Energy System and Scenario Analysis Toolkit
- 2.1. Assess current country plans, policies, practices, and capacities
- 2.2. Compile lessons learned and good practices from ongoing and previous sustainable development efforts in the country
- 2.3. Assess public and private sector capacity to support initiatives
- 2.4. Assess and improve the national GHG inventory and other economic and resource data as needed for LEDS development
- Greenhouse Gas Inventory Development Toolkit
- 3a. Analytical Decision Making - Developing BAU Scenario
- 3b. Analytical Decision Making - Assessing Opportunities
- 3b.1. Assess technical potential for sector technologies
- Renewable Energy Technical Potential Toolkit
- Building Energy Assessment Toolkit
- Power System Screening and Design Toolkit
- Land Use Assessment Toolkit
- Bioenergy Assessment Toolkit
- Transportation Assessment Toolkit
- 3b.2. Assess economic and market potential of technologies and initiatives
- Clean Energy Market Analysis Toolkit
- 3b.3. Prioritize development options
- 3c. Analytical Decision Making - Developing and Assessing Low Emissions Development Scenarios
- 3c.1. Develop low emissions growth scenarios
- 3c.2. Assess institutional framework for LEDS
- Financing Initiatives Toolkit
- Policy and Program Design Toolkit
- 3c.3. Assess in-depth contributions of selected scenarios to goals across sectors
- Land-use Scenario Analysis Toolkit
- Energy System and Scenario Analysis Toolkit
- 3c.4. Perform multi-criteria impact analysis and assess stakeholder responses
- Clean Energy Impact Assessment Tool
- Sustainable Land-use Impact Assessment Toolkit
- Understanding approaches
- Common models/methods
- Choosing a method/model
- Data resources
- Training and support
- MAC Curve
- Indo-US Low Carbon Modeling Workshop
Common models used for quantitative pathways analysis are usually divided among bottom-up, top-down and hybrid models which combine elements of each.
- Bottom-up approaches provide great sectoral detail and are most concerned with the impacts of a given policy on that sector and, to an extent, on the linkages of that sector with the wider economy. They are "partial" equilibrium models, partial in the sense that they do not capture the multitude of interactions between the major economic variables following the implementation of a given policy.
- Top-down approaches are most concerned with how the economy as a whole responds to a given policy. While they may contain some sectoral detail, they are more concerned with providing a comprehensive picture of an economy. General equilibrium models fall under this category, general in the sense that they attempt to represent the interactions of economic variables across the entire economy following the implementation of a policy. Their weakness may be in their aggregate nature that can often hides underlying details that would emerge in the sector-specific bottom-up model. Bottom-up models can be linked to top-down models to provide sectoral detail that feeds into a comprehensive analysis.
Scenario or pathways analysis can also be performed in a qualitative manner when data needs are unavailable or are unreliable. Most existing energy models assess only the quantitative aspects of energy systems. Qualitative aspects are usually ignored in the analysis. However, these impacts can play an important role in the viability of energy systems.
The following literature provides useful background on the similarities and differences as well applications of pathways analysis models.
- Ecofys - Sectoral Emission Mitigation Potential: Comparing Bottom-Up and Top-Down Approaches
- IDS - Modelling Energy Systems for Developing Countries
- Stanford - Energy Modeling Forum
- Stanford - Energy Modeling Forum 22 Special Issue
- Lawrence Berkeley National Laboratory - Climate Change Mitigation in the Energy and Forestry Sectors of Developing Countries
- UNEP-Risoe - Issues in Conducting GHG Mitigation Assessments in Developing Countries
- UNFCCC - Mitigation Methods and Tools in the Energy Sector
- Energy and Power Evaluation Program (ENPEP-BALANCE)
- ENPEP-BALANCE is a software tool developed at Argonne National Laboratory that allows users to evaluate the entire energy system (supply and demand sides) and the environmental implications of different energy strategies.
- The Advanced Energy System Analysis Computer Model, or EnergyPLAN, is a free model developed at the Aalborg University's Department of Development and Planning in Denmark. The model runs on Windows computers and optimizes national or regional energy systems. It is a deterministic model based on simulations in hourly time steps that helps answer questions about the impact of renewable energy targets and other energy regulations on a country or region's energy supply.
- Integrated Global System Modeling Framework
- This comprehensive tool developed by the MIT Joint Program on Science and Policy of Global Change analyzes interactions among humans and the climate system. It is used to study causes of global climate change and potential social and environmental consequences.
