Search Results (111)

Global land degradation metrics often rely on trend-based categories that miss cumulative severity, frequently misclassifying degraded areas as stable. To overcome this, we developed a Land Degradation Index (LDI) to assess degradation across Brazil on a 500 m grid for 2001 and 2021. The LDI integrates land-cover change legacy (deforestation age), ecosystem functioning (Gross Primary Productivity), and soil condition (Soil Organic Carbon) into a six-level gradient ranging from conserved to highly degraded. Results reveal that between 2001 and 2021, Brazil lost 50.5 million hectares of conserved land, while intermediate and severe degradation expanded by 53.5 million hectares. Conservation remained concentrated in the Amazon and Pantanal, whereas degradation intensified across the Atlantic Forest, Cerrado, and Caatinga, particularly along agricultural frontiers. Furthermore, while Indigenous Lands and Quilombola Territories act as vital conservation cores, the LDI reveals intensified degradation in their immediate surroundings, highlighting the intersection of biophysical decline, land conflicts, and socio-environmental vulnerability. The proposed index advances beyond conventional indicators, such as SDG 15.3.1, by incorporating both the intensity and variation of degradation processes into a unified analytical framework, providing a robust, reproducible framework to support Land Degradation Neutrality (LDN) targets, inform public policies, and guide inclusive territorial planning. Full article

Cadmium (Cd) and lead (Pb) co-contamination in construction and demolition waste landfill soils presents a significant challenge to ecosystem health, necessitating effective remediation strategies. This study investigated a synergistic approach combining a composite amendment (compost, superphosphate, desulfurized gypsum) with seven plant species to elucidate the interactions driving metal immobilization and phytoextraction. The amendment significantly altered soil properties: it reduced total Cd while increasing its bioavailability, and enhanced soil fertility (e.g., elevated organic matter and total nitrogen). Plant responses varied: Solanum americanum Mill. and Tagetes patula L. exhibited high Cd phytoextraction capacity, whereas Lolium perenne L. sequestered Cd/Pb primarily in roots. The bacterial community shifted from an oligotrophic, stress-tolerant state (e.g., Sphingomonas-dominated) in contaminated soil to a copiotrophic, functionally active state (e.g., Streptomyces-enriched) in amended soil. Community structure was strongly correlated with available Cd, pH, and nutrient levels. Key microbial biomarkers were specifically enriched in different plant rhizospheres. In contrast, the fungal community exhibited minimal responsiveness. These findings demonstrate that remediation efficiency is governed by an integrated “amendment–plant–microbe” framework: amendments regulate metal bioavailability, plants execute extraction or stabilization, and the restructured microbiome supports nutrient cycling and plant health. This integrated remediation strategy directly supports the Sustainable Development Goals of the 2030 Agenda, especially on environmentally sound management of chemicals and wastes and land degradation neutrality. This mechanistic understanding underscores the necessity of combined biological and chemical strategies for sustainable remediation of co-contaminated soils, ultimately enabling ecological reclamation and safe recycling of such urban marginal lands into productive uses. Full article

Land productivity assessments used in SDG 15.3.1 commonly rely on NDVI trends, which may be strongly influenced by precipitation variability and can therefore misrepresent actual land condition change, particularly in dryland environments where vegetation productivity responds rapidly to rainfall fluctuations. To address this issue, this study presents a land degradation assessment (2000–2022) using a fully reproducible Google Earth Engine workflow integrating high-resolution 30 m Landsat time-series NDVI, precipitation, land cover, and soil organic carbon datasets. The core methodological contribution is a precipitation-conditioned hybrid productivity framework that dynamically selects among NDVI trends, Rain-Use Efficiency (RUE), and Residual Trends (RESTREND) according to local rainfall dynamics. By adapting productivity metrics to precipitation conditions, the framework reduces precipitation-driven misinterpretation of vegetation trends, operationalizes a more climate-aware implementation of the land productivity (LP) sub-indicator within SDG 15.3.1, and enables systematic comparison of productivity metrics under contrasting rainfall regimes. Results for the 2015–2022 monitoring period, which included multiple drought years, indicate that $18\%$ of land showed declining productivity, $75\%$ remained stable, and $6\%$ showed improvement. Decline was spatially concentrated in arid and semi-arid regions, whereas irrigated and managed landscapes exhibited localized improvements. The hybrid indicator provides an additional option for LP assessment that explicitly accounts for precipitation variability, supporting more climate-sensitive interpretation of productivity trends. This transferable, reproducible methodology strengthens national capacity for SDG 15.3.1 reporting and offers a scalable framework for land degradation assessments in other drought-prone regions. Full article

