Conversion of dryland to paddy fields (CDPF) is an effective way to transition from rain-fed to irrigated agriculture, helping to mitigate the effects of climate change on agriculture and increase yields to meet growing food demand. However, the suitability of CDPF is spatio-temporally dynamic but has often been neglected in previous studies. To fill this knowledge gap, this research developed a novel method for quantifying the suitability of CDPF, based on the MaxEnt model for application in Northeast China. We explored the spatiotemporal characteristics of the suitability of CDPF under the baseline scenario (2010–2020), and future projections (2030–2090) coupled with climate change and socioeconomic development scenarios (SSP126, SSP245, and SSP585), and revealed the driving factors behind it. Based on this, we identified potential priority areas for future CDPF implementation. The results show that the suitability of CDPF projects implemented in the past ten years is relatively high. Compared with the baseline scenario, the suitability of CDPF under the future scenarios will decline overall, with the lightest decrease in the RCP585 and the most severe decrease in the RCP245. The key drivers affecting the suitability of CDPF are elevation, slope, population count, total nitrogen, soil organic carbon content, and precipitation seasonality. The potential priority areas for the future CDPF range from 6,284.61 km2 to 37,006.02 km2. These findings demonstrate the challenges of CDPF in adapting to climate change and food security, and provide insights for food-producing regions around the world facing climate crises.
Ending extreme poverty and achieving sustainable development by 2030 poses a significant challenge for developing countries. In the past decade, China has pioneered the Targeted Poverty Alleviation (TPA) strategy and implemented a range of anti-poverty programs, aiming to reconcile poverty reduction with environmental restoration. However, the effectiveness of the TPA strategy in facilitating sustainable development in the poor areas of China (PAC) remains unclear. Drawing on a perspective of systems, this study compiles a panel dataset of 832 nationally designated poverty-stricken counties in China from 2013 to 2020 and employ the coupling coordination degree model to examine the coupling and coordination relationships among economic, social, and environmental systems in the PAC. We find that during the TPA period, the socioeconomic level developed rapidly, while the environmental quality was slightly improved in the PAC. The TPA strategy promotes the coordinated development of social, economic, and ecological systems in the PAC, shifting the relationship between human and environment from imbalance to coordination. Our findings underscore the necessity for the Chinese government to persist in its environmental restoration efforts in the PAC to guarantee a sustained development progress.
In recent years, there has been a pronounced increase in the frequency of extreme weather events. To comprehensively examine the impact of extreme weather on ecosystem services within the Wuhan Urban Agglomeration (WUA), this study utilized meteorological station data, the Mann-Kendall (MK) test, and the Standardized Precipitation-Evapotranspiration Index (SPEI) to quantify the variation trends in heatwaves (HW) and droughts from 1961 to 2020. Then the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model was employed to evaluate and compare the differences in water yield and climate regulation ecosystem services under various HW, droughts, and HW-drought combination scenarios. The results show that over the past 60 years, the temperature, duration, and frequency of HW have significantly increased in the WUA. Specifically, the highest HW temperature, total HW days, HW frequency, and average HW temperature showed changing trend of +0.17 ℃/decade, +1.4 day/decade, +0.19 event/decade, and +0.07 ℃/decade, respectively. The year 2000 was identified as a mutation year for HW, characterized by increased frequency and heightened severity thereafter. The SPEI value exhibited an insignificant upward trend, with 1980 marked as a mutation year, indicating a decreasing trend in drought occurrences after 1980. Heatwaves have a weakening effect on both water yield and climate regulation services, while drought significantly weakened water yield and had a relatively modest effect on climate regulation. During HW-drought composite period, the average monthly water yield showed a notable discrepancy of 60 mm compared to humid years. Besides, as heatwaves intensify, the area of low aggregation for ecosystem services expands, whereas the area of high aggregation decreases. This study provides a preliminary understanding of the impact of urban extreme weather on urban ecosystem services under changing climatic conditions.
