As the Chinese government ratified the Paris Climate Agreement in 2016, the goal of reducing carbon dioxide emissions per unit of gross domestic product (carbon intensity) from 60% to 65% of 2005 levels must now be achieved by 2030. However, as numerous factors influence Chinese carbon intensity, it is key to assess their relative importance in order to determine which are most important. As traditional methods are inadequate for identifying key factors from a range acting simultaneously, machine learning is applied in this research. The random forest (RF) algorithm based on decision tree theory was proposed by Breiman (2001); this algorithm is one of the most appropriate because it is insensitive to multicollinearity, robust to missing and unbalanced data, and provides reasonable predictive results. We therefore identified the key factors influencing Chinese carbon intensity using the RF algorithm and analyzed their evolution between 1980 and 2014. The results of this analysis reveal that dominant factors include the scale and proportion of energy-intensive industries as well as fossil energy proportion and technical progress between 1980 and 1991. As the Chinese economy developed rapidly between 1992 and 2007, effects on carbon intensity were enhanced by service industry proportion and the fossil fuel price such that the influence of traditional residential consumption also increased. The Chinese economy then entered a period of deep structural adjustment subsequent to the 2008 global financial crisis; energy-saving emission reductions were greatly enhanced over this period and effects on carbon intensity were also rapidly boosted by the increasing availability of new energy and its residential consumption. Optimization of energy and industrial structures, promotion of technical progress, green consumption, and the reduction and management of emissions will be key to cutting future carbon intensity levels within China. These approaches will all help to achieve the 2030 goal of reducing carbon emission intensity from 60% to 65% of 2005 levels.

Since the reform and opening up in 1978, China has created a miracle of long-term high-speed economic growth, but the relationship between man and nature has suffered a serious damage, which is highlighted by the excessive consumption of resources and the intensification of environmental pollution. As a result, China is facing a slowdown in development. At the same time, China needs to maintain a certain speed of development in order to realize the dream of a powerful nationality entering the ranks of developed countries in 2050. To this end, China is facing transformation development. Now Chinese scholars and governments need to answer this kind of question: What economic growth rate is expected along with the corresponding development modes or means of regulation in the medium and long term? The growth development mode of the national economy is influenced and even dominated by the resource and environment support system. This study is intended to reveal the coupling relationship between economic growth, development modes and the supporting system, simulate the interaction process between them, explore the possible options for future economic growth and its requirements for the resource and environmental support system (the main factors), and provide early warning regarding China's environmental and development status. The results show that in order to achieve the development goal of entering the ranks of developed countries in 2050 and maintaining a fine ecological environment, the suitable growth rate for China's economy is 3.8%-6.3% on the premise that technological progress will improve resource utilization efficiency and reduce pollution emissions. Within this speed range, on the one hand, the smaller development velocity may be adopted to reduce the pressure on resources and environment, on the other hand, higher velocity can be adopted given that we are optimistic about the technological advances. The model proposed could help to compare different development scenarios and determine a better development mode; this way provides decision support for sustainable development. This study is a response to the "Future Earth" framework document. It develops the theoretical system of the resource and environmental carrying capacity in terms of development speed. It has important theoretical exploration significance and application value.

As the main form of China new urbanization, urban agglomerations are the important platform to support national economic growth, promote regional coordinated development and participate in international competition and cooperation, but they are also the core area of air pollution. This paper selects PM_2.5 data from NASA atmospheric remote sensing image inversion from 2000 to 2015, and uses GIS spatial analysis and Spatial Durbin Model to reveal the temporal and spatial evolution pattern characteristics and main controlling factors of PM_2.5 in China's urban agglomerations. The main conclusions are as follows: (1) From 2000 to 2015, the PM_2.5 concentration of China urban agglomerations showed a volatility growth trend. In 2007, there was an inflection point. The number of low-concentration cities declined, and the number of high-concentration cities increased. (2) The concentration of PM_2.5 in urban agglomerations was in the pattern of high in the east and and low in the west, with the "Hu Huanyong Line" as the boundary. The spatial difference between urban agglomerations is significant, and the difference is increasing. The concentration of PM_2.5 is growing faster in urban agglomerations in the eastern and northeastern regions. (3) The urban agglomeration of PM_2.5 has a significant spatial concentration. The hot spots are concentrated to the east of the "Hu Huanyong Line", and the number of cities continues to rise. The cold spots are concentrated to the west of the "Hu Huanyong Line", and the number of cities continues to decline. (4) There is a significant spatial spillover effect of PM_2.5 pollution among cities within urban agglomerations. The main controlling factors of PM_2.5 pollution in different urban agglomerations have significant differences. Industrialization and energy consumption have a significant positive impact on PM_2.5 pollution. Foreign direct investment has a significant negative impact on PM_2.5 pollution in the southeast coastal and border urban agglomerations. Population density has the significant positive impact on PM_2.5 pollution in the region, and has the opposite result in the neighbouring areas. Urbanization level has a negative impact on PM_2.5 pollution in national-level urban agglomerations, and has the opposite result in regional and local urban agglomerations. The high degree of industrial structure has a significant negative impact on PM_2.5 pollution in the region, and has the opposite result in the neighboring regions. Technical support has a significant impact on PM_2.5 pollution, but there are also lag effects and rebound effects.

Virtual water transfers can redistribute water resources among different regions, hence to reduce or aggregate water scarcity in one region. The Beijing-Tianjin-Hebei region, which is located in the North China Plain, has long been suffered from water scarcity. Knowledge on virtual water trades within and beyond this region is vital for understanding water resources problems and making responding strategies. In this study, water footprints and virtual water in Beijing, Tianjin and Hebei are computed based on the multi-regional input-output table and provincial water uses in China in 2010. Spatial patterns and characteristics of virtual water flows are analyzed, with an emphasis on separate accounting of local and external virtual water transfers. In addition, the relationships between virtual water transfers and distances from Beijing, Tianjin and Hebei to provinces where virtual water is from are examined. Main results include: (1) Water use intensities of different sectors in Beijing, Tianjin and Hebei present a big variability. Agricultural water use intensity is the highest. (2) Per capita water footprints in Beijing, Tianjin and Hebei present large difference, which are 405 m ³, 568 m ³ and 191 m ³, respectively. (3) Imported virtual water in Beijing, Tianjin and Hebei come from different provinces all over China. Local and external water footprints are 9.14 billion m ³ and 19.85 billion m ³, respectively. The region contributing the largest share of external virtual water to Beijing-Tianjin-Hebei region is the western region in China. (4) Overall, virtual water inflows in Beijing, Tianjin and Hebei tend to come from neighbouring provinces. Average distances of virtual water inflows in Beijing, Tianjin and Hebei are 1049 km, 1297 km and 688 km, respectively. (5) Beijing and Tianjin import net virtual water, indicating that the socio-economic development in Beijing and Tianjin heavily relies on external water resources. Hebei exports net virtual water, providing water resources to Beijing, Tianjin and other provinces in China. Net virtual water export in Hebei aggregates local water scarcity. The results benefit policy implications on sustainable water resources management under the framework of virtual water trade. Solutions possibly relieving water scarcity in Beijing-Tianjin-Hebei region include increasing water use efficiency, upgrading industrial structure, promoting low water footprint consumption mode, and implementing virtual water strategy.