ITRC Fast Track Analysis

Introduc)on to the UK Infrastructure Transi)ons Research Consor)um

Prof Jim Hall FREng Principal Inves.gator ITRC

Director, Environmental Change Ins.tute, University of Oxford

29 February 2012

What are the desirable a@ributes of a Na)onal Infrastructure?

•  Priori.sing the capacity constraints to economic prosperity

•  Achieving carbon reduc.on commitments •  Ensuring energy security •  Well adapted to a changing climate •  Resilient to natural and man-‐made hazards •  Robust to a full range of future uncertain.es •  Financially feasible •  Within an appropriate governance framework

2011 Na)onal Infrastructure Plan

•  Underlines the economic importance of na.onal infrastructure

•  Sets out a detailed plan for infrastructure delivery in the coming years

•  Explores new sources of funding and finance

•  Proposes metrics for monitoring infrastructure performance 3

Challenges in delivering the vision

•  Analysing the long term state of NI systems •  Uncertain.es e.g. in demand, economic condi.ons, costs, performance

•  The complexity of mul.ple governance arrangements and projects

•  The capacity of UK industry to compete in globalised markets for infrastructure services

ITRC Aim and Ambi)on

Aim: To develop and demonstrate a new genera.on of simula.on models and tools to inform the analysis, planning and design of na.onal infrastructure Ambi%on: Enabling a revolu.on in the strategic analysis of NI provision in the UK… whilst at the same .me becoming an interna.onal landmark programme recognised for novelty, research excellence and impact.

ITRC Key Ques)ons and Objec)ves

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1.  How can infrastructure capacity and demand be balanced in an uncertain future?

2. What are the risks of infrastructure failure and how can we adapt Na.onal Infrastructure to make it more resilient?

3.  How do infrastructure systems evolve and interact with society and the economy?

4. What should the UK's strategy be for integrated provision of NI in the long term?

Programme Overview

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Consor)um

Lead Universi)es •  Cardiff University •  University of Leeds •  University of Southampton •  Newcastle University •  University of Oxford •  University of Sussex •  University of Cambridge Support •  Engineering and Physical Science Research Council Programme Grant £4.7 million •  University contribu.ons £1 million •  Industry contribu.ons £1.6 million

Partnership Over 40 partners in industry and government: •  Contractors •  Engineering & mul.-‐disciplinary consultants •  Engineering ins.tu.ons •  Government departments, agencies & local authori.es •  Insurers •  NGOs •  U.lity companies On-‐going collabora.on and dissemina.on arrangements

CuQng through the complexity

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The ITRC Fast Track Analysis

Objec.ves: 1. Ensure that the ITRC research programme is building upon exis.ng knowledge. 2. Refine the scope of the ITRC research. 3. Pilot and communicate new analysis concepts. 4. Strengthen the rela.onship between the research team and the consor.um’s partners in government and industry.

Agenda

16:40 Harnessing stakeholder and partner par%cipa%on in the co-‐produc%on of transi%on strategies

16:50 Fast Track Analysis Methodologies and Results 17.10 ITRC complex systems concepts, methodologies and

the modelling framework 17.30 Discussion session 18:00 Drink recep%on

Harnessing stakeholder and partner par.cipa.on

Ben Kidd

ITRC Partners

Over 40 partners in industry and government: •  Government departments,

agencies and local authori.es •  U.lity companies •  Engineering and mul.-‐disciplinary

consultants •  Contractors •  Insurers •  Research organisa.ons and data

providers •  Engineering ins.tu.ons •  NGOs

Coordinated research & knowledge exchange

Linking in with affiliated and other similar projects: •  ARCC Coordina.on Network

•  LWEC Infrastructure Challenge

•  EPSRC-‐funded projects •  EU-‐funded projects

ITRC Stakeholder communica)ons

•  Newslefer (over 450 contacts)

