Carbon Emissions as Threats to Environmental Sustainability Exploring Conventional and Technology-Based Remedies
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Abstract
Carbon emissions are serious threats to the environment. This study examines carbon emissions as threats to the achievable extent of environmental sustainability. It relies on closely related secondary data that are subjected to content analysis and thematic review. It is anchored on Kuznets’ Environmental Kuznets Curve theory. The EKC holds that the effects of environmental degradation, such as those of gas emissions, greatly affect a society’s socio-economic growth, health wellbeing and sustainable development. Leaning on the insights from EKC theory, the study argues that the adverse effects of carbon emissions continually threaten environmental sustainability, thereby limiting the supposed significant extent of its realization. Drawing evidence from extant studies, the paper shows that efficacious and strategic conventional and technology-based techniques can be leveraged for remedies to carbon emissions and the attendant effects. It concludes that carbon emissions are indeed threats to environmental sustainability, while AI and other cutting-edge technologies and strategic conventional measures can significantly mitigate the threats. The study charges stakeholders to combine conventional and technology-based techniques for remedies to carbon emissions and the attendant effects on humans and the nonhumans alike.
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References
Akpulu, E. (2021, September). Singer’s polemical perspectives in environmental ethics. BA project submitted to the Department of Philosophy, Faculty of Arts, Federal University of Lafia, Nigeria, in partial fulfilment of the requirements for the award of Bachelor of Arts Degree in Philosophy.
Alghamdi, H. S. (2022). Towards explainable deep neural networks for the automatic detection of diabetic retinopathy. Appl. Sci., 12, 9435.
Ali, R., Hardie, R. C., Narayanan, B. N., & Kebede, T. M. (2022). IMNets: Deep learning using an incremental modular network synthesis approach for medical imaging applications. Appl. Sci., 12, 5500.
Baduge, S. K., Thilakarathna, S., Perera, J. S., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A., & Mendis, P. (2022). Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automation in Construction, 141, 104440.
Banuri, T., & Opschoor, H. (2007). Climate change and sustainable development. Economic & Social Affairs, DESA Working, no.56. ST/ESA/2007/DWP/56
Bidhendi, A. & Azizi, M. (2021). Application of machine learning in project management. 12th International Congress on Civil Engineering, Ferdowsi University of Mashhad, Mashhad, Iran, 12-14 July.
Bjorndalen, N., Mustafiz, S., Rahman, M. H. & Islam, M. (2005). No- flare design: Coverting waste to value addition. Energy Source, vol.27, iss. 4, 371-380. Doi.org/10.1080/0090830390424160
Bodin, Ö. (2021). Has sustainability science turned left?. Sustainability Science, https://doi.org/10.1007/s11625-021-01017-7
Bulama, L., & Shirivastata, M. (2022). The role of information & communication technology towards protection of lives and property in northern Nigeria: A focus on Maiduguri Borno State in vidyabharti.International Interdisciplinary Research Journal, vol.14, no.1, 1–9.
Chakravarty, D., & Mandal, S. K. (2016). Estimating the relationship between economic growth and environmental quality for the brics economies: A dynamic panel data approach. Journal of Developing Areas, Tennessee State University, College of Business, vol. 50(5), special, I. 119-130.
Dakwale, V. A., & Ralegaonkar, R. V. (2012). Review of carbon emission through buildings: threats, causes and solution. International Journal of Low-Carbon Technologies, 7, 143–148. doi:10.1093/ijlct/ctr032
Ericsson, A. (2022). Methane emissions and economic growth: An N-shaped Environmental Kuznets Curve for the G20 countries? Degree project in Economics, 30 hp, Master of Science in Business and Economics, 240 hp, Umeå University, Sweden.
George, R. M., Nalluri, M. R. & Anand, K. B. (2022). Application of ensemble machine learning for construction safety risk assessment. J. Inst. Eng. India, Ser. A, vol.103, 989-1003. https://doi.org/10.1007/s40030-022-00690-w.
George, R. M., Nalluri, M. R. & Anand, K. B. (2022). Application of ensemble machine learning for construction safety risk assessment. J. Inst. Eng. India, Ser. A, vol.103, 989-1003. https://doi.org/10.1007/s40030-022-00690-w.
Getty Images (2021). Shot of gas flare burns in pit at oil well. At Williston Basin / North... [online] Available at: https://www.gettyimages.com/detail/video/shot-of-gas-flare-burns-in-pit-at-oil-well-at-stockvideo-footage/505958697
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577-586.
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577-586.
Koyuncu, T., Beşer, M. K., & Alola, A. A. (2021). Environmental sustainability statement of economic regimes with energy intensity and urbanization in Turkey: A threshold regression approach. Environ. Sci. Pollut. Res., 28(31), 42533–42546.
Kuznets, S. (1955). Economic growth and income inequality. The American Economic Review, 45, 1-28.
McEwen, J. D.N., & Johnson, M. R. (2012). Black carbon particulate matter emission factors for buoyancy-driven associated gas flares. Journal of the Air & Waste Management Association, 62(3), 307-321. DOI: 10.1080/10473289.2011.650040
Naess, A. (1973). The shallow and the deep, long-range ecology movement, a summary. Inquiry, 16, 95-100.
Næss, A. (1999). Intuition, intrinsic value and deep ecology. In N. Witoszek & A. Brennan (eds.), Philosophical dialogues: Arne Naess and the progress of ecophilosophy (166-170). Rowman & Littlefield Publishers Inc.
Naess, A. (2001). The deep ecological movement: some philosophical aspects. In M. J. Zimmerman, B. Callicott, G. Sessions, K. Warren & J. Clark (eds.), Environmental philosophy. Prentice-Hall.
