IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i15p4531-d602314.html
   My bibliography  Save this article

Influence of Population Income on Energy Consumption for Heating and Its CO 2 Emissions in Cities

Author

Listed:
  • Pedro J. Zarco-Periñán

    (Departamento de Ingeniería Eléctrica, Escuela Superior de Ingeniería, Universidad de Sevilla, Camino de los Descubrimientos, s/n, 41092 Sevilla, Spain)

  • Irene M. Zarco-Soto

    (Departamento de Ingeniería Eléctrica, Escuela Superior de Ingeniería, Universidad de Sevilla, Camino de los Descubrimientos, s/n, 41092 Sevilla, Spain)

  • Fco. Javier Zarco-Soto

    (Departamento de Ingeniería Eléctrica, Escuela Superior de Ingeniería, Universidad de Sevilla, Camino de los Descubrimientos, s/n, 41092 Sevilla, Spain)

  • Rafael Sánchez-Durán

    (Endesa, Avenida de la Borbolla, 5, 41004 Sevilla, Spain)

Abstract

As a result of the increase in city populations, and the high energy consumption and emissions of buildings, cities in general, and buildings in particular, are the focus of attention for public organizations and utilities. Heating is among the largest consumers of energy in buildings. This study examined the influence of the income of inhabitants on the consumption of energy for heating and the CO 2 emissions in city buildings. The study was carried out using equivalized disposable income as the basis for the analysis and considered the economies of scale of households. The results are shown per inhabitant and household, by independently considering each city. Furthermore, to more clearly identify the influence of the population income, the study was also carried out without considering the influence of the climate. The method was implemented in the case of Spain. For this purpose, Spanish cities with more than 50,000 inhabitants were analyzed. The results show that, both per inhabitant and per household, the higher the income of the inhabitants, the greater the consumption of energy for heating and the greater the emissions in the city. This research aimed to help energy utilities and policy makers make appropriate decisions, namely, planning for the development of facilities that do not produce greenhouse gases, and enacting laws to achieve sustainable economies, respectively. The overall aim is to achieve the objective of mitigating the impact of emissions and the scarcity of energy resources.

