How does artificial intelligence promote industrial upgrading? Evidence from China's practice of building the modern industrial system

Qinghe Song; Luge Ma; Qinghe Song; Luge Ma

doi:10.3934/QFE.2026022

Quantitative Finance and Economics

2026, Volume 10, Issue 3: 571-599. doi: 10.3934/QFE.2026022

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Research article

How does artificial intelligence promote industrial upgrading? Evidence from China's practice of building the modern industrial system

Qinghe Song ¹,
Luge Ma ^{2
,
,}

1.
School of Future Technology, Harbin Institute of Technology, Harbin 150001, China
2.
Laboratory of Intelligent Decision-making for National Education Development Strategy, Beijing Normal University, Beijing 100875, China

Received: 03 June 2026 Revised: 08 June 2026 Accepted: 12 June 2026 Published: 18 August 2026
JEL Codes: O33, O14, R11, C33

Whether productivity gains from artificial intelligence (AI) translate into broader industrial upgrading remains unclear. Using panel data for 285 Chinese cities from 2011 to 2023, we examine this question in the context of China's modern industrial system, which emphasizes the coordinated development of the real economy, technological innovation, modern finance, and human resources. We construct a city-level index of modern industrial system development and measure local AI development using AI patent applications. An additional 1,000 AI patent applications is estimated to raise the MIS index by 0.367 points, equivalent to approximately 3.7% of its sample mean. The estimate remains stable across a range of robustness and endogeneity checks. Channel evidence suggests that AI facilitates technical-efficiency catch-up, improves the alignment of sectoral output and employment, and expands producer-service capacity. The estimated effect is stronger in less-developed regions and lower-tier cities and varies nonlinearly with the local industrial base, digital infrastructure, and governance intensity. Overall, the findings suggest that the broader industrial effects of AI depend on its diffusion across sectors and the complementary local conditions that support its productive use.
- artificial intelligence,
- industrial upgrading,
- modern industrial system,
- threshold effects
Citation: Qinghe Song, Luge Ma. How does artificial intelligence promote industrial upgrading? Evidence from China's practice of building the modern industrial system[J]. Quantitative Finance and Economics, 2026, 10(3): 571-599. doi: 10.3934/QFE.2026022

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Abstract

Whether productivity gains from artificial intelligence (AI) translate into broader industrial upgrading remains unclear. Using panel data for 285 Chinese cities from 2011 to 2023, we examine this question in the context of China's modern industrial system, which emphasizes the coordinated development of the real economy, technological innovation, modern finance, and human resources. We construct a city-level index of modern industrial system development and measure local AI development using AI patent applications. An additional 1,000 AI patent applications is estimated to raise the MIS index by 0.367 points, equivalent to approximately 3.7% of its sample mean. The estimate remains stable across a range of robustness and endogeneity checks. Channel evidence suggests that AI facilitates technical-efficiency catch-up, improves the alignment of sectoral output and employment, and expands producer-service capacity. The estimated effect is stronger in less-developed regions and lower-tier cities and varies nonlinearly with the local industrial base, digital infrastructure, and governance intensity. Overall, the findings suggest that the broader industrial effects of AI depend on its diffusion across sectors and the complementary local conditions that support its productive use.

