Climate and land use changes impact the trajectories of ecosystem service bundles in an urban agglomeration: Intricate interaction trends and driver identification under SSP-RCP scenarios.

The delivery of ecosystem services (ESs), particularly in urban agglomerations, faces substantial threats from impending future climate change and human activity. Assessing ES bundles (ESBs) is critical to understanding the spatial allocation and interactions between multiple ESs. However, dynamic projections of ESBs under various future scenarios are still lacking, and their underlying driving mechanisms have received insufficient attention. This study examined the Beijing-Tianjin-Hebei urban agglomeration and proposed a framework that integrates patch-generating land use simulation into three shared socioeconomic pathway (SSP) scenarios and clustering analysis to assess spatiotemporal variations in seven ESs and ESBs from 1990 to 2050. The spatial trajectories of ESBs were analyzed to identify fluctuating regions susceptible to SSP scenarios. The results indicated that (1) different scenarios exhibited different loss rates of regulating and supporting services, where the mitigation of degradation was most significant under SSP126. The comprehensive ES value was highest under SSP245. (2) Bundles 1 and 2 (dominated by regulating and supporting services) had the largest total proportion under SSP126 (51.92 %). The largest total proportion of Bundles 4 and 5 occurred under SSP585 (48.96 %), with the highest provisioning services. The SSP126 scenario was projected to have the least ESB fluctuation at the grid scale, while the most occurred under SSP585. (3) Notably, synergies between regulating/supporting services were weaker under SSP126 than under either SSP245 or SSP585, while trade-offs between water yield and non-provisioning services were strongest. (4) Forestland and grassland proportions significantly affected carbon sequestration and habitat quality. Climatic factors (precipitation and temperature) acted as the dominant drivers of provisioning services, particularly water yield. Our findings advocate spatial strategies for future regional ES management to address upcoming risks.

Multiple scenario land use simulation based on a coupled MOGA-PLUS model: a case of the Yellow River Basin.

Land use changes have profoundly influenced global environmental dynamics. The Yellow River (YR), as the world's fifth-longest river, significantly contributes to regional social and economic growth due to its extensive drainage area, making it a key global player. To ensure ecological stability and coordinate land use demand, modeling the future land allocation patterns of the Yellow River Basin (YRB) will assist in striking a balance between land use functions and the optimization of its spatial design, particularly in water and sand management. In this research, we used a multi-objective genetic algorithm (MOGA) with the PLUS model to simulate several different futures for the YRB's land use between 1990 and 2020 and predict its spatial pattern in 2030. An analysis of the spatiotemporal evolution of land use changes in the YRB indicated that construction land expansion is the primary driver of landscape pattern and structure changes and ecological degradation, with climate change also contributing to the expansion of the watershed area. On the other hand, the multi-scenario simulation, constrained by specific targets, revealed that economic development was mainly reflected in land expansion for construction. At the same time, grassland and woodland were essential pillars to support the region's ecological health, and increasing the development of unused land emerged as a potential pathway towards sustainable development in the region. This study could be used as a template for the long-term growth of other large river basins by elucidating the impacts of human activities on land use and rationalizing land resource allocation under various policy constraints.

How has carbon storage changed in the Yili-Tianshan region over the past three decades and into the future? What has driven it to change?

Predicting future land use changes and assessing carbon storage remain challenging. Nowadays, how nature and socioeconomics drive changes in carbon storage is a hot topic in research. In this study, through the projection of land use type and the integration of the PLUS, Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), and Geodetector models, we constructed a framework for assessing carbon storage in different land use scenarios. Utilizing this framework, it is possible to project land use change and estimate carbon storage based on different development scenarios. We applied the framework to the Yili Tianshan region and identified the main driving forces for carbon storage change. Further, we estimated the carbon storage in the Yili Tianshan region in 2035 under four scenarios (RE, NE, EP, and CLP). The results showed the following: 1) Between 1990 and 2020, there was an increase in the forest area and water bodies in the Yili-Tianshan region, mainly from bare land. 2) As shown on the time scale, carbon storage increases in the Yili-Tianshan region with a W-shaped fluctuation by converting grasslands and bare land into forests. On a spatial scale, the carbon storage was lower in the center and higher on both sides in the Yili-Tianshan region. 3) In 2035- RE, 2035-ND, and 2035-EP scenarios, the carbon storage was increased by 4.30 Tg, 6.67 Tg, and 12.08 Tg; in the 2035-CLP scenario, it was decreased by 14.63 Tg. The Yili-Tianshan region experienced a notable rise in carbon storage under the 2035-EP scenario compared to the other three scenarios. 4) Soil type played a significant role in the spatial differentiation of carbon storage in Yili-Tianshan (q value 0.5958), followed by population density (0.5394). The changes in carbon storage in the Yili-Tianshan region are the result of synergistic effects of multiple factors, in which the soil type∩soil erosion intensity are the most important. This research could provide a reference method for improving regional carbon storage.

