Abstract
This paper studies the impact of bilateral participation strategy on the dynamic emission-reducing behaviors and associated performance of low-carbon supply chains using differential game models. We first consider a dyadic supply chain comprising one supplier and one manufacturer in the base model and derive the equilibrium solutions of dynamic emission abatement in decentralized and centralized systems, respectively. In the decentralized supply chain, we consider both unilateral sharing and bilateral participation contracts and examine the feedback equilibrium strategies and trajectories of emission abatement of all parties. By comparison, we find that the bilateral contract leads to lower emission abatement and higher profits of all players and the system than that under unilateral sharing contract. Meanwhile, we show that the properly-designed bilateral participation contract can coordinate the decentralized dyadic supply chain. By extending the base model, we further figure out the equilibria and optimal trajectories for the dynamic cooperative emission abatement and the associated mechanism design in a supply chain with two competing suppliers and one manufacturer. Interestingly, we find that the bilateral participation contract can also coordinate the competing channels with competitive or complementary components at the upstream level. We uncover that the bilateral participation contract is effective in coordinating supply chains with dynamic cooperative emission abatement since it can overcome the disadvantage of uneven distribution of emission-reducing capability endowments among chain members. Finally, we also conduct computational and sensitivity analyses to illustrate the previous results. These findings provide potential implications for firms’ dynamic cooperation in emission abatement.
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Acknowledgements
The authors sincerely thank the editors and three anonymous reviewers for their time and effort devoted to handling this paper. Their constructive comments and suggestions have helpfully improved the study. This work is partially supported by National Natural Science Foundation of China (NSFC) Grants (Nos.91646118, 71501108, 71602142, 71701144) and The Science & Technology Pillar Key Program of Tianjin Key Research and Development Plan (20YFZCGX00640).
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Appendix
Appendix
Proof of Propostion 1
Maximizing the right side of Eq. (9) produces
Substituting Eqs. (89) and (90) into Eq. (9), respectively gives
Inferred from Eq. (91), the linear function of \(x\) and \(y\) is the solution of the HJB equation. Set
where \(a_{1}\), \(b_{1}\) and \(c_{1}\) are constants. From Eq. (92), we know that
Substitute Eqs. (93) and (94) into Eq. (92) respectively, then collate and compare the coefficients of the similar terms on the left and right sides, and we have
Substituting Eq. (94) into Eqs. (89) and (90) respectively, the static feedback equilibrium strategy of emission reduction efforts of suppliers and manufacturer under centralized decision making is
Thus, Proposition 1 can be proved.
Proof of Proposition 2
Substituting the optimal strategies \(Z_{S}^{C*} { = }\frac{{(U_{M} + U_{S} )\alpha \eta \gamma }}{{\mu_{S} (\rho + \beta )^{2} }}\) and \(Z_{M}^{C*} { = }\frac{{(U_{M} + U_{S} )\delta \eta }}{{\mu_{M} (\rho + \beta )}}\) into Eq. (2) in the case of centralized decision making of Proposition 1, we can solve the following differential equations
Eq. (103) is expressed in terms of vectors as follows.
Introduce vectors \({\mathbf{X}} = (x,y)^{T} {,}\) \({\mathbf{b(t)}} = (\alpha Z_{S} ,\delta Z_{M} )^{T}\) and matrix \({\mathbf{A = }}\left( {\begin{array}{*{20}c} { - \beta } & 0 \\ \gamma & { - \beta } \\ \end{array} } \right)\), where there are homogeneous system of differential equations.
Then the general solution of differential equations Eq. (97) consists of a special solution and the general solution of Eq. (98).
