Sustainability

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Open AccessArticle

Exploring the Sustainable Cultivation Pathways of Pre-Service Teachers’ AI Literacy Based on the TAM-IDT Integrated Model

by Shuai Cao and Yanlin Zheng

Sustainability 2026, 18(14), 7206; https://doi.org/10.3390/su18147206 (registering DOI) - 14 Jul 2026

Abstract

With the deep integration of artificial intelligence technology into education, AI literacy has emerged as a core competence indispensable for pre-service teachers. However, its formation mechanisms and sustainable cultivation pathways remain to be further explored. This study integrates the Technology Acceptance Model (TAM) [...] Read more.

With the deep integration of artificial intelligence technology into education, AI literacy has emerged as a core competence indispensable for pre-service teachers. However, its formation mechanisms and sustainable cultivation pathways remain to be further explored. This study integrates the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT) to construct a theoretical model, in which Individual Innovation (II) and Self-Efficacy (SE) serve as antecedents, Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) as mediators, Behavioral Intention (BI) as a proximal variable, AI literacy as the outcome variable, gender and major as moderating variables, and grade and AI exposure time as control variables, exploring the influencing factors and mechanisms of pre-service teachers’ AI literacy. Through a questionnaire survey of 778 pre-service teachers, Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) were employed for sequential empirical analysis. The PLS-SEM results reveal that II and SE were significantly and positively associated with AI literacy through the serial mediation of PU, PEOU, and BI. The fsQCA further identified four distinct equifinal configurations associated with high AI literacy: “High-efficacy Practice-Oriented”, “High-Behavioral-Intention-Oriented”, “High-Innovativeness-Oriented”, and “Long-Term-Development-Oriented”. The findings demonstrate that the improvement of pre-service teachers’ AI literacy follows multiple equifinal mechanisms, necessitating a shift beyond the single-training mindset. Accordingly, this study proposes differentiated cultivation pathways, providing theoretical foundations and practical references for normal universities to deliver targeted and sustainable AI literacy training. It also offers empirical evidence and strategic support for the sub-goals of SDG 4 concerning teacher capacity-building and the digital transformation of education, which aim to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. Full article

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Open AccessArticle

From Industrial Enclaves to Urban Integration: A Paired Comparison of China’s Third Front Cities

by Yizhuo Gao and Gangyi Tan

Sustainability 2026, 18(14), 7205; https://doi.org/10.3390/su18147205 (registering DOI) - 14 Jul 2026

Abstract

The long-term transformation of mono-industrial cities has become a critical issue in sustainable urban development, particularly where urban growth was initially shaped by state-led industrialization and strategic security concerns. This paper examines China’s Third Front Construction, a large-scale Cold War programme that relocated [...] Read more.

The long-term transformation of mono-industrial cities has become a critical issue in sustainable urban development, particularly where urban growth was initially shaped by state-led industrialization and strategic security concerns. This paper examines China’s Third Front Construction, a large-scale Cold War programme that relocated industrial and defence facilities to inland regions, through a paired comparison of Yuan’an and Xiaogan in Hubei Province. Focusing on Base 066 as a city-forming enterprise, the study combines archival research, local gazetteers, factory records, field investigation, historical satellite imagery, and urban morphological analysis to examine how policy shifts reshaped urban form, industrial layout, infrastructure, and public facilities. The findings show that Yuan’an developed as a dispersed, mountain-based industrial enclave structured by concealment, air defence requirements, and work unit organization, whereas Xiaogan evolved into a more compact and integrated urban industrial district after the relocation of Base 066. This transformation changed not only production space but also urban–rural relations, residential organization, and public service provision. The study demonstrates that Third Front cities should be understood as policy-produced urban systems whose later decline or integration reflects the changing relationship between security, industry, and urban sustainability. It further suggests that industrial heritage, adaptive reuse, and intercity memory networks can support the regeneration of former mono-industrial settlements. Full article

(This article belongs to the Section Tourism, Culture, and Heritage)

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Open AccessArticle

Assessing the Value of Reactive Power Injection from Photovoltaic Generation in Optimal Sizing Studies

by Juan M. Lujano-Rojas, Rodolfo Dufo-López, Jesús S. Artal-Sevil and José L. Bernal-Agustín

Sustainability 2026, 18(14), 7204; https://doi.org/10.3390/su18147204 (registering DOI) - 14 Jul 2026

Abstract

The transition toward a sustainable society requires the large-scale integration of renewable energy resources into modern power systems. Among the available technologies, photovoltaic distributed generation (PDG) plays a key role in achieving these goals. This paper proposes an optimization model for the sizing [...] Read more.