[Impact of Land Use Change on Carbon Storage in Urban Agglomerations in the Guanzhong Plain].

It is important to study the impact of land use change on terrestrial ecosystem carbon stocks in urban agglomerations for the optimization of land use structure and sustainable development in urban agglomerations. Based on the patch-generating land use simulation (PLUS) model and integrated valuation of ecosystem services and trade-offs (InVEST) model, a simulation was developed that predicted the land use change and carbon stock of the Guanzhong Plain urban agglomeration in 2040 under different scenarios and further analyzed the impact of land use change on carbon stock. The results showed that:① The land use types of the Guanzhong Plain urban agglomeration were mainly cultivated land, forest land, and grassland, which accounted for more than 90 % of the total study area. ② From 2000 to 2020, the carbon stock in the Guanzhong Plain showed a continuous downward trend, with cropland, woodland, and grassland being the main sources of carbon stock in the Guanzhong Plain, and the overall carbon stock declined by 15.12×106 t, with the spatial distribution presenting the distribution characteristics of "high in the north and south and low in the middle." ③ By 2040, the carbon stock would decrease the most under the urban development scenario, with a total reduction of 27.08×106 t, and the least under the ecological development scenario, with a total reduction of 4.14×106t. The research results can provide data support for the high-quality development and rational land use planning of the Guanzhong Plain urban agglomeration.

[Spatio-temporal Evolution and Prediction of Carbon Storage in Huaibei City Based on InVEST-PLUS Model].

This study aimed to investigate the impact of spatiotemporal changes in land use on ecosystem carbon storage. The study analyzed the spatiotemporal changes in carbon storage in the study area based on land use data from five periods (1985, 1995, 2005, 2015, and 2020) using the InVEST model. The PLUS model was used to predict land use changes in the study area under four different scenarios (natural development, farmland protection, ecological protection, and double protection of farmland and ecology) in 2035, and the ecosystem carbon storage under different scenarios was estimated. The results of the study indicated that the farmland in the area under investigation had been decreasing consistently from 1985 to 2020, with a more rapid rate of change observed between 2015 and 2020. During this period, the overall dynamic attitude towards land use reached 34.62 %. Additionally, the carbon storage in the area showed a decreasing trend over the years, with a decrease of 1.55×105 t from 1985 to 2020. Between 2005 and 2015, the carbon storage showed a decrease of 1.22×105 t, with an average annual decrease of 1.22×104 t. The areas with higher carbon storage were located in the eastern part of the study area, whereas areas with lower carbon storage were found in the central and northwestern parts. Although the proportion of carbon storage in farmland decreased from 66.89 % to 57.73 %, farmland remained the most important carbon pool in the study area. The conversion of other land use types to grassland and forestland was advantageous for increasing ecosystem carbon storage. Finally, the study projected that by 2035, the carbon storage in the natural development scenario, the farmland protection scenario, the ecological protection scenario, and the dual protection scenario would be 81.77×105, 82.45×105, 82.82×105, and 82.51×105 t, respectively.

Evaluating net primary productivity dynamics and their response to land-use change in the loess plateau after the 'Grain for Green' program.

