Dynamic Systems Model for Evaluating Atmospheric Greenhouse Gas Emissions in Accordance with USEPA CFR PART 98 Subpart W

Marcelo Cruz dos Santos; Claudinei de Souza Guimarães; Amarildo da Cruz Fernandes

doi:doi:10.11648/j.ajep.20231204.12

| Peer-Reviewed

Dynamic Systems Model for Evaluating Atmospheric Greenhouse Gas Emissions in Accordance with USEPA CFR PART 98 Subpart W

Marcelo Cruz dos Santos, Claudinei de Souza Guimarães, Amarildo da Cruz Fernandes

Published in American Journal of Environmental Protection (Volume 12, Issue 4)

Received: 5 June 2023 Accepted: 4 July 2023 Published: 17 July 2023

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

The oil industry has a relevant role in the generation of Greenhouse Gases (GHG) in its various segments, among them the Exploration and Production of Oil and Natural Gas (E&P). There are several methodologies for GHG inventories, each with different degrees of uncertainty, which makes the quantification of emissions complex, given the large number of variables to be analyzed. According to the Compendium of Greenhouse Gas Emissions Methodologies for the Oil and Gas Industry of the American Petroleum Institute (API), all GHG emissions should be calculated as a product of an "activity factor" by an appropriate "emission factor". That is, the amount of fuel used, considering how it is used. The product between the activity data and the emission factors provides an estimate of the GHG emissions associated with the company's activities. Based on this premise, this paper presents a model developed in System Dynamics (SD) for the preparation of inventories of CO₂ and CH₄ emissions, the main GHG emitted by the oil industry. The model was developed to meet the requirements of "Subpart W" of the United States Environmental and Protection Agency (USEPA) CFR Part 98, which states that oil and gas E&P facilities that emit at least 25 x 10³ t CO₂e/year, must report their estimates of total annual GHG emissions, annual individualized emissions of each GHG, and annual individualized emissions of each GHG broken down by source type expressed in metric tons of CO₂e. The proposed model goes beyond the USEPA requirements in that it also allows estimation of emissions of CO₂, of CH₄ and their equivalence in CO₂e from specific sources and groups of sources, generating an estimate of the emissions profile over the entire lifetime of the inventoried facility.

Published in	American Journal of Environmental Protection (Volume 12, Issue 4)
DOI	10.11648/j.ajep.20231204.12
Page(s)	92-108
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Systems Dynamics, Greenhouse Gases, Global Warming, Oil and Natural Gas

