With the speedy growth of economic development, the imbalance of energy supply and demand pose a critical challenge for the energy security of our country. Meanwhile, the increasing and excessive energy consumption lead to the greenhouse effect and atmospheric pollution, greatly threatening the survival and development of human beings. This study integrated two nighttime light remote sensing datasets, namely Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data and Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) data, to extend the temporal coverage of the study. Then, the distributions of China’s energy consumption from 1995 to 2016 at a 1-km resolution were estimated using different models and the spatiotemporal variations of energy consumption were explored on the basis of the best estimated results. Next, the factors influencing China’s energy intensity on the provincial level were investigated based on the spatial econometric model. The results show that: (1) The integrated nighttime light datasets can be successfully applied to estimate the dynamic changes of energy consumption. Moreover, the panel data model established in our research performed better than the quadratic polynomial model. (2) During the observation period, the energy consumption in China significantly increased, especially in the Yangtze River Delta, the Pearl River Delta, the Beijing–Tianjin–Hebei region, eastern coastal cities, and provincial capitals. (3) Different from the random spatial distribution pattern of energy consumption on the provincial level, the spatial distribution of energy consumption on the prefectural level has significant clusters, and its spatial agglomeration was strengthened year by year during the research period. (4) The spatial Durbin model (SDM) with a spatial fixed effect has been proved to be more suitable to explore the impact mechanism of China’s energy consumption. Among the four socio-economic factors, industrial structure has the greatest impact on the provincial energy intensity in China. Moreover, the changes in industrial structure and foreign direct investment (FDI) can not only influence the local energy intensity but also affect the energy intensity of the neighboring provinces.

One of the most critical development problems in China is the existence of a considerable number of contiguous destitute areas (also known as regional poverty), and the causes behind such regional poverty might structurally differ by geographical characteristics. To deal with the problem, this paper, based on the “Three Nature” theory of New Economic Geography, first establishes an analytical index system of poverty causes, and then uses the gray relational method to identify the causes of poverty in 14 contiguous destitute areas. The results show that, at present, the main poverty causes in the mountain areas in eastern part of China, such as the Dabie mountain area, the Yanshan-Taihang mountain area and the southern Greater Khingan mountain area, are the shortage of human capital and information technology. In contrast, the main factors behind regional poverty in the central mountain areas such as the LuoXiao mountain area, the Lvliang mountain area and the Wuling mountain area are poor transport, locational and other natural factors. The western mountain regions such as Tibet, Tibetan areas in four provinces, South Xinjiang’s three districts and the West Yunnan border area are especially affected by natural endowments, but each area’s specific cause for poverty is different from one another. Finally, this paper discusses relevant policy issues regarding the fact that poverty causes are different by distinct natural factors.

Global climate change is likely to affect reference evapotranspiration (ET₀), which is important for agricultural water resources and food security. Analysis of ET₀ changes is also important for understanding climate change and its impacts on hydrology. In this study, ET₀ was calculated from 2020 to 2099 in nine agricultural regions and the entire country of China utilizing the Penman–Monteith method based on bias‐corrected climate data from the global climate models MIROC‐ESM‐CHEM, NorES1‐M, IPSL‐CM5A‐LR, GFDL‐ESM 2M and HadGEM2‐ES under future climate change scenarios. The spatiotemporal characteristics of seasonal and annual ET₀ and climatic variables were analysed based on the Mann–Kendall test with trend‐free prewhitening (TFPW‐MK) and Sen's slope estimator. Sensitivity coefficient and multivariate regression were used to identify factors controlling the change in ET₀. The results showed that seasonal and annual air temperature and precipitation both increased under all climate scenarios, and the majority of relative humidity and wind speed values showed downward trends during 2020–2099. The lowest annual ET₀ values were found in the NECR and QTR, and the SCR had the maximum ET₀. Both the TFPW‐MK and slope statistics showed an upward trend of seasonal ET₀ during 2020–2059 under all scenarios. During 2060–2099, seasonal ET₀ values showed a slight and nonsignificant downward trend under RCP 2.6, while upward trends were produced in other scenarios. Areas with high ET₀ values were located in the SCR, GXR and HHHR, while low ET₀ values mainly occurred in the NECR and QTR. The ET₀ for the four seasons in the SCR was higher than that in the other regions, and the summer ET₀ was the highest compared with that in the other seasons overall. For the attribution assessment, T_avg is the main controlling variables to ET₀ trends under the interaction of multiple climatic factors though RH is the most sensitive.

To assess whether China’s emissions will peak around 2030, we forecast energy consumption and carbon emissions in China. We use an agent-based model driven by enterprises’ innovation. Results show some differences in both energy consumption peaks and carbon emission peaks when we compare trends at different levels. We find that carbon emissions and energy consumption will peak in 2027 and 2028, respectively. However, the primary, secondary, and tertiary industries will reach energy consumption in different years: 2023, 2029, and 2022, respectively, and reach carbon emission peaks in 2022, 2028, and 2022, respectively. At the sectoral level, we find a wider range of energy consumption peaks and carbon emission peaks. Peak energy consumption occurs between 2020 and 2034, and peak carbon emissions between 2020 and 2032. Commercial and catering businesses, utilities and resident services, and finance and insurance achieve peak energy consumption and carbon emissions earliest in 2020, while building materials and other non-metallic mineral products manufacturing and metal products manufacturing are the two latest sectors to reach peak energy consumption and emissions, in 2034 and 2032, respectively.

Estimation of the capacity of provincial forest carbon sinks in chinese mainland using the co2fix model provides data support for the effective management of provincial regions. according to china’s sixth national forest inventory, we estimate the capacities of original and new afforestation carbon sinks under the assumption of using the country’s non-forest land for afforestation and reforestation to achieve a new forested area of 57323200 ha. the carbon absorption capacity of china’s forest ecosystems estimated from 2005 to 2050 reaches 8.4 gtc. the absorption capacities of original forest and new afforestation respectively are 4.9 and 3.5 gtc. the annual capacity of all forest carbon sinks has a roughly decreasing trend. inner mongolia autonomous region, yunnan, sichuan and heilongjiang provinces make major contributions to the carbon sink capacity.