Sustainability

27 pages, 1063 KB

Open AccessArticle

Digital Finance and Corporate ESG Disclosure–Practice Consistency: The Roles of Corporate Digitalization and Executives’ Digital Background

by Yong Li and Shiming Shi

Sustainability 2026, 18(11), 5263; https://doi.org/10.3390/su18115263 (registering DOI) - 23 May 2026

Abstract

In the digital era, sustainable finance is increasingly expected not only to expand financial access, but also to strengthen ESG transparency, accountability, and the alignment between corporate disclosure and actual practice. Against this backdrop, this study examines whether digital finance enhances corporate ESG [...] Read more.

In the digital era, sustainable finance is increasingly expected not only to expand financial access, but also to strengthen ESG transparency, accountability, and the alignment between corporate disclosure and actual practice. Against this backdrop, this study examines whether digital finance enhances corporate ESG disclosure–practice consistency by mitigating corporate ESG decoupling. Using Chinese A-share listed firms from 2011 to 2024 as the sample, we further investigate the moderating roles of corporate digitalization and executives’ digital background. The results show that digital finance significantly reduces corporate ESG decoupling, and this finding remains robust after alternative variable specifications, sample adjustments, stricter fixed-effects settings, and instrumental-variable estimation. Across the environmental, social, and governance dimensions, digital finance exhibits a stronger mitigating effect on social and governance decoupling. Corporate digitalization and executives’ digital background, acting as key micro-level enabling mechanisms through which regional digital finance translates into firm-level governance improvement, both significantly strengthen the mitigating effect of digital finance on corporate ESG decoupling. Further analysis shows that this effect mainly operates through easing financing constraints and reducing information asymmetry. This study contributes to the literature on sustainable finance, digital governance, and corporate sustainability by providing new evidence on how digital finance can narrow the ESG disclosure–practice gap and improve the consistency between corporate ESG disclosure and actual performance. It also offers practical implications for advancing the high-quality development of digital finance, strengthening firms’ digital capabilities, and enhancing the digital literacy of corporate executives. Full article

(This article belongs to the Special Issue Sustainable Finance, Technologies, and Regulatory Frameworks: Advancing Sustainability in a Digital Era)

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20 pages, 1576 KB

Open AccessArticle

A Spatial Modelling Framework for Integrating Forest Ecosystem Services into Public Health Strategies: Evidence from Zhejiang Province, China

by Yu Zhang and Guoshuang Tian

Sustainability 2026, 18(11), 5262; https://doi.org/10.3390/su18115262 (registering DOI) - 23 May 2026

Abstract

The relationship between forest ecosystem services and human health has emerged as a key topic in forest economics and health policy research. This study develops a spatial modelling framework to quantify the health benefits of forest ecosystem services and proposes policy mechanisms to [...] Read more.

The relationship between forest ecosystem services and human health has emerged as a key topic in forest economics and health policy research. This study develops a spatial modelling framework to quantify the health benefits of forest ecosystem services and proposes policy mechanisms to incorporate these benefits into governmental health strategies. Using county-level panel data from 66 administrative units in Zhejiang Province, China, covering the period 2013–2023, we analyse the relationship between forest-mediated air purification services and two population health outcomes: the incidence of respiratory diseases and cardiovascular disease mortality. We employ a Spatial Durbin Model (SDM) to estimate both direct and spatial spillover effects across county boundaries. The findings indicate that forest ecosystem services exert significant negative effects on adverse health outcomes, with spillover effects extending beyond administrative boundaries. The monetised health benefit of forests is estimated at approximately RMB 1108.6 per hectare per year, substantially exceeding current ecological compensation standards and suggesting systematic undervaluation of forest health services. Heterogeneity analysis reveals that health benefits are greater in urbanised regions and among vulnerable population groups, including the elderly. These findings provide an empirical basis for reforming health-oriented ecological compensation mechanisms and offer implications for sustainable land use governance aligned with SDG 3 (Good Health and Well-being) and SDG 15 (Life on Land). Full article

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23 pages, 2485 KB

Open AccessReview

A Review of the Application of Machine Learning Models in Groundwater Resources Management and Quality Assessment

by Qiyuan Liu, Kunjie Liang, Fu Xia, Zhichao Yun, Sheng Deng, Xu Han, Yu Yang and Yonghai Jiang

Sustainability 2026, 18(11), 5261; https://doi.org/10.3390/su18115261 (registering DOI) - 23 May 2026

Abstract

Machine learning (ML) has evolved into an indispensable tool for uncovering hidden patterns and deducing correlations. Currently, ML is having a profound impact on the field of groundwater resources and environment research by enhancing predictive accuracy and optimizing management strategies. In this study, [...] Read more.

