Club Convergence and Factors of Per Capita Transportation Carbon Emissions in China

Abstract

China is the largest carbon dioxide emitter in the world, and reducing China’s transportation carbon emissions is of great significance for the world. Using the Chinese provincial data from 2005–2015, this article analyzes the convergence characteristics of per capita transportation carbon emissions in China. It employs the log t regression test method and the club clustering algorithm developed by Phillips and Sul (2007) to separate the provinces and municipalities in China into three convergence clubs with different transportation carbon emission levels and one divergent group. Among them, the divergent group consisted of Beijing and Liaoning; the high carbon emission club consisted of Shanghai and Inner Mongolia; the low carbon emission club consisted of Jiangxi, Henan, Shandong, Hebei, and Sichuan; the medium carbon emission club consisted of the remaining 21 provinces and municipalities. On this basis, this article adopts the Ordered Logit model to explore factors influencing the formation of the convergence clubs. The regression results showed that the per capita transportation carbon emissions in the provinces with a high energy intensity of the transportation sector, a high urbanization level, or a high fixed assets investment intensity of the transportation sector tended to converge into the high carbon emission club.

House Price Convergence on the Primary and Secondary Markets: Evidence from Polish Provincial Capitals

“…(1) is only a cross-secional regression. Moreover, the convergence parameter is biased and inconsistent due to omitted variables and endogeneity (BAI et al 2019). The second classical approach of studying convergence is based on the so-called σ-convergence.…”

“…An innovative approach has been proposed by PHILLIPS and SUL (2007), who created the log t regression test. In comparison to the traditional tests, this method has the following advantages: it takes into consideration the heterogeneity among cities and allows this heterogeneity to change over time; it overcomes the problem of biased and inconsistent convergence parameters; it does not impose any special assumptions concerning trend stability or random non-stationarity; it allows the presence of convergence clubs to be analyzed if all cities do not share a common trend (BAI et al 2019;DU 2017). In order to outline the details of the method introduced by PHILLIPS and SUL 2007, it is necessary to decompose ln as follows:…”

Spatial Non-Equilibrium and Its Solidification Effect of China’s Per Capita Transportation Carbon Emissions

“…For example, in the period from 2005 to 2015, the eastern region had higher per capita transportation carbon emissions than the central and western regions, but had a lower average annual growth rate [13]. Therefore, the per capita transportation carbon emissions in various provinces could converge to the different clubs with different carbon emission levels [14]; moreover, different provinces are also in different positions and play different roles in the spatial association network of transportation carbon emissions [15]. In general, the spatial non-equilibrium implies that there are significant differences of per capita transportation carbon emissions at the regional level in China.…”

Agricultural Convergence Clubs in Africa Countries: A Note

“…Evidence of convergence implies a reduction in dispersion or differences in economic variables across the countries over a certain period. The concept has been applied to indicators such as income per capita, total factor productivity, research and development, poverty, carbon emission, and nutrition (Ogundari and Ito, 2015;Bai et al, 2019;Churchill et al, 2019;Kindberg-Halon and Okou 2020;Ravallion 2012;Sala-i-Martin 1996;Regmi et al, 2008). Four different tests are used to investigate the convergence hypothesis in the literature.…”