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The Annals of Regional Science

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08-05-2024 | Special Issue Paper

Industrial clusters and carbon emission reduction: evidence from China

Authors: Ye Liu, Yiyun Wu, Xiwei Zhu

Published in: The Annals of Regional Science | Issue 2/2024

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Abstract

In the context of carbon peaking and neutrality goals, this paper examines the impact of industrial clusters on firms’ carbon emissions based on listed manufacturing firms in China from 2008 to 2020. To avoid the endogeneity issue, we constructed an industrial cluster database ten years earlier than the sample period. The results of the empirical analysis suggest that industrial clusters are conducive to decreasing carbon emissions. The stronger the industrial cluster, the lower the carbon intensity. Examining the channel reveals that industrial clusters have an emission reduction effect through digital transformation. The heterogeneity analysis demonstrates that the variations in industrial clusters, including pollutant emissions, energy intensity, and energy consumption structure, significantly affect a firm's carbon emission reduction performance. In addition, environmental policies and the internal and external green development impetus of firms also affect emission reduction behavior. Our findings provide the rationale for governments to promote green transformation and enhance digital competitiveness through industrial cluster development.

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For example, data from the Hubei Science and Technology Department show that the total revenue of national creative clusters reached 254.794 billion yuan, contributing 3.294 billion yuan to provincial tax revenue in 2021.

Local cluster support projects include the "415X" advanced cluster cultivation program in Zhejiang province, a characteristic industrial cluster cultivation program in Jiangsu province, etc.

According to China Academy of Information and Communications Technology (CAICT), the scale of China's digital economy accounted for 39.2 trillion yuan in 2020, and its growth rate reached 9.7%, which is the key driver for China's stable economic growth (Wang et al. 2022).

Brynjolfsson E, Wang J, Wan X (2023) Information technology, firm size, and industrial concentration, NBER working paper, No. 31065.

Report on Chinese regional and urban digital economy development. Retrieved from http://www.caict.ac.cn/kxyj/qwfb/ztbg/202101/t20210104_367593.htm.

The shared platforms include common technical service institutions, industrial sharing intelligent manufacturing factories, and warehousing logistics distribution systems.

According to the world-class advanced manufacturing clusters Whitebook (2022), the Yueqing Electric Industrial Cluster, the Xuzhou Mechanical Engineering Industrial Cluster, and the Ningbo Green Petrochemical Industrial Cluster are all typical examples of industrial clusters digitalization.

Retrieved from https://www.ccidgroup.com/info/1096/21329.htm.

According to the China Wind Carbon Emissions Database, only 375 listed firms declared their total carbon emissions for the 2021 fiscal year, accounting for approximately 7% of the total listed firms.

The stock market value is calculated based on the closing price on December 31, 2021. In China, a stock market value of more than 50 billion is considered to represent a large firm. A value between 10 and 50 billion is indicative of a medium-sized company, whereas a value of less than 10 billion is representative of a small company.

Coal is the predominant source of energy consumption in China. Conventionally, the carbon emission coefficient is calculated based on coal burning in Chinese studies, such as Wang et al (2023). The carbon emission coefficient for 1 ton of coal completed combustion is 0.68 in this paper, and 1 ton of carbon is burned in oxygen to produce about 3.667 tons of carbon dioxide, so the calculation coefficient is 0.68 × 3.667 = 2.493.

According to the guidelines on accounting methods and reporting of greenhouse gas emissions published by the China Development and Reform Commission, the firm’s carbon emissions come from combustion and escape, production process, waste emissions, and land use conversion (such as forest to industrial land).

Compared to the listed firms, the China Annual Industry Survey contains more small and medium-sized firms, and the industrial cluster calculated from this database is more representative. However, due to variable availability, we use listed firm data to assess firms' carbon emissions.

To avoid the influence of extreme values, we excluded 0.5% of samples from the word frequency-related indicators, including digital, intelligent, and governance.

The relative variation of standard deviation is calculated as follows: (standard deviation of independent variable × estimated coefficient)/standard deviation of the dependent variable, i.e., 2.2% = (0.46 × 0.059/1.233) × 100%. The following values are obtained by the same method, and the standard errors of the variables are shown in Table 1.

