Shaping a low-carbon future: Uncovering the spatial-temporal effect of population aging on carbon emissions in China.

Background: With the accelerated development of the aging trend in Chinese society, the aging problem has become one of the key factors affecting sustainable economic and social development. Given the importance of controlling carbon emissions for achieving global climate goals and China's economic transformation, studying the spatial and temporal effects of population aging on carbon emissions and their pathways of action is of great significance for formulating low-carbon development strategies adapted to an aging society.

Objective: This paper aims to explore the spatial-temporal effects of population aging on carbon emissions, identify the key pathways through which aging affects carbon emissions, and further explore the variability of these effects across different regions. The findings will provide theoretical support and empirical evidence for government departments to formulate policies to promote the coordinated development of a low-carbon society and an aging society.

Methods: Based on the panel data of 30 provinces in China from 2004 to 2022, this paper systematically investigates the impact of population aging on carbon emission intensity from both spatial and temporal dimensions by using the spatial Durbin model and the mediating effect model. The direct effect of aging on carbon emission intensity, the spatial spillover effect, and the indirect effect through mediating variables such as residents' consumption, environmental regulation, and new urbanization are analyzed in depth.

Results: The study found that population aging in China has significant spatial and temporal effects on carbon emissions. From the spatial dimension, there is a significant spatial spillover effect of the effect of aging on carbon emissions, and aging reduces local carbon emissions but increases carbon emissions in adjacent regions. From the time dimension, the effect of aging on carbon emissions shows a stage characteristic, initially it will reduce carbon emissions, but with the deepening of aging, its effect may tend to weaken. In addition, this study identifies a number of key pathways through which aging affects carbon emissions, including reducing residential consumption, promoting new urbanization, and increasing the intensity of environmental regulations. Finally, this study explores the regional heterogeneity of the impact of aging on carbon emissions and its mechanism of action.

Conclusion: This study is instructive: first, the complex impact of population aging on carbon emissions should be fully recognized to formulate a comprehensive low-carbon development strategy; second, attention should be paid to the spatial spillover effect of aging on carbon emissions to strengthen inter-regional cooperation and coordination; and lastly, differentiated low-carbon policies should be formulated to address the characteristics of aging in different regions and stages in order to promote the synergistic development of a low-carbon society and an aging society.