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

Guo, Sida and Zhong, Ziqi ORCID: 0000-0002-3919-9999 (2025) Artificial intelligence-driven optimization of carbon neutrality strategies in population studies: employing enhanced neural network models with attention mechanisms. Journal of Organizational and End User Computing, 37 (1). ISSN 1546-2234

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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.

Item Type:	Article
Divisions:	LSE
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences H Social Sciences > HD Industries. Land use. Labor Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited:	17 Mar 2025 14:54
Last Modified:	25 Mar 2025 17:05
URI:	http://eprints.lse.ac.uk/id/eprint/127570

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