The government implemented low-carbon pilot cities in batches in 2010, 2013, and 2017 during the sample period.

Aboura S, Chevallier J (2014) Cross-market spillovers with ‘volatility surprise.’ Rev Financ Econ 23(4):194–207. https://doi.org/10.1016/j.rfe.2014.08.002CrossRef

Agrawal A, Gans S, Goldfarb A (2019) Artificial intelligence: the ambiguous labor market impact of automating prediction. J Econ Perspect 33(2):31–49. https://doi.org/10.1257/jep.33.2.31CrossRef

Arouri M, Youssef B, M’henni A et al (2012) Energy consumption, economic growth and CO2 emissions in middle east and north African countries. Energy Policy 45:342–349. https://doi.org/10.1016/j.enpol.2012.02.042CrossRef

Auffhammer M, Sun W, Wu J et al (2016) The decomposition and dynamics of industrial carbon dioxide emissions for 287 Chinese cities in 1998–2009. J Econ Surv 30(3):460–481. https://doi.org/10.1111/joes.12158CrossRef

Baptista R, Swann P (1998) Do firms in clusters innovate more? Res Policy 27(5):525–540. https://doi.org/10.1016/S0048-7333(98)00065-1CrossRef

Bettiol M, Capestro M, De Marchi V et al (2021) Industrial districts and the fourth industrial revolution. Compet Rev 31(1):12–26. https://doi.org/10.1108/CR-12-2019-0155CrossRef

Bloom N, Sadun R, Van Reenen J (2012) Americans do IT better: US multinationals and the productivity miracle. Am Econ Rev 102(1):167–201. https://doi.org/10.1257/aer.102.1.167CrossRef

Brown J, Martinsson G, Petersen BC (2012) Do financing constraints matter for R & D? Eur Econ Rev 56(8):1512–1529. https://doi.org/10.1016/j.euroecorev.2012.07.007CrossRef

Brynjolfsson E, Rock D, Syverson C (2021) The productivity J-curve: how intangibles complement general purpose technologies. Am Econ J-Macroecon 13(1):333–372. https://doi.org/10.1257/mac.20180386CrossRef

Cainelli G, Lacobucci D (2012) Agglomeration, related variety, and vertical integration. Econ Geogr 88(3):255–277. https://doi.org/10.1111/j.1944-8287.2012.01156.xCrossRef

Cárdenas Rodríguez M, Dupont-Courtade L, Oueslati W (2016) Air pollution and urban structure linkages: evidence from European cities. Renew Sust Energ Rew 53:1–9. https://doi.org/10.1016/j.rser.2015.07.190CrossRef

Chapple L, Clarkson PM, Gold DL (2013) The cost of carbon: capital market effects of the proposed emission trading scheme (ETS). Abacus 49(1):1–33. https://doi.org/10.1111/abac.12006CrossRef

Chen Y (2020) Improving market performance in the digital economy. China Econ Rev 62:101482. https://doi.org/10.1016/j.chieco.2020.101482CrossRef

Combes P, Duranton G, Gobillon L et al (2006) Labour pooling, labour poaching, and spatial clustering. Reg Sci Urban Econ 36(1):1–28. https://doi.org/10.1016/j.regsciurbeco.2005.06.003CrossRef

Costantini V, Crespi F, Martini C et al (2015) Demand-pull and technology-push public support for Eco-innovation: the case of the biofuels sector. Res Policy 44(3):577–595. https://doi.org/10.1016/j.respol.2014.12.011CrossRef

Cui M, Pan S (2015) Developing focal capabilities for e-commerce adoption: a resource orchestration perspective. Inform Manage-Amster 52(2):200–209. https://doi.org/10.1016/j.im.2014.08.006CrossRef

Dranove D, Forman C, Goldfarb A et al (2014) The trillion dollar conundrum: complementarities and health information technology. Am Econ J-Econ Polic 6(4):239–270. https://doi.org/10.1257/pol.6.4.239CrossRef

Duranton G, Overman HG (2005) Testing for localization using micro-geographic data. Rev Econ Stud 72(4):1077–1106. https://doi.org/10.1111/0034-6527.00362CrossRef

