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The Influence of Built Environment and Socio-Economic Factors on Commuting Energy Demand: A Path Analysis-Based Approach

Soufiane Boukarta
Soufiane Boukarta
 and 
Ewa Berezowska-Azzag
Ewa Berezowska-Azzag
   | Dec 29, 2022
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Published Online: Dec 29, 2022
Page range: 19 - 39
Received: Sep 24, 2021
DOI: https://doi.org/10.14746/quageo-2022-0039
Keywords
© 2022 Soufiane Boukarta et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Study area and the neighbourhoods of the city according to their period of creation.Source: own composition based on Bouzidi et Boukhari 2013.
Study area and the neighbourhoods of the city according to their period of creation.Source: own composition based on Bouzidi et Boukhari 2013.

Fig. 2

Conceptual model explaining the need for commuting.
Conceptual model explaining the need for commuting.

Fig. 3

The hypothesised model.In red: positive causal relation; in black: negative causal relationSource: own study.
The hypothesised model.In red: positive causal relation; in black: negative causal relationSource: own study.

Fig. 4

Comparison between the survey and census data.LPG – liquefied petroleum gasSource: own study.
Comparison between the survey and census data.LPG – liquefied petroleum gasSource: own study.

Fig. 5

Employment and housing locations.green: workplace, yellow: housing, white: centre of the citySource: authors’ compilation based on Google Earth (2015).
Employment and housing locations.green: workplace, yellow: housing, white: centre of the citySource: authors’ compilation based on Google Earth (2015).

Fig. 6

Direct effects of driving factors explaining the energy consumption generated by commuting. R2 = 0.61.Source: own compilation.
Direct effects of driving factors explaining the energy consumption generated by commuting. R2 = 0.61.Source: own compilation.

Fig. 7

Effect of SE and BE driving factors per unit of measurement on energy consumption of commuting to work.BE – built environment, SE – socio-economicSource: own compilation.
Effect of SE and BE driving factors per unit of measurement on energy consumption of commuting to work.BE – built environment, SE – socio-economicSource: own compilation.

Direct and indirect effects of the driving factors of energy consumption generated by commuting.

Effect Number of cars Home-to-work distance Round trip frequency Profession Built density Energy consumption (kWh person−1 × year−1)
Number of floors Direct 0.255 0.000 0.000 0.000 0.000 0.000
Indirect 0.000 −0.030 0.009 0.000 0.000 0.097
Total 0.255 −0.030 0.009 0.000 0.000 0.097
Housing type Direct 0.295 0.000 −0.139 0.227 0.664 0.000
Indirect 0.000 −0.035 0.010 0.000 0.000 0.016
Total 0.295 −0.035 −0.129 0.227 0.664 0.016
Income Direct 0.425 0.000 0.000 0.000 0.000 0.000
Indirect 0.000 −0.050 0.014 0.000 0.000 0.161
Total 0.425 −0.050 0.014 0.000 0.000 0.161
Occupancy rate per housing Direct 0.000 0.130 0.000 0.000 0.000 0.000
Indirect 0.000 0.000 −0.037 0.000 0.000 0.076
Total 0.000 0.130 −0.037 0.000 0.000 0.076
Bus rotation Direct 0.000 0.427 0.000 0.000 0.000 0.000
Indirect 0.000 0.000 −0.122 0.000 0.000 0.249
Total 0.000 0.427 −0.122 0.000 0.000 0.249
Distance to centre Direct 0.000 0.330 0.000 0.000 −0.331 0.000
Indirect 0.000 0.000 −0.094 0.000 0.000 0.241
Total 0.000 0.330 −0.094 0.000 −0.331 0.241
Number of cars Direct 0.000 −0.117 0.000 0.000 0.000 0.448
Indirect 0.000 0.000 0.033 0.000 0.000 −0.068
Total 0.000 −0.117 0.033 0.000 0.000 0.379
Education level Direct 0.000 0.000 0.000 0.000 0.111 0.000
Indirect 0.000 0.000 0.000 0.000 0.000 −0.016
Total 0.000 0.000 0.000 0.000 0.111 −0.016
Bus frequency Direct 0.000 0.000 −0.105 −0.155 0.000 0.000
Indirect 0.000 0.000 0.000 0.000 0.000 −0.054
Total 0.000 0.000 −0.105 −0.155 0.000 −0.054
Distance to national road Direct 0.000 0.000 0.000 0.000 0.252 0.000
Indirect 0.000 0.000 0.000 0.000 0.000 −0.037
Total 0.000 0.000 0.000 0.000 0.252 −0.037
Home-to-work distance Direct 0.000 0.000 −0.285 0.000 0.000 0.661
Indirect 0.000 0.000 0.000 0.000 0.000 −0.077
Total 0.000 0.000 −0.285 0.000 0.000 0.584
Respondent age Direct 0.000 0.000 0.000 0.000 0.000 −0.119
Indirect 0.000 0.000 0.000 0.000 0.000 0.000
Total 0.000 0.000 0.000 0.000 0.000 −0.119
Round–trip frequency Direct 0.000 0.000 0.000 0.000 0.000 0.271
Indirect 0.000 0.000 0.000 0.000 0.000 0.000
Total 0.000 0.000 0.000 0.000 0.000 0.271
Profession Direct 0.000 0.000 0.000 0.000 0.000 0.165
Indirect 0.000 0.000 0.000 0.000 0.000 0.000
Total 0.000 0.000 0.000 0.000 0.000 0.165
Built density Direct 0.000 0.000 0.000 0.000 0.000 −0.145
Indirect 0.000 0.000 0.000 0.000 0.000 0.000
Total 0.000 0.000 0.000 0.000 0.000 −0.145

