Advances in Integrating Urban Form and Energy-

Economy Modeling for Simulating Transportation

GHG-Energy Policies

by

Thomas Budd

B.A. (Hons., Economics), Simon Fraser University, 2017

Project Submitted in Partial Fulfillment of the

Requirements for the Degree of

Master of Resource Management

in the

School of Resource and Environmental Management

Faculty of Environment

Report No. 732

© Thomas Budd 2019

SIMON FRASER UNIVERSITY

Summer 2019

Copyright in this work rests with the author. Please ensure that any reproduction or re-use is done in accordance with the relevant national copyright legislation.

ii

Approval

Name: Thomas Budd

Degree: Master of Resource Management

Report Number: 732

Title: Advances in Integrating Urban Form and Energy-Economy Modeling for Simulating Transportation GHG-Energy Policies

Examining Committee: Chair: Aaron Pardy PhD Student, Resource and Environmental Management

Mark Jaccard Senior Supervisor Professor

Rose Murphy Supervisor Postdoctoral Fellow

Date Defended/Approved: July 26, 2019

iii

Abstract

Vancouver, British Columbia is one of many leading municipal jurisdictions that has set

ambitious GHG emission and renewable energy targets. This analysis uses Vancouver’s

Renewable City Strategy as a case study of municipal policy that affects land use,

transportation infrastructure, and population densification to assess the impact of urban

form and density on transportation GHG emissions, energy use, mode-choice, and travel

demand. The CIMS-Urban energy-economy model is used to provide realistic

estimations of the effect of municipal policies on technology use and personal mobility

behaviour that account for most urban transportation energy demand. The results

indicate that improvements to urban form, in the absence of other policies that target

vehicle energy efficiency and fuel switching to renewable sources, will not provide

sufficient reductions in GHG emissions to achieve ambitious decarbonization targets.

Additionally, urban density policy must be accompanied with mixed-use land zoning

changes to be effective.

Keywords: urban form; population density; fuel switching; energy efficiency;

transportation mode-shift; travel demand

iv

Acknowledgements

I would like to acknowledge all those who supported me during my studies at

SFU’s School of Resource and Environmental Management and made my research

activities at SFU EMRG a success. My deepest gratitude to my Senior Supervisor, Dr.

Mark Jaccard. Thank you, Mark, for your mentorship and encouragement in helping me

succeed in my academic pursuits. Ever since I enrolled in your undergraduate course,

REM 350, I have continued to find inspiration, under your leadership, on how I may

contribute and give back to our global community. To my second supervisor, Dr. Rose

Murphy, thank you for your valued feedback in helping me improve the quality of my

research.

Thank you to Navius Research and Managing Partner, Jotham Peters, for inviting

me to spend time at their office while developing my energy modeling improvements. I

especially grateful to Michael Wolinetz and Brett Zuehlke for their generosity in helping

me to learn the CIMS-Urban model and all of its complexities.

I would like to recognize Mark and Jotham, in particular, for funding my research

endeavours through the Mitacs Accelerate internship.

Thank you to all my fellow team members at EMRG for their friendship and for

making my experience fun and memorable; To Tiffany, Morgan and Mikela for

introducing me to our lab’s interesting research; To Aaron P. and Shahid for being my

cohort compatriots and greatest study-buddies; to Emily, Franzi, and Aaron H. for their

enthusiasm in continuing our lab’s research and enhancing our energy model’s to new

heights; and to Brad for the continual support he offers all of us at EMRG.

Finally, thank you to my ever-supportive parents Rebecca and Don, my

grandmother Frances, and my entire extended family for being there for me throughout

all my education and activities at SFU.

v

Table of Contents

Approval ............................................................................................................................ ii

Abstract ............................................................................................................................. iii

Acknowledgements .......................................................................................................... iv

Table of Contents .............................................................................................................. v

List of Tables .................................................................................................................... vi

List of Figures................................................................................................................... vii

List of Equations .............................................................................................................. viii

List of Acronyms ............................................................................................................... ix

Chapter 1. Introduction ................................................................................................ 1

Chapter 2. Review of Available Models ...................................................................... 6

2.1. CIMS Stock Turnover Module ................................................................................. 6

2.2. CIMS Spatial Module .............................................................................................. 9

Chapter 3. Model Development ................................................................................. 14

3.1. CIMS Market-Share Estimation ............................................................................. 14

3.2. CIMS Spatial Module Intangible Cost Estimation .................................................. 16

3.3. Urban Population Growth and Density .................................................................. 18

3.4. Person Kilometers Travelled Estimation ............................................................... 19

3.5. Road Congestion Rebound Effect ......................................................................... 21

Chapter 4. Test Simulations ...................................................................................... 23

4.1. Test Policy Scenarios ............................................................................................ 23

4.2. Model Assumptions, Data Inputs and Calibration ................................................. 28

Chapter 5. Results and Discussion .......................................................................... 32

5.1. Greenhouse Gas Emissions ................................................................................. 32

5.2. Energy Consumption ............................................................................................. 35

5.3. Emissions and Energy Consumption Decomposition ........................................... 37

5.4. Travel Demand ...................................................................................................... 39

5.5. Transportation Mode Share .................................................................................. 41

5.6. Road Congestion Rebound Effect ......................................................................... 44

Chapter 6. Conclusions ............................................................................................. 46

6.1. Summary of Model Development and Test Simulation Findings........................... 46

6.2. Limitations and Opportunities for Future Research ............................................... 48

References ..................................................................................................................... 51

Appendix A. Spatial Algorithm Calibration Variables .......................................... 55

Appendix B. Transportation Baseline Lifecycle Costs ........................................ 56

vi

List of Tables

Table 1. Scenario summary matrix ........................................................................ 23

Table 2. Data sources used in the development of CIMS-Urban .......................... 29

Table 3. GHG emissions reductions from 2010 to 2050 ........................................ 34

Table 4. Energy consumption reductions from 2010 to 2050 ................................ 37

Table 5. GHG emissions & energy consumption reductions with NoPol Scenario from 2010 to 2050 ................................................................................... 39

Table 6. PKT changes 2010 to 2050 ..................................................................... 41

Table 7. Percent change in PKT by mode type from 2010 to 2050: UniformGrowth ................................................................................................................. 43

Table 8. UniformGrowth Scenario reductions in results from 2010 to 2050 with rebound effect .......................................................................................... 45

vii

List of Figures

Figure 1. Vancouver Neighbourhood Zones (red line) and Census Dissemination Areas (dashed lines) ................................................................................ 17

Figure 2. Land use and bike network changes proposed in the Vancouver Renewable City Strategy. Reprinted from Vancouver’s Renewable City Strategy: Economic and Policy Analysis, by Brett Maynard Zuehlke, retrieved from http://summit.sfu.ca/. Copyright Brett Maynard Zuehlke 2017, Simon Fraser University. Reprinted with permission. .................... 25

Figure 3. Population density scenarios and 2010 baseline estimation ................... 27

Figure 4. Total and per capita greenhouse gas emissions in the Current Policy (CurrentPol), and Renewable City Strategy (VanRen) urban policies, for UniformGrowth, HighDensity, and LowDensity population scenarios ..... 33

Figure 5. Total and per capital energy consumption in the Current Policy (CurrentPol), and Renewable City Strategy (VanRen) urban policies, for UniformGrowth, HighDensity, and LowDensity population scenarios ..... 36

Figure 6. Total greenhouse gas emissions and energy consumption in the No Policy (NoPol), Current Policy (CurrentPol), and Renewable City Strategy (VanRen) urban policies, for UniformGrowth, HighDensity, and LowDensity population scenarios ............................................................ 38

Figure 7. Total and per capita travel demand (Person Kilometers Travelled) in the Current Policy (CurrentPol), and Renewable City Strategy (VanRen) urban policies, for UniformGrowth, HighDensity, and LowDensity population scenarios ................................................................................ 40

Figure 8. Transportation mode share changes from 2010 to 2050 in the Current Policy (CurrentPol), and Renewable City Strategy (VanRen) urban policies, for UniformGrowth population scenario ..................................... 42

Figure 9. Uniform Growth Scenario percentage change in results between 2010 and 2050 with rebound effect .................................................................. 45

viii

List of Equations

Equation 1. CIMS Market Share Algorithm ................................................................... 7

Equation 2. Declining Capital Cost Function ................................................................ 8

Equation 3. Declining Intangible Cost Function ............................................................ 8

Equation 4. Network Quality Algorithm for Driving Quality ......................................... 10

Equation 5. Network Quality Algorithm for Walking Quality ........................................ 11

Equation 6. Network Quality Algorithm for Transit Quality .......................................... 11

Equation 7. Network Quality Algorithm for Cycling Quality ......................................... 12

Equation 8. PKT Index Calculator Algorithm .............................................................. 20

Equation 9. Updated Network Quality Algorithm for Driving Quality ........................... 21

ix

List of Acronyms

BC British Columbia

CIMS Canadian Integrated Modeling System

CurrentPol Current Policy Scenario

DA Census Dissemination Area

EIA The United States Energy Information Administration

EMRG Energy and Materials Research

GHG Greenhouse Gas

GIS Geographic Information System

GJ Gigajoule

HighDensity High Density Growth Population Density Scenario

HOV High Occupancy Vehicle

KtCO2e Kilotonne Carbon Dioxide Equivalents

LowDensity Low Density Growth Population Density Scenario

NoPol No Policy Scenario

NRCAN Natural Resources Canada

OLS Ordinary-Least-Squares Regression Analysis

PKT Person-Kilometers Travelled

RLCFRR Renewable and Low Carbon Fuel Requirements Regulations

SFU Simon Fraser University

tCO2e Tonne Carbon Dioxide Equivalents

UBC IRES University of British Columbia Institute for Resources Environment and Sustainability

UniformGrowth Uniform Growth Population Density Scenario

VanRen Vancouver Renewable City Strategy Policy Scenario

1

Chapter 1. Introduction

Cities, within Canada and worldwide, are embracing the critical need to reduce

GHG emissions within their jurisdictions by committing to transition their energy systems

to renewable sources (Global Covenant of Mayors for Climate & Energy, 2017). Within

Canada, the municipal government of Vancouver, British Columbia, has taken the lead

in setting an ambitious GHG reduction target of 80% by 2050, along with a pledge to

change their energy sources to 100% renewable through their Renewable City Strategy

(City of Vancouver, 2015a). In response to these announced targets, researchers from

Simon Fraser University’s Energy and Materials Research Group (EMRG) have

engaged in research to analyze the capacity of municipal governments to enact policies

that achieve their intended climate goals. For my research project, I have contributed to

advancing methodology in energy-economy models for simulating urban transportation

and the effect of land-use density policies on personal mobility within the municipal

context.

Cities have several policy instruments to ameliorate transportation GHGs. First,

they can introduce new transportation infrastructure such as public transit lines and bike

lanes, while simultaneously restricting personal vehicle access to specific areas of a city.