•  Website (www.itrc.org.uk) •  Twifer

Co-‐produc)on in prac)ce

Good engagement via FTA report development –  Produc.ve stakeholder review workshop (30th Oct 2011) –  Electronic and paper-‐based reviews of drais of the FTA report

–  “Comments Log”, providing an audit trail

ITRC impact -‐ Informing policy and prac)ce

Early impacts: •  Infrastructure UK engagement

•  ICE State of the Na.on: Water resources engagement

•  England Waste Strategy (Defra, ICE)

•  RSSB futures work

Fast Track Analysis Methodologies and Results

Dr Jus)n Henriques

Overview of the FTA Methodology

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Highgrowth

Medium growth

Lowgrowth

Capacity-intensive (CI)

strategy

Capacity-constrained

(CC) strategyDecentralisation

(DC) strategy

Decision-maker goals & key questions

Sector analysismodels

Policy & technologyevaluation of performance

FTA growth scenarios Cross-sectoral transition strategies Performance evaluation

Figure 1

HIGH

HIGH

HIGH

LOW

LOW

LOW

Populationgrowth

Economicg rowth

Energycosts

Populationgrowth

Economicg rowth

Energycosts

Developing Scenarios: Drivers of Change

Primary drivers of change •  Demographic change •  Energy prices •  Economic growth

Secondary drivers •  Climate change •  Carbon emission targets •  EU direc.ves and Na.onal

standards •  Others

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Scenarios

Figure 2

Developing Scenarios

Low Growth •  Popula.on (Fig 4) •  GDP growth: 1.6% •  Energy costs: DECC high* Medium growth •  Popula.on (Fig 4) •  GDP growth: 2.3% •  Energy costs: DECC central* High Growth •  Popula.on (Fig 4) •  GDP growth: 3.0% •  Energy costs: DECC low*

*assump.ons of fossil fuel price

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Scenarios

Population of Great Britain projections 2008–2100

2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

120

90

60

30

GB High projectionGB Principal projectionGB Low projection

Popu

latio

n (m

illio

ns)

90+85–8980–8475–7970–7465–6960–6455–5950–5445–4940–4435–3930–3425–2920–2415–1910–14

5–90–4

Figure 3

Demand and Supply Models

For each sector… •  Created demand projec.ons

based on the three scenarios

•  Constructed supply-‐side and demand management op.ons for each strategy

•  Evaluated performance –  Common set of performance

measures, including cost, emissions, and security of supply

Sector Models

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Water demand projections and capacity implications from climate change

20,000

15,000

10,000

5000

02010 2020 2030 2040 2050

Population scenarios: Low growth Medium growth High growth

Climate change Low impact Central impact High impact

Wat

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eman

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cap

acity

(MI/d

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Figure 4

Transi)on Strategies: Key Ques.ons

•  growing demand for infrastructure services?

•  investment constraints and infrastructure capacity?

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Strategies

What are the implica)ons of…

•  a carbon-‐constrained future?

•  a decentralised na)onal infrastructure system?

•  interdependence between infrastructure sectors?

Transi)on Strategies: Dimensions

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Strategies

High investment

Low investment

Centralised provision Decentralised provision


Capacity-constrained (CC)

Decentralisation(DC)

Capacity-‐intensive

High investment in new capacity to keep up with demand and maintain good security of supply (except transport)

Decentralisa%on Reorienta.on to more distributed systems involving a combina.on of supply and demand-‐side measures

Capacity-‐constrained

Emphasis on demand management measures, low infrastructure investment Figure 5

Performance Evalua)on

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Cost

Emssions

Security of supply

Low growth scenario

Medium growth scenario

High growth scenario

High performance(e.g. low cost, low emissions, high supply security)

Medium performance

Low performance(e.g. high cost, high emissions,low supply security)

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Figure 7

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Decentralisation (DC)

