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Canadian Energy Research Institute

THE ECONOMIC

EFFECTIVENESS OF

DIFFERENT CARBON

PRICING OPTIONS

TO REDUCE

CARBON DIOXIDE

EMISSIONS

STUDY NO. 189AUGUST 2020

THE ECONOMIC EFFECTIVENESS OF DIFFERENT CARBON PRICING OPTIONS TO REDUCE CARBON DIOXIDE EMISSIONS

The Economic Effectiveness of Different Carbon Pricing Options to Reduce Carbon Dioxide Emissions

Authors: Dinara Millington, Nurul Hossain, Anna Vypovska, Kiana Goddard, Mateo Sanin

Recommended Citation (Author-date style):

Millington, Dinara, A K M Nurul Hossain, Anna Vypovska, Kiana Goddard, and Mateo Sanin. 2020. “The

Economic Effectiveness of Different Carbon Pricing Options to Reduce Carbon Dioxide Emissions.” Study

No. 189. Calgary, AB: Canadian Energy Research Institute.

https://ceri.ca/assets/files/Study_189_Full_Report.pdf

Recommended Citation (Numbered style):

D. Millington, A.N. Hossain, A. Vypovska, K. Goddard, and M. Sanin, “The Economic Effectiveness of

Different Carbon Pricing Options to Reduce Carbon Dioxide Emissions,” Canadian Energy Research

Institute, Calgary, AB, Study No. 189, 2020. URL: https://ceri.ca/assets/files/Study_189_Full_Report.pdf

Copyright © Canadian Energy Research Institute, 2020

Sections of this study may be reproduced in magazines and newspapers with acknowledgment to the

Canadian Energy Research Institute

August 2020

Printed in Canada

Acknowledgements:

The authors of this report would like to extend their thanks and sincere gratitude to all CERI staff involved

in the production and editing of the material. The authors would also like to acknowledge the following

reviewers for providing helpful and valuable insights for this study:

• Marla Orenstein, Canada West Foundation

• Clayton Munnings, International Emissions Trading Association

• Katie Sullivan, International Emissions Trading Association

• Dr. Alastair Lucas, Canadian Institute of Resources Law

• Dr. Safoura Moeeni, Department of Economics, University of Regina

Responsibility for any errors, interpretations, or omissions lies solely with CERI.

ABOUT THE CANADIAN ENERGY RESEARCH INSTITUTE

Founded in 1975, the Canadian Energy Research Institute (CERI) is an independent, registered charitable

organization specializing in the analysis of energy economics and related environmental policy issues in

the energy production, transportation, and consumption sectors. Our mission is to provide relevant,

independent, and objective economic research of energy and environmental issues to benefit business,

government, academia, and the public. For more information about CERI, visit www.ceri.ca.

CANADIAN ENERGY RESEARCH INSTITUTE

150, 3512 – 33 Street NW, Calgary, Alberta T2L 2A6

Email: [email protected] Phone: 403-282-1231

http://www.ceri.ca/

mailto:[email protected]

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The Economic Effectiveness of Different Carbon Pricing Options to Reduce Carbon Dioxide Emissions iii

Table of Contents List of Figures ............................................................................................................................................... vi

List of Tables ............................................................................................................................................... vii

Acronyms and Abbreviations ....................................................................................................................... ix

Executive Summary ...................................................................................................................................... xi

Chapter 1: Introduction and Background ..................................................................................................... 1

Background: Global Concerns and Major Policies to Reduce GHG Emissions ......................................... 1

Carbon Tax System ............................................................................................................................... 2

Emissions Trading System .................................................................................................................... 2

Other Policies and Additional Measures for GHG Emissions Reduction .............................................. 5

Study Scope and Objectives ..................................................................................................................... 6

Chapter 2: Overview of Carbon Management Policies in Selected Jurisdictions ......................................... 8

European Union Emissions Trading System ............................................................................................. 8

Carbon Management Policies in Other Studied Countries ..................................................................... 11

California (Linked with Quebec) Cap-and-Trade System ........................................................................ 13

Carbon Management Policies in Canada ................................................................................................ 17

British Columbia Carbon Tax System ................................................................................................. 27

Alberta Specified Gas Emitters Regulation ........................................................................................ 27

Emissions Targets and Achievements by Country .................................................................................. 28

Chapter 3: Impacts of Carbon Management Policies: What Does the Literature Suggest ......................... 31

Evidence on the European Union Emissions Trading Scheme ............................................................... 31

Evidence on British Columbia’s Carbon Tax System ............................................................................... 32

Evidence on the US Regional Greenhouse Gas Initiative (RGGI) ............................................................ 33

Evidence on Other Jurisdictions ............................................................................................................. 34

New Zealand’s Emissions Trading System .......................................................................................... 34

Ukraine’s Carbon Tax ......................................................................................................................... 34

Sweden’s Tax Policy ........................................................................................................................... 34

Panel Studies ...................................................................................................................................... 35

Summary of Findings .............................................................................................................................. 36

Chapter 4: Historical Trend Analysis: Economic Performance and GHG Emissions Reduction .................. 41

European Union ...................................................................................................................................... 41

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The United States ................................................................................................................................... 46

Canada .................................................................................................................................................... 50

Canadian Sectors ................................................................................................................................ 54

Chapter 5: Modelling Methodology and Assumptions ............................................................................... 61

Methodology .......................................................................................................................................... 61

The Difference-in-Differences (DiD) Approach .................................................................................. 61

Parallel Trend Assumption ................................................................................................................. 64

Econometric Specification .................................................................................................................. 64

Data ........................................................................................................................................................ 65

Estimation Sample .............................................................................................................................. 65

EU-ETS System .................................................................................................................................... 66

California Cap-and-Trade System ....................................................................................................... 66

BC Carbon Tax .................................................................................................................................... 66

Alberta SGER System .......................................................................................................................... 67

Variable Definition .................................................................................................................................. 67

EU-ETS System .................................................................................................................................... 67

California Cap-And-Trade System ...................................................................................................... 68

BC Carbon Tax .................................................................................................................................... 69


Chapter 6: Empirical Results ....................................................................................................................... 72

Common Trend Assumption ................................................................................................................... 72

Fixed Effect Regressions: Effect on Emissions Efficiency ........................................................................ 76

Empirical Evidence for Emission Trading System ............................................................................... 76

Empirical Evidence for Carbon Tax System ........................................................................................ 79

Empirical Evidence for Hybrid System ............................................................................................... 80

Fixed Effect Regressions: Effect on Real GDP ......................................................................................... 81

EU-ETS System .................................................................................................................................... 81


BC Carbon Tax System ........................................................................................................................ 83

Alberta SGER system .......................................................................................................................... 84

Fixed Effect Regressions: Effect on Emissions ........................................................................................ 85

EU-ETS System .................................................................................................................................... 85

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The Economic Effectiveness of Different Carbon Pricing Options to Reduce Carbon Dioxide Emissions v


BC Carbon Tax .................................................................................................................................... 87


Results Summary .................................................................................................................................... 90

Placebo Test Using Data for Prior Periods .............................................................................................. 90

EU-ETS System .................................................................................................................................... 91


BC Carbon Tax .................................................................................................................................... 93


Study Limitations and Comparison with Other Studies .......................................................................... 94

Chapter 7: Conclusions ............................................................................................................................... 96

Bibliography .............................................................................................................................................. 100

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List of Figures Figure 1.1: Carbon Pricing Initiatives Around the World: a Summary Map ................................................. 4

Figure 1.2: A Spectrum of Possibilities Between Emissions Trading Schemes and a Carbon Tax ................ 5

Figure 4.1: Emissions efficiency in the EU-25 countries and other studied jurisdictions (the control group),

billion USD/Mt CO2e ................................................................................................................................... 42

Figure 4.2: Emissions efficiency growth in the EU-25 countries and the control group, percentage ........ 43

Figure 4.3: GDP in the EU-25 countries and the control group, billion USD ............................................... 43

Figure 4.4: GDP growth rate in the EU-25 countries and the control group, percentage .......................... 44

Figure 4.5: GHG emissions in the EU-25 countries and the control group, Mt CO2e ................................. 45

Figure 4.6: GHG emissions growth rate in the EU-25 countries and the control group, percentage ......... 46

Figure 4.7: Emissions efficiency in the US states, billion USD/Mt CO2e ..................................................... 47

Figure 4.8: Emission efficiency growth in the US states, percentage ......................................................... 47

Figure 4.9: GDP in the US states, billion USD .............................................................................................. 48

Figure 4.10: GDP growth rate in the US states, percentage ....................................................................... 48

Figure 4.11: GHG emissions in the US states, Mt CO2e .............................................................................. 49

Figure 4.12: GHG emissions growth rate in the US states, percentage ...................................................... 50

Figure 4.13: Emissions efficiency in selected Canadian provinces, billion CAD/Mt CO2e .......................... 51

Figure 4.14: Emissions efficiency growth in selected Canadian provinces, percentage ............................. 51

Figure 4.15: GDP in selected Canadian provinces, billion CAD ................................................................... 52

Figure 4.16: GDP growth rate in selected Canadian provinces, percentage .............................................. 52

Figure 4.17: GHG emissions in selected Canadian provinces, Mt CO2e/year ............................................ 53

Figure 4.18: GHG emissions growth rate in selected Canadian provinces, percentage ............................. 54

Figure 4.19: Sector-level GDP in selected Canadian provinces, million CAD .............................................. 56

Figure 4.20: Sector-Level GHG Emissions in Selected Canadian Provinces, Mt CO2e ................................ 60

Figure 5.1:Graphical Representation of DiD Model .................................................................................... 63

Figure 6.1: Common Trend Assessment for the EU-ETS Case Study .......................................................... 73

Figure 6.2: Common Trend Assessment for the California Cap-and-Trade Case Study ............................. 74

Figure 6.3: Common Trend Assessment for the BC Carbon Tax Case Study .............................................. 75

Figure 7.1: Summary of Carbon Pricing Initiatives Implemented in the Studied Jurisdictions ................... 97

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The Economic Effectiveness of Different Carbon Pricing Options to Reduce Carbon Dioxide Emissions vii

List of Tables Table 2.1: Key Characteristics of National Carbon Tax Policies Implemented in the European Union

Member Countries ...................................................................................................................................... 10

Table 2.2: Key Carbon Management Policies and Initiatives in Other Studied Jurisdictions ..................... 12

Table 2.3: Key Federal, Regional and State Carbon Management Policies and Initiatives in the United States

.................................................................................................................................................................... 15

Table 2.4: Carbon Pricing Systems Implemented in Canada ...................................................................... 19

Table 2.5: Key Carbon Management Policies Implemented in Canada ...................................................... 21

Table 2.6: GHG Emissions Reduction Targets for Canadian Jurisdictions ................................................... 29

Table 2.7: GHG Emissions Reduction Targets for Other Studied Jurisdictions ........................................... 30

Table 3.1: Summary of Findings from the Overall Assessment of the Considered ETSs ............................ 35

Table 3.2: Summary of Key Findings from the Evidence-Based Literature ................................................. 37

Table 4.1: Sector-Level Emissions Efficiency in Selected Canadian Provinces, Billion CAD/kt CO2e .......... 55

Table 4.2: Sector-Level GDP in Selected Canadian Provinces, Million CAD ................................................ 57

Table 4.3: Sector-Level GHG emissions in Selected Canadian Provinces, Mt CO2e ................................... 58

Table 5.1: Variable Definition for the EU-ETS Case Study ........................................................................... 68

Table 5.2: Variable Definition for the California Cap-and-Trade System Case Study ................................. 69

Table 5.3: Variable Definition for The BC Carbon Tax Case Study .............................................................. 70

Table 5.4: Variable Definition for the Alberta SGER Case Study ................................................................. 71

Table 6.1: Effect of the EU-ETS on Emissions Efficiency ............................................................................. 76

Table 6.2: Effect of California Cap-and-Trade System on Emissions Efficiency .......................................... 78

Table 6.3 Effect of BC Carbon Tax on Emissions Efficiency ......................................................................... 80

Table 6.4 Effect of Alberta SGER System on Emissions Efficiency .............................................................. 81

Table 6.5: Effect of the EU-ETS on Real GDP............................................................................................... 82

Table 6.6: Effect of California Cap-and-Trade System on Real GDP ........................................................... 83

Table 6.7: Effect of BC Carbon Tax on Real GDP ......................................................................................... 84

Table 6.8: Effect of Alberta SGER System on Real GDP .............................................................................. 85

Table 6.9: Effect of the EU-ETS on Emissions Reduction ............................................................................ 86

Table 6.10: Effect of California Cap-and-Trade System on Emissions Reduction ....................................... 87

Table 6.11: Effect of BC Carbon Tax on Emissions Reduction ..................................................................... 88

Table 6.12:Effect of Alberta SGER System on Emissions Reduction ........................................................... 89

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Table 6.13: Summary Results ...................................................................................................................... 90

Table 6.14: Placebo Test on EU-ETS ............................................................................................................ 91

Table 6.15: Placebo Test on California Cap-and-Trade System .................................................................. 92

Table 6.16: Placebo Test on BC Carbon Tax ................................................................................................ 93

Table 6.17: Placebo Test on the Alberta SGER System ............................................................................... 94

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The Economic Effectiveness of Different Carbon Pricing Options to Reduce Carbon Dioxide Emissions ix

Acronyms and Abbreviations BAU Business as Usual

BC British Columbia

CAD Canadian Dollar

CCIR Carbon Competitiveness Incentive Regulation (Alberta)

CEPA Canadian Environmental Protection Act

CERI Canadian Energy Research Institute

CO2 Carbon Dioxide

CO2e Carbon Dioxide Equivalent (including all greenhouse gases)

DiD Difference-in-Differences

ECCC Environment and Climate Change Canada

EE Emissions Efficiency

EITE Energy Intensive and Trade Exposed

EPS Emissions Performance Standards

ETS Emissions Trading System

EU The European Union

GDP Gross Domestic Product

GHG Greenhouse Gas

ICAP International Carbon Action Partnership

IEA International Energy Agency

kt Kilotonnes (thousand tonnes)

LULUCF Land Use, Land Use Change, and Forestry

Mt Megatonnes (million tonnes)

NCEL National Caucus of Environmental Legislators (US)

NDC Nationally Determined Contributions

OBPS Output-Based Pricing System

OECD Organization for Economic Cooperation and Development

PSS Performance Standards System (Newfoundland and Labrador)

RGGI Regional Greenhouse Gas Initiative

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x Canadian Energy Research Institute

SGER Specified Gas Emitters Regulation (Alberta)

t tonne

TCI Transportation and Climate Initiative (US)

TIER Technology Innovation and Emissions Reduction (Alberta)

UN United Nations

UNFCCC United Nations Framework Convention on Climate Change

US The United States

USD US Dollar

WGOCPM Working Group on Carbon Pricing Mechanisms

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The Economic Effectiveness of Different Carbon Pricing Options to Reduce Carbon Dioxide Emissions xi

Executive Summary This report sheds light on the effectiveness of carbon management policies in terms of their effect on the

economy and/or sector-level emissions and economic performance and adds to the growing volume of

other empirical studies. Studies based on empirical evidence of the effectiveness of different carbon

policies were sparse and hard to find, with most researchers taking a qualitative approach to the

evaluation of the various systems in place.

This Canadian Energy Research Institute (CERI) study takes the quantitative approach and analyzes the

impact of major carbon management initiatives across the globe. It reviews the design, analyzes the

impact, and identifies the lessons learned from key carbon management policies/systems for the four

case studies in terms of their impacts on emissions efficiency, emissions reduction, and economic output.

The four case studies include the European Union Emissions Trading System (EU ETS), California (linked

with Quebec) Cap-and-Trade System, British Columbia (BC) Carbon Tax System, and Alberta (AB) Specified

Gas Emitters Regulation (SGER)1.

Overall, the emissions trading system (ETS) policy was found to be more effective at reducing greenhouse

gas (GHG) emissions than the Carbon Tax policy or a Hybrid policy. Evidence suggests that while gross

domestic product (GDP) is also negatively impacted in the EU case, the magnitude of the effect on GDP is

smaller than the effect on overall emissions; in other words, the impact of the ETS is larger on emissions

than on the economic growth. California-Quebec Cap-and-Trade analysis suggests that the system is

effective at reducing emissions and thus increasing emissions efficiency without negatively impacting the

economic growth.

BC carbon tax policy boosted economic activity but had no effect on emissions. Since the objective of

regulatory policy is to reduce emissions, our results suggest that the carbon tax policy in British Columbia

failed to achieve its goal. In fact, oil prices have been found to have a bigger effect on emissions in BC than

carbon tax. Alberta SGER policy did not reduce GHG emissions as well. In fact, the SGER policy had a

statistically significant positive impact on GHG emissions. Consistent with other literature, we find a

positive correlation between GDP and GHG emissions indicating that an increase in economic activity

generally increases GHG emissions.

Altogether, our results can be summarised as shown in Table E.1.

1 Alberta SGER has been repealed and replaced by the Carbon Competitiveness Incentive Regulation (CCIR) in 2017, and then by the Technology Innovation and Emissions Reduction (TIER) Regulation in 2019 (CCIR) – see Chapter 2 for details. The other three carbon pricing systems studied in this report are currently in force.

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xii Canadian Energy Research Institute

Table E.1: Summary Results

Jurisdictions Indicators

Emissions efficiency GDP GHG Emissions

EU-ETS (+) 0.80 billion per Kt CO2e (+3.0% )

(-) 25,084 billion USD (-4.1%)

(-)9.9Mt CO2e (-4.9%)

Cap-and-trade (+)0.16 million USD/kt CO2e (+3.6%)

(+)60.78 billion USD (+4.7%)

(-)9.07 Mt CO2e (-3.4%)

BC Carbon tax --- (+)11.49 billion CAD (+5.55)

---

Alberta SGER --- (+)40.97 billion CAD (+14.6%)

(+)37.95 Mt CO2e (16.3%)

Note: “-----” means no impact

It should be noted that some of the topics related to the carbon pricing options were beyond the scope

of the current study and would be a subject for further CERI research:

- Social acceptability and political palatability of action in ensuring outcomes is not considered;

- The administrative process and cost of the instruments are not explicitly considered or evaluated;

- Carbon leakage associated with each instrument and options for its mitigation have not been

evaluated.

This study emphasizes the necessity to design policies based on lessons learned from experience and

empirical evidence from already established policies around the world. The CERI findings align well with

conclusions and suggestions from other reviewed literature (Burtraw and Themann 2018; Carbon Pricing

Leadership Coalition 2017; Christensen and Olhoff 2019; Harrison 2019; Schmalensee and Stavins 2017;

Raymond 2019; World Bank 2019) on the lessons that can strengthen the functioning of carbon markets

and can be applicable to Canada. Both are summarized below:

- Both carbon tax and emissions trade systems have a great capacity to reduce GHG emissions;

however, a level at which they are utilized is not adequate for significant change towards low-

carbon economies;

- Strengthening existing and adding new carbon policies and actions, especially those that can deal

with carbon leakage, is needed;

- Current carbon prices in many jurisdictions remain insufficient to achieve the objectives of the

Paris Agreement, even with extended carbon pricing policies in place to align with the specific

GHG reduction targets2;

- Stronger complementary policies and actions are needed to achieve the total reductions in GHG

emissions in a case of the BC carbon tax;

- Lessons from ETS systems, especially California’s cap-and-trade system, has revealed that the

economy-wide approach can be more efficient than managing specific sectors differently.

2 According to (Carbon Pricing Leadership Coalition 2017), a minimal direct price on GHG emissions needs to be in the range of US$40–80/t CO2e by 2020 and US$50–100/t CO2e by 2030 to meet the objectives of the Paris Agreement, under the condition that an ambitious climate policy is in place for the specific jurisdiction.

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The Economic Effectiveness of Different Carbon Pricing Options to Reduce Carbon Dioxide Emissions xiii

- Linkage of a cap-and-trade system with those in other jurisdictions (such as California’s cap-and-

trade system linked with Quebec) could potentially reduce abatement costs, price volatility, and

market power.

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xiv Canadian Energy Research Institute

August 2020

The Economic Effectiveness of Different Carbon Pricing Options to Reduce Carbon Dioxide Emissions 1

Chapter 1: Introduction and Background

Background: Global Concerns and Major Policies to Reduce GHG Emissions

The accumulation of greenhouse gases (GHGs) influenced by human activities, aggregated into carbon

dioxide equivalent (CO2e) emissions, and the resulting rise in global temperatures and climate change,

are attracting a great deal of attention globally. To limit the adverse impacts of climate change, several

countries, including Canada, under the Paris Agreement, 2015, committed to implementing policies to

keep global temperatures from rising more than two degrees Celsius above pre-industrial levels.

Consequently, policy actions to reduce GHG emissions are accelerating around the world. As of April 1,

2020, the global carbon markets cover around 12 billion tonnes (Gt) of CO2e emissions, representing

approximately 22.3% of global GHG emissions (World Bank 2020a).

A variety of related efforts to reduce GHGs are available or being developed on the international, national,

and sub-national levels. Carbon pricing is an instrument that applies market mechanisms to shift the cost

of emitting GHGs to industry emitters (the “polluter pays” principle). It has been considered as a critical

policy tool for reducing GHG emissions due to its efficiency and transparency. There are different

approaches to establishing a carbon price, which can take various shapes and forms. GHG emissions can

be generally priced through a carbon tax or an ETS. The former directly sets the price of carbon (per ton

of carbon dioxide equivalent); however, an environmental outcome remains uncertain. The latter directly

sets the quantity of GHG emissions (and therefore, provides more certainty regarding the environmental

outcome); however, the price of carbon is flexible and determined by a market. Nevertheless, some

carbon tax systems may contain elements of trading, and some ETSs may include elements of price

certainty (Carbon Pricing Leadership Coalition 2018; Environment and Climate Change Canada [ECCC]

2019b; Whitmore 2013; Working Group on Carbon Pricing Mechanisms [WGOCPM] 2016; World Bank

2020b).

A key concern for policymakers, while choosing among various policies, is to understand the features and

limitations of each policy and their impact on both emissions reduction and economic performance.

Summary information for the two most common carbon pricing systems based on the analysis presented

in (Burtraw and Themann 2018; Carbon Pricing Leadership Coalition 2017; CERI 2018; Narassimhan et al.

• Carbon pricing has been considered as a critical policy tool for reducing greenhouse gas emissions

due to its efficiency and transparency.

• A carbon tax and an emissions trading system (ETS) are the most common approaches to establish

a carbon price. However, many policies and initiatives around the world can best be defined as

positioned on a spectrum of carbon pricing possibilities between these two systems.

• A combination of carbon pricing policies with other approaches for carbon emissions

management (such as direct non-pricing regulations, indirect pricing options and other

complementary climate actions) can be more efficient.

• Currently, carbon tax systems have been implemented in 20, and different types of ETS systems

in 13 out of the 60 jurisdictions selected for this study.

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2 Canadian Energy Research Institute

2018; National Caucus of Environmental Legislators [NCEL] 2019b; Organization for Economic Cooperation

and Development [OECD] n.d.; WGOCPM 2016; Wood 2018; World Bank 2020b), as well as a brief

discussion of other policies and additional measures aimed at GHG emissions reduction, are provided

below.

Carbon Tax System

A carbon tax puts a direct fixed price on GHG emissions and requires the industry to pay for each ton of

CO2e released into the atmosphere. The system provides certainty about the price and guarantees a

maximum cost per unit of pollution in the sectors covered by the carbon tax. Nevertheless, there is some

uncertainty regarding the magnitude of GHG emissions reduction, and the carbon tax rate may need to

be adjusted (increased) over time to achieve established GHG emissions reduction targets.

The system allows industry participants to adjust their behaviour in response to the carbon price and

decide if they want to invest in emissions reduction technologies. Carbon taxes create financial incentives

to cut emissions by choosing cleaner fuels or more efficient processes. They can be applied not only to

fossil fuels combustion emissions but also to those from industrial processes and venting (non-combustion

emissions).

Carbon taxes can be developed based on the existing taxes and are substantial sources of revenues3. They

are often easier to administer than an ETS system, as it doesn’t require imposing rules to prevent market

manipulations. However, the broad application of a carbon tax to include non-combustion emissions

could increase both administrative and compliance costs.

As of April 1, 2020, carbon tax systems have been implemented in 20 out of the 60 jurisdictions selected

for this study, primarily on the national level (Canada, 11 EU-member states, three non-EU countries) as

well as the subnational level (four Canadian provinces and one territory).

Emissions Trading System

An ETS allows industry emitters to trade emission units to comply with their GHG emissions targets. ETS

participants can choose to implement internal measures to reduce emissions or purchase emission units

in the carbon market. A market price for GHG emissions can be established by creating demand and supply

for those emissions allowances.

With a well-functioning ETS, emissions will decrease each year by a predictable amount, as they are

limited to a specific level (the emissions cap). A double benefit for GHG emissions reductions may be

obtained where the ETS policy results in mitigation, while auction revenues are being reinvested in other

activities to minimize emissions (e.g., cap-and-invest systems). With proper allocation of allowances,

potential carbon leakage, and negative impacts on competitiveness, can also be minimized. Emission

trading systems become more efficient with broader coverage across sectors and geographical locations.

3 Revenue positive carbon pricing add new revenue for the jurisdiction, which can be reinvested or distributed between other government programs and funds. Revenue neutral pricing schemes don’t bring new revenue, but they offer rebates to consumers directly or replace other taxes (NCEL 2019b).

August 2020


There are two main types of ETSs:

- Cap-and-trade systems, which have a fixed upper limit on GHG emissions (cap), and limited

emissions permits (allowances) are either distributed for free according to specific criteria or

auctioned off (can be purchased and traded), up to the amount equivalent to the GHG emissions

cap.

