IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i18p13694-d1239279.html
   My bibliography  Save this article

Spatio-Temporal Characteristics of Industrial Carbon Emission Efficiency and Their Impacts from Digital Economy at Chinese Prefecture-Level Cities

Author

Listed:
  • Lyu Jun

    (Seoul School of Integrated Sciences and Technologies, Seoul 03767, Republic of Korea)

  • Shuang Lu

    (College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China)

  • Xiang Li

    (College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China)

  • Zeng Li

    (Guangdong Public Laboratory of Geographic Spatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou 510070, China)

  • Chenglong Cao

    (College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China)

Abstract

In the pursuit of China’s dual carbon goals, identifying spatio-temporal changes in industrial carbon emission efficiency and their influencing factors in cities at different stages of development is the key to effective formulation of countermeasures to promote the low-carbon transformation of Chinese national industry and achieve high-quality economic development. In this study, we used balanced panel data of 270 Chinese cities from 2005 to 2020 as a research object: (1) to show spatio-temporal evolution patterns in urban industrial carbon emission efficiency; (2) to analyze the aggregation characteristics of industrial carbon emission efficiency in Chinese cities using Global Moran’s I statistics; and (3) to use the hierarchical regression model for panel data to assess the non-linear impact of the digital economy on the industrial carbon emission efficiency of cities. The results show the following: (1) the industrial carbon emission efficiency of Chinese cities exhibited an upward trend from 2005 to 2020, with a spatial distribution pattern of high in the south and low in the north; (2) China’s urban industrial carbon emission efficiency is characterized by significant spatial autocorrelation, with increasing and stabilizing correlation, and a relatively fixed pattern of spatial agglomeration; (3) there is a significant inverted-U-shaped relationship between the digital economy and the industrial carbon emission efficiency of cities. The digital economy increases carbon emissions and inhibits industrial carbon emission efficiency in the early stages of development but inhibits carbon emissions and promotes industrial carbon emission efficiency in mature developmental stages. Therefore, cities at all levels should reduce pollution and carbon emissions from high-energy-consuming and high-polluting enterprises, gradually reduce carbon-intensive industries, and accelerate the digital transformation and upgrading of enterprises. Western, central, and eastern regions especially should seek to promote the sharing of innovation resources, strengthen exchanges and interactions relating to scientific and technological innovation, and jointly explore coordinated development routes for the digital economy.

