CIMS COMMUNITY ENERGY AND GREENHOUSE GAS EMISSIONS...

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 2.5 Canada License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/2.5/ca/

CIMS COMMUNITY ENERGY AND GREENHOUSE GAS EMISSIONS FORECASTING TOOL: USER DOCUMENTATION

SEPTEMBER, 2012

PREPARED BY:

Navius Research with support from the

Energy and Materials Research Group at Simon Fraser University

FUNDED BY:

Contact: www.NaviusResearch.com

Phone: 778-868-3744 Fax: 604-683-1253

Email: [email protected]

CIMS Community Model User Documentation

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TABLE OF CONTENTS

INTRODUCTION .................................................................................................................................... 3

HOW TO USE THE MODEL ...................................................................................................................... 5

RESULTS AND THEIR INTERPRETATION ....................................................................................................... 6

CIMS COMMUNITY METHODOLOGY ....................................................................................................... 9

STRENGTHS AND LIMITATIONS OF THE CIMS COMMUNITY MODEL .............................................................. 13

KNOWN ISSUES AND FUTURE IMPROVEMENT ........................................................................................... 15

APPENDIX: SECTOR SPECIFIC METHODOLOGY .......................................................................................... 16

Residential Sector ..................................................................................................................... 16

Commercial/Institutional/Small Industrial Sector .................................................................... 18

Personal and Freight Transportation Sectors ........................................................................... 20

Solid Waste Sector .................................................................................................................... 22


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INTRODUCTION

The CIMS Community Energy and Greenhouse Gas Emissions Forecasting Tool (the CIMS Community model for short) is designed to assist British Columbian communities with their energy and greenhouse gas emissions planning. This tool allows them to forecast the energy consumption and emissions of any community covered by the British Columbian Community Energy and Emissions Inventories (CEEI) under various future assumptions and scenarios. Therefore, it can be used to estimate the effects of policies on energy consumption and emissions and to help assess both short and long term targets for these indicators.

WHAT CAN THIS TOOL DO?

CIMS Community simulates the purchase, use and retirement of the energy using technologies, such as light bulbs, cars or furnaces, found in communities from the present to 2050. Consequently, it also simulates energy consumption and greenhouse gas emissions during this period. It covers the residential, commercial/institutional, solid waste, personal transportation and freight transportation sectors and can be quickly set to represent specific communities. Therefore, CIMS Community can:

Produce reference scenario energy and emissions forecasting for communities based on the CEEI reports and user defined assumptions.

Provide realistic analysis of individual and multiple policies that affect energy and emission.

Generate visual and numeric scenario results.

WHAT DOES REALISTIC POLICY ANALYSIS MEAN?

CIMS Community provides realistic policy analysis. Therefore, the model considers both the technologies and human behaviour that result in energy consumption and emissions. This consideration includes the lifespan of existing technologies, the emergence of new technologies and the diversity of human decision making and behaviour when purchasing, using and retiring these technologies

HOW CAN THE TOOL BE USED?

CIMS Community can be used by local governments to:

Produce a standalone forecast for a community that has not completed an energy and emissions plan.

Update an existing energy and emissions analysis if there are changes in fundamental assumptions, senior government policy, mandates for emissions targets, or the community vision for emissions abatement.

Test the sensitivity of an energy and emissions plan to a range of assumptions or to a different methodology.

Produce assumptions to inform other analyses, such as the impact of a specific policy or changes in reference case technologies (e.g., change in average vehicle fuel efficiency through time).


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Furthermore, CIMS Community can be used as a communication and education tool by:

Facilitating the communication, visualization and real-time feedback of outcomes of alternative planning models and assumptions in local contexts.

Providing a tool that can be used by anyone to better understand how current conditions, future assumptions, and market and policy drivers may affect the energy system.

WHAT DOES CIMS MEAN?

CIMS once stood for the Canadian Integrated Modelling System. However, the CIMS methodology has been applied to many regions (community, provincial, national international). Therefore, the acronym no longer applies and CIMS is now a proper name describing models that realistically represent technologies and human behaviour.

DOES IT COST ANYTHING TO USE THIS MODEL AND IS SUPPORT AVAILABLE?

Because the development of the CIMS Community model was funded by the Pacific Institute for Climate Solutions, the model and its documentation may be used and shared freely. Both the CIMS Community Excel model and this documentation are licensed under the Creative Commons Attribution, Non-Commercial, and No Derivatives license. They can be freely shared and used so long as they are attributed to the developers and are not used for commercial purposes or used to produce a derivative work.1

Most common problems can be resolved quickly at no cost, so contact us if you encounter any errors using the model. We would love to hear from those who are using the model. We are also open to providing more involved support, such as extending the model's capabilities or interpreting the results through individual/customized contracts. However, any future model or documentation updates that occur through this work will be covered by the Creative Commons license noted above.

OVERVIEW OF THIS DOCUMENT

This document describes the use and methodology of the CIMS Community model in several levels of detail. To learn how to use the model, read the following section, titled How to Use the ModelError! eference source not found.. This section describes the steps involved in producing a forecast with CIMS community and simulating the impact of greenhouse gas abatement policies.

