The Sustainable Development Goals (SDGs) are significantly off-course as we reach the midpoint of their 2030 deadline. From a scientific perspective, the critical challenge in achieving the SDGs lies in the need for more scientific principles to understand the complex socio-ecological systems (SES) and their interactions influencing the 17 SDGs. Here, we propose a scientific framework to clarify the common scientific principles and the rational treatment of diversity under these principles. The framework’s core is revealing the complex mechanisms underlying the achievement of each Sustainable Development Goal (SDG) and SDG interactions. Building upon the identified mechanisms, complex SES models can be established, and the implementation of SDGs can be formulated as a multi-objective optimization problem, seeking a compromise in competition between essential costs and desired benefits. Our framework can assist countries, and even the world in accelerating progress towards the SDGs.

Urban vegetation in China has changed substantially in recent decades due to rapid urbanization and dramatic climate change. Nevertheless, the spatial differentiation of greenness among major cities of China and its evolution process and drivers are still poorly understood. This study examined the spatial patterns of vegetation greenness across 289 cities in China in 2000, 2005, 2010, 2015, and 2018 by using spatial autocorrelation analysis on the Normalized Difference Vegetation Index (NDVI); then, the influencing factors were analyzed by using the optimal parameters-based geographical detector (OPGD) model and 18 natural and anthropogenic indicators. The findings demonstrated a noticeable rise in the overall greenness of the selected cities during 2000–2018. The cities in northwest China and east China exhibited the rapidest and slowest greening, respectively, among the six sub-regions. A significant positive spatial correlation was detected between the greenness of the 289 cities in different periods, but the correlation strength weakened over time. The hot and very hot spots in southern and eastern China gradually shifted to the southwest. While the spatial pattern of urban greenness in China is primarily influenced by wind speed (WS) and precipitation (PRE), the interaction between PRE and gross domestic product (GDP) has the highest explanatory power. The explanatory power of most natural factors decreased and, conversely, the influence of anthropogenic factors generally increased. These findings emphasize the variations in the influence strength of multiple factors on urban greenness pattern, which should be taken into account to understand and adapt to the changing urban ecosystem.}

Increased hazards threatening the United Nations Educational, Scientific and Cultural Organization (UNESCO)-designated sites and endangering cultural heritage and community well-being require attention and action. Considering the pivotal role of UNESCO sites in conservation and development, this study assessed their levels of disaster preparedness. The absence of studies assessing disaster awareness, risk perception, and preparedness among UNESCO site actors, as well as the pivotal place of preparedness within the Disaster Risk Management (DRM) cycle justifies this research. Applying the tenets of the Person-Relative-to-Event framework, we hypothesized that a strong positive correlation exists between perceived risks, resources, and disaster preparedness. To collect pertinent data, we employed an embedded mixed-method design and conducted an online questionnaire survey yielding 141 responses from 59 countries. From the results of relevant analyses, wildfires, floods, and droughts are top hazards occurring frequently in UNESCO sites, with significant concerns about pollution and habitat loss during future events. Smartphones emerged as the most available crucial DRM resource, with higher availability of DRM resources correlating positively and significantly with sites’ preparedness. Our findings contribute valuable insights to address missing links for disaster-ready and resilient UNESCO sites, promoting their preservation for future generations.}

The Anthropocene era is characterized by the escalating impact of human activities on the environment, as well as the increasingly complex interactions among various components of the Earth system. These factors greatly affect the Earth’s evolutionary trajectory. Despite notable strides in sustainable development practices worldwide, it remains unclear to what extent we have achieved Earth sustainability. Consequently, there is a pressing need to enhance conceptual and methodological frameworks to measure sustainability progress accurately. To address this need, we developed an Earth Vitality Framework that aids in tracking the Earth sustainability progress by considering interactions between spheres, recognizing the equal relationship between humans and nature, and presenting a threshold scheme for all measures. We applied this framework at global and national scales to demonstrate its usefulness. Our findings reveal that the current Earth Vitality Index is 63.74, indicating that the Earth is in a “weak” vitality. Irrational social institutions, unsatisfactory life experiences and the poor state of the biosphere and hydrosphere have remarkably affected the Earth vitality. Additionally, inequality exists between high-income and low-income countries. Although most of the former exhibit poor human-nature interaction, all of them enjoy good human well-being, while the opposite is true for the latter. Finally, we summarize the challenges and possible options for enhancing the Earth vitality in terms of coping with spillover effects, tipping cascades, feedback, and heterogeneity.}

New rainfall records were registered in the southeastern region of Brazil during February 2023. The amount of rain in the north coast region of the State of São Paulo was more than 650 mm in less than two days. Landslides and tragedies with a socioeconomically vulnerable population marked this climatic extreme. The country has a regulatory system that suggests the elaboration and implementation of municipal public policies aimed at territorial organization, environmental conservation, and the prevention of disasters induced by natural hazards. In addition, both federal and state funds earmarked for such hazard's prevention have been underutilized over the last decade. In addition to this current devastating climate episode, other events were registered in this decade, reinforcing that financial governance is a key challenge to face the climate crisis in Brazil. The predicted future extreme events in different regions of Brazil will require a different governance system to minimize social inequality, seek sustainable alternatives for urban environments and manage to adapt cities for the challenges posed by climate change.}

Urban green spaces (UGS) are relevant to city well-being, as recognized by the United Nations’ Sustainable Development Goals (SDGs). However, few studies have studied the temporal use of UGS. This work assessed the seasonal, weekly, and daily use of three urban green spaces (Vingis Park, Bernardino Garden, and Jomantas Park) in Vilnius (Lithuania). The study is based on an on-site observation-based survey, which recorded users’ characteristics, activities, and weather conditions during summer and winter. The results showed that UGS’s seasonal, weekly, and daily use differed according to park and users’ characteristics. Parks with a higher diversity of facilities had a high seasonal difference in the number of observed activities. User numbers were higher in the summer for activities with children, social activities, sports, and water activities than in the winter. Jomantas Park had the lowest variability in user characteristics. Weather variables were linked to changes in users’ activities. Higher precipitation and lower temperature were associated with reducing the number of users and the diversity of registered activities. Most of the stationary activities were observed during summer. The diversity of the observed activities was associated with the available facilities rather than the park size. The distribution of stationary activities was spatially correlated with facility/equipment (benches, playgrounds, sports, and fitness equipment) and proximity to water features. The results of this study are relevant for UGS design, planning, and management.}

