Intergenerational Emissions Inequality in Germany: Are the Younger Generations more Environmentally Conscious?

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1 Intergenerational Emissions Inequality in Germany: Are the Younger Generations more Environmentally Conscious? Dragana Nikodinoska 33 RD USAEE/IAEE North American Conference: The Dynamic Energy Landscape Pittsburg, 26 th October,

2 Overview The research question and motivation Similar research and results Data and methodology Empirical results Conclusion and further work 2

3 The research question Investigating the determinants of residential CO 2 emissions in Germany: Cohort (year of birth effects) Age Period Income Education Urban density Other controls such as household size, dwelling size, car ownership, electric appliances, etc. 3

4 Motivation The household sector is the second largest emitter of CO 2, after the industry sector responsible for 24% of total CO 2 emissions in Germany Realistic predictions of emissions will help in calibrating policies compatible with Germany s GHG emissions targets 40% reduction by 2020 relative to 1990 levels 55% reduction by 2030 Investigating the emissions from different energy sources can be helpful in designing carbon mitigation policies these policies should not increase income inequality or energy poverty among the households 4

5 Similar research and previous findings Hargreaves et al. (2013) find that CO 2 emissions are strongly correlated with income: the richest 10 percent emit three times more than the poorest 10 percent in Great Britain find the following factors associated with higher emissions: multi-adults households and couples, middle aged households (35-60 years), households containing multiple workers, households that use oil for heating, and properties in rural areas Fahmy et al. (2011) find that domestic fuel emissions account for 60% of total household CO 2 emissions from all sources in the UK find that income inequalities are one important determining factor of emissions and energy consumption conclude that social equity concerns would trigger policies which aim at reduction of energy consumption among the groups, which are overconsuming relative to the population as a whole speculatively, the perfect candidates for carbon mitigation policies would be taxing private vehicle and aviation transport among the rich households 5

6 Similar research and previous findings II Chancel (2014) finds that the richest 10% emit around three times more CO 2 emissions than the poorest 10% in the US and in France the emissions gap decreased only slightly over time employs APCD model and finds no evidence of the effect of year of birth on emissions in the US finds evidence that strong cohort effects are present in France : the cohorts born are the highest CO 2 emitters, even after introducing other control variables Papathasopoulou and Jackson (2009) investigate the fossil fuel inequalities amongst the income quantiles in the UK between 1968 and 2000 calculate resource based Gini coefficient by using input output resource allocation model the rise in inequality for total fossil fuel consumption was around 24%, and was mainly driven by rising demand for fuel and lighting, car use, recreation and travel by the richest households 6

7 My research and results De-trended Age Period Cohort Model for CO 2 emissions is estimated for Germany with a rich dataset: 4 waves of the Income and Expenditure Survey (IES) data are used, in combination with consumer prices and carbon emissions factors Significant cohort effects are found: cohorts born have on average 5% higher CO 2 emissions than the average German household including additional controls indicates that the generations born are the highest emitters Age, income, household size, dwelling size, education, and urban density are important factors in explaining residential CO 2 emissions 7

8 The data I German Income and Expenditure Survey (IES) Source : Federal Statistical Office Conducted every 5 years Each of the 4 waves contains around 50,000 households Period: Detailed information on: expenditures on durables and non durables sources of income demographic characteristics socio-economic characteristics of households 8

9 The data II: Descriptive statistics Variable adults children age ydisp 28,710 31,426 33,897 34,692 Gini index educ educ TVs PCs and notebooks refrigirators and freezers dishwashers washing mascines and driers dwelling size cars_new cars_old expenditures on electricity expenditures on gas expenditures on car fuels observations 38,378 47,747 41,045 42,315 9

10 Methodology I Households direct CO 2 emissions are calculated from the IES data, using carbon emissions factors (household i and time t subscripts are omitted for simplicity): (1) CO 2,e = θ e expenditure e price e e is the relevant energy good: electricity, gas or car fuels θ e denotes the carbon factor De-trended Age Period Cohort Model (APCD) used to separate the effect of date of birth (cohort), age, and period on the variable of interest (total CO 2 emissions of households) the goal of APCD models is the detection of intrinsic cohort effects 10

11 Methodology II The empirical De-trended Age Period Cohort Model (APCD) model takes the following form (see Chancel (2014) and Chauvel (2011)): 2 ln ( CO 2 ) apc = μ 0 + α a + π p + γ c + β j X j + ε j ln ( CO 2 ) apc is the logarithm of direct CO 2 emissions of household, with household head of age a, belonging to cohort c, surveyed in period p μ 0 is the general intercept α a is the age effect π p is the period effect γ c is the cohort effect X j are the additional control variables: income, household size, etc ε is the error term 11