The transition toward a sustainable society requires the large-scale integration of renewable energy resources into modern power systems. Among the available technologies, photovoltaic distributed generation (PDG) plays a key role in achieving these goals. This paper proposes an optimization model for the sizing of PDG in rural distribution systems (DSs). The active power contribution of PDG reduces the loading of the DS, while reactive power injection through Volt–VAR control improves the voltage profile. The optimization problem incorporates probabilistic constraints associated with voltage regulation, line ampacity, and reverse power flow at the substation. The proposed methodology, based on the enhanced snow geese algorithm (ESGA), was validated using two rural DSs with 95 and 170 buses. For the 95-bus system, the results demonstrated a significant improvement in the voltage profile and a 22.9% reduction in the annual energy supplied by the substation. For the 170-bus system, ESGA achieved a high-quality solution with an objective function value only 1.4% higher than that obtained by PSO. The resulting PV penetration levels reached 27.3% and 30.8%, respectively. These results demonstrate the capability of ESGA to provide solutions comparable to those obtained with well-established optimization techniques. Full article

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Open AccessArticle

Trade-Offs Between Environmental Sustainability and Occupational Safety: Carbon Monoxide Emissions in Enclosed and Open Composting Systems

by Karolina Sobieraj, Karolina Giez and Andrzej Białowiec

Sustainability 2026, 18(14), 7203; https://doi.org/10.3390/su18147203 (registering DOI) - 14 Jul 2026

Abstract

This study investigates carbon monoxide (CO) emission potential and kinetics in two biowaste composting facilities, one of which implements process hermetization in a closed hall, in accordance with the Best Available Techniques (BAT) for Waste Treatment, representing a more sustainable waste management approach [...] Read more.

This study investigates carbon monoxide (CO) emission potential and kinetics in two biowaste composting facilities, one of which implements process hermetization in a closed hall, in accordance with the Best Available Techniques (BAT) for Waste Treatment, representing a more sustainable waste management approach due to the reduction in uncontrolled gaseous emissions to the environment. The flux chamber method was used to measure cumulative CO concentrations before and after compost turning across 10 compost piles located either indoors or outdoors. Maximum cumulative CO concentrations (C_COmax) and CO production rate constants (k) were calculated. Results indicate that indoor composting leads to significantly higher net CO emissions, both before and after pile turning. In all indoor piles, cumulative CO concentrations exceeded the United States Environmental Protection Agency (EPA) 1 h and 8 h exposure limits. Post-turning cumulative CO levels reached over 3000 mg CO·m⁻³, with averages ranging from above 15 to over 70 mg CO·m⁻³. These levels pose a serious health risk, potentially causing headaches, collapse, or even loss of consciousness in workers after approximately two hours of exposure. Additionally, compost turning in closed systems resulted in slower CO production, prolonging exposure. The study demonstrated that, although BAT-compliant enclosed systems are beneficial for the environment and support sustainable waste management practices, they may create hazardous conditions for workers. Therefore, continuous monitoring of gas concentrations is essential in closed composting facilities to ensure that environmentally sustainable waste treatment technologies are implemented together with adequate occupational safety measures, supporting a more comprehensive and balanced approach to sustainability. Full article

(This article belongs to the Special Issue Atmospheric Pollution and Microenvironmental Air Quality)

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Open AccessArticle

Reducing the Sensing Burden: A Sensor-Light Machine Learning Framework for Thermal Comfort Assessment

by Christos Mountzouris, Grigorios Protopsaltis, Nikos Andriopoulos, Dimitrios Koukiasas and John Gialelis

Sustainability 2026, 18(14), 7202; https://doi.org/10.3390/su18147202 (registering DOI) - 14 Jul 2026

Abstract

Thermal comfort shapes occupant health, well-being, and productivity and influences the sustainability and energy efficiency of the built environment. The Predicted Mean Vote (PMV) is the most widely used thermal comfort index, yet four of its six input parameters—globe temperature, clothing insulation, metabolic [...] Read more.

Thermal comfort shapes occupant health, well-being, and productivity and influences the sustainability and energy efficiency of the built environment. The Predicted Mean Vote (PMV) is the most widely used thermal comfort index, yet four of its six input parameters—globe temperature, clothing insulation, metabolic rate, and air velocity—require specialized, costly equipment or occupant self-reporting, which has long limited its practical large-scale application. This study introduces a machine learning (ML) framework aimed at estimating these four parameters using indoor and outdoor air temperature and relative humidity as its only sensor inputs, complemented by readily available contextual information and individual activity profiles. It exploits the climatic coupling of globe temperature and air velocity to the indoor–outdoor environment and the temperature- and activity-driven behavioral patterns that govern clothing insulation and metabolic rate. The proposed framework achieved strong predictive performance, explaining 85% of the variance in actual PMV values (R² = 0.85), with a near-zero mean residual (−0.041) and a residual standard deviation of 0.286. Approximately 91% of absolute errors fell below 0.5 PMV units—a deviation unlikely to shift the assigned thermal comfort category. Mapped to thermal comfort categories, predictions reached 80% accuracy, with a macro-averaged precision of 0.81 and recall of 0.80, exhibiting the highest performance for neutral and warm conditions while performing less accurately for cool discomfort. These results suggest that standard temperature and humidity sensors, combined with basic contextual information and individual activity profiles, could support reliable PMV-based thermal comfort assessment, advancing scalable, sensor-light comfort monitoring for energy-efficient, sustainable buildings. Full article

