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The impact of China’s artificial intelligence development on urban energy efficiency
Caglar, A. E., Gönenç, S. & Destek, M. A. Toward a sustainable environment within the framework of carbon neutrality scenarios: evidence from the novel Fourier-NARD approach. Sustain. Dev. 32, 6643–6655 (2024).
Caglar, A. E., Daştan, M., Ahmed, Z., Mert, M. & Avci, S. B. The synergy of renewable energy consumption, green technology, and environmental quality: designing < scp > sustainable development goals policies. Nat. Resour. Forum. (2024).
Wang, C. H. & Juo, W. An environmental policy of green intellectual capital: green innovation strategy for performance sustainability. Bus. Strat Env. 30, 3241–3254 (2021).
D’Adamo, I., Di Carlo, C., Gastaldi, M., Rossi, E. N. & Uricchio, A. F. Economic performance, environmental protection and social progress: A cluster analysis comparison towards sustainable development. Sustain. (Switz). 16, 5049–5049 (2024).
Ali, I., Rahaman, A., Ali, M. J. & Rahman, F. The growth–environment nexus amid geopolitical risks: cointegration and machine learning algorithm approaches. Discov. Sustain. 6, (2025).
Caglar, A. E., Daştan, M., Ahmed, Z., Mert, M. & Avci, S. B. A novel panel of European economies pursuing carbon neutrality: do current climate technology and renewable energy practices really pass through the Prism of sustainable development? Gondwana. Res. (2025).
Rasoulinezhad, E. & Taghizadeh-Hesary, F. Role of green finance in improving energy efficiency and renewable energy development. Energy Effic. 15, (2022).
Lee, C. C. & Lee, C. C. How does green finance affect green total factor productivity? Evidence from China. Energy Econ. 107, 105863 (2022).
Wang, J. & Hao, S. The Spatial impact of carbon trading on harmonious economic and environmental development: evidence from China. Environ. Geochem. Health. 45, 6495–6515 (2023).
Hong, Q., Cui, L. & Hong, P. The impact of carbon emissions trading on energy efficiency: evidence from quasi-experiment in china’s carbon emissions trading pilot. Energy Econ. 110, 106025–106025 (2022).
Chen, Z., Song, P. & Wang, B. Carbon emissions trading scheme, energy efficiency and rebound effect – Evidence from china’s provincial data. Energy Policy. 157, 112507–112507 (2021).
Song, M., Du, J. & Tan, K. H. Impact of fiscal decentralization on green total factor productivity. Int. J. Prod. Econ. 205, 359–367 (2018).
Liu, J., Cheng, Z. & Zhang, H. Does industrial agglomeration promote the increase of energy efficiency in china?? J. Clean. Prod. 164, 30–37 (2017).
Yuan, H., Feng, Y., Lee, C. C. & Cen, Y. How does manufacturing agglomeration affect green economic efficiency? Energy Econ. 92, 104944 (2020).
Ali, I., Islam, M. & Ceh, B. Assessing the impact of three emission (3E) parameters on environmental quality in canada: A provincial data analysis using the quantiles via moments approach. Int. J. Green. Energy. 1–19. (2024).
Jianda, W., Kangyin, D., Xiucheng, D. & Farhad, T. H. Assessing the digital economy and its carbon-mitigation effects: the case of China. Energy Econ. 113, (2022).
Rinku, N., Singh, N. G., Artificial intelligence in sustainable energy industry: status quo, challenges, and opportunities. J. Clean. Prod. 289, 234–237 (2023).
Viskovic, A., Franki, V. & Jevtic, D. Artificial Intelligence as a facilitator of the energy transition. In international convention on information and communication technology. Electron. Microelectron. 494–499. (2022).
Xue, Y., Tang, C., Wu, H., Liu, J. & Hao, Y. The emerging driving force of energy consumption in china: does digital economy development matter? Energy Policy. 165, 112997 (2022).
Liu, Z. et al. Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: challenges and future perspectives. Energy AI. 10, 100195–100195 (2022).
Hussain, M., Yang, S., Maqsood, U. S. & Zahid, R. M. A. Tapping into the green potential: the power of artificial intelligence adoption in corporate green innovation drive. Bus. Strat Env. 33, 4375–4396 (2024).
Farzaneh, H. et al. Artificial intelligence evolution in smart buildings for energy efficiency. Appl. Sci. 11, 763 (2021).
