Sustainability

22 pages, 5926 KB

Open AccessArticle

Evaluation and Enhancement of Landscape Resilience in Mountain–Water Towns from the Perspective of Cultural and Tourism Integration: Case Study of Yinji Town, Wugang City

by Huaijing Wu, Shuo Liu, Hu Li, Wenqi Wang, Lijuan Niu and Hong Zhang

Sustainability 2025, 17(21), 9806; https://doi.org/10.3390/su17219806 (registering DOI) - 3 Nov 2025

Abstract

Rural tourism in China is advancing rapidly, with cultural and tourism integration (CTI) becoming a vital pathway for sustainability. Mountain–water towns, given their special geographical conditions, face numerous challenges in CTI development, which need to enhance landscape resilience. This study proposes the theoretical [...] Read more.

Rural tourism in China is advancing rapidly, with cultural and tourism integration (CTI) becoming a vital pathway for sustainability. Mountain–water towns, given their special geographical conditions, face numerous challenges in CTI development, which need to enhance landscape resilience. This study proposes the theoretical framework of landscape resilience in mountain–water towns from the perspective of CTI. Taking Yinji Town of Wugang City as an example, it constructs a resilience evaluation system including three dimensions: cultural landscape, natural landscape, and social systems. The study uses the AHP–Entropy Weight combined method to determine indicator weights. Indicator scores are obtained through field research and GIS analysis, which are substituted into the preparedness–vulnerability resilience model to calculate resilience level, and the Jenks Natural Breaks method is used for level classification. Finally, the Obstacle Degree Model is applied to identify the primary obstacle factors affecting landscape resilience. The results indicate the following: (1) The average landscape resilience (RI) score of the 19 villages in Yinji Town is 0.84 (RI < 1), indicating a generally low level. Two villages are in the high-level range, while four villages are in the low-level range. (2) Cultural landscape resilience is the primary weakness, with the lowest average score (0.70), while natural landscape resilience is the highest (1.03). (3) Major obstacles include such as the number of cultural inheritors, the degree of susceptibility to natural disasters, and the distance to core mountain–water resources. The study contributes a CTI-based evaluation framework and methodology for assessing landscape resilience, offering enhancement strategies through increased preparedness and reduced vulnerability. Full article

(This article belongs to the Special Issue Moving beyond Sustainable Tourism Rediscovery through Regenerative Travel)

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15 pages, 475 KB

Open AccessArticle

Factors Affecting Fish Production in Saudi Arabia

by Mohammed Al-Mahish and Fatimah Alsafra

Sustainability 2025, 17(21), 9805; https://doi.org/10.3390/su17219805 (registering DOI) - 3 Nov 2025

Abstract

Governmental organizations, projects, and initiatives in Saudi Arabia have focused specifically on the fisheries and the aquaculture sector to reduce reliance on imports, achieve self-sufficiency, and significantly contribute to food security. To accommodate the annual population increase, Saudi Arabia needs to enhance its [...] Read more.

Governmental organizations, projects, and initiatives in Saudi Arabia have focused specifically on the fisheries and the aquaculture sector to reduce reliance on imports, achieve self-sufficiency, and significantly contribute to food security. To accommodate the annual population increase, Saudi Arabia needs to enhance its fish production. This study aims to illustrate the impact of credit on the fisheries sector by examining the factors that affect fish output in Saudi Arabia, both in general and in specific contexts. The research employed annual time series data to estimate the Cobb–Douglas production function. The study computed the Cobb–Douglas model in an error correction format due to the stationarity characteristic of the data. The results show that fish production in Saudi Arabia is significantly enhanced by the number of fishermen, marine fisheries, aquaculture farms, and financial resources. Furthermore, the results reveal that economies of scale play a crucial role in the Saudi fishing industry. Nevertheless, since the data indicates that the influence of marine fisheries on fish output in Saudi Arabia in the long run surpasses that of aquaculture farms, the researchers recommend an increase in aquaculture production. Sustainable methods for fish production, such as minimizing overfishing and bycatch, improving water and environmental quality, and promoting the traceability of fish populations, should be prioritized in the advancement of the fisheries sector. Full article

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21 pages, 4651 KB

Open AccessArticle

The Influence of Carbonate Binder Content on the Mechanical and Physical Properties of Artificial Lightweight Aggregates Produced by Carbonization Using Wood Waste Fly Ash

by Vitoldas Vidikas and Algirdas Augonis

Sustainability 2025, 17(21), 9804; https://doi.org/10.3390/su17219804 (registering DOI) - 3 Nov 2025

Abstract

Large amounts of wood waste fly ash (WWFA) are generated in bioenergy plants, yet their potential for reuse in construction materials remains underexplored. In this study, artificial lightweight aggregates (ALWAs) were produced by cold-bonded granulation of WWFA with hydrated lime, followed by carbonation [...] Read more.

