China has achieved the poverty reduction goal of the United Nations 2030 Agenda for Sustainable Development 10 years ahead of schedule, contributing significantly to global poverty reduction. Despite extended efforts in poverty elimination, there is a lack of quantitative studies categorizing and comparing poverty-elimination counties (PECs) based on their processes. This study proposes an innovative framework for analyzing PECs’ development paths from the perspective of population-land-industry (PLI). We quantify the PLI matching degree of PECs in China during the critical phase of the battle against poverty through a multivariate matching model, classify PECs via K-means clustering according to the consistency in PLI matching degree evolution, and summarize the typical development patterns of PECs. Results indicate that the PLI matching degree of PECs in China increased substantially from 2015 to 2020, particularly in eastern areas, while the western region, including the Qinghai-Xizang Plateau and southwestern Xinjiang, shows untapped potential for improvement. Five types of PECs are identified, with the majority (30.1 %) showing sustained moderate PLI matching and a minority (9.6 %) experiencing long-term PLI mismatch. Industry is the shortfall of various PECs, and effective strategies to facilitate all types of PECs include the development of emerging businesses and the expansion of secondary and tertiary industries. Additionally, enriching rural labor force and increasing farmland use efficiency are essential for optimal PLI matching and positive interaction, ultimately ensuring poverty elimination and sustainable development.

The relationship between ecosystem services (ES) and human well-being (HWB) is fundamental to the science and practice of sustainability. However, studies have shown conflicting results, which has been attributed to the influences of indicators, contexts, and scales. Yet, another potential factor, which has been overlooked, may be the mixed use of spatial and temporal approaches. Using twelve ES and seven well-being indicators and multiple statistical methods, we quantified and compared the spatial and temporal ES–HWB relationships for Inner Mongolia, China. The spatial and temporal relationships differed in both correlation direction and strength. Most relationships of economic and employment-related indicators with food provisioning and supporting services were temporally positive but spatially nonsignificant or negative. Some relationships of economic and employment-related indicators with water retention, sandstorm prevention, and wind erosion were temporally negative but spatially complex. However, the spatial and temporal ES–HWB relationships could also be similar in some cases. We conclude that although both the spatial and temporal approaches have merits, space generally cannot substitute for time in the study of ES–HWB relationship. Our study helps reconcile the seemingly conflicting findings in the literature, and suggests that future studies should explicitly distinguish between the spatial and temporal ES–HWB relationships.

In a warming world, climate extremes tend to be more frequent and intense. The exceptional response of ecosystems triggered by extreme climate events under a warmer and wetter climate in northwest China (NWC) has aroused growing concern. However, understanding the responses of vegetation to climate extremes from the compound events perspective remains challenging. In this study, we identify the climate dynamics in NWC during 1971–2020 based on daily meteorological observations, focusing on the changes in compound hot-dry events (CHDEs) during the warmer and wetter period. We further explore the effects of CHDEs on vegetation by examining vegetation anomalies and recovery time using daily gross primary productivity (GPP) data. The results show a clear warmer and wetter period in NWC during 2000–2020. No signs of a hiatus in CHDEs increase are observed during this period, and even the duration of CHDEs in western NWC keeps showing an increasing tendency. Vegetation in eastern NWC, with a lower probability of GPP anomalies, exhibits stronger resistance of ecosystems to CHDEs than in western NWC. In NWC, vegetation typically returns to its normal state in 5.50 days on average, but exhibits greater resilience in the western region, where it takes less recovery time (4.82 days). Vegetation in the central region shows the lowest probability of GPP anomalies and relatively longer recovery time, likely due to its higher altitudes. Our research underscores the imperative to address the considerable impacts of CHDEs on vegetation growth even as the regional climate becomes increasingly warmer and wetter.