Secondly, they can alter land-use zoning to increase density around urban centres and

transportation hubs. Within municipal land-use policy, cities can also introduce mixed

land-use strategies to create “walkable cities” where residences are placed in proximity

to commercial and employment districts (Southworth, 2005). Academic literature

indicates that such policies may induce ‘mode-shift’ away from personal vehicles (Ding

et al., 2017; Potoglou & Kanaroglou, 2008). Such mode-shift has been shown to

improve transportation energy efficiency and decrease GHG emissions per person-

kilometer-travelled (Jaccard et al., in press; Cuenot, Lew, & Staub, 2012). Furthermore,

studies have found that higher concentrations in urban density and mixed-use urban

form may lead to lower per-capita transportation energy consumption and reduced use

of fossil fuel-based travel devices (Jaccard et al., in press; Gudipudi et al., 2016; Chester

et al., 2013).

2

Notwithstanding the growing political will of cities to address climate change, it is

critical to identify the policy limitations of municipal jurisdictions in the areas of personal

vehicle energy efficiency and fuel switching. Reports indicate that vehicle energy

efficiency improvements have been a successful contributor to mitigating per-capita

transportation energy use (Natural Resources Canada, 2013). Switching vehicle fuel

sources from carbon emitting fossil fuel-based energy to non-GHG sources of electricity,

biofuels and hydrogen have also been a critical requirement for GHG reductions

(Lepitzki & Axsen, 2018; Melton, Axsen, & Sperling, 2016). Policies that support

necessary increases in alternative fuel use and enhanced energy efficiency include

carbon pricing, targeted subsidy programs, and regulations over fuels and vehicle

technology (Santos, 2017). Within the Canadian context, cities do not appear to have

the jurisdictional authority to mandate these policies to the same degree as higher levels

of government (Jaccard et al., in press; Zuehlke et al., 2017).

In response to the ambitious targets announced by cities, like Vancouver, which

are limited in their ability to directly regulate upstream sources of GHG emissions, my

study addressed the research question: What can be the effect of changes to urban form

and population density on transportation GHG emissions and energy use? To answer

this question, I required an energy and transportation model that would simulate how

city-dwellers may choose alternative transportation modes and vehicle technologies

under different urban form policies and density scenarios. To effectively evaluate the

GHG and energy reducing potential of these initiatives, an energy-economy model was

needed to assess the effect of changes to vehicle technology and energy use, and a

spatial model to account for the unique dynamics of urban form and transportation

infrastructure.

Energy-economy models specialize in analyzing capital stock turnover for

specific energy-using devices and energy supply infrastructure, and may incorporate

parameters to realistically simulate consumer behaviour (Murphy & Jaccard, 2011).

Collectively, the attributes of these models provide an understanding of how individuals

may use their available energy-using devices and potentially adopt new technology

(Mundaca et al., 2010). Despite the strengths of energy-economy models in simulating

expected energy use of a particular economy, they generally do not integrate spatial

relationships of municipal infrastructure and land-use and their potential for non-

uniformity across an urban landscape (Li, Bataille, & Pye, 2019).

3

Existing transportation spatial models, such as land-use-transport models, have

typically focused on consumption of mobility services and energy resources. Such

models are traditionally comprised of interconnected sub-modules that are linked to

forecast urban activities (Wegener, 2004). These models are generally based on

microeconomic preference maximizing principals or on ‘heuristic decision’ simulations

that estimate the likely choices of city dwellers (Acheampong & Silva, 2015; Bhat et al.,

2004; Arentze & Timmermans, 2000; Pendyala et al., 1995). However, most

transportation models lack the analysis of energy supply systems that account for

expected fuel use and capital stock turn-over within urban areas (Keirstead, Jennings, &

Sivakumar, 2012).

Researchers at EMRG have engaged in research to mitigate the limitations

attributed to both energy-economy models and spatial transportation models by

integrating a spatial GIS urban land-use module within their energy-economy modeling

system, known as CIMS (Jaccard et al., in press). CIMS was developed in the 1990’s as

a model that combined aggregated information on relationships between the energy

supply and demand sectors with individual energy services and technologies (Murphy &

Jaccard, 2011). The result was a technologically detailed economic model which

incorporates the potential behaviour of individuals. Integrating CIMS with a GIS land-use

and infrastructure component enables the newly titled ‘CIMS-Urban’ to provide realistic

estimations of technology use and personal mobility behaviour that account for most

sources of urban energy demand and simulate the expected outcomes of municipal

policies (Jaccard et al., in press).

To test the capabilities CIMS-Urban, EMRG researchers conducted a study on

the City of Vancouver and its Renewable City Strategy, as initiated and promoted in the

period 2015-2017, as a case study of municipal policy that affects urban form and

transportation infrastructure (Jaccard et al., in press). The study produced promising

results, but even with the recent advances in urban energy modeling by EMRG,

methodological limitations remained. Under CIMS-Urban’s existing state of development,

there was no capacity to assess the spatial impacts of population growth and urban

density scenarios on travel demand and mode choice. The model assumed that the

relative density and location of population clusters in an urban landscape would remain

constant across time. Additionally, there was an assumption that transportation

conveyances would be able to accommodate any additional population growth within a

4

city. While this may be a reasonable assumption for walking, transit and cycling

infrastructure, it is impractical for personal vehicles given that expected increases in road

congestion would begin disincentivizing urban automobility. A second limitation of CIMS-

Urban is the model’s inability to endogenously calculate travel demand. All future

demand for transport in the form of person-kilometers travelled (PKT) was externally

calculated without incorporating the spatial impacts of population growth and land-use

policy on personal mobility within the model. A third limitation of CIMS-Urban was a lack

of capacity to integrate road congestion rebound effect on personal vehicle use.

Research shows that any policy which would reduce congestion via decreases in travel

demand or vehicle mode-shift would also incentivize countervailing traffic increases that

at least partially offset the effect of the policy, a phenomenon which traffic modelers

sometimes refer to as rebound effect. (Hymel, Small, & Van Dender, 2010; Coulombel et

al., 2019).

I improved the CIMS-Urban model to overcome these limitations and accomplish

the following research objectives:

1) Estimate changes to urban transportation GHG emissions, energy use, travel demand, and mode-choice under different urban form and population density scenarios.

2) Estimate the relative contributions of vehicle energy efficiency and fuel switching to GHG emissions and energy use when assessing the impacts of municipal urban form and density policies.

3) Estimate the impact of congestion rebound effect on vehicle use under different urban policy scenarios.

To meet the above research objectives, I replicated the EMRG Study on the City of

Vancouver and its Renewable City Strategy (Jaccard et al., in press) and incorporated

different population densification scenarios that explored where population growth could

occur.

The follow sections of this report outline the details and outcomes of my

research. Chapter 2 provides a review of the available versions of CIMS-Urban that I

used in my study. Chapter 3 describes the methodological improvements I applied in

advancing CIMS-Urban. Chapter 4 describes the City of Vancouver’s policies that I

applied to CIMS-Urban to test my methodology and to answer my research question. In

Chapter 5, I present and discuss the results of my test simulations. In Chapter 6, I

5

provide a summary of my methodological contributions to CIMS-Urban, comment on the

findings of my test simulations and their potential for informing urban policy

development, identify the limitations of my study and recommend opportunities for future

research.

6

Chapter 2. Review of Available Models

As previously noted, CIMS-Urban is comprised of a capital stock turnover module

and a GIS spatial module integrated within its modeling system. For my study, I

improved a version of the CIMS Stock Turnover Module applied in a previous study to

the City of Vancouver (Wolinetz, 2017) and integrated it with a spatial module developed

by SFU EMRG. The initial version of the CIMS Stock Turnover Module was equipped to

answer questions pertaining to the residential, commercial, industrial, freight

transportation and personal transportation sectors of Vancouver. I adapted a subset of

this model that related to personal transportation and hard-linked it to the CIMS Spatial

Module. By way of background, what follows is a detailed description of the CIMS Stock

Turnover Module and the CIMS-Spatial Module, as was initially available to me, prior to

any improvements produced during my study.

2.1. CIMS Stock Turnover Module

CIMS integrates interactions between energy supply and demand sectors. It is

technologically explicit, meaning that it accounts for changes to capital stock and

technology over time, including ‘vintage-specific’ characteristics of each type of device,

equipment or infrastructure (Jaccard et al., in press; Jaccard et al., 2003). Additionally,

CIMS incorporates behavioural parameters, based on past research, that account for

consumer preferences in decision-making (Rivers & Jaccard, 2006; Rivers & Jaccad,

2005; Horne, Jaccard, & Tiedemann, 2005). The model is run over any simulation

period, in discreet five-year increments. At each simulation year, a percentage of

existing capital stock is retired based on its expected life span. When new capital stock

is required, either to replace retired stock or to accommodate increases in demand, the

model introduces new technologies into the economy through competition using a

market share algorithm (Equation 1), which simulates decision making of consumers

and firms (Jaccard et al., in press; Rivers & Jaccad, 2005). CIMS is comprised of a

decision-tree that nests these energy technology choices in multiple levels of

competition that link various energy services within an integrated supply chain (Bataille,

7

et al., 2007). The model produces results on GHG emissions, as well as energy use and

costs.

Equation 1. CIMS Market Share Algorithm

K

k

v

kkknk

v

jjjnj

j

iECMCr

rCC

iECMCr

rCC

MS

k

j

1 )1(1*

)1(1*

The CIMS market share algorithm determines new market share (MSj ) for a

particular energy service technology (j) by comparing its lifecycle costs to those of all

competing technologies (k) that provide the same service in a given market. Life cycle

costs are comprised of explicit financial costs including capital cost (CCj ) annualized

over nj number of years, annual maintenance and operation cost (MCj) and annual

energy cost (ECj) (Bataille, et al., 2007; Rivers & Jaccad, 2005). Non-financial costs are

represented by intangible-costs (i) which account for ‘technology-specific’ preferences of

the consumers or firms who determine technology choices at a given competition node

(Jaccard et al., 2003). Consumer and firm preferences can manifest as risk-averse

behavior in purchasing new technologies or perception of the quality of service provided,

such as the convenience of using a personal vehicle over public transit. The absence of

an intangible-costs variable would render the market share algorithm unrealistic as

consumers rarely make purchasing decisions based on deterministic financial costs

alone. A discount rate (r) represents consumer time preferences and is used to

annualize capital costs. Time preferences of consumers can sometimes differ from that

of firms, and even from one type of energy service decision to another. Thus, while only

one time preference is assumed for all decision makers at a given node, different time

preference rates can be applied at other nodes, depending on the type of decision

(Rivers & Jaccard, 2006). A market heterogeneity parameter (v) helps simulate how the

personal preferences of individuals may be dissimilar across a given economy whereby

each consumer, when faced with an identical set of energy services and costs, is likely

to make different choices (Horne, Jaccard, & Tiedemann, 2005; Rivers & Jaccad, 2005).