12 Emission inequalities I: Poor versus Rich year first decile of equivalent income tenth decile of equivalent income fifth decile of equivalent income 95% lower and upper CI 95% lower and upper CI 95% lower and upper CI the emissions of the poorest households have declined by 50% between1993 and 2008 the CO 2 emissions of the richest households have decreased by 20% the emissions inequality is quite apparent in Germany: in 1993 the rich emitted 71% more CO 2 emissions than the poor while in 2008 even 172% more CO 2 emissions 12

13 Emission inequalities II: Urban versus Rural year Rural household Urban household 95% lower and upper CI 95% lower and upper CI the emissions of the rural households have declined by 30% between1993 and 2008 the CO 2 emissions of the urban households have decreased by 44% in 1993 the rural emitted 16% more CO 2 emissions than the urban while in 2008 even 60% more emissions 13

14 Results I: Cohort effects from the APCD without additional controls the cohorts born emit on average 5% more direct energy related CO 2 than the average German household independent of age and period the cohort born in 1943 emit even 7% more CO 2 the generation born in 1973 emits 6% less emissions than the average household and the generation born in 1978 emits 8% less CO 2 14

15 Results II: Cohort effects from the APCD with additional controls the generations born between 1933 and 1968 have 1.8% higher CO 2 total energy related emissions than the average German household, holding everything else constant the cohorts born before 1933 and after 1968 emit 2.5% less CO 2 than the average household the generations born after 1968 exhibit more environmentally conscious behavior income, household size, dwelling size, and rural residential area are associated with higher total energy related emissions high school education or university degree of the household s leader is found to be related with lower energy related emissions 15

16 Results III: Cohort effects for electricity, gas and car fuels emissions separately 0,05 0,05 0,03 0,03 0,01 0,01-0,01-0,01-0,03-0,03-0,05-0,05 Electricity Gas 0,06 0,02-0,02-0,06-0,10 Car fuels the cohorts born between 1923 and 1953 are responsible for emitting more electricity related CO 2 emissions than the average German household the generations are responsible for emitting higher gas related emissions the cohorts appear to be the highest emitters of car fuels related emissions 16

17 Conclusions and further work Cohort effects are found to be statistically significant Cohorts born have 5% higher CO 2 emissions than the average household Including additional controls in the model indicates that the generations born emit around 2% more CO 2 Age, income, household size, dwelling size, education, and urban density are important factors in explaining energy related residential CO 2 emissions Work ahead: include the IES for 2013 (and also IES from before 1993) in the analyses and examine the changes in the results 17

18 18 Thank you for your attention!

19 References Chancel, L. (2014): Are Younger Generations Higher Carbon Emitters than their Elders? Inequalities, generations and CO 2 emissions in France and in the USA, Ecological Economics, 100: Chauvel, L. (2012): APCD: Stata Module for Estimating Age Period Cohort Effects with Detrended Coefficients, Age-Period-Cohort De-trended APC-D model, Statistical Software Components, Boston College Department of Economics Fahmy, E., Thumim, J. and V. White (2011): The Distribution of UK Household CO 2 Emissions: Interim Report. JRF program paper: Climate change and social justice Hargreaves, K., Preston, I., White, V and J. Thumim (2013): The Distribution of Household CO 2 Emissions in Great Britain. Supplementary Project Paper No. 1, JRF Program Paper: Climate change and social justice Papathanasopoulou, E. and T. Jackson ( 2009): Measuring fossil resource inequality A case study for the UK between 1968 and 2000, Ecological Economics, 68:

20 Appendix I Age Period Cohort (APC) model usually serves for modeling incidence and mortality rates defines those rates as a sum of (non-linear) age- period- and cohort-effects The APC model takes the form: A1 y apc = f a + g p + h(c) a refers to the age variable p refers to the period variable c refers to the cohort variable f(), g() and h() are functions y apc is the variable of interest *APC model suffers from a direct relationship between the explanatory variables, namely c = p a. *The model needs to be constrained so that the components of this model can be uniquely determined. 20

