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Unveiling land use-carbon Nexus: Spatial matrix-enhanced neural network for predicting commercial and residential carbon emissions

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  • Luo, Haizhi
  • Zhang, Yiwen
  • Gao, Xinyu
  • Liu, Zhengguang
  • Song, Xia
  • Meng, Xiangzhao
  • Yang, Xiaohu

Abstract

Carbon emissions play a pivotal role in driving global warming, with commercial and residential sectors ranking as the third-largest source after industrial and transportation sectors. Consequently, establishing a framework for spatial characterizing and predicting carbon emissions from these sectors is vital for effective carbon reduction planning. This study proposes an optimized Backpropagation Neural Network, incorporating a Spatial Weight Matrix, to model the relationship between land use and carbon emissions, taking spatial effects into account. Additionally, an optimized Random Forest model with variable search step lengths is developed for large-scale and multi-class land use prediction. Beijing, China serves as the study site, yielding the following findings: (1) The optimized Backpropagation Neural Network outperforms traditional models, achieving goodness of fit of 0.999 and 0.997 in training and testing datasets, respectively. (2) The optimized Random Forest model, while reducing prediction precision for ecological areas (Root Mean Square Error increases by 4.12 %), enhances overall model performance by 73.95 % in a single iteration. (3) By 2025, commercial and residential carbon emissions in Beijing are projected to reach 8.91 million tons. (4) The study characterizes the spatial patterns of commercial and residential carbon emissions at a 1 km resolution for the next 15 years, revealing statistical features. (5) Expansion of mixed commercial and residential land relies on service-oriented development, positively impacting regional carbon emission intensity control.

Suggested Citation

  • Luo, Haizhi & Zhang, Yiwen & Gao, Xinyu & Liu, Zhengguang & Song, Xia & Meng, Xiangzhao & Yang, Xiaohu, 2024. "Unveiling land use-carbon Nexus: Spatial matrix-enhanced neural network for predicting commercial and residential carbon emissions," Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:energy:v:305:y:2024:i:c:s0360544224014956
    DOI: 10.1016/j.energy.2024.131722
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    References listed on IDEAS

