IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i16p10053-d887788.html
   My bibliography  Save this article

Impact of Producer Service Agglomeration on Carbon Emission Efficiency and Its Mechanism: A Case Study of Urban Agglomeration in the Yangtze River Delta

Author

Listed:
  • Yaoshan Ma

    (School of Sociology, Shanghai University, Shanghai 200444, China)

  • Qingyu Yao

    (School of Economics, Shanghai University, Shanghai 200444, China)

Abstract

As an important part of the economic system of urban agglomeration, the agglomeration of producer services (APS) has become a key contributor to regional low-carbon development. This study analyzes the spatial effect of APS on carbon emission efficiency (CEE) as well as its mechanism and heterogeneity using the panel data of 41 cities in the Yangtze River Delta (YRD) region from 2005 to 2019. First, a U-shaped relationship exists between APS and CEE in both local and neighboring areas. Second, the non-linear relationship between APS and CEE is generated by allocation effects, structural effects and technology effects. Third, the effect of APS on CEE is constrained by the heterogeneity of urban characteristics, in which human capital, fiscal expenditure, and information infrastructure all support and positively moderate the energy-saving and carbon-reduction effect of APS. Fourth, the impact of externalities of APS on CEE varies, both the Marshall–Arrow–Romer (MAR) and Porter externalities having a U-shaped relationship with the CEE of neighboring areas but Jacobs externalities having no significant influence on the CEE of the surrounding areas. The findings of this study indicate that increasing the scale of APS in urban agglomeration, promoting the diversification and division of labor and the cooperation of industries across areas, and promoting the process of city–industry integration are important for achieving the goal of carbon peaking and carbon neutrality in the YRD region.

