Sustainability

22 pages, 2116 KB

Open AccessArticle

Smart City Innovation in Local Government: A Stakeholder Analysis

by Ali Roziqin, Rio Aji Prasetyo, Rifki Muhammad Bintang, Bramantyo Tri Asmoro, Setyo Budi Kurniawan and Muhammad Fauzul Imron

Sustainability 2026, 18(14), 7347; https://doi.org/10.3390/su18147347 (registering DOI) - 17 Jul 2026

Abstract

Smart city innovation has emerged as a strategic concept in urban governance to improve the quality of life, requiring collaboration among diverse stakeholders. This study examines smart city development from a stakeholder perspective, focusing on Malang City, Indonesia. A qualitative approach was employed, [...] Read more.

Smart city innovation has emerged as a strategic concept in urban governance to improve the quality of life, requiring collaboration among diverse stakeholders. This study examines smart city development from a stakeholder perspective, focusing on Malang City, Indonesia. A qualitative approach was employed, including in-depth interviews, policy document analysis, and field observations, guided by stakeholder theory to understand how various actors influence policy and implementation. The findings indicate that Malang has implemented several smart initiatives, including e-government services, digital infrastructure, traffic management, and environmental monitoring. These efforts are generally perceived to enhance efficiency and transparency. However, challenges persist, particularly in ensuring inclusivity, strengthening inter-agency coordination, and improving digital literacy. The local government plays a leading role, while the private sector, academia, and civil society contribute technical expertise and community engagement. Overall, smart city innovation in Malang is progressing but remains in its early stages and requires stronger collaboration, institutional capacity, and accountability. This study underscores the importance of stakeholders as supporting actors in advancing collaborative and inclusive smart city governance. Full article

(This article belongs to the Special Issue Sustainable Urban Development Prospective for Smart Cities)

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29 pages, 6548 KB

Open AccessArticle

Influence of Renewable Glycine max on Engine Performance, Combustion and Emission: A Combined Model Data Validation and Machine Learning Approach

by Md Nurun Nabi, Fazlur Rashid, Nashia Ahmed Nabila, Nahina Islam, Roksana Yasmin, Biplob Ray, Inji Kenawy and Quddus Tushar

Sustainability 2026, 18(14), 7346; https://doi.org/10.3390/su18147346 (registering DOI) - 17 Jul 2026

Abstract

With increasing energy demand and environmental emission concerns, biodiesels have emerged as a promising alternative to traditional fossil diesel. This study investigates the effects of soybean (Glycine max) biodiesel–diesel blends containing 0–80% biodiesel on the performance, combustion, and emission characteristics of [...] Read more.

With increasing energy demand and environmental emission concerns, biodiesels have emerged as a promising alternative to traditional fossil diesel. This study investigates the effects of soybean (Glycine max) biodiesel–diesel blends containing 0–80% biodiesel on the performance, combustion, and emission characteristics of a diesel engine. The performance parameters evaluated include brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), brake power (BP), and brake thermal efficiency (BTE). Combustion characteristics are assessed through in-cylinder pressure, heat release rate (HRR), and cumulative heat release fractions (HRFs), while emission characteristics are analysed in terms of smoke opacity and nitrogen oxides (NOx) emissions. The primary objective of this study is to evaluate the impact of high-percentage biodiesel and fuel injection timing on overall engine characteristics. A one-dimensional (1D) model was developed using Diesel-RK (Version: 4.30.189) and GT-Suite software (Version: GT-ISE 2026). The models were calibrated and validated through model to model and further validated against experimental data. Using the simulation generated dataset, a machine learning-based surrogate prediction model was developed to rapidly estimate engine performance and emission characteristics directly from engine operating conditions and biodiesel blend ratios, eliminating the need for repeated Diesel-RK simulations. Multiple regression algorithms were evaluated, with the XGBoost tree ensemble model providing the highest prediction accuracy across the investigated performance parameters. Moreover, the Diesel-RK data were validated with experimental data. The results demonstrated model-to-model data and between model to experimental data, confirming the reliability of the developed models. The proposed Diesel-RK simulation and machine learning framework enables rapid and accurate prediction of engine performance and emissions, providing an efficient tool for evaluating biodiesel blends and supporting future engine design and optimisation. Full article

(This article belongs to the Special Issue Sustainable Advances In and Development of Energy Systems in Buildings)

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25 pages, 2185 KB

Open AccessArticle

AI Multimodal Capability and Inclusive Innovation: The Mediating Roles of AI-Enhanced Customer Empathy and Generative Inclusive Design and the Moderating Roles of AI Ethical Awareness and Data Governance Quality

by Hongchang Yan and Yu Gao

Sustainability 2026, 18(14), 7345; https://doi.org/10.3390/su18147345 (registering DOI) - 17 Jul 2026

Abstract

The rapid advancement of artificial intelligence (AI) offers significant opportunities for innovation, yet its benefits remain unequally distributed, with digitally marginalized groups often left behind. Based on the attention-based view and dynamic capability theory, this study investigates how firms may leverage AI’s multimodal [...] Read more.