Direct effects between driving factors.

Effect P
Number of cars < – - Income 0.425 ***
Number of cars < – - Number of floors 0.255 0.019**
Number of cars < – - Housing type 0.295 0.004**
Home-to-work distance < – - Distance to centre 0.330 ***
Home-to-work distance < – - Number of bus rotation 0.427 ***
Home-to-work distance < – - Occupancy rate per housing 0.130 0.070*
Home-to-work distance < – - Number of cars −0.117 0.071*
Built density < – - Distance to national road 0.252 0.014**
Profession < – - Bus frequency −0.155 0.032**
Built density < – - Distance to centre −0.331 0.002**
Built density < – - Education level 0.111 0.061*
Profession < – - Housing type 0.227 0.002**
Round trip frequency < – - Housing type −0.139 0.057*
Built density < – - Housing type 0.664 ***
Round trip frequency < – - Bus frequency −0.105 0.149
Round trip frequency < – - Home-to-work distance −0.285 ***
Energy consumption (kWh × person−1 × year−1) < – - Home-to-work distance 0.661 ***
Energy consumption (kWh × person−1 × year−1) < – - Profession 0.165 ***
Energy consumption (kWh × person−1 × year−1) < – - Round trip frequency 0.271 ***
Energy consumption (kWh × person−1 × year−1) < – - Number of cars 0.448 ***
Energy consumption (kWh × person−1 × year−1) < – - Built density −0.145 0.007**
Energy consumption (kWh × person−1 × year−1) < – - Respondent age −0.119 0.032**

Descriptive statistics.

Variables N Variable type Minimum Maximum Average SD
Accessibility
Outward journey time (min) 175 Continuous 2 200 20.59 18.26
Home–work distance (m) 162 Continuous 50 18,000 2,206.91 1,966.96
Number of bus rotations 150 Continuous 0 3 1.20 0.556
Density
Plot ratio* 139 Continuous 0.17 1.00 0.59 0.32
Built density* 139 Continuous 0.60 3.20 2.02 0.74
Design
Distance to centre (m)* 148 Continuous 17.59 2,659.18 1,219.54 696.57
Distance from national road (m)* 151 Continuous 25.62 2,569.23 1,163.83 629.94
Average number of floors. (n)* 139 Continuous 2.00 6.00 3.79 1.08
Block's area (m2)* 139 Continuous 770 36.061 7,398.83 9,150.32
Housing type (1: collective, 2: individual) 184 Nominal 1 2 1.54 0.50
Distance to public transport
Distance to public transport (housing zone)(0: <300, 4: >1 km) 173 Ordinal 1.00 4.00 2.3237 0.98
Distance to public transport (work zone)(0: <300, 4: >1 km) 172 Ordinal 0.00 4.00 1.6919 0.97
Bus frequency 184 Continuous 0.00 5.00 2.4620 1.56
Diversity
Mixed use index (from 5 to 40)* 175 Continuous 12 36 24.23 5.38
Households’ SE characteristics
Household's average age* 42 Continuous 16.33 43.80 27.1681 8.44
Respondent age 120 Continuous 27 70 43.95 10.82
Round-trip frequency 172 Continuous 1 4 1.66 0.51
Household's education level 46 Continuous 2.00 5.00 3.5230 0.77
Respondent's education level 174 Ordinal 0 4 3.26 1.06
Number of cars owned 184 Continuous 0 2 0.60 0.57
Profession (1: public, 2: liberal) 181 Nominal 1 3 1.19 0.52
Income (from 15,000 to + 60,000 Da) 181 Ordinal 1 4 2.15 0.95
Occupancy rate per housing. 139 Continuous 2 12 5.34 1.87
Modal share
Public transport (TC (1: TC, 0: other) 184 Nominal 0.00 1.00 0.31 0.46
Car (1: Voiture, 0: other) 184 Nominal 0.00 1.00 0.26 0.44
Walking (1: MAP, 0: other) 184 Nominal 0.00 1.00 0.42 0.49

