Abstract
Under the background of urban connectivity, whether there are similarities and differences in the impacts of local industrial agglomeration and inter-city industrial agglomeration borrowing performance on carbon emission intensity(CI), and how cities can fully utilize the external force-borrowing performance to reduce local CI, these issues are of great significance for the cost saving and efficiency enhancement in the process of carbon emission (CE) reduction. Based on panel data of 280 prefecture-level cities in China from 2003 to 2020, the panel dual fixed-effect model, instrumental variable method, and adjustment effect model were used to analyze the impacts of the manufacturing agglomeration (MA), producer service agglomeration (PA), and the collaborative agglomeration (CA) on the CI from the perspective of individual cities and the urban system. The results showed that the influence of MA on CI presents a significant inverted U-shaped relationship, PA significantly reduces CI, and the CA of the two industries increases CI. Further analysis showed that the borrowing MA performance improves CI, especially in newer industrial-based cities, non-resource-based cities, and medium and big cities; the borrowing PA performance reduces CI, especially in old industrial-based cities, non-resource-based cities, and large cities; and the borrowing CA performance has no significant effect on CI. In addition, the development of the Internet strengthens the influence of borrowing performance in MA and PA on CI.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Data will be made available on request.
References
Alonso W (1973) Urban zero population growth. Daedalus 102:191–206
Burger MJ, Meijers EJ, Hoogerbrugge MM, Tresserra JM (2015) Borrowed size, agglomeration shadows and cultural amenities in North-West Europe. Eur Plan Stud 23:1090–1109. https://doi.org/10.1080/09654313.2014.905002
Camagni R, Capello R, Caragliu A (2016) Static vs. dynamic agglomeration economies. Spatial context and structural evolution behind urban growth. Pap Reg Sci 95:133–158. https://doi.org/10.1111/pirs.12182
Cao Z, Derudder B, Dai L, Peng Z (2022) ‘Buzz-and-pipeline’ dynamics in Chinese science: the impact of interurban collaboration linkages on cities’ innovation capacity. Reg Stud 56:290–306. https://doi.org/10.1080/00343404.2021.1906410
Chen C, Sun Y, Lan Q, Jiang F (2020a) Impacts of industrial agglomeration on pollution and ecological efficiency—a spatial econometric analysis based on a big panel dataset of China’s 259 cities. J Clean Prod 258:120721. https://doi.org/10.1016/j.jclepro.2020.120721
Chen J, Gao M, Cheng S et al (2020b) County-level CO2 emissions and sequestration in China during 1997–2017. Sci Data 7:391. https://doi.org/10.1038/s41597-020-00736-3
Ding J, Liu B, Shao X (2022) Spatial effects of industrial synergistic agglomeration and regional green development efficiency: evidence from China. Energy Econ 112:106156. https://doi.org/10.1016/j.eneco.2022.106156
Dong F, Li Y, Li K et al (2022) Can smart city construction improve urban ecological total factor energy efficiency in China? Fresh evidence from generalized synthetic control method. Energy 241:122909. https://doi.org/10.1016/j.energy.2021.122909
Ellison G, Glaeser EL (1997) Geographic concentration in U.S. manufacturing industries: a Dartboard approach. J Polit Econ 105:889–927. https://doi.org/10.1086/262098
Geng Y, Liu W, Wu Y (2021) How do zombie firms affect China’s industrial upgrading? Econ Model 97:79–94. https://doi.org/10.1016/j.econmod.2021.01.010
Guo R, Yuan Y (2022) Research on the influence mechanism of internet development on industrial co-agglomeration. Stat Res 39:52–67. https://doi.org/10.19343/j.cnki.11-1302/c.2022.06.004
Guo A, Liu P, Zhong F et al (2022a) Borrowing size and urban green development efficiency in the city network of China: impact measures and size thresholds. Land 11:493. https://doi.org/10.3390/land11040493
Guo Q, Wang Y, Dong X (2022b) Effects of smart city construction on energy saving and CO2 emission reduction: evidence from China. Appl Energy 313:118879. https://doi.org/10.1016/j.apenergy.2022.118879
Han F, Xie R (2017) Does the agglomeration of producer services reduce carbon emissions?. J Quant Tech Econ 34:40–58. https://doi.org/10.13653/j.cnki.jqte.2017.03.003
He Z, Chen Z, Feng X (2022) Different types of industrial agglomeration and green total factor productivity in China: do institutional and policy characteristics of cities make a difference? Environ Sci Eur 34:64. https://doi.org/10.1186/s12302-022-00645-9
