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Journals / Journal of Business Economics and Finance / 2026 / Volume: 15 Issue: 1

DIGITAL FINANCIAL INCLUSION AND URBAN CARBON EMISSIONS: AN EMPIRICAL STUDY BASED ON NONLINEAR CHARACTERISTICS AND MECHANISM TESTING

Jinkun Huo(Guangdong University)0009-0001-4706-826X
Yongyi Gu(Guangdong University)0000-0002-6651-1714
Wei Guo(Guangdong University)0009-0005-0982-4721
Weilun Xu(Guangdong University)0009-0007-5327-2308
Pages
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Publication Date
2026-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Findings- The results indicate that DFI significantly reduces carbon emission intensity, but this effect follows a distinct "U-shaped" pattern, with a turning point at an index value of 137, revealing diminishing marginal returns in emission reduction benefits.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Purpose- This study intends to investigate the nonlinear relationship between Digital Financial Inclusion (DFI) and carbon emission intensity in Chinese cities.
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Key Findings & Data:Methodology- Applying balanced panel data from 270 Chinese cities spanning 2012 to 2022, the study employs a two-way fixed effects model and the system Generalized Method of Moments (GMM) for empirical analysis.
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Conclusion & Impact:Findings- The results indicate that DFI significantly reduces carbon emission intensity, but this effect follows a distinct "U-shaped" pattern, with a turning point at an index value of 137, revealing diminishing marginal returns in emission reduction benefits.
🔬Design:Modeling & Simulation
📍Scope:2012 Verileri
⚙️Variables:Digital financial inclusion · carbon emission intensity · U-shaped curve

Abstract

Purpose- This study intends to investigate the nonlinear relationship between Digital Financial Inclusion (DFI) and carbon emission intensity in Chinese cities. It seeks to uncover the potential "U-shaped" pattern of DFI's environmental effects and examine the underlying mechanisms, including technological innovation and urbanization, while considering variations across different city types. Methodology- Applying balanced panel data from 270 Chinese cities spanning 2012 to 2022, the study employs a two-way fixed effects model and the system Generalized Method of Moments (GMM) for empirical analysis. The research examines the nonlinear impact of DFI on carbon emission intensity and explores the mediating role of technological innovation and the moderating effect of urbanization. Findings- The results indicate that DFI significantly reduces carbon emission intensity, but this effect follows a distinct "U-shaped" pattern, with a turning point at an index value of 137, revealing diminishing marginal returns in emission reduction benefits. Technological innovation is identified as a key mediating channel. Urbanization negatively moderates the emission reduction effect, reflecting an "inclusive compensation" characteristic of DFI in less developed areas. Furthermore, resource-based cities exhibit a stronger initial emission reduction effect but face more severe U-shaped rebound risks. Conclusion- We provide empirical evidence supporting the design of differentiated emission reduction strategies. It highlights the importance of considering the nonlinear dynamics of DFI and local characteristics—such as urbanization and city type—in formulating effective environmental policies.

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Cite This Article

Huo, J., Gu, Y., Guo, W., Xu, W. (2026). DIGITAL FINANCIAL INCLUSION AND URBAN CARBON EMISSIONS: AN EMPIRICAL STUDY BASED ON NONLINEAR CHARACTERISTICS AND MECHANISM TESTING. Journal of Business Economics and Finance, Volume: 15 Issue: 1. https://doi.org/10.17261/Pressacademia.2026.2035

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References (26)