(This article belongs to the Special Issue Sustaining Occupant Well-Being: Thermal Comfort and Air Quality in Indoor Environments)

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Open AccessArticle

Spatio-Temporal Assessment of Vegetation Dynamics for Forest Sustainability in Ouled Yagoub Forest, Khenchela, Algeria, from 1994 to 2025, Using GIS and Remote Sensing

by Oussama Meghithi, Toufik Aliat and Mohamed S. Shokr

Sustainability 2026, 18(14), 7201; https://doi.org/10.3390/su18147201 (registering DOI) - 14 Jul 2026

Abstract

Mediterranean and semi-arid mountain forests are increasingly affected by recurrent drought, wildfire, overgrazing, and anthropogenic pressure, with direct implications for forest sustainability. This study assesses the spatio-temporal dynamics of vegetation cover in the Ouled Yagoub Forest, Khenchela Province, northeastern Algeria, from 1994 to [...] Read more.

Mediterranean and semi-arid mountain forests are increasingly affected by recurrent drought, wildfire, overgrazing, and anthropogenic pressure, with direct implications for forest sustainability. This study assesses the spatio-temporal dynamics of vegetation cover in the Ouled Yagoub Forest, Khenchela Province, northeastern Algeria, from 1994 to 2025, using GIS and remote sensing. Multi-temporal satellite images, including Landsat data for historical periods and Sentinel-2 data for recent years, were processed to calculate NDVI, classify NDVI-derived vegetation-cover classes, and detect vegetation changes before and after the 2021 wildfire. Vegetation-cover classes were quantified in hectares and percentages, and NDVI change maps were produced for the periods 1994–2000, 2000–2010, 2010–2020, 2020–2021, 2021–2022, 2021–2025, and 1994–2025. Results showed that dense vegetation increased from 14.15% in 1994 to 20.71% in 2020, indicating improved pre-fire vegetation conditions. After the 2021 wildfire, dense vegetation decreased to 17.44% in 2021 and 13.44% in 2022, while very low vegetation increased sharply to 29.79% in 2022. The 2021–2022 period showed the strongest negative vegetation response, with 32.65% of the mapped area classified as vegetation decrease. By 2025, partial recovery was observed, with vegetation increase covering 20.14% of the mapped area between 2021 and 2025. However, low vegetation remained dominant, indicating incomplete and spatially heterogeneous recovery. These findings highlight the usefulness of NDVI-based multi-temporal analysis for monitoring forest degradation, post-fire recovery, and priority areas for restoration planning in semi-arid Mediterranean mountain forests, while also supporting sustainability-oriented forest management in other fire-prone regions with comparable ecological constraints. Full article

(This article belongs to the Special Issue Conservation of Biodiversity in Forest and Agroecosystems Under Climate Change)

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Open AccessArticle

Exploring the Relationship Between Digital Transformation and Sustainable Development Goals in Slovenian SMEs

by Jurij Verhovnik, Simona Stojanova, Nina Cvar, Andrej Kos and Emilija Stojmenova Duh

Sustainability 2026, 18(14), 7200; https://doi.org/10.3390/su18147200 (registering DOI) - 14 Jul 2026

Abstract

Digital transformation is increasingly recognized as an important enabler of sustainable development and competitiveness in small- and medium-sized enterprises (SMEs). However, evidence on how different dimensions of digital transformation relate to the achievement of Sustainable Development Goals (SDGs) remains limited. This study explores [...] Read more.

Digital transformation is increasingly recognized as an important enabler of sustainable development and competitiveness in small- and medium-sized enterprises (SMEs). However, evidence on how different dimensions of digital transformation relate to the achievement of Sustainable Development Goals (SDGs) remains limited. This study explores the relationship between digital transformation and selected Sustainable Development Goals (SDGs) using an explanatory sequential mixed-methods design, in which quantitative findings informed the subsequent qualitative exploration and interpretation of managerial perspectives. The quantitative phase combined data from Eurostat’s ICT Usage Survey (2020–2024), including 60 sustainability-related indicators, with an analysis of the relationship between the Digital Economy and Society Index (DESI) and selected SDG indicators across 27 EU member states using Spearman’s rank correlation. The quantitative analysis suggests that Slovenia performs close to the EU average in overall digitalization, while significant associations were identified between digitalization and SDG 9. The qualitative phase consisted of semi-structured interviews with managers from ten Slovenian SMEs from different sectors. The findings indicate that managers perceive digital technologies, process digitalization, data-driven decision-making, and employee digital competencies as important contributors to sustainability-related outcomes, particularly in relation to SDG 8, SDG 9, SDG 12, and SDG 13. The study contributes to the literature on sustainability-oriented digital transformation in SMEs by integrating quantitative benchmarking with managerial perspectives. The findings highlight the importance of organizational capabilities, digital competencies, and strategic alignment in translating digital transformation initiatives into sustainability-related outcomes. The results provide practical implications for SMEs and policymakers seeking to support sustainable and digitally enabled business development. Full article

(This article belongs to the Special Issue Achieving Sustainability: Role of Technology and Innovation)