For guidelines on how to use and interpret CIMS Community results, refer to Results and their Interpretation. To understand how CIMS Community produces a forecast, users should read the CIMS Community Methodology. This section qualitatively describes how the model forecasts the evolution of the energy using technologies, equipment and infrastructure in each community. Following this methodology is a discussion of Strengths and Limitations of the CIMS Community Model. Known Issues and Future Improvement are noted following this discussion. Finally, for an in-depth description of the structure and assumptions in each sector model, refer to the “Error! Reference source not found.”.

1 Creative Commons Attribution-NonCommercial-NoDerivs 2.5 Canada License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/2.5/ca/


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HOW TO USE THE MODEL

CIMS Community provides a detailed and comprehensive analysis, but it is also easy to use. A forecast of the energy consumption and greenhouse gas emissions of any community or regional district covered in the British Columbia CEEI reports can be set up in only a few minutes. To run a simple reference case and policy scenario forecast, follow these steps on the “Control Panel” sheet in the model:

1. From the “Control Panel” sheet, select the community for which you would like to generate. Retrieve a community’s inventory by pushing the “Get Regional Districts” button (reads in all possible regional districts from the 2007 BC CEEI reports). Select the desired regional district, local government name and local government type. When complete, push the “Get Community Inventory button” to load the CEEI data.

2. CIMS Community uses a set of default assumptions to forecast the future energy and emissions of communities. However, user can further customize a forecast by entering their inputs for key assumptions on the “User Inputs” sheet. All required user inputs are described in detail in the model. Note that the user defined assumptions for the solid waste sector are typically required. Specifically these are the annual precipitation at the landfill and the fraction of landfill gas captured in the base year (2007). After entering inputs and assumptions, return to the “Control Panel”.

3. Press the “Calibrate Model” button to align the model with the 2007 CEEI data and user inputs. Calibration ensures the forecast of energy consumption by fuel and greenhouse gas emissions begins from the correct starting conditions. Calibration may take several minutes, but this process only needs to be repeated if the CEEI inventory is changed, or if based year user assumptions (prior to 2010) are updated.

4. Press the “Run Model” button to simulate the reference scenario forecast for the community.

5. Select the policies to simulate in the policy scenario. Policies can be activated and defined from the “Policy Summary” sheet. This sheet summarizes the active policies and provides access to where the user can modify the policy conditions, such as start year and stringency. After defining the policy scenario, return to the “Control Panel”.

6. Press the “Run Policies” button to simulate the policy scenario for the community. Note that several policies can be run at the same time as a policy package. This is useful for identifying interactions or overlap between policies or groups of policies since CIMS Community is designed to not double count the impact of any given policy.

7. Press the “Results” button to view the outputs from the reference and policy scenario forecast on the “Results” sheet. A description of each specific result is in the model. Below, we provide a summary of how to interpret the results.

In each simulation, the model produces one reference and one policy forecast for one community. If users require multiple reference and policy scenarios, or wish to produce forecasts for several communities, they can copy and paste results to another spreadsheet for later use. Alternatively, users may save a version of the CIMS Community model with another file name that notes the community and scenario it contains.


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RESULTS AND THEIR INTERPRETATION

Forecasting models such as CIMS community are useful, albeit imperfect, tools that can assist planning and decision making. Models do not predict the future. Careful use of a model involves a critical interpretation of the results. Ultimately, a model organizes assumptions in a consistent methodology and provides a framework for understanding and discussing the key variables and outcomes of energy and emissions planning. Using this model:

Ensures all important assumptions are accounted for

Allows people to indentify the impact of changing a specific assumption

Helps people discuss their differing assumptions rather than arbitrarily disagreeing with results

Lets users determine which assumptions are most important to the results of an analysis.

To facilitate good modelling practice we provide a brief description of the results produced by CIMS Community as well as advice on how to understand and analyse these results.

CIMS COMMUNITY RESULTS

Primary results include GHG emissions and energy consumption. These results are reported by fuel and sector. To provide context to these results, the model also produces:

Measures of activity by sector (how much residential floor space, how much travel etc.),

Measures of energy efficiency (e.g., average vehicle fuel efficiency)

Technology market shares (e.g., percent of homes broken down by their building envelope energy efficiency)

Use of specific emissions abatement actions (e.g., landfill gas capture and utilization)

Descriptions of specific results are documented in the model.

UNDERSTANDING THE RESULTS: WHY DO ENERGY CONSUMPTION AND EMISSIONS CHANGE THROUGH TIME?

Energy consumption and greenhouse gas emissions are linked to changes in population and demand for energy services. However, improved energy efficiency and variations in the relative costs of different fuels can decouple energy use and emissions from their primary drivers. Furthermore, a policy can change demand for energy services and change the incentive to use alternative technologies and fuels relative to the reference scenario, resulting in a different forecast of energy and emissions.

