Future cropland loss from urban-rural construction land expansion in China

Land-use transition (LUT), a pivotal vector for anthropogenic intervention in the carbon cycle, profoundly influences the formation and evolution of regional carbon emission patterns. This study focuses on Hainan, China's sole tropical island, and establishes a model accounting for carbon emissions associated with LUT based on related remote-sensing data, socioeconomic statistics, and energy consumption-related data between 2000 and 2025. We combine spatial autocorrelation analysis, an extended logarithmic mean Divisia index decomposition model, and the Tapio decoupling model to systematically elucidate the spatiotemporal features of LUT-associated carbon emissions, their driving factors, and their decoupling relation with economic growth. Notably, Hainan Province features an LUT involving decreasing and increasing proportions of carbon-sink land and carbon-source land, respectively, with construction land expansion being the primary transition mode driving carbon emission growth. The associated carbon emission response features a spatial differentiation pattern of high values concentrated in the north and west and low values localized in the south and east. In addition, carbon sources and sinks demonstrate considerable spatial agglomeration. Economic output is the core driver promoting carbon emission growth, with improvements in land-use efficiency and energy intensity being critical for carbon emission mitigation. During the examined period, the correlation between LUT-associated carbon emissions and economic growth evolves from weak to strong decoupling, demonstrating the remarkable efficacy of peak carbon and carbon neutrality goals in guiding emission reduction-focused LUT. Overall, this research provides a scientific basis for coordinating LUT and low-carbon development in the Hainan Free Trade Port initiative.

As a critical ecological-economic nexus along China's Belt and Road Initiative, the Kashgar region exemplifies tensions between rapid socioeconomic development and ecological fragility in arid Central Asia. Landscape degradation monitoring in arid regions is severely constrained by data scarcity; most studies rely on 15-20year windows insufficient for detecting decadal-scale threshold behaviors. This study fills a critical research gap by integrating multi-method landscape analysis with a relatively long 43-year land use record (1980-2023). Our integrated analytical framework combines land use dynamic degree, intensity indices, transfer matrices, landscape fragmentation metrics, and spatial autocorrelation analysis, with all results validated through sensitivity analyses (parameterization robustness Spearman rs > 0.96; scale robustness rs > 0.93). We quantified unidirectional anthropogenic landscape reorganization: cultivated and construction land expanded 3,671.78 km2 and 973.41 km2 respectively, while natural landscapes (forest, grassland, water bodies) collectively declined 2,115.82 km2. Construction land displayed the highest transformation intensity (8.41% annual dynamic degree), with peak land use intensity coinciding with China's Western Development Strategy (2000-2010: ΔLa = 3.31, R = 2.14%). Quantitative landscape fragmentation escalated markedly: patch density increased 68% (0.056 → 0.095/km2), shape complexity increased 33% (LSI: 56.097 → 74.590), and spatial connectivity declined 3.25% (CONTAG: 66.870% → 64.698%). Transfer analysis demonstrated that construction land exhibited a persistent unidirectional inflow imbalance, while unused land showed a consistent unidirectional outflow imbalance; other land types underwent bidirectional transitions. Landscape ecological security exhibited distinctive three-phase dynamics (improvement → degradation → recovery) with strong persistent spatial autocorrelation (Moran's I: 0.78-0.81, p < 0.001), generating stable "high-high" clusters in oases and vulnerable "low-low" clusters in desert margins. Quantified attribution analysis (Grey Relational Analysis, Spearman correlations) revealed that GDP growth was the strongest driver of construction land expansion (GRG = 0.87), while population growth was the primary driver of cultivated land expansion (GRG = 0.85). Our preliminary observations suggest a potential monitoring reference zone (ΔLa ≈ 2.5-3.5 per decade) for early-warning systems; rigorous cross-regional validation is essential before establishing universal thresholds. Persistence of ecological degradation into 2010-2020 despite reduced land use change (LC: 1.20% → 0.15%) indicates hysteresis dynamics in arid systems. This 43-year dataset and validated analytical framework provide critical baseline data and evidence-based quantitative thresholds for early-warning systems and territorial spatial planning in ecologically fragile arid zones.