Machine learning (ML) has evolved into an indispensable tool for uncovering hidden patterns and deducing correlations. Currently, ML is having a profound impact on the field of groundwater resources and environment research by enhancing predictive accuracy and optimizing management strategies. In this study, we conducted a bibliometric review using CiteSpace and a global-scale analysis of ML methods applied to groundwater resources and quality based on 1326 records. The findings suggest that ML applications in groundwater resources and water environment research are still in their infancy compared with other environmental science fields. This paper then provides a systematic summary of the specific applications of machine learning methodologies within groundwater research, focusing primarily on the prediction of groundwater levels and water quality, along with the extraction of feature importance. Furthermore, a comparison was made of the pros and cons of several prevalent ML techniques used in groundwater level and water quality studies, with an emphasis on the significance of aligning data with models during the application of ML. Finally, the challenges encountered by ML tools in groundwater research were addressed, along with opportunities for the future. The significant potential of employing ML methodologies in groundwater is proposed to make the invisible visible. Full article

(This article belongs to the Special Issue Impact of Anthropogenic Pressures on the Groundwater Quality and Sustainability)

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19 pages, 11076 KB

Open AccessArticle

Soil Organic Carbon Stability and Its Controlling Factors in Typical Permafrost Wetlands in the Great Hing’an Mountains, Northeast China

by Hao Liu, Xingfeng Dong, Miao Li, Dongyu Yang, Haoran Man, Ruitong Zhang, Junxiang Lu and Fan Qi

Sustainability 2026, 18(11), 5260; https://doi.org/10.3390/su18115260 (registering DOI) - 23 May 2026

Abstract

The stability of soil organic carbon (SOC) in high-latitude permafrost regions plays a critical role in the global carbon balance. However, a systematic understanding of SOC pool fractions and their response to warming across different wetland types in the Great Hing’an Mountains remains [...] Read more.

The stability of soil organic carbon (SOC) in high-latitude permafrost regions plays a critical role in the global carbon balance. However, a systematic understanding of SOC pool fractions and their response to warming across different wetland types in the Great Hing’an Mountains remains lacking. In this study, soil samples were collected from forested, shrub, and herbaceous wetlands at depths of 0–60 cm and incubated at 5, 10 and 15 °C. A three-pool first-order kinetic model was employed to analyze SOC mineralization characteristics, carbon pool fractions, and influencing factors. The results showed that SOC mineralization rates exhibited a pattern of rapid increase followed by a peak and gradual decline over time, decreased with soil depth, and increased with temperature. The mineralization potential followed the order of shrub wetlands > herbaceous wetlands > forest wetlands. The temperature sensitivity (Q₁₀) was lowest in the deep soil layer of shrub wetlands (1.2), whereas a deeper soil layer of forest wetlands exhibited the highest Q₁₀ value (3.5). Across the three wetland types, SOC was dominated by the inert carbon pool (61–72%), with forest wetlands showing the highest proportion of inert carbon (72%). The active carbon pool in shrub wetlands was most sensitive to warming, while herbaceous wetlands had the largest inert carbon stock. Soil pH was significantly negatively correlated with the inert carbon pool, whereas soil moisture content showed a significantly positive correlation. Path analysis further revealed that SOC had the largest total effect on inert carbon accumulation, whereas available nitrogen and pH showed the strongest direct associations with Q₁₀. Wetland type was indirectly associated with inert carbon stocks through its influence on soil moisture, pH, SOC, and available nitrogen. These results highlight that both direct and indirect pathways jointly influence SOC stability in permafrost wetlands. Overall, Wetland type and soil physicochemical properties jointly regulate SOC stability and its response to warming. These results suggest that although forest wetlands possess stronger carbon stability, their stable carbon pools may become increasingly vulnerable under climate warming. Full article

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27 pages, 572 KB

Open AccessArticle

How Does Executive AI Adoption Impact Corporate Persistent Green Innovation? New Evidence from the BERT Model

by Gongmin Zhao, Minrong Chen and Yongjie Wu

Sustainability 2026, 18(11), 5259; https://doi.org/10.3390/su18115259 (registering DOI) - 23 May 2026

Abstract

With the rapid growth of the digital economy, the application of artificial intelligence (AI) technology has injected new momentum into persistent green innovation. Using data on Chinese A-share listed companies from 2010 to 2023, this article aims to investigate whether senior executives’ adoption [...] Read more.

With the rapid growth of the digital economy, the application of artificial intelligence (AI) technology has injected new momentum into persistent green innovation. Using data on Chinese A-share listed companies from 2010 to 2023, this article aims to investigate whether senior executives’ adoption of AI technology influences companies’ persistent green innovation and to identify the specific mechanisms underlying this relationship. To improve measurement accuracy, this paper employs the BERT model to conduct an in-depth analysis of corporate annual report texts to construct an executive AI adoption metric. The findings reveal that executive AI adoption significantly promotes corporate persistent green innovation, and this effect is primarily achieved through enhanced data factor allocation capabilities. Moreover, strategic agility positively moderates the relationship between executive AI adoption and corporate persistent green innovation. Specifically, the higher the level of strategic agility, the stronger the mediating role of data factor allocation in the relationship between executive AI adoption and corporate persistent green innovation. In particular, executive AI adoption plays a more significant role in fostering persistent green innovation among firms with higher total factor productivity and those facing intense market competition. Full article

(This article belongs to the Special Issue Achieving Sustainability Goals Through Artificial Intelligence)

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21 pages, 2271 KB

Open AccessArticle

AHP in Design for Six Sigma Project Selection

by Marcin Nakielski and Grzegorz Ginda

Sustainability 2026, 18(11), 5258; https://doi.org/10.3390/su18115258 (registering DOI) - 23 May 2026

Abstract

Effective project selection is a critical determinant of success for Design for Six Sigma (DFSS), particularly in automotive environments defined by high technical complexity and constrained resources. Because these selection tasks involve competing priorities, they are fundamentally multi-criteria decision-making (MCDA) problems that directly [...] Read more.

