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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 $15 billion. When linked to a macro-
economic model, the GDP impact of CIMS is 3% while that of MARKAL is less than 1%. This difference would have
been slightly larger had all target assumptions of the two models been identical.
� 2002 Elsevier Science B.V. All rights reserved.
Keywords: Decision analysis; Large-scale optimization; Energy; Environment
1. Introduction
Modeling the cost of greenhouse gas (GHG)
abatement has many sources of uncertainty. One
of these is due to contrasting methods of modeling
technological change. On the one hand, optimi-
zation models are recognized as powerful tools
for finding that economic equilibrium path which
minimizes the financial cost of reducing GHG
emissions. These models can provide policy-mak-
ers with an ideal portfolio of technologies. In thissense, they are described as normative or pre-
scriptive. On the other hand, behavioral simu-
lation models are recognized as critical for
European Journal of Operational Research 145 (2003) 148–164
www.elsevier.com/locate/dsw
*Corresponding author. Tel.: +1-604-291-4219; fax: +1-604-
291-4968.
E-mail address: jaccard@sfu.ca (M. Jaccard).
0377-2217/03/$ - see front matter � 2002 Elsevier Science B.V. All rights reserved.
PII: S0377 -2217 (01 )00402 -7
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
M. Jaccard et al. / European Journal of Operational Research 145 (2003) 148–164 151
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.
152 M. Jaccard et al. / European Journal of Operational Research 145 (2003) 148–164
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).
M. Jaccard et al. / European Journal of Operational Research 145 (2003) 148–164 153
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.
154 M. Jaccard et al. / European Journal of Operational Research 145 (2003) 148–164
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.
M. Jaccard et al. / European Journal of Operational Research 145 (2003) 148–164 155
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.
156 M. Jaccard et al. / European Journal of Operational Research 145 (2003) 148–164
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.
M. Jaccard et al. / European Journal of Operational Research 145 (2003) 148–164 157
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).
158 M. Jaccard et al. / European Journal of Operational Research 145 (2003) 148–164
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.
M. Jaccard et al. / European Journal of Operational Research 145 (2003) 148–164 159
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
160 M. Jaccard et al. / European Journal of Operational Research 145 (2003) 148–164
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.
M. Jaccard et al. / European Journal of Operational Research 145 (2003) 148–164 161
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.
162 M. Jaccard et al. / European Journal of Operational Research 145 (2003) 148–164
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.
References
Analysis and Modelling Group, 1999. Canada’s Emissions
Outlook: An Update. National Climate Change Process.
Ottawa.
Berger, C., Dubois, R., Haurie, A., Lessard, E., Loulou, R.,
Waaub, J.-P., 1992. Canadian MARKAL: An advanced
linear programming system for energy and environment
modelling. INFOR 20, 114–125.
Canadian Department of Finance, 2000. A Computable Gen-
eral Equilibrium Analysis of Greenhouse Gas Reduction
Paths and Scenarios. Economic Studies and Policy Analysis
Division, Ottawa.
Energy Research Group/MK Jaccard and Associates (ERG/
MKJA). 2000a. Integration of GHG emission reduction
options using CIMS. Report to Analysis and Modelling
Group of the Canadian National Climate Change Imple-
mentation Process, ERG/MKJ, Vancouver.
HALOA, 2000. Integrated analysis of options for GHG
emission reduction with MARKAL. Report to Analysis
and Modelling Group of the Canadian National Climate
Change Implementation Process, HALOA, Montreal.
Huntington, H., Schipper, L., Sanstad, A., 1994. Is There An
Energy–Efficiency Gap? Energy Policy 22 (10) (special
issue).
Informetrica, 2000. Macroeconomic impacts of GHG reduction
options: National and provincial effects. Report to Analysis
and Modelling Group of the Canadian National Climate
Change Implementation Process. Informetrica, Ottawa.
Jacobsen, H., 1998. Integrating the bottom-up and top-down
approach to energy-economy modelling: The case of Den-
mark. Energy Economics 20 (4), 443–461.
Jaccard, M., Bailie, A., Nyboer, J., 1996. CO2 emission reduc-
tion costs in the residential sector: Behavioral parameters in
21 We include the previously discussed differences in uptake
of CO2 sequestration as part of the behavioral difference in that
while both models have the same basic technology representa-
tion, this action penetrates more readily in MARKAL as
expected.
M. Jaccard et al. / European Journal of Operational Research 145 (2003) 148–164 163
a bottom-up simulation model. The Energy Journal 17 (4),
107–134.
Kanudia, A., Loulou, R., 1999. Advanced bottom-up model-
ling for national and regional energy planning in response to
climate change. International Journal of Environment and
Pollution 12 (2/3), 191–216.
Nyboer, J., 1997. Simulating Evolution of Technology: An Aide
to Policy Analysis. Ph.D. Thesis, Simon Fraser University,
Vancouver.
US Department of Energy, 1994. The national energy modeling
system: An overview. Energy Information Administration,
Washington, DC.
US Department of Energy, 1998. Impacts of the Kyoto
protocol on US energy markets and economic activity.
Energy Information Administration, Washington, DC.
Weyant, J., Hill, J., 1999. The Costs of the Kyoto Protocol: A
Multi-model Evaluation. The Energy Journal 22 (special
issue).
164 M. Jaccard et al. / European Journal of Operational Research 145 (2003) 148–164