The influence of built environment and socio-economic factors on commuting energy demand. A path analysis-based approach

• Authors: Soufiane Boukarta , Ewa Berezowska-Azzag

Abstracts

EN

Transport is the second energy consumer sector after housing in Algeria. In this article, we explore the ener- gy implication of commuting by considering a panel of socio-economic (SE) and built environment (BE) driving factors. The method is based on four steps: (i) The first step is to identify the main and potential drivers from the literature review and to propose a model that summarises the main assumptions that could explain the volume of commuting and the resulting energy consumption. (ii) In the second step, we designed and distributed 700 questionnaires in the municipality of Djelfa and retained 184 valid questionnaires in the final study sample. (iii) In the third step, we developed a method adapted to urban areas to quantify energy consumption as a function of the distance travelled, the type and density of occupation by means of transport and the type of fuel. (iv) The fourth step is to check the fit of the hypothetical model with a path analysis-based approach. The model developed identifies 15 factors, of which five have a direct impact and 10 have an indirect impact on the energy consumption of commuting. The model shows that building density and the age of the respondent can reduce the energy consumption of commuting by up to −15% and −12% respectively; whereas the number of cars by housing and the round-trip frequency could increase the energy consumption up to 38% and 27% respectively. Our results suggest a structuring role of the socio-economic characteristics of households in explaining the energy consumption of commuting.

Keywords

EN

Djelfa (Algeria); energy consumption; path analysis; sensitivity analysis; commuting

• Publisher: Uniwersytet im. Adama Mickiewicza w Poznaniu
• Journal: Quaestiones Geographicae
• Year: 2022
• Volume: 41
• Issue: 4
• Pages: 19-39

Dates

published

2022

Contributors

author

Soufiane Boukarta

• Institute of Architecture and Urban Planning, University of Blida, Algeria; Laboratoire, Ville, Urbanisme et développement durable (VUDD), Algeria

• https://orcid.org/0000000330947740

author

Ewa Berezowska-Azzag

• Institute of Architecture and Urban Planning, University of Blida, Algeria; Ecole Polytechnique d’Architecture et d’Urbanisme (EPAU), Algeria

