Understanding the Impact of the Key Determinants of Change in Household Emissions in The European Union: Index Decomposition Analysis

Year 2024, Volume: 9 Issue: 1, 113 - 144, 29.02.2024

Burcu Hiçyılmaz

https://doi.org/10.25229/beta.1368760

Abstract

Sectoral and household activities are the main drivers of greenhouse gas emissions caused by human activity. Still, household emissions are often overlooked and no concentrated effort is undertaken. However, in order to achieve global climate mitigation and the net zero target, household emissions must be reduced. This study intends to investigate the change in emissions caused by household activities in 27 countries of the European Union, which is a pioneer in emission reduction. In the study, the Log Mean Divisia Index (LMDI) approach is employed to analyse changes in household emissions, which are separated into four major impacts (emission intensity, energy intensity, consumption, and population). The findings show that in most EU-27 countries, emission intensity and energy intensity factors reduce emissions, whereas consumption effect and population effect factors increase emissions and negatively affect household emission reduction performance. In such a case, where final consumption by households per capita increases emissions, interventions focused at guiding consumer behaviour would be preferable. As a result, it is concluded that programmes encouraging sustainable consumption habits, providing incentives for access to low-carbon items, and other similar policies will be appropriate policy practises for EU-27 countries.

Keywords

Households, Greenhouse gas emissions, European Union, Index decomposition analysis, LMDI

Ethical Statement

Çalışmanın, etik kurul izni ve/veya yasal/özel izin gerektirmeyen bir çalışma olduğunu ve akademik etik kurallara uygun olarak yazıldığını beyan ederim

Avrupa Birliği’nde Hanehalkı Emisyonlarındaki Değişimin Temel Belirleyicilerinin Etkisini Anlamak: İndeks Ayrıştırma Analizi

Abstract

Sektörel faaliyetler ve hanehalkı faaliyetleri, insan faaliyetlerinden kaynaklanan sera gazı emisyonlarının ana etkenleridir. Yine de hanehalkı emisyonları sıklıkla göz ardı edilmekte ve uyumlu bir çaba gösterilememektedir. Ancak küresel iklim azaltımına ve net sıfır hedefine ulaşmak için hanehalkı emisyonlarının azaltılması gerekmektedir. Bu çalışmada emisyon azaltımında öncü olan Avrupa Birliği'ndeki 27 ülkede hanehalklarının faaliyetlerinden kaynaklanan emisyonlardaki değişimin incelenmesi amaçlanmıştır. Logaritmik Ortalama Divisia Endeksi (LMDI) yöntemi, çalışmada dört ana etkiye (emisyon yoğunluğu, enerji yoğunluğu, tüketim, nüfus) ayırılan hanehalkı emisyonlarındaki değişimi analiz etmek için kullanılmaktadır. Elde edilen bulgular, AB-27 ülkelerinin çoğunda, emisyon yoğunluğu ve enerji yoğunluğu faktörlerinin emisyonları azaltıcı etkide bulunduğunu, harcama etkisi ve nüfus etkisi faktörlerinin ise emisyonları artıran ve hanehalklarının emisyon azaltım performansını olumsuz etkileyen iki temel etken olduğunu göstermektedir. Hanehalkının kişi başına nihai tüketiminin emisyonları artırdığı böyle bir durumda, tüketici davranışını yönlendirmeye odaklanan müdahaleler tercih edilebilir. Sonuç olarak sürdürülebilir tüketim alışkanlıklarını teşvik eden, düşük karbonlu ürünlere erişimi teşvik eden programların ve benzeri politikaların AB-27 ülkeleri için uygun politika uygulamaları olacağı sonucuna varılmıştır.