Suggested Citation

  • Pedro J. Zarco-Periñán & Irene M. Zarco-Soto & Fco. Javier Zarco-Soto & Rafael Sánchez-Durán, 2021. "Influence of Population Income on Energy Consumption for Heating and Its CO 2 Emissions in Cities," Energies, MDPI, vol. 14(15), pages 1-18, July.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4531-:d:602314
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/15/4531/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/15/4531/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gejirifu De & Wangfeng Gao, 2018. "Forecasting China’s Natural Gas Consumption Based on AdaBoost-Particle Swarm Optimization-Extreme Learning Machine Integrated Learning Method," Energies, MDPI, vol. 11(11), pages 1-20, October.
    2. Sharma, Susan Sunila, 2011. "Determinants of carbon dioxide emissions: Empirical evidence from 69 countries," Applied Energy, Elsevier, vol. 88(1), pages 376-382, January.
    3. Chen, Jiandong & Wang, Ping & Cui, Lianbiao & Huang, Shuo & Song, Malin, 2018. "Decomposition and decoupling analysis of CO2 emissions in OECD," Applied Energy, Elsevier, vol. 231(C), pages 937-950.
    4. Golley, Jane & Meng, Xin, 2012. "Income inequality and carbon dioxide emissions: The case of Chinese urban households," Energy Economics, Elsevier, vol. 34(6), pages 1864-1872.
    5. Alam, M. Shahid, 2006. "Economic Growth with Energy," MPRA Paper 1260, University Library of Munich, Germany.
    6. Fátima Lima & Paula Ferreira & Vítor Leal, 2020. "A Review of the Relation between Household Indoor Temperature and Health Outcomes," Energies, MDPI, vol. 13(11), pages 1-24, June.
    7. Chancel, Lucas, 2014. "Are younger generations higher carbon emitters than their elders?," Ecological Economics, Elsevier, vol. 100(C), pages 195-207.
    8. Hansi Liu & Sheng Zhou & Tianduo Peng & Xunmin Ou, 2017. "Life Cycle Energy Consumption and Greenhouse Gas Emissions Analysis of Natural Gas-Based Distributed Generation Projects in China," Energies, MDPI, vol. 10(10), pages 1-14, October.
    9. Deyun Wang & Yanling Liu & Zeng Wu & Hongxue Fu & Yong Shi & Haixiang Guo, 2018. "Scenario Analysis of Natural Gas Consumption in China Based on Wavelet Neural Network Optimized by Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 11(4), pages 1-16, April.
    10. Hekkenberg, M. & Benders, R.M.J. & Moll, H.C. & Schoot Uiterkamp, A.J.M., 2009. "Indications for a changing electricity demand pattern: The temperature dependence of electricity demand in the Netherlands," Energy Policy, Elsevier, vol. 37(4), pages 1542-1551, April.
    11. Lyons, Seán & Pentecost, Anne & Tol, Richard S. J., 2012. "Socioeconomic Distribution of Emissions and Resource Use in Ireland," Papers WP426, Economic and Social Research Institute (ESRI).
    12. Li, Danny H.W. & Yang, Liu & Lam, Joseph C., 2012. "Impact of climate change on energy use in the built environment in different climate zones – A review," Energy, Elsevier, vol. 42(1), pages 103-112.
    13. Mustafa Akpinar & M. Fatih Adak & Nejat Yumusak, 2017. "Day-Ahead Natural Gas Demand Forecasting Using Optimized ABC-Based Neural Network with Sliding Window Technique: The Case Study of Regional Basis in Turkey," Energies, MDPI, vol. 10(6), pages 1-20, June.
    14. Paula Ala-Kotila & Terttu Vainio & Jarmo Laamanen, 2020. "The Influence of Building Renovations on Indoor Comfort—A Field Test in an Apartment Building," Energies, MDPI, vol. 13(18), pages 1-18, September.
    15. Federico Scarpa & Vincenzo Bianco, 2017. "Assessing the Quality of Natural Gas Consumption Forecasting: An Application to the Italian Residential Sector," Energies, MDPI, vol. 10(11), pages 1-13, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Haijiang Zou & Siyu Guo & Ruifeng Wang & Fenghao Wang & Zhenxing Shen & Wanlong Cai, 2023. "Numerical Investigation of the Long-Term Load Shifting Behaviors within the Borehole Heat Exchanger Array System," Energies, MDPI, vol. 16(5), pages 1-19, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lina Liu & Jiansheng Qu & Tek Narayan Maraseni & Yibo Niu & Jingjing Zeng & Lihua Zhang & Li Xu, 2020. "Household CO 2 Emissions: Current Status and Future Perspectives," IJERPH, MDPI, vol. 17(19), pages 1-19, September.
