Vol.3 No.1 2000 Spring 002 intuitive CVM CVM 1 CVM CVM CVM NISHIDA, Masaru
CVM 2 2.1 CVM 1 1 Revealed Preference Stated Preference CVM CVM CVM 2 2 2 CVM NOAA 3 CVM 4 5 CVM 2.2 CVM CVM CVM2 CVM 2 Vol.3 No.1 2000 Spring 003
3CVM 3.1 2 1 6 30 Case1 EV Case2 3.2 NOAA CVM Case1Case2 20 480 2 380480 7 3.3 20 Case1 WTA CV 20 1 2 301 004 Vol.3 No.1 2000 Spring
Vol.3 No.1 2000 Spring 005 1 Case1 2 4 3 3.4 3 80 4 4.1 80 4050 4.2 1 2 3 4 5 1 2 34 5 1,000 2,000 5,000 10,000 1 2 500 1,000 2,000 5,000 10,000 20,000 11 4 4 54 61 4 4 4 8 72 56 8 13 13 8 29 46 4 Case1
Case2 0 4 0 48 41 7 0 6 6 83 61 0 4 4 8 72 56 8 4.3 Case1 7 35 39 7 0 0 Case2 6 33 38 17 14 12 Case2 Case1 Case2 4.4 8 P yes X exp[ X/ P yes X [ exp 1 2 X -1 [ [ P yes (X )X X 1 2 closedend1 1 1 2 31 37 41 19 38.8 46.3 51.3 63.3 9.511 1.891 3.609 5.868 1.151 0.764 1.056 0.972 31.059 51.988 49.677 25.199 0.710 0.716 0.683 0.579 6,918 1,171 2,550 4,025 9,018 2,280 3,544 5,722 31 37 41 19 38.8 46.3 51.3 63.3 1 2.249 0.825 1.598 1.701 (4.22) (2.36) (3.69) (2.99) 2 0.296 0.656 0.527 0.362 4.07) 4.98) 5.27) 3.61) 32.824 65.145 50.767 26.897 0.710 0.716 0.683 0.579 7,586 1,257 3,032 4,701 7,924 1,811 3,381 5,153 t 1 2 0 006 Vol.3 No.1 2000 Spring
Vol.3 No.1 2000 Spring 007 0.60.7 2 3 Case2 0 1.0 0.8 0.6 0.4 0.2 0.0 5,000 10,000 15,000 20,000 Case1 0 1.0 0.8 0.6 0.4 0.2 0.0 5,000 10,000 15,000 20,000 Case1 36 45.0 2.598 1.155 46.807 0.75 1,892 2,489 36 45.0 2.717 0.45 44.406 0.611 1,202 4,474 50 62.5 3.187 0.664 54.706 0.64 1,836 3,980 18 60.0 0.687 0.309 21.413 0.806 0 2,902 1 2 36 45.0 1.770 (4.00) 0.899 4.97) 50.229 0.75 1,969 2,144 36 45.0 0.130 (0.38) 0.181 3.43) 50.73 0.625 715 4,029 50 62.5 0.607 (1.74) 0.302 4.29) 57.404 0.62 2,012 3,438 18 60.0 0.530 (1.03) 0.165 2.38) 26.897 0.806 0 2,669 0 1.0 0.8 0.6 0.4 0.2 0.0 5,000 10,000 15,000 20,000 Case2 0 1.0 0.8 0.6 0.4 0.2 0.0 5,000 10,000 15,000 20,000 Case2
0Case2 34 0100 0100 500 0 0 1 50Case2 0 50 2 4.5 4 Case1 Case2 A,B,C,D A ABC DE Case1 A A,B 4.6 Case1 Case2 Case1 8,7001,200 1,040 4,4003,200 1,400 2,440 Case2 2,5001,200 340 3,8003,200 1,200 1,540 Case11.5km 113 1 1 Case2 008 Vol.3 No.1 2000 Spring
Vol.3 No.1 2000 Spring 009 4.7 1 warm glow Case1 2 3 4 Case1 Case2 5 Case2 3 5 CVM 2 4 CVM 2 NOAA 70 CVM CVM CVM CVM CVM
CVM CVM 1[1998]No.21(2) 2Mitchell, R. C. and R. T. Cameron [1989],Using Surveys to Value Public Goods: The Contingent Valuation Method, Washington D.C., Resources for the Future. 3Arrow, K., et. al. [1993], Report of the NOAA Panel on Contingent Valuation, Federal Register 58. 4Carson, R. T., W. M. Hanemann, R. J. Kopp, J. A. Krosnick, R. C. Mitchell, S. Presser, P. A. Ruud and V. K. Smith, Was the NOAA Panel Correct about Contingent Valuation?, Discussion Paper 96-20, Resource for the Future, Washington DC, May 1996. 5[1998] Vol.83, 19986. 61984 7[1999]CVM Vol.45,No.1. 819989 Appraising non-market values in Transport Infrastructure Development By Masaru NISHIDA, Newjec Inc. Application of cost benefit analysis has been increasingly commonplace in appraisal of public works projects. However, evaluation of non-market values is in part left behind due to the difficulties in its measurement. Contingent valuation method is expected to play a role in non-market value assessment despite its inherent problems. This paper introduces a study on an impact of elevated motorways on landscape as an example of CVM application. The result of the surveys is analysed to discuss the applicability of the method to subjects with different levels/types of utility change brought to respondents. Key Words ; contingent valuation method (CVM), non-market value, project appraisal 010 Vol.3 No.1 2000 Spring
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Prob(No, No)=1P(X i1 ) XP(X) i X i1 N L= ln [ i 1 PX i 2 i 2 PX i 1 PX i 2 N i 1, i 2, i 3 X i 2 Prob(Yes, Yes)=P(X i2 ) P(X) Prob(Yes, No)=Prob(No,Yes )=P(X i1 )P(X i2 ) i=1 i 31PX i 1 [ Vol.3 No.1 2000 Spring 013