  1. Badeeb, R. A., Lean, H. H., & Clark, J. (2017). The evolution of the natural resource curse thesis: A critical literature survey. Resources Policy, 51, 123-134.
  2. Balli, H. O., & Sørensen, B. E. (2013). Interaction effects in econometrics. Empirical Economics, 45(1), 583-603.
  3. Chen, Y. The nonlinear impact of digital finance on carbon emission efficiency: Evidence from Chinese cities. Frontiers in Environmental Science, 14, 1739323.
  4. Dong, X., Lv, S., & Wu, S. (2025). The green promise of digital finance: Exploring its effects and mechanisms on carbon reduction. China Economic Review, 90, 102374.
  5. Feng, S., Zhang, R., & Li, G. (2022). Environmental decentralization, digital finance and green technology innovation. Structural Change and Economic Dynamics, 61, 70-83.
  6. Gao, X., & Li, J. (2024). China’s digital economy: A dual mission of carbon-emission reduction and efficiency enhancement. Sustainability, 16(6), 2351.
  7. He, Q., & Jiang, H. (2024). Digital inclusive finance, digital technology innovation, and carbon emission intensity. Sustainability, 16(15), 6407.
  8. Jiang, Y., Zhao, R., & Qin, G. (2024). How does digital finance reduce carbon emissions intensity? Evidence from chain mediation effect of production technology innovation and green technology innovation. Heliyon, 10(9), 373-399.
  9. Kang, J., Duan, S. Y., Yan, S. X., Xue, S. Y., Li, Y. H., Li, N.,. & Liu, G. H. (2025). Impact of Digital Inclusive Finance on Carbon Emissions under the Dual-Carbon Goal: Evidence from Nine Regions in the Yellow River basin of China. Water Economics and Policy, 2440011.
  10. Lange, S., Pohl, J., & Santarius, T. (2020). Digitalization and energy consumption. Does ICT reduce energy demand? Ecological Economics, 176, 106760.
  11. Le, T. H., Le, H. C., & Taghizadeh-Hesary, F. (2020). Does financial inclusion impact CO2 emissions? Evidence from Asia. Finance Research Letters, 34, 101451.
  12. Li, Y. (2025). How does digital financial inclusion affect households’ CO2? Micro-evidence from an emerging country. Journal of Economics and Business, 133, 106222.
  13. Li, Z., & Wang, J. (2022). The dynamic impact of digital economy on carbon emission reduction: evidence city-level empirical data in China. Journal of Cleaner Production, 351, 131570.
  14. Pu, Z., Qian, Y., & Liu, R. (2025). Is digital technology innovation a panacea for carbon reduction?. Technological and Economic Development of Economy, 31(2), 496-524.
  15. Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy, 98(5), 71-102.
  16. Sadorsky, P. (2012). Information communication technology and electricity consumption in emerging economies. Energy Policy, 48, 130-136.
  17. Sachs, J. D., & Warner, A. M. (2001). The curse of natural resources. European Economic Review, 45(4-6), 827-838.
  18. Shaheen, R. (2025). Investigating the relationship between financial inclusion and CO2 emissions in G20 countries: a quantile-on-quantile approach. Social Responsibility Journal, 21(10), 2152-2172.
  19. Stiglitz, J. E., & Weiss, A. (1981). Credit rationing in markets with imperfect information. The American Economic Review, 71(3), 393-410.
  20. Unruh, G. C. (2000). Understanding carbon lock-in. Energy Policy, 28(12), 817-830.
  21. Wan, J., Pu, Z., & Tavera, C. (2023). The impact of digital finance on pollutants emission: evidence from Chinese cities. Environmental Science and Pollution Research, 30(15), 42923-42942.
  22. Wang, S., Zhou, A., Zhang, Y., Yang, G., & Peng, X. (2024). Enhancing carbon emission performance in resource-based cities through technological innovation under China’s digital economic development strategy. Frontiers in Environmental Science, 12, 1397063.
  23. York, R., Rosa, E. A., & Dietz, T. (2003). STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecological Economics, 46(3), 351-365.
  24. Zheng, F., Chen, S., & Wang, X. (2025). How the impact and mechanisms of digital financial inclusion on agricultural carbon emission intensity: new evidence from a double machine learning model. Frontiers in Environmental Science, 13, 1549623.
  25. Zhou, B., & Wang, Y. L. (2024). The nonlinear effects of digital finance on carbon performance: Evidence from China. Journal of Innovation & Knowledge, 9(2), 100484.
  26. Kang, J., Duan, S. Y., Yan, S. X., Xue, S. Y., Li, Y. H., Li, N.,. & Liu, G. H. (2025). Impact of Digital Inclusive Finance on Carbon Emissions under the Dual-Carbon Goal: Evidence from Nine Regions in the Yellow River basin of China. Water Economics and Policy, 2440011.