Evaluation of a SATOYAMA Forest Using a Voluntary Labor Supply Curve Version: c 2003 Taku Terawaki, Akio Muranaka URL: http
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1 Evaluation of a SATOYAMA Forest Using a Voluntary Labor Supply Curve Version: c 2003 Taku Terawaki, Akio Muranaka URL: 1 [14] 3 [10] 3 2 Andreoni[1] Duncan[7] 1
2 CVM Contingent Valuation Method Geographic Information Systems GIS GIS GIS Z Q Z Z(V,Q) V n v i V := n i=1 v i Q Z Z(V,0) = 0 i Z(V,0)/ v i =0 Z 5 x i l i U i (x i,l i,z(v,q),v i ) Q i U i (x i,l i,z(v,0),v i )/ v i =0 c i m i t v w i y i V i max U i (x i,l i,z(v,q),v i ) (2.1) x i,l i,v i ( s. t. y i + w i t l i v i v ) im i x i + c iv i (2.2) t v t v x i 0, t > l i 0, t > v i 0 (2.3) 3 CVM Swallow and Woudyalew[13] Echessah et al.[8] Hadker et al.[9] Kamuanga et al.[11] 4 GIS [15] 5 Andreoni[1] impurely altruistic 2
3 t x i l i v i (2.2) y i + w i t x i + w i l i +(c i /t v + w i + w i m i /t v )v i c i /t v + w i + w i m i /t v v i w i w i { L(x i,l i,v i )=U i (x i,l i,z(v,q),v i )+λ y i + w i (t l i v i ) x i c } iv i (2.4) t v L = U i λ 0, x i 0 (2.5) x i x i L = U i λw i 0, l i 0 (2.6) l i l i L = U i v i Z Z + U ( i ci λ + w i + w ) im i 0, v i 0 (2.7) v i v i t v t v L λ = w(t l i v i ) x i cv i 0, λ 0 (2.8) (2.7) v i > 0 c i + w i + w ( im i Ui = t v t v Z Z V + U ) / i λ (2.9) v i U i / x i > 0 x i > 0 (2.5) λ (2.9) v i c i /t v + w i + w i m i /t v v i (2.9) x i l i v i x i (2.9) c i /t v + w i + w i m i /t v v i v i 2 Z/ vi 2 < 0 2 U i / Z 2 < 0 2 U i / vi 2 < 0 x i l i v i V i (2.1) (2.2) (2.3) V i i 6 Q =0 Q =0 Z/ v i =0 U i / v i =0 (2.7) v i =0 x i c i /t v + w i + w i m i /t v w i 6 Bergstrom, Blume, and Varian[4] 3
4 1 x i v i (2.2) (v i,x i ) (t v (t + l i )/(t v + m i ),y i c i (t + l i )/(t v + m i )) l i v i l i y i =0 4 1 w i w i x i 0 v i tv(t + l i),y i ci(t + l i) t v + m i t v + m i 1: w i 2 w i 4
5 x i 0 v i tv(t + l i),y i ci(t + l i) t v + m i t v + m i 2: w i v = v(c i /t v + w i + w i m i /t v ) c i /t v + w i + w i m i /t v 3 7 Poisson regression model Prob(Y i = y i )= exp( λ i)λ yi i y i! y i =0, 1, 2 (3.1) ln λ i = x iβ (3.2) Y i i y i x i i β x i Y i λ i β (3.2) λ i (3.2) (3.3) 7 Cameron and Trivedi[6] ln λ i = x iβ + ε i (3.3) 5
6 ε i exp(ε i ) 1 α i.i.d. exp(ε i ) Y i ( ) Prob(Y i = y i )= Γ(α 1 + y i ) α 1 α 1 ( ) yi λ i Γ(α 1 )Γ(y i +1) α 1 + λ i α 1 + λ i y i =0, 1, 2 (3.4) ln λ i = x iβ (3.5) negative binomial regression model Y i λ i λ i (1 + αλ i ) exp(ε i ) α overdispersion α 0 (3.3) ε i σ 2 i.i.d. exp(ε i ) exp(σ 2 /2) σ 2 (exp(σ 2 ) 1) i.i.d. Y i Prob(Y i = y i )= Prob(Y i ε i ) 1 ( σ φ εi ) dε i σ y i =0, 1, 2 (3.6) ln λ i = x i β (3.7) Prob(Y i ε i ) φ( ) Poisson-(log)normal mixture model Poisson-gamma mixture model Y i λ i exp(σ 2 /2) λ i exp(σ 2 /2) + λ 2 i (exp(2σ2 ) exp(σ 2 )) σ σ ha ha 6
7 GIS 9 JMC GIS CSV 10 ESRI Arc View 3.2 GIS UTM : 9 GIS Bateman et al.[2] Bateman et al.[3] 10 URL 11 UTM NI-53 UTM
