| 基于STIRPAT模型的山地城市碳达峰预测与实证：以重庆市数据为例 |
| 摘要点击 274 全文点击 10 投稿时间：2025-03-17 修订日期：2025-06-11 |
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| 中文关键词 碳排放达峰 STIRPAT模型 山地城市 多情景分析 减排路径 |
| 英文关键词 peak carbon emissions STIRPAT model mountain cities multi-scenario analysis emission reduction pathways |
| DOI 10.13227/j.hjkx.202503185 |
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| 中文摘要 |
| 针对山地城市碳达峰路径中区域结构性变量刻画不足、情景设计缺乏政策适配性和西部案例代表性薄弱等问题，以重庆市为典型案例，基于STIRPAT模型引入产业结构、能源强度与能源结构等区域关键变量，结合中国碳核算数据库（CEADs），采用岭回归方法优化参数估计，并构建7种多情景组合预测2023~2050年碳排放演变. 结果表明：①人口规模、产业结构和能源结构是重庆市碳排放的核心驱动因素，其中人口规模的边际效应最为显著；②基准情景下碳排放量持续上升，达峰时间为2037年（以CO2计，峰值达208.009×106 t）；在“低速增长+高效降碳”情景中，通过协同推进能源强度加速下降与产业结构深度调整，碳排放量最早可于2035年达峰，峰值降至202.040×106 t，较基准情景降低约2.9%. ③“低速增长+智能化驱动+高效降碳”路径是实现重庆市及相似区域碳达峰的最优策略. |
| 英文摘要 |
| Based on the 2001-2022 panel data of Chongqing Municipality， we employed the extended STIRPAT （population-affluence-technology-impact） model， incorporating six driving variables. The model parameters were robustly estimated using ridge regression， combining the CEADs carbon emission accounting data and the Chongqing Statistical Yearbook. The six driving variables were categorized as follows： population size， GDP per capita， and urbanization rate （socioeconomic factors） and industrial structure coefficient， energy intensity per unit of GDP， and proportion of coal consumption （technological and structural factors）. Relying on the 14th Five-Year Plan and regional development needs， we designed three growth rates （low， medium， and high） for each driver and combined them into seven scenarios， using a full factorial design， to predict the evolution of CO2 emission trajectories in Chongqing from 2023 to 2050. The results showed that population size elasticity （0.956， influence degree， same as below）， industrial structure coefficient （0.568）， and proportion of coal consumption （-0.483） exerted the most significant influence on carbon emissions， while the roles of GDP per capita （0.082） and energy intensity （-0.156） were comparatively weaker. Under the baseline （medium-speed） scenario， carbon emissions continued to rise and peak at 208.009×106 t （in terms of CO2， same as below） in 2037. Under the “low-speed growth+efficient carbon reduction” scenario， the peak could achieve earlier peaking in 2035， and the peak was reduced to 202.040×106 t， which was two years earlier than in the baseline scenario， a reduction of 2.9%. The scenario analysis indicated that suppressing excessive population and urbanization growth， while simultaneously accelerating improvements in energy consumption efficiency and optimizing industrial structure， can significantly advance and reduce the peak carbon level. For mountainous， complex industrial cities， a low-carbon development path combining structural reform and technological progress can provide a practical policy reference for realizing the regional peak carbon target. |
