Journal Description
Sustainability
Sustainability
is an international, peer-reviewed, open-access journal on environmental, cultural, economic, and social sustainability of human beings, published semimonthly online by MDPI. The Canadian Urban Transit Research & Innovation Consortium (CUTRIC), International Council for Research and Innovation in Building and Construction (CIB) and Urban Land Institute (ULI) are affiliated with Sustainability and their members receive discounts on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE and SSCI (Web of Science), GEOBASE, GeoRef, Inspec, RePEc, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q2 (Environmental Studies) / CiteScore - Q1 (Geography, Planning and Development)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.9 days after submission; acceptance to publication is undertaken in 3.6 days (median values for papers published in this journal in the second half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our editors and authors say about Sustainability.
- Companion journals for Sustainability include: World, Sustainable Chemistry, Conservation, Future Transportation, Architecture, Standards, Merits, Bioresources and Bioproducts, Accounting and Auditing, Environmental Remediation and Green.
- Journal Cluster of Environmental Science: Sustainability, Land, Clean Technologies, Environments, Nitrogen, Recycling, Urban Science, Safety, Air, Waste, Aerobiology and Toxics.
Impact Factor:
3.3 (2024);
5-Year Impact Factor:
3.6 (2024)
Latest Articles
Enacting Entrepreneurial Agency in Practice: Taking Consequential Actions to Sustain Educational Innovation After a Change Laboratory
Sustainability 2026, 18(11), 5326; https://doi.org/10.3390/su18115326 (registering DOI) - 25 May 2026
Abstract
Educational systems are increasingly required not only to innovate but to sustain innovation over time. While research on Change Laboratory (CL) interventions has extensively examined the development of new models and the emergence of transformative agency, less is known about how such agency
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Educational systems are increasingly required not only to innovate but to sustain innovation over time. While research on Change Laboratory (CL) interventions has extensively examined the development of new models and the emergence of transformative agency, less is known about how such agency is enacted through concrete actions in everyday practice. This study addresses this gap by examining consequential actions as expressions of entrepreneurial agency in the implementation of open work in a kindergarten following a CL intervention. Drawing on semi-structured interviews with 17 staff members, the study adopts a theoretically informed inductive approach to identify types of agentive actions and interpret them in relation to EntreComp competences and activity system components. The findings show that entrepreneurial agency is a distributed and situated process enacted through coordinated material, relational, and organizational actions toward the tools and community, highlighting the importance of environmental reconfiguration and collaboration in sustaining change. The study also shows that agency is unevenly distributed across roles and that newcomers participate differently in the implementation process. Overall, sustaining educational innovation appears to depend less on the design of models than on the collective capacity to continuously enact and transform them in practice.
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(This article belongs to the Special Issue Transformative Agency for Sustainability: Curriculum Design and Learning Landscape Design with Living Infrastructures)
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Open AccessArticle
An Integrated Lean–QMS–SPC Analytical Framework for Process Stability and Sustainable Manufacturing
by
Mariusz Niekurzak and Jerzy Mikulik
Sustainability 2026, 18(11), 5324; https://doi.org/10.3390/su18115324 (registering DOI) - 25 May 2026
Abstract
This study addresses the growing need to integrate operational excellence with sustainability objectives in manufacturing systems. Despite extensive research on Lean Management and Quality Management Systems (QMSs), their combined impact on process performance and resource efficiency remains insufficiently explored, particularly in real industrial
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This study addresses the growing need to integrate operational excellence with sustainability objectives in manufacturing systems. Despite extensive research on Lean Management and Quality Management Systems (QMSs), their combined impact on process performance and resource efficiency remains insufficiently explored, particularly in real industrial contexts. The aim of this study is to develop and apply an integrated Lean–QMS–SPC analytical framework linking process performance improvement with sustainability-related outcomes. A case study was conducted in a high-volume manufacturing environment. The study combined process analysis, system-level assessment, and root cause identification to support targeted improvement actions. The results indicate that the implementation of Lean-oriented practices and supporting methods was associated with improved process stability, reduced variability, and decreased occurrence of nonconformities. These improvements translate into enhanced operational performance and reduced resource consumption associated with rework and defects. A scenario-based estimation model, based on observed defect reduction, is used to assess the potential impact on energy consumption and CO2 emissions. The study contributes to the literature by operationally integrating SPC analysis, QMS assessment, root cause analysis, and Lean-oriented improvement activities within an industrial manufacturing context. The findings highlight that quality-driven process improvements may support operational efficiency while contributing to resource-efficiency performance.
