Mandatory CBA endorses the residents' expectation that dams will be built and therefore accelerates the occurrence of the dynamic inconsistency problem. They showed the mechanism of how the dynamic inconsistency problem arises and the sufficient conditions under which the. Even in cases that do not involve CBA, the number of quantitative analyzes of the dynamic inconsistency problem is extremely limited (Kiuchi, 2005).4.
Under such circumstances, the present study makes an analysis using the example of coastal embankment improvement in order to show the degree of inefficiency of the dynamic inconsistency problem caused by the use of CBA. On the other hand, the quantitative results show the degree of the dynamic inconsistency problem with the assumption that the implemented plan corresponds to the actual land use before the earthquake.
Companies
Land owners
To calculate the annual amount of damage, initially consider the flood water depth by tsunami height by bank height for each zone and then consider the probability of occurrence of a tsunami by the tsunami run-up height to calculate the tsunami damage, namely, the risk of damage to homes and the risk of death. 2, and the run-up height means the height reached by the tsunami that landed as shown in Fig. The run-up height in the Takata area in the tsunami simulation is calculated on the assumption that the same earthquake on the same scale as the three previous large earthquakes, which were different in run-up height, namely the.
Because we need to set an accurate headwater height for our simulation to calculate the flood water depth in each zone, we assume that the headwater height based on their simulation is 10m. K is higher than the headwater height Φ , the flood water depth in all zones will be zero as shown in Eq. First, the data on the run-up heights from previous tsunamis to Sanriku.
17 There are new flooded zones in the event of a larger tsunami with a run-up height of over 10m, which is the estimated run-up height in the simulation. For these zones, the flood water depth is set to 0m where the run-up height of the arriving tsunami is 10m and set to the part that exceeds 10m where the run-up height of the tsunami exceeds 10m. Only in the case where the runoff heightΦ exceeds the fixed coastal embankment heightK.
Multiply this amount by the probability of a tsunami occurring by the run-up height ( )T Φ to determine the annual deduction per person DiDeath in the zone (only if the run-up height Φ is greater than the set coastal dike heightK.
Calculation of the coastal levee improvement cost
Finally, we add the welfare of residents and the welfare of absentee landowners to obtain gross social welfare. To compare with the cost of the coastal levee improvement, which is a state variable, obtain the benefit by converting it to present value using a social discount rate of 4%. The cost function and cost of the coastal embankment improvement against the set height of the embankment, calculated using regression analysis28, is shown by the regression line in the figure.
The coastal dikes to be discussed for improvement in this study are the dikes with a total length of 1,977 meters that are planned to be developed (by Iwate Prefecture) along the coast of the Takata area. The location of the improved coastal dikes is shown by the blue line in Figure 7.
Formation of the standard
Simulation
Simulation for each of the two cases
Simulation results
Explanation: Increase/decrease refers to the difference with the standard, where the coastal dike height is 6 meters, as a result of the increase/decrease in the coastal dike height. In the case of dynamic inconsistency, the government determines the coastal dike height via CBA after residents have strategically migrated. When the population of the Takata area is increased to about 1.5 times (13,080 people) over the optimal social case (8,440 people), the improved coastal dike height was 11 meters above T.P., 1 meter higher than in the optimal social case .
Discussion
8, the vertical axis (A) represents the improved height of the coastal embankment, the vertical axis (B) represents the net benefit to the Takata area, and the horizontal axes for (A) and (B) represent the population of the Takata area. On the horizontal axis, the minimum value is the population of the Takata area before the earthquake, and the maximum value is about twice that. Curve (a) is the result in the optimal social case and shows the population of the Takata area when the equilibrium population stabilizes after the height of the coastal embankment is determined.
Curve (b) is the result in the case of dynamic inconsistency and shows the improved coastal dykes determined through CBA after the Takata area population has been determined. Curve (c) shows the benefit to the Takata area for each improved coastal levee height after comparing the amount of reduced damage for each improved coastal levee height with the case of the coastal levee height of 6m above T.P. Tables 7 and 8 (and Fig. 8) clarify that the improved coastal embankment height determined through CBA is 11m when the population of the Takata area is 13,080, whereas the equilibrium population is 8,645 when the improved coastal embankment height is 11m.
