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Eco-Friendly Office Platform: Leveraging Machine Learning and GIS for Carbon Footprint Management and Green Space Analysis

Author

Listed:
  • Wanida Saetang

    (Faculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, Surat Thani 84000, Thailand)

  • Supaporn Chai-Arayalert

    (Faculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, Surat Thani 84000, Thailand)

  • Siriwan Kajornkasirat

    (Faculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, Surat Thani 84000, Thailand)

  • Jinda Kongcharoen

    (Faculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, Surat Thani 84000, Thailand)

  • Aekarat Saeliw

    (Faculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, Surat Thani 84000, Thailand)

  • Kritsada Puangsuwan

    (Faculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, Surat Thani 84000, Thailand)

  • Supattra Puttinaovarat

    (Faculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, Surat Thani 84000, Thailand)

Abstract

This research focuses on developing an innovative platform to manage carbon footprint data related to office activities and monitor green spaces, integrating geoinformatics and machine learning technologies. The platform addresses the lack of automated systems for tracking carbon emissions, particularly in high paper consumption environments, such as those involving printing and photocopying. Additionally, it monitors green spaces within corporate settings, an aspect often overlooked in existing systems. The study demonstrates the platform’s capability to automate carbon footprint calculations and provide accurate assessments of green areas, achieving a high accuracy rate of 96.22% and a Kappa coefficient of 0.92. The results confirm the platform’s ability to deliver both comprehensive and granular environmental insights, supporting decision making for more sustainable office environments. The key novelty of this study lies in the integration of real-time data capture with geoinformatics and machine learning to efficiently track both carbon footprints and green spaces. This approach offers a practical solution to a gap in environmental data management in office settings, enabling organizations to align their practices with sustainability goals. The platform’s precise, automated system contributes significantly to the development of eco-friendly workplaces, highlighting its academic and practical value in the field of environmental sustainability.

Suggested Citation

  • Wanida Saetang & Supaporn Chai-Arayalert & Siriwan Kajornkasirat & Jinda Kongcharoen & Aekarat Saeliw & Kritsada Puangsuwan & Supattra Puttinaovarat, 2024. "Eco-Friendly Office Platform: Leveraging Machine Learning and GIS for Carbon Footprint Management and Green Space Analysis," Sustainability, MDPI, vol. 16(21), pages 1-20, October.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:21:p:9424-:d:1510020
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    References listed on IDEAS

    as
    1. Simplice Igor Noubissie Tientcheu & Shyama P. Chowdhury & Thomas O. Olwal, 2019. "Intelligent Energy Management Strategy for Automated Office Buildings," Energies, MDPI, vol. 12(22), pages 1-27, November.
    2. Victoria Linn Lygum & Katia Dupret & Peter Bentsen & Dorthe Djernis & Sidse Grangaard & Yun Ladegaard & Charlotte Petersson Troije, 2023. "Greenspace as Workplace: Benefits, Challenges and Essentialities in the Physical Environment," IJERPH, MDPI, vol. 20(17), pages 1-19, August.
    3. Philip Stessens & Frank Canters & Ahmed Z. Khan, 2021. "Exploring Options for Public Green Space Development: Research by Design and GIS-Based Scenario Modelling," Sustainability, MDPI, vol. 13(15), pages 1-52, July.
    4. Ilya Stepanov & Igor Makarov & Ekaterina Makarova & Elizaveta Smolovik, 2023. "Climate change and challenges to sustainable development in the Russian Arctic," Climatic Change, Springer, vol. 176(4), pages 1-18, April.
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