References

[1]	Acemoglu D (2025) The simple macroeconomics of AI. Econ Policy 40: 13–58. https://doi.org/10.1093/epolic/eiae042 doi: 10.1093/epolic/eiae042
[2]	Acemoglu D, Restrepo P (2019) Automation and new tasks: How technology displaces and reinstates labor. J Econ Perspect 33: 3–30. https://doi.org/10.1257/jep.33.2.3 doi: 10.1257/jep.33.2.3
[3]	Acemoglu D, Restrepo P (2020) Robots and jobs: Evidence from US labor markets. J Polit Econ 128: 2188–2244. https://doi.org/10.1086/705716 doi: 10.1086/705716
[4]	Aghion P, Howitt P (1992) A model of growth through creative destruction. Econometrica 60: 323–351. https://doi.org/10.2307/2951599 doi: 10.2307/2951599
[5]	Agrawal A, Gans JS, Goldfarb A (2019) Artificial intelligence: The ambiguous labor market impact of automating prediction. J Econ Perspect 33: 31–50. https://doi.org/10.1257/jep.33.2.31 doi: 10.1257/jep.33.2.31
[6]	Agrawal AK, Gans JS, Goldfarb A (2023) Similarities and differences in the adoption of general purpose technologies. NBER Working Paper No. 30976. https://doi.org/10.3386/w30976
[7]	Antohi VM, Barbuta-Misu N, Georgescu PL, et al. (2026) Digital transformation, innovation, and green transitions: A panel data analysis of structural drivers in the European economy. Green Financ 8: 468–500. https://doi.org/10.3934/GF.2026017 doi: 10.3934/GF.2026017
[8]	Arnold JM, Javorcik BS, Lipscomb M, et al. (2016) Services reform and manufacturing performance: Evidence from India. Econ J 126: 1–39. https://doi.org/10.1111/ecoj.12206 doi: 10.1111/ecoj.12206
[9]	Babina T, Fedyk A, He A, et al. (2024) Artificial intelligence, firm growth, and product innovation. J Financ Econ 151: 103745. https://doi.org/10.1016/j.jfineco.2023.103745 doi: 10.1016/j.jfineco.2023.103745
[10]	Baqaee DR, Farhi E (2020) Productivity and misallocation in general equilibrium. Q J Econ 135: 105–163. https://doi.org/10.1093/qje/qjz030 doi: 10.1093/qje/qjz030
[11]	Bonfiglioli A, Crinò R, Gancia G, et al. (2025) Artificial intelligence and jobs: Evidence from US commuting zones. Econ Policy 40: 145–194. https://doi.org/10.1093/epolic/eiae059 doi: 10.1093/epolic/eiae059
[12]	Bresnahan TF, Trajtenberg M (1995) General purpose technologies: Engines of growth? J Econometrics 65: 83–108. https://doi.org/10.1016/0304-4076(94)01598-T doi: 10.1016/0304-4076(94)01598-T
[13]	Brynjolfsson E, Rock D, Syverson C (2021) The productivity J-curve: How intangibles complement general purpose technologies. Am Econ J Macroecon 13: 333–372. https://doi.org/10.1257/mac.20180386 doi: 10.1257/mac.20180386
[14]	Comin D, Mestieri M (2018) If technology has arrived everywhere, why has income diverged? Am Econ J Macroecon 10: 137–178. https://doi.org/10.1257/mac.20150175 doi: 10.1257/mac.20150175
[15]	Czarnitzki D, Fernandez GP, Rammer C (2023) Artificial intelligence and firm-level productivity. J Econ Behav Organ 211: 188–205. https://doi.org/10.1016/j.jebo.2023.05.008 doi: 10.1016/j.jebo.2023.05.008
[16]	David JM, Hopenhayn HA, Venkateswaran V (2016) Information, misallocation, and aggregate productivity. Q J Econ 131: 943–1005. https://doi.org/10.1093/qje/qjw006 doi: 10.1093/qje/qjw006
[17]	Deng Y, Lu M, Zhou X, et al. (2025) Statistical analysis and applications of financial network data in the era of digital intelligence. Data Sci Financ Econ 5: 536–556. https://doi.org/10.3934/DSFE.2025021 doi: 10.3934/DSFE.2025021
[18]	Dos Santos DLDJS, Dos Santos GC (2025) Technological convergence in financial auditing: A systematic literature review. Data Sci Financ Econ 5: 440–465. https://doi.org/10.3934/DSFE.2025018 doi: 10.3934/DSFE.2025018
[19]	Filippucci F, Gal P, Schief M (2026) Aggregate productivity gains from artificial intelligence: A sectoral perspective. AEA Pap Proc 116: 31–35. https://doi.org/10.1257/pandp.20261035 doi: 10.1257/pandp.20261035
[20]	Francois J, Woerz J (2008) Producer services, manufacturing linkages, and trade. J Ind Compet Trade 8: 199–229. https://doi.org/10.1007/s10842-008-0043-0 doi: 10.1007/s10842-008-0043-0
