Global warming has brought increased attention to the relationship between carbon emissions and economic development. Research on the driving factors of carbon emissions from energy consumption can provide a scientific basis for regional energy savings, as well as emissions reduction and sustainable development. Henan Province is a major agricultural province in China, and it is one of most populous provinces. Industrial development and population growth are the causes of carbon emissions. The STIRPAT model was conducted for analyzing carbon emissions and the driving factors for future carbon emission in Henan Province. The results show that: carbon emissions and energy consumption in Henan Province presented a rising trend from 1995 to 2014; Energy consumption due to population growth is the main contributor to carbon emissions in Henan Province. As every 1% increase in the population, GDP per-capita, energy intensity, and the level of urbanization development will contribute to the growth of emissions by 1.099, 0.193, 0.043, and 0.542%, respectively. The optimization of the industrial structure can reduce carbon emissions in Henan Province, as suggested by the results, when the tertiary sector increased by more than 1%, the total energy consumption of carbon emissions reduced by 1.297%. The future pattern of carbon emissions in Henan Province is predicted to increase initially and then follows by a decreasing trend, according to scenario analysis; and maintaining a low population growth rate, and a high growth rate of GDP per-capita and technical level is the best mode for social and economic development.

Land is an increasingly scarce resource that plays a critical role in achieving many Sustainable Development Goals (SDGs). Land scarcity, namely the imbalance state between cropland availability and demand, can be mitigated by the trade of agricultural products, but how effective it is remains unclear. Here, by integrating grid-level data on cropland into multi-regional input–output analysis, this paper accounts for the scarce land footprint and virtual scarce land flows within China at a 1 km × 1 km resolution. Results show that over 70% of China’s land footprint and scarce land footprint can be attributed to less than 20% of the land, and nearly 38% of the land footprint and scarce land footprint hotspot clusters are found to cross provinces. Generally, while virtual land trade mitigates the land scarcity of land-importing provinces by 50.8%, it disproportionately aggravates the land scarcity of land-exporting provinces by 119.8%. These findings challenge the dominant thinking about food trading and call for new policies to improve land resources management and promote collaborative governance across administrative boundaries. Our study also highlights the critical importance of considering land scarcity, shedding lights on how it may be integrated into environmental footprints to better assist the SDG framework.

The net ecosystem productivity (NEP) of drainage basins plays an important role in maintaining the carbon balance of those ecosystems. In this study, the modified CASA (Carnegie Ames Stanford Approach) model and a soil microbial respiration model were used to estimate net primary productivity (NPP) and NEP of the Yellow River Basin’s (YRB) vegetation in the terrestrial ecosystem (excluding rivers, floodplain lakes and other freshwater ecosystems) from 1982 to 2015. After analyzing the spatiotemporal variations in the NEP using slope analysis, the coefficient of variation, and the Hurst exponent, precipitation was identified as the main factor limiting vegetation growth in the YRB. Hence, precipitation was treated as the control variable and a second-order partial correlation method was used to determine the correlation between diurnal asymmetric warming and the YRB’s NEP. The results indicate that: (i) diurnal asymmetric warming occurred in the YRB from 1982 to 2015, with nighttime warming (T_min) being 1.50 times that of daytime warming (T_max). There is a significant correlation between variations in NPP and diurnal warming; (ii) the YRB’s NEP are characterized by upward fluctuations in terms of temporal variations, large differences between the various vegetation types, high values in the western and southeastern regions but low values in the northern region in terms of spatial distribution, overall relative stability in the YRB’s vegetation cover, and changes in the same direction being more dominant than those in the opposite direction (although the former is not sustained); and (iii) positive correlations between the NEP and nighttime and daytime warming are approximately 48.37% and 67.51% for the YRB, respectively, with variations in nighttime temperatures having more extensive impacts on vegetation cover.