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(This article belongs to the Special Issue Industry 4.0 and Application of Artificial Intelligence System in Operation Management)
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Open AccessArticle
Sustainable Operation of Wind–Solar–Hydrogen-Integrated Energy Systems Considering Lifetime Degradation: Hybrid Electrolyzer Power Allocation and Array Rotation Strategies
by
Liye Ma, Kangle Yan, Shisheng Bai and Jiaxu Wang
Sustainability 2026, 18(11), 5322; https://doi.org/10.3390/su18115322 (registering DOI) - 25 May 2026
Abstract
As global industrialization and energy demands rise, excessive reliance on fossil fuels escalates carbon emissions, making clean energy alternatives an urgent priority for sustainable development. As a key transition pathway, wind and solar power can be converted into hydrogen via electrolyzers for electricity
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As global industrialization and energy demands rise, excessive reliance on fossil fuels escalates carbon emissions, making clean energy alternatives an urgent priority for sustainable development. As a key transition pathway, wind and solar power can be converted into hydrogen via electrolyzers for electricity generation, thermal supply, or natural gas synthesis. This enables flexible multi-energy coordination and improves overall renewable energy utilization efficiency. However, conventional electrolyzer scheduling approaches typically assume fixed hydrogen production efficiency, failing to account for dynamic variations in operating conditions, efficiency attenuation, and lifetime degradation under fluctuating renewable inputs. This inadequacy compromises the long-term sustainability of green hydrogen systems. To address these challenges, this paper proposes a hybrid AEL-PEM electrolyzer power allocation and operating condition array rotation strategy. Piecewise linear models are established to characterize the efficiency and full life cycle degradation of both electrolyzer types across normal operation, overload, and start–stop transitions. A mixed-integer linear programming (MILP) model is formulated with an objective function incorporating energy purchase costs, start–stop penalty costs, and electrolyzer lifetime degradation costs, and is solved using the Gurobi solver. Simulation validation is conducted using a 24 h typical summer day dataset with a 15 min resolution. Three comparative schemes are evaluated to verify the strategy’s effectiveness in minimizing total system operation costs and enhancing renewable energy utilization efficiency through optimized operating condition management. Results demonstrate that the proposed strategy reduces total system costs by 23%, entirely eliminates renewable energy curtailment, and balances electrolyzer lifespan degradation across all units, collectively advancing the economic efficiency, asset sustainability, and long-term operational reliability of green hydrogen systems.
Full article
(This article belongs to the Special Issue AI-Driven Low-Carbon Sustainable Energy Systems: System Design, Computational Strategies, and Emerging Innovations)
Open AccessArticle
Beyond the Single Horizon: Ecological Footprint Convergence in the Big Ten Emerging Economies Using Discrete Wavelet Transform
by
Hamza Çeştepe, Havanur Ergün Tatar and Volkan Bektaş
Sustainability 2026, 18(11), 5320; https://doi.org/10.3390/su18115320 (registering DOI) - 25 May 2026
Abstract
This study investigates the ecological footprint (EF) convergence dynamics of the “Big Ten Emerging Economies” (BTEs) over the period 1967–2024. Employing the Maximum Overlap Discrete Wavelet Transform (MODWT) in conjunction with the Fourier KPSS (FKPSS) stationarity test, the analysis decomposes the EF series
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This study investigates the ecological footprint (EF) convergence dynamics of the “Big Ten Emerging Economies” (BTEs) over the period 1967–2024. Employing the Maximum Overlap Discrete Wavelet Transform (MODWT) in conjunction with the Fourier KPSS (FKPSS) stationarity test, the analysis decomposes the EF series into short-, medium-, and long-term frequency components, allowing the stochastic convergence hypothesis to be examined separately across multiple time horizons. The empirical results reveal that convergence is largely absent in the original series, with stochastic convergence detected only for India, Indonesia, and Türkiye at the aggregate level. Once the series are decomposed, convergence becomes considerably more visible. In the short run, convergence is supported for Argentina, Indonesia, Mexico, Poland, and Türkiye. The medium run emerges as the most robust convergence horizon, with all ten economies exhibiting stochastic convergence—a result that becomes visible only after accounting for nonlinear structural breaks through the Fourier framework. In the long run, convergence is supported for Argentina, Brazil, China, Korea, Poland, and South Africa, while India, Indonesia, Mexico, and Türkiye exhibit persistent divergence. No single country maintains convergence consistently across all time horizons, underscoring the heterogeneous and frequency-dependent nature of EF dynamics in major emerging economies. The robustness analysis based on the Fourier ADF and standard ADF tests supports the primary findings. These results contribute to the EF convergence literature by demonstrating that environmental convergence is a multi-layered and frequency-dependent phenomenon, and offer empirical insights relevant to the design of long-run sustainability policies for emerging economies.
Full article
(This article belongs to the Section Air, Climate Change and Sustainability)
Open AccessArticle
Multi-Objective Optimization for the Time-Dependent Green Vehicle Routing Problem with Time Windows
by
Jipeng Wang, Weiquan Huang, Chenming Liu, Gaosen Dong, Fenglian Yuan, Yan Yang and Yongjun Ma
Sustainability 2026, 18(11), 5319; https://doi.org/10.3390/su18115319 (registering DOI) - 25 May 2026
Abstract
In the context of urban distribution, given the complexity of express delivery and the variability of distribution conditions, vehicle routing problems with time-dependent characteristics have received increasing attention. This study incorporates a cross-period travel time estimation method for road segments that accounts for
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In the context of urban distribution, given the complexity of express delivery and the variability of distribution conditions, vehicle routing problems with time-dependent characteristics have received increasing attention. This study incorporates a cross-period travel time estimation method for road segments that accounts for temporal and weather-dependent variations in vehicle speed. Building upon this foundation, this study establishes an multi-objective optimization model for the green vehicle routing problem that systematically incorporates intricate constraints, including time-varing vehicle speed, fuel consumption, carbon emissions, and customer servive time windows. This model aims to achieve three primary objectives: (1) minimizing the fleet size, (2) minimizing the overall delivery expenses, which include fuel consumption and carbon emissions, and (3) maximizing the average customer satisfaction. To solve this model, we develop an improved Non-Dominated Sorting Genetic Algorithm III (INSGA-III). To effectively prevent the algorithm from becoming trapped in local optima, we propose a dual-criteria selection mechanism. Meanwhile, we introduce a destroy-and-repair variable neighborhood search strategy to enhance the algorithm’s optimization capability under complex constraints. Experimental evaluations conducted on Solomon benchmark instances as well as real-world case studies indicate that the proposed INSGA-III algorithm surpasses widely utilized multi-objective optimization methods across all assessed performance metrics. This highlights the significant potential of the presented INSGA-III algorithm for practical applications in urban delivery scenarios, which is closely linked to the sustainable development of cities.
