bluuebunny/crossref_metadata_2025 huggingface dataset:
Influencing Factors for Consumers’ Intention to Reduce Plastic Packaging in Different Groups of Fast-Moving Consumer Goods in Germany
Authors: Siddiqui Shahida Anusha , Profeta Adriano , Decker Thomas , Smetana Sergiy , Menrad Klaus
Published: May 2023

The greatly increased global use of plastic has serious negative environmental consequences. This study aims to analyse the influence of environmental attitudes, personal norms, social norms, and socioeconomic and demographic characteristics on consumers’ intention to reduce plastic packaging in different groups of fast-moving consumer goods (FMCGs) in Germany. Data on plastic packaging use for food, clothing, cosmetics, cleaning goods, and furniture were collected from 299 German families in a four-stage survey. The findings show differing consumer intentions to reduce plastic packaging between food products and other FMCG. In particular, environmental attitudes can differentiate between consumer groups with high and low intentions to reduce plastic packaging in nearly all the product groups analysed, while social norms, gender and age only show statistically significant differences in some product groups. Personal norms did not contribute to group differentiation in any of the groups of FMCG analysed. The results are helpful for managers of FMCGs to develop more ecological packaging solutions in the future and for state authorities to derive political activities in this field.
Capacity and Mechanisms of Pb(II) and Cd(II) Sorption on Five Plant-Based Biochars
Authors: Yu Yan , He Jiangtao , Sun Jingyang , Pei Zixuan , Wu Qidong , Yu Rui
Published: May 2023

China is a large agricultural country that produces a large amount of crop straw every year. Thus, the development of cost-effective and economic application of invasive plants is warranted. Biochars derived from crop straw have been proven to be promising for adsorbent materials. However, less studies have focused on biochar derived from different types of crop straw as adsorbent under the same conditions to compare their adsorption performance. Here, we characterized the five biochars in the same system (600 °C). In results, GBC has higher ash content, pH, CEC, specific surface area, mineral composition and oxygen-containing functional groups. The adsorption kinetics can be explained adequately by the pseudo-second-order model and the Langmuir model, indicating that the adsorption behavior of the biochar is both physical adsorption and chemical adsorption; the adsorption process includes complexation reaction, cationic π bond, ion precipitation and electrostatic adsorption. In conclusion, GBC exhibited higher metal equilibrium adsorption capacities (125 mg·g−1 for Pb2+, 29 mg·g−1 for Cd2+). The solution pH, biochar dosing, pyrolysis temperature and the properties of these heavy metals were responsible for adsorption capacity, thus showing stronger affinity and better adsorption effect. Our results are important for the selection and utilization of plant-based biochar for different heavy metals.
Improved Understanding of Learning Characteristics among International Students in Post-Baccalaureate Medical Education for Sustainable Development
Authors: Hsu Hsiang-Chin , Sung Tzu-Ching
Published: May 2023

Ensuring the success of medical students in post-baccalaureate training is crucial for providing sustainable, high-quality healthcare worldwide. However, international medical students encounter unique sustainability challenges that may affect their ability to learn and excel in medical schools. Therefore, this study aimed to investigate and assess the key attributes of learning styles and attitudes among first- and second-year medical students from a university in Southern Taiwan using the Attitudes Toward Thinking and Learning Survey (ATTLS). In November 2022, a cross-sectional survey and purposive sampling were used to recruit all 43 international medical students. Factor analysis was employed to identify and simplify complex personality traits associated with learning styles and attitudes. The resulting four-factor solution, which accounted for 70.364% of the total variance, was labeled as follows: Factor 1 “Understanding, Interaction Insights, and Empathy”, Factor 2 “Respect, Trust, and an Objective and Rational Vision of Multicultural Diversity”, Factor 3 “Comment, Debate, and Declare One’s Position”, and Factor 4 “Critical Thinking, Logical and Rational Problem Solving, and Rigorous Inference”. This study highlights the significance of connected knowers, who possess both Factor 1 and Factor 2, in providing holistic empathy and multicultural insight for future pedagogy. The four factors identified in this research can serve as a guide for developing teaching strategies that consider students’ diverse learning preferences and needs. By cultivating connected knowers, this research contributes to the sustainable improvement of pedagogical quality and the reduction of non-intellectual challenges in the classroom.
Rural Energy Transition for Cooking in India—Revisiting the Drivers
Authors: Goswami Anandajit , Bandyopadhyay Kaushik Ranjan , Singh Preeti , Gurtu Amulya
Published: May 2023