Large amounts of wood waste fly ash (WWFA) are generated in bioenergy plants, yet their potential for reuse in construction materials remains underexplored. In this study, artificial lightweight aggregates (ALWAs) were produced by cold-bonded granulation of WWFA with hydrated lime, followed by carbonation curing (20 °C, 64% RH, 19% CO₂). The aggregates were evaluated according to EN 13055:2016 classification criteria, with testing performed following the relevant European standards, including EN 1097-3 and EN 1097-6 for density and water absorption, EN 1097-11 for crushing resistance, and EN 1367-7 for freeze–thaw resistance. All ALWAs met the lightweight aggregate classification, with bulk densities of 1010.9–1060.0 kg/m³ and crushing resistances up to 2.74 N/mm², exceeding that of lightweight expanded clay aggregate (LECA) (1.26 N/mm²). XRD confirmed CaCO₃ formation, SEM revealed binder- and w/m-dependent porosity and crystal morphology, and freeze–thaw resistance indicated suitability for non-structural applications. These results demonstrate that WWFA-based ALWAs are a sustainable alternative to natural aggregates, combining waste valorization with competitive performance. Full article

(This article belongs to the Special Issue Innovative Building Solutions for Decarbonized and Sustainable Construction)

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30 pages, 4396 KB

Open AccessArticle

Advancing Sustainability Through an IoT-Driven Smart Waste Management System with Software Engineering Integration

by Reem Alnanih, Lamiaa Elrefaei and Ayman Al-Ahwal

Sustainability 2025, 17(21), 9803; https://doi.org/10.3390/su17219803 (registering DOI) - 3 Nov 2025

Abstract

Sustainability in software engineering encompasses environmental, human, social, and economic dimensions, each essential for ensuring software’s positive and lasting impact. This paper presents an innovative Internet of Things (IoT)-based Smart Waste Management (SWM) system. The proposed system addresses key limitations in existing solutions, [...] Read more.

Sustainability in software engineering encompasses environmental, human, social, and economic dimensions, each essential for ensuring software’s positive and lasting impact. This paper presents an innovative Internet of Things (IoT)-based Smart Waste Management (SWM) system. The proposed system addresses key limitations in existing solutions, including lack of real-time responsiveness, inefficient routing, inadequate emergency detection, and limited user-centric design. While prior studies have investigated IoT applications in SWM, challenges remain in achieving dynamic, integrated, and scalable systems for sustainable urban development. The proposed solution introduces a holistic architecture that enables real-time monitoring of waste bin levels and fire incidents through Waste Bin Level Monitoring Units (BLMUs) equipped with ultrasonic and flame sensors. Data is transmitted via Wi-Fi to a centralized City Command and Control Center (4C), allowing for automated alerts and dynamic route optimization. A dual-platform software suite supports both administrative and operational workflows: a desktop web application and a role-based Android mobile app developed in Flutter, and integrated with Google Cloud Firestore, enabling centralized data management and efficient resource allocation. We validated the system through a working prototype, demonstrating notable contributions including enhanced emergency responsiveness, optimized waste collection routes, and improved stakeholder engagement. This research contributes to the advancement of sustainable urban infrastructure by offering a scalable, data-driven SWM framework grounded in software engineering principles and aligned with smart city objectives. This paper presents an innovative IoT-based Smart Waste Management (SWM) system that addresses key limitations in existing solutions, including insufficient real-time responsiveness, inefficient routing, inadequate emergency detection, and limited user-centric design. Full article

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19 pages, 6085 KB

Open AccessArticle

Study on Sustainable Sludge Utilization via the Combination of Electroosmotic Vacuum Preloading and Polyacrylamide Flocculation

by Heng Zhang, Chongzhi Tu and Cheng He

Sustainability 2025, 17(21), 9802; https://doi.org/10.3390/su17219802 (registering DOI) - 3 Nov 2025

Abstract

Dredged sludge is characterized by a high water content, low permeability, and poor load-bearing capacity, which hinder its sustainable utilization as an engineering filler. During the stabilization process using vacuum preloading (VP), fine-grained sludge readily clogs drainage channels, thereby prolonging consolidation duration and [...] Read more.

Dredged sludge is characterized by a high water content, low permeability, and poor load-bearing capacity, which hinder its sustainable utilization as an engineering filler. During the stabilization process using vacuum preloading (VP), fine-grained sludge readily clogs drainage channels, thereby prolonging consolidation duration and compromising drainage efficiency. To address these persistent challenges, this study proposes an improved method that combines electroosmosis, VP, and polyacrylamide (PAM) to enhance the consolidation performance of dredged sludge. Column settling experiments demonstrated that the optimal application dosages of anionic polyacrylamide (APAM) and calcium chloride (CaCl₂) were 0.25% and 4.0% of dry sludge mass, respectively. Excessive dosage of either APAM or CaCl₂ disturbed the agglomeration and sedimentation of fine-grained particles due to surface charge inversion. Electroosmotic VP (EVP) facilitated the directional movement of pore water, which increased the cumulative water discharge mass by 37.3%. The combination of APAM and CaCl₂ enhanced particle flocculation via adsorption and bridging effects, significantly improving soil permeability and dewatering performance. Driven by an electric field, Ca²⁺ ions transported water molecules toward the cathode. Subsequently, these Ca²⁺ ions participated in reactions to generate cementitious agents. Compared with VP, this integrated method increased the sludge shear strength by 108.1% and produced a much denser microstructure. Full article

