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Energy and Environmental indicators in the WIOD System of Satellite Accounts

Energy and Environmental indicators in the WIOD System of Satellite Accounts Dataset version 1: Energy & Emissions to Air.

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Energy and Environmental indicators in the WIOD System of Satellite Accounts

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  1. Energy and Environmental indicators in the WIOD System of Satellite Accounts Dataset version 1: Energy & Emissions to Air Frederik Neuwahl and Aurélien Genty Institute for Prospective Technological Studies (IPTS), Seville, Spainhttp://ipts.jrc.ec.europa.euKurt KratenaAustrian Institute of Economic Research (WIFO), Vienna, Austriahttp://www.wifo.ac.at

  2. Outline • Scope and definition of satellite accounts covered • Available information and data reconciliation effort • Construction of Energy and Air Emission Accounts: methodology • Completion of vers. 1 dataset: discussion of results • Modelling: bridging monetary to physical information

  3. Energy-environment satellite accounts in WIOD Core indicators Energy use, 25 energy commodities including Oil and gas Electricity and heat Coal and coal derivatives Refinery products Renewables and waste Air emissions Global warming potential (CO2, N2O, CH4) Acidification potential (SO2, NOX, NH3) Tropospheric ozone formation potential (NOX, NMVOC, CO, CH4) Additional indicators Water consumption Land use Resource use

  4. Target: Energy & emissions satellite accounts NAMEA concept: framework fully compliant with the accounting conventions of the SUT system. Allows integrated economy-environment analysis/modelling NAMEA-AIR: emissions to air by pollutant and by sector NAMEA-E: energy use by energy commodity and by sector, with a range of coexisting concepts: net energy use, gross energy use, emission relevant energy use Different methodologies: energy first, inventory first Additionally: summary energy balances and energy supply

  5. The starting blocks Energy information is widely available in two main forms: IEA extended energy balances: ~100 flows, ~60 energy commodities. In TJ. Complemented by some (less rich) price information. Use table of the national accounts: ~60 sectors, ~4 energy-related commodities. In $. In addition to this basic data situation Some countries already publish energy NAMEA Wider availability of air emission NAMEA (inventory first); Eurostat provides either official NSI data or estimations for the whole range of EU countries.

  6. The Energy NAMEA DE 2000. Source: DESTAT

  7. Key task in the estimation of NAMEA-E Main issue: reconciling the classification mismatch between IEA balances and national accounts However, different approaches are possible: Enrich the use table with additional rows containing energy carrier use in physical unit; uses partial price information, but without allowing an immediate link between the entries of rows 10, 11, 23 and 40 and the physical flows. Full reconciliation between IEA balances and the monetary information of the use table. Requires the estimation of a full vector of energy prices by energy commodity, de facto is equivalent to the disaggregation of rows 10,11, 23 and 40 in a finer product classification, and to building the equivalent PIOT rows. Also, data inconsistencies would require changes either in the UT or in the EB

  8. From energy balances to NAMEA-E: General issues of concordance to be addressed Autoproduction of electricity Assignment of road transport Territorial vs. Residence principle; affects: Marine transport Aviation Road transport Tourism statistics Military use Extraterritorial organisations Splitting of non energy intensive sectors to target classification information in use table } International bunkering } } Stems from discordant conceptual definition of sector Equivalent problem in National accounts

  9. Alignment with official data Deviation of results from officially available data: NAMEA-E (DE, NL, AT, DK) Own estimation fully calibrated to official data at the available level of sector, energy commodity and time label Extrapolation to target sector, energy commodity and time series by deriving from own estimation growth indices and split shares Deviation of results from officially available data: NAMEA-AIR (all EU countries) Emission coefficients scaled to replicate official data of emissions by sector Or: statistical difference allowed (CO2: small range is physically possible)

  10. Results for energy : DEU 2000; total energy use by sector, vs. NAMEA-E

  11. Results for energy : Accuracy tested with NL, DE, AT, DK (countries that publish a NAMEA-E) Total values generally match within ~2% Deviations in energy intensive sectors and households are typically limited (less than 10%) Deviations in other sectors can be large in percentage, but not very influential in absolute value Deviations by individual energy commodity can be larger in percentage, especially with small absolute values In some cases the totals can have larger deviations (20%), due to energy information used by NSI different from that reported to IEA (vintage problem)

  12. CO2 emissions by country: comparison with official data

  13. CO2 emissions by sector: comparison with official data DEU 2000, kt CO2

  14. (continued)

  15. Results for CO2: Accuracy can be tested for most EU countries, against NAMEA-AIR data reported to Eurostat Total values generally match within few % Deviations in energy intensive sectors should again be small (less than 10%) In some cases the totals can have significant deviations, and the emissions from energy intensive sectors (power sector) can have large deviations (20%), unexplainable (cannot stem from wrong allocation: no process emissions or classification discordance) unless the energy information used by NSI is different from that reported to IEA. Data vintages issue.

  16. non-CO2 emissions: The modelling of emission coefficients is complex, combines parameters such as changes of fuel quality, combustion technology, end of pipe abatement techniques Use whenever possible official statistical sources for emissions by industry Draw on EXIOPOL experience for the modelling of emissions (explain emissions vs fuel substitution, technology)

  17. SOx vs energy (emitting fuels) inputs in EU countries: whole national economy (EU15 countries), trend

  18. SOx vs energy (emitting fuels) inputs in EU countries: SOx intensive industries, aggregate EU15, trend

  19. SOx vs energy (emitting fuels) inputs in EU countries: Basic metals sector, EU15 countries, year 2006

  20. Consistency between monetary (SUT) and physical (NAMEA energy) data Different data sets with identical or linked variables The consistency problem: Energy inputs from NAMEA energy (in TJ, 25 energy carriers) by NACE industry combined with energy prices (OECD/IEA) yield energy inputs  aggregating to CPA energy commodities  monetary energy inputs CPA*WIOD industry Energy inputs from SUT: monetary energy inputs CPA*WIOD industry

  21. Consistency between monetary (SUT) and physical (NAMEA Energy) data Consistency of classifications: energy commodities Energy data for 25 energy carriers in energy units by NACE industry  CPA/Energy * WIOD industry CPA 10: Coal (without coke) CPA 11: Crude Oil & Natural Gas; identification of crude oil by user, only 23 (refineries) uses crude oil CPA 23: Oil products & coke CPA 40: Electricity & Heat, gas distribution

  22. Consistency between monetary (SUT) and physical (NAMEA energy) data Comparison of energy inputs in monetary units: long term energy price increase

  23. Consistency between monetary (SUT) and physical (NAMEA energy) data Comparison of energy inputs in monetary units: ‘no problem’ case

  24. Consistency between monetary (SUT) and physical (NAMEA energy) data Comparison of energy inputs in monetary units: the post 2004 energy price boom

  25. Consistency between monetary (SUT) and physical (NAMEA energy) data Comparison of energy inputs in monetary units: the post 2004 energy price boom

  26. Consistency between monetary (SUT) and physical (NAMEA energy) data ‘Soft link’ between SUT (WP1/WIOD) and Energy satellite accounts (WP4/WIOD) Start with NAMEA energy, apply absolute prices (per energy unit) of 25 energy carriers, from Energy Prices & Taxes (OECD/IEA) Calculate NAMEA energy inputs in monetary units for CPA energy commodities by WIOD-industry (compare to SUT energy inputs in monetary units) Create a ‘soft link’ between - calculated (implicit) deflators of CPA energy commodities from NAMEA energy inputs in monetary units and in energy units - SUT energy deflators of CPA energy commodities

  27. Thank you for your kind attention

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