Modeling the relationship between carbon emissions and environmental sustainability during COVID-19: a new evidence from asymmetric ARDL cointegration approach

Abstract

The study aims to examine the CO 2 emissions by considering the implication of COVID-19 under strict lockdown in India. The nonlinear (asymmetric) relationship is investigated between CO 2 emission and COVID-19 with its specific determinants. The positive and negative asymmetries of COVID-19 determinants are also captured by using econometric techniques. The daily data series of CO 2 emission, new confirmed cases, confirmed deaths, and lockdown as dummy variables from January 30, 2020, to December 1, 2020, for India is analyzed by employing the nonlinear autoregressive distributed lag model. This research revealed a significant nonlinear relationship between CO 2 emission and COVID-19. The bound test and asymmetric coefficients are confirmed by the variables' long- and short-run relationships. The dynamic multiplier graphs present that India's strict lockdown due to the rapid increase in COVID-19 patients significantly reduces toxic gas emissions, especially CO 2 emissions. This asymmetric relationship has been proficiently declared that unhealthy public routine, extra traffic, and unhygienic gases released in the air become the reason for environmental destruction. The lockdown is practically imposed for specific periods and reasons, contributing to reducing toxic emissions, but it is not a permanent solution for environmental sustainability. The government of India, policymakers, and environmentalists should make people aware of unhealthy and environmentally envying activities and policies and long-term applicable strategies should be designed to upgrade the environment's quality.

Do environmental pollutants carrier to COVID-19 pandemic? A cross-sectional analysis

“…The results are also in contrast with the results of Wu et al ( 2020 ). The earlier studies confirmed the negativity of the environmental pollutants leading to COVID-19 deaths, i.e., Sarfraz et al ( 2021 ), Cicala et al ( 2021 ), and Magazzino et al ( 2020 ). The stated studies argued that ecological factors, including environmental pollutants, provide a channel to exacerbate coronavirus cases, requiring stringent environmental regulations and sound healthcare policies to improve community health Table 7 .…”

COVID-19 and Greenhouse Gas Emission Mitigation: Modeling the Impact on Environmental Sustainability and Policies

“…Table 3 contains the result of the bound test, which is generally used to check the binding of variables for long-term and shortterm interactions. This test is based on the mixture of integrated results of ADF to avoid spurious results (Naseem et al, 2021;Sarfraz et al, 2021). The value of F-statistics is higher than tabulated, which confirms the significance of all variables at 1% significance level.…”

“…The short-run ARDL model is employed, and the results are presented in Table 4. Before explaining the results, it is necessary to mention that the software automatically selected the lags and behavior of variables and delivered the final form (Sarfraz et al, 2020;Naseem et al, 2021;Sarfraz et al, 2021), which is presented in Table 4 under consideration of a specific set of variables. The first and third interacted variables toward the independent variable are NO 2 and SO 2 , which authorize the significance of confirmed cases and deaths of COVID-19 at a 1% level of significance.…”

“…The COVID-19 parameters for structuring the models are lockdown, and confirmed cases and confirmed deaths of COVID-19. The last term of the equation ε t is an indication of an error term that measures data disturbance (Dar et al, 2021;Naseem et al, 2021;Sarfraz et al, 2021).…”

Shallot Market Cointegration Between Markets in Province West Southeast and East Nusa Tenggara

“…Cointegration is closely related to the problem of determining a long-term relationship or longterm balance. If the time-series data are cointegrated, there is a long-term relationship between the time series data (Arnold & Glushko, 2021;Chisti, Shakeel, & Ganai, 2020;Engle & Granger, 1987;Maysami, Howe, & Rahmat, 2005;Roy, Ratya, Nuhfil, & Wahib, 2018;Sarfraz, Mohsin, Naseem, & Kumar, 2021), cointegration is a long-term relationship between variables which, although individually not stationary, but linear conditions between these variables can be stationary. Cointegration occurs when the independent and dependent variables are both a trend (time series), so they are not stationary.…”