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Modelling the spatial forest-thinning planning problem considering carbon sequestration and emissions

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  • Liu, Wan-Yu
  • Lin, Chun-Cheng
  • Su, Ke-Hong

Abstract

Appropriate forest thinning is beneficial for growing forests and protecting ecological environments. To determine a beneficial approach in both economic and environmental aspects, carbon sequestration and emissions caused by forest-thinning activities can be traded. However, previous research on forest planning has not considered a detailed and sophisticated calculation for forest thinning and carbon sequestration. Hence, this study proposes a spatial forest-thinning planning problem involving carbon sequestration and emissions, which determine forest-thinning schedules over a planning period so that the total thinned timber volume over the period and the revenue from carbon sequestration and emissions can be maximized under certain spatial constraints. For this research, we first created a novel mathematical programming model, which can generally solve only small-scale problems. Therefore, this study also proposes an improved simulated annealing heuristic approach (ISA), which iteratively searches for a near optimal solution with two improved designs: a spatial local search operator and a neighborhood search scheme. The simulation results obtained using 300 forestland instances revealed that the proposed ISA can achieve better results through the proposed spatial local search operator, and run more efficiently through the proposed neighborhood search scheme. In addition, the decision regarding carbon sequestration and emissions was verified to be clearly advantageous for the cycling and sustainability of forest resources.

Suggested Citation

  • Liu, Wan-Yu & Lin, Chun-Cheng & Su, Ke-Hong, 2017. "Modelling the spatial forest-thinning planning problem considering carbon sequestration and emissions," Forest Policy and Economics, Elsevier, vol. 78(C), pages 51-66.
    • Handle: RePEc:eee:forpol:v:78:y:2017:i:c:p:51-66
    DOI: 10.1016/j.forpol.2017.01.002
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    References listed on IDEAS

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