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Developing a Water Bills Projection Model:
Integrated Final Report
Defra
March 2015
NERA Economic Consulting
CONFIDENTIALITY
We understand that the maintenance of confidentiality with respect to our clients’ plans and
data is critical to their interests. NERA Economic Consulting rigorously applies internal
confidentiality practices to protect the confidentiality of all client information.
Similarly, our approaches and insights are proprietary and so we look to our clients to protect
our interests in our proposals, presentations, methodologies and analytical techniques. Under
no circumstances should this material be shared with any third party without the prior written
consent of NERA Economic Consulting.
© NERA Economic Consulting
Integrated Final Report Contents
NERA Economic Consulting
Contents
Executive Summary i
1. Background and Introduction 1
2. Development of the Water Bills Projection Model 2
2.1. Formulation of Model 2
2.2. Model Scope and Limitations 3
2.3. Conceptual Structure of the Model 5
2.4. Model Inputs 7
2.5. Assumptions 12
2.6. Model Operation and Outputs 19
3. Model Specification 21
3.1. Model Technical Structure 22
3.2. Baseline Model Variables and Scenario 30
3.3. Sources for Driver Variables 32
3.4. Policy Scenarios 44
3.5. Output Specification 59
4. Key Model Results 61
4.1. Introduction 61
4.2. Limitations 61
4.3. Inputs and Assumptions 62
4.4. Baseline Results and Exogenous Variable Sensitivities 68
4.5. Policy-Specific Results - using baseline exogenous variables 78
4.6. Non-Baseline Policies and Policy-Based Sensitivities 84
4.7. Intermediate Baseline Outputs 94
5. Possible Model Developments 101
5.1. Dataset Upgrades 102
5.2. Structural Upgrades 104
5.3. Policy Upgrades 106
5.4. Output Upgrades and Output Analysis Tools 108
Appendix A. Elasticity Effects 110
Appendix B. Specification Issues Agreed with TSG 114
Appendix C. Glossary 118
Integrated Final Report Executive Summary
NERA Economic Consulting i
Executive Summary
This report presents work by NERA to develop the Defra Water Bills Projection Model. The
model has been designed to assist decision making in the water sector by showing the impact
of policy, regulatory, and company investment choices on final customer bills in England &
Wales over the period from 2015-50. It allows users to project bills under a range of
scenarios about the sector-policy environment, and about relevant macroeconomic and
environmental factors including the drivers of sector-costs, reporting the associated typical
bills for classes of customer by company or by region. The projections depend completely on
the assumptions input by the user such as the projected investment profile, as well as on other
assumptions that are implicit in taking a modelling approach, including the assumption that
the projected situation can be financed and can be compliant with relevant future laws.
The model was designed in an iterative process with a technical steering group (TSG)
consisting of industry stakeholders. These included representatives from Defra, Ofwat, the
EA, and from five water companies. During the six months of the project, several iterations
of the draft model and memos setting out options have been presented to the TSG. Their
feedback has usefully been incorporated into the final model. Several water companies also
provided longer-term data which we used to generate longer term trends to populate the
model from 2020 and beyond.
Figure 1.1 sets out an overview of the model’s structure. The structure is based on the cost
and revenue characteristics that we observe in the industry. The impacts on bills are derived
from simplified regulatory profit and loss account building blocks for wholesale value chain
components and application of a margin for retail.
Figure 1.1
Conceptual Structure of the Model
Source: NERA illustration
Abstraction
Reform
WRMP
Demand
WRMP
Supply
Properties
Climate Change
and RSA
RPEs
Retail
Competition
Activity-
Based
Costing
EU Directives
and UK statute
Upstream
Competition
GDP
Population
Financials:
COD/COE
PAYG
Gearing
Tax
Leakage
Supply Pipe
Adoption
Inflation
Baseline
Variables
Modelled
Demand
PAYG
RCV
WACC
Runoff & New
Depreciation
Drivers
Variables
Metering
Policy
Scenarios
Interest Rates
Modelled
Supply
Internal Calculations
Modelled
Opex &
Capex
Cost Impact on Bills
Modelled
Finance Costs
Tax
NHH Real Bill
Impact
Compound
Cost Impacts
Regulatory
Mechanisms &
efficiency
effects
Greeness
HH PCC targets
Wholesale Costs
NHH Allowed Retail
Costs
Wholesale Costs
attributed to HH
HH Retail Cost to
Serve
Wholesale Costs
attributed to NHH
NHH Retail
Margins
HH Retail Margins
HH Real Bill
Impact
HH and NHH
Nominal Bill
Impact
Greater
Resilience
Integrated Final Report Executive Summary
NERA Economic Consulting ii
Figure 1.2 displays the average household bill levels under a range of scenarios for the factors
that are thought to matter most to costs in the sector, hence to bills. In interpreting these
results it should be borne in mind that:
There is little information available about the more distant future. For many of our
external input variables, such as cost inflation, there are no long-run forecasts directly
suitable for use as data inputs. There is also considerable uncertainty about the likely
regulatory and policy environment, for example about the long-run statutory requirements
to improve wastewater discharges. Assumptions must be made by the model user - we try
to be transparent about those used in this report;
In making assumptions to form long-term data inputs, we face the difficulty that the future
may be different from the past. For some important input data series such as capital
maintenance expenditure requirements we use current average levels and short/medium-
term forecasts made by water companies as a basis for long-run forecasts, though this
relationship might change, for example capital maintenance needs might increase more
than expected as assets become older and change with the service-quality and climate
context. An unforeseen change in the underlying relationships could make our results
misleading, when hindsight can be applied;
Though the model is designed to reflect available data, it is also only a model, one also
designed to be within the computational capacity of Excel avoiding use of macros.
Consequently the most granular level of data, relationship, and result treated in the
model is the company value chain level and an annual time step; no effects at finer levels
are modelled; few feedbacks are covered within the model.
Although the model does have limitations, it nonetheless remains a comprehensive and robust
set of scenario forecasts based on the best available evidence and a degree of industry
expertise.
Figure 1.2 shows that by 2050, the range of national average annual household bills goes
from £237 in the lower scenario setting, to £553 in the upper scenario, in real terms. The
black line shows the estimate of real household bills produced under the “baseline” driver
assumptions and scenarios in the model, falling from £355 in 2015 to £343 by 2050.
Integrated Final Report Executive Summary
NERA Economic Consulting iii
Figure 1.2
Baseline and Sensitivity Range for Average Household Bills – Real
Source: NERA
The dark blue area around this baseline projection shows the national household average bill
range – in real terms - from setting all of the driver variables (e.g. GDP, population growth,
construction cost inflation, etc.) and baseline policies (retail competition in 2017, upstream
reform in 2020, and regulatory mechanisms) to their low or high sensitivities. The high
sensitivity includes the WFD Scenario 3 cost estimates which includes non-cost beneficial
solutions (this scenario also accounts for the cost spike in 2015).
The light blue areas at the top and bottom of the fan correspond to the “upper” and “lower”
modelled scenarios, which result from setting all of the drivers, policies and scenarios (e.g.
greater resilience ) to their high and low settings respectively. The impact of setting all the
policy drivers to their “low” settings is more muted, as for some policies the low setting is the
same as having them turned “off” in the baseline, and there is therefore no change from them
in the “lower” scenario. In contrast, the “upper” scenario measures include substantial extra
resilience expenditure from 2020 as well as maintaining the EA WFD Scenario 3 cost
estimates. For these “upper” and “lower” scenarios we emphasise that currently unforeseen
policies and/or extreme conditions could have impacts that are currently not captured by the
model.
Figure 1.3 shows the modelled evolution of baseline average household combined water and
sewerage bills in real terms in 2015, 2030 and 2050 for each of the WASC regions. South
West Water’s average bills are the highest, while Severn Trent Water’s are the lowest. There
is some convergence towards the industry average due to a greater decrease in the bills in
regions which initially have higher-than-average bills. The decline in bill levels from decade
to decade matches the declining baseline average bill level presented above in Figure 1.2.
This is the result of all the baseline assumptions in concert, principally through their effect on
sector cost levels.
200
250
300
350
400
450
500
550
600
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
2019
2021
2023
2025
2027
2029
2031
2033
2035
2037
2039
2041
2043
2045
2047
2049
£/P
rop
ert
y (2
01
2/1
3 p
rice
s)
High/Low Bill Scenarios Range Upper/Lower Scenarios Range Model Baseline
Historic Forecast
AMP6
Integrated Final Report Executive Summary
NERA Economic Consulting iv
Figure 1.3
Baseline Water and Sewerage Household Average Bills
by WASC in 2015, 2030 and 2050 – Real
Source: NERA
Figure 1.4 sets out the totex projection by cost component. The opex component is roughly
the same size as the combined enhancement and maintenance capex spend. The figure shows
that the gradual decline in totex is largely due to a reduction in enhancement capex spending.
This reduction in capex roughly coincides with the 2027 conclusion of the river basin
management plan cycles in the current WFD. At present, no known large scale capex
programme is projected after the WFD, but it is possible that further quality or environmental
improvements will need to be implemented. As a result, the decline in totex should be taken
to represent a starting point from which additional options will be considered.
Figure 1.4
Projected Totex by Cost Component - Baseline Case
Source: NERA
Figure 1.5 shows the net changes to the RCV over the modelling horizon. The figure
displays the industry’s enhancement expenditure added to the share of CM that is added to
the RCV. The RCV is growing when this sum is greater than the depreciation series, which
is the case in the first ten years of the horizon. Following that point, the RCV stabilises in the
Integrated Final Report Executive Summary
NERA Economic Consulting v
base case as the increasing levels of CM additions are offset by a gradual decline in
enhancement expenditure.
Figure 1.5
Net Changes to the RCV
Source: NERA
Figure 1.6 shows the key input assumptions underlying the baseline case. The model
produces an overview sheet summarising the key inputs (macroeconomic and long term cost
efficiency savings assumptions) as well as additional displaying the WFD inputs that feed
into the modelled scenario so that the user can check them for plausibility and internal
consistency. Note that the cost efficiency incentive rate is presented as a negative value to
represent its decreasing effect on costs.
Figure 1.6
Key Baseline Inputs
Source: NERA
-2.5%
-1.5%
-0.5%
0.5%
1.5%
2.5%
3.5%
4.5%
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
Gro
wth
Rat
e %
GDP Growth RPIReal Price Effect: Opex Real Price Effect: CapexCost Efficiency Incentive Effect CPI (if applicable)Risk-free Rate
Integrated Final Report Background and Introduction
NERA Economic Consulting 1
1. Background and Introduction
This report presents work by NERA to develop a Water Bills Projection Model for Defra.
The project comes in the context of a number of challenges to the water sector in England
and Wales. In the short term, water companies will need to invest to meet the demands of
stricter environmental standards and growing populations, while regulatory and structured
changes such as retail competition for non-households and abstraction reform will affect the
way they do business. Over the longer term, the impacts of climate change are projected to
increase the need for resilience in both the supply of water and treatment of wastewater and
the network infrastructure that delivers it. The consequences of these and other challenges
for water and sewerage companies’ costs – and therefore customers’ bills – will depend on
interactions between them. To adequately assess the costs and benefits to the public of any
environmental or regulatory policy that affects the water sector, it is important to understand
this process of interaction and quantify its implications for customer bills, both now and in
the future. A major objective of this project is to provide Defra, Ofwat, and the EA with a
tool to help do this.
The Water Bills Projection Model has been designed to assist decision making in the water
sector by showing the impact of policy, regulatory, and company investment choices on final
customer bills in England & Wales over the period from 2015-50.1 The first five years
coincides with the AMP6 price review period where bills the regulatory nature of the industry
ensures that bills will be very similar to the levels set in the Ofwat Final Determinations. As
a result, the modelled baseline “projections” could be considered to begin in 2020.2 The
model allows users to project bills under a range of policy scenarios and assumptions about
relevant macroeconomic and environmental factors, reporting typical bills for classes of
customer by company or by region.
This report is structured as follows:
Chapter 2 describes the stages of formulation and the structure of the model, its scope and
limitations, and the inputs and assumptions underlying its results;
Chapter 3 sets out an abridged and updated version of the model’s technical specification
report;
Chapter 4 provides a comprehensive set of the model’s outputs;
Chapter 5 sets out potential model upgrades.
1 From this point onwards when describing the model we will always refer to the financial year beginning when we
reference years. For example, 2015 refers to the financial year 2015/16 beginning on 1 April 2015. The final year of
the horizon is therefore 2049/50 which ends on 31 March 2050 and which we will refer to as 2049.
2 Although this version of the model is based on the DDs and may therefore change when updated for the FDs (and any
subsequent CMA determinations that might override them, if they occur).
Integrated Final Report Development of the Water Bills Projection Model
NERA Economic Consulting 2
2. Development of the Water Bills Projection Model
This chapter sets out the stages of formulation of the model. This model formulation process
is described through the data inputs and sources drawn upon and the main assumptions used.
The structure of this chapter is as follows:
Section 2.1 describes the process that NERA followed to develop the model;
Section 2.2 sets out the scope of the model as well as its limitations;
Section 2.3 provides a description of the model’s structure;
Section 2.4 briefly describes the inputs and the sources from which these were drawn;
Section 2.5 sets out the main assumptions underlying the model; and
Section 2.6 provides a high-level overview of the modelling operations and outputs.
2.1. Formulation of Model
In this section we describe the four major stages of model formulation in turn.
2.1.1. Technical specification process
The formulation of the model began with a specification phase where the model’s technical
aspects were agreed. This phase involved considerable participation by the Technical
Steering Group (TSG) including workshops and comments on various specification proposals
that were circulated by email. The model structure, policies, and variables to be included
were agreed at this point, as well as the data sources and sensitivity specifications. The
summary report on the model specification has been merged into this report, see Chapter 3.
2.1.2. Preliminary model
A preliminary version of the model was presented to the TSG in July 2014. The purpose of
the model presentation session was to demonstrate, at an early stage, a functional but
rudimentary version of the model. This allowed the TSG to see how the model would work
and to suggest structural or technical changes as appropriate.
The presentation was also used as an opportunity to highlight some of the more technical
modelling issues and trade-offs faced by the modelling team and to seek stakeholder views on
how to resolve them. During the session several issues were clarified, and others were
addressed through follow-up memos which set out the potential options and suggested our
preferred modelling approach, leading to an agreed decision.
2.1.3. Draft model
The first draft model was a fully functional model delivered to Defra and capable of
projecting water bills under any combination of seven policy scenarios and any user user-
results driver variable series or sensitivity.3 The draft model was delivered alongside a draft
3 For detail on the policies and driver variables, please see section 2.4.2.
Integrated Final Report Development of the Water Bills Projection Model
NERA Economic Consulting 3
results report and draft model handbook. The draft (November 2014) version of the model
preceded final quality assurance stage and was circulated for comments prior to the
finalisation of the model’s development, the external quality assurance, and the subsequent
finalisation of this report.
2.1.4. Final model
The model was finalised following a final iteration of comments from stakeholders and
external quality assurance checks performed by Vivid Economics. The changes resulting
from stakeholder feedback and comments received from Vivid include:
The addition of an inputs tab where the model displays an overview of the key
(macroeconomic, WFD, and long term assumption) inputs feeding into the model to
check them for plausibility and internal consistency;
The addition of a WFD tabs where the user can view the incurred level of WFD for the
modelled scenario at the industry or the RB level respectively;
Changes to the cost efficiency savings assumptions (see section 3.3.4 for more details) as
well as to the long term opex and capex RPEs and the RPI input forecasts;
Adjustments to the definition of the baseline and preset high/low and upper/lower
scenarios;
Corrections to the magnitude of the PR14 regulatory mechanisms and incentives to
include the effects of moving to separate the HH and NHH retail controls from their
wholesale counterparts (which are additional to the effects from moving to NHH retail
competition);
The addition of a second Macro that resets all assumptions to their preset levels; and
Several small corrections to formulae and formatting as pointed out by Vivid economics.4
2.2. Model Scope and Limitations
The modelling horizon extends from 2015 to 2050 via annual time increments, over which
the available input data falls into one of three categories. Short-term data covers the period
from 2015 to 2020, and is mostly sourced from reputable forecasts and published PR14
documents. The longer-term data from 2020 to 2040 is based on WRMP data or forecasts
using long-run averages. From 2040 to 2050, all data is forecasted for this project as none of
the datasets available contain useable figures that go that far into the future.
The model draws on data covering a specific set of cost drivers under a range of states of the
world. While some drivers can be added by future users, the process requires making the
new variables feed through the model appropriately, which requires relatively advanced
modelling skills and an in-depth understanding of the model.
4 As an output from their external quality assurance of the model, Vivid provided a traffic light-based comments log for
their findings where green items were not a major concern, amber items needed attention as they may be cause for
confusion, and a small number of red items which were labelled as potentially serious issues. As the total number of
items was modest, NERA implemented all the changes suggested regardless of whether green, amber, or red.
Integrated Final Report Development of the Water Bills Projection Model
NERA Economic Consulting 4
The model’s granularity extends to each company’s region’s fourteen value chain elements –
seven for water and seven for sewerage as listed in Table 2.1 below. The model provides two
spare value chain elements to accommodate further value chain element disaggregation.5
Table 2.1
The Model Contains 14 Value Chain Elements
Water Sewerage
Water resources Sewerage network
Raw water distribution Sewerage treatment
Water treatment Sludge treatment
Treated water distribution Sludge disposal
Spare water value chain element Spare sewerage value chain element
Retail water households Retail sewerage households
Retail water non-households Retail sewerage non-households
Source: NERA analysis of Ofwat (2008) “Accounting Separation: Consultation on allocation of
activities between business units
These value-chain or cost-driver components, given for each company region, function as the
“atoms” from which the model constructs a revenue requirement and in turn customer bills.
It calculates total expenditures at this “atomic” level and then combines them into different
price controls, company-region representations, or, potentially, regional aggregations at the
river basin level. The model is unable to represent effects other than those that can be
constituted from these basic elements, such as within-company or within-company region
impacts.
The model has a pre-specified set of seven policy options, set out in more detail in section
2.4.2.2 The user can add up to three additional policy options relatively simply from a
technical point of view, as these feed through the model through a set of predetermined
channels in which policies must be defined prior to being added. The “Water Bills Projection
Model: User Guide” describes the process for performing these policy option additions.
The model faces three major limiting factors:
A lack of available information on the more distant future. For many variables, there
are no long-run forecasts of external factors such as cost inflation that are directly suitable
for use as data inputs, so there is a need to make assumptions about how these inputs
should be forecast. There is also considerable uncertainty surrounding the likely
regulatory and policy environments in the future, such as the need for wastewater
improvement, so even the available longer term company forecasts may be affected by
variations in company assumptions about the context.
Linked to the last factor, the reliance on a set of assumptions to generate input
forecasts. For the longer-term horizon of the model, the model’s projections are
5 The spare value chains are value chain elements (left blank in the model) where no costs are currently realised but
which may be used in future modelling work if desired.
Integrated Final Report Development of the Water Bills Projection Model
NERA Economic Consulting 5
dependent on a set of assumptions that may not accurately reflect future events. In
particular, for many of the long-run input series in the model such as capital maintenance
needs we adopt a long-run average approach – including the available forecasts - to
projecting these variables. We also make several key assumptions regarding the relative
ongoing stability of the sector and of the climate, each of which could make the model
misleading if their realisation was very different from our assumption.
The model is designed to reflect available data, and to be within the computational
capacity of Excel avoiding use of macros. Consequently the most granular level of data
and results in the model is the company value chain level and an annual time step; no
effects at finer levels are modelled; few feedback effects are covered.
For this reason many of our input factors have in-built sensitivity ranges (low, medium, high)
and so do many of our policy effects have in-built “strength of effect” ranges (low, central,
high). Accordingly, as a general matter we consider it better to present the resulting bill
projections as ranges covering the low and high scenarios (i.e. from £Y/year to £X/year) than
as point figures.
2.3. Conceptual Structure of the Model
The model is structured according to the cost and regulatory characteristics of each of twelve
value chain elements contained within water and sewerage services. These characteristics
shape the influence of quantity and quality changes on costs hence bills. In particular, value
chain components are each assumed to have their own fixed and variable proportions of costs.
For example, the water resources components, when flexed to increase supplies, will exhibit
increasing marginal costs – based on the cost solutions set out in the latest company Water
Resource Management Plans (WRMPs) and other company documents.The model structure
is based on the cost and revenue characteristics that we observe in the industry. The water
resources component has increasing unit costs, whereas the water and sewerage networks and
the treatment and disposal components have roughly constant incremental costs.6 The retail
components are characterised by constant average cost to serve (ACTS). The impacts on bills
are derived from simplified regulatory profit and loss account building blocks for upstream
components and application of a margin for retail. In addition to the value chain elements
that are already reported by companies, Table 2.1 shows that the model also contains one
additional spare wholesale element for each service.
Figure 2.1 sets out an overview of the water resources and treatment component’s structure.
We focus a disproportionate amount of time on setting out this component of the model due
to its complexity relative to the other elements. As shown in the figure, the baseline and
driver input variables interact with user-selected policy scenarios to calculate projected
supply and demand requirements, which then feed through to modelled costs. These costs are
computed at the value-chain level according to each of the revenue building blocks. These
are then added together to determine real bills at the household metered, household
unmetered, weighted average household (metered & unmetered), and non-household level for
each company.
6 The assumptions of increasing marginal costs in water resources and roughly constant incremental costs in the networks,
treatment and disposal compoenents was agreed with the project’s technical steering group.
Integrated Final Report Development of the Water Bills Projection Model
NERA Economic Consulting 6
Figure 2.1
Conceptual Structure of the Model
Source: NERA illustration
The dashed lines in the figure split this component of the model into five distinct segments:
baseline variables, driver variables, policy scenarios, internal calculations, and cost impacts
on bills. The baseline variables form the projections which are the foundation of the model
and are based on the company WRMPs and Draft determinations (intended to be final when
available). The driver variables are a mix of company-specific or macroeconomic variables
that allow the user to test sensitivities around the baseline. The policy scenarios are switches
that affect the model’s drivers or cost functions, and allow exploration of the impacts of
policy options.
Abstraction
Reform
WRMP
Demand
WRMP
Supply
Properties
Climate Change
and RSA
RPEs
Retail
Competition
Activity-
Based
Costing
EU Directives
and UK statute
Upstream
Competition
GDP
Population
Financials:
COD/COE
PAYG
Gearing
Tax
Leakage
Supply Pipe
Adoption
Inflation
Baseline
Variables
Modelled
Demand
PAYG
RCV
WACC
Runoff & New
Depreciation
Drivers
Variables
Metering
Policy
Scenarios
Interest Rates
Modelled
Supply
Internal Calculations
Modelled
Opex &
Capex
Cost Impact on Bills
Modelled
Finance Costs
Tax
NHH Real Bill
Impact
Compound
Cost Impacts
Regulatory
Mechanisms &
efficiency
effects
Greeness
HH PCC targets
Wholesale Costs
NHH Allowed Retail
Costs
Wholesale Costs
attributed to HH
HH Retail Cost to
Serve
Wholesale Costs
attributed to NHH
NHH Retail
Margins
HH Retail Margins
HH Real Bill
Impact
HH and NHH
Nominal Bill
Impact
Greater
Resilience
Integrated Final Report Development of the Water Bills Projection Model
NERA Economic Consulting 7
2.4. Model Inputs
This section describes the dataset used in the model as well as the baseline and sensitivity
inputs. Section 2.4.1 describes the construction of the model dataset by combining data from
a wide range of public and institutional sources, and forecasting it out to 2050 whenever data
is absent. Section 2.4.2 then describes the cost drivers and policy options that determine the
supply-demand balance which underlie the model’s bill projections.
2.4.1. Constructing the Dataset
The following subsection briefly describes the data on non-cost variables that NERA
compiled from public sources. When this data was absent or when the available data series
came to an end, we forecast the remaining values according to the approaches set out in
Section 2.4.1.2.
2.4.1.1. Data provided by companies and agencies
We compiled data from various publically available sources. The bulk of the data came from
the available draft determinations, WRMP data tables, and August Submissions (all 2014). A
summary of the data obtained is displayed in Table 2.2.
Table 2.2
Variables and Sources
Draft Determinations
WRMP Tables August Submissions
W & WW Water Water Wastewater
W & WW RCV Allocation Population growth rate Addressing low pressure Sewer Flooding
Risk-free Rate Metering Meeting Lead Standards Private Sewers
Notional Gearing Leakage
Ecological Improvements at Abstractions
Sludge treatment and disposal
Effective tax rate Water Available for Use Improving Taste / Odour / Colour WFD Compliance
PAYG PCC New Development and growth
Depreciation rate and method
RSA Revocations and Modifications
Additional Environmental Capex
RCV run-off rate Target Headroom Capital Maintenance
Cost to serve (CTS) Distribution Input SEMD
SEMD Properties Resilience
Source: NERA
In addition to the above sources, we also compiled data from reputable national sources such
as the ONS, OBR, the Bank of England, and DECC. More details on the variables used can
be found in section 3.3.
2.4.1.2. Forecasting input data series
We allow the user to select from a series of approaches to forecast any data needed that are
absent from the input datasets. The model detects absent data (opposed to zero entries)
through the use of periods “.” in the data input cells. Where data is absent, the user can select
from the following five options to forecast each variable:
Integrated Final Report Development of the Water Bills Projection Model
NERA Economic Consulting 8
Roll over the latest available data cell to maintain the future values at a constant level;
Roll forward the future values at a constant level equal to the average of the 2015-2020
values;
Take the compound annual growth rate (CAGR) of the past “x” years, where “x” can be
specified to be any number of years for which data is available. The CAGR is then
applied to forecast future values at a steady compounding rate; or
Specify an alternative variable whose growth rate the forecast series tracks.
Alternatively, the user can input a custom series of privately held data, or public data that is
not already available in the model, as a “custom” sensitivity and choose to use this in the user
controls. The user manual describes the approach to using custom data in more detail.
2.4.2. Defining the Baseline and Sensitivities
2.4.2.1. Cost Drivers
This section summarises the data sources for the generic cost drivers in the model’s scenarios
sheet. In some cases, a data source is consistent throughout the short-term and long-term
modelling horizon. In other cases, we use different sources for the short-term and long-term.
Table 2.3 lists the data sources and assumptions used in the model’s baseline as well as the
sources for the high and low sensitivities for the short and long term periods.