By introducing virtual water (VW) flow correlation coefficients and risk indicators, this study examines the VW transmission relationship between urban agglomerations and cities in the Yellow River Basin (YRB) and its impact on regional water resources pressure. The results show that: except for the Shandong Peninsula Urban Agglomeration (SPUA) and Central Plains Urban Agglomeration (CPUA), the other urban agglomerations primarily act as VW exporting regions, while virtual water-importing cities are concentrated in the eastern regions. Notably, the Ningxia Urban Agglomeration (NUA) demonstrates significantly higher VW impact and sensitivity coefficients values than the remaining six urban agglomerations. First-tier cities generally display lower virtual water impact and sensitivity coefficients, whereas emerging cities exhibit the opposite trend. Additionally, we observe uneven risk variations between VW importing and exporting regions. Taking NUA as an example, the risk increase resulting from VW exports significantly exceeds the risk reduction associated with VW imports in the corresponding regions. It’s important to highlight that first-tier cities consistently decrease water resource risk through VW imports in the study years. However, among second and third-tier cities, only 38.9 % experience reduced water resource risk through VW imports. Therefore, we recommend a focused examination of VW imports and exports in western region urban agglomerations, cities, and second and third-tier cities within the watershed. Leveraging virtual water’s asymmetric and high-value transfer can alleviate water resource pressure and scarcity risks in high-pressure regions by shifting them to lower-pressure regions, thus mitigating water resource stress across regions.

On-time mapping dynamics of crop area, yield, and production is important for global food security. Such information, however, is often not available. Here, we used satellite information, a spectral-phenology integration approach for mapping crop area, and a machine learning model for predicting yield in the war-stricken Ukraine. We found that in Ukraine crop area and production declined in 2022 relative to 2017–2021 and 2021 for winter-triticeae crops, which was invaded before the cropping season in February of that year. At the same time, crop area and production for rapeseed increased in Ukraine, with yields consistently lower by 6.5 % relative to 2021. The low precipitation and the Russian-Ukrainian conflict-related factors contributed to such yield variations by -1.3 % and -0.9 % for winter-triticeae crops and -4.2 % and -0.5 % for rapeseed in 2022. We demonstrate a robust framework for monitoring country-wide crop production dynamics in near real-time, serving as an early-food-security-warning system.

Understanding vegetation water availability can be important for managing vegetation and combating climate change. Changes in vegetation water availability throughout China remains poorly understood, especially at a high spatial resolution. Standardized Precipitation Evapotranspiration Index (SPEI) is an ideal water availability index for assessing the spatiotemporal characteristics of drought and investigating the vegetation-water availability relationship. However, no high-resolution and long-term SPEI datasets over China are available. To fill this gap, we developed a new model based on machine learning to obtain high-resolution (1 km) SPEI data by combining climate variables with topographical and geographical features. Here, we analyzed the long-term drought over the past century (1901–2020) and vegetation-water availability relationship in the past two decades (2000–2020). The century-long drought trend analyses indicated an overall drying trend across China with increasing drought frequency, duration, and severity during the past century. We found that drought events in 1901–1961 showed a larger increase than that in 1961–2020, with the Qinghai-Xizang Plateau showing a significant drying trend during 1901–1960 but a wetting trend during 1961–2020. There were 13.90 % and 28.21 % of vegetation in China showing water deficit and water surplus respectively during 2000–2020. The water deficit area significantly shrank from 2000 to 2020 across China, which is dominated by the significant decrease in water deficit areas in South China. Among temperature, precipitation, and vegetation abundance, temperature is the most important factor for the vegetation-water availability dynamics in China over the past two decades, with high temperature contributing to water deficit. Our findings are important for water and vegetation management under a warming climate.

Grassland ecosystems are experiencing severe deterioration due to ongoing climate fluctuation and human disturbance. Although numerous research centers on the patterns, processes, and functioning of degraded grassland, there is still a lack of standards for defining and assessing degraded grassland, which restricts the cognition of the degraded grassland mechanisms and restoration practices. Therefore, we review current grassland degradation research for the sake of the common definitions and assessment methods worldwide. Grassland degradation definitions are divided into three stages, including biotic/abiotic factors, ecosystem functions, and ecosystem services/sustainability, and further combine the concept of “nature’s contributions to people” with the definition of grassland degradation. Moreover, grassland degradation assessment methods and indicators are diverse across scales. Additionally, we systematically explore the climate change and social system factors that affect grassland degradation, and reveal that grassland management policies play an essential role in grassland degradation and restoration. Overall, this review advances our understanding of grassland degradation and calls for a unified and effective global definition and assessment criteria, which will contribute to the sustainable management of the grassland ecosystem.

