Dataset Information

International organizations and climate change adaptation: A new dataset for the social scientific study of adaptation, 1990-2017.

ABSTRACT: This article introduces a new dataset on the climate change adaptation activities of international organizations (IOs). While climate change adaptation has been studied at the local level and in the context of major climate organizations, such as the United Nations Framework Convention on Climate Change, we provide a first quantitative dataset on non-environmental IOs that can be linked to different social scientific datasets relevant for adaptation. Our new dataset contains information on the governance activities of 30 IOs from 1990 to 2017. Based on this dataset, we introduce different types of adaptation-related activities and develop a quantitative measure of IOs' climate adaptation engagement. We map the adaptation engagement of the 30 IOs across organizations, across issue areas, and over time. This dataset can be used to compare adaptation activities across and within IOs, but also as an empirical foundation for the emerging research field of global adaptation governance, for which IO climate change adaptation activities are relevant.

SUBMITTER: Kural E

PROVIDER: S-EPMC8432833 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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International organizations and climate change adaptation: A new dataset for the social scientific study of adaptation, 1990-2017.

Kural Ece E Dellmuth Lisa Maria LM Gustafsson Maria-Therese MT

PloS one 20210910 9

This article introduces a new dataset on the climate change adaptation activities of international organizations (IOs). While climate change adaptation has been studied at the local level and in the context of major climate organizations, such as the United Nations Framework Convention on Climate Change, we provide a first quantitative dataset on non-environmental IOs that can be linked to different social scientific datasets relevant for adaptation. Our new dataset contains information on the g ...[more]

PMID: 34506547

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Project description:Climate change affects biophysical processes related to the transmission of many infectious diseases, with potentially adverse consequences for the health of communities. While our knowledge of biophysical associations between meteorological factors and disease is steadily improving, our understanding of the social processes that shape adaptation to environmental perturbations lags behind. Using computational modeling methods, we explore the ways in which social cohesion can affect adaptation of disease prevention strategies when communities are exposed to different environmental scenarios that influence transmission pathways for diseases such as diarrhea. We developed an agent-based model in which household agents can choose between two behavioral strategies that offer different levels of protection against environmentally mediated disease transmission. One behavioral strategy is initially set as more protective, leading households to adopt it widely, but its efficacy is sensitive to variable weather conditions and stressors such as floods or droughts that modify the disease transmission system. The efficacy of the second strategy is initially moderate relative to the first and is insensitive to environmental changes. We examined how social cohesion (defined as average number of household social network connections) influences health outcomes when households attempt to identify an optimal strategy by copying the behaviors of socially connected neighbors who seem to have adapted successfully in the past. Our simulation experiments suggest that high-cohesion communities are able to rapidly disseminate the initially optimal behavioral strategy compared to low-cohesion communities. This rapid and pervasive change, however, decreases behavioral diversity; i.e., once a high cohesion community settles on a strategy, most or all households adopt that behavior. Following environmental changes that reduce the efficacy of the initially optimal strategy, rendering it suboptimal relative to the alternative strategy, high-cohesion communities can fail to adapt. As a result, despite faring better early in the course of computational experiments, high-cohesion communities may ultimately experience worse outcomes. In the face of uncertainty in predicting future environmental stressors due to climate change, strategies to improve effective adaptation to optimal disease prevention strategies should balance between intervention efforts that promote protective behaviors based on current scientific understanding and the need to guard against the crystallization of inflexible norms. Developing generalizable models allows us to integrate a wide range of theories multiple datasets pertaining to the relationship between social mechanisms and adaptation, which can provide further understanding of future climate change impacts. Models such as the one we present can generate hypotheses about the mechanisms that underlie the dynamics of adaptation events and suggest specific points of measurement to assess the impact of these mechanisms. They can be incorporated as modules within predictive simulations for specific socio-ecological contexts.

Project description:BackgroundDrylands, which are among the biosphere's most naturally limiting and environmentally variable ecosystems, constitute three-quarters of the African continent. As a result, environmental sustainability and human development along with vector-borne disease (VBD) control historically have been especially challenging in Africa, particularly in the sub-Saharan and Sahelian drylands. Here, the VBD burden, food insecurity, environmental degradation, and social vulnerability are particularly severe. Changing climate can exacerbate the legion of environmental health threats in Africa, the social dimensions of which are now part of the international development agenda. Accordingly, the need to better understand the dynamics and complex coupling of populations and environments as exemplified by drylands is increasingly recognized as critical to the design of more sustainable interventions.Main bodyThis scoping review examines the challenge of vector-borne disease control in drylands with a focus on Africa, and the dramatic, ongoing environmental and social changes taking place. Dryland societies persisted and even flourished in the past despite changing climates, extreme and unpredictable weather, and marginal conditions for agriculture. Yet intrusive forces largely out of the control of traditional dryland societies, along with the negative impacts of globalization, have contributed to the erosion of dryland's cultural and natural resources. This has led to the loss of resilience underlying the adaptive capacity formerly widely exhibited among dryland societies. A growing body of evidence from studies of environmental and natural resource management demonstrates how, in light of dryland system's inherent complexity, these factors and top-down interventions can impede sustainable development and vector-borne disease control. Strengthening adaptive capacity through community-based, participatory methods that build on local knowledge and are tailored to local ecological conditions, hold the best promise of reversing current trends.ConclusionsA significant opportunity exists to simultaneously address the increasing threat of vector-borne diseases and climate change through methods aimed at strengthening adaptive capacity. The integrative framework and methods based on social-ecological systems and resilience theory offers a novel set of tools that allow multiple threats and sources of vulnerability to be addressed in combination. Integration of recent advances in vector borne disease ecology and wider deployment of these tools could help reverse the negative social and environmental trends currently seen in African drylands.

Dataset Information

International organizations and climate change adaptation: A new dataset for the social scientific study of adaptation, 1990-2017.

Publications

International organizations and climate change adaptation: A new dataset for the social scientific study of adaptation, 1990-2017.

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