Supplementary information files for "Optimising and evaluating centralised infrastructure transitions: A case study of wastewater treatment plants consolidations in the UK"

<p dir="ltr">Supplementary files for article "Optimising and evaluating centralised infrastructure transitions: A case study of wastewater treatment plants consolidations in the UK"<br><br>Rational planning is essential for infrastructure transitions in network industries due to the substantial investment costs involved. This study evaluates the economic feasibility of transitioning to a centralised wastewater treatment paradigm. We propose a hybrid Wastewater Infrastructure Transition Optimisation and Evaluation (WWITOE) framework that integrates mathematical optimisation and Cost-Benefit Analysis (CBA), explicitly accounting for geographic, topographic, and hydraulic cost uncertainties. The transition strategy explored involves cascading consolidations of WastewaterTreatment Plants (WWTPs), whereby small-scale facilities are closed, and their flows redirected to larger, centralisedhubs. While economies of scale at these hubs are expected to reduce treatment costs, the construction of extended collection networks may offset these savings—necessitating a balanced, cost-effective approach.</p><p dir="ltr">We formulate the optimisation model as an Uncapacitated Transportation and Facility Location Problem (UNCAP-TFLP) using integer Linear Programming (LP), and solve it through a Nearest Neighbour Search Algorithm (NNS-A) supported by GIS tools to address spatial feasibility and routing.</p><p dir="ltr">The WWITOE framework is applied to a real-world case study of 63 WWTPs operated by Anglian Water Services Ltd (AWS) in Lincolnshire, UK. Over a 25-year planning horizon, the Benefit-to-Cost Ratios (BCRs) from the CBA reveal that fully centralised consolidation does not deliver economic advantages in this flat, semi-rural region. The findings demonstrate that WWITOE provides a practical decision-support tool for evaluating wastewater infrastructure transitions, with scalability to other contexts. However, the results reinforce that optimal degrees of centralisation are highly context-dependent, warranting further research into hybrid and adaptive transition pathways.<br><br>© The Author(s), CC BY 4.0</p>