Interrelated Climate Change Adaptation Measures and Factors

Climate is changing. While worldwide societies experience climate-induced hazards, progress in climate change adaptation – both public and private – remains slow. Understanding who, when, and how pursues Climate Change Adaptation (CCA) is essential. Factors explaining adaptation by different actors (governments, communities, households, farmers, and individuals) may vary across countries and per type of adaptation (incremental vs. transformational). Worldwide rich qualitative research reveals factors influencing these different adaptation decisions. Thousands of articles report valuable insights, and the literature reporting mounting evidence constantly grows. To address this challenge, we use a novel approach to elicit patterns in decision factors for various actors and types of adaptation. Specifically, using natural language processing, thematic coding books, and network analysis, we consolidate empirical evidence fragmented across various textual sources. Here, we provide two exemplar datasets derived using this approach. Both databases provide systematic overviews of various adaptation factors associated (positively, negatively, or neutrally) with particular adaptation measures. The first dataset provides a systematic overview of factors associated with CCA to floods and sea-level rise by actor (see Dataset 1). The second set of data provides a systematic overview of factors associated with transformational vs. incremental adaptation by farmers (Dataset 2). <p>Both datasets rely on the textual information reported in peer-reviewed articles and derived using our algorithm Gil-Clavel & Filatova (2024). The coding book for adaptation factors per actor group is provided in Gil-Clavel et al. (2024), and the coding book for classifying CCA measures as transformational adaptation is described in Gil-Clavel et al. (2023). We go beyond the traditional automatic analysis of textual data focused on the word count and topic modeling. Instead, we strive to elicit meaningful associations between the type of adaptation and factors reported to influence those decisions. We do that based on the detailed code-books designed with domain experts and on the computational Natural Language Processing algorithms that elicit reported relationships and classify them as positive, negative, or neutral. </p> <p><b>Related work</b>:</p> <p>Gil-Clavel, Sofia, and Tatiana Filatova. “Using Natural Language Processing and Networks to Automate Structured Literature Reviews: An Application to Farmers Climate Change Adaptation.” arXiv, July 3, 2024. http://arxiv.org/abs/2306.09737. </p> <p>Gil-Clavel, Sofia, Thorid Wagenblast, Joos Akkerman, and Tatiana Filatova. “Patterns in Reported Adaptation Constraints: Insights from Peer-Reviewed Literature on Flood and Sea-Level Rise,” April 26, 2024. https://doi.org/10.31235/osf.io/3cqvn </p> <p>Gil-Clavel, Sofia, Thorid Wagenblast, and Tatiana Filatova. “Incremental and Transformational Climate Change Adaptation Factors in Agriculture Worldwide: A Natural Language Processing Comparative Analysis,” November 24, 2023. https://doi.org/10.31235/osf.io/3dp5e</p> <p>How to cite the datasets: Gil-Clavel, Sofia; Filatova, Tatiana, 2024, “Interrelated Climate Change Adaptation Measures and Factors”, https://doi.org/10.17026/SS/PYZCXK, DANS Data Station Social Sciences and Humanities </p> <p>Funding This work was supported by the Netherlands Organization for Scientific Research NWO VIDI grant number 191015.</p>气候正在发生变化。当前全球各地社会均面临气候引发的灾害，而公共与私人层面的气候变化适应（Climate Change Adaptation, CCA）推进进程依然缓慢。明确何类主体、何时以及以何种方式开展气候变化适应，是至关重要的课题。 不同行动主体（政府、社区、家庭、农户与个人）的气候变化适应行为影响因素，不仅因国家而异，也会因适应类型（渐进式 vs. 变革式）的不同而存在差异。全球范围内已有大量质性研究揭示了影响各类气候变化适应决策的因素，数千篇相关文献提供了极具价值的研究洞见，且此类积累实证证据的文献数量仍在持续增长。为应对这一研究挑战，我们采用了一种创新性方法，以挖掘不同行动主体与不同适应类型的决策影响因素模式。具体而言，我们借助自然语言处理（Natural Language Processing, NLP）、主题编码手册与网络分析技术，对分散在各类文本来源中的实证证据进行整合。 本文提供了基于该方法生成的两份示例数据集。两份数据库均系统梳理了与特定适应措施相关（呈正相关、负相关或中性相关）的各类适应影响因素。第一份数据集按行动主体分类，系统梳理了与洪涝及海平面上升相关的气候变化适应影响因素（详见数据集1）。第二份数据集则系统梳理了农户开展变革式与渐进式气候变化适应的相关影响因素（数据集2）。 两份数据集均基于同行评议期刊论文中的文本信息，并通过我们提出的Gil-Clavel & Filatova（2024）算法生成。针对不同行动主体群体的适应影响因素编码手册详见Gil-Clavel等人（2024）的研究，而将气候变化适应措施归类为变革式适应的编码手册则见于Gil-Clavel等人（2023）的成果。本研究跳出了传统文本数据自动分析仅关注词频与主题建模的局限，致力于挖掘适应类型与影响适应决策的报告因素之间的实质性关联。我们通过与领域专家共同设计的详细编码手册，结合可提取报告关联并将其归类为正相关、负相关或中性相关的计算型自然语言处理算法，实现了这一目标。 **相关研究**： Gil-Clavel, Sofia 与 Tatiana Filatova. 《利用自然语言处理与网络技术实现结构化文献综述自动化：以农户气候变化适应研究为例》，arXiv，2024年7月3日。http://arxiv.org/abs/2306.09737. Gil-Clavel, Sofia、Thorid Wagenblast、Joos Akkerman 与 Tatiana Filatova. 《报告中的适应约束模式：来自洪涝与海平面上升相关同行评议文献的洞见》，2024年4月26日。https://doi.org/10.31235/osf.io/3cqvn Gil-Clavel, Sofia、Thorid Wagenblast 与 Tatiana Filatova. 《全球农业领域渐进式与变革式气候变化适应影响因素：基于自然语言处理的比较分析》，2023年11月24日。https://doi.org/10.31235/osf.io/3dp5e 数据集引用方式：Gil-Clavel, Sofia; Filatova, Tatiana, 2024, 《相互关联的气候变化适应措施与影响因素》，https://doi.org/10.17026/SS/PYZCXK, DANS人文社科数据站 资助信息：本研究得到荷兰科学研究组织（Netherlands Organization for Scientific Research, NWO）VIDI项目（项目编号191015）的资助。