QuickBooks 2000 5.1G serial key or number

• Mitigation Methods and Tools in the Energy Sector

• To introduce different approaches for GHG
  mitigation assessment in the energy sector.
• To review the benefits and drawbacks of different
  approaches.
• To introduce various software tools that may be
  useful for GHG mitigation analysis.
• To provide participants with information to help
  them choose an appropriate tool for their own
  assessments.
• NB will NOT provide in-depth training in the use
  of any one tool.
• Separate, in-depth training will be likely
  required for any tools selected.

1. Approaches for Energy Sector Mitigation Modeling
2. Review of Modeling Tools
3. MARKAL
4. ENPEP-BALANCE
5. LEAP
6. RETScreen
7. Conclusions

• a) Approaches for Energy Sector Mitigation
  Modeling

• Decision 17/CP.8, para 38
• Based on national circumstances, NA1 Parties are
  encouraged to use whatever methods are available
  and appropriate

• Bottom-up
• Use detailed data on fuels, technologies and
  policies
• Assess costs/benefits of individual technologies
  and policies
• Can explicitly include administration and program
  costs
• Dont assume efficient markets, overcoming market
  barriers can offer cost-effective energy savings
• Capture interactions among projects and policies
• Commonly used to assess costs and benefits of
  projects and programs

• Top-down
• Use aggregated economic data
• Assess costs/benefits through impact on output,
  income, GDP
• Implicitly capture administrative, implementation
  and other costs.
• Typically assume efficient markets, and no
  efficiency gap
• Capture intersectoral feedbacks and interactions
• Commonly used to assess impact of carbon taxes
  and fiscal policies
• Less suitable for examining technology-specific
  policies.

• Examine general impact on economy of GHG
  mitigation.
• Important where GHG mitigation activities will
  cause substantial changes to an economy.
• Typically examine variables such as GDP,
  employment, imports, exports, public finances,
  etc.
• Assume competitive equilibrium and optimizing
  behavior in consumers and producers.
• Should also consider role of informal sector,
  which may be important in many non-Annex 1
  countries.
• Can be used in conjunction with bottom-up
  approaches to help check consistency.
• E.g. energy sector investment requirements from a
  bottom-up energy model used in macroeconomic
  assessment to iteratively check the GDP forecasts
  driving the energy model.

• Types of top-down approaches
• Simplified macroeconomic assessment seeks
  consistency between sectoral forecasts and
  informs baseline scenarios.
• Input-output captures intersectoral feedbacks
  but not structural changes in economies (assume
  no shifts between sectors).
• Computable general equilibrium captures
  structural changes, assume market clearing.
• 2 3 require more expertise and more data, which
  may not be available in many non-Annex 1
  countries.
• All models are abstractions. Assumptions may not
  reflect real-world market conditions.
• Macroeconomic models tend to be country-specific.
  Off-the-shelf software not typically available.

• Optimization Models e.g. MARKAL
• Iterative Equilibrium/Simulation Models e.g.
  ENPEP-BALANCE
• Hybrid Modelse.g. MARKAL-MACRO
• Accounting Frameworks e.g. LEAP

• UNFCCC Guidelines do not specify which approach
  is appropriate for national communications on
  mitigation.
• Both Top-Down and Bottom-up models can yield
  useful insights on mitigation.
• Top-down models are most useful for studying
  broad macroeconomic and fiscal policies for
  mitigation such as carbon or other environmental
  taxes.
• Bottom-up models are most useful for studying
  options that have specific sectoral and
  technological implications.
• The lack of off-the-shelf top-down models, the
  greater availability of physical, sectoral and
  technological data, and the focus on identifying
  potential projects has meant that most mitigation
  modeling has so far focused on bottom-up
  approaches.