Keywords

Hanehalkları, Sera gazı emisyonları, Avrupa Birliği, İndeks ayrıştırma analizi, LMDI

References

• Ang, B. W. (2004). Decomposition analysis for policymaking in energy:: which is the preferred method? Energy Policy, 32(9), 1131–1139. https://doi.org/10.1016/S0301-4215(03)00076-4
• Ang, B. W. (2005). The LMDI approach to decomposition analysis: a practical guide. Energy Policy, 33(7), 867–871. https://doi.org/10.1016/J.ENPOL.2003.10.010
• Ang, B. W., & Choi, K.-H. (1997). Decomposition of aggregate energy and gas emission intensities for industry: a refined divisia index method on JSTOR. The Energy Journal, 18(3). https://www.jstor.org/stable/41322738
• Anser, M. K., Alharthi, M., Aziz, B., & Wasim, S. (2020). Impact of urbanization, economic growth, and population size on residential carbon emissions in the SAARC countries. Clean Technologies and Environmental Policy, 22(4), 923–936. https://doi.org/10.1007/S10098-020-01833-Y/FIGURES/3
• Bataille, C. G. F. (2020). Physical and policy pathways to net-zero emissions industry. Wiley Interdisciplinary Reviews: Climate Change, 11(2). https://doi.org/10.1002/WCC.633
• Berrill, P., Gillingham, K. T., & Hertwich, E. G. (2021). Drivers of change in US residential energy consumption and greenhouse gas emissions, 1990-2015. In Environmental Research Letters (Vol. 16, Issue 3). IOP Publishing Ltd. https://doi.org/10.1088/1748-9326/abe325
• Bin, S., & Dowlatabadi, H. (2005). Consumer lifestyle approach to US energy use and the related CO2 emissions. Energy Policy, 33(2), 197–208. https://doi.org/10.1016/S0301-4215(03)00210-6
• Cansino, J. M., Sánchez-Braza, A., & Rodríguez-Arévalo, M. L. (2015). Driving forces of Spain׳s CO2 emissions: A LMDI decomposition approach. Renewable and Sustainable Energy Reviews, 48, 749–759. https://doi.org/10.1016/J.RSER.2015.04.011
• Cellura, M., Longo, S., & Mistretta, M. (2012). Application of the Structural Decomposition Analysis to assess the indirect energy consumption and air emission changes related to Italian households consumption. Renewable and Sustainable Energy Reviews, 16, 1135–1145. https://doi.org/10.1016/j.rser.2011.11.016
• Chen, C., Liu, G., Meng, F., Hao, Y., Zhang, Y., & Casazza, M. (2019). Energy consumption and carbon footprint accounting of urban and rural residents in Beijing through Consumer Lifestyle Approach. Ecological Indicators, 98, 575–586. https://doi.org/10.1016/J.ECOLIND.2018.11.049
• Chen, G. Q., Wu, X. D., Guo, J., Meng, J., & Li, C. (2019). Global overview for energy use of the world economy: Household-consumption-based accounting based on the world input-output database (WIOD). Energy Economics, 81, 835–847. https://doi.org/10.1016/J.ENECO.2019.05.019
• Chen, J., Lin, Y., Wang, X., Mao, B., & Peng, L. (2022). Direct and indirect carbon emission from household consumption based on LMDI and SDA model: a decomposition and comparison analysis. Energies, 15(14). https://doi.org/10.3390/en15145002
• Chen, L., Xu, L., Xia, L., Wang, Y., & Yang, Z. (2022). Decomposition of residential electricity-related CO2 emissions in China, a spatial-temporal study. In Journal of Environmental Management (Vol. 320). Academic Press. https://doi.org/10.1016/j.jenvman.2022.115754
• Christis, M., Breemersch, K., Vercalsteren, A., & Dils, E. (2019). A detailed household carbon footprint analysis using expenditure accounts – Case of Flanders (Belgium). Journal of Cleaner Production, 228, 1167–1175. https://doi.org/10.1016/J.JCLEPRO.2019.04.160
