Biodiversity net gain (BNG) is a "net outcome" planning policy which aims for development projects to leave biodiversity in a better state than before they started. Understanding the origins and history of existing mandatory BNG is necessary to understand the drivers and barriers that have influenced the policy to date and could inform the development and implementation of future BNG policies. Biodiversity net gain legislation was first discussed in Parliament in England through the passage of the Environment Act (2021) and became a mandatory requirement for most terrestrial and intertidal developments in February 2024. The policy uses habitat attributes as a proxy for biodiversity and represented the widest reaching net outcome policy in the world at the point of its introduction. As such, it is expected to have a significant impact on future land use decisions in England. This paper uses a mixture of literature review and the knowledge of those involved in the early stages of this BNG policy development in England to present a timeline of the stages that have led to mandatory biodiversity net gain. In doing so, we highlight formative events and documents, as an important first step in understanding its history and understanding how this can be used to inform future biodiversity policy.

ABSTRACT Soil erosion strips away fertile topsoil, posing challenges to global ecology and food security, particularly in the karst regions of southern China. However, research on soil erosion risk in karst areas under the dual influences of climate and land use changes remains scarce. Specifically, the question of how soil erosion risk in karst regions will change probabilistically in the future has not been effectively addressed. This study proposes a novel assessment framework from a risk probability perspective. Within this innovative framework, we employ Bayesian Belief Networks (BBN) to link future Land Use and Land Cover (LULC), climatic factors, and soil erosion management. By integrating various SSP‐RCP scenario data through Geographic Information Systems, we assess the probabilistic risk of soil erosion induced by future climate and socio‐economic changes. BBN sensitivity analysis reveals that LULC, slope, and precipitation are key factors influencing soil erosion. Compared to 2020, the probability of various soil erosion levels under four future scenarios generally shows a declining trend, with the greatest decrease observed under the SSP126 scenario. High‐risk areas for future soil erosion are primarily concentrated in the northern and western parts of the study area. Spatial planning for soil erosion control should focus on adjusting the key states of key variables for different soil erosion risk levels in these regions to reduce the likelihood of soil erosion risk. This study provides a promising visual decision‐making tool for developing climate‐adaptive soil erosion risk management plans and broadens new perspectives for future soil and water conservation planning from a risk probability standpoint.

Land use changes in Germany mainly affect agricultural land. Demands on these areas are increasing and exceeding the amount of land available. Previous efforts to reduce the use of open spaces and move towards a circular economy have failed to meet political targets (e.g., the 30-hectare target). Unlike other land uses, arable land is less protected by law. Therefore, spatial planning plays a special role in securing (high-quality) agricultural land. However, the demands on agricultural land extend (far) beyond spatial planning and pose challenges for the planning and implementation of future land use options. Against this background, the article analyses the spatial planning specifications for safeguarding agricultural land and assesses them in terms of land-related spatial claims and aspects of food security. In particular, from a spatial balance perspective, it highlights the contribution that regional planning currently makes to avoiding irreversible losses of productive land to limit supra-regional land displacement effects with negative consequences for near-natural areas. The results confirm a fundamental need for discussion about the role of spatial planning in balancing land-related spatial demands.

In ecologically fragile semiarid loess hilly agricultural regions, water resources constitute a critical constraint on sustainable development. Previous studies have demonstrated that landuse changes significantly affect the spatiotemporal distribution of water through vegetation cover modifications and hydrological process shifts. This study aims to predict future landuse changes and assess their impacts on water supply and demand, thereby providing a basis for sustainable water resource management. The current study employed an integrated PLUS-Markov chain approach (with a high validation accuracy, OA > 0.9 and Kappa > 0.83) complemented by the InVEST model to project landuse arrangements under three scenarios (NIS, FSS, and EDS) for Guyuan city in 2030, 2040, and 2050, and to analyze the consequent spatiotemporal evolution of water supply and demand risks. The results indicated that by 2050, the cropland under the NIS scenario decreased by 6.7%, primarily transitioning to grassland. In contrast, the FSS scenario led to a substantial increase in cropland by 10.7%, resulting in an overall reduction in built-up area. Meanwhile, the EDS scenario drove rapid urbanization, with a built-up area expansion rate reaching 2.99km²/year, largely at the expense of cropland. By 2050, landuse change was projected to exert minor influences on the regional water supply, with only a 7.8% variation projected compared with 2030 levels, whereas substantial impacts were projected for the water demand, which increased by 43.3% during the same period. Notably, approximately 90% of Guyuan's area may face water security risks by 2050, particularly in ecological reserves and urban zones, with the risk severity increasing over time. Several adaptive strategies were proposed to reconcile land-water relationships, thereby offering practical solutions for sustainable agroecosystem management in semiarid loess hilly regions.

