Methodological contrasts in costing greenhouse gas abatement policies: Optimization and simulation...

O.R. Applications

Methodological contrasts in costing greenhouse gasabatement policies: Optimization and simulation modeling

of micro-economic effects in Canada

Mark Jaccard a,*, Richard Loulou b, Amit Kanudia b, John Nyboer a,Alison Bailie a, Maryse Labriet b

a Energy and Materials Research Group (EMRG), School of Resource and Environmental Management, Simon Fraser University,

Vancouver, BC, Canada V5A 1S6b Groupe d’�eetudes et de recherche en analyse des d�eecisions (GERAD), McGill University and Universit�ee de Montr�eeal, Montr�eeal, Canada

Received 23 May 2001; accepted 18 October 2001

Abstract

The national process in Canada for greenhouse gas abatement selected contrasting models to estimate costs, pro-

viding a rare opportunity to assess the importance of methodological differences in cost estimates when other input

assumptions are the same. MARKAL is a well-known optimization model of the energy-economy system; CIMS is a

policy simulation model developed initially for Canada. The models require the same technology and financial data, but

CIMS, which does not assume financial cost minimization, also requires information on technology preferences, risk

perceptions, tax effects and other critical factors in the decision making of firms and households in order to simulate

their likely response to policies. Given the market inertia that is incorporated in a CIMS simulation, it estimates higher

costs of emission reduction than MARKAL. CIMS’ present value cost estimate for Canada to achieve its Kyoto target

of 6% below 1990 emissions by 2010 is $45 billion (CDN) while MARKAL’s is $15 billion. When linked to a macro-

economic model, the GDP impact of CIMS is 3% while that of MARKAL is less than 1%. This difference would have

been slightly larger had all target assumptions of the two models been identical.

� 2002 Elsevier Science B.V. All rights reserved.

Keywords: Decision analysis; Large-scale optimization; Energy; Environment

1. Introduction

Modeling the cost of greenhouse gas (GHG)

abatement has many sources of uncertainty. One

of these is due to contrasting methods of modeling

technological change. On the one hand, optimi-

zation models are recognized as powerful tools

for finding that economic equilibrium path which

minimizes the financial cost of reducing GHG

emissions. These models can provide policy-mak-

ers with an ideal portfolio of technologies. In thissense, they are described as normative or pre-

scriptive. On the other hand, behavioral simu-

lation models are recognized as critical for

European Journal of Operational Research 145 (2003) 148–164

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*Corresponding author. Tel.: +1-604-291-4219; fax: +1-604-

291-4968.

E-mail address: [email protected] (M. Jaccard).

0377-2217/03/$ - see front matter � 2002 Elsevier Science B.V. All rights reserved.

PII: S0377 -2217 (01 )00402 -7

mail to: [email protected]

estimating how far policies can actually move

the economy given the realities of firm and

household decision-making. In this sense, they are

referred to as descriptive or predictive. Cost esti-

mates from these latter models include additional

costs related to the inertia in the economy, someof them resulting from difficult-to-estimate, non-

financial preferences of consumers.

Both types of models have important roles to

play. Optimization models are especially strong in

finding the equilibrium conditions for complex

interrelated systems, as is the case when energy

supply and demand are integrated with macro-

economic demands and even international trade.Many countries are using these kinds of models for

probing the significance of GHG emission abate-

ment policies on trade in energy and other com-

modities. Simulation models are especially strong

for exploring the direct effects of technology-spe-

cific packages of policies that seek to move mar-

kets slightly further in one direction or another.

Electric utilities turned to these kinds of models inthe 1980s when they needed to know the market

impact and cost impact of their demand-side

management programs.

When choosing one or more models for esti-

mating GHG emission abatement costs, policy-

makers would benefit greatly if they could learn

the importance of model choice in determining

differences in cost estimates. Unfortunately, thisquestion is rarely examined. Modelers lack the

time and resources to compare their models in

controlled conditions, i.e. testing identical input

assumptions and scenarios. A rare exception to

this is the effort by the Energy Modeling Forum in

the US, which organizes this kind of comparative

analysis from time to time (Weyant and Hill,

1999).In this paper, we report on a unique opportu-

nity for model comparison in a real policy analysis

context. In 1998, the Canadian government initi-

ated the National Climate Change Implementation

Process (NCCIP), which led to the establishment

of 17 consultative Issue Tables composed of ex-

perts, interest groups and government officials.

The Issue Tables produced an inventory of actionsand measures in all sectors that could contribute

to the national commitment of reducing GHG

emissions to 6% below 1990 levels by 2010. 1 In

mid-1999, two modeling teams were selected to

integrate these actions and test for the effect of

different implementation policies and different

assumptions about external developments (e.g.,

different international prices for trading GHGemission permits). The models are deliberately

contrasting in method: one is the Canadian version

of MARKAL (Berger et al., 1992; Kanudia and

Loulou, 1999), developed and maintained by a

team from the Groupe d’�eetudes et de recherche enanalyse des d�eecisions (McGill University in col-

laboration with researchers from Universit�ee du

Qu�eebec �aa Montr�eeal); the other is CIMS (Jaccardet al., 1996; Nyboer, 1997), operated by the Energy

and Materials Research Group at Simon Fraser

University. 2 MARKAL is a technology-explicit,

optimization model, versions of which are used

in many countries. CIMS is a technology-explicit,

behavior simulation model. It has similarities to

the NEMS model (US Department of Energy,

1994) of the US government and to simulationmodels used by electric and gas utilities and several

governments for detailed energy policy design and

forecasting.

Both of these models have a macro-economic

equilibrium capability in that they can calculate

changes in the demand for final and intermediate

products and services as technologies and costs

change. However, these options were disabledin the models so that the direct results could be

later used as inputs for two macro-economic

models: the CaSGEM (Canadian Department of

Finance, 2000) general equilibrium model of

the Canadian government and the TIMS model

1 An action is defined as doing something (purchasing or

using equipment, switching to a different fuel) to change

emissions from what they otherwise would be and a measure is

defined as the combination of this action with the policy that

motivated it (a grant, a tax, a regulation or a system of tradable

emission permits).2 MARKAL is applied by Loulou, Kanudia and Labriet.

CIMS is applied by Jaccard, Nyboer and Bailie. CIMS received

its name in 1998, having evolved from an earlier, less integrated,

model called ISTUM. The Energy and Materials Research

Group was the Energy Research Group prior to 2000.