- International Institute for Applied Systems Analysis (IIASA) Models
- The International Institute for Applied Systems Analysis is an independent institution that conducts climate change, energy security, and sustainable development research. In the energy sector, the institute has developed several analytical tools for its research, which are described on the website and in publications available for download. The institute also provides access to greenhouse gas databases.
- MARKet ALlocation (MARKAL)
- MARKAL is a computer-driven, dynamic optimization model that uses upwards of 10,000 equations and constraints to foster strategic energy planning developed at Brookhaven National Laboratory.
- Long-range Energy Alternatives Planning System (LEAP)
- LEAP is a Windows application for medium- to long-term energy planning and greenhouse gas mitigation assessment developed by the Stockholm Environment Institute (SEI). It is used by government agencies, academics, non-governmental organizations, consulting companies, and energy utilities for integrated resource planning and greenhouse gas mitigation assessments from city-scale analyses to analyses on a national, regional, and global scale. The model can track energy consumption, production and resource extraction in all sectors of an economy, and can account for greenhouse gas emission sources and sinks in both the energy and non-energy sectors.
- Object-Oriented Energy, Climate, and Technology Systems (ObjECTS) Global Change Assessment Model (GCAM)
- "GCAM is a partial equilibrium model of the world with 14 regions. GCAM is used to project energy consumption and greenhouse gas emissions and to investigate the impact of climate change policies and technologies for emissions mitigation. GCAM includes an agriculture land-use module and a reduced-form carbon cycle and climate module. It also addresses demographics, resources, and energy production and consumption." - Source: http://www.pnl.gov/atmospheric/modeling_tools.stm
- Model for Analysis of Energy Demand (MAED-2)
- "MAED evaluates future energy demands based on medium to long term scenarios of socioeconomic, technological, and demographic development. Energy demand is disaggregated into a large number of end-use categories corresponding to different goods and services. The influences of social, economic, and technological driving factors from a given scenario are estimated. These are combined for an overall picture of future energy demand growth." - Source: http://www.iaea.org/Publications/Factsheets/English/capacity.pdf
- Wien Automatic System Planning (WASP) Package
- "WASP is the most widely used model in developing countries for power system planning (over 100 countries). Within constraints defined by the user, WASP determines the optimal long term expansion plan for a power generating system. Constraints may include limited fuel availability, emission restrictions, system reliability requirements, and other factors. Optimal expansion is determined by minimizing discounted total costs." - Source: http://www.iaea.org/Publications/Factsheets/English/capacity.pdf
- Model for Energy Supply System Alternatives and their General Environmental Impacts (MESSAGE)
- "MESSAGE is used to formulate and evaluate alternative energy supply strategies for a country or region. The model finds the optimal energy supply strategy for user defined constraints on, for example, new investment limits, market penetration rates for new technologies, fuel availability and trade, environmental emissions, etc. MESSAGE is extremely flexible and can also be used to analyze energy/electricity markets and climate change issues." - Source: http://www.iaea.org/Publications/Factsheets/English/capacity.pdf
- Prospective Outlook on Long-Term Energy Systems (POLES)
- "POLES purpose is to produce detailed, long-term simulations on the world energy demand, supply, and price projections by regions and large countries." - Source: http://www.enerdata.net/docssales/press-office-20th-world-energy-congress.pdf
The following table provides a list of common tools used for pathways analysis in developing countries. You can use the information on data requirements, outputs and example products to help choose a pathways analysis model or method for your country. The next tab also includes information on available training materials and international support services for use of the tools.
