IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i10p6133-d818385.html
   My bibliography  Save this article

Effects of the INDC and GGRMA Regulations on the Impact of PM 2.5 Particle Emissions on Maritime Ports: A Study of Human Health and Environmental Costs

Author

Listed:
  • Ching-Chih Chang

    (Department of Transportation and Communication Management Science, The Research Center for Energy Technology and Strategy, National Cheng Kung University, No. 1, University Road, Tainan 70101, Taiwan)

  • Yu-Wei Chang

    (Department of Transportation and Communication Management Science, National Cheng Kung University, No. 1, University Road, Tainan 70101, Taiwan)

  • Po-Chien Huang

    (Department of Transportation and Communication Management Science, National Cheng Kung University, No. 1, University Road, Tainan 70101, Taiwan)

Abstract

This study employs an activity-based model to estimate the PM 2.5 particle emissions from ships, cargo-handling equipment, and heavy-duty vehicles in the Port of Kaohsiung, Taiwan. External health costs, the index of health impact (IHI), and external environmental costs are assessed to quantify the impact of PM 2.5 particle emissions. The mitigation regulations applied in this study are the Intended Nationally Determined Contribution Act (INDC) and the Greenhouse Gas Reduction and Management Act (GGRMA). The provisions in these acts are incorporated into Scenario-INDC and Scenario-GGRMA. The results are as follows: from 2005 to 2017, PM 2.5 particle emissions caused an external health cost of 3238.30 DALY (disability-adjusted life year), an IHI value of 8.53%, and environmental cost of USD 2176.04 million annually. For Scenario-INDC and Scenario-GGRMA, it is predicted that PM 2.5 -related external health costs, IHI value, and external environmental cost will decrease by 927.64 DALY, 2.45%, and USD 608.86 million and by 1736.28 DALY, 4.58%, and USD 1139.84 million, respectively, as compared to BAU-2030 and BAU-2050. The results indicate that compliance with INDC and GGRMA regulations will lead to a significant mitigation of PM 2.5 particle emissions, resulting in a significant improvements in air quality and human health in addition to a reduction in environmental costs.