Assessing net primary productivity (NPP) dynamics and the contribution of land-use change (LUC) to NPP can help guide scientific policy to better restore and control the ecological environment. Since 1999, the "Green for Grain" Program (GGP) has strongly affected the spatial and temporal pattern of NPP on the Loess Plateau (LP); however, the multifaceted impact of phased vegetation engineering measures on NPP dynamics remains unclear. In this study, the Carnegie-Ames-Stanford Approach (CASA) model was used to simulate NPP dynamics and quantify the relative contributions of LUC and climate change (CC) to NPP under two different scenarios. The results showed that the average NPP on the LP increased from 240.7 gC·m-2 to 422.5 gC·m-2 from 2001 to 2020, with 67.43% of the areas showing a significant increasing trend. LUC was the main contributor to NPP increases during the study period, and precipitation was the most important climatic factor affecting NPP dynamics. The cumulative amount of NPP change caused by LUC (ΔNPPLUC) showed a fluctuating growth trend (from 46.23 gC·m-2 to 127.25 gC·m-2), with a higher growth rate in period ΙΙ (2010-2020) than in period Ι (2001-2010), which may be related to the accumulation of vegetation biomass and the delayed effect of the GGP on NPP. The contribution rate of LUC to increased NPP in periods Ι and ΙΙ was 101.2% and 51.2%, respectively. Regarding the transformation mode, the transformation of grassland to forest had the greatest influence on ΔNPPLUC. Regarding land-use type, the increased efficiency of NPP was improved in cropland, grassland, and forest. This study provides a scientific basis for the scientific management and development of vegetation engineering measures and regional sustainable development.

Spatiotemporal evolution and driving factors of ecosystem service bundle based on multi-scenario simulation in Beibu Gulf urban agglomeration, China.

Rapid urbanization is profoundly impacting the ecological environment and landscape patterns, leading to a decline in ecosystem services (ES) and posing threats to both ecological security and human well-being. This study aimed to identify the spatial and temporal patterns of ecosystem service bundles (ESB) in the Beibu Gulf urban agglomeration from 2000 to 2030, analyze the trajectory of ESB evolution, and elucidate the drivers behind ESB formation and evolution. We utilized the Patch-generating Land Use Simulation (PLUS) model to establish baseline (BLS), carbon sequestration priority (CPS), and urbanization priority (UPS) scenarios for simulating land use patterns in 2030. Following the assessment of ecosystem service values (ESV) through the equivalent factor method, we identified the spatiotemporal distribution patterns of ESB using the K-means clustering algorithm. By employing stability mapping and landscape indices, we identified and analyzed various types of ESB evolutionary trajectories. Redundancy analysis (RDA) was employed to pinpoint the drivers of ESB formation and evolution. The results revealed that from 2000 to 2030, land use changes were primarily observed in cropland, forestland, and construction land. Between 2000 and 2020, 92.88% of the region did not experience shifts in ESB types. In UPS, the ESB pattern in the study area underwent significant changes, with only 76.68% of the region exhibiting stabilized trajectories, while the other two scenarios recorded percentages higher than 80%. Key drivers of ESB-type shifts included initial food provision services, elevation, slope, changes in the proportion of construction land, and population change. This multi-scenario simulation of ESB evolution due to land use changes aids in comprehending potential future development directions from diverse perspectives and serves as a valuable reference for formulating and changing ecological management policies and strategies.

Spatiotemporal variations of ecosystem services in the Aral Sea basin under different CMIP6 projections.

The Aral Sea, located in Central Asia, has undergone significant reduction in surface area owing to the combined impacts of climate change and human activities. This reduction has led to a regional ecological crisis and profound repercussions on ecosystem services. Investigating the spatiotemporal variations and synergistic trade-offs of ESs in the Aral Sea basin is crucial for fostering the integrated development of the region's socioeconomic ecology. This study utilizes the Future Land-Use Simulation and InVEST models to analyze future land-use scenarios, integrating CMIP6 projections to assess the quality of four key ecosystem services: water production, soil conservation, carbon storage, and habitat quality over two timeframes: the historical period (1995-2020) and the projected future (2021-2100). Employing Spearman correlation, the study explores the trade-offs and synergies among these ecosystem services. Findings reveal that the primary forms of land-use change in the Aral Sea basin are the reduction in water area (- 49.59%) and the rapid expansion of urban areas (+ 504.65%). Temporally, habitat quality exhibits a declining trend, while carbon storage shows an increasing trend, and water production and soil retention fluctuate initially decreasing and then increasing. Spatially, water production and carbon storage demonstrate an increasing trend from the northwest to the southeast. Habitat quality exhibits a higher spatial pattern in the southeast and south, contrasting with lower spatial patterns in the north and west. Low-level soil conservation is predominantly distributed in the northwest, while medium to low-level soil conservation is prevalent in the east of the basin. The trade-off and synergy analysis indicates that between 1995 and 2020, a trade-off relationship existed between carbon storage and habitat quality and water production, whereas synergies were observed between soil conservation and carbon storage, water production and habitat quality, and soil conservation. The correlation between water production and soil conservation emerges as the strongest, whereas the correlation between carbon storage and habitat quality appears to be the weakest. The dynamic spatiotemporal changes, trade-offs, and collaborative relationships of ESs constitute major aspects of ecosystem service research, holding substantial implications for the effective management of the regional ecological environment.

