我国自2010年启动低碳城市试点政策以来,持续探索城市低碳转型路径,为各城市提供了示范案例。为考察评估试点城市低碳转型的实际成效,在综合评估试点政策的基础上,采用机器学习方法构建试点城市碳排放模型,尝试还原各试点城市的目标排放轨迹,并将其与城市实际碳减排成效进行对比分析。结果表明,低碳试点城市在碳排放强度和碳排放总量控制方面的实际表现总体优于全国平均水平,但仅有4个试点城市实现碳排放总量如期达峰,另有5个试点城市的实际碳排放峰值已超过规划峰值。试点城市实际碳减排效果与规划目标之间普遍存在差距,可能使碳排放峰值较规划目标值增加约16%,并延后达峰时间,加大下一阶段全国城市低碳转型的压力。这一差距可能源于经济增长与低碳转型之间的矛盾以及吉登斯悖论的强化。未来可通过刚性约束碳排放量、将碳减排目标纳入地方政府绩效考核、建立碳排放轨迹动态预警机制、差异化调整减排政策重点等方式,推动试点城市实际减排成效向规划目标收敛,降低碳达峰进程的不确定性。
China launched the Low-Carbon City Pilot program in 2010 to explore pathways for urban low-carbon transition and provide demonstrative experiences. To evaluate the actual performance of pilot cities against their planned targets, this study first comprehensively assesses pilot performance and then employs machine learning to construct carbon emission models for pilot cities, aiming to reconstruct the trajectories of target carbon intensity and total emissions and compare them with actual emission reduction outcomes during urban development. The results show that pilot cities generally performed better than the national average in controlling both carbon emissions intensity and total carbon emissions. However, only four pilot cities achieved carbon peaking as scheduled, while five cities had already exceeded their planned peak emission levels based on their observed maximum emissions. Substantial gaps generally remain between the actual emission-reduction outcomes of pilot cities and their planned targets, potentially resulting in an approximately 16% increase in peak carbon emissions relative to the planned levels and a delay in the timing of carbon peaking. These deviations may also place greater pressure on the low-carbon transition of cities nationwide in the next stage. This gap stems from the structural tension between economic growth and low-carbon transition and is further reinforced by the Giddens paradox. Future efforts should strengthen the rigid constraints of dual carbon controls, incorporate carbon reduction targets into local government performance evaluations, establish dynamic early-warning mechanisms for carbon emission trajectories, and differentiate emission reduction priorities according to cities' stages of carbon peaking, so as to promote convergence between actual emission reduction outcomes and planned targets and reduce uncertainty in the carbon peaking process.
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