Abstract
Waste disposal has always been a challenge for organisations around the world, especially, the widely accepted detrimental consequences to the environment from inefficient waste management. The United Nations Sustainable Development Goals are heavily focused on global sustainability where responsible waste management is heavily embedded in these goals. A review of worldwide studies related to waste management is conducted in this paper to identify the level of development of solutions in improving waste management practices. The research method adopted in the review of studies exhibited characteristics of a systematic review which was based on the steps of (1) Does the research investigate innovations for improving waste management systems? and (2) Was the study conducted during the period 2000–2023? A thematic analysis (TA) method was adopted to analyse the reviewed studies based on the criteria of Year, Geography, Research Method adopted, Problems identified for waste management systems and the solutions/innovations developed to overcome the problems for each reviewed study. For all the reviewed studies, an inability amongst the public or businesses to dispose of waste appropriately in terms of metal, glass, plastic, medical and general waste was highlighted as a major issue in waste management. Machine Learning (ML) and Artificial Intelligence (AI) based waste management programs were identified as common solutions amongst the reviewed studies. It should be noted that the highlighted findings from the reviewed studies on challenges and solutions for waste management were not exclusive to a business context. However, limited/no solutions for waste management were identified amongst studies in the United Arab Emirates (UAE). Therefore, a paucity in literature was identified for studies investigating challenges and solutions related to waste management for businesses based in the UAE. Based on the findings from the literature review, a ML based program was developed and proposed for waste management in the context of the UAE and possibly the rest of the world. The findings from this research make policy based (UN SDGs) and practice based (Waste Management ML program to improve business efficiency) contributions through the development of a Waste Management ML program.
| Original language | English |
|---|---|
| Title of host publication | Technology-Driven Business Innovation |
| Publisher | Springer Nature |
| Pages | 453-464 |
| Number of pages | 11 |
| DOIs | |
| Publication status | Published (VoR) - 24 Mar 2024 |
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