This page is a method.
The page identifier is Op_en3151
|Moderator:Heta (see all)|
Give your opinion to the peer rating of the content of this page.
The E3ME(Energy-Environment-Economy Model for Europe) model is a multi-country econometric macro-sectoral simulation model for EU-25 countries plus Norway and Switzerland. The model combines the features of an annual short- and medium-term sectoral model estimated by formal econometric methods. E3ME solves for a very detailed set of inter-region and inter-industry relationships. The econometric grounding of E3ME enables to address the short-term and medium-term economic effects of E3 policies as well as the long-term effects of such policies.
An in-depth treatment of changes in the input-output structure of the economy over the forecast period incorporates the effects of technological change, relative price movements and changes in the composition of each industry's output. The model has been used for general macro analysis and for a wide range of policy analyses including: greenhouse gas mitigation, incentives for industrial energy efficiency, transport policy, technological change, sustainable investment and sustainable household consumption.
The model provides economy forecasts to the year 2020 for 27 European regions including the EU25, for industry output, investment prices, exports, imports, employment and intermediate demand at a 42-industry level including 20 service industries, and for consumer expenditures in 28 categories.
- 1 Result
- 1.1 The advantages of the E3ME model lies in three areas:
- 1.2 Typical Model Applications:
- 1.3 Model structure:
- 1.4 Sectoral coverage:
- 1.5 Consumption categories:
- 1.6 Energy-environment module:
- 1.7 Dynamic structure:
- 1.8 Linkage between regions and countries:
- 1.9 Market Structure:
- 1.10 Main model results:
- 1.11 Required technical infrastructure:
- 1.12 Structure of Input Data and Data Sources:
- 1.13 Links to other Models, Projects, Networks:
- 1.14 Regional Scope:
- 2 See also
- 3 References
The advantages of the E3ME model lies in three areas:
- Model disaggregation. The detailed nature of the model allows it to represent fairly complex scenarios, in particular scenarios which are differentiated according to sector and to country. Similarly, the impact of policies can be represented in a detailed way.
- Econometric pedigree. The econometric grounding of the models gives it a better capability in representing and forecasting performance in the short to medium run. It therefore provides information which is closer to the time horizon of many policy makers than pure CGE models.
- E3 linkages. An interaction (two-way feedback) between the economy, energy demand/supply and environmental emissions is an undoubted advantage over other models which may either ignore the interaction completely or only assume a one-way causation.
The E3ME model has been developed under European Commission funding through different projects between 1995 and 2004. (Note that the NEMESIS model, by the Belgian Bureau Fédéral du Plan and Chambre de Commerce et d'Industrie de Paris, has been based on the earlier version of E3ME 2.0, with an earlier version of the data, a smaller set of industries (32 rather than the current 42), and with a more limited regional coverage of the old EU-15 Member States.) The E3ME version 4.0 of the model includes induced technological change, a more elaborate treatment of the EU Emission Trading Scheme and a full treatment of Structural Indicators, as defined by the European Commission.
Typical Model Applications:
- Macro top-down and industrial bottom-up simulation analysis of the economy, allowing industrial factors to influence macroeconomic variables.
- Dynamic multiplier analysis, illustrating the response of the main economic indicators, industrial outputs and prices to standard changes in the assumption, e.g. changes in world oil prices, income taxes, government spending, and exchange rates.
- Scenario analysis (differentiated according to sector and to country), across a range of greenhouse gas mitigation and energy policies at the European level, including carbon taxes and permit trading.
- Analysis of long-term structural change in energy demand and supply and in the economy focused on the contribution of research and development, and associated technological innovation, on the dynamics of growth and change.
- Providing short- and medium-term economic and industrial forecasts for business and government based on a system of dynamic equations estimated on annual data and calibrated to recent outcomes and short-term forecasts.