|Name||Sector||Type||Data Needs||Cost||Experience||Output||Example Product||Transparency and Clarity|
|ENPEP BALANCE- Energy and Power Evaluation Program||Energy||Simulation Model||Medium – baseline energy statistics (pro-duction and consumption), structure of energy system, projected en-ergy demand, technical and policy data,
emission factor defaults included
|Free||5 day training - $10,000||Integrated energy and GHG scenarios - Energy system responses to change in price and demand, GHG emissions and local air pollutants||Zambia Long Term Generation Expansion Study|
|MARKAL-TIMES-MARKet ALlocation Figure 1||Energy||Bottom-up, Optimization Model (there are also a number of hybrid MARKAL models such as MARKAL-MACRO)||Medium - Technology cost and performance data, input cost and price elasticity supply side data (e.g., fuel), market demand side data, emission inventory and emission factors||$8500-$15,000||8 day training – $30-40,000||Integrated energy economy and GHG scenarios - Estimates of energy prices and demand, marginal value of technologies within the system, fuel and technology mixes, GHG emissions and mitigation costs, optimizes investment in the economy and maximizes consumer welfare||World Bank low carbon growth country study model for South Africa - pg. 13||Medium||High - applied in over 30 countries for complete energy system (can be adapted for rural economy)|
|LEAP - Long Range Energy Alternatives Planning System||Energy||Bottom-up, Accounting Framework||Low - Model includes the Technology and Environmental Database (TED) which has energy technology data for performance and cost as well as environmental impacts for many technologies. Model also includes IPCC emission factors and energy and GHG baselines.||Free||5 day training ($5000); free online training materials available||Integrated energy and GHG scenarios - showing interactions between different policies and measures, transformation analysis, social cost benefit analysis||World Bank low carbon growth country study model for Mexico - pg. 13||High||High - applied in 85 countries|
|Model for Energy Supply System Alternatives and their General Environmental Impacts (MESSAGE)||Energy||Hybrid, Energy supply-economy-climate model||Medium- Energy supply and demand data, technology cost and performance, policy and technology constraints||Primary and final energy mix, emissions and waste streams, health and environmental impacts, resource use, land use, import dependence, investment requirement||Application of the MESSAGE model in Greater Mekong Subregion|
|RETScreen||Energy||Renewable energy climate decision model||Low - RETScreen has fully integrated product, project, hydrology and climate databases, as well as links to worldwide energy resource maps. And, to help the user rapidly commence analysis, RETScreen has built in an extensive database of generic clean energy project templates.||Free||RETScreen offers free training materials online and 3-days on-site courses that range at about $1500.||Pre-tax and pos-tax cash flow, financial statistics, and carbon value||Example Validation of the PV Project Model||High|
Data requirements are an important consideration in choosing the model or method to be used for pathways or energy system analysis. Some models and methods are much more data intensive than others.
At a minimum, energy supply and demand data as well as IPCC emission factors and GHG baselines are often required. LEAP is a model with very low data requirements as it includes a technology and environment database (TED) with cost and performance data for energy technologies as well as IPCC emission factors and energy and GHG baselines.
Other models are much more data intensive such as hybrids that combine top-down and bottom-up models requiring both engineering and input-output data for the economy.
The following data sources provide a starting point for understanding which models or methods might be most appropriate for your country. In the “Choosing a method/model” tab, these data are matched to models to help with the model choice process.
GHG emissions data
Energy system statistics
- The U.S. Department of Energy's Energy Information Administration Country Energy Profiles website provides access to official U.S. government energy statistics for 215 countries. The statistics include crude oil production, oil consumption, natural gas production and consumption, electricity generation and consumption, primary energy, energy intensity, carbon dioxide emissions, and fuel imports and exports for each country. In addition, Country Analysis Briefs are availalbe for 54 countries.
- Energy flow charts developed by Lawrence Livermore National Laboratory show the relative size of primary energy resources and end uses in the United States, with fuels compared on a common energy unit basis. Similar energy flow charts can be developed for other countries to feed into energy system modeling activities.
- Ecofys country fact sheets updated in December 2009 provide information on greenhouse gas emissions, energy use, sectoral trends, emission reduction costs and climate policies for 61 countries as well as for the world, Annex I and non Annex I countries.
- Ecofys is working to develop templates to assist developing countries in planning sectoral emission baselines under a post-Kyoto climate regime.
Energy technology cost and performance data
- This NREL webpage presents a graph published in May 2009 indicating the recent cost estimates and capacity factors for renewable and other energy technologies.
- This databook provide cost and performance characterizations for a number of clean energy technologies
- The Renewables Global Status Report prepared by the Renewable Energy Policy Network for the 21st Century (REN21) describes the status of renewable energy investment and installations worldwide.
- These data indicates the range of recent cost estimates for renewable energy and other technologies.
Marine and Hydrokinetic Technology
Tool Training and Support Resources
Below is a listing of training and other resources to support the use of common pathways analysis models presented in the previous tab.
|Tool||Software||Data||Training resources and user guides||Case studies and lessons learned|
|MARKAL||Information on obtaining the software||Country-specific database contacts Energy technology data source||Introductions and overviews||National Model Applications|
International Support Programs
The following international programs support pathways analysis activities in developing countries.
