中国工业固废的演化与驱动机制:数据驱动分析OA
Evolution and Driving Mechanisms of Industrial Solid Waste in China:A Data-Driven Analysis
在工业化持续推进与"双碳"目标约束并行的背景下,我国工业固体废物的产生呈现长期高位增长、结构调整和治理转型并存的复杂态势.厘清工业固废长期演化阶段与主导驱动力,对于制定差异化、阶段性控废政策具有重要意义.本研究系统分析了 2003-2019年间我国城市工业固体废物产生的长期演化阶段结构及其驱动力在不同阶段中的变化特征.研究表明,工业固体废物产生经历了 2003-2007、2008-2012和 2013-2019年三个稳定的阶段,尽管总量持续增长,但增速显著放缓.人口规模与经济富裕度构成的规模效应在各阶段持续增强,占比达 33.4%.然而增速放缓的主要原因在于产业结构效应由正向拉动(+1.13×104 万吨)转为显著的负向抵消(-2.90×104 万吨),与此同时,强度效应保持相对稳定且发挥一定的抑制作用.在区域层面,各地通过结构或强度降低等不同路径抵消增长的拉动作用,东北地区在后期阶段出现了强度效应由抑制转为正向的特殊情况;城市层面上,工业固体废物产生对工业活动强度相关因素的短期年际变化更为敏感,而对规模与结构变量的年际变化响应有限.在多指标竞争条件下,治理绩效和过程性指标表现出更高的稳定性,更适合作为城市年度固废治理的操作性工具.本研究结果为不同阶段和尺度上协调结构性调控与过程性管理,提升工业固体废物治理的针对性与可执行性提供了有力的实证支持.
Against the backdrop of continued industrialization and China's"dual-carbon"targets,industrial solid waste(ISW)generation has exhibited persistent growth amidst structural adjustments and governance transitions.Understanding whether this growth follows a stable trajectory or undergoes stage-specific shifts is essential for interpreting governance outcomes and designing differentiated waste management strategies.This study applies a data-driven analytical framework to a city-level panel dataset to identify the long-term stage structure of urban ISW generation in China from 2003 to 2019 and to examine how its driving mechanisms evolve across different stages and spatial scales.First,unsupervised time-series structural learning is employed to detect endogenous stage boundaries in national ISW generation without predefined breakpoints.Second,inter-stage changes are decomposed into population scale,economic affluence,industrial structure,and composite intensity effects using the Logarithmic Mean Divisia Index(LMDI)method.Third,city-level dynamics are analyzed using two-way fixed effects models and a random forest-based feature selection procedure to identify governance-relevant signals that remain stable at the annual municipal scale.We identify three stable stages:2003–2007,2008–2012,and 2013–2019.Total ISW continued to increase in all stages;however,the growth rate declined over time.Scale effects remained persistently positive,strengthened across stages,and explained 33.4%of cumulative growth.The post-2013 slowdown primarily reflects a structural transition.The industrial structure effect shifted from a positive contribution(+113 Mt)to a substantial negative contribution(-290 Mt).In contrast,the intensity effect was negative across all stages,with minimal variation in magnitude,indicating a stable offset rather than a trigger for a stage change.Regionally,all major regions shared a common temporal stage structure but relied on different pathways to mitigate scale-driven growth.In the later stage(2013–2019),the Eastern and Central regions primarily depended on intensity-related mitigation,whereas the Western regions exhibited stronger structural offsets.Northeast China,however,demonstrated a notable deviation in driving mechanisms in the later stage,during which the intensity effect turned positive and became the dominant contributor to regional ISW changes.City-level evidence suggests this pattern is consistent in direction across cities but highly concentrated in magnitude,with a small number of cities accounting for most of the positive intensity contribution.At the city scale,ISW generation is more responsive to short-term interannual changes in industrial activity intensity than to variations in economic scale or industrial structure,once time-invariant city characteristics and common shocks are controlled for(coefficient=0.0787,p=0.032).Under multi-indicator competition,performance-and process-oriented indicators,such as comprehensive ISW utilization,demonstrate greater stability and explanatory power in annual variations,standing out as especially informative for municipal operations.Overall,the findings suggest that the recent slowdown in China's ISW growth primarily reflects a structural transition rather than a weakening of scale pressures.Effective governance,therefore,requires aligning policy instruments with stage-specific driving mechanisms,emphasizing structural adjustment at the macro level while strengthening process-oriented management at the urban scale.
袁嘉翼;陈楚珂;吴悦菡;陈思晨;常慧敏;杨航;徐明
清华大学 环境学院,北京 100084清华大学 环境学院,北京 100084清华大学 环境学院,北京 100084清华大学 环境学院,北京 100084清华苏州环境创新研究院 天工智库中心,江苏 苏州 215163清华大学 环境学院,北京 100084清华大学 环境学院 钢铁工业环境保护全国重点实验室,北京 100084
资源环境
工业固体废物阶段划分结构性转型城市异质性随机森林
Industrial solid wasteStage IdentificationStructural transitionUrban heterogeneityRandom Forest
《能源环境保护》 2026 (2)
62-73,12
国家自然科学基金资助项目(52400239)
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