References

[1]	Forster, P. et al., 2021: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi: 10.1017/9781009157896.009.
[2]	Gulev, S. K. et al., 2021: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 287–422, doi: 10.1017/9781009157896.004.
[3]	Ward, D. S. and N. M. Mahowald, 2015: Local sources of global climate forcing from different categories of land use activities. Earth System Dynamics, 6 (1), 175–194, doi: 10.5194/esd-6-175-2015.
[4]	Kärcher, B. Formation and radiative forcing of contrail cirrus. Nat Commun 9, 1824 (2018), doi: 10.1038/s41467-018-04068-0.
[5]	Lee, D. S., Fahey D. W., Skowron, A., Allen, M. R., Burkhardt, U., Chen, Q., Doherty, S. J., Freeman, S., Forster, P. M., Fuglestvedt J., Gettelman, A., De León, R. R., Lim, L. L., Lund, M. T., Millar, R. J., Wen, B., Penner, J. E., Pitari, G., Prather, M. J., Sausen, R., Wilcox, L. J., The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018, Atmospheric Environment, Volume 244, 2021, 117834, ISSN 1352-2310, https://doi.org/10.1016/j.atmosenv.2020.117834.
[6]	Myhre, G. et al., 2013: Anthropogenic and Natural Radiative Forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
[7]	National Oceanic and Atmospheric Administration, The NOAA Annual Greenhouse Gas Index (AGGI). 2022.
[8]	Carter, T. R. et al., 2007: New Assessment Methods and the Characterization of Future Conditions. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, 133-171.
[9]	Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R. K. Pachauri and L. A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.
[10]	Ara Begum, R. et al., 2022: Point of Departure and Key Concepts. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 121–196, doi: 10.1017/9781009325844.003.
[11]	Friedlingstein, P. et al., 2022: Global Carbon Budget 2022, Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022.
[12]	WMO Greenhouse Gas Bulletin. The State of Greenhouse Gases in the Atmosphere Based on Global Observations through 2021. No. 18 - 26 October 2022.
[13]	World Greenhouse Gas Emissions: 2018. Available in https://www.wri.org/data/world-greenhouse-gas-emissions-2018.
[14]	World Energy Outlook 2022. International Energy Agency, Available in https://iea.blob.core.windows.net/assets/7e42db90-d8ea-459d-be1e-1256acd11330/WorldEnergyOutlook2022.pdf.
[15]	Kenner, D., Heede, R., White knights, or horsemen of the apocalypse? Prospects for Big Oil to align emissions with a 1.5°C pathway, Energy Research & Social Science, Volume 79, 2021, 102049, ISSN 2214-6296, https://doi.org/10.1016/j.erss.2021.102049.
[16]	Griffin, P. CDP Report. July 2017. Avaiable in http://www.truevaluemetrics.org/DBpdfs/Carbon/CDP/CDP-Carbon-Majors-Report-2017.pdf. (Accessed April 16, 2023).
[17]	Clarke, L. et al., 2022: Energy Systems. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK and New York, NY, USA. doi: 10.1017/9781009157926.008.
[18]	2011-2021 Greenhouse Gas Reporting Program Industrial Profile: Petroleum and Natural Gas Systems. https://www.epa.gov/system/files/documents/2022-10/subpart_w_2021_sector_profile.pdf.
[19]	40 CFR Part 98. Mandatory Reporting of Greenhouse Gases: Petroleum and Natural Gas Systems; Final Rule. November 30, 2010. Available in http://www.gpo.gov/fdsys/pkg/FR-2010-11-30/pdf/2010-28655.pdf. (Accessed January 10, 2023).
[20]	API 2009. American Petroleum Institute (API). Compendium of Greenhouse Gas Emissions Estimation Methodologies for the Oil and Gas Industry; August 2009. Available in https://www.api.org/~/media/files/ehs/climate-change/2009_ghg_compendium.ashx.
[21]	API 2021. American Petroleum Institute (API). Compendium of Greenhouse Gas Emissions Estimation Methodologies for the Oil and Gas Industry, November 2021. Available in https://www.api.org/~/media/Files/Policy/ESG/GHG/2021-API-GHG-Compendium-110921.pdf.
[22]	Forrester, J. W. Industrial Dynamics. New York, London. The MIT Press and John Willey & Sons, 1961.
[23]	Zuckerman O. (2004): System blocks: Learning about systems concepts through hands-on modeling and simulation. MA Thesis, MIT, Cambridge, MA.
[24]	Forrester, J. W. Designing the Future. Universidad de Sevilla, Sevilla, España. December 15, 1998.
