Choice and implementation of models for ma policy

Transcription

Deliverable D.3
“Choice and implementation of Models for
Mitigation / Adaptation policy portfolios”
Lead Beneficiary
Included Overviews
Overview of Models in use for Mitigation/Adaptation policy
Selection of Models for Mitigation / Adaptation policy
PROMITHEAS-4: “Knowledge transfer and research needs
for preparing mitigation/adaptation policy portfolios”
PROMITHEAS – 4
“Overview of Models in Use for
Mitigation/Adaptation Policy”
Task Leader: Prof. Bernhard Felderer,
Institute of Advanced Studies (IHS),
Vienna, August 2011
This report has been read, commented and approved by all members of the PROMITHEAS-4
Scientific Committee.
It was also disseminated for comments, through BSEC – PERMIS and BSEC – BC, to all
relevant governmental and business authorities and partners before its finalization.
Partners from the beneficiary countries* of the consortium were encouraged to contact direct
national authorities, agencies, institutions and market stakeholder for comments before the
finalization of this report (Annex 1).
Members of the PROMITHEAS – 4 Scientific Committee:
1. Prof. Dimitrios MAVRAKIS, NKUA – KEPA (GREECE) -Editor
2. Dr. Popi KONIDARI, NKUA – KEPA (GREECE) – Assistant to the editor
3. Dr. Harry KAMBEZIDIS, NOA (GREECE)
4. Prof. Bernhard FELDERER, IHS (AUSTRIA)
5. Prof. Bilgin HILMIOGLU, TUBITAK – MAM (TURKEY)
6. Prof. Vahan SARGSYAN, SRIE – ESC (ARMENIA)
7. Prof. Dejan IVEZIC, UB – FMG (SERBIA)
8. Prof. Mihail CHIORSAK, IPE ASM (MOLDOVA)
9. Prof. Agis PAPADOPOULOS, AUT – LHTEE (GREECE)
10. Prof. Alexander ILYINSKY, FA (RUSSIA)
11. Prof. Anca POPESCU, ISPE (ROMANIA)
12. Prof. Andonaq LAMANI, PUT (ALBANIA)
13. Prof. Elmira RAMAZANOVA, GPOGC (AZERBAIJAN)
14. Dr. Lulin RADULOV, BSREC (BULGARIA)
15. Prof. Arthur PRAKHOVNIK, ESEMI (UKRAINE)
16. Prof. Sergey INYUTIN, SRC KAZHIMINVEST (KAZAKHSTAN)
17. Prof. Alvina REIHAN, TUT (ESTONIA)
*Turkey, Armenia, Serbia, Moldova, Russia, Romania, Albania, Azerbaijan, Bulgaria, Ukraine,
Kazakhstan, Estonia.
The EU, the Consortium of PROMITHEAS – 4 and the members of the Scientific Committee do not undertake any
responsibility for copyrights of any kind of material used by the Task Leaders in their report. The responsibility is
fully and exclusively of the Task Leader and the his/her Institution.
Acknowledgments: The Task Leader of this report acknowledges the contribution of
Mr. Michael-Gregor Miess and Mr. Stefan Schmelzer for the development of this
overview.
Table of Contents
Table of Abbreviations
3
Introduction
4
Integrated Scenarios
6
Integrated Assessment Models for Adaptation/Mitigation
8
MARKAL/TIMES
12
Specific Characteristics of MARKAL
12
Specific Characteristics of TIMES
14
Evaluation of MARKAL/TIMES
14
ENPEP-BALANCE
16
Specific Characteristics of ENPEP-BALANCE
16
Evaluation of ENPEP-BALANCE
17
MESSAGE
17
Specific Characteristics of MESSAGE
18
Evaluation of MESSAGE
19
LEAP
21
Specific Characteristics of LEAP
21
Evaluation of LEAP
22
IMAGE
24
Specific Characteristics of IMAGE
24
Evaluation of IMAGE
25
MERCI
25
Specific Characteristics of MERCI
26
Evaluation of MERCI
27
Conclusion
27
References
29
2
PROMITHEAS-4: “Knowledge transfer and research needs for
preparing mitigation/adaptation policy portfolios”
ADAM
BAU
CCS
CEEESA
CES
EARDF
EC
EFOM
ENPEP
ERDF
ESF
ETSAP
FAIR
GAMS
GDP
GHG
IAEA
IAM
IHS
IIASA
IEA
IMAGE
IPCC
LEAP
M/A
MARKAL
MERCI
MESSAGE
MNP
NAPA
OECD
R&D
RES
RES
RIVM
SEI
TED
TIMES
UNFCCC
WEM
Title of the “Adaptation and Mitigation Strategies: Supporting European Climate Policy” project
Business As Usual
Carbon Capture and Storage
Centre for Energy, Environmental and Economic Systems Analysis
Constant Elasticity of Substitution
European Agricultural Rural Development Fund
European Commission
Energy Flow Optimization Model
Energy and Power Evaluation program
European Regional Development Fund
European Social Fund
Energy Technology and Systems Analysis Program
Framework to Assess International Regimes for differentiation of commitments
General Algebraic Modeling System
Gross Domestic Product
GreenHouse Gas
International Atomic Energy Agency
Integrated Assessment Model
Institut für Höhere Studien (Institute for Advanced Studies)
International Institute for Applied Systems Analysis
International Energy Agency
Integrated Model to Assess the Global Environment
Intergovernmental Panel on Climate Change
Long-range Energy Alternatives Planning
Mitigation/Adaptation
Market Allocation (Model)
Model for Evaluating Regional Climate change Impacts
Model for Energy Supply Strategy Alternatives and their General Environmental (Impact)
Milieu en Natuur Planbureau (Netherlands Environmental Assessment Agency)
National Adaptation Programs for Action
Organisation for Economic Co-operation and Development
Research and Development
Reference Energy Scenario
Renewable Energy Sources
Rijksinstituut voor Volksgezondheid en Milieu (National Institute for Health and Environment)
Stockholm Environment Institute
Technology and Environmental Database
The Integrated MARKAL-EFOM System
United Nations Framework Convention on Climate Change
World Energy Model
Introduction
This report provides an overview of models that should be considered to be used for
developing and quantifying adaptation and mitigation scenarios for the countries of the Black
Sea Region. These models are evaluated according to the specific aspects to be regarded for
the emerging economies of the countries in question, according to the advantages or
disadvantages when using them for scientific and public purposes, as well as according to
their terms and costs of use.
The challenges of developing a stable and sustainable energy system are manifold. Research
in the industrial countries was conducted on a very broad range, however, it still has to be
clarified if and how these research results can be applied to the situation in emerging
economies. This is the focus of the PROMITHEAS-4 project: to develop and evaluate
mitigation and adaptation policy portfolios, together with a characterization of research needs
and gaps in this area. Countries of the Black Sea Region are the predominantly targeted
emerging economies of this project: Albania, Armenia, Azerbaijan, Bulgaria, Moldova,
Romania, Russian Federation, Serbia, Turkey and Ukraine. Estonia and Kazakhstan are also
included in the beneficiary countries of this project. Their economic characteristics, however,
are comparable to the ones of the Black Sea Region. The assessment of the main
characteristics of emerging economies in this region will be crucial for the suitability of
energy models for the use in generating M/A policy portfolios in these countries. As Urban F.
et al (2007) have elaborated, there is reason to question the use of energy models developed
in/for industrialised countries in developing countries. They find that the characteristics of the
energy systems and economies of developing countries differ from those in industrialised
countries in the following aspects, which apply to developing countries’ energy systems:
•
•
•
•
•
•
•
•
The electricity supply of the economy is not functioning optimally;
The electrification rates are much lower relative to industrialized countries;
Predominant use of traditional bio-fuels;
The tariffs are often below long-term marginal cost of production and many bills will
never be paid;
A widespread informal economy;
Developing countries may not follow the same trajectory towards industrialisation as
today’s industrialized countries did;
The urban-rural divide causing high distribution differences within countries and regions;
Abuse or inadequate use of subsidies;
(Urban F. et al. 2007, p. 3474 ff).
Data from “Procedures, sources, and data for Mitigation / Adaptation for policy portfolios”
report could be used to clarify the following question: What exactly are the specific
requirements for emerging economies? There is little literature dealing with this specific
issue.
The situation in the Black Sea Region is of course a different one to that of developing
countries. However, it is important to bear in mind that energy models in use for industrialised
countries may yield insufficient or misleading outcomes for emerging economies. Therefore,
a short description of the economic situation in the Black Sea Region is important for the
comparison of different energy modelling tools. After defining the characteristics the models
should incorporate, we will analyse them from this perspective.
4
A concise description of the current economic situation in the Black Sea Region can be found
in a policy report prepared by the Commission on the Black Sea (Gavras, 2010). After 1999
the region experienced a period of high growth rates, growing government credibility,
improvement of the legal system, low government deficits, and rapid movement towards a
well functioning market-oriented economic system.
However, the financial crisis of 2008 brought this upward trend to a halt. Especially the large
decrease in investment inflows into the region caused serious problems, although the situation
differs among the countries. Obtaining funds became difficult, since investment into the
region’s countries is perceived to carry higher risks and risk aversion is reaching high levels
during the current financial crisis. Through lower credit ratings (there are other factors
determining a country’s risk as well, but this is the most prominent one) the cost of financing
budget deficits and undertaking investments rises. Furthermore, the economies have little
possibilities to conduct effective stimulus programmes (Gavras p. 7ff, p. 13ff).
After having provided a very brief sketch of the economic framework within which any
model employed by the PROMITHEAS-4 project will operate, we have to look at what kinds
of models are eligible for use. The final set of models included in this study consist of the
following models: MARKAL/TIMES, ENPEP-BALANCE, MESSAGE, LEAP, IMAGE and
MERCI.
A wide spectrum of models has been developed over the past thirty (30) years, which by far
cannot be covered within this project. We therefore selected the most renowned models that,
in advance, were considered to be best suited for the PROMITHEAS-4 project. Moreover, the
set of models is such that at least one simulation, economic equilibrium or optimisation1
model is included. PRIMES, for example, which is also a renowned simulation model, is not
included because it is similar to ENPEP-BALANCE. Similarly, EnergyPLAN is also a
simulation model, which was not included, since otherwise the simulation models would be
overrepresented. Important for the inclusion of a model within our study was its suitability for
emerging economies, such as evolved by Bhattacharyya and Timilsina (2010), as well as by
Urban et al. (2007). E.g. PRIMES and EnergyPLAN are not mentioned within these studies.
Another considered criterion was the availability of model applications on a national level and
moreover, in countries of the Black Sea region (e.g. PRIMES is predominantly used at
European level and databases are not available for many countries; the World Energy Model
(WEM), although mentioned by Urban et al. (2007), is mainly employed at a global scale and
therefore not included herein).
Summarising, there is a vast number of models that could be included within this overview.
However, we decided to choose the most representative ones for each modelling category
(simulation, optimisation and economic equilibrium), as well as those with the highest
probability to be suited for emerging economies within the Black Sea region.
As the selected model will be used to design integrated M/A scenario portfolios, comprising
environmental, technological, economic and policy problems, we have to focus on integrated
assessment models that are able to deliver this purpose. Thus, before we can begin with a
modelling overview, we have to look at the requirements these models have to fulfil, i.e. what
we understand as integrated scenarios.
1
These categories will be explained in more detail in the next two sections.
5
Integrated Scenarios
As it is elaborated on in the final report of the ADAM (Adaptation and Mitigation Strategies:
Supporting European Climate Policy) project, adaptation and mitigation, even though some
consider them as alternative strategies to deal with climate change, should not be regarded as
mutually exclusive (Hulme et al., p. 8). Thus, any policy portfolio designed for the
PROMITHEAS-4 project should involve mitigation as well as adaptation strategies, not only
trying to limit the global temperature increase to 2°C (which would involve already a high
level of mitigation activities, see Hulme et al., p. 8), but also include measures to deal with
the effects of this temperature increase or a larger one of up to 4°C.
Generally, a scenario should be seen as time paths of key variables, which are exogenously
specified and then used as inputs driving other parts of an assessment (Parson and FisherVanden, 1997, p. 595). Viewed in the context of energy-economy-environmental modelling,
then, scenarios are story-lines about how an energy system might evolve over time2.
However, an M/A scenario will not only have to consider the development of the energy
system and the emission patterns it induces (mitigation), but also take into account the
regional aspects of adaptive measures.
Therefore, while mitigation, seen as identification of the appropriate set of energy efficient
and renewable energy technologies, has been intensively studied and has a common
knowledge base, e.g. a unified technological database, defined expectations for technological
progress, etc., adaptation includes responses to the predicted impact of climate change and is
highly dependent on local conditions and on national priorities. Thus, any modelling effort
has to be supported by a model that is flexible enough to consider regional, local, and national
aspects of adapting to climate change.
As has emerged from the recent discussion on climate change policies (both at European and
global level), adaptation represents a new priority for policy makers, which also has to be
taken into account when constructing policy portfolios. This requires not only a change in
attitude, but also a change in the scale of priorities for both policy makers and scientists. In a
complex financial framework such as that present for the European Union, a change in the
scale of priorities, to be effective, must correspond to a shift of budgetary allocations. This
step has high-level political implications and therefore needs to be dealt with in a wider
framework which critically evaluates all current EU policies and priorities (e.g. cohesion,
competitiveness, growth, infrastructure, etc.) (Lavalle, 2009, p. 4).
In order to take on this matter, the European Commission has issued a White Paper,
“Adapting to climate change: Towards a European framework for action” (EC White Paper in
the following) in 2009 as its latest formal proposal. This White Paper is based on a phased
approach, the first of which will last for the period 2009-2012, preparing a comprehensive EU
adaptation strategy, which is then to be implemented in phase 2 starting from 2013 (see EC
White Paper, p. 7). Phase 1 is based on four (4) main pillars (EC White Paper, p. 7):
1) building a solid knowledge base on the impact and consequences of climate change for the
EU;
2) integrating adaptation into EU key policy areas;
3) employing a combination of policy instruments to ensure effective delivery of adaptation
and;
2
UNFCCC “Module 5.1 – Mitigation Methods and Tools in the Energy Sector” 2006:55
6
4) stepping up international cooperation on adaptation.
In order for this phase 1 to work, national, regional and local authorities have to cooperate
closely. The EU provides funding for financing adaptation measures, amongst other through
the following sources:
•
•
•
•
The Common Agricultural Policy and Rural Development (EARDF);
The Structural Fund (ERDF);
The European Solidarity Fund (ESF);
Civil Protection Mechanism.
To encourage developing countries and emerging economies to prepare for the expected
impacts of climate change, the United Nations Framework Convention on Climate Change
(UNFCCC) proposes to develop National Adaptation Programs for Action (NAPA)3,
including information, among other things, on climate change induced natural hazards like
floods, droughts, heat waves, heavy rainfall, hurricanes, and tornadoes.
Even though the NAPAs are rather designed for less developed countries, which stand in
contrast to the emerging economies of the Black Sea region, it seems clear that any adaptation
strategy is subject to a larger framework at the EU and global level, which is being developed
at this very moment.
All of these facts point to the conclusion that only a selected number of Integrated Assessment
Models for Adaptation/Mitigation (IAMs) are eligible for use by the PROMITHEAS-4
project. However, one has to say that most models primarily focus on mitigation issues, and
that adaptation, if present in the models, can only be implicitly depicted in the majority of
cases. Therefore, adaptation issues probably will have to be dealt with also outside of a formal
modelling environment, defining the adaptation part of an M/A policy portfolio
predominantly based on regional economic and environmental specifics.
After an introduction into the class of IAMs, a selection of those that come into question for
the PROMITHEAS-4 project will be evaluated.
3
http://unfccc.int/national_reports/napa/items/2719.php
7
Integrated Assessment Models for Adaptation/Mitigation
Before one can talk of an assessment model, it is crucial to define this term. If one follows the
literature, assessment is described as
“social processes that bridge the domains of knowledge and decision-making,
assembling and synthesizinig expert scientific or technical knowledge to advise policy
or decision-making”.