Farboodi M, Mihet R, Philippon T et al (2019) Big data and firm dynamics. AEA Pap Proc 109:38–42. https://doi.org/10.1257/pandp.20191001CrossRef

Glaeser EL, Kerr WR (2009) Local industrial conditions and entrepreneurship: how much of the spatial distribution can we explain? J Econ Mange Strat 18(3):623–663. https://doi.org/10.1111/j.1530-9134.2009.00225.xCrossRef

Glaser EL, Kahn ME (2010) The greenness of cites: carbon dioxide emissions and urban development. J Urban Econ 67(3):404–418. https://doi.org/10.1016/j.jue.2009.11.006CrossRef

Glaser EL, Rosenthal S, Strange C (2010) Urban economics and entrepreneurship. J Urban Econ 67(1):1–14. https://doi.org/10.1016/j.jue.2009.10.005CrossRef

Glaser EL (2011) Triumph of the city: how our greatest invention makes US richer, smarter, greener, healthier, and happier. Penguin Press, Maryland

Goldfarb A, Tucker C (2019) Digital economics. J Econ Lit 57(1):3–43. https://doi.org/10.1257/jel.20171452CrossRef

Goldsmith-Pinkham P, Sorkin I, Swift H (2020) Bartik instruments: what, when, why, and how. Am Econ Rev 110(8):2586–2624. https://doi.org/10.1257/aer.20181047CrossRef

Götz M, Jankowska B (2017) Clusters and industry 4.0 - do they fit together? Eur Plan Stud 25(9):1633–1653. https://doi.org/10.1080/09654313.2017.1327037CrossRef

Hassan OAG, Marston C (2019) Corporate financial disclosure measurement in the empirical accounting literature: a review article. Int J Account 54(2):1950006. https://doi.org/10.1142/S1094406019500069CrossRef

Hervas-Oliver J, Estelles-Miguel S, Peris-Ortiz M et al (2023) Does regional innovation policy really work for industry 4.0? Evidence for industrial districts. Eur Plan Stud 31(7):1358–1376. https://doi.org/10.1080/09654313.2022.2135368CrossRef

Holland SP, Hughes JE, Knittel CR (2009) Greenhouse gas reductions under low carbon fuel standards? Am Econ J-Econ Polic 1(1):106–146. https://doi.org/10.1257/pol.1.1.106CrossRef

Imbert C, Seror M, Zhang Y et al (2022) Migrants and firms: evidence from China. Am Econ Rev 112(6):1885–1914. https://doi.org/10.1257/aer.20191234CrossRef

Jiang T, Yu Y, Jahanger Y et al (2022) Structural emissions reduction of China’s power and heating industry under the goal of ‘double carbon’: a perspective from input-output analysis. Sustain Prod Consump 31:346–356. https://doi.org/10.1016/j.spc.2022.03.003CrossRef

Jirčíková E, Pavelková D, Bialic-Davendra M et al (2013) The age of clusters and its influence on their activity preferences. Technol Econ Dev Eco 19(4):621–637. https://doi.org/10.3846/20294913.2013.837115CrossRef

Jin Y, Shi R, Wu B et al (2023) What does the function of industrial clusters enable industrial platforms come from? The perspective of function development and trust construction. Manage World 5:127–144. https://doi.org/10.19744/j.cnki.11-1235/f.2023.0061 (in Chinese)CrossRef

Kasahara H, Li B (2020) Grain exports and the causes of China’s great famine, 1959–1961: county-level evidence. J Dev Econ 146:102513. https://doi.org/10.1016/j.jdeveco.2020.102513CrossRef

Ketels C, Protsiv S (2021) Cluster presence and economic performance: a new look based on European data. Reg Stud 55(2):208–220. https://doi.org/10.1080/00343404.2020.1792435CrossRef

Klump R, Mcadam P, Willman A (2007) Factor substitution and factor-augmenting technical progress in the United States: a normalized supply-side system approach. Rev Econ Stat 89(1):183–192. https://doi.org/10.1162/rest.89.1.183CrossRef