Driven by the global energy transition and carbon neutrality goals, hybrid power systems have become a core technical path for energy conservation and carbon reduction in the transportation and power sectors, and the performance of energy management strategies directly determines the system’s overall energy efficiency. Traditional energy management methods have inherent bottlenecks of high model dependence and poor adaptability, making it difficult to satisfy real-time decision-making requirements under complex operating conditions. Deep Reinforcement Learning (DRL) provides an innovative solution to this technical bottleneck, and has become a cutting-edge research direction in this field. However, existing reviews have not yet constructed a full-chain analysis framework covering its algorithms, applications, verification, challenges and prospects. Focusing on the engineering application of DRL in the real-time energy management of hybrid power systems, this paper systematically sorts out domestic and international research results up to the first quarter of 2026. The core quantitative findings of this review are as follows: (1) DRL-based strategies can achieve 93–99.5% of the Dynamic Programming (DP) theoretical global optimum in fuel economy, which is 5–25% higher than rule-based methods; (2) DRL strategies only have 3.1–4.8% performance degradation under unseen operating conditions, which is significantly better than the 10.3–14.7% degradation of the Equivalent Consumption Minimization Strategy (ECMS); (3) Actor–Critic (AC) algorithms (Twin Delayed Deep Deterministic Policy Gradient (TD3)/Soft Actor–Critic (SAC)) have become the mainstream in this field, with a 3–5 times higher sample efficiency than value function-based algorithms; and (4) offline DRL and transfer learning can reduce the training time of DRL strategies by more than 80% while maintaining equivalent optimization performance. This paper first analyzes the essential attributes and core technical challenges of hybrid power system energy management; second, classifies DRL algorithms from the perspective of control engineering and analyzes their technical characteristics; third, disassembles the application design logic of DRL around four major scenarios: land vehicles, water vessels, aerial vehicles and fixed microgrids; fourth, summarizes the mainstream verification platforms and evaluation systems; fifth, analyzes core bottlenecks and cutting-edge solutions; and finally, prospects the development trends of next-generation intelligent energy management systems combined with cross-fusion technologies. This paper aims to build a complete technical system map for this field and promote the engineering deployment and practical application of intelligent energy management technologies integrating data and knowledge. Full article

The development of bioenergy crops on saline–alkaline land has been recognized as a potential pathway for both land restoration and combating global warming. However, the role of soil organic carbon (SOC) dynamics under such conditions remains insufficiently quantified in long-term assessments. In this study, an exploratory assessment was conducted to evaluate the long-term soil carbon sequestration (SCS) potential and life-cycle greenhouse gas (GHG) emissions of sustainable aviation fuel (SAF) produced from Arundo donax in the Bohai Rim region of China. The CENTURY model was integrated with Long Short-Term Memory (LSTM) time series forecasting to simulate SOC dynamics under future climate scenarios (2024–2035). Compared with the original CENTURY simulation, the LSTM model yielded a substantially more conservative estimate of SOC accumulation, with an Ensemble Mean SCS rate of 0.032 t C/ha/a and a 95% confidence interval ranging from −0.079 to 0.143 t C/ha/a. This result indicates a positive regional average tendency toward soil carbon sequestration, while also suggesting that some locations may behave as carbon sources under less favorable climatic conditions. The total SCS potential across the study area was estimated at 0.615 Tg C. When these soil carbon benefits were incorporated into the life-cycle assessment of Fischer–Tropsch (F-T) SAF, the pathway could become potentially net-negative under the adopted assumptions, reaching −32.1 g CO₂e/MJ, which corresponds to a potential reduction of 136.1% relative to fossil aviation fuel. These results should be interpreted as exploratory and scenario-based, given that large-scale cultivation of Arundo donax has not yet been established in the Bohai Rim region and the assessment therefore relies on assumptions. Beyond GHG mitigation, the cultivation of Arundo donax on degraded saline–alkaline soils may also have potential relevance to broader sustainability objectives, including SDG 13 (Climate Action) and SDG 15 (Life on Land). These findings highlight the possible synergies among energy crop cultivation, soil restoration, and climate neutrality goals, and provide preliminary insights for integrating marginal land utilization into sustainable land management and low-carbon aviation strategies. Full article

