Digital Economy Development, Industrial Structure Upgrading and Green Total Factor Productivity: Empirical Evidence from China’s Cities
Abstract
:1. Introduction
2. Literature Review
3. Theoretical Analysis
4. Materials and Methods
4.1. Data Source and Sample Selection
4.1.1. Relevant Indicators for GTFP Measurement
4.1.2. Digital Economy Indicators
4.1.3. Intermediary Variables
4.1.4. Control Variables
4.2. Research Strategy
4.3. Research Methods
4.3.1. Measuring Method of GTFP
4.3.2. Tobit Model
4.3.3. Quantile Regression Model
4.3.4. PVAR Model
4.3.5. Mediating Effect Model
5. Results
5.1. Tobit and OLS Regression Results and Analysis
5.2. Quantile Regression Results and Analysis
5.3. Dynamic Impact of Digital Economy on GTFP
- (1)
- The response of GTFP in national and central cities to the impact of digital economy shows positive promotion, and the response curve first rises and then falls and gradually converges to zero. The response of peripheral cities is first suppressed and then promoted, the cumulative effect is positive, and the response curve finally converges to zero. This shows that the digital economy has indeed played a positive role in promoting China’s GTFP. In recent years, the Chinese government has launched a series of forward-looking digital infrastructure construction policies, especially the comprehensive implementation of the network power strategy and the national big data strategy, which has successfully transformed China’s super-large market and demographic dividends into data dividends. Through data circulation, cooperation and sharing, the upstream and downstream blockages of the supply chain are opened up, resource allocation is optimized, and GTFP is improved.
- (2)
- In the face of an orthogonal shock of the digital economy, the response intensity of GTFP in the whole country, central cities and peripheral cities decreases successively, with the response peak values of 0.0021 in the third period, 0.0075 in the first period and 0.0015 in the fourth period, respectively. In addition, the peripheral cities have a negative response in the early stage. Central cities have the fastest response speed and the greatest response intensity, which shows that the digital economy has the best effect on promoting GTFP of central cities. The response speed of peripheral cities is slower than the national average, and the response intensity is also lower than the national average. The digital economy has a negative impact on the GTFP of peripheral cities in the early stage, and this gradually turns to a positive impact after the first stage. This shows that there is still much room for digital economy in peripheral cities to improve GTFP. Mitrovic (2020) stated that digital information has the characteristics of spillover and sharing, it is easier to catch up with digital informatization [80]. Therefore, peripheral cities should firmly grasp the development opportunities of digital economy, strive to narrow the digital divide and fully stimulate the role of digital economy in promoting city’s GTFP.
- (3)
- The cumulative effects of GTFP in the whole country, central cities and peripheral cities facing the shock of digital economy are 0.0104, 0.0207 and 0.0063, respectively. The cumulative effect of central cities is the largest, while that of peripheral cities is lower than the national average. This shows that, from the dynamic perspective of long period span, the digital economy has the greatest effect on the improvement of GTFP in central cities and the smallest effect on the improvement of GTFP in peripheral cities. The digital economic dividend presents a distribution pattern with more central cities and fewer peripheral cities. Compared with central cities, the digital infrastructure of peripheral cities is weaker, the development of digital industry still lags far behind that of central cities, and the integration level of digital economy and traditional industry is also lower than that of central cities. Peripheral cities should learn from the development experience of digital economy in central cities and combine their own comparative advantages to firmly seize the important opportunity of the digital economy and strive to overtake in corners.
5.4. Robustness Test
5.5. Influence Mechanism Test
6. Discussion and Policy Recommendations
- (1)
- The influence coefficients of the digital economy on the GTFP of the whole country, the eastern and central region and the western region are 0.3549, 0.4194 and −0.1495, respectively. This shows that the digital economy can significantly promote China’s GTFP. China has gradually explored a digital economy development path that is suitable for the development environment of emerging markets and different from western developed countries. The digital economy has become an important engine to promote the high-quality development of China’s economy. However, there are clear regional differences. The digital economy in the eastern and central regions has a good effect on promoting the GTFP, while the role of digital economy in promoting GTFP in the western region has not yet appeared.
- (2)
- The influence characteristic of the digital economy on the conditional distribution of city’s GTFP is shown as follows: with the increase of the quantile of GTFP, the influence coefficient of digital economy on it gradually increases, with the value ranging from 0.2678 to 0.5929. This shows that the digital economy has a clear effect on the promotion of city’s GTFP, and there are significant differences among cities. The higher the GTFP, the greater the promotion effect of the digital economy on the city’s GTFP.