(Parson, Fisher-Vanden, 1997, p. 590, see also Parson, 1995, and Weyant et al., 1996)
One way to achieve such an assessment is by employing a formal modelling environment that
represents the complex relationships underlying the to-be-assessed problem field, as opposed
to e.g. deliberation by interdisciplinary expert panels (see Parson, Fisher-Vanden, 1997, p.
591). The Promitheas-4 project has chosen to rely mainly on a formal modelling environment
to construct M/A policy portfolios for the beneficiary countries.
Integrated assessment models, now, can deliver this purpose by combining socio-economic
dimensions of climate change with systemic aspects of technological alternatives in order to
address policy options and environmental impacts of climate change. In general, IAMs
attempt to employ one or several of three methods associated with each other, in a combined
or stand-alone form, to project emissions and with them climate change (Parson, FisherVanden, 1997, p. 595): emission scenarios (externally specified), an accurate bottom-up
representation of technologies for the production of energy and other goods, and economic
modelling in an aggregate form (e.g. taking account of economic equilibrium conditions).
Common to all these approaches are predictions of the future in a speculative form, who only
“differ in the detail and explicitness of different components of the projections” (Parson,
Fisher-Vanden, 1997, p. 595).
Given the speculative nature of these models, they serve as a means to estimate costs and
benefits of policy options, always related to a possible future development of the social,
economical and environmental system, all of them being dependent on each other to a certain
extent. Hence, they can be used to (Dickinson, 2007, p.7):
• Assess climate change control policies (Weyant et al., 1996);
•
Create interdisciplinary frameworks;
•
Address climate change problems including determining influential forces that make
sectors sensitive to climate change;
•
Quantify environmental and non-environmental problems resulting from climate
change by ranking climate change control benefits and detriments in developed and
emerging economies, as well as developing countries (IPCC, 2001).
IAMs can be divided into three (3) types of models: simulation, economic equilibrium and
optimisation models (see e.g. Urban F. et al., p.3479). However, Bhattacharyya and Timilsina
(2010) use a different type of categorisation, especially designed for energy system models
(see Bhattacharyya, p. 501):
• bottom-up, optimisation-based models (such as e.g. MARKAL);
• bottom-up, accounting models (such as e.g. LEAP);
8
• top-down, econometric models;
• hybrid models (such as e.g. MERCI);
• electricity system models.
Which categorisation is used will depend on the characteristics one wants to include in the
differentiation of the models. For the purposes of this report, the categorisation from Urban F.
et al. (2007), which essentially is an adaptation of a model classification system proposed by
Van Beeck (1999,2003) and not much different to the one of Bhattacharyya and Timilsina
(2010), has been chosen as the most suitable. The different aspects to and specifics of the
different model classes are described in brief in Table 1 below.
Table 1: Integrated Assessment Models Comparison Chart
Economic Equilibrium
MODEL
Simulation IAMs
Optimisation IAMs
IAMs
To simulate and
To assess overall
approximate the
To identify optimal policies
economic development
environmental results
e.g. of climate change
Use
and ecological impacts
of a selected policy
control options
simultaneously
option
Individual portfolio
Scale
of policy options or
Global or national level
Usually global
chosen scenarios
Through the use of
Finds a new economic
Determines the policy path
scenarios based on
equilibrium based on
that maximizes utility, or
user-defined
exogenously specified
minimizes costs, while
Description
assumptions, a
scenarios, endogenously
imitating the effects of
portfolio of policy
finding optimal control
mitigation on the
options is produced
variables
global/local economy
LEAP, IMAGE,
MARKAL/TIMES,
Examples
MERCI
ENPEP
MESSAGE
Source: Dickinson, 2007, p.8, Urban F. et al, p. 3479, Authors.
Integrated Assessment models were first designed beginning from the 1990’s, when the focus
of policy makers and the scientific community shifted towards energy-environment
interactions and climate change related issues (Bhattacharyya and Timilsina, 2010, p. 498).
To depict these environmental issues, some extensions to energy system models were close at
hand (Bhattacharyya and Timilsina, 2019, p. 498):
•
•
•
accounting models, i.e. models based on energy balances (see Bhattacharyy and Timilsina,
2010, p. 496), were able to incorporate environmental effects in relation to energy
production, conversion and use by including an appropriate set of environmental
coefficients;
network-based models, i.e. models extending the energy balance framework to a network
description of the energy system capturing all activities involved in the entire supply chain
(see Bhattacharyy and Timilsina 2010, p. 496), could similarly estimate environmental
burdens employing environmental pollution coefficients and evaluating the economic
impacts by considering costs of mitigation;
energy models with macro linkage could analyse the allocation issues taking account of the
overall economic implications.
9
Bhattacharyya and Timilsina (2010) come to the conclusion that what they call models of
bottom-up accounting type (or simulation IAMs, in the classification of this report) are best
suited for the representation of energy systems for developing countries, mostly because of
their flexibility, limited skill requirements (Bhattacharyya and Timilsina, 2010, p. 501), and
because they can account for several specifics of developing countries.
Under the conditions delineated above, potential integrated scenario-based assessment models
that include mitigation and adaptation were considered as a result of an extensive literature
search, parts of which are presented in the next sessions and after communications with key
Partners during the kick-off meeting, 3rd-4th March 2011, Athens.
Figure 1: Criteria for Models to be Included in this Overview
Source: Authors.
The models subsequently described in this overview are presented in Table 2 below. The
following overview is based on (a) model documentations, (b) existing research reports, (c)
articles in scientific journals and (d) model websites.
10
MODEL NAME
Table 2: List of Models to be surveyed
Organisation / Author
FURTHER INFORMATION
Availability
MARKAL /
TIMES
The Energy Technology Systems Analysis
Program (ETSAP), IEA
www.etsap.org
Source code free
/Simulators to be
purchased
ENPEPBALANCE
Argonne National Laboratory. Energy and
Power Evaluation Program
http://www.dis.anl.gov/projects/Enpepwin.html
Free to Download
MESSAGE III
IIASA, Laxenburg, Austria
Messner S., Strubegger M., (1995), User's Guide for
MESSAGE III, IIASA, WP-95-069
Commercial
LEAP
Stockholm Environment Institute – Boston
Center
http://www.energycommunity.org/
Free for developing
countries
IMAGE
the Netherlands Environmental Assessment
Agency
http://www.rivm.nl/bibliotheek/rapporten/500110002
.pdf
Upon a cooperation
agreement
MERCI /
ATHDM E3
IHS, Vienna, Austria
Miess M., Schmelzer S., Balabanov T. (2010), The
Austrian Hybrid Dynamic Model E3: Methodology,
Application and Validation, IHS internal WP
Work in progress
Source: Authors.
PROMITHEAS-4: “Knowledge transfer and research needs for preparing mitigation/adaptation policy
portfolios”
MARKAL/TIMES
The acronym “MARKAL” stands for MARKet ALlocation. This model was first
developed by the Brookhaven National Laboratory in the late 1970’s. In 1978 the
Energy Technology and Systems Analysis Program (ETSAP) was established by the
International Energy Agency (IEA) to pursue further development of the model. Since
then, a whole family of models was created and most recently the TIMES (The
Integrated MARKAL-EFOM4 System) model was introduced as a successor of
MARKAL5.
Source: Loulou et al. (2004); Bhattacharyya and Timilsina (2010)
Specific Characteristics of MARKAL
The MARKAL model facilitates the analysis of different future energy system pathways over a
medium to long term, by integrating energy, environmental, and economic factors. Since the
development of MARKAL, many extensions were introduced. The model originally started
out using a linear programming approach that focused entirely on the integrated assessment
of energy systems. The developed amplifications went from the introduction of non-linear
programming, combining a 'bottom-up6' modelling technique with a 'top-down' macroeconomic view, to the application of stochastic programming, which allowed addressing
future uncertainties, to model multiple regions (Seebregts et al. 2001).
The model works with a user defined map (Reference Energy System) of the energy system
which contains information on the following features (Seebregts et al. 2001):
4
EFOM (Energy Flow Optimization Model) is another bottom-up energy model on which the TIMES
model is based upon.
5
However, MARKAL still can be used if necessary.
6
I.e., the specific technological features of the energy sector are accounted for explicitly.
12
PROMITHEAS-4: “Knowledge transfer and research
needs for preparing mitigation/adaptation policy portfolios”
•
Conversion of energy (e.g. power plants, refineries, solar plants)
•
Primary supply of energy carriers (e.g. mining, petroleum extraction);
•
Consumption of energy (e.g. industrial energy use, vehicles);
•
Demand7 (exogenous, forecasts have to be produced outside the model);
•
Technical characteristics;
•
Technology costs.
With respect to the exogenously given end-use energy demand level, the model estimates a
discrete supply curve. Therefore, all quantities and prices are in equilibrium (suppliers
produce exactly the amount demanded by consumers) (Loulou et al. 2004). A portfolio is
provided with a cost minimising set of energy resources, energy carriers, transformation
technologies, etc; which satisfy the user defined constraints (e.g. energy balance, electrical
system operation, emission caps, technology portfolio standards, taxes, etc.). More
importantly, the model quantifies the environmental emissions resulting from this portfolio
(Seebregts et al. 2001; Johnson 2004).
The MARKAL model has typically been employed to address issues related to carbon dioxide
emission reduction, technology dynamics and R&D (Seebregts et al. 2001). “The specification
of new technologies, which are less energy- or carbon-intensive, allows the user to explore
the effects of these choices on total system costs, changes in fuel and technology mix, and
the levels of greenhouse gases and other emissions.” (Seebregts et al., 2001)
Johnson (2004) presents exemplary questions which can be investigated by the MARKAL
model:
•
What happens if a new technology becomes available, or if an old one becomes
cheaper or more efficient?
•
What are the implications of a technology forcing policy (e.g., a renewable portfolio
standard)?
•
How do changes in technology, environmental policy, and resource availability/costs
interact?
As Bhattacharyya and Timilsina (2010) conclude, this model is among the better suited ones
to be used for the specific needs in developing countries, and in our case, emerging
economies, however, if compared to a bottom-up accounting type of model (as LEAP) it
misses some important features of these countries (e.g. including the degree of the informal
sector, energy shortages, the degree of economic transition).
7
Demand can be disaggregated by sector and by functions in the sector.
13
Specific Characteristics of TIMES
The TIMES model, as it was previously stated, is based on the MARKAL modelling
paradigm. The “Documentation for the TIMES Model Part I” (Loulou et al. 2004, p.
52) summarises the main differences between the MARKAL and the TIMES model.
The main are:
•
User defined period lengths (e.g. small steps within the first few periods,
greater durations thereafter);
•
Greater user flexibility in input data specification independent of time
periods
(matching is done by the model);
•
User-chosen time-slices of commodities;
•
Processes in different Reference Energy System sectors have the same basic
features,
activated by data specification;
•
Greater specification possibility of commodity-related criteria;
•
Investment payments can be timed more accurately and it is possible to
define timedependent discount rates.
The TIMES model operates with user-provided estimates of energy related equipment
in all sectors, characteristics of available technologies, together with present and
future energy sources and their potentials (Loulou et al. 2004, p.7).
Evaluation of MARKAL/TIMES
Urban et al. (2007; p.3478) find that MARKAL accounts for a medium number of
developing countries characteristics, such as: electrification, traditional-bio fuels,
urban-rural divide, subsidies, emission training and a wide assessment of renewable
energies.
The MARKAL or TIMES models, respectively, have been used in numerous national
and regional studies. The European Commission has used the TIMES model for the
evaluation of the Renewable Energy Strategy for 2020 (RES2020). Further, the
TIMES model (among others) was applied to optimise the Electricity, Heat and
Natural Gas Markets of the EU-25. Sulkan et al. (2010) found the MARKAL model
useful in modelling alternative energy futures for Turkey. Various Estonian doctoral
theses8 and studies have been conducted using MARKAL, among them “Reduction of
CO2 emissions in Estonia during 2000-2030” by Agabus et al. (2007). Also in
Moldova MARKAL was applied to investigate energy efficiency measures and
renewable energy sources implementation possibilities. The preliminary results of this
study can be found in “MARKAL Application for Analysis of Energy Efficiency
measures and Renewable Energy Sources” by Robu et al. (2010)
Evaluation
Criteria
Description
Methodology
Optimisation approach creating a dynamic partial equilibrium including all user
provided energy sector specifics
8
“Long-Term Capacity Planning and Feasibility of Nuclear Power in Estonia Under Uncertain
Conditions” by Landsberg (2008) and “Large-Scale Integration of Wind Energy into the Power System
Considering the Uncertainty Information” by Agabus (2009).
14
Transparency,
complexity,
easiness of use
Technology-rich energy/economic/environmental model that requires long
preparatory work
Data requirements
&
software
requirements
Medium to high data requirement, including estimates of: energy related
equipment in all sectors, characteristics of available technologies, present and
future energy sources and their potentials; GAMS (General Algebraic Modeling
System) is required; Windows based
Costs
Cost per user for educational license: €1.200– €3.000; for the 12 beneficiaries
the licensing costs could reach € 30.000
Level of coverage of
M/A issues
Compliance of
outputs with
projects objectives
Availability of
training and
technical support
International
recognition
MARKAL-MACRO: provides for endogenous and price responsive demands,
and estimates of GDP impact and feedbacks;
Allows certain behavioural characteristics of observed markets to be reproduced
Used to simulate European Commission integrated policies on the use of
renewable sources, climate change mitigation and energy efficiency
improvement, the so called 20–20–20 targets, and far more stringent M/A
targets in the longer term at the national and pan EU level
The most demanding and expensive part of MARKAL/TIMES is the training of
8 days €22.000-€30.000; however, broad documentation is available resulting
from the wide use of the model; Costs of Technical support: €500-€1.800 for
one year
Most widely used bottom-up optimisation model; used in > 40 countries
Sources: http://www.etsap.org/; Loulou et al.2004; UNFCCC 2006.
15
ENPEP-BALANCE
The Energy and Power Evaluation Program (ENPEP) was developed in 1999 by the
Centre for Energy, Environmental and Economic Systems Analysis (CEEESA9 Argonne National Laboratory in the USA) and the U.S. Department of Energy (DOE).
It is now used in over 80 countries. BALANCE is one of ten (10)10 integrated energy,
environmental and energy analysis tools named by the UNFCCC in its 2006 report on
mitigation assessment (UNFCCC 2006, p.39).
Sources: Argonne National Laboratory 2008; UNFCCC 2006 “
Specific Characteristics of ENPEP-BALANCE
The ENPEP-BALANCE Model uses the following input parameters: energy system
structure, base year energy statistics (with production levels, consumption levels and
prices included), energy demand growth projections as well as technical and policy
constraints. With this information, an energy network is created graphically and
configured by the user. The developers stress the importance of the model applying a
market share algorithm. Through this it is possible to estimate the penetration of
supply alternatives (Argonne National Laboratory 2008, p.1). “The equilibrium
solution develops an energy system configuration that balances the conflicting
demands, objectives, and market forces without optimizing across all sectors of the
economy” (Argonne National Laboratory 2008, p.2).
This equilibrium solution, i.e., the set of market clearing prices and quantities, is
found by the simultaneous intersection of supply and demand curves for all energy
forms, as depicted in the network structure (Argonne National Laboratory 2008, p.2).
Regarding environmental issues, BALANCE calculates green house gas emissions,
local air pollutants (such as SOX, NOX, CO, CO2, and methane), water pollution and
land use (UNFCCC 2006, p. 39; Argonne National Laboratory 2008, p.3).
9
CEEESA – Center for Energy, Environmental, and Economic Systems Analysis;
The nine modules are: MACRO-E, MAED, LOAD, PC-VALORAGUA, WASP, GTMax, ICARUS,
IMPACTS and DAM.
10
16
The model allows for an annual analysis over a time horizon of up to 75 years
(Connolly 2010:1069).
The model, being a simulation type of model, allows for better incorporation of nonprice factors in the analysis. This is of particular importance if emerging or
developing country features are being considered.
