Sustainability

29 pages, 1892 KB

Open AccessArticle

Research on the Impact of Non-Agricultural Employment on Agricultural Carbon Emission Reduction: An Empirical Analysis Based on 295 Prefecture-Level Cities in China

by Hui Yang and Ruifang Zheng

Sustainability 2026, 18(13), 6883; https://doi.org/10.3390/su18136883 - 6 Jul 2026

Abstract

In the context of China’s Carbon Peaking and Carbon Neutrality Goals, understanding how non-agricultural employment affects agricultural carbon emission intensity is critical for achieving green and sustainable agricultural development. This study asks whether the reallocation of labor away from agriculture increases or reduces [...] Read more.

In the context of China’s Carbon Peaking and Carbon Neutrality Goals, understanding how non-agricultural employment affects agricultural carbon emission intensity is critical for achieving green and sustainable agricultural development. This study asks whether the reallocation of labor away from agriculture increases or reduces agricultural carbon emission intensity, through which transformation-related pathways this relationship may operate, and whether the relationship changes across development stages and spatially connected regions. Using panel data from 295 prefecture-level cities in China from 2011 to 2023, this study constructs a factor-substitution framework and applies fixed-effects models, potential pathway analysis, threshold models, and Spatial Durbin Models. The results show that non-agricultural employment has a significant inverted U-shaped association with agricultural carbon emission intensity: at relatively low levels it is associated with higher emission intensity, whereas beyond the estimated turning point it is associated with lower emission intensity. Non-agricultural employment is also systematically associated with agricultural structural adjustment, mechanization transformation, and AI-related patenting activity, which are interpreted as potential pathways rather than definitive causal mediation channels. Threshold results indicate that economic development and urbanization condition the marginal effect of non-agricultural employment, while spatial estimates show that the nonlinear relationship extends to neighboring regions through significant spillover effects. These findings suggest that agricultural carbon-reduction policies should distinguish between cities at different stages of labor reallocation, promote low-carbon forms of capital and technology substitution, and strengthen cross-regional coordination in agricultural producer services and green technology diffusion. Full article

(This article belongs to the Special Issue Agricultural Economic Transformation and Sustainable Development: 2nd Edition)

23 pages, 671 KB

Open AccessArticle

LLM-Assisted and Rule-Based Assessment of ESG Disclosure Quality and Its Association with External ESG Ratings: Exploratory Evidence from S&P 500 Energy Firms

by Hae Sun Jung, Shaopeng Che and Haein Lee

Sustainability 2026, 18(13), 6882; https://doi.org/10.3390/su18136882 - 6 Jul 2026

Abstract

Environmental, social, and governance (ESG) disclosure has become an important source of information for external stakeholders. As sustainability reporting has expanded, distinguishing disclosure quantity from disclosure quality has become increasingly important. This study examines how sustainability disclosure quality is associated with external ESG [...] Read more.

Environmental, social, and governance (ESG) disclosure has become an important source of information for external stakeholders. As sustainability reporting has expanded, distinguishing disclosure quantity from disclosure quality has become increasingly important. This study examines how sustainability disclosure quality is associated with external ESG evaluation outcomes among Standard & Poor’s (S&P) 500 Energy Sector firms. ESG-related claims were identified and classified from sustainability reports using large language model (LLM)-assisted structured content analysis. Based on the resulting corpus, three disclosure quality indicators were constructed: the Quantitative Evidence Ratio (QER), the Target Accountability Ratio (TAR), and the Reporting Infrastructure Score (RIS). These indicators were integrated into a composite Disclosure Quality Index (DQI) and examined in relation to S&P Global ESG Scores using Spearman’s rank correlation. The results indicated limited positive associations for the individual indicators, whereas the composite DQI showed a more pronounced positive relationship. Disclosure–rating divergence patterns were also observed, indicating that relatively favorable disclosure quality positions do not consistently correspond to higher ESG Score rankings. Overall, the findings suggested that sustainability disclosure quality may be multidimensional and that its association with external ESG evaluation outcomes became more apparent when disclosure characteristics were considered in combination. However, because the analysis is restricted to a small sample of S&P 500 Energy Sector firms, the findings should be interpreted as exploratory sector-specific evidence with limited generalizability. Full article

(This article belongs to the Topic AI for Sustainable Development: Innovations, Challenges, and Real-World Applications)

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28 pages, 36110 KB

Open AccessArticle

Landslide Susceptibility Mapping Assessment Method Based on the IVM-BiTCN–Transformer Model

by Zian Lin, Yuanfa Ji and Zhijie Chen

Sustainability 2026, 18(13), 6881; https://doi.org/10.3390/su18136881 - 6 Jul 2026

Abstract

Landslide susceptibility assessment acts as a core technical tool for geological disaster governance, ecological protection and long-term risk mitigation strategies. This modeling approach quantifies the possibility of slope-collapse events and delivers objective decision-making support for regional geologic environment supervision. To overcome the low [...] Read more.