UNDERSTANDING THE REFERENCE SCENARIO FORECAST

Changes in population and demand for energy services will drive changes energy consumption and greenhouse gas emissions. A community’s energy consumption and greenhouse gas emissions generally rise and fall in step with the population. However, energy consumption in communities may increase independently of population growth as people increase their demand for energy services such as buying and using a second car or purchasing additional home electronics and appliances. These changes have historically been linked to a rising standard of living


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On the other hand, improved energy efficiency may act against the influence of increasing population or demand for energy services. Energy costs, especially for petroleum fuels, and more recently, public concern over energy consumption (e.g. regarding the environment or energy security) have led to a steady decline in the energy intensity of individual technologies (e.g., the average fuel efficiency of vehicles has increased in response to higher fuel prices). Therefore, even in the reference scenario without the influence of a specific policy, improved energy efficiency can slow or reverse the rise in energy consumption and emissions.

However, improvements in energy efficiency are constrained by the lifespan of capital stock. While more efficient technologies can reduce ongoing costs, this benefit is rarely large enough to warrant early retirement of still useful equipment. Therefore, once purchased, many technologies will be used for years or decades, “locking-in” a certain amount of energy consumption. For example, a furnace is usually replaced when it no longer works and significant energy improvements to a building envelope may occur infrequently during a major renovation.

Finally, greenhouse gas emissions may change at a rate that is different than population growth or energy consumption if people begin to use different energy sources (i.e. fuels). This fuel switching is driven primarily by the interplay of energy prices and the upfront costs of the technologies that use the energy. For example, if one anticipates a prolonged period of low natural gas prices and increasing electricity rates, then it would be reasonable to expect that more people will purchase technologies that use natural gas rather than electricity, when the choice is available. In this case, there would be a switch from a low emissions fuel, such as hydroelectricity, to a higher emissions fuel like natural gas, and emissions will rise faster than energy consumption. The reverse would also be true if electricity rates were below natural gas prices. In this case we would see energy consumption rise faster than emissions.

WHY ARE POLICY SCENARIO RESULTS DIFFERENT?

Policies can change the demand for energy services and change the technologies and fuels that people use. A policy may reduce growth in demand for energy services, encourage switching to low-emissions fuels and increase the adoption of more energy efficient technologies. Therefore the policy scenario results will show a reduction in energy consumption and greenhouse gas emissions compared to the reference scenario. However, because this decline requires the turnover or retrofit of capital stock, the effect of policies relative to the reference scenario will start small and grow over time.

WHY ARE INDIRECT GHG EMISSIONS NOT INCLUDED?

Indirect emissions are the emissions generated from the production of energy such as the emissions generated from the production of electricity or gasoline. Indirect emissions associated with the production of energy consumed within a community are important to community energy and emissions planning. However, they are not within the scope of the current version of CIMS Community. Nevertheless, the model forecasts energy consumption by fuel which allows users to estimate indirect emissions according to their own assumptions.

A full forecast of indirect emissions should focus on all fuels. Inventories typically only cover indirect emissions from electricity generation. Electricity is by the far the largest source of indirect emissions in most jurisdictions, but the emissions intensity of electricity in British Columbia is currently very low. Consequently, then indirect emissions from electricity may be comparable to those from other fuels (e.g., natural gas and petroleum fuels).


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CAN THE MODEL PRODUCE OUTPUTS NOT SHOWN ON THE RESULTS SHEET?

The results shown on the “Result” sheet are drawn from a larger set of raw results. They were chosen because of their utility to the user; they best explain the outcome of a scenario to a broad audience. However, other results are possible including capital costs, energy costs, different technology market shares or measures of energy efficiency. These outputs may be added to the results in the future. If there is an expressed need for a specific output it could be added to the model as part of an individual or customized contract with Navius Research.


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CIMS COMMUNITY METHODOLOGY

CONCEPTUAL FRAMEWORK

The CIMS Community model describes the energy system of communities as a collection of “energy services” (e.g., transportation, space heating) that are supplied using “capital stock” (e.g., cars, furnaces). Energy services represent the services people and business use that require energy. These services include space and water heating, lighting, service from appliances and electronics, mobility within or between municipalities and the transportation of goods. Demand for these energy services is supplied using technologies, equipment, buildings and infrastructure, collectively known as “capital stock”.

The energy consumption and greenhouse gas emissions of each community depend on the demand for energy services and the capital stock used to supply these services. For example, the emissions that result from home heating depend on the amount of heat required, related to the number and size of homes, the climate and personal heating needs, and the technology used to supply and retain the heat, such as a baseboard electric heater or a natural gas furnace that supply heat retained by a building.

CIMS Community simulates how policy and market forces affect demand for energy services and the technologies used to supply these services. For example, if a policy (e.g., legislation, regulation and incentives from governments) or market conditions (e.g., energy prices, technology costs etc.) change the cost of driving, people may opt to drive less and may eventually buy a more fuel efficient vehicle.

Finally, the model describes how a community will actually react to policies instead of prescribing the optimal response. CIMS Community model simulates realistic consumer and firm decision making with regard to the purchase and use of energy consuming capital stock rather than assuming these decisions are financially cost-optimized (i.e. that consumers and firms have perfect information to purchase the least cost option).

