Artificial intelligence-driven optimization of carbon neutrality strategies in population studies: employing enhanced neural network models with attention mechanisms

Sida Guo and Ziqi Zhong

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: With the growing severity of global climate change, achieving carbon neutrality has become a central focus worldwide. The intersection of population studies and carbon neutrality introduces significant challenges in predicting and optimizing energy consumption, as demographic factors play a crucial role in shaping carbon emissions. This paper proposes a model based on a Region-based Convolutional Neural Network (RCNN) and Generative Adversarial Network (GAN), enhanced with a dual-stage attention mechanism for optimization. The model automatically extracts key features from complex demographic and carbon emission data, leveraging the attention mechanism to assign appropriate weights, thereby capturing the behavioral patterns and trends in energy consumption driven by population dynamics more effectively. By integrating multi-source data, including historical carbon emissions, population density, demographic trends, meteorological data, and economic indicators, experimental results demonstrate the model's outstanding performance across multiple datasets.

Keywords: Carbon Neutral; Artificial Intelligence; Data Analysis; Fusion model; Two-Stage Attention Optimization; Deep Learning (search for similar items in EconPapers)
JEL-codes: J01 R14 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2025-03-05
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Published in Journal of Organizational and End User Computing, 5, March, 2025, 37(1). ISSN: 1546-2234

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:127570

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