Sustainability

25 pages, 4018 KB

Open AccessArticle

Streatery Interface Design for Healthy and Inclusive Streets: A Scenario-Based Experimental Study of Perceived Spatial Publicness and Emotional Restoration

by Yan He, Li Zhu, Haoyu Deng, Ni Zhang, Quhan Chen, Siyu Zhang, Xiangxiang Chen and Chenxi Song

Sustainability 2026, 18(13), 6927; https://doi.org/10.3390/su18136927 - 7 Jul 2026

Viewed by 468

Abstract

Although streateries, defined here as outdoor commercial extensions of dining, café, or retail activities into street-edge pedestrian spaces, can enliven urban streets, their commercial use of public pedestrian space may raise concerns about openness, shareability, and inclusion. This study examines how different streatery [...] Read more.

Although streateries, defined here as outdoor commercial extensions of dining, café, or retail activities into street-edge pedestrian spaces, can enliven urban streets, their commercial use of public pedestrian space may raise concerns about openness, shareability, and inclusion. This study examines how different streatery interface designs affect pedestrians’ perceived spatial publicness and emotional restoration, and whether perceived spatial publicness mediates this relationship. Drawing on publicness studies and Restorative Environment Theory, a scenario-based between-subjects experiment was conducted using four standardized visual stimuli: boundaryless open, fully enclosed, flexible permeable, and hybrid covered interfaces. Based on 420 valid questionnaires, Welch’s ANOVA, Games–Howell post hoc tests, independent-sample t-tests, and PROCESS mediation analysis were performed. The results show that prior streatery consumption experience significantly increased perceived spatial publicness but did not significantly affect emotional restoration. Interface type had significant effects on both outcomes, following a non-monotonic pattern: hybrid covered and flexible permeable interfaces performed best, the fully enclosed interface performed worst, and the boundaryless open interface was not necessarily optimal. Perceived spatial publicness partially mediated the relationship between interface type and emotional restoration, indicating one psychological pathway through which interface design shapes restorative experience. These findings suggest a possible perceptual-level emotional compensation pathway, in which perceived spatial publicness serves as one tested route linking streatery interface design with emotional restoration in southern Chinese commercial street contexts. The study offers context-specific evidence for public-experience-oriented streatery design in southern Chinese commercial streets. Full article

(This article belongs to the Special Issue Sustainable Urban Design and Resilient Communities)

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30 pages, 750 KB

Open AccessArticle

From Experimentation to Sustainability Transformation: Developing a Tool to Better Anticipate Upscaling of Urban Innovation Experiments

by Marc Dijk, Francesca Cellina, Nicola da Schio, Thomas Höflehner and Mario Diethart

Sustainability 2026, 18(13), 6926; https://doi.org/10.3390/su18136926 - 7 Jul 2026

Viewed by 347

Abstract

Urban experiments are increasingly embraced for their potential to transform incumbent socio-technical systems by offering multifaceted, ‘high-quality’ learning. The early literature on sustainability transitions painted an optimistic picture of the impact of experiments, prescribing their role in managing transitions. More recently, scholars have [...] Read more.

Urban experiments are increasingly embraced for their potential to transform incumbent socio-technical systems by offering multifaceted, ‘high-quality’ learning. The early literature on sustainability transitions painted an optimistic picture of the impact of experiments, prescribing their role in managing transitions. More recently, scholars have elaborated on the different purposes and functions of experiments; however, they generally stress that, as of yet, there is scarce evidence for their effectiveness concerning transformation in practice. This paper develops a tool for more effective follow-ups after an experiment in practice, by anticipating contextual constraints on upscaling innovations. The tool has been developed through a design science research method by first doing action research on sustainable mobility innovations in four European cities and subsequently testing the prototype of the tool in five other places. Our findings suggest that this new tool improves conditions for wider implementation of the innovation being experimented with, and associated transformation. This is one key starting point for increasing the impact of experiments and accelerating urban sustainability transformation. Full article

(This article belongs to the Special Issue Sustainable Urban Green Transport and Mobility: Lessons from Practice)

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18 pages, 624 KB

Open AccessArticle

AI Use Quality and Sustainable Educational Equity: Evidence on the Socioeconomic Gap in Deep Learning Approach Among Chinese High School Students

by Ziqi Zhang and Fuhai An

Sustainability 2026, 18(13), 6925; https://doi.org/10.3390/su18136925 - 7 Jul 2026

Viewed by 602

Abstract

Sustainable educational equity, the principle behind United Nations Sustainable Development Goal 4, calls for ensuring that disadvantaged students benefit from emerging educational technologies rather than being pushed further behind. As AI learning tools become routine in secondary schools, whether they reduce or widen [...] Read more.

Sustainable educational equity, the principle behind United Nations Sustainable Development Goal 4, calls for ensuring that disadvantaged students benefit from emerging educational technologies rather than being pushed further behind. As AI learning tools become routine in secondary schools, whether they reduce or widen socioeconomic gaps in learning has become a pressing question for sustainable educational policy. Building on digital divide theory and the resource substitution hypothesis, we tested whether family socioeconomic status (SES) moderates the link between students’ AI use quality and deep learning approach—specifically, whether high-quality AI use is more strongly associated with deep learning approach for students from lower-SES backgrounds. Data came from 548 students at three public high schools in Hangzhou, China. AI use quality was operationalized as a three-part construct (seeking, evaluating, applying). Deep learning approach was measured with the deep approach subscale of the R-SPQ-2F. We tested moderation with hierarchical regression and probed the interaction with simple slopes. Two results stood out. First, both SES and AI use quality positively predicted deep learning approach. Family SES moderated the association between AI use quality and deep learning approach: the link between AI use quality and deep learning approach was stronger for low-SES students than for their higher-SES peers, and when AI use quality was high, the deep learning gap across SES levels was correspondingly narrower. The data support the equalizer hypothesis: high-quality AI use can narrow the SES-related gap in deep learning approach and serve as a lever for sustainable educational equity. Schools that want AI to advance equity should treat AI literacy as an instructional priority across subjects, not as something students are expected to figure out on their own. Full article

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54 pages, 1589 KB

Open AccessArticle

Assessing the Investment Attractiveness of Metallurgical Enterprises to Improve the Efficiency of Their Sustainable Investment Activities

by Tatyana Semenova, Ivan Volkov, Alexey Novikov, Juan Yair Martínez Santoyo, Dmitrii Gloukhov and Elena Stepuk

Sustainability 2026, 18(13), 6924; https://doi.org/10.3390/su18136924 - 7 Jul 2026

Viewed by 461

Abstract

The objective of this study is to develop a methodological approach to the integral assessment of the investment attractiveness of metallurgical enterprises to improve the efficiency of investment activities and the implementation of projects and ensure sustainable development. The metallurgy industry faces the [...] Read more.

The objective of this study is to develop a methodological approach to the integral assessment of the investment attractiveness of metallurgical enterprises to improve the efficiency of investment activities and the implementation of projects and ensure sustainable development. The metallurgy industry faces the challenge of balancing efficiency goals and sustainable objectives (ESG) and risks. Our approach takes into account the relationship between investment potential, realized opportunities, and the level of risk. Based on a systematic analysis of theoretical approaches, an integral investment attractiveness index is proposed that aggregates investment potential (consisting of seven sub-potentials), an assessment of the results of project implementation, and an aggregated risk index. Assessing investment attractiveness is important for ensuring the sustainable implementation of effective projects and determining their priority. A panel dataset was constructed using data from two metallurgy companies. The relationship between investment attractiveness and classical indicators (ROIC, EVA, MVA, Tobin’s Q, and P/BV) is examined through panel regression with fixed effects, cross-correlation analysis of the temporal structure of relationships, a CUSUM test for model stability, and decomposition of investment attractiveness changes. Decomposition of investment attractiveness changes makes it possible to quantify the contribution of potential, opportunities, and risk to the dynamics of investment attractiveness across various periods, including crisis and post-crisis ones describing the specifics of the metallurgic industry. The presented methodology is relevant for increasing the efficiency of project implementation within the framework of an integral company policy and contributes to the acceleration of industrial implementation of sustainable projects in the metallurgy sector. Full article

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36 pages, 12729 KB

Open AccessArticle

Integrating Smart Port System and Blue Economy Principles for the Sustainable Maritime Development of an Island Region in Indonesia: A Bayesian Network Approach

by Akhmad Fauzi, Kastana Sapanli, Gatot Yulianto and Tomi Ramadona

Sustainability 2026, 18(13), 6923; https://doi.org/10.3390/su18136923 - 7 Jul 2026

Viewed by 437

Abstract

The global maritime sector is undergoing rapid transformation, creating an urgent need to align digital port technologies with a sustainable development framework. However, existing research on smart ports and the blue economy is fragmented and predominantly driven by deterministic approaches that overlook systemic [...] Read more.