M. Jaccard et al. / European Journal of Operational Research 145 (2003) 148–164 149

(Informetrica, 2000) of Informetrica, a Canadian

consulting firm. The macro-economic part of the

analysis is not presented in detail here although we

report the aggregate GDP impacts when the out-

puts of MARKAL and CIMS are used by the

TIMS model.The goal of this paper is to detail first the

methodological similarities and differences of the

two models. This then provides the basis for un-

derstanding the differing GHG abatement cost

estimates that they produce for Canada, hopefully

in a way that will help to inform and guide policy

makers (HALOA, 2000; ERG/MKJA, 2000a). 3

In the final section, we comment more generallyon the broad lessons from the exercise, and on how

the models might be used as contrasting and

complementary tools for policy analysis.

2. Method

2.1. Situating MARKAL and CIMS among energy

models

Both MARKAL and CIMS are in the category

of models that keep track explicitly of technologies

and their turnover. In this sense, they differ from

those economic modeling tools conventionally

known as top-down models. The latter focus on

aggregate relationships (measured as shares ofexpenditures in constant monetary units) between

inputs and outputs of the economy, linking these

in an equilibrium portrayal of the economy’s

feedback loops. Energy forms, with their associ-

ated GHG emissions, are inputs for which a rela-

tionship is estimated between relative costs and

relative use, yielding a production function for

firms and a consumption function for households(from which are derived elasticities of substitu-

tion). In the ideal, these relationships are statisti-

cally estimated from market data, meaning that

they provide revealed consumer and firm prefer-

ences about technology choices.

But how useful for policy-making is it to have a

historically verified relationship between the rela-

tive costs of inputs and their use levels in the

economy? First, the future mix of available tech-nologies may differ fundamentally from that of the

past. This may affect costs and consumer prefer-

ences. Second, some policies to be explored, like

regulations, grants and tax concessions, focus on

individual technologies. Together, these factors

can lead to significantly different aggregate elas-

ticities of substitution in the future. Yet, with such

models, it is close to impossible to estimate howthe future elasticities might differ from those of the

past.

Technology-explicit models have been devel-

oped to deal with this problem. These bottom-up

models focus on the apparent financial costs of

technologies that, if widely deployed to meet the

energy service needs of firms and households,

would lead to dramatic reductions in GHG emis-sions. 4 However, the most simplistic forms of

bottom-up models tend to be simple accounting

devices that add up all of the best technologies

for providing the various products and services of

the economy without any estimation of critical

system-wide or interactive effects. In reality, the

choice of an energy using technology depends on

the simultaneous choice of energy supply tech-nologies and vice versa. A model that integrates

supply and demand is needed to solve for the op-

timal combination, in which decisions in one sec-

tor are dependent on all other decisions. Moving

toward a more general equilibrium framework, we

also know that energy supply and demand deci-

sions have an effect on the total and relative de-

mand for products and services in the economy.Simple bottom-up models are far from taking all

3 These documents provide complete descriptions of the

methods and results of the two models in the application

reported here.

4 A useful way to contrast bottom-up and top-down models

is to examine the way each describes the production function of

a sector of the economy. Top-down models adopt a closed-form

functional expression that allows the production factors of a

sector to substitute for one another via the use of elasticities of

substitution. In bottom-up models, the production function is

defined implicitly, as the model selects the mix of technologies

to use in each sector.

150 M. Jaccard et al. / European Journal of Operational Research 145 (2003) 148–164

of these feedback loops into account, whereas both

MARKAL and CIMS do.

In Section 2.2, we explain further how MAR-

KAL and CIMS are similar. Then, in Section

2.3, we explain the key differences in the two

models.

2.2. Key similarities of MARKAL and CIMS

Both MARKAL and CIMS are technology-

explicit models. For all of Canada, they each

include over 4000 technologies, with about 10

characteristics for each technology. These charac-

teristics are basically the same for the two models.They include:

• size, in terms of annual output of service or

product;

• capital cost;

• non-energy operating cost (operations and

maintenance costs);

• energy use per unit of output;• emissions per unit of output;

• lifespan;

• year of market availability;

• current market share;

• linkage to other services and products, technol-

ogies and processes;

• special market constraints; and

• other information, such as an annual availabil-ity factor, etc.

The technologies are allocated to the energy

using sectors – residential, commercial/institu-

tional, industrial and transportation – and the

energy producing and transforming sectors – en-

ergy mineral extraction, oil refining, natural gas

processing and electricity generation.While technology information in the two mod-

els differed somewhat as recently as two years ago,

the national climate change process in Canada has

contributed to a substantial harmonization. The

terms of reference for the national process required

that both teams modify their models to conform to

the technology information and market assump-

tions developed by the Issue Tables during theperiod 1998–1999. In the electricity sector,

MARKAL had been used by that Issue Table; its

technology details were transferred into CIMS.

For the industrial sector, both groups were asked

to conduct a special estimate for the Industrial

Table; in this case, much of the sector-specific

industry technology data in CIMS was incorpo-

rated into MARKAL. For the other sectors, bothmodels incorporated technology information from

the Issue Tables and de-activated technologies

that were inconsistent with the views of the Issue

Tables. The overall result is a technology corre-

spondence between the two models of greater than

95% for the analysis reported in this paper. In

terms of geographical disaggregation, both models

included separate modules for six Canadianprovinces, with the four Atlantic provinces com-

bined as a seventh region. While the characteristics

of most technologies are common to the whole

country, there are some technologies whose tech-

nical characteristics and even costs must reflect the

differences from one region to another in Canada

(e.g., hydropower, CO2 sequestration in deep sa-

line aquifers, transit).As noted above, both MARKAL and CIMS

attempt to capture system interactive effects. This

occurs both within the firm or household and

within a sector; thus, the choice of lighting tech-

nology may impact the choice of heating tech-

nology in buildings. This interaction also occurs

between energy supply and demand; thus, changes

in energy supply technologies may affect the rela-tive prices of energy (or the aggregate price of all

forms of energy) with an impact on the choice of

energy using technologies. One can say, therefore,

that both models pursue an equilibrium represen-

tation of the micro-economic feedbacks related to

energy use.

At the same time, neither model is general

equilibrium as generally defined by economicmodelers. First, neither model currently includes

the broader, macro-economic relationships that are

common to general equilibrium models: links be-

tween energy supply and demand, on the one hand,

and investment, government expenditure, interest

rates, employment levels and trade on the other.

Second, while both models have the capability to

model how changes to the costs of products orservices may change their demands, this capabil-

ity (service and product demand elasticities) was


disabled for this project. 5 In this application,

therefore, the models must be referred to as partial

equilibrium.

