Sectoral Cost-Benefit Analysis for Clean Technology Demonstrators: Insights for Decarbonizing Hard-to-Abate Industries

Maryam Sadighi et al.

Environmental and Resource Economics2026https://doi.org/10.1007/s10640-025-01051-4article
AJG 3ABDC A
Weight
0.50

Abstract

Adopting disruptive technologies to decarbonize hard-to-abate industrial sectors requires experimentation through demonstration (pilot) projects. However, from an economic perspective, the potential long-term benefits and the challenges of designing effective public policies are often overlooked in standard valuations of these projects. This paper demonstrates that conducting cost-benefit analysis (CBA) at the sectoral level offers valuable insights to address these issues. It accounts for knowledge spillovers from the pilot project across the industry and incorporates technical changes in value-added cost components of adjacent activities. Such an analysis identifies the optimal trajectory for decarbonizing the sector. Our proposed CBA framework also determines the relevant abatement cost for the pilot project, a critical indicator for public policy. Applied to France’s large-scale, high-quality container glass sector, the CBA estimates an abatement cost of approximately €200/tCO2 for the pilot deploying a decarbonized hybrid technology—45% lower than previous standard approaches. Furthermore, we demonstrate that subsidizing the pilot project, combined with a commitment to transfer knowledge to follower plants, is sufficient to achieve the social optimum, provided governments implement an emissions tax that internalizes environmental costs. This approach can be extended to other hard-to-abate sectors, enabling the early deployment of disruptive innovations and facilitating the design of effective public policies.

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https://doi.org/https://doi.org/10.1007/s10640-025-01051-4

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@article{maryam2026,
  title        = {{Sectoral Cost-Benefit Analysis for Clean Technology Demonstrators: Insights for Decarbonizing Hard-to-Abate Industries}},
  author       = {Maryam Sadighi et al.},
  journal      = {Environmental and Resource Economics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s10640-025-01051-4},
}

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R · text relevance †0.50 × 0.4 = 0.20

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