Study on the coordinated development of urban competitiveness and energy-carbon emission reduction in China.

The urban competitiveness (UC) evaluation system is multidimensional and complex. This paper takes the simulated annealing (SA) model as the projection pursuit (PP) optimization to achieve a comprehensive assessment of competitiveness of 277 Chinese cities from 2011 to 2019, accompanied by energy saving and carbon-emission reduction (ESCER) as environmental measurements, to explore whether the two can meet the Porter hypothesis through coupling coordination degree (CCD). Further using spatiotemporal autocorrelation and obstacle degree model to uncover spatiotemporal features and interfering factors of coordinated development.

A dual-core system dynamics approach for carbon emission spillover effects analysis and cross-regional policy simulation.

As carbon emission continue to rise and climate issues grow increasingly severe, countries worldwide have taken measures to reduce carbon emission. However, carbon dioxide is continuously flowing in the atmosphere and is easily influenced by neighboring cities' policies. Therefore, how to solve the problem of carbon emission spillover effect has become the key to improve policy efficiency.

Study on the coupling coordination development of China's multidimensional digital economy and industrial carbon emission efficiency.

Exploring the coupling coordination between China's digital economy (DE) and industrial carbon emission efficiency (ICEE) is of great significance for achieving sustainable development goals. In the study, a multidimensional indicator system was established to evaluate DE, and spatiotemporal analysis and network analysis methods were used to reveal the dynamic evolution characteristics of DE and ICEE. The coupling coordination model and convergence model were adopted to explore the development trend of coupling coordination between DE and ICEE.

Industrial carbon emission efficiency prediction and carbon emission reduction strategies based on multi-objective particle swarm optimization-backpropagation: A perspective from regional clustering.

Against the backdrop of global climate change, industrial carbon emission reduction has become an important pathway to for global low-carbon development. This study constructs a framework of geographic spatial constraints regionalization and multi-objective machine learning to predict future industrial carbon emission efficiency (ICEE) and explore strategies for carbon emission reduction. Firstly, the ICEE of 285 Chinese cities were calculated by the super-efficiency slacks-based measure.

Dynamic simulation of carbon emission under different policy scenarios in Pearl River Delta urban agglomeration, China.

Global climate continues to warm; by reducing carbon emission (CE) to cope with climate warming has become a global consensus. The influencing factors of CE exhibit diversification and spatial characteristics, and the complexity of the CE system poses challenges to green and low-carbon development and the realization of China's dual-carbon goals. Taking the Pearl River Delta urban agglomeration as an example, this study explored the influencing factors of CE and designed emission reduction schemes with the help of multi-scale geographically weighted regression (MGWR).