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Table 2.3
Short- and Long-Term Driver Sources
Driver Short-Term Baseline
Source Period Low7 High
GDP Growth PWC PR14 Risk Analysis 2015-2020 PWC Low Case PWC High Case
RPI PWC PR14 Risk Analysis 2015-2020 PWC Low Case PWC High Case
Energy Prices DECC E&E - ref prices 2015-2030 DECC E&E - Low price DECC E&E - High price
RPE: Opex8 Assumption: 0%
Assumption: 0.5%
2015-2020
2020-2025
0%
0%
0%
1%
RPE: Capex9 Assumption: 0%
Assumption: 0.5%
2015-2020
2020-2025
0%
0%
0%
1%
Cost Efficiency Incentive Assumption: 0%
Assumption: 1%
2015-2020
2020-2025
0%
2%
0%
0.5%
CPI BOE CPI Forecast 2015-2017 Same increase factor as
RPI10
Same increase factor as
RPI
Driver Long-Term Baseline
Source Period Low High
GDP Growth OBR long run average:
2.3% 2021-2049 2% 2.7%
RPI OBR long run average:
3% 2021-2049 2.5% 4.5%
Energy Prices DECC E&E- ref Price
with CAGR 2031-2049
DECC E&E-low Price
with CAGR
DECC E&E- High Price
with CAGR
RPE: Opex Assumption: 0.5% 2025-2049 0% 1%
RPE:Capex Assumption: 0.5% 2025-2049 0% 1%
Cost Efficiency Incentive Assumption: 0.5% 2025-2049 1% 0%
CPI BOE CPI target: 2% 2018-2049 1.7% 3%
Source: NERA
7 The cost efficiency incentive is put in place to account for technological change and improvements in management over
time. The low and high sensitivities were defined in terms of their effects on bills. For example, the low cost efficiency
incentive has a larger magnitude (and therefore results in lower bills) than the high cost efficiency incentive.
8 Assumption was cross-checked against a long term observed Opex RPE trend based on three elements: energy, labour,
and materials cost. The sources used for the cross check are for Energy: DECC E&E - ref prices; for Labour: ASHE
Long Run Average; for Materials: ONS long run average. We use a 10.3% weight on energy prices, a 12.4% weight on
labour prices, and a 6.0% weight on materials prices based on the opex expenditure in each of these categories for a
sample of the larger companies (we used Anglian, Yorkshire, Thames, Severn Trent, Southern) during 2010/11 (JR
data). RPEs are defined as relative to RPI, so the model’s RPE figures are consistent even after any changes to the RPI
assumptions used in the model.
9 Assumption was cross-checked against a long term observed trend of the BIS COPI (All work) index. We assumed
0.5% (compounding) based on the observation that the 25 year (1987-2011) average arithmetic average growth rate was
0.65%. We assumed the range of the high and low sensitivity values following a review of the periods with the highest
and lowest 10, 15, or 20 year rates observed within the 25 year window.
10 We quantify the relative CPI high vs CPI base case scenario by using the same proportional factor as the RPI high vs
RPI base case. The same applies to the CPI low case. For example, if RPI base is 3%, RPI high is 4%, and CPI base is
2% then we set CPI high as (4/3)*2%.
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2.4.2.2. Policy Scenarios and Impacts
Table 2.4 sets out the model’s policy options/ scenarios and features. The first two columns
list and briefly describe the options. The option that is specified in the model’s baseline is
listed in the third column – items listed as “off” are not included in the baseline scenario.
The fourth column lists the scenarios that can be selected by the user, as well as some of the
additional features available for each measure .
Table 2.4
Policy Options and Features
Policy option/scenario Description Baseline Option Alternative Options and
Features
Retail Competition
Adoption of non-domestic retail competition in England (all) &
Wales (>50Ml/D) with voluntary separation for NHH retail
WSL Reforms and Voluntary
Separation
WSL reforms only option (without exit)
Regulatory Mechanisms This measure shows the effect of
alternative incentives
Regulatory Mechanisms
(PR14)
Low (PR09) or high (2*PR14) and
customisable cost of capital impact
Upstream Competition Policy Switch for upstream
competition reforms Upstream
Reforms option
Timing of implementation. Varying levels of
competition and CoC
WFD Cost of meeting WFD compliance Based on DDs and Company
Long-Term data
EA Sc3: All Feasible Measures; EA Sc4: Cost-
Beneficial Measures; and Strength Options
Private Supply Pipes Adoption of private supply pipes Off Strength options only
PCC targeting Reductions to the volume of HH
water demand Off Strength options only
Abstraction Reform
Policy Switch for abstraction reform (midway option is
average of water shares and system plus)
Off Water Shares; System
Plus; or Midway options
Greater-Resilience
Switch for significant increase of industry resilience, through
higher headroom and resilience costs
Off
Strength options with customisable Ml/d
target and cost adjustments
Source: NERA, often based on policy Impact Assessment studies
Note: All policies have low/central/high strength options, and all but the regulatory
mechanisms and WFD costs are able to be switched off by the user
The model is designed such that policies can affect the model’s projections through the
following four impact categories by value-chain element:
Botex effects: percentage changes to capital maintenance and operating expenditure
costs;
Enhancement effects: percentage changes to the cost of new capital expenditure;
Volume effects: percentage changes to the level of water supplied or demanded; and
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WACC effects: percentage point adjustments to the weighted average cost of capital (NB
– these are input as percentage point adjustments in contrast to the other effects).
Table 2.5 displays the impacts that correspond to each of the seven policies/scenarios in the
model, and a brief description of these impacts.
Table 2.5
Modelled Policy/Scenario Impacts11
Policy/scenario Cause of Impact
Retail Competition
Botex costs: Regulation fees, acquisition & retention, settlement & switching
Botex benefits: Wholesale efficiency, retail efficiency savings, bundling benefits
Volume Reductions: Demand reductions through customer demand management
Regulatory Mechanisms Botex benefits: Totex cost assessment, menu regulation, water trading, separate retail HH and NHH controls
Upstream Competition
Botex costs: Regulation fees, increased scrutiny costs
Botex benefits: Efficiency catch-up and ongoing improvements
Enhancement benefits: Efficiency catch-up and ongoing improvements
WACC Increases: Higher borrowing costs to reflect greater risk
Private Supply Pipes Botex costs: HH & NHH Repair and replacement costs, HH & NHH administration costs
Per Capita Consumption Targeting
Volume Reductions: Household demand reductions resulting from water saving technology or increased importance of water conservation
Abstraction Reform
Botex costs: Transition costs
Botex benefits: Government and business admin cost savings, improved gross margins
Volume Increases: Increased water supply availability at current abstraction sites
Greater-Resilience
Volume Increases: Increased target headroom requiring additional supply capacity
Botex costs: Additional costs for resilience (doubling of pipes, etc.)
Enhancement costs: Additional costs for resilience in water and sewerage (doubling of pipes, installations of larger sewers to prevent overflows at bottleneck locations, etc.)
Source: NERA
Many of the quantitative policy effects in the model were obtained by NERA from analysis
of policy impact assessments from Defra or others (e.g. for retail competition, abstraction
reform, upstream competition, private supply pipe adoption, and regulatory mechanisms).
Additional scenarios such as greater resilience and PCC targeting are based on assumptions,
developed with the TSG , defined around existing company costs/volumes, and contain
sensitivities around the base case.
11 Note that the WFD costs feed through thte model like any other cost variable (e.g. as would, say, sewer flooding
expenditure) hence they do not feed through the model’s policy channels. Instead, because the model considers the
WFD costs as a driver variable (which allows sensitivities / alternative levels of WFD costs), it feeds through the model
as a cost rather than as an impact on costs. As a result, WFD scenarios are not included in this table.
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2.5. Assumptions
2.5.1. Allocation of Initial RCV across Value Chain Elements
We used two approaches to allocate the initial RCV across the different value chain elements:
a focused and an unfocused approach. Both approaches are based on an allocation of each
company’s net modern equivalent asset values (MEAV), and are described in more detail
below. The unfocused approach is used in the baseline, but the user is able to switch to the
focused approach if desired.
The unfocused approach entails calculating the value-chain proportions of the net MEAVs,
and allocating the RCV to each value chain element using those proportions. This approach
is very simple and transparent. However, if competition is introduced, entrants to the
competitive parts of the supply chain will need to earn a return on the full net MEAV in order
to enter the market. For this reason, it may not be a suitable approach to RCV allocation in
modelled scenarios where competition is introduced.
Under the focused approach, the distribution of the RCV to each value-chain element is
carried out in a two-stage process. The first stage applies the full net MEAV for each
contestable element of the value chain (that is, for the upstream competition policy, resources
and treatment). The second stage then allocates the remaining share of RCV to the non-
contestable elements: treated and untreated water distribution and sewerage networks.
2.5.2. Allocation of TOTEX across Value Chain Elements
We allocate TOTEX across value-chain elements in different ways for different cost
components. For opex and capital maintenance (CM), we allocate costs according to
companies’ allocation of opex and CM across the value chains in their 2013/14 regulatory
accounts. For enhancement cost lines, we allocate each item to the value chains on a line-by-
line basis. The allocation proportions have been sense checked by the TSG including the
water company representatives.
2.5.3. Modelling Water Supply-Demand Balance
Many of the policies’ and variables’ cost effects lead to a shift in either supply or demand,
which the model must reconcile. Increases in demand that cannot be met by a company’s
supply during a given year automatically trigger a supply investment such that the demand is
met (and the target level of headroom is maintained). As a simplification, the model assumes
that these supply additions can be brought online during the year in which they are required.
In contrast, excess supply capacity is assumed to be “sunk”, in the sense that supply capacity
cannot be reversed. The effect of having excess supply capacity is that it defers the need for
further investment until all of the excess capacity “slack” becomes used up. The model does
not attribute operating expenditure costs to “non-utilised” capacity, but the corresponding
capex does depreciate at the same rates as utilised capex.
2.5.4. Supply and Supply Curves
The model makes the following general assumptions about company supply:
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Regardless of demand patterns, baseline existing water and sewerage capacity is always
retained and assumed to be included in company cost figures.
Whenever increases in demand require companies to meet supply headroom targets, they
install additional capacity based on the lowest whole-life cost option from their respective
WRMPs (see below). This capacity is never subsequently retired.
Leakage reductions are modelled as supply-increasing measures, with all other non-
revenue water fixed as projected in the WRMP.
The cost of any measures to increase capacity – reflected by the company “supply curves” –
is estimated as follows:
Supply curves are approximated by company average incremental cost (AIC) curves,
which give the per-unit cost of available measures to increase supply, on the assumption
that these are installed in ascending unit-cost order. Thus, for example, if a water
company has two available programmes, one that increases its supply by 7 Ml/day for a
cost of £700m and another that raises supply by 5 Ml/day for £1bn, the company’s AIC is
£100m per Ml/day for the first 7Ml/day it installs above its existing capacity and £200m
per Ml/day for the next 5Ml/day.
For those companies that include water supply enhancement programmes in their
WRMPs, the AIC of additional water capacity is calculated by adding the capital and
operating costs of the programmes from Table 3 of the WRMP (e.g. WRP3).
For other companies the AIC of additional capacity is estimated using the cost curves
from neighbouring or similar companies. These cost curve mappings are displayed in
Table 2.6.
Table 2.6
Additional Water Capacity Supply Curve Mappings
Mapped To Mapped From
South West Water United Utilities
Welsh Water United Utilities
Yorkshire Water South East Water
Cambridge Water & South Staffs Portsmouth Water
Sembcorp Bournemouth Portsmouth Water
Dee Valley Water Portsmouth Water
Northumbrian Water Wessex Water
Source: NERA assumption
2.5.5. Demand Elasticities and Metering
Volumes demanded are driven by population growth and the baseline trends in per capita
consumption (PCC) in the model, which are to a large extent determined by the user-
specified scenarios. However, the following aspects are imposed on the model when the
corresponding sensitivities are selected:
The demand response to a change in water or energy price is lagged by a year – so if
water bills rise by 10% in one year, customers reduce their demand in response to this in
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the following year. This year-long lag reduces processing time, as the model would
otherwise need to solve equilibrium supply and demand responses simultaneously.
Demand responses to water and energy prices can be turned off and on in the model
controls – they are off in the baseline;
The price elasticities of demand for water for household and non-household customers are
taken from a 2003 UKWIR report prepared by NERA12
, which found the domestic price
elasticity estimate in England & Wales to be -0.14.13
Changes to water consumption also
result in equivalent changes in sewerage service demand. The model uses this figure as
the elasticity for both the household and non-household sectors;
The cross-price elasticity of demand for water with respect to energy prices is based on a
study of Danish water demand14
and the relative proportions of water heated by electricity
in Denmark and the UK. The value we use from that report is -0.17;
The policy that reduces PCC is assumed to be costless – representing for example a shift
in consumer views about water conservation due to climate events;
Any new metering is assumed to lead to a 12.5% reduction in consumption by newly
metered households,15
and for WaSCs, the growth rate of measured sewerage customers’
volume is assumed to be the same as that of water customers’ volume.
The costs of metering comprise the capital costs of installation and maintaining new
meters (attributed to water wholesale), and the operational cost of reading them
(attributed to water retail). These costs are based on a recent metering report published
by Ofwat. 16
2.5.6. Capital Maintenance
The cost of maintaining companies’ existing capital stocks are rolled forward from the most
recent levels in the model’s baseline. Capital maintenance associated with any additional
enhancement generated by different scenarios modelled is assumed to occur based on a
renewal cycle of 10 to 100 years depending on the value chain element.17
This additional
CM is assumed to be incurred smoothly in each year after the asset is built, in order to
incorporate the longer term cost effects smoothly over the horizon rather than as lumpy
renewals at set intervals, some of which might be outside of the modelled horizon. The asset
life assumptions used for additional CM by value chain are displayed in Table 2.7.
12 Baker et al., “The Impact of Household Metering on Consumption: Further Analysis”, UKWIR 2003, page 81.
13 The interpretation of this is that a price increase of 1% in the current year leads to a decrease in demand by 0.1412% in
the following year.
14 Hansen (1996), “Water and Energy Price Impacts on Residential Water Demand in Copenhagen”, Land Economics,
p.66.
15 Ofwat (2011) "Exploring the costs and benefits of faster, more systematic water metering in England and Wales", p.8
16 Ofwat (2011) "Exploring the costs and benefits of faster, more systematic water metering in England and Wales", p.26-
28
17 Any CM on additional assets resulting from the modelled scenarios are therefore based on the assumed asset life for the
value chain and hence not based on an assessment of the actual cost of maintenance required.
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Table 2.7
Asset Lives for Additional Capital Maintenance by Value Chain
Water Value Chain Asset Life (years) Sewer Value Chain Asset Life (years)
Water Resources 80 Sewage Network 100
Raw Water Distribution 80 Sewage Treatment 20
Water Treatment 20 Sludge Treatment 20
Treated Water Distribution 80 Sludge Disposal 10
Source: NERA
The additional CM expenditure is calibrated to be equal to the NPV from incurring the full
cost of each additional item again at the end of the item’s asset life (with no CM in the
meantime).18
For example, the capital maintenance expenditure associated with a £100m
enhancement project with a 20 year asset life leads to additional CM expenditure of £6.57m
per year beginning in the year after the asset is built and lasting in perpetuity. The NPV of an
£6.57m annuity is equal to a single renewal of £100m twenty years in the future. This
additional CM includes renewal costs, so the additional CM is incurred in perpetuity on the
basis that the additional asset is maintained in a “like-new” state. This smoothed approach
makes changes to bill levels easier to interpret, as renewal expenditure is not incurred during
a single year.
2.5.7. Financial Parameters
The model uses financial parameters to calculate the weighted average cost of capital
(WACC). The model applies the WACC to the regulated capital value (RCV) to generate the
return on capital (e.g. WACC x RCV) component of the wholesale building block for each
wholesale value chain element.
The financial parameters used by the model include the asset beta, the cost of debt premium,
the notional level of gearing, the total market return and the risk-free rate. These parameters,
with the exception of the risk-free rate, are held fixed at the levels provided by Ofwat in the
Risk and Reward Guidance for PR14 (to be updated to final determination levels when
known). In effect, the model assumes that the level of investment risk associated with
investing in the water sector is constant relative to the market. Changes to the risk free rate
over time drive the bulk of the variability of the WACC, and a small amount of additional
variation occurs due to some policy scenarios’ assumed impacts on investors’ perception of
investment risk in the sector.
The risk-free rate for 2015-20 is held at the level provided by Ofwat in the Risk and Reward
Guidance for PR14 of 1.25%. After AMP6, the model reverts back to the long-run average
18 We use a discount rate of 3.5% to compute the NPV cost.
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risk-free rate for the UK from the Dimson, Marsh and Staunton database of 2.30%.19
The
model combines the risk-free rate with the debt premium to compute the cost of debt, and
applies the risk-free rate with the other Capital Asset Pricing Model (CAPM) parameters to
compute the cost of equity.
2.5.8. Driver Interdependencies
In this section, we discuss the key interdependency relationships that are integrated into the
model. In some cases, one variable has a causal effect on another without being affected by it
in return. In other cases a pair or group of variables have an impact on one another.
In order to establish which driver interdependencies are necessary we considered each
combination of drivers. We then set out for the TSG our view on all the non-negligible
relationships between drivers, which we provide for reference in the bulleted list below. We
do not discuss those where we have determined no relationship between drivers, except
where this warrants explanation.
Macroeconomic drivers are likely to have most impact on: water companies’ financing,
as was seen for example during the recent credit crisis, which led to favourable financing
costs in the industry affecting companies’ WACCs; on property growth as there is
empirical evidence that higher economic growth supports household formation and
reduces the size of average households; and on willingness to pay for environmental
goods. Macroeconomic drivers may also affect quality levels, as higher GDP per capita
may make customers more willing to pay for service improvements. Although
macroeconomic drivers can also impact population; meter penetration, the elasticity of
water demand; and regulation, by affordability concerns increasing regulatory scrutiny.
Macroeconomic conditions are also likely to affect NHH demand..
Demography will have the greatest impact on the demand for water and correspondingly
on leakage; and on companies’ physical assets, as new capacity is required in response to
changes in demand. Demography could also affect, although to a lesser extent, climate
change, as larger population leads to larger GHG emissions; and regulation, as for
example Ofwat cite future population growth as a core regulatory challenge. We note
that demography would have an effect on macroeconomic drivers as, for example,
increases in the young population generally increase GDP. However, we would expect
this effect to be sufficiently lagged in comparison with population’s effect on water use,
since it takes a long-time horizon for new births to become productive, that this does not
need representation in the model.
Regulation may have an effect on all core industry specific categories, since Ofwat and
the EA’s regulatory purview includes ensuring the general well-functioning of the system.
Quality levels would be expected to have its main impacts on regulation, as failing
quality standards would force Ofwat or the EA to change incentives or otherwise in their
regulatory arrangements; and company physical assets, as changing quality standards
would require investment in new assets and technology.
19 DMS (February 2014), “Credit Suisse Global Investment Returns Yearbook 2014”
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Financing may have a modest effect on regulation; and company physical assets, as
financial constraints may limit companies’ ability to invest in new assets and
infrastructure. It is possible that financing constraints may also impact on success on
quality levels and compliance. However we expect this to be covered by financing’s
effect on company assets, which in turn, as described below, affects quality levels.
Climate change has the potential to have a broad range of effects, many of which are
uncertain and difficult to quantify given the range of probabilities over various climate
change scenarios. We would expect the UK CP09 projections or later updates to be
useful in coordinating these relationships. In general, we would expect climate change to
affect water availability and demand, as environmental factors make existing sources
more vulnerable; macroeconomic drivers, if carbon abatement slows economic growth;
water use, if climate change campaigns convince people to reduce water consumption and
drive companies to reduce leakage; regulation, as Ofwat and the EA evolve their
regulatory approaches to deal with climate change; quality levels, as increased climate
volatility may make it more challenging to meet quality standards; and company physical
assets, if climate change requires companies to invest more in flood defences or storage
capacity to cope with droughts. There may be some harder to discern financial risks
affecting financing that result from climate change.
Implementing a built in dependency relationship between certain driver variables can make
the model more realistic and internally consistent. However, this internal consistency comes
at the cost of model tractability and complexity without necessarily adding significant
benefits. As a result, following TSG views especially from the specification workshop
meeting, this section sets out our model structure based on introducing the minimum degree
of complexity required to determine a projection of water bills.
Table 2.8 outlines our judgements on the modelled inter-dependencies. We incorporate the
interdependencies on a year by year basis without a lag.
Table 2.8
Interdependencies Imposed Between Model Drivers
Source: NERA
Of these four model drivers, the properties and greenness variable are modelled as fully
functional interdependencies. They are different from the other two variables in the sense
GDP
Industrial Demand
Affecting
Driver
Risk Free Interest Rates
Allowed Rate of Return
Greeness
Affected
Aspect Reasoning
Economic growth leads to higher rates of household formation
Properties
As incomes increase the demand for environmental goods increases
E conomic growth leads to increased production
Interest rates are an important component of the weighted average cost of capital
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that they are driver variables in themselves, and can be subject to their own sensitivities
independently from those that are associated with the variable with which they are linked. In
contrast, industrial demand is not a variable in the model that can be run at a higher or lower
sensitivity other than through GDP, which it assumed to track. The same can be said for the
allowed rate of return – it is conditional on the risk free rates (and also to a smaller extent on
certain policy effects) but cannot have sensitivities tested on it through any other channel.
2.5.8.1. GDP Impact on Property Growth
The modelled interaction between GDP and property growth is taken from the Department
for Communities and Local Government’s report, “Estimating Housing Need”. The report
estimates the elasticity of household formation with respect to individual real incomes to be
0.088, and we use GDP as a proxy for real incomes.
2.5.8.2. GDP Impact on Greenness
Forecasting future desire for environmental goods and services requires the underlying
determinants of environmental valuation to be identified. However, few studies have
investigated the specific effects of increases in income on water-related environment
improvements. A study by Pearce (2003)20
collates evidence on the income elasticity of
willingness to pay for improvement in environmental attributes. The evidence shows positive
income elasticity, suggesting that willingness to pay for environmental goods and services
rises with income, but the magnitude is inconclusive. The empirical evidence presented in
the paper states a range of 0.3-0.7 as the income elasticity of WTP for various types of
environmental improvements.
Another study, by Hökby and Söderqvist (2002) on the willingness to pay for various
environmental services in Sweden, provides a median elasticity of demand estimate of 0.46.21
This aligns with the Pearce findings, as well as those from Kahn and Matsusaka (1995)22
and
Kriström and Riera (1996)23
that this parameter is generally less than unity. We rely on the
Hökby and Söderqvist estimate of 0.46 with a range of +/-0.10 as the magnitude for the high
and low scenario sensitivities. We use +/-0.10 as this is equivalent to half of the range from
the Pearce study, which corresponded to a greater spectrum of environmental goods and
services.
20 Pearce (2003), “Conceptual Framework for Analysing the Distributive Impacts of Environmental Policies”
21 Hökby, S and Söderqvist, T (2002). “Elasticities of Demand and Willingness to Pay for Environmental Services in
Sweden, Paper to 11th Annual Conference of the European Association of Environmental and Resource Economists,
Southampton, 2001. The study was based on 21 elasticities for various environmental issues such as deterioration of
angling due to eutrophication, opportunities for moose hunting, access to forest areas, and preventing the extinction of a
type woodpecker in Sweden. Of the 21 cases, one was negative and four were greater than 1, while the remaining 16
fell within a range of 0.20 to 0.91. The median was 0.46 and the mean was 0.68, but we prefer the median as the mean
was biased upwardly by the four positive outliers, the highest of which was 2.68.
22 Kahn and Matusaka (1995), “Demand for Environmental Goods: Evidence from Voting Patterns on California
Initiatives”
23 Kriström, B. and Riera, P. (1996), “Is the Income Elasticity of Environmental Improvements Less Than One?”.
Environmental and Resource Economics 7, 45-55.
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We implement the greenness link through a relationship in the growth in income (i.e. GDP)
to demand for environmental improvement and hence company expenditure. There is an
underlying assumption that increased environmental demand effectively translates to higher
environmental spending through elicitation of customer views, for example using WTP
studies. Hence, an increase in income of 1% leads to an increase in companies’
environmental spending by 0.46% in the base case. This spending relates to water
expenditure on ecological improvements at abstractions sites and additional environmental
capex, and to wastewater expenditure on odour, WFD compliance, and additional
environmental capex. We include this effect in the baseline and provide the functionality for
the user to switch off this effect if desired.
2.5.8.3. GDP Impact on Industrial Demand
The relationship between the growth rates of GDP growth and industrial demand is assumed
to be 1:1 – that is, all else being equal, a 1% increase in GDP leads to a 1% increase in
industrial demand for water and sewerage.
2.6. Model Operation and Outputs
The model generates two sets of results based on the two scenario combinations selected by
the user in the Scenario Manager. For each scenario combination, the model retains:
A complete set of the input baseline data based on the datasets and data forecasting
methods as specified by the user in the Dataset Manager. The spreadsheet flags any data
items that have been overwritten by the user;
A complete set of the input scenario data after the model has applied the effects of
policy scenarios and cost driver sensitivities for the current scenario combination
selection;
RCV account for each company region disaggregated to the level of individual wholesale
and retail value chain elements;
Revenue requirements for each company disaggregated to the level of individual
wholesale and retail value chain elements; and
Average levels of bills for each of the three customer groups of metered household
customers, unmetered household customers and non-household customers.
The model uses these five output elements to create a set of standard graphical results that
compare modelling outputs from the two scenario combinations. The user can create
additional graphical outputs on the basis of the five output elements using standard Excel
tools.
In the subsections that follow, we describe the disaggregated modelling results in more detail.
2.6.1. Results
Results by company regions are available for the following categories of outputs:
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Average water and sewerage bill figures in nominal terms for the household (HH)
measured, HH unmeasured, and non-household (NHH) customer groups;
Average water and sewerage bill figures in real terms for each customer group, broken
down by value chain element or by service;
Expenditure forecasts including TOTEX by wholesale cost component or by value chain;
Average year RCV broken down across each of the wholesale value chains;
Wholesale revenue requirements; and
The proportion of population whose water, sewerage, or combined bills exceed a set
percentage of their income.
The results by river basins use an apportionment of company-regions across each river basin
based on data provide by the Environment Agency. Results are available for the following
categories of outputs:
Average water and sewerage bill figures in nominal terms for each customer group; and
Average water and sewerage bill figures in real terms for each customer group, broken
down by service.
Aggregated results for England and Wales are available for the following categories of
outputs:
Average water and sewerage bill figures in nominal terms for each customer group;
Average water and sewerage bill figures in real terms for each customer group, broken
down by service;
Expenditure forecasts including TOTEX by wholesale cost component or by value chain;
Average year RCV broken down across each of the wholesale value chains;
Wholesale revenue requirements;
The proportion of population whose water, sewerage, or combined bills exceed a set
percentage of their income;24
Industry-wide depreciation compared to Enhancement and CM additions to RCV;
Industry distribution input (DI) (i.e. the volume of water demanded); and
Industry water available for use (WAFU) (i.e. the volume of available water supply).
A complete set of output results are presented in Section 4.
24 Income forecasts by region are taken from the Family Resources Survey Almanac 2012-13. Used directly for the
period to 2020, after which an average regional growth rate is used. These forecasts are roughly consistent with the
baseline GDP growth used in the model. Forecasts are available at:
https://www.gov.uk/government/publications/family-resources-survey-2012-to-2013
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3. Model Specification
This model specification chapter provides details on the model’s technical structure,
including data sources and policy scenarios, and the selected model options that were chosen,
after consultation with the TSG.
