Unlocking information technology infrastructure for promoting climate resilience and environmental quality

We examine the role of Information Technological Infrastructure (ITI) in promoting climate resilience and environmental quality in OECD countries. Our empirical strategy is framed in the advanced econometrics methodology. Our analysis begins with standard specification, which controls for unobserved factors in the panel data. We then explore the variables' long and short-run relationships using the Generalised Method of Moment dynamic family specifications. We also evaluate the heterogeneity drivers of environmental quality using Quantile via Moment. The analysis is also extended using alternative and complementary statistical procedures by Hausman-Taylor and Feasible Generalised Least Squares as robustness checks. Our findings indicate that ITI and renewable energy significantly mitigates carbon emissions and can be helpful in achieving a net-zero target. In contrast, the empirical evidence reveals that economic growth and non-renewable energy usage are harmful to the environment. The finding also suggests a significant degree of heterogeneity exists in the covariates on the conditional distribution of environmental quality and its driven factors. While the findings reaffirm the significance of ITI in ensuring careful planning and monitoring of critical infrastructure, they also show that ITI can be used to balance the entire system by creating resilience. We strongly suggest that policymakers should use ITI to spur innovation and drive better solutions for energy transition and environmental improvement.

Automated summary

This study examines the role of Information Technological Infrastructure (ITI) in promoting climate resilience and environmental quality in OECD countries. The findings suggest that ITI and renewable energy significantly mitigate carbon emissions and contribute to achieving a net-zero target, while economic growth and non-renewable energy usage harm the environment. The study also reveals the heterogeneity in the covariates on the conditional distribution of environmental quality and its driven factors.