Geography and Sustainability最新文献

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.

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.

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.

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.

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.

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.

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.

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 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.