The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
The impact of AI on carbon emissions: evidence from 66 countries
Abstract
This study aims to address debate in previous studies on whether AI has a positive or negative effect on carbon emission reduction. We used quantile regression and PSTR models to study the diverse impacts of AI on carbon emissions in 66 countries from 1993–2019. There were three main findings in this paper. First, the impact of AI on carbon emissions varies across countries, and its effect on carbon reduction is mainly found in high-carbon emission and high-income countries. Second, the industrial structure environment of different countries affects the role of AI in carbon reduction, with its marginal effect in limiting emissions decreasing with the rise of secondary industrial structures. Third, the impact of AI varies in countries based on their different demographic structures. The marginal effect of AI on carbon emission reduction increases in places with older populations. This study offers unique insight into the heterogeneous impact of AI on CO2 emissions. Our analysis confirms the importance of industrial and demographic structures in promoting carbon emission reduction. We provide effective policy recommendations for economic development and environmental governance.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“in Norilsk, Russia, a nickel factory released 350,000 tons of sulfur dioxide yearly, heavily polluting the city.”
From Artificial Intelligence Colonialism: Environmental Damage, Labor Exploitation, and Human Rights Crises in the Global South (2024) · cites this paper as [99]
In the citing paper
“In the Philippines, seventeen nickel mines were shut down due to environmental issues”
From Artificial Intelligence Colonialism: Environmental Damage, Labor Exploitation, and Human Rights Crises in the Global South (2024) · cites this paper as [99]
Citations
Cites
Robots and Jobs: Evidence from US Labor Markets
Acemoglu, D., & Restrepo, P. (2020) · Journal of Political Economy · doi:10.1086/705716 · Cited by 4,088
The role of artificial intelligence in achieving the Sustainable Development Goals
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2020) · Nature Communications · doi:10.1038/s41467-019-14108-y · Cited by 3,294
Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda
Nishant, R., Kennedy, M., & Corbett, J. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102104 · Cited by 1,203
Implications of population ageing for economic growth
Bloom, D. E., Canning, D., & Fink, G. (2010) · Oxford Review of Economic Policy · doi:10.1093/oxrep/grq038 · Cited by 971
Zhu, H., Duan, L., Guo, Y., & Yu, K. (2016) · Economic Modelling · doi:10.1016/j.econmod.2016.05.003 · Cited by 914
Li, R., Wang, Q., Liu, Y., & Jiang, R. (2021) · Sustainable Production and Consumption · doi:10.1016/j.spc.2021.02.031 · Cited by 590
Wang, Q., Zhang, F., & Li, R. (2022) · Environmental Research · doi:10.1016/j.envres.2022.114637 · Cited by 564
The carbon impact of artificial intelligence
Dhar, P. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0219-9 · Cited by 509
Artificial intelligence and industrial innovation: Evidence from German firm-level data
Rammer, C., Fernández, G. P., & Czarnitzki, D. (2022) · Research Policy · doi:10.1016/j.respol.2022.104555 · Cited by 489
Impacts of urbanization on carbon emissions: An empirical analysis from OECD countries
Wang, W., Liu, L.-C., Liao, H., & Wei, Y. (2021) · Energy Policy · doi:10.1016/j.enpol.2021.112171 · Cited by 472
Cited by
Sorted by citations
A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical UsagePreprint
Hadi, M. U., Al-Tashi, Q., Qureshi, R., Shah, A., Muneer, A., Irfan, M., Zafar, A., Shaikh, M. B., Akhtar, N., Hassan, S. Z., et al. (2023) · doi:10.36227/techrxiv.23589741.v1 · Cited by 475
Wang, Q., Li, Y., & Li, R. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03520-5 · Cited by 280
Hadi, M. U., Al-Tashi, Q., Qureshi, R., Shah, A., Muneer, A., Irfan, M., Zafar, A., Shaikh, M. B., Akhtar, N., Hassan, S. Z., et al. (2023) · doi:10.36227/techrxiv.23589741.v4 · Cited by 193
Wang, Q., Li, Y., & Li, R. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-02736-9 · Cited by 108
Zhang, F., Li, R., & Wang, Q. (2025) · Sustainable Development · doi:10.1002/sd.70312 · Cited by 92
Alam, G. M. I., Tanim, S. A., Sarker, S. K., Watanobe, Y., Islam, M. R., Mridha, M. F., & Nur, K. M. (2025) · Scientific Reports · doi:10.1038/s41598-025-87233-y · Cited by 101
Hadi, M. U., Al-Tashi, Q., Qureshi, R., Shah, A., Muneer, A., Irfan, M., Zafar, A., Shaikh, M. B., Akhtar, N., Wu, J., et al. (2023) · doi:10.36227/techrxiv.23589741 · Cited by 111
How does digital inclusive finance promote the journey of common prosperity in China?
