Development of a Decision Support Model Based on Machine Learning for Applying Greenhouse Gas Reduction Technology

Sungwoo Lee and Sungho Tae
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Sungwoo Lee: Department of Architectural Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan 15588, Korea
Sungho Tae: Department of Architectural Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan 15588, Korea

Sustainability, 2020, vol. 12, issue 9, 1-19

Abstract: Multiple nations have implemented policies for greenhouse gas (GHG) reduction since the 21st Conference of Parties (COP 21) at the United Nations Framework Convention on Climate Change (UNFCCC) in 2015. In this convention, participants voluntarily agreed to a new climate regime that aimed to decrease GHG emissions. Subsequently, a reduction in GHG emissions with specific reduction technologies (renewable energy) to decrease energy consumption has become a necessity and not a choice. With the launch of the Korean Emissions Trading Scheme (K-ETS) in 2015, Korea has certified and financed GHG reduction projects to decrease emissions. To help the user make informed decisions for economic and environmental benefits from the use of renewable energy, an assessment model was developed. This study establishes a simple assessment method (SAM), an assessment database (DB) of 1199 GHG reduction technologies implemented in Korea, and a machine learning-based GHG reduction technology assessment model (GRTM). Additionally, we make suggestions on how to evaluate economic benefits, which can be obtained in conjunction with the environmental benefits of GHG reduction technology. Finally, we validate the applicability of the assessment model on a public building in Korea.

Keywords: GHG reduction technology; machine learning; GBRT; SVM; DNN; K-ETS (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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