Landslide susceptibility assessment acts as a core technical tool for geological disaster governance, ecological protection and long-term risk mitigation strategies. This modeling approach quantifies the possibility of slope-collapse events and delivers objective decision-making support for regional geologic environment supervision. To overcome the low computational efficiency and weak capacity of conventional evaluation frameworks to extract multi-level spatial grid rules, this paper takes Nanning City, the capital and largest city of the Guangxi Zhuang Autonomous Region in southern China, as the research object. Ten types of terrain and geological control factors combined with historical landslide inventory records are adopted to build a two-stage coupled evaluation framework integrating the information value method (IVM), a Bidirectional Temporal Convolutional Network (BiTCN) and Transformer, named IVM-BiTCN–Transformer. The hierarchical framework first adopts IVM to finish preliminary hazard grading and calculate factor contribution weights, then inputs classified grid samples into the BiTCN-Transformer module to realize local terrain feature and global factor fusion, which significantly lifts the overall identification precision. Ten widely adopted landslide evaluation algorithms are selected for contrast simulation, with multiple quantitative metrics adopted to judge model reliability. Experimental outcomes prove that the presented IVM-BiTCN–Transformer framework obtains superior hazard discrimination capacity, which can raise the precision and stability of landslide zoning and offer reliable technical support for targeted regional geological disaster prevention. Full article

(This article belongs to the Special Issue Sustainable Assessment and Risk Analysis on Landslide Hazards)

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32 pages, 4173 KB

Open AccessArticle

Tracing Methanogenesis Pathways via Stable Carbon Isotopes for Sustainable Biogas Production in Continuous-Flow Open Systems

by Michał Bucha, Anna Detman-Ignatowska, Aleksandra Chojnacka, Ewa Łupikasza, Łukasz Pleśniak, Wojciech Drzewicki, Marta Jakubiak, Adriana Trojanowska-Olichwer, Beata Berbeć, Dominika Kufka, Anna Sikora and Mariusz Orion Jędrysek

Sustainability 2026, 18(13), 6880; https://doi.org/10.3390/su18136880 - 6 Jul 2026

Abstract

The common products of acidogenesis, the key stage in the process of anaerobic digestion, are lactate, butyrate, propionate, and acetate. They were decomposed in the Up-flow Anaerobic Sludge Blanket bioreactors working in continuous-flow open systems. A comprehensive analysis of variations in both isotopic [...] Read more.

The common products of acidogenesis, the key stage in the process of anaerobic digestion, are lactate, butyrate, propionate, and acetate. They were decomposed in the Up-flow Anaerobic Sludge Blanket bioreactors working in continuous-flow open systems. A comprehensive analysis of variations in both isotopic ratios and concentrations of organic acids in the effluents was conducted to enhance comprehension of methanogenic processes. The analysis of carbon isotope fractionation in the CO₂-CH₄ system, as evidenced by the α¹³C_CO2-CH4 factor, has indicated that acetate decarboxylation has occurred. Furthermore, a decline in CO₂ levels was observed, accompanied by the predominance of butyrate and propionate, despite the presence of acetic acid in the effluents from all the bioreactors. Butyric acid demonstrated the greatest resistance to decomposition, resulting in ¹³C-enrichment of DIC. Lactic acid was utilised almost entirely. The observations presented above were subsequently validated through statistical analysis. A comparative analysis of the δ¹³C(CH₄) and δ¹³C(CO₂) values of our study with those of other natural substrates (detritic lignite, xylite, maize silage, and cattle manure) was undertaken, and it was found that isotope fractionation differs significantly in closed (potential thermodynamic processes) and open systems (expected Rayleigh processes). In the context of open systems, the isotope fractionation factor α¹³C_CO2-CH4 during methaneogenesis has been observed to attain values that are consistent with those observed in CH₄ oxidation. The study revealed that the presence of acetate in the substrate (i.e., the M4 bioreactor) led to the generation of CO₂ with a higher proportion of light carbon isotopes. This, in turn, resulted in a shift in the isotope fractionation factor (i.e., α¹³C_CO2-CH4) to values below 1.03. Our results suggest that methanogenic pathway signatures in open, continuous-flow systems may only be partially apparent. This is because substrate depletion drives Rayleigh-type isotope enrichment, while the dominance of a single substrate and its constant inflow stabilise pathway expression and shift control towards substrate dynamics rather than intrinsic microbial changes. Our finding suggests that isotope-based diagnostics could enhance process control in biogas plants by identifying substrate-driven limitations and facilitating more efficient and stable CH₄ production. Full article

(This article belongs to the Topic Advanced Bioenergy and Biofuel Technologies)

27 pages, 8373 KB

Open AccessArticle

Ecological Assessment of Temperature’s Influence on CO₂ Efflux from Lawn Soils in Case of Its Pronounced Dynamics

by Andrey V. Stepanov, Sergey N. Kivalov and Ivan I. Vasenev

Sustainability 2026, 18(13), 6879; https://doi.org/10.3390/su18136879 - 6 Jul 2026

Abstract

The carried-out microfield model research was aimed at identifying patterns in the dynamics of soil CO₂ effluxes depending on the locally occurring hydrothermal regimes of regenerated lawn ecosystems on peat–sand substrates with different peat contents. Monitoring was carried out every ten days [...] Read more.

