Economic Research Institute, State Grid Shanghai Muncipale Electric Power Company
School of Economics and Management, North China Electric Power University

随着全球及我国对可持续发展和生态环境保护重视程度的不断提升,上海市作为我国的经济中心城市,在能源消费模式转型和减少碳排放方面扮演着至关重要的角色。通过收集上海市2001—2020年的能源消费数据,运用LMDI和基于注意力机制优化后的CNN-BiGRU神经网络模型,对上海市历年能源消费数据进行了深入的分析与预测。结合经济、社会及技术发展等多种因素,对上海市2021—2035年的碳排放量进行了情景分析预测,并提出了上海市节能减排措施建议,旨在促进上海市能源结构的优化升级,提高能源利用效率,减少碳排放,推动上海市经济向绿色低碳转型,为实现我国乃至全球的碳中和目标贡献力量。

In light of the growing significance of sustainable development and ecological environmental protection on a global and Chinese scale, Shanghai, as the economic hub of China, occupies a pivotal position in the transition of energy consumption patterns and the reduction of carbon emissions. This paper collates energy consumption data for Shanghai from 2001 to 2020. It employs the LMDI and an optimized CNN-BiGRU neural network model based on the attention mechanism to provide an in-depth analysis and prediction of Shanghai's energy consumption data over the years. A variety of factors, including economic, social and technological development, are taken into account in the scenario analysis, which predicts the carbon emissions of Shanghai from 2021 to 2035. The analysis also puts forward suggestions for energy-saving and emission reduction measures in Shanghai, with the aim of promoting optimization. Furthermore, the optimization of Shanghai′s energy structure, enhancement of energy utilization efficiency, reduction of carbon emissions and facilitation of the transformation of Shanghai′s economy to a green and low-carbon status will contribute to the realization of China′s and the world's carbon-neutral goals.

北京市自然科学基金资助项目(9234031)

王琼, 蒋庆南, 王沛雯, 姚润坤. 基于注意力机制改进CNN-BiGRU和LMDI的上海市能源消费与碳排放预测分析. 煤炭经济研究. 2024, 44(12): 6-15

WANG Qiong, JIANG Qingnan, WANG Peiwen, YAO Runkun. Analysis of energy consumption and carbon emission prediction of shanghai municipality based on attention mechanism improved CNN-BiGRU and LMDI. Coal Economic Research. 2024, 44(12): 6-15