Evaluation of ENPEP-BALANCE
The model was applied in various studies11, among them the following involving
participating economies of PROMITHEAS-4: A regional European project to
evaluate various GHG mitigation options conducted studies for 10 countries including
Bulgaria, Turkey and Ukraine; A World Bank Project to develop an Energy and
Environmental Review for Bulgaria (The World Bank 2001); A CEEESA project
together with the Romanian Institute of Power Studies and Design to develop a longterm energy strategy for Romania (Koritarov et al. 1998); And a CEEESA project to
analyse carbon mitigation policies in Turkey conducted for the World Bank
(Conzelmann et al. 2002). Furthermore, ENPEP-III (an older version of the model) is
applied in Moldova.12 It is already evident from this summary that the ENPEPBALANCE model is mostly employed to analyse national (versus regional) energysystems (Argonne National Laboratory 2008, p.4f; Connolly et al. 2010, p.1069).
Evaluation Criteria
Methodology
Costs
Data requirements
&
software
requirements
M/A issues
Compliance
of
outputs
with
projects objectives
Availability
of
training
and
technical support
International
recognition
Description
Non-linear, equilibrium energy system model with economic and
environmental modules; determines the response of various segments of the
energy system to changes in energy prices and demand levels
Can be downloaded for free from <www.dis.anl.gov/projects/Enpepwin.html>;
However, training costs for 5 days amount to ~ € 7000; Costs for technical
support amount to another € 7000
Medium to high: energy statistics, energy demand growth projections,
technology coefficients; Windows based
The emphasis is on mitigation studies; some were already conducted in
PROMITHEAS-4 participating economies
Used for green - house-gas (GHG) emissions projections and modelling the
regional electricity networks; analysis of mitigation strategies
Typical training duration is 5 days for basic applications and two weeks for
advanced applications; Technical support is provided via phone or email (€
7.000 for 80 hours)
Used in over 80 countries
Sources: UNFCCC 2006, p.23, Argonne National Laboratory 2008, p. 4f;
<http://www.dis.anl.gov/projects/Enpepwin.html#balance>.
MESSAGE
The acronym “MESSAGE” stands for Model for Energy Supply Strategy Alternatives
and their General Environmental Impact. The model was developed by the
11
An extensive list can be found on the developer’s web page:
<http://www.dis.anl.gov/projects/Enpepwin.html#balance>
12
See “Greenhouse Gas reduction for scenarios of power sources development of the Republic of
Moldova” by Robu and Comendant (2010).
17
International Institute for Applied Systems Analysis (IIASA) in the 1980s and is
widely used by the International Atomic Energy Agency (IAEA) and its member
states (Connolly et al. 2010:1072). The model operates similar to the
MARKAL/TIMES model and the actual version is MESSAGE IV.
Sources: IIASA, Connolly et al. 2010
Specific Characteristics of MESSAGE
The user determines all the system-inherent and physical constraints and a Reference
Energy System, where all the necessary configurations of the energy network are
represented. Moreover, the necessary input data includes the performance
characteristics of the technologies. The model then creates various energy system
scenarios, which minimise total system costs, from resource extraction to the end-use.
This is done starting from the base year leading up to the end of the time horizon
(max. 120 years) in five to ten year steps13 (Connolly et al. 2010, p. 1072). “All
thermal generation, renewable, storage/conversion, transport technologies, and costs
(including SO2 and NOX costs) can be simulated by MESSAGE as well as carbon
sequestration (Connolly et al. 2010, p. 1072).”
Moreover, a stochastic energy system model was developed to assess key
uncertainties within the energy system. This includes uncertainties concerning
technological, socio-economic and climate change specifications into the modelling
structure.
MESSAGE was further linked with the MACRO14 model to allow for a specific
treatment of the impact of policies on energy costs, GDP and on energy demand.
Another important model development includes extension of the model to cover all
six (6) Kyoto GHGs, their drivers and mitigation technologies. In their research
project about further developments of the Kyoto-Protocol, Nakicenovic and Riahi
applied the “macroeconomic model MACRO [...] to assess the economic impact and
13
http://www.iiasa.ac.at/Research/ECS/docs/models.html [Accessed 12/05/2011];
“MACRO corresponds to the macroeconomic module of the top-down macroeconomic model
MERGE” (Manne, Richels 1992).
14
18
price-induced changes of energy demand due to carbon abatement policies
(Nakicenovic, Riahi 2003, p.4).”
Evaluation of MESSAGE
The report by Urban et al. (2010, p. 3478) assessing energy models for developing
countries finds that many developing countries (again, similarities arise regarding
IAM characteristics for emerging economies) characteristics are included. These are
electrification, traditional bio-fuels, urban-rural divide, subsidies, clean development
mechanism, emission trading and renewable energies.
The model was applied in the following research projects: The development of global
energy transition pathways for the World Energy Council (Nakicenovic, N., Riahi, K.,
2001); GHG emission scenarios for the Intergovernmental Panel on Climate Change
(Nakicenovic et al. 2000); Energy supply options in the Baltic states (IAEA, 2007);
Moreover research projects are currently being undertaken using MESSAGE in
Moldova.
Evaluation Criteria
Methodology
Transparency,
complexity,
easiness of use
Costs
Data requirements &
software
requirements
Description
Systems engineering optimisation model; Technology-rich energy systems
model with economic and environmental modules
Time demanding development of case studies
Free for academic purposes
Data: Energy/Economic/Environmental database, which corresponds to the EU
statistical standards.
Software: A free Linear Programming (LP) solver is provided. However
depending on the problem complexity, more powerful LP and Non-Linear
19
Availability of the
model and the Data
M/A issues
Compliance
of
outputs with projects
objectives
Availability
of
training and technical
support
International
recognition
Programming (NLP) solvers can be seamlessly used by the software; Windows
based
Economic/Energy/Environmental Data base corresponds to the EU statistical
standards
The model is mostly used to estimate global or regional multi-sector mitigation
strategies
With emphasis on mitigation a multitude of national studies has been
completed, e.g. on options for increasing the use of renewable energy for
China or energy supply options in the Baltic States, etc
The training (also conducted by IAEA) takes approximately 2 weeks; most
demanding part after the initial training is the development of case studies: this
can take up to half a year with IIASA team’s support
Several hundred users; wide use in IAEA member countries
Sources: Connolly et al. 2010:1072; <http://www.energycommunity.org/default.asp?action=71> [Accessed
12/05/2011].
20
LEAP
The Long-range Energy Alternatives Planning (LEAP) model was developed in 1980
in the USA. Later, the Stockholm Environment Institute (SEI) took over the
maintenance and further development of the model.
Sources: Bhattacharyya and Timilsina 2010, Connolly et al. 2010:1071, UNFCCC 2006, p. 50f.
Specific Characteristics of LEAP
LEAP offers broad modelling possibilities: The whole range of sectors, technologies
and costs within energy-systems can be simulated. Questions regarding externalities
for any pollutant, decommissioning costs and unmet demand costs can be answered.
The time horizon for the evaluation of national energy-systems in LEAP typically lies
between 20 and 50 years, but can be extended unlimitedly. The analysis is conducted
on an annual basis (Connolly et al. 2010, p. 1071).
The model requires relatively low data inputs, e.g. there is a possibility to assess
energy systems and GHG emission without further information on technology costs
(UNFCCC 2006, p.50).
Different approaches are taken to model the demand and supply side. On the demandside a spectrum from bottom-up, end-use accounting technique to top-down
macroeconomic modelling is covered. The supply side offers a spectrum of physical
energy and environmental accounting as well as simulation methodologies, which are
used for developing a clear picture of the electricity power generation and for
planning capacity expansions (Connolly et al. 2010, p.1071; UNFCCC 2006, p.51).
The LEAP output consists of the following details: fuel demands, technology costs,
unit productions, resource extraction, GHG emissions, air-pollutants, full system
social-cost-benefit analysis and non-energy sector sources and sinks. “Usually, these
results are then used to compare an active policy scenario versus a policy neutral
business-as-usual baseline scenario (Connolly et al. 2010, p.1071; UNFCCC 2006,
p.50).”
Bhattacharyya and Timilsina (2010) consider this type of model to be the most suited
one for addressing developing countries’ characteristics. The accounting framework
makes the model very flexible regarding data requirement. However, especially the
21
underlying scenario-based structure (versus an optimisation approach) makes them
recommend this model for developing countries (Bhattacharyya and Timilsina 2010,
p. 508; 513). Moreover the United Nations Framework Convention on Climate
Change (UNFCCC), in its Training Handbook on Mitigation Assessment, describes
the calculations of the model as non-controversial because of their simple verification
and high transparency (UNFCCC 2006, p. 51).
Evaluation of LEAP
LEAP was also reviewed by Urban et al. (2007, p. 3478). They found that it includes
a large number of developing countries’ characteristics (therefore, also characteristics
of emerging economies), such as performance of the power sector, electrification,
traditional bio-fuels, urban-rural divide, subsidies, individual assumptions per
country, emission trading, clean development mechanism, renewable energies and
rural energy programmes.
The applications of the LEAP model are numerous (Community for Energy,
Environment and Development - URL). Recently, an assessment of CCS (Carbon
capture and storage) potential was conducted in Greece, to analyse the emission
mitigation strategies for 2050 (Bellona Foundation 2011). Moldova used LEAP for
preparing the “Second National Communication of the Republic of Moldova to
UNFCCC”15. In Estonia, two studies were prepared recently on the basis of LEAP:
“Energy Planning Models Analysis and Their Adaptability for Estonian Energy
Sector” by Dementjeva and “Analysis of current Estonian energy situation and
adaptability of LEAP model for Estonian energy sector” by Dementjeva and Siirde.
Another notable study was prepared by the SEI, analysing how Europe can show
leadership in keeping global climate change under the limit of 2°C higher warming.
Evaluation Criteria
Methodology
Transparency,
complexity,
easiness of use
Costs
Description
Accounting type; Optimisation model was released on May 7th
Notable for its flexibility, transparency and user-friendliness
Free to qualified users, but there is a cost for OECD (Organisation for
15
This can be downloaded at:
<http://unfccc.int/essential_background/library/items/3599.php?rec=j&priref=7159&suchen=n >
22
Data requirement &
software requirement
M/A issues
Compliance of outputs
with
projects
objectives
Economic Co-operation and Development) based users; Paid license for
EU27/free for developing countries; Whether costs arise for emerging
economies has yet to be clarified.
Total cost arising for the project € 8.800
Data: Provides national "starter" data sets; Includes a built-in Technology and
Environmental Database (TED) for a variety of technologies
Software: Windows
For adaptation macroeconomic indicators (price, GDP, etc.)
Final and useful energy demand analyses; Stock-turnover for transport;
Scenarios of energy and non-energy sector emissions and sinks
Availability of training
and technical support
Online training is available but not sufficient;
One week of trainer-led training is recommended;
Technical support provided against fee;
International
recognition
Currently LEAP has over 5000 users in 169 countries
Source: Connolly D. et al. 2010, UNFCCC “Module 5.1 – Mitigation Methods and Tools in the Energy Sector”
2006:50ff.
23
IMAGE
The Integrated Model to Assess the Global Environment (IMAGE) was developed in
the late 1980s16 by the National Institute for Public Health and the Environment
(RIVM) in the Netherlands and is currently maintained by the Netherlands
Environmental Assessment Agency (MNP). The latest version, incorporating many
enhancements and extensions, created in close co-operation with different institutions
in this area, is IMAGE 2.4.
Main facts
Sources: Bouwman et al. 2006, p.5, Urban et al. 2007, p. 3479.
Specific Characteristics of IMAGE
The IMAGE 2.4 model is one of the most complex modelling frameworks developed
until now. Different independent models can be combined for various purposes: For
example, the TIMER hybrid model investigates the energy supply and energy demand
side of the economies and the FAIR model analyses policy options (PBL –
Netherlands Environmental Assessment Agency - URL). Human activities in areas
such as industry, housing, transport, agriculture or forestry have various implications
on human and natural systems. In IMAGE these interactions are specified and
explored thoroughly. The “key-drivers” of the model are defined as change,
population and macro economy (Bouwman et al. 2006, p. 8).
The specific features of the IMAGE 2.4 framework can be divided into three (3)
interacting categories (Bouwman et al. 2006, p.13):
-
-
-
16
17
Socio-economic system: This category includes demographics, energy supply and
demand, agricultural demand and trade, as well as the broad category “world
economy”.
Earth system: This category contains an explicit land use and land cover model,
including the carbon, nitrogen and water cycle, as well as the atmosphere and ocean
systems17.
Impacts: This category offers options for evaluating climate policies, using the policy
decision-support model FAIR18. Hence, climate impacts, land degradation issues,
water stress, biodiversity, as well as water & air pollution can be addressed.
It was called Integrated Model to Assess the Greenhouse Effect.
An additional model, GLOBIO 3, can be used to address biodiversity issues.
24
Evaluation of IMAGE
Urban F. et al. (2007), in their assessment of developing countries’ features that are
accounted for energy models, described the IMAGE model as incorporating a medium
number of developing countries’ characteristics, namely electrification, traditional bio
fuels, urban-rural divide, clean development mechanism, emission trading and a wide
assessment of renewable energies (Urban et al. 2007; p.3478).
Research conducted on the basis of the IMAGE model includes: IPCC Special Report
on Emissions Scenarios (SERS; IPCC Special Report on Emissions Scenarios, 2000),
EUruralis study focusing on future prospects for agriculture and the rural areas of the
EU-25 countries19, and various Greenhouse Gas Reduction studies20.
Evaluation Criteria
Methodology
Transparency, complexity,
easiness of use
Costs
Data requirements & software
requirements
Level of coverage of M/A
issues
Compliance of outputs with
projects objectives
Availability of training and
technical support
International recognition
Description
Hybrid simulation model
Comprehensive Integrated Assessment Model (IAM) consisting of variety of
sub-modules
The model is useable only in close cooperation with the IMAGE developers,
costs are therefore not specified;
IMAGE cannot be provided as a "ready to use package" to others: Much of the
performance of IMAGE actually comes from design of scenario assumptions
and its translation into actual model input. Therefore a lot of expert knowledge
is necessary.
Provides insights into the full range of adaptation and mitigation options,
including the costs, benefits and risks of different climate futures, policies and
socio-economic development pathways, etc.
Applied in assessing climate mitigation strategies
The developers are open for serious collaborations with other institutes, to share
model results, to work together on projects or model development.
Multitude of studies analysing scenarios of global and regional environmental
change.
Sources: Bouwman et al. 2006; http://themasites.pbl.nl/en/themasites/image/overview/index.html.
MERCI
The Model for Evaluating Regional Climate change Impacts (MERCI) was developed
by the Institute for Advanced Studies (IHS) Vienna in 2009, and is still being refined
and advanced during its use in diverse projects. Since then it has been used in applied
research for different Austrian ministries. MERCI is a multisectoral dynamic hybrid
top-down bottom-up model, currently implemented at a national level.
The main strength of MERCI lies in its ability to simultaneously depict overall
economic circumstances, as well as such concerning the energy sector at a detailed
technological level.
Currently MERCI can be used on a national level, or for an entire region with a
18
“FAIR is widely used to assess the environmental and abatement cost implications of international
regimes for the differentiation of future emission reductions of greenhouse gases. The model links
long-term climate targets and global reduction objectives with regional emission allowances and
abatement costs, accounting for the Kyoto Mechanisms.” Bouwman et al. 2006, p.16
19
Initiated in 2004; http://www.eururalis.eu/
20
E.g.:http://www.pbl.nl/en/publications/2000/Global-and-Regional-Greenhouse-Gas-EmissionsScenarios [accessed 14/05/2011]; Studies mentioned in: Bouwman et al. 2006, An extensive list can be
found here: http://themasites.pbl.nl/en/themasites/image/publications/articles/index.html
[accessed 14/05/2011]
25
homogenous economic structure, due to its multisectoral composition and the
precondition of regional equality in prices and the state of technology.
Source: Authors
Specific Characteristics of MERCI
MERCI makes use of the hybrid, top-down, bottom-up modelling approach suggested
by Böhringer and Rutherford (2008). The top-down part of the model consists of
(currently 13) cost minimizing production sectors, an infinitely lived representative
agent, who maximizes total lifetime utility, i.e. a composite of consumption and
leisure, a government agent in charge of various political instruments such as taxes,
subsidies and quotas, and an artificial agent representing foreign trade. All production
and utility functions are in the form of Constant Elasticity of Substitution (CES)
functions. The theoretical underlying is the classical structure of the small open
economy Ramsey model.