The carried-out microfield model research was aimed at identifying patterns in the dynamics of soil CO₂ effluxes depending on the locally occurring hydrothermal regimes of regenerated lawn ecosystems on peat–sand substrates with different peat contents. Monitoring was carried out every ten days from 21 April 2019 to 30 October 2019 and included measurements of soil and air temperature, soil moisture, and CO₂ efflux every 3 h during the day. The weather conditions of the 2019 growing season in Moscow, with air temperature close to the annual average and increased precipitation, made it possible to clarify quantitative patterns of the temperature influence on CO₂ efflux from lawn soils in case of their pronounced dynamics without real soil moisture deficit. To study relationships between CO₂ efflux and soil and air temperatures, three empirical CO₂ efflux models (Exponential, Raich–Hashimoto and Lloyd–Taylor) were used with comparative assessment of their results. The conducted investigation showed that both peat content, local hydrothermal regime, and type of vegetation cover play a significant role in efflux modulation, with the temperature factor dominating on both seasonal (72% impact) and intraday (51–94% impact) scales. The lawn substrate factor accounts for up to 10% of CO₂ efflux variability on the intraday scale. The lawn vegetation cover (with the lower and higher diversity) significantly affects the soil hydrothermal regime depending on the peat content (a higher impact with a lower peat content due to the soil pH difference). The denser vegetation reduces the soil temperature, providing better protection, and at the same time reduces soil moisture by transpiration, which provides the combined effect on the CO₂ efflux reduction (up to 1 g CO₂ m⁻² day⁻¹ reduction for the lower-pH soils). Full article

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

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29 pages, 16892 KB

Open AccessArticle

Sustainable Power-Quality Governance Through Adaptive Voltage Sag Compensation and Tripartite Commercial Operation: A Bi-Level Nash Bargaining Approach to Avoided-Loss Benefit Allocation

by Bin Yang, Yongbiao Yang and Qingshan Xu

Sustainability 2026, 18(13), 6878; https://doi.org/10.3390/su18136878 - 6 Jul 2026

Abstract

Power-quality resilience is an important component of sustainable industrial electricity use, as voltage sag events can cause production interruptions, equipment damage, and inefficient allocation of mitigation costs and benefits among stakeholders. However, high initial investment costs and the lack of a viable commercial [...] Read more.

Power-quality resilience is an important component of sustainable industrial electricity use, as voltage sag events can cause production interruptions, equipment damage, and inefficient allocation of mitigation costs and benefits among stakeholders. However, high initial investment costs and the lack of a viable commercial operation scheme have hindered the large-scale deployment of mitigation devices. To support sustainable power-quality governance, this study proposes an integrated framework that connects the technical compensation performance of the mitigation device with the economic foundation of a tripartite commercial operation model. First, an adaptive switching compensation strategy dynamically shifts between different modes based on the real-time voltage sag depth, establishing a mapping relationship with avoided-loss benefits. Then, a bi-level Nash bargaining model is constructed to allocate costs and benefits among the government, the enterprise, and the user, deriving closed-form analytical solutions for both the upper- and lower-level games. Through pilot operations at a large public service facility, economic losses of 480,000 CNY caused by a single voltage sag can be effectively avoided. Meanwhile, under the proposed scheme, all three parties achieve positive net present values. Compared to the user self-funding mode, the user’s NPV increases by 21.9%. Furthermore, unlike bilateral or equal-sharing alternatives, the Nash bargaining solution ensures all parties remain within the strong feasible region. The government and enterprise recover their costs within 4.14 and 6.20 years, respectively. These results indicate that the proposed framework can enhance the economic sustainability of power-sensitive users, encourage shared public–private investment in power-quality improvement, and support more resilient and efficient industrial electricity use. Full article

(This article belongs to the Section Energy Sustainability)

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33 pages, 3896 KB

Open AccessArticle

Digital Twin-Guided Multi-Source State Estimation via Physics-Constrained DDPM for Renewable-Integrated Distribution Networks

by Yixian Li, Xudong Zhu, Lingxiao Yang and Ning Zhang

Sustainability 2026, 18(13), 6877; https://doi.org/10.3390/su18136877 - 6 Jul 2026

Abstract

Reliable state estimation is essential for the secure and efficient operation of sustainable energy systems, especially under the increasing integration of renewable energy, distributed resources, and heterogeneous sensing devices. However, in practical power systems, SCADA, PMU, and AMI measurements often have different sampling [...] Read more.

