Towards sustainable household consumption? Structural Decomposition Analysis of carbon footprint household consumption in Finland

Transcription

1 Towards sustainable household consumption? Structural Decomposition Analysis of carbon footprint household consumption in Finland Hannu Savolainen a,b, Marja Salo a, Ilmo Mäenpää b, Ari Nissinen a & Juha Nurmela c a Finnish Environment Institute SYKE, Finland; b Oulu Business School, University of Oulu, Finland; c Statistics Finland, Finland hannu.savolainen@ymparisto.fi Futures Conference 2018 Energizing Futures Tampere

2 Outline Motivation Our objectives and contribution Data and EEIO model Structural decomposition analysis Main results Most important take-aways Previous studies Policy implications

3 Motivation Greenhouse gas emissions of household consumption contribute more than 60 % of global GHG emissions (Ivanova et al. 2016) In order to reach 2 degree climate goals, we need policy measures to decrease carbon footprint of household consumption GHG emissions of HH consumption per capita in Finland exceed EU average (Ivanova et al. 2017) Finland s goal: cut per capita carbon footprint with 50 % by 2030 (Ministry of Environment 2017) We need to build understanding concerning how consumption-based GHG emissions evolve over time which commodity groups are most GHG intensive what determinants explain the changes in GHG emissions

4 Our objectives and contribution Our objective is to study 1. how the carbon footprint of Finnish household consumption has changed between , 2. which product groups are the main contributors for GHG emissions, and 3. which are the key drivers of historical development We construct the time-series of GHG coefficients for consumption commodities and embodied (direct and indirect) GHG emissions of household consumption in Finland between Our study highlights the challenges of sustainable development in Finland (and abroad) from perspective of GHG emissions of household consumption

5 Data Household consumption expenditure data from national accounts (Statistics Finland) timeframe Constant prices 2015 Government individual and collective consumption expenditure not included (public healthcare, education, administration, defense etc.) 59 consumption commodities or commodity groups

6 EEIO model ENVIMAT15 Environmentally extended input-output model (EEIO) ENVIMAT15 is able to take account embodied (direct and indirect) GHG emissions of consumption commodities GHG coefficients/intensities (kg CO2e / ) for commodities are estimated with in constant prices 2015 for years Detailed aggregation: 148 industries, 229 products Embodied GHG coefficients of imported goods are based on LCIA databank Ecoinvent Consumption of capital goods not included in present results (real estates etc.) Technological changes: the changes in the key parameters related to GHG emissions taken into account

7 Structural decompostion analysis (SDA) SDA is used to break down the growth in some variable into the changes in its determinants A comparative static method that differs from other decomposition methods in that it makes use of input-output data Similar to growth accounting

8 Structural decompostion analysis (SDA) E = E t E 0 = e r Q + e Q r + q e, where E is GHG emissions E is the change in GHG emissions (Mkg CO2e) between the start (0) and terminal (t) year e is the vector of GHG coefficients, q is the vector of commodity-wise expenditure, Q is the sum of consumption expenditure and r = q/q is the expenditure share of a commodity Determinants of change in GHG emissions Consumption growth (Level effect) e r Q Changes in consumption patterns / commodity mix (Mix effect) e Q r Changes in technology; commodity-wise GHG intensity coefficients (Intensity change) q e We follow Dietzenbacher & Los (1998) in average calculating

9 Mkg CO2e, M Finnish household consumption GHG emissions and expenditure Growth rates 2016/ Household consumption expenditure M (constant prices 2015) 38 % GHG emissions of household consumption Mkg CO2e 12 % Population 6 % GHG emissions of household consumption Mkg CO2e Household consumption expenditure M (constant prices 2015)

10 2000 = GHG emissions, expenditure and GHG intensity of household consumption (volume index) GHG emissions of household consumption, year 2000 = 100 Household consumption expenditure (constant prices 2015), year 2000 = 100 GHG intensity of household consumption, year 2000 = 100

