【摘要】 随着中国污染减排和碳排放控制形势日益严峻，研究减污降碳协同效应的时空演变特征对推动传统工业城市可持续发展具有重要意义。本研究以绍兴市为例，构建减污降碳协同评价模型，分析其2010—2023年减污降碳协同效应的演变趋势；采用对数平均迪氏指数(LMDI)模型分解减污降碳驱动因素；并利用时空地理加权回归(GTWR)模型揭示各县(市、区)驱动因素的空间敏感性差异。结果表明：1)绍兴市减污降碳协同指数年际波动明显，2016—2020年较2011—2015年有所提升，但交叉弹性分析显示，不同年份减排状态差异较大，部分时段甚至出现协同增排，表明协同机制尚未稳固建立；2)经济发展效应和能源强度效应是绍兴市工业源碳排放主要的影响因素；3)人均国内生产总值(GDP)和常住人口对减污降碳协同指数存在负向影响，而研究与试验发展(R&D)经费占比则表现为正向影响。基于此，本文提出针对绍兴市减污降碳协同治理的优化路径与政策建议，为传统工业城市低碳转型提供科学依据。更多还原

【Abstract】 As pollution reduction and carbon emission control have become increasingly urgent in China, investigating the spatiotemporal evolution of synergistic effects of pollution reduction and carbon reduction is of great significance for promoting sustainable development in traditional industrial cities. Taking Shaoxing as a case study, a synergistic evaluation model was constructed to analyze the temporal trends of synergistic pollution reduction and carbon reduction from 2010 to 2023. The driving factors were decomposed using the logarithmic mean divisia index（LMDI） method, and the geographically and temporally weighted regression（GTWR） model was employed to reveal spatial heterogeneity in the sensitivity of these driving factors across counties（districts and cities）. The results indicated that: 1) The synergistic effect in Shaoxing exhibited significant interannual fluctuations, with the average value during 2016-2020 slightly higher than that during 2011-2015. However, cross elasticity analysis revealed substantial variation in emission reduction states across years, with some periods even showing co-increases in pollutants and CO₂,indicating that a stable synergy mechanism has not yet been established. 2) Economic development and energy intensity were identified as the dominant factors influencing industrial CO₂ emissions. 3) Per capita gross domestic product（GDP） and permanent population exerted negative effects on the synergistic index, whereas the share of research and experimental development（R&D） expenditure demonstrated a consistently positive influence. Based on these findings, optimized pathways and policy recommendations for synergistic governance in Shaoxing were proposed, providing a scientific basis for low-carbon transformation in traditional industrial cities.更多还原