TOWARD A LOW-CARBON ECONOMY - Economic Policy … · RPP BIO BIO σ TF σ TBIO σTRPP K RPP ... The...

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TOWARD A LOW-CARBON ECONOMY The costs and benefits of cap-and-trade APPENDICES

Transcript of TOWARD A LOW-CARBON ECONOMY - Economic Policy … · RPP BIO BIO σ TF σ TBIO σTRPP K RPP ... The...

TOWARD A LOW-CARBON ECONOMY The costs and benefits of cap-and-trade

APPENDICES

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APPENDICES

APPENDIX A

The GEEM model Provided by Navius Research

GEEM is a computable general equilibrium (CGE) model of Canada and the United States, which simulates how the economy evolves under different economic conditions. In the GEEM model, households and sectors that produce goods and services (e.g., electricity generation, pulp and paper, and petroleum refining) are explicitly represented. Each sector is characterized by what it produces (e.g., electricity) and the inputs required in production (i.e., capital, labour, energy and materials). Commodities that are produced can then be sold to other producers (as intermediate inputs), to households (the final consumers of goods produced in the economy), or to other regions and the rest of the world as exports. Commodities can also be imported from other regions or the rest of the world.

As the model steps through time, it ensures that markets clear for all commodities and factors by adjusting prices. For example, growth in pulp and paper production may increase demand for electricity in a single region, which must be generated provincially or imported. The price for electricity increases or decreases until supply matches demand.

Due to their framework, CGE models show how policies or different economic conditions alter the structure and growth of the economy. A policy leading to the contraction of one sector has a ripple effect throughout the economy as all sectors of the economy return to equilibrium. For example, a policy causing an increase in the cost of producing pulp and paper or refined petroleum products (assuming the prices for these goods remain constant) can lead to a loss of competitiveness and lower production levels. In turn, lower production would reduce the output from sectors that supply these sectors with goods and services, and capital and labour would be reallocated throughout the economy.

APPENDICES

TOWARD A LOW-CARBON ECONOMY: THE COSTS AND BENEFITS OF CAP-AND-TRADE 3

Detailed model descriptionThe key economic flows in the GEEM model are shown below in Exhibit A1.

The GEEM model is recursive and can solve in selected incre-ments from 2002 to 2050. One of the benefits of using a recursive model is it can simulate policies that change over time. For example, GEEM can simulate carbon taxes that rise over time, or regulatory policies (e.g., requirements for carbon capture and storage) implemented in a certain year. Furthermore, the model simulates capital stock turnover over time. The data underlying the model is derived primarily from the Statistics Canada System of National Accounts.

The following sections describe the model’s representation of industry and consumers.

IndustrySectors can be disaggregated into various industries across North America (see examples in Exhibit 4). All industrial sectors in the GEEM model are represented by constant elasticity of substitution (CES) functions, which represent the technologies that industry can use to produce goods and ser-vices. Central to this function are the elasticity of substitution parameters which represent how easily a sector can substitute between different inputs while maintaining a given level of production. For example, the model simulates a trade-off between energy consumption and value added (i.e., capital and labour) in each industry through an elasticity of substitu-tion parameter. A low value for this parameter indicates that capital and labour are not very substitutable for energy, and as a result, the energy intensity of the sector is largely unaffected by new economic conditions or policies. A high value for this parameter indicates greater substitution possibilities, and economic conditions or policies that raise the price for energy relative to the price of capital and labour will induce improve-ments in energy efficiency.

EXHIBIT A1 Overall structure of the GEEM model

Imports

From other regionsand rest of the world

Imports

Production for export

Final demand

Capital, labourTo other regionsand rest of the world

Capital from other regions Capital to other regions

Intermediateinputs

Leisure WelfareProductionfor domesticconsumption

Foreign exchangefrom international

exports used to buyinternational imports

ArmingtonAggregator

Exports Industry

Welfare

Consumer

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We discuss the energy end uses in more detail below.

