Post on 31-Mar-2015
transcript
HOW DO LAND USE TRENDS AFFECT CBA OUTCOME?
Peter Almström
Svante Berglund
Maria Börjesson
Daniel Jonsson
27 november 2009, 1
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. • Assumed influence 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
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
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
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
Investments
27 november 2009, 6
Land-use scenarios
27 november 2009, 7
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
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
Compared to what?
Land use 2030
Trend Central Perifer
Investments
Base
E4L
FÖR
ROS…
Car [vkm] and Transit [pkm]
Centr
al
Tre
nd
Peri
fer
Centr
al
Tre
nd
Peri
fer
Centr
al
Tre
nd
Peri
fer
Centr
al
Tre
nd
Peri
fer
Centr
al
Tre
nd
Peri
fer
Centr
al
Tre
nd
Peri
fer
Centr
al
Tre
nd
Peri
fer
Centr
al
Tre
nd
Peri
fer
JA JA2 E4L STL NAC SYD FÖR ROS
14500
15000
15500
16000
16500
17000
17500
18000
15802
16494
17125
15795
16484
17122
15897
16550
17179
15778
16466
17102
15785
16471
17106
15793
16492
17125
16369
17075
17734
15786
16482
17113
Travel distance, car [mill vkm/year]
Centr
al
Tre
nd
Peri
fer
Centr
al
Tre
nd
Peri
fer
Centr
al
Tre
nd
Peri
fer
Centr
al
Tre
nd
Peri
fer
Centr
al
Tre
nd
Peri
fer
Centr
al
Tre
nd
Peri
fer
Centr
al
Tre
nd
Peri
fer
Centr
al
Tre
nd
Peri
fer
JA JA2 E4L STL NAC SYD FÖR ROS
15200
15400
15600
15800
16000
16200
16400
16049
15779
15584
16123
15845
15650
16091
15832
15643
16140
15864
15663
16216
15945
15747
16088
15807
15615
16024
15768
15573
16201
15931
15739
Passenger kilometers [mill pkm]
Consumer surplus
Central Trend Perifer Central Trend Perifer Central Trend Perifer Central Trend Perifer Central Trend Perifer Central Trend PeriferE4L STL NAC SYD FÖR ROS
0
500
1000
1500
2000
2500
3000
3500
435366 398
48 64 81202 220 181
68 45 59
2896 2955 2996
144 153 128
Sum of benefits
Net present value ratio
FÖR
ROS
E4L
STL
NAC
SYD
-2.50 -2.00 -1.50 -1.00 -0.50 0.00 0.50 1.00
PeriferTrendCentral
User benefits in LUTI models
Land use 2030
Base Adj. to investment
Investments Base
Some investment
Land use and consumer surplus
Base Adj FOR Adj Base & FOR
Base Adj FOR Adj Base & FOR
Base Adj FOR Adj Base & FOR
Trend Central Perifer
0
500
1000
1500
2000
2500
3000
3500
569 595 595 562 563 560 593 594 610
126 127 129 108 109 108 138 145 147
429 430 431407 407 406
444 442 434
152 157 157152 153 152
152 152 156258 259 259
250 251 251253 254 255
1421 1438 14341415 1412 1405
1416 1418 1419
Time, Driver Cost, Driver Toll, Driver Total, Passenger Total, Transit Total, Goods
Land use and NPVR
Förbifart Förbifart MA Förbifart JA MA
Förbifart Förbifart MA Förbifart JA MA
Förbifart Förbifart MA Förbifart JA MA
Trend Central Perifer
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.31
0.35 0.34
0.30 0.29 0.29
0.330.32
0.36
Sometimes
Land use
Central Trend Perifer
Investments
Base
Big transit
Big road
LUTI adjusted
LUTI adjusted
LUTI adjusted
LUTI adjusted
LUTI adjusted
LUTI adjusted
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, Förbifart Stockholm, increases with more sprawl.
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 translate directly to NPVR
• Conclusions apply to Stockholm, where the public transport system is well structured/developed.
Thank you
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
Model system
Accessibility Total population by type of housing and zone
Exogenous data: Transport networkAggregated population forecast, share of house typesEconomic development
Transport model
Demand/mode (5)
/destination/trip purpose
Assignment/peak/low
/car/transit
PopulationForecast
Population by: age, sex and zone
Land-use model
Demand/type of housing
Supply of land
Model for car ownership
and license holding
Data
Models
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
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