Abstract
The cooperative emergence of low-carbon technologies is a dynamic evolutionary process in which manufacturing enterprises adopt such technologies for production and drive large-scale diffusion across networks. Considering the clustering characteristics of the relational network among manufacturing enterprises, this paper integrated the scale-free networks with variable clustering into the evolutionary model to explorer the driving effect of the cooperative emergence of low-carbon technology. The results indicated that the expected payoffs of manufacturing enterprises under various technology choices are influenced by both their own decisions and the strategic behaviors of neighbors in the manufacturer network. When carbon reduction costs are low, the expansion of the manufacturer network tends to promote the cooperative emergence of low-carbon technology, whereas when carbon reduction costs are high, the expansion of the network may hinder cooperation. Although carbon taxes effectively promote the cooperative emergence of low-carbon technology among manufacturing enterprises, they simultaneously lead to varying reductions in the expected payoffs. Stronger technology spillover effects have facilitated the cooperative emergence of low-carbon technology among manufacturing enterprises, while expected payoffs are projected to rise steadily. As low-carbon technology costs decline, excessive industrial clustering is not invariably beneficial, the policymakers should reform market structures and support small and medium-sized manufacturers. Once consumer preferences for low-carbon products can be effectively demonstrated, which will significantly accelerate the emergence of low-carbon technology co-operation among manufacturing enterprises, then policymakers should strengthen demand-side policies by improving carbon footprint and labeling systems.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
All data generated or analyzed during this study are included in this article.
References
Ahmadi, Y., Yamazaki, A., & Kabore, P. (2022). How do carbon taxes affect emissions? Plant-level evidence from manufacturing. Environmental and Resource Economics, 82(2), 285–325. https://doi.org/10.1007/s10640-022-00678-x
Bai, X. (2024). Energy conservation and carbon reduction in key industries are being advanced in an orderly manner, and energy efficiency levels continue to rise. China Economic Guide. https://doi.org/10.28095/n.cnki.ncjjd.2024.000445.
Barabasi, Albert, L. Albert&Reka, (1999). Emergence of Scaling in Random Networks. Science. https://doi.org/10.1515/9781400841356.349A.-L
Carbon Trading Network Industry carbon intensity advanced value data query list. [in Chinese]. 2014 [cited 2025 0120]; Available from: www.tanpaifang.com/tanjiliang/2014/0504/31846.html
Cepni, O., Gil-Alana, L. A., Gupta, R., & Polat, O. (2025). Time-variation in the persistence of carbon price uncertainty: The role of carbon policy uncertainty. The Quarterly Review of Economics and Finance, 102, Article 102004. https://doi.org/10.1016/j.qref.2025.102004
Chen, W. (2018). Using evolutionary game theory to study governments and manufacturers’ behavioral strategies under various carbon taxes and subsidies. Journal of Cleaner Production, 201, 123–141. https://doi.org/10.1016/j.jclepro.2018.08.007
China Net Finance The total trading volume of the national carbon market exceeded 180 million tons in 2024.[in Chinese]. 2024 [cited 2025 0120]; Available from: https://finance.china.com.cn/esg/20250101/6202067.shtml
Cholda, P. (2012). The structure of complex networks: Theory and applications. Computing Reviews, 53(10), 598–599.
Escoto, X., Gebrehewot, D., & Morris, K. C. (2022). Refocusing the barriers to sustainability for small and medium-sized manufacturers. Journal of Cleaner Production, 338, Article 130589.
Fan, R., Chen, R., Wang, Y., Wang, D., & Chen, F. (2022). Simulating the impact of demand-side policies on low-carbon technology diffusion: A demand-supply coevolutionary model. Journal of Cleaner Production, 351, Article 131561. https://doi.org/10.1016/j.jclepro.2022.131561
Global Energy Review (2025). 2025 [cited 2025 0324]; Available from: https://www.iea.org/reports/global-energy-review-2025
Han, T., Lu, J., Zhang, H., & Zhang, K. (2025). Co-evolution analysis of low-carbon cooperation between service providers and demanders. Journal of Cleaner Production, 490, Article 144711. https://doi.org/10.1016/j.jclepro.2025.144711
Holme, P., & Kim, B. J. (2002). Growing scale-free networks with tunable clustering. Physical Review E, 65(2), Article 026107.
