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Volume 17
Issue 9
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Sustainability, Volume 17, Issue 9 (May-1 2025) – 491 articles

Cover Story (view full-size image): Persistent environmental contaminants pose a serious threat to agroecosystems' security, necessitating innovative solutions for their evaluation and mitigation. Using a multi-scale approach, this study integrates UAV-based multi/hyperspectral and thermal imaging with in situ analyses to assess the effects of soil contamination (with heavy metals and organic pollutants) on maize crop. The findings underscore how bioindication and integrated remote sensing technologies can be effectively used to monitor the impact of soil pollution in agricultural ecosystems. Using an interdisciplinary approach, this research highlights how technological advancements can revolutionize environmental monitoring by bridging conventional soil assessment methods with sustainable agricultural management, thus ensuring resilient food systems and ecosystem health. View this paper
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31 pages, 4306 KiB  
Article
Sustainable Digital Rural Development: Measurements, Dynamic Evolutions, and Regional Disparities—A Case Study of China
by Ming Lei, Xinyu Yang, Shuifeng Hong, Dandan Wang, Wei Zhang and Hui Chen
Sustainability 2025, 17(9), 4250; https://doi.org/10.3390/su17094250 - 7 May 2025
Viewed by 315
Abstract
Amid the Fourth Industrial Revolution and the 2030 Sustainable Development Goals (SDGs), China’s digital village initiative has emerged as a localized implementation for achieving multidimensional sustainability. However, the progress of digital villages in China remains uneven, posing challenges to achieving sustainable rural transformation. [...] Read more.
Amid the Fourth Industrial Revolution and the 2030 Sustainable Development Goals (SDGs), China’s digital village initiative has emerged as a localized implementation for achieving multidimensional sustainability. However, the progress of digital villages in China remains uneven, posing challenges to achieving sustainable rural transformation. This study develops a multidimensional index system at four levels: rural digital infrastructure, the digital development environment in rural areas, the digital industry in rural areas, and agricultural production digitalization. Entropy weighting was used to evaluate digital village progress across 30 Chinese provinces (2013–2022). Kernel density estimation, the Dagum Gini coefficient, and the obstacle degree model were used to study China’s spatiotemporal dynamics, regional disparities, and digital village development barriers. The results show that between 2013 and 2022, digital villages in China advanced (the average annual growth rate: 9.43%), with a spatial distribution pattern of “east superior, west inferior, south prosperous, and north declining”. National and regional digital villages have advanced yearly, with absolute and relative disparities increasing, extensibility increasing, and multi-polarizing rising. Digital village development is becoming increasingly imbalanced, with inter-regional differences driving “east, central, and west” disparity and intra-regional disparities driving North–South disparity. Ranking the average hurdle levels: the digital industry in rural areas (45.94%) > the digital development environment in rural areas (24.83%) > rural digital infrastructure (21.85%) > agricultural production digitalization (7.38%). Taobao villages are a major restraint on China’s digital village development. Full article
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23 pages, 3251 KiB  
Article
Financial Globalization and Energy Security: Insights from 123 Countries
by Liyun Liu and Simei Zhou
Sustainability 2025, 17(9), 4248; https://doi.org/10.3390/su17094248 - 7 May 2025
Viewed by 164
Abstract
In this paper, a panel smooth transition regression model is used to examine the nonlinear effects of financial globalization on energy security. These effects are examined in 123 countries for the period of 2000–2018. Control variables are armed forces, industrialization rate, trade value [...] Read more.
In this paper, a panel smooth transition regression model is used to examine the nonlinear effects of financial globalization on energy security. These effects are examined in 123 countries for the period of 2000–2018. Control variables are armed forces, industrialization rate, trade value share, and urbanization rate, and the conversion variable is the financial globalization index in the following year. The results of the financial globalization effects can be obtained from both time and space. The results show that financial globalization has a positive nonlinear effect on energy security. When the logarithm of financial globalization in the previous year exceeds 0.0467, the coefficient between financial globalization and energy security will decrease from 0.0467 to 0.0209. Temporal variation analyses show that the positive effect followed a “decrease, increase, decrease” trend between 2000 and 2018. Spatial variation analyses show that the positive effect is greatest in Oceania and the Americas (with an effect coefficient of 0.0467) and smallest in Europe (with an effect coefficient of 0.0391). According to the results of the regional heterogeneity research, the Organization of the Petroleum Exporting Countries (OPEC) countries see a stronger nonlinear impact of financial globalization on energy security than non-OPEC countries. Full article
(This article belongs to the Special Issue Low Carbon Innovation: Energy Policy and Strategic Technology Planning)
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22 pages, 3386 KiB  
Article
Research on the Distribution Dynamics and Convergence of Renewable Energy in China
by Dongxiao Yang and Lingjie Liu
Sustainability 2025, 17(9), 4247; https://doi.org/10.3390/su17094247 - 7 May 2025
Viewed by 186
Abstract
It is important to study the difference, distribution dynamics and convergence of China’s renewable energy development level to stimulate its potential. Based on China’s provincial panel data from 2006 to 2021, this paper analyzes the regional characteristics of China’s renewable energy development using [...] Read more.
It is important to study the difference, distribution dynamics and convergence of China’s renewable energy development level to stimulate its potential. Based on China’s provincial panel data from 2006 to 2021, this paper analyzes the regional characteristics of China’s renewable energy development using the Dagum Gini coefficient, kernel density estimation, σ convergence and spatial β convergence, and draws the following conclusions: Firstly, renewable energy in the country and the four major regions have achieved stable growth. Secondly, the intra-group differences in China and the four major regions are gradually decreasing, and the contribution rate of inter-group differences to the overall differences is gradually increasing, which is the main source of the overall differences. Thirdly, the national renewable energy development has a positive spatial correlation. Fourthly, there are σ convergence and spatial β convergence in the whole country and the four major regions; the σ convergence coefficient gradually decreases, and the β convergence regression coefficient is significantly negative. Therefore, this paper proposes the following recommendations: formulate government policies according to local conditions, strengthen technical exchanges and cooperation among regions, and encourage investment in renewable energy development; thus, we can promote a more efficient realization of the “dual carbon” goal. Full article
(This article belongs to the Section Sustainable Products and Services)
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28 pages, 2951 KiB  
Article
Coupling Agricultural Carbon Emission Efficiency and Economic Growth: Evidence from Jiangxi Province, China
by Lulu Yang, Xieqihua Liu, Xiaolan Kang, Yuxia Zhu, Chaobao Wu, Bin Liu and Wen Li
Sustainability 2025, 17(9), 4246; https://doi.org/10.3390/su17094246 - 7 May 2025
Viewed by 219
Abstract
Exploring the law and evolution mechanism of coupling and coordination between agricultural carbon emission efficiency (ACE) and agricultural economic growth (AEG) can provide a reference basis for agricultural low-carbon transformation. This study takes 11 cities in Jiangxi Province as the research object; measures [...] Read more.