Trajectories of socio-ecological systems: A case study in the tropical Andes.

Scenario and policy assessments in socioeconomic and environmental studies face significant challenges in socio-ecological systems (SES). There are a limited number of studies that have looked at the impact of different scenarios within integrated approaches, and many have used a static approach with a single driver of change. The present work analyzes the SES dynamics for a strategic basin in the Colombian Andes when implementing and analyzing scenarios and policies related to land cover and land use change using a system dynamics simulation model. The model includes natural, ecosystem services, sociocultural, and economic components. Scenarios and policy options are analyzed both individually and jointly to identify synergies or trade-off effects between the different SES components. The results showed the different trajectories of the socio-ecological system according to the cases studied, and its impact on different variables in the analyzed components. Some counterintuitive effects were also identified, such as the importance of intrinsic motivations in decision-making processes, and determinants in land management and policy design.

Ecosystem service valuation and multi-scenario simulation in the Ebinur Lake Basin using a coupled GMOP-PLUS model.

The Ebinur Lake Basin is an ecologically sensitive area in an arid region. Investigating its land use and land cover (LULC) change and assessing and predicting its ecosystem service value (ESV) are of great importance for the stability of the basin's socioeconomic development and sustainable development of its ecological environment. Based on LULC data from 1990, 2000, 2010, and 2020, we assessed the ESV of the Ebinur Lake Basin and coupled the grey multi-objective optimization model with the patch generation land use simulation model to predict ESV changes in 2035 under four scenarios: business-as-usual (BAU) development, rapid economic development (RED), ecological protection (ELP), and ecological-economic balance (EEB). The results show that from 1990 to 2020, the basin was dominated by grassland (51.23%) and unused land (27.6%), with a continuous decrease in unused land and an increase in cultivated land. In thirty years, the total ESV of the study area increased from 18.62 billion to 67.28 billion yuan, with regulation and support services being the dominant functions. By 2035, cultivated land increased while unused land decreased in all four scenarios compared with that in 2020. The total ESV in 2035 under the BAU, RED, ELP, and EEB scenarios was 68.83 billion, 64.47 billion, 67.99 billion, and 66.79 billion yuan, respectively. In the RED and EEB scenarios, ESV decreased by 2.81 billion and 0.49 billion yuan, respectively. In the BAU scenario, provisioning and regulation services increased by 6.05% and 2.93%, respectively. The ELP scenario, focusing on ecological and environmental protection, saw an increase in ESV for all services. This paper can assist policymakers in optimizing land use allocation and provide scientific support for the formulation of land use strategies and sustainable ecological and environmental development in the inland river basins of arid regions.

Research on the evolution characteristics, driving mechanisms and multi-scenario simulation of habitat quality in the Guangdong-Hong Kong-Macao Greater Bay based on multi-model coupling.

Since the 20th century, the global urbanization has led to a series of pollution issues, posing a severe threat to the habitat quality of human habitat. The quality of habitat determines whether ecosystems can provide suitable living conditions for humans and other species. Therefore, systematic study of the habitat quality is essential for the maintenance of sustainable development. In this study, we coupled models such as SD, InVEST and PLUS with a series of indicators to analyze the characteristics of land cover and habitat quality evolution in the Guangdong-HongKong-Macao Greater Bay Area (GBA) from 2000 to 2020 and deconstruct the driving mechanisms of habitat quality. Then simulate the evolution of land cover and habitat quality under different scenarios in 2030. The results show that: 1) Over the historical research period, the GBA exhibited "rapid expansion of artificial surfaces and rapid shrinkage of ecological land". Artificial surfaces increased by approximately 4878.95km2,while ecological land, such as agricultural land, decreased by about 3095.93km2.2) The degradation of habitat quality gradually accelerated and the habitat quality was characterized by "stepwise decline from the periphery to the interior", which was directly related to the land cover changes brought about by the topographic gradient effect in the Bay Area.3) Pollution control driven by environmental investments has had a moderating effect on habitat degradation, but it has not been able to change the overall degradation trend. 4) Scenario analysis suggests that future habitat quality in the GBA will degrade to a certain extent due to the impact of artificial surface expansion. We deduce that this will affect the structure of the city's ecological network as well as the conservation function of the ecological zones. This study provides a scientific basis for understanding the historical and future trends of habitat quality in the GBA, offering new insights into the intrinsic driving mechanisms of habitat quality. It also provides a theoretical support for relevant authorities to undertake sustainable development initiatives.