The rapid advancement of artificial intelligence (AI) offers significant opportunities for innovation, yet its benefits remain unequally distributed, with digitally marginalized groups often left behind. Based on the attention-based view and dynamic capability theory, this study investigates how firms may leverage AI’s multimodal capability to foster inclusive innovation, the moderating effects of AI ethical awareness and data governance quality, and the mediating effects of AI-enhanced customer empathy and generative inclusive design. Using survey data from 233 Chinese firms, we employ hierarchical regression and bootstrap analysis to test the hypotheses. The results show that AI multimodal capability is positively associated with inclusive innovation: AI ethical awareness and data governance quality positively moderate this relationship, while AI-enhanced customer empathy and generative inclusive design mediate it. These findings suggest that inclusive innovation is not merely a social imperative but a strategically achievable outcome through deliberate use of AI, and offer actionable insights for firms’ AI-enabled innovation practices. Full article

(This article belongs to the Section Economic and Business Aspects of Sustainability)

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36 pages, 4946 KB

Open AccessArticle

Climate Risk Contagion and Financial Stability During the Low-Carbon Transition: A Multiscale Vine-Copula Analysis

by Li Zeng and Jinghui Huang

Sustainability 2026, 18(14), 7344; https://doi.org/10.3390/su18147344 (registering DOI) - 17 Jul 2026

Abstract

As the global economy accelerates toward low-carbon transformation, climate financial risks are emerging as a key challenge to monetary policy design and financial stability oversight. This study examines the contagion effects and dynamic interdependencies among domestic climate-sensitive industries, financial climate risk indices, and [...] Read more.

As the global economy accelerates toward low-carbon transformation, climate financial risks are emerging as a key challenge to monetary policy design and financial stability oversight. This study examines the contagion effects and dynamic interdependencies among domestic climate-sensitive industries, financial climate risk indices, and international climate markets. Using daily data from April 2020 to April 2025, we apply a multiscale tail risk modeling framework that integrates wavelet decomposition, conditional volatility modeling, and vine-copula techniques to capture time-varying and asymmetric dependence structures across markets. The results show that the three markets display volatility clustering, fat tails, and nonlinear dependence. The international climate market shows weaker and more volatile connections with the two domestic markets, suggesting that external climate expectations operate mainly through indirect dependence across market states. The risk spillover results further show that climate financial risk contagion differs between upside and downside states and varies across short and medium horizons. These findings have important implications for integrating climate risk into macroprudential surveillance. Central banks and regulators should strengthen early warning mechanisms, climate stress testing, and scenario analysis by considering market-specific, nonlinear, and multiscale risk spillovers. The main contribution of this study is to integrate multiscale decomposition, conditional volatility modelling, and vine-copula dependence analysis into a unified empirical framework for identifying climate financial risk contagion across markets. The findings offer useful evidence for climate stress testing, early warning systems, and financial stability monitoring in emerging markets. Full article

25 pages, 2763 KB

Open AccessArticle

Coupling Coordination of County-Level Urban Resilience and Drought Risk and Its Obstacle Factors: Evidence from Ningxia Province

by Fei Zhang, Na Zhou, Yinxin Zhao, Wei Mei, Jian Yuan, Heyin Tong, Xiaoqin Pang and Banxiao Ruan

Sustainability 2026, 18(14), 7343; https://doi.org/10.3390/su18147343 (registering DOI) - 17 Jul 2026

Abstract

Under the context of global climate change, extreme droughts constrain sustainable development in arid and semi-arid regions of Northwest China. This study took 22 counties in Ningxia as research units and established urban resilience and drought risk evaluation systems. The urban resilience evaluation [...] Read more.

Under the context of global climate change, extreme droughts constrain sustainable development in arid and semi-arid regions of Northwest China. This study took 22 counties in Ningxia as research units and established urban resilience and drought risk evaluation systems. The urban resilience evaluation system includes economy, society, ecology, infrastructure and governance, while the drought risk evaluation system covers hazard factors, vulnerability of the disaster-forming environment and exposure of hazard-affected bodies. It applied an entropy method, improved coupling coordination degree model and obstacle degree model to examine their coupling coordination and key obstacles from 2015 to 2022. The results were as follows. First, the overall urban resilience of county-level regions in Ningxia increased steadily from 2015 to 2022, with an average annual growth rate of 7.32%, while drought risk showed phased fluctuation. Second, the coupling coordination degree between urban resilience and drought risk generally evolved from a relatively high level to a high level, with an average annual growth rate of 2.39%. Its spatial distribution also exhibited marked county-level differences. Crucially, this elevated coordination represents optimized structural balance and narrowed gaps in relative development levels between the two systems, rather than an automatic, synchronous improvement in absolute resilience or risk mitigation. Third, the key factors restricting improvements in coupling coordination included land for cultivated land area, land for water conservancy facilities and medical resources. At the criterion layer, exposure of hazard-affected bodies was the primary obstacle. The findings imply that despite higher system coordination, resilience enhancement and risk prevention do not progress in a fully synchronized manner, remaining constrained by structural bottlenecks like resource endowments and public services. Therefore, tailored disaster-specific governance and operations are highly required for sustainable county-level development. Full article

