运筹与管理 ›› 2021, Vol. 30 ›› Issue (9): 232-239.DOI: 10.12005/orms.2021.0304

• 管理科学 • 上一篇    

中国碳排放与经济发展存在倒U型关系吗?——考虑时间相关效应和异质性的研究

田成诗, 刘怡   

  1. 东北财经大学 统计学院,辽宁 大连 116025
  • 收稿日期:2017-11-14 出版日期:2021-09-25
  • 作者简介:田成诗(1971-),辽宁大连人,教授,博士,研究方向:统计建模;刘怡(1994-),山东菏泽人,硕士研究生,研究方向:统计建模。
  • 基金资助:
    2020年辽宁省高等学校创新人才支持计划;辽宁省教育厅科学研究项目(LN2019Z12)

Is There an Inverted U-shaped Relationship between Carbon Emissions and Economic Development in China? ——Accounting Time-related Effects and Heterogeneity

TIAN Cheng-shi, LIU Yi   

  1. College of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
  • Received:2017-11-14 Online:2021-09-25

摘要: 随着我国经济进入高质量发展阶段,经济发展与碳排放之间的关系日益受到关注。本文基于1997~2016年省级面板数据,运用非参数广义加性混合模型研究了中国碳排放与经济发展的关系。文中,不可观测的时间相关效应和残差自相关结构被作为独立变量加入模型,收入效应和时间相关效应对碳排放的影响可能存在异质性也予以考虑。实证结果显示,东部和中部地区的最适模型中收入效应具有异质性,西部地区的最适模型中包含异质性时间相关效应;中国碳排放与经济发展之间不存在倒U型关系;在未来的节能减排工作中,应充分考虑中国经济发展阶段性、区域差异性及碳排放驱动因素的异质性。

关键词: 碳排放, 广义加性混合模型, 时间相关效应, 异质性

Abstract: As economy enters a period of high-quality development, the relationship between economic development and carbon emission has been paid more and more attention. The paper empirically explores the relationship between carbon emissions and economic development in China by nonparametric Generalized Additive Mixed Models based on the panel data of provinces from 1997 to 2016.In the paper, as independent variables the unobserved time-related effects and the residual auto-correlation structure are taken into the model and the possibly heterogeneous of the income and time-related effects across provinces are considered. The empirical results show that the income effects in the eastern and central regions is heterogeneous and time-related effects in the western region is heterogeneous. There is no inverted U-shaped relationship between carbon emissions and economic development. We should take into account the economic development stages,regional differences and the heterogeneity of drivers of carbon emissions in the energy-saving and emission-reduction in the future.

Key words: carbon emission, generalized additive mixed models, time-related factors, heterogeneity

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