The dawn of the Artificial Intelligence (AI) era presents a plethora of new possibilities for analyzing regional economic development. The present article provides an in-depth exploration of the methods employed in this field, highlighting the immense opportunities that AI offers while also addressing potential challenges. The role of AI is crucial in complex data handling, enabling efficient analyses of intricate regional economic patterns. This capacity is paramount in shaping economic policies and strategies that are reflective of each region's unique needs and potential. The article firstly explores various AI methods used in economic analysis, including but not limited to machine learning, deep learning, and natural language processing. It delves into the application of these methods in discerning development trends, predicting economic shifts, and identifying strategic economic drivers unique to various regions. Subsequently, the potential of AI to transform regional economic analysis is discussed, encompassing its capability to process large and complex datasets, its power to predict future trends based on past and present data, and its ability to aid in strategic decision-making. However, this new era of AI-driven economic analysis is not without challenges. The latter part of this article thus confronts the issues related to data privacy, ethical use of AI, and the necessity of interdisciplinary skills in AI and economics. This exploration contributes to a broader understanding of how AI is transforming the landscape of regional economic development analysis, illuminating both its present use and future implications. By understanding these dynamics, we can better harness the potential of AI to advance economic prosperity in various regions around the globe.
Damaševičius, R. Regional Economic Development in the AI Era: Methods, Opportunities, and Challenges. Journal of Regional Economics, 2023, 2, 11. https://doi.org/10.58567/jre02020001
Damaševičius R. Regional Economic Development in the AI Era: Methods, Opportunities, and Challenges. Journal of Regional Economics; 2023, 2(2):11. https://doi.org/10.58567/jre02020001
Damaševičius, Robertas 2023. "Regional Economic Development in the AI Era: Methods, Opportunities, and Challenges" Journal of Regional Economics 2, no.2: 11. https://doi.org/10.58567/jre02020001
Damaševičius, R. (2023). Regional Economic Development in the AI Era: Methods, Opportunities, and Challenges. Journal of Regional Economics, 2(2), 11. https://doi.org/10.58567/jre02020001
Article Access Statistics
S. J. Bickley, H. F. Chan, and B. (2022). Torgler. Artificial intelligence in the field of economics. Scientometrics 127(4), 2055–2084. https://doi.org/10.1007/s11192-022-04294-w
X. Ding, P. Shi, and X. Li. (2021). Regional smart logistics economic development based on artificial intelligence and embedded system. Microprocessors and Microsystems 81. https://doi.org/10.1016/j.micpro.2020.103725
J. Zhang, L. Shu, and P. Liao. (2021). An empirical analysis of Beijing-tianjin-hebei regional economic development level based on unsupervised machine learning. In Proceedings-2021 International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2021, pages 159–164. https://doi.org/10.1109/ISPDS54097.2021.00038
Y. Pu, M. Liu, and C. Yan. (2021). Economic evaluation of the sichuan-chongqing region based on machine learning. In Proceedings-2021 International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2021, pages 207–213. https://doi.org/10.1109/ISPDS54097.2021.00047
Z. Jiang. (2022). Prediction and management of regional economic scale based on machine learning model. Wireless Communications and Mobile Computing, 2022. https://doi.org/10.1155/2022/2083099
S. Bl¨othner and M. Larch. (2022). Economic determinants of regional trade agreements revisited using machine learning. Empirical Economics 63(4):1771–1807. https://doi.org/10.1007/s00181-022-02203-x
E. Okewu, S. Misra, J. Okewu, R. Damaˇseviˇcius, and R. Maskeliu¯nas. (2019). An intelligent advisory system to support managerial decisions for a social safety net. Administrative Sciences 9(3). https://doi.org/10.3390/admsci9030055
