How do land use trends affect CBA outcome?

1. How do land use trends affect CBA outcome? Peter Almström Svante Berglund Maria Börjesson Daniel Jonsson 27 november 2009, 1

2. Background • CBA - number of assumptions about input factors • population, land-use, economic growth, vehicle characteristics, fuel prices and public transport ticket prices • Induces uncertainty in the outcome. • Assumedinfluence on relative ranking of road and rail is often controversial. • CBA questioned by some practitioners/policy-makers. • Specifically, we concentrate on land-use assumptions. 27 november 2009, 2

3. Backgrund II • Does new investment tend to "create its own demand" through long-term land use effects. • Rail investments - structured land use patterns • Road investments - increase the risk of urban sprawl. • Underestimation of benefits of rail investments? 27 november 2009, 3

4. Purpose of the study • How future land-use policy affect uncertainty in CBA outcome. • Uncertainty can mean many things. We concentrate one the ranking of rail and road projects. • A second purpose: how the ranking is affected by the fact that investments tend to create its own demand through land use changes. 27 november 2009, 4

5. Method • Large-scale integrated land-use and transport model calibrated for the Stockholm region • Ranking of the CBA outcome • Six rail and road investments • Three general land use scenarios for the period 2006-2030: • a trend scenario, a central scenario and a periphery scenario. • We investigate also how the demand induced by changes in location patterns affects the relative ranking. 27 november 2009, 5

6. Investments 27 november 2009, 6

7. Land-use scenarios Trend • 58 % of the population growth in multi family housing, the same as the trend during the last 30 years • The tolerance for density is such that the current development structure is preserved • Accessibility by public transport is important for localization of new multi family housing, accessibility by car is important for localization of new single family housing Central • 78 % of the population growth in multi family housing • The tolerance for high population and work place density is considerable • Accessibility by public transport is important for localization of new housing and work places Perifer • 27 % of the population growth in multi family housing, single family houses are built in the same pace as in the 70’s • Low tolerance for high density, considerable exploitation of unused land • Accessibility by car is important for localization of new housing and work places 27 november 2009, 7

8. Compared to what?

9. Car [vkm] and Transit [pkm]

10. Consumer surplus

11. Net present valueratio

12. Userbenefits in LUTI models

13. Land use and consumer surplus

14. Land use and NPVR

15. Sometimes LUTI adjusted LUTI adjusted LUTI adjusted LUTI adjusted LUTI adjusted LUTI adjusted

16. Conclusions • Land-use scenario effects are small in the time- perspective on 10-30 years. Has limited impact on CBA outcome. • It is not obvious how the relative merits of rail and road investments are influenced by planning policy. • The benefit of a large road investment, FörbifartStockholm, increases with more sprawl.

17. Conclusions • Consistent with Zhao and Kockelman (2001) Pradhan and Kockelman (2002) -larger impact on land-use than on transport patterns. In the transport network differences smooth out. • Induced demand: • The consumer benefit (on the transport market) increases if the land-use is adjusted to the investment – but the benefit of induced demand is small in the time perspective of 10-30 years. • Does not translatedirectly to NPVR • Conclusions apply to Stockholm, where the public transport system is well structured/developed.

18. Thank you

19. User benefits in LUTI models • If the land-use/transport model was an integrated nested logit model (if models calculate rents clearing the market): • But • No explicit land-use prices in the land-use model. • The land-use choice does not use the total benefits appearing in the transport model. • We calculate only benefits in the transport market • Land use adjusts to accessibility with investment built

20. Model system Exogenous data: Transport network Aggregated population forecast, share of house types Economic development Transport model Land-use model Demand /type of housing Demand /mode (5) /destination /trip purpose Models Model for car ownership and license holding Population Forecast Population by: age, sex and zone Supply of land Assignment /peak/low /car /transit Data Total population by type of housing and zone Accessibility

21. Literature • De Jong et al. (2007) finds that uncertainty due to input factors are larger than uncertainty due to model errors. • That uncertain socioeconomic forecasts are a significant source of uncertainty in the model outcome (Rodier and Johnston, 2001; Thompson et al, 1997; Harvey and Deakin, 1995). • Zhao and Kockelman (2001) Pradhan and Kockleman (2002). • Rodier (2000; 2005) show that land use changes induced by highway investments accounts for about 50 percent of the increases in travel demand due to the investments. Marshal and Grady (2002) find, on the contrary, that land use impact have little effect on travel. • Land-use density has an impact on travel and emissions - if combined with appropriate transit investments and auto pricing policies (Ewing and Cervero, 2001; Cervero 2001, Kenworthy and Newman, 1989; Rodier et al. 2002; Wegener and Fürst, 1999; Cervero and Kockelman, 1997; Ferdman 2005; Rodier et al. 2002; Rodier and Johnston 1997; Jonston and Ceerla, 1995; Rodier and Johnstson 2002). 27 november 2009, 24

22. Model characteristics • Transport model • Lutrans (Land use Transport Model) is a simplified version of the national transport model Sampers. • Simplified with regard to the number of trip types 2 – Work and other • Lutrans was developed and used for the regional plan for Stockholm – Mälardalen • Car ownership model • Sensitive to land use characteristics in the zone i.e. the share of single family houses and density. • We also have ”the usual variables” age structure, income (square root). • Population forecast or population disaggregation model • Uses the age of the houses and share of single/multi family houses in a zone to calculate the disaggregated population forecast • Land-use model • Allocates a fixed number of inhabitants/workplaces to county • Accessibility, density • By single/multi family houses • Special model for conversion of summerhouses to permanent housing