The effect of energy patents on China's carbon emissions: Evidence from the STIRPAT model

Junbing Huang, Xinghao Li, Yajun Wang and Hongyan Lei

Technological Forecasting and Social Change, 2021, vol. 173, issue C

Abstract: China has been the world's largest carbon emitter since 2009. Thus, there is considerable pressure on it to control its carbon emissions. Technological progress is a factor that most influences the reduction of carbon emissions. Unlike existing literature, this study narrows the technological progress to the energy field and focuses specifically on energy patents. Additionally, as there is significant heterogeneity among different types of energy patents, we classify them based on performers and purposes. Conventional estimators including Driscoll and Kraay, fixed effects with instrumental variables, and difference generalised method of moments are applied to estimate the effect of energy patents on carbon emissions which was based on the stochastic impact by regression on population, affluence, and technology (STIRPAT) model. The results indicate that energy patents are not a powerful instrument for controlling carbon emissions. However, energy patents originating from enterprises and scientific institutions exert a positive effect in reducing carbon emissions, and energy patents originating from higher education institutions enhance these effects. The utility type of energy patents significantly reduces carbon emissions, but the creation type offsets this positive effect. Further, the dynamic panel threshold model is employed in which human capital is incorporated as the threshold variable. The estimates indicate that carbon emissions can be positively reduced by increasing the level of human capital.

Keywords: Energy patents; Carbon emissions; STIRPAT; China (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:173:y:2021:i:c:s0040162521005436

DOI: 10.1016/j.techfore.2021.121110

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