To address the global issue of climate change and create focused mitigation plans,accurate CO_(2)emissions forecasting is essential.Using CO_(2)emissions data from 1990 to 2023,this study assesses the predicting perfo...To address the global issue of climate change and create focused mitigation plans,accurate CO_(2)emissions forecasting is essential.Using CO_(2)emissions data from 1990 to 2023,this study assesses the predicting performance of five sophisticated models:Random Forest(RF),XGBoost,Support Vector Regression(SVR),Long Short-Term Memory networks(LSTM),and ARIMA To give a thorough evaluation of the models’performance,measures including Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Mean Absolute Percentage Error(MAPE)are used.To guarantee dependable model implementation,preprocessing procedures are carried out,such as feature engineering and stationarity tests.Machine learning models outperform ARIMA in identifying complex patterns and long-term associations,but ARIMA does better with data that exhibits strong linear trends.These results provide important information about how well the model fits various forecasting scenarios,which helps develop data-driven carbon reduction programs.Predictive modeling should be incorporated into sustainable climate policy to encourage the adoption of low-carbon technologies and proactive decisionmaking.Achieving long-term environmental sustainability requires strengthening carbon trading systems,encouraging clean energy investments,and enacting stronger emission laws.In line with international climate goals,suggestions for lowering CO_(2)emissions include switching to renewable energy,increasing energy efficiency,and putting afforestation initiatives into action.展开更多
文摘To address the global issue of climate change and create focused mitigation plans,accurate CO_(2)emissions forecasting is essential.Using CO_(2)emissions data from 1990 to 2023,this study assesses the predicting performance of five sophisticated models:Random Forest(RF),XGBoost,Support Vector Regression(SVR),Long Short-Term Memory networks(LSTM),and ARIMA To give a thorough evaluation of the models’performance,measures including Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Mean Absolute Percentage Error(MAPE)are used.To guarantee dependable model implementation,preprocessing procedures are carried out,such as feature engineering and stationarity tests.Machine learning models outperform ARIMA in identifying complex patterns and long-term associations,but ARIMA does better with data that exhibits strong linear trends.These results provide important information about how well the model fits various forecasting scenarios,which helps develop data-driven carbon reduction programs.Predictive modeling should be incorporated into sustainable climate policy to encourage the adoption of low-carbon technologies and proactive decisionmaking.Achieving long-term environmental sustainability requires strengthening carbon trading systems,encouraging clean energy investments,and enacting stronger emission laws.In line with international climate goals,suggestions for lowering CO_(2)emissions include switching to renewable energy,increasing energy efficiency,and putting afforestation initiatives into action.
