Jiangtao Xiao

Abandoned cropland is a crucial strategic reserve to reconcile food security (SDG 2) and climate mitigation (SDG 13). However, maximizing these co‐benefits in ecologically fragile hilly regions is constrained by intense spatial competition between food and carbon. In this study, we proposed a spatial decision‐making framework that integrates suitability assessment and Pareto optimization to quantify trade‐offs and identify synergistic pathways in a typical hilly province of Southwest China. The results showed that although the remaining abandoned cropland had substantial potential for either food (4.0 × 10 ¹¹ kcal yr ⁻¹ ) or carbon (1.38 × 10 ⁶ t CO ₂ yr ⁻¹ ), the dual‐suitability overlap rate reached 69.1%, leading to high opportunity costs under single‐objective management. Crucially, scenario simulations and Pareto analysis identified Pareto‐efficient compromise solutions for balancing regional benefits. When spatial prioritization was combined with intensified management, food and carbon potentials could be increased by 125.0% and 51.6%, respectively, without expanding beyond abandoned cropland. These findings highlight that governing abandoned cropland in complex terrain requires spatially differentiated zoning that distinguishes food‐dominant, carbon‐dominant, and synergistic management zones to achieve regional synergistic gains.

The Upper Minjiang River Basin is a critical ecological barrier in the upper Yangtze River, where forest ecosystems play a vital role in carbon sequestration, water conservation, and soil retention. Given that different forest types exhibit significant variations in community structure, species composition, and ecological processes, their ecosystem service (ES) supplies and trade-off/synergy relationships are also expected to show distinct heterogeneity. However, systematic research on the trade-offs and synergies of ESs across different forest types remains limited, constraining the development of precision forest management and differentiated management strategies. To deal with this, we used the InVEST model and calculated five key services across the basin: carbon stock (CS), water yield (WY), soil conservation (SC), habitat quality (HQ), and forest stock volume (FSV). We then applied Spearman’s correlation, root mean square deviation (RMSD), and the GeoDetector model to analyze trade-offs and uncover driving mechanisms. Finally, we used spatially constrained K-means clustering to map different management zones. The results indicate that the Upper Minjiang River Basin stored 1.78 × 10⁸ t of carbon, retained 2.98 × 10⁸ t of soil, produced 6.48 × 10⁹ m³ of water yield, maintained a mean habitat quality of 0.78, and supported a forest stock volume of 1.20 × 10⁸ m³. Coniferous forests exhibited the highest CS (181.07 t ha⁻¹) and FSV (176.37 m³ ha⁻¹), whereas shrublands contributed the largest share (52.17%) of regional water yield. At the regional scale, CS and FSV showed the strongest synergy (r = 0.71, p < 0.01), while WY displayed significant trade-offs with most other services. GeoDetector analysis revealed that forest type acts as the primary driver shaping the relationships among services, while elevation and precipitation play supporting roles. Based on the ES bundles identified via spatially constrained K-means clustering, the Upper Minjiang River Basin was divided into four distinct management zones: a carbon sequestration core zone, an ecological balance zone, an ecologically fragile zone, and a multifunctional conservation zone. Therefore, findings from the Upper Minjiang River Basin may provide insights applicable to other mountain forest ecosystems facing similar environmental and management challenges.