Against the backdrop of global climate change and the "dual-carbon" goals, optimizing the spatial pattern of carbon storage in terrestrial ecosystems is essential for achieving regional carbon balance and carbon neutrality. This study coupled the PLUS and InVEST models to simulate land use change and carbon storage distribution in the Wujiang River Basin by 2030 under four scenarios: urban development, ecological protection, cultivated land protection, and natural development. Furthermore, it utilized a Bayesian network model to conduct spatial optimization of the carbon storage pattern in the study area. The results show the following: (1) Carbon storage in the basin first declined and then rose, exhibiting a spatial pattern of high values in the north-central part and low values in the southwest, with significant spatial autocorrelation. (2) In 2030, carbon storage levels differ markedly among the four scenarios: the ecological-protection scenario registers a pronounced increase, whereas the other three scenarios exhibit decreases; high-carbon areas are mainly concentrated in the northwest and central regions. (3) By screening key variables through the Bayesian network, the basin is zoned into ecological protection zones, ecological buffer zones, restricted development zones, and economic development zones. This study offers scientific guidance for decision-makers seeking to optimize ecosystem carbon storage in the Wujiang River Basin of China, which is essential for formulating future land use policies and achieving the "dual carbon" strategic goal.

ABSTRACT Under the combined influence of climate change and human activities, analysing and predicting water and sediment dynamics is essential for watershed management. This study investigates the Tao River Basin (TRB) on the Tibetan Plateau and the Zuli River Basin (ZRB) on the Loess Plateau—both major tributaries of the upper Yellow River with distinct eco‐geographical features. Using the SWAT model to simulate runoff and sediment processes, and partial least squares structural equation modelling (PLS‐SEM) to quantify driving mechanisms, we compared how these basins respond to future climate and land use changes. SWAT parameter comparisons revealed that TRB processes are more influenced by vegetation cover and baseflow, whereas ZRB processes are more sensitive to soil properties, moisture transport and channel erosion. PLS‐SEM results showed that in the TRB, runoff is mainly driven by precipitation (positive effect) and temperature (suppressive effect), with the positive role of NDVI increasing over time. Sediment dynamics are predominantly controlled by precipitation, with its indirect effects (via NDVI and runoff) outweighing direct meteorological impacts. In the ZRB, runoff is the primary driver of sediment, while precipitation influences both runoff and sediment mainly through vegetation. Temperature has little effect on sediment, and vegetation consistently reduces sediment yield. Scenario analyses indicated distinct trends. Under the low‐emission scenario combined with ecological‐economic land use, both runoff and sediment are projected to decrease. In contrast, under medium‐ and high‐emission scenarios, both are projected to increase. Notably, the TRB, with its superior vegetation cover, exhibited significantly smaller fluctuations in runoff and sediment across all scenarios. This highlights the key buffering role of vegetation against climate impacts. Therefore, forest and grassland restoration can mitigate climate effects on water and sediment processes. However, sediment control measures may also reduce river runoff, underscoring the need to sustain runoff resources while balancing ecological and socio‐economic water needs.

