Future LNG competition and trade using an agent-based predictive model
<p dir="ltr">Liquified Natural Gas (LNG) is an alternative method to transport natural gas (NG), more versatile than pipeline gas, and helps increasing the availability, affordability and use of NG compared to carbon-intensive coal and oil. In the LNG market, various expansions proje...
محفوظ في:
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| مؤلفون آخرون: | , , |
| منشور في: |
2021
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| الموضوعات: | |
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| _version_ | 1864513551778971648 |
|---|---|
| author | Abel Meza (17191222) |
| author2 | Ibrahim Ari (12058516) Mohammed Saleh Al-Sada (17191225) Muammer Koç (8350053) |
| author2_role | author author author |
| author_facet | Abel Meza (17191222) Ibrahim Ari (12058516) Mohammed Saleh Al-Sada (17191225) Muammer Koç (8350053) |
| author_role | author |
| dc.creator.none.fl_str_mv | Abel Meza (17191222) Ibrahim Ari (12058516) Mohammed Saleh Al-Sada (17191225) Muammer Koç (8350053) |
| dc.date.none.fl_str_mv | 2021-11-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.esr.2021.100734 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Future_LNG_competition_and_trade_using_an_agent-based_predictive_model/24339262 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Economics Econometrics Engineering Resources engineering and extractive metallurgy LNG market LNG projects Agent-based model Simulation |
| dc.title.none.fl_str_mv | Future LNG competition and trade using an agent-based predictive model |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Liquified Natural Gas (LNG) is an alternative method to transport natural gas (NG), more versatile than pipeline gas, and helps increasing the availability, affordability and use of NG compared to carbon-intensive coal and oil. In the LNG market, various expansions projects have been planned and underway for the 2020s. Although some projects were put hold or delayed due to the demand shocks from the COVID-19 pandemic, the LNG market demonstrates the potential to expand further in the future. This study employs an Agent-Based Model (ABM) to evaluate these prospects of expansion in demand and supply, competition among various suppliers, and potential trade challenges in the coming decades. This model combines the usual contractual engagements of the LNG market and a representation of the spot market to simulate the possible traded quantities. The model is validated by comparing simulations with the historical record of the LNG trade in 2016 and 2018, reflecting its accuracy in replicating such real data. Proceeding with the results for the time horizon until 2030, the model represents the preponderance of Qatar as the most competitive LNG supplier, even when new LNG infrastructure comes online everywhere. The US is an emergent competitor with multiple projects finding demand in all the LNG regions, while Australia would still highly depend on the Asian Pacific basin. Other smaller exporters would struggle to find importing markets but collectively would open new regional markets. The model projects around 510+ MTPA of LNG trade by 2030, fairly similar to other projections.</p><h2>Other Information</h2><p dir="ltr">Published in: Energy Strategy Reviews<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.esr.2021.100734" target="_blank">https://dx.doi.org/10.1016/j.esr.2021.100734</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_447196f77513f99eaf40dfd8118fab19 |
| identifier_str_mv | 10.1016/j.esr.2021.100734 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24339262 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Future LNG competition and trade using an agent-based predictive modelAbel Meza (17191222)Ibrahim Ari (12058516)Mohammed Saleh Al-Sada (17191225)Muammer Koç (8350053)EconomicsEconometricsEngineeringResources engineering and extractive metallurgyLNG marketLNG projectsAgent-based modelSimulation<p dir="ltr">Liquified Natural Gas (LNG) is an alternative method to transport natural gas (NG), more versatile than pipeline gas, and helps increasing the availability, affordability and use of NG compared to carbon-intensive coal and oil. In the LNG market, various expansions projects have been planned and underway for the 2020s. Although some projects were put hold or delayed due to the demand shocks from the COVID-19 pandemic, the LNG market demonstrates the potential to expand further in the future. This study employs an Agent-Based Model (ABM) to evaluate these prospects of expansion in demand and supply, competition among various suppliers, and potential trade challenges in the coming decades. This model combines the usual contractual engagements of the LNG market and a representation of the spot market to simulate the possible traded quantities. The model is validated by comparing simulations with the historical record of the LNG trade in 2016 and 2018, reflecting its accuracy in replicating such real data. Proceeding with the results for the time horizon until 2030, the model represents the preponderance of Qatar as the most competitive LNG supplier, even when new LNG infrastructure comes online everywhere. The US is an emergent competitor with multiple projects finding demand in all the LNG regions, while Australia would still highly depend on the Asian Pacific basin. Other smaller exporters would struggle to find importing markets but collectively would open new regional markets. The model projects around 510+ MTPA of LNG trade by 2030, fairly similar to other projections.</p><h2>Other Information</h2><p dir="ltr">Published in: Energy Strategy Reviews<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.esr.2021.100734" target="_blank">https://dx.doi.org/10.1016/j.esr.2021.100734</a></p>2021-11-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.esr.2021.100734https://figshare.com/articles/journal_contribution/Future_LNG_competition_and_trade_using_an_agent-based_predictive_model/24339262CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/243392622021-11-01T00:00:00Z |
| spellingShingle | Future LNG competition and trade using an agent-based predictive model Abel Meza (17191222) Economics Econometrics Engineering Resources engineering and extractive metallurgy LNG market LNG projects Agent-based model Simulation |
| status_str | publishedVersion |
| title | Future LNG competition and trade using an agent-based predictive model |
| title_full | Future LNG competition and trade using an agent-based predictive model |
| title_fullStr | Future LNG competition and trade using an agent-based predictive model |
| title_full_unstemmed | Future LNG competition and trade using an agent-based predictive model |
| title_short | Future LNG competition and trade using an agent-based predictive model |
| title_sort | Future LNG competition and trade using an agent-based predictive model |
| topic | Economics Econometrics Engineering Resources engineering and extractive metallurgy LNG market LNG projects Agent-based model Simulation |