Atmospheric and Oceanic Sciences

This study presents the results of comprehensive functional-spatial analyses conducted using cellular models in relation to the cities of the GZM Metropolis and its surroundings. The Abbreviation “GZM” stands for Górnośląsko-Zagłębiowska Metropolia, due to its location, which in English has been recognized as the GZM Metropolis. The GZM Metropolis, the largest metropolitan area in Poland, has a complex administrative and spatial structure that includes 41 very diverse municipalities, which poses a significant challenge in interpreting data and understanding its complexity. The research was conducted by a multi-person and interdisciplinary team using various tools, including geographic information systems (GIS) and statistical data. The spatial models built on the basis of the collected data were visualized using augmented reality tools to facilitate data interpretation. Special attention was paid to environmental aspects, especially blue-green infrastructure, which plays a key role in maintaining this heavily urbanized area. Furthermore, the authors developed urbanization scenario models for the GZM Metropolis based on their own approaches to cellular modeling and examined the integration of artificial intelligence techniques to further refine these forecasts.

Traditional villages and covered bridge heritage are key components of rural cultural landscapes, yet their spatial relationship and driving mechanisms remain insufficiently quantified at the provincial scale. Taking Hunan Province, China as a case study, we integrated heritage inventories with multi-source socioeconomic and cultural indicators. Kernel density estimation and spatial autocorrelation were used to characterize clustering and spatial association. A coupling–coordination model quantified coupling intensity and coordination level, and GeoDetector identified dominant factors and their interactions. Results reveal significant association but prevalent spatial mismatch between the two heritage types, with a marked pattern of high coupling but low coordination and strong intra-provincial heterogeneity. Socioeconomic development, urbanization, rural revitalization, and cultural diversity are key drivers, and factor interactions generally explain the pattern better than single factors. These findings suggest that conservation and revitalization should be tailored to mismatch areas and coordinated with rural revitalization and cultural innovation initiatives to improve spatial coordination and support sustainable heritage-based development.

Urbanization in developing countries has intensified ecological degradation and reduced the availability of Urban Green Spaces (UGS), including in Bogor City, Indonesia, where public UGS covers only 4.26%—far below the national minimum requirement of 20%. Agroforestry is increasingly recognized as a viable strategy to enhance the ecological, economic, and social functions of limited urban green areas. This study assesses the sustainability of agroforestry practices in Bogor City’s public UGS using the Multi-Aspect Sustainability Analysis (MSA) method across five aspects: ecological, economic, social, infrastructure–technology, and legal–institutional. This study is grounded in three principal hypotheses: (i) the implementation of agroforestry exerts a positive effect on ecological, social, and infrastructural–technological sustainability; (ii) economic and legal–institutional dimensions constitute the major limiting factors affecting overall sustainability performance; and (iii) strategic improvements targeting key leverage factors can significantly enhance the composite sustainability index. Primary data were collected through field observations, interviews, and surveys, supplemented by secondary policy and spatial data. Results show an overall sustainability score of 51.84%, categorized as “sustainable”. Ecological (66.71%), social (60.71%), and infrastructural–technological (60.50%) aspects were sustainable, while economic (26.14%) and legal–institutional (45.14%) aspects were less sustainable. Key leverage factors influencing sustainability include microclimate regulation, canopy density, biodiversity, tourism management, consumer dependence on agroforestry products, product quality standardization, availability of processing industries, and the presence of management institutions and SOPs. Scenario analysis demonstrates that targeted improvements in these levers can substantially increase sustainability scores, with optimistic scenarios raising the aggregate index to 78.45%. Strengthening economic value chains, regulatory frameworks, management institutions, and data infrastructure is essential to enhance the adaptive capacity and long-term viability of urban agroforestry in Bogor City.

