Li L, Hong X (2017) Spatial effects of energy carbon emission and environmental pollution: a spatial Dubin measure model based on energy intensity and technological progress. Hu Y, Guichun L, Kong X et al (2016) Analysis of spatial and temporal differences in China’s carbon emission intensity.

įu Y, Ma S, Song Q (2015) Spatial econometric analysis of regional carbon emission intensity in China. ĭoudou B, Fang Y, Xie M, Tang Y, Lin Z (2015) Temporal and spatial evolution of China’s provincial service industry innovation level and its dynamic mechanism: An empirical study based on spatial econometric model. ĭeng J, Liu X, Wang Z (2014) Analysis and disintegration of regional disparities and evolution characteristics of carbon emissions in China. Ĭonley TG, Ligon E (2002) Economic distance and cross-country spillovers. Ĭheng Y, Wang Z, Ye X, Wei YD (2014) Spatiotemporal dynamics of carbon intensity from energy consumption in China. (00)00349-9Ĭheng Y, Wang Z, Shouzhi Z et al (2013) Spatial measurement of carbon emission intensity of energy consumption in China and its influencing factors. Īnselin L (2001) Rao’s score test in spatial econometrics. The results show that: China’s provincial carbon emission intensity has obvious spatial agglomeration characteristics, and regional differences are improving, and the spatial spillover effect of some influencing factors is obvious innovation indicators such as the number of patent authorizations, technical market turnover, and foreign direct investment, and GDP have a significant negative impact on carbon intensity, and the effects of general scale variables such as urbanization rate, energy consumption, and population density on carbon intensity are significantly positive.Īndersson FNG, Karpestam P (2013) CO2, emissions and economic activity: short- and long-run economic determinants of scale, energy intensity and carbon intensity. Then, from an innovation-driven perspective, combining the data of innovative technologies and scale factors to construct a spatial panel model to explore the main influencing factors of carbon emission intensity and its spatial spillover effect. First, the temporal and spatial pattern evolution of China’s carbon emission intensity was analyzed using spatial statistics.

This study estimates the carbon emission intensity of China’s provinces during the period from 2000 to 2015.

Hausman fixed random stata 14.0