4.7 Article

Voluntary disclosure of greenhouse gas emissions by cities under carbon disclosure project: A sustainable development approach

期刊

SUSTAINABLE DEVELOPMENT
卷 29, 期 4, 页码 719-727

出版社

WILEY
DOI: 10.1002/sd.2169

关键词

carbon disclosure project; GDP; greenhouse gas emissions; population; sustainable development

资金

  1. National Natural Science Foundation of China [71603049]
  2. Social Science Foundation of Ministry of Education of China [16YJC790006]
  3. program for young Excellent Talents, University of International Business and Economics [18YQ07]
  4. Key Research Project Foundation of Beijing Finance Society

向作者/读者索取更多资源

This study examines the impact of population and GDP on carbon emissions in cities, revealing that these factors are significant predictors of carbon emissions. The findings suggest that economic growth and population increase in cities contribute to rising carbon emissions.
Today's world is facing a serious challenge of high carbon emissions. A large number of studies are examining the deteriorating impact of carbon emissions at the country or firm level, but the literature on the voluntary carbon disclosure project (CDP) by cities is not rich. Therefore, this study seeks to examine the effect of the population (Pop) and gross domestic product (GDP) on carbon emissions of cities by using data of CDP. The dataset refers to a set of numerous cities from around the world, spanning the period from 2012 to 2016. Random effects panel regression and generalized method of moments (GMM), and quantile regressions techniques have been employed to empirically investigate the discussed relationship and to control the potential endogeneity issues, respectively. According to the results, population and GDP are substantial predictors of carbon emissions in sampled cities. Findings show that not only factories contribute to increasing carbon emissions rate, but cities are equally responsible for these emissions as they put no checks on their economic growth and population. The findings are also robust for policy implications.

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