Both MARKAL and CIMS are based on a

stock accounting process. Technologies are ac-

counted for based on the energy service or physi-cal product they provide. Then, their evolution is

explicitly accounted for as a function of time-

dependent retirement and changing service and

product demands, together culminating in new

stock acquisition. Five basic steps are involved in

accounting for stock turnover, a process that both

models execute in five-year segments:

1. A base case macro-economic forecast drives

the model runs. Because this forecast is usually

produced by a macro-economic model, the

monetary estimates of sectoral economic growth

must be translated into growth forecasts of the

physical products and energy services used by

the models. This is the critical link between con-

ventional economists’ measures of economic ac-tivity and the physical measures of interest to

bottom-up modelers because it is the physical

services that are linked to the output of technol-

ogies. The forecast creates a demand for ser-

vices and products in the future. If the service

and product feedback elasticities of the models

have been disabled, as in this application, the

models look to this forecast for the productand service demands in each future period of

a run.

2. In each future period, some portion of the ini-

tial-year’s stock of technologies is retired. Re-

tirement is time-dependent, although both

models include functions that can accelerate re-

tirement based on economic conditions: chang-

ing costs may lead to premature retirement or

retrofit of technologies because of economic ob-

solescence. The outputs of residual (unretired)

technology stocks in each time period are sub-

tracted from the forecast energy service and

product demand (from the macro-economicforecast) and this difference determines the

amount of new technology stocks in which to

invest. 6

3. Prospective technologies compete for this new

investment. The competition is based on finan-

cial information and other factors. However,

this is where the two models differ fundamen-

tally, so the specifics of technology competitionare explained in the section below that describes

their major differences.

4. In each time period, a competition also occurs

to determine if any technologies will be retrofit-

ted or prematurely retired. Again, each model

does this differently, as explained below.

5. In each time period, the models determine the

supply–demand equilibrium. However, CIMSdoes this differently than MARKAL. Thus,

the models are similar in having the same steps

of accounting for stock turnover, but they differ

in how they execute these steps. Once the final

stocks are determined, both models add up

the energy use, costs, emissions and other out-

puts that can be calculated from the contribu-

tion of each technology to the economy’sservice and product needs.

As this description of the stock turnover/stock

accounting procedure suggests, a special issue in

using MARKAL and CIMS for economic policy

analysis is that, while these models use financial

and technical information to determine technology

stock shares, they actually measure stocks andoutput in material and energy units, not monetary

units. This link of technology models to economic

measures is a challenge for GHG emission abate-

ment modeling. However, it is a challenge that

cannot be avoided if GHG policy-making is to be5 In order to separate the analysis into micro and macro

components, the total product and service output of the

economy, both final and intermediate, did not change from

the base case forecast for the phase reported here – except for

energy services and products. The only other exception was the

demand for personal mobility, expressed as kilometers traveled

(see Section 3.2). Otherwise, the demands of final consumers for

products and services, and the output of each industrial sector,

did not change in this micro-economic analysis.

6 As noted, there is no constraint or feedback effect on total

investment, which one would have in a general equilibrium

model. However, such a constraint, based on various macro-

economic parameters, could be built into the models without

great difficulty.


enriched by an explicit representation of existing

and emerging technologies, technologies that may

be very different from those of the past. The

transition from physical flows to financial flows

has been partially addressed by the two models

during the national climate change process, byusing the physical commodity flows and the com-

modity prices to compute monetary flows in and

out of each sector.

2.3. Major differences between MARKAL and

CIMS

In spite of their many similarities, MARKALand CIMS differ in terms of how they determine

the technology choices for meeting new stock re-

quirements and how they find equilibrium between

energy supply and demand. These differences can

be attributed to the differing objectives of the two

models. MARKAL seeks to be prescriptive in

terms of the technology outcome that would

minimize society’s costs based on the basic finan-cial and efficiency information provided for each

technology option. In contrast, CIMS seeks to

predict how firms and households will respond to

various policies to induce changes in their tech-

nology choices; thus, it must focus not just on

the basic technology-specific financial informa-

tion but also on the combined effect of this with

other factors that influence firm and householdtechnology decision-making. This latter includes

intangible consumer preferences for certain tech-

nologies, differences in perceived risks of technol-

ogies (new vs. conventional), and time preferences

that differ from the social discount rate (short

payback vs. long payback). 7

These different objectives lead to different al-

gorithms for estimating technology choices. Themajor differences are outlined below:

1. MARKAL’s calculation is based on the basic

financial costs of technologies. In contrast, CIMS

attempts to include monetary proxies for the in-

tangible values that firms and households may

attach to certain technologies. Economists refer

to this in part as the lost consumers’ surplus thatoccurs when consumers are forced away from a

technology that they originally favored. Research

shows that this especially applies to households as

final consumers, for whom many technologies that

may appear to provide the same service or product

are in fact not seen as perfect substitutes. For ex-

ample, mass transit and cars both provide mobility,

but consumers may be willing to pay a premiumto use a car (or must be paid compensation to

give up using the car). Over the last two decades,

there has been considerable research on the value

of such premiums for different types of energy-

using equipment (Huntington et al., 1994). Some

research focuses on attributes that may remain

somewhat stable over time. For example, the trade-

off between horsepower and efficiency in privatevehicles that existed in the past may continue into

the future. There is research to reveal the magni-

tude of this revealed preference for the average

consumer. Some research focuses on new products

with new attributes and asks consumers to estimate

hypothetically what they would be willing to pay

(or demand in compensation) for these new attri-

butes. For example, consumers may require com-pensation before willingly switching from a

gasoline-driven car to an electric car. Of course,

both of these types of research lead to highly un-

certain parameters. Because of this inherent un-

certainty, sensitivity testing of CIMS’ behavioral

parameters is an important part of its use as a

model for policy analysis. In sum, the difference of

MARKAL and CIMS with respect to costs meansthat the estimates of GHG abatement costs of the

two models must be interpreted carefully.

2. Like any optimization model, MARKAL

calculates new stock technology shares on the

basis of winner-take-all. Small changes in costs can

lead to dramatic changes in outcomes, referred to

as penny-switching. MARKAL modelers are able,

however, to offset this characteristic to some de-gree by segmenting each sub-sector of demand into

differentiated sub-segments and by the careful

7 In this effort to include firm and household preferences,

CIMS shares attributes with top-down models. The major

challenge is to estimate the monetary value of these preference

differences for technologies that have just, or not even yet,

emerged on the market. This general approach has been

referred to as hybrid modeling or integration of top-down

and bottom-up modeling (Jacobsen, 1998).


https://www.researchgate.net/publication/4944096_Integrating_the_bottom-up_and_top-down_approach_to_energy-economy_modelling_the_case_of_Denmark?el=1_x_8&enrichId=rgreq-ff3c9fda-9b10-4a73-bb29-bb78d3ecbc66&enrichSource=Y292ZXJQYWdlOzIyMjgyNTkxNztBUzo5OTczNDI3ODYzOTYyNUAxNDAwNzg5OTEzNTU5

application of additional technology market share

constraints if desired by the decision-maker or user.

