Digital technologies have become a core instrument for advancing green, low-carbon development. To address fragmented, cross-disciplinary evidence on where and how these tools deliver measurable efficiency gains, this study conducted a bibliometric analysis of 2082 Web of Science records using a reproducible toolchain and a unified terminology policy. The analysis quantified citation baselines, mapped clustered co-occurrence structures, and detected burst-driven trend evolution. The findings reveal a compact core of authors and hub institutions, a three-phase progression from measurement digitization to process digitalization and AI-enabled digital innovation, and a divergence between publication volume from per-article influence via average citation scores. The scientific value-added lies in integrating these hotspots and trends into interpretable maps that link areas of concentrated impact to existing gaps. Future efforts should prioritize interoperable data infrastructure and outcome-based incentives, scale high-return use cases through digital twins governed by large models, and establish open, replicable benchmarks to accelerate translation to measurable efficiency gains.
Citation: Wanchang Chen, Xue Zhang, Youqing Fan, Kai Yang, Hua Wang, Qingtai Xiao. Bibliometric investigation of energy efficiency improvement from digitalization to smart efficiency[J]. AIMS Energy, 2025, 13(5): 1167-1194. doi: 10.3934/energy.2025043
Digital technologies have become a core instrument for advancing green, low-carbon development. To address fragmented, cross-disciplinary evidence on where and how these tools deliver measurable efficiency gains, this study conducted a bibliometric analysis of 2082 Web of Science records using a reproducible toolchain and a unified terminology policy. The analysis quantified citation baselines, mapped clustered co-occurrence structures, and detected burst-driven trend evolution. The findings reveal a compact core of authors and hub institutions, a three-phase progression from measurement digitization to process digitalization and AI-enabled digital innovation, and a divergence between publication volume from per-article influence via average citation scores. The scientific value-added lies in integrating these hotspots and trends into interpretable maps that link areas of concentrated impact to existing gaps. Future efforts should prioritize interoperable data infrastructure and outcome-based incentives, scale high-return use cases through digital twins governed by large models, and establish open, replicable benchmarks to accelerate translation to measurable efficiency gains.
| [1] |
Yu W, Patros P, Young B, et al. (2022) Energy digital twin technology for industrial energy management: Classification, challenges and future. Renewable Sustainable Energy Rev 161: 112407. https://doi.org/10.1016/j.rser.2022.112407 doi: 10.1016/j.rser.2022.112407
|
| [2] |
Li J, Wei W, Zhen W, et al. (2019) How green transition of energy system impacts China's mercury emissions. Earth's Future 7: 1407–1416. https://doi.org/10.1029/2019EF001269 doi: 10.1029/2019EF001269
|
| [3] |
Bettini G, Karaliotas L (2013) Exploring the limits of peak oil: Naturalizing the political, de-politicizing energy. Geogr J 179: 331–341. https://doi.org/10.1111/geoj.12024 doi: 10.1111/geoj.12024
|
| [4] |
Xiao H, Sun K, Bi H, et al. (2019) Changes in carbon intensity globally and in countries: Attribution and decomposition analysis. Appl Energy 235: 1492–1504. https://doi.org/10.1016/j.apenergy.2018.09.158 doi: 10.1016/j.apenergy.2018.09.158
|
| [5] |
Fawzy S, Osman A, Doran J, et al. (2020) Strategies for mitigation of climate change: A review. Environ Chem Lett 18: 2069–2094. https://doi.org/10.1007/s10311-020-01059-w doi: 10.1007/s10311-020-01059-w
|
| [6] |
