Sustainable Policy Modelling - CIMS
EMRG / Mark Jaccard and Associates
2.1 Energy Demand
3.1 Technological Detail
4.1 Energy and Production Cost Effects of Energy Policy
This introduction presents CIMS developed by the Energy and Materials Research Group and M.K. Jaccard and Associates (EMRG/MKJ) at the School of Resource and Environmental Management at Simon Fraser University.
CIMS is a state-of-the-art, integrated set of economic and energy models capable of the widest range of combined energy and economic analysis available in Canada.
Energy flows are at the centre of CIMS; like its predecessor, the ISTUM family of models, it tracks the flow of energy, beginning with production processes, through to eventual end-use by individual technologies. Unlike the partial equilibrium ISTUM models, which compete technologies against each other to serve prespecified demands for end-uses, CIMS is a full equilibrium system that incorporates macroeconomic demand feedbacks, second order macroeconomic effects, demand dependent energy supply costs and energy trade.
CIMS' focus on detailed energy flows through technologies makes it ideal for modelling air quality and greenhouse gas emissions. Emission levels of all pollutants are technology specific; unless a model operates on an individual technology basis, as CIMS does, the emission estimates can only be approximated by economic activity.
The following diagram describes CIMS. It is a system of modules, each of which may contain one or several specific models. This system has various options to allow users to select the degree of disaggregation or the type of representation they wish to explore.
2.1 Energy Demand
On the right side of the diagram is the energy demand module. The four models in the demand module comprise the industrial, residential, commercial and transportation sectors. In the default version of CIMS, these models are all written in the same code and include similar options, an approach known under the name ISTUM, the Intra-Sectoral Technology Use Model, developed in the mid-1980s. ISTUM allows the user to simulate endogenously technology acquisition decisions as a function of observable financial costs, as well as intangible costs and cost risks. The industrial model is by far the largest because of its heterogeneous processes and technologies. EMRG /MKJ is known nationally and internationally as among the most advanced and detailed modellers of industrial energy use. The residential, commercial (/institutional) and transportation models are updated to include the latest available disaggregated data from government and independent agencies across the country. In addition to the default version, the user has many options for demand-side modelling.
2.2 Community Energy Management
The link between the macro-economic module / scenario and the demand module is mediated by key assumptions about land use planning and urban infrastructure development; this occurs within the Community Energy Management module. This module was developed in 1995 by EMRG / MKJ. It links broad assumptions about the level of effort made by different levels of government to influence the long term evolution of urban form and urban infrastructure. Efforts in this domain have implications for the level of energy services to be met by the energy demand module. For example, efforts to improve the proximity to work and shopping for some types of urban residents (say apartment dwellers) will influence the need for vehicle kilometers traveled, an input to the transportation demand model. There is currently no potential for endogenous simulation of behaviour in the CEM module (although this can be easily changed). The module links policy assumptions about land use planning and zoning, transportation, energy and other infrastructure development, and site design requirements, to forecasted levels of energy service demand that then feed into the demand module.
2.3 Energy Supply
On the left side of the diagram is the energy supply module. This includes both energy supply markets and major energy conversion processes. In the default version of CIMS, the energy supply markets (coal, oil, natural gas, renewables) are modelled based on estimates of Canadian resources and global markets. Linkages to U.S. markets are options. In the default version of CIMS, both electricity generation and production of refined petroleum products are modelled using a version of the ISTUM model. However, in addition to this default version, the user has other options for supply-side modelling.
2.4 Macro-economic Links
At the top of the diagram is the macro-economic module. In the past, CIMS simulations were driven by one or several macro-economic scenarios about structural change, economic growth and other key assumptions (regulations, technologies, international prices, trade, etc.). However, this approach does not allow for feed-backs as changes in the costs of industrial inputs and consumer products may lead to structural shifts (one major sector or industrial branch grows relative to another) and changes in overall economic activity (the key indirect effects of GHG reduction policies). The current CIMS framework provides options for the degree of detail that the user wishes to explore in the key macro-economic linkages. In the default version of CIMS, we use estimated energy service price elasticities (following the approach of Richard Loulou at McGill), and a few key macro-economic linkages, to simulate the structural and total output feedbacks from changes in costs of energy services (resulting form policies that affect energy prices and/or the choice of technologies by firms and households). However, other options are available.