Phenology shifts influence regional climate by altering energy, and water fluxes through biophysical processes. However, a quantitative understanding of the phenological control on vegetation’s biophysical feedbacks to regional climate remains elusive. Using long-term remote sensing observations and Weather Research and Forecasting (WRF) model simulations, we investigated vegetation phenology changes from 2003 to 2020 and quantified their biophysical controls on the regional climate in Northeast China. Our findings elucidated that earlier green-up contributed to a prolonged growing season in forests, while advanced green-up and delayed dormancy extended the growing season in croplands. This prolonged presence and increased maximum green cover intensified climate-vegetation interactions, resulting in more significant surface cooling in croplands compared to forests. Surface cooling from forest phenology changes was prominent during May’s green-up (-0.53 ± 0.07 °C), while crop phenology changes induced cooling throughout the growing season, particularly in June (-0.47 ± 0.15 °C), July (-0.48 ± 0.11 °C), and September (-0.28 ± 0.09 °C). Furthermore, we unraveled the contributions of different biophysical pathways to temperature feedback using a two-resistance attribution model, with aerodynamic resistance emerging as the dominant factor. Crucially, our findings underscored that the land surface temperature (LST) sensitivity, exhibited substantially higher values in croplands rather than temperate forests. These strong sensitivities, coupled with the projected continuation of phenology shifts, portend further growing season cooling in croplands. These findings contribute to a more comprehensive understanding of the intricate feedback mechanisms between vegetation phenology and surface temperature, emphasizing the significance of vegetation phenology dynamics in shaping regional climate pattern and seasonality.
Given the reality of climate-driven migration, the net effectiveness of existing spatially fixed protected areas (PAs) to biodiversity conservation is expected to decline, while the potential of non-PA habitats (non-PAs, i.e., natural, altered, or artificial ecosystems that are not formally designated as PAs) for biodiversity conservation is gaining attention. However, the contribution of non-PAs to biodiversity conservation remains poorly understood. With the aim of comprehensively assessing the effectiveness of non-PAs as transient refugia and steppingstones during future climate-change-induced migration of species in China, a six-metric integrated framework was applied and statistics of these metrics for PAs and non-PAs are compared. Results reveal that, a greater area of non-PAs has a low velocity of climate change (VoCC) compared to that of PAs, and can therefore serve as temporary refugia for species. The disappearing climate index (DCI) and novel climate index (NCI) results show that some 17 % of the subdivided climate classes within the PAs have changed. However, the displacement index (DI) results imply that nearly half (48.98 %) of the PAs need non-PAs to provide transient refugia for climate-driven migration of species in PAs. The higher ratio of effective steppingstones measured using the climate corridor score (CCS) and landscape current flow (LCF) further emphasizes that non-PAs play a more significant role as steppingstones for climate-driven migration than do PAs in terms of both their structural and functional connectivity. Our research further demonstrates that a conservation approach that improves connectivity among PAs and considers Other Effective area-based Conservation Measures (OECMs) is essential for long-term biodiversity adaptation to climate change.
Establishing and maintaining protected areas is a pivotal strategy for attaining the post-2020 biodiversity target. The conservation objectives of protected areas have shifted from a narrow emphasis on biodiversity to encompass broader considerations such as ecosystem stability, community resilience to climate change, and enhancement of human well-being. Given these multifaceted objectives, it is imperative to judiciously allocate resources to effectively conserve biodiversity by identifying strategically significant areas for conservation, particularly for mountainous areas. In this study, we evaluated the representativeness of the protected area network in the Qinling Mountains concerning species diversity, ecosystem services, climate stability and ecological stability. The results indicate that some of the ecological indicators are spatially correlated with topographic gradient effects. The conservation priority areas predominantly lie in the northern foothills, the southeastern, and southwestern parts of the Qinling Mountain with areas concentrated at altitudes between 1,500–2,000 m and slopes between 40°–50° as hotspots. The conservation priority areas identified through the framework of inclusive conservation optimization account for 22.9 % of the Qinling Mountain. Existing protected areas comprise only 6.1 % of the Qinling Mountain and 13.18 % of the conservation priority areas. This will play an important role in achieving sustainable development in the region and in meeting the post-2020 biodiversity target. The framework can advance the different objectives of achieving a quadruple win and can also be extended to other regions.
