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Given the increasingly severe challenges of global climate change and sustainable development, accurate prediction of daily carbon dioxide (CO 2) emissions has become crucial. However, current research on real-time daily forecasts remains relatively scarce, often limited to annual predictions and point estimates. This paper proposes an innovative integrated model, based on quantile regression and Attention-Bidirectional Long Short-Term Memory (BILSTM), specifically designed for probabilistic prediction of daily carbon emissions. Testing the model on daily carbon emission data from major carbon-emitting countries including China, the United States, India, Russia, and Italy has demonstrated its superior performance across various metrics, including mean squared error (MSE), mean absolute error (MAE), coefficient of determination (R2), and normalized average width of prediction intervals (PINAW), compared to other benchmark models. This achievement not only highlights the model's effectiveness in predicting daily carbon emissions but also provides policymakers with a powerful tool for real-time monitoring and assessment of carbon emissions. Furthermore , the application of probabilistic forecasting methods enables decision-makers to gain a more comprehensive understanding of the potential range of future carbon emissions, providing strong support for the formulation of targeted policies. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are efficient tools for modeling species geographic distribution under climate change scenarios. Due to differences among predictions of these models, their results are combined using consensus methods to form an ensemble model. This paper provides an optimal combination of the common SDMs according to accuracy and correlation to model the climatic suitability of Quercus brantii in the west of Iran and projects it into the years 2050 and 2070. This is done using 1000 samples of the species presence and absence, 4 bioclimatic variables related to temperature and precipitation, and 10 modeling algorithms. An ensemble combination of Global Climate Models (GCMs) and 4 optimistic and pessimistic greenhouse-gas emissions scenarios were utilized to identify the climatically suitable areas in the years 2050 and 2070. These models were combined using three common statistics, including mean, median, and weighted mean. The predictive accuracies of the single-models and the consensus methods were assessed using the area under the curve (AUC) metric that validates the acceptable performance of the 9 out of the 10 models studied. Applying the genetic algorithm, the best combination of the models was selected including 4 algorithms with accuracy and correlation equal 0.95 and 0.30 respectively. The results show that the Random Forest (RF) model causes less error in the ensemble model and also compensates other models' errors more. Projections into the years 2050 and 2070 showed that in both time periods and under all scenarios, changes will occur in the spatial distribution of this species, and the most severe one would be a 55.6% loss under the most pessimistic scenario in 2070. • Modeling climate change effects on Zagros forests by species distribution models. • Using the genetic algorithm to find the optimum combination of models. • Ensemble modeling approach provides more accurate predictions. • Suitable climatic habitats of the species will decrease over time. [ABSTRACT FROM AUTHOR]