Within the bottom-up part of the model, the electricity sector is split up in currently
eight (8) different technologies, all producing the same consumption good, electricity.
These technologies require different input structures of labour, capital and other
intermediate input goods for production, which determine their different production
costs. Energy demand is taken from the top-down equilibrium, and, subject to
resource and capacity constraints of each technology (e.g. locations for hydro power
plants, plant capacities of processing raw energy, etc.), the most cost efficient
technology mix is found within the bottom-up solution process.
The top-down and the bottom-up parts of the model are solved simultaneously,
generating a set of activity levels (i.e. output quantities) of production sectors and
technologies, and prices of all goods and factors, such that demand meets supply in all
markets.
MERCI is designed to assess different possible future developments in a complex
economic and ecological sense, and to evaluate them with respect to the criteria
important to the user. Based on an equilibrium data set in the base year, and a
calibrated long term reference path (Business As Usual, BAU), a shock is imposed on
the economy, and a new equilibrium path is computed. These shocks, or scenarios,
typically include unforeseen changes in the economic structure, or political
instruments, in order to design economy and environment. The range is broad, and
can be adapted according to nearly any focus of interest.
Currently imposed scenarios are amongst others changes or introductions of taxes on
26
fossil fuels, subsidies or quotas for renewable energy sources, or a price raise in raw
energy commodities.
The results (sectoral output, consumer prices, wage rates, energy mix, etc.) of the
newly computed equilibrium path are compared with the BAU reference scenario.
Due to the general equilibrium structure of the model, results are always in the
context of the overall economy, in the form of a new equilibrium, so that important
economic interdependencies are never left out. In this way the scenario effects can be
analysed from different angles, with respect to several kinds of evaluation criteria.
Evaluation of MERCI
Evaluation Criteria
Transparency, complexity, easiness of use
Cost
Data requirements & software
requirements
Level of coverage of M/A issues
Compliance of outputs with projects
objectives
Availability of training and technical
support
International recognition
Description
The model is formulated as a mixed complementarity problem
(MCP) within the programming surrounding GAMS. Currently
there is no graphical user interface, so detailed GAMS knowledge
and mathematical skills are required for use.
Training costs for MERCI would be free for this project.
The model is available at a development stage and needs
comprehensive national level Input/Output tables and technological
data
The top-down part is analyzing the adaptation options of the overall
economy, while the bottom-up part depicts the technological
processes on the energy level.
Could provide us national trends in the interdependency between
macroeconomic issues and mitigation and adaptation strategies.
Model development is still in progress; training personnel and
technical support are currently only partly available.
MERCI was used in two studies for Austrian ministries at a national
level.
Source: Authors.
Conclusion
In short, the following advantages and disadvantages of the six models surveyed in
this deliverable are summarised:
•
•
•
•
MARKAL/TIMES is widely used in the research community and comes with extensive
documentation. However, it requires high computer skills and other models may be
better suited for use in emerging countries.
ENPEP-BALANCE offers a graphical interface and was already employed in various
studies, including countries within the Black Sea Region. However, neither the study
by Urban et al. (2007) nor the study by Bhattacharyya and Timilsina (2010) analyse
ENPEP-BALANCE for its developing countries’ features. Therefore no conclusions can
be drawn regarding this issue.
One of the drawbacks of MESSAGE is the lengthy preparation of scenarios. However,
Urban et al. (2007) find MESSAGE to be the most suited model, together with LEAP,
among the ones analysed that addresses developing countries’ specifics.
Urban et al. (2007) and Bhattacharyya & Timilsina (2010) find that LEAP is the most
suited model available to address issues related to developing countries. Also the
low costs and the broad user-base are notable advantages. Still, until recently, LEAP
did not incorporate an optimisation tool. The actual version provides this feature.
However, this optimization module is still a work in progress, as has been noted by
27
•
•
the developers, and should be handled with reservation. However, it has already
become quite clear that LEAP is the most suited model for the mitigation/adaptation
analysis to be conducted in the PROMITHEAS-4 project.
IMAGE 2.4’s main disadvantage regarding the focus of the PROMITHEAS-4 project is
that it is not possible to provide it as a ‘ready-to-use’ software. However, it is
capable of incorporating a medium number of developing countries’ characteristics
into the scenario analysis.
MERCI is a complex modelling tool, whith high-level theoretical background,
flexibility of use and hybrid structure allow for a comprehensive cost-benefit analysis
when it comes to environmental and energy questions within the economy.
However, it has only been set up for Austria. Consequently, the process of
transferring the database has not been standardized yet. This may bring some
unexpected problems with it. Furthermore, MERCI has no graphical user interface
that can be easily explained to trainees, and, being a highly complex modelling tool,
would thus probably prolong the training process. Another point is that a
computable general equilibrium model, often imposing rigorous assumptions on the
economy, might not be best suited for use in emerging economies.
The next deliverable (D 2.2) will evaluate the models presented, also taking into
account the conclusion from this report that the LEAP model is the most suited one
for the analysis, using the following criteria, which were decided upon in the ad-hoc
working group’s protocol – “Chain of activities for concluding with policy portfolios”
– 4th of March, 2011, Athens:
a. The choice will be restricted to models used at European level;
b. The wideness of the model in covering mitigation/adaptation issues (The model that
is closer in covering these issues will be taken into consideration);
c. Transparency, complexity and easiness in using the model;
d. Availability of inputs (available in statistics books, national accounts);
e. Flexibility of the model in building scenarios (e.g.: a simulation model does not
impose bias in modelling outputs);
f. Compliance of outputs with our contractual obligations (socio-economic,
technological penetration);
g. Cost of acquiring the model;
h. International recognition of the model (used by governments);
i. Training and technical support.
28
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31
PROMITHEAS – 4
“Selection of Models for
Mitigation/Adaptation Policy”
Task Leader: Ptof. Bernhard Felderer
Institute of Advanced Studies (IHS),
Vienna, August 2011
32
This report has been read and commented by all members of the PROMITHEAS-4 Scientific
Committee.
It was also disseminated for comments, through BSEC – PERMIS and BSEC – BC, to all
relevant governmental and business authorities and partners before its finalization.
Partners from the beneficiary countries* of the consortium were encouraged to contact direct
national authorities, agencies, institutions and market stakeholder for comments before the
finalization of this report (Annex 1).
List of PROMITHEAS – 4, Scientific Committee:
18. Prof. Dimitrios MAVRAKIS, NKUA – KEPA (GREECE) -Editor
19. Dr. Popi KONIDARI, NKUA – KEPA (GREECE) – Assistant to the editor
20. Dr. Harry KAMBEZIDIS, NOA (GREECE)
21. Prof. Bernhard FELDERER, IHS (AUSTRIA)
22. Prof. Bilgin HILMIOGLU, TUBITAK – MAM (TURKEY)
23. Prof. Vahan SARGSYAN, SRIE – ESC (ARMENIA)
24. Prof. Dejan IVEZIC, UB – FMG (SERBIA)
25. Prof. Mihail CHIORSAK, IPE ASM (MOLDOVA)
26. Prof. Agis PAPADOPOULOS, AUT – LHTEE (GREECE)
27. Prof. Alexander ILYINSKY, FA (RUSSIA)
28. Prof. Anca POPESCU, ISPE (ROMANIA)
29. Prof. Andonaq LAMANI, PUT (ALBANIA)
30. Prof. Elmira RAMAZANOVA, GPOGC (AZERBAIJAN)
31. Dr. Lulin RADULOV, BSREC (BULGARIA)
32. Prof. Arthur PRAKHOVNIK, ESEMI (UKRAINE)
33. Prof. Sergey INYUTIN, SRC KAZHIMINVEST (KAZAKHSTAN)
34. Prof. Alvina REIHAN, TUT (ESTONIA)
*Turkey, Armenia, Serbia, Moldova, Russia, Romania, Albania, Azerbaijan, Bulgaria,
Ukraine, Kazakhstan, Estonia.
The EU, the Consortium of PROMITHEAS – 4 and the members of the Scientific Committee do not undertake
any responsibility for copyrights of any kind of material used by the Task Leaders in their report. The
responsibility is fully and exclusively of the Task Leader and the his/her Institution.
Acknowledgments: The Task Leader of this report acknowledges the contribution of
Mr. Michael-Gregor Miess and Mr. Stefan Schmelzer for the development of this
overview.
33
Table of contents
35
Introduction
37
Introduction
37
Mitigation and Adaptation
39
ENPEP-BALANCE
41
MARKAL/TIMES
45
MERCI
51
LEAP
54
IMAGE
58
MESSAGE
62
Conclusion
65
Definition of Terms
68
References
69
URLs:
72
Communication
72
34
ADAM
AOS
CEEESA
CGE
CO2
COMMEN
D
EC
EFOM
EIS
ENPEP
ETSAP
FAIR
GAMS
GDP
GHG
GNU
GWP
IAEA
IAM
IHS
IIASA
IEA
IER
IMAGE
INPRO
IPCC
IPE
LEAP
M/A
MAGICC
MARKAL
MERCI
MESSAGE
MNP
NAPA
NEEDS
NPV
OECD
PBL
PET
Pg C
PPP
R&D
RES
Title of the “Adaptation and Mitigation Strategies: Supporting European Climate Policy” project
Title of the “Adaptation
and Mitigation Strategies: Supporting European Climate Policy” project
Atmosphere-Ocean
System
Centre for Energy, Environmental and Economic Systems Analysis
Computable General Equilibrium
Carbon Dioxide
Community for Energy, Environment and Development
European Commission
Energy Flow Optimization Model
Energy-Industry System
Energy and Power Evaluation program
Energy technology and Systems Analysis Program
Framework to Assess International Regimes for differentiation of commitments
General Algebraic Modeling System
Gross Domestic Product
GreenHouse Gas
GNU’s not Unix
Global Warming Potential
International Atomic Energy Agency
Integrated Assessment Model
Institut für Höhere Studien (Institute for Advanced Studies)
International Institute for Applied Systems Analysis
International Energy Agency
Institut für Energiewirtschaft und Rationelle Energieanwendung
Integrated Model to Assess the Global Environment
International Project on Innovative Nuclear Reactors and Fuel Cycles
Intergovernmental Panel on Climate Change
Institute of Power Engineering
Long-range Energy Alternatives Planning
Mitigation/Adaptation
Model to Assess Greenhouse-gas Induced Climate Change
Market Allocation (Model)
Model for Evaluating Regional Climate change Impacts
Model for Energy Supply Strategy Alternatives and their General Environmental (Impact)
Milieu en Natuur Planbureau (Netherlands Environmental Assessment Agency)
National Adaptation Programs for Action
New Energy Externalities Developments for Sustainability
Net Present Value
Organisation for Economic Co-operation and Development
Planbureau voor de Leefomgeving (Netherlands Environmental Assessment
Pan European TIMES model
Petagrams (1015 g) of Carbon
Purchasing Power Parity
Research and Development
Reference Energy System/Scenario
35
RES
RIVM
SAM
SEI
TES
TIMES
TIMER
UNEP
UNFCCC
USAID
VEDA
Renewable Energy Sources
Rijksinstituut voor Volksgezondheid en Milieu (National Institute for Health and Environment)
Social Accounting Matrix
Stockholm Environment Institute
Terrestrial Environment System
The Integrated MARKAL-EFOM System
The Regionalized Energy Model of IMAGE 2.4
United Nations Environment Programme
United Nations Framework Convention on Climate Change
United States Agency for International Development
VErsatile Data Analyst
36
Introduction
The purpose of this report is to present an in-depth classification of the previously presented
models (ENPEP-BALANCE, MARKAL/TIMES, MESSAGE, MERCI, LEAP and IMAGE;
see work package 2.1) according to the following criteria:
j. The choice will be restricted to models used at European level;
k. The wideness of the model in covering mitigation/adaptation issues (The model that is closer
in covering these issues will be taken into consideration);
l. Transparency, complexity and easiness in using the model;
m. Availability of inputs (available in statistics books, national accounts);
n. Flexibility of the model in building scenarios (e.g.: a simulation model does not impose bias in
modelling outputs);
o. Compliance of outputs with our contractual obligations (socio-economic, technological
penetration);
p. Cost of acquiring the model;
q. International recognition of the model (used by governments);
r. Training and technical support.
Considering them in turns:
Since the Black Sea area and its economies are largely influenced by the European
Community, it is reasonable to use models that are recommended and appreciated within
these countries (analysed under criterion a. Except for MESSAGE, where detailed
information for applications at the European level is lacking, and MERCI, which only has
been used on a national scale (Austria) so far, all models were already used within the
European Community. This is not surprising, since all of the considered models are very well
known within the academic community.
Also the international recognition (criterion h.) of the tools plays an important role, when
deciding what model to apply in our analysis. Most models are known world-wide, especially
MARKAL, LEAP and ENPEP-BALANCE.
The second criterion to be investigated is the ability of the model to cover mitigation and
adaptation issues. Regarding the models analysed here, Patt et al. 2009 (p. 385) conclude that
“process-oriented models [e.g. IMAGE and MARKAL] with considerable physical detail” are
less suited for dealing with adaptation issues. Simpler models calculating mitigation costs and
climate damages at an aggregate scale are regarded to be more useful here. After reviewing
the relevant literature, the following statement can be made: All tools can and were used for
mitigation analysis already. However, the exact conduct of modelling adaptation measures is
harder to assess, since documentations make no reference to the relevant issues. Regarding the
point ‘Transparency, complexity and easiness in using the model’ (c. criterion): The models
under consideration here are all bottom-up models, some of which include or can include topdown aspects (hybrid models). They differ in the modelling approach: optimisation or
accounting. The bottom-up structure enables the models to provide a “detailed technological
37
representation of the energy system and can be used to analyse the environmental effects as
well” (Bhattacharyya and Timilsina 2010, p. 512).
All tools considered herein have transparent structures, and most of them provide a graphical
user interface (MERCI is an exception; however, a graphical user interface is being
constructed). More complex models, requiring expert knowledge, include:
MARKAL/TIMES, MERCI and IMAGE. MESSAGE can be described as an intermediate
model, regarding its complexity. On the contrary, ENPEP-BALANCE and especially LEAP
are convenient to use and highly transparent.
The next criterion, namely the availability of inputs for model runs, (criterion d.), is a relevant
issue, especially for the PROMITHEAS-4 investigation, as some details may be unattainable.
This section therefore presents the relevant data inputs of each model. The optimisation
models are usually more data intensive, i.e., require substantial information on demand and
supply levels. Moreover, base year statistics have to include a wide range of energy specifics.
Accounting type, simulation models do not need as much information, and the data
requirements are determined by the type of analysis the user is about to conduct (see LEAP).
It is further important for the assessment of the quality of the set of models to determine how
flexible they are in building scenarios, (criterion e.). The easiness with which models can
develop new scenarios follows from the model structure that will be presented in detail at the
beginning of each model’s section. Nowadays, due to the rising demand for scenario analysis,
this feature has been incorporated into many IAMs (Integrated Assessment Models).
Especially flexible tools for scenario analysis are LEAP and IMAGE, noting that both apply a
simulation modelling approach.
Analysing the compliance of outputs with our contractual obligations, (criterion f.), it can be
observed that the set of models was already chosen such that socio-economic aspects can be
analysed with considerable detail. However, only IMAGE incorporates a detailed feed-back
mechanism, mapping the effects of emissions back to the earth system.
An important aspect of our considerations is the suitability of models with respect to
emerging economies characteristics. This analysis is based on the description of Urban et al.
(2007) and Bhattaacharyya and Timilsina (2010).
Regarding the financial aspects of the models, (criterion g.), there is great variability between
them. Also the training and support costs differ substantially among the models.
First, an overview of mitigation and adaptation specific issues arising with integrated
assessment models is presented. Then, the report is structured as follows: Each section starts
with a short review of the model in question. Each model is then analysed subject to the
different criteria. The paper concludes with a summary and the selection of the most suited
model, according to our contractual obligations.
38
Mitigation and Adaptation
The focus of the PROMITHES-4 project is the analysis of mitigation and adaptation
possibilities within the Black Sea region’s countries, Kazakhstan and Estonia. Therefore, the
model that is about to be chosen for the analysis has to be suited for these requirements. This
section shall depict in more detail the difficulties that arise for such an analysis, especially
with the requirement to model adaptation measures.