- Baseline-and-credit systems, where is no fixed limit on GHG emissions, but baseline emissions

levels are established for individual entities under regulations. If regulated entities reduce their

GHG emissions below the baseline, they are entitled to credits that can be sold to other polluters

who exceed their baseline levels and need those credits to comply with regulations.

As of April 1, 2020, different types of ETS systems have been implemented in 13 out of the 60 jurisdictions

selected for this study, including two regional initiatives (the European Union [EU] ETS and the Regional

Greenhouse Gas Initiative (RGGI) in the US), five national systems (Canada and four non-EU countries),

and six subnational ETSs (in four Canadian provinces and two US states4).

Figure 1.1 provides a summary map of major carbon pricing initiatives (ETS and carbon tax) that are

implemented, scheduled for implementation or under consideration on regional, national and subnational

levels (World Bank 2020c).

4 If individual US states – participants of the RGGI are considered, then amount of subnational ETSs among the studied jurisdictions will be equal 15 (four Canadian jurisdictions and 11 US states), and total for the jursdictions with implemented ETSs will be 22 out of the 60 studied ones.

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Figure 1.1: Carbon Pricing Initiatives Around the World: a Summary Map

Source: (World Bank 2020c)

It should be noted that many carbon pricing policies and initiatives around the world cannot be identified

as an ETS or a carbon tax system only. Instead, they can best be defined as positioned on a spectrum of

carbon pricing possibilities, where pure emissions trading represents one end of this spectrum, and a pure

carbon tax is on the other end of it (Whitmore 2013). Figure 1.2 shows examples of four carbon pricing

schemes within the scope of our study (see below) placed in the order of increasing the inclusion of fixed

price components (or limits to price ranges) when moving from an ETS to a carbon tax. At the same time,

the inclusion of credit trading components (e.g., offsets or generated credits) increases in the opposite

direction, from a carbon tax to an ETS.

As can be seen from the figure (and discussed in detail further in Chapter 2), the European Union ETS (EU

ETS) is the almost pure ETS with minimal limits on price, while British Columbia carbon tax is an example

of a simple carbon tax system with minimum provisions for offsets. Both California and Quebec cap-and-

trade systems include floor prices and price ceilings and are shifted from the pure ETS end of the spectrum

toward the carbon tax end of it. SGER is a hybrid carbon pricing system that sets up a baseline for

emissions intensity and includes a carbon tax applicable to emissions that exceed these intensity-based

GHG emissions targets. Thus, the Alberta SGER system can be placed closer to the middle of the spectrum

for carbon pricing options (Whitmore 2013).

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Figure 1.2: A Spectrum of Possibilities Between Emissions Trading Schemes and a Carbon Tax

Source: Adapted from (Whitmore 2013).

Other Policies and Additional Measures for GHG Emissions Reduction

Carbon tax and ETS policies themselves may not be entirely satisfactory in achieving GHG emissions

reduction targets. A combination of carbon pricing policies with other approaches for carbon emissions

management, such as direct non-pricing regulations, may be more efficient. Different jurisdictions may

select different policy tools depending on national and local circumstances (Carbon Pricing Leadership

Coalition 2017). Such approaches are widely observed in Canadian jurisdictions. Regulations generally

require taking specific actions that will result in emissions reduction, including (CERI 2018):

- setting mandatory limits on GHG emissions (e.g., establishing a hard cap on the emissions of the

electricity sector in Nova Scotia; capping annual oil sands emissions in Alberta);

- defining standards for specific GHG emissions performance or GHG intensity targets for various

sectors (e.g., federal regulations limiting carbon dioxide emissions from the natural gas-fired

generation of electricity; regulations to address methane in the oil and gas sector; heavy-duty

vehicle GHG regulations);

- adopting a specific type of technology or prescribing the use of low-carbon technologies (e.g.,

renewable portfolio standards in Saskatchewan, New Brunswick and Nova Scotia; proposed

Federal Clean Fuel Standards; renewable fuel regulations); and

- eliminating a certain type of emissions-intensive technology (e.g., federal regulations on the

reduction of carbon dioxide emissions that will result in phasing out traditional coal-fired

generation of electricity).

Complementary climate actions such as increasing energy efficiency through implementing energy

efficiency regulations and programs and tightening energy efficiency standards (which are established for

each regulated product in the specific sections of energy efficiency regulations) can help with cutting GHG

emissions while also reducing demand for electricity (ECCC 2016a).

August 2020


Other options that indirectly price carbon are subsidies and public investments, which can use public funds

to support specific actions, technologies or behaviours to reduce GHG emissions (ECCC 2020; Office of the

Parliamentary Budget Officer 2016). Examples of these options include but are not limited to:

- financial support for green infrastructure and green innovation projects, and research and

development in clean-tech sectors;

- funding programs for energy efficiency;

- funding for deployment of emissions reduction technology, such as carbon capture and storage;

- provincial renewable feed-in tariffs (FITs) that provide payments for each kilowatt-hour of

renewable electricity generated; and

- public investments to purchase assets such as electricity grids to enable emissions reductions.

Study Scope and Objectives

For policymakers, choosing the right policy for their economies is challenging. It involves a comprehensive

assessment of the major carbon management policies established around the world with similar economic

and environmental characteristics. Choosing a policy that works in a non-carbon intensive jurisdiction may

not be right for a jurisdiction with a higher carbon intensity.

Given the rising importance of climate policy and its adoption globally, a growing number of studies has

used a variety of approaches, including the computable general equilibrium modelling and the input-

output model, to analyze the economic and emission impacts of each policy. These approaches are

forward-looking, and they focus mainly on the potential impacts of carbon management policies. Several

recent studies, on the other hand, used econometric approaches such as the difference-in-differences

(DiD), and panel regression techniques to provide evidence on how carbon management policies, such as

carbon tax, or cap-and-trade system have impacted some of the macroeconomic variables such as

unemployment, firm’s profitability, etc.

This study takes the quantitative approach and analyzes the impact of major carbon management

initiatives across the globe. In doing so, it analyzes jurisdictions that are important environmentally and/or

economically, and for which the performances are well documented for a long period5.

The report reviews the design, analyzes the impact, and identifies the lessons learned from key carbon

management policies/systems in terms of their impacts on environmental and economic indicators from

the following four cases:

1. The European Union Emissions Trading System

2. California (linked with Quebec) Cap-and-Trade System

3. British Columbia Carbon Tax System

5 For a detailed list of jurisdictions and selection criteria, please see the Data section in Chapter 5.

August 2020


4. Alberta Specified Gas Emitters Regulation6

More specifically, the objectives of the study are as follows:

- Providing a review of the major carbon management policies around the world focusing on the

case studies

- Estimating the impact of carbon management policies on emissions efficiency, emissions

reduction, and economic output in the regulated jurisdictions

- Identifying key lessons learned for future applications of the studied policy instruments in Canada

It should be noted that some of the topics related to the carbon pricing options were beyond the scope

of the current study and would be a subject for further CERI research:

- Social acceptability and political palatability of action in ensuring outcomes is not considered

- The administrative process and cost of the instruments are not explicitly considered or evaluated

- Carbon leakage associated with each instrument and options for its mitigation have not been

evaluated

6 Alberta SGER has been repealed and replaced by the Carbon Competitiveness Incentive Regulation (CCIR) in 2017, and then by the Technology Innovation and Emissions Reduction (TIER) Regulation in 2019 (CCIR) – see Chapter 2 for details. The other three carbon pricing systems studied in this report are currently in force.

August 2020


Chapter 2: Overview of Carbon Management Policies in Selected Jurisdictions

This chapter provides an up-to-date review of carbon management policies in the studied jurisdictions,

with an emphasis on carbon pricing systems for the four studied cases (EU ETS, California cap-and-trade

system, BC carbon tax, and AB SGER). The overview also summarizes other key policies in the studied

jurisdictions, including the control group, as specified in the Methodology Section in Chapter 5.

The carbon management policies for various jurisdictions are generally categorized by the type of policy;

status and/or year of implementation; sectors affected by the policy; GHGs covered; and carbon price ($/t

CO2e), where applicable. For some policies and jurisdictions, other specific characteristics (e.g., the

proportion of GHG emissions covered by the particular policy) are also provided.

European Union Emissions Trading System

The EU ETS introduced in 2005 is the first major carbon market in the world, and the largest one. It has

also been the world’s first international ETS that inspired the development of similar carbon pricing

systems in other jurisdictions. Since 2005, the three trading phases of the EU ETS (2005-2007; 2008-2012,

and 2013-2020) have been implemented. Phase 4 (2021-2030) is scheduled for the implementation in

January 2021. The key features of the EU ETS are as follows (European Commission 2015; 2016;

International Carbon Action Partnership [ICAP] 2020a; World Bank 2020a):

- the system is based on the cap-and-trade principle, with free benchmark-based allocations and

auctioning;

- a single cap for stationary sources (2,084 megatonnes [Mt] CO2e) was established EU-wide at the

beginning of Phase 3, with annual reduction by 1.74% (i.e., 1816 Mt CO2e in 2020). In Phase 4,

from 2021 on, the cap will decrease annually by 2.2%;

• The EU ETS (2005) is the first major carbon market in the world, the world’s first international ETS,

and the largest one (covers approximately 45% of the EU’s emissions).

• The California cap-and-trade program (2012) is a crucial element of California’s climate plan and

covers approximately 85% of California’s emissions.

• Alberta SGER (2007), a hybrid carbon pricing system, was the first regulatory regime in North

America governing GHG emissions. The British Columbia (BC) carbon tax system (2008) became

the first North American broad-based carbon tax.

• All studied jurisdictions except Nunavut have their 2030 GHG emissions reduction targets as

required by the Paris Agreement of 2015. Some jurisdictions (the EU, Switzerland, California, Nova

Scotia) set up an ambitious goal to reach carbon neutrality by 2050.

August 2020


- the total amount of emission allowances is determined top-down and decreases annually. From

2021 onwards, the number of free allowances will decline; the power sector is not eligible for any

free allocation;

- it operates in all 28 EU member states, as well as three European Economic Area-European Free

Trade Association (EEA-EFTA) countries: Iceland, Liechtenstein and Norway;

- the EU ETS applies to CO2e emissions from industry, power and aviation (only flights within the

European Economic Area);

- it covers approximately 45% of the EU’s emissions from more than 10,700 liable entities (as of

June 1, 2020);

- approximately 8% of covered GHG emissions overlap with other carbon pricing initiatives in the

European Union, mainly with the existing national carbon taxes in the EU member states (see

Table 2.1 for details);

- GHG covered include carbon dioxide (CO2) from all affected sectors; nitrous oxide (N2O) from

certain chemical sectors; and perfluorocarbons (PFCs) from primary aluminum production; and

- the current allowance price level (as of April 1, 2020) is USD 18.54 t/CO2e.

Recent system developments and updates (ICAP 2020a; World Bank 2020a):

- In 2019, the European Commission implemented the Market Stability Reserve, which is the tool

to address the supply-demand imbalance of allowances in the EU ETS.

- The United Kingdom (UK) withdrew from the EU ETS on January 31, 2020, after the Brexit and its

departure from the EU. However, the transition period for the UK will last until December 31,

2020, and after that, linking the UK national ETS with the EU ETS will be considered.

- In January 2020, the EU ETS became linked with the Swiss ETS, the first linking of this kind, after

10 years of negotiation.

In addition to the EU ETS, many of the EU member states implemented their own national carbon tax

policies which can be multi-sectoral or affect individual sectors only, have different GHGs covered and

also different proportions of emissions covered by a national carbon tax in the specific jurisdiction, or

overlapping with the GHG emissions covered by the EU ETS. Table 2.1 presents all these details for the 11

EU member countries.

As can be seen from the table, the year of carbon tax implementation in these jurisdictions ranges

between 1990 (Finland, Poland) and 2015 (Portugal), with Finland being the first country in the world to

introduce the carbon tax (World Bank 2020a). The carbon price ranges from as low as USD 0.1/t CO2e

(Poland) to as high as USD 119.4/t CO2e (Sweden). Three EU countries have two separate carbon tax rates

in their jurisdictions: one for fluorinated GHGs (such as hydrofluorocarbons, perfluorocarbons and sulphur

hexafluoride), and another one for fossil fuels in Denmark, as well as one for transport fuels, and another

one for the other fossil fuels in Finland and Ireland. The proportion of GHG emissions covered by the

national carbon tax also varies from 3% in Estonia and Spain to 49% in Ireland.

It should also be mentioned that Germany is scheduled to introduce its national ETS for heating and

transport fuels in 2021 that will complement the EU ETS. This ETS will have a fixed price per t CO2e in

2021-2025 and will be implemented gradually (World Bank 2020a).

August 2020


Table 2.1: Key Characteristics of National Carbon Tax Policies Implemented in the European Union Member Countries

Jurisdiction Year of Carbon Tax Implemen-

tation

Sectors Affected GHG Covered1

Carbon Price2, USD/t CO2e

Proportion of Jurisdiction’s GHG Emissions, %:

Covered by Carbon Tax

Overlapping with Other

CO2e Pricing

Denmark 1992 Buildings, Transport All GHGs 21.98 (F-gases3) 25.93 (FF)

40 N/A

Estonia 2000 Industry, Power CO2 2.19 3 N/A

Finland 1990 Industry, Transport, Buildings

CO2 57.96 (OFF) 67.80 (TF)

36 37

France 2014 Industry, Transport, Buildings

CO2 48.77 35 N/A

Ireland 2010 Multi-sectoral CO2 21.87 (OFF) 28.43 (TF)

49 40

Latvia 2004 Industry, Power CO2 9.84 15 N/A

Poland 1990 Multi-sectoral All GHGs 0.07 4 N/A

Portugal 2015 Industry, Transport, Buildings

CO2 25.83 29 N/A

Slovenia 1996 Buildings, Transport All GHGs 18.92 24 N/A

Spain 2014 Multi-sectoral F-gases 16.40 3 N/A

Sweden 1991 Buildings, Transport CO2 119.43 40 N/A

Data source: (World Bank 2020a)

Notes:

1. All GHGs (or CO2e) include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), perfluorocarbons (PFCs), hydrofluorocarbons (HFCs), sulphur hexafluoride (SF6) and nitrogen trifluoride (NF3).

2. Prices are current as of April 1, 2020.

3. F-gases include the following fluorinated GHGs: hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulphur hexafluoride (SF6).

4. Acronyms presented in the table stand for: FF – fossil fuels; OFF – other fossil fuels; TF – transport fuels.

August 2020


Carbon Management Policies in Other Studied Countries

Table 2.2 summarizes the main characteristics of the key carbon management policies and initiatives that

are implemented, scheduled for the implementation or under consideration in other studied jurisdictions

(a control group for the EU ETS). As defined in (World Bank 2019), carbon pricing initiatives are considered

as scheduled for implementation after they have been legally adopted and have an official start date.

Carbon pricing initiatives are under consideration if “the government has announced its intention to work

towards the implementation of a carbon pricing initiative, and this has been formally confirmed by official

government sources.”

As can be seen from the table, six out of nine jurisdictions from the control group have implemented

either ETS (Australia, Kazakhstan, New Zealand) or carbon tax (Japan, Ukraine). Switzerland implemented

both types of carbon pricing with different sectors and GHG covered, and different prices for each type of

policy. In addition to the carbon tax existing in Ukraine, this jurisdiction has also scheduled the

implementation of an ETS policy. Japan and Turkey have their ETSs under consideration (for Japan, it will

be in addition to the existing carbon tax). Belarus and the Russian Federation are the two jurisdictions

from the control group that currently do not have any carbon pricing initiatives under consideration.

For the six studied countries, the year of carbon pricing implementation varies between 2008 (Switzerland

and New Zealand) and 2016 (Australia), with the price ranging from USD 0.4/t CO2e (Ukraine) to USD

99.4/t CO2e (Switzerland).

August 2020


Table 2.2: Key Carbon Management Policies and Initiatives in Other Studied Jurisdictions

Jurisdiction Policy, Regulation or Initiative Type of Policy

Year of Implementation

Sectors Affected GHG Covered

Carbon Price1, USD/t CO2e

Implemented Carbon Pricing Policies and Initiatives

Australia ERF Safeguard Mechanism ETS 2016 Industry, Power All GHGs 10.20

Japan Tax for Climate Change Mitigation Carbon tax 2012 Multi-sectoral CO2 2.69

Kazakhstan Environmental Code of the Republic of Kazakhstan National GHG Emission Quota Allocation Plan 2018-2020 Rules for the Allocation of Quotas for GHG emissions Rules of Trading GHG Emission Quota and Carbon Units

ETS 2013 Industry, Power CO2 1.11

New Zealand Climate Change Response Act 2002, Part 4 - New Zealand Greenhouse Gas Emissions Trading Scheme

ETS 2008 Multi-sectoral All GHGs 14.30

Switzerland Federal Act on the Reduction of CO2 Emissions Ordinance on the Reduction of CO2 Emissions

ETS 2008 Industry, Power All GHGs 18.80

Switzerland Swiss CO2 Levy (CO2 Act) Carbon tax 2008 Multi-sectoral CO2 99.44

Ukraine Ukrainian Tax Code – Environmental Tax on Air Pollution from Stationary Sources

Carbon tax 2011 Industry, Power, Buildings

CO2 0.38

Carbon Pricing Policies and Initiatives Under Development or Scheduled for Implementation

Ukraine Law on the Principles of MRV of GHG Emissions ETS Proposed Multi-sectoral All GHGs N/A

Carbon Pricing Under Consideration

Japan Long Term Low-Carbon Vision, 2017 ETS Under consideration

Multi-sectoral CO2 N/A

Turkey Turkish MRV Legislation ETS Under consideration

Industry, Power CO2 N/A

Data sources: (ICAP 2020a; World Bank 2019; 2020a)

Notes:


2. Acronyms presented in the table stand for: ERF – Emissions Reduction Fund; MRV – Monitoring, Reporting and Verification.

August 2020


California (Linked with Quebec) Cap-and-Trade System

The California Cap-and-Trade Program was initiated in 2012 and is a crucial element of California’s climate

plan7. The system creates a price signal necessary to attract long-term investment in cleaner fuels and

more efficient use of energy. It also intends to help the state meet its GHG emissions reduction targets.

Since the beginning of the Program in 2013, the third compliance period (2018-2020) is currently in force.

The next five two-year compliance periods are scheduled from 2021 to 2031. The key features of the

California Cap-and-Trade Program are as follows (CARB 2015; 2020a; ICAP 2020a; World Bank 2020a):

- The California Air Resources Board (CARB) distributes allowances to the Program via two major

mechanisms: direct allocation to the covered entities and auctioning sales to all market

participants.

- Allowances are distributed differently to each of the three capped sectors (industry receives

about 90% of its allowances for free, the utility sector receives free allowances but must sell them

at auctions, and the transport sector must purchase all allowances).

- The cap established for the system has been declining since 2015 by approximately 12 Mt CO2e

annually, reaching 334.2 Mt CO2e in 2020. The cap will decline by about 13.4 Mt CO2e/year during

the period from 2021 to 2030.

- The California cap-and-trade system applies to CO2e emissions from the industry, power,

transport and building sectors (large facilities that emit ≥25 kilotonnes [kt] CO2e/year).

- The Program covers approximately 80% to 85% of California’s emissions from more than 500 liable

entities (approximately 600 entities with reporting obligations).

- GHGs covered include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and

perfluorocarbons (PFCs) such as HFCs, PFCs, NF3, and other fluorinated GHGs.

- The current allowance price level (as of April 1, 2020) is USD 15.30 t/CO2e.

- In 2018, the CARB approved some key reforms (including an addition of a price ceiling, no

continued free allocation, and reduced use of offsets) that came into force in 2019.

The California Cap-and-Trade Program’s design enables it to link with similar trading programs in other

states and/or regions. California has participated in the Western Climate Initiative (WCI)8 since 2007, and

linked its system with Quebec’s Cap-and-Trade system in January 2014. It was linked to Ontario’s system

as well for a short period in 2018 until Ontario terminated its system in mid-2018 (CARB 2020b; ICAP

2020a; 2020c; World Bank 2020a).

The Quebec cap-and-trade system has been implemented since 2013, and covers industry, power,

transport, building, and natural gas distribution sectors, accounting for approximately 85% of Quebec’s

GHG emissions. By June 1, 2020, Quebec and California have had a total of 23 joint auctions of GHG

emission allowances (Government of Canada 2019; World Bank 2020a).

7 In California, a few other non-cap-and-trade policies were enacted in 2012. The most important of them were the California’s renewable portfolio standard, the low carbon fuel standard, and energy efficiency measures. The cap-and-trade system was to offer a backstop to help ensure that total emissions would not rise above the established levels (Bang, Victor, and Andresen 2017). 8 Other WCI participating jurisdictions include Quebec and Nova Scotia.

August 2020


As the background information and for the purposes of a better understanding of the existing/planned

policies in the US states that represent a control group (see Methodology section), Table 2.3 provides a

summary of key federal, regional and state-level carbon management policies and initiatives in the United

States. Similarly to Table 2.2, the policies and initiatives are split into three main categories: those that

are implemented, scheduled for implementation, or under consideration, as defined in (World Bank

2019).

As can be seen from the table, only two US states (California and Massachusetts) have implemented direct

carbon pricing policies (ETS) so far. On the regional level, the RGGI has been the first mandatory market-

based ETS program in the US (implemented in 2009) to cap and reduce GHG emissions from the power

sector. There are currently 10 states (Connecticut, Delaware, Maine, Maryland, Massachusetts, New

Hampshire, New Jersey, New York, Rhode Island, and Vermont) that are participants of the RGGI. The

allowance price level (as of April 1, 2020) for the RGGI is USD 5.13 t/CO2e (RGGI 2020; World Bank 2020a).

It should be noted that Massachusetts’ ETS covers the same sectors and GHG emissions as the RGGI does.

There is no direct carbon pricing policy implemented in the US on the federal level, though some policies

that propose either a carbon tax or ETS for different sectors are under development (see Table 2.3). On

the regional level, two initiatives, the Carbon Cost Coalition and the Transportation and Climate Initiative

(TCI), are proposed for implementation. The former includes representatives from 12 US states

(Connecticut, Hawaii, Maine, Maryland, Massachusetts, New Hampshire, New York, Oregon, Rhode

Island, Utah, Vermont, and Washington). It is an initiative aimed to take action on climate change and

reduce GHG emissions through carbon pricing either in the form of a carbon tax, ETS, or direct non-pricing

policies (NCEL 2019a). The latter is a carbon pricing initiative in the form of ETS for the transportation

sector for 12 US states (Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New

Jersey, New York, Pennsylvania, Rhode Island, Vermont, and Virginia) as well as Washington, DC (TCI

2020). Three US states have their individual carbon pricing policies under development: a proposed

carbon tax system in Connecticut and a proposed ETS in Pennsylvania and Virginia. It should be noted that

Pennsylvania plans to join RGGI by 2022, and Virginia could join the initiative by the end of 2020 (ICAP

2020a).

In seven US states, state-level carbon pricing initiatives are currently under consideration: a carbon tax in

Massachusetts, New Hampshire and Rhode Island, and an ETS in North Carolina, New Mexico, Oregon and

Washington. There is a future possibility to link California’s cap-and-trade system with Oregon or

Washington once their ETSs are implemented (ICAP 2020a; World Bank 2020a).

Nine out of the 14 US states defined as a control group for the purposes of this study (specifically, Florida,

Georgia, Illinois, Louisiana, Indiana, Michigan, Missouri, Ohio and Texas) do not have any carbon pricing

initiatives under consideration.

August 2020


Table 2.3: Key Federal, Regional and State Carbon Management Policies and Initiatives in the United States

Jurisdiction Policy, Regulation or Initiative Type of Policy Year of Implementation


Implemented Policies and Initiatives

Federal Greenhouse Gas Reporting Program Direct non-pricing 2010 Multi-sectoral All GHGs

Federal Light-Duty Vehicle GHG Emission Standards and Corporate Average Fuel Economy (CAFE) Standards

Direct non-pricing 2010 Transportation CO2

Regional (10 states)1

Regional Greenhouse Gas Initiative (RGGI) ETS 2009 Power CO2

California Global Warming Solutions Act, 2006 Regulation for the California Cap on Greenhouse Gas Emissions and Market-Based Compliance Mechanisms, 2010

ETS 2012 Multi-sectoral All GHGs

California Mandatory Greenhouse Gas Emissions Reporting Regulation Direct non-pricing 2008 Multi-sectoral All GHGs

Massachusetts Limits on Emissions from Electricity Generators ETS 2018 Power CO2

Policies and Initiatives Under Development or Scheduled for Implementation

Federal Energy Innovation and Carbon Dividend Act (introduced 01/24/2019)

Carbon tax Proposed Multi-sectoral All GHGs

Federal Healthy Climate and Family Security Act (introduced 03/28/2019)

ETS Proposed Energy (fossil fuels)

All GHGs

Federal Safer Affordable Fuel-Efficient (SAFE) Vehicles Final Rule for Model Years 2021-2026



Carbon Cost Coalition Carbon tax/ETS Direct non-pricing

Proposed Multi-sectoral All GHGs


Transportation and Climate Initiative (TCI) ETS Proposed for 2022

Transportation CO2

Connecticut Act Establishing a Carbon Price for Fossil Fuels Sold in Connecticut (Bill HB5363), 2018

Carbon tax Proposed Energy (fossil fuels)

All GHGs

Pennsylvania Executive Order 2019-07 Air Pollution Control Act

ETS Proposed for 2022

Power CO2

August 2020


Jurisdiction Policy, Regulation or Initiative Type of Policy Year of Implementation


Virginia Regulation for Emissions Trading Programs Virginia Clean Economy Act

ETS Proposed for 2020

Power CO2

Policies and Initiatives Under Consideration

Massachusetts Act Relative to Carbon Pricing, 2019 (Bill HD1580) Act Combating Climate Change (Bill S1821) Act to Promote Green Infrastructure, Reduce Greenhouse Gas Emissions and Create Jobs (Bill H1726)

Carbon tax Under consideration

Multi-sectoral All GHGs

New Hampshire

Act establishing a carbon cashback fee collection and distribution program (Bill HB735)


Energy (fossil fuels)

All GHGs

New Mexico Executive Order on Addressing Climate Change and Energy Waste Prevention (EO 2019-003)

ETS Under consideration


North Carolina Executive Order to Develop Policy Options for Reducing Power Sector Emissions (EO No. 80) Clean Energy Plan


Power CO2

Oregon Oregon Climate Action Program Oregon Greenhouse Gas Initiative (LC19) Cap and Invest Bill (SB1507 & HB4001)



Rhode Island Clean Energy Investment and Carbon Pricing Act of 2017 (Bills H5369 and S0365)


Energy (fossil fuels)

All GHGs

Washington Clean Air Rule, 2016 ETS Under consideration


Data sources: (ICAP 2020a; NCEL 2019a; 2019b; RGGI 2020; TCI 2020; World Bank 2019; 2020a)

Notes:

1. The Regional Greenhouse Gas Initiative (RGGI) currently includes the following participating states: Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Rhode Island, and Vermont.