Effective project selection is a critical determinant of success for Design for Six Sigma (DFSS), particularly in automotive environments defined by high technical complexity and constrained resources. Because these selection tasks involve competing priorities, they are fundamentally multi-criteria decision-making (MCDA) problems that directly impact a company’s economic performance. This paper proposes a hybrid decision-support framework that integrates the Analytic Hierarchy Process (AHP) with a normalized scoring model. In this approach, classical AHP pairwise comparisons are used to derive consistent criteria weights, while project alternatives are evaluated on a 1–10 normalized scale to ensure the model remains scalable and practical for an industrial setting. The framework was empirically validated through a case study in an automotive company evaluating twelve DFSS project concepts. The results reveal that experts prioritize Product Quality (33%) and Cost/Functionality (33%) above all other factors, with these two criteria accounting for 66% of the total decision weight. Furthermore, the study established classification rules where projects scoring above 7.2 showed high implementation potential, while those below 5.2 were frequently discontinued. This structured approach enables a transparent and justifiable prioritization process that supports economic and operational sustainability by significantly reducing wasted engineering hours and prototype costs. Full article

(This article belongs to the Special Issue Innovative Development and Application of Sustainable Management)

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33 pages, 1811 KB

Open AccessArticle

Towards Sustainable Urban Mobility in Medium-Sized Cities: A Multi-Actor and Multi-Criteria Comparative Analysis

by David Ramos-Pacheco, José Gomes, João Monteiro, Anabela Ribeiro, Juan Francisco Coloma and Marta García

Sustainability 2026, 18(11), 5257; https://doi.org/10.3390/su18115257 (registering DOI) - 23 May 2026

Abstract

The transition towards sustainable urban mobility requires planning approaches that integrate accessibility, social inclusion, environmental quality, and stakeholder preferences, particularly in medium-sized cities, where mobility challenges differ from those of large metropolitan areas. However, comparative evidence on how different stakeholder groups prioritize sustainable [...] Read more.

The transition towards sustainable urban mobility requires planning approaches that integrate accessibility, social inclusion, environmental quality, and stakeholder preferences, particularly in medium-sized cities, where mobility challenges differ from those of large metropolitan areas. However, comparative evidence on how different stakeholder groups prioritize sustainable mobility strategies in such cities remains limited. This paper addresses this gap by applying a comparative Multi-Actor Multi-Criteria Analysis (MAMCA) to two medium-sized European cities: Cáceres, Spain, and Coimbra, Portugal. The analysis involved five stakeholder groups (citizens, entrepreneurs, public institutions, mobility operators, and academics) and used a common framework comprising five objectives, fifteen sub-objectives, and eight strategic alternatives for each city. The results show that both cities share strong priorities related to accessibility for vulnerable groups, safety, environmental quality, and public space. However, their preferred strategic pathways differ. In Coimbra, the highest support is associated with pedestrian infrastructure, public space improvements, and integrated spatial planning, whereas in Cáceres, the leading priorities are public transport connectivity, territorial integration, and accessibility for vulnerable groups. The study confirms the usefulness of MAMCA as a transferable decision-support framework for incorporating stakeholder preferences into sustainable mobility planning. Full article

(This article belongs to the Special Issue Sustainable Urban Transport Planning: Challenges and Solutions)

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25 pages, 6533 KB

Open AccessArticle

Fine-Grained Perception and Spatial Heterogeneity Analysis of Streetscapes Within Beijing’s 5th Ring Road Based on a Multi-Task Fine-Tuning Framework

by Yuhe Hu, Haiming Qin, Nan Chen, Linhe Song, Shuo Wang and Weiqi Zhou

Sustainability 2026, 18(11), 5256; https://doi.org/10.3390/su18115256 (registering DOI) - 23 May 2026

Abstract

Deep learning-powered Street View Imagery (SVI) analytics provides a critical mechanism for smart city perception within the framework of Sustainable Development Goal 11 (SDG 11), effectively bridging the gap left by traditional remote sensing in fine-grained street-level observation. Over the years, deep learning-based [...] Read more.