Substantial tracts of fallow farmland remain unutilized across southwestern China throughout winter and spring. To explore a high-yield planting pattern for utilizing such fallow land, a cereal–legume intercropping experiment was conducted in Chengdu in 2021–2022 and in 2022–2023. This involved five different intercropping ratios of oat (Avena sativa) and common vetch (Vicia sativa) including 100:0, 75:25, 50:50, 25:75, and 0:100 based on seed number per unit area. The relative density, LER (land equivalent ratio), hay yield, nutritional composition and in vitro fermentation characteristics were assessed. The study revealed that the combination of oat and common vetch led to a significant enhancement in the production performance over the monocultures. At the flowering stage, the most balanced interspecific competition was observed at a ratio of 50:50. The ratio of 50:50 had the higher LER in the mixture—from 1.018 to 1.873—which was significantly higher than the other two intercropping ratios in 2021–2022. At the flowing development stage in 2021–2022, the harvesting of mixed crops at the 50:50 ratio resulted in a significant higher crude protein yield, 1454.7 kg/hm², than the other intercropping ratios. As the growth stage continued, the mixture hay neutral detergent fiber and acid detergent fiber contents increased, while the relative feed value and crude fat content decreased. The soluble sugar content increased with the prolongation of the growth stage and peaked at the jointing stage, and decreased with the decrease in the proportion of oat in the mixture. Additionally, the gas production showed an overall decreasing trend with the increase in the proportion of common vetch. The dry matter degradation rate in the mixture hay was overall higher than that of the monocultures, and the NH₃-N content showed an overall trend of increasing with the decrease with the intercropping ratio of oat. Consequently, the 50:50 ratio may be recommended as an oat-common vetch intercropping ratio suitable for utilizing fallow fields in southwestern China from October to April to produce high-quality forage. Full article

Land use and land cover (LULC) change strongly influences national carbon dynamics and the effectiveness of forest-based climate mitigation strategies, particularly in mountainous developing countries. This study integrates scenario-based LULC modeling, spatially explicit carbon accounting, and economic valuation to assess how alternative development pathways affect carbon storage and its economic value in Nepal over the 2020–2050 period. LULC projections for four scenarios: Business-as-Usual (BAU), Rapid Urban Development (RUD), Forest Degradation and Terai Contraction (FDTC), and Agricultural Land Abandonment and Ecological Recovery (ALER), were generated using the TerrSet Land Change Modeler, with 2020 as the baseline. These projections were then used as inputs to the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Carbon Storage and Sequestration model to estimate changes in ecosystem carbon stocks, integrating aboveground biomass, belowground biomass, soil organic carbon, and dead organic matter pools. Carbon stock changes were monetized using a constant carbon price of USD 5/tCO₂e and a 3% discount rate to estimate net present values (NPV). Results reveal strong divergence across scenarios. National carbon storage remains near-neutral under BAU (−0.46% by 2050), declines under RUD (−2.42%) and FDTC (−5.32%), and increases substantially under ALER (+11.74%). These biophysical outcomes translate into contrasting economic values: BAU yields a small negative NPV, RUD and FDTC generate large discounted losses, and ALER produces a strongly positive NPV exceeding USD 800 million by 2050. Spatially, forest and other wooded land dominate national carbon dynamics, while urban expansion and forest degradation drive disproportionate losses. Overall, the study results demonstrate that recovery-oriented land-use pathways offer substantially greater long-term carbon and economic benefits than development trajectories dominated by urban expansion or forest degradation, providing a policy-relevant framework to support Reducing Emissions from Deforestation and Forest Degradation, together with conservation, sustainable forest management, and enhancement of forest carbon stocks (REDD+) planning and long-term mitigation assessment in Nepal. Full article

