From National Goals to Industry Action: AI-Driven Forecasting of India’s Carbon Emissions

Anushka Chaubey, Tushar Shinde

Keywords: Carbon emissions, Physics-Informed Neural Networks, PINNs, Time series forecasting, ESG reporting, Climate TRACE, India emissions data, XGBoost, Prophet, ARIMA, SARIMA, Holt-Winters, Random Forest, Gradient Boosting, SVR, Hybrid modeling, Machine learning, Missing value imputation, Distribution-aware imputation, Lag features, MAPE, Mean Absolute Percentage Error, Residual correction, Emission forecasting, Sustainability, Climate goals, Environmental data, Seven sectors, Waste management, Manufacturing, Fossil fuels, Transportation, Power generation, Agriculture, Buildings, National-level emissions, Industry-level forecasting, Carbon transparency, Sustainable development, Neural networks, Classical time series models, Predictive modeling, Climate data analysis, ESG compliance, Emission trends, AI-driven forecasting

Submission Number: 194