The soil organic carbon (SOC) pool in farmland is changing rapidly due to human activities, thereby greatly affecting the regional and global environment, as well as influencing soil fertility and crop yields. The present study investigated the effects of underground coal mining on the regional SOC pool in farmland in the Jiuli Mining Area of Xuzhou City in China as a typical coal mining region based on field sampling, chemical analysis, model construction, and spatial analysis using the software of ArcGIS. The results showed that in the mining subsidence area, spatial variations in the SOC content and soil bulk density were mainly caused by structural factors (mining subsidence, subsidence waterlogging, and other structural factors due to coal mining) at a regional scale. SOC storage in farmland soil decreased sharply in non-waterlogged subsidence farmland and seasonally waterlogged subsidence farmland in the areas with mining, whereas the SOC storage increased in waterlogged wetland after coal mining. The SOC was reduced by 102,882 tonnes (32.81%) compared with the original SOC stock as a consequence of coal mining, and thus the effect of underground coal mining on the regional SOC pool in farmland was characterized as a carbon loss process. Land-use changes, soil degradation and erosion contributed almost equally to the carbon loss process in the study area. The results of this study may facilitate evaluations of low-carbon land reclamation and ecological compensation in mining areas.

Rivers and streams are recognized as potential greenhouse gas (GHGs: CO₂, CH₄, and N₂O) sources, contributing to global warming. However, GHG emissions from rivers and streams have received insufficient attention compared to other ecosystems (forests, grasslands, wetlands, etc.). In this study, dissolved GHG concentrations were measured in the Qingyijiang River, the longest tributary in the lower reaches of the Yangtze River, during two campaigns in September 2020 and April 2021. Our results showed that the Qingyijiang River was oversaturated with dissolved GHGs. The dissolved GHG concentration in the surface river water ranged from 8.70 to 67.38 μM CO₂, 0.03 to 2.06 μM CH₄, and 12.30 to 32.22 nM N₂O. The average diffusive GHG emission rates were 31.89 ± 22.23 mmol CO₂ m⁻² d⁻¹, 697.22 ± 939.82 μmol CH₄ m⁻² d⁻¹, and 18.12 ± 7.73 μmol N₂O m⁻² d⁻¹. The total emissions (CO₂-e) were CO₂ (58%) dominated, while CH₄ (38%) played a moderate role in total emissions. Temporally, average GHG concentrations and fluxes from the studied river in April were higher than those in September. The concentration and flux of CH₄ exhibited high spatial variability, similar to those in most rivers. In contrast, we found that there was no obvious spatial variability in CO₂ and N₂O concentrations but a significant difference among reaches in N₂O fluxes. We found that water temperature and flow velocity were the potential drivers for the regulating spatial variability in GHGs. However, no other observed limnological parameters were found in governing the spatial patterns of GHGs, suggesting a complex combination of factors governing GHG fluxes; thus, these inconspicuous mechanisms underscore the need for further research. Overall, our study suggests that this river acts as a minor source of GHGs relative to other rivers, and CH₄ cannot be ignored when considering aquatic carbon emissions.

Land-use carbon emissions are typically analyzed within administrative or economic regions; however, natural temperature gradients may modulate how socioeconomic and policy drivers translate into emissions. Using China’s land-use, energy, and meteorological data (1990–2022), we applied a coupled nature–economy–policy modeling framework across six temperature zones to test whether temperature acts as a nonlinear moderator rather than a direct determinant. The warm temperate zone accounted for >55% of national emissions and, together with the mid-temperate zone, this share reached ~80%, while the Qinghai–Tibet Plateau remained a net sink. Driver contributions varied systematically: economic factors dominated tropical and subtropical zones (74.3%), natural constraints dominated the plateau (41.7%), and policy interactions dominated mid- and warm-temperate zones (58.4%). Temperature acted nonlinearly, with emission effects rising from 0.277 to 1.212 t CO₂ per °C above a 26.65 °C summer mean (a 4.4-fold amplification). A generalized additive model confirms this significant nonlinearity. Scenario simulations suggest that the business-as-usual scenario peaks in 2035–2040, whereas the ecological protection and low-carbon scenarios peak around 2030. Under RCP4.5, the model projects a potential shift of the plateau from sink to source, but this result is conditional on simplified climate–carbon feedbacks and should be interpreted as a plausible risk rather than a prediction. These findings indicate that temperature zones offer a complementary natural gradient—not a replacement for administrative divisions—for understanding how similar human activities produce different carbon outcomes under contrasting thermal conditions. Given the use of partly uniform emission factors, the results represent macro-scale differentiation; governance implications are therefore conditional on scenario assumptions and require local feasibility assessment.