2. The Carbon Cost Coalition currently includes representatives from the following states: Connecticut, Hawaii, Maine, Maryland, Massachusetts, New Hampshire, New York, Oregon, Rhode Island, Utah, Vermont, and Washington.

3. The Transportation and Climate Initiative (TCI) currently includes the following participating jurisdictions: the states of Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, and Virginia, and Washington, DC.

August 2020


Carbon Management Policies in Canada

Carbon pricing is a central component of federal and some provincial carbon management initiatives. In

2016, the federal government released the Pan-Canadian Approach to Pricing Carbon Pollution, the

benchmark outlining the criteria that carbon pricing systems implemented by provinces and territories

need to meet. The Greenhouse Gas Pollution Pricing Act (GHGPPA), enacted in 2018, introduced the

federal carbon pollution pricing system that includes two key elements (ECCC 2016b; Government of

Canada 2018):

- a charge on fossil fuels that would be paid by fuel producers or distributors, and

- an output-based pricing system (OBPS) for industrial facilities with high levels of emissions.

As of April 1, 2020, carbon pricing systems have been in place in all Canadian provinces and territories,

either as provincial/territorial or the federal system. The federal carbon pollution pricing system (“federal

carbon price backstop”) applies in whole or in part in jurisdictions that requested it (opted-in), or that did

not implement their own carbon pricing regime (which meets the federal benchmark)9. All revenues

generated from the federal carbon pollution pricing system are being returned to the jurisdiction of origin

(Government of Canada 2019).

As can be seen from Table 2.4, five jurisdictions have implemented their own carbon pricing systems that

fully met the federal stringency requirements and are not the subject of the federal backstop:

- Newfoundland and Labrador launched its provincial carbon tax for a number of sectors, along

with a provincial performance standards system (PSS) in January 2019.

- Nova Scotia introduced its cap-and-trade program in January 2019.

- Quebec has had its cap-and-trade system in place since 2013 (linked with the California cap-and-

trade system since January 2014).

- British Columbia implemented its provincial carbon tax in 2008, and launched its baseline-and-

credit system in 2016, enabled by the BC Greenhouse Gas Industrial Reporting and Control Act.

- Northwest Territories implemented a territorial carbon tax in fall 2019.

Five other jurisdictions have both components of the federal backstop (the fuel charge and the OBPS) with

the following status:

- Fully imposed – Ontario and Manitoba

- Partially imposed – Saskatchewan (as it has its own provincial OBPS that covers specific sectors

and partially meets the federal benchmark)

- Opted-in (Nunavut and Yukon)

9 It might be worthwhile noting that there are some relatively hard constraints on instrument choice, particularly constitutional limits, that impact federations like Canada. It is why the federal GHGPPA was designed as a backstop, focused on a national standard carbon price with provincial opt out if federal equivalency criteria are met. It also explains the provincial constitutional jurisdiction judicial challenges by Ontario, Saskatchewan and Alberta. This is a significant uncertainty factor. A federal loss would result in a need to redesign the federal legislation and perhaps raise questions about the limits of the federal taxation power, something that was not clearly addressed in the provincial judicial decisions.

August 2020


It should be noted that Ontario has developed its own emissions performance standards (EPS) program

for large industrial emitters; however, it is not implemented yet. Manitoba enacted its own small-

emissions tax on July 1, 2020, so the federal fuel charge is not applicable in this province after that date.

For the remaining three jurisdictions, only one component of the federal backstop was implemented:

- Prince Edward Island opted-in for the federal OBPS, as it introduced the provincial carbon levy in

April 2019.

- New Brunswick has had the federal OBPS imposed since January 1, 2019. The province is no longer

a subject of the federal fuel charge, as it introduced the provincial fuel charge on April 1, 2020.

- Alberta has had the federal fuel charge imposed since January 1, 2020. The province is not a

subject of the federal OBPS, as it has its own OBPS system that came into force on January 1,

2020.

Table 2.4 also provides information on the current carbon prices in Canada, ranging from $20/t CO2e in

four jurisdictions, to $40/t CO2e in British Columbia, with the federal carbon tax established as $30/t CO2e

(rising $10 a year, on April 1 of each year until it hits $50 a tonne in 202210).

In Canada, the nature of the carbon management policy instruments and their stringency varies by

jurisdiction. In addition to carbon pricing policies, all provinces and territories have already implemented

a number of other non-pricing policies and incentives to reduce GHG emissions. In addition, federal,

provincial, and territorial governments continued to make progress on implementing a multitude of

complementary actions to reduce GHG emissions. As a quick overview, information on the key carbon

management policies implemented in Canada (by jurisdiction) is summarized in Table 2.5. In the table,

the policy tools highlighted are split into the following categories:

- Direct carbon pricing policies (further separated as a carbon tax/levy or a cap-and-trade program)

- Direct non-pricing regulations

- Indirect pricing policies

From the information provided in Tables 2.4 and 2.5, substantial progress has been made to implement

carbon management policies in many Canadian jurisdictions since 2016, when the Pan-Canadian

Framework on Clean Growth and Climate Change was released, and especially for the last two years

(2018-2020).

10 The built-in legislation review for the federal carbon tax rate is scheduled for 2022.

August 2020


Table 2.4: Carbon Pricing Systems Implemented in Canada

Jurisdiction Type of Carbon Pricing System1 Year of Implementation

Carbon Price2, CAD/t CO2e

Canada (Federal) Carbon tax/ETS (Fuel charge/ OBPS) 2019 30

Newfoundland and Labrador

Provincial carbon tax/ Provincial PSS 2019 20

Prince Edward Island Provincial carbon levy/ Federal OBPS (opt-in)

2019 20

Nova Scotia Provincial cap-and-trade 2019 20 3

New Brunswick Provincial carbon tax4/ Federal OBPS (imposed) 2020/ 2019 30

Quebec Provincial cap-and-trade 2013 22.40 5

Ontario Federal fuel charge/ Federal OBPS (imposed) Provincial EPS under consideration

2019 30

Manitoba Federal fuel charge6/ Federal OBPS (imposed) 2019 30

Saskatchewan Federal fuel charge/ Federal OBPS (partially)7 Provincial OBPS (specific sectors)

2019 30

Alberta Federal fuel charge/ Provincial OBPS (TIER)8 2020 30

British Columbia Provincial carbon tax/Provincial ETS9 2008/ 2016 40 10

Nunavut Federal fuel charge/ Federal OBPS (opt-in) 2019 30

Northwest Territories Territorial carbon tax 2019 20

Yukon Federal fuel charge/ Federal OBPS (opt-in) 2019 30

Data Sources: (Government of Canada 2019; ICAP 2020a; World Bank 2019; 2020a)

Notes:

1. Acronyms for the carbon pricing systems presented in the table stand for: ETS – Emissions Trading System; OBPS – Output-Based Pricing System; PSS – Performance Standards System; EPS – Emissions Performance Standards; TIER - Technology Innovation and Emissions Reduction system.


3. The minimum carbon price in Nova Scotia will be $20 per emission allowance for auctions held in 2020 (beginning June 10, 2020).

4. New Brunswick was a subject to the federal fuel charge, but on April 1, 2020, it switched to its own provincial tax on carbon-emitting products that meets the federal benchmark for the sources it covers.

5. Current allowance price as of April 1, 2020.

6. In March 2020, Manitoba decided to enact its own small-emissions tax ($25/t CO2e) starting July 1, 2020.

7. Saskatchewan implemented its own provincial OBPS that covers large industrial facilities and partially meets the federal benchmark. The federal OBPS covers electricity generation and natural gas transmission pipelines that are not subject to the provincial system.

8. Alberta SGER, the first Alberta hybrid pricing system, was in place since 2007, and was replaced by the Carbon Competitiveness Incentive Regulation (CCIR) in 2017. Subsequently, the CCIR was replaced by the TIER in 2020.

August 2020


9. In British Columbia, only LNG operations are currently regulated under the provincial ETS, which makes it very limited in scope.

10. British Columbia intended to raise the tax to $45/t CO2e on April 1, 2020; however, in response to the situation with COVID-19 pandemic, it remains at the 2019 level ($40/t CO2e) until further notice.

August 2020


Table 2.5: Key Carbon Management Policies Implemented in Canada

Policy/Regulation Type of Policy Year of Implementation/

Repeal

Sectors Affected

GHG Covered1

Canada (Federal)

Greenhouse Gas Pollution Pricing Act (GHGPPA), 2018 Output-Based Pricing System Regulations, 2019 (under GHGPPA)

Carbon tax/ Cap-and-trade 2019 Multi-sectoral All GHGs

Pan-Canadian Framework on Clean Growth and Climate Change (by ECCC), 2016


Greenhouse Gas Reporting Program (under Section 46 of the Canadian Environmental Protection Act [CEPA])

Direct non-pricing 2004 Multi-sectoral All GHGs

Regulations Respecting Reduction in the Release of Methane and Certain Volatile Organic Compounds (Upstream Oil and Gas Sector) (under CEPA), 2018

Direct non-pricing 2020 Oil and Gas CH4, VOCs

Reduction of Carbon Dioxide Emissions from Coal-Fired Generation of Electricity Regulations (under CEPA), 2012

Direct non-pricing 2015 Electricity CO2

Regulations Amending the Reduction of Carbon Dioxide Emissions from Coal‐fired Generation of Electricity Regulations (under CEPA), 2018 2


Regulations Limiting Carbon Dioxide Emissions from Natural Gas-fired Generation of Electricity (under CEPA), 2018


Heavy-duty Vehicle and Engine Greenhouse Gas Emission Regulations (under CEPA), 2013

Direct non-pricing 2013 Transportation CO2, CH4, N2O

Passenger Automobile and Light Truck Greenhouse Gas Emission Regulations (under CEPA), 2010

Direct non-pricing 2010 Transportation CO2, CH4, N2O

Renewable Fuel Regulations (under CEPA), 2010 Direct non-pricing 2010 Transportation CO2

Regulation of Hydrofluorocarbons Direct non-pricing 2019 Multi-sectoral HFCs

Energy Efficiency Regulations, 2016 Indirect pricing 2018 Multi-sectoral CO2, CH4, N2O

Proposed Regulatory Approach for the Clean Fuel Standard, 2019 Indirect pricing 2022 Multi-sectoral CO2, CH4

August 2020



Repeal

Sectors Affected

GHG Covered1


Made-in-Newfoundland and Labrador Approach to Carbon Pricing, 2018 Carbon tax/ Cap-and-trade 2019 Multi-sectoral All GHGs

Management of Greenhouse Gas Act, 2016 Management of Greenhouse Gas Reporting Regulations (under the Act), 2018


The Way Forward on Climate Change in Newfoundland and Labrador Direct non-pricing 2019 Multi-sectoral All GHGs

Prince Edward Island

Climate Leadership Act, Climate Leadership Regulations

Carbon tax 2019 Multi-sectoral CO2

Climate Change Action Plan 2018-2023 Direct non-pricing 2018 Multi-sectoral CO2, CH4, N2O

Renewable Energy Act Direct non-pricing 2005 Electricity CO2

Building Code Act Direct non-pricing 2020 Buildings CO2

Nova Scotia

Nova Scotia Cap-and-Trade Program, 2018 Cap-and-trade 2019 Multi-sectoral All GHGs

Cap-and-Trade Program Regulations (under the Environment Act), 2018 Cap-and-trade 2019 Multi-sectoral All GHGs

Greenhouse Gas Emissions Regulations (under the Environment Act) Direct non-pricing 2009 Electricity All GHGs

Renewable Electricity Regulations (under the Electricity Act) Direct non-pricing 2010 Electricity CO2

New Brunswick

Amendments to the Gasoline and Motive Fuel Tax Act Carbon tax 2020 Multi-sectoral All GHGs

Energy Efficiency Act and Regulations Direct non-pricing 1992 Multi-sectoral CO2

The Electricity Act, 2013 Renewable Portfolio Standard Regulation

Direct non-pricing 2014 Electricity CO2, N2O

Climate Change Act Direct non-pricing 2018 Multi-sectoral All GHGs

August 2020



Repeal

Sectors Affected

GHG Covered1

Quebec

Cap-and-trade System for Greenhouse Gas Emission Allowances Cap-and-trade 2013 Multi-sectoral All GHGs

Regulation respecting a cap-and-trade system for greenhouse gas emission allowances (2011, last amended 2017)

Cap-and-trade 2013 Multi-sectoral All GHGs

2013‐2020 Climate Change Action Plan 2013‐2020 Government Strategy for Climate Change Adaptation

Direct non-pricing 2013 (R 2020) Multi-sectoral All GHGs

Zero Emission Vehicle Act (2016) Direct non-pricing 2018 Transportation CO2

Ontario

Climate Change Mitigation and Low-carbon Economy Act, 2016 Cap-and-trade Program Regulation (under the Act), 2016 Cap and Trade Cancellation Act, 2018

Cap-and-trade 2016 (R 2018) Multi-sectoral All GHGs

Green Energy and Green Economy Act, 2009 Direct non-pricing 2009 (R 2019) Electricity CO2

A Made-in-Ontario Environment Plan Direct non-pricing 2018 Multi-sectoral All GHGs

Manitoba

Climate Change and Emissions Reductions Act, 2008 Direct non-pricing 2008 Multi-sectoral All GHGs

Manitoba Carbon Savings Account (under the Climate and Green Plan Act), 2018


Efficiency Manitoba Act, 2017 Direct non-pricing 2018 Multi-sectoral CO2, CH4, N2O

Saskatchewan

Saskatchewan Prairie Resilience Strategy Cap-and-trade 3 Direct non-pricing

2017 Industry All GHGs

Management and Reduction of Greenhouse Gases Act (MRGHGA), 2010: - Management and Reduction of Greenhouse Gases (Standards and Compliance) Regulations (under the MRGHGA), 2019

Cap-and-trade 3 2019 Industry All GHGs

August 2020



Repeal

Sectors Affected

GHG Covered1

Management and Reduction of Greenhouse Gases Act (MRGHGA), 2010: - Management and Reduction of Greenhouse Gases (General and Electricity Producer) (under the MRGHGA), 2017

- Management and Reduction of Greenhouse Gases (Reporting and General) Regulations (under the MRGHGA), 2018


Saskatchewan Methane Action Plan Direct non-pricing 2019 Oil and Gas CH4

Oil and Gas Emissions Management Regulations (under the Oil and Gas Conservation Act), 2019

Direct non-pricing 2019 Oil and Gas CH4

Alberta

Climate Leadership Act, 2016 Carbon Tax Repeal Act, 2019

Carbon tax 2017 (R 2020) Multi-sectoral All GHGs

Part 1 of the Greenhouse Gas Pollution Pricing Act Regulations (Alberta) (under the federal GHGPPA), 2019

Carbon tax 2020 Multi-sectoral All GHGs

Emissions Management and Climate Resilience Act (EMCRA) 4, 2003:

- Specified Gas Emitters Regulation (SGER), under EMCRA, 2007

Carbon tax/ Cap-and-trade 2008 (R 2017) Multi-sectoral All GHGs

Emissions Management and Climate Resilience Act (EMCRA) 4, 2003:

- Carbon Competitiveness Incentive Regulation (CCIR), under EMCRA, 2017

Carbon tax/ Cap-and-trade 2018 (R 2019) Multi-sectoral All GHGs

Technology Innovation and Emissions Reduction (TIER) Regulation, 2019, under the TIER Implementation Act


Emissions Management and Climate Resilience Act (EMCRA) 4, 2003: Renewable Fuels Standard Regulation, under EMCRA, 2010


Oil Sands Emissions Limit Act, 2016 Direct non-pricing 2017 Oil and Gas CO2

Methane Emission Reduction Regulation (under the Environmental Protection and Enhancement Act), 2018

Direct non-pricing 2020 Oil and Gas CH4

British Columbia

August 2020



Repeal

Sectors Affected

GHG Covered1

Greenhouse Gas Reduction (Cap-and-trade) Act, 2008 Cap-and-trade 2008 (R 2015) Multi-sectoral All GHGs

Greenhouse Gas Industrial Reporting and Control Act, 2014 Cap-and-trade 2016 Oil and Gas CO2, CH4

Carbon Tax Act, 2008 Carbon tax 2008 Multi-sectoral All GHGs

Climate Change Accountability Act 5 Direct non-pricing 2018 Multi-sectoral All GHGs

Greenhouse Gas Emission Control Regulation, 2016 Direct non-pricing 2016 Multi-sectoral CO2, CH4

Zero‐Emission Vehicles Act, 2019 Direct non-pricing 2020 Transportation CO2, N2O

Renewable and Low Carbon Fuel Requirements Regulation, 2008 Direct non-pricing 2008 Transportation CO2

Methane Reduction Policy, 2017 Direct non-pricing 2020 Oil and Gas CH4

Nunavut

Energy Management Program Direct non-pricing 2007 Buildings CO2

Northwest Territories

Act to Amend the Petroleum Products Tax Act, 2019 Carbon tax 2019 Multi-sectoral CO2

Yukon

Yukon Independent Power Production Policy Direct non-pricing 2019 Electricity CO2, CH4, N2O

Data sources: (Government of Canada 2019; ICAP 2020a; World Bank 2019; 2020a)

Notes: 1. All GHGs (or CO2e) include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), perfluorocarbons (PFCs), hydrofluorocarbons (HFCs), sulphur

hexafluoride (SF6) and nitrogen trifluoride (NF3).

2. Some provinces, including Nova Scotia and Saskatchewan, are currently working with the federal government on equivalency agreements in lieu of the amended coal-fired electricity regulations, or have already signed those agreements.

3. Saskatchewan introduced a sector-specific OBPS system for large industrial emitters as part of the province’s Prairie Resilience strategy. The OBPS will

apply to more than 40 Saskatchewan industrial facilities.

4. Formerly, the Climate Change and Emissions Management Act, 2003.

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5. Formerly, the Greenhouse Gas Reduction Targets Act, 2007. Information in the table is current as of April 1, 2020.

August 2020


British Columbia Carbon Tax System

The British Columbia carbon tax system implemented in 2008 by the Carbon Tax Act became the first

North American broad-based carbon tax. The escalating tax started at a rate of $10/t CO2e and was

scheduled to increase by $5/t CO2e each year, in order to reach $50/t CO2e in 202111. The BC carbon tax

covers GHG emissions from multiple sectors (with some exemptions for the industry, aviation, transport

and agriculture sectors). It applies to the purchase and use of fossil fuels, and the coverage is planned to

be expanded to include fugitive emissions and some other emissions (e.g., from the burning of certain

forestry residues). The use of fuel includes all uses (even if the fuel is not combusted). The share of

provincial GHG emissions covered by this tax is approximately 70% (Province of BC 2020a; 2020b; World

Bank 2020a).

The BC carbon tax used to be revenue-neutral, so one hundred percent of the revenue generated was

returned to taxpayers through various tax reductions and tax credits. In 2017, this requirement of the

revenue neutrality was eliminated (Government of BC 2017). Revenues generated from the carbon tax

will be focused on three broad areas (Province of BC 2020a):

1. Carbon tax relief for low- and moderate-income British Columbians, and protection of

affordability

2. Support for emissions intense industry to transition to a low-carbon economy, in order to

maintain industry competitiveness

3. New green initiatives to grow innovation and investment

Alberta Specified Gas Emitters Regulation

SGER was a hybrid carbon pricing system based on rewarding emissions intensity reductions. It had

elements similar to the carbon tax and elements similar to the cap-and-trade system. In 2007, Alberta

became the first province in North America to establish a regulatory regime governing GHG. The Climate

Change and Emissions Management Act and the SGER sought to reduce emissions intensity from large

emitters (producing ≥ 100 kt CO2e/year) by 20% in 2017. These entities contributed to approximately half

of Alberta’s emissions. The price rate had been established as $15/t CO2e from 2007 to 2015, $20/t CO2e

in 2016, and $30/t CO2e from 2017 forward.

Under the SGER, GHG emissions limits were established according to the facility-specific baselines that

were based on historical data for each facility (the first three years of operation for the new facility). All

industrial emissions that exceeded that pre-determined cap were subject to the carbon pricing. An

intensity-based limit on GHG emissions from industrial facilities was created by requiring certain large

emitters to reduce their emissions intensity by 12% annually (aiming to reduce total emissions to 50% of

1990 levels by 2020). For emitters to meet their intensity-based GHG emissions targets, there were several

mechanisms established by the SGER, including improvements in operations during the applicable period,

emission offsets, emission performance credits and fund credits (Osler, Hoskin & Harcourt LLP 2015):

11 The scheduled carbon tax increase to $45/t CO2e in 2020 was delayed due to COVID-19 pandemic, and as of June 1, 2020 it remained at the 2019 level of $40/t CO2e until further notice.

August 2020


The main limitations of SGER were not rewarding top performers and a lack of incentives to shift to lower

emissions production. Each producer had to reduce emissions in comparison to their baseline only, which

could initially be of high or low emissions intensity. These factors made the SGER ineffective in achieving

its goals (Pembina Institute 2019). In 2017, the SGER was replaced by the Carbon Competitiveness

Incentive Regulation (CCIR) as part of Alberta’s new Climate Leadership Plan. The CCIR imposed an output-

based benchmark on all large emitting entities within the same industry (Alberta Climate Leadership Panel

2015; Government of Canada 2019). Subsequently, on January 1, 2020, CCIR was replaced by the new

Technology Innovation and Emissions Reduction (TIER) regulation12.

Emissions Targets and Achievements by Country

The overall objective of carbon management policies for each jurisdiction is to reduce GHG emissions.

Tables 2.6 and 2.7 show that similar to the numerous carbon management policies, every jurisdiction has

its own target (current as of April 1, 2020). Two Canadian provinces and the three territories have no long-

term target, and this is the case for six other jurisdictions within the scope of this study. Some jurisdictions,

such as the European Union, Switzerland, California, Nova Scotia, set up an ambitious goal to reach carbon

neutrality by mid-century. All studied jurisdictions except Nunavut have their 2030 GHG emissions

reduction targets as required by the Paris Agreement of 2015, and as stated in intended nationally

determined contributions (NDCs) (United Nations Framework Convention on Climate Change [UNFCCC]

2020). Most of the countries, as well as seven Canadian provinces, have also established the emissions

reduction targets by 2020. However, as analyzed in the United Nations Environment Programme report

(UNEP 2019), even with progress on climate policy in many jurisdictions, GHG emissions worldwide

continue to rise with no significant changes in the global trend.

Global GHG emissions have almost reached the level projected for 2020 under the no-policy (“business as

usual” [BAU]) scenario, with the emissions gap larger than ever (Christensen and Olhoff 2019). For the

unconditional NDC targets for 2030, the European Union, Russia and Turkey are projected to meet the

target with currently implemented policies, while Australia, Japan, Canada and the US are expected to

meet the unconditional 2030 target only with additional policies or stricter enforcement of existing

policies implemented (UNEP 2019). It should be noted that in the analysis by the UNEP, only progress of

the G20 countries towards achieving the 2030 targets was reviewed, and this information was not

available for some other countries within the scope of CERI’s study.

12 This report does not evaluate either CCIR or TIER regulations.

August 2020


Table 2.6: GHG Emissions Reduction Targets for Canadian Jurisdictions

Jurisdiction GHG Emission Reduction Targets by:

2020 2030 2050

Canada (Federal) 17% below 2005 30% below 2005 Net-zero emissions from federal operations1

Newfoundland and Labrador 10% below 1990 35-45% below 1990 75-85% below 2001

Prince Edward Island 10% below 1990 35-45% below 1990 75-80% below 2001

Nova Scotia 10% below 1990 53% below 2005 Net-zero emissions

New Brunswick 10% below 1990 (≤14.8 Mt/CO2e)

35% below 1990 (≤10.7 Mt/CO2e)

80% below 2001 (≤5.0 Mt/CO2e)

Quebec 20% below 1990 37.5% below 1990 80-95% below 1990

Ontario 15% below 1990 30% below 2005 N/A

Manitoba 1 Mt CO2e reduction (2018-2022)

1/3 over 2005 1/2 over 2005

Saskatchewan N/A 40% below 2005 for GHG emissions from electricity1

N/A

Alberta N/A Targets for specific sectors1,2

N/A

British Columbia N/A 40% below 2007 80% below 2007

Nunavut N/A N/A N/A

Northwest Territories N/A 30% below 2005 N/A

Yukon N/A 30% below 2010 N/A

Data sources: (Climate Action Tracker 2019; Government of Canada 2019; NEG/ECP 2015)

Notes:

1. No target established for total GHG emissions, however, a plan to achieve net-zero emissions by 2050 will be developed.

