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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
CAS
PubMed
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
Song, M., Du, J. & Tan, K. H. Impact of fiscal decentralization on green total factor productivity. Int. J. Prod. Econ. 205, 359–367 (2018).
Article
Google Scholar
Liu, J., Cheng, Z. & Zhang, H. Does industrial agglomeration promote the increase of energy efficiency in china?? J. Clean. Prod. 164, 30–37 (2017).
Article
Google Scholar
Yuan, H., Feng, Y., Lee, C. C. & Cen, Y. How does manufacturing agglomeration affect green economic efficiency? Energy Econ. 92, 104944 (2020).
Article
Google Scholar
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).
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
Farzaneh, H. et al. Artificial intelligence evolution in smart buildings for energy efficiency. Appl. Sci. 11, 763 (2021).
Article
CAS
Google Scholar
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).
Article
Google Scholar
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).
Article
CAS
Google Scholar
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).
Article
CAS
PubMed
Google Scholar
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).
Article
Google Scholar
Wei, W. et al. Embodied greenhouse gas emissions from Building china’s large-scale power transmission infrastructure. Nat. Sustain. 4, 739–747 (2021).
Article
Google Scholar
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).
Article
Google Scholar
Du, L. & Lin, W. Does the application of industrial robots overcome the Solow paradox? Evidence from China. Technol. Soc. 68, 101932 (2022).
Article
Google Scholar
Cheng, H., Jia, R., Li, D. & Li, H. The rise of robots in China. J. Econ. Perspect. 33, 71–88 (2019).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
PubMed
Google Scholar
Lu, J. & Li, H. Can digital technology innovation promote total factor energy efficiency? Firm-level evidence from China. Energy 293, 130682–130682 (2024).
Article
Google Scholar
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).
Article
Google Scholar
Pan, W., Xie, T., Wang, Z. & Ma, L. Digital economy: an innovation driver for total factor productivity. J. Bus. Res. 139, 303–311 (2022).
Article
Google Scholar
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).
Article
CAS
PubMed
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
Wu, J., Zhang, Z. & Zhou, S. X. Credit rating prediction through supply chains: A machine learning approach. Prod. Oper. Manag. 31, 1613–1629 (2022).
Article
Google Scholar
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).
Article
Google Scholar
Brooks, R. A. Intelligence without representation. Artif. Intell. 47, 139–159 (1991).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
CAS
Google Scholar
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).
Article
Google Scholar
Pawanr, S. & Gupta, K. A. Review on recent advances in the energy efficiency of machining processes for sustainability. Energies 17, 3659–3659 (2024).
Article
Google Scholar
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).
CAS
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
Jenne, C. A. & Cattell, R. K. Structural change and energy efficiency in industry. Energy Econ. 5, 114–123 (1983).
Article
Google Scholar
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).
Article
CAS
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
CAS
Google Scholar
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).
Article
Google Scholar
Wilson, B., Trieu, L. H. & Bowen, B. Energy efficiency trends in Australia. Energy Policy. 22, 287–295 (1994).
Article
Google Scholar
Charnes, A., Cooper, W. W. & Rhodes, E. Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2, 429–444 (1978).
Article
MathSciNet
Google Scholar
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).
Article
ADS
Google Scholar
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).
Article
Google Scholar
Tone, K. A strange case of the cost and allocative efficiencies in DEA. J. Oper. Res. Soc. 53, 1225–1231 (2002).
Article
Google Scholar
Tone, K. A slacks-based measure of super-efficiency in data envelopment analysis. Eur. J. Oper. Res. 143, 32–41 (2002).
Article
MathSciNet
Google Scholar
Acemoglu, D. & Restrepo, P. Robots and jobs: evidence from US labor markets. J. Political Econ. 128, 2188–2244 (2020).
Article
Google Scholar
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).
Article
Google Scholar
Mann, K. & Püttmann, L. Benign effects of automation: new evidence from patent texts. Rev. Econ. Stat. 105, 562–579 (2021).
Article
Google Scholar
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).
Article
Google Scholar
Henderson, J. V. Marshall’s scale economies. J. Urban Econ. 53, 1–28 (2003).
Article
Google Scholar
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).
Article
Google Scholar
Kathuria, V. Informal regulation of pollution in a developing country: evidence from India. Ecol. Econ. 63, 403–417 (2007).
Article
Google Scholar
Pargal, S. & Wheeler, D. Informal regulation of industrial pollution in developing countries: evidence from Indonesia. J. Political Econ. 104, 1314–1327 (1996).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
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).
Article
Google Scholar
Borusyak, K., Hull, P. & Jaravel, X. Quasi-experimental shift-share research designs. Rev. Econ. Stud. 89, 181–213 (2021).
Article
MathSciNet
PubMed
PubMed Central
Google Scholar
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).
Article
Google Scholar