Soil salinity is recognized as a critical abiotic stress that limits plant growth on marginal lands. The cup plant (Silphium perfoliatum L.), a perennial bioenergy species with high biomass potential, has been proposed for cultivation on saline-degraded soils; however, its physiological responses to different types of salinity stress, particularly alkaline and neutral salt stress, remain insufficiently characterized. In the present study, the physiological responses of the cup plant to neutral (NaCl) and alkaline (NaHCO₃) salt stress at concentrations of 100, 200, and 300 mM were evaluated in a pot experiment conducted under controlled conditions. The assessed indicators included relative chlorophyll content (CCI), chlorophyll fluorescence parameters (F_v/F_m, F_v/F₀, PI), and gas exchange characteristics, namely net photosynthetic rate (P_N), stomatal conductance (g_s), transpiration rate (E), and intercellular CO₂ concentration (C_i). Salinity reduced most physiological parameters, although some, such as maximum photochemical efficiency of PSII (F_v/F_m) and transpiration rate (E), did not show a clear dose-dependent response. Alkaline salt stress induced more pronounced reductions in the physiological parameters than neutral salt stress. At the first measurement, at the highest salt concentration, the chlorophyll content decreased by 49.0% and the P_N parameter by 77.8% under NaHCO₃ treatment, whereas under NaCl conditions the decreases were 29.0% and 51.3%, respectively, compared to the control. At 300 mM NaHCO₃, the chlorophyll content and photosynthetic rate were substantially reduced compared with those recorded under the corresponding NaCl treatment. Even at the moderate salinity level of 100 mM NaHCO₃, reductions in photosynthetic performance were detected relative to the control. Overall, photosynthetic efficiency and gas exchange in the cup plant were markedly impaired by salinity, particularly under conditions of high bicarbonate concentration. The results offer a deeper understanding of the physiological limitations of S. perfoliatum under acute salt stress and demonstrate that alkaline salinity, associated with elevated pH due to HCO₃⁻, exacerbates stress effects beyond the osmotic and ionic impacts of neutral salinity. These results highlight the potential of S. perfoliatum for sustainable biomass production on salt-affected soils, supporting renewable energy generation and environmentally responsible land use. Full article

Many cities worldwide face decline due to mineral-resource exhaustion, with mining-induced subsidence and land degradation posing urgent land use challenges. At the same time, carbon neutrality has become a global agenda, promoting ecological restoration, emissions reduction, and green transformation in resource-exhausted cities. However, empirical evidence on how carbon neutrality strategies drive land use transition remains scarce. Taking Xuzhou, China, as a case study, we integrate the GeoSOS–FLUS land use simulation model with a Markov chain model to project land use patterns in 2030 under three scenarios: natural development (ND), land recovery (LR), and carbon neutrality (CN). Using emission factors and a land use carbon inventory, we quantify spatial distributions and temporal shifts in carbon emission and sequestration. Results show that LR’s rigid recovery policies restrict broader transitions, while the CN scenario effectively reshapes land use by enhancing the competitiveness of low-carbon types such as forests and new-energy land. Under CN, built-up land expansion is curbed, forests and new-energy land are maximized, and emissions fall by 4.95% from 2020. Carbon neutrality offers opportunities for industrial renewal and ecological restoration in resource-exhausted cities, steering transformations toward approaches that balance ecological function and carbon benefits. Long-term monitoring is required to evaluate policy sustainability and effectiveness. Full article