29 pages, 2990 KB

Open AccessReview

Biostimulants for Sustainable and Resilient Agriculture

by Boujemaa Fassih, Raja Ben-Laouane, Abdessamad Fakhech, Mohamed Ait-El-Mokhtar and Said Wahbi

Sustainability 2026, 18(14), 7342; https://doi.org/10.3390/su18147342 (registering DOI) - 17 Jul 2026

Abstract

The growing pressure exerted by global food demand, combined with the excessive use of chemical and synthetic inputs, is prompting the agricultural sector to seek innovative and sustainable solutions to improve, or at least maintain, crop yields in a context of increased abiotic [...] Read more.

The growing pressure exerted by global food demand, combined with the excessive use of chemical and synthetic inputs, is prompting the agricultural sector to seek innovative and sustainable solutions to improve, or at least maintain, crop yields in a context of increased abiotic stress linked to climate change. Among the promising approaches, biostimulants are attracting growing interest, particularly those derived from natural sources such as seaweed extracts, humic acids, and beneficial microorganisms. These products work through various mechanisms, including osmotic regulation, activation of antioxidant systems, stimulation of root growth, and improvement of nutrient absorption. Many recent research and review articles have explored the optimal combinations of raw materials, formulation processes, target crops, and environmental conditions to maximize beneficial effects on plant growth, soil health, and tolerance to abiotic stresses. As a result, a growing range of commercial products is emerging, with diverse chemical compositions, formulations, and modes of application. However, the precise relationships between the biochemical composition of biostimulants and their physiological effects remain poorly understood, suggesting a key role for molecular synergies. This review provides a concise overview of recent advances in biostimulant research and their potential to enhance food security by improving crop resilience in the context of climate change. Full article

(This article belongs to the Section Sustainable Agriculture)

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31 pages, 654 KB

Open AccessArticle

How Does Industrial Robot Application Promote Sustainable Green Innovation of Enterprises?

by Guangsheng Zhang, Chuanwang Zhang and Zhijia Xu

Sustainability 2026, 18(14), 7341; https://doi.org/10.3390/su18147341 (registering DOI) - 17 Jul 2026

Abstract

As digital technologies continue to evolve, intelligent manufacturing technology represented by industrial robots has been increasingly integrated into various stages of enterprise production and operational activities. This trend has created new opportunities for enterprises to advance green transformation and enhance the level of [...] Read more.

As digital technologies continue to evolve, intelligent manufacturing technology represented by industrial robots has been increasingly integrated into various stages of enterprise production and operational activities. This trend has created new opportunities for enterprises to advance green transformation and enhance the level of sustainable green innovation. Based on data from Chinese A-share listed firms, this study empirically examines the effect of industrial robot application on sustainable green innovation of enterprises. The study finds that industrial robot application significantly enhances sustainable green innovation of enterprises. The underlying mechanisms mainly operate through the optimization of human capital structure and government-specific subsidies. Meanwhile, industrial robot application exhibits significant peer spillover effect, which can generate demonstration and pressure transmission effects among enterprises within the same industry and region, thereby promoting the coordinated improvement of sustainable green innovation. Heterogeneity analysis further reveals that the promoting effect of industrial robot application on sustainable green innovation is more pronounced in non-state-owned enterprises, labor-intensive enterprises, enterprises whose executives have an environmental background, and enterprises in regions with stricter environmental regulation. Furthermore, the establishment of regional environmental courts can strengthen the positive effect of industrial robot application on sustainable green innovation of enterprises. The results of the extended analysis further demonstrate that the contribution of industrial robot application to sustainable green innovation mainly stems from its ‘’leverage effect’’ on overall corporate innovation activities, rather than from the ‘’crowding-out effect’’ caused by compressing other technological innovation activities. This study not only enriches the literature on the environmental effects of industrial robot application but also provides new insights into how enterprises can leverage intelligent technologies such as industrial robots to achieve green transformation. Full article

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17 pages, 661 KB

Open AccessArticle

The Impact of Urban–Rural Integrated Development on Agricultural Green Total Factor Productivity: Empirical Evidence from China

by Zhao Liu and Hui-Ming Jiang

Sustainability 2026, 18(14), 7340; https://doi.org/10.3390/su18147340 (registering DOI) - 17 Jul 2026

Abstract

Urban–rural integration, as a pivotal strategy for reshaping the relationship between cities and the countryside and promoting balanced regional development in the current era, carries considerable weight for the green transition of agricultural systems. This study utilizes panel data from 31 Chinese provincial [...] Read more.