Zou, J., Yao, L., Wang, B., Zhang, Y., & Deng, X. (2024) · Cities · doi:10.1016/j.cities.2024.105083 · Cited by 74
Regilme, S. S. F. (2024) · SAIS Review of International Affairs · doi:10.1353/sais.2024.a950958 · Cited by 65
Iqbal, A., Zhang, W., & Jahangir, S. (2025) · Sustainability · doi:10.3390/su17030990 · Cited by 66
Hadi, M. U., Al-Tashi, Q., Qureshi, R., Shah, A., Muneer, A., Irfan, M., Zafar, A., Shaikh, M. B., Akhtar, N., Hassan, S. Z., et al. (2023) · doi:10.36227/techrxiv.23589741.v3 · Cited by 71
Robots for sustainability: Evaluating ecological footprints in leading AI-driven industrial nations
Liu, L., Rasool, Z., Ali, S., Wang, C., & Nazar, R. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102460 · Cited by 50
Kandhare, P., Kurlekar, M., Deshpande, T., & Pawar, A. (2025) · Drugs and Drug Candidates · doi:10.3390/ddc4010009 · Cited by 58
Hadi, M. U., Al-Tashi, Q., Qureshi, R., Shah, A., Muneer, A., Irfan, M., Zafar, A., Shaikh, M. B., Akhtar, N., Hassan, S. Z., et al. (2023) · doi:10.36227/techrxiv.23589741.v2 · Cited by 54
Luo, H., & Sun, Y. (2024) · Energy · doi:10.1016/j.energy.2024.132081 · Cited by 43
Shan, T., Feng, S., Li, K., Chang, R., & Huang, R. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.123657 · Cited by 43
Zhang, G., Chang, F., & Liu, J. (2024) · Sustainable Cities and Society · doi:10.1016/j.scs.2024.105776 · Cited by 40
Advances and challenges in energy and climate alignment of AI infrastructure expansion
Lal, A., & You, F. (2025) · Advances in Applied Energy · doi:10.1016/j.adapen.2025.100243 · Cited by 35
Zhang, F., Wang, Q., & Li, R. (2025) · Energy Economics · doi:10.1016/j.eneco.2025.108785 · Cited by 36
Wang, Z., Zhou, F., & Zhong, J. (2024) · Economic Analysis and Policy · doi:10.1016/j.eap.2024.09.028 · Cited by 30
Self-citationBergougui, B. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.103029 · Cited by 30
How does artificial intelligence change carbon emission intensity? A firm lifecycle perspective
Wu, Q., & Zhou, P. (2025) · Applied Economics · doi:10.1080/00036846.2025.2482927 · Cited by 28
Bergougui, B. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.103021 · Cited by 28
Wang, Q., Li, Y., Pata, U. K., & Li, R. (2025) · Geoscience Frontiers · doi:10.1016/j.gsf.2025.102072 · Cited by 27
Does income inequality undermine the carbon abatement benefits of artificial intelligence?
Dong, Z., Zhang, L., Tan, C. P.-P., Luo, Q., & Zhang, L. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.143437 · Cited by 25
Can AI grow green? Evidence of a Kuznets curve among AI, renewable energies and emissions
Melguizo, Á., Katz, R. L., & Jung, J. (2025) · Energy Policy · doi:10.1016/j.enpol.2025.114883 · Cited by 23
Towards carbon neutrality: Will artificial intelligence and green bond become catalysts?
