Does Energy Consumption, Economic Growth, and Foreign Direct Investment Contribute to CO2 Emission? Evidence from Bangladesh

This work used the Johansen Cointegration Test and the Vector Error Correction Model (VECM) cointegration methodology to assess the long-run cointegrating relationship and short-run dynamics in Bangladesh between energy consumption economic growth, foreign direct investment, urbanization, population growth, and carbon emissions. To assess the long-term association between these variables, we examined data from 1972 to 2014, and empirical estimation revealed that all factors are significant at the 1% level of significance in the case of Bangladesh. Thus, energy consumption, economic growth, foreign direct investment, urbanization, population growth, and carbon emissions  all have shown the predicted sign and are statistically significant, indicating that increased energy consumption, gross domestic product, and population increase all are responsible for increased carbon emissions in Bangladesh. Higher FDI inflows, on the other hand, cut per capita carbon emissions in Bangladesh. On the other hand, the empirical outcome has revealed that there is no substantial causal association between carbon emissions and urbanization. Keywords: CO2; FDI; GDP; population growth; energy consumption; VECM DOI: 10.7176/JESD/12-12-05 Publication date:June 30th 202

Causal Inference with Missingness in Confounders

Causal inference is the process of uncovering causal connection between the effect variable and disease outcome in epidemiologic research. Confounders that influence both the effect variable and outcome need to be accounted for when obtaining the causal effect in observational studies. In addition, missing data often arise in the data collection procedure, working with complete cases often results in biased estimates. We consider the estimation of causal effect in the presence of missingness in the confounders under the missing at random assumption. We investigate how different estimators namely regression, G-estimation, propensity score-based estimators including matching, stratification, weighting, propensity regression and finally doubly robust estimator, perform when applying complete-case analysis or multiple imputation. Due to the uncertainty of imputation model and computational challenge for large number of imputations, we propose an expectation-maximization (EM) algorithm to estimate the expected values of the missing confounder and utilize weighting approach in the estimation of average treatment effect. Simulation studies are conducted to see whether there is any gain in estimation efficiency under the proposed method than complete case analysis and multiple imputation. The analysis identified EM method as most efficient and accurate method for dealing missingness in confounder except for propensity score matching and inverse weighting estimators. In these two estimators, multiple imputation is found as efficient, however EM is efficient for inverse weighting when the outcome is binary. Real life data application is shown for estimating the effect of adjuvant radiation treatment on patient's survival status after 10 years of breast cancer diagnosis. Under missing completely at random (MCAR) mechanism, EM is found as the most accurate method for handling missingness in confounder than multiple imputation

Does globalization escalate the carbon emissions? Empirical evidence from selected next-11 countries

Adverse consequences are observed in developing countries due to the impact of the globalization process. Therefore, our study aims to empirically verify whether globalization escalates carbon dioxide emissions in selected N-11 (next-11) countries between 1990 and 2019. The study also analyzes how per capita GDP, per capita GDP2, population growth, and renewable energy consumption affect carbon emissions. For this reason, the researchers used several econometric methods, including the slope homogeneity test, the cross-sectional dependency test, the panel unit root test, the panel cointegration test, the method of moment’s panel quantile regression analysis, and the Wald test. The estimated results of panel quantile regression show how carbon emissions change across a range of quantiles (0.1 to 0.9). The findings show that per capita GDP significantly impacts the overabundance of carbon emissions in N-11 countries. Over time, the study found that the positive coefficient value of per capita GDP decreased from the first to the last (7.41 to 5.87), leading to the validation of the EKC hypothesis. The adverse correlation between per capita GDP2 and environmental contamination confirms that the Environmental Kuznets Curve hypothesis is valid for selected N-11 countries. Globalization deteriorates the environment by directly affecting CO2 emissions. It increases monotonically from the lower quantile to the upper quantile (0.972 to 1.002). At the quantile level of 0.1 to 0.9, population growth and renewable energy consumption increase impede carbon dioxide emissions in these selected countries. Coefficient values in the quantile 0.1 to 0.9 (-0.35 to -0.53) suggest that governments can reduce carbon emissions more due to renewable energy consumption over time. But the negative coefficient values of the population (-0.97, -0.93, -0.90, -0.88, -0.86, -0.85, -0.83, -0.81, and -0.77) decrease from the lower quantile to the upper quantile. The Wald test supports the asymmetric effects of different quantiles. As a robustness check of estimators, the study used FMOLS, DOLS, and CCR, which show the variables’ long-run elasticity. The research developed targeted policy recommendations for sustainably mitigating carbon emissions based on the above results