Throwing green into the mix: how the EU Emissions Trading System impacted the energy mix of French manufacturing firms (2000–16)

Rayan Chebbi-Giovanetti

Journal of Energy Markets2023https://doi.org/10.21314/jem.2023.022article
AJG 1
Weight
0.41

What the paper says

Public policies that aim to reduce carbon emissions act as a constraint on production decisions. Much of the empirical literature has shown that environmental policies reduce carbon emissions, but the source of that reduction is not explained. This paper investigates the hypothesis that environmental policy directly affects production decisions. Hence, we look at the energy mix of firms, emphasizing the choice firms face when deciding which energy input to use. We build a difference-indifferences model to show how the implementation of the first three phases of the European carbon market (that is, the EU Emissions Trading System) influenced the composition of the energy mix in favor of carbon-neutral energy inputs (ie, firms substitute between energy inputs in favor of less carbon-intensive inputs). To do this, we use French microdata on manufacturing firms, specifically information on energy usage, carbon emissions and assignment to treatment. We show that when considering the energy mix, firms that were subject to the European carbon market tended to use more carbon-neutral energy inputs during the three phases of the policy, with the magnitude varying according to the specification.

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https://doi.org/https://doi.org/10.21314/jem.2023.022

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@article{rayan2023,
  title        = {{Throwing green into the mix: how the EU Emissions Trading System impacted the energy mix of French manufacturing firms (2000–16)}},
  author       = {Rayan Chebbi-Giovanetti},
  journal      = {Journal of Energy Markets},
  year         = {2023},
  doi          = {https://doi.org/https://doi.org/10.21314/jem.2023.022},
}

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.17 × 0.4 = 0.07
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.