Carbon Capture in Developing Economies: The Role of AI in Addressing Infrastructure and Cost Challenges

References

[1] A.K. Priya, Balaji Devarajan, Avinash Alagumalai, Hua Song. (2023). Artificial intelligence enabled carbon capture: A review. Science of The Total Environment, Volume 886, 163913, ISSN 0048-9697. https://doi.org/10.1016/j.scitotenv.2023.163913.

[2] Ahmed, Bashir & Sano, Nadeem. (2024). AI-Powered Solutions for Carbon Removal and Compliance in the Oil and Gas Industry.

[3] Ali, Baber & Badi, Sadi. (2024). Artificial Intelligence and Carbon Removal: A Sustainable Approach to Reducing Environmental Impact in the US Energy Sector. 10.13140/RG.2.2.32444.55685.

[4] Ampomah, W., Morgan, A., Koranteng, D. O., & Nyamekye, W. I. (2024). CCUS Perspectives: Assessing Historical Contexts, Current Realities, and Future Prospects. Energies, 17(17), 4248. https://doi.org/10.3390/en17174248

[5] Anu Adeoye. (2024). Energy poverty and funding hurdles hold back Africa’s green transition. Financial Times. Retrieved from https://www.ft.com/content/bb1e4f4f-7709-4441-8f64-472763d679e7?utm

[6] Anyebe, Abraham & Yeboah, Owura & Bakinson, Oladipupo & Tayo, Yusuf & Okafor, Francisca. (2024). Optimizing Carbon Capture Efficiency through AI-Driven Process Automation for Enhancing Predictive Maintenance and CO2 Sequestration in Oil and Gas Facilities. American Journal of Environment and Climate. 3. 44-58. 10.54536/ajec.v3i3.3766.

[7] Ajao (2024). Driving Carbon Capture in Parallel with Industrialization in Developing Countries: Opportunities, Challenges and Strategies. European Journal of Technology. ISSN 2520-0712 (online) Vol.8, Issue 5, pp 21 - 43. https://doi.org/10.47672/ejt.2450 21

[8] Baloch, Mumtaz & Sano, Nadeem. (2024). Sustainability Through AI-Driven Evaluation Systems in Carbon Removal for the Oil and Gas Industry.

[9] Bartosz Dziejarski, Renata Krzyżyńska, Klas Andersson. (2023). Current status of carbon capture, utilization, and storage technologies in the global economy: A survey of technical assessment. Fuel, Volume 342, 127776, ISSN 0016-2361. https://doi.org/10.1016/j.fuel.2023.127776.

[10] Betiku, Adedola & Bassey, Bassey. (2022). Exploring the Barriers to Implementation of Carbon Capture, Utilisation and Storage in Nigeria. 10.2523/IPTC-22387-MS.

[11] Blessing Ameh. (2024). Digital tools and AI: Using technology to monitor carbon emissions and waste at each stage of the supply chain, enabling real-time adjustments for sustainability improvements International Journal of Science and Research Archive, 13(01), 2741–2754. DOI: https://doi.org/10.30574/ijsra.2024.13.1.1995

[12] CECO Enviro. (2024). Barriers to successfully implementing carbon capture, utilization, and storage (CCUS). Retrieved from https://www.cecoenviro.com/barriers-to-successfully-implementing-carbon-capture-utilization-and-storage-ccus/

[13] Chatterjee J, Dethlefs N. (2022). Facilitating a smoother transition to renewable energy with AI. Patterns (N Y). 2022 Jun 10;3(6):100528. doi: 10.1016/j.patter.2022.100528. PMID: 35755866; PMCID: PMC9214339.

[14] Colin van Noordt, Luca Tangi. (2023). The dynamics of AI capability and its influence on public value creation of AI within public administration. Government Information Quarterly, Volume 40, Issue 4, 101860, ISSN 0740-624X. https://doi.org/10.1016/j.giq.2023.101860.

[15] Enobong Hanson, Chukwuebuka Nwakile, Victor Oluwafolajimi Hammed. (2025). Carbon capture, utilization, and storage (CCUS) technologies: Evaluating the effectiveness of advanced CCUS solutions for reducing CO2 emissions. Results in Surfaces and Interfaces, Volume 18, 100381, ISSN 2666-8459. https://doi.org/10.1016/j.rsurfi.2024.100381.