This was the case for many technologies in the

application reported here. In contrast, the inten-

tion with CIMS is to reproduce the probabilistic

character of firm and household technology choi-ces, as revealed by consumer choice research. Thus,

market shares are a probabilistic function of the

financial costs and other preferences of consumers.

The technology that is favored on this basis will

capture the largest new stock market share. How-

ever, if its advantage over one or more competing

technologies is only marginal, market shares will

be almost equal. Only as the advantage becomessubstantial do market shares differ significantly.

Even then, technologies that appear significantly

unfavorable will in many cases still capture a non-

negligible fraction of the market, again consistent

with market research. 8 The shape of the market

share trade-off curve, for any given set of compet-

ing technologies, is only known for certain energy

services and technologies, again because the focusis often on new technologies for which there is little

or no historical data. However, the curve can be

approximated from market behavior evidence of

the relative importance of financial cost differences

in various types of technology choices. 9

3. In addition to how technology costs and

preferences are characterized, and then how tech-

nology market shares result from these character-izations, the twomodels differ in their basic solution

algorithm. As an optimization model, MARKAL

generates a global solution that simultaneously

minimizes the objective function and satisfies all

constraints for all time periods and all sectors. This

solution is optimal from the point of view of all

information available to the model; in other words,

every technology choice is informed by all other

technology choices in all time periods. In contrast,

as a simulation model, CIMS tries to reflect the

bounded rationality of market decision-making.Its equilibrium solution is found by iterating

between, for example, the supply and demand sec-

tors. Changes in one sector (energy demand) induce

changes in another sector (energy price), which re-

quire rerunning of the initial sector. The model it-

erates until it converges to an outcome in which

changes in all sectors are very small. This is com-

pleted for one time period and then the modelmoves on to the next time period. The solution in

that next time period has no bearing on the previous

one. This difference between MARKAL and CIMS

also explains why optimization models are more

amenable to modeling large integrated systems. As

more linked systems are integrated in a simulation

model like CIMS it becomes more of a challenge to

simulate an equilibrium solution. 10

4. Consistent with its optimization logic, all

prices in MARKAL are based on marginal costs,

the cost of the last unit of product or service pro-

vided in every sector. For example, an economy-

wide constraint on GHG emissions will cause new

electricity sector investments, whose marginal cost

is likely higher than the average cost of produc-

ing electricity. This marginal cost then sets thenew price of electricity, which is the price faced by

all electricity consumers for all units consumed.

CIMS, in contrast, sets electricity prices based on

an assessment of average production costs. In the

Canadian economy today, most electricity prices

are regulated and based on average cost although

the pricing scheme may change in the future due

to deregulation in some provinces. Thus, highercost investments to reduce GHG emissions in the

electricity sector will lead to higher electricity pri-

ces, but only to the extent that average costs are

driven up by the higher cost incremental invest-

8 There are many reasons for this. First, actual financial costs

can differ between locations; this can be due to differences in

delivery costs, or installation costs or even the degree of

competition between suppliers. Second, perceptions of financial

costs may differ from reality. Third, some consumers have very

different preferences than the average, whether with respect to

relative risk, or payback period, or the particular qualities of

certain technologies and energy forms.9 A rather flat trade-off curve implies that many other factors

are important, while a vertical curve will lead to penny-

switching outcomes like an optimization model.

10 See the discussion below on interprovincial electricity

trade and internationally linked models.


ments. This is likely to be significantly less of an

increase than with marginal cost pricing. 11

In addition to these fundamental differences

between an optimization and a simulation model,

the models also had other differences in this par-

ticular application. These relate more to how theterms of reference for the national climate change

process were interpreted and the current state of

each model’s development.

1. The Canadian version of MARKAL has the

capability to include interprovincial electricity

trade and trade with the US. 12 The terms of ref-

erence called for disabling the trade link to the US.

However, the interprovincial electricity trade wasincluded. CIMS does not currently have this ca-

pability. It could simulate the effect of alternative

scenarios of interprovincial electricity trade, but

this was not tested in this application.

2. Another difference arose because the two

teams interpreted differently one of the terms of

reference. While domestic output of crude oil was

assumed to remain constant in both models, asrequired by the client, the MARKAL modelers as-

sumed that reductions in domestic demand for re-

fined petroleum products (under the GHG emission

reduction policies) would lead to the closure of some

domestic refineries, with the surplus domestic crude

oil being exported. The CIMS modelers assumed

that such reductions would lead to no change in do-

mestic refinery output, with an increase in exports ofrefined petroleumproducts as domestic demand fell.

3. Some of the runs for this project called for

both models to find a least-cost solution across all

sectors and all regions of the country that would

achieve the country’s Kyoto commitment for

GHG emissions reduction (6% reduction from

1990 levels). The models solve for this in different

ways. With the inclusion of a global constraintthat matches the GHG emission target, the

MARKAL model is assured of finding a solution.

The shadow price of the GHG constraint repre-

sents the marginal cost of GHG emission abate-

ment. The CIMS approach differs in that a global

constraint is not possible with this type of model.

Instead, the model is run several times at different

imputed costs of GHG emissions. These are re-ferred to as shadow prices for GHG emissions; to

an economist, they are effectively the same as the

shadow price associated with the global emission

constraint in MARKAL. However, this led to a

difference in how the two models treated the

emission target in subsequent periods (beyond

2010). While both models covered the period

2000–2020 in their runs, MARKAL maintainedthe Kyoto constraint beyond 2010 all the way to

2020; this means that GHG emissions remained

constant. CIMS maintained the shadow price

throughout this period, but this did not prevent

GHG emissions from rising somewhat after 2010.

In the results and concluding sections, we return

to these differences between the model methodol-

ogies and applications in order to assess their rel-ative contributions to differences in the cost

estimates. However, here in the methodology sec-

tion one can anticipate the directional effect of

these differences. Indeed, all but one of the meth-

odological differences should lead to lower cost

estimates from MARKAL.

1. While MARKAL’s cost estimates are restrictedto basic financial costs, those from CIMS in-

clude monetary estimates of some intangible

financial costs, effectively a part of what econo-

mists call consumers’ surplus. This would make

CIMS’ cost estimates higher.