The rapid urbanization intensifies the conflict between land use and ecological protection. While existing studies acknowledge the role of ecological networks in mitigating human-land conflicts, most focus on one-way impact analyses and neglect the dynamic feedback mechanisms between land use change and ecological network evolution. This study develops a bidirectional feedback framework integrating ecological networks and land use simulation. By quantifying the ecological network's constraint effect, we adjust the development probabilities in land use simulations, update predicted land use patterns, and synchronously update the ecological network structure. Through iterative feedback, we identify core areas critical to regional ecological security. Results reveal a strong feedback mechanism between land use change and ecological network structure, with spatially differentiated mutual influences. The bidirectional feedback suppresses construction land expansion near ecological networks, redirecting development to distant areas. Applying the framework to Huanggang City, we identify 2257.55 km2 of key ecological source areas and 20.56 km2 of core ecological corridors as future ecological security core areas (FESCA). This study advances dynamic feedback analysis of ecological network-land use co-evolution, offering scientific insights for regional spatial planning and ecological conservation.

Dietary transition is reshaping cropland demand and intensifying the challenge of matching food demand with land supply in rapidly urbanizing regions. This study examines how different dietary structure scenarios generate differentiated cropland demand, how these demands match with land supply under alternative development pathways, and how the land system responds when diet-driven demand is incorporated into land-use simulation. Using Jiangsu Province, China, as a case study, we developed a coupled diet–land simulation framework. On the demand side, five dietary structure scenarios—current, balanced, U.S., Japanese, and Greek—were constructed based on seven food categories, and their cropland demand in 2035 and 2050 was estimated using the cropland footprint approach and LSTM forecasting. On the supply side, the GeoSOS-FLUS model was used to simulate future land-use patterns under four development scenarios: natural development, cultivated land protection, ecological protection, and economic development. The cropland demand associated with each dietary scenario was then introduced into the land-use simulation process as an external demand constraint to identify land-system feedbacks and scenario differences. The results show that cropland demand differs markedly across dietary scenarios, forming a clear gradient from moderate-demand to high-demand diets. These differences are driven primarily by changes in the composition of key food categories, especially grains, livestock and poultry meat, plant oils, and fruits, rather than by proportional increases across all foods. In terms of supply–demand matching, the cultivated land protection scenario provides the strongest support for high-demand diets, whereas the natural development, ecological protection, and economic development scenarios are more compatible with moderate-demand dietary pathways. Once diet-driven demand is incorporated into land-use simulation, the land system shows clear sensitivity and strong scenario dependence. High-demand dietary scenarios intensify cropland compensation pressure and trigger structural reallocation among cultivated land and flexible land types. Under natural development, the response is mainly reflected in cropland expansion and grassland compression; under cultivated land protection and ecological protection, it is expressed more through substitutions among grassland, water bodies, and unused land; under economic development, the most prominent feedback is the competitive reallocation among cultivated land, construction land, and water bodies, with high dietary demand even constraining construction land expansion. Overall, the robustness of cropland supply–demand matching depends not only on the scale of dietary demand but also on how different dietary pathways interact with development-oriented land-use structures.

The Linpan in western Sichuan is a composite rural landscape of “household-water-forest-field” on the Chengdu Plain. Under the interference of human activities, problems such as landscape fragmentation and ecological function degradation have become increasingly serious, threatening regional ecological security. The specific components involved in the “study on ecological risk sequence” include landscape disturbance degree, landscape vulnerability degree, landscape connectivity, and human activity intensity. Given the lack of long-term ecological risk research on the Linpan landscape in Chongzhou City to support conservation decisions, this study takes it as the object. Based on five phases of land use data from 2003 to 2023, a landscape ecological risk assessment model was constructed. This model is a deterministic and nonlinear comprehensive evaluation model. The determinism is reflected in the fact that, based on specific influencing factors, a unique and definite result can be obtained through a fixed indicator system and calculation method. The nonlinearity is reflected in the fact that the comprehensive risk index does not involve a simple linear superposition of the various factors; instead, the evaluation result is obtained by integrating the factors through nonlinear approaches such as weighted coupling. Using ArcGIS and spatial analysis methods, based on a temporal resolution of 5 years and a spatial resolution of 30 m, the spatiotemporal evolution characteristics were revealed. The results show that: (1) From 2003 to 2023, the Linpan landscape pattern in Chongzhou City underwent significant evolution, characterized by “reduction in agricultural land, expansion of construction land, and slight recovery of ecological land”. Landscape fragmentation intensified, connectivity decreased, but overall aggregation remained stable. (2) The evolution of the landscape pattern drove the ecological risk to show a stable pattern of “low in the northwest and high in the southeast”. The global Moran’s I value decreased from 0.887 to 0.832, indicating that risk aggregation intensified in the early period and was alleviated in the later period. (3) Landscape disturbance degree is the key factor dominating the change in the comprehensive ecological risk index. Compared with similar studies, this research shares the commonality of urbanization-driven fragmentation exacerbation risk, but also exhibits the uniqueness of Linpan structural resilience and conservation policies promoting a reduction in high-risk areas. This study can provide a scientific basis for Linpan protection, land use optimization, and ecological security pattern construction in Chongzhou City.