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(This article belongs to the Special Issue Sustainability of Supply Chain and Logistics System: Opportunities and Challenges)
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Open AccessArticle
Sustainable Municipal Solid Waste Treatment in a Central Asian City: A Geographic Information System and Scenario-Based Framework for Technology Prioritization in Shymkent, Kazakhstan
by
Akbota Aitimbetova and Zhaksylyk Pernebayev
Sustainability 2026, 18(11), 5318; https://doi.org/10.3390/su18115318 (registering DOI) - 25 May 2026
Abstract
Sustainable municipal solid waste (MSW) treatment in rapidly urbanizing secondary cities requires evidence-based, district-level prioritization of technologies. We integrate GIS hotspot mapping, Random Forest, and AnyLogic System Dynamics into a decision-support framework and apply it to Shymkent, Kazakhstan (population 1.19 million; ≈301,400 tonnes
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Sustainable municipal solid waste (MSW) treatment in rapidly urbanizing secondary cities requires evidence-based, district-level prioritization of technologies. We integrate GIS hotspot mapping, Random Forest, and AnyLogic System Dynamics into a decision-support framework and apply it to Shymkent, Kazakhstan (population 1.19 million; ≈301,400 tonnes of MSW in 2025). This is the first application of such a framework to MSW management in a Kazakhstani secondary city and, to our knowledge, the first regional application across Central Asia; the integration concept has prior precedents in Latin American, South Asian, and East Asian metropolitan studies, and the present contribution lies in empirical calibration to a Central Asian upper-middle-income context using 2015–2025 morphological audits, air-quality and soil monitoring, and Sentinel-2 NDVI. Random Forest (n = 80, 9 predictors) achieved R2 = 0.976 ± 0.011 under 5-fold cross-validation; a complementary GroupKFold protocol confirms the model is Shymkent-calibrated while the methodology remains transferable. AnyLogic simulation shows an Infrastructure/Waste-to-Energy pathway reduces the 2030 annual landfilled volume to ≈201 kt, environmental risk by 70%, and methane emissions by 86% (≈270 kt CO2-eq/year) relative to the Inertial baseline. The principal deliverable is a District × Technology × Phase prioritization matrix for sequencing sustainable investment under realistic budget constraints.
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(This article belongs to the Special Issue Advances in Research on Sustainable Waste Treatment and Technology)
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Open AccessReview
Microalgae-Based Treatment of Cheese Whey Wastewater for Circular Bioeconomy Applications
by
Tugba Atatoprak-Gonçalves, Bruno Esteves and Luísa Cruz-Lopes
Sustainability 2026, 18(11), 5317; https://doi.org/10.3390/su18115317 (registering DOI) - 25 May 2026
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Cheese production generates large volumes of whey, and high-strength wastewater with elevated organic load, salinity, and nutrient content. Although whey contains valuable components including lactose, proteins, and minerals, approximately half of global production remains underutilized, contributing to eutrophication and oxygen depletion in aquatic
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Cheese production generates large volumes of whey, and high-strength wastewater with elevated organic load, salinity, and nutrient content. Although whey contains valuable components including lactose, proteins, and minerals, approximately half of global production remains underutilized, contributing to eutrophication and oxygen depletion in aquatic ecosystems. Conventional physicochemical and biological treatment methods are limited by high operational costs, energy demands, and secondary waste generation. Microalgae-based bioremediation has emerged as a promising sustainable strategy for whey valorization, enabling simultaneous nutrient removal and biomass production. Through a focused review of the current literature, this study analyzes microalgal strains commonly applied in whey remediation, their cultivation modes (photoautotrophic, heterotrophic, and mixotrophic), nutrient uptake mechanisms, and operational conditions. The review highlights cultivation systems, biomass recovery techniques, and potential conversion of microalgal biomass into high value bioproducts, including biofuels, pigments, proteins, and biofertilizers. Critically, a major research gap exists: no studies systematically examine whey-grown microalgal biomass for bioplastic or film production, despite its elevated polysaccharide and protein content. Future development requires integrated biorefinery approaches, optimized cultivation strategies, and supportive policy frameworks to enable large-scale circular economy implementation within the dairy industry.
