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Towards sustainable household consumption? − Structural Decomposition Analysis of carbon footprint household consumption in Finland
Hannu Savolainena,b, Marja Saloa, Ilmo Mäenpääb, Ari Nissinena & Juha Nurmelac
aFinnish Environment Institute – SYKE , Finland; bOulu Business School, University of Oulu, Finland; cStatistics Finland, Finland
Futures Conference 2018 – Energizing Futures 13.6.2018 Tampere
Outline
• Motivation
• Our objectives and contribution
• Data and EEIO model
• Structural decomposition analysis
• Main results• Most important take-aways
• Previous studies
• Policy implications
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
Our objectives and contribution
• Our objective is to study 1. how the carbon footprint of Finnish household consumption has changed
between 2000–2016,2. which product groups are the main contributors for GHG emissions, and3. 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 2000–2016
• Our study highlights the challenges of sustainable development in Finland (and abroad) from perspective of GHG emissions of household consumption
Data
• Household consumption expenditure data from national accounts (Statistics Finland)• timeframe 2000–2016
• Constant prices 2015
• Government individual and collective consumption expenditure not included (public healthcare, education, administration, defense etc.)
• 59 consumption commodities or commodity groups
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 2000–2016
• 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
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
Structural decompostion analysis (SDA)• ∆𝐸 = 𝐸𝑡 − 𝐸0 = 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
Growth rates2016/2000
Household consumption
expenditure M€(constant prices
2015)
38 %
GHG emissions of household consumption
Mkg CO2e
12 %
Population 6 %0
20 000
40 000
60 000
80 000
100 000
120 000
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Mkg
CO
2e,
M€
Finnish household consumption GHG emissions and expenditure 2000–2016
GHG emissions of household consumption Mkg CO2e Household consumption expenditure M€ (constant prices 2015)
0
20
40
60
80
100
120
140
160
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
2000 = 100
GHG emissions, expenditure and GHG intensity of householdconsumption 2000–2016 (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
Consumptioncommodity
groups
Growth rate2016/2000
Shares in 2016
Housing and energy
-9 % 29 %
Travel 2% 30 %
Other goods and services
35 % 22 %
Food 13 % 19 %
Total 6 %
0
2 000
4 000
6 000
8 000
10 000
12 000
14 000
16 000
18 000
20 000
22 000
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
kg CO2e per capita,€ per capita in 2015 prices
Average consumption expenditure and carbon footprint of consumptionper capita
Food and non-alcoholic beverages Other goods and services
Travel Housing and energy
Consumption expenditure per capita, € (constant prices 2015)
-8000
-6000
-4000
-2000
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
Change in GHG emissions ΔE Level effect Mix effect Intensity change
Mkg
CO
2e
Structural decomposition analysis of GHG emissions of household consumption 2000–2016
2000–2008 2008–2016 2000–2016
-6000
-5000
-4000
-3000
-2000
-1000
0
1000
2000
3000
4000
Food Housing and energy TransportMkg CO2e
SDA of GHG emissions change in consumption commodity groups (COICOP 2-number aggregation)
Change in GHG emissions 00-16
Level effect 00-08
Level effect 08-16
Mix effect 00-08
Mix effect 08-16
Intensity change 00-08
Intensity change 08-16
Main results
• Total GHG emissions of household consumption have increased between 2000–2016, 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 (+ 16 400 Mkg CO2e)
• Technological change (intensity change) is encouraging, but cannot compensate level effect (- 6 700 Mkg CO2)
• Change in consumption commodity mix (mix effect) contributes to emissions reduction, but less than technological change (- 3 100 Mkg CO2)
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
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)
References• Dietzenbacher, E. & Los, B. 1998. Structural decomposition techniques, sense and sensitivity. Economic
Systems Research, vol. 10, 307–323.
• Hoekstra, R. & van den Bergh, J.C. 2002. 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. 2017. 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. 2016. Environmental Impact Assessment of Household Consumption. Journal of Industrial Ecology, 20: 526-536.
• Miller, R. & Blair, P. 2009. Input-output analysis, Foundations and extensions. Second edition, Cambridge.
• Ministry of Environment. 2017. 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. 2016. 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. 2009. 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. 2011. An assessment of greenhouse gas emissions and material flows causedby the Finnish economy using the ENVIMAT model. J. Clean. Prod. 19.
