This research study investigates the extent of heavy metal pollution and pollution trends in agricultural soil in mining areas during different time periods. A total of 125 soil samples were collected from two mining areas in China, the Chengchao iron mine and Tonglushan ancient copper mine. The samples were analyzed for various potentially toxic elements (PTEs). The index of geoaccumulation (Igeo), pollution index ($P_i$), potential ecological risk index ($E_r^i$), and hazard index (HI) were calculated to evaluate the pollution status of PTEs in the farmland around the two mining areas. The sources of PTEs were inferred by pollution distribution, and the pollution conditions of the two mining areas were compared. The results showed that the pollution of ancient copper mines was relatively severe. The main pollution elements were Cu, Cd, and As, and their average $P_i$ values were 3.76, 4.12, and 1.84, respectively. These PTEs mainly came from mining and transportation. There are no particularly polluted elements in the Chengchao iron mine and the average $P_i$ of all PTEs were classified as light pollution and had a wide range of sources. The findings suggest that the ancient copper mine, due to outdated mining techniques and insufficient mine restoration efforts, resulted in the spread and accumulation of PTEs in the soil over an extended period, making the farmland soil around the ancient copper mine more polluted compared to the Chengchao iron mine. In the two mining areas, there is no risk of cancer for adults and children. However, the RI values of Cr in adults and children are higher than 10⁻⁴, which indicates that the carcinogenic risk of Cr in these soils is very high. The non-carcinogenic effects of PTEs on the human body in the soil of ancient copper mine are also higher than that of the Chengchao iron mine.

The Yangtze River Basin serves as the socioeconomic core of China, and rapid development in recent years has intensified the conflict in the area between economic growth and ecological conservation. This study evaluated the spatiotemporal evolution of the land ecological security (LES) across 11 provinces and municipalities in the Yangtze River Basin from 2008 to 2023 by using the framework of the drivers, pressures, state, impact, and response model of intervention. We forecasted the trends of LES (2024–2033) by using a grey prediction model and identified the key obstacles to it through an obstacle degree model. The findings revealed the following: (1) Economic density (D3) and per capita water resources (S4) had significantly high weights, disproportionately impacting LES. Shanghai scored highest for Drivers, Impact, and Response subsystems, while Tibet led in Pressures and State. (2) Basin-wide LES scores improved from “less safe” to “critical safe” but saw no fundamental breakthrough. LES exhibited a three-tier spatial pattern: higher in the middle-lower reaches (e.g., Shanghai, Jiangsu) and lower in the upper reaches (e.g., Qinghai). Tibet remained “critical safe” with minor fluctuations; other regions improved gradually yet mostly remained “less safe” or “critical safe”. (3) Forecasts (2024–2033) indicate continued overall LES improvement. Shanghai and Jiangsu are projected to reach “safe” status, Qinghai will remain “unsafe”, while most others persist as “critical safe”. Basin LES remains fragile, requiring intervention. (4) The Drivers (D) and State (S) subsystems were the primary constraints on LES. Critical obstacle indicators included economic pressure (per capita GDP (D2), D3), resource availability (S4, ratio of effectively irrigated area (I1)), land productivity (agricultural/forestry output per unit area (I3)), and forest coverage rate (R6). Enhancing LES necessitates implementing regionally tailored policies addressing spatial variations, prioritizing urban economic optimization, strengthening water resource management, and ensuring effective cross-regional governance.

Thorough analysis of regional carbon emissions is essential, given their substantial contribution to overall carbon emissions, as it underpins the formulation of targeted low-carbon development strategies. This study investigates whether Hubei Province can achieve carbon peaking by 2030 and explores feasible emission reduction pathways. To address the limitations of existing regional carbon emission studies—particularly the direct use of decomposition factors in prediction models and the lack of logical separation between mechanism analysis and forecasting—a hybrid analytical-predictive framework is proposed. Specifically, the logarithmic mean Divisia index (LMDI) method is first employed to decompose historical carbon emissions and identify the driving forces, while the STIRPAT model combined with the Lasso regression is subsequently used to screen key influencing factors for emission prediction, thereby avoiding the direct use of decomposition factors in forecasting. Based on the selected factors, a genetic algorithm–optimized backpropagation neural network (GA-BP) is developed to predict carbon emissions in Hubei Province from 2024 to 2035. The predictive performance of the GA-BP model is validated using three statistical indicators (R², MAPE, and RMSE) and compared with Extreme Learning Machine (ELM), Support Vector Regression (SVR), and conventional BP models. Furthermore, six development scenarios are designed in accordance with provincial policy objectives to assess the feasibility of carbon peaking. The results indicate the following: (1) Based on the results of the LMDI decomposition, Lasso–STIRPAT analysis, and model sensitivity analysis, per capita GDP is identified as the primary driving factor of carbon emissions in Hubei Province. (2) The GA-BP model demonstrates superior predictive accuracy compared with benchmark models and (3) carbon peaking by 2030 can only be achieved under Scenario 6, highlighting the necessity of coordinated structural and technological interventions. Based on these findings, targeted policy recommendations for carbon emission reduction are proposed.