scholarly journals Assisted tree migration in North America: policy legacies, enhanced forest policy integration and climate change adaptation

2016 ◽  
Vol 32 (6) ◽  
pp. 535-543 ◽  
Author(s):  
Adam Wellstead ◽  
Michael Howlett
Climate Law ◽  
2011 ◽  
Vol 2 (3) ◽  
pp. 375-394 ◽  
Author(s):  
Martin O. Oulu

Mainstreaming climate change adaptation into policies and development planning processes is widely acknowledged and advocated as an important means of addressing the myriad impacts of climate change.Kenya, like many developing countries, is very vulnerable to climate change and urgently needs to adapt. However, the country’s adaptation mainstreaming efforts are still nascent and largely insufficient. Through a literature review and key informant interviews, this paper identifies Kenya’s potential climateadaptation mainstreaming entry-points and investigates the normative, organizational, and procedural mainstreaming strategies employed. This is done from a horizontal Climate Policy Integration perspective. Three potential mainstreaming entry-points, among them Kenya Vision 2030, the current development blueprint, are identified. The results indicate that while political commitment to, and strategic vision on, climate adaptation is present as exemplified by high-profile champions and the development of the National Climate Change Response Strategy, institutional set-ups remain fragmented and inadequate. Of particular importance is the need to anchor coordination efforts for climate change adaptation in a highlevel and cross-sectoral office. Ex-ante assessment procedures, such as Strategic Environment Assessment and Environment Impact Assessment, should incorporate robust climate vulnerability assessments and adaptation requirements.


2020 ◽  
Vol 20 (3) ◽  
Author(s):  
Robbert Biesbroek ◽  
Shashi Badloe ◽  
Ioannis N. Athanasiadis

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.


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