2. The winner-take-all modeling approach of

MARKAL means that the lowest cost choice

is always taken. With its probabilistic approach,CIMS will allow higher cost technologies to

capture part of the market. This would make

CIMS’ cost estimates higher.

3. The bounded rationality of CIMS in time and

space would also lead to higher costs. In MAR-

KAL, firms and households choose technolo-

gies that are optimal for all time periods. In

CIMS, they choose technologies under currentprices that may prove sub-optimal at future

time periods, resulting in higher operating costs

11 Prices could theoretically be set on the basis of marginal

costs in CIMS, although the probabilistic nature of technology

market shares makes it difficult to identify the source of the

marginal kWh. The user must define which single technology or

combination of technologies represents marginal cost.12 This reflects the greater ease with which systems can be

linked in an optimization model.


or new retrofit investment costs. This would

make CIMS’ cost estimates higher.

4. The marginal cost pricing in MARKAL ensures

that least cost pricing decisions are made

throughout the economy for all types of deci-sions. With its average cost pricing of electricity

supply, CIMS sends a different signal through

the economy. Its shadow price for GHG emis-

sions does send a common signal for this exter-

nality, but decisions depend on all costs, not

just those of GHG emissions, and the marginal

cost of MARKAL ensures that uniform signal.

This difference may lead to slightly higher costsin CIMS, but its effect is especially felt in the al-

location of costs between sectors. Thus, mar-

ginal cost pricing of electricity in MARKAL

results in a transfer of revenue (if unchecked)

from consumer sectors to the electricity sector.

5. Endogenous interprovincial trade in electricity,

to the extent this option is adopted in MAR-

KAL, would lead to lower costs in that model.Without this option, CIMS must find invest-

ments to reduce GHG emissions elsewhere,

and these would be higher cost if the MAR-

KAL model already ignored them in favor of

increased interprovincial electricity trade. Sensi-

tivity analysis suggests that this is fairly impor-

tant in explaining the cost differences in the two

models, although not nearly as important asfactors 1–3 above.

6. Likewise, allowing high cost refineries to shut-

down, as permitted in MARKAL, would lead

to lower costs, although this depends on the

profit margins assumed for refineries relative

to those of crude oil exports. Again, CIMS

would have to find equivalent reductions from

other measures that must be higher cost if ig-nored by MARKAL.

7. Finally, one methodological difference works in

the opposite direction in terms of cost estimates;

MARKAL sustains GHG emissions at their

2010 level through to the year 2030. MARKAL

must look to increasingly higher cost actions as

the normal growth in the economy pushes up

emissions. This is less of a requirement in theCIMS runs, as the shadow price is sustained

but emissions are allowed to rise. The magni-

tude of the difference is at least reduced in that

the net present value results of the two models

only extend to costs incurred up to 2020. Thus,

it is only the discounted costs from 2010 to 2020

that push up MARKAL costs relative to CIMS.

3. Input data and model parameters

3.1. Calibration

The first task for both models was to calibrate to

an external energy and emission forecast provided

by the Analysis and Modeling Group (AMG), 13

called Canada’s Emissions Outlook – an Update

(CEOU, AMG, 1999), covering the period 1990–

2020. To do so, the models were first calibrated for

the past periods, 1995–2000, and then run. For

each sector, the differences in energy consumption

observed in 2010 and 2020 between the models’

results and the forecast were noted, and the models’

parameters adjusted to reduce these differences. InMARKAL, the main tool to force the model to

conform to a forecast is the imposition of con-

straints to limit the penetration of some fuels in

some sectors. There were relatively few such con-

straints needed. In CIMS, the behavioral parame-

ters were adjusted until the model replicated the

forecast within an acceptable margin. Table 1

shows the results of the emission calibration forboth models, although the calibration was con-

ducted at a much more detailed level for each sub-

sector and each energy form. The reader is invited

to examine the full reports for a complete set of

calibration results.

3.2. Actions and measures from issue tables

As mentioned in Section 1, the main objective

of the process was the integration of the numerous

sectoral measures and actions proposed by the

Issue Tables into a coherent micro-economic

framework, in order to reach the Kyoto emission

target. To do so, each model’s database was al-

tered to include the technological and behavioral

13 The national climate change process created an Analysis

and Modeling Group to provide analytical support for the Issue

Tables and to integrate the research into final reports.


changes described in the Issue Tables’ option pa-pers. There were more than 300 actions in total,

spread over all sectors of the economy. We give

below a brief summary of the different categories

of measures and actions in each sector:

• Electricity sector. The single micro measure

modeled is a subsidy on the purchasing of elec-

tricity from lower emission, emerging technolo-gies. However, the models were also equipped

with many substitution possibilities that would

be brought into play as soon as a carbon pricing

mechanism were utilized (such as a cap or tax on

CO2e14 emissions, see Paths 1–4 in the following

section).

• Upstream oil and gas, oil refining, and industry.

The Industry Table identified a number of tech-nological improvements and good practices.

Three measures were defined by the AMG: en-

hanced voluntary, enhanced cogeneration, and

capital subsidy for all actions with a cost per

tonne of CO2e less than a threshold value. This

value was varied from $75 in Path 0 to $300 in

Paths 1 and 3.

• Transportation. A large number of measureswere identified, which included actions such

as: fuel economy, change in vehicle consump-

tion, decrease in kilometers traveled, and

switching to bio fuels (ethanol). The policies

triggering these changes were of several types,

including: incentives, educational programs,

regulations, infrastructure improvements, and

a fuel tax for road transportation. 15

• Residential, commercial and institutional. Here

too, there were a large number of GHG re-

duction actions such as building shell im-

provements, replacement of heating, cooling

and lighting equipment with improved versions,and better operating practices. The policies trig-

gering these actions included building code

changes, incentives aimed at existing buildings,

and labeling and educational programs.

• Municipalities. The measures were aimed at re-

ducing water consumption, better treatment of

waste water, capture and use of land fill gas

(from dumps), increased cogeneration of heatand power, and land use changes.

• Others. These included forestry programs to af-

forest and reforest specific areas, and best agri-

cultural practices to enhance retention of

carbon in agricultural soils. In addition, the nat-

ural carbon sequestration potential of Cana-

dian forests and soils was assessed.