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Open AccessArticle
A Predict–Optimize–Evaluate Framework for Sustainable Traffic Safety Resource Allocation: LSTM Forecasting with Triangulated Enforcement Elasticity in Saudi Arabia
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Majed H. Moosa, Fawaz Alharbi, Meshal Almoshaogeh, Osama M. Irfan and Walid M. Shewakh
Sustainability 2026, 18(11), 5316; https://doi.org/10.3390/su18115316 (registering DOI) - 25 May 2026
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Road traffic crashes remain a global public health burden and a persistent resource allocation problem that undermines progress toward the sustainable development of safe, equitable mobility systems. Saudi Arabia’s Vision 2030 targets fewer than 10 fatalities per 100,000 population, a goal aligned with
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Road traffic crashes remain a global public health burden and a persistent resource allocation problem that undermines progress toward the sustainable development of safe, equitable mobility systems. Saudi Arabia’s Vision 2030 targets fewer than 10 fatalities per 100,000 population, a goal aligned with United Nations Sustainable Development Goal 3.6 (halving road traffic deaths) and SDG 11.2 (safe and sustainable transport), yet a gap persists between crash prediction research and how agencies deploy enforcement resources. This paper builds a closed-loop predict–optimize–evaluate framework connecting Long Short-Term Memory (LSTM) neural networks to a goal-distance gap metric and constrained optimization, feeding forecast outputs directly into enforcement scheduling decisions. Using monthly casualty data from official Saudi sources covering the entire kingdom (all 13 administrative regions) from 2010 through 2024 (N = 42,856 fatal and serious injuries across 180 monthly observations), we validate LSTM forecasting against five benchmarks plus a GRU and a Transformer baseline, apply gap analysis as a standardized goal-distance metric, optimize enforcement allocation with triangulated elasticity estimates, and evaluate past policy reforms through multi-method counterfactual analysis. A headline finding is that roughly 28% of fatal and serious injuries cluster within only about 6% of weekly hours, creating an unusually concentrated target for enforcement reallocation. The LSTM achieves RMSE = 2.47 with MASE = 0.83, beating ARIMA by 35% while maintaining robustness during COVID disruptions (RMSE = 2.38 in the post-acute period 2022–2024 versus 2.61 in the acute period 2020–2021). Temporal analysis confirms 28% of fatalities (95% CI: 26.0–30.0%) cluster within 6% of weekly hours. Enforcement elasticity triangulated from three independent sources converges at α ≈ 0.31 (90% CI: 0.25–0.40). The optimization model allocates 56% of enforcement resources to Thursday–Friday midnight-to-4 AM windows, projecting a 17.1% casualty reduction (90% CI: 13.5–20.6% under Monte Carlo uncertainty in α). Monte Carlo sensitivity analysis with 10,000 iterations confirms a median benefit-cost ratio of 1.88 (90% CI: 1.18–2.97), with P (BCR > 1.0) = 98.9%, using locally calibrated VSL = SAR 4.2 million (equivalent to approximately USD 1.12 million at the SAMA-pegged rate of 3.75 SAR/USD, in constant 2024 prices). Counterfactual evaluation finds that the post-2018-reform period was associated with a 22.1% casualty reduction (95% CI: 16.4–27.8%), with magnitude robust across four methods (LSTM counterfactual, Bayesian Structural Time-Series, Synthetic Control, and an inverse-variance-weighted synthesis of the three); we stress, however, that attribution to the driving reform itself cannot be cleanly separated from concurrent Saher camera expansion, public awareness campaigns, and trauma-care improvements. By translating prediction into evidence-based, resource-efficient enforcement, the framework supports sustainable road safety policy in middle-income and rapidly motorizing settings.
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Open AccessReview
Spatial Attributes and Level-Based Assessment of Age-Friendly Built Environments: A Scoping Review for Sustainable Urban Development
by
Agnieszka Ptak-Wojciechowska
Sustainability 2026, 18(11), 5315; https://doi.org/10.3390/su18115315 (registering DOI) - 25 May 2026
Abstract
Despite an ageing society emerging as a global challenge, urban spaces still do not adequately address the spatial needs of older citizens. Numerous studies analyse built environment characteristics in relation to the mobility of older citizens, yet studies on the quality of older
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Despite an ageing society emerging as a global challenge, urban spaces still do not adequately address the spatial needs of older citizens. Numerous studies analyse built environment characteristics in relation to the mobility of older citizens, yet studies on the quality of older pedestrians’ perception of spatial attributes with their levels are scarce. This scoping review of 2855 records from 2013 to 2023, exported from Scopus and Web of Science, aimed to identify common patterns with respect to the aspects used in the assessment of the quality of urban spaces for older adults, with the emphasis placed on spatial attributes measured through different levels. Following PRISMA-ScR, the analysis was conducted in AsReview, a scientific tool using ML. Inclusion criteria were: peer-reviewed English-language journal articles and conference papers; the inclusion of spatial attributes in urban planning, measuring the perception of pedestrians, using a conjoint experiment, or urban digital twins; and taking into account an ageing society. The author performed the coding of 115 eligible records in four iterative rounds with the use of Atlas.ti. The findings show that Land Use & Buildings/Destinations, Sidewalk and Amenities, and Aesthetics/Urban Form were the most frequently occurring aspects. Attribute levels were proposed only in 10 records. No study incorporated stated preference and 3D walk-through environments to quantify older adults’ perception of walkability-related attributes. This represents a methodological gap for future research on older adults’ walkability perception. Urban planners and other decision-makers may use the findings of this study to support the design and management of age-friendly, sustainable, and inclusive street environments.
Full article
(This article belongs to the Section Health, Well-Being and Sustainability)
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Open AccessArticle
The Energy-Environmental Kuznets Curve: Evidence from a Time-Varying Parametric Framework
by
Ibrahim N. Khatatbeh, Ahmed Alrashed, Abdullah Alsadan and Mohammed N. Abu Alfoul
Sustainability 2026, 18(11), 5314; https://doi.org/10.3390/su18115314 (registering DOI) - 25 May 2026
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Reconciling economic growth with environmental sustainability and energy security is a defining challenge for resource-constrained emerging economies. This study examines whether Jordan follows the Environmental Kuznets Curve (EKC) and the Energy Kuznets Curve (EnKC)—two hypotheses positing that as an economy grows, its environmental
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Reconciling economic growth with environmental sustainability and energy security is a defining challenge for resource-constrained emerging economies. This study examines whether Jordan follows the Environmental Kuznets Curve (EKC) and the Energy Kuznets Curve (EnKC)—two hypotheses positing that as an economy grows, its environmental degradation and energy consumption follow an inverted U-shaped curve in relation to per capita GDP—as counterparts to the original Kuznets curve. While these relationships have been investigated in cross-country settings, little attention has been given to individual emerging economies such as Jordan, where energy and environmental issues are among the most pressing challenges of the new century. The existence of EKC and EnKC curves is tested using a “time-varying parametric (TVP) framework”—specifically, the unobserved components model (UCM), utilizing annual data from 1980 to 2024. Further tests are carried out to validate the nonlinearity hypothesis using the variable-addition test and non-nested model selection tests. Moreover, we augment the EKC and EnKC by incorporating trade openness and urbanization as control variables. For robustness, we support the UCM results with the Dynamic OLS (DOLS) long-run estimator. The results support the EnKC across the entire battery of tests, with a turning point of roughly USD 4000–4650 depending on specification. For the EKC, the OLS quadratic estimation does not exhibit a clear inverted-U; however, once a stochastic trend (UCM) or appropriate covariates (Trade, Urban) are introduced, the inverted-U re-emerges with a turning point near USD 4149–4874. This study contributes novel empirical evidence on the EKC and EnKC for Jordan using a TVP framework. Whereas prior studies have explored the EKC in Jordan, this study systematically validates both the energy and environmental variants of the Kuznets curve using robust econometric strategies. The results offer valuable policy insights for sustainable development in Jordan and other resource-constrained emerging economies facing analogous development–environment trade-offs within international climate transition frameworks.