Exhibit A3 shows the energy end uses in GEEM. Electric-only end uses captures the ability of industry to improve efficiency. The transportation end use captures the ability to improve the efficiency of vehicles (e.g., freight trucks), but it further captures the ability of the sector to substitute between refined petroleum products and biofuels. The structure of process heat is described in more detail below.

The structure of process heat is shown in Exhibit A4. The model captures that ability to substitute between different fuels (e.g., natural gas, coal and refined petroleum products); as well as potential improvements to the efficiency of heating services. In addition to using process heat for direct industrial processes (e.g., space heating in commercial buildings or to meet the heating requirements for a refinery), heat can be used to gener-ate electricity.

Exhibit A2 through Exhibit A4 show the structure for each industry. The model uses a generic structure to represent every industry, while elasticities of substitution and the inputs are specific to each industry. In other words, the model captures different industrial structures for energy consumption, the consumption of other goods and/or services, and abilities to substitute between inputs.

Exhibit A2 shows the key end uses captured in GEEM. These are comprised of six complementary (i.e., substitution is not possible) end uses: 1) electric only end uses (e.g., lighting or electric motors); 2) transportation; 3) process heat; 4) demand for non-energy intermediate goods (e.g., cement or services); 5) value-added unrelated to energy consumption; and 6) non-combustion GHG emissions (i.e., GHG emissions unre-lated to energy consumption, such as venting/flaring in natural gas extraction or process emissions in aluminum smelting).

Electric only Transportation Process heat Non-energyimtermediate Value-added Non-combustion

GHG

Sector output

EXHIBIT A2 End uses in GEEM

Electric only Transportation Process heat

Sector output

EXHIBIT A3 Energy end uses in GEEM

Heat

ELE

σEELE

RPP BIO

BIO

σTF

σTBIOσTRPP

K

KRPP K

Heat

Cogen Con

Cogen

σCOG

RPPGas

Gas

σHF

σHGAS

CHTS

Coal

K

K

Heat

Electric only

Process heat

EXHIBIT A4 Process heat in GEEM

Sector output

TOWARD A LOW-CARBON ECONOMY: THE COSTS AND BENEFITS OF CAP-AND-TRADE 5

The elasticities of substitution in GEEM implicitly represent the ability of different sectors to improve energy efficiency as well as substitute between fuels. To link the CIMS and GEEM models, the values for the most elasticities of substitution have been statistically estimated from CIMS. In other words, GEEM provides a reasonable approximation of the technological responses observed in CIMS.

Although energy efficiency and fuel switching capture a large portion of the abatement opportunities in the economy,

some sectors have opportunities to directly control their GHG emissions. Examples of these opportunities include carbon capture and storage, capture of landfill gas, and efforts by the aluminum industry to reduce the emission of Perfluorocarbons. These actions are captured in GEEM by using discrete technol-ogies. For example, the “heat” services produced by natural gas (see Exhibit A4) are available in two options: with and without carbon capture and storage. Likewise, a sector may have multi-ple representations for non-combustion emissions with greater or fewer GHG emissions (see Exhibit A2).

GEEM Code Description

PEXT Crop and animal production Forestry and logging Fishing, hunting and trapping Support activities for agriculture and forestryOCHY Heavy crude oil extractionOCLM Light and medium crude oil extractionOSMIN Mined bitumen extractionOSIS In-situ bitumen extractionBITUP Bitumen upgradingCNGAS Conventional natural gas extractionTNGAS Tight natural gas extractionSNGAS Shale natural gas extractionEOROIL Enhanced oil recoveryCOALMIN Coal miningMINING Mineral miningOGSER Support activities for mining and oil and gas extractionCELEC Conventional electric power generationRELEC Renewable electric power generationELDIS Electric power distributionNGDIS Natural gas distributionPAPER Paper manufacturingWOODPM Wood product manufacturingREFLOL Petroleum products manufacturing from light crudeREFHOL Petroleum products manufacturing from heavy crudePETCHEM Petrochemical manufacturingOBCHEM Other basic chemical manufacturingFERT Fertilizer manufacturingBIOFUEL Biofuels manufacturingCEMMAN Cement manufacturingLIMMAN Lime manufacturingIRONST Primary iron and steel manufacturingALMAN Primary aluminum manufacturingOPMMAN Other primary metal manufacturing