2. Capping annual oil sands emissions to 100 Mt (from 2017 on); reducing methane emissions 45% below

2014 levels by 2025; retiring coal-fired generation.

August 2020


Table 2.7: GHG Emissions Reduction Targets for Other Studied Jurisdictions

Jurisdiction GHG Emission Reduction Targets by:

2020 2030 2050

United States (Federal) 26-28% below 2005 by 2025

26-28% below 2005 by 2025

N/A

California Return to 1990 levels 40% below 1990 Carbon neutrality by 2045

European Union 20% below 1990 40% below 1990* Carbon neutrality

Australia 5% below 2000 26-28% below 2005 N/A

Belarus N/A 28% below 1990 N/A

Japan 3.8% below 2005 26% below 2013 N/A

Kazakhstan 15% below 1992 15% below 1990 25% below 1992

New Zealand 5% below 1990 30% below 2005 Net-zero emissions, excluding CH4 emissions

Russian Federation N/A 25-30% below 1990 N/A

Switzerland 20-30% below 1990 50% below 1990 Carbon neutrality

Turkey N/A 21% below BAU scenario N/A

Ukraine 20% below 1990 40% below 1990 50% below 1990

Data sources: (Climate Action Tracker 2020; ICAP 2020b; UNFCCC 2020; World Bank 2020a).

Note: *43% below 2005 for GHG emissions regulated by the EU ETS.

August 2020


Chapter 3: Impacts of Carbon Management Policies: What Does the Literature Suggest

In efforts to better understand the effectiveness of existing carbon management policies around the

world, an evidence-based literature review was conducted to evaluate their effectiveness. The

effectiveness was evaluated based on key economic and environmental variables that were identified in

published literature, focusing on more recent studies within the last five years (2015 to present).

Evidence on the European Union Emissions Trading Scheme

Bel and Joseph (2015) looked to unravel the causes of emission abatement that were due to the EU ETS

or attributable to the production decrease as a result of the financial crisis. They used a dynamic panel

data approach on a sample data of 30 countries over a time span of 2005 to 2012. They found that the

total emissions abatement due to the EU ETS in that time span ranged from 33.78 to 40.76 Mt of the total

294.5 Mt of emissions reduction. However, this study indicated that most of the reduction in emissions

was due to the economic recession rather than the EU ETS having major implications on the system. They

argued that the market for emissions allowances became oversupplied, and their prices fell, reducing the

incentive for those to invest in low-carbon technology. To mitigate the effects, they suggested to lighten

the emissions caps or cancel future allocations.

Dechezleprêtre, Nachtigall, and Venmans (2018) investigated the joint impact of the EU ETS on carbon

emissions and the economic performance of companies regulated by the ETS to understand the

effectiveness of the EU ETS from a firm-level perspective. They investigated the emissions in the UK,

France, Netherlands, and Norway using regression analysis to estimate the policy’s impact on the

emissions from installations13 and a firm’s economic factors. They found a 6% reduction of carbon

emissions in the first phase and 15% in the second phase of the EU ETS between 2005 and 2012. They

found that the larger installations had a stronger effect on emissions reductions due to the capital-

intensive nature of pollution control.

13 According to Article 3(e) of the EU ETS Directive, an installation is a stationary technical unit where one or more activities under the scope of the European Union Emissions Trading Scheme (EU ETS) and any other directly associated activities which have a technical connection with the activities carried out on that site and which could have an effect on emissions and pollution.

• Overall, there is evidence that when correctly implemented, ETSs and carbon taxing are effective

measures towards reducing carbon emissions.

• Common trends in the research showed that the introduction of exceptions to carbon policies

tended to undermine the effectiveness of systems that were implemented.

• The research strongly suggests that moving towards a more complex linked system with more

participants would have a much more significant impact on emissions trends and avoid problems

of false signaling from limited markets in which carbon emissions can be traded.

August 2020


They supported the view of (Bel and Joseph 2015), and noted that a greater amount of free allowances

lowers the impact of the EU ETS, and those over-allocated installations have not contributed to a decrease

in emissions. Moreover, this study evaluated the impact of the EU ETS on firm performance and found

that the economic impact on these companies under the ETS led to a significant increase in revenue. They

argued that the increase in revenue might be driven by the rise in productivity, which was linked to the

ETS regulated companies’ higher investment in purchasing more carbon-saving technologies. They stated

that the effects of the ETS are most substantial for larger installations, supporting the idea that pollution

control is capital intensive and involves high fixed costs. The study also addressed the potential “risk of

relocation”, leading to leakage, and found that companies at risk of relocation and carbon leakage do

suffer more, as their assets reduced by around 17%, employment decreased by 13%, and profits did as

well. Within these at-risk sectors, however, the companies that are regulated by the EU ETS did

outperform those that are not, indicating that the free allowances may be having a positive impact like

the policy intended for.

More recently, (Bayer and Aklin 2020) evaluated the effectiveness of the EU ETS using a synthetic control

method. They found that the EU ETS reduced CO2 emissions beyond just the decrease due to the

economic crisis, which contradicts the study by (Bel and Joseph 2015) that was conducted five years prior.

Their estimates showed that the EU ETS abated a cumulative amount of around 1.2 billion tons CO2

between 2008 and 2016, which is roughly 3.8% relative to total emissions at that time. They argued that

showed that carbon markets could work, even when prices are low. They advised a strong political

commitment to continued carbon regulation in the future and increased scarcity of carbon credits in the

markets to maintain the effectiveness of this ETS.

Evidence on British Columbia’s Carbon Tax System

In 2008, BC implemented a carbon tax for the province, which was one of the first within North America.

In 2015 and 2016, British Columbia had the most significant economic growth of all the provinces in

Canada; in 2017, their gross domestic product (GDP) increased to 3.9% (Richards and Statistique Canada

2018). Output in the province was a contributor to this expansion in their economy, which also led to an

increase in GHG emissions of 1.2% since 2016, bringing the 2017 total to 64.5 million tonnes of CO2e in

the province (Government of BC 2019).

A study conducted by (Murray and Rivers 2015) reviewed existing evidence to examine the effects of the

carbon tax on GHG emissions, the economy, and income distribution in BC. The study used empirical and

simulation models to suggest that the decrease in emissions was due to the implementation of the carbon

tax. They also found that the tax has reduced emissions in BC between 5% and 15% since implementation

while having negligible effects on the economy. They argued that while BC has experienced a higher

growth rate in comparison to the rest of Canada between 2008 and 2013, it is not confirmed that this is

due to the carbon tax. Murray and Rivers (2015) critiqued that carbon tax exemptions have been granted

to particular sectors instead of making broad-based tax cuts. They argued that these targeted tax credits

towards specific industries are likely to reduce the cost effectiveness of the tax overall.

Yamazaki (2017) examined the employment impact of the revenue-neutral BC carbon tax. With an

empirical strategy, a simple labour market model was used to illustrate how a revenue-neutral carbon tax

August 2020


affects employment. The study found that the carbon tax increased employment by 0.74% between 2007

and 2013 as an aggregation across industries. Yamazaki (2017) argued that the impact on employment

differs across industries due to the differences in output effect and redistribution effect caused by the tax.

The study also found a negative output effect in emission and/or trade intensive industries, causing a

negative employment impact, while the labour-intensive industries experienced job gains that outweigh

the negative impacts on employment driven by the carbon tax.

A recent study by Bernard, et al. (2018) tested the impact of gasoline and diesel carbon taxes on the GDP

changes in the province using monthly data in British Columbia from January 1987 to December 2016.

Using a vector autoregression model, this study looked at the relationships between oil prices and the

economy, evaluating key variables such as the price of gasoline and diesel and per capita GDP in the

province. Their evidence found that there was no statistically significant effect of the carbon taxes on the

monthly GDP change in the province. The authors concluded that to measure the effectiveness of the

carbon tax better, there needs to be a more comprehensive understanding of emissions from other fossil

fuels that were not included in this study (e.g., natural gas, coal) as well as the availability of monthly price

data for the other fuels. It is anticipated that the impacts of this tax will be spread over time and evolve

at relatively long horizons and is recommended that future work should continue to assess the effects of

the carbon tax as more data becomes available.

Evidence on the US Regional Greenhouse Gas Initiative (RGGI)

The RGGI was established in 2009 as a mandatory market-based program with participation by 10 US

states (Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York,

Rhode Island, Vermont and Virginia) to cap the amount of CO2 produced by power plants. Funds collected

by the program were to be earmarked for strategic energy development and consumer benefit programs.

While CO2 emissions had declined markedly since the implementation of the policy, several exogenous

factors could have contributed to this: the economic recession and subsequent decline in economic

activity; the increased abundance of natural gas; and complementary environmental policies put in place

at a local, regional and federal level.

Murray and Maniloff (2015) used state-year data from 1990 to 2012 for all 48 contiguous US states to

examine the effects of the RGGI on CO2 emissions. The authors found that the RGGI program shows a

significant effect on CO2 emissions in participating states even after controlling for marked changes in

fuel prices, economic uncertainty, and complementary policies. The study also examined the possibility

of there being an “announcement effect” by which firms facing the implications of the carbon pricing

program acted by retooling power plants or enacting policies that would result in reduced emissions

before the program came into effect. The model suggested that the influence of this effect is of almost

the same magnitude as the impact post the RGGI’s implementation. Therefore, when considering only

the direct impact of the RGGI program without considering the announcement effect, the impact of the

program is found to be statistically insignificant.

Overall, the study found that the RGGI was effective at reducing CO2 emissions in the participating states,

estimating that emissions would have been approximately 50% higher by 2012 in participating states if

August 2020


not for the combination of the natural gas market, macroeconomic factors and carbon policy. When

excluding exogenous factors, the model suggests that emissions would have been 24% higher in

participating states had the RGGI program not been implemented. The authors also indicated the

possibility of emissions leakage to neighbouring states or Canadian provinces, considering that there are

power transmission linkages; however, this was beyond the scope of their research.

Evidence on Other Jurisdictions

New Zealand’s Emissions Trading System

The report by (New Zealand Ministry for the Environment and Ministry for the Environment 2016)

presents the Ministry’s findings regarding the effectiveness of the ETS after several years of execution. To

achieve this, the authors relied on a “Context-Input-Product” model, and used data from the national GHG

inventories and reports under the UNFCCC, international reviews and reports of the New Zealand ETS,

Statistics New Zealand’s Business Operations Survey of 2012, and stakeholder surveys. The report found

that the higher emission unit prices in the first few years of the New Zealand ETS influenced the

development of new carbon sinks, particularly in forestry development. Net GHG emissions were also

reduced below the business as usual (BAU) levels. However, this reduction was found to be small,

especially when compared to the Kyoto Protocol commitment period one (2008-2012), which is to reduce

emissions to 5% below the 1990 level. There is uncertainty on how much of these reductions can be

attributed to the ETS or the various exogenous economic factors present over this period.

Ukraine’s Carbon Tax

Using a computable general equilibrium model, Frey (2017) evaluated the effectiveness of Ukraine’s

carbon tax and the potential impact of different levels of the carbon tax assuming perfect competition

and constant returns to scale and balanced budgets for the model’s agents. They used economic data

from the Ukrainian National Accounts and input-output tables, and International Energy Agency (IEA)’s

“Energy Balances of Non-OECD Countries” database to obtain and segregate the data required by the

model. Considering several scenarios, including BAU and the carbon tax regime under effect at the time,

the author showed that the current tax level of $0.02/t CO2 did not result in a significant decrease in CO2

when compared to BAU. The study suggested that a carbon tax of around $3.46/t CO2 is required to

achieve the goals set by Ukraine’s national government.

Sweden’s Tax Policy

Brännlund et al. (2014) investigated the effectiveness of the environmental policy on sector-wide

emissions and output in Sweden for the period starting from 1991 to 2014. They found that the Swedish

manufacturing industry became 45% less carbon-intensive by reducing emissions by 10% and increasing

output by 35%. They found that both fossil fuel prices and the carbon tax had an effect on carbon intensity

performance with a higher sensitivity towards carbon tax, thus suggesting that carbon tax was an effective

instrument for improving carbon intensity performance in Sweden.

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Panel Studies

Villoria-Sáez et al. (2016) carried out an analysis to determine the effectiveness of GHG Trading Schemes

in achieving the Protocol’s 2020 target emissions and how effective the penalties set forth by the ETSs

were at curbing GHG emissions. A total of six major regions that are currently employing ETSs were

selected for analysis: The European Union, Australia, New Zealand, Japan, the United States, and Canada.

Using a linear regression model, the authors found that in all regions considered, on average, carbon

emissions were reduced by 1.6% per year since the implementation of the ETS. Around 23.4% reduction

of CO2 can be reached in 10 years of implementation for all regions studied. The suggested that U$90.22/t

CO2 is the optimal penalty in achieving the maximum carbon reduction, based on the data available. The

authors recognized that many of the ETSs that were analyzed had not been in effect for a long enough

time to have a comprehensive dataset allowing for in-depth evaluation, and it would be necessary to

conduct further analysis.

Narassimhan et al. (2018) conducted a review of several existing ETSs from different jurisdictions around

the world, which had been in effect for a long enough period to present insights into the effectiveness of

the policies. They considered systems that included the EU ETS, Switzerland ETS, the US RGGI, the ETS

operated between California and Quebec (and Ontario, briefly), and New Zealand ETS. The researchers

carried out a qualitative assessment of these systems, focusing on several primary variables, which would

lead to an overall conclusion on the effectiveness of the system. Table 3.1 presents a summary of the

findings from this review.

Table 3.1: Summary of Findings from the Overall Assessment of the Considered ETSs

Source: (Narassimhan et al. 2018)

As an overall assessment, the authors found that the California-Quebec linked system shows the best

environmental performance with almost full coverage of key emitting sectors and continuous tightening

of the cap. The authors also find New Zealand and Chinese pilot systems to perform the poorest due to

significant exemption of energy intensive and trade exposed (EITE) industries.

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In conclusion, the authors remark on the difficulty of evaluating the systems because unique contexts and

opportunities lead to no two ETSs being alike. While patterns can be recognized, which lead to crucial

policy insights such as the importance of sharing experience between participating jurisdictions and the

importance of the “double dividend,” lack of consistent quality data makes it difficult to arrive at a

straightforward answer.

Summary of Findings

Evaluation of the different carbon policies presented mixed results, with some showing a marked impact

on emissions since the policy’s implementation. In contrast, others proved to be ineffective in meeting

the regions’ commitments to GHG emissions reductions. Overall, the studies tended to show better results

from ETSs over taxes on carbon emissions, especially those with linked carbon markets such as California

and Quebec, and the RGGI. The research strongly suggests that moving towards a more complex linked

system with more participants would have a much more significant impact on emissions trends and avoid

problems of false signaling from limited markets in which carbon emissions can be traded.

Common trends in the research showed that the introduction of exceptions to carbon policies tended to

undermine the effectiveness of systems that were implemented. Cases such as New Zealand, Japan and

Ukraine provided evidence that half-measures and tailoring policy to protect what governments

considered to be “vulnerable” industries undermined the policy’s intended effect. Similarly, the EU ETS

identified these at-risk industries and developed plans that allow companies to remain competitive.

Furthermore, these mitigations lower the potential for carbon leakage due to companies moving

elsewhere in efforts to avoid the impacts of the policy on their performance.

While it is difficult to attribute results directly due to the number of variables involved, there is evidence

that when correctly implemented, ETSs and carbon taxing are effective measures towards reducing

carbon emissions. Research suggests that well thought-out programs that rely on past experiences of

other jurisdictions and are flexible to the different particularities of their local economies tend to meet

the goals for which they were designed, effectively and efficiently. Contrarily, systems that hold

exemptions for specific industries or are restricted tend to perform poorly and tend to not contribute

significantly to the fulfillment of emissions reduction goals.

Studies based on empirical evidence of the effectiveness of different carbon policies were sparse and hard

to find, with most researchers taking a qualitative approach to the evaluation of the various systems in

place. Table 3.2 summarizes the literature addressed in this chapter.

August 2020


Table 3.2: Summary of Key Findings from the Evidence-Based Literature

Study Jurisdiction(s)/ Policy

Objectives Methods Used Findings Conclusions/ Policy Suggestions

(Bayer and Aklin 2020)

EU ETS Measure the effectiveness of the carbon market on the emissions reduction

Synthetic control method • The EU ETS reduced CO2 emissions beyond just the decrease due to the financial crisis.

• The EU ETS saved a cumulative amount of ~1200 Mt CO2 between 2008 and 2016

• A strong political commitment to continued carbon regulation in the future.

• Increased scarcity in the markets is required.

(Dechezleprêtre, Nachtigall, and Venmans 2018)

EU ETS Investigate the joint impact of the EU ETS on carbon emissions and the economic performance of regulated companies

Quantitative assessment utilizing difference-in-differences approach.

The ETS led to an increase in revenue and a reduction of CO2 emissions for the companies.

Pollution control is capital intensive and involves high fixed costs.

(Bel and Joseph 2015)

EU ETS Evaluate the impact of the policy on GHG emissions during the first two trading phases.

Quantitative assessment on data from installations under the EU ETS.

• The impact of the ETS is 11-14% of the total GHG emissions reductions.

• Most of the reductions in emissions are due to the economic recession

• Tighten the emissions cap.

• Cancel future allocations.

(Narassimhan et al. 2018)

ETSs in the EU, Switzerland, RGGI, California-Quebec, New Zealand, South Korea and China (Chinese Pilot Programs)

Provide a comparative review of existing ETS systems

Qualitative assessment based on five criteria: environmental effectiveness, economic efficiency, market management, stakeholder engagement and revenue management.

• The California-Quebec ETS has the best overall performance.

• The poorest performing ETSs are New Zealand and the Chinese Pilot Programs.

• Each national context creates unique opportunities and constraints. ETSs must be tailored to the specific context in which they are to be used.

August 2020




(Villoria-Sáez et al. 2016)

ETSs in Australia, Canada, the EU, Japan, New Zealand, the US

Determine the effectiveness of ETSs in different parts of the world.

• A five-step methodology was developed, focusing on progress towards achieving Kyoto goals.

• A linear regression analysis on the EU, US and Canada data was carried out to establish an optimal carbon price for ETS.

• All ETS were effective in reducing carbon emissions with the exception of Japan.

• The European Union, US and Canada (California-Quebec, Alberta) showed to have the most effective ETS in terms of emissions reduction.

• The implementation of the ETS impacted emissions trends negatively.

(Bernard, Kichian, and Islam 2018)

BC carbon tax Investigate the impact of the gasoline and diesel carbon taxes on the GDP changes in the province.

• Quantitative assessment using a vector autoregression model,

• No statistically significant effect of the carbon taxes on the monthly GDP change in the province.

• To better measure the effectiveness of the carbon tax, there needs to be a more comprehensive understanding of the leaks in natural gas, coal, oil products as well as the availability of monthly price data.

(Yamazaki 2017) BC carbon tax Examine the employment impact of the BC carbon tax.

Empirical strategy to illustrate the three channels through which a carbon tax affects employment.

• 0.74% annual increase in employment between 2007 and 2013 as an aggregation across industries .

• A negative output effect in EITE industries, causing a negative employment impact.

• Labour-intensive industries experience job gains

August 2020




(Murray and Rivers 2015)

BC carbon tax Review the existing evidence on the effect of the carbon tax on GHG emissions, the economy, and income distribution.

Quantitative assessment applying applied empirical and simulation

• The tax has reduced emissions in BC between 5 and 15%. While BC has experienced a growth rate between 2008 and 2013 higher than the rest of Canada, it is not confirmed that this is due to the carbon tax.

Supporting certain industries with targeted tax credits is likely to reduce the cost effectiveness of the tax overall.

(Murray and Maniloff 2015)

The US RGGI Determine if the decline in GHG emissions in RGGI states can be attributed to the RGGI initiative.

Panel econometric methods to estimate emissions inside and outside the RGGI as a function of range, policy, market and environmental variables.

• GHG emissions would have been about 50% higher by 2012 in RGGI states were it not for the combination of policy, and other related variables. Once recession, natural gas price and RTS policies are factored in, the simulation suggests emissions would be 24% higher if RGGI program not in effect.

A tightly targeted cap and trade program such as was installed through RGGI is effective at reducing emission levels.

(Brännlund, Lundgren, and Marklund 2014)

Carbon tax in Sweden

Determine the effectiveness of carbon policy in Sweden in terms of CO2 reduction.

Quantitative assessment using sector-wide emissions and output data in Sweden for the period starting from 1991 to 2014.

• During 1991-2004, Swedish manufacturing became 45% less carbon-intensive by reducing emissions by 10% and increasing production by 35%.

• The evidence points to CO2 tax being a significant reason for this development.

• The carbon tax was an effective tool in curbing CO2 emissions in Sweden.

(New Zealand Ministry for the Environment and Ministry for the

The New Zealand ETS

Evaluate the performance of the New Zealand ETS

The "Context Input Process Product Model," which considers: a context for policy; resource required; process; product;

• Higher emission unit prices in the first few years of the New Zealand ETS reduced net GHG emissions below BAU emissions.

• New Zealand is efficiently supporting the government's commitments in the Kyoto Protocol.

August 2020




Environment 2016)

emissions impact; business impact; behaviour impact and impact of price on emissions.

• There is uncertainty over the exact amount of reductions caused by carbon pricing, compared to other economic factors.

(Frey 2017) Ukraine’s Carbon Tax

Assess the impacts of different carbon tax levels in Ukraine

Computable general equilibrium model with the following inputs: CO2 emissions, indirect taxes and subsidies, household expenditures, tariffs, crude oil and gas use, energy volume, energy price

• The current tax level ($0.02/t CO2) does not result in a significant decrease in CO2.

• To achieve the goals set by the national government, the tax should be increased to $3.46 /t CO2.

August 2020


Chapter 4: Historical Trend Analysis: Economic Performance and GHG Emissions Reduction

European Union

Figure 4.1 displays the trend of emissions efficiency, measured as the level of economic output (measured

in GDP) per unit of GHG emission (Mt CO2e) in the EU-2514, and other countries from 1991 to 2017. Long-

run emissions efficiency grew most rapidly for the EU-25, and Switzerland after 2000, while it has been

relatively steady at a very low level (below 1) in countries including Ukraine, Kazakhstan, Belarus, and

Russian Federation. Japan had a relatively high emission efficiency in 1991 (3.79), climbing moderately

until 2008 (4.37), falling precipitously during and after the global recession between 2010 to 2012, and

recovering since then. In general, emissions efficiencies ranged between 0.40 in Ukraine in 1991 to 13.88

in Switzerland (shown in the secondary axis) in 2017. The largest improvement in emissions efficiency was

witnessed in Switzerland, where the factor grew from 7.76 in 1991 to 13.88 in 2017 followed by the EU,

whose emission efficiency has improved from 2.3 in 1991 to 4.3 in 2017.

14 See the definition of the EU-25 countries in Chapter 1.

• Emissions efficiency varies significantly across countries. Between 1991 and 2017, the largest

improvement in emissions efficiency was witnessed in Switzerland followed by the EU-25 in

our EU ETS analysis.

• GHG emissions in the state of California are about 10% higher than in 1997 resulting from the

continued economic growth.

• Across Canadian provinces, the largest improvement in emissions efficiency is seen in Ontario,

followed by Quebec between 1997 and 2017.

• Overall, emissions from the utility sector show a declining trend while that of transportation

shows an increasing trend in most of the Canadian provinces except few.

• The oil and gas sector is the largest emitter in Alberta, followed by the utility sector, and the

transportation sector in 2017. In contrast, emissions from the transportation sector are the

largest contributor to Ontario’s GHG emissions.

August 2020


Figure 4.1: Emissions efficiency in the EU-25 countries and other studied jurisdictions (the control group), billion USD/Mt CO2e

Data Source: (UNFCCC 2019), CERI. Figure by CERI.

Figure 4.2 depicts the average growth in emissions efficiency in the jurisdictions under consideration for

the periods before the EU-ETS was enforced (1992-2004) and after (2005-2017). Emissions efficiency in

the EU-25 averaged 3.4% growth per year between 2005 and 2017, increasing from 1.57% growth rate

per year between 1992 and 2004. Belarus (3.34%) and Switzerland (3.11%) were other countries that

achieved over 3% growth during these periods. On the other hand, Kazakhstan suffered the biggest drop

in the growth rate of emission efficiency from 5.77% per year between 1992 and 2004 to 1.77% per year

between 2005 and 2017.

1

3

5

7

9

11

13

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2

2.5

3

3.5

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5

1991 1994 1997 2000 2003 2006 2009 2012 2015

Australia Belarus

Japan Kazakhstan

New Zealand Russian Federation

Turkey Ukraine

EU-avg Switzerland (secondary axis)

August 2020


Figure 4.2: Emissions efficiency growth in the EU-25 countries and the control group, percentage

Data Source:(UNFCCC 2019), CERI. Figure by CERI.

The following subsection and Figures 4.3 and 4.4 present some general trends of output, measured by

GDP, to provide a general overview of the economy. Overall, although several general trends can be

observed, the evolution of output remains highly diverse across countries, suggesting that country-

specific factors play a pivotal role in the functioning of the economy.