SIMULATION SEQUENCE

CIMS Community tracks the evolution of capital stock in a given community through time. The capital stock changes as consumers and firms retire stock, retrofit stock and purchase new stock. The following simulation sequence is used to determine the total capital stock for each forecasted year:

1. Total demand for the technologies needed for each energy service is assessed.

2. Capital stock at the end of its useful life is retired. For example, we assume a new home will be replaced after 75 years (although the technologies used within the home may change more frequently).

3. Where applicable, the remaining stock may undergo a retrofit. For example, an existing home may receive a retrofit that improves the energy efficiency of the building shell. This retrofit would only occur if induced by market or policy conditions.

4. The gap between the supply and demand for each energy service is assessed. In the residential sector, the model would assess the difference in the supply and demand of floor space. Essentially, it would determine if the construction of new homes were needed.


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5. New stock is acquired to satisfy demand for energy services. For example, new homes are built to satisfy demand for housing.

The energy consumption and emissions associated with each energy service depend on sequential purchases or retrofits made over many years and will not respond immediately to new policies or different market conditions. For most energy services, only a fraction of the applicable capital stock is retired or retrofitted in a given period and most is acquired in the past. Consequently, for most communities, new policy or market conditions will gradually redirect the forecasted trends in energy consumption and emissions.

ACQUISITION OF NEW CAPITAL STOCK

CIMS represents the technologies that make up capital stock as archetypes that can be compared based on their life cycle cost (LCC). For example, for lighting, the model represents distinct types of lighting technologies (e.g., incandescent, CFL or LED) rather than every light-bulb on the market. Within a set of similar technologies the LCC of each specific technology archetype determines which ones are used to satisfy demand for new capital stock. For example, if new refrigerators are required, then the types of fridges purchased, as defined by energy performance (standard, high and ultra-high efficiency), is based on a comparison of their LCC.

CIMS Community uses a modified lifecycle cost (LCC) for each technology archetype that accounts for preferences and perceptions to better represent human decision making. A standard LCC typically accounts for the purchase cost, the operating cost, and the energy and emissions costs of a technology. However, people rarely make decisions based on long-term financial cost minimization. When making purchases, people will consider many factors in addition to the financial cost.

The example of a compact hybrid car illustrates the preferences and perceptions a buyer may towards a technology that go beyond its sticker price:

Do I like the product? Individuals may not want a hybrid car if it does not meet their needs such as high power or off-road capability.

Do I trust the product? Individuals may be wary of buying a newer technology such as a hybrid car if they believe it will be an unreliable vehicle. Although hybrid vehicles have been available for over a decade, some consumers are still concerned that the battery will fail before the end of the vehicle’s life.

Do I know enough about the product? Individuals may not have the time to learn about the hybrid car or perhaps they don’t know of a mechanic that services the car. The time needed to address their concerns may be worth more than the potential benefits of the hybrid car. This is especially true of technologies when they are first commercially available and least known.

Am I willing to spend a lot of money now in order to achieve savings over several years? Energy efficient technologies often have higher upfront costs. Most people prefer saving money in the present and this behaviour is reasonable in an uncertain world. For example the future cost of gasoline is uncertain, so the benefit of owning a hybrid car is difficult to quantify. Furthermore, the additional money spent on the hybrid car might be needed for an unforeseen emergency, or perhaps the car will be ruined in an accident before its higher fuel economy has paid off.

CIMS Community accounts for these factors by modifying the LCC of each technology with an “intangible cost” and a “revealed discount rate”:


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The intangible cost is a non-financial costs or benefits of a technology that are not covered by the upfront cost and the operating cost. These include the “costs” associated with the preferences, concerns and information issues described above.

The revealed discount rate represents how people choose between current savings versus future savings and it is based on the analysis of real purchasing behaviour. These parameters have been estimated through surveys, market research and calibration to past trends.

Finally, CIMS community assumes that the market for any given technology is heterogeneous. In other words, technologies with similar energy performance may have different financial costs and individuals may use different intangible costs and discount rates. This means that there is variation in the LCC of each technology, and there may be several “best” technology choices to satisfy new demand for each energy service.

EFFECT OF POLICIES ON TECHNOLOGY CHOICES

A policy scenario is developed by applying policies that change the LCCs or constraints on technologies relative to the reference scenario. An example would be a carbon price that increases the operating costs of technologies that use fossil fuels (by adding an emissions price) or a subsidy that reduces the upfront cost of buying an alternative technology. An example of a changing constraint would be a building code that requires a higher standard of energy efficiency from new construction. When the LCCs or technologies constraints change, the set of technologies that satisfy new demand for each energy service will change relative to the reference scenario. Consequently, energy consumption and emissions will also change.

CIMS Community represents two relationships that may increase how policies can change technology choices:

1. New technologies become cheaper as more people use them. This relationship represents the economies of scale and experience that reduce the cost of new technologies. A policy may encourage or require greater adoption of an emerging technology, which ultimately makes it less costly. This process is driven by innovation and experience across many regions, so the implicit assumption is that climate policy is being implemented at a broad scale rather than only in one community.