    as
    1. Lubowski, Ruben N. & Plantinga, Andrew J. & Stavins, Robert N., 2006. "Land-use change and carbon sinks: Econometric estimation of the carbon sequestration supply function," Journal of Environmental Economics and Management, Elsevier, vol. 51(2), pages 135-152, March.
    2. Jiqun Wen & Xiaowei Chuai & Shanchi Li & Song Song & Yuanwei Li & Mengjie Wang & Shuosheng Wu, 2019. "Spatial Heterogeneity of the Carbon Emission Effect Resulting from Urban Expansion among Three Coastal Agglomerations in China," Sustainability, MDPI, vol. 11(17), pages 1-20, August.
    3. Qian, Long & Xu, Xiaolin & Sun, Ying & Zhou, Yunjie, 2022. "Carbon emission reduction effects of eco-industrial park policy in China," Energy, Elsevier, vol. 261(PB).
    4. Yu, Feng & Xu, Xiaozhong, 2014. "A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network," Applied Energy, Elsevier, vol. 134(C), pages 102-113.
    5. Fang, Guochang & Gao, Zhengye & Tian, Lixin & Fu, Min, 2022. "What drives urban carbon emission efficiency? – Spatial analysis based on nighttime light data," Applied Energy, Elsevier, vol. 312(C).
    6. J. Loisel & A. V. Gallego-Sala & M. J. Amesbury & G. Magnan & G. Anshari & D. W. Beilman & J. C. Benavides & J. Blewett & P. Camill & D. J. Charman & S. Chawchai & A. Hedgpeth & T. Kleinen & A. Korhol, 2021. "Author Correction: Expert assessment of future vulnerability of the global peatland carbon sink," Nature Climate Change, Nature, vol. 11(4), pages 362-362, April.
    7. Clifford Lam & Pedro C.L. Souza, 2020. "Estimation and Selection of Spatial Weight Matrix in a Spatial Lag Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(3), pages 693-710, July.
    8. Luo, Haizhi & Wang, Chenglong & Li, Cangbai & Meng, Xiangzhao & Yang, Xiaohu & Tan, Qian, 2024. "Multi-scale carbon emission characterization and prediction based on land use and interpretable machine learning model: A case study of the Yangtze River Delta Region, China," Applied Energy, Elsevier, vol. 360(C).
    9. Huang, Ren & Zhang, Sufang & Wang, Peng, 2022. "Key areas and pathways for carbon emissions reduction in Beijing for the “Dual Carbon” targets," Energy Policy, Elsevier, vol. 164(C).
    10. Jianling Jiao & Yufei Yang & Yu Bai, 2018. "The impact of inter-industry R&D technology spillover on carbon emission in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(3), pages 913-929, April.
    11. Lede Niu & Jingzhi Lin & Lifang Zhou & Anlin Li & Yan Zhou, 2023. "The prediction of carbon emissions from construction land in central Yunnan urban agglomeration area based on multiple linear regression model," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 45(4/5), pages 349-365.
    12. Jingjing Chen & Yangyang Lin & Xiaojun Wang & Bingjing Mao & Lihong Peng, 2022. "Direct and Indirect Carbon Emission from Household Consumption Based on LMDI and SDA Model: A Decomposition and Comparison Analysis," Energies, MDPI, vol. 15(14), pages 1-22, July.
    13. Formolli, M. & Kleiven, T. & Lobaccaro, G., 2023. "Assessing solar energy accessibility at high latitudes: A systematic review of urban spatial domains, metrics, and parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 177(C).
    14. Ahmed Gassar, Abdo Abdullah & Yun, Geun Young & Kim, Sumin, 2019. "Data-driven approach to prediction of residential energy consumption at urban scales in London," Energy, Elsevier, vol. 187(C).
    15. Qu, Xi & Lee, Lung-fei, 2015. "Estimating a spatial autoregressive model with an endogenous spatial weight matrix," Journal of Econometrics, Elsevier, vol. 184(2), pages 209-232.
    16. Lopez, Anthony & Mai, Trieu & Lantz, Eric & Harrison-Atlas, Dylan & Williams, Travis & Maclaurin, Galen, 2021. "Land use and turbine technology influences on wind potential in the United States," Energy, Elsevier, vol. 223(C).
    17. Hao, Jian Li & Ma, Wenting, 2023. "Evaluating carbon emissions of construction and demolition waste in building energy retrofit projects," Energy, Elsevier, vol. 281(C).
    18. Yiwen Zhang & Haizhi Luo & Jiami Xie & Xiangzhao Meng & Changdong Ye, 2023. "The Influence and Prediction of Built Environment on the Subjective Well-Being of the Elderly Based on Random Forest: Evidence from Guangzhou, China," Land, MDPI, vol. 12(10), pages 1-16, October.
    19. Wang, Jiaxin & Lu, Feng, 2021. "Modeling the electricity consumption by combining land use types and landscape patterns with nighttime light imagery," Energy, Elsevier, vol. 234(C).
    20. Luo, Haizhi & Li, Yingyue & Gao, Xinyu & Meng, Xiangzhao & Yang, Xiaohu & Yan, Jinyue, 2023. "Carbon emission prediction model of prefecture-level administrative region: A land-use-based case study of Xi'an city, China," Applied Energy, Elsevier, vol. 348(C).
    21. J. Loisel & A. V. Gallego-Sala & M. J. Amesbury & G. Magnan & G. Anshari & D. W. Beilman & J. C. Benavides & J. Blewett & P. Camill & D. J. Charman & S. Chawchai & A. Hedgpeth & T. Kleinen & A. Korhol, 2021. "Expert assessment of future vulnerability of the global peatland carbon sink," Nature Climate Change, Nature, vol. 11(1), pages 70-77, January.
    22. Wang, Shouxiang & Zhang, Na & Wu, Lei & Wang, Yamin, 2016. "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method," Renewable Energy, Elsevier, vol. 94(C), pages 629-636.
    23. Wang, Ailun & Hu, Shuo & Li, Jianglong, 2022. "Using machine learning to model technological heterogeneity in carbon emission efficiency evaluation: The case of China's cities," Energy Economics, Elsevier, vol. 114(C).
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