[25]	SANTOS, A. C. K.; CHO, Y,; ARAUJO, I. S.; GONÇALVES, G. P. Some possibilities of using the principles of Forrester systems in Physics topics, through the STELLA quantitative modeling tool. Cad. Cat. Ens. Fís., v. 17, n. 1: p. 81-95, abr. 2000.
[26]	Amega, K., Moumouni, Y., Lare, Y.. A System Dynamics Modelling of a Long-term Residential Electricity Consumption in Lomé, Togo. International Journal of Energy and Power Engineering. Vol. 10, No. 6, 2021, pp. 141-150. doi: 10.11648/j.ijepe.20211006.17.
[27]	Forrester, J. W. “Some Basic Concepts in System Dynamics,” pp. 1–17, 2009.
[28]	Simoneto, E. O.; Lobler, M. L. Simulation based on system dynamics to evaluate scenarios about the generation and disposal of municipal solid waste. African Journal of Business Management Vol. 6 (50), pp. 11976-11985, 19 December, 2012.
[29]	R. Rehan, M. A. Knight, C. T. Haas, A. J. A. Unger, Application of system dynamics for developing financially self-sustaining management policies for water and wastewater systems, Water Research, Volume 45, Issue 16, 2011, Pages 4737-4750, ISSN 0043-1354, https://doi.org/10.1016/j.watres.2011.06.001.
[30]	Mirchi A, Madani K, Watkins D, Ahmad S. 2012. Synthesis of system dynamics tools for holistic conceptualization of water resources problems. Water Resour Manage (2012) 26: 2421–2442 DOI 10.1007/s11269-012-0024-2.
[31]	Sterman, J. Business dynamics: systems thinking and modeling for a complex world. Copyright 2000 by The McGraw-Hill Companie. ISBN-13: 978-0-07-231135-8.
[32]	Moumouni, Y., Ahmad, S., Jacob Baker, R. A System Dynamics Model for Energy Planning in Niger. International Journal of Energy and Power Engineering. Vol. 3, No. 6, 2014, pp. 308-322. doi: 10.11648/j.ijepe.20140306.14.
[33]	Epe Shari, B., Moumouni, Y., Momodu, A. S. Low Carbon Transition of Residential Electricity Consumption in Nigeria: A System Dynamics Modeling Approach. International Journal of Energy and Power Engineering. Vol. 9, No. 1, 2020, pp. 11-21. doi: 10.11648/j.ijepe.20200901.12.
[34]	Momodu, A. S., Adepoju, T. D. System Dynamics Model of the Kinetics of Biogas Production from Vegetal Matter. International Journal of Energy and Power Engineering. Vol. 9, No. 2, 2020, pp. 22-28. doi: 10.11648/j.ijepe.20200902.11.
[35]	Mupfumira, P., Wirjodirdjo, B. An Economically Sustainable Urban Public Transport Framework: System Dynamics Modelling Approach to Reduce Public Transport Liberalisation Impact in Harare City. International Journal of Science, Technology and Society. Special Issue: Design Innovations for Sustainable Development. Vol. 3, No. 2-2, 2015, pp. 11-21. doi: 10.11648/j.ijsts.s.2015030202.13.
[36]	Zhihong, L., Xueping, L., Ming, S. A Study on the Effect of Telecom Terminal Customization Service Strategy on Conversion Tendency Based on the System Dynamics. Science Innovation. Vol. 2, No. 3, 2014, pp. 27-36. doi: 10.11648/j.si.20140203.12.
[37]	Hajiheydari, N., Khakbaz, S. B., Ojaki, A. A. Developing Responsive Marketing System Concept: Applying System Dynamics Approach. Science Journal of Business and Management. Vol. 2, No. 3, 2014, pp. 91-96. doi: 10.11648/j.sjbm.20140203.11.
[38]	Ming-Feng H., Fang-Chen K., Chang, J. Study on Cash Flow of Volunteer Soldiers Retirement & Compensation Fund – From System Dynamics Perspective, Science Journal of Business and Management. Volume 10, Issue 1, March 2022, pp. 13-19. doi: 10.11648/j.sjbm.20221001.13.
[39]	Ouyang, Y. Leininger, T. D., Hatten, J., Parajuli, P. B. A STELLA Model to Estimate Soil CO₂ Emissions from a Short-Rotation Woody Crop. Water Air Soil Pollut 224, 1392 (2013). https://doi.org/10.1007/s11270-012-1392-1.
[40]	Sharif, S. V., Moshfegh, P. H., Morshedi, M. A., Kashani, H. Modeling the impact of mitigation policies in a pandemic: A system dynamics approach, International Journal of Disaster Risk Reduction, Volume 82, 2022, 103327, ISSN 2212-4209, https://doi.org/10.1016/j.ijdrr.2022.103327.
[41]	Rodriguez Lara, D. V., Pfaffenbichler, P., Rodrigues da Silva, A. N. Modeling the resilience of urban mobility when exposed to the COVID-19 pandemic: A qualitative system dynamics approach, Sustainable Cities and Society, Volume 91, 2023, 104411, ISSN 2210-6707, https://doi.org/10.1016/j.scs.2023.104411.
