Who shapes climate impacts research? An NLP-based network analysis of global hubs and bridges

Principles of justice and equity in climate impacts research are widely recognized as essential for the legitimacy and effectiveness of international climate agreements. Yet, quantitative evidence on global imbalances in climate knowledge production remains limited. In this study, we leverage recent advances in Natural Language Processing to provide a large-scale, data-driven assessment of global inequalities in climate impacts research, with particular focus on disparities between the Global North and the Global South, as well as differences across country income groups as defined by the World Bank’s gross national income–based classification. We compile a dataset of over 40,000 open- and closed-access scientific publications from OpenAlex related to the thematic scope of IPCC Working Group II on societal impacts, vulnerability, and adaptation. The relevance of publications within our database is identified using a machine-learning pipeline. Building on the relevant articles, we analyze global co-authorship networks to identify key research hubs, bridges, and communities across countries and regions. Our preliminary results show that climate impacts’ research is predominantly led by high-income countries, which dominate the top ten global research hubs and account for more than 60% of total authorships. Research communities exhibit strong geographic clustering, with countries collaborating more intensively with continental neighbors. However, high-income countries play a disproportionate intermediary role in global collaboration networks: despite its geographic distance, the United Kingdom intermediates twice as many scientific collaborations within the African climate impacts research community as South Africa. We further quantify structural inequalities in collaboration using temporal homophily measures in co-authorship networks. While cross-income and North–South collaborations have increased over time, income-based homophily remains stable once research productivity is accounted for, indicating that high-income countries continue to preferentially co-author with one another. This suggests that increased connectivity has not translated into more equitable research output. By using NLP-based literature mapping and network analysis, this work highlights their combined potential for diagnosing structural biases in climate change knowledge production. Our findings aim to provide empirical evidence to support more equitable research collaborations, and more coherent international climate change policy frameworks.