Abstract
Against the background of carbon neutrality and sustainable manufacturing, manufacturing faces multiple challenges in improving carbon emission efficiency, with the integration of digital technology and industrial internet platforms as a key solution. Given the digital technology availability, this study focuses on the internal mechanism and dynamic decision-making issues of how the industrial internet platform can enhance the manufacturing carbon emission efficiency. This study has constructed a differential game model involving manufacturing enterprises, the government, and industrial internet platforms. This study incorporates random interfering factors. This method addresses the limitations of neglected external uncertainties and overly loose assumptions. The innovation enables the capture of stochastic disturbances in carbon emission systems, such as market fluctuations and policy adjustments. The realism of matching equilibrium strategies to the enhancement of manufacturing carbon emission efficiency is improved. The findings are as follows: (1) Carbon emission system benefit coefficient, operational cost coefficient, and compliance cost coefficient negatively impact game strategies, while digital technology maturity coefficient has a positive effect; (2) Government subsidies under intermediate dependence enhance the effort of enterprises and platforms, and advanced symbiosis achieves optimal effort of the three subjects and system efficiency through in-depth digital technology availability empowerment; (3) The advanced symbiotic decision-making mechanism model is regarded as the optimal embodiment and practical application of the digital technology availability theory. Reasonable benefit distribution can fully unleash the potential value of digital technology availability.
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Data availability
The datasets generated during and the current study are available from the corresponding author on reasonable request.
References
Canal, V. L., Longo, M. & Mura, M. Are the European manufacturing and energy sectors on track for achieving net-zero emissions in 2050? An empirical analysis. Energy Policy 156, 112464. https://doi.org/10.1016/j.enpol.2021.112464 (2021).
Mun, J., Yun, E. & Choi, H. A study of linkage effects and environmental impacts on information and communications technology industry between South Korea and USA: 2006–2015. Processes 9(6), 1043. https://doi.org/10.3390/pr9061043 (2021).
Kang, H. & Zoh, H. D. Classifying regional and industrial characteristics of GHG emissions in South Korea. Energies 15(20), 7777. https://doi.org/10.3390/en15207777 (2022).
Tale, M. & Li, T. Industrial intelligence and carbon emission reduction: Evidence from China’s manufacturing industry. Sustainability 16(15), 6573. https://doi.org/10.3390/su16156573 (2024).
Luca, M., Rama, K. R. K. & Giovanni, E. Not everything is as it seems: Digital technology affordance, pandemic control, and the mediating role of sociomaterial arrangements. Gov. Inf. Q. 38(4), 101599. https://doi.org/10.1016/j.giq.2021.101599 (2021).
Satish, N., Mike, W. & Maryann, F. The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes. Res. Policy 48(8), 103773. https://doi.org/10.1016/j.respol.2019.03.018 (2019).
Li, Z. Y. & Yang, G. L. Research on the impact of “Internet+” on China’s manufacturing industry agglomeration—An empirical research based on the mediating effect model of provincial panel data. Mob. Inf. Syst. 2022, 8871632. https://doi.org/10.1155/2022/8871632 (2022).
Zhang, X. Y., Ming, X. G., Bao, Y. G. & Liao, X. G. System construction for comprehensive industrial ecosystem oriented networked collaborative manufacturing platform (NCMP) based on three chains. Adv. Eng. Inform. 52, 101538. https://doi.org/10.1016/j.aei.2022.101538 (2022).
Su, Y. F. & Xu, G. H. Can intelligent equipment optimization improve the carbon emissions efficiency of the equipment-manufacturing industry?. Processes 13(5), 1543. https://doi.org/10.3390/pr13051543 (2025).
Su, Y. F. & Xu, G. H. The influencing mechanism of intelligent production on carbon emission efficiency of equipment manufacturing industry in China. Processes 13(4), 1102. https://doi.org/10.3390/pr13041102 (2025).
Chi, C. et al. A compatible carbon efficiency information service framework based on the industrial internet identification. Digit. Commun. Netw. 10(4), 884–894. https://doi.org/10.1016/j.dcan.2023.06.005 (2024).
