Resource-exhausted cities face dual crises of economic stagnation and ecological degradation, which is primarily attributable to the inefficient use of industrial land. The redevelopment of industrial land has emerged as a crucial solution to the “resource depletion-urban decline” dilemma. The issue of inefficient industrial land use in resource-exhausted cities is of great significance as it directly impacts both economic development and ecological protection. Therefore, finding effective ways to redevelop this land is essential for the sustainable development of these cities. This research takes Hegang, a representative resource-exhausted city in China, as a case study. A multi-dimensional evaluation framework and an adaptive redevelopment strategy system are constructed in this research. By integrating data related to land use status, land use efficiency, policy constraints, and development potential, a parcel-scale assessment model is established. This model consists of 4 primary indicators and 13 secondary indicators. Through this model, 11.01 km² of inefficient industrial land in the main urban area of Hegang is identified. Standard deviation ellipse and kernel density analysis are employed to reveal the spatial pattern of inefficient land. The results show that the inefficient industrial land in Hegang exhibits a pattern of “overall dispersion with localized agglomeration”. It is found that idle and abandoned land are the dominant types of inefficient industrial land in Hegang’s main urban area, accounting for 69.7% of the total. This finding provides a clear understanding of the nature of the inefficient land use problem in resource-exhausted cities. A strategic framework is proposed, which incorporates classified governance, dynamic restoration, and multi-stakeholder collaboration. This framework offers a governance toolkit with both theoretical depth and practical value for resource-exhausted cities. Breaking the locked relationship between industrial land and resource dependence promotes the deep integration of spatial restructuring and sustainable transformation. The findings of this research provide significant scientific insights for similar cities worldwide to address the challenges they face and achieve harmony between human activities and land use. Future research could focus on further refining the evaluation framework and redevelopment strategies based on different regional characteristics and resource endowments.

National soil-carbon assessments often emphasize carbon density without fully integrating soil depth and spatial extent. Here, we quantified the soil organic carbon (SOC) stock represented by the complete 1 km CSDLv2 raster domain across mainland China using six depth layers spanning 0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm. Across 13,810,230 valid grid cells, the 0–200 cm soil profile stored 215.82 Pg C, of which 57.72 Pg C occurred in the upper 0–30 cm and 158.09 Pg C in the 30–200 cm subsoil. Although 73.2% of the total stock occurred below 30 cm, this large contribution primarily reflected the greater cumulative thickness of the subsoil interval; thickness-normalized SOC storage was substantially higher in the surface layer than in the subsoil (20.34 vs. 9.83 kg C m⁻² m⁻¹). Northwest China contained the largest integrated SOC stock (90.79 Pg C), whereas Northeast China exhibited the highest mean profile carbon density (33.17 kg C m⁻²), revealing a clear regional reversal between carbon density and total stock. Surface–subsoil decoupling and relative subsoil-carbon distance (CSD) further characterized regional differences in vertical SOC organization. Cross-dataset evaluation against an independent 2010–2024 SOC compilation comprising 17,990 depth intervals from 5870 profiles yielded an original-scale RMSE of 4.73 kg C m⁻², a mean bias of 0.44 kg C m⁻², and a Pearson correlation of 0.44. Replacing the 1 km layers with 90 m layers changed the national stock estimate by only 0.02%, while alternative DI thresholds and CSD reference quantiles affected screening magnitudes but preserved the broad regional patterns. These results highlight pronounced regional differences in the amount and vertical organization of soil carbon and support differentiated management priorities, including deep-carbon conservation in Northeast China, profile-structure diagnosis in North China and the Loess Plateau, water–soil coordination in Southern China, context-dependent water–carbon management in Northwest China, and disturbance avoidance and natural recovery in Southwest China and Tibet.

Ensuring stable soybean supply requires understanding how production patterns evolve and whether policy consistently dominates production dynamics. This study examines 36 prefecture-level units in Northeast China from 2000 to 2024 by integrating production growth decomposition, the production concentration index, center-of-gravity and standard deviational ellipse models, spatial autocorrelation (Moran’s I), and the geographical detector. The results indicate the following: (1) Production fluctuated upward, with its spatial core consolidating in the northern high-latitude zone and the regional centroid moving 123.3 km northeastward overall. Concentration increased more than sown-area concentration, indicating a gradual, spatially uneven recovery in yield contributions rather than a completed shift to yield-led growth. (2) Spatial differentiation was fundamentally governed by hydrothermal endowments rather than monotonic policy dominance. Annual mean temperature consistently exhibited the strongest independent explanatory power (reaching q = 0.39 in 2024), followed by agricultural machinery power (q = 0.22) and fertilizer input (q = 0.24). (3) The influence of agricultural fiscal support intensity was stage-specific and conditional (significant in 2010 and 2020, but waning to q = 0.14 in 2024), functioning primarily through synergistic interactions with natural endowments and input levels. Consequently, regional soybean layout reflects a multifactor interplay where climatic suitability sets the production ceiling, while factor configuration and targeted fiscal incentives regulate localized capacity. Policy interventions should shift from uniform area expansion subsidies toward differentiated, climate-resilient input optimization in core and marginal regions.