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Figure 6

Summary of the FTA methodology

1. Developing scenarios •  IdenAfy the primary drivers that impact

the future demand and capacity of infrastructure services

•  Construct three possible futures through varia.on of these drivers to 2050

2. Sector modelling •  Build models to project future demand

across the three scenarios for each NI sector

•  Construct three transi)on strategies and IdenAfy key performance metrics

3. Evalua)on •  Evaluate the performance of the

transi.on strategies across the scenarios •  Construct visualisa.on summary of

performance

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Highgrowth

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Lowgrowth


strategy



(DC) strategy





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Summary of results

Page 27 Cost

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Medium performance


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Sample of cross-‐sectoral findings

Decentraliza)on •  Greater diversity of supply could lead

to greater supply security

•  Capitalize on interdependencies (local waste to energy conversion);

Constrained investment •  cost, however, erosion of supply security,

especially in high growth scenario

•  Demand reduc.on may improve efficiently (energy), but can also adversely impact economy and society (transport)

Page 28

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Capacity-Intensive (CI) Capacity-Constrained (CC) Decentralisation (DC)

Implications of…

Contribu)ons

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1.  Iden.fica.on of key drivers of change of demand for NI services

2.  Enabling the cross-‐sectoral analysis of NI strategies under long term uncertainty

3.  Incorporates a process-‐based understanding of NI demand and capacity

4.  Visualisa.on of NI strategy performance over mul.ple metrics and .me periods

ITRC complex systems concepts, methodologies and the modelling

framework

Alex Lorenz

General methodology of Capacity/Demand modelling within ITRC

System

Surroundings

System

System System

System of Systems

System of Systems Analysis Strategies future conditions

(“Scenarios”) Surroundings

smaller set of drivers

Decision analysis

increasing returns to scale for the nonemitting technologies, therate at which agents learn from one another about the perfor-mance of new technologies, the agents’ risk aversion, and theheterogeneity of the agents’ price-performance preferences fornew technologies. Then, we viewed a series of interactive computer visualizations using different combinations of these fourkey uncertain inputs as independent variables (as well as a fifthuncertainty, the damages caused by climate change), each onecomparing the performance of the ‘‘Limits-Only’’ and ‘‘Com-bined Strategy.’’ Each visualization showed the performance ofthe two strategies as surface plots, measured as the present valueof the GDP over the 21st century (reflecting both the costs andbenefits of each strategy) as a function of two of the uncertain-ties, with the other inputs held constant at fixed values. A clearpattern emerged: the Limits-Only strategy is preferable in a

world where the agents’ technology preferences are homoge-neous, imperfect information effects are small, and the damagescaused by climate change emerge slowly. When these conditionsdo not hold, the Combined (tax and subsidy) Strategy quicklybecomes more attractive.

The robust region map in Fig. 5 summarizes these results. Thefigure shows the expectations about the future that should causea decision-maker to prefer the Limits-Only strategy to theCombined Strategy. The horizontal axis represents the range ofexpectations a decision-maker might have for how likely itis—from very unlikely (Left) to very likely (Right)—that factorssuch as the potential number of early adopters and the amountof increasing returns to scale will significantly influence thediffusion of new technologies. The vertical axis represents therange of expectations a decision-maker might have that there

Fig. 3. Adaptive decision strategy for adjusting carbon taxes (Left) and technology incentives (Right) over time.

Fig. 4. A Landscape of Plausible Futures showing a wide range of future GHG emissions paths, all of which are consistent with available information.[Reproduced with kind permission from figure 3 of ref. 18 (Copyright 2000, Kluwer Academic Publishers).]

7312 ! www.pnas.org"cgi"doi"10.1073"pnas.082081699 Lempert

Lempert et al., 2002

Large ensemble simulation Robust control approach

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S T R AT E G I E S F O R N AT I O N A L I N F R A S T R U C T U R E P R O V I S I O N I N G R E AT B R I TA I N : E X E C U T I V E S U M M A R Y

Figure 12: Summary

of transition strategy

performance assessment

using cross-sectoral metrics

of cost, emissions and

security of supply. In the

transport sector, the ‘security

of supply’ metric relates to

congestion.