GEEM Code Description

OMAN Other chemical manufacturing Other non-metallic mineral product manufacturing Miscellaneous manufacturingLQDNG Liquefied natural gas productionWRTD Wholesale trade Retail tradeTRANSIT Transit and ground passenger transportationTRANS Truck transportation Pipeline transportation Rail transportation Water transportation Other transportationSERV Warehousing and storage Water and other utilities Construction Information and cultural industries Finance, insurance, real estate and rental and leasing Professional, scientific and technical services Administrative and support services Educational services Health care and social assistance Arts, entertainment and recreation Accommodation and food services Other services (except public administration) Operating, office, cafeteria, and labouratory supplies Travel and entertainment, advertising and promotion Non-profit institutions serving householdsWASTE Waste management and remediation servicesTRMARGIN Transportation marginsGOVT Government sector

EXHIBIT A5 Example of sector coverage in GEEM

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is shown in Exhibit A6 (note that the methodologies for space heating, appliances and other goods are similar to the transportation methodology, and so are not shown in detail). Most of the elasticity values (shown as σ in Exhibit A6) have been econometrically estimated from Navius Research’s CIMS energy-economy model, while the values representing the substitutability between an end use and other goods (σTST) are from Paltsev (2005).1 The representative agent in GEEM maximizes his/her welfare subject to available income through Lagrangian optimization.

In the GEEM model, all industries maximize profits (i.e., revenue minus costs of production) subject to technology constraints through Lagrangian optimization.

ConsumersGEEM uses a representative agent framework, where all households are represented by a single representative agent. In this framework, the representative agent maximizes his/her welfare, where welfare is a function of consumption of various commodities, savings (i.e., future consumption) and leisure. The structure of the household welfare model

σCL

σTST

σTE

σNE

σLQD

Transportation

Consumption

Energy fromexisting vehicle

New vehicles

Transit

Space heating

Savings

Liquids

Biofuel NGRPP

Households

LeisureCons/savings

OtherAppliances

Vehicle

New vehicles Energy

Electricity

EXHIBIT A6 Structure of household welfare

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APPENDIX B

Special notes regarding the modelling

In any modeling exercise, it is important to understand how uncertainty in the forecast could affect the results.

The forecast generated for this study relies on many different assumptions and therefore represents one possible projection for Ontario’s economy and GHG emissions. Should the economy evolve differently, the results may be impacted.

Uncertainty is important to the extent that it may change the recommendations or results of the analysis. The key uncertainties and their potential impacts are summarized below:

• If Ontario pursues a different approach with its climate policy than projected here, the impacts will be different. As the details for Ontario’s plan are not publicly available, this analysis uses the experiences in California and other jurisdictions to draw insight into the way Ontario may implement its policy. However, the results are likely to differ if the analysis had examined the exact details of Ontario’s planned policy.

• If the price for allowances in California and Québec is different than assumed, the economic impacts of the policy could be different. A different price for carbon in California and Québec would affect the benefit of participating in emissions trading with those jurisdictions. In general, the greater the difference between the jurisdictions, the greater the benefit from trading.

• The GHG price in the WCI is uncertain because the cost of abatement in Ontario and other jurisdictions is not fully known. This analysis used an external forecast provided by Californiacarbon.info. This forecast for the carbon price embodies many assumptions about the rate of economic growth and the cost of abatement from various sectors of the economy.2 In this analysis, Ontario imports allowances under emissions trading because the cost of abatement in Ontario is estimated to be greater than that in California and Québec (as estimated by Californiacarbon.info’s forecast).3 However, the cost of abatement is uncertain in both Ontario and other WCI partners. Greater compliance costs in California or Québec would lead to greater reductions achieved in Ontario relative to what is shown.

