• Graduate Programs
  • Research
  • Browse our Courses
  • Events
    • Events Calendar
    • Events Archive
    • Summer School
      • Applied Public Policy Evaluation
      • Deep Learning
      • Development Economics
      • Economics of Blockchain and Digital Currencies
      • Economics of Climate Change
      • The Economics of Crime
      • Foundations of Machine Learning with Applications in Python
      • From Preference to Choice: The Economic Theory of Decision-Making
      • Inequalities in Health and Healthcare
      • Marketing Research with Purpose
      • Markets with Frictions
      • Modern Toolbox for Spatial and Functional Data
      • Sustainable Finance
      • Tuition Fees and Payment
      • Business Data Science Summer School Program
    • Tinbergen Institute Lectures
    • 2026 Tinbergen Institute Opening Conference
    • Annual Tinbergen Institute Conference
  • News
  • Summer School
    • Applied Public Policy Evaluation
    • Deep Learning
    • Development Economics
    • Economics of Blockchain and Digital Currencies
    • Economics of Climate Change
    • The Economics of Crime
    • Foundations of Machine Learning with Applications in Python
    • From Preference to Choice: The Economic Theory of Decision-Making
    • Inequalities in Health and Healthcare
    • Marketing Research with Purpose
    • Markets with Frictions
    • Modern Toolbox for Spatial and Functional Data
    • Sustainable Finance
    • Tuition Fees and Payment
  • Alumni

Jiang, Y. and Tol, RichardS.J. (2026). Environmental regulation, green innovation, and productivity: Crowding-out or reallocation? Economic Modelling, 155:1--17.


  • Journal
    Economic Modelling

Does environmental regulation enhance firm productivity through reallocation toward green innovation, conditional on firms' pollution intensity and productivity levels? We analyze Chinese listed firms (2010–2018) using a Crépon–Duguet–Mairesse recursive framework, a three-equation causal chain that links regulation, innovation, and productivity. We find that a variety of environmental policies raise compliance costs, crowding-out average R\&D investment. Non-green innovation yields higher productivity returns in low-pollution firms and exhibits a U-shaped return pattern across the productivity distribution, whereas green innovation's returns follow an inverted-U-shaped profile, peaking at medium-high productivity—where, for high-pollution firms, they exceed those of non-green innovation, boosting TFP. These results provide mechanism-based evidence for the strong Porter hypothesis as a context-dependent reconfiguration of technological and productive efficiency under regulatory stimuli. Policy support for green innovation should be targeted at high-pollution, mid-to-high-productivity firms based on verifiable outcomes, turning the crowding-out of R\&D into productive reallocation.