[21]	Furman J, Seamans R (2019) AI and the economy. Innov Policy Econ 19: 161–191. https://doi.org/10.1086/699936 doi: 10.1086/699936
[22]	Gao XY, Lu CP, Mao JH (2020) Effects of urban producer service industry agglomeration on export technological complexity of manufacturing in China. Entropy 22: 1108. https://doi.org/10.3390/e22101108 doi: 10.3390/e22101108
[23]	Gong M, Zeng Y, Zhang F (2023) New infrastructure, optimization of resource allocation and upgrading of industrial structure. Financ Res Lett 54: 103754. https://doi.org/10.1016/j.frl.2023.103754 doi: 10.1016/j.frl.2023.103754
[24]	Graetz G, Michaels G (2018) Robots at work. Rev Econ Stat 100: 753–768. https://doi.org/10.1162/rest_a_00754 doi: 10.1162/rest_a_00754
[25]	Guo F, Wang JY, Wang F, et al. (2020) Measuring China's digital financial inclusion: Index compilation and spatial characteristics. China Econ Q 19: 1401–1418. https://doi.org/10.13821/j.cnki.ceq.2020.03.12 [in Chinese] doi: 10.13821/j.cnki.ceq.2020.03.12
[26]	Han Q, Deng C (2025) Evaluating the development of China's modern industrial system. Financ Res Lett 74: 106676. https://doi.org/10.1016/j.frl.2024.106676 doi: 10.1016/j.frl.2024.106676
[27]	Hang L, Lu W, Ge X, et al. (2024) R & D innovation, industrial evolution and the labor skill structure in China manufacturing. Technol Forecast Soc Change 204: 123434. https://doi.org/10.1016/j.techfore.2024.123434 doi: 10.1016/j.techfore.2024.123434
[28]	Hu L, Gong Y, Zhu L (2025a) The impact of new digital infrastructure on total factor productivity in the education service industry: Evidence from China. Natl Account Rev 7: 309–339. https://doi.org/10.3934/NAR.2025014 doi: 10.3934/NAR.2025014
[29]	Hu L, Guo Y, Zhu L (2025b) New digital infrastructure boosts the inclusive growth of China's economy. Quant Financ Econ 9: 853–886. https://doi.org/10.3934/QFE.2025030 doi: 10.3934/QFE.2025030
[30]	Jabeen M, Jafar RMS, Li Z (2026) Leveraging metaverse technologies for a sustainable future: The role of knowledge management practices and technology readiness. Technol Soc 84: 103122. https://doi.org/10.1016/j.techsoc.2025.103122 doi: 10.1016/j.techsoc.2025.103122
[31]	Jin LQ, Cao KR, Li JY, et al. (2024) Information infrastructure construction and optimization of resources allocation among firms: Evidence from "Broadband China" strategy. Int Rev Econ Financ 91: 36–53. https://doi.org/10.1016/j.iref.2024.01.007 doi: 10.1016/j.iref.2024.01.007
[32]	Jones CI (2011) Intermediate goods and weak links in the theory of economic development. Am Econ J Macroecon 3: 1–28. https://doi.org/10.1257/mac.3.2.1 doi: 10.1257/mac.3.2.1
[33]	Juhász R, Lane N, Rodrik D (2024) The new economics of industrial policy. Annu Rev Econ 16: 213–242. https://doi.org/10.1146/annurev-economics-081023-024638 doi: 10.1146/annurev-economics-081023-024638
[34]	Koch M, Manuylov I, Smolka M (2021) Robots and firms. Econ J 131: 2553–2584. https://doi.org/10.1093/ej/ueab009 doi: 10.1093/ej/ueab009
[35]	Kumar S, Russell RR (2002) Technological change, technological catch-up, and capital deepening: Relative contributions to growth and convergence. Am Econ Rev 92: 527–548. https://doi.org/10.1257/00028280260136381 doi: 10.1257/00028280260136381
[36]	Leng T, Liu Y, Xiao Y, et al. (2023) Does firm financialization affect optimal real investment decisions? Evidence from China. Pac-Basin Financ J 79: 101970. https://doi.org/10.1016/j.pacfin.2023.101970 doi: 10.1016/j.pacfin.2023.101970
[37]	Li Z, Guo F, Du Z (2025a) Learning from peers: How peer effects reshape the digital value chain in China? J Theor Appl Electron Commer Res 20: 41. https://doi.org/10.3390/jtaer20010041 doi: 10.3390/jtaer20010041
[38]	Li Z, Xu Y, Du Z (2025b) Valuing financial data: The case of analyst forecasts. Financ Res Lett 75: 106847. https://doi.org/10.1016/j.frl.2025.106847 doi: 10.1016/j.frl.2025.106847
[39]	Li Z, Xu Y, Zou W (2026) Signal differences in Chinese central bank communication channels. Econ Syst 50: 101377. https://doi.org/10.1016/j.ecosys.2026.101377 doi: 10.1016/j.ecosys.2026.101377