Liu, J. X., Xie, R. B., Kang, G. S. & Wen, Y. P. Spatial‐temporal aware service composition for production factors under industrial internet. Concurrency Comput. Pract. Exp. 35(12), e7689. https://doi.org/10.1002/cpe.7689 (2023).
Menon, K., Kärkkäinen, H., Wuest, T. & Gupta, J. P. Industrial internet platforms: A conceptual evaluation from a product lifecycle management perspective. Proc. Inst. Mech. Eng. B J. Eng. Manuf. 233(5), 1390–1401. https://doi.org/10.1177/0954405418760651 (2019).
Liu, Z. M. & Li, J. X. A trusted computing resources optimal scheduling algorithm in industrial internet and healthcare integrating Drl, blockchain and Een-edge-cloud. J. Mech. Med. Biol. 23(04), 2340056. https://doi.org/10.1142/S0219519423400560 (2023).
Chen, W., Meng, W. & Zhang, L. L. Evolutionary machine learning driven big data analysis and processing for industrial internet. Fractals 31(06), 2340100. https://doi.org/10.1142/S0218348X2340100X (2023).
Zhao, J. & Wu, D. The risk assessment on the security of industrial internet infrastructure under intelligent convergence with the case of GE’s intellectual transformation. Math. Biosci. Eng. 19(3), 2896–2912. https://doi.org/10.3934/mbe.2022133 (2022).
Li, Y. S. & Zhang, Y. Digital twin for industrial internet. Fundam. Res. 4(1), 21–24. https://doi.org/10.1016/j.fmre.2023.01.005 (2024).
Wang, L. et al. The growth model of industrial internet platform in industrial 4.0. Wirel. Commun. Mob. Comput. https://doi.org/10.1155/2022/5145641 (2022).
Dou, K. Q., Li, J. & Zhou, Y. Research on design and monitoring of a development index of an industrial internet platform based on a fixed-base index method. Electronics 11(2), 274. https://doi.org/10.3390/electronics11020274 (2022).
Yu, F. F. & Chen, J. Q. The impact of industrial internet platform on green innovation: Evidence from a quasi-natural experiment. J. Clean. Prod. 414, 137645. https://doi.org/10.1016/j.jclepro.2023.137645 (2023).
He, J. J. & Liu, X. C. Study on the impact and mechanism of industrial internet pilot on digital transformation of manufacturing enterprises. Sustainability 15(10), 7872. https://doi.org/10.3390/su15107872 (2023).
Hu, R., Shahzad, F., Abbas, A. & Xu, N. Empirical analysis of the impact of industrial internet development environment on open green innovation of manufacturing enterprises. Front. Environ. Sci. 10, 947675. https://doi.org/10.3389/fenvs.2022.947675 (2022).
Cao, Y. J., Liu, S. & Deng, A. X. Digital carbon neutrality: A new way explored by industrial internet. Environ. Dev. Sustain. 27(11), 27125–27142. https://doi.org/10.1007/s10668-024-04824-x (2024).
Jin, B. L. & Han, Y. Influencing factors and decoupling analysis of carbon emissions in China’s manufacturing industry. Environ. Sci. Pollut. Res. 28(45), 64719–64738. https://doi.org/10.1007/s11356-021-15548-0 (2021).
Hou, F. M., Pei, R. T. & Zhang, Y. Z. Research on the influence of China’s manufacturing industry embedding in global value chain on the embodied carbon emissions in trade. J. Environ. Prot. Ecol. 23(6), 2693–2700 (2022).
Liu, M. et al. Carbon emission structure decomposition analysis of manufacturing industry from the perspective of input-output subsystem: A case study of China. Environ. Sci. Pollut. Res. 30(7), 19012–19029. https://doi.org/10.1007/s11356-022-23334-9 (2023).
Zhang, L. et al. Digital economy, energy efficiency, and carbon emissions: Evidence from provincial panel data in China. Sci. Total Environ. 852, 158403. https://doi.org/10.1016/j.scitotenv.2022.158403 (2022).
