Spatiotemporal decoupling CH4 emission from economic growth and future trend in categorized Chinese cities

Abstract

As the second largest greenhouse emission, the spatiotemporal decoupling CH4 emission from economic growth and future trends received limited attentions, resulting in reduced policy effectiveness towards climate change mitigation. To fill the gaps, the study presented an in-depth investigation on the spatiotemporal decoupling CH4 emission and economic growth nexus and corresponding drivers based on the developed spatiotemporal decoupling models in a case of 289 Chinese cities classified by economic structure and population size over 2005–2022. Further, several advanced deep learning models were employed for projecting city-level CH4 emissions. The results suggested that, first, CH4 emission intensity and GDP per capita were the largest drivers contributing to both temporal changes and spatial differences in CH4 emission. Second, the spatiotemporal decoupling states and corresponding drivers between CH4 emission and GDP exhibited a certain extent of heterogeneity across different types of cities. For instance, in large industrial cities, CH4 emissions were more closely linked to energy consumption patterns, while in service-oriented cities, resources support may play a more prominent role. Third, multiple forecasting models suggest overall increasing trends for city-level CH4 emission by 2030 across the country. The study highlighted the importance of reducing CH4 emission from socioeconomic perspectives while being cautious about multi-model-based policy formulations towards carbon neutrality for countries like China.