CIMS represents dynamic changes in the perceived and real costs of

technologies by adjusting the cost values used within the market share algorithm. A

declining capital cost function (Equation 2) endogenously incorporates the effect of

8

economies-of-scale and learning-by-doing by reducing the capital costs of a technology

over time (CC(t)) as the cumulative production (N(t)) of that technology increases

(Jaccard et al., in press; Bataille, et al., 2007; Jaccard et al., 2003). The rate of cost

reduction is determined by a progress ratio (PR), which represents the percentage cost

lowered for a doubling of cumulative production. A declining intangible cost function

(Equation 3) endogenously incorporates the ‘neighbour effect’ phenomenon on

consumer preferences where increased market share and familiarity of a technology

reduces its perceived risk and perhaps increases its attractiveness with product

development and marketing experience (Jaccard et al., in press; Bataille, et al., 2007;

Jaccard et al., 2003). In CIMS, this phenomenon is simulated by setting intangible costs

at a given time (i(t)) for a technology as a function of that technology’s market share of

the previous time period (MSt–1) and calibration parameters (A, k) which governs the rate

at which costs are reduced.

Equation 2. Declining Capital Cost Function

Equation 3. Declining Intangible Cost Function

)(log2

)0(

)()0()(

PR

N

tNCCtCC

1*1

)0()(

tMSkAe

iti

The version of the CIMS Stock Turnover Module I adopted for my study

examines Vancouver’s personal transportation sector over a simulation period from 2010

to 2050. The module is organized as a tree of energy service nodes that supplies an

exogenous demand for aggregate travel in the City of Vancouver, measured in units of

Person-Kilometers Travelled (PKTs) (Wolinetz, 2017; Bataille, et al., 2007). Travel

demand units are allocated to four specific modes of travel, through the market share

algorithm, including Single Occupancy Vehicle, High Occupancy Vehicle, Public Transit,

and Active Transportation (walking/cycling). Vehicle services are supplied from a

competition amongst a variety of vehicle choices of diverse efficiencies and fuel-types,

including conventional gasoline and diesel, biofuels, hydrogen and electricity. Transit

services are supplied through rapid transit and local bus service nodes, which also rely

on a selection of different fuel types. The intangible cost parameter is the primary

variable representing personal preferences and trade-offs. Perceptions related to

transportation mode choice rely on intangible cost values assigned for each mode of

travel to account for the comfort or convenience of using personal vehicles verses public

transit, cycling or walking (Horne, Jaccard, & Tiedemann, 2005). These intangible cost

9

values are determined externally to the CIMS Stock Turnover Module and have

previously been derived through guess-and-check calibration in prior studies (Wolinetz,

2017). Within the CIMS-Urban model framework, such mode choice intangible cost

values are generated by the spatial module.

2.2. CIMS Spatial Module

The CIMS Spatial Module is comprised of a series of GIS map layers

representing: 1) residential population through Census Dissemination Areas provided by

Statistics Canada; 2) commercial land-use districts; and 3) transportation infrastructure

including roads, transit routes and bike lanes. Using a system of spatial calculations, the

availability of transportation options and local commercial areas to city residents are

converted into intangible costs. Unique intangible cost values are calculated for each of

the five-year simulation periods between 2010 and 2050, representing the convenience

and comfort of each mode of travel that specific types of urban form provide to

commuters. Within the Spatial Module, the non-uniformity of the urban landscape is

captured through its capacity to measure the effect that a new road, transit route, bike

lane or commercial district may have on a specific city neighbourhood. The model

assigns lower intangible costs to those areas which offer a favorable urban form to

specific modes of travel and higher intangible costs to neighbourhoods that do not have

access to transportation infrastructure. For example, a high concentration of commercial

districts would lower the intangible costs of walking for local residents. Similarly, the

close proximity of high-quality transit service and bike lanes would lower the intangible

costs of taking transit and cycling respectively for those neighbourhoods. All intangible

costs produced by the Spatial Module are entered into the CIMS market share algorithm

governing the competition of transportation mode shares in the CIMS Stock Turnover

Module. Intangible costs related to vehicle and fuel choice are not provided by the

Spatial Module given that such decisions are not as motivated by spatial considerations.

Instead, these costs were sourced externally through empirical research in previous

studies.

The CIMS Spatial Module calculates intangible costs for each transportation

mode type (driving, transit, cycling and walking) and simulation time period via a two-

step process. First, the spatial relationships for each transportation mode type,

contained within a GIS projection of a city’s urban form, are converted to a measurable

10

network quality index that permit neighbourhoods to be ranked according to their ability

to support personal mobility. Spatially exclusive neighbourhood zones are designed

within the model to reflect the requirement for spatial disaggregation for a particular

study. Neighbourhoods that contain transportation infrastructure networks and land-use

patterns that improve the accessibility or quality of service of a specific mode type are

given a higher index value. In the second step of the process, the calculated indexes for

each time period are converted into intangible costs by multiplying those indexes with a

network quality coefficient, estimated within the model, for each of the four transportation

mode types. Once calculated, the intangible costs for each transportation mode type

and simulation year are entered into the CIMS Stock Turnover Module.

Network quality indexes are calculated in four separate categories representing

each transportation mode type. These categories are: driving network quality for

personal vehicle users, walking network quality for pedestrians, public transit network

quality, and cycling network quality. The algorithms used to calculate each type of

network quality are represented in equations 4 to 7 (Jaccard et al., in press). Transit,

cycling and walking quality indexes are calculated for every Census Dissemination Area

(DA) in a given city provided by Statistics Canada (Statistics Canada, 2016). The

calculated indexes in each DA can be averaged across the study’s larger neighbourhood

zones. Driving network qualities are calculated for a neighbourhood zone directly.

Equation 4. Network Quality Algorithm for Driving Quality

𝑄𝐷𝑟𝑖𝑣𝑒 =∑ 𝑞 × 𝑙𝑛1

𝑛

𝑝

𝑎⁄

q=road quality, l=road length, n=number of road segments in a neighbourhood, p=neighbourhood population, a=land area of a neighbourhood

Driving quality (QDrive) is calculated by determining the average road quality for a

neighbourhood in relation to its population density characterised by the local population

(p) divided by the neighbourhood’s measured land area (a). Road quality is determined

by assigning a quality value (q) for a given type of road segment multiplied by its length

(l). Quality values are categorized according to road type, including: freeway, arterial,

collector, local, lane/strata, and trail/restricted/other. Population density is used as a

proxy for road congestion that reduces driving quality.

11

Equation 5. Network Quality Algorithm for Walking Quality

𝑄𝑊𝑎𝑙𝑘 =∑1

1 + 𝑒ln(19)200

×𝑑𝑚𝑖𝑛−200

𝑛

1

n=number of commercial/institutional districts, dmin=minimum distance between census dissemination area and a commercial/institutional district

Walking quality (Qwalk) is calculated for a single DA as a function of the minimum

distance between the DA boundary and a commercial district (dmin). A logistic decay

function is used within the algorithm to produce a value which reflects how longer

distances have a disproportionally larger effect in disincentivizing individuals to walk

compared to shorter distances. All distances are measured as straight lines between the

DAs and commercial areas based on the assumption that all road networks are grid-

based and have sidewalks and paths that are readily accessible to pedestrians. The

calculated values for every commercial district in a city are summed together to produce

the walking quality index for each DA.

Equation 6. Network Quality Algorithm for Transit Quality

𝑄𝑇𝑟𝑎𝑛𝑠𝑖𝑡 = ∑1

1 + 𝑒ln(19)200

×0.5×(𝑑𝑚𝑖𝑛+𝑑𝑚𝑎𝑥)× 𝑓 + ∑

1

1 + 𝑒ln(19)400

×0.5×(𝑑𝑚𝑖𝑛+𝑑𝑚𝑎𝑥)× 𝑓

𝑛𝑅𝑎𝑝𝑖𝑑𝑇𝑆

1

𝑛𝑅𝑒𝑔𝑇𝑆

1

nRegTS/nRapidTS=number of regular/rapid transit stops, dmin /dmax =minimum/maximum distance between census dissemination area and regular/rapid transit stop, f=frequency of transit service

Transit quality is calculated for a single DA as a function of the minimum distance

between the geographic centre of the DA and a transit stop (0.5 x (dmin + dmax)) multiplied

by its service frequency (f). Transit stops consist of two categories, rapid (RapidTS) and

regular transit (RegTS), each requiring a distinct algorithm. Rapid transit is defined as a

fast conveyance operating on reliable routes that have a dedicated right-of-way while

regular transit is comprised of the remaining bus routes in a city (Jaccard et al., in

press). The regular transit algorithm employs the identical logistical distance decay

function used for calculating the walking quality index. The rapid transit function decays

over a longer distance reflecting how individuals are willing to walk farther to use rapid

transit. The calculated values for every rapid and regular transit stop are summed

together to produce the transit quality index for each DA.

12

Equation 7. Network Quality Algorithm for Cycling Quality

𝑄𝐶𝑦𝑐𝑙𝑒 =∑1

0.5 × (𝑑𝑚𝑖𝑛 + 𝑑𝑚𝑎𝑥)× 𝑙 × 𝑞𝑘

𝑛

1

n=number of bicycle lanes, dmin ,/dmax =minimum/maximum distance between census dissemination area and a bike lane, l=bike lane length, q=bike lane quality, k=calibration value

Cycle quality (Qcycle) is calculated by an inverse distance function between the

geographic centre of a DA and the closest point of a bike lane (0.5 x (dmin + dmax))

multiplied by the length of the lane (l) and a value representing the quality of the route

(q). Route quality is categorized according to lane type, including: separated lanes and

lanes that share vehicle road space. The calculated values for every bike lane are

summed together to produce the Cycling Quality Index for each DA.

Network quality coefficients, used to convert network quality indexes into

intangible costs, are estimated separately for each of the four transportation mode types

by way of a linear ordinary-least-squares (OLS) regression method. The OLS analysis

estimates a best-fit trendline between network quality index data for a given baseline

year in the GIS map and baseline intangible costs derived through exogenously provided

census data on transportation mode choice for each DA corresponding to the baseline

year. The data points used in the regression are organized into location-specific panel-

data, whereby network quality indexes and intangible cost data are averaged across the

neighbourhood zones designed for the study. The network quality coefficients are

estimated as negative values, which converts higher network quality indexes,

representing better transportation infrastructure and land-use patterns, into lower

intangible costs. Once calculated, the four coefficients are held constant across all

simulation years in the model allowing for intangible cost changes in each mode type,

caused by policy induced improvements in network quality, to be comparable across

different time periods relative to the baseline year.

The CIMS market share algorithm is used to produce baseline transportation

intangible costs from census data for each of the four transportation mode types and for

every neighbourhood zone considered in the analysis. Aggregated transportation mode

share census data and empirically derived financial costs are used to solve city-wide

intangible costs. The estimated baseline costs are determined under the constraint that

driving a personal vehicle incurs an intangible cost of zero with all other transportation

13

intangible costs calculated relative to this driving cost value. Baseline intangible costs

are extrapolated for each neighbourhood from the city-wide cost estimates using the

Nelder and Mead optimization technique. Nelder and Mead optimization is used to solve

non-linear equations, such as the CIMS market share algorithm, by generating potential

solutions using previously calculated values as inputs in subsequent calculations.

(Nelder & Mead, 1965). The process is repeated until the input values and output

solutions are equal, indicating that the equations have been optimized. Using this

technique, city-wide intangible costs are used as an initial starting point for the

optimization method, which are then recalculated until they solve for the neighbourhood

specific census transportation mode share data. Neighbourhood intangible costs are

related to each other by virtue of those costs originating from a single set of city-wide

intangible cost estimates. Once determined, the neighbourhood baseline intangible

costs are utilized in the OLS regressions that determine the network quality coefficients.