The recent analysis from IEA (International Energy Agency) on energy transition in India highlights that cooking continues to be the weakest link in the energy transition process for rural households and that rural energy transition of households to cleaner fuel is nonlinear in nature. Several programs have been designed to plague the voids and address this nonlinearity, but the transition to cleaner alternatives for cooking did not happen in the pace it should ideally have. Therefore, an empirical exercise was carried out at a national level to revisit the disconnect between the income growth and energy transition and identify the drivers of the energy transition process in cooking at the national as well as at the subnational state level for a developing country. The paper adds to the current scholarship on drivers of household energy transition by analyzing the relationship between household energy choices and non-income determinants and proves the nonlinearity in energy consumption of rural households of Bihar. Analyzing unit level record from National Sample Survey, an empirical exercise was carried out by using multinomial logit model to identify the potential determining factors at the individual household and group level. The group effect analysis through fixed and random effect has been conducted purposely to understand if social and cultural norms or community level factors within a village society have any effect on the cooking energy transition of rural households and if that offsets the effect of household income in energy transition for cooking. Furthermore, to statistically examine the perceived non-linearity in the consumption of cooking fuel such as firewood by rural households, Brock–Dechert–Scheinkman (BDS) test was conducted for rural households of 38 districts of Bihar. The analysis helps in inferring that subsidy on modern fuel and/or other cooking alternatives alone may not suffice to drive the transition process, but more targeted intervention rooted in the local cultural context in consonance with social and cultural norms or community level factors could be more effective for sustained rural energy transition.
A Review of Sustainable Pillars and their Fulfillment in Agriculture, Aquaculture, and Aquaponic Production
Authors: Schoor Mark , Arenas-Salazar Ana Patricia , Torres-Pacheco Irineo , Guevara-González Ramón Gerardo , Rico-García Enrique
Published: May 2023

Focusing on new food production methods and sustainable pillars’ accomplishments has changed the definition of sustainable pillars themselves. Moreover, some general characteristics of the main pillars can be redefined in separate dimensions to better explain their positive sustainable impacts. Therefore, the main objective of this research is to redefine the sustainable pillars linked to food production and review the most important cultural and technological sustainability impacts they have, in addition to the three classic pillars: economic, social, and environmental sustainability. Cultural and technological sustainability are increasingly important complements to the traditional sustainability concept. Furthermore, new food production technologies and systems are influenced by ancient production methods, as well as by profitable crop selection. Traditional agricultural and aquaculture production in relation to more recent aquaponic production concepts are still a major part of global food security, but the better usage of waste materials or residues generates a more favorable agroecological impact. In conclusion, constantly redefining the sustainable pillars in the context of sustainable food production methods and proving the viability of their general production impacts is important.
A Two-Level Variable Neighborhood Descent for a Split Delivery Clustered Vehicle Routing Problem with Soft Cluster Conflicts and Customer-Related Costs
Authors: Xu Rui , Huang Yumiao , Xiao Wei
Published: May 2023

This paper introduces Split Delivery Clustered Vehicle Routing Problem with Soft cluster conflicts and Customer-related costs (SDCVRPSC) arising in automotive parts of milk-run logistics with supplier cluster distribution in China. In SDCVRPSC, customers are divided into different clusters that can be visited by multiple vehicles, but each vehicle can only visit each cluster once. Penalty costs are incurred when traveling between clusters. The transportation cost of a route is calculated as the maximum direct shipment cost between customers on the route plus the total drop costs. The SDCVRPSC aims to minimize the sum of transportation costs and penalty costs by determining the assignment of customers to vehicles and the visiting order of clusters. We propose an integer linear programming model and a two-level variable neighborhood descent algorithm (TLVND) that includes two-stage construction, intensification at cluster and customer levels, and a perturbation mechanism. Experimental results on designed SDCVRPSC benchmark instances demonstrate that TLVND outperforms the Gurobi solver and two adapted algorithms at the business operation level. Moreover, a real case study indicates that TLVND can bring significant economic savings compared to expert experience decisions. TLVND has been integrated into the decision support system of the case company for daily operations.
Climate Mitigation Strategies: The Use of Cool Pavements
Authors: Giorio Martina , Paparella Rossana
Published: May 2023

Recent statistical reports highlight an accelerating phenomenon of radical urbanization, and the forecasts estimate that within the next ten years, about 70% of the world’s population will be located in urban areas, compared with 54% currently living there. This phenomenon will result in an increase in the constructed volume, with foreseeable adverse effects on the climate, the environment, and residents’ health. The resulting growth of the emissions and the use of energy resources, combined with changes in the soil condition and absorption characteristics, leads to a focus on issues related to the sustainable development of cities. The effects of anthropogenic activity influence the materials’ surface and air temperatures, contributing to the phenomenon of the increase of the average atmospheric temperatures near the earth’s surface, with the consequent generation of the phenomenon of the urban heat islands (UHIs). This paper aims to examine, in a neighborhood context, the effects of mitigation strategies implemented through the application of cool materials on urban surfaces. Through simulations carried out in a case study, with the support of software such as ENVI-met and tools for Grasshopper such as Ladybug, and with the observation of data related to the evolution of the surface temperatures, the air temperatures, and other microclimatic parameters, the outcomes obtained with the use of cool pavements were analyzed. Finally, the comparison between the two scenarios, the current and the projected, allowed the evaluation of the overall efficiency of the proposed interventions.
AI-Enabled Energy Policy for a Sustainable Future
Authors: Danish Mir Sayed Shah , Senjyu Tomonobu
Published: May 2023