E3ME comprises the accounting balances for commodities from input-output tables, for energy carriers from energy balances and for institutional incomes and expenditures from the national accounts. It includes environmental emission flows and 20 sets of time-series econometric equations. These can be grouped by:
- aggregate energy demands (1 set of equations)
- fuel substitution equations for coal, heavy oil, gas and electricity (4 set of equations)
- commodity exports and imports (4 set of equations)
- total consumers' expenditure (1 set of equations)
- disaggregated consumers' expenditure (1 set of equations)
- industrial fixed investment (1 set of equations)
- industrial employment (1 set of equations)
- industrial hours worked (1 set of equations)
- labour participation (1 set of equations)
- industrial prices (1 set of equations)
- export and import prices (2 set of equations)
- industrial wage rates (2 set of equations)
- residual incomes (2 set of equations)
- investment in dwellings (2 set of equations)
Each set of equation is estimated for the corresponding disaggregation level. For example, Employment is estimated in each region for each of the 41 E3ME 4.0 industries. This produces a rich set of econometrically estimated parameters: 558,647 parameters in a total of 17,015 equations. Each of the set of equations is estimated using panel-data techniques on time-series/cross-section data. Energy supplies and population stocks and flows are treated as exogenous.
(1) Agriculture etc, (2) Coal, (3) Oil & Gas etc, (4) Other Mining, (5) Food, Drink & Tobacco, (6) Textiles, Clothing & Leather, (7) Wood & Paper, (8) Printing & Publishing, (9) Manufactured Fuels, (10) Pharmaceuticals, (11) Chemicals nes, (12) Rubber & Plastics, (13) Non-Metallic Mineral Products, (14) Basic Metals, (15) Metal Goods, (16) Mechanical Engineering, (17) Electronics, (18) Electrical Engineering & Instruments, (19) Motor Vehicles, (20) Other Transport Equipment, (21) Manufacturing nes, (22) Electricity, (23) Gas Supply, (24) Water Supply, (25) Construction, (26) Distribution, (27) Retailing, (28) Hotels & Catering, (29) Land Transport etc, (30) Water Transport, (31) Air Transport, (32) Communications, (33) Banking & Finance, (34) Insurance, (35) Computing Services, (36) Professional Services, (37) Other Business Services, (38) Public Administration & Defence, (39) Education, (40) Health & Social Work, (41) Miscellaneous Services, (42) Unallocated.
28 consumption goods
(1) Food, (2) Drink, (3) Tobacco, (4) Clothing and Footwear, (5) Gross Rent and Water, (6) Electricity, (7) Gas, (8) Liquid Fuels, (9) Other Fuels, (10) Furniture etc., (11) Household Textile etc., (12) Major Appliances, (13) Hardware, (14) Household Operation, (15) Domestic Services, (16) Medical Care etc., (17) Cars etc, (18) Petrol etc., (19) Rail Transport, (20) Buses and Coaches, (21) Air Transport, (22) Other Transport, (23) Communication, (24) Equipment etc., (25) Entertainment etc, (26) Exp. Rest and Hotel, (27) Misc. Goods and Services, (28) Unallocated.
E3ME considers interactions between the economy, energy demand/supply and environmental emissions. Energy and environmental industries and fuel types are highly disaggregated. The emissions captured are: CO2, SO2, NOx, CO, methane (CH4), black smoke, VOC, nuclear - air, lead - air, CFCs, N20, HFCs, PFCs, and SFCs.
- Engle-Granger cointegration with error-correction dynamic models.
- Annual forecasts to the year 2020.
- Technical progress is measured by cumulative gross investment and data on R&D expenditure. Technology change in industry input-output coefficients through logistic growth.
Linkage between regions and countries:
For most commodities there is a European `pool' into which region supplies part of its production and from which each region satisfies part of its demands. The demand for a region's exports of a commodity depends on the relative prices, on an activity level of the main external EU export markets, and on the domestic demand for the commodity in all other EU regions, weighted by their economic distance.
