Under the Kyoto Protocol, a global governmental response to climate change, protocol signatories make an effort to cut their greenhouse gas emissions. South Korea is not included in the list of Annex I countries; yet, South Korea is the seventh highest emitter of CO2. The South Korean government has enacted various institutional policies to encourage greenhouse gas reductions. While previous studies have focused on the guidance that reflects the stance of suppliers in the carbon market, this study focuses on South Korean firms' actual demand for forest carbon credits. By applying the contingent valuation method, we estimated domestic firms' willingness to pay for forest carbon credits. We then applied a rank-ordered logistic regression to confirm whether the rank of forest carbon credits, as compared to any other carbon credit, is influenced by a firm's characteristics. The results showed that Korean firms are willing to pay 5.45 USD/tCO2 and 7.77 USD/tCO2 for forest carbon credits in domestic and overseas forest carbon projects, respectively. Therefore, the introduction of forest carbon credits in the Korean carbon market seems reasonable. Analysis of the priority rankings of forest carbon credits, however, demonstrated that forestry projects were least likely to be ranked by firms as their first priority. Although relative preferences for forest carbon credits were influenced by individual firms' characteristics such as prior experience of environmental CSR related activities and whether the firm established an emissions reduction plan, the impact of perceived behavior control, whether the firm was included in the emissions target management scheme on forest carbon credits was negligible. Therefore, forest carbon credits are not a feasible solution without strong government support or institutional instruments. The results of this study are expected to provide policy makers with realistic approaches to formulate climatic change-related policies.
Keywords: Carbon market; Forest carbon; Offset; Rank-ordered logistic regression; Willingness to pay.
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