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European Low-Carbon Policy: Impact on fossil energy markets

Author

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  • Jacques Minlend

    (Université de Rennes, CNRS, CREM-UMR6211, F-35000 Rennes, France)

Abstract

This paper proposes text-as-data methods relying on unsupervised machine learning algorithms applied to European Union (EU) law acts and newspapers. These are used to construct two monthly indices over a reference period 1997-2021: (i) First, a news-based index which underlies a conjunctural uncertainty about the international context in which the global energy and environment policy evolves (EnvPU). (ii) Second, a laws-based index which reflects structural changes of the European energy and environment regulations (EnvP). The main findings suggest both indices display, in some extent, a common evolutionary pattern around salient events in the history of the EU energy and environment policy. Moreover, EnvPU index appears to be more volatile and is driven in the short-run by EnvP index. Given the support of such a policy to carbon phase-out, we further examine, in what extent, each index relates to price uncertainty dynamics in fossil energy markets (oil, gas, and coal). As a result, we uncover that, increase in news-based EnvPU index has a positive impact on price uncertainty of all fossil energy markets, the effect being stronger and more significant for gas and coal markets. In contrast, while an exogenous shock in laws-based EnvP index has a negative effect on price uncertainty in oil and gas markets, it tends to increase the coal price uncertainty. Overall, EnvP index depicts a stabilizing effect on fossil energy prices.

Suggested Citation

  • Jacques Minlend, 2023. "European Low-Carbon Policy: Impact on fossil energy markets," Economics Working Paper Archive (University of Rennes & University of Caen) 2023-04, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.
  • Handle: RePEc:tut:cremwp:2023-04
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    More about this item

    Keywords

    Energy and Environment Policy; News and media; Text-mining; Unsupervised machine learning; Commodity markets; Structural VAR.;
    All these keywords.

    JEL classification:

    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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