Urban–rural integration, as a pivotal strategy for reshaping the relationship between cities and the countryside and promoting balanced regional development in the current era, carries considerable weight for the green transition of agricultural systems. This study utilizes panel data from 31 Chinese provincial units over the 2011–2023 period to construct a four-dimensional (economic, social, spatial, and ecological) assessment framework for urban–rural integration. It then applies the SBM-GML model to estimate agricultural green total factor productivity and systematically investigates the impact and underlying mechanisms of integration on that productivity. The principal findings are as follows: (1) Urban–rural integration significantly boosts agricultural green total factor productivity, and this outcome remains robust after a variety of sensitivity checks and corrections for potential endogeneity. (2) Mechanism tests further reveal that the primary conduits through which such integration raises AGTFP are the upgrading of human capital and the proliferation of agricultural socialized services. (3) Analysis of moderating effects indicates that both innovation in agricultural science and technology and government fiscal support for farming positively reinforce the facilitative impact of urban–rural integration on agricultural green total factor productivity. (4) Results from heterogeneity analysis suggest that the green productivity effect of such integration is strongest in major grain-producing areas, weaker in major grain-selling areas, and not statistically detectable in grain production–marketing balance regions. Full article

21 pages, 802 KB

Open AccessArticle

The Influence of Environmental Education on Higher Education Students’ Ecological Behavior: A Moderated Chain Mediation Model

by Jianjun Ni, Lei Chen, Zhiquan Qian and Haijing Zheng

Sustainability 2026, 18(14), 7339; https://doi.org/10.3390/su18147339 (registering DOI) - 17 Jul 2026

Abstract

Given the increasingly prominent environmental issues that adversely affect global sustainable development, this study, grounded in situated cognition theory and social cognition theory, constructs a moderated serial mediation model to investigate the mechanisms and boundary conditions through which environmental education influences higher education [...] Read more.

Given the increasingly prominent environmental issues that adversely affect global sustainable development, this study, grounded in situated cognition theory and social cognition theory, constructs a moderated serial mediation model to investigate the mechanisms and boundary conditions through which environmental education influences higher education students’ ecological behavior. Based on a survey and analysis of 3961 higher education students, the findings reveal that (1) theoretical education and practical education exert significantly different pathways to ecological behavior, with practical education demonstrating a significant positive direct behavioral driving force, whereas the direct effect of theoretical education is not significant and operates mainly through indirect psychological transmission; (2) ecological values and ecological cognition serve as serial mediators between environmental education and ecological behavior; (3) the degree of theory-practice transformation positively moderates the relationship between practical education and ecological cognition, while ecological action competence positively moderates the relationship between ecological cognition and ecological behavior. The results indicate that while strengthening students’ environmental education, universities should also focus on cultivating their ecological action competence—the key competence that transforms ecological cognition into actual behavior—and strive to enhance the teaching effectiveness of transforming theoretical education into practical experience. Full article

(This article belongs to the Section Psychology of Sustainability and Sustainable Development)

19 pages, 5264 KB

Open AccessArticle

Towards Sustainable Flood Management: Diagnosing River–Lake Interactions and Proposing a Separation Scheme for the Huaihe River–Hongze Lake System

by Chenguang Xiao and Zengyuan Chai

Sustainability 2026, 18(14), 7338; https://doi.org/10.3390/su18147338 (registering DOI) - 17 Jul 2026

Abstract

The middle–lower Huaihe River Basin faces persistent flood and waterlogging threats, with river–lake interactions being a critical yet underexplored factor constraining flood discharge capacity. This study investigates the flood discharge capacity and erosion–deposition dynamics in the Bengbu–Hongze Lake reach and proposes sustainable management [...] Read more.

The middle–lower Huaihe River Basin faces persistent flood and waterlogging threats, with river–lake interactions being a critical yet underexplored factor constraining flood discharge capacity. This study investigates the flood discharge capacity and erosion–deposition dynamics in the Bengbu–Hongze Lake reach and proposes sustainable management solutions. By analyzing long-term hydrological data (1954–2020) and cross-sectional measurements (1971–2025), we quantified changes in channel morphology and flood behavior. The results reveal that while upstream inflow has remained stable (annual runoff 20.5–33.3 billion m³), sediment concentration has continuously declined by approximately 80%—from 0.474 kg/m³ in the 1950s to 0.094 kg/m³ in the 2020s. The main channel exhibits persistent incision totaling 135.7 × 10⁶ m³, while floodplains have undergone progressive aggradation of 35.1 × 10⁶ m³, reflecting a sediment-starved river system in geomorphic disequilibrium. Critically, the riverbed leading to Hongze Lake exhibits an adverse slope, rising from –10 m at Fushan to over +9 m at Laozishan, while the lake’s sedimentation has reduced its storage capacity by 29% since the 1980s (from 31.27 × 10⁸ m³ to 22.15 × 10⁸ m³). Despite extensive engineering interventions, significant issues persist—including the backwater effect of Hongze Lake, prolonged high water levels during moderate floods (in 2020, water level at Fushan reached 18.34 m at only 61% of the design discharge), and exacerbated waterlogging in riparian lowlands. Therefore, we advocate for a paradigm shift towards a river–lake separation scheme, specifically, an inner-lake embankment approach. This nature-based solution aims to restore the river’s physical structure and harness its self-shaping morphological function for long-term flood management and ecological sustainability. Our findings provide a quantitative basis for re-evaluating the river–lake relationship and offer a strategic direction for sustainable flood management in highly altered alluvial river systems. Full article

