Transnational dependency networks shape trade-offs between decarbonization and economic growth

Enhancing carbon emission efficiency (CEE) is crucial for balancing economic growth with low-carbon transformation. Transnational regional dependencies from global integration offer both challenges and opportunities for collaborative decarbonization. This study presents a multi-objective optimization framework incorporating transnational dependency networks to improve CEE, reduce carbon emissions, and sustain economic development. First, a three-stage super-efficiency model quantifies national CEE. Second, a gravity model integrates economic, population, emission, and geographic factors to build spatio-temporal dependency networks. Third, based on network features, surrogate models for CEE, carbon emissions (CE), and gross domestic product (GDP) are constructed, using explainable machine learning and multi-objective algorithms to identify optimal strategies. A case study of 112 Belt and Road countries (2011–2020) shows: (1) Surrogate models incorporating cross-border dependencies perform well (R² for CEE, CE, GDP: 0.908, 0.927, 0.918); (2) Multi-factor optimization raises CEE by 12.8%, cuts CE by 29.2%, but reduces GDP by 22.7%; (3) Single-factor optimization lessens economic losses (0.48% CEE increase, 28.3% CE and 24.3% GDP reductions). The study’s novelty is: (a) proposing a transnational dependency network framework for efficiency measurement, modeling, and optimization; (b) developing three-dimensional optimization algorithms prioritizing CEE. This research advances theory and methods for low-carbon transition in Belt and Road countries, offering guidance for transnational decarbonization policies.