Research article

The moderate level of digital transformation: from the perspective of green total factor productivity

  • Received: 26 October 2023 Revised: 17 December 2023 Accepted: 25 December 2023 Published: 12 January 2024
  • In the context of accelerated development of the digital economy, whether enterprises can drive green total factor productivity (GTFP) through digital technology has become the key to promoting high-quality development of the economy and achieving the goal of "dual-carbon", However, the relationship between digital transformation and GTFP is still controversial in existing studies. Based on the data of 150 listed companies in China's A-share energy industry from 2011 to 2021, this study empirically analyzes the impact of digital transformation on GTFP using a fixed-effect model. The study shows an inverted U-shaped nonlinear effect of digital transformation on enterprises' GTFP, and the conclusion still holds after a series of robustness tests. Mechanism analysis shows that enterprise investment efficiency and labour allocation efficiency play a significant mediating role in the above inverted U-shaped relationship, in which the inverted U-shaped relationship between digital transformation and GTFP mainly stems from the influence of enterprise investment efficiency. Heterogeneity analysis finds that the inverted U-shaped relationship between digital transformation and GTFP of enterprises is more significant in large-scale enterprises, new energy enterprises and enterprises in central and western regions. The study's findings provide important insights for enterprises to promote digital transformation and realize the green and high-quality development of the energy industry.

    Citation: Kaiwei Jia, Lujun Li. The moderate level of digital transformation: from the perspective of green total factor productivity[J]. Mathematical Biosciences and Engineering, 2024, 21(2): 2254-2281. doi: 10.3934/mbe.2024099

    Related Papers:

  • In the context of accelerated development of the digital economy, whether enterprises can drive green total factor productivity (GTFP) through digital technology has become the key to promoting high-quality development of the economy and achieving the goal of "dual-carbon", However, the relationship between digital transformation and GTFP is still controversial in existing studies. Based on the data of 150 listed companies in China's A-share energy industry from 2011 to 2021, this study empirically analyzes the impact of digital transformation on GTFP using a fixed-effect model. The study shows an inverted U-shaped nonlinear effect of digital transformation on enterprises' GTFP, and the conclusion still holds after a series of robustness tests. Mechanism analysis shows that enterprise investment efficiency and labour allocation efficiency play a significant mediating role in the above inverted U-shaped relationship, in which the inverted U-shaped relationship between digital transformation and GTFP mainly stems from the influence of enterprise investment efficiency. Heterogeneity analysis finds that the inverted U-shaped relationship between digital transformation and GTFP of enterprises is more significant in large-scale enterprises, new energy enterprises and enterprises in central and western regions. The study's findings provide important insights for enterprises to promote digital transformation and realize the green and high-quality development of the energy industry.