The global maritime sector is undergoing rapid transformation, creating an urgent need to align digital port technologies with a sustainable development framework. However, existing research on smart ports and the blue economy is fragmented and predominantly driven by deterministic approaches that overlook systemic complexity and uncertainty. This study develops a smart port system model grounded in blue economy principles, using a Bayesian network to analyze causal relationships among operational, environmental, and governance variables under uncertainty. The model incorporates key factors including port operational efficiency, logistics reliability, environmental compliance systems, coastal employment, and regulatory enforcement. The findings indicate that operational and logistical factors are the primary drivers of the system, while environmental and socioeconomic variables strongly shape sustainability outcomes. Scenario analysis shows that coordinated interventions targeting these key variables generate the greatest improvements in Smart Port–Blue Economy integration. Sensitivity analysis further identifies coastal economic output, regional competitiveness, and marine ecosystem health as the most responsive outcome variables. The research offers lessons for policymakers to enhance port management by integrating logistics and technological considerations with blue economy principles to design adaptive and resilient policies, particularly in island regions. Full article

(This article belongs to the Special Issue Sustainable Logistics Management: Research Focus on Port and Maritime Transportation)

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23 pages, 2111 KB

Open AccessArticle

Regime-Dependent Financial Inclusion, Energy Intensity, and Trade Openness in Saudi Arabia: An ARDL–Structural Break Analysis of CO₂ Emissions and the Sustainable Development Goals

by Amira Houaneb, Aarif Mohammad Khan, Mohammad Junaid Alam, Dorra Talbi, Fatima Thamer Al-Otaibi and Amal Oyun Saud Alhuthayli

Sustainability 2026, 18(13), 6922; https://doi.org/10.3390/su18136922 - 7 Jul 2026

Viewed by 382

Abstract

Background: Whether financial deepening and trade integration support or hinder environmental sustainability in hydrocarbon-dependent economies remains contested. Methods: This study examines the relationships among financial inclusion, energy intensity, trade openness, and CO₂ emissions per capita in Saudi Arabia for 1980–2020. The empirical [...] Read more.

Background: Whether financial deepening and trade integration support or hinder environmental sustainability in hydrocarbon-dependent economies remains contested. Methods: This study examines the relationships among financial inclusion, energy intensity, trade openness, and CO₂ emissions per capita in Saudi Arabia for 1980–2020. The empirical strategy combines ARDL bounds testing, FMOLS, DOLS, CCR robustness, Toda–Yamamoto causality, and a battery of structural-break tests comprising Zivot–Andrews unit-root tests, Bai–Perron sup-F tests, and Chow tests. To address the mechanical correlation between carbon productivity and GDP, the per capita emissions specification (LNCP) is used as the primary outcome; carbon productivity (LNES) is reported for robustness. The small-sample sub-period results are stress-tested using ridge regression, residual-bootstrap confidence intervals, a GDP-augmented (scale-control) specification, and a break-date sensitivity analysis. Results: Cointegration is established. The Chow test identifies a significant break in the cointegrating relationship at 2001 (F = 7.36, p < 0.001 for LNCP), supported by the Zivot–Andrews endogenous-break dates for the financial-inclusion series (2000) and trade-openness series (2005), and by the Bai–Perron sup-F test (sup-F = 26.37 at 1990, exceeding the 1% Andrews critical value). Sub-sample re-estimation around 2001 shows that energy intensity, urbanisation, and trade openness are robust drivers of per capita emissions only after the break, while financial inclusion is statistically insignificant in both regimes once the GDP–carbon-productivity mechanical relationship is removed. Conclusions: The Saudi finance–environment relationship is structurally unstable, and policy assessments based on full-sample averages can be misleading. The evidence is best read as describing regime-dependent, conditional long-run associations rather than as identifying structural causal effects. By exposing the interactions, synergies, and trade-offs among financial deepening (SDG 8), energy efficiency (SDG 7), sustainable consumption and production (SDG 12), and climate action (SDG 13), the study shows how this descriptive quantitative evidence can inform—rather than directly identify—an instrument-level policy discussion. The findings are consistent with a Vision 2030 mix that prioritises energy efficiency and green-finance reform, with implications for SDG Targets 7.3, 8.10, 12.2, and 13.2 across oil-exporting economies. Full article

(This article belongs to the Special Issue Innovating for the SDGs: Scientific and Technological Pathways to Sustainable Development)

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23 pages, 3368 KB

Open AccessArticle

Supplier Selection Framework in Circular Supply Chains: Combining BWM, AHP Ratings, and Risk Analysis

by Claudemir Leif Tramarico, Antonella Petrillo and Valério Antonio Pamplona Salomon

Sustainability 2026, 18(13), 6921; https://doi.org/10.3390/su18136921 - 7 Jul 2026

Viewed by 452

Abstract

Selecting suppliers for circular supply chains is an important requirement, demanding evaluation frameworks that capture reuse, reverse flows, and waste minimization beyond traditional metrics. This paper introduces a structured model designed to assess suppliers against specific circularity-oriented criteria. The Best-Worst Method (BWM) derives [...] Read more.

Selecting suppliers for circular supply chains is an important requirement, demanding evaluation frameworks that capture reuse, reverse flows, and waste minimization beyond traditional metrics. This paper introduces a structured model designed to assess suppliers against specific circularity-oriented criteria. The Best-Worst Method (BWM) derives criteria weights, the Analytic Hierarchy Process (AHP) ratings evaluate alternatives, and a risk assessment stage consolidates the final ranking. The primary insights of this research include: (i) the development of a structured supplier evaluation model that encompasses dimensions like closed-loop integration, end-of-life management, material efficiency, and waste management into a multi-criteria perspective; (ii) applying BWM to derive consistent criteria weights, clarifying how circular performance attributes shape supplier prioritization; (iii) applying AHP ratings and risk assessment to consolidate the evaluation into a final ranking of alternatives; and (iv) demonstrating the operational feasibility and applicability of the framework through a real-world case analysis, providing empirical evidence for assessing circular supplier performance in industrial environments. Full article

(This article belongs to the Special Issue Sustainable Operations and Green Supply Chain)

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23 pages, 24826 KB

Open AccessArticle

Spatial and Temporal Patterns of Environmental Noise in Two Colombian Urban Typologies: A Comparative SoundPLAN-Based Study Between a Metropolitan City (Soledad) and a Mining-Industrial City (Montelíbano)

by Samuel Pinto Argel, Mauricio Rosso Pinto and Humberto Tavera Quiróz

Sustainability 2026, 18(13), 6920; https://doi.org/10.3390/su18136920 - 7 Jul 2026

Viewed by 402

Abstract

This study compares the spatial and temporal dynamics of environmental noise in two Colombian municipalities with contrasting urban typologies: Soledad (Atlántico, >600,000 inhabitants; traffic and airport dominated) and Montelíbano (Córdoba, ~86,647 inhabitants; ferronickel mining and heavy transport dominated). A two-tier methodology integrated field [...] Read more.

