Global climate change driven by carbon dioxide (CO2) emissions necessitates accurate forecasting for effective mitigation strategies. Traditional forecasting models, such as ARIMA and statistical methods, often struggle to handle the non-linear and long-term temporal dependencies in CO2 emission data. This paper evaluates the performance of advanced Recurrent Neural Network (RNN) architectures, specifically Long Short-Term Memory (LSTM), Stacked LSTM, Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) with attention mechanisms, in forecasting long-term CO2 emissions. These models are compared against traditional machine learning algorithms like Random Forest and Gradient Boosted Trees (GBT). The Random Forest and Gradient Boosted Trees, implemented using PySpark framework for distributed computing, are leveraged for efficient big data handling, enabling scalable solutions for CO2 emission forecasting. Our experimental results, using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), show that the Stacked LSTM model achieved the lowest MAE of 25.795, RMSE of 80.678, and MAPE of 1.072, outperforming Random Forest (MAE: 72.4, RMSE: 310.5, MAPE: 1.32) and GBT (MAE: 75.6, RMSE: 356.8, MAPE: 1.57). The findings demonstrate that advanced RNN models, particularly the Stacked LSTM and Bi-LSTM with attention, provide superior performance over traditional models, effectively capturing complex temporal patterns in the data. This study offers valuable insights into CO2 emission forecasting approaches, contributing to the development of accurate tools for climate policy and strategy formulation.

Climate change and global warming demand immediate global action to protect future generations. Although deep learning is crucial for solving complex tasks, its high energy consumption and carbon emissions from extensive training processes necessitate improvements in efficiency to reduce its environmental impact. This research addresses the need for accurate carbon emission tracking to mitigate climate change and promote sustainable development. We evaluated deep learning models—specifically, the Temporal Fusion Transformer (TFT), Attention-based LSTM (Att-LSTM), and standard LSTM—in predicting CO2 emissions using datasets from World Bank Indicators (WBI) and Our World in Data (OWID). The results highlight TFT’s superiority, achieving the lowest mean squared error (MSE) in validation (0.0029) compared to Att-LSTM (0.0070) and LSTM (0.0072). Additionally, TFT demonstrates a lower root mean squared error (RMSE) in validation (0.0539) than both Att-LSTM (0.0712) and LSTM (0.0909), showcasing its enhanced predictive accuracy across evaluation tools like CarbonTracker, eco2AI, and CodeCarbon. Furthermore, TFT consistently outperforms LSTM in computational efficiency and carbon emissions, despite LSTM’s lower power usage but higher emissions in specific scenarios. These findings underscore TFT's pivotal role in formulating effective climate policies and promoting renewable energy sources, emphasizing its potential to significantly contribute to global warming mitigation and sustainability.

Accurate forecasting of critical pollutants such as NO2and PM2.5 is essential in the ongoing battle against air pollution. This paper introduces ConvTrans, a novel hybrid architecture that integrates convolutional and Transformer technologies to enhance the precision of PM2.5 and NO2predictions. Our evaluations demonstrate that ConvTrans improves PM2.5 prediction accuracy, increasing the R-squared value by 3.6%, reducing RMSE by 5.4%, MSE by 7.5%, and MAE by 17.3%. Similarly, for NO2predictions, the model shows a 4.2% increase in R-squared, along with a 5.4% reduction in RMSE, a 13.7% decrease in MSE, and a 5.6% reduction in MAE. These results highlight the model’s effectiveness in short-term forecasting, addressing key challenges in environmental management.

This research work introduces a new strategy to enhance computation offloading in wireless-powered Edge-Enabled Mobile Computing (EEMC) networks by utilizing Dueling Double Deep Q-Networks with Attention (Dueling DDQN-A). EEMC facilitates the offloading of computational tasks from mobile devices to local edge servers, leading to decreased latency and improved energy efficiency. However, the dynamic and stochastic nature of wireless environments presents significant challenges for real-time task offloading decisions. To address this, we enhance the Dueling DDQN framework by integrating an attention mechanism to prioritize the most relevant features in the state space. The proposed Dueling DDQN-A model enhances decision-making by separating state value estimation from action advantage estimation. Additionally, the model uses attention to prioritize key state features, leading to improved adaptability in dynamic wireless channel and network environments. We frame the offloading decision problem as a Markov Decision Process (MDP) and apply deep reinforcement learning to optimize both the computation rate and energy efficiency. We compare and evaluate the model through comprehensive simulations using the latest techniques like Simple DQN, Double DQN, and Dueling DDQN. Our results demonstrate that Dueling DDQN-A achieves a 59.12% improvement in average computation rate and a 16.19% reduction in energy consumption over the baseline models. Additionally, it significantly outperforms the baselines in terms of training loss and convergence speed. These findings suggest that Dueling DDQN-A offers a robust and highly efficient solution for real-time computation offloading in wireless-powered EEMC networks, making it a strong candidate for deployment in real-world scenarios.

Datacenter carbon emissions are rising, which poses a serious issue that must be addressed quickly. We may see differences in emissions when we look at the carbon intensity of the electrical system since various areas have different energy sources. We can time the execution of workloads to occur during periods when the carbon intensity is lower by using this temporal variability. This paper aims to address the challenge of reducing carbon emissions in cloud computing by proposing a framework for workload shifting based on low carbon intensity periods in the power grid. The study focuses on four countries and their carbon production in the year 2022, calculating the carbon intensity for each country. Additionally, the paper identifies different cloud computing workloads and integrates constraints such as power, SLA, carbon emissions, and routing into the framework. The crucial factors considered during workload shifting include geo-distributed load balancing and right-sizing the data center.A simulation is developed to evaluate the proposed framework, simulating scenarios with shiftable workloads. The results are compared and analyzed, assessing the framework’s effectiveness in reducing carbon emissions while meeting the specified constraints. The findings highlight the potential benefits of workload shifting in reducing carbon emissions and improving environmental sustainability. Overall, this research contributes to the advancement of green computing and offers insights into the development of sustainable cloud computing practices.

Climate change and global warming are urgent environmental issues demanding immediate action to safeguard future generations. The major contributor to the greenhouse effect, carbon dioxide $\left(\mathrm{CO}_{2}\right)$, primarily originates from industrial and transportation fossil fuel combustion. International agreements, like the Paris Agreement, call for a 30-35% reduction in CO2emissions compared to 2005 levels. This research aims to predict CO2emissions and raise awareness among SAARC nations and governments about the increasing trend. We introduce a novel predictive framework using Attention-based Long Short-Term Memory (A-LSTM) for CO2emissions analysis. The Attention mechanism assigns variable weights to input data, facilitating indirect connections between LSTM outputs and pertinent inputs. This enhances resource allocation in the A-LSTM model, overcoming computational constraints. We integrate input parameters encompassing CO2emissions from land-use changes, oil, natural gas, and coal combustion to forecast CO2emissions and correlate them with population and per capita GDP. Our comparative analysis conclusively demonstrates the superior performance of A-LSTM models over baseline LSTM models when applied to the CO2emission dataset sourced from Our World in Data (OWID) and World Bank Indicator database. Specifically, the LSTM model registers a MAPE of 24.968 and an RMSE of 0.34, whereas the Attention-based LSTM model showcases a marked improvement of 57% with a considerably lower MAPE of 10.5902 and an RMSE of 0.107.