Deep learning-powered Street View Imagery (SVI) analytics provides a critical mechanism for smart city perception within the framework of Sustainable Development Goal 11 (SDG 11), effectively bridging the gap left by traditional remote sensing in fine-grained street-level observation. Over the years, deep learning-based semantic segmentation of urban streetscapes has become the dominant paradigm. However, when scaling to megacity measurements, current research faces the dual bottlenecks of “computational redundancy” and the “geographical domain shift” caused by the blind application of pre-trained models based on Western datasets. To address these challenges, this study is the first to systematically quantify the performance trade-off between Multi-Task Learning (MTL) and Single-Task Learning (STL) in megacity scenarios. Using this as a baseline, we constructed and validated a “low-computation, high-robustness” framework for streetscape semantic perception and spatial measurement. Relying on an integrated ResNeXt101-FPN MTL architecture and an ultra-low-cost fine-tuning strategy to overcome geographical domain shift, we extracted and analyzed the spatial heterogeneity of five core semantic elements—vegetation, sky, building, road, and vehicle—across the road network within Beijing’s 5th Ring Road. The results indicate the following: (1) We explicitly defined the computation-accuracy trade-off of MTL and STL in megacity perception. While utilizing only 1/5 of the parameters of STL, the MTL framework achieved a 5.34-fold increase in inference speed with a negligible 0.1% loss in overall mean Intersection over Union (mIoU); however, a 27.13% decrease in boundary segmentation accuracy was observed. (2) We established a low-cost, localized correction paradigm to overcome domain shift. Utilizing a minimal annotation cost (only 200 local images) significantly improved cross-domain adaptability, boosting the overall mIoU by 8.92% and significantly mitigating the geographical domain shift problem. (3) Multi-dimensional measurement and spatial analysis revealed a significant spatial decoupling pattern in Beijing’s streetscapes. The visual proportion of vegetation exhibited a pronounced “north-high, south-low” spatial differentiation, whereas built environment elements (e.g., building and road) displayed a typical “center-periphery” concentric gradient. This objectively reflects the spatial inequality of urban street greenery resources and the monocentric development characteristics of the built environment. The proposed framework therefore serves as a low-cost, AI-driven computational paradigm for smart city perception in resource-constrained regions. Furthermore, the revealed spatial heterogeneity offers data-driven insights for formulating sustainable urban renewal policies aligned with SDG 11. Full article

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

20 pages, 1349 KB

Open AccessArticle

Shadow Size Distribution Analysis for Automated Classification of Wood Chip Particle Size Distribution Under Bulk Conditions

by Thomas Gasperini, Manuela Mancini, Elena Provinciali, Gloria Ficosecco and Giuseppe Toscano

Sustainability 2026, 18(11), 5255; https://doi.org/10.3390/su18115255 (registering DOI) - 23 May 2026

Abstract

Italy is one of Europe’s largest consumers of wood pellets, while domestic production remains comparatively limited. In parallel, wood chips (WC) represent a strategic biofuel for power generation, where particle size distribution (PSD) affects handling and storage. Conventional PSD assessment relies on time-consuming [...] Read more.

Italy is one of Europe’s largest consumers of wood pellets, while domestic production remains comparatively limited. In parallel, wood chips (WC) represent a strategic biofuel for power generation, where particle size distribution (PSD) affects handling and storage. Conventional PSD assessment relies on time-consuming methodology. This study proposes a patent-pending image-processing approach (Shadow Size Distribution—SSD analysis) for PSD classification of WC under bulk conditions. One hundred samples were characterized via both standard analysis and SSD. PSD data were aggregated into fine and coarse macro-fractions and used to define binary class labels. Multivariate analyses (PERMANOVA, PCA) and Support Vector Classifier (SVC) models were employed to evaluate the discriminative capability of SSD features. PCA revealed coherent relationships between PSD macro-variables and key shadow descriptors, particularly shadow number and area. The best SVC configuration achieved 0.77 test accuracy, with strong recall for coarse samples. Although overall performance was constrained by dataset size and imbalance, the results demonstrate that SSD features retain meaningful granulometric information, supporting further development toward automated, in-line PSD monitoring systems. From a sustainability perspective, the proposed SSD-based approach enables faster and potentially in-line monitoring of biomass quality, supporting more efficient combustion processes, reduced emissions, and improved resource management in bioenergy systems. Full article

(This article belongs to the Special Issue Sustainable Development Goal 7: Biofuel Production from Biomass Conversion)

20 pages, 584 KB

Open AccessArticle

The Impact of ESG on Firm Financial Performance: Empirical Evidence from Companies in the UK FTSE350

by George Giannopoulos, Ha Phuong Vu, Farooq Mahmood, Ioannis Salmon and Rebecca Salti

Sustainability 2026, 18(11), 5254; https://doi.org/10.3390/su18115254 (registering DOI) - 23 May 2026

Abstract

This study examines the relationship between Environmental, Social, and Governance (ESG) and financial performance in the United Kingdom context, an area of increasing importance in both academic and practical domains. ESG is measured using the London Stock Exchange Group (LSEG) disclosure score. Using [...] Read more.

This study examines the relationship between Environmental, Social, and Governance (ESG) and financial performance in the United Kingdom context, an area of increasing importance in both academic and practical domains. ESG is measured using the London Stock Exchange Group (LSEG) disclosure score. Using a dataset of firms in the FTSE 350 index over a 5-year period from 2020 to 2024, a panel regression model is employed to analyse the relationship between ESG and financial performance. The results of this study are mixed. When using ROA as a proxy for financial performance, the results suggest a statistically significant and positive relationship between ESG and financial performance, although the ESG coefficient indicates a relatively modest effect. However, when ROE is used as a proxy, the results are insignificant, suggesting that the impact of ESG may vary depending on the financial performance measure used. These findings contribute to the literature by providing evidence from the UK during a period of economic disruption, highlighting ESG’s role in operational performance rather than shareholder returns. However, the results should be interpreted with caution due to the use of disclosure-based ESG measures and a limited set of control variables. Full article

