Sustainable Economic Growth Through Artificial Intelligence -Driven Tax Frameworks Nexus on Enhancing Business Efficiency and Prosperity; An Appraisal
Article Sidebar
Main Article Content
Abstract: The article examines the nexus between Artificial Intelligence (AI)-driven tax frameworks and sustainable economic growth, with a focus on enhancing business efficiency and prosperity. The research made use of explorative method. As governments and businesses face challenges like climate change, resource depletion, and income inequality, AI offers transformative potential in optimizing tax frameworks. By leveraging AI technologies such as machine learning, data analytics, and natural language processing, tax systems can become more efficient, equitable, and transparent. The paper proposed optimized tax collection strategies driven by artificial intelligence. The paper recommended the need to address technical, regulatory, and operational challenges by focusing on strategies targeting each of them can tax authorities and businesses employ the full potential of AI-driven tax frameworks.
Downloads
Downloads
References
Acemoglu, D., & Robinson, J. A. (2012). Why nations fail: The origins of power, prosperity, and poverty. Crown Business. DOI: https://doi.org/10.1355/ae29-2j
Acemoglu, D., & Restrepo, P. (2020). "Artificial Intelligence, Automation, and Work." Journal of Economic Perspectives, 34(4), 3-30.
Alonso, A., & Li, J. (2021). "AI in Tax Administration: Challenges and Opportunities." Journal of Tax Administration, 7(2), 45-61.
Anwansedo, F., Gbadebo, A. D., & Akinwande, O. T. (2024). Exploring the Role of AI-Enhanced Online Marketplaces in Facilitating Economic Growth: An Impact Analysis on Trade Relations between the United States and Sub-Saharan Africa. Revista De Gestão Social E Ambiental, 18(6), e07494. https://doi.org/10.24857/rgsa.v18n6-139 DOI: https://doi.org/10.24857/rgsa.v18n6-139
Bloom, N., Schankerman, M., & Van Reenen, J. (2019). "Identifying technology spillovers and product market rivalry." Econometrica, 87(5), 1341-1371.
Brynjolfsson, E., & McAfee, A. (2017). "The Business of Artificial Intelligence: What it Can and Cannot – Do for Your Organization." Harvard Business Review.
Chen, H., Chiang, R. H. L., & Storey, V. C. (2019). "Business Intelligence and Analytics: From Big Data to Big Impact." MIS Quarterly, 36(4), 1165-1188 DOI: https://doi.org/10.2307/41703503
Cockfield, A. J. (2020). "Big Data and Tax Haven Secrecy." Canadian Business Law Journal, 61(2), 206-226.
Eichhorst, W., & Marx, P. (2021). "AI and the Future of Work: How Artificial Intelligence is Shaping Labor Markets." IZA Journal of Labor Policy, 10(1), 12-25
Eichner, T., & Pethig, R. (2019). "Renewable energy subsidies: Second-best policy or fatal aberration for efficient mitigation?" Journal of Public Economics, 170, 67-79.
Elliott, L. (2019). "Green Growth: Ideology, Political Economy and the Alternatives." International Journal of Political Economy, 48(1), 1-14.
Hoffman, L. F., et al. (2020). "AI and Taxation: The Potential for Transformative Change." International Journal of Economic Policy in Emerging Economies, 13(3), 267-284.
Kokina, J., & Davenport, T. H. (2017). "The Emergence of Artificial Intelligence: How Automation is Changing Tax Preparation." Journal of Emerging Technologies in Accounting, 14(1), 115-122. DOI: https://doi.org/10.2308/jeta-51730
Oladele, I., Orelaja, A., & Akinwande, O., (2024). Ethical Implications and Governance of Artificial Intelligence in Business Decisions: A Deep Dive into the Ethical Challenges and Governance Issues Surrounding the Use of Artificial Intelligence in Making Critical Business Decisions. International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), Volume XIII, Issue II, DOI: https://doi.org/10.51583/IJLTEMAS.2024.130207 DOI: https://doi.org/10.51583/IJLTEMAS.2024.130207
Piketty, T. (2014). Capital in the twenty-first century. Harvard University Press. DOI: https://doi.org/10.4159/9780674369542
Sachs, J. D. (2015). "The Age of Sustainable Development." Columbia University Press. DOI: https://doi.org/10.7312/sach17314
Slemrod, J., & Gillitzer, C. (2013). Tax systems. MIT Press. DOI: https://doi.org/10.7551/mitpress/9780262026727.001.0001
Stiglitz, J. E. (2018). "The Welfare State in the Twenty-First Century." Journal of Public Economics, 162, 4-17. DOI: https://doi.org/10.7312/ocam18544-004
Tadesse, B. W., & Shiferaw, M. (2021). "Machine Learning for Tax Evasion Detection: An Overview." International Journal of Public Administration in the Digital Age, 8(1), 1-12.
Veale, M., & Brass, I. (2019). "Administration by Algorithm? Public Management Meets Public Sector Machine Learning." International Journal of Public Administration, 42(13), 1151 1167. DOI: https://doi.org/10.31235/osf.io/mwhnb
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.