The Twin Transition and Carbon Emissions per Capita: Digital-Green Complementarities and Configuration Effects
Year: 2026
Type: article
Abstract: The twin transition is widely expected to support decarbonization, yet the conditions under which it does so remain poorly understood. This study examines whether and how the twin transition is associated with per-capita CO 2 emissions across countries, distinguishing between aggregate transition progress and structurally distinct configurations: digital-green synergy, transition imbalance, and directional emphasis. Using an original composite Twin Transition Index constructed from digital connectivity and renewable-energy indicators, and a two-way fixed-effects framework with Driscoll-Kraay standard errors applied to a panel of 74 countries over 2000-2020, the analysis produces four main findings. First, aggregate twin transition progress carries no stable unconditional association with per-capita emissions; environmental outcomes depend critically on transition structure rather than transition scale. Second, digital-green synergy is robustly associated with lower per-capita CO 2 emissions, while digital-first trajectories are associated with higher emissions; the absolute gap between dimensions carries no independent effect. Third, the emission-reducing association of coordinated transition progress strengthens after 2010, consistent with a structural shift in the digital-energy-emissions nexus as renewable technologies became cost-competitive. Fourth, mechanism tests indicate that effects operate through changes in energy composition and efficiency rather than reductions in aggregate demand and are amplified in countries with stronger economic and infrastructural readiness. These findings demonstrate that the twin transition is associated with lower emissions only when digital and green capabilities advance in coordination and establish transition configuration (not transition speed) as the central determinant of environmental performance.
Authors: Rebean Al-Silefanee, Ramazan Uctu, Serife Koc
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7203758092