This study investigates the interplay between urban morphology, vegetation, and thermal environments by integrating mobile air temperature (AT) measurements with satellite-derived land surface temperature (LST). The case study is the city of Bologna (Italy). Correlation analysis revealed strong multicollinearity among morphological indicators, with building density and floor area ratio nearly collinear, while vegetation cover (PV) remained the most independent predictor. A composite urban density indicator (CUDI), derived through principal component analysis, was introduced to address redundancy among morphological metrics. Ordinary least squares regressions demonstrated significant associations, with PV exerting a pronounced cooling effect and CUDI amplifying both AT and LST. Model diagnostics confirmed statistical robustness, though residual spatial autocorrelation necessitated spatial regression approaches. Spatial lag models (SLMs) substantially improved explanatory power, highlighting spatial spillovers and neighborhood effects as central to understanding urban heat dynamics. Comparative analysis with spatial error models reinforced the dominance of SLM in capturing localized dependencies. Despite limitations in spatial coverage, temporal scope, and indicator transferability, findings emphasize the critical roles of vegetation and urban compactness in shaping thermal environments. This work underscores the necessity of integrating greening strategies with urban form management for effective heat mitigation and provides a methodological framework for analyzing urban heat islands through multi-source thermal and morphological data.

In recent decades, the business paradigm has been transforming into the face of global challenges such as climate change and resource scarcity, consolidating sustainability as a strategic pillar that integrates the economic, environmental, and social dimensions. In parallel, Lean Philosophy, focused on eliminating waste and creating value, has been widely adopted as an effective management model. Despite the potential for its integration, literature reveals significant gaps, especially regarding the social dimension, which is often underexplored compared to the environmental and economic dimensions. To address this gap, this study identifies, analyzes, and synthesizes scientific literature on the integration between Lean and sustainability, with a special focus on the social dimension, using a systematic literature review conducted according to the PRISMA guidelines. A total of 132 articles published between 2011 and 2024 were analyzed, obtained from the Scopus, Web of Science, and ScienceDirect databases. The results demonstrate a growing convergence between the two concepts, highlighting the centrality of the human factor, namely well-being, safety and health at work, and ethical practices, and identifying challenges and opportunities for future research focused on a more holistic approach to organizational sustainability.

Short-Term Residential Load Forecasting (STRLF) is a core task in smart grid dispatching and energy management, and its accuracy directly affects the economy and stability of power systems. Current mainstream methods still have limitations in addressing issues such as complex temporal patterns, strong stochasticity of load data, and insufficient model interpretability. To this end, this paper proposes an explainable and efficient forecasting framework named KAN+Transformer, which integrates Kolmogorov–Arnold Networks (KAN) with Transformers. The framework achieves performance breakthroughs through three innovative designs: constructing a Reversible Mixture of KAN Experts (RMoK) layer, which optimizes expert weight allocation using a load-balancing loss to enhance feature extraction capability while preserving model interpretability; designing an attention-guided cascading mechanism to dynamically fuse the local temporal patterns extracted by KAN with the global dependencies captured by the Transformer; and introducing a multi-objective loss function to explicitly model the periodicity and trend characteristics of load data. Experiments on four power benchmark datasets show that KAN+Transformer significantly outperforms advanced models such as Autoformer and Informer; ablation studies confirm that the KAN module and the specialized loss function bring accuracy improvements of 7.2% and 4.8%, respectively; visualization analysis further verifies the model’s decision-making interpretability through weight-feature correlation, providing a new paradigm for high-precision and explainable load forecasting in smart grids. Collectively, the results demonstrate our model’s superior capability in representing complex residential load dynamics and capturing both transient and stable consumption behaviors. By enabling more accurate, interpretable, and computationally efficient short-term load forecasting, the proposed KAN+Transformer framework provides effective support for demand-side management, renewable energy integration, and intelligent grid operation. As such, it contributes to improving energy utilization efficiency and enhancing the sustainability and resilience of modern power systems.

The integration of distributed renewable energy sources into distribution networks is a key approach to achieving sustainable and low-carbon power systems. However, high renewable penetration significantly increases the volatility and uncertainty of distribution systems, posing challenges to renewable energy accommodation and reliable operation. To address these challenges, active control of distribution networks is required, which in turn relies on accurate system states. In practice, the limited number and accuracy of measurement devices in distribution networks make dynamic state estimation a critical technology for sustainable distribution systems. In this paper, a novel dynamic state estimation method for sustainable distribution systems is proposed, incorporating spatiotemporal data correlation and adaptiveness to process and measurement noise. A CNN-BiGRU-Attention model is developed to reconstruct high-accuracy real-time pseudo-measurements, compensating for insufficient sensing infrastructure. Furthermore, a noise adaptive dynamic state estimation method is proposed based on an improved unscented Kalman filter. An amplitude modulation factor (AMF) is applied to track time-varying process noise, while an evaluation method based on robust Mahalanobis distance (RMD) is embedded to deal with non-Gaussian measurement noise. Finally, simulation studies on the IEEE 33-bus three-phase unbalanced distribution network demonstrate the effectiveness and robustness of the proposed method.