To find the general solution of Eq. (98), the eigenvalue of A is obtained firstly as double root \(\lambda_{1} = \lambda_{2} = - \beta\). From the properties of the eigenvalue, one of the eigenvectors is \({\mathbf{v}} = (0,1)^{T}\). The corresponding linear independent solution can be solved as \({\mathbf{x}}_{{\mathbf{1}}} {\mathbf{(t)}} = \left( {_{1}^{0} } \right)e^{ - \beta t}\). Let another corresponding linear independent solution is \({\mathbf{x}}_{{\mathbf{2}}} {\mathbf{(t)}} = ({\mathbf{m}} + {\mathbf{n}}t)e^{ - \beta t}\). The conditions that \({\mathbf{x}}_{{\mathbf{2}}} {\mathbf{(t)}}\) is the solution of Eq. (97) are \({\mathbf{n + m}}l{\mathbf{ = Am}}\) and \({\mathbf{n}}l{\mathbf{ = An}}\), set \({\mathbf{n = v}}\), we can get \({\mathbf{m}} = (1{/}\gamma ,1)^{T}\). Here, the general solution of homogeneous system of differential equations Eq. (98) is
Let the special solution of Eq. (97) be \(\tilde{x} = (f_{1} {; }f_{2} )\),where \(f_{1}\) and \(f_{2}\) are constants, and substitute them into Eq. (2), then we can get
From Eqs. (99) and (100), the general solution of differential equations Eq. (97) is as follows:
When \(t = 0\), substituting the initial values \(x(0) = x_{0}\) and \(y(0) = y_{0}\) into Eq. (101) yields
where \(x_{{_{SS} }}^{C*} = \alpha Z_{S} {/}\beta\) and \(y_{{_{SS} }}^{{\text{C*}}} = (\gamma x_{ss}^{C*} + \delta Z_{M} ){/}\beta\).
Substitute Eqs. (102) and (103) into Eq. (101), and we can get
Thus, Proposition 2 has been proved.
Proof of Proposition 3
Maximizing the right side of Eqs. (18) and Eq. (19) gives
Substitute Eqs. (106) and (107) into Eqs. (18) and (19), respectively, and collate it. Then we can get
It is inferred from the Eqs. (108) and (109) that the linear function of x and y is the solution of the HJB equation. Set
Then \(V_{Sx} = a_{2}\), \(V_{Sy} = b_{2}\), \(V_{Mx} = a_{3}\), \(V_{My} = b_{3}\). Substituting Eqs. (110) and (111) into Eqs. (108) and (109) respectively produces
Collating Eq. (112) and comparing its coefficients of the counterpart terms in both sides yields
Doing the same thing in Eq. (113) gives
Then substitute them into Eqs. (106) and (107), we can get
So, the Proposition 3 has been proved.
Proof of Proposition 4
Similar to the proof of Proposition 2, substituting optimal strategies \(Z_{S}^{D*} { = }U_{S} \alpha \eta \gamma {/[}\mu_{S} (\rho + \beta )^{2} {]}\) and \(Z_{M}^{D*} { = }U_{M} \delta \eta {/[}\mu_{M} (\rho + \beta ){]}\) into Eq. (2) respectively, in the case of decentralized decision making of Proposition 3, we can solve the following differential equations:
Then the optimal trajectory of Proposition 4 can be obtained.
Proof of Proposition 5
Maximizing the right side of Eqs. (34) and (35) shows us
Substituting Eqs. (118) and (119) into Eqs. (34) and (35) respectively shows
It can be inferred from the Eqs. (120) and (121) that the linear function of x and y is the solution of the HJB equation. Set
Then \(V_{Sx}^{B} = a_{4}\), \(V_{Sy}^{B} = b_{4}\), \(V_{Mx}^{B} = a_{5}\), and \(V_{My}^{B} = b_{5}\). Substituting Eqs. (122) and (123) into Eqs. (120) and (121), respectively, yields
Collate Eq. (124) and compare its coefficients of the similar terms on the left and right sides, and we can get
Collating Eq. (125) and compare its coefficients of the similar terms in the left and right sides gives
Then substitute them into Eqs. (118) and (119), we can get
Coordinating the supply chain system can make the optimal emission reduction efforts of suppliers and manufacturer under bilateral decision making equal to those under centralized decision making, so \(Z_{S}^{C*} = Z_{S}^{B*}\), \(Z_{M}^{C*} = Z_{M}^{B*}\), i.e., \(\frac{{(U_{M} + U_{S} )\alpha \eta \gamma }}{{\mu_{S} (\rho + \beta )^{2} }} = \frac{{U_{S} \alpha \eta \gamma }}{{(1 - \omega )\mu_{S} (\rho + \beta )^{2} }}\), \(\frac{{(U_{M} + U_{S} )\delta \eta }}{{\mu_{M} (\rho + \beta )}} = \frac{{U_{M} \delta \eta }}{{(1 - \psi )\mu_{M} (\rho + \beta )}}\).
