Advancing the Industrial Circular Economy: The Integrative Role of Machine Learning in Resource Optimization

Abstract

In the face of escalating resource scarcity driven by the consumption of non-renewable resources, the industrial circular economy (ICE) emerges as a vital paradigm shift, pivotal for fostering resource-efficient societies and ensuring national resource security. This integrative review aims to critically assess the evolution and challenges inherent within the ICE over recent years, with a specific focus on the burgeoning role of machine learning (ML) in this domain. By synthesizing extant literature, this examination reveals several key findings. Firstly, the ICE significantly contributes to cost reduction through enhanced recycling and secondary utilization, underscoring its environmental stewardship. Secondly, it is evident that ML exhibits substantial promise in the manufacturing sector, not only augmenting production processes but also elevating product precision, reducing defect rates, and minimizing the likelihood of production mishaps. Most crucially, the application of ML within the ICE is identified as a potent catalyst, driving advancements across various facets -data analysis, model development, technological innovation, and equipment refinement. This analysis further elucidates the intrinsic value of ML in resource recycling and waste management, yielding improvements in resource recycling rates and methodologies, which in turn curtails production costs and amplifies output efficiency. Despite the strides made in replacing traditional industrial models with more sustainable ICE practices, challenges persist, particularly regarding the suboptimal levels of resource recycling and the continued generation of industrial waste. The integration of ML within ICE frameworks is posited as a transformative approach, offering not only enhanced resource recycling capabilities and superior product quality but also a sustainable trajectory for future industrial development. This study, therefore, contributes to the growing discourse on sustainable industrial practices, underscoring the synergistic potential of ML in revolutionizing the ICE, thereby aligning with the broader objectives of sustainable economic development.

Sustainable Development and Competitiveness: Is There a Need for GCI Reconstruction?

“…The EU aims to promote resource efficiency through the use of clean energy, a circular economy, and the prevention of environmental destruction and climate change, as well as to reverse biodiversity loss and reduce pollution. The circular economy, encompassing the social, environmental, and economic facets of sustainable development, presents a viable solution to mitigate the resource constraints and socio-environmental ramifications inherent in the prevailing linear economic model (Lin and Wei, 2023). The European Union (EU) has fully committed to achieving the goals of the 2030 Agenda, which are reflected in numerous European working plans and roadmaps.…”