我们如何从低碳经济中获益?来自中国区域劳动力市场的证据

IF 5 2区 经济学 Q1 ECONOMICS Structural Change and Economic Dynamics Pub Date : 2024-09-04 DOI:10.1016/j.strueco.2024.09.001
Chuanhui Wei , Yongji Zhang , Minghui Lan , Zhi Su , Heran Du , Ke Wang
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引用次数: 0

摘要

现有关于低碳经济对就业影响的研究主要关注部门动态而非地区动态,这为地级市的政策制定提供了有限的参考。我们采用固定效应模型和 2006 年至 2020 年中国城市数据集弥补了这一不足。我们的研究结果凸显了低碳经济通过产业集聚效应和创新效应对地区就业的重大贡献。低碳经济对就业的促进作用存在明显的异质性,环境执法宽松、教育投资充足、非资源依赖型结构的城市受益更明显。值得注意的是,低碳经济对中等就业人口城市的就业促进作用更大。此外,我们还发现了低碳经济的共同效益,包括提高平均工资和缓解老龄化趋势。这些见解有助于人们更好地理解低碳经济,并为都道府县政府制定有针对性的低碳经济战略提供了宝贵的指导。
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How could we benefit from a low-carbon economy? Evidence from Chinese regional labor markets

Existing studies on the employment effects of a low-carbon economy predominantly focus on sectoral dynamics instead of regions, providing limited references for prefectural policymaking. We address this gap by employing a fixed effects model and a dataset of Chinese cities from 2006 to 2020. Our findings highlight the significant contribution of a low-carbon economy to regional employment through the industry agglomeration effect and innovation effect. Heterogeneities are evident in the employment-promoting influence of a low-carbon economy, as cities with lenient environment enforcement, ample education investment, and non-resource-dependent structures benefit more significantly. Notably, a low-carbon economy exhibits a disproportionately higher employment promotion effect in cities with mid-range employment populations. In addition, we identify co-benefits of a low-carbon economy, including higher average salaries and mitigation of aging trends. These insights foster a better understanding of the low-carbon economy and offer valuable guidance for prefectural governments seeking to develop targeted low-carbon economy strategies.

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来源期刊
CiteScore
9.60
自引率
4.90%
发文量
159
期刊介绍: Structural Change and Economic Dynamics publishes articles about theoretical, applied and methodological aspects of structural change in economic systems. The journal publishes work analysing dynamics and structural breaks in economic, technological, behavioural and institutional patterns.
期刊最新文献
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