pith. sign in

arxiv: 2606.09006 · v1 · pith:JKIEJSSInew · submitted 2026-06-08 · 💻 cs.SI · cs.AI· cs.CY· cs.ET

Sustainability and Artificial Intelligence: Necessary, Challenging, and Promising Intersections

Han-Teng Liao , Zijia Wang This is my paper

Pith reviewed 2026-06-27 14:33 UTC · model grok-4.3

classification 💻 cs.SI cs.AIcs.CYcs.ET
keywords artificial intelligencesustainabilitybibliometric analysisgreen technologysustainable developmentinterdisciplinary researchwicked problems
0
0 comments X

The pith

Bibliometric analysis of 541 papers shows AI and sustainability research intersecting on complex problems that bridge disciplines.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper maps the growing overlap between artificial intelligence and sustainability studies using data from 541 papers. It argues these fields converge because both tackle complex, interconnected issues that evolve over time. A sympathetic reader would care since clearer maps of this overlap could steer AI applications toward better environmental and social outcomes. The analysis identifies a central body of work on green and sustainable science and technology that connects different fields, journals, and concepts.

Core claim

Based on 541 bibliographic records from the Web of Science database, the findings reveal an increasingly central body of work on green and sustainable science and technology that bridges various disciplines, main journals, and key topics and concepts, showing that the interactions between sustainability and AI research are necessary, challenging, and promising.

What carries the argument

Bibliometric analysis of 541 papers from the Web of Science that maps research structure and identifies bridging topics across AI and sustainability.

If this is right

  • The identified intersections can inform efforts to address wicked problems in environmental, social, and governance aspects of development.
  • Research communities should diversify and expand practices around AI for sustainable development.
  • Expected application areas for AI should include more sustainability-focused topics and institutions.
  • Mapping these links helps clarify how AI shapes the evolution of sustainability features.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The mapping could help identify priority journals for funding interdisciplinary AI-sustainability projects.
  • Extending the analysis over time might reveal whether certain application areas are accelerating faster than others.
  • If the convergence pattern holds, it could encourage hybrid methods that apply AI tools directly to sustainability metrics.
  • Comparing results across multiple databases would test how robust the observed centrality of green technology themes remains.

Load-bearing premise

The papers retrieved through the chosen search terms in one database give a representative and unbiased sample of all relevant intersections between AI and sustainability.

What would settle it

Repeating the analysis with a substantially different set of search terms or an additional database that produces a less central or less bridged structure for green and sustainable science topics would challenge the central claim.

read the original abstract

Both digital economy and digital technology researchers increasingly recognize the need to better address the role that artificial intelligence (AI) plays in shaping the evolution of the environmental, social and governance aspects of development. It appears that sustainability and AI research converge on the features of wicked problems that are complex, interconnected and dynamic. Building off such convergence, this article aims to map out the necessary, challenging, and promising intersections by providing an overview of the state of art research. Based on 541 bibliographic data collected from the Web of Science (WoS) database, the findings reveal the increasingly central body of work on green and sustainable science and technology in bridging various disciplines, main journals and key topics and concepts. The findings reveal how such interactions can be necessary, challenging, and promising. The article concludes with few general arguments regarding how to diversify and expand the community of practice regarding AI for sustainable development, especially in the areas of expected AI application areas and institutions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The manuscript claims to map the necessary, challenging, and promising intersections between sustainability and artificial intelligence research via a bibliometric overview of 541 records collected from the Web of Science database. It concludes that work on green and sustainable science and technology occupies an increasingly central position, bridging disciplines, journals, and concepts, and offers general arguments for expanding the community of practice in AI for sustainable development.

Significance. A rigorous bibliometric mapping of AI-sustainability intersections would be valuable given the timeliness of the topic. If the corpus were demonstrably representative, the descriptive findings on centrality and bridging could usefully inform future research directions. The current lack of methodological transparency, however, prevents any such assessment and thereby limits the contribution.

major comments (1)
  1. [Abstract] Abstract: The claim that the 541 WoS records reveal the centrality of green and sustainable science and technology (and thereby demonstrate the necessary/challenging/promising character of the intersections) rests on the assumption that the corpus is a valid, unbiased sample. No search terms, keywords, date range, inclusion/exclusion criteria, or query string are supplied, making it impossible to evaluate selection bias or coverage and rendering the central empirical claims unverifiable.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive comment on methodological transparency. We agree that the absence of search details limits verifiability and will revise the manuscript to address this.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that the 541 WoS records reveal the centrality of green and sustainable science and technology (and thereby demonstrate the necessary/challenging/promising character of the intersections) rests on the assumption that the corpus is a valid, unbiased sample. No search terms, keywords, date range, inclusion/exclusion criteria, or query string are supplied, making it impossible to evaluate selection bias or coverage and rendering the central empirical claims unverifiable.

