IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i5p1212-d329129.html
   My bibliography  Save this article

Analysis of CO 2 Drivers and Emissions Forecast in a Typical Industry-Oriented County: Changxing County, China

Author

Listed:
  • Yao Qian

    (Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
    University of Chinese Academy of Sciences, Beijing 100049, China
    State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100049, China)

  • Lang Sun

    (Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Quanyi Qiu

    (Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China)

  • Lina Tang

    (Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China)

  • Xiaoqi Shang

    (Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Chengxiu Lu

    (College of Geography and Environment, Shandong Normal University, Jinan 250014, China)

Abstract

Decomposing main drivers of CO 2 emissions and predicting the trend of it are the key to promoting low-carbon development for coping with climate change based on controlling GHG emissions. Here, we decomposed six drivers of CO 2 emissions in Changxing County using the Logarithmic Mean Divisia Index (LMDI) method. We then constructed a model for CO 2 emissions prediction based on a revised version of the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and used it to simulate energy-related CO 2 emissions in five scenarios. Results show that: (1) From 2010 to 2017, the economic output effect was a significant, direct, dominant, and long-term driver of increasing CO 2 emissions; (2) The STIRPAT model predicted that energy structure will be the decisive factor restricting total CO 2 emissions from 2018 to 2035; (3) Low-carbon development in the electric power sector is the best strategy for Changxing to achieve low-carbon development. Under the tested scenarios, Changxing will likely reach peak total CO 2 emissions (17.95 million tons) by 2030. Measures focused on optimizing the overall industrial structure, adjusting the internal industry sector, and optimizing the energy structure can help industry-oriented counties achieve targeted carbon reduction and control, while simultaneously achieving rapid economic development.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1212-:d:329129
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/5/1212/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/5/1212/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ang, B.W & Zhang, F.Q & Choi, Ki-Hong, 1998. "Factorizing changes in energy and environmental indicators through decomposition," Energy, Elsevier, vol. 23(6), pages 489-495.
    2. 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.
    3. Urban, Frauke, 2018. "China's rise: Challenging the North-South technology transfer paradigm for climate change mitigation and low carbon energy," Energy Policy, Elsevier, vol. 113(C), pages 320-330.
    4. Ang, B. W., 2005. "The LMDI approach to decomposition analysis: a practical guide," Energy Policy, Elsevier, vol. 33(7), pages 867-871, May.
    5. Hatzigeorgiou, Emmanouil & Polatidis, Heracles & Haralambopoulos, Dias, 2008. "CO2 emissions in Greece for 1990–2002: A decomposition analysis and comparison of results using the Arithmetic Mean Divisia Index and Logarithmic Mean Divisia Index techniques," Energy, Elsevier, vol. 33(3), pages 492-499.
    6. Binod Vaidya & Hussein T. Mouftah, 2020. "Connected Autonomous Electric Vehicles as Enablers for Low-Carbon Future," Chapters, in: Alfredo Vaccaro & Ahmed F. Zobaa & Prabhakar Karthikeyan Shanmugam & Kannaiah Sathish Kumar (ed.), Research Trends and Challenges in Smart Grids, IntechOpen.
    7. Jin-Wei Wang & Hua Liao & Bao-Jun Tang & Ruo-Yu Ke & Yi-Ming Wei, 2017. "Is the CO2 Emissions Reduction from Scale Change, Structural Change or Technology Change? Evidence from Non-metallic Sector of 11 Major Economies in 1995-2009," CEEP-BIT Working Papers 101, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    8. Lin, Boqiang & Du, Kerui, 2014. "Decomposing energy intensity change: A combination of index decomposition analysis and production-theoretical decomposition analysis," Applied Energy, Elsevier, vol. 129(C), pages 158-165.