Shahbaz, M., Wang, J., Dong, K. & Zhao, J. The impact of digital economy on energy transition across the globe: the mediating role of government governance. Renew. Sustain. Energy Rev. 166, 112620–112620 (2022).
Yi, M., Liu, Y., Sheng, M. S. & Wen, L. Effects of digital economy on carbon emission reduction: new evidence from China. Energy Policy. 171, 113271 (2022).
Li, X., Li, S., Cao, J. & Spulbar, A. C. Does artificial intelligence improve energy efficiency? Evidence from provincial data in China. Energy Econ. 108149–108149. (2024).
Zhang, L. et al. Digital economy, energy efficiency, and carbon emissions: evidence from provincial panel data in China. Sci. Total Environ. 852, 158403–158403 (2022).
Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A. & De Felice, F. Artificial intelligence and machine learning applications in smart production: progress, trends, and directions. Sustain. (Switz). 12, 492 (2020).
Wei, W. et al. Embodied greenhouse gas emissions from Building china’s large-scale power transmission infrastructure. Nat. Sustain. 4, 739–747 (2021).
Dong, K., Sun, R., Hochman, G. & Li, H. Energy intensity and energy conservation potential in china: A regional comparison perspective. Energy 155, 782–795 (2018).
Du, L. & Lin, W. Does the application of industrial robots overcome the Solow paradox? Evidence from China. Technol. Soc. 68, 101932 (2022).
Cheng, H., Jia, R., Li, D. & Li, H. The rise of robots in China. J. Econ. Perspect. 33, 71–88 (2019).
Tao, W., Weng, S., Chen, X., ALHussan, F. B. & Song, M. Artificial intelligence-driven transformations in low-carbon energy structure: evidence from China. Energy Econ. 136, 107719 (2024).
Caglar, A. E., Avci, S. B., Gökçe, N. & Destek, M. A. A sustainable study of competitive industrial performance amidst environmental quality: new insight from novel fourier perspective. J. Environ. Manage. 366, 121843 (2024).
Lu, J. & Li, H. Can digital technology innovation promote total factor energy efficiency? Firm-level evidence from China. Energy 293, 130682–130682 (2024).
Luo, S. et al. Digitalization and sustainable development: how could digital economy development improve green innovation in china?? Bus. Strat Environ. 32, 1847–1871 (2023).
Pan, W., Xie, T., Wang, Z. & Ma, L. Digital economy: an innovation driver for total factor productivity. J. Bus. Res. 139, 303–311 (2022).
Lyu, Y., Wang, W., Wu, Y. & Zhang, J. How does digital economy affect green total factor productivity? Evidence from China. Sci. Total Environ. 857, 159428 (2023).
Hanafizadeh, P. & Nik, M. R. H. Configuration of data monetization: A review of literature with thematic analysis. Glob J. Flex. Syst. Manag. 21, 17–34 (2019).
Jackson, I., Ivanov, D., Dolgui, A. & Namdar, J. Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation. Int. J. Prod. Res. 62, 6120–6145 (2024).
Mitra, R., Saha, P. & Kumar Tiwari, M. Sales forecasting of a food and beverage company using deep clustering frameworks. Int. J. Prod. Res. 62, 3320–3332 (2023).
Wu, J., Zhang, Z. & Zhou, S. X. Credit rating prediction through supply chains: A machine learning approach. Prod. Oper. Manag. 31, 1613–1629 (2022).
Chien, C. F., Lin, Y. S. & Lin, S. K. Deep reinforcement learning for selecting demand forecast models to empower industry 3.5 and an empirical study for a semiconductor component distributor. Int. J. Prod. Res. 58, 2784–2804 (2020).
Brooks, R. A. Intelligence without representation. Artif. Intell. 47, 139–159 (1991).
Raees, N. The effect of ventilation and economizer on energy consumptions for air source heat pumps in schools. Am. J. Eng. Appl. Sci. 7, 58–65 (2014).
Zhu, S. et al. Intelligent computing: the latest advances, challenges, and future. Intell. Comput. 2, (2023).
Liu, J., Qian, Y., Yang, Y. & Yang, Z. Can artificial intelligence improve the energy efficiency of manufacturing companies? Evidence from China. IJERPH 19 (2022).
Li, P., Yang, J., Islam, M. A., Ren, S. Making AI less ‘Thirsty’: Uncovering and addressing the secret water footprint of AI models. arXiv 2304.03271 (2023).
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).