Assessment of SDG11.3.1 indicator of the United Nations Sustainable Development Goals (SDGs) is a valuable tool for policymakers in urban planning. This study aims to enhance the accuracy of the SDG11.3.1 evaluation and explore the impact of varying precision levels in urban built-up area on the indicator’s assessment outcomes. We developed an algorithm to generate accurate urban built-up area data products based on China’s Geographical Condition Monitoring data with a 2 m resolution. The study evaluates urban land-use efficiency in China from 2015 to 2020 across different geographical units using both the research product and data derived from other studies utilizing medium and low-resolution imagery. The results indicate: (1) A significant improvement in the accuracy of our urban built-up area data, with the SDG11.3.1 evaluation results demonstrating a more precise reflection of spatiotemporal characteristics. The indicator shows a positive correlation with the accuracy level of the built-up area data; (2) From 2015 to 2020, Chinese prefecture-level cities have undergone faster urbanization in terms of land expansion relative to population growth, leading to less optimal land resource utilization. Only in extra-large cities does urban population growth show a relatively balanced pattern. However, urban population growth in other regions and cities of various sizes lags behind land urbanization. Notably, Northeast China and small to medium cities encounter significant challenges in urban population growth. The comprehensive framework developed for evaluating SDG11.3.1 with high-precision urban built-up area data can be adapted to different national regions, yielding more accurate SDG11.3.1 outcomes. Our urban area and built-up area data products provide crucial inputs for calculating at least four indicators related to SDG11.
Ecosystems play a pivotal role in advancing Sustainable Development Goals (SDGs) by providing indispensable and resilient ecosystem services (ESs). However, the limited analysis of spatiotemporal heterogeneity often restricts the recognition of ESs’ roles in attaining SDGs and landscape planning. We selected 183 counties in the Sichuan Province as the study area and mapped 10 SDGs and 7 ESs from 2000 to 2020. We used correlation analysis, principal component analysis, Geographically and Temporally Weighted Regression model, and self-organizing maps to reveal the spatiotemporal heterogeneity of the impacts of the bundle of ESs on the SDGs and to develop spatial planning and management strategies. The results showed that (1) SDGs were improved in all counties, with SDG 1 (No Poverty) and SDG 3 (Good Health and Well-being) exhibiting poor performance. Western Sichuan demonstrated stronger performance in environment-related SDGs in the Sichuan Province, while the Sichuan Basin showed better progress in socio-economic-related SDGs; (2) habitat quality, carbon sequestration, air pollution removal, and soil retention significantly influenced the development of 9 SDGs; (3) supporting, regulating, and provisioning service bundles have persistent and stable spatiotemporal heterogeneity effects on SDG1, SDG8, SDG11, SDG13, and SDG15. These findings substantiate the need for integrated management of multiple ESs and facilitate the regional achievement of SDGs in geographically intricate areas.
The rapid expansion of tobacco farming poses a significant threat to biodiversity in Yunnan Province, China, a region known for its rich biodiversity. This study aims to understand the trade-offs between tobacco farming and higher plant species diversity, and to identify priority counties for conservation. We employed an integrated approach combining species distribution modeling, GIS overlay analysis, and empirical spatial regression to empirically assess the impact of tobacco farming intensity on biodiversity risk. Our findings reveal a compelling negative spatial correlation between tobacco farming expansion and higher plant species diversity. Specifically, southern counties in Wenshan and Honghe prefectures are major priority areas of conservation that exhibit significant spatial correlations between biodiversity risks and high tobacco farming intensity. Quantitatively, at county level, a 1 % increase in tobacco farming area corresponds to a 0.094 % decrease in endemic higher plant species richness across the entire province. These results underscore the need for targeted and region-specific regulations to mitigate biodiversity loss and promote sustainable development in Yunnan Province. The integrated approach used in this study provides a comprehensive assessment of the tobacco-biodiversity trade-offs, offering actionable insights for policymaking.