• Diakoulaki, D., & Mandaraka, M. (2007). Decomposition analysis for assessing the progress in decoupling industrial growth from CO2 emissions in the EU manufacturing sector. Energy Economics, 29(4), 636–664. https://doi.org/10.1016/J.ENECO.2007.01.005
• Donglan, Z., Dequn, Z., & Peng, Z. (2010). Driving forces of residential CO 2 emissions in urban and rural China: An index decomposition analysis. Energy Policy, 38, 3377–3383. https://doi.org/10.1016/j.enpol.2010.02.011
• Duarte, R., Mainar, A., & Sánchez-Chóliz, J. (2013). The role of consumption patterns, demand and technological factors on the recent evolution of CO2 emissions in a group of advanced economies. Ecological Economics, 96, 1–13. https://doi.org/10.1016/j.ecolecon.2013.09.007
• Duarte, R., Miranda-Buetas, S., & Sarasa, C. (2021). Household consumption patterns and income inequality in EU countries: Scenario analysis for a fair transition towards low-carbon economies. Energy Economics, 104. https://doi.org/10.1016/j.eneco.2021.105614
• Dünya Bankası. (2023a). Hanehalkı ve NPISH’lerin nihai tüketim harcamaları (sabit 2015 ABD doları). https://databank.worldbank.org/source/world-development-indicators
• Dünya Bankası. (2023b). Nüfus (toplam). https://databank.worldbank.org/source/world-development-indicators Eurostat. (2022). NACE Rev. 2 faaliyetine göre hava emisyon hesapları. https://ec.europa.eu/eurostat/databrowser/view/ ENV_AC_AINAH_R2__custom_7251403/default/table
• Eurostat. (2023). Yakıt tipine göre toplam hanehalkı nihai enerji tüketimi. https://ec.europa.eu/eurostat/databrowser/view/TEN00125/default/table
• Gill, B., & Moeller, S. (2018). GHG Emissions and the Rural-Urban Divide. A Carbon Footprint Analysis Based on the German Official Income and Expenditure Survey. Ecological Economics, 145, 160–169. https://doi.org/10.1016/J.ECOLECON.2017.09.004
• Golley, J., & Meng, X. (2012). Income inequality and carbon dioxide emissions: The case of Chinese urban households. Energy Economics, 34(6), 1864–1872. https://doi.org/10.1016/j.eneco.2012.07.025
• González, P. F., Presno, M. J., & Landajo, M. (2024). Tracking the change in Spanish greenhouse gas emissions through an LMDI decomposition model: A global and sectoral approach. Journal of Environmental Sciences, 139, 114–122. https://doi.org/10.1016/J.JES.2022.08.027
• Han, L., Xu, X., & Han, L. (2015). Applying quantile regression and Shapley decomposition to analyzing the determinants of household embedded carbon emissions: Evidence from urban China. Journal of Cleaner Production, 103, 219–230. https://doi.org/10.1016/j.jclepro.2014.08.078
• Hoekstra, R., & van der Bergh, J. J. C. J. M. (2003). Comparing structural decomposition analysis and index. Energy Economics, 25(1), 39–64. https://doi.org/10.1016/S0140-9883(02)00059-2
• Huo, T., Ma, Y., Yu, T., Cai, W., Liu, B., & Ren, H. (2021). Decoupling and decomposition analysis of residential building carbon emissions from residential income: Evidence from the provincial level in China. Environmental Impact Assessment Review, 86. https://doi.org/10.1016/j.eiar.2020.106487
• IEA. (2020). Energy Technology Perspectives 2020. https://www.iea.org/reports/energy-technology-perspectives-2020
• IPCC. (2023). Summary for Policymakers. In: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change.
• Ivanova, D., & Büchs, M. (2020). Household sharing for carbon and energy reductions: the case of EU countries. Energies 2020, Vol. 13, Page 1909, 13(8), 1909. https://doi.org/10.3390/EN13081909