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An integrated Capacity & Demand framework

Dem

and

man

agem

ent

Capital investment &

other scenario variables

Demographics Economics

Scenarios

Performance measures

Energy

Transport

Water

Waste water

Waste

Demand

Household Industry

Supply Capacity

Demand modelling Household appliances (amenity)

Electricity

Gas

Biomass

Biogas

(Delivered) heat

Oil (petrol, diesel)

Solar

LPG

Biofuel

Hydrogen

Private vehicle transportation (cars, vans, motorcycles)

Freight transportation - LGV, HGV, rail, ship

Aviation - private, business travel, commercial (cargo)

Water purification

Water extraction & delivery

Waste water collection & processing

Waste collection and disposal - transportation

Waste processing

H2

LPG

Waste

(Waste)Water

Digital communication (network, servers, routers)

Data center operation

Computing services - excl. cloud/data centers

Transport

Mass Transportation - Rail, Bus

Agriculture

Plowing, harvesting & fertilizer application

Irrigation - water extraction, pumping

Food processing

Residential

Industry / Commercial

Mechanical - motor, drives, cranes Process heating - low & high temperature

Drying/separation

Compressed air processes

Site transportation (raw/processed material moving)

Other (inc. power production)

ICT

Cooking

Lighting (illumination)

Space heating (thermal comfort)

Space cooling/Air-conditioning (thermal comfort)

Water heating

H2

H2

H2

H2

Issues for an integrated Modelling framework

Issues Want to integrate the sector CDAMs + interdependency

Combined single model is computa.onal infeasible

General equilibrium approach (soi link) also infeasible

Trade off between single model detail and level of integra.on

Linking architecture

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Work Stream 1 (WS1) is developing a system of quanti!ed capacity/demand assessment modules (CDAM) for analysis of long term strategies for infrastructure provision. In that sense it will resemble the FTA but will be based upon more quanti!ed and more fully integrated models including:

• A micro-simulation model for generation of high resolution demographic and demand scenarios.

• A regional economic model that will generate regional multi-sectoral projections of industrial demand for infrastructure services.

• A model of the UK electricity and gas networks and a new disaggregated energy demand module.

• A national strategic model of trunk road, rail, port and airport infrastructure.

• A national water resources system model, coupled with a model of wastewater treatment systems.

• A national solid waste assessment model.

ICT will be excluded from the WS1 analysis, as the FTA has illustrated that new capacity has being provided historically and this can be expected to continue for the foreseeable future. Further the demand is very sensitive to unforeseen technological developments which makes future analysis di"cult.

These models will be coupled in an overall simulation framework in which the main scenario uncertainties are extensively sampled, expanding upon the small number of scenarios analysed in the FTA. A set of infrastructure investment options will be developed for each sector and assembled #exibly into cross-sectoral packages, representing a major extension of the three transition strategies analysed in the FTA. New tools will be developed to explore and visualise the results of the analysis.

High resolution demographic projections

Regional multi-sectoral economic model

National infrastructuredatabase andanalysis archive

Module for:1 Sampling scenarios & uncertainties 2 Specifying options & strategies for infrastructure provisions3 Specifying CDAM model runs4 Post-processing & visualising results

Capacity/Demand Assessment Module (CDAM) for each NI sector

Energy

Transport

Water & wastewater

Solid waste

Figure 14: Structure of the

system of assessment models

and databases now under

development in Work Streams 1

and 4 of ITRC.

