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Identification of key energy efficiency drivers through global city benchmarking: A data driven approach

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  • Wang, Xin
  • Li, Zhengwei
  • Meng, Haixing
  • Wu, Jiang

Abstract

A majority of carbon dioxide is emitted from cities. However, due to the complexity of urban energy consumption behaviour and lack of sufficient data, the influencing mechanisms of urban energy efficiency has not been fully understood. This paper presents a systematic study to identify the key energy efficiency drivers related with major urban performance aspects, such as R&D, livability, etc. First, the performance indicators of 25 global cities are collected from the Global Power City Index (GPCI) report and other data sources. Second, a set of data driven techniques (data envelopment analysis, clustering, and decision tree) are deployed to analyse the data. The results show that, on average, energy efficiency of European cities is the highest, 19% higher than that of North American cities and 90% higher than that of Asian cities. Livability (reflected by the number of doctors per capita) is the most important influencing factor of energy efficiency, followed by sustainability (reflected by the percentage of renewable energy use) and R&D ability (reflected by the number of top universities). On average, the efficiency of cities with high livability and high sustainability exceeds cities with low livability and low sustainability by 46% and 26%, respectively. To improve energy efficiency, optimizing the industry structure, raising public concern over energy sustainability, and promoting energy efficient equipment and technologies are the most effective measures.

Suggested Citation

  • Wang, Xin & Li, Zhengwei & Meng, Haixing & Wu, Jiang, 2017. "Identification of key energy efficiency drivers through global city benchmarking: A data driven approach," Applied Energy, Elsevier, vol. 190(C), pages 18-28.
    • Handle: RePEc:eee:appene:v:190:y:2017:i:c:p:18-28
    DOI: 10.1016/j.apenergy.2016.12.111
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    1. Yoshiki Yamagata & Daisuke Murakami & Kazuhiro Minami & Nana Arizumi & Sho Kuroda & Tomoya Tanjo & Hiroshi Maruyama, 2016. "Electricity Self-Sufficient Community Clustering for Energy Resilience," Energies, MDPI, vol. 9(7), pages 1-13, July.
    2. Sampaio, Henrique César & Dias, Rubens Alves & Balestieri, José Antônio Perrella, 2013. "Sustainable urban energy planning: The case study of a tropical city," Applied Energy, Elsevier, vol. 104(C), pages 924-935.
    3. Feng, Y.Y. & Chen, S.Q. & Zhang, L.X., 2013. "System dynamics modeling for urban energy consumption and CO2 emissions: A case study of Beijing, China," Ecological Modelling, Elsevier, vol. 252(C), pages 44-52.
    4. Chen, Guangwu & Wiedmann, Thomas & Wang, Yafei & Hadjikakou, Michalis, 2016. "Transnational city carbon footprint networks – Exploring carbon links between Australian and Chinese cities," Applied Energy, Elsevier, vol. 184(C), pages 1082-1092.
    5. Fang, Delin & Chen, Bin, 2017. "Linkage analysis for the water–energy nexus of city," Applied Energy, Elsevier, vol. 189(C), pages 770-779.
    6. Yamagata, Yoshiki & Seya, Hajime, 2013. "Simulating a future smart city: An integrated land use-energy model," Applied Energy, Elsevier, vol. 112(C), pages 1466-1474.
    7. Hsu, David, 2015. "Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data," Applied Energy, Elsevier, vol. 160(C), pages 153-163.
    8. 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.
    9. Thellufsen, Jakob Zinck & Lund, Henrik, 2016. "Roles of local and national energy systems in the integration of renewable energy," Applied Energy, Elsevier, vol. 183(C), pages 419-429.
    10. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    11. Chen, Qin & Song, Zheng, 2014. "Accounting for China's urbanization," China Economic Review, Elsevier, vol. 30(C), pages 485-494.
    12. Tan, Sieting & Yang, Jin & Yan, Jinyue & Lee, Chewtin & Hashim, Haslenda & Chen, Bin, 2017. "A holistic low carbon city indicator framework for sustainable development," Applied Energy, Elsevier, vol. 185(P2), pages 1919-1930.
    13. Keirstead, James, 2013. "Benchmarking urban energy efficiency in the UK," Energy Policy, Elsevier, vol. 63(C), pages 575-587.
    14. Phdungsilp, Aumnad, 2010. "Integrated energy and carbon modeling with a decision support system: Policy scenarios for low-carbon city development in Bangkok," Energy Policy, Elsevier, vol. 38(9), pages 4808-4817, September.
    Full references (including those not matched with items on IDEAS)

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