This study compares the spatial and temporal dynamics of environmental noise in two Colombian municipalities with contrasting urban typologies: Soledad (Atlántico, >600,000 inhabitants; traffic and airport dominated) and Montelíbano (Córdoba, ~86,647 inhabitants; ferronickel mining and heavy transport dominated). A two-tier methodology integrated field monitoring under Resolution 627 of 2006 at 80 points (Soledad) and 30 points (Montelíbano), with calibrated SoundPLAN 6.0 dispersion models implementing ISO 9613-2 propagation. The central finding is that urban typology produces fundamentally different acoustic fingerprints: Soledad exhibits a strong day–night gradient (working-day mean LAeq diurnal = 73.2 dB(A), nocturnal = 68.1 dB(A); mean ΔLAeq = −5.1 dB(A)), while Montelíbano displays a near-flat profile (diurnal = 67.1 dB(A), nocturnal = 67.0 dB(A); ΔLAeq = −0.1 dB(A)), reflecting continuous mining-industrial operations. Non-compliance rates reach 83.8% (Soledad day), 96.2% (Soledad night), 60.0% (Montelíbano day) and 100% (Montelíbano night). Model validation meets international ISO 9613-2 benchmarks for Montelíbano (75% of residuals within ±5 dB(A); mean residuals −2.72/−2.92 dB(A) diurnal/nocturnal); Soledad shows higher scatter (mean residuals +5.78/+1.43 dB(A)), consistent with the greater acoustic heterogeneity of a large metropolitan environment. These results demonstrate that typology-differentiated noise management policies are needed for effective implementation of Colombia’s Anti-Noise Law (Law 2450 of 2025). Full article

(This article belongs to the Special Issue Sustainable Air Quality Management and Monitoring)

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31 pages, 2797 KB

Open AccessArticle

From Facility Provision to Process Embeddedness: Micro-Renewal Strategies for Informal Street Rest Spaces for Food Delivery Riders

by Chenxi Song, Li Zhu, Haoyu Deng, Quhan Chen, Siyu Zhang and Xiangxiang Chen

Sustainability 2026, 18(13), 6919; https://doi.org/10.3390/su18136919 - 7 Jul 2026

Viewed by 389

Abstract

Food delivery riders face a structural shortage of informal street rest spaces in urban public environments, yet existing facilities often fail to match their highly mobile labor processes. Taking the Hexi University Town commercial district in Changsha as a case study, this research [...] Read more.

Food delivery riders face a structural shortage of informal street rest spaces in urban public environments, yet existing facilities often fail to match their highly mobile labor processes. Taking the Hexi University Town commercial district in Changsha as a case study, this research examines how rest-space conditions are associated with riders’ occupational dignity and work environment satisfaction. Based on 365 valid questionnaires, field observations, and informal interviews, structural equation modeling, bootstrap mediation analysis, and grouped regression analysis were conducted within a spatial justice framework. The results show that spatial justice perceptions are associated with satisfaction through differentiated pathways. Spatial embeddedness is associated with work environment satisfaction, while facility suitability operates partly through occupational dignity and has the highest mediation proportion. Procedural justice is insignificant in formal spaces but has a strong effect in informal spaces, revealing a mismatch between institutional provision and practical accessibility. The findings indicate that riders’ rest-space dilemma stems not only from insufficient facilities but also from the disembedding of spatial rights from mobile labor processes. This study extends spatial justice research from resource distribution to labor-process embeddedness and proposes micro-renewal strategies that shift from facility provision to process embeddedness, offering implications for inclusive public-space planning, sustainable urban design, and urban governance. Full article

(This article belongs to the Special Issue Sustainable Urban Design and Resilient Communities)

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34 pages, 1252 KB

Open AccessArticle

Adaptive Resilience in Shrinking Regions: Emerging Firm-Level Patterns of Authentic Leadership and Endogenous Renewal in a Resource-Constrained Legacy Organization

by Soichiro Furuki and Norihiro Nishimura

Sustainability 2026, 18(13), 6918; https://doi.org/10.3390/su18136918 - 7 Jul 2026

Viewed by 516

Abstract

Since the late 1970s, regional areas in Japan have experienced prolonged contraction driven by population decline, aging, and industrial shrinkage. Prior research has shown that some localities exhibit adaptive resilience under these conditions, yet the firm-level processes underlying such resilience remain insufficiently understood. [...] Read more.

Since the late 1970s, regional areas in Japan have experienced prolonged contraction driven by population decline, aging, and industrial shrinkage. Prior research has shown that some localities exhibit adaptive resilience under these conditions, yet the firm-level processes underlying such resilience remain insufficiently understood. This study examines Nagano International Country Club (NICC), a regionally central growth-era firm in Nagano Prefecture that increased its visitor numbers to 165% of the 2013 level despite severe financial constraints and flat performance among nearby competitors. Using semi-structured member interviews (n = 3), employee surveys (n = 5), and a reflexively governed autoethnographic analysis, the study explores how stakeholders perceived NICC’s recovery trajectory. Under extreme resource scarcity, the manager repeatedly engaged in a low-cost, labor-intensive practice of personally repairing divots. Participants interpreted this sustained practice as an authentic expression of leadership that appeared to foster trust, activate or generate a sense of belonging, and encourage voluntary participation in course maintenance. These processes were perceived as contributing to spontaneous value co-creation that emerged without crisis framing or financial incentives. The study offers a context-specific interpretation of how endogenous, trust-based value co-creation may be experienced within a resource-constrained legacy firm and suggests that early contours of adaptive resilience observed at the regional level may also manifest at the firm level. Full article

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27 pages, 1077 KB

Open AccessReview

Advances in Resilience Assessment and Adaptive Strategies for Watershed Non-Point Source Pollution Systems Under Climate Change

by Bao-Ling Liu, Chun-Xue Yang, Shao-Peng Yu, Chuan-Qi Shi and Jian-Lin Rong

Sustainability 2026, 18(13), 6917; https://doi.org/10.3390/su18136917 - 7 Jul 2026

Viewed by 546

Abstract

The changing climate raises the level of hydroclimatic non-stationarity and export of pollutants at the event scale in agricultural, mixed-land-use, and urbanizing watersheds. In this review, there is an emphasis on nitrogen, phosphorus, and sediment; however, selective references are made to pesticides, pathogens, [...] Read more.

The changing climate raises the level of hydroclimatic non-stationarity and export of pollutants at the event scale in agricultural, mixed-land-use, and urbanizing watersheds. In this review, there is an emphasis on nitrogen, phosphorus, and sediment; however, selective references are made to pesticides, pathogens, microplastics, and wet-weather mixed-source processes when characteristics similar to event-driven transport, threshold exceedance, and adaptive control are identified. Drawing on a structured literature search of studies published from 2000 to December 2025, this narrative review synthesizes evidence from 138 selected references on how extreme rainfall, drought–rewetting, warming, and freeze–thaw processes alter source activation, hydrological connectivity, biogeochemical processing, and receiving-water hazards. Our resilience assessment is based on resistance, recovery, robustness, and persistence, which we interpret using exposure, sensitivity, and adaptive capacity. It is shown that standard average-load and fixed-baseline measurements may not detect short pollution pulses, cross-scenario failure, and long-term drift; operational measurement must thus involve event thresholds, recovery trajectories, tail-risk measures, and propagation of uncertainty. Extrapolation, interpretability, data demand, and applicability for data-sparse basins are used to compare process-based, data-driven, and hybrid models. Adaptation options are associated with measurable triggers as part of a monitoring–trigger–action cycle with location-specific instructions for monsoon-agricultural, cold-region, semi-arid and urban systems. The novel aspect of this framework is the integration of mechanism-based evidence, quantitative resilience indicators, model uncertainty, and adaptive governance into one decision-focused workflow. This sustainability-oriented framework advances long-term watershed management by linking water-quality protection and resilient development. Full article

(This article belongs to the Special Issue Advancing Climate Risk Management: Strategies for Sustainability and Adaptation)

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26 pages, 32045 KB

Open AccessArticle

Time Series Decomposition-Based Prediction Model for Sustainable Reservoir Operation and Flood Risk Management in Backwater Reaches

by Shihan Pan, Qiong Wu, Hanzhi Wang, Shu Chen and Li Zhang

Sustainability 2026, 18(13), 6916; https://doi.org/10.3390/su18136916 - 7 Jul 2026

Viewed by 384

Abstract

Water level prediction for the backwater reaches of large reservoirs is a critical step for many tasks of reservoir operation and flood control, directly affecting the sustainability of water–energy–ecosystem balance. The problem is very challenging due to arbitrarily complicated hydrodynamic mechanisms and various [...] Read more.