WS1 Scenario Genera)on Process

1.  Compiling a list of interesting, high level policy relevant questions about the future performance of the interdependent infrastructure system

2.  Deriving a set of dimensions relevant for answering the policy questions and partitioning of these dimensions into small sets of significantly different “levels”

3.  Combining the “level” values of different dimensions to an overall strategy (accompanied by a narrative that accounts for synchronisation and consistency across the strategy dimensions)

4.  Link the “level” values within the strategy dimensions to the “strategy variables” on the modelling level

Future conditions

Strategy variables

External Variables

smaller set of drivers

Policy Questions

Strategy Dimensions

WS1 Scenario Generation

1.  Partitioning the space of external assumptions into “strategy variables” and “future conditions”

2.  Linking the “future conditions” to a shorter list of underlying drivers

3.  Sampling the underlying drivers (acknowledging interdependencies between different drivers)

Itera)ve ensemble approach

Scenario # Decision variables

Underlying Drivers

d1 d2 … dM r1 r2 … rK

2 1 1 … 1 of BM 1 1 … 1

3 2 1 … 1 of BM 1 1 … 2

3 3 1 … … 3

4 … …

5 … …

… … …

7 … …

9


High investment

Low investment

Centralised provision Decentralised provision



Decentralisation(DC)

Figure 6: Relative location of

the FTA infrastructure transition

strategies in relation to

investment requirements and

centralisation/decentralisation.

Highgrowth

Medium growth

Lowgrowth


strategy



(DC) strategy


FTA drivers: Population growth • Economic growth • Energy cost




Figure 7: Summary of the Fast

Track Analysis methodology.

Single Scenario

FTA-‐like small set with narra.ves

Larger Monte Carlo Ensembles

Complete Factor analysis

Sampling Procedures – Future condi)ons (Scenarios)

unit

Time (units) temporal resolution

}

1 2 3 4

11+ aT be.g.

Parameterised time series

•  Linking CDAM input parameters to a set of underlying drivers •  Quantifying uncertainty of the underlying drivers •  Implementing a formal sampling procedure

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THE ITRC FAST TRACK ANALYSIS ME THODOLOGY

National Infrastructure systems have to cope with the implications of long term changes in population, the economy, society and the environment. The nature of these changes is hard to predict in the long term, so the ITRC is adopting an approach in which plausible ranges of these future changes are analysed. A simpli!ed version of this methodology has been developed for the FTA, in which three primary scenario dimensions that are common to all infrastructure sectors have been analysed: demographic change, energy prices and economic growth (Figure 4).

Whilst the ITRC modelling tools that are now under development will enable the analysis of many combinations of these and other scenario dimensions, in the FTA the analysis has been restricted to only three combinations, representing low, medium, and high growth scenarios (Table 2, and Figure 5).

Table 2: Summary of the FTA scenarios

Low growth scenario



GB population (see Figure 5)

Low ONS projection

Principal ONS projection

High ONS projection

GDP growth per year

1.6% 2.3% 3.0%

Energy cost1 DECC High fossil fuel prices

DECC Central fossil fuel price

DECC Low fossil fuel price

1 DECC (2010). Updated energy and emissions projections. Department for Energy and Climate Change, London: TSO.

Figure 4: The dimensions of the

FTA scenario space.

HIG

H

HIGH

HIGH

LOW

LOW

LOW

Population growth

Economicgrowth

Energy costs

FTA driver space

Revisi)ng the level of resolu)on issue

LAD Regions

Finding acceptable levels of resolution for each sector

Twin-Track approach of low- and high resolution CDAM versions

Investigating the impact of NI management on different levels (e.g. Waste CDAM)

Incorpora)on of Interdependencies D

eman

d m

anag

emen

t

Capital investment &

other scenario variables

Demographics Economics

Scenarios

Performance measures

Energy

Transport

Water

Waste water

Waste

Demand

Household Industry

Supply Capacity

Transport (Southampton)

Water (Newcastle)

Solid Waste (Southampton)

Waste Water (Newcastle)

Energy (Oxford/Cardiff)

2… 3 1… Evaluation order

Sector CDAMs

Potential for interdependencies Current approach: •  Common set of assumptions

•  Solving order allows for one way dependencies

Iterative solution of the modelling framework…

Visuliza)on of Results 352 | THE ATLAS OF ECONOMIC COMPLEXITY

2008

ZIMBABWE

EVOLUTION OF EXPORT COMPOSITION

PRODUCT SPACE

2008

PRODUCT EXPORTED WITH RCA>1

PRODUCT NOT EXPORTED WITH RCA>1

NODE SIZE IS PROPORTIONAL TO WORLD TRADE

ECONOMIC COMPLEXITY INDEX [2008] ! -0.327 / (80/6) EXPECTED GDPPC GROWTH * ! 3.79% / (6/1)

