IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v307y2015icp32-47.html
   My bibliography  Save this article

Statistical-thermodynamics modelling of the built environment in relation to urban ecology

Author

Listed:
  • Mohajeri, Nahid
  • Gudmundsson, Agust
  • Scartezzini, Jean-Louis

Abstract

Various aspects of the built environment have important effects on ecology. Providing suitable metrics for the built forms so as to quantify and model their internal relations and external ecological footprints, however, remains a challenge. Here we provide such metrics focusing on the spatial distribution of 11,418 buildings within the city of Geneva, Switzerland. The size distributions of areas, perimeters, and volumes of the buildings follow approximately power laws, whereas the heights of the buildings follow a bimodal (two-peak) distributions. Using the Gibbs–Shannon entropy formula, we calculated area, perimeter, volume, and height entropies for 16 neighbourhoods (zones) in Geneva and show that they have positive correlations (R2=0.43–0.84) with the average values of these parameters. Furthermore, the entropies of area, perimeter, and volume themselves are all positively correlated (R2=0.87–0.91). Deriving entropy from Helmholtz free energy, we interpret entropy as a measure of spreading or expansion and provide an analogy between the entropy increase during the expansion of a solid and the entropy increase with the expansion of the built-up area in Geneva. Compactness of cities is widely thought to affect their ecology. Here we use the density of buildings and transport infrastructure as a measure of compactness. The results show negative correlation (R2=0.39–0.54) between building density and the entropies of building area, perimeter, and volume. The calculated length-size distributions of the street network shows a negative correlations (R2=0.70–0.76) with the number of streets per unit area as well as with the total street length per unit area. The number of buildings as well as populations (number of people) show sub-linear relations with both the annual heat demand (MJ) and CO2 emissions (kg) for the 16 neighbourhoods. These relations imply that the heat demand and CO2 emissions grow at a slower rate than either the number of buildings or the population. More specifically, the relations can be interpreted so that 1% increase in the number of buildings or the population is associated with some 0.8–0.9% increase in heat demand and CO2 emissions. Thus, in terms of number of buildings and populations, large neighbourhoods have proportionally less ecological footprints than smaller neighbourhoods.