Exploring the law and evolution mechanism of coupling and coordination between agricultural carbon emission efficiency (ACE) and agricultural economic growth (AEG) can provide a reference basis for agricultural low-carbon transformation. This study takes 11 cities in Jiangxi Province as the research object; measures the level of ACE based on the panel data from 2008 to 2022; and analyzes the development and influencing factors of the coupling and coordination between ACE and AEG by using the coupling coordination degree model, the Dagum Gini coefficient decomposition method, and the Tobit regression model. The results reveal the following: (1) The overall ACE in Jiangxi Province displays a significant upward trend, with the average efficiency value increasing from 0.172 to 0.624, reflecting an average annual growth rate of 72.43%. Nonetheless, there remains clear regional heterogeneity, characterized by lower efficiencies in Central and Southern Jiangxi compared to the higher efficiencies found in Northern and Western Jiangxi. (2) Despite gradual improvements in regional coordination, the Central and Southern Jiangxi regions still lag Northern and Western Jiangxi in terms of the linked coordination between ACE and AEG, symptoms of which had been previously misaligned. (3) The results of Dagum’s Gini coefficient decomposition show that inter-regional disparities are the main source of overall disparities, with a contribution of 37.43%, which is higher than the synergistic effect of intra-regional disparities and hyper-variable densities, corroborating the core contradiction of uneven development across regions. (4) The Tobit model reveals that government investment, industrial structure optimization, urbanization, and educational attainment exert a significant positive influence on promoting coupling coordination. To establish a scientific basis for achieving a low-carbon agricultural transformation and equitable AEG in Jiangxi Province, this research recommends bolstering regional cooperation, fostering innovations in agricultural science and technology, optimizing the industrial structure, and enhancing farmers’ awareness of low-carbon practices. This study expands the theoretical system of agricultural low-carbon transition in terms of research methods and scales to provide a scientific basis for agricultural provinces to realize agricultural low-carbon transition and balanced economic development. Full article
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21 pages, 637 KiB  
Article
Green Finance Policies and Corporate Biodiversity Disclosure: Evidence from China
by Ting Yang and Kai Wu
Sustainability 2025, 17(9), 4245; https://doi.org/10.3390/su17094245 - 7 May 2025
Viewed by 263
Abstract
This study examines the impact of green finance policies on corporate biodiversity disclosures, focusing on China’s Green Finance Reform and Innovation Pilot Zones (GFPZs). Utilizing a comprehensive dataset of Chinese-listed firms from 2010 to 2022, we apply textual analysis to annual reports to [...] Read more.
This study examines the impact of green finance policies on corporate biodiversity disclosures, focusing on China’s Green Finance Reform and Innovation Pilot Zones (GFPZs). Utilizing a comprehensive dataset of Chinese-listed firms from 2010 to 2022, we apply textual analysis to annual reports to quantify biodiversity-related disclosures. Our findings reveal that GFPZ policies significantly reduce biodiversity disclosures, suggesting a trade-off between carbon-focused financial incentives and broader environmental transparency. Cross-sectional analysis indicates that firms with higher R&D intensity and those in regions with stricter environmental enforcement exhibit fewer negative effects. Mechanism analysis highlights that carbon production intensity and green information disclosure quality mediate this relationship. Robustness checks, including propensity score matching, confirm these results. Our study underscores the need for policymakers to integrate biodiversity considerations into green finance frameworks to ensure balanced ESG priorities. Full article
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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18 pages, 5413 KiB  
Article
Dynamic Estimation of Travel Time Reliability for Road Network Using Trajectory Data
by Jiayu Hang, Tianpei Tang and Jiawen Wang
Sustainability 2025, 17(9), 4244; https://doi.org/10.3390/su17094244 - 7 May 2025
Viewed by 172
Abstract
To evaluate the operation of an urban transportation system by accurately analyzing the reliability of a road network, with the aim of reducing the substantial fluctuation of travel time, a method for dynamically estimating the reliability of road network travel time is proposed. [...] Read more.
To evaluate the operation of an urban transportation system by accurately analyzing the reliability of a road network, with the aim of reducing the substantial fluctuation of travel time, a method for dynamically estimating the reliability of road network travel time is proposed. First, the definition of travel time reliability is given by referring to system reliability theory: the possibility that all travelers in the road network reach their destination within a predetermined time. The travel time reliability is numerically expressed as the probability that the ratio of delay to travel time (RODT) is less than a certain value. Then, actual data are used to prove that the RODT of vehicles in the road network obeys the normal distribution, based on which a data-driven method of travel time reliability estimation is proposed. The travel time reliability of a real-world network is estimated based on the trajectory. Finally, the variation in travel time reliability under different road network capacities is studied, and the accuracy of the estimated travel time reliability under different trajectory data penetration rates is analyzed. The dynamic estimation method of travel time reliability proposed in this paper supports better understanding of the operation efficiency of urban road traffic systems, to help better evaluate the performance of road network systems and provide a basis for road network reliability optimization. Full article
(This article belongs to the Special Issue Sustainable Transportation and Logistics Optimization)
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24 pages, 598 KiB  
Article
Exploring Stakeholder and Organizational Influences on ESG Management in the Logistics Sector
by Byung-Cheol Yoo
Sustainability 2025, 17(9), 4243; https://doi.org/10.3390/su17094243 - 7 May 2025
Viewed by 263
Abstract
As the global emphasis on sustainability intensifies, logistics companies face mounting pressure from stakeholders to adopt environmental, social, and governance (ESG) practices. Despite this growing interest, few studies have investigated how both external pressures and internal organizational factors jointly influence ESG management and [...] Read more.
As the global emphasis on sustainability intensifies, logistics companies face mounting pressure from stakeholders to adopt environmental, social, and governance (ESG) practices. Despite this growing interest, few studies have investigated how both external pressures and internal organizational factors jointly influence ESG management and its outcomes in the logistics sector. This study aims to examine the effects of international, governmental, investor, and customer pressures on three ESG dimensions—environmental management, social responsibility, and governance practices. Furthermore, the study evaluates how these ESG dimensions affect corporate image and organizational performance. Data were collected from 352 logistics professionals through a structured online survey. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to test the proposed research model. The findings reveal that investor and customer pressures are the most influential drivers of comprehensive ESG engagement. As an internal factor, hierarchy culture significantly enhances organizational performance and strengthens the impact of corporate image on performance. Environmental and governance management contribute to both image and performance, while social responsibility primarily enhances corporate image. These results provide valuable insights for logistics companies and managers seeking to align ESG strategy with stakeholder expectations and operational excellence. Full article
(This article belongs to the Special Issue Sustainable Management of Logistic and Supply Chain)
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26 pages, 2700 KiB  
Article
Mental Models Matter: Conceptualizations of the Human–Nature Relationship Predict Pro-Environmental Attitudes and Behavioral Intentions
by Joan J. H. Kim and John D. Coley
Sustainability 2025, 17(9), 4242; https://doi.org/10.3390/su17094242 - 7 May 2025
Viewed by 222
Abstract
Mental models—internal, dynamic, incomplete representations of the external world that people use to guide cognitive processes such as reasoning, decision making, and language comprehension—have practical implications for predicting attitudes and behaviors across various domains. This study examines how mental models of the human–nature [...] Read more.