14

Chapter 3. Model Development

To complete my research objectives, I contributed to the methodological

development of CIMS-Urban to address key limitations within the model. As previously

discussed, CIMS-Urban was limited in its ability to assess the impacts of population

growth and urban density. Moreover, changes to travel demand were not endogenously

determined within the model, as a function of land use improvements, and no

consideration was made to the potential impact of road congestion rebound effect. In

this chapter, I outline the enhancements I made within the CIMS-Urban model

framework. Section 3.1 describes alterations made to the existing model components

within the CIMS Stock Turnover Module that governs the market share competition of

transportation mode choices. Section 3.2 describes improvements made within the

CIMS Spatial Module pertaining to the way intangible costs are estimated. Section 3.3

provides an overview of how urban densification and population growth are now

considered within CIMS-Urban. Section 3.4 outlines how a person-kilometers-travelled

(PKT) calculator was incorporated into the model to endogenize travel demand within

CIMS-Urban. Section 3.5 describes how road congestion rebound effect was added

within my analysis.

3.1. CIMS Market-Share Estimation

The CIMS Stock Turnover Module was designed to represent four basic

categories of transportation modes: Single Occupancy Vehicle, High Occupancy

Vehicle, Public Transit, and Active Transportation (walking/cycling) (Wolinetz, 2017).

Each of these travel options compete based on their respective life cycle costs. For my

research, I altered the number and type of transportation mode options to enhance the

model’s realism in simulating travel behavior.

As a first step to altering the market share components of the CIMS Stock

Turnover Module, I removed the Active Transportation mode choice and replaced it with

two additional travel mode options representing walking and cycling as separate nodes

within the CIMS decision tree. Previously, Active Transportation captured walking and

15

cycling behavior as a single choice which competed against personal vehicle use and

transit. Walking and cycling intangible costs produced by the Spatial Module were

averaged into one cost value before being entered as Active Transportation within the

CIMS market share algorithm. Under this competitive framework, the model was unable

to independently determine how a change in the urban bike route network may affect

cycling mode share as an independent choice from walking. Similarly, the impact of any

changes to land-use that creates a more favourable walking environment could not be

directly identified if occurring alongside improvements to the cycling network. Creating

separate decision nodes for walking and cycling within the CIMS market share algorithm

allowed the intangible costs for both travel options to be directly incorporated into the

mode share competition without the need for averaging or alteration. This enhancement

allowed the model to produce more realistic results in determining mode share splits

between transportation options. Having an equal number of travel mode choices

between the CIMS Stock Turnover Module and the Spatial Module permitted a more

precise calibration of baseline transportation mode shares in both components of the

model before running simulations.

The other alteration I made was to redefine the personal vehicle travel mode

choices of Single Occupancy Vehicle and High Occupancy Vehicle. In the original

version of the model, Single Occupancy Vehicles represent personal vehicles operated

by a driver without any passengers. High Occupancy Vehicles represent the remaining

personal vehicles on the road which transport more than one individual as part of a

carpool. Both mode choices share driving intangible costs estimated by the Spatial

Module because of the earlier model design assumption that all car users, whether they

be solo drivers or part of a carpool, experience similar levels of comfort during their

commute. To account for generally lower occurrences of carpooling relative to solo

driving, a fixed intangible cost is applied to the High Occupancy Vehicle decision node to

make the option of sharing a vehicle less attractive in the model simulation.

For my study, I replaced the above personal vehicle transportation decision

modes in the model with ‘Personal Vehicle Drivers’ and ‘Personal Vehicle Passengers.’

In this configuration, the number of Vehicle-Kilometers Travelled is a function of the

share of Personal Vehicle Drivers, estimated by the mode share competition algorithm.

Individuals represented under Personal Vehicle Passengers do not influence the number

of Vehicle-Kilometers Travelled and instead experience the inconvenience of arranging

16

transportation with another vehicle owner. A higher fixed intangible cost, relative to

driving, is applied to the Personal Vehicle Passengers decision node to reflect the

difficulty of finding transportation as a vehicle passenger. My rationale for this alteration

was to ensure that the transportation mode share results produced by the CIMS Stock

Turnover Module aligned to data produced by the Spatial Module. Intangible costs

produced by the Spatial Module are based on DA level census data derived from survey

responses, produced by Statistics Canada, indicating the number of commuters for

every transportation mode choice. This census data provided information on the number

of individual drivers and passengers living within each DA. Specific data on carpooling

behavior was unavailable at the level of spatial disaggregation necessary for my study.

Matching the transportation competition components in the Stock Turnover Module to

available census data contained within the Spatial Module enabled me to integrate and

calibrate CIMS-Urban to produce meaningful results for city-bound transportation within

Vancouver.

3.2. CIMS Spatial Module Intangible Cost Estimation

I enhanced the existing components of the CIMS Spatial Module by increasing its

spatial disaggregation. In a previous study of Vancouver by SFU EMRG, the spatial

module was calibrated to produce results for three unique neighbourhood archetypes

(Downtown, Inner City, and Suburban) (Jaccard et al., in press). For my research, I

increased the number of neighbourhood areas to twenty-two, representing each of

Vancouver’s planning neighbourhoods (Figure 1). I incorporated a new manual

neighbourhood feature within the Spatial Module to accept additional neighbourhood

designs. Future users of CIMS-Urban will have the benefit of determining an appropriate

amount of spatial disaggregation by drawing neighbourhood zones on the Spatial

Module’s GIS map and assigning DAs to those specific areas. In my study, I assigned

each of Vancouver’s 992 DAs into their respective neighbourhood areas.

My rationale for increasing CIMS-Urban’s spatial disaggregation was two-fold.

First, determining the effect of urban population densification requires a model capable

of accounting for changes to transportation intangible costs within various

neighbourhoods. Without the capacity of producing unique results over multiple

neighbourhoods in a city, the impact of intra-city population movements on travel

behavior would not be recorded in the model. Secondly, the addition of multiple

17

neighbourhood zones, as new data points, enhanced the statistical significance of the

OLS regressions calculating network quality coefficients.

Figure 1. Vancouver Neighbourhood Zones (red line) and Census Dissemination Areas (dashed lines)

When operating the model, I calculated baseline intangible costs and network

qualities for each of Vancouver’s twenty-two neighbourhoods by averaging DA census

mode share data and network quality indexes across each neighborhood zone. These

calculations generated twenty-two data points, for each transportation mode choice, that

were regressed with the OLS method to produce the network quality coefficients. When

determining future intangible costs during policy simulations, the model produced unique

intangible cost values for each neighbourhood and transportation mode choice.

18

City-wide intangible costs for each simulation year and transportation mode were

produced by averaging the results from Vancouver’s neighbourhoods.

3.3. Urban Population Growth and Density

The analysis of population growth and density required new components to be

added within CIMS-Urban. I incorporated a population growth feature into the Spatial

Module. This feature has the capability of capturing changes in population density by

location and can inform how intangible costs should be weighted before being averaged

into city-wide costs and entered into the CIMS Stock Turnover Module. New spatial

layers were added to the Vancouver GIS maps which assigned population data to all

DAs. The policy scenario map permits the user to manually input population values in

every DA location for every five-year future time period. The baseline map used to

calculate network quality coefficients was designed to accept current DA level census

data population. City-wide population can be increased by gradually inputting higher

population values over multiple years. Higher densities are simulated by entering high

population values in adjacent DAs over a spatial landscape.

Urban densities were incorporated into the intangible cost calculations by

summing the population of each DA into groups corresponding to the twenty-two

neighbourhood zones used in my study. For each simulation year, the transportation

intangible costs for every neighbourhood were multiplied by the population in that

neighbourhood. The result was summed across all neighborhoods and divided by

Vancouver’s total estimated population to produce city-wide per-person intangible cost

values. Weighting neighbourhood-specific intangible costs by population provides an

accurate account of the degree to which local population growth and density may

influence transportation mode choice at the city level.

Integrating the new population growth and density feature into CIMS-Urban

required alterations to the Driving Network Quality Algorithm (Equation 4). As

previously discussed, road quality for each neighbourhood zone is divided by its

population density, which is a measurement of local population divided by land area. In

previous iterations of the model, population density for each neighbourhood was held

constant for all simulation years (Jaccard et al., in press). To incorporate CIMS-Urban’s

new ability to alter density within the Driving Quality Algorithm, the GIS spatial layers

19

containing neighbourhood-specific population data was linked to the algorithm’s

population variable.

The method of calculating land area in the Driving Network Quality Algorithm was

also altered to only measure the drivable area of each neighbourhood zone. The

drivable area represents a subset of the land-use map excluding areas where vehicles

are prohibited such as parks, recreational areas and institutional zones. Previous

calculation methods incorporated all land in a neighbourhood, which resulted in

inconsistent estimates for road congestion across Vancouver. Restricting population

density measurements to a neighbourhood’s drivable area better reflects the potential

impact of personal vehicle driving on shared road space.

3.4. Person Kilometers Travelled Estimation

I integrated a PKT calculation algorithm (Equation 8) within CIMS-Urban to

determine the effect of future changes in population density and land-use on travel

demand. Baseline year per-person PKT values are exogenously sourced from data and

transformed into estimated future PKTs by way of calculated index values that replicate

percent changes in travel demand over time. The calculation methodology is based on

an assumption that PKT is a function of the relative spatial relationships between an

individual’s residence and the surrounding commercial and institutional land-use

districts. Commercial and institutional districts act as a proxy for local employment

zones and essential local services such as grocery stores and medical clinics. When the

average distance between the residence and all surrounding commercial and

institutional districts decreases, I assumed that the travel requirement to reach those

districts would decrease proportionately. Additionally, I assumed that if there are local

commercial districts providing adequate services and employment to residents, it follows

that the establishment of similar commercial districts at a greater distance would not

affect local travel demand. Likewise, if new commercial districts are developed locally

for residents, those individuals would discontinue travelling to comparable districts

located farther away in favour of the newly established areas. When integrating the

effect of population growth and density, I assumed that the amount of population living in

a given land parcel proportionately impacts the aggregate amount of travel demand

anticipated from that parcel.

20

Equation 8. PKT Index Calculator Algorithm

𝑃𝐾𝑇𝐼𝑛𝑑𝑒𝑥𝑡,𝐷𝐴 = ∑ (𝑑𝐷𝐴,𝑘)𝑊 × 𝑝𝐷𝐴

𝑛

𝑘=1

t = time period, DA= census dissemination area, k= commercial-institutional district,

n = number of closest commercial-institutional districts, W= weighted value,

p=DA population

The algorithm calculates a PKT index value for a specific DA and time period

(PKT Indext,DA) by summing the distances (dDA,k) between the DA and closest

commercial and institutional districts (k). Each distance calculated can be weighted (W)

to account for the preferences that residents may have for traveling to closer districts.

