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The impact of green technology innovation, environmental taxes, and renewable energy consumption on ecological footprint in Italy: Fresh evidence from novel dynamic ARDL simulations

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  • Javed, Aamir
  • Rapposelli, Agnese
  • Khan, Feroz
  • Javed, Asif

Abstract

Over the last few decades, environmental hazards have grown as a result of the increasing ecological pressure exerted by humanity, as calculated by its ecological footprint (EFP). Green technology innovation (GTI), renewable energy consumption (REN) and environmental taxes (ET) now represent the best approaches to attaining sustainable development without harming the environment. In this context, the main objective of this paper is to investigate the effect of GTI, ET, REN, TO and GDP on the EFP in Italy during the period 1994–2019. We employ a novel dynamic simulated ARDL (DYARDL) model to evaluate the short and long-run run connections among the above variables. Our study also utilizes the frequency domain causality (FDC) test to analyze the direction of causality. The results of the study clearly indicate the existence of a long-term cointegration among variables. Further, the results demonstrate that GTI, ET, and REN significantly enhance the quality of the environment by lowering the EFP. However, TO and GDP have a positive significant influence on EFP and degrades the environment. Finally, FDC test results also confirmed that GTI, ET, REN, TR, and GDP granger cause EFP. The results obtained suggest that government and policy makers should promote the investment in green innovation and renewable energy sectors to accomplish the ecological sustainability.

Suggested Citation

  • Javed, Aamir & Rapposelli, Agnese & Khan, Feroz & Javed, Asif, 2023. "The impact of green technology innovation, environmental taxes, and renewable energy consumption on ecological footprint in Italy: Fresh evidence from novel dynamic ARDL simulations," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:tefoso:v:191:y:2023:i:c:s0040162523002196
    DOI: 10.1016/j.techfore.2023.122534
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