智能城市2026，Vol.12Issue(8)：1-6,6.DOI:10.19301/j.cnki.zncs.2026.08.001
大数据驱动的城市居民低碳行为画像与碳普惠激励机制研究
Research on low-carbon behavior profiling and carbon-inclusive incentive mechanisms for urban residents driven by big data
摘要
Abstract
Based on multi-source behavioral data from 326 000 residents in a fourth-tier Chinese city throughout 2024,this study integrates four categories and seventeen indicators covering energy consumption,travel trajectories,green consumption,and community participation.The K-Means clustering algorithm is employed to construct a low-carbon behavior profiling system,which classifies residents into five typical groups:full-domain high practice,travel-led low carbon,household energy saving dominant,potential convertible,and high-carbon inertial.The findings reveal significant differentiation among these groups in terms of behavioral shortcomings,emission reduction potential,and behavioral stability.Among them,the potential convertible group exhibits the largest emission reduction space(10.36 kg per capita per month),while the high-carbon inertial group accounts for the highest proportion(26.68%)and shows the lowest stability.Accordingly,differentiated carbon credit accounting rules and tiered incentive schemes are designed,providing quantitative analytical tools and policy design references for low-carbon governance at the urban resident level.关键词
城市居民/低碳行为/行为画像/碳普惠/激励机制Key words
urban residents/low-carbon behavior/behavior profiling/carbon inclusion/incentive mechanism分类
资源环境引用本文复制引用
王鹏,皇甫政彤,许峰..大数据驱动的城市居民低碳行为画像与碳普惠激励机制研究[J].智能城市,2026,12(8):1-6,6.基金项目
2022年海南省哲学社会科学规划课题(HNSK(YB)22-23) （HNSK(YB）
2025年中国城市商业网点建设管理联合会课题(CUCO2025LXYK0041) （CUCO2025LXYK0041）