2008 EXPORT OPPORTUNITY SPECTRUM

FRACTION OF PRODUCTS WITH RCA > 1

22% OF WT

6% OF WT

2% OF WT

0.8% OF WT

0.06% OF WT

*Expected annual average for the 2009-2020 period.

0.9 0.91 0.92 0.93 0.94

-2

-1

0

1

2

3

Distance

Avera

ge Co

mplex

ity of

Miss

ing Pr

oduc

ts

0.01 0.02 0.03 0.04 0.05 0.06

-2

-1

0

1

2

3

Opportunity Gain

Avera

ge Co

mplex

ity of

Miss

ing Pr

oduc

ts

MAPPING PATHS TO PROSPERITY | 353

2927 (12%) Flora 1212 (5.5%) Wholly or partly strippedtobacco

1222 (1.6%) Cigarretes

1211 1213

2631 (5.5%) Raw cotton 0612 (1.7%)Refined sugar

0572(0.99%)Fresh or driedcitrus N.E.S

0813

2872 (18%) Nickel 2731 2789

3232 (0.99%)

6612 (0.97%)

6672 (0.9%)Not mounteddiamonds

7821 (3.5%) Trucks& vans

8743 (3.3%) Gas,liquid & electriccontrol instruments

8946

6991

6954 7188

6716 (3%) Ferro-alloys 7711 (2.6%) Electricaltransformers

6513 (3.4%) Cotton yarn

6535 (0.74%)

8422 (2.5%) Men'ssuits

8219 (1%) 2482(0.98%)

2471

8960 (2.7%) Worksof art

0545 (1.6%)Other fresh orchilledvegetables

0586 0546(0.78%)

6421

5137 (1.7%) Monocarboxylicacids & derivatives

8748 (1.5%) Electricalmeasuring & controllinginstruments N.E.S.

5232(0.71%)

5231(0.67%)

8928 (4.1%) Printed matterN.E.S.

* Data are from 2008. Numbers indicate: Value (World Ranking / Regional Ranking). Sub-Saharan Africa.

2008

1968

EXPORTTREEMAP

EXPORT TREEMAP

* Numbers indicate SITC-4 rev 2 codes. Parenthesis indicate percentage of total exports. Treemap Headers show: Total Trade/Total World Trade (share of world trade represented by the country).

TOTAL EXPORTS: 24.65 M / 228.33 B (0.01%) 1988 EXPORT TREEMAP TOTAL EXPORTS: 1.13 B / 2.79 T (0.04%)

TOTAL EXPORTS: 1.72 B / 15.56 T (0.01%)

ELEC

TRON

ICS

MACH

INER

Y

AIRCR

AFT

BOILE

RS

SHIPS

META

L PRO

DUCT

S

CONS

TR. M

ATL.

& EQ

PT.

HOME

& OF

FICE

PULP

& PA

PER

CHEM

ICALS

& HE

ALTH

AGRO

CHEM

ICALS

OTHE

R CHE

MICA

LS

INOR

. SAL

TS &

ACID

S

PETR

OCHE

MICA

LS

LEAT

HER

MILK

& CH

EESE

ANIM

AL FI

BERS

MEAT

& EG

GS

FISH &

SEAF

OOD

TROP

ICAL A

GRIC.

CERE

ALS &

VEG.

OILS

COTT

ON/R

ICE/S

OY &

OTHE

RS

TOBA

CCO

FRUI

T

MISC

. AGR

ICULT

URE

NOT C

LASS

IFIED

TEXT

ILE &

FABR

ICS

GARM

ENTS

FOOD

PROC

ESSIN

G

BEER

/SPIR

ITS &

CIGS

.

