IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/127570.html
   My bibliography  Save this paper

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

Author

Listed:
  • Guo, Sida
  • Zhong, Ziqi

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.

Suggested Citation

  • Guo, Sida & Zhong, Ziqi, 2025. "Artificial intelligence-driven optimization of carbon neutrality strategies in population studies: employing enhanced neural network models with attention mechanisms," LSE Research Online Documents on Economics 127570, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:127570
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/127570/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Carbon Neutral; Artificial Intelligence; Data Analysis; Fusion model; Two-Stage Attention Optimization; Deep Learning;
    All these keywords.

    JEL classification:

    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ehl:lserod:127570. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.