2.5 CIMS Convergence Procedure
The convergence procedure, as currently designed, works in the following way. This procedure can be easily modified to suit the preferences of the user. The current default version of CIMS uses standard spreadsheet software to iterate between the demand, supply and macro modules in five year intervals (which can be easily converted to year-by-year results). However, the current, simple version is generally not transparent to the user. Discussions have been undertaken with various consultants to develop a high quality, user friendly interface for the user to establish and track the interactions between the modules. The following steps comprise the current, default, CIMS convergence procedure.
1. Set baseline macro-economic drivers (including the CEM scenario), energy supply drivers, and energy service technology characteristics for the desired time frame (15 years, 20 years, etc) in five year periods. (Note that the CEM scenario does not change as part of the feedback mechanisms, but output from it may adjust automatically because of changes in the inputs from the macro-economic model.)
Macro-economic drivers include income, economic output, structural evolution, interest rates, employment rates, regulations, etc. This provides all energy service demands for the energy demand module.
Energy supply drivers include international prices, domestic market developments, forecasts of costs and availability of marginal supplies, and resulting domestic prices.
Energy demand drivers include date of availability of technology and process options, technology energy efficiencies and capital / operating costs, firm and household time preferences (discount rates) for discretionary and non-discretionary expenditures (new and retrofit), and other behavioural parameters (differences in intangible costs, perceived risks and consumer surpluses of different technologies, interdependence of certain technology choices that serve different energy services, such as domestic water heating and domestic space heating).
2. Run energy demand module (industry, commercial, residential and transportation) for first five year period based on all initial forecast driver values (including energy supply prices).
3. The demand models then send the demand for energy to the supply models. They will send back corresponding energy prices. If the changes in energy prices are over a pre-set threshold, rerun the demand models with the new energy prices. Repeat this process until the energy price changes fall under their respective thresholds.
4. If, due to the changes in energy prices, the lifecycle costs of some energy services have changed beyond a pre-set threshold the changes are sent to the macro model. Within the macro model price elasticities for these services will be applied, thereby changing the demand for these services. If the change is more than a pre-set threshold the new demand is sent back to the supply and demand models for re-calculation. The macro model will also calculate a general change in the cost of energy and energy services, and make a corresponding change to overall demand, investment, employment and consumption.
5. The model reiterates step 2 , 3 and 4 until all variables stabilize.
2.6 Comparison to NEMS
The CIMS approach is very similar to the National Energy Modelling System (NEMS) developed by the U.S. government, in its structure, in its approach to modelling technology acquisition decision making (see below) and in its convergence procedure. The most significant differences of the CIMS default version from NEMS are the following.
3. The Technology Simulation Approach: Technologically Explicit and Behaviourally Realistic Simulation of Energy Demand and Supply
CIMS is based on the approach to modelling that dominates the ISTUM set of energy demand models. It includes considerable technological detail to better probe the range of future technology policy options for government. However, it also seeks to be behaviourally realistic, for without this the model will be incapable of helping decision makers assess the true costs and other impacts of the alternatives for GHG emission reduction and capture.
3.1 Technological Detail
A lack of technological disaggregation reduces one's ability to portray the true extent of technological responses to a new situation, such as a threat of climate change. The ability to model technological detail, on the other hand, permits the analyst to ascertain which industries are more vulnerable to new policies or changed economic conditions such as the development of emission credit trading or markets for emissions reduction. The needs are quite specific:
CIMS' technology database includes levels of existing stock in terms of physical characteristics such as energy, emissions and costs. To adequately reflect potential changes in energy consumption, CIMS's technology databases are constantly updated with any new or developing technologies that could affect energy consumption.
Data on technology stock and characteristics come from a myriad of sources: existing databases completed for other studies, utilities, consultants in the field and experts in the sector. All of the data is specific to Canada. For example, in the industrial energy use model, the Canadian Industry Energy End-use Data and Analysis Centre, operated by EMRG, maintains and continually updates detailed sector-by-sector data on the processes, technologies and technology prospects of Canadian industry.
Although databases that describe technology characteristics are increasing, few databases define active stocks of technologies for any particular year. No such data are known to exist for Canada, except those developed for ISTUM. Through continuous interaction with utility and industry engineers, the EMRG / MKJ group have continually updated and refined the technology-specific information in ISTUM, thereby reducing uncertainty about our Canadian technology data.
