Structural Associations of Digital Economy Components with Green Economic Efficiency in China

Digital transformation may improve productive efficiency while increasing energy and material demand. We examine its association with green economic efficiency (GEE) in a balanced panel of 30 Chinese provincial-level regions from 2012 to 2023. GEE is measured using an undesirable-output, output-oriented, constant-returns-to-scale super-efficiency ε-based measure (EBM), and digital development is represented by a total index and three entropy-weighted components. Dynamic fixed-effects error-correction models include province and year effects and province-clustered standard errors. With year effects, the total index is positively associated with GEE nationally. Baseline industrial digitalization is positive, but this finding is suggestive rather than robust because its significance varies across sensitivity checks; digital industrialization is insignificant. Within the primary model and most reported specifications retaining year effects, infrastructure shows the most stable positive long-run pattern, with a negative short-run association in the primary model; the no-year-effects specification is an explicit exception. Omitting year effects also reverses the aggregate sign and produces a negative digital-industrialization coefficient. The findings are conditional dynamic associations, not causal mechanism estimates.