Suggested Citation

  • Yaoshan Ma & Qingyu Yao, 2022. "Impact of Producer Service Agglomeration on Carbon Emission Efficiency and Its Mechanism: A Case Study of Urban Agglomeration in the Yangtze River Delta," Sustainability, MDPI, vol. 14(16), pages 1-23, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10053-:d:887788
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/16/10053/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/16/10053/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Romer, Paul M, 1986. "Increasing Returns and Long-run Growth," Journal of Political Economy, University of Chicago Press, vol. 94(5), pages 1002-1037, October.
    2. Liu, Yunqiang & Zhu, Jialing & Li, Eldon Y. & Meng, Zhiyi & Song, Yan, 2020. "Environmental regulation, green technological innovation, and eco-efficiency: The case of Yangtze river economic belt in China," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    3. 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).
    4. James P. LeSage & R. Kelley Pace, 2014. "The Biggest Myth in Spatial Econometrics," Econometrics, MDPI, vol. 2(4), pages 1-33, December.
    5. Yuan, Huaxi & Feng, Yidai & Lee, Chien-Chiang & Cen, Yan, 2020. "How does manufacturing agglomeration affect green economic efficiency?," Energy Economics, Elsevier, vol. 92(C).
    6. Wang, Keying & Wu, Meng & Sun, Yongping & Shi, Xunpeng & Sun, Ao & Zhang, Ping, 2019. "Resource abundance, industrial structure, and regional carbon emissions efficiency in China," Resources Policy, Elsevier, vol. 60(C), pages 203-214.
    7. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    8. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2020. "Spatial and temporal correlation analysis of wind power between different provinces in China," Energy, Elsevier, vol. 191(C).
    9. Kena Mi & Rulong Zhuang, 2022. "Producer Services Agglomeration and Carbon Emission Reduction—An Empirical Test Based on Panel Data from China," Sustainability, MDPI, vol. 14(6), pages 1-19, March.
    10. Senlin Hu & Gang Zeng & Xianzhong Cao & Huaxi Yuan & Bing Chen, 2021. "Does Technological Innovation Promote Green Development? A Case Study of the Yangtze River Economic Belt in China," IJERPH, MDPI, vol. 18(11), pages 1-18, June.
    11. Canh, Nguyen Phuc & Schinckus, Christophe & Thanh, Su Dinh & Chong, Felicia Hui Ling, 2021. "The determinants of the energy consumption: A shadow economy-based perspective," Energy, Elsevier, vol. 225(C).
    12. YIN,Chao & HUANG,Ningyang, 2018. "Spatial Differences in the Development Level of Agricultural Producer Services in China," Asian Agricultural Research, USA-China Science and Culture Media Corporation, vol. 10(06), June.
    13. Zhao, Jun & Jiang, Qingzhe & Dong, Xiucheng & Dong, Kangyin & Jiang, Hongdian, 2022. "How does industrial structure adjustment reduce CO2 emissions? Spatial and mediation effects analysis for China," Energy Economics, Elsevier, vol. 105(C).
    14. Haochang Yang & Faming Zhang & Yixin He, 2021. "Exploring the effect of producer services and manufacturing industrial co-agglomeration on the ecological environment pollution control in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(11), pages 16119-16144, November.
    15. Rui Xie & Siling Yao & Feng Han & Jiayu Fang, 2019. "Land Finance, Producer Services Agglomeration, and Green Total Factor Productivity," International Regional Science Review, , vol. 42(5-6), pages 550-579, September.
    16. Shao, Shuai & Tian, Zhihua & Yang, Lili, 2017. "High speed rail and urban service industry agglomeration: Evidence from China's Yangtze River Delta region," Journal of Transport Geography, Elsevier, vol. 64(C), pages 174-183.
    17. Feng Han & Rui Xie & Mingyong Lai, 2018. "Traffic density, congestion externalities, and urbanization in China," Spatial Economic Analysis, Taylor & Francis Journals, vol. 13(4), pages 400-421, October.
    18. Tuochen Li & Dongri Han & Shaosong Feng & Lei Liang, 2019. "Can Industrial Co-Agglomeration between Producer Services and Manufacturing Reduce Carbon Intensity in China?," Sustainability, MDPI, vol. 11(15), pages 1-15, July.
    19. Shahid Yusuf & Kaoru Nabeshima & Shoichi Yamashita, 2008. "Growing Industrial Clusters in Asia : Serendipity and Science," World Bank Publications - Books, The World Bank Group, number 6429, December.
    20. 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.
    21. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    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. Lizhi Gui & Xiaowen Hu & Xiaorui Li & Ming Zheng, 2022. "Study on the Influence of Undertaking Industrial Transfer on the Sustainability Development of Wanjiang City Belt," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
    2. Zheng Li & Ruoyao Tang & Hanbin Qiu & Linwei Ma, 2023. "Smart Energy Urban Agglomerations in China: The Driving Mechanism, Basic Concepts, and Indicator Evaluation," Sustainability, MDPI, vol. 15(15), pages 1-23, August.