2. Preferences towards new technologies will change as more people use them. As described above, most people will prefer to use familiar technologies. However, as new technologies become common, people’s aversion to them will decrease. The hybrid car example demonstrates this effect: a technology that was once unfamiliar, unknown, and potentially risky became desirable after 15 years on the market.

FURTHER READING

To learn more about the CIMS methodologies please see the Energy and Materials Research Group website at www.emrg.sfu.ca or explore the following resources:

Jaccard, M. 2009 “Combining top down and bottom up in energy economy models.” In International Handbook on the Economics of Energy. In. J. Evans & L.C. Hunt (Eds.). pp.311-331. Northampton, MA: Edward Elgar.

http://www.emrg.sfu.ca/


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Bataille, C, Wolinetz, M, Peters, J, Bennett, M, Rivers, R. October 2009. “Exploration of two Canadian greenhouse gas emissions targets: 25% below 1990 and 20% below 2006 levels by 2020.” Prepared for the David Suzuki Foundation and the Pembina Institute by MK Jaccard and Associates, Inc. Accessible at http://www.davidsuzuki.org/publications/reports/2009/exploration-of-two-canadian-greenhouse-gas-emissions-targets-25-below-1990-and-2/

Bataille, C., M. Jaccard, J. Nyboer, and N. Rivers. 2006. “Towards general equilibrium in a technology-rich model with empirically estimated behavioral parameters.” Energy Journal, 27, Special Issue on Hybrid Modeling of Energy-Environment Policies: Reconciling Bottom-up and Top-Down: 93-112.


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STRENGTHS AND LIMITATIONS OF THE CIMS COMMUNITY MODEL

STRENGTHS

CIMS Community is easy to use and provides a rigorous forecast of energy consumption and greenhouse gas emissions. This model:

Can be operated by local governments allowing them to quickly produce a forecast for their jurisdiction or to better understand how different assumptions would change an existing energy and emissions forecasts.

Simulates realistic consumer and firm decision making with regard to the purchase and use of energy consuming technologies and energy services (e.g. mobility or space heating) in response to energy policy, rather than assuming these decisions are financially cost-optimized.

Produces results based on user input of market conditions (e.g. energy prices) and policies rather than using subjective assumptions regarding the achievable potential of emissions and energy reduction actions.

Contains a detailed depiction of current and emerging technologies while tracking the service life of individual units of capital stock.

Incorporates dynamic costs and changing preferences towards new technologies (e.g. hybrid cars have become less expensive and people are more familiar with them and thus less averse to the technology).

Corrects for subsidy free-ridership, where an action would have been taken without a subsidy, by forecasting the effect of a policy relative to the reference scenario.

Explicitly accounts for positive and negative interactions among all policies (i.e. CIMS accounts for enabling policies but does not double count the same action if it would occur in response to multiple policies).

Is consistent with the modelling framework used for provincial energy and emissions planning (e.g. the quantitative analysis used in the 2007 British Columbia Climate Action Plan used the CIMS modelling framework).

LIMITATIONS

REPRESENTATION OF LAND USE

This model specializes in realistically forecasting the evolution of technologies within a community, but the impact of changing urban form is based upon user assumptions. These assumptions include:

Transportation demand (e.g., how far people need to travel in their daily lives)

Building type (e.g., attached vs. detached house)

Building area (e.g., reducing growth in average home area)

With these assumptions as inputs, CIMS Community can show their implication on energy consumption and GHG emissions. However, the model does not simulate how land-use policies will change the urban form.


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KNOWLEDGE OF THE PRESENT AND FUTURE

Like any forecasting tool, CIMS community is limited by imperfect knowledge of the present and the future. CIMS Community requires external forecasts of activity in each sector (e.g. growth in number of households in the residential sector) and fuel price forecasts on which to base the analysis. These inputs are uncertain and so the results are also uncertain. Therefore, the model does not predict the future. Instead it provides a robust analysis of how current decisions and expected trends may affect the future.

Furthermore, CIMS Community contains a considerable level of technological detail which introduces further uncertainty. This detail enables CIMS to simulate the adoption of alternative technologies and to ensure that reference and policy scenarios are grounded in technological and economic reality. While care has been taken in representing the technologies in CIMS, the exact parameters of specific technologies are uncertain. This uncertainty becomes larger over time. Additionally, CIMS only contains technologies that are known today and by definition, does not include technologies that have not yet been invented.

REPRESENTATION OF HUMAN BEHAVIOUR

The technology choice algorithm of CIMS Community accounts for human decision making as revealed by real-world technology acquisition behaviour. Incorporating behavioural realism is critical to forecasting how people will respond to policies. However the preferences and behaviour of consumers and firms are difficult to estimate, and can change over time. Therefore, there is uncertainty associated with the representation of behaviour in CIMS.

Additionally, CIMS Community does not model how people use the technologies they acquire in the simulation (the exception is the simulated switching between cars, transit, biking and walking). For example, CIMS does not represent how a household may reduce their comfort by choosing to use less heating in their home. The implications of this limitation are small. Behaviour is notoriously hard to change and is generally affected only by severe price shocks (e.g. oil crises).

