2,795 research outputs found
An approach to advance circular practices in the maritime industry through a database as a bridging solution
The concept of maritime circularity has gained increasing attention to address challenges arising from the net-zero targets of the maritime industry. The circular economy provides potential solutions to address these challenges through reuse, remanufacturing, and recycling practices. However, the industry faces complex challenges, including inefficient reverse supply chains, a lack of awareness about circular economy principles, standardisation issues, and the need for digital infrastructure to provide vital information in the sector. These challenges prevent the implementation of circularity practices, as access to crucial data throughout the vesselâs life cycle is obstructed. This novel research aims to create a robust first-of-its-kind database solution specifically designed to support the industryâs shift towards circularity. The database will facilitate fast and transparent information flow between the stakeholders, providing foundations for asset tracking and a robust reverse supply chain. A case study was conducted to show that a database could help extract higher financial value from end-of-life ships by over 80%. The ageing fleet increases the urgency of utilising such a database, which could be a pivotal strategy for a sustainable and circular industry. This digital solution offers significant benefits to all industry stakeholders and allows holistic resource management, influencing maritime operationsâ sustainability, resilience, and profitability
Organizing sustainable development
The role and meaning of sustainable development have been recognized in the scientific literature for decades. However, there has recently been a dynamic increase in interest in the subject, which results in numerous, in-depth scientific research and publications with an interdisciplinary dimension. This edited volume is a compendium of theoretical knowledge on sustainable development. The context analysed in the publication includes a multi-level and multi-aspect analysis starting from the historical and legal conditions, through elements of the macro level and the micro level, inside the organization. Organizing Sustainable Development offers a systematic and comprehensive theoretical analysis of sustainable development supplemented with practical examples, which will allow obtaining comprehensive knowledge about the meaning and its multi-context application in practice. It shows the latest state of knowledge on the topic and will be of interest to students at an advanced level, academics and reflective practitioners in the fields of sustainable development, management studies, organizational studies and corporate social responsibility
An Overview of the Sustainable Recycling Processes Used for Lithium-Ion Batteries
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An Overview of the Sustainable Recycling Processes Used for Lithium-Ion Batteries
by Daniele Marchese 1,*ORCID,Chiara GiosuĂš 2,*ORCID,Antunes Staffolani 3,4,5ORCID,Massimo Conti 6,Simone Orcioni 6,Francesca Soavi 3,4,5ORCID,Matteo Cavalletti 1 andPierluigi Stipa 2ORCID
1
MIDAC S.p.A., Via Alessandro Volta 2, Soave, 37038 Verona, Italy
2
Department of Science and Engineering of Matter, Environment and Urban Planning (SIMAU), Polytechnic University of Marche, INSTM Research Unit, 60131 Ancona, Italy
3
Department of Chemistry âGiacomo Ciamicianâ, Alma Mater Studiorum University of Bologna, 40126 Bologna, Italy
4
ENERCube, Centro Ricerche Energia, Ambiente e Mare, Centro Interdipartimentale per la Ricerca Industriale Fonti Rinnovabili, Ambiente, Mare ed Energia (CIRI-FRAME)âAlma Mater Studiorum University of Bologna, Viale Ciro Menotti, 48, 48122 Marina di Ravenna, Italy
5
National Reference Center for Electrochemical Energy Storage (GISEL)âINSTM, Via G. Giusti 9, 50121 Firenze, Italy
6
Department of Information Engineering (DII), Polytechnic University of Marche, INSTM Research Unit, 60131 Ancona, Italy
*
Authors to whom correspondence should be addressed.
