May 7, 2008 - 2 The Coupling of Top-down and Bottom-up Models. 7. 3 Review of ..... Table 5.3: GEMINI-E3 sectors. #. Designation. 1. Coal. 2. Oil. 3. Gas. 4.
A master program that will drive the coupling of GEMINI-E3 and MARKAL/TIMES models
May 7, 2008
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Contents Contents
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1 Introduction
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2 The Coupling of Top-down and Bottom-up Models
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3 Review of existing methodology 3.1 Linking models . . . . . . . . 3.2 Top-Down oriented approaches 3.3 Bottom-up oriented approach . 3.4 Integrated Model . . . . . . .
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4 Brief presentation of the two models 4.1 GEMINI-E3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 TIMES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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5 Coupling Methodology 5.1 Algorithm . . . . . . . . . . . . . . . . . . . 5.2 Coupling Regions . . . . . . . . . . . . . . . 5.3 Connecting productions sectors and demands 5.4 Database comparison . . . . . . . . . . . . .
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6 Implementation in GEMINI-E3 6.1 Domestic production in the standard version . 6.2 Integration of fuel-mix coming from TIMES fuel sectors . . . . . . . . . . . . . . . . . . 6.3 Representation of fossil fuel sectors . . . . . 6.4 Rewriting Household consumption . . . . . .
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7 Implementation in TIMES 7.1 Extracting sectoral technical progress and fuel mixes . . . . . . 7.2 Computing coupling variables . . . . . . . . . . . . . . . . . . 2
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Contents 7.3
3 Useful Demands Specification . . . . . . . . . . . . . . . . . .
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A Indicator by sector and region
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Bibliography
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Chapter 1 Introduction This report presents the conjoint work carrying out by Laurent Drouet and Marc Vielle (the GEMINI-E3 team, REME) and Maryse Labriet and Richard Loulou (the ETSAP-TIMES, KANLO team) in order to build a master program which drives the coupling of the top-down model GEMINI-E3 and the bottom-up model ETSAP-TIMES. Alain Haurie and Jean-Philippe Vial are also gratefully thanks for their helpful comments. After an introduction of the basis of the coupling of top-down and bottom-up models in Chapter 2, we review an non-exhaustive list of existing couplings in the literature in Chapter 3. Chapter 4 provides a short description of the two models. Chapter 5 describes the coupling procedure along with the connections established between the models and, finally, specific modifications in GEMINIE3 and ETSAP-TIMES are exposed in Chapter 6 and Chapter 7, respectively.
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Chapter 2 The Coupling of Top-down and Bottom-up Models Among the tools used by the modelers to assess energy and climate policies two families of models prevail: the top-down (TD) models and the bottom-up (BU) models. Top-down models are economy-wide models whilst bottom-up models are energy system models. In their review of integrated assessment models, Grubb et al. (1993) already distinguish the top-down and bottom-up models by their valuations of the CO2 emission reduction costs where TD have higher costs and BU have lower costs because of their technological description. Both of these families have their own strengths and weaknesses which actually determine their usages in the field of policy assessment. Böhringer and Rutherford (2006) resume the terms “top-down” and “bottom-up” as shorthand for aggregate and disaggregated models. Bottom-up models are generally mathematical programming problems or more specifically linear problems. It consists in minimizing the energy system total cost subject to satisfy the demands. These kind of problems are rather easy to solve and the size of the problem can be very large. Thus, the current and future technological choices and the description of the reference energy system are very detailed in this type of models. Energy flows and conversion are described in physical terms. This methodology is really adapted to represent the technological options. However, bottom-up models fail to represent the complex market interactions for the reason that it doesn’t incorporate the key economy components such as labor, investment, capital and consumption. Traditionally, top-down models are either computable general equilibrium 7
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CHAPTER 2. THE COUPLING OF TOP-DOWN AND BOTTOM-UP MODELS
(CGE) models, or long-term macroeconomic growth models which cover a larger economy framework. They comprise an explicit representation of the main economy factors and are based on sound microeconomic foundations. They also supply a basis to describe the consumers behavior in the economy and represent the market effects such as the income effects for the households and the government. The production is often formed by a CES production function with an energy aggregate. The technologies are described as a response to the different prices by the substitution between the energies and the others production factors (labor, capital). Moreover, there a non-market factor, named autonomous improvements in energy (AEEI) which reduces energy demands per unit of GDP. The economy flows, including the energy flows, are here represented by economy accounting in constant or current prices. Top-down models lack of detailed technological information on the energy system, specially for energy production and conversion. This information are essential in the valuation of the quantitative effects of the energy policies. In addition, top-down models can break some physical boundaries such as physical energy conservation principles (Böhringer and Rutherford, 2005). The particular advantages and disadvantages of the bottom-up and top-down approaches explain ongoing efforts to marry the technological-oriented approach of bottom-up models with the economy-oriented approach of top-down models within a hybrid modeling framework. Thus, the hybrid model will accept structural changes in the energy system and in the economy considering technical changes and alternative energy policies. From this point of view, the modelers main effort is to profit from both types of models by creating hybrid models.
Chapter 3 Review of existing methodology Various methods and approaches allow for the development of hybrid models. This chapter provides a non-exhaustive information list of methodologies used to create hybrid models in the literature. We retain four main categories to classify the methodologies of coupling top-down and bottom-up models. The first type of hybrid models creates links between existing top-down and bottom-up models. A second type of hybrid models aims at incorporate a reduced bottom-up model within an existing top-down model. At the opposite, the third type incorporates some equations coming from a top-down model inside an existing bottom-up model. Finally, the fourth type of hybrid models is the integration of the two models within the same optimization framework either by a monolithic program or by a decomposition method.
3.1
Linking models
This methodology aims at minimize the number of structural changes of the original models. It is a flexible method which allows supplementary model development easier. Basically, the two models are exchanging data and one hope the “convergence” of the two models. The models are then executed consequently. The first attempt to couple bottom-up and top-down models uses this methodology. Hoffman and Jorgenson (1977) have built a framework for US energy policies where energy cost paiements (capital charges, operation and maintenance, and fuel costs for all technologies), resources and technologies used, price 9
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CHAPTER 3. REVIEW OF EXISTING METHODOLOGY
of services and product, environment effects and capital requirements coming from the BU model are sent to the TD model. Then demands in energy services and products coming from the TD model are sent to the BU model. The procedure iterates until a solution is found; i.e. a value of output of each sector is equal to the input of that sector. MESSAGE-MACRO (Messner and Schrattenholzer, 2000) is a model that links a macroeconomic model (MACRO) with a energy supply model (MESSAGE). “The purpose of the linkage is to consistently reflect the influence of energy supply costs as calculated by the energy supply model in the optimal mix of production factors included in the macroeconomic model.” Drouet et al. (2005) have built a framework where to couple a bottom-up model (Swiss MARKAL) restricted to the housing sector in Switzerland with a global top-down model (GEMINI-E3) where the region Switzerland sees its energy consumptions in housing fixed. Böhringer and Rutherford (2005) underline the lack of theoretical background of this simple methodology like the inconsistencies in behavioral assumptions and accounting concepts for the whole framework.
3.2
Top-Down oriented approaches
These models are either top-model models that are calibrated with the responses of top-down model at some conditions, or top-down models which integrate discrete and detailed technology choices.