The Turkish economy has been affected by recurring populist cycles and resultant economic crises, which have, in turn, unfavorably influenced the growth performance of the country. Inspired by the Turkish experience, this study attempts to investigate the effects of changes in exchange rate on the growth performance of the Turkish economy by using the production function framework. The data is sourced from the World Development Indicators and Penn World Table. Modern time series techniques are utilized to estimate the production function. Our findings reveal that there is a long-term but unfavorable relationship between changes in the exchange rate and economic growth in Turkey over the 1980–2019 period. Beyond its macroeconomic implications, the findings highlight that persistent exchange rate instability undermines macroeconomic sustainability by distorting the price mechanism, weakening investment incentives, and reducing long-term productive capacity. In this context, exchange rate stability emerges as a critical prerequisite for achieving sustainable economic growth in emerging economies such as Turkey.

Effective management of Environmental, Social, and Governance (ESG) practices within the framework of transparency and accountability in businesses is crucial for enhancing their compliance capacity in the face of regulatory pressures and contributing to the early detection of environmental and social risks. This study aims to evaluate the ESG disclosure-based performance of businesses operating in the textile, clothing, and leather sectors in Turkey by examining their ESG indicators from a transparency and accountability perspective. The CRITIC (Criteria Importance Through Intercriteria Correlation) method was used to determine the relative importance levels of the indicators, while the MABAC (Multi-Attributive Border Approximation Area Comparison) and COPRAS (Complex Proportional Assessment) methods were used to rank the performance of businesses within the framework of these indicators. Finally, clustering analysis was used to classify businesses with similar characteristics. The findings show that corporate governance principles are the most important indicator, and that Kordsa Teknik Tekstil A.Ş. and Söktaş Tekstil Sanayi ve Ticaret A.Ş. exhibit a significant and positive difference in terms of transparency and accountability in their ESG practices compared to other businesses. The combined use of CRITIC, MABAC, COPRAS, and cluster analysis offers an innovative, robust decision-making approach and holistic methodological integration for assessing ESG disclosure-based performance in the context of transparency and accountability for businesses.

With the widespread adoption of diesel engine technology, the problem of pollutant emissions has become increasingly prominent. Especially in the cold start stage of the diesel engine, the instantaneous pollutant emissions may be several times or even tens of times that of stable operation, which adds to deterioration of the environment. Therefore, the combustion characteristics and emissions of a two-cylinder diesel engine at high altitudes and low temperatures were explored and analyzed in this research. By adjusting the injection timing and compression ratio (CR) experimentally, the optimal combination of parameters to simulate the emission at high-altitude and low-temperature conditions was determined. The results show that advancing the injection timing can improve the combustion efficiency, but higher CR and injection timing significantly influence the hydrocarbon (HC)/nitrogen oxide (NOX) trade-off. While delaying the injection timing can reduce NOX emissions, it can increase HC emissions. Increasing CR from 18.5 to 20.5 raised peak instantaneous NOX emissions by approximately 27.7% but contributed to a reduction in HC emissions. In the cold start stage, HC concentration peaked sharply and gradually stabilized, while NOX concentration rose rapidly with more fluctuations. Under high altitude conditions, HC emission normally rises with altitude. When reaching 4000 m, the HC emissions increased by 27.9% compared with 0 m but the concentration decreased at 5000 m, the NOX emission decreased with elevation, and ambient temperature had little effect.

Mid-channel bars are fundamental fluvial geomorphic units that regulate sediment transport, channel stability, and riparian ecosystems, and their spatiotemporal evolution provides critical insights for sustainable river management. This study examines the structural reorganization and migration dynamics of mid-channel bars along the mainstem of the transboundary Yalu River using multi-temporal Sentinel-2 imagery acquired in 2019, 2022, and 2024. An automated extraction framework combining a dense atrous U-Net (DA-UNet) with multispectral indices was developed to robustly identify mid-channel bars under complex water–land transition conditions. Based on the extracted results, changes in bar number, area, size composition, morphological characteristics, and centroid migration were systematically analyzed. The results reveal a pronounced reorganization of mid-channel bars systems over the study period: although the number of bars increased from 111 to 136, the total area decreased from 168.97 km2 to 165.00 km2, indicating a transition from a “few-large” to a “many-small” configuration. Size-based analysis further shows an increase in small and medium bars, while large bars remained relatively stable, leading to a more differentiated multi-scale structure. These findings highlight the effectiveness of integrating multi-temporal remote sensing and deep learning for long-term monitoring of geomorphic dynamics and provide scientific evidence to support sustainable river regulation and transboundary watershed management.