There are urgent calls for new approaches to map the global urban conditions of complexity, diffuseness, diversity, and connectivity. However, existing methods mostly focus on mapping urbanized areas as bio physical entities. Here, based on the continuum of urbanity framework, we developed an approach for cross-scale urbanity mapping from town to city and urban megaregion with different spatial resolutions using the Google Earth Engine. This approach was developed based on multi-source remote sensing data, Points of Interest – Open Street Map (POIs-OSM) big data, and the random forest regression model. This approach is scale-independent and revealed significant spatial variations in urbanity, underscoring differences in urbanization patterns across megaregions and between urban and rural areas. Urbanity was observed transcending traditional urban boundaries, diffusing into rural settlements within non-urban locales. The finding of urbanity in rural communities far from urban areas challenges the gradient theory of urban-rural development and distribution. By mapping livelihoods, lifestyles, and connectivity simultaneously, urbanity maps present a more comprehensive characterization of the complexity, diffuseness, diversity, and connectivity of urbanized areas than that by land cover or population density alone. It helps enhance the understanding of urbanization beyond biophysical form. This approach can provide a multifaceted understanding of urbanization, and thereby insights on urban and regional sustainability.}

Riparian land use/land cover (LULC) plays a crucial role in maintaining riverine water quality by altering the transport of pollutants and nutrients. Nevertheless, establishing a direct relationship between water quality and LULC is challenging due to the multi-indicator nature of both factors. Water quality encompasses a multitude of physical, chemical, and biological parameters, while LULC represents a diverse array of land use types. Riparian habitat quality (RHQ) serves as an indicator of LULC. Yet, it remains to be seen whether RHQ can act as a proxy of LULC for assessing the impact of LULC on riverine water quality. This study examines the interplay between RHQ, LULC and water quality, and develops a comprehensive indicator to predict water quality. We measured several water quality parameters, including pH (potential of hydrogen), TN (total nitrogen), TP (total phosphorus), Twater (water temperature), DO (dissolved oxygen), and EC (electrical conductivity) of the Yue and Jinshui Rivers draining to the Han River during 2016, 2017 and 2018. The water quality index (WQI) was further calculated. RHQ is assessed by the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model. Our study found noticeable seasonal differences in water quality, with a higher WQI observed in the dry season. The RHQ was strongly correlated with LULC compositions. RHQ positively correlated with WQI, and DO concentration and vegetation land were negatively correlated with Twater, TN, TP, EC, cropland, and construction land. These correlations were stronger in the rainy season. Human-dominated land, such as construction land and cropland, significantly contributed to water quality degradation, whereas vegetation promoted water quality. Regression models showed that the RHQ explained variations in WQI better than LULC types. Our study concludes that RHQ is a new and comprehensive indicator for predicting the dynamics of riverine water quality.}

Ecological restoration projects implemented over the past 20 years have substantially increased forest coverage in China, but the high tree mortality of new afforestation forest remains a challenging but unsolved problem. It is still not clear how much vegetation can be sustained by the forest lands with given water, energy and soil conditions, i.e., the carrying capacity for vegetation (CCV) of forest lands, which is the prerequisite for planning and implementing forest restoration projects. Here, we used a simplified method to evaluate the CCV across forest lands nationwide. Specifically, based on leaf area index (LAI) dataset, we use boosted regression tree and multiple linear regression model to analyze the CCV during 2001–2020 and 2021–2030 and explore the contribution of environmental factors. We find that there are three typical regions with lower CCV located in the Loess Plateau and the southern region of the Inner Mongolia Plateau, the Hengduan Mountain region, and the Tianshan Mountains. More importantly, the vegetation in the regions near the dry-wet climate transition zone show excess local carrying capacity for vegetation over the past two decades and they are more susceptible to potential climatic stress. In comparison, in the Greater Khingan Mountains and Hengduan Mountains, there is high potential to improve the forest growth. Temperature, precipitation and soil affects the CCV by shaping the vegetation in the optimal range. This indicates that more consideration should be given to restrictions of regional environmental constraints when planning afforestation and forest management. This study has important implications for guiding future forest scheme in China.}

In the pursuit of sustainable urbanization, Bike-Sharing Services (BSS) emerge as a pivotal instrument for promoting green, low-carbon transit. While BSS is often commended for its environmental benefits, we offer a more nuanced analysis that elucidates previously neglected aspects. Through the Dominant Travel Distance Model (DTDM), we evaluate the potential of BSS to replace other transportation modes for specific journey based on travel distance. Utilizing multiscale geographically weighted regression (MGWR), we illuminate the relationship between BSS’s environmental benefits and built-environment attributes. The life cycle analysis (LCA) quantifies greenhouse gas (GHG) emissions from production to operation, providing a deeper understanding of BSS’s environmental benefits. Notably, our study focuses on Xiamen Island, a Chinese “Type II large-sized city” (1–3 million population), contrasting with the predominantly studied “super large-sized cities” (over 10 million population). Our findings highlight: (1) A single BSS trip in Xiamen Island reduces GHG emissions by an average of 19.97 g CO2-eq, accumulating monthly savings of 144.477 t CO2-eq. (2) Areas in the southwest, northeast, and southeast of Xiamen Island, characterized by high population densities, register significant BSS environmental benefits. (3) At a global level, the stepwise regression model identifies five key built environment factors influencing BSS’s GHG mitigation. (4) Regionally, MGWR enhances model precision, indicating that these five factors function at diverse spatial scales, affecting BSS’s environmental benefits variably.}