- (3)
- The results of dynamic influence of the digital economy on GTFP are as follows: From a dynamic long-term perspective, the digital economy has indeed positively promoted China’s GTFP. The digital economy had the best effect on promoting GTFP in central cities. There is still much room for the digital economy of peripheral cities to improve the GTFP. The digital economic dividend presents a distribution pattern with more central cities and fewer peripheral cities.
- (4)
- Through the Stepwise causality method of intermediary effect, Sobel test and bootstrap intermediary effect test, we verified that digital economy can improve a city’s GTFP by optimizing and upgrading industrial structure. The digital economy can accelerate the transformation of new and old kinetic energy by empowering the transformation and upgrading of traditional industries and promote the green development of the economy.At the same time, the digital economy facilitates the transformation of the organizational form of the manufacturing industry chain and reshapes the value distribution form of the manufacturing industry chain. The rise of the digital economy platform has opened up new market space for the industrialization of digital technology, spawned a number of new industries and new business forms, injecting vitality into economic development and promoting the economy to take a path of sustainable development.
- (1)
- China should seize the opportunity of digital economy development, build new competitive advantages of the country and promote GTFP through digital economy development so as to realize green economic development. It is necessary to promote the deep integration of digital technology and the real economy, fully tap the huge potential of data as a production factor, unswervingly build digital China, strengthen key core technology research, give full play to the advantages of China’s new nationwide system and super-large-scale market, grasp the autonomy of developing digital economy, avoid the “bottleneck” problem, accelerate the construction of digital infrastructure, open up the information “main artery” of economic and social development, promote the rapid flow of various resource elements and enhance the resilience of China’s economic development.
- (2)
- In the process of promoting GTFP through the digital economy, China should pay attention to the important mechanism of industrial structure upgrading through vigorously developing the digital economy, assisting the transformation and upgrading of traditional industries, eliminating outdated production capacity and promoting the iterative upgrade of new and old kinetic energy. The digital economy helps China to walk a sustainable development path, improve the green development level of China’s economy and lay a solid foundation for China to realize peak carbon dioxide emissions by 2030 and carbon neutrality by 2060.Efforts should be made to promote the digitalization of manufacturing, service and agriculture industries as well as to use digital technology to comprehensively transform traditional industries, reduce energy consumption, increase GTFP, promote the development of digital industry in key areas, improve the competitiveness of key links in the industrial chain, unblock the upstream and downstream blocking points in the industrial chain, improve the GTFP and gradually explore a digital economy development path that suits the development environment of emerging markets.
- (3)
- Efforts should be made to narrow the gap between the western region and the eastern and central regions as well as between peripheral cities and central cities in terms of digital economy promoting GTFP. China needs to make overall plans for digital infrastructure represented by technologies, such as the internet, big data, cloud computing and artificial intelligence; intensify the construction of digital infrastructure in relatively backward areas; and strive to narrow the gap of digital infrastructure among regions. Late-developing regions should seize the important opportunity of the digital economy to narrow the gap with developed regions.It is necessary to optimize the regional layout and achieve differentiated positioning for different regions, give full play to the comparative advantages of the regions and realize the complementary advantages of the regions. It is necessary to make full use of the radiating and leading role of central cities, extend the digital industry chain to peripheral cities, strengthen the industrial cooperation between cities, build digital economy demonstration cities, promote cities with a high level of digital economy development and backward cities to build digital economy platforms together, realize the cross-city and barrier-free flow of data elements and smooth the circulation of domestic data elements.