The Impact of Income Level on Household Greenhouse Gas Emissions: A Case Study for Turkey

Öz

This study examines the effects of income levels on household greenhouse gas (GHG) emissions. For this aim, the impact of income level on household carbon footprints was analysed. Based on a largescale household survey in Ankara, direct and some indirect GHG emissions at the household level were estimated. Furthermore, the results were examined with the Environmental Kuznets Curve (EKC) Hypothesis which was studied in Turkey. Results showed that there is a significant relationship between household GHG emissions and income level groups. Depending on the level of income, consumption growth, and per capita, energy usage results in an increase in greenhouse gas emissions. Household average emission is found as 6.934 tons of CO2e per capita. Household carbon footprint varied across Ankara’s district according to income level. Wealthy districts usually have a higher per capita carbon footprint than poor districts. Besides the wealthiest socio-economic level was found to emit 1.87 times as much CO2e as the lowest. It is identified that emissions from heat production take the larger share of household carbon footprints. It is also determined that the only emission that decreases with higher income levels is indirect emissions from water consumption.

Anahtar Kelimeler

• ANOVA Analysis
• Greenhouse Gas Emissions
• Household Income

Gelir Düzeyinin Hanehalkı Sera Gazı Emisyonlarına Etkisi: Türkiye için Bir Vaka Çalışması

Öz

Bu çalışma, gelir düzeylerinin hane halkı sera gazı emisyonları (SGE) üzerindeki etkilerini incelemektedir. Bu amaçla gelir düzeyinin hane halkı karbon ayak izleri üzerindeki etkisi analiz edilmiştir. Ankara'da gerçekleştirilen geniş ölçekli bir hane halkı araştırmasına dayalı olarak, hane düzeyinde doğrudan ve bazı dolaylı sera gazı emisyonları tahmin edilmiştir. Diğer taraftan sonuçlar Türkiye için çalışılan Çevresel Kuznets Eğrisi (EKC) Hipotezi ile incelenmiştir. Buna göre hane halkı sera gazı emisyonları ile gelir düzeyi arasında önemli bir ilişki olduğu tespit edilmiştir. Gelir düzeyine, tüketim artışına ve kişi başına düşen enerji kullanımına bağlı olarak, enerji kullanımı sera gazı emisyonlarında artışa neden olmaktadır. Hane halkı ortalama emisyonu 6.934 ton CO2e olarak tespit edilmiştir. Hane halkı karbon ayak izi, Ankara ili genelinde gelir düzeyine göre farklılık göstermiştir. Genellikle yüksek gelire sahip ilçeler, düşük gelire sahip ilçelere göre daha yüksek kişi başına karbon ayak izine sahiptir. Ayrıca en yüksek sosyo-ekonomik düzeyin, en düşük sosyo-ekonomik düzeye göre 1.87 kat daha fazla CO2e saldığı tespit edilmiştir. Isı üretiminden kaynaklanan emisyonların hane halkı karbon ayak izi içerisinde en büyük paya sahip olduğu belirlenmiştir. Diğer taraftan gelir düzeyi yükseldikçe azalan tek emisyonun su tüketiminden kaynaklanan dolaylı emisyonlar olduğu da tespit edilmiştir. 