Wang, X., Safi, A., & Ge, F. (2025) · Energy Economics · doi:10.1016/j.eneco.2025.108711 · Cited by 17
Wang, X., Khan, M. A. S., & Mir, F. W. (2025) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2025.146331 · Cited by 16
Artificial Intelligence in Energy Economics Research: A Bibliometric Review
Jiao, Z., Zhang, C., & Li, W. (2025) · Energies · doi:10.3390/en18020434 · Cited by 14
Yang, T., Han, M., Zhong, Y., Zhong, J., & Zhang, Q. (2023) · Economic Change and Restructuring · doi:10.1007/s10644-023-09544-2 · Cited by 14
Self-citationJiang, W., Hu, Y., & Zhao, X. (2025) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2025.123973 · Cited by 16
Survey on AI Ethics: A Socio‐Technical Perspective
Mbiazi, D., Bhange, M., Babaei, M., Sheth, I., Kenfack, P. J., & Kahou, S. E. (2025) · Computational Intelligence · doi:10.1111/coin.70149 · Cited by 39
Broby, D., & Yang, Z. (2025) · Journal of risk and financial management · doi:10.3390/jrfm18070379 · Cited by 17
Driving the Green Transformation of Enterprises: The Role of Patent Insurance
Zhong, J., Wang, Z., & Deng, Y. (2024) · Managerial and Decision Economics · doi:10.1002/mde.4439 · Cited by 11
Self-citationXu, X., Wang, Y., Deng, Y., Jiang, M., & Shi, V. (2026) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2026.124765 · Cited by 11
Suppliers’ AI adoption and customers’ carbon emissions: firm-level evidence from China
Han, F. L., Qi, Q., & Zhou, S. (2025) · Applied Economics · doi:10.1080/00036846.2025.2482246 · Cited by 13
Vasilescu, L. G., Sichigea, M., Sitnikov, C. S., & Mihai, L. (2025) · Economies · doi:10.3390/economies13090271 · Cited by 11
The impact of AI on enterprise energy management: from the perspective of carbon emissions
Yang, G., Yang, G., & Yang, W. (2024) · Science and Technology for Energy Transition · doi:10.2516/stet/2024096 · Cited by 10
Towards Sustainable Development: Can Industrial Intelligence Promote Carbon Emission Reduction
Xu, H., Cao, Z., & Han, D. (2025) · Sustainability · doi:10.3390/su17010370 · Cited by 10
AI Analytics for Carbon-Neutral City Planning: A Systematic Review of Applications
Cong, C., Page, J., Kwak, Y., Deal, B. M., & Kalantari, Z. (2024) · Urban Science · doi:10.3390/urbansci8030104 · Cited by 14
Hasanov, F., Sbia, R., Paparas, D., & Kostakis, I. (2024) · Energy Reports · doi:10.1016/j.egyr.2024.11.054 · Cited by 9
Narimani, S., Hoff, S. R., Kurz, K. D., Gjesdal, K., Geisler, J., & Grøvik, E. (2025) · Scientific Reports · doi:10.1038/s41598-025-92863-3 · Cited by 9
Ahmad, M., Rana, J., Islam, M. F., Jihad, M. N. I., Rashed, M., & Uddin, M. K. (2026) · Business Strategy and the Environment · doi:10.1002/bse.70748 · Cited by 8
How does artificial intelligence technology affect ESG performance? Evidence from China
Li, X., & Bian, J. (2025) · Sustainability Accounting Management and Policy Journal · doi:10.1108/sampj-03-2024-0272 · Cited by 7
Zambrano‐Monserrate, M. A., Soto, G. H., & Subramaniam, Y. (2025) · Energy Sources Part B Economics Planning and Policy · doi:10.1080/15567249.2025.2558529 · Cited by 7
KONAT, G., SALİHOĞLU, E., & Han, A. (2026) · Carbon Balance and Management · doi:10.1186/s13021-025-00383-4 · Cited by 7
Zhong, J., Tang, S. Y., & Albitar, K. (2026) · Corporate Social Responsibility and Environmental Management · doi:10.1002/csr.70387 · Cited by 6
Self-citationYu, D., & Xu, B. (2026) · Economic Analysis and Policy · doi:10.1016/j.eap.2026.01.060 · Cited by 8
Technological innovations fuel carbon prices and transform environmental management across Europe
Balcılar, M., Elsayed, A. H., Khalfaoui, R., & Hammoudeh, S. M. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.123663 · Cited by 6
Beba, H., & Öztürk, Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-29724-6 · Cited by 5
Çağlar, A. E., Radulescu, M., & Uche, E. (2025) · Sustainable Development · doi:10.1002/sd.70147 · Cited by 5
Bergougui, B., & Sulimany, H. G. H. (2026) · Business Strategy and the Environment · doi:10.1002/bse.70702 · Cited by 4
Usman, M. Z., Huang, Y., & Makhdum, M. S. A. (2026) · Sustainable Development · doi:10.1002/sd.71024 · Cited by 4
Li, X., Liang, F., Pi, Y., & Chen, D. (2024) · PLoS ONE · doi:10.1371/journal.pone.0310476 · Cited by 4
Thematic Analysis of Blockchain and Artificial Intelligence in Sustainable Finance
