What are the impacts of demographic structure on CO2 emissions? A regional analysis in China via heterogeneous panel estimates

This study comprehensively investigated the impacts of demographic structure on CO2 emissions in China at the national level and the regional level for the first time. Panel cointegration modeling was employed to test the long-run relationships between CO2 emissions and six demographic structure variables, namely, dependency ratio, sex ratio, higher education ratio, industrial employment ratio, urbanization ratio, and average household size. The fully modified ordinary least squares method was then applied to estimate the long-run elasticity of CO2 emissions for the six demographic structure variables. The results suggested that long-run relationships between CO2 emissions and demographic structure existed at both the national level and the regional level. Dependency ratio was found to exert negative effects on CO2 emissions in China and its three sub-regions. Positive associations between sex ratio and CO2 emissions were revealed to exist in China and West China, and CO2 emissions elasticity for sex ratio was relatively high in West China. Higher education ratio had a positive effect on CO2 emissions in East China. Industrial employment ratio was found to positively correlate with CO2 emissions in China, East China, and Central China. Urbanization ratio was demonstrated to increase CO2 emissions at the national level and the regional level, and CO2 emissions elasticity for urbanization ratio decreased from West China to Central China, and then to East China. Negative correlations between average household size and CO2 emissions were detected at both the national level and the regional level. Based on the findings of this study, several practical recommendations were proposed, including optimizing age structure, promoting gender equality, advocating low-carbon lifestyles and low-carbon consumption patterns, promoting industrial upgrading and industrial structure optimization, building low-carbon cities and less carbon-intensive public infrastructure systems, and improving residential energy efficiency. (C) 2018 Elsevier B.V. All rights reserved.