Abstract
Digital technology has become a key driver of industrial transformation and resource utilization. However, no consensus has been reached on the exact relationship between digital technology and energy utilization. This study adopted a comprehensive index system to investigate the impact of digital technologies on energy utilization across 30 provinces in China. The results reveal a non-linear relationship between digital technologies and energy efficiency in China (represented by an N-curve), which is validated by robustness tests. This indicates digital technology exerts a fast-slow-rapid impact on improving energy efficiency throughout its initial-rapid-stable developmental stages. Geographically, this effect is more pronounced in eastern and central China, as well as in areas with lower energy efficiency. Furthermore, the impact of digital technology on total energy consumption can be characterized by an inverted N-shaped curve. As regional energy efficiency improves, the energy consumption associated with the development of digital technologies gradually decreases. These findings can contribute valuable insights for enhancing energy efficiency and provide practical guidance for the formulation of energy and digital technology policies.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11356-024-32538-0/MediaObjects/11356_2024_32538_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11356-024-32538-0/MediaObjects/11356_2024_32538_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11356-024-32538-0/MediaObjects/11356_2024_32538_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11356-024-32538-0/MediaObjects/11356_2024_32538_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11356-024-32538-0/MediaObjects/11356_2024_32538_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11356-024-32538-0/MediaObjects/11356_2024_32538_Fig6_HTML.png)
Similar content being viewed by others
Data Availability
Data are available from the first author (Linrong You) upon a reasonable request.
References
Alam MdM, Murad MdW (2020) The impacts of economic growth, trade openness and technological progress on renewable energy use in organization for economic co-operation and development countries. Renewable Energy 145:382–390. https://doi.org/10.1016/j.renene.2019.06.054
Andersson DE, Ekman A, Huila A, Tell F (2023) Industrial design rights and the market value of firms. Technol Forecast Soc Change 196:122827. https://doi.org/10.1016/j.techfore.2023.122827
Arendt L (2008) Barriers to ICT adoption in SMEs: how to bridge the digital divide? J Syst Inf Technol 10:93–108. https://doi.org/10.1108/13287260810897738
Awan U, Hannola L, Tandon A, et al (2022) Quantum computing challenges in the software industry. A fuzzy AHP-based approach. Inf Softw Technol 147:106896. https://doi.org/10.1016/j.infsof.2022.106896
Bamakan SMH, Babaei Bondarti A, Babaei Bondarti P, Qu Q (2021) Blockchain technology forecasting by patent analytics and text mining. BLOCKCHAIN-RES APPL 2:100019. https://doi.org/10.1016/j.bcra.2021.100019
Bashir MF, Ma B, Shahbaz M et al (2021) Unveiling the heterogeneous impacts of environmental taxes on energy consumption and energy intensity: empirical evidence from OECD countries. Energy 226:120366. https://doi.org/10.1016/j.energy.2021.120366
Belkhir L, Elmeligi A (2018) Assessing ICT global emissions footprint: Trends to 2040 & recommendations. J Clean Prod 177:448–463. https://doi.org/10.1016/j.jclepro.2017.12.239
Bryukhovetskaya S, Artamonova K, Gibadullin A et al (2020) Management of digital technology development in the national economy. IOP Conference Series: Environ Earth Sci 421:042018. https://doi.org/10.1088/1755-1315/421/4/042018
Bu C, Zhang K, Shi D, Wang S (2022) Does environmental information disclosure improve energy efficiency? Energy Policy 164:112919. https://doi.org/10.1016/j.enpol.2022.112919
Cetindamar D, Can O, Pala O (2006) Technology management activities and tools: the practice in Turkey. 2006 Technology Management for the Global Future - PICMET 2006 Conference 92–98. https://doi.org/10.1109/PICMET.2006.296557
Chen X, Despeisse M, Johansson B (2020) Environmental sustainability of digitalization in manufacturing: a review. Sustainability 12:10298. https://doi.org/10.3390/su122410298
Chong C-Y, Kumar SP (2003) Sensor networks: evolution, opportunities, and challenges. Proc IEEE 91:1247–1256. https://doi.org/10.1109/JPROC.2003.814918
