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Evaluating optimal solutions to environmental breakdown.

Stafford, R., Croker, A., Rivers, E., Cantarello, E., Costelloe, B., Ginige, T. A., Sokolnicki, J., Kang, K., Jones, P., McKinley, E. and Shiel, C., 2020. Evaluating optimal solutions to environmental breakdown. Environmental Science and Policy, 112 (October), 340-347.

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DOI: 10.1016/j.envsci.2020.07.008


The severity of environmental threats, especially climate change, biodiversity loss and pollution, are well established, as is the urgent need for them to be addressed. These threats act both in isolation as well as synergistically to contribute to overall ‘environmental breakdown’. Debate exists around the most optimal governance and policy approaches to address these threats and, to date, little quantitative evidence exists to compare the different approaches. Using a modified Bayesian belief network model to assess the probability of environmental threats, we compare and contrast a range of proposed policy solutions to a selection of contemporary environmental problems that have been identified as having the potential to contribute to, or indeed may lead to environmental breakdown. Through interrogation of the models, we conclude that policies that prioritise economic growth at the expense of nature would be largely ineffective, whereas a more integrated approach, adopting comprehensive ‘Green New Deal’ policies combined with nature-based solutions would be the most effective approaches to preventing environmental breakdown, as they address societal and environmental issues simultaneously. We therefore recommend that decision makers take an integrated approach to decision making and policy development, accounting for social, economic and environmental drivers that ensure delivery of multiple benefits and real change.

Item Type:Article
Uncontrolled Keywords:Climate change; Biodiversity loss; Pollution; Nature-based solutions; Green New Deal; Economic growth
Group:Faculty of Science & Technology
ID Code:34270
Deposited By: Symplectic RT2
Deposited On:09 Jul 2020 10:54
Last Modified:14 Mar 2022 14:23


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