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Dynamic Computable General Equilibrium Model and Sensitivity Analysis for Shadow Price of Water Resource in China

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  • Jing He
  • Xikang Chen
  • Yong Shi
  • Aihua Li

Abstract

A novel dynamic computable general equilibrium model based on National Water Resource Input Holding Output Table is proposed to calculate the Shadow Price of water resource in China. Unlike previous approaches, the dynamic Shadow Price of water resource is largely based on the scarcity extent and can reflect the marginal long-term value in the balanced growth path of China. Firstly, the basic concepts of dynamic Input Output analysis and Turnpike Theory are reviewed. Then, Dynamic Computable General Equilibrium (DCGE) is elaborated to calculate the Shadow Price, including the definition and computer-based algorithm. Furthermore, Shadow Price of water resource in China from 1949 to 2050 is calculated based on the DCGE. Also the sensitivity analysis of the DCGE for Shadow Price of water resource in China is presented. Dynamic Shadow Price of water resource has two meanings for China government: (1) Project evaluation. Every large-scale project in China must have national economic evaluation and the dynamic Shadow Price is prerequisite for national economic evaluation. (2) Market price of water resource. A lesson from this paper is that Shadow Price of water resource in domestic market of China should be rewritten according to the dynamic Shadow Price. In addition, the parallel computations approach could also be used to solve these problems in different countries or for different natural resources. Copyright Springer Science+Business Media, Inc. 2007

Suggested Citation

  • Jing He & Xikang Chen & Yong Shi & Aihua Li, 2007. "Dynamic Computable General Equilibrium Model and Sensitivity Analysis for Shadow Price of Water Resource in China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(9), pages 1517-1533, September.
    • Handle: RePEc:spr:waterr:v:21:y:2007:i:9:p:1517-1533
    DOI: 10.1007/s11269-006-9102-7
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    1. Fu, Xue & Lahr, Michael & Yaxiong, Zhang & Meng, Bo, 2017. "Actions on climate change, Intended Reducing carbon emissions in China via optimal industry shifts: Toward hi-tech industries, cleaner resources and higher carbon shares in less-develop regions," Energy Policy, Elsevier, vol. 102(C), pages 616-638.
    2. Zheng, Jiajia & Kamal, Muhammad Abdul, 2020. "The effect of household income on residential wastewater output: Evidence from urban China," Utilities Policy, Elsevier, vol. 63(C).
    3. Lijin Zhong & Arthur Mol, 2010. "Water Price Reforms in China: Policy-Making and Implementation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(2), pages 377-396, January.
    4. Dinar, Ariel, 2012. "Economy-wide implications of direct and indirect policy interventions in the water sector: lessons from recent work and future research needs," Policy Research Working Paper Series 6068, The World Bank.
    5. Zhao, Jing & Ni, Hongzhen & Peng, Xiujian & Li, Jifeng & Chen, Genfa & Liu, Jinhua, 2016. "Impact of water price reform on water conservation and economic growth in China," Economic Analysis and Policy, Elsevier, vol. 51(C), pages 90-103.
    6. Tamaki, Tetsuya & Shin, Kong Joo & Nakamura, Hiroki & Fujii, Hidemichi & Managi, Shunsuke, 2018. "Shadow prices and production inefficiency of mineral resources," Economic Analysis and Policy, Elsevier, vol. 57(C), pages 111-121.

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