Reliable state estimation is essential for the secure and efficient operation of sustainable energy systems, especially under the increasing integration of renewable energy, distributed resources, and heterogeneous sensing devices. However, in practical power systems, SCADA, PMU, and AMI measurements often have different sampling rates, accuracies, communication delays, and availability levels, which makes reliable data completion and multi-source fusion difficult. This paper focuses on the state estimation problem of renewable-integrated distribution networks under multi-source heterogeneous measurement conditions. In such distribution networks, the increasing penetration of distributed renewable energy resources and the joint deployment of multiple measurement devices, including SCADA, PMU, and AMI, may lead to incomplete measurements, asynchronous sampling, differences in measurement accuracy, and reduced system observability. To address these issues, this paper proposes a model-based digital twin reference-guided physics-constrained DDPM framework to improve the quality of missing-measurement completion and the reliability of state estimation in distribution-network scenarios. A four-layer simulation-oriented cyber–physical framework is first constructed to integrate physical sensing, model-based digital twin reference mapping, AI-based measurement completion, and state estimation feedback. Within this framework, a physics-constrained self-supervised denoising diffusion probabilistic model is developed to recover missing measurements by combining observed data, digital twin reference measurements, real-time topology information, and power system operational constraints. The completed pseudo-measurements and physical measurements are then fused through a credibility-aware weighting strategy that considers timeliness, data integrity, measurement accuracy, and virtual–real consistency verification under simulation settings. Simulation results on the IEEE 14-bus system show that the proposed method improves pseudo-measurement completion and supports more reliable voltage magnitude and phase angle estimation under different measurement configurations. Under the tested simulation settings and multi-source measurement configurations, the results indicate that the proposed method can improve pseudo-measurement completion and support more reliable voltage magnitude and phase angle estimation. However, its performance under frequent topology switching, high missing-data ratios, and complex abnormal data conditions remains to be further evaluated. Full article

(This article belongs to the Special Issue Digital Twin-Driven Energy Systems Optimization: From Algorithm Innovation to Low-Carbon Operation)

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24 pages, 57891 KB

Open AccessArticle

Assessing Road Changes by AHP Approach with GIS: Insight into Economic Sustainability in the Qiantang River Basin of China

by Shiyi Xie, Jinzhao Fan, Guanmin Qiao, Zucheng Wu and Pingbin Jin

Sustainability 2026, 18(13), 6876; https://doi.org/10.3390/su18136876 - 6 Jul 2026

Abstract

Assessing the sustainability of urban development, including road changes, is increasing from year to year and requires clear indicators for robust decision-making tools to gain knowledge across regions. This study conducts the selection of transportation routes over a longer period as an example [...] Read more.

Assessing the sustainability of urban development, including road changes, is increasing from year to year and requires clear indicators for robust decision-making tools to gain knowledge across regions. This study conducts the selection of transportation routes over a longer period as an example to evaluate the sustainability of historical official routes in achieving economically cost-efficient operation and maintenance. Official ways in the Qiantang River Basin connected the Jiangnan region, the economic center of China, with surrounding provinces were assessed. During the past six hundred years, the official road network in this area gradually simplified, evolving from valley roads to river banks, which covered longer distances. However, this transformation lacks a systematic explanation. By applying the analytic hierarchy process (AHP) with geographic information system (GIS), quantitative analysis was gained and the results are as follows: (1) Among the influencing factors, the weights of transportation cost and population related to economic needs are 39.54% and 29.52% respectively, with a combined total of 69.06%. (2) The official road network is often designed for governing the people, but in places such as the Qiantang River Basin, economic logic superseded political imperatives, becoming the dominant factor in reshaping the official ways. (3) In the pre-industrial era characterized by limited technological capacity, the physical environment had a greater impact on economic costs, ultimately reshaping the spatial configuration of official route networks. Overall, the evolution of official routes reflects the decline in their military-political function, driven by sustained peace and long-term decline in strategic position. The evolution of the official ways in the Qiantang River Basin reveals the importance of economic benefits in road selection. Full article

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30 pages, 2375 KB

Open AccessArticle

Multidimensional Analysis of Alerts Reported in the Safety Gate System (RAPEX) in 2005–2025

by Marcin Pigłowski

Sustainability 2026, 18(13), 6875; https://doi.org/10.3390/su18136875 - 6 Jul 2026

Abstract

The safety of non-food products is embedded in the United Nations 2030 Agenda for Sustainable Development and the European Union (EU) framework, supporting health protection, responsible production and consumption, and market surveillance. The EU Rapid Alert System for dangerous non-food products, known as [...] Read more.

The safety of non-food products is embedded in the United Nations 2030 Agenda for Sustainable Development and the European Union (EU) framework, supporting health protection, responsible production and consumption, and market surveillance. The EU Rapid Alert System for dangerous non-food products, known as Safety Gate (formerly RAPEX), was established in 2005 to facilitate the exchange of information on products posing risks within the internal market. The aim of this study was to present the interdependencies reported in the Safety Gate system/RAPEX in 2005–2025, considering: product category, type of risk, country of origin, notifying country and year, as well as measures taken. The VOSviewer 1.6.20 and Statistica 13.3 were used. The results highlighted the following problems: toys from China with chemical, choking and injury risks; electrical appliances also from China with electric shock hazards; motor vehicles from Germany with injury risks; cosmetics from Italy with chemical and microbiological risks; and clothing from Turkey with suffocation risks. Reporting is expected to continue under existing regulatory frameworks, although changing the name of the system from RAPEX to “Safety Gate” may reduce its recognition. The findings highlight the need for targeted enforcement, improved risk profiling by product category and origin, and ongoing monitoring of emerging safety risks. Full article

19 pages, 1461 KB

Open AccessArticle

More Grain-Oriented, but Limited Efficiency Gains? Smallholder Grain Production Under Farm-Scale Expansion in Rural China

by Jing Li, Zhiqi Shen and Zixin Xiong

Sustainability 2026, 18(13), 6874; https://doi.org/10.3390/su18136874 - 6 Jul 2026

Abstract

Farm-scale expansion is widely viewed as a means of improving agricultural efficiency and linking smallholders to modern agriculture. Yet whether it improves the conditions under which smallholders participate in grain production remains unclear. Using panel data from the China Rural Revitalization Survey (CRRS) [...] Read more.