3.2 Behavioural Realism
In spite of such detailed input, there are no systematic data to confirm estimations on both equipment stock or technology characteristics. To ameliorate the problem, data on existing stock are calibrated to disaggregated, industry-specific, energy consumption data and estimates of end-use technology allocation. This data is reviewed and assessed in detail by experienced industry and consumer personnel. Through our work on various Canadian-specific projects, EMRG/MKJ has been able to develop relationships with a broad team of industry and sectoral experts across the country. In this way, not only are the model databases maintained but model structure and function are tested for credibility.
It is impossible to verify in any definitive way the behavioural parameters of a model that is seeking to simulate how firms and household will behave when faced with future technology choices that may differ somewhat or a great deal from past technology choices. This problem is complicated by the fact that energy consumers' stated preferences for the characteristics of energy consuming equipment are often very different from their revealed preferences, as demonstrated by purchasing data. However, it is possible to have some confidence about key elements of such decisions, allowing some structuring of the technology acquisition process for modelling purposes.
First, there has been a certain consistency over time in the way that firms and households have responded to trade-offs between up-front and operating cost differences; a rather short payback period is required before consumers will risk higher up-front costs for promised operating cost savings (repeated evidence shows a shorter payback required by households relative to firms). Also, there has been a certain consistency over time in the way that firms and households have responded to new technologies relative to well known technologies; again any incremental cost of a new technology must earn a relatively quick payback. This kind of information, based on extensive research by academics, consultants and industry, is incorporated into the technology acquisition decision parameters of the demand models.
Second, there is also considerable empirical information on how consumers look upon discretionary as opposed to non-discretionary expenditures (shorter payback required for discretionary expenditures), and this is incorporated into the technology acquisition decision parameters.
Third, there is substantial behavioural evidence on decision factors such as the interdependence of certain technology choices. For example, evidence shows that the choice of energy form for domestic hot water heating and the choice of energy form for domestic space heating are more closely linked than would be suggested by two separate analyses, one focusing on space heating, the other on domestic water heating.
Fourth, for physical reasons, the market shares of some technologies can be limited a priori. For example, a forecast of the likely extension of the natural gas grid provides an upper limit for customer conversions to natural gas. Another example, from industry, is that variable speed drives will not be adopted for motive force applications that only require a constant speed.
Fifth, risk plays an integral part in any equipment purchasing decision. What if it breaks? Your loss is minimized by buying the least expensive equipment, which tends to be the least energy efficient. Aversion to risk can also be an argument for buying energy efficient equipment, in that it minimizes your exposure to energy price changes.
4. CIMS' Analytic Capabilities
CIMS and its demand component, ISTUM, are both well suited to many different types of analyses. CIMS would be used in the where one would like to account for demand and supply feed-backs and macroeconomic effects, while ISTUM would be used alone where detailed technological analysis is sufficient or where these feedback effects are likely to be small.
4.1 Energy and Production Cost Effects of Energy Policy
CIMS and ISTUM can both model the production costs effects of new standards, marketing programs, and other energy policies. These policies are modelled through market share constraints and technology diffusion controls. It should be noted that EMRG / M.K. Jaccard and Associates regularly monitors the latest research in energy consumer behaviour, and these findings are incorporated into our technology penetration methodologies.
4.2 Greenhouse Gas Emission Policy
Both CIMS and its demand component, ISTUM, are well suited to rapid and detailed analyses of various GHG emissions taxes or permit trading systems. Both will deliver emissions levels, marginal costs and changes in the technology stock over time from a business as usual scenario.
4.3 Macro-economic Effects of Energy Policy
In the short run (one year or less), energy is typically a small proportion of total energy production, housing and transportation costs, and as such has a small effect on the economy. In the medium to long term, however, energy prices have a definitive impact on both the demand for products and serviced derived from energy and the overall level of employment and consumption in the economy. CIMS models both direct demand effects, technology stock structure effects and second order employment, consumption and investment effects.
4.4 Air Quality Policy
CIMS is also capable of air quality modelling, through emission rates, for some pollutants (SOx & NOx). Given more resources and lead time this capability could be expanded.