The present time is a seminal decade for the transition of the energy sector through the deployment of green energy and the optimization of efficiencies using the power of automation and artificial intelligence (AI), which demands competitive policies to handle multidimensional endeavors via a single platform. The failure of energy policies can have far-reaching socioeconomic consequences when policies do not meet the energy and climate goals throughout the lifecycle of the policy. Such shortcomings are reported to be due to inadequate incentives and poor decision making that needs to promote fairness, equality, equity, and inclusiveness in energy policies and project decision making. The integration of AI in energy sectors poses various challenges that this study aims to analyze through a comprehensive examination of energy policy processes. The study focuses on (1) the decision-making process during the development stage, (2) the implementation management process for the execution stage, (3) the integration of data science, machine learning, and deep learning in energy systems, and (4) the requirements of energy systems in the context of substantiality. Synergistically, an emerging blueprint of policy, data science and AI, engineering practices, management process, business models, and social approaches that provides a multilateral design and implementation reference is propounded. Finally, a novel framework is developed to develop and implement modern energy policies that minimize risks, promote successful implementation, and advance society’s journey towards net zero and carbon neutral objectives.
Geothermal Exploration Using Remote Sensing, Surface Temperature, and Geophysical Data in Lunayyir Volcanic Field, Saudi Arabia
Authors: Alqahtani Faisal , Aboud Essam , Ehsan Muhsan , Naseer Zohaib , Abdulfarraj Murad , Abdelwahed Mohamed F. , El-Masry Nabil
Published: May 2023

Energy consumption worldwide has undergone a recent shift, with hydrocarbons, coal, and new energy sources taking center stage. However, fossil fuels face criticism due to their negative impacts on the environment through air pollution. Geothermal energy is a clean, renewable, and eco-friendly alternative that can be harnessed for power generation. The Lunayyir volcanic field is located in the western part of Saudi Arabia and has been identified as a potential geothermal resource. In this study, a comprehensive analysis of the gravity, magnetic, and land surface temperature data collected over the study area is conducted to investigate the geothermal potential. The gravity data are processed using standard techniques to correct for instrument drift, diurnal variations, and topographic effects and to filter out high-frequency noise. The magnetic data are processed using standard procedures. Analysis of the gravity data shows that the Lunayyir volcanic field is characterized by a series of gravity anomalies, which can be interpreted as indicative of the presence of subsurface geologic structures that are similar in geothermal areas, such as faults, fractures, and intrusions. The magnetic data, on the other hand, show that the volcanic field has a relatively low magnetic susceptibility, which is consistent with the presence of volcanic rocks implying shallow heat sources. Landsat satellite data are utilized to calculate the land surface temperature through the use of GIS software. Additionally, the normalized difference vegetation index (NDVI) is calculated using the near-infrared and red bands of the Landsat satellite. The land surface temperature of the year 2007 shows the relatively high temperature at the surface as compared to 2000 and 2021, which is an indication of volcanic activities in the subsurface. It is suggested that these structures may provide conduits for hot fluids, which could potentially be exploited for geothermal energy production. The obtained results emphasize the importance of combining different geophysical datasets to obtain a better understanding of underground geology and geothermal systems. In general, this study improves the assessment of the Lunayyir volcanic field in the western part of Saudi Arabia for geothermal energy.
Techno-Economic Analysis of a Hydrogen-Based Power Supply Backup System for Tertiary Sector Buildings: A Case Study in Greece
Authors: Tziritas Dimitrios , Stavrakakis George M. , Bakirtzis Dimitris , Kaplanis George , Patlitzianas Konstantinos , Damasiotis Markos , Zervas Panagiotis L.
Published: May 2023