Oganesyan, G., Nava, L., Ghirlanda, G. & Celotti, A. (2017). Detection of Low-energy Breaks in Gamma-Ray Burst Prompt Emission Spectra. The Astrophysical Journal, vol.846, iss.137, 1-22. DOI 10.3847/1538-4357/aa831e
Ojijiagwo, E. N. (2017). Development of a sustainable framework to manage flare gas in an oil and gas environment: A case study of Nigeria. A thesis submitted in partial fulfilment of the requirement of the University of Wolverhampton for the Degree of Doctor of Philosophy. Faculty of Science and Engineering University of Wolverhampton United Kingdom.
Okoye, L. U. et al. (2022). Effect of gas flaring, oil rent and fossil fuel on economic performance: The case of Nigeria. Resour. Policy 77, 102677. https://doi.org/10.1016/j.resourpol.2022.102677.
Onifade, S. T., & Alola, A. (2022). Energy transition and environmental quality prospects in leading emerging economies: The role of environmental-related technological innovation. Sustain. Dev., 30(2), 1-13. https://doi.org/10.1002/sd.2346.
Ostic, D., Twum, A. K., Agyemang, A. O. & Boahen, H. A. (2022). Assessing the impact of oil and gas trading, foreign direct investment inflows, and economic growth on carbon emission for OPEC member countries. Environ. Sci. Pollut. Res., 29(28), 43089–43101.
Pasupuleti, V. (2022). Advancing healthcare through language models for enhanced conversational AI and knowledge extraction. World Journal of Innovation and Modern Technology, 6(1). E-ISSN 2756-5491 P-ISSN 2682-5910. www.iiardjournals.org
Pasupuleti, V., & Inyang, L. (2022). Mitigating pancreatic cancer through data-driven AI techniques, holistic health record, iHELP and integrative systems. International Journal of Health and Pharmaceutical Research, 7(2), 63-77. E-ISSN 2545-5737 P-ISSN 2695-2165. www.iiardjournals.org
Policy and Legal Advocacy Centre [PLAC] (2004). Environmental Impact Assessment Act.
Regona, M., Yigitcanlar, T., Xia, B. & Li, R. Y. M. (2022). Opportunities and adoption challenges of AI in the construction industry: A PRISMA review. Journal of Open Innovation: Technology, Market, and Complexity, 8(1), Article number 45.
Rodrigues, A. C. C. (2022). Decreasing natural gas flaring in Brazilian oil and gas industry. Resour. Policy, 77, 102776.
Seo, J., Han, S., Lee, S. & Kim, H. (2015). Computer vision techniques for construction safety and health monitoring. Advanced Engineering Informatics, 29(2), 239-251.
Srivastava, A. (2021). The application & impact of artificial intelligence (AI) on E-commerce. Contemporary Issues in Commerce and Management, 1(1), 165-75.
Srivastava, A. (2021). The application & impact of artificial intelligence (AI) on E-commerce. Contemporary Issues in Commerce and Management, 1(1), 165-75.
Talluri, K. K., Fiedler, M.-A., & Al-Hamadi, A. (2022). Deep 3D convolutional neural Network for facial micro-expression analysis from video images. Appl. Sci., 12, 11078.
Thakkar, A., & Lohiya, R. (2021). A survey on intrusion detection system: Feature selection, model, performance measures, application perspective, challenges, and future research directions. Artificial Intell Rev., 55(1), 453–563. https://doi.org/10.1007/S10462-021-10037-9
Thuraka, B. (2021). Machine learning, advanced health informatics, and diagnostic improvement opportunities. Interdisciplinary Journal of African & Asian Studies (IJAAS), 7(2), 1-10. (ISSN: 2504-8694).
Thuraka, B. (2022). Impact of technological innovations on healthcare service delivery in US: Medicare and Medicaid as facilitators. Interdisciplinary Journal of African & Asian Studies (IJAAS), 8(1).
Thuraka, B. (2022). Impact of technological innovations on healthcare service delivery in US: Medicare and Medicaid as facilitators. Interdisciplinary Journal of African & Asian Studies (IJAAS), 8(1), 1-10. (ISSN: 2504-8694).
Tvaronavičienė, M. (2021). Effects of climate change on environmental sustainability. E3S Web of Conferences, 250, 01005. https://doi.org/10.1051/e3sconf/202125001005
Van, H., Kauchak, D., & Leroy, G. (2020). AutoMeTS: The auto complete for medical text simplification. Computation and Language (cs.CL), Cornell University. https://doi.org/10.48550/arXiv.2010.10573
Wang, P. (2019). On defining artificial intelligence. Journal of Artificial General Intelligence, 10(2), 1-37.
Wusu, G. E., Alaka, H., Yusuf, W., Mporas, I., Toriola-Coker, L. & Oseghale, R. (2022). A machine learning approach for predicting critical factors determining adoption of offsite construction in Nigeria. Smart and Sustainable Built Environment(ahead-of-print).
Xu, Y., Zhou, Y., Sekula, P., & Ding, L. (2021). Machine learning in construction: From shallow to deep learning. Developments in the Built Environment, 6, 100045.
Yigitcanlar, T., Desouza K. C., Butler L., & Roozkhosh F. (2020). Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature. Energies, 13(6). Doi:10.3390/en13061473
Zimmerman, M. E. (2004). What can continental philosophy contribute to environmentalism? In B. Foltz & R. Frodeman (eds.), Nature revisited: New essays in environmental philosophy. Indiana University Press.