connecting you to the future Developing a panel database for global energy-environment-economy modelling of climate change mitigation Terry Barker Faculty of Economics, University of Cambridge, And Cambridge Econometrics November 2005

connecting you to the future Outline Context: global climate-change mitigation Use of data in the modelling Data sources Data quality Conclusions

connecting you to the future Global climate-change mitigation IPCC: greenhouse gas emissions from human activity are likely to be responsible for climate change Since the problem and solution are global and long- run, the modelling should –Distinguish the large single-country emitters (US, China) and those promising early action within a classification covering all countries –Focus on the dynamic aspects (long-run development) The data set must cover energy, environment and economy (E3) variables in sectoral detail –Widespread use of energy, unevenly across sectors –fuels have different carbon contents –Many relevant technologies

connecting you to the future Engineering-Energy-Environment-Economy statistics and interactions ECONOMY as in national accounts TECHNOLOGY specifications & costs ENVIRONMENTAL EMISSIONS as in environmental statistics ENERGY as in energy statistics damage to health and buildings e.g. industrial emissions of SF6 funding R&D prices and activity low-carbon processes & products feedback energy-saving equipment etc fuel use pollution- abatement equipment

connecting you to the future Economic theory and data Prevailing theory: neoclassical general equilibrium with use of Computable General Equilibrium (CGE) models –The practice of drawing parameter estimates from literature is ad hoc which ones? short- or long-run estimates? With same data and same parameters, different functional forms yield different policy outcomes (McKitrick, 1998) –Functional forms are chosen for tractability and stable unique solutions –Time-series data are generally ignored Alternative theory: economic behaviour is institutional, with choices dominated by inertia and “satisficing” –Behaviour is highly place- and time-specific –Use of formal econometric techniques –Very data-intensive approach McKitrick, Ross R. (1998), ‘The econometric critique of applied general equilibrium modelling: the role of functional forms’, Economic Modelling, 15, pp

connecting you to the future E3 model at the Global level (E3MG) features Structural, econometric, dynamic, non-equilibrium, simulation E3 global model –projecting annually to 2020 and every 10 years to 2100 –20 world regions, 21 energy users, 12 energy carriers, 41 industries, 14 atmospheric emissions,… Use of time-series data with cointegration techniques to identify long-run trends Use of cross-section data –input-output tables for 2000 for industrial demands –bilateral trade flows for export and import weights –detailed emissions (various dates) for GHG and atmospheric pollutants (SO2, NOx, PM10)

connecting you to the future Availability of energy data: IEA and US EIA –IEA Energy balances Comprehensive coverage at broad level (power, industry, households, transportation) Many gaps at a more detailed level –IEA Energy prices and taxes Very partial data –US EIA Easy to use Full country coverage by geographical region Secondary source

connecting you to the future Availability of economic data University of Purdue: GTAP (Global Trade Analysis Project) –Global trade model with a database that contains bilateral trade info for over 40 countries for 50 sectors, 2001 OECD: STAN industry analysis –Main data source for OECD, covers many industrial variables, with detailed 2-digit level sectors, DG Economics and Finance: AMECO –Secondary data source, covers most world economic data, –Main data source for macro variables (eg exchange rates). World Bank: World Development Indicators (WDI) –Tertiary data source covers all countries and most economic data, some breakdowns, between Secondary data source for macro variables (eg exchange and interest rates).

connecting you to the future Availability of economic data: Conclusion: no suitable, up-to-date, global data base available GTAP6 for general equilibrium models but – for one year (2001) –sectors suitable for studies of tariffs –regions suitable for trade not environmental analysis –quality for some regions and dates? STAN for industry and R&D studies but –for OECD countries –selected (industrial) variables –no constant-priced trade data AMECO for macroeconomic data but –EU focus WDI comprehensive and incl. developing countries but –mainly aggregates –quality ?

connecting you to the future Social Accounting Matrices (including input-output tables) GTAP (Global Trade Analysis Project) –IO tables from varying dates adjusted to 2001 STAN –Consistent tables for 16 OECD countries, plus Brazil and China –From 1990s EUROSTAT –Estimated tables for 2000 –EU MSs

connecting you to the future Organising the data “Original” data collected from primary sources (e.g. OECD) Missing data interpolated from shares and totals Processed for E3MG definitions, conventions and classifications Stored as 2-dimensional matrices on several databanks –standard variable and parameter names –accessible by estimation and solution software –sector by year or sector by sector

connecting you to the future Problems in constructing the database comparability of data –across countries –over time –matching cross-section and time-series data order of precedence of sources quality of data missing data –for former Soviet Union countries before 1990 –missing series, e.g. employees in India –gaps in series

connecting you to the future Precedence for data sources STAN is the default preferred choice for data AMECO is preferred if no Stan-based matrix is available and for macro data: exchange and interest rates and taxes Eurostat and national sources are required for consumption WDI is used if nothing else is available

connecting you to the future Conclusions Massive exercise for a small research team Very variable quality of data –deteriorates as they go back in time –and for countries as per capita incomes fall Major problems with missing data –techniques developed to minimise errors of interpolation when totals available –not always possible e.g. former Soviet Union countries However, even with these problems, using time-series integrated with cross-section data is better than using just one-year’s data Urgently needed: improvements in coverage and quality –especially large emitting developing countries ie India –OECD STAN leading the way, but slow progress