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Open AccessSystematic Review
Human–AI Collaboration Across Decision Support, Autonomous Systems, and LLM Agents: A Systematic Review and Collaboration Convergence Framework
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Aqi Dong, Peng Li, Yanbing Chen, Shanan Gibson, Lin Zhao and Meiling He
Sustainability 2026, 18(11), 5313; https://doi.org/10.3390/su18115313 (registering DOI) - 25 May 2026
Abstract
Across four decades of AI deployment, the same six human challenges (trust calibration, reliance behavior, cognitive engagement, skill retention, accountability, and transparency) recur, yet fragmentation across research communities obscures this continuity and limits knowledge transfer. Functionally similar phenomena are repeatedly relabeled (a jangle
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Across four decades of AI deployment, the same six human challenges (trust calibration, reliance behavior, cognitive engagement, skill retention, accountability, and transparency) recur, yet fragmentation across research communities obscures this continuity and limits knowledge transfer. Functionally similar phenomena are repeatedly relabeled (a jangle fallacy): what aviation researchers call “automation complacency,” decision scientists call “algorithm appreciation,” and LLM researchers describe as “over-reliance.” This systematic review synthesizes 152 papers spanning aviation, healthcare, manufacturing/supply chain, and cross-domain contexts across three AI technology generations: decision support systems, autonomous systems, and large language model (LLM) agents. We introduce the Collaboration Convergence Framework (CCF), a 6 × 3 matrix with solution-maturity indicators that maps each challenge across generations. The framework shows that Gen 3 designers can transfer decades of evidence from automation and decision support research (particularly reliance calibration, cognitive forcing, and skill maintenance) rather than rediscovering them. Cross-generational synthesis also isolates three Gen 3 phenomena without direct precedent in earlier generations: epistemia (attributing genuine knowledge to LLMs based on surface fluency), attribution ambiguity in co-creation, and motivational withdrawal. We distill twelve transferable design principles and propose ten research directions, prioritizing skill-retention interventions and accountability frameworks. These findings carry direct sustainability implications aligned with Industry 5.0: protecting workforce capability under increasing automation (SDG 8), reducing duplicated research effort through cross-generational knowledge reuse (SDG 9), and supporting responsible deployment by treating collaboration risks as predictable rather than novel (SDG 12). The CCF provides conceptual infrastructure for cumulative learning across AI generations and industries.
Full article
(This article belongs to the Special Issue AI for Sustainable Development: Applications and Impacts across Industries)
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Open AccessArticle
Green Digital Technologies as Catalysts for Sustainable Business Transformation: Institutional Drivers of IFRS-Aligned Climate Disclosure in an Emerging Capital Market
by
Amal Alharthi, Ahmad Alomari, Fawwaz Alrwabdah, Mashael Bakhit, Iman Babiker and Mohamed Ahmed M. Ali Ramadan
Sustainability 2026, 18(11), 5312; https://doi.org/10.3390/su18115312 (registering DOI) - 25 May 2026
Abstract
This paper explores how green digital technologies (GDTs)—ERP systems, cloud software, IoT, artificial intelligence, and big data analytics—can be used to improve the quality of ESG disclosures of industrial listed companies in the Amman Stock Exchange (ASE). Based on the institutional isomorphism theory,
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This paper explores how green digital technologies (GDTs)—ERP systems, cloud software, IoT, artificial intelligence, and big data analytics—can be used to improve the quality of ESG disclosures of industrial listed companies in the Amman Stock Exchange (ASE). Based on the institutional isomorphism theory, we examine how the relationship between coercive, mimetic, and normative institutional pressures and adopting green technology interacts to effect sustainability reporting practices. Using panel data pertaining to 30 ASE-listed industrial companies during the 2020–2024 period (N = 146 firm-year observations), we applied pooled OLS and random effects frameworks characterized by a strong clustering of standard errors. The findings show that the Green Digital Technology Index is positively and significantly associated with ESG disclosure scores (Pooled OLS: β = 5.448, t = 2.367, p = 0.019; Random Effects: β = 5.941, t = 2.507, p = 0.024), with adopting firms having an average score that is 1.73 points higher. Its largest effect is on the environmental dimension (β = 3.460, p = 0.074). Institutional pressures do not moderate the GDT–disclosure relationship; however, mediation analysis indicated that institutional pressure significantly predicts GDT adoption (β = 0.098, p < 0.001), suggesting that institutional forces are linked to disclosure quality through their association with technology adoption rather than through direct effects, indicating that institutional forces exert their influence through technology adoption. Disclosure quality is negatively associated with CEO duality (β = −4.863, p < 0.001). These results are consistent with the interpretation that green digital technologies serve as a transmission channel through which institutional pressures are associated with enhanced sustainability disclosure in emerging markets.
