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School of Economics, Hiroshima University, 1-2-1 Kagamiyama, Higashihiroshima 739-8525, Hiroshima, Japan
Dr. Syed Abidur Rahman
College of Business Administration, University of Sharjah, Sharjah 27272, United Arab Emirates
Dr. Naheed Rabbani
Department of Banking and Insurance, University of Dhaka, Dhaka 1000, Bangladesh
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Global Perspectives: AI as a Driver of Structural Shifts in the Economy

Abstract submission deadline
30 April 2027
Manuscript submission deadline
30 June 2027
Viewed by
4047

Topic Information

Dear Colleagues,

This Topic is dedicated to exploring how Artificial Intelligence (AI) is reshaping economic structures worldwide. From productivity growth and labor market transformations to global value chains, financial stability, and inequality, AI is driving profound shifts with far-reaching macroeconomic and policy implications.

In addition to these core themes, this issue also seeks to highlight how AI adoption differs across advanced, emerging, and developing economies, reflecting diverse institutional capacities, regulatory frameworks, and societal challenges. Questions of inclusivity, resilience, and sustainability will be central, particularly in understanding whether AI accelerates convergence or widens global divides. This issue also welcomes perspectives on the ethical and geopolitical implications of AI diffusion, especially in areas such as global governance, competition, and technological sovereignty.

By bringing together interdisciplinary insights, this Topic aims to advance understanding of AI’s role as a key driver of structural change in the 21st-century economy. It invites contributions from economics, policy studies, business, and technology to offer a comprehensive perspective on how societies can navigate and adapt to the opportunities and challenges of an AI-driven future.

Dr. Mostafa Saidur Rahim Khan
Dr. Syed Abidur Rahman
Dr. Naheed Rabbani
Topic Editors

Keywords

  • artificial intelligence (AI)
  • structural change
  • global economy
  • productivity
  • labor markets
  • inequality
  • trade and value chains
  • economic policy
  • financial stability
  • investment dynamics

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
AI
ai 		6.5 7.3 2020 20.4 Days CHF 1800 Submit
Businesses
businesses 		- - 2021 22.3 Days CHF 1000 Submit
Economies
economies 		2.3 5.2 2013 23.3 Days CHF 1800 Submit
Journal of Risk and Financial Management
jrfm 		- 5.5 2008 18.3 Days CHF 1600 Submit
Sustainability
sustainability 		4.1 8.9 2009 16.9 Days CHF 2400 Submit

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Published Papers (2 papers)

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22 pages, 700 KB  
Article
How Can New Quality Productive Forces Reshape the Industrial Landscape?—The Dual Enabling Effects of Factor Endowments and Synergies
by Qingling Wu, Mingtao He, Yiliang Li and Bishan Zou
Economies 2026, 14(3), 83; https://doi.org/10.3390/economies14030083 - 6 Mar 2026
Cited by 1 | Viewed by 1323 | Correction
Abstract
China’s economy has shifted from high-speed development to high-quality development as the marginal benefits of the traditional growth model decline. This study explores how new quality productive forces (NQPFs) drive industrial structure upgrading via factor endowment upgrading and factor synergy, with the goal [...] Read more.
China’s economy has shifted from high-speed development to high-quality development as the marginal benefits of the traditional growth model decline. This study explores how new quality productive forces (NQPFs) drive industrial structure upgrading via factor endowment upgrading and factor synergy, with the goal of addressing development bottlenecks. Using 2013–2024 provincial panel data, NQPFs are measured through the entropy weight-TOPSIS method (factor endowment upgrading) and a coupled coordination model (factor synergy), integrated into a comprehensive index, and analyzed via two-stage least squares (2SLS) regression. The empirical results show that China’s NQPFs are in their initial stage, with factors still in the process of accumulation and adaptation. NQPFs significantly promote industrial structure advancement and rationalization, with factor synergy outperforming factor endowment upgrading. Its promotion of advancement is universal, but its impact on rationalization is regionally heterogeneous, with inadequate transmission in underdeveloped and high-environmental-regulation provinces and ineffective dual mechanisms in manufacturing-led provinces. Notably, the driving effect of NQPFs on manufacturing upgrading is stronger than that on services. This study provides guidance for NQPF cultivation and contributes to the theory and Chinese practice of industrial structure upgrading from a dual-factor perspective. Full article
(This article belongs to the Topic Global Perspectives: AI as a Driver of Structural Shifts in the Economy)
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21 pages, 311 KB  
Article
Can Artificial Intelligence Enhance Corporate Financial Risk-Taking Capacity? A Perspective on Innovation Resilience and the Environment
by Kelin Du, Yubing Wei and Shanyue Jin
Sustainability 2026, 18(4), 1840; https://doi.org/10.3390/su18041840 - 11 Feb 2026
Viewed by 1318
Abstract
In the current global competition, innovation-driven strategies are considered crucial to enhance corporate productivity. Financial risk-taking serves as the baseline for corporate survival. This study scrutinizes the consequences of artificial intelligence on corporate financial risk-taking capacity and elucidates the pathways and mechanisms involved. [...] Read more.
In the current global competition, innovation-driven strategies are considered crucial to enhance corporate productivity. Financial risk-taking serves as the baseline for corporate survival. This study scrutinizes the consequences of artificial intelligence on corporate financial risk-taking capacity and elucidates the pathways and mechanisms involved. Using data on local publicly traded entities for the period spanning 2015–2024, this study employs text mining methods and fixed-effects regression analysis to investigate the influence of artificial intelligence on corporate financial risk-taking capacity. The outcomes suggest that AI advances corporate financial risk-taking capacity; specifically, it improves corporate innovation and strengthens innovation resilience (i.e., stability dimension). Furthermore, environmental uncertainty suppresses the constructive influence of AI on monetary risk-taking, whereas high-level environmental information disclosure exerts a positive impact. This study uncovers the underlying processes through which machine learning boosts business financial risk-taking capacity and provides theoretical and practical insights for balancing innovation and risk during corporate digital transformation. In summary, this study makes three key contributions: First, it develops a novel theoretical chain linking AI adoption to enhanced corporate financial risk-taking through the mediating mechanism of innovation resilience. Second, it reveals that the positive effect of AI is attenuated by environmental uncertainty but amplified by environmental information disclosure, integrating external factors into the framework. These findings offer strategic insights for managers and policymakers in the digital era. Full article
(This article belongs to the Topic Global Perspectives: AI as a Driver of Structural Shifts in the Economy)
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