Water level prediction for the backwater reaches of large reservoirs is a critical step for many tasks of reservoir operation and flood control, directly affecting the sustainability of water–energy–ecosystem balance. The problem is very challenging due to arbitrarily complicated hydrodynamic mechanisms and various types of influencing factors. This paper proposes a method based on time series decomposition for feature extraction from data samples by a novel neural architecture. To accurately quantify the complex hydraulic conditions of large reservoirs, we investigate a type of neural basis expansion to incorporate exogenous variables (e.g., reservoir regulation and storage, upstream confluence, and flow travel time). Unlike the traditional LSTM-based methods, our method is free from recurrent architecture. It can exploit backward and forward residual links as a backbone to ensure the validity and structural distribution of the information during the model training. Extensive experiments on real data of the Three Gorges Reservoir are implemented to evaluate the performance of the proposed method. The results show that the proposed method shows state-of-the-art performance on all evaluation metrics and can provide reliable technical support for the refined and sustainable operation of large reservoirs. Full article

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20 pages, 548 KB

Open AccessArticle

What Drives Sustainable Business Models? A Hierarchy of Pathways for SMEs

by Aiman Noor, Chuanmin Mi and Hasan Farid

Sustainability 2026, 18(13), 6915; https://doi.org/10.3390/su18136915 - 7 Jul 2026

Viewed by 510

Abstract

Institutional theory assumes that coercive, normative, and mimetic pressures operate as parallel forces driving organizational isomorphism. This study tests this assumption by exploring the impact of pressures on eco-innovation and environmental performance in manufacturing SMEs. A two-wave, time-lagged survey of 271 manufacturing SMEs [...] Read more.

Institutional theory assumes that coercive, normative, and mimetic pressures operate as parallel forces driving organizational isomorphism. This study tests this assumption by exploring the impact of pressures on eco-innovation and environmental performance in manufacturing SMEs. A two-wave, time-lagged survey of 271 manufacturing SMEs in China was analyzed using structural equation modeling (SEM) using IBM SPSS Statistics 27 and AMOS 21.0. The findings demonstrate a significant impact of institutional forces. Specifically, mimetic pressure (competitive pressure) most strongly influences eco-innovation, while normative pressure (stakeholder pressure) most strongly influences environmental performance. Coercive pressure (environmental regulations) is relatively weak. This research advances institutional theory by measuring the mediation magnitudes, revealing that these pressures affect both direct and innovation-mediated pathways. Mimetic pressure is most dependent on eco-innovation for performance (32.5% mediated), while normative pressure mostly uses direct channels (10% mediated). The fsQCA results complement these findings by providing interchangeable pathways to achieve Environmental Performance. By considering all pressures together, this study establishes a hierarchy of influence where competition stimulates eco-innovation, and stakeholders stimulate performance. This study provides evidence that these pressures are not substitutes because they affect different degrees of reliance on eco-innovation. This study shows that, under fragmented enforcement, non-regulatory pressures may be more significant than regulatory ones. Full article

(This article belongs to the Topic Advancing the Circular Transition: Digitalization and Material Innovation for a Sustainable Built Environment)

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19 pages, 3358 KB

Open AccessArticle

Assessing the Performance of a Rural Water Supply System: Case Study of Matatani Village, Vhembe District Municipality, South Africa

by Elelwani Tshivhase, Shudufhadzo Godlive Mukwevho, Tuwani Petrus Malima and Rachel Makungo

Sustainability 2026, 18(13), 6914; https://doi.org/10.3390/su18136914 - 7 Jul 2026

Viewed by 435

Abstract

This study assessed the performance of a rural water supply system. Performance assessment of water supply systems is important to ensure the long-term sustainability of water services. The study addressed a critical gap in assessing performance while accounting for water disruptions and their [...] Read more.

This study assessed the performance of a rural water supply system. Performance assessment of water supply systems is important to ensure the long-term sustainability of water services. The study addressed a critical gap in assessing performance while accounting for water disruptions and their effects on water quality in nonlinear rural water supply systems. This is critical, especially in rural areas where reliable access to water is limited. A questionnaire survey was conducted to collect data on the reliability and accessibility of the water supply system. Questionnaire responses were analysed using the Statistical Package for Social Sciences version 25. Spearman’s rank correlation was used to determine the relationship between the socio-economic variables and the performance indicators. and the variables. Turbidity, electrical conductivity (EC), total dissolved solids (TDS), and pH were measured in the field. Escherichia coli (E. coli) and total coliforms were analysed using the membrane filtration method. A paired two-tailed t-test was used to determine if there is a significant difference in water quality between the dry and wet seasons. Key performance indicators on reliability and accessibility were assessed by comparing benchmarks. Most households receive an inadequate quantity of water, with 84.3% using less than the recommended basic need of 1500 L per week. Travel distances to the source exceeded the recommended benchmark of 100 m. The majority of respondents (81.4%) reported frequent water supply disruptions, indicating poor reliability of the source. EC and pH were within the South African National Standards (SANS) 241 guideline for drinking water. TDS, turbidity, and microbial parameters failed to meet safe drinking water standards, except for E. coli during the dry season. There was no significant difference in the water quality between the dry and wet seasons. The water supply system demonstrated poor performance. Measures such as implementing low-cost filtration systems to reduce turbidity, raising community awareness about water safety, and decentralising maintenance activities to improve system sustainability due to financial constraints. These interventions will reduce physical burdens and increase effective water usage. Full article

(This article belongs to the Section Sustainable Water Management)

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30 pages, 7148 KB

Open AccessArticle

Impact of Landscape Composition and Configuration on Urban Heat Island Intensity in Zhengzhou Urban Area: Based on Nonlinear Response Patterns and Region-Specific Thresholds

by Guojie Wei, Shuhui Wang and Qindong Fan

Sustainability 2026, 18(13), 6913; https://doi.org/10.3390/su18136913 - 7 Jul 2026

Viewed by 360

Abstract

Rapid urbanization has significantly altered urban landscape composition and configuration, making it a key driver exacerbating the urban heat island (UHI) effect. As a rapidly expanding inland city in Central China, Zhengzhou is highly sensitive to changes in landscape composition and spatial configuration. [...] Read more.