Fine-scale accounting of land use carbon budgets and identification of their driving factors provides an essential scientific basis for constructing green and low-carbon territorial spatial systems. This is of great significance for optimizing territorial spatial structure and promoting low-carbon development in urban agglomerations. Taking the Central Guizhou Urban Agglomeration as the study area, this study employed a composite carbon coefficient method to construct a 30 m × 30 m grid-based carbon budget index and quantitatively assessed carbon budget changes induced by land use transitions from 2000 to 2024. POI data and a quantile regression model were further integrated to analyze the dominant spatial characteristics associated with carbon budgets, and a carbon budget monitoring and early-warning index was developed to delineate risk zones. The results show that: (1) From 2000 to 2024, the total area of land use change reached 0.95 × 104 km2 in the Central Guizhou Urban Agglomeration, accounting for 17.68% of the total land area, and leading to a net increase of 2.3821 million tons of carbon emissions. This increase was primarily associated with the conversion of cultivated land to construction land, with an accelerated growth rate observed in the later period. (2) The spatial patterns of carbon budgets and carbon emission risk levels exhibit a distinct “core–periphery” structure, with high carbon emission levels concentrated in built-up urban areas and lower levels observed in peripheral ecological land. (3) The expansion of construction land is the dominant contributor to the increase in net carbon emissions; industrial, transportation, and residential spaces exert significant positive driving effects, whereas commercial and service spaces show a negative association. (4) Carbon budget risk zoning based on dominant spatial characteristics identifies Guiyang and Anshun as extremely high-risk areas. The results further suggest that reducing carbon-increment spaces and increasing carbon-reduction spaces may play an important role in territorial carbon budget optimization. The integrated “accounting–driving–monitoring” analytical framework established in this study provides a scientific basis for territorial spatial optimization and carbon emission reduction in mountainous urban agglomerations.

Geographical-XGBoost and SHAP reveal ecosystem service supply-demand responses to landscape patterns in karst regions.

Analysis of carbon storage response characteristics and driving mechanisms to land use change in mountainous and hilly regions

Retraction notice to “Impact of rapid urban construction land expansion on spatial inequalities of ecosystem health in China: Evidence from national, economic regional, and urban agglomeration perspectives” [Ecol. Indic. 172 (2025) 113196

Resource-based cities face unique land use challenges due to resource dependence and path lock-in, yet the driving mechanisms and future trajectories of their land use transitions remain underexplored. This study examines the Huaihai Economic Zone (HEZ), a representative coal-rich region in eastern China, to analyze land use changes from 2000 to 2023 and simulate 2036 scenarios under different development pathways. Using land use transfer matrices, dynamic degree metrics, and the Patch-generating Land Use Simulation (PLUS) model, we systematically identified spatiotemporal evolution patterns, quantified the contributions of driving factors, and projected multi-scenario future land use patterns. Results reveal that land use change in the study area was dominated by the conversion of cultivated land to construction land, alongside spatial restructuring from a monocentric to a polycentric network pattern. Notably, construction land expansion was least evident in the central Mining-Affected Zone, where land use changes remained relatively sluggish compared to other sub-regions. Driving factor analysis indicates that socio-economic factors primarily influenced changes in construction and cultivated land, while natural factors strongly affected ecological land and unused land. Multi-scenario simulations for 2036 demonstrate diverging trajectories: an urban development scenario would accelerate cultivated land loss and unused land expansion; a natural development scenario would maintain current pressures; and an ecological protection scenario would effectively curb urban sprawl while actively promoting ecological land recovery. This study concludes that transcending simple land use control to actively orchestrate “mining-urban-rural-ecological” spatial synergy is critical for achieving a sustainable transition in resource-based regions facing similar transformation pressures.