Mental models—internal, dynamic, incomplete representations of the external world that people use to guide cognitive processes such as reasoning, decision making, and language comprehension—have practical implications for predicting attitudes and behaviors across various domains. This study examines how mental models of the human–nature relationship predict pro-environmental behavioral intentions directly and indirectly as mediated through anthropocentric and biocentric environmental attitudes. To address these aims, participants were asked about mental model components of the human–nature relationship (human exceptionalism, beliefs about human impact on nature, and beliefs about nature’s impact on humans), pro-environmental attitudes (biocentric and anthropocentric), and their pro-environmental behavioral intentions (protection and investment). We found that protection intentions were (1) directly predicted by human exceptionalism beliefs (negatively) and perceived human impact on nature (positively) and (2) indirectly predicted by mental model components via biocentric attitudes. Investment intentions were directly predicted by nature’s perceived impact on humans, and were similarly indirectly predicted by mental model components via biocentric attitudes. The results suggest that mental models of the human–nature relationship provide a cognitive foundation for environmental behavioral intentions both directly and through their association with environmental attitudes. These findings have implications for pro-environmental interventions that deal with conceptual and attitudinal change. Full article
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30 pages, 2887 KiB  
Article
Evaluating the Role of Next-Generation Productive Forces in Mitigating Carbon Lock-In: Evidence from Regional Disparities in China
by Chenchen Song, Zhiling Guo, Xiaoyue Ma, Jijiang He and Zhengguang Liu
Sustainability 2025, 17(9), 4241; https://doi.org/10.3390/su17094241 - 7 May 2025
Viewed by 195
Abstract
Carbon lock-in (CLI), defined as the structural persistence of fossil-fuel-based systems, poses a significant barrier to decarbonization. As CLI continues to impede China’s progress toward carbon neutrality, understanding the role of next-generation productive forces (NGPFs) in breaking this path dependence has become increasingly [...] Read more.
Carbon lock-in (CLI), defined as the structural persistence of fossil-fuel-based systems, poses a significant barrier to decarbonization. As CLI continues to impede China’s progress toward carbon neutrality, understanding the role of next-generation productive forces (NGPFs) in breaking this path dependence has become increasingly urgent; however, it remains underexplored in empirical research. This study examines the impact of NGPFs on CLI using provincial panel data from 2012 to 2022. Composite indices for NGPFs and CLI are constructed using the entropy weight method. The analysis applies instrumental variable estimation (IV-GMM) to address potential endogeneity, feasible generalized least squares (FGLS) to account for heteroskedasticity, and spatial Durbin models (SDMs) to capture spatial dependence. In addition, quantile regression is used to explore distributional effects, and subsample regressions are conducted to assess regional heterogeneity. The results show that (1) a 1% increase in NGPFs leads to approximately a 0.9643% reduction in CLI, effectively mitigating CLI. (2) NGPF levels are high in Beijing, Shanghai, and Guangdong, while being constrained in Heilongjiang, Gansu, and Qinghai. Provinces like Jiangsu, Zhejiang, and Shandong are rapidly catching up. (3) Shanxi, Inner Mongolia, and Shandong struggle with high comprehensive CLI from carbon-heavy industries; in contrast, Beijing, Shanghai, and Hainan show low CLI. (4) As CLI levels increase (90th percentile), the effectiveness of NGPFs in reducing CLI gradually diminishes (−0.2724). (5) The impact of NGPFs on CLI is not significant in the Eastern region, while in the Central and Western regions, the effects are −1.1365 and −1.0137, respectively. This study offers vital insights for shaping policies that promote sustainable growth and mitigate CLI in China. Full article
(This article belongs to the Special Issue Advances in Energy Economics, Energy Policy and Sustainability Transition)
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18 pages, 3341 KiB  
Article
From River to Reservoir: The Impact of Environmental Variables on Zooplankton Assemblages in Karst Ecosystems
by Binbin Li, Qiuhua Li, Pengfei Wang, Xiaochuan Song, Jinjuan Li, Mengshu Han and Si Zhou
Sustainability 2025, 17(9), 4240; https://doi.org/10.3390/su17094240 - 7 May 2025
Viewed by 188
Abstract
Zooplankton are ubiquitous in aquatic ecosystems and play crucial roles in material cycling and energy flow. However, the mechanisms governing zooplankton community assembly, particularly habitat-specific differences, remain poorly understood. In this two-year study, we monitored zooplankton communities across reservoir and river habitats within [...] Read more.
Zooplankton are ubiquitous in aquatic ecosystems and play crucial roles in material cycling and energy flow. However, the mechanisms governing zooplankton community assembly, particularly habitat-specific differences, remain poorly understood. In this two-year study, we monitored zooplankton communities across reservoir and river habitats within the Chayuan watershed, a representative karst region in southwest China. Our findings revealed significant spatial divergence in water-quality variables (including water temperature, pH, total nitrogen, total phosphorus, permanganate index, dissolved oxygen, chlorophyll-a, and ammonia nitrogen) between habitats. Twenty-nine dominant zooplankton species were identified in reservoir and river communities, with only eight shared between the two habitats. The mechanisms underlying the corresponding zooplankton community structures showed distinct segregation between habitats, with deterministic processes predominating in reservoir communities (explaining 25.1% of the variation) and stochastic processes predominating in river communities (3.4% of the variation explained). Environmental drivers differed substantially between habitats: reservoir communities were primarily influenced by total nitrogen, dissolved oxygen, and chlorophyll-a concentrations, whereas river communities responded predominantly to ammonia nitrogen levels. This study provides novel insights into the divergent mechanisms governing zooplankton community assembly in lentic versus lotic systems within a shared karst watershed, offering theoretical foundations for ecosystem-specific management strategies in fragile karst environments. Future research should focus on key climatic variables (e.g., extreme precipitation) and hydrological dynamics (such as flow velocity and water residence time) to further elucidate the mechanisms behind zooplankton community assembly, providing deeper insights to facilitate effective ecosystem management in karst environments. Full article
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23 pages, 29458 KiB  
Article
Study on Temporal and Spatial Distribution Characteristics of Biogenic Pollutant Fluxes in Ten Main Rivers Discharging into the Sea in Eastern China
by Lu Wang, Shuqin Ma, Shuo Liu, Yan Chen, Wei Gao and Yuan Zhang
Sustainability 2025, 17(9), 4239; https://doi.org/10.3390/su17094239 - 7 May 2025
Viewed by 146
Abstract
Rapid economic development, accelerated urbanization, and agricultural modernization in eastern China have exacerbated pollution in rivers discharging into the sea, challenging regional ecological security and water resource sustainability. This study investigates ten main rivers in eastern China using monthly water quality and hydrological [...] Read more.
Rapid economic development, accelerated urbanization, and agricultural modernization in eastern China have exacerbated pollution in rivers discharging into the sea, challenging regional ecological security and water resource sustainability. This study investigates ten main rivers in eastern China using monthly water quality and hydrological data from 2021 to 2023. Pollutant fluxes for permanganate index (CODMn), ammonia nitrogen (AN), total phosphorus (TP), and total nitrogen (TN) were calculated, and their temporal and spatial variations were analyzed using descriptive statistics, two-way analysis of variance (ANOVA), and principal component analysis (PCA). Results show significant spatial heterogeneity, with the Yangtze (YAR) and Pearl Rivers (PER) exhibiting the highest fluxes due to high basin runoff and intense human activities. Seasonal variations significantly affect CODMn, TP, and TN fluxes, with summer runoff and agricultural activities enhancing pollutant transport. Moreover, flood periods markedly increase pollutant fluxes compared to non-flood periods. PCA further reveals that the pollutant flux patterns of YAR and PER are clearly distinct from those of the other rivers, indicating the joint influence of geographic conditions and anthropogenic activities. This study provides quantitative evidence for regional water environment management and offers crucial guidance for developing sustainable, differentiated pollution control strategies. Full article
(This article belongs to the Special Issue Sustainable Water Management: Innovations in Wastewater Treatment)
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23 pages, 2216 KiB  
Article
AI vs. ESG? Uncovering a Bidirectional Struggle in China’s Sustainable Finance
by Zizhe Du and Chao Chen
Sustainability 2025, 17(9), 4238; https://doi.org/10.3390/su17094238 - 7 May 2025
Viewed by 297
Abstract
As global discourse increasingly centers on environmental, social, and governance considerations, ESG investment has become a major trend in financial markets. Artificial intelligence (AI), through its rapid evolution, has exerted a transformative influence that continues to reshape the fundamental structures of this domain. [...] Read more.