Rapid urbanization has significantly altered urban landscape composition and configuration, making it a key driver exacerbating the urban heat island (UHI) effect. As a rapidly expanding inland city in Central China, Zhengzhou is highly sensitive to changes in landscape composition and spatial configuration. Therefore, clarifying the nonlinear relationship between landscape patterns and the urban thermal environment is of great significance for sustainable urban planning and thermal environment regulation. Taking the main urban area of Zhengzhou as the study area, this paper retrieves land surface temperature (LST) using the radiative transfer equation method based on Landsat 8 remote sensing images from August 2015 to August 2024, and constructs the surface urban heat island intensity (SUHII) index. By integrating multi-dimensional landscape pattern indices, the XGBoost machine learning model, and the SHAP interpretability method, this study systematically analyzes the nonlinear response mechanisms of landscape composition and configuration to SUHII, key regulatory thresholds, and their changes between 2015 and 2024. The results show that: (1) The SUHII in Zhengzhou was substantially higher in 2024 than in 2015. The area proportions of strong and extremely strong heat islands were higher in 2024 (26.16% and 2.34%) than in 2015 (2.22% and 0.12%), and the thermal environment differed between 2015 and 2024, shifting from a localized patch pattern to a more continuously expanding pattern. (2) Landscape area-related indices are the key factors. The areas of green space and water bodies, along with the landscape diversity index, show significant negative correlations, while built-up area and aggregation index show significant positive correlations. (3) SHAP feature importance indicates that water body area is the primary cooling factor, whereas built-up area is the primary warming factor, jointly dominating the spatial pattern of the thermal environment in Zhengzhou. (4) Landscape composition and configuration exhibit significant nonlinear responses to SUHII with region-specific thresholds, and these thresholds were higher/lower in 2024 than in 2015, suggesting a possible association with urban expansion. Specifically, stable cooling effects occurred when the water body area exceeded 3.5 km² in 2015, with the threshold rising to 4.2 km² in 2024. The warming threshold for built-up area decreased from 18.8 km² to 8.5 km², suggesting a higher sensitivity of the thermal environment to built-up area expansion in 2024 compared to 2015, characterized by a regulation pattern of “dominant scale effect and weakened configuration effect”. This study identifies thresholds specific to Zhengzhou’s main urban area at two time points (2015 and 2024), providing quantitative support and scientific basis for blue–green space optimization, precise heat island mitigation, and territorial spatial planning in Zhengzhou. These findings are based on a comparison of two time points (2015 and 2024) and do not directly capture continuous temporal dynamics. Full article

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44 pages, 7222 KB

Open AccessArticle

Mapping Strategic Innovation Capacity and Sustainable Development in the European Union: Evidence from Grey Clustering

by Corina Ioanăș, Bianca-Raluca Cibu, Paul Diaconu, Florinel-Marian Sgărdea and Camelia Delcea

Sustainability 2026, 18(13), 6912; https://doi.org/10.3390/su18136912 - 7 Jul 2026

Viewed by 467

Abstract

This paper evaluates the extent to which European Union member states show alignment between strategic innovation capacity and sustainable development outcomes. To achieve this objective, indicators were collected from Eurostat for two dimensions: strategic capacity for innovation (public expenditure on research and development, [...] Read more.

This paper evaluates the extent to which European Union member states show alignment between strategic innovation capacity and sustainable development outcomes. To achieve this objective, indicators were collected from Eurostat for two dimensions: strategic capacity for innovation (public expenditure on research and development, human resources in science and technology, and the higher education graduation rate) and sustainable development outcomes (real GDP per capita, employment rate, risk of poverty or social exclusion, and greenhouse gas emissions). Going beyond traditional literature, we develop an analysis based on grey clustering using multiple scenarios to illustrate the complex, non-linear relationships and structural bottlenecks in member states. The stability of the classifications was further examined through threshold sensitivity testing across all scenarios and through 200,000 weight-perturbation simulations for an illustrative boundary case. The results reveal distinct performance typologies: a resilient group of “systemic leaders” (including Denmark, Sweden, and the Netherlands) demonstrating consistent excellence across all applied prioritization scenarios, and a stagnant core facing structural challenges regarding both innovation and sustainability (such as Romania and Hungary). The dynamic analysis covering 2021–2024 suggests that strong innovation-capacity indicators are not necessarily associated with equally strong sustainability-outcome indicators, while certain economies in Central and Eastern Europe show positive convergence trends. Supported by stability simulations conducted across multiple scenarios, the study highlights significant alignment gaps between innovation-capacity indicators and sustainability-outcome indicators across the European Union and offers public policy recommendations to stimulate sustainable cohesion and technology adoption. Full article

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19 pages, 1136 KB

Open AccessArticle

Integrated Assessment of Energy Recovery Strategies and Sustainable Management for Municipal Solid Waste

by Raül Emili Sanchis-Gonzàlez and Francesc Hernández-Sancho

Sustainability 2026, 18(13), 6911; https://doi.org/10.3390/su18136911 - 7 Jul 2026

Viewed by 391

Abstract

High-value components in the organic fraction of both municipal and industrial waste are still underused. In fact, there are two components in organic matter with high energy and emission value: carbohydrates (up to 46%) and fats (3.9–25%). The technological potential of using an [...] Read more.

High-value components in the organic fraction of both municipal and industrial waste are still underused. In fact, there are two components in organic matter with high energy and emission value: carbohydrates (up to 46%) and fats (3.9–25%). The technological potential of using an integrated sequential biorefinery route, including lipid extraction for HVO/SAF, carbohydrate fermentation for bioethanol, and pyrolysis for renewable hydrogen generation, is not fully exploited. The objective of this work is to propose an approach based on a systematic six-step engineering methodology to determine the feasibility of its recovery. This integrated strategy achieves an attractive economic performance, with payback periods between 1.97 and 3.00 years, significantly outperforming traditional waste-to-energy models such as anaerobic digestion or composting. While current green hydrogen production costs range from USD 4.28 to USD 6.86, our model positions urban waste as a competitive feedstock for energy transition, achieving a selling price of 4.84 EUR/kg at midpoint values. For the remaining organic matter, a definitive thermal barrier for the 100% removal of microplastics is proposed, to prevent them from reaching agricultural soils. At the same time, efficient waste characterization, aligned with the European RED III directive, will allow the identification of high-value products and the application of the best available techniques for their extraction and use. Full article

(This article belongs to the Section Waste and Recycling)

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21 pages, 13626 KB

Open AccessArticle

Green Industrial Zones and Ports: A 100% Renewable Energy Transition Model

by Mario Mihetec, Maja Pokrovac, Zvonimir Šoša, Goran Stunjek and Goran Krajačić

Sustainability 2026, 18(13), 6910; https://doi.org/10.3390/su18136910 - 7 Jul 2026

Viewed by 396

Abstract

Energy industrial zones can act as a transformative model for industrial decarbonization by integrating renewable energy infrastructure directly with industrial production. By combining energy industrial zones with the energy community framework and peer-to-peer (P2P) energy trading, this study proposes a pathway toward 100% [...] Read more.

Energy industrial zones can act as a transformative model for industrial decarbonization by integrating renewable energy infrastructure directly with industrial production. By combining energy industrial zones with the energy community framework and peer-to-peer (P2P) energy trading, this study proposes a pathway toward 100% renewable energy sources. The model was tested using a techno-economic assessment applied to the Bravar-Jasenice case study in Croatia featuring 12 MW of solar PV, 10 MW of wind power, and a 9.3 MW biogas cogeneration plant. This integrated approach can achieve 80–90% energy self-sufficiency and reduce electricity expenditures for participating enterprises by approximately 15%. Furthermore, the system facilitates an annual reduction of roughly 20,000 tonnes of CO₂ emissions, thus directly supporting European Green Deal objectives. The study also highlights the potential for industrial symbiosis, including green hydrogen production, data centre integration, and waste heat recovery. Ultimately, the proposed framework provides a robust strategy for enhancing industrial competitiveness and ensuring energy security through localized, sustainable energy management. Full article

(This article belongs to the Section Energy Sustainability)

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21 pages, 2145 KB

Open AccessArticle

Circularity Without Redistribution? North–South Inequality in Recycled Aluminum Value Chains

by Javier Arévalo-Royo, Óscar Martín-Llorente, Eduardo Martínez-Cámara, Francisco-Javier Flor-Montalvo and Julio Blanco-Fernández

Sustainability 2026, 18(13), 6909; https://doi.org/10.3390/su18136909 - 7 Jul 2026

Viewed by 433

Abstract

The transition towards sustainable aluminum manufacturing is commonly assessed through recycling rates, energy savings, and resource efficiency, but its distributive effects across global value chains remain insufficiently examined. This study evaluates whether recycled aluminum value chains contribute to both circularity and north–south redistribution, [...] Read more.