Figure 4.3: GDP in the EU-25 countries and the control group, billion USD


0

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2000

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4000

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6000

7000

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800

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1400

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1991 1994 1997 2000 2003 2006 2009 2012 2015

Australia Belarus

Kazakhstan New Zealand

Russian Federation Switzerland

Turkey Ukraine

EU-Avergae Japan (Secondary axis)

August 2020


As highlighted in Figure 4.4, between 2005 and 2017, the GDP growth, on average, increased in both EU-

25, and non-EU countries. Economic growth in the EU-25 region averaged 1.3 % in the post-policy period

(2006-2017), a slight improvement from the pre-policy growth rate (0.64%). Turkey and Kazakhstan led

the improvement in GDP growth during the post-policy period with an average growth rate of 5.5%, and

5.4% per year, respectively, up from the pre-policy level. All countries included in the sample not only

enjoyed growth during the post-policy periods, but also had an improvement in the growth rate except

for Australia, Japan, and New Zealand who saw a decline in the GDP growth rate in post-policy periods.

As can be seen from Figure 4.5, the EU-25 continued its strong progress with decarbonization as the

average GHG emissions (measured by CO2 equivalent or CO2e emissions) in 2017 present the lowest

average emission level achieved in the EU-25 between 1991 and 2017. The main reasons for reductions

were decreases in energy and carbon intensity and a switch to less emission-intensive fuels, which

overcompensated emission increases due to rising population, economic growth, and rising energy

demand15. Ukraine saw the largest % decline in GHG emissions in 2017 on a country basis – from 852.4 Mt

in 1991 to 320.6 Mt in 2017, a fall of 531.8 Mt, or 62.4% from 1991 level; the fastest pace of decline by

any country over that period. Russian Federation spearheaded the decline in emissions in the sample; its

emissions fell by 8% to 2,155.5 Mt of CO2 in 2017 from 3,024.3 Mt of CO2e in 1991, the largest decline by

any country in the sample over that period.

Figure 4.4: GDP growth rate in the EU-25 countries and the control group, percentage


15 For details, see (European Environment Agency 2011)

-2.50%

-1.50%

-0.50%

0.50%

1.50%

2.50%

3.50%

4.50%

5.50%

Australia Belarus Japan Kazakhstan New Zealand RussianFederation

Switzerland Turkey Ukraine EU-Avergae

Growth rate (1992-2005)


August 2020


Figure 4.5: GHG emissions in the EU-25 countries and the control group, Mt CO2e


Between 2005 and 2018, average GHG emissions decreased by 19.1 % (an average of 1.47% per year) for

EU-25 as seen in Figure 4.6. In 2018, the EU-25 emitted an average of 165.4 Mt CO2e, 87.4 Mt (34.5%)

CO2e less compared to 1991 (252.8 Mt CO2e). Similarly, Ukraine reduced GHG emissions by 62.4%

between 1991 and 2017, from 852.4 Mt CO2e in 1991 to from 320.6 Mt CO2e in 2017. On the other hand,

countries like Belarus, despite having a similar level of GHG emissions in 1991 (226.5 Mt CO2e), failed to

reduce emissions and emitted 526.3 Mt CO2e in 2018 (a rise by about 132%). EU-25 emissions decreased

by an average of 1.67% between 1991 and 2004, and 1.47% between 2005 and 2017. Other countries

including Japan, Switzerland, and Ukraine also experienced a reduction in their total GHG emissions

growth between 2005 and 2017, with Ukraine being the most successful in reducing emission (2.21% per

year). Australia and Turkey experienced a positive emission growth rate through the whole sample periods

(1991-2017), as well as in the subsamples periods (1992-2004), and (2005-2017).

0

500

1000

1500

2000

2500

3000

3500

-100

100

300

500

700

900

1100

1300

1500

1700

1991 1994 1997 2000 2003 2006 2009 2012 2015

Australia BelarusJapan KazakhstanNew Zealand SwitzerlandTurkey UkraineEU-avg Russian Federation (secondary axis)

August 2020


Figure 4.6: GHG emissions growth rate in the EU-25 countries and the control group, percentage


The United States

Figures 4.7 and 4.8 show changes in emissions efficiency in the state of California and other states in the

U.S. over the period of 1997 to 2017. Between 2013 and 2017, emissions efficiency increased by 6.9 % per

year in the state of Indiana (GDP increase of 1.6 % while emissions decreased by 4 %), up by about 3.1%

from its 1998-2012 value (3.8%). During the same time, the emissions efficiency in the state of Ohio, and

California increased by 4.6% (GDP increase of 1.9 % while emissions decreased by 2.4 %), and 2.9% (GDP

increase of 4% while emissions increased by 0.8%) per year, respectively. The state of Louisiana

experienced negative growth (-2% per year) in the emission efficiency during this period, while the state

of Illinois experienced almost no growth during this time. The states of Indiana, Ohio, California, and

Florida improved their emissions efficiency growth in recent times. On the other hand, the states of Texas,

Pennsylvania, and Illinois failed to maintain the high growth they achieved between 1998-2012.

-5.00%

-4.00%

-3.00%

-2.00%

-1.00%

0.00%

1.00%

2.00%

3.00%

4.00%

Australia Belarus Japan Kazakhstan NewZealand

RussianFederation

Switzerland Turkey Ukraine EU-avg



August 2020


Figure 4.7: Emissions efficiency in the US states, billion USD/Mt CO2e

Data Source: (US EIA 2019), CERI. Figure by CERI.

Figure 4.8: Emission efficiency growth in the US states, percentage

Data Source: (US EIA 2019), CERI. Figure by CERI.

Figure 4.9 shows the historical GDP for the selected states in the U.S. from 1997 to 2017. As can be seen,

remarkable increases in GDP occurred primarily in the states of California, Texas, and Florida during this

period. For the state of California, GDP almost doubled from USD 1,378.6 billion in 1997 to USD 2,610.8

Billion in 2017, while GDP in Texas and Florida increased to USD 1,646.3 billion, and USD 896.1 Billion,

respectively.

-2.50%

-1.50%

-0.50%

0.50%

1.50%

2.50%

3.50%

4.50%

5.50%

6.50%

7.50%

IN OH TX CA Rest PA FL MI IL LA



0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

1997 2000 2003 2006 2009 2012 2015

CA FLIL INLA MIOH PATX Rest of the USA

August 2020


Figure 4.9: GDP in the US states, billion USD

Data Source:(US EIA 2019), CERI. Figure by CERI.

There was a slowdown of output during the financial crisis in 2008 and 2009, but since then, most states

have recovered from the downturn. Thus, apart from short interruptions in years of global recessions,

state-level GDP has been increasing steadily in the decades under consideration. It also shows that while

the average GDP growth between 2013 and 2017 in the states under consideration has been fairly similar

to their previous level (1998 to 2012), the state of Louisiana saw a negative growth of 0.2% during this

period. Conversely, California, Texas, and Florida saw a large annual growth in GDP of 4%, and 3.1%,

respectively (see Figure 4.10).

Figure 4.10: GDP growth rate in the US states, percentage


0

1000

2000

3000

4000

5000

6000

0

500

1000

1500

2000

2500

3000

1997 2000 2003 2006 2009 2012 2015

CA FLIL INLA MIOH PATX Rest of the USA (secondary axis)

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

CA TX FL Rest PA IN IL OH MI LA



August 2020


As can be seen in Figure 4.11, total GHG emissions in the state of California reached 475.8 Mt of CO2e in

2017, with an average annual growth of 0.8% (significantly higher than the average of 0.4% from 1998 to

2012 (Figure 4.12). Overall, present GHG emissions in the state of California are about 10% higher than in

1997. This increase occurred while the economic growth between 2013 and 2018 continued to grow at

an average annual growth rate of 4% (Figure 4.10). The states of Indiana and Ohio are the only states that

were successful in reducing emissions for the whole period. Total GHG emissions in the state of Indiana

decreased by 80.8 Mt (31% less from 1997 value), from 260.6 Mt in 1997 to 179.8 Mt in 2017. On the

other hand, the Ohio state reduced its GHG emissions by 82.1 Mt (27% less from its’ 1997 value), from

305.7 Mt in 1997 to 223.6 Mt in 2017. Moreover, both states of Indiana and Ohio were successful in

accelerating emission reduction growth to 3.9% and 2.4%, respectively, between 2013 and 2017. All other

states in the sample failed to reduce absolute GHG emissions between 2013 and 2017 with emissions

growth being highest in the state of Louisiana at about 2.6%.

Figure 4.11: GHG emissions in the US states, Mt CO2e


0

200

400

600

800

1000

1200

1400

1600

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250

300

350

400

450

500

550

600

1997 2000 2003 2006 2009 2012 2015

CA FLIL INLA MIOH PATX (secondary axis) Rest (secodnary axis)

August 2020


Figure 4.12: GHG emissions growth rate in the US states, percentage


Canada

Figure 4.13 depicts that a remarkable increase in emissions efficiency that occurred in Quebec, Ontario,

and British Columbia between 1997 and 2017 (see Figure 4.13). In general, emissions efficiencies ranged

between 0.089 in Saskatchewan in 1997 to 4.94 in Quebec in 2017. The largest improvement in emissions

efficiency is seen in Ontario from 2.47 in 1997 to 4.80 in 2017, followed by Quebec, whose emission

efficiency has improved from 3.01 in 1997 to 4.94 in 2017. Also, emissions efficiency in Ontario increased

by 3.9 % per year between 2008 and 2017. Quebec, British Columbia, and the rest of Canadian provinces

within the control group (Manitoba, Newfoundland and Labrador, New Brunswick, Nova Scotia, and Prince

Edward Island) enjoyed over 2% growth in emissions efficiency per year during this period. All other

provinces except for Ontario suffered a decline in the growth of emissions efficiency between 2008 and

2017, compared to their values in the 1998 and 2007, possibly due to a relatively low level of economic

growth during the financial crisis (see Figure 4.14).

Figure 4.15 shows that provincial GDP in Ontario grew steadily from CAD 477.1 billion in 1997 to CAD

761.8 billion in 2017, with a notable drop in 2009 as the financial and economic crisis led to lower

economic activity. Between 2008 and 2017, the GDP in Ontario increased by 1.5% per year; half of its

annual growth between 1997 and 2008. GDP in British Columbia also increased steadily during this time

except for the recession in 2008. However, the average annual growth in 2008-2017 was 2.1%,

substantially lower than its 1998-2007 value of 3.5% (see Figure 4.16). Also, apart from a short

interruption in years of global recessions in 2009, and a significant drop in the oil prices in 2014, provincial

GDP in Alberta have been increasing steadily during the period. Consequently, the average annual GDP

growth in Alberta fell from 3.65% (1998 to 2007) to 2.06% (2008 to 2017). Other provinces, including

Quebec and Saskatchewan, also experienced a fall in GDP growth between 2007 and 2017, compared to

their previous level from 1998 to 2007.

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

IN OH MI PA Rest IL TX LA FL CA



August 2020


Figure 4.13: Emissions efficiency in selected Canadian provinces, billion CAD/Mt CO2e

Data Source: (ECCC 2019a), CERI. Figure by CERI.

Figure 4.14: Emissions efficiency growth in selected Canadian provinces, percentage


0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

4.00%

4.50%

Ontario British Columbia Quebec Rest Alberta Saskatchewan

Growth rate (1998-2007


0.75

1.25

1.75

2.25

2.75

3.25

3.75

4.25

4.75

5.25

1997 2000 2003 2006 2009 2012 2015

Alberta British Columbia

Ontario Quebec

Saskatchewan Rest of Canada

August 2020


Figure 4.15: GDP in selected Canadian provinces, billion CAD

Data Source: (Statistics Canada 2020), CERI. Figure by CERI.

Figure 4.16: GDP growth rate in selected Canadian provinces, percentage


Figure 4.17 shows historical emission levels up to 2017 (the last available year of historical emissions

numbers under the National Inventory Report at the time of writing) for the five selected provinces, as

well as rest of Canada. As can be seen, between 1998 and 2017, GHG emissions increased mostly in

Alberta and Saskatchewan. Alberta experienced the largest increase (27.6%) from 213.7 Mt CO2e in 1998

50

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350

450

550

650

750

1997 2000 2003 2006 2009 2012 2015

Alberta British ColumbiaOntario QuebecSaskatchewan Rest of Canada

0.00%

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2.00%

2.50%

3.00%

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4.00%

Alberta British Columbia Saskatchewan Ontario Rest of Canada Quebec



August 2020


to 272.7 Mt CO2e in 2017. The second-largest emitter, Ontario, enjoyed a significant drop in the GHG

emissions during this time, from 193.4 Mt CO2e in 1998 to 158.6 Mt CO2e in 2017, with a large part of

this due to a provincial coal-fired electricity phase-out. Emissions from other provinces including British

Columbia, Quebec, and the rest of Canada are still relatively low compared to Alberta.

Figure 4.18 shows that most Canadian provinces except Saskatchewan reduced their GHG emission

growth rate in the 2008–2017 period compared to 1999–2007. Although the emissions growth rate

decreased in the provinces of Alberta, British Columbia, and Saskatchewan, absolute emissions still

increased between 2008 and 2017. Ontario, Quebec, and the rest of the provinces enjoyed an overall

reduction in GHG emission as indicated by the negative growth rate of GHG emissions in Figure 4.18. The

province of Ontario reduced its emission growth rate by 2.17% per year followed by Quebec (1.02% per

year) between 2008 and 2017. Alberta and Saskatchewan increased GHG emissions by 1.06%, and 1.21%

per year, respectively.

Figure 4.17: GHG emissions in selected Canadian provinces, Mt CO2e/year


50

100

150

200

250

1998 2001 2004 2007 2010 2013 2016

Alberta British Columbia

Ontario Quebec

Saskatchewan Rest of Canada

August 2020


Figure 4.18: GHG emissions growth rate in selected Canadian provinces, percentage


Canadian Sectors

Table 4.1 shows sector-level emissions efficiency (in billion CAD/Kt CO2e) in 1997 and 2017 and their

average annual percentage change in the major sectors in Canadian provinces. In 2017, emissions

efficiency in Alberta ranges from 0.11 in the utility sector to 9.79 in the construction sector. Overall, there

is a substantial variation in the emission efficiency across provinces ranging from a very low value of 0.04

in the oil and gas sector in Ontario, to a massive value of 267.9 in the coal sector of Saskatchewan in 2017.

In general, the oil and gas sector has the lowest value of emissions efficiency in all provinces while the

manufacturing and construction sectors have higher values of emissions efficiency. For the oil and gas

sector, Alberta and Saskatchewan suffered negative growth in the emissions efficiency while the

manufacturing sector saw growth in the emissions efficiency in all provinces except for Manitoba. Overall,

New Brunswick, Newfoundland and Labrador, Nova Scotia, and Ontario enjoyed a growth in the emissions

efficiency in every sector between 1997 and 2017.

-2.00%

-1.50%

-1.00%

-0.50%

0.00%

0.50%

1.00%

1.50%

Alberta British Columbia Ontario Quebec Saskatchewan Rest of Canada



August 2020


Table 4.1: Sector-Level Emissions Efficiency in Selected Canadian Provinces, Billion CAD/kt CO2e

Province/ Year Agriculture Coal Utility Manufacturing Construction Oil and Gas

Transportation Waste

Alberta

1997 0.13 1.32 0.06 1.08 2.52 0.61 0.43 2.79

2017 0.30 2.53 0.11 1.46 9.79 0.56 0.47 4.17

Average annual growth

4.1% 3.1% 2.7% 1.4% 6.7% -0.5% 0.5% 1.9%

Saskatchewan

1997 0.32 206.80

0.08 1.19 4.15 0.44 0.43 0.92

2017 0.39 267.88

0.11 1.48 9.32 0.50 0.33 1.23


1.0% 1.2% 1.5% 1.1% 3.9% 0.7% -1.3% 1.4%

Manitoba

1997 0.19

5.75 5.03 4.21 0.13 0.47 0.76

2017 0.45

29.32 4.97 3.79 1.91 0.54 1.46


4.3%

8.1% -0.1% -0.5% 13.5% 0.7% 3.1%

Ontario

1997 0.32

0.51 1.82 2.44 0.01 0.41 2.20

2017 0.62

6.19 3.02 5.70 0.04 0.52 4.73


3.2%

12.6% 2.4% 4.1% 5.8% 1.1% 3.7%

New Brunswick

1997 0.90 0.12 1.57 2.25 0.30 0.81

2017 1.98 0.39 3.99 5.89 0.41 2.30


3.8% 6.0% 4.5% 4.7% 1.5% 5.1%

Nova Scotia

1997 0.83 0.45 0.09 2.82 2.90 0.21 0.23 0.56

2017 2.04 1.40 0.11 4.77 6.27 0.45 0.25 1.50


4.4% 5.6% 1.0% 2.5% 3.7% 3.7% 0.3% 4.8%


1997 2.80 0.37 0.42 7.16 0.03 0.22 0.26

2017 2.89 0.42 1.44 12.40 2.88 0.22 0.64


0.2% 0.6% 6.0% 2.7% 23.4% 0.1% 4.3%

Data Source: (ECCC 2019a), CERI. Table by CERI.

August 2020


Figure 4.19: Sector-level GDP in selected Canadian provinces, million CAD


In 2017, Alberta’s GDP from the oil and gas sector grew to more than 74.6 billion, an increase of 1.7% per

year since 1997, the base year used in the study. Since 1997, except for coal, sector-level GDP in Alberta

increased annually by 3.6% in construction, 1.8% in manufacturing, and 3.2% in the transportation sector,

the next biggest sectors in Alberta. In contrast, overall GDP from these sectors in Ontario grew from CAD

165.7 billion in 1997 to CAD 218 billion in 2017, despite a sudden CAD 186.3 billion reduction in 2009

because of the global financial crisis. The construction sector, one of the main drivers of Ontario’s

economic growth, grows rapidly during this time, from CAD 27.1 billion in 1997 to CAD 51 billion in 2017,

registering 3.0% compounded annual growth. The transportation sector in Ontario also saw a significant

growth of about 2.25% per year since 1997 while that the manufacturing sector, the largest sector in

Ontario, saw only 0.2 % growth during this period (see Table 4.2). The oil and gas sector in Newfoundland

and Labrador grew by more than CAD 8.2 billion, from CAD 45 million in 1997 to CAD 8.25 billion in 2017,

generating a rapid compounded annual growth rate of 28.2%. GDP from the coal sector suffered negative

growth in most of the provinces except Newfoundland and Labrador and Saskatchewan.

0.00

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250,000.00

19

97

20

05

20

10

20

17

19

97

20

05

20

10

20

17

19

97

20

05

20

10

20

17

19

97

20

05

20

10

20

17

19

97

20

05

20

10

20

17

19

97

20

05

20

10

20

17

19

97

20

05

20

10

20

17

Ontario Alberta Saskatchewan Manitoba Newfoundlandand Labrador

New Brunswick Nova Scotia

Agriculture Coal

Utility Manufacturing

Construction Oil and Gas


August 2020


Table 4.2: Sector-Level GDP in Selected Canadian Provinces, Million CAD

Province/ Year

Agriculture

Coal Utility Manufacturing

Construction

Oil and Gas


Alberta

1997 2786.7 1177.2 3015.5 17523.0 12967.3 52405.1 7888.9 3471.3

2017 6144.4 1064.1 4672.4 25325.8 27496.0 74644.2 15188.8 8063.9


3.8% -0.5% 2.1% 1.8% 3.6% 1.7% 3.2% 4.1%

Saskatchewan

1997 4526.9 8306.2 1250.4 3017.5 3529.6 9727.6 2393.3 613.0

2017 7092.2 8539.6 1770.4 5010.5 6013.8 11783.7 3661.0 884.8


2.2% 0.1% 1.7% 2.4% 2.6% 0.9% 2.0% 1.8%

Manitoba

1997 1357.1 953.7 1587.3 5173.1 2323.6 242.1 2383.1 533.1

2017 3399.8 953.2 2069.3 6045.5 4592.7 1025.7 3974.2 1029.8


4.5% 0.0% 1.3% 0.7% 3.3% 7.1% 2.5% 3.2%

Ontario

1997 4244.4 7181.6 13028.9 83377.9 27148.6 178.2 18259.1 12307.1

2017 7683.1 6800.5 13438.3 87291.9 51011.3 346.3 28679.2 22787.0


2.9% -0.3% 0.1% 0.2% 3.0% 3.2% 2.2% 3.0%

New Brunswick

1997 634.7 1466.1 1000.0 2633.7 1470.6 0.0 1256.2 490.1

2017 1041.5 295.2 1188.7 3035.7 2036.9 0.5 1542.8 1164.1


2.4% -7.3% 0.8% 0.7% 1.6% 1.0% 4.2%

Nova Scotia

1997 666.3 375.1 712.3 2174.8 1533.3 131.5 1063.7 387.5

2017 904.9 134.1 750.1 2511.9 2323.8 144.2 1267.4 739.3


1.5% -4.8% 0.2% 0.7% 2.0% 0.4% 0.8% 3.1%


1997 379.4 990.0 461.5 705.5 1386.0 45.0 673.4 173.4

2017 289.9 2496.9 639.0 839.8 3263.2 8250.3 892.8 359.1


-1.3% 4.5% 1.6% 0.8% 4.2% 28.2% 1.4% 3.5%

Data Source: (Statistics Canada 2020), CERI. Table by CERI.

Table 4.3 shows that the oil and gas sector accounts for 133.8 Mt CO2e emissions, followed by the utility

sector with 43.5 Mt CO2e emissions, and the transportation sector with 32.2 Mt CO2e emissions in 2017

in Alberta. In contrast, emissions from the transportation sector are the largest contributor to Ontario’s

GHG emissions, accounting 55.3 Mt CO2e emission in 2017, up 1.1% per year from its 1997 value.

August 2020


Emissions from the manufacturing sector, the second-largest emitting sector in Ontario, decreased by

23 Mt between 1997 and 2017. Ontario has been most successful in decarbonizing its utility sector where

emissions decreased from 25.5 Mt in 1997 to 2.2 Mt in 2017, with an average annual rate of -11.1% due

to the retirement of its coal power generation units. In general, emissions in Ontario show a declining

trend since 2005 while that of other large emitters including Alberta and Saskatchewan show an increasing

trend (see Figure 4.20). Emissions in Saskatchewan’s agriculture sector increased by 4 Mt overall between

1997 and 2017, up 1.2% per year while the transportation sector remains the biggest emitter in this

province with 23.5 Mt CO2e emissions in 2017. Overall, emissions from the utility sector show a declining

trend in most of the provinces except Newfoundland and Labrador and Saskatchewan, while that of

transportation shows an increasing trend in most of the provinces except New Brunswick (see

Figure 4.20).

Table 4.3: Sector-Level GHG emissions in Selected Canadian Provinces, Mt CO2e

Province/ Year

Agriculture Coal Utility Manufacturing

Construction Oil and Gas

Transportation

Waste

Alberta

1997 21.3 0.9 49.6 16.2 5.1 85.4 18.5 1.2

2017 20.2 0.4 43.5 17.3 2.8 133.8 32.2 1.9


-0.2% -3.5% -0.6% 0.3% -2.8% 2.2% 2.7% 2.1%

Saskatchewan

1997 14.1 0.0 15.0 2.5 0.9 22.3 5.6 0.7

2017 18.1 0.0 15.6 3.4 0.6 23.5 11.2 0.7


1.2% -1.1% 0.2% 1.4% -1.3% 0.2% 3.3% 0.3%

Manitoba

1997 7.3 0.0 0.3 1.0 0.6 1.8 5.1 0.7

2017 7.6 0.0 0.1 1.2 1.2 0.5 7.4 0.7


0.2%

-6.3% 0.8% 3.8% -5.6% 1.8% 0.1%

Ontario

1997 13.2 0.0 25.5 45.8 11.1 13.0 44.4 5.6

2017 12.3 0.0 2.2 28.9 8.9 7.7 55.3 4.8


-0.3%

-11.1% -2.2% -1.0% -2.4% 1.1% -0.7%

New Brunswick

1997 0.7 0.0 8.6 1.7 0.7 1.5 4.2 0.6

2017 0.5 0.0 3.0 0.8 0.3 3.5 3.7 0.5


-1.4%

-4.9% -3.7% -3.0% 4.0% -0.5% -0.9%

Nova Scotia

1997 0.8 0.8 7.8 0.8 0.5 0.6 4.7 0.7

2017 0.4 0.1 6.7 0.5 0.4 0.3 5.2 0.5


-2.8% -9.8% -0.7% -1.8% -1.7% -3.2% 0.5% -1.6%

August 2020



1997 0.1 0.0 1.2 1.7 0.2 1.3 3.1 0.7

2017 0.1 0.0 1.5 0.6 0.3 2.9 4.0 0.6


-1.4%

1.0% -4.9% 1.5% 3.9% 1.3% -0.7%

Data Source: (ECCC 2019a), CERI. Table by CERI.

August 2020


Figure 4.20: Sector-Level GHG Emissions in Selected Canadian Provinces, Mt CO2e


0

50

100

150

200

250

300

19

97

20

05

20

10

20

17

19

97

20

05

20

10

20

17

19

97

20

05

20

10

20

17

19

97

20

05

20

10

20

17

19

97

20

05

20

10

20

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97

20

05

20

10

20

17

19

97

20

05

20

10

20

17

Alberta Ontario Saskatchewan Manitoba Nova Scotia New Brunswick Newfoundlandand Labrador

Waste Utility

Transportation Oil and Gas

Manufacturing Coal

Agriculture Construction

August 2020


Chapter 5: Modelling Methodology and Assumptions

This chapter presents the modelling methodology and assumptions for analyzing how effective various

policy instruments have been in the studied jurisdictions.