PREC

IOUS

STON

ES

COAL OI

L

MINI

NG

GDP ! USD 4.2 B / (124/22)

GDPPC ! USD 341 / (125/23)

EXPORTS PER CAPITA ! USD 138 / (119/19)

EXPORTS AS SHARE OF GDP ! 41 % (44/9)

POPULATION ! 12 M / (62/17)

TOTAL EXPORTS ! USD 1.7 B / (118/22)

MIT Atlas of economic complexity

Investigating different methods and methodologies for complex data visualisation…

Visulisa)on of Results

14


Figure 12: Summary

of transition strategy

performance assessment

using cross-sectoral metrics

of cost, emissions and

security of supply. In the

transport sector, the ‘security

of supply’ metric relates to

congestion.

Cost

Emssions

Security of supply

Low growth scenario




Medium performance


L

M

H

L

LL

L

LL

M

M

M

M

M

M

HH

H

HH

H

L

LL

L

LL

M

M

M

M

M

M

HH

H

HH

H

L

LL

L

LL

M

M

M

M

M

M

HH

H

HH

H

Energy

2010–2030

2030–2050


L

LL

L

LL

M

M

M

M

M

M

HH

H

HH

H

L

LL

L

LL

M

M

M

M

M

M

HH

H

HH

H

L

LL

L

LL

M

M

M

M

M

M

HH

H

HH

H

Transport

2010–2030

2030–2050


L

LL

L

LL

M

M

M

M

M

M

HH

H

HH

H

L

LL

L

LL

M

M

M

M

M

M

HH

H

HH

H

L

LL

L

LL

M

M

M

M

M

M

HH

H

HH

H

Water

2010–2030

2030–2050


L

LL

L

LL

M

M

M

M

M

M

HH

H

HH

H

L

LL

L

LL

M

M

M

M

M

M

HH

H

HH

H

L

LL

L

LL

M

M

M

M

M

M

HH

H

HH

H

Wastewater

2010–2030

2030–2050


L

LL

L

LL

M

M

M

M

M

M

HH

H

HH

H

L

LL

L

LL

M

M

M

M

M

M

HH

H

HH

H

L

LL

L

LL

M

M

M

M

M

M

HH

H

HH

H

Solid waste

2010–2030

2030–2050


L

LL

L

LL

M

M

M

M

M

M

HH

H

HH

H

L

LL

L

LL

M

M

M

M

M

M

HH

H

HH

H

L

LL

L

LL

M

M

M

M

M

M

HH

H

HH

H

ICT

2010–2030

2030–2050


352 | THE ATLAS OF ECONOMIC COMPLEXITY

2008

ZIMBABWE

EVOLUTION OF EXPORT COMPOSITION

PRODUCT SPACE

2008

PRODUCT EXPORTED WITH RCA>1

PRODUCT NOT EXPORTED WITH RCA>1

NODE SIZE IS PROPORTIONAL TO WORLD TRADE

ECONOMIC COMPLEXITY INDEX [2008] ! -0.327 / (80/6) EXPECTED GDPPC GROWTH * ! 3.79% / (6/1)

2008 EXPORT OPPORTUNITY SPECTRUM

FRACTION OF PRODUCTS WITH RCA > 1

22% OF WT

6% OF WT

2% OF WT

0.8% OF WT

0.06% OF WT

*Expected annual average for the 2009-2020 period.

0.9 0.91 0.92 0.93 0.94

-2

-1

0

1

2

3

Distance

Avera

ge Co

mplex

ity of

Miss

ing Pr

oduc

ts

0.01 0.02 0.03 0.04 0.05 0.06

-2

-1

0

1

2

3

Opportunity Gain

Avera

ge Co

mplex

ity of

Miss

ing Pr

oduc

ts

MIT Atlas of economic complexity

…and applying them to the different WS1 outputs.

www.itrc.org.uk

Education

ITRC Fast Track Analysis