Farm-scale expansion is widely viewed as a means of improving agricultural efficiency and linking smallholders to modern agriculture. Yet whether it improves the conditions under which smallholders participate in grain production remains unclear. Using panel data from the China Rural Revitalization Survey (CRRS) for 2020 and 2022, this study examines how village-level farm-scale expansion affects smallholder grain production. The results show that a 0.1 increase in village-level farm-scale expansion intensity is associated with a 0.81-percentage-point higher grain-sown share, but without corresponding improvements in production conditions. Farm-scale expansion is also associated with lower mechanization, a lower share of spending on purchased agricultural services, greater reliance on household-owned machinery, and higher family labor input. We describe this pattern as constrained grain-oriented adjustment: an increase in grain-sown share without corresponding improvements in mechanization or external service support, leaving production more dependent on household-based resources. Cooperative membership is associated with less severe mechanization and cost pressures. Overall, a higher grain-sown share under farm-scale expansion does not necessarily imply improved conditions for smallholder grain production. To promote inclusive agricultural modernization, policy efforts should focus not only on farm-scale operations, but also on strengthening smallholders’ access to mechanized, service-based, and organizational support. Full article

(This article belongs to the Section Sustainable Agriculture)

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32 pages, 1141 KB

Open AccessSystematic Review

A Systematic Literature Review on Bipolar Fuzzy Soft Sets in Environmental Sustainability and the Conceptual Development of Weighted BFSS

by Ema Carnia, Sukono, Dwi Susanti, Mohd Zaki Awang Chek, Mugi Lestari, Audrey Ariij Sya’imaa HS and Moch Panji Agung Saputra

Sustainability 2026, 18(13), 6873; https://doi.org/10.3390/su18136873 - 6 Jul 2026

Abstract

Effective environmental sustainability decision-making requires the consideration of both positive and negative information, as well as the integration of weighting methods, to ensure decisions are accurate and representative of real-world conditions. This study presents a Systematic Literature Review (SLR) on the development of [...] Read more.

Effective environmental sustainability decision-making requires the consideration of both positive and negative information, as well as the integration of weighting methods, to ensure decisions are accurate and representative of real-world conditions. This study presents a Systematic Literature Review (SLR) on the development of the Weighted Bipolar Fuzzy Soft Set (WBFSS) framework for environmental sustainability decision-making. Articles were retrieved from three databases: Scopus, ScienceDirect, and Dimensions. The screening process adhered to PRISMA 2020 guidelines and identified 27 relevant articles. VOSviewer was subsequently used to conduct a bibliometric analysis, mapping keyword co-occurrences and the structural landscape of research topics. The analysis examined the evolution of Bipolar Fuzzy Soft Set (BFSS) frameworks, their application domains, and the integration of weighting methods within BFSS and related Fuzzy Soft Set (FSS) frameworks. The review found that, although 11 studies addressed environmental sustainability applications, only three explicitly employed BFSS-based frameworks, indicating that the application of BFSS in this domain remains limited. Furthermore, the incorporation of explicit weighting techniques within BFSS remains scarce, particularly for objective, data-driven weighting approaches. These findings provide a comprehensive overview of current research trends, identify important methodological gaps, and support the conceptual development of the WBFSS framework as a direction for future research rather than an established decision-making framework. This study highlights opportunities to advance decision-support methods for environmental sustainability, which may support future climate-related and sustainability-oriented decision-making in the context of Sustainable Development Goal 13 (Climate Action). Full article

22 pages, 579 KB

Open AccessArticle

Labor Constraints and Sustainability of the Economic Growth in Croatia—An Input–Output Approach

by Davor Mikulić, Željko Lovrinčević and Damira Keček

Sustainability 2026, 18(13), 6872; https://doi.org/10.3390/su18136872 - 6 Jul 2026

Abstract

After EU accession, Croatia has leveraged the advantages of EU membership, such as access to a large market and EU funds, to accelerate economic growth and reduce the development gap in comparison to advanced EU economies. Although EU membership has stimulated economic growth, [...] Read more.