Methodology

For the empirical analysis of the effect of different carbon pricing options around the globe, the study will

conduct four separate econometric analyses: investigating the environmental and economic impacts of

the EU-ETS on the EU member countries; the California cap-and-trade on the state of California and

Quebec; BC carbon tax on the province of BC; and and the SGER on the sectors in Alberta. We differentiate

these policies into three categories: the EU-ETS and California (linked with Quebec) cap-and-trade fall

under the emission trading system; the BC carbon tax system falls under the carbon pricing system; and

the SGER represents a hybrid system.

The Difference-in-Differences (DiD) Approach

The primary purpose of the analysis is to evaluate whether the implementation of each carbon

management policy, such as ETS, carbon tax, or a hybrid approach, curbs GHG emissions (measured by

CO2e emissions). In an ideal research setting, the status of participation in any of these policies will be

randomly assigned across jurisdictions, creating variations uncorrelated with baseline characteristics. In

the absence of a randomized controlled trial, the most intuitive way is to compare the GHG emissions of

different regions before and after the implementation of the carbon management policy.

In order to evaluate the impact of carbon management policies on GHG emissions in the regulated

jurisdictions, it is required not only to look at the differences before and after but also to determine

whether the changes are caused by a specific policy. In recent times, the (DiD) approach, originally put

forward by Ashenfelter and Card (1985), has been widely used to evaluate policy effectiveness. In doing

so, we build on a large body of literature [see, for example, Angrist and Krueger (1999), Besley and Case

(2000), and Lee and Kang (2006), among many others] that takes an econometric approach to control for

the systematic differences between the treatment group and the control group and analyze the changes

in the treatment group before and after the implementation of a certain policy. One key advantage of this

• The main objective is to capture the causal effect of carbon management policies on the

economic and environmental performance in the jurisdictions under consideration.

• The report takes the fixed effect difference-in-differences (DiD) approach to identify the

treatment effects of an environmental policy from two angles: the cross-sectional difference,

and the time-series difference.

• The report pays explicit attention to the parallel trend assumption necessary for the

implementation of the DiD approach.

August 2020


approach is its ability to identify the treatment effects of a policy from two angles: the cross-sectional

difference and the time-series difference.

The main objective is to capture the causal effect of carbon management policies on the economic and

environmental performance in the jurisdictions under consideration. The superior performance of this

approach in evaluating the environmental policy impact has been demonstrated by a large number of

studies. See, for example, Bertrand et al. (2004) for a brief survey, and Abadie (2005) and Lee and Kang

(2006) for some recent development.

For a simple DiD model, let 𝑌𝑇1and 𝑌𝐶

1 denote the average outcome variable at treated and control groups

after the implementation, respectively. Similarly, let 𝑌𝑇0and 𝑌𝐶

0 denote the average outcome variable at

treated and control groups before the implementation, respectively. The classical DiD technique estimates

the average impact as follows:

𝐷𝑖𝐷 = E (𝑌𝑇1 − 𝑌𝑇

0) − 𝐸 (𝑌𝐶1 − 𝑌𝐶

0) [1]

More specifically, the DiD method calculates the average difference in outcomes separately for treatment

and nontreatment groups over the same period.

Figure 5.1 shows a general graphical representation of the DiD model used in this report. The line chart

shows the outcome variable over time. At the implementation of a policy, the treatment group is expected

to experience a change in outcome over any secular trend experienced by the control group. The

difference in the outcome levels after the implementation of a policy represents the treatment effect or

the excess change in outcome variable resulting from the policy.

August 2020


Figure 5.1:Graphical Representation of DiD Model

To conduct the empirical analysis, we collected the data on total GHG emissions and GDP at the

country/jurisdiction level in both treatment and control groups, and used the data for a single good output

(GDP) and a single bad output (CO2e) in country j. We then calculated emissions efficiency (EE) index for

each country, j, at a time, t as

𝐸𝐸𝑗𝑡 =𝐺𝐷𝑃𝑗𝑡

𝐶𝑂2𝑒𝑗𝑡 ⁄ , 𝑗 = 𝑐𝑜𝑢𝑛𝑡𝑟𝑦, 𝑡 = 𝑡𝑖𝑚𝑒 [2]

A positive (negative) change in EE means that emission efficiency or real value of output per unit of

emissions has increased (decreased) between period t and (t+1). Replacing the outcome variable in

equation (1) with our emissions efficiency index, we can calculate the average impact of the policy as

follows:

𝐷𝑖𝐷 = E (𝐸𝐸𝑇1 − 𝐸𝐸𝑇

0) − 𝐸 (𝐸𝐸𝐶1 − 𝐸𝐸𝐶

0) [3]

More specifically, the DiD method calculates the average difference in outcomes separately for treatment

and nontreatment groups over the period.

Y

YT0

YC1

YC0

YT1

Treatment Effect

Treatment

Control

time t0 t1 Policy

Intervention

August 2020


Parallel Trend Assumption

One of the underlying assumptions in the DiD model is that the treatment and control groups were

behaving similarly prior to the policy intervention. A violation of this assumption would be seen if the

outcome variable (the emissions efficiency, as in our case) for the treated jurisdictions was following a

different trend line than emission efficiency in other jurisdictions prior to the policy implementation year.

Visual inspection by looking at the average annual emission efficiency in all treated groups in comparison

with that of non treated groups was conducted and presented in the next Chapter.

It is important to discover that emissions efficiency in treated groups was trending similarly to control

groups before the policies were implemented. This would suggest that there was no violation of the

assumption that the treatment and control groups were on a similar trajectory before the policy

intervention. However, the visual evidence from these graphics is not enough to satisfy the parallel trend

assumption so a series of placebo tests are conducted to ensure that treated jurisdictions were not on a

different trajectory than other states before the policy was enacted. These tests are used to check for pre-

existing trends in emissions efficiency. It is expected that, if the carbon management policy had an effect

on emissions efficiency as hypothesized, positive and statistically significant effects would be found in the

basic results with a full set of controls but not in the placebo tests.

Econometric Specification

Chakraborty and Chatterjee (2017) argued that in order to eliminate a series of competitive hypotheses,

DiD regression analysis needs to consider the fixed effect. The fixed effect of a region is added to the

model to avoid the pseudo-correlation between policy issuance and implementation effect caused by

regional differences.

The report follows Brännlund et al. (2014) for the econometric specification and uses the fixed-effect DiD

approach to investigate the causal effect of the carbon management policy on emissions efficiency as

follows:

𝑌𝑗𝑡 = 𝛼𝑗 + 𝛽1(𝑡𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑗 × 𝑝𝑜𝑠𝑡_𝑝𝑜𝑙𝑖𝑐𝑦𝑡) + 𝑋𝑗𝑡−1́ 𝛾 + τ𝑗𝑡−1𝜗́ + 𝜃𝑗𝑡́ + 𝜖𝑗𝑡 [4]

The variable, 𝑡𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑗, is a dummy for the treatment group, and 𝑝𝑜𝑠𝑡_𝑝𝑜𝑙𝑖𝑐𝑦𝑡 is a time dummy for the

policy implementation. More specifically,

𝑡𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑗 = {1, 𝑖𝑓 𝑡ℎ𝑒 𝑝𝑜𝑙𝑖𝑐𝑦 𝑎𝑝𝑝𝑙𝑖𝑒𝑑 𝑡𝑜 𝑡ℎ𝑒 𝑗𝑢𝑟𝑖𝑠𝑑𝑖𝑐𝑡𝑖𝑜𝑛

0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

𝑝𝑜𝑠𝑡_𝑝𝑜𝑙𝑖𝑐𝑦𝑡 = {1, 𝑖𝑓 𝑡ℎ𝑒 𝑦𝑒𝑎𝑟 ≥ 𝑝𝑜𝑙𝑖𝑐𝑦 𝑖𝑚𝑝𝑙𝑒𝑚𝑒𝑛𝑡𝑎𝑡𝑖𝑜𝑛 𝑦𝑒𝑎𝑟

0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

In the above equation, 𝑌𝑗𝑡 , is the emissions efficiency index of country j at time t that includes as defined

in equation (2).

Our key variable of interest is the DiD estimator, which is the coefficient of the interaction term, 𝛽1. It

shows the effectiveness of the policy by estimating the difference in emissions efficiency between the

August 2020


control group and the treatment group before and after the policy was implemented in the treatment

group. The coefficient of the interaction 𝛽1 indicates whether the implementation of a carbon

management policy had a significant impact on the emissions efficiency. More specifically, a significant

positive value of 𝛽1 will indicate that the implementation of the policy has increased the output per unit

of emissions in the treated group, therefore improving the environmental performance in the region.

In addition to the dependent variables (emissions efficiency) and the independent variable of interest,

𝑡𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑗 × 𝑝𝑜𝑠𝑡_𝑝𝑜𝑙𝑖𝑐𝑦𝑡 , that captures the implementation, our model also controls for country (or

state/province/state) level economic factors believed to be associated with emissions efficiency level

and/or higher CO2e emissions. These control variables are included to increase the precision of the

estimates.

The vector 𝑋𝑗𝑡−1 is a set of location-specific characteristics to control for any observable differences that

might confound the analysis. Our premise is that emissions efficiency is governed by the change in real

GDP, CO2e emissions, or both. Consequently, we follow Brännlund et al. (2014) and regress the efficiency

measure on a set of explanatory variables including: energy price; fossil fuel dependency measured by the

percentage of fossil fuel in total fuel consumption by the economy; industry structure measured by the

percentage of industry value added in total output; and capital intensity measured by the gross capital

stock over total population. In addition, we control for additional carbon-reducing policies such as carbon

tax that might have existed during the study period.

The geographical fixed effect, 𝛼𝑗 , is included in the model to control for time-invariant unobservable

macroeconomic characteristics that affect emissions efficiency. We added a general time trend that

captures technological progress and increased environmental awareness/pressure on countries during

the time period studied. Prior literature has shown that including location-specific linear (or quadratic)

time trends can matter to estimates produced in difference-in-difference models [see for instance,

Brännlund et al. (2014),]. Our models also incorporate linear state trends as it is possible that emission

efficiency levels could follow different time-based trends in different geographic locations. We also add a

dummy for 2008-2009 global financial crisis as Bel and Joseph (2015) suggest that economic recession

played a major role in emission reductions.

Finally, as in Brännlund et al. (2014), all explanatory variables in equation (4) are in one period lag as we

aim to avoid possible endogeneity problems that may be present, and to account for dynamic effects.

Under this setup, the coefficient, 𝛽1 fully captures the emissions efficiency difference between the two

groups after the implementation of a carbon management policy or the effectiveness of the

environmental policy.

Data

Estimation Sample

Motivated by this DiD approach, this study considers each policy implementation as the independent

“natural experiment”, with the regulated jurisdictions considered as the treatment group. The control

group consists of similar jurisdictions that are not affected by the policy. Under this setup, the

implementation of a carbon management policy would inevitably change the environmental conditions

August 2020


in the treated group, thereby leading to fluctuations of these groups around time, while jurisdictions in

the control group are not influenced. The effectiveness of the policy is then represented by the difference

in outcomes between the two groups before and after the policy implementation.

EU-ETS System

We consider country-level GHG emissions data from 1990 to 2017 from the United Nations Framework

Convention on Climate Change (UNFCCC). Since the EU-ETS was first implemented in the EU in 2005, we

define all members of the EU in 2005, i.e. the EU-25, as the treatment group. We excluded current EU

member countries such as Romania, Bulgaria, Croatia, Iceland, and Norway, as they joined the EU after

2005 and this can change the composition of both groups, therefore violating one of the key assumptions

of the DiD approach. Since European countries have many commonalities in terms of economy,

environmental policies and more, we chose non-EU European countries including Belarus, Russian

Federation, Switzerland, Turkey, Ukraine and some other non-European countries including Australia,

Japan, Kazakhstan, and New Zealand who has comparable economic and environmental policies as the

control group.

The key advantages of this control group are: (i) the emissions efficiency in both groups is assumed to

experience similar country-wide economic shocks in the absence of the emission trading system, EU-ETS;

and (ii) the EU-ETS should not influence the environmental performance outside of the EU. Overall, in our

final estimation, we included a total of 34 countries, including 25 EU member countries in 2005, and nine

countries in the control group.

California Cap-and-Trade System

For our analysis on the California cap-and-trade system, we considered a state-level panel covering the

years from 1997 to 2017 for 18 states based on the size of the economy and level of CO2e emissions. Since

the California cap-and-trade system was first implemented in California and Quebec in 2013, we consider

California, and Quebec as the treatment group. All other relevant US states including Florida, Georgia,

Illinois, Massachusetts, Maryland, New England, New Jersey, New York, North Carolina, Ohio,

Pennsylvania, Texas, Louisiana, Indiana, Michigan, Missouri, Virginia, and Washington are considered as

the control group as they were not regulated by this policy. However, we exclude New England as it lacks

the detailed CO2e emissions data. The control group is selected based on their similarity with the

treatment group in terms of environmental performance, and economic performance in the absence of

the cap-and-trade system. Moreover, the cap-and-trade system is expected to have no influence on the

environmental performance outside California, and Quebec. In our final estimation, we include a total of

17 states as our control group covering the years from 1997 to 2017.

BC Carbon Tax

The BC carbon tax was first implemented in the province of British Columbia in 2008, therefore we

consider a province-level panel covering the years from 1995 to 2017 for 10 Canadian provinces. The

province of British Columbia is directly affected by the carbon tax and considered as the treatment group;

all other Canadian provinces (except Quebec as they had CO2e emission reduction policies in place) those

were not regulated by this policy are considered as the control group. However, we exclude Canadian

August 2020


territories (Northwest Territories, Nunavut, and Yukon) as they lack key macroeconomic data necessary

for the study. Altogether our control group includes Alberta, Manitoba, New Brunswick, Newfoundland

and Labrador, Nova Scotia, Ontario, Prince Edward Island, Quebec, and Saskatchewan.

Alberta SGER System

For our analysis on the policy effectiveness of the Alberta SGER system, we consider sector level provincial

data covering the years 1997 to 2017 across Canadian provinces. We consider major emitting sectors

including agriculture, utilities, mining, quarrying, oil and gas extraction, manufacturing, construction,

transportation, and waste management, for which the relevant data were available. Since the SGER

system was implemented in Alberta in 2007, we consider the aforementioned sectors in Alberta as the

treatment group, and the same sectors in all other Canadian provinces that were not regulated by this

policy as the control group. However, we exclude British Columbia, and Quebec from our sample as they

already had CO2e emission reduction policies in place. We also exclude Prince Edward islands, Northwest

territories, Nunavut, and Yukon because of lack of available data.

Altogether, our control group includes agriculture, utilities, mining, quarrying, oil and gas extraction,

manufacturing, construction, transportation, and waste management sectors in other Canadian provinces

including Manitoba, New Brunswick, Newfoundland and Labrador, Nova Scotia, Ontario, and

Saskatchewan. These sectors in Alberta are regulated by the SGER while the same sectors in other

provinces were not regulated and therfore should not be influenced by the SGER system, making them an

ideal control group. Overall, in our final estimation we include a total of seven provinces, and seven

sectors covering the years 1997 to 2017.

Variable Definition

Our analysis also selects energy price (as measured by oil price) and fossil fuel dependency (the ratio of

fossil fuel to total energy consumption), industrialization (the ratio of industrial production to total

output), capital intensity (gross fixed capital divided by total labor), as a set of control variables to avoid

estimation errors caused by missing variables. Throughout the analyses, all economic data are adjusted

for inflation whenever needed. Natural logs are taken for all values in order to address the skewedness of

the distributions of these variables. Finally, all independent economic variables are lagged one period to

avoid possible endogeneity.

EU-ETS System

We measure environmental performance or emissions efficiency by a ratio of real GDP (in billion 2010

USD) per kt CO2e emissions (total GHG emissions without land use, land-use change, and forestry

(LULUCF)). This data is collected from the UNFCCC GHG Data Interface. The data on carbon tax (measured

in USD/tonne of CO2e) are collected from the carbon pricing dashboard. The Brent crude oil price (in USD

per barrel) from BP Statistical Review is selected as our measure of energy price. To achieve full

comparability of GDP among the countries, we follow Liddle (2010) and use the GDP at purchasing power

parity. This data is collected from the IEA. The industry structure is the proportion of value-added by the

industry in a country’s total GDP, and the dependence on fossil fuel is calculated as the proportion of fossil

fuel to total energy consumption. Finally, capital intensity is measured as the ratio of gross fixed capital

August 2020


formation is measured in constant 2010 US dollars to the total population. To control for the impact of

the global recession in 2008 and 2009, we use a recession dummy (equals to 1 in 2008, and 2009; 0,

otherwise). These data are collected from the World Bank’s database.

Detailed information for these variables can be found in Table 5.1.

Table 5.1: Variable Definition for the EU-ETS Case Study

Variable name

Variable long name Variable description Source

EP Emissions efficiency or GDP/CO2e

Annual greenhouse gas Indicators, in billions USD / kt CO₂ equivalent

UNFCCC GHG Data Interface

Carbon Tax USD/Tonnes Carbon tax in USD per Tonnes Carbon Pricing Dashboard

Real GDP Real GDP Real Gross domestic product at purchasing power parities

The U.S. Energy Information Administration (EIA)

Fossil Fossil fuel energy consumption (% of total)

Fossil fuel comprises coal, oil, petroleum, and natural gas products

World Bank

Industry Industry, value added (% of GDP)

Industry corresponds to ISIC divisions 10-45 and includes manufacturing (ISIC divisions 15-37)

World Bank national accounts data, and OECD National Accounts data files

Capital Intensity

Gross fixed capital formation (constant 2010 US$) per capita

(Gross fixed capital formation includes land improvements, plant, machinery, and equipment purchases; and the construction of roads, railways, and the like) /total population

World Bank national accounts data, and OECD National Accounts data files

Energy Price

Crude oil import prices

This indicator is measured in USD per barrel of oil. The real price was calculated using the deflator for GDP at market prices and rebased with reference year 1970 = 100

OECD National Accounts data files

California Cap-And-Trade System

We measure environmental performance or emissions efficiency by a ratio of real GDP (in billion 2012

USD) per kt CO2e emissions (total GHG emissions without LULUCF). We calculate the index based on the

GDP data from Bureau of Economic Analysis, and GHG emissions data from world resource institute. Fossil

fuel dependency is calculated from the fossil fuel consumption data by the U.S. Energy Information

Administration’s State Energy Data System (SEDS). Total energy average price is also collected from the

same source. The industry structure, defined as the proportion of private industrial production to total

GDP, is calculated from industrial production data from the Bureau of Economic Analysis. Finally,

merchandise exports data are collected from the Foreign Trade Division, U.S. Census Bureau. Details of all

data sources are provided in Table 5.2.

August 2020


Table 5.2: Variable Definition for the California Cap-and-Trade System Case Study

Variable name


EP Emission efficiency or GDP/CO2e

Billions USD / kt CO₂ equivalent

Authors’ calculation from GDP and emissions data

GHG emissions

GHG emissions Total GHG Emissions Excluding LULUCF

1990-2014: WRI, CAIT 2.0. 2014. Climate Analysis Indicators Tool. Available at: http://cait2.wri.org. 2015-2017: Authors’ calculation

Real GDP Real GDP Real GDP by state: All industry total Bureau of Economic Analysis


Fossil fuel comprises coal, oil, petroleum, and natural gas products.

U.S. Energy Information Administration, State Energy Data System

Industry Private industry output (% of GDP)

Real GDP by state: Private industries

Bureau of Economic Analysis

Energy price

Total energy average price

Dollars per Million Btu

U.S. Energy Information Administration, State Energy Data System

Exports Merchandise Exports

NAICS Total All Merchandise Exports to World in USD

Foreign Trade Division, U.S. Census Bureau

BC Carbon Tax

Environmental performance or emissions efficiency is measured by a ratio of real GDP (in billion 2012

USD) per kt CO2e emissions (total GHG emissions without LULUCF). This data is collected from the

Environment and Climate Change Canada (ECCC) Database. Monthly average retail prices for gasoline and

fuel oil from Statistics Canada (Table 18-10-0001-01) is selected as our measure of energy price. Provincial

GDP and gross fixed capital formation data are collected from the statistics Canada website. The industry

structure is defined as the proportion of industrial production in a province’s total GDP. The data on

industrial production is also collected from the Statistics Canada website. The dependence of fossil fuel is

calculated as the proportion of fossil fuel in total energy consumption using the data from Statistics

Canada (Table 25-10-0029-01). Table 5.3 provides detailed information on data and variables use.

http://cait2.wri.org/

August 2020


Table 5.3: Variable Definition for The BC Carbon Tax Case Study

Variable name



Billions USD / kt CO₂ equivalent Authors’ calculation from GDP and emissions data

GHG emissions

GHG emissions Total GHG Emissions Excluding LULUCF Environment and Climate Change Canada


Fossil fuel comprises coal, oil, petroleum, and natural gas products

Statistics Canada

Real GDP Real GDP in chained (2012) dollars

Gross domestic product (GDP) at basic prices, by industry, provinces and territories

Statistics Canada

Industry Industrial Production(% of GDP)

Industrial Production Chained (2012) dollars millions divided by GDP

Statistics Canada

Capital Intensity

Gross fixed capital formation (constant 2012 US$) per capita

Gross fixed capital formation Chained (2012) dollars millions/ total population

Statistics Canada

Trade balance

Export less import In millions dollars Statistics Canada

Energy price

Retail price of fuel oil

Monthly average retail prices for gasoline and fuel oil

Statistics Canada

Alberta SGER System

We measure environmental performance or emissions efficiency by a ratio of real GDP (in million 2012

USD) per kt CO2e emissions (total GHG emissions). We calculate the index based on the GDP data from

Statistics Canada and GHG emissions data from ECCC. Capital intensity is measured as the fixed non-

residential capital and is calculated using data from Statistics Canada. Energy average price is proxied by

the oil price for which we collect monthly average retail prices for gasoline and fuel oil from Statistics

Canada. Table 5.4 provides detailed information on data and variables use.

August 2020


Table 5.4: Variable Definition for the Alberta SGER Case Study

Variable name



Million dollars / kt CO₂ equivalent Authors’ calculation from GDP and emissions data

GHG emissions

GHG emissions Total GHG Emissions by sector by province Environment and Climate Change Canada

Real GDP Real GDP in chained (2012) dollars

Gross domestic product (GDP) at basic prices, by industry, provinces and territories

Statistics Canada

Capital intensity

Fixed non-residential capital (Chained (2012) dollars) per capita

Flows and stocks of fixed non-residential capital, by industry and type of asset, Canada, provinces and territories/ total population

Statistics Canada

Energy price

Retail price of fuel oil

Monthly average retail prices for gasoline and fuel oil

Statistics Canada

August 2020


Chapter 6: Empirical Results

Common Trend Assumption

As discussed in the previous Chapter, one of the underlying assumptions in the DiD model is that the

treatment and control groups must behave similarly before the policy intervention. A violation of this

assumption would be seen if the emissions efficiency index for the EU countries was on a different trend

line than control groups before 2005. Figure 6.1 shows that the average emissions efficiency (in natural

log) in the EU was trending similarly to that of the control group before the implementation of EU-ETS in

2005. The visual evidence from the graph shows that the average emission efficiency in the EU member

countries was trending similarly to the non-EU countries before the implementation of the emission

trading system in 2005. It can also be observed that the trend lines diverge around the time of the

implementation, around 2005, suggesting that there is no violation of the assumption that the treatment

and control groups were on a similar trajectory before the policy intervention.

• The ETS policy was found to be more effective at reducing GHG emissions than the Carbon

Tax policy or a Hybrid policy. However, EU-ETS had a statistically significant negative effect on

real GDP.

• California-Quebec cap-and-trade system is effective at reducing emissions and thus increasing

emissions efficiency without negatively impacting the economic growth.

• BC carbon tax and AB SGER boosted economic activity but had no effect on emissions.

August 2020


Figure 6.1: Common Trend Assessment for the EU-ETS Case Study

Figure 6.2 shows that the average emissions efficiency of the treatment group consisting of California and

Quebec diverged after the time of the implementation. It is not clear from the Figure whether it was

trending similarly to that of the control group before the implementation of California cap-and-trade

system in 2013. Nevertheless, the visual evidence does not suggest that there is a violation of the

assumption that the treatment and control groups were on a similar trajectory before the policy

intervention, suggesting the use of the difference-indifference model.

1

1.5

2

2.5

3

3.5

4

4.5

1990 1993 1996 1999 2002 2005 2008 2011 2014 2017

Emis

sio

ns

effi

cien

cy (

in n

atu

ral l

og) Policy intervention

Control

Treatment

August 2020


Figure 6.2: Common Trend Assessment for the California Cap-and-Trade Case Study

Figure 6.3 shows that the average emissions efficiency in British Columbia was trending almost similarly

to that of the control group before the implementation of the BC carbon tax in 2008. However, it is not

clear whether the trend lines diverge around the time of the implementation. Nevertheless, the visual

evidence does not suggest that the treatment and control groups were not on a similar trajectory before

the policy intervention, suggesting the use of the difference-indifference model.

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

1997 2000 2003 2006 2009 2012 2015

Emis

sio

ns

effi

cien

cy (

in n

atu

ral l

og)

Policy intervention

Control

Treatment

August 2020


Figure 6.3: Common Trend Assessment for the BC Carbon Tax Case Study

Figure 6.4 shows that the average emissions efficiency in Alberta might have diverged after the time of

the implementation. It is not clear from the Figure whether it was trending almost similarly to that of the

control group before the implementation of Alberta SGER system in 2007.