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Han, J.; Sun, Y.W.; Chen, X. Analysis of the development path of China’s digital economy in the post-pandemic era. Comp. Econ. Syst. 2020, 5, 16–24. [Google Scholar]
- Chen, X.D.; Yang, X.X. The influence of the development of the digital economy on the upgrading of industrial structure: A study based on the grey relation entropy and the theory of dissipative structure. Reform 2021, 3, 26–39. [Google Scholar]
- Gereffi, G.; Humphrey, J.; Sturgeon, T. The governance of global value chains. Rev. Int. Political Econ. 2005, 12, 78–104. [Google Scholar] [CrossRef]
- Tapscott, D.; McQueen, R. The digital economy: Promise and peril in the age of networked intelligence. Bambook 1996, 10, 69–71. [Google Scholar]
- Clifton, N.; Fuzi, A.; Loudon, G. Coworking in the digital economy: Context, motivations, and outcomes. Futures 2019, 135, 102439. [Google Scholar] [CrossRef]
- Ding, C.; Liu, C.; Zheng, C.; Li, F. Digital economy, technological innovation and high-quality economic development: Based on spatial effect and mediation effect. Sustainability 2022, 14, 216. [Google Scholar] [CrossRef]
- Guo, L. The impact mechanism of the digital economy on China’s total factor productivity: An uplifting effect or a restraining effect? South China J. Econ. 2021, 40, 9–27. [Google Scholar]
- Kim, B.; Barua, A.; Whinston, A.B. Virtual field experiments for a digital economy: A new research methodology for exploring an information economy. Decis. Support Syst. 2002, 32, 215–231. [Google Scholar] [CrossRef]
- Quah, D. Digital Goods and the New Economy; CEP Discussion Paper: London, UK, 2002; p. 563. [Google Scholar]
- Freidman, T. The World Is Flat; Farrar, Straus and Giroux: New York, NY, USA, 2005; p. 488. [Google Scholar]
- Organisation for Economic Co-operation and Development. Measuring the Digital Economy: A New Perspective; OECD Publishing: Paris, France, 2014; pp. 45–49. [Google Scholar]
- Barua, A.; Chellappa, R.; Whinston, A.B. Creating a collaboratory in cyberspace: Theoretical foundation and an implementation. J. Organ. Comput. Electron. Commer. 1995, 5, 417–442. [Google Scholar] [CrossRef]
- Barua, A.; Chellappa, R.; Whinston, A.B. The design and development of Internet and Intranet-based collaboratories. Int. J. Electron. Commer. 1996, 1, 32–58. [Google Scholar] [CrossRef]
- Choi, S.Y.; Stahl, D.O.; Whinston, A.B. The Economics of Electronic Commerce; Macmillan Technical Publishing: Indianapolis, Indiana, 1997; p. 626. [Google Scholar]
- Roller, L.H.; Waverman, L. Telecommunications infrastructure and economic development: A simultaneous approach. Am. Econ. Rev. 2001, 91, 909–923. [Google Scholar] [CrossRef] [Green Version]
- Antonelli, C. The digital divide: Understanding the economics of new information and communication technology in the global economy. Inf. Econ. Policy 2003, 15, 173–199. [Google Scholar] [CrossRef]
- Oliner, S.D.; Sichel, D.E.; Stiroh, K.J. Explaining a productive decade. J. Policy Modeling 2008, 30, 633–673. [Google Scholar] [CrossRef] [Green Version]
- Greenstein, S.; McDevitt, R.C. The broadband bonus: Estimating broadband Internet’s economic value. Telecommun. Policy 2011, 35, 617–632. [Google Scholar] [CrossRef]
- Jimenez, M.; Matus, J.A.; Martinez, M.A. Economic growth as a function of human capital, Internet and work. Appl. Econ. 2014, 46, 3202–3210. [Google Scholar] [CrossRef]
- Ivus, O.; Boland, M. The employment and wage impact of broadband deployment in Canada. Can. J. Econ. 2015, 48, 1803–1830. [Google Scholar] [CrossRef]
- Jorgenson, A.K. Environment, development, and ecologically unequal exchange. Sustainability 2016, 8, 227. [Google Scholar] [CrossRef] [Green Version]
