Since the approval of the Agenda 2030, researchers and policy makers have pointed out the need to understand interactions among the Sustainable Development Goals (SDGs)—suggesting that progress or the lack of progress toward one goal will affect other goals through systemic interactions, producing synergies and trade-offs. However, most of the methods used to account for these interactions rely on hard systems thinking approaches, which are limited by the absence of needed data below national levels. Moreover, a general lack of data also constrains the scope of analysis to the 17 Goals, ignoring their 169 underlying targets. Given these challenges, we report on an experiment using an example of a soft systems thinking methodology: the SDG Synergies approach, which is based not only on available information but also on the elicitation of stakeholder and expert opinions. Thus, the approach allows for analysis of target-to-target interactions at subnational scales. The study, the first of its kind, assessed interactions at two scales: the national level in Colombia and the subnational level in the department of Antioquia. The results reveal profound differences between the two scales, suggesting that national-scale analysis of SDG interlinkages is not certain to capture local-level realities. The findings raise important issues for understanding and managing cross-scale interactions. Our work suggests that soft systems thinking is more appropriate for assessing SDG interactions because such an approach lends itself to conducting target-level analysis at various scales in the face of limited data availability.
Climate change impacts on populations have increased the number of affected people and climate migrants worldwide. Although the nexus between climate change and migration is not monolithic, analyses of individual-level factors at the local scale that reveal the specific drivers of migration are lacking. Here, we show that people are motivated by individual calculations, prioritizing economic and social factors when deciding to migrate. We use data from 53 structured interviews to decompose the assessment of the decision-making process of people deciding to migrate from a region highly vulnerable to climate change, assessing the internal and external migratory potential. The assessment of migration potential evidenced that potential migrants react and make decisions based on perceptions and preferences among economic, social, environmental, and cultural factors when migrating and value these factors differently. Our spatial multi-criteria model reports disaggregation in that people prioritize economic factors, such as unemployment, job opportunities, and lack of income, over other migration-related factors, while environmental factors are generally considered underlying. Our results demonstrate that migration is not monolithic but a mixture and amalgam of multiple interacting factors, which causes people to migrate or stay in one place despite vulnerability and climate change impacts.
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