Arslan, M., Faizulayev, A., Nauman, M., & Wisker, Z. L. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0355-0.ch002 · Cited by 5
The positive effects of the higher education expansion policy on urban innovation in China
Zhang, Q., Chen, Y., Zhong, Y., & Zhong, J. (2024) · AIMS Mathematics · doi:10.3934/math.2024147 · Cited by 4
Self-citationHamadou, I., Wicaksono, T. Y., & Hamidi, M. L. (2026) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.128500 · Cited by 5
Bottom-up green spillovers: how corporate ESG drives green transformation in supply chains
Zhang, Q., Albitar, K., Zhang, H., & Han, M. (2026) · Applied Economics · doi:10.1080/00036846.2025.2612270 · Cited by 4
Self-citationClimate target announcements as catalysts for artificial intelligence advancement
Zhou, Y., Cai, X., Yu, J., & Ji, X. (2026) · Energy Economics · doi:10.1016/j.eneco.2026.109247 · Cited by 4
Environmental outcomes of artificial intelligence
SenGupta, S., & Menegaki, A. (2026) · Journal of Environmental Management · doi:10.1016/j.jenvman.2026.130557 · Cited by 3
Meng, C., Yin, Q., & Lin, Y. (2024) · Applied Economics Letters · doi:10.1080/13504851.2024.2398715 · Cited by 3
Sustainable Innovation and the Circular Economy: Regulatory and Economic Drivers
Balsalobre‐Lorente, D., Rong, N., Shah, S. A. R., Huseynova, R., & Abbas, N. (2025) · Springer eBooks · doi:10.1007/978-3-032-00437-6_4 · Cited by 2
Driving tourism growth through AI adoption in G-7 economies: a quantile regression approach
Zhao, S., Javed, M. A., & Gao, X. (2025) · Information Technology & Tourism · doi:10.1007/s40558-025-00342-2 · Cited by 2
Ma, J., & George, T. (2026) · Sustainable Development · doi:10.1002/sd.70766 · Cited by 2
Sun, Y.-P., Nguyen, D., & Zhang, S.-F. (2026) · Sustainable Development · doi:10.1002/sd.70822 · Cited by 2
On using large language models to support social research for climate action
Nabavi, E., Maier, H. R., Razavi, S., Hindes, A., Howden, M. S., Grant, W. J., & Raman, S. (2026) · npj Climate Action · doi:10.1038/s44168-026-00375-1 · Cited by 2
Artificial Intelligence and Economic Development: A Systematic Review of Patterns and Pathways
Ho, S., & Giwa, F. (2026) · Journal of the Knowledge Economy · doi:10.1007/s13132-026-03385-w · Cited by 2
Liu, B., Qiu, Z., Peng, J., & Hu, Y. (2024) · Expert Systems · doi:10.1111/exsy.13755 · Cited by 3
Ou, Y., Li, Y., & Zhang, T. (2025) · Sustainability · doi:10.3390/su17177918 · Cited by 2
Green finance and sustainability in China: Myth or reality?
Anees, A., Meo, M. S., & Saleem, A. (2025) · Sustainable Futures · doi:10.1016/j.sftr.2025.101426 · Cited by 2
Lee, H. S., Kuang, K. S., Liew, P. X., & Har, W. M. (2024) · International Conference on Mechatronics and Robotics Engineering (ICMRE) · doi:10.1109/icmre60776.2024.10532166 · Cited by 2
AI and carbon pricing in turbulent times: Navigating market dynamics for a sustainable future
Elsayed, A. H., Khalfaoui, R., Zhang, D., & Urquhart, A. (2025) · International Review of Financial Analysis · doi:10.1016/j.irfa.2025.104632 · Cited by 1
Narimani, S., Hoff, S. R., Kurz, K. D., Gjesdal, K., Geisler, J., & Grøvik, E. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01947-z · Cited by 1
Ahmad, S., Wu, X., Rahman, A. U., & Ullah, A. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-06230-8 · Cited by 1
Corporate Decarbonization via Technology and Management
Montero, H., Sinkovics, R. R., & Kuivalainen, O. (2026) · Business Strategy and the Environment · doi:10.1002/bse.70648 · Cited by 1
The Green Illusion? How Artificial Intelligence and Finance Shape Environmental Futures
Sönmez, Y., Balcı, N., Doğan, M., Köylüoğlu, A. S., & ÇERÇİ, S. (2026) · Sustainability · doi:10.3390/su18104907 · Cited by 1
Nguyen, T. P., & Duong, T. T. T. (2026) · Discover Sustainability · doi:10.1007/s43621-026-03998-w · Cited by 1
Nabavi, E., Maier, H. R., Razavi, S., Hindes, A., Howden, M. S., Grant, W. J., & Raman, S. (2024) · doi:10.22541/essoar.171136837.71755629/v1 · Cited by 1
Identifying Important AI Sub-Pillars on Carbon Dioxide Emission Using Data Mining Techniques
Lee, H. S., Kuang, K. S., Khor, K., Ooi, B. C., & Lee, S. Y. (2025) · IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) · doi:10.1109/iscaie64985.2025.11080796
Balancing AI’s energy consumption with commitments to clean energy (SDG 7): Is there a trade-off?
