Pakistan CO(2) Emission Modelling and Forecasting: A Linear and Nonlinear Time Series Approach

Pakistan is considered among the top five countries with the highest CO(2) emissions globally. This calls for pragmatic policy implementation by all stakeholders to bring finality to this alarming situation since it contributes greatly to global warming, thereby leading to climate change. This study is an attempt to make a comparative analysis of the linear time series models with nonlinear time series models to study CO(2) emission data in Pakistan. These linear and nonlinear time series models were used to model and forecast future values of CO(2) emissions for a short period. To assess and select the best model among these linear and nonlinear time series models, we used the root mean square error (RMSE) and the mean absolute error (MAE) as performance indicators. The outputs showed that the nonlinear machine learning models are the best among all other models, having the lowest RMSE and MAE values. Based on the forecasted value of the nonlinear machine learning neural network autoregressive model, Pakistan's CO(2) emissions will be 1.048 metric tons per capita by 2028. The increasing trend in emissions is a frightening and clear warning, suggesting that innovative policies must be initiated to reduce the trend. We encourage the Pakistan government to price CO(2) emissions by companies and entities per ton, adapt electricity production from hydro, wind, and different sources with no emissions of CO(2), initiate rigorous planting of more trees in the populated areas of Pakistan as forest covers, provide incentives to companies, organisations, institutions, and households to come out with clean technologies or use technologies with no CO(2) emissions or those with lower ones, and fund more studies to develop clean and innovative technologies with less or no CO(2) emissions.