Integrated assessment and prediction of ecological security in typical ecologically fragile areas.

In order to safeguard and restore ecological security in ecologically fragile regions, a regionally appropriate land use structure and ecological security pattern should be constructed. Previous ecological security research models for ecologically fragile areas are relatively homogenous, and it is necessary to establish a multi-modeling framework to consider integrated ecological issues. This study proposes a coupled "PLUS-ESI-Circuit Theory" framework for multi-scenario ecological security assessment of the Ningxia Hui Autonomous Region (NHAR). Firstly, the PLUS model was used to complete the simulation of four future development scenarios. Secondly, a new ecological security index (ESI) is constructed by synthesizing ecological service function, ecological health, and ecological risk. Finally, the Circuit Theory is applied to construct the ecological security pattern under multiple scenarios, and the optimization strategy of ecological security zoning is proposed. The results show that (1) from 2000 to 2030, the NHAR has about 80% of grassland and farmland. The built-up area is consistently growing. (2) Between 2000 and 2030, high ecological security areas are primarily located in Helan Mountain, Liupan Mountain, and the central part of NHAR, while the low ecological security areas are dominated by Shapotou District and Yinchuan City. (3) After 2010, the aggregation of high-security areas decreases, and the fragmentation of patches is obvious. Landscape fragmentation would increase under the economic development (ED) scenario and would be somewhat ameliorated by the ecological protection (EP) and balanced development (BD) scenarios. (4) The number of sources increases but the area decreases from 2000 to 2020. The quantity of ecological elements is on the rise. Ecological restoration and protection of this part of the country will improve its ecological security.

Spatiotemporal dynamics of wetlands and their future multi-scenario simulation in the Yellow River Delta, China.

Wetlands, known as the "kidney of the earth", are an important component of global ecosystems. However, they have been changed under multiple stresses in recent decades, which is especially true in the Yellow River Delta. This study examined the spatiotemporal change characteristics of wetlands in the Yellow River Delta from 1980 to 2020 and predicted detailed wetland changes from 2020 to 2030 with the patch-generating land use simulation (PLUS) model under four scenarios, namely, the natural development scenario (NDS), the farmland protection scenario (FPS), the wetland protection scenario (WPS) and the harmonious development scenario (HDS). The results showed that wetlands increased 709.29 km2 from 1980 to 2020 overall, and the wetland types in the Yellow River Delta changed divergently. Over the past four decades, the tidal flats have decreased, whereas the reservoirs and ponds have increased. The gravity center movement of wetlands differed among the wetland types, with artificial wetlands moving to the northwest and natural wetlands moving to the south. The movement distance of the gravity center demonstrated apparent phase characteristics, and an abrupt change occurred from 2005 to 2010. The PLUS model was satisfactory, with an overall accuracy (OA) value greater than 83.48 % and an figure of merit (FOM) value greater than 0.1164. From 2020 to 2030, paddy fields and tidal flats decreased, whereas natural water, marshes and reservoirs and ponds increased under the four scenarios. The WPS was a relatively ideal scenario for wetlands, and the HDS was an alternative scenario for wetland restoration and food production. In the future, more attention should be paid to restoring natural wetlands to prevent further degradation in the Yellow River Delta. This study provides insights into new understandings of historical and future changes in wetlands and may have implications for wetland ecosystem protection and sustainable development.

Optimization of land use planning under multi-objective demand-the case of Changchun City, China.

Modeling and scenario analysis are the core elements of land use change research, and in the face of the increasingly serious ecological and environmental problems in urbanization, it is important to carry out land use simulation studies under different protection constraints for scientific planning and policy formulation. Taking Changchun City, the capital of Jilin Province, a pilot national eco-province, as an example, a CLUE-S model with coupled landscape ecological security patterns was constructed to predict and simulate the land use structure and layout under multi-objective optimization scenarios in the planning target year (2030), and the results were analyzed based on landscape index evaluation. The study found the following: (i) the proportion of ecological land area under low, medium, and high security levels in the study area was 8.7%, 64.8%, and 26.5%, respectively; (ii) under the current development trend scenario, the trend of increasing fragmentation of cultivated land patches in Changchun in 2030 will remain unchanged, with construction land spreading along the periphery in a compact and continuous pattern, while ecological land will be seriously encroached upon; and (iii) in the 2030 multi-objective optimization scenario, land use patches of all types will begin to show a tendency to cluster, with less landscape fragmentation and more connectivity, while cultivated land and construction land will also begin to converge and do not deteriorate as a result of spatial conflicts over ecological land.