- LBNL Climate Change and International Studies
- The Lawrence Berkeley National Laboratory's (LBL) Climate Change and International Studies website provides a list of climate change, country study, energy efficiency standards, and industrial energy analysis projects of the laboratory.
- NREL International Activities Bilateral Partnerships
- The National Renewable Energy Laboratory's (NREL) International Bilateral Partnerships website lists and describes the laboratory's international activities in different countries and regions of the world. The website also provides access to websites describing other NREL international activities.
- PNNL Advanced International Studies Unit Energy Efficiency Centers
- The Pacific Northwest National Laboratory (PNNL) Energy Efficiency Centers website lists the six independent energy efficiency centers that the laboratory's Advanced International Studies Unit helped to establish. The website includes links to each of the center's website and to a publication describing the centers.
Search all resources
Below you can search all pathways analysis training materials included in the Energy System and Scenario Analysis Toolkit.
A marginal abatement cost curve (MAC) illustrates the marginal cost of achieving a stated reduction of carbon emissions relative to a business-as-usual (BAU) emission level for a given year in the future. MAC curves can be very popular with policy makers because they clearly illustrate carbon mitigation economics. A policy maker can decide on a level of carbon mitigation and see the carbon price required to achieve that level of mitigation. Vice Versa, the policy maker can choose an “acceptable” price and see the expected level of mitigation. However, there are disadvantages to the MAC curve concept. First, it may lack transparency with respect to how the BAU emissions level was calculated, the technology or mitigation assumptions, and the calculated mitigation costs and potential. Second, the MAC curve represents an abatement cost for a given year, but does not detail the required investments and their costs in preceding years that would be required to achieve the level of mitigation desired. Third, there is generally limited representation of uncertainty in market conditions (i.e. GDP growth, fuel costs, etc.) and in future technology characteristics. Finally, the costs do not give consideration to ancillary benefits of carbon mitigation such as improved air quality or increased energy security.
Advantages and Disadvantages of MAC Curve Concept
|Present the marginal abatement cost for any given total reduction amount||Limited to one point in time|
|Give the total cost necessary to abate a defined amount of carbon emissions||No representation of path dependency|
|Allow the calculation of average abatement costs||Limited representation of uncertainty|
|Lacking transparency of assumptions|
|No consideration of ancillary benefits|
Expert-based MAC curves rely on expert judgment on the likely BAU future energy system development and the corresponding cost to reduce those emissions through different energy technologies, fuel switching, efficiency improvements, etc. The measures are lined up according to cost and the lowest cost measures are considered first when the expert decides the potential market share that the technology can gain and, thus, how much mitigation potential it can achieve. The expert-based MAC curve provides a lot of information in a single chart and may be the “ultimate” PowerPoint slide. This approach gained significant attention from the detailed country level studies done by McKinsey & Company in 2010. EPA has developed expert-derived MAC curves for non-CO2 gases.
The advantages of the expert-based approach is that it includes a significant level of detail for individual technologies and mitigation measures. However, this approach is based on a technical cost and potential and does not incorporate the behavioral aspect of consumer and commercial decision making. Many expert-based MAC curves show negative cost mitigation potential (i.e. the measure saves money). In most cases, these are the result of market imperfections in which those who make decisions about mitigation technologies do not directly benefit from them (i.e. the landlord pays to replace a furnace but the tenant pays the utility bills) or where the measure has a considerable upfront investment that takes many years of energy bill savings to pay for it. In other cases the consumer may not consider future energy costs when purchasing an appliance such as a television.
As mentioned above, the expert-based approach considers the mitigation potential of technologies and policy measures from least cost to highest cost. This will tend to understate the mitigation potential of higher cost measures. For example, at low carbon prices, switching from coal to gas to generate electricity may offer a large amount of mitigation potential. However, at higher prices renewable generation technologies would show even more mitigation potential than gas-fired generation. The mitigation potential for renewable technologies, however, will be reduced in order to prevent double counting of carbon savings, given that the analysis would have allocated more mitigation potential to gas-fired generation. As such, expert-based MAC curves will tend to provide a distorted view of the mitigation measures and technologies for higher levels of desired mitigation.