Mitigation, in the context of strategies for the reduction of climate change related damage,
was defined as follows by the Fourth Assessment Report of the Intergovernmental Panel on
Climate Change (IPCC): Mitigation consists of “[t]echnological change and substitution that
reduce resource inputs and emissions per unit of output. Although several social, economic
and technological policies would produce an emission reduction, with respect to climate
change, mitigation means implementing policies to reduce GHG emissions and enhance
sinks” (IPCC 2007, Annex I, p.818).
Further, the IPCC defined adaptation as “[i]nitiatives and measures to reduce the
vulnerability of natural and human systems against actual or expected climate change effects.
Various types of adaptation exist, e.g. anticipatory and reactive, private and public, and
autonomous and planned. Examples are raising river or coastal dikes, the substitution of
more temperature shock resistant plants for sensitive ones, etc” (IPCC 2007, Annex I, p.
809). It consists of adjustments in ecological, social, or economic systems to a new or
changing environment. Adaptation measures therefore seek to reduce harm or exploit
beneficial opportunities in the context of climate change (IPCC 2001, Glossary, p. 708).
The UNFCCC points out that mitigation21 can be investigated by two (2) different modelling
approaches: top-down and bottom-up. The top-down approach is better suited for broader
macroeconomic and fiscal policies (e.g. carbon taxes), while the bottom-up approach
(ENPEP-BALANCE, MARKAL/TIMES, MESSAGE) ensures a specific sectoral and
technology-based perspective (UNFCCC 2006, p. 10). However, this is no strict dichotomy.
Many models started to incorporate both aspects. These are called hybrid models (MERCI,
MARKAL-MACRO, ENPEP, IMAGE). Moreover, there exists a different category of
models, namely accounting type models (LEAP), which have yet another approach towards
integrated assessment modelling.
In assessing mitigation strategies, it is important to analyse the implementation of mitigation
measures
in
the
three
(3)
energy
end-use
sectors,
which
are
commercial/residential/institutional buildings, transportation and industry. Together with the
energy-supply side of the economy, agriculture, forestry and waste management sectors, these
sectors constitute the necessary structure for an appropriate analysis of policy scenarios (IPCC
1996, p. 3).
An important issue arises with the appropriation of climate-related benefits of mitigation and
adaptation. These benefits accrue at different geographic levels. Benefits from mitigation
actions can be enjoyed at a global scale, while adaptation related benefits arise mostly at an
individual, organisational or local level. This implies that mitigation involves many top-down
21
Of course, many other issues can be addressed with these different modelling approaches. However, mitigation
and adaptation are the focal points of this study.
39
characteristics, while adaptation can be investigated better via a bottom-up approach (Patt et
al. 2009, p. 390).
Looking at the structure of integrated assessment models it is challenging to account for
adaptation policies appropriately. IAMs are used on a broad range of assessments of climate
change related mitigation scenarios; difficulties arise, however, when it comes to adaptation.
Various approaches have been considered, with different and, mostly, unsatisfying results.
The range covered by IAMs goes from including no adaptation at all, modelling it implicitly
and, recently, to account explicitly for adaptation. First, in modelling the damage from climate
change, IAMs included estimates of the amount of adaptation that was likely to be
undertaken. Second, effort was directed towards implicit assumptions about the amount of
adaptation necessary to minimise climate change damages. However, this approach, known as
the ‘Ricardian analysis’, was criticised because of its sole focus on partial equilibrium22
analysis and for its missing representation of frictional costs, when shifting from one
production system to another23. Third, lately models have begun to include adaptation
explicitly. This is done by formulating adaptation in terms of a control variable. These
models, however, have shortcomings too, as there has been little progress in “including
adaptation as a more nuanced variable” (Patt et al. 2009, p. 385ff; last citation from p. 388).
22
„[I]t does not consider changes in prices of different commodities as the entire production shifts.“ (Patt et al.
2009, p.387)
23
The Ricardian approach ignores these shifts between production systems (Patt et al. 2009, p.387).
40
ENPEP-BALANCE
The Energy and Power Evaluation Program (ENPEP), used in conjecture with BALANCE, is
one of ten24 available integrated energy, environmental and economic analysis tools. It is a
bottom-up simulation, iterative equilibrium model, applicable locally, regionally and globally.
It imitates consumer and producer behaviour, considering different constraints and signals
(UNFCCC 2006, p. 13, 39).
The analyst using ENPEP-BALANCE builds a representation of the region’s (or the nation’s)
energy system in the software’s graphical user interface. This makes the model accessible
without having to acquire extensive syntax training. Within the energy network, each sector is
modelled separately, consisting of a variety of nodes connected by links between them. The
node types available are depicted in Figure 1 below:
Figure 1. [Source: CEEESA, p. 7]
The range of nodes reaches from energy supply, via economic and resource processes, as well
as conversion mechanisms, to energy demand. These nodes are submodels described by sets
of quantity and price equations. This decentralized decision making process allows for
optimal energy choices according to the decision makers’ own needs (CEEESA I, p.5ff, 12;
CEEESA 2008, p. 1).
Moreover, “[t]he model employs a market share algorithm to estimate the penetration of
supply alternatives”(CEEESA 2008, p.1). It can account for various competing fuels and
technologies at decision nodes. Each market share reacts to changes in prices relative to the
prices of alternative commodities. Together with the market share algorithm, an additional lag
factor allows for the possibility of delays in capital stock turnover. These features lead to the
24
The nine tools are: MACRO-E, MAED, LOAD, PC-VALORAGUA, WASP, GTMax, ICARUS, IMPACTS
and DAM.
41
nonlinear, iterative equilibrium solution, subject to various policy constraints, of ENPEPBALANCE (CEEESA p. 12; CEEESA 2008, p. 2).
After the specification of all constraints and adding the available information, ENPEPBALANCE finds market clearing prices. This equilibrium balances demand and supply
curves for the whole energy network (CEEESA I p.2, UNFCCC 2006, p. 13).
a. Use of the model at European level
A whole list of ENPEP-BALANCE applications in Europe can be found on CEEESA’s
homepage (CEEESA I25). The wide use of the model in assessing future options for a sensible
treatment of climate change related issues in eastern and south-eastern Europe is striking.
Among them are eight countries for which mitigation and adaptation policy portfolios shall be
developed within the Promitheas-4 project: Albania26, Armenia27, Bulgaria28, Moldova29,
Romania30, Russia31, Turkey32 and Ukraine33. Moreover, ENPEP-BALANCE was employed
in two projects involving Greece34. These studies analysed GHG (Green House Gas) emission
projections and mitigation scenarios, investigated the cost of meeting EU environmental
standards (on Turkey’s fossil-fired power plants), conducted energy and nuclear power
planning scenarios, energy and environmental reviews and characterised old and inefficient
technologies (CEEESA I).
b. The wideness of the model in covering mitigation/adaptation issues
As was mentioned previously under a.), ENPEP-BALANCE was applied in various studies
investigating mitigation issues. “Numerous countries used ENPEP to help prepare GHG
mitigation assessments as part of their national communications to the UNFCCC” (UNFCCC
2006, p. 46).
As was described in the introduction above, it is of great importance for an integrated
assessment model regarding mitigation to describe the energy end-use and supply side sectors
in detail. Herein lies ENPEP-BALANCE’s strength, since the user defines the energy network
most appropriate for the region or nation in focus.
ENPEP-BALANCE enables the calculation of environmental residuals associated with a predefined energy system configuration. Air pollutants, such as GHGs, sulphur oxide (SOx),
nitrogen oxide (NOx), carbon monoxide (CO) and carbon dioxide (CO2), methane (CH4),
volatile organic compounds (VOC), leads (Pb) and others can be considered within these
25
http://www.dis.anl.gov/news/EnpepwinAppsEurope.html
Project title: “Capacity Building in GHG Mitigation Analysis for Balkan Countries”;
27
Project title: “Energy and Nuclear Power Planning Study for the Period up to 2020”; also used within the IAEA TC Project (2000-2003);
28
Project titles: “Bulgaria Energy and Environmental Review (EER)”, “Bulgaria GHG Emission Projections”, “Bulgaria UNFCCC National
Communications”, “Infrastructure Development and Nuclear Competitiveness”, “Capacity Building in Energy and Power
Systems Analysis in Bulgaria”;
29
Project titles: “Moldova UNFCCC First National Communication”, “UNDP Project "Climate Change Enabling Activity
(Phase II)" Technology Needs Assessment”; See report “Overview of models in use for Mitigation / Adaptation policy”, for details.
30
Project title: “Capacity Building in Energy and Power Systems Analysis in Romania”, “Developing a Fuel Policy for Romania”,
“Romania UNFCCC National Communication”;
31
Project title: “Modeling of Heat Sources in Power System Expansion Planning”;
32
Project title: “Finding the Most Cost-Effective Sulfur Control Strategy for Turkey's Yatagan Lignite-Fired Power Plant”, “Capacity
Building in Energy and Environmental Systems Analysis in Turkey”, “Analyzing Turkey's GHG Mitigation Options”, “Costs of
26
Meeting EU Environmental Standards;
on Turkey's Fossil-Fired Power Plants”, “Infrastructure Development and Nuclear Competitiveness”, “Providing
Modeling Support for Turkey's First National Communication to the UNFCCC”;
33
Project title: “Modeling and Analysis of GHG Emissions in Ukraine: Selecting and Adapting the ENPEP Program
to Ukrainian Conditions and Test Modeling”;
34
Project titles: “Integrated Resource Planning for the Island of Crete”; “Capacity Building in GHG Mitigation
Analysis for Balkan Countries”;
42
residuals. Noteworthy, however, is the possibility to analyse the effect of water pollution,
waste generation and land use via these environmental residuals (CEEESA 2008, p. 3). This
provides a particularly informative model when calculating the costs and benefits of planned
mitigation measures.
c. Transparency, complexity and easiness in using the model
From the description of the model, it can be concluded right away that ENPEP-BALANCE is
a transparently structured tool. The model’s graphical user interface adds to the easiness in
using the model. The user, constructing a RES (Reference Energy System), can build a
graphical map of the energy system, where various drop-down menus assist the configuration
of the mentioned nodes and links. Some complexity arises, however, from large data
requirements. This makes building new scenarios somehow more time consuming.
d. Availability of inputs
The required data inputs consist of information on the energy system structure, especially
base year energy statistics. These have to include production and consumption levels, as well
as prices. Moreover, it is important to provide estimates of future energy demand growth
exogenously. Additional constraints on technology and policy issues are of course further
decision parameters for the users (CEEESA 2008, p. 1). Characterising behavioural
relationships, which is necessary in simulation models, can be challenging as knowledge of
these parameters is lacking, and even more so in countries where time series data is missing
(UNFCCC 2006, p. 13).
e. Flexibility of the model in building scenarios
The ENPEP-BALANCE model is well suited for scenario building. This feature is
documented in the various reports35 from mitigation studies (many of which regard GHG
emissions or carbon mitigation).
Moreover, since ENPEP-BALANCE is a simulation model, it is easier to account for nonprice factors in the analysis compared to optimisation models (UNFCCC 2006, p. 13).
f.
Compliance of outputs with our contractual obligations
Although the UNFCCC clearly recommends using this model, neither Bhattacharyya and
Timilsina nor Urban et al. make any reference to ENPEP-BALANCE in the context of
developing countries issues. From the list of applications, it can be concluded, however, that
this model is especially useful and flexible for usage in developing countries, as well as in
developed countries. (See h.)
As was previously mentioned, the model incorporates a market share algorithm to calculate
the penetration of energy technologies. This percentage market share of a supply option is
sensitive to the price of the commodity relative to the prices of other commodities.
Furthermore, the market share algorithm is sensitive to user defined constraints, government
policies, consumer preferences and the “ability of markets to respond to price signals over
time” (CEEESA 2008, p. 1f). However, no feedback mechanism from the resulting emission
level from the production and consumption side of the economy is incorporated.
g. Cost of acquiring the model
The model can be downloaded free of charge from:
<www.dis.anl.gov/projects/Enpepwin.html>
h. International recognition of the model
35
An extensive list is provided in CEEESA 2008, p.4f;
43
ENPEP-BALANCE found use in over 50 countries, where it was employed, among many
other institutions36, by energy and environmental ministries. Although the list of applications
of ENPEP-BALANCE is long and applications can be found all over the world, in
North/South America, Africa, Europe as well as in Asia, some exemplary studies are
mentioned below:
In the United States, CEEESA conducted a study analysing carbon emission mitigation
strategies. Venezuela, Jordan and Kazakhstan investigated their GHG mitigation options as
part of their national communication to the UNFCCC. Further, Egypt constructed an energy
plan of the transport sector considering especially environmental issues (CEEESA II).
i.
Training and technical support
The model developers recommend at least five days of training. The associated costs amount
to around 7000 €. Technical support is offered by phone, e-mail or online. Basic support is
provided for free, an extensive premium support package can be acquired for approximately
7000 € (UNFCCC 2006, p. 47).
36
Electric utilities, power merchants, transmission companies, consulting companies, lending agencies and research
institutions. (CEEESA 2008, p. 4)
44
MARKAL/TIMES
The MARKet ALlocation (MARKAL) model is currently maintained by the Energy
Technology and Systems Analysis Program (ETSAP), which was established by the
International Energy Agency (IEA) in 1978. A whole family of models evolved is based on
MARKAL and the most prominent model version used today is the TIMES (The Integrated
MARKAL-EFOM37 System) model, which was developed in 1999 and is expected to replace
MARKAL over time.
The MARKAL modelling technique can be summarised as follows: The bottom-up model
employs an optimisation method (MARKAL: linear programming; MARKAL-MACRO: nonlinear programming; MARKAL with uncertainties: Stochastic programming), i.e., it develops
options for energy supply services which minimise the total cost of the energy supply system.
This minimisation is undertaken considering user imposed constraints, such as limits on
technology, CO2, etc., and for an exogenously specified demand. For each technology either
the utility maximising (MARKAL-MACRO) or the producer/consumer surplus maximising
(MARKAL/TIMES) prices and quantities are calculated over the entire planning horizon.
Through the optimisation approach, the model is an especially useful tool where many
technical aspects have to be studied and the future development of costs is well known
(UNFCCC 2006, p. 12, 26; Bhattacharyya and Timilsina 2010, p. 512).
Before continuing with a description of TIMES, a few details about EFOM, the Energy Flow
Optimization Model, will complete the analysis. Developed in the 1970s by Finon at the
‘Institut Economique et Juridique de l’Energie’ at Grenoble, France, EFOM became a
prominent model around the world. In short, it is a multi-period system optimisation model. It
uses linear programming as the solution mechanism to minimise total discounted system
costs, constrained on an exogenously given energy demand level. Each sector can be either
investigated separately (“single-sector mode of analysis”) or, using a different configuration,
the whole energy system can be analysed (“multi-sector model”). Together with the fact that
the electricity industry is extensively covered by the model, this makes it especially useful for
the analysis in developing countries (Bhattacharyya and Timilsina 2010, p. 511).
TIMES, the latest development within this family of models, merges features of both, the
MARKAL and the EFOM model. The analyst can either decide to calculate the least-cost
solution for the whole system or can focus on a specific sector. Moreover, investment and
operating decisions can be included in the analysis. On the one hand, demand drivers’
characteristics and elasticities of demand (regarding the demand drivers and prices), that are
exogenously specified, facilitate a sensible demand-side analysis. On the other hand, a supplyside analysis is conducted using a set of supply curves for the spectrum of available resources.
As elaborated above, the TIMES model maximises producer and consumer surplus. This
yields a partial equilibrium solution. Summarising, it results that TIMES is a more flexible
replacement for MARKAL (Bhattacharyya and Timilsina 2010, p. 512f, Seebergts et al.
2001).