Assessing regional economic development is key for advancing towards the Sustainable Development Goals and ensuring sustainable societal progress. Traditional evaluation methods focus on basic economic metrics like population and capital, which may not fully reflect the complexities of economic activities. Nighttime light (NTL) has been validated as an alternative indicator for regional economic development, yet limitations persist in its evaluation. This study integrates OpenStreetMap (OSM) data and NTL data, providing a novel data integration approach for evaluating economic development. The study uses mainland of China as a case, applying ordinary least squares (OLS) and geographically weighted regression (GWR) to evaluate OSM and NTL data across provincial, municipal, and county levels. It compares OSM, NTL, and their combined use, providing key empirical insights for enhancing data fusion models. The study results reveal: (1) NTL data is more accurate for provincial-level economic activity, while OSM data excels at the county level. (2) GWR demonstrates superior capability over OLS in revealing the spatial dynamics of economic development across scales. (3) Through the integration of both datasets, it is observed that, compared to single-data modeling, the performance is enhanced at the city scale and county scale. The study demonstrates that combining OSM and NTL data effectively assesses economic development in both developed and underdeveloped areas at provincial, municipal, and county levels. The study offers a straightforward and efficient approach to data integration. The findings offer new research perspectives and scientific support for sustainable regional economic growth, particularly valuable in data-scarce, underdeveloped areas.

Central Asia (CA) faces escalating threats from increasing temperature, glacier retreat, biodiversity loss, unsustainable water use, terminal lake shrinkage, and soil salinization, all of which challenge the balance between ecological integrity and socio-economic development essential for achieving Sustainable Development Goals. However, a comprehensive understanding of priority areas from a multi-dimensional perspective is lacking, hindering effective conservation and development strategies. To address this, we developed a comprehensive assessment framework with a tailored indicator system, enabling a spatial evaluation of CA’s priority areas by integrating biodiversity, ecosystem services (ESs), and human activities. Combining zonation and geographical detectors, this approach facilitates spatial prioritization and examines ecological and socio-economic heterogeneity. Our findings reveal a heterogeneous distribution of priority areas across CA, with significant concentrations in eastern mountainous regions, river valleys, and oasis agricultural lands. We identified 184 key districts crucial for ecological and societal sustainability. Attribution analysis shows that natural factors like soil types, precipitation, and evapotranspiration significantly shape these areas, influencing human activities and the distribution of biodiversity and ESs. Multi-dimensional analysis indicates existing protected areas cover only 15 % of the top 30 % priority areas, revealing substantial conservation gaps. Additionally, a 38 % overlap between ESs and human activities, along with 63.25 % congruence in integrated areas, underscores significant human impacts on ecological systems and their dependency on ESs. Given CA’s limited resources, it is crucial to implement measures that strengthen conservation efforts, align ecological preservation with socio-economic demands, and enhance resource efficiency through sustainable integrated land and water resource management.

The Sustainable Development Goals (SDGs) are crucial in tackling the sustainability challenges and emerging issues faced by humanity, with government attention being a significant factor in promoting their successful achievement. However, there is limited quantitative research systematically examining the impacts of government attention on SDGs progress. This study employs text analysis and a panel regression model to analyze the impacts of government attention intensity, text similarity, and tone on the achievement of SDGs, utilizing data extracted from China’s Government Work Reports spanning the decade from 2010 to 2020. The findings reveal that the Chinese government attention to the SDGs has generally increased over time. The heightened focus has notably bolstered the achievement of the SDGs, with the most significant impact observed post-2015. Government attention intensity was identified as the most impactful factor. Moreover, government attention intensity, text similarity, and tone have positively influenced the coupling coordination relationship between 17 SDGs, as measured by the coupling coordination degree, leading to a more harmonious and balanced achievement of socioeconomic and environmental goals in China. Financial investment served as a moderating factor, enhancing the positive impacts of attention intensity, text similarity and tone on the promotion of SDGs attainment. The effects of government attention on SDGs progress were notably positive in the eastern region, exhibiting greater significance in areas with stronger governance capacity compared to those with weaker governance capacity. This study provides insightful information for enhancing the modernization and efficiency of China’s national governance system, promoting SDGs at local and global scales, and fostering sustainable transformation.