• Types of Bottom-Up Models

• Use mathematical programming to identify
  configurations of energy systems that minimize
  the total cost of providing energy services.
• Cost-minimization is performed within constraints
  (e.g. limits on CO2 emissions, technology
  availability, foreign exchange, etc.).
  Constraints also ensure balance of supply and
  demand.
• May optimize over all time periods (perfect
  foresight) or year-on-year (myopic).
• Select among technologies based on their relative
  costs.
• Dual solution yields estimates of energy prices.
• Can yield extreme knife edge solutions (model
  allocates all market share to cheapest technology
  even if only slightly cheaper)
• Must be constrained to yield reasonable
  results by using hurdle rates, by
  disaggregating demands into homogenous groups, or
  by constraining market allocations.
• Typically assume perfect competition/energy cost
  is only factor in technology choice.
• Useful where complex options need to be analyzed
  and costs are well known.
• Cost-minimization assumptions may be
  inappropriate for simulating most likely
  evolution of real-world energy systems in a
  baseline scenario.
• Data intensive
• Complex so hard to apply where expertise is
  limited.
• Examples MARKAL/TIMES

• Simulate behavior of energy consumers and
  producers under various signals (e.g. price,
  income levels) and constraints (e.g. limits on
  rate of stock replacement).
• Easier to include non-price factors in analysis
  compared to optimizing models.
• Balances demand and supply by calculating
  market-clearing prices.
• Prices and quantities are adjusted endogenously
  using iterative calculations to seek equilibrium
  prices.
• Behavioral relationships can be controversial and
  hard to parameterize. Crucial parameters are
  highly abstracted or poorly known, especially in
  countries where time series data is lacking.
• Example ENPEP-BALANCE

• Examines macroeconomic impacts of energy system
  on the wider economy.
• Changes in the energy system can feed-back to
  effect macroeconomic growth and structure.
• Production functions allow for substitution among
  capital, labor and different forms of energy.
• Useful energy demands are endogenous to the
  model.
• Example MARKAL-MACRO

• Account for flows of energy in a system based on
  simple engineering relationships (e.g.
  conservation of energy).
• Rather than simulating decisions of energy
  consumers and producers, user explicitly accounts
  for outcomes of those decisions.
• Simple, transparent, intuitive easy to
  parameterize.
• Evaluation and comparison of policies are largely
  performed externally by the analyst framework
  serves primarily as a sophisticated
  calculator/database/reporting tool.
• Framework ensures physical consistency but not
  economic consistency.
• Example LEAP

• Tools must be
• widely applied in a variety of international
  settings,
• thoroughly tested and generally found to be
  credible,
• actively being developed and professionally
  supported,
• primarily designed for integrated energy and GHG
  mitigation analysis, or screening of energy
  sector technologies.

• LEAP
• Long-range Energy Alternatives Planning system
• Primary Developer Stockholm Environment
  Institute
• ENPEP
• Energy and Power Evaluation Program
• Primary Developers Argonne National Laboratory
  and the International Atomic Energy Authority
  (IAEA)
• MARKAL and MARKAL-MACRO
• MARKet Allocation model
• Primary Developers IEA/ETSAP
• RETSCREEN
• Renewable Energy Technology Screening
• Primary Developers Natural Resources Canada
• All are integrated scenario modeling tools except
  RETSCREEN, which screens renewable and CHP
  technologies.
• Other tools and approaches may be appropriate.
• Full Disclosure Dr. Heaps is the developer of
  LEAP reviewed here.

• Developed International Energy Agency, Energy
  Technology Systems Analysis Programme
  (IEA/ETSAP).
• Generates energy, economic, engineering, and
  environmental equilibrium models.
• Models are represented as Reference Energy
  Systems (RES), which describe an entire energy
  system from resource extraction, through energy
  transformation and end-use devices, to the demand
  for useful energy services.
• Calculates the quantity and prices of each
  commodity that maximize either the utility
  (MARKAL-MACRO) or the producer/consumer surplus
  (MARKAL) over the planning horizon, thereby
  minimizing totally energy system cost.
• Note TIMES The Integrated MARKAL-EFOM System
  is gradually expected to replace MARKAL and
  MARKAL-MACRO.