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Open AccessArticle

The Chimborazo Fauna Production Reserve: Biocultural Landscapes, Sustainability Challenges, and Integrative Governance in High-Andean Protected Areas

by Carlos Renato Chávez-Velasquez, Jhony F. Cruz-Román, Alba M. Sinaluisa-Pilco, Sandra P. Miranda-Salazar, Alden Yépez-Noboa, José Sánchez-Agudo, Emilio R. Díaz-Varela and Fausto O. Sarmiento

Sustainability 2026, 18(14), 7199; https://doi.org/10.3390/su18147199 (registering DOI) - 14 Jul 2026

Abstract

High-mountain protected areas are increasingly exposed to climate change, governance fragmentation, and limited integration of cultural dimensions into conservation strategies. In this context, this study examines the Chimborazo Fauna Production Reserve (Ecuador) as a high-Andean biocultural landscape, integrating ecological, geological, climatic, and cultural [...] Read more.

High-mountain protected areas are increasingly exposed to climate change, governance fragmentation, and limited integration of cultural dimensions into conservation strategies. In this context, this study examines the Chimborazo Fauna Production Reserve (Ecuador) as a high-Andean biocultural landscape, integrating ecological, geological, climatic, and cultural evidence within a common interpretive framework based on the concepts of integrity and authenticity. Using an interdisciplinary synthesis of secondary sources, the analysis explores six analytical dimensions encompassing geodiversity, ecosystems, biodiversity, climate dynamics, archaeological evidence, and cultural practices. The results suggest that the Chimborazo system maintains a notable degree of structural and functional coherence across its ecological and geomorphological components, while also experiencing increasing pressures associated with climate change, particularly glacier retreat and hydrological variability. At the same time, the persistence of archaeological records, Andean epistemologies, ritual practices, and biocultural traditions supports the interpretation of a landscape characterized by continuity of cultural meaning and long-term human–environment interaction. Rather than proposing a definitive or directly transferable model, this study offers a conceptual perspective that highlights the interdependence between ecological processes and cultural values in high-mountain environments. Interpreting Chimborazo as a biocultural landscape offers a useful approach for exploring sustainability-oriented governance and adaptive management in Andean protected areas. The findings may inform ongoing discussions on the integration of biodiversity conservation, cultural continuity, and climate adaptation, while further empirical validation is required to assess the broader applicability of this approach in other mountain contexts. Full article

(This article belongs to the Section Tourism, Culture, and Heritage)

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Open AccessArticle

Does Farmer Knowledge of Soil Quality Influence Input Allocation Decisions and Productivity Outcomes? Implications for Sustainability

by Michael Msukuma, Chisomo Mkwanda, Robertson R. B. Khataza, Harry Mathanda, Wisdom Richard Mgomezulu and Godswill Makombe

Sustainability 2026, 18(14), 7198; https://doi.org/10.3390/su18147198 (registering DOI) - 14 Jul 2026

Abstract

Land degradation, characterized by declining soil fertility and erosion, is a major constraint of maize productivity in Malawi, where more than half of the arable land is degraded. Although knowledge of soil fertility is critical for efficient input allocation, most smallholder farmers rely [...] Read more.

Land degradation, characterized by declining soil fertility and erosion, is a major constraint of maize productivity in Malawi, where more than half of the arable land is degraded. Although knowledge of soil fertility is critical for efficient input allocation, most smallholder farmers rely on subjective assessments of soil quality, which could potentially lead to imprecise decisions. This study examines how farmers’ perceptions of soil fertility and erosion influence input allocation and maize productivity among smallholder farmers in Malawi. Using plot-level data comprising 6370 maize plots from the Malawi Integrated Household Survey, we apply a Conditional Mixed Process estimator and stochastic frontier analysis to assess input use behaviour and technical efficiency. Results indicate that farmers allocate more labour and inorganic fertilizer to plots perceived as fertile, and adoption of improved maize varieties is lower on plots perceived as poor. Organic manure is more frequently applied on degraded plots to recondition the soils. Mean technical efficiency is estimated at 0.63, indicating substantial inefficiency relative to the production frontier. Further, technical efficiency declines with worsening soil conditions. These findings highlight sustainability risks associated with soil degradation and inefficient input allocation. Improved soil diagnostic services, integrated soil fertility management practices, and targeted extension programmes could promote sustainable land management and efficient matching of inputs to soil conditions. Full article

(This article belongs to the Special Issue Agricultural Sustainability and Economic Viability: The Role of Technology)

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Open AccessArticle

Sustainable Intra-Campus Micromobility at an Ecuadorian University: Multinomial Logit and Mixed Logit Models for Bicycle and E-Scooter Choice

by Víctor Núñez, Hugo Salazar, Julio Galarraga, Diego Naunay and Nury Ortiz

Sustainability 2026, 18(14), 7197; https://doi.org/10.3390/su18147197 (registering DOI) - 14 Jul 2026

Abstract

Expanding university campuses face a dual challenge: meeting higher internal travel demand while reducing congestion and emissions. Using stated-preference data (412 respondents; 9 choice tasks per person), this study quantifies the expected adoption of two low-emission micromobility options—bicycle and electric scooter—relative to walking [...] Read more.

