IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i15p9524-d879278.html
   My bibliography  Save this article

Carbon Emissions in the Yellow River Basin: Analysis of Spatiotemporal Evolution Characteristics and Influencing Factors Based on a Logarithmic Mean Divisia Index (LMDI) Decomposition Method

Author

Listed:
  • Ke Liu

    (School of Economics and Management, Zhengzhou University of Light Industry, Science Avenue 136, Zhengzhou 450007, China)

  • Xinyue Xie

    (School of Economics and Management, Zhengzhou University of Light Industry, Science Avenue 136, Zhengzhou 450007, China)

  • Mingxue Zhao

    (School of Economics and Management, Zhengzhou University of Light Industry, Science Avenue 136, Zhengzhou 450007, China)

  • Qian Zhou

    (Economics School, Zhongnan University of Economics and Law, Nanhu Avenue 182, Wuhan 430073, China)

Abstract

The “14th Five-Year Plan” period is a critical period and a window to obtain emission peak and carbon neutrality in China. The Yellow River Basin, a vital location for population activities and economic growth, is significant to China’s emission peak by 2030. Analyzing carbon emissions patterns and decomposing the influencing factors can provide theoretical support for reducing carbon emissions. Based on the energy consumption data from 2000–2019, the method recommended by Intergovernmental Panel on Climate Change (IPCC) is used to calculate the carbon emissions in the Yellow River Basin. The Logarithmic Mean Divisia Index (LMDI) decomposition method decomposes the influence degree of each influencing factor. The conclusions are as follows: First, The Yellow River Basin has not yet reached the peak of carbon emissions. Regional carbon emissions trends are different. Second, Shandong, Shanxi, Henan and Inner Mongolia consistently ranked in the top four in total carbon emissions, with low carbon emission efficiency. Third, Economic development has the most significant contribution to carbon emissions; other factors have various effects on nine provinces.

Suggested Citation

  • Ke Liu & Xinyue Xie & Mingxue Zhao & Qian Zhou, 2022. "Carbon Emissions in the Yellow River Basin: Analysis of Spatiotemporal Evolution Characteristics and Influencing Factors Based on a Logarithmic Mean Divisia Index (LMDI) Decomposition Method," Sustainability, MDPI, vol. 14(15), pages 1-18, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9524-:d:879278
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/15/9524/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/15/9524/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Jinying & Li, Sisi, 2020. "Energy investment, economic growth and carbon emissions in China—Empirical analysis based on spatial Durbin model," Energy Policy, Elsevier, vol. 140(C).
    2. Zhang, Fan & Deng, Xiangzheng & Phillips, Fred & Fang, Chuanglin & Wang, Chao, 2020. "Impacts of industrial structure and technical progress on carbon emission intensity: Evidence from 281 cities in China," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    3. 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.
    4. Peters, Glen P., 2008. "From production-based to consumption-based national emission inventories," Ecological Economics, Elsevier, vol. 65(1), pages 13-23, March.
    5. Li, Fujia & Dong, Suocheng & Li, Zehong & Li, Yu & Li, Shantong & Wan, Yongkun, 2012. "The improvement of CO2 emission reduction policies based on system dynamics method in traditional industrial region with large CO2 emission," Energy Policy, Elsevier, vol. 51(C), pages 683-695.
    6. Vaninsky, Alexander, 2014. "Factorial decomposition of CO2 emissions: A generalized Divisia index approach," Energy Economics, Elsevier, vol. 45(C), pages 389-400.
    7. Kennedy, Christopher & Steinberger, Julia & Gasson, Barrie & Hansen, Yvonne & Hillman, Timothy & Havránek, Miroslav & Pataki, Diane & Phdungsilp, Aumnad & Ramaswami, Anu & Mendez, Gara Villalba, 2010. "Methodology for inventorying greenhouse gas emissions from global cities," Energy Policy, Elsevier, vol. 38(9), pages 4828-4837, September.
    8. Cheng, Zhonghua & Li, Lianshui & Liu, Jun, 2018. "Industrial structure, technical progress and carbon intensity in China's provinces," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2935-2946.
    9. Zhou, Wenhao & Zeng, Bo & Wang, Jianzhou & Luo, Xiaoshuang & Liu, Xianzhou, 2021. "Forecasting Chinese carbon emissions using a novel grey rolling prediction model," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    10. Yong Wang & Yu Zhou & Lin Zhu & Fei Zhang & Yingchun Zhang, 2018. "Influencing Factors and Decoupling Elasticity of China’s Transportation Carbon Emissions," Energies, MDPI, vol. 11(5), pages 1-29, May.
    11. Zhou, Xiaoyan & Zhang, Jie & Li, Junpeng, 2013. "Industrial structural transformation and carbon dioxide emissions in China," Energy Policy, Elsevier, vol. 57(C), pages 43-51.
    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. Shan Yang & Shangkai Zhu & Gao Deng & Huan Li, 2022. "Study on Influencing Factors and Spatial Effects of Carbon Emissions Based on Logarithmic Mean Divisia Index Model: A Case Study of Hunan Province," Sustainability, MDPI, vol. 14(23), pages 1-19, November.

