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Exploring driving force factors of building energy use and GHG emission using a spatio-temporal regression method

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  • Zhang, Yan
  • Teoh, Bak Koon
  • Zhang, Limao

Abstract

Building energy consumption and GHG emission have significant influence on urban sustainable development. However, research works that investigate the temporal information in energy prediction are limited. This study bridged these gaps and assessed the energy use intensity (EUI) and GHG intensity (GHGI) based on three aspects by considering spatio-temporal dimensions. A GTWR model capturing both spatial and temporal information was employed to assess ten driving forces towards buildings EUI and GHGI. A case study in Seattle was used to demonstrate the effectiveness and validate the proposed approach. Results indicate that (1) The temporal heterogeneity has significant influence on EUI and GHGI, where the value of R2 in EUI experienced a remarkable improvement of 21.82% in the GTWR model compared to the GWR model, and that of for GHGI has been increased 13.92% from GWR model to GTWR model; (2) Buildings with large GFA was found to positively impact the EUI and GHGI, while the population under poverty and the number of floors have a negative impact. The novelty of this study lies in establishing a comprehensive framework for predicting energy usage with spatio-temporal information and quantifying the impacts of the ten driving forces at a regional level.

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  • Zhang, Yan & Teoh, Bak Koon & Zhang, Limao, 2023. "Exploring driving force factors of building energy use and GHG emission using a spatio-temporal regression method," Energy, Elsevier, vol. 269(C).
    • Handle: RePEc:eee:energy:v:269:y:2023:i:c:s036054422300141x
    DOI: 10.1016/j.energy.2023.126747
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    1. Zafar, Muhammad Wasif & Shahbaz, Muhammad & Sinha, Avik & Sengupta, Tuhin & Qin, Quande, 2020. "How Renewable Energy Consumption Contribute to Environmental Quality? The Role of Education in OECD Countries," MPRA Paper 100259, University Library of Munich, Germany, revised 08 May 2020.
    2. Li, Wenliang & Zhou, Yuyu & Cetin, Kristen & Eom, Jiyong & Wang, Yu & Chen, Gang & Zhang, Xuesong, 2017. "Modeling urban building energy use: A review of modeling approaches and procedures," Energy, Elsevier, vol. 141(C), pages 2445-2457.
    3. Borck, Rainald, 2016. "Will skyscrapers save the planet? Building height limits and urban greenhouse gas emissions," Regional Science and Urban Economics, Elsevier, vol. 58(C), pages 13-25.
    4. Shimei Wu & Xinye Zheng & Chu Wei, 2017. "Measurement of inequality using household energy consumption data in rural China," Nature Energy, Nature, vol. 2(10), pages 795-803, October.
    5. Ma, Jun & Cheng, Jack C.P., 2016. "Identifying the influential features on the regional energy use intensity of residential buildings based on Random Forests," Applied Energy, Elsevier, vol. 183(C), pages 193-201.
    6. Zhang, Yan & Teoh, Bak Koon & Wu, Maozhi & Chen, Jiayu & Zhang, Limao, 2023. "Data-driven estimation of building energy consumption and GHG emissions using explainable artificial intelligence," Energy, Elsevier, vol. 262(PA).
    7. Meng, Ting & Hsu, David & Han, Albert, 2017. "Estimating energy savings from benchmarking policies in New York City," Energy, Elsevier, vol. 133(C), pages 415-423.
    8. Kialashaki, Arash & Reisel, John R., 2013. "Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks," Applied Energy, Elsevier, vol. 108(C), pages 271-280.
    9. Ma, Jun & Cheng, Jack C.P., 2016. "Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology," Applied Energy, Elsevier, vol. 183(C), pages 182-192.
    10. Cayla, Jean-Michel & Maizi, Nadia & Marchand, Christophe, 2011. "The role of income in energy consumption behaviour: Evidence from French households data," Energy Policy, Elsevier, vol. 39(12), pages 7874-7883.
    11. Lin, Jinyao & Lu, Siyan & He, Xiaoyu & Wang, Fang, 2021. "Analyzing the impact of three-dimensional building structure on CO2 emissions based on random forest regression," Energy, Elsevier, vol. 236(C).
    12. Daioglou, Vassilis & van Ruijven, Bas J. & van Vuuren, Detlef P., 2012. "Model projections for household energy use in developing countries," Energy, Elsevier, vol. 37(1), pages 601-615.
    13. Park, Hyo Seon & Lee, Minhyun & Kang, Hyuna & Hong, Taehoon & Jeong, Jaewook, 2016. "Development of a new energy benchmark for improving the operational rating system of office buildings using various data-mining techniques," Applied Energy, Elsevier, vol. 173(C), pages 225-237.
    14. Fuerst, Franz & Warren-Myers, Georgia, 2018. "Does voluntary disclosure create a green lemon problem? Energy-efficiency ratings and house prices," Energy Economics, Elsevier, vol. 74(C), pages 1-12.
    15. Liddle, Brantley, 2014. "Impact of population, age structure, and urbanization on carbon emissions/energy consumption: Evidence from macro-level, cross-country analyses," MPRA Paper 61306, University Library of Munich, Germany.
    16. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    17. Mingfang Tang & Xiao Fu & Huiming Cao & Yuan Shen & Hongbing Deng & Gang Wu, 2016. "Energy Performance of Hotel Buildings in Lijiang, China," Sustainability, MDPI, vol. 8(8), pages 1-12, August.
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