ABSTRACT Amid ongoing industrial upgrading and the integration of Production–Living–Ecological Space (PLES) in coastal economic and technological development zones (ETDZs), investigating the spatiotemporal evolution patterns and driving factors of PLES is essential for achieving spatial optimization. To support this research objective, coastal ETDZs require a more refined PLES classification system and identification approach. However, existing research rarely addresses these aspects. This study considered the Weifang Coastal ETDZ as a case study. A novel PLES classification framework tailored to ETDZs was developed based on traditional PLES and industrial classification theory. Subsequently, a time-series PLES recognition method was constructed based on Self-Organizing Map neural network and k-means algorithm. The evolutionary characteristics of time-series PLES were analysed using change trajectory reclassification, and the influencing factors were examined through a spatial Multinominal logit regression method. The results indicate that from 2005 to 2020, Technology-Intensive Production Space, Ecology–Living Integration Space, Capital-Intensive Production Space, and Ecological Space increased significantly, primarily transformed from Resource-Intensive Production Space. The Living Expansion Type (LET), Ecological Expansion Type (EET), and Industrial Upgrading Type (IUT) exhibited pronounced spatial agglomeration characteristics. The construction land expansion types (LET and IUT) were generally distributed in areas characterized by convenient transportation access and poor soil quality. EET was primarily distributed in areas with favourable ecological conditions, such as riverbanks and coastal zones. These results demonstrate the effectiveness of the proposed PLES recognition method and provide a scientific reference for optimizing the functional spatial layout of ETDZs in the future.

Land-use and land-cover change (LULCC) is one of the key drivers altering terrestrial ecosystem carbon storage. Accurate simulation of LULCC is crucial for assessing ecosystem sustainability and formulating global climate change mitigation strategies. Within this context, this study proposes a novel deep learning model integrating U-Net architecture, an attention mechanism, and ConvLSTM-termed U-Net-Attention-ConvLSTM (UNA-CL), to enhance the accuracy of LULCC simulation. The model's effectiveness was validated using land use and land cover (LULC) data from Kunming (2000-2020) and compared with a widely applied convolutional neural network (CNN) model (CNNA-CL) and Random Forest-Cellular Automata (RF-CA) model. Furthermore, this study coupled the UNA-CL model with the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, which is designed for ecosystem service assessment, to jointly reveal the spatiotemporal evolution characteristics of future LULC patterns and carbon storage. The results indicate that (1) the UNA-CL model outperformed the comparative models in classification accuracy, achieving an overall accuracy (OA) of 94.36%, which is 5.72% and 0.5% higher than the CNNA-CL and RF-CA models, respectively; (2) in terms of spatial allocation accuracy, the UNA-CL model not only accurately simulated land cover categories with complex distribution patterns but also mitigated the simulation bias caused by spatial heterogeneity in the RF-CA model; (3) from 2000 to 2030, the net increase in carbon storage was 3.74 Mega tons (Mt), exhibiting a trend of increase followed by decrease. Specifically, the conversion of grassland and cultivated land to forest land led to an accumulation of 3.81 Mt during 2000-2020. However, from 2020 to 2030, a combination of forest land loss and continued construction land expansion resulted in a net decrease of 0.07 Mt.