Calibrate nested CES functions with simulations coming from bottom-up model In the transport sector Schäfer and Jacoby (2005, 2006) adapt the transport sector representation of a CGE model (EPPA) to be consistent with the technological specifications of a Bottom-up model (MARKAL). Figure 3.1 summarizes their approach. They also augment the standard top-down/bottom-up framework with a new module (named modal split model) describing the modal splits in the transport sector. MARKAL does not substitute endogenously between the transportation modes (car, bus, rail, airplane); each mode is rather set exogenously
3.2. TOP-DOWN ORIENTED APPROACHES
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and correspond to a unique useful demand. The modal split model (Schäfer and Victor, 2000) supposes that people tend to dedicate a fixed share of their time to transport. With an increasing demand of transport, the modal split model substitute per example light-duty vehicle by aircraft. In EPPA, the transportation
Figure 3.1: Linking model system (Pizer et al., 2003b) demand is split into own transport and purchased transport with a CES function (see figure 3.2), own transport consumption is then disaggregated into refined oil consumption (REFOIL) and other industries and services consumption (OIND). On these bases, the CES functions of the EPPA model are recalibrated by runs
Figure 3.2: Structure of transportation within the Household sector (Pizer et al., 2003b)
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CHAPTER 3. REVIEW OF EXISTING METHODOLOGY
coming from MARKAL and the modal split model. The coupling procedure used is the following: • Run the three models with common baseline assumptions (oil price, transport demand, etc); • Calibrate the share parameter in the CES function by using the modal split results on household-own and household-purchased transport ; • Run the MARKAL model with 11 oil price scenarios at seven future years and compute energy use and capital services to calibrate the CES elasticies of substitution of EPPA ; • Adjust the energy use for the two transportation demand categories with the autonomous energy efficiency variable (AEEI) from MARKAL. The resulting coupled model is an EPPA model with transport demands that are calibrated on the basis of technological information.
In the electricity sector Pizer et al. (2003a,b) estimate the elasticities of the production sectors of a CGE model, with simulations coming from 4 sectoral models representing respectively the electricity sector, the industrial sector, the household transportation sector and the rest of energy demand (commercial uses, and energy linked to building). In their CGE model, the authors represent energy demand in the production sector and in household consumption with nested CES functions as shown shown in figures 3.3 and 3.4.
Figure 3.3: Production Sectors in the CGE model developed by RFF (Pizer et al., 2003b)
3.2. TOP-DOWN ORIENTED APPROACHES
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Figure 3.4: Nesting of Aggregate Consumption Good in the CGE model developed by RFF (Pizer et al., 2003b)
For each sectoral model, a reduced-form sectoral model from an Input-Output framework is simulated with a carbon tax varying between zero and 60 USD. With these runs, two energy elasticities σe and σtop are estimated (see figures 3.3 and 3.4). Elasticities are determined to minimize the sum of squared errors in percent changes in fuel given by the reduced-form model (xe,n ) compared to the detailed sectoral model results (xe,n ), weighting by fuel expenditures (we ). They minimize the following equation:
X
(we (xe,n − xe,n )2
(3.1)
e,n
In practical terms the same percentage changes in fuel price are applied and the same fuel consumption changes with the reduced-form model by choosing the elasticity parameters. The rest of the economy is represented and calibrated with standard functional forms and elasticities. An important limitation of their model is linked to the dynamics. The model is static, and the simulations tested are supposed modeled 10 years into the future. Löschel and Soria (2007) have also adopted this methodology, they calibrate the electricity module of the CGE model PACE with runs coming from the bottom-up model POLES.
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CHAPTER 3. REVIEW OF EXISTING METHODOLOGY
Incorporate technology details into a computable general equilibrium model Böhringer (1998) presents a first way of modeling computable general equilibrium model for “a hybrid description of economy-wide production possibilities where energy sectors are represented by bottom-up activity analysis and the other production sectors are characterized by top-down regular functional forms typically belonging to the constant-elasticity-of-substitution CES family” using the complementarity format. He presents a sample application where technological details are entered in the electricity and housing sectors. The aim of Wing (2006) is to represent better the electricity production within a Computable General Equilibrium (CGE) Model. He adapts the CES production function of the electricity sector with elements coming from technological considerations. The new module concerning electric power takes into account three distinct and complementarity activities: electricity generation, electricity transmission and electricity distribution. Each of them produce a goods with separate labor, capital, material and energy inputs. The generation activity is a CES aggregation of generation technologies with an elasticity of substitution equal to 10. Each generation technology is in turn produced with a Leontieff production function (fixed-coefficient) taking into account capital, labor, fossil fuel. An important work must be done on the database of the model in order to calibrate this new production module. Data are mainly coming from the electricity sector (existing capacities, cost of generation, etc) and are formatted in a Social Accounting Matrix framework (Pyatt, 1988). A main difficulty concerns the static and dynamic aspects of technological allocations. The trick, cited from the article, is to suppose that generation capacities are mobile between technologies (retrofit decisions). But the inter-temporal consistency of the solution adopted is not really clear and as it is pointed out by the author it is unlikely to be the last word on this issue.
3.3
Bottom-up oriented approach
In this approach, a bottom-up model is augmented by equations coming from top-down models, typically a economy-wide production function. This methodology has been typically developed by bottom-up modelers to introduce others economy aspects in their models.
3.4. INTEGRATED MODEL
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The MARKAL-MACRO model (Hamilton et al., 1992) is an hybrid model combining the technological detail of MARKAL with a simplified representation of the economy consisting of a single producing sector, the MACRO module from used in ETA-MACRO (Manne, 1981) and the MERGE integrated assessment model (Manne and Richels, 1992).
3.4
Integrated Model
In this case, a framework contains the top-down and the bottom-up parts and are solved in an unique optimization session.
Monolithic Program The first integrated models are models written in the same language format. They are close in some aspects to the two last approaches (top-down oriented and bottom-up oriented) but here it is more difficult to tell which parts is prominent. Böhringer and Rutherford (2005) proposes a writing of a complete top-down and a complete bottom-up model under the form of a mixed complementary problem. This has been done for relative reduced-size models but, in case of complex models, it still requires too much computational power even for largescale computers. Nevertheless, the formulation of the problem in “a unifying framework for combining technological details of bottom-up models and economic richness of top-down models” is economically consistent and it gives a good insight of what will be a hard-coupled TD/BU model when the algorithms and computer will be able to solve them.
Decomposition Method As it is too difficult to solve at the same time the TD model and the BU model, the decomposition method makes existing models to communicate by means of an exchange of variables and parameters. The communication is ensured by a separate module, which optimize a meta-model to ensure both the consistency of the final solution and the convergence towards an optimal solution.
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CHAPTER 3. REVIEW OF EXISTING METHODOLOGY
Böhringer and Rutherford (2006) propose an method of combining a topdown and bottom-up model. The top-down model is a CGE model written in mixed complementarity format and the bottom-up model is a quadratic nonlinear optimization problem. The energy prices are sent from the BU model to the TD model to build demands functions for the energy sector outputs. The energy system inputs and outputs are sent from the TD to the BU model. With this decomposition method, very complex problems (with a lot of technological choices) can be solved whereas it would be very hard or impossible to solve them within a monolithic framework.
forthcoming workarounds Among the methods that allow for coupling different types of models, the ProximalACCPM method has proven to be very efficient (Babonneau et al., 2006), notably to couple climate models with a top-down model (Drouet et al., 2006). It should be possible to use a similar method to perform an optimization which would bring closer a top-down model and a bottom model by using a likelihood function. By solving this likelihood function, a meta-problem “will estimate unknown parameters based on known outcomes” coming from the models.