[42]	Payne-Sturges, D. C., Ballard, E., Cory-Slechta, D. A., Thomas, S. B., Hovmand, P. Making the invisible visible: Using a qualitative system dynamics model to map disparities in cumulative environmental stressors and children's neuro development, Environmental Research, Volume 221, 2023, 115295, ISSN 0013-9351, https://doi.org/10.1016/j.envres.2023.115295.
[43]	Luo, Z. Simulation of tourism carbon emissions based on system dynamics model, Physics and Chemistry of the Earth, Parts A/B/C, Volume 129, 2023, 103346, ISSN 1474-7065, https://doi.org/10.1016/j.pce.2022.103346.
[44]	Ahmed Nouh Meshref, ELsayed Abdel Fattah Ahmed Elkasaby, Ahmed Abdel Kader Mohamed Farid. Reducing construction waste in the construction life cycle of industrial projects during design phase by using system dynamics, Journal of Building Engineering, Volume 69, 2023, 106302, ISSN 2352-7102, https://doi.org/10.1016/j.jobe.2023.106302.
[45]	Anggoro, P. W., Yuniarto, T., Bawono, B., Setyohadi, D. B., Murdapa, P. S., Jamari, J. System dynamics modelling for calculation of carbon footprint on a non-homogeneous production system: A case in a ceramic studio, Results in Engineering, Volume 17, 2023, 100812, ISSN 2590-1230, https://doi.org/10.1016/j.rineng.2022.100812.
[46]	Ritter, K., Lev-On, M., Shires, T. Development of a Consistent Methodology for Estimating Greenhouse Gas Emissions from Oil and Gas Industry Operations, Presented at the 11th Emissions Inventory Conference of the U. S. Environmental Protection Agency, Atlanta, GA, April 2002.
[47]	INGAA. Greenhouse gas emission estimation guidelines for natural gas transmission and storage. Volume 1 – GHG emission estimation methodologies and procedures. Revision 2. September 28, 2005.
[48]	Offshore Petroleum and Natural Gas Production. Final Rule: Petroleum and Natural Gas Production (40 CFR 98, Subpart W). 2012. Available in https://www.epa.gov/sites/default/files/2017-09/documents/subpartw_offshoreinfosheet_508.pdf. (Accessed February 01, 2023).
[49]	Wilson, D., R. Billings, R. Oommen, B. Lange, J. Marik, S. McClutchey, and H. Perez. 2010. Year 2008 Gulfwide emission inventory study. U.S. Dept. of the Interior, Bureau of Ocean Energy Management, Regulation and Enforcement, Gulf of Mexico OCS Region, New Orleans, LA. OCS Study BOEMRE 2010-045. 186 pp.
[50]	IBP. The Brazilian oil and gas sector in a context of net zero emissions (net zero emissions). COPPE/UFRJ, PPE/UFRJ, 2022.
[51]	Offshore Oil and Gas Platform Report - Final Report. TCEQ Contract No. 582-7-84003. Work Order No. 582-7-84003-FY10-25. Eastern Research Group, Inc. 1600 Perimeter Park Drive. Morrisville, NC 27560. August 2010.
[52]	Discussion Paper for a Natural Gas Transmission and Distribution Greenhouse Gas Reporting Protocol. Final Draft Report. Prepared for California Climate Action Registry and World Resources Institute. Prepared by URS Corporation and The LEVON Group. June 6, 2007.
[53]	Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. Fugitive Emissions from Oil and Natural Gas Activities. 2000. Available in http://www.ipcc-nggip.iges.or.jp/public/gp/bgp/2_6_Fugitive_Emissions_from_Oil_and_Natural_Gas.pdf. (Accessed December 18, 2023).
[54]	PSI. Pipeline Systems Incorporated. Annual Methane Emissions Estimate of the Natural Gas Systems in the United States, Phase 2. For Radian Corporation, September 1990.
[55]	AP-42: Emission Factor Documentation for Compilation of air Pollutant Emission factors, Volume I: Stationary Point and Area Sources. Fifth Edition, AP-42, Section 1.4, Natural Gas Combustion, U.S. Environmental Protection Agency, Office of Air Quality Planing and Standards. Research Triangle Park, NC, March 1998.
[56]	Sustainability Report 2021. PETROBRAS – Petróleo Brasileiro S. A. – 2021. Available in https://www.investidorpetrobras.com.br/en/presentations-reports-and-events/annual-reports/. (Accessed January 30, 2023).
[57]	Newell, D. and Tiesinga, E. (2019), The International System of Units (SI), 2019 Edition, Special Publication (NIST SP), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.SP.330-2019 (Accessed March 29, 2023).
[58]	Doyle, J. K., Ford, D. N. (1998). Mental models concepts for system dynamics research. Syst. Dyn. Rev., 14 (1), 3–29.