Guaranteeing the ecological security of the Dongting Lake Basin is of paramount importance for national-scale programs, such as the Yangtze River Economic Belt and aquatic conservation projects. Within this framework, carbon storage and its determining drivers act as essential indicators of regional ecological stability. However, the historical trajectory of carbon pools and their response to future multi-scenario land-use transitions remain insufficiently understood. Therefore, this study aims to quantify the spatiotemporal evolution of carbon storage in the Dongting Lake Basin from 2000 to 2020 and project its future dynamics under diverse development pathways. This study, utilizing land use data from 2000 to 2020 and the carbon density database of the Dongting Lake Basin, assessed land use changes over two decades and determined the spatiotemporal distribution of carbon storage. Additionally, using 17 driving factors and various spatial policies, the study projected the land use and land cover changes (LUCC) for 2030 under four scenarios: natural development, ecological protection, economic development, and planned development. The spatiotemporal distribution of carbon storage and its response mechanisms were analyzed for each scenario. The results showed that carbon storage was directly impacted by LUCC, with an overall “decrease-increase-decrease” trend from 2000 to 2020, resulting in a net increase of 3.685 × 106 t. By 2030, the changes in carbon storage under the natural development, ecological protection scenario, economic development, and planned development scenarios were projected to be −1.008 × 107 t, 1.276 × 107 t, 3.292 × 108 t, and −1.200 × 105 t, respectively. Notably, the ecological protection scenario showed a significant positive growth in carbon storage, primarily driven by an increase in forest and wetland areas. Additionally, the spatial distribution of carbon storage exhibited a pattern of “high in the west and low in the east”. These results imply that to achieve the “Dual Carbon Strategy”, future land use planning in the Dongting Lake Basin should prioritize ecological protection and planned development models, including strict control of construction land expansion, increasing ecological land area, and enhancing carbon storage.

In the process of rapid urbanization, high-speed rail has become pivotal in shaping urban land-use patterns by facilitating factor mobility. This research examines the spatial–temporal dynamics of high-speed rail accessibility and urban construction land across 200 counties in the Beijing–Tianjin–Hebei region. Utilizing fixed panel models and geographical detectors, the study delineates the effects and the underlying drivers of high-speed rail on county-level construction land expansion. Key findings include (1) high-speed rail significantly enhances transportation accessibility between urban nodes across counties, evident in a strip-like expansion pattern along rail corridors. (2) Urban construction land initially increases and then decreases, displaying strong spatial clustering with expansion hotspots stretching southeast along the Beijing–Tianjin axis, increasingly concentrating and widening within Tianjin. Spatial variation in construction land expands, yet spatial correlations intensify, particularly in the southeast–northwest direction. (3) High-speed rail development correlates closely with the dynamics of county-level urban construction land, contributing to a 23.5% increase in such land, with pronounced effects in municipal districts and lesser in county-level cities and counties. A cubic relationship between high-speed rail factors and construction land area is identified, with population and gross domestic product interactions indicating a nonlinear amplification of effects. This study offers new insights into the interplay between railway transportation and land use, aiding in understanding the high-speed rail’s role in urban land expansion.

In China, ports have long served as a key engine of growth for coastal cities. Increases in coastal port throughput inevitably lead to port spatial expansion, which in turn drives construction land expansion in port cities. Consequently, ports are a critical factor shaping construction land expansion in coastal cities, with direct implications for spatial planning and sustainable development in coastal port cities. Therefore, it is necessary to examine how ports influence construction land expansion in coastal cities. This paper using multiple linear regression and binary logistic regression models and incorporating landscape metrics explores the impacts of ports on the expansion of urban construction land in coastal port cities. The findings reveal distinct characteristics of land expansion in port cities compared to non-port cities: (1) Macro-level changes: The expansion of construction land is driven by industrial restructuring, real estate development, port cargo traffic, population growth, and GDP growth. Industrial restructuring is the primary driver, while real estate development plays a significant role in land expansion. Port cargo demand serves as a unique driving factor compared to non-port cities, whereas population and GDP growth have minimal effects. (2) Micro-level spatial expansion: Land expansion is influenced by proximity to port shorelines, transportation infrastructure, and the degree of base construction land expansion. Expansion tends to concentrate along the port shoreline, transport hubs, and established urban areas. Elevation and slope are significant factors for coastal port cities, while rivers and proximity to core urban areas predominantly impact estuarine port cities. (3) Temporal patterns of expansion: Port development follows a phased pattern of land expansion: “Decline → Increase → Decline”. Ports also influence landscape patterns, with increased distance from the port shoreline leading to decreased patch density and higher landscape fragmentation. The results of this paper help to address gaps in existing research on how ports shape the spatial expansion of coastal cities. Furthermore, this paper provides insights for effective land use strategies, spatial planning, and port-city management, promoting coordinated land and marine development. It offers a foundation for addressing the integration of land and sea spatial planning in the “One Map” initiative.