This chapter is structured as follows:
Section 3.1 describes the model’s technical structure;
Section 3.2 describes the baseline variables and scenarios to be used in the model;
Section 3.3 lists and discusses data sources for the drivers;
Section 3.4 sets out the policy scenarios that will be able to be chosen in the model;
Section 3.5 describes the output specifications.
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3.1. Model Technical Structure
This section describes the technical structure including a general view of model
functionalities and relationships between model elements.
Figure 3.1 displays the technical structure of the spreadsheet model in terms of the five types
of worksheets. These include the user control interface (displayed in orange), the data input
sheets (in green), the input processing tabs (in purple), the internal computation sheets (in
light blue), and the outputs (in dark blue).
Figure 3.1
Technical Structure of the Model
Source: NERA illustration
Scenario Manager
PR14 DDsPR14 FDs
August Submissions
Final WRMPs
DataManager
Supply Curve
Draft WRMPs
Current Co Data
Trend Data
Macro Scenarios
Scenario Shock
Current Policy
Drivers and Policies
RCV Calculation
Regulatory P&L
Average Bills
Summary Outputs
Company Bills by VC
Bills -Company and
National
Dist Effects -National
Rev Building Blocks
Wholesale
Policy Effect Costs
Policy Effect Benefits
Combined Shocks
Forecast Co Data
Override
ScenarioInputs
Water and WW Totex
SDB Returns
Dist. Input and WAFU
Policy Effect Net
Assumptions
Baked in Policy
VC Depreciation
Bills - RiverBasin
Dist Effects -Company
Input Summary
RB Mapping
River BasinWFD Cost
WFD Costs
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A high-level description of the five sheet categories is:
User control interface – containing a switch for each driver variable setting and policy
option setting. There is a scenario selector with a database of initial plus user-defined
scenarios containing a complete set of switch identifiers to define each scenario;
Raw data inputs – providing an interface for generic data input and company-specific or
geography-specific data input. The generic data input are for data that are common across
all companies while the company data input are for data that are company specific. There
are separate sections for different baseline inputs (e.g. past datasets) and a range of delta
values corresponding to driver variable sensitivities;
Input processing tabs – compute the final dataset to be run through the model based on
the selections made in the user control interface and the set of assumptions.
Internal computation sheet – performing calculations to convert switch selections and
raw data inputs into final bill impact; and
Output visualiser sheet – recording the detailed modelling inputs and outputs for the
current baseline and one chosen alternative scenario, with a set of graphical tools to
compare chosen aspects of the results from the baseline with those of the chosen scenario.
Each of the categories is described in more detail in the following sections.
3.1.1. Control Interface
The scenario manager worksheet allows a user to define scenarios using two sets of
switches: one set of switches for choosing from an exhaustive list of the levels offered for
each driver variable, and another set of switches for choosing from an exhaustive list of the
available choices for each policy. The user is also able to assign one of the defined scenarios
as the comparison group (typically the model baseline) against which the user is able to
compare the results from one other scenario, including a high or low sensitivity.
The scenario manager automatically assigns a unique ID for each defined scenario to ensure
consistent referencing across the model. The unique ID contains the model version number,
which will be set out on a version control worksheet, in order to facilitate replication of the
results when the model is updated. The user will have the ability to assign a customised
name for each scenario. The unique ID appears on any output sheets for easy reference.
We expect there to be updates to input data, for example as Ofwat makes PR14 final
determinations and companies’ water resources management plans are updated. The control
interface therefore also contains a dataset manager tab to allow the user to specify which
data to run through the model. There is a switch to allow the user to alternate between data
inputs from different dates and sources.
3.1.2. Raw Data Inputs
The model block contains several worksheets which correspond to assumption parameters,
company data inputs, generic macro scenario data, and the scenario sensitivities around the
model’s driver variables. This section corresponds to the green tabs displayed in Figure 3.1.
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The assumptions tab contains populated input cells corresponding to items that NERA has
input, such as elasticity parameters, metering (cost of installation, volume impact, etc.), and
industry financial parameters. It also contains inputs on the policy effect range for the high
and low policy sensitivities. These are usually calibrated based on the effects in the high and
low case of the associated IA compared to the IA’s central estimate.
The assumptions sheet contains a set of cost allocation assumptions on a company-specific or
industry-wide basis. For the company-specific cost allocations we draw on the regulatory
accounts for 2013/14 to obtain the opex and capital maintenance costs by value chain element.
We allocate industry-wide splits of RCV by value chain element according to the WASC
industry-average regulatory account MEAVs for 2012/13. Some of the remaining cost items
are assumed to be well-represented by the value chain splits so we assign the same split as
that used to split the RCV into value chains. Other cost items are assigned based on our
judgement and input from the TSG. Table 3.1 and Table 3.2 display the industry-wide cost
allocations for water and sewerage respectively.25
Table 3.1
Water Cost and RCV Value Chain Allocations
Water Cost Item
Water Resources
Raw Water Distribution
Water Treatment
Treated Water
Distribution
Capex for growth and unallocated enhancement
10.86% 4.97% 6.69% 77.48%
Addressing low pressure 0.00% 0.00% 0.00% 100.00%
Meeting Lead Standards 0.00% 0.00% 20.00% 80.00%
Improving Taste / Odour / Colour
0.00% 0.00% 50.00% 50.00%
Ecological Improvements at Abstractions
100.00% 0.00% 0.00% 0.00%
Additional Environmental Capex
10.86% 4.97% 6.69% 77.48%
Additional Quality Driver 10.86% 4.97% 6.69% 77.48%
Leakage 0.00% 5.00% 0.00% 95.00%
Allocated growth opex 50.00% 0.00% 50.00% 0.00%
Allocated leakage opex 0.00% 5.00% 0.00% 95.00%
RCV allocation unfocussed 10.86% 4.97% 6.69% 77.48%
RCV allocation focussed 48.03% 0.96% 32.11% 18.90%
Source: NERA analysis of net MEAVs in 2012/13 Regulatory Accounts (all non-round numbers); and NERA
assumptions (round numbers)
25 The allocation assumptions have a small effect on the relative magnitudes of the value chain elements, but they have
very little impact on the overall bill levels. The company-specific opex and CM cost allocations are too numerous to
display here, but interested readers can view them on the model’s Assumptions sheet.
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Table 3.2
Sewer Cost and RCV Value Chain Allocations
Sewerage Cost Item Sewerage Network
Sewage Treatment
Sludge Treatment
Sludge Disposal
Capex for growth and unallocated enhancement
91.72% 6.99% 1.25% 0.03%
Sewer Flooding 100.00% 0.00% 0.00% 0.00%
Private Sewers 100.00% 0.00% 0.00% 0.00%
Odour26 0.00% 84.80% 15.20% 0.00%
WFD Compliance 10.00% 90.00% 0.00% 0.00%
Additional Environmental Capex 91.72% 6.99% 1.25% 0.03%
Sludge treatment and disposal 0.00% 0.00% 80.00% 20.00%
RCV allocation (unfocussed) 91.72% 6.99% 1.25% 0.03%
RCV allocation (focussed) 32.97% 56.42% 10.32% 0.29% Source: NERA analysis of net MEAVs in 2012/13 Regulatory Accounts (all non-round numbers); and NERA
assumptions (round numbers)
The raw data input worksheets also contain the company-specific data worksheets. As we
expect company data to evolve with current regulatory processes, we have created separate
company data input worksheets for each data update to ensure source- and time-consistency
of company inputs. The categories of inputs in a company data input worksheet include:
Quality variables – e.g. additional environmental capex, adoption of private sewers and
WFD compliance measures;
Customer variables – e.g. metering and number of properties;
WRMP-related variables – e.g. baseline water supply and demand balance, baseline
capex, economic level of leakage, RSA initiatives; and
Financials – e.g. RCV, PAYG, gearing, initial costs by value chain element.
The dataset manager (discussed in section 3.1.1) allows the user to select new company data
after it is added to the model. If the dataset manager is set to use the most recent data
available, then any changes to the input data automatically feed through the data processing
block provided it has been correctly dated on the input sheet.
The scenarios worksheet contains categories of inputs that are common across all companies.
Within the worksheet, there are separate sections for each different baseline and a range of
delta values for scenarios. The categories of inputs in this worksheet include:
Macroeconomic variables – e.g. interest rates, RPI/CPI inflation rates, GDP growth
rates, and population growth rates;
26 Odour was split across the sewerage and sludge treatment value chains only, according to their relative shares of the
unfocussed RCV split.
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Efficiency Assumptions – assumed cost-savings efficiency rates;
Enhancement expenditure – sensitivity ranges for the high and low cases of each
enhancement cost item (e.g. low pressure, sewer flooding, etc.) on a line-by-line basis;
and
Financial parameters – e.g. the time profile of the risk free rate in the baseline, high and
low cases and the level of notional gearing.
The model only processes the data for one scenario at a time. If the data change affects the
results for both the comparison and live scenario, then we advise the user to rerun the model
to simultaneously generate outputs for both the baseline and the selected input scenario for
the scenario comparison tool.
3.1.3. Input Processing
The input processing sheets are designed to gather the raw input data and convert it into the
forms required by the model. This section corresponds to the purple tabs displayed in Figure
3.1. There are three main strands of input processing: policy effects, driver shocks, and
company data forecasting. The policy effects sheets compute the policy effects that are
implicitly baked into the input figures, as well as separately computing the policy effects
selected in the scenario manager. Each policy can be chosen to have benefits-only, costs-
only, and net effects. The current policy tab then computes the net effect of the policies that
is above and beyond any effect that has already been implicitly included in the input data.
The driver shock sheets convert the driver variable and enhancement expenditure sensitivities
into shocks. These shocks are then fed into a compound shock sheet where the
interdependencies between them experience compounding effects. After the interdependency
effects, the shocks feed into the volume and cost calculation sheets described in section 3.1.4.
The final strand of input processing relates to compiling and creating a complete data set for
each company. This is done by aggregating the company data contained in the model and
forecasting any missing items or figures in later years of the horizon. Once the company
dataset is complete, the data feeds into a final sheet where it can be overwritten by the user
prior to feeding into the model’s internal computation sheets.
3.1.4. Internal Computation
The next model block further processes the input data in three different stages of computation.
This section corresponds to the pale blue tabs displayed in Figure 3.1. The model processes
one scenario at a time to reduce the model size and computation time.
3.1.4.1. Stage 1
The first stage involves making intermediary calculations to convert switch selections and
processed inputs into a current scenario dataset. This stage of internal computation is mainly
focussed within the Supply-Demand sheet. This stage is different for water than for sewerage
due to some service-specific cost drivers.
The first stage for water includes:
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Water demand projection: The model contains each company’s baseline water demand
projection over the modelling horizon. Each company’s baseline demand is based on the
dry year annual average demand as reported in the company’s WRMP. Where a selection
of a policy option or driver variable in the Scenario Manager influences water demand,
the model applies the corresponding delta values from the scenarios worksheet to the
baseline demand.
Water supply projection: The model contains each company’s baseline water supply
projection over the modelling horizon. Each company’s baseline supply is based on the
dry year annual average demand plus headroom as reported in the company’s WRMP.
Where a selection of a policy option or driver variable in the Scenario Manager influences
water supply, the model applies the corresponding delta values from the scenarios
worksheet to the baseline supply.
Water supply-demand balance: The model calculates each company’s water supply-
demand balance as the difference between the company’s supply and demand projections
from above. Where there is a supply-demand deficit, the model calculates the additional
capacity and cost required to restore the supply-demand balance to zero for the company
in the year of the deficit (as below).
Water totex projection: The model holds each company’s annual water totex
expenditure programme over the modelling horizon from two data sources. The first
source is Ofwat’s PR14 DDs which contain annual base totex expenditure (botex) and
annual enhancement expenditure for the period from 2015 to 2020. The model calculates
botex in subsequent years based on AMP6 Opex levels from the DDs and forward-
looking CM long-term trend data (derived from company submissions) subject to the
industry-wide assumed level of efficiency savings. Supply-demand enhancement totex in
subsequent years are based on long-run average incremental cost (LRAIC) from WRMP
multiplied by projected new volume of supplies required to balance water supply and
demand. An advantage of using LRAIC from WRMP is that it already includes capital
maintenance costs so no assumptions on capital maintenance on new assets are required.
The first stage for sewerage includes:
Sewage volume projection: The model contains each company’s baseline sewage
volume projection over the modelling horizon. The August Return submitted by
companies to Ofwat for PR14 contains a sewage volume projection for each company,
which forms the baseline in the model. Where the selection of a scenario in the Scenario
Manager influences sewage volume, the model applies the corresponding delta values
from the scenarios worksheet to the baseline volume.
Sewerage totex projection: The model contains each company’s annual sewerage totex
expenditure programme over the modelling horizon from two data sources. The first
source is Ofwat’s PR14 which contains annual base totex expenditure (botex) and annual
enhancement expenditure for the period from 2015 to 2020. The model calculates botex
in subsequent years by based on AMP6 Opex levels from the DDs and forward-looking
CM long term trend data subject to an industry-wide annual efficiency factor. Totex for
growth in sewage volume is based on average unit cost multiplied by projected volume
growth.
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3.1.4.2. Stage 2
The second stage involves collating all outputs from different processes in the first stage to
construct a full dataset for each company that the model uses to generate revenue
requirements in the third stage. This stage corresponds to the water and sewer totex and
returns tabs.
The worksheet contains forecasts of:
Water supply and demand balance for each company after exports and imports;
Macroeconomic assumptions including inflation and RPEs;
Sewage volume projection for each company;
Water totex projection and sewerage totex projection for each value chain element for
each company;
Number of billed metered and unmetered household customers for each company;
Number of billed non-household customers for each company; and
Financing assumptions including PAYG Ratio, runoff/depreciation rate, and WACC.
Key outputs from stage 1 and 2 are then fed into the scenario inputs tab where they will then
be drawn on during stage 3.
3.1.4.3. Stage 3
The third stage converts the full dataset in Stage 2 into revenue requirements, real and
nominal, disaggregated by company and element of value chain. There is one worksheet for
each type of calculation to reduce potential for errors and allow flexibility to amend the
model at later stage. Calculations in this stage include:
The RCV calculation: The RCV for wholesale water and wholesale sewerage in 2015 is
based on Ofwat’s PR14 final determination for each company. Annual additions to the
RCV over the modelled period are based on the projected water totex and sewerage totex
in the model multiplied by a PAYG ratio. Depreciation allowances are deducted from the
RCV to compute closing RCV for the year for the value chain element according to a
runoff rate (for the pre-2015 RCV) and a depreciation rate (for the post 2015 RCV).
The regulatory P&L: The calculation of each company’s revenue requirement is based
on the assumption of continued application of Ofwat’s building block approach, allowing
for a margin-approach to retail. We model a regulatory view of companies’ profits and
losses through eight separate P&Ls per company – one for each of the wholesale water
and sewerage value chain elements.
For each P&L, the worksheet first calculates pre-tax revenue requirements by drawing
from the RCV worksheet for PAYG, return on RCV, and depreciation. Then, it back-
solves for taxes using the imputed pre-tax revenues to avoid introducing tax-revenue
circularity. Adding the taxes to the pre-tax revenues yields total revenue requirement. The
model calculates revenue requirements in real terms (it later inflates them into nominal
terms in the average bills sheet). Revenue requirements are then aggregated across water
and sewerage value chains to arrive at company allowed revenue figures.
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Average Bills: The bills for household and non-household customers are based on initial
and projected company-specific ratios from households and non-households. The model
disaggregates average household bills by according to the following customer groups: HH
measured, HH unmeasured, HH weighted average of measured and unmeasured, NHH.
After the stage 3 computation is complete, the output sheets repopulate and generate
summary charts. The outputs are described in more detail and displayed in section 4.
3.1.5. Outputs Snapshot
The model runs twice for each model run; once to calculate the baseline results and another
time to calculate the input scenario results. After each run the model will capture the results
and underlying input details in a separate output sheet.
An Output sheet for each run contains a section for the regions of each of the ten water and
sewerage companies (WaSCs) and each of the nine independent water-only companies
(WOCs). Each output section contains projected water totex and sewerage totex;
disaggregated building blocks for wholesale water, wholesale sewerage, retail water and
retail sewerage; disaggregated RCV for wholesale water and wholesale sewerage; build-up of
water bills and sewerage bills across contributions from different elements in the value chains.
The output visualiser worksheets draw from the output sheets to aggregate and present the
results in easily navigable summary tables and charts. The model contains functionality to
allow the user to aggregate the results by industry, company region or, potentially, river basin
areas. Several model results are displayed in section 4.
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3.2. Baseline Model Variables and Scenario
This section describes the variables used to form the baseline projections.
3.2.1. Baseline Variables
3.2.1.1. Baseline Supply and Demand
We use the supply and demand data from companies’ WRMP “Water Resource Planning
Guideline supply-demand workbook” tables (hereafter referred to as WRPG tables). The
supply figures provided in these tables are based on each company’s best projection of the
most likely resource availability conditions over the modelled period and its investment
programme. It therefore incorporates the companies’ view on climate and on new legal or
regulatory requirements. However we would expect the Final Determination figures for
demand and for supply/demand totex to be used when available for the period up to 2020.
The WRMP demand projections embody the companies’ best view on longer-run population,
properties, industry demand, leakage, water imports and exports, reductions to restore
sustainable abstractions, and metering - all of which have annual figures going out to 2040.27
3.2.1.2. Baseline Costs and Financing
To model company costs we rely on the draft determination (DD) figures for the period up to
2020, expecting these to be updated when the final determinations become available. We
then bring these figures forward using long term trends based on a set of industry data
provided for this purpose.
The baseline financing cost projections required by the model are the cost of equity (COE),
cost of debt (COD), PAYG ratio, runoff/depreciation ratio, notional gearing, and a simplified
tax rate. This set of assumptions enables us to provide a sufficient but minimum financial
representation of companies for the purpose of estimating water bills.
The PAYG ratio and financial parameters in the business planning process and in the DDs are
used in the model. The PAYG ratio from 2015-2025 is taken directly from the DD data
published by Ofwat. After this point, we model the ratio based on the approximate company-
specific total opex + capex costs incurred (which will be adjustable to reflect changes in the
proportion of capex/opex spending changes across each five year period). As requested at the
first technical steering group (TSG) meeting, the model is based on notional rather than
company specific financial figures.
3.2.2. The Baseline Scenario
The baseline scenario represents a central view on each of the variables that influence supply,
demand, and costs. Some of these central figures are contained in the WRPG tables (such as
population, properties, supply, etc.) whereas others are available from the DDs, or were
requested from the companies or the EA.
27 See for example South West Water’s data tables, available at:
https://www.southwestwater.co.uk/media/pdf/7/s/Water_Resources_Management_Plan_Tables_June_2014.pdf
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The long-term baseline scenario for the model will be the one set out as the baseline in the
latest WRMPs. In this a prime measure of demand is the dry year annual average demand,
which is a period of low rainfall and unconstrained demand following WRMP practice.28
Table 3.3 summarises the demand measures that the EA requires companies to consider
and/or publish. We represent the dry year annual average demand as it is the basis for a
company’s WRMP investment needs. However, the weighted annual average demand is also
in the plan and is closer to the basis for the company’s revenue forecast when Ofwat sets
price limits. 29
We draw on the dry year demand and the weighted annual average demand
scenarios for the supply/demand capacity investment and the annual cost elements of the
model respectively.
Table 3.3
Demand Measures Published in WRMPs
Scenario Calculate Publish in the plan
Dry year annual average Yes Yes
Dry year critical period Optional – company dependant
Optional – only if there is a deficit
Normal year Yes No
Weighted annual average demand Yes Yes
Utilisation Yes- only if an option is required
Yes – only if an option is required
Source: EA 2012 Water Resource Planning Guideline
The latest WRMPs contain data sources going out to 2040. For the period beyond that year
we forecast the variables based on either the GDP growth rate or the population growth rate.
3.2.2.1. Baseline (Central) Values for All Drivers
Any changes to the values of the baseline variables, to reflect external variable sensitivities or
policy change scenarios, are reflected within the model as ‘delta changes’ to volumes of
supply or demand, or shifts in the cost of service provision. As a result, all variables have a
“central” value as part of the baseline. We try to ensure all baseline variables are internally
consistent in all years. Wherever possible, the central values are taken from the assumptions
stated in the WRMPs or BPs/DDs.
The EA’s guidance documents set out planning principles that companies should use as the
underlying conditions for their WRMP baselines. These principles were not prescriptive to
the point of setting out firm assumptions on GDP growth and inflation that companies should
use. As a result, companies have taken a range of approaches to their baseline scenarios.
For our baseline model inputs we use national estimates from reputable sources. We provide
the functionality for the model to allow the user to specify alternative baseline inputs so that
28 EA June 2012 Water Resource Planning Guideline, page 21
29 EA June 2012 Water Resource Planning Guideline, page 22
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different assumptions can be used - including baselines that are based on regional
conditions.30
We discuss this issue further in the following section.
3.2.3. Policy Assumptions in the Baseline
Some of the policy effects described in this document are also contained in the baseline. We
define the baseline as containing all of the existing regulatory and statutory elements that are
currently in place as well as the policies that Defra views as likely to occur as these are
implicitly factored into industry views and long term figures.
Reforms changing the arrangements governing retail competition for all NHH customers in
England from 2017 are contained in the Water Act and the baseline.31
We understand that
upstream competition reforms are not embodied in the current legal framework. However,
we include Defra’s preferred option for upstream competition in the baseline. The baseline
policies also include assumed effects of PR14 incentive mechanisms and regulatory
mechanisms which apply to 2020 and beyond if not specifically changed by policy switches.
3.3. Sources for Driver Variables
This section describes the data that we propose to use for each driver variable.32
In Table 3.4 and Table 3.5 we list the drivers by category and list our data source for the
baseline, high and low cases. The table shows that we typically obtained this information
from Ofwat or EA documents or company DDs. or accounts. In some cases stakeholders
were asked to provide information or make consistency checks. In others cases simple and
transparent assumptions were made, especially for long term projections. We discussed these
proposals with Defra, the EA, company representatives, and Ofwat during a technical
steering group meeting on June 19th
2014.
We define each variable’s sensitivities in terms of its effect on average bills. Where the user
is uncertain of the direction of a variable’s sensitivity, we suggest that they consult the
“scenarios” sheet and compare the corresponding variable to its base values.
30 For example, Severn Trent’s draft WRMP is based on regional forecasts from Experian’s standard UK Regional
Planning Service (RPS). This source provides detailed regional data and forecasts for the period 1982-2026.
31 Retail competition in Wales continues to apply only those NHH customers purchasing over 50 megaliters per day, as
this was not changed by the Act
32 A driver variable is defined as either an exogenous variable (e.g. GDP growth) that affects items in the model or an
endogenous variable that is influenced by a process within the model but also has an effect on some other aspects of it
(e.g. leakage).
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Table 3.4 - Driver Variables and Data Sources (1 of 2)
Source: NERA
Variable Case 2015-2019 2020-2039 2040-2049 Notes
Low PWC low case PWC low case; then base - 0.3% Baseline - 0.3%
BasePWC Scenario Assumptions -
Appendix 1
PWC Appendix 1 2020-21; then OBR
long run average 2022-39OBR long run average
High PWC high case PWC low case; then base + 0.4% Baseline + 0.4%
Low PWC low case PWC low case; then base - 0.5% Baseline - 0.5%
BasePWC Scenario Assumptions -
Appendix 1
PWC Appendix 1 (2020 to 2021);
then OBR long run average 2022-39OBR long run average
High PWC high case PWC low case; then base + 1.5% Baseline + 1.5%
LowDECC E&E projections- low
prices
DECC E&E - low prices 2020-30; then
DECC E&E - low price average rate
DECC E&E projection - low price average
rate
Base DECC E&E - ref pricesDECC E&E - ref prices 2020-30; then
DECC E&E - ref price average rateDECC E&E - ref price average rate
HighDECC E&E projections- high
prices
DECC E&E - high prices 2020-30; then
DECC E&E - high price average rate
DECC E&E projection - high price average
rate
Low Same as Base Baseline -0.5% Baseline -0.5%
Base0% Assumption (assumed to be
included in DD figures)0.5% Assumption 0.5% Assumption
High Same as Base Baseline +0.5% Baseline +0.5%
Low Same as Base Baseline -0.5% Baseline -0.5%
Base0% Assumption (assumed to be
included in DD figures)0.5% Assumption 0.5% Assumption
High Same as Base Baseline +0.5% Baseline +0.5%
Low Baseline -0.5% Baseline - 1% Baseline - 1%
Base Ofwat R&R figure (1.25%) DMS long run average (2.3%) DMS long run average (2.3%)
High Ofwat R&R (Comp Max 2.1%) Baseline + 1% Baseline + 1%
Low Same as Base Same as Base Same as Base
Base Ofwat DDs Rolled the 2019 DD Figure Rolled the 2019 DD Figure
High Same as Base Same as Base Same as Base
Low Baseline -1% Baseline -1% Baseline -1%
Base WRMP tables row 51FP WRMP tables row 51FP Extrapolation based on WRMP row 51FP
High Baseline + 1% Baseline + 1% Baseline + 1%
Low Baseline -1% Baseline -1% Baseline -1%
Base WRMP tables row 48FP WRMP tables row 48FP Extrapolation based on WRMP row 48FP
High Baseline + 1% Baseline + 1% Baseline + 1%
We apply a compounding growth rate from
2031-2050 based on the average growth rate
(2.82% in base case). Relative to RPI
RPE: Capex
Assumption was cross checked against COPI
yearly (1955-2012) index for all new
constructions, repair and maintanence.
Relative to RPI
The distance from baseline in the low/high
cases from 2020-49 is based on the range
provided by Experian for UK GDP as reported
by SVT's dWRMP
The distance from baseline in the low/high
cases from 2022-49 is an assumption based on
recent observed high/low RPI rates over 10
year historical periods. RPI (CHAW) 1987-2013
The ranges are 1% above and below the
baseline but the user will be able to input any
other desired range
The ranges are 1% above and below the
baseline but the user will be able to input any
other desired range
Based on Ofwat Draft Determinations.
Company Financial Models - Executive
Summary Tab, Income Statement
From 2020 we use Dimson, Marsh and
Staunton long run average. DMS (February
2014), “Credit Suisse Global Investment
Assumption was cross-checked against a long
term observed Opex RPE trend based on three
elements: energy, labour, and materials cost.
Materials - ONS PPI (1996-2013) for industry
"Collection, purification and distribution of
water" (MC3U); Labour - ASHE annual survey
of hours and earning E&W, full time workers,
annual gross pay from 1998-2013; Energy -
DECC (as above). Relative to RPI.