ENERGY SUPPLY AND DEMAND INTEGRATION

CIMS Community is not an integrated energy supply and demand model. Therefore, the model does not simulate the cost of producing the energy used in the community. While the model will account for the impact of a carbon tax on fuel costs, it cannot simulate how energy prices may change if new market and policy conditions change the cost of energy production. For example, if a climate policy requires all oil extraction, upgrading and refining to use carbon capture, the additional cost of using this technology could be passed on to consumers through the fuel price. This price impact is generally smaller than pricing the direct carbon content of the fuel.


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KNOWN ISSUES AND FUTURE IMPROVEMENT

KNOWN ISSUES

To date, we have discovered three problems with CIMS Community that affect its ability to produce a forecast for any British Columbian community:

1. Data omissions in individual CEEI reports can create errors in the calibration and simulation code. For example, the Sechelt Indian Band has no population listed and this omission causes problems for the model calibration and simulation.

2. Some communities (Grand Forks, Penticton, and Kelowna) purchase bulk electricity and sell it via a local utility. These communities have a large amount of electricity consumption listed under “wholesale” and the current model cannot automatically accommodate this categorization.

3. Some regions have limited natural gas distribution. CIMS community has difficulty calibrating to these inventories where natural gas is available but highly constrained.

Please contact us if you discover further problems at:

[email protected]

FUTURE IMPROVEMENTS

This model will evolve through time, acting as a platform to analyze new ideas and concepts in the field of community energy and emissions planning. User feedback will be an important input to this process, so contact us if you have ideas or improvements for this model. Updates to the model may include:

A representation of district energy

A neighbourhood version of the model

A building retrofit policy/action

Inclusion of cost outputs

Inclusion of local air pollution outputs


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APPENDIX: SECTOR SPECIFIC METHODOLOGY

RESIDENTIAL SECTOR

MODEL STRUCTURE

Figure 1 shows the structure of the residential sector model and the energy services it simulates. The primary driver of energy use in the residential sector is the number of households. Future estimates of household growth are based on population growth forecasts. The energy consumption and greenhouse gas emissions from these households result from the amount of floor space that requires lighting, heating and cooling, from the use of appliances in the households and from hot water consumption.

Figure 1: Residential Model Structure

Floor space is based on the number of households and the assumption for the average area of each household. We assume a fixed amount of lighting is required per unit of floor space, but the technologies that provide this lighting (incandescent, compact fluorescent, LED) are a simulated result. Floor space also determines the maximum area available for rooftop solar panels. Based on solar cell costs, electricity prices and policies, the model will simulate the degree of adoption of home solar panels.

Residential

Blower/Pump

Furnaces

Water Heating

Air Cond.

Room

Central

Clothes Drying

Hang Drying

Clothes Dryers

Households

Other Appliances

Refrigerators

Freezers

Clothes Washers

Tap Hot Water

Ranges

Floorspace

Lighting

Solar Electricity

Detached Home

Attached Home

Building Envelope

Apartment

Mobile

Demand for space

heating and cooling is

driven by the number

of buildings, type of

buildings, and energy

efficiency rating of the

building envelope

Water heating is driven by

demand for hot water

Dish Washer

If a building does not

use electric resistance

to satisfy the heating

load, it requires a

furnaces and a blower

or pump.


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Estimates of the heating and cooling requirements of the sector are also based on the floor space. The energy consumption and emissions related to heating and cooling are a function of floor space and the assumed share of buildings types (detached, attached, apartment, mobile). The energy consumed for heating and cooling is also determined by the simulated choice of building envelope energy efficiency, and the simulated choice between heating/cooling equipment (e.g., baseboard electric, various furnaces and furnace fans, heat pumps, air conditioners).

Energy use by appliances is based on the assumed number of each appliance per household and the simulated choice of which appliances are used (e.g., low versus high efficiency refrigerators). For dishwashing and clothes drying, the model contains an assumption of the amount of each service performed without appliances (i.e., hand washing dishes and air drying clothes).

Hot water production is based on simulated hot water demand from appliances and taps. The simulated choice of appliances and tap flow rates determine the amount of hot water required. The simulated choice of hot water heaters determines the amount and type of energy required for meeting hot water demand.

DATA USE AND ENERGY DECOMPOSITION

To forecast residential energy use, the 2007 energy consumption by community listed in the CEEI reports is allocated to the specific energy services shown in Figure 1. This “energy decomposition” is based on data and assumptions derived from the British Columbian residential comprehensive energy use database published by the Office of Energy Efficiency at Natural Resources Canada (NRCAN). This decomposition determines the starting conditions from which a community specific forecast begins. The following data and assumptions are used:

We assume floor space in the community is based on the average British Columbian dwelling size by building type in a given year unless modified by the user input.

We assume the provincial average lighting energy intensity (GJ/m2 of floor space) applies to all communities.

We assume the provincial average energy intensity (GJ/dwelling) for appliances applies to all communities.

