IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i3p1716-d1039083.html
   My bibliography  Save this article

Impact of Population Aging on Carbon Emissions in China: An Empirical Study Based on a Kaya Model

Author

Listed:
  • Hua Xiang

    (Labor Economics, School of Labor Economics, Capital University of Economics and Business, Beijing 100072, China)

  • Xueting Zeng

    (Institute of Population Economics, Capital University of Economics and Business, Beijing 100072, China)

  • Hongfang Han

    (Labor Economics, School of Labor Economics, Capital University of Economics and Business, Beijing 100072, China)

  • Xianjuan An

    (Labor Economics, School of Labor Economics, Capital University of Economics and Business, Beijing 100072, China)

Abstract

As the world’s largest developing country, China is facing the serious challenge of reducing carbon emissions. The objective of this study is to investigate how China’s aging population affects carbon emissions from the production and consumption sides based on an improved Kaya model. The advantage of the Kaya model is that it links economic development to carbon dioxide generated by human activities, which makes it possible to effectively analyze carbon emissions in relation to the structure of energy consumption and human activities. Based on different energy consumption structures and technological innovation levels, a threshold effect model is constructed. The results show that: (1) There is an inverted U-shaped curve relationship between population aging and carbon emissions in China. (2) Energy consumption structure and technological innovation thresholds can be derived for the impact of population aging on carbon emissions, with thresholds of 3.275 and 8.904 identified, respectively. (3) Population aging can reduce carbon emissions when the energy consumption structure does not exceed the threshold value. (4) There is no significant intervention effect of technological innovation on the relationship between population aging and carbon emissions. Based on the research results, some countermeasures and suggestions to reduce carbon emissions are proposed.

Suggested Citation

  • Hua Xiang & Xueting Zeng & Hongfang Han & Xianjuan An, 2023. "Impact of Population Aging on Carbon Emissions in China: An Empirical Study Based on a Kaya Model," IJERPH, MDPI, vol. 20(3), pages 1-20, January.
  • Handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:1716-:d:1039083
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/3/1716/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/20/3/1716/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fang, Kai & Tang, Yiqi & Zhang, Qifeng & Song, Junnian & Wen, Qi & Sun, Huaping & Ji, Chenyang & Xu, Anqi, 2019. "Will China peak its energy-related carbon emissions by 2030? Lessons from 30 Chinese provinces," Applied Energy, Elsevier, vol. 255(C).
    2. Eisenack, Klaus & Edenhofer, Ottmar & Kalkuhl, Matthias, 2012. "Resource rents: The effects of energy taxes and quantity instruments for climate protection," Energy Policy, Elsevier, vol. 48(C), pages 159-166.
    3. Yao Qian & Lang Sun & Quanyi Qiu & Lina Tang & Xiaoqi Shang & Chengxiu Lu, 2020. "Analysis of CO 2 Drivers and Emissions Forecast in a Typical Industry-Oriented County: Changxing County, China," Energies, MDPI, vol. 13(5), pages 1-21, March.
    Full references (including those not matched with items on IDEAS)