The number of commercial and institutional districts included within the algorithm (n) can

be adjusted to reflect how many of these districts’ residents may consider as likely

destinations in their travel decisions. The calculated index values are then multiplied by

the DA’s local population to account for the impacts of population density and growth.

The population growth feature, discussed above, permits density changes across all

simulation years to be incorporated into the algorithm.

City-wide per capita index values representing change in travel demand are

extrapolated by averaging all DA-specific indexes in Vancouver and dividing by total

population at every simulation year. The per-person PKT index values for each

simulation year are normalized relative to the baseline year, thereby permitting all

changes in mobility to be reported as a city-wide percent change in per capita travel

demand. The normalized per-person PKT index values are multiplied by the

exogenously derived baseline year per-person PKT estimate to produce aggregated per

capita travel demand values for Vancouver. The aggregated per capita PKTs are

multiplied by the corresponding total population in Vancouver for every simulation year

to produce travel demand stock values that are entered into the CIMS personal

transportation sector market share algorithms. As discussed above, the aggregate

PKTs are divided by the CIMS market share algorithms among transportation mode

choices, specifically Personal Vehicle Drivers, Personal Vehicle Passengers, Transit,

Walking and Cycling.

21

3.5. Road Congestion Rebound Effect

I incorporated road congestion rebound effect into CIMS-Urban by updating the

Driving Network Quality Algorithm to include two additional variables representing

internal feedbacks within the model (Equation 9). The algorithm was altered based on

the assumption that increasing the number of drivers on the road decreases driving

network quality, thereby increasing the intangible costs of using personal vehicles.

Similarly, fewer drivers on the road would decrease driving intangible costs.

The first internal feedback incorporated within the Driving Network Quality

Algorithm represents total travel demand (PKTs). Changes to total PKT would affect the

number of people driving on the road, just as it would influence the frequency with which

people walk, cycle or take transit. If new land-use policy was enacted to reduce travel

demand with development of mixed-use residential and commercial neighbourhoods, it

follows that the number of vehicles on the road would decrease, thereby increasing

driving network quality and reducing driving intangible costs.

The second internal feedback that influences driving quality is vehicle mode-shift

substitution. Drivers who choose to switch from personal vehicles to alternative forms of

transportation leave additional space for vehicles on the road, thus increasing driving

network quality and decreasing driving intangible costs relative to other transportation

mode choices. The combined effect of taking into account changes to travel demand

and mode substitution is an environment whereby decreases in personal vehicle use

would lead to a simultaneous and countervailing rebound effect in driving, due to a

relative increase in favorable driving conditions.

Equation 9. Updated Network Quality Algorithm for Driving Quality

𝑄𝐷𝑟𝑖𝑣𝑒 =(∑ 𝑞 × 𝑙)/𝑛𝑛

1𝑝𝑎× 𝑑 × 𝑐

q=road quality, l=road length, n=number of road segments, p= population, a= drivable land area, d= per person travel demand (PKTs), c=drive share coefficient

I improved the Driving Network Quality Algorithm by adding two additional

variables:1) a PKT Demand coefficient (d) representing percent changes to travel

demand; and 2) a Drive Share coefficient (c) representing driving mode share

substitution effect. Neighbourhood specific per-person PKT index values are generated

22

within the model for each simulation year and entered into the PKT Demand coefficient

of the corresponding Driving Quality Algorithm. The Drive Share coefficients, for all

estimated simulation years, are calculated as part of a feedback loop programed into the

model. City-wide driving mode share results produced by CIMS-Urban, as a percentage

of total travel demanded, is used to populate the Drive Share coefficients for each of the

twenty-two neighbourhood zones as a method of estimating real time changes in vehicle

use. The consequential changes in driving intangible costs caused by the two

congestion feedback coefficients are run through the model to produce updated mode

share results. CIMS-Urban recalculates driving mode share results through multiple

iterations until an equilibrium value for percentage vehicle-use is found where the

model’s driving mode share result outputs are equal to what is fed back into the Drive

Quality Algorithm’s Drive Share coefficients.

23

Chapter 4. Test Simulations

I conducted test simulations to meet the objectives of my study and to validate

the methodical improvements I made to CIMS-Urban in the course of my research. I

developed a robust experiment designed to isolate and measure the relative

contributions of change to transportation infrastructure, land-use and population density

when applied simultaneously on Vancouver’s urban landscape. The scenarios were

designed to be illustrative examples that demonstrate the outcomes of GHG emissions,

energy use, travel demand and mode choice under contrasting urban form policies. To

increase the relevance of the simulation results, I updated the data sources and

underlying assumptions that form the basis of CIMS-Urban’s estimations. In Section 4.1,

I describe the test policy scenarios used in my study, while in Section 4.2, I provide an

overview of the data sources and modeling assumptions I utilized when conducting the

simulations.

4.1. Test Policy Scenarios

I used two urban policy scenarios derived from a previous study by SFU EMRG

(Jaccard et al., in press) and independently developed three population density

scenarios for the City of Vancouver. Collectively, the population scenarios and urban

policies formed a total of six combinations to test each incremental effect on

transportation behaviour under different urban forms and population densities. All

scenarios were simulated using a version of CIMS-Urban that included my

enhancements outlined in Chapter 3. A matrix outlining each of the scenarios is

displayed in Table 1.

Table 1. Scenario summary matrix

Population Density Scenarios

Uniform Growth High Density Growth

Low Density Growth

Van

cou

ver

Mu

nic

ipal

Po

licie

s

Current Policy (CurrentPol)

CurrentPol (UniformGrowth)

CurrentPol (HighDensity)

CurrentPol (LowDensity)

Renewable City Strategy (VanRen)

VanRen (UniformGrowth)

VanRen (HighDensity)

VanRen (LowDensity)

24

The two urban policy scenarios were sourced from the EMRG study on

Vancouver’s Renewable City Strategy to capture urban change mandated under this

strategic plan (Jaccard et al., in press). CurrentPol is a reference scenario representing

current urban form policy in Vancouver, as of 2010, held constant throughout the model

simulation to 2050. Transit service quality and bike lane networks do not improve, but

the model assumes that the carrying capacity of both conveyances will be expanded to

accommodate increases in population over time. Land-use policy dictating the location

of commercial, institutional, and mixed-use districts remains unchanged. Shifts in

consumer choice regarding the use of vehicle technologies and fuel types are permitted

within the model, reflecting the natural progression of the economy during the forty-year

scenario.

The VanRen Scenario shows gradual changes to land-use, bike lane networks,

transit routes and roads to 2050 (Jaccard et al., in press). In this scenario, land-use is

changed to incorporate additional instances of mixed-use residential and commercial

districts located in major transit corridors. Bike lanes and transit route networks are

expanded and improved in quality with increased frequency of bus and rapid transit.

Road surface area decreases in select neighbourhoods to accommodate the

improvement of the bike lane network. The VanRen Scenario was developed in

previous research studies by SFU EMRG and contains hypothetical transportation

network changes consistent with the stringency and design of Vancouver’s

Transportation 2040 (City of Vancouver, 2012) and Translink’s Metro Vancouver 10-year

plan (Mayors’ Council on Regional Transportation, 2014; Jaccard et al., in press). A

mapping of the Renewable City Strategy between 2015 and 2050is depicted in Figure 2.

25

Figure 2. Land use and bike network changes proposed in the Vancouver Renewable City Strategy. Reprinted from Vancouver’s Renewable City Strategy: Economic and Policy Analysis, by Brett Maynard Zuehlke, retrieved from http://summit.sfu.ca/. Copyright Brett Maynard Zuehlke 2017, Simon Fraser University. Reprinted with permission.

26

All population scenarios were based on forecast data for Vancouver provided by

BC Stats Sub-Provincial Population Projections extending to 2041 (BC Stats, 2017).

Population forecasts to 2050 were extrapolated based on an assumption that yearly

growth rates will remain constant at 0.525%, as estimated for 2041, until 2050. City-wide

population is recorded in the model as increasing from 631,789 in 2010 to 860,501 by

2050. I developed each of the three density scenarios by altering the location where

population growth is simulated to take place. I manually entered new population figures

within the census DA boundaries in a pattern that generated each of the three density

scenarios, while ensuring city-wide population growth remained consistent with BC Stats

forecasts for all simulation years. A mapping of the three density scenarios is depicted in

Figure 3.

The UniformGrowth scenario represents Vancouver with no changes in relative

population densities. All DAs proportionately increase in population to match BC Stats

population forecasts. The rationale behind UniformGrowth was to develop a neutral

scenario to determine the impact of land-use and transportation network improvements

absent of any relative changes in population density. The HighDensity scenario,

restricts growth to DAs that contained 50% of the 2010 population living in the highest

density neighbourhoods. Specific DAs were selected by ranking them according to

population density. The densest DAs representing half of Vancouver’s population were

chosen to receive population increases in future simulation years. In this scenario,

population increases in approximately one-third of Vancouver’s DAs located in proximity

to the downtown core and along the Expo-Line SkyTrain corridor. The objective of the

HighDensity scenario was to provide an extreme example of how maximum increases in

density within Vancouver’s urban landscape has the potential to impact transportation

GHG emissions, energy use, travel demand and mode choice. The LowDensity

scenario restricts growth to the remaining DAs that contained 50% of the 2010

population living in Vancouver’s lowest density neighbourhoods, which represented

approximately two-thirds of remaining DAs in the city. However, population was added

to each low density DA such that its density never exceeded 7500 persons per square

kilometer, the lowest value of the high density DAs located downtown and along the

Expo Line.

27

Figure 3. Population density scenarios and 2010 baseline estimation

28

4.2. Model Assumptions, Data Inputs and Calibration

I applied a consistent set of energy data, senior government policies, and other

discretionary modeling assumptions across all policy scenarios, to create a modeling

framework and calibration procedure with CIMS-Urban capable of producing accurate

results.

Table 2 outlines the data sources used in my study. Exogenous data inputs for

Vancouver were used in the CIMS Stock Turnover Module in the form of costs, energy

prices, PKT baseline year estimates, and share of rapid transit use. Additionally, GIS

data from the previous EMRG study (Jaccard et al., in press) was applied to develop the

baseline spatial mappings used in the CIMS Spatial Module in combination with

population data used to develop the urban density scenarios.

Data applied in the CIMS Stock Turnover Module were derived from a variety of

sources used in previous research. I sourced energy and fuel wholesale price data from

the Vancouver Energy and Emissions Forecast report by Navius Research Inc

(Wolinetz, 2017). From this report, I utilized their optimistic assumptions for bioenergy

based on estimates from Fortis BC (2017), the Canadian Gas Association (2014), Jones

et al. (2013), and Wolinetz (2017). My rationale for using this data was to calculate the

hypothetical maximum impact that Vancouver’s GHG reduction policy could achieve

under the most favorable economic circumstances. Wholesale prices for gasoline,

diesel and natural gas were converted to local retail prices through a method developed

by Wolinetz (2017) which added costs from refining and processing, and associated

GST and excise taxes. Electricity price projections were determined by applying British

Columbia’s 2015 yearly average electricity consumption estimates to BC Hydro’s two-

step rates. Electricity prices in the model were then increased according to BC Hydro’s

announced rate increases until reaching an average retail price of 110 $/MWh plus 5%

GST by 2020. All electricity prices were held constant thereafter. It was assumed that

Vancouver is a ‘price-taking’ economy, meaning that the economic activities of

Vancouver were not expected to affect national or international energy prices. Based on

this assumption, the energy price data, once converted to local retail levels, were not

further manipulated or altered within the model with any economic feedback functions

within CIMS-Urban.

