Dynamic correlation of environmental regulation, technological innovation, and corporate carbon emissions: empirical evidence from China listed companies

Linjie Feng, Yi Shi, Zhenjie Yang, Johnny F.I. Lam, Su Lin, Jinghui Zhan, Huangxin Chen

Research output: Contribution to journalArticlepeer-review

Abstract

The notable rise in carbon emissions has profoundly affected humanity’s sustainable development. Achieving the "dual-carbon" goal requires understanding how enterprises can effectively reduce their carbon footprint. To elucidate the dynamic correlation of environmental regulation, corporate technological innovation, and corporate carbon emissions, this study employs a Panel Vector Autoregression model to analyze data from listed firms between 2005 and 2021, using GMM regression, impulse response analysis, and variance decomposition. The key findings are: (1) Environmental regulation, corporate technological innovation, and corporate carbon emissions exhibit self-reinforcing mechanisms, though the effect weakens over time. (2) Environmental regulation reduces corporate carbon emissions significantly, and this effect is partially mediated through corporate technological innovation. (3) A bidirectional relationship exists between corporate technological innovation and corporate carbon emissions, where corporate technological innovation has a stronger positive effect on reducing corporate carbon emissions. (4) Environmental regulation promotes corporate technological innovation, while corporate technological innovation gradually mitigates the stringency of environmental regulation. The findings herein offer actionable insights for enterprises to adopt cleaner production strategies and offer a scientific basis for policymakers to enhance environmental regulations in pursuit of a low-carbon economy.

Original languageEnglish
Article number8433
JournalScientific Reports
Volume15
Issue number1
DOIs
Publication statusPublished - Dec 2025

Keywords

  • Corporate carbon emissions
  • Corporate technological innovation
  • Environmental regulation
  • Impulse response
  • Panel Vector Autoregression

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