(This article belongs to the Special Issue Soil Stabilization and Geotechnical Engineering Sustainability)

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24 pages, 5518 KB

Open AccessArticle

PropNet-R: A Custom CNN Architecture for Quantitative Estimation of Propane Gas Concentration Based on Thermal Images for Sustainable Safety Monitoring

by Luis Alberto Holgado-Apaza, Jaime Cesar Prieto-Luna, Edgar E. Carpio-Vargas, Nelly Jacqueline Ulloa-Gallardo, Yban Vilchez-Navarro, José Miguel Barrón-Adame, José Alfredo Aguirre-Puente, Dalmiro Ramos Enciso, Danger David Castellon-Apaza and Danny Jesus Saman-Pacamia

Sustainability 2025, 17(21), 9801; https://doi.org/10.3390/su17219801 (registering DOI) - 3 Nov 2025

Abstract

Liquefied petroleum gas (LPG), composed mainly of propane and butane, is widely used as an energy source in residential, commercial, and industrial sectors; however, its high flammability poses a critical risk in the event of accidental leaks. In Peru, where LPG constitutes the [...] Read more.

Liquefied petroleum gas (LPG), composed mainly of propane and butane, is widely used as an energy source in residential, commercial, and industrial sectors; however, its high flammability poses a critical risk in the event of accidental leaks. In Peru, where LPG constitutes the main domestic energy source, leakage emergencies affect thousands of households each year. This pattern is replicated in developing countries with limited energy infrastructure. Early quantitative detection of propane, the predominant component of Peruvian LPG (~60%), is essential to prevent explosions, poisoning, and greenhouse gas emissions that hinder climate change mitigation strategies. This study presents PropNet-R, a convolutional neural network (CNN) designed to estimate propane concentrations (ppm) from thermal images. A dataset of 3574 thermal images synchronized with concentration measurements was collected under controlled conditions. PropNet-R, composed of four progressive convolutional blocks, was compared with SqueezeNet, VGG19, and ResNet50, all fine-tuned for regression tasks. On the test set, PropNet-R achieved MSE = 0.240, R² = 0.614, MAE = 0.333, and Pearson’s r = 0.786, outperforming SqueezeNet (MSE = 0.374, R² = 0.397), VGG19 (MSE = 0.447, R² = 0.280), and ResNet50 (MSE = 0.474, R² = 0.236). These findings provide empirical evidence that task-specific CNN architectures outperform generic transfer learning models in thermal image-based regression. By enabling continuous and quantitative monitoring of gas leaks, PropNet-R enhances safety in industrial and urban environments, complementing conventional chemical sensors. The proposed model contributes to the development of sustainable infrastructure by reducing gas-related risks, promoting energy security, and strengthening resilient, safe, and environmentally responsible urban systems. Full article

(This article belongs to the Special Issue Data-Driven Approaches and Decision Support Tools for Sustainable and Resilient Infrastructure Systems)

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25 pages, 6411 KB

Open AccessArticle

An Evolutionary Game Perspective for Promoting Utilization of Crop Straw as Energy: A Case Study in Guangdong

by Yuexiang Yang, Leixin Zhang, Jiale Ren, Wen Wang and Xudong Sun

Sustainability 2025, 17(21), 9800; https://doi.org/10.3390/su17219800 (registering DOI) - 3 Nov 2025

Abstract

The industrialization of using crop straw as energy is currently hindered by systemic bottlenecks, including high collection and storage costs, a poorly coordinated industrial chain, and underdeveloped market mechanism. This study takes Guangdong province as a case study to construct a tripartite evolutionary [...] Read more.

The industrialization of using crop straw as energy is currently hindered by systemic bottlenecks, including high collection and storage costs, a poorly coordinated industrial chain, and underdeveloped market mechanism. This study takes Guangdong province as a case study to construct a tripartite evolutionary game model on the transition of straw to energy among the government, enterprises, and farmers. Different from previous studies that focused on the strategy of penalizing the open burning of straw by farmers, this work investigated the cooperation of farmers for straw removal from field, the operational strategies of enterprises for straw utilization as energy, and the selection of government-guided incentive policies. It analyzes the behavioral evolution of these stakeholders under various incentive policies and cooperative scenarios. Numerical simulations were performed to identify the system’s evolutionary stable strategies and assess the potential of expanding straw for energy utilization. It indicated that mild government intervention could lead to a stable equilibrium through facilitating the removal of straw from fields and the utilization of straw as energy by enterprises. Farmers were sensitive to the fluctuation of acquisition price, and their willingness to cooperate would be negatively impacted by a large-scale price reduction. Enterprise expansion was exposed to significant risk under intensive policy intervention. The feasible pathway to increase the proportion of straw utilization as energy in Guangdong began at a small scale. Under mild incentive policies, a scenario targeting a 20% increase was more likely to achieve a market equilibrium for large-scale production than that targeting a 55% increase. The government should draw up positive incentive policies to promote the utilization of straw as energy. By guiding farmers in straw removal from the field and improving the energy enterprises’ competitiveness, the government should curb irrational industry expansion and corporate speculation, and shift from investment support to incentive policies. Meanwhile, the ecological construction of industry and supply chains should be enhanced, and the scale should be used to reduce the high supply-side costs of the straw. It would overcome the central barrier to the commercialization of straw utilization as energy. This work sets an example for conducting dynamic analysis of multi-stakeholder interactions for straw utilization. Full article