[16] Eslam G. Al-Sakkari, Ahmed Ragab, Hanane Dagdougui, Daria C. Boffito, Mouloud Amazouz. (2024). Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities. Science of The Total Environment, Volume 917, 170085, ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2024.170085.

[17] Fareed, Ghulam & Badi, Sadi. (2024). Leveraging Artificial Intelligence for Carbon Removal: Advancing Sustainability in the US Oil and Gas Industry. 10.13140/RG.2.2.29089.11367.

[18] Giorgio Cabrera, Alex Dickson, Alain-Désiré Nimubona, John Quigley. (2022) Carbon capture, utilisation and storage: Incentives, effects and policy. International Journal of Greenhouse Gas Control, Volume 120, 103756, ISSN 1750-5836. https://doi.org/10.1016/j.ijggc.2022.103756.

[19] Hope McLaughlin, Anna A. Littlefield, Maia Menefee, Austin Kinzer, Tobias Hull, Benjamin K. Sovacool, Morgan D. Bazilian, Jinsoo Kim, Steven Griffiths. (2023). Carbon capture utilization and storage in review: Sociotechnical implications for a carbon reliant world. Renewable and Sustainable Energy Reviews, Volume 177, 113215, ISSN 1364-0321. https://doi.org/10.1016/j.rser.2023.113215.

[20] Hugo Dixon. (2024). Many roadblocks delay the journey to a zero-carbon world. Reuters. Retrieved from https://www.reuters.com/breakingviews/many-roadblocks-delay-journey-zero-carbon-world-2024-10-28/?utm

[21] IEA (2023), Financing Clean Energy in Africa, IEA, Paris https://www.iea.org/reports/financing-clean-energy-in-africa Licence: CC BY 4.0

[22] International Energy Agency (IEA). (2020). CCUS in clean energy transitions: CCUS technology innovation. Retrieved from https://www.iea.org/reports/ccus-in-clean-energy-transitions/ccus-technology-innovation

[23] International Energy Agency. (2021). Nigerian CCUS event summary. Retrieved from https://iea.blob.core.windows.net/assets/a196066a-3225-42ab-b341-d432452058f0/IEAOVP_NigerianCCUS_Event_Summary_Final.pdf

[24] International Energy Agency (IEA). (2022). Nigeria - Emissions. Retrieved from https://www.iea.org/countries/nigeria/emissions

[25] International Energy Agency. (2023). CO2 Emissions in 2023: Executive Summary. Retrieved from https://www.iea.org/reports/co2-emissions-in-2023/executive-summary

[26] International Energy Agency. (2023). Carbon capture, utilisation and storage (CCUS). Retrieved from https://www.iea.org/energy-system/carbon-capture-utilisation-and-storage

[27] International Energy Agency. (2023). CCUS in Clean Energy Transitions: CCUS in the Transition to Net-Zero Emissions. Retrieved from https://www.iea.org/reports/ccus-in-clean-energy-transitions/ccus-in-the-transition-to-net-zero-emissions

[28] International Energy Agency. (2023). CCUS in clean energy transitions: A new era for CCUS. Retrieved from https://www.iea.org/reports/ccus-in-clean-energy-transitions/a-new-era-for-ccus

[29] International Energy Agency. (2023). UNFCCC and IEA launch new phase of cooperation on tackling climate change. Retrieved from https://www.iea.org/news/unfccc-and-iea-launch-new-phase-of-cooperation-on-tackling-climate-change

[30] International Renewable Energy Agency (IRENA). (2023). World Energy Transitions Outlook 2023. Retrieved from https://www.irena.org/Digital-Report/World-Energy-Transitions-Outlook-2023

[31] International Energy Agency. (2024). CO2 Transport and Storage. Retrieved from https://www.iea.org/energy-system/carbon-capture-utilisation-and-storage/co2-transport-and-storage

[32] International Energy Agency. (2024). Carbon Capture, Utilisation and Storage. Retrieved from https://www.iea.org/energy-system/carbon-capture-utilisation-and-storage

[33] Jain, Varun & Balakrishnan, Anandaganesh & Beeram, Divya & Najana, Madhavi & Chintale, Pradeep. (2024). Leveraging Artificial Intelligence for Enhancing Regulatory Compliance in the Financial Sector. International Journal of Computer Trends and Technology. 72. 124-140. 10.14445/22312803/IJCTT-V72I5P116.

