To ensure adequate oil supply and mitigate geopolitical uncertainties, China has diversified its sources of crude oil imports in recent years. Central Asia is a neighbor region of China, rich in oil and natural gas reserves, which can play an important role in China’s strategy to reduce its dependence on energy supplies from the Middle East. The geopolitical attributes of energy and the geopolitical situation in Central Asia determine that Central Asia’s energy development and cooperation are disturbed by domestic and foreign factors, and also face the risks of complex energy structural evolution and geopolitical games, which create a unique energy geopolitical pattern in Central Asia. This study proposes an analysis framework for energy geopolitics in Central Asia, arguing that the complexity of energy geopolitics in Central Asia can be demonstrated from the perspectives of both the main countries of energy development (game actors) and the whole-industry-chain of energy development (game themes). The most obvious feature of Central Asian energy geopolitics is the fierce game that is played between key countries and regions, Russia, the United States, China, the European Union (EU), Japan, India, Iran, and Turkey. Strategic geopolitical considerations and resource requirements necessitate the active participation of these players in Central Asian energy development and mean that the foreign policy agendas of states in this region are likely to become ever more crowded. Therefore, the energy cooperation between China and Central Asia faces the geopolitical risks of the great power games. It is necessary to develop reasonable and effective policies to establish guarantee mechanisms to minimize the risks of energy cooperation. This study characterizes the energy geopolitical strategies of Russia, the United States, China, and other related powers in Central Asia. It also assesses the country risks faced by energy cooperation between China and Central Asian countries. To withstand possible geopolitical and country risks, this study develops a series of policy frameworks which can be used to fortify the stability of the energy cooperation between China and Central Asian countries, and can also be effective in responding to the array of risks that might be encountered in the coming years.

Quantitative and accurate urban land information on regional and global scales is urgently required for studying socioeconomic and eco-environmental problems. The spatial distribution of urban land is a significant part of urban development planning, which is vital for optimizing land use patterns and promoting sustainable urban development. Composite nighttime light (NTL) data from the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) have been proven to be effective for extracting urban land. However, the saturation and blooming within the DMSP-OLS NTL hinder its capacity to provide accurate urban information. This paper proposes an optimized approach that combines NTL with multiple index data to overcome the limitations of extracting urban land based only on NTL data. We combined three sources of data, the DMSP-OLS, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), to establish a novel approach called the vegetation–water-adjusted NTL urban index (VWANUI), which is used to rapidly extract urban land areas on regional and global scales. The results show that the proposed approach reduces the saturation of DMSP-OLS and essentially eliminates blooming effects. Next, we developed regression models based on the normalized DMSP-OLS, the human settlement index (HSI), the vegetation-adjusted NTL urban index (VANUI), and the VWANUI to analyze and estimate urban land areas. The results show that the VWANUI regression model provides the highest performance of all the models tested. To summarize, the VWANUI reduces saturation and blooming, and improves the accuracy with which urban areas are extracted, thereby providing valuable support and decision-making references for designing sustainable urban development.

Previously published studies on population distribution were based on the provincial level, while the number of urban-level studies is more limited. In addition, the rough spatial resolution of traditional nighttime light (NTL) data has limited their fine application in current small-scale population distribution research. For the purpose of studying the spatial distribution of populations at the urban scale, we proposed a new index (i.e., the road network adjusted human settlement index, RNAHSI) by integrating Luojia 1-01 (LJ 1-01) NTL data, the enhanced vegetation index (EVI), and road network density (RND) data based on population density relationships to depict the spatial distribution of urban human settlements. The RNAHSI updated the high-resolution NTL data and combined the RND data on the basis of human settlement index (HSI) data to refine the spatial pattern of urban population distribution. The results indicated that the mean relative error (MRE) between the population estimation data based on the RNAHSI and the demographic data was 34.80%, which was lower than that in the HSI and WorldPop dataset. This index is suitable primarily for the study of urban population distribution, as the RNAHSI can clearly highlight human activities in areas with dense urban road networks and can refine the spatial heterogeneity of impervious areas. In addition, we also drew a population density map of the city of Shenzhen with a 100 m spatial resolution for 2018 based on the RNAHSI, which has great reference significance for urban management and urban resource allocation.

As one of the most developed and competitive metropolitan areas in the world, the contradiction between resource depletion and sustainable development in the Guangdong-Hong Kong-Macao Greater Bay Area (GHMGBA) has become a crucial issue nowadays. This paper analyzed the natural capital utilization patterns in GHMGBA during 2009–2016 based on a three-dimensional ecological footprint model. Ecological carrying capacity intensity (EC_intensity) was calculated to optimize the accounting of ecological carrying capacity (EC). Ecological footprint depth (EF_depth) and EC_intensity were quantitatively investigated and influencing factors were further explored based on a partial least squares (PLS) model. Results showed that GHMGBA had been operating in a deficit state due to the shortage of natural capital flow and accumulated stock depletion. The highest EF_depth occurred in Macao (17.11~26.21) and Zhongshan registering the lowest (2.42~3.58). Cropland, fossil energy and construction land constituted the most to total ecological deficit, while woodland was continuously in a slight surplus. Natural capital utilization patterns of 11 cities were divided into four categories through hierarchical clustering analysis. Driving factors of EF_depth, EC_intensity and three-dimensional ecological deficit (ED_3D) were mainly students in primary and secondary education, disposable income, consumption expenditure, R&D personnel and freight volume. Our findings could provide guidance for decision-makers to develop resource utilization portfolios in GHMGBA.