Conventional measures of economic growth do not account for environmental degradation and natural capital depletion, potentially overstating development performance in rapidly transforming economies like India. This study examines the environmental efficiency of economic growth by analysing the Green-Brown GDP gap, defined as the divergence between conventional GDP and environmentally adjusted Green GDP, over the period 1990-2020. The Green-Brown GDP gap averaged 7.1% during the study period, indicating that environmentally adjusted output remained consistently below conventional GDP. The empirical strategy combines a linear Autoregressive Distributed Lag (ARDL) model with a nonlinear ARDL (NARDL) framework to examine the effects of forest cover, renewable energy consumption, carbon emissions, urbanization, and trade openness on environmentally adjusted growth. The linear results indicate that a one percentage point increase in forest cover reduces the Green-Brown GDP gap by approximately 3.1% in the long run, while a 1% increase in trade openness increases the gap by about 0.026%. The error-correction term is negative and statistically significant, indicating that approximately 54.5% of short-run disequilibrium is corrected annually. The nonlinear analysis provides stronger evidence of cointegration and reveals important asymmetries. Positive shocks to carbon emissions widen the Green-Brown GDP gap, whereas reductions contribute to narrowing it. Trade openness also exhibits asymmetric effects, with negative trade shocks worsening environmental efficiency more strongly than positive shocks improve it. The nonlinear adjustment process corrects approximately 85.2% of disequilibrium within one year. The findings suggest that sustainability depends on the composition and environmental intensity of growth rather than its pace, highlighting the importance of natural capital preservation and structural transformation.
Payasi et al. (Wed,) studied this question.