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Optimal timing of carbon capture policies under learning-by-doing
Affiliation:1. Department of Environmental Biology, Sapienza University of Rome, Italy;2. Department of Botany and Zoology, Masaryk University, Brno, Czech Republic;3. Geography Department, Humboldt-Universität zu Berlin, Germany;4. Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys), Humboldt-Universität zu Berlin, Germany;5. LSCE-IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France;6. Department of Ecological Sciences, VU University, 1081 HV Amsterdam, the Netherlands
Abstract:Using a standard Hotelling model of resource exploitation, we determine the optimal energy consumption paths from three options: dirty coal, which is non-renewable and carbon-emitting; clean coal, which is also non-renewable but carbon-free thanks to carbon capture and storage (CCS); and solar energy, which is renewable and carbon-free. We assume that the atmospheric carbon stock cannot exceed an exogenously given ceiling. Taking into account learning-by-doing in CCS technology, we show the following results: (i) clean coal exploitation cannot begin before the outset of the carbon constrained phase and must stop strictly before the end of this phase; (ii) the energy price path can evolve non-monotonically over time; and (iii) when the solar cost is low enough, an unusual energy consumption sequence along with solar energy is interrupted for some time and replacement by clean coal may exist.
Keywords:Climate change  Energy substitution  Carbon capture and storage  Learning-by-doing
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