    9. Pasurka, Carl Jr., 2006. "Decomposing electric power plant emissions within a joint production framework," Energy Economics, Elsevier, vol. 28(1), pages 26-43, January.
    10. Hong, Taehoon & Jeong, Kwangbok & Koo, Choongwan, 2018. "An optimized gene expression programming model for forecasting the national CO2 emissions in 2030 using the metaheuristic algorithms," Applied Energy, Elsevier, vol. 228(C), pages 808-820.
    11. Wang, Qunwei & Chiu, Yung-Ho & Chiu, Ching-Ren, 2015. "Driving factors behind carbon dioxide emissions in China: A modified production-theoretical decomposition analysis," Energy Economics, Elsevier, vol. 51(C), pages 252-260.
    12. Barton, John & Davies, Lloyd & Dooley, Ben & Foxon, Timothy J. & Galloway, Stuart & Hammond, Geoffrey P. & O’Grady, Áine & Robertson, Elizabeth & Thomson, Murray, 2018. "Transition pathways for a UK low-carbon electricity system: Comparing scenarios and technology implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2779-2790.
    13. Burandt, Thorsten & Xiong, Bobby & Löffler, Konstantin & Oei, Pao-Yu, 2019. "Decarbonizing China’s energy system – Modeling the transformation of the electricity, transportation, heat, and industrial sectors," Applied Energy, Elsevier, vol. 255(C).
    14. Luís Carmo-Calado & Manuel Jesús Hermoso-Orzáez & Roberta Mota-Panizio & Bruno Guilherme-Garcia & Paulo Brito, 2020. "Co-Combustion of Waste Tires and Plastic-Rubber Wastes with Biomass Technical and Environmental Analysis," Sustainability, MDPI, vol. 12(3), pages 1-19, February.
    15. Geng, Yong & Zhao, Hongyan & Liu, Zhu & Xue, Bing & Fujita, Tsuyoshi & Xi, Fengming, 2013. "Exploring driving factors of energy-related CO2 emissions in Chinese provinces: A case of Liaoning," Energy Policy, Elsevier, vol. 60(C), pages 820-826.
    16. Jan Weinzettel & Jan Kovanda, 2011. "Structural Decomposition Analysis of Raw Material Consumption," Journal of Industrial Ecology, Yale University, vol. 15(6), pages 893-907, December.
    17. Wang, Zhaohua & Liu, Wei, 2015. "Determinants of CO2 emissions from household daily travel in Beijing, China: Individual travel characteristic perspectives," Applied Energy, Elsevier, vol. 158(C), pages 292-299.
    18. Ang, B.W. & Liu, Na, 2007. "Energy decomposition analysis: IEA model versus other methods," Energy Policy, Elsevier, vol. 35(3), pages 1426-1432, March.
    19. Nieves, J.A. & Aristizábal, A.J. & Dyner, I. & Báez, O. & Ospina, D.H., 2019. "Energy demand and greenhouse gas emissions analysis in Colombia: A LEAP model application," Energy, Elsevier, vol. 169(C), pages 380-397.
    20. Xu, X.Y. & Ang, B.W., 2013. "Index decomposition analysis applied to CO2 emission studies," Ecological Economics, Elsevier, vol. 93(C), pages 313-329.
    21. Dhakal, Shobhakar, 2009. "Urban energy use and carbon emissions from cities in China and policy implications," Energy Policy, Elsevier, vol. 37(11), pages 4208-4219, November.
    22. Yu, Jinglei & Shao, Chaofeng & Xue, Chenyang & Hu, Huaqing, 2020. "China's aircraft-related CO2 emissions: Decomposition analysis, decoupling status, and future trends," Energy Policy, Elsevier, vol. 138(C).
    23. Sumabat, Ana Karmela & Lopez, Neil Stephen & Yu, Krista Danielle & Hao, Han & Li, Richard & Geng, Yong & Chiu, Anthony S.F., 2016. "Decomposition analysis of Philippine CO2 emissions from fuel combustion and electricity generation," Applied Energy, Elsevier, vol. 164(C), pages 795-804.
    24. Burandt, Thorsten & Xiong, Bobby & Löffler, Konstantin & Oei, Pao-Yu, 2019. "Decarbonizing China’s energy system – Modeling the transformation of the electricity, transportation, heat, and industrial sectors," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 255, pages 1-17.
    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. 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.
    2. 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).
    3. 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.

