Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Exploring the nonlinear relationship between robotics manufacturing and urban carbon emissions
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 01 April 2026

Exploring the nonlinear relationship between robotics manufacturing and urban carbon emissions

  • Jie Lin1,
  • Yizhi Xie2 &
  • Jianfu Shen3 

Scientific Reports , Article number:  (2026) Cite this article

  • 23 Accesses

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Environmental sciences
  • Environmental social sciences

Abstract

Robotics is widely regarded as a key driver of digital transformation and green industrial upgrading, yet its environmental implications remain ambiguous. Existing studies primarily focus on the emission effects of robot applications, while largely neglecting robotics manufacturing as an energy- and resource-intensive industrial activity. Using panel data from 277 Chinese prefecture-level cities from 2008 to 2019, this study examines the relationship between robotics manufacturing development and urban carbon emissions. We find a robust inverted U-shaped relationship: carbon emissions initially increase with the expansion of robotics manufacturing but decline once the industry reaches a moderate scale. Mechanism analysis reveals a stage-dependent sequential pathway, whereby mature robotics manufacturing promotes robot adoption, improves urban energy efficiency, and ultimately reduces carbon emissions, while this channel is inactive at early stages. Heterogeneity analysis shows that carbon-mitigation effects are more pronounced in the central region than in the eastern region, suggesting a latecomer advantage in green industrialization. Subsector analysis further indicates that system integration delivers earlier and stronger carbon-reduction effects than ontology manufacturing. These findings highlight the importance of considering industrial life-cycle stages and value-chain positions when designing policies to align high-tech industrial development with carbon-reduction goals.

Similar content being viewed by others

Industrial robots application and collaborative governance of pollution reduction and carbon reduction

Article Open access 25 May 2025

Industrial robots reduce carbon emissions in manufacturing through global value chains

Article Open access 29 July 2025

The impact of industrial robot adoption on corporate green innovation in China

Article Open access 31 October 2023

Data availability

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Li, Y. Path-breaking industrial development reduces carbon emissions: Evidence from Chinese Provinces, 1999–2011. Energy Policy 167, 113046 (2022).

    Google Scholar 

  2. Zhang, Y. J. & Da, Y. B. The decomposition of energy-related carbon emission and its decoupling with economic growth in China. Renew. Sustain. Energy Rev. 41, 1255–1266 (2015).

    Google Scholar 

  3. IPCC Sections. In Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team (eds Lee, H. & Romero, J.) 35–115 (IPCC,Geneva, 2023).

    Google Scholar 

  4. Javaid, M., Haleem, A., Singh, R. P. & Suman, R. Substantial capabilities of robotics in enhancing industry 4.0 implementation. Cognitive Robotics 1, 58–75 (2021).

    Google Scholar 

  5. Wang, Y., Wang, Z. & Zameer, H. Structural characteristics and evolution of the international trade-carbon emissions network in equipment manufacturing industry: International evidence in the perspective of global value chains. Environ. Sci. Pollut. Res. 28(20), 25886–25905 (2021).

    Google Scholar 

  6. Li, L. et al. Life cycle greenhouse gas emissions for last-mile parcel delivery by automated vehicles and robots. Environ. Sci. Technol. 55(16), 11360–11367 (2021).

    Google Scholar 

  7. Liu, J., Liu, L., Qian, Y. & Song, S. The effect of artificial intelligence on carbon intensity: Evidence from China’s industrial sector. Socioecon. Plann. Sci. 83, 101002 (2022).

    Google Scholar 

  8. Zhou, W., Zhuang, Y. & Chen, Y. How does artificial intelligence affect pollutant emissions by improving energy efficiency and developing green technology. Energy Econ. 131, 107355 (2024).

    Google Scholar 

  9. Yu, L., Wang, Y., Wei, X. & Zeng, C. Towards low-carbon development: The role of industrial robots in decarbonization in Chinese cities. J. Environ. Manage. 330, 117216 (2023).

    Google Scholar 

  10. Xu, T. & Ding, C. J. The path of urban sustainable development: The mechanism and heterogeneous effects of ecological industrial parks in energy efficiency. Energy Policy 209, 114959 (2026).

    Google Scholar 

  11. Xu, T., Ding, C. J. & Ahmed, A. D. The dark side of green innovation policy on energy consumption: From technology substitution effect perspective. Energy Policy 208, 114894 (2026).

    Google Scholar 

  12. Li, Q., Liu, Y., Li, W. & Zheng, L. Will industrial robots terminate enterprise innovation?—An empirical evidence from China’s enterprise robot penetration. Journal of the Knowledge Economy https://doi.org/10.1007/s13132-024-02310-3 (2024).

    Google Scholar 

  13. Li, X., Li, Z. & Gao, L. Paths for the digital transformation and intelligent upgrade of China’s discrete manufacturing industry. Strateg. Study Chin. Acad. Eng. 24(2), 64–74 (2022).

