Machine learning for research on climate change adaptation policy integration: an exploratory UK case study
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ORIGINAL ARTICLE
Machine learning for research on climate change adaptation policy integration: an exploratory UK case study Robbert Biesbroek 1
&
Shashi Badloe 2 & Ioannis N. Athanasiadis 3
Received: 20 August 2019 / Accepted: 18 June 2020 # The Author(s) 2020
Abstract Understanding how climate change adaptation is integrated into existing policy sectors and organizations is critical to ensure timely and effective climate actions across multiple levels and scales. Studying climate change adaptation policy has become increasingly difficult, particularly given the increasing volume of potentially relevant data available, the validity of existing methods handling large volumes of data, and comprehensiveness of assessing processes of integration across all sectors and public sector organizations over time. This article explores the use of machine learning to assist researchers when conducting adaptation policy research using text as data. We briefly introduce machine learning for text analysis, present the steps of training and testing a neural network model to classify policy texts using data from the UK, and demonstrate its usefulness with quantitative and qualitative illustrations. We conclude the article by reflecting on the merits and pitfalls of using machine learning in our case study and in general for researching climate change adaptation policy. Keywords Machine learning . Quantitative text analysis . Climate change adaptation . Policy and decision making . Mainstreaming . Artificial intelligence
Introduction Studies from across the globe suggest that countries, regions, and cities are increasingly developing dedicated climate change adaptation policies, strategies, and measures to adapt to current and projected climate change impacts. Examples include raising awareness of climate risks, developing novel financial schemes to increase resilience, changing existing legislation, and implementing ‘hard’ and ‘soft’ adaptation measures on the ground (Bauer et al. 2012; Clar and Steurer 2019.; EEA 2014; Henstra 2017; Lesnikowski et al. 2016, Lesnikowski et al. 2015;
Communicated by Chandni Singh * Robbert Biesbroek [email protected] 1
Public Administration and Policy group, Wageningen University & Research, Wageningen, Netherlands
2
Bioinformatics group, Wageningen University & Research, Wageningen, Netherlands
3
Geo-Information Science and Remote Sensing Laboratory, Wageningen University & Research, Wageningen, Netherlands
Uittenbroek et al. 2019). Consequently, it has become increasingly clear that the success of adaptation actions is influenced by the ability of governments to integrate or ‘mainstream’ a focus on climate change across relevant sectors, domains, and levels (Runhaar et al. 2018). Mainstreaming adaptation—or adaptation policy integration—refers to the process whereby climate change concerns become an integral part of the structural dimensions of sectoral public bureaucracies (e.g. changing sectoral policies) and influence the ways in which public governance actors perceive the p
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