The integration of digital technologies into early childhood education extends beyond mere technical necessity; it constitutes a fundamental pillar of social sustainability within the teaching profession. Yet, a persistent paradox remains in teacher education: the “Attitude–Competence Gap,” where pre-service teachers’ enthusiasm for technology fails to translate into practical proficiency. This study interrogates this disconnect within a STEAM framework, specifically examining whether digital competence is driven by general technological attitudes or domain-specific pedagogical beliefs. Utilizing an explanatory sequential mixed-methods design, we analyzed data from 200 Child Development students, followed by in-depth semi-structured interviews with 15 participants who exhibited high attitudes but low initial competence. Hierarchical regression analysis yielded a critical insight: while general attitudes toward digital storytelling did not predict competence (p > 0.05), pedagogical beliefs regarding the use of children’s literature in mathematics were a strong predictor of technical proficiency (β = 0.35, p < 0.001). Qualitative evidence corroborated that students overcame technical limitations not through technological affinity but through a motivation to concretize abstract mathematical concepts via storytelling. These findings suggest that to foster sustainable STEAM education, teacher training curricula must prioritize the “why” (pedagogical conviction) over the “how” (technical mechanics), thereby closing the gap between digital intention and action. This study uniquely demonstrates that domain-specific pedagogical convictions, rather than general technological enthusiasm, are the fundamental drivers of digital competence in STEAM, providing an empirical basis for more resilient teacher education models.

Intangible environmental externalities in informal economies are hard to detect, attribute, and regulate because transaction records and evidentiary trails are fragmented. This conceptual paper reframes pollution control from improving model performance to designing institutions for verifiability and examines how generative AI (GAI) can both strengthen and undermine that verifiability. Integrating transaction-structure theory, institutional economics, and digital-governance research, we derive four propositions: (P1) standardized, interoperable evidence and hybrid auditing allow GAI to lower verification costs; (P2) opaque, multi-tier transactions and concentrated data control enable plausible falsification; (P3) detection reduces pollution only when linked to remediation through enforcement capacity; and (P4) incentives must reward verified, not merely claimed, circularity to deter greenwashing. We illustrate feasibility and boundary conditions through three precedents: Amazon’s unit-level identifiers and sustainability labeling, India’s CPCB extended producer responsibility portal for plastic packaging, and Brazil’s nationwide e-invoicing infrastructure (NF-e/SPED). The framework offers actionable design principles, testable hypotheses, and measurable indicators (evidence linkage, audit-log completeness, time-to-remediation) for future empirical work. The framework is intended to support analytic generalization for policy and practice across contexts.

Global agriculture faces the dual challenges of resource constraints and international competition, making the transition from quantitative expansion to quality upgrading a central imperative. While trade competitiveness is widely considered a key driver of agricultural transformation, the pathways and mechanisms through which it influences agricultural quality upgrading are far more complex than conventionally understood. Against this backdrop, this study constructs a moderated nonlinear mediation theoretical framework. Empirical analysis based on China’s provincial panel data (2014–2023) yields three key findings: (1) Trade competitiveness exerts a significant inverted U-shaped effect on high-quality agricultural development, revealing a dynamic trade-off between “competitive escape” and “competitive suppression.” (2) Optimization of the Agricultural Sectoral Structure serves as a mediating pathway, and this mediation itself exhibits nonlinear characteristics, further underscoring the nuanced nature of “structural dividends.” (3) Regional innovation capacity significantly moderates the latter stage of this pathway (from Optimization of the Agricultural Sectoral Structure to quality development). Viewed through the lens of appropriate technology theory, a robust regional innovation system can deploy context-appropriate technologies and knowledge, thereby mitigating the potential adverse impacts of agricultural structural transformation on quality-related outcomes. This research thus provides a new strategic framework for achieving sustainable, high-quality agricultural growth.