    Google Scholar 

  14. Lin, B. & Zheng, Q. Energy efficiency evolution of China’s paper industry. J. Clean. Prod. 140, 1105–1117 (2017).

    Google Scholar 

  15. Zhu, H., Bao, W. & Yu, G. How can intelligent manufacturing lead enterprise low-carbon transformation? Based on China’s intelligent manufacturing demonstration projects. Energy https://doi.org/10.1016/j.energy.2024.134032 (2024).

    Google Scholar 

  16. Li, Y. & Zhang, Y. What is the role of green ICT innovation in lowering carbon emissions in China? A provincial-level analysis. Energy Econ. 127, 107112 (2023).

    Google Scholar 

  17. Schulte, P., Welsch, H. & Rexhäuser, S. ICT and the demand for energy: Evidence from OECD countries. Environ. Resource Econ. 63, 119–146 (2016).

    Google Scholar 

  18. Wang, Y., Zhao, W. & Ma, X. The spatial spillover impact of artificial intelligence on energy efficiency: Empirical evidence from 278 Chinese cities. Energy 312, 133497 (2024).

    Google Scholar 

  19. Zhao, M. & Sun, T. Dynamic spatial spillover effect of new energy vehicle industry policies on carbon emission of transportation sector in China. Energy Policy 165, 112991 (2022).

    Google Scholar 

  20. Haini, H. Examining the impact of ICT, human capital and carbon emissions: Evidence from the ASEAN economies. Int. Econ. 166, 116–125 (2021).

    Google Scholar 

  21. Li, L., Hong, X. & Peng, K. A spatial panel analysis of carbon emissions, economic growth and high-technology industry in China. Struct. Change Econ. Dyn. 49, 83–92 (2019).

    Google Scholar 

  22. Grant, D., Jorgenson, A. K. & Longhofer, W. How organizational and global factors condition the effects of energy efficiency on CO2 emission rebounds among the world’s power plants. Energy Policy. 94, 89–93 (2016).

    Google Scholar 

  23. Jin, X. & Yu, W. Information and communication technology and carbon emissions in China: The rebound effect of energy intensive industry. Sustain. Prod. Consum. 32, 731–742 (2022).

    Google Scholar 

  24. Zhou, X., Zhou, D., Wang, Q. & Su, B. How information and communication technology drives carbon emissions: A sector-level analysis for China. Energy Econ. 81, 380–392 (2019).

    Google Scholar 

  25. Strubell, E., Ganesh, A. & McCallum, A. Energy and policy considerations for modern deep learning research. Proc. AAAI Conf. Artif. Intell. 34(09), 13693–13696 (2020).

    Google Scholar 

  26. Wen, X., Sun, Y., Ma, H. L. & Chung, S. H. Green smart manufacturing: Energy-efficient robotic job shop scheduling models. Int. J. Prod. Res. 61(17), 5791–5805 (2022).

    Google Scholar 

  27. Park, Y., Meng, F. & Baloch, M. A. The effect of ICT, financial development, growth, and trade openness on CO2 emissions: An empirical analysis. Environ. Sci. Pollut. Res. 25, 30708–30719 (2018).

    Google Scholar 

  28. Xu, B. & Lin, B. Does the high–tech industry consistently reduce CO2 emissions? Results from nonparametric additive regression model. Environ. Impact Assess. Rev. 63, 44–58 (2017).

    Google Scholar 

  29. Liu, K., Guo, X., Nie, A. & Ding, C. J. Technological progress and labour welfare: Evidence from robot adoption in China. Appl. Econ. 57(36), 5444–5459 (2024).

    Google Scholar 

  30. Huang, G., He, L. Y. & Lin, X. Robot adoption and energy performance: Evidence from Chinese industrial firms. Energy Econ. 107, 105837 (2022).

    Google Scholar 

  31. Li, Y., Zhang, Y., Pan, A., Han, M. & Veglianti, E. Carbon emission reduction effects of industrial robot applications: Heterogeneity characteristics and influencing mechanisms. Technol. Soc. 70, 102034 (2022).

    Google Scholar 

  32. Zhou, X., Li, G., Wang, Q. & Zhou, D. U-shaped relationship between digitalization and low-carbon economy efficiency: Mediation and spillover effects. J. Clean. Prod. 458, 142535 (2024).

    Google Scholar 

  33. Luan, F., Yang, X., Chen, Y. & Regis, P. J. Industrial robots and air environment: A moderated mediation model of population density and energy consumption. Sustain. Prod. Consum. 30, 870–888 (2022).

    Google Scholar 

  34. Lin, J. et al. The spatial pattern evolution and influencing factors of China’s robotics industry. Stat. Decis. 41 (13), 76–82 (2025).

    Google Scholar 

  35. Andreoni, A., Frattini, F. & Prodi, G. Getting robots in ‘our own hands’: Structural drivers, spatial dynamics and multi-scalar industrial policy in China. Competition & Change https://doi.org/10.1177/10245294241261878 (2024).

    Google Scholar 

  36. Yuan, H. et al. How does manufacturing agglomeration affect green economic efficiency? Energy Econ. 92, 104944 (2020).