Hu Y, Jiang H, Zhong Z (2020) Impact of green credit on industrial structure in China: theoretical mechanism and empirical analysis. Environ Sci Pollut Res 27:10506–10519. https://doi.org/10.1007/s11356-020-07717-4
Huang Y, Hong T, Ma T (2020) Urban network externalities, agglomeration economies and urban economic growth. Cities 107:102882. https://doi.org/10.1016/j.cities.2020.102882
Jiang N, Jiang W, Zhang J, Chen H (2022) Can national urban agglomeration construction reduce PM2.5 pollution? Evidence from a quasi-natural experiment in China. Urban Clim 46:101302. https://doi.org/10.1016/j.uclim.2022.101302
Lan F, Sun L, Pu W (2021) Research on the influence of manufacturing agglomeration modes on regional carbon emission and spatial effect in China. Econ Model 96:346–352. https://doi.org/10.1016/j.econmod.2020.03.016
Li H, Liu B (2022) The effect of industrial agglomeration on China’s carbon intensity: evidence from a dynamic panel model and a mediation effect model. Energy Rep 8:96–103. https://doi.org/10.1016/j.egyr.2022.05.070
Lin B, Huang C (2023) Promoting variable renewable energy integration: the moderating effect of digitalization. Appl Energy 337:120891. https://doi.org/10.1016/j.apenergy.2023.120891
Lin B, Zhou Y (2021) Does fiscal decentralization improve energy and environmental performance? New perspective on vertical fiscal imbalance. Appl Energy 302:117495. https://doi.org/10.1016/j.apenergy.2021.117495
Liu X, Zhang X, Sun W (2022) Does the agglomeration of urban producer services promote carbon efficiency of manufacturing industry? Land Use Policy 120:106264. https://doi.org/10.1016/j.landusepol.2022.106264
Liu B, Zheng K, Zhu M et al (2023) Towards sustainability: the impact of industrial synergistic agglomeration on the efficiency of regional green development. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-023-28449-1
Ma D, Zhu Q (2022) Innovation in emerging economies: research on the digital economy driving high-quality green development. J Bus Res 145:801–813. https://doi.org/10.1016/j.jbusres.2022.03.041
Ma S, Zhang Y, Lv J et al (2020) Big data driven predictive production planning for energy-intensive manufacturing industries. Energy 211:118320. https://doi.org/10.1016/j.energy.2020.118320
Meijers EJ, Burger MJ (2017) Stretching the concept of ‘borrowed size.’ Urban Studies 54:269–291. https://doi.org/10.1177/0042098015597642
Meijers EJ, Burger MJ, Hoogerbrugge MM (2016) Borrowing size in networks of cities: city size, network connectivity and metropolitan functions in Europe. Pap Reg Sci 95:181–198. https://doi.org/10.1111/pirs.12181
Meijers EJ (2013) Metropolitan labor productivity and urban spatial structure. In: Klaesson J, Johansson B, Karlsson C (eds) Metropolitan Regions: Knowledge Infrastructures of the Global Economy. Springer, Berlin, Heidelberg, 141–166. https://doi.org/10.1007/978-3-642-32141-2_7
National Bureau of Statistics. Notice of the National Bureau of Statistics on the Issuance of the Statistical Classification of Producer Services (2019). https://www.stats.gov.cn/sj/tjbz/gjtjbz/202302/t20230213_1902776.html
Nie M, Zhao H (2013) Does industrial agglomeration promote low-carbon development?—an empirical study based on China’s manufacture industry. Econ Manag 27:70–75
Otsuka A (2020) Inter-regional networks and productive efficiency in Japan. Pap Reg Sci 99:115–133. https://doi.org/10.1111/pirs.12474
Peng H, Lu Y, Wang Q (2023) How does heterogeneous industrial agglomeration affect the total factor energy efficiency of China’s digital economy. Energy 268:126654. https://doi.org/10.1016/j.energy.2023.126654
Phelps NA, Fallon RJ, Williams CL (2001) Small firms, borrowed size and the urban-rural shift. Reg Stud 35:613–624. https://doi.org/10.1080/00343400120075885
Shetewy N, Shahin AI, Omri A, Dai K (2022) Impact of financial development and internet use on export growth: New evidence from machine learning models. Res Int Bus Financ 61:101643. https://doi.org/10.1016/j.ribaf.2022.101643
Song Y, Yang T, Li Z et al (2020) Research on the direct and indirect effects of environmental regulation on environmental pollution: Empirical evidence from 253 prefecture-level cities in China. J Clean Prod 269:122425. https://doi.org/10.1016/j.jclepro.2020.122425
Song C, Zhang Z, Xu W, Elshkaki A (2023) The spatial effect of industrial transfer on carbon emissions under firm location decision: a carbon neutrality perspective. J Environ Manage 330:117139. https://doi.org/10.1016/j.jenvman.2022.117139
