IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i14p8765-d865256.html
   My bibliography  Save this article

A Calculation Model of Carbon Emissions Based on Multi-Scenario Simulation Analysis of Electricity Consumption

Author

Listed:
  • Xiaoli Chen

    (Management Science Research Institute, Guangdong Power Grid Corporation, Guangzhou 510062, China)

  • Zhiwei Liao

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China)

  • Zhihua Gao

    (Strategic Planning Department, Guangdong Power Grid Corporation, Guangzhou 510699, China)

  • Qian Li

    (School of Accounting, Guangdong University of Foreign Studies, Guangzhou 510420, China)

  • Peng Lv

    (Guangzhou Power Supply Bureau, Guangdong Power Grid Corporation, Guangzhou 510630, China)

  • Guangyu Zheng

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China)

  • Kun Yang

    (Management Science Research Institute, Guangdong Power Grid Corporation, Guangzhou 510062, China)

Abstract

In order to reach the peak of carbon emission in China by 2030 and to meet the low-carbon conversion of energy and the growing demand for electricity, this study aims to propose a more accurate and scientific method to calculate the carbon emissions of the entire power industry chain. This paper analyzes the historical actual operation data of the energy and power industry from 2000 to 2020, and originally proposes a carbon emission calculation model based on a multi-scenario simulation analysis of electricity consumption. This paper is an original study from the perspective of the whole industry chain of electricity production, transmission, and consumption. Firstly, a carbon emission model of the power system is established based on the carbon emission composition and transmission mechanism of the whole power industrial chain, which consists of calculation models for carbon emissions from overall electricity demand and carbon emissions from electricity network losses. Secondly, the concept of carbon emission coefficient is proposed, and the key parameters of the carbon emission coefficient of the power system are obtained through the econometric model. On this basis, the carbon emission coefficient is obtained by regression fitting of multiple key parameters according to historical data. Finally, electricity consumption per unit output value (ECPUOV) and per capita electricity consumption (PCEC) are used to predict electricity consumption in the next 15 years. This paper also makes a quantitative analysis of the relationship between CO 2 emissions from the power system and electricity consumption. This paper takes G province, which ranks first in total energy consumption and economic aggregate in China, as an example and calculates its CO 2 emissions and achievement of peak CO 2 emissions by multi-scenario analysis. The case study results show that the low carbon scenario(LC) is the best route for G province to peak CO 2 emissions from energy consumption. The method proposed in this paper can set an achievable goal of 2030 carbon peaking for the government and industry policymakers, and find a feasible implementation path.