• Modeling uncertainty could affect the results, but the direction is unclear. It is important to acknowledge uncertainty in simulating Ontario’s GHG emissions until 2030. Alternative assumptions for economic growth and growth by sector are likely to lead to different projections. While the model provides a reasonable forecast, the effect of alternative assumptions was not examined.

• Uncertainty in policy design. As discussed above, this analysis examines an option for cap-and-trade without insight into the province’s final plan. The impact of the final plan is likely to differ from what is shown here.

• Uncertainty in modeling. As with economic activity, it is important to acknowledge uncertainty in simulating Ontario’s GHG emissions until 2030. Alternative assumptions for economic growth and growth by sector are likely to lead to different projections. While the model provides a robust forecast, the effect of alternative assumptions was not examined.

APPENDIX C

Special thanks

The Institute would like to thank Californiacarbon.info for providing an allowance price forecast.

The Institute would also like to thank Cleantech Group for information regarding the number of cleantech companies and, cleantech companies with patents, by region.

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GDPNote: 2013 Exchange rates were used: 6.51 SEK = 1 USD; 1.03 CAD = 1 USD.

Bank of Canada, Annual Average Exchange Rates – 2013.

Statistics Canada, Gross domestic product, expenditure-based, provincial and territorial, annual, CANSIM Table 384-0038.

Statistics Sweden, GDP: expenditure approach (ESA2010) by type of use. Year 1980-2015.

US Bureau of Economic Analysis, Regional Economic Accounts – Annual Gross Domestic Product (GDP) by State.

Emissions by sectorNote: In Sweden, due to the use of combined heat and power, electricity emissions are recorded together with emissions from buildings:

California Environmental Protection Agency (Air Resources Board), “California Greenhouse Gas Inventory for 2000-2013 – by Category as Defined in the 2008 Scoping Plan,” last mod-ified April, 2015, accessed February 11, 2016, http://www. arb.ca.gov/cc/inventory/data/tables/ghg_inventory_scoping-plan_2000-13_20150831.pdf.

Environmental Commissioner of Ontario, Feeling the Heat: Greenhouse Gas Progress Report 2015, 2015.

Swedish Environmental Protection Agency, National Inventory Report Sweden 2015: Greenhouse Gas Emission Inventories 1990-2013, 2015.

Electricity Generation Mix:California Energy Commission, “Energy Almanac: Total Electricity System Power,” last modified September, 2015, accessed February 11, 2016, http://energyalmanac.ca.gov/electricity/total_system_power.html.

Statistics Sweden, “Monthly Electricity Supply,” last modified February, 2016, accessed February 11, 2016, http://www. scb.se/en_/Finding-statistics/Statistics-by-subject-area/Energy/Energy-supply-and-use/Monthly-electricity-statistics/Aktuell-Pong/6381/391694/.

APPENDIX D

References for Exhibit 16: Economic prosperity and GHG reductions can exist together

Total population:Statistics Canada, Estimates of population, by age group and sex for July 1, Canada, provinces and territories, annual, CANSIM Table 051-0001.

Statistics Sweden “Preliminary Population Statistics 2016,” last modified March 2016, accessed February 11, 2016, http://www.scb.se/en_/Finding-statistics/Statistics-by-subject-area/Population/Population-composition/Population-statistics/Aktuell-Pong/25795/Monthly-statistics--The-whole-country/25890/.

United States Census Bureau, State Totals: Vintage 2015: Annual Estimates of the Resident Population for the United States, Regions, States, and Puerto Rico.

Global emissionsWorld Resources Institute, “CAIT – Historical Emissions Data (Countries, U.S. States UNFCCC),” accessed March 10, 2016, http://www.wri.org/resources/data-sets/cait-historical-emissions-data-countries-us-states-unfccc.

Total emissions Note: Emissions exclude Land-Use, land-use change and for-estry (LULUCF)

California Environmental Protection Agency (Air Resources Board), “California Greenhouse Gas Emission Inventory – 2015 Edition,” last modified June, 2015, accessed February 11, 2016, http://www.arb.ca.gov/cc/inventory/data/data.htm.