3.3. Scenarios and paths to Kyoto

Apart from the business-as-usual (BAU) sce-

nario, described by the CEOU, three scenarios

Table 1

Calibration results: Model emissions vs. Canadian emissions outlook

Sector CEOU MARKAL CIMS

1995 2010 2020 1995 2010 2020 1995 2010 2020

Upstream 98.0 123.0 137.0 104.3 124.9 124.6 –a –a –a

Electricity 100.0 119.0 120.0 101.8 124.3 120.1 102.0 128.6 150.0

Industry (w/o upstream) 128.0 137.0 154.0 133.0 139.4 160.9 233.0a 254.7a 281.5a

Residential 51.0 48.0 51.0 49.7 46.0 49.2 50.0 46.8 49.8

Commercial +waste 51.0 58.0 60.0 54.9 58.7 62.8 48.0 51.8 55.0

Transportation 159.0 197.0 228.0 159.1 190.3 221.8 160.0 198.2 232.9

Othersb 63.0 79.0 90.0 63.0 79.0 90.0 63.0 80.0 90.0

Total 650.0 761.0 840.0 665.7 762.6 829.4 656.0 760.0 859.0

aCIMS modeled upstream and downstream oil and gas as a single industry.b Includes: agriculture, forests, land use change, propellants, and HFCs, which are not modeled.

14 CO2e is a convention for converting all GHGs into CO2

equivalents in terms of greenhouse effect.

15 As noted, the measures that decreased kilometers traveled

were the only case in which a service or product demand was

allowed to decrease as part of the micro-economic analysis. The

decision to make this exception was made by the AMG.


about Canada and US GHG emission policies

were studied. The scenario parameters are sum-

marized in Table 2.

• In the ‘Canada acts alone’ (CA) scenario, it is

assumed that Canada pursues GHG emissionreductions while other countries do not. There-

fore, the energy markets are similar to what

they were in the BAU scenario, and Canada

must realize all of its targeted reductions do-

mestically because there is no international

market for emission permits.

• In the ‘Kyoto-tight’ (KT) scenario, it is assumed

that other countries also pursue GHG emissionreductions and that there is a market for inter-

national permits, with a permit price of $60/

tonne CO2e. In this scenario, the prices and

quantities of gas and electricity exported by

Canada are higher than in BAU, and the price

and quantity of exported oil are lower.

• In the ‘Kyoto-loose’ (KL) scenario, inter-

national permits are available at $25/tonneCO2e, denoting a broader market for permits.

At the same time, the gas and electricity export

markets are less favorable to Canada than in

KT (but more favorable than in BAU).

For each scenario, the Kyoto target was pur-

sued in several ways – called paths, depending on

how the models were allowed to choose among theIssue Tables’ actions and on the breadth of the

policy instrument(s) selected to induce the actions.

We now briefly describe the five paths:

• Path 0. The Issue Tables’ actions were imposed

on the models. Because some of the Issue Tables

could not come up with sufficient actions to

achieve their sector’s proportion of the national

target, the collective effect was that Canada fellshort of achieving its Kyoto reduction target.

• Path 1. The Issue Tables’ actions were imposed

and the models were asked to find additional re-

duction actions so that each sector emitted 94%

of the 1990 emissions in 2010. This amounts to

imposing a cap on each sector separately.

• Path 2. The Issue Tables’ actions were avail-

able, but the models were free to choose or ig-nore them in order to reach the Kyoto target

for Canada as a whole (and not sector by sec-

tor). This amounts to imposing a global cap

on Canadian emissions with trading allowed be-

tween sectors. However, no specific initial allo-

cation of permits to sectors was defined.

• Path 3. This is a hybrid between Path 1 and

Path 2 in which the electricity and industrialsectors had a common reduction target for

2010 (equal to 94% of their joint 1990 emis-

sions) and the other sectors were treated as in

Path 1, i.e., with individual targets.

• Path 4. This is also a hybrid of Paths 1 and 2 in

which most sectors (totaling more than 80% of

emissions in 1990) are under the same cap and

the rest have individual targets.

All five paths were simulated under the CA

scenario, but only Paths 2 and 4 were simulated

under the KT and KL scenarios. In this article we

Table 2

Specification of Kyoto-loose and Kyoto-tight scenarios (% change relative to BAU)

Condition Kyoto-loose Kyoto-tight

2005 2010 2020 2005 2010 2020

Natural gas export prices 1 0 13 1 18 38

Natural gas export volumes )1 )1 3 0 7 9

Crude oil export prices )1 )4 – )2 )11 )9Crude oil export volumes 0 0 1 )1 )1 0

Average electricity export price 2 20 30 23 49 45

Coal import price 2 3 3 2 2 1

Carbon permit price (US $1996/tonne) – 67 99 – 163 141

CO2 permit price (CDN $1996/tonne) – 25 36 – 60 52

Source: US DOE/EIA (US Department of Energy, 1998).


do not present an exhaustive set of results, but

instead focus on selected results that best indicate

the contribution of modeling methodology to di-

vergent cost estimates. 16

4. Results and discussion

In this section we examine the emissions and the

costs of each case and discuss a few technologies

that play a major role in the models’ solutions.

4.1. Canadian emissions

As shown in Table 3, of total Canadian emis-

sions in 2010, Paths 0 and 1 do not reach the 94%

Kyoto target for Canada as a whole.

In Path 0, only 71% of the Kyoto Gap is filled

via Issue Tables’ measures in the MARKAL run

and 75% in the CIMS run. This is due in great part

to the lack of options costing less than $75/tonne

in industry and upstream oil and gas. In addition,recall that only one relatively minor action was

included in the electricity sector in Path 0. In Path

1, industry as a whole still falls short of its sectoral

target, signaling that industry actions costing up to

$300/tonne CO2e are still not sufficient to reach the

94% target in this sector. All other sectors reach

their reduction target. Overall, MARKAL fills

90% of the Kyoto gap and CIMS 84%. By con-

struction, Paths 2–4 reach the Canadian Kyoto

target.

Under KT, MARKAL shows few permits being

purchased (indeed some are even sold in 2010)

while CIMS requires that Canada purchase a

substantial quantity of permits. However, underKL, the position is reversed; MARKAL shows

30% more permit purchases by Canada than

CIMS. This indicates that more of the actions

represented in MARKAL have costs in the $25–

$60/tonne CO2e range. Fig. 1 provides a rough

illustration of this effect, showing how the relative

costing of actions and behavioral representation in

the two models can result in shadow price trajec-tories that have different shapes and may cross.