[34] Kasper Storrs, Ivar Lyhne, Rikke Drustrup. (2023). A comprehensive framework for feasibility of CCUS deployment: A meta-review of literature on factors impacting CCUS deployment. International Journal of Greenhouse Gas Control, Volume 125, 103878, ISSN 1750-5836. https://doi.org/10.1016/j.ijggc.2023.103878.

[35] Li, G., Luo, T., Liu, R., Song, C., Zhao, C., Wu, S., & Liu, Z. (2024). Integration of Carbon Dioxide Removal (CDR) Technology and Artificial Intelligence (AI) in Energy System Optimization. Processes, 12(2), 402. https://doi.org/10.3390/pr12020402

[36] M. Yahaya-Shiru, O. Igwe, S. Obafemi. (2022). 3D structural and stratigraphic characterization of X field Niger Delta: implications for CO2 sequestration. J. Pet. Explor. Prod. Technol., 12 (4) (2022), pp. 959-977

[37] Maghdid RS, Kareem SM, Salih Hama Y, Waris M, Naveed RT. (2024). Moderating role of political stability and economic policy uncertainty between country governance practice and stock market performance. A comparative analysis of Pakistan and Kurdistan Region of Iraq. PLoS One. 2024 Apr 16;19(4):e0301698. doi: 10.1371/journal.pone.0301698. PMID: 38626026; PMCID: PMC11020357.

[38] McKinsey. (2023). The world needs to capture, use, and store gigatons of CO₂: Where and how. McKinsey & Company. Retrieved from https://www.mckinsey.com/industries/oil-and-gas/our-insights/the-world-needs-to-capture-use-and-store-gigatons-of-co2-where-and-how

[39] Md. Altab Hossin, Hermas Abudu, Johnson Katsekpor, Mu Lei, Elvis Banoemuleng Botah. (2024). Tracking sustainable energy indicators in Africa: New evidence from technique for order of preference by similarity to ideal solution. Renewable Energy, Volume 239, 122167, ISSN 0960-1481. https://doi.org/10.1016/j.renene.2024.122167.

[40] Mehdi Nassabeh, Zhenjiang You, Alireza Keshavarz, Stefan Iglauer. (2024). Sub-surface geospatial intelligence in carbon capture, utilization and storage: A machine learning approach for offshore storage site selection. Energy, Volume 305, 132086, ISSN 0360-5442. https://doi.org/10.1016/j.energy.2024.132086.

[41] Nabeel, Muhammad. (2024). AI-Enhanced Project Management Systems for Optimizing Resource Allocation and Risk Mitigation: Leveraging Big Data Analysis to Predict Project Outcomes and Improve Decision-Making Processes in Complex Projects. Asian Journal of Multidisciplinary Research & Review. 5. 53-91. 10.55662/AJMRR.2024.5502.

[42] Nejat Rahmanian, Gregory Mwenketishi, Hadj Benkreira. (2024). Criteria for Selecting Carbon Subsurface and Ocean Storage Site in Developing Countries: A Review. DOI: 10.4236/ajcc.2024.132007

[43] Nasija Suhail, Zied Bahroun, Vian Ahmed (2024). Augmented reality in engineering education: enhancing learning and application. Sec. Augmented Reality. Volume 5 - 2024 | https://doi.org/10.3389/frvir.2024.1461145

[44] Nassef, A. M. (2023). Improving CO2 Absorption Using Artificial Intelligence and Modern Optimization for a Sustainable Environment. Sustainability, 15(12), 9512. https://doi.org/10.3390/su15129512

[45] Nathalie Spath, Tian Xia. (2022). Between economic growth and climate change. Retrieved from https://www.deginvest.de/DEG-Dokumente/%C3%9Cber-uns/Was-wir-bewirken/Between-economic-growth-and-climate-change_paper.pdf

[46] Nembe, Joseph & Atadoga, Joy & Mhlongo, Noluthando & Falaiye, Titilola & Odeyemi, Olubusola & Daraojimba, Andrew & Oguejiofor, Bisola. (2024). THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENHANCING TAX COMPLIANCE AND FINANCIAL REGULATION. Finance & Accounting Research Journal. 6. 241-251. 10.51594/farj.v6i2.822.