Expanding university campuses face a dual challenge: meeting higher internal travel demand while reducing congestion and emissions. Using stated-preference data (412 respondents; 9 choice tasks per person), this study quantifies the expected adoption of two low-emission micromobility options—bicycle and electric scooter—relative to walking and car for intra-campus trips at an Ecuadorian university. Under Random Utility Theory, we estimate a multinomial logit (MNL) and a panel mixed logit (MIXL) model, treating MIXL as the preferred specification. Simulated maximum likelihood with 12,000 draws shows MIXL substantially improves fit and reveals marked heterogeneity in time sensitivity. In out-of-sample prediction (TEST), the two prospective modes achieve comparable average choice probabilities (bicycle = 0.289; electric scooter = 0.286), with a joint acceptance of 0.575, supporting the feasibility of a micromobility program under the evaluated scenarios. Environmental impact (gCO₂/km) exhibits limited aggregate influence compared with operational factors (time and cost). A complementary WTP-space MIXL provides VOT estimates and confirms that 10–20% time reductions increase expected acceptance. Overall, results indicate that adoption is most likely when sustainability objectives are translated into tangible service improvements in effective speed, reliability, and affordability. Full article

(This article belongs to the Special Issue Advancing Public Transport and Urban Infrastructures for Micro-Mobility in Sustainable Cities)

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Open AccessArticle

Social–Ecological Performance Evaluation of Old Neighborhoods in Mountainous Cities and Sustainable Renewal Research: A Case Study of Daxigou Subdistrict, Chongqing

by Qiao Yu and Siyuan Zhong

Sustainability 2026, 18(14), 7196; https://doi.org/10.3390/su18147196 (registering DOI) - 14 Jul 2026

Abstract

Old neighborhoods in China’s mountainous cities generally face the dual challenge of a decline in social vitality intertwined with the fragility of ecosystem functions; there is an urgent need to develop a socio-ecological performance evaluation tool suited to the mountainous context to guide [...] Read more.

Old neighborhoods in China’s mountainous cities generally face the dual challenge of a decline in social vitality intertwined with the fragility of ecosystem functions; there is an urgent need to develop a socio-ecological performance evaluation tool suited to the mountainous context to guide decisions on sustainable regeneration. Taking nine typical aging communities in Daxigou Subdistrict, Yuzhong District, Chongqing as case studies, this study, based on socio-ecological systems theory, constructed an evaluation system comprising 31 indicators across social, ecological, and spatial environmental subsystems. The entropy-weighted TOPSIS method was employed to quantitatively measure the communities’ comprehensive socio-ecological performance, whilst K-means clustering was used for community classification and the identification of weaknesses. The results indicate that the overall socio-ecological performance of Daxigou Subdistrict is relatively low, with scores across the various subsystems distributed unevenly. Correlation analysis reveals the decisive role of resource-related factors in community performance. Based on the evaluation results, an integrated and operational decision making framework combining diagnosis, classification, and policy implementation was established, providing a reference for the targeted regeneration and sustainable development of aging communities in mountainous cities. Full article

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Open AccessArticle

ESG Rating for SMEs: A Tool to Measure Sustainability Performance and Support Credit Assessment

by Giuseppe Andrea Troiano and Federica Ielasi

Sustainability 2026, 18(14), 7195; https://doi.org/10.3390/su18147195 (registering DOI) - 14 Jul 2026

Abstract

The European sustainable finance agenda has increased the demand for ESG information, yet most small and medium-sized enterprises (SMEs) remain outside mandatory sustainability reporting requirements, which are largely designed for large listed firms. This creates an information gap for banks required to integrate [...] Read more.

The European sustainable finance agenda has increased the demand for ESG information, yet most small and medium-sized enterprises (SMEs) remain outside mandatory sustainability reporting requirements, which are largely designed for large listed firms. This creates an information gap for banks required to integrate ESG risks into credit assessment, while SMEs often lack proportionate tools to disclose and signal their sustainability-related practices. This paper addresses this gap by examining how an SME-oriented ESG rating can structure sustainability information in bank lending and which criteria and data can support a proportionate assessment framework for resource-constrained firms. Using Banca Etica’s internal socio-environmental rating model and a unique dataset of 2395 Italian SMEs, the study provides descriptive evidence on ESG score patterns by firm size, legal form and economic sector. The results suggest that Social and Governance dimensions are more readily assessable within the rating model, as they rely on observable organisational practices such as labour conditions, gender balance, stakeholder relations and governance structures. By contrast, Environmental scores are systematically lower, suggesting that environmental practices are more difficult to formalise and document through standardised assessment tools, especially when they require monitoring systems, certifications, technological adaptation and upfront investments. The paper contributes to the literature by linking the SME regulatory gap with a parallel gap in ESG rating research and by documenting how an internal bank-based ESG rating can structure SME sustainability information and support credit assessment processes. Rather than providing direct evidence of improved creditworthiness, the study shows how such ratings may function as potential signalling mechanisms for SMEs within relationship-based lending. Full article

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Open AccessArticle

Improving Landslide Susceptibility Mapping with IF-KMeans Negative Sampling for Geological Disaster Prevention

by Shouhua Wang, Xiang Chen, Boyang Fan, Haifeng Huang, Yuanfa Ji and Xiyan Sun

Sustainability 2026, 18(14), 7194; https://doi.org/10.3390/su18147194 (registering DOI) - 14 Jul 2026

Abstract

Reliable landslide susceptibility mapping (LSM) depends not only on classifier selection but also on the construction of non-landslide samples. Conventional random or buffer-based sampling can retain candidate negatives that are environmentally similar to landslides, increasing label ambiguity and reducing model reliability. This study [...] Read more.

