“双碳”目标下闽三角碳排放脱钩状态及驱动机制分析

快速城市化背景下,建设低碳城市群是实现"双碳"目标的最佳方式。在碳排放核算的基础上,使用Tapio脱钩模型和LMDI方法对闽三角以及厦门、漳州和泉州的脱钩状态和碳排放的驱动机制进行了研究。主要结论如下：（1）2005-2017年闽三角碳排放和人均碳排放均持续增加,二者有相同的变化趋势。闽三角的工业中心泉州有最高的碳排放和人均碳排放。发展型城市漳州碳排放最低,但碳排放和人均碳排放增长率均最高。服务型城市厦门碳排放增长率最低。（2）闽三角的脱钩状态逐渐改善,平均脱钩系数为1.03,脱钩状态为扩张性连接。厦门、漳州和泉州的平均脱钩系数分别为0.45、2.70和1.10,3个城市分别以弱脱钩、扩张负脱钩和扩张性连接状态为主。（3）人均GDP和人口规模是闽三角碳排放的正向因素,能源结构和能源强度是负向因素。正向因素的贡献在下降,负向因素的贡献在升高。人均GDP和能源结构分别对漳州和厦门碳排放有最强的促进和抑制效应。能源强度对3个城市碳排放变化的效应不同。（4）人口扩张促进碳排放增加,使碳排放与经济发展无法脱钩。人口规模对闽三角碳减排无脱钩努力。能源结构优化和能源强度下降有助于碳排放与经济发展脱钩,是闽三角碳减排的强脱钩努力和弱脱钩努力。能源强度对泉州碳减排无脱钩努力。优化能源结构是闽三角实现碳减排和"双碳"目标的关键。已经脱钩的厦门宜尽早制定碳达峰行动计划,引领闽三角的碳达峰行动。漳州可通过升级产业结构实现减排。泉州必须提升能源效率才能降低碳排放。

Decoupling status and driving mechanisms of carbon emissions in the Golden Triangle of Southern Fujian under "carbon peaking and neutrality" goals

Developing low-carbon urban agglomeration is the best way to achieve "carbon peaking" and "carbon neutrality" goals under the background of rapid urbanization. Based on carbon emissions accounting, the Tapio decoupling model and the Logarithmic Mean Divisia Index (LMDI) method are utilized to analyze the decoupling status and driving mechanisms of carbon emissions of the Golden Triangle of Southern Fujian (GTSF), Xiamen, Zhangzhou and Quanzhou. The main findings are as follows:(1) Carbon emissions and carbon emissions per capita both kept increasing from 2005 and 2017, with the same trend. Quanzhou, an industrial center of the GTSF, has the highest carbon emissions and carbon emissions per capita. Zhangzhou, a developing city, has the least carbon emissions but the highest growth rate of carbon emissions and carbon emissions per capita. Xiamen, a service-oriented city, holds the lowest growth rate of carbon emissions. (2) Decoupling status of the GTSF has improved. The average decoupling index of the GTSF is 1.03, and the decoupling status is dominated by the expansive connection. The average decoupling indices of Xiamen, Zhangzhou and Quanzhou are 0.45, 2.70 and 1.10, respectively. The dominant decoupling status of the three cities is weak decoupling, expansive negative decoupling, and the expansive connection, respectively. (3) Gross domestic product (GDP) per capita and population size are positive factors of carbon emissions of the GTSF, while energy structure and energy intensity are negative factors. The contribution of the positive factors is decreasing, while the contribution of the negative factors is increasing. GDP per capita and energy structure have the strongest promoting effects and inhibiting effects on carbon emissions of Zhangzhou and Xiamen, respectively. The effects of energy intensity on carbon emissions of the three cities are different. (4) Population expansion leads to an increase in carbon emissions, which is not conducive to the decoupling of carbon emissions and economic development. Population size has no decoupling efforts on carbon emissions reduction of the GTSF. The optimization of energy structure and the decline of energy intensity contribute to the decoupling of carbon emissions and economic development. Energy structure and energy intensity are the strong decoupling efforts and the weak decoupling efforts of carbon emissions reduction of the GTSF, respectively. Energy intensity has no decoupling efforts on carbon emissions reduction of Quanzhou. For the GTSF, optimizing energy structure is the key to realize carbon emissions reduction and "carbon peaking and neutrality" goals. It is suggested that Xiamen, which has been decoupled, should formulate carbon emissions peaking action plans, and lead the peaking actions of the GTSF. Zhangzhou can achieve carbon emissions reduction by upgrading industrial structure. Carbon emissions reduction of Quanzhou depends on the improvement of energy efficiency.