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GREENHOUSE GAS EMISSION AND THEIR TREND PREDICTION USING AIS AND TRADE DATA

Cilt: 9 Sayı: Special Issue 2nd International Symposium of Sustainable Logistics “Circular Economy” 9 Aralık 2022
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GREENHOUSE GAS EMISSION AND THEIR TREND PREDICTION USING AIS AND TRADE DATA

Öz

Due to decarbonization and greenhouse gas (GHG) emission reduction attempts nowadays, liquefied natural gas (LNG) has become widely used as an alternative marine fuel. As Japan is the top global LNG importer and one of the largest crude oil importers, this study focuses on LNG and tanker shipping and their emissions in Japan, and import volumes. In this study, the emission estimation model is constructed based on the Holtrop-Mennen power prediction method. Using automatic identification system (AIS) data, fuel consumption and GHG emissions are estimated. Next, long term GHG emission is predicted using hthe Japan trade statistics. Combining the vessel movement data and trade statistics, GHG emission in Japan is projected to decline over years for tankers, and to remain stable for LNG carriers. The results could be considered in formulating environmental and trade policy. It is hoped the study will provide useful insights for zero emission projects and implementations in Japan.

Anahtar Kelimeler

Kaynakça

  1. Benamara, H., Hoffmann, J. and Youssef, F. (2019). Maritime Transport: The Sustainability Imperative. Psaraftis, H. N. (Ed.). in: Sustainable Shipping A Cross-Disciplinary View, Springer Nature Switzerland AG, pp. 1-31.
  2. Bereta, K., Chatzikokolakis, K. and Zissis, D. (2021). Maritime Reporting Systems. Artikis, A. and Zissis, D. (Ed.). in: Guide to Maritime Informatics, Springer Nature Switzerland AG, pp. 3-30.
  3. Cerdeiro, Komaromi, Liu and Saeed. (2020). AIS Data Collected by MarineTraffic. available at UN COMTRADE Monitor https://comtrade.un.org/data/ais (accessed 07 September 2022).
  4. Goldsworthy, B. (2017). Spatial and Temporal Allocation of Ship Exhaust Emissions in Australian Coastal Waters using AIS Data: Analysis and Treatment of Data Gaps. Atmospheric Environment, 163, 77-86.
  5. Harvald, S.A. (1983). Resistance and Propulsion of Ships. John Wiley & Sons.
  6. Holtrop, J. (1984). A Statistical Re-Analysis of Resistance and Propulsion Data. International Shipbuilding Progress, 31, 363.
  7. Holtrop, J. and Mennen, G.G.J. (1982). An Approximate Power Prediction Method. International Shipbuilding Progress, 29, 335.
  8. International Energy Agency. (2017). Energy efficiency 2017. Paris: IEA. available at: https://www.iea.org/reports/energy-efficiency-2017 (accessed 20 September 2022).

Ayrıntılar

Birincil Dil

İngilizce

Konular

-

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

9 Aralık 2022

Gönderilme Tarihi

28 Eylül 2022

Kabul Tarihi

21 Kasım 2022

Yayımlandığı Sayı

Yıl 2022 Cilt: 9 Sayı: Special Issue 2nd International Symposium of Sustainable Logistics “Circular Economy”

Kaynak Göster

APA
Win, T. K., Watanabe, D., & Torıumı, S. (2022). GREENHOUSE GAS EMISSION AND THEIR TREND PREDICTION USING AIS AND TRADE DATA. Toros Üniversitesi İİSBF Sosyal Bilimler Dergisi, 9(Special Issue 2nd International Symposium of Sustainable Logistics “Circular Economy”), 107-121. https://doi.org/10.54709/iisbf.1181251
AMA
1.Win TK, Watanabe D, Torıumı S. GREENHOUSE GAS EMISSION AND THEIR TREND PREDICTION USING AIS AND TRADE DATA. Toros Üniversitesi İİSBF Sosyal Bilimler Dergisi. 2022;9(Special Issue 2nd International Symposium of Sustainable Logistics “Circular Economy”):107-121. doi:10.54709/iisbf.1181251
Chicago
Win, Thuta Kyaw, Daisuke Watanabe, ve Shigeki Torıumı. 2022. “GREENHOUSE GAS EMISSION AND THEIR TREND PREDICTION USING AIS AND TRADE DATA”. Toros Üniversitesi İİSBF Sosyal Bilimler Dergisi 9 (Special Issue 2nd International Symposium of Sustainable Logistics “Circular Economy”): 107-21. https://doi.org/10.54709/iisbf.1181251.
EndNote
Win TK, Watanabe D, Torıumı S (01 Aralık 2022) GREENHOUSE GAS EMISSION AND THEIR TREND PREDICTION USING AIS AND TRADE DATA. Toros Üniversitesi İİSBF Sosyal Bilimler Dergisi 9 Special Issue 2nd International Symposium of Sustainable Logistics “Circular Economy” 107–121.
IEEE
[1]T. K. Win, D. Watanabe, ve S. Torıumı, “GREENHOUSE GAS EMISSION AND THEIR TREND PREDICTION USING AIS AND TRADE DATA”, Toros Üniversitesi İİSBF Sosyal Bilimler Dergisi, c. 9, sy Special Issue 2nd International Symposium of Sustainable Logistics “Circular Economy”, ss. 107–121, Ara. 2022, doi: 10.54709/iisbf.1181251.
ISNAD
Win, Thuta Kyaw - Watanabe, Daisuke - Torıumı, Shigeki. “GREENHOUSE GAS EMISSION AND THEIR TREND PREDICTION USING AIS AND TRADE DATA”. Toros Üniversitesi İİSBF Sosyal Bilimler Dergisi 9/Special Issue 2nd International Symposium of Sustainable Logistics “Circular Economy” (01 Aralık 2022): 107-121. https://doi.org/10.54709/iisbf.1181251.
JAMA
1.Win TK, Watanabe D, Torıumı S. GREENHOUSE GAS EMISSION AND THEIR TREND PREDICTION USING AIS AND TRADE DATA. Toros Üniversitesi İİSBF Sosyal Bilimler Dergisi. 2022;9:107–121.
MLA
Win, Thuta Kyaw, vd. “GREENHOUSE GAS EMISSION AND THEIR TREND PREDICTION USING AIS AND TRADE DATA”. Toros Üniversitesi İİSBF Sosyal Bilimler Dergisi, c. 9, sy Special Issue 2nd International Symposium of Sustainable Logistics “Circular Economy”, Aralık 2022, ss. 107-21, doi:10.54709/iisbf.1181251.
Vancouver
1.Thuta Kyaw Win, Daisuke Watanabe, Shigeki Torıumı. GREENHOUSE GAS EMISSION AND THEIR TREND PREDICTION USING AIS AND TRADE DATA. Toros Üniversitesi İİSBF Sosyal Bilimler Dergisi. 01 Aralık 2022;9(Special Issue 2nd International Symposium of Sustainable Logistics “Circular Economy”):107-21. doi:10.54709/iisbf.1181251