Drivers of carbon emissions in the era of artificial intelligence: case of Japan
DOI: https://doi.org/10.3846/tede.2026.26996Abstract
This research is driven by the absence of a unified consensus regarding the relationship between artificial intelligence, energy consumption, and economic growth and their impact on CO2 emissions. Not clear whether AI increases or decreases CO2. The novelty – identification of the two-way causal link between the implementation of AI and carbon emissions, a dynamic not previously confirmed in the literature. The originality – the model tested on a scale of Japan as developed country which still around 90% depending on fossil fuels. Data period: 1995–2024. An ARDL-based econometric approach to analyze the long-term and short-term impacts and several diagnostics to improve the precision and reliability of the study results. Tests used: the Breusch-Godfrey Serial Correlation, Ramsey RESET, Breusch-Pagan-Godfrey, CUSUMSQ and CUSUM. The study outputs reveal that in Japan, AI and energy consumption are associated with an increase in carbon emissions, while exports – with decrease in emission levels. In developed economies governments recommended to lower CO2 emissions by speeding up the shift toward renewable energy sources and investing in environmentally friendly AI technologies. Policymakers should adopt an integrated approach that links AI, energy, environmental, and economic policies, supported by regulatory reforms to promote sustainability and achieve carbon neutrality.
First published online 8 June 2026
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Japan, artificial intelligence, economic development, energy use, carbon emissions, ARDLHow to Cite
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References
Adebayo, T. S., Awosusi, A. A., Oladipupo, S. D., Agyekum, E. B., Jayakumar, A., & Kumar, N. M. (2021). Dominance of fossil fuels in Japan’s national energy mix and implications for environmental sustainability. International Journal of Environmental Research and Public Health, 18(14), Article 7347. https://doi.org/10.3390/ijerph18147347
Afroz, R., Alofaysan, H., Sarabdeen, M., Muhibbullah, M. D., & Muhammad, Y. B. (2024). Analyzing the influence of energy consumption and economic complexity on carbon emissions: Evidence from Malaysia. Energies, 17(12), Article 2900. https://doi.org/10.3390/en17122900
Ali, M., & Kirikkaleli, D. (2022). The asymmetric effect of renewable energy and trade on consumption-based CO2 emissions: The case of Italy. Integrated Environmental Assessment and Management, 18(3), 784–795. https://doi.org/10.1002/ieam.4516
Appiah, M., Li, M., Onifade, S. T., & Gyamfi, B. A. (2024). Investigating institutional quality and carbon mitigation drive in Sub-Saharan Africa: Are growth levels, energy use, population, and industrialization consequential factors? Energy and Environment, 35(4), 2031–2057. https://doi.org/10.1177/0958305X221147602
Ayhan, F., Kartal, M. T., Kılıç Depren, S., & Depren, Ö. (2023). Asymmetric effect of economic policy uncertainty, political stability, energy consumption, and economic growth on CO2 emissions: Evidence from G-7 countries. Environmental Science and Pollution Research, 30, 47422–47437. https://doi.org/10.1007/s11356-023-25665-7
Brock, W. A., & Taylor, M. S. (2005). Economic growth and the environment: A review of theory and empirics. In Handbook of economic growth (Vol. 1, pp. 1749–1821). Elsevier. https://doi.org/10.1016/S1574-0684(05)01028-2
Chatti, W., Majeed, M. T., Khoj, H., Miraz, M. H., & Ali, A. (2024). Towards smart and sustainable transportation: The role of artificial intelligence and new technologies in mitigating passenger car CO2 emissions in European countries. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-024-05685-0
Chen, P. Y., Chen, S. T., Hsu, C. S., & Chen, C. C. (2016). Modeling the global relationships among economic growth, energy consumption and CO2 emissions. Renewable and Sustainable Energy Reviews, 65, 420–431. https://doi.org/10.1016/j.rser.2016.06.074