Under the framework of the Rural Revitalization Strategy, optimizing the layout of rural settlements in mountainous areas and guiding their sustainable development must be based on a deep understanding of the evolution characteristics of rural settlements and suitability evaluations. This study focuses on Lixian County, located in the southwestern part of China, Sichuan Province, as the research area and employs methods such as the average nearest neighbor index, kernel density analysis, and landscape pattern index to analyze the spatiotemporal evolution characteristics of rural settlements in 2000, 2010, and 2020. Additionally, the Maxent model, based on ecological niche theory, is applied to evaluate the suitability of rural settlements. The results reveal the following: (1) Rural settlements in Lixian County exhibit a spatial distribution characterized by “sparser in the west, denser in the east, and a belt-like pattern”, with a clustered distribution trend. The number and area of settlement patches increased, with settlement distribution becoming more centralized, shapes becoming more complex, and connectivity between settlements improving. (2) The area of highly suitable land for rural settlements has decreased annually, with over 85% of the land classified as unsuitable for rural settlement layout. Suitability transitions mostly occur between adjacent levels, and it is difficult for unsuitable land to become suitable. (3) In earlier years, settlement suitability was significantly influenced by the distance to cultivated land, slope, and distance to geological hazard sites. By 2020, however, the distance to roads had become the second most important environmental factor, following the distance to cultivated land. Natural environmental factors, particularly topographic features such as elevation and slope, were found to exert a greater influence than socioeconomic factors in evaluating the suitability of rural settlements in Lixian County. These findings provide a scientific foundation for optimizing rural settlement layouts in mountainous regions, offering valuable insights into rural transformation and sustainable development not only in the upper Minjiang River area but also for reference in other similar mountainous regions.

Cropland abandonment (CA) is an increasingly severe global issue, with significant implications for achieving the Sustainable Development Goal of Zero Hunger. In China, widespread CA is particularly evident in remote mountainous regions. However, the rugged terrain and highly fragmented cropland pose significant challenges in mapping abandoned cropland with high precision using remote sensing technology. Moreover, CA is the result of multi-level factors, yet previous studies have primarily analyzed its driving factors from a single level, leading to a lack of comprehensive understanding of the underlying mechanisms. We took Sichuan Province, located in the mountainous regions of Western China, as a case study, utilizing satellite-derived high-precision CA maps to reveal the spatiotemporal patterns of CA. Additionally, we employed hierarchical linear model to explore the determinants of CA and their interactions at both county and municipal levels. The results indicate that the CA rate decreased continuously from 6.75% in 2019 to 4.47% in 2023, with abandoned cropland exhibiting significant spatial clustering. High-value clusters were predominantly concentrated in the western mountainous areas, and hotspots of CA exhibited a general migration trend from the northeast to the southwest. Furthermore, we found that CA is influenced by multi-level factors, with 61% and 39% of the variance in CA being explained at the county and municipal levels, respectively. The agglomeration index of cropland (AI) is a key determinant at the county level, with the Digital Elevation Model (DEM) and the distance to roads also playing significant roles. At the municipal level, urbanization rate and the proportion of non-agricultural employment (PNAE) are dominant factors, and an increase in PNAE weakens the negative impact of AI on CA rates. To curb CA in mountainous areas, we recommend implementing land consolidation projects, improving rural land transfer markets, and strengthening legal mechanisms to combat CA. Our study has broad application prospects, providing critical support for assessing the ecological and environmental consequences of CA and exploring the potential of reutilizing abandoned cropland for food production, bioenergy, and carbon sequestration.

This study focuses on the coastal city of Xiamen, examining the factors and driving mechanisms influencing land use changes and spatial patterns. Spatial logistic regression and Statistical Package for the Social Sciences (SPSS) software were employed using grid data with a resolution of 100m to analyze the spatial relationships between six driving factors (such as elevation and slope) and five land use types within the study area. Regression models were established for each factor, and all Relative Operating Characteristic (ROC) tests were passed. Based on the results of the logistic regression analysis, land use changes and spatial distribution were simulated using the updated Conversion of Land Use and its Effects (CLUE) model so as to validate the driving mechanisms. The findings indicate that the six driving factors effectively explain the spatial patterns of land use in the study area. The distance to the coastline is the primary influencing factor in the evolution of spatial patterns, particularly impacting built-up land and farmland, while for forest land, slope is the main factor affecting the spatial distribution. The simulation and accuracy analysis revealed an overall simulation accuracy ranging from 73% to 90.1%, demonstrating that the selected driving factors have effective explanatory power for the spatial distribution of land use. Thus, this study’s results provide valuable insights into the complexity of land use changes and serve as a reference for relevant departments in land use management and planning.