    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. 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.
    2. Wang, Qunwei & Wang, Yizhong & Zhou, P. & Wei, Hongye, 2017. "Whole process decomposition of energy-related SO2 in Jiangsu Province, China," Applied Energy, Elsevier, vol. 194(C), pages 679-687.
    3. Tan, Xianchun & Dong, Lele & Chen, Dexue & Gu, Baihe & Zeng, Yuan, 2016. "China’s regional CO2 emissions reduction potential: A study of Chongqing city," Applied Energy, Elsevier, vol. 162(C), pages 1345-1354.
    4. Wang, Qunwei & Chiu, Yung-Ho & Chiu, Ching-Ren, 2015. "Driving factors behind carbon dioxide emissions in China: A modified production-theoretical decomposition analysis," Energy Economics, Elsevier, vol. 51(C), pages 252-260.
    5. Wang, Qunwei & Hang, Ye & Su, Bin & Zhou, Peng, 2018. "Contributions to sector-level carbon intensity change: An integrated decomposition analysis," Energy Economics, Elsevier, vol. 70(C), pages 12-25.
    6. Wang, Miao & Feng, Chao, 2017. "Analysis of energy-related CO2 emissions in China’s mining industry: Evidence and policy implications," Resources Policy, Elsevier, vol. 53(C), pages 77-87.
    7. Liu, Xiao & Zhou, Dequn & Zhou, Peng & Wang, Qunwei, 2017. "What drives CO2 emissions from China’s civil aviation? An exploration using a new generalized PDA method," Transportation Research Part A: Policy and Practice, Elsevier, vol. 99(C), pages 30-45.
    8. Dequn Zhou & Xiao Liu & Peng Zhou & Qunwei Wang, 2017. "Decomposition Analysis of Aggregate Energy Consumption in China: An Exploration Using a New Generalized PDA Method," Sustainability, MDPI, vol. 9(5), pages 1-13, April.
    9. Azam, Muhammad & Younes, Ben Zaied & Hunjra, Ahmed Imran & Hussain, Nazim, 2022. "Integrated Spatial-Temporal decomposition analysis for life cycle assessment of carbon emission intensity change in various regions of China," Resources Policy, Elsevier, vol. 79(C).
    10. Tan, Ruipeng & Lin, Boqiang, 2018. "What factors lead to the decline of energy intensity in China's energy intensive industries?," Energy Economics, Elsevier, vol. 71(C), pages 213-221.
    11. Cansino, José M. & Sánchez-Braza, Antonio & Rodríguez-Arévalo, María L., 2018. "How can Chile move away from a high carbon economy?," Energy Economics, Elsevier, vol. 69(C), pages 350-366.
    12. Zha, Donglan & Yang, Guanglei & Wang, Qunwei, 2019. "Investigating the driving factors of regional CO2 emissions in China using the IDA-PDA-MMI method," Energy Economics, Elsevier, vol. 84(C).
    13. Fernández González, P. & Presno, M.J. & Landajo, M., 2015. "Regional and sectoral attribution to percentage changes in the European Divisia carbonization index," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1437-1452.
    14. Li, Hao & Zhao, Yuhuan & Qiao, Xiaoyong & Liu, Ya & Cao, Ye & Li, Yue & Wang, Song & Zhang, Zhonghua & Zhang, Yongfeng & Weng, Jianfeng, 2017. "Identifying the driving forces of national and regional CO2 emissions in China: Based on temporal and spatial decomposition analysis models," Energy Economics, Elsevier, vol. 68(C), pages 522-538.
    15. Román-Collado, Rocío & Cansino, José M. & Botia, Camilo, 2018. "How far is Colombia from decoupling? Two-level decomposition analysis of energy consumption changes," Energy, Elsevier, vol. 148(C), pages 687-700.
    16. Jaruwan Chontanawat, 2019. "Driving Forces of Energy-Related CO 2 Emissions Based on Expanded IPAT Decomposition Analysis: Evidence from ASEAN and Four Selected Countries," Energies, MDPI, vol. 12(4), pages 1-23, February.
    17. Patiño, Lourdes Isabel & Alcántara, Vicent & Padilla, Emilio, 2021. "Driving forces of CO2 emissions and energy intensity in Colombia," Energy Policy, Elsevier, vol. 151(C).
    18. Kang, Jidong & Zhao, Tao & Liu, Nan & Zhang, Xin & Xu, Xianshuo & Lin, Tao, 2014. "A multi-sectoral decomposition analysis of city-level greenhouse gas emissions: Case study of Tianjin, China," Energy, Elsevier, vol. 68(C), pages 562-571.
    19. Du, Kerui & Xie, Chunping & Ouyang, Xiaoling, 2017. "A comparison of carbon dioxide (CO2) emission trends among provinces in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 19-25.
    20. Song, Yi & Huang, Jianbai & Zhang, Yijun & Wang, Zhiping, 2019. "Drivers of metal consumption in China: An input-output structural decomposition analysis," Resources Policy, Elsevier, vol. 63(C), pages 1-1.

    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:13:y:2020:i:5:p:1212-:d:329129. 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.