29

Table 2. Data sources used in the development of CIMS-Urban

Data requirement Data source

CIMS Stock Turnover Module data sources

Vehicle biogas and natural gas price forecasts

Biogas Gas: Current price from Fortis BC (2017); Forecast from Canadian Gas Association (2014)

Natural Gas: Based on Henry Hub forecasts from EIA’s Annual Energy Outlook (EIA, 2017)

Liquid fuel price Gasoline and Diesel Fuel based on West Texas Intermediate Spot Price from EIA’s Annual Energy Outlook (EIA, 2017); Adjusted to Western Canadian Select price (Sproule and Associates, 2015)

Renewable Gasoline and Diesel based on Jones et al. (2013)

Ethanol and Biodiesel based on International Renewable Energy Agency Report (2012)

Electricity price forecasts From BC Hydro 10-year plan announcement. (BC Hydro, 2013)

Average electricity use in BC NRCAN Comprehensive Energy Use Database (Natural Resources Canada, 2015)

Vehicle motor prices and fuel use Estimates based on data used in City of Vancouver Energy and Emissions Forecast by Navius Research Inc. (Wolinetz, 2017), UBS Global Research (2017) and Axsen & Kurani (2013)

Share of rail rapid transit Assumptions based on study by Jaccard et al. (in press)

CIMS Spatial Module data sources

Population growth Sub-Provincial Population Projections for Vancouver School District (BC Stats, 2017)

Dissemination areas Statistics Canada’s Dissemination Area Boundary Files (2016)

Transportation mode share by dissemination area

Canadian Census Analyzer provided by the Canadian Socio-Economic Information Management System (2016)

Land-use data Metro Vancouver’s Open Data Catalogue (2015)

Road network data Province of BC’s Digital Road Atlas (GeoBC, 2015)

Transit data TransLink Open API Google GTFS data (TransLink, 2015)

Bike routes, Vancouver’s local neighbourhood areas

City of Vancouver’s Open Data Catalogue (City of Vancouver, 2015b)

Persons Kilometer Travelled (PKT) baseline year

UBC IRES Greenest City Initiative Report: Based on geographic method, scope 1 and scope 3 (Shakouri, et al., 2015)

Financial and intangible costs, and fuel consumption rates representing the

technological progression of vehicle types and motors were sourced from Wolinetz

(2017). Battery technology cost for electric and plug-in hybrid electric personal vehicles

was set to decrease to 125$/kwh by 2029. The expected split in transit ridership

30

between bus and rail rapid transit to 2050 was set exogenously in CIMS-Urban for

Vancouver to align with figures used in the study by Jaccard et al. (in press). Rail rapid

transit use was set to 30% at 2010 and increased to 35% and 40% in 2025 and 2035

respectively.

Spatial Data sources produced between 2015 and 2016 were used, in

combination with 2016 Census DA data, to form the basis of the GIS mappings and

parameters that calculated the network quality indexes and coefficients, and mode share

intangible costs as described in Chapter 2. An important caveat is that the 2016 mode

share census data at the DA level used to calculate baseline intangible costs provided

information only on commuting to work. Data specifying trip demand for other purposes,

including essential services, exist within the Vancouver Transportation Panel survey,

and TransLink Trip Diary survey, but not at the spatially disaggregated level necessary

for my study (Zuehlke, 2017). The remaining discretionary calibration variables within the

Network Quality and PKT Index algorithms (Equations 4 to 9) are outlined in

Appendix A.

For my study, I entered Senior Federal and Provincial Government policies that

were announced as of Fall 2015 for all simulation years to 2050. I applied a provincial

carbon tax that increases to a maximum of $28 per tonne of carbon dioxide equivalent

by 2015 and declines annually by 2% thereafter to reflect the impacts of inflation on the

real value of the tax. Provincial Renewable and Low Carbon Fuel Requirements

Regulations (RLCFRR) are set to 2010 levels of 4% and 5% of fuel volume for diesel

and gasoline respectively and subsequently increased to 10% by 2020. Federal Vehicle

Emissions Standards are set in the model to increase to the 2016 standard for light duty

vehicles and to the 2017 standard for heavy duty vehicles. The announced federal light

duty vehicle emissions standard to 2025 was not applied. My intent on using these

policy assumptions was to determine the extent to which local governments could

independently reduce GHG emissions and energy use absent of any additional senior

government support.

I calibrated CIMS-Urban though a three-step process. First, I generated 2010

annual baseline financial costs for each transportation mode type by conducting an initial

test run of the CIMS Stock Turnover Module, produced by Wolinetz (2017), once the

above data and assumptions were entered into the model. I relied on Wolinetz’s existing

31

model parameters for vehicles in Vancouver, which he adjusted until his results

approximated BC Hydro’s electricity consumption data, Fortis BC’s natural gas

consumption data, and the BC Government and City of Vancouver’s energy and

emissions inventories. Secondly, I calculated Vancouver’s city-wide transportation mode

type baseline intangible costs with the CIMS market share algorithm using mode share

information from census data and baseline financial costs generated from the CIMS

Stock Turnover Module in the first calibration step. I used a non-linear equation

calculator to solve for the intangible costs and normalized them by equating driving

intangible costs to 0. Lastly, I entered the calculated annual financial costs and

intangible costs for each of the transportation mode types into the CIMS Spatial Module

as the final parameters required to run the model. These cost values are outlined in

Appendix B.

When calculating network quality coefficients in the CIMS Spatial Module, I

excluded two Vancouver neighbourhoods for specific transportation mode types as they

produced outliers which biased the model’s results. I removed the Downtown

neighbourhood from the OLS regressions calculating transit and cycling coefficients and

I excluded the Killarney neighbourhood from regressions calculating transit and driving

coefficients. The Downtown core contained a high concentration of cycling and transit

routes, which serve as a transportation conduit between other neighbourhoods. As a

result, the cycling and transit network quality index values were overestimated relative to

their frequency of use by downtown residents. The Killarney neighbourhood generated

transit and driving baseline intangible cost values that were unreasonably low while

producing cycling and walking intangible costs that aligned to the regression trendlines.

The likely reason for this occurrence is an artifact of the non-linearity of the CIMS market

share algorithm. Census data from Killarney had the highest driving mode shares in

Vancouver relative to the three other transportation mode types. Extreme intangible cost

values were required to solve the market share algorithms for that neighbourhood.

32

Chapter 5. Results and Discussion

To meet my research objectives, I first display expected GHG emissions and

energy consumption trajectories for each of my six test scenarios and illustrate the

impacts of how different combinations of population density and urban form may assist

in reducing Vancouver’s carbon footprint and energy use. Next, I present estimated

contributions to GHG emission and energy use reductions from vehicle energy efficiency

and fuel switching relative to the expected impacts from changes in urban form and

population density. Following the energy and emissions results, I show the estimated

impacts of urban form and population density policies on travel demand and mode

choice, discussing the extent to which such policies may influence travel behavior.

Lastly, I explore the impact of road congestion rebound effect and its potential to

compromise mode-shift from personal vehicles. All results display emissions from end-

use energy consumption of vehicles. No upstream life-cycle emissions pertaining to fuel

production and processing are captured in the model.

5.1. Greenhouse Gas Emissions

Figure 4 shows the estimated total and per capita GHG emissions results of the

six scenarios. Table 3 displays a summary of the percent emissions reductions from

2010 by 2050 for each of the scenarios. Aggregate GHG results support a supposition

that changes to urban form contribute to GHG reductions, albeit at moderate amounts

relative to the existing business-as-usual trends. It is estimated that CurrentPol will

reduce GHG emission between 43% and 48% while VanRen will generate a modest

additional reduction of 12 to 13 percentage points. The per capita results indicate that

increases in population diminish the ability of Vancouver to abate GHG emissions under

VanRen by approximately 10 percentage points. My results clearly show that population

growth will likely put upward pressure on the carbon footprint of Vancouver’s personal

transportation sector in the absence of stringent countervailing policies.

33

Figure 4. Total and per capita greenhouse gas emissions in the Current Policy (CurrentPol), and Renewable City Strategy (VanRen) urban policies, for UniformGrowth, HighDensity, and LowDensity population scenarios

34

Table 3. GHG emissions reductions from 2010 to 2050

Scenarios UniformGrowth HighDensity LowDensity

Reductions/Gains

From Density

Percent Total Reductions

CurrentPol -45.7% -47.8% -43.1% -2.1% / +2.6%

VanRen -58.3% -60.1% -56.1% -1.8% / +2.2%

Percent Difference

-12.6% -12.3% -13.0% +0.3% / -0.4%

Percent Per Capita Reductions

CurrentPol -60.1% -61.6% -58.2% -0.5% / +1.9%

VanRen -69.4% -70.7% -67.7% -1.3% / +1.7%

Percent Difference

-9.3% -9.1% -9.5% +0.2% / -0.2%

When considering population densities, HighDensity population growth is

consistently shown to cause a further decrease in emissions, whereas LowDensity

growth causes emissions to increase compared to other density scenarios. The model

predicted that restricting growth to high density neighbourhoods allows for a greater

proportion of Vancouver’s population to be in proximity to higher quality transportation

infrastructure and mixed-use urban form, which promotes reductions in the use of GHG

emitting personal vehicles. Increases in population living in low density neighbourhoods

are projected to experience higher intangible costs of using low-emission alternative

transportation and are therefore more likely to meet their travel needs by using personal

vehicles. However, when accounting for the magnitude of impact that density may have

on GHG emissions, the introduction of HighDensity and LowDensity population

scenarios are estimated to vary total and per capita emissions by no more than a few

percentage points by 2050 for either CurrentPol or VanRen urban form

policy. Therefore, increasing urban density is unlikely to adequately counteract the

increased GHG emissions produced through Vancouver’s expected population growth.

As it turns out, all scenarios fail to realize Vancouver’s target of 80% GHG

emissions reduction by 2050. This is the case even though I calibrated CIMS-Urban to

produce the most favorable economic conditions for alternative fuel production and

vehicle technology shift. Under the VanRen-HighDensity scenario, planned urban form

improvements in combination with high population density, allow Vancouver to reach

approximately three-quarters of the GHG emissions reductions necessary to achieve its

climate target. It follows that, under circumstances with less favorable energy economic

35

conditions for alternative fuels and vehicle technologies, Vancouver’s planned GHG

emissions reduction strategies will fall far short of its 2050 target.

5.2. Energy Consumption

Figure 5 shows the estimated total and per capita energy consumption results of

the six test scenarios. Table 4 displays a summary of the percent reductions in energy

consumption by 2050 for each scenario. Much like the GHG emissions results, the

contribution of energy savings by VanRen is relatively smaller than existing energy

reduction trends under CurrentPol. Per capita energy consumption results, in

comparison with total energy use, indicate that additional energy demand from

population growth reduces energy savings by approximately 16%. The reduction in

energy saving capacity is likely a contributing factor to why population increases may

reduce the GHG emission abatement capacity of Vancouver.