Reliable landslide susceptibility mapping (LSM) depends not only on classifier selection but also on the construction of non-landslide samples. Conventional random or buffer-based sampling can retain candidate negatives that are environmentally similar to landslides, increasing label ambiguity and reducing model reliability. This study proposes an IF-KMeans negative sampling framework to refine candidate non-landslide samples for LSM in Wuzhou City, China. Isolation Forest was trained using 395 mapped landslides and then applied to 2000 candidate negative samples to remove samples with high similarity to the landslide feature space; K-Means clustering was subsequently used to stratify the retained candidates and select representative negative samples. The optimized samples were evaluated using six classifiers, including LR, SVM, MLP, RF, XGBoost, and LightGBM, and compared with conventional buffer-based sampling. The IF-KMeans framework consistently improved AUC across the six classifiers, with gains of 0.041–0.081, and the IF-KMeans-RF model achieved the highest AUC of 0.944. Additional diagnostics showed that the IF-removed samples were closer to known landslides in environmental feature space and were located in areas with higher local landslide density, indicating higher potential confusion risk. These findings suggest that positive-sample-guided negative-sample refinement can reduce ambiguity in LSM training data and improve the reliability of susceptibility mapping for geological disaster prevention and risk mitigation. Full article

(This article belongs to the Special Issue A Research on Sustainable Prevention and Management of Geological Disasters in Engineering)

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Open AccessArticle

Hybrid Machine Learning-Geostatistical Framework for Sustainable Spatiotemporal Temperature Forecasting

by Nouman Iqbal, Sandra De Iaco and Monica Palma

Sustainability 2026, 18(14), 7193; https://doi.org/10.3390/su18147193 (registering DOI) - 14 Jul 2026

Abstract

Temperature plays a key role in climate systems, with significant implications for environmental sustainability, agricultural productivity, and water resource management. Accurate spatiotemporal forecasting of temperature is, therefore, essential for supporting sustainable development and informing climate adaptation strategies. However, traditional modeling approaches often treat [...] Read more.

Temperature plays a key role in climate systems, with significant implications for environmental sustainability, agricultural productivity, and water resource management. Accurate spatiotemporal forecasting of temperature is, therefore, essential for supporting sustainable development and informing climate adaptation strategies. However, traditional modeling approaches often treat spatial and temporal dimensions separately, thereby limiting their ability to capture the full spectrum of spatiotemporal dependencies. This paper proposes a hybrid approach that integrates a machine learning model with a geostatistical prediction technique to forecast daily mean air temperature over the study area. The best-performing spatiotemporal correlation model is selected among various time series and machine learning models including Holt–Winters, Seasonal Autoregressive Integrated Moving Average (SARIMA), Neural Network Autoregression (NNAR), and Artificial Neural Networks (ANNs). The findings demonstrate that the proposed hybrid approach consistently outperforms traditional, non-integrated methods. Importantly, this study contributes to sustainability by enabling high-resolution temperature forecasting that supports climate-resilient agriculture, efficient water resource allocation, and evidence-based environmental management. The generated predictive maps provide actionable insights for policymakers and stakeholders, enhancing adaptive capacity and promoting sustainable resource management under changing climate conditions. Full article

(This article belongs to the Section Air, Climate Change and Sustainability)

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Open AccessArticle

Digital and Operational Modernization and the Sustainability and Performance of the Intermodal Transport Chain: A Pre–Post Analysis of Nine Serbian Firms

by Aleksandar Jovanović, Đorđe Vranješ, Branka Bursać Vranješ, Violeta Lukić Vujadinović, Goran Tričković, Filip Dobrić, Lazar Veljković, Nebojša Ćurčić and Aleksandar Gošić

Sustainability 2026, 18(14), 7192; https://doi.org/10.3390/su18147192 (registering DOI) - 14 Jul 2026

Abstract

Intermodal freight transport is an integral, strategic foundation of the European Union’s vision to decarbonize Europe, but in non-EU countries such as Republic of Serbia, the split in modalities remains heavily focused on road haul, while terminal infrastructure remains poorly interconnected and digital [...] Read more.

