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Nature-based secondary resource recovery under climate change uncertainty: a robust multi-objective optimisation methodology

Alshehri, Khaled, Basirati, Mohadese, Sapsford, Devin ORCID: https://orcid.org/0000-0002-6763-7909, Harbottle, Michael ORCID: https://orcid.org/0000-0002-6443-5340 and Cleall, Peter ORCID: https://orcid.org/0000-0002-4005-5319 2024. Nature-based secondary resource recovery under climate change uncertainty: a robust multi-objective optimisation methodology. Sustainability 16 (16) , 7720. 10.3390/su16167220

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Abstract

The management of high-volume (HV) waste poses a persistent challenge in sustainable materials management and represents an untapped opportunity in circular economy models. This study proposes a conceptual decision-making framework to operationalise a novel circular economy strategy for HV waste, involving temporary storage to facilitate nature-based secondary resource recovery. Using an illustrative case study of a candidate HV waste (legacy mining waste), we apply a robust multi-objective spatial optimisation approach at a national scale, employing an exact solution approach. Our methodology integrates mixed-integer linear programming to evaluate the economic viability, social benefits, and impacts of climate change uncertainties on nature-based solutions (NbS) implementation across diverse scenarios. The results demonstrate that NbS can enhance economic feasibility by incorporating carbon sequestration and employment benefits while demonstrating resilience against climate change projections to ensure long-term sustainability. The findings suggest that although NbS can improve the circular economy of HV nationally, it is essential to assess additional ecosystem services and address multiple uncertainties for effective macro-level sustainability assessment of HV management. This study offers a robust decision-making framework for policymakers and stakeholders to plan and implement nature-based circular economy strategies for HV waste streams at a national level while effectively managing long-term planning uncertainties.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Engineering
Publisher: MDPI
ISSN: 2071-1050
Date of First Compliant Deposit: 23 August 2024
Date of Acceptance: 15 August 2024
Last Modified: 04 Sep 2024 10:15
URI: https://orca.cardiff.ac.uk/id/eprint/171557

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