Journal Description
Sustainability
Sustainability
is an international, peer-reviewed, open-access journal on environmental, cultural, economic, and social sustainability of human beings, published semimonthly online by MDPI. The Canadian Urban Transit Research & Innovation Consortium (CUTRIC), International Council for Research and Innovation in Building and Construction (CIB) and Urban Land Institute (ULI) are affiliated with Sustainability and their members receive discounts on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE and SSCI (Web of Science), GEOBASE, GeoRef, Inspec, AGRIS, RePEc, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q2 (Environmental Studies) / CiteScore - Q1 (Geography, Planning and Development)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 20 days after submission; acceptance to publication is undertaken in 3.4 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our editors and authors say about Sustainability.
- Companion journals for Sustainability include: World, Sustainable Chemistry, Conservation, Future Transportation, Architecture, Standards and Merits.
Impact Factor:
3.3 (2023);
5-Year Impact Factor:
3.6 (2023)
Latest Articles
Applicability of a Modified Gash Model for Artificial Forests in the Transitional Zone between the Loess Hilly Region and the Mu Us Sandy Land, China
Sustainability 2024, 16(19), 8709; https://doi.org/10.3390/su16198709 (registering DOI) - 9 Oct 2024
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Afforestation in the transitional zone between the loess hilly area and the Mu Us Sandy Land of China has reshaped the landscape and greatly affected eco-hydrological processes. Plantations are crucial for regulating local net rainfall inputs, thus making it necessary to quantify the
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Afforestation in the transitional zone between the loess hilly area and the Mu Us Sandy Land of China has reshaped the landscape and greatly affected eco-hydrological processes. Plantations are crucial for regulating local net rainfall inputs, thus making it necessary to quantify the closure loss of plantation species in drought and semi-arid areas. To quantify and model the canopy interception of these plantations, we conducted rainfall redistribution measurement experiments. Based on this, we used the modified Gash model to simulate their interception losses, and the model applicability across varying rainfall types was further compared and verified. Herein, Caragana korshinskii, Salix psammophila, and Pinus sylvestris plantations in the Kuye River mountain tract were chosen to measure the precipitation distribution from May to October (growing season). The applicability of a modified Gash model for different stands was then evaluated using the assessed data. The results showed that the canopy interception characteristics of each typical plantation were throughfall, interception, and stemflow. The relative error of canopy interception of C. korshinskii simulated by the modified Gash model was 8.79%. The relative error of simulated canopy interception of S. psammophila was 4.19%. The relative error of canopy interception simulation of P. sylvestris was 13.28%, and the modified Gash model had good applicability in the Kuye River Basin. The modified Gash model has the greatest sensitivity to rainfall intensity among the parameters of the C. korshinskii and S. psammophila forest. The sensitivity of P. sylvestris in the modified Gash model is that the canopy cover has the greatest influence, followed by the mean rainfall intensity. Our results provide a scientific basis for the rational use of water resources and vegetation restoration in the transitional zone between the loess hilly region and the Mu Us Sandy Land. This study is of import for the restoration and sustainability of fragile ecosystems in the region.
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Open AccessArticle
Analysis of Industrial Water Use Efficiency Based on SFA–Tobit Panel Model in China
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Han Liu, Heng Liu and Leihua Geng
Sustainability 2024, 16(19), 8708; https://doi.org/10.3390/su16198708 (registering DOI) - 9 Oct 2024
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Over the past two decades, the industrial sector of China has experienced rapid development, which has correspondingly led to a significant increase in water resource consumption. To better understand the dynamics of industrial water use, and formulate appropriate water resource conservation and management
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Over the past two decades, the industrial sector of China has experienced rapid development, which has correspondingly led to a significant increase in water resource consumption. To better understand the dynamics of industrial water use, and formulate appropriate water resource conservation and management policies, it is necessary to evaluate the evolution of industrial water use efficiency and its influencing factors in China. Given the high sensitivity and accuracy of the stochastic frontier analysis (SFA) model for efficiency assessment, the Tobit model is more suitable for regression analyses of truncated data. This study employed the SFA–Tobit panel model to evaluate the industrial water use efficiency of provinces in China from 2003 to 2021. The results indicate that national industrial water use efficiency improved from 0.41 to 0.65 during the study period. All provinces showed significant improvements, with developed provinces exhibiting higher industrial water use efficiency than undeveloped provinces. Regionally, the eastern areas demonstrated superior industrial water use efficiency compared to the western regions, with the central regions having the lowest overall water use efficiency. Moreover, the efficiency gap between regions has been narrowing. The national industrial water-saving potential is estimated at 31.306 billion cubic meters, with Jiangsu province having the highest saving potential at 3.709 billion cubic meters. In comparison, Beijing has the lowest at just 32,000 cubic meters. The Tobit regression results reveal that economic development and technological progress positively contribute to increased industrial water use efficiency. In contrast, water use intensity, openness, and urbanization levels negatively impacted the improvement of industrial water use efficiency. Therefore, it is necessary to increase investment in technological innovation, strictly control industrial water intensity, appropriately balance import and export trade with urbanization levels, and promote sustainable economic development. This study can provide effective support for the subsequent green transformation of China’s industry.
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Open AccessArticle
A Study of Chinese University Students’ English Learning Motivation, Anxiety, Use of English and English Achievement
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Meihua Liu and Ning Du
Sustainability 2024, 16(19), 8707; https://doi.org/10.3390/su16198707 (registering DOI) - 9 Oct 2024
Abstract
Research has found that contact with a second language (L2), L2 motivation and foreign language anxiety are important variables affecting L2 learning. Nevertheless, not much research has explored the relationships among these variables and their interactive effects on L2 learning outcomes. The present
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Research has found that contact with a second language (L2), L2 motivation and foreign language anxiety are important variables affecting L2 learning. Nevertheless, not much research has explored the relationships among these variables and their interactive effects on L2 learning outcomes. The present large-scale survey study examined the relationships between English learning motivation, English classroom anxiety and use of English, as well as their predictive effects on Chinese university students’ English achievement. A total of 439 randomly sampled students from two Chinese universities in Beijing answered the background information questionnaire, the 8-item English Classroom Anxiety Scale and the 35-item English Learning Motivation Questionnaire. The major findings were as follows: (a) English learning motivation, English classroom anxiety and use of English were significantly correlated with one another, and (b) English classroom anxiety, use of English and English learning motivation generally significantly predicted the students’ English achievements. These findings confirm the importance of L2 motivation, foreign language anxiety and contact with the L2 for second language learners. Hence, specific suggestions (e.g., reading and listening to more of a second language, creating a supportive and relaxed L2 classroom environment, etc.) are discussed regarding how to enhance L2 learning motivation, reduce foreign language anxiety and increase contact with the L2 in second language learning, so as to ensure sustainable development in second language learning.
Full article
(This article belongs to the Topic Education and Learning in the Context of the New Normal: Sustainable Development and the Path to Sustainability)
Open AccessArticle
Distributed Secondary Control of DC Microgrid with Power Management Based on Time-of-Use Pricing and Internal Price Rate
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Muhammad Alif Miraj Jabbar, Dat Thanh Tran and Kyeong-Hwa Kim
Sustainability 2024, 16(19), 8705; https://doi.org/10.3390/su16198705 (registering DOI) - 9 Oct 2024
Abstract
This paper presents a novel approach to manage distributed DC microgrids (DCMG) by integrating a time-of-use (ToU) electricity pricing scheme and an internal price rate calculation mechanism. The proposed power-management system is designed to effectively handle uncertainties such as utility grid (UG) availability,
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This paper presents a novel approach to manage distributed DC microgrids (DCMG) by integrating a time-of-use (ToU) electricity pricing scheme and an internal price rate calculation mechanism. The proposed power-management system is designed to effectively handle uncertainties such as utility grid (UG) availability, fluctuating electricity prices, battery state of charge (SOC) levels, and frequent plug-ins and plug-outs of electric vehicles (EVs). Uncertainties in DCMG systems often lead to inefficiencies, power imbalances, and inexact voltage regulation issues within DCMGs. In addition, to maintain the power balance and constant voltage regulation under various operational states, the proposed scheme also incorporates secondary control into the DCMG power-management system. Unlike the existing approaches that often fail to adapt dynamically to changing conditions, the proposed method is the first approach to consider the concept of internal price rate in designing the DCMG power management. To address this challenge, this approach proposes a more resilient power-management strategy to enhance the efficiency and adaptability of DCMG systems. Extensive simulations and experimental validations demonstrate the practicality and adaptability of the proposed control strategy under diverse test conditions, including operation transitions between grid-connected mode (GCM) and islanded mode (IM), low battery SOC condition, operation transition from the current control mode (CCM) to distributed secondary control mode (DSCM), and EV plug-in scenarios. The test results confirm that the proposed method enhances the reliability, efficiency, and economic viability of DCMG systems, making it a promising solution for future smart grid and renewable energy integrations.
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(This article belongs to the Special Issue Smart Grids and Microgrids in Smart Cities: Operation, Control, Protection and Security)
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Open AccessReview
Microplastics and Nanoplastics as Environmental Contaminants of Emerging Concern: Potential Hazards for Human Health
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Rita Khanna, Abhilash Chandra, Shaundeep Sen, Yuri Konyukhov, Erick Fuentes, Igor Burmistrov and Maksim Kravchenko
Sustainability 2024, 16(19), 8704; https://doi.org/10.3390/su16198704 (registering DOI) - 9 Oct 2024
Abstract
With nearly 40% of the total plastics produced being used for packaging, up to five trillion plastic bags are consumed in the world annually. The inadequate disposal of plastic waste and its persistence has become a serious challenge/risk to the environment, health, and
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With nearly 40% of the total plastics produced being used for packaging, up to five trillion plastic bags are consumed in the world annually. The inadequate disposal of plastic waste and its persistence has become a serious challenge/risk to the environment, health, and well-being of living creatures, including humans. The natural degradation of plastics is extremely slow; large pieces of plastic may break down into microplastics (MPs) (1 μm–5 mm) or nanoplastics (NPs) (<1000 nm) after protracted physical, chemical, and/or biological degradations. A brief overview of the transport of micro- and nanoplastics in the aquatic, terrestrial, and atmospheric environments is presented. Details are provided on the exposure routes for these waste materials and their entry into humans and other biota through ingestion, inhalation, and dermal contact. The greatest concern is the cumulative impact of the heterogeneous secondary MPs and NPs on planetary and human health. Inhaled MPs and NPs have been shown to affect the upper respiratory tract, lower respiratory tract, and alveoli; prolonged exposure can lead to chronic inflammatory changes and systemic disease. These can also lead to autoimmune diseases and other chronic health conditions, including atherosclerosis and malignancy. Sustainable mitigation strategies to reduce the impact of MPs/NPs include source reduction, material substitution, filtration and purification, transformation of plastic waste into value-added materials, technological innovations, etc. Multidisciplinary collaborations across the fields of medicine, public health, environmental science, economics, and policy are required to help limit the detrimental effects of widespread MPs and NPs in the environment.
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(This article belongs to the Section Environmental Sustainability and Applications)
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The Path Driving China’s Energy Structure Transformation from the Perspective of Policy Tools
by
Jintao Li, Hui Sun, Long Cheng and Lei Chu
Sustainability 2024, 16(19), 8703; https://doi.org/10.3390/su16198703 (registering DOI) - 9 Oct 2024
Abstract
Combing through Chinese energy-related policy texts and exploring the development path of energy restructuring are significant steps towards a better understanding of the history of energy restructuring in the process of building a moderately prosperous society in all aspects. To explore the various
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Combing through Chinese energy-related policy texts and exploring the development path of energy restructuring are significant steps towards a better understanding of the history of energy restructuring in the process of building a moderately prosperous society in all aspects. To explore the various paths driving the transformation of China’s energy structure, the energy policies promulgated at the national level from 2001 to 2020 in China were systematically compiled. Based on the policy tool perspective, a theoretical framework for influencing China’s energy structure transition was proposed in three dimensions: objectives, tools, and intensity. A total of 39 national energy policy texts were analyzed using a fuzzy set qualitative comparative analysis method. The results show that (1) the consistency levels of individual preconditions were all below 0.9, which does not constitute a necessary condition for the transformation of China’s energy structure; (2) the sufficiency analysis identified three paths of China’s energy structure transformation, which can be described as models driven by “multiple objectives + information”, “single objective + information”, and “single objective”; (3) energy restructuring is a goal-oriented process; the clarity and certainty of the efficient and green objectives of energy policies have a facilitating effect on energy restructuring, and the support of information technology provides an important guarantee for energy restructuring; (4) compared with European and American countries, it was found that China’s energy policy tools were characterized by an emphasis on macro-planning, insufficient connectivity, and marked tendencies in practice.
Full article
(This article belongs to the Special Issue Advancing Carbon Reduction Technology Innovation: Pathways to Global Carbon Neutrality and Sustainable Development)
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Impact of Enterprise Supply Chain Digitalization on Cost of Debt: A Four-Flows Perspective Analysis Using Explainable Machine Learning Methodology
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Hongqin Tang, Jianping Zhu, Nan Li and Weipeng Wu
Sustainability 2024, 16(19), 8702; https://doi.org/10.3390/su16198702 (registering DOI) - 9 Oct 2024
Abstract
Rising costs, complex supply chain management, and stringent regulations have created significant financial burdens on business sustainability, calling for new and rapid strategies to help enterprises transform. Supply chain digitalization (SCD) has emerged as a promising approach in the context of digitalization and
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Rising costs, complex supply chain management, and stringent regulations have created significant financial burdens on business sustainability, calling for new and rapid strategies to help enterprises transform. Supply chain digitalization (SCD) has emerged as a promising approach in the context of digitalization and globalization, with the potential to reduce an enterprise’s debt costs. Developing a strategic framework for SCD that effectively reduces the cost of debt (CoD) has become a key academic challenge, critical for ensuring business sustainability. To this end, under the perspective of four flows, SCD is deconstructed into four distinct features: logistics flow digitalization (LFD), product flow digitalization (PFD), information flow digitalization (IFD), and capital flow digitalization (CFD). To precisely measure the four SCD features and the dependent variable, COD, publicly available data from Chinese listed manufacturing enterprises such as annual report texts and financial statement data are collected, and various data mining technologies are also used to conduct data measurement and data processing. To comprehensively investigate the impact pattern of SCD on CoD, we employed the explainable machine learning methodology for data analysis. This methodology involved in-depth data discussions, cross-validation utilizing a series of machine learning models, and the utilization of Shapley additive explanations (SHAP) to explain the results generated by the models. To conduct sensitivity analysis, permutation feature importance (PFI) and partial dependence plots (PDPs) were also incorporated as supplementary explanatory methods, providing additional insights into the model’s explainability. Through the aforementioned research processes, the following findings are obtained: SCD can play a role in reducing CoD, but the effects of different SCD features are not exactly the same. Among the four SCD features, LFD, PFD, and IFD have the potential to significantly reduce CoD, with PFD having the most substantial impact, followed by LFD and IFD. In contrast, CFD has a relatively weak impact, and its role is challenging to discern. These findings provide significant guidance for enterprises in furthering their digitalization and supply chain development, helping them optimize SCD strategies more accurately to reduce CoD.
Full article
(This article belongs to the Special Issue Sustainable Innovation in Logistics and Supply Chain Management, 2nd Edition)
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Open AccessArticle
Driver Behavior Mechanisms and Conflict Risk Patterns in Tunnel-Interchange Connecting Sections: A Comprehensive Investigation Based on the Behavioral Adaptation Theory
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Chenwei Gu, Xingliang Liu and Nan Mao
Sustainability 2024, 16(19), 8701; https://doi.org/10.3390/su16198701 (registering DOI) - 9 Oct 2024
Abstract
Tunnel-interchange sections are characterized by complex driving tasks and frequent traffic conflicts, posing substantial challenges to overall safety and efficiency. Enhancing safety in these areas is crucial for the sustainability of traffic systems. This study applies behavior adaptation theory as an integrated framework
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Tunnel-interchange sections are characterized by complex driving tasks and frequent traffic conflicts, posing substantial challenges to overall safety and efficiency. Enhancing safety in these areas is crucial for the sustainability of traffic systems. This study applies behavior adaptation theory as an integrated framework to examine the impact of environmental stimuli on driving behavior and conflict risk in small-spaced sections. Through driving simulation, 19 observation indicators are collected, covering eye-tracking, heart rate, subjective workload, driving performance, and conflict risk. The analysis, using single-factor ranking (Shapley Additive Explanation), interaction effects (dependence plots), and multi-factor analysis (Structural Equation Modeling), demonstrates that driving workload and performance dominate the fully mediating effects between external factors and conflict risk. High-load environmental stimuli, such as narrow spacing (≤500 m) and overloaded signage information (>6 units), significantly elevate drivers’ stress responses and impair visual acuity, thereby increasing task difficulty and conflict risk. Critical factors like saccade size, heart rate variability, lane deviation, and headway distance emerge as vital indicators for monitoring and supporting driving decisions. These findings provide valuable insights for the operational management of small-spacing sections and enhance the understanding of driving safety in these areas from a human factor perspective.
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(This article belongs to the Collection Accident Prevention and Risk Management for Safe and Sustainable Transportation)
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Open AccessReview
Empowering Communities to Act for a Change: A Review of the Community Empowerment Programs towards Sustainability and Resilience
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Diana Dushkova and Olga Ivlieva
Sustainability 2024, 16(19), 8700; https://doi.org/10.3390/su16198700 (registering DOI) - 9 Oct 2024
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At the global level, significant efforts have been made to address societal challenges and improve the lives of people and restore the planet’s ecosystems through sustainability and resilience programs. These programs, however, tend to be driven by governments, private sectors, and financial institutions,
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At the global level, significant efforts have been made to address societal challenges and improve the lives of people and restore the planet’s ecosystems through sustainability and resilience programs. These programs, however, tend to be driven by governments, private sectors, and financial institutions, and therefore often lack a process of empowerment to ensure that the local communities can participate actively in co-designing and implementing these programs. More knowledge is needed on how to develop such programs and how the process of empowerment can be organized so that it supports in the long run sustainability transformation. Against this background, the paper explores the role of community empowerment programs as a critical tool for sustainability management strategies and practices. A semi-systematic review of 21 community empowerment programs for sustainability and resilience is conducted. The analysis reveals that the programs mostly aimed to address challenges such as the lack of education and capacity, limited access to basic services and resources, and poor governance and management. The programs initiators involve a diverse set of actors, especially through established partnerships and networks. Most of the programs address the specific needs of vulnerable or marginalized groups or communities. The structure of the programs typically follows a phased methodological approach, beginning with awareness-raising and problem identification, followed by capacity building that allows for making decisions collaboratively and for co-creating innovative solutions based on local knowledge and values. Also, monitoring and evaluation of transformative impact are mentioned as important structural elements. Specifically, the analysis highlights four main focus areas of empowerment: (1) capacity building, (2) self-reliance, control, ownership, responsibility, and independence, (3) participation, engagement, and collective action, and (4) integration of local knowledge and values. However, there is no one-size-fits-all approach to such programs. Instead, successful empowerment programs towards sustainability depend on a deep understanding of local contexts and the ability to tailor strategies to meet specific community needs. The review also identified knowledge gaps that require further investigation to enhance the effectiveness of empowerment programs for both people and nature.
Full article
(This article belongs to the Special Issue Sustainability Management Strategies and Practices—2nd Edition)
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Open AccessArticle
Coupling SWAT and Transformer Models for Enhanced Monthly Streamflow Prediction
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Jiahui Tao, Yicheng Gu, Xin Yin, Junlai Chen, Tianqi Ao and Jianyun Zhang
Sustainability 2024, 16(19), 8699; https://doi.org/10.3390/su16198699 (registering DOI) - 9 Oct 2024
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The establishment of an accurate and reliable predictive model is essential for water resources planning and management. Standalone models, such as physics-based hydrological models or data-driven hydrological models, have their specific applications, strengths, and limitations. In this study, a hybrid model (namely SWAT-Transformer)
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The establishment of an accurate and reliable predictive model is essential for water resources planning and management. Standalone models, such as physics-based hydrological models or data-driven hydrological models, have their specific applications, strengths, and limitations. In this study, a hybrid model (namely SWAT-Transformer) was developed by coupling the physics-based Soil and Water Assessment Tool (SWAT) with the data-driven Transformer to enhance monthly streamflow prediction accuracy. SWAT is first constructed and calibrated, and then its outputs are used as part of the inputs to Transformer. By correcting the prediction errors of SWAT using Transformer, the two models are effectively coupled. Monthly runoff data at Yan’an and Ganguyi stations on Yan River, a first-order tributary of the Yellow River Basin, were used to evaluate the proposed model’s performance. The results indicated that SWAT performed well in predicting high flows but poorly in low flows. In contrast, Transformer was able to capture low-flow period information more accurately and outperformed SWAT overall. SWAT-Transformer could correct the errors of SWAT predictions and overcome the limitations of a single model. By integrating SWAT’s detailed physical process portrayal with Transformer’s powerful time-series analysis, the coupled model significantly improved streamflow prediction accuracy. The proposed models offer more accurate and reliable predictions for optimal water resource management, which is crucial for sustainable economic and societal development.
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Open AccessArticle
The Impact of Rapid Urbanization on the Efficiency of Industrial Green Water Use in Urban Agglomerations around Poyang Lake
by
Huirong Li, Xiaoke Zhao, Xuhui Ding and Runze Zhang
Sustainability 2024, 16(19), 8698; https://doi.org/10.3390/su16198698 (registering DOI) - 9 Oct 2024
Abstract
The construction of urban agglomerations around Poyang Lake is an important starting point of the strategy for the improvement of central China, but the spatial agglomeration of industry and population brings great pressure to the ecological environment. It is of great practical value
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The construction of urban agglomerations around Poyang Lake is an important starting point of the strategy for the improvement of central China, but the spatial agglomeration of industry and population brings great pressure to the ecological environment. It is of great practical value to explore the impact of rapid urbanization on the water use efficiency of important ecological functional areas. Considering the undesired output of industrial production, this paper adopts the SE-SBM model to measure industrial green water use efficiency, comprehensively considers different aspects of urbanization of the urban agglomeration around Poyang Lake, empirically tests its inhibiting or boosting effect on industrial green water use and explores its spatial spillover effect with the help of a spatial metrology model. The results show that (1) the industrial green water use efficiency of urban agglomerations shows an overall upward trend, and the efficiency value of central cities is significantly higher than that of non-central cities and continues to show a state of diffusion; (2) social urbanization, environmental urbanization, and balanced urbanization can significantly improve industrial green water use efficiency, while industrial urbanization or industrialization inhibits the improvement in water use efficiency; (3) considering the spatial spillover factor, there are significant positive local effects between population urbanization and balanced urbanization, and significant positive spatial spillover effects between industrial urbanization and environmental urbanization; (4) the original model can pass the significance test by replacing the output-oriented water use efficiency with the input-oriented or non-oriented water use efficiency; the study area is extended to Jiangxi Province, and the impact of urbanization on industrial water use efficiency is basically consistent. We should adhere to the new type of urbanization that improves well-being and is friendly to the environment, rationally plan the industrial spatial pattern of urban agglomerations, adhere to the ecological and environmental threshold on undertaking industrial transfer, and promote the flow and sharing of green production factors.
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(This article belongs to the Special Issue Sustainable Water Management in Rapid Urbanization)
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Open AccessArticle
Ease of Recycling in Glendale, Salt Lake City, Utah: Dissecting Recycling Efforts by Household Size, Age, Income and Gender
by
Ivis García
Sustainability 2024, 16(19), 8697; https://doi.org/10.3390/su16198697 (registering DOI) - 9 Oct 2024
Abstract
This study investigates the perceived ease of recycling in Glendale, Salt Lake City, Utah, USA, by household size, age, income, and gender. While existing research has broadly explored how sociodemographic factors impact recycling, there is a lack of comprehensive studies analyzing these factors
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This study investigates the perceived ease of recycling in Glendale, Salt Lake City, Utah, USA, by household size, age, income, and gender. While existing research has broadly explored how sociodemographic factors impact recycling, there is a lack of comprehensive studies analyzing these factors within specific local contexts. This study aims to identify specific barriers and motivators across different demographics to enhance local recycling efforts using Glendale as a case study. Data were collected through an online survey of 111 respondents and analyzed using both quantitative and qualitative methods. The survey included questions about the demographic information, perceptions of recycling ease, and barriers to recycling. The analysis revealed that one-person households and young adults (18–35) face constraints such as limited space for recyclables, a lack of access to recycling bins in rental units, or high costs. Older adults (56 years or older) are highly committed but may face physical challenges. Higher-income households report higher participation due to better access and awareness, whereas lower-income households encounter significant barriers such as limited facility access and insufficient information. Gender differences indicate that women are slightly more proactive and committed to recycling compared to men. Recommendations include expanding recycling facilities, targeted educational campaigns, and economic incentives to encourage lower-income households, males, younger, and older adults. Addressing these demographic-specific barriers can improve recycling rates and contribute to more sustainable communities. Future studies should include in-person surveys as one of the limitations of this study is that an online survey format may introduce biases and the exclusion of residents without internet access.
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(This article belongs to the Section Waste and Recycling)
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Urban Flooding Disaster Risk Assessment Utilizing the MaxEnt Model and Game Theory: A Case Study of Changchun, China
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Fanfan Huang, Dan Zhu, Yichen Zhang, Jiquan Zhang, Ning Wang and Zhennan Dong
Sustainability 2024, 16(19), 8696; https://doi.org/10.3390/su16198696 (registering DOI) - 9 Oct 2024
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This research employs the maximum entropy (MaxEnt) model alongside game theory, integrated with an extensive framework of natural disaster risk management theory, to conduct a thorough analysis of the indicator factors related to urban flooding. This study conducts an assessment of the risks
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This research employs the maximum entropy (MaxEnt) model alongside game theory, integrated with an extensive framework of natural disaster risk management theory, to conduct a thorough analysis of the indicator factors related to urban flooding. This study conducts an assessment of the risks associated with urban flooding disasters using Changchun city as a case study. The validation outcomes pertaining to urban flooding hotspots reveal that 88.66% of the identified flooding sites are situated within areas classified as high-risk and very high-risk. This finding is considered to be more reliable and justifiable when contrasted with the 77.73% assessment results derived from the MaxEnt model. Utilizing the methodology of exploratory spatial data analysis (ESDA), this study applies both global and local spatial autocorrelation to investigate the disparities in the spatial patterns of flood risk within Changchun. This study concludes that urban flooding occurs primarily in the city center of Changchun and shows a significant agglomeration effect. The region is economically developed, with a high concentration of buildings and a high percentage of impervious surfaces. The Receiver Operating Characteristic (ROC) curve demonstrates that the MaxEnt model achieves an accuracy of 90.3%. On this basis, the contribution of each indicator is analyzed and ranked using the MaxEnt model. The primary determinants affecting urban flooding in Changchun are identified as impervious surfaces, population density, drainage density, maximum daily precipitation, and the Normalized Difference Vegetation Index (NDVI), with respective contributions of 20.6%, 18.1%, 13.1%, 9.6%, and 8.5%. This research offers a scientific basis for solving the urban flooding problem in Changchun city, as well as a theoretical reference for early warnings for urban disaster, and is conducive to the realization of sustainable urban development.
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Open AccessArticle
Is It Possible to Establish an Economic Trend Correlating Territorial Assessment Indicators and Earth Observation? A Critical Analysis of the Pandemic Impact in an Italian Region
by
Maria Prezioso
Sustainability 2024, 16(19), 8695; https://doi.org/10.3390/su16198695 (registering DOI) - 9 Oct 2024
Abstract
The paper is set within the methodological framework of the Territorial Impact Assessment (TIA) process, which is an instrument designed to facilitate sustainable and cohesive policy-making choices at the European level. The article is developed within the context of a European H2020-RICE cooperative
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The paper is set within the methodological framework of the Territorial Impact Assessment (TIA) process, which is an instrument designed to facilitate sustainable and cohesive policy-making choices at the European level. The article is developed within the context of a European H2020-RICE cooperative project, which utilises the STeMA (Sustainable Territorial Economic/Environmental Management Approach) TIA methodology to investigate the potential relationship between statistical economic indicators, specifically Gross Domestic Product, and related parameters (metadata), and Earth Observation (EO) data. The objective is to provide evidence of socioeconomic trends during the Coronavirus 2019 pandemic in the Lazio Region (Italy), with a particular focus on the metropolitan area of the Rome capital city Rome. In line with the pertinent European context and the scientific literature on the subject, the paper examines the potential for combining classical and Earth observation indicators to assess macroeconomic dimensions of development, specifically in terms of gross domestic product (GDP). The results of the analysis indicate the presence of certain correlations between grey data and EO information. The STeMA-TIA approach allows for the measurement and correlation of both qualitative and quantitative statistical indicators with typological functional areas (in accordance with European Commission-EC and Committee of Ministers responsible for Spatial/Regional Planning—CEMAT guidance) at the NUTS (Nomenclature des unités territoriales statistiques) 2 and 3 levels. This facilitates the territorialisation of information, enabling the indirect comparison of data with satellite data and economic trends. A time series of data was gathered and organised for the purpose of facilitating comparison between different periods, beginning with 2019 and extending to the present day. In order to measure and monitor the evolution of the selected territorial economies (the Lazio Region), a synthetic index (or composite indicator) was developed in the economic and epidemic dimensions. This index combines single values of indicators according to a specific STeMA methodology. It is important to note that there are some critical observations to be made about the impact on GDP, due to the discrepancy between the indicators in the two fields of observation.
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(This article belongs to the Special Issue Development Economics and Sustainable Economic Growth)
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Link between Digital Technologies Adoption and Sustainability Performance: Supply Chain Traceability/Resilience or Circular Economy Practices
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Aylin Duman Altan, Ömer Faruk Beyca and Selim Zaim
Sustainability 2024, 16(19), 8694; https://doi.org/10.3390/su16198694 (registering DOI) - 9 Oct 2024
Abstract
Technological progress and digitalization have ushered in significant transformations in business strategies. At present, research is scarcely focused on the influence of the adoption of digital technologies (DTs) on establishing comprehensive relationships within the context of a circular economy (CE), and the supply
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Technological progress and digitalization have ushered in significant transformations in business strategies. At present, research is scarcely focused on the influence of the adoption of digital technologies (DTs) on establishing comprehensive relationships within the context of a circular economy (CE), and the supply chain (SC) framework to contribute to the Resource-Based View (RBV) theory. This study utilizes survey data collected from 235 manufacturing practitioners employed by Turkish manufacturing enterprises to explore a model elucidating the relationship between DTs adoption and sustainability performance (SP) through supply chain traceability (SCT), supply chain resilience (SCR), and circular economy practices (CEPs), based on 10R strategies. Through this linkage, this research accentuates that the exclusive integration of CEPs with digital technology solutions is insufficient for industrial enterprises to attain their long-term sustainability goals. It underscores the necessity of ensuring SCT and/or SCR in this context.
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(This article belongs to the Special Issue Sustainable Supply Chain Management in Industry 4.0)
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Global Climate Change, Mental Health, and Socio-Economic Stressors: Toward Sustainable Interventions across Regions
by
Peng Nie, Ke Zhao, Dawei Ma, Huo Liu, Saqib Amin and Iftikhar Yasin
Sustainability 2024, 16(19), 8693; https://doi.org/10.3390/su16198693 (registering DOI) - 9 Oct 2024
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Global climate change’s pervasive impacts extend beyond the environment, significantly affecting mental health across diverse regions. This study offers a comprehensive multi-regional analysis spanning Asia, Africa, Oceania, Europe, and the Americas, addressing three critical gaps in existing research: (i) the necessity of a
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Global climate change’s pervasive impacts extend beyond the environment, significantly affecting mental health across diverse regions. This study offers a comprehensive multi-regional analysis spanning Asia, Africa, Oceania, Europe, and the Americas, addressing three critical gaps in existing research: (i) the necessity of a global scope given climate change’s widespread impact, (ii) the under-researched mental health dimension compared to general health effects, and (iii) the integration of climate and mental health data. Using data from 1970 to 2020, we found a strong correlation between climate change and rising mental disorders globally. Regional patterns emerged, with Asia, Africa, and Oceania showing broader associations with various mental health issues, while Europe and the Americas saw increases in anxiety and depression. This study contributes to a more comprehensive understanding of the interconnectedness between climate change, mental health, and sustainability. By addressing the mental health impacts of climate change, we can identify sustainable solutions that promote both environmental well-being and human well-being. Our findings highlight the urgent need for global action to mitigate climate change’s mental health effects and provide insights for tailored interventions and public health strategies. Additionally, socio-economic factors like unemployment, urbanisation, GDP growth, and globalisation are incorporated to explore the intricate interplay between climate change, mental health, and societal contexts, offering a clearer understanding of the mechanisms at play.
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Regional Economic Development, Climate Change, and Work Force in a Gender Perspective in Chile: Insights from the Input–Output Matrix
by
Sergio Soza-Amigo and Jean Pierre Doussoulin
Sustainability 2024, 16(19), 8692; https://doi.org/10.3390/su16198692 (registering DOI) - 9 Oct 2024
Abstract
Most nations fulfilled the commitment to reduce their emissions after the Paris Climate Agreement, and as a result, each nation has produced suitable plans to reach those goals. In this sense, Chile is hardly an exception. The emission multiplier product matrix (eMPM
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Most nations fulfilled the commitment to reduce their emissions after the Paris Climate Agreement, and as a result, each nation has produced suitable plans to reach those goals. In this sense, Chile is hardly an exception. The emission multiplier product matrix (eMPM) and labor multiplier product matrix (lMPM), which are associated with the gender differences in the labor market, a state-of-the-art technique that integrates CO2 emissions with multi-region input–output table (MRIO) databases and elasticity to estimate the pollution caused by inter-industrial activity in the nation’s various north, center, and south regions, are used in this article to analyze the emissions of Chilean industries. This approach, by studying the economic territorial consistency (ETC) issue, is expected to establish a connection between each region’s production structure and interregional relationships between gender and the main industries that produce emissions. Indeed, the study aims to determine which regions foster economic development from an equitable perspective through the ETC study. The ETC in Chile depends on some variables, such as labor force, gender and CO2 emissions. The improvement in terms ofion will depend on the use of technology and the proper state regulation in line with the promises gained by Chile following the convening of COP25.