A new assessment framework to forecast land use and carbon storage under different SSP-RCP scenarios in China.

The vision of achieving "carbon neutrality" has created new requirements for the projection of land use and land cover (LULC), as well as the carbon storage (CS) of terrestrial ecosystem. Global-scale LULC scenario assessments with coarser resolution introduces uncertainties to national and regional-scale studies, which in turn has a negative impact on CS analysis based on land use perspective. Therefore, we proposed a new framework for scenario-based assessment that integrates the global-scale Land Use Harmonization (LUH2) dataset, Patch-generating Land Use Simulation (PLUS) model, and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, which we called LUH2-PLUS-InVEST (LPI) model. Our aim is to investigate the potential impacts of the combinations of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) on China's future LULC and CS. By calibrating the demands, we generated structural predictions that were consistent with the actual land use. Furthermore, we explored the spatial heterogeneity of potential land use changes using 500 m × 500 m downscale simulations. Additionally, we developed a quantitative evaluation of CS from a spatiotemporal perspective and made recommendations on potential ecological threats. Our findings indicate that the basic characteristics of LULC and CS are determined by the natural context and that the prospects of land use distribution and carbon sequestration capacity are influenced by global emission pressure, regional competition, and China's unique development pattern. The results demonstrate that the LUH2-PLUS-INVEST model can provide an effective method for modeling the feedbacks of LULC and CS to the climate-society system.

[Simulation of land use and assessment of ecosystem service value in the eastern coastal cities of Zhejiang Province, China]. / 浙江东部沿海城市土地利用模拟及生态系统服务价值评估.

Simulating the change of ecosystem service values (ESV) caused by land use/cover change (LUCC) in the eastern coastal cities of Zhejiang Province is of great significance for regional sustainable development and ecological security. Based on remote sensing images of land use and Statistics Yearbook of 2000, 2010, and 2020, we analyzed the influence of LUCC on ESV in the study area during 2000-2020. We used the PLUS model to simulate land use change under three scenarios, including inertial development, ecological protection, and urban development in 2030, analyzed the spatial distribution and concentration degree of ESVs based on grid scale, and clarified the sensitivity characteristics of ESVs. The results showed that the construction land area showed an increasing trend during 2000-2020. The area of forest, cultivated land and water decreased significantly, resulting in a continuous downward trend of ESVs, which decreased by 160×108 yuan. Under the simulation of three scenarios of inertial development, ecological development, and urban development, the construction land area would increase by 93624, 54927, and 111966 hm2, respectively. The eastern plain would become the agglomeration area of construction land expansion. The ESVs of those three scenarios was 1693×108, 1729×108, and 1688×108 yuan, respectively, which were all lower than the ESVs of the study area in 2020. The decline rate of ESV in the ecological protection scenario slowed down. The spatial distribution of ESVs in the study area was high in the west and low in the east. Hot spots and cold spots of ESVs were distributed in a large range with strong agglomeration. Hot spots were mainly concentrated in the west, while cold spots were mainly distributed in the east and north.

Multi-scenario simulation of carbon budget balance in arid and semi-arid regions.

The carbon budget has emerged as a central focus in global carbon cycle research. The limited understanding of carbon budget balance dynamics has led to an increasing imbalance between ecological and socio-economic benefits. Building upon a comprehensive analysis of carbon storage and emission in Lanzhou from 2000 to 2020, this study develops a novel deep learning model (CNN-LSTM) to simulate carbon budget under various scenarios from 2030 to 2050. Additionally, scientifically grounded recommendations for carbon compensation are provided. The results demonstrate several key findings: (1) The deep learning model exhibits outstanding performance, with an average overall accuracy exceeding 0.93. The coupled model outperforms individual models, underscoring the significance and necessity of incorporating both temporal and spatial features in land use simulation. (2) Under the ecological protection redline scenario from 2030 to 2050, a noteworthy augmentation in carbon storage and a proficient constraint on carbon emissions are observed. This substantiates the effectiveness of ecological protection interventions. (3) Carbon compensation payment areas are predominantly concentrated in built-up land, with the extent of these areas expanding over time. (4) The disparities in carbon balance effects of forest were more conspicuous than that of built-up land across diverse temporal and scenarios.