Fuel conversion to kWh per km per person.

Means of commuting
Car Bus
Fuel type Diesel Petrol LPG Diesel
Consumption (L × km−1) 0.063* 0.075* 0.075* 0.2**
Rate of occupation per vehicle 1.27* 1.27* 1.27* 28**
Density (Tonne/m3) 0.825**** 0.735***** 0.55 0.825
Conversion factor 1 (T fuel – >Toe)*** 1,015 1,054 1,084 1,015
Conversion factor 2 (Toe – >KWh) 11,630***
Consumption KWh × km−1 0.61 0.68 0.52 1.95
Consumption KWh per person per Km per one-way trip 0.48 0.53 0.41 0.07
Consumption KWh per person per Km per year (225 day) 108 119.25 92.25 15.75

Fit indices of the energy consumption of commuting model.

df c2 Probability level RMSEA NFI CFI
Commuting energy consumption model 53 47.785 0.677 0.000 0.948 1.000

Description of the papers chosen for the literature review.

Authors Country Period Data sources Study scale Sensitivity analysis method Sample size Mobility type drivers explanatory power of the modal
C S L SE BE
Van Acker and Witlox (2010) Belgium 2000–2001 Survey on behaviour of travellers in Ghent on people aged 18 and over. City SEM 2,500 households × × × × × R2 = 20.1%
Breheny (1995) UK – Wales 1961–1991 Aggregated data from Ecotec project (1993). National Interpolation from data from Ecotec project (1993). Ecotec project sample (1993) × × × × –
Brownstone and Golob (2008) California 2001 National Household Transportation Survey. Aggregate data. National SEM 2,079 households × × × × × R2 = 0.37 and 0.42
Calabrese et al. (2012) Massachusetts, USA 2011 Deducted by detecting signal of mobile phones carried out by AirSag. Metropolitan area Multiple linear regression 1,101 households × × × × × R2 = 49.40% and 56.48%
Newman et al. (1989) 32 cities of different countries 1980 Collection of fuel consumption data and calculation of density excluding rural areas. Urban planning agency of different countries. Aggregate data. City Bivariate correlation analysis 32 cities × × × × × /
Cervero and Murakami (2010) USA 2003 Data collected from Highway Statistics. Department of Commerce. National SEM 370 urban areas × × × × CFI (>0: 900) 0.969NFI (>0: 950) 0.961NNFI (>0: 900) 0.942
Cervero and Radisch (1995) USA 1990–1991 Bay Area Travel questionnaire survey. Neighbourhood Binary logistic regression 2 Neighbourhoods: 620 households for non commuting. And 840 households for commuting × × × × × Pseudo R2 = 0.29,Predicted cases = 88.6%.
Chen et al. (2007) NY, USA 1997/1998 Household survey Metropolitan area SEM 2,089 trips × × × R2 = 0.45 and 0.58
Dargay (2004) UK 1970–1995 Surveys of family spending. National Semi-logistic regression 256 pseudo panels × × × × × R2 = 0.989
Dieleman et al. (2002) Netherlands 1996 National Mobility Survey in the Netherlands National Multinomial logistic regression 70,000 households × × × × × R2 = 0.31
Ding et al. (2017) Baltimore USA 2001 Household survey Metropolitan area SEM 3,519 households × × × /
Feng et al. (2013) China and Netherlands 2008 Household survey on mobility in both countries. City Multiple linear regression 2,989 respondents for 10 districts in China and 1,322 respondents for Randstad. × × × × × China:R2 = 0.115RandstadR2 = 0.124
Handy et al. (2005) California (US) 2003 E-mail questionnaire carried out on eight neighbourhoods. District in metropolitan area Linear regression 1,466 respondents × × R2 = 0.16R2 adjusted = 154
Holden and Norland (2005) Oslo, Borway 2003 Questionnaire distributed by mail. Regional linear regression 650 for daily trips, 778 for leisure travel, <100 respondents per zone (eight zones selected for the study). × × × × R2 = 0.231 for commuting