Glavas, D. (2025) · International Journal of Green Energy · doi:10.1080/15435075.2025.2546047
Makur, P., & Sur, D. (2025) · Journal of Electronic Business & Digital Economics · doi:10.1108/jebde-02-2025-0017
Enhancing Predictive Analysis with Large Language Models in the Digital Innovation World
Kohli, S., Dhir, S., & Kumud (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2474-6.ch008
Makur, P. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83662-570-420251014
Hussain, I., Manna, S., & Banerjee, A. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83662-570-420251015
Idris, A., Aung, S., Shah, H., & Giribabu, N. (2025) · International Journal of Clinical Medical Research · doi:10.61466/ijcmr3060002
Brivá-Iglesias, V. (2025) · Tradumàtica tecnologies de la traducció · doi:10.5565/rev/tradumatica.504
Metal organic frameworks in ionic liquid composites allow efficient CO2 capture
Guo, N., An, T., Li, Z., Gao, D., & Yang, J. (2026) · Chemical Engineering Journal · doi:10.1016/j.cej.2026.174638
Social trust and corporate default risk: evidence from a machine learning approach
Zhang, Q., Chen, Y., Zhong, J., & Albitar, K. (2026) · Journal of the Asia Pacific Economy · doi:10.1080/13547860.2026.2638287
Self-citationLu, X., Liao, L., Luo, X., & Zhao, B. (2026) · Sustainability · doi:10.3390/su18073494
Han, X., Alajlani, S. E., Dong, Z., Chong, K. M., & Pardaev, J. (2026) · Sustainable Development · doi:10.1002/sd.71167
How Does AI Technology Innovation Boost Carbon Productivity? Evidence from China
Du, Z., Luo, S., Alhidi, A. M., & Zhao, L. (2026) · Sustainability · doi:10.3390/su18104984
Liu, Z., & Zhao, C. (2026) · Polish Journal of Environmental Studies · doi:10.15244/pjoes/217903
Artificial Intelligence: facilitator or destroyer of carbon neutrality? Evidence from China
Su, C., Liu, W., Qin, M., & Lin, C.-T. (2026) · Climate Policy · doi:10.1080/14693062.2026.2678306
Deeb, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6866873
Saleem, F., & Xu, L. (2026) · Journal of Tax Reform · doi:10.15826/jtr.2026.12.2.250
Scientific literacy and generative AI: embracing nature of science for social justice
Yacoubian, H. A. (2026) · International Journal of Science Education · doi:10.1080/09500693.2026.2701380
Rahman, M. M., Islam, K. S., Islam, M. F., Alam, M. S., & Lukács, E. (2026) · Discover Sustainability · doi:10.1007/s43621-026-04081-0
Islam, M. S., Zheng, G., Islam, M. F., Muhammad, F., & Rahman, S. (2026) · International Review of Economics & Finance · doi:10.1016/j.iref.2026.105667
Aslam, Y., Sanoe, A. A., & Ghazi, W. (2026) · Springer eBooks · doi:10.1007/978-981-92-2732-7_7
Sustainable Digital Banking as a Catalyst for Climate-Responsive Financial Services
Vohra, A., & Purohit, S. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-9745-0.ch005
Metadata
- Authors
- Zhong, J., Zhong, Y., Han, M., Yang, T., & Zhang, Q.
- Affiliations
- South China University of Technology (CN)
- Guangzhou Huashang College (CN)
- Corresponding author
- Yang, Tianjian
- Venue
- Applied Economics
- Publisher
- Informa UK Limited
- Published
- 2023-04-19
- Volume
- 56
- Issue
- 25
- Pages
- 2975-2989
- ISSN
- 0003-6846 (ISSN-L)
- 1466-4283
- OpenAlex
- W4366490174
- Cited by
- 105
- References
- 38
- Citations by year
- 2026: 38 · 2025: 41 · 2024: 20 · 2023: 6
- FWCI
- 21.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Economics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- The National Social Science Youth Fund of China (20CJY009)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Energy, Environment, Economic Growth
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic Growth and Development
- Keywords
- artificial intelligence, CO2 emissions, carbon emission reduction, heterogeneous effects, industrial structure, demographic structure, population aging, high-income countries, environmental governance, economic development
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review