In view of the European Union’s strategy on hydrogen for decarbonization and buildings’ decarbonization targets, the use of hydrogen in buildings is expected in the future. Backup power in buildings is usually provided with diesel generators (DGs). In this study, the use of a hydrogen fuel cell (HFC) power supply backup system is studied. Its operation is compared to a DG and a techno-economic analysis of the latter’s replacement with an HFC is conducted by calculating relevant key performance indicators (KPIs). The developed approach is presented in a case study on a school building in Greece. Based on the school’s electricity loads, which are calculated with a dynamic energy simulation and power shortages scenarios, the backup system’s characteristics are defined, and the relevant KPIs are calculated. It was found that the HFC system can reduce the annual CO2 emissions by up to 400 kg and has a lower annual operation cost than a DG. However, due to its high investment cost, its levelized cost of electricity is higher, and the replacement of an existing DG is unviable in the current market situation. The techno-economic study reveals that subsidies of around 58–89% are required to foster the deployment of HFC backup systems in buildings.
Managing Disruptions in a Biomass Supply Chain: A Decision Support System Based on Simulation/Optimisation
Authors: Piqueiro Henrique , Gomes Reinaldo , Santos Romão , de Sousa Jorge Pinho
Published: May 2023

To design and deploy their supply chains, companies must naturally take quite different decisions, some being strategic or tactical, and others of an operational nature. This work resulted in a decision support system for optimising a biomass supply chain in Portugal, allowing a more efficient operations management, and enhancing the design process. Uncertainty and variability in the biomass supply chain is a critical issue that needs to be considered in the production planning of bioenergy plants. A simulation/optimisation framework was developed to support decision-making, by combining plans generated by a resource allocation optimisation model with the simulation of disruptive wildfire scenarios in the forest biomass supply chain. Different scenarios have been generated to address uncertainty and variability in the quantity and quality of raw materials in the different supply nodes. Computational results show that this simulation/optimisation approach can have a significant impact in the operations efficiency, particularly when disruptions occur closer to the end of the planning horizon. The approach seems to be easily scalable and easy to extend to other sectors.
Analysis of Multi-Temporal Shoreline Changes Due to a Harbor Using Remote Sensing Data and GIS Techniques
Authors: Zoysa Sanjana , Basnayake Vindhya , Samarasinghe Jayanga T. , Gunathilake Miyuru B. , Kantamaneni Komali , Muttil Nitin , Pawar Uttam , Rathnayake Upaka
Published: May 2023

Coastal landforms are continuously shaped by natural and human-induced forces, exacerbating the associated coastal hazards and risks. Changes in the shoreline are a critical concern for sustainable coastal zone management. However, a limited amount of research has been carried out on the coastal belt of Sri Lanka. Thus, this study investigates the spatiotemporal evolution of the shoreline dynamics on the Oluvil coastline in the Ampara district in Sri Lanka for a two-decade period from 1991 to 2021, where the economically significant Oluvil Harbor exists by utilizing remote sensing and geographic information system (GIS) techniques. Shorelines for each year were delineated using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager images. The Normalized Difference Water Index (NDWI) was applied as a spectral value index approach to differentiate land masses from water bodies. Subsequently, the Digital Shoreline Analysis System (DSAS) tool was used to assess shoreline changes, including Shoreline Change Envelope (SCE), Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR). The results reveal that the Oluvil coast has undergone both accretion and erosion over the years, primarily due to harbor construction. The highest SCE values were calculated within the Oluvil harbor region, reaching 523.8 m. The highest NSM ranges were recorded as −317.1 to −81.3 m in the Oluvil area and 156.3–317.5 m in the harbor and its closest point in the southern direction. The maximum rate of EPR was observed to range from 3 m/year to 10.7 m/year towards the south of the harbor, and from −10.7 m/year to −3.0 m/year towards the north of the harbor. The results of the LRR analysis revealed that the rates of erosion anomaly range from −3 m/year to −10 m/year towards the north of the harbor, while the beach advances at a rate of 3 m/year to 14.3 m/year towards the south of the harbor. The study area has undergone erosion of 40 ha and accretion of 84.44 ha. These findings can serve as valuable input data for sustainable coastal zone management along the Oluvil coast in Sri Lanka, safeguarding the coastal habitats by mitigating further anthropogenic vulnerabilities.
Influence of Parameters in SDM Application on Citrus Presence in Mediterranean Area
Authors: Catalano Giuseppe Antonio , D’Urso Provvidenza Rita , Maci Federico , Arcidiacono Claudia
Published: May 2023

