Trapped in Tradition or Trailblazing Change? Unveiling the Dual Nature of Path Dependence in Manufacturing Pollution Control

Climate Policy and Green Economy Journal

Articles

Trapped in Tradition or Trailblazing Change? Unveiling the Dual Nature of Path Dependence in Manufacturing Pollution Control

Downloads

https://doi.org/10.54963/cpgej.v1i1.1850
Wang, S., Wang, S., & Sun, Y. (2025). Trapped in Tradition or Trailblazing Change? Unveiling the Dual Nature of Path Dependence in Manufacturing Pollution Control. Climate Policy and Green Economy Journal, 1(1), 1–18. https://doi.org/10.54963/cpgej.v1i1.1850
Volume 1 Issue 1 (2025)
Copyright (c) 2025 Sufeng Wang, Shuyan Wang, Yinan Sun
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Copyright

The authors shall retain the copyright of their work but allow the Publisher to publish, copy, distribute, and convey the work.

License

Climate Policy and Green Economy Journal (CPGEJ)  publishes accepted manuscripts under Creative Commons Attribution 4.0 International (CC BY 4.0). Authors who submit their papers for publication by Climate Policy and Green Economy Journal (CPGEJ) agree to have the CC BY 4.0 license applied to their work, and that anyone is allowed to reuse the article or part of it free of charge for any purpose, including commercial use. As long as the author and original source is properly cited, anyone may copy, redistribute, reuse and transform the content.

Authors

  • Sufeng Wang

    School of Economy and Management, Anhui Jianzhu University, Hefei 230601, China
  • Shuyan Wang

    School of Economy and Management, Anhui Jianzhu University, Hefei 230601, China
  • Yinan Sun

    School of Computer and Information Science, Anqing Normal University, Anqing 246011, China

Received: 20 April 2025; Revised: 9 June 2025; Accepted: 13 June 2025; Published: 25 June 2025

This study employs panel data from 1884 listed manufacturing companies in China (2009–2021) to investigate the environmental effects of path dependence on atmospheric pollution emissions. Using dictionary-based textual analysis of annual reports, we measure three dimensions of path dependence—technological, institutional, and managerial—and examine their non-linear relationships with sulfur dioxide emissions through fixed-effects models. Our findings reveal consistent U-shaped patterns across all dependence types: moderate levels initially reduce emissions (the “honey phase”) while excessive reliance leads to increased pollution (the “arsenic phase”). The analysis demonstrates that technological path dependence operates through sunk costs and learning effects, institutional dependence reflects regulatory inertia, and managerial dependence stems from organizational routines. Robustness tests using alternative pollution measures and instrumental variable approaches confirm these relationships. The study identifies significant heterogeneity in these effects. Non-state-owned enterprises exhibit stronger path dependence impacts due to greater flexibility, while high-maturity firms show amplified U-curves reflecting their accumulated experience. Conversely, capital-intensive enterprises display attenuated effects, suggesting diminishing returns to scale in pollution control. These findings highlight the dual nature of path dependence as both a stability mechanism and potential barrier to innovation. The policy implication is that manufacturing pollution control strategies should account for both dependence levels and firm-specific characteristics, maintaining path dependence within optimal ranges to harness stabilization benefits without impeding technological transitions. This research contributes to environmental governance literature by extending path dependence theory to pollution control and offering a multidimensional analytical framework for sustainable manufacturing transformation.

Keywords:

Technological Dependence Institutional Dependence Management Dependence Air Pollution Manufacturing Industry