37
EFOM - Energy Flow Optimization Model
45
Figure 2. [Source: ETSAP II]
As depicted in Figure 2 the MARKAL and the TIMES model generators can be used more or
less equivalently. However, as was previously outlaid in the model overview (W.P. 2.1) and
above, there are some important technical differences in TIMES:
•
•
•
•
•
•
•
•
•
•
User specified, completely flexible time period lengths;
Data is decoupled from the initial period, i.e., “user provides technical and cost data at those
past years when the investment actually took place, and the model takes care of calculating
how much capacity remains in the various modelling periods.” Changing the initial period or
the period length is therefore much easier than in MARKAL;
User chooses time slices for every commodity/process (seasonal/monthly, weekly, daily);
Processes in all RES (Reference Energy Systems) have the same basic features, which are
activated via data specification;
Completely flexible Processes;
Investment and dismantling lead-times costs;
Vintage processes and age-dependent parameters;
Commodity related parameters (e.g. total production, total consumption, flow variables);
This way, the user imposes limits and costs on commodities;
More accurate and realistic depiction of investment cost payments;
The concentration of CO2, radiative forcing and global temperature change (stemming from
GHG emissions) is endogenised through a set of variables and equations.
(Loulou et al. 2005, p. 52ff)
The TIMES model was used in the PET model (Pan European TIMES model), to enable the
analysis of the renewable energy targets set by the European Union for 2020. This is a
technically oriented model which illustrates in detail the whole energy system of the EU-27
member states38 for the period from 2000 to 2050. This project characterised future options
38
Iceland, Norway and Switzerland were also included in the study.
46
for policies and measures. Moreover, specific targets for the renewable energy sources’
contribution were calculated that can be achieved by implementation of these options.
Additionally, the implications of the achievement of these targets to the European economy
was investigated (RES2020).
MARKAL/TIMES tools are used in the USAID project “E&E Regional Energy Security and
Market Development” which is conducted by Armenia, Georgia, Moldova and Ukraine.39
Further, the TIMES model generator was employed within the Pan European TIMES model
of the New Energy Externalities Developments for Sustainability (NEEDS) project. “The
ultimate objective of the NEEDS Integrated Project [was] to evaluate the full costs and benefits (i.e.
direct + external) of energy policies and of future energy systems, both at the level of individual
countries and for the enlarged EU as a whole” (NEEDS).
Another noteworthy use of the TIMES model at EU level is the ‘EU30 TIMES-Electricity and
Gas supply model’, which seeks to optimise the electricity, heat and natural gas markets of the
EU member states. Studies using this model are mostly conducted at the ‘Institut für
Energiewirtschaft und Rationelle Energieanwendung’ (IER) at Stuttgart University, Germany
(IEA/ETSAP 2008, p. 58).
European countries using MARKAL/TIMES include: Belgium, Finland, France, Germany,
Italy, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and the United
Kingdom (IEA/ETSAP 2008, p. 77 - 177).
MARKAL/TIMES can support the following list of mitigation measures: transportation,
energy demand, energy conversion and supply, energy sector emissions, non-energy sector
industrial process emissions, solid waste management, geological sequestration and the value
of carbon rights. Moreover, MARKAL-MACRO can inform about the effect of
macroeconomic policies, such as carbon taxes or emission caps (UNFCCC 2006, p. 29).
Additionally, TIMES gives the analyst a powerful tool in addressing climate issues through a
set of variables and equations that endogenise the concentration of CO2, calculate radiative
forcing and global temperatures change (from GHG emissions and accumulation) (Loulou et
al. 2005, p. 55) .
A key advantage of MARKAL/TIMES is its transparency: “Data assumptions are open and
each result may be traced to its technological roots” (Johnson 2004, p. 10). The user defines a
Reference Energy System (RES), wherein all energy sources, conversion processes and enduse possibilities are included. However, the MARKAL/TIMES model generators require high
skilled users or analysts.40
On the one hand, the high data requirements add some complexity to the usage of the model.
On the other hand, the lengthy training and the sophisticated programming approach hamper
the easiness of the model, although they have advantages for different types of analyses.
39
40
http://www.winrock.org/fact/facts.asp?CC=5830&bu=
See criterion i.)
47
As indicated by UNFCCC (2006, p. 12, 23) MARKAL and the hybrid sister-model
MARKAL-MACRO are quite data intensive (medium-high data requirement). This
necessitates a data and results handling system, as can be seen in Figure 2. The two databases
that can be linked to the model are ANSWER and VEDA-FE/VEDA-BE. These two systems
are quite different substitutes. ANSWER is more user friendly, but this comes at the cost of
pre-defined output tables. Contrary to this, VEDA-BE gives the user full flexibility in
structuring and exploring the model’s results. It is recommended to use VEDA-BE for the
TIMES model, that uses ANSWER as a data handling system (IEA/ETSAP II).
As previously mentioned under criterion c., scenarios are based upon RES. This, together with
the modelling approach of optimisation, implies some bias in the model’s output. However,
the developers managed to make energy demand price-responsive, so that a more realistic tax
policy analysis or an investigation about the effects of emission constraints is possible.
Furthermore, TIMES can account for multiple periods and various regions, as well as explore
uncertainties connected to future energy system development trajectories (Bhattacharyya and
Timilsina 2010, p. 512f).
f.
MARKAL/TIMES model generators use a bottom-up optimisation approach towards their
least-cost solution. Bhattacharyya and Timilsina (2010, p. 501), conclude that, although
bottom-up models are better suited for developing countries characteristics than top-down
models, optimising models are less useful than accounting-type models. Neither MARKAL,
nor TIMES can include informal sector characteristics, or such important features (for the
context of developing/emerging countries) as energy shortages or subsidies (possible, but
typically not included). However, they do account for rural energy features, rural-urban divide
and non-price policies. Moreover, TIMES can include economic transition variables
(Bhattacharyya and Timilsina 2010, p. 503).
The MARKAL model constructs optimal future scenarios by either optimising over all time
periods, i.e., under the assumption of perfect foresight, or year-on-year, i.e., using myopic
expectations (UNFCCC 2006, p. 12).41 The time horizon is user controlled. Typically, the
development of a scenario is analysed over a period of 20-50, sometimes 100, years
(Connolly et al. 2010, p. 1072).
MARKAL/TIMES quantifies the sources of emissions from the associated energy system and
calculates estimates of energy and material prices, demand activity, technology and fuel
mixes, the marginal value of individual technologies to the energy system, GHG and other
emission levels, as well as mitigation and control costs (Johnson 2004, p. 11, UNFCCC 2006,
p. 28).
According to Mr. Goldstein, from the Department of Energy in the United States and expert
on MARKAL/TIMES models, the level of CO2 emissions is calculated and reported in the
units, in which the user has specified the emissions. Further, MARKAL/TIMES computes
total costs of the energy system. Apart from this, no extra social costs are calculated (unless
this is reflected in the cost data fed into the model). Moreover, the costs resulting from
mitigation/adaptation scenarios can be calculated for different target groups, such as industry,
agriculture, households, government, etc., assuming that data is provided for the relevant
sectors. The model can further calculate the percentage of the penetration of renewable energy
sources or the penetration of energy efficient technologies as far as these technologies are
41
Agents having myopic expectations act short-sighted, i.e., they do not have perfect foresight over the entire model horizon.
48
specified by the user. Finally, the model only calculates administrative costs, if the cost data is
integrated into the model.
The source code for the MARKAL/TIMES model generators is distributed for free42. Since
the code is written using the commercial software package GAMS (General Algebraic
Modeling System), this has to be purchased. Moreover, a data handling system (ANSWER or
VEDA-FE) and a solver (MINOS, CPLEX, XPRESS, GUROBI or CONOPT) has to be
acquired. This leads to total costs of around 1.275 €-3.170 € for an educational license and
around 9.825 € - 15.200 € for a commercial license (Connolly et al. 2010, p. 1072;
IEA/ETSAP I).
MARKAL is currently used in around 70 countries by 250 institutions. In the category of
optimisation models, it is probably the one most widely used and definitely the best known
(Connolly et al. 2010, p. 1071, Seebregts et al. 2001, Bhattacharyya and Timilsina 2010, p.
512). Figure 3 below gives a clear picture of the huge acknowledgment MARKAL/TIMES
have received. Moreover, the Final report of Annex X (2005-2008) “Global Energy Systems
and Common Analyses” summarises the studies and projects using MARKAL/TIMES as
modelling tools. ‘The IEA Energy Technology Perspective Project’ and ‘The ETSAP TIMES
Integrated Assessment Model’ used Global (TIAM). These are/were two prominent global
applications (IEA/ETSAP 2008, p. 21ff; 26ff).
At national level, the MARKAL-family models were employed in the following country
studies:
MARKAL-MACRO was used to conduct a model for Kazakhstan, a TIMES model was
developed for the Russian Federation, the MARKAL model was used for energy modelling in
Portugal (together with LEAP), Spain investigated future energy policies under the European
Energy and Climate Policy Framework, Norway modelled a GHG emission reduction of 75%
until 2050, France calculated CO2 emissions reduction using MARKAL/TIMES, etc
(IEA/ETSAP 2008, p. 77 - 177).
i.
Training in using this model is the quite demanding, as it can take some months (Connolly et
al. 2010, p. 1072). The costs for an eight (8) days training workshop vary between 20.000 €
and 30.000 €, and the support costs amount to 350-1.800 € per year (UNFCCC 2006, p. 24).
42
A letter of Agreement has to be signed.
49
Figure 3. [Source: IEA/ETSAP 2008, p. ii]
50
PROMITHEAS-4: “Knowledge transfer and research needs for preparing mitigation/adaptation policy
portfolios”
MERCI
The Model for Evaluating Regional Climate change Impacts is a dynamic hybrid top-down
bottom-up Computable General Equilibrium (CGE) model. The combined model structure is
based on Böhringer and Rutherford (2008). The Institute for Advanced Studies in Vienna,
(IHS), has adapted this hybrid modelling structure for developing MERCI since 2009
(Balabanov et al., 2010). There is also a static version of the model, focussing primarily on
issues concerning labour market analysis.
MERCI is a very young model, and therefore is currently still only applicable at a national
level for Austria. Extensions for neighbouring countries or the European level are currently
being implemented. MERCI has been used for 2 national evaluations for Austrian ministries
in the last year:
In a study for the Federal Ministry of Labour, Social Affairs and Consumer Protection, the
potential of “green jobs” in the near future, as well as general impacts of energy and
environmental policies on labour market issues were analysed (Balabanov et al. 2010).
Most recently, in a study for the Federal Ministry for Economy, Family and Youth, an
analysis of the overall economic impacts of investment incentives created through public
subsidies in specific sectors of the economy, as well as the impact of subsidies on the actual
investment is being conducted with the help of the static version of MERCI (Miess et al.
2011).
MERCI is a scenario based general equilibrium model. A “business as usual” scenario is
being compared to a set of scenarios dealing e.g. with different mitigation and adaptation
strategies. Various scenario tools like subsidies for sustainable energy resources, green
quotas, carbon taxes or other emission taxes, emission caps and control limits, exogenous
shocks on resource prices, etc., are included in the modelling framework.
The sectoral structure allows for a detailed assessment of emissions not only of the energy
sector and its different technologies, but also for industrial process emissions within the intrasectoral chain of intermediate consumption. Transportation, detailed depiction of industrial
production, sectoral and household energy consumption demand, energy conversion and
supply, are some of the mitigation/adaptation measures that are incorporated in the model, and
make MERCI a powerful tool for mitigation and adaptation analysis on a regional level.
The variety and flexibility in using and defining different scenario variables not only allow
MERCI to optimize and create a dynamic adjustment path in order to reach a predefined
future goal, it is also possible to set certain scenario variables to specific predefined values,
and “see what happens” until the end of the model horizon. While using the model in any of
these analysing procedures, the total economic and social abatement costs are calculated
automatically (in the form of GDP, welfare, sectoral growth, emissions, etc.), due to the
general equilibrium framework used in MERCI.
MERCI does not have any kind of user interface. Therefore, it is necessary for the potential
user not only to be familiar with the modelling software GAMS, but also to have at least basic
51
knowledge in General Equilibrium theory, and an understanding of Mixed Complementarity
Problems in order to use the model with already incorporated scenarios. Building new
scenarios requires even more knowledge of the aforementioned technical issues, and generally
requires a serious amount of preparation/training time for each user. Therefore, all analyses
conducted with the model until now were carried out by IHS personnel only. Until a proper
user interface is being developed, distribution of the model to other institutions would only be
appropriate if general equilibrium modellers (and a corresponding amount of their time) are
available at the receiving institution.
The main data structure used in MERCI is that of standard input/output tables, which are
released by most of the countries in the world nowadays. A slight preparation of these
datasets in the form of a Social Accounting Matrix (SAM43) is required, which takes some
weeks’ time, but is not hard to conduct, given that the data needed for the procedure are
available. Energy statistics in the base year, concerning all available technologies for energy
production, are also necessary. This data is implemented at the technological level in a similar
way as the I/O tables on the sectoral level.
The second type of data required in MERCI, are elasticities. The range is broad as in any
Computable General Equilibrium (CGE) model, covering price elasticities in production,
consumption elasticities of households, elasticities between leisure and consumption, etc.
These elasticities may vary strongly between countries, and not all of them might be available
in all countries of the Black Sea region, Kazakhstan and Estonia. Estimating missing
elasticities can be a hard and expensive task, adapting elasticities from countries with similar
structures can be dangerous and misleading when analysing results, due to wrong assumptions
on habits and economic structures.
As previously described under b), MERCI is a very powerful tool when developing scenarios.
Political, environmental, or economic scenarios can be implemented with various different
scenario tools. The structure of the model, and the way of using it, by directly changing
source code whenever trying a new model run, allows implementing whatever scenario
instrument the modeller can literally imagine. This being the greatest freedom, it is also the
greatest disadvantage, because developing new scenarios usually needs expertise in economic
theory, strong mathematical background and some programming skills.
f.
Reductions of GHG emissions are measured in tCO2 equivalents, and can be displayed for
each sector in the economy, each electricity or energy technology, or end user. The display of
marginal abatement cost curves is optional within MERCI, depending on the focus of prespecified scenario definitions. Social costs can be derived from various model outputs, like an
explicit measurement of general welfare, GHG emissions, and additional tax burdens, or even
financial ease (e.g. subsidies) for the specific household agents in the model, who represent
the whole population. However there is not an aggregate measure for social costs in Euro.
Due to the sectoral model structure costs for different target groups as industry, services,
agriculture and other economic sectors, as well as different household groups, and the
government sector (administrative costs) can be displayed in high detail. Still, while such
implementation costs of specified scenarios can be measured in monetary units (Euro), effects
of GHG Emissions, or temperature changes can not.
43
For more information on the SAM, please see e.g. King (1985), Pyatt (1988), or Reinert and Roland-Holst (1997)
52
A main focus of MERCI lies on the detailed depiction of the electricity and energy sector.
Due to that, the percentage of RES penetration or the penetration of energy efficient
technologies, can be delineated more than sufficiently.
Licenses for the most recent GAMS distribution and for the PATH solver (which can be
purchased online) are required to use the model properly. A single user license is available for
$640 for academic and $3,200 for commercial use. Also multiple user licenses are available
(GAMS-SALES). IHS would provide the source code of MERCI for free for this project. Yet
the training time that would be required would definitely exceed the person months allocated
to IHS.
Recently MERCI was used in 2 national studies for the Austrian government, as already
described in criterion a. (Balabanov et al. 2010), (Miess et al. 2011).
i.
Model development for MERCI is still in progress. Training personnel and technical support
of IHS are currently only partly available. However there is a huge community of modellers,
(teachers and students alike) available on the internet, which provides technical support for
particular problems encountered when using or developing CGE models implemented in
GAMS. (GAMS-L)
53
LEAP
The Long-range Energy Alternatives Planning model (LEAP) was developed in 1980. The
Stockholm Environment Institute (SEI) is currently in charge of the model. Noteworthy is the
wide use of this model: its user-base counts over 5000 users in 169 countries (Connolly et al.
2010).
LEAP’s technique proceeds at two steps: First, the straightforward and consistent energy,
emissions and cost-benefit accounting problems are solved by a calculator-like tool. At the
second step, the analyst specifies further aspects of the model he/she wants to analyse
including tabular formulas. These then help to define time-varying data or multi-variable
models. Based on this, econometric analysis and simulation can be conducted (Heaps 2008,
p.6).