Krugman R (1991) Geography and trade. MIT press, MA

Lewbel A (1997) Constructing instruments for regressions with measurement error when no additional data are available, with an application to patents and R&D. Econometrica 65(5):1201–1213. https://doi.org/10.2307/2171884CrossRef

Mads B, Torben D (2013) Cluster facilitation from a cluster life cycle perspective. Eur Plan Stud 21(4):556–574. https://doi.org/10.1080/09654313.2012.722953CrossRef

Mao R (2016) Industry clustering and financial constraints: a reinterpretation based on fixed asset liquidation. Econ Dev Cult Change 64(4):795–821. https://doi.org/10.1086/686582CrossRef

Moyer JD, Hughes BB (2012) ICTs: do they contribute to increased carbon emissions? Technol Forecast Soc 79(5):919–931. https://doi.org/10.1016/j.techfore.2011.12.005CrossRef

Mueller A, Varkek K, Titus J et al (2012) Pioneering orientation and firm growth: knowing when and to what degree pioneering makes sense. J Manage 36(5):1517–1549. https://doi.org/10.1177/0149206310380249CrossRef

Otsuka E, Goto M, Sueyoshinergy T (2014) Energy efficiency and agglomeration economics: the case of Japanese manufacturing industries. Reg Sci Policy Pract 6(2):195–212. https://doi.org/10.1111/rsp3.12039CrossRef

Overman G, Puga D (2010) Labor pooling as a source of agglomeration: an empirical investigation. University of Chicago press, ChicagoCrossRef

Park S (2018) The fourth industrial revolution and implications for innovative cluster policies. AI & Soc 33(3):433–445. https://doi.org/10.1007/s00146-017-0777-5CrossRef

Pflüger M (2021) City size, pollution and emission policies. J Urban Econ 162(2):103391. https://doi.org/10.1016/j.jue.2021.103391CrossRef

Qin B, Wu J (2015) Does urban concentration mitigate CO2 emissions? Evidence from China 1998–2008. China Econ Rev 35:220–231. https://doi.org/10.1016/j.chieco.2014.02.006CrossRef

Qiu L, Zhou M, Wei X (2018) Regulation, innovation, and firm selection: the Porter hypothesis under monopolistic competition. J Environ Econ Manag 92:638–658. https://doi.org/10.1016/j.jeem.2017.08.012CrossRef

Regnier C, Legras S (2018) Urban structure and environmental externalities. Environ Resour Econ 70(1):31–51. https://doi.org/10.1007/s10640-016-0109-0CrossRef

Rosenthal SS, Strange WC (2001) The determinants of agglomeration. J Urban Econ 50(2):191–229. https://doi.org/10.1006/juec.2001.2230CrossRef

Shao S, Yang L, Gan C et al (2016) Using an extended LMDI model to explore techno-economic drivers of energy-related industrial CO2 emission changes: a case study for Shanghai (China). Renew Sust Energ Rev 55:516–536. https://doi.org/10.1016/j.rser.2015.10.081CrossRef

Shao S, Zhang K, Dou J (2019) Effects of economic agglomeration on energy saving and emission reduction: theory and empirical evidence from China. Manage Word 1:36–60. https://doi.org/10.19744/j.cnki.11-1235/f.2019.0005. (in Chinese)CrossRef

Snyder J, Strömberg D (2010) Press coverage and political accountability. J Polit Econ 118(2):355–408. https://doi.org/10.1086/652903CrossRef

Tzeremes P (2018) Time-varying causality between energy consumption, CO2 emissions, and economic growth: evidence from US States. Environ Sci Pollut R 25(6):6044–6060. https://doi.org/10.1007/s11356-017-0979-xCrossRef

Valero-Gil J, Surroca A, Tribo A et al (2023) Innovation vs standardization: the conjoint effects of Eco-innovation and environmental management systems on environmental management systems on environmental performance. Res Policy 52(4):104737. https://doi.org/10.1016/j.respol.2023.104737CrossRef

Wang Z, Fu H, Ren X (2023) Political connections and corporate carbon emission: new evidence from Chinese industrial firms. Technol Forecast Soc Chang 188:122326. https://doi.org/10.1016/j.techfore.2023.122326CrossRef