Chapter 4 Brief presentation of the two models 4.1
GEMINI-E3
The GEMINI-E3 is a dynamic-recursive CGE model which represents the world economy in 28 regions and 18 sectors, and contains a highly detailed representation of indirect taxation (Bernard and Vielle, 1998, 2008). The version of GEMINI-E3 used in this study is formulated as a Mixed Complementarity Problem (MCP), which is solved using GAMS and the PATH solver (Ferris and Munson, 2000; Ferris and Pang, 1997). GEMINI-E3 is built on a comprehensive energy-economy data set, the GTAP-6 database (Dimaranan, 2007), that provides a consistent representation of energy markets in physical units, a detailed Social Accounting Matrix (SAM) for a large set of countries or regions, and bilateral trade flows between them. The reference year of the database is 2001. Also, we have complemented the data from the GTAP database with information on indirect taxation and government expenditures from the International Energy Agency (2002a,b, Quartely Statitics 2005), the Organisation For Economic Cooperation and Development (2005, 2003) and the International Monetary Fund (2004). For non CO2 greenhouse gases (GHG), data on emissions and abatement costs comes from the United States Environmental Protection Agency (2006). The first version of GEMINI-E3 and its successors, have been especially designed to calculate the social Marginal Abatement Costs1 (Bernard and Vielle, 2003). The original version of GEMINI-E3 is described in Bernard and Vielle (2008) 1
MAC, i.e. the welfare loss of a unit increase in pollution abatement.
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CHAPTER 4. BRIEF PRESENTATION OF THE TWO MODELS
and on the internet2 . Various versions of the model have been used to analyze the implementation of economic instruments allowing for GHG emissions reductions in a second-best setting (Bernard and Vielle, 2000). The following studies are examples of various analyses carried out with GEMINI-E3: the assessment of the strategic allocation of GHG emission allowances in the enlarged EU market (Viguier et al., 2006), the analysis of Russia’s behavior with respect to the Kyoto Protocol ratification process (Bernard et al., 2003), the assessment of the Protocol implementation cost in Switzerland with and without international emissions trading schemes (Bernard et al., 2005), the assessment of oil prices increase effects on global en regional GHG emissions (Vielle and Viguier, 2007). Apart from a comprehensive description of indirect taxation, the specificity of the model is to simulate all relevant markets: e.g. commodities (through relative prices), labour (through wages) as well as domestic and international savings (through interest rates and exchange rates). Terms of trade (i.e. real income transfers between countries resulting from variations of imports and exports relative prices) and “real” exchange rates can also be accurately represented. Time periods are linked in the model through endogenous real interest rates, which are determined by the equilibrium between savings and investments. GEMINI-E3 is a model based on recursive dynamics. Expectations of agents are based on adaptive rules and the model does not presume perfect foresight. National and regional models are linked by endogenous real exchange rates resulting from constraints on foreign trade deficits or surpluses. Finally, the 14 regions of the model are presented in Table 4.1. For more information on GEMINI-E3, please refer to the TOCSIN deliverables D3.1, D3.2 and D3.3.
2
For a complete description of the model, please refer to all technical documents available at: http://gemini-e3.epfl.ch.
4.2. TIMES
19 Table 4.1: GEMINI-E3 Regional Description
4.2
Name
Countries
EUR XEU FSU USA CAN AUZ JAP MEX CHI IND ASI LAT MID AFR
European Union (25) Other European Countries Former Soviet Union (except Baltic States) United States of America Canada Australia and New Zealand Japan Mexico China India Rest of Asia Central and Latin America Middle East Africa
TIMES
ETSAP-TIAM is an instance of the TIMES (The Integrated Markal-Efom System) model generator, developed by ETSAP members over the last decade (ETSAP website, www.etsap.org/documentation). TIMES is a technology based, technology-rich bottom-up model of the third generation, that integrates the entire energy/emission system of a country/region or set of regions, into a single model, including the procurement, transformation, trade, and consumption of a large number of energy forms. TIMES’ economic paradigm is the computation of a dynamic inter-temporal partial equilibrium on energy/emission markets based on the maximization of total surplus, defined as the sum of suppliers and consumers surpluses. In the Base (or Reference) case, the model is driven by user provided demands for a detailed set of energy services in all sectors of the economy. The demands are provided over the entire time horizon of the instance. In Alternate (or Policy) scenarios, the demands are elastic to their own prices, and are thus endogenously re-computed by the model as a result of an endogenous increase or decrease in demand prices. The price elasticities are set to 0 at the initial (usually past) period, and at low values for the few initial years of the horizon. In the version used for the coupling, the demands elasticities are always set to 0.
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CHAPTER 4. BRIEF PRESENTATION OF THE TWO MODELS
The equilibrium is computed via Linear Programming, using the GAMS matrix generation language and the CPLEX or XPRESS optimizers. The GAMS program that contains all TIMES equations and variables is owned by ETSAP and is available only from that organization. The optimization is conducted as the minimization of the negative of the surplus, which is usually called the system ‘cost’. Additional optional features of TIMES include the following: • Inclusion of damage functions in the cost (or surplus) expression; • Decision under risk via stochastic programming, with several alternate criteria; • Modeling of ‘lumpy’ investments via Mixed Integer Programming (MIP); • Modeling of Endogenous technological Learning via MIP; • Integrated TIMES-MACRO model. None of these extra features are used for the coupling. TIAM is a global model with 15 regions covering the entire planet. Figure 4.1 contains a list of the regions. TIAM was meant from the beginning to cover a rather long horizon, extending up to 2100. For the coupling, the relevant horizon extends to 2050. For more information on GEMINI-E3, please refer to the TOCSIN deliverables D2.1, and D2.2.
4.2. TIMES
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Figure 4.1: ETSAP-TIMES Regional Sescription Name
Countries
USA CAN MEX CSA WEU EEU FSU AFR AUS IND SKO CHI JPN MEA ODA
USA Canada Mexico Rest of america Western Europe Eastern Europe Former USSR Africa Australia + New Zealand India South Corea China Japan Middle-East Rest of Asia
Chapter 5 Coupling Methodology 5.1
Algorithm
In order to couple the top-down model GEMINI-E3 and the bottom-up mode TIMES, we use the following procedure. Let the coupling variables be vectors with a value at each coupling period (2005, 2010, 2020, 2030, 2040, 2050) : • D : useful demands (indexed over regions, economy sectors and periods) • P : energy prices (indexed over regions, commodities and periods) • F : fuel mixes (indexed over regions, economy sectors, commodities and periods) • θ : technical progress (indexed over regions, economy sectors and periods) The method for the coupling of the two models is a Gauss-Seidel Method where we look after a fixed point for the useful demands through a iterative procedure. First, we run TIMES with given useful demands D0 . They correspond to the values obtained during a calibration phase of the two models. From this run, we obtain a fuel mix and a technical progress for each sector and the energy prices. We run GEMINI-E3 with these values and we get the GDP and the industrial outputs that are used by some demand functions to generate new useful demands D1 . Then, we run again TIMES and so forth until the convergence of 23
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CHAPTER 5. COUPLING METHODOLOGY
the demands. We compute a criteria which corresponds to the norm of the difference of the demands in iteration k and the demands in iteration k − 1. The procedure stops if the norm is smaller than fixed . The algorithm is summarized in Figure 5.1 and Table 5.1.
fuel mixes technical progress energy prices
?