As global discourse increasingly centers on environmental, social, and governance considerations, ESG investment has become a major trend in financial markets. Artificial intelligence (AI), through its rapid evolution, has exerted a transformative influence that continues to reshape the fundamental structures of this domain. This study investigates the dynamic relationship between AI and ESG investment indices in China, aiming to reveal the bidirectional causal linkages and time-dependent interactions between these two critical areas. In methods, we used four different parameter stability tests to indicate that the Granger causality test based on the full-sample VAR model may produce biased results. Therefore, we employed a bootstrap rolling-window subsample Granger causality test using data from January 2013 to September 2024 in China. The results reveal a significant dynamic relationship between ESG investment and AI. In key findings, we find that AI exerts a negative impact on ESG investment. AI development attracts substantial capital inflows that favor technological advancement and commercialization over long-term ESG investments. Meanwhile, ESG investment shows both positive and negative effects on AI. The positive effect indicates that ESG investment promotes AI research and applications emphasizing energy efficiency, data privacy, and fairness, thereby supporting the sustainable development of AI technologies. However, driven by short-term economic returns, strict ESG standards and compliance requirements may, in the short term, constrain the development of certain energy-intensive or emerging AI technologies. In economic and political implications, our study provides policymakers with scientific evidence to improve the ESG investment environment and to design balanced policies that support both AI development and sustainable investment practices. It underscores the necessity of promoting coordinated development between AI and ESG investment to achieve global sustainability goals and recommends measures to align short-term economic interests with long-term ESG objectives. This study is expected to serve as a scientific basis for ESG goal-setting and contribute to the realization of China’s dual-carbon goals. In particular, it facilitates the convergence of artificial intelligence technologies with sustainable development initiatives and tells the importance of responsible technological progress for global sustainable development. Full article
(This article belongs to the Special Issue The Role of Digitalization and Artificial Intelligence in Low-Carbon Energy Transition and Achieving Carbon Neutrality: Interdisciplinary Perspectives)
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22 pages, 5006 KiB  
Article
Data-Driven Online State Prediction Method for the Traction Motors of Electric Multiple Units (EMUs)
by Yuchen Liu, Chaoxu Li and Man Li
Sustainability 2025, 17(9), 4237; https://doi.org/10.3390/su17094237 - 7 May 2025
Viewed by 127
Abstract
With the high-density operations of high-speed trains, predicting the health status of core components such as traction motors is crucial for enhancing the safety and sustainability of trains. Currently, traditional maintenance mechanisms such as periodic inspections and fixed-threshold alarm systems are hindered by [...] Read more.
With the high-density operations of high-speed trains, predicting the health status of core components such as traction motors is crucial for enhancing the safety and sustainability of trains. Currently, traditional maintenance mechanisms such as periodic inspections and fixed-threshold alarm systems are hindered by delayed abnormality detection and inadequate real-time responsiveness. This paper proposes a dynamic prediction method for traction motor states based on an Online Gated Recurrent Unit (OGRU), which considers various influencing factors and updates model parameters in real-time. Experimental results demonstrate that the online prediction model significantly reduces the RMSE compared to offline methods and exhibits increased prediction stability under different conditions and step sizes. Notably, it decreases computational time by 23.3% relative to the Online Long Short-Term Memory (OLSTM) approach. The proposed method enhances preventive maintenance strategies, optimizes resource utilization, extends equipment lifespan, and reduces costs, thereby making a substantial contribution to the sustainable operation of high-speed railways. By improving energy efficiency, safety, and economic viability, this approach supports a transition toward greener rail transportation. Based on this study, the developed method can facilitate real-time maintenance decision-making, enabling the intelligent operation and maintenance of high-speed trains. Full article
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25 pages, 1380 KiB  
Review
Understanding Food Waste Sorting Practices: Insights from a Systematic Review
by Gediminas Naujokas and Viktorija Bobinaite
Sustainability 2025, 17(9), 4236; https://doi.org/10.3390/su17094236 - 7 May 2025
Viewed by 229
Abstract
Approximately 2.5 billion tons of waste are generated annually worldwide, with food waste constituting a significant portion: 88 million tons in the European Union (EU) alone. Food waste has severe societal, economic, and environmental consequences, contributing 15–16% of greenhouse gas (GHG) emissions from [...] Read more.
Approximately 2.5 billion tons of waste are generated annually worldwide, with food waste constituting a significant portion: 88 million tons in the European Union (EU) alone. Food waste has severe societal, economic, and environmental consequences, contributing 15–16% of greenhouse gas (GHG) emissions from the food supply chain. In response, many countries, including EU member states, the United States of America (USA), and China, have introduced policies mandating food waste sorting. These regulations are informed by scientific research on waste prevention, environmental impact assessments, and cost–benefit analyses of waste reduction strategies. For example, studies on organic waste treatment technologies, economic incentives for waste sorting, and the effectiveness of landfill bans have influenced the development of the EU Waste Framework Directive (2008/98/EC), China’s National Waste Classification Policy (2017), and the USA Food Recovery Act (2015). As waste management continues to evolve, understanding the economic, technological, and policy dimensions of food waste sorting remains crucial for achieving sustainable development and circular economy goals globally. This study systematically reviews the international literature on food waste sorting, analyzing sorting behaviors and identifying theoretical frameworks that explain these behaviors. Using the PSALSAR systematic review methodology, 67 relevant studies from diverse geographic regions were analyzed. The findings highlight the critical influence of external factors in shaping sorting behaviors, such as financial incentives and infrastructure, alongside internal drivers, such as environmental awareness and social norms. While external measures often yield immediate compliance, internal motivation fosters long-term behavioral changes. Moreover, significant regional and cultural variations in food waste sorting practices were identified. The Theory of Planned Behavior (TPB) emerged as a dominant framework in the study of waste sorting behaviors, often complemented by other models such as Social Cognitive Theory (SCT). Policy recommendations emphasize the need for tailored interventions that address regional and demographic differences, community-driven educational initiatives, and the integration of innovative waste sorting technologies. Future research should focus on assessing the economic and psychological impacts of waste sorting policies across different socio-cultural contexts and exploring innovative strategies to enhance global public participation in food waste management. Full article
(This article belongs to the Section Waste and Recycling)
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28 pages, 40848 KiB  
Article
Lane Change Trajectory Planning for Intelligent Electric Vehicles in Dynamic Traffic Environments: Aiming at Optimal Energy Consumption
by Lin Hu, Jie Wang, Jing Huang, Pak Kin Wong and Jing Zhao
Sustainability 2025, 17(9), 4235; https://doi.org/10.3390/su17094235 - 7 May 2025
Viewed by 158
Abstract
With the reduction in battery costs and the widespread application of artificial intelligence, the adoption of new-energy vehicles is accelerating. Integrating energy consumption optimization into the process of intelligent development is of great significance for sustainable development. This paper, considering the regenerative braking [...] Read more.
