How digitalization, renewable energy, and natural resources shape environmental excellence? Evidence from China using a Quantile-on-Quantile framework

• Digitalization, Renewable Energy, and Natural Resources on Ecological Footprint in China. • Novel Quantile-on-Quantile regression, and cross-quantile, and partial cross-quantile correlation models. • The estimated analysis discloses nuanced dependencies across various quantiles. • China’s environmental quality is influenced by digitalization, renewable energy, and natural resources. Natural resources, green energy, and sustainable development are closely linked with concepts that carry mutual goals to endorse social equity, economic prosperity, and ecological stability while curtailing the harmful influence on the globe. However, the recognition of the Sustainable Development Goals (SDG-7, SDG-13) is closely entangled with digital economy. In this pursuit, this study scrutinizes the effect of digitalization, renewable energy, and natural resources on the ecological footprint in China from 1990Q1-2022Q4. The empirical analyses are carried out by employing the Quantile-on-Quantile regression, and cross-quantile and partial cross-quantile correlation approaches to inspect the tail dependence of model parameters. The empirical outcomes highlight how China’s environmental quality is influenced by exogenous variables, including digitalization index, renewable energy consumption, and natural resources. Digitalization has adverse impact on the ecological footprint in lower quantiles, while insignificant in higher quantiles. Moreover, a strong adverse association exists between ecological footprint and renewable energy, which syndicate all the quantiles of renewable energy with linking over lower to middle quantiles and weak in higher quantiles of ecological footprint. Besides, the estimated analysis discloses nuanced dependencies across various quantiles. Similarly, it can be found that the strong negative effect of natural resources on ecological footprint in initial quantiles, moderate in middle quantiles, and less positive effect in higher quantiles. By explaining these dynamics, the current study offers valuable intuitions designed at controlling China toward its dual-carbon target and encouraging the development of a sustainable digital and green economy and thereby, continuing towards achieving SDG-7, and SDG-13 objectives.