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Machine learning for research on climate change adaptation policy integration: an exploratory UK case study

Authors: Biesbroek, Robbert; Badloe, Shashi; Athanasiadis, Ioannis N.;

Machine learning for research on climate change adaptation policy integration: an exploratory UK case study

Abstract

AbstractUnderstanding 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.

Country
Netherlands
Related Organizations
Subjects by Vocabulary

Microsoft Academic Graph classification: Computer science Climate change Machine learning computer.software_genre Adaptation (computer science) Artificial neural network business.industry Public sector Nature Conservation Artificial intelligence Climate change adaptation business computer

Keywords

Artificial intelligence, Mainstreaming, mainstreaming, Climate change adaptation, WASS, adaptation, Laboratory of Geo-information Science and Remote Sensing, Machine learning, Laboratorium voor Geo-informatiekunde en Remote Sensing, climate change adaptation, Global and Planetary Change, WIMEK, Public Administration and Policy, Policy and decision making, PE&RC, quantitative text analysis, climate change, machine learning, policy and decision making, Bestuurskunde, Quantitative text analysis, policy

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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    31
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    31
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
31
Top 10%
Top 10%
Top 10%
Green
hybrid