Guang, F. T., Deng, Y. T., Wen, L., Sharp, B. & Hong, S. F. Impact of regional energy allocation distortion on carbon emission efficiency: Evidence from China. J. Environ. Manage. 342, 118241. https://doi.org/10.1016/j.jenvman.2023.118241 (2023).
Liu, D. D. Convergence of energy carbon emission efficiency: Evidence from manufacturing sub-sectors in China. Environ. Sci. Pollut. Res. 29(21), 31133–31147. https://doi.org/10.1007/s11356-022-18503-9 (2022).
Zhang, C., Weng, X. Y. & Guo, Y. L. Digital infrastructure construction and household energy efficiency: Based on a quasi-natural experiment in China. Sci. Total Environ. 911, 168544. https://doi.org/10.1016/j.scitotenv.2023.168544 (2024).
Han, J. & Jiang, T. H. Does the development of the digital economy improve carbon emission efficiency?. Front. Ecol. Evol. 10, 1031722 (2022).
Li, R. R., Han, X. Y. & Wang, Q. Do technical differences lead to a widening gap in China’s regional carbon emissions efficiency? Evidence from a combination of LMDI and PDA approach. Renew. Sustain. Energy Rev. 182, 113361. https://doi.org/10.1016/j.rser.2023.113361 (2023).
Feng, X. C., Zhao, Y. P. & Yan, R. Y. Does carbon emission trading policy has emission reduction effect? - An empirical study based on quasi-natural experiment method. J. Environ. Manag. 351, 119791. https://doi.org/10.1016/j.jenvman.2023.119791 (2024).
Zeng, Y., Zhang, W. G., Sun, J. W., Sun, L. A. & Wu, J. Research on regional carbon emission reduction in the Beijing–Tianjin–Hebei urban agglomeration based on system dynamics: Key factors and policy analysis. Energies 16(18), 6654. https://doi.org/10.3390/en16186654 (2023).
Hou, J., Shi, C. X., Fan, G. L. & Xu, H. X. Research on the impact and intermediary effect of carbon emission trading policy on carbon emission efficiency in China. Atmos. Pollut. Res. 15(4), 102045. https://doi.org/10.1016/j.apr.2024.102045 (2024).
Tian, G. L., Yu, S. W., Wu, Z. & Xia, Q. Study on the emission reduction effect and spatial difference of carbon emission trading policy in China. Energies 15(5), 1921. https://doi.org/10.3390/en15051921 (2022).
Chatterjee, S., Moody, G. D., Lowry, P. B., Chakraborty, S. & Hardin, A. Information technology and organizational innovation: Harmonious information technology affordance and courage-based actualization. J. Strateg. Inf. Syst. 29(1), 101596. https://doi.org/10.1016/j.jsis.2020.101596 (2020).
Sun, Z., Zhao, L., Mehrotra, A., Salam, M. A. & Yaqub, M. Z. Digital transformation and corporate green innovation: An affordance theory perspective. Bus. Strateg. Environ. 34(1), 433–449. https://doi.org/10.1002/bse.3991 (2025).
Xie, W. H., Zou, Y. K., Guo, H. Z. & Li, Z. S. What drives digital innovation cycles? Evidence from manufacturing enterprises in China. Technol. Forecast. Soc. Change 204, 123449. https://doi.org/10.1016/j.techfore.2024.123449 (2024).
Zhao, H. X., Xu, G. M., Liu, L., Shi, C. C. & Zhao, H. J. Low-carbon technology innovation decision making of manufacturing companies in the industrial internet platform ecosystem. Sustainability 15(4), 3555. https://doi.org/10.3390/su15043555 (2023).
Tim, Y., Pan, S. L., Bahri, S. & Fauzi, A. Digitally enabled affordances for community‐driven environmental movement in rural Malaysia. Inf. Syst. J. 28(1), 48–75. https://doi.org/10.1111/isj.12140 (2018).
Liu, Y., Dong, J. Y., Mei, L. & Shen, R. Digital innovation and performance of manufacturing firms: An affordance perspective. Technovation 119, 102458. https://doi.org/10.1016/j.technovation.2022.102458 (2023).