GEMINI-E3
ETSAP-TIMES 6 -
industrial outputs GDP
DEMAND FUNCTIONS
useful demands
Figure 5.1: Coupling framework
Table 5.1: Coupling Algorithm 1. Set first demands D0 Set k = 0 2. Run TIMES with demands Dk Get fuel mixes Fk , energy prices Pk and technical progress θk 3. Run GEMINI-E3 with Fk , Pk and θk Get GDP and industrial outputs Compute demands Dk+1 4. Compute criteria γ = kDk+1 − Dk k 5. Increment k 6. If γ ≥ then go to 2.
5.2. COUPLING REGIONS
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Table 5.2: Coupling Regions USA Canada Mexico Rest of america Western Europe Eastern Europe Former USSR Africa Australia + New Zealand India South Corea China Japan Middle-East Rest of Asia
5.2
Coupling Regions
The coupling regions correspond to the TIMES regions, as presented in Table 5.2.
5.3
Connecting productions sectors and demands
First, Table 5.3 enumerates the production sectors of GEMINI-E3 model.
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CHAPTER 5. COUPLING METHODOLOGY Table 5.3: GEMINI-E3 sectors #
Designation
1 2 3 4 5
Coal Oil Gas Petroleum Products Electricity
6 7 8 9 10 11 12 13 14 15 16 17 18
Agriculture Forestry Mineral Products Chemical, rubber, Plastic Metal and Metal products Paper products publishing Transport nec Sea Transport Air Transport Consuming goods Equipment goods Services Dwellings
19
Household
The five first sectors produce commodities : coal, oil, gas, petroleum products and electricity. In TIMES, these sectors are not connected with useful demands but rather with commodities. Table 5.4 details these connections. Notice that no-carbon emitters commodities are not explicitly represented by a sector. Table 5.4: Connections between TIMES commodities and GEMINI-E3 sectors TIMES Commodities
Connection with #
Coal Crude oil Natural gas Liquified natural gas Refined oil electricity non fossil-fuels commodities (biomass,solar,wind,. . . )
1 2 3 3 4 5 —
5.3. CONNECTING PRODUCTIONS SECTORS AND DEMANDS
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Connections between TIMES useful demands and GEMINI-E3 sectors are shown in Table 5.5. The household sector which is not a production sector is connected with the useful demands in autos, two wheelers and residential facilities. The transportation, residential, commercial segments of useful demands are then easily connected. The agriculture corresponds to the GEMINI-E3 sectors agriculture and forestry. The industrial segments is less evident to connect (except for the chemicals and paper ones). The demand in metal and non-metal products is connected to the sectors 10 and 8 respectively. Others industries useful demands are linked with the sectors of consuming and equipment goods production. The “dwelings” sector is not connected since it doesn’t consume any energy commodities.
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CHAPTER 5. COUPLING METHODOLOGY
Table 5.5: Connections between TIMES useful demands and GEMINI-E3 sectors TIMES useful demands
Code(s)
Unit
#
TRT TRB TRL TRC TRM TRH TRW TRE TAI TAD TTF TTP TWD PJ/year NEU
Billion vehicle-km/year Billion vehicle-km/year Billion vehicle-km/year Billion vehicle-km/year Billion vehicle-km/year Billion vehicle-km/year Billion vehicle-km/year Billion vehicle-km/year PJ/year PJ/year PJ/year PJ/year PJ/year TWI PJ/year
19 12 12 12 12 12 19 12 14 14 12 12 13 13 12
RH1, RH2, RH3, RH4 RC1, RC2, RC3, RC4 RWH RL1, RL2, RL3, RL4 RK1, RK2, RK3, RK4 RRF RCW RCD RDW PJ/year ROT
PJ/year PJ/year PJ/year PJ/year PJ/year PJ/year PJ/year PJ/year PJ/year REA PJ/year
19 19 19 19 19 19 19 19 19 19 19
CH1, CH2, CH3, CH4 CC1, CC2, CC3, CC4 CHW CLA CCK PJ/year COE COT
PJ/year PJ/year PJ/year PJ/year PJ/year CRF PJ/year PJ/year
17 17 17 17 17 17 17 17
AGR
PJ/year
6,7
IIS INF ICH ILP INM IOI
Millions tonnes/year Millions tonnes/year PJ/year Millions tonnes/year PJ/year PJ/year
ONO
15,16
Transportation segments (15) Autos Buses Light trucks Commercial trucks Medium trucks Heavy trucks Two wheelers Three wheelers International aviation Domestic aviation Freight rail transportation Passengers rail transportation Internal navigation International navigation (bunkers) Non-energy uses in transport Residential segments (11) Space heating Space cooling Hot water heating Lighting Cooking Refrigerators and freezers Cloth washers Cloth dryers Dish washers Miscellaneous electric energy Other energy uses Commercial segments(8) Space heating Space cooling Hot water heating Lighting Cooking Refrigerators and freezers Electric equipments Other energy uses Agriculture segment (1) Agriculture Industrial segments (6) Iron and steel Non ferrous metals Chemicals Pulp and paper Non metal minerals Other industries Other segment (1) Other non specified energy consumption
10 10 9 11 8 15,16
5.4. DATABASE COMPARISON
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Coupling commodities and sectors To summarize, we define two sets for the coupled model: commodities and economy sectors. They are defined in Table 5.6. The corresponding production sectors are indicated between parenthesis. Table 5.6: Definition of sets for the coupled model Commodities (5) COAL COIL CGAS CPET CELE
Coal (1) Crude Oil (2) Gas (3) Refined Petroleum Products (4) Electricity (5) Economy sectors (11)
AGRI MINE CHEM META PAPE TRAN SEAT AIRT CONS SERV HOUS
5.4
Agriculture and forestry (6,7) Mineral Products (8) Chemical, rubber, Plastic (9) Metal and Metal products (10) Paper products publishing (11) Transport nec (12) Sea Transport (13) Air Transport (14) Consuming & Equipment goods (15,16) Services (17) Household (19)
Database comparison
After establishing the classification mapping between GEMINI-E3 and TIMES, we compare in this section the fuel shares of the different sectors described by the two models. We do this comparison for the reference year of the two models: 2001 concerning GEMINI-E3 and 2005 for TIMES. We compute, for each sector and each region, an indicator Ii,r giving the squared euclidian distance between the two models data, this indicator is equal to :
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CHAPTER 5. COUPLING METHODOLOGY
Ii,r =
X j
j αi,r − βi,r
2
(5.1)
j
where i, r, and j stand for sector, region, and fuel (coal, crude oil, natural gas, j petroleum products and electricity) respectively. αi,r represents the GEMINI-E3 j fuel share in percentage and βi,r the TIMES fuel share in percentage. The table 5.7 gives the mean by sector of this indicator. Appendix A gives this indicator by sector and region. Table 5.7: Mean by sector of the Indicator Ii,. Electricity Agriculture Mineral Products Chemical Metal Products Paper Products Transport nec Sea Transport Air Transport Consuming goods and equipment goods Services Households
0.06 0.02 0.08 0.66 0.21 0.26 0.02 0.02 0.03 0.23 0.10 0.03
Chapter 6 Implementation in GEMINI-E3 6.1
Domestic production in the standard version
Figure 6.1 represents the structure of the production sector in GEMINI-E3. Production technologies are described through nested CES functions.