Hu, X., Yang, Z., Sun, J., & Zhang, Y. (2020). Carbon tax or cap-and-trade: Which is more viable for Chinese remanufacturing industry? Journal of Cleaner Production, 243, Article 118606. https://doi.org/10.1016/j.jclepro.2019.118606
Hua, J., Wang, K., Lin, J., & Qian, Y. (2024). Carbon tax vs. carbon cap-and-trade: Implementation of carbon border tax in cross-regional production. International Journal of Production Economics, 274, Article 109317. https://doi.org/10.1016/j.ijpe.2024.109317
Huang, C., Du, S., Wang, B., & Tang, W. (2023). Accelerate or hinder it? Manufacturer transformation under competition and carbon emission trading. International Journal of Production Research, 61(18), 6230–6250. https://doi.org/10.1080/00207543.2022.2058434
Huang, Y., He, P., Cheng, T. C. E., Xu, S., Pang, C., & Tang, H. (2024). Optimal strategies for carbon emissions policies in competitive closed-loop supply chains: A comparative analysis of carbon tax and cap-and-trade policies. Computers & Industrial Engineering, 195, Article 110423. https://doi.org/10.1016/j.cie.2024.110423
Jauhari, W. A., Novia Ramadhany, S. C., Nur Rosyidi, C., Mishra, U., & Hishamuddin, H. (2023). Pricing and green inventory decisions for a supply chain system with green investment and carbon tax regulation. Journal of Cleaner Production, 425, Article 138897. https://doi.org/10.1016/j.jclepro.2023.138897
Jiang, X., Sun, L., & Wang, Y. (2023). Technology spillover and incremental cost effects of carbon transfers on optimal carbon quotas in supply chains. Journal of Cleaner Production, 419, Article 138171. https://doi.org/10.1016/j.jclepro.2023.138171
Jiang, X., Sun, L., & Wang, Y. (2024). Effects of interactions between technology spillovers and carbon transfers on carbon emission reduction decisions and coordination within supply chains. Journal of Business & Industrial Marketing, 39(7), 1497–1512. https://doi.org/10.1108/JBIM-04-2023-0215
Ju, L., Yin, Z., Yang, S., Zhou, Q., & Lu, X. (2022). Bi-level electricity–carbon collaborative transaction optimal model for the rural electricity retailers integrating distributed energy resources by virtual power plant. Energy Reports, 8, 9871–9888. https://doi.org/10.1016/j.egyr.2022.07.171
Li, W., Xu, J., Ostic, D., Yang, J., Guan, R., & Zhu, L. (2021). Why low-carbon technological innovation hardly promote energy efficiency of China? – Based on spatial econometric method and machine learning. Computers & Industrial Engineering, 160, Article 107566. https://doi.org/10.1016/j.cie.2021.107566
Lin, B., & Lei, X. (2015). Carbon emissions reduction in China’s food industry. Energy Policy, 86(NOV.), 483–492.
Liu, J., Ke, H., & Gao, Y. (2022). Manufacturer’s R &D cooperation contract: Linear fee or revenue-sharing payment in a low-carbon supply chain. Annals of Operations Research, 318(1), 323–355. https://doi.org/10.1007/s10479-022-04869-z
Liu, Q., & Zhu, X. (2023). Carbon reduction decisions in green technology collaborative R&D and spillover time lag effects. Journal of Cleaner Production, 429, Article 139595. https://doi.org/10.1016/j.jclepro.2023.139595
Liu, Q., & Zhu, X. (2024). How Carbon Tax Policy Affects the Carbon Emissions of Manufacturers with Green Technology Spillovers? Environmental Modeling & Assessment, 29(5), 971–985. https://doi.org/10.1007/s10666-024-09965-x
Liu, W., Liu, M., & Li, Y. (2024). How to promote China’s green economic development? The combination effects of consumption tax and carbon tax policies. Energy & Environment 35(3).