We assume the provincial average energy intensity (GJ/m2) for air conditioning and the fraction of floor space that is air conditioned applies to all communities, unless modified by the user. We also assume a slowly growing share of air conditioned floor space during the simulation period.

We assume the provincial average energy intensity (GJ/household) for water heating, by building type, applies to all communities. Total energy use for water heating is the sum of the number of households by building type multiplied by the applicable water heating energy intensity. Based on NRCAN data, in households that use a fossil fuel for space and water heating, roughly 35% of the fuel is used for water heating. Therefore we assumed that 35% of each of the fossil fuels used in the residential sector of a given community were for water heating. The remaining energy consumption (difference between total water heating energy consumption and the share of fossil fuels used for water heating) and was assumed to be electricity. Using the methodology described above, roughly 20% of energy consumption in any community covered by the CEEI is used for water heating.

For space heating, we did not assume that the provincial average applies to all communities. We chose to not make this assumption since climate and residential buildings vary substantially across


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the province. Instead, the energy used for space heating is the difference between residential energy consumption by fuel in the CEEI and the sum of all other energy consumption already accounted for by this methodology. Thus, we determine the unique fuel share and heating energy required for each community. Using this methodology, the heating energy intensity (GJ/m2 of residential floor space) for a given community is typically within 25% of the provincial average.

COMMERCIAL/INSTITUTIONAL/SMALL INDUSTRIAL SECTOR

MODEL STRUCTURE

Figure 2 shows the structure of the commercial/small industrial/institutional building model. The amount of building floor space covered by this sector model is linked to the population forecast and an estimate of per and the capita floor space that is calibrated using the CEEI data. The energy consumption and greenhouse gas emissions from these buildings result from the amount of floor space that requires lighting, heating, cooling and ventilation, and from the use of appliances, electronics and hot water.

The energy consumption and emissions related to heating and cooling are a function of the floor space covered by the model and the assumed share of buildings by activity types (i.e., wholesaling, retailing, offices, schools, healthcare, etc.). The energy consumed for heating and cooling is also determined by the simulated choice of building energy efficiency, and the simulated choice between heating, ventilation and cooling equipment (e.g., electric resistance heating, high or low efficiency natural gas heating, heat pumps, variable speed fan drives, variable air volume systems, building automation and energy monitoring systems etc.).

We assume a fixed amount of lighting, hot water, and appliance/electronics use is required per unit of floor space, but the technologies that provide these energy services are a simulated result. For example, lighting can make use of different lamps and ballast to change the amount energy used per unit of light provided. Likewise, hot water production can make use of boilers with different levels of efficiency using either electricity or natural gas, and can incorporate solar thermal heating and heat pumps. Furthermore, hot water systems can be designed with service load reductions to reduce the total volume of hot water needed.


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Figure 2: Commercial/Institutional/Small Industrial Model Structure


The floor space covered by this sector approximates the total commercial and institutional gross building area reported for each region or community in the BC Assessment. However, it is difficult to obtain and organize this data. Therefore the floor space in each community is adjusted until the energy consumption reported by the model in the base year (2007) matches the energy consumption for commercial buildings reported in the CEEI.

The implicit assumption of this calibration method is that the average demand for energy services (heating, cooling, water heating, ventilation etc.) per m2 of building area in British Columbia applies throughout the province. Although this assumption is likely inaccurate, the demand for energy services will vary less than the amount of commercial/institutional floor space in each community. For example, a community that is a commercial center for a region will have more Floorspace per capita than a more rural community. Assuming the demand for energy services per unit of buildings area is fixed in the base year (2007) while varying the amount of floor space produces a more realistic model. The average demand for energy services and the share of floor space by building activity are drawn from the British

Commercial, Small Industrial, Institutional

Lighting Building Envelope

Service lighting

General Area Wholesale Trade

Retail Trade

High-Bay Transportation,

Warehousing

Offices

Educational

Services

Information,

Cultural

Other Services

Accommodation

and Food

Services

Health Care

Arts,

Entertainment and

Recreation

Hot Water Appliances and

Electronics

HVAC: Heating,

Ventilation and Air

Conditioning

Equipment


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Columbian commercial comprehensive energy use database published by the Office of Energy Efficiency at Natural Resources Canada (NRCAN).

PERSONAL AND FREIGHT TRANSPORTATION SECTORS

MODEL STRUCTURE

Figure 3 shows the structure of the personal transportation and freight transportation sectors. The personal transportation sector simulates urban transportation by light-duty vehicle, transit, and walking or cycling. Total transportation demand is measured as person kilometers travelled per year. This demand is the product the of user defined forecast of population and per capita travel in the community.

The model simulates which transportation modes are used for this travel (e.g. single occupant vehicle, high occupant vehicle, transit and walk/cycle). The choice between transportation modes is determined by the implicit preference for each mode (based on data in supporting indicators that accompany the CEEI) and the ongoing costs of each mode (e.g. transit fare versus fuel costs). Total transportation demand is converted into vehicle kilometers travelled according the fraction of vehicle trips with one occupant versus multiple occupants.