Li, Z. & Wang, J. The dynamic impact of digital economy on carbon emission reduction: evidence City-level empirical data in China. J. Clean. Prod. 351, 131570–131570 (2022).
Diamantoulakis, P. D., Kapinas, V. M. & Karagiannidis, G. K. Big data analytics for dynamic energy management in smart grids. Big Data Res. 2, 94–101 (2015).
Pawanr, S. & Gupta, K. A. Review on recent advances in the energy efficiency of machining processes for sustainability. Energies 17, 3659–3659 (2024).
Balakrishnan, D., Sharma, P., Bora, B. J. & Dizge, N. Harnessing biomass energy: advancements through machine learning and AI applications for sustainability and efficiency. Chem. Eng. Res. Des. 191, 193–205 (2024).
Mahmood, S. et al. Integrating machine and deep learning technologies in green buildings for enhanced energy efficiency and environmental sustainability. Sci. Rep. 14, (2024).
Villarreal, J. A. S., Mendoza, V. S., Acosta, J. A. N. & Ruiz, E. R. Energy consumption outlier detection with AI models in modern cities: a case study from north-eastern Mexico. Algorithms 17, 322–322 (2024).
Wang, E. Z., Lee, C. C. & Li, Y. Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries. Energy Econ. 105, 105748 (2022).
Lin, B. & Xu, C. The effects of industrial robots on firm energy intensity: from the perspective of technological innovation and electrification. Technol. Forecast. Soc. Chang. 203, 123373–123373 (2024).
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).
Acemoglu, D., Autor, D., Dorn, D., Hanson, G. H. & Price, B. Return of the Solow paradox?? IT, productivity, and employment in US manufacturing. am. Econ. Rev. 104, 394–399 (2014).
Barbieri, N., Marzucchi, A. & Rizzo, U. Knowledge sources and impacts on subsequent inventions: do green technologies differ from non-green ones? Res. Policy. 49, 103901–103901 (2019).
Ouyang, X., Li, Q. & Du, K. How does environmental regulation promote technological innovations in the industrial sector? Evidence from Chinese provincial panel data. Energy Policy. 139, 111310–111310 (2020).
Jenne, C. A. & Cattell, R. K. Structural change and energy efficiency in industry. Energy Econ. 5, 114–123 (1983).
Hu, L., Yuan, W., Jiang, J., Ma, T. & Zhu, S. Asymmetric effects of industrial structure rationalization on carbon emissions: evidence from Thirty Chinese provinces. J. Clean. Prod. 428, 139347–139347 (2023).
Xue, L. et al. Impacts of industrial structure adjustment, upgrade and coordination on energy efficiency: empirical research based on the extended STIRPAT model. Energy Strategy Rev. 43, 100911 (2022).
Li, B., Jiang, F., Xia, H. & Pan, J. Under the background of AI application, research on the impact of science and technology innovation and industrial structure upgrading on the sustainable and High-Quality development of regional economies. Sustain. (Switz). 14, 11331 (2022).
Su, Y. & Fan, Q. Renewable energy technology innovation, industrial structure upgrading and green development from the perspective of china’s provinces. Technol. Forecast. Soc. Chang. 180, 121727–121727 (2022).
Du, K., Cheng, Y. & Yao, X. Environmental regulation, green technology innovation, and industrial structure upgrading: the road to the green transformation of Chinese cities. Energy Econ. 98, 105247–105247 (2021).
Yu, H. et al. How does green technology innovation influence industrial structure? Evidence of heterogeneous environmental regulation effects. Environ. Dev. Sustain. 26, 17875–17903 (2023).
Peng, H., Shen, N., Ying, H. & Wang, Q. Can environmental regulation directly promote green innovation behavior?—— based on situation of industrial agglomeration. J. Clean. Prod. 314, 128044 (2021).
Chen, L., Li, W., Yuan, K. & Zhang, X. Can informal environmental regulation promote industrial structure upgrading? Evidence from China. Appl. Econ. 54, 2161–2180 (2021).
Huang, S. & Ge, J. Are there heterogeneities in environmental risks among different types of resource-based cities in china?? Assessment based on environmental risk field approach. Int. J. Disaster Risk Reduct. 104810–104810. (2024).
Wang, K., Chen, X. & Wang, C. The impact of sustainable development planning in resource-based cities on corporate ESG–Evidence from China. Energy Econ. 127, 107087 (2023).
