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.
Highlights
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
Since the aim of our study is to identify and map adaptation from policy texts, we make use of a distinct class of algorithms within the general machine learning literature: artificial neural networks (ANN)
In this article we explored the use of machine learning methods for the study of climate change adaptation policy
Summary
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. 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). Adaptation policy integration aims to reduce trade-offs across sectors and to promote synergies; reduce under- and overreaction by departments, organizations, or ministries in response to climate change impa cts; prevent inefficient investments of (scarce) resources; and promote coherence and consistency in implementing actions on the ground (Candel and Biesbroek 2016, 2018; Cejudo and Michel 2017; Lenschow et al 2018; Maor et al 2017; Tosun and Lang 2017)
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