On the economics of carbon pricing: Insights from econometric modeling with industry-level data

Boqiang Lin () and Presley K. Wesseh

Energy Economics, 2020, vol. 86, issue C

Abstract: The literature which tests the Porter hypothesis by studying the role of environmental policies on innovation is not only fragmented, but mostly employs broad measures of environmental regulations. In order to draw consensus in this literature and sufficiently incentivize the pursuit of environmental policy objectives, further contributions on the economics of environmental regulations utilizing alternative methods and data are needed. Therefore, our analysis takes the Chinese national emission trading scheme (ETS) as specific example of such a policy. In the econometric analysis, Chinese carbon prices from this market are used and their impact on innovation is tested using a dynamic panel data regression. The main findings are that while a carbon price appears to stimulate R&D intensity (innovation input), the boost in R&D does not result into increased sales and profits for industries (innovation output). High energy consuming industries have higher tendencies to innovate but this takes place at the distribution and marketing stages of the value chain. However, since the energy requirement at these levels is less relative to the actual production stage, the energy costs saving may be limited. Therefore, to better the economics of carbon pricing in a competitive world in which risk aversion is a possibility, policies which encourage energy intensive industries to innovate at the production stage would present opportunities. In general, these findings contribute to our understanding of innovation behavior and have the potential to stimulate new debates in the literature.

Keywords: Carbon pricing; Innovation input; Innovation output; Energy consumption; China (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:86:y:2020:i:c:s0140988320300177

DOI: 10.1016/j.eneco.2020.104678

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