Due to the described methodological environment, the LEAP model ends up being a powerful
analytical tool. The accounting framework enables simple, transparent and intuitive
investigation of the complex matter of energy policies (UNFCCC 2006, p.15; Heaps 2008,
p.5).
This year (2011) a new version of LEAP was published: Optimization modelling is now
supported. The additional feature was developed in collaboration with various institutions44
and is based on the Open Source Energy Modeling System (OSeMOSYS). This again evolved
from the GNU45 Linear Programming Kit (GLPK)46. Although, both software packages are
open source, they were incorporated into LEAP. Therefore, LEAP’s optimisation extension is
well integrated into the stand-alone modelling tool (Heaps 2011, p. 1; SEI 2011, p. 4).
Why use this optimisation tool47? “Typically you will use this new capability to calculate the
optimal expansion and dispatch of power plants for an electric system, where optimal is defined as the
energy system with the lowest total net present value of the social costs of system over the entire
period of calculation (from the base year through the end year).” (Heaps 2011, p. 1)
Among the high quantity of LEAP users are various European countries. As previously
mentioned in the report “Overview of models in use for Mitigation / Adaptation policy”,
researchers in Estonia, Moldova, Albania and Greece have already carried out studies and
research using LEAP. The most recently published report is “A Bridge to a Greener Greece”
(Bellona Foundation 2011). Furthermore, Heaps et al. examined the role Europe can play in
keeping global climate change targets in a study conducted at SEI “Europe’s Share of the
Climate Challenge - Domestic Actions and International Obligations to Protect the Planet”
(Heaps et al. 2009).
44
The Stockholm Environment Institute worked together with the International Atomic Agency (IAEA), the United Nations
Industrial Development Organization (UNIDO), the UK Energy Research Center and the Royal Technical University (KTH)
in Sweden.
45
46
GNU stands for “GNU’s not Unix”
“[A] software toolkit intended for solving large scale linear programming problems by means of the revised simplex
method.” (Heaps 2011, p. 1)
47
The optimisation extension in LEAP2011 is not yet fully finished and “should be used for testing purposes only.” (Heaps
2011, p. 1)
54
As an accounting model LEAP is able to consider mitigation issues in a highly flexible and
realistic way. In contrast to an optimization based model, it does not impose a clear bias in
modelling scenarios. Various studies with the focus on investigating mitigation issues have
already been based on LEAP. Therein, a comparison between a business-as-usual scenario
and one or more mitigation policy scenario(s) was prepared. These analyses outline how
aggressive mitigation measures to reduce energy demand affect the energy composition of a
country or region and the corresponding emission levels affect the economy. LEAP can
compute a preliminary mitigation assessment using essential production-based emission
variables and elaborated production statistics, i.e., ‘Tier 1’ emission factors, provided by the
IPCC, which are included in LEAP. The analysis becomes more precise as more data on local
and regional emission sources and levels are provided. Great emphasis is put on the provision
of data regarding chemical compositions of the used fuels, since this specifies the emission
estimates (Davis 2010a and 2010b, SEI 2006, p. 4).
As with adaptation, LEAP does not assess the cost of it. There is, however, the possibility to
include externality values, which are associated with different GHGs and local air pollutants.
In turn, these externality costs may be integrated into the overall net present value (NPV)
calculation.
Figure 4. [Source: COMMEND (II)]
LEAP is a highly transparent and flexible tool. It is an accounting type model, i.e., its
modelling approach is based on the quantitative representation of flows of energy. These are
defined through simple engineering relations. The demand for energy is modelled through
various methods: Either a bottom-up, an end-use accounting or a top-down macroeconomic
55
approach is applied. To model electric sector generations and calculate capacity expansion on
the supply side in a satisfying way, accounting and simulation methods are employed.
Moreover, these methods allow for the inclusion of results from other models, which
investigate these supply side factors in a more thorough way (UNFCCC 2006, p. 15; Heaps
2008, p. 6).
Transparency for the user is guaranteed by the accessible user interface, which is designed as
depicted in Figure 4. This is the ‘Analysis View’, where the data structure provided by the
analyst is organized in the hierarchical tree on the left-hand side and the data for a specific
section of the energy system (here: household demand) can be viewed in the window on the
right-hand side. The great advantage of this tool can be seen in the third window: immediately
a chart can be drawn to provide a solid graphical interpretation of the data (Heaps 2008, p. 8).
LEAP incorporates even more tools for data and result presentation. Additionally to charts,
also tables and maps can be developed right away (and can even be exported to Microsoft
Excel, Word and PowerPoint).
The data requirements of a LEAP based model will crucially depend on the type of analysis
the researcher wants to investigate. Therefore, no exhaustive list of necessary data can be
provided. Furthermore, since some/many elements48 of LEAP can be added or removed by the
user, and while the user decides whether he/she is about to conduct a top-down or bottom-up
analysis, these decisions will shape the structure of the required data set (SEI 2006, p. 1).
Next, it is important to make sure that the investigation is not determined by the available
data. Instead, the detail necessary for our study of mitigation/adaptation issues would have to
be clearly defined and data would have to be provided accordingly. The analyst therefore,
does not conduct his/her study according to the available data, but seeks the necessary data for
his/her study. A full overview of data requirements is given in SEI (2006). Data concerning
demographics, the economic situation, the energy system, demand characteristics,
transformation possibilities, the environmental variables and fuel specifics can be
incorporated within the analysis.
As already described under b.), analysts using LEAP can build various scenarios: First, they
will develop a baseline scenario, which will then be used for comparison. Second, they create
scenarios with differing levels of mitigation and other measures. These are long-term
scenarios, accounting for the socio-economic and environmental impacts of the possibly
undertaken policies. It is very convenient to build scenarios using LEAP. The model software
provides a Scenario Manager, where policies can be investigated for their individual effects or
for their composite effects (Heaps 2008, p. 6).
f.
It was previously mentioned in the report “Overview of models in use for Mitigation /
Adaptation policy”, how useful LEAP is in covering specific characteristics of developing
countries. Reviewing them shortly, LEAP can include and evaluate variables such as
electrification, traditional bio-fuels, urban-rural divide, individual assumptions per country,
emission trading, renewable energies, rural energy programmes. These can be accounted for
explicitly. Moreover, implicitly modelled characteristics are the performance of the power
sector and subsidies (Urban et al. 2007, p. 3478).
48
Such as transformation analysis, pollution and GHG emissions analysis, cost analysis, and non-energy sector GHG
accounting.
56
Users of LEAP can deduct various cost-specific outcomes from the cost-benefit summary
report provided as one part of the output. Therein, a summary of costs and benefits for each
computed scenario relative to the pre-specified baseline (or “business as usual”) scenario is
delivered.49
The cost summary compares “total cumulative emissions of all greenhouse gases avoided by
each scenario (shown in terms of the global warming potential of those pollutants in tonnes of
CO2 equivalent)” (SEI 2011, p. 22, emphasis by authors). Furthermore, LEAP can calculate
the individual and combined Global Warming Potential (GWP) of one or more greenhouse
gases. For this calculation, the user can choose among two measurement units: Carbon (C)
equivalents and Carbon Dioxide (CO2) equivalents.
According to Mr. Heaps, developer of LEAP, social cost for the different scenarios can be
calculated in any currency. Moreover, they can be presented in discounted or in real terms.
LEAP calculates the total social net present values of a computed scenario against a prespecified baseline scenario. However, it does not specify the actual costs accruing to different
groups, i.e. it deals with costs rather than prices. However, the NPV can be split up to
determine the streams from each sector (households, industry, transport, services, electric
supply, etc.). LEAP can calculate the percentage of RES penetration, as well as the
penetration of energy efficient technologies. Finally, administrative costs can be included
optionally, provided that the necessary data is included.
LEAP is free for analysts in developing countries. However, users from OECD (Organisation
for Economic Co-operation and Development) countries are charged a fee for the use of the
model (Connolly et al. 2010, p. 1071).
This model is widely recognized among government agencies, academics, non-governmental
organizations, consulting companies and energy utilities. Together with the mentioned
magnitude of users (over 5000) this makes LEAP a standard model for integrated resource
planning and greenhouse gas mitigation assessment (Heaps 2008, p. 5).
i.
Support for any occurring problems with LEAP is easy to access by phone, email or a web
forum. It is provided for free to registered users. COMMEND – Community for Energy,
Environment and Development and the respective web site <www.energycommunity.org> is
a rich source on various LEAP and energy related issues, especially for developing countries’
analysts. Moroever, the community itself provides trainings and support (UNFCCC 2006, p.
66f).
Although LEAP is an intuitive tool, some training is necessary nevertheless. A minimum of
five days is recommended and offered for free, however, paying for the expenses (travel costs,
etc. for the trainer).
49
The user can freely change the monetary unit of the summary.
57
IMAGE
IMAGE, the Integrated Model to Assess the Global Environment, was developed in the late
1980s at the National Institute for Public Health and the Environment (RIVM), Netherlands.
Today, a separate institution, the Netherlands Environmental Assessment Agency (MNP) is
maintaining and further developing the model. After a series of revisions, the latest version on
the way to IMAGE 3 is 2.4 (MNP 2006, p. 5).
IMAGE can be described as a hybrid model, incorporating both the top-down and the bottomup views of energy and economic systems. The main focus of the model is to investigate the
global interconnectedness of the natural/biophysical and the human/socio-economic systems.
The targeted issue of IMAGE is therefore a very broad one, particularly different from the
other models included in this study.
The model investigates “direct and indirect pressures on human and natural systems closely related
to human activities in industry, housing, transport, agriculture and forestry” (MNP 2006, p. 8). On
the one hand, the socio-economic conditions and interactions are analysed focusing on 24regions (plus Greenland and Antarctica).50 On the other hand, the climate, land-cover and
land-use dynamics are modelled by applying a geographically explicit approach, namely a
grid resolution of 0.5 by 0.5 degrees. This feature contributes to the strength of the model in
the analysis of relations within the earth energy system (MNP 2006, p. 8f).
The model connects three modules for the system analysis: The Energy-Industry System
(EIS), the Terrestrial Environment System (TES) and the Atmosphere-Ocean System (AOS).
The EIS calculates emissions for all the regions, considering industrial and energy sources.
This calculation is undertaken by the TIMER51 energy model. The TES provides emission
quantities related to global land-cover changes and other indicators ((agro-)economic and
climate characteristics). The resulting emission levels from the calculations within the EIS
and the TES are then used to simulate the greenhouse-gas stock in the atmosphere, using AOS
(MNP 2006, p. 9ff).
The modules themselves consist of a complex structure that is depicted in Figure 5. The
socio-economic effects on land use change affect the climate, which in turn again affects the
human system. Demographics, energy supply and demand levels, as well as agricultural
production and economic interactions at the world level produce emissions and imply a
certain land allocation. These emission levels and the land use affect the biophysical system.
The resulting climate impacts, the land degradation, the degree of water pollution, the effects
on biodiversity as well as the air pollution are identified. Based on these results the FAIR52
model investigates possible policy options (MNP 2006, p. 12).
50
The regions are: Canada, USA, Mexico, Central America, Brazil, Rest of South America, Northern Africa, Western Africa,
Eastern Africa, Southern Africa, Western Europe, Central Europe, Turkey, Ukraine region, Kazakhstan region, Russia,
Middle East, South Asia, Korea region, East Asia, Southeast Asia, Indonesia, Japan, Oceania, Greenland and Antarctica.
51
TIMER = The Targets IMage Energy Regional Model, developed in connection with the IMAGE 2.2 version.
52
FAIR = Framework to Assess International Regimes for differentiation of commitments
58
Figure 5. Source: MNP 2006, p. 13
IMAGE was applied within the EUruralis study, which was initiated in 2004. This study
investigated future prospects for agriculture and the rural areas of the EU-25 countries.
Moreover, the project “Greenhouse Gas Reduction Pathways in the UNFCCC Process up to
2025” employed an older version of the model, IMAGE 2.2, in the analysis.
Further, IMAGE was applied in the ADAM (Adaptation and Mitigation) project, funded by
the Framework 6 Programme by the European Community, in the development of two
mitigation and adaptation scenarios.
Finally, IMAGE was used in the ENSEMBLES project, also financed by the Framework 6
Programme, to prepare an ‘ambitious climate policy scenario’ (MNP I).
The results of the climate model IMAGE can be fed into FAIR to analyse environmental and
mitigation issues. This model then calculates the implied abatement costs of different future
59
emission reduction scenarios. These costs are calculated by marginal abatement cost curves of
the energy- and industry-related CO2 emissions (MNP 2006, p. 187).
“The model has been developed to explore and evaluate the environmental and abatement cost
implications of various international regimes with respect to the differentiation of future commitments
for meeting long-term climate targets, such as the stabilization of atmospheric GHG concentrations”
(MNP 2006, p. 187).
The model computes who shall contribute which quantity in reaching climate stabilization
goals. However, the analysis of these questions is not solely focused on cost and technical
assessment; the emphasis is put also on responsibility and equity matters (as far as they can be
assessed in a quantitative way) (MNP 2006, p. 187).
The most recent studies conducted with IMAGE to assess mitigation potentials are listed at
the MNP web page (MNP II). The list includes ‘Oil and natural gas prices and greenhouse gas
emission mitigation’ (van Ruijven and van Vuuren 2009) and ‘Assessment of bottom-up
sectoral and regional mitigation potentials. Background report’ (Hoogwijk M. et al. 2008).
This description of the model leads to the conclusion that IMAGE 2.4 is a complex model
with far-reaching implications. Additionally to an intuitive user interface, the model’s
structure is easy to understand. Applying this model, however, necessitates quite a wide range
of specialised and committed users. Therefore, it can be used only in research projects carried
out together with MNP (see g. & i.).
Under the auspices of the Netherlands Environmental Assessment Agency researchers
developed a History database of the Global Environment (HYDE). This database provides
historical time series (mainly for the period between 1890-2000, for some data, between
1700-2000) for land-use and land-cover data, population data, livestock, GDP, energyspecific data, production levels, estimates of energy consumption levels, as well as figures of
atmosphere, oceans and terrestrial environment characteristics (MNP 2006, p. 94).
Although, the database provides all this information for many countries, further and more
recent information has to be provided regardless.
IMAGE is a simulation-type model and therefore produces less biased scenarios than
optimisation-type models. The wide use of IMAGE in scenario development, within projects
at the MNP, projects conducted by the European Community or the IPCC confirms this
suitability (MNP 2006, p. 6).
f.
Urban et al. (2007) came to the conclusion that IMAGE is a moderate tool in modelling
developing countries characteristics. Explicitly traditional bio-fuels, clean development
mechanism, emission trading and a broad spectrum of renewable energies can be included.
Implicitly, electrification and the urban-rural divide are modelled (Urban et al. 2007, p. 3478).
According to Mr. van Vuuren, Senior researcher at Netherlands Environmental Assessment
Agency (PBL), IMAGE does not account for social costs of different scenarios. Neither does
it compute the costs resulting from mitigation/adaptation scenarios differentiated along
sectors (households, industry, production, agriculture), but the emission reduction by sector is
60
calculated naturally. The administrative costs of the scenarios are not included.53 IMAGE
does, however, compute the penetration of renewable energy sources.
The cost implications of different scenarios produced with IMAGE are analysed by the FAIR
sub-model. The model uses marginal abatement cost curves calculated by the data (on energyand industry-related carbon dioxide emissions) provided by the TIMER54 2.0 model and the
IMAGE model (that provides data on carbon sinks, MNP 2006, p. 187). As a measure of
potential economic impacts of emission reduction, the FAIR model applies the ratio of
abatement costs to GDP in PPP (MNP 2006, p. 195). The calculations related to GHG
emission reduction are conducted in Petagrams of Carbon (Pg C) but can be calculated in
percentage terms as well.
The cost of acquiring the model is not specified, since the model cannot be provided as a
‘ready-to use’ software package. IMAGE can be used only by close cooperation with MNP
and other partner institutions (MNP IV).
As mentioned under e., IMAGE was used in various studies and research projects around the
world. It was a central constituent within the IPCC Special report on Emission Scenarios,
IPCC Representative Concentration Pathways, the UNEP Third and Fourth Global
Environment Outlook, the Millennium Ecosystem Assessment, the OECD Environmental
Outlook, as well as many more (MNP III).
i.