Court V, Sorrell S (2020) Digitalisation of goods: a systematic review of the determinants and magnitude of the impacts on energy consumption. Environ Res Lett 15:043001. https://doi.org/10.1088/1748-9326/ab6788
Dong C, Franklin R (2021) From the digital internet to the physical internet: a conceptual framework with a stylized network model. J BUS LOGIST 42:108–119. https://doi.org/10.1111/jbl.12253
Fu Z, Zhou Y, Li W, Zhong K (2023) Impact of digital finance on energy efficiency: empirical findings from China. Environ Sci Pollut Res 30:2813–2835. https://doi.org/10.1007/s11356-022-22320-5
Ghorbal S, Farhani S, Youssef SB (2022) Do renewable energy and national patents impact the environmental sustainability of Tunisia? Environ Sci Pollut Res 29:25248–25262. https://doi.org/10.1007/s11356-021-17628-7
Guo J, Wang L, Zhou W, Wei C (2022a) Powering green digitalization: evidence from 5 G network infrastructure in China. Resour Conserv Recycl 182:106286. https://doi.org/10.1016/j.resconrec.2022.106286
Guo Q, Wang Y, Dong X (2022b) Effects of smart city construction on energy saving and CO2 emission reduction: evidence from China. Appl Energy 313:118879. https://doi.org/10.1016/j.apenergy.2022.118879
Gupta S, Motlagh M, Rhyner J (2020) The digitalization sustainability matrix: a participatory research tool for investigating digitainability. Sustainability 12:9283. https://doi.org/10.3390/su12219283
Han H, Shiwakoti RK, Jarvis R et al (2023) Accounting and auditing with blockchain technology and artificial intelligence: a literature review. Int J Account Inf Sy 48:100598. https://doi.org/10.1016/j.accinf.2022.100598
Hao Y, Gai Z, Wu H (2020) How do resource misallocation and government corruption affect green total factor energy efficiency? Evid China Energy Pol 143:111562. https://doi.org/10.1016/j.enpol.2020.111562
Hao Y, Guo Y, Wu H (2022) The role of information and communication technology on green total factor energy efficiency: does environmental regulation work? Bus Strateg Environ 31:403–424. https://doi.org/10.1002/bse.2901
Hong J, Shi F, Zheng Y (2023) Does network infrastructure construction reduce energy intensity? Based on the “Broadband China” strategy. Technol Forecast Soc Change 190:122437. https://doi.org/10.1016/j.techfore.2023.122437
Huang J, Zhang D, Zhang Z et al (2023) Trapped in dilemma: inverted N-shaped EKC evidence of economic growth and ecological land in a spatial spillover perspective. Appl Geogr 161:103145. https://doi.org/10.1016/j.apgeog.2023.103145
Ishida H (2015) The effect of ICT development on economic growth and energy consumption in Japan. Telematics Inform 32:79–88. https://doi.org/10.1016/j.tele.2014.04.003
Jan Z, Ahamed F, Mayer W et al (2023) Artificial intelligence for Industry 4.0: systematic review of applications, challenges, and opportunities. Expert Syst Appl 216:119456. https://doi.org/10.1016/j.eswa.2022.119456
Jiang P, Fan YV, Klemeš JJ (2021) Impacts of COVID-19 on energy demand and consumption: challenges, lessons and emerging opportunities. Appl Energy 285:116441. https://doi.org/10.1016/j.apenergy.2021.116441
** C, Xu A, Zhu Y, Li J (2023) Technology growth in the digital age: evidence from China. Technol Forecast Soc Change 187:122221. https://doi.org/10.1016/j.techfore.2022.122221
Joyce PJ, Finnveden G, Håkansson C, Wood R (2019) A multi-impact analysis of changing ICT consumption patterns for Sweden and the EU: indirect rebound effects and evidence of decoupling. J Clean Prod 211:1154–1161. https://doi.org/10.1016/j.jclepro.2018.11.207
Khan H, ur R, Usman B, Zaman K, et al (2022) The impact of carbon pricing, climate financing, and financial literacy on COVID-19 cases: go-for-green healthcare policies. Environ Sci Pollut Res 29:35884–35896. https://doi.org/10.1007/s11356-022-18689-y
Lange S, Pohl J, Santarius T (2020) Digitalization and energy consumption Does ICT reduce energy demand? Ecol Econ 176:106760. https://doi.org/10.1016/j.ecolecon.2020.106760
Li J, Just RE (2018) Modeling household energy consumption and adoption of energy efficient technology. Energy Econ 72:404–415. https://doi.org/10.1016/j.eneco.2018.04.019
Lin B, Du K (2013) Technology gap and China’s regional energy efficiency: a parametric meta frontier approach. Energy Econ 40:529–536. https://doi.org/10.1016/j.eneco.2013.08.013