Many actions may be responsible for this. One

notable example is the sequestration of CO2. In

this action, electric utilities inject CO2 into deep

saline aquifers or old oil and gas wells. The cost of

sequestering one tonne of CO2 is estimated at

Table 3

Canadian emissions and Kyoto gaps in 2010 (Mt CO2e/year)

1990 2010 GAP in 2010

MARKAL CIMS MARKAL CIMS

BAU 601.5 746.8 743.2 181.8 178.7

Path 0 601.5 618.4 608.4 53.5 43.9

Path 1 601.5 583.1 592.8 18.1 28.3

Path 2 601.5 565.5 562.8 0.5 )1.7Path 3 601.5 564.8 561.1 )0.2 )3.4Path 4 601.5 564.9 562.3 0.0 )2.1Path 2 KT 601.5 572.5 592.9 7.6a 28.5a

Path 4 KT 601.5 562.6 592.7 )2.4b 28.2a

Path 2 KL 601.5 644.2 613.4 79.2a 48.9a

Path 4 KL 601.5 630.5 613.3 65.6a 48.8a

aA positive gap is filled by purchasing emission permits from abroad.bA negative gap means that some permits are sold to other countries.

16 See the original reports for greater detail. Fig. 1. Comparison of shadow price for GHG abatement.


about $35/tonne CO2e and falls in the range

mentioned, between $25 and $60/tonne CO2e. In

MARKAL, at the permit price of $25/tonne CO2e,

all of the sequestration reductions are not invoked,

whereas this action in CIMS does not play so

significant a role and is not totally eliminated whenpermit prices are at $25 (see Section 4.2).

In summary, both models exhibit a very similar

and consistent set of emissions in all nine simu-

lated cases. Any differences can be explained by

MARKAL’s propensity to require lower shadow

prices than CIMS, as expected from the model’s

objective of minimizing financial costs.

4.2. Sectoral shares of emission reductions

We now focus on the five Canada-alone paths

to discuss sectoral emission reductions as simu-

lated by the two models. The results for scenarios

KT and KL do not add much to the insights

gained by our analysis.

In Path 0, it was expected that the CIMS andMARKAL reductions in all sectors except indus-

try should be similar since the path consists in

applying the Issue Tables’ measures in each sector

without much freedom left to the models. The

exception is industry, where a GHG shadow price

of $75/tonne CO2e was applied which enabled

MARKAL to exhibit its financial cost minimizing

character and thus show greater reductions thanCIMS at that price. This translates into a larger

share of reduction by industry in the MARKAL

results and correspondingly smaller shares in all

other sectors.

In Paths 1 and 3 (quite similar in design, see

Section 3), emissions in all sectors except industry

are again identical in both models. In industry,

MARKAL achieves more reductions (as it did inPath 0) and therefore the resulting Kyoto gap is

smaller in MARKAL’s results than in CIMS’.

In Paths 2 and 4, characterized by a global cap

on emissions (quasi-global in the case of Path 4),

MARKAL shows markedly larger reductions in

the Electricity sector than CIMS and correspond-

ingly smaller reductions in Transportation. Two

features of the models explain this. First, the in-terprovincial trading of electricity is allowed to

vary in MARKAL, but remains fixed across all

CIMS simulations. MARKAL increases the trade

of hydro electricity from hydro rich provinces

(Newfoundland, Quebec, Manitoba and BC) to

other regions as an important part of its emission

reduction strategy. Second, MARKAL shows a

larger take-up of the geological sequestration ofCO2 from flue gases of coal-fired electricity plants

than CIMS (43 Mt CO2 in 2010 vs. 27 Mt). The

reason for the larger take-up is again the absence

of behavioral inertia in MARKAL (see Table 4).

In summary, the two models’ results are gen-

erally close together, with the remaining dif-

ferences explained by the models’ portrayal of

business and consumer decision making charac-teristics. It may be said that the divergent results

provide a range of estimates for the emission re-

Table 4

Sectoral shares of emission reductions in the five Canada-alone paths

Electricity (%) Industry (%) Residential +

commercial (%)

Transportation (%) Other (%)

Path 0 MARKAL 11 29 10 41 8

CIMS 14 21 12 46 8

Path 1 MARKAL 20 36 7 30 7

CIMS 24 24 6 38 7

Path 2 MARKAL 57 17 11 9 5

CIMS 43 16 11 24 6

Path 3 MARKAL 40 20 7 27 6

CIMS 43 17 4 31 6

Path 4 MARKAL 55 19 11 10 6

CIMS 43 16 11 24 6


ductions that might be obtained in each sector

under the various paths.

4.3. GHG shadow prices

We have already noted how certain differencesin reductions by the two models are explained by

the different shadow prices needed to achieve the

required reductions. Table 5 shows these shadow

prices in all five paths; the shadow prices in the KT

and KL scenarios are exogenously set at the price

of CO2e permits (approximately $60 and $25 per

tonne as shown in Table 2).

In Path 0, there are no shadow prices because aset of actions were simply imposed in each sec-

tor. The exception is industry where the shadow

price is set exogenously at $75/tonne CO2e in both

models. In Path 1, each sector has a different price

of GHG since there are individual sectoral caps

(except again in industry, where the price was set

at $300/tonne CO2e.17 As expected, MARKAL

requires lower shadow prices than CIMS since itsleast financial cost nature allows the model to

reach the caps at lower cost. Similar observations

apply to Paths 2–4. 18

4.4. Cost of abatement

The cost of abatement of a particular path is the

difference between that path’s total system costs

and the total system costs of the BAU run. We

show in Table 6 the total discounted present value

in 2000 of Canadian GHG reduction costs. The

discount rate is 10% and the units are 1998 Ca-

nadian dollars. As noted previously, CIMS’ cost

estimates incorporate some of the intangible wel-fare costs (lost consumers’ surplus) of firms and

households who purchase equipment that they

would not otherwise purchase in the absence of an

incentive or regulation.

