Academic Journal

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

التفاصيل البيبلوغرافية
العنوان: Machine learning for research on climate change adaptation policy integration: an exploratory UK case study
المؤلفون: Biesbroek, Robbert, Badloe, Shashi, Athanasiadis, Ioannis N.
المصدر: Regional Environmental Change 20 (2020) 3 ; ISSN: 1436-3798
سنة النشر: 2020
المجموعة: Wageningen UR (University & Research Centre): Digital Library
مصطلحات موضوعية: Artificial intelligence, Climate change adaptation, Machine learning, Mainstreaming, Policy and decision making, Quantitative text analysis
الوصف: 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.
نوع الوثيقة: article in journal/newspaper
وصف الملف: application/pdf
اللغة: English
Relation: https://edepot.wur.nl/526545
DOI: 10.1007/s10113-020-01677-8
الاتاحة: https://research.wur.nl/en/publications/machine-learning-for-research-on-climate-change-adaptation-policy
https://doi.org/10.1007/s10113-020-01677-8
Rights: https://creativecommons.org/licenses/by/4.0/ ; Wageningen University & Research
رقم الانضمام: edsbas.E95A2E35
قاعدة البيانات: BASE
الوصف
DOI:10.1007/s10113-020-01677-8