It is never an easy task for China to feed 1.4 billion people with only 7% of the world’s arable land. With nearly 30% of the world’s nitrogen (N) fertilizer applied, China achieves high crop yields while facing N pollution resulting from excessive N input. Here, we calculate the farmland N budget on the national and regional scales. The N use efficiency (NUE) in China increased by 28.0% during 2005–2018. This improvement is due to the reduction in fertilization and the improvement of crop management. The fragmented farmland is changing to large-scale farmland with the increase in cultivated land area per rural population and the development of agricultural mechanization. This opportunity brings more possibilities for precision farmland management, thus further improving NUE. The goal of an NUE of 0.6 may be achieved in the 2040s based on the current development trend. This striking N use shift in China has important implications for other developing countries.}

The Himalayas and their surrounding areas boast vast glaciers rivaling those in polar regions, supplying vital meltwater to the Indus, Ganges, and Brahmaputra rivers, supporting over a billion downstream inhabitants for drinking, power, and agriculture. With changing runoff patterns due to accelerated glacial melt, understanding and projecting glacio-hydrological processes in these basins is imperative. This review assesses the evolution, applications, and key challenges in diverse glacio-hydrology models across the Himalayas, varying in complexities like ablation algorithms, glacier dynamics, ice avalanches, and permafrost. Previous findings indicate higher glacial melt contributions to annual runoff in the Indus compared to the Ganges and Brahmaputra, with anticipated peak melting in the latter basins — having less glacier cover — before the mid-21st century, contrasting with the delayed peak expected in the Indus Basin due to its larger glacier area. Different modeling studies still have large uncertainties in the simulated runoff components in the Himalayan basins; and the projections of future glacier melt peak time vary at different Himalaya sub-basins under different Coupled Model Intercomparison Project (CMIP) scenarios. We also find that the lack of reliable meteorological forcing data (particularly the precipitation errors) is a major source of uncertainty for glacio-hydrological modeling in the Himalayan basins. Furthermore, permafrost degradation compounds these challenges, complicating assessments of future freshwater availability. Urgent measures include establishing comprehensive in situ observations, innovating remote-sensing technologies (especially for permafrost ice monitoring), and advancing glacio-hydrology models to integrate glacier, snow, and permafrost processes. These endeavors are crucial for informed policymaking and sustainable resource management in this pivotal, glacier-dependent ecosystem.}

The Yellow River Basin (YRB) has experienced severe floods and continuous riverbed elevation throughout history. Global climate change has been suggested to be driving a worldwide increase in flooding risk. However, owing to insufficient evidence, the quantitative correlation between flooding and climate change remains ill-defined. We present a long time series of maximum flood discharge in the YRB dating back to 1843 compiled from historical documents and instrument measurements. Variations in yearly maximum flood discharge show distinct periods: a dramatic decreasing period from 1843 to 1950, and an oscillating gentle decreasing from 1950 to 2021, with the latter period also showing increasing more extreme floods. A Mann-Kendall test analysis suggests that the latter period can be further split into two distinct sub-periods: an oscillating gentle decreasing period from 1950 to 2000, and a clear recent increasing period from 2000 to 2021. We further predict that climate change will cause an ongoing remarkable increase in future flooding risk and an ∼44.4 billion US dollars loss of floods in the YRB in 2100.}

This study uses logistic and Poisson regression models to examine the factors influencing the adoption of sustainable land management (SLM) practices in Mali using two rounds of the nationally representative survey Enquête Agricole de Conjoncture Intégrée aux Conditions de Vie des Ménages. The SLMs considered include the application of organic fertilizers, the application of inorganic fertilizers, the use of improved seeds, and the practice of intercropping. On average the application of organic fertilizers (39.2%), and inorganic fertilizers (28.7%) are the most frequent SLM practices among Malian farmers, and between 2014 and 2017, we observe a decline in the practice of intercropping. The regression results show that farmers’ adoption of different SLMs is significantly associated with biophysical factors (average temperature, climate type, plot size, plot shape, and location), demographic factors (age, gender, education, household size), and socioeconomic factors (number of cultivated plots, livelihood diversification, type of crop grown, market access, credit access, economic shocks, and social capital). Our findings suggest that policymakers and agricultural development agencies in Mali need to adopt a multidimensional policy framework to unlock the untapped potential of SLM practices in promoting sustainable agriculture and food security.}

China’s Grain to Green Program (GTGP), which is one of the largest payments for ecosystem services (PES) in the world, has made significant ecological improvements to the environment. However, current understanding of its outcomes on the social-ecological system (SES) remains limited. Therefore, taking the South China Karst as an example, a SES resilience evaluation index system was constructed followed by an exploratory spatial analysis, root mean square error, and Self-Organizing Feature Map to clarify the spatiotemporal changes and relationship of SES resilience, achieve the zoning of SES resilience and provide restoration measures. The results showed an upward trend in social resilience from 2000 to 2020, especially its subsystem of social development. Regional ecological resilience was stable, owing to a slightly declined ecosystem services and increased landscape pattern. Spatially, nearly half of the counties exhibited a distribution mismatch in SES resilience. There was an obvious inverted U-shaped relationship of SES resilience, indicating a clear threshold effect, and the constraint relationship of SES resilience eased over time, demonstrating the effectiveness of the ecological restoration program. GTGP played a positive role in reducing regional SES trade-off, but this positive effect was limited, reflecting the limitations of overemphasizing the conversion from farmland to forest and grassland. Regional SES resilience can be divided into four clusters, which were the key optimization zone for social system, the SES resilience safety zone, the key restoration zone for SES resilience, and the key optimization zone for ecological system. Adaptive adjustments for the GTGP in these zones should be taken to achieve maximum SES benefits in the future.}