    Authors: We agree that the manuscript does not supply the required methodological details. In the revised version we will add a dedicated Data and Methods section that reports the precise Web of Science query string, the full list of keywords and search terms, the date range, and all inclusion/exclusion criteria used to obtain the 541 records. This addition will allow readers to assess selection bias and coverage directly. revision: yes

Circularity Check

0 steps flagged

No circularity: purely descriptive bibliometric summary

full rationale

The paper conducts a bibliometric overview of 541 WoS records to describe intersections between AI and sustainability research. No equations, derivations, predictions, fitted parameters, or uniqueness theorems appear in the provided text or abstract. All claims reduce directly to observed patterns in the collected corpus without any self-referential construction or load-bearing self-citation. The analysis is self-contained as an empirical mapping exercise.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the assumption that Web of Science captures the relevant literature and that keyword-based retrieval accurately identifies intersections; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Web of Science database search with unspecified terms yields a representative sample of AI-sustainability literature
    Basis for collecting the 541 bibliographic records used for all findings

pith-pipeline@v0.9.1-grok · 5697 in / 1078 out tokens · 28107 ms · 2026-06-27T14:33:45.667918+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

33 extracted references · 27 canonical work pages

  1. [1]

    Computational Sustainability and Artificial Intelligence in the Developing World,

    J. Quinn, V. Frias -Martinez, and L. Subramanian, “Computational Sustainability and Artificial Intelligence in the Developing World,” AIMag, vol. 35, no. 3, pp. 36 –47, Sep. 2014, doi: 10.1609/aimag.v35i3.2529

    work page doi:10.1609/aimag.v35i3.2529 2014

  2. [2]

    United Nations General Assembly, Road map for digital cooperation: implementation of the recommendations of the High -level Panel on Digital Cooperation. 2020

    2020

  3. [3]

    Proposed programme of work and budget for the biennium 2020‒ 2021,

    UNEP, “Proposed programme of work and budget for the biennium 2020‒ 2021,” Nairobi, UNEP/EA.4/4, Mar. 2019

    2020

  4. [4]

    Artif icial intelligence for Sustainable Development,

    UNESCO, “Artif icial intelligence for Sustainable Development,” UNESCO, Paris, France, Synthesis report ED -2019/WS/39, 2019. Accessed: Nov. 25, 2019. [Online]. Available: https://unesdoc.unesco.org/ark:/48223/pf0000370308

    2019

  5. [5]

    Digital Transformation: An IEEE Digital Reality Initiative White Paper

    M. Borst, Ed., “Digital Transformation: An IEEE Digital Reality Initiative White Paper.” IEEE Digital Reality Initiative, 2020, [Online]. Available: https://digitalreality.ieee.org/images/files/pdf/digital-transformation- white-paper.pdf

    2020

  6. [6]

    Research fronts,

    E. Garfield, “Research fronts,” Current Comments, Oct. 1994, Accessed: Jan. 27, 2020. [Online]

    1994

  7. [7]

    The geography of science: disciplinary and national mappings,

    H. Small and E. Garfield, “The geography of science: disciplinary and national mappings,” Journal of Information Science , vol. 11, no. 4, pp. 147–159, Oct. 1985, doi: 10.1177/016555158501100402

    work page doi:10.1177/016555158501100402 1985

  8. [8]

    Bibliographic coupling and its application to research-front and other core documents,

    B. Jarneving, “Bibliographic coupling and its application to research-front and other core documents,” Journal of Informetrics, vol. 1, no. 4, pp. 287– 307, 2007, doi: https://doi.org/10.1016/j.joi.2007.07.004

    work page doi:10.1016/j.joi.2007.07.004 2007

  9. [9]

    New Web of Science categories reflect ever -evolving research,

    M. BOLETTA, “New Web of Science categories reflect ever -evolving research,” Web of Science Core Collection Help , Jan. 24, 2019. https://clarivate.com/webofsciencegroup/article/new-web-of-science- categories-reflect-ever-evolving-research/ (accessed Nov. 01, 2020)

    2019

  10. [10]

    A Design -to-Device Pipeline for Data-Driven Materials Discovery,

    J. M. Cole, “A Design -to-Device Pipeline for Data-Driven Materials Discovery,” Acc. Chem. Res., vol. 53, no. 3, pp. 599–610, Mar. 2020, doi: 10.1021/acs.accounts.9b00470

    work page doi:10.1021/acs.accounts.9b00470 2020

  11. [11]