(This article belongs to the Special Issue Sustainable Biomass Utilization for Renewable Energy)

29 pages, 2608 KB

Open AccessArticle

Assessing the Sustainable Development of the Tourism Industry Based on Fuzzy AHP and Grey Relational TOPSIS

by Qiyong Yang, Jidan Huang and Wenyan Pan

Sustainability 2025, 17(21), 9799; https://doi.org/10.3390/su17219799 (registering DOI) - 3 Nov 2025

Abstract

As tourism develops, more study focuses on tourism sustainable development assessment. To solve ambiguous indicators and subjective weight distributions in such evaluations, this paper proposes a hybrid model combining Fuzzy AHP (FAHP) and Grey Relational TOPSIS (GR-TOPSIS). A 13-secondary-indicator evaluation system is established [...] Read more.

As tourism develops, more study focuses on tourism sustainable development assessment. To solve ambiguous indicators and subjective weight distributions in such evaluations, this paper proposes a hybrid model combining Fuzzy AHP (FAHP) and Grey Relational TOPSIS (GR-TOPSIS). A 13-secondary-indicator evaluation system is established across four dimensions (economy, society, environment, culture), distinguishing positive/negative indicators based on tourism’s local impacts. FAHP builds a triangular fuzzy judgment matrix, with confidence ranking to determine index weights and consistency tests to ensure weight rationality. Grey relational theory improves TOPSIS, which integrates Euclidean distance and grey relational degree to form a hybrid closeness index, overcoming traditional TOPSIS’s poor fuzzy data handling. Verified with seven tourist regions in our cases, the method yields indicator weights and final superiority–inferiority rankings. Among the seven evaluated regions, Lijiang Qinghsui (P4) achieves the highest sustainable development level (hybrid closeness: 0.693), while P6 performs the poorest. Among the 13 indicators, Tourism Revenue Contribution is the most important (weight: 0.189) and Tourists’ Cultural Respect Degree (F13) is the least important (weight: 0.015). Compared with traditional TOPSIS, this innovative model quantifies sustainable tourism development levels, offering a scientific basis for regional tourism decision-making. Full article

15 pages, 1040 KB

Open AccessArticle

Source Apportionment of Soil Heavy Metals in Urban Agglomerations Based on the APCS-MLR Model

by Yanjie Zhang, Yunxia Wang, Yuan Zhang, Xinmiao Wang, Min Li and Lei Yang

Sustainability 2025, 17(21), 9798; https://doi.org/10.3390/su17219798 (registering DOI) - 3 Nov 2025

Abstract

In order to study the differential characteristics of heavy metal contamination levels and their sources in soils under various land use types and anthropogenic activities at a regional scale, this study focused on the Beijing–Tianjin–Hebei (BTH) urban agglomeration in North China. We analyzed [...] Read more.

In order to study the differential characteristics of heavy metal contamination levels and their sources in soils under various land use types and anthropogenic activities at a regional scale, this study focused on the Beijing–Tianjin–Hebei (BTH) urban agglomeration in North China. We analyzed heavy metal content in three land use types (urban green spaces, croplands, and vegetable fields/orchards) through field sampling and laboratory analysis, with content determined by inductively coupled plasma mass spectrometry (ICP-MS). The sources of heavy metals were quantitatively apportioned their sources using the absolute principal component score–multiple linear regression (APCS-MLR) method. Results of this study are as follows: (1) Heavy metal content varied among different soil types, with vegetable fields/orchards soils showing relatively higher content. Urban green spaces and cropland soils exhibited comparable heavy metal levels, though urban green spaces displayed higher spatial heterogeneity, while cropland soils showed more homogeneous distributions. (2) The APCS-MLR model identified five pollution sources: mixed traffic–coal combustion sources, industrial sources, agricultural sources, natural sources, and unknown sources. Natural sources were consistently the dominant contributors of arsenic (As), chromium (Cr), and nickel (Ni) across all three land use types, with contribution rates of 32.62–70.26%. Traffic and coal combustion emissions were the primary sources of lead (Pb) and copper (Cu) in urban green spaces, accounting for 40.28–66.26%, while industrial activities showed the highest contributions to zinc (Zn) and cadmium (Cd) in urban green spaces, at 45.88–65.25%. Agricultural activities contributed similarly to Cd accumulation in both cropland and vegetable fields/orchards soils (41.68–51.32%), but their contributions to Cu and Zn in vegetable fields/orchards soils (46.62–55.58%) were significantly higher than those in cropland (9.21–13.40%). Notably, unexplained sources accounted for 18.64–42.59% of heavy metals in vegetable fields/orchards soils, suggesting particularly complex sources in these systems. This study provides a scientific basis for sustainable soil management strategies and promoting coordinated pollution control in urban agglomeration regions. Full article

