@article{Barr2026, 
author = {Mohammad Barr and Tawfeeq Shawly and Ahmed A. Alsheikhy and Sahbi Boubaker},
title = {A dual encounter logarithmic path neural network for precision carbon emission monitoring and mitigation},
year = {2026},
journal = {AIMS Mathematics},
volume = {11},
number = {4},
pages = {9655-9685},
keywords = {DeepCarbonNet, climate change, sustainability, carbon emission, carbon footprint monitoring, emission reduction optimization, real-time monitoring, logistics},
url = {https://www.sciopen.com/article/10.3934/math.2026400},
doi = {10.3934/math.2026400},
abstract = {Monitoring and reducing carbon footprints are crucial for achieving sustainability and effectively tackling climate change. Given the urgent global need to address climate change and to participate in Saudi Arabia's Vision 2030, we present DeepCarbonNet (EcoNet), a novel deep learning (DL) framework to monitor, analyze, and reduce carbon emissions. The framework is built using a new Dual Encounter Logarithmic Path Neural Network (DELPNN) architecture, including a novel Spatial Encounter Pathway (SEP), which processes high-resolution satellite images through a Logarithmic Convolutional Encoder (LCE) to extract multi-scale spatial features, and a new Temporal Encounter Pathway (TEP), which processes sequential Internet of Things (IoT) sensor and energy consumption data via a Gated Logarithmic Recurrent Unit (GLRU) and a central Feature Fusion Operator (FFO) that integrates the spatial and temporal features using cross-attention mechanisms and projects them into a logarithmic latent space to capture intricate, non-linear emission dynamics. This approach enables the precise capture of spatial and temporal dependencies within carbon emission data, achieving an outstanding level of accuracy. The simulation experimental results demonstrated that EcoNet attains a high accuracy of 98.7% in estimating carbon footprints after training the model on two public datasets. Furthermore, the model employed a reinforcement learning (RL)-based optimization strategy, enabling a 29.4% reduction in emissions through adaptive mitigation techniques. EcoNet was designed to adapt to changing conditions and promote environmental sustainability continuously. Beyond monitoring, EcoNet achieved a 32.8% improvement in energy efficiency. Additionally, the framework demonstrated robust performance across weather conditions, with 97.0-98.7% accuracy and an accuracy of emission intensities between 94.2–99.1%. These results showed that EcoNet is a solution for artificial intelligence (AI)-driven environmental sustainability, which offers immediate practical value for industrial monitoring, smart city management, logistic services to reduce fuel consumption, and national sustainability programs.}
}