    Connecting circular economy and industry 4.0,

    S. Rajput and S. P. Singh, “Connecting circular economy and industry 4.0,” International Journal of Information Management , vol. 49, pp. 98 –113, Dec. 2019, doi: 10.1016/j.ijinfomgt.2019.03.002

    work page doi:10.1016/j.ijinfomgt.2019.03.002 2019

  12. [12]

    Industry 4.0 to Accelerate the Circular Economy: A Case Study of Electric Scooter Sharing,

    T. T. Pham et al., “Industry 4.0 to Accelerate the Circular Economy: A Case Study of Electric Scooter Sharing,” Sustainability, vol. 11, no. 23, p. 6661, Nov. 2019, doi: 10.3390/su11236661

    work page doi:10.3390/su11236661 2019

  13. [13]

    Digitalizing the Circular Economy: Circular Economy Engineering Defined by the Metallurgical Internet of Things,

    M. A. Reuter, “Digitalizing the Circular Economy: Circular Economy Engineering Defined by the Metallurgical Internet of Things,” Metall and Materi Trans B , vol. 47, no. 6, pp. 3194 –3220, Dec. 2016, doi: 10.1007/s11663-016-0735-5

    work page doi:10.1007/s11663-016-0735-5 2016

  14. [14]

    IoT Vulnerability Assessment for Sustainable Computing: Threats, Current Solutions, and Open Challenges,

    P. Anand, Y. Singh, A. Selwal, M. Alazab, S. Tanwar, and N. Kumar, “IoT Vulnerability Assessment for Sustainable Computing: Threats, Current Solutions, and Open Challenges,” IEEE Access , vol. 8, pp. 168825–168853, 2020, doi: 10.1109/ACCESS.2020.3022842

    work page doi:10.1109/access.2020.3022842 2020

  15. [16]

    On big data, artificial intelligence and smart cities,

    Z. Allam and Z. A. Dhunny, “On big data, artificial intelligence and smart cities,” Cities, vol. 89, pp. 80 –91, Jun. 2019, doi: 10.1016/j.cities.2019.01.032

    work page doi:10.1016/j.cities.2019.01.032 2019

  16. [17]

    Disease Diagnosis in Smart Healthcare: Innovat ion, Technologies and Applications,

    K. Chui, W. Alhalabi, S. Pang, P. Pablos, R. Liu, and M. Zhao, “Disease Diagnosis in Smart Healthcare: Innovat ion, Technologies and Applications,” Sustainability, vol. 9, no. 12, p. 2309, Dec. 2017, doi: 10.3390/su9122309

    work page doi:10.3390/su9122309 2017

  17. [18]

    Advances in Multiple Criteria Decision Making for Sustainability: Modeling and Applications,

    K.-Y. Shen and G. -H. Tzeng, “Advances in Multiple Criteria Decision Making for Sustainability: Modeling and Applications,” Sustainability, vol. 10, no. 5, p. 1600, May 2018, doi: 10.3390/su10051600

    work page doi:10.3390/su10051600 2018

  18. [19]

    A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation,

    S. S. Kamble, A. Gunasekaran, A. Ghadge, and R. Raut, “A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation,” International Journal of Production Economics , vol. 229, p. 107853, Nov. 2020, doi: 10.1016/j.ijpe.2020.107853

    work page doi:10.1016/j.ijpe.2020.107853 2020

  19. [20]

    The Influence of Smart Manufacturing towards Energy Conservation: A Review,

    S. Terry et al., “The Influence of Smart Manufacturing towards Energy Conservation: A Review,” Technologies, vol. 8, no. 2, p. 31, May 2020, doi: 10.3390/technologies8020031

    work page doi:10.3390/technologies8020031 2020

  20. [21]

    Smart Manufacturing Systems and Applied Industrial Technologies for a Sustainable Industry: A Systematic Literature Review,

    R. Cioffi, M. Travaglioni, G. Piscitelli, A. Petrillo, and A. Parmentola, “Smart Manufacturing Systems and Applied Industrial Technologies for a Sustainable Industry: A Systematic Literature Review,” Applied Sciences, vol. 10, no. 8, p. 2897, Apr. 2020, doi: 10.3390/app10082897

    work page doi:10.3390/app10082897 2020

  21. [22]

    Smart manufacturing,

    A. Kusiak, “Smart manufacturing,” International Journal of Production Research, vol. 56, no. 1 –2, pp. 508 –517, Jan. 2018, doi: 10.1080/00207543.2017.1351644

    work page doi:10.1080/00207543.2017.1351644 2018

  22. [23]

    Sustainability accounting and reporting in the industry 4.0,

    K. Tiwari and M. S. Khan, “Sustainability accounting and reporting in the industry 4.0,” Journal of Cleaner Production , vol. 258, p. 120783, Jun. 2020, doi: 10.1016/j.jclepro.2020.120783

    work page doi:10.1016/j.jclepro.2020.120783 2020

  23. [24]

