アルゴリズム的権力と気候外交：パキスタンの地球規模気候ガバナンスにおける人工知能の影響評価

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:AIと気候外交の交差領域の理論枠組みと指標化の試みとして有用。

🏢実務担当者:途上国での気候データ整備やAI活用の投資判断に示唆。

🏛政策担当者:気候交渉におけるデジタル格差と国際協力の政策立案に参考。

This study examines the implications of artificial intelligence (AI) for Pakistan’s role in global climate governance by introducing the concept of “algorithmic power.” Traditional climate diplomacy relies on economic size, technical expertise, and alliance networks, yet overlooks how data infrastructure and AI tools reshape negotiation leverage. We propose a framework comprising three dimensions Data Capacity, Analytical Capacity, and Diplomatic Capacity operationalized through three composite indices: Algorithmic Readiness Index (ARI), Diplomatic Leverage Ratio (DLR), and Algorithmic Influence Score (AIS). Using hypothetical scores calibrated against regional benchmarks, Pakistan’s ARI is estimated at 0.40, indicating limited algorithmic readiness, while its DLR of 1.25 suggests relatively effective conversion of this readiness into diplomatic influence. However, the overall AIS of 0.50 reveals modest aggregate influence, underscoring structural constraints in data and analytical capacities. Qualitative analysis of Pakistan’s climate policy documents and UNFCCC submissions shows limited explicit reference to AI, reliance on traditional indicators, and emerging but underdeveloped awareness of digital tools. The findings highlight that while diplomatic initiative and coalition-building can partially offset algorithmic weaknesses, long-term influence in an increasingly data-driven climate regime requires targeted investments in climate data systems, AI literacy, modelling institutions, and equitable international digital cooperation. References Ahmad, H., & Muhammad, F. (2026). The AI–climate nexus in international relations: Climate diplomacy, energy transition, and emerging technology governance. Journal of Global Social Transformation, 2(8), 283–293. Al Kium, A., Sarker, S., Shikha, S. A., Kamal, M. A. T., Jabed, M. I. K., Munifa, N. 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