Environment Canada, National Inventory Report: Greenhouse Gas Sources and Sinks in Canada 1990-2013, 2015.

Swedish Environmental Protection Agency, National Inventory Report Sweden 2015: Greenhouse Gas Emission Inventories 1990-2013, 2015.

TOWARD A LOW-CARBON ECONOMY: THE COSTS AND BENEFITS OF CAP-AND-TRADE 9

ENDNOTES

1 MIT Joint Program on the Science and Policy of Global Change “IGSM – Integrated

Global System Modeling Framework,” accessed February 11, 2016, http://global-

change.mit.edu/research/IGSM#EPPA.

2 CaliforniaCarbon.Info, 2030 California-Québec carbon allowance price forecast,

August 11, 2015.

3 Although this analysis does not compare the cost of abatement between Ontario

and California and Québec, it is quite possible that the cost of abatement is slightly

higher in Ontario. California has a significant opportunity to reduce its emissions by

reducing the import of high GHG intensity electricity, while this opportunity is more

limited in Ontario. California is a significant net importer of electricity, with over

20 percent of electricity consumption coming from imports. Ontario, on the other

hand, is a significant net exporter of electricity and has already largely decarbon-

ized its electricity sector. The electricity sector is believed to have lower costs of

abatement relative to other sectors, with cost of closing a coal plant and building a

new natural gas fired plant costing around $30 per tonne CO2e. Therefore greater

opportunity to reduce emissions in the electricity sector is likely to lead to lower

costs in California relative to Ontario. Source: Energy Information Administration

(EIA), Annual Energy Outlook, 2015.

Independent Electricity System Operator, “Supply Overview,” last updated December, 2015, accessed February 11, 2016, http://www.ieso.ca/Pages/Power-Data/Supply.aspx.

International Energy Agency, Energy Policies of IEA countries: Sweden 2013 Review, 2013.

Cleantech sectorCleanTech Group, “i3connect: The Innovation Network that Drives Sustainability,” accessed February 11, 2016, https://i3connect.com.

Carbon pricingNote: February 11, 2016 exchange rates were used: 1 SEK = 0.12 USD; 1 Euro = 1.13 USD.

Climate Policy Initiative, “California Carbon Dashboard,” accessed February 11, 2016, http://calcarbondash.org/?gclid=CjwKEAjwq6m3BRCP7IfMq6Oo9gESJACRc0b-N5cyuLNsxHKOsiULk4EmIMqcVPQWFXVKRCOYWWY_UbRoCka_w_wcB.

Global Environmental Exchange, “European Emission Allowances,” accessed February 11, 2016, https://www.eex.com/en/market-data/emission-allowances/spot-market/european-emission-allowances#!/2016/03/10.

International Energy Agency, “Addressing Climate Change, Policies and Measures Databases, Sweden, Energy, Carbon Dioxide and Sulphur Taxation,” last modified June 26, 2015, accessed February 11, 2016, http://www.iea.org/policiesandmeasures/pams/sweden/name-21011-en.php?s=dHlwZT1jYyZzdGF0dXM9T2s,&return=PG5hdiB-pZD0iYnJlYWRjcnVtYiI-PGEgaHJlZj0iLyI-SW50ZXJuYXRp-b25hbCBFbmVyZ3kgQWdlbmN5Jnp3bmo7PC9hPjxzcGFu-PiAmZ3Q7IDwvc3Bhbj48YSBocmVmPSIvcG9saWNpZXNhbmRtZWFzdXJlcy8iPlBvbGljaWVzIGFuZCBNZWFzdXJlczwvYT48c3Bhbj4gJmd0OyA8L3NwYW4-PGEgaHJlZj0iL3BvbGljaWVzYW5kbWVhc3VyZXMvY2xpbWF0ZWNoYW5nZS8iPkNsaW1hdGUgQ2hhbmdlPC9hPjxzcGFuIGNsYXNzPSJsYXN0Ij48L3N-wYW4-PC9uYXY-.