Anahtar Kelimeler

• : ANOVA Analizi
• Hane Halkı Geliri
• Sera Gazı Emisyonları

Kaynakça

1. AbdelHady, M. (2019). Income, inequality, and households' emissions: an assessment of the environmental kuznets curve in Canada. Concordia University Department of Economics, Research paper, (12-50).
2. Adriana P, R., Pratama, A. (2020). Greenhouse gas emission estimations for Depok’s (West Java, Indonesia) middle-class household water end-uses. IOP Conference series earth and environmental science.
3. Aliağaoğlu, A., Mirioğlu, G. (2019). Balıkesir Şehrinde Su Tüketimi: Coğrafi Bir Yaklaşım (Water Consumption in Balıkesir City: A Geographical Approach). Coğrafi Bilimler Dergisi / Turkish Journal of Geographical Sciences, 17(2), 260-280.
4. Barrett, J., Peters, G., Wiedmann, T., Scott, K., Lenzen, M., Roelich, K., Le Quéré, C. (2013). Consumption-based GHG emission accounting: a UK case study. Climate policy.
5. Climate Data, 2020. Ankara - Climate graph, temperature graph, weather by month. https://tr.climate-data.org/asya/turkiye/ankara/ankara-172/ last access 06.04.2022.
6. Cong, R., Saito, M., Hirata, R., Ito, A. (2019). Spatiotemporal analysis on CO2 emissions from households in Japan. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W16, 2019, PIA19+MRSS19 – Photogrammetric image analysis & Munich Remote Sensing Symposium, 18–20 September 2019, Munich, Germany.
7. Dam, M. M., Karakaya E. & Bulut, Ş. (2014). Çevresel Kuznets Eğrisi ve Türkiye: Amprik Bir Analiz. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, Eyi Özel Sayısı. 85-96.
8. EN ISO 14064-1 (2019). Greenhouse Gases-Part 1: Specification with guidance at the organization level for quantification and reporting of greenhouse gas emissions and removals.