• Ivanova, D., Vita, G., Steen-Olsen, K., Stadler, K., Melo, P. C., Wood, R., & Hertwich, E. G. (2017). Mapping the carbon footprint of EU regions. Environmental Research Letters, 12. https://doi.org/10.1088/1748-9326/aa6da9
• Karmellos, M., Kosmadakis, V., Dimas, P., Tsakanikas, A., Fylaktos, N., Taliotis, C., & Zachariadis, T. (2021). A decomposition and decoupling analysis of carbon dioxide emissions from electricity generation: Evidence from the EU-27 and the UK. Energy, 231. https://doi.org/10.1016/j.energy.2021.120861
• Kaya, Y. (1990). Impact of carbon dioxide emission control on GNP growth: interpretation of proposed scenarios. IPCC energy and industry subgroup, response strategies working group.
• Kerkhof, A. C., Benders, R. M. J., & Moll, H. C. (2009). Determinants of variation in household CO2 emissions between and within countries. Energy Policy, 37(4), 1509–1517. https://doi.org/10.1016/J.ENPOL.2008.12.013
• Lee, J., Taherzadeh, O., & Kanemoto, K. (2021). The scale and drivers of carbon footprints in households, cities and regions across India. Global Environmental Change, 66. https://doi.org/10.1016/j.gloenvcha.2020.102205
• Lévay, P. Z., Vanhille, J., Goedemé, T., & Verbist, G. (2021). The association between the carbon footprint and the socio-economic characteristics of Belgian households. Ecological Economics, 186, 107065. https://doi.org/10.1016/J.ECOLECON.2021.107065
• Li, J., Chen, Y., Li, Z., & Huang, X. (2019). Low-carbon economic development in Central Asia based on LMDI decomposition and comparative decoupling analyses. Journal of Arid Land, 11(4), 513–524. https://doi.org/10.1007/S40333-019-0063-0/METRICS
• Liu, L. C., Wu, G., Wang, J. N., & Wei, Y. M. (2011). China’s carbon emissions from urban and rural households during 1992–2007. Journal of Cleaner Production, 19(15), 1754–1762. https://doi.org/10.1016/J.JCLEPRO.2011.06.011
• Liu, L., Qu, J., Clarke-Sather, A., Maraseni, T. N., & Pang, J. (2017). Spatial Variations and Determinants of Per Capita Household CO2 Emissions (PHCEs) in China. Sustainability , 9(7), 1277. https://doi.org/10.3390/SU9071277
• Liu, X., Wang, X., Song, J., Wang, H., & Wang, S. (2019). Indirect carbon emissions of urban households in China: Patterns, determinants and inequality. Journal of Cleaner Production, 241, 118335. https://doi.org/10.1016/J.JCLEPRO.2019.118335
• Mousavi, B., Lopez, N. S. A., Biona, J. B. M., Chiu, A. S. F., & Blesl, M. (2017). Driving forces of Iran’s CO2 emissions from energy consumption: An LMDI decomposition approach. Applied Energy, 206, 804–814. https://doi.org/10.1016/j.apenergy.2017.08.199
• Moutinho, V., Moreira, A. C., & Silva, P. M. (2015). The driving forces of change in energy-related CO2 emissions in Eastern, Western, Northern and Southern Europe: The LMDI approach to decomposition analysis. In Renewable and Sustainable Energy Reviews (Vol. 50, pp. 1485–1499). Elsevier Ltd. https://doi.org/10.1016/j.rser.2015.05.072
• Pang, Q., Dong, X., Peng, S., & Zhang, L. (2022). Sector linkages and driving forces of Chinese household CO2 emissions based on semi-closed input–output model. Environmental Science and Pollution Research, 29(23), 35408–35421. https://doi.org/10.1007/s11356-021-18039-4
• Qu, J., Liu, L., Zeng, J., Maraseni, T. N., & Zhang, Z. (2022). City-level determinants of household CO2 emissions per person: an empirical study based on a large survey in China. Land, 11(6), 925. https://doi.org/10.3390/LAND11060925/S1