Constantinescu, A., Kraujalienė, L., Socol, A. G., Marin-Pantelescu, A., & Roman, M. D. (2025). Economic, social and technological determinants of foreign direct investment inflows: A worldwide analysis. Economic Computation and Economic Cybernetics Studies and Research, 59(3), 114–130. https://doi.org/10.24818/18423264/59.3.25.07
Crippa, M., Guizzardi, D., Pagani, F., Banja, M., Muntean, M., Schaaf, E., Becker, WE., Monforti-Ferrario, F., Quadrelli, R., Risquez Martin, A., Taghavi-Moharamli, P., Köykkä, J., Grassi, G., Rossi, S., Melo, J., Oom, D., Branco, A., San-Miguel, J., & Vignati, E. (2023). GHG emissions of all world countries (JRC/IEA Report). Publications Office of the European Union. https://doi.org/https://edgar.jrc.ec.europa.eu/
DeAngelo, J., Azevedo, I., Bistline, J., Clarke, L., Luderer, G., Byers, E., & Davis, S. J. (2021). Energy systems in scenarios at net-zero CO2 emissions. Nature Communications, 12, Article 6096. https://doi.org/10.1038/s41467-021-26356-y
De Bolle, M. (2024). AI’s carbon footprint appears likely to be alarming. Peterson Institute for International Economics. https://www.piie.com/blogs/realtime-economics/2024/ais-carbon-footprint-appears-likely-be-alarming
Delanoë, P., Tchuente, D., & Colin, G. (2023). Method and evaluations of the effective gain of artificial intelligence models for reducing CO2 emissions. Journal of Environmental Management, 331, Article 117261. https://doi.org/10.1016/j.jenvman.2023.117261
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427–431. https://doi.org/10.1080/01621459.1979.10482531
Dissanayake, H., Perera, N., Abeykoon, S., Samson, D., Jayathilaka, R., Jayasinghe, M., & Yapa, S. (2023). Nexus between carbon emissions, energy consumption, and economic growth: Evidence from global economies. PLoS ONE, 18(6), Article e0287579. https://doi.org/10.1371/journal.pone.0287579
Dong, K., Hochman, G., & Timilsina, G. R. (2018). Are driving forces of CO2 emissions different across countries? Insights from identity and econometric analyses. (Policy Research Working Paper No. 8477). World Bank Group. https://documents1.worldbank.org/curated/en/777121529433693727/pdf/WPS8477.pdf
Dong, M., Wang, G., & Han, X. (2023). Artificial intelligence, industrial structure optimization, and CO2 emissions. Environmental Science and Pollution Research, 30, 108757–108773. https://doi.org/10.1007/s11356-023-29859-x
Dong, M., Wang, G., & Han, X. (2024). Impacts of artificial intelligence on carbon emissions in China: In terms of artificial intelligence type and regional differences. Sustainable Cities and Society, 113, Article 105682. https://doi.org/10.1016/j.scs.2024.105682
Glass, D. V., (1976). [Review of Thomas Robert Malthus: An essay on the principle of population., by P. Appleman]. Population Studies, 30(2), 369–369. https://doi.org/10.2307/2173616
Global Carbon Atlas. (2024). Carbon emissions in MtCO2. https://globalcarbonatlas.org/emissions/carbon-emissions/
Hamed, L. M. M., Dhaouadi, L., Zehri, F., Tiba, S., Besser, H., Karbout, N., & Emara, E. I. R. (2024). Examining the relationship between the economic growth, energy use, CO2 emissions, and water resources: Evidence from selected MENA countries. Journal of the Saudi Society of Agricultural Sciences, 23(6), 415–423. https://doi.org/10.1016/j.jssas.2024.04.002
Hasanov, F. J., Liddle, B., & Mikayilov, J. I. (2018). The impact of international trade on CO2 emissions in oil exporting countries: Territory vs consumption emissions accounting. Energy Economics, 74, 343–350. https://doi.org/10.1016/j.eneco.2018.06.004
Haug, A. A., & Ucal, M. (2019). The role of trade and FDI for CO2 emissions in Turkey: Nonlinear relationships. Energy Economics, 81, 297–307. https://doi.org/10.1016/j.eneco.2019.04.006
International Monetary Fund. (2025). IMF Executive Board Concludes 2025 Article IV Consultation with Japan (Press Release No. 25/084). https://www.imf.org/en/News/Articles/2025/04/01/pr25084-japan-imf-executive-board-concludes-2025-article-iv-consultation-with-japan?
