Effect of Environmental Tax on Ecological Quality Using Machine Learning Algorithm

Abstract

Many developing nations are considering environmental taxes as a way to raise money and fulfill their obligations in combating climate change and promote sustainable development. Recently, government of Ghana tax policy of E-levy as well as emissions tax has received mixed reaction by various stakeholders. This has called for the need to look at various forms of tax avenues that can serve as alternative tax policy to achieve the government developmental agenda for the years 2000 to 2019. Notwithstanding, it is indispensable to guise at the effect of the introduction of the environmental tax policy on environment-growth correlation in trying to achieve Sustainable Development Goals 8 and 13. The study employed machine learning algorithm such as Kernel-based Regularized Least Squares (KRLS) techniques to scrutinize the causal-upshot connection. One conspicuous result is that, environmental tax was found not significant but the parameter shows that, 1% change have inverse connection with ecological quality. That is, the change reduces the emission level by 0.037. Further findings vividly disclosed that, population and economic expansion have snowballing peripheral effects on emission level. Hence, there is a need to hone green tax policies to provide stronger spurs for industries within the state to adopt much greener habit in their activities. This policy strategy can be achieved through the provision of reducing the environmental rate for industries emitting emission within a certain threshold as well as educating industries on emission reduction strategies especially on how to benefit from carbon trade if they reduce their emission level.