Figure 6.4: Common Trend Assessment for the Alberta SGER Case Study

0

0.0005

0.001

0.0015

0.002

0.0025

0.003

0.0035

0.004

0.0045

1995 1998 2001 2004 2007 2010 2013 2016

Emis

sio

ns

effi

cien

cy (

in n

atu

ral l

og)

Policy intervention

Treatment

Control

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

1998 2001 2004 2007 2010 2013 2016

Emis

sio

ns

effi

cien

cy (

in n

atu

ral l

og)

Policy interventionTreatmentControl

August 2020


Fixed Effect Regressions: Effect on Emissions Efficiency

This section presents the results for the models described in the previous Chapter. In particular, this

section presents the modelling results for the emission efficiency parameter. All models are estimated

with robust standard errors using panel data with fixed effects (FE) and random effects (RE). We report

the results from the fixed effect only as the Hausman test supports the estimation of fixed effects. The

results were obtained using statistical platform R version 3.6.2.

Empirical Evidence for Emission Trading System

EU-ETS System

Table 6.1 shows the results of our main DiD estimation for the EU-ETS. Our base model (model 1) reports

results when the trend and recession dummy are included as the control variables, while model 2 to model

5 report estimation results obtained with additional control variables. Our chosen model, with a full set

of controls and the geographical fixed effects, is shown in model 5. More specifically, model 2 is equivalent

to the basic model plus oil price and carbon tax. Model 3 adds fossil fuel dependency, while model 4 adds

industry structure, and model 5 adds capital intensity to the prior model.

Table 6.1: Effect of the EU-ETS on Emissions Efficiency

Dependent variable (1) (2) (3) (4) (5)

Emissions Efficiency





Post*EU 0.0358*** (0.0019)

0.0319** (0.0192)

0.0292** (0.0351)

0.0350*** (0.0093)

0.0301** (0.0175)

Energy price 0.0012 (0.9176)

0.0035 (0.7797)

-0.0003 (0.9771)

-0.0137 (0.2062)

Policies 0.0009*** (0.0000)

0.0008*** (0.0000)

0.0007*** (0.0000)

0.0008*** (0.0000)

Fossil -0.0585 (0.3212)

-0.0382 (0.5094)

-0.1268* (0.0685)

Industry 0.1873*** (0.0000)

0.0073 (0.8962)

Capital intensity 0.1658*** (0.000)

Trend 0.0216*** (0.0000)

0.0273*** (0.0000)

0.0270*** (0.0000)

0.0294*** (0.0000)

0.0248*** (0.0000)

Recession Dummy

Yes Yes Yes Yes Yes

Country fixed effects

Yes Yes Yes Yes Yes

No. of observations

896 896 896 896 896

Adjusted R2

0.804 0.805 0.805 0.811 0.841

Note: Heteroskedasticity-consistent p-values are in parentheses. The time period is 1990-2017. ∗Significant at 10% level; ∗∗significant at 5% level; ∗∗∗significant at 1% level.

The estimates from Table 6.1 shows that the EU-ETS program resulted in higher emissions efficiency than

would be expected based on the secular trend. As described in the methodology section, our coefficient

August 2020


of interest is the DiD estimate — the coefficient on Post*EU shows the effect of the EU-ETS on emissions

efficiency in the EU-25. The significant positive coefficient of the interaction term (Post*EU) of the

treatment/control group dummy, which represents the policy effectiveness of the EU-ETS, indicates that

compared to countries without the ETS, the growth of emissions efficiency speeds up in the EU-ETS where

the policy was implemented.

As can be seen from all the specifications in Table 6.1, the coefficient of Post*EU, is statistically significant

at a conventional level with the values ranging from 0.030 (with p- value of 0.017 in model 5) to 0.035

(with p- value of 0.001 in model 1). The result from our preferred model, model 5, suggests that the EU-

25 member countries improved their emission efficiency by an additional 3.0% (over and above the

improvement made by non-EU countries). The 2004 mean value for emissions efficiency in the EU was

USD 2.79 billion per kt CO2e. Hence, a 3.0% (100*e0.030 -1) increase in this mean level of emissions

efficiency is USD 0.08 billion per kt CO2e. All specifications in Table 6.1 show a substantial and significant

increases in emissions efficiency levels following the enactment of EU-ETS in 2005.

It is clear from Table 6.1 that the coefficients on each control variable corresponds to the expected

economic explanation. For example, our results in model 5 suggest that increased capital intensity helps

economies to establish less emitting technology, therefore increasing emissions efficiency (coefficient of

0.166 with a p-value of 0.000 in model 5). Our results also suggest that fossil fuel dependency has negative

impacts on emissions efficiency (coefficient of -0.127 with a p-value of 0.068 in model 5). Higher

dependency in fossil fuel makes it difficult for the industries to switch to a different input mix, therefore

resulting in lower emissions efficiency. Similarly, a change in oil price has no significant impact on the

emissions efficiency in the EU while the presence of other emissions reduction policy, for example, the

carbon tax, increases the emissions efficiency in the EU-25 (coefficient of 0.001 with a p-value of 0.000 in

model 5). The time trend is positive and significant.


This section presents the effect of California cap-and-trade system on emissions efficiency using a fixed

effect model. The base model (model 1) is the basic DiD model estimation with control variables including

the time trend, recession, and policy dummy. Model 2 is equivalent to the basic model with an addition

of the energy price. Model 3 adds state level exports as a percentage of GDP to model 2. Fossil fuel

dependency measured by share of fossil fuel in total energy consumption is added in model 4. Finally,

model 5 represents our main regression result which includes the industry structure measured by the

share of private industries in total output to the prior model.

August 2020


Table 6.2: Effect of California Cap-and-Trade System on Emissions Efficiency

Dependent variable (1) (2) (3) (4) (5)






Post*CA 0.0427** (0.0134)

0.0430** (0.0268)

0.0567*** (0.0093)

0.0347* (0.0537)

0.0364* (0.0548)

Policies 0.0886*** (0.0000)

0.0852*** (0.0000)

0.0789*** (0.0000)

0.0621*** (0.0003)

0.0628*** (0.0004)

Energy price 0.0707** (0.0371)

0.0742** (0.0282)

0.1695*** (0.0000)

0.1707*** (0.0000)

Fossil -0.4019* (0.0823)

-0.9100*** (0.0002)

-0.8984*** (0.0002)

Exports -0.1775*** (0.0000)

-0.1733*** (0.0000)

Industry -0.0622 (0.5935)

Trend 0.0185*** (0.0000)

0.0152*** (0.0000)

0.0138*** (0.0000)

0.0151*** (0.0000)

0.0149*** (0.0000)

Recession Dummy

Yes Yes Yes Yes Yes

State fixed effects

Yes Yes Yes Yes Yes

No. of observations

380 380 380 344 344

Adjusted R2 0.695 0.701 0.704 0.713 0.712


The outcome variable is the state level CO2e emissions efficiency measured in real GDP (in billions USD)

per kt CO2e emissions. The estimated treatment effect, Post*CA, shows the average impact of the

California cap-and-trade system on the state/province level emissions efficiency in California and Quebec.

A positive treatment effect would mean that the regulated jurisdiction, i.e., California and Quebec, were

more emission efficient than the unregulated jurisdictions. In contrast, a negative treatment effect would

imply that the ETS makes the regulated state less emission efficient than the unregulated states.

It is evident from Table 6.2 that the California cap-and-trade has a significant and positive impact on

emissions efficiency, with the value of the coefficient (Post*CA) ranging from 0.035 with a p-value of 0.054

in model 4 to 0.057 with a p-value of 0.009 in model 3. Moreover, the coefficient of Post*CA is statistically

significant at any conventional level for all specifications. The result from model 5 suggests that members

of California cap-and-trade system, i.e. California and Quebec, improved their emission efficiency by an

additional 0.036% over and above the improvement made by other US states. The 2012 mean value for

emissions efficiency in California and Quebec was USD 4.55 million per Kt CO2e. A 3.6% (100*e0.036 -1)

increase in this mean level of emissions efficiency equates to USD 0.16 million per Kt CO2e.

Regression results in all models are theoretically consistent. For example, an increase in the average

energy price increases the emission efficiency, meaning that a 1% increase in the energy price increases

August 2020


the emission efficiency by 0.17% (coefficient estimate of 0.171 with a p-value of 0.000). This implies that

in the case of California’s system, an increase in energy price may result in input substitution and induces

less consumption of energy use, therefore reducing emissions. Similarly, a higher dependency in fossil

fuels makes input substitution difficult, therefore reducing the emissions efficiency as indicated by the

significant and negative coefficient of -0.898 with a p-value of 0.000. Additional policy intervention to

reduce CO2e emissions increases emissions efficiency as indicated by the coefficient of 0.062 with a p-

value of 0.001.

Empirical Evidence for Carbon Tax System

BC Carbon Tax

Table 6.3 shows the results of the BC carbon tax’s impact on emission efficiency where the base model is

the basic DiD model estimation with control variables including carbon reducing policies across Canadian

provinces, oil price, time trend, and a recession dummy. Model 1 is equivalent to the basic model plus

capital intensity as measured by the gross fixed capital formation per capita. Model 2 adds provincial trade

balances to model 1, while fossil fuel dependency measured by share of fossil fuel in total energy

consumption is added in model 3. Finally, model 4 represents our main regression result which includes

the industry structure measured by the share of industrial production in total output to the prior model.

The outcome variable is the province specific emissions efficiency measure in real GDP (in billions CAD)

per kt CO2e emissions. The estimated treatment effects show the average impact of the BC carbon tax on

the province specific emissions efficiency. Our coefficient of interest is the DiD estimate—the coefficient

on Post2008*BC. In the Table 6.3, the coefficient of Post2008*BC is statistically insignificant at any

conventional level for all specifications implying that the BC carbon tax has no impact on the emissions

efficiency. This basic result is found throughout the analyses of the program from base model (with

Post2008*BC = -0.028 with a p-value of 0.147 in the base model, and -0.009 with a p-value of 0.699 in the

model 4).

The other explanatory variables, such as oil price and capital intensity, are found to be statistically

significant in all the econometric analyses. The statistically significant and positive coefficient on the oil

price indicates that the oil price increases the emission efficiency, meaning that s 1% increase in the oil

price increases the emission efficiency by 0.01% (coefficient estimate of 0.0118 with a p-value of 0.000).

This implies that higher oil price improves the emissions efficiency in British Columbia compared with the

other provinces, even after controlling for the impact of other relevant variables. Similarly, a higher

dependency on capital reduces the emissions efficiency as indicted by the significant and negative

coefficient of -0.1048 with a p-value of 0.0166.

August 2020


Table 6.3 Effect of BC Carbon Tax on Emissions Efficiency

Dependent variable Base (1) (2) (3) (4)






Post*BC -0.0284 (0.1471)

-0.0180 (0.4401)

-0.0120 (0.7113)

-0.0129 (0.5961)

-0.0091 (0.6995)

Energy price 0.0892** (0.0135)

0.0680** (0.0172)

0.0720* (0.0509)

0.0721** (0.0134)

0.0118*** (0.0002)

Capital intensity -0.1359*** (0.0000)

-0.1455*** (0.0000)

-0.1504*** (0.0000)

-0.1454*** (0.0003)

Trade balance -0.0001 (0.2107)

-0.0001 (0.1956)

-0.0001* (0.0592)

Fossil 0.0696 (0.5341)

0.0181 (0.5815)

Industry 0.0108*** (0.0024)

Trend 0.0301*** (0.0000)

0.0324*** (0.0000)

0.0325*** (0.0000)

0.0329*** (0.0000)

0.0341*** (0.0000)

Recession Dummy

Yes Yes Yes Yes Yes

Province fixed effects

Yes Yes Yes Yes Yes

Policy Dummy

Yes Yes Yes Yes Yes

No. of observations

170 170 170 170 170

Adjusted R2

0.776 0.833 0.833 0.833 0.846

Note: Heteroskedasticity-consistent p-values are in parentheses. The time period is 2001-2017. ∗∗significant at 5% level; ∗∗∗significant at 1% level.

Empirical Evidence for Hybrid System

Alberta SGER System

This section presents the effect of Alberta SGER system on the provincial sector-level emissions efficiency.

In Table 6.4, the base model is the basic DiD model estimation with control variables including the time

trend and a recession dummy. Model 1 is equivalent to the basic model with an addition of average energy

price, while model 2 adds provincial sector level capital intensity measured by fixed capital per worker.

The DiD estimator allows a comparison of outcomes and emissions efficiency, measured in real GDP (in

billions CAD) per kt CO2e emissions. In this case, measured before and after a policy change for a group

affected by the change (the sectors included in the SGER, the so-called treatment group), compared with

those of a group not affected by the change (the control group). A positive treatment effect would mean

that the sectors in the regulated province or jurisdiction, i.e., Alberta, were more emission-efficient than

the unregulated sectors, i.e., same sectors in other provinces.

August 2020


Table 6.4 Effect of Alberta SGER System on Emissions Efficiency

Dependent variable (1) (2) (3)




Post 2007*AB -0.0480 (0.1420)

-0.0501 (0.1208)

-0.0554 (0.1000)

Energy price 0.0502 (0.5833)

0.0619 (0.5007)

Capital Intensity 0.0573* (0.0757)

Trend 0.0255*** (0.0000)

0.0235*** (0.0000)

0.0220*** (0.0000)

Recession Dummy

Yes Yes Yes

State fixed effects

Yes Yes Yes

No. of observations

980 980 380

Adjusted R2

0.178 0.177 0.178


In Table 6.4, our coefficient of interest - the coefficient on Post2007*AB is statistically insignificant at any

conventional level for all specifications, implying that the Alberta SGER had no impact on the emissions

efficiency. This result remains consistent as we add more control variables in model 2 and model 3 (with

Post2007*AB = -0.048 with a p-value of 0.142 in model 1, and -0.055 with a p-value of 0.100 in model 3).

Regarding the control variables, the results show that energy price is insignificant in terms of affecting the

emissions efficiency. However, the emissions efficiency is affected positively by capital intensity (Capital

intensity = 0.0573 with a p-value of 0.0757 in model 3).

Fixed Effect Regressions: Effect on Real GDP

This section presents the empirical evidence of the effect of the policy on real GDP.

EU-ETS System

Next, we consider the effect of EU-ETS on the economic performance measured by real GDP. These results

are shown in Table 6.5. We find a negative and statistically significant effect of EU-ETS on real GDP at the

conventional level for our model with all control variables (model 4). The results from model 4 suggest

that the DiD coefficient equals -0.04 (Post2005*EU = -0.041 with p-value 0.000), which means that the

real GDP of EU member countries was reduced by 0.042 points as compared to the non-EU countries. The

2004 mean value of real GDP in the EU was USD 627,114 billion, consequently, a 4.1% (100*e0.041 -1)

decrease in this mean level of real GDP translates to USD 25,084 billion reduction. It is important to note

that these estimations do not suggest that the average GDP in the EU-25 reduced by 2.2% (base model)

to 4.1% (model 4) since 2005. The EU-25 economy, in fact, has grown by 1.3% since 2005. Instead these

August 2020


reductions are estimates of reduction in GDP against the control group who was not affected by the EU-

ETS.

For the other control variables, the signs are as expected and remain consistent over the different model

specifications. For example, an increase in the use of fossil fuel increases the real GDP (Fossil = 0.214 with

p-value 0.000 in model 4), meaning that a 1% increase in the use of energy increases the real GDP by

0.21%, supporting the vast literature suggesting the positive relationship between energy use and

economic output. Similarly, a higher level of capital intensity increases the real GDP (Capital Intensity =

0.331 with p-value 0.000 in model 4), suggesting the importance of capital formation on economic growth.

Table 6.5: Effect of the EU-ETS on Real GDP


Real GDP Real GDP Real GDP Real GDP Real GDP

Post2005*EU -0.0225 (0.1623)

-0.0592*** (0.0058)

-0.0431** (0.0115)

-0.0315** (0.0394)

-0.0413*** (0.0005)

Energy price 0.0080*** (0.0000)

0.0664*** (0.0000)

0.0586*** (0.0000)

0.0318*** (0.0000)

Policies -0.0007*** (0.0004)

-0.0003 (0.1021)

-0.0006*** (0.0028)

-0.0004** (0.0155)

Fossil 0.3508*** (0.0000)

0.3915*** (0.0000)

0.2147*** (0.0000)

Industry 0.3747*** (0.0004)

0.0155 (0.7201)


Trend 0.0263*** (0.0000)

0.0222*** (0.0000)

0.0241*** (0.0000)

0.0288*** (0.0000)

0.0196*** (0.0000)

Recession

Yes Yes Yes Yes Yes


Yes Yes Yes Yes Yes

No. of observations

896 896 896 896 896

Adjusted R2

0.706 0.721 0.729 0.758 0.893



The effects of the California cap-and-trade system on economic performance measured by the real GDP

are shown in Table 6.6. The positive coefficient of the interaction term, Post2013*CA, of the

treatment/control dummy represents the economic effect of the cap-and-trade system. The size of the

estimated treatment effects also decreases when controlling for confounding factors from 0.0777 (with a

p-value of 0.0011) in baseline model to 0.0470 (with a p-value of 0.0185) in model 4. Our preferred DiD

model (model 4), with all the control variables, indicates a 0.047% increase in the real GDP in California

August 2020


and Quebec due to the cap-and-trade compared to other states without such initiative. The 2013 mean

value real GDP in California and Quebec was USD 1,293 billion, hence, a 4.7% (100*e0.047 -1) increase in

this mean level of real GDP equates to USD 60.78 billion per year increase as compared to other US states

without the regulation.

Table 6.6: Effect of California Cap-and-Trade System on Real GDP



Post2013*CA 0.0777*** (0.0011)

0.0778*** (0.0017)

0.0618** (0.0139)

0.0419** (0.0261)

0.0470** (0.0185)

Energy price 0.0259 (0.1992)

0.0218 (0.2938)

0.0705*** (0.0005)

0.0741*** (0.0004)

Policies 0.0037 (0.7348)

0.0025 (0.8193)

0.0098 (0.3786)

0.0022 (0.8244)

0.0043 (0.6618)

Fossil 0.4649*** (0.0053)

0.0751 (0.6481)

0.1099 (0.5210)

Exports -0.1327*** (0.0000)

-0.1203*** (0.0000)

Industry -0.1867** (0.0370)

Trend 0.0136*** (0.0000)

0.0148*** (0.0000)

0.0165*** (0.0000)

0.0190*** (0.0000)

0.0162*** (0.0000)

Recession Dummy

Yes Yes Yes Yes Yes


Yes Yes Yes Yes Yes

No. of observations

380 380 380 300 344

Adjusted R2

0.745 0.745 0.752 0.718 0.746


BC Carbon Tax System

Next, we consider the effect of the BC carbon tax on the economic performance measured by real GDP.

These results are shown in Table 6.7. For the real GDP, we find statistically and economically significant

treatment effects for all specifications. The size of the estimated treatment effects ranges from 0.038

(with a p-value of 0.017 in base model) to 0.064 (with a p-value of 0.0008 in model 1). However, our

preferred DiD model (model 4) with all the control variables (Post2008*BC = 0.055 with p-value 0.000),

indicates a 0.056% increase in the real GDP in British Columbia due to the carbon tax compared to other

provinces without carbon tax. The 2007 mean value for real GDP in British Columbia was CAD 209.4 billion,

hence, a 5.5% (100*e0.055 -1) increase in this mean level of real GDP is about CAD 11.49 billion. The

positive correlation between capital intensity and real GDP indicates that the growth in capital intensity

August 2020


will translate to a 10% increase in real GDP in British Columbia. Industry structure and energy intensity

are found to have significant and positive impacts on economic growth.

Table 6.7: Effect of BC Carbon Tax on Real GDP



Post2008*BC 0.0376** (0.0176)

0.0640*** (0.0008)

0.0589*** (0.0001)

0.0549*** (0.0004)

0.0555*** (0.0000)

Energy price -0.0921*** (0.0004)

-0.0553** (0.0419)

-0.0557** (0.0392)

0.0407** (0.0119)


0.0777** (0.0289)

0.1025*** (0.0000)

Fossil 0.2385*** (0.0046)

0.2125*** (0.0000)

Industry 0.2401*** (0.0000)

Trend 0.0195*** (0.0000)

0.0177*** (0.0000)

0.0156*** (0.0000)

0.0175*** (0.0000)

0.0194*** (0.0000)

Recession Dummy

Yes Yes Yes Yes Yes


Yes Yes Yes Yes Yes

Policy Dummy

Yes Yes Yes Yes Yes

No. of observations

170 170 220 220 170

Adjusted R2

0.788 0.799 0.814 0.819 0.938


Alberta SGER system

Next, consider the effect of the Alberta SGER system on the economic performance measured by real

GDP . These results are shown in Table 6.8. For the real GDP, we also find statistically and economically

significant treatment effects for all specifications. The size of the estimated treatment effects remains

significant when controlling for confounding factors, ranging from 0.1437 (with a p-value of 0.0000) in

model 2 to 0.1468 (with a p-value of 0.0000) in model 1. Our preferred DiD model is model 3 with all the

control variables. It indicates a 14.63% increase in the real GDP in Alberta due to the SGER system

compared to other provinces without such initiative. On the other hand, energy price and capital intensity

were found to have no significant impact on real GDP in Alberta sectors.

August 2020


Table 6.8: Effect of Alberta SGER System on Real GDP

Dependent variable (1) (2) (3)

Real GDP Real GDP Real GDP

Post2007*AB 0.1468*** (0.0000)

0.1437*** (0.0000)

0.1463*** (0.0000)

Energy price 0.0724 (0.1538)

0.0665 (0.1945)

Capital Intensity -0.0287 (0.2025)

Trend 0.0139*** (0.0000)

0.0111*** (0.0000)

0.0118*** (0.0000)

Recession Dummy

Yes Yes Yes

State fixed effects

Yes Yes Yes

No. of observations

980 980 980

Adjusted R2

0.206 0.207 0.207


Fixed Effect Regressions: Effect on Emissions

This section presents the empirical evidence of the effect of the policy on emissions.

EU-ETS System

In Table 6.9, we summarize the results of the impact of EU-ETS on environmental performance as

measured by GHG emissions. As can be seen from our base model to model 4 with full set of controls, the

EU-ETS has negative and statistically significant treatment effects for all specifications. The size of the

estimated treatment effects ranges from -0.049 (with a p-value of 0.000 in model 4) to -0.089 (with a p-

value of 0.000 in model 1). However, our preferred DiD model (model 4) with all the control variables

(Post2005*EU = -0.0498 with p-value 0.000), indicates a 5% decrease in GHG emissions in the EU due to

the EU-ETS compared to other countries without the ETS in place. The 2005 mean value for GHG emissions

in the EU was 201.1 Mt CO2e, hence, a 5 % (100*e0.0498 -1) decrease in this mean level of GHG emissions

is about 9.9 Mt CO2e. Consequently, the EU-ETS is associated with substantial decarbonization of 9.9 Mt

(4% of 1991 level) between 2005 and 2018.

While the true emission reduction was 35.7 Mt during this time, this estimated emissions reduction

resulted directly from the establishment of the EU-ETS, in addition to other emission reductions in non-

EU-ETS countries. These emission reductions are in addition to any emission reductions from reduced

economic activity during the recession, as well as oil price. Consistent with general economic sense, our

results suggest that economic activity measured by real GDP significantly increases GHG emissions (GDP

= 0.4788 with a p-value of 0.000 in model 4). Similarly, GHG emissions increase as the dependency of fossil

August 2020


fuel increases (Fossil = 0.2430 with a p-value of 0.003), implying that as the share of fossil fuels in total

energy consumption increases by 1%, the CO2e emissions increase by 0.24%.

Table 6.9: Effect of the EU-ETS on Emissions Reduction


GHG Emissions GHG Emissions GHG Emissions GHG Emissions GHG Emissions

Post2005*EU -0.0574*** (0.0000)

-0.0895*** (0.0000)

-0.0705*** (0.0000)

-0.0696*** (0.0000)

-0.0498*** (0.0000)

Energy price 0.0775*** (0.0005)

0.0613*** (0.0007)

0.0441*** (0.0005)

0.0288*** (0.0041)

Policies -0.0017*** (0.0003)

-0.0012*** (0.0008)

-0.0012*** (0.0004)

-0.0010*** (0.0001)

Fossil 0.4137*** (0.0000)

0.3458*** (0.0000)

0.2430*** (0.0032)

Industry 0.0074 (0.881)

0.0002 (0.999)


0.0065 (0.7964)

GDP 0.4788*** (0.0000)

Trend -0.0013 (0.2344)

-0.0051*** (0.0007)

-0.0028** (0.0415)

-0.0050*** (0.0005)

-0.014*** (0.0003)

Recession

Yes Yes Yes Yes Yes


Yes Yes Yes Yes Yes

No. of observations

896 896 896 896 896

Adjusted R2

0.033 0.106 0.152 0.310 0.404



We report the impact of the California cap-and-trade system on CO2e emissions reduction in Table 6.10.

Consistent with our other findings, the results show that the California cap-and-trade system beneficially

reduced CO2e emissions as compared to other states that are not regulated (Post2013*CA = -0.0340 with

a p-value of 0.074 in model 4). The introduction of other control variables including fossil fuel

dependency, GDP, oil price, level of industrialization, etc. in the DiD models makes the research

conclusions more robust. Our preferred DiD model (model 4), with all the control variables (Post2013*CA

= -0.034 with p-value 0.074), indicates a 3.4% decrease in the GHG emissions in California and Quebec

due to the California cap-and-trade system compared to other states without such ETS in place.

Overall, the effect of cap-and-trade on emissions efficiency is economically meaningful. The 2012 mean

value for GHG emissions in California and Quebec was 266.9 Mt CO2e, resulting in a 3.4% (100*e0.034 -

1) decrease in this mean level of GHG emissions translates to about 9.07 Mt CO2e.

