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| 区域碳排放分解预测及“双碳”路径规划研究 |
| Decomposition-forecasting and pathway planning for regional carbon emissions toward dual-carbon goals |
| 投稿时间：2024-04-07 |
| DOI：10.13259/j.cnki.eri.2025.04.002 |
| 中文关键词: “双碳”目标 路径分析 Kaya模型 碳排放量预测 |
| 英文关键词:dual-carbon goals pathway analysis Kaya model carbon-emission forecasting |
| 基金项目:国家自然科学基金资助项目（72701130、71871144）；上海理工大学大学生创新计划资助项目（XJ2023156） |
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| 中文摘要: |
| 当前中国经济增长仍高度依赖能源消耗，碳减排任务艰巨，因此亟需探索经济增长与碳排放脱钩的可行路径。经典Kaya模型虽在碳排放因素分解方面具有基础性作用，但其结构相对简单，难以反映多经济部门与复杂能源结构对碳排放的影响，也未提供预测及路径分析功能，因而在支撑区域差异化减排策略方面存在局限。对Kaya模型进行了系统性扩展，构建了涵盖经济、人口及多能源消费部门的碳排放指标体系，并结合对数平均迪氏指数(LMDI)分解法定量评估各因素对碳排放的贡献程度。引入STIRPAT模型分析变量间关联关系，借助岭回归方法消除异方差影响，提升模型拟合质量与预测稳健性。构建基于PATH-STIRPAT模型的碳减排路径分析框架，研判出实现“碳达峰”与“碳中和”需要面对的主要挑战并规划出“双碳”路径。以我国东南沿海某区域“十二五”至“十三五”期间数据为对象的实证研究表明，该方法能够清晰识别该地区碳排放的关键驱动与抑制因素，准确预测“碳达峰”时间，并甄别出当前最优先且有效的减排路径。研究成果为区域层面精准制定碳减排政策提供了科学的理论依据与实践指导。 |
| 英文摘要: |
| Current economic growth in China remains highly dependent on energy consumption, making carbon emission reduction a challenging task and necessitating the exploration of viable pathways to decouple economic growth from carbon emissions. Although the classical Kaya model plays a fundamental role in decomposing the factors of carbon emissions, its structure is relatively simple and fails to adequately reflect the impact of multiple economic sectors and complex energy structures on carbon emissions. Moreover, it lacks capabilities for forecasting and pathway analysis, thereby limiting its effectiveness in supporting regionally differentiated emission-reduction strategies. This study systematically extends the Kaya model by constructing a carbon-emission indicator system that encompasses economic, demographic, and multi-energy consumption sectors. Combined with the LMDI decomposition method, the contribution of each factor to carbon emissions is quantitatively assessed. The STIRPAT model is introduced to analyze the interrelationships among variables, and ridge regression is employed to mitigate heteroscedasticity, thereby enhancing model-fitting quality and forecasting robustness. A carbon-reduction pathway analysis framework based on the PATH-STIRPAT model is developed to identify the main challenges in achieving carbon peak and carbon neutrality and to plan corresponding dual-carbon pathways. An empirical study using data from a coastal region in southeastern China during the 12th and 13th Five-Year Plan periods demonstrates that the proposed approach can clearly identify the key driving and restraining factors of carbon emissions in the region, accurately predict the timing of carbon peak, and identify the most immediate and effective emission-reduction pathways. It provides a scientific theoretical basis and practical guidance for the precise formulation of carbon-reduction policies at the regional level. |
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