• https://orcid.org/0000000343382021

References

• Abouri R., Siagh H., 2016. Production de l'essence sans plomb par amélioration du procédé de reforming catalytique. Master Thesis, Kasdi Merbah University, Algeria.
• Algerian accreditation body, 2016, Certificate of Accreditation N 1-2-016, Technical Annex, Algerac, Algeria.
• APRUE, 2007. The National Agency for the Promotion and Rationalisation of Energy Use, Final consumption, key figures for 2005.
• APRUE, 2014. The National Agency for the Promotion and Rationalisation of Energy Use, Final consumption, key figure for the year 2012.
• APRUE, 2015. The National Agency for the Promotion and Rationalisation of Energy Use, The National Energy Situation, 2013 figures, 2015 edition.
• APRUE, 2017. The National Agency for the Promotion and Rationalisation of Energy Use, The National Energy Situation, 2015 figures, 2017 edition.
• Bakour M., 2016. La mobilité urbaine et la planification des réseaux de transport dans une logique de renouvellement urbain, le cas d'Alger. PHD thesis, EPAU, Algeria.
• Boukarta S., 2019. Déterminants de la forme urbaine générant le potentiel de maîtrise de l'énergie en zone semi-aride. Application sur le cas de Djelfa en Algérie. PhD Dissertation, Ecole polytechnique d'architecture et d'urbanisme d'Alger, Algeria.
• Boukarta S., Berezowska-Azzag E., 2018. Assessing household gas and electricity consumption, case study of Djelfa, Algeria. Quaestiones Geographicae 37(4): 111-129. DOI https://doi.org/10.2478/quageo-2018-0034
• Boukarta S., Berezowska-Azzag E., 2020. Exploring the role of socio-economic and built environment driving factors in shaping the commuting modal share: A path-analysis-based approach. Quaestiones Geographicae 39(4): 87-107. DOI https://doi.org/10.2478/quageo-2020-0032
• Boutaud Aurélien, Penser le changement ou changer le pansement. Dissertation thesis, 2005, Rochelle University, France.
• Breheny M., 1995. The compact city and transport energy consumption. Transactions of the Institute of British Geographers: 20(1): 81-101. DOI https://doi.org/10.2307/622726
• Brownstone D., Golob T.F., 2008. The impact of residential density on vehicle usage and energy consumption. Journal of Urban Economics 65(1): 91-98. DOI https://doi.org/10.1016/j.jue.2008.09.002
• Calabrese F., Diao M., Di Lorenzo G., Ferreira J. Jr., Ratti C., 2012. Understanding individual mobility patterns from urban sensing data: A mobile phone trace example. Transportation Research Part C: Emerging Technologies 26: 301-313. DOI https://doi.org/10.1016/j.trc.2012.09.009
• Cervero R., Kockelman K., 1997. Travel demand and the 3Ds: Density, diversity, and design. Transportation Research Part D: Transport and Environment 2: 199-219. DOI https://doi.org/10.1016/S1361-9209(97)00009-6
• Cervero R., Murakami J., 2010. Effects of built environments on vehicle miles traveled: Evidence from 370 US urbanized areas. Environment and Planning A 42(2): 400-418. DOI https://doi.org/10.1068/a4236
• Cervero R., Radisch C., 1995. Travel choices in pedestrian versus automobile oriented neighborhoods. Transport Policy 3(3): 127-141. DOI https://doi.org/10.1016/0967-070X(96)00016-9
• Chen C., Gong H., Paaswell R., 2007. Role of the built environment on mode choice decisions: Additional evidence on the impact of density. Transportation 35(3): 285-299. DOI https://doi.org/10.1007/s11116-007-9153-5
• Dargay J., 2004. The effect of prices and income on car travel in the UK. Transportation Research Part A: Policy and Practice 41(10): 949-960. DOI https://doi.org/10.1016/j.tra.2007.05.005
• Dieleman F.M., Dijst M., Burghouwt G., 2002. Urban form and travel behaviour: Micro-level household attributes and residential context. Urban Studies 39(3): 507-527. DOI https://doi.org/10.1080/00420980220112801
• Ding C., Wang D., Liu C., Zhang Y., Yang J., 2017. Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance. Transportation Research Part A: Policy and Practice 100: 65-80. DOI https://doi.org/10.1016/j.tra.2017.04.008
• Ewing R., Cervero R., 2002. Travel and the built environment: A synthesis. Transportation Research Record: Journal of the Transportation Research Board 1780: 87-114. DOI https://doi.org/10.3141/1780-10
• Ewing R., Cervero R., 2010. Travel and the built environment: A meta-analysis. Journal of the American Planning Association 76(3): 265-294. DOI https://doi.org/10.1080/01944361003766766
• Feng J., Dijst M., Prillwitz J., Wissink B., 2013. Travel time and distance in international perspective: A comparison between Nanjing (China) and the Randstad (The Netherlands). Urban Studies 50(14): 2993-3010. DOI https://doi.org/10.1177/0042098013482504
• Handy S., Cao X., Mokhtarian P., 2005. Correlation or causality between the built environment and travel behavior? Evidence from Northern California. Transportation Research Part D: Transport and Environment 10(6): 427-444. DOI https://doi.org/10.1016/j.trd.2005.05.002
• Holden E., Norland I.T., 2005. Three challenges for the compact city as a sustainable urban form: Household consumption of energy and transport in eight residential areas in the greater Oslo region. Urban Studies 42(12): 2145-2166. DOI https://doi.org/10.1080/00420980500332064
