| 基于STIRPAT和CNN-LSTM组合模型的福建省碳达峰预测 |
| 摘要点击 2076 全文点击 268 投稿时间：2024-01-09 修订日期：2024-04-11 |
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| 中文关键词 碳排放 碳达峰 STIRPAT模型 CNN-LSTM模型 政策 |
| 英文关键词 carbon emission carbon peaking STIRPAT model CNN-LSTM model policy |
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| 中文摘要 |
| 碳达峰对中国实现“双碳”目标、推动经济社会绿色转型具有重要意义.基于改进的STIRPAT模型分析影响福建省碳排放的主要因素,设置3种情景方案,利用CNN-LSTM神经网络混合模型对福建省2022~2035年碳排放量进行预测.结果表明：①人口、人均GDP和产业结构对福建省碳排放有正向驱动作用,能源强度、能源结构和对外贸易度则起负向驱动作用；②基准情景下于2033年实现碳达峰,达峰值为361.107 9 Mt,低碳情景和优化情景可以提早1 a达峰且达峰值均有不同程度下降,分别为333.028 4 Mt和301.748 3 Mt；③对比优化情景和低碳情景,调整产业和能源结构能够控制福建省碳峰值降低10.37%,加快推动能源和产业结构优化转型是解绑碳排放与经济发展之间束缚的关键所在.最后,结合福建省当前政策规划和发展现状,从能源减排、产业结构和制度体系等角度提出低碳发展建议. |
| 英文摘要 |
| Carbon peaking is of great significance for China to achieve the “dual carbon” target goal and promote the green transformation of the economy and society. Based on the improved STIRPAT model, to analyze the main factors affecting carbon emissions in Fujian Province, we set up three scenarios and predicted the carbon emissions in Fujian Province from 2022 to 2035 using the hybrid CNN-LSTM neural network model. The results showed that ① Population, GDP per capita, and industrial structure positively drove carbon emissions in Fujian Province, while energy intensity, energy structure, and foreign trade degree negatively drove them. ② The baseline scenario achieved peak carbon in 2033 with a peak value of 361.107 9 Mt； the low-carbon scenario and the optimization scenario could reach the peak one year earlier, and the peak value decreased to a different extent, with 333.028 4 Mt and 301.748 3 Mt, respectively. ③ Comparing the optimization scenario and the low carbon scenario, adjusting the industrial and energy structure could control the reduction in the peak carbon value in Fujian Province by 10.37%, and accelerating the promotion of the optimization and transformation of the energy and industrial structure is the key to unlocking the constraints between carbon emissions and economic development. Finally, combined with the current policy planning and development status of Fujian Province, we put forward suggestions for low-carbon development from the perspectives of energy emission reduction, industrial structure, and institutional system. |
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