Owing to the far-reaching environmental consequences of agriculture and food systems, such as their contribution to climate change, there is an urgent need to reduce their impact. International and national governments set sustainability targets and implement corresponding measures. Nevertheless, critics of the globalized system claim that a territorial administrative scale is better suited to address sustainability issues. Yet, at the sub-national level, local authorities rarely apply a systemic environmental assessment to enhance their action plans. This paper employs a territorial life cycle assessment methodology to improve local environmental agri-food planning. The objective is to identify significant direct and indirect environmental hotspots, their origins, and formulate effective mitigation strategies. The methodology is applied to the administrative department of Finistere, a strategic agricultural region in North-Western France. Multiple environmental criteria including climate change, fossil resource scarcity, toxicity, and land use are modeled. The findings reveal that the primary environmental hotspots of the studied local food system arise from indirect sources, such as livestock feed or diesel consumption. Livestock reduction and organic farming conversion emerge as the most environmentally efficient strategies, resulting in a 25% decrease in the climate change indicator. However, the overall modeled impact reduction is insufficient following national objectives and remains limited for the land use indicator. These results highlight the innovative application of life cycle assessment led at a local level, offering insights for the further advancement of systematic and prospective local agri-food assessment. Additionally, they provide guidance for local authorities to enhance the sustainability of planning strategies.}

Understanding the spatial interaction among residents, cooling service, and heat risk area in complex urban areas is conducive to developing targeted management. However, traditional urban thermal environment assessments typically relied on simple linear integration of associated indicators, often neglecting the spatial interaction effect. To explore the spatial interaction among the three elements, this study proposes an accessibility-based urban thermal environment assessment framework. Using Zhengzhou, a rapidly urbanizing city, as an example, remotely sensed images from three periods (2010, 2015 and 2020) were applied to extract urban green space (UGS) and hot island area (HIA). An improved two-step floating catchment area (2SFCA) method and bivariate local Moran’s I were employed to explore whether residents’ clustering locations are more likely to access cooling service or to be exposed to heat risk. The results demonstrate that the UGS in the city has been expanding, whereas the HIA shrank within the inner city in 2015 and then increased in 2020. Even though the urban thermal environment may have improved in the last decade, the spatial interaction among the residents, cooling service and heat risk area could be exacerbated. Spatial autocorrelation shows an increase in locations that are disadvantageous for resident congregation. Even when sufficient cooling services were provided, residents in these areas could still be exposed to high heat risk. The developed urban thermal environment framework provides a novel insight into the residents’ heat risk exposure and cooling service accessibility, and the findings could assist urban planners in targeting the improvement of extra heat exposure risk locations.}

The agricultural production space, as where and how much each agricultural product grows, plays a vital role in meeting the increasing and diverse food demands. Previous studies on agricultural production patterns have predominantly centered on individual or specific crop types, using methods such as remote sensing or statistical metrological analysis. In this study, we characterize the agricultural production space (APS) by bipartite network connecting agricultural products and provinces, to reveal the relatedness between diverse agricultural products and the spatiotemporal characteristic of provincial production capabilities in China. The results show that core products are cereal, pork, melon, and pome fruit; meanwhile the milk, grape, and fiber crop show an upward trend in centrality, which is in line with diet structure changes in China over the past decades. The little changes in community components and structures of agricultural products and provinces reveal that agricultural production patterns in China are relatively stable. Additionally, identified provincial communities closely resemble China’s agricultural natural zones. Furthermore, the observed growth in production capabilities in North and Northeast China implies their potential focus areas for future agricultural production. Despite the superior production capabilities of southern provinces, recent years have witnessed a notable decline, warranting special attentions. The findings provide a comprehensive perspective for understanding the complex relationship of agricultural products’ relatedness, production capabilities and production patterns, which serve as a reference for the agricultural spatial optimization and agricultural sustainable development.}

Vegetation greening has long been acknowledged, but recent studies have pointed out that vegetation greening is possibly stalled or even reversed. However, detailed analyses about greening reversal or increased browning of vegetation remain scarce. In this study, we utilized the normalized difference vegetation index (NDVI) as an indicator of vegetation to investigate the trends of vegetation greening and browning (monotonic, interruption, and reversal) through the breaks for the additive season and trend (BFAST) method across China’s drylands from 1982 to 2022. It also reveals the impacts of ecological restoration programs (ERPs) and climate change on these vegetation trends. We find that the vegetation displays an obvious pattern of east-greening and west-browning in China’s drylands. Greening trends mainly exhibits monotonic greening (29.8 %) and greening with setback (36.8 %), whereas browning shows a greening to browning reversal (19.2 %). The increase rate of greening to browning reversal is 0.0342/yr, which is apparently greater than that of greening with setback, 0.0078/yr. This research highlights that, under the background of widespread vegetation greening, vegetation browning is progressively increasing due to the effects of climate change. Furthermore, the ERPs have significantly increased vegetation coverage, with the increase rate in 2000–2022 being twice as much as that of 1982–1999 in revegetation regions. Vegetation browning in southwestern Qingzang Plateau is primarily driven by adverse climatic factors and anthropogenic disturbances, which offset the efforts of ERPs.}

The exponential growth of food demand due to the increasing global population has the potential to seriously threaten the quality and quantity of food supplies due to climate change. This study explores the utilisation of green urban spaces for achieving food self-sufficiency by investigating the extent to which sustainable urban farms could be used to reduce the consumption of imported produce in the UK. It also examines urban farming stakeholders’ perspective on how food self-sufficiency can help realise the SDGs especially SDG 2 (Zero hunger) and SDG 13 (Climate action). The study adopts a mixed method approach through a survey with 115 respondents and semi-structured interviews conducted with 12 respondents from 4 different urban farming stakeholder groups. The findings of this study presented a strong correlation between stakeholders who had concerns about where their food came from and the carbon footprint of imported produce. The research shows that, urban farms will no doubt play a vital role in the future of food security in our cities/communities and that the SDGs could be realised through sustainable urban farms implemented within the relevant planning regulations/policies.}