Wang, C. C., Liu, Y. B., Wan, Y. K., Hu, S. & Xia, H. B. How does the digital economy impact the green upgrading of manufacturing? Perspectives on technological innovation and resource allocation. Appl. Econ. 57(34), 5049–5064. https://doi.org/10.1080/00036846.2024.2364112 (2025).
Bo, Q. S., Liu, H. & Zheng, J. W. Research on the mechanism of the green innovation of enterprises empowered by digital technology from the perspective of value co-creation. Sustainability 16(20), 9065. https://doi.org/10.3390/su16209065 (2024).
Li, L. X., Zhou, H. D., Yang, S. L. & Teo, T. S. H. Leveraging digitalization for sustainability: An affordance perspective. Sustain. Prod. Consum. 35, 624–632. https://doi.org/10.1016/j.spc.2022.12.011 (2023).
Yang, G. Q., Nie, Y. M., Li, H. G. & Wang, H. S. Digital transformation and low-carbon technology innovation in manufacturing firms: The mediating role of dynamic capabilities. Int. J. Prod. Econ. 263, 108969. https://doi.org/10.1016/j.ijpe.2023.108969 (2023).
Novales, A., Mocker, M., van Heck, E. & Dul, J. Realizing desired effects from digitized product affordances: A case study of key inhibiting factors. Decis. Support Syst. 189, 114365. https://doi.org/10.1016/j.dss.2024.114365 (2025).
Markus, M. L. & Silver, M. S. A foundation for the study of IT effects: a new look at DeSanctis and Poole’s concepts of structural features and spirit. J. Assoc. Inf. Syst. 9(10), 609–632. https://doi.org/10.17705/1jais.00176 (2008).
Baudry, M., Faure, A. & Quemin, S. Emissions trading with transaction costs. J. Environ. Econ. Manag. 108, 102468. https://doi.org/10.1016/j.jeem.2021.102468 (2021).
Chen, D., Zhang, Y., Hong, X., Chen, Q. F. & Zhang, J. Non-cooperative game and cooperative operation of multi-level supply chain under background of carbon emission reduction. IEEE Access 10, 33015–33025. https://doi.org/10.1109/ACCESS.2022.3156639 (2022).
Sun, R., He, D. Y. & Yan, J. J. Dynamic analysis of green technology innovation in products and processes under supply chain competition scenarios-A study based on stochastic differential game model. J. Environ. Manag. 373, 123545. https://doi.org/10.1016/j.jenvman.2024.123545 (2024).
Wei, Z. J., Yi, Y. X. & Fu, C. Y. Cournot competition and “green” innovation under efficiency-improving learning by doing. Phys. A 513, 121762. https://doi.org/10.1016/j.physa.2019.121762 (2019).
Wang, Y. F., Shi, J. & Qu, G. H. Research on collaborative innovation cooperation strategies of manufacturing digital ecosystem from the perspective of multiple stakeholders. Comput. Ind. Eng. 190, 110003. https://doi.org/10.1016/j.cie.2024.110003 (2024).
Acknowledgements
This work was supported by 2025 Liaoning Provincial Department of Education Project "Research on the Mechanism and Path of Enabling Data Elements to Improve the Quality and Efficiency of Green Finance in Liaoning Province" (Project number: LJ112510142004).
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Conceptualization, H.Q. and H.S.; methodology, H.Q.; software, H.Q.; validation, D.L.; formal analysis, D.L.; investigation, H.Q.; resources, H.Z.; data curation, H.S.; writing—original draft preparation, H.S.; writing—review and editing, H.Q.; visualization, H.S.; supervision, H.S.; project administration, H.Z.; funding acquisition, H.Q. All authors have read and agreed to the published version of the manuscript.
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Qin, H., Shi, H., Zhang, H. et al. Research on industrial internet platforms empowering carbon emission efficiency improvement in manufacturing: based on digital technology availability. Sci Rep (2026). https://doi.org/10.1038/s41598-026-45672-1
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DOI: https://doi.org/10.1038/s41598-026-45672-1

