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

Author

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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)

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.

Suggested Citation

  • Sungwoo Lee & Sungho Tae, 2020. "Development of a Decision Support Model Based on Machine Learning for Applying Greenhouse Gas Reduction Technology," Sustainability, MDPI, vol. 12(9), pages 1-19, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:9:p:3582-:d:351561
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    References listed on IDEAS

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    1. Roh, Seungjun & Tae, Sungho, 2017. "An integrated assessment system for managing life cycle CO2 emissions of a building," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 265-275.
    2. Peng, Jinqing & Lu, Lin & Yang, Hongxing, 2013. "Review on life cycle assessment of energy payback and greenhouse gas emission of solar photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 255-274.
    3. Singh, Devesh & Basu, Chandrajit & Meinhardt-Wollweber, Merve & Roth, Bernhard, 2015. "LEDs for energy efficient greenhouse lighting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 139-147.
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    Cited by:

    1. Sungwoo Lee & Sungho Tae & Hyungjae Jang & Chang U. Chae & Youngjin Bok, 2021. "Development of Building Information Modeling Template for Environmental Impact Assessment," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    2. Simona Andreea Apostu & Elena Mirela Nichita & Cristina Lidia Manea & Alina Mihaela Irimescu & Marcel Vulpoi, 2023. "Exploring the Influence of Innovation and Technology on Climate Change," Energies, MDPI, vol. 16(17), pages 1-13, September.
    3. Caterina De Lucia & Pasquale Pazienza & Mark Bartlett, 2020. "Does Good ESG Lead to Better Financial Performances by Firms? Machine Learning and Logistic Regression Models of Public Enterprises in Europe," Sustainability, MDPI, vol. 12(13), pages 1-29, July.

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