- Acemoglu, D.; Restrepo, P. The race between man and machine: Implications of technology for growth, factor shares, and employment. Am. Econ. Rev. 2018, 108, 1488–1542. [Google Scholar] [CrossRef] [Green Version]
- Graetz, G.; Michaels, G. Robots at work. Rev. Econ. Stat. 2018, 100, 753–768. [Google Scholar] [CrossRef] [Green Version]
- Sutherland, E. Trends in regulating the global digital economy. In Proceedings of the 4th Annual Competition and Economic Regulation Conference, Johannesburg, South Africa, 16–20 July 2018; 2018. [Google Scholar]
- Chakpitak, N.; Maneejuk, P.; Chanaim, S.; Sriboonchitta, S. Thailand in the era of digital economy: How does digital technology promote economic growth? In International Conference of the Thailand Econometrics Society; Springer: Cham, Germany, 2018; pp. 350–362. [Google Scholar]
- Pan, W.; Xie, T.; Wang, Z.; Ma, L. Digital economy: An innovation driver for total factor productivity. J. Bus. Res. 2022, 139, 303–311. [Google Scholar] [CrossRef]
- Li, T.; Han, D.; Ding, Y.; Shi, Z. How does the development of the Internet affect green total factor productivity: Evidence from China. IEEE Access 2020, 8, 216477–216490. [Google Scholar] [CrossRef]
- Zhou, Y.; Wang, H.Y. The impact of digital economy on industrial green total factor productivity: Based on the moderating effect of regional basic absorptive capacity. Sci. Technol. Econ. 2021, 34, 81–85. [Google Scholar]
- Bukht, R.; Heeks, R. Defining, Conceptualising and Measuring the Digital Economy; Development Informatics Working Paper: Manchester, UK, 2017; p. 68. [Google Scholar]
- Guo, H. The Path for the integration of digital economy and real economy to promote high-quality development. J. Xi’an Univ. Financ. Econ. 2020, 33, 20–24. [Google Scholar]
- Yang, J.; Li, X.M.; Huang, S.J. Impacts on environmental quality and required environmental regulation adjustments: A perspective of directed technical change driven by big data. J. Clean. Prod. 2020, 275, 124–126. [Google Scholar] [CrossRef]
- Liang, Q.; Xiao, S.P.; Li, M.X. Has the development of digital economy improved the ecological efficiency of cities?: Based on the perspective of industrial structure upgrading. Inq. Econ. Issues 2021, 6, 82–92. [Google Scholar]
- Liang, G.; Yu, D.; Ke, L. An empirical study on dynamic evolution of industrial structure and green economic growth: Based on data from China’s underdeveloped areas. Sustainability 2021, 13, 8154. [Google Scholar] [CrossRef]
- Drucker, J. Regional industrial structure concentration in the United States: Trends and implications. Econ. Geogr. 2011, 87, 421–452. [Google Scholar] [CrossRef]
- Zhao, J.; Tang, J. Industrial structure change and economic growth: A China-Russia comparison. China Econ. Rev. 2018, 47, 219–233. [Google Scholar] [CrossRef]
- Mah, J.S. Industrial policy and economic development: Korea’s experience. J. Econ. Issues 2007, 41, 77–92. [Google Scholar] [CrossRef]
- Timmer, M.P.; Szirmai, A. Productivity growth in Asian manufacturing: The structural bonus hypothesis examined. Struct. Chang. Econ. Dyn. 2000, 11, 371–392. [Google Scholar] [CrossRef]
- Zhong, K. Does the digital finance revolution validate the Environmental Kuznets Curve? Empirical findings from China. PLoS ONE 2022, 17, e0257498. [Google Scholar] [CrossRef] [PubMed]
- Borgersen, T.A.; King, R.M. Export-led growth in transition economies: The role of industrial structure, productivity growth differentials, and cross-sectoral subsidies. East. Eur. Econ. 2014, 52, 33–54. [Google Scholar] [CrossRef]
- Pei, T.; Gao, L.; Yang, C.; Xu, C.; Tian, Y.; Song, W. The impact of FDI on urban PM2. 5 pollution in China: The mediating effect of industrial structure transformation. Int. J. Environ. Res. Public Health 2021, 18, 9107. [Google Scholar] [CrossRef] [PubMed]
- Wang, S.; Liang, Y.C.; Li, W.D.; Cai, X.T. Big data enabled intelligent immune system for energy efficient manufacturing management. J. Clean. Prod. 2018, 195, 507–520. [Google Scholar] [CrossRef]
- Laitner, J.A.; Berners-Lee, M. Smarter 2030: ICT solutions for 21st century challenges. Glob. E-Sustain. Initiat. Tech. Rep. 2015. Available online: https://smarter2030.gesi.org/downloads/Full_report.pdf (accessed on 8 January 2022).