Jiang, Z., Yuan, C. & Xu, J. The impact of digital government on energy sustainability: empirical evidence from prefecture-level cities in China. Technol. Forecast. Soc. Chang. 209, 123776–123776 (2024).
Lu, S., Zhang, W., Yu, J. & Li, J. The identification of spatial evolution stage of resource-based cities and its development characteristics. Acta Geogr. Sin. 75 2180–2191 (2020).
Wang, L. & Shao, J. Digital economy, entrepreneurship and energy efficiency. Energy 269, 126801–126801 (2023).
Wu, Y., Shi, K., Chen, Z., Liu, S. & Chang, Z. Developing improved Time-Series DMSP-OLS-Like data (1992–2019) in China by integrating DMSP-OLS and SNPP-VIIRS. IEEE Trans. Geosci. Remote Sens. 60, 1–14 (2021).
Lin, Y. & Cheung, A. Climate policy uncertainty and energy transition: evidence from prefecture-level cities in China. Energy Econ. 107938–107938 (2024).
Renshaw, E. F. Energy efficiency and the slump in labour productivity in the USA. Energy Econ. 3, 36–42 (1981).
Wilson, B., Trieu, L. H. & Bowen, B. Energy efficiency trends in Australia. Energy Policy. 22, 287–295 (1994).
Charnes, A., Cooper, W. W. & Rhodes, E. Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2, 429–444 (1978).
Li, M. J. & Tao, W. Q. Review of methodologies and Polices for evaluation of energy efficiency in high energy-consuming industry. Appl. Energy. 187, 203–215 (2017).
Muhammad, S., Pan, Y., Agha, M. H., Umar, M. & Chen, S. Industrial structure, energy intensity and environmental efficiency across developed and developing economies: the intermediary role of primary, secondary and tertiary industry. Energy 247, 123576–123576 (2022).
Tone, K. A strange case of the cost and allocative efficiencies in DEA. J. Oper. Res. Soc. 53, 1225–1231 (2002).
Tone, K. A slacks-based measure of super-efficiency in data envelopment analysis. Eur. J. Oper. Res. 143, 32–41 (2002).
Acemoglu, D. & Restrepo, P. Robots and jobs: evidence from US labor markets. J. Political Econ. 128, 2188–2244 (2020).
Beaudry, P., Doms, M. & Lewis, E. Should the personal computer be considered a technological revolution?? Evidence from U.S. Metropolitan areas. J. Polit. Econ. 118, 988–1036 (2010).
Mann, K. & Püttmann, L. Benign effects of automation: new evidence from patent texts. Rev. Econ. Stat. 105, 562–579 (2021).
Autor, D., Chin, C., Salomons, A. & Seegmiller, B. New frontiers: the origins and content of new work, 1940–2018. Q. J. Econ. 139, 1399–1465 (2024).
Henderson, J. V. Marshall’s scale economies. J. Urban Econ. 53, 1–28 (2003).
Xiong, M., Li, W., Xian, B. T. S. & Yang, A. Digital inclusive finance and enterprise innovation—Empirical evidence from Chinese listed companies. J. Innov. Knowl. 8, 100321 (2023).
Kathuria, V. Informal regulation of pollution in a developing country: evidence from India. Ecol. Econ. 63, 403–417 (2007).
Pargal, S. & Wheeler, D. Informal regulation of industrial pollution in developing countries: evidence from Indonesia. J. Political Econ. 104, 1314–1327 (1996).
Jia, R., Shao, S. & Yang, L. High-speed rail and CO2 emissions in urban china: A Spatial difference-in-differences approach. Energy Econ. 99, 105271–105271 (2021).
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).
Shi, D. & Li, S. Emissions trading system and energy use efficiency: Measurements and empirical evidence for cities at and above the prefecture level. China Industrial Economics 5–23 (2020).
Goldsmith-Pinkham, P., Sorkin, I. & Swift, H. Bartik instruments: what, when, why, and how. am. Econ. Rev. 110, 2586–2624 (2020).
Borusyak, K., Hull, P. & Jaravel, X. Quasi-experimental shift-share research designs. Rev. Econ. Stud. 89, 181–213 (2021).
Lee, C. C., Fang, Y., Quan, S. & Li, X. Leveraging the power of artificial intelligence toward the energy transition: the key role of the digital economy. Energy Econ. 135, 107654 (2024).
AI Makes Up Cases, Court Says ‘Sure, Why Not’: Judge signed off on party’s proposed order. Apparently didn’t bother to check the made up cases.