Even though the residents who migrated to the Takata area to take advantage of the coastal dike improvement, many people leave the Takata area again to eventually reach the equilibrium. In the present study, since the utility function of the residents is assumed, this equilibrium can be projected. Finally, the infrastructure improvement cost and the degree of the dynamic inconsistency problem are analyzed to discuss the dynamic inconsistency problem in infrastructure other than seawalls.
Therefore, although many residents have migrated to the Takata area to increase the population to a level of about 1.5 times the optimal population as a result of the strategic behavior of the developers, the coastal embankment height has changed from an optimal social height of 10m above the T.P.
Conclusions
Appendices
Land use data creation
Traffic volume data creation
Regression analysis
Shown below are the results of the regression analysis and the data when the coastal levee improvement cost function was obtained. 205 “Reports on the Reconstruction Process after the 1960 Tsunami Disaster in Chile” of Iwate Prefecture and an approximation of the cost of coastal embankment improvement (12.5 m above T.P.) as of 2012. The cost function of coastal embankment improvement shown in Eq. 24) can be obtained based on the regression analysis results and the 1977 m extension of the improved coastal dikes in the Takata area designated by Iwate Prefecture.
Cabinet Office, 2007, Research Report on Economic Analysis of Damage and Loss Caused by Traffic Accidents http://www8.cao.go.jp/koutu/chou-ken/19html/gaiyou.html. Iwate Prefecture, 1969. "Report on the Recovery Process of the 1960 Chilean Earthquake Tsunami Disaster." Chapter 7, pp Ministry of Land, Infrastructure, Transport and Tourism, Tokyo Metro Area Person Trip Survey in 1998 (in Japanese).
Ex ante free mobility, ex post immobility and time consistency in a federal system," Journal of Public Economics. Dynamic commitment and the soft budget constraint: an empirical test," American Economic Journal: Economic Policy.
Supplement
Use number of trips per person by means k by purpose n by age group m αknm generated using the National Passenger Travel Survey (1999). Multiply the population by area and the number of trips generated per person by purpose together to create the data for the number of trips generated by area (days of the week). Multiply the number of trips generated by area (weekdays) and the holiday-to-weekday ratio (the ratio of the number of trips generated per person by purpose on holidays to those on weekdays) together to create the data for number of trips generated by area (holidays).
To calculate the number of trips generated by zone with funds k by purpose n by age group m on weekdays, multiply the number of trips per person αknm and the population by zone by age group POPimsammen. To calculate the number of trips generated by zone using means k by purpose n by age group m on holidays, multiply the number of trips generated by zone on weekdaysTiwkm and the ratio of holidays to weekdaysβntogether. Appendix Table 2: The ratio between the number of journeys on public holidays and on weekdays Commuters to.
In the following, in addition to creating data on the time required to travel between zones by means of transport and the number of generated trips that were created, create data on the number of employees at destination j EOPj (only in the case of driving - to school purposes, population by age group m at destination j POPjm) and the distance decay parameter by means k by age group n by purpose m λwknm. Using these data, estimate the number of OD trips between regions in the bottleneck model. If both generated volume and concentrated volume were obtained, a dual constraint model can be established to estimate the number of OD trips.
Consequently, using the number of employees (only in the case of commuting purposes to school, the population at the destination) as an alternative index to the concentrated volume, create the function for the number of employees (only in the case of commuting to work. -purposes to school , the population at the destination) and the distance resistance (time required) as shown in the equation below to proportionally distribute the generated volume. For OD trips for commuting/private/business purposes, proportionally distribute the number of trips generated in relation to the number of employees at the destination and traffic resistance. For OD trips for school travel purposes, proportionally distribute the number of trips generated in relation to the population at destinationPOPj and traffic resistance.