[40]	Liu R, Cambria E (2025) Integrating financial sentiment analysis with coreference resolution: A comprehensive empirical framework. Data Sci Financ Econ 5: 577–600. https://doi.org/10.3934/DSFE.2025023 doi: 10.3934/DSFE.2025023
[41]	Lv G, Xiao S (2026) Digital government construction and corporate ESG performance: Evidence from the "Internet + Government Services" pilot as a quasi-natural experiment. Green Financ 8: 298–325. https://doi.org/10.3934/GF.2026011 doi: 10.3934/GF.2026011
[42]	Lyu YP, Lin HL, Ho CC, et al. (2019) Assembly trade and technological catch-up: Evidence from electronics firms in China. J Asian Econ 62: 65–77. https://doi.org/10.1016/j.asieco.2019.04.002 doi: 10.1016/j.asieco.2019.04.002
[43]	McMillan M, Rodrik D, Verduzco-Gallo I (2014) Globalization, structural change, and productivity growth, with an update on Africa. World Dev 63: 11–32. https://doi.org/10.1016/j.worlddev.2013.10.012 doi: 10.1016/j.worlddev.2013.10.012
[44]	Noy S, Zhang W (2023) Experimental evidence on the productivity effects of generative artificial intelligence. Science 381: 187–192. https://doi.org/10.1126/science.adh2586 doi: 10.1126/science.adh2586
[45]	Ren XH, Zeng GD, Gozgor G (2023) How does digital finance affect industrial structure upgrading? Evidence from Chinese prefecture-level cities. J Environ Manage 330: 117125. https://doi.org/10.1016/j.jenvman.2022.117125 doi: 10.1016/j.jenvman.2022.117125
[46]	Rognini D, Lecca P, Díaz-Lanchas J (2025) Constant price input-output and productivity surplus. Natl Account Rev 7: 630–648. https://doi.org/10.3934/NAR.2025026 doi: 10.3934/NAR.2025026
[47]	Shen HS, Qin MY, Li TY, et al. (2024) Digital finance and industrial structure upgrading: Evidence from Chinese counties. Int Rev Financ Anal 95: 103442. https://doi.org/10.1016/j.irfa.2024.103442 doi: 10.1016/j.irfa.2024.103442
[48]	Tan Y, Dallas M, Farrell H, et al. (2025) Driven to self-reliance: Technological interdependence and the Chinese innovation ecosystem. Int Stud Q 69: sqaf017. https://doi.org/10.1093/isq/sqaf017 doi: 10.1093/isq/sqaf017
[49]	Tian J (2025) Corporate financialization and innovation investment in China: Disentangling the crowding-out effect and reservoir effect under economic policy uncertainty. Systems 13: 115. https://doi.org/10.3390/systems13020115 doi: 10.3390/systems13020115
[50]	Xia L, Han QJ, Yu S (2024) Industrial intelligence and industrial structure change: Effect and mechanism. Int Rev Econ Financ 93: 1494–1506. https://doi.org/10.1016/j.iref.2024.04.002 doi: 10.1016/j.iref.2024.04.002
[51]	Xiong MZ, Zhang F, Zhang HJ, et al. (2023) Digital economy, credit expansion, and modernization of industrial structure in China. Financ Res Lett 58: 104500. https://doi.org/10.1016/j.frl.2023.104500 doi: 10.1016/j.frl.2023.104500
[52]	Xu LG, Shu HC, Lu XL, et al. (2024) Regional technological innovation and industrial upgrading in China: An analysis using interprovincial panel data from 2008 to 2020. Financ Res Lett 66: 105621. https://doi.org/10.1016/j.frl.2024.105621 doi: 10.1016/j.frl.2024.105621
[53]	Zeng K, Luo L, Li H (2025) Does policy finance promote domestic industrial gradient relocation? Evidence from China. Quant Financ Econ 9: 810–831. https://doi.org/10.3934/QFE.2025028 doi: 10.3934/QFE.2025028
[54]	Zhai S, Yang Y, Chan KC (2023) Artificial intelligence technology innovation and firm productivity: Evidence from China. Financ Res Lett 58: 104437. https://doi.org/10.1016/j.frl.2023.104437 doi: 10.1016/j.frl.2023.104437
[55]	Zhang YC (2025) AI-driven industrial structure upgrading: The moderating mechanism of inclusive finance development and regional differences analysis. Financ Res Lett 80: 107327. https://doi.org/10.1016/j.frl.2025.107327 doi: 10.1016/j.frl.2025.107327
[56]	Zhu Q, Zhang K (2026) The driving mechanism and challenges of artificial intelligence innovation to the alternate with industrial upgrading from multiple perspectives. Data Sci Financ Econ 6: 121–146. https://doi.org/10.3934/DSFE.2026005 doi: 10.3934/DSFE.2026005

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