Wang Q, Zhou B, Zhang C, et al. (2021) Do energy subsidies reduce fiscal and household non-energy expenditures? A regional heterogeneity assessment on coal-to-gas program in China. Energy Policy 155: 112341. https://doi.org/10.1016/j.enpol.2021.112341 doi: 10.1016/j.enpol.2021.112341
|
| [7] |
Liu X, Qin CH, Liu B, et al. (2024) The economic and environmental dividends of the digital development strategy: Evidence from Chinese cities. J Clean Prod 440: 140398. https://doi.org/10.1016/j.jclepro.2023.140398 doi: 10.1016/j.jclepro.2023.140398
|
| [8] |
Du K, Cheng Y, Yao X (2021) Environmental regulation, green technology innovation, and industrial structure upgrading: The road to the green transformation of Chinese cities. Energy Econ 98: 105247. https://doi.org/10.1016/j.eneco.2021.105247 doi: 10.1016/j.eneco.2021.105247
|
| [9] |
Wang A, Hu S, Li J (2021) Does economic development help achieve the goals of environmental regulation? Evidence from partially linear functional-coefficient model. Energy Econ 103: 105618. https://doi.org/10.1016/j.eneco.2021.105618 doi: 10.1016/j.eneco.2021.105618
|
| [10] |
Chen D, Hu X, Li Y, et al. (2023) Nodal conservation principle of potential energy flow analysis for energy flow calculation in energy internet. Energy 263: 125562. https://doi.org/10.1016/j.energy.2022.125562 doi: 10.1016/j.energy.2022.125562
|
| [11] |
Zhao X, Ma X, Chen B, et al. (2022) Challenges toward carbon neutrality in China: Strategies and countermeasures. Resour Conserv Recycl 176: 105959. https://doi.org/10.1016/j.resconrec.2021.105959 doi: 10.1016/j.resconrec.2021.105959
|
| [12] |
Yang T, Chen W, Zhou K, et al. (2018) Regional energy efficiency evaluation in China: A super efficiency slack-based measure model with undesirable outputs. J Clean Prod 198: 859–866. https://doi.org/10.1016/j.jclepro.2018.07.098 doi: 10.1016/j.jclepro.2018.07.098
|
| [13] |
Zhang H, Zhou P, Sun X, et al. (2024) Disparities in energy efficiency and its determinants in Chinese cities: From the perspective of heterogeneity. Energy 289: 129959. https://doi.org/10.1016/j.energy.2023.129959 doi: 10.1016/j.energy.2023.129959
|
| [14] |
Wei Y, Chen K, Kang J, et al. (2022) Policy and management of carbon peaking and carbon neutrality: A literature review. Engineering 14: 52–63. https://doi.org/10.1016/j.eng.2021.12.018 doi: 10.1016/j.eng.2021.12.018
|
| [15] |
Du B, Lund DP, Wang J (2022) Improving the accuracy of predicting the performance of solar collectors through clustering analysis with artificial neural network models. Energy Rep 8: 3970–3981. https://doi.org/10.1016/j.egyr.2022.03.013 doi: 10.1016/j.egyr.2022.03.013
|
| [16] |
Wang H, Gao L, Jia Y (2022) The predicament of clean energy technology promotion in China in the carbon neutrality context, Lessons from China's environmental regulation policies from the perspective of evolutionary game theory. Energy Rep 8: 4706–4723. https://doi.org/10.1016/j.egyr.2022.03.142 doi: 10.1016/j.egyr.2022.03.142
|
| [17] |
Zhang R, Fu Y (2022) Technological progress effects on energy efficiency from the perspective of technological innovation and technology introduction: An empirical study of Guangdong, China. Energy Rep 8: 425–437. https://doi.org/10.1016/j.egyr.2021.11.282 doi: 10.1016/j.egyr.2021.11.282
|
| [18] |
Chiang C, Young C (2022) An engineering project for a flood detention pond surface-type floating photovoltaic power generation system with an installed capacity of 32,600.88 kWp. Energy Rep 8: 2219–2232. https://doi.org/10.1016/j.egyr.2022.03.005 doi: 10.1016/j.egyr.2022.03.005
|
| [19] |
He P, Sun Y, Shen H, et al. (2019) Does environmental tax affect energy efficiency? An empirical study of energy efficiency in OECD countries based on DEA and Logit model. Sustainability 11: 3792. https://doi.org/10.3390/su11143792 doi: 10.3390/su11143792
|
| [20] |
Zhao H, Li J (2021) Energy efficiency evaluation and optimization of industrial park customers based on PSR model and improved Grey-TOPSIS method. IEEE Access 9: 76423–76432. https://doi.org/10.1109/ACCESS.2021.3081142 doi: 10.1109/ACCESS.2021.3081142