GDP growth
rates
RPI Inflation
Input Prices:
Energy
Tax
Risk Free
Rates
RPE: Opex
Properties
Population
Integrated Final Report Model Specification
NERA Economic Consulting 34
Table 3.5 - Driver Variables and Data Sources (2 of 2)
Source: NERA
Driver Variable Case 2015-2019 2020-2039 2040-2049 Notes
Low Baseline -5% Baseline -5% Baseline -5%
Base WRMP tables row 40FP WRMP tables row 40FP Extrapolation of WRMP row 40FP
High Baseline +5% Baseline +5% Baseline +5%
Low Baseline -20% Baseline -20% Baseline -20%
Base WRMP table row 8.2BL WRMP table row 8.2BL Extrapolation of WRMP row 8.2BL
High Baseline +20% Baseline +20% Baseline +20%
Low Baseline -20% Baseline -20% Baseline -20%
Base WRMP table row 13FP WRMP table row 13FP Extrapolation of WRMP row 13FP
High Baseline +20% Baseline +20% Baseline +20%
Low Same as Base N/A N/A
BaseOfwat 2014/15 year end value;
then internal model calculationInternal model calculation Internal model calculation
High Same as Base N/A N/A
Low Same as Base Same as Base Same as Base
Base Ofwat DDsOfwat DDs from 2020-25; then
modelled as (IRE + Opex)/TotexModelled as (IRE + Opex)/Totex
High Same as Base Same as Base Same as Base
Low Baseline -1% Baseline -1% Baseline -1%
Base Ofwat DDsOfwat DDs from 2020-25; then
based on AMP7 asset livesBased on AMP7 asset lives
High Baseline +1% Baseline +1% Baseline +1%
Low Baseline -0.5% Baseline -0.5% Baseline -0.5%
Base Ofwat R&R (nominal) Ofwat R&R (nominal) Ofwat R&R (nominal)
High Baseline +0.5% Baseline +0.5% Baseline +0.5%
Low Baseline -1% Baseline -1% Baseline -1%
Base Ofwat R&R (real) Ofwat R&R (real) Ofwat R&R (real)
High Baseline +1% Baseline +1% Baseline +1%
Low Same as Base Baseline -1% Baseline -0.5%
Base0% Assumption (assumed to be
included in DD figures)-1% Assumption 2020-25 -0.5% Assumption 2025-49
High Same as Base Baseline +0.5% Baseline +0.5%
Low Baseline -1% Baseline -1% Baseline -1%
Base WRMP tables row 45FP WRMP tables row 45FPExtrapolation based on WRMP row
45FP
High Baseline + 1% Baseline + 1% Baseline + 1%
NHH Retail MarginsThe user is able to input any other series of values
including a series that changes over time
The user is able to input any other series of values
including a series that changes over time
The user is able to input any other series of values
including a series that changes over time
We model the high and low sensitivities as a level step
up or down.
RSA Reduction of
Water Availability
HH Retail MarginsThe user is able to input any other series of values
including a series that changes over time
Cost efficiency
improvements
The assumptions used for cost efficiency savings follow
from discussions with the TSG - See cost efficiency
sensitivity section for more details.
Metering The ranges are 1% above and below the baseline but
the user will be able to input any other desired range.
Value of RCV
We obtained the 2014/15 year end RCV value by
company from the Ofwat RCV update spreadsheets
available on the Ofwat website. We split the RCV
according to the relative share of company value chain
net MEAVs using the 2012/13 regulatory accounts.
Depreciation
We modelled value-chain specific depreciation rates
that arrive at totals that are consistent with Ofwat DD
depreciation rates by service. The user can
alternatively use the DD service-specific depreciation
PAYG The user is able to input any other series of values
including a series that changes over time
Climate Change
Reduction of Water
Availability
Leakage
Integrated Final Report Model Specification
NERA Economic Consulting 35
For ease of reference, Figure 3.2 shows the key input assumptions underlying the baseline
case. The model produces an overview sheet summarising the key inputs (macroeconomic
and long term cost efficiency savings assumptions) as well as additional displaying the WFD
inputs that feed into the modelled scenario so that the user can check them for plausibility and
internal consistency.
Figure 3.2
Baseline Inputs
Source: NERA
The remainder of this section sets out the data sources that we have identified for use as the
central variable values or as the basis for informing the magnitude of the driver sensitivities.
We set out the drivers according to the following categories:
Macroeconomic drivers;
Demographic data;
Regulation and statute;
Quality levels;
Financing; and
Climate Change.
Water quality levels are not modelled and are assumed to be maintained at acceptable levels
through Ofwat’s regulatory mechanisms and incentives. The model also assumes that
sewerage capacity is always maintained.
-2.5%
-1.5%
-0.5%
0.5%
1.5%
2.5%
3.5%
4.5%
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
Gro
wth
Rat
e %
GDP Growth RPIReal Price Effect: Opex Real Price Effect: CapexCost Efficiency Incentive Effect CPI (if applicable)Risk-free Rate
Integrated Final Report Model Specification
NERA Economic Consulting 36
3.3.1. Macroeconomic Variables
3.3.1.1. GDP
Company WRMPs appear to use GDP growth as a factor in determining non-household
demand. Several companies’ assumptions on GDP growth were not available from public
sources. Of those that were, Severn Trent’s draft WRMP update to Appendix G provided a
transparent presentation of GDP assumptions based on regional forecasts from Experian.
These regional forecasts were consistent with Experian’s UK forecasts, which are displayed
in Table 3.6.
Table 3.6
Experian - UK GDP Growth Forecasts33
Source: Severn Trent dWRMP - Appendix G
Based on our review of the available data, we propose to use the central GDP assumptions
from the Ofwat Business Plan GDP figures published in PWC’s Economic Assumptions for
PR14 Risk Analysis for the baseline from 2015 to 2022, and on the ONS long-run average for
the period from 2023 to 2050. We inform the magnitude of the range for the long-run high
and low cases of GDP from the 2020-40 Experian UK forecasts. Our GDP growth forecasts
are displayed in Table 3.7. Users can optionally input their own GDP assumptions which
they can design following cyclical or any other desired patterns.
Table 3.7
Proposed Model GDP Growth Forecasts (%)
2015 – 2021 2022 - 2050
Low PWC low GDP
estimates Ranging from -0.1% to 2.2%
OBR LR Av minus 0.3% 2.0%
Central PWC central GDP
estimates Ranging from 1.7% to 2.2%
OBR LR Average 2.3%
High PWC high GDP
estimates Ranging from 2.0% to 3.1%
OBR LR Av plus 0.4% 2.7%
Sources: (1) BP estimates from PWC Economic Assumptions for PR14 risk analysis (2) OBR historical long-run
average 1982-2013 for: Central Case for 2022-2050; (3) Experian for: High/Low Ranges for 2021-2050
3.3.1.2. Inflation
As was the case with GDP growth, several companies’ draft WRMPs assumptions on the
magnitude of RPI growth were not available from public sources. Of those that were
33 These estimates were published in Severn Trent’s updated draft WRMP Appendix G, page 62. Available at:
http://www.severntrent.com/draft-wrmp-documents
2013-2020 2020-2040
Central High Low Central High Low
GDP Growth Rate (%) 2.0 2.5 1.6 2.4 2.8 2.1
Integrated Final Report Model Specification
NERA Economic Consulting 37
available, Severn Trent’s draft WRMP update to Appendix G provided a transparent
presentation of RPI assumptions based on Experian’s UK estimates, which can be seen in
Table 3.8.
Table 3.8
Experian - UK RPI Forecasts34
2013-2020 2020-2040
Central High Low Central High Low
RPI Inflation Rate (%) 3.5 5.3 1.2 2.9 5.0 0.1
Source: Severn Trent dWRMP - Appendix G
We use the Ofwat Business Plan RPI figures published in PWC’s Economic Assumptions for
PR14 Risk Analysis for the baseline to 2021. We then use the OBR long-run average (3%)
for the remaining duration of the modelling window.35
These estimates are reasonably close
to the central Experian estimates set out above.
Rather than relying on the high and low Experian sensitivity values for RPI, we instead
inform the range for the long-term high and low estimates of inflation using an assumption
calibrated to approximate the highest/lowest 10 year periods of inflation during the period
from 1987-2013. Our proposed low, central, and high forecasts are displayed in Table 3.9.
We also enable the model to allow the user to specify any other RPI growth forecast.
Table 3.9
Proposed Model RPI Forecasts (%)
2015 -2021 2022 -2050
Low BP low RPI estimate OBR LR Average –0.5%
Central BP central RPI estimate OBR LR Average
High BP high RPI estimate OBR LR Average +1.5%
Sources: (1) BP estimates from PWC Economic Assumptions for PR14 risk analysis (2) OBR: Central Case for
2021-2050
3.3.1.3. Input Prices
Real price effects (RPE) represent the change in particular water input prices relative to the
level of inflation in the economy as measured by the retail price index (RPI). A review of
companies’ WRMPs, draft PR14 BPs, and Ofwat’s BP data table templates suggests that
input price forecasts are not provided by the companies in any documents in the public
domain. Instead, we draw upon reputable sources in order to construct indices of real prices
effects (RPEs), for opex and for capex. These RPE figures are net of RPI, so that they are in
34 These estimates were published in Severn Trent’s updated draft WRMP Appendix G, page 62. Available at:
http://www.severntrent.com/draft-wrmp-documents
35 We calculate the OBR RPI long-run average as the compound annual growth rate from 2000 to 2013.
Integrated Final Report Model Specification
NERA Economic Consulting 38
addition to (and therefore consistent with) any modelled RPI scenarios. We determine these
indices based on the following sources:
Energy price inflation (for opex): The Department for Energy and Climate Change
(DECC) annually publishes long-term projections of growth in energy prices. The most
recent edition (September 2013) contains energy price projections up to the year 2030.
See Figure 3.3 below. We draw on this publication for the energy price forecasts used to
determine the energy price elasticity effects, as well as using it to cross-check the OPEX
RPE assumptions used in the model;
Figure 3.3
DECC Energy Price Scenarios
Source: DECC Updated Energy & Emissions Projections - September 2013
Materials inflation (for opex): The ONS publishes the producer price index (PPI) for
“Collection, purification & distribution of water” industry group. To cross-check the
OPEX RPE assumptions used in the model’s baseline we use a long-run average of
historical PPI inflation for the industry group as a proxy for future inflation in prices of
materials;
Labour price inflation (for opex): The ONS publishes the results of its Annual Survey
of Hours and Earnings (ASHE). The Survey contains estimates of the rate of growth in
the earnings of employees in different regions of the UK which we use to cross-check the
OPEX RPE assumptions used in the model’s baseline we in order to proxy for regional
labour price inflation; and
Construction price inflation (for capex): The Department for Business, Innovation and
Skills (BIS) quarterly publishes construction output price indices (COPI). To cross-check
the CAPEX RPE assumptions used in the model’s baseline we use a long-run historical
compound average rate of change in the COPI as a proxy for future inflation in prices of
construction.
0
1
2
3
4
5
6
7
8
9
10
Wh
ole
sale
Ele
ctr
icit
y P
rice (
£ / k
Wh
)
Reference prices Low Prices High Prices
Integrated Final Report Model Specification
NERA Economic Consulting 39
We apply the growth rates to the relevant costs items with the opex cost indices applying to
the approximate share of opex corresponding to energy, materials, and labour.36
To determine the high and low sensititivities, we cross-checked our assumption of +/- 0.5%
relative to the baseline for the Opex and Capex RPEs respectively by examining the 10, 15
and 20 year periods coinciding with the highest and lowest COPI price inflation.
3.3.1.4. Interest Rates
Interest rates influence the allowed rate of return set by Ofwat based on the weighted average
cost of capital approach employed at each price review. We propose using:
Figures specified by Ofwat that underpin the company BPs and DDs for the period from
2015 to 2020; and
Long-run historical average for the period from 2020 to 2050: This forecast is based
on taking a very long term historical average of the real rate of return on UK risk-free
assets to avoid introducing market volatility into the forecast. We use the long-run
historical average of 2.3% from the Dimson, Marsh and Staunton (DMS) database, which
provides estimates for the period from 1900 to 2013. We add the PR14 debt premium.
3.3.1.5. Effective Taxation
The TSG agreed that detailed elements of taxation are not required within the scope of the
model. Instead, we use PR14 effective rates to 2020, then an approximation of the actual tax
rates faced by companies. To do this we take an average of the proportion of actual taxes
paid during AMP6.
3.3.2. Demographic Variables
3.3.2.1. Population and Property Data
The WRPG tables include projections for population to 2040 which we use as the basis for
the model. In order to determine the baseline population inputs from 2040 to 2050, we use
the compound annual growth rate from the preceding ten years. Population shocks affect
household size which has a knock on effect on overall consumption, even in the absence of
property growth.
Similarly, we draw on the WRMP figures for our properties figures from 2015 to 2040. We
then grow properties in line with GDP growth for the period from 2040 to 2050, given that
household formation has been shown to be positively correlated with economic growth.
Additional properties are assumed to consume the same volume of water as the average
consumption for that type of property.
36 Weightings for Opex RPE are: 10% for energy, 12% for labour, 6% for materials, and the remaining 72% is assumed to
grow with RPI (i.e. with no RPE effect). These are based on the expenditure incurred by a sample of the larger
companies (we used Anglian, Yorkshire, Thames, Severn Trent, Southern data) during 2010/11 (JR data)
Integrated Final Report Model Specification
NERA Economic Consulting 40
3.3.2.2. Metering Penetration Rates and Leakage
The WRPG tables provide annual forecast data on meter optants, compulsory metering, and
selective metering as well as the number of metered properties which we propose to use in
the baseline. Users can build sensitivities based on simple and transparent assumptions (e.g.
increase in expected metering by 1%, saturated at 95%) or based around established views on
rates of metering uptake. We base the costs and effects of metering on demand on Ofwat’s
“Exploring the costs and benefits of faster, more systematic water metering in England and
Wales”.37
We allow the user to test sensitivities around reducing leakage beyond the sustainable
economic level of leakage (SELL).38
The effect of this reduction beyond the SELL, by
definition of the SELL, is to reduce the level of distribution input while raising the cost of
service provision. We estimate the cost of leakage reductions using each company’s capacity
addition costs from their WRMP.
3.3.3. Capex Expenditure Categories
We set out below the list of capex cost drivers or categories used by Ofwat in its PR14
methodology and in the August submissions. To estimate cost by enhancement category, we
draw on more granular longer term non-public data received by Defra directly from
companies on a confidential basis. We received this data from ten different companies
covering varying durations from 2015 to 2050. We drew on these submissions to determine
reasonable long-term average industry forecasts which we then applied to all companies
(including those which submitted long-term data). The cost categories used in the model for
water and sewerage are displayed in Table 3.10 and Table 3.11.
37 See http://www.ofwat.gov.uk/future/customers/metering/pap_tec201110metering.pdf
38 The SELL is the lowest service-cost level by construction and may change as innovation and input prices change over
time. As a result, maintaining the SELL level of leakage may entail some leakage reductions or increases that have a
cost impact.
Integrated Final Report Model Specification
NERA Economic Consulting 41
Table 3.10
Water Service Capex Cost Drivers
Defra Water Bills Model Ofwat PR14 BP Submissions – Table W3 Lines
1. Capital maintenance 4. Maintaining the long term capability of the infrastructure assets
5. Maintaining the long term capability of the non-infrastructure assets
2. New development and growth
9. New developments
3. Addressing low pressure 2. Addressing low pressure
4. Improving taste/colour/odour
3. Improving taste/colour/odour
5. Meeting lead standards 6. Meeting lead standards
6. Enhancement to SDB 7. Enhancements to the supply / demand balance (dry year critical / peak conditions)
8. Enhancements to the supply / demand balance (dry year annual average conditions)
7. W WFD Related 1. Making ecological improvements at abstractions (habitats directive, SSSI, BAPs)
10. Investment to address raw water deterioration (THM, nitrates, Crypto, pesticides, others)
8. Additional environmental capex
13. NEP - Flow monitoring at water treatment works
14. NEP - Drinking water protected areas
9. SEMD 12. SEMD
10. Resilience 11. Resilience
Source: NERA based on Ofwat PR14 BP Data Tables
Integrated Final Report Model Specification
NERA Economic Consulting 42
Table 3.11
Sewerage Service Capex Cost Drivers
DefraWater Bills Model Ofwat PR14 BP Submissions – Table S3 Lines
1. Capital maintenance 4. Maintaining the long term capability of infrastructure assets 5. Maintaining the long term capability of sewage treatment works 6. Maintaining the long term capability of sewage pumping stations 7. Maintaining the long term capability of management and general
assets 8. Maintaining the long term capability of all other non-infrastructure
assets
2. New development and growth 23. New development and growth
3. Sewer flooding 27. Reduce flooding risk for properties
4. Private sewers 28. Private sewers
5. First time sewerage 1. First time sewerage
6. Sludge treatment and disposal
2. Sludge treatment & disposal - enhancement 3. Sludge treatment & disposal - base
7. Odour 22. Odour
8. WFD compliance 10. NEP - Event Duration Monitoring at intermittent discharges 13. NEP - Storage schemes to reduce spill frequency at CSOs, storm
tanks, etc. 14. NEP - Chemicals removal pilot/full-scale demonstration plants 15. NEP - Groundwater schemes 16. NEP - Investigations 17. NEP - Nutrients (N removal) 18. NEP - Nutrients (P removal at activated sludge STWs) 19. NEP - Nutrients (P removal at filter bed STWs) 20. NEP - Reduction of sanitary parameters 21. NEP - UV disinfection (or similar)
9. Additional environmental capex
9. NEP - Conservation drivers 11. NEP - Flow monitoring at sewage treatment works 12. NEP - Monitoring of pass forward flows at CSOs
10. SEMD 27. SEMD
11. Resilience 26. Resilience
12. Company specific -
Source: NERA based on Ofwat PR14 BP Data Tables
For the AMP6 period, we distributed the totex figures into these cost categories according to
the average proportionate share of those companies that submitted confidential data, while
ensuring that the corresponding totex figures were consistent with those from the DDs. For
expenditure in the period from 2020 to 2050, our approach to using the longer term data was
to calculate an industry level trend based on the companies who have provided data, and
apply that an industry-wide trend to all companies in the model. For each enhancement
capex item, in each AMP, we calculate the submitted confidential data from that item as a
proportion of AMP6 opex, and use that proportion for each subsequent AMP period. Capital
maintenance is calculated as a percentage change on expenditure over the previous AMP
3.3.4. Cost Efficiency Effects
Over the history of the industry since privatisation, companies have increasingly become
more cost efficient. We model this cost efficiency effect based on assumptions calibrated to
reflect diminishing levels of efficiency improvement over time. We assume modest levels of
improvement during AMP7, with lower levels of improvement thereafter to reflect the
increasing maturity of the industry since privatisation. For AMP6 we assume that the Ofwat
Integrated Final Report Model Specification
NERA Economic Consulting 43
DDs reflect the perceived efficiency improvements that are likely to occur, so we do not
assume any above and beyond those implicitly contained in the input data for this period.
In the baseline, we assume that companies experience a cost savings reduction of 1% per
annum (with a compounding effect) on all of their expenditure from 2020 to 2025, and 0.5%
thereafter. In the high/low case we assume a 0.5%/2% improvement per annum for 2020 to
2025 and 0%/1% for the remainder of the horizon. These assumptions are presented in Table
3.12.39
Table 3.12
Cost Efficiency – Strength of Effects
2015-20 2020-25 2025-49
Low 0% 2% 1%
Baseline 0% 1% 0.5%
High 0% 0.5% 0%
Source: NERA Assumption based on TSG Discussions
These compounding levels of cost efficiency assumptions can have large effects on company
expenditure, particularly during the later stages of the horizon. The model also enables users
to experiment with any custom levels of assumed efficiency effects if desired.
3.3.5. Financial Parameters
Finance parameters feed into the calculation of companies’ projected allowed revenues which
drive final bills. These finance parameters include:
The allowed rate of return (ARoR): ARoR is estimated as the PR14 figure to 2020 then
the projected cost of capital. For wholesale elements ARoR is multiplied by companies’
RCVs to derive the returns building block. The approach requires projections of the cost
of debt, cost of equity, and gearing. We use the parameters in Ofwat’s Risk and Reward
publication as a baseline to 2020,40
and update the parameters to reflect the projected
market cost of debt (based on changes to the risk free rate, while holding the debt
premium constant) where relevant for later years;
Value of RCV: Ofwat publishes companies’ opening RCVs as part of the draft
determinations. We draw on these opening values, and update them using the PR14 RCV
updating method after that (i.e. add 1-PAYG, subtract runoff and depreciation);
Retail margins: Ofwat provided guidance on retail net margins for PR14 in its Risk and
Reward publication. Ofwat’s views of the appropriate net margins, which we will use as
39 The model considers these effects as negative values to represent their effect of reducing costs. We show them as
positive here to avoid confusion for the reader.
40 Ofwat ( January 2014), “Setting Price Controls for 2015-20 – Risk and Reward Guidance”
Integrated Final Report Model Specification
NERA Economic Consulting 44
the baseline, are 1% for household retail and 2.5% for non-household retail.41
We hold
these constant over the modelling horizon; and
PAYG ratio, and runoff and depreciation rates: These parameters affect the profiling
of companies’ allowed revenues, and therefore final bills to customers. We draw from
companies’ proposed PR14 PAYG ratios, RCV run-off rates, and depreciation rates to
2025 based on the figures available in the DDs. From 2025 onwards we model the
PAYG ratio based on the share of opex+IRE over totex. We roll forward the runoff rates
at their 2025 values throughout the horizon, and we use the calibrated AMP7 value chain-
specific depreciation rates for the 2020 to 2050 period.
3.3.6. Climate Change and Restoring Sustainable Abstraction (RSA) Effect on Water Available for Use
We draw on the WRPG tables to inform the baseline availability of water ready for use
(WAFU). Additionally we make simple assumptions for the driver variable sensitivities that
affect water availability (e.g. for the high sensitivity we increase company estimates of the
impact of climate change/RSA on water availability by 20%). The model allows the user to
input any percentage increase or reduction in these variables as a custom scenario.42
We were able to identify data relating to restoring sustainable abstractions in the Water
Resource Planning Guideline supply-demand workbook table WRP1. We use this data in the
baseline and allow the user to test sensitivities using a high, low, or custom sensitivity.
3.4. Policy Scenarios
This section describes the main policy scenarios that the user can model using policy
switches. The baseline scenario set is the foundation of the model. The policy switches have
a ‘delta effect’ interpretation –as changes from the baseline set. This ‘delta effect’ treatment
works in much the same way as that caused by changes to individual “external” driver
variables. We are attentive to whether variables within the baseline already reflect views on
some of the policy reforms in order to ensure that these are not double-counted. Table 3.13
lists the policy scenarios built into the model.
41 We use adjusted NHH retail margin figures provided by Ofwat for Welsh Water and Dee Valley Water since they are
not subject to the same changes as the English companies in the Water Act 2014. For Welsh Water’s NHH retail
margins we use 1.23% and for Dee Valley Water we use 1.36%.
42 Falling water availability may result in shortfalls in the supply-demand balance which will result in bringing forward
the investment profile and therefore increase totex.
Integrated Final Report Model Specification
NERA Economic Consulting 45
Table 3.13
Policy Matrix
Topic User Choice Additional Options
Contained in model
Retail Competition
Policy Switch for non-domestic retail competition in England (all) & Wales (>50Ml/D) with voluntary separation for NHH retail
WSL reforms (no exit) Yes, central option (with NHH exit) in
the baseline
Regulatory Mechanisms
The PR14 mechanisms are in the baseline. The switch shows the effect of stronger incentives , from increased retention or ODIs or duration, or weaker (e.g. PR09)
Adjustments to strength of incentives (efficiency) and
cost of capital
Yes, PR14 mechanisms are in
the baseline
Upstream Competition
Policy Switch for upstream competition reforms
Upstream reforms option only. Timing of
implementation. Cost of capital levels
Yes, central upstream reforms is
in the baseline
Private Supply Pipes
Adoption of private supply pipes Strength options only Yes, not in baseline
PCC targeting Switch for per capita consumption targeting Strength options only Yes, not in baseline
Abstraction Reform
Policy Switch for abstraction reform (midway option is average of water shares and system plus)
Water shares, system plus, and midway option. Timing
of implementation Yes, not in baseline
Greater-Resilience
Switch for significant increase of industry resilience
Strength options with adjustments to (Ml/d)
headroom targets and capex costs
Yes, not in baseline
Source: NERA
Note: All policies have low/base/high strength options, and all but the regulatory mechanisms are able to be
switched off by the user
The following sections describe the policies that will be included in the model.
3.4.1. Baseline Scenario Set
We use our current understanding of the PR14 measures that will be introduced from 2015 as
the model baseline regulatory mechanisms “policy”. This includes Totex benchmarking,
reward/penalty bulk water trading, the new SIM measures, and expected known changes
resulting from the Water Act. These are described in more detail in section 3.4.7.
The baseline scenario set provides a standardised set of outputs corresponding to the
company investment plans described by the WRMPs and BPs/DDs. Any changes to these are
‘delta effects’ - changes that move away from the baseline scenarios.
3.4.2. Retail Competition
Retail competition for non-households from 2017 is in the baseline and is likely to have an
impact on the sector through many channels, many of which are already implicit in the PR14
BP/DD cost and demand and margin levels. Based on the Water Bill and studies including
Defra’s 2011 Impact Assessment (IA) and the Cave Review, the following are the most
important impacts to model:
Integrated Final Report Model Specification
NERA Economic Consulting 46
Reductions in retail costs for water and sewerage arising from cost efficiencies driven
by competitive pressure. These will be most pronounced in the contestable section of the
market (non-households), though there are likely to be spillovers to the non-contestable
market due to the spread of best practice and information gathered by Ofwat. In theory,
retail exit could either increase efficiency spillovers (because it gives incumbents stronger
incentives to invest in their retail activities) or reduce them (in the event of non-household
exit, a household retailer is no longer subject to the same competitive pressure). In the
model we rely on spillover assumptions in the voluntary exit option in the IA. We note
that this option has recently been revised to reflect NHH-only exit. Although the
underpinning assumptions related to abatement (proportion of companies that exit at
market opening and over time) and the size of the household spillover have been updated,
the headline impact in terms of net benefits is broadly identical to the original option.
Hence our analysis in terms of bill impacts is broadly representative of the revised option.
Reductions in wholesale costs for water and sewerage, resulting from effective
consumer advocacy from retailers and the revelation of information on wholesale costs
that follows from wholesale/retail splits (and Ofwat’s subsequent ability to better regulate
these costs).
Margin reductions as competitive pressure deprives retailers of excess returns.
Reductions in water demand as retailers compete by offering their customers assistance
with water efficiency measures. This will primarily affect bills by reducing the amount of
capital investment needed in water and (to a lesser extent) sewerage network and
treatment.
Regulatory costs associated with Ofwat setting up and then enforcing market codes. We
expect these to raise customer bills via licence fee contributions.
Settlement and switching costs related to setting up and then operating a central market
authority, which we assume are paid for by retailers, with associated retail activity costs.
Customer acquisition expenditure by retailers on promotional activity aimed at
attracting new customers and retaining existing ones.