Textualism/Originalism May Be Bankrupt, But Like Donald Trump Always Is… Ignoring Costs, Harming People Who Trust Them In Good Faith, And Barreling Forward To The Next Crisis Of Their Own Making: Justice Breyer delivers well-crafted critiques that misunderstand that proponents aren’t trying to win the argument, they’re trying to have smart people treat them like they have ideas worth engaging.
John Roberts Replies On Cue: The Chief Justice took time out of his busy schedule to clarify that people like Breyer may have detailed, powerful, constitutionally valid criticisms, but they’re losers because SCOREBOARD! SIX VOTES, SUCKAS!
Diddy Covers ‘RICO’ Sauve: Prosecutors reached for racketeering. Missed.
The Definition Of Psychosis…: Speaking of doing the same thing over and over and expecting a different result, Trump appeals Perkins Coie loss.
Law Firms Exhibit Serious ‘I Got Dumped, So Let’s Get Married’ Energy: After losing 60 attorneys, Biglaw firm entertains merger talks.
Trump’s Lawyers Play Iowa Civ Pro Roulette: Team Trump’s legal eagles realized they needed to drop and refile their lawsuit. Probably a day late.
Biglaw Firm Parties Like It’s 2019: Another Biglaw firm decides what associates really need is more fluorescent lighting.
Budget Bill Limits Student Loans: If law school is harder to pay for… maybe they’ll lower tuition?
Lei Jun, founder and chairman of Xiaomi Corp., the only tech company to have successfully diversified into carmaking, couldn’t resist.
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AI Insights
Undervalued and Profitable: This Artificial Intelligence (AI) Stock Has Soared 73% in 2025, and It Could Still Jump Higher
Storage solutions provider Seagate Technology (STX -1.59%) has registered an outstanding rally on the stock market in 2025, rising an incredible 73% year to date and beating the Nasdaq Composite index’s 7% return by a massive margin.
This impressive performance can be attributed to robust growth in the demand for storage in data centers running artificial intelligence (AI) workloads. Let’s dig into how AI is fueling Seagate’s growth and see how it could pave the way for more upside in this technology stock.
Image source: Getty Images.
Seagate Technology is growing at an incredible pace, and it can sustain its momentum
Seagate Technology’s revenue in the first nine months of its fiscal 2025 increased almost 43% year over year to $6.65 billion. Even better, the company’s non-GAAP (adjusted) income from operations has jumped more than fourfold during this period, thanks to higher margins.
Management attributes this fantastic growth to the healthy demand for mass capacity storage in the cloud, which has created a tight supply environment and led to an increase in prices. Management remarked on the company’s April earnings call that the growing storage demand “aligns with the cloud capex investment cycle and ongoing build-out of data center infrastructure to support AI transformations.”
Specifically, 90% of the storage in large-scale data centers is done with hard drives because of their cost efficiency and scalability. With the storage requirement in data centers expected to more than double between 2024 and 2028, Seagate estimates this could push annual revenue for the data center storage market to $23 billion by 2028, up from $13 billion last year.
Seagate is in a solid position to make the most of this growth opportunity considering its 40% share of the global storage market. Not surprisingly, Seagate’s outlook for the recently concluded fiscal fourth quarter was an impressive one. The company guided for $2.4 billion in revenue at the midpoint of its range, along with $2.40 per share in earnings.
The top-line guidance is good for a 27% year-over-year increase, while earnings are on track to more than double from the prior-year period’s reading of $1.05 per share.
A solid jump in the company’s earnings points toward more gains
For the full fiscal year, Seagate could grow revenue 38%, while its adjusted earnings will jump more than sixfold to $7.91 per share. Importantly, the company should be able to sustain this momentum, thanks to the tailwinds discussed above, and that sets the stage for strong returns.
Data by YCharts.
The potential earnings growth combined with Seagate’s incredibly attractive valuation makes the stock a no-brainer buy. It is now trading at just 21 times trailing earnings and 16 times forward earnings estimates. The Nasdaq 100 index, meanwhile, has an average forward earnings multiple of 29, which means the stock trades at a significant discount to the tech sector overall.
Investors looking for a fast-growing AI stock that’s also reasonably priced would do well to buy Seagate before it flies higher.
Harsh Chauhan has no position in any of the stocks mentioned. The Motley Fool has no position in any of the stocks mentioned. The Motley Fool has a disclosure policy.
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