Spatial heterogeneity and scenario simulation of carbon budget on provincial scale in China.

BACKGROUND: Conducting an extensive study on the spatial heterogeneity of the overall carbon budget and its influencing factors and the decoupling status of carbon emissions from economic development, by undertaking simulation projections under different carbon emission scenarios is crucial for China to achieve its targets to peak carbon emissions by 2030 and to achieve carbon neutrality by 2060. There are large disparities in carbon emissions from energy consumption, the extent of land used for carbon absorption, and the status of decoupling of emissions from economic development, among various regions of China. RESULTS: Based on night light data and land use data, we investigated carbon budget through model estimation, decoupling analysis, and scenario simulation. The results show that the carbon deficit had a continuous upward trend from 2000 to 2018, and there was a significant positive spatial correlation. The overall status of decoupling first improved and then deteriorated. Altogether, energy consumption intensity, population density of built-up land, and built-up land area influenced the decoupling of carbon emissions from economic development. There are significant scenarios of carbon emissions from energy consumption for the study area during the forecast period, only in the low-carbon scenario will the study area reach the expected carbon emissions peak ahead of schedule in 2027; the peak carbon emissions will be 6479.27 million tons. CONCLUSIONS: China's provincial-scale carbon emissions show a positive correlation with economic development within the study period. It is necessary to optimize the economic structure, transforming the economic development mode, and formulating policies to control the expansion of built-up land. Efforts must be made to improve technology and promote industrial restructuring, to effectively reduce energy consumption intensity.

Guiding the landscape patterns evolution is the key to mitigating river water quality degradation.

Consensus has emerged that landscape pattern evolution significantly impacts the river environment. However, there remains unclear how the landscape pattern evolves possible to achieve a balance between land resource use and water conservation. Thus, simulating future landscape patterns under different scenarios to predict river eutrophication level is critical to propose targeted landscape planning programs and alleviate river water quality degradation. Here, we coupled five water quality parameters (TOC, TN, NO3--N, NH4+-N, TP), collected from October 2020 to September 2021, to construct the river eutrophication index (EI) to assess river water quality. Meanwhile, based on redundancy analysis, patch-generating land use simulation model, and stepwise multiple linear regression model comprehensively analyze the Fengyu River watershed landscape patterns evolution and their impact on river eutrophication. Results indicated that current rivers reach eutrophic levels, and EI reaches 40.7. The landscape patterns explain 88.2 % of river eutrophication variation, while the LPI_Con metric is critical and individually explained 21.5 %. Furthermore, eutrophication in the watershed will increase in 2040 under the natural development (ND) scenario, and the EI will reach 44.4. In contrast, farmland protection (FP) scenarios and environmental protection (EP) scenarios contribute to mitigating eutrophication, the EI values are 38.2 and 38.1, respectively. The results provide a potential mechanistic explanation that river eutrophication is a consequence of unreasonable landscape pattern evolution. Guiding the landscape patterns evolution based on critical driver factors from a planning perspective is conducive to mitigating river water quality degradation.

Relocating built-up land for biodiversity conservation in an uncertain future.

Land use changes associated with habitat loss, fragmentation, and degradation exert profoundly detrimental impacts on biodiversity conservation. Urban development is one of the prevailing anthropogenic disturbances to wildlife habitat, because these developments are often considered permanent and irreversible. As a result, the potential benefits of built-up land relocation for biodiversity conservation have remained largely unexplored in environmental management practices. Here, we analyze recent built-up land relocation in Shanghai and explore how such restoration programs can affect future land change trajectories with regards to biodiversity conservation. Results show that 187.78 km2 built-up land in Shanghai was restored to natural habitat between 2017 and 2020. Further simulation analysis highlights that relocating built-up land can substantially promote conserve biodiversity. In particular, there would be less habitat loss, better natural habitat quality and more species habitat-suitable range under the scenarios with built-up land relocation. Species extinction assessment suggest that amphibians, mammals, and reptiles will all have an increasingly high extinction risk without built-up land relocation. However, there will even be a marginal decrease in extinction risk over time for mammals and reptiles if the relocation of built-up land is permitted, but still a moderate increase in extinction risk for amphibians. This study highlights the importance of incorporating rigorous conservation planning prior to development activities, thereby underpinning a sustainable approach to environmental management.