23 pages, 2718 KB

Open AccessArticle

Explainable AI for Water Leakage Detection in Urban Water Distribution Networks Using Real and Simulated Data

by Khalid Alharbi

Sustainability 2026, 18(14), 7337; https://doi.org/10.3390/su18147337 (registering DOI) - 17 Jul 2026

Abstract

Water leakage in urban water distribution networks (WDNs) poses significant challenges for sustainable resource management and infrastructure reliability. Traditional detection methods are often reactive and difficult to scale in modern sensor-rich environments. This paper proposes a hybrid data-driven framework for early leak detection [...] Read more.

Water leakage in urban water distribution networks (WDNs) poses significant challenges for sustainable resource management and infrastructure reliability. Traditional detection methods are often reactive and difficult to scale in modern sensor-rich environments. This paper proposes a hybrid data-driven framework for early leak detection that integrates physics-informed simulation with machine learning and explainable analytics. A region-aware EPANET-style simulator is developed to generate realistic hydraulic data under varying demand patterns, environmental conditions, and pressure-dependent leak scenarios. To enhance generalizability, the synthetic dataset is combined with a BATADAL-inspired benchmark, enabling both in-domain and cross-domain evaluation. A feature engineering pipeline is introduced to capture temporal, spatial, and hydraulic relationships, expanding raw sensor signals into a high-dimensional representation. Six machine learning models, including Random Forest, Gradient Boosting, Support Vector Machine, Logistic Regression, Isolation Forest, and a PCA-Based Autoencoder, are systematically evaluated under constrained false-positive requirements. The results show that tree-based ensemble models achieve strong detection performance while maintaining low false-alarm rates (FPR ≤ 0.05). Importantly, cross-domain experiments demonstrate that models trained on simulated data retain competitive performance when applied to benchmark datasets, indicating robust transferability. Finally, explainability analysis reveals that pressure-based temporal statistics and spatial gradients are key indicators of leakage, providing interpretable insights for system monitoring. The proposed framework offers a scalable and generalizable approach for intelligent leak detection in modern water distribution systems. Full article

(This article belongs to the Special Issue Leveraging AI and Deep Learning for Smart Cities: Challenges, Opportunities, and Applications to Sustainable Development)

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47 pages, 6344 KB

Open AccessArticle

How Community Public Space Promotes Older Adults’ Social Health: An Intra-Community Study of the Mediating Role of Social Activities

by Si Zhang, Xinyi Fei and Shuyu Liu

Sustainability 2026, 18(14), 7336; https://doi.org/10.3390/su18147336 (registering DOI) - 17 Jul 2026

Abstract

Against the background of healthy aging, age-friendly communities, and social sustainability, community public spaces are important everyday settings for older adults’ outdoor activities and social connections. However, it remains insufficiently explained how public space environments are translated into social health benefits through specific [...] Read more.

Against the background of healthy aging, age-friendly communities, and social sustainability, community public spaces are important everyday settings for older adults’ outdoor activities and social connections. However, it remains insufficiently explained how public space environments are translated into social health benefits through specific social activities. From an environment-behavior-health perspective, this study examines Chongwen Jincheng Community in Nanjing through a concentrated sample design within a single large community. Based on 217 valid questionnaires from residents aged 60 years and above, an AMOS latent-variable structural equation model was constructed, and a standardized composite-variable Bootstrap model was used for robustness checking. The results show that the community public space environment has a significant total association with older adults’ social health. After social activities are included, the direct associations between traffic convenience, environmental comfort and safety, service facility supportiveness, and social health become non-significant, whereas multiple indirect paths through social activities remain significant, supporting a full mediation pattern in this case study. Traffic convenience is mainly linked to social health through passive and brief social interaction, environmental comfort and safety through brief and sustained social interaction, and service facility supportiveness through passive and sustained social interaction. These findings suggest that, within the cross-sectional SEM framework, the social health value of community public spaces is mainly linked to everyday encounters, weak-tie contact, brief communication, and sustained participation. Full article

(This article belongs to the Section Health, Well-Being and Sustainability)

19 pages, 1090 KB

Open AccessArticle

Accelerating Robust Power Grid Dispatch in Sustainable Energy Systems: Worst-Case Scenario Generation via a Physics-Guided Conditional Diffusion Model

by Shiqi Liu, Rong Yan, Nan Lou, Zhengbo Shan, Ke Wang, Shengmin Qiu, Sitao Wang, Ruopu Yang, Dawei Liao, Yutong He, Sihan Zhou, Yu Yao and Jun Zhang

Sustainability 2026, 18(14), 7335; https://doi.org/10.3390/su18147335 (registering DOI) - 17 Jul 2026

Abstract

The high penetration of sustainable energy integration leads to severe source-side uncertainty challenges in power system dispatch, and two-stage robust optimization is a critical tool for addressing this issue. However, traditional solution methods suffer from significant iterative computational bottlenecks and fail to meet [...] Read more.

