Distributed Causality in the SDG Network: Evidence from Panel VAR and Conditional Independence Analysis
Md Muhtasim Munif Fahim, Md Jahid Hasan Imran, Luknath Debnath, Tonmoy Shill, Md. Naim Molla, Ehsanul Bashar Pranto, Md Shafin Sanyan Saad, Md Rezaul Karim

TL;DR
This paper develops a causal network model of SDG dependencies using Panel VAR and conditional independence tests across 168 countries, revealing complex interrelations and the importance of coordinated multi-goal strategies for sustainable development.
Contribution
It introduces a novel causal discovery framework combining Panel VAR and PCMCI+ to map SDG dependencies, challenging the idea of a single keystone SDG and proposing a tiered priority framework.
Findings
Identified 10 significant causal relationships among SDGs
Education to Inequality is the most significant direct effect
Effect magnitudes vary by income level
Abstract
The achievement of the 2030 Sustainable Development Goals (SDGs) is dependent upon strategic resource distribution. We propose a causal discovery framework using Panel Vector Autoregression, along with both country-specific fixed effects and PCMCI+ conditional independence testing on 168 countries (2000-2025) to develop the first complete causal architecture of SDG dependencies. Utilizing 8 strategically chosen SDGs, we identify a distributed causal network (i.e., no single 'hub' SDG), with 10 statistically significant Granger-causal relationships identified as 11 unique direct effects. Education to Inequality is identified as the most statistically significant direct relationship (r = -0.599; p < 0.05), while effect magnitude significantly varies depending on income levels (e.g., high-income: r = -0.65; lower-middle-income: r = -0.06; non-significant). We also reject the idea that…
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Taxonomy
TopicsEnergy, Environment, Economic Growth · Sustainability and Climate Change Governance · Qualitative Comparative Analysis Research
