![]() 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. ![]() For your reference, I mention below the steps in Stata and R that I followed for the analysis.This study estimates the carbon emission intensity of China’s provinces during the period from 2000 to 2015. So, I was wondering whether " plm" package has the default "fixed effect" first and then "random effect" second. However, I didn't see any such restriction in the "plm" package. The point here is that Stata requires fixed effect to be estimated first followed by random effect. A similar test is also available for the Stata. One of the important test in this package for choosing between "fixed effect" or "random effect" model is called Hausman type. ![]() I have been using " plm" package of R to do the analysis of panel data. ![]()
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