Incumbent restructuring costs incurred in order to ensure compliance with competition
law and regulatory requirements.
Table 3.14 below outlines how these effects are quantified, primarily based on the IA’s
analysis.
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Table 3.14
Effects of Retail Competition Policy
Impact Description Category Approx. value Comments
Contestable retail efficiency
Driven by retail mergers, reduction in bad debts, reduction in metering, IT,
telecoms cost.
Water / sewerage retail
2.5% initial saving +0.38% per annum.
From IA: “No separation” scenario where 50%* “abatement factor” applied to savings under legal
separation.
Non-cont retail efficiency
Spillovers from spread of best practice, info gathered by Ofwat,
mergers of retailers.
Water / sewerage retail.
0.63% initial then .094% per annum.
From IA: Assumes 19%* of proportional effect on contestable retail spills over. Merger impact
dubious?
Wholesale efficiency
Separation reveals costs, and retailers champion consumer interests.
Water / sewerage opex
0.13% applied to “in-house” opex.
From IA: 25% of the 0.5% efficiency savings of legal separation.
Bundling savings
Combine water, w/w, and other utilities.
Water / sewerage retail
£15 per customer p.a.; 7-year transition to full
bundling. From IA: 25% of £15 per customer assumed.
Demand reductions
Incentive for companies to offer water efficiency advice.
Water / sewerage capex
5-year transition to 0.5% usage reduction.
From IA: 25% of full 2% reduction.
Margin effects Competitive pressure reduces
margins. Retail margins. PR14 level.
Possible sources: analysis of comparable sectors.
Regulatory Costs
Developing market codes and then ongoing monitoring.
Licence fees £5.7m upfront; £4.87m
p.a. ongoing.
From IA: set-up costs equivalent to those incurred by WICS; ongoing costs 400% of WICS
given competition enforcement.
Incumbent costs
Costs associated with managing switches and customer contacts.
Water / sewerage retail
5 FTEs per WaSC, 2 FTE per large (>1m
customers) WOC, 1FTE per small WOC.
From IA: do not include costs of renegotiating bonds etc. on assumption these aren’t voluntarily
incurred.
Settlement & switching
Setting up and operation of central authority.
Water / sewerage retail
£6.4m one-off, then £5m p.a.
From IA: based on double cost observed in Scotland.
Acquisition & retention
Expenditure on obtaining new customers
Water / sewerage retail
5% of contestable cost base p.a.
From IA.
Source: NERA Analysis of Defra 2011 Retail Competition Impact Assessment
*The model was calibrated using the 2011 IA option with voluntary separation excluding the finance costs of separation. However, we understand that the results are very
similar to the updated IA. As a result, we present the updated parameters used in Defra’s Oct 2014 IA updated exit option.
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In the IA, most impacts comprise an initial cost effect followed by an ongoing trend, and
sensitivities are conducted as follows:
Abatement factors have a low/medium/high range of 0.15/0.25/0.5;43
“Baseline” efficiency gains (i.e. pre-application of abatement factors) have
low/medium/high ranges of:
− Contestable retail one-off: 5%/10%/15% of cost base.
− Contestable ongoing: 1%/1.5%/2% of cost base per annum.
− Non-contestable spillover: 0.25/0.5/0.75 of contestable savings.44
− Wholesale spillover: 0.25%/0.5%/0.75% of cost base.
We draw on these ranges to construct our high and low ranges for retail competition effects.
In addition, we base retail exit on an adjusted version of the voluntary exit option in the IA.45
3.4.3. Regulatory Mechanisms and Nature of Control
Ofwat’s Future Price Limits statement of principles includes an impact assessment of changes
to the regulatory framework, in NPV terms over a 30 year period.46
This assessment has also
been evaluated for Ofwat by PwC.47
We use the assumptions set out in these documents to
gauge the cost effects of changes to regulatory incentives and mechanisms over the modelling
horizon away from the “continued PR14” baseline. We describe the main elements of the
regulatory mechanisms in the following subsections, but we do not model these individually.
Instead, we model the regulatory mechanisms as a package based on the estimated aggregated
effects of the elements on cost efficiency at the totex level.
3.4.3.1. Totex cost assessment and menu regulation
Ofwat has introduced a totex cost assessment approach, removing the “capex bias” under the
previous regime. Under PwC’s scenario based analysis, this could lead to net sector benefits
of £60m (which could range between £10m and £310m) in NPV over 30 years.48
This gain is
implicit in the PR14 mechanisms baseline.
43 In the updated Defra IA the central abatement figure used is 0.5 and the central non-contestible spillover used is 0.19.
We understand that these changes roughly cancel out leading to a very similar size of impact. We also note that the
updated IA has not yet been published and is still being reviewed by the Regulatory Policy Committee.
44 See previous footnote.
45 We did not include financing cost which accounted for roughly one quarter of the retail costs (NPV£199m in the Defra
IA) on the basis that much of this was motivated by the possibility of debt renegotiation or default triggered by the need
for separation, whereas the voluntary approach to exit (eventually adopted in the Water Act) should allow much of that
cost to be avoided
46 Ofwat (2011), “Future price limits – statement of principles Appendix1: Impact Assessment”.
47 PwC (2013) “Updated Price Limits Assessment: Water Services Regulatory Authority (Ofwat)”.
48 This calculation is based on between 2% and 10% of capex being replaced by opex, and whole-life costs being 1%-5%
higher under the current regime with capex bias.
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Menu regulation is also being introduced at the Totex level to incentivise companies to
truthfully reveal costs, and incentivise outperformance on these costs. The incremental
savings of this reform above the existing efficiency profiles and savings accruing due to
reforms such as totex cost assessment are highly uncertain, but are estimated to be of the
order of £0.5bn to £2bn.49
We assume that the baseline benefit is £1.1bn.50
.
3.4.3.2. Water trading incentives
Given Ofwat’s desire to increase efficient bulk water trading between companies, include a
policy switch affecting the relationship between regulation and water trading, away from the
baseline level assuming the AIM.
The baseline contains the current view of bulk imports and exports, from the latest WRMP
tables. Table 3.15 displays current estimates of water supply accounted for by imports or
export from one company to another, based on draft WRMPs.
Table 3.15
Bulk Water Trading (Ml/d)
Source: Water company draft WRMPs for 2015-2040 and/or supporting tables (WP1)
To explore variations away from the baseline we rely on Ofwat’s 2011 study of upstream
markets in the E&W water sector. This document suggests that trades currently not in place
could yield net economic benefits of at least £959m in NPV terms.51
The enhanced incentive
49 PwC (2013) “Updated Price Limits Assessment: Water Services Regulatory Authority (Ofwat)”, page 40. We assume
this impact is calculated as an NPV over 30 years.
50 We assume that this mid-point approximately represents the regulatory mechanisms that underlie the PR14 price review.
PwC (2013) “Updated Price Limits Assessment: Water Services Regulatory Authority (Ofwat)” page We would
advise a comparison of FD expectations with the PWC estimates as a check when the FDs become available.
51 Ofwat (2011) “A study on potential benefits of upstream markets in the water sector in England and Wales”
Ml/D ANH AFW BRL BWH DVW NES NWT PRT SES SEW SRN SST SVT SWT TMS WSH WSX YKY Total
ANH 91.0 1.2 8.0 100.2
AFW 8.1 36.0 0.1 2.9 47.1
BRL 11.7 11.7
BWH 0.2 0.2
DVW 0.1 0.1
NES 3.1 0.0 0.7 3.7
NWT 0.0 80.0 80.0
PRT 4.5 4.5
SES 0.1 0.1
SEW 0.0
SRN 1.3 31.1 0.3 32.7
SST 1.4 1.4
SVT 0.0 0.1 48.6 48.7
SWT 0.0
TMS 14.5 91.0 5.0 0.0 110.5
WSH 0.0 338.3 338.3
WSX 1.1 1.1
YKY 0.3 0.3
Total 11.5 106.8 1.1 0.0 0.0 92.2 0.7 0.0 41.0 31.1 4.6 0.0 347.7 0.0 0.1 80.0 15.2 48.6 780.5
Importer
Exp
ort
er
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scheme in place for PR14 is estimated to realise savings in the NPV range £160m to £690m.
We use the midpoint of this range as the baseline net benefit estimate..
3.4.3.3. Abstraction incentive mechanism (AIM)
Ofwat will put in place the AIM over Amp6, as a reputational incentive on companies to
reduce abstraction from water resources with high environmental costs at certain
locations/points in time. These sites will be identified in agreement with the EA, but the
scale of the impact will depend on the number of sites included in the AIM, and the strength
of the incentives. As the AIM is not currently proposed to be incentivised financially, the
strength of these incentives is highly uncertain, but may be substantial at a regional level. We
do not directly incorporate any financial effects of the AIM into the model’s policy impacts
but they may be implicitly considered to be part of the family of regulatory mechanisms.
3.4.3.4. Separation of wholesale and HH and NHH retail controls
HH retailers face strengthened financial related to the provision of low-cost service (as far
below the average cost to serve as possible). The separation of the retail and wholesale
controls will make company performance more transparent and help drive efficiencies in this
segment of the value chain. The benefits from this part of the PR14 impact assessment are
estimated at £1190m.52
Similarly, the separation of the NHH retail controls are expected to generate significant
additional benefits. The reforms based on the default tariffs and levels of service framework
will enable companies to drive down their retail costs where possible while protecting
customers in high-cost areas from bill increases. Many of the efficiencies achieved due to
competitive pressure are expected to spillover into the HH and wholesale segments. The
benefits assumed from the separation of the NHH retail control are estimated at £360m. 53
3.4.4. Upstream Competition
We expect that the main upstream competition reform will not be implemented by legislative
change in advance of the PR19 price review. We correspondingly use 2020 as the baseline
year of implementation, but the user can select any other implementation year.
The upstream competition reforms are perhaps the most significant of those being presently
considered.
For our purposes upstream competition is defined as:
Upstream Water: Enabling water competitors and new entrants to abstract and treat
water and input it into the water network; and
Upstream Sewerage: Enabling sewage competitors and new entrants to remove and treat
sewage from the sewerage network and treat and dispose of sludge.
52 PwC (2013) “Updated Price Limits Assessment: Water Services Regulatory Authority (Ofwat)” page 41.
53 PwC (2013) “Updated Price Limits Assessment: Water Services Regulatory Authority (Ofwat)”, page 46.
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We base policy impacts for introducing water upstream competition on the most recently
updated Impact Assessment from 2013. There is currently only one policy option being
considered. It is termed “Upstream Reforms”, and is a package of reforms to the current
water supply licencing (WSL) regime that will encourage competition in the provision of new
water and sewerage treatment capacity and water resources.54
This reform will be achieved in
part by unbundling licences so that an entrant can input water into the network without
needing to have a corresponding final customer to sell it to. We include this option in the
baseline.
The effects of the upstream competition reforms can be described as follows:
Synergy and interaction effects:
− Links may exist between abstraction reform and upstream competition in
water:55
it is unclear whether these reforms influence each other so we do not model
any explicit synergy effects.
− The degree of competition expected would depend on the access pricing levels
and perhaps in turn on the treatment of RCV by value chain element. See
section 2.5.1 for details on the focussed and unfocussed approaches to attribution of
the RCV;
Transition costs and benefits:
− No transition costs incurred by the government: The Cave review suggested that
there would be very few changes to the regulatory framework under these reforms and
so no exceptional costs are expected during the transition year;
− A large transition benefit arises from company efficiency quickly catching up to
the frontier: The IA assumes that the presence of competition forces companies to
quickly improve their efficiency to the point of catching up to the efficiency frontier
for contestable elements. This effect is calculated as a reduction in the upstream cost
base subject to effective competition56
of 12% for annual capex costs and 10% for
annual opex. (We note that the efficiencies generated by the reform are expected to
contribute to a decline in RCV due to reductions in capex spending.) We implement
these as sector-wide improvements but the model allows the user to input company-
specific efficiency improvements;
Ongoing costs and benefits:
− Ongoing regulatory costs will increase: Ofwat will incur about 10% more opex
costs, corresponding to £2 million, which will be funded by the water companies and
therefore feed into water bills. Similarly, the DWI and the EA will each require an
additional £0.33 million;
54 We note that this option does not entail mandatory separation of upstream activities.
55 By upstream we mean treatment capacity (and the water for it).
56 The upstream cost base subject to effective competition is calculated by taking 20% of the opex and capex spent on
resources and treatment.
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− Company finance costs will increase significantly: the IA estimates a higher cost of
capital for capex enhancement expenditure subject to effective competition as a result
of the additional risks that investors will bear. The estimated increase in the cost of
capital is equal to 2.5% in the low costs case, 1% in the central costs case, and 0.5%
in the high costs case which is applied to the capex enhancement expenditure
proportion of the cost base that is subject to effective competition. The proportion of
costs subject to effective competition is 10% in the low benefits case, 20% in the
central benefits case, and 30% in the high benefits case. Post-implementation
additions to RCV (i.e. post-2020 additions) in each of the value chains57
that are
subjected to competition will be subject to the adjusted WACC. The share of higher-
WACC RCV items are pooled with the unaffected-WACC RCV items to give a
weighted average WACC that applies to the entire value chain element in
question;5859
− Company ongoing costs will increase as a result of the extra scrutiny required to
run their businesses: the IA followed the Cave review assumption that companies
will incur additional annual opex costs of £1.11 million;60
− Efficiency improvements in delivering upstream services: The existence of
competition is expected to deliver ongoing efficiency gains through the development
ofdelivering upstream services at lower cost, substituting demand management
measures, or adopting measures to control pollution at source. These effects are
quantified as the following reductions to capex and opex costs for new and
replacement capacity:
− New capacity Capex: 0.48%;
− Replacement capacity Capex: 0.33%
− New capacity Opex: 0.36%;
− Replacement capacity Opex: 0.24%.
The upstream competition option that is incorporated into the model contains high/low
variants as well as a user-specified parameters on the strength of the WACC effect in those
sensitivities.
57 The value chains that are expected to be subjected to competition are the non-network elements: water resources and
treatment, sewer treatment, and sludge treatment and disposal.
58 The upstream cost base for effective competition is calculated by taking 20% of the opex and capex spent on resources
and treatment. Of this share, only the capex enhancement and MNI will go into the RCV and constitute an additional
cost effect through the effect on WACC. As a result, the proportion of this cost base relative to the RCV is likely to be
relatively small, thus bringing down the percentage effect on the cost of capital to much lower levels. For example, if
the RCV is nine times the size of the capex enhancement +MNI cost subject to effective competition then the central
case WACC would only be 0.1% higher.
59 We note that an error in the treatment of depreciation underlying the IA figure leads to higher IA costs than those that
feed through the model. This is caused by underestimating the amount of depreciation, hence overestimating the
amount of RCV subject to an increase in WACC. This leads to higher cost estimates in the IA. The resulting magnitude
of the model’s upstream NPV net benefit is therefore larger than that from the IA.
60 £0.95 million in 2009 prices adjusted up by RPI CHAW factor 1.170332.
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Table 3.16
Effects of Upstream Competition Policy (IA)
Impact Description Category Approx. value Comments
Synergy effect on competition
The links between upstream and abstraction reform are not
clear. N/A N/A
We do not model any synergies between abstraction reform and upstream
competition. This is due to uncertainty about whether linkages exist.
Efficiency catch-up
Companies efficiency levels are expected to quickly rise to the frontier for the upstream
segments
Water and sewerage capex
12% of capex cost base subject to effective competition, which is 20% of
resources and treatment capex. Applies to transition year only
Applies to transition year only. Efficiency assessments may provide a basis for which companies will see greater cost
reductions
Efficiency catch-up
Companies efficiency levels are expected to quickly rise to the frontier for the upstream
segments
Water and sewerage opex
10% of opex cost base subject to effective competition, which is 20% of
resources and treatment opex. Applies to transition year only
Applies to transition year only. Efficiency assessments may provide a basis for which companies will see greater cost
reductions
Government administration cost savings
Ofwat will incur an additional £2m p.a. and EA and DWI will
each incur an extra £0.33m p.a.
Water and sewerage opex
£2.66 million p.a. Results from regulation costs being
passed onto companies.
Increased company finance costs
Companies will face higher finance costs due to bearing
additional risks WACC
0.5%, 1.0%, or 2.5% increase in the proportion of WACC related to
enhancement capex
Results from additional risks of competition
Increased costs related to company scrutiny
The effect of competition will force companies to be more
attentive to market conditions
Water and sewerage opex
£1.11 million p.a. Additional costs related to scrutiny of the
market
Ongoing efficiency gains
Efficiencies related to ongoing development of cost savings and demand management
Water and sewerage opex
and capex
Reductions of 0.48% in enhancement capex; 0.33% for capital maintenance; 0.36 for new capacity opex; and 0.24%
for replacement capacity opex
Development of new ways of delivering upstream services at lower cost,
substituting demand management measures, or adopting measures to
control pollution at source
Source: NERA Analysis of Defra 2013 Upstream Competition Impact Assessment. We note that an error in the treatment of depreciation underlying the IA figure leads to
higher IA costs than those that feed through the model. The resulting magnitude of the model’s upstream NPV net benefit is therefore larger than that from the IA.
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In the baseline we assume the implementation of upstream reforms in the year 2020, though
this is still uncertain. We understand that policy decisions on the implementation date for
upstream reform have not yet been taken, so we provide the flexibility for the user to input
any other start date - which will shift the entire impact profile backwards or forwards in time.
At this stage it remains unclear whether the entire upstream market will be made competitive
or whether competition is phased in for new incremental capacity only. We follow the
analysis underlying the IA by assuming that only the incremental capacity will be subjected
to the new WACC.
3.4.5. Adoption of Private Supply Pipes
The model includes a policy switch to activate additional costs associated with the transfer of
private water supply pipes (PWSP). Defra recently carried out a public consultation on
supply pipes. The evidence and views showed that there are benefits to be gained from
transferring ownership of private supply pipes to water supply companies. However, there is
less certain evidence about the range of potential impacts on water bills for various customers
and geographical regions. Defra decided not to carry out further work on transferring
ownership of supply pipes at the current time due to the Coalition Government’s commitment
to maintain pressure to keep household bills down. Hence we propose to leave this policy out
of the baseline.
The transfer of PWSP would raise the cost of providing water services, as the responsibility
for repairs and maintenance would be passed on to water companies. The benefits would be
better management of supply pipes, potentially resulting in reduced leakage and savings to
residents who would have incurred repair costs. These benefits are not captured by the model.
This policy switch would therefore raise costs for the provision of a given level of supply.
Impacts that this reform is likely to have on the model are:
Increases in capital maintenance costs as water companies become responsible for
maintaining supply pipes for households and non-households;
Increased administration opex costs required to allow reporting of faults and subsequent
repair coordination;
Likely increases in some quality of service measures as, for example, more faults are
tackled rather than customers ignoring problems, possible associated reductions in
leakage levels and avoidance of more serious (and therefore more costly) issues;
All opportunities to exploit economies of scale in repair and maintenance of the supply
pipes are taken.
We inform the magnitude of the effects from this policy switch based on the recent
assumptions calculated by Defra as part of the Water Bill analysis that considered this topic.61
61 Defra Impact Assessment, “Transfer of private water supply pipes to Water and Sewerage Company ownership
(WaSCs)”, 2013.
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3.4.6. Per Capita Consumption Targeting
Company TSG members suggested the addition of a policy switch to test sensitivities around
having per capita consumption targets implemented in the future. These were thought to be
important due to the relative importance of PCC changes over time in order for companies to
meet their demand targets without bringing too much supply
We defined the PCC targeting “policy” as a cost-free reduction in the volume of water
consumed. The cost-free assumption that underlies this policy effect is unlikely to be
realistic and should be refined in subsequent upgrades of the model. The baseline reduction
PCC targeting reduction is 10%, with the high and low sensitivities set at 5% and 15%
respectively.
3.4.7. Abstraction Management System Reform
Defra are aiming to legislate for abstraction reform early in the next Parliament (from 2015).
Our scenarios use 2025 as the year of implementation of the reform, but the user is able to
select any other implementation year. The abstraction reform focus is intra catchment trading
- unlike Ofwat’s enhanced water trading incentives which are also in the baseline and can be
inter catchment.
Two options were considered for the consultation IA under the heading of abstraction licence
reform. The first option is “Current System plus”, which takes many of the characteristics of
the existing system such as flow-based restrictions, but arguably makes the system more
flexible, more responsive to water availability, more supportive of trading, and fairer for
abstractors. The second option is “Water Shares” which entails similar changes as those in
the current system plus but introduces a share based licencing system which aligns
abstractors’ interest in a jointly managed variable resource. These options are very similar in
terms of how they may impact bills and we feed them through the model in the same way.
We understand that a third option which may constitute a hybrid of the water shares and
system plus options is being considered. To reflect this we add a ‘midway’ policy option to
the model by taking the average of the two other options. This midway option can then be
overwritten when the final IA is published in early 2015. Note that for this reason, the current
cost - benefit profile for the abstraction license reform options are subject to change.
Abstraction reform is by itself not expected to yield large effects on water bills. The main
goal of the reform is to achieve environmental objectives more efficiently and to enable a
more functional upstream market that is more conducive to upstream competition.62
We
therefore consider two types of abstraction management system reform effects: transition
costs and ongoing costs/benefits.
We use the most recent IA (produced in 2013) as the basis for informing the model inputs
which are affected by either policy option switch. The only relevant difference between the
two policy options relates to the magnitudes of the effects. For each of the policy options we
62 We consider abstraction reform to pertain only to licence trading and allocation trading. We consider investment into
new treatment plants at the abstraction site to fall under upstream competition.
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consider high and low cases based on the figures provided in the IA. Note that this IA does
not contain the cost breakdown in terms of percentages of different cost categories, so we rely
on the NPV cost estimates to derive proportional shares of the respective opex and capex cost
categories for the average annual year costs.63
The effects of abstraction reform can be described as follows:
Transition costs:
− Transition costs incurred by the government: the EA and Natural Resources Wales
will incur costs related to moving the existing abstraction licences to a new system.
In order to be conservative, we assume that water companies will ultimately bear and
pass on these costs in the form of water bills. While we realise that these costs will
likely be shared across several groups of abstractors, we assume that they are paid for
entirely by the water industry as they are relatively insignificant and only occur
during the year of implementation. We reflect these costs during the transition year as
an increase in company opex equivalent to £21.0 million and £23.5 million in 2013
NPV terms for system plus and water shares respectively;
Ongoing benefits and costs:
− Reductions in administration costs to government: the EA and Natural Resources
Wales will benefit from lower ongoing administration costs as a result of the change
to the new system which we expect will be passed through to companies. We pass an
opex cost savings equivalent to £74.2 million and £62.8 million in 2013 NPV terms
for system plus and water shares respectively;
− Reductions in administration costs to businesses: according to the IA, the
administration costs of moving to the new system will be reduced relative to their
current levels, representing an ongoing cost reduction to businesses’ opex equal to
£37.6 million and £36.9 million in 2013 NPV terms for system plus and water shares
respectively;
− Reductions in the NPV capital investment profile (and associated operating
costs): water companies will be able to take advantage of the market for licences to
manage their supply and demand balance as the climate changes. This will allow
them to postpone the profile of their capital investment requirements , according to
the IA creating a capex benefit of £214 million and £219.4 million in 2013 NPV terms
for system plus and water shares respectively;
− Improved gross margins for business related to increased access to high river
flows and abstraction trading (but not selling abstraction rights): these additional
benefits relate to opex savings which are equivalent to £0.7 million and £1.3 million
in 2013 NPV terms for system plus and water shares respectively. We understand
that these benefits may not go to the water industry but we continue to include them in
the current modelling work as they are negligible;
Additional ongoing costs not contained in the IA:
63 We attribute all the transitional costs to the single transitional cost category.
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− Increased abstraction charges to businesses: water companies and other abstractors
will face a cost that is more reflective of water scarcity and will therefore be different
than the current (near-zero) abstraction costs they face. We do not consider these
changes to charges within the current scope of this project;
− Windfall gains from the sale of abstraction licences: We assume that the transition
will be managed such that no windfall gains are realised by abstractors. We therefore
view this item as outside the current scope.
Table 3.17
Abstraction Reform Policy Effect (IA)
Impact Description Category Approx. value Comments
Synergy effect on competition
It is unclear whether there are synergies between abstraction reform and upstream
competition.
N/A N/A
Due to the uncertainty about whether synergies
exist we do not model them.
Transition costs incurred by government
Costs of moving to the licence trading system
Water opex
All transition costs
Applies to transition year only.
Government administration cost savings
Lower ongoing administration costs
resulting from simplified system
Water opex
22.7% or 19.6% of total p.a. net
benefits
System plus or water shares
percentages, or midpoint
Business administration cost savings
Lower ongoing business administration costs
resulting from simplified system
Water opex
11.5% or 11.5% of total p.a. net
benefits
System plus or water shares
percentages, or midpoint
Capital investment deferral savings
Lower annual average capex due to deferring capital enhancement
spending
Water capex
65.5% or 68.5% of total p.a. net
benefits
System plus or water shares
percentages, or midpoint
Increased gross margins due to additional water availability
Cost savings related to increased access to high
river flows and abstraction trading
Water opex
0.2% or 0.4% of total p.a. net
benefits
System plus or water shares
percentages, or midpoint
Source: NERA Analysis of Defra 2013 Abstraction Reform Consultation Stage Impact Assessment
To obtain our estimates we use the transition cost and average annual costs from the IA for
the system plus and water shares options respectively, and also use the midpoint between the
two options as the baseline ‘midway’ option. We attribute a 46.9% share of the total costs
and benefits to the water industry since that is its share of the total volume of water
abstracted.64
65
64 We obtained the proportion of the water industry’s share of total water abstracted from the IA, page 11. This figure
appears to be consistent with Figure A1 of Defra’s “Abstraction Reform Consultation Technical Detail Annex C:
Additional detail on specific elements of reform”, page 6.
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In the model we take the net annual benefit for the industry and divide it out among each
water company in proportion to the volume of water that they abstract. (This simplification
may not properly represent the distribution of benefits as they are only realised in ‘enhanced’
catchments, i.e. generally those facing water stress in the South/South East, as categorised in
the IA. The number of ‘enhanced’ catchments increases over time).
Future versions of the model may include a model upgrade to allow the model to contain
information related based on catchment benefits – see section 5for potential upgrade
descriptions. The high/low figures in the IA are used to inform the strength options in the
model. These figures are not statistical representations; they are based on taking estimates
from equally likely climate change and socio economic scenarios.
The abstraction reform implementation date is set at 2025. We provide the flexibility for the
user to input any start date for the reforms to take place. Changes to the start date will shift
the entire net benefits profile backward or forward in time.
3.4.8. Greater-Resilience
The model contains a switch to represent a possible shift from current levels of resilience in
case these are perceived to be insufficient in the future (for example due to greater pressures
arising from climate change). We implement the effects of companies responding to a new
increased resilience scenario through changes to levels of target headroom as well as to the
costs that companies incur to maintain and improve their services. This greater resilience
(GR) scenario is not included in the model baseline.