After EU accession, Croatia has leveraged the advantages of EU membership, such as access to a large market and EU funds, to accelerate economic growth and reduce the development gap in comparison to advanced EU economies. Although EU membership has stimulated economic growth, it has also brought negative effects, such as labor emigration to more developed EU economies with higher wages and increased inflation due to price convergence and the adoption of the Euro. The weak growth of labor productivity in Croatia is a consequence of the slow transformation towards technology-intensive industries, the dominance of traditional labor-intensive sectors such as construction and hospitality, and the rapid growth of employment in the public sector. The novelty of the research lies in applying an input–output model to estimate direct and indirect labor requirements in Croatia, an example of a small, service-oriented economy that, after joining the EU, witnessed a significant increase in final demand. Research is based on the Eurostat FIGARO database. The increase in gross value added across industries during 2015–2024 is separated into price and real growth effects. Analysis indicates that the current Croatian growth model is unsustainable because of high labor requirements and slow productivity growth. Results imply that European Union membership brings many advantages, but if not coupled with an adequate industrial development strategy, economic growth based exclusively on increasing final demand could reach its limits. Labor constraints and continued demand growth without substantial structural changes could result in rising wages and prices rather than real GDP growth. Full article

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

26 pages, 11129 KB

Open AccessArticle

Multi-Hazard Risk Assessment of Critical Infrastructure Using Pan-European Open Datasets: A Unified Framework Applied to Schools Under Flood, Earthquake, and Landslide Hazards

by Stavroula Fotopoulou, Anna Karatzetzou, Paraskevi Tsoumani, Stella Karafagka and Dimitris Pitilakis

Sustainability 2026, 18(13), 6871; https://doi.org/10.3390/su18136871 - 6 Jul 2026

Abstract

Recent evidence shows that multi-hazard events are becoming more frequent across Europe, highlighting the need to move beyond single-hazard approaches and toward integrated risk assessment. Despite recent advances, four key gaps persist: limited quantitative research on hazard interactions; model complexity that restricts large-scale [...] Read more.

Recent evidence shows that multi-hazard events are becoming more frequent across Europe, highlighting the need to move beyond single-hazard approaches and toward integrated risk assessment. Despite recent advances, four key gaps persist: limited quantitative research on hazard interactions; model complexity that restricts large-scale applicability; narrow hazard coverage with insufficient integration of climate change scenarios; and neglect of cumulative impacts from sequential events. This study makes two complementary contributions. First, it proposes a scalable, unified multi-hazard risk assessment framework applicable at regional and European scales. In this framework, multi-hazard considerations are embedded throughout the entire assessment process—from study domain definition and loss metrics, through hazard characterization and conceptual incorporation of dynamic vulnerability, to the probabilistic treatment of hazard interactions and compound effects via a probabilistic, conditional-dependency framework conceptually represented as a Bayesian network. Second, based on the literature review conducted in this study, no prior European study was identified that combines flood, earthquake, and earthquake-triggered landslide hazards at the asset level for educational facilities. Therefore, this work is, to the best of the authors’ knowledge, among the first such quantitative, asset-level multi-hazard risk assessments. The framework is demonstrated for over 1700 school buildings in the Region of Central Macedonia, Greece, using pan-European open-access datasets (ESHM20, ESRM20, JRC, GIRI, and ELSUS v2), making it readily transferable across Europe. By supporting risk-informed prioritization of mitigation and resilience investments, this work is consistent with the broader objectives of the Sendai Framework and the UN Sustainable Development Goals, particularly SDG 11 and SDG 13. Full article

(This article belongs to the Section Hazards and Sustainability)

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20 pages, 690 KB

Open AccessArticle

Associations Between Digital Life Balance, Eco-Emotions and Readiness to Change for Sustainability

by Gianmarco Barsotti, Marina Baroni, Andrea Guazzini, Anna Enrica Tosti, Giulia Valdrighi and Mirko Duradoni

Sustainability 2026, 18(13), 6870; https://doi.org/10.3390/su18136870 - 6 Jul 2026

Abstract

Given the growing impact of the climate crisis on mental health, it is necessary to explore the domain of eco-emotions, the affective responses to environmental change. In keeping with this, the present exploratory study aimed to investigate the association between Digital Life Balance [...] Read more.

Given the growing impact of the climate crisis on mental health, it is necessary to explore the domain of eco-emotions, the affective responses to environmental change. In keeping with this, the present exploratory study aimed to investigate the association between Digital Life Balance (DLB) and eco-emotions, while also examining whether Readiness to Change (RtC) dimensions were involved in possible indirect associations within exploratory cross-sectional statistical mediation models. Data were collected through the use of an anonymous online survey, and the final sample consisted of 257 participants (59.9% cisgender women; 39.3% cisgender men; 0.8% people belonging to the LGBTQIA+ community; mean age = 32.99, SD = 14.640). From a methodological perspective, correlation analysis, MANOVA and exploratory cross-sectional statistical mediation models were performed. The results showed small positive correlations between DLB and eco-emotions in terms of anger, isolation, anxiety, and sorrow. Exploratory statistical mediation models suggested possible uncorrected indirect associations involving perceived importance of the environmental problem (PI; RtC). However, these specific indirect associations did not remain significant after Benjamini–Hochberg correction for multiple testing. Accordingly, the findings should be interpreted as small, preliminary, unadjusted cross-sectional associations between subjective online–offline balance, selected eco-emotions, and sustainability-related psychological readiness. In conclusion, this work provides an initial basis for future longitudinal and covariate-adjusted studies examining how subjective digital–offline balance may be linked to eco-emotional experiences and sustainability-related psychological readiness. Full article