Within the context of Agriculture 4.0, the importance of predicting species distribution is increasing due to climatic change. The use of predictive species distribution models represents an essential tool for land planning and resource conservation. However, studies in the literature on Suitability Distribution Models (SDMs) under specific conditions are required to optimize the model accuracy in a specific context through map inspection and sensitivity analyses. The aim of this study was to optimize the simulation of the citrus distribution probability in a Mediterranean area based on presence data and a random background sample, in relation to several predictors. It was hypothesized that different parameter settings affected the SDM. The objectives were to compare different parameter settings and assess the effect of the number of input points related to species presence. Simulation of citrus occurrence was based on five algorithms: Boosted Regression Tree (BRT), Generalized Linear Model (GLM), Multivariate Adaptive Regression Splines (MARS), Maximum Entropy (MaxEnt), and Random Forest (RF). The predictors were categorized based on 19 bioclimatic variables, terrain elevation (represented by a Digital Terrain Model), soil physical properties, and irrigation. Sensitivity analysis was carried out by (a) modifying the values of the main models’ parameters; and (b) reducing the input presence points. Fine-tuning the parameters for each model according to the literature in the field produced variations in the selection of predictors. Consequently, probability changed in the maps and values of the accuracy measures modified. Results obtained by using refined parameters showed a reduced overfitting for BRT, yet associated with a decrease in the AUC value from 0.91 to 0.81; minor variations in AUC for GLM (equal to about 0.85) and MARS (about 0.83); a slight AUC reduction for MaxEnt (from 0.86 to 0.85); a slight AUC increase for RF (from 0.88 to 0.89). The reduction in presence points produced a decrease in the surface area for citrus probability of presence in all the models. Therefore, for the case study analyzed, it is suggested to keep input presence points above 250. In these simulations, we also analyzed which covariates and related ranges contributed most to the predicted value of citrus presence, for this case study, for different amounts of input presence points. In RF simulations, for 250 points, isothermality was one of the major predictors of citrus probability of presence (up to 0.8), while at increasing of the input points the contribution of the covariates was more uniform (0.4–0.6) in their range of variation.
How Do Tourism Stakeholders Support Sustainable Tourism Development: The Case of Iran
Authors: Wu Xiaoyuan , Hashemi Shiva , Yao Yiyue , Kiumarsi Shaian , Liu Danping , Tang Jinquan
Published: May 2023

The aim of this paper is to empirically examine the effect of emotional solidarity, stakeholders’ attitude, stakeholders’ commitment, perceived economic benefit, and cost on the sustainable tourism development in the Iranian tourism sector. Data were collected from surveying 258 Iranian stakeholders. The analysis was completed by using Partial Least Squares—Structural Equation Modeling (PLS-SEM). The findings show that there is a direct effect of emotional solidarity and stakeholders’ attitude on perceived economic benefit. Stakeholders’ attitude and commitment influence perceived cost, and perceived economic benefits and sustainable tourism development were highly associated. Moreover, perceived economic benefit plays the mediator role between emotional solidarity, stakeholders’ attitude, and supports sustainable tourism development. This study makes significant contributions to the body of tourism literature by confirming the link between emotional solidarity, stakeholders’ attitude, stakeholders’ commitment, perceived economic benefit and cost on support in sustainable tourism development. Furthermore, this study offers several practical implications for local authorities and tourism policies aiming to improve support and engagement in tourism planning for aiding sustainable tourism development in Iran.
Elements of Food Service Design for Low-Carbon Tourism-Based on Dine-In Tourist Behavior and Attitudes in China
Authors: Lai Yingjie , Yoo Chaemoon , Zhou Xiaomin , Pan Younghwan
Published: May 2023

One of the key issues in sustainable tourism research is the gap between tourists’ expressed friendly attitudes towards sustainable behaviors and their actual behaviors. Although many “low-carbon” themed restaurants have emerged during the low-carbon transformation of the Chinese tourism industry, low-carbon food services have not been significantly improved. This study takes food as the entry point to explore tourists’ behavior and attitudes towards low-carbon tourism in relation to food. We conducted two interviews. The first interview was a semi-structured contextual interview with 120 tourists who had experiences in food streets, aiming to identify the core user group: low-carbon attitude-friendly tourists with high-carbon food behaviors. The second interviews was an in-depth interview based on grounded theory with 29 core users, analyzing the four main reasons for their high-carbon food behaviors and their requirements for low-carbon food services in tourism. Based on this, we extracted four design elements for low-carbon tourism food services: low-carbon information show service, low-carbon service product attractiveness improvement, low-carbon food environment atmosphere creation, and service providers’ low-carbon behaviors. Through these four service elements, we constructed a low-carbon tourism food service design framework based on the core users’ needs, discussed the mechanism of service elements, and provided service design suggestions accordingly. The research results can be helpful for tourism providers, low-carbon tourism researchers, and designers.
Coherence Analysis of National Maritime Policy of Pakistan across Shipping Sector Governance Framework in the Context of Sustainability
Authors: Aslam Bushra , Iqbal Kanwar Muhammad Javed , Akhtar Nadia , Mottaeva Angela Bahauovna , Fedorovna Turutina Tatyana , Barykin Sergey , Khan Muhammad Irfan
Published: May 2023

