Data-driven complementary indices and metrics for assessing national progress on climate risk and adaptation

Abstract

Climate change is a complex, multidimensional issue requiring decision-making and governance supported by extensive data from social and natural systems. Large cross-country datasets are available, and various methods are used to transform this data into information relevant for policy and decision-making. Summary indices provide insights into adaptation, mitigation, vulnerability, and risk, helping track countries’ climate-related ambitions and progress. However, many existing methods for constructing indices do not fully exploit the multivariate structures within the data, leading to potential redundancies and overlaps. We develop a set of complementary, non-overlapping indices using Principal Component Analysis to capture distinct dimensions of societal and climate interactions. These data-driven indices account for underlying data structures, ensuring each provides unique and independent insights. Our analysis includes harmonized country-level datasets, metrics relevant to loss and damage, public perceptions of climate change, and projections of economic damages. The application of these indices is illustrated with dissonance metrics that assess the alignment between a country’s adaptation capacities, societal concerns, and risks. The proposed approach for index construction can be valuable across various policy contexts and for informing climate-related strategies. An online tool is provided to visualize and access the results presented in this paper.