Batteries 2024, 10(1), 27; https://doi.org/10.3390/batteries10010027
Submission received: 25 November 2023 / Revised: 21 December 2023 / Accepted: 6 January 2024 / Published: 11 January 2024
(This article belongs to the Special Issue Toward Next-Generation Rechargeable Lithium-Ion Batteries: Current Status and Future Prospects)
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Abstract
Lithium-ion batteries (LIBs) can play a crucial role in the decarbonization process that is being tackled worldwide; millions of electric vehicles are already provided with or are directly powered by LIBs, and a large number of them will flood the markets within the next 8â10 years. Proper disposal strategies are required, and sustainable and environmental impacts need to be considered. Despite still finding little applicability in the industrial field, recycling could become one of the most sustainable options to handle the end of life of LIBs. This review reports on the most recent advances in sustainable processing for spent LIB recycling that is needed to improve the LIB value chain, with a special focus on green leaching technologies for Co-based cathodes. Specifically, we provide the main state of the art for sustainable LIB recycling processes, focusing on the pretreatment of spent LIBs; we report on Life Cycle Assessment (LCA) studies on the usage of acids, including mineral as well as organic ones; and summarize the recent innovation for the green recovery of valuable metals from spent LIBs, including electrochemical methods. The advantage of using green leaching agents, such as organic acids, which represent a valuable option towards more sustainable recycling processes, is also discussed. Organic acids can, indeed, reduce the economic, chemical, and environmental impacts of LIBs since post-treatments are avoided. Furthermore, existing challenges are identified herein, and suggestions for improving the effectiveness of recycling are defined
Digital Innovations for a Circular Plastic Economy in Africa
Plastic pollution is one of the biggest challenges of the twenty-first century that requires innovative and varied solutions. Focusing on sub-Saharan Africa, this book brings together interdisciplinary, multi-sectoral and multi-stakeholder perspectives exploring challenges and opportunities for utilising digital innovations to manage and accelerate the transition to a circular plastic economy (CPE).
This book is organised into three sections bringing together discussion of environmental conditions, operational dimensions and country case studies of digital transformation towards the circular plastic economy. It explores the environment for digitisation in the circular economy, bringing together perspectives from practitioners in academia, innovation, policy, civil society and government agencies. The book also highlights specific country case studies in relation to the development and implementation of different innovative ideas to drive the circular plastic economy across the three sub-Saharan African regions. Finally, the book interrogates the policy dimensions and practitioner perspectives towards a digitally enabled circular plastic economy.
Written for a wide range of readers across academia, policy and practice, including researchers, students, small and medium enterprises (SMEs), digital entrepreneurs, non-governmental organisations (NGOs) and multilateral agencies, policymakers and public officials, this book offers unique insights into complex, multilayered issues relating to the production and management of plastic waste and highlights how digital innovations can drive the transition to the circular plastic economy in Africa.
The Open Access version of this book, available at https://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license
Exploring the barriers to Gen Yâs non-shopping behaviors toward second-hand clothes
The global textile industry has experienced remarkable growth in recent years, leading to a surge in textile waste that poses a significant environmental threat. Researchers have explored the potential of using second-hand clothes (SHC) as an alternative to new garments to address this issue. Among different customer segments, Generation Y has emerged as a critical group with high purchasing power and contribution to the growth of the SHC market. However, the non-purchasing behaviors of this generation towards SHC remain understudied.
This thesis investigates the internal and external barriers that impede Generation Y's adoption of SHC. The study began with a historical overview of the SHC market and the characterization of Generation Y's shopper profiles, followed by a review of existing literature on consumer behaviors. The qualitative research method used semi-structured questionnaires and thematic analysis of individual interviews. Despite a small sample size, participants came from diverse educational, professional, and cultural backgrounds.
The findings revealed some disparities between Generation Y shoppers and general shoppers in terms of the most frequently cited barriers, including concerns about unseen defects, cleanliness, unknown sources, and specific items, a lack of patience and time, and limited availability of clothes in terms of style, size, and quality. The research contributes to the literature on SHC consumption behavior and generational cohorts. It provides practical recommendations for SHC businesses to tailor their business models and product offerings to different customer segments within Generation Y
Image-based Decision Support Systems: Technical Concepts, Design Knowledge, and Applications for Sustainability
Unstructured data accounts for 80-90% of all data generated, with image data contributing its largest portion. In recent years, the field of computer vision, fueled by deep learning techniques, has made significant advances in exploiting this data to generate value. However, often computer vision models are not sufficient for value creation. In these cases, image-based decision support systems (IB-DSSs), i.e., decision support systems that rely on images and computer vision, can be used to create value by combining human and artificial intelligence. Despite its potential, there is only little work on IB-DSSs so far.
