About the project
The project aims to improve matching algorithms to better account for industry needs and inputs, using data-driven methods for evaluating which firms can collaborate efficiently and reuse resources; develop dynamic pricing and smart contract mechanisms for efficient transactions, utilising algorithmic game theory methods and mechanism design; and design and develop budget-balanced taxation and subsidy schemes to encourage environmentally sustainable circular economy relations.
This project advances sustainable practices in the circular economy through the innovative application of state-of-the-art AI methods.
The circular economy is a sustainable economic model, aiming to minimise waste and maximise resource reuse through closed-loop systems, in contrast to the traditional linear model of extracting raw material, producing, and then sending waste to pollute the environment. Instead, the circular economy prioritises the continual use and regeneration of materials, products, and resources to extend their lifecycle, reducing environmental impact.
In this context, matching algorithms play a crucial role in facilitating the identification of synergies among industry stakeholders, ensuring optimal collaboration and resource reuse, thereby contributing to the overarching goal of creating environmentally sustainable circular economies. This needs to be complemented by smart methods for managing established relations (dynamic contracts) and requires support from policymakers in terms of incentives and subsidy schemes.
This PhD project offers a unique opportunity to contribute to the forefront of sustainable circular economies through AI innovation.