Understanding the complex interactions between urbanization and ecosystem services (ESs) is crucial for optimizing planning policies and achieving sustainable urban management. While previous research has largely focused on highly urbanized areas, little attention has been given to the phased effect of progressive urbanization on ES networks. This study proposes a conceptual framework that utilizes the network method and space-time replacement to examine the effect of urbanization on the complex relationships among ESs at different stages, with a particular emphasis on the progressive evolution of the process. We apply this framework to the Horqin area, a typical eco-fragile area in China. Results demonstrate that the connectivity of the ES synergy network exhibits a non-stationary characteristic, initially increasing, then decreasing, and subsequently strengthening. Meanwhile, its modularity shows a rising trend during periods of accelerated urbanization. The performance of the trade-off network displays the opposite pattern. Additionally, we observe a gradual replacement of provisioning and regulation services by cultural services in terms of dominance in the synergy network as urbanization advances. By providing guidance for identifying key planning initiatives and implementing ecological protection policies at different stages of development, this study contributes a pathway that can inform development strategies in other regions undergoing progressive urbanization.
Ensuring the provision of accessible, affordable, and high-quality public services to all individuals aligns with one of the paramount aims of the United Nations’ Sustainable Development Goals (SDGs). In the face of escalating urbanization and a dwindling rural populace in China, reconstructing rural settlements to enhance public service accessibility has become a fundamental strategy for achieving the SDGs in rural areas. However, few studies have examined the optimal methods for rural settlement reconstruction that ensure accessible and equitable public services while considering multiple existing facilities and service provisions. This paper focuses on rural settlement reconstruction in the context of the SDGs, employing an inverted MCLP-CC (maximal coverage location problem for complementary coverage) model to identify optimal rural settlements and a rank-based method for their relocation. Conducted in Changyuan, a county-level city in Henan Province, China, this study observed significant enhancements in both accessibility and equity following rural settlement reconstruction by utilizing the MH3SFCA (modified Huff 3-step floating catchment area) and the spatial Lorenz curve method. Remarkably, these improvements were achieved without the addition of new facilities, with the accessibility increasing by 44.21 %, 4.97 %, and 3.11 %; Gini coefficients decreasing by 19.53 %, 1.64 %, and 3.18 %; Ricci-Schutz coefficients decreasing by 21.09 %, 2.09 %, and 4.33 % for educational, medical, and cultural and sports facilities, respectively. It indicated that rural settlement reconstruction can bolster the accessibility and equity of public services by leveraging existing facilities. This paper provides a new framework for stakeholders to better reconstruct rural settlements and promote sustainable development in rural areas in China.
Global warming has led to the frequent occurrence of extreme climatic events (ECEs) in the ecologically fragile Qinghai-Xizang Plateau. Rural households face strong barriers in adaption, and food production is seriously threatened. Current methods for increasing household adaptability take a holistic point of view, but do not accurately identify groups experiencing different adaptive barriers. To better identify different barriers, this paper examines natural, economic, cognitive, and technical barriers. A total of 17 indicators were selected to comprehensively evaluate the degree of barriers to crops adaptation in response to ECEs. Key factors were further analyzed to identify paths to break down the barriers. The results showed the following. (1) Natural barriers were present at the highest degree, economic barriers appear to be smallest, and the overall barriers were biased towards the lower quartile. 10.82 % of the households with the highest barriers. (2) 67.38 % of households report taking adaptive measures in crops production. The increase of the barriers leads to an increase and then a decrease in the possibility of adaptive behavior. (3) Addressing technical barriers is key to rapidly increasing household adaptive behavior in response to ECEs. The study provides recommendations for local governments to improve household adaptation behavior from two perspectives: short-term and long-term optimization pathways. This study can help governments quickly locate households with different classes of barriers, and propose more targeted adaptation policies. The ultimate goal is to ensure the sustainability of crops production and the well-being of households in northeastern part of the Qinghai-Xizang Plateau.