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28 pages, 2772 KB

Open AccessArticle

Embodied Environmental and Social Impacts: A Regionalised Sectoral Method for Low-Carbon Construction Materials in Italy

by Elisabetta Palumbo and Francesco Pomponi

Sustainability 2025, 17(21), 9797; https://doi.org/10.3390/su17219797 (registering DOI) - 3 Nov 2025

Abstract

The decarbonisation of the built environment has increased reliance on Environmental Life Cycle Assessment (E-LCA) to evaluate the impacts of construction materials. However, social aspects—particularly those affecting workers—remain underexplored. This study presents a regionalised approach to support socially and environmentally informed decision-making in [...] Read more.

The decarbonisation of the built environment has increased reliance on Environmental Life Cycle Assessment (E-LCA) to evaluate the impacts of construction materials. However, social aspects—particularly those affecting workers—remain underexplored. This study presents a regionalised approach to support socially and environmentally informed decision-making in the Italian construction sector. For this purpose, we have integrated worker health and safety indicators into the E-LCA of two representative building products assessed across key life cycle stages. These indicators are incorporated into the evaluation of Global Warming Potential (GWP), thus serving as a decision-support tool during the design phase. From a design perspective, the aim is to promote a broader understanding of sustainability—encompassing both environmental and social dimensions—within building projects. Methodologically, the contribution is twofold. First, it addresses the current gap in context-specific data on the critical indicator of worker health and safety in the construction sector, an essential requirement for robust and scientifically recognised S-LCA studies. To this end, the study develops a regionalised scoring system based on publicly available occupational health and safety data from the Italian National Accident Database (INAIL), disaggregated by sector and region. Second, we propose a framework to combine these social indicators with LCA-based environmental impact metrics, which remain central to building-scale E-LCA. It is clear that no single region performs best, while a critical need for multi-criteria decision-making in sustainable design is evident. Full article

(This article belongs to the Topic Construction Project Management and Infrastructure Sustainability)

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30 pages, 1531 KB

Open AccessReview

Low-Energy Regeneration Technologies for Industrial CO₂ Capture: Advances, Challenges, and Engineering Applications

by Le Ren, Sihong Cheng, Tao Xie, Qianxuan Zhang, Rui Li, Tao Yue and Changqing Cai

Sustainability 2025, 17(21), 9796; https://doi.org/10.3390/su17219796 (registering DOI) - 3 Nov 2025

Abstract

High carbon dioxide (CO₂) emissions from industrial processes have intensified the need for large-scale, sustainable, and low-energy-consumption carbon capture technologies. Amine-based chemical absorption is a promising method for large-scale CO₂ reduction, but it faces challenges like high regeneration energy consumption, [...] Read more.

High carbon dioxide (CO₂) emissions from industrial processes have intensified the need for large-scale, sustainable, and low-energy-consumption carbon capture technologies. Amine-based chemical absorption is a promising method for large-scale CO₂ reduction, but it faces challenges like high regeneration energy consumption, technical limitations, and commercialization difficulties. To reduce energy consumption in regeneration, this paper reviews low-energy regeneration methods, including absorbent optimization, catalytic regeneration, process waste heat recovery, and calcium-based chemical desorption, and explains the energy-saving mechanisms of each approach. Focusing on technical development bottlenecks, this paper provides a comprehensive review of the technical advantages, application limitations, and key challenges associated with various methods. Based on commercialization needs, this paper thoroughly investigates the development process and industrialization status of carbon capture technology in the iron and steel industry. Research has revealed that optimized absorbent designs reduce regeneration heat loads, catalytic acid sites promote proton transfer and lower desorption temperature, utilization of waste heat reduce additional energy consumption, and calcium-based compounds offer both low energy consumption and economic advantages in desorption. This article constructs a theoretical framework for low-energy regeneration technology, identifies innovation priorities, and analyzes scalability challenges and development pathways, providing theoretical support and technical guidance for industrial implementation. Full article

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27 pages, 1220 KB

Open AccessArticle

Optimizing Sustainable Supply Chain Resource Integration: A Multi-Objective Approach Considering Consumer Green Preferences and Policy Interventions

by Yuchen Shao, Hongmin Li and Jianming Yao

Sustainability 2025, 17(21), 9795; https://doi.org/10.3390/su17219795 (registering DOI) - 3 Nov 2025

Abstract

In the context of escalating environmental awareness and the rise of green consumer preferences, enterprises are confronted with the complex challenge of aligning supply and demand while maintaining the quality and long-term value of sustainable supply chain (SSC) resource integration. This study introduces [...] Read more.

