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- W4307748813 abstract "Abstract The performance of climate technology implemented under Clean Development Mechanism (CDM) is varied in terms of Certified Emission Reductions (CERs) issuance rate, a ratio of the actual CER issued of the total CER expected, ranged between 2% and 193%. In this study, we propose a novel approach for estimating the CER issuance rate (i.e., lower or higher than 100%) by using random forest algorithms. Unlike previous studies, we associate 1443 indicators related to economy, environment and society from 28 countries with the outcome of CER issuance rate of 3338 CDM projects and examine mixed effects of local context-driven indicators. Data preprocessing and feature selection techniques are applied to extract important indicators with which classification and prediction are conducted across different types of CDM technologies. The highest accuracy and F-measure of the developed prediction model are achieved from wind projects (84%, 92%, respectively). The obtained results exhibit possibilities of capturing the performance gap through machine learning algorithm. Moreover, we recommend adopting local demand-driven indicators to enhance the usability of the proposed approach." @default.
- W4307748813 created "2022-11-05" @default.
- W4307748813 creator A5033812772 @default.
- W4307748813 date "2022-10-31" @default.
- W4307748813 modified "2023-10-16" @default.
- W4307748813 title "Random Forest-based prediction of performance gap of the Clean Development Mechanism projects" @default.
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- W4307748813 doi "https://doi.org/10.21203/rs.3.rs-2140692/v1" @default.
- W4307748813 hasPublicationYear "2022" @default.
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