|
| [21] |
Zhang W, Cheng J, Liu X, et al. (2022) Heterogeneous industrial agglomeration, its coordinated development and total factor energy efficiency. Environ Dev Sustainability 25: 5511–5537. https://doi.org/10.1007/s10668-022-02277-8 doi: 10.1007/s10668-022-02277-8
|
| [22] |
Jin W, Han J (2018) An energy integrated dispatching strategy of multi-energy based on energy internet. IOP Conference Series: Earth and Environmental Science 112: 012011. https://doi.org/10.1088/1755-1315/112/1/012011 doi: 10.1088/1755-1315/112/1/012011
|
| [23] |
Yong C, Shao M, Zhang X, et al. (2022) Research on supporting mechanism of ancillary service of PV system to grid energy efficiency based on multi-time and space-time operation. PLoS One 17: e0268173. https://doi.org/10.1371/journal.pone.0268173 doi: 10.1371/journal.pone.0268173
|
| [24] |
Patankar N, Fell GH, Rodrigo de Queiroz A, et al. (2022) Improving the representation of energy efficiency in an energy system optimization model. Appl Energy 306: 118083. https://doi.org/10.1016/j.apenergy.2021.118083 doi: 10.1016/j.apenergy.2021.118083
|
| [25] |
Liu J, Qian Y, Yang Y, et al. (2022) Can artificial intelligence improve the energy efficiency of manufacturing companies? Evidence from China. Inl J Environ Res Public Health 19: 2091. https://doi.org/10.3390/ijerph19042091 doi: 10.3390/ijerph19042091
|
| [26] |
Wang Q, Zhao T, Wang R (2021) Carbon neutrality ambitions and reinforcing energy efficiency through OFDI reverse technology spillover, Evidence from China. Pol J Environ Stud 31: 315–328. https://doi.org/10.15244/pjoes/139739 doi: 10.15244/pjoes/139739
|
| [27] |
Jalo N, Johansson I, Kanchiralla MF, et al. (2021) Do energy efficiency networks help reduce barriers to energy efficiency? A case study of a regional Swedish policy program for industrial SMEs. Renewable Sustainable Energy Rev 151: 111579. https://doi.org/10.1016/j.rser.2021.111579 doi: 10.1016/j.rser.2021.111579
|
| [28] |
Kamel T, Tian Z, Zangiabadi M, et al. (2022) Smart soft open point to synergistically improve the energy efficiencies of the interconnected electrical railways with the low voltage grids. Int J Electr Power Energy Syst 142: 108288. https://doi.org/10.1016/j.ijepes.2022.108288 doi: 10.1016/j.ijepes.2022.108288
|
| [29] |
Wang X, Zhou D (2023) The underlying drivers of energy efficiency, a spatial econometric analysis. Environ Sci Pollut Res 30: 13012–13022. https://doi.org/10.1007/s11356-022-23037-1 doi: 10.1007/s11356-022-23037-1
|
| [30] |
Nielsen T, Lund H, Ostergaard P, et al. (2021) Perspectives on energy efficiency and smart energy systems from the 5th SESAAU2019 conference. Energy 216: 119260. https://doi.org/10.1016/j.energy.2020.119260 doi: 10.1016/j.energy.2020.119260
|
| [31] |
Dranka G, Ferreira P, Vaz A (2022) Co-benefits between energy efficiency and demand-response on renewable-based energy systems. Renewable Sustainable Energy Rev 169: 112936. https://doi.org/10.1016/j.rser.2022.112936 doi: 10.1016/j.rser.2022.112936
|
| [32] |
Wu Z, Hou G, Xin B (2020) The causality between participation in GVCs, renewable energy consumption and CO2 emissions. Sustainability 12: 1237. https://doi.org/10.3390/su12031237 doi: 10.3390/su12031237
|
| [33] |
Adly B, El-Khouly T (2022) Combining retrofitting techniques, renewable energy resources and regulations for residential buildings to achieve energy efficiency in gated communities. Ain Shams Eng J 13: 101772. https://doi.org/10.1016/j.asej.2022.101772 doi: 10.1016/j.asej.2022.101772
|
| [34] |
Tanaka K, Managi S (2021) Industrial agglomeration effect for energy efficiency in Japanese production plants. Energy Policy 156: 112442. https://doi.org/10.1016/j.enpol.2021.112442 doi: 10.1016/j.enpol.2021.112442
|
| [35] |