This study’s goal is to present a dynamic portrait of the farm-buildings environment in Occitania, in Southern France, in order to better identify the transitions underway in agri-food chains. To this end, we undertook a territorial diagnosis based on actor statements, using 28 semi-structured interviews across Occitania. This diagnosis was enriched by graphic modelling, which enabled the spatialization of the dynamics described. We show that the process of standardisation of farm buildings prevails in the majority of the territories studied. This phenomenon has intensified in recent years with the development of vast photovoltaic-roofed sheds, accentuating the farmland conversion and soil sealing. At the same time, in areas with strong environmental, landscape and heritage contexts, a ‘new adventure in farm buildings’ (2022 survey) is taking shape. It is primarily driven by local short food chains, which rely on self-construction, repurposing and refurbishment, the sharing of tools and equipment, and which favour the use and reuse of local resources. This study shows that farm-buildings dynamics crystallise many challenges confronting the reterritorialisation of agriculture and food production.}

It is essential to better integrate wilderness representations of different stakeholders into wilderness conservation. The way in which local residents and other stakeholders frame the construction of wilderness of protected areas in developing countries are poorly understood. In these areas, land use policy and decision may lead to conflicts. This study aims to explore existing public wilderness representations using a questionnaire survey (n = 514) administered amongst tourists and other stakeholders in the Wuyishan National Park, in southeast China. The spatial differences in public representations of wilderness across different stakeholder groups were compared against expert knowledge. We found that integrated wilderness representation maps of different stakeholder groups were consistent, namely ‘area where wild animals live’, ‘area with no human influence’, ‘a barren and lonely area’. However, three sub-representations of the individual stakeholders varied significantly. Moreover, expert-based wilderness mapping did not reflect public representations accurately, and an integrated wilderness quality map considering wilderness representations across both stakeholders and experts can better identify detailed wilderness areas. Our study provides new insights and technical support for future exploration of wilderness conservation and mapping in China and other countries with insufficient awareness of wilderness values and investigations in a regional scale.}

Equal access to social infrastructures is a fundamental prerequisite for sustainable development, but has long been a great challenge worldwide. Previous studies have primarily focused on the accessibility to social infrastructures in urban areas across various scales, with less attention to rural areas, where inequality can be more severe. Particularly, few have investigated the disparities of accessibility to social infrastructures between urban and rural areas. Here, using the Changsha–Zhuzhou–Xiangtan urban agglomeration, China, as an example, we investigated the inequality of accessibility in both urban and rural areas, and further compared the urban-rural difference. Accessibility was measured by travel time of residents to infrastructures. We selected four types of social infrastructures including supermarkets, bus stops, primary schools, and health care, which were fundamentally important to both urban and rural residents. We found large disparities in accessibility between urban and rural areas, ranging from 20 min to 2 h. Rural residents had to spend one to two more hours to bus stops than urban residents, and 20 min more to the other three types of infrastructures. Furthermore, accessibility to multiple infrastructures showed greater urban-rural differences. Rural residents in more than half of the towns had no access to any infrastructure within 15 min, while more than 60% of the urban residents could access to all infrastructures within 15 min. Our results revealed quantitative accessibility gap between urban and rural areas and underscored the necessity of social infrastructures planning to address such disparities.}

Landscape fragmentation is generally viewed as an indicator of environmental stresses or risks, but the fragmentation intensity assessment also depends on the scale of data and the definition of spatial unit. This study aimed to explore the scale-dependence of forest fragmentation intensity along a moisture gradient in Yinshan Mountain of North China, and to estimate environmental sensitivity of forest fragmentation in this semi-arid landscape. We developed an automatic classification algorithm using simple linear iterative clustering (SLIC) and Gaussian mixture model (GMM), and extracted tree canopy patches from Google Earth images (GEI), with an accuracy of 89.2% in the study area. Then we convert the tree canopy patches to forest category according to definition of forest that tree density greater than 10%, and compared it with forest categories from global land use datasets, FROM-GLC10 and GlobeLand30, with spatial resolutions of 10 m and 30 m, respectively. We found that the FROM-GLC10 and GlobeLand30 datasets underestimated the forest area in Yinshan Mountain by 16.88% and 21.06%, respectively; and the ratio of open forest (OF, 10% < tree coverage < 40%) to closed forest (CF, tree coverage > 40%) areas in the underestimated part was 2:1. The underestimations concentrated in warmer and drier areas occupied mostly by large coverage of OFs with severely fragmented canopies. Fragmentation intensity of canopies positively correlated with spring temperature while negatively correlated with summer precipitation and terrain slope. When summer precipitation was less than 300 mm or spring temperature higher than 4 °C, canopy fragmentation intensity rose drastically, while the forest area percentage kept stable. Our study suggested that the spatial configuration, e.g., sparseness, is more sensitive to drought stress than area percentage. This highlights the importance of data resolution and proper fragmentation measurements for forest patterns and environmental interpretation, which is the base of reliable ecosystem predictions with regard to the future climate scenarios.

Wind power has been developing rapidly as a key measure to mitigate human-driven global warming. The understanding of the development and impacts of wind farms on local climate and vegetation is of great importance for their rational use but is still limited. In this study, we combined remote sensing and on-site investigations to identify wind farm locations in Inner Mongolia and performed landscape pattern analyses using Fragstats. We explored the impacts of wind farms on land surface temperature (LST) and vegetation net primary productivity (NPP) between 1990 and 2020 by contrasting these metrics in wind farms with those in non-wind farm areas. The results showed that the area of wind farms increased rapidly from 1.2 km2 in 1990 to 10,755 km2 in 2020. Spatially, wind farms are mainly clustered in three aggregation areas in the center. Further, wind farms increased nighttime LST, with a mean value of 0.23 °C, but had minor impacts on the daytime LST. Moreover, wind farms caused a decline in NPP, especially over forest areas, with an average reduction of 12.37 GC/m². Given the impact of wind farms on LST and NPP, we suggest that the development of wind farms should fully consider their direct and potential impacts. This study provides scientific guidance on the spatial pattern of future wind farms.}