• Identifies least-cost solutions for energy system
  planning.
• Evaluates options within the context of the
  entire energy/materials system by
• balancing all supply/demand requirements,
• ensuring proper process/operation,
• monitoring capital stock turnover, and
• adhering to any environmental policy
  constraints.
• Selects technologies based on life-cycle costs of
  alternatives.
• Provides estimates of
• energy/material prices
• demand activity
• technology and fuel mixes
• marginal value of individual technologies to the
  energy system
• GHG and other emission levels, and
• mitigation and control costs.

• Macroeconomic policies (e.g. carbon taxes)
• Transportation
• Energy demand
• Energy conversion and supply
• Energy sector emissions
• Non-energy sector industrial process emissions
• Solid waste management
• Geological sequestration
• Value of carbon rights

• MARKAL-MACRO (M-M) is an extension of the MARKAL
  model that simultaneously solves the energy and
  economic systems.
• Can be thought of as a hybrid model as merges
  elements of top-down and bottom-up analysis.
• Has price responsive demands (i.e., determined
  endogenously) while MARKAL does not (i.e.,
  demands are exogenously defined).
• Maximizes consumer welfare over the solution
  period, optimizes aggregate investment in the
  economy and provides least cost energy system
  configurations to meet endogenously determined
  demands.
• Energy service costs, energy service demands, and
  energy prices are determined simultaneously
  during optimization.
• Relative energy costs determine types and levels
  of substitution between fuels and technologies.

• Windows PC with 512 MB RAM.
• MARKAL/TIMES source code (written in GAMS)
• GAMS modeling language and a Solver
• Data Management and Reporting User Interface
• Two available ANSWER and VEDA
• Cost of software US 8,500-15,000 depending on
  institutional arrangements.

• International Energy Agency (IEA) technology
  detail for the World Energy Outlook scenarios.
• U.S. DOE/SAGE an analytic framework for the
  International Energy Outlook.
• European Union 25 state European model examines
  externalities and life cycle assessment issues.
• Six New England States Analysis of Clean Air Act
  goals and support for climate change commitments.
• USAID establishing a common framework for
  assessing demand-side management.
• IEA/ETSAP partner institutions supporting their
  national governments planning (Canada, UK, Italy,
  U.S. DOE EPA)
• China and India examining reform and energy
  sector evolution to meet economic development
  goals, and developing multi-region national
  models.
• APEC cost-effective levels of renewable
  generation in 4 APEC economies.
• ASEAN 8 countries participating in a AusAID
  sponsored energy planning initiative
• Three Central America countries baselines and
  opportunities within the realm of Climate Change.
• Bolivia GHG reduction strategies, including
  modeling of forestation as a carbon reduction
  option.
• South Africa National energy and environmental
  planning.

• Useful Energy Demands, and own price elasticities
  for MED or demand decoupling factors for MACRO
• Costs
• Resource, investment, fixed, variable, fuel
  delivery, hurdle rates
• Technology Profiles
• Fuels in/out, efficiency, availability
• Resource supply steps, cumulative resources
  limits, installed capacity, new investment
• Environmental Impacts
• Unit emissions per resource, technology,
  investment
• System and other parameters
• Discount rate, seasonal/day-night fractions,
  electric reserve margin

• Technical support offered by phone and email.
• Cost is US 500-2500 depending on institutional
  arrangements.
• Training is offered through ETSAP and its
  partners in different parts of the world.
• A minimum of 2 trainings of 4 days each are
  recommended, with follow-up support included.
• Cost is US 15,000-40,000 plus expenses.