Aggregated inputs Xir is realized with four aggregated inputs : capital (Kir ), labor (Lir ), energy (Eir ), and material (M Air ). Demand for these factors are then equal to:
Kir ·
kt θir
Lir ·
l t θir
Eir ·
et θir
"
= Xir · λir ·
k αir
P Dir · k −t P Kir · λir · θir
#σir
(6.1)
P Dir l = Xir · λir · αir · l −t P Lir · λir · θir
#σir
"
#σir
"
= Xir · λir ·
e αir
P Dir · e −t P Eir · λir · θir
31
(6.2)
(6.3)
32
CHAPTER 6. IMPLEMENTATION IN GEMINI-E3 Total demand σx Import
Domestic production
σi
σpf σpp
Fixed factor
Crude oil
Other factors σ
Material
Energy
Capital
Labor
σe
σmm Transport
Other inputs
Fossil energy
σt
σm
σf e
Electricity
Inputs 12-14 Inputs 6-11 & 15-18 Inputs 1-4 Figure 6.1: Structure of the Production Sector in GEMINI-E3
"
M Air ·
mt θir
= Xir · λir · (1 −
k αir
−
l αir
−
e αir )
P Dir · m −t P Mir · λir · θir
#σir
(6.4)
k l e m where θir , θir , θir and θir represent the technical progress incorporated respectively in capital, labor, energy and material.
Energy consumption by sector Demand for energy (Eir ) is allocated between aggregate fossil fuel consumption (EFir ) and electricity (IC5ir ): "
EFir = Eir ·
λeir
·
ee αir
P Eir · e λir · P EFir
#σ e
ir
(6.5)
6.2. INTEGRATION OF FUEL-MIX COMING FROM TIMES IN THE NON FOSSIL FUEL SECTORS 33 "
IC5ir = Eir ·
λeir
· (1 −
ee αir )
P Eir · e λir · P IC5ir
#σe
ir
(6.6)
and demand for each fuel through another CES function : "
ICkir = EFir ·
6.2
λef ir
·
ef αkir
P EFir · ef λir · P ICkir
#σef ir
∀k = 1, 2, 3, 4
(6.7)
Integration of fuel-mix coming from TIMES in the non fossil fuel sectors
Rewriting CES functions ef e We assume that the elasticities σir and σir are equal to zero. The CES functions become then Leontieff functions with fixed coefficients. We can rewrite the equations 6.5, 6.6 and 6.7:
ee EFir = αir · Eir
(6.8)
ee IC5ir = (1 − αir ) · Eir
(6.9)
ef ICkir = αkir · EFir ∀k = 1, 2, 3, 4
(6.10)
ef ee where αir is the share of fossil fuel energy in energy consumption, and αkir are the shares of each fossil fuel energy. These share are determined by the model TIMES.
Introducing the fuel mix We use the parameters ψkir which link energy consumption in monetary units (ICkir ) to energy consumption in ton oil equivalent (IOEN ERkir ) in the GEMINI-
34
CHAPTER 6. IMPLEMENTATION IN GEMINI-E3
E3 model. These parameters are computed on the reference year (2001) and are equal to:
ψkir =
IOEN ERkir ∀k = 1, 2, 3, 4, 5 ICkir
(6.11)
ef ee and αkir on the basis of the fuel mix Fkir in We compute the parameters αir petajoule coming from TIMES:
F5ir ee αir = 1 − Pψ5ir Fkir
(6.12)
k ψkir
ef αkir
=
Fkir ψkir P Flir l ψlir
∀k = 1, 2, 3, 4,
(6.13)
Introducing the technical progress on energy The next step is to modify the technical progress associated to the energy goods which determine the amount of energy needed for the production activity. This e on the basis of TIMES runs (see secis done by computing the parameter θir tion 3.2).
Calibration of CES function For the base year of the GEMINI-E3 model it is necessary to recalibrate the CES functions of the aggregated inputs. Effectively taking into account the fuel mix of the TIMES model, modifies the value of the total energy consumption (i.e. P Eir · Eir ) and this requires to recompute the parameters of the equations 6.1, 6.2, 6.3 and 6.4. We suppose that the variation of energy consumption in respect to the baseline figures is compensated by variation of labor remuneration in order to maintain the value of the domestic production constant. k l e We recompute the coefficients αir , αir , αir , λir on the basis of the set of data [Kir , Lir , Eir , M Air , P Kir , P Lir , Eir , M Air ] where Eir and Lir are the variables which have been modified.
6.3. REPRESENTATION OF FOSSIL FUEL SECTORS
6.3
35
Representation of fossil fuel sectors
In the GEMINI-E3 model the fossil fuel production (coal, crude oil and natural gas) is represented through the nested CES functions represented in the figure 6.1. In the coupling procedure, we suppose that the prices of fossil fuel energy are determined by the TIMES model which take into account a very detailed representation of energy resources and extraction costs. It is why the prices of fossil fuel energy in GEMINI-E3 (respectively P DP F1r , P DP F2r and P DP F3r ) are exogenous and fixed by TIMES. In GEMINI-E3, we suppose in this way that substitution between the fix factor (F Fir ) and the other factors (ENir , M Air , KAVir and LAVir ), is equal to zero (σpf = 0) and we compute the remuneration of the fix factor in order to balance the inputs remuneration on the basis of the exogenous production price:
F Fir · P Fir = P DP Fir · XDP Fir − ENir · P Eir − M Air · P Mir −KAVir · P Kir − LAVir · P Lir ∀i = 1, 2, 3
6.4
Rewriting Household consumption
In the standard version of GEMINI-E3, the household consumption is represented by a Linear Expenditure System, this formulation is not enough flexible and it was decided to replace this formulation by a nested CES function. The figure 6.2 represents the nested CES architecture retained. Total consumption is divided into three aggregated consumptions: housing, transportation and other consumption. Housing is again splited between energy and equipment. This energy nest excludes purchases of transport fuel included in the transportation nest. Equipment represents household consumption for housing minus energy consumption. Concerning transportation consumption we distinguish own-supplied transport and purchased transport (water travel, air travel and land travel). Own-supplied transport are provided using energy and equipment (mainly purchases of vehicles). Finally, the others consumptions are described through a CES function between the 10 non energy goods (agriculture, forestry, mineral, ...).
