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Carbon Peak Control Strategies and Pathway Selection in Dalian City: A Hybrid Approach with STIRPAT and GA-BP Neural Networks

Author

Listed:
  • Linghui Zheng

    (College of Management, Shenyang Jianzhu University, Shenyang 110168, China)

  • Yanli Sun

    (College of Management, Shenyang Jianzhu University, Shenyang 110168, China)

  • Yang Yu

    (Centre for Infrastructure Engineering, Western Sydney University, Penrith, NSW 2751, Australia
    Centre for Infrastructure Engineering and Safety, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
    Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang 110870, China)

Abstract

Mitigating the rate of global warming is imperative to preserve the natural environment upon which humanity relies for survival; greenhouse gas emissions serve as the principal driver of climate change, rendering the promotion of urban carbon peaking and carbon neutrality a crucial initiative for effectively addressing climate change and attaining sustainable development. This study addresses the inherent uncertainties and complexities associated with carbon dioxide emission accounting by undertaking a scenario prediction analysis of peak carbon emissions in Dalian, utilizing the STIRPAT model in conjunction with a GA-BP neural network model optimized through a genetic algorithm. An analysis of the mechanisms underlying the influencing factors of carbon emissions, along with the identification of the carbon emission peak, is conducted based on carbon emission accounting derived from nighttime lighting data. The GA-BP prediction model exhibits significant advantages in addressing the nonlinear and non-stationary characteristics of carbon emissions, attributable to its robust mapping capabilities and probabilistic analysis proficiency. The findings reveal that energy intensity, tertiary industry value, resident population, and GDP are positively correlated with carbon emissions in Dalian, ranked in order of importance. In contrast, population density significantly reduces emissions. The GA-BP model predicts carbon emissions with 99.33% accuracy, confirming its excellent predictive capability. The recommended strategy for Dalian to achieve its carbon peak at the earliest is to adopt a low-carbon scenario, with a forecasted peak of 191.79 million tons by 2033.

Suggested Citation

  • Linghui Zheng & Yanli Sun & Yang Yu, 2024. "Carbon Peak Control Strategies and Pathway Selection in Dalian City: A Hybrid Approach with STIRPAT and GA-BP Neural Networks," Sustainability, MDPI, vol. 16(19), pages 1-23, October.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8657-:d:1493445
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    References listed on IDEAS

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    1. Qifan Guan, 2023. "Decomposing and Decoupling the Energy-Related Carbon Emissions in the Beijing–Tianjin–Hebei Region Using the Extended LMDI and Tapio Index Model," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    2. York, Richard & Rosa, Eugene A. & Dietz, Thomas, 2003. "STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts," Ecological Economics, Elsevier, vol. 46(3), pages 351-365, October.
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