When comparing the percentage point difference in energy savings between

VanRen and CurrentPol (16.7-17.5%) to the percentage point difference in GHG

emissions reductions (12.3-13%), the results indicate that changes to urban form have

the effect of increasing GHG emission intensity per unit of energy consumed. A likely

explanation may be that, in my modeling scenarios, personal vehicles are decarbonizing

at a faster rate than transit vehicles. In this circumstance, mode-shift from driving to

transit, in the absence of stringent transit vehicle fuel switching policies, may not provide

the impactful GHG emissions reductions as originally anticipated.

Changes in population density yield a similar effect on energy use as were recorded in

the GHG emissions results, with lower density populations using a greater amount of

total and per capita energy compared to higher density populations. Once again,

however, the magnitude of that variation is within a few percentage points of the

UniformGrowth scenario for total and per capita energy use. It may be concluded that

population moving to different neighbourhoods in Vancouver, without accompanying

urban form and land use change, is unlikely to cause meaningful energy or GHG

emissions reductions.

36

Figure 5. Total and per capital energy consumption in the Current Policy (CurrentPol), and Renewable City Strategy (VanRen) urban policies, for UniformGrowth, HighDensity, and LowDensity population scenarios

37

Table 4. Energy consumption reductions from 2010 to 2050

Scenarios UniformGrowth HighDensity LowDensity

Reductions/Gains

From Density

Percent Total Reductions

CurrentPol -24.8% -27.5% -21.4% -2.7% / +3.4%

VanRen -41.9% -44.2% -38.9% -2.3% / +3.0%

Percent Difference

-17.1% -16.7% -17.5% +0.4% / -0.4%

Percent Per Capita Reductions

CurrentPol -44.8% -46.8% -42.3% -2.0% / +2.5%

VanRen -57.3% -59.0% -55.1% -1.7% / +2.2%

Percent Difference

-12.5% -12.2% -12.8% +0.3% / -0.3%

5.3. Emissions and Energy Consumption Decomposition

To distinguish the effect of energy efficiency and fuel switching in emissions and

energy consumption reductions, I calculated a No Policy (NoPol) trajectory by holding

constant all transportation fuel switching, mode-shifting, and land use zoning to 2010

levels. This NoPol scenario is plotted with the GHG emissions and energy consumption

results in Figure 6. Expected total energy and emissions reductions to 2050 are

summarised in Table 5. All reductions in emissions and energy consumption under

NoPol is strictly associated with energy efficiency gains in transit and personal vehicles.

When comparing the NoPol and CurrentPol urban form policies, as part of a

decomposition analysis, the results show that increases in energy efficiency from 2010

to 2050 are estimated to constitute over half of the GHG emissions reductions and

nearly all energy use reductions of the CurrentPol scenario. The remaining GHG

reductions in CurrentPol can be attributed to vehicles switching to zero or low emission

energy sources. The small discrepancy between the CurrentPol and NoPol energy use

results is attributed to negligible amounts of mode-shifting that are estimated to occur

when no changes are made to urban form policy. Notwithstanding the potential

contributions of vehicle energy efficiency, the results show that efficiency gains are

expected to make contributions to GHG emission decreases between 2010 and 2030

with limited additional reductions thereafter. The results indicate that, while policies that

influence urban form are a contributing factor to GHG emissions and energy use

reductions, policies that induce fuel switching or energy efficiency gains are potentially a

larger contributor to a city’s ability to reach their climate goals. Furthermore, it is

38

estimated that vehicle technology innovations are expected to have a diminishing impact

on energy efficiency improvements, indicating that vehicle fuels switching is expected to

be the primary driver of GHG emissions reductions over the long term.

Figure 6. Total greenhouse gas emissions and energy consumption in the No Policy (NoPol), Current Policy (CurrentPol), and Renewable City Strategy (VanRen) urban policies, for UniformGrowth, HighDensity, and LowDensity population scenarios

39

Table 5. GHG emissions & energy consumption reductions with NoPol Scenario from 2010 to 2050

Scenarios UniformGrowth HighDensity LowDensity

Reductions/Gains

From Density

Percent Total GHG Reductions

NoPol -23.7% -25.6% -21.2% -1.9% / +2.5%

CurrentPol -45.7% -47.8% -43.1% -2.1% / +2.6%

VanRen -58.3% -60.1% -56.1% -1.8% / +2.2%

Percent Total Energy Consumption Reductions

NoPol -23.7% -25.6% -21.2% -1.9% / +2.5%

CurrentPol -24.8% -27.5% -21.4% -2.7% / +3.4%

VanRen -41.9% -44.2% -38.9% -2.3% / +3.0%

5.4. Travel Demand

Figure 7 shows the calculated total and city-wide per capita travel demand of the

six policy scenarios. Table 6 displays a summary of the estimated percent gains and

reductions in travel demand for each scenario. CIMS-Urban indicates that per capita

PKT demand remains constant between 2010 and 2050 in the CurrentPol-

UniformGrowth scenario, an outcome of no improvements to land-use patterns or

changes in population density. Under VanRen, land-use improvements that increase the

accessibility of commercial and institutional land parcels to city residents reduces PKT

demand in all population density scenarios. However, total PKT results indicate that the

estimated 228,712-person increase in Vancouver’s population is estimated to negate all

decreases in individual travel by increasing city-wide mobility in both the CurrentPol and

VanRen policy scenarios. Hence, the Vancouver Renewable City Strategy is estimated

to mitigate expected growth in future travel demand but may not prevent Vancouver’s

requirement for increased mobility. The ‘step’ pattern shown in the graph is a result of

CIMS-Urban measuring changes to PKT in discreet five-year increments.

Once again, factoring in population densities are shown to influence total and

city-wide per capita PKT estimates by no more than a few percentage points for

CurrentPol and VanRen. High density growth has a negative impact on per capita PKT,

whereas low density growth increases per capita PKT relative to the UniformGrowth

scenario. When comparing the relative impacts of land-use change and population

density, the results indicate that the main contributor to decreasing an individual’s travel

demand is the existence of mixed-use land zoning and the accessibility of commercial

40

districts to residential areas. Changes in population density without accompanying land-

use reforms is shown to have a limited impact on travel demand.

Figure 7. Total and per capita travel demand (Person Kilometers Travelled) in the Current Policy (CurrentPol), and Renewable City Strategy (VanRen) urban policies, for UniformGrowth, HighDensity, and LowDensity population scenarios

41

Table 6. PKT changes 2010 to 2050

Scenarios UniformGrowth HighDensity LowDensity Reductions/Gains

From Density

Percent Total Gains

CurrentPol +36.2% +33.0% +40.2% -3.2% / +4.0%

VanRen +13.5% +11.1% +16.9% -2.4% / +3.4%

Percent Difference

-22.7% -21.9% -23.3% +0.8% / -0.6%

Percent Per Capita Reductions

CurrentPol 0.0% -2.3% +2.9% -2.3% / +2.9%

VanRen -16.7% -18.5% -14.1% -1.8% / +2.6%

Percent Difference

-16.7% -16.2% -17.0% +0.5% / -0.3%

5.5. Transportation Mode Share

Figure 8 and Table 7 show the UniformGrowth population scenario as an

illustrative example for transportation mode-shift between the CurrentPol and VanRen

policies. I have omitted results for the HighDensity and LowDensity scenarios due to the

negligible differences in mode-shift compared to UniformGrowth. Without the

implementation of urban form policies, a small amount of mode-shift from personal

vehicles towards transit and walking is expected under CurrentPol, while cycling remains

relatively unchanged. In terms of total kilometers travelled, all transportation modes are

shown to increase with transit, walking and cycling collectively capturing 60% of the

expected PKT growth by 2050 and personal vehicle use accounting for the remaining

40%. The CurrentPol results indicate that without addressing overall increases in travel

demand, relying solely on mode-shift may not successfully address Vancouver’s reliance

on personal vehicles due to expected increases in population.

42

Figure 8. Transportation mode share changes from 2010 to 2050 in the Current Policy (CurrentPol), and Renewable City Strategy (VanRen) urban policies, for UniformGrowth population scenario

43

Table 7. Percent change in PKT by mode type from 2010 to 2050: UniformGrowth

Mode Type Scenarios Percent

Difference CurrentPol VanRen

Vehicle Driver 28.2% -2.1% -30.3%

Vehicle Passenger 38.9% 4.4% -34.5%

Transit 43.6% 15.4% -28.2%

Walking 43.6% 26.5% -17.1%

Cycling 43.6% 100.7% +57.1%

Between 2010 and 2050, the VanRen policy facilitates a greater amount of

personal vehicle mode-shift, mostly towards cycling. In this scenario, a small portion of

personal vehicle drivers who switched to transit or carpool over time in CurrentPol chose

instead to walk or cycle. This result suggests that a number of individuals shifting from

personal vehicles to a different form of travel may have been predisposed to alternative

transportation. When measuring the expected GHG emission impact of bike lane

network expansions or improvements in neighbourhood walkability, there is a likelihood

that a portion of the new cyclists or pedestrians may have previously planned to reduce

their car use by other means. When considering total kilometers travelled, personal

vehicle use was projected to remain virtually constant under the VanRen scenario from

2010 to 2050, with walking, cycling and transit capturing the majority of the estimated

increase in PKT demand.

The results show that mode-shift and reductions in travel demand provided by

alternative transportation infrastructure and the introduction of mixed-use land zoning

reforms may prevent an expected increase in driving but is not expected to succeed in

significantly reducing the total number of vehicle-kilometres in a city with a growing

population and an extensive road network. Such urban policy initiatives are estimated to

accommodate the anticipated growth in travel needs as Vancouver’s population

increases over time, while maintaining personal vehicle use at current levels. The

parameter values I used in this study suggest that personal vehicles will remain an

important mobility device in the foreseeable future, indicating that vehicle fuel switching

is the only likely means for completely decarbonizing the personal transportation sector.

44

5.6. Road Congestion Rebound Effect

My results include estimated road congestion rebound effect. As discussed in

Section 3.5, CIMS-Urban with my modifications has the capacity to isolate and display

this effect on personal vehicle mode share through internal feedback loops within the

model that account for changes in total travel demand (PKT) and vehicle mode-shift

substitution. When vehicle use decreases, CIMS-Urban detects additional road space

that can be used by other drivers. As an illustrative example I provide rebound effect

results for the UniformGrowth population density scenario. Similar to the mode share

results, the HighDensity and LowDensity scenarios show negligible differences in

rebound effect compared to UniformGrowth.