    加载中


    [1] S. Mantravadi, J. S. Srai, C. Møller, Application of MES/MOM for Industry 4.0 supply chains: A cross-case analysis, Comput. Ind., 148 (2023), 103907. https://doi.org/10.1016/j.compind.2023.103907 doi: 10.1016/j.compind.2023.103907
    [2] E. Lafuente, Y. Vaillant, R. Rabetino, Digital disruption of optimal co-innovation configurations, Technovation, 125 (2023), 102772. https://doi.org/10.1016/J.TECHNOVATION.2023.102772 doi: 10.1016/J.TECHNOVATION.2023.102772
    [3] Y. Peng, C. Tao, Can digital transformation promote enterprise performance?-From the perspective of public policy and innovation, J. Innovation Knowl., 7 (2022), 100198. https://doi.org/10.1016/J.JIK.2022.100198 doi: 10.1016/J.JIK.2022.100198
    [4] B. Dou, S. Guo, X. Chang, Y. Wang, Corporate digital transformation and labor structure upgrading, Int. Rev. Financ. Anal., 90 (2023), 102904. https://doi.org/10.1016/J.IRFA.2023.102904 doi: 10.1016/J.IRFA.2023.102904
    [5] X. Guo, M. Li, Y. Wang, A. Mardani, Does digital transformation improve the firm's performance? From the perspective of digitalization paradox and managerial myopia, J. Bus. Res., 163 (2023), 113868. https://doi.org/10.1016/J.JBUSRES.2023.113868 doi: 10.1016/J.JBUSRES.2023.113868
    [6] R. Bohnsack, A. Hanelt, H. Kurtz, Re-examining path dependency in the digital age:A longitudinal case study in the car industry, Acad. Manage. Annu. Meet. Proc., 2019 (2019). https://doi.org/10.5465/AMBPP.2019.17439abstract doi: 10.5465/AMBPP.2019.17439abstract
    [7] S. Liu, J. Yan, S. Zhang, H. Lin, Can digital transformation in enterprise management improve input output efficiency, J. Manage. World, 37 (2021), 170–190. https://doi.org/10.19744/j.cnki.11-1235/f.2021.0072 doi: 10.19744/j.cnki.11-1235/f.2021.0072
    [8] B. Huang, H. Li, J. Liu, J. Lei, Digital technology innovation and the high-quality development of chinese enterprises: Evidence from enterprise's digital patents, Econ. Res. J., 58 (2023), 97–115.
    [9] L. Grewal, A. T. Stephen, N. V. Coleman, When posting about products on social media backfires: The negative effects of consumer identity signaling on product interest, J. Mark. Res., 56 (2019), 197–210. https://doi.org/10.1177/0022243718821960 doi: 10.1177/0022243718821960
    [10] Y. Qi, C. Cai, Research on the multiple effects of digitalization on the performance of manufacturing enterprises and their mechanisms, Study Explor., (2020), 108–119. https://doi.org/10.3969/j.issn.1002-462X.2020.07.013
    [11] J. Shao, L. Wang, Can new-type urbanization improve the green total factor energy efficiency? Evidence from China, Energy, 262 (2023), 125499. https://doi.org/10.1016/J.ENERGY.2022.125499 doi: 10.1016/J.ENERGY.2022.125499
    [12] C. Jiakui, J. Abbas, H. Najam, J. Liu, J. Abbas, Green technological innovation, green finance, and financial development and their role in green total factor productivity: Empirical insights from China, J. Cleaner Prod., 382 (2023), 135131. https://doi.org/10.1016/J.JCLEPRO.2022.135131 doi: 10.1016/J.JCLEPRO.2022.135131
    [13] C. Bai, H. Liu, R. Zhang, C. Feng, Blessing or curse? Market-driven environmental regulation and enterprises' total factor productivity: Evidence from China's carbon market pilots, Energy Econ., 117 (2023), 106432. https://doi.org/10.1016/j.eneco.2022.106432 doi: 10.1016/j.eneco.2022.106432
    [14] C. Li, Y. Qi, S. Liu, X. Wang, Do carbon ETS pilots improve cities' green total factor productivity? Evidence from a quasi-natural experiment in China, Energy Econ., 108 (2022), 105931. https://doi.org/10.1016/J.ENECO.2022.105931 doi: 10.1016/J.ENECO.2022.105931
    [15] J. Cai, H. Zheng, M. Vardanyan, Z. Shen, Achieving carbon neutrality through green technological progress: Evidence from China, Energy Policy, 173 (2023), 113397. https://doi.org/10.1016/j.enpol.2022.113397 doi: 10.1016/j.enpol.2022.113397
    [16] L. Wang, J. Shao, Digital economy, entrepreneurship and energy efficiency, Energy, 269 (2023), 126801. https://doi.org/10.1016/j.energy.2023.126801 doi: 10.1016/j.energy.2023.126801
    [17] Y. Lyu, W. Wang, Y. Wu, J. Zhang, How does digital economy affect green total factor productivity? Evidence from China, Sci. Total Environ., 857 (2023), 159428. https://doi.org/10.1016/J.SCITOTENV.2022.159428 doi: 10.1016/J.SCITOTENV.2022.159428
    [18] X. Chen, J. Wang, Unleashing the power of informatization: How does the "information benefiting people" policy affect green total factor productivity?, J. Environ. Manage., 341 (2023), 118083. https://doi.org/10.1016/j.jenvman.2023.118083 doi: 10.1016/j.jenvman.2023.118083
