How does green innovation drive urban carbon emission efficiency? —Evidence from the Yangtze River Economic Belt
This paper explores the relationship between green innovation and urban carbon emission efficiency (CEE). Based on the panel data of 108 cities in the Yangtze River Economic Belt, the spatiotemporal evolution pattern of green innovation and urban CEE is investigated. The spatial Durbin model shows t...
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Veröffentlicht in: | Journal of cleaner production 2022-11, Vol.375, p.134196, Article 134196 |
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Sprache: | eng |
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Zusammenfassung: | This paper explores the relationship between green innovation and urban carbon emission efficiency (CEE). Based on the panel data of 108 cities in the Yangtze River Economic Belt, the spatiotemporal evolution pattern of green innovation and urban CEE is investigated. The spatial Durbin model shows that green innovation has a positive and significant effect on CEE. The conclusions are robust after considering different spatial weight matrices and possible patent hysteresis. Furthermore, it is found that environmental regulation and industrial upgrading play a significant role in strengthening the relationship between green innovation and CEE. Heterogeneity analysis shows that green innovation has a more significant effect on CEE in the middle and lower reaches of the Yangtze River and in the period of 2012–2017. In addition, the impact of green innovation on CEE is linear, while the impact of general innovation on CEE is U-shaped, reflecting the important driving role of green innovation for CEE. The findings in this study offer a reference for improving the efficiency of spatial allocation of technical elements, which are conducive to urban green and low-carbon transformation.
•The spatiotemporal pattern of green innovation and urban CEE is investigated.•Evident heterogeneity exists in the promotion effect of green innovation.•The impact of general innovation on CEE is U-shaped.•The synergistic effect of industrial upgrading is further discussed. |
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ISSN: | 0959-6526 1879-1786 |
DOI: | 10.1016/j.jclepro.2022.134196 |