Iqbal, A., Tang, X., & Rasool, S. F. (2023). Investigating the nexus between CO2 emissions, renewable energy consumption, FDI, exports and economic growth: Evidence from BRICS countries. Environment, Development and Sustainability, 25, 2234–2263. https://doi.org/10.1007/s10668-022-02128-6
Karaaslan, A., & Çamkaya, S. (2022). The relationship between CO2 emissions, economic growth, health expenditure, and renewable and non-renewable energy consumption: Empirical evidence from Turkey. Renewable Energy, 190, 457–466. https://doi.org/10.1016/j.renene.2022.03.139
Khan, Z., Ali, S., Umar, M., Kirikkaleli, D., & Jiao, Z. (2020). Consumption-based carbon emissions and international trade in G7 countries: The role of environmental innovation and renewable energy. Science of the Total Environment, 730, Article 138945. https://doi.org/10.1016/j.scitotenv.2020.138945
Khan, I., Hou, F., Zakari, A., Tawiah, V., & Ali, S. A. (2022). Energy use and urbanization as determinants of China’s environmental quality: Prospects of the Paris climate agreement. Journal of Environmental Planning and Management, 65(13), 2363–2386. https://doi.org/10.1080/09640568.2021.1972797
Khan, I., Zhong, R., Khan, H., Dong, Y., & Nuţă, F. M. (2024). Examining the relationship between technological innovation, economic growth and carbon dioxide emission: Dynamic panel data evidence. Environment, Development and Sustainability, 26, 18161–18180. https://doi.org/10.1007/s10668-023-03384-w
Kirikkaleli, D., & Adebayo, T. S. (2021). Do public-private partnerships in energy and renewable energy consumption matter for consumption-based carbon dioxide emissions in India? Environmental Science and Pollution Research, 28, 30139–30152. https://doi.org/10.1007/s11356-021-12692-5
Kirikkaleli, D., Güngör, H., & Adebayo, T. S. (2022). Consumption-based carbon emissions, renewable energy consumption, financial development and economic growth in Chile. Business Strategy and the Environment, 31(3), 1123–1137. https://doi.org/10.1002/bse.2945
Kraujalienė, L. (2019). Efficiency evaluation of technology transfer process in higher education institutions [Doctoral dissertation, Vilnius Gediminas Technical University]. https://doi.org/10.20334/2019-045-M
Kraujalienė, L., & Gruodis, A. (2025). Digital indicators for evaluation of the organizational development: American and Finnish methodologies. In I. Bartuseviciene, B. Antanas, A. Karasavvoglou, & P. Polychronidou, (Eds.), Building economic resilience: Strategies for sustainable growth and competitiveness (pp. 151–180). Springer. https://doi.org/10.1007/978-3-031-96428-2_7
Kraujalienė, L., & Gruodis, A. (2026). LeadWinO self-assessment model for managers activity: A feed-forward neural network-based indicator system. Administrative Sciences, 16(5), Article 197. https://doi.org/10.3390/admsci16050197
Kraujalienė, L., Yaseen, A., & Bilinskienė, I. (2025). The impact of industrialization and self-renewing energy utilization on carbon discharges in the country economics. Technological and Economic Development of Economy, 31(5), 1665–1685. https://doi.org/10.3846/tede.2025.24578
Kraujalienė, L., Yaseen, A., Marin-Pantelescu, A., & Topor, D. I. (2026). The impact of industrialization, information and communication technology, economic activity, and trade openness on emissions in Europe: Evidence from Lithuania. Sustainability, 18(3), Article 1314. https://doi.org/10.3390/su18031314
Luan, F., Yang, X., Chen, Y., & Regis, P. J. (2022). Industrial robots and air environment: A moderated mediation model of population density and energy consumption. Sustainable Production and Consumption, 30, 870–888. https://doi.org/10.1016/j.spc.2022.01.015
Magazzino, C., & Mele, M. (2025). A new machine learning algorithm to explore the CO2 emissions-energy use-economic growth trilemma. Annals of Operations Research, 345, 665–683. https://doi.org/10.1007/s10479-022-04787-0
Mehboob, M. Y., Ma, B., Sadiq, M., & Zhang, Y. (2024). Does nuclear energy reduce consumption-based carbon emissions: The role of environmental taxes and trade globalization in highest carbon emitting countries. Nuclear Engineering and Technology, 56(1), 180–188. https://doi.org/10.1016/j.net.2023.09.022
Nawaz, F., Kayani, U., ElRefae, G. A., Hasan, F., & Bazai, H. S. K. (2025). Economic growth and carbon emissions nexus: Environmental sustainability a case of Japan from East Asia. Discover Sustainability, 6, Article 177. https://doi.org/10.1007/s43621-025-00941-3
Ohta, H., & Barrett, B. F. (2023). Politics of climate change and energy policy in Japan: Is green transformation likely?. Earth System Governance, 17, Article 100187. https://doi.org/10.1016/j.esg.2023.100187
Organisation for Economic Co-operation and Development. (n.d.). Triadic patent families. https://www.oecd.org/en/data/indicators/triadic-patent-families.html?
