Green technology innovations, urban innovation environment and CO2 emission reduction in China: Fresh evidence from a partially linear functional-coefficient panel model

Boqiang Lin () and Ruiyang Ma

Technological Forecasting and Social Change, 2022, vol. 176, issue C

Abstract: Green technology innovations are deemed as effective channels through which economic growth and environmental governance are balanced. However, empirical research on the nexus between green technology innovations and CO2 emissions, especially in developing countries, remains scant. Employing panel data on 264 prefecture-level cities from 2006 to 2017 in China, we explore the impact of the urban innovation environment on the effect of green technological innovations on CO2 emissions. The empirical results indicate that green technology innovations have a heterogeneous impact in different types of cities. Meanwhile, green technological innovations can contribute to CO2 emission mitigation after 2010, while the effect is not significant in Chinese cities before 2010. Secondly, green technology innovations can reduce CO2 emissions indirectly through industrial structure upgrading. Thirdly, when the urban innovation environment is considered, government fiscal expenditure cannot significantly impact the marginal effect of green technologies. Meanwhile, the marginal mitigation effect of green technology innovations on CO2 emissions is only significant when the human capital level of a city has reached a certain level. There is a better carbon emission reduction effect in cities with higher human capital levels. The results provide important enlightenment to realize the coordination and unity of economic transition to innovation-driven and green and low-carbon development.

Keywords: Green technology innovations; CO2 emissions; Urban innovation environment; Partially linear functional-coefficient models (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (72)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:176:y:2022:i:c:s0040162521008659

DOI: 10.1016/j.techfore.2021.121434

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