(This article belongs to the Special Issue Beyond Moral Motivation: Psychological Ergonomics of Pro-Environmental Behaviour)

26 pages, 820 KB

Open AccessArticle

Identifying Strategies for Balancing Profitability and Sustainability: An Exploratory Case from the Apparel Supply Chain

by Anuradha Colombage and Darshana Sedera

Sustainability 2026, 18(13), 6869; https://doi.org/10.3390/su18136869 - 6 Jul 2026

Abstract

Business models have historically prioritized efficiency and profitability, often at the expense of environmental and social concerns. Balancing profitability and sustainability remain a central challenge in global apparel supply chains, amid increasing competitive, regulatory, and consumer scrutiny. This study explores how organizations navigate [...] Read more.

Business models have historically prioritized efficiency and profitability, often at the expense of environmental and social concerns. Balancing profitability and sustainability remain a central challenge in global apparel supply chains, amid increasing competitive, regulatory, and consumer scrutiny. This study explores how organizations navigate this tension through an interpretive case study of a South Asia-based apparel manufacturer that piloted multi-tier traceability technologies, combining physical and digital tracking to ensure a traceable chain of custody. In-depth interviews with senior decision-makers, guided by a deductive approach using strategic duality theory as a sensitizing device, identified four mechanisms for reconciling economic and environmental goals: (1) achieving economies of scale through digital platformatization; (2) enhancing authentication and verification; (3) creating benchmarking matrices linking sustainability and performance; and (4) enabling data-driven decision making. The study contributes to theory and practice by demonstrating how traceability technologies position sustainability as a long-term driver of profitability, innovation, and ethical value creation. Full article

(This article belongs to the Special Issue Digital Twin–Enabled Sustainable Supply Chains for an Environmentally Friendly Economy)

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22 pages, 9392 KB

Open AccessArticle

Desertification Safety Levels Assessment by Geospatial Methods in the Uzbekistan Part of the Khorezm Oasis

by Muzaffar Matchanov, Ana Teodoro, Otabek Matchanov, Rifat Boymurodov and Ikrom Gulimmatov

Sustainability 2026, 18(13), 6868; https://doi.org/10.3390/su18136868 - 6 Jul 2026

Abstract

Desertification is a serious environmental challenge in regions with desert landscapes, such as the Khorezm Oasis in the Republic of Uzbekistan. Low precipitation rates and shortages of irrigation water have driven dynamic changes in desert-related land use and land cover (LULC) classes, threatening [...] Read more.

Desertification is a serious environmental challenge in regions with desert landscapes, such as the Khorezm Oasis in the Republic of Uzbekistan. Low precipitation rates and shortages of irrigation water have driven dynamic changes in desert-related land use and land cover (LULC) classes, threatening environmental and food security. This study aims to assess desertification safety levels in the Khorezm oasis using geospatial technologies to better understand spatiotemporal dynamics and to support sustainable agricultural management. A multi-criteria decision-making (MCDM) approach was used for desertification assessment. Annual mean values of key indicators—land surface temperature, vegetation index, groundwater (GW) depth, wind speed, soil erodibility (K-factor), precipitation, normalized enhanced sand index, maximum air temperature, and LULC classes—were analyzed for the period 2000–2023. The results indicate that the normalized enhanced sand index and LULC classes exert the strongest influence on desertification processes. Areas classified as high to very high desertification hazard are predominantly concentrated in the Republic of Karakalpakstan, covering a total area of 2345.65 km². Ongoing water shortages in the Amu Darya River basin pose a significant risk of further expansion of desertified areas. The findings provide valuable insights for regional land management and desertification mitigation planning. Full article

(This article belongs to the Section Sustainability in Geographic Science)

30 pages, 18230 KB

Open AccessArticle

From Benchmark Accuracy to Field Performance: Hybrid Deep Learning-Based Plant Disease Classification with IoT-Enabled Environmental Monitoring

by Jalampelli Thirupathi, Nandagopal Malarvizhi and Potula Sree Brahmanandam

Sustainability 2026, 18(13), 6867; https://doi.org/10.3390/su18136867 - 6 Jul 2026

Abstract

Accurate detection of plant leaf diseases is essential for enhancing crop productivity and supporting global food security. In addition to disease classification, understanding how environmental and soil conditions affect model performance is important for developing robust real-world agricultural monitoring systems. Although deep learning [...] Read more.