(This article belongs to the Special Issue Fostering Sustainability: Business Innovation and Consumer Choices)

32 pages, 837 KB

Open AccessSystematic Review

Designing IoT Sensor Networks for Microclimate Monitoring Across the Urban–Forest Gradient: From Urban Heat Drivers to Forest Buffering Mechanisms

by Iulia Diana Arion, Irina M. Morar, Alina M. Truta, Elena Cervelli, Rusu Aniela Brîndușa and Felix H. Arion

Sustainability 2026, 18(11), 5253; https://doi.org/10.3390/su18115253 (registering DOI) - 23 May 2026

Abstract

Urbanization intensifies microclimatic heterogeneity along the urban–forest gradient, where built morphology, vegetation structure, and hydrological processes interact to shape local thermal conditions. This systematic review synthesizes advances in IoT-based microclimate monitoring across open urban environments, urban forests, and peri-urban forest ecosystems. Following PRISMA [...] Read more.

Urbanization intensifies microclimatic heterogeneity along the urban–forest gradient, where built morphology, vegetation structure, and hydrological processes interact to shape local thermal conditions. This systematic review synthesizes advances in IoT-based microclimate monitoring across open urban environments, urban forests, and peri-urban forest ecosystems. Following PRISMA 2020 guidelines, 426 records were identified, of which 63 met the eligibility criteria, and 34 core studies were analyzed in depth. In open urban environments, air temperature and relative humidity are predominantly governed by urban morphology and radiative properties. In contrast, forest microclimate is regulated through structural and ecohydrological mechanisms, where canopy structure, edge effects, and water availability determine the stability and depth of microclimatic buffering. Structural simplification and disturbance reduce buffering capacity, whereas canopy continuity enhances thermal stability. IoT-based and low-cost sensor networks enable high-resolution, multi-scale monitoring of these dynamics; however, methodological heterogeneity limits cross-site comparability. By integrating urban climate research with forest microclimate ecology, this review proposes a conceptual and methodological framework for designing distributed sensor networks capable of capturing microclimatic variability along the urban–forest gradient and supporting climate adaptation strategies. Full article

(This article belongs to the Special Issue Agro-Ecosystem Approaches to Sustainable Land Use and Food Security)

35 pages, 1032 KB

Open AccessArticle

HydraLight: A Global-Context Spatio-Temporal Graph Transformer Framework for Scalable Multi-Agent Traffic Signal Control

by Ahmed Dabbagh, Guray Yilmaz, Esra Calik Bayazit and Ozgur Koray Sahingoz

Sustainability 2026, 18(11), 5252; https://doi.org/10.3390/su18115252 - 22 May 2026

Abstract

Urban traffic congestion presents a complex challenge driven by intricate spatial dependencies and non-stationary temporal dynamics. While Multi-Agent Deep Reinforcement Learning has shown promise for Traffic Signal Control, existing approaches often struggle with partial observability and fail to coordinate effectively across large-scale, heterogeneous [...] Read more.

Urban traffic congestion presents a complex challenge driven by intricate spatial dependencies and non-stationary temporal dynamics. While Multi-Agent Deep Reinforcement Learning has shown promise for Traffic Signal Control, existing approaches often struggle with partial observability and fail to coordinate effectively across large-scale, heterogeneous road networks. In this paper, we propose HydraLight (HYbrid Deep Reinforcement Learning Architecture for Traffic Lights), a novel spatio-temporal framework that integrates Graph Attention Networks and Temporal Transformers. To overcome the localized myopia of standard graph methods, HydraLight introduces a Global Pooling Context module that broadcasts macroscopic, citywide traffic summaries, enabling agents to proactively mitigate systemic gridlock. Furthermore, to facilitate robust multi-scenario training, we introduce a Unified Prioritized Experience Replay (Unified PER) module that normalizes Temporal-Difference errors, preventing task dominance across diverse topologies. Extensive experiments on the RESCO benchmark across five synthetic and real-world networks demonstrate that HydraLight consistently outperforms state-of-the-art baselines (including X-Light and CoSLight).Byreducing traffic congestion, travel delays, and idle waiting times, the proposed framework also contributes to more sustainable urban mobility through improved traffic flow efficiency, lower fuel consumption, and reduced vehicular carbon emissions. Notably, the proposed architecture excels in structurally irregular environments, achieving up to 13.07% reduction in average travel time on complex arterial networks and consistently improving queue stability and waiting-time minimization across both synthetic and real-world RESCO benchmarks compared to state-of-the-art baselines. Full article

(This article belongs to the Section Sustainable Transportation)

33 pages, 1802 KB

Open AccessArticle

How Rural E-Commerce Shapes Agricultural Carbon Emissions: Evidence from a Quasi-Natural Experiment in China

by Jingbang Hu and Guojun Yin

Sustainability 2026, 18(11), 5251; https://doi.org/10.3390/su18115251 - 22 May 2026

Abstract

Rural e-commerce is reshaping agricultural markets, yet its environmental consequences remain insufficiently understood. This study examines how the Rural E-commerce Comprehensive Demonstration (RECD) program affects agricultural carbon outcomes in China. Using a balanced panel of 2152 counties from 2010 to 2022, we employ [...] Read more.

