INTERGENRATIONAL EMISSIONS INEQUALITY IN GERMANY: ARE THE
YOUNGER GENERATIONS MORE ENVIRONMENTALLY CONSCIOUS?
Dragana Nikodinoska, Christian-Albrechts-University of Kiel, Germany,
+49(0)15155290379, d.nikodinoska@economics.uni-kiel.de
Overview
In recent years, many countries in Europe have experienced growth in residential energy consumption and hence
increase in the anthropogenic carbon dioxide (CO2) emissions. However, the increase in consumption and emission
has not been equally distributed among the households and seems to be highly dependent on the level of income.
There appears to be wide gap in energy consumption and pollution between the rich and the poor, even in developed
countries such as Germany. These differences in the levels of pollution among individual groups or households are
known as impact inequalities. Understanding the impact inequalities and their causes among households will help us
better understand inequalities in climate change and strive towards their reduction possibly through change in
lifestyles. This paper investigates first and foremost the effects of date of birth on households’ direct energy related
CO2 emissions in Germany, as well as the impact of income and education on emissions. The role of cohorts (groups
of individuals born in the same year) in determining energy consumption and the related CO2 emissions is important
but has long been overlooked in economic research.
Methods
Data from consumer Income and Expenditure Surveys (IES) for the years 1993-2008, in combination with the
emisssions factors, is used to calculate direct energy related CO2 emissions of German households. De-trended Age
Period Cohort (APCD) models are used to separate the effect of date of birth, age, period and other demographic,
socio-economic and life-style variables on CO2 emissions. The goal of Age Period Cohort (APC) models is the
detection of “intrinsic” cohort effects on the variable of interest. The improved APCD method distinguishes between
linear trends and fluctuations by delivering a de-trended cohort effect, having a zero-sum zero-slope shape and an
unique solution. The variables of interest for this paper are direct electricity, gas, and car-fuels related CO2 emissions
as well as total energy related emissions. Education, household size, apartment size, number of electric appliances,
type of heating system and income are used as additional control variables in the equation for electricity and gas
related emissions. Income, education, number of new and used cars, and household size are used as additional
control variables in the equation for car fuels related CO2 emissions.
Results
The estimations from the APCD model demonstrate that cohorts born between 1928 and 1958 have a stronger
tendency to emit direct energy related CO2 than the cohorts born before 1928 and after 1958. If we look at the
separate energy goods, the cohorts born in 1923-53 emit more electricity and gas related emissions than their
predeccessors or successors while for car fuels the cohorts 1928-58 emit highest levels of CO2 emissions. The age
variable also show that households with leaders aged 30 and below or 70 and above emit less energy related CO2 in
the atmosphere. The existence of strong and statistically siginificant generational emissions inequalities in Germany
can be explained by the fact that generations born between 1928 and 1958 are wealthier and live in bigger, energy
inefficient dwellings. The generational effect is still present once income, education, household size, apartment size,
type of heating system, number of electric appliances and cars are controlled for. The effects shifts this time among
the generations born between 1933 and 1968, it is smaller in size but still statistically significant. It seems that the
cohorts born in the period 1933-68 have difficulties in adapting to more energy efficient consumption patterns and
lifestyles.
Conclusions
First, the direct CO2 emissions of German households are derived using households’ consumer data on energy goods
expenditures as well as emissions factors. Secondly, the relationship between income and emissions among
households was analysed. The results show that the richest 10% of the German population emits 72% more CO2
than the poorest 10%. Moreover, the gap between the CO2 emissions of the rich and the poor increases over time.
Thirdly, the analyses focus on the role of year of birth or cohort effect in driving direct energy related emissions with
the help of De-trended Age Period Cohort (APCD) model. The results indicate the presence of clear cohort effects
on CO2 emissions among German households. The 1928-1958 cohorts are found to be the highest CO2 emitters,
while the cohorts 1913-23 and 1963-78 emit lower amounts of CO2.
Moreover, the cohort effects for the emissions coming from different energy sources: electricity, gas and car fuels are
analysed separately. For electricity related emissions, the cohorts born between 1923 and 1953 are responsible for
emitting more emissions than the average German household. Similarly the generations 1928-53 are responsible for
emitting higher gas related CO2 emissions. The cohort effects for car fuels related emissions do not exibit a clear
pattern as the cohort effect changes sign several times, but again the cohorts 1928-1958 appear to be the highest CO2
emissions emitters. Inserting further controls into the model demonstrates that the generational effect is still present
for total energy emissions and it is statistically significant. The cohort effect is reflection of energy inefficient
infrastructures and higher number of motor vehicles used by older generations as well as continuous economic
marginalization of the younger generations. The younger German generations appear to be more environmentally
conscious and to have more energy efficient life styles and this could partly explain the CO2 emissions inequality.
References
Chancel, L. (2014): „Are Younger Generations Higher Carbon Emiters than their Elders? Inequalities, generations
and CO2 emissions in France and in the USA,“ Ecological Economics, 100: 195-207
Chauvel, L. (2012): „APCD: Stata Module for Estimating Age Period Cohort Effects with Detrended Coefficients,
Age-Period-Cohort Detrended APC-D model, “ Statistical Software Components, Boston College Department of
Economics
Grossman, G. and A. Krueger (1995): „Economic Growth and the Environment, “ Quarterly Journal of Economics,
110 (2): 353-377
Kuznets, S. (1955): „Economic growth and income inequality, “ American Economic Review, 45(1): 1-28
Nikodinoska. D. and C. Schröder ( 2015): „On the Emissions-Inequality Trade-Off in Energy Taxation: Evidence on
the German Car Fuels Tax, “ Frei Universität Berlin, School of Business and Economics, Discussion paper 6.
Papathanasopoulou, E. and T. Jackson ( 2009): „Measuring fossil resource inequality—A case study for
the UK between 1968 and 2000, “ Ecological Economics, 68:1213-1225
Reiss, P., and M. White (2005): „Household Electricity Demand, Revisited,“ Review of Economic Studies, 72 (3):
853-883
Rutherford, M.J, Lambert, P.C. and J.R.Thompson (2010): „Age-Period-Cohort Modeling ,“ The Stata Journal,
10(4): 606-627
Sasieni, P.D. (2012): „ Age-Period-Cohort Models in Stata,“ The Stata Journal, 12(1): 45-60
Sterner, T. (2011): „Fuel Taxes and the Poor: The distributional consequences of gasoline taxation and their
implications for climate policy,“ Routledge Journals, Taylor & Francis Ltd
Schulhofer-Wohl, S. and Y. Yang (2006): „APC: Stata module for estimating age-period-cohort effects, “
Statistical Software Components, Boston College Department of Economics
Yang, Y., W. J. Fu, and K.C. Land. (2004): „A Methodological Comparison of Age-Period- Cohort Models:
Intrinsic Estimator and Conventional Generalized Linear Models, “ Sociological Methodology 34: 75-110
Yang, Y., Schulhofer-Wohl, S. Wenjiang J. Fu, and K. C. Land (2008): „The Intrinsic Estimator for Age-PeriodCohort Analysis: What It Is and How To Use It, “ American Journal of Sociology, 113
Yang, Y., Land, Yang, Y. and K. C. Land (2006): „A mixed models approach to the age-periodcohort analysis of
repeated cross-section surveys, with an application to data on trends in verbal test scores, “ Sociological
Methodology, 36: 75–97