Mining-resource-based cities, as distinctive human–environment systems, face urgent challenges from intensified urbanization and mining, leading to land imbalance and ecosystem service degradation. To enhance resilience, it is essential to identify the evolution and drivers of ecosystem services and construct targeted ecological compensation models. This study focuses on Xinzhou, a representative mining city in China, and systematically analyzes three aspects: (1) spatiotemporal dynamics of land use and ecosystem service value (ESV) from 2000 to 2023 using Markov chains, equivalent factor method, hotspot and sensitivity analyses; (2) identification of ESV driving mechanisms through an integrated “stepwise regression + geographical detector” framework; and (3) formulation of ecological compensation models via quantification of priority indices, demand intensity coefficients, and compensation standards. Key findings indicate that land conversion was concentrated in coalfield zones and surrounding built-up areas, involving 2,518,341.75 hm2 (35.76% of total area), primarily characterized by a reduction in farmland and expansion of forest, grassland, and construction land. ESV showed a striped spatial pattern, with higher values in mountainous zones and lower values in valleys and basins with frequent human activity. The northwest coalfield region experienced an initial decline followed by a recovery in ESV. Annual mean temperature emerged as the dominant driver, while DEM influence increased annually. All factor interactions exhibited synergistic effects, with natural variables exerting greater influence than socio-economic ones. Ecological compensation demand was high overall, especially in Wutai, Kelan, and Pianguan counties, with high-value compensation areas mainly distributed in the eastern and central parts of Xinzhou. Looking ahead, a compensation framework prioritizing ecological–economic optimization should be developed, guided by zoned, typological, and dynamic configurations. By analyzing ecosystem governance from the perspective of a mining-resource-based city, this study enhances global ecosystem service evaluation frameworks and offers a replicable model to advance transnational ecological cooperation and green urban transformation.

The impact of land use change on habitat quality is of great significance for advancing regional ecological civilization construction. In this study， Jiangxi Province， a national ecological civilization pilot zone， was selected as the study area. Land use data from 2000， 2010， and 2020 were used to analyze the characteristics of land use changes. Three representative scenarios from CMIP6， namely SSP119， SSP245， and SSP585， were adopted to simulate the patterns of land use change in 2040 and 2060 using the SD-PLUS model. Subsequently， the variations of habitat quality were assessed using the InVEST model. The results showed that： ① Between 2000 and 2020， land use change in Jiangxi Province exhibited a pattern of "rapid at first， then slowing down，" with land conversion rate in the first decade being 4.67 times that in the second decade. The primary transitions included mutual conversions among paddy field， dryland， forested land， and sparse forest land， as well as the transfer to urban land and other construction land. ② During this period， the mean habitat quality decreased by 3.57%， with medium， good， and excellent quality levels predominating. In contrast， poor and relatively poor levels were primarily concentrated in urban built-up areas and exhibited a "large-character" diffusion pattern along major transportation corridors. ③ Future scenarios indicated a continued reduction in cultivated land and an expansion of construction land. Under SSP119， forest land was significantly increased， resulting in the highest habitat quality. Under SSP245， a more balanced land-use pattern was observed， with moderate habitat quality. Under SSP585， the most pronounced expansion of construction land occurred， leading to the lowest habitat quality. ④ The improvement in habitat quality was primarily driven by the increase in forested land and sparse forest land. Conversely， the expansion of construction land and the conversion of forest land to cultivated land were the main factors contributing to the decline in habitat quality. These findings provide a scientific basis for ecological governance and sustainable development in Jiangxi Province.

Understanding the spatiotemporal impacts of land use transition on carbon emissions is crucial for achieving regional carbon neutrality. This study presents an integrated analytical framework that combines dynamic land use modeling, the Geo-detector method (GDM), and Geographically and Temporally Weighted Regression (GTWR) to analyze land use transition and carbon emission dynamics in China's Pearl River Delta (PRD) from 2000 to 2020. Key findings include: (1) Construction land expansion was the dominant explicit transition, with land conversion sources shifting from cropland-centric patterns to diverse transfers involving woodland and water bodies. (2) The implicit land use transition index exhibited an annual growth rate of 15.6%, progressing through three phases-rapid development (2000-2010), structural adjustment (2010-2015), and high-quality transition (2015-2020). (3) Regional carbon emissions increased by 186.96%, exhibiting spatial disparities between core and peripheral regions. Construction land expansion and GDP density were primary drivers. This research advances the theoretical integration of land system science and low-carbon governance, offering actionable insights for spatially differentiated emission reduction strategies in megacity clusters.

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.