Suggested Citation

  • Xiaoli Chen & Zhiwei Liao & Zhihua Gao & Qian Li & Peng Lv & Guangyu Zheng & Kun Yang, 2022. "A Calculation Model of Carbon Emissions Based on Multi-Scenario Simulation Analysis of Electricity Consumption," Sustainability, MDPI, vol. 14(14), pages 1-20, July.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8765-:d:865256
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/14/8765/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/14/8765/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wenxiu Wang & Yaoqiu Kuang & Ningsheng Huang, 2011. "Study on the Decomposition of Factors Affecting Energy-Related Carbon Emissions in Guangdong Province, China," Energies, MDPI, vol. 4(12), pages 1-24, December.
    2. Wang, Miao & Feng, Chao, 2017. "Decomposition of energy-related CO2 emissions in China: An empirical analysis based on provincial panel data of three sectors," Applied Energy, Elsevier, vol. 190(C), pages 772-787.
    3. Nizar Harrathi & Ahmed Almohaimeed, 2022. "Determinants of Carbon Dioxide Emissions: New Empirical Evidence from MENA Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 12(1), pages 469-482.
    4. Jinpeng Liu & Delin Wei, 2020. "Analysis and Measurement of Carbon Emission Aggregation and Spillover Effects in China: Based on a Sectoral Perspective," Sustainability, MDPI, vol. 12(21), pages 1-22, October.
    5. York, Richard & Rosa, Eugene A. & Dietz, Thomas, 2003. "STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts," Ecological Economics, Elsevier, vol. 46(3), pages 351-365, October.
    6. Wang, Ping & Wu, Wanshui & Zhu, Bangzhu & Wei, Yiming, 2013. "Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China," Applied Energy, Elsevier, vol. 106(C), pages 65-71.
    7. Jianhui Jian & Xiaojie Fan & Pinglin He & Hao Xiong & Huayu Shen, 2019. "The Effects of Energy Consumption, Economic Growth and Financial Development on CO 2 Emissions in China: A VECM Approach," Sustainability, MDPI, vol. 11(18), pages 1-16, September.
    8. Li, Lechen & Meinrenken, Christoph J. & Modi, Vijay & Culligan, Patricia J., 2021. "Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features," Applied Energy, Elsevier, vol. 287(C).
    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. Xu, Chao & Haase, Dagmar & Su, Meirong & Yang, Zhifeng, 2019. "The impact of urban compactness on energy-related greenhouse gas emissions across EU member states: Population density vs physical compactness," Applied Energy, Elsevier, vol. 254(C).
    2. Decai Tang & Yan Zhang & Brandon J. Bethel, 2019. "An Analysis of Disparities and Driving Factors of Carbon Emissions in the Yangtze River Economic Belt," Sustainability, MDPI, vol. 11(8), pages 1-13, April.
    3. Roula Inglesi-Lotz & Luis Diez del Corral Morales, 2017. "The Effect of Education on a Country’s Energy Consumption: Evidence from Developed and Developing Countries," Working Papers 201733, University of Pretoria, Department of Economics.
    4. Wang, Yuan & Zhang, Xiang & Kubota, Jumpei & Zhu, Xiaodong & Lu, Genfa, 2015. "A semi-parametric panel data analysis on the urbanization-carbon emissions nexus for OECD countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 704-709.
    5. Bo Yang & Minhaj Ali & Shujahat Haider Hashmi & Mohsin Shabir, 2020. "Income Inequality and CO 2 Emissions in Developing Countries: The Moderating Role of Financial Instability," Sustainability, MDPI, vol. 12(17), pages 1-24, August.
    6. Liang, Xiaoying & Min Fan, & Xiao, Yuting & Yao, Jing, 2022. "Temporal-spatial characteristics of energy-based carbon dioxide emissions and driving factors during 2004–2019, China," Energy, Elsevier, vol. 261(PA).
    7. Liquan Xu & Yong Geng & Dong Wu & Chenyi Zhang & Shijiang Xiao, 2021. "Carbon Footprint of Residents’ Housing Consumption and Its Driving Forces in China," Energies, MDPI, vol. 14(13), pages 1-16, June.
    8. Li, Ke & Lin, Boqiang, 2015. "Impacts of urbanization and industrialization on energy consumption/CO2 emissions: Does the level of development matter?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1107-1122.
    9. Vélez-Henao, Johan-Andrés & Font Vivanco, David & Hernández-Riveros, Jesús-Antonio, 2019. "Technological change and the rebound effect in the STIRPAT model: A critical view," Energy Policy, Elsevier, vol. 129(C), pages 1372-1381.
    10. Bo Li & Xuejing Liu & Zhenhong Li, 2015. "Using the STIRPAT model to explore the factors driving regional CO 2 emissions: a case of Tianjin, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 76(3), pages 1667-1685, April.
    11. Wang, Shaojian & Zeng, Jingyuan & Liu, Xiaoping, 2019. "Examining the multiple impacts of technological progress on CO2 emissions in China: A panel quantile regression approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 140-150.
    12. Wang, Shaojian & Fang, Chuanglin & Guan, Xingliang & Pang, Bo & Ma, Haitao, 2014. "Urbanisation, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China’s provinces," Applied Energy, Elsevier, vol. 136(C), pages 738-749.
    13. Ying Han & Baoling Jin & Xiaoyuan Qi & Huasen Zhou, 2021. "Influential Factors and Spatiotemporal Characteristics of Carbon Intensity on Industrial Sectors in China," IJERPH, MDPI, vol. 18(6), pages 1-18, March.
    14. Yang, Di & Luan, Weixin & Qiao, Lu & Pratama, Mahardhika, 2020. "Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery," Applied Energy, Elsevier, vol. 268(C).
    15. Xing, Licong & Khan, Yousaf Ali & Arshed, Noman & Iqbal, Mubasher, 2023. "Investigating the impact of economic growth on environment degradation in developing economies through STIRPAT model approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    16. Wei Sun & Junli Li & Wenxi Qu, 2022. "Sustainability evolution and factors based on ecological footprint: A case study of Rizhao, China," Growth and Change, Wiley Blackwell, vol. 53(1), pages 132-150, March.
    17. Minda Ma & Ran Yan & Weiguang Cai, 2017. "An extended STIRPAT model-based methodology for evaluating the driving forces affecting carbon emissions in existing public building sector: evidence from China in 2000–2015," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(2), pages 741-756, November.
    18. Xu, Bin & Lin, Boqiang, 2015. "How industrialization and urbanization process impacts on CO2 emissions in China: Evidence from nonparametric additive regression models," Energy Economics, Elsevier, vol. 48(C), pages 188-202.
    19. Kong-Qing Li & Ran Lu & Rui-Wen Chu & Dou-Dou Ma & Li-Qun Zhu, 2018. "Trends and Driving Forces of Carbon Emissions from Energy Consumption: A Case Study of Nanjing, China," Sustainability, MDPI, vol. 10(12), pages 1-13, November.
    20. Nor Salwati Othman & Hussain Ali Bekhet, 2021. "Dynamic Effects of Malaysia's Government Spending on Environment Quality: Bridging STIRPAT and EKC Hypothesis," International Journal of Energy Economics and Policy, Econjournals, vol. 11(5), pages 343-355.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jsusta:v:14:y:2022:i:14:p:8765-:d:865256. 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.