This study developed a heat transfer model and systematically simulated heat conduction behavior during flame disinfection to optimize surface flame disinfection (SFD) technology targeting Bradysia odoriphaga larvae. By determining pest mortality rates at various temperatures, we identified 40 °C as the critical threshold. When temperature increased from 30 °C to 65 °C, the time required to achieve 50% (LT50, median lethal time, represents the baseline threshold for control efficacy) mortality dropped sharply from 131 s to merely 6 s, while the time to reach 95% mortality (LT95, i.e., 95% lethal time, represents the standard for complete control in the field) decreased from 279 s to 12 s. The model demonstrated that higher surface temperatures enabled heat to penetrate deeper into the soil. For every 20 °C increase in temperature, lethal depth increased by 2.1 cm, and heat conduction depth increased by 1.2 cm. Soil thickness exhibited a dual effect; although deeper soil could increase lethal depth, it also created thermal resistance that slowed heat penetration. In practical applications, heating a 20 cm thick soil layer to 163 °C could achieve effective pest control at a depth of 32.5 cm. This framework provides support for achieving precise flame disinfection and promotes sustainable pest management with reduced chemical pesticide use.

Resilience to climate change is a complex concept, especially in metropolitan areas where diverse services and stakeholders interact. Promoting sustainable climate adaptation, a resilience assessment method focused on regional areas and nature-based solutions is presented, along with its open-access, web-based platform, supporting resilience assessment, planning, and monitoring. Floods, droughts, heat or cold waves, windstorms, and forest fires can be assessed. A framework for holistic assessment and other framework, addressing critical infrastructure, are integrated. Four resilience dimensions are assessed: organizational (governance, social aspects, finance); spatial (exposure, impacts, and mapping); functional (service management, interdependencies); and physical (infrastructure robustness, redundancy). Strategic services comprise, e.g., water, waste, and natural areas. Resilience capacities, e.g., to prevent, respond, and recover from disruptions, are also assessed. The paper emphasizes new developments and assessment. Practical step-by-step guidance aligned with assessment purposes is included, aiming to address observed limitations (e.g., fragmented service provision, communication silos, data constraints). Overall results of a Spanish metropolitan area (AMB) and an exploratory application to an Austrian rural case (SLR) are also presented. Following the guidelines, AMB progressed from an essential to a comprehensive assessment. Overall, almost 1/3 of the metrics are advanced or progressing. SLR assessed its resilience capabilities regarding electrical infrastructure.

Building resilient cities that can survive, adapt, and thrive amid climate and ecological challenges has become a global priority, yet achieving this goal requires adequate financial support. This study investigates the impact of green finance on urban ecological resilience (UER) by exploiting the establishment of China’s Green Finance Reform and Innovation Pilot Zones (GFPZs) as a policy shock. Using a DPSIR-based (driving force–pressure–state–impact–response) evaluation framework and a staggered difference-in-differences approach with panel data from 277 cities (2011–2022), the empirical results show that (1) the GFPZ policy significantly enhances UER; (2) green finance improves UER through three transmission channels—government environmental governance, green technological innovation, and public environmental participation; (3) the policy effects display clear spatial and structural heterogeneity, with stronger impacts in southern, less-developed, and non-traditional industrial cities, as well as positive local effects, negative spatial spillovers, and significant synergies with national big data pilot zones. This study clarifies how financial instruments contribute to building resilient cities and offers insights for embedding green finance into urban ecological strategies.

In the ready-to-wear industry, accurate estimation of fabric consumption is essential for cost efficiency, particularly for bias tapes, which constitute a notable share of overall material usage. Although bias tape is widely used across the apparel industry, no formal mathematical model exists for calculating its consumption, and current practices rely heavily on technician experience and sample garments. Because bias tape consumption in multi-size garment manufacturing involves geometric adjustments and decision steps that are not captured by existing methods, this study develops the first mathematically structured model for bias tape estimation and implements it in a software tool, providing a more reliable and standardized alternative to manual calculations. The model was validated using data collected in a real production environment from 20 garment models (13,122 units), and statistical analyses confirmed that the software produced significantly more accurate estimates than conventional methods, resulting in fabric savings of up to 39.5%. These findings demonstrate that the system enhances material utilization and supports data-driven planning in apparel manufacturing.