[A. Bertoletti, J. Berbegal-Mirabent, and T. Agasisti. (2022). Higher education systems and regional economic development in europe: A combined approach using econometric and machine learning methods. Socioeconomic planning sciences 82. https://doi.org/10.1016/j.seps.2022.101231
C. Cheng and H. Huang. (2022). Evaluation and analysis of regional economic growth factors in digital economy based on the deep neural network. Mathematical Problems in Engineering 2022. https://doi.org/10.1155/2022/1121886
Q. Li, C. Yu, and G. Yan. (2022). A new multipredictor ensemble decision frame- work based on deep reinforcement learning for regional gdp prediction. IEEE Access 10, 45266–45279. https://doi.org/10.1109/ACCESS.2022.3170905
F. Xu, Y. Li, and S. Xu. (2020). Attentional multi-graph convolutional network for regional economy prediction with open migration data. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 2225–2233. https://doi.org/10.1145/3394486.3403273
C. Xiong, T. Song, and C. Zhou. (2022). A study on the relationship between artificial intelligence and 5g network construction and the level of economic development of regional cities. Wireless Communications and Mobile Computing 2022. https://doi.org/10.1155/2022/8020388
D. Zhu. (2020). The application of artificial intelligence-based iot technology in regional economic statistics. In Journal of Physics: Conference Series, volume 1648. https://doi.org/10.1088/1742-6596/1648/2/022042
H. Lan, T. Zhuang, Z. Meng, and X. Zu. (2019). Chinese regional economic cooperative development model based on network analysis and multimedia data visualization. Multimedia Tools and Applications 78(4), 4743–4765. https://doi.org/10.1007/s11042-018-6870-z
J. Lu, Z. Zhang, and N. Sai. (2021). Using machine learning method to qualify and evaluate the regional economy. In Proceedings-2021 International Conference on Computer, Blockchain and Financial Development, CBFD 2021, pages 277–280.
P. Luming, T. Kaiyang, and S. Yuanchen. (2021). Research on the quality evaluation of regional economic development in jiangsu, zhejiang and shanghai based on machine learning. In Proceedings-2021 International Conference on Computer, Blockchain and Financial Development, CBFD 2021, pages 78–83. https://doi.org/10.1109/CBFD52659.2021.00023
Y. Bai, Z. Song, and W. Cui. (2022). Studying the coupling and coordination of regional economic and university development levels based on a deep learning model. Mathematical Problems in Engineering 2022. https://doi.org/10.1155/2022/1480173
E. Du and M. Ji. (2021). Analyzing the regional economic changes in a hightech industrial development zone using machine learning algorithms. PLoS ONE 16(6 June). https://doi.org/10.1371/journal.pone.0250802
L. Zhu, Z. Yu, and H. Zhan. (2021). Impact of industrial agglomeration on regional economy in a simulated intelligent environment based on machine learning. IEEE Access 9, 20695–20702. https://doi.org/10.1109/ACCESS.2020.3047830
Q. Li, G. Yan, and C. Yu. (2022). A novel multifactor three-step feature selection and deep learning framework for regional gdp prediction: Evidence from china. Sustainability 14. https://doi.org/10.3390/su14084408
X. Liu. (2022). A new machine learning algorithm for regional low-carbon economic development analysis based on data mining. Journal of Function Spaces. https://doi.org/10.1155/2022/5692666
B. Ma. (2023). The impact of environmental pollution on residents’ income caused by the imbalance of regional economic development based on artificial intelligence. Sustainability 15(1). https://doi.org/10.3390/su15010637
L. Xing. (2023). Evaluation of the impact of artificial intelligence and intelligent internet of things on population mobility on regional economic differences. Soft Computing. https://doi.org/10.1007/s00500-023-08351-1
X. Wang, R. Shi, and Q. Shi. (2022). Spatiotemporal evolution of regional green economy under administrative division adjustment on applications of artificial intelligence: A case study of the guangdong-hong kong-macao greater bay area. Wireless Communications and Mobile Computing, 2022. https://doi.org/10.1155/2022/9217915