The unique geographical conditions in southwestern China lead to a fragile ecological balance and uneven geographical distribution of water resources. This study takes the Aba Tibetan and Qiang Autonomous Prefecture as its research subject, investigating the connection between water resources and the ecological environment in the Aba Tibetan and Qiang Autonomous Prefecture, which is of paramount importance. Given this, the current study constructs the remote sensing ecological index and water footprint for the period spanning from 2000 through 2020, analyses the coordination status of the two by using the coupling coordination degree, and then combines it with a detector to determine the primary drivers impacting the state of the ecological environment. The outcomes reveal that: (1) The ecological environment of the Aba Tibetan and Qiang Autonomous Prefecture gradually improved from 2000 through 2020, and the excellent ecological environment area observed within the study’s scope is primarily focused in the eastern part. The poor ecological environment area is focused mainly in the northwestern part within the study’s scope. (2) The total water footprint of the Aba Tibetan and Qiang Autonomous Prefecture has increased year by year, with agricultural water consumption comprising an immense 78% proportion, dominating the total water footprint. (3) The coupled coordination degree of the remote sensing ecological index and the water footprint shows a rising trend in general, turning from a barely coordinated stage to an intermediate coordinated stage. (4) The geodetector results show that all factor interactions were dual-factor enhancement or nonlinear enhancement, and the WET, NDBSI and NDVI factors contributed the most. The above results can provide important guidance for utilizing water resources and protecting the ecological environment in the Aba Tibetan and Qiang Autonomous Prefecture.

China has undergone a long period of rapid urbanization from 2000 to 2020, resulted in huge conversion of arable land into built-up areas. This situation has raised concerns regarding food security, including issues like reduced food supply and challenges to achieving food self-sufficiency, particularly in certain regions and during special period. To address these issues, this study focuses on Xiamen, a rapidly urbanizing coastal city in China, and examined both the food supply and demand. On hand of the supply, this study mainly analyzed the attributes of the food availability, indicators like the food production (FP) of the ecosystem service value (ESV), per capita grain production, imports and exports along with food price were calculated. The findings revealed a significant expansion of built-up areas in Xiamen, mostly at the expense of arable land. The declining arable land coupled with population growth due to rapid urbanization contributed to a decrease in per capita grain production, drop to 6 kg in 2018, which is merely 1% of China’s average. A comparison of per capita grain production across various regions, as well as changes in China’s grain import and export, highlighted a notable discrepancy between urbanization rates and food security in Xiamen. It has become challenging for Xiamen to rely on outside for sustained food import. Therefore, it is imperative for Xiamen to implement a range of measures and policy interventions to ensure a certain level of food self-sufficiency and protect basic arable land.

Southwest China is an important carbon sink area in China. It is critical to track and assess how human activity (HA) and climate change (CC) affect plant alterations in order to create effective and sustainable vegetation restoration techniques. This study used MODIS NDVI data, vegetation type data, and meteorological data to examine the regional and temporal variations in the normalized difference vegetation index (NDVI) in Southwest China from 2000 to 2020. Using trend analysis, the study looks at the temporal and geographical variability in the NDVI. Partial correlation analysis was also used to assess the effects of precipitation, extreme climate indicators, and mean temperature on the dynamics of the vegetation. A new residual analysis technique was created to categorize the effects of CC and HA on NDVI changes while taking extreme climate into consideration. The findings showed that the NDVI in Southwest China grew at a rate of 0.02 per decade between 2000 and 2020. According to the annual NDVI, there was a regional rise in around 85.59% of the vegetative areas, with notable increases in 36.34% of these regions. Temperature had a major influence on the northern half of the research region, but precipitation and extreme climate had a notable effect on the southern half. The rates at which climatic variables and human activity contributed to changes in the NDVI were 0.0008/10a and 0.0034/10a, respectively. These rates accounted for 19.1% and 80.9% of the variances, respectively. The findings demonstrate that most areas displayed greater HA-induced NDVI increases, with the exception of the western Sichuan Plateau. This result suggests that when formulating vegetation restoration and conservation strategies, special attention should be paid to the impact of human activities on vegetation to ensure the sustainable development of ecosystems.