Karathodorou et al. (2010) 42 countries 1995 Millennium Cities Database for Sustainable Transport (1999) for 100 countries. And car occupancy from Mobility in Cities database (2006). Cities Linear regression 84 cities × × × × R2 = 0.61
Khan et al. (2014) Seatle, USA 2006 Questionnaires/Puget Sound Regional Council Metropolitan area Regression modelling 10,510 respondents of 4,741 households. × × × × /
Kitamura et al. (1997) San Francisco, USA 1994 Questionnaire, And land use information is obtained from the Metropolitan Transportation Commission. Neighbourhood Multiple linear regression 5 Neighbourhoods, 640 respondents, × × × × R2 = 0.2125
Limtanakool et al. (2006) Netherlands 1996 National Mobility Survey conducted by telephone interview and questionnaire Regional Binary logistic regression Commuting: 2,326Shopping: 932Leisure: 3,072 × × × × ×
Ma et al. (2014) China 2007 Questionnaires Neighbourhoods Logistic regression 60 households, 699 trips of 10 neighbourhoods. × × × × × Pseudo R2 = 0.16
Manaugh et al. (2009) Montréal, Canada 2003 Origin-destination survey, Neighbourhoods Linear regression 17,000 trips × × × SE: R2 = 0.06.SE+BE modal:R2 = 0.40
Marique (2013) Belgium 2001 2001 SE survey National Multiple linear regression 966.247 respondents. × × × R2 = 0.457
Næss (2010) Hangzhou, China 2005 Qualitative interview and questionnaire in 40 urban areas. Urban zone Multiple linear regression 28 interviews3,150 questionnaire respondents. × × × R2 = 0.189
Naess (2014) Hangzhou, China and Copenhague, Danemark 2005 Interview and questionnaire Regional Linear regression 1932 et 3150 questionnaire × × × CopenhagueR2 = 0.233HangzhouR2 = 0.095
Pan et al. (2009) Shanghai, China 2001 Questionnaires Neighbourhood Multiple logistic regression 1,709 respondents in 4 Neighbourhoods × × × × Pseudo R2 = 0.2714
Zhang et al. (2014) Zhongshan, China 2010 Questionnaires Neighbourhoods Linear regression 25,618 respondents × × × × × Pseudo R2 = 0.2823
Bakour (2016) Algiers, Algeria 2004 Household survey conducted by an organisation City Linear regression 1,200 respondents × R2 = 0.5 à 0.9

Pearson's bivariate correlation.

Commuting energy consumption (KWh × person−1 × year−1)
Correlation Sig No.
Home-work distance (m) 0.561* 0.000 162
Outward journey time (min) 0.035* 0.661 163
Number of bus rotations 0.247* 0.004 136
Built density −0.135* 0.133 125
Plot ratio −0.120* 0.184 125
Housing type (1: collective, 2: individual) −0.153* 0.051 164
Distance from national road (m) 0.256* 0.003 136
Distance to centre (m) 0.280* 0.001 133
Block's area (m2) 0.224* 0.012 125
Average number of floors 0.143* 0.112 125
Mixed use index −0.049* 0.545 156
Distance to public transport (housing zone) −0.152* 0.059 154
Distance to public transport (work zone) −0.145* 0.064 164
Round trip frequency 0.118* 0.141 156
Profession (1: public, 2: liberal) 0.169* 0.031 163
Respondent age −0.265* 0.005 109
Respondent's education level 0.107* 0.183 156
Household's education level 0.102* 0.541 138
Income 0.136* 0.082 163
Household's average age −0.307* 0.064 137
Number of cars owned 0.379* 0.000 163
Occupancy rate per housing −0.073* 0.418 126

Descriptive statistics of the energy consumption generated by commuting to work.

Minimum Maximum Average SD
Home-to-work distance 50 18,000 2,206.91 1,966.96
Daily consumption (kWh × person−1) 0.00 3.60 0.4853 0.788
Annual consumption (kWh × person−1 × an) 0.00 810.00 109.1854 177.31
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