Carbon dioxide is believed widely to be the major contributor to global warming. Policymakers worldwide are turning to tax policies in an effort to abate carbon emissions. China is the largest emitter of carbon emissions on our planet. The central government, as well as the local official, has introduced a series of environmental regulations, such as environmental protection tax and emissions trading system, to reduce carbon emissions and improve environmental quality. In the near future, the carbon emission tax is also expected to be implemented by the Chinese government. In order to analyze and predict the effect of the carbon emission tax on environmental and economic systems, we developed a four department dynamic stochastic general equilibrium model, which includes households, enterprises, the government, and the environment. The dynamic parameters were obtained using maximum likelihood estimation. In the comparative static-s analysis, we found that after the introduction of carbon emission tax, the level in environmental quality was substantially improved, whereas most economic variables were significantly reduced. Moreover, we used impulse responses functions to evaluate how one shock to the carbon emission tax affects the steady static values for these endogenous variables in our model. We found that the carbon emission tax shock has an instantaneous effect on the majority of economic variables, but it does not affect the environmental quality immediately. In addition, we tested the Porter hypothesis and found no evidence suggesting the statement regarding this hypothesis. Finally, we applied Bayesian estimation to assure our findings in this study, again.
Researcher and analyst are often interested in estimating the effect of an intervention or treatment, which takes place at the aggregate level and affect one single unit, such as country and region. Thus, comparative case studies would be their first choice in practice. However, comparative case studies could fail to yield an estimate in the effect that is unbiased and consistent, as in some contexts; there are not suitable control units that are similar to the treated. The econometric literature has taken synthetic control methods and panel data approaches to this problem. In this study, we developed a principal covariate regression estimator, which exploits the cross-sectional correlation, as well as the temporal dependency, to reproduce the dynamics of the treated in the absence of an event or policy. From a theoretical perspective, we introduce the statistical literature on dimensional reduction to make a causal inference. From a technique perspective, we combine the vertical regression and the horizontal regression. We constructed an annual panel of 38 states, to evaluate the effect of Proposition 99 on beer sales in California, using the principal covariate regression estimator proposed here. We find that California’s tobacco control program had a significant negative and robust effect on local beer consumption, suggesting that policymakers could reduce the use of cigarette and alcohol in the public using one common behavioral intervention.
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