[47] Ogbo, Ojiako & Onuoha, David & Odoh, Chijioke. (2024). Carbon Capture and Storage (CCS) in Nigeria: A Review of Challenges and Opportunities. British Journal of Multidisciplinary and Advanced Studies. 5. 1-18. 10.37745/bjmas.2022.04148.

[48] Onwusinkwue, Shedrack & Osasona, Femi & Ahmad, Islam & Anyanwu, Anthony & Dawodu, Samuel & Obi, Ogugua & Hamdan, Ahmad. (2024). Artificial intelligence (AI) in renewable energy: A review of predictive maintenance and energy optimization. World Journal of Advanced Research and Reviews. 21. 2487-2799. 10.30574/wjarr.2024.21.1.0347.

[49] Organisation for Economic Co-operation and Development. (2023). Emissions from oil and gas operations in net-zero transitions. Retrieved from https://www.oecd.org/en/publications/emissions-from-oil-and-gas-operations-in-net-zero-transitions_317cbf59-en.html

[50] Osmond I. Nwali, Micheal A. Oladunjoye, Olatunbosun A. Alao. (2024). A review of atmospheric carbon dioxide sequestration pathways; processes and current status in Nigeria. Carbon Capture Science & Technology, Volume 12, 100208, ISSN 2772-6568. https://doi.org/10.1016/j.ccst.2024.100208.

[51] Otieno, Daniel. (2020). Cyber security challenges: The Case of Developing Countries.

[52] Pacer Guobadia and Rasheed Belo-Osagie (2023, August 22). Carbon capture and storage in Nigeria. Mondaq. Retrieved January 3, 2025, from https://www.mondaq.com/nigeria/renewables/1317622/carbon-capture-and-storage-in-nigeria

[53] Pengcheng Jiao, Amir H. Alavi. (2020). Artificial intelligence in seismology: Advent, performance and future trends. Geoscience Frontiers, Volume 11, Issue 3, Pages 739-744, ISSN 1674-9871. https://doi.org/10.1016/j.gsf.2019.10.004.

[54] Plevris, V. (2024). AI-Driven Innovations in Earthquake Risk Mitigation: A Future-Focused Perspective. Geosciences, 14(9), 244. https://doi.org/10.3390/geosciences14090244

[55] Rebecca O'Rourke. (2024). Bridging the clean energy talent gap: Challenges and solutions for a sustainable future. Retrieved from https://www.nesfircroft.com/resources/blog/bridging-the-clean-energy-talent-gap--challenges-and-solutions-for-a-sustainable-future/

[56] Samuel Yeboah. (2023). “Balancing Growth and Green: Strategies for Sustainable Development in Developing Economies". Sunyani Technical University, Sunyani Ghana. https://mpra.ub.uni-muenchen.de/118180/1/Balancing%20Growth%20and%20Green%20FOR%20MPRA%20FINAL.pdf

[57] Sharm El Sheikh. (2022). Background note on draft technical paper on assessing the impacts of potential new businesses and industries resulting from the implementation of response measures (KCI7_3_b_Background note TP new industries_web.pdf). Retrieved from https://unfccc.int/sites/default/files/resource/KCI7_3_b_Background%20note%20TP%20new%20industries_web.pdf

[58] Sivasubramanian Manikandan, Rangarajan Sindhu Kaviya, Dhamodharan Hemnath Shreeharan, Ramasamy Subbaiya, Sundaram Vickram, Natchimuthu Karmegam, Woong Kim, Muthusamy Govarthanan (2024). Artificial intelligence-driven sustainability: Enhancing carbon capture for sustainable development goals– A review. https://doi.org/10.1002/sd.3222