Agri-environmental subsidies had been implemented to promote sustainable agriculture in regions such as the EU and the U.S. prior to the year 2000. Contract-Based Agri-Environmental Schemes (AESs) are designed to promote green, sustainable agriculture by employing environmentally friendly farming practices (EFFPs) to reduce pollution and meet other environmental goals. A central challenge, however, is the limited inclusion of small farmers, who are key to agricultural sustainability and form the backbone of production, particularly in developing countries. This study aims to investigate the preferences and participation of small farmers in AESs to enable effective policy design. Using discrete choice experiments (DCEs) and a latent class model (LCM) on survey data collected in 2017 from three key rice-producing counties in China—Fangzheng (Heilongjiang), Qingtongxia (Ningxia), and Yixing (Jiangsu)—allowed us to identify two distinct preference classes: “experienced adopters” and “potential adopters”. The results confirmed (1) a high participation rate of small farmers in AESs. Compensation can further motivate them to sign a contract. (2) There is significant heterogeneity among small farmers’ preferences on various EFFPs, so flexible and modulated schemes are needed; (3) those with experience in EFFPs are more likely to participate in AESs; and (4) the modular AES contract with progressive subsidy ties makes payments directly based on EFFP adoption, addressing the shortcomings of China’s current area-based subsidy system. The results of this paper can help policymakers fine-tune farming policies that effectively engage smallholders, thereby alleviating tensions over production–pollution cycles and fostering a more targeted and sustainable agricultural policy system.

With the integration of high-proportion renewable energy, the operation modes of the power system are becoming increasingly complex and diverse. The typical operation modes selected with manual experience cannot comprehensively represent system operating characteristics. To more accurately analyze system operating characteristics, an analysis method for power system operation modes based on autoencoder clustering is proposed. Compared to other clustering methods, the autoencoder clustering method can adapt to data of different types and structures, extract features and perform clustering in a reduced-dimensional space, and suppress noise in the data to a certain extent. First, multi-dimensional analysis metrics for power system operation modes are proposed. The metrics are used to evaluate system characteristics such as cleanliness, security, flexibility, and adequacy. The evaluation metrics for clustering are designed based on the metrics. Second, an operation mode analysis framework is constructed. The framework uses an autoencoder to extract implicit coupling relationships between system operation variables. The encoded feature vectors are used for clustering, which helps to find the internal similarities of the operation modes. Regulation resources such as pumped hydro storage are also considered in the framework. Finally, the proposed method is tested on the IEEE 39-node system. In the test, the comparison of clustering evaluation metrics and operation mode analysis errors shows that the proposed method has the best clustering performance and operation mode analysis effect compared to other clustering methods. The results prove that the proposed method can effectively extract the inner correlations and coupling relations of high-dimensional operating vectors, form consistent operation mode clusters, select typical operation modes, and accurately assess the characteristics and risks of the power system with high-proportion renewable energy integration. This paper helps to build a stronger power system that can integrate a higher proportion of renewable energy, replace fossil fuel generation, and contribute to a higher level of sustainable development.

Corporate greenwashing poses a significant challenge to global sustainability efforts. Drawing on firm-level data from China, this study explores the effect of social trust as a key informal institution on inhibiting greenwashing behavior. We find that social trust significantly reduces the level of greenwashing. Our mechanism analysis suggests that social trust restrains greenwashing primarily by enhancing corporate information transparency, alleviating managerial short-termism, and easing financial constraints. Further heterogeneity tests show that the effect is stronger in firms not audited by Big Four auditors and those without voluntary environmental disclosure, as well as in regions with weaker formal institutional environments. We also examine multidimensional trust and find that generalized trust plays a dominant role in curbing greenwashing, whereas personalized and institutional trust show limited effects. These findings highlight the importance of social capital and informal institutional forces in promoting corporate environmental accountability and advancing sustainable development goals.

Collaboration in cooperatives helps farmers strengthen their economic position in dynamic agri-food markets. Unlike other types of businesses, agricultural cooperatives are user-owned, user-controlled, and user-benefitting enterprises. Their dual nature as business enterprises and social groups of members complicates performance evaluation. This study attempts to bridge the gap by developing a micro-level conceptual framework for benchmarking agricultural cooperatives. Based on a systematic literature review of 77 empirical studies published in 1987–2025 and thematic analysis, the authors propose an eight-dimensional conceptual framework encompassing competitive, financial, educational, efficiency, environmental, governance, operational, and social performance indicators. The review reveals that existing research prioritises financial indicators while overlooking cooperative-specific characteristics arising from their dualistic nature. The conceptual framework offers a structured conceptual basis for assessing the performance of agricultural cooperatives across sectors and countries. Although applying the framework is beyond the scope of this paper, the authors highlight prospective indicators for future empirical work and practical implementation.