References

  1. Xiao, W.L.; Qiu, T.Z.; Liu, Q.; et al. Manufacturing crisis and twin-oriented manufacturing. J. Manuf. Syst. 2024, 73, 205–22. DOI: https://doi.org/10.1016/j.jmsy.2024.02.002
  2. Zhou, D.; Zhong, Z.; Chen, L.; et al. Can the joint regional air pollution control policy achieve a win-win outcome for the environment and economy? Evidence from China. Econ. Anal. Policy 2022, 74, 13–33. DOI: https://doi.org/10.1016/j.eap.2022.01.011
  3. Chow, J.; Liu, T.; Du, C.D.; et al. From research to policy recommendations: A scientometric case study of air quality management in the Greater Bay Area, China. Environ. Sci. Policy 2025, 165, 104025. DOI: https://doi.org/10.1016/j.envsci.2025.104025
  4. Drechsler, M.; Wätzold, F. Biodiversity conservation in a dynamic world may lead to inefficiencies due to lock-in effects and path dependence. Ecol. Econ. 2020, 173, 106652. DOI: https://doi.org/10.1016/j.ecolecon.2020.106652
  5. Huang, J. Resources, innovation, globalization, and green growth: The BRICS financial development strategy. Geosci. Front. 2024, 15, 101741. DOI: https://doi.org/10.1016/j.gsf.2023.101741
  6. Kotlikoff, L.; Kubler, F.; Polbin, A.; et al. Can today’s and tomorrow’s world uniformly gain from carbon taxation? Eur. Econ. Rev. 2024, 168, 104819. DOI: https://doi.org/10.1016/j.euroecorev.2024.104819
  7. Jin, W. Path dependence, self-fulfilling expectations, and carbon lock-in. Resour. Energy Econ. 2021, 66, 101263. DOI: https://doi.org/10.1016/j.reseneeco.2021.101263
  8. Feng, W.; Wang, Z.; Xiao, T.; et al. Adaptive weighted dictionary representation using anchor graph for subspace clustering. Pattern Recogn. 2024, 151, 110350. DOI: https://doi.org/10.1016/j.patcog.2024.110350
  9. Zhao, C.; Zhong, C.; Liu, C.; et al. How the digital economy is empowering green strategies for breaking carbon lock-in. J. Environ. Manage. 2024, 365, 121670. DOI: https://doi.org/10.1016/j.jenvman.2024.121670
  10. Biddau, F.; Rizzoli, V.; Cottone, P.; et al. “These industries have polluted consciences; we are unable to envision change”: Sense of place and lock-in mechanisms in Sulcis coal and carbon-intensive region, Italy. Glob. Environ. Change 2024, 86, 102850. DOI: https://doi.org/10.1016/j.gloenvcha.2024.102850
  11. Trencher, G.; Okubo, Y.; Mori, A. Phasing out carbon not coal? Identifying coal lock-in sources in Japan’s power utilities. Clim. Policy 2024, 24, 766–784. DOI: https://doi.org/10.1080/14693062.2024.2302324
  12. Eitan, A.; Hekkert, M.P. Locked in transition? Towards a conceptualization of path-dependence lock-ins in the renewable energy landscape. Energy Res. Soc. Sci. 2023, 106, 103316. DOI: https://doi.org/10.1016/j.erss.2023.103316
  13. Klitkou, A.; Bolwig, S.; Hansen, T.; et al. The role of lock-in mechanisms in transition processes: The case of energy for road transport. Environ. Innov. Soc. Transit. 2015, 16, 22–37. DOI: https://doi.org/10.1016/j.eist.2015.07.005
  14. Hetemi, E.; Jerbrant, A.; Mere, J.O. Exploring the emergence of lock-in in large-scale projects: A process view. Int. J. Proj. Manag. 2020, 38, 47–63. DOI: https://doi.org/10.1016/j.ijproman.2019.10.001
  15. Gitelman, V.; Kaplan, S.; Hakkert, S. The causation–prevention chain in infrastructure safety measures: A consideration of four types of policy lock-ins. Accid. Anal. Prev. 2024, 195, 107399. DOI: https://doi.org/10.1016/j.aap.2023.107399
  16. Goldstein, J.E.; Neimark, B.; Garvey, B.; et al. Unlocking “lock-in” and path dependency: A review across disciplines and socio-environmental contexts. World Dev. 2023, 161, 106116. DOI: https://doi.org/10.1016/j.worlddev.2022.106116
  17. Wu, H.; Wang, L.; Peng, F. Does it pay to be green? The impact of emissions reduction on corporate tax burden. J. Asian Econ. 2024, 91, 101707. DOI: https://doi.org/10.1016/j.asieco.2024.101707
  18. Belaounia, S.; Tao, R.; Zhao, H. Director foreign experience: Geographic specificity and value implication. Int. Rev. Financ. Anal. 2024, 91, 102998. DOI: https://doi.org/10.1016/j.irfa.2023.102998
  19. Song, P.; Lu, H.; Zhang, Y. Unveiling tone manipulation in MD&A: Evidence from ChatGPT experiments. Financ. Res. Lett. 2024, 67, 105837. DOI: https://doi.org/10.1016/j.frl.2024.105837
  20. Wang, F.; Wang, X.; Li, B.; et al. Ownership structure and eco-innovation: Evidence from Chinese family firms. Pac.-Basin Financ. J. 2023, 82, 102158. DOI: https://doi.org/10.1016/j.pacfin.2023.102158
  21. Chen, F.; Zhang, T.; Chen, Z. Assessment of environmental concern for enterprise pollution reduction. Econ. Anal. Policy 2024, 81, 772–786. DOI: https://doi.org/10.1016/j.eap.2024.01.003
  22. Zhou, L.; Fan, J.; Hu, M.; et al. Clean air policy and green total factor productivity: Evidence from Chinese prefecture-level cities. Energy Econ. 2024, 133, 107512. DOI: https://doi.org/10.1016/j.eneco.2024.107512
  23. Talebzadehhosseini, S.; Garibay, I. The interaction effects of technological innovation and path-dependent economic growth on countries’ overall green growth performance. J. Clean Prod. 2022, 333, 130134. DOI: https://doi.org/10.1016/j.jclepro.2021.130134
  24. Ai, H.; Wang, M.; Zhang, Y.; et al. How does air pollution affect urban innovation capability? Evidence from 281 cities in China. Struct. Change Econ. Dyn. 2022, 61, 166–178. DOI: https://doi.org/10.1016/j.strueco.2022.02.012
  25. Jiang, Z.; Liu, Z. Policies and exploitative and exploratory innovations of the wind power industry in China: The role of technological path dependence. Technol. Forecast. Soc. Change 2022, 177, 121519. DOI: https://doi.org/10.1016/j.techfore.2022.121519