In the model we assume that the central greater resilience scenario entails a tripling of all
resilience costs in water and sewerage (e.g. expenditure to reduce flood risk, SEMD, and any
enhancement expenditure falling under the Ofwat definition of resilience) as well as a 20%
increase in the level of target headroom in the base case GR option.66
The additional
resilience costs are a reflection of the doubling of water supply pipes, installation of larger
sewers to prevent overflows at bottleneck locations, and other such additions that affect
reliability through the network and/or resources but do not necessarily influence the level of
output. The increase to the level of target headroom reflects spending on resilience of the
network and/or resources (for example by adding capacity from multiple sources) and for
increasing capacity at existing sites as a form of protection against external risks such as
natural disasters or harsh climatic conditions.
65 We note that the use of the water industry share of total abstraction ignores issues of consumptiveness (the public water
supply sector is considerably more consumptive than many of the other abstracting agents) and also displaces water
considerably further from the point of abstraction.
66 More specifically, we defined the WASC average proportional expenditure on “resilience” as (Resilience + SEMD) /
(Net Capex + Opex) for water and (Resilience + SEMD + Expenditure to Reduce Flood Risk) / (Net Capex + Opex) for
sewerage. We then uplifted all companies’ actual costs by double this proportion in the base case; making the industry
average resilience spend three times larger in the base case option. For the low case, we double average resilience and
increase target headroom by 10%, and for the high case we quadruple resilience costs and increase target headroom by
40%.
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3.5. Output Specification
The model incorporates all drivers to generate forecasts of final bills for customers under a
range of macroeconomic, demographic, climate and policy scenarios from 2015 to 2050.
This section sets out how we structure the outputs of the model.
3.5.1. Intermediate and Final Outputs
The model allocates each company’s modelled annual allowed revenues into three revenue
baskets for each service: household metered customers, household non-metered customers,
and non-household customers. From these allocated revenues, the model calculates average
bills for each company, customer category and service, reported in real and nominal terms
annually from 2015 to 2050.67
We also plot the average historical bills for the years 1989-
2015 for a comparison of the headline weighted average household bill results.
In addition, the model presents an approximate aggregation of bills by river basin using river
basin mapping data provided by the EA.
The model also reports the build-up of average bills, across contributions from different
elements of the water and sewerage value chains. It shows cost breakdowns such as splits of
wholesale totex into enhancement, capital maintenance, and opex.
These outputs are displayed in easily navigable summary tables and charts, each comparing
the (baseline) projection results with the results for the user’s chosen scenario. So that users
can further analyse output results, it is possible to view the output results, including tables
and charts, for both the baseline and the user’s chosen scenario.
3.5.2. Distributional Impacts
The distributional impacts of bills form an important consideration in policy decision making.
The model is able to assess some of the distributional impacts of projected total water and
sewerage bills in each company region by determining the proportion of households where
the average bills are above a user-defined level of average disposable income. We enable the
user to specify the threshold level of disposable income that projected bills are reported
against. We do not specify any particular threshold level as part of the baseline.
Our approach to calibrating this assessment is to start with the initial distribution of
household income in each UK statistical region from the Family Resources Survey as
published by the Department for Work and Pensions. This data is mapped to company
regions from the UK statistical regions in the Survey.
To project the income distribution forward, as a first approximation we adopt the simple
assumption that all households experience income growth at the regional average rate of
economic growth. We project regional economic growth based on a projection of gross value
added (GVA) growth rates by region from the UK Employment and Skills Almanac 2011.
67 The model also calculates national and river basin average bills according to the weighted average of all household
customers, based on the revenue baskets and property figures corresponding to metered and unmetered households.
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The model then computes nominal wage estimates based on the user’s choice of either RPI or
CPI inflation. The nominal bill levels are then compared with nominal income and the
proportion of people paying above each threshold of income are displayed in a chart. We
provide the functionality to consider these distributional effects at the company or industry
level.
3.5.3. Sensitivity Checks
Partly to help provide assurance on robustness, we offer the ability to easily report sensitivity
results as “deltas” from the baseline levels. When variants from the baseline drivers are set
as inputs, through policy switches or sensitivities, the model routinely displays the baseline
scenario outputs alongside those for the desired variant. This helps ensure that the model
responds to changes in the baseline as one would generally expect.
To the extent possible, we perform scalable or replicable calculations within the same
worksheet to ensure model flow consistency and accessibility. All calculation sheets have a
system of embedded internal checks to help ensure accuracy of calculations performed.
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4. Key Model Results
4.1. Introduction
The Water Bills Projection Model has been designed to assist decision making in the water
sector by showing the impact of policy, regulatory, and company investment choices on final
customer bills in England & Wales over the period from 2015-50. The model allows users to
project bills under a range of policy scenarios and assumptions about relevant
macroeconomic and environmental factors, reporting typical bills for various classes of
customer by company or by region. Along with this report and the excel model itself, there is
also an accompanying model user guide.
4.2. Limitations
This section briefly sets out some of the main caveats to the results which are presented in
this chapter. When considering the results, it must be borne in mind that the model implicitly
assumes the projected situations are institutionally feasible, as the model makes no test of
sector compliance with sector laws such as compliance with WFD requirements transposed
into UK law, makes no test of compliance with competition laws, and makes no definitive
test of the financeability of the projections (though it is easy to construct projections which
would fairly obviously not be financeable).
In terms of WFD costs, the input data is fairly consistent with EA projections for scenario 4
cost. 68
In this projection, approximately 90% of capex costs were realised prior to the “full
compliance” date of 2027. While this forms the basis of our projections, we recognise that
the target full compliance date may shift. There may also be another phase of environmental
regulation post 2027, that is currently unknown - particularly if challenging climate
conditions arise. There is no available evidence to inform our modelling assumptions, but this
type of additional environmental expenditure could have a significant effect on the later
years of the modelling horizon. In contrast, Opex costs are expected to be incurred over a
longer time horizon. When applying EA WFD scenario estimates we model Opex costs at a
constant level throughout the modelled horizon.
Climate change impacts are represented via the WRMP inputs, but the higher climate risk
scenarios covered by other sources (e.g. EA Case for Change) were not incorporated into the
model due to a lack of suitable sources on cost implications for the industry. These climate
impacts constitute a significant area of uncertainty for the effects and we recommend that
further work be undertaken to specify plausible ranges of effects and corresponding levels of
investments. We have attempted to cover off the bill effects arising from the need for greater
levels of resilience through an additional scenario. However, we note that, unlike for the
water service, we have not directly considered capacity additions for sewerage due to climate
change or the potential need for sizable sewerage replacement expenditure beyond our input
data. The input data contains a declining amount of enhancement expenditure - this situation
68 EA, “Water for Life and Livelihoods: A consultation on the draft update to the river basin management plan - Part 3:
Economic analysis”, 2014
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may be perceived to be a reflection of failure to allow for ‘‘known unknown’’ cost and
quality drivers that may materialise in the future.
4.3. Inputs and Assumptions
This section sets out the historical and forecast inputs used in the model as well as listing key
assumptions that significantly impact the results.
We have quality assured the model to ensure that results are robust and incorporated
stakeholder feedback through several iterations. Stakeholder feedback was gathered through
workshop sessions with the TSG. Any issues where agreement was not reached during these
workshops were resolved through follow-up emails or memos listing potential approach
options.
The model baseline was constructed to be consistent with draft determinations for PR14.
Most of the expenditure categories follow directly from AMP6 estimates used in Ofwat’s
DDs, which we believe provides an extremely robust platform from which to build the
dataset. There has been an internal audit of the model formulae and functionality, spot
checks on the expenditure predictions compared to historic spend, and detailed comparisons
to forecast expenditure from other sources. The inputs and assumptions were also double-
checked by Vivid Economics as part of the model’s external quality assurance checks and
any Vivid comments related to the inputs and assumptions were revised.69
Some of the major cross-check we performed were based on comparisons with previous
water models. Figure 4.1 shows a high level comparison of the main expenditure categories
between the water bills projection model and the “Changing Course” study carried out by
Severn Trent.70
In each of the three five year blocks, the dark grey (high) “business as usual”
SVT case is presented on the left, followed by the pale gray (low) SVT “Alternative course”
on its right and the high/baseline/low model scenarios shown in increasingly lighter shades of
blue in the third, fourth and fifth columns.
69 In particular, Vivid drew attention to the RPI, RPE and cost efficiency variables, as well as some series that had
inconsistent trends. We have reviewed our approach to RPI, RPEs, and cost efficiency assumptions as a result. We
have also changed some of the forecasting methods in the cases where Vivid pointed out trend issues.
70 Severn Trent Water, “Changing Course – Delivering a sustainable future for the water industry in England and Wales”,
2010.
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Figure 4.1
Comparison of Expenditure in Water Bills Projection Model and SVT Model
Source: NERA output from water bills projection model and SVT “Changing Course”
We understand that the Severn projections were based on a fuller definition of WFD
compliance which made it likely to produce high estimates of expenditure.71
As a result, it
may be most appropriate to consider it against the “upper” model scenario. The model’s
expenditure levels are generally lower than those from Severn. This appears to be largely due
to the magnitude of Severn’s CM and Opex forecasts, which appear to be turning out to be
excessive (at least for the period to 2020 for which the model’s baseline estimates are much
lower).72
Figure 4.2 shows a similar comparison with the Ofwat’s Water Industry Forward Look
(WIFL).73
The WIFL report contained figures at the level of capex rather than CM and
enhancement expenditure, so we combine those cost categories from the model to make the
comparison. Figure 4.2 shows the high/average/low charts from the WIFL report in shades of
brown on the left side of each block, and compares these to the blue high/baseline/low
estimates from the model on the right.
71 We understand that the Severn projections included non-cost beneficial items and was therefore closer to the EA’s
scenario 3, described in more detail in section 4.6.5
72 Note that the Model “Baseline” figures for 2015-20 are relatively accurate as they are consistent with the DDs. The
2015-20 period can therefore be used to examine the validity/bias in the severn figures if we assume that any bias
persists into the later periods.
73 Ofwat, “Water Industry Forward Look 2010-30 – Some Possible Views on the Future”, 2006.
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Figure 4.2
Comparison of Expenditure in Water Bills Projection Model and WIFL
Source: NERA output from water bills projection model and Ofwat’s Water Industry Forward Look 2010-30
Both models appear to have broadly similar levels of capital expenditure. The model capex
projections start out at a very similar level for 2015-20 and, with the exception of the high
scenario, they stay roughly in line with them to 2030. The high scenario capex is
considerably higher – in part due to the elevated EA Scenario 3 WFD costs which are largely
incurred prior to 2027. For opex however, the model estimates are roughly 20% lower than
the WIFL figures in their respective central cases and they remain lower than the respective
WIFL equivalents throughout the horizon.
Overall the Defra projection model has lower levels of total expenditure, and the difference
between the respective central scenarios is 8%, 4%, and 1% for the AMP6, AMP7, and
AMP8 windows respectively. Given that we can have significantly more confidence in the
AMP6 figures from the model compared to those that were available at the time of the WIFL
publication in 2006, it appears that the WIFL model overstated near-term expenditure levels,
particularly for opex. As a result, the similar differences for AMP7 and AMP8 with the best
available comparator suggests a degree of plausibility in the results from the Defra model.
4.3.1. Description of Input Data
The model is populated with the most current data available from public sources or company
projections. We used companies’ final WRMP public data whenever available as well as a
complete set of figures from all companies Draft Determinations as published by Ofwat in the
companies’ respective populated financial models.74
The only companies for whom final
74 Note that where we identified inconsistencies across the DD and WRMP data inputs we favoured the DDs. We also
favoured using projections of DD values over actual WRMP figures where we had noted these inconsistencies during
the AMP6 period. Our assumpitons tended to result in higher HH bill levels over time and therefore can be seen as
conservative. We recommend a model upgrade that reviews and develops a complete and fully internally consistent
data set in section 5.1.
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WRMP were unavailable are Southern Water, United Utilities, and the Essex and Suffolk
region of Northumbrian water. For these companies or areas we have used the draft WRMPs.
In addition to the public data mentioned above, we also requested some granular longer term
non-public data directly from companies on a confidential basis. We received data responses
from ten different companies covering varying durations from 2015 to 2050. We drew on
these submissions to determine reasonable long-term average industry forecasts which we
then applied to all companies (including those which submitted long-term data). This
approach makes use of the confidential data while not preventing the model from being
circulated due to confidentiality agreements. It also means that the model’s projections for
some companies will be different from their own best views.75
In addition to the above-mentioned data sources, we also received company mappings onto
River Basins and WFD compliance cost estimates broken down into opex and capex costs
from the Environment Agency. We used this data to test the long term trend data that we
forecasted based on the approach described in the preceding paragraph. These tests are
described in more detail in section 4.6.5.
4.3.2. WFD Inputs
This section briefly sets out the WFD costs which underlie the baseline. The WFD costs are
implicitly included in the DD figures and the longer term input cost forecasts and this section
aims to make them more transparent. In order to arrive at the WFD costs presented, we asked
companies and the EA to tell us what proportion of each of their enhancement cost items
corresponded to WFD compliance measures. We note that some company forecasts may
only apply to the WFD requirements that are currently known. As a result, the longer term
expenditure forecasts (which are based on the weighted average company submissions) for
the period beyond 2027 (when the current WFD cycle is expected to conclude) may fail to
account for future expenditure requirements. To determine the share of opex costs relating to
WFD compliance we assumed that the ratio of WFD opex to capex costs was approximately
equal to that arising from the opex-capex breakdown of scenario 4 WFD costs provided by
the EA.
Figure 4.3 displays our WFD input assumptions at the industry level for water and sewerage.
We assumed that all water quality expenditure were allocated to sewerage treatment and all
water resource expenditure was allocated to the water resource value chain element. The
figure shows that the costs are much greater on the sewerage side and that these are also more
front-loaded.
75 Our approach to using the longer term data is to calculate an industry level trend based on the companies who have
provided data, and fit this an industry-wide trend to all company regions in the model. For enhancement capex, we
calculate the submitted enhancement cost of all companies that submitted confidential data, and calculated it as a
proportion of AMP6 opex from those same companies. We then repeated the process for the companies that submitted
data for each subsequent AMP period. We then hold this industry level proportion constant across all companies to get
an estimate of long term enhancement expenditure. Capital maintenance is calculated as a percentage change on
expenditure over the previous AMP.
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Figure 4.3
Baseline WFD Input Cost Figures
Source: NERA inputs based on DDs and long term input forecasts over a set of cost categories relating to WFD
We discuss WFD costs in more detail and perform a cross check with the EA’s WFD cost
estimates for Scenarios 3 and 4 in section 4.6.5. We also suggest that a further exercise be
undertaken in the coming months in order to more accurately reflect WFD costs over the long
term.
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4.3.3. Table of key assumptions
Table 4.1 sets out some of the key assumptions used in the model. These primarily pertain to
policy effects or the functionality of other modelling shocks.
Table 4.1
Key Assumptions
Topic Description
Intra-Policy Effects Benefits and costs within all policies have additive effects; the net policy effect is derived from adding the cost (negative) and the benefit
Inter-Policy Effects All policies are treated as independent and therefore multiplicative
Upstream Finance Costs Cost calibrated based on Projected Baseline 2020 RCVs
Leakage and Growth Costs Leakage and growth costs are based on the same supply curve – i.e. the cost of reducing leakage and the cost of adding capacity are the same
DYAA and DYCP data Model uses DYAA and weighted average annual figures - no direct use of DYCP data
Growth expenditure costs Companies with no supply data in their WRMP are modelled using closest reasonable comparator76
Baseline policy effects Baseline effects are implicitly incorporated in input figures
Non-baseline policy effects Non-baseline effects are additional to the input figures
Variable forecasts and shocks
The ordering assumption used is that variables are forecast first, then shocked
Efficiency savings are additive to company data inputs
We assume that companies achieve a varying level of cost efficiency saving that is above and beyond any cost input to the model, including the DD figures and longer-term estimates – we examine a sensitivity to this in Section 4.6.4
Long Term trend forecasts Long term data is forecast using additional data submissions from a subset of companies77
Policy effects at cost category level
Effects must be calibrated to cost category (e.g. botex or enhancement) by value chain
Source: NERA
4.3.4. Outputs Conditional on Current Inputs
The modelling results produced for this document are based on our best endeavours to
compile a robust set of input data from public and private sources. We believe that this data
set represents the best currently available set of public data. We understand that the inputs
76 SWW/WSH get UU curve; NES gets WSX; YKY gets SEW; CW/SB/DVW get PRT
77 See description of input data in Section 4.3.1.
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will be further updated by Defra as the final few WRMPs are published and after Ofwat
produce company Final Determinations..
In interpreting these results it should be borne in mind that:
There is little information available about the more distant future. For many of
our external input variables, such as cost inflation, there are no long-run forecasts
directly suitable for use as data inputs. There is also considerable uncertainty about
the likely regulatory and policy environment, for example about the long-run statutory
requirements to improve wastewater discharges. Assumptions must be made by the
model user - we try to be transparent about those used in this report;
In making assumptions to form long-term data inputs, we face the difficulty that the
future may be different from the past. For some important input data series such as
capital maintenance expenditure requirements we use current average levels and
short/medium-term forecasts made by water companies as a basis for long-run
forecasts, though this relationship might change, for example capital maintenance
needs might increase more than expected as assets become older and change with the
service-quality and climate context. An unforeseen change in the underlying
relationships could make our results misleading, when hindsight can be applied;
Though the model is designed to reflect available data, it is also only a model, one
also designed to be within the computational capacity of Excel avoiding use of
macros. Consequently the most granular level of data, relationship, and result
treated in the model is the company value chain level and an annual time step; no
effects at finer levels are modelled; few feedbacks are covered within the model.
4.4. Baseline Results and Exogenous Variable Sensitivities
This section displays the baseline average bill results as well as those from sensitivities
applying to all of the exogenous drivers at once. In all cases presented in this section the
baseline policies of retail competition, abstraction reform, and upstream competition are all
held at their central “baseline” impact levels. This section also includes some intermediate
output results that show the constituents of water bills in more detail.
4.4.1. Headline Results from a Range of Scenarios
In this section we present the average household bill levels under a range of scenarios for the
factors that are thought to matter most to costs in the sector, hence to bills.
Figure 4.4 shows that by 2050, the range of national average annual household bills goes
from £240 in the lower scenario setting, to £553 in the upper scenario, in real terms. The
black line shows the estimate of real household bills produced under the “baseline” driver
assumptions and scenarios in the model, falling from £355 in 2015 to £343 by 2050. The
variation displayed during the AMP6 period is largely illustrative since the average bills for
this period will be determined by Ofwat’s FDs (unless overruled by Ofwat Interim
Determinations of K or Competition and Markets Authority (CMA) Determinations).
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Figure 4.4
Baseline and Sensitivity Range for Average Household Bills – Real
Source: NERA
The dark blue area around this baseline projection shows the national household average bill
range – in real terms - from setting all of the driver variables (e.g. GDP, population growth,
construction cost inflation, etc.) and baseline policy options (retail competition in 2017,
upstream reform in 2020, and regulatory mechanisms) to their low or high sensitivities. The
high sensitivity includes the WFD Scenario 3 cost estimates which includes non-cost
beneficial solutions (this scenario also accounts for the cost spike in 2015).
The light blue areas at the top and bottom of the fan correspond to the “upper” and “lower”
modelled scenarios, which result from setting all of the drivers and all of the policies to their
high and low settings respectively.78
The impact of setting all the policy drivers to their “low”
settings is more muted, as for some policies the low setting is the same as having them turned
“off” in the baseline, and there is therefore no change from them in the “lower” scenario. In
contrast, the “upper” scenario measures include substantial extra resilience expenditure from
2020 as well as the EA WFD Scenario 3 cost estimates. For these “upper” and “lower”
scenarios we emphasise that currently unforeseen policies and/or extreme conditions could
have impacts that are currently not captured by the model.
78 The upper/lower scenarios includes the non-baseline policies (abstraction reform, greater resilience, PCC targeting, and
private supply pipe adoption) in addition to the baseline policies (retail competition, upstream reform, and regulatory
mechanisms).
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In addition, the input data contained a declining amount of enhancement expenditure - a
situation that may be perceived to be a reflection of failure to allow for ‘‘unknown unknown’’
cost and quality drivers that are likely to materialise in the future. Figure 4.31 in section
4.7.4 displays the declining share of enhancement that results from this situation and leads to
the resulting baseline reduction in average HH bills.
Table 4.2 displays the average household bill levels of the five scenarios at three intervals in
time. The “upper” scenario brings bills up to £553 by 2049, 61% higher than the baseline
estimate for that year. The “lower” scenario bill for 2049 is £237, or 31% below the baseline
by 2049, reflecting the lower levels of bill level reduction uncertainty.
Table 4.2
Average HH Bill Projections by Scenario (Real)
Scenario 2015 2030 2049
Upper £446 £480 £553
High £446 £439 £487
Baseline £355 £364 £343
Low £340 £316 £252
Lower £340 £299 £237
Source: NERA
The individual effects of each policy are discussed in more detail in the following sections.
4.4.2. Results from baseline exogenous variables and sensitivities, holding baseline policies constant
The following subsections describe the model average bill projections for household and non-
household consumers respectively. The sensitivities labelled “high” and “low” indicate that
the high or low sensitivities are applied to all driver variables and baseline policies.
4.4.2.1. Weighted Average Household Bills
Figure 4.5 displays the weighted average HH bills in the baseline compared to the high and
low scenarios over the course of the modelling horizon. In the high case, average HH bills
start higher than the baseline bills with an initial spike in 2015 corresponding to the EA
Scenario 3 WFD costs. After coming down from the initial spike they then grow slowly for
the rest of the horizon. In the baseline scenario the bill levels gradually decline over the
horizon, but the period of gradual decline does not set in until after AMP7 – the point where
additional cost and quality drivers are likely to materialise in the future. The gradual
divergence of bills across these scenarios results from the driver and policy differences used
to form the high case - all drivers (including GDP growth, efficiency effects, enhancement
capex, opex, etc.) and all baseline policies (retail competition, upstream competition, and
regulatory mechanisms) are specified at their higher sensitivity levels in the high case. The
peak bill for the baseline occurs in 2020 at £370, before declining to £342 by the end of the
period. The high case average HH bill peaks at £491 in 2049.
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Figure 4.5
HH Bills - Weighted Average – Baseline, Low and High Scenarios – Real
Source: NERA
The low case HH bills follow an opposite pattern; starting slightly lower than the baseline,
remaining fairly constant over the early years before setting off in a stronger decline from
2020 until the end of the horizon. The difference in bill effects results from the combination
of driver and policy differences. The peak bill for the low case occurs in 2020 at £343. The
end of period bills for the baseline and low drivers cases are £343 and £252 respectively.
The high, low, and baseline cases all remain relatively similar over the AMP6 period
reflecting a degree of consistency of the inputs with the Ofwat DDs. The efficiency rates and
RPEs – two of the most powerful real macroeconomic cost drivers – are held at the same
levels for this period. Any differences to these series can mostly be attributed to assumed
changes in the risk free rates, higher/lower enhancement expenditure levels relative to those
set out in the DDs, or from relatively benign sensitivities over population and property
growth. In concert these divergences result in a relatively narrow and constant bill corridor
over the AMP6 period, which then fans out to reflect the additional uncertainty present
beyond 2020, as can be seen in above (or in context with the historical bills in Figure 4.4).
Given that bills remain roughly constant over the AMP6 period (albeit at slightly different
levels) we present snapshots of the evolution of bills at specific intervals with percentage
change relative to their respective levels in 2020. We display these changes in Table 4.3.
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Table 4.3
Cumulative Effect of the High and Low
Sensitivities on Average HH Bills (Real)
Base Case Low Case High Case
Year Average Bill % Change from 2020
Average Bill % Change from 2020
Average Bill % Change from 2020
2020 £370 £344 £414
2030 £364 -1.8% £316 -8.0% £439 5.9%
2049 £343 -7.5% £252 -26.7% £487 17.6%
Source: NERA
Figure 4.6 shows the weighted average nominal household bill levels in the baseline case
compared to the high and low sensitivities respectively. The effect of RPI, and to a lesser
extent CPI, generates a much wider range of bills than that seen in the real bill charts above.79
In the baseline, RPI is assumed to range between 3.1% - 3.5% during the period to 2021, after
which we hold it constant at 3%. In the low and high cases, it ranges from 1.8% - 3.1% and
3.1% - 4.0% in the early years, before remaining constant at 2.5% and 4.5% respectively. In
contrast, in the period to 2021 CPI is assumed to range between 1.8% - 2% in the baseline,
1.6% - 2% in the low case, and 1.8% - 2.3% in the high case. From 2022 onwards CPI is
assumed to remain constant at 2%, 1.7%, and 3% in the baseline, low, and high cases
respectively.
Figure 4.6
HH Bills – Weighted Average – Baseline, Low, and High Cases Based on RPI and CPI
Inflation – Nominal
Source: NERA
79 We note that the actual nominal bills are determined according to RPI inflation, as stated in company licences. The
CPI-based nominal bills are therefore illustrative only.
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4.4.2.2. Non-Household Bills
Figure 4.7 displays a comparison of the baseline with the high and low non-household real
bill levels respectively over the course of the modelling horizon. In the high scenario there is
an initial spike coinciding to the EA Scenario 3 WFD costs, following which bills drop
sharply and then modestly increase in bills over time. In the baseline they gradually decline
except for a slight increase coinciding with the start of AMP7. This jump in 2020 is caused
in part by an increase in the risk-free rate to bring it back to its long-term average level,
which feeds into a higher return on capital (see section 4.7).
Figure 4.7
Average NHH Bills – Baseline, Low and High Scenarios – Real
Source: NERA
The cumulative change in non-household bills over the period to 2030, from 2030-2050, and
over the entire horizon are shown in Table 4.4.
Table 4.4
Cumulative Effect of the High Drivers and Low Drivers
Sensitivities on Average NHH Bills
Base Case Low Case High Case
Average Bill
% Change from 2020
Average Bill % Change from 2020
Average Bill % Change from 2020
2020 £1,817 £1,593 £1,965
2030 £1,657 -8.8% £1,438 -9.7% £2,066 5.1%
2049 £1,523 -16.2% £1,120 -29.7% £2,246 14.3%
Source: NERA
Figure 4.8 shows the nominal non-household bill levels in the baseline case compared to the
high and low sensitivities respectively. As was the case for HH bills, the case-varying effect
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of CPI and RPI on nominal bills creates quite a large range in average NHH bills despite the
much smaller underlying variation in real bills seen in the previous figures. 80
Figure 4.8
Average NHH Bills – Baseline, Low and High Scenarios Based on RPI and CPI
Inflation - Nominal
Source: NERA
4.4.3. Annual Company-Specific Bills and Other Outputs
This section briefly describes the model capabilities in terms of modelling companies at an
individual level. The model is capable of generating company-specific average bills broken
down by wholesale value chain element and retail component for measured HH, unmeasured
HH, weighted average HH, and NHH customer groups. It can produce these outputs for
water-only, sewer-only, or combined services.