ABSTRACT Accurately predicting future land use dynamics and their impacts on ecosystem service value (ESV) is a critical prerequisite for achieving regional sustainable development and optimizing territorial spatial layout. This study proposed an innovative assessment framework integrating system dynamics (SD), mixed‐cell cellular automata (MCCA), and map comparison statistic (MCS) methods, overcoming the limitations of traditional models in representing mixed land use structure and continuous dynamic changes. It systematically simulated the evolution of land use and ESV under various climate and socioeconomic (SSP‐RCP) scenarios and, for the first time, evaluated the uncertainty of ESV across different scenarios and spatial scales. The spatial pattern of ESV exhibited a distribution characterized by “low center–high periphery”, with the forested northern foothills of the Qinling Mountains serving as a core ecological barrier. Between 2020 and 2080, land use change generally exerted a negative influence on ESV, with the intensity of impact following the order of SSP126 > SSP245 > SSP585 under development pathways. Uncertainty increased with decreasing spatial scale, peaking at the grid scale; scenario‐based uncertainty exceeded interannual variability and intensified over longer time periods. The uncertainty associated with ESV responses to land use change demonstrated significant local heterogeneity and scale dependence, while maintaining consistency across scales. We recommend that territorial spatial planning should adopt a low‐carbon ecological orientation, with systematic integration of ecological conservation objectives into decision‐making processes to mitigate the potential risks of high‐carbon pathways. Concurrently, a comprehensive system for preventing and controlling land degradation should be established to enhance the synergistic maintenance of ecological land structure and function, effectively curbing cropland loss and the fragmentation of ecological spaces, thereby safeguarding territorial system stability and ecological security.

Research on carbon storage is crucial for guiding regional sustainable development. However, Sichuan Province lacks long-term systematic analyses of carbon storage, and the driving mechanisms behind its changes remain unclear. This study systematically examines the spatiotemporal evolution of LUCC(land use/cover change) and carbon storage in Sichuan from 1980 to 2020, analyzes driving factors of carbon storage changes, and simulates future carbon storage distribution under different scenarios, based on LUCC data and 13 driving factors. Key findings include: (1) Over the 40-year period, land use was dominated by grassland, forest land, and farmland, maintaining a stable "grassland/forest land in the west, farmland in the east" pattern, with notable farmland and water body shrinkage alongside grassland and construction land expansion. (2) Total carbon storage showed minor fluctuations (9,201.53-9,209.52 Tg) but exhibited significant spatial heterogeneity, persistently displaying a "high in the west and low in the east" distribution. Water body-to-grassland and farmland-to-forest land conversions substantially increased carbon storage, while forest land-to-grassland and farmland-to-construction land transitions decreased it. (3) Spatial autocorrelation analysis revealed a negative correlation between carbon storage and land use intensity, with pronounced spatial clustering-High-High clusters concentrated in western regions and Low-Low clusters distributed peripherally. (4) Temperature and Digital Elevation Model emerged as dominant factors, while transportation accessibility and precipitation showed minimal influence. Human activities demonstrated moderate regulatory effects, with factor interactions significantly enhancing explanatory power, indicating multi-factor driven changes. (5) Multi-scenario projections (2030-2050) maintained the "high in the west and low in the east" pattern. Compared to 2020, SSP1-1.9 (Shared Socioeconomic Pathway 1-1.9) showed minimal change (10,711.94 ~ 10,712.16 Tg), SSP2-4.5 (Shared Socioeconomic Pathway 2-4.5) exhibited the largest decline (9,243.73 ~ 9,202.01 Tg), and SSP5-8.5 (Shared Socioeconomic Pathway 5-8.5) also decreased notably (9,015.01 ~ 8,980.07 Tg). This study provides a scientific basis for future land use optimization and carbon sink management in Sichuan Province.

The ecological threshold has not yet formed a unified definition, and there is no definition for “the threshold of the supply and demand of ecosystem services (TrSD)”, leading to no limitation of the negative impact of production and life behavior on the supply and demand of ecosystem services. This study defined and set TrSD, and took Urumqi as an example to carry out a case study. Firstly, the concept of TrSD was elaborated referred to multiple definitions of the ecological threshold based on “the difference between the supply and demand of ecosystem services (ESr)”. Then, the geographical simulation and optimization system- future land use simulation (GeoSOS-FLUS) software was used to simulate future land use. After that, the Land Use and Land Cover (LULC) matrix model was applied to calculate ESr. Finally, the TrSD was determined via the inflection point analysis of ESr. This study concludes that the proposed TrSD and its systematic calculation method are innovative and rational. The results can be used for ecosystem service management and ecological valuation, which helps the sustainability progress of the global.

