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Green Transition and Waste Management in the Digital Era: Strategies, Learnings, Challenges and Future Trends

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 2169

Special Issue Editor


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Guest Editor
Department of Business Development and Technology, Aarhus University, 8000 Aarhus, Denmark
Interests: green transition; sustainability; circular economy; digitalization; operations management

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue entitled “Green Transition and Waste Management in the Digital Era: Strategies, Learnings, Challenges and Future Trends”.

These days, there is an urgent interest in our society and business to achieve the 2030 Agenda for Sustainable Development through the UN’s Sustainable Development Goals (SDGs). In addition, another challenge to transform the world to be more sustainable for the current and future generations is the European Union's strategy to be climate-neutral by 2050 through an economy with net-zero greenhouse gas emissions. In order to attend to these complex challenges, green transition and waste management strategies play an essential role.

However, although some developed countries have been leading the implementation of pragmatic actions in the short-, medium- and long-term to address these challenges, several countries worldwide are facing several difficulties and challenges. Therefore, it is the appropriate moment to examine the current strategies that have been successfully implemented around the world, and the lessons generated from these practical implementations to benchmark other countries and companies. This sharing of learnings and strategies can potentially contribute to cross-fertilizing and accelerating the dissemination of green transition and waste management strategies in several contexts. This Special Issue attempts to extend the current empirical literature on green transition and waste management strategies.

The key objectives of this Special Issue are the following: (1) to identify a comprehensive body of empirical-oriented knowledge to support the implementation of green transition and waste management strategies in different contexts, (2) to identify the essential empirical-oriented learnings and the key challenges that countries and companies can face while implementing green transition and waste management strategies and how these challenges can be overcome or minimized, and (3) to understand the main future trends for achieving a sustainable world through green transition and waste management strategies.

To address the objectives of this call, we invite scholars, practitioners, policymakers and other agents working on green transition and waste management to submit original studies based on case studies, action research papers, design science research and intervention-based papers providing empirical insights into the theme. Moreover, this Special Issue welcomes comprehensive systematic literature reviews employing rigorous protocols and meta-analyses studies able to discover empirical insights and knowledge that meet the three objectives of this call.

Research areas may include (but are not limited to) the following:

  • Best practices and lessons learned for green transition and waste management realization.
  • Applications, effects and strategies when applying cutting-edge Industry 4.0 technologies (e.g., Artificial Intelligence, Big Data, Internet of Things, Additive Manufacturing, Machine Learning, Blockchain, among others) supporting green transition and waste management realization.
  • Waste management and waste valorization methods, practices and strategies.
  • Strategies and methods for the successful green transition.
  • Circular economy for green transition and waste management.
  • Barriers and challenges for green transition and waste management realization.
  • The role of cyber–physical systems for green transition and waste management systems.
  • Implementing green transition and waste management through production systems and supply chains.
  • Green transition and waste management in developed and developing economies.
  • Re-manufacturing for green transition and waste management.
  • The role of international standards (e.g., ISO 14000 family standards, BS 8001, ISO 22526, among others).
  • Future trends and further developments for green transition and waste management.
  • Other related topics.

We look forward to receiving your contributions.

Dr. Diego Augusto De Jesus Pacheco
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • waste management
  • green transition
  • circular economy
  • green operations
  • green supply chains

Published Papers (1 paper)

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Research

22 pages, 5489 KiB  
Article
Integrating Life Cycle Assessment and Machine Learning to Enhance Black Soldier Fly Larvae-Based Composting of Kitchen Waste
by Muhammad Yousaf Arshad, Salaha Saeed, Ahsan Raza, Anum Suhail Ahmad, Agnieszka Urbanowska, Mateusz Jackowski and Lukasz Niedzwiecki
Sustainability 2023, 15(16), 12475; https://doi.org/10.3390/su151612475 - 16 Aug 2023
Cited by 4 | Viewed by 1898
Abstract
Around 40% to 60% of municipal solid waste originates from kitchens, offering a valuable resource for compost production. Traditional composting methods such as windrow, vermi-, and bin composting are space-intensive and time-consuming. Black soldier fly larvae (BSFL) present a promising alternative, requiring less [...] Read more.
Around 40% to 60% of municipal solid waste originates from kitchens, offering a valuable resource for compost production. Traditional composting methods such as windrow, vermi-, and bin composting are space-intensive and time-consuming. Black soldier fly larvae (BSFL) present a promising alternative, requiring less space and offering ease of handling. This research encompasses experimental data collection, life cycle assessment, and machine learning, and employs the Levenberg–Marquardt algorithm in an Artificial Neural Network, to optimize kitchen waste treatment using BSFL. Factors such as time, larval population, aeration frequency, waste composition, and container surface area were considered. Results showed that BSFL achieved significant waste reduction, ranging from 70% to 93% by weight and 65% to 85% by volume under optimal conditions. Key findings included a 15-day treatment duration, four times per day aeration frequency, 600 larvae per kilogram of waste, layering during feeding, and kitchen waste as the preferred feed. The larvae exhibited a weight gain of 2.2% to 6.5% during composting. Comparing the quality of BSFL compost to that obtained with conventional methods revealed its superiority in terms of waste reduction (50% to 73% more) and compost quality. Life cycle assessment confirmed the sustainability advantages of BSFL. Machine learning achieved high accuracy of prediction reaching 99.5%. Full article
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