Tang C, Chai J (2022) Spatial-temporal evolution and proximity mechanism of urban networks in China from the multiplicity perspective. Front Phys 10. https://doi.org/10.3389/fphy.2022.879218
Tian X, Bai F, Jia J et al (2019) Realizing low-carbon development in a developing and industrializing region: Impacts of industrial structure change on CO2 emissions in southwest China. J Environ Manage 233:728–738. https://doi.org/10.1016/j.jenvman.2018.11.078
Volgmann K, Rusche K (2020) The geography of borrowing size: exploring spatial distributions for German urban regions: the geography of borrowing size. Tijds Voor Econ En Soc Geog 111:60–79. https://doi.org/10.1111/tesg.12362
Wang L, You J (2023) An integrated perspective on the spatial–temporal characteristics of China’s manufacturing carbon emissions at the regional and industry levels. Energy Rep 10:1688–1701. https://doi.org/10.1016/j.egyr.2023.08.034
Wang Y, Yan W, Ma D, Zhang C (2018) Carbon emissions and optimal scale of China’s manufacturing agglomeration under heterogeneous environmental regulation. J Clean Prod 176:140–150. https://doi.org/10.1016/j.jclepro.2017.12.118
Wang L, Yue Y, Xie R, Wang S (2020) How global value chain participation affects China’s energy intensity. J Environ Manage 260:110041. https://doi.org/10.1016/j.jenvman.2019.110041
Wang K-L, Sun T-T, Xu R-Y et al (2022) How does internet development promote urban green innovation efficiency? Evidence from China. Technol Forecast Soc Chang 184:122017. https://doi.org/10.1016/j.techfore.2022.122017
Wu R, Lin B (2021) Does industrial agglomeration improve effective energy service: an empirical study of China’s iron and steel industry. Appl Energy 295:117066. https://doi.org/10.1016/j.apenergy.2021.117066
Xu H, Liu W, Zhang D (2023) Exploring the role of co-agglomeration of manufacturing and producer services on carbon productivity: an empirical study of 282 cities in China. J Clean Prod 399:136674. https://doi.org/10.1016/j.jclepro.2023.136674
Yang R, Hu Z, Hu S (2023) The failure of collaborative agglomeration: from the perspective of industrial pollution emission. J Clean Prod 387:135952. https://doi.org/10.1016/j.jclepro.2023.135952
Yao C, Song D, Fan X (2020) Does the small size of cities restrict economic growth? a re-examination from the perspective of two kinds of ‘borrowed-size.’ Chin J Popul Res Environ 30:62–71. https://doi.org/10.12062/cpre.20200117
Yao C, Song D (2019) Borrowed-size, network externalities and agglomeration economies in the urban agglomerations. Ind Econ Res 76–87. https://doi.org/10.13269/j.cnki.ier.2019.02.007
Zeng P, Shang L, Xing M (2023) Spatial correlation between producer services agglomeration and carbon emissions in the Yangtze River Economic Belt based on point-of-interest. Sci Rep 13:5606. https://doi.org/10.1038/s41598-023-32803-1
Zhao J, Dong X, Dong K (2021) How does producer services’ agglomeration promote carbon reduction?: the case of China. Econ Model 104:105624. https://doi.org/10.1016/j.econmod.2021.105624
Zhao R, Fang C, Liu J, Zhang L (2022) The evaluation and obstacle analysis of urban resilience from the multidimensional perspective in Chinese cities. Sustain Cities Soc 86:104160. https://doi.org/10.1016/j.scs.2022.104160
Zhou F, Wang X (2022) The carbon emissions trading scheme and green technology innovation in China: a new structural economics perspective. Econ Anal Policy 74:365–381. https://doi.org/10.1016/j.eap.2022.03.007
Zhu Y, Xia Y (2019) Industrial agglomeration and environmental pollution: evidence from China under New Urbanization. Energy Environ 30:1010–1026. https://doi.org/10.1177/0958305X18802784
Funding
The authors gratefully acknowledge the National Social Science Fund of China (Grant No. 21BGL269) and Qingdao Social Science Planning Research Project (Grant No. QDSKL2301057).
Author information
Authors and Affiliations
Contributions
Conceptualization, methodology, writing—review and editing, and supervision: Dongjing Chen. Methodology, software, data curation, writing—original draft, and writing—review and editing: Yachong Wang.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Consent for publication
We do not have any individual person’s data in any form.
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Ilhan Ozturk
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Chen, D., Wang, Y. Influence mechanism of industrial agglomeration on carbon emission intensity—a perspective on borrowing performance. Environ Sci Pollut Res 31, 21737–21751 (2024). https://doi.org/10.1007/s11356-024-32425-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-024-32425-8