Daniel Goodwin
Cranfield University
10 Papers
7 Citations
Daniel Goodwin is an academic researcher from Cranfield University. The author has contributed to research in topics: Reuse & Risk management. The author has an hindex of 5, co-authored 5 publications.
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Papers
Collaboration on risk management: the governance of a non-potable water reuse scheme in London
TL;DR: In this paper, a case study is used to explore the challenges for an operational sewer mining scheme in London, where reclaimed non-potable water is used for irrigation and toilet flushing at the site of the London 2012 Olympic Park.
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Informing public attitudes to non-potable water reuse - The impact of message framing.
TL;DR: Evaluating how different ways of framing messages about the safety of recycled water might impact on public attitudes helps isolate the effects of specific message frames, and inform the debate on whether an increased understanding of risk positively or negatively influences willingness to support water reuse schemes.
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What is the evidence linking financial assistance for drought-affected agriculture and resilience in tropical Asia? A systematic review
Daniel Goodwin,Ian P. Holman,Liwa Pardthaisong,Supattra Visessri,Chaiwat Ekkawatpanit,Dolores Rey Vicario +5 more
TL;DR: In this paper , the authors identify and review 43 regionally specific articles that describe a range of financial interventions to mitigate drought-related impacts and adaptation towards longer-term resilience in tropical Asia.
Stakeholder evaluations of risk interventions for non-potable recycled water schemes: A case study.
TL;DR: The study concludes that contemporary risk management guidance would benefit from more explicitly outlining constructive ways to engage stakeholders in scheme evaluation.
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Indicator-to-impact links to help improve agricultural drought preparedness in Thailand
Maliko Tanguy,Michael P. Eastman,Eugene Magee,Lucy Barker,Thomas Chitson,Chaiwat Ekkawatpanit,Daniel Goodwin,Jamie Hannaford,Ian P. Holman,Liwa Pardthaisong,Simon Parry,Dolores Rey Vicario,Supattra Visessri +12 more
TL;DR: In this paper , a combination of correlation analysis and machine learning techniques (random forest) is used to analyze the link between meteorological drought indicators and high-resolution remote sensing vegetation indices used as proxies for crop yield and forest growth impacts.
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