Osobajo, O. A., Otitoju, A., Otitoju, M. A., & Oke, A. (2020). The impact of energy consumption and economic growth on carbon dioxide emissions. Sustainability, 12(19), Article 7965. https://doi.org/10.3390/SU12197965
Our World in Data. (n.d.). https://ourworldindata.org/
Pata, U. K. (2018). The influence of coal and noncarbohydrate energy consumption on CO2 emissions: Revisiting the environmental Kuznets curve hypothesis for Turkey. Energy, 160, 1115–1123. https://doi.org/10.1016/j.energy.2018.07.095
Pesaran, M. H., & Shin, Y. (1999). An autoregressive distributed lag modelling approach to cointegration analysis. In S. Strøm (Ed.). Econometrics and economic theory in the 20th century: The Ragnar Frisch Centennial Symposium (pp. 371–413). Cambridge University Press. https://doi.org/10.1017/CCOL521633230.011
Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. https://doi.org/10.1002/jae.616
Pratiwi, S., & Juerges, N. (2020). Review of the impact of renewable energy development on the environment and nature conservation in Southeast Asia. Energy, Ecology and Environment, 5, 221–239. https://doi.org/10.1007/s40974-020-00166-2
Puntoon, W., Tarkhamtham, P., & Tansuchat, R. (2022). The impacts of economic growth, industrial production, and energy consumption on CO2 emissions: A case study of leading CO2 emitting countries. Energy Reports, 8, 414–419. https://doi.org/10.1016/j.egyr.2022.10.219
Rahman, M. M., Alam, K., & Velayutham, E. (2022). Reduction of CO2 emissions: The role of renewable energy, technological innovation and export quality. Energy Reports, 8, 2793–2805. https://doi.org/10.1016/j.egyr.2022.01.200
Rahman, M. M., & Alam, K. (2022). CO2 Emissions in Asia-Pacific region: Do energy use, economic growth, financial development, and international trade have detrimental effects? Sustainability, 14(9), Article 5420. https://doi.org/10.3390/su14095420
Raihan, A., Begum, R. A., Nizam, M., Said, M., & Pereira, J. J. (2022). Dynamic impacts of energy use, agricultural land expansion, and deforestation on CO2 emissions in Malaysia. Environmental and Ecological Statistics, 29, 477–507. https://doi.org/10.1007/s10651-022-00532-9
Raihan, A., Tanchangya, T., Rahman, J., & Ridwan, M. (2024). The influence of agriculture, renewable energy, international trade, and economic growth on India’s environmental sustainability. Journal of Environmental and Energy Economics, 3, 37–53. https://doi.org/10.56946/jeee.v3i1.324
Rasheed, M. Q., Yuhuan, Z., Haseeb, A., Ahmed, Z., & Saud, S. (2024). Asymmetric relationship between competitive industrial performance, renewable energy, industrialization, and carbon footprint: Does artificial intelligence matter for environmental sustainability? Applied Energy, 367, Article 123346. https://doi.org/10.1016/j.apenergy.2024.123346
Safi, N., Rashid, M., Shakoor, U., Khurshid, N., Safi, A., & Munir, F. (2023). Understanding the role of energy productivity, eco-innovation and international trade in shaping consumption-based carbon emissions: A study of BRICS nations. Environmental Science and Pollution Research, 30, 98338–98350. https://doi.org/10.1007/s11356-023-29358-z
Salazar-Núñez, H. F., Venegas-Martínez, F., & Lozano-Díez, J. A. (2022). Assessing the interdependence among renewable and non-renewable energies, economic growth, and CO2 emissions in Mexico. Environment, Development and Sustainability, 24, 12850–12866. https://doi.org/10.1007/s10668-021-01968-y
Saqib, N., Abbas, S., Ozturk, I., Murshed, M., Tarczyńska-Łuniewska, M., Alam, M. M., & Tarczyński, W. (2024). Leveraging environmental ICT for carbon neutrality: Analyzing the impact of financial development, renewable energy and human capital in top polluting economies. Gondwana research, 126, 305–320. https://doi.org/10.1016/j.gr.2023.09.014