In the context of escalating environmental awareness and the rise of green consumer preferences, enterprises are confronted with the complex challenge of aligning supply and demand while maintaining the quality and long-term value of sustainable supply chain (SSC) resource integration. This study introduces consumer green preference as a pivotal moderating factor that influences demand variability across supply chain networks. To address this challenge, a multi-objective optimization model is proposed, designed to simultaneously maximize supply-demand matching utility, enhance the quality of SSC resource integration, and control the cost of the supply chain. The model also incorporates the dual impact of policy interventions on both consumer behavior and enterprise operations, thereby offering a comprehensive framework for improving SSC sustainability. The optimization problem is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which yields Pareto-optimal solutions that balance the competing objectives. A numerical case study is presented to demonstrate the feasibility and practical applicability of the proposed model. This research contributes to the literature by integrating consumer behavior and policy factors into the design of SSCs. Specifically, the numerical case demonstrates that targeted policy interventions can enhance supply-demand matching utility while reducing integration costs, thereby providing actionable insights for organizations aiming to achieve sustainability through enhanced resource integration strategies and long-term value optimization. Full article

(This article belongs to the Special Issue Supply Chain Management in a Sustainable Business Environment)

35 pages, 1059 KB

Open AccessArticle

The Impacts of Green Supply Chain Management and Product Innovation on Marketing Performance in Thailand’s Processed Food Industry

by Kamonthip Parichatnon, Surakiat Parichatnon, Poranee Loatong and Manote Rithinyo

Sustainability 2025, 17(21), 9794; https://doi.org/10.3390/su17219794 (registering DOI) - 3 Nov 2025

Abstract

This research investigates the synergistic relationships between Green Supply Chain Management (GSCM) practices and product innovation in marketing performance and organizational sustainability within Thailand’s processed food industry. Building upon Resource-Based View theory and Stakeholder Theory, this study addresses a critical gap in understanding [...] Read more.

This research investigates the synergistic relationships between Green Supply Chain Management (GSCM) practices and product innovation in marketing performance and organizational sustainability within Thailand’s processed food industry. Building upon Resource-Based View theory and Stakeholder Theory, this study addresses a critical gap in understanding how environmental practices interact with innovation strategies to create sustainable competitive advantages in emerging markets. The research employs a comprehensive mixed-methods approach, integrating qualitative insights from industry expert interviews with quantitative analysis through Structural Equation Modeling (SEM). Primary data were systematically collected from 300 strategically selected enterprises representing small (≤50 employees), medium (51–200 employees), and large-scale (>200 employees) operations across diverse product categories within Thailand’s processed food sector. The analytical framework examines three core GSCM dimensions—green purchasing, green production, and green distribution—alongside three innovation aspects—quality innovation, safety innovation, and sustainability innovation. Eleven hypothesized relationships were rigorously tested to examine direct and indirect effects on marketing performance indicators (sales growth, market share expansion, brand enhancement, customer satisfaction, and cost optimization) and organizational sustainability metrics (environmental impact reduction, regulatory compliance, competitive positioning, and resource efficiency). SEM results revealed that Green Production practices significantly enhance marketing performance (β = 0.16, p < 0.01), demonstrating the strategic value of environmentally responsible production processes in achieving market success. Conversely, Green Distribution exhibited negative effects on both marketing performance (β = −0.106, p < 0.10) and organizational sustainability (β = −0.152, p < 0.05), indicating potential operational trade-offs and infrastructure limitations that require strategic optimization. The model demonstrated excellent fit indices (GFI = 0.929, CFI = 1.000, TLI = 1.000, RMSEA = 0.000, RMR = 0.034), validating the theoretical framework’s robustness. However, modest explanatory power (R² MP = 0.050, R² OS = 0.029) suggests that additional contextual factors, firm-specific capabilities, and market dynamics significantly influence these outcomes, warranting future investigation of mediating and moderating variables. Full article

(This article belongs to the Special Issue Innovation Management and Organizational Performance for Sustainable Future—2nd Edition)

27 pages, 1108 KB

Open AccessArticle

Deepfake-Style AI Tutors in Higher Education: A Mixed-Methods Review and Governance Framework for Sustainable Digital Education

by Hanan Sharif, Amara Atif and Arfan Ali Nagra

Sustainability 2025, 17(21), 9793; https://doi.org/10.3390/su17219793 (registering DOI) - 3 Nov 2025

Abstract

Deepfake-style AI tutors are emerging in online education, offering personalized and multilingual instruction while introducing risks to integrity, privacy, and trust. This study aims to understand their pedagogical potential and governance needs for responsible integration. A PRISMA-guided, systematic review of 42 peer-reviewed studies [...] Read more.

