Year: 2024
Type: article
Abstract: This paper explores the application of Recurrent Neural Networks (RNNs) for forecasting emission trends, a critical aspect of addressing climate change and formulating effective environmental policies. Traditional forecasting methods, such as Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing, often fail to capture the complex nonlinear relationships and temporal dependencies inherent in emission data. In contrast, RNNs, particularly Long Short-Term Memory (LSTM) networks, are designed to recognize patterns in sequential data, making them well-suited for time series forecasting tasks. This study employs a comprehensive methodology that includes data collection from reputable sources, feature selection, RNN model design, and rigorous evaluation using various performance metrics. The results indicate that RNNs significantly outperform traditional forecasting techniques in terms of accuracy, providing valuable insights into future emission trajectories. The findings underscore the potential of RNNs as powerful tools for policymakers and researchers, facilitating more informed decision-making in the fight against climate change.
Source: Jisuanji shenghuojia.
DOI: 10.54097/ezvnav34
Authors: Xuetian Zou, Kangli Wang, Jiawei Lu, Dili Wu
Language: English
Field: Decision Sciences
Domain: Social Sciences
SDG: Climate action
Open Access status: hybrid
OpenAlex ID: W4402961418