With the reduction in battery costs and the widespread application of artificial intelligence, the adoption of new-energy vehicles is accelerating. Integrating energy consumption optimization into the process of intelligent development is of great significance for sustainable development. This paper, considering the regenerative braking characteristics of electric vehicles and the time-varying nature of surrounding obstacle vehicles during lane changes, proposes a segmented real-time trajectory-planning method combining optimal control and quintic polynomials. At the beginning of the lane change, a safe intermediate position is calculated based on the speed and position information of the ego vehicle and the leading obstacle vehicle in the current lane. The trajectory optimization problem from the starting point to the intermediate position is formulated as an optimal control problem, resulting in the first segment of the trajectory. Upon reaching the intermediate position, the endpoint range is determined based on the speed and position information of the leading and trailing obstacle vehicles in the target lane. Multiple trajectories are then generated using quintic polynomials, and the optimal trajectory is selected as the second segment of the lane-changing trajectory. Experimental results from a driving simulator show that the proposed method can reduce energy consumption by approximately 40%. Full article
(This article belongs to the Section Sustainable Transportation)
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29 pages, 2775 KiB  
Article
Will Participation in Dual Value Chains Promote Manufacturing Upgrades and Green Development?
by Shi Wang and Shanshan Wang
Sustainability 2025, 17(9), 4234; https://doi.org/10.3390/su17094234 - 7 May 2025
Viewed by 159
Abstract
The global and domestic divisions of labor have had a great influence on the economy and environment in China during the last decade. With the refinement of production processes, national value chains (NVCs) coexist with global value chains (GVCs), enabling regions to participate [...] Read more.
The global and domestic divisions of labor have had a great influence on the economy and environment in China during the last decade. With the refinement of production processes, national value chains (NVCs) coexist with global value chains (GVCs), enabling regions to participate in dual value chains (DVCs) simultaneously. This study calculates the NVCs and GVCs participation of manufacturing sectors in China’s provinces. On this basis, this research adopts a fixed effects model to analyze the impact of GVCs and NVCs participation and their interaction effect on manufacturing upgrades and green development. The results show, first, that significant regional differences in GVCs participation exist among provinces in China. In comparison, provincial NVCs participation demonstrates fewer regional differences. Second, there are significant sectoral differences of GVCs participation in China’s manufacturing industry—high-tech manufacturing is more embedded than other manufacturing industries. The sectoral differences in NVCs participation are relatively small. Third, GVCs and NVCs participation and their interaction effect have significantly promoted the upgrading and green development of manufacturing sectors in provinces of China, and this impact exhibits significant heterogeneity across regions, industries, and NVCs participation modes. The conclusions of this study provide empirical evidence and policy recommendations for the upgrading and green development of China’s manufacturing industry. Full article
(This article belongs to the Special Issue Advances in Economic Development and Business Management)
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17 pages, 4998 KiB  
Article
Mechanisms of Cu(II) Adsorption onto Biochars Derived from Fallen and Non-Fallen Maple Leaves
by Kyung Bin Oh, Saerom Park, Ye Jin Kim, Gyu Won Lee, Jeong Wook Jo, Jae Hun Kim, Ji Eun Kim, Gwangnam Kang, Sang Hyun Lee, Hyung Joo Kim and Yong-Keun Choi
Sustainability 2025, 17(9), 4233; https://doi.org/10.3390/su17094233 - 7 May 2025
Viewed by 149
Abstract
The ability of biochars derived from fallen (F-BC) and non-fallen (NF-BC) maple leaves to adsorb Cu2+ ions from aqueous solutions was examined. Biochars were produced at pyrolysis temperatures of 350, 550, and 750 °C. Higher pyrolysis temperatures resulted in enhanced specific surface [...] Read more.
The ability of biochars derived from fallen (F-BC) and non-fallen (NF-BC) maple leaves to adsorb Cu2+ ions from aqueous solutions was examined. Biochars were produced at pyrolysis temperatures of 350, 550, and 750 °C. Higher pyrolysis temperatures resulted in enhanced specific surface areas and promoted CaCO3 crystallization while limiting MgCO3 formation. The Cu2+ adsorption capacity depended on the biochar type and pyrolysis conditions. Although the Cu2+ adsorption efficiency of NF-BCs decreased with increasing pyrolysis temperature, F-BC550 exhibited a higher Cu2+ adsorption capacity than F-BC750. Additionally, the Cu2+ adsorption performance of both NF-BC350 and F-BC550 improved with increasing solution pH. Cu2+ adsorption onto NF-BC350 and F-BC550 followed the two-compartment first-order (involving fast and slow adsorption compartments) and Langmuir (meaning homogeneous monolayer adsorption) models, respectively. The maximum Cu2+ adsorption capacity of F-BC550 (147.3 mg Cu/g BC) was 7.8-fold higher than that of NF-BC350 (18.8 mg Cu/g BC), as determined by isotherm studies. The enhanced adsorption performance of F-BC550 may be attributable to physical adsorption facilitated by increased surface area and multiple mechanisms, including cationic attraction via functional groups, ion exchange (Ca and Mg), and van der Waals interaction facilitated by increased surface area. These findings suggest that F-BC550, derived from waste biomass, exhibits superior Cu2+ adsorption performance compared to NF-BCs, making it a promising adsorbent for wastewater treatment applications. Full article
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33 pages, 761 KiB  
Article
Natural Environmental Change and Firm Sustainable Development in China: The Moderating Effect of Slack Resources and Digital Transformation
by Shouquan Xu, Ming Tian, Yujie Cai and Xuan Fu
Sustainability 2025, 17(9), 4232; https://doi.org/10.3390/su17094232 - 7 May 2025
Viewed by 162
Abstract
The existing research lacks a comprehensive framework to explain the impact of natural environmental change on corporate sustainable development. After analyzing 2010–2023 data from 4816 Shanghai/Shenzhen A-share firms (39,271 firm-year observations), fixed-effects models reveal that natural environmental change improves financial performance but harms [...] Read more.
The existing research lacks a comprehensive framework to explain the impact of natural environmental change on corporate sustainable development. After analyzing 2010–2023 data from 4816 Shanghai/Shenzhen A-share firms (39,271 firm-year observations), fixed-effects models reveal that natural environmental change improves financial performance but harms environmental–social performance. Absorbed slack resources weaken the positive influence of natural environmental change on financial performance and the negative influence on environmental–social performance; unabsorbed slack resources strengthen the influence of natural environmental change on financial performance but weaken the negative influence on environmental–social performance. Digital transformation diminishes the positive financial effects of natural environmental change. Findings suggest that firms should prioritize strategic slack resource allocation to manage environmental uncertainty, as digital initiatives currently demonstrate limited effectiveness in mitigating these challenges. Full article
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19 pages, 2884 KiB  
Article
Efficient Approach for the Sectorization of Water Distribution Systems: Integrating Graph Theory and Binary Particle Swarm Optimization
by Sabrina da Silva Corrêa Raimundo, Elizabeth Amaral Pastich and Saulo de Tarso Marques Bezerra
Sustainability 2025, 17(9), 4231; https://doi.org/10.3390/su17094231 - 7 May 2025
Viewed by 139
Abstract
The accelerated expansion of urban areas has significantly increased the complexity of managing water distribution systems. Network sectorization into smaller, independently controlled areas is often highlighted as an important measure to enhance operational security and reduce water losses in networks. However, identifying the [...] Read more.