August 2020


The covariates affect the GHG emissions in the expected direction. For example, the estimates of the

economic activity measured by real GDP reveal a positive and significant impact on GHG emissions (GDP

= 0.5188 with p-value 0.000), consistent with theory and literature. Similarly, fossil fuel dependency, and

industrialization significantly increases emissions as can be seen from the estimates from model 4 (Fossil

= 0.7533 with p-value 0.032), and (Industry = 0.2011 with p-value 0.0784), respectively. The magnitude of

the impact is also very similar across different specifications.

Table 6.10: Effect of California Cap-and-Trade System on Emissions Reduction


GHG Emissions

GHG Emissions

GHG Emissions

GHG Emissions

GHG Emissions

Post2013*CA -0.0142 (0.3091)

-0.0292* (0.0800)

-0.0295* (0.0836)

-0.0345* (0.0536)

-0.0340* (0.0744)

Policies -0.1219*** (0.0000)

-0.1138*** (0.0000)

-0.1134*** (0.0000)

-0.1161*** (0.0000)

-0.1045*** (0.0000)

GDP 0.4441*** (0.0000)

0.4098*** (0.0000)

0.4112*** (0.0000)

0.4323*** (0.0000)

0.5188*** (0.0000)

Energy Price -0.0072 (0.7632)

-0.0205 (0.4648)

-0.0482 (0.1607)

Fossil 0.5171* (0.0568)

0.5203* (0.0584)

0.4719* (0.0781)

0.7553** (0.0320)

Industry 0.2477** (0.0228)

0.2011* (0.0784)

Export 0.0440 (0.1314)

Trend -0.0134*** (0.0001)

-0.0121*** (0.0000)

-0.0131*** (0.0000)

-0.0109*** (0.0002)

-0.0124*** (0.0000)

Recession Dummy

Yes Yes Yes Yes Yes


Yes Yes Yes Yes Yes

No. of observations

380 380 380 380 344

Adjusted R2

0.427 0.440 0.439 0.444 0.466


BC Carbon Tax

Table 6.11 represents the estimated results of the impact of environmental regulation, i.e., carbon tax on

GHG emissions in British Columbia. The result shows that the coefficient of BC carbon tax (Post2008*BC

= 0.0179 with a p-value of 0.4415 in model 4) is statistically insignificant, indicating an absence of any

effect of BC carbon tax on GHG emissions. Considering that the objective of the regulatory policy is to

reduce emissions, our results from table 6.11 suggest that the carbon tax policy in British Columbia failed

to achieve its goal. Consistent with our results from other jurisdictions, we find that economic activity

August 2020


increases GHG emissions (GDP = 1.0358 with a p-value of 0.000 in model 4). While an increase in oil price

tends to reduce the GHG emissions (Oil price = -0.1562 with a p-value of 0.000 in model 4) as higher oil

price induces a possible input substitution and a reduction in oil consumption, resulting in a fall in GHG

emissions.

Table 6.11: Effect of BC Carbon Tax on Emissions Reduction


GHG Emissions

GHG Emissions

GHG Emissions

GHG Emissions

GHG Emissions

Post2008*BC -0.0195 (0.3508)

0.0348* (0.0705)

0.0337 (0.1380)

0.0178 (0.4416)

0.0179 (0.4415)

Real GDP 0.9940*** (0.0000)

0.8927*** (0.0000)

0.7590*** (0.0000)

1.0328*** (0.0000)

1.0358*** (0.0000)

Oil price -0.1769*** (0.0000)

-0.1201*** (0.0001)

-0.1561*** (0.0000)

-0.1562*** (0.0000)


0.1375*** (0.0005)

0.1383*** (0.0006)

Industry -0.1254*** (0.0045)

-0.1259*** (0.0047)

Fossil -0.0176 (0.8794)

Trend -0.0256*** (0.0000)

-0.0271*** (0.0000)

-0.0288*** (0.0000)

-0.0343*** (0.0000)

-0.0344*** (0.0000)

Recession Dummy

Yes Yes Yes Yes Yes


Yes Yes Yes Yes Yes

Policy Dummy

Yes Yes Yes Yes Yes

No. of observations

170 170 170 170 170

Adjusted R2

0.432 0.497 0.577 0.602 0.600


Alberta SGER System

Results reported in Table 6.12 indicate that Alberta SGER policy failed to reduce GHG emissions. In fact,

we find the SGER policy has a statistically significant positive impact on GHG emissions, which is opposite

of the policy makers’ expectation. It is for this reason, the emissions efficiency in Alberta has decreased

compared to other provinces who did not have such policy. Consistent with our study, we find a positive

correlation between GDP and GHG emissions indicating that increase in economic activity generally

increases emissions (GDP = 0.2618 with a p-value of 0.000 in model 4). For all these specifications we find

a significant and positive impact of GDP on GHG emissions.

August 2020


Table 6.12:Effect of Alberta SGER System on Emissions Reduction

Dependent variable Base (1) (2) (3)

GHG Emissions GHG Emissions GHG Emissions GHG Emissions

Post2007*AB 0.1948*** (0.0000)

0.1939*** (0.0000)

0.1555*** (0.0000)

0.1634*** (0.0000)

Energy price 0.0221 (0.8013)

0.0027 (0.9741)

-0.0128 (0.8825)

GDP 0.2673*** (0.0001)

0.2618*** (0.0002)

Capital Intensity -0.0785** (0.0068)

Trend -0.0115*** (0.0000)

-0.0124** (0.0038)

-0.0154*** (0.0003)

-0.0133*** (0.0028)

Recession Dummy

Yes Yes Yes Yes

State fixed effects

Yes Yes Yes Yes

No. of observations

980 980 980 980

Adjusted R2

0.012 0.011 0.049 0.046


August 2020


Results Summary

Altogether, our results can be summarised as shown in Table 6.13. It is important to note that while Table

6.13 provides the impacts of different carbon management policies on some key economic, and

environamental variables, readers should be careful while comparing the results as the jurisdictions vary

significantly in terms of their sample sizes, and time since the policy was implemented, etc.

Table 6.13: Summary Results

Jurisdictions Indicators

Emissions efficiency GDP GHG Emissions

EU-ETS (+) 0.80 billion per Kt CO2e (+3.0% )

(-) 25,084 billion USD (-4.1%)

(-)9.9Mt CO2e (-4.9%)

Cap-and-trade (+)0.16 million USD/kt CO2e (+3.6%)

(+)60.78 billion USD (+4.7%)

(-)9.07 Mt CO2e (-3.4%)

BC Carbon tax --- (+)11.49 billion CAD (+5.55)

---

Alberta SGER --- (+)40.97 billion CAD (+14.6%)

(+)37.95 Mt CO2e (16.3%)

Note: “-----” means no impact

Overall, the ETS policy was found to be more effective at reducing GHG emissions than the Carbon Tax

policy or a Hybrid policy. Evidence suggests that while GDP is also negatively impacted in the EU case, the

magnitude of the effect on GDP is smaller than the effect on overall emissions; in other words, the impact

of the ETS is larger on emissions than on the economic growth.

The California-Quebec Cap-and-Trade analysis suggests that the system is effective at reducing emissions

and thus increasing emissions efficiency without negatively impacting the economic growth.

The BC carbon tax policy boosted economic activity but had no effect on emissions. Since the objective of

the regulatory policy is to reduce emissions, our results suggest that the carbon tax policy in British

Columbia failed to achieve its goal. In fact, oil prices have been found to have a bigger effect on emissions

in British Columbia than carbon tax.

Alberta SGER policy failed to reduce GHG emissions as well. In fact, the SGER policy had a statistically

significant positive impact on GHG emissions. It is for this reason, the emissions efficiency in Alberta has

decreased compared to other provinces who did not have such policy.

Consistent with other literature, we find a positive correlation between GDP and GHG emissions indicating

that an increase in economic activity generally increases GHG emissions.

Placebo Test Using Data for Prior Periods

As mentioned in the methodology section, one assumption of the DiD method is that the trends of the

treatment and control groups would be equal in the absence of the treatment. Because we cannot prove

this assumption, we perform a placebo test to check whether the identified effects are due to such

August 2020


treatment and endorse the correct selection of the control and treatment groups. These tests are run to

ensure that the DiD model is picking up the effect of the carbon management policy, not some other trend

that was present prior to the implementation policy. For the placebo test, we conducted a falsification

test using data for prior periods, i.e., constructing a hypothetical time (pre-treatment) when the policy is

implemented. In Tables 6.14 through 6.18, we apply the placebo test using fake-treatment years for the

policies, knowing that the emissions efficiency should not be affected before the implementation of the

policy. It is the expectation that models with fake treatment years will yield insignificant results for the

DiD estimator, which would indicate no prior trends in the treated group that are different from the

control group.

EU-ETS System

In Table 6.1416 we apply the placebo test using a fake-treatment year for the EU-ETS, beginning from 1992

to 2004, knowing that the emissions efficiency should not be affected in these years as the policy was

implemented in 2005. The results show that before the implementation of the EU-ETS, there is no

statistically significant difference between the treatment group and their control groups in the emissions

efficiency. Therefore, it can be inferred that the treatment group and the experimental group share a

common time trend before the implementation of the carbon market in the EU supporting the robustness

of our results. In addition, we also estimated the model using a subsample ranging from 1996 to 201717

as a test of robustness. The results are consistent even with the subsample.

Table 6.14: Placebo Test on EU-ETS

Fake Treatment Year

1992 2002 2003 2004

Dependent variable Emissions efficiency

Emissions efficiency



Post2005*EU -0.0328 (0.4102)

0.0062 (0.5806)

0.0076 (0.5121)

0.0188 (0.1273)

Energy price -0.0060 (0.5267)

-0.0053 (0.5925)

-0.0061 (0.5551)

-0.0101 (0.3515)

Policies 0.0009*** (0.0000)

0.0009*** (0.0000)

0.0009*** (0.0071)

0.0008*** (0.0005)

Fossil -0.1471** (0.0402)

-0.1500** (0.0345)

-0.1483** (0.0368)

-0.1394** (0.0472)

Industry -0.0009 (0.9868)

0.0018 (0.9744)

0.0021 (0.9695)

0.0045 (0.9363)


0.1660*** (0.0000)

0.1661*** (0.0000)

0.1657*** (0.0000)

Trend 0.0253*** (0.0000)

0.0249*** (0.0000)

0.0249*** (0.0000)

0.0249*** (0.0000)

Recession

Yes Yes Yes Yes


Yes Yes Yes Yes

No. of observations 896 896 896 896

16 For brevity, we report only 1992 as one of the beginning years. 17 Results are available upon request.

August 2020


Adjusted R2

0.840 0.840 0.840 0.840



We conducted a series of Placebo tests on the California cap-and-trade system. Here, we impose all years

(2001-2012) as the adoption time of the treatment. We report on the most recent three years, 2010-2012,

and one of the initial years in the sample, 2001, in Table 6.15. In other words, the years 1998-2000, 1998-

2009, 1998-2010, 1998-2011 are the false pre-program and the years after 2001, 2010, 2011, and 2012

are the false post-program. Table 6.15 summarizes the results which corroborate our

hypothesis: the DiD estimator are not statistically significant in any of the models which reveals that there

are no systematic differences in the time trends between the treatment and control group.

Table 6.15: Placebo Test on California Cap-and-Trade System

Fake Treatment Year

Dependent variable

2001 2010 2011 2012





DID -0.0290 (0.1375)

0.0130 (0.4985)

0.0157 (0.4274)

0.0135 (0.5348)

Energy price 0.1711*** (0.0000)

0.1711*** (0.0000)

0.1710*** (0.0000)

0.1713*** (0.0000)

Exports -0.1756*** (0.0000)

-0.1756*** (0.0000)

-0.1755*** (0.0000)

-0.1759*** (0.0000)

Fossil -0.8725*** (0.0003)

-0.8725*** (0.0002)

-0.8730*** (0.0002)

-0.8724*** (0.0000)

Industry -0.0544 (0.6397)

-0.0544 (0.6397)

-0.0552 (0.6344)

-0.0543 (0.6994)

Trend 0.0152*** (0.0000)

0.0152*** (0.0000)

0.0152*** (0.0000)

0.0152*** (0.00000)

Recession Dummy

Yes Yes Yes Yes


Yes Yes Yes Yes

Polcieis Yes Yes Yes Yes

No. of observations

344 344 344 344

Adjusted R2

0.712 0.712 0.712 0.712


August 2020


BC Carbon Tax

In the case of the BC carbon tax we assume all years in pre-treatment period, i.e., 2003 to 2007 as false

program years, and present the results obtained from 2003, 2006, and 2007 as representative of all results

in Table 6.16. More specifically, the years 2001-2002,2001-2005, and 2001-2006 are considered as the

false pre-program, and we expect that the DiD estimates to statistically insignificant to pass the

falsification test. As can be seen from Table 6.16, the estimated coefficient on DiD is statistically

insignificant implying that the treatment and control group were on similar time trends before the

implementation of the policy making our results robust. This result indicates that the emissions efficiency

did not evolve in a statistically significant way for treated and non-treated provinces before the

implementation of the policy.

Table 6.16: Placebo Test on BC Carbon Tax

Fake Treatment Year

2003 2006 2007




DID 0.0357 (0.2195)

0.0351 (0.1152)

0.0118 (0.6298)

Energy price 0.1088*** (0.0004)

0.1032*** (0.0012)

0.1114*** (0.0005)

Capital intensity -0.1493*** (0.0000)

-0.1526*** (0.0000)

-0.1480*** (0.0000)

Trade Balance -0.0001** (0.0419)

-0.0001** (0.0327)

-0.0001** (0.0430)

Fossil 0.0600 (0.6001)

0.0600 (0.6018)

0.0593 (0.6040)

Industry 0.1082*** (0.0023)

0.1078*** (0.0025)

0.1084*** (0.0023)

Trend 0.0338*** (0.0000)

0.0337*** (0.0000)

0.0339*** (0.0000)

Recession Dummy

Yes Yes Yes


Yes Yes Yes

Policy Dummy

Yes Yes Yes

No. of observations

170 170 170

Adjusted R2

0.847 0.847 0.846


Alberta SGER System

For the Alberta SGER system, we suppose all years in the pre-treatment period, i.e., 2000 to 2006 as the

adoption time of the treatment. In other words, the years 1998-1999, 1998-2003, 1998-2004, and 1998-

August 2020


2005 are the false pre-program and the years after 2000, 2004, 2005, and 2006 are the false post-program.

Table 6.18 summarizes the results which corroborate our hypothesis: the DiD estimator is not statistically

significant in any of the models. The fact that emissions efficiencies before the tax was implemented were

not statistically different indicates that the two groups evolved analogously and suggests that they could

have evolved similarly had the tax not been implemented.

Table 6.17: Placebo Test on the Alberta SGER System

Fake Treatment Year

2000 2004 2005 2006





DID 0.0837 (0.5732)

0.0111 (0.7837)

-0.0099 (0.7919)

-0.0349 (0.3255)

Energy price -0.0922 (0.4564)

0.0531 (0.5687)

0.0578 (0.5347)

0.0624 (0.5008)

Capital Intensity 0.0798 (0.2043)

0.0546* (0.0880)

0.0551* (0.0857)

0.0563* (0.0807)

Trend 0.0303*** (0.0000)

0.0217*** (0.0000)

0.0217*** (0.0000)

0.0218*** (0.0000)

Recession Dummy

Yes Yes Yes Yes


Yes Yes Yes Yes

No. of observations

980 980 980 980

Adjusted R2

0.177 0.177 0.177 0.178

Note: Heteroskedasticity-consistent p-values are in parentheses. The time period is 1998-2017.

Study Limitations and Comparison with Other Studies

Despite the carbon tax policy relevance of the findings of this study, we note some important limitations

of our analysis. The analyses of the report assume no anticipatory effects, which result from the gap

between the announcement and the implementation of the policy. If an economy responds to the policy

before its implementation, the documented effects could, at best, serve as a lower bound. On the other

hand, carbon leakage may occur between jurisdictions under the carbon policy and other jurisdictions

with no policy or a less stringent policy. It results from the combination of two effects: (i) relocation, when

the treated firms shift their production to the non-treated locations to evade the increased production

cost imposed by the environmental policy; and (ii) changes in market shares, when treated firms lose

market share to unregulated foreign competitors.

While we have shown that the EU-ETS and California cap-and-trade contributed substantially to efforts to

decarbonize their economies, we cannot rule out that some of these reductions were achieved by moving

production outside of the EU and California and Quebec, respectively, and into countries/states without

carbon markets. If such leakage happens, the net effect of the EU-ETS and California cap-and-trade on

August 2020


global emissions must be weaker than its local effect, therefore serves as an upper bound of the true

effect.

Our results from the EU-ETS are in line (albeit differs in magnitude) with a recent study by Bayer and Aklin

(2020) who find that the EU-ETS reduced CO2 emissions by about 1.2 billion tons between 2008 and 2016

relative to a world without carbon markets. Similar results are also evident from Petrick and Wagner

(2014) and Dechezleprêtre et al. (2018) who find that the EU ETS had a significant impact on emissions

reduction. Our results further support findings from country specific studies on the effect of

environmental policies in the EU. For example, Brännlund et al. (2014), who finds the Swedish carbon tax

to have a significant and positive effect on carbon intensity performance at firm level, and U. Wagner et

al. (2014), who finds a statistically significant reduction in CO2 emissions in France. On the other hand,

our findings on the GDP impact of EU-ETS differs significantly from Petrick and Wagner (2014) and LLschel

et al. (2016), who do not find a statistically negative significant effect of the EU ETS on output.

Empirical evidence on the effect of BC carbo tax is mixed, and still in progress. For example, (Metcalf 2019)

finds that the tax policy has reduced the province’s CO2 emissions by 5%–10% with little negative impact

on the economy, while Murray and Rivers (2015) focused on the emissions leakage. On the other hand,

Yamazaki (2017) and Yip (2018) analyzed the labor market impact of carbon tax and found that a carbon

tax can have a heterogeneous effect on the labor market. Our findings, however, support Pretis (2019)

and Metcalf (2019) and oppose the findings from Bernard et al. (2018) in the sense that we find that the

policy did not reduce output, but questions whether emissions have been reduced because of the policy.

August 2020


Chapter 7: Conclusions

This report sheds light on the effectiveness of carbon management policies in terms of their effect on the

economy and/or sector-level emissions and economic performance. While there is widespread agreement

among economists on carbon emissions pricing, much debate remains regarding the choice of specific

carbon-pricing policy instruments, with some supporting carbon taxes (Nordhaus, 2007) and others cap-

and-trade or ETS (Ellerman et al., 2003). See Goulder and Schein (2013), and Stavins (2008), for a detailed

discussion. This report investigates the impact of the EU-ETS, the California cap-and-trade, the BC carbon

tax, and the Alberta SGER on three critical areas: i) emissions efficiency, ii) GDP, and iii) emissions

reduction; and contributes to the small but rapidly growing scholarly literature on the ex-post evaluation

of different carbon management policies.

In doing so, we built on a body of literature that takes the DiD estimation process to control for the

systematic differences between the treatment group and the control group and analyzed the changes in

the treatment group before and after the implementation of a certain policy. We paid explicit attention

to the underlying assumption in the DiD model, i.e., the treatment and control groups were behaving

similarly prior to the policy intervention. We examined whether the carbon management policies have

been effective in meeting the policy goals in the EU-25, the state of California and the province of Quebec,

and the provinces of British Columbia and Alberta.

Figure 7.1 presents a summary of the key information on the carbon pricing policies in the selected

jurisdictions under the scope of this study.

• The EU-ETS and the cap-and-trade have had a robust impact on emissions reduction.

• The BC carbon tax and Alberta SGER have shown no impact on GHG emissions.

• Both EU-ETS and California cap-and-trade improved emissions efficiency while the BC carbon

tax and Alberta’s SGER failed to have any significant impact.

• Only California Cap-and-Trade was successful at increasing GDP while reducing emissions.

• The study notes some methodological limitations in terms of anticipatory effects, carbon

leakages, and nonrandom nature of participation of firms.

• The study emphasizes the use of firm-level microdata from a large set of countries for a better

understanding of the impacts of different policies. Nonetheless, country/state-level analysis,

as in this report, is able to produce a comprehensive economy-wide estimation that is easy to

communicate to various stakeholders, including policymakers.

August 2020


Figure 7.1: Summary of Carbon Pricing Initiatives Implemented in the Studied Jurisdictions

Source: Figure by CERI

The study answers a question of how these policies affected three major indicators: i) emissions efficiency,

as measured by GDP per unit of CO2e emissions, ii) economic performance, as measured by real GDP; and

iii) GHG emissions.

First, concerning the issue of GHG emissions, our evidence suggests that the EU-ETS and the cap-and-

trade have had a robust impact on emissions reduction. In contrast, the BC carbon tax and Alberta SGER

failed to reduce GHG emissions. Second, Both EU-ETS and California cap-and-trade improved the

emissions efficiency while BC carbon tax and Alberta’s SGER did not have any significant impact. Third,

only California cap-and-trade was successful at increasing GDP while reducing emissions. This result

confirms the so-called “double dividends” concept for California cap-and-trade. While the EU-ETS is being

successful in reducing emissions, it comes with a cost of a reduction in output. On the other hand, BC

carbon tax and Alberta SGER, despite failing to reduce emissions, did not have an adverse effect on the

economic performance in those respective jurisdictions.

The results of the fixed effect DiD approach can be summarized as follows:

Carbon prices range for the implemented initiatives:

Carbon tax: USD 0.1 to USD 119.4 ETS: USD 1.1 to USD 18.8

Range of the implementation timelines for the studied jurisdictions:

Carbon tax: 1990 to 2020 ETS: 2005 to 2019

Percentage of the global GHG emissions covered by the implemented policies in the studied jurisdictions: 15.3% (2019)

Carbon pricing policies implemented in the studied jurisdictions:

Regional: European UnionNational: Canada, 11 EU countries, 6

non-EU countriesSubnational: 7 Provinces (CA), 1

Territory (CA), 12 states (US)

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The EU-ETS system:

- improved the emissions efficiency in the EU-25 by 3% or 0.80 billion per Kt CO2e;

- had an adverse impact on GDP in order of USD 25,084 billion (a decrease in the mean level of real

GDP); and

- had the desired outcome of reducing emissions by about 9.9 Mt CO2e (a decrease in the mean

level of GHG emissions) over the studied period.

The California cap-and-trade system:

- improved emissions efficiency in California and Quebec by an additional 3.6% or 0.16 million

USD/kt CO2e;

- had a positive impact on GDP of USD 60.78 billion (an increase in the mean level of real GDP); and

- had the desired outcome of reducing emissions by about 9.07 Mt CO2e (a decrease in the mean

level of GHG emissions) over the studied period.

The BC carbon tax:

- had no impact on provincial emissions efficiency;

- had a positive impact on GDP of about CAD 11.49 billion CAD (an increase in the mean level of

real GDP); and

- had no impact on the emission reductions over the studied period.

The Alberta SGER system:

- had no impact on the emissions efficiency;

- had a positive impact on GDP of about CAD 40.97 billion (an increase in the mean level of real

GDP); and

- had the unwanted outcome of increasing emissions by about 37.95 Mt CO2e (an increase in the

mean level of GHG emissions) over the studied period18.

As a research endeavour, impact analysis of the EU-ETS, California cap-and-trade, and BC carbon tax is still

very much a work in progress while that of Alberta SGER is rare. That is because most of the carbon pricing

options are ongoing and continuously evolving policy instruments. Also, some methodological challenges

arise from the policy design itself—particularly the fact that participation of firms in any of the market-

based carbon pricing options is not random. Thus, applying firm-level microdata from a large set of

countries will improve our understanding of the impacts of different policies on emission reductions and

other variables of interest. While the use of microdata at the firm or plant level solves the possible

aggregation error problem, country/state-level analysis, as in our report, is able to produce an estimate

of the economy-wide emission reductions impact that is comprehensive and easy to communicate to

various stakeholders, including policymakers.

18 It should be noted that the SGER system was designed to improve efficiency of existing large emitting projects. As there was a great deal of project development over this time with associated emissions it would be challenging for the system to address them.

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This study emphasizes the necessity to design policies based on lessons learned from experience and

empirical evidence from already established policies around the world. The CERI findings align well with

conclusions and suggestions from other reviewed literature (Burtraw and Themann 2018; Carbon Pricing

Leadership Coalition 2017; Christensen and Olhoff 2019; Harrison 2019; Schmalensee and Stavins 2017;

Raymond 2019; World Bank 2019) on the lessons that can strengthen the functioning of carbon markets

and can be applicable to Canada. Both are summarized below:

- Both carbon tax and emissions trade systems have a great capacity to reduce GHG emissions;

however, a level at which they are utilized is not adequate for significant change towards low-

carbon economies.

- Strengthening existing and adding new carbon policies and actions, especially those that can deal

with carbon leakage, is needed.

- Current carbon prices in many jurisdictions remain insufficient to achieve the objectives of the

Paris Agreement, even with extended carbon pricing policies in place to align with the specific

GHG reduction targets19.

- Stronger complementary policies and actions are needed to achieve the total reductions in GHG

emissions in a case of the BC carbon tax.

- Lessons from ETS systems, especially California’s cap-and-trade system, has revealed that the

economy-wide approach can be more efficient than managing specific sectors differently.

- Linkage of a cap-and-trade system with those in other jurisdictions (such as California’s cap-and-

trade system linked with Quebec) could potentially reduce abatement costs, price volatility, and

market power.

19 According to (Carbon Pricing Leadership Coalition 2017), a minimal direct price on GHG emissions need to be in the range of US$40–80/t CO2e by 2020 and US$50–100/t CO2e by 2030 to meet the objectives of the Paris Agreement, under the condition that an ambitious climate policy is in place for the specific jurisdiction.

August 2020


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