Based on the super-efficiency SBM model to measure the carbon emission efficiency (CEE) of China's chemical industry across 30 provinces from 2007 to 2021, this study employs spatial analysis methods and kernel density estimation to characterize the spatiotemporal evolution patterns of CEE at both the national and regional levels. Furthermore, a Tobit regression model is applied to identify its influencing factors. Although the CEE of China's chemical industry exhibited a fluctuating upward trend during the study period, the overall level remained relatively low, with a mean value of 0.629. Moreover, a persistent regional disparity was observed, following the order of eastern China (0.750) > western China (0.584) > central China (0.530). The spatial distribution of CEE ultimately displayed a "southwest-northeast" orientation, with significant shifts in spatial patterns gradually forming a "tripartite balance" structure, though most regions remained at low efficiency levels. Additionally, the mean Gini coefficient was 0.322, indicating substantial spatial heterogeneity overall. The CEE in eastern China surpassed that of the central and western regions, with hypervariable density identified as the primary source of regional disparities. The overall evolutionary trend of CEE in the chemical industry was positive, with interprovincial gaps gradually narrowing. While the trends in eastern, central, and western China were generally favorable, attention should be paid to the increasing divergence in the western region. Industrial agglomeration, energy structure, and economic development level significantly promoted CEE, with the energy structure of the chemical industry having the strongest impact which coefficient is 0.9942. Therefore, the government should prioritize optimizing the energy structure of the chemical industry while fully leveraging the positive effects of industrial agglomeration and regional economic development. Additionally, enhancing interregional collaboration and formulating region-specific policies are crucial for further improving the CEE of the chemical industry.
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |