Resources Environment and Sustainability最新文献

{"title":"What can egocentric network measures contribute to stated preference analyses? An exploration","authors":"Solomon Geleta ,&nbsp;John Janmaat ,&nbsp;John Loomis","doi":"10.1016/j.resenv.2023.100132","DOIUrl":"https://doi.org/10.1016/j.resenv.2023.100132","url":null,"abstract":"<div><p>While economics has long recognized concern for others (e.g. altruism and bequest motives), explicit inclusion of social network metrics in non market valuation models is relatively recent. Social network effects on willingness to pay can propagate through the entire network and bias willingness to pay (WTP) estimates. However, social networks are complex systems of relationships between individuals, and measuring them can be difficult. We explored the potential for egocentric social network (ESN) measures to help explain variations in preference for the status quo. Using simulated random networks, we demonstrate that respondents more likely to choose an alternative to the status quo are part of more dense ESNs. A strong influence of an attitude toward the impact of economic development on the environmental goods and services is consistent with network structure and preference for environmental improvements being jointly determined.</p></div>","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":"14 ","pages":"Article 100132"},"PeriodicalIF":0.0,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49837317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid machine learning-metaheuristic model for sustainable agri-food production and supply chain planning under water scarcity","authors":"Mobina Mousapour Mamoudan ,&nbsp;Ali Jafari ,&nbsp;Zahra Mohammadnazari ,&nbsp;Mohammad Mahdi Nasiri ,&nbsp;Maziar Yazdani","doi":"10.1016/j.resenv.2023.100133","DOIUrl":"10.1016/j.resenv.2023.100133","url":null,"abstract":"<div><p>Agriculture is of great importance in all societies, serving as the fundamental basis for producing food and ensuring the survival of human populations. The process of agricultural production and the associated logistical elements face numerous difficulties, which are further intensified by the worldwide water scarcity resulting from climate change. Nevertheless, the existing body of literature has not sufficiently addressed the consequences of water scarcity on agri-food supply chains. To bridge this research gap and contribute to mitigating the global water crisis induced by climate change, this study proposes a hybrid model that combines optimized neural networks based on meta-heuristic algorithms and mathematical optimization for a sustainable agricultural supply chain. The proposed model integrates particle swarm optimization (PSO) for feature selection and a hybrid convolutional neural network (CNN)-gated recurrent unit (GRU) with a genetic algorithm (GA) optimized structure to predict water consumption. Leveraging the model’s results, a multi-objective sustainable agriculture supply chain model is developed to optimize supply chain profitability while simultaneously addressing environmental pollutants, production waste, food waste, water usage, and manufacturing costs and time. To evaluate the effectiveness of the proposed approach, a real case study in Iran is employed, providing both theoretical and practical insights into the design of agriculture supply chain optimization that incorporates sustainability factors and effectively tackles the growing challenge of water scarcity. The findings of this study hold implications for managers and policymakers in countries where the importance of sustainability is growing. By integrating advanced optimization techniques and predictive models, this research offers a novel framework for enhancing the sustainability of agricultural supply chains and addressing the pressing issues of water scarcity induced by climate change.</p></div>","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":"14 ","pages":"Article 100133"},"PeriodicalIF":0.0,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45028893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining awareness, attitudes and behaviours of stakeholders in Irish Fishing towards plastic","authors":"Stephen Kneel ,&nbsp;Caroline Gilleran Stephens ,&nbsp;Alec Rolston ,&nbsp;Suzanne Linnane","doi":"10.1016/j.resenv.2023.100131","DOIUrl":"https://doi.org/10.1016/j.resenv.2023.100131","url":null,"abstract":"<div><p>This paper explores the awareness, knowledge, attitudes and behaviour of members of the Irish fishing community towards environmental topics such as; microplastics, plastic pollution and recycling. We conducted a mixed method survey consisting of 26 questions (2021) involving members of the Irish fishing community (fishers, aquaculturists etc.). Respondents were generally aware of microplastics and the threats they can pose to different environmental matrices. They noticed litter frequently when engaged in their fishing activities (0% never noticed litter) and in large quantities (35% of respondents noticed over 10+ items) but they were likely (likely 40% and highly likely 35%) to remove it from the environment. Durability was the main reason for the selection of most fishing plastics used by respondents (ranked first in 4 of 5 plastic items) while recyclability played a lesser role. Respondents also viewed plastics as cheap and convenient with these terms accounting for 48% of positive connotations related to the word ‘plastic’, however, in general associated plastic with negative phrases. Barriers to the recycling of used fishing plastics were most frequently identified as being due to a lack of knowledge on how to or a lack of facilities. This study provides novel insight into a previously unstudied cohort in Irish society towards plastics and recycling and can serve as guidance for further work on this group.</p></div>","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":"14 ","pages":"Article 100131"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49837318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining awareness, attitudes and behaviour of stakeholders in Irish Fishing towards plastic","authors":"Stephen Kneel, Caroline Gilleran Stephens, A. Rolston, S. Linnane","doi":"10.1016/j.resenv.2023.100131","DOIUrl":"https://doi.org/10.1016/j.resenv.2023.100131","url":null,"abstract":"","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43177855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-resolution maps of intensive and extensive livestock production in China","authors":"Mingjin Cheng ,&nbsp;Jiawei Quan ,&nbsp;Jiaheng Yin ,&nbsp;Xuewei Liu ,&nbsp;Zengwei Yuan ,&nbsp;Lin Ma","doi":"10.1016/j.resenv.2022.100104","DOIUrl":"10.1016/j.resenv.2022.100104","url":null,"abstract":"<div><p>Reliable and detailed information on livestock distribution is essential for studies of food security, environmental change, and even sustainable development. However, insufficient accuracy and inadequate validation currently remain in high-resolution livestock distribution datasets primarily resulting from using spatially-continuous models and deficient data. This study presents, for the first time to our knowledge, a spatially detailed dataset on intensive (point) and extensive (30”<span><math><mo>×</mo></math></span> 30” grid) livestock production in China (HIEL-China) in 2017 based on an improved model and multi-scale data. Technical validation shows high accuracy in spatial distribution and farm-size simulation. Based on the more reliable depiction of livestock farms, we addressed the obvious underestimation of livestock density in previous datasets, and found different structures of livestock production systems in urban, peri-urban and rural areas. This study accordingly contributes to an essential data basis for livestock-associated analyses targeting at sustainable development of food systems, especially for the largest contributor to global livestock production.</p></div>","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":"12 ","pages":"Article 100104"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48079480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing machine learning models to estimate energy savings from an industrial energy system","authors":"Eva McLaughlin,&nbsp;Jun-Ki Choi","doi":"10.1016/j.resenv.2022.100103","DOIUrl":"10.1016/j.resenv.2022.100103","url":null,"abstract":"<div><p>Energy audits are an important part of reducing energy usage, costs, and carbon emissions, but there have been discrepancies in the quality of audits depending upon the auditor, which can negatively affect the impacts and credibility of the energy assessment. In this paper, historical energy auditing data from a U.S. Department of Energy sponsored research program was gathered and analyzed with a machine-learning algorithm to predict demand savings from a compressed air system assessment recommendation in industrial manufacturing facilities. Different energy auditors calculate savings for repairing leaks in compressed air systems in various ways, so the energy demand savings have been calculated differently throughout the historical assessment recommendations. Machine learning models are utilized in order to enhance the accuracy of the existing practice and reduce variations resulting from the abovementioned discrepancies. A large set of historical assessment recommendation data was used to train five unique machine learning models. Four base learner models and one metalearner model were devised and compared. Results showed that the distributed random forest model best predicted compressed air energy demand savings against the new scenarios within an error of 17%. This indicates that the distributed random forest model can more accurately quantify savings from repairing leaks in compressed air systems. In addition, the results from this study provide insight into the important factors contributing to leaks in the compressed air systems and why it is crucial to repair those leaks regularly to save money and energy while decreasing emissions.</p></div>","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":"12 ","pages":"Article 100103"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43110637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Promoting and maintaining public participation in waste separation policies — A comparative study in Shanghai, China","authors":"Lishan Xiao ,&nbsp;Bo Fu ,&nbsp;Tong Lin ,&nbsp;Liang Meng ,&nbsp;Ouwen Zhang ,&nbsp;Lijie Gao","doi":"10.1016/j.resenv.2023.100112","DOIUrl":"10.1016/j.resenv.2023.100112","url":null,"abstract":"<div><p>Waste management is an evolutionary system, but few studies have explored how and why public willingness to participate in waste separation changes, or explored possible paths to increase residents’ willingness to participate. This paper took Shanghai as a case study of a city that has experienced recent environmental policy change to embrace an evolutionary feedback perspective. The results showed that after the policy implementation, resident’s satisfaction with waste management increased by only 5.3%, and participation willingness actually decreased by 5.4%. A Geodetector model showed that both separation attitude and knowledge are highly important both before and after policy implementation. Residents’ satisfaction with community waste management showed the largest increase soon after the policy implementation while the importance of time occupied by waste separation dropped significantly. Policy simulation by a system dynamics model showed that the community-driven scenario was able to achieve 95% participation 2 years earlier than baseline scenario. Improving community satisfaction by improving waste management infrastructure can offset the adverse effects of an increased number of waste categories. The study helps to quantify the interaction between institutional change and public participation and find effective measures to maintain public participation in environmentally-friendly behaviors.</p></div>","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":"12 ","pages":"Article 100112"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46463813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determining the influence of food user value on the intention to waste tomatoes at home","authors":"Gina Tüfer,&nbsp;Thomas A. Brunner","doi":"10.1016/j.resenv.2023.100111","DOIUrl":"10.1016/j.resenv.2023.100111","url":null,"abstract":"<div><p>To date, there is no evidence on how food user value influences the intention to waste food at home. We experimentally tested the influence of the freshness of tomatoes and them being grown in/on one’s garden/balcony on the intention to waste tomatoes at home (n = 454). We uncovered a significantly lower intention to waste them if they were described as still fresh (versus no longer fresh) and a lower intention to waste them if they were homegrown (versus bought). It did not make a difference whether fresh tomatoes were store-bought or homegrown. However, once the tomatoes were no longer fresh, the purchased tomatoes were much more likely to be thrown away than the homegrown tomatoes.</p></div>","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":"12 ","pages":"Article 100111"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42926848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the Earth system in the Anthropocene","authors":"Christopher E. Ndehedehe","doi":"10.1016/j.resenv.2023.100113","DOIUrl":"10.1016/j.resenv.2023.100113","url":null,"abstract":"","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":"12 ","pages":"Article 100113"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41345261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Formation mechanism of NO2 distribution heterogeneity at different spatial scales","authors":"Hancong Zhu ,&nbsp;Li Yang","doi":"10.1016/j.resenv.2022.100106","DOIUrl":"10.1016/j.resenv.2022.100106","url":null,"abstract":"<div><p>NO₂ is one of the main pollutants in the atmospheric environment, which can directly or indirectly cause harm to human health. Jiangsu province is economically developed and densely populated, and the conflict between economic development and ecological environment protection is more prominent, which causes its mean annual NO₂ concentration to be higher than other neighboring provinces. The study takes the formation mechanism of NO₂ concentration distribution heterogeneity at different spatial scales as the research object. By using OMI satellite remote sensing data and NO₂ concentration ground monitoring data in Jiangsu Province from 2015 to 2020, the paper studies NO₂ concentration distribution heterogeneity at macroscopic and microscopic scales, respectively, to analyze the formation mechanism. The results show that: (1) The spatial distribution characteristics of NO₂ concentration obtained based on the analysis of two data sources have good consistency, with the overall performance of high NO₂ concentration in the south of Jiangsu where the concentration of NO₂ column reached 16.3 × 10¹⁵ molec/cm ² and the monitoring concentration reached <span><math><mrow><mn>46</mn><mo>.</mo><mn>3</mn><mspace></mspace><mi>μ</mi><mi>g</mi></mrow></math></span>/m³, while the concentration of NO₂ in central Jiangsu is relatively low, with average concentrations of 6 × 10¹⁵ molec/cm ² and <span><math><mrow><mn>26</mn><mspace></mspace><mi>μ</mi><mi>g</mi></mrow></math></span>/m³. (2) Under the macroscopic spatial scale, the average NO₂ column concentration in the south of Jiangsu is 9 × 10¹⁵ molec/cm ² higher and the monitoring concentration is <span><math><mrow><mn>15</mn><mspace></mspace><mi>μ</mi><mi>g</mi></mrow></math></span>/m³ than that in the north of Jiangsu; natural factors such as temperature are the important influencing factors for the heterogeneity of NO₂ concentration distribution under this scale. (3) At the microscopic spatial scale, NO₂ concentration is 5–10% higher in industrial concentrations in southern Jiangsu, some heating cities in northern Jiangsu, and industrial parks in northern Jiangsu. In addition, NO₂ concentration decreases gradually with distance from this area; anthropogenic factors such as population density, GDP, and car ownership are important factors to influence the heterogeneity of NO₂ concentration distribution at this scale. Based on the above findings, the paper proposes to adjust the industrial structure, limit the number of high energy-consuming and high-emission enterprises, and develop seasonal emission reduction measures.</p></div>","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":"12 ","pages":"Article 100106"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47751455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}