    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. Senlin Hu & Gang Zeng & Xianzhong Cao & Huaxi Yuan & Bing Chen, 2021. "Does Technological Innovation Promote Green Development? A Case Study of the Yangtze River Economic Belt in China," IJERPH, MDPI, vol. 18(11), pages 1-18, June.
    2. Kena Mi & Rulong Zhuang, 2022. "Producer Services Agglomeration and Carbon Emission Reduction—An Empirical Test Based on Panel Data from China," Sustainability, MDPI, vol. 14(6), pages 1-19, March.
    3. Dongchu Han & Mianfang Liu, 2022. "How Does the Digital Economy Contribute to Regional Green Development in China? Evidence-Based on the Intermediary Effect of Technological Innovation," Sustainability, MDPI, vol. 14(18), pages 1-14, September.
    4. Yuan, Huaxi & Feng, Yidai & Lee, Chien-Chiang & Cen, Yan, 2020. "How does manufacturing agglomeration affect green economic efficiency?," Energy Economics, Elsevier, vol. 92(C).
    5. Peng, Hui & Lu, Yaobin & Wang, Qunwei, 2023. "How does heterogeneous industrial agglomeration affect the total factor energy efficiency of China's digital economy," Energy, Elsevier, vol. 268(C).
    6. Wang, Xuliang & Xu, Lulu & Ye, Qin & He, Shi & Liu, Yi, 2022. "How does services agglomeration affect the energy efficiency of the service sector? Evidence from China," Energy Economics, Elsevier, vol. 112(C).
    7. Yuanyuan Wu & Zhanhua Jia & Tingting Yu, 2022. "Tourism and Green Development: Analysis of Linear and Non-Linear Effects," IJERPH, MDPI, vol. 19(23), pages 1-22, November.
    8. Hangang Hu & Lisha Pan & Xin Jing & Guan Li & Yuefei Zhuo & Zhongguo Xu & Yang Chen & Xueqi Wang, 2022. "The Spatiotemporal Non-Stationary Effect of Industrial Agglomeration on Urban Land Use Efficiency: A Case Study of Yangtze River Delta, China," Land, MDPI, vol. 11(5), pages 1-27, May.
    9. Wei Qian & Yongsheng Wang, 2022. "How Do Rising Labor Costs Affect Green Total Factor Productivity? Based on the Industrial Intelligence Perspective," Sustainability, MDPI, vol. 14(20), pages 1-19, October.
    10. Yuxi Chen & Mengting Zhang & Chencheng Wang & Xin Lin & Zhijie Zhang, 2023. "High-Tech Industrial Agglomeration, Government Intervention and Regional Energy Efficiency: Based on the Perspective of the Spatial Spillover Effect and Panel Threshold Effect," Sustainability, MDPI, vol. 15(7), pages 1-29, April.
    11. Cheng, Xiaoqiang & Yao, Dingjun & Qian, Yuanyuan & Wang, Bin & Zhang, Deliang, 2023. "How does fintech influence carbon emissions: Evidence from China's prefecture-level cities," International Review of Financial Analysis, Elsevier, vol. 87(C).
    12. Shuhua Zhang & Jian Li & Bao Jiang & Tianmiao Guo, 2023. "Government Intervention, Structural Transformation, and Carbon Emissions: Evidence from China," IJERPH, MDPI, vol. 20(2), pages 1-19, January.
    13. Samargandi, Nahla & Fidrmuc, Jan & Ghosh, Sugata, 2015. "Is the Relationship Between Financial Development and Economic Growth Monotonic? Evidence from a Sample of Middle-Income Countries," World Development, Elsevier, vol. 68(C), pages 66-81.
    14. Ruijing Zheng & Yu Cheng & Haimeng Liu & Wei Chen & Xiaodong Chen & Yaping Wang, 2022. "The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation," IJERPH, MDPI, vol. 19(15), pages 1-22, July.
    15. Zhang, Xiaobei & Wang, Xiaojun, 2021. "Measures of human capital and the mechanics of economic growth," China Economic Review, Elsevier, vol. 68(C).
    16. Dong, Kangyin & Dong, Xiucheng & Jiang, Qingzhe & Zhao, Jun, 2021. "Assessing energy resilience and its greenhouse effect: A global perspective," Energy Economics, Elsevier, vol. 104(C).
    17. Abdulqadir, Idris A. & Asongu, Simplice A., 2022. "The asymmetric effect of internet access on economic growth in sub-Saharan Africa," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 44-61.
    18. Shang, Hua & Jiang, Li & Pan, Xianyou & Pan, Xiongfeng, 2022. "Green technology innovation spillover effect and urban eco-efficiency convergence: Evidence from Chinese cities," Energy Economics, Elsevier, vol. 114(C).
    19. Pietrobelli, Carlo, 2019. "Modern industrial policy in Latin America: Lessons from cluster development policies," MERIT Working Papers 2019-031, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    20. Xiaoxue Liu & Fuzhen Cao & Shuangshuang Fan, 2022. "Does Human Capital Matter for China’s Green Growth?—Examination Based on Econometric Model and Machine Learning Methods," IJERPH, MDPI, vol. 19(18), pages 1-27, September.

    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:16:p:10053-:d:887788. 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.