The transition towards sustainable aluminum manufacturing is commonly assessed through recycling rates, energy savings, and resource efficiency, but its distributive effects across global value chains remain insufficiently examined. This study evaluates whether recycled aluminum value chains contribute to both circularity and north–south redistribution, or whether they reproduce unequal patterns of value capture, industrial upgrading, employment quality, and trade dependency. The analysis combines UN Comtrade trade data for HS 7601–7616, OECD ICIO 2025 value added indicators, ILOSTAT labor statistics, and UN SDG data for the 2018–2020 three-year average. Eighty economies are classified into four groups: advanced industrial economies, emerging industrial economies, lower-middle-income economies, and low-income economies. A composite indicator linked to SDGs 8, 9, 10, and 12, with SDG 17 incorporated only as a trade dependency context, is constructed from normalized industrial, circular material flow, distributive, and job-quality variables. The results show a clear north–south hierarchy: advanced economies concentrate a larger share of exports in aluminum manufactures, while low-income economies remain more dependent on scrap flows. Group A captures most chain value added, whereas Groups C and D retain only marginal shares. Labor productivity falls sharply from advanced to low-income economies, while working poverty increases substantially. By contrast, circularity scores vary less strongly across groups, suggesting that participation in circular material flows does not necessarily imply equitable industrial upgrading. This study shows that circularity in recycled aluminum value chains does not automatically generate redistribution and provides a replicable framework for distinguishing material circularity from distributive justice. Full article

(This article belongs to the Section Development Goals towards Sustainability)

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53 pages, 11904 KB

Open AccessReview

AI-Powered Digital Twins for Building Energy Management: Modeling Frameworks, Validation and Uncertainty Quantification, Smart Grid Integration, and Deployment Roadmap

by Łukasz Łach

Sustainability 2026, 18(13), 6908; https://doi.org/10.3390/su18136908 - 7 Jul 2026

Viewed by 1504

Abstract

The global buildings and construction sector remains a dominant contributor to anthropogenic climate change, and deep decarbonization has positioned digital twin technology as a transformative pathway for intelligent building energy management. Despite considerable research momentum, the field lacks a coherent synthesis mapping AI [...] Read more.

The global buildings and construction sector remains a dominant contributor to anthropogenic climate change, and deep decarbonization has positioned digital twin technology as a transformative pathway for intelligent building energy management. Despite considerable research momentum, the field lacks a coherent synthesis mapping AI capabilities onto the full digital twin lifecycle—from sensor-driven calibration through real-world deployment to district-scale operation. This review addresses this gap through six objectives: analyzing AI-enhanced modeling approaches for building digital twins; examining data infrastructure and interoperability requirements; evaluating validation, calibration, and uncertainty quantification practices; synthesizing real-world implementation evidence across diverse building typologies; assessing integration with renewable energy systems and smart grids; and identifying challenges, research gaps, and a strategic deployment roadmap. Physics-based, data-driven, and hybrid modeling strategies occupy distinct and complementary roles. Physics-informed surrogate models preserve thermodynamic interpretability while reducing computational overhead; deep learning architectures—including recurrent networks and reinforcement learning agents—deliver adaptive control; and federated learning frameworks enable privacy-preserving optimization across distributed building portfolios. Rigorous multi-metric validation aligned with established calibration standards proves essential for trustworthy deployment, while Bayesian and ensemble-based uncertainty quantification methods emerge as indispensable components of operationally credible digital twins. Evidence from real-world deployments in residential, commercial, healthcare, and industrial facilities confirms that AI-powered digital twins consistently deliver substantial energy savings and measurable improvements in occupant comfort. Scaling to district and urban levels introduces challenges in data governance, computational architecture, and multi-stakeholder coordination, yet federated digital twin frameworks are beginning to demonstrate viable pathways. The paper concludes with a decade-long strategic roadmap spanning technological maturation, market development, regulatory alignment, and decarbonization impact—positioning AI-enhanced digital twins not as incremental optimization tools, but as the foundational infrastructure for the coordinated transformation of the global building stock. Full article

(This article belongs to the Special Issue Advancing Energy-Efficient Buildings for Net-Zero Carbon Emission Goals)

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18 pages, 9163 KB

Open AccessArticle

Mitigating Shallow Earthquake Risk: A Reliable Seismicity Rate Model for Southern Sumatra and West Java

by Wahyu Triyoso and Shindy Rosalia

Sustainability 2026, 18(13), 6907; https://doi.org/10.3390/su18136907 - 7 Jul 2026

Viewed by 369

Abstract

This study offers a new approach to probabilistic earthquake hazard assessment (PEHA) in the densely populated regions of Southern Sumatra and West Java, Indonesia. While much attention is given to powerful, offshore megathrust earthquakes, this research focuses on a different yet equally dangerous [...] Read more.

This study offers a new approach to probabilistic earthquake hazard assessment (PEHA) in the densely populated regions of Southern Sumatra and West Java, Indonesia. While much attention is given to powerful, offshore megathrust earthquakes, this research focuses on a different yet equally dangerous threat: shallow, moderate-magnitude earthquakes (4.5 ≤ M_w ≤ 6.5) that occur on land. These events, often caused by unmapped faults, pose a significant risk due to their proximity to major cities and infrastructure. To develop a more reliable model, a best-fit earthquake rate model was estimated using declustered shallow earthquake events as a reference. This model enhances existing methods by offering a more precise depiction of where these shallow, damaging earthquakes are likely to occur. We accomplished this by analyzing a comprehensive probability of exceedance (PoE) of earthquakes with magnitudes up to 6.5 and depths up to 50 km that occurred between 1963 and 2022, mapping and modeling both the known active faults and the historical seismic activity in the region, and using advanced statistical methods to create a highly reliable, integrated seismicity rate model. The final product, the Integrated Most Reliable Spatial Seismicity Rate Model (Model_IMRSSR), is proposed as a useful tool for government authorities and urban planners. It can be used to create detailed seismic hazard maps that highlight areas of highest risk, especially those with unmapped faults. By guiding development away from these high-risk zones and identifying specific locations for physical reinforcement, this research provides a framework for sustainable investment. The proactive use of these findings can lead to more resilient communities and a significant reduction in potential damage and loss of life from future earthquakes. Full article

(This article belongs to the Special Issue Building Resilience: Sustainable Approaches in Disaster Management)

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37 pages, 9273 KB

Open AccessArticle

Geometric Optimal Transport for Sustainable Closed-Loop Supply Chain: A Fused Gromov–Wasserstein Framework for Structural and Attribute Inefficiency Diagnosis

by Iman Seyedi, Antonio Candelieri and Francesco Archetti

Sustainability 2026, 18(13), 6906; https://doi.org/10.3390/su18136906 - 7 Jul 2026

Viewed by 377

Abstract

Designing sustainable closed-loop supply chain (CLSC) networks requires jointly assessing node-level operational attributes (recovery efficiency, processing capacity, unit cost) and inter-node spatial structure. Existing methods, including mixed-integer programming, multi-objective metaheuristics, and graph-matching, typically optimize a single cost dimension and do not decompose structural [...] Read more.

