Artificial intelligence, machine learning, deep learning, and big data for circular economy, resilience, and sustainable development

Abstract

The increasing environmental destruction, resource exploitation, and the climate change issues require the major revolutionary change in how the societies generate, consume, and govern resources. The conventional models of the linear economy have failed to deal with the intricate sustainability issues of the twenty first century. This review of the literature provides how Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) and Big Data analytics help to facilitate Circular Economy (CE) principles, improve resilience, and benefit Sustainable Development Goals (SDGs). The review includes a variety of applications in the domain of waste management optimization, resources efficiency improvement, supply chain circularity, predictive maintenance systems, environmental monitoring. The highlights of the findings indicate that AI-based solutions have the potential to decrease waste by 30-45, improve resource use by 25-40, and improve the accuracy of the decisions in the circular systems by up to 60. High-yield learning algorithms along with Internet of Things (IoT) sensors allow monitoring and adaptive control of the processes of the circular economy in real time. Nevertheless, there are still a lot of challenges, such as data quality, bias in algorithms, AI models consumption, and the lack of implementation in developing economies. The review can point to research gaps relating to critical areas of cross-sectoral integration, the use of AI ethically, and solutions scalability to small and medium enterprises. The results prove that the systematic application of smart technologies can spur the desire to build sustainable, resilient, and circular economic frameworks and provide for various SDGs at the same time.