A Differential Game of Transboundary Pollution Control and Ecological Compensation in a River Basin

This paper investigates a Stackelberg differential game between an upstream region and a downstream region for transboundary pollution control and ecological compensation (EC) in a river basin. Among them, the downstream region as the leader chooses its abatement investment level and an ecological compensation rate to encourage upstream investing in water pollution control firstly. After then, the upstream region as the follower determines its abatement investment level to maximize welfare. FFurthermore, we take into consideration the effects of efficiency-improving and cost-reducing learning by doing which are originated from abatement investment activity of both regions simultaneously. The results show the following. (i) There is an optimal ecological compensation rate and under which a Pareto improvement result can be obtained. (ii) Carrying out EC will shift some abatement investment from the downstream region into the upstream region. (iii) The efficiency-improving and cost-reducing learning by doing derived from abatement investment activity of both regions can decrease the optimal ecological compensation rate, increase abatement investment,and improve the social welfare.

[1]  G. Martín-Herrán,et al.  Second-best taxation for a polluting monopoly with abatement investment , 2018, Energy Economics.

[2]  R. Schwarzenbach,et al.  Global Water Pollution and Human Health , 2010 .

[3]  Xiaojun Pan,et al.  Production , Manufacturing and Logistics Dynamic optimal control of process – product innovation with learning by doing , 2015 .

[4]  J. Farley,et al.  Determining when payments are an effective policy approach to ecosystem service provision , 2010 .

[5]  Ngo Van Long,et al.  Pollution control: A differential game approach , 1992, Ann. Oper. Res..

[6]  Zhendong Li,et al.  Optimal control for transboundary pollution under ecological compensation: A stochastic differential game approach , 2019, Journal of Cleaner Production.

[7]  Shuhua Chang,et al.  Dynamic optimal strategies in transboundary pollution game under learning by doing , 2018 .

[8]  U. Moslener,et al.  Optimal Abatement in Dynamic Multi-Pollutant Problems When Pollutants Can Be Complements or Substitutes , 2005 .

[9]  U. Moslener,et al.  The dynamics of optimal abatement strategies for multiple pollutants-An illustration in the Greenhouse , 2009 .

[10]  K. Hasund.  Developing environmental policy indicators by criteria – indicators on the public goods of the Swedish agricultural landscape , 2011 .

[11]  Suresh P. Sethi,et al.  Optimal Abatement and Emission Permit Trading Policies in a Dynamic Transboundary Pollution Game , 2018, Dyn. Games Appl..

[12]  E. Dockner,et al.  International Pollution Control: Cooperative versus Noncooperative Strategies , 1993 .

[13]  Carmen Camacho,et al.  Carbon capture and storage and transboundary pollution: A differential game approach , 2014, Eur. J. Oper. Res..

[14]  J. Simon,et al.  The economic implications of learning-by-doing for population size and growth. , 1984, European economic review.

[15]  Da-ming You,et al.  A differential game approach to dynamic optimal control strategies for watershed pollution across regional boundaries under eco-compensation criterion , 2019, Ecological Indicators.

[16]  Yanpeng Cai,et al.  A hybrid game theory and mathematical programming model for solving trans-boundary water conflicts , 2019, Journal of Hydrology.

[17]  Yongxi Yi,et al.  Cournot competition and “green” innovation under efficiency-improving learning by doing , 2019, Physica A: Statistical Mechanics and its Applications.

[18]  U. Moslener,et al.  Optimal Abatement in Dynamic Multi-Pollutant Problems when Pollutants are Complements or Substitutes , 2007 .

[19]  F. Ploeg,et al.  International aspects of pollution control , 1992 .

[20]  Shuhua Chang,et al.  Modelling and Computation in the Valuation of Carbon Derivatives with Stochastic Convenience Yields , 2015, PloS one.

[21]  Christoph H. Glock,et al.  Production Economics and the Learning Curve: A Meta-Analysis , 2014 .

[22]  S. Wunder,et al.  Designing payments for environmental services in theory and practice: An overview of the issues , 2008 .

[23]  S. Rosen.  Learning by Experience as Joint Production , 1972 .

[24]  E. Corbera,et al.  Reconciling theory and practice: An alternative conceptual framework for understanding payments for environmental services , 2010 .

[25]  K. Arrow.  The Economic Implications of Learning by Doing , 1962 .

[26]  Huiquan Li,et al.  A differential game analysis of multipollutant transboundary pollution in river basin , 2019 .

[27]  Shoude Li,et al.  A Differential Game of Transboundary Industrial Pollution with Emission Permits Trading , 2014, J. Optim. Theory Appl..

[28]  Georges Zaccour,et al.  Incentive equilibrium strategies and welfare allocation in a dynamic game of pollution control , 1999, Autom..

[29]  G. Zaccour,et al.  Time-consistent Shapley value allocation of pollution cost reduction , 1999 .

[30]  Shoude Li,et al.  A dynamic general equilibrium model of pollution abatement under learning by doing , 2014 .

[31]  L. Lambertini,et al.  Cournot Competition and 'Green' Innovation: An Inverted-U Relationship , 2014 .

[32]  S. Mourato,et al.  Community conservation and a two-stage approach to payments for ecosystem services , 2011 .

[33]  Xin Gao,et al.  An evolutionary game analysis of governments' decision-making behaviors and factors influencing watershed ecological compensation in China. , 2019, Journal of environmental management.