Cite This Article

Plain Text BibTeX RIS

APA Style

Marcelo Cruz dos Santos, Claudinei de Souza Guimarães, Amarildo da Cruz Fernandes. (2023). Dynamic Systems Model for Evaluating Atmospheric Greenhouse Gas Emissions in Accordance with USEPA CFR PART 98 Subpart W. American Journal of Environmental Protection, 12(4), 92-108. https://doi.org/10.11648/j.ajep.20231204.12

Copy | Download

ACS Style

Marcelo Cruz dos Santos; Claudinei de Souza Guimarães; Amarildo da Cruz Fernandes. Dynamic Systems Model for Evaluating Atmospheric Greenhouse Gas Emissions in Accordance with USEPA CFR PART 98 Subpart W. Am. J. Environ. Prot. 2023, 12(4), 92-108. doi: 10.11648/j.ajep.20231204.12

Copy | Download

AMA Style

Marcelo Cruz dos Santos, Claudinei de Souza Guimarães, Amarildo da Cruz Fernandes. Dynamic Systems Model for Evaluating Atmospheric Greenhouse Gas Emissions in Accordance with USEPA CFR PART 98 Subpart W. Am J Environ Prot. 2023;12(4):92-108. doi: 10.11648/j.ajep.20231204.12

Copy | Download

@article{10.11648/j.ajep.20231204.12,
  author = {Marcelo Cruz dos Santos and Claudinei de Souza Guimarães and Amarildo da Cruz Fernandes},
  title = {Dynamic Systems Model for Evaluating Atmospheric Greenhouse Gas Emissions in Accordance with USEPA CFR PART 98 Subpart W},
  journal = {American Journal of Environmental Protection},
  volume = {12},
  number = {4},
  pages = {92-108},
  doi = {10.11648/j.ajep.20231204.12},
  url = {https://doi.org/10.11648/j.ajep.20231204.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajep.20231204.12},
  abstract = {The oil industry has a relevant role in the generation of Greenhouse Gases (GHG) in its various segments, among them the Exploration and Production of Oil and Natural Gas (E&P). There are several methodologies for GHG inventories, each with different degrees of uncertainty, which makes the quantification of emissions complex, given the large number of variables to be analyzed. According to the Compendium of Greenhouse Gas Emissions Methodologies for the Oil and Gas Industry of the American Petroleum Institute (API), all GHG emissions should be calculated as a product of an "activity factor" by an appropriate "emission factor". That is, the amount of fuel used, considering how it is used. The product between the activity data and the emission factors provides an estimate of the GHG emissions associated with the company's activities. Based on this premise, this paper presents a model developed in System Dynamics (SD) for the preparation of inventories of CO2 and CH4 emissions, the main GHG emitted by the oil industry. The model was developed to meet the requirements of "Subpart W" of the United States Environmental and Protection Agency (USEPA) CFR Part 98, which states that oil and gas E&P facilities that emit at least 25 x 103 t CO2e/year, must report their estimates of total annual GHG emissions, annual individualized emissions of each GHG, and annual individualized emissions of each GHG broken down by source type expressed in metric tons of CO2e. The proposed model goes beyond the USEPA requirements in that it also allows estimation of emissions of CO2, of CH4 and their equivalence in CO2e from specific sources and groups of sources, generating an estimate of the emissions profile over the entire lifetime of the inventoried facility.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Dynamic Systems Model for Evaluating Atmospheric Greenhouse Gas Emissions in Accordance with USEPA CFR PART 98 Subpart W
AU  - Marcelo Cruz dos Santos
