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Dynamic Efficiency in Experimental Emissions Trading Markets with Investment Uncertainty

Author

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  • Timothy N. Cason
  • Frans P. de Vries

Abstract

This study employs a laboratory experiment to assess the performance of tradable permit markets on dynamic efficiency arising from cost-reducing investment. The permit allocation rule is the main treatment variable, with permits being fully auctioned or grandfathered. The experimental results show significant investment under both allocation rules in the presence of ex ante uncertainty over the actual investment outcome. However, auctioning permits generally provides stronger incentives to invest in R&D, leading to greater dynamic efficiency compared to grandfathering.

Suggested Citation

  • Timothy N. Cason & Frans P. de Vries, 2018. "Dynamic Efficiency in Experimental Emissions Trading Markets with Investment Uncertainty," Purdue University Economics Working Papers 1313, Purdue University, Department of Economics.
    • Handle: RePEc:pur:prukra:1313
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    File URL: https://business.purdue.edu/research/working-papers-series/2018/1313_Dyn_Eff_Perm.pdf
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    Cited by:

    1. Lidia Vidal-Meliá & Carmen Arguedas & Eva Camacho-Cuena & José Luis Zofío, 2022. "An Experimental Analysis of the Effects of Imperfect Compliance on Technology Adoption," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 81(3), pages 425-451, March.
    2. Timothy N. Cason & John K. Stranlund & Frans P. de Vries, 2022. "Investment Incentives in Tradable Emissions Markets with Price Floors Approach," Purdue University Economics Working Papers 1331, Purdue University, Department of Economics.
    3. Lidia Vidal-Melia & Eva Camacho-Cuena & Till Requate & Israel Waichman, 2026. "Timing of Environmental Regulation and the Adoption of Low-Pollution Technologies: An Experiment," CESifo Working Paper Series 12539, CESifo.
    4. Requate, Till & Camacho-Cuena, Eva & Kean Siang, Ch'ng & Waichman, Israel, 2019. "Tell the truth or not? The montero mechanism for emissions control at work," Journal of Environmental Economics and Management, Elsevier, vol. 95(C), pages 133-152.
    5. Arsenii Vilkov & Gang Tian, 2023. "Blockchain’s Scope and Purpose in Carbon Markets: A Systematic Literature Review," Sustainability, MDPI, vol. 15(11), pages 1-27, May.
    6. Timothy N. Cason & Steven Y. Wu, 2019. "Subject Pools and Deception in Agricultural and Resource Economics Experiments," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 73(3), pages 743-758, July.
    7. Stephanie Rosch & Sharon Raszap Skorbiansky & Collin Weigel & Kent D. Messer & Daniel Hellerstein, 2021. "Barriers to Using Economic Experiments in Evidence‐Based Agricultural Policymaking," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(2), pages 531-555, June.

    More about this item

    Keywords

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    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy

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