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
[1] | Xiaoming Su, Jiahui Wang, Adiya Bao . Stability analysis and chaos control in a discrete predator-prey system with Allee effect, fear effect, and refuge. AIMS Mathematics, 2024, 9(5): 13462-13491. doi: 10.3934/math.2024656 |
[2] | Kottakkaran Sooppy Nisar, G Ranjith Kumar, K Ramesh . The study on the complex nature of a predator-prey model with fractional-order derivatives incorporating refuge and nonlinear prey harvesting. AIMS Mathematics, 2024, 9(5): 13492-13507. doi: 10.3934/math.2024657 |
[3] | Nehad Ali Shah, Iftikhar Ahmed, Kanayo K. Asogwa, Azhar Ali Zafar, Wajaree Weera, Ali Akgül . Numerical study of a nonlinear fractional chaotic Chua's circuit. AIMS Mathematics, 2023, 8(1): 1636-1655. doi: 10.3934/math.2023083 |
[4] | A. Q. Khan, Ibraheem M. Alsulami . Complicate dynamical analysis of a discrete predator-prey model with a prey refuge. AIMS Mathematics, 2023, 8(7): 15035-15057. doi: 10.3934/math.2023768 |
[5] | Xiao-Long Gao, Hao-Lu Zhang, Xiao-Yu Li . Research on pattern dynamics of a class of predator-prey model with interval biological coefficients for capture. AIMS Mathematics, 2024, 9(7): 18506-18527. doi: 10.3934/math.2024901 |
[6] | Weili Kong, Yuanfu Shao . The effects of fear and delay on a predator-prey model with Crowley-Martin functional response and stage structure for predator. AIMS Mathematics, 2023, 8(12): 29260-29289. doi: 10.3934/math.20231498 |
[7] | Asharani J. Rangappa, Chandrali Baishya, Reny George, Sina Etemad, Zaher Mundher Yaseen . On the existence, stability and chaos analysis of a novel 4D atmospheric dynamical system in the context of the Caputo fractional derivatives. AIMS Mathematics, 2024, 9(10): 28560-28588. doi: 10.3934/math.20241386 |
[8] | Yao Shi, Zhenyu Wang . Bifurcation analysis and chaos control of a discrete fractional-order Leslie-Gower model with fear factor. AIMS Mathematics, 2024, 9(11): 30298-30319. doi: 10.3934/math.20241462 |
[9] | Guilin Tang, Ning Li . Chaotic behavior and controlling chaos in a fast-slow plankton-fish model. AIMS Mathematics, 2024, 9(6): 14376-14404. doi: 10.3934/math.2024699 |
[10] | Xuyang Cao, Qinglong Wang, Jie Liu . Hopf bifurcation in a predator-prey model under fuzzy parameters involving prey refuge and fear effects. AIMS Mathematics, 2024, 9(9): 23945-23970. doi: 10.3934/math.20241164 |
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.
Throughout the paper, we work over an algebraically closed field
Σk=Σk(C,L)⊆Pr |
of
Assume that
σk+1:Ck×C⟶Ck+1 |
be the morphism sending
Ek+1,L:=σk+1,∗p∗L, |
which is a locally free sheaf of rank
Bk(L):=P(Ek+1,L) |
equipped with the natural projection
H0(Bk(L),OBk(L)(1))=H0(Ck+1,Ek+1,)=H0(C,L), |
and therefore, the complete linear system
βk:Bk(L)⟶Pr=P(H0(C,L)). |
The
It is clear that there are natural inclusions
C=Σ0⊆Σ1⊆⋯⊆Σk−1⊆Σk⊆Pr. |
The preimage of
Theorem 1.1. Let
To prove the theorem, we utilize several line bundles defined on symmetric products of the curve. Let us recall the definitions here and refer the reader to [2] for further details. Let
Ck+1=C×⋯×C⏟k+1times |
be the
Ak+1,L:=Tk+1(L)(−2δk+1) |
be a line bundle on
The main ingredient in the proof of Theorem 1.1 is to study the positivity of the line bundle
Proposition 1.2. Let
In particular, if
In this section, we prove Theorem 1.1. We begin with showing Proposition 1.2.
Proof of Proposition 1.2. We proceed by induction on
Assume that
rz,k+1,L:H0(Ck+1,Ak+1,L)⟶H0(z,Ak+1,L|z) |
is surjective. We can choose a point
rz,k+1,L:H0(Ck+1,Ak+1,L)⟶H0(z,Ak+1,L|z) |
where all rows and columns are short exact sequences. By tensoring with
rz,k+1,L:H0(Ck+1,Ak+1,L)⟶H0(z,Ak+1,L|z) |
in which we use the fact that
Since
Lemma 2.1. Let
Proof. Note that
B′/A′⊗A′A′/m′q=B′/(m′qB′+A′)=B′/(m′p+A′)=0. |
By Nakayama lemma, we obtain
We keep using the notations used in the introduction. Recall that
αk,1:Bk−1(L)×C⟶Bk(L). |
To see it in details, we refer to [1,p.432,line –5]. We define the relative secant variety
Proposition 2.2. ([2,Proposition 3.15,Theorem 5.2,and Proposition 5.13]) Recall the situation described in the diagram
αk,1:Bk−1(L)×C⟶Bk(L). |
Let
1.
2.
3.
As a direct consequence of the above proposition, we have an identification
H0(Ck+1,Ak+1,L)=H0(Σk,IΣk−1|Σk(k+1)). |
We are now ready to give the proof of Theorem 1.1.
Proof of Theorem 1.1. Let
b:˜Σk:=BlΣk−1Σk⟶Σk |
be the blowup of
b:˜Σk:=BlΣk−1Σk⟶Σk |
We shall show that
Write
γ:˜Σk⟶P(V). |
On the other hand, one has an identification
ψ:Ck+1⟶P(V). |
Also note that
ψ:Ck+1⟶P(V). |
Take an arbitrary closed point
α−1(x)⊆π−1k(x″)∩β−1k(x′). |
However, the restriction of the morphism
[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
![]() |