Deepfake-style AI tutors are emerging in online education, offering personalized and multilingual instruction while introducing risks to integrity, privacy, and trust. This study aims to understand their pedagogical potential and governance needs for responsible integration. A PRISMA-guided, systematic review of 42 peer-reviewed studies (2015–early 2025) was conducted from 362 screened records, complemented by semi-structured questionnaires with 12 assistant professors (mean experience = 7 years). Thematic analysis using deductive codes achieved strong inter-coder reliability (κ = 0.81). Four major themes were identified: personalization and engagement, detection challenges and integrity risks, governance and policy gaps, and ethical and societal implications. The results indicate that while deepfake AI tutors enhance engagement, adaptability, and scalability, they also pose risks of impersonation, assessment fraud, and algorithmic bias. Current detection approaches based on pixel-level artifacts, frequency features, and physiological signals remain imperfect. To mitigate these challenges, a four-pillar governance framework is proposed, encompassing Transparency and Disclosure, Data Governance and Privacy, Integrity and Detection, and Ethical Oversight and Accountability, supported by a policy checklist, responsibility matrix, and risk-tier model. Deepfake AI tutors hold promise for expanding access to education, but fairness-aware detection, robust safeguards, and AI literacy initiatives are essential to sustain trust and ensure equitable adoption. These findings not only strengthen the ethical and governance foundations for generative AI in higher education but also contribute to the broader agenda of sustainable digital education. By promoting transparency, fairness, and equitable access, the proposed framework advances the long-term sustainability of learning ecosystems and aligns with the United Nations Sustainable Development Goal 4 (Quality Education) through responsible innovation and institutional resilience. Full article

(This article belongs to the Special Issue Advancing Sustainable Education Through AI and Technological Breakthroughs)

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14 pages, 2680 KB

Open AccessArticle

Coastal Bathing Water Evaluation Under Contrasting Tourism Pressures at Herradura Bay (S-W Mediterranean)

by Miguel María Granados-Fernández, Salvador Arijo, Andreas Reul, Francisco Guerrero, Juan Diego Gilbert, Jorge García-Márquez, Begoña Bautista and María Muñoz

Sustainability 2025, 17(21), 9792; https://doi.org/10.3390/su17219792 - 3 Nov 2025

Abstract

Coastal water quality is crucial for ecosystem services, supporting biodiversity and tourism. However, high tourist influxes often overwhelm wastewater treatment plant (WWTP) capacities, leading to untreated discharge and eutrophication, which severely impacts bathing water. Water quality monitoring is currently limited to selected points [...] Read more.

Coastal water quality is crucial for ecosystem services, supporting biodiversity and tourism. However, high tourist influxes often overwhelm wastewater treatment plant (WWTP) capacities, leading to untreated discharge and eutrophication, which severely impacts bathing water. Water quality monitoring is currently limited to selected points at the beach and oceanographic sampling, which requires depths >20 m offshore, leaving a gap of measurements between 1 and 50 m from the beach. To resolve this gap, our study proposes a low cost-effective sampling and monitoring method by using a kayak with a submersible fluorometer FlowCAM, as well as fecal bacteria detection and quantification. The kayak sampling was carried out during high- and low-tourism seasons in coastal bathing waters surrounded by Marine Protected Areas. The results show a patchy phytoplankton distribution, with chlorophyll a concentration up to 5.5 μg/L, indicating local fertilization. The observed floating organic matter patches were fecal bacteria free, while effluents of the WWTP to the Jate river and shore exceeded the legal limits for bathing water. These results suggest that wastewater treatment was overwhelmed during the high-tourism season, likely discharging wastewater into the river that flows into the shore. These findings are discussed in a sustainable development and socioeconomical context. Full article

(This article belongs to the Special Issue Biodiversity, Biologic Conservation and Ecological Sustainability—2nd Edition)

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18 pages, 585 KB

Open AccessArticle

Does ESG Uncertainty Disrupt Inventory Management? Evidence from an Emerging Market

by Salem Hamad Aldawsari

Sustainability 2025, 17(21), 9791; https://doi.org/10.3390/su17219791 - 3 Nov 2025

Abstract

The growing prominence of environmental, social, and governance (ESG) considerations has introduced new challenges for firms worldwide. While ESG practices are often framed as long-term drivers of competitiveness, uncertainty surrounding their regulatory requirements has created significant operational risks. The primary objective of this [...] Read more.

The growing prominence of environmental, social, and governance (ESG) considerations has introduced new challenges for firms worldwide. While ESG practices are often framed as long-term drivers of competitiveness, uncertainty surrounding their regulatory requirements has created significant operational risks. The primary objective of this study is to examine how ESG uncertainty (ESG) affects inventory management in listed firms. The study analyzed data from Chinese A-share listed companies over the period 2010 to 2024. A series of econometric estimations, including fixed effect models, two-stage least squares (2SLS), and system GMM, were employed to ensure the robustness of the results and to address issues of heteroscedasticity, endogeneity, and dynamic effects. The empirical results consistently revealed that ESG uncertainty exerted a significant negative effect on inventory management. Firms facing greater unpredictability in ESG-related requirements experienced disruptions in supply chain coordination, difficulties in demand forecasting, and inefficiencies in inventory turnover. Beyond this, larger firms and those with higher environmental expenditures exhibited weaker inventory efficiency, while debt ratio, cost of capital, and firm performance were positively associated with improved inventory outcomes. For corporate managers, the study highlighted the importance of embedding sustainability considerations into inventory strategies and adopting flexible procurement systems, predictive analytics, and stronger governance mechanisms. The findings underscored the broader societal need for clarity and stability in ESG regulations. For this, reducing policy unpredictability could enable firms to align sustainability commitments with operational efficiency, thereby improving competitiveness while minimizing waste and resource misallocation. This study was among the first to empirically establish the link between ESG uncertainty and inventory management, bridging the gap between sustainability research and operational efficiency. Full article