Lin B, Huang C (2023) Nonlinear relationship between digitization and energy efficiency: evidence from transnational panel data. Energy 276:127601. https://doi.org/10.1016/j.energy.2023.127601
Lin B, Zhou Y (2022) Does energy efficiency make sense in China? Based on the perspective of economic growth quality. Sci Total Environ 804:149895. https://doi.org/10.1016/j.scitotenv.2021.149895
Liu Y, Yang Y, Li H, Zhong K (2022) Digital economy development, industrial structure upgrading and green total factor productivity: empirical evidence from China’s cities. Int J Environ Res Public Health 19:2414. https://doi.org/10.3390/ijerph19042414
Liu B, Zhan J, Zhang A (2023) Empowering rural human settlement: digital economy’s path to progress. J Clean Prod 427:139243. https://doi.org/10.1016/j.jclepro.2023.139243
Lv Y, Chen W, Cheng J (2020) Effects of urbanization on energy efficiency in China: new evidence from short run and long run efficiency models. Energy Policy 147:111858. https://doi.org/10.1016/j.enpol.2020.111858
Markard J (2020) The life cycle of technological innovation systems. Technol Forecast Soc Change 153:119407. https://doi.org/10.1016/j.techfore.2018.07.045
Morley J, Widdicks K, Hazas M (2018) Digitalisation, energy and data demand: the impact of internet traffic on overall and peak electricity consumption. Energy Res Soc Sci 38:128–137. https://doi.org/10.1016/j.erss.2018.01.018
Parviainen P, Tihinen M, Kääriäinen J, Teppola S (2017) Tackling the digitalization challenge: how to benefit from digitalization in practice. IJISPM-INT J INF SYS and Project Management 5:63–77. https://doi.org/10.12821/ijispm050104
Peng G, Tang Y, Tian K (2023a) Understanding the nonlinear impact of information and communication technology on carbon emissions in the logistics industry of China. Sustainability 15:13351. https://doi.org/10.3390/su151813351
Peng H-R, Zhang Y-J, Liu J-Y (2023b) The energy rebound effect of digital development: evidence from 285 cities in China. Energy 270:126837. https://doi.org/10.1016/j.energy.2023.126837
Peterson S, Kinyeki C, Mutai J, Ndungu C (1996) Computerizing accounting systems in develo** bureaucracies: lessons from Kenya. Public Budg Finance 16:45–58. https://doi.org/10.1111/1540-5850.01085
Ren S, Liu Z, Hanbayev RZ, Du M (2022) Does internet development put pressure on energy-saving potential for environmental sustainability? Evidence from China. Econ Anal Policy 1:49–65
Salahuddin M, Alam K, Ozturk I (2016) The effects of Internet usage and economic growth on CO2 emissions in OECD countries: a panel investigation. Renew Sust Energ Rev 62:1226–1235. https://doi.org/10.1016/j.rser.2016.04.018
Samuel G, Lucivero F, Somavilla L (2022) The Environmental Sustainability of Digital Technologies: Stakeholder Practices and Perspectives. Sustainability 14:3791. https://doi.org/10.3390/su14073791
Sood A (2010) Technology S-Curve. In: Wiley International Encyclopedia of Marketing. John Wiley & Sons, Ltd
Taylor M, Taylor A (2012) The technology life cycle: conceptualization and managerial implications. Int J Prod Econ 140:541–553. https://doi.org/10.1016/j.ijpe.2012.07.006
Wan D, Xue R, Linnenluecke M et al (2021) The impact of investor attention during COVID-19 on investment in clean energy versus fossil fuel firms. Finance Res Lett 43:101955. https://doi.org/10.1016/j.frl.2021.101955
Wang E-Z, Lee C-C (2022) The impact of information communication technology on energy demand: some international evidence. Int Rev Econ Financ 81:128–146. https://doi.org/10.1016/j.iref.2022.05.008
Wang J, Wang W, Ran Q et al (2022) Analysis of the mechanism of the impact of internet development on green economic growth: evidence from 269 prefecture cities in China. Environ Sci Pollut Res 29:9990–10004. https://doi.org/10.1007/s11356-021-16381-1
Wang J, Liu Y, Wang W, Wu H (2023a) How does digital transformation drive green total factor productivity? Evidence from Chinese listed enterprises. J Clean Prod 406:136954. https://doi.org/10.1016/j.jclepro.2023.136954
Wang X, Li Y, Tian L, Hou Y (2023b) Government digital initiatives and firm digital innovation: evidence from China. Technovation 119:102545. https://doi.org/10.1016/j.technovation.2022.102545
Wei X, Jiang F, Yang L (2023) Does digital dividend matter in China’s green low-carbon development: environmental impact assessment of the big data comprehensive pilot zones policy. EIA Review 101:107143. https://doi.org/10.1016/j.eiar.2023.107143
