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Nonlinear impacts of industrial intelligence on synergistic reduction of pollution and carbon emissions: The role of technological factors

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  • Zhu, Bangzhu
  • Chen, Gang
  • Wang, Ping

Abstract

The integration of digital and intelligent technologies into industrial processes positions industrial intelligence as a potential driver of improved environmental performance. However, its role in the synergistic reduction of pollution and carbon emissions (PCSR) under varying technological conditions remains underexplored. This study compiles data on industrial intelligence, greenhouse gas emissions, and pollutant emissions across China, and quantifies both the level of industrial intelligence and the degree of PCSR. By incorporating technological factors into the transition function, we employ panel smooth transition regression (PSTR) and instrumental variable PSTR models to examine the nonlinear effects of industrial intelligence on PCSR. Results show that industrial intelligence significantly improves PCSR, but its marginal effect declines after technological thresholds are exceeded. Moreover, the effect of industrial intelligence on PCSR differs significantly across technological threshold levels. Specifically, key thresholds are identified for R&D investment (14.940), digital economy development (0.284), and innovation output (2.761). Mechanism analysis reveals that green technological innovation, total factor productivity improvements, and industrial structure upgrading are key channels through which industrial intelligence promotes PCSR. Significant regional heterogeneity is also observed. The positive impact of industrial intelligence on PCSR is substantially greater in coastal regions than in inland areas. In regions with stricter environmental regulations, policy pressure further amplifies this effect. In regions with a higher share of clean energy consumption, industrial intelligence yields the most pronounced emission reduction benefits. Our findings provide effective pathways and policy recommendations for achieving synergistic emissions reduction.

Suggested Citation

  • Zhu, Bangzhu & Chen, Gang & Wang, Ping, 2025. "Nonlinear impacts of industrial intelligence on synergistic reduction of pollution and carbon emissions: The role of technological factors," Energy Economics, Elsevier, vol. 149(C).
    • Handle: RePEc:eee:eneeco:v:149:y:2025:i:c:s0140988325006243
    DOI: 10.1016/j.eneco.2025.108797
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    References listed on IDEAS