Rural e-commerce is reshaping agricultural markets, yet its environmental consequences remain insufficiently understood. This study examines how the Rural E-commerce Comprehensive Demonstration (RECD) program affects agricultural carbon outcomes in China. Using a balanced panel of 2152 counties from 2010 to 2022, we employ a multi-period difference-in-differences (DID) model to identify the effect of the RECD policy. The results show that the RECD policy significantly increases total agricultural carbon emissions. Evidence for production expansion and production restructuring suggests that improved market access and stronger price incentives encourage output expansion and a shift toward more market-oriented production, thereby raising aggregate emissions. At the same time, the RECD policy significantly reduces the carbon emission intensity and improves the carbon emission efficiency, indicating better carbon performance per unit of agricultural output. Further analysis shows that this dual result reflects the coexistence of efficiency gains and scale expansion, with the scale effect dominating the technical effect at the current stage. The emission-increasing effect is more pronounced in balanced agricultural areas, poverty-designated counties, counties with weaker initial e-commerce foundations, and counties with higher initial emission levels, while stronger environmental regulation and green technological innovation significantly mitigate this effect. In addition, the RECD policy generates spillover effects on neighboring counties within 50 km. These findings provide empirical evidence on the effects of the RECD policy on agricultural carbon emissions and offer policy guidance for integrating rural e-commerce policies with low-carbon agricultural transformation. Full article

(This article belongs to the Special Issue Integration of Digitalization and Green Economy)

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31 pages, 1688 KB

Open AccessArticle

The Sustainable Evaluation and Improvement of Age-Friendly Outdoor Thermal Environments in Rural Xi’an: A Perspective on Spatiotemporal Variations in Elderly Daily Activity

by Wuxing Zheng, Lu Liu, Yingluo Wang, Ranran Feng, Jiaying Zhang, Teng Shao, Seigen Cho, Haonan Zhou and Jingqiu Cui

Sustainability 2026, 18(11), 5250; https://doi.org/10.3390/su18115250 - 22 May 2026

Abstract

Elderly individuals in rural China are highly vulnerable to extreme weather events and temperature fluctuations due to inadequate infrastructure in the built environment and constrained economic conditions, thereby increasing their health risks. Outdoor spaces represent one of the primary daily activity settings for [...] Read more.

Elderly individuals in rural China are highly vulnerable to extreme weather events and temperature fluctuations due to inadequate infrastructure in the built environment and constrained economic conditions, thereby increasing their health risks. Outdoor spaces represent one of the primary daily activity settings for rural older adults. However, existing research rarely links spatiotemporal patterns of outdoor activities to evidence-based thermal environment optimization, leaving a critical knowledge gap for age-friendly and sustainable rural design. This study focuses on the spatiotemporal differentiation patterns of daily outdoor activities among elderly people aged 60 years and above in rural Xi’an, as well as the optimization of spatial variations in thermal environments. Using on-site interviews, thermal environment measurements, thermal comfort questionnaires, continuous thermal environment monitoring, and machine learning based on random forest, this study drew the following conclusions: (1) outdoor activities in winter were concentrated between 9:00–11:00 and 13:00–17:00, while in summer, they shifted to the morning and evening periods, namely 6:00–9:00 and 17:00–21:00. (2) Models for outdoor clothing adjustment, thermal sensation, and thermal acceptability among elderly residents were established. The calculated neutral temperature was 10.19 °C, with a 90% outdoor thermal acceptability range of 9.6–27.2 °C and an 80% outdoor thermal acceptability range of 6.2–30.6 °C. These findings differ from those documented in regions with distinct climate zones and geographical settings. This discrepancy stems from regional climatic features, lifestyle variations between urban and rural older adults, and differences in the thermal environment quality of elderly-oriented outdoor activity spaces. (3) In winter, the acceptable period of the Universal Thermal Climate Index (UTCI) at south-facing entrances (10:30–16:30) was significantly longer than that in the courtyard (13:30–14:00). In summer, the comfortable period in the courtyard (before 10:00 and after 20:00) was longer than that at north-facing entrances (before 09:00). A random forest model for thermal sensation was established, and the relative importance of each parameter influencing thermal sensation was analyzed. On this basis, priority improvement pathways and strategies for the thermal environment, as well as suggestions for the subjective adaptive behaviors of elderly residents, were proposed. The research results of this study can provide technical solutions for age-friendly thermal environment design in rural areas, thereby safeguarding the comfort, health, and social well-being of the elderly population in rural areas. Full article

(This article belongs to the Special Issue Sustainable Human Settlement Design and Assessment)

63 pages, 5259 KB

Open AccessSystematic Review

Scientometric and Systematic Review with SWOT Analysis of the Application and Performance of Synthetic and Composite Textile Waste-Derived Materials in Flexible Pavements

by Nura Shehu Aliyu Yaro, Zesizwe Ngubane, Suleiman Abdulrahman, Aliyu Usman, Nasir Khan, Ashiru Mohammed, Bonga PraiseGod Khuzwayo and Jacob Adedayo Adedeji

Sustainability 2026, 18(11), 5249; https://doi.org/10.3390/su18115249 - 22 May 2026

Abstract

The dramatic increase in the volume of postconsumer textile waste poses not only a major environmental problem but also an untapped opportunity for the development of sustainable infrastructure through the use of synthetic and composite textile waste-derived materials (SCTWDMs) in the field of [...] Read more.

