Proceedings of the 3rd International Conference on Big Data Economy and Digital Management, BDEDM 2024, January 12–14, 2024, Ningbo, China

Research Article

Analyzing Residential Energy Consumption in the Greater Bay Area: A STIRPAT Model Approach

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  • @INPROCEEDINGS{10.4108/eai.12-1-2024.2347177,
        author={Yi  Yan},
        title={Analyzing Residential Energy Consumption in the Greater Bay Area: A STIRPAT Model Approach},
        proceedings={Proceedings of the 3rd International Conference on Big Data Economy and Digital Management, BDEDM 2024, January 12--14, 2024, Ningbo, China},
        publisher={EAI},
        proceedings_a={BDEDM},
        year={2024},
        month={6},
        keywords={stirpat model residential energy consumption greater bay area socio-economic factors urban energy efficiency},
        doi={10.4108/eai.12-1-2024.2347177}
    }
    
  • Yi Yan
    Year: 2024
    Analyzing Residential Energy Consumption in the Greater Bay Area: A STIRPAT Model Approach
    BDEDM
    EAI
    DOI: 10.4108/eai.12-1-2024.2347177
Yi Yan1,*
  • 1: Tsinghua University
*Contact email: 2482516799@qq.com

Abstract

This paper utilizes the STIRPAT model to analyze residential energy consumption in the Greater Bay Area, focusing on the interplay of population growth, economic development, and urban residential infrastructure. Incorporating factors such as per capita population, GDP, residential building area, and climatic variables like cooling degree days and geographical coordinates, the study reveals significant correlations between these factors and per capita electricity consumption in residential buildings. Empirical analysis indicates that increases in both per capita population and GDP significantly elevate residential electricity consumption, whereas changes in residential building area show minimal impact. The research highlights the importance of considering socio-economic and environmental factors in urban energy planning, providing insights for developing sustainable energy strategies in urban settings. The findings particularly emphasize the need for targeted energy efficiency measures in rapidly urbanizing areas, taking into account the diverse socio-economic and climatic conditions prevalent in such regions.