E. Okewu, S. Misra, R. Maskeliunas, R. Dama¸sevi¸cius, and L. Fernandez-Sanz. (2017). Optimizing green computing awareness for environmental sustainability and economic security as a stochastic optimization problem. Sustainability 9(10). https://doi.org/10.3390/SU9101857
L. Dong. (2021). Support vector regression method for regional economic mid- and long-term predictions based on wireless network communication. Wireless Communications and Mobile Computing, 2021. https://doi.org/10.1155/2021/1837681
Y. You. (2022). Data mining of regional economic analysis based on mobile sensor network technology. Journal of Sensors, 2022. https://doi.org/10.1155/2022/3415055
T. M. Adeyemi-Kayode, S. Misra, R. Maskeliunas, and R. Damasevicius. (2023). A bibliometric review of grid parity, energy transition and electricity cost research for sustainable development. Heliyon 9(5). https://doi.org/10.1016/j.heliyon.2023.e15532
X. Xu and Z. Zeng. (2021). Analysis of regional economic evaluation based on machine learning. Journal of Intelligent and Fuzzy Systems 40(4), 7543–7553. https://doi.org/10.3233/JIFS-189575
B. Peng. (2022). Regional economy using hybrid sequence-to-sequence-based deep learning approach. Complexity, 2022. https://doi.org/10.1155/2022/9235012
S. F. Kvamsdal, I. Belik, A. O. Hopland, and Y. Li. (2021). A machine learning analysis of the recent environmental and resource economics literature. Environmental and Resource Economics 79(1), 93–115. https://doi.org/10.1007/s10640-021-00554-0
S. Y. W. Chai, F. J. F. Phang, L. S. Yeo, L. H. Ngu, and B. S. How. (2022). Future era of techno-economic analysis: Insights from review. Frontiers in Sustainability 3. https://doi.org/10.3389/frsus.2022.924047
D. C. Parkes and M. P. Wellman. (2015). Economic reasoning and artificial intelligence. Science 349(6245), 267–272. https://doi.org/10.1126/science.aaa8403
E. Okewu, S. Misra, L. F. Sanz, R. Maskeliu¯nas, and R. Damaˇseviˇcius. (2018). An e-environment system for socio-economic sustainability and national security. Problemy Ekorozwoju 13(1). 121–132.
B. Mittelstadt, P. Allo, M. Taddeo, S. Wachter, and L. Floridi. (2016). The ethics of algorithms: Mapping the debate. Big Data and Society 3(2), 1–21. https://doi.org/10.1177/2053951716679679
Tiago Cravo Oliveira Hashiguchi, Jillian Oderkirk, and Luke Slawomirski. (2022). Fulfilling the promise of artificial intelligence in the health sector: Let’s get real. Value in Health 25(3), 368–373. https://doi.org/10.1016/j.jval.2021.11.1369
A. Rao, J. A. Vazquez, and M. DeSantis. (2022). Artificial intelligence in the developing world: Theorizing its impact on economic development. The Information Society 38(1), 52–64.
E. Okewu, P. Adewole, S. Misra, R. Maskeliunas, and R. Damasevicius. (2021). Artificial neural networks for educational data mining in higher education: A systematic literature review. Applied Artificial Intelligence 35(13), 983–1021. https://doi.org/10.1080/08839514.2021.1922847
M. Tzanou. (2020). Addressing big data and ai challenges. In The Datafication of Health, pages 1–19. Routledge.
R. P. Hall, R. Ashford, N. A. Ashford, and J. Arango-Quiroga. (2019). Universal basic income and inclusive capitalism: Consequences for sustainability. Sustainability 11(16). https://doi.org/10.3390/su11164481
Anna K. Przegalinska and Robert E. Wright. (2021). Ai: Ubi income portfolio adjustment to technological transformation. Front. Hum. Dyn. https://doi.org/10.3389/fhumd.2021.725516
T. Thaipisutikul, Y.-C. Chen, L. Hui, S.-C. Chen, P. Mongkolwat, and T.-K. Shih. (2020). The matter of deep reinforcement learning towards practical ai applications. IEEE. https://doi.org/10.1109/Ubi-Media.2019.00014
Mathias Durand. (2015). Policy challenges. In Palgrave Macmillan.
Klaus Schwab. (2017). The Fourth Industrial Revolution. Currency.
Y. Zhang, Y. Li, Y. Liu, Y. Liu, and H. Chen. (2022). A study on the impact of the belt and road initiative on the industrial structure upgrade in the pearl river delta urban agglomeration. Cities 118, 103292.