Lou, W., Wu, B., Chen, F., & Zhu, B. (2024). Exploring the dynamic diffusion of low-carbon technology research and development among enterprises: Based on an evolutionary game model with a two-level heterogeneous social network. Managerial and Decision Economics, 45(7), 5182–5195. https://doi.org/10.1002/mde.4309
Luo, J., Hu, M., Huang, M., & Bai, Y. (2023). How does innovation consortium promote low-carbon agricultural technology innovation: An evolutionary game analysis. Journal of Cleaner Production, 384, Article 135564. https://doi.org/10.1016/j.jclepro.2022.135564
Metcalf, G. E. (2021). Carbon taxes in theory and practice. Annual Review of Resource Economics, 13(1), 245–265. https://doi.org/10.1146/annurev-resource-102519-113630
National Standardization Management Committee of China GB/T 4754–2017. National Economic Industry Classification (in Chinese). 2017 [cited 2025 0120]; Available from: https://openstd.samr.gov.cn/bzgk/gb/newGbInfo?hcno=A703F0E23DD165A5A1318679F312D158
Nie, P. Y., Wang, C., & Wen, H. X. (2021). Technology spillover and innovation. Technology Analysis and Strategic Management, 34(2), 210–222. https://doi.org/10.1080/09537325.2021.1893294
Publication: State and Trends of Carbon Pricing 2025. (2025) [cited 2025 0701]; Available from: https://openknowledge.worldbank.org/entities/publication/e5f6e755-e6a6-4d2c-927a-23b5cc8a9b03
Regulation, E. U. (2023). 2023/956 of the European Parliament and of the Council of 10 May 2023 establishing a carbon border adjustment mechanism. [cited; Available from: https://eur-lex.europa.eu/eli/reg/2023/956/oj
Shi, Y., Wei, Z., Shahbaz, M., & Zeng, Y. (2021). Exploring the dynamics of low-carbon technology diffusion among enterprises: An evolutionary game model on a two-level heterogeneous social network. Energy Economics, 101, Article 105399. https://doi.org/10.1016/j.eneco.2021.105399
Shrestha, R. M., & Shrestha, R. (1998). Environmental and electricity planning implications of carbon tax and technological constraints in a developing country. Energy Policy, 26(7), 527–533. https://doi.org/10.1016/S0301-4215(97)00144-4
Sun, T., & She, D. (2016). Research on the relationship between the international competitiveness of China’s manufacturing industry and factor price -- based on the empirical analysis of 28 manufacturing industries(In Chinese). Shanghai economic research. 05, 10–18 + 57.
Timilsina, G. R. (2022). Carbon Taxes. Journal of Economic Literature, 60(4), 1456–1502. https://doi.org/10.1257/jel.20211560
Uzzi, B. (2008). A social network’s changing statistical properties and the quality of human innovation. Journal of Physics A: Mathematical and Theoretical, 41(22), 224023. https://doi.org/10.1088/1751-8113/41/22/224023
Wang, C., Wang, L., Wang, W., Xiong, Y., & Du, C. (2023). Does carbon emission trading policy promote the corporate technological innovation? Empirical evidence from China’s high-carbon industries. Journal of Cleaner Production, 411, Article 137286. https://doi.org/10.1016/j.jclepro.2023.137286
Wang, H. (2017). Study on purification of food grade carbon dioxide impurities by adsorption technology. Chinese and Foreign Wine Industry · Beer Science and Technology, 11, 20–25.
Wang, J., Cheng, S., Guo, X., Xu, X., & Wang, Z. (2024). An evolutionary analysis of the diffusion of low-carbon technology innovation in supply networks. Research in International Business and Finance, 70, Article 102400. https://doi.org/10.1016/j.ribaf.2024.102400
Wang, J., Xu, H., & Wang, M. (2025). Technology diffusion considering technological progress and multiple policy combinations based on evolutionary game theory. Technology in Society, 81, Article 102799. https://doi.org/10.1016/j.techsoc.2024.102799
Wang, K. (2024). The achievements and prospects of China’s carbon market construction. China Economic Report, 01, 107–119.
