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基于STIRPAT模型和Tapio脱钩模型的废水排放驱动因素研究——以重庆市为例 被引量:6

Research on driving factors of wastewater discharge based on the STIRPAT model and Tapio decoupling model:a case in Chongqing
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摘要 利用重庆市2000—2015年废水年排放量及社会经济统计数据,根据STIRPAT模型,通过相关性分析和岭回归分析获得人口数量、城镇化率、财富水平、COD年排放量、非第三产业占比与废水年排放量的关系,并采用Tapio脱钩模型界定人均GDP与废水年排放量之间的关系。结果表明:导致废水年排放量变化的3个主要驱动因素为财富水平、COD年排放量和人口数量;在观测时段内,重庆市存在"倒U"型环境库兹涅茨曲线。对重庆市废水年排放量进行分解,发现重庆市废水年排放量较为稳定,工业废水年排放量呈缓慢下降的趋势,而生活废水年排放量呈逐步上升的趋势,并逐渐成为重庆市废水排放的主要来源;重庆市经济增长与工业废水年排放量处于强脱钩与弱脱钩交替状态,而经济增长与生活废水年排放量始终处于弱脱钩状态。 In this paper,correlation analysis and ridge regression analysis were conducted using the STIRPAT model to analyze the connection between annual wastewater discharge and population size,urbanization rate,affluence,annual emissions of COD and share of non-tertiary industry from 2000 to 2015.And the Tapio decoupling model was assumed to define the relationship between per capita GDP and annual wastewater discharge.The results showed that the three main factors leading to the change of annual wastewater discharge were population size,affluence,and annual emissions of COD.The overall findings supported the“inverted U-shape”environment Kuznets curve hypothesis.During the study period,annual wastewater discharge in Chongqing remained stable,annual industrial wastewater discharge decreased slowly and annual domestic wastewater discharge showed a trend of escalation and gradually become the main source of wastewater discharge in Chongqing.The economic growth of Chongqing and the annual industrial wastewater discharge were in an alterate strong state and weak state of decoupling while the economic growth and the annual domestic wastewater discharge were always in a weak state of decoupling.
作者 胡家僖 管宏友 HU Jiaxi;GUAN Hongyou(College of Resources and Environment,Southwestern University,Chongqing 400716)
出处 《环境污染与防治》 CAS CSCD 北大核心 2018年第3期355-359,共5页 Environmental Pollution & Control
基金 国家社科基金资助一般项目(No.15BMZ098) 重庆市社科基金资助一般项目(No.2014YBSH040)
关键词 废水排放 STIRPAT模型 Tapio脱钩模型 岭回归 wastewater discharge STIRPAT model Tapio decoupling model ridge regression
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