In this thesis, we develop technical foundations and design knowledge for IBDSSs and demonstrate the possible positive effect of IB-DSSs on environmental sustainability. The theoretical contributions of this work are based on and evaluated in a series of artifacts in practical use cases: First, we use technical experiments to demonstrate the feasibility of innovative approaches to exploit images for IBDSSs.
We show the feasibility of deep-learning-based computer vision and identify future research opportunities based on one of our practical use cases. Building on this, we develop and evaluate a novel approach for combining human and artificial intelligence for value creation from image data. Second, we develop design knowledge that can serve as a blueprint for future IB-DSSs. We perform two design science research studies to formulate generalizable principles for purposeful design â one for IB-DSSs and one for the subclass of image-mining-based decision support systems (IM-DSSs). While IB-DSSs can provide decision support based on single images, IM-DSSs are suitable when large amounts of image data are available and required for decision-making. Third, we demonstrate the viability of applying IBDSSs to enhance environmental sustainability by performing life cycle assessments for two practical use cases â one in which the IB-DSS enables a prolonged product lifetime and one in which the IB-DSS facilitates an improvement of manufacturing processes.
We hope this thesis will contribute to expand the use and effectiveness of imagebased decision support systems in practice and will provide directions for future research
Creating shared value:An operations and supply chain management perspective
Focusing solely on short-term profits has caused social, environmental, and economic problems. Creating shared value integrates profitability with social and environmental objectives, offering a holistic solution. This dissertation examines two areas where this integration is crucial. The first topic explores servicizing business models for a transition to a more circular economy, emphasizing environmental benefits and firm profitability. Initially, we focus on pricing policies, comparing pricing schemes across consumer segments to identify win-win-win strategies that meet all people, planet, and profit objectives. Our research reveals that pay-per-use schemes outperform pay-per-period schemes for cost-inefficient or small-scale providers. A win-win (profit and planet) strategy can be achieved by offering a pay-per-use policy to high usage-valuation consumers, but a win-win-win strategy is unattainable. We then investigate consumer choices in servicizing models by conducting a conjoint experiment on payment scheme, price, minimum contract duration, and entry label attributes. The payment scheme emerges as the most influential attribute, with purchasing and pay-per-use schemes being popular options. The second topic focuses on drug shortages. Specifically, we examine the impact of tendering on shortages. Our findings demonstrate that tendering reduces prices but increases shortages, particularly at the beginning of contracts. However, shortages are less severe when alternative suppliers are available, and the market is less concentrated. To address this issue, we propose allowing multiple winners, regionalizing tenders, increasing the time between tender and contract initiation, and incorporating a reliability measure as a winning criterion to mitigate shortages
Application of reinforcement learning in robotic disassembly operations
Disassembly is a key step in remanufacturing. To increase the level of automation in disassembly, it is necessary to use robots that can learn to perform new tasks by themselves rather than having to be manually reprogrammed every time there is a different job. Reinforcement Learning (RL) is a machine learning technique that enables the robots to learn by trial and error rather than being explicitly programmed.
In this thesis, the application of RL to robotic disassembly operations has been studied. Firstly, a literature review on robotic disassembly and the application of RL in contact-rich tasks has been conducted in Chapter 2.
To physically implement RL in robotic disassembly, the task of removing a bolt from a door chain lock has been selected as a case study, and a robotic training platform has been built for this implementation in Chapter 3. This task is chosen because it can demonstrate the capabilities of RL to pathfinding and dealing with reaction forces without explicitly specifying the target coordinates or building a force feedback controller. The robustness of the learned policies against the imprecision of the robot is studied by a proposed method to actively lower the precision of the robots. It has been found that the robot can learn successfully even when the precision is lowered to as low as ±0.5mm. This work also investigates whether learned policies can be transferred among robots with different precisions. Experiments have been performed by training a robot with a certain precision on a task and replaying the learned skills on a robot with different precision. It has been found that skills learned by a low-precision robot can perform better on a robot with higher precision, and skills learned by a high-precision robot have worse performance on robots with lower precision, as it is suspected that the policies trained on high-precision robots have been overfitted to the precise robots.