Amid climate change and land-use transformation, the scientific identification of high-quality arable land reserves is critical for safeguarding both cropland quantity and quality. Conventional approaches, largely based on spatial autocorrelation and heterogeneity theories, inadequately capture the multi-scale integration of ecological functions and carbon cycling, particularly in ecologically high-risk areas where systematic identification and mechanism analysis are lacking. To address these challenges, this study introduces a geographically similar “grain-carbon” synergistic framework, paired with a “bidirectional optimization” strategy (negative elimination + positive selection), to overcome the shortcomings of traditional methods and mitigate grain–carbon trade-offs in high-risk areas. Using land-use data from Yunnan’s mountainous areas (2000–2020), integrated with InVEST-PLUS model outputs, multi-source remote sensing, and carbon pool datasets, we developed a dynamic land-use–carbon storage simulation framework under four policy scenarios: natural development, urban expansion, arable land protection, and ecological conservation. High-quality arable lands were identified through a geographic similarity analysis with the Geo detector, incorporating ecological vulnerability and landscape risk indices to delineate priority high-risk zones. Carbon storage degradation trends and land-use pressures were further considered to identify optimal areas for cropland-to-forest conversion, facilitating the implementation of the bidirectional optimization strategy. Multi-scenario simulations revealed an increase of 454.33 km² in high-quality arable land, with the optimized scenario achieving a maximum carbon storage gain of 23.54 × 10⁶ t, reversing carbon loss trends and enhancing both farmland protection and carbon sequestration. These findings validate the framework’s effectiveness, overcoming limitations of traditional methods and providing a robust strategy for coordinated optimization of carbon storage and arable land conservation in ecologically high-risk regions, with implications for regional carbon neutrality and food security. Full article

Soil degradation in arid ecosystems is a major threat to sustainable development and food security, especially under accelerating climate change. Kazakhstan, where more than 70% of agricultural land suffers from salinisation, erosion, and humus loss, offers a representative case for studying climate-driven degradation. This study quantitatively assessed the influence of air temperature, precipitation, aridity index, and extreme climatic events on soil properties in the arid regions of western Kazakhstan (Atyrau and Mangystau). The analysis integrated long-term meteorological time series (1941–2023) with field and laboratory data (1967–2024) into a harmonised dataset of 1330 records. Principal component analysis (PCA) identified four degradation gradients explaining 73.6% of total variance, while Random Forest and SHAP algorithms quantified variable importance. Mean annual temperature, frequency of arid years, and aridity index were the strongest predictors of humus, salinity, pH, and CO₂ parameters, with climate factors accounting for up to 30% of soil variability. The findings demonstrate that climatic stressors are the main drivers of soil degradation in arid zones, with climate factors explaining up to 30% of the variability in key soil properties (humus, salinity, pH, and CO₂)—a substantial proportion that underscores their dominant role relative to local geochemical and anthropogenic influences. The proposed hybrid PCA—Random Forest/SHAP framework provides a robust tool for analysing climate–soil interactions and supports the design of adaptive land-use strategies to achieve Land Degradation Neutrality (LDN) in Kazakhstan and other arid regions. Full article

The lower Mahanadi basin in eastern India is experiencing significant land and soil transformations that directly influence agricultural sustainability and ecosystem resilience. In this study, we used geospatial techniques to analyze the spatial-temporal variability of soil quality and land cover between 2011 and 2020 in the lower Mahanadi basin. The results revealed that the cropland decreased from 39,493.2 to 37,495.9 km², while forest cover increased from 12,401.2 to 13,822.2 km², enhancing soil organic carbon (>290 g/kg) and improving fertility. Grassland recovered from 4826.3 to 5432.1 km², wastelands declined from 133.3 to 93.2 km², and water bodies expanded from 184.3 to 191.4 km², reflecting positive land–soil interactions. Soil quality was evaluated using the Simple Additive Soil Quality Index (SQI), with core indicators bulk density, organic carbon, and nitrogen, selected to represent physical, chemical, and biological components of soil. These indicators were chosen as they represent the essential physical, chemical, and biological components influencing soil functionality and fertility. The SQI revealed spatial variability in texture, organic carbon, nitrogen, and bulk density at different depths. SQI values indicated high soil quality (SQI > 0.65) in northern and northwestern zones, supported by neutral to slightly alkaline pH (6.2–7.4), nitrogen exceeding 5.29 g/kg, and higher organic carbon stocks (>48.8 t/ha). In contrast, central and southwestern regions recorded low SQI (0.15–0.35) due to compaction (bulk density up to 1.79 g/cm³) and fertility loss. Clay-rich soils (>490 g/kg) enhanced nutrient retention, whereas sandy soils (>320 g/kg) in the south increased leaching risks. Integration of LULC with soil quality confirms forest expansion as a driver of resilience, while agricultural intensification contributed to localized degradation. These findings emphasize the need for depth-specific soil management and integrated land-use planning to ensure food security and ecological sustainability. Full article

