School of Management, China University of Mining and Technology, Xuzhou 221116, China.
Int J Environ Res Public Health. 2018 Oct 24;15(11):2343. doi: 10.3390/ijerph15112343.
The reductions of industrial pollution and greenhouse gas emissions are important actions to create an ecologically stable civilization. However, there are few reports on the interaction and variation between them. In this study, the vertical and horizontal scatter degree method is used to calculate a comprehensive index of industrial pollution emissions. Then based on carbon density, a geographically and temporally weighted regression (GTWR) model is developed to examine the interaction between industrial pollution emissions and carbon emissions. The results specify that there exists spatial autocorrelation for carbon density in China. Overall, the average effect of industrial pollution emissions on carbon density is positive. This indicates that industrial pollution emissions play a driving role in carbon density on the whole, while there are temporal and spatial differences in the interactions at the provincial level. According to the Herfindahl index, neither time nor space can be neglected. Moreover, according to the traditional division of eastern, central and western regions in China, the situation in 30 provinces is examined. Results show that there is little difference in the parameter-estimated results between neighboring provinces. In many provinces, the pull effect of industrial pollution emissions on carbon density is widespread. Thus, carbon emissions could be reduced by controlling industrial pollution emissions in more than 60% of regions. In a few other regions, such as Shanghai and Heilongjiang, the industrial pollution emissions do not have a pull effect on carbon density. But due to spatial and temporal heterogeneity, the effects are different in different regions at different times. It is necessary to consider the reasons for the changes combined with other factors. Finally, the empirical results support pertinent suggestions for controlling future emissions, such as optimizing energy mix and reinforcing government regulation.
减少工业污染和温室气体排放是创建生态稳定文明的重要举措。然而，关于它们之间的相互作用和变化的报道很少。在本研究中，采用垂直和水平散射度方法计算工业污染排放的综合指数。然后，基于碳密度，建立一个地理和时间加权回归（GTWR）模型来检验工业污染排放与碳排放之间的相互作用。结果表明，中国的碳密度存在空间自相关。总的来说，工业污染排放对碳密度的平均影响是正的。这表明工业污染排放对碳密度整体上起着驱动作用，而在省级层面上，相互作用存在时间和空间差异。根据赫芬达尔指数，时间和空间都不能被忽视。此外，根据中国东部、中部和西部地区的传统划分，对 30 个省份的情况进行了检验。结果表明，相邻省份的参数估计结果差异不大。在许多省份，工业污染排放对碳密度的拉动作用普遍存在。因此，在 60%以上的地区通过控制工业污染排放，可以减少碳排放。在上海和黑龙江等其他一些地区，工业污染排放对碳密度没有拉动作用。但是，由于时空异质性，在不同的时间和不同的地区，影响是不同的。有必要结合其他因素考虑变化的原因。最后，实证结果支持了控制未来排放的相关建议，如优化能源组合和加强政府监管。