Wu H, Hao Y, Ren S (2020) How do environmental regulation and environmental decentralization affect green total factor energy efficiency: evidence from China. Energy Econ 91:104880. https://doi.org/10.1016/j.eneco.2020.104880
Wu H, Hao Y, Ren S et al (2021) Does internet development improve green total factor energy efficiency? Evid China Energ Pol 153:112247. https://doi.org/10.1016/j.enpol.2021.112247
Yu C, Long H, Zhang X et al (2023) Regional integration and city-level energy efficiency: evidence from China. Sustain Cities Soc 88:104285. https://doi.org/10.1016/j.scs.2022.104285
Zhang Y, Li X (2022) Environmental regulation and high-quality economic growth: quasi-natural experimental evidence from China. Environ Sci Pollut Res 29:85389–85401. https://doi.org/10.1007/s11356-022-21832-4
Zhao S, Hafeez M, Faisal CMN (2022a) Does ICT diffusion lead to energy efficiency and environmental sustainability in emerging Asian economies? Environ Sci Pollut Res 29:12198–12207. https://doi.org/10.1007/s11356-021-16560-0
Zhao S, Peng D, Wen H, Wu Y (2022b) Nonlinear and spatial spillover effects of the digital economy on green total factor energy efficiency: evidence from 281 cities in China. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-022-22694-6
Zhou Y, Yang Q, Lu S (2023) Research on the identification and formation mechanism of the main path of digital technology diffusion: empirical evidence from China. Technol Soc 75:102398. https://doi.org/10.1016/j.techsoc.2023.102398
Zhu J, Lin B (2022) Economic growth pressure and energy efficiency improvement: empirical evidence from Chinese cities. Appl Energy 307:118275. https://doi.org/10.1016/j.apenergy.2021.118275
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Linrong You and Weicong Xu, and the manuscript was written by Linrong You. Zhide Jiang and Ao Chen were involved in the revision and suggested guides towards the developed analyses. The author Weicong Xu contributed with the idea and to the revision of the document and supervised the whole research. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethical approval and consent to participate.
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Philippe Loubet
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
Appendix 1
Table 11
Appendix 2
We adopt the entropy method to measure the level of digital technology development, which can avoid the inaccuracy of the subjective assignment method measurement. The measurement method is as follows:
Standardization of the positive indicators:
where t represents the year; i and j represent the region and indicator, respectively. xitj and Xij indicate the value of indicator before and after standardized treatment in year.
Indicator normalization:
Pitj indicates the normalized value of the indicator.
Calculate entropy value:
Determine each indicator’s weight:
Calculate digital technology level:
Appendix 3
Table 12
Appendix 4
The digital technology–related words in the provincial government work reports include digital economy, intelligent economy, information economy, knowledge economy, smart economy, digitized information, modern information networks, information and communication technology (ICT), communication infrastructure, internet, cloud computing, blockchain, Internet of Things (IoT), digitization, digital rural areas, digital industry, e-commerce, 5G, digital infrastructure, artificial intelligence, e-business, big data, digitization, industrial digitization, digital industrialization, data assetization, smart cities, cloud services, cloud technology, cloud computing, e-government, mobile payments, online, information industry, software, information infrastructure, information technology, and digital lifestyle.
Appendix 5
The northeast region includes Liaoning, Jilin, and Heilongjiang; the eastern region includes Bei**g, Tian**, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan; the central region includes Shanxi, Anhui, Jiangxi, Henan, Hubei, and Hunan; the western region includes Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and **njiang.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
You, L., Jiang, Z., Chen, A. et al. Energy saving effects of digital technologies from a life-cycle-analytical perspective: evidence from China. Environ Sci Pollut Res 31, 21811–21828 (2024). https://doi.org/10.1007/s11356-024-32538-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-024-32538-0