Accurate mapping and timely monitoring of urban redevelopment are pivotal for urban studies and decision-makers to foster sustainable urban development. Traditional mapping methods heavily depend on field surveys and subjective questionnaires, yielding less objective, reliable, and timely data. Recent advancements in Geographic Information Systems (GIS) and remote-sensing technologies have improved the identification and mapping of urban redevelopment through quantitative analysis using satellite-based observations. Nonetheless, challenges persist, particularly concerning accuracy and significant temporal delays. This study introduces a novel approach to modeling urban redevelopment, leveraging machine learning algorithms and remote-sensing data. This methodology can facilitate the accurate and timely identification of urban redevelopment activities. The study’s machine learning model can analyze time-series remote-sensing data to identify spatio-temporal and spectral patterns related to urban redevelopment. The model is thoroughly evaluated, and the results indicate that it can accurately capture the time-series patterns of urban redevelopment. This research’s findings are useful for evaluating urban demographic and economic changes, informing policymaking and urban planning, and contributing to sustainable urban development. The model can also serve as a foundation for future research on early-stage urban redevelopment detection and evaluation of the causes and impacts of urban redevelopment.}

Understanding stakeholders’ differences in perceptions of ecosystem services (ES) is crucial for guiding ecological conservation and planning. However, the variations of ES perception amongst different types of residents in urbanizing areas along an urban-rural gradient are still poorly understood. Combining a questionnaire-based survey, redundancy analysis, and statistical tests, we delineated the urban-rural gradient according to local residents’ socio-economic characteristics, and investigated the differences in local residents’ perceptions of ES and potential factors affecting them in the Guanting Reservoir basin, a rapidly urbanizing basin in China. The results showed that residents living in urban-rural transitional areas attached great importance to provisioning services of providing food and domestic water, regulating services of carbon sequestration and air purification, and cultural services of providing education and training, which were 0.7%–13.1%, 0.7%–9.1% and 2.5%–21.2% higher than that of residents in other areas, respectively. Age and occupation were major factors affecting residents’ perceptions. In terms of land-use types that deliver ES, the difference in perceptions of ES delivered by grassland was the greatest amongst residents. Our results support recommendations for policymakers to take into account the stakeholders’ diverse perceptions, thus promoting residents’ sense of gain on ES.}

Long-term exposure to high surface ozone (O3) concentrations, a complex oxidative atmospheric pollutant, can adversely impact human health. Based on O3 monitoring data from 261 cities worldwide in 2020, generalized additive model (GAM) and spatial data analysis (SDA) methods were applied in this study to quantitatively evaluate the spatiotemporal distribution of O3 concentration, exposure risk, and dominant meteorological factors. Results indicated that over 40% of the cities worldwide were exposed to harmful O3 concentration ranges (40–60 µg/m3), with most cities distributed in China and India. Moreover, significant seasonal variations in global O3 concentrations were observed, presenting as summer (45.6 µg/m3) > spring (47.3 µg/m3) > autumn (38.0 µg/m3) > winter (33.6 µg/m3). Exposure analysis revealed that approximately 12.2% of the population in 261 cities were exposed to an environment with high O3 concentrations (80–160 µg/m3), with about 36.32 million people in major countries. Thus, the persistent increase in high O3 levels worldwide is a critical factor contributing to threats to human health. Furthermore, GAM results indicated temperature, relative humidity, and wind speed as primary determinants of O3 variability. The synergy of meteorological factors is critical for understanding O3 changes. Our findings are important for enforcing robust air quality policies and mitigating public risk.}

Regional inequality significantly influences sustainable development and human well-being. In China, there exists pronounced regional disparities in economic and digital advancements; however, scant research delves into the interplay between them. By analyzing the economic development and digitalization gaps at regional and city levels in China, extending the original Cobb-Douglas production function, this study aims to evaluate the impact of digitalization on China’s regional inequality using seemingly unrelated regression. The results indicate a greater emphasis on digital inequality compared to economic disparity, with variable coefficients of 0.59 for GDP per capita and 0.92 for the digitalization index over the past four years. However, GDP per capita demonstrates higher spatial concentration than digitalization. Notably, both disparities have shown a gradual reduction in recent years. The southeastern region of the Hu Huanyong Line exhibits superior levels and rates of economic and digital advancement in contrast to the northwestern region. While digitalization propels economic growth, it yields a nuanced impact on achieving balanced regional development, encompassing both positive and negative facets. Our study highlights that the marginal utility of advancing digitalization is more pronounced in less developed regions, but only if the government invests in the digital infrastructure and education in these areas. This study’s methodology can be utilized for subsequent research, and our findings hold the potential to the government’s regional investment and policy-making.

The application of biofertilizers is becoming an inevitable trend to substitute chemical fertilizers for sustainable agriculture. To better understand the development of biofertilizers from 1980 to 2022, we used bibliometric mining to analyze 12,880 journal articles related to biofertilizer. The network cooccurrence analysis suggested that the biofertilizers research can be separated into three stages. The first stage (1980–2005) focused on nitrogen fixation. The second stage (2006–2015) concentrated on the mechanisms for increasing plant yield. The third stage (2016–2022) was the application of biofertilizers to improve the soil environment. The keyword analysis revealed the mechanisms of biofertilizers to improve plant-growth: biofertilizers can impact the nutritional status of plants, regulate plant hormones, and improve soil environments and the microbiome. The bacteria use as biofertilizers, included Pseudomonas, Azospirillum, and Bacillus, were also identified through bibliometric mining. These findings provide critical discernment to aid further study of biofertilizers for sustainable agriculture.}

The world is facing dramatic challenges related to environmental sustainability at an accelerating pace. In this context, the field of economic geography (EG) has been playing an important role in understanding both the socioeconomic and technological dimensions of these challenges, as it deals with a variety of complementary notions and perspectives. Departing from this lens, our aim is to explore a conceptual framework that can help us to understand environmental changes relating to multi-dimensional territorial development, notably in economic contexts where inequality is high, and stratification based on hierarchies regulate social and economic life. Based on the territory concept, we propose the original notion of a hierarchical regional innovation system (HRIS) that emphasises the pervasive role of hierarchies (powers) in regional innovation systems and illustrate its value with evidence and case studies from extant literature on sustainability transitions. The HRIS can help us understand and promote development paths considering the contribution of inclusive eco-innovations (another original conceptual amalgam). Through some empirical cases from other studies in low-carbon transitions, we show the application of the HRIS (and inclusive eco-innovation) framework. In conclusion, we provide incentives to explore new regional innovation systems, alongside the HRIS, adapted to different regions worldwide and centred on the inclusiveness of people and places.}