Wang J, Dong K, Done X et al (2022) Assessing the digital economy and its carbon-mitigation effects: the case of China. Energ Econ 113:106198. https://doi.org/10.1016/j.eneco.2022.106198CrossRef

Wang R, Liang Q, Li G (2018) Virtual agglomeration: a new form of spatial organization for the deep integration of new generation information technology and real economy. Manage Word 34(2):13–21. https://doi.org/10.19744/j.cnki.11-1235/f.2018.02.002. (in Chinese)CrossRef

Wei J, Gu Q, Xiang Y et al (2009) China industrial cluster development report. China Machine Press, Beijing

Wu F, Hu H, Lin H et al (2021) Enterprise digital transformation and capital market performance: empirical evidence from stock liquidity. Manage Word 7:130–144. https://doi.org/10.19744/j.cnki.11-1235/f.2021.0097. (in Chinese)CrossRef

Wu J, Reimer J (2016) Pollution mobility, productivity disparity, and the spatial distribution of polluting and nonpolluting firms. J Reg Sci 56(4):615–634. https://doi.org/10.1111/jors.12266CrossRef

Wu J, Segerson B, Wang C (2023) Is environmental regulation the answer to pollution problems in urbanizing economies? J Environ Econ Manage 117:102754. https://doi.org/10.1016/j.jeem.2022.102754CrossRef

Wu Y, Zhu X (2017) Industrial policy and economic geography: evidence from China. J Asia Pac Econ 22(1):173–190. https://doi.org/10.1080/13547860.2016.1261485CrossRef

Xu L, Fan M, Yang L et al (2021) Heterogeneous green innovations and carbon emission performance: evidence at China’s city level. Energ Econ 99:105269. https://doi.org/10.1016/j.eneco.2021.105269CrossRef

Yadav MS, Prabhu JC, Chandy RK (2007) Managing the future: CEO attention and innovation outcomes. J Mark 71(4):84–101. https://doi.org/10.1509/jmkg.71.4.84CrossRef

Yi M, Liu Y, Sheng M et al (2022) Effects of digital economy on carbon emission reduction: new evidence from China. Energy Policy 171:113271. https://doi.org/10.1016/j.enpol.2022.113271CrossRef

Yu L, Wang Y, Wei X et al (2023) Towards low-carbon development: the role of industrial robots in decarbonization in Chinese cities. J Environ Manage 330(6):117216. https://doi.org/10.1016/j.jenvman.2023.117216CrossRef

Zhang X, Ruan J (2011) Evolution and development of industrial clusters in China. Zhejiang University Press, Hangzhou

Zhang Y, Peng Y, Ma C, et al (2017) Can environmental innovation facilitate carbon emissions reduction? Evidence from China. Energy Policy 100:18–28. https://doi.org/10.1016/j.enpol.2016.10.005

Zhang W, Liu X, Wang D et al (2022) Digital economy and carbon emission performance: evidence at China’s city level. Energy Policy 165:112927. https://doi.org/10.1016/j.enpol.2022.112927CrossRef

Zhao Z, Dai Y, Fan Q et al (2022) Digitalization of national industrial clusters in China: estimation from online industrial belts. Bull Chin Acad Sci 37(12):1757–1769 (in Chinese)

Zeng D, Zhao L (2009) Pollution havens and industrial agglomeration. J Environ Econ Manag 58(2):141–153. https://doi.org/10.1016/j.jeem.2008.09.003CrossRef

Zhu X, Liu Y, He M et al (2019) Entrepreneurship and industrial clusters: evidence from China industrial census. Small Bus Econ 52(3):595–616. https://doi.org/10.1007/s11187-017-9974-3CrossRef

Title: Industrial clusters and carbon emission reduction: evidence from China
Authors: Ye Liu
Yiyun Wu
Xiwei Zhu
Publication date: 08-05-2024
Publisher: Springer Berlin Heidelberg
Published in: The Annals of Regional Science / Issue 2/2024
Print ISSN: 0570-1864
Electronic ISSN: 1432-0592
DOI: https://doi.org/10.1007/s00168-024-01268-z

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