Sustainable maritime development needs coherent governance, particularly for the shipping sector, to ensure a stable maritime economy. However, maritime governance in Pakistan is considered inadequate and incoherent and thus poses a serious challenge to sustainability. The National Maritime Policy (NMP) was launched two decades ago, in 2002, to provide an overarching governance framework for long-term benefits, but it is now outdated. This study aimed at analyzing the extent to which the NMP of 2002 is coherent with provincial, national, and international obligations and addresses emerging requirements for the development of a sustainable shipping sector in Pakistan. An analytical framework based on three (03) guiding principles, seven (07) criteria, and twenty-eight (28) indicators was employed using a combination of qualitative and quantitative methods. Coherence across twenty-one (21) governing instruments was analyzed, scored (0–5), and rated on a ratio scale to represent the coherence index for each indicator. Thus, a matrix representing the coherence index score for each indicator individually and in pairwise comparison with the NMP was developed. An overall coherence index was calculated as 2.1, which shows poor coherence across governing instruments. This indicates that the NMP 2002 is unable to address emerging governance needs for sustainability issues such as climate response, pollution control, ballast water management, ecosystem conservation, social well-being, stakeholder engagement, and economic diversification, thus requiring revision to meet the challenges created by incoherent governing instruments at all levels and among segments of the maritime economy.
Predictive and Prescriptive Analytics in Identifying Opportunities for Improving Sustainable Manufacturing
Authors: Relich Marcin
Published: May 2023

Environmental issues and sustainability performance are more and more significant in today’s business world. A growing number of manufacturing companies are searching for changes to improve their sustainability in the areas of products and manufacturing processes. These changes should be introduced in the design process and affect the whole product life cycle. This paper is concerned with developing a method based on predictive and prescriptive analytics to identify opportunities for increasing sustainable manufacturing through changes incorporated at the product design stage. Predictive analytics uses parametric models obtained from regression analysis and artificial neural networks in order to predict sustainability performance. In turn, prescriptive analytics refers to the identification of opportunities for improving sustainability performance in manufacturing, and it is based on a constraint programming implemented within a constraint satisfaction problem (CSP). The specification of sustainability performance in terms of a CSP provides a pertinent framework for identifying all admissible solutions (if there are any) of the considered problem. The identified opportunities for improving sustainability performance are dedicated to specialists in product development, and aim to reduce both resources used in manufacturing and negative effects on the environment. The applicability of the proposed method is illustrated through reducing the number of defective products in manufacturing.
A Framework to Design and Evaluate Green Contract Mechanisms for Forestry Supply Chains
Authors: Teymourifar Aydin , Trindade Maria A. M.
Published: May 2023

Green contracting mechanisms are utilized to integrate sustainable and environmentally protective goals into business objectives. This study proposes a framework for the design of green contract mechanisms in forestry supply chain management. We assumed that there was an applicant for harvesting timber in a forest, and that the owner tried to evaluate different scenarios to design a green contracting mechanism. We also assumed that the owner of a forest cared about green goals, such as carbon dioxide absorption, in the forest. We regarded the interests of the parties, such as the profit of the applicant as well as the green goals of the forest owner. We used multi-attribute decision-making techniques such as the weighted sum, normalized weighted sum, TOPSIS, and VIKOR to evaluate the various scenarios. In the literature, another approach was taken to solve a similar problem based on multi-objective techniques and the Pareto optimality concept. We compared the outcomes of the determined framework with the outputs of the previously employed methods. The recommended framework can provide more interpretable results since it considers the interests of different sides. The framework can assist businesses in designing contracts that promote sustainable operations and support compliance with the United Nations’ Sustainable Development Goals.
Evaluating Environmental Impact of Natural and Synthetic Fibers: A Life Cycle Assessment Approach
Authors: Gonzalez Victoria , Lou Xingqiu , Chi Ting
Published: May 2023

This research aims to analyze the environmental impact of six fibers in the textile industry: conventional and organic cotton, silk, jute, flax, and polyester. The study used a life cycle assessment (LCA) methodology with a cradle-to-gate system boundary and analyzed the stages of agriculture, spinning, weaving, and dyeing. In agriculture production, five impact categories (i.e., fossil resource scarcity, global warming, land use, terrestrial ecotoxicity, and water consumption) have the most significant differences across these fibers. Polyester production significantly impacted the terrestrial ecotoxicity impact category, while stratospheric ozone depletion had a minor impact. In yarn preparation and spinning, silk has the most significant impact in most categories, followed by conventional cotton, while jute had the most minimal impact. In weaving, the most visible differences were in fossil resource scarcity, global warming, land use, terrestrial ecotoxicity, and water consumption. Conventional cotton dyeing showed significant impacts on global warming potential and terrestrial ecotoxicity. This study contributes to the limited literature on existing LCA research in the textile industry. Adding updated information will help increase the comprehension of LCA research and guide stakeholders in transitioning fashion supply chains more sustainably.
Study on the Spatial and Temporal Evolution of the Ecological Environmental Quality in Linghekou Wetland
Authors: Cheng Qian , Wang Tieliang , Chen Fujiang
Published: May 2023