The high penetration of sustainable energy integration leads to severe source-side uncertainty challenges in power system dispatch, and two-stage robust optimization is a critical tool for addressing this issue. However, traditional solution methods suffer from significant iterative computational bottlenecks and fail to meet the timeliness requirements of real-time grid dispatch. Therefore, this paper proposes a data-model hybrid-driven fast solution method for two-stage robust optimization. First, the method abandons the traditional iterative solution mode and directly identifies the worst-case scenarios within the uncertainty set based on a conditional diffusion model, which transforms the two-stage robust optimization into a single-stage deterministic optimization. Then, it constructs a physics-guided conditional diffusion model and proposes a worst-case scenario physics-guided operator to apply directional adversarial guidance during the reverse denoising stage to approach the worst-case operational boundary. Finally, by establishing a sensitivity allocation model for global active power imbalance, it reformulates the non-differentiable risk constraints in the gradient operator into efficient algebraic matrix operations. Experimental results show that the proposed method accurately generates worst-case scenarios and reduces the computation time to approximately 20% of the original while ensuring near-optimal economy, which provides a new solution paradigm for balancing decision robustness and timeliness. Full article

(This article belongs to the Topic AI-Enabled Operation and Control of Modern Power and Energy Systems)

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32 pages, 21329 KB

Open AccessArticle

Aligning ESG Rankings in Maritime Ports: A TOPSIS–PROMETHEE Approach to Academic and Port Administration Perspectives

by Marco Marto, Seyedeh Azadeh Alavi-Borazjani, Eduardo B. Couto, José Moreira and Helena Alvelos

Sustainability 2026, 18(14), 7334; https://doi.org/10.3390/su18147334 (registering DOI) - 17 Jul 2026

Abstract

Environmental, social, and governance (ESG) reports are increasingly important for maritime ports and large service and industrial companies, as they measure sustainability, social responsibility, and integration with society. This study aims to strengthen collaboration between port administrations and academia by identifying differences between [...] Read more.

Environmental, social, and governance (ESG) reports are increasingly important for maritime ports and large service and industrial companies, as they measure sustainability, social responsibility, and integration with society. This study aims to strengthen collaboration between port administrations and academia by identifying differences between industry needs and academic focus. The research analyzes ESG indicators from sustainability reports of 23 port administrations and keywords from 34 SCOPUS publications (2020–2025). Indicators and keywords were grouped into ESG dimensions and thematic clusters, generating two criteria: the number of indicators used by ports and the number of keywords in academic research. Two multicriteria decision analysis methods, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), were applied to rank and compare topics across clusters and criteria. Results show similarities in rankings but also reveal differences in priorities between ports and academia, highlighting the need for stronger collaboration and common ESG indicators. Key priorities identified include data and innovation, ranked 1st and 2nd in the governance pillar, employment and job benefits, ranked 1st in the social pillar, and energy efficiency and management, ranked 1st in the environmental pillar. Differences in some environmental and social topics suggest further efforts are needed to align academic research with port administration priorities. Full article

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30 pages, 1469 KB

Open AccessArticle

Photo-Electrocatalysis to Mitigate the Environmental Impact of Nitrogen Compound Pollution in the Water and into the Atmosphere in Recirculating Aquaculture Systems for Trout

by Eleonora Buoio, Luca Maistrello, Simone Livolsi, Alessia Di Giancamillo, Lucia Aidos, Giorgio Mirra, Chiara Bazzocchi, Raffaella Rossi, Daniela Bertotto, Giuseppe Radaelli, Nadia Cherif, Tarek Temraz, Gian Luca Chiarello and Annamaria Costa

Sustainability 2026, 18(14), 7333; https://doi.org/10.3390/su18147333 (registering DOI) - 17 Jul 2026

Abstract

Aquaculture has rapidly expanded, surpassing capture fisheries and playing a vital role in global food security. However, this growth raises environmental concerns, especially regarding nitrogen waste accumulation in recirculating aquaculture systems (RASs). Nitrogen compounds from uneaten feed and fish excreta, mainly ammonia (NH [...] Read more.