Several actions from both the environmental and human viewpoints have already been made to meet the sustainability goals targeted at food systems. Still, new place-based ideas to improve sustainability are needed. Agroecological symbiosis (AES), a novel food system model, is an example of a suggested system-level change to attain sustainability targets; it is a symbiosis of food production and processing using renewable energy that uses its own feedstock. AES has already been found advantageous from the ecological and biophysical viewpoints, but a regional economic evaluation of the model is still lacking. Thus, the aim of our paper is to assess the regional economic impact of a possible systemic change in the food system using the network of agroecological symbiosis (NAES) as an example. We applied scenarios representing different ways of moving towards envisioned NAES models in Mäntsälä, Finland, and a computable general equilibrium model to evaluate the regional economic impact. According to our results, both regional economy and employment would increase, and the regional production base would diversify with NAES implementation applied to the region, but the extent of the benefits varies between scenarios. The scenario that includes change in both public and private food demand, production of bioenergy and utilization of by-products would cause the largest impacts. However, realizing NAES requires investments that may influence the actual implementation of such models. Nonetheless, a change towards NAES can promote an economically and spatially just transition to sustainability, as NAES seems to be economically most beneficial for rural areas.}

Changing climate will jeopardize biodiversity, particularly the geographic distribution of endemic species. One such species is the Javan Hawk-Eagle (JHE, Nisaetus bartelsi), a charismatic raptor found only on Java Island, Indonesia. Thus, it is crucial to develop an appropriate conservation strategy to preserve the species. Ecological niche modeling is considered a valuable tool for designing conservation plans for the JHE. We provide an ecological niche modeling approach and transfer its model to future climate scenarios for the JHE. We utilize various machine learning algorithms under sustainability and business-as-usual (BAU) scenarios for 2050. Additionally, we investigate the conservation vulnerability of the JHE, capturing multifaceted pressures on the species from climate dissimilarities and human disturbance variables. Our study reveals that the ensemble model performs exceptionally well, with temperature emerging as the most critical factor affecting the JHE distribution. This finding indicates that climate change will have a significant impact on the JHE species. Our results suggest that the JHE distribution will likely decrease by 28.41% and 40.16% from the current JHE distribution under sustainability and BAU scenarios, respectively. Furthermore, our study reveals high-potential refugia for future JHE, covering 7,596 km2 (61%) under the sustainability scenario and only 4,403 km2 (35%) under the BAU scenario. Therefore, effective management and planning, including habitat restoration, refugia preservation, habitat connectivity, and local community inclusivity, should be well-managed to achieve JHE conservation targets.}

Sustainable rural development is predicated on a delicate balance between rural territorial functions and ecological constraints. While various functions can catalyze economic growth in rural settings, aligning them with inherent ecological variables is essential. An exhaustive analysis of Beijing’s Pinggu District elucidated specific supply-demand thresholds and evaluated the adaptability of current utilization within these boundaries, culminating in a novel optimization strategy. Notably, while locals place significant emphasis on production functions, the area’s topography and soil characteristics present palpable challenges to achieving these goals. We discovered that ‘shackle type’ predominantly characterize individual rural territorial functions, while ‘diversified weak function optimization type’ define multifunctional aspects, showcasing varied development paths and uncovering potential strategies for sustainable improvement. This study contributes a groundbreaking perspective to sustainable rural development, offering key insights and strategic recommendations for policy formulation.

Ecosystem degradation is one of the critical constraints for the sustainable development of our planet. However, recovering an ecosystem to a pre-impairment condition is often not practical. The International Restoration Standards provide the first framework for practical guidance on what constitutes the process of ecological repair and how this repair process can be influenced to improve net ecological benefits. In these Standards, Restorative Continuum is highlighted and it recognises that many do not, yet there is still value in aspiring to improvements to the highest extent possible, with some sites potentially being able to be improved in a stepwise manner. Here we elaborate on these Standards by providing a cross-ecosystem theoretical framework of Stepwise Ecological Restoration (STERE) for promoting higher environmental benefits. STERE allows the selection of suitable restorative modes by considering the degree of degradation while encouraging a transition to a higher state. These models include environmental remediation for completely modified and degraded ecosystems, ecological rehabilitation for highly modified and degraded ecosystems, and ecological restoration for degraded native ecosystems. STERE requires selecting tailored restorative modes, setting clear restorative targets and reference ecosystems, applying a systematic-thinking approach, and implementing a continuous monitoring program at all process stages to achieve a resilient trajectory. STERE allows adaptive management in the context of climate change, and when the evidence is available, to “adapt to the future” to ensure climate resilience. The STERE framework could assist in initiating and implementing restoration projects worldwide, especially in developing countries.}

The increase in extreme precipitation (EP) may pose a serious threat to the health and safety of population in arid and semi-arid regions. The current research on the impact of EP on population in Central Asia (CA) is insufficient and there is an urgent need for a comprehensive assessment. Hence, we opted for precipitation and temperature data under two Shared Socioeconomic Pathways (SSP2–4.5 and SSP5–8.5) from ten Global Climate Models (GCMs), which were obtained from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6). By integrating population data in 2020 and 2050 (SSP2 and SSP5), we investigated the future changes in EP and population exposure in CA under 1.5 °C and 2 °C global warming scenarios (GWSs). Our analysis indicates that EP in CA is projected to increase with global warming. Under the SSP5–8.5, the maximum daily precipitation (Rx1day) exhibits an average response rate to global warming of 3.58 %/K (1.99–4.06 %/K). With rising temperatures, an increasing number of areas and populations in CA will be impacted by EP, especially in the Fergana valley. Approximately 25% of the population (land area) in CA is exposed to Rx1day with increases of more than 8.31% (9.32%) under 1.5 °C GWS and 14.18% (13.25%) under 2 °C GWS. Controlling temperature rise can be effective in reducing population exposures to EP. For instance, limiting the temperature increase to 1.5 °C instead of 2 °C results in a 2.79% (1.75%–4.59%) reduction in population exposure to Rx1day. Finally, we found that climate change serves as the predominant factor influencing the population exposure to EP, while the role of population redistribution, although relatively minor, should not be disregarded. Particularly for prolonged drought, the role of population redistribution manifests negatively.}