21 Appendix II Chauvel (2012) defines the De-trended Age Period Cohort Model (APCD) model as: (A2) y apc = α a + π p + γ c + α 0 + γ 0 + μ + β j X j + ε i j y apc is the household direct CO 2 emissions at period p, age a and cohort c α a is the de-trended age effect π p is the de-trended period effect γ c is the de-trended cohort effect α 0 is the inter-period, inter-cohort linear slope of age γ 0 is the inter-period, inter-age linear slope of cohort μ is the general intercept X j are the additional control variables ε i is the error term Several constraints have to be imposed in order to allow for unique estimates of the detrended cohort effect and to solve the identification problem: A3 p = a + c A4 a α a = p π p = c γ c = 0 A5 slope a α a = slope p π p = slope c γ c = 0 where slope a α a is the linear slope of the α a estimates A6 min c < c < max(c) 21

22 Appendix III: APCD estimation results without additional controls with additional controls Ln (CO 2 emissions) Coef. Std. Err Coef. Std. Err coh_ coh_ coh_ coh_ coh_ coh_ coh_ coh_ coh_ coh_ coh_ coh_ coh_ coh_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ age_ without additional controls with additional controls Ln (CO 2 emissions) Coef. Std. Err Coef. Std. Err per_ per_ per_ per_ rescacoh rescaage _cons lnydisp hhsize lnh_qm educ educ rural

23 Appendix IV: Electricity, gas and car fuels emissions Electricity Gas Car fuels ln(co 2 emissions) Coef. Std. Err Coef. Std. Err Coef. Std. Err coh_1913-0,023 0,000-0,047 0,000 0,003 0,001 coh_1918-0,003 0,000-0,014 0,000-0,051 0,000 coh_1923 0,001 0,000-0,017 0,000-0,036 0,000 coh_1928 0,002 0,000 0,017 0,000 0,003 0,000 coh_1933 0,017 0,000 0,011 0,000 0,015 0,000 coh_1938 0,011 0,000 0,019 0,000 0,008 0,000 coh_1943 0,012 0,000 0,028 0,000 0,040 0,000 coh_1948 0,015 0,000 0,037 0,000 0,028 0,000 coh_1953 0,005 0,000 0,022 0,000 0,026 0,000 coh_1958-0,006 0,000 0,018 0,000 0,029 0,000 coh_1963-0,014 0,000 0,008 0,000 0,009 0,000 coh_1968-0,029 0,000-0,024 0,000 0,010 0,000 coh_1973-0,017 0,000-0,027 0,000 0,003 0,000 coh_1978 0,028 0,000-0,031 0,000-0,087 0,000 age_0025-0,002 0,000 0,008 0,000-0,012 0,000 age_0030-0,017 0,000-0,014 0,000-0,003 0,000 age_0035-0,012 0,000-0,009 0,000-0,030 0,000 age_0040-0,014 0,000-0,005 0,000-0,061 0,000 age_0045-0,010 0,000-0,005 0,000-0,021 0,000 age_0050 0,023 0,000 0,006 0,000 0,023 0,000 age_0055 0,027 0,000 0,019 0,000 0,045 0,000 age_0060 0,036 0,000 0,016 0,000 0,070 0,000 age_0065 0,020 0,000 0,008 0,000 0,054 0,000 age_0070 0,001 0,000-0,004 0,000 0,058 0,000 age_0075-0,019 0,000-0,006 0,000 0,035 0,000 age_0080-0,009 0,000-0,013 0,000-0,039 0,000 age_0085-0,024 0,000-0,002 0,000-0,118 0, Electricity Gas Car fuels ln(co 2 emissions) Coef. Std. Err Coef. Std. Err Coef. Std. Err per_1993 0,028 0,000-0,150 0,000-0,073 0,000 per_1998-0,065 0,000 0,157 0,000 0,099 0,000 per_2003 0,045 0,000 0,137 0,000 0,022 0,000 per_2008-0,008 0,000-0,144 0,000-0,048 0,000 rescacoh -0,491 0,001-3,463 0,001-0,674 0,001 rescaage -0,180 0,000-1,618 0,000-0,705 0,000 lnydisp 0,106 0,000 0,119 0,000 0,390 0,000 hhsize 0,104 0,000 0,040 0,000 0,040 0,000 educ2-0,048 0,000-0,007 0,000-0,011 0,000 educ3-0,065 0,000 0,014 0,000-0,044 0,000 lnh_qm 0,295 0,000 0,437 0,000 distant -0,411 0,000 0,270 0,000 central -0,305 0,000 0,403 0,000 i_6 0,030 0,000 i_7 0,029 0,000 i_8 0,068 0,000 i_9 0,101 0,000 i_10 0,103 0,000 cars_new 0,355 0,000 cars_old 0,386 0,000 rural -0,004 0,000 0,045 0,000 0,100 0,000 _cons -2,226 0,001-1,714 0,001-3,908 0,001