Aquaculture has rapidly expanded, surpassing capture fisheries and playing a vital role in global food security. However, this growth raises environmental concerns, especially regarding nitrogen waste accumulation in recirculating aquaculture systems (RASs). Nitrogen compounds from uneaten feed and fish excreta, mainly ammonia (NH₃) and nitrite (NO₂⁻), lead to water pollution, eutrophication, and greenhouse gas emissions. This study describes the setup and the efficiency of a new photo-electrocatalytic (PEC) system in reducing nitrogen waste in a high-density RAS for rainbow trout (30 kg/m³). The PEC system, an evolution of a pure photocatalytic system, was integrated in the units of the RAS and tested for the first time in field conditions, combining photocatalysis and electrochemical oxidation to convert toxic nitrogen species (NH₃) into less harmful nitrogen forms (NO₃⁻ and N₂), aiming to mitigate both water and atmospheric pollution. Over a 4-week period, water nitrogen compounds, ammonia and greenhouse gases (carbon dioxide, nitrous oxide and methane) emitted by water were continuously monitored in two groups of three tanks (PEC vs. control). Each tank was equipped as an independent RAS unit. PEC treatment led to significantly lower NH₃ concentrations (0.96 ± 0.2 mg/L vs. 1.78 ± 0.2 mg/L, p < 0.01), lower NO₂⁻ levels and higher NO₃⁻ levels (61.77 ± 2.14 mg/L vs. 53.10 ± 2.14 mg/L, p < 0.01) in water, indicating efficient nitrogen oxidation. Gaseous emissions were also reduced: NH₃ (1.49 vs. 2.64 mg/m²/day, p < 0.05) and N₂O (1.44 vs. 2.88 mg/m²/day, p < 0.05). These results support PEC technology as a promising solution for improving nitrogen management in intensive aquaculture. Although challenges remain in optimizing energy use and scalability, PEC offers a valuable strategy for reducing environmental impact while sustaining productivity in the aquaculture industry. Full article

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30 pages, 1643 KB

Open AccessArticle

Revenue-Oriented Smart Greenhouse Regulation Using Agro-Economic State Representation and Deep Reinforcement Learning

by Jiawen Yang, Xijue Zhang, Zhengyang Li, Yibin Yu, Shuwen Yin, Xiaohan Lou and Yihong Song

Sustainability 2026, 18(14), 7332; https://doi.org/10.3390/su18147332 (registering DOI) - 17 Jul 2026

Abstract

Smart greenhouse management requires decisions that balance yields, costs, resource use, emissions, and revenue. Existing threshold-based controls and many learning models use sensor data mainly for environmental feedback or yield prediction, leaving limited support for operational decisions tied to farm profitability and resource [...] Read more.

Smart greenhouse management requires decisions that balance yields, costs, resource use, emissions, and revenue. Existing threshold-based controls and many learning models use sensor data mainly for environmental feedback or yield prediction, leaving limited support for operational decisions tied to farm profitability and resource efficiency. This study proposes AER-SFRDNet, a multisource sensor fusion and deep reinforcement learning framework for revenue-aware greenhouse regulation. Crop images, environmental sensor sequences, management records, resource consumption data, and economic variables from facility tomato production are integrated into a unified agro-economic state representation. A yield–cost–revenue prediction module estimates the yield, resource consumption, production cost, sales revenue, and net revenue. These outputs guide a reinforcement learning module that optimizes continuous actions including irrigation, ventilation, supplemental lighting, heating, and

C O_{2}

application. A comparison with traditional machine learning, unimodal deep learning, multimodal learning, and reinforcement learning baselines shows lower prediction errors and improved regulation outcomes. AER-SFRDNet achieves a yield MAE of

0.486

, yield RMSE of

0.674

, yield

R^{2}

of

0.893

, cost RMSE of

0.613

, and net revenue MAPE of

10.82

. In regulation experiments, the cumulative net revenue and input–output ratio reach

1.276

and

1.263

, while the normalized energy use, water use, and carbon emissions per unit yield decrease to

0.752

,

0.781

, and

0.741

, respectively. The results suggest that agro-economic state modeling can support greenhouse decisions that consider both revenue and sustainability-related resource efficiency. Full article

25 pages, 9572 KB

Open AccessArticle

YOLO-MR: An Efficient Forest Fire Detection Framework with Lightweight Design for UAV-Based Sustainable Intelligent Forestry Monitoring

by Weiyu Zhong, Zonglai Liu, Xiaogeng Wei, Nan-Feng Li and Wei Feng

Sustainability 2026, 18(14), 7331; https://doi.org/10.3390/su18147331 (registering DOI) - 17 Jul 2026

Abstract

Forest fires pose a serious threat to forest ecosystems, biodiversity, carbon sequestration, and sustainable socioeconomic development. Timely and accurate detection is essential for effective early warning and emergency response. However, existing deep learning-based methods often face challenges in balancing detection accuracy and computational [...] Read more.

