长江流域资源与环境 >> 2020, Vol. 29 >> Issue (6): 1290-1297.doi: 10.11870/cjlyzyyhj202006004

• 区域可持续发展 • 上一篇    下一篇

武汉市劳动力转移的环境效应分析

刘少慧1, 2,刘传江1*   

  1. (1. 武汉大学经济发展研究中心,湖北 武汉 430072; 2. 武汉大学经济与管理学院,湖北 武汉 430072)
  • 出版日期:2020-06-20 发布日期:2020-07-20

A Study on Environmental Effects of Labor Force Transfer in Wuhan City

LIU Shao-hui 1, 2, LIU Chuan-jiang 1   

  1. (1.Center for Economic Development Research, Wuhan University, Wuhan 430072, China;2. Economic and Management School, Wuhan University, Wuhan 430072, China)
  • Online:2020-06-20 Published:2020-07-20

摘要: 摘  要:改革开放以来,农村劳动力被大量释放到城市,这部分劳动力在促进国民经济快速发展的同时,也对城市的生态环境产生了一定的影响。尤其是在城市常住人口不断增加、污染日益恶化的发展背景下,从劳动力转移的视角分析其生态环境效应具有重要价值和现实意义。通过利用武汉市转移人口调查数据,采用碳足迹分析方法从生产和消费两个方面研究外来劳动力对武汉市的环境影响,并进一步探究劳动力迁入前后的碳足迹差异及影响因素。结果表明,外来劳动力对武汉市的环境压力为1 768.53万t碳足迹,其中消费碳足迹194.72万t,生产碳足迹1 573.81万t;与迁入前相比,分别增加了45.58和1 439.41万t碳当量。这种碳足迹差异主要受到劳动力性别、年龄、非农工作时间和技术水平的影响,其中性别和年龄与碳足迹差异具有显著的负相关关系,而非农工作时间和技术水平具有正向影响。此外,劳动合同的签订及与当地居民语言交流的困难程度也增加了转移前后的碳足迹差异。

Abstract: Abstract:Since the reform and opening up, rural labor forces have been poured into the city in large quantities. As promoting national economy, those labor forces have also exerted some influences on the urban ecological environment. With resident population increasing and environment pollution aggravating, it is of great value and practical significance to study the effects from the perspective of labor transfer. In this research, carbon footprint methods are adopted to analyze the outcomes in terms of production and consumption based on transferred population survey in Wuhan City, and to further explore the differences and related factors of carbon footprint before and after labor migration. The results show that 17.69 million tons carbon emissions are produced by nonlocal labor force per year, in which the consumption carbon footprint is 1.95 million tons, and production carbon footprint is 15.74 million tons. Compared with that before the move, carbon emissions of 0.46 million tons and 14.39 million tons are increased respectively. This difference is mainly affected by gender, age, off-farm working hours and technical skills. It is negatively correlated with gender and sex, while positively correlated with working hours and technical skills. Besides, contract and language communication could also have positive effects.

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