Full article
(This article belongs to the Special Issue Circular Economy and Sustainable Business Management: The Catalytic Role of Green Technologies)
Open AccessArticle
Sustainable Entrepreneurship Orientation: Application of a Formative Measurement Model
by
Padmaka Mirihagalla and Gyula Vastag
Sustainability 2026, 18(11), 5311; https://doi.org/10.3390/su18115311 (registering DOI) - 25 May 2026
Abstract
Background: Despite growing scholarly interest in Sustainable Entrepreneurship Orientation (SEO), the field lacks a theoretically grounded measurement approach, limiting the generalizability and comparability of SEO adaptation. Methods: This paper proposes an evidence-based formative measurement approach to assess the degree of SEO
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Background: Despite growing scholarly interest in Sustainable Entrepreneurship Orientation (SEO), the field lacks a theoretically grounded measurement approach, limiting the generalizability and comparability of SEO adaptation. Methods: This paper proposes an evidence-based formative measurement approach to assess the degree of SEO within an enterprise. A multidisciplinary literature review identified four SEO dimensions, namely Entrepreneurial, People, Environmental, and Communal Orientation (EPEC), and their observable firm behavior indicators. A Multiple Indicators and Multiple Causes (MIMIC) model framework is used to position SEO as a latent formative construct rated across defined maturity levels. A longitudinal single-case study of a Hungarian private medical clinic, conducted over four quarterly measurement cycles using onsite observations and semi-structured interviews, was used to demonstrate the feasibility of the instrument, its ability to rate SEO adaptation levels consistently across independent raters from different organizational roles, and its ability to generate meaningful, trackable SEO maturity data that evolves. Results: Fleiss’ Kappa values confirmed substantial inter-rater agreement across 21 raters, and progressive SEO maturity was observed across all four quarters. Conclusions: The paper offers a theoretically grounded, methodologically replicable measurement instrument with potential applications for researchers, practitioners, and policymakers, subject to further validation across diverse organizational and cultural contexts.
Full article
(This article belongs to the Special Issue Building Sustainable Social Economy & Social-Ecological Systems for Inclusive Futures)
Open AccessArticle
Land Use Change and Landscape Ecological Risks in a Dynamic Landscape: Nonlinear Effects and Spatial Drivers in the Li River Basin
by
Yaming Fan, Zimei Su, Xia Chen, Minghang Wei, Yixin Zhao and Shizhen Cao
Sustainability 2026, 18(11), 5310; https://doi.org/10.3390/su18115310 (registering DOI) - 25 May 2026
Abstract
Rapid urbanization and tourism-driven economic development have accelerated land-use/land-cover change (LUCC), reshaped landscape structure and increased landscape ecological risk (LER). Unraveling the spatiotemporal evolution of LER and its drivers is critical for regional ecological restoration and sustainable development. Using LUCC data for the
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Rapid urbanization and tourism-driven economic development have accelerated land-use/land-cover change (LUCC), reshaped landscape structure and increased landscape ecological risk (LER). Unraveling the spatiotemporal evolution of LER and its drivers is critical for regional ecological restoration and sustainable development. Using LUCC data for the Li River Basin (LRB) during 2000–2020, this study constructed an LER assessment model based on landscape pattern indices and employed an XGBoost–MGWR framework to identify key natural and socioeconomic drivers, as well as their nonlinear effects and spatial mechanisms. The results showed that: (1) forestland and cropland dominated the basin throughout the study period. Cropland and built-up land expanded, whereas forestland, grassland, and water areas contracted, with significant mutual conversions between forestland and cropland. (2) Relatively low- and middle-risk areas dominated the LER structure and exhibited a spatial pattern of “higher in the center and lower in the periphery”. Middle- and relatively high-risk areas expanded, while low and high-risk areas contracted, indicating a shift toward middle to relatively high risk levels. (3) Land-use intensity (LUI) and the digital elevation model (DEM) were the core drivers of LER change, while tourism intensity (TRI) showed an increasing influence over time. These findings provide a scientific basis for regional ecological management and sustainable development.
Full article
(This article belongs to the Special Issue Applications of Remote Sensing and Artificial Intelligence in Land Cover Mapping and Ecosystem Monitoring)
Open AccessArticle
Sustainable Corporate Governance Under Organizational Complexity and Decentralized Structures: Evidence from Two Emerging Capital Markets
by
Ruaa BinSaddig, Hilal Rabayah, Reem Khamis and Bahaa Subhi Awwad
Sustainability 2026, 18(11), 5309; https://doi.org/10.3390/su18115309 (registering DOI) - 25 May 2026
Abstract
Despite extensive research on corporate governance compliance and firm-level outcomes, limited attention has been paid to how internal organizational structures, particularly business complexity and decentralization, shape governance effectiveness across institutionally differentiated emerging markets. This study examines these relationships within the Palestinian and Jordanian
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Despite extensive research on corporate governance compliance and firm-level outcomes, limited attention has been paid to how internal organizational structures, particularly business complexity and decentralization, shape governance effectiveness across institutionally differentiated emerging markets. This study examines these relationships within the Palestinian and Jordanian capital markets, which provide a relevant comparative setting due to differences in governance enforcement, institutional maturity, and sustainable governance adaptation. Grounded in agency theory, transaction cost theory, and contingency theory, the study adopts a comparative cross-sectional design using documentary data from non-financial firms listed on the Palestine Exchange (PEX) and the Amman Stock Exchange (ASE) for the 2023 fiscal year. Composite indices for governance effectiveness, decentralization, and business complexity were constructed using binary-coded governance disclosures. The empirical analysis employs descriptive statistics, correlation analysis, regression models, and moderation testing. The findings reveal substantial cross-market heterogeneity. In the Palestinian market, decentralization and business complexity are positively associated with governance effectiveness when examined independently, whereas the interaction effect is not supported. In the Jordanian market, business complexity emerges as the primary determinant of governance effectiveness, while decentralization shows no significant effect. Across both markets, the hypothesized moderating role of business complexity is not supported. The study contributes to the sustainable corporate governance literature by demonstrating that governance effectiveness in emerging markets is not merely a compliance issue, but also a sustainability-related organizational capability that supports transparency, accountability, institutional resilience, and responsible long-term decision-making. The findings provide context-sensitive implications for regulators and firms seeking to strengthen sustainable corporate governance practices within institutionally heterogeneous emerging-market environments.