- Imperfect monopolistic competition
- Firms set prices (mark-ups over marginal costs)
- Demand for employment partially adjusts to output growth, costs of labour and technology index.
- Participation rate in the labour force depend on reservation wage.
- Wage-setting decisions are depend on union activities across different regions of Europe. Unions choose wage rates to maximise utility subject to the labour-demand constraint.
- Hours worked adjust ot technological change. Other adjustments to hours worked arise from short-run output adjustments.
Main model results:
Macro economic results (EU-wide and country level):
- Industry output, investment prices, exports, imports, employment, intermediate demand
- Consumer expenditure
Sectoral economic results (EU-wide and country level):
- Industry output, investment prices, exports, imports, employment, intermediate demand (in 41 categories)
- Consumer expenditure (in 28 categories)
- GHG emissions by energy-using sectors (21) and energy carriers (12)
- SO2, NOx, PM10 by energy-using sectors (21) and energy carriers (12)
Required technical infrastructure:
Construction and solution use the software package IDIOM, while the stochastic parameters are estimated using a general-to-specific (GETS) model estimation software designed in Ox.
Structure of Input Data and Data Sources:
- Exogenous data: world (extra European) data, population stocks and flows, economic policies (e.g. tax rates)
- Parameter matrices, such as the parameters of the investment functions
- Classification converters (sparse matrices)
- Calibration values, lagged values, initialisation values
- Data sources: EUROSTAT, OECD/IEA, OECD/STAN, IMF, QUEST
Links to other Models, Projects, Networks:
- The Transition to Sustainable Economic Structures Network (TRANSUST).
- Other projects funding the development of E3ME: SEAMATE, TIPMAC, COMETR.
- Joint simulation with the EU transport models SCENES and ASTRA
- Links to models MDM and E3MG.
E3ME 4.0: 27 European regions corresponding to EU-25 countries, Norway and Switzerland.
E3ME 3.3: 19 European regions: EU-15 countries, Norway, Switzerland, Germany (divided into east and west) and Italy (divided into north and south)
- Cambridge Econometrics, Covent Garden CB1 2HS, UK
- More information
- Full text of Barker, Terry and Knut Einar Rosendahl (2000)
- JRC: IA TOOLS. Supporting inpact assessment in the European Commission. 
European Commission DG XII (1995), E3ME An Energy-Environment-Economy Model for Europe, EUR 16715EN, Brussels.
Barker, Terry, Sebastian de-Ramon, Ben Gardiner and Hector Pollitt (2004), An Energy-Environment-Economy Model for Europe: E3ME Version 3.1 (E3ME31) MODEL DESCRIPTION, Cambridge Econometrics, March 2004.
Barker, Terry and Knut Einar Rosendahl (2000), Ancillary Benefits of GHG Mitigation in Europe: SO2, NOx and PM10 reductions from policies to meet Kyoto targets using the E3ME model and EXTERNE valuations, Ancillary Benefits and Costs of Greenhouse Gas Mitigation, Proceedings of an IPCC Co-Sponsored Workshop, March, OECD, Paris.
Barker, Terry (1999), Achieving a 10% cut in Europe's carbon dioxide emissions using additional excise duties: coordinated, uncoordinated and unilateral action using the econometric model E3ME, Economic Systems Research, Vol. 11, No. 4: 401-421.
Barker, T S (1998), Use of energy-environment-economy models to Inform greenhouse gas mitigation policy, Impact Assessment and Project Appraisal, Vol. 16, No. 2: 123-131.
Barker, T S, Ekins, P and N Johnstone (1995), Global Warming and Energy Demand, Routledge, London.
Bruvoll, A, Ellingsen, G and K E Rosendahl (2000), Inclusion of 6 greenhouse gases and other pollutants into the E3ME model, Statistics Norway.
de-Ramon, Sebastian and Richard Lewney (2004), Macroeconomic and Structural Impacts of IST, International Journal of Technology, Policy and Management (IJTPM), forthcoming.