Simulating Streamflow Scenarios Using Hydrological Modeling Integrated with Future Land Use and CMIP6 Climate Projections

Lead transformation to plumbojarosite remains stable following lime addition for lead and arsenic contaminated soils.

Previous efforts to assess uncertainty in Land Use Cover Change (LUCC) models have primarily focused on increasing model accuracy and hence the credibility of future simulations. However, such an approach does not adequately incorporate the inherent, deep, uncertainty associated with long-term future projections of LUCC. By contrast, exploratory modelling approaches, which capture this uncertainty through the simulation of multiple potential future scenarios, offer better potential to support decision-making. This paper proposes and demonstrates a framework for exploratory modelling of LUCC scenarios to identify the main factors of change and map decision boundaries to support planning. Specifically, this framework projects a large ensemble of future LUCC simulations to then apply Global Sensitivity Analysis (GSA) in two different settings: Factor prioritisation and Factor mapping to quantify the influence of all possible types of factors driving LUCC: external uncertainties, planning decisions and stochasticity. The framework is demonstrated through the projection of future urban development scenarios for the Lausanne-Morges agglomeration in Switzerland, under major transport infrastructure development. This case study illustrates the potential value of the framework for planning support by highlighting that new infrastructure development supports densification efforts over a wide range of future scenarios, although the magnitude of this effect exhibits clear spatial variations.

Since land use considerably affects the spatial variation of PM2.5 levels, it is crucial to predict PM2.5 concentrations under future land use changes. However, prior research has primarily concentrated on meteorological factors influencing PM2.5 predictions, while neglecting the effect of land use configurations. Consequently, in our study, a novel Patch-generating Land Use Simulation–Land Use Regression (PLUS-LUR) method was developed by integrating the PLUS model’s dynamic prediction capability with the LUR model’s spatial interpretation strength. The incorporation of landscape indices as key variables was essential for predicting PM2.5 concentrations. First, the random forest-optimized LUR method was trained with PM2.5 datasets from the Pearl River Delta (PRD) monitoring stations and multi-source spatial datasets. We assessed the modeling accuracy with and without considering landscape indices using the test dataset. Subsequently, the PLUS approach was applied to forecast land use as well as associated landscape indices in 2028. Based on these projections, grid-scale influencing factors were input into the previously constructed LUR model to forecast future PM2.5 distributions at a grid scale. The results reveal a spatial pattern with higher PM2.5 levels in central areas and lower levels in peripheral regions. Furthermore, the PM2.5 concentrations in the PRD are all below the Grade II threshold of the China Ambient Air Quality Benchmark in 2028. Notably, the predictions incorporating landscape indices demonstrate higher accuracy and reliability compared to those excluding them. These results provide methodological support for future PM2.5 assessment and land use management.

As a core city in central China and a key node of the Changsha–Zhuzhou–Xiangtan (CZT) Metropolitan Area, Changsha has experienced profound territorial space restructuring amid rapid urbanization and high-quality development. This study focuses on the spatiotemporal evolution characteristics, driving mechanisms, and future optimization paths of production–living–ecological space (PLES) in Changsha, using three key time nodes: 2010, 2020, and 2025. Based on updated land use data (30 m spatial resolution), socioeconomic statistics, and the latest territorial spatial planning policies, we integrated multiple research methods including the land use transfer matrix, dynamic degree model, Logistic regression, and FLUS (Future Land Use Simulation) model. The results reveal the evolutionary law of PLES space from “rapid expansion” (2010–2020) to “quality improvement” (2020–2025) in Changsha and simulate the 2035 PLES layout under three scenarios (natural development, cultivated land protection, and ecological protection) incorporating rigid policy constraints such as urban development boundaries and ecological conservation red lines. This research provides updated scientific support for the coordinated and sustainable development of territorial space in new first-tier cities and metropolitan area cores.