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(This article belongs to the Special Issue Sustainable Corporate Governance and Firm Performance)
Open AccessArticle
EGS Sustainability: Deconstructing UtahForge Engineered Geothermal System Flow Data
by
Peter Leary
Sustainability 2026, 18(11), 5308; https://doi.org/10.3390/su18115308 (registering DOI) - 25 May 2026
Abstract
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Show Figures
Engineered geothermal system (EGS) cross-well flow of 30 L/s producing heat at a rate of Q~20 MW for 30 days was achieved by the UtahForge project in 2024. The cross-well flow doublet measured ℓ~400 m in length at L~100 m vertical offset. A
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Engineered geothermal system (EGS) cross-well flow of 30 L/s producing heat at a rate of Q~20 MW for 30 days was achieved by the UtahForge project in 2024. The cross-well flow doublet measured ℓ~400 m in length at L~100 m vertical offset. A first-order question is how sustainable the doublet’s 20 MW heat extraction is. Where once the answer would be framed in terms of pipe-like cubic-law flow along stress-aligned fault-scale planar heat exchange surfaces, UtahForge flow data rule out this heat exchange picture. The EGS flow data indicate aquifer-like volumetric cross-well flow with heat exchange at the grain scale. More specifically, the EGS flow data indicate no cross-well flow for a dozen hydrofrack attempts, while the 30 L/s flow occurred when the 400 m doublet wells were rendered effectively open to the crustal formation by drilling out all hydrofrack gear. An essential further observation is that the producer well flowed at only 70% of the injector rate: 30% of injected fluid was lost to flow heterogeneity in the cross-well volume. A four-step deconstruction of these observations explicitly characterizes the flow heterogeneous volume: (i) flow stimulation of the cross-well volume, (ii)wellbore-centric flow in/out of cross-well volume along the 400 m open well reach, (iii) heat advection in the cross-well volume, and (iv) sustainability-specific heat conduction into the cross-well volume. EGS stimulation process step (i) is attested by microseismic emissions (Meqs) registered on downhole sensors. Meq size and spatial correlations in turn reflect the flow heterogeneity of the cross-well volume. EGS step (iv), crustal heat conduction sustainability, is approximated by assuming radial heat energy extraction at rate Q/ℓ by a central line-sink of radius R < L/2. The line-sink analytic solution yields heat reservoir sustainability of ~3–10 years. Greater sustainability at Q/ℓ rate requires larger cross-well offsets L. The intimate relation between fluid flow and seismic emissions enables downhole seismic sensor data to image EGS flow stimulation activity. The future of EGS heat extraction depends to a large degree on feasible sizes of cross-well offset L in the flow-heterogeneous crust.
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Open AccessSystematic Review
Efficacy Beliefs and Natural Resource Conservation: A Systematic Review and Meta-Analytic Investigation
by
Giulia Scaglioni, Davide Albertoni, Nicoletta Cavazza and Margherita Guidetti
Sustainability 2026, 18(11), 5307; https://doi.org/10.3390/su18115307 (registering DOI) - 25 May 2026
Abstract
Environmental degradation represents a critical global challenge. Given its profound impact on ecosystems and societies, understanding the psychological factors that motivate individuals to engage in natural resource conservation behaviors has become increasingly important. Because efficacy beliefs (i.e., self-efficacy, response efficacy, and collective efficacy)
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Environmental degradation represents a critical global challenge. Given its profound impact on ecosystems and societies, understanding the psychological factors that motivate individuals to engage in natural resource conservation behaviors has become increasingly important. Because efficacy beliefs (i.e., self-efficacy, response efficacy, and collective efficacy) are key psychological drivers of both plans and actions, a meta-analytic approach was used to estimate the associations between efficacy beliefs and conservation-related intentions and behaviors. The moderating roles of data collection method, population type, culture, and participants’ gender were also examined. Five meta-analyses synthesized the findings from 50 studies on conservation intentions and behaviors, revealing medium-sized positive associations with self-efficacy (intention, r = 0.47; behaviors, r = 0.41) and response efficacy (intention, r = 0.36; behaviors, r = 0.34), whereas the association with collective efficacy was small (single index, r = 0.28). Although substantial heterogeneity was observed, none of the tested moderators reached statistical significance, highlighting the need for future studies. Overall, these findings underscore the importance of strengthening individuals’ beliefs in their ability to engage in conservation behaviors.
