Uncovering complexity in the economic assessment of derogations from the European industrial emissions directive
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Elsevier
Abstract
Environmental regulation forces many industrial sectors to explore alternative innovative compliance investments to internalize environmental externalities without hindering competitiveness. The impacts of air emissions associated with climate change and other global and local sources on health and ecosystems are a main concern. Under the Industrial Emissions Directive (IED), the European Union requires that industrial facilities obtain an operating permit linked to specific emissions levels that can be reached through application of the best available techniques (BAT). Some flexibility is allowed in the choice of these BATs, and in some cases, derogations can be obtained if a cost-benefit analysis (CBA) accounting for all private and social costs and benefits reveals that the BAT option results in disproportionate costs. CBAs depend on complex variables that are subject to a high degree of uncertainty, such as the social discount rate (SDR), the private weighted average cost of capital (WACC), and the value of the social or external costs derived from changes in air emissions. Through a case study comparing two alternative compliance options in a fictitious nonferrous metal plant (a BAT with disproportionate costs and a more cost-efficient and cost-effective derogation proposal), this study applied fuzzy-set qualitative comparative analysis (fsQCA) to improve the understanding of how the former uncertain variables affect the surplus net present value (SNPV) of the derogation option. The results can be considered by plant operators and policy-makers to design more transparent and balanced licensing processes, which can improve environmental protection without damaging economic competitiveness.
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Romero-Castro, N., López-Cabarcos, M. Á., & Piñeiro-Chousa, J. (2022). Uncovering complexity in the economic assessment of derogations from the European industrial emissions directive. Journal of Innovation & Knowledge, 7(1), 100159.
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https://doi.org/10.1016/j.jik.2021.11.001Sponsors
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© 2021 The Author(s). Published by Elsevier España, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY-NC-ND license








