Elite hospitals represent the highest level of Chinese hospitals in medical service and management, medical quality and safety, technical level and efficiency, which are also one of the important indicators reflecting high-quality medical resources in the region, and their spatial allocation is directly related to the fairness of health resource allocation. We explored the allocation pattern of high-quality resources and its influencing factors in the development of China's health system using geographic weighted regression (GWR), Multi-scale Geographically Weighted Regression (MGWR), GWR and MGWR with Spatial Autocorrelation(GWR-SAR and MGWR-SAR), spatial lag model (SLM), and spatial error model (SEM). The results of OLS regression showed that city level, number of medical colleges, urbanization rate, permanent population and GDP per capita were its significant variables. And spatial auto-correlation of elite hospitals in China is of great significance. Further, its spatial agglomeration phenomenon was confirmed through SLM and SEM. Among them, the city level is the most important factor affecting the spatial allocation of elite hospitals in China. Its action intensity shows a solid and weak mosaic trend in the Middle East, relatively concentrated in some areas with medium intensity and concentrated in the West China. Obviously, China's elite hospitals are unevenly distributed and have evident spatial heterogeneity. Therefore, we suggest that we should pay attention to the spatial governance of high-quality medical resources, attract medical elites in the region, increase investment in medical education in the scarce areas of elite hospitals and develop tele-medicine service.

The transport sector is one of the most important and potential sectors to achieve low-carbon development in China. As economic growth is desirable, but high-level traffic CO₂ emissions are not. This paper estimated the on-road traffic CO₂ emissions and investigated the decoupling states of traffic CO₂ emissions from economic growth for six cities in Hebei province from 1995 to 2015. In 2015, the on-road traffic CO₂ emissions were ranked, as follows: Tangshan (4.75 Mt) > Handan (3.38 Mt) > Baoding (1.38 Mt) > Zhangjiakou (1.05 Mt) > Langfang (1.01 Mt) > Chengde (0.46 Mt). Two turning points of traffic CO₂ emissions during the study period were found. From 2008 to 2013, the traffic CO₂ emissions increased more rapidly than before. After 2013, the traffic CO₂ emissions of three cities (Baoding, Handan and Chengde) began to decrease, and the traffic CO₂ emissions’ growth rates of the other three cities (Zhangjiakou, Langfang and Tangshan) became lower than before. The decoupling states during 1996–2015 can be divided into four phases: decoupling-coupling concurrence stage (1996–2000), decoupling dominant stage (2001–2008), coupling dominant stage (2009–2013), and improvement stage (2014–2015). Chengde and Baoding were identified due to their good local practice on decoupling CO₂ emissions in transport sector from economic growth. These results will enrich the greenhouse gas inventory of China at city level and provide scientific support to achieve the mitigation of CO₂ emissions in the transport sector.

Studying the carbon emissions resulting from digital transformation can provide a reference for the realization of the goals of carbon peaking and carbon neutrality in the era of the digital economy. This study calculated the value added to the digital economy and carbon emissions for 97 industry divisions from 1997 to 2018. Using the input–output model, we estimated the carbon emissions induced by the digital transformation of different industries, and used the structural decomposition analysis (SDA) to identify their driving factors. The results show that the carbon emissions induced by the digital economy in agriculture, forestry, animal husbandry, and fishery decreased in 2010, those from mining increased year by year, and those from scientific research and technical services showed a decreasing trend from 2011 to 2015. The induced rate of digital economy carbon emissions for production and supply of electricity, heat, gas, and water has persistently remained high. At present, digital economy labor productivity has not shown a promoting effect on carbon emission reduction. China should strengthen the construction of a digital platform for ecological and environmental governance and build a green and low-carbon industrial chain and supply chain to promote the realization of the goals of carbon peaking and carbon neutrality.