36
CHAPTER 6. IMPLEMENTATION IN GEMINI-E3
Total Consumption
Housing
Energy Equipment
Transport
Purchased
Others
Private
... AgricultureMineral Forestry Services
Equipment Coal Natural Gas Electricity Air Energy Oil Petroleum Sea Other
Coal
Petroleum Electricity Natural gas Oil
Figure 6.2: Structure of the Household Sector in GEMINI-E3
Chapter 7 Implementation in TIMES 7.1
Extracting sectoral technical progress and fuel mixes
TIMES outputs are very disaggregated, so the extraction essentially consists in summing up values following the connections established in Table 5.5 and the energy classifications of The International Energy Agency at http://www. iea.org/Textbase/stats/defs/defs.htm. The consumed commodities are aggregated into coupling commodities as shown in Table 7.1 where the symbols * stands for the prefixes “AGR”, “COM”, Table 7.1: Consumed commodities aggregation Description
Coupling
TIMES nomenclature
Coal Crude oil Gas Petroleum products
COAL COIL CGAS CPET
Electricity Uranium Hydraulic Others
CELE CURA CHYD COTH
*COA, INDCOG, INDCOK INDCRD *NGA, INDNGL *DST,*GSL,*HFO,*KER,*LPG, ELCCGO, ELCGOI, INDOIL, INDETH, INDNAP, INDPTC, TRAAVG, TRAETH, TRAJTK, TRAMET *ELC, INDSTM ELCNUC, INDNUC ELCHYD, INDHYD *BIO,*GEO,*HET, *SOL, ELCBGS, ELCBMU, ELCCRP, ELCSLD, ELCTDL, ELCWIN, INDBFG, INDOXY, INDTDL, INDWIN
37
38
CHAPTER 7. IMPLEMENTATION IN TIMES
“ELC”, “IND”, “RES” and “TRA”. Table 7.2 shows the processes which produce a commodity. Table 7.2: Processes aggregation by output Coupling
TIMES nomenclature
AGRI MINE
AGR000 INF000 (Non-Ferrous), INM000 (Non-Metals), ICH000 (Chemical) IIS000 (Iron & Steel) ILP (Pulp & Paper) TRB*, TRC*, TRE*, TRH*, TRL*, TRM*, TTF000, TTP000 TWD000, TWI000 TAD000, TAI000 IOI000 (Other Industries), ONO000 CC*,CH*,CLA*,COE*,COT*,CRF* RC*, RDW, REA, RH*, RK*, RL*, ROT, RRF, TRT, TRW E*, CHP*
CHEM META PAPE TRAN SEAT AIRT CONS SERV HOUS ELEC
Table 7.3 shows the processes which consume commodities by sector. Table 7.3: Processes aggregation by input Coupling
TIMES nomenclature
AGRI MINE
AGR000 IENF*, IMNF*, IONF*, IPNF*, ISNF* (Non-Ferrous), IENM*, IMNM*, IONM*, IPNM*, ISNM* (Non-Metals) IECH*, IMCH*, IOCH*, IPCH*, ISCH* (Chemical) IEIS*, IFIS*, IMIS*, IOIS*, IPIS*, ISIS* (Iron & Steel) IELP*, IMLP*, IOLP*, IPLP*, ISLP* (Pulp & Paper) TRB*, TRC*, TRE*, TRH*, TRL*, TRM*, TTF000, TTP000 TWD000, TWI000 TAD000, TAI000 IEOI*, IMOI*, IOOI*, IPOI*, ISOI* (Other Industries), ONO000 CC*,CH*,CLA*,COE*,COT*,CRF* RC*, RDW, REA, RH*, RK*, RL*, ROT, RRF, TRT, TRW E*, CHP*
CHEM META PAPE TRAN SEAT AIRT CONS SERV HOUS ELEC
7.2. COMPUTING COUPLING VARIABLES
7.2
39
Computing coupling variables
Let be the following extracted data: • conso_com(r, s, c, p): consumption of the commodity c of the sector s for region r in period p. • conso(r, s, p): total consumption of the sector s for region r in period p. • prod(r, s, p): total production of the sector s for region r in period p. • eff (r, s, p): efficiency of the sector s at period p for region r. The fuel mix share is obtained by sector in value (PJ) with this expression: F (r, s, c, p) =
conso_com(r, s, c, p) , conso(r, s, p)
(7.1)
Economy sector efficiency is obtained by : eff (r, s, p) =
prod(r, s, p) . conso(r, s, p)
(7.2)
Technical progress is the ratio of the sector efficiency at period p over the base year sector efficiency (2005). θ(r, s, p) =
7.3
eff (r, s, p) . eff (r, s, 2005)
(7.3)
Useful Demands Specification
In TIMES, Useful demands are built from socioeconomic hypotheses. Thus, each useful demand depends to a specific economic driver such as population, number of households, GDP, GDP per capita. . . The useful demands DM are computed as following DM(d, r, t) = DR(d, r, t)elas(d,r,t)
(7.4)
where, DM(d, r, t) is the useful demands for the energy service d, for the region r and the period t, DR is the economic driver and elas is the elasticity of the
40
CHAPTER 7. IMPLEMENTATION IN TIMES
demand to the driver. GEMINI-E3 sectors and TIMES sectors are not exactly the same, the equivalences used to compute the TIMES useful demands from the coupling sectorial outputs are shown in Table 7.4. GEMINI-E3 computes Table 7.4: Relation between useful demands and economy drivers Useful demands
Coupling sector/Driver
NEO, NEU, ONO, TAD, TAI, TRC, TRH, TRM, TTF, TWD, TWI RL1, RL2, TRT, RL3, RL4, RCW∗ , ROT∗ , RRF∗
GDP
AGR
AGRI
ICH
CHEM
IIS, INF
META
ILP, INM
MINE + PAPE
IOI
CONS
CC1,CC2,CC3,CC4 CCK, CH1, CH2, CH3, CH4 CHW, CLA, COE, COT, CRF
SERV
∗
For a selection of countries only
the GDP and the sectorial economy outputs (aggregated as defined in Table 5.6) at each iteration k of the coupling. The useful demand I00 has no driver and the useful demands which are not listed in Table 7.4 because they depends to population growth. At each iteration k of the coupling, new useful demands are computed as follows DMk+1 (d, r, t) =
DRk+1 (d, r, t) DRk (d, r, t)
!elas(d,r,t)
× DMk (d, r, t)
(7.5)
Appendix A Indicator by sector and region The following tables contain the indicator Ir,i details.