Shah, S. A., Ye, X., Wang, B., & Wu, X. (2024). Dynamic linkages among carbon emissions, artificial intelligence, economic policy uncertainty, and renewable energy consumption: Evidence from East Asia and Pacific countries. Energies, 17(16), Article 4011. https://doi.org/10.3390/en17164011
Sikder, M., Wang, C., Yao, X., Huai, X., Wu, L., KwameYeboah, F., Wood, J., Zhao, Y., & Dou, X. (2022). The integrated impact of GDP growth, industrialization, energy use, and urbanization on CO2 emissions in developing countries: Evidence from the panel ARDL approach. Science of the Total Environment, 837, Article 155795. https://doi.org/10.1016/j.scitotenv.2022.155795
Solmaz, A. R., Emikönel, M., Soylu, Ö. B., & Radulescu, M. (2025). Artificial intelligence and energy efficiency in the EU: A dynamic panel approach. International Journal of Energy Research, Article 3409830. https://doi.org/10.1155/er/3409830
Song, D., Hu, Y., Zhang, Q., Wang, S., & Bi, C. (2025). Harnessing AI for renewable energy transition: Threshold effects on China’s economic growth. Sustainable Futures, 10, Article 101349. https://doi.org/10.1016/j.sftr.2025.101349
Sreenu, N. (2022). Impact of FDI, crude oil price and economic growth on CO2 emission in India: – symmetric and asymmetric analysis through ARDL and non -linear ARDL approach. Environmental Science and Pollution Research, 29, 42452–42465. https://doi.org/10.1007/s11356-022-19597-x
Stankevičienė, J., & Kraujalienė, L. (2017, May 11–12). COPRAS approach for efficiency assessment of R&D expenditures in technology transfer process. In Proceedings of the 5th International Scientific Conference Contemporary issues in business, management and education’2017 (pp. 1–11). Vilnius, Lithuania. Vilnius Gediminas Technical University. https://doi.org/10.3846/cbme.2017.066
Stankevičienė, J., & Kraujalienė, L. (2021). Evaluation of the efficiency of the technology transfer process with DEA tool in Lithuanian higher education institutions. In K. Nermend, M. Łatuszyńska, & E. Thalassinos (Eds.), Decision-making in management: Methods and behavioral tools (pp. 263–286). Springer. https://doi.org/10.1007/978-3-030-67020-7_15
Sufyanullah, K., Ahmad, K. A., & Sufyan Ali, M. A. (2022). Does emission of carbon dioxide is impacted by urbanization? An empirical study of urbanization, energy consumption, economic growth and carbon emissions – Using ARDL bound testing approach. Energy Policy, 164, Article 112908. https://doi.org/10.1016/j.enpol.2022.112908
Tagwi, A. (2022). The impacts of climate change, carbon dioxide emissions (CO2) and renewable energy consumption on agricultural economic growth in South Africa: ARDL approach. Sustainability, 14(24), Article 16468. https://doi.org/10.3390/su142416468
Tang, Z., Tang, S., & Zou, J. (2025). Artificial intelligence and global embodied carbon flow: Evidence from the application of industrial robots. Habitat International, 165, Article 103560. https://doi.org/10.1016/j.habitatint.2025.103560
Tomlinson, B., Black, R. W., Patterson, D. J., & Torrance, A. W. (2024). The carbon emissions of writing and illustrating are lower for AI than for humans. Scientific Reports, 14, Article 3732. https://doi.org/10.1038/s41598-024-54271-x
Tripathi, S., Kolodziej, C. P., Masum, F., Al-Ghussain, L., Lu, Z., Gomez, D. D. C., He, X., Jin, E., Bouchard, J., Hawkins, T., & Wang, M. (2025). Life cycle greenhouse gas emissions and cost of marine transport with conventional fuels and methanol. Energy Conversion and Management: X, 27, Article 101116. https://doi.org/10.1016/j.ecmx.2025.101116
Voumik, L. C., Islam, M. A., Ray, S., Mohamed Yusop, N. Y., & Ridzuan, A. R. (2023). CO2 emissions from renewable and non-renewable electricity generation sources in the G7 countries: Static and dynamic panel assessment. Energies, 16(3), Article 1044. https://doi.org/10.3390/en16031044