• Gary Goldstein
• International Resources Group
• Sag Harbor, New York, 11963, USA
• Phone 1 (631) 725-1869
• Fax 1 (631) 725-1869
• Email ggoldstein_at_irgltd.com
• www.etsap.org

• The Energy and Power Evaluation Program (ENPEP)
  is a set of ten integrated energy, environmental,
  and economic analysis tools.
• Here the focus is on one tool, BALANCE, which is
  most frequently used for the integrated
  assessment of energy and GHG emissions.
• BALANCE is a market-based simulation that
  determines how various segments of the energy
  system may respond to changes in energy prices
  and demands.
• BALANCE consists of a system of simultaneous
  linear and nonlinear relationships that specify
  the transformation of energy quantities and
  energy prices through the various stages of
  energy production, processing, and use.
• BALANCE also calculates emissions of GHGs and
  local air pollutants.
• BALANCE can be run in combination with other
  ENPEP tools, such as MAED and WASP.

• Matches demands for energy with available
  resources and technologies. 
• The user creates an energy network that traces
  the flow of energy from primary resources to
  useful energy demands.
• Networks are constructed graphically using
  various nodes and links.
• Nodes represent resources, conversion processes,
  energy demands, and economic processes.
• Links connect the nodes and transfer information
  among nodes.

• A logit function estimates the market share of
  supply alternatives based on commoditys price
  relative to alternatives.
• Other constraints (e.g., capacity limits),
  government policies (taxes, subsidies, etc.), and
  the ability of markets to respond to price
  signals can also be modeled.
• Consumer preferences can also be included via a
  premium multiplier variable.
• Simultaneously balances supply and demand curves
  for all fuels.
• Equilibrium is reached at market clearing prices
  and quantities.
• Does not minimize costs. Instead, simulates the
  response of consumers and producers.

• MACRO-E feedbacks between the energy sector and
  the wider economy.
• MAED a bottom-up energy demand model.  
• LOAD hourly electric loads and generates load
  duration curves for use in other ENPEP modules.
• PC-VALORAGUA optimal generating strategy for
  mixed hydro-thermal electric power systems.  
• WASP least-cost electric generation expansion
  paths. 
• GTMax marketing and system operational issues in
  deregulated energy markets.  
• ICARUS reliability and economic performance of
  alternative electric generation expansion paths.
• IMPACTS physical and economic damages from air
  pollution (now part of BALANCE).
• DAM a decision analysis tool used to analyze
  tradeoffs between technical, economic, and
  environmental concerns.

• ENPEP has been used extensively in Africa, Asia,
  Europe and North and South America for a variety
  of integrated energy analyses.
• Many countries used ENPEP to help prepare GHG
  mitigation assessments as part of their initial
  national communications to the UNFCCC.
• Numerous ENPEP applications are described at the
  ENPEP web site, in most cases with links to
  related reports.

• Technical support offered by phone, email, or
  on-line.
• Basic support is free premium support packages
  available for up to US 10,000 per year.
• Training is offered by the developers on-site or
  at ANL.
• Since 1978, ANL has trained over 1300 experts
  from over 80 countries.
• Minimum of 5 days training is recommend.
• Cost is US 10,000 plus expenses.

• Guenter Conzelmann
• Center for Energy, Economic, and Environmental
  Systems Analysis (CEEESA), Argonne National
  Laboratory (ANL)
• 9700 South Cass Avenue, Argonne, IL 60439, USA
• Phone 1 (630) 252-7173
• Fax 1 (630) 252-6073
• Email guenter_at_anl.gov
• http//www.dis.anl.gov/CEEESA/ENPEPwin.html

• LEAP Long-range Energy Alternatives Planning
  System

• An integrated energy-environment, scenario-based
  modeling system.
• Based on simple physical accounting and
  simulation modeling approaches.
• Flexible and intuitive data management and
  advanced reporting.
• Scope demand, transformation, resource
  extraction, GHG emissions and local air
  pollutants, full system social cost-benefit
  analysis, non-energy sector sources and sinks.
• Annual time-step, unlimited number of years.
• Methodology physical accounting for energy
  demand and supply via a variety of methodologies.
  
• Optional specialized methodologies for modeling
  of certain sectors/issues. E.g. stock/turnover
  modeling for transport analyses.
• Links to MS-Office (Excel, Word and PowerPoint).
• Low initial data requirements (for example costs
  not required for simplest energy and GHG
  assessment). Many aspects optional.