Full article
(This article belongs to the Section Psychology of Sustainability and Sustainable Development)
Open AccessArticle
Measurement, Evolution, and Market Potential Enhancement Effects of New Quality Productivity in Enterprises—A Study Based on the Three Major Eastern Urban Agglomerations
by
Jiaying Shi, Shuaihang Yi, Yi Chai, Xing Wang and Yiniu Cui
Sustainability 2026, 18(11), 5306; https://doi.org/10.3390/su18115306 (registering DOI) - 25 May 2026
Abstract
At a time when China confronts the dual challenges of intensifying international competition and urgent industrial transformation, enhancing enterprises’ new quality productivity (NQP) has become a critical pathway to strengthening market competitiveness. This study constructs a comprehensive micro-level NQP index system for enterprises,
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At a time when China confronts the dual challenges of intensifying international competition and urgent industrial transformation, enhancing enterprises’ new quality productivity (NQP) has become a critical pathway to strengthening market competitiveness. This study constructs a comprehensive micro-level NQP index system for enterprises, encompassing three core dimensions: revolutionary breakthroughs in science and technology, deep transformation and upgrading of industrial systems, and innovative allocation of production factors. Using panel data from listed enterprises in China’s three major eastern urban agglomerations (Beijing–Tianjin–Hebei, Yangtze River Delta, and Guangdong–Hong Kong–Macao Greater Bay Area), we systematically examine the spatiotemporal evolution patterns and market expansion effects of enterprise NQP. The results reveal that while enterprises’ NQP has shown a generally upward trend, significant regional disparities and pronounced polarization persist across the three urban agglomerations. Development is notably path-dependent and spatially correlated, being easily influenced by neighboring cities. More importantly, empirical evidence from benchmark regression and spatial Durbin models indicates that enhancing NQP significantly boosts enterprises’ market potential, with substantial positive spatial spillover effects. This study contributes to the literature by developing a novel micro-level measurement framework for new quality productivity and providing robust evidence that NQP serves as a powerful driver for expanding market potential in an era of technological and industrial transformation.
Full article
Open AccessArticle
Effect of Ion Polarity Regime and Ventilation on Particle Removal Efficiency
by
Justinas Masionis, Darius Čiužas, Edvinas Krugly, Martynas Tichonovas, Tadas Prasauskas, Justina Kukelkaitė and Dainius Martuzevičius
Sustainability 2026, 18(11), 5305; https://doi.org/10.3390/su18115305 (registering DOI) - 25 May 2026
Abstract
Ensuring the effective removal of airborne particles is essential for maintaining indoor air quality, particularly in environments with limited ventilation. This study examines how ion polarity regime, voltage, and relative humidity influence aerosol particle removal in a controlled, room-sized chamber (35.8 m3
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Ensuring the effective removal of airborne particles is essential for maintaining indoor air quality, particularly in environments with limited ventilation. This study examines how ion polarity regime, voltage, and relative humidity influence aerosol particle removal in a controlled, room-sized chamber (35.8 m3) using a custom-built air ionizer. Experiments were conducted under stagnant and ventilated conditions (0.5 h−1) while varying ionizer polarity (positive, negative, bipolar, alternating), voltage (6 kV, 10 kV), humidity (40%, 70%), and aerosol type (incense smoke, nebulized KCl). Positive and negative unipolar ionization achieved over 90% removal within 60 min, with decay rates of 0.04–0.05 min−1, half-lives of 13–17 min, and clean air delivery rates (CADR) of 60–90 m3 h−1. Bipolar ionization was less efficient due to ion-ion recombination, yielding CADR values below 25 m3 h−1, while alternating polarity improved deposition (40–70 m3 h−1) by reducing recombination losses. Relative humidity had a minimal influence on unipolar performance but moderated efficiency in bipolar and alternating modes. Under low ventilation, unipolar negative ionization sustained high removal (96.7%), while ozone remained below the detection limits of the methods used. These findings indicate that ion polarity control and field strength strongly influence particle removal and that unipolar or alternating-polarity operation can provide effective particle removal under controlled chamber conditions, including a low-ventilation case of 0.5 h−1.
Full article
Open AccessArticle
A Novel Inland Barge Practice for Sustainable Freight in the Pearl River Delta: Pricing Strategies for Outsourcing Leftover Shipping Demands
by
Wenxue Cai, Wenzhuo Wang, Yan Liu, Yimiao Gu and Hui Shan Loh
Sustainability 2026, 18(11), 5304; https://doi.org/10.3390/su18115304 (registering DOI) - 25 May 2026
Abstract
The Pearl River Delta region suffers from congestion in the urban road network, noise, air pollution, and other “urban diseases”. Vigorously developing inland water transportation can greatly alleviate these “urban diseases”. However, it is difficult to take advantage of the inland waterway transportation
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The Pearl River Delta region suffers from congestion in the urban road network, noise, air pollution, and other “urban diseases”. Vigorously developing inland water transportation can greatly alleviate these “urban diseases”. However, it is difficult to take advantage of the inland waterway transportation cost advantages due to the Pearl River Delta’s short haul distance characteristics. In recent business practice, a novel, environment-friendly, and competitiveness-enhanced inland waterway transportation mode has emerged in the area, called the leftover-cargo mode in this paper. This mode is composed of first-tier (big companies) and second-tier (small companies) inland barge companies, which establish a cooperative relationship and jointly meet the needs of shippers and can lead to a modal shift from inland truck to inland waterway transportation. In real practice, the pricing methods of this novel mode still rely on experience. We propose four pricing game theory models based on channel leadership in order to investigate how decision-making impacts the pricing and income of the two-tier companies. We find that, if the market price ceiling is low, second-tier inland barge companies always benefit more than first-tier companies, which is very interesting and counter to the existing literature. These findings offer pricing insights into economically viable leftover-cargo cooperation and its role in supporting sustainable road-to-waterway freight modal shift in the Pearl River Delta.
Full article
(This article belongs to the Special Issue Green and Smart Synergies in Port, Shipping and Water Transportation)
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