Asymmetric linkages between pandemic uncertainty and environmental quality: Evidence from emerging economies

Abstract

The current COVID-19 pandemic was a huge shock, influencing a wide range of socioeconomic measures, including the environment. The issue of how the uncertainty caused by pandemics will influence environmental quality is critical. This research examines the nonlinear relationship between pandemic uncertainty and environmental quality across leading polluted emerging economies (China, India, Russia, Indonesia, Brazil, Mexico, Iran, Saudi Arabia, South Africa, and Turkey). Using data ranging from 1996 to 2020, a distinctive approach, ‘Quantile-on-Quantile’, is used. Greenhouse gas emissions (GHG) are adopted as a proxy for environmental quality. The outcomes analyze how pandemic uncertainty's quantiles influence the quantiles of GHG asymmetrically, giving an efficient paradigm for grasping the entire dependence structure. The findings show that pandemic uncertainty improves environmental quality by decreasing GHG in our sample economies at diverse quantiles. Higher levels of GHG (75th–90th quantiles) suggest a strong negative association between pandemic uncertainty and GHG in the majority of nations. The magnitude of the coefficients helps to explain why pandemic uncertainty has a significantly greater impact on GHG in Mexico and Turkey (with a coefficient size of −2) compared to Russia, India, and South Africa, where the effect is considerably smaller (with a coefficient size of −0.05). Furthermore, the rank of asymmetry in our chosen variables fluctuates by nation, underscoring the prominence of governments exercising caution and prudence while implementing pandemic-based uncertainty and environmental quality measures.

Asymmetric nexus between pandemic uncertainty and public health spendings: Evidence from quantile estimation

“…The influence of PNU on PHS is anticipated to exhibit variability, contingent upon the degree of PNU, thereby highlighting an intricate and evolving relationship that becomes more pronounced with higher levels of PNU. Building on our hypothesis, we propose that the nonlinear distribution of attributes would result in nonlinear oscillations between PNU and PHS, consequently giving rise to nonlinear disparities in economic indicators (Wentao et al., 2023).…”