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<p>This meta-analysis synthesizes evidence from 89 empirical studies to examine how distinct environmental regulation types influence green innovation performance (GIP). Grounded in Institutional Theory, the Resource-Based View, and the Porter Hypothesis, the study classifies regulations as command-and-control, market-based, voluntary/self-regulation, or governance-related, and innovation outcomes as patenting, process efficiency, market commercialization, or composite performance. Using a PRISMA-compliant approach and 160 effect sizes from 273,505 firm-level observations, the overall analysis confirms a statistically significant positive relationship between ER and GIP. Voluntary schemes yield the strongest innovation gains, while prescriptive and market-based instruments also perform well under credible enforcement. Patent and efficiency innovations are more responsive to policy signals than market-based outcomes, which often stall without complementary demand-side support. The study offers an evidence-based framework to guide policymakers in aligning regulatory instruments with specific innovation goals, institutional contexts, and sectoral priorities, maximizing both environmental and economic returns.</p>

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Regulating for innovation: a meta-analysis of environmental policy effects on green innovation performance

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月8日

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  • Owen Kembabazi,
  • Grace Sojourner,
  • Tom Patrick Mugizi,
  • Godwin Mwesigye Ahimbisibwe,
  • Dedrix Stephenson Bindeeba

摘要

This meta-analysis synthesizes evidence from 89 empirical studies to examine how distinct environmental regulation types influence green innovation performance (GIP). Grounded in Institutional Theory, the Resource-Based View, and the Porter Hypothesis, the study classifies regulations as command-and-control, market-based, voluntary/self-regulation, or governance-related, and innovation outcomes as patenting, process efficiency, market commercialization, or composite performance. Using a PRISMA-compliant approach and 160 effect sizes from 273,505 firm-level observations, the overall analysis confirms a statistically significant positive relationship between ER and GIP. Voluntary schemes yield the strongest innovation gains, while prescriptive and market-based instruments also perform well under credible enforcement. Patent and efficiency innovations are more responsive to policy signals than market-based outcomes, which often stall without complementary demand-side support. The study offers an evidence-based framework to guide policymakers in aligning regulatory instruments with specific innovation goals, institutional contexts, and sectoral priorities, maximizing both environmental and economic returns.