Steep-slope cropland plays a vital role in food production, economic development, ecosystem diversity, and European cultural heritage. However, these systems are susceptible to extreme weather events. The 2022 summer drought significantly impacted European agriculture, but the specific effects on steep-slope crops remain uncertain. Clarifying this is essential for comprehending similar future events and for implementing effective water management strategies to ensure the sustainability of steep-slope agriculture and associated ecosystem services. This study quantitatively analyzes the spatial distribution of twelve major European steep-slope (>12%) crops and assesses agricultural drought severity during the 2022 events using open-access spatial data. The satellite-based Vegetation Health Index (VHI) is utilized to identify critical hotspots. Results show that olive grove is the most widespread crop in steep slope agriculture (34% of total area), followed by wheat (24%), maize (16%), and vineyard (11%). Almost half of the steep-slope agriculture in Europe suffered drought during summer 2022. Vineyards were hardest affected at 79%, primarily in northern Portugal, northern Spain, southern France, and central Italy. Sunflowers followed at 62%, mainly in Spain, central Italy, southern France, and northern Romania. Olive groves ranked third at 59%, with the most impact in northern Portugal, southern and central Spain, and southern Italy. Maize was also significantly affected at 54%. In this paper, we therefore highlight the need to increase steep-slope agriculture resilience by improving water management and promoting sustainable land practices.}

Terracing is a widely adopted agricultural practice in mountainous regions around the world that aims to conserve soil and water resources. Soil nutrients play a crucial role in determining soil quality, particularly in landscapes prone to drought. They are influenced by factors such as land-use type, slope aspect, and altitude. In this study, we sought to examine the impact of terracing on soil nutrients (soil organic content (SOC), total nitrogen (TN), nitrate-nitrogen (NO3–-N), ammonium nitrogen (NH4+-N), total phosphorus (TP), available phosphorus (AP), total potassium (TK), and available potassium (AK)) and how they vary with environmental factors in the Chinese Loess Plateau. During the growing season, we collected 540 soil samples from the 0 to 100 cm soil layer across five major land-use types, different slope aspects, and varying altitudes. Additionally, a meta-analysis of literature data further corroborated the effective accumulation of soil nutrients through terracing in the Loess Plateau. Our findings are as follows: (1) Terraced fields, regardless of land-use type, showed a significant improvement in SOC and TN content. (2) Soil nutrient contents within terraced fields were predominantly higher on sunny slopes. (3) Terraces at lower altitudes are characterized by elevated SOC concentrations. (4) A meta-analysis of literature data pertaining to terracing and soil nutrients in this region confirmed the effective accumulation of soil nutrients through terracing. The elucidated outcomes of this study offer a profound theoretical underpinning for the accurate planning and management of terraces, the scientific utilization of land resources, and the enhancement of land productivity.}

The study explores the intricate interplay between land use land cover (LULC), normalized difference vegetation index (NDVI), and land surface temperature (LST) within the Lower Son River Basin in India from 1991 to 2020. The region’s ecological balance has been increasingly strained due to rapid urbanization and changing land use patterns. Through a combination of Landsat TM & OLI/TIRS satellite imageries and geospatial analysis techniques, this study unveils the intricate connection between land use and land cover changes, vegetation, and land surface temperature variations. The study area is classified into three altitudinal zones (Zone I: 39–300 m, Zone II: 301–600 m and Zone III: 601–1,247 m) to examine the changes in depth. The area has seen significant changes in LULC, vegetation and LST in all the three altitudinal zones. The findings hold significant implications for sustainable land management and environmental conservation strategies in the Lower Son River Basin. As per the result, 103,438 ha of vegetation was converted into agriculture land and 82,572 ha of agricultural land was transformed into settlements from 1991 to 2020. This trend shows human pressure on the land resource in the study area. Minor increase in water body is seen which is attributed to commissioning of Bansagar dam. Zone I has seen highest settlement growth while Zone III experienced severe deforestation of around 15%. Zone II and III needs attention for holistic sustenance. Analysis of LST shows that it has increased by 0.82 °C from 1991 to 2020 which is a red flag. The study underscores the critical importance of balanced land use practices to preserve ecological integrity and mitigate the adverse effects of urbanization and climate change.}

Coastal land transformation has been identified as a topic of research in many countries around the world. Several studies have been conducted to determine the causes and impacts of land transformation. However, much less is understood about coupling change detection, factors, impacts, and adaptation strategies for coastal land transformation at a global scale. This review aims to present a systematic review of global coastal land transformation and its leading research areas. From 1,741 documents of Scopus and Web of Science, 60 studies have been selected using the PRISMA-2020 guideline. Results revealed that existing literature included four leading focus areas regarding coastal land transformation: change detection, driving factors, impacts, and adaptation measures. These focus areas were further analyzed, and it was found that more than 80% of studies used Landsat imagery to detect land transformation. Population growth and urbanization were among the major driving factors identified. This review further identified that about 37% of studies included impact analysis. These studies identified impacts on ecosystems, land surface temperature, migration, water quality, and occupational effects as significant impacts. However, only four studies included adaptation strategies. This review explored the scope of comprehensive research in coastal land transformation, addressing change detection, factor and impact analysis, and mitigation-adaptation strategies. The research also proposes a conceptual framework for comprehensive coastal land transformation analysis. The framework can provide potential decision-making guidance for future studies in coastal land transformation.}