22 pages, 5451 KB

Open AccessArticle

Global Multi-Faceted Application and Evaluation of Three Commonly Used NDVI Products for Terrestrial Ecosystem Monitoring

by Qi Liu, Zehao Pan, Ziyue Wang, Jiali Tang, Junda Qiu, Jiaqi Han, Haozhong Zheng and Shijie Li

Sustainability 2025, 17(21), 9790; https://doi.org/10.3390/su17219790 - 3 Nov 2025

Abstract

The Normalized Difference Vegetation Index (NDVI) is a fundamental metric for monitoring terrestrial ecosystem dynamics and assessing ecological responses to climate change. However, uncertainties persist across NDVI products, and a comprehensive assessment of their consistency is lacking. This study conducts a multi-faceted evaluation [...] Read more.

The Normalized Difference Vegetation Index (NDVI) is a fundamental metric for monitoring terrestrial ecosystem dynamics and assessing ecological responses to climate change. However, uncertainties persist across NDVI products, and a comprehensive assessment of their consistency is lacking. This study conducts a multi-faceted evaluation of three NDVI products, GIMMS V1.2 NDVI (NDVI_3g+), PKU GIMMS NDVI (NDVI_pku), and MODIS NDVI (NDVI_mod), to elucidate their performance across ecosystem applications. Our analysis encompasses a comparative analysis of NDVI values, trends, sensitivity to root-zone soil moisture (RSM), and performance in tracking photosynthesis benchmarked against solar-induced chlorophyll fluorescence (SIF). Our results reveal that NDVI_3g+ deviates notably from NDVI_pku and NDVI_mod over cold climates and Evergreen Broadleaf Forest (EBF). Additionally, NDVI_3g+ exhibits significant global browning, in contrast to the significant greening observed for NDVI_pku and NDVI_mod. Although their responses to RSM are generally uncertain, consistent positive responses appear in Drylands, with NDVI_mod showing the highest sensitivity. Additionally, the three NDVI products have high seasonality consistency with SIF, except over EBF, and NDVI_pku and NDVI_3g+ achieve the highest and lowest overall anomaly consistency with SIF, respectively. Furthermore, converting NDVI_3g+, NDVI_pku, and NDVI_mod to the corresponding kernel NDVIs improves seasonality consistency with SIF across 85% of the globe. Full article

(This article belongs to the Special Issue Research on Ecological and Environmental Sustainability Based on Remote Sensing and Geographic Information Systems)

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6 pages, 183 KB

Open AccessEditorial

Application of Remote Sensing and GIS for Promoting Sustainable Geoenvironment

by Hariklia D. Skilodimou, George D. Bathrellos and Konstantinos G. Nikolakopoulos

Sustainability 2025, 17(21), 9789; https://doi.org/10.3390/su17219789 - 3 Nov 2025

Abstract

The continuous growth of Earth’s population poses increasingly complex challenges to the physical environment [...] Full article

(This article belongs to the Special Issue Application of Remote Sensing and GIS for Promoting Sustainable Geoenvironment)

22 pages, 2920 KB

Open AccessArticle

Grassland Tourism Evolves from Quantity- to Quality-Oriented with Lessening Ecological Disturbance: Evidence from Hulunbuir, China

by Lu Han, Boyu Wang, Baohui Dong, Bochuan Zhao, Yuhui Xu and An Chang

Sustainability 2025, 17(21), 9788; https://doi.org/10.3390/su17219788 - 3 Nov 2025

Abstract

Tourism, a key driver of regional economies and perceived “green industry,” faces challenges from irrational resource allocation and spatial overlaps, undermining sustainability. This study examines 825 tourism resources in China’s Hulunbuir Grassland, analyzing spatiotemporal patterns, influencing factors, and ecological impacts using GPP and [...] Read more.

Tourism, a key driver of regional economies and perceived “green industry,” faces challenges from irrational resource allocation and spatial overlaps, undermining sustainability. This study examines 825 tourism resources in China’s Hulunbuir Grassland, analyzing spatiotemporal patterns, influencing factors, and ecological impacts using GPP and NDVI data. Three development phases emerged: essential development, rapid growth, and upgrading. They present a spatial pattern with Hailar and Chen Barag as the center, and multiple other points, mainly affected by ethnic minority population proportions, tourist reception, tourist attraction density, and river networks. Ecological analysis reveals that tourism-induced disturbances cause less environmental stress than other human activities, with grassland NDVI in tourism areas improving during upgrading. However, the NDVI of grasslands under non-tourism disturbance is still superior to that of grasslands under tourism disturbance. The findings emphasize the need for optimized resource allocation and proactive monitoring of tourism’s ecological footprint to advance sustainable grassland tourism. Full article

(This article belongs to the Section Tourism, Culture, and Heritage)

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