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(This article belongs to the Special Issue Regional and Local Economic Development and Policies in Sustainable Economy)
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Natural Gas Consumption Forecasting Based on Homoheterogeneous Stacking Ensemble Learning
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Qingqing Wang, Zhengshan Luo and Pengfei Li
Sustainability 2024, 16(19), 8691; https://doi.org/10.3390/su16198691 (registering DOI) - 9 Oct 2024
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Natural gas consumption is an important indicator of energy utilization and demand, and its scientific and high-accuracy prediction plays a key role in energy policy formulation. With the development of deep neural networks and ensemble learning, a homoheterogeneous stacking ensemble learning method is
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Natural gas consumption is an important indicator of energy utilization and demand, and its scientific and high-accuracy prediction plays a key role in energy policy formulation. With the development of deep neural networks and ensemble learning, a homoheterogeneous stacking ensemble learning method is proposed for natural gas consumption forecasting. Firstly, to obtain the potential data characteristics, a nonlinear concave and convex transformation-based data dimension enhancement method is designed. Then, with the aid of a stacking ensemble learning framework, the multiscale autoregressive integrated moving average (ARIMA) and high-order fuzzy cognitive map (HFCM) methods are chosen as the base learner models, while the meta learner model is constructed via a well-designed deep neural network with long short-term memory (LSTM) cells. Finally, with the natural gas energy consumption data of national and 30 provinces (where the data of Xizang are unavailable) of China from 2000 to 2019, the numerical results show the proposed algorithm has a better forecasting performance in accuracy, robustness to noise, and sensitivity to data variations than the seven compared traditional and ensemble methods, and the corresponding model applicability rate could achieve more than 90%.
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The Process of Visionary Leadership Increases Innovative Performance among IT Industry 4.0 for SMEs for Organizational Sustainability: Testing the Moderated Mediation Model
by
Le Wang, Xiu Jin and Jinyoung Jinnie Yoo
Sustainability 2024, 16(19), 8690; https://doi.org/10.3390/su16198690 (registering DOI) - 9 Oct 2024
Abstract
In the era of the fourth industrial revolution, competition among IT industries is becoming increasingly fierce. If a company is weak in its competitive advantage, it can have a huge negative impact on its organizational sustainability and ultimately lead to organizational failure. In
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In the era of the fourth industrial revolution, competition among IT industries is becoming increasingly fierce. If a company is weak in its competitive advantage, it can have a huge negative impact on its organizational sustainability and ultimately lead to organizational failure. In such a research background, we highlighted that employees’ innovative performance via creativity acts as an alternative lubricant to increase the sustainable competitive advantage and promote overall organizational sustainability. In particular, employees’ innovative performance has a great relationship with organizational culture and types of leadership. Hence, we focused on exploring the ways in which visionary leadership increases employees’ innovative performance, presenting and verifying a moderated mediation research model related to the innovative performance. To verify our research model, this study conducted a survey of 303 employees from the information technology (IT) industry of SMEs in China. According to the results, visionary leadership was found to have a positive influence on both psychological empowerment and employees’ innovative performance. In addition, psychological empowerment partially mediated the impact of visionary leadership on the innovation performance. Finally, the organizational learning culture moderated the effects of psychological empowerment and the innovation performance. Overall, this study contributes to revealing the process that leads to employees’ innovative performance and contributes to expanding the research field by verifying the moderated mediation model for a sustainable competitive advantage and organizational sustainability in IT Industry 4.0 for SMEs.
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(This article belongs to the Special Issue Sustainability and Innovation in SMEs)
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The Spatial Spillover Effects of Transportation Infrastructure on Regional Economic Growth—An Empirical Study at the Provincial Level in China
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Fan Yin, Yongsheng Qian, Junwei Zeng and Xu Wei
Sustainability 2024, 16(19), 8689; https://doi.org/10.3390/su16198689 (registering DOI) - 9 Oct 2024
Abstract
This study examines the spatial spillover effects of transportation infrastructure on regional economic growth, utilizing panel data from 31 provincial-level administrative divisions in China from 2003 to 2022. Using the spatial Durbin model (SDM) and three distinct spatial weight matrices—0–1 adjacency, spatial economic–geographical
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This study examines the spatial spillover effects of transportation infrastructure on regional economic growth, utilizing panel data from 31 provincial-level administrative divisions in China from 2003 to 2022. Using the spatial Durbin model (SDM) and three distinct spatial weight matrices—0–1 adjacency, spatial economic–geographical nested, and GDP-based economic distance matrices—this study comprehensively analyzes the multifaceted impacts of transportation infrastructure. The results show that transportation infrastructure significantly promotes economic growth in both local and neighboring regions across all spatial weight matrices. The total effect is most pronounced in geographically proximate regions, with a coefficient of 7.845 (p < 0.01). Regions with similar economic development levels also show strong collaborative effects, with a coefficient of 2.074 (p < 0.01), although the marginal effect of transportation infrastructure diminishes. Furthermore, adjustments in industrial structure and innovation inputs demonstrate a short-term inhibitory effect on economic growth, highlighting the need for synchronized development of transportation infrastructure alongside industrial and innovation policies. This study incorporates multi-dimensional spatial weight matrices to systematically reveal the direct and indirect impacts of transportation infrastructure on regional economies, providing essential empirical support for regional coordination and infrastructure investment policies. The findings offer valuable insights for infrastructure planning in other regions, particularly in formulating policies that promote cross-regional economic cooperation and optimize resource allocation.
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