• Unlike ENPEP and MARKAL, LEAP does not require
  the user to subscribe to a particular view of how
  an energy system behaves (e.g. least cost
  optimization, market-clearing equilibrium).
• Instead LEAP is based on relatively simple
  physical energy and environmental accounting
  principles.
• Thus all of the basic calculations in LEAP are
  non-controversial and can be easily verified,
  making the system highly transparent.
• Instead of the model endogenously calculating
  market shares of devices, in LEAP the user must
  tell the software how those shares will evolve in
  each scenario.
• Thus instead of using a complex tool that tells
  you whats best, the approach in LEAP is to use
  a relatively simple tool that makes it quick and
  easy for the user to explore the implications
  (cost, GHGs, etc.) of different hypothetical
  scenarios.

• Basic non-controversial energy-environment
  accounting relationships are built-in to LEAP.
• Data are specified using spreadsheet-like
  expressions.
• Expressions can be simple static values or they
  can be time-series functions that describe how
  variables change over time in different
  scenarios.
• Expressions can also be used to create
  relationships between variables allowing for
  engineering, econometric or simulation models.
• Expressions can also be used to create live links
  to Excel spreadsheets allowing LEAP to function
  as an overall organizing and integrating
  framework for separate spreadsheet analyses.

• Growth(3.2)Exponential growth after the base
  year.
• Interp(2000, 40, 2010, 65, 2020, 80)Interpolates
  between specified data points.
• Step(2000, 300, 2005, 500, 2020, 700)Discrete
  changes in particular years.
• GrowthAs(Income,e)Future years calculated from
  rate of growth in variable Income and an
  elasticity variable, e.
• Interp(c\sample.xls,Importrange)Interpolate
  based on values in range importrange from sheet
  sample.xls

• Scenarios are story-lines about how an energy
  system might evolve over time. Can be used for
  analysis of alternative policy assumptions and
  for sensitivity analysis.
• In LEAP, the Scenario Manager is used to create a
  hierarchy of scenarios.
• Typically users create one baseline scenario, and
  one or more scenarios used to screen individual
  policies or measure.
• These policy scenarios are then combined to form
  overall integrated mitigation scenarios, which
  examine the interactions between measures.

• Default expressions are inherited from one
  scenario to another, thus minimizing data entry
  and allowing common assumptions to be edited in
  one place.
• On screen, expressions are color coded to show
  which have been entered explicitly in a scenario
  (blue), which are inherited from a parent
  scenario (black), and which are inherited from
  another region (purple).

• The tree is the main data structure used for
  organizing data and models, and for reviewing
  results.
• Icons indicate the types of data (e.g.,
  categories, technologies, fuels and environmental
  effects).
• Users can edit the tree on-screen using standard
  editing functions (copy, paste, drag drop)
• Structure can be detailed and end-use oriented,
  or highly aggregate (e.g. sector by fuel).
• Detail can be varied from sector to sector.

• Process analysis of energy conversion,
  transmission and distribution, and resource
  extraction.
• Capacity additions specified by user or added
  automatically by model to maintain planning
  reserve margin.
• Choice of methods for simulation of electric
  dispatch to meet peak power requirements and load
  shape.
• Calculates imports, exports and primary resource
  requirements.
• Tracks costs and environmental loadings.