Strengths and Weaknesses of MAC Curves Based on Expert Judgment
|Extensive technological detail||No integration of behavioural factors|
|Possibility of taking into account technology specific market distortions||No integration of interactions and dependencies between mitigation measures|
|Easy understanding of technology-specific abatement curves||Possibility of inconsistent baseline emissions|
|No representation of intertemporal interactions|
|Limited representation of uncertainty|
|In some cases, limited to one economic sector without the possibility to accumulate abatement curves across sectors|
|No representation of macroeconomic feedbacks|
|Simplified technological cost structure|
Model-derived MAC curves are generated by using an energy/economic model to generate a BAU emissions path. Then the model will be run several different times with different carbon price assumptions. The results of the carbon price emission paths are then compared to the BAU path and the differences are translated into a MAC curve. However, unlike an expert-based MAC curve, the carbon mitigation cannot be assigned to individual technologies or measures since, at each carbon price, a range of mitigation measures are typically being made. Depending on the level of technology detail in the model used, the changes in technology investment over time and different carbon prices can be broken out.
Energy/economic models are the tools that analysts use to project the potential impact of policy on energy consumption and environmental emissions. They can be categorized into top-down and bottom-up models. In general, top-down models, which model the entire economy, are good at capturing the impact of changes in the broader economy (i.e. macroeconomic feedback and change in GDP) but have less technical detail in the energy system. Conversely, bottom-up models tend to focus primarily on the energy system and have a very detailed representation of energy technologies. However, bottom up-models tend to have little, if any, detail on the rest of the economy.
Strengths and Weaknesses of model-derived MAC Curves
|Model explicitly maps energy technologies in detail||No macroeconomic feedbacks|
|Direct cost in the energy sector|
|Risk of penny-switching|
|No reflection of indirect rebound effect|
|Macroeconomic feedbacks and costs considered||Model lacks technological detail|
|Possible unrealistic physical implications|
|Interactions between measures included||No technological detail in representation of MAC curve|
|Consistent baseline emission pathway||Assumption of a rational agent, disregarding most market distortions|
|Intertemporal interactions incorporated|
|Possibility to represent uncertainty|
|Incorporation of behavioural factors|
|Comparably quick generation|
This discussion is a summary of Fabian Keisicki’s paper “Marginal Abatement Cost Curves for Policy Making – Expert-Based vs. Model-Derived Curves”. The full paper can be found at: http://www.homepages.ucl.ac.uk/~ucft347/Kesicki_MACC.pdf
A sample expert derived MAC curve can be found at: http://www.mckinsey.com/Client_Service/Sustainability/Latest_thinking/~/media/McKinsey/dotcom/client_service/Sustainability/cost%20curve%20PDFs/poland_greenhouse_gas_abatement_potential_summary.ashx
EPA's non-CO2 MAC curves are available at http://epa.gov/climatechange/EPAactivities/economics/nonco2mitigation.html
In response to a request from the Government of India’s Planning Commission, the Indo-US Low Carbon Modeling Workshop was held in late September 2011. This workshop provided a forum for an exchange on modeling frameworks for low emissions growth, a team of US government and national laboratory staff experts with their Indian counterparts. Links to the presentations made by the U.S. team of experts are below.
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|This talk will examine some key lessons from economic modeling of cap & trade policies, and further examine the implications of moving from economy wide policies to sectoral policies. The analysis uses the Intertemporal General Equilibrium Model (IGEM) and Applied Dynamic Analysis of the Global Economy (ADAGE) models to analyze the American Power Act (Kerry – Lieberman).|
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|This presentation explores the sensitivity of carbon mitigation to energy technology R&D and Carbon prices. A series of marginal abatement curve for different technology assumptions is derived using Brookhaven National Laboratory’s MARKAL model.|
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|Clean Energy Standards (CES) require retail electricity suppliers to meet a certain percentage of their sales with electricity generated from “clean” resources, like wind, biomass, solar, and geothermal. This paper analyzes a generic CES policy using the Brookhaven National Laboratory’s our 10-region U.S. MARKAL model.|
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|This presentation gives some background on why analysis about the energy future should be concerned with the impact of technology and market risk and uncertainty. The presentation shows the estimation of technology costs and performance risk and uncertainty using expert elicitations, including challenges and lessons learned. Some examples of the results from the use of probabilistic simulations using stochastic models are also presented.|
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|This presentation addresses analytical approaches for capturing EE and RE potential and presents LBNL experience on capturing EE and RE potential in India|