Thakre K, Mohanty BK, Kommukuri SV, et al. (2022) Modified cascaded multilevel inverter for renewable energy systems with less number of unidirectional switches. Energy Rep 8: 5296–5304. https://doi.org/10.1016/j.egyr.2022.03.167 doi: 10.1016/j.egyr.2022.03.167
|
| [36] |
Nylén D, Holmström J (2015) Digital innovation strategy: A framework for diagnosing and improving digital product and service innovation. Bus Horiz 58: 57–67. https://doi.org/10.1016/j.bushor.2014.09.001 doi: 10.1016/j.bushor.2014.09.001
|
| [37] |
Yang K, Wang Y, Li M, et al. (2023) Modeling topological nature of gas-liquid mixing process inside rectangular channel using RBF-NN combined with CEEMDAN-VMD. Chem Eng Sci 267: 118353. https://doi.org/10.1016/j.ces.2022.118353 doi: 10.1016/j.ces.2022.118353
|
| [38] |
Ju Y, Wu L, Li M, et al. (2022) A novel hybrid model for flow image segmentation and bubble pattern extraction. Measurement 192: 110861. https://doi.org/10.1016/j.measurement.2022.110861 doi: 10.1016/j.measurement.2022.110861
|
| [39] |
Yang K, Zhang X, Li M, et al. (2022) Measurement of mixing time in a gas-liquid mixing system stirred by top-blown air using ECT and image analysis. Flow Meas Instrum 84: 102143. https://doi.org/10.1016/j.flowmeasinst.2022.102143 doi: 10.1016/j.flowmeasinst.2022.102143
|
| [40] |
Wang L, Shao J (2024) The energy saving effects of digital infrastructure construction, Empirical evidence from Chinese industry. Energy 294: 130778. https://doi.org/10.1016/j.energy.2024.130778 doi: 10.1016/j.energy.2024.130778
|
| [41] |
Stroud D, Evans C, Weinel M (2020) Innovating for energy efficiency, Digital gamification in the European steel industry. Eur J Ind Relat 26: 419–437. https://doi.org/10.1177/0959680120951707 doi: 10.1177/0959680120951707
|
| [42] |
Bornmann L (2020) Bibliometrics-based decision tree (BBDT) for deciding whether two universities in the Leiden ranking differ substantially in their performance. Scientometrics 122: 1255–1258. https://doi.org/10.1007/s11192-019-03319-1 doi: 10.1007/s11192-019-03319-1
|
| [43] |
Chen X, Xie H, Li Z, et al. (2022) Leveraging deep learning for automatic literature screening in intelligent bibliometrics. Int J Mach Learn Cybern 14: 1483–1525. https://doi.org/10.1007/s13042-022-01710-8 doi: 10.1007/s13042-022-01710-8
|
| [44] |
Gou X, Xu X, Xu Z, et al. (2024) Circular economy and fuzzy set theory: a bibliometric and systematic review based on Industry 4.0 technologies perspective. Technol Econ Dev Econ 30: 489–526. https://doi.org/10.3846/tede.2024.20286 doi: 10.3846/tede.2024.20286
|
| [45] |
Xu X, Gou X, Zhang W, et al. (2023) A bibliometric analysis of carbon neutrality: Research hotspots and future directions. Heliyon 9: e18763. https://doi.org/10.1016/j.heliyon.2023.e18763 doi: 10.1016/j.heliyon.2023.e18763
|
| [46] |
Yin S, Wang Y, Zhang Q (2025) Mechanisms and implementation pathways for distributed photovoltaic grid integration in rural power systems: A study based on a multi-agent game theory approach. Energy Strategy Rev 60: 101801. https://doi.org/10.1016/j.esr.2025.101801 doi: 10.1016/j.esr.2025.101801
|
| [47] |
Yin S, Yuan Y (2024) Integrated assessment and influencing factor analysis of energy-economy-environment system in rural China. AIMS Energy 12: 1173–1205. https://doi.org/10.3934/energy.2024054 doi: 10.3934/energy.2024054
|
| [48] |
Wang X, Fang Z, Sun X (2016) Usage patterns of scholarly articles on Web of Science, A study on Web of Science usage count. Scientometrics 109: 917–926. https://doi.org/10.1007/s11192-016-2093-0 doi: 10.1007/s11192-016-2093-0
|
| [49] |
Simsek Z, Vaara E, Paruchuri S, et al. (2019) New ways of seeing big data. Acad Manag J 62: 971–978. https://doi.org/10.5465/amj.2019.4004 doi: 10.5465/amj.2019.4004
|
| [50] |
Zupic I, Čater T (2015) Bibliometric methods in management and organization. Organ Res Methods 18: 429–472. https://doi.org/10.1177/1094428114562629 doi: 10.1177/1094428114562629
|
| [51] |