    2. Yulin Liu & Min Zhang & Rujia Liu, 2020. "The Impact of Income Inequality on Carbon Emissions in China: A Household-Level Analysis," Sustainability, MDPI, vol. 12(7), pages 1-22, March.
    3. Pedro J. Zarco-Periñán & Fco Javier Zarco-Soto & Irene M. Zarco-Soto & José L. Martínez-Ramos & Rafael Sánchez-Durán, 2022. "CO 2 Emissions in Buildings: A Synopsis of Current Studies," Energies, MDPI, vol. 15(18), pages 1-10, September.
    4. Irene M. Zarco-Soto & Fco. Javier Zarco-Soto & Pedro J. Zarco-Periñán, 2021. "Influence of Population Income on Energy Consumption and CO 2 Emissions in Buildings of Cities," Sustainability, MDPI, vol. 13(18), pages 1-18, September.
    5. Zhang, Weishi & Xu, Ying & Wang, Can & Streets, David G., 2022. "Assessment of the driving factors of CO2 mitigation costs of household biogas systems in China: A LMDI decomposition with cost analysis model," Renewable Energy, Elsevier, vol. 181(C), pages 978-989.
    6. Shi, Xunpeng & Wang, Keying & Cheong, Tsun Se & Zhang, Hongwu, 2020. "Prioritizing driving factors of household carbon emissions: An application of the LASSO model with survey data," Energy Economics, Elsevier, vol. 92(C).
    7. Wang, Keying & Cui, Yongyan & Zhang, Hongwu & Shi, Xunpeng & Xue, Jinjun & Yuan, Zhao, 2022. "Household carbon footprints inequality in China: Drivers, components and dynamics," Energy Economics, Elsevier, vol. 115(C).
    8. Zhang, Hongwu & Shi, Xunpeng & Wang, Keying & Xue, Jinjun & Song, Ligang & Sun, Yongping, 2020. "Intertemporal lifestyle changes and carbon emissions: Evidence from a China household survey," Energy Economics, Elsevier, vol. 86(C).
    9. Kangyin Dong & Xiucheng Dong & Qingzhe Jiang, 2020. "How renewable energy consumption lower global CO2 emissions? Evidence from countries with different income levels," The World Economy, Wiley Blackwell, vol. 43(6), pages 1665-1698, June.
    10. Reza Hafezi & Amir Naser Akhavan & Mazdak Zamani & Saeed Pakseresht & Shahaboddin Shamshirband, 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand," Energies, MDPI, vol. 12(21), pages 1-22, October.
    11. Qiao, Weibiao & Liu, Wei & Liu, Enbin, 2021. "A combination model based on wavelet transform for predicting the difference between monthly natural gas production and consumption of U.S," Energy, Elsevier, vol. 235(C).
    12. Valadkhani, Abbas & Nguyen, Jeremy & Bowden, Mark, 2019. "Pathways to reduce CO2 emissions as countries proceed through stages of economic development," Energy Policy, Elsevier, vol. 129(C), pages 268-278.
    13. Zhang, Yimeng & Wang, Feng & Zhang, Bing, 2023. "The impacts of household structure transitions on household carbon emissions in China," Ecological Economics, Elsevier, vol. 206(C).
    14. Yixi Xue & Jie Ren & Xiaohang Bi, 2019. "Impact of Influencing Factors on CO 2 Emissions in the Yangtze River Delta during Urbanization," Sustainability, MDPI, vol. 11(15), pages 1-19, August.
    15. Olonscheck, Mady & Walther, Carsten & Lüdeke, Matthias & Kropp, Jürgen P., 2015. "Feasibility of energy reduction targets under climate change: The case of the residential heating energy sector of the Netherlands," Energy, Elsevier, vol. 90(P1), pages 560-569.
    16. Lucas Chancel & Thomas Piketty, 2015. "Carbon and inequality: From Kyoto to Paris Trends in the global inequality of carbon emissions (1998-2013) & prospects for an equitable adaptation fund World Inequality Lab," World Inequality Lab Working Papers halshs-02655266, HAL.
    17. Xu, Xinkuo & Han, Liyan & Lv, Xiaofeng, 2016. "Household carbon inequality in urban China, its sources and determinants," Ecological Economics, Elsevier, vol. 128(C), pages 77-86.
    18. Xibao Xu & Yan Tan & Shuang Chen & Guishan Yang & Weizhong Su, 2015. "Urban Household Carbon Emission and Contributing Factors in the Yangtze River Delta, China," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-21, April.
    19. Hongwu Zhang & Lequan Zhang & Keying Wang & Xunpeng Shi, 2019. "Unveiling Key Drivers of Indirect Carbon Emissions of Chinese Older Households," Sustainability, MDPI, vol. 11(20), pages 1-17, October.
    20. Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4531-:d:602314. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.