Enhancing the spatio-temporal connectivity of dynamic landscapes is crucial for species to adapt to climate change. However, the spatio-temporal connectivity network approach considering climate change and species movement is often overlooked. Taking Tibetan wild ass on the Qinghai-Xizang Plateau as an example, we simulated species distribution under current (2019) and future scenarios (2100), constructed spatio-temporal connectivity networks, and assessed the spatio-temporal connectivity. The results show that under the current, SSP2–4.5 and SSP3–7.0 scenarios, suitable habitats for the Tibetan wild ass account for 21.11 %, 21.34 %, and 20.95 % of the total area, respectively, with increased fragmentation projected by 2100. 78.35 % of the habitats which are predicted to be suitable under current conditions will remain suitable in the future, which can be regarded as stable climate refuges. With the increase in future emission intensity, the percentage of auxiliary connectivity corridors increases from 27.65 % to 33.57 %. This indicates that more patches will function as temporary refuges and the auxiliary connectivity corridors will gradually weaken the dominance of direct connectivity corridors. Under different SSP-RCP scenarios, the internal spatio-temporal connectivity is always higher than direct connectivity and auxiliary connectivity, accounting for 42 %–43 %. Compared with the spatio-temporal perspective, the purely spatial perspective overestimates network connectivity by about 28 % considering all current and future patches, and underestimates network connectivity by 16 %–21 % when only considering all current or future patches. In this study, a new approach of spatio-temporal connectivity network is proposed to bridge climate refuges, which contributes to the long-term effectiveness of conservation networks for species’ adaptation to climate change.

Measuring the lifecycle of low-carbon energy technologies is critical to better understanding the innovation pattern. However, previous studies on lifecycle either focus on technical details or just provide a general overview, due to the lack of connection with innovation theories. This article attempts to fill this gap by analyzing the lifecycle from a combinatorial innovation perspective, based on patent data of ten low-carbon energy technologies in China from 1999 to 2018. The problem of estimating lifecycle stages can be transformed into analyzing the rise and fall of knowledge combinations. By building the international patent classification (IPC) co-occurrence matrix, this paper demonstrates the lifecycle evolution of technologies and develops an efficient quantitative index to define lifecycle stages. The mathematical measurement can effectively reflect the evolutionary pattern of technologies. Additionally, this article relates the macro evolution of lifecycle to the micro dynamic mechanism of technology paradigms. The sign of technology maturity is that new inventions tend to follow the patterns established by prior ones. Following this logic, this paper identifies different trends of paradigms in each technology field and analyze their transition. Furthermore, catching-up literature shows that drastic transformation of technology paradigms may open “windows of opportunity” for laggard regions. From the results of this paper, it is clear to see that latecomers can catch up with pioneers especially when there is a radical change in paradigms. Therefore, it is important for policy makers to capture such opportunities during the technology lifecycle and coordinate regional innovation resources.