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    1. Wolfgang Cramer & Joël Guiot & Marianela Fader & Joaquim Garrabou & Jean-Pierre Gattuso & Ana Iglesias & Manfred A. Lange & Piero Lionello & Maria Carmen Llasat & Shlomit Paz & Josep Peñuelas & Maria , 2018. "Climate change and interconnected risks to sustainable development in the Mediterranean," Nature Climate Change, Nature, vol. 8(11), pages 972-980, November.
    2. Fouquau, Julien & Hurlin, Christophe & Rabaud, Isabelle, 2008. "The Feldstein-Horioka puzzle: A panel smooth transition regression approach," Economic Modelling, Elsevier, vol. 25(2), pages 284-299, March.
    3. Du, Longzheng & Lin, Weifen, 2022. "Does the application of industrial robots overcome the Solow paradox? Evidence from China," Technology in Society, Elsevier, vol. 68(C).
    4. Wang, Jianda & Wang, Bo & Dong, Kangyin & Dong, Xiucheng, 2022. "How does the digital economy improve high-quality energy development? The case of China," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    5. Wei, Xinyang & Tong, Qing & Magill, Iain & Vithayasrichareon, Peerapat & Betz, Regina, 2020. "Evaluation of potential co-benefits of air pollution control and climate mitigation policies for China's electricity sector," Energy Economics, Elsevier, vol. 92(C).
    6. Li, Zihao & Bai, Tingting & Qian, Jingwen & Wu, Haitao, 2024. "The digital revolution's environmental paradox: Exploring the synergistic effects of pollution and carbon reduction via industrial metamorphosis and displacement," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
    7. Zhu, Yuke & Lan, Mudan, 2023. "Digital economy and carbon rebound effect: Evidence from Chinese cities," Energy Economics, Elsevier, vol. 126(C).
    8. Xu, Yingying & Shao, Xuefeng & Tanasescu, Cristina, 2024. "How are artificial intelligence, carbon market, and energy sector connected? A systematic analysis of time-frequency spillovers," Energy Economics, Elsevier, vol. 132(C).
    9. Wang, Shaojian & Xie, Zihan & Wu, Rong & Feng, Kuishang, 2022. "How does urbanization affect the carbon intensity of human well-being? A global assessment," Applied Energy, Elsevier, vol. 312(C).
    10. William D. Nordhaus, 2021. "Are We Approaching an Economic Singularity? Information Technology and the Future of Economic Growth," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(1), pages 299-332, January.
    11. Yin, Zi Hui & Zeng, Wei Ping, 2023. "The effects of industrial intelligence on China's energy intensity: The role of technology absorptive capacity," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    12. Liu, Bing & Yin, Weijun & Chen, Gang & Yao, Jing, 2023. "The threshold effect of climate risk and the non-linear role of climate policy uncertainty on insurance demand: Evidence from OECD countries," Finance Research Letters, Elsevier, vol. 55(PA).
    13. Álvaro Escribano & Oscar Jordá, 2001. "Testing nonlinearity: Decision rules for selecting between logistic and exponential STAR models," Spanish Economic Review, Springer;Spanish Economic Association, vol. 3(3), pages 193-209.
    14. Liang, Chao & Wang, Qi, 2023. "The relationship between total factor productivity and environmental quality: A sustainable future with innovation input," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    15. González, Andrés & Teräsvirta, Timo & van Dijk, Dick & Yang, Yukai, 2005. "Panel Smooth Transition Regression Models," SSE/EFI Working Paper Series in Economics and Finance 604, Stockholm School of Economics, revised 11 Oct 2017.
    16. Lin, Boqiang & Xu, Chongchong, 2024. "Enhancing energy-environmental performance through industrial intelligence: Insights from Chinese prefectural-level cities," Applied Energy, Elsevier, vol. 365(C).
    17. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    18. Shahbaz, Muhammad & Song, Malin & Ahmad, Shabbir & Vo, Xuan Vinh, 2022. "Does economic growth stimulate energy consumption? The role of human capital and R&D expenditures in China," Energy Economics, Elsevier, vol. 105(C).
    19. Zhu, Bangzhu & Chen, Gang & Wang, Ping, 2024. "How does green fiscal policy promote the synergy of pollution mitigation and carbon reduction? Evidence from China," Energy, Elsevier, vol. 313(C).
    20. Cao, Jing & Gong, Yazhen & Liu, Qingfeng, 2025. "Coordinating climate mitigation and pollution control policies: Insights from China's SO2 reduction mandates," Energy Economics, Elsevier, vol. 144(C).
    21. Wang, Linhui & Wang, Hui & Cao, Zhanglu & He, Yongda & Dong, Zhiqing & Wang, Shixiang, 2022. "Can industrial intellectualization reduce carbon emissions? — Empirical evidence from the perspective of carbon total factor productivity in China," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    22. Zhang, Bingbing & Wang, Ning & Yan, Zhijun & Sun, Chuanwang, 2023. "Does a mandatory cleaner production audit have a synergistic effect on reducing pollution and carbon emissions?," Energy Policy, Elsevier, vol. 182(C).
    23. Zhou, Yi & Zhuo, Chengfeng & Deng, Feng, 2021. "Can the rise of the manufacturing value chain be the driving force of energy conservation and emission reduction in China?," Energy Policy, Elsevier, vol. 156(C).
    24. Yan, Yaxue & Zhang, Xiaoling & Zhang, Jihong & Li, Kai, 2020. "Emissions trading system (ETS) implementation and its collaborative governance effects on air pollution: The China story," Energy Policy, Elsevier, vol. 138(C).
    25. Zhou, Xiaoyong & Zhou, Dequn & Wang, Qunwei & Su, Bin, 2019. "How information and communication technology drives carbon emissions: A sector-level analysis for China," Energy Economics, Elsevier, vol. 81(C), pages 380-392.
    26. Tian, Lingyue & Chai, Jian & Zhang, Xiaokong & Pan, Yue, 2024. "Spatiotemporal evolution and driving factors of China's carbon footprint pressure: Based on vegetation carbon sequestration and LMDI decomposition," Energy, Elsevier, vol. 310(C).
    27. Wang, Chen & Engels, Anita & Wang, Zhaohua, 2018. "Overview of research on China's transition to low-carbon development: The role of cities, technologies, industries and the energy system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1350-1364.
    28. Kheiri, Farshad & Haberl, Jeff S. & Baltazar, Juan-Carlos, 2023. "Impact of outdoor humidity conditions on building energy performance and environmental footprint in the degree days-based climate classification," Energy, Elsevier, vol. 283(C).
    29. Lin, Boqiang & Zhang, Aoxiang, 2023. "Government subsidies, market competition and the TFP of new energy enterprises," Renewable Energy, Elsevier, vol. 216(C).
    30. Zhou, Wei & Zhuang, Yan & Chen, Yan, 2024. "How does artificial intelligence affect pollutant emissions by improving energy efficiency and developing green technology," Energy Economics, Elsevier, vol. 131(C).
    31. Gu, Gaoxiang & Wang, Zheng, 2018. "Research on global carbon abatement driven by R&D investment in the context of INDCs," Energy, Elsevier, vol. 148(C), pages 662-675.
    32. Saia, Artjom, 2023. "Digitalization and CO2 emissions: Dynamics under R&D and technology innovation regimes," Technology in Society, Elsevier, vol. 74(C).
    33. Gao, Kang & Yuan, Yijun, 2021. "The effect of innovation-driven development on pollution reduction: Empirical evidence from a quasi-natural experiment in China," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    34. Yang, Siying & Liu, Fengshuo, 2024. "Impact of industrial intelligence on green total factor productivity: The indispensability of the environmental system," Ecological Economics, Elsevier, vol. 216(C).
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