    Google Scholar 

  37. Keshvarparast, A., Battini, D., Battaia, O. & Pirayesh, A. Collaborative robots in manufacturing and assembly systems: Literature review and future research agenda. J. Intell. Manuf. 35(5), 2065–2118 (2024).

    Google Scholar 

  38. Nibedita, B. & Irfan, M. The role of energy efficiency and energy diversity in reducing carbon emissions: Empirical evidence on the long-run trade-off or synergy in emerging economies. Environ. Sci. Pollut. Res. 28 (40), 56938–56954 (2021).

    Google Scholar 

  39. Sgobbi, A., Simoes, S. G., Magagna, D. & Nijs, W. Assessing the impacts of technology improvements on the deployment of marine energy in Europe with an energy system perspective. Renewable Energy 89, 515–525 (2016).

    Google Scholar 

  40. Weber, G. & Cabras, I. The transition of Germany’s energy production, green economy, low-carbon economy, socio-environmental conflicts, and equitable society. J. Clean. Prod. 167, 1222–1231 (2017).

    Google Scholar 

  41. Morrison, B. & Golden, J. S. Life cycle assessment of co-firing coal and wood pellets in the Southeastern United States. J. Clean. Prod. 150, 188–196 (2017).

    Google Scholar 

  42. Acemoglu, D. & Restrepo, P. Robots and jobs: Evidence from US Labor Markets. J. Polit. Econ. 128(6), 2188–2244 (2020).

    Google Scholar 

  43. Baron, R. M. & Kenny, D. A. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51(6), 1173–1182 (1986).

    Google Scholar 

  44. Li, R., Wang, Q., Liu, Y. & Jiang, R. Per-capita carbon emissions in 147 countries: The effect of economic, energy, social, and trade structural changes. Sustain. Prod. Consum. 27, 1149–1164 (2021).

    Google Scholar 

  45. Mohsin, M. et al. Developing low carbon economies: An aggregated composite index based on carbon emissions. Sustain. Energy Technol. Assess. 35, 365–374 (2019).

    Google Scholar 

  46. Bin, G. Technology acquisition channels and industry performance: An industry-level analysis of Chinese large-and medium-size manufacturing enterprises. Res. Policy. 37 (2), 194–209 (2008).

    Google Scholar 

  47. Mitić, P., Fedajev, A., Radulescu, M. & Rehman, A. The relationship between CO2 emissions, economic growth, available energy, and employment in SEE countries. Environ. Sci. Pollut. Res. 30 (6), 16140–16155 (2023).

    Google Scholar 

  48. Asumadu-Sarkodie, S. & Owusu, P. A. Carbon dioxide emissions, GDP, energy use, and population growth: A multivariate and causality analysis for Ghana, 1971–2013. Environ. Sci. Pollut. Res. 23(13), 13508–13520 (2016).

    Google Scholar 

  49. Hao, Y., Ba, N., Ren, S. & Wu, H. How does international technology spillover affect China’s carbon emissions? A new perspective through intellectual property protection. Sustainable Prod. Consum. 25, 577–590 (2021).

    Google Scholar 

  50. Sun, W. & Huang, C. How does urbanization affect carbon emission efficiency? Evidence from China. J. Clean. Prod. 272, 122828 (2020).

    Google Scholar 

  51. Zhang, F., Deng, X., Phillips, F., Fang, C. & Wang, C. Impacts of industrial structure and technical progress on carbon emission intensity: Evidence from 281 cities in China. Technol. Forecast. Soc. Change 154, 119949 (2020).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Business School, Shantou University, 5 Cuifeng Road, Shantou, 515821, Guangdong, China

    Jie Lin

  2. Department of Social Science and Policy Studies, Education University of Hong Kong, Ting Kok, Hong Kong, China

    Yizhi Xie

  3. Department of Building and Real Estate, Hong Kong Polytechnic University, Hung Hom, Hong Kong, China

    Jianfu Shen

Authors
  1. Jie Lin
    View author publications

    Search author on:PubMed Google Scholar

  2. Yizhi Xie
    View author publications

    Search author on:PubMed Google Scholar

  3. Jianfu Shen
    View author publications

    Search author on:PubMed Google Scholar

Contributions

J.L. conceptualized and designed the study. J.S. designed the methodology. Y.X. performed the data analysis. J.L. and Y.X. drafted the main manuscript text. All authors reviewed and approved the final manuscript.

Corresponding author

Correspondence to Jie Lin.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (download DOCX )

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, J., Xie, Y. & Shen, J. Exploring the nonlinear relationship between robotics manufacturing and urban carbon emissions. Sci Rep (2026). https://doi.org/10.1038/s41598-026-46922-y

Download citation

  • Received: 17 July 2025

  • Accepted: 28 March 2026

  • Published: 01 April 2026

  • DOI: https://doi.org/10.1038/s41598-026-46922-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Robotics manufacturing industry
  • Carbon emissions
  • Inverted U-shaped relationship
  • Energy efficiency
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on X
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com footer links

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Anthropocene