Human-wildlife conflict (HWC) and its socioeconomic impacts are a pressing global issue. Accurately quantifying HWCs and their interaction with residential development is crucial for rural revitalization and biodiversity conservation efforts. This study investigates the interplay between rural residential expansion (RRE) with human-elephant conflict (HEC) in southern Yunnan Province using high-accuracy yearly land use/land cover data and Asian elephant accident data. A piecewise regression along with several metrics, including expansion intensity, rate of rural residential land, and residential density, were employed to analyze the spatial-temporal change characteristics of RRE. Then, a geographical detector and a bivariate spatial autocorrelation model were used to reveal the driving mechanisms of RRE, with particular emphasis on the spatial relations between RRE and HECs. The results indicate that HECs had a significant negative impact on RRE, exhibiting higher expansion intensity and rate of rural residential land in non-HEC areas than in HEC areas. High spatiotemporal consistency between accelerated RRE and intensified HECs occurred from 2010 onwards, which aligns with the year when the trend of settlement area expansion changed. RRE activities and ensuing land use conversions led to increased occurrences of HECs, which negatively affected the RRE. Compared to HECs, topography and locational factors exhibited a secondary effect on RRE activities. The findings underscore reciprocal feedback mechanisms between RRE and HECs and the elevated risk of adverse interactions between humans and elephants within the range of China’s wild elephants, providing theoretical support for coordinating conservation initiatives for Asian elephants with rural revitalization in the border areas of Southwest China.

Sustainable Development Goal 2 (SDG 2, zero hunger) highlights that global hunger and food insecurity have worsened since 2015, driven in part by growing imbalance. Addressing the challenge of achieving SDG 2 in the face of rapid global population growth requires sustained attention to global and national cropland changes. Accurately quantifying the correlation between population and cropland area (i.e., SDG 2.4.1 per capita cropland) and analyzing the trends of global cropland imbalance are essential for a comprehensive understanding of SDG 2. In this study, we utilized a new global 30 m land-cover dynamic dataset (GLC_FCS30D) to analyze cropland dynamics, quantify per capita cropland and its changes across various countries and levels of development. Our results indicate that the global cropland area expanded by 0.944 million km² from 1985 to 2022, with an average expansion rate of 2.42 × 10⁴ km²/yr. However, the global per capita cropland area decreased from 0.347 ha in 1985 to 0.217 ha in 2022, mainly due to a higher population increase of nearly 65 % in the same period. In the context of globalization, cropland expansion and per capita cropland exhibited spatial imbalances globally, particularly in developing countries. Developing countries saw an increase in total cropland area by 7.09 % but a significant decrease in per capita cropland area by 37.38 %. From a temporal perspective, the global imbalance has been steadily increasing with the Gini index rising from 0.895 in 1985 to 0.909 in 2022. Consequently, this study reveals an increasing imbalance of global per capita cropland across various countries, which threatens the attainment of the targets of SDG 2.

China’s commitment to carbon neutrality by 2060 has made decarbonization a key principle for spatial planning (also referred to as urban/city/town planning). Although the mitigation effect of spatial planning in urban areas has been well documented, its significance in rural development has yet to be investigated. This paper addresses this research gap by empirically examining the influence of town planning on rural direct residential CO₂ emissions (DRCEs) across 30 provinces in China. Based on various quantitative models, this study not only confirms the significant impact of town planning on rural DRCEs and the moderation effect of plan implementation capacity but also discloses that different dimensions of town planning have disparate roles in rural DRCE reduction. Additionally, regional variations in the mitigation effects of town planning on rural DRCEs were observed. The study also reveals spatial spillover effects, indicating that the influence of town planning on rural DRCEs extends beyond individual areas. Overall, China’s experiences demonstrate that well-managed town planning could play an essential role in low-carbon rural revitalization or, otherwise, it may augment rural DRCEs per capita. Consequently, governments should ascribe great importance to low-carbon town planning and allocate sufficient resources to towns, especially those in the central and western regions, so that they can afford professional planning consultation and adequate staffing in plan implementation. Moreover, governments should cooperate to promote knowledge sharing and transferring of low-carbon planning.