AU  - Claudinei de Souza Guimarães
AU  - Amarildo da Cruz Fernandes
Y1  - 2023/07/17
PY  - 2023
N1  - https://doi.org/10.11648/j.ajep.20231204.12
DO  - 10.11648/j.ajep.20231204.12
T2  - American Journal of Environmental Protection
JF  - American Journal of Environmental Protection
JO  - American Journal of Environmental Protection
SP  - 92
EP  - 108
PB  - Science Publishing Group
SN  - 2328-5699
UR  - https://doi.org/10.11648/j.ajep.20231204.12
AB  - The oil industry has a relevant role in the generation of Greenhouse Gases (GHG) in its various segments, among them the Exploration and Production of Oil and Natural Gas (E&P). There are several methodologies for GHG inventories, each with different degrees of uncertainty, which makes the quantification of emissions complex, given the large number of variables to be analyzed. According to the Compendium of Greenhouse Gas Emissions Methodologies for the Oil and Gas Industry of the American Petroleum Institute (API), all GHG emissions should be calculated as a product of an "activity factor" by an appropriate "emission factor". That is, the amount of fuel used, considering how it is used. The product between the activity data and the emission factors provides an estimate of the GHG emissions associated with the company's activities. Based on this premise, this paper presents a model developed in System Dynamics (SD) for the preparation of inventories of CO2 and CH4 emissions, the main GHG emitted by the oil industry. The model was developed to meet the requirements of "Subpart W" of the United States Environmental and Protection Agency (USEPA) CFR Part 98, which states that oil and gas E&P facilities that emit at least 25 x 103 t CO2e/year, must report their estimates of total annual GHG emissions, annual individualized emissions of each GHG, and annual individualized emissions of each GHG broken down by source type expressed in metric tons of CO2e. The proposed model goes beyond the USEPA requirements in that it also allows estimation of emissions of CO2, of CH4 and their equivalence in CO2e from specific sources and groups of sources, generating an estimate of the emissions profile over the entire lifetime of the inventoried facility.
VL  - 12
IS  - 4
ER  -

Copy | Download

Author Information

Marcelo Cruz dos Santos

Polytechnic School, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
Claudinei de Souza Guimarães

Polytechnic School, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
Amarildo da Cruz Fernandes

Polytechnic School, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Marcelo Cruz dos Santos, Claudinei de Souza Guimarães, Amarildo da Cruz Fernandes. (2023). Dynamic Systems Model for Evaluating Atmospheric Greenhouse Gas Emissions in Accordance with USEPA CFR PART 98 Subpart W. American Journal of Environmental Protection, 12(4), 92-108. https://doi.org/10.11648/j.ajep.20231204.12

Copy | Download

ACS Style

Marcelo Cruz dos Santos; Claudinei de Souza Guimarães; Amarildo da Cruz Fernandes. Dynamic Systems Model for Evaluating Atmospheric Greenhouse Gas Emissions in Accordance with USEPA CFR PART 98 Subpart W. Am. J. Environ. Prot. 2023, 12(4), 92-108. doi: 10.11648/j.ajep.20231204.12

Copy | Download

AMA Style

Marcelo Cruz dos Santos, Claudinei de Souza Guimarães, Amarildo da Cruz Fernandes. Dynamic Systems Model for Evaluating Atmospheric Greenhouse Gas Emissions in Accordance with USEPA CFR PART 98 Subpart W. Am J Environ Prot. 2023;12(4):92-108. doi: 10.11648/j.ajep.20231204.12