Accurate detection of plant leaf diseases is essential for enhancing crop productivity and supporting global food security. In addition to disease classification, understanding how environmental and soil conditions affect model performance is important for developing robust real-world agricultural monitoring systems. Although deep learning (DL) models achieve high accuracy on benchmark datasets, their performance in real-world settings is often limited by variations in illumination, background complexity, and environmental conditions. This study proposes a smart DL framework for detecting and classifying multiple leaf diseases in tomato, potato, and pepper plants. The framework combines U²-Net-based leaf segmentation with a Convolutional Neural Network–Bidirectional Gated Recurrent Unit (CNN–Bi-GRU) architecture. MobileNetV2 is employed as the feature extraction backbone to capture spatial characteristics, while Bi-GRU layers model sequential feature dependencies, forming a spatio-temporal network whose architectural design prioritizes parameter efficiency through depthwise separable convolutions and reduced gating complexity. The model was trained and validated using the PlantVillage benchmark dataset and achieved a classification accuracy of 99.8% with a macro-averaged F1-score of 94%, outperforming several state-of-the-art architectures. To assess robustness under real-world conditions, the trained model was further tested on leaf images collected from open-field environments near Eluru, South India. The field evaluation revealed a reduction in classification accuracy to 61.97%, indicating the impact of domain shift and environmental variability. To investigate potential contributing factors, soil parameters, including pH, temperature, moisture, and NPK levels, were monitored using an IoT-based Arduino sensing system over ten consecutive days. Rather than serving as direct inputs to the disease classification model, these environmental measurements were analyzed to assess their potential influence on disease symptom expression and the observed reduction in model performance under field conditions. The results suggest that environmental conditions may influence disease symptom expression and model transferability. This study highlights the importance of integrating DL-based disease recognition with environmental monitoring for reliable field-level agricultural applications. Nevertheless, computational complexity metrics, including inference latency and memory footprint, were not evaluated in the present work and are identified as a priority for future edge deployment studies. Full article

(This article belongs to the Special Issue Application of Remote Sensing and Machine Learning in Sustainable Agriculture)

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20 pages, 2544 KB

Open AccessArticle

Synergistic Reduction of Carbon and Pollutants in China’s Coal Chemical Industry Using Renewable H₂ and O₂

by Yuanyuan Sun, Yue Zhang, Yichen Li, Qi Qiao and Lu Bai

Sustainability 2026, 18(13), 6866; https://doi.org/10.3390/su18136866 - 6 Jul 2026

Abstract

The coal chemical industry is a major emitter of carbon and pollutants in China, yet the synergistic potential of decarbonization options remains unclear. This study integrates life-cycle assessment (LCA) and techno-economic analysis (TEA) to evaluate the synergistic reduction potential of substituting conventional coal-based [...] Read more.

The coal chemical industry is a major emitter of carbon and pollutants in China, yet the synergistic potential of decarbonization options remains unclear. This study integrates life-cycle assessment (LCA) and techno-economic analysis (TEA) to evaluate the synergistic reduction potential of substituting conventional coal-based H₂/O₂ with renewable-powered electrolytic H₂/O₂ across eight scenarios for 2030 and 2050, explicitly accounting for green H₂ supply constraints. We find that full life-cycle emissions reached 1.29 Gt CO₂eq and 20.43 Mt of pollutants in 2023 (≈10% of national GHG emissions), projected to rise to 2.49 Gt and 41.46 Mt by 2050. While the theoretical maximum carbon reduction potential reaches 95%, a severe green H₂ supply gap limits near-term feasibility: achievable reductions are only 12% (carbon) and 1% (pollutants) by 2030, rising to 42% and 11% by 2050, with abatement costs of –380 billion to 3.6 trillion CNY. The wind- and solar-powered pathways are most cost-effective (marginal abatement costs as low as 195 CNY/t CO₂eq). We recommend prioritizing deployment in renewable-rich regions and aligning electrolysis scale-up with grid decarbonization to enable a pragmatic transition toward a green H₂-integrated coal chemical industry. Full article

(This article belongs to the Special Issue Energy Transition and the Collaborative Governance for Reduction of Pollution and Carbon Emissions)

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23 pages, 1419 KB

Open AccessArticle

Green Product Design Methodology with TRIZ Evolutionary Trends

by Hsin Rau, Katrina Mae Procopio, Jia-Jhe Wu and Imam Santoso

Sustainability 2026, 18(13), 6865; https://doi.org/10.3390/su18136865 - 6 Jul 2026

Abstract

With the increasing importance of green design in the business landscape, designers are compelled to shift towards eco-design practices. However, existing methodologies face challenges related to resource requirements, abstract concepts, and industry specificity. To address these challenges and stimulate innovation, this study proposes [...] Read more.

With the increasing importance of green design in the business landscape, designers are compelled to shift towards eco-design practices. However, existing methodologies face challenges related to resource requirements, abstract concepts, and industry specificity. To address these challenges and stimulate innovation, this study proposes a green design methodology that integrates TRIZ concepts and is anchored in TRIZ evolutionary trends. The methodology includes function and attribute analysis, the introduction of green features, the identification of TRIZ trends through a two-stage process, and the use of a developed system to improve calculation efficiency. Detailed design solutions are generated by combining green features, TRIZ trends, and inventive principles. A case study validates the methodology, showcasing its value in promoting sustainable development. By leveraging the evolutionary potential of products and incorporating TRIZ, the methodology offers a promising approach to address sustainability challenges and drive innovation. This research serves as a starting point for a practical and efficient design methodology that utilizes TRIZ concepts and a computer-aided application tool. Future steps involve stress-testing the methodology and exploring its application in different domains. Full article

(This article belongs to the Section Sustainable Products and Services)

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