The abandonment of rural activities in the Mediterranean mid-mountains has led to the activation of revegetation processes, as well as the subsequent implementation of various management measures to mitigate the associated ecosystem disservices. Focusing on soil environment and its growing importance in a climate change scenario, it is of great interest to study how land management and landscape changes can affect, not only the soil carbon storage process, but also its dynamics. A study was conducted in La Rioja (Iberian System, Spain), comparing three post-abandonment management strategies: secondary succession, forest management, and shrub clearing and extensive grazing. These strategies were analysed in two types of soil environments (acid and alkaline) and for two depth ranges (0–20 cm and 20–40 cm). Laboratory analyses were performed on aggregate stability and soil organic carbon fractionation with regard to three aggregate sizes (< 2 mm, 2–5 mm, > 5 mm) and three density fractions (free labile, occluded, and heavy fraction). The results showed that: 1) SOC content in aggregates < 2 mm (relative to total SOC) increases with shrub clearing and grazing strategy in acid environments; 2) aggregate stability benefits from the implementation of afforestation in acid environments and from all three study strategies in alkaline ones; 3) in acid environments, the percentage of labile fractions (free and occluded) in afforested sites is significantly higher compared with shrubland, while in alkaline environments, recalcitrant SOC is significantly higher in shrub clearing sites. Thus, land management should be focused on SOC storage after land abandonment in Mediterranean mountainous environments.

Reptile fauna should be considered a conservation objective, especially in respect of the impacts of climate change on their distribution and range’s dynamics. Investigating the environmental drivers of reptile species richness and identifying their suitable habitats is a fundamental prerequisite to setting efficient long-term conservation measures. This study focused on geographical patterns and estimations of species richness for herpetofauna widely spread Z. vivipara, N. natrix, V. berus, A. colchica, and protected in Latvia C. austriaca, E. orbicularis, L. agilis inhabiting northern (model territory Latvia) and southern (model territory Ukraine) part of their European range. The ultimate goal was to designate a conservation network that will meet long-term goals for survival of the target species in the context of climate change. We used stacked species distribution models for creating maps depicting the distribution of species richness under current and future (by 2050) climates for marginal reptilepopulations. Using cluster analysis, we showed that this herpeto-complex can be divided into “widespread species” and “forest species”. For all forest species we predicted a climate-driven reduction in their distribution range both North (Latvia) and South (Ukraine). The most vulnerable populations of “forest species” tend to be located in the South of their range, as a consequence of northward shifts by 2050. By 2050 the greatest reduction in range is predicted for currently widely spread Z. vivipara (by 1.4 times) and V. berus (by 2.2 times). In terms of designing an effective protected-area network, these results permit to identify priority conservation areas where the full ensemble of selected reptile species can be found, and confirms the relevance of abiotic multi-factor GIS-modelling for achieving this goal.

The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them. The interplay between such variables is crucial for modelling urban growth to closely reflects reality. Despite extensive research, ambiguity remains about how variations in these input variables influence urban densification. In this study, we conduct a global sensitivity analysis (SA) using a multinomial logistic regression (MNL) model to assess the model’s explanatory and predictive power. We examine the influence of global variables, including spatial resolution, neighborhood size, and density classes, under different input combinations at a provincial scale to understand their impact on densification. Additionally, we perform a stepwise regression to identify the significant explanatory variables that are important for understanding densification in the Brussels Metropolitan Area (BMA). Our results indicate that a finer spatial resolution of 50 m and 100 m, smaller neighborhood size of 5 × 5 and 3 × 3, and specific density classes—namely 3 (non-built-up, low and high built-up) and 4 (non-built-up, low, medium and high built-up)—optimally explain and predict urban densification. In line with the same, the stepwise regression reveals that models with a coarser resolution of 300 m lack significant variables, reflecting a lower explanatory power for densification. This approach aids in identifying optimal and significant global variables with higher explanatory power for understanding and predicting urban densification. Furthermore, these findings are reproducible in a global urban context, offering valuable insights for planners, modelers and geographers in managing future urban growth and minimizing modelling.