    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. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    2. Meng Guo & Shukai Cai, 2022. "Impact of Green Innovation Efficiency on Carbon Peak: Carbon Neutralization under Environmental Governance Constraints," IJERPH, MDPI, vol. 19(16), pages 1-18, August.
    3. Lili Sun & Huijuan Cui & Quansheng Ge, 2021. "Driving Factors and Future Prediction of Carbon Emissions in the ‘Belt and Road Initiative’ Countries," Energies, MDPI, vol. 14(17), pages 1-21, September.
    4. Concetta Castiglione & Davide Infante & Maria Teresa Minervini & Janna Smirnova, 2014. "Environmental taxation in Europe: What does it depend on?," Cogent Economics & Finance, Taylor & Francis Journals, vol. 2(1), pages 1-8, December.
    5. Zhang, Lixiao & Yang, Min & Zhang, Pengpeng & Hao, Yan & Lu, Zhongming & Shi, Zhimin, 2021. "De-coal process in urban China: What can we learn from Beijing's experience?," Energy, Elsevier, vol. 230(C).
    6. Zheng, Li & Yuan, Ling & Khan, Zeeshan & Badeeb, Ramez Abubakr & Zhang, Leilei, 2023. "How G-7 countries are paving the way for net-zero emissions through energy efficient ecosystem?," Energy Economics, Elsevier, vol. 117(C).
    7. Ye, Rui-Ke & Gao, Zhuang-Fei & Fang, Kai & Liu, Kang-Li & Chen, Jia-Wei, 2021. "Moving from subsidy stimulation to endogenous development: A system dynamics analysis of China's NEVs in the post-subsidy era," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    8. Wanghu Sun & Yuning Sun & Xiaochun Hong & Yuan Zhang & Chen Liu, 2023. "Research on Biomass Waste Utilization Based on Pollution Reduction and Carbon Sequestration," Sustainability, MDPI, vol. 15(5), pages 1-15, March.
    9. Zhang, Xi & Geng, Yong & Shao, Shuai & Dong, Huijuan & Wu, Rui & Yao, Tianli & Song, Jiekun, 2020. "How to achieve China’s CO2 emission reduction targets by provincial efforts? – An analysis based on generalized Divisia index and dynamic scenario simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    10. Zhong, Shengyuan & Wang, Xiaoyuan & Zhao, Jun & Li, Wenjia & Li, Hao & Wang, Yongzhen & Deng, Shuai & Zhu, Jiebei, 2021. "Deep reinforcement learning framework for dynamic pricing demand response of regenerative electric heating," Applied Energy, Elsevier, vol. 288(C).
    11. Jiang Zhu & Xiang Li & Huiming Huang & Xiangdong Yin & Jiangchun Yao & Tao Liu & Jiexuan Wu & Zhangcheng Chen, 2023. "Spatiotemporal Evolution of Carbon Emissions According to Major Function-Oriented Zones: A Case Study of Guangdong Province, China," IJERPH, MDPI, vol. 20(3), pages 1-20, January.
    12. Zhang, Boling & Wang, Qian & Wang, Sixia & Tong, Ruipeng, 2023. "Coal power demand and paths to peak carbon emissions in China: A provincial scenario analysis oriented by CO2-related health co-benefits," Energy, Elsevier, vol. 282(C).
    13. Li, Dezhi & Huang, Guanying & Zhu, Shiyao & Chen, Long & Wang, Jiangbo, 2021. "How to peak carbon emissions of provincial construction industry? Scenario analysis of Jiangsu Province," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    14. Lili Sun & Hang Yu & Qiang Liu & Yanzun Li & Lintao Li & Hua Dong & Caspar Daniel Adenutsi, 2022. "Identifying the Key Driving Factors of Carbon Emissions in ‘Belt and Road Initiative’ Countries," Sustainability, MDPI, vol. 14(15), pages 1-16, July.
    15. Guo, Xuepeng & Pang, Jun, 2023. "Analysis of provincial CO2 emission peaking in China: Insights from production and consumption," Applied Energy, Elsevier, vol. 331(C).
    16. Xie, Minghua & Min, Jialin & Fang, Xingming & Sun, Chuanwang & Zhang, Zhen, 2022. "Policy selection based on China's natural gas security evaluation and comparison," Energy, Elsevier, vol. 247(C).
    17. Zhe Zhao & Xin Xuan & Fan Zhang & Ying Cai & Xiaoyu Wang, 2022. "Scenario Analysis of Renewable Energy Development and Carbon Emission in the Beijing–Tianjin–Hebei Region," Land, MDPI, vol. 11(10), pages 1-13, September.
    18. Genovaitė Liobikienė & Mindaugas Butkus & Kristina Matuzevičiūtė, 2019. "The Contribution of Energy Taxes to Climate Change Policy in the European Union (EU)," Resources, MDPI, vol. 8(2), pages 1-23, April.
    19. Longyu Shi & Fengmei Yang & Lijie Gao, 2020. "The Allocation of Carbon Intensity Reduction Target by 2030 among Cities in China," Energies, MDPI, vol. 13(22), pages 1-14, November.
    20. Meng Yang & Yisheng Liu & Jinzhao Tian & Feiyu Cheng & Pengbo Song, 2022. "Dynamic Evolution and Regional Disparity in Carbon Emission Intensity in China," Sustainability, MDPI, vol. 14(7), pages 1-15, March.

    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:jijerp:v:20:y:2023:i:3:p:1716-:d:1039083. 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.