Forecasting CO2 Emissions in India: A Time Series Analysis Using ARIMA

This study evaluates the capability of the ARIMA (Auto Regressive Integrated Moving Average) to predict CO2 emissions in India using data from 1990 to 2023, addressing a critical need for accurate forecasting amid various economic and environmental uncertainties. It is observed that ARIMA yields high accuracy with respect to the prediction, and hence, it is reliable for environmental forecasting. These predictions give policymakers evidence-based information to aid in implementing sustainable climate policies within India. To ensure reliable predictions, the study methodology utilizes the Box–Jenkins approach, which encompasses model identification, estimation, and diagnostic checking. The initial step in the study is the Augmented Dickey–Fuller (ADF) test, which assesses data stationarity as a prerequisite for precise time series forecasting. Model selection is guided by the Akaike Information Criterion (AIC), which balances prediction accuracy with model complexity. The efficiency of the ARIMA model is assessed by comparing the actual observed values to the predicted CO2 emissions and the results demonstrate ARIMA’s effectiveness in forecasting India’s CO2 emissions, validated by statistical measures that confirm the model’s robustness. The value of the present study lies in its focused assessment of the relevance of the ARIMA model to the specific environmental and economic context of India, with actionable insight for policymakers. This study enhances prior research by incorporating a focused approach to data-driven policy formulation that increases climate resilience. The establishment of a reliable model for the forecasting of CO2 will aspire to support informed decision making in environmental policy and help India move forward toward sustainable climate goals. This study only serves to highlight the applicability of ARIMA in terms of environment-based forecasting and permits further emphasis on how much this method can be a useful data-based tool in climate planning.