Coastal wetlands are located in the overland area between land and sea and play an important ecological role, but social and economic development and the acceleration of urbanization have resulted in the degradation of the ecological functions of coastal wetlands and serious pollution within the wetlands. The study of the spatial and temporal changes in the ecological environmental quality of coastal wetlands can suggest feasible response strategies for the ecological construction of coastal wetlands. This study uses RS and GIS technology, based on the PSR model, AHP method and InVEST model, to study the spatial and temporal changes in the ecological environmental quality of the Linghekou wetland and to propose early warning on future ecological and environmental conditions. The results show the following: (1) The natural wetland area decreased, the landscape fragmentation index increased and the wetland landscape showed a degradation trend between 2005 and 2020. (2) The composite indices for the evaluation of the ecological environmental quality of the Linghekou wetland in 2005, 2010, 2015 and 2020 were 0.473, 0.380, 0.353 and 0.378, respectively, with the ecological environmental quality declining from a sub-healthy state in 2005 to a sub-sick state in 2020, with increasing interference from human activities, increasing differentiation of the internal organization of the wetlands and degradation of ecological services. (3) The habitat quality of the Linghekou wetland has improved since 2015 due to the implementation of local conservation measures, but the habitats are still under strong pressure from anthropogenic disturbance. (4) The predicted results for 2025 and 2030 show that the ecological environmental quality of the Linghekou wetland will continue to deteriorate in the future, especially in the northwestern and central-eastern parts of the study area, where anthropogenic disturbance will continue to intensify and habitat degradation will become more severe in the future. This study provides a scientific reference for coastal wetland management and ecological construction, and also enriches the research results on coastal wetlands in the field of ecological and environmental assessment.
Trends and Causes of Regional Income Inequality in China
Authors: Yan Xiao , Mohd Saidatulakmal
Published: May 2023

Although China has been successful in reducing national income inequality over the past decade, regional income inequality shows a fluctuating trend. The pre-tax income shares of the top 10% and 1% have grown since 1978 and reached about 40% and 15% of the total income share in 2015. Meanwhile, the pre-tax income shares of the bottom 50% have been falling, having dropped from one-quarter of the total income share to less than that of the top 1% in 2015. With this background, this study investigated the trends of income inequality from 2000 to 2020 in west, central, northeast, and east regions in China and analysed their influence factors. Income data from 271 prefecture-level cities in mainland China between 2010 and 2019 were used to calculate the Theil index in each province and region. The analysis was segregated based on urban and rural areas in four regions: east, northeast, central, and west. The Theil index indicated that the income inequality of different regions in China showed a declining trend in rural areas, and a declining and then increasing trend in urban areas. Furthermore, local economic development has a positive impact on income inequality, whereas the urbanization rate and fiscal spending rate have negative impacts on income inequality.
Effects of Urban Planning on Ecosystem Services: The Umbria Region Seismic Crater
Authors: Cattani Chiara , Montaldi Cristina , Di Pietro Gianni , Zullo Francesco
Published: May 2023

The earthquake in central Italy in 2016 led to a profound urban and natural landscape transformation. The role of territorial planning in this kind of situation is extremely important because it allows the orientation of future settlement choices through appropriate interpretative keys of the existing territorial dynamics. This work aims to analyze the effects of the planning choices made in the post-earthquake period in the seismic crater municipalities of the Umbria region. Using the InVEST models, these studies regard the comparison of the effects of in-force plans on ecosystem services such as habitat quality and carbon storage. The data about the mosaic of the municipal urban planning tools are derived from specific actions produced under two LIFE projects (SUNLIFE and IMAGINE). The comparison makes it possible to identify how and to what extent the transformative scenarios, linked to the new condition, change the spatial planning compared to the previous one and the effects on the provision of ecosystem services. The knowledge of the latter aspect allows optimization of the methods of urban transformation that will be implemented. Moreover, this process of optimizing the provision of essential ecosystem services could certainly play a key role in the enhancement and economic recovery of these areas.
How Does the Degree of Competition in an Industry Affect a Company’s Environmental Management and Performance?
Authors: Chang Yu Jin , Yoo Jae Wook
Published: May 2023

