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The Impact of Spatial Patterns in Road Traffic Externalities on Willingness-to-Pay Estimates

Abstract

Studying traffic related externalities in the city of Gent (Belgium), we find little to no evidence that observed spatial dependencies in actual (objective) externality levels play a direct role in determining spatial dependencies in the willingness to pay (WTP) for changes in the city’s mobility policy. Investigating alternative factors that can influence WTP-estimates, however, reveals that higher stated (subjective) externality levels are positively correlated with higher WTP for reducing exposure to noise, air and odor pollution. Our results suggest complex interactions between housing decisions, perceived externality levels and WTP-estimates. Thus, allowing for subjective perceptions, sorting behavior and patterns in individuals’ characteristics can result in WTP-estimates that are not spatially correlated even though the underlying externalities are spatially correlated.

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Fig. 1
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Notes

  1. 1.

    Hanley et al. (2003) find no significant distance decay effect for the generally called ‘river in the Thames valley’, while they do find a significant distance effect for a specific river, i.e. the Mimram.

  2. 2.

    https://stad.gent/mobiliteitsplan/over-het-mobiliteitsplan/wat-en-waarom.

  3. 3.

    The basic descriptive statistics of the three actual externality variables are included in Table 10 in the “Appendix”.

  4. 4.

    Gent uses the “Nederlandse berekeningsmethode RMW 2002” or “standaardrekenmethode II” (i.e. SRM II) method to extrapolate the LDEN point measurements. This method accounts for (a.o.) the material type of the top layer of the road, speed limit, geometry of the environment, presence of noise reflecting or noise absorbing objects, type of traffic and traffic intensity.

  5. 5.

    The PM10 data are modelled with RIO-IFDM v420 and interpolated via triangulation to squares.

  6. 6.

    More details on the resulting likelihood function and the assumptions needed to calculate the WTP-scores can be found in Czajkowski et al. (2017).

  7. 7.

    http://statbel.fgov.be/nl/statistieken/cijfers/verkeer_vervoer/verkeer/ongevallen_slachtoffers/ (2012).

  8. 8.

    http://financien.belgium.be/nl/particulieren/belastingaangifte/gemeentebelasting/.

  9. 9.

    Some basic descriptive statistics of the main background variables and the reported externality levels are included in the tables in the “Appendix”.

  10. 10.

    For example, the invitation was posted on the official Facebook page of Stad Gent, Gent fietst, Leven in Gentbrugge en Ledeberg, Gents Klimaatverbond, Autopia, Netwerk Duurzame Mobiliteit and GMF (Gents MilieuFront).

  11. 11.

    Several respondents turned to live outside our geographic scope and four respondents were younger than 18.

  12. 12.

    Since only 11 respondents were between 18 and 21 years old, we opted to keep them in the sample.

  13. 13.

    Note that the Moran’s I and Geary’s c tests did not show any evidence of spatial correlation for the WTP-scores for travel time and accident risks as well.

  14. 14.

    The built area is defined as the ratio between the built surface of the neighborhood (‘wijk’) and the total surface of the neighborhood, expressed in percentages. The data are freely available from the following site: https://gent.buurtmonitor.be/.

  15. 15.

    We also estimated an extended Model 1 with interactions between actual externality levels. However, since the coefficients of these interactions were never statistically significant, we opted not to report these results.

  16. 16.

    To ensure that our findings are not driven by the design of our spatial weight matrices, alternative specifications (such as the 10 nearest neighbors and distance band matrices) were also tested. Analyzing potential spatial dependencies of the WTP based on the alternative weight matrices, revealed only one significant result (the Lagrange multiplier test for the presence of a spatial error for the WTP for a joint reduction of noise and odor pollution was significant at the 10% level when 10 nearest neighbors matrix was used), indicating that our result are quite robust for the specification of the weight matrix.

  17. 17.

    The correlation coefficient between high-income and Lden is -0.0622, between high-income and PM10 is -0.0621, and between high-income and odor is 0.0242.

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Acknowledgements

We would like to thank the administrators of the following Facebook pages: Stad Gent, Gent fietst, Leven in Gentbrugge en Ledeberg, Gents Klimaatverbond, Autopia, Netwerk Duurzame Mobiliteit, GMF. They allowed us to share our survey to their members. Further, we would like to thank the Environmental Department Gent (especially France Raulo) for providing us with the necessary contacts and data as well as Donald Chapman for his linguistic help. We would also like to thank the reviewers and editors for their many constructive suggestions and remarks. Marieke Franck also acknowledges the financial support of the BOF-KUBrussel.

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Correspondence to Sandra Rousseau.

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Appendix

Appendix

See Tables 9 and 10.

Table 9 Summary statistics for the dummy variables
Table 10 Summary statistics for the continuous variables

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Rousseau, S., Franck, M. & De Jaeger, S. The Impact of Spatial Patterns in Road Traffic Externalities on Willingness-to-Pay Estimates. Environ Resource Econ 75, 271–295 (2020). https://doi.org/10.1007/s10640-019-00348-5

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Keywords

  • Discrete choice experiments
  • Road traffic externalities
  • Subjective exposure
  • Objective exposure
  • Spatial patterns