41
Electricity XEU GEMINI-E3 Coal Oil Natural Gas Petroleum Products Electricity TIMES Coal Oil Natural Gas Petroleum Products Electricity Distance Indicator
USA
JAP
CAN
MEX
AUZ
IND
CHI
MID
ASI
LAT
AFR
EUR
FSU
58% 0% 23% 13% 7%
71% 0% 20% 6% 3%
37% 3% 38% 17% 5%
65% 0% 19% 11% 4%
13% 0% 29% 55% 3%
85% 0% 11% 1% 2%
90% 0% 5% 2% 3%
90% 0% 1% 5% 3%
14% 11% 47% 25% 3%
40% 0% 35% 22% 3%
15% 0% 40% 41% 4%
58% 0% 26% 13% 2%
58% 0% 26% 12% 4%
26% 0% 63% 8% 2%
38% 0% 9% 54% 0%
74% 0% 22% 4% 0%
30% 0% 58% 12% 0%
82% 0% 13% 6% 0%
4% 0% 11% 86% 0%
90% 0% 6% 4% 0%
86% 0% 3% 11% 0%
95% 0% 0% 4% 0%
15% 0% 53% 32% 0%
45% 0% 29% 26% 0%
17% 0% 48% 35% 0%
65% 0% 21% 14% 0%
63% 0% 26% 11% 0%
17% 0% 81% 3% 0%
0.23
0.00
0.05
0.04
0.13
0.01
0.01
0.00
0.02
0.01
0.01
0.01
0.00
0.05
Agriculture XEU GEMINI-E3 Coal Oil Natural Gas Petroleum Products Electricity TIMES Coal Oil Natural Gas Petroleum Products Electricity Distance Indicator
USA
JAP
CAN
MEX
AUZ
IND
CHI
MID
ASI
LAT
AFR
EUR
FSU
3% 0% 8% 70% 20%
0% 0% 0% 100% 0%
0% 0% 0% 98% 2%
0% 0% 12% 66% 22%
0% 0% 0% 75% 25%
1% 0% 0% 79% 20%
0% 0% 1% 0% 99%
26% 0% 0% 52% 21%
0% 0% 0% 75% 25%
1% 0% 0% 87% 12%
0% 0% 0% 85% 14%
5% 0% 0% 67% 28%
4% 0% 15% 65% 16%
1% 0% 5% 46% 48%
0% 0% 6% 81% 13%
0% 0% 21% 63% 16%
0% 0% 0% 98% 3%
0% 0% 15% 71% 15%
0% 0% 0% 77% 23%
0% 0% 0% 81% 19%
0% 0% 1% 5% 94%
0% 0% 0% 76% 24%
0% 0% 0% 81% 19%
0% 0% 0% 90% 10%
0% 0% 0% 86% 14%
0% 0% 0% 73% 27%
0% 0% 19% 68% 13%
0% 0% 6% 54% 40%
0.02
0.21
0.00
0.01
0.00
0.00
0.00
0.13
0.01
0.00
0.00
0.01
0.00
0.01
Mineral Products XEU GEMINI-E3 Coal Oil Natural Gas Petroleum Products Electricity TIMES Coal Oil Natural Gas Petroleum Products Electricity Distance Indicator
USA
JAP
CAN
MEX
AUZ
IND
CHI
MID
ASI
LAT
AFR
EUR
FSU
18% 0% 31% 32% 19%
28% 0% 34% 10% 28%
38% 0% 3% 39% 19%
7% 0% 32% 28% 33%
0% 0% 24% 44% 32%
15% 0% 24% 37% 24%
70% 0% 1% 17% 12%
66% 0% 1% 17% 16%
13% 0% 28% 42% 17%
59% 0% 5% 26% 10%
14% 0% 21% 46% 18%
23% 0% 6% 27% 44%
12% 0% 37% 31% 20%
16% 0% 42% 14% 29%
41% 0% 36% 11% 13%
64% 0% 27% 1% 8%
62% 0% 4% 19% 16%
84% 0% 9% 1% 6%
33% 0% 20% 40% 7%
45% 0% 40% 4% 11%
77% 0% 0% 5% 18%
85% 0% 0% 6% 9%
12% 0% 22% 28% 39%
77% 0% 3% 14% 6%
49% 0% 20% 22% 9%
68% 0% 19% 1% 12%
31% 0% 43% 10% 16%
36% 0% 45% 3% 17%
0.10
0.19
0.10
0.79
0.17
0.24
0.02
0.05
0.07
0.05
0.20
0.38
0.09
0.07
Chemical XEU GEMINI-E3 Coal Oil Natural Gas Petroleum Products Electricity TIMES Coal Oil Natural Gas Petroleum Products Electricity Distance Indicator
USA
JAP
CAN
MEX
AUZ
IND
CHI
MID
ASI
LAT
AFR
EUR
FSU
3% 0% 39% 37% 20%
4% 0% 35% 45% 17%
3% 0% 6% 78% 13%
0% 0% 42% 46% 12%
0% 0% 27% 55% 18%
4% 0% 59% 28% 9%
10% 0% 28% 51% 12%
24% 2% 11% 37% 25%
1% 0% 52% 44% 4%
8% 0% 22% 58% 11%
1% 4% 31% 52% 11%
30% 0% 36% 24% 10%
2% 0% 28% 52% 18%
3% 2% 46% 16% 33%
80% 0% 1% 0% 19%
77% 0% 0% 4% 19%
50% 0% 0% 0% 50%
77% 0% 0% 0% 23%
65% 0% 24% 3% 8%
80% 0% 0% 4% 16%
77% 0% 0% 0% 23%
79% 0% 0% 3% 18%
52% 0% 30% 5% 12%
58% 0% 4% 14% 24%
68% 0% 6% 7% 19%
73% 0% 11% 4% 12%
28% 0% 43% 6% 24%
55% 0% 11% 0% 33%
0.87
0.82
0.98
0.99
0.70
0.99
0.80
0.44
0.46
0.50
0.72
0.28
0.31
0.42
Metal Products XEU GEMINI-E3 Coal Oil Natural Gas Petroleum Products Electricity TIMES Coal Oil Natural Gas Petroleum Products Electricity Distance Indicator
USA
JAP
CAN
MEX
AUZ
IND
CHI
MID
ASI
LAT
AFR
EUR
FSU
6% 0% 18% 22% 54%
5% 0% 46% 12% 37%
3% 0% 11% 45% 40%
3% 0% 26% 14% 57%
0% 0% 38% 25% 37%
13% 0% 32% 14% 42%
37% 0% 3% 25% 35%
27% 0% 1% 42% 30%
4% 0% 33% 37% 26%
20% 0% 14% 30% 35%
10% 0% 26% 25% 40%
26% 0% 10% 18% 46%
8% 0% 27% 27% 39%
4% 0% 27% 16% 53%
53% 0% 19% 5% 23%
28% 0% 36% 4% 32%
48% 0% 12% 11% 29%
45% 0% 16% 3% 36%
54% 0% 32% 7% 7%
33% 0% 21% 14% 33%
60% 0% 0% 3% 37%
74% 0% 0% 6% 19%
10% 0% 18% 47% 24%
41% 0% 5% 20% 35%
45% 0% 11% 26% 18%
34% 0% 7% 24% 34%
38% 0% 24% 5% 33%
24% 0% 23% 6% 47%
0.35
0.07
0.33
0.24
0.41
0.06
0.10
0.36
0.04
0.06
0.20
0.02
0.14
0.05
Paper Products XEU GEMINI-E3 Coal Oil Natural Gas Petroleum Products Electricity TIMES Coal Oil Natural Gas Petroleum Products Electricity Distance Indicator
USA
JAP
CAN
MEX
AUZ
IND
CHI
MID
ASI
LAT
AFR
EUR
FSU
2% 0% 22% 19% 58%
8% 0% 40% 13% 38%
16% 0% 6% 37% 41%
0% 0% 25% 16% 59%
0% 0% 10% 32% 58%
14% 0% 37% 4% 45%
47% 0% 3% 7% 43%
53% 0% 0% 7% 39%
3% 0% 24% 55% 18%
26% 0% 7% 34% 33%
7% 0% 17% 32% 44%
9% 0% 13% 40% 38%
6% 0% 34% 10% 50%
15% 0% 56% 7% 23%
57% 0% 11% 1% 31%
79% 0% 7% 0% 13%
39% 0% 28% 5% 28%
55% 0% 9% 2% 35%
48% 0% 15% 15% 22%
62% 0% 6% 1% 31%
43% 0% 3% 0% 54%
77% 0% 2% 1% 21%
1% 0% 13% 22% 63%