Figure 9 and Table 8 display changes to personal vehicle mode share, GHG

emissions and energy consumption between 2010 and 2050, accounting for road

congestion rebound effect. In the absence of rebound effect, the difference between the

CurrentPol and VanRen policy scenarios is estimated to be a reduction in GHG

emissions of 114,503 tCO2e, energy use of approximately 2.38 million GJ and a 5.1%

decrease in personal vehicle use relative to other transport options. In this scenario,

reductions in road congestion were not incorporated within the CIMS market share

algorithm for personal transportation mode choice. When road congestion feedbacks

were activated, the estimated magnitude of decreases in GHG emissions, energy use

and personal vehicle mode share were reduced by 9,431 tCO2e, 138,967 GJ, and 1.5%

respectively. When comparing the impacts of rebound effect as part of a decomposition

analysis isolating the relative contributions of city-wide travel demand changes and

vehicle mode-shift substitution, both caused increases to GHGs, energy use and vehicle

use by nearly equal amounts.

45

Figure 9. Uniform Growth Scenario percentage change in results between 2010 and 2050 with rebound effect

Table 8. UniformGrowth Scenario reductions in results from 2010 to 2050 with rebound effect

Scenarios Current

Pol VanRen

Total Difference

Difference from

Rebound Effect

No Rebound Effect

GHG Emission (tCO2e) -378,951 -493,454 -114,503 0

Energy Consumption (GJ) -3,249,424 -5,628,395 -2,378,971 0

Personal Vehicle Mode Share (percent)

-2.8% -7.9% -5.1% 0%

Travel Demand Rebound Effect

GHG Emission (tCO2e) -378,951 -488,731 -109,780 +4,723

Energy Consumption (GJ) -3,249,424 -5,558,739 -2,309,315 +69,656

Personal Vehicle Mode Share (percent)

-2.8% -7.1% -4.3% +0.8%

Mode-shift Rebound Effect

GHG Emission (tCO2e) -378,951 -488,746 -109,795 +4,708

Energy Consumption (GJ) -3,249,424 -5,559,084 -2,309,660 +69,311

Personal Vehicle Mode Share (percent)

-2.8% -7.2% -4.4% +0.7%

Travel Demand + Mode-shift Rebound Effect

GHG Emission (tCO2e) -378,951 -484,023 -105,072 +9,431

Energy Consumption (GJ) -3,249,424 -5,489,428 -2,240,004 +138,967

Personal Vehicle Mode Share (percent)

-2.8% -6.4% -3.6% +1.5%

46

Chapter 6. Conclusions

6.1. Summary of Model Development and Test Simulation Findings

In this study, I used the CIMS-Urban energy-economy model to determine how

urban form and population density policies may assist in addressing climate change. To

accomplish my research objectives, I contributed to the methodological development of

CIMS-Urban by incorporating new components within the model capable of measuring

the spatial distribution of population growth, determining future travel demand based on

expected land-use changes, and accounting for road congestion rebound effect. To test

CIMS-Urban’s new capabilities, I replicated a previous study by SFU EMRG (Jaccard et

al., in press) which examined the expected impacts of Vancouver’s Renewable City

Strategy on GHG emissions, energy consumption, travel demand and mode choice.

Additionally, I examined the extent to which high or low population densities may help or

hinder Vancouver in achieving its climate goals.

My results verified the previous EMRG study by indicating that urban form

changes have a relatively minor impact on GHG emissions compared to fuel switching

and energy efficiency. Urban form changes resulted in a modest mode-shift from

personal vehicles to transit, walking and cycling. New findings from my study indicate

that when mode-shift is combined with estimated changes to PKT, the total number of

personal vehicles and drivers were not estimated to decrease significantly under the

Vancouver Renewable City Strategy by 2050. At most, the expected urban form

changes within Vancouver are estimated to accommodate the anticipated increases in

travel demand due to population growth.

If urban form initiatives are to be pursued by government, they should be

circumspect of strategies that prioritize population density concentrations as a leading

means to achieve climate targets. Urban density, as a stand-alone policy that

concentrates population within an urban landscape, is shown to have a relatively small

effect on GHG emissions, energy consumption and travel demand in comparison with

the adoption of mixed-use land zoning policies. Results from my study suggest that

47

urban density must be accompanied with appropriate mixed-use land zoning changes

that encourage a reduction in the need to travel between personal residences and

commercial districts when commuting for work or traveling for local services. However,

despite the potential for land-use policy to decrease travel demand, my study indicates

urban form changes will not reduce the need for mobility to the degree necessary for

Vancouver to achieve deep decarbonization in the personal transportation sector.

Municipal governments or, if necessary, higher levels of government, will likely

need to adopt more stringent policies than what has been proposed by Vancouver’s

Renewable City Strategy. Specifically, policies that are intended to significantly reduce

transportation GHG emissions must either: 1) decarbonize personal mobility devices

through a transition to zero or low emission fuels; or 2) be designed to dramatically

decrease the use of personal vehicles. The results of my study indicate that changes to

urban form, in the absence of any other policies that reduce and ultimately eliminate the

use of carbon emitting personal vehicles, will not provide adequate reductions in GHG

emissions. Furthermore, regulations that induce short-term gains in vehicle energy

efficiency may be pursued to compliment fuel switching initiatives but cannot be relied on

to achieve GHG emissions targets.

My results on Road Congestion Rebound Effect suggest that policies designed

to address transportation emissions by reducing personal vehicle use will need to

include regulations or urban form changes that restrict personal vehicles from operating

on city roads. Otherwise, the availability of road space may be too enticing for

automobility. Without policies that directly target personal vehicles, congestion rebound

effect will persistently prevent meaningful mode-shift away from driving. Alternatively, if

large reductions in personal vehicle use is deemed too challenging or unrealistic for the

city’s social environment, the remaining solution is the introduction of a suite of policies

that reduce the carbon intensity of driving. Within the Canadian context, municipalities

will need to seek the support of senior level governments to decarbonize available

transportation fuel stocks or phase out the sale of fossil fuel-based vehicles to be

replaced with zero-emission electricity, biofuels and perhaps hydrogen. Cities may also

explore policies within their jurisdictional authority that favour an energy transition to zero

or low emitting vehicles, such as parking restrictions or road pricing on carbon emitting

vehicles.

48

6.2. Limitations and Opportunities for Future Research

Consideration must be given to the reality that CIMS-Urban, as a model, is a

simplified representation of the true complexity of urban dynamics. In the development

of CIMS Urban, the spatial resolution of the model was set to record results at the

neighbourhood level. Future research could explore whether further spatial detail could

enhance the analytical capabilities the model. Additionally, assumptions have been

made with respect to data inputs and parameter values imbedded within the model,

including urban form changes and energy prices. Some of these assumptions are

associated with uncertainties that can influence the model’s results. Future researchers

may wish to test how uncertainty may modify the findings of my study by altering and

updating my spatial policy scenarios within the model and running additional simulations

under different energy price forecasts.

A second limitation of my study was that the version of CIMS-Urban I used

simulated Vancouver as a subset of a greater metropolitan area. Any inter-city

transportation to and from Vancouver and its suburbs was not included in the analysis.

Future research could incorporate a project which develops a CIMS-Urban model for the

Metro Vancouver region that can capture mobility behaviour at the metropolitan scale.

When conducting this research, a method for differentiating between regional and local

transportation within the CIMS Spatial Module could be developed. A distinction

between commuting for employment and travelling for local commercial services could

be added to the model, which would allow the behavioural attributes of each type of

transportation to be calibrated independently.

Future research that explores modeling a metropolitan region may benefit from

re-evaluating my decision to redesign the CIMS personal transportation market share

algorithm to create separate Personal Vehicle Drivers and Personal Vehicle Passengers

decision nodes instead of the original configuration of Single and High-Occupancy

Vehicles. My approach would present limitations, as part of a regional study, when

modeling the effect of how HOV lanes and other initiatives may encourage commuters to

form carpools instead of driving individually. However, given the specific context of my

study to evaluate local travel within Vancouver, my alterations to the model were

appropriate in order to calibrate the model to available census data. Future studies

which intend to explore questions pertaining to regional commuting and carpooling

49

should consider restoring the CIMS transportation market share algorithm and obtain the

appropriate High Occupancy Vehicle data to properly calibrate CIMS-Urban for best

results.

CIMS-Urban is undergoing continual improvements as part of an ongoing

development project to integrate spatial analysis into urban applications of energy-

economy modeling. Future research should endeavour to base the parameters and

assumptions within the CIMS Spatial Module on further empirical research about

technologies and firm and household decision-makers. Some of the model’s current

parameters are based on informed guesswork and experimentation that was conducted

during my research and by previous masters student researchers in EMRG (Zuehlke,

2017). The CIMS Spatial Module could benefit from external research that improves the

methodology of converting public preferences of urban form into travel demand forecasts

and mode choice intangible costs. Improvements to the network quality and PKT index

algorithms could be informed by public surveys which quantify how an individual’s spatial

perception of land use zoning and transportation infrastructure may influence their travel

demand and decision to chose different modes of travel.

Future research could also improve the model’s process of estimating network

quality coefficients which convert index values to intangible costs. Currently, the

relationship between the spatial network quality indexes and intangible costs are

assumed to be linear, supporting the rational for using an OLS regression method for

calculating network quality coefficients. On viewing intangible costs regression

scatterplots in the CIMS Spatial Module, some relationships appear to be non-linear.

Future research could inform a new methodology of generating intangible costs that

captures the complex non-linearities of individual travel preferences as influenced by

transportation infrastructure and land use patterns.

Additional future improvements could include integrating a network analysis

approach to the CIMS Spatial Module that traces roadways and pathways within cities

for each mode of travel. A GIS network analysis program would constrain the spatial

module to calculate distances and travel behavior along predetermined routes to

improve the simulation of walking, cycling, driving and transit use. CIMS-Urban’s

method of measuring road congestion rebound effect could be enhanced with a network

analyzer which captures road quality, driving use, and congestion. With this added

50

capability, future research could potentially track the volume of use on specific roads,

transit routes and bike path networks. Moreover, CIMS-Urban could assess trip

destinations in addition to its current capability of calculating the relative proximity

between transportation infrastructure and residences.

51

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Appendix A. Spatial Algorithm Calibration Variables

Table A1. Driving Network Quality Algorithm Calibration Coefficients

Variable Road Type Coefficient

q=road quality

Arterial 5

Collector/Ramp 4

Local 3

Alleyway/Driveway/Lane/Service/Strata 1

Freeway/Recreation/Restricted/Trail/Other 0

Table A2. Cycling Network Quality Algorithm Calibration Variables

Variable Bike Lane Type Coefficient

q =bike lane quality

Shared Lane 1

Local Street/Painted Bike Lane 3

All ages and abilities local street 4

Separated Lanes or Paved Path 5

k = quality calibration value 1.5

Table A3. PKT Index Calculator Algorithm Calibration Variables

Variable Coefficient

n = number of closest commercial-institutional districts 3,593

W= weighted value 1

Baseline year annual PKT estimate 11547.6

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Appendix B. Transportation Baseline Lifecycle Costs

Table B1. Baseline City-Wide Annualized Financial and Intangible Costs

Cost Type Mode Type Coefficient

Financial Costs

Personal Vehicle Driver $12,616.63

Personal Vehicle Passenger $0.00

Transit $1,240.98

Walking $0.00

Cycling $200.00

Intangible Costs

Personal Vehicle Driver $0.00

Personal Vehicle Passenger $19,244.40

Transit $12,383.25

Walking $15,655.49

Cycling $17,559.62