    [19] T. Liang, Y. Zhang, W. Qiang, Does technological innovation benefit energy firms' environmental performance? The moderating effect of government subsidies and media coverage, Technol. Forecast. Soc. Change, 180 (2022), 121728. https://doi.org/10.1016/j.techfore.2022.121728 doi: 10.1016/j.techfore.2022.121728
    [20] Z. Yang, Y. Shen, The impact of intelligent manufacturing on industrial green total factor productivity and its multiple mechanisms, Front. Environ. Sci., 10 (2023), 1058664. https://doi.org/10.3389/FENVS.2022.1058664 doi: 10.3389/FENVS.2022.1058664
    [21] Y. Liu, J. Dong, L. Mei, R. Shen, Digital innovation and performance of manufacturing firms: An affordance perspective, Technovation, 119 (2023), 102458. https://doi.org/10.1016/J.TECHNOVATION.2022.102458 doi: 10.1016/J.TECHNOVATION.2022.102458
    [22] J. Wang, Y. Liu, W. Wang, H. Wu, How does digital transformation drive green total factor productivity? Evidence from Chinese listed enterprises, J. Cleaner Prod., 406 (2023), 136954. https://doi.org/10.1016/j.jclepro.2023.136954 doi: 10.1016/j.jclepro.2023.136954
    [23] C. Lee, Z. He, Z. Yuan, A pathway to sustainable development: Digitization and green productivity, Energy Econ., 124 (2023), 106772. https://doi.org/10.1016/J.ENECO.2023.106772 doi: 10.1016/J.ENECO.2023.106772
    [24] Z. Zou, M. Ahmad, Economic digitalization and energy transition for green industrial development pathways, Ecol. Inf., 78 (2023), 102323. https://doi.org/10.1016/J.ECOINF.2023.102323 doi: 10.1016/J.ECOINF.2023.102323
    [25] Q. Zhang, F. Zhang, Q. Mai, Robot adoption and green productivity: Curse or Boon, Sustainable Prod. Consumption, 34 (2022), 1–11. https://doi.org/10.1016/J.SPC.2022.08.025 doi: 10.1016/J.SPC.2022.08.025
    [26] A. Berner, S. Bruns, A. Moneta, D. I. Stern, Do energy efficiency improvements reduce energy use? Empirical evidence on the economy-wide rebound effect in Europe and the United States, Energy Econ., 110 (2022), 105939. https://doi.org/10.1016/j.eneco.2022.105939 doi: 10.1016/j.eneco.2022.105939
    [27] L. Kong, G. Hu, X. Mu, G. Li, Z. Zhang, The energy rebound effect in households: Evidence from urban and rural areas in Beijing, Appl. Energy, 343 (2023), 121151. https://doi.org/10.1016/J.APENERGY.2023.121151 doi: 10.1016/J.APENERGY.2023.121151
    [28] S. Lange, J. Pohl, T. Santarius, Digitalization and energy consumption. Does ICT reduce energy demand?, Ecol. Econ., 176 (2020), 106760. https://doi.org/10.1016/j.ecolecon.2020.106760 doi: 10.1016/j.ecolecon.2020.106760
    [29] B. Lin, C. Huang, Nonlinear relationship between digitization and energy efficiency: Evidence from transnational panel data, Energy, 276 (2023), 127601. https://doi.org/10.1016/j.energy.2023.127601 doi: 10.1016/j.energy.2023.127601
    [30] Y. Zhang, M. Wang, L. Cui, Impact of the digital economy on green total factor productivity in chinese cities, Econ. Geogr., 42 (2022), 33–42.
    [31] D. Sheng, W. Pu, The usage of robots and enterprises' pollution emissions in china, J. Quant. Technol. Econ., 39 (2022), 157–176.
    [32] Z. Ye, J. Yang, N. Zhong, X. Tu, J. Jia, J. Wang, Tackling environmental challenges in pollution controls using artificial intelligence: A review, Sci. Total Environ., 699 (2020), 134279. https://doi.org/10.1016/j.scitotenv.2019.134279 doi: 10.1016/j.scitotenv.2019.134279
    [33] W. Zhang, N. Xu, C. Li, X. Cui, H. Zhang, W. Chen, Impact of digital input on enterprise green productivity: Micro evidence from the Chinese manufacturing industry, J. Cleaner Prod., 414 (2023), 137272. https://doi.org/10.1016/J.JCLEPRO.2023.137272 doi: 10.1016/J.JCLEPRO.2023.137272
    [34] S. Zhang, X. Wei, Does information and communication technology reduce enterprise's energy consumption-evidence from chinese manufacturing enterprises survey, China Ind. Econ., (2019), 155–173.
    [35] L. Wang, Digital transformation and total factor productivity, Finance Res. Lett., 58 (2023), 104338. https://doi.org/10.1016/j.frl.2023.104338 doi: 10.1016/j.frl.2023.104338
    [36] G. Li, F. Liao, Input digitalization and green total factor productivity under the constraint of carbon emissions, J. Cleaner Prod., 377 (2022), 134403. https://doi.org/10.1016/j.jclepro.2022.134403 doi: 10.1016/j.jclepro.2022.134403
    [37] L. Vazhenina, E. Magaril, I. Mayburov, Digital management of resource efficiency of fuel and energy companies in a circular economy, Energies, 16 (2023), 3498. https://doi.org/10.3390/en16083498 doi: 10.3390/en16083498