The dramatic increase in the volume of postconsumer textile waste poses not only a major environmental problem but also an untapped opportunity for the development of sustainable infrastructure through the use of synthetic and composite textile waste-derived materials (SCTWDMs) in the field of asphalt pavement engineering, contributing to the achievement of the United Nations Sustainable Development Goals (SDGs 9, 11, 12, and 13). This systematic review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A systematic search of the literature in the field of SCTWDMs in asphalt pavement engineering was performed between 2010 and 2025 using the Web of Science and Scopus databases. A total of 65 studies were identified and analysed according to the inclusion and exclusion criteria of the current review. The quality of the studies and the risk of bias were assessed according to the transparency of the methods and the reporting of the results. The triangulated methodological framework consisted of bibliometric analysis, systematic review, and SWOT analysis. The bibliometric analysis was carried out via VOSviewer software version 1.6.20. The results of this study indicate an increase in the number of publications in SCTWDMs; however, there is fragmentation in the field. This denotes poor interrelationships among themes, insufficient collaboration across research streams, and scattered networks of keyword associations, suggesting a lack of a coherent research framework for SCTWDM research. The results of this study indicate that SCTWDMs generally improve the rheological properties, cracking resistance, and mechanical characteristics of asphalt mixtures. However, variability in fibre properties, optimisation of dosage, and limited field validation remain major challenges in SCTWDMs. The SWOT analysis also highlights important technical, institutional, and standardisation barriers, as well as opportunities for further development in sustainable pavement technologies. Despite this, the body of evidence is limited by heterogeneity in study design and a lack of long-term results. The review is not preregistered, but all the methodological procedures are transparently described. In conclusion, this body of evidence offers a strategic direction for further research, policy development, and industry practice, highlighting the importance of linking laboratory results to applications to position SCTWDMs as a viable option within the global sustainability agenda. Full article

(This article belongs to the Special Issue Innovative and Sustainable Pavement Materials and Technologies)

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34 pages, 71033 KB

Open AccessArticle

Green Intervention with a Hydroxyapatite-Based Sustainable Eco-Material: Case Study of the Apos Architecture Summer School

by Alina Moșiu, Iasmina Onescu, Rodica-Mariana Ion, Lorena Iancu, Ramona Marina Grigorescu and Daniel Johannes Burileanu Tellman

Sustainability 2026, 18(11), 5248; https://doi.org/10.3390/su18115248 - 22 May 2026

Abstract

Current challenges in the construction field emphasize the need for compatible and durable materials for heritage interventions. Traditional lime-based mortars often exhibit limitations under environmental exposure, particularly in terms of water absorption and freeze–thaw resistance. This article investigates the performance of hydroxyapatite (HAp)-modified [...] Read more.

Current challenges in the construction field emphasize the need for compatible and durable materials for heritage interventions. Traditional lime-based mortars often exhibit limitations under environmental exposure, particularly in terms of water absorption and freeze–thaw resistance. This article investigates the performance of hydroxyapatite (HAp)-modified lime mortars applied in a real-scale heritage context, namely a student built micro-museum developed within the Apoș Architecture Summer School. Following the premature degradation of a conventional lime mortar layer applied at roof level, HAp-modified formulations were introduced as a protective and consolidating solution. The experimental approach combines laboratory testing and in situ evaluation, including compressive strength measurements, water absorption, capillarity tests, chromatic analysis, and freeze–thaw assessment. The results indicate a reduction in water absorption from approximately 22% to 12%, an increase in compressive strength from 6.57 MPa to 19.95 MPa and a significant improvement in freeze–thaw resistance, reflected by a decrease in gelivity from 61.2% to 5.73%, compared to traditional lime mortars. In addition, the contact angle increased from 36° to 82°, indicating enhanced hydrophobic behavior. These improvements are associated with pore structure refinement, reduced capillary uptake, and enhanced interfacial bonding within the mortar matrix. The study also highlights the role of real-scale educational environments in validating sustainable material solutions. Full article

(This article belongs to the Special Issue Latest Review Papers in Section ‘Sustainable Engineering and Science’)

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19 pages, 2590 KB

Open AccessArticle

An AOD-Integrated Remote Sensing Ecological Index for Assessing Ecological Quality Dynamics and Management Zoning in the Shenyang Metropolitan Area (2000–2025)

by Tuo Shi, Fangyuan Li, Mingyu Wang, Chunjiao Li, Li Qi, Yuzhu Dong and Lingxue Hu

Sustainability 2026, 18(11), 5247; https://doi.org/10.3390/su18115247 - 22 May 2026

Abstract

To better capture ecological quality under aerosol pollution stress, an AOD-integrated Remote Sensing Ecological Index (ARSEI) was developed for the Shenyang Metropolitan Area (2000–2025). Using Google Earth Engine, multi-source MODIS products were compiled to generate an annual growing-season ARSEI through PCA, combining PC1 [...] Read more.