Additionally, the model also produces company-specific totex, allowed revenue, and average-
year RCV outputs, many of which are discussed at the industry level in section 4.7.
Figure 4.9 shows the decomposition of industry-wide allowed household revenue over the
modelling horizon by company. The water only companies make up a small share – just
seven per cent – of industry level allowed revenues, while the larger WASCs account for the
lion’s share of revenues; e.g. Thames Water81
, Severn Trent Water, Anglian Water and
United Utilities comprise 54-55 per cent of allowed revenues in each of the intervals between
2015 and 2050.
80 We note that the actual nominal bills are determined according to RPI inflation, as stated in company licences. The
CPI-based nominal bills are therefore illustrative only.
81 We include the Thames Tideway Tunnel whenever we make reference to Thames Water in the model and the report.
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Figure 4.9
Allowed HH Revenue by Region
Source: NERA
Figure 4.10 and Figure 4.11 show the evolution of average household bills in real terms in
2015, 2030 and 2050 by service.
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Northumbrian Water
South West Water
Southern Water
Yorkshire Water
Welsh Water
United Utilities
Anglian Water
Severn Trent Water
Thames Water
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Figure 4.10
Average Water Bills by Region in 2015, 2030 and 2050 - Real
Source: NERA
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Integrated Final Report Key Model Results
NERA Economic Consulting 77
Figure 4.11
Average HH Sewer Bills by Region in 2015, 2030 and 2050 - Real
Source: NERA
Figure 4.12 shows the modelled evolution of baseline average household combined water and
sewerage bills in real terms in 2015, 2030 and 2050 for each of the WASC regions. South
West Water’s average bills are the highest, while Severn Trent Water’s are the lowest. There
is some convergence towards the industry average due to a greater decrease in the bills in
regions which initially have higher-than-average bills, with the exception of Welsh Water
which moves away from the average over time. South West Water has the highest average
HH bills while Severn Trent Water has the lowest. Most company bills are relatively stable,
although there are some modest decreases for Anglian Water and United Utilities and an
increase for Welsh, Thames and Yorkshire Water. Thames Tideway costs are included in the
figures. The decline in bill levels toward the end of the horizon matches the declining
baseline average bill level presented in Figure 4.4. This is the result of all the baseline
assumptions in concert, principally through their effect on sector cost levels.
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Thames Water Southern Water Severn Trent Water Yorkshire Water
United Utilities Wessex Water Industry Weighted Average
Integrated Final Report Key Model Results
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Figure 4.12
Combined (Water and Sewer) Average HH Bills
Source: NERA
4.5. Policy-Specific Results - using baseline exogenous variables
The following sections show the magnitude of each of the baseline policies. The effects that
these policies have were taken from their respective IA’s. The types of effects described in
the IA were of three types:
One-off: a single effect (typically in the year of implementation);
Productive: an effect that has a constant impact in every year; and
Dynamic: an effect that becomes more and more important over time (in a compounding
fashion).
The policies typically have several effects of different types taking place at the same time.
This can cause the magnitude of the bill effects to reverse (typically after the first year),
diminish, or grow over time.
4.5.1. Retail Competition
We demonstrate the reduction in bills from retail competition policy by displaying the
percentage difference between the model baseline excluding retail competition and the
unadjusted model baseline.82
We adopt this approach to obtain bill effect figures that are
reflective of bill levels that are as accurate as possible, given any synergies between policies,
in contrast to comparing the effects with the other baseline policies switched off. The figures
displayed assume implementation of retail competition in 2017 as specified by the 2014
Water Act.
82 Note that the model baseline includes the effects of retail competition, PR14 regulatory mechanisms, and upstream
competition policies. The driver variables (e.g. GDP, RPI, etc.) are held at their central values.
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NERA Economic Consulting 79
The green and blue bars in Figure 4.13 display the real bill reduction of retail competition
over time for all HH and NHH bills respectively. Positive (negative) changes can be
interpreted as percentage increases (decreases) in bills caused by the retail competition policy.
Most of the effect of this policy is the result of efficiency spillovers from NHH competition
onto HH bills. The effects begin in 2017 when retail competition is implemented.
Figure 4.13
Reduction in Average HH and NHH Bills from NHH Retail Competition
Source: NERA
As can be seen in Figure 4.13, the policy reduces average HH and NHH bills by an
increasingly large amount over the course of the modelling horizon. This follows from the
assumptions underlying the IA, which include compounding “dynamic effects”. The
reduction in the final year of the horizon for HHs is 1.1%, which is equivalent £4 per
household. For NHHs, the savings reach 1.4% in 2049, which is equivalent to £21 per NHH
property.
The impact of the policy relative to the baseline bill levels over time and the cumulative
savings for HH and NHH properties are displayed in Table 4.5 and Table 4.6 respectively.
-0.50%
-0.25%
0.00%
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Table 4.5
Effect of Retail Competition on Average HH Bills
Baseline
Change in Bills w/ Retail Competition
Change in Bills w/o Retail Competition
Cumulative Bill Impact from retail
competition
Average Bills % Change from 2015 % Change from 2015 Savings from 2015
2015 £355
2030 £364 2.4% 2.7% £3
2049 £343 -3.5% -2.4% £47
Source: NERA.
Table 4.6
Effect of Retail Competition on Average NHH Bills
Baseline
Change in Bills w/ Retail Competition
Change in Bills w/o Retail Competition
Cumulative Bill Impact from retail
competition
Average Bills % Change from 2015 % Change from 2015 Savings from 2015
2015 £1,758
2030 £1,657 -5.7% -5.4% £14
2050 £1,523 -13.4% -12.2% £269
Source: NERA
4.5.2. Upstream Reforms
We demonstrate the effects of the upstream reform policy by displaying the percentage
difference between the model baseline excluding upstream reform and the unadjusted model
baseline. 83
Figure 4.14 displays the HH and NHH bill reduction percentages resulting from upstream
reform relative to the baseline scenario. Positive changes can be interpreted as percentage
reductions in bills caused by the upstream reform policy. The savings are principally driven
by ongoing totex efficiencies and an assumed efficiency catch-up from less efficient firms as
a result of the competitive pressures arising in the year of the reforms. The figures displayed
assume implementation of upstream reforms in 2020.
83 Note that the model baseline includes the effects of retail competition, PR14 regulatory mechanisms, and upstream
competition policies. The driver variables (e.g. GDP, RPI, etc.) are held at their central values.
Integrated Final Report Key Model Results
NERA Economic Consulting 81
Figure 4.14
Reduction in HH and NHH Bills from Upstream Reforms
Source: NERA
As can be seen in the figure, the policy reduces average HH bills by an increasingly large
amount over the course of the modelling horizon. The reduction in the final year of the
horizon is 3.7%, or £13, per HH and 4%, or £62, per NHH property relative to the baseline.84
The impact of the policy relative to the baseline bill levels over time and the cumulative
savings for HH and NHH properties are displayed in Table 4.7 and Table 4.8 respectively.
Table 4.7
Effect of Upstream Reforms on Average HH Bills
Baseline
Change in Bills w/ Upstream Reform
Change in Bills w/o Upstream Reform
Cumulative Bill Impact from
upstream reform
Average Bills % Change from 2015 % Change from 2015 Savings from 2015
2015 £355
2030 £364 2.4% 4.0% £33
2049 £343 -3.5% 0.1% £216
Source: NERA
84 Underlying this average bill reduction, in the final year the cost base is reduced by anywhere between zero and 17%
depending on the value chain element and cost component (botex or enhancement). The enhancement effects are
roughly 45% larger than the botex effects.
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Table 4.8
Effect of Upstream Reforms on Average NHH Bills
Baseline
Change in Bills w/ Upstream Reform
Change in Bills w/o Upstream Reform
Cumulative Bill Impact
fromupstream refrom
Average Bills % Change from 2015 % Change from 2015 Savings from 2015
2015 £1,758
2030 £1,657 -5.7% -4.2% £166
2049 £1,523 -13.4% -9.9% £1,050
Source: NERA
4.5.3. Regulatory mechanisms
We demonstrate the effects of the regulatory mechanisms corresponding to PR14 incentives
by displaying the percentage difference between the model baseline with PR09 incentives and
the unadjusted model baseline.85
The regulatory mechanism reforms for PR14 affect bills through five main channels which
we grouped together into one aggregate effect. These channels are: the move to Totex,
menu-regulation, water trading incentives, and the separation of the HH and the NHH retail
controls from the wholesale controls. The abstraction incentive mechanism is also
considered by the IA but it does not quantify financial incentives and therefore has no direct
effect on the bill impacts presented here. The IA bill impacts assume the implementation of
retail competition and upstream reform as their counterfactual - so the effects shown are in
addition to those resulting from those policies.
Figure 4.15 displays the real bill effects of the PR14 incentives and regulatory mechanisms
on HH and NHH bills. Positive percentage changes can be interpreted as decreases in bills
caused by the PR14 incentives. The figures displayed compare the effect of moving from
PR09 strength incentives to those from PR14 over the entire modelling horizon.
85 Note that the model baseline includes the effects of retail competition, PR14 regulatory mechanisms, and upstream
competition policies. The driver variables (e.g. GDP, RPI, etc.) are held at their central values.
Integrated Final Report Key Model Results
NERA Economic Consulting 83
Figure 4.15
Reduction in HH and NHH Bills due to PR14 Incentives and Regulatory Mechanisms
Source: NERA
The effect of the incentives is to reduce bills at a gradually increasing rate with a modest
jump in 2025. The changes in bill reduction percentages in 2025 is not a result of changes to
the incentives themselves (as these are held constant through the horizon); they result from
the knock on effects of changes to expenditure due to other causes (such as changes in
expenditure across AMPs, policy effects, changes in driver variable levels to reflect long term
averages, etc.).
The impact of the policy relative to the baseline bill levels over time and the cumulative
savings for HH and NHH properties are displayed in Table 4.9 and Table 4.10 respectively.
Table 4.9
Effect of PR14 Incentives and Regulatory Mechanisms on Average HH Bills
Baseline
Change in Bills w/ PR14 Incentives
Change in Bills w/o PR14 Incentives
Cumulative Bill Impact of PR14
Incentives
Average Bills % Change from 2015 % Change from 2015 Savings from 2015
2015 £355
2030 £364 2.4% 4.0% £68
2049 £343 -3.5% -1.8% £183
Source: NERA
0.0%
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Table 4.10
Effect of PR14 Incentives and Regulatory Mechanisms on Average NHH Bills
Baseline
Change in Bills w/ PR14 Incentives
Change in Bills w/o PR14 Incentives
Cumulative Bill Impact of PR14
Incentives
Average Bills % Change from 2015 % Change from 2015 Savings from 2015
2015 £1,758
2030 £1,657 -5.7% -5.1% £316
2049 £1,523 -13.4% -12.6% £837
Source: NERA
4.6. Non-Baseline Policies and Policy-Based Sensitivities
This section begins by describing the non-baseline policies which we assume to not currently
be factored in to company expectations. These policies include abstraction reform, private
water supply pipe adoption, and additional per capita consumption targeting measures.
In addition, this section also describes some key sensitivities that have been incorporated into
the model through the channel of policy effects. These sensitivities can be interpreted as
exogenous scenarios, as plausible policies, as checks of the input data that were simplest to
perform through the model’s policy channel, or as checks over combinations of policy effects
in unison. These include a greater resilience scenario, checks on the baseline WFD costs, and
a counterfactual scenario sense check that aims to identify the joint effect of implementing
the three baseline policies.
4.6.1. Abstraction Reform
We demonstrate the effects of the abstraction reform policy by displaying the percentage
difference between the unadjusted model baseline and the model baseline with abstraction
reform. 86
Figure 4.16 displays the real bill effects of abstraction reform on HH and NHH bills. Positive
(negative) percentage changes can be interpreted as decreases (increases) in bills caused by
the abstraction reform policy. The figures displayed assume implementation of abstraction
reforms in 2025. The effect of the policy is to increase bills in the first year of the reform but
then reduce them thereafter. The directional effect on the respective HH and NHH customers
is very similar. The economic significance of this reform on bill levels is very minor.
86 Note that the model baseline includes the effects of retail competition, PR14 regulatory mechanisms, and upstream
competition policies. The driver variables (e.g. GDP, RPI, etc.) are held at their central values.
Integrated Final Report Key Model Results
NERA Economic Consulting 85
Figure 4.16
Impact on HH and NHH Bills from Abstraction Reform – Relative to the Baseline
Scenario
Source: NERA
As can be seen in the axis, the policy increases average HH and NHH bills by a very small
amount (0.1%) in the year of the reform to cover the implementation costs, followed by an
even smaller (0.04%) and roughly constant cost savings over the remainder of the modelling
horizon. The impact of the policy relative to the baseline bill levels over time and the
cumulative savings for HH and NHH properties are displayed in Table 4.11 and Table 4.12
respectively.
Table 4.11
Effect of Abstraction Reform on Average HH Bills
Baseline
Change in Bills w/o Abstraction Reform
Change in Bills w/ Abstraction Reform
Cumulative Potential Impact
Average Bills % Change from 2015 % Change from 2015 Savings from 2015
2015 £355
2030 £364 2.4% 2.4% £0
2049 £343 -3.5% -3.5% £3
Source: NERA
-0.12%
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Table 4.12
Effect of Abstraction Reform on Average NHH Bills
Baseline
Change in Bills w/o Abstraction Reform
Change in Bills w/ Abstraction Reform
Cumulative Potential Impact
Average Bills % Change from 2015 % Change from 2015 Savings from 2015
2015 £1,758
2030 £1,657 -5.7% -5.7% £1
2049 £1,523 -13.4% -13.4% £12
Source: NERA
4.6.2. Private Supply Pipe Adoption
We demonstrate the effects of the private supply pipe adoption by displaying the percentage
difference between the unadjusted model baseline and the model baseline with the supply
pipe adoption policy switched on.87
Figure 4.17 displays the real bill effects of supply pipe adoption over time for household bills.
The negative “reduction” in bills can be interpreted as bill percentage increases caused by the
private supply pipe adoption policy. The figures displayed assume that the adoption of
private supply pipes is undertaken in 2020.
Figure 4.17
Private Supply Pipe Adoption Increases Average HH and NHH Bills – Relative to the
Baseline Scenario
Source: NERA
The effect of the policy is to increase bills slightly in the first year with an additional gradual
increase over time. The additional costs are driven by increasing repair and replacement costs
87 Note that the unadjusted model baseline includes the effects of retail competition, PR14 regulatory mechanisms, and
upstream competition policies. The driver variables (e.g. GDP, RPI, etc) are held at their central values.
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and increased scrutiny costs. The increase in the final year of the horizon is 0.4% per HH and
0.4% per NHH property. The impact of the policy relative to the baseline bill levels over
time and the cumulative bill increases for HH and NHH properties are displayed in Table
4.13 and Table 4.14 respectively.
Table 4.13
Effect of Private Supply Pipe Adoption on Average HH Bills
Baseline
Change in Bills w/o Supply Pipe Adoption
Change in Bills w/ Supply Pipe Adoption
Cumulative Potential Impact
Average Bills % Change from 2015 % Change from 2015
Increases from 2015
2015 £355
2030 £364 2.4% 2.7% £6
2049 £343 -3.5% -3.1% £29
Source: NERA
Table 4.14
Effect of Private Supply Pipe Adoption on Average NHH Bills
Baseline
Change in Bills w/o Supply Pipe Adoption
Change in Bills w/ Supply Pipe Adoption
Cumulative Potential Impact
Average Bills % Change from 2015 % Change from 2015
Increases from 2015
2015 £1,758
2030 £1,657 -5.7% -5.5% £28
2049 £1,523 -13.4% -13.0% £126
Source: NERA
4.6.3. Greater Resilience
We demonstrate the effects of a potential need for greater resilience by displaying the
percentage difference between the model baseline with the greater resilience scenario and the
unadjusted model baseline.88
Greater resilience can validly be considered to be an exogenous
scenario where resilience expenditure is increased without a specific intervention.
Greater resilience is defined as a tripling of the resilience costs for the water and sewerage
services and a 20% increase in the level of target headroom in the base case option.89
The
additional resilience costs are a reflection of the doubling of water supply pipes, installation
of larger sewers to prevent overflows at bottleneck locations, and other such additions that
affect reliability through the network and/or resources but do not necessarily influence the
88 Note that the unadjusted model baseline includes the effects of retail competition, PR14 regulatory mechanisms, and
upstream competition policies. The driver variables (e.g. GDP, RPI, etc) are held at their central values.
89 More specifically, we defined the WASC average proportional expenditure on “resilience” as (Resilience + SEMD) /
(Net Capex + Opex) for water and (Resilience + SEMD + Expenditure to Reduce Flood Risk) / (Net Capex + Opex) for
sewerage. We then uplifted all companies’ actual costs by double this proportion in the base case; making the industry
average resilience spend three times larger in the base case option. For the low case, we double average resilience and
increase target headroom by 10%, and for the high case we quadruple resilience costs and increase target headroom by
40%.
Integrated Final Report Key Model Results
NERA Economic Consulting 88
level of output. The increase to the level of target headroom reflects spending on resilience
of the network and/or resources (for example by adding capacity from multiple sources) and
for increasing capacity at existing sites as a form of protection against external risks such as
natural disasters or harsh climatic conditions.
Figure 4.18 displays the bill percentage effects of greater resilience over time for HH and
NHH average bills. Negative changes can be interpreted as percentage increases to bills
caused by the greater resilience policy. The figures displayed assume implementation of the
change in 2020.
Figure 4.18
Greater Resilience Increases Average HH Bills – Real
Source: NERA
The effect of the policy is to increase HH bills significantly in the first year with an additional
gradual increase over time. The economic significance of this reform is considerable, even in
the base case option (which is the case presented in the figures). As a result, this policy has a
large impact on the overall results when all policies are switched on. In particular, the high
greater resilience sensitivity plays a large role in the magnitude of the upper range of the fan
chart of average HH bills displayed Figure 4.4.
As can be seen in Figure 4.18, the policy increases average HH and NHH bills by
approximately 4% initially, and this cost gradually increases to around 8% in the final year of
the horizon. It is also interesting to note that the model projects falling average HH and NHH
bills in real terms for this scenario, even with this costly additional resilience requirement.
The impact of the policy relative to the baseline bill levels over time and the cumulative
savings for HH and NHH properties are displayed in Table 4.15 and Table 4.16.
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Table 4.15
Cumulative Effect of Greater Resilience on Average HH Bills
Baseline
Change in Bills w/o Greater Resilience
Change in Bills w/ Greater Resilience
Cumulative Potential Impact
Average Bills % Change from 2015 % Change from 2015
Increases from 2015
2015 £355
2030 £364 2.4% 8.3% £170
2049 £343 -3.5% 4.1% £657
Source: NERA
Table 4.16
Cumulative Effect of Greater Resilience on Average NHH Bills
Baseline
Change in Bills w/o Greater Resilience
Change in Bills w/ Greater Resilience
Cumulative Potential Impact
Average Bills % Change from 2015 % Change from 2015
Increases from 2015
2015 £1,758
2030 £1,657 -5.7% 0.0% £844
2049 £1,523 -13.4% -6.0% £3,180 Source: NERA
4.6.4. Cost Efficiency Sensitivity
This section briefly considers a sensitivity on the cost efficiency effect assumptions used in
the model to account for innovation and technological change. This parameter compounds
annually and can therefore have a significant effect on bills, particularly at the later stages of
the horizon. The cost efficiency parameter is applied to opex and capex spending. We have
observed that there is an established precedent for using a 1% efficiency rate reduction – for
example, 1% was the rate assumed in the 2011 Defra retail competition IA.90
91
However,
following discussions with the TSG we note that there is a degree of consensus that a 1%
compounding efficiency rate may not be appropriate over a longer term horizon. In addition,
because Ofwat has already factored in efficiency improvement when arriving at the DDs,
they are also implicitly contained in the DD figures used as inputs in the model.
90 Defra, “Introducing Competition in the Water Sector”, 2011, page 36.
91 We understand that the same rate was assumed for upstream competition and (implicitly) for the PR14 regualtory
mechanisms IA which used retail competition and upstream reform as its counterfactual. The Water Industry
Commission for Scotland also makes this assumption on controllable costs in its SR10 price review, available at:
http://www.scottishwater.co.uk/assets/about%20us/files/strategic%20projections/appendix14costsandefficiency.pdf
Integrated Final Report Key Model Results
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Table 4.17 displays the efficiency rate assumptions that we have assumed for the model. We
make no adjustment during the AMP6 period to be consistent with the DDs by avoiding
double-counting the efficiency savings. In the baseline, we use the 1% efficiency savings
during AMP7 before moving to 0.5% from 2025 onwards. We assume that the high
sensitivity has twice the level of efficiency savings as the baseline. The low case has half as
much during AMP7 and no further savings from 2025. 92
Table 4.17
Assumed Cost Efficiency Rate
Period 2015-2020 2020-2025 2025-2049
Base 0% 1.0% 0.5%
High 0% 2.0% 1.0%
Low 0% 0.5% 0.0%
Source: NERA Assumption based on TSG discussions
Figure 4.19 displays the baseline case with both of the cost efficiency rate sensitivities as well
as a zero efficiency scenario. The fan is approximately symmetric for the high and low
efficiency sensitivities. The low efficiency case has slightly lower bills than the no efficiency
savings scenario, since the effects of the low efficiency sensitivity’s AMP7 savings carry on
throughout the horizon.
Figure 4.19
The Removal of the Cost Efficiency Assumption Increases Average HH Bills – Real
Source: NERA
92 In the model the high and low cases of the cost efficiency incentives are inverted - the high case represents high bills
(and therefore lower efficiency savings). The model’s efficiency rates are also presented as negative values. We do not
present them in this way in the report to avoid confusion.
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Given the importance of the cost efficiency assumptions that go into the model we would
advise that more detailed further work be carried out to reach a long term profile for expected
efficiency savings. This could also be particularly valuable as a basis for the (typically 30+
year horizons) that underlie the NPV estimates in most IAs.
4.6.5. EA’s WFD Scenario Cost Sensitivities
This section examines the WFD costs in the baseline as well as the consistency of the input
WFD data from the DDs and company long-term projections compared to the EA’s scenario
3 and scenario 4 estimates of the cost of WFD compliance by river basin. 93
94
We apply the
EA WFD data as a sensitivity (rather than the baseline) as these figures are EA’s preliminary
estimates.95
The baseline WFD figures are those implicitly contained in the DDs and the
long-term company data submissions that were drawn upon to obtain industry cost trends.
The EA’s scenario 3 pertains to all technically feasible WFD measures, while scenario 4
relates to all cost-beneficial measures only.
Figure 4.20 presents the WFD costs incurred in the baseline scenario.96
As can be seen from
the figure, the sewerage costs are much greater than those for water. The costs are front-
loaded, for sewerage in particular, as WFD planning cycles extend to 2027.
93 EA, “Water for Life and Livelihoods: A consultation on the draft update to the river basin management plan - Part 3:
Economic analysis”, 2014
94 We do not examine the other EA WFD scenarios due to a lack of data.
95 The EA WFD figures used in the sensitivities described in this section are based on water industry figures extracted by
the EA from the National Appraisal Summary Spreadsheet prepared for the Draft Impact Assessment of the 2nd round
of River Basin Management Plans. These figures are preliminary estimates that should be viewed as within +/- 30% of
the true costs.
96 These costs incurred are “outputs” – they are slightly different from the baseline WFD inputs.
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Figure 4.20
Baseline WFD Costs
Source: Model outputs based on DDs and long term data submission
Figure 4.21 shows the impact of the baseline WFD costs on HH bills. It can be seen that the
costs peak at about £10 per property in the final years of AMP7 before dropping to £4-£7
level for the remainder of the horizon. As visible in Figure 4.20, the cost of WFD
compliance is front-loaded in the first two AMPs which results in larger bill impacts from
2015-24.
Figure 4.21
WFD Cost Impact on HH Bills
Source: NERA – difference in projected bill levels in the baseline with and without the NERA baseline WFD
costs
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ills
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The costs were forecast using company long term data submissions, so the estimates
presented may not be particularly accurate. For example, some companies provided constant
AMP6 costs for the entire horizon, therefore implying constant WFD costs.Other companies
may have assumed that no further environmental regulations will be required after the current
WFD cycle concludes.97
We also assume that WFD opex costs are held constant over the
entire horizon.
This sensitivity using the EA figures therefore provides a useful cross-check of whether the
baseline WFD inputs are of the right magnitude. In order to perform the sensitivity, NERA
allocated the river basin cost estimates (provided by the EA) amongst the companies
according to a river basin mapping, which was also provided by the EA. This mapping was
consistent with the EA’s published estimates at the national level (but nothing more granular),
so we examined these sensitivities at the national level only. At the service/value chain levels
used in the Defra model, we allocated all water resource and water quality WFD expenditure
to the water resources and the sewage treatment value chain elements respectively.
The EA figures for scenarios 3 and 4 were estimated at the opex/capex level of granularity.
We applied the capex figures to the model according to the annual profile provided by the
EA.98
For the opex figures, we calculated the annuity value of the total NPV cost and applied
it equally across every year of the horizon. When applying the EA WFD capex figures we
avoid double-counting by:
Netting off from the DD baseline the implied WFD capex expenditure using the
breakdown of costs contained in the company long-term data submissions;
Netting off from the DD baseline a share of opex corresponding to WFD costs, which we
estimate as a proportion from the DD baseline of the implied WFD capex contained in the
company long-term data submissions.99
Figure 4.22 displays the totex projections comparing the baseline to the EA’s scenario 3. The
scenario 3 costs are substantially higher than those in the model baseline. This illustrates the
potential implications of pursing WFD objectives without any provision for less stringent
objectives on the basis of disproportionate expense. The scenario 3 costs peak in 2015 and
2025, when a considerable amount of the capex spending is assumed to be incurred (and was
not spread out in the preliminary EA projections we received). We present the figures as
provided in the EA cost estimates, as interpreted by NERA, although we are aware that this
time profile of the expenditure is not intended to be realistic, particularly for 2015. In terms
of the overall cost profile the scenario 3 costs are higher than those in the baseline (even
without the spikes).
97 Although companies may also have anticipated other equivalent costs in place of those corresponding to the WFD. We
suggest further work on this issue to arrive at more precise estiamtes of the WFD’s impact.
98 The WFD capex includes renewal costs corresponding to the level of WFD costs in the baseline scenario. However, the
difference in capex from the EA WFD cost sensitivities is fed through the model as an input (rather than a driver or
policy shock), such that it does not trigger additional renewal capital maintenance expenditure.
99 For consistency with the opex implied in the EA scenarios, we calculate opex on an annuity basis both when applying
EA cost estimates and when netting off opex costs from company long-term data submissions.