In Chapter 4, the approach of using a digital-twin-assisted simulation-to-reality transfer to accelerate the learning performance of the RL has been investigated. To address the issue of identifying the system parameters, such as the stiffness and damping of the contact models, that are difficult to measure directly but are critical for building the digital twins of the environments, system identification method is used to minimise the discrepancy between the response generated from the physical and digital environments by using the Bees Algorithm. It is found that the proposed method effectively increases RL's learning performance. It is also found that it is possible to have worse performance with the sim-to-real transfer if the reality gap is not effectively addressed. However, increasing the size of the dataset and optimisation cycles have been demonstrated to reduce the reality gap and lead to successful sim-to-real transfers.
Based on the training task described in Chapters 4 and 5, a full factorial study has been conducted to identify patterns when selecting the appropriate hyper-parameters when applying the Deep Deterministic Policy Gradient (DDPG) algorithm to the robotic disassembly task. Four hyper-parameters that directly influence the decision-making Artificial Neural Network (ANN) update have been chosen for the study, with three levels assigned to each hyper-parameter. After running 241 simulations, it is found that for this particular task, the learning rates of the actor and critic networks are the most influential hyper-parameters, while the batch size and soft update rate have relatively limited influence.
Finally, the thesis is concluded in Chapter 6 with a summary of findings and suggested future research directions
Annual Climate Report 2023
According to the Climate Act, the Government submits the Annual Climate Report to Parliament every year. The Report examines the trends in emissions and sinks, sufficiency of the planned measures to achieve the emission reduction targets, need for further measures, and implementation of the targets and measures of the Medium-term Climate Plan and Climate Plan for the Land Use Sector. The sufficiency and implementation of measures in the National Adaption Plan are also discussed.
Total emissions without the land use sector decreased in 2022 compared to 2021. Emissions from the emissions trading sector decreased clearly from the previous year. Emissions from the effort-sharing sector decreased as well, and they were within Finlandâs annual emission allocations in 2021 and 2022.
The land use sector was a small net sink in 2022. The fact that the sector turned from a source of net emissions in 2021 to a net sink is due the smaller felling volume in 2022. Net emissions, i.e. emissions and sinks from all sectors combined (including the land use sector), decreased in 2022 compared to the previous year.
The pace of emission reductions is in line with the emission reduction target for 2030 set in the Climate Act. If no further measures are taken in the land use sector, Finland is not likely to achieve the EU commitments under the LULUCF Regulation without buying emission credits from other Member States. Achieving the national climate neutrality target requires further measures in the land use sector and other sectors
RISK PERCEPTION AND INVESTMENT INTENTION IN ISLAMIC BANKS' TERM DEPOSITS: AN EMPIRICAL STUDY IN MOROCCO
This study focuses on the impact of risk perception on the intention to invest in Islamic banks' term deposits in Morocco. Using quantitative methodologies, including statistical analysis, Cronbach's reliability tests, and structural equation modelling, the study assesses the influence of various factors, including perceived quality and perceived value, on the intention to invest. Contrary to expectations and the existing literature, the results suggest that risk perception has no statistically significant effect on the intention to invest in these financial products. However, perceived value shows a significant relationship with intention to invest, indicating its crucial role in financial decision-making within this specific context. These results have important implications for Islamic banks, policymakers, and researchers, as they challenge the conventional emphasis on risk perception in the Islamic finance literature. The results suggest that other factors, such as perceived quality and value, may play a more influential role in the intention to invest in Islamic term deposits. JEL: G11, G 21, Z12, D81 Article visualizations
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