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Commitment vs Credibility: Macroeconomic Effects of Climate Policy Uncertainty

Author

Listed:
  • Fulvia Marotta
  • Maria Sole Pagliari
  • Jasper de Winter

Abstract

This paper introduces a novel media-based index of climate policy uncertainty – the CPU-Concern index – that captures both the prevalence of climate policy uncertainty and the intensity of public concern. Using data from the Netherlands, a setting charac- terized by ambitious climate targets and persistent credibility challenges, we document how policy announcements shape perceived uncertainty through signaling effects. The CPU-Concern index rises during contested policy debates and declines following for- mal ratification, with heterogeneous responses depending on the policy’s ambition and credibility. We show that climate policy uncertainty primarily transmits through shifts in business and consumer sentiment, affecting stock market prices, investments and real activity. Furthermore, negative CPU shocks generate more persistent economic drag than positive ones, while the opposite holds true for nominal variables, thus highlighting asymmetries in how uncertainty shapes behavior and potential policy reactions. Our findings underscore the importance of credible and transparent policy communication in reducing uncertainty and supporting the low-carbon transition.

Suggested Citation

  • Fulvia Marotta & Maria Sole Pagliari & Jasper de Winter, 2025. "Commitment vs Credibility: Macroeconomic Effects of Climate Policy Uncertainty," Working Papers 840, DNB.
  • Handle: RePEc:dnb:dnbwpp:840
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    References listed on IDEAS

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    1. Ben S. Bernanke & Mark Gertler & Mark Watson, 1997. "Systematic Monetary Policy and the Effects of Oil Price Shocks," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 28(1), pages 91-157.
    2. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    3. James H. James & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," Working Papers 2005-2, Princeton University. Economics Department..
    4. Robert B. Barsky & Eric R. Sims, 2012. "Information, Animal Spirits, and the Meaning of Innovations in Consumer Confidence," American Economic Review, American Economic Association, vol. 102(4), pages 1343-1377, June.
    5. Campiglio, Emanuele & Lamperti, Francesco & Terranova, Roberta, 2024. "Believe me when I say green! Heterogeneous expectations and climate policy uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 165(C).
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    Keywords

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    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy
    • E66 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General Outlook and Conditions
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation

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