研究采用2000~2021年中国30个省份能源耗费数据,基于ARIMA模型和BP神经网络模型,测算并预测了2000~2035年中国30个省份碳排放总量,采用ArcGIS和标准椭圆差对时空演变特征进行了可视化分析,进一步利用LMDI模型对影响碳排放的驱动因素进行了分解。研究结果表明:(1) 2000~2035年,我国碳排放总量逐年递增,但碳排放增长率逐渐降低;碳排放结构为“第二产业 > 居民生活 > 第三产业 > 第一产业”,第二产业和居民生活碳的增长速度较快,第一产业和第三产业变化趋势较小;(2) 我国各省碳排放的空间分布呈现典型的“东部 > 中部 > 西部”,“北部 > 南部”的分布格局,碳排放中心有向西北移动的趋势;(3) 数字经济、产业结构高级化以及新质生产力发展水平较高的地区碳排放相对较少,具有显著的组别差异效应;(4) 能源消费强度效应是驱动碳排放不断增长主要因素,人均GDP和能源消费结构效应是抑制碳排放的主要因素,产业结构和人口规模效应的影响相对较小。基于研究结论,从能源结构、产业结构、新质生产力和数字经济等方面提出了政策建议。Based on the energy consumption data of 30 provinces in China from 2000 to 2021, the total carbon emissions of 30 provinces in China from 2000 to 2035 were measured and predicted based on the ARIMA model and BP neural network model. The results show that: (1) From 2000 to 2035, China’s total carbon emissions will increase year by year, but the growth rate of carbon emissions will gradually decrease;The carbon emission structure is “secondary industry > residents’ daily life > tertiary industry > primary industry”, the secondary industry and residents’ living carbon growth rate is relatively fast, and the change trend of the primary industry and the tertiary industry is small. (2) The spatial distribution of carbon emissions in various provinces in China presents a typical distribution pattern of “eastern > central > western” and “northern > south”, and the carbon emission center has a tendency to move to the northwest. (3) The carbon emissions of the regions with higher levels of digital economy, industrial structure and new productivity are relatively small, which has a significant group difference effect. (4) The energy consumption intensity effect is the main factor driving the continuous growth of carbon emissions, the per capita GDP and energy consumption structure effect are the main factors inhibiting carbon emissions, and the impact of industrial structure and population scale effect is relatively small. Based on the research conclusions, policy suggestions are put forward from the aspects
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