34% 0% 5% 28% 33%
52% 0% 12% 33% 2%
19% 0% 55% 0% 26%
21% 0% 38% 1% 40%
13% 0% 49% 5% 33%
0.41
0.68
0.22
0.40
0.39
0.35
0.02
0.09
0.33
0.01
0.38
0.37
0.04
0.02
Transport nec XEU GEMINI-E3 Coal Oil Natural Gas Petroleum Products Electricity TIMES Coal Oil Natural Gas Petroleum Products Electricity Distance Indicator
USA
JAP
CAN
MEX
AUZ
IND
CHI
MID
ASI
LAT
AFR
EUR
FSU
0% 0% 3% 91% 6%
0% 0% 7% 91% 2%
0% 0% 0% 95% 4%
0% 0% 13% 84% 3%
0% 0% 0% 99% 1%
0% 0% 3% 94% 3%
0% 0% 0% 96% 3%
12% 0% 1% 84% 4%
0% 0% 0% 98% 1%
0% 0% 1% 98% 1%
0% 0% 4% 94% 1%
0% 0% 2% 96% 2%
0% 0% 1% 94% 5%
0% 0% 34% 54% 12%
0% 0% 0% 95% 5%
0% 0% 0% 99% 1%
0% 0% 0% 97% 3%
0% 0% 0% 100% 0%
0% 0% 0% 100% 1%
0% 0% 0% 98% 2%
0% 0% 0% 97% 3%
10% 0% 0% 88% 2%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 1% 99% 0%
0% 0% 0% 98% 2%
0% 0% 0% 97% 3%
0% 0% 0% 87% 13%
0.00
0.01
0.00
0.04
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.23
Sea Transport XEU GEMINI-E3 Coal Oil Natural Gas Petroleum Products Electricity TIMES Coal Oil Natural Gas Petroleum Products Electricity Distance Indicator
USA
JAP
CAN
MEX
AUZ
IND
CHI
MID
ASI
LAT
AFR
EUR
FSU
0% 0% 0% 97% 3%
0% 0% 1% 97% 2%
0% 0% 0% 99% 1%
0% 0% 9% 90% 1%
0% 0% 0% 100% 0%
8% 0% 2% 84% 6%
0% 0% 0% 97% 3%
0% 0% 0% 99% 0%
0% 0% 0% 99% 1%
0% 0% 0% 99% 0%
0% 0% 1% 98% 1%
0% 0% 1% 97% 2%
0% 0% 0% 98% 1%
0% 0% 31% 64% 6%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
4% 0% 0% 96% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0.00
0.00
0.00
0.02
0.00
0.02
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.23
Air Transport XEU GEMINI-E3 Coal Oil Natural Gas Petroleum Products Electricity TIMES Coal Oil Natural Gas Petroleum Products Electricity Distance Indicator
JAP
CAN
MEX
AUZ
IND
CHI
MID
ASI
LAT
AFR
EUR
FSU
0% 0% 0% 99% 0%
0% 0% 0% 99% 1%
0% 0% 12% 86% 2%
0% 0% 0% 99% 1%
0% 0% 1% 99% 1%
0% 0% 0% 97% 3%
0% 0% 0% 99% 1%
0% 0% 0% 99% 1%
0% 0% 0% 99% 1%
0% 0% 3% 95% 2%
0% 0% 2% 97% 2%
0% 0% 1% 98% 2%
0% 0% 35% 57% 8%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0% 0% 0% 100% 0%
0.00
0.00
0.00
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.31
g goods and equipment goods XEU GEMINI-E3 Coal 4% Oil 0% Natural Gas 31% Petroleum Products 25% Electricity 40% TIMES Coal 86% Oil 0% Natural Gas 14% Petroleum Products 0% Electricity 0% Distance Indicator
USA
0% 0% 1% 95% 4%
USA
0.94
JAP
CAN
MEX
AUZ
IND
CHI
MID
ASI
LAT
AFR
EUR
FSU
5% 0% 38% 14% 43%
5% 0% 13% 30% 52%
1% 0% 50% 22% 26%
0% 0% 7% 36% 57%
13% 0% 29% 9% 49%
14% 0% 5% 33% 48%
41% 0% 2% 18% 39%
6% 0% 29% 42% 23%
14% 0% 10% 35% 42%
5% 1% 17% 36% 41%
9% 0% 13% 43% 35%
4% 0% 37% 17% 42%
5% 0% 19% 7% 70%
88% 0% 11% 1% 0%
9% 0% 86% 5% 0%
100% 0% 0% 0% 0%
90% 0% 10% 0% 0%
100% 0% 0% 0% 0%
100% 0% 0% 0% 0%
98% 0% 0% 2% 0%
28% 0% 8% 64% 0%
91% 0% 6% 3% 0%
97% 0% 3% 0% 0%
99% 0% 1% 1% 0%
6% 0% 94% 0% 0%
76% 0% 24% 0% 0%
0.98
0.87
1.35
1.27
1.08
1.08
0.50
0.19
0.87
1.17
1.13
0.53
1.00
Services XEU GEMINI-E3 Coal Oil Natural Gas Petroleum Products Electricity TIMES Coal Oil Natural Gas Petroleum Products Electricity Distance Indicator
USA
JAP
CAN
MEX
AUZ
IND
CHI
MID
ASI
LAT
AFR
EUR
FSU
4% 0% 22% 26% 48%
1% 0% 35% 12% 53%
0% 0% 7% 55% 38%
0% 0% 36% 26% 38%
0% 0% 5% 47% 48%
3% 0% 18% 24% 55%
18% 0% 2% 9% 71%
23% 0% 2% 41% 34%
2% 0% 31% 43% 24%
10% 0% 9% 36% 44%
1% 1% 11% 28% 60%
4% 0% 8% 51% 36%
1% 0% 29% 23% 47%
3% 0% 37% 21% 39%
7% 0% 41% 8% 44%
1% 0% 35% 7% 58%
2% 0% 20% 34% 45%
0% 0% 45% 20% 35%
0% 0% 0% 51% 49%
2% 0% 18% 12% 68%
18% 0% 2% 0% 80%
13% 0% 9% 45% 33%
0% 0% 15% 17% 68%
0% 0% 14% 37% 48%
0% 0% 12% 14% 75%
11% 0% 1% 10% 78%
0% 0% 35% 18% 46%
2% 0% 65% 2% 31%
0.07
0.01
0.07
0.01
0.00
0.03
0.01
0.02
0.29
0.01
0.04
0.35
0.01
0.12
Households XEU GEMINI-E3 Coal Oil Natural Gas Petroleum Products Electricity TIMES Coal Oil Natural Gas Petroleum Products Electricity Distance Indicator
USA
JAP
CAN
MEX
AUZ
IND
CHI
MID
ASI
LAT
AFR
EUR
FSU
2% 0% 19% 37% 43%
0% 0% 23% 55% 22%
0% 0% 9% 62% 29%
0% 0% 27% 45% 28%
0% 0% 2% 83% 15%
0% 0% 15% 55% 30%
11% 0% 1% 69% 18%
38% 0% 6% 35% 22%
2% 0% 22% 57% 19%
4% 0% 13% 63% 21%
0% 0% 11% 65% 24%
3% 0% 7% 70% 21%
2% 0% 29% 46% 23%
6% 0% 36% 13% 45%
0% 0% 4% 88% 8%
0% 0% 1% 92% 7%
0% 0% 0% 93% 7%
0% 0% 1% 94% 5%
0% 0% 0% 98% 2%
0% 0% 0% 91% 9%
0% 0% 0% 91% 9%
0% 0% 2% 92% 6%
0% 0% 3% 85% 12%
0% 0% 1% 92% 7%
0% 0% 1% 92% 8%
0% 0% 0% 98% 2%
0% 0% 0% 95% 5%
0% 0% 8% 82% 10%
0.40
0.21
0.15
0.36
0.04
0.19
0.07
0.49
0.12
0.12
0.11
0.12
0.35
0.69
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