Green Transition at What Cost? : A comparative case study of community inclusion and social conflict in climate change mitigation projects

Detta är en Master-uppsats från Uppsala universitet/Institutionen för freds- och konfliktforskning

Sammanfattning: Projects aiming to mitigate climate change, e.g. forest conservation and renewable energy projects, are increasing in numbers across the world while often being praised in international settings. However, on a local level, climate change mitigation projects may have unintended consequences due to a lack of meaningful community inclusion, while justifying their operations with an environmental discourse. A research gap is identified concerning the link and causal pathways between local community inclusion and social conflict in mitigation projects. This study thus aims to answer the question: how does the degree of community inclusion in projects aiming to mitigate climate change affect the likelihood of social conflict? The theoretical argument follows that project implementers not addressing recognition and representation increase the likelihood of social conflict. The causal mechanism is community perception of project legitimacy which is decisive for community acceptance or opposition. The theoretical argument is tested in a small-n comparative case study using the structured focused comparison method and the most-similar systems method. Pairs of cases from Senegal and Lao PDR are compared. The empirical findings suggest that the hypothesis receives partial support. Although the theorized link between the degree of inclusion and social conflict is observable, the data is limited and thus only gives weak support to the hypothesis. Legitimacy is found to be a potential causal mechanism, although limited data on community perceptions result in weak support. Lastly, the findings have policy relevance in terms of prioritizing recognition and representation of all groups affected by the mitigation project, particularly gender. Future research paths include field studies to improve evidence of community experiences and a large-n study to improve generalizability.

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