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Adapting sustainable forest management to climate policy uncertainty: A conceptual framework

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  • Zhou, Mo

Abstract

Sustainable forest management delivers ecological benefits critical to mitigating climate change impacts and can produce carbon offset credits tradable at the market price, generating additional income to forest landowners. However, due to high uncertainty in the climate policy of the United States, the economic potential of sustainably managing forests for offset credits is uncertain, discouraging landowners from participating in such practices. Also uncertain are the ecological consequences, especially in terms of forest carbon stocks. Here a conceptual framework was proposed which, with a regime-switching process, modeled the price of carbon credits as a proxy of the climate policy. Uncertainty in policy was translated into a limited number of scenarios regarding the timing and magnitude of policy regime switches. This model was then incorporated into a Markov decision process model of forest management, which accounted for multiple forms of risk and uncertainty affecting forest functioning and management. Using linear programming, this framework quantified the economic and ecological potentials of forest carbon management in various policy scenarios and determined optimal harvesting rules adaptive to policy shifts. A simple numerical example was provided to demonstrate the application of this framework.

Suggested Citation

  • Zhou, Mo, 2015. "Adapting sustainable forest management to climate policy uncertainty: A conceptual framework," Forest Policy and Economics, Elsevier, vol. 59(C), pages 66-74.
  • Handle: RePEc:eee:forpol:v:59:y:2015:i:c:p:66-74
    DOI: 10.1016/j.forpol.2015.05.013
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    2. Xiongfeng Pan & Jing Zhang & Changyu Li & Rong Quan & Bin Li, 2018. "Exploring Dynamic Impact of Foreign Direct Investment on China’s CO $$_{2}$$ 2 Emissions Using Markov-Switching Vector Error Correction Model," Computational Economics, Springer;Society for Computational Economics, vol. 52(4), pages 1139-1151, December.

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