8 4.4 2 i TRC i VLC i TMC i TRC i = d ig f (4.1) VLC i = w i h (4.2) TRC i = d iw i s (4.3) d i i g f w i i h s d i w i d i w i i g f km/h 14 h 4 s km/l 15 VLC i TMC i v = v(θ 1 TRC i +θ 2 VLC i +θ 3 TMC i ) θ 1 θ 2 θ 3 θ 2 /θ 1 θ 3 /θ 1 VLC i TMC i 16 (4.1) (4.2) (4.3) TMC i = f sgh TRC i VLC i (4.4) TRC i VLC i TMC i (4.4) TMC i TRC i VLC i TRC VLC TMC AIC AIC McConnel and Strand[12] 8
9 1: t t t constant TRC VLC TMC VLC overdispersion AIC AIC TRC VLC VLC TRC V = exp( VLC VLC ) (5.1) V = exp( trc TRC ) (5.2) 4 voluntary labor cost travel cost number of activities number of activities (a) VLC (b) TRC 4: (a) VLC (b) TRC 5.2 9
10 1 Prob(Y i = y i Y i > 0) = Prob(Y i = y i ) Prob(Y i > 0) = ln λ i = x iβ exp( λ i )λ yi i y i!{1 exp( λ i )} y i =0, 1, 2 (5.3) 2 (5.4) 2: t t t TRC TMC VLC overdispersion AIC AIC VLC TRC V = exp( vlc ) (5.5) V = exp( TRC ) (5.6) 5 voluntary labor cost travel cost number of activities number of activities (a) VLC (b) TRC 5: 10
11 TRC VLC 5.3 TRC 1 TRC V = exp(ˆα + ˆβTRC) TRC i i CS i CS i = TRC i exp(ˆα + ˆβTRC)dT RC (5.7) = 1ˆβ exp(ˆα + ˆβTRC i ) (5.8) = 1ˆβ V (TRC i ) (5.9) CS i /V (TRC i ) 1/ ˆβ Bockstael, Strand, and Hanemann[5] [1] Andreoni, J., Impure Altruism and Donations to Public Goods: A Theory of Warm-glow Giving, The Economic Journal, Vol.100, 1990, pp [2] Bateman, I.J., A.P.Jones, A.A.Lovett, I.R.Lake, and B.H.Day, Applying Geographical Information Systems(GIS) to Environmental and Resource Economics, Environmental and Resource Economics, Vol.22, 2002, pp [3] Bateman, I.J., A.A.Lovett, J.S.Brainard, Applied Environmental Economics: A GIS Approach to Costbenefit Analysis, Cambridge Univ. Press,
12 [4] Bergstrom, T., L.Blume, and H.Varian, On the Private Provision of Public Goods, Journal of Public Economics, Vol.29, 1986, pp [5] Bockstael, N.F., I.E.Strand, and W.M.Hanemann, Time and the Recreational Demand, American Journal of Agricultural Economics, Vol.69, 1987, pp [6] Cameron, A.C. and P.K.Trivedi, Regression Analysis of Count Data, Cambridge Univ. Press, [7] Duncan, B., Modeling Charitable Contributions of Time and Money, Journal of Public Economics, Vol.72, 1999, pp [8] Echessah, P.N., B.M.Swallow, D.W.Kamara, and J.J.Curry, Willingness to Contribute Labor and Money to Tsetse Control: Application of Contingent Valuation in Busia District, Kenya, World Development, Vol.25, 1997, pp [9] Hadker, N., S.Sharma, A.David, and T.R.Muraleedharan, Willingness to Pay for Borivi National Park: Evidence from a Contingent Valuation, Ecological Economnics, Vol.21, 1997, pp [10] 2002 [11] Kamuanga, M., B.M.Swallow, H.Sigue, and B.Bauer, Evaluating Contingent and Actual Contributions to a Local Public Good: Tsetse Control in the Yale Agro-pastoral Zone, Burkina Faso, Ecological Economnics, Vol.39, 2001, pp [12] McConnel,K.E. and I.Strand, Measuring the Cost of Time in Recreation Demand Analysis: An Application to Sportfishing, American Journal of Agricultural Economics, Vol.63, 1981, pp [13] Swallow, B.M. and M.Woudyalew, Evaluating Willingness to Contribute to a Local Public Good: Application of Contingent Valuation to Tsetse Control in Ethiopia, Ecological Economnics, Vol.11, 1994, pp [14] 2001 [15] GIS,
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