Forest fires pose a serious threat to forest ecosystems, biodiversity, carbon sequestration, and sustainable socioeconomic development. Timely and accurate detection is essential for effective early warning and emergency response. However, existing deep learning-based methods often face challenges in balancing detection accuracy and computational efficiency under UAV-assisted forestry monitoring scenarios. This study proposes YOLO-MR, a compact and efficient forest fire detection framework based on YOLOv8n for UAV-assisted intelligent forestry monitoring. The proposed method incorporates a multi-scale deep dilated spatial pyramid fast pooling module, GhostConv, an improved ReC2f module, and an additional P2 detection layer, as well as PIoUv2 loss, enabling more effective multi-scale feature representation and improved target localization. Evaluation using the public M4SFWD dataset together with the self-constructed UFFD dataset verifies the effectiveness and generalization capability of YOLO-MR. Compared against YOLOv8n, the proposed YOLO-MR delivers increases of 1.3%, 2.1%, 1.3%, and 2.1% for Precision, Recall, mAP50, and mAP50-90, respectively, on the M4SFWD dataset, with corresponding gains of 0.9%, 1.8%, 1.8%, and 2.5% on the UFFD dataset. Meanwhile, the proposed model requires only 2.85 M parameters, which is 5.3% fewer than the baseline, demonstrating improved parameter efficiency. The experimental results demonstrate that YOLO-MR provides an effective trade-off between detection performance and computational efficiency, offering a promising solution for Unmanned Aerial Vehicle (UAV)-assisted forest fire monitoring and early warning. By enabling timely wildfire detection with a compact and efficient architecture, the proposed framework supports sustainable intelligent forestry monitoring, contributes to ecosystem protection and disaster prevention, and promotes sustainable forest management. Full article

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15 pages, 2974 KB

Open AccessReview

A New Era in Sustainability: Te- and Pb-Free Thermoelectrics

by Kivanc Saglik, Yannan Lu and Xizu Wang

Sustainability 2026, 18(14), 7330; https://doi.org/10.3390/su18147330 (registering DOI) - 17 Jul 2026

Abstract

Nearly two-thirds of global energy is lost as waste heat. Thermoelectric materials enable the direct conversion of heat into electricity, offering a promising recovery strategy. However, many high-performance systems rely on toxic or scarce elements such as Pb and Te, limiting sustainability. This [...] Read more.

Nearly two-thirds of global energy is lost as waste heat. Thermoelectric materials enable the direct conversion of heat into electricity, offering a promising recovery strategy. However, many high-performance systems rely on toxic or scarce elements such as Pb and Te, limiting sustainability. This review compares transport mechanisms in bulk and thin-film thermoelectrics, highlights environmentally benign material alternatives and fabrication routes, and discusses key challenges and future opportunities for sustainable energy applications. To provide a comprehensive overview of tellurium- and Pb-free bulk thermoelectrics, recent developments are summarized in a comprehensive table detailing material composition, peak zT values, temperature differences (ΔK), efficiencies (η), power output (P_out) (or maximum power density (P_max)), and device geometries. Full article

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21 pages, 1343 KB

Open AccessArticle

Non-Financial Factors and Risk Appetite in Early-Stage Entrepreneurship in Romania: Implications for Sustainable Business Development

by Camelia-Cristina Dragomir-Pânzaru, Tiberiu Foris, Geanina-Bianca Savu, Loredana-Nuți Rogoz and Larisa-Elena Stanciu

Sustainability 2026, 18(14), 7329; https://doi.org/10.3390/su18147329 (registering DOI) - 17 Jul 2026

Abstract

Early-stage entrepreneurship involves making decisions under conditions of high uncertainty, which makes risk appetite an important component of the entrepreneurial decision-making process. This paper analyzes the influence of non-financial factors on risk appetite in early-stage entrepreneurship in Romania and their relevance in shaping [...] Read more.

Early-stage entrepreneurship involves making decisions under conditions of high uncertainty, which makes risk appetite an important component of the entrepreneurial decision-making process. This paper analyzes the influence of non-financial factors on risk appetite in early-stage entrepreneurship in Romania and their relevance in shaping the entrepreneurial decision-making process. The research uses an exploratory qualitative approach, based on focus groups and semi-structured interviews conducted with entrepreneurs in the early stages of business development. The results highlight that entrepreneurs’ risk appetite is not merely an impulsive reaction or the exclusive result of economic reasoning, but reflects a process of evaluation and adaptation to conditions of uncertainty, influenced by the interaction between psychological and human capital factors, such as previous experience and entrepreneurial education. In the analyzed context, the results suggest a moderate risk appetite of the entrepreneurs participating in the study, which favors the adoption of entrepreneurial decisions oriented towards adaptability, continuity of activity and sustainable business development. Based on the results, the authors propose an integrated conceptual framework that highlights the relationship between entrepreneurial education, previous experience, psychological factors, risk appetite and the entrepreneurial decision-making process, with implications for sustainable business development in the context of an emerging economy. Full article

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