9. Endeksa, 2020. Research on Ankara income level, https://www.endeksa.com/tr/analiz/ankara/demografi, last access 26.09.2020.
10. European Environment Agency (2015). Greenhouse gas emissions induced by household consumption, per Euro spent of expenditure in 12 household consumption categories, 2000-2007. https://www.eea.europa.eu/data-and-maps/figures/direct-and-indirect-green-house-1
11. GHG Protocol (2021). Technical Guidance for Calculating Scope 3 Emissions, 3-9
12. Gökpur, H., Zıba, C. A., Dolaz, M. (2019). Kahramanmaraş İli Dulkadiroğlu Bölgesi Katı Atık Bileşenlerinin Araştırılması. Mühendislik Bilimleri ve Tasarım Dergisi, 7(2), 345-351.
13. Hargreaves K, Preston I, White V, Thumim J. (2013). The distribution of Household CO2 emissions in Great Britain. JRF Programme Paper Climate Change and Social Justice.
14. IPCC (2006). In: Eggleston, H.S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K. (Eds.). Guidelines for National Green House Gas Inventories Volume 2 Chapter 2 Stationary Combustion, IGES, Japan.
15. Kemal, M.B., Hizarci, B.B. (2017). The relationship between energy consumption, CO2 emissions and GDP per capita: A revisit of the evidence from Turkey. Alphanumeric Journal, 5, 353-368.
16. Kennedy, C, Steinberger, J, Gasson, B, Hillman, T., Havránek, M., Hansen, Y, Pataki, D, Phdungsilp, A., Ramaswami, A., Villalba Mendez, G. (2010). Methodology for inventorying greenhouse gas emissions from global cities. Energy Policy 37 (9).
17. Kijewska A, Bluszcz A. (2016). Analysis of greenhouse gas emissions in the European Union member states with the use of an agglomeration algorithm, The Silesian University of Technology, Faculty of Mining and Geology, 44-100 Gliwice, ul. Akademicka 2, Poland.
18. Lebe, F. (2016). Çevresel Kuznets Eğrisi Hipotezi: Türkiye için Eşbütünleşme ve Nedensellik Analizi. Doğuş Üniversitesi Dergisi, 17(2), 177-194
19. Li, J., Huang, X., Yang H., Chuai, X., Li a, Y., Qu d, J., Zhang, Z. (2016). Situation and determinants of household carbon emissions in Northwest China. Habitat International, (51) 178-187.
20. Liu L.N., Zeng Qu, J.J. & Wang Q.H., Wang L. (2013). Analysis the influence factors of China’s household carbon intensity. Environment, Energy and Sustainable Development – Sung, Kao & Chen (eds), DOI: 10.1201/b16320-94.
21. Long, Y., Yoshida, Y., Fang, K., Zhang, H. & Dhondt, M. (2019). City-level household carbon footprint from purchaser point of view by a modified input-output model. Appl. Energy, 236, 379–387.
22. Mert, M., Bölük, G. (2016). Do foreign direct investment and renewable energy consumption affect the CO2 emissions? New evidence from a panel ARDL approach to Kyoto Annex countries. Environ. Sci. Pollut. Res. 23 (21), 21669–21681
23. Mi, Z., Zheng, J., Meng, J. et al. (2020). Economic development and converging household carbon footprints in China. Nat Sustain 3, 529–537. https://doi.org/10.1038/s41893-020-0504-y
24. Miehe, R., Scheumann, R., Jones, C. M., Kammen, D. M., & Finkbeiner, M. (2016). Regional carbon footprints of households: A German case study. Environment, Development and Sustainability, 18, 577–591. https://doi.org/10.1007/s10668-015-9649-7
25. Özcan M, Öztürk S. (2015). Türkiye’nin Elektrik Enerjisi Üretimi Kaynaklı Sera Gazı Emisyonunda Beklenen Değişimler ve Karbon Vergisi Uygulaması. EMO.
26. Per Capita Electricity Consumption Per Household in Turkey, https://gazelektrik.com/faydali-bilgiler/elektrik-tuketimi last access 22.08.2020.
27. Plassmann, K., Norton, A., Attarzadeh, N., Jensen, M. P., Brenton, P., Edwards-Jones, G. (2010). Methodological complexities of product carbon footprinting: a sensitivity analysis of key variables in a developing country context. Environmental Science & Policy, 13(5), 393-404.
28. Premanandh, J. (2011). Factors affecting food security and contribution of modern technologies in food sustainability. J. Sci. Food Agric., 91: 2707-2714.
29. Soneja, S., Breyssel, P., Tielsch, J. (2013). Assessment of household emissions due to cookstoves in southern Nepal, conference paper.
30. Turkish Statistical Institute (2020a), Greenhouse Gas Emissions Statistics. Available at: http://www.tuik.gov.tr/PreHaberBultenleri.do?id=24588 (accessed 10 August 2020)
31. Turkish Statistical Institute, (2020b). Income distribution and living conditions statistics for Turkey, from http://www.tuik.gov.tr/PreTablo.do?alt_id=1011, last access 06.08.2020.
32. Turkish Statistical Institute (2020c). Income. Life. Consumption and Poverty. Inflation and Price. Ankara. Retrieved July 13. 2020. from http://www.tuik.gov.tr/UstMenu.do?metod=kategorist
33. Turkish Statistical Institute (2020d). Environment with Statistics, 2018. Ankara. Retrieved August 4, 2020 from http://www.tuik.gov.tr/PreHaberBultenleri.do?id=33675
34. Türkeş, M. (2001). Küresel iklimin korunması iklim değişikliği çerçeve sözleşmesi ve Türkiye, Tesisat Mühendisliği. TMMOB Makina Mühendisleri Odası, Süreli Teknik Yayın, 61: 14-29.
35. Uzar, U.; Eyuboglu, K. (2019). The nexus between income inequality and CO2 emissions in Turkey. J. Cleaner Production, 227, 149–157
36. Vardopoulos, I., Konstantinou, Z. (2017). Study of the possible links between CO2 emissions and employment status. Sustainable Development, Culture, Traditions Journal, Volume 1b.
37. Wang, H., Liu, G., Shi, K. (2019). What are the driving forces of urban CO2 emissions in China? A refined scale analysis between national and urban agglomeration levels. Int. J. Environ. Res. Public Health, 16, 3692
38. Wang, S., Li, G., Fang, C. (2018). Urbanization, economic growth, energy consumption, and CO2 emissions: empirical evidence from countries with different income levels. Renew. Sust. Energ. Rev., 81, 2144–2159.
39. World Bank Data, 2020. https://data.worldbank.org/ last access:14 October 2020.
40. Yatarkalkmaz, M.M., Özdemir, M.B. (2019). The calculation of greenhouse gas emissions of a family and projections for emission reduction. Journal of Energy Systems, 3(3): 96-110, DOI: 10.30521/jes.566516
41. Zhang H.W., Shi X.P., Wang K.Y., Xue J.J., Song L.G., Sun Y.P. (2020). Intertemporal lifestyle changes and carbon emissions: evidence from a China household survey. Energy Econ, 86:104655 https://doi.org/10.1016/j.eneco.2019.104655.
42. Zhu L., Huang Y., Chen W. (2019). Household carbon emission characteristics and adaptive strategies for low carbon community planning. E3S Web of Conferences, 136, 04058 https://doi.org/10.1051/e3sconf/2019136040 E 3S