Moving beyond passive legal compliance is a critical challenge for the global shipping industry in protecting seafarers’ rights. Drawing on interviews with 32 Chinese shipping executives, this study conceptualizes Environmental, Social, and Governance (ESG) strategy not merely as a disclosure tool, but as a critical “governance translation” mechanism. We propose a theoretical model where external accountability pressures drive the institutionalization of seafarers’ rights protection (SRP), mediated effectively by corporate ESG engagement. We find that this process is positively moderated by three boundary conditions: the localization of international conventions, the intensity of Port State Control (PSC) enforcement, and the maturity of organizational governance. Practically, the findings suggest that policymakers should prioritize the clear localization of international standards to reduce ambiguity. For managers, the study demonstrates that embedding SRP into board oversight and digital monitoring systems is essential for transforming labor rights from a cost center into a sustainable strategic advantage.

Green Infrastructure (GI) is crucial for urban climate adaptation, providing ecosystem services like mitigating the urban heat island effect and enhancing stormwater management, alongside benefits for public health and biodiversity. Effective GI implementation remains challenging, particularly in dense, rapidly urbanized mid-Adriatic coastal cities, classified as climate hotspots like other Mediterranean contexts. This paper presents a replicable applied trans-scalar methodology for detailed GI design scenarios, developed through the EU-funded LIFE+ A_GreeNet project to bridge the theory–practice gap and enable pilot implementations in multiple Italian mid-Adriatic coastal municipalities. The research details a comprehensive, multi-disciplinary, five-phase process applied to the Sant’Antonio district of San Benedetto del Tronto—a dense, trafficked urban area projected to face “extremely strong heat stress” by 2050. Design interventions included spatial optimization, strategic species replacement, the creation of vegetated bioretention basins, and systematic pavement de-sealing. The application of the model demonstrated significant improvements: a substantial increase in permeable surface area (+194%), a measurable reduction in the UTCI index (average ENVI-MET simulated reduction of 1.17 °C by 2030), and a series of benefits resulting from increased green space and enhanced meteorological water management. This research offers local authorities a tangible model to accelerate climate-adaptive solutions, showing how precise GI design creates resilient, comfortable, and human-centered urban spaces.

The degradation of wetlands and forests is still a threat to the supply and recovery of ecosystem services in the tropics. Studies comparing restoration measures and ecosystem service recoveries are fragmented. This study investigated the spatial extent and drivers of wetland/forest degradation, and assessed the effects of restoration measures on the recovery of ecosystem services and resilient livelihoods. A cross-sectional household survey was conducted targeting households adjacent to restored and unrestored wetland/forest ecosystems. The data was analyzed using a Binary Logistic regression to characterize earlier and recovered ecosystem services between forest and wetland ecosystems. High spatial-resolution optical satellite imagery from the Airbus constellation was obtained and analyzed to examine wetland and forest degradation. Our findings revealed that the spatial extent of degraded land under wetlands and forests decreased between 2023 and 2025. Ecosystem service degradation was primarily driven by chronic poverty, excessive water abstraction, population growth, burning practices, overharvesting of resources, overgrazing, cultivation, infrastructure development, and the invasion of alien species (p < 0.05). The counteractive ecosystem restoration activities undertaken included mobilization and sensitization of communities on wetland restoration, wetland demarcation, revegetation, establishment of flood control measures, and provision of alternative livelihoods (p ≤ 0.05). The multiple direct and indirect ecosystem service recoveries reported were provisioning services (increases in pasture, enhanced livestock production, increased soil productivity, health-related benefits from crops and livestock products) and regulating services (improved water quality/quantity). The ecosystem service recoveries were more significant in the restored wetlands than the forests. The indicators of enhanced ecosystem-based resilient livelihoods included increased household incomes, higher livestock yields, increased crop productivity, improved health from crop/livestock products, improved water quality/quantity, and enhanced scenic beauty and tourism (p < 0.05). The restoration activities in degraded wetland systems had more potential to facilitate full recovery of the wetland ecosystem compared to the absence of interventions. This evidence highlights the need to restore high-ecological-sensitive ecosystems to sustain the delivery of ecosystem services for community and environmental resilience.