The gap between the projected urban areas in the current trend (UAC) and those in the sustainable scenario (UAS) is a critical factor in understanding whether cities can fulfill the requirements of sustainable development. However, there is a paucity of knowledge on this cutting-edge topic. Given the extensive and rapid urbanization in the United States (U.S.) over the past two centuries, accurately measuring this gap between UAS and UAC is of critical importance for advancing future sustainable urban development, as well as having significant global implications. This study finds that although the 740 U.S. cities have a large UAC in 2100, these cities will encompass a significant gap from UAC to UAS (approximately 165,000 km²), accounting for 30 % UAC at that time. The study also reveals the spatio-temporal heterogeneity of the gap. The gap initially increases before reaching a inflection point in 2090, and it disparates greatly from −100 % to 240 % at city level. While cities in the Northwestern U.S. maintain UAC that exceeds UAS from 2020 to 2100, cities in other regions shift from UAC that exceeds UAS to UAC that falls short of UAS. Filling the gap without additional urban growth planning could lead to a reduction of crop production ranging from 0.3 % to 3 % and a 0.68 % loss of biomass. Hence, dynamic and forward-looking urban planning is essential for addressing the challenges of sustainable development posed by urbanization, both within the U.S. and globally.
This paper addresses urban sustainability challenges amid global urbanization, emphasizing the need for innovative approaches aligned with the Sustainable Development Goals. While traditional tools and linear models offer insights, they fall short in presenting a holistic view of complex urban challenges. System dynamics (SD) models that are often utilized to provide holistic, systematic understanding of a research subject, like the urban system, emerge as valuable tools, but data scarcity and theoretical inadequacy pose challenges. The research reviews relevant papers on recent SD model applications in urban sustainability since 2018, categorizing them based on nine key indicators. Among the reviewed papers, data limitations and model assumptions were identified as major challenges in applying SD models to urban sustainability. This led to exploring the transformative potential of big data analytics, a rare approach in this field as identified by this study, to enhance SD models’ empirical foundation. Integrating big data could provide data-driven calibration, potentially improving predictive accuracy and reducing reliance on simplified assumptions. The paper concludes by advocating for new approaches that reduce assumptions and promote real-time applicable models, contributing to a comprehensive understanding of urban sustainability through the synergy of big data and SD models.
Changes in food production, often driven by distant demand, have a significant influence on sustainable management and use of land and water, and are in turn a driving factor of biodiversity change. While the connection between land use and demand through value chains is increasingly understood, there is no comprehensive conceptualisation of this relationship. To address this gap, we propose a conceptual framework and use it as a basis for a systematic review to characterise value-chain connection and explore its influence on land-use and -cover change. Our search in June 2022 on Web of Science and Scopus yielded 198 documents, describing studies completed after the year 2000 that provide information on both value-chain connection and land-use or -cover change. In total, we used 531 distinct cases to assess how frequently particular types of land-use or -cover change and value-chain connections co-occurred, and synthesized findings on their relations. Our findings confirm that 1) market integration is associated with intensification; 2) land managers with environmental standards more frequently adopt environmentally friendly practices; 3) physical and value-chain distances to consumers play a crucial role, with shorter distances associated with environmentally friendly practices and global chains linked to intensification and expansion. Incorporating these characteristics in existing theories of land-system change, would significantly advance understanding of land managers’ decision-making, ultimately guiding more environmentally responsible production systems and contributing to global sustainability goals.