Designing sustainable closed-loop supply chain (CLSC) networks requires jointly assessing node-level operational attributes (recovery efficiency, processing capacity, unit cost) and inter-node spatial structure. Existing methods, including mixed-integer programming, multi-objective metaheuristics, and graph-matching, typically optimize a single cost dimension and do not decompose structural connectivity from attribute-level inefficiency. We propose a Fused Gromov–Wasserstein (FGW) diagnostic framework that combines the Wasserstein distance (attribute similarity) and the Gromov–Wasserstein distance (structural alignment) via a convex trade-off parameter α, solved using the conditional gradient algorithm. Supply–capacity imbalances are resolved by marginal rescaling, with residual unabsorbed mass reported as a diagnostic indicator of infrastructure shortfall. The framework is applied to an eight-echelon PET bottle recovery and filament manufacturing network across 24 synthetic benchmark instances at three scale classes. The FGW cost decomposes exactly into feature and structural components, allowing bottleneck arcs to be diagnosed as attribute-driven or structure-driven. Under this benchmark, bottleneck cost decreases with network size, the most frequent bottleneck arc shifts from the collection interface in small networks to the mid-chain processing handoff in large networks, and attribute heterogeneity accounts for the majority of FGW cost (57.9%, conditional on the normalization and weighting scheme used) across all 144 arc–instance combinations. These results position FGW as a tractable, interpretable diagnostic layer for circular supply chain analysis, complementing rather than replacing classical CLSC design models. Full article

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21 pages, 10300 KB

Open AccessArticle

Urban Green Space and Its Socio-Economic and Demographic Determinants: A Meta-Analysis for Urban Sustainability

by Haowen Zheng, Yongping Wei, Shuanglei Wu and Kunshu Yang

Sustainability 2026, 18(13), 6905; https://doi.org/10.3390/su18136905 - 7 Jul 2026

Viewed by 305

Abstract

Urban green space (UGS) with multidimensional attributes provides crucial ecosystem services and public health benefits. However, there is no systemic knowledge on how these diverse UGS attributes meet needs with different socio-economic and demographic characteristics. This study conducts a meta-analysis of the existing [...] Read more.

Urban green space (UGS) with multidimensional attributes provides crucial ecosystem services and public health benefits. However, there is no systemic knowledge on how these diverse UGS attributes meet needs with different socio-economic and demographic characteristics. This study conducts a meta-analysis of the existing UGS case studies from the Web of Science database to reveal the knowledge breadth, depth and applicability for future research and practice in UGS. We evaluated how 75 socio-economic and demographic indicators shape 73 UGS indicators across 48 global case studies using a combination of relational analysis, meta-analytical effect and spatiotemporal distribution. The relation analysis reveals significant imbalances of UGS knowledge across attributes (Accessibility, security and amenities) and users’ characteristics (e.g., age), the meta-analytical effect size analysis indicates that many associations have not yet received consistent and conclusive empirical support for direct application to UGS planning and design, and the spatiotemporal analysis indicates that the concentration of the sampled studies in China and the United States further constrains the external applicability of the synthesized evidence. These findings recognize the multidimensional nature of UGS and the need for context-sensitive consideration and highlight the urgency of developing a unified assessment framework to improve knowledge discovery and applicability in UGS planning for urban sustainability. Full article

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32 pages, 1717 KB

Open AccessReview

Emotional Intelligence as a Driver of Pro-Environmental Behavior: A Conceptual Review for Climate Action

by Plinio Limata, Beatrice Cianfanelli, Antonino Callea, Giovanni Ferri and Marco Costanzi

Sustainability 2026, 18(13), 6904; https://doi.org/10.3390/su18136904 - 7 Jul 2026

Viewed by 358

Abstract

This paper examines whether the persistent difficulty in addressing the eco-social crisis may partly stem from an inadequate representation of human decision-making within mainstream economic models. Although pro-environmental behaviors (PEBs) and sustainable consumption are increasingly recognized as essential for sustainability transitions, neoclassical economics [...] Read more.

This paper examines whether the persistent difficulty in addressing the eco-social crisis may partly stem from an inadequate representation of human decision-making within mainstream economic models. Although pro-environmental behaviors (PEBs) and sustainable consumption are increasingly recognized as essential for sustainability transitions, neoclassical economics still largely relies on the homo oeconomicus paradigm, which assumes fully rational and utility-maximizing decision-making. Building on contributions from psychology, behavioral economics, neuroscience, and sustainability studies, this integrative narrative review examines how cognitive biases challenge the foundational assumptions of homo oeconomicus and explores the potential role of emotional intelligence in sustainability-related decision-making. Adopting the integrative narrative review approach, this paper integrates literature on (1) cognitive biases and bounded rationality; (2) emotional intelligence and judgment bias; and (3) emotional intelligence, pro-environmental behaviors, and sustainable consumption. The evidence reviewed suggests that sustainability-related decisions are strongly shaped by cognitive and emotional processes operating under uncertainty and socially embedded consumption patterns. Within this framework, EI may represent a psychological resource capable of influence of cognitive biases by supporting emotional regulation, impulse control, self-awareness, and long-term orientation. Overall, the paper proposes a conceptual framework linking cognitive biases, emotional intelligence, and sustainable behavior beyond the traditional homo oeconomicus paradigm. Full article

(This article belongs to the Special Issue Circular Economy and Green Technology for Sustainable Development)

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18 pages, 2171 KB

Open AccessArticle

Integration of Circular Systemic Solutions for Wood and Plastic Waste Valorisation in the Production of Insulation Materials: An Environmental/Sustainability Assessment

by Chrysa Politi, Vittoria Benedetti, Xenia Chaidemenou, Francesco Patuzzi, Marco Baratieri, Kamil Maszczyk, Mateusz Imiela and Antonis Peppas

Sustainability 2026, 18(13), 6903; https://doi.org/10.3390/su18136903 - 7 Jul 2026

Viewed by 295

Abstract

This study presents an environmental and circularity assessment of an integrated insulation-production system that valorises plastic waste and wood packaging waste as secondary material and energy resources. The analysis evaluates the recovery of incoming waste streams and their reintegration into a new production [...] Read more.

This study presents an environmental and circularity assessment of an integrated insulation-production system that valorises plastic waste and wood packaging waste as secondary material and energy resources. The analysis evaluates the recovery of incoming waste streams and their reintegration into a new production cycle, while the downstream end-of-life of the resulting insulation product remains outside the assessed system boundary. The process chain includes mechanical pre-treatment of wood (grinding, metal separation, and pelletising); thermochemical conversion via wood gasification and gas combustion; and post-combustion CO₂ capture. The captured CO₂ is used in the subsequent polymer processing stages, which comprise mixing, extrusion, thermal treatment, and cooling. Environmental impacts are evaluated through Life Cycle Assessment (LCA), while circularity indicators are assessed within the framework of EN 15804+A2. The results demonstrate the environmental and circularity potential of valorising wood packaging and plastic waste in the context of carbon capture and utilisation (CCU) and sustainable material development. Full article

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15 pages, 278 KB

Open AccessArticle

External Assurance of Sustainability Reporting and ESG Performance: Evidence from Saudi Listed Firms

by Khaled S. Aljaaidi, Neef F. Alwadani and Eyad H. Abutheeb

Sustainability 2026, 18(13), 6902; https://doi.org/10.3390/su18136902 - 7 Jul 2026

Viewed by 435

Abstract

This paper examines the association between external verification of sustainability reports and ESG performance of Saudi-listed firms from the years 2014–2021. With regard to the Saudi stock exchange (Tadawul) dataset consisting of 188 firm-year observations, it is concluded that external sustainability report verification [...] Read more.