The accelerated expansion of urban areas has significantly increased the complexity of managing water distribution systems. Network sectorization into smaller, independently controlled areas is often highlighted as an important measure to enhance operational security and reduce water losses in networks. However, identifying the optimal sectorization strategy is challenging due to the vast number of possible combinations, and existing methods still present practical limitations. This study proposes a hybrid model for the optimal design of district-metered areas in water distribution systems. The methodology combines graph theory, the Dijkstra shortest path algorithm (DSP), and the meta-heuristic binary particle swarm optimization (BPSO) algorithm. Structuring the topology of the water distribution network using graphs allows the identification of existing connections between the network components. By DSP, the shortest paths from the reservoir to the consumption points were determined, while the proposed BPSO sought the best combination of pipe conditions (open or closed) while meeting the constraint conditions. The application of the model to three real water distribution systems in João Pessoa, in northeastern Brazil, demonstrated its efficiency in sectorization projects, providing optimal solutions that meet the imposed constraints. The results highlight the model’s potential to optimize costs and enhance decision-making in water utility projects. Full article
(This article belongs to the Special Issue Sustainable Water Resources Management and Water Supply)
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28 pages, 7533 KiB  
Article
TeaNet: An Enhanced Attention Network for Climate-Resilient River Discharge Forecasting Under CMIP6 SSP585 Projections
by Prashant Parasar, Poonam Moral, Aman Srivastava, Akhouri Pramod Krishna, Richa Sharma, Virendra Singh Rathore, Abhijit Mustafi, Arun Pratap Mishra, Fahdah Falah Ben Hasher and Mohamed Zhran
Sustainability 2025, 17(9), 4230; https://doi.org/10.3390/su17094230 - 7 May 2025
Viewed by 233
Abstract
The accurate prediction of river discharge is essential in water resource management, particularly under variability due to climate change. Traditional hydrological models commonly struggle to capture the complex, nonlinear relationships between climate variables and river discharge, leading to uncertainties in long-term projections. To [...] Read more.
The accurate prediction of river discharge is essential in water resource management, particularly under variability due to climate change. Traditional hydrological models commonly struggle to capture the complex, nonlinear relationships between climate variables and river discharge, leading to uncertainties in long-term projections. To mitigate these challenges, this research integrates machine learning (ML) and deep learning (DL) techniques to predict discharge in the Subernarekha River Basin (India) under future climate scenarios. Global climate models (GCMs) from the Coupled Model Intercomparison Project 6 (CMIP6) are assessed for their ability to reproduce historical discharge trends. The selected CNRM-M6-1 model is bias-corrected and downscaled before being used to simulate future discharge patterns under SSP585 (a high-emission scenario). Various AI-driven models, such as a temporal convolutional network (TCN), a gated recurrent unit (GRU), a support vector regressor (SVR), and a novel DL network named the Temporal Enhanced Attention Network (TeaNet), are implemented by integrating the maximum and minimum daily temperatures and precipitation as key input parameters. The performance of the models is evaluated using the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2). Among the evaluated models, TeaNet demonstrates the best performance, with the lowest error rates (RMSE: 2.34–3.04; MAE: 1.13–1.52 during training) and highest R2 (0.87–0.95), outperforming the TCN (R2: 0.79–0.88), GRU (R2: 0.75–0.84), SVR (R2: 0.68–0.80), and RF (R2: 0.72–0.82) by 8–15% in accuracy across four gauge stations. The efficacy of the proposed model lies in its enhanced attention mechanism, which successfully identifies temporal relationships in hydrological information. In determining the most relevant predictors of river discharge, the feature importance is analyzed using the proposed TeaNet model. The findings of this research strengthen the role of DL architectures in improving long-term discharge prediction, providing valuable knowledge for climate adaptation and strategic planning in the Subernarekha region. Full article
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19 pages, 471 KiB  
Article
Climate Policy Uncertainty and Enterprise Working Capital Management Efficiency
by Xiangyi Sui
Sustainability 2025, 17(9), 4229; https://doi.org/10.3390/su17094229 - 7 May 2025
Viewed by 151
Abstract
This study explores the effect of climate policy uncertainty on corporate working capital management efficiency. Investigating this problem may provide advice to mitigate the impact of climate policy uncertainty on firms. We used Chinese A-share listed companies’ data, spanning from 2007 to 2023, [...] Read more.
This study explores the effect of climate policy uncertainty on corporate working capital management efficiency. Investigating this problem may provide advice to mitigate the impact of climate policy uncertainty on firms. We used Chinese A-share listed companies’ data, spanning from 2007 to 2023, and discovered that climate policy uncertainty reduced companies’ working capital management efficiency. Mechanism research found that climate policy uncertainty reduced firms’ working capital management efficiency by increasing the transaction costs, lowering specific asset investment, and increasing inventory turnover days. Furthermore, our heterogeneity analysis indicated that the impact of climate policy uncertainty on working capital management efficiency was more pronounced in enterprises in the western and central regions, areas with lower marketization levels, and regions with lower highway density. By exploring the influence of climate policy uncertainty on working capital management efficiency, we have expanded the understanding of how climate policy uncertainty affects corporations and enriched research about corporate working capital management efficiency. We recommend that the government enhance the transparency of climate policies and reduce the frequency of policy changes. Furthermore, we advise enterprises to maintain close relationships with their customers and suppliers to mitigate the impact of climate policy uncertainty on working capital management efficiency. Full article
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44 pages, 2691 KiB  
Review
Technology, Behavior, and Governance: Far Away, Yet So Close! A Comprehensive Review of the Sustainable Mobility and Transportation Literature
by Ioannis Kanakis, Stathis Arapostathis and Stelios Rozakis
Sustainability 2025, 17(9), 4228; https://doi.org/10.3390/su17094228 - 7 May 2025
Viewed by 154
Abstract
Within the multidisciplinary field of Sustainable Mobility and Transport(ation) (SMT), there are few review studies that analyze the vast and complex literature in a comprehensive manner, often paying limited attention to the key structural and interpretive elements and their interrelationships. Aiming to fill [...] Read more.
Within the multidisciplinary field of Sustainable Mobility and Transport(ation) (SMT), there are few review studies that analyze the vast and complex literature in a comprehensive manner, often paying limited attention to the key structural and interpretive elements and their interrelationships. Aiming to fill this research gap, the present study offers a thorough review of the literature from the past thirty years (1992–2020), analyzing and organizing it to ultimately provide a unified synthesis. Bibliometric network visualization of the SMT literature (2084 peer-reviewed journal articles) and content analysis of its most influential subset (220 articles) are combined using a mixed-methods approach. Based on this synthesis, three main bibliographic clusters are identified: “technology”, “behavior change”, and “policy–governance”, each addressing twenty-one bibliographic themes. These structural elements (clusters and themes) are then interpreted through three main narratives and twelve sub-narratives, revealing their dynamic interactions. The entire set of clusters, themes, narratives, and sub-narratives, along with their interconnections, constitutes a conceptual framework of the SMT literature. This study highlights the importance of fostering interdisciplinarity through deeper collaboration between researchers from applied sciences, social sciences, and the humanities, and identifies key thematic research areas and topics for future exploration. Full article
(This article belongs to the Section Sustainable Transportation)
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23 pages, 11864 KiB  
Article
Utilizing Remote Sensing and Random Forests to Identify Optimal Land Use Scenarios and Address the Increase in Landslide Susceptibility
by Aditya Nugraha Putra, Jaenudin, Novandi Rizky Prasetya, Michelle Talisia Sugiarto, Sudarto, Cahyo Prayogo, Febrian Maritimo and Fandy Tri Admajaya
Sustainability 2025, 17(9), 4227; https://doi.org/10.3390/su17094227 - 7 May 2025
Viewed by 210
Abstract
Massive land use changes in Indonesia driven by deforestation, agricultural expansion, and urbanization have significantly increased landslide susceptibility in upper watersheds. This study focuses on the Sumber Brantas and Kali Konto sub-watersheds where rapid land conversion has destabilized slopes and disrupted ecological balance. [...] Read more.