Suggested Citation

  • Ching-Chih Chang & Yu-Wei Chang & Po-Chien Huang, 2022. "Effects of the INDC and GGRMA Regulations on the Impact of PM 2.5 Particle Emissions on Maritime Ports: A Study of Human Health and Environmental Costs," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6133-:d:818385
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/10/6133/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/10/6133/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chi Zhang, 2017. "Population in China," Europe-Asia Studies, Taylor & Francis Journals, vol. 69(8), pages 1333-1334, September.
    2. Chang, Ching-Chih & Huang, Po-Chien & Tu, Jhih-Sheng, 2019. "Life cycle assessment of yard tractors using hydrogen fuel at the Port of Kaohsiung, Taiwan," Energy, Elsevier, vol. 189(C).
    3. Holt, Charles C., 2004. "Forecasting seasonals and trends by exponentially weighted moving averages," International Journal of Forecasting, Elsevier, vol. 20(1), pages 5-10.
    4. Zhu, Jianyun & Chen, Li & Wang, Bin & Xia, Lijuan, 2018. "Optimal design of a hybrid electric propulsive system for an anchor handling tug supply vessel," Applied Energy, Elsevier, vol. 226(C), pages 423-436.
    5. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sawaeng Kawichai & Susira Bootdee & Sopittaporn Sillapapiromsuk & Radshadaporn Janta, 2022. "Epidemiological Study on Health Risk Assessment of Exposure to PM2.5-Bound Toxic Metals in the Industrial Metropolitan of Rayong, Thailand," Sustainability, MDPI, vol. 14(22), pages 1-17, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuxin Zhang & Yifei Yang & Xiaosi Li & Zijing Yuan & Yuki Todo & Haichuan Yang, 2023. "A Dendritic Neuron Model Optimized by Meta-Heuristics with a Power-Law-Distributed Population Interaction Network for Financial Time-Series Forecasting," Mathematics, MDPI, vol. 11(5), pages 1-20, March.
    2. Simona Mikšíková & David Ulčák & František Kuda, 2022. "Analysis of Malfunctions in Selected Parking Systems in the Czech Republic," Sustainability, MDPI, vol. 14(3), pages 1-10, February.
    3. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    4. Hossein Yousefi & Mohammad Hasan Ghodusinejad & Armin Ghodrati, 2022. "Multi-Criteria Future Energy System Planning and Analysis for Hot Arid Areas of Iran," Energies, MDPI, vol. 15(24), pages 1-25, December.
    5. Dyna Heng & Anna Ivanova & Rodrigo Mariscal & Ms. Uma Ramakrishnan & Joyce Wong, 2016. "Advancing Financial Development in Latin America and the Caribbean," IMF Working Papers 2016/081, International Monetary Fund.
    6. Kang, Wensheng & Ratti, Ronald A. & Vespignani, Joaquin L., 2016. "The implications of monetary expansion in China for the US dollar," Journal of Asian Economics, Elsevier, vol. 46(C), pages 71-84.
    7. Kim, Yochan & Park, Jinkyun & Jung, Wondea, 2017. "A quantitative measure of fitness for duty and work processes for human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 595-601.
    8. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    9. Guo-hua Ye & Mirxat Alim & Peng Guan & De-sheng Huang & Bao-sen Zhou & Wei Wu, 2021. "Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-13, March.
    10. Ahmed Belhadjayed & Grégoire Loeper & Frédéric Abergel, 2016. "Forecasting Trends With Asset Prices," Post-Print hal-01512431, HAL.
    11. Karzan Mahdi Ghafour & Abdulqadir Rahomee Ahmed Aljanabi, 2023. "The role of forecasting in preventing supply chain disruptions during the COVID-19 pandemic: a distributor-retailer perspective," Operations Management Research, Springer, vol. 16(2), pages 780-793, June.
    12. Fieger, Peter & Rice, John, 2016. "Modelling Chinese Inbound Tourism Arrivals into Christchurch," MPRA Paper 75468, University Library of Munich, Germany.
    13. Koopman, Siem Jan & Ooms, Marius, 2006. "Forecasting daily time series using periodic unobserved components time series models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 885-903, November.
    14. Albrecht, Tobias & Rausch, Theresa Maria & Derra, Nicholas Daniel, 2021. "Call me maybe: Methods and practical implementation of artificial intelligence in call center arrivals’ forecasting," Journal of Business Research, Elsevier, vol. 123(C), pages 267-278.
    15. Sprangers, Olivier & Schelter, Sebastian & de Rijke, Maarten, 2023. "Parameter-efficient deep probabilistic forecasting," International Journal of Forecasting, Elsevier, vol. 39(1), pages 332-345.
    16. Kosuke Kawakami & Hirokazu Kobayashi & Kazuhide Nakata, 2021. "Seasonal Inventory Management Model for Raw Materials in Steel Industry," Interfaces, INFORMS, vol. 51(4), pages 312-324, July.
    17. Hu, Yuntong & Xiao, Fuyuan, 2022. "A novel method for forecasting time series based on directed visibility graph and improved random walk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    18. Xianbo Li, 2022. "Sequence Model and Prediction for Sustainable Enrollments in Chinese Universities," Sustainability, MDPI, vol. 15(1), pages 1-25, December.
    19. Andrea Kolková & Petr Rozehnal, 2022. "Hybrid demand forecasting models: pre-pandemic and pandemic use studies," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 17(3), pages 699-725, September.
    20. Feng Xu & Mohamad Sepehri & Jian Hua & Sergey Ivanov & Julius N. Anyu, 2018. "Time-Series Forecasting Models for Gasoline Prices in China," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(12), pages 1-43, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6133-:d:818385. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.