The comparison gives \(U_{M} + U_{S} = U_{S} {/(}1 - \omega {)}\) and \(U_{M} + U_{S} = U_{M} {/(}1 - \psi {)}\). Namely, \(\omega^{*} = U_{M} {/(}U_{M} + U_{S} {)}\), and \(\psi^{*} = U_{S} {/(}U_{M} + U_{S} {)}\). Thus, Proposition 5 can be proved.
Proof of Proposition 6
Maximizing the right side of Eq. (50) yields
Substituting Eqs. (130) and (131) into Eq. (50) derives
Inferred from the Eq. (132), the linear function of x and y is the solution of HJB equation. Let
Then \(V_{{Tx_{i} }}^{C} = a_{6i}\) and \(V_{Ty}^{C} = b_{6}\). Substituting Eq. (133) into Eq. (132) attains
Comparing its coefficients of the similar terms of the left and right sides in Eq. (134) yields
Then substituting them into Eqs. (130) and (131), we have
Thus, Proposition 6 can be proved.
Proof of Proposition 7
Plugging optimal strategies \(Z_{{S_{i} }}^{C*} = (U_{M} + \sum\nolimits_{i = 1}^{2} {v_{i} U_{{S_{i} }} } )\alpha_{i} \eta \gamma_{i} v_{i} {/[}\mu_{{S_{i} }} (\rho + \beta )^{2} {]}\) and \(Z_{M}^{C*} = (U_{M} + \sum\nolimits_{j = 1}^{2} {v_{j} U_{{S_{j} }} } )\eta \delta {/[}\mu_{M} (\rho + \beta ){]}\) into Eq. (2) respectively, in the case of centralized decision making of Proposition 6, we can solve the following differential equations
Then the optimal trajectory of Proposition 7 can be obtained.
Proof of Proposition 8
Maximizing the right side of Eqs. (62) and (63) gives
Substituting Eqs. (140) and (141) into Eqs. (62) and (63), respectively, we have
Inferred from Eqs. (142) and (143), the linear function of x and y is the solution for HJB equation.
Then \(V_{{S_{i} }}^{D} = a_{7ji}\) (\(j \ne i\)), \(V_{{S_{i} y}}^{D} = b_{7i}\), \(V_{{S_{i} x_{i} }}^{D} = a_{7ii}\), \(V_{{Mx_{i} }}^{D} = a_{8i}\), and \(V_{My}^{D} = b_{8}\).
Substituting Eqs. (144) and (145) into Eqs. (142) and (143), respectively, we have
From Eq. (146), comparing its coefficients of the similar terms on the left and right sides shows us
Collating Eq. (147) and comparing its coefficients of the similar terms on the left and right sides gives
Comparing the coefficients of terms on the left and right sides in Eq. (148) shows us
Then substitute them into Eqs. (140) and (141), we can get
Thus, Proposition 8 can be proved.
Proof of Proposition 9
Similar to the proof of Proposition 2, substituting the optimal strategies \(Z_{{S_{i} }}^{D*} { = }U_{{S_{i} }} \alpha_{i} \eta \gamma_{i} v_{i}^{2} {/[}\mu_{{S_{i} }} (\rho + \beta )^{2} {]}\) and \(Z_{M}^{D*} { = }U_{M} \delta \eta {/[}\mu_{M} (\rho + \beta ){]}\) into Eq. (2) in the case of decentralized decision making of Proposition 8, we can solve the following differential equations
Then the optimal trajectory of Proposition 9 can be obtained.
Proof of Proposition 10
Maximizing the right side of Eqs. (77) and (78) gives
Substituting Eqs. (156) and (157) into Eqs. (77) and (78) respectively attains
From Eqs. (158) and (159), The HJB equation has the linear function of \(x\) and \(y\) as the solution. Set
Then \(V_{{S_{i} x_{i} }}^{B} = a_{9ii}\), \(V_{{S_{i} x_{j} }}^{B} = a_{9ji}\) Eq. (\(j \ne i\)); \(V_{{S_{i} y}}^{B} = b_{9i}\), \(V_{{M_{1} x_{i} }}^{B} = a_{10i}\), \(V_{{M_{1} y}}^{B} = b_{10}\).