Massive land use changes in Indonesia driven by deforestation, agricultural expansion, and urbanization have significantly increased landslide susceptibility in upper watersheds. This study focuses on the Sumber Brantas and Kali Konto sub-watersheds where rapid land conversion has destabilized slopes and disrupted ecological balance. By integrating remote sensing, Cellular Automata-Markov (CA-Markov), and Random Forest (RF) models, the research aims to identify optimal land use scenarios for mitigating landslide hazards. Three scenarios were analyzed: business as usual (BAU), land capability classification (LCC), and regional spatial planning (RSP) using 400 field-validated landslide data points alongside 22 topographic, geological, environmental, and anthropogenic parameters. Land use analysis from 2017 to 2022 revealed a 1% decline in natural forest cover, which corresponded to a 1% increase in high and very high landslide hazard areas. From 2017 to 2022, landslide risk increased as the “High” category rose from 33.95% to 37.59% and “Very High” from 10.24% to 12.18%; under BAU 2025, they reached 40.89% and 12.48%, while RSP and LCC reduced the “High” category to 44.12% and 34.44%, respectively. These findings highlight the critical role of integrating geospatial analysis and machine learning in regional planning to promote sustainable land use, reduce landslide hazards, and enhance watershed resilience with high model accuracy (>81%). Full article
(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction, 2nd Edition)
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26 pages, 2692 KiB  
Review
Redefining Corporate Social Responsibility: The Role of Strategic Communication Practices
by Umaru Kargbo, Biju Terrence and Timothy B. Palmer
Sustainability 2025, 17(9), 4226; https://doi.org/10.3390/su17094226 - 7 May 2025
Viewed by 475
Abstract
Corporate Social Responsibility (CSR) and its sustainability-focused communications are now recognized as essential corporate activities. As society increasingly holds firms accountable for their social, environmental, and sustainability impacts, academic interest in CSR communications has similarly grown, with scholars exploring how CSR communication influences [...] Read more.
Corporate Social Responsibility (CSR) and its sustainability-focused communications are now recognized as essential corporate activities. As society increasingly holds firms accountable for their social, environmental, and sustainability impacts, academic interest in CSR communications has similarly grown, with scholars exploring how CSR communication influences stakeholder engagement and corporate strategies. In response to this growing interest, we conducted a systematic literature review utilizing bibliometric analysis to identify and examine publication trends and patterns in CSR and CSR-associated communications, drawing from a robust dataset of 3513 documents extracted from Scopus and Web of Science. The analysis was conducted using the Biblioshiny R package and Excel to ensure methodological precision and analytical depth. We explored the characteristics of publications related to topics such as business, authorship, and journals over a four-decade period spanning from 1984 to 2024. Our results reveal four strategic clusters of CSR disclosure, reflecting a shift from symbolic to strategic and stakeholder-focused communication. Thematic evolution highlights the growing integration of ESG frameworks and digital reporting practices. This study is significant not only in its methodological rigor but also in its timely contribution to the intersection of CSR, sustainability, and strategic communication. Also, this study introduces a new theoretical framework through the CSR strategic disclosure indicator metric, which connects the level of disclosure maturity with the focus on different stakeholder groups. We discuss the implications of our findings not only for future scholarly research in CSR but also for corporate sustainability practitioners who look to academia for insights on emerging trends in CSR and CSR reporting. Full article
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35 pages, 26705 KiB  
Article
Living Inheritance of Traditional Knowledge and Practical Wisdom of Severe Cold-Region Traditional Villages: A Case Study of Jinjiang Chalet Village in the Changbai Mountain Area
by Hongyu Zhao, Jiandong Fang, Zhanlve Lin, Jiajun Tang, Shinan Zhen, Huijia Shi, Xiaoyu Hui and Yuesong Liu
Sustainability 2025, 17(9), 4225; https://doi.org/10.3390/su17094225 - 7 May 2025
Viewed by 235
Abstract
Despite traditional knowledge’s (TK’s) potential to mitigate climate-induced vulnerabilities across diverse climates, cold-region communities remain critically understudied. To bridge that gap, this study adopts the pressure–state–response (PSR) framework to analyze how Indigenous knowledge in China’s Jinjiang Chalet Village—a 300-year-old cold-region settlement—embodies dynamic resilience [...] Read more.
Despite traditional knowledge’s (TK’s) potential to mitigate climate-induced vulnerabilities across diverse climates, cold-region communities remain critically understudied. To bridge that gap, this study adopts the pressure–state–response (PSR) framework to analyze how Indigenous knowledge in China’s Jinjiang Chalet Village—a 300-year-old cold-region settlement—embodies dynamic resilience across ecological, climatic, social, and economic dimensions. Combining semi-structured interviews with Indigenous Elders, UAV-based multispectral analysis, and environmental simulations, we identify strategies rooted in sustainable wisdom: ecosystem stewardship, climate-responsive architecture, community governance, and adaptive economic practices. A key innovation lies in the Eco-Wisdom Laboratory—a pilot project operationalizing TK through modern passive design and participatory education, demonstrating how traditional woodcraft and microclimate management can be integrated with contemporary technologies to achieve scalable, low-carbon solutions. Crucially, we advance the concept of living inheritance by showcasing how such hybrid practices decolonize static preservation paradigms, enabling communities to codify TK into tangible, future-oriented applications. This study provides a replicable framework for embedding TK into global sustainability agendas, particularly for severe cold regions facing similar stressors. Our findings advocate for policy reforms centering Indigenous agency in climate adaptation planning, offering actionable insights for architects, policymakers, and educators working at the nexus of cultural heritage and ecological resilience. Full article
(This article belongs to the Special Issue Traditional Land-Based Knowledge and Practice as Environmental Sustainability)
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28 pages, 4219 KiB  
Review
Adoption of Innovative Technologies for Sustainable Agriculture: A Scoping Review of the System Domain
by Rocco Addorisio, Roberta Spadoni and Giulia Maesano
Sustainability 2025, 17(9), 4224; https://doi.org/10.3390/su17094224 - 7 May 2025
Viewed by 374
Abstract
The agricultural sector is undergoing a profound transformation driven by the integration of innovative technologies and practices, but the adoption of these technologies remains uneven. Holistic approaches to the diffusion of innovative technologies in agriculture are seen as crucial for effective adoption and [...] Read more.
The agricultural sector is undergoing a profound transformation driven by the integration of innovative technologies and practices, but the adoption of these technologies remains uneven. Holistic approaches to the diffusion of innovative technologies in agriculture are seen as crucial for effective adoption and sustainable development. In this context, the systemic dimension of technology adoption is characterized by the interactions between actors that create knowledge and promote the process of technology adoption. Therefore, the overall objective of this study is to provide a comprehensive analysis of the current state of the art in relation to the systemic dimension of the process of technology adoption in developed countries. Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension protocol for scoping reviews, we examined the literature to capture the role of the systems dimension in the process of technology adoption. We conducted a two-analysis, bibliometric and content network analysis to identify the concepts and thematic clusters that define the systemic dimension and represent the main drivers of technology adoption for sustainable development in agriculture. The results show that the factors influencing the adoption of agricultural technologies are treated inconsistently in the literature, with a focus on technological and economic aspects rather than systemic elements such as governance and stakeholder interactions. Full article
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22 pages, 1743 KiB  
Review
Microbial Degradation of Lignocellulose for Sustainable Biomass Utilization and Future Research Perspectives
by Mengke Chen, Qinyu Li, Changjun Liu, Er Meng and Baoguo Zhang
Sustainability 2025, 17(9), 4223; https://doi.org/10.3390/su17094223 - 7 May 2025
Viewed by 246
Abstract
Lignocellulose, as Earth’s most abundant renewable biomass, represents a crucial resource for the production of biofuels and biochemicals, it is of great significance for sustainable development. Microbial degradation offers a promising pathway for transforming lignocellulose into valuable products. This review explores the diversity [...] Read more.