While the CIMS results include some of the es-

timated losses due to lost consumers’ surplus from

switching technologies, a second type of consum-

ers’ surplus loss is incurred when GHG emissionreduction policies involve a reduction in service

demand. For example, some of the transportation

measures reduce the demand for person–kilome-

tres traveled as opposed to just switching between

transportation technologies for the same level of

demand. These service demand losses were not

estimated for both models and thus are not re-

ported here. 19

As expected, MARKAL’s cost estimates are

significantly lower than CIMS’ for all paths except

Path 0 where they are close. The difference is small

in those paths that are highly constrained (Paths 0,

1 and 3), and much larger for unconstrained

paths (Paths 2 and 4), where the least financial cost

nature of MARKAL manifests itself more com-

pletely.The estimated changes in investment and op-

erating costs that are associated with these cost

estimates of MARKAL and CIMS then provided

inputs for applications of the TIMS macro-

economic model to estimate the effect on total

economic activity in the Canadian economy. Re-

call that industrial output, with the exception of

electricity, was held constant for the runs by CIMSand MARKAL. With TIMS, industrial output

was allowed to change in response to changing

input costs, final demands, government budgets

17 This was set as an arbitrary limit because industry was still

not reaching its target in CIMS and the marginal abatement

cost curve was getting too steep for higher prices to make much

difference. It is important to remember that industrial output

was not allowed to change in response to rising costs of

production. This is good in terms of allowing a clear compar-

ison of the two models, but at these high shadow prices it

renders the exercise less plausible. Fortunately, this is less of an

issue at the prices in Paths 3 and 4.18 In CIMS, the marginal cost of GHG reduction has a

somewhat different interpretation than in MARKAL. Whereas

in MARKAL, the shadow price is the cost of reduction of the

marginal tonne of GHG throughout the system, in CIMS, it

represents the cost incurred by the marginal agent to implement

the marginal tonne of reduction. In MARKAL, all agents that

happen to implement the last tonne of reduction have identical

behavior. In CIMS, they do not: there is a distribution of

agents, each with their own implicit cost for accepting to effect

the last tonne of reduction.

19 Both models can provide this. Indeed, MARKAL auto-

matically computes loss of this consumers’ surplus due to

reduced demands. However, these losses are not reported here

because they were only available for the road travel segment of

the transportation sector (at the request of the AMG) and, in

any case, were not calculated by the CIMS modeling team.


and net exports. In this sequential linking of bot-tom-up models with a macro-economic model –

for the least constrained Paths 2 and 4 – the results

from the CIMS inputs showed a 3% reduction in

GDP over the 10 years between 2000 and 2010

while those from the MARKAL inputs showed a

less than 1% reduction in GDP. 20

5. Conclusions

Climate change policy makers face many

uncertainties. One apparently significant uncer-

tainty is the impact of modeling method on esti-

mating the costs of reducing GHG emissions.

Unfortunately, policy makers are rarely able to

test for the importance of this particular uncer-tainty because models are rarely used in identical

circumstances.

In the period 1998–2000, the Canadian national

climate change process used two different kinds of

micro-economic models to explore the marginal

and total costs of paths that integrated actions to

reduce GHG emissions from all sectors of the

economy. The models were used with almost iden-tical assumptions about business-as-usual trends in

the economy, technology options and specific ac-

tions to reduce GHG emissions. Yet one model,

MARKAL, is an optimization model while the

other model, CIMS, is a behavior simulationmodel.

As expected, the MARKAL cost estimates are

substantially lower than those of CIMS. In Paths 2

and 4 – the least constrainted paths – MARKALestimates a net present direct cost (excluding

macro-economic effects) to the Canadian economy

of $14–$20 billion (1998 C$) while CIMS estimates

a cost of about $45 billion.

From our detailed analyses, and other sensi-

tivity tests, we estimate that this difference is pri-

marily attributable to the way in which technology

Table 6

GHG reduction costs in 2000 (NPV in billions of 1998 C$,

2000–2022)

Total

Path 0 MARKAL 45.5

CIMS 42.2

Path 1 MARKAL 51.8

CIMS 61.1

Path 2 MARKAL 13.6

CIMS 44.5

Path 3 MARKAL 35.9

CIMS 46.6

Path 4 MARKAL 19.7

CIMS 44.9

Table 5

Shadow price required to reach target by sector ($/tonne CO2e)

Model Economy Electricity Industry Commercial/instituitional/

munipalites/residential

Transport

Path 0 MARKAL 75a

CIMS 75a

Path 1 MARKAL 22.7 300a 5.1 69.6

CIMS 30 300a 10 300b

Path 2 MARKAL 56.5

CIMS 120

Path 3 MARKAL 32 32c 5 84

CIMS 110 110c 10 300b

Path 4 MARKAL 48.7a

CIMS 120a

a Set exogenously.bReflects the marginal cost of the highest cost measure included in the assessment, fuel tax level at $50/tonne CO2e.c Valid only for sub-sectors covered by the cap, see Table 2.

20 Participants recognized that running the micro- and

macro-models sequentially instead of iterating to an equilib-

rium is misleading in that industrial output might have changed

significantly in some sectors before undertaking high cost

actions. However, the pedagogical benefit of seeing the separate

effects of the micro-economic and macro-economic models was

valued more highly at this stage of the national process.


choices are modeled, that is optimization versus

behavioral simulation. 21 Other factors that lead to

lower costs with MARKAL are its allowance of

increased interprovincial electricity trade and the

closure of petroleum refineries as the domestic

demand for refined petroleum products decreases.As an offsetting effect, the difference in results

would have been even greater if CIMS had been

required to sustain the year 2010 emissions at the

same level through to 2020, as MARKAL did. We

estimate that this could have pushed the difference

up by 25%, increasing the CIMS cost estimate into

the $55 billion range.

Although the direct cost estimates of one modelare three times those of the other, both results

appear to have a relatively modest impact on the

Canadian economy, depending on the assump-

tions about international developments and mac-

ro-economic vulnerabilities. When used as inputs

to a major Canadian macro-economic model

(TIMS), neither model’s results suggest that

achieving the Kyoto target would reduce by morethan 3% what would be almost 30% of cummula-

tive economic growth over a 10-year period (2000–

2010). This is an important overall lesson because

there have still been suggestions that the Kyoto

commitment would have a much more dramati-

cally negative impact on the Canadian economy.

That two models with such different methodolo-

gies would both reach this general conclusionprovides some reassurance to decision makers with

respect to this particular concern.

Continuing to use the models as currently

designed and applied is one way of providing

ongoing information of the importance of meth-

odological differences to the cost estimates. An

alternative strategy is to explore how the models

can be brought closer together. For example, in-tangible costs and other behavioral inertia could

be incorporated into the MARKAL model. Also,

the CIMS model could apply a form of marginal

cost pricing as well as changing its treatment of

interprovincial electricity trade and refinery clo-

sure decisions. Another issue for CIMS is to assess

how not just capital costs but also intangible costs

might decrease as new technologies gain market

share. Some of these modifications are currently

being explored.

Acknowledgements

The authors wish to acknowledge the important

contribution to the modeling exercise of Chris

Bataille, Roberto D’Abate, Alison Laurin, Mi-

chael Margolick, Rose Murphy, Mallika Nanduri,Bryn Sadownik, Amy Taylor, Kathleen Vaillan-

court, and all the members of the Analysis and

Modeling Group including the macro-economic

modelers.

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Methodological contrasts in costing greenhouse gas abatement policies: Optimization and simulation...

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