    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. Luan, Bingjiang & Zou, Hong & Chen, Shuxing & Huang, Junbing, 2021. "The effect of industrial structure adjustment on China’s energy intensity: Evidence from linear and nonlinear analysis," Energy, Elsevier, vol. 218(C).
    2. Changyou Zhang & Wenyu Zhang & Weina Luo & Xue Gao & Bingchen Zhang, 2021. "Analysis of Influencing Factors of Carbon Emissions in China’s Logistics Industry: A GDIM-Based Indicator Decomposition," Energies, MDPI, vol. 14(18), pages 1-23, September.
    3. Lina Liu & Yunyun Zhang & Bei Liu & Pishi Xiu & Lipeng Sun, 2022. "How to Achieve Carbon Neutrality: From the Perspective of Innovative City Pilot Policy in China," IJERPH, MDPI, vol. 19(24), pages 1-20, December.
    4. You, Jianmin & Zhang, Wei, 2022. "How heterogeneous technological progress promotes industrial structure upgrading and industrial carbon efficiency? Evidence from China's industries," Energy, Elsevier, vol. 247(C).
    5. Zhao, Jun & Dong, Xiucheng & Dong, Kangyin, 2021. "How does producer services’ agglomeration promote carbon reduction?: The case of China," Economic Modelling, Elsevier, vol. 104(C).
    6. Bu, Yan & Wang, Erda & Möst, Dominik & Lieberwirth, Martin, 2022. "How population migration affects carbon emissions in China: Factual and counterfactual scenario analysis," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    7. Lin, Boqiang & Zhou, Yicheng, 2021. "How does vertical fiscal imbalance affect the upgrading of industrial structure? Empirical evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    8. Lazarus, Michael & Chandler, Chelsea & Erickson, Peter, 2013. "A core framework and scenario for deep GHG reductions at the city scale," Energy Policy, Elsevier, vol. 57(C), pages 563-574.
    9. Jianguo Zhou & Baoling Jin & Shijuan Du & Ping Zhang, 2018. "Scenario Analysis of Carbon Emissions of Beijing-Tianjin-Hebei," Energies, MDPI, vol. 11(6), pages 1-17, June.
    10. Su, Yongxian & Chen, Xiuzhi & Li, Yong & Liao, Jishan & Ye, Yuyao & Zhang, Hongou & Huang, Ningsheng & Kuang, Yaoqiu, 2014. "China׳s 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 231-243.
    11. Liu, Zhu & Feng, Kuishuang & Hubacek, Klaus & Liang, Sai & Anadon, Laura Diaz & Zhang, Chao & Guan, Dabo, 2015. "Four system boundaries for carbon accounts," Ecological Modelling, Elsevier, vol. 318(C), pages 118-125.
    12. Weidong Sun & Zhigang Chen & Danyang Wang, 2019. "Can Land Marketization Help Reduce Industrial Pollution?," IJERPH, MDPI, vol. 16(12), pages 1-16, June.
    13. Xue, Ruoyu & Wang, Shanshan & Long, Wenqi & Gao, Gengyu & Liu, Donghui & Zhang, Ruiqin, 2021. "Uncovering GHG emission characteristics of industrial parks in Central China via emission inventory and cluster analysis," Energy Policy, Elsevier, vol. 151(C).
    14. Wu, Linfei & Sun, Liwen & Qi, Peixiao & Ren, Xiangwei & Sun, Xiaoting, 2021. "Energy endowment, industrial structure upgrading, and CO2 emissions in China: Revisiting resource curse in the context of carbon emissions," Resources Policy, Elsevier, vol. 74(C).
    15. 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).
    16. Haiying Liu & Avik Sinha & Mehmet Akif Destek & Majed Alharthi & Muhammad Wasif Zafar, 2022. "Moving toward sustainable development of sub‐Saharan African countries: Investigating the effect of financial inclusion on environmental quality," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(6), pages 2015-2024, December.
    17. Chen, Shaoqing & Long, Huihui & Chen, Bin & Feng, Kuishuang & Hubacek, Klaus, 2020. "Urban carbon footprints across scale: Important considerations for choosing system boundaries," Applied Energy, Elsevier, vol. 259(C).
    18. Ling Luo & Yang Fu & Hui Li, 2023. "Do Urban Innovation Policies Reduce Carbon Emission? Empirical Evidence from Chinese Cities with DID," Sustainability, MDPI, vol. 15(8), pages 1-17, April.
    19. Fan, Wei & Li, Li & Wang, Feiran & Li, Ding, 2020. "Driving factors of CO2 emission inequality in China: The role of government expenditure," China Economic Review, Elsevier, vol. 64(C).
    20. Wang, Jianlong & Wang, Weilong & Liu, Yong & Wu, Haitao, 2023. "Can industrial robots reduce carbon emissions? Based on the perspective of energy rebound effect and labor factor flow in China," Technology in Society, Elsevier, vol. 72(C).

    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:15:p:9524-:d:879278. 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.