Copy | Download

@article{10.11648/j.ajep.20231204.12,
  author = {Marcelo Cruz dos Santos and Claudinei de Souza Guimarães and Amarildo da Cruz Fernandes},
  title = {Dynamic Systems Model for Evaluating Atmospheric Greenhouse Gas Emissions in Accordance with USEPA CFR PART 98 Subpart W},
  journal = {American Journal of Environmental Protection},
  volume = {12},
  number = {4},
  pages = {92-108},
  doi = {10.11648/j.ajep.20231204.12},
  url = {https://doi.org/10.11648/j.ajep.20231204.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajep.20231204.12},
  abstract = {The oil industry has a relevant role in the generation of Greenhouse Gases (GHG) in its various segments, among them the Exploration and Production of Oil and Natural Gas (E&P). There are several methodologies for GHG inventories, each with different degrees of uncertainty, which makes the quantification of emissions complex, given the large number of variables to be analyzed. According to the Compendium of Greenhouse Gas Emissions Methodologies for the Oil and Gas Industry of the American Petroleum Institute (API), all GHG emissions should be calculated as a product of an "activity factor" by an appropriate "emission factor". That is, the amount of fuel used, considering how it is used. The product between the activity data and the emission factors provides an estimate of the GHG emissions associated with the company's activities. Based on this premise, this paper presents a model developed in System Dynamics (SD) for the preparation of inventories of CO2 and CH4 emissions, the main GHG emitted by the oil industry. The model was developed to meet the requirements of "Subpart W" of the United States Environmental and Protection Agency (USEPA) CFR Part 98, which states that oil and gas E&P facilities that emit at least 25 x 103 t CO2e/year, must report their estimates of total annual GHG emissions, annual individualized emissions of each GHG, and annual individualized emissions of each GHG broken down by source type expressed in metric tons of CO2e. The proposed model goes beyond the USEPA requirements in that it also allows estimation of emissions of CO2, of CH4 and their equivalence in CO2e from specific sources and groups of sources, generating an estimate of the emissions profile over the entire lifetime of the inventoried facility.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Dynamic Systems Model for Evaluating Atmospheric Greenhouse Gas Emissions in Accordance with USEPA CFR PART 98 Subpart W
AU  - Marcelo Cruz dos Santos
AU  - Claudinei de Souza Guimarães
AU  - Amarildo da Cruz Fernandes
Y1  - 2023/07/17
PY  - 2023
N1  - https://doi.org/10.11648/j.ajep.20231204.12
DO  - 10.11648/j.ajep.20231204.12
T2  - American Journal of Environmental Protection
JF  - American Journal of Environmental Protection
JO  - American Journal of Environmental Protection
SP  - 92
EP  - 108
PB  - Science Publishing Group
SN  - 2328-5699
UR  - https://doi.org/10.11648/j.ajep.20231204.12
AB  - The oil industry has a relevant role in the generation of Greenhouse Gases (GHG) in its various segments, among them the Exploration and Production of Oil and Natural Gas (E&P). There are several methodologies for GHG inventories, each with different degrees of uncertainty, which makes the quantification of emissions complex, given the large number of variables to be analyzed. According to the Compendium of Greenhouse Gas Emissions Methodologies for the Oil and Gas Industry of the American Petroleum Institute (API), all GHG emissions should be calculated as a product of an "activity factor" by an appropriate "emission factor". That is, the amount of fuel used, considering how it is used. The product between the activity data and the emission factors provides an estimate of the GHG emissions associated with the company's activities. Based on this premise, this paper presents a model developed in System Dynamics (SD) for the preparation of inventories of CO2 and CH4 emissions, the main GHG emitted by the oil industry. The model was developed to meet the requirements of "Subpart W" of the United States Environmental and Protection Agency (USEPA) CFR Part 98, which states that oil and gas E&P facilities that emit at least 25 x 103 t CO2e/year, must report their estimates of total annual GHG emissions, annual individualized emissions of each GHG, and annual individualized emissions of each GHG broken down by source type expressed in metric tons of CO2e. The proposed model goes beyond the USEPA requirements in that it also allows estimation of emissions of CO2, of CH4 and their equivalence in CO2e from specific sources and groups of sources, generating an estimate of the emissions profile over the entire lifetime of the inventoried facility.
VL  - 12
IS  - 4
ER  -

Copy | Download