Li Guo-Zhu, Huang Qiao-Hui
School of Economics, Hebei GEO University, Shijiazhuang 050031, China.
Natural Resources Asset Capital Research Centre, Hebei GEO University, Shijiazhuang 050031, China.
Huan Jing Ke Xue. 2025 Feb 8;46(2):636-646. doi: 10.13227/j.hjkx.202309001.
The Beijing-Tianjin-Hebei Region is a cluster of energy consumption and carbon emissions in China. Reducing carbon emissions and achieving a carbon peak are the primary goals of the region. Considering the carbon emission data of the Beijing-Tianjin-Hebei Region from 1995 to 2021, and the data on influencing factors on carbon emissions as research samples, the decoupling value of carbon emissions and economic growth in the three regions of the Beijing-Tianjin-Hebei Region was first calculated, and the decoupling state was divided. Secondly, considering the complexity of factors affecting carbon emissions, the Lasso variable selection method was used to determine the key factors affecting carbon emissions in each region of the Beijing-Tianjin-Hebei Region. The selected key factor values were considered the inputs of the GRNN and BP neural networks, and the network output was the carbon emission values of the corresponding places. The Lasso-GRNN and Lasso-BP carbon emission models of each region were analyzed and compared, and the Lasso-GRNN prediction results were superior to those of the Lasso-BP model after comprehensive analysis and comparison in all aspects. Therefore, the Lasso-GRNN model was selected to further set the baseline scenario, factor regulation scenario, and comprehensive regulation scenario for analysis and prediction. The results showed that: ① The economic growth and carbon emissions of Beijing and Tianjin achieved strong decoupling, whereas Hebei Province was in a weak decoupling state, and the overall economic development state was not ideal, which needs to be adjusted and optimized. ② Under each scenario setting, the carbon peak in Beijing was 138 439 800 tons in 2010, and Tianjin achieved the peak carbon value of 211.154 8 million tons in 2013. Hebei Province, under the comprehensive factor control scenario, was predicted to achieve the peak of carbon in 2029, with a peak of 9 240.286 million tons. Based on the research results, reasonable suggestions were put forward for the economic development of Beijing-Tianjin-Hebei, optimizing the industrial structure, and developing low-carbon paths in a differentiated way to further strengthen the cooperation between Beijing-Tianjin-Hebei and promote the innovation of a low-carbon cooperation system and mechanism.
京津冀地区是中国能源消耗和碳排放的聚集区。减少碳排放并实现碳达峰是该地区的首要目标。以1995年至2021年京津冀地区的碳排放数据以及碳排放影响因素数据作为研究样本,首先计算京津冀地区三个区域碳排放与经济增长的脱钩值,并划分脱钩状态。其次,考虑到影响碳排放因素的复杂性,采用Lasso变量选择方法确定京津冀地区各区域影响碳排放的关键因素。将所选关键因素值作为广义回归神经网络(GRNN)和BP神经网络的输入,网络输出为相应地区的碳排放值。对各区域的Lasso-GRNN和Lasso-BP碳排放模型进行分析比较,经全面综合分析比较,Lasso-GRNN预测结果优于Lasso-BP模型。因此,选择Lasso-GRNN模型进一步设定基准情景、因素调控情景和综合调控情景进行分析预测。结果表明:①北京和天津的经济增长与碳排放实现了强脱钩,而河北省处于弱脱钩状态,整体经济发展状况不理想,需要进行调整优化。②在各情景设定下,北京碳达峰出现在2010年,峰值为13843.98万吨,天津在2013年实现碳峰值21115.48万吨。河北省在综合因素控制情景下预计2029年实现碳达峰,峰值为92402.86万吨。基于研究结果,对京津冀经济发展、优化产业结构、差异化发展低碳路径提出合理建议,以进一步加强京津冀协同合作,推动低碳合作体系和机制创新。