This paper examines the association between external verification of sustainability reports and ESG performance of Saudi-listed firms from the years 2014–2021. With regard to the Saudi stock exchange (Tadawul) dataset consisting of 188 firm-year observations, it is concluded that external sustainability report verification and ESG performance are positively associated. This study constructs the premise that the enhancement of credibility and transparency of sustainability reports in turn fosters stakeholder confidence. This paper documents a positive association between voluntary assurance and ESG performance from an emerging market perspective, which broadens the scope of the ESG literature. This observation particularly justifies the need to endorse more assurance services in support of sustainable development and to strengthen the reporting frameworks and policies. The study results support the objectives of Vision 2030, specifically the pillars of promoting environmental sustainability, corporate transparency, and governance. The evidence aligning national goals to encourage transparency in corporate systems and sustainability in assurance services is the positive relationship between ESG and sustainability reporting assurance. Moreover, the results highlight Saudi Arabia’s dedication to the United Nations Sustainable Development Goals, specifically SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action), as they underscore the role of assurance and disclosure practices in fostering sustainable business practices in Saudi Arabia. Full article

(This article belongs to the Section Environmental Sustainability and Applications)

13 pages, 212 KB

Open AccessArticle

Decarbonizing Through Innovation: The Role of AI Investment in Reducing CO₂ Emissions in the Top AI-Investing Economies

by Cemal Egemen and Dervis Kirikkaleli

Sustainability 2026, 18(13), 6901; https://doi.org/10.3390/su18136901 - 7 Jul 2026

Viewed by 355

Abstract

This research explores the effect of artificial intelligence (AI) investment on carbon dioxide (CO₂) emissions relating to worldwide industrialization and environmental concerns. CO₂ emissions have risen due to fossil fuel usage, leading to global warming, environmental degradation, and climate change. [...] Read more.

This research explores the effect of artificial intelligence (AI) investment on carbon dioxide (CO₂) emissions relating to worldwide industrialization and environmental concerns. CO₂ emissions have risen due to fossil fuel usage, leading to global warming, environmental degradation, and climate change. While most previous studies have concentrated on components such as economic progress, energy consumption, and technological improvement, little attention has been given to the impact of artificial intelligence on the levels of carbon dioxide emissions. Artificial intelligence technology enables systems to perform complicated duties including reasoning and decision making. This article intends to fill this research gap by examining the impact of AI on carbon emissions in the eight highest AI-investing countries (the USA, China, the United Kingdom, Israel, Germany, India, Canada, and Korea) from 2012 to 2022. OECD data on average trade, AI investment, and consumption-based CO₂ emissions were analyzed in this study. The Augmented Mean Group (AMG) approach was employed to analyze the study findings, with the results showing that GDP had a beneficial influence on CO₂ emissions in the Top AI-Investing Economies. The results also showed that AI investment and trade had adverse impacts on CO₂ emissions. The findings supported the idea that investment on AI has a positive impact on decreasing CO₂ emissions, thus leading to a more sustainable environment. Full article

33 pages, 3889 KB

Open AccessReview

From Decision-Support Tools to Digital Twins: A Review of Digital Farming, Data Platforms, and AI for Sustainable Dairy Systems

by Yijing Gong, Eduardo Noronha de Andrade Freitas and Victor E. Cabrera

Sustainability 2026, 18(13), 6900; https://doi.org/10.3390/su18136900 - 7 Jul 2026

Viewed by 460

Abstract

Sustainability targets for livestock require decision support that is both scientifically credible and operationally usable on farms. This integrative narrative review synthesizes the broader peer-reviewed literature on digital farming, artificial intelligence and machine learning, simulation modeling, optimization, and digital-twin concepts as applied to [...] Read more.

Sustainability targets for livestock require decision support that is both scientifically credible and operationally usable on farms. This integrative narrative review synthesizes the broader peer-reviewed literature on digital farming, artificial intelligence and machine learning, simulation modeling, optimization, and digital-twin concepts as applied to sustainable dairy systems, and uses selected peer-reviewed dairy studies from one integrated research program as illustrative worked examples that show how these elements can be connected end-to-end. We organize the synthesis around a data-to-decision pipeline that links data foundations and interoperability, governance and trust, analytics, decision engines, and deployment, comparing model classes by data needs, temporal resolution, interpretability, and deployment maturity. Recurring barriers to impact—weak ground truth, data drift, fragmented identifiers, and misaligned incentives—are highlighted alongside the design principles that address them. The contribution of the review is the transferable pipeline framework, demonstrated through worked examples drawn from one integrated research program; the program’s studies appear repeatedly because they together trace decisions across all pipeline layers, not because they constitute the field. We conclude with a practical roadmap and implementation checklist for designing and scaling decision-intelligence systems with transparent tradeoffs and measurable sustainability outcomes. Full article

(This article belongs to the Special Issue Innovative Strategies for Sustainable Livestock Production)

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20 pages, 1291 KB

Open AccessArticle

Understanding the Drivers of Egyptian Farmers’ Intention to Adopt Biodegradable Plastic Mulch: A Structural Equation Modeling Approach

by Hazem S. Kassem, Ahmed Mosa, Mondira Bhattacharya, Mohammed AbouElnaga, Moshira Elagamy, Doaa Atiya, Belal Elgamal and Henny Osbahr

Sustainability 2026, 18(13), 6899; https://doi.org/10.3390/su18136899 - 7 Jul 2026

Viewed by 238

Abstract

The biodegradable plastic mulch (BDM) was advanced as a promising alternative to address the environmental and management issues associated with conventional polyethylene mulch. However, its uptake remains low, and empirical evidence on the sociopsychological drivers of BDM adoption among farmers in Egypt is [...] Read more.

The biodegradable plastic mulch (BDM) was advanced as a promising alternative to address the environmental and management issues associated with conventional polyethylene mulch. However, its uptake remains low, and empirical evidence on the sociopsychological drivers of BDM adoption among farmers in Egypt is limited. This study incorporates an extended theory of planned behavior (TPB) to predict farmers’ intention to adopt BDM. Three hundred and sixty farmers were selected in three governorates using a multistage sampling technique. Data analysis involved using partial least squares structural equation modeling (PLS-SEM). The findings indicated that the extended TPB model accounted for 51% of the total variation in predictive power. Three variables, including subjective norms, perceived behavioral control, and perceived self-identity, positively affected farmers’ intentions to adopt BDM, with the influence of attitudes being not statistically significant. The most critical barriers to adopting BDM from farmers’ perspectives encompassed limited local availability (71%), limited knowledge (56%), elevated cost (46%), field durability (39%), and limited use among other farmers (34%). These findings underscore the importance of broadening the focus beyond the technical benefits of BDM to urge and accelerate adoption. Accordingly, policymakers should focus on reducing adoption barriers and emphasize sociopsychological factors more through well-designed interventions to promote the adoption of BDM in agriculture. Full article

(This article belongs to the Section Sustainable Agriculture)

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30 pages, 1520 KB

Open AccessArticle

Environmental Taxes and Corporate Green Transition: Evidence from Chinese Manufacturing Firms

by Xi Wang, Dan Zhao and Zicheng Wei

Sustainability 2026, 18(13), 6898; https://doi.org/10.3390/su18136898 - 7 Jul 2026

Viewed by 349

Abstract

In China, the environmental protection tax constrains and incentivizes firms to cut emissions and lift efficiency. To examine the effect and mechanism of environmental regulation as a driver of corporate green transformation, this study uses data on Chinese listed manufacturing firms from 2011 [...] Read more.

In China, the environmental protection tax constrains and incentivizes firms to cut emissions and lift efficiency. To examine the effect and mechanism of environmental regulation as a driver of corporate green transformation, this study uses data on Chinese listed manufacturing firms from 2011 to 2022. It takes the 2018 environmental fee-to-tax reform as a quasi-natural experiment and employs a difference-in-differences model. The core DID coefficient is 0.0088 (p < 0.05). After the reform was implemented, manufacturers in higher-tax regions achieved better green transformation by increasing pollution costs, adjusting investment and improving executives’ green awareness. The policy effects were more pronounced for low-profit, non-state-owned, non-patent and labor-intensive firms in regions with higher tax burdens. Additionally, the policy effect exhibited a time lag. The incentive effect was stronger for heavily polluting enterprises, and the policy simultaneously boosted corporate economic performance. Accordingly, we propose broadening the taxable scope, tightening supervision, optimizing tax incentives and adopting targeted policies to support corporate green transformation. Full article

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