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Figure 4.22
Projected Totex by Cost Component – Baseline including NERA WFD Baseline Costs or
Baseline with EA WFD Scenario 3 Costs
Source: NERA analysis of preliminary EA WFD cost estimate profile
Figure 4.23 displays the Totex projections comparing the baseline to the EA’s scenario 4.
This scenario relates to all cost-beneficial WFD measures. The EA scenario 4 totex costs, as
interpreted by NERA, are similar to those implied by the DD/company submissions overall.
The most notable exception occurs in 2015, when they are considerably higher as the result of
significant capex spending in the EA projections for that year. These figures are preliminary
at this stage, and we understand that they are subject to a range of error of +/-30%. For the
remainder of the horizon they are similar to the baseline - reflecting a reasonable degree of
consistency between the DDs/company figures and the EA estimates.
Figure 4.23
Projected Totex by Cost Component – Baseline including NERA WFD Baseline Costs or
Baseline with EA WFD Scenario 4 Costs
Source: NERA analysis of preliminary EA WFD cost estimate profile
4.7. Intermediate Baseline Outputs
This section describes some of the intermediate outputs for the baseline scenarios. These
include allowed revenue by building blocks, RCVs, aggregate supply, aggregate demand and
different constituents of Totex.
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4.7.1. Total Allowed Revenue Building Blocks
This section compares the bill elements in terms of the respective shares of the allowed
revenue building blocks. These include the PAYG element of Totex, the capital depreciation
element, the portion corresponding to the return on capital (WACC multiplied by RCV), the
tax allocation, and the retail element. Figure 4.24 displays each of these elements for the
baseline case.
Figure 4.24
Total Allowed Revenue by Building Blocks – Baseline
Source: NERA
The figure shows that the industry’s total allowed revenue level grows slightly in the first ten
years of the horizon and then remains roughly constant to the end of the horizon. The return
on capital element increases in 2020 as a result of the assumed risk-free rate returning to its
long-term average rate.100
Each of the revenue building blocks remains fairly constant for the
second half of the period.
4.7.2. Average Year RCV
This section considers the changes to average year RCV over the course of the modelling
horizon. Figure 4.25 and Figure 4.26 display the level of depreciation against enhancement
spending and capital maintenance respectively. This gives an indication of the factors
underlying the changes to the RCV over time. The enhancement capex adds to the RCV
while depreciation reduces it. The CM adds to it, but only the share proportional to one
minus the PAYG ratio (which we make clearer in the subsequent figure).
100 During AMP6, we set the risk-free rate according to the figures reported by PWC in a technical appendix to Ofwat’s
business planning expectations document.
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Tax Retail Depreciation Return on Capital PAYG Element of Totex
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Figure 4.25
Depreciation and Enhancement Capex – Baseline Case
Source: NERA
Figure 4.26
Depreciation and Capital Maintenance – Baseline Case
Source: NERA
Figure 4.27 shows the net changes to the RCV over the modelling horizon. The figure
displays the industry’s enhancement expenditure (as shown in Figure 4.25) added to the share
of CM that is added to the RCV. The RCV is growing when this sum is greater than the
depreciation series, which is the case in the first ten years of the horizon. Following that
point, the RCV stabilises in the base case as the increasing levels of CM additions are offset
by a gradual decline in enhancement expenditure.
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Figure 4.27
Net Changes to the RCV
Source: NERA
Figure 4.28 displays the aggregate year-average RCV for the industry over time. The
baseline assumption is that the initial RCV allocation is based on the unfocussed approach,
where the RCV is allocated proportionally to each value chain’s MEAV. As a result, it can
be seen from the figure that the year-beginning 2015 RCV is heavily weighted toward the
network elements (sewage network and treated water distribution). Over time, the RCVs are
continually depreciated and any costs incurred are allocated to the relevant value chains,
thereby producing a slightly larger share of sewage treatment, sludge treatment, and water
resources within RCV.
Figure 4.28
RCV by Value Chain Element - Baseline Case – Unfocussed Case
Source: NERA
4.7.3. Supply and Demand Volumes
Figure 4.29 displays the aggregate supply and demand picture for the industry. The supply
measure displayed is the volume of water available for use (WAFU). The demand volume is
based on the modelled volume of distribution input (DI). The gap between the two measures
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Water Resources Raw Water Distribution Water Treatment Treated Water DistributionSewerage Network Sewage Treatment Sludge Treatment Sludge Disposal
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constitutes the level of headroom. When an individual company cannot meet its target level
of headroom it builds additional capacity. In the aggregate, capacity is roughly constant. The
volume of DI however initially slumps reflecting the large scale metering roll-outs planned
by most companies. After an initial decline that coincides with aggressive metering
programmes, DI gradually climbs to reflect the underlying population and property growth
(eroding any excess headroom for those companies that have it) and it appears that additional
capacity will be required at the end of the modelled horizon.
Figure 4.29
Aggregate Supply and Aggregate Demand - Baseline Case
Source: NERA
4.7.4. Outputs by River Basin
The model is also capable of presenting outputs by river basin area. Table 4.18 displays the
AMP6 bills for each of the eleven river basin areas. Bills for the Humber and Severn river
basins are the lowest while those for South West and Western Wales are the highest. The
mapping of bills from company regions to river basin areas was performed using river basin
data provided by the EA.
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Table 4.18
AMP6 Average HH Bills by River Basin Area (Real)
River Basin Area 2015 2016 2017 2018 2019 AMP6
average
Anglian RB 377 373 369 368 363 370 South East RB 378 376 377 374 374 376 Thames RB 336 344 344 341 344 342 Humber RB 320 314 315 320 329 319 Northumbria RB 342 352 356 357 357 353 Severn RB 338 335 332 328 330 332 South West RB 443 444 443 435 430 439 Dee RB 368 368 367 365 366 367 North West RB 361 359 360 363 368 362 Western Wales RB 421 420 414 409 408 414 Solway Tweed RB 360 361 363 364 369 363
Source: NERA
4.7.5. Constituents of Totex
4.7.5.1. Baseline Totex by Value Chain and Cost Components
Figure 4.30 sets out the Totex projection by value chain element. The largest component for
water is treated water distribution. For sewerage, the largest value chains elements are
sewage treatment and the sewerage network. The categories that are declining faster than
average have lower ongoing costs allocated to them relative to the level of depreciation
(which is based on the assumed value chain assets lives). Table 4.19 presents the figures
underlying the figure in 2015, 2030, and 2049.
Figure 4.30
Projected Totex by Value Chain Element - Baseline Case
Source: NERA
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£m (
2012
/13)
Water Resources Raw Water Distribution Water Treatment Treated Water DistributionSewerage Network Sewage Treatment Sludge Treatment Sludge Disposal
Integrated Final Report Key Model Results
NERA Economic Consulting 100
Table 4.19
Baseline Projected Totex Expenditure Figures by Value Chain – Real (£m)
Value Chain 2015 2030 2030 % change
from 2015 2049
2049 % change
from 2015
Water Resources £562 £519 -8% £448 -20%
Raw Water Distribution
£147 £130 -11% £129 -12%
Water Treatment £989 £979 -1% £1,001 1%
Treated Water Distribution
£2,176 £1,947 -11% £1,987 -9%
Sewerage Network
£1,630 £1,390 -15% £1,392 -15%
Sewage Treatment
£1,690 £1,533 -9% £1,671 -1%
Sludge Treatment £507 £462 -9% £504 -1%
Sludge Disposal £153 £104 -32% £97 -37%
Industry Total £7,854 £7,064 -10% £7,228 -8%
Source: NERA
Figure 4.31 sets out the totex projection by cost component. The opex component is roughly
the same size as the combined enhancement and maintenance capex spend. Table 4.20
presents the figures underlying the figure in 2015, 2030, and 2049. The figure shows that the
gradual decline in totex is largely due to a reduction in enhancement capex spending. This
reduction in capex roughly coincides with the 2027 conclusion of the river basin management
plan cycles in the current WFD. At present, no known large scale capex programme is
projected after the WFD, but it is possible that further quality or environmental improvements
will need to be implemented. As a result, the decline in totex should be taken to represent a
starting point from which additional options will be considered.
Integrated Final Report Possible Model Developments
NERA Economic Consulting 101
Figure 4.31
Projected Totex by Cost Component - Baseline Case
Source: NERA
Table 4.20
Baseline Projected Totex Expenditure Figures– Real (£m)
Value Chain 2015 2030 2030 %
change from 2015
2049 2049 %
change from 2015
Operating Expenditure
£3,314 £3,213 -3.1% £3,024 -8.8%
Capital Maintenance
£2,662 £2,791 4.8% £3,416 28.3%
Enhancement £1,877 £1,060 -43.5% £789 -58.0%
Industry Total £7,854 £7,064 -10.1% £7,228 -8.0%
Source: NERA
5. Possible Model Developments
This chapter lists some of the upgrade options that may be desirable in the future. The model
contains a wealth of easily accessible information in its current form, but the results it
provides often raise additional questions. These further questions will sometimes require
additional model development which raises the question of answering which developments
constitute a sufficiently productive use of Defra resources.
This section is set out as follows:
Section 5.1: Lists some dataset updates that are likely to become desirable;
Section 5.2: Describes some potential structural upgrades to the model;
Section 5.3: Sets out a range of possible policy option additions or refinements; and
Section 5.4: Displays a non-exhaustive set of possibly useful output additions and further
output analysis tools.
0
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01
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3)
Opex Capital Maintenance Enhancement Capex
Integrated Final Report Possible Model Developments
NERA Economic Consulting 102
5.1. Dataset Upgrades
This section describes some relatively straightforward dataset. These typically involve small
changes to the input sheets and potential refinements of the data processing or pre-processing
(the later relating to processing of data prior to inputting it into the model). Figure 5.1
displays the relevant model areas for these upgrades superimposed onto the model’s technical
structure map.
Figure 5.1
Dataset Upgrades Mapping
Source: NERA
Table 5.1 displays some of the more relevant potential dataset updates or upgrades. These
include updating the dataset following the publication of Ofwat’s Final Determinations,
updating the WFD cost figures that are fed into the model, and developing a more robust
approach to building a long term dataset.
Integrated Final Report Possible Model Developments
NERA Economic Consulting 103
Table 5.1
Potential Model Upgrades: Data Updates
Item Description Resource
Implication
Dataset update following release of the
FDs
Data update and quality assurance following the December 2014 release of company Final Determinations and any subsequent CMA
Determinations
Low
WFD or other specific cost updates
Refinement of WFD or other specific cost data and testing over a more comprehensive range of options
and sensitivities Medium
Constructing a Future WFD scenario
Working with stakeholders to identify key assumptions for constructing a future WFD scenario that could begin after the 2027 ‘full implementation’
date of the current WFD programme
Low/ Medium
Development of a new long term dataset
Incorporate long-range inputs derived from any new sector long-range scenario studies and conduct full
review / update of consistency across all data inputs Medium
Review of long term RfR, RPEs, and Cost Efficiency forecast
assumptions
Perform a thorough review of the compounding parameters that have a large bearing on company expenditure in the long term - including the rate of efficiency assumptions, risk free rate, and real price
effects
Medium
Update retail policy effects
Formally update the retail policy effects using the updated (Oct 2014) retail competition IA101
Low/ Medium
Dataset update matching retail reform
developments
Update system costs, wholesale cost allocations, and retail margin projections, as they become known
Model: Low Data: Medium
Source: NERA
101 We understand that, due to the offsetting nature of the changes in the 2014 update, the bill impacts are likely to be
almost unchanged.
Integrated Final Report Possible Model Developments
NERA Economic Consulting 104
5.2. Structural Upgrades
This section describes some of the structure upgrades that would require changes to the core
of the model. These would likely involve major changes to the model calculation tabs and
potentially some refinements to the user interface and macro scenarios sheets. Figure 5.2
displays the relevant model areas for these upgrades.
Figure 5.2
Structural Upgrades Can be Mapped to Various Areas of the Model
Source: NERA
Table 5.2 displays a set of potential structural model upgrades. These include adding a value
chain element, allowing for companies to split or merge into smaller or larger entities, and
developing confidence intervals using Monte Carlo simulations.
Integrated Final Report Possible Model Developments
NERA Economic Consulting 105
Table 5.2
Potential Model Upgrades: Structural
Item Description Resource
Implication
Addition of Flood Assets Value Chain
Adding flood asset value chain modelling by converting one of the spare value chain elements
Model: Low Data: Unknown
Allowing De-Integration and/or Merger
Adding the functionality to have company regions break apart into smaller units or be merged
Medium/ High
Company-Specific Policy Effects
Add controls allowing the user to select company-specific policy-impact factors that transform the industry-wide effects into a more tailored effect
across each of the companies
Medium
Options to Impose Constraints into the
Model
Develop model interface options that allow the user to set variables (such as the PAYG ratio or the level
of the RCV) as fixed Low
Align borrowing conditions with Totex
and RCV level
Develop industry-wide or company-specific WACC rates based on existing totex and RCV levels
Medium/ High
Research on focussed vs unfocussed RCV
Determine potential consequences of focussed and unfocussed approaches to splitting the RCV, in
particular regarding impact of upstream competition Medium
Develop links between climate/population and
resilience/capacity
Build in interactions between climate and population growth conditions with resilience costs and network
capacity Medium
Tidying formulas for transparency
Changes to model formula to reduce the existence of #N/A or #Value errors in places where there is no data available (i.e. which are not modelling errors)
and addition of more checks
Low
Economies of Scale: Fixed and Variable
costs switch
Build in an assumption switch which allows users to set the level of sewerage treatment and other costs which are based entirely on fixed unit costs versus those which have decreasing unit costs for higher
levels of treatment due to economies of scale
Medium
Monte Carlo Simulation
Allowing a loop to form and simulate a series of scenarios relating to climate and macroeconomic
conditions (GDP, RPI, WACC, etc.) and report results as distributions
Medium/ High
Source: NERA
Integrated Final Report Possible Model Developments
NERA Economic Consulting 106
5.3. Policy Upgrades
This section describes some of the policy upgrades that would require changes within the
model’s policy impact sheets. In addition, these upgrades would involve a significant amount
of work on pre-processing the new policy effects into inputs that can be fed into the model.
Figure 5.3 displays the relevant model areas for these upgrades.
Figure 5.3
Policy Upgrade Mapping
Source: NERA
Table 5.3 lists various additional policy options with widely ranging resource implications.
These include refinements to the current baseline policies and model upgrades to account for
different types of innovation and potential bulk water trading solutions.
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NERA Economic Consulting 107
Table 5.3
Potential Model Upgrades: Further Policy Options
Policy Item Description Resource
Implication
HH Retail Competition Add household retail competition, as an option Medium
NHH Retail Competition Add non-household retail competition as an option for all E&W
Low
More Granular Representation of Upstream Competition
Add options representing the impacts of upstream competition on water, sewerage, and sludge individually
Medium
Abstraction Reform Add options for different degrees of trading: “enhanced”, all zones
Medium
Abstraction Reform Review policy impacts using updated estimates as they become available and revisit assumptions underlying all impact categories
Low/ Medium
Levels of Innovation Options for various forms of innovation in detail Low
Representation of Regulatory Mechanisms Individually
Analysing the effects of each regulatory mechanism independently (Totex, AIM, ODIs, SIM,…), including potential RCV penalties
High
Integrate Bulk Water Trading
Make exports and imports decided within the model
Very High
Source: NERA
Integrated Final Report Possible Model Developments
NERA Economic Consulting 108
5.4. Output Upgrades and Output Analysis Tools
This section describes some of the policy upgrades that would require changes within the
model’s output tabs. These upgrades could also potentially consist of standalone excel
documents that model results can automatically be fed into for analysis. Figure 5.4 displays
the relevant model areas for these upgrades.
Figure 5.4
Ouput Upgrade Mapping
Source: NERA
The model’s approach for storing outputs allows the user to produce custom graphical results
using standard Excel tools. Table 5.4 outlines some possible upgrades to the model for a
richer visualisation of results beyond the set currently provided.
Integrated Final Report Possible Model Developments
NERA Economic Consulting 109
Table 5.4
Potential Model Upgrades: Further Outputs
Item Description Resource
Implication
Detailed Model Architecture
Addition of a Logic Tree schematic showing the interaction of the model’s key elements.
Low
Formula Description Description of key formulas in words for ease of model comprehension and auditing
Medium
Influence Diagrams Schematic that shows the relative impacts of each of the different macroeconomic and driver assumptions
Medium
Measured vs Unmeasured HH bill differential analysis
Analysis companring the size of HH vs NHH bills by service for each company region or river basin.
Low
Effect of bill changes at the value chain level
Expand the bill change outputs by providing charts and tables showing the changes and their effects at the value chain level (at the industry or company level)
Medium
Capital enhancement analysis by VC
Breakdown of capital enhancement expenditure by value chain
Low
Aggregation and comparison of bills
Comparisons of bills by region (i.e. South and east vs north and west averages), all bills comparisons over time (e.g. UM-HH, M-HH, NHH, 2020 vs 2030 average)
Low
Tool for identifying and analysing policy effects
Detailed comparison of differences between bill effects for policy option A and policy option B
Low
Collecting and comparing multiple scenarios
Tool for analysing multiple scenarios simultaneously Medium
Source: NERA
Integrated Final Report Elasticity Effects
NERA Economic Consulting 110
Appendix A. Elasticity Effects
Economic theory suggests that customers respond to changes in product prices by adjusting
their final demand for that product. The measure of customer responsiveness to price
changes is termed price elasticity of demand.
Because the price elasticity of demand for water is not zero, an ideal water bill model should
contain a feedback loop between projected price and projected demand. The feedback loop
would take the model projected price, given a set of projected totex, and apply the price
elasticity figure to calculate the change in demand given the change in price. Then, it would
calculate the change in totex given the change in demand, and use the revised totex to
calculate a new price. The process is iterated until the change in the level of demand as a
result of a change in price is within a tolerance limit.
To capture the effects of prices on demand, while keeping the model as simple and flexible as
possible, we will include a forward-only feedback effect (where demand levels at t+1 depend
on prices at t). The forward-only feedback effect will allow the user to test for the effects of
prices changes as a result of metering, for example. This effect will apply to increases and
reductions in prices. This simplification compensates for the omission of an in-period
feedback loop while making the model considerably simpler and more flexible to update.
Each of the elasticities is off in the baseline. This section describes the effects of these price
elasticities that can optionally be activated in the model. There are three elasticity options:
measured HH elasticity with respect to price, NHH elasticity with respect to price, and
elasticity with respect to energy prices. We examine each of these individually in the
following subsections.
5.4.1. Measured HH Elasticity
This section displays the impact of the price elasticity on HHs. This elasticity is a volume
consumption response to the change in price observed the previous year. For example, if
average measured bills increase by 1% from the year 2015 to 2016, then measured HH
consumers reduce their volumetric consumption in 2017 by the magnitude of the elasticity
(defined at 0.14%) from what they otherwise would have been. The lag in the volume
adjustment was specified in order to make the model faster to run and simpler, as no
equilibrium dynamics are required.
Figure A.1 displays the effect of Measured HH price elasticity compared to the baseline on
average HH bills. It can be seen from the axis that the elasticity effects have very little
overall impact. There are a few small jumps coinciding with years where sizable falls or
increases in bills occur. Most notably, there is a bill reduction in 2021 following the increase
in average bills that occurs in 2020 (the 2020 increase occurs for reasons unrelated to the
price elasticity). The measured HH price elasticity has a similarly minor effect on average
NHH bills.
Integrated Final Report Elasticity Effects
NERA Economic Consulting 111
Figure A.1
Reduction of Average HH Bills from HH Price Elasticity - Real
Source: NERA
5.4.2. NHH Elasticity
This section displays the impact of the price elasticity on NHH customers. This elasticity is a
volume consumption response to the change in price observed the previous year. For
example, if average measured bills increase by 1% from the year 2015 to 2016, then NHH
consumers reduce their volumetric consumption in 2017 by the magnitude of the elasticity
(defined at 0.14%) from what they otherwise would have been. As was the case for the
measured HH elasticity, the lag in the volume adjustment was specified in order to make the
model faster to run and simpler.
Figure A.2 displays the effect of the NHH price elasticity on NHHs. It can be seen from the
axis figures that the elasticity effects have very little impact. This is because the effects tend
to cancel out across companies and over time. The NHH elasticity also has no visible impact
on HH bills.
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Difference Between "Baseline" and "Baseline w/ HH Elasticity" (left axis) Baseline Average Bills (right axis)
Integrated Final Report Elasticity Effects
NERA Economic Consulting 112
Figure A.2
Reduction of Average NHH Bills from NHH Price Elasticity – Real
Source: NERA
5.4.3. Elasticity with Respect to Energy Prices
The model’s third elasticity relates to HHs reducing their volumetric water consumption as a
result of wanting to cut back on energy costs. NHHs are not subject to this elasticity in the
model. For HHs, the elasticity is a volume consumption response to the change in energy
prices observed the previous year, as measured by a DECC energy index forecast. For
example, if the energy cost index increases by 1% from the year 2015 to 2016, then measured
HH consumers reduce their volumetric consumption in 2017 by the magnitude of the
elasticity (defined at 0.17%) from what they otherwise would have been. As before, the lag
in the volume adjustment was specified in order to make the model faster to run and simpler.
Figure A.3 and Figure A.4 display the effects of HH energy price elasticity on HHs and
NHHs respectively. The volumetric response for households leads to a decrease in water use
and an overall decrease in average HH bills. Contrastingly, NHHs experience slightly higher
average bills because their proportional share of water consumed increases as a result of the
HH volume reduction.
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Difference Between "Baseline w/ NHH Elasticity" and "Baseline" (left axis) "Baseline" Average Bills (right axis)
Integrated Final Report Elasticity Effects
NERA Economic Consulting 113
Figure A.3
Reduction of Average HH Bills from HH Energy Price Elasticity – Real
Source: NERA
Figure A.4
Reduction of Average NHH Bills from HH Energy Price Elasticity – Real
Source: NERA
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Difference Between "Baseline" and "Baseline w/ Energy Elasticity" (left axis) "Baseline" Average Bills (right axis)
Integrated Final Report Specification Issues Agreed with TSG
NERA Economic Consulting 114
Appendix B. Specification Issues Agreed with TSG
NERA invited comment on certain unresolved aspects of the proposed modelling structure
during the TSG meeting scheduled for June 19th
. These issues and a brief summary of the
conclusions reached by the TSG are listed below:
Representation of the AMP6 period (2015-2020): during the period to 2020 the
model’s projected bill levels in the baseline case will be those in the BPs/DDs/FDs as
available. When alternative scenarios are modelled we propose that the bills flex
annually, to reflect changes in the drivers. We propose that this be done in the alternative
scenario for the AMP6 years’ bills as well, for modelling simplicity, even though those
bills will be a poor approximation. The alternative modelling approach is that in the
alternative scenario the AMP6 bills be overwritten by the PR14-set bills, perhaps with a
recourse adjustment in 2020 for the outturn differences. We propose not to include these
recourse effects in the model to keep it simple.
− The TSG agreed with our view that the building block approach should be used
throughout the modelled period, including during PR14.
Representation of regulatory mechanisms: in the baseline the model will implicitly
assume that the PR14 regulatory mechanisms continue to apply, annually, throughout the
modelled period unless a policy change having a different strength of regulatory
incentives is switched in by the user. As a result, bills will immediately adjust to reflect
changes in costs caused by (for example) a change in energy prices. This is clearly
approximate but is simple to model and seems fit for the purpose of the model.
− The TSG confirmed that this was a sensible approach.
No explicit representation of entrants: we propose to model a sole notional supplier of
each value chain element in each company-region in each year – the incumbent – with
final bills reflecting the associated costs. The alternative is to explicitly represent the
entrants as well, but this substantially increases the size and complexity of the model.
− The TSG confirmed that modelling entrants was outside the scope of the project.
Representation of price elasticity: we propose not to find an equilibrium supply-
demand position in each scenario in each year. This is to keep the modelling simple. We
intend to investigate whether a forward-only price elasticity effect can be introduced
(where demand levels at t+1 depend on prices at t), without slowing the model too much.
− The TSG agreed with our view but request that we prioritise integrating a forward-
only price elasticity effect.
Representation of upstream competition options: we propose to model the options
from the 2013 IA, with the preferred option in the baseline. However the eventual
implementation here seems very uncertain. Confirmation or alternative suggestions are
sought.
− The TSG confirmed that this was a sensible approach.
Representation of retail competition including the exit possibility: we propose to
model NHH retail competition from 2017 in the baseline, with the ability to switch in
greater or lesser strength effects. However exit from retail is now being discussed.
Confirmation, or suggestion of a varied option to model, is sought.
Integrated Final Report Specification Issues Agreed with TSG
NERA Economic Consulting 115
− The TSG requested that we have a policy option for voluntary exit based on the
corresponding ‘optional separation’ option in the latest IA, adjusted to remove the
effects of HH competition.
Providing and confirming long run data, and value-chain data: for some drivers we
believe that water and sewerage companies will have a longer term view of the likely
expenditure requirements than has been published and perhaps than is held by Ofwat or
the EA. Examples of such areas might be capital maintenance, and resilience, and LoS
areas. We also believe that in some cases the companies will have a better idea on how
expenditures should be attributed to different value chain elements. We propose to ask
companies to check our proposed data inputs for such items, and to suggest their own
figures where these are better. We invite discussion on company preparedness to
undertake this exercise, and on any confidentiality conditions that might be necessary for
the exercise.
− Company representative of the TSG thought that the companies would likely be
willing to supply data for the project provided the exercise is not too onerous and
conditional on their other obligations regarding the price review.
Extent of Representation of Interdependencies: many elements of the model are in
reality interdependent to an extent. We propose to model only the clear and major causal
effects. This is to keep the modelling simple and to avoid having so many “automatic
adjustments” that it becomes hard to trace the effect of an input change on an output
variable. We invite comment on this approach; in particular, we invite suggestions as to
the interdependencies that TSG members have found to be important in previous
modelling work of this sort.
− The TSG suggested two additional interdependencies which we will consider
incorporating into the model: (1) Greenness which could be made to reduce demand;
and (2) GDP which could be linked to non-PWS water demand hence the cost at the
abstraction point.
Integrated Final Report Glossary
NERA Economic Consulting 116
Appendix C. Glossary
AMP – Asset Management Plan (AMP6 corresponds to 2015-2020)
BP – Business Plan
CoC – Cost of Capital
DD – Draft Determinations
Defra – Department for Environment, Food & Rural Affairs
EA – Environment Agency
FD – Final Determinations
HH – Household
NHH – Non-Household
IA – Impact Assessment
IRE – Infrastructure Renewals Expenditure
LoS – Levels of Service
MNI – Maintenance Non-Infrastructure
Ofwat – The Water Services Regulation Authority
RB – River Basin
RCV – Regulatory Capital Value
RfR – Risk Free Rate
RPE – Real Price Effect
WASC – Water and Sewerage Company
WFD – Water Framework Directive
WOC – Water-only Company
WACC – Weighted Average Cost of Capital
Integrated Final Report
NERA Economic Consulting 117
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