Close collaboration with the developers is necessary to use this model (as mentioned under
g.). Further, the model’s developers point out that a high level of “expert knowledge is
necessary
to
make
good
use
of
it”
(MNP
IV).
53
54
Transaction costs in international trading are, however, included.
TIMER – The Regionalized Energy Model of IMAGE 2.4
61
MESSAGE
The Model for Energy Supply Strategy Alternatives and their General Environmental Impact
(MESSAGE) came into being at the International Institute for Applied System Analysis
(IIASA) in Austria around 1980. It is widely used in the member countries of the International
Atomic Agency (IAEA). Two major extensions were later developed: ‘The Macroeconomic
Model MACRO’ and ‘The Model to assess Greenhouse-gas Induced Climate Change’
(MAGICC). Moreover, a stochastic and a myopic model exist (IIASA; Connolly et al. 2010).
MESSAGE is a ‘systems engineering’ optimisation tool. It determines energy scenarios,
which minimise total system costs considering user imposed constraints on the energy system.
Further, MESSAGE analyses ‘how much of the available technologies and resources are
actually used to satisfy a particular end-use demand [...].’ Based on this and further
information provided by the analyst, an energy system configuration is developed for the
entire planning horizon (base year – > user defined end of the time horizon) (IIASA II).
The technological details supplied by the user will vary from geographical and temporal
characteristics of the problem under consideration. MESSAGE is rather flexible with respect
to the provided degree of this information. When utilizing this model generator, the analyst
describes a RES, specifying all relevant links and nodes within the energy system. This RES
should include all necessary performance characteristics of technologies (IIASA II).
As was previously mentioned above, two extensions of MESSAGE exist: First, ‘The
Macroeconomic Model MACRO’ was developed based on the top-down macroeconomic
model MERGE (Model for Evaluating the Regional and Global Effects of GHG Reduction
Policies). At IIASA, MACRO was modified to achieve full compatibility with MESSAGE,
and MACRO is now predominantly used in connection with MESSAGE. MACRO solves the
inter-temporal utility maximisation problem of a representative producer-consumer for each
region. Introducing this top-down view on the economy the effect of policies on the energy
system (e.g.: energy costs, GDP, energy demand) can be investigated. “The link is established
by using MESSAGE results on total and marginal costs of energy supply to derive the quadratic
demand functions for MACRO. The linked model is iterated until MACRO’s resulting energy demands
do not deviate from MESSAGE’s by more than a given fraction” (IIASA I).
The second extension, The Model to Assess Greenhouse-gas Induced Climate Change
(MAGICC), estimates aggregate climate impacts of Energy, Economic and Environment
scenarios. By incorporating a carbon cycle model55 and estimating resulting emissions,
implied net carbon flows and atmospheric CO2 concentrations, changes in radiative forcing,
temperature and sea level relative to 1990 can be calculated (IIASA I). Rao and Riahi (2006,
p. 179) used MAGICC in connection with MESSAGE to achieve scenario consistency
regarding the proposed forcing target. The previously obtained emission levels from
MESSAGE are provided as inputs for MAGICC. This tool then estimated the hypothetical
forcing resulting from the given emission levels. The result of this calculation, the new CO2
concentration56 limit, is then returned back to MESSAGE. Consistency between the GHG
55
Describes how atmospheric inputs (emissions) and outputs (physical and chemical sink processes) affect changes in the
atmospheric carbon concentration (IIASA I).
56
http://www.iaea.org/INPRO/index.html
62
emissions from MESSAGE and the forcing target is obtained through iteratively repeating
this procedure (Rao and Riahi 2006, p.179f).
Our partner institution from Moldova, the Institute of Power Engineering (IPE ASM), pointed
out that its personnel are being trained in using the model for further research uses. Also,
partners from the Energy Strategy Center of Scientific Research Institute of Energy (SRIEESC) in Armenia have used the model within their studies. Moreover, local studies were
conducted in Armenia using this model.
MESSAGE is currently used in the International Project on Innovative Nuclear Reactors and
Fuel Cycles (INPRO) founded by the IAEA. The set of members consists of all 32 IAEA
member states as well as the European Commission.
MESSAGE is primary a tool for mitigation analysis: Instead of focusing on climate targets,
MESSAGE estimates the effects of mitigation strategies at regional and global levels within
various sectors. (Connolly et al. 2010, p. 1072) Moreover, MESSAGE was applied to
“develop climate mitigation scenarios aimed at achieving long-term stabilization of global
radiative forcing” (Rao and Riahi 2006, p. 177).
In MESSAGE it is possible to impose emission control limits on individual plants and on one
or more groups of plants. Although it is possible to model emission trading among plants or
utilities, this is a more complicated issue (Rogner 2002, p. 37).
The current version, MESSAGE V, incorporates a user interface for data entries and program
calls. The IAEA characterises it as “an extremely flexible model”, however the analyst has to
develop the RES and clarify the relevant policy questions (Rogner 2002, p. 40).
As an optimisation tool, with demanding data requirements, the scenario development can be
quite challenging. The data structure and modelling technique, however, provide a transparent
analysis framework.
The research necessary to provide data for MESSAGE shall obtain information on the
available energy resources, future technological developments and the evolution of
technological parameters over time (Messner and Strubegger 1995b, p. 11). Moreover,
information on energy demand needs to be provided exogenously, together with seasonal
variation in demand. An advantage of MESSAGE is the absolute flexibility in energy and fuel
demand specification for the user (Rogner 2002, p. 21).
Two scenario databases are frequently used in conjunction with the MESSEAGE model
generator: IPCC RCP (Representative Concentration Pathways) scenario database and
Greenhouse Gas Initiative (GGI) scenario database. Among them, the analyst chooses the
appropriate baseline scenario to build their study on (IIASA III).
For example, within the study on the effects of non-CO2 GHGs on climate change mitigation,
Rao and Riahi (2006, p. 179) used the B2 scenario developed by the IPCC in the Special
Report on Emissions. This scenario describes “local solutions to economic, social and
environmental sustainability” (Rao and Riahi 2006, p. 179).
MESSAGE facilitates modelling of all energy technologies. Moreover, the following
characteristics can be accounted for: multiple inputs and outputs, seasonal variation &
63
storage, efficiency and costs varying with time, limits on production and resource extraction,
nested and linked constraints, capacity build-up constraints, market penetration and
environmental regulation (Rogner 2002, p. 25).
A drawback in using MESSAGE, however, is the time consuming development of case
studies, which has to be conducted with IAEA’s cooperation and can take several months.
f.
MESSAGE is among57 the best suited models to address developing countries issues
according to Urban et al. (2007, p. 3478). Characteristics such as electrification, traditional
bio-fuels, urban-rural divide, structural economic change, subsidies, emission trading, clean
development mechanism and renewable energies can be addressed (Urban et al. 2007; p.
3478, Table 2).
MESSAGE focuses on the calculation of GHG related emissions. The main focus thereby lies
on CO2 and CH4 and local pollutants like SOx and NOx. Further, Rao and Riahi (2006, p. 179)
applied MESSAGE to calculate the impact of non-CO2 GHG, more specifically, of all six
Kyoto GHG emission (CO2, CH4, N2O, HFCs, PFCs and SF6) on climate change mitigation.
Cost-effective targets of GHG emission limits and reduction options are specified (Connolly
et al. 2010, p. 1072).
According to Mr. Jalal, Senior Energy/Nuclear Power Planner in the Planning and Economic
Studies Section, Department of Nuclear Energy at the IAEA, MESSAGE is a very flexible
tool that enables the construction of energy models with varying details. The wideness of the
model in fulfilling the requirements necessary for the PROMITHEAS-4 mitigation/adaptation
analysis will depend on how the model is constructed. The necessary cost accounting
(calculation of mitigation/adaptation costs, social costs, etc.) can be conducted using
MESSAGE, however, the analyst has to induce the model to do so. Also, the penetration rates
of different technologies/sources can be outputs of the model.
The model package is provided free of charge to the public sector, non-profit organisations
and research organisations, i.e. academia (COMMEND).
MESSAGE, was prominently used in the Environmentally Compatible Energy Strategies
Project at IIASA, in cooperation with the World Energy Council, to assess the implications of
global economic development on energy and environmental impacts (Messner and Strubegger
1995b, p. 10). As was mentioned under criterion a., it is used within the INPRO project by the
IAEA member states as well as the European Commission.
i.
IAEA provides in-depth training courses for IAEA member states taking approximately 2
weeks for basic applications (Connolly 2010, p. 1072).
57
Together with LEAP, RETScreen and WEM.
64
Conclusion
This report analyses the previously presented six models, ENPEP-BALANCE,
MARKAL/TIMES, MERCI, LEAP, IMAGE and MESSAGE, according to the following
catalogue of criteria:
a. The choice will be restricted to models used at European level;
b. The wideness of the model in covering mitigation/adaptation issues (The model that is closer
in covering these issues will be taken into consideration);
c. Transparency, complexity and easiness in using the model;
d. Availability of inputs (available in statistics books, national accounts);
e. Flexibility of the model in building scenarios (e.g.: a simulation model does not impose bias in
modelling outputs);
f. Compliance of outputs with our contractual obligations (socio-economic, technological
penetration);
g. Cost of acquiring the model;
h. International recognition of the model (used by governments);
i. Training and technical support.
Below, we summarize and list the models according to their capabilities to comply with the
relevant criteria.
The first question regards the use of the models at the European level. All models comply
with this requirement. ENPEP(-BALANCE) was already used in many PROMITHEAS-4
partner countries. MARKAL/TIMES was applied in several EU projects and various
European countries. MERCI is currently under construction and was used so far only in
Austrian projects. The wide spectrum of countries applying the LEAP model within policy
research projects includes European countries such as Greece, Estonia, Albania and Moldova.
Within a number of projects conducted by the European Community, such as the ADAM
(Adaptation and Mitigation) project, scenarios were developed with IMAGE. MESSAGE is
used in a number of countries, mostly IAEA member and partner countries. Summarising, all
the models seem to be widely accepted within the European community, noting that MERCI
is still in the development process and has only been implemented on a national scale so far.
An especially important subject from the PROMITHEAS-4 perspective is the wideness of the
model in covering mitigation/adaptation issues. After having presented in detail the
possibilities of IAMs to take into account mitigation, but more importantly the difficulties
with modelling adaptation in a separate section, the best suited model to account for both is
LEAP. However, all other models can include mitigation in their investigations.
Moving on to the next topic, the transparency, complexity and easiness of the tools, an
informal ranking can be constructed: MARKAL/TIMES is the most complex, however,
transparent model for well trained analysts, followed by IMAGE, which requires a lot of
expert knowledge. While MERCI is in the development phase, its usage is open only to
analyst in collaboration with the developers. MESSAGE can well be categorised as an
intermediate tool as far as transparency and complexity are concerned. ENPEP-BALANCE
and LEAP, two simulation models, are the most transparent and easy to use tools in our set.
Both have an intuitive user interface for fluent scenario construction.
65
The analysis of country specific mitigation and adaptation policies needs a solid data base.
The models, however, vary quite significantly regarding the specifics necessary for a sensible
investigation. MARKAL/TIMES, MESSAGE, MERCI and ENPEP-BALANCE have
medium to extensive data requirements, while the data needs for LEAP and IMAGE will
depend on the analysis the user is interested in. Moreover, there are databases available to be
used especially in connection to LEAP and IMAGE respectively.
It is evident that the set of models consists of powerful tools for scenario development. They
will vary, however, in terms of flexibility in building these scenarios. To aid the decision
process, also here an informal ranking is provided: Somewhat less flexible tools are
MARKAL/TIMES and MESSAGE. ENPEP-BALANCE, MERCI and IMAGE offer more
flexibility. The most flexible tool is LEAP, based on how easy scenarios can be adapted.
Regarding the question, how these models take into account our contractual obligations, i.e.
socio-economic aspects, social costs, administrative costs, costs for target groups,
technological penetration rates and emerging/developing countries characteristics, the model
that is best suited for this task is, again, LEAP. It is possible to account for a high number of
developing countries specifics using LEAP, which will facilitate our analysis. Moreover, the
model is highly flexible in addressing and calculating the relevant costs. MESSAGE also
corresponds well to these criteria, followed by MARKAL/TIMES and IMAGE. MERCI and
ENPEP-BALANCE can especially consider technological penetrations, but are useful tools
for calculations of the other above mentioned aspects as well.
Of course the respective cost of the models plays a crucial role, since the licence has to be
acquired for twelve countries. MARKAL/TIMES, as the most sophisticated model in our set,
is also the most costly one (within the range of €1.200 and €3.000). LEAP is free for
developing countries; costs arise, however, for OECD countries. MESSAGE and ENPEPBALANCE can be obtained free of charge. For MERCI no costs were specified so far.
Developers of IMAGE, since it is only available in close cooperation with the developers, do
not determine any costs for the use of this model.
The most renowned model is definitely MARKAL/TIMES, while LEAP is fast approaching.
However, all the other models, except for MERCI, have found wide international recognition.
Turning to the last issue, the availability of training and support has to be evaluated. Although
broad support and training are available for MARKAL/TIMES-users, the costs of both are
high: basic training cost can amount up to €30.000. Training and support for the ENPEPBALANCE model is available at €7.000 respectively. LEAP training is provided for free
(expenses for the trainers have to be covered), support can be obtained against a fee. Costs for
training and support for MESSAGE (training is conducted by the IAEA), IMAGE and
MERCI are not specified.
The striking aspects of this summary are, of course, the many advantages of LEAP for the
purposes of the PROMITHES-4 project. The choice therefore, is not a difficult one and we
can finalise this report with the conclusion that LEAP will be used for mitigation and
adaptation analysis within the PROMITHEAS-4 project.
66
Table 1. [Source: Author]
Transparency,
Easiness
Required
Data
intensity
Flexibility
in
building
scenarios
Compliance
with
contractual
obligations
Cost
International
recognition
Training,
Technical
support
(cost)
Moderate
High
Moderate
Moderate
Moderate
Low
Moderate
Moderate
High
Moderate
Low
High
Low
Moderate
High
High
High
MERCI
Low
Moderate
Moderate
Moderate
High
Moderate
Low
Low
Moderate
LEAP
High
High
High
Low
High
High
Low
High
Low
IMAGE
High
Moderate
Moderate
Low
Moderate
Low
N/A
Moderate
Not
specified
MESSAGE
Moderate
Moderate
Moderate
Moderate
Low
High
Low
Moderate
N/A
Use at
European
Level
Ability to
cover M/A
issues
ENPEPBALANCE
High
MARKAL/
TIMES
67
PROMITHEAS-4: “Knowledge transfer and research needs for preparing mitigation/adaptation policy portfolios”
Definition of Terms
Bottom-up models
This category encompasses models that “represent reality by aggregating
characteristics of specific activities and processes, considering technological,
engineering and cost details” (IPCC 2007, Annex I, p. 810).
Top-down models
This are models “applying macroeconomic theory, econometric and optimization
techniques to aggregate economic variables. Using historical data on consumption,
prices, incomes, and factor costs, top-down models assess final demand for goods and
services, and supply from main sectors, such as the energy sector, transportation,
agriculture, and industry. Some top-down models incorporate technology data,
narrowing the gap to bottom-up models” (IPCC 2007, Annex I, p. 821).
Accounting-type models
Models applying an accounting framework consider flows of energy in a system
determined through simple engineering relationships. The user determines all the
relevant technology parameters and their values. The model is primarily a
‘sophisticated calculator’, whereby it can present various technology allocation
scenarios (UNFCCC 2006, p. 15).
68
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%29%20in%20Europe.htm> [accessed 26/05/2011]
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(II)
–
world
wide
applications
list
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http://www.energycommunity.org/default.asp?action=71
COMMEND (II). http://www.energycommunity.org/default.asp?action=72
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Communication
The following model developers were contacted for further information:
Gary Goldstein – MARKAL/TIMES
Charles Heaps - LEAP
Ahmed Irej Jalal - MESSAGE
Detlef van Vuuren – IMAGE
72

Choice and implementation of models for ma policy

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