Applying the established methodology, the spatial–temporal distribution of carbon emissions and driving forces in 21 cities of Guangdong province are calculated and analyzed.
4.2. Results of the GTWR Model
Before applying the GTWR model, the correlation test was conducted between carbon emission intensity (CI) and the six independent variables (
Figure 5). The results show that all the six explanatory variables are significant at least at the 10% significance level. Then, we tested the multicollinearity of variables. The variance inflation factor (VIF) of all variables was less than 10 (
Table 4), which indicates that there is no multicollinearity in the model and the setting is reasonable. Therefore, we applied the GTWR method to explore the spatiotemporal heterogeneity of the influencing factors of industrial carbon emissions of Guangdong. A summary of estimated coefficients is given in
Table 5, which shows that the values of the bandwidth and AICc are 0.115 and −188.899, respectively. R
2 is equal to 0.9919, and adjusted R
2 is equal to 0.9916; thus, the fitting effect is good.
4.2.1. Comparisons with Other Conventional and Spatiotemporal Models
To further understand the effects of spatial and temporal information on model performance improvement, conventional OLS, GWR and TWR models were fitted using the same variables as those used in the GTWR models. The results of these models are listed in
Table 6. The R
2 of the GTWR model is larger than the other; thus, it is obvious that the GTWR model outperforms the other models.
4.2.2. Time Evaluation of Carbon Intensity Influencing Factors
In this paper, we estimate the contribution coefficient of each driving factor to the carbon emission intensity and plot and observe the box diagram of each coefficient and its evolution trend over time (
Figure 6).
During the study period, the impact of economic development level (PIVA) on carbon emissions is mostly negatively correlated. That is, the CI decreases as the level of economic development improves. The average of the regression coefficient rises over time, reaching a maximum in 2017 and then leveling off, which shows that the negative influence of PIVA on carbon emission is gradually weakening. With the improvement of the level of economic development and implementation of a series of low-carbon policies and energy policies, the carbon emissions caused by economic development can be curbed, and the fluctuation of the impact degree will be reduced.
The impact of population scale (IPOP) on CI is basically negative, indicating that industrial population agglomeration is conducive to energy saving and emission reduction. Similar to the change in the coefficient of economic development level, the average of the regression coefficient rises over time, reaching a maximum in 2017 and then leveling off. The industrial population agglomeration guides economic activities and production factors to agglomerate in space by exerting cost optimization effects, thereby improving the comprehensive utilization efficiency of energy and resources. At the same time, it also saves the cost of emission reduction to the greatest extent and facilitates the centralized supervision of government departments, which provides the possibility for centralized control of carbon emission problems.
The contribution rate of energy intensity (EI) to CI is positive, showing that EI has a positive impact on CI in the industrial sector. The average regression coefficient is 0.9242, which presents an upward trend during the research period. On the one hand, the government has been insisting on increasing energy conservation and enhancing energy efficiency in the first place through low-carbon production technologies. On the other hand, with the rapid development of the industry, urban infrastructure construction generates a large demand for heavy products such as steel and cement, and energy consumption has further increased, resulting in a tremendous amount of carbon emissions. Therefore, the overall energy intensity regression coefficient shows an upward trend. In conclusion, EI has a strong impact on CI. In general, improving energy efficiency and insisting on energy conservation and emission reduction are important ways to achieve carbon emission reduction.
The average regression coefficient of urbanization level (UL) is −0.0279, which indicates that UL has a negative correlation with CI. In the early stage of the study, due to the deployment and implementation of urbanization development strategy, the improvement of urbanization level curbed carbon emissions to a certain extent, and the regression coefficient was negative, while at the later stage of the study, the regression coefficient of UL began to change from negative to positive. This may be because with resumption of industrial production in some areas, the agglomeration of population, industry and various economic activities led to the carbon emissions increased.
The impact of industrial structure (IS) on CI is basically negative. That is, CI will increase with the decrease in the added value of the industrial sector. During the study period, the fluctuation of the regression coefficient of the industrial structure decreases, indicating that the influence of industrial structure on carbon emissions is slowly diminishing, and industrialization is still the main factor that aggravates carbon emissions.
The contribution rate of energy consumption structure (ES) to CI is mostly positive, indicating that the energy structure in most areas of Guangdong is optimized and reasonable, which effectively promotes industrial carbon emission reduction. The overall trend of the energy consumption structure coefficient drops and then rises. Since the “Twelfth Five-Year Plan” period, China has implemented a series of policies to optimize the energy structure so that non-fossil energy sources, such as hydropower, solar power, nuclear power, wind power, etc., occupy a certain proportion of energy consumption. Therefore, there is a mostly positive correlation between ES and CI. However, in the later stage of the study, the energy consumption has further increased due to the start-up or resumption of high-energy-consuming projects in some cities of Guangdong [
48], which leads to the coefficient rising briefly.
4.2.3. Spatial Heterogeneity of Carbon Intensity Influencing Factors
In order to visualize the discrepancy in the spatial distribution of each influence factor more intuitively, the average fitting results of each influencing factor in each region are selected for visualization in this paper (
Figure 7).
The impact of economic development level (PIVA) on regional industrial carbon emissions is negative, which indicates that PIVA is effective in curbing carbon emissions reduction. In terms of spatial distribution, PIVA has great influence on reducing carbon emissions in Shanwei, Heyuan and Jieyang, mainly because the improvement of industrial development level will lead to technological progress, and then technological progress will enhance energy utilization rate, optimize energy structure and reduce industrial carbon emissions.
The regions with positive impact of population scale (IPOP) on CI are Shaoguan and Meizhou. The rest are negatively affected. In general, the impact of IPOP on regional carbon emissions is negative. The reason is that the industrial population agglomeration effect leads to the spatial agglomeration of economic activities and production factors, thus enhancing the comprehensive utilization efficiency of energy and resources and curbing carbon emission.
Energy intensity (EI) has great influence on carbon emissions in Shantou, Chaozhou, Jieyang, and Meizhou, mainly concentrated in Eastern Guangdong. This indicates that those areas have higher energy intensity, which has somewhat inhibited progress in reducing carbon emissions. The regression coefficient of energy intensity ranges from 0.8584 to 0.9703, showing a consistent positive correlation, and the span is small. This means that the impact of EI on carbon emissions in different regions of Guangdong are not much different. Overall, the impact of EI on regional carbon emissions is greatly positive. In terms of spatial distribution, the spatial distribution of the average regression coefficient of EI presents a gradient trend, which is low in the middle and high on both sides.
The impact of urbanization level (UL) on regional carbon emission is mostly negative. which shows that the urbanization level can curb carbon emissions. However, the UL has a significant positive impact on carbon emissions in Shantou, Chaozhou, Jieyang, and Meizhou, of which are mainly concentrated in Eastern Guangdong. This indicates that the urbanization process will aggravate carbon emissions, mainly due to massive concentrations of population, industry and various economic activities in those regions.
The industrial structure (IS) has both positive and negative effects on CI, which shows that the industrial structure has a different correlation with the CI. In terms of spatial distribution, IS has great influence on carbon emissions in Shaoguan and Zhanjiang, whose absolute value is relatively large. This is mainly because those regions are dominated by high energy-consuming industries, which accounts for a relatively high proportion in those regions, and the demand for energy is relatively high.
The energy consumption structure (ES) has great impact on industrial carbon emissions in Zhanjiang and Maoming, mainly due to the fact that heavy industries are mainly concentrated in Western Guangdong, where the consumption of coal resources is relatively high, leading to relatively high carbon emissions in this region. In terms of spatial distribution, the regression coefficient of ES gradually increases from northeast to southwest.