This study adopts an integrated simulation framework based on Cellular Automata and Artificial Neural Networks (CA-ANN) to model land use and land cover (LULC) transitions driven by urban expansion. By combining machine learning with spatial modeling, the approach enables the forecasting of urban growth dynamics and supports data-driven urban planning. The objective is to assess urban sprawl in the city of Passo Fundo, southern Brazil, using LULC change simulations from 2002 to 2043. Satellite imagery from 2002 to 2023 indicate for supervised classification of three land cover classes-urbanized areas, forests, and non-urbanized areas-alongside key spatial variables, including hypsometry, proximity to water bodies, railways, central business districts, and road networks. These variables served as inputs to the CA-ANN model to simulate future land use scenarios for 2033 and 2043. Results indicate a 45% increase in urbanized areas from 2002 to 2023, with projections reaching 66% growth by 2043, absolute land area expansion. This urban expansion primarily occurs at the expense of agricultural and forest areas, underscoring the risks of landscape fragmentation, biodiversity loss, and pressure on agricultural lands. The findings highlight the urgency of integrating spatial intelligence into sustainable land governance strategies, particularly in regions where urbanization intersects with agribusiness territories and food security systems.

Land use and land cover change strongly influences hydrological processes and watershed functioning in rapidly transforming landscapes. This study modeled future land use and land cover change and its hydrological implications in the Dabus watershed of southwest Ethiopia for the period 2019–2029. Historical land use and land cover data from 2019 were used to simulate the 2029 scenario using an empirical and dynamic land use modeling framework implemented in the TerrSet Geospatial Monitoring and Modeling System. The projected land use and land cover distribution for 2029 indicates expansion of shrubland to 510.9 km² (25.9%), water bodies to 149.3 km² (7.6%), and built up areas to 980.5 km² (49%), while forest land declines to 31.1 km² (1.6%), cultivated land to 199.2 km² (10.1%), barren land to 85.4 km² (4.3%), and grassland to 18.6 km² (0.9%). Relative to 2019, shrubland, water bodies, and built-up areas increase by 35.7 km², 13.1 km², and 0.9 km², respectively, whereas forest land, cultivated land, barren land, and grassland decrease by 5.5 km², 31.3 km², 11.6 km², and 1.3 km². These changes, except for barren land, are likely to accelerate surface runoff and increase flood susceptibility in the watershed. The expansion of built-up areas and the continued loss of forest and cultivated land highlight the need for immediate land and water resource management interventions. The integrated empirical and dynamic modeling approach provides a valuable decision support framework for sustainable watershed management in southwest Ethiopia.

The ecological protection and high-quality development of the Yangtze River Basin are major strategic initiatives of the central government. As a critical tributary of the upper Yangtze River， the Chishui River plays an essential role in maintaining regional ecological security. Investigating the spatiotemporal evolution of land use and predicting future scenarios in the Chishui River Basin is not only significant for local development planning but also offers valuable insights for ecological protection and efficient land use in the Yangtze River Basin and similar regions. This study uses the Chishui River Basin （Yunnan section） as a representative case to analyze land use changes and their driving factors while exploring future land use trends under different scenarios. The findings aim to provide scientific guidance for ensuring ecological security and rational land resource utilization. Based on Landsat satellite imagery from 1985 to 2020 and field survey data from 2022 to 2023， land use data were obtained using a supervised classification method and analyzed for spatiotemporal dynamics. Quantitative analysis of the driving factors of land use change was conducted using the PLUS model and the Geodetector model to reveal underlying mechanisms. The PLUS model was then used to predict the spatial distribution of land use in 2035 under different development scenarios. The results showed that： ① Between 1985 and 2020， the overall trend of land use changes in the basin included an initial increase followed by a decrease in cultivated land， a decrease and subsequent stabilization in grassland， an initial decrease followed by an increase in forest and shrubland， stability in water bodies， and a continuous increase in built-up land. ② The expansion of built-up land was primarily influenced by proximity to secondary roads and slope， with the distance to administrative centers being the most decisive factor. Moreover， interactive effects between factors demonstrated synergistic enhancement， with the interaction between natural environment and regional accessibility being the most significant. ③ Under the natural development scenario， cultivated land and forest areas decreased， while built-up land and water areas increased. Under the ecological protection scenario， ecological land areas increased overall， whereas under the farmland protection scenario， rapid growth in cultivated land significantly encroached on other land types. Future land use patterns under different scenarios showed notable variation. As a source area of the Yangtze River， future land use planning and development of the basin should prioritize ecological protection and sustainable development， emphasizing the synergy between environmental conservation and economic growth. The results of this study provide scientific references for land use planning and ecological protection policy-making in the Yangtze River Basin and other ecologically sensitive regions.

