로그 회귀분석 및 CART를 활용한 수력사업의 CDM 승인여부예측 모델에 관한 연구

Translated title of the contribution: Predicting the success of CDM Registration for Hydropower Projects using Logistic Regression and CART

Research output: Contribution to journalArticlepeer-review

Abstract

The Clean Development Mechanism (CDM) is the multi-lateral 'cap and trade' system endorsed by the Kyoto Protocol. CDM allows developed (Annex I) countries to buy CER credits from New and Renewable (NE) projects of non-Annex countries, to meet their carbon reduction requirements. This in effect subsidizes and promotes NE projects in developing countries, ultimately reducing global greenhouse gases (GHG). To be registered as a CDM project, the project must prove 'additionality,' which depends on numerous factors including the adopted technology, baseline methodology, emission reductions, and the project's internal rate of return. This makes it difficult to determine ex ante a project's acceptance as a CDM approved project, and entails sunk costs and even project cancellation to its project stakeholders. Focusing on hydro power projects and employing UNFCCC public data, this research developed a prediction model using logistic regression and CART to determine the likelihood of approval as a CDM project. The AUC for the logistic regression and CART model was 0.7674 and 0.7231 respectively, which proves the model's prediction accuracy. More importantly, results indicate that the emission reduction amount, MW per hour, investment/Emission as crucial variables, whereas the baseline methodology and technology types were insignificant. This demonstrates that at least for hydro power projects, the specific technology is not as important as the amount of emission reductions and relatively small scale projects and investment to carbon reduction ratios.
Translated title of the contributionPredicting the success of CDM Registration for Hydropower Projects using Logistic Regression and CART
Original languageKorean
Pages (from-to)65-76
Number of pages12
Journal한국건설관리학회 논문집
Volume16
Issue number2
DOIs
StatePublished - Mar 2015

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