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Climatic Change

, Volume 94, Issue 1–2, pp 157–168 | Cite as

The feasibility of carbon incentives to private forest management in Korea

  • KiJoo Han
  • Yeo-Chang Youn
Article

Abstract

Forest management is regarded as one possible approach to reducing greenhouse gases by absorbing carbon at a relatively low cost. In Korea, the forest comprises 64% of the total land area, so forests are expected to play a key role in mitigating climate change on the one hand. On the other hand, since 70% of the forest area is owned by the private sector, there is considerable uncertainty about managing forests for the national carbon sink strategy. The objective of this study is to examine the levels of carbon incentives to private forest management for the purpose of maximizing forests’ carbon absorption. First, in the context of present forest management policies, this study discusses applicable measures for the promotion of carbon sequestration in private forests. Next, considering the implications of policies related to forestry, the study develops a hypothetical carbon incentive scheme to compensate for economic revenue loss derived from accepting a rotation period that maximizes carbon sequestration. Carbon incentive levels are estimated by assessing the difference of financial revenue between a financially optimal rotation plan and a carbon-sink maximizing rotation plan. This study found that for red pine forests, the levels of the carbon incentives vary US$2–6 at 5% discount rate and US$ 34–88 at 7% discount rate while the values for oak forests are differing US$2–22 at 5% discount rate and US$ 20–52 at 7% discount rate. The study concludes that the carbon incentive scheme could be effective for increasing the carbon sink. However, given related governmental policies, it may not be desirable to employ the scheme without considering changes in government policy toward land use and regional development.

Keywords

Discount Rate Forest Management Rotation Period Forest Owner Site Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Department of Forest Resources ManagementUniversity of British ColumbiaVancouverCanada
  2. 2.Department of Forest SciencesSeoul National UniversitySeoulSouth Korea

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