The global energy transition is expected to require three to twenty times more land areas than fossil fuel-based power generation, making the availability of suitable land for the global energy transition a key challenge. Based on different types of energy resources, this study designs a telecoupling multi-regional input–output (MRIO) model to analyze cross-border electricity-driven embodied land appropriation patterns. The results show that the land footprint associated with renewable energy is substantially lower than that associated with conventional power generation. However, the growth rate of this footprint is 2.18 times higher than that of conventional electricity generation. China and Germany are identified as key export markets for wind- and solar- driven embodied land. The share of electricity-driven embodied land from China to the United States, Japan, and Germany declined, whereas the embodied land flowing to countries including South Korea, India, and Singapore increased. Embodied land-exporting nations face trilemma issues related to environmental degradation chain reactions, resource consumption threshold lines, and social distribution tensions, which may significantly affect decarbonization progresses. By integrating renewable power infrastructures and land use occupation, this analytical framework is expected to advance the understanding of energy–land nexus dynamics, providing theoretical foundations for cross-system governance in the implementation of carbon neutrality. Full article

Experts have long associated West Africa’s drylands with extensive and severe land degradation. In fact, the term “desertification” was coined in reference to the great Sahelian droughts of the 1970s and 1980s. Thus, much research has focused on Sahelian countries where there have also been numerous large-scale projects to combat desertification. Wetter, southern Sudanian savannas have received less attention. At the same time, scientific experts and policymakers have seriously questioned desertification as a concept and advocate for a new paradigm of land degradation neutrality (LDN). This entails assessing both land degradation and rehabilitation. The northern Sudanian savannas of Togo had been previously identified as an area with widespread and increasing land degradation based on regional analyses with coarse satellite imagery. Little or no rehabilitation had been either studied or detected. This study sought to follow up on these previous works to investigate local-scale patterns of both land degradation and rehabilitation. Fieldwork entailed a place-based approach using unmanned aerial vehicles (UAVs or drones) and participatory exercises with local stakeholders across nine sites. The spatial analysis incorporated local perceptions to classify the drone imagery. Results indicate that LDN varies markedly among the communities and that patterns of LDN are highly heterogeneous at these local scales. Full article

Red-bed desertification represents a critical form of land degradation in subtropical regions, yet the coupled soil–vegetation processes remain poorly understood. This study integrates Sentinel-2 vegetation indices with soil fertility gradients to assess vegetation–soil interactions in the Nanxiong Basin of South China. By combining Normalized Difference Vegetation Index (NDVI)-based vegetation classification with comprehensive soil property analyses, we aim to uncover the spatial patterns and driving mechanisms of degradation. The results revealed a clear gradient from intact forests to exposed red-bed bare land (RBBL). NDVI classification achieved an overall accuracy of 77.8% (κ = 0.723), with mixed forests being identified most reliably (97.1%), while Red-Bed Bare Land (RBBL) exhibited the highest omission rate. Along this gradient, soil organic matter, available nitrogen, and phosphorus declined sharply, while pH shifted from near-neutral in forests to strongly acidic in bare lands. Principal component analysis (PCA) identified a dominant fertility axis (PC1, explaining 56.7% of the variance), which clustered forested sites in nutrient-rich zones and isolated RBBL as the most degraded state. The observed vegetation–soil pattern aligns with a “weathering–transport–exposure” sequence, whereby physical disintegration and selective erosion during monsoonal rainfall drive organic matter depletion, soil thinning, and acidification, with human disturbance further accelerating these processes. To our knowledge, this study is the first to directly couple PCA-derived soil fertility gradients with vegetation patterns in red-bed regions. By integrating vegetation indices with soil fertility gradients, this study establishes a process-based framework for interpreting red-bed desertification. These findings underscore the utility of remote sensing, especially NDVI classification, as a powerful tool for identifying degradation stages and linking vegetation patterns with soil processes, providing a scientific foundation for monitoring and managing land degradation in monsoonal and semi-arid regions. Full article