Mukherjee D, Lim WM, Kumar S, et al. (2022) Guidelines for advancing theory and practice through bibliometric research. J Bus Res 148: 101–115. https://doi.org/10.1016/j.jbusres.2022.04.042 doi: 10.1016/j.jbusres.2022.04.042
|
| [52] |
Zhang L, Ling J, Lin M (2023) Carbon neutrality, a comprehensive bibliometric analysis. Environ Sci Pollut Res 30: 45498–45514. https://doi.org/10.1007/s11356-023-25797-w doi: 10.1007/s11356-023-25797-w
|
| [53] |
Kut P, Pietrucha-Urbanik K (2024) Bibliometric analysis of renewable energy research on the example of the two European countries, insights, challenges, and future prospects. Energies 17: 176. https://doi.org/10.3390/en17010176 doi: 10.3390/en17010176
|
| [54] |
Hasan M, Abedin MZ, Bin Amin M, et al. (2023) Sustainable biofuel economy, A mapping through bibliometric research. J Environ Manag 336: 117644. https://doi.org/10.1016/j.jenvman.2023.117644 doi: 10.1016/j.jenvman.2023.117644
|
| [55] |
Sharma R, Jabbour C, Jabbour A (2021) Sustainable manufacturing and industry 4.0: what we know and what we don't. J Enterp Inform Manag 34: 230–266. https://doi.org/10.1108/JEIM-01-2020-0024 doi: 10.1108/JEIM-01-2020-0024
|
| [56] |
Donthu N, Kumar S, Mukherjee D, et al. (2021) How to conduct a bibliometric analysis: An overview and guidelines. J Bus Res 133: 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070 doi: 10.1016/j.jbusres.2021.04.070
|
| [57] |
Öztürk O, Kocaman R, Kanbach DK (2024) How to design bibliometric research: an overview and a framework proposal. Rev Manag Sci 18: 3333–3361. https://doi.org/10.1007/s11846-024-00738-0 doi: 10.1007/s11846-024-00738-0
|
| [58] |
Chen Y, Lin M, Zhuang D (2022) Wastewater treatment and emerging contaminants: Bibliometric analysis. Chemosphere 297: 133932. https://doi.org/10.1016/j.chemosphere.2022.133932 doi: 10.1016/j.chemosphere.2022.133932
|
| [59] |
Zhang Z, Hu G, Mu X, et al. (2022) From low carbon to carbon neutrality: a bibliometric analysis of the status, evolution and development trend. J Environ Manage 322: 116087. https://doi.org/10.1016/j.jenvman.2022.116087. doi: 10.1016/j.jenvman.2022.116087
|
| [60] |
Zhang L, Ling J, Lin M (2022) Artificial intelligence in renewable energy: A comprehensive bibliometric analysis. Energy Rep 8: 14072–14088. https://doi.org/10.1016/j.egyr.2022.10.347 doi: 10.1016/j.egyr.2022.10.347
|
| [61] |
Linnenluecke MK, Marrone M, Singh AK (2020) Conducting systematic literature reviews and bibliometric analyses. Aust J Manag 45: 175–194. https://doi.org/10.1177/0312896219877678 doi: 10.1177/0312896219877678
|
| [62] |
Agyekum E, Odoi-Yorke F, Abbey A, et al. (2024) A review of the trends, evolution, and future research prospects of hydrogen fuel cells—A focus on vehicles. Int J Hydrogen Energy 72: 918–939. https://doi.org/10.1016/j.ijhydene.2024.05.480 doi: 10.1016/j.ijhydene.2024.05.480
|
| [63] |
Kraus S, Kumar S, Lim W, et al. (2023) From moon landing to metaverse: Tracing the evolution of Technological Forecasting and Social Change. Technol Forecast Soc Change 189: 122381. https://doi.org/10.1016/j.techfore.2023.122381 doi: 10.1016/j.techfore.2023.122381
|
| [64] |
Zeba G, Dabic M, Cicak M, et al. (2021) Technology mining: Artificial intelligence in manufacturing. Technol Forecast Soc Change 171: 120971. https://doi.org/10.1016/j.techfore.2021.120971 doi: 10.1016/j.techfore.2021.120971
|
| [65] |
Jia C, Mustafa H (2023) A bibliometric analysis and review of nudge research using VOSviewer. Behav Sci 13: 19. https://doi.org/10.3390/bs13010019 doi: 10.3390/bs13010019
|
| [66] |
Zhou W, Kou A, Chen J, et al. (2018) A retrospective analysis with bibliometric of energy security in 2000–2017. Energy Rep 4: 724–732. https://doi.org/10.1016/j.egyr.2018.10.012 doi: 10.1016/j.egyr.2018.10.012
|
| [67] |
Huang L, Kelly S, Lv K, et al. (2019) A systematic review of empirical methods for modelling sectoral carbon emissions in China. J Clean Prod 215: 1382–1401. https://doi.org/10.1016/j.jclepro.2019.01.058 doi: 10.1016/j.jclepro.2019.01.058
|