Suggested Citation

  • Mohajeri, Nahid & Gudmundsson, Agust & Scartezzini, Jean-Louis, 2015. "Statistical-thermodynamics modelling of the built environment in relation to urban ecology," Ecological Modelling, Elsevier, vol. 307(C), pages 32-47.
  • Handle: RePEc:eee:ecomod:v:307:y:2015:i:c:p:32-47
    DOI: 10.1016/j.ecolmodel.2015.03.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380015001258
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2015.03.014?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sciubba, Enrico & Ulgiati, Sergio, 2005. "Emergy and exergy analyses: Complementary methods or irreducible ideological options?," Energy, Elsevier, vol. 30(10), pages 1953-1988.
    2. Reid Ewing & Robert Cervero, 2010. "Travel and the Built Environment," Journal of the American Planning Association, Taylor & Francis Journals, vol. 76(3), pages 265-294.
    3. A. S. MacDougall & K. S. McCann & G. Gellner & R. Turkington, 2013. "Diversity loss with persistent human disturbance increases vulnerability to ecosystem collapse," Nature, Nature, vol. 494(7435), pages 86-89, February.
    4. Del Giudice, Emilio & Pulselli, Riccardo M. & Tiezzi, Enzo, 2009. "Thermodynamics of irreversible processes and quantum field theory: An interplay for the understanding of ecosystem dynamics," Ecological Modelling, Elsevier, vol. 220(16), pages 1874-1879.
    5. Aura Reggiani & Peter Nijkamp (ed.), 2009. "Complexity and Spatial Networks," Advances in Spatial Science, Springer, number 978-3-642-01554-0.
    6. Bristow, David N. & Kennedy, Christopher A., 2013. "Maximizing the use of energy in cities using an open systems network approach," Ecological Modelling, Elsevier, vol. 250(C), pages 155-164.
    7. Filippo Simini & Marta C. González & Amos Maritan & Albert-László Barabási, 2012. "A universal model for mobility and migration patterns," Nature, Nature, vol. 484(7392), pages 96-100, April.
    8. Alan G Wilson, 2006. "Ecological and Urban Systems Models: Some Explorations of Similarities in the Context of Complexity Theory," Environment and Planning A, , vol. 38(4), pages 633-646, April.
    9. Sciubba, Enrico, 2010. "On the Second-Law inconsistency of Emergy Analysis," Energy, Elsevier, vol. 35(9), pages 3696-3706.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Moazami, Amin & Nik, Vahid M. & Carlucci, Salvatore & Geving, Stig, 2019. "Impacts of future weather data typology on building energy performance – Investigating long-term patterns of climate change and extreme weather conditions," Applied Energy, Elsevier, vol. 238(C), pages 696-720.
    2. Jørgensen, Sven E. & Nielsen, Søren Nors & Fath, Brian D., 2016. "Recent progress in systems ecology," Ecological Modelling, Elsevier, vol. 319(C), pages 112-118.
    3. Mohajeri, Nahid & Upadhyay, Govinda & Gudmundsson, Agust & Assouline, Dan & Kämpf, Jérôme & Scartezzini, Jean-Louis, 2016. "Effects of urban compactness on solar energy potential," Renewable Energy, Elsevier, vol. 93(C), pages 469-482.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aura Reggiani, 2022. "The Architecture of Connectivity: A Key to Network Vulnerability, Complexity and Resilience," Networks and Spatial Economics, Springer, vol. 22(3), pages 415-437, September.
    2. Boeing, Geoff, 2017. "Methods and Measures for Analyzing Complex Street Networks and Urban Form," SocArXiv 93h82, Center for Open Science.
    3. Patterson, Murray G., 2012. "Are all processes equally efficient from an emergy perspective?," Ecological Modelling, Elsevier, vol. 226(C), pages 77-91.
    4. Marvuglia, Antonino & Benetto, Enrico & Rios, Gordon & Rugani, Benedetto, 2013. "SCALE: Software for CALculating Emergy based on life cycle inventories," Ecological Modelling, Elsevier, vol. 248(C), pages 80-91.
    5. Noronha Vaz, E. de & Nainggolan, D. & Nijkamp, P. & Painho, M., 2011. "A complex spatial systems analysis of tourism and urban sprawl in the Algarve," Serie Research Memoranda 0003, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    6. Lucia, Umberto & Sciubba, Enrico, 2013. "From Lotka to the entropy generation approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3634-3639.
    7. Zhang, Xiaohu, 2021. "Beyond expected regularity of aggregate urban mobility: A case study of ridesourcing service," Journal of Transport Geography, Elsevier, vol. 95(C).
    8. Sciubba, Enrico, 2011. "What did Lotka really say? A critical reassessment of the “maximum power principle”," Ecological Modelling, Elsevier, vol. 222(8), pages 1347-1353.
    9. Liao, Wenjie & Heijungs, Reinout & Huppes, Gjalt, 2012. "Thermodynamic analysis of human–environment systems: A review focused on industrial ecology," Ecological Modelling, Elsevier, vol. 228(C), pages 76-88.
    10. John Stanley & Janet Stanley, 2023. "Improving Appraisal Methodology for Land Use Transport Measures to Reduce Risk of Social Exclusion," Sustainability, MDPI, vol. 15(15), pages 1-18, August.
    11. Li, Jingjing & Kim, Changjoo & Sang, Sunhee, 2018. "Exploring impacts of land use characteristics in residential neighborhood and activity space on non-work travel behaviors," Journal of Transport Geography, Elsevier, vol. 70(C), pages 141-147.
    12. Ding, Yu & Lu, Huapu, 2016. "Activity participation as a mediating variable to analyze the effect of land use on travel behavior: A structural equation modeling approach," Journal of Transport Geography, Elsevier, vol. 52(C), pages 23-28.
    13. Toşa, Cristian & Sato, Hitomi & Morikawa, Takayuki & Miwa, Tomio, 2018. "Commuting behavior in emerging urban areas: Findings of a revealed-preferences and stated-intentions survey in Cluj-Napoca, Romania," Journal of Transport Geography, Elsevier, vol. 68(C), pages 78-93.
    14. Regine Gerike & Caroline Koszowski & Bettina Schröter & Ralph Buehler & Paul Schepers & Johannes Weber & Rico Wittwer & Peter Jones, 2021. "Built Environment Determinants of Pedestrian Activities and Their Consideration in Urban Street Design," Sustainability, MDPI, vol. 13(16), pages 1-21, August.
    15. Chetan Doddamani & M. Manoj, 2023. "Analysis of the influences of built environment measures on household car and motorcycle ownership decisions in Hubli-Dharwad cities," Transportation, Springer, vol. 50(1), pages 205-243, February.
    16. Jie Gao & Dick Ettema & Marco Helbich & Carlijn B. M. Kamphuis, 2019. "Travel mode attitudes, urban context, and demographics: do they interact differently for bicycle commuting and cycling for other purposes?," Transportation, Springer, vol. 46(6), pages 2441-2463, December.
    17. He, Mingwei & He, Chengfeng & Shi, Zhuangbin & He, Min, 2022. "Spatiotemporal heterogeneous effects of socio-demographic and built environment on private car usage: An empirical study of Kunming, China," Journal of Transport Geography, Elsevier, vol. 101(C).
    18. Mouratidis, Kostas & Ettema, Dick & Næss, Petter, 2019. "Urban form, travel behavior, and travel satisfaction," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 306-320.
    19. Hamdi Lemamsha & Chris Papadopoulos & Gurch Randhawa, 2018. "Perceived Environmental Factors Associated with Obesity in Libyan Men and Women," IJERPH, MDPI, vol. 15(2), pages 1-16, February.
    20. Kevin Credit & Elizabeth Mack, 2019. "Place-making and performance: The impact of walkable built environments on business performance in Phoenix and Boston," Environment and Planning B, , vol. 46(2), pages 264-285, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:307:y:2015:i:c:p:32-47. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.