Coastal tourism holds substantial development potential. However, coastal ecosystems are affected by tourism development, which limits the supply of ecosystem services (ES). This study aims to conduct a systematic literature review on the impacts of tourism on coastal and marine ES using the Preferred Reporting Items for Systematic Reviews and Meta-alpha Methods. We initially identified 640 studies by searching titles, abstracts, and keywords. After screening, only 50 studies met the criteria for inclusion in the review. The results showed a significant increase in publications between 2011 and 2023. Most studies were conducted in Europe, Asia, and North and Central America. The most used ES classifications were MEA and CICES. Most studies concentrated on the ES supply dimension (43 studies; 86 %). Cultural ES (47 studies; 94 %) were researched more than provisioning (28 studies; 56 %) and regulating & maintenance (29 studies; 58 %) sections. Regarding cultural ES, most studies were focused on “Physical and experiential interactions with the natural environment” (34 studies; 68 %) and on provisioning ES on “Wild animals (terrestrial and aquatic) for nutrition, materials or energy” (18 studies; 36 %). Quantitative and mixed methods were the most used in the reviewed studies. Most studies identified pressures from “Tourism, urbanisation, and population increase” (27 studies; 54 %) and focused on “Integrative/ common management strategies” (20 studies; 40 %). Only a few of the studies’ results have been validated by external data (10 studies; 20 %). This study provides an overview of the most assessed marine and coastal ES, where studies are needed with more comprehensive geographic coverage.

Protecting rare, endemic, and endangered species requires careful habitat evaluation to set strategic plans for mitigating biodiversity loss and prioritizing conservation goals. The endangered Asian elephant (Elephas maximus) exemplifies the urgent need for targeted conservation efforts, given its challenging habitat conditions. This study examines the impact of climate and land use changes on the suitable habitat distribution of Asian elephants. Utilizing ten predictor variables, including climatic, topographic, and land use data, and employing six ensemble Species Distribution Models (SDMs) alongside Coupled Model Intercomparison Project Phase 6 data, the study estimates spatial changes and potential habitat expansions for Asian elephants across Tropical Asia. Occurrence data were gathered from field surveys in Bangladesh and the Global Biodiversity Information Facility database for Sri Lanka, Myanmar, Bhutan, Cambodia, India, Laos, Nepal, Thailand, and Vietnam. To evaluate habitat suitability, the analysis considered two distinct socioeconomic pathways (SSP 245 and SSP 370) across two future periods (2041–2060 and 2061–2080). Results reveal a strong correlation between isothermality and habitat suitability, with higher isothermality enhancing the habitat conditions for Asian elephants. Among the SDMs, the random forest model demonstrated the highest performance. Projected scenarios indicate significant habitat fragmentation by 2061–2080, heightening the risk of species’ vulnerability. Specifically, in SSP 245, the north zone is anticipated to experience a higher rate of habitat loss (588.443 km²/year), whereas, in SSP 370, the west zone is expected to face a more severe rate of habitat loss (1,798.56 km²/year). The eastern zone, which includes Cambodia, Vietnam, Laos, Thailand, and southern Myanmar, is notably at risk, with an estimated habitat loss of 14.8 million hectares. Anticipated changes in climate and land cover will impact the availability of essential resources such as food, water, and shelter, potentially driving the species to relocate to different elevation belts. The outcomes of the consensus map highlighting critical habitats and future fragmentation scenarios will support effective conservation and management strategies for the species.

Based on the frequency of themes covered at the 35th International Geographical Congress (IGC) and the 2024 American Association of Geographers Annual Meeting (AAG-AM), we present an integrated analysis of current research hotspots in geography. The interdisciplinary approach of geography in tackling global challenges, including climate change, urbanization, and sustainable development is highlighted. Hotspot analysis of the 35th IGC reveals the prominence of “Tourism, Leisure, and Global Change,” and “Urban Geography” as key themes, whereas the 2024 AAG-AM placed more emphasis on “GeoAI and Deep Learning,” and “Geospatial Data Science for Sustainability.” Frontier analysis, based on emerging research beyond the two conferences, highlights major critical issues being confronted by geographers, notably Earth’s surface systems, spatial patterns of human activities, intelligent remote sensing, climate change adaptation, biodiversity conservation, hazards and disaster risk, planetary boundaries, coupled human and natural systems, and global and regional sustainability. The analysis demonstrates that geographical research is becoming more diverse and systematic, and artificial intelligence technology is increasingly being harnessed. This not only reflects specific regional interests and priorities but also shows the dynamic development of geographical research and its important role in dealing with the challenges of the 21st century.