Environmental management, which was recognized as a functional part of corporate management, has recently been recognized as a strategic element of all business activities. It can be defined as a series of management activities to improve environmental performance throughout the business process and simultaneously achieve profitability and sustainability. Accordingly, businesses are now abandoning the existing management philosophy that economic and environmental feasibility are inevitably in conflict and establishing strategies and methods to achieve both. Meanwhile, industry affects the business performance of individual companies. Since the performance of a company tends to be influenced by the intensity of competition in an industry, it is necessary to analyze the structural factors that determine the intensity of competition in an industry in order to predict the future performance of a company. Therefore, we studied how environmental management affects corporate performance given the level of industry competition. The results are as follows. First, environmental management positively impacts corporate performance. Second, a high level of competition within an industry moderates the relationship between environmental management and corporate performance. By verifying the influence of the industry to which a company belongs, that is, the level of competition within the industry, we confirm that a company’s environmental management can be used strategically to gain a competitive advantage. With the finding that the impact of a company’s environmental practices differs by industry in line with the degree of competition, we expect this study to be helpful for future research into strategic ESG activities.
Modeling of Predictive Maintenance Systems for Laser-Welders in Continuous Galvanizing Lines Based on Machine Learning with Welder Control Data
Authors: Choi Jin-Seong , Choi So-Won , Lee Eul-Bum
Published: May 2023

This study aimed to develop a predictive maintenance model using machine learning (ML) techniques to automatically detect equipment failures before line shutdowns due to equipment malfunctions, explicitly focusing on laser welders in the continuous galvanizing lines (CGLs) of a steel plant in Korea. The study selected an auto-encoder (AE) as a base model, which has the strength of applying normal data and a long short-term memory (LSTM) model for application to time series data, such as equipment operation data. Here, a laser welder predictive maintenance model (LW-PMM) based on the LSTM-AE algorithm was developed by combining the technical advantages of both algorithms. Approximately 1500 types of data were collected, and approximately 200 were selected through preprocessing. The training and testing datasets were split at a ratio of 8:2, and the model parameters were optimized using 10-fold cross-validation. The performance evaluation of the LW-PMM resulted in an accuracy rate of 97.3%, a precision rate of 79.8%, a recall rate of 100%, and an F1-score of 88.8%. The precision of 79.8% compared to the 100% recall value indicated that although the model predicted all failures in the equipment as failures, 20.2% of them were duplicate values, which can be interpreted as one of the five failure signals being not an actual failure. As a result of the application to an actual CGL operation site, equipment abnormalities were detected for the first time 27 h before failure, resulting in a reduction of 18 h compared with the existing process. This study is unique because it started as a proof of concept (POC) and was validated in a production setting as a pilot system for the predictive maintenance of laser welders. We expect this study to be expanded and applied to steel production processes, contributing to digital transformation and innovation in the steel industry.
A Review of Machine Learning Approaches to Soil Temperature Estimation
Authors: Taheri Mercedeh , Schreiner Helene Katherine , Mohammadian Abdolmajid , Shirkhani Hamidreza , Payeur Pierre , Imanian Hanifeh , Cobo Juan Hiedra
Published: May 2023

Soil temperature is an essential factor for agricultural, meteorological, and hydrological applications. Direct measurement, despite its high accuracy, is impractical on a large spatial scale due to the expensive and time-consuming process. On the other hand, the complex interaction between variables affecting soil temperature, such as topography and soil properties, leads to challenging estimation processes by empirical methods and physical models. Machine learning (ML) approaches gained considerable attention due to their ability to address the limitations of empirical and physical methods. These approaches are capable of estimating the variables of interest using complex nonlinear relationships with no assumptions about data distribution. However, their sensitivity to input data as well as the need for a large amount of training ground truth data limits the application of machine learning approaches. The current paper aimed to provide a review of ML techniques implemented for soil temperature modeling, their challenges, and milestones achieved in this domain.
How about Now? Changes in Risk Perception before and after Hurricane Irma
Authors: Billman Matthew , Atoba Kayode , Thompson Courtney , Brody Samuel
Published: May 2023

Risk reduction behaviors are often voluntary and influenced by how at-risk an individual personally feels, known as risk perception. This paper examines how risk perception changed from before and six months after Hurricane Irma, a Category 3 storm that narrowly missed Sarasota County, FL. Both surveys asked about residents’ hurricane risk perceptions, evacuation behaviors, mitigation plans, and attitudes about self-efficacy. For each question found in both surveys, the research used t-tests (ɑ = 0.05) to assess whether significant changes in risk perceptions occurred between responses. The results suggest that Hurricane Irma had a notable impact on risk perception. The changes were most evident in reported levels of self-efficacy as residents were less likely to feel able to sufficiently prepare for or recover from hurricane impacts after Hurricane Irma. Respondents were also more likely to believe individuals are responsible for preparing for hurricane impacts. The findings have implications for public risk communicators, who may find it effective and sustainable to appeal to residents’ lowered self-efficacy or sense of responsibility for the dangers of hurricanes while implementing policies and communication strategies.