    [38] M. Onifade, J. A. Adebisi, A. P. Shivute, B. Genc, Challenges and applications of digital technology in the mineral industry, Resour. Policy, 85 (2023), 103978. https://doi.org/10.1016/j.resourpol.2023.103978 doi: 10.1016/j.resourpol.2023.103978
    [39] F. Wu, H. Hu, H. Lin, X. Ren, Digital transformation of enterprises and capital market performance: Empirical evidence from stock liquidity, J. Manage. World, 37 (2021), 130–144. https://doi.org/10.19744/j.cnki.11-1235/f.2021.0097 doi: 10.19744/j.cnki.11-1235/f.2021.0097
    [40] P. Huo, L. Wang, Digital economy and business investment efficiency: Inhibiting or facilitating?, Res. Int. Bus. Finance, 63 (2022), 101797. https://doi.org/10.1016/j.ribaf.2022.101797 doi: 10.1016/j.ribaf.2022.101797
    [41] S. Liu, Y. Wu, X. Yin, B. Wu, Digital transformation and labour investment efficiency: Heterogeneity across the enterprise life cycle, Finance Res. Lett., 58 (2023), 104537. https://doi.org/10.1016/J.FRL.2023.104537 doi: 10.1016/J.FRL.2023.104537
    [42] S. F. Wamba, A. Gunasekaran, S. Akter, S. J. Ren, R. Dubey, S. J. Childe, Big data analytics and firm performance: Effects of dynamic capabilities, J. Bus. Res., 70 (2017), 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009 doi: 10.1016/j.jbusres.2016.08.009
    [43] A. Lateef, F. O. Omotayo, Information audit as an important tool in organizational management: A review of literature, Bus. Inf. Rev., 36 (2019), 15–22. https://doi.org/10.1177/0266382119831458 doi: 10.1177/0266382119831458
    [44] D. Wadley, Technology, capital substitution and labor dynamics: global workforce disruption in the 21st century?, Futures, 132 (2021), 102802. https://doi.org/10.1016/j.futures.2021.102802 doi: 10.1016/j.futures.2021.102802
    [45] D. Acemoglu, P. Restrepo, Robots and jobs: Evidence from US labor markets, J. Political Econ., 128 (2020), 2188–2244. https://doi.org/10.1086/705716 doi: 10.1086/705716
    [46] K. Tone, A slacks-based measure of super-efficiency in data envelopment analysis, Eur. J. Oper. Res., 143 (2002), 32–41. https://doi.org/10.1016/S0377-2217(01)00324-1 doi: 10.1016/S0377-2217(01)00324-1
    [47] D. Oh, A global Malmquist-Luenberger productivity index, J. Prod. Anal., 34 (2010), 183–197. https://doi.org/10.1007/s11123-010-0178-y doi: 10.1007/s11123-010-0178-y
    [48] W. Zhang, W. Jing, Digital economy supervision helps enterprises digital transformation: A balance analysis based on benefits and costs, J. Quant. Technol. Econ., (2023), 1–22. https://doi.org/10.13653/j.cnki.jqte.20231117.007
    [49] X. Yang, H. Wu, S. Ren, Q. Ran, J. Zhang, Does the development of the internet contribute to air pollution control in China? Mechanism discussion and empirical test, Struct. Change Econ. Dyn., 56 (2021), 207–224. https://doi.org/10.1016/J.STRUECO.2020.12.001 doi: 10.1016/J.STRUECO.2020.12.001
    [50] J. T. Lind, H. Mehlum, With or without U? The appropriate test for a U‐shaped relationship, Oxford Bull. Econ. Stat., 72 (2010), 109–118. https://doi.org/10.1111/j.1468-0084.2009.00569.x doi: 10.1111/j.1468-0084.2009.00569.x
    [51] A. V. Singh, G. Bansod, M. Mahajan, P. Dietrich, S. P. Singh, K. Rav, et al., Digital transformation in toxicology: Improving communication and efficiency in risk assessment, ACS omega, (2023), 21377–21390. https://doi.org/10.1021/ACSOMEGA.3C00596
    [52] C. Cinelli, C. Hazlett, Making sense of sensitivity: Extending omitted variable bias, J. R. Stat. Soc., Ser. B: Stat. Methodol., 82 (2020), 39–67. https://doi.org/10.1111/rssb.12348 doi: 10.1111/rssb.12348
    [53] R. Sharma, A. B. L. de Sousa Jabbour, V. Jain, A. Shishodia, The role of digital technologies to unleash a green recovery: Pathways and pitfalls to achieve the European Green Deal, J. Enterp. Inf. Manage., 35 (2022), 266–294. https://doi.org/10.1108/JEIM-07-2021-0293 doi: 10.1108/JEIM-07-2021-0293
    [54] T. Ni, Y. Wang, Regional administrative integration, factor marketization, and firms' resource allocation efficiency, J. Quant. Technol. Econ., 39 (2022), 136–156.
    [55] M. T. Ballestar, Á. Díaz-Chao, J. Sainz, J. Torrent-Sellens, Impact of robotics on manufacturing: A longitudinal machine learning perspective, Technol. Forecast. Soc. Change, 162 (2021), 120348. https://doi.org/10.1016/j.techfore.2020.120348 doi: 10.1016/j.techfore.2020.120348
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(535) PDF downloads(51) Cited by(0)

Article outline

Figures and Tables

Figures(2)  /  Tables(6)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog