Green credit as a catalytic mechanism for carbon intensity reduction in China: A novel GDI-ARDL modeling approach.

China's path to carbon neutrality hinges on a delicate trade-off: slashing CO2 emissions without stifling the economic vitality essential for its modernization goals. In this context, carbon intensity (CI) measured as CO2 emissions per unit of GDP serves as a key metric for assessing economic-environmental efficiency. While existing research has attempted to apply the Generalized Divisia Index (GDI) to examine CI determinants, it has overlooked the role of green credit as a critical policy instrument, let alone systematically examining its short- and long-term impact mechanisms. To address these gaps, this paper proposes a novel GDI-Autoregressive Distributed Lag (ARDL) model. The results reveal significant regional heterogeneity in CI mitigation, with the Eastern region driving substantial national reductions while the Northeast and Western regions underperformed. The GDI decomposition analysis reveals that while carbon emission effects raised CI, credit-based emission reduction effects dominated the offsetting process, complemented by the regulatory potential of credit structure effects optimization. The ARDL results indicate that green credit exerts a "long-term effective but short-term lagged" inhibitory effect on CI, with the error correction term (ECT = -0.4837) driving 48.37% of short-term disequilibrium back to long-run equilibrium per period.