- Hu, A.G.; Zheng, J.H.; Gao, Y.N.; Ning, Z.; Haiping, X. Provincial technical efficiency ranking considering environmental factors (1999–2005). Q. Econ. 2008, 3, 933–960. [Google Scholar]
- Chen, S.Y. Energy consumption, carbon dioxide emissions and the sustainable development of China’s industry. Econ. Res. J. 2009, 44, 41–55. [Google Scholar]
- Yang, L.; Ouyang, H.; Fang, K.; Ye, L.; Zhang, J. Evaluation of regional environmental efficiencies in China based on super-efficiency-DEA. Ecol. Indic. 2015, 51, 13–19. [Google Scholar] [CrossRef]
- Chen, C.F. China’s industrial green total factor productivity and its influencing factors: An empirical study based on the ML productivity index and dynamic panel model. Stat. Res. 2016, 33, 53–62. [Google Scholar]
- Zhang, J.; Li, Z.F. Does foreign direct investment promote China’s green total factor productivity growth: An empirical test based on dynamic system GMM estimation and threshold model. J. Int. Trade 2020, 7, 159–174. [Google Scholar]
- Zhang, J.; Wu, G.Y.; Zhang, J.P. China’s inter-provincial material capital stock estimation: 1952–2000. Econ. Res. J. 2004, 10, 35–44. [Google Scholar]
- Young, A. Gold into base metals: Productivity growth in the People’s Republic of China during the reform period. J. Political Econ. 2003, 111, 1220–1261. [Google Scholar] [CrossRef] [Green Version]
- Lu, L.W.; Song, D.Y.; Li, X.F. Research on the green efficiency of urban development in the Yangtze River economic zone. China Popul. Resour. Environ. 2016, 26, 35–42. [Google Scholar]
- Lin, B.Q. Power consumption and China’s economic growth: A study based on the production function. Manag. World 2003, 11, 18–27. [Google Scholar]
- Zhao, T.; Zhang, Z.; Liang, S.K. Digital economy, entrepreneurial activity and high-quality development: Empirical evidence from Chinese cities. Manag. World 2020, 36, 65–76. [Google Scholar]
- Guo, F.; Wang, J.Y.; Wang, F.; Kong, T.; Zhang, X.; Cheng Z., Y. Measuring the development of China’s digital financial inclusion: Index compilation and spatial characteristics. China Econ. Q. 2020, 19, 1401–1418. [Google Scholar]
- Fu, L.H. An empirical study on the relationship between the industrial structure advancement and economic growth in China. Stat. Res. 2010, 27, 79–81. [Google Scholar]
- Zhang, J.P.; Chen, S.Y. Financial development, environmental regulation and economic green transformation. Financ. Res. 2021, 47, 78–93. [Google Scholar]
- Ihaka, R.; Gentleman, R.R. A language for data analysis and graphics. J. Comput. Graph. Stat. 1996, 5, 299–314. [Google Scholar]
- Fare, R.; Grosskopf, S.; Pasurka, C.A. Environmental production functions and environmental directional distance functions. Energy 2007, 32, 1055–1066. [Google Scholar] [CrossRef]
- Chung, Y.H.; Fare, R.; Grosskopf, S. Productivity and undesirable outputs: A directional distance function approach. J. Environ. Manag. 1997, 51, 229–240. [Google Scholar] [CrossRef] [Green Version]
- Oh, D.H. A global malmquist-luenberger productivity index. J. Product. Anal. 2010, 34, 183–197. [Google Scholar] [CrossRef]
- Mulungu, K.; Ngombe, J.N. Sources of economic growth in Zambia, 1970–2013: A growth accounting approach. Economies 2017, 5, 15. [Google Scholar] [CrossRef] [Green Version]
- Roe, T.L.; Rodney, S.; Choi, D. Introduction to Growth Accounting as a Diagnostic; University of Minnesota: Minneapolis, MN, USA, 2014. [Google Scholar]
- Coelli, T.J.; Rao, D.S.P. Total factor productivity growth in agriculture: A malmquist index analysis of 93 countries, 1980–2000. Agric. Econ. 2005, 32, 115–134. [Google Scholar] [CrossRef] [Green Version]
- Li, J.; Tang, D.; Tenkorang, A.P.; Shi, Z. Research on environmental regulation and green total factor productivity in Yangtze River Delta: From the perspective of financial development. Int. J. Environ. Res. Public Health 2021, 18, 12453. [Google Scholar] [CrossRef] [PubMed]
- Xu, X.; Huang, X.; Huang, J.; Gao, X.; Chen, L. Spatial-temporal characteristics of agriculture green total factor productivity in China, 1998–2016: Based on more sophisticated calculations of carbon emissions. Int. J. Environ. Res. Public Health 2019, 16, 3932. [Google Scholar] [CrossRef] [Green Version]
- Shair, F.; Shaorong, S.; Kamran, H.W.; Hussain, M.S.; Nawaz, M.A.; Nguyen, V.C. Assessing the efficiency and total factor productivity growth of the banking industry: Do environmental concerns matters? Environ. Sci. Pollut. Res. 2021, 28, 20822–20838. [Google Scholar] [CrossRef]
- Zhong, K.; Wang, Y.; Pei, J.; Tang, S.; Han, Z. Super efficiency SBM-DEA and neural network for performance evaluation. Inf. Process. Manag. 2021, 58, 102728. [Google Scholar] [CrossRef]
- Zhong, K.; Li, C.; Wang, Q. Evaluation of bank innovation efficiency with data envelopment analysis: From the perspective of uncovering the black box between input and output. Mathematics 2021, 9, 3318. [Google Scholar] [CrossRef]
- Tobin, J. Estimation of relationships for limited dependent variables. Econom. J. Econom. Soc. 1958, 26, 24–36. [Google