Carbon stocks in terrestrial ecosystems are significantly affected by land use/cover change （LUCC）， and exploring the role of LUCC on carbon stocks of regional terrestrial ecosystems is of great importance for improving land use structure and achieving the goals of carbon peaking and carbon neutrality. Based on the land use data between 2000 and 2020， this study established four development scenarios， namely natural development （ND）， ecological protection （EP）， economic development （ED）， and comprehensive development （CD）， by integrating 13 key drivers. Combined with the PLUS and InVEST models， the dynamic adjustment of land use types and the spatial and temporal evolution of carbon stocks in the Sichuan-Chongqing Region were simulated. The study findings revealed that： ① From 2000 to 2020， the areas of grassland and cropland decreased by 83.65×104 hm2 and 46.41×104 hm2， respectively， while the areas of forest land， construction land， and water increased by 65.37×104， 50.55×104， and 13.41×104 hm2， respectively， and the area of unutilized land remained stable. ② In 2000， 2010， and 2020， the carbon stocks were 1 968.88×107， 1 996.90×107， and 1 998.59×107 t， respectively， and the total carbon stock increased by 29.71×107 t. The conversion of cropland and grassland to forest land was the primary driver for increased carbon stocks. ③ According to the study， compared to that in 2020， carbon stocks under the ND， ED， EP， and CD scenarios increased by 1.48×107， 27.75×107， 43.62×107， and 50.32×107 t， respectively. The CD scenario exhibited higher carbon stocks and a more excellent total value than the other scenarios， making it the optimal development model. The results of the study provide a reference for decision-makers on ecosystem carbon stock optimization from the perspective of land use， which will provide strong support for the formulation of future land use policies and the realization of the goal of carbon peaking and carbon neutrality.

ABSTRACT Aim Terrestrial biodiversity is impacted by both climate and land use change. Yet, future biodiversity projections have rarely considered these two drivers in combination. In this study, we aim to assess the individual and combined impact of future climate and land use change on global terrestrial vertebrate diversity under a ‘sustainability’ (SSP1‐RCP2.6) and an ‘inequality’ (SSP4‐RCP6.0) scenario. Location Global land, excluding Antarctica. Time Period 1995, 2080. Major Taxa Studied Amphibians, birds, and mammals. Methods We combined global climate‐driven species distribution model (SDM) projections of 13,903 vertebrates (amphibians, birds, and mammals) with future and present land use projections from the Land Use Harmonisation 2 (LUH2) project. We refined the SDM outputs by the habitat requirements of each species using a land use filtering approach. We then analyzed future species richness changes globally, per region, and per land use category, and looked at taxon‐specific effects. Results Under both scenarios, decreases in future species richness dominate at low and mid‐latitudes, with climate and land use change playing an equally important role. Land use change can be either an alleviating (SSP1‐RCP2.6) or an exacerbating (SSP4‐RCP6.0) factor of climate‐induced biodiversity loss. Sub‐Saharan Africa is projected to become a high‐risk area for future land use‐driven biodiversity loss under the SSP4‐RCP6.0. Under SSP1‐RCP2.6, forested and non‐forested land areas increase, while SSP4‐RCP6.0 leads to higher rates of deforestation and pasture expansion. Mammals experience the largest climate‐driven losses, affecting 56.4% of land area under SSP4‐RCP6.0, while amphibians are particularly vulnerable to land use‐driven losses, especially under SSP4‐RCP6.0. Main Conclusions Our results suggest that both climate and land use pressures on biodiversity will be highest in lower latitudes, which harbor the highest levels of biodiversity.

Integrating future land use uncertainty and spatial scales into ecosystem service degradation risk assessment for urban ecological management