    Publicising Food: Big Data, Precision Agriculture, and Co - Experimental Techniques of Addition,

    M. Carolan, “Publicising Food: Big Data, Precision Agriculture, and Co - Experimental Techniques of Addition,” Sociol. Rural., vol. 57.0, no. 2, p. 135, Apr. 2017, doi: 10.1111/soru.12120

    work page doi:10.1111/soru.12120 2017

  24. [25]

    Agri -food 4.0: A survey of the supply chains and technologies for the future agriculture,

    M. Lezoche, J. E. Hernandez, M. del M. E. Alemany Dí az, H. Panetto, and J. Kacprzyk, “Agri -food 4.0: A survey of the supply chains and technologies for the future agriculture,” Computers in Industry, vol. 117, p. 103187, May 2020, doi: 10.1016/j.compind.2020.103187

    work page doi:10.1016/j.compind.2020.103187 2020

  25. [26]

    A Path From Sustainable Nutrition to Nutritional Sustainability of Complex Food Systems,

    S. M. Smetana, S. Bornkessel, and V. Heinz, “A Path From Sustainable Nutrition to Nutritional Sustainability of Complex Food Systems,” Front. Nutr., vol. 6, p. 39, Apr. 2019, doi: 10.3389/fnut.2019.00039

    work page doi:10.3389/fnut.2019.00039 2019

  26. [27]

    Food traceability system from governmental, corporate, and consumer perspectives in the European Union and China: A comparative review,

    J. Qian et al., “Food traceability system from governmental, corporate, and consumer perspectives in the European Union and China: A comparative review,” Trends in Food Science & Technology, vol. 99, pp. 402–412, May 2020, doi: 10.1016/j.tifs.2020.03.025

    work page doi:10.1016/j.tifs.2020.03.025 2020

  27. [28]

    Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders,

    J.-S. Chou and D. -S. Tran, “Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders,” Energy, vol. 165, pp. 709 –726, Dec. 2018, doi: 10.1016/j.energy.2018.09.144

    work page doi:10.1016/j.energy.2018.09.144 2018

  28. [29]

    Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption,

    K. T. Chui, M. D. Lytras, and A. Visvizi, “Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption,” Energies, vol. 11, no. 11, p. 2869, Oct. 2018, doi: 10.3390/en11112869

    work page doi:10.3390/en11112869 2018

  29. [30]

    Big Data Analytics for Dynamic Energy Management in Smart Grids,

    P. D. Diamantoulakis, V. M. Kapinas, and G. K. Karagiannidis, “Big Data Analytics for Dynamic Energy Management in Smart Grids,” Big Data Research, vol. 2, no. 3, pp. 94 –101, Sep. 2015, doi: 10.1016/j.bdr.2015.03.003

    work page doi:10.1016/j.bdr.2015.03.003 2015

  30. [31]

    Time to seize the digital evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs,

    L.-W. Wong, L.-Y. Leong, J.-J. Hew, G. W.-H. Tan, and K.-B. Ooi, “Time to seize the digital evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs,” International Journal of Information Management , vol. 52, p. 101997, Jun. 2020, doi: 10.1016/j.ijinfomgt.2019.08.005

    work page doi:10.1016/j.ijinfomgt.2019.08.005 2020

  31. [32]

    Multi -Period E -Closed-Loop Supply Chain Network Considering Consumers’ Preference for Products and AI-Push,

    Duan, Xiu, and Yao, “Multi -Period E -Closed-Loop Supply Chain Network Considering Consumers’ Preference for Products and AI-Push,” Sustainability, vol. 11, no. 17, p. 4571, Aug. 2019, doi: 10.3390/su11174571

    work page doi:10.3390/su11174571 2019

  32. [33]

    Role of technological dimensions of green supply chain management practices on firm performance,

    S. Bag, S. Gupta, S. Kumar, and U. Sivarajah, “Role of technological dimensions of green supply chain management practices on firm performance,” JEIM, vol. ahead -of-print, no. ahead -of-print, Apr. 2020, doi: 10.1108/JEIM-10-2019-0324

    work page doi:10.1108/jeim-10-2019-0324 2020

  33. [34]

    Sustainable Supply Chains in the Age of AI and Digitization: Research Challenges and Opportunities,

    N. R. Sanders, T. Boone, R. Ganeshan, and J. D. Wood, “Sustainable Supply Chains in the Age of AI and Digitization: Research Challenges and Opportunities,” J Bus Logist, vol. 40, no. 3, pp. 229 –240, Sep. 2019, doi: 10.1111/jbl.12224

    work page doi:10.1111/jbl.12224 2019