To better capture ecological quality under aerosol pollution stress, an AOD-integrated Remote Sensing Ecological Index (ARSEI) was developed for the Shenyang Metropolitan Area (2000–2025). Using Google Earth Engine, multi-source MODIS products were compiled to generate an annual growing-season ARSEI through PCA, combining PC1 and PC2 by variance-weighted contributions. Long-term trends were assessed with Theil–Sen slope estimation and the Mann–Kendall test, future persistence with the Hurst index, and drivers with an optimal parameter geographical detector. ARSEI closely matched conventional RSEI in multi-year pixel means (R² = 0.98, p < 0.001) but identified larger “poor” (+0.4%) and “moderate” (+3.4%) areas from 2000 to 2025, indicating higher sensitivity to pollution-related stress. Ecological quality improved overall, with high grades in eastern mountainous forests and low grades in the central built-up core and surrounding croplands. Improvement was dominant (31.08% significant, 38.27% slight), while degradation was limited (4.27% significant, 13.92% slight) and concentrated in peri-urban expansion belts. Elevation was the strongest natural control, whereas land use and population were the leading socioeconomic drivers with increasing influence over time. Finally, we delineated differentiated management zones based on current status and projected trajectories to support targeted regional governance. Full article

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

28 pages, 693 KB

Open AccessArticle

Determinants of Employment in the Digital Economy: Evidence from EU Countries with Implications for Inclusive Labour Market and Sustainable Development

by Olena Ivashko, Iryna Tsymbaliuk, Nataliia Pavlikha, Kamila Ćwik and Piotr Czarnecki

Sustainability 2026, 18(11), 5246; https://doi.org/10.3390/su18115246 - 22 May 2026

Abstract

This study examines the impact of digitalisation, innovation activity, demographic factors, and macroeconomic variables on employment in European Union countries within the framework of sustainable development. The empirical analysis is based on Eurostat panel data for 2015–2023 and applies regression analysis to identify [...] Read more.

This study examines the impact of digitalisation, innovation activity, demographic factors, and macroeconomic variables on employment in European Union countries within the framework of sustainable development. The empirical analysis is based on Eurostat panel data for 2015–2023 and applies regression analysis to identify the key determinants of employment. The results indicate that digitalisation demonstrates the strongest positive statistical association with employment, confirming its important role in labour market transformation and inclusive economic development. Expenditures on research and development also show a positive effect, highlighting the significance of innovation activity for employment growth. At the same time, GDP per capita does not exhibit a statistically significant relationship with employment, while education expenditure demonstrates a negative short-term effect. The findings suggest that digitalisation and innovation contribute not only to employment growth but also to the expansion of labour market participation opportunities for diverse social groups. The study contributes to the analysis of SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), and SDG 10 (Reduced Inequalities) by identifying the structural factors associated with employment dynamics in the digital economy. Full article

(This article belongs to the Special Issue Management for Sustainable Future: Challenges, Innovations and Organizational Performance)

25 pages, 5919 KB

Open AccessArticle

Groundwater Springs in Young Glacial Areas and Their Role in Sustainable Environmental Development (Case Study—North Poland)

by Izabela Chlost, Stanisław Chmiel, Roman Cieśliński, Joanna Fac-Beneda, Ivan Kirvel and Alicja Olszewska

Sustainability 2026, 18(11), 5245; https://doi.org/10.3390/su18115245 - 22 May 2026

Abstract

This article presents the results of a field study conducted in 2022 on groundwater outflows located at the edge of the Kashubian Lake District and the Reda-Łeba Proglacial Stream Valley in northern Poland. The recharge of numerous springs was found to occur from [...] Read more.

This article presents the results of a field study conducted in 2022 on groundwater outflows located at the edge of the Kashubian Lake District and the Reda-Łeba Proglacial Stream Valley in northern Poland. The recharge of numerous springs was found to occur from the first aquifer, locally supported by a deeper aquifer connected to the first one near the bowl of Lubowidzkie Lake. Groundwater drainage occurs by gravity. It is relatively abundant for young glacial areas and averages 82 dm³·s⁻¹, making the springs capable of acting as a drinking water reservoir. This assessment is based on major ions and nutrients only; microbiological and trace-organic/metal analyses are required before any drinking-water designation. Spring water is important in the lake’s supply, accounting for 18.0% of the total inflow to the basin. The hydrochemical characteristics of these waters keep the lake in ecological balance. The waters from the springs are characterized by little variation in chemical composition, with the Ca-HCO₃ hydrochemical type. They represent young infiltration waters associated with direct recharge from precipitation (the average age of the water is 60 years). Currently, low nitrate and chloride suggest limited agricultural and urban influence, but phosphate levels and observed human activities warrant caution. Forest management is gradually developing in its catchment, which may result in a reduction of the spring yield and a deterioration of their quality in the future. This may result in a disturbance of the hydrological balance of structures hydraulically connected to spring recharge and to groundwater inflow (river, lake). Although the springs studied are local hydrological phenomena, their functioning and the need for protection are closely linked to global challenges in the field of sustainable development. This primarily concerns the protection of groundwater-dependent ecosystems and, more broadly, water security and increased resilience to climate change. Full article

(This article belongs to the Special Issue Impact of Anthropogenic Pressures on the Groundwater Quality and Sustainability)

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