Geography and Sustainability最新文献

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.

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 We present a long time series of maximum flood discharge in the YRB dating back to 1843 compiled from historical documents and instrument measurements.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.

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.

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.

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.

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), T_water (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 T_water, 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.

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.

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.

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.

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.