Intermodal freight transport is an integral, strategic foundation of the European Union’s vision to decarbonize Europe, but in non-EU countries such as Republic of Serbia, the split in modalities remains heavily focused on road haul, while terminal infrastructure remains poorly interconnected and digital maturity remains low. This research study investigates the adoption of new digital and operational technologies along the intermodal transport chain. It is measured in the context of enhancing ecological and operational performance in the Serbian environment. Research data was collected from a sample of nine medium and large Serbian intermodal logistics companies, who operate along Pan-European Corridor X and the Rhine–Danube Corridor. Three ecological and three operational key performance indicators (KPIs) were defined to measure the level on enhancements reached through sustainability efforts. The study is an uncontrolled, firm-level pre–post comparison: three directional hypotheses were evaluated using paired-sample t-tests, exact Wilcoxon signed-rank tests, and bootstrap confidence intervals on pre- and post-modernization firm-level aggregates. In the post-modernization period, mean CO₂ intensity per TEU (twenty-foot equivalent unit) was 17.4% lower, terminal energy intensity (kWh/TEU) 14.3% lower, and terminal dwell time 22.5% lower than in the pre-modernization period (exact p ≤ 0.001; 95% bootstrap confidence intervals excluding zero). Implications were discussed for EU Green Deal alignment, Trans-European Transport Network (TEN-T) extension to the Western Balkans, and future research has been initially defined. Given the pre/post design without a control group, the reported effects are associations rather than causal estimates and should be read as preliminary, exploratory evidence. Full article

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Open AccessArticle

Spatiotemporal Dynamics of Landscape Ecological Resilience and Adaptive Cycle Categories Under Multi-Scenario Urbanization: Evidence from the Changsha–Zhuzhou–Xiangtan Urban Agglomeration

by Yang Ying, Huaizhen Peng, Yigao Tan, Huachao Lou, Weiwei Wang, Qingying He, Yifan Liu, Yuefeng Cai and Polang Liu

Sustainability 2026, 18(14), 7191; https://doi.org/10.3390/su18147191 (registering DOI) - 14 Jul 2026

Abstract

Urban agglomerations are complex adaptive social–ecological systems, and understanding the spatiotemporal evolution of landscape ecological resilience is important for their sustainable management. This study extended the previously developed Risk–Potential–Connectivity (RPC) framework for the Changsha–Zhuzhou–Xiangtan Urban Agglomeration (CZXUA) by identifying adaptive cycle categories at [...] Read more.

Urban agglomerations are complex adaptive social–ecological systems, and understanding the spatiotemporal evolution of landscape ecological resilience is important for their sustainable management. This study extended the previously developed Risk–Potential–Connectivity (RPC) framework for the Changsha–Zhuzhou–Xiangtan Urban Agglomeration (CZXUA) by identifying adaptive cycle categories at the grid scale using Local Moran’s I and explicit classification rules. Previously generated SD–PLUS land use projections were used to evaluate future changes in the RPC dimensions, integrated resilience, and adaptive cycle composition. (1) From 2000 to 2020, mean landscape ecological resilience decreased from 0.3642 to 0.3345, representing a net decline of 8.15%. Global Moran’s I increased from 0.746 to 0.787, indicating stronger spatial clustering. The spatial pattern remained relatively stable, with higher resilience in peripheral ecological areas, lower resilience in urban cores, and a persistent northwest–southeast orientation. (2) Adaptive cycle composition changed only moderately. The conservation (K) category accounted for more than 45% of the study area, while the release (Ω) category accounted for approximately 19% and was concentrated mainly in urban cores. The exploitation (r) category declined after 2010, whereas the smaller reorganization (α) and transitional (t) categories were more sensitive to classification settings. (3) Using previously generated land use projections as scenario inputs, future RPC responses differed clearly among scenarios. SSP126 maintained more intact ecological patches, relatively stable connectivity, and the smallest resilience change, with increases in r and K. SSP245 showed the greatest connectivity decline, expansion of areas with declining resilience, a reduction in r, and increases in Ω and α. SSP585 produced the highest landscape ecological risk, the largest declines in ecological potential and integrated resilience, reductions in r and K, and increases in α and t. These conditional results indicate that limiting construction land expansion, protecting ecological land, and maintaining ecological connectivity may reduce resilience losses under future urban development. The RPC adaptive cycle framework can support the identification of priorities for ecological protection, restoration, connectivity enhancement, and risk reduction. Full article

(This article belongs to the Special Issue Designing Resilient Cities: Landscape-Based Architecture and Green Space Strategies for Urban Sustainability)

2382 KB

Open AccessArticle

A Phenology–Spectral Dual-Constrained Strategy for Fine-Scale Crop Mapping in Middle-to-High Latitude Agricultural Basins

by Youli Ma, Mingchang Wang, Lai Wei, Xunhua Zheng, Yi Sun and Zhaopei Chu

Sustainability 2026, 18(14), 7190; https://doi.org/10.3390/su18147190 (registering DOI) - 14 Jul 2026

Abstract

Accurate crop mapping in middle-to-high latitude agricultural basins is essential for food security, agricultural management, and sustainable land-use planning. However, crop classification in these regions remains challenging because fragmented field patterns, mixed pixels, and overlapping phenological stages often lead to severe spectral confusion [...] Read more.