• Greenhouse Gas Mitigation Studies Argentina,
  Bolivia, Cambodia, Ecuador, El Salvador, Lebanon,
  Mali, Mongolia, Korea, Senegal, Tanzania, Vietnam
  and many others through US and Danish Country
  Studies Programs and as part of UNFCCC national
  communications.
• USA Greenhouse Gas Emissions Mitigation studies
  in California, Washington, Oregon and Rhode
  Island.
• U.S. DOE ongoing project to construct a global
  end-use oriented energy model.
• USEPA Integrated Environmental Strategies
  Described yesterday by Jack and Jose. LEAP used
  for parts of IES analyses.
• Energy and Carbon Scenarios Chinese Energy
  Research Institute (ERI) and U.S. DOE.
• U.S. Light Duty Vehicle Energy Use and Emissions
  Various U.S. transportation NGOs.
• APERC Energy Outlook Energy forecasts for each
  APEC economy.
• East Asia Energy Futures Project Study of energy
  security issues in East Asian countries including
  the Koreas, China, Mongolia, Russia, Japan.
• U.N. Millennium Project Costs of meeting a
  parallel millennium development goal (MDG) for
  energy.
• Integrated Resource Planning Brazil, Malaysia,
  Indonesia, Ghana, South Africa.
• City Level Energy Strategies Cape Town South
  Africa.
• Transportation Studies Texas (Tellus) and 7
  Asian Cities (AIT).
• Sulfur Abatement Scenarios for China Chinese
  EPA/UNEP.
• Rural Wood Energy Planning in South Asia FAO.

• Societal perspective of costs and benefits (i.e.
  economic not financial analysis).
• Avoids double-counting by drawing consistent
  boundary around analysis (e.g. whole system
  including.
• Cost-benefit analysis calculates the Net Present
  Value (NPV) of the differences in costs between
  two scenarios.
• NPV sums all costs in all years of the study
  discounted to a common base year.
• Optionally includes externality costs.

• Technical support offered by phone, email and web
  forum.
• Free to registered users.
• Minimum of 5 days training is recommended
• On-site training is offered by the developers
  (SEI) and by regional partners at cost.
• Regular regional trainings also being organized.
  Cost to attend is minimal, but participants must
  cover travel expenses.

• Four year initiative (2003-2006) sponsored by the
  Govt. of the Netherlands to build capacity and
  foster a community among developing country
  energy analysts working on sustainability issues.
• Managed by the Stockholm Environment Institute in
  collaboration with regional partners in Africa,
  Europe and Latin America.
• Open to everyone at no charge.
• Activities
• Regional training workshops (Africa, Latin
  America, Planned in Asia).
• Community web site
• Technical support for Southern energy analysts
• LEAP development maintenance
• Semi-annual newsletter
• http//www.energycommunity.org

• Dr. Charles Heaps
• Stockholm Environment Institute Boston Center
• 11 Arlington Street, Boston, MA, 02116, USA
• Phone 1 (617) 266 8090
• Fax 1 (617) 266 8303
• Email leap_at_tellus.org
• http//www.energycommunity.org

• Evaluates the energy production, life-cycle costs
  and GHG emissions reductions from renewable
  energy and energy efficient technologies.
• Intended primarily for project-level analysis
  (screening/feasibility), not for national-level
  integrated analyses.
• Does allow options to be compared to a
  counter-factual situation, but this is primarily
  a static comparison.
• Complements other tools reviewed here.
• Can be used for screening of options before
  inclusion in integrated assessments, or for
  detailed project-level assessments. Can help
  develop the technical, cost and performance
  variables required in other models.

• Structured as a set of separate modules, each
  with a common look and approach.
• Each module is developed in Microsoft Excel
• Modules include
• Wind energy
• Small hydro
• Photovoltaics
• Combined heat power
• Biomass heating
• Solar air heating
• Solar water heating
• Passive solar heating
• Ground-source heat pumps
• Energy efficiency measures (coming soon)

• Data requirements are those needed for a
  technical and financial assessment of any clean
  energy project.
• This includes location data, meteorological data,
  equipment data, cost data, and financial data.
• RETScreen includes both meteorological and
  product cost and performance databases which help
  determine the amount of clean energy that can be
  delivered (or saved) by a project, and help
  calculate parameters such as heating loads.
• The weather database has data from 4,720
  meteorological stations around the world.
• The product database is linked online to
  continuously updated data.

• Free support is available via email or a
  web-based forum.
• Because RETScreen is developed in Excel, training
  requirements are minimal.
• Users with little experience of the technologies
  being analyzed, will need to study the
  introductory training materials available for
  free on the website
• Free training materials include slides,
  teachers notes, e-textbooks, online manual, case
  studies.
• An online distance-learning course is also freely
  available to all registered users.
• A network of trainers conducts other training
  events, which are posted on the RETScreen
  Website.

