Quantifying Climate Policy Action and Its Links to Development Outcomes: A Cross-National Data-Driven Analysis

TL;DR

This paper introduces a quantitative, NLP-based framework to classify and analyze climate policies across nations, linking policy focus to development outcomes with high accuracy and revealing key correlations.

Contribution

It develops a multilingual transformer-based model to classify climate policies and links these classifications to development data, providing a scalable, quantitative assessment tool.

Findings

Mitigation policies are linked to higher GDP and GNI.

Disaster risk management correlates with increased GNI and debt but less FDI.

Adaptation and loss and damage have limited measurable effects.

Abstract

Addressing climate change effectively requires more than cataloguing the number of policies in place; it calls for tools that can reveal their thematic priorities and their tangible impacts on development outcomes. Existing assessments often rely on qualitative descriptions or composite indices, which can mask crucial differences between key domains such as mitigation, adaptation, disaster risk management, and loss and damage. To bridge this gap, we develop a quantitative indicator of climate policy orientation by applying a multilingual transformer-based language model to official national policy documents, achieving a classification accuracy of 0.90 (F1-score). Linking these indicators with World Bank development data in panel regressions reveals that mitigation policies are associated with higher GDP and GNI; disaster risk management correlates with greater GNI and debt but reduced foreign direct investment; adaptation and loss and damage show limited measurable effects. This integrated NLP-econometric framework enables comparable, theme-specific analysis of climate governance, offering a scalable method to monitor progress, evaluate trade-offs, and align policy emphasis with development goals.