Suggested Citation

  • Lyu Jun & Shuang Lu & Xiang Li & Zeng Li & Chenglong Cao, 2023. "Spatio-Temporal Characteristics of Industrial Carbon Emission Efficiency and Their Impacts from Digital Economy at Chinese Prefecture-Level Cities," Sustainability, MDPI, vol. 15(18), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13694-:d:1239279
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/18/13694/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/18/13694/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kangni Lyu & Shuwang Yang & Kun Zheng & Yao Zhang, 2023. "How Does the Digital Economy Affect Carbon Emission Efficiency? Evidence from Energy Consumption and Industrial Value Chain," Energies, MDPI, vol. 16(2), pages 1-20, January.
    2. Iftikhar, Yaser & Wang, Zhaohua & Zhang, Bin & Wang, Bo, 2018. "Energy and CO2 emissions efficiency of major economies: A network DEA approach," Energy, Elsevier, vol. 147(C), pages 197-207.
    3. Hanhua Shao & Jixin Cheng & Yuansheng Wang & Xiaoming Li, 2022. "Can Digital Finance Promote Comprehensive Carbon Emission Performance? Evidence from Chinese Cities," IJERPH, MDPI, vol. 19(16), pages 1-18, August.
    4. Zhang, Wei & Liu, Xuemeng & Wang, Die & Zhou, Jianping, 2022. "Digital economy and carbon emission performance: Evidence at China's city level," Energy Policy, Elsevier, vol. 165(C).
    5. Ang, James B., 2007. "CO2 emissions, energy consumption, and output in France," Energy Policy, Elsevier, vol. 35(10), pages 4772-4778, October.
    6. Matthew A. Cole & Eric Neumayer, 2003. "Examining the Impact of Demographic Factors On Air Pollution," Labor and Demography 0312005, University Library of Munich, Germany, revised 13 May 2004.
    7. Xiaodie Liu & Xiangqian Wang & Xiangrui Meng, 2023. "Carbon Emission Scenario Prediction and Peak Path Selection in China," Energies, MDPI, vol. 16(5), pages 1-17, February.
    8. Yi, Ming & Liu, Yafen & Sheng, Mingyue Selena & Wen, Le, 2022. "Effects of digital economy on carbon emission reduction: New evidence from China," Energy Policy, Elsevier, vol. 171(C).
    9. Luo, Kang & Liu, Yaobin & Chen, Pei-Fen & Zeng, Mingli, 2022. "Assessing the impact of digital economy on green development efficiency in the Yangtze River Economic Belt," Energy Economics, Elsevier, vol. 112(C).
    10. Marco Springmann & Da Zhang & Valerie Karplus, 2015. "Consumption-Based Adjustment of Emissions-Intensity Targets: An Economic Analysis for China’s Provinces," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 61(4), pages 615-640, August.
    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. Umar Nawaz Kayani & Mochammad Fahlevi & Roohi Mumtaz & Reema Al Qaruty & Muzaffar Asad, 2023. "The Nexus between Carbon Emissions and Per Capita Income of Households: Evidence from Japanese Prefectures," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 567-572, November.
    2. Ruiqing Yuan & Xiangyang Xu & Yanli Wang & Jiayi Lu & Ying Long, 2024. "Evaluating Carbon-Emission Efficiency in China’s Construction Industry: An SBM-Model Analysis of Interprovincial Building Heating," Sustainability, MDPI, vol. 16(6), pages 1-16, March.
    3. Pinjie Xie & Yue Lu & Yuwen Xie, 2024. "The Influencing Factors of Carbon Emissions in the Industrial Sector: Empirical Analysis Based on a Spatial Econometric Model," Sustainability, MDPI, vol. 16(6), pages 1-24, 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. Bai, Ling & Guo, Tianran & Xu, Wei & Liu, Yaobin & Kuang, Ming & Jiang, Lei, 2023. "Effects of digital economy on carbon emission intensity in Chinese cities: A life-cycle theory and the application of non-linear spatial panel smooth transition threshold model," Energy Policy, Elsevier, vol. 183(C).
    2. Yukun Ma & Shaojian Wang & Chunshan Zhou, 2023. "Can the Development of the Digital Economy Reduce Urban Carbon Emissions? Case Study of Guangdong Province," Land, MDPI, vol. 12(4), pages 1-13, March.
    3. Xiaoli Wu & Yaoyao Qin & Qizhuo Xie & Yunyi Zhang, 2022. "The Mediating and Moderating Effects of the Digital Economy on PM 2.5 : Evidence from China," Sustainability, MDPI, vol. 14(23), pages 1-17, November.
    4. Yanan Wu & Zinb Abduljabbar Mohamed Al-Duais & Biyu Peng, 2023. "Towards a low-carbon society: spatial distribution, characteristics and implications of digital economy and carbon emissions decoupling," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    5. Zhenhua Xu & Fuyi Ci, 2023. "Spatial-Temporal Characteristics and Driving Factors of Coupling Coordination between the Digital Economy and Low-Carbon Development in the Yellow River Basin," Sustainability, MDPI, vol. 15(3), pages 1-23, February.
    6. Nan Li & Beibei Shi & Rong Kang, 2023. "Analysis of the Coupling Effect and Space-Time Difference between China’s Digital Economy Development and Carbon Emissions Reduction," IJERPH, MDPI, vol. 20(1), pages 1-25, January.
    7. Jingrong Tan & Lin Chen, 2022. "Spatial Effect of Digital Economy on Particulate Matter 2.5 in the Process of Smart Cities: Evidence from Prefecture-Level Cities in China," IJERPH, MDPI, vol. 19(21), pages 1-20, November.
    8. Le Hoang Phong & Dang Thi Bach Van & Ho Hoang Gia Bao, 2018. "The Role of Globalization on CO2 Emission in Vietnam Incorporating Industrialization, Urbanization, GDP per Capita and Energy Use," International Journal of Energy Economics and Policy, Econjournals, vol. 8(6), pages 275-283.
    9. Kangni Lyu & Shuwang Yang & Kun Zheng & Yao Zhang, 2023. "How Does the Digital Economy Affect Carbon Emission Efficiency? Evidence from Energy Consumption and Industrial Value Chain," Energies, MDPI, vol. 16(2), pages 1-20, January.
    10. Ruiyuan Dong & Xiaowei Zhou, 2023. "Analysis of the Nonlinear and Spatial Spillover Effects of the Digital Economy on Carbon Emissions in the Yellow River Basin," Sustainability, MDPI, vol. 15(6), pages 1-18, March.
    11. Charfeddine, Lanouar & Ben Khediri, Karim, 2016. "Financial development and environmental quality in UAE: Cointegration with structural breaks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 1322-1335.
    12. Yaqiao Xu & Jiayi Hu & Liusan Wu, 2023. "Efficiency Evaluation of China’s Provincial Digital Economy Based on a DEA Cross-Efficiency Model," Mathematics, MDPI, vol. 11(13), pages 1-11, July.
    13. Jayasooriya, Sujith, 2020. "Nexus of Demographic Change, Structural Transformation and Economic Growth in South Asia," MPRA Paper 100831, University Library of Munich, Germany.
    14. Pingguo Xu & Leyi Chen & Huajuan Dai, 2022. "Pathways to Sustainable Development: Corporate Digital Transformation and Environmental Performance in China," Sustainability, MDPI, vol. 15(1), pages 1-21, December.
    15. Liang Liu & Yuhan Zhang & Xiujuan Gong & Mengyue Li & Xue Li & Donglin Ren & Pan Jiang, 2022. "Impact of Digital Economy Development on Carbon Emission Efficiency: A Spatial Econometric Analysis Based on Chinese Provinces and Cities," IJERPH, MDPI, vol. 19(22), pages 1-21, November.
    16. Li, Ji & Zhou, Quan & He, Xu & Chen, Wan & Xu, Hongming, 2023. "Data-driven enabling technologies in soft sensors of modern internal combustion engines: Perspectives," Energy, Elsevier, vol. 272(C).
    17. Onafowora, Olugbenga A. & Owoye, Oluwole, 2014. "Bounds testing approach to analysis of the environment Kuznets curve hypothesis," Energy Economics, Elsevier, vol. 44(C), pages 47-62.
    18. Shahbaz, Muhammad & Jalil, Abdul & Dube, Smile, 2010. "Environmental Kuznets curve (EKC): Times series evidence from Portugal," MPRA Paper 27443, University Library of Munich, Germany.
    19. Haiyan Lei & Suiping Zeng & Aihemaiti Namaiti & Jian Zeng, 2023. "The Impacts of Road Traffic on Urban Carbon Emissions and the Corresponding Planning Strategies," Land, MDPI, vol. 12(4), pages 1-20, March.
    20. Menggen Chen & Songyangyang Zhao & Jiawen Wang, 2023. "The Impact of the Digital Economy on Regional Carbon Emissions: Evidence from China," Sustainability, MDPI, vol. 15(20), pages 1-34, October.

    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:15:y:2023:i:18:p:13694-:d:1239279. 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.