connecting you to the future developing a panel database 1970-2003 for global...

connecting you to the future

Developing a panel database 1970-2003 for global energy-environment-economy modelling

of climate change mitigation

Terry BarkerFaculty of Economics, University of Cambridge,And Cambridge Econometrics

November 2005


Outline

• Context: global climate-change mitigation

• Use of data in the modelling

• Data sources

• Data quality

• Conclusions


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


Engineering-Energy-Environment-Economy statistics and interactions

ECONOMYas in national

accounts

TECHNOLOGYspecifications &

costs

ENVIRONMENTALEMISSIONS

as in environmentalstatistics

ENERGYas in energy

statistics

damage to health and buildings

e.g. industrial emissions of SF6

funding R&D

pricesandactivity

low-carbonprocesses &products

feedback

energy-savingequipment etc

fuel use

pollution-abatementequipment


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. 543-573.


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 1971-2003 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)


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


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, 1970-2003.

• DG Economics and Finance: AMECO– Secondary data source, covers most world economic data, 1960-2006

– 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 1960-2001. Secondary data source for macro variables (eg exchange and interest rates).


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 ?


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


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


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


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


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

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