Agricultural drought, characterized by insufficient soil moisture crucial for crop growth, poses significant challenges to food security and economic sustainability, particularly in water-scarce regions like Senegal. This study addresses this issue by developing a comprehensive geospatial monitoring system for agricultural drought using the Regional Hydrologic Extremes Assessment System (RHEAS). This system, with a high-resolution of 0.05°, effectively simulates daily soil moisture and generates the Soil Moisture Deficit Index (SMDI)-based agricultural drought monitoring. The SMDI derived from the RHEAS has effectively captured historical droughts in Senegal over the recent 30 years period from 1993 to 2022. The SMDI, also provides a comprehensive understanding of regional variations in drought severity (S), duration (D), and frequency (F), through S-D-F analysis to identify key drought hotspots across Senegal. Findings reveal a distinct north-south gradient in drought conditions, with the northern and central Senegal experiencing more frequent and severe droughts. The study highlights that Senegal experiences frequent short-duration droughts with high severity, resulting in extensive spatial impact. Additionally, increasing trends in drought severity and duration suggest evolving climate change effects. These findings emphasize the urgent need for sustainable interventions to mitigate drought impacts on agricultural productivity. Specifically, the study identifies recurrent and intense drought hotspots affecting yields of staple crops like maize and rice, as well as cash crops like peanuts. The developed high-resolution drought monitoring system for Senegal not only identifies hotspots but also enables prioritizing sustainable approaches and adaptive strategies, ultimately sustaining agricultural productivity and resilience in Senegal’s drought-prone regions.
A significant number and range of challenges besetting sustainability can be traced to the actions and interactions of multiple autonomous agents (people mostly) and the entities they create (e.g., institutions, policies, social network) in the corresponding social-environmental systems (SES). To address these challenges, we need to understand decisions made and actions taken by agents, the outcomes of their actions, including the feedbacks on the corresponding agents and environment. The science of complex adaptive systems—complex adaptive systems (CAS) science—has a significant potential to handle such challenges. We address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science, the generic features of CAS, and the key advances and challenges in modeling CAS. Artificial intelligence and data science combined with agent-based modeling promise to improve understanding of agents’ behaviors, detect SES structures, and formulate SES mechanisms.
We demonstrate a multi-method approach towards discovering and structuring sustainability transition knowledge in marginalized mountain regions. By employing reflective thinking, artificial intelligence (AI)-powered text summarization and text mining, we synthesize experts’ narratives on sustainable development challenges and solutions in Kardüz Upland, Türkiye. We then analyze their alignment with the UN Sustainable Development Goals (SDGs) using document embedding. Investment in infrastructure, education, and resilient socio-ecological systems emerged as priority sectors to combat poor infrastructure, geographic isolation, climate change, poverty, depopulation, unemployment, low education levels, and inadequate social services. The narratives were closest in substance to SDG 1, 3, and 11. Social dimensions of sustainability were more pronounced than environmental dimensions. The presented approach supports policymakers in organizing loosely structured sustainability transition knowledge and fragmented data corpora, while also advancing AI applications for designing and planning sustainable development policies at the regional level.
Geography is a discipline that touches multiple sciences and has been key to bridging numerous fields of knowledge. This gives geography the advantage of connecting natural (e.g., biology, ecology, climatology, geomorphology) with social and human (e.g., education, demography, sociology) sciences. The spatialisation of information from different sciences allows us to understand distribution patterns and connections between different realities. Thus, geographical knowledge is essential for an integrated and consistent understanding of our world. The Sustainable Development Goals (SDGs) established by the United Nations (UN) in 2015 were essential to unifying the world towards a common goal. To achieve these, 17 goals and 169 targets were created, and knowledge from multiple sciences is needed to support them. It is a huge challenge, and different knowledge branches are needed to connect. Geography and geographical knowledge have this capacity and support all 17 goals and 169 targets. Although this is a reality, as it will be explained in this editorial, SDG’s achievement for some is becoming utopic and unrealistic due to our world’s differences. It is time to think about the post-2030 SDGs, in which geography and geographic knowledge will be essential unequivocally.