• Robaina, M., & Neves, A. (2021). Complete decomposition analysis of CO2 emissions intensity in the transport sector in Europe. Research in Transportation Economics, 90. https://doi.org/10.1016/j.retrec.2021.101074
• Song, C., Zhao, T., & Xiao, Y. (2022). Temporal dynamics and spatial differences of household carbon emissions per capita of China’s provinces during 2000–2019. Environmental Science and Pollution Research, 29(21), 31198–31216. https://doi.org/10.1007/s11356-021-17921-5
• Su, S., Ding, Y., Li, G., Li, X., Li, H., Skitmore, M., & Menadue, V. (2023). Temporal dynamic assessment of household energy consumption and carbon emissions in China: From the perspective of occupants. Sustainable Production and Consumption, 37, 142–155. https://doi.org/10.1016/J.SPC.2023.02.014
• Wang, H., Ang, B. W., & Su, B. (2017). A multi-region structural decomposition analysis of global CO2 emission intensity. Ecological Economics, 142, 163–176. https://doi.org/10.1016/J.ECOLECON.2017.06.023 Weber, C. L., & Matthews, H. S. (2008). Quantifying the global and distributional aspects of American household carbon footprint. Ecological Economics, 66, 379–391. https://doi.org/10.1016/j.ecolecon.2007.09.021
• Wen, H. xing, Chen, Z., Yang, Q., Liu, J. yi, & Nie, P. yan. (2022). Driving forces and mitigating strategies of CO2 emissions in China: A decomposition analysis based on 38 industrial sub-sectors. Energy, 245, 123262. https://doi.org/10.1016/J.ENERGY.2022.123262
• Wier, M. (1998). Sources of Changes in emissions from energy: a structural decomposition analysis. Economic Systems Research, 10(2), 99–112. https://doi.org/10.1080/09535319808565469
• Xie, J., Zhou, S., Teng, F., & Gu, A. (2023). The characteristics and driving factors of household CO2 and non-CO2 emissions in China. Ecological Economics, 213, 107952. https://doi.org/10.1016/j.ecolecon.2023.107952
• Xu, X., Han, L., & Lv, X. (2016). Household carbon inequality in urban China, its sources and determinants. Ecological Economics, 128, 77–86. https://doi.org/10.1016/J.ECOLECON.2016.04.015
• Yamakawa, A., & Peters, G. P. (2011). Structural decomposition analysis of greenhouse gas emissions in Norway 1990-2002. Economic Systems Research, 23(3), 303–318. https://doi.org/10.1080/09535314.2010.549461
• Yang, Z., Fan, Y., & Zheng, S. (2016). Determinants of household carbon emissions: Pathway toward eco-community in Beijing. Habitat International, 57, 175–186. https://doi.org/10.1016/J.HABITATINT.2016.07.010
• Yeo, Y., Shim, D., Lee, J. D., & Altmann, J. (2015). Driving forces of CO2missions inmerging countries: LMDIecomposition analysis on China and India’s residential sector. Sustainability (Switzerland), 7(12), 16108–16129. https://doi.org/10.3390/su71215805
• Zen, I. S., Uddin, M. S., Al-Amin, A. Q., Majid, M. R. Bin, Almulhim, A. I., & Doberstein, B. (2022). Socioeconomics determinants of household carbon footprint in Iskandar Malaysia. Journal of Cleaner Production, 347, 131256. https://doi.org/10.1016/J.JCLEPRO.2022.131256
• Zhang, J., Li, F., Sun, M., Sun, S., Wang, H., Zheng, P., & Wang, R. (2021). Household consumption characteristics and energy-related carbon emissions estimation at the community scale: A study of Zengcheng, China. Cleaner and Responsible Consumption, 2. https://doi.org/10.1016/j.clrc.2021.100016
• Zhang, J., Yu, B., Cai, J., & Wei, Y. M. (2017). Impacts of household income change on CO2 emissions: An empirical analysis of China. Journal of Cleaner Production, 157, 190–200. https://doi.org/10.1016/j.jclepro.2017.04.126