• Hooper D., Coughlan J., Mullen M.R., 2008. Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business research methods 6(1): 53-60.
• Hu L.T., Bentler P.M., 1999. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal 6(1): 1-55. DOI https://doi.org/10.1080/10705519909540118
• Iacobucci D., 2010. Structural equations modeling: Fit indices, sample size, and advanced topics. Journal of Consumer Psychology 20(1): 90-98. DOI https://doi.org/10.1016/j.jcps.2009.09.003
• Official Gazette (61), 2010. Law No. 10-02 approving the National Spatial Planning Scheme (SNAT 2030).
• Karathodorou N., Graham D.J., Noland R.B., 2010. Estimating the effect of urban density on fuel demand. Energy Economics 32(1): 86-92.
• DOI https://doi.org/10.1016/j.eneco.2009.05.005
• Khan M., Kockelman K.M., Xiong X., 2014. Models for anticipating non-motorized travel choices, and the role of the built environment. Transport Policy 35: 117-126. DOI https://doi.org/10.1016/j.tranpol.2014.05.008
• Kitamura R., Mokhtarian P.L., Laidet L., 1997. A micro-analysis of land use and travel in five neighborhoods in the San Francisco Bay Area. Transportation 24(2): 125-158.
• Kline R., 1998. Principles and practice of structural equation modeling. The Guilford Press, New York, NY.
• Leslie K., Bolstein R., Crow M., 1965. Statistical formulas and citations for sample size and result analyzer. American Statistical 52(2): 41.
• Limtanakool N., Dijst M., Schwanen T., 2006. The influence of socioeconomic characteristics, land use and travel time considerations on mode choice for medium-and longer-distance trips. Journal of Transport Geography 14(5): 327-341. DOI https://doi.org/10.1016/j.jtrangeo.2005.06.004
• Ma J., Mitchell G., Heppenstall A., 2014. Daily travel behaviour in Beijing, China: An analysis of workers' trip chains, and the role of socio-demographics and urban form. Habitat International 43: 263-273. DOI https://doi.org/10.1016/j.habitatint.2014.04.008
• Manaugh K., Miranda-Moreno L.F., El-Geneidy A.M., 2009. The effect of neighbourhood characteristics, accessibility, home-work location, and demographics on commuting distances. Transportation 37(4): 627-646. DOI https://doi.org/10.1007/s11116-010-9275-z
• Manaugh K., Kreider T., 2013. What is mixed use? Presenting an interaction method for measuring land use mix. Journal of Transport and Land Use 6(1): 63-72. DOI https://doi.org/10.5198/jtlu.v6i1.291
• Marique A.F., 2013. Méthodologie d'évaluation énergétique des quartiers périurbains. Perspectives pour le renouvellement périurbain wallon (Doctoral dissertation, Liège university, Belgique)
• Marique A.F., Reiter S., 2012. A method for evaluating transport energy consumption in suburban areas. Environmental Impact Assessment Review 33(1): 1-6. DOI https://doi.org/10.1016/j.eiar.2011.09.001
• Matec, 2010. National Inventory of Greenhouse Gas Emissions for the year 2000.
• MEAT, 2001. Development of the National Climate Change Strategy and Action Plan.
• Muñiz I., Galindo A., 2005. Urban form and the ecological footprint of commuting. The case of Barcelona. Ecological Economics 55(4): 499-514. DOI https://doi.org/10.1016/j.ecolecon.2004.12.008
• Næss P., 2010. Residential location, travel, and energy use in the Hangzhou metropolitan area. Journal of Transport and Land Use 3(3): 27-59. DOI https://doi.org/10.5198/jtlu.v3i3.98
• Naess P., 2014. Tempest in a teapot: The exaggerated problem of transport-related residential self-selection as a source of error in empirical studies. Journal of Transport and Land Use 7(3): 57-79. DOI https://doi.org/10.5198/jtlu.v7i3.491
• Newman P.W.G., Kenworthy J.R., 1989. Cities and automobile dependence: An international sourcebook. Gower, Aldershot, UK.
• Nguyen Q.S., 2014. Mobilités spatiales et ségrégation dans un contexte de métropolisation: Le cas de Hanoi. Phd Dissertation. Lyon 2 university.
• Pan H., Shen Q., Zhang M., 2009. Influence of urban form on travel behaviour in four neighbourhoods of Shanghai. Urban Studies 46(2): 275-294. DOI https://doi.org/10.1177/0042098008099355
• Rogers R.G., Gumuchdjian P., 2008. Des Villes durables pour une petite planète. Ed Moniteur, France.
• Schreiber J.B., Nora A., Stage F.K., Barlow E.A., King J., 2006. Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research 99(6): 323-338. DOI https://doi.org/10.3200/JOER.99.6.323-338
• Stead D., 2001. Relationships between land use, socioeconomic factors, and travel patterns in Britain. Environment and Planning B: Planning and Design 28(4): 499-528. DOI https://doi.org/10.1068/b2677
• Van Acker V., Witlox F., 2010. Car ownership as a mediating variable in car travel behaviour research using a structural equation modelling approach to identify its dual relationship. Journal of Transport Geography 18(1): 65-74. DOI https://doi.org/10.1016/j.jtrangeo.2009.05.006
• Zhang Y., Wu W., Li Y., Liu Q., Li C., 2014. Does the built environment make a difference? An investigation of household vehicle use in Zhongshan metropolitan area, China. Sustainability 6(8): 4910-4930. DOI https://doi.org/10.3390/su6084910

Identifiers

DOI

10.14746/quageo-2022-0039

Biblioteka Nauki

15805631