• RETScreen has gt 65,000 users in 207 countries
  around the world.
• Some examples are
• Canada, Archemy Consulting, Solar/wind electric -
  Solar thermal, 21 kW
• Canada, DGV Engineering Services, Small hydro, 35
  MW
• Canada, WindShare, Wind energy, 750 kW
• Australia, Power and Water, Photovoltaics Wind
  energy, 890 kW 50 kW
• Brazil, Negawatt, Small hydro, 4 MW
• Czech Republic, Hydrohrom, Small hydro, 2 MW
• France, Electricité de France, Small hydro wind
  energy, 27 MW 7 MW
• Ireland, Sustainable Energy Authority, Wind
  energy, 100 MW
• India, IT Power India, Photovoltaics Small
  hydro, 89 kW 1 MW
• Italy, Seriana Servizi, Biomass power, 48 MW
• Nicaragua, Comisión Nacional de Energía, Mini
  hydro, 12 MW
• Russia, SKIF-TECH., Earth energy, 320 kW
• Romania, SPERIN, Wind solar thermal, 8.4 MW
  80 m2
• Senegal, ASERA, Wind energy Photovoltaics, 9 kW
  5 kW
• United States, Artha Renewable Energy, Solar
  water heating, 560 m2

• RETScreen Customer Support
• Natural Resources Canada
• 1615 Boulevard Lionel-Boulet, Varennes, QC,
  J3X1S6, Canada
• Phone 1 (450) 652-4621
• Fax 1 (450) 652-5177
• Email rets_at_nrcan.gc.ca
• http//www.retscreen.net

• MARKAL is a good choice if
• Already have MARKAL modeling experience.
• Technical and statistical data are relatively
  plentiful.
• A large number of complex and interacting
  technology options need to be assessed.
• Assessment team is familiar with concepts of
  optimization.
• Assumptions of optimizing models are reasonable
  in the study context.
• Assessment will be conducted over a relatively
  long time frame (e.g. one year) and able to
  invest considerable human resources in the
  assessment.
• Cost of software support is acceptable.

• ENPEP-BALANCE is a good choice in similar
  situations to MARKAL
• particularly if there is need to take a
  market-simulation approach, and optimization
  assumptions are not appropriate,
• LEAP is a good choice if
• Data is less plentiful.
• Team has less modeling expertise.
• Time frame for analysis is relatively short.
• Inherent assumptions of MARKAL/ENPEP are not
  appropriate.
• Assessment will focus on both technology choice
  and other mitigation options.
• RETScreen, is complementary to all of the
  integrated/national level tools.
• Country-specific approaches, using spreadsheets
  or other models may make sense for many Parties.

• Sathaye, J. and Meyers, S. 1995. Greenhouse Gas
  Mitigation Assessment A Guidebook
  Kluwer.http//ies.lbl.gov/iespubs/iesgpubs.html
  
• Halsnaes, K. Callaway, J.M. Meyer, H.J. 1999.
  Economics of Greenhouse Gas Limitations
  Methodological Guidelines. UNEP Collaborating
  Centre on Energy and Environment, Denmark.
  http//uneprisoe.org/EconomicsGHG/MethGuidelines.
  pdf
• Swisher, J. Januzzi, G. Redlinger, R.Y. 1997.
  Tools and Methods for Integrated Resource
  Planning. UNEP Collaborating Centre on Energy and
  Environment, Denmark. http//www.uneprisoe.org/IRP
  Manual/IRPmanual.pdf
• Heaps, C. 2005. User Guide for LEAP 2005.
  SEI-Boston. http//forums.seib.org/leap

• What additional information do you need to allow
  you to decide on a modeling approach?
• How well do the existing models fit the needs of
  your national communications assessments?
• How can training needs best be addressed in your
  country?

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