[59] Statista. (2023). Global carbon capture cost by technology. Retrieved from https://www.statista.com/statistics/1304575/global-carbon-capture-cost-by-technology/

[60] Statista. (2024). Natural gas flaring emissions in Nigeria. Retrieved from https://www.statista.com/statistics/1478377/natural-gas-flaring-emissions-in-nigeria/

[61] Ting Xiao, Ting Chen, Zhiwei Ma, Hailong Tian, Saro Meguerdijian, Bailian Chen, Rajesh Pawar, Lianjie Huang, Tianfu Xu, Martha Cather, Brian McPherson. (2024). A review of risk and uncertainty assessment for geologic carbon storage. Renewable and Sustainable Energy Reviews, Volume 189, Part B, 113945, ISSN 1364-0321. https://doi.org/10.1016/j.rser.2023.113945.

[62] Ukoba, K., Olatunji, K. O., Adeoye, E., Jen, T.-C., & Madyira, D. M. (2024). Optimizing renewable energy systems through artificial intelligence: Review and future prospects. Energy & Environment, 35(7), 3833-3879. https://doi.org/10.1177/0958305X241256293

[63] United Nations Department of Economic and Social Affairs. (2024). Energy transition in extractives. Retrieved from https://financing.desa.un.org/sites/default/files/2024-10/CRP%2043%20Energy%20transition%20in%20extractives%201-10-2024.pdf

[64] Val Hyginus Udoka Eze, John Saah Tamba, Martin C Eze, Wisdom O Okafor, Foday Hassan Bawor, (2024). Integration of carbon capture utilization and storage into sustainable energy policies in Africa: the case of Liberia, Oxford Open Energy, Volume 3, 2024, oiae011, https://doi.org/10.1093/ooenergy/oiae011

[65] World Bank. (2022). P1775140051e090a80bfd20b5f4ae1dabc0. Retrieved from https://documents1.worldbank.org/curated/en/099300001062330554/pdf/P1775140051e090a80bfd20b5f4ae1dabc0.pdf

[66] World Economic Forum. (2024). Why COP29 must secure a trillion-dollar climate finance goal. Retrieved from https://www.weforum.org/stories/2024/11/why-cop29-must-secure-trillion-dollar-climate-finance-goal/

@article{1706951,
      author = {Angela Ndalaku Ibemenem},
      title = {Carbon Capture in Developing Economies: The Role of AI in Addressing Infrastructure and Cost Challenges},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {7},
      pages = {396-409},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706951.pdf},
      abstract = {Adopting carbon capture, utilization, and storage (CCUS) technologies in developing economies faces challenges limiting their contribution to global climate goals. These nations often face significant obstacles such as inadequate infrastructure, high costs, limited technical expertise, and weak policy frameworks. Compounding these issues are competing priorities, where immediate economic development, poverty alleviation, and energy access are prioritized over climate-focused initiatives. This article examines these barriers and explores how artificial intelligence (AI) can serve as a transformative tool to address them effectively.  AI technologies offer innovative solutions to optimize CCUS adoption in resource-constrained environments. From infrastructure mapping and cost reduction to skills development and scalable deployment, AI-driven models provide actionable pathways for overcoming critical barriers.  AI facilitates efficient decision-making by leveraging data analytics, predictive modeling, and process optimization, thereby enabling CCUS technologies to become more accessible and sustainable. To contextualize these opportunities, Nigeria is presented as a case study. The country?s significant emissions profile, policy environment, and industrial activities are analyzed in light of the challenges and opportunities associated with CCUS implementation. These examples show how AI can enable developing economies to transition toward sustainable development while aligning with global climate targets. This article emphasizes the critical need for strategic collaborations, policy reforms, and capacity-building efforts to maximize the potential of AI-enhanced CCUS in developing economies. By addressing infrastructure gaps, reducing costs, and supporting skills development, AI technologies offer a promising pathway for these nations to position themselves as leaders in innovative climate solutions.},
      keywords = {Carbon Capture, Utilization, and Storage (CCUS), Artificial Intelligence (AI), Developing Economies, Sustainable Development, Nigeria, Emissions Reduction, Infrastructure Optimization, Cost Efficiency.},
      month = {January},
  }