Plugging Eqs. (160) and (161) into Eqs. (158) and (159) respectively gives
Collating Eq. (162) and compare its coefficients of the similar terms on both sides yields
Collating Eq. (163) and comparing its coefficients of the terms on the left and right sides gives
Comparing its coefficients of the similar terms on both sides in Eq. (164) produces
Coordinating the supply chain system can make the optimal emission reduction efforts of suppliers and manufacturer under bilateral contract equal to those under centralized case, so \(Z_{{S_{1} }}^{C*} = Z_{{S_{1} }}^{B*}\),\(Z_{{S_{2} }}^{C*} = Z_{{S_{2} }}^{B*}\),\(Z_{M}^{C*} = Z_{M}^{B*}\). Namely, \(\frac{{(U_{M} + v_{1} U_{{S_{1} }} + v_{2} U_{{S_{2} }} )\alpha_{1} \eta \gamma_{1} v_{1} }}{{\mu_{{S_{1} }} (\rho + \beta )^{2} }} =\)\(\frac{{v_{1} U_{{S_{1} }} \alpha_{1} \eta \gamma_{1} v_{1} }}{{(1 - \omega_{1} )\mu_{{S_{2} }} (\rho + \beta )^{2} }}\), \(\frac{{(U_{M} + v_{1} U_{{S_{1} }} + v_{2} U_{{S_{2} }} )\alpha_{2} \eta \gamma_{2} v_{2} }}{{\mu_{{S_{2} }} (\rho + \beta )^{2} }} = \frac{{v_{2} U_{{S_{2} }} \alpha_{2} \eta \gamma_{2} v_{2} }}{{(1 - \omega_{2} )\mu_{{S_{2} }} (\rho + \beta )^{2} }}{,}\)\(\frac{{(U_{M} + v_{1} U_{{S_{1} }} + v_{2} U_{{S_{2} }} )\delta \eta }}{{\mu_{{M_{1} }} (\rho + \beta )}}\)\(= \frac{{U_{M} \delta \eta }}{{(1 - \psi_{1} - \psi_{2} )\mu_{M} (\rho + \beta )}}{.}\) By comparison, we have \(U_{M} + v_{1} U_{{S_{1} }} + v_{2} U_{{S_{2} }} = v_{1} U_{{S_{1} }} {/(}1 - \omega_{1} {)}\), \(U_{M} + v_{1} U_{{S_{1} }} + v_{2} U_{{S_{2} }} = v_{2} U_{{S_{2} }} {/(}1 - \omega_{2} {),}\) \(U_{M} + v_{1} U_{{S_{1} }} + v_{2} U_{{S_{2} }} = U_{M} {/(}1 - \psi_{1} - \psi_{2} {)}\). So we get \(\omega_{1}^{*} = \frac{{U_{M} + v_{2} U_{{S_{2} }} }}{{U_{M} + v_{1} U_{{S_{1} }} + v_{2} U_{{S_{2} }} }}\), \(\omega_{2}^{*} = \frac{{U_{M} + v_{1} U_{{S_{1} }} }}{{U_{M} + v_{1} U_{{S_{1} }} + v_{2} U_{{S_{2} }} }}\), \(\psi_{1}^{*} + \psi_{2}^{*} = \frac{{v_{1} U_{{S_{1} }} + v_{2} U_{{S_{2} }} }}{{U_{M} + v_{1} U_{{S_{1} }} + v_{2} U_{{S_{2} }} }}\). Thus, Proposition 10 can be proved.
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He, L., Yuan, B., Bian, J. et al. Differential game theoretic analysis of the dynamic emission abatement in low-carbon supply chains. Ann Oper Res 324, 355–393 (2023). https://doi.org/10.1007/s10479-021-04134-9
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DOI: https://doi.org/10.1007/s10479-021-04134-9