Wang, M., Li, Y., Li, M., Shi, W., & Quan, S. (2019). Will carbon tax affect the strategy and performance of low-carbon technology sharing between enterprises? Journal of Cleaner Production, 210, 724–737. https://doi.org/10.1016/j.jclepro.2018.10.321
Wang, W., Zhao, D., & Chen, Y. (2011). Research on the current situation, problems and development models of low-carbon technology in China. China Soft Science., 12, 84–91.
Wei, J., Li, Y., & Liu, Y. (2024). Tripartite evolutionary game analysis of carbon emission reduction behavior strategies under government regulation. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-024-04972-0
Wu, D., & Zhu, C. (2025). Evaluating carbon quota allocation mechanisms for manufacturers’ green technology investment strategies under technology spillovers. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-025-06111-9.
Wu, Y., Liu, Z., Wang, X., Yin, A., & Zhang, S. (2026). Research on the diffusion of low-carbon technologies through trilateral evolutionary game based on incentive mechanisms. Applied Mathematics and Computation, 516, Article 129873. https://doi.org/10.1016/j.amc.2025.129873
Wu, Y., Liu, Z., Wang, X., Zhang, S., & Feng, J. (2024). An evolutionary dynamical analysis of low-carbon technology diffusion among enterprises in the complex network. Technological Forecasting and Social Change, 208, Article 123726. https://doi.org/10.1016/j.techfore.2024.123726
Wu, Y., Wang, X., Liu, Z., & Zhao, X. (2023). Research on low-carbon technology diffusion among enterprises in networked evolutionary game. Chaos, Solitons & Fractals, 174, Article 113852. https://doi.org/10.1016/j.chaos.2023.113852
Xu, Y., Wang, J., & Cheng, A. (2025). How to Achieve Cooperative Emission Reduction in Low-Carbon Service Supply Chains: An Evolutionary Game-Theoretic Study. Managerial and Decision Economics, 46(3), 1602–1627. https://doi.org/10.1002/mde.4453
Yang, J., Zhong, J., & Zhang, K. (2013). Beer carbon footprint and calculation case. Beer Science and Technology, 08, 6–14.
Zeng, M. (2025). The national carbon market marks its fourth anniversary with the first expansion of its coverage. Chinese financier, 08, 64–65. https://doi.org/10.19294/j.cnki.cn11-4799/f.2025.08.016
Zheng, J., Li, K., Zhou, Y., & Zhou, X. (2025). The effect of the carbon tax on the low-carbon level under different market powers. Environment Development and Sustainability, 27(1), 1545–1573. https://doi.org/10.1007/s10668-023-03929-z
Zhou, S., & Shan, F. (2023). Discovery of innovation effect and spillover effect: Evidence from intelligent manufacturing promoting low-carbon development. Journal of Innovation & Knowledge, 8(3), Article 100383. https://doi.org/10.1016/j.jik.2023.100383
Zhu, S., Ji, J., Huang, Q., Li, S., Ren, J., He, D., & Yang, Y. (2024). Optimal scheduling and trading in joint electricity and carbon markets. Energy Strategy Reviews, 54, Article 101426. https://doi.org/10.1016/j.esr.2024.101426
Funding
This research was supported by Hainan Provincial Natural Science Foundation of China (Grant number: 724QN275) and the National Social Science Fund of China (Grant number: 21XGL020).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Competing interests
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.
Additional information
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, W., Hu, ZH. Cooperative Emergence of Low-Carbon Technologies Among Manufacturing Enterprises in Scale-Free Networks With Adjustable Clustering. Comput Econ (2026). https://doi.org/10.1007/s10614-026-11373-0
Received:
Accepted:
Published:
Version of record:
DOI: https://doi.org/10.1007/s10614-026-11373-0