11 Average consumption expenditure and carbon footprint of consumption kg CO2e per capita, per capita in 2015 prices per capita Consumption commodity groups Growth rate 2016/2000 Shares in Housing and energy -9 % 29 % Travel 2% 30 % Other goods and services 35 % 22 % Food and non-alcoholic beverages Other goods and services Food 13 % 19 % Total 6 % Travel Housing and energy Consumption expenditure per capita, (constant prices 2015)

12 Mkg CO2e Structural decomposition analysis of GHG emissions of household consumption Change in GHG emissions ΔE Level effect Mix effect Intensity change

13 SDA of GHG emissions change in consumption commodity groups (COICOP 2-number aggregation) Mkg CO2e 4000 Food Housing and energy Transport Change in GHG emissions Level effect Level effect Mix effect Mix effect Intensity change Intensity change 08-16

14 Main results Total GHG emissions of household consumption have increased between , but evolution has been volatile Relative decoupling is taking place (GHG intensity decreasing) Per capita carbon footprint following abovementioned evolution Housing and energy, transport and food main contributors among commodity groups Consumption growth (level effect) is the most important determinant in increasing GHG emissions ( Mkg CO2e) Technological change (intensity change) is encouraging, but cannot compensate level effect ( Mkg CO2) Change in consumption commodity mix (mix effect) contributes to emissions reduction, but less than technological change ( Mkg CO2)

15 Previous studies Hoekstra & Van Den Bergh 2002: review of 27 SDA studies of physical flows (including CO2 emissions in several papers) The final demand effect is the most important long-term determinant of increased physical flows The final demand mix effect is responsible of only minor reductions Changes in technology is the most important source of downward pressure on material throughput Housing, transport and food are the most important contributors (Salo et al. 2016; Ivanova et al. 2016; Seppälä et al. 2011) Our results are in line with previous studies

16 Policy implications Policy measures should be targeted on technology and both the level and the commodity mix of household consumption Changes in technology are encouraging, but the pace of change should be considerably faster Heat and power generation is significant factor affecting almost all production chains Electrification of economy and society continues in the future Are measures reducing the consumption level out of the question? Important to steer consumption towards more sustainable and less GHG intensive commodity mix How changing prices affect? Possible rebound problem, since the savings rate is negative? On sectors outside emissions trading scheme no effective price mechanism at the moment (cf. possible carbon tax)

17 References Dietzenbacher, E. & Los, B Structural decomposition techniques, sense and sensitivity. Economic Systems Research, vol. 10, Hoekstra, R. & van den Bergh, J.C Structural Decomposition Analysis of Physical Flows in the Economy Environmental and Resource Economics 23: 357. Ivanova, D., Vita, G., Steen-Olsen, K., Stadler, K., Melo, P. C., Wood, R. and Hertwich E. G Mapping the carbon footprint of EU regions. Environmental Research Letters 12. Ivanova, D., Stadler, K., Steen Olsen, K., Wood, R., Vita, G., Tukker, A. and Hertwich, E. G Environmental Impact Assessment of Household Consumption. Journal of Industrial Ecology, 20: Miller, R. & Blair, P Input-output analysis, Foundations and extensions. Second edition, Cambridge. Ministry of Environment Government Report on Medium-term Climate Change Plan for 2030 Towards Climate-Smart Day-to-Day Living. Reports of the Ministry of the Environment 21/2017. Salo, M., Nissinen, A., Mäenpää, I. and Heikkinen, M Kulutuksen hiilijalanjäljen seurantaa tarvitaan. Tieto&Trendit 1/2016. Seppälä, J., Mäenpää, I., Koskela, S., Mattila, T., Nissinen, A., Katajajuuri, J-M, Härmä, T., Korhonen, M-R, Saarinen, M. and Virtanen, Y Suomen kansantalouden materiaalivirtojen ympäristövaikutusten arviointi ENVIMAT-mallilla. Suomen ympäristö 20/2009. Seppälä, J., Mäenpää, I., Koskela, S., Mattila, T., Nissinen, A., Katajajuuri, J-M, Härmä, T., Korhonen, M-R, Saarinen, M. and Virtanen, Y An assessment of greenhouse gas emissions and material flows caused by the Finnish economy using the ENVIMAT model. J. Clean. Prod. 19.

18 Thank you! Questions or comments?