Accurate crop mapping in middle-to-high latitude agricultural basins is essential for food security, agricultural management, and sustainable land-use planning. However, crop classification in these regions remains challenging because fragmented field patterns, mixed pixels, and overlapping phenological stages often lead to severe spectral confusion among major dryland crops. To address this issue, this study developed a Phenology–Spectral Dual-Constrained Strategy (PS-DCS) by integrating agronomic knowledge with physically constrained spectral features. The proposed framework identified August as the optimal observation window based on crop phenological divergence. Wheat was first extracted using a spectral fingerprint combining the Chlorophyll Index Red Edge (CI_RE) and Redness index. Subsequently, maize and soybean were separated within the non-wheat mask using the B6 red-edge band selected through feature separability analysis. Validation based on Sentinel-2 time-series imagery and 1056 independent field samples collected in 2025 yielded an Overall Accuracy of 95.36% with a Kappa coefficient of 0.928. Compared with RF, XGBoost, and CNN models, PS-DCS maintained competitive classification performance while substantially reducing dependence on large training datasets and complex parameter tuning. Cross-year validation during 2022–2024 further demonstrated stable spatial transferability without threshold recalibration. These results indicate that translating agronomic mechanisms into physically interpretable remote sensing rules provides an effective and transparent framework for high-precision crop mapping and long-term agricultural monitoring in complex agricultural landscapes. Full article

(This article belongs to the Special Issue Urban Environmental, Spatial and Land Use Assessment by Remote Sensing and GIS)

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Open AccessArticle

Climate Risks, Resilience Resources, and High-Quality Development of the Grain Industry: Evidence from China

by Shuangyu Hu and Kun Chai

Sustainability 2026, 18(14), 7189; https://doi.org/10.3390/su18147189 (registering DOI) - 14 Jul 2026

Abstract

Climate change has increased the frequency of natural disasters, posing growing challenges to the high-quality development of the grain industry. Using China‘s provincial panel data from 2010 to 2023, this study examines the direct, moderating, and spatial effects of natural disasters on the [...] Read more.

Climate change has increased the frequency of natural disasters, posing growing challenges to the high-quality development of the grain industry. Using China‘s provincial panel data from 2010 to 2023, this study examines the direct, moderating, and spatial effects of natural disasters on the high-quality development of the grain industry. A multidimensional index of high-quality development is constructed using the CRITIC weighting method. The results show that natural disasters significantly hinder the high-quality development of the grain industry, and this finding remains robust across a series of robustness tests. Further analysis indicates that agricultural irrigation and rural internet penetration mitigate the adverse effects of natural disasters, suggesting that both engineering resilience and digital resilience enhance the adaptive capacity of grain production systems. Heterogeneity analysis reveals that the negative impacts are more pronounced in major grain-producing regions and in the central, western, and northeastern regions of China. In addition, the spatial econometric results reveal significant negative spatial spillover effects, indicating that disaster shocks extend beyond directly affected regions through spatial interactions. This study contributes to the literature by extending disaster-impact research from grain output losses to the high-quality development of the grain industry, integrating engineering and digital resilience resources within a unified analytical framework, and providing new evidence on the spatial spillover effects of natural disasters. Full article

(This article belongs to the Special Issue Agricultural Environment and Sustainable Management)

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Open AccessArticle

Governing Energy Transitions Under System Complexity:Why Coordination Failures Persist Despite Networked Governance

by Mikael Johnson

Sustainability 2026, 18(14), 7188; https://doi.org/10.3390/su18147188 (registering DOI) - 14 Jul 2026

Abstract

Energy transitions depend on coordination across interdependent actors, infrastructures and practices. As electrification, sector coupling and decentralisation reshape energy systems, governance challenges have shifted from control-oriented delivery towards problems of interaction, responsibility and system coordination, yet governance arrangements continue to struggle with persistent [...] Read more.

Energy transitions depend on coordination across interdependent actors, infrastructures and practices. As electrification, sector coupling and decentralisation reshape energy systems, governance challenges have shifted from control-oriented delivery towards problems of interaction, responsibility and system coordination, yet governance arrangements continue to struggle with persistent coordination failures across institutional contexts. This article argues that such difficulties cannot be explained as implementation problems or insufficient cooperation alone. Coordination failures are conceptualised as manifestations of a mismatch between how energy systems function and how they are governed, distinguishing supply-chain governance assumptions from energy systems operating as service networks. The analysis shows how prevailing governance remains oriented towards linear responsibility and asset-based performance, even as outcomes increasingly depend on coordinated action and resource integration in use. Network-based arrangements represent partial adaptations that acknowledge interdependence without reconfiguring underlying governance logics. Building on this diagnosis, three design principles—relational accountability, alignment-based performance measurement, and constitutive coordination—clarify what a genuine shift in governance logic would entail, as distinct from adaptive overlays. The framework is illustrated through regional energy governance experiences in Sweden and comparable European settings, clarifying why coordination failures persist in sustainability transitions characterised by complex socio-technical interdependence. Full article

(This article belongs to the Special Issue Energy Transition Amidst Climate Change and Sustainability)

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