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ESRC grant RES-000-22-4083

Using the EFS/LCF for estimating CO2 emissions - methodological considerations Nick Bardsley, Milena Buchs and Sylke V. Schnepf RSS Workshop 5 July 2012. ESRC grant RES-000-22-4083. Background. A range of studies have used the EFS/LCF (UK) to estimate household emissions or policy impacts

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ESRC grant RES-000-22-4083

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  1. Using the EFS/LCF for estimating CO2 emissions - methodological considerationsNick Bardsley, Milena Buchs and Sylke V. SchnepfRSS Workshop 5 July 2012 ESRC grant RES-000-22-4083

  2. Background • A range of studies have used the EFS/LCF (UK) to estimate household emissions or policy impacts • E.g. Dresner/Ekins 2006; DEFRA 2008; Druckman/Jackson 2008, 2009; Baiocchi 2010; Preston et al. 2010, Fahmyet al 2011; Gough et al. 2011; • Cross-section data particularly important for examining the role of socio-economic factors and distributional issues • But so far no detailed discussion of methodological problems in the literature

  3. Methodological issues • Converting expenditure into emissions – and limitations • Comparing different methods of conversion • Infrequency of purchase • Need to discuss impact on estimation of total emissions, mean, median, measures of variance, regression coefficients, standard errors

  4. Converting expenditure to emissions • Our dataset: merged the Expenditure and Food Survey 2006 and 2007 with the Living Costs and Food Survey 2008 and 2009; total household sample size 24,446 • How can we convert expenditure into CO2 emissions? • Use external price statistics (home energy, motor fuels); passenger km (public transport) to estimate units of consumption. Apply DECC conversion factors to estimate CO2 • Use the Resources and Energy Analysis database (REAP) from the University of York/SEI to estimate CO2/£ expenditure for 56 COICOP consumption categories • We used a combination of both and also compared them

  5. Limitations • Expenditure ≠ consumption/emissions • E.g. home energy: no information on provider, tariff, etc.; price of transport tickets not strictly related to km travelled • External price data can account for some variation but are still relatively aggregated • “Product quality” problem – high income hh buy more expensive products  overestimation of emissions likely (see Girod/Haan 2010)

  6. Comparison of household CO2 emissions, 2006-2009 average

  7. Comparing estimates

  8. Infrequency of purchase • Infrequency of purchase • Very common problem but little discussion in lit on emissions • EFS/LCF collects data through diaries and a hhsurvey for less frequent expenditures • Expenditure during diary window is likely to over- or underrepresent consumption for a range of items • In theory, zero expenditure and higher-than-consumption expenditure cancel out • Totals, means and regression coefficients unaffected but percentiles, measures of variance and standard errors inflated

  9. Exploring the extent of infrequency of purchase • Home energy (5.8% zero) • Flights and public transport (59%/98.7% and 50.2% zero) • Motor fuels (36% zeros) (comparison with NTS) • Can we distinguish IoP from “true” zeros?

  10. Home energy • Home energy: prepayments for electricity and gas are collected through the diary, in contrast to all other payment methods • Home energy expenditure for oil, wood, coal, etc. are also collected through the diary, but only make up ca. 2% of sample • 8.08% and 7.03% of households have zero electricity or gas expenditure • 48% and 51% of households who pre-pay for electricity and gas have zero expenditure • 31% of households in the lowest income quartile are on prepayment meters

  11. Zero public transport and flights, 2007 Percentage of households in sample who have zero expenditure (EFS diary)/zero usage (NTS survey) in 2007

  12. Motor fuels • 18.2 % of households who have a vehicle do not have an expenditure on motor fuels in the EFS/LCF (2-week diary) • We use the National Travel Survey (NTS) to cross check: In the NTS, 39% of households who have a vehicle did not purchase motor fuel during the 1-week diary period • The NTS also records mileage driven through a survey  Only 0.1% of households who have a vehicle recorded zero miles travelled by car

  13. Range Distortion from Infrequency of Purchase Range 0-60,000kg Median: 2,500kg CO2 Range 0-60,000kg Median: 2,500kg CO2 CoV: 1.1 Range 0-100,000kg Median: 0kg CO2 CoV: 1.6 Range 0-100,000kg Median: 0kg CO2 National Travel Survey diary data National Travel Survey interviewdata

  14. “False” motor fuel zeros

  15. “False” motor fuel zeros

  16. Conclusions • Using expenditure surveys like the EFS/LCF has limitations for estimating CO2 emissions  but currently the only available option for examining the distribution of hh emissions and role of hh characteristics • Infrequency of purchase is unlikely to affect estimates for total and mean emissions but likely to affect order statistics and measures of variance which are important in the analysis of distribution/inequality • Improved data collection required for transport and some areas of home energy

  17. Limitations: product quality and price Source: Defra 2011 http://www.defra.gov.uk/statistics/foodfarm/food/familyfood/datasets/, authors’ calculations LCFS 2006 to 2009

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