Wang, J., Rickman, D. S., & Yu, Y. (2022). Dynamics between global value chain participation, CO2 emissions, and economic growth: Evidence from a panel vector autoregression model. Energy Economics, 109, Article 105965. https://doi.org/10.1016/j.eneco.2022.105965
Wang, Q., Li, Y., & Li, R. (2024a). Ecological footprints, carbon emissions, and energy transitions: The impact of artificial intelligence (AI). Humanities and Social Sciences Communications, 11, Article 1043. https://doi.org/10.1057/s41599-024-03520-5
Wang, Q., Zhang, F., Li, R., & Sun, J. (2024b). Does artificial intelligence promote energy transition and curb carbon emissions? The role of trade openness. Journal of Cleaner Production, 447, Article 141298. https://doi.org/10.1016/j.jclepro.2024.141298
Wang, S., Pan, M., & Wu, X. (2023). Sustainable development in the export trade from a symbiotic perspective on carbon emissions, exemplified by the case of Guangdong, China. Sustainability, 15(12), Article 9667. https://doi.org/10.3390/su15129667
Weili, L., Khan, H., Khan, I., & Han, L. (2022). The impact of information and communication technology, financial development, and energy consumption on carbon dioxide emission: Evidence from the Belt and Road countries. Environmental Science and Pollution Research, 29, 27703–27718. https://doi.org/10.1007/s11356-021-18448-5
World Bank. (n.d.). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators
Xu, H., Cao, Z., & Han, D. (2025). Towards sustainable development: Can industrial intelligence promote carbon emission reduction. Sustainability, 17(1), Article 370. https://doi.org/10.3390/su17010370
Yahyaoui, I. (2022). Does the interaction between ICT diffusion and economic growth reduce CO2 emissions? An ARDL approach. Journal of the Knowledge Economy, 15, 661–681. https://doi.org/10.1007/s13132-022-01090-y
Yan, Y., Li, X., Wang, R., & Pan, A. (2023). Global value chain and export-embodied carbon emissions: New evidence from foreign-invested enterprises. Economic Modelling, 127, Article 106449. https://doi.org/10.1016/j.econmod.2023.106449
Zafar, M. W., Saleem, M. M., Destek, M. A., & Caglar, A. E. (2022). The dynamic linkage between remittances, export diversification, education, renewable energy consumption, economic growth, and CO2 emissions in top remittance-receiving countries. Sustainable Development, 30(1), 165–175. https://doi.org/10.1002/sd.2236
Zhao, S., & Zhang, X. (2025). Did digitalization of manufacturing industry improved the carbon emission efficiency of exports: Evidence from China. Energy Strategy Reviews, 57, Article 101614. https://doi.org/10.1016/j.esr.2024.101614
Zhang, H. (2021). Technology innovation, economic growth and carbon emissions in the context of carbon neutrality: Evidence from BRICS. Sustainability, 13(20), Article 11138. https://doi.org/10.3390/su132011138
Zhang, Y. Q., Li, L., Sadiq, M., & Chien, F. S. (2024a). The impact of non-renewable energy production and energy usage on carbon emissions: Evidence from China. Energy and Environment, 35(4), 2248–2269. https://doi.org/10.1177/0958305X221150432
Zhang, Z., & Sharifi, A. (2024). Analysis of decoupling between CO2 emissions and economic growth in China’s provincial capital cities: A Tapio model approach. Urban Climate, 55, Article 101885. https://doi.org/10.1016/j.uclim.2024.101885
Zhu, J. X., Sun, M., Wei, S. X., & Ye, F. Y. (2023). Characterizing patent big data upon IPC: A survey of triadic patent families and PCT applications. Journal of Big Data, 10, Article 85. https://doi.org/10.1186/s40537-023-00778-5
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