Lignocellulose, as Earth’s most abundant renewable biomass, represents a crucial resource for the production of biofuels and biochemicals, it is of great significance for sustainable development. Microbial degradation offers a promising pathway for transforming lignocellulose into valuable products. This review explores the diversity and classification of lignocellulose-degrading microorganisms, focusing on fungi and bacteria and their respective enzyme systems responsible for breaking down cellulose, hemicellulose, and lignin. Key factors influencing degradation efficiency, including environmental conditions, substrate complexity, and microbial interactions, are thoroughly analyzed. Limitations in microbial degradation are also discussed, notably the need for identifying high-activity strains. Additionally, the review outlines future research directions, emphasizing the application of advanced technologies such as genomics, synthetic biology, and machine learning to optimize microbial degradation processes. These insights aim to enhance lignocellulose utilization efficiency, fostering its broader industrial and agricultural applications. Full article
(This article belongs to the Section Resources and Sustainable Utilization)
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29 pages, 2196 KiB  
Review
Exploring Capabilities for Digital Transformation in the Business Context: Insight from a Systematic Literature Review
by Afrin Fauzya Rizana, Iwan Inrawan Wiratmadja and Muhammad Akbar
Sustainability 2025, 17(9), 4222; https://doi.org/10.3390/su17094222 - 7 May 2025
Viewed by 325
Abstract
Digital transformation is considered a high-risk investment due to the fact that as much as 80% of its initiatives fail. To effectively manage and execute digital transformation, organizations must establish capabilities tailored to this process. Thus, this study aims to identify capabilities essential [...] Read more.
Digital transformation is considered a high-risk investment due to the fact that as much as 80% of its initiatives fail. To effectively manage and execute digital transformation, organizations must establish capabilities tailored to this process. Thus, this study aims to identify capabilities essential for digital transformation in the business context. A systematic literature review (SLR) was conducted following the PRISMA. An initial search across major academic databases yielded 542 articles. After applying inclusion and exclusion criteria, 43 relevant articles were selected for in-depth analysis. Descriptive, co-occurrence, and qualitative analyses were then applied. The findings reveal five core dimensions of digital transformation capability: digital dynamic capability, digital leadership capability, employee digital capability, digital technology and operational capability, and digital investment capability. These capabilities demonstrate that successful digital transformation depends not only on technology, but also on leadership, human capital, strategy, and investment that ensure resource readiness. This study contributes to digital transformation theory by identifying essential organizational capabilities and provides insights into how organizations can develop these capabilities to achieve successful digital transformation. Full article
(This article belongs to the Special Issue Digital Transformation of Supply Chain Innovation)
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20 pages, 2557 KiB  
Article
Improving Reusability of Biocatalysts by Exploiting Cross-Linked Enzyme Aggregates (CLEAs) with Commercial Cellulolytic Cocktails for Hydrolysis of Green Coconut Waste
by Jéssica R. F. Morais, Isabela O. Costa, Carlos E. A. Padilha, Nathália S. Rios and Everaldo S. dos Santos
Sustainability 2025, 17(9), 4221; https://doi.org/10.3390/su17094221 - 7 May 2025
Viewed by 174
Abstract
Efficient hydrolysis of cellulose in agricultural waste (e.g., coconut fiber) is critical for biorefining processes such as second-generation bioethanol (2G ethanol) production. However, free cellulases suffer from low thermal stability and challenges in recovery. To address this, we developed cross-linked enzyme aggregates (CLEAs) [...] Read more.
Efficient hydrolysis of cellulose in agricultural waste (e.g., coconut fiber) is critical for biorefining processes such as second-generation bioethanol (2G ethanol) production. However, free cellulases suffer from low thermal stability and challenges in recovery. To address this, we developed cross-linked enzyme aggregates (CLEAs) combined with magnetic nanoparticles (magnetic CLEAs, m-CLEAs) to enhance enzyme stability and reusability. In this context, solutions of ethanol, acetone, and ammonium sulfate were used to prepare enzymatic aggregates, with subsequent use of glutaraldehyde and magnetic nanoparticles to obtain the biocatalysts. The addition of bovine serum albumin (BSA) protein was also tested to improve immobilization. Biocatalysts with ethanol and acetone performed better. Acetone (AC) and BSA yielded the highest enzymatic activities (287.27 ± 42.59 U/g for carboxymethyl cellulase (CMCase) with Celluclast; 425.37 ± 48.11 U/g for CMCase with Cellic CTec2). Magnetic nanoparticles were incorporated to expand the industrial applicability, producing m-CLEAs with excellent thermal stability and high catalytic activities. The m-CLEA–Celluclast–AC–BSA–GA 5% maintained 58% of its activity after 72 h at 70 °C. The m-CLEA–Celluclast-AC–BSA–GA 2.5% proved effective in hydrolyzing coconut fiber and isolated cellulose, producing up to 0.91 ± 0.01 g/L of glucose and 2.7 ± 0.15 g/L of glucose, respectively, after 72 h. Therefore, this approach supports sustainability by using coconut fiber, which is often discarded into the environment. Full article
(This article belongs to the Special Issue Utilization of Biomass: Energy, Catalysts, and Applications)
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16 pages, 783 KiB  
Article
Dual Mediation Mechanisms of Ownership Climate on Safety Behavior in Construction Workers: Evidence from China
by Chun Fu and Jialing Tan
Sustainability 2025, 17(9), 4220; https://doi.org/10.3390/su17094220 - 7 May 2025
Viewed by 174
Abstract
As a high-risk industry characterized by persistently high accident and casualty rates, the construction sector has been extensively studied in terms of individual behavioral safety, organizational safety culture, and safety climate. However, existing research remains fragmented, lacking an integrative perspective to systematically explore [...] Read more.
As a high-risk industry characterized by persistently high accident and casualty rates, the construction sector has been extensively studied in terms of individual behavioral safety, organizational safety culture, and safety climate. However, existing research remains fragmented, lacking an integrative perspective to systematically explore the interconnections between these interrelated dimensions. This study investigates the mechanisms through which the ownership climate influences safety behaviors among construction workers in China. Applying self-determination theory (SDT) and the theory of planned behavior (TPB), we propose a dual-mediation model with team building and risk perception as parallel mediators. Empirical data were collected from 312 frontline workers through structured surveys and analyzed using structural equation modeling (SEM) and bootstrapping techniques. The results demonstrate that a sense of ownership climate not only directly enhances the safety behaviors of construction workers but also functions via dual mechanisms: strengthening team building to improve both compliance with and the execution of safety protocols and heightening risk perception awareness to reduce the propensity for risk-taking behaviors. These findings highlight the need for strategies integrating ownership climate cultivation, team collaboration, and risk awareness training to optimize safety outcomes. This study extends the existing literature by bridging motivational (SDT) and cognitive (TPB) frameworks, offering culturally grounded solutions for transient workforce management in high-risk industries. Full article
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