Triantafyllidis, CP;
Koppelaar, RHEM;
Wang, X;
van Dam, KH;
Shah, N;
(2018)
An integrated optimisation platform for sustainable resource and infrastructure planning.
Environmental Modelling and Software
, 101
pp. 146-168.
10.1016/j.envsoft.2017.11.034.
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Abstract
It is crucial for sustainable planning to consider broad environmental and social dimensions and systemic implications of new infrastructure to build more resilient societies, reduce poverty, improve human well-being, mitigate climate change and address other global change processes. This article presents resilience.io, 2 a platform to evaluate new infrastructure projects by assessing their design and effectiveness in meeting growing resource demands, simulated using Agent-Based Modelling due to socio-economic population changes. We then use Mixed-Integer Linear Programming to optimise a multi-objective function to find cost-optimal solutions, inclusive of environmental metrics such as greenhouse gas emissions. The solutions in space and time provide planning guidance for conventional and novel technology selection, changes in network topology, system costs, and can incorporate any material, waste, energy, labour or emissions flow. As an application, a use case is provided for the Water, Sanitation and Hygiene (WASH) sector for a four million people city-region in Ghana.
| Type: | Article |
|---|---|
| Title: | An integrated optimisation platform for sustainable resource and infrastructure planning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.envsoft.2017.11.034 |
| Publisher version: | https://doi.org/10.1016/j.envsoft.2017.11.034 |
| Language: | English |
| Additional information: | Copyright © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
| Keywords: | Integrated Environmental Modelling (IEM), Decision support, Sub-Saharan Africa, WASH, Agent-Based Modelling, Mathematical Programming |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10043930 |
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