CIMS Community also simulates which kinds of vehicles are used. Options include body types (small cars, large cars, light trucks) and engines/motors (low to high efficiency internal combustion engines, hybrid drive trains, plug-in hybrid drive trains and battery electric motors). Finally, various blends of petro- and bio-fuels can be used to fuel vehicles that require liquid fuel.


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Figure 3: Personal and Freight Transportation Model Structure

The freight transportation sector simulates the energy demand from the commercial vehicles and tractor trailers attributed to the community in the CEEI. The activity in this sector is based on the user defined population forecast and per capita freight requirements in the community taken from the CEEI. Total freight moved is divided among commercial vehicles (e.g., delivery trucks) and tractor trailer vehicles.

Energy consumption and GHG emissions from freight vehicles is based on the amount of freight moved and the types of vehicles used. Transportation by commercial vehicles may be provided using low and high efficiency internal combustion engines, hybrid drive trains, plug-in hybrid drive trains and battery electric motors. We assume heavy freight transport in tractor trailers is powered by internal combustion engines. However, the energy efficiency of new tractor trailers portrayed in the model can range from standard to extra-high efficiency, representing a range of fuel saving modification to a truck’s engine, drive train, body and tires. Like the personal transportation sector, the vehicles that use liquid fuels can also used specified blends of petro- and bio-fuels.

Personal Transportation

Community Travel

Public Transit

Walk/Cycle

Multi-Passenger

Vehicle

One Passenger

Vehicle

Passenger Vehicles Public Transit

Rapid Transit

BusNew Vehicles

Existing Vehicles

Vkm from cars or

trucks

Passenger Vehicle

Motors

Gas and Diesel

Motors

Fuel Blends

Freight Transportation

Road Freight

Commercial

Vehicles

Tractor Trailers

Fuel Blends

Fuel Blends


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Total personal transportation and per capita transportation demand is determined for the base year (2007) using the population and vehicle kilometres travelled in the CEEI combined with estimates of transportation modes taken from the supporting indicators (e.g. single occupant vehicle, high occupant vehicle, transit and walk/cycle). The base year transportation modes, vehicle body types and average fuel efficiency of each vehicle type (small car, large car, truck) are also calibrated to the data from the CEEI and supporting indicators.

Total freight moved is estimated from the vehicle kilometres travelled listed in the CEEI using assumptions for vehicle payload. We assume commercial vehicles carry an average of 1.25 tonnes while tractor trailer carry 6.4 tonnes, based on the British Columbian data in the comprehensive energy use database published by the Office of Energy Efficiency at Natural Resources Canada (NRCAN). The fuel efficiency (L/100km) of each commercial vehicles and tractor trailers are individually calibrated to match the CEEI data for 2007.

SOLID WASTE SECTOR

MODEL STRUCTURE

CIMS Community accounts for the production and abatement of methane from anaerobic decomposition of organic matter in landfills. Emissions from incineration facilities and liquid waste are not included. As in the CEEI, CO2 emissions from decomposition are treated as “carbon neutral” since they are biological in origin.

Abatement of landfill gas emissions may occur by capturing and combusting the landfill gas. The amount of landfill gas capture can be specified for the reference and policy scenario for each time period. The user can also specify whether the captured gas is flared or used for electricity generation. The methane component of the gas contains 0.037 GJ/m3 and may be converted to electricity at an efficiency of 25% (e.g., using an internal combustion or reciprocating engine).

Abatement can also be achieved by reducing the amount of organic matter entering the landfill. We account for a reduction in organic matter by reducing the methane generation potential of the waste. For example, if half of the organic matter is removed from the waste, we assume the total amount of methane the waste can produce is also halved.

The abatement of landfill gas emissions is not price driven and will not respond to a carbon price. The cost of each abatement action is highly variable between communities so we did not attempt to estimate the GHG price that would encourage landfill gas capture or organics separation. Furthermore, current policy in British Columbia regulates the emissions of landfill gas rather than applying a carbon price to them.

DATA USE AND METHODOLOGY

The methane emissions are forecasted using the Waste-in-Place methodology that is used to produce the CEEI inventory solid waste emissions. This is the same methodology used by the US Environmental Protection Agency LandGEM model. This method calculates the emissions from waste according to the year in which it was deposited. Emissions from a given amount of waste are highest when waste it is first deposited, trending towards zero over time as the organic component decomposes. The total


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amount of methane that can come from a given piece of waste is based on the British Columbian average outlined in the Technical Methods and Guidance Document for the 2007 CEEI Reports. The rate at which methane is produced depends on moisture and the precipitation at the landfill. The precipitation is defined by the user resulting in a methane generation rate that is appropriate to the community in question.

The model calibrates to GHG emissions in 2007 by adjusting the amount of historical waste (pre-2000) in the landfill attributed to each community. Given that the age of this historical waste is not known, we assume the methane emissions it generates decline linearly through time.

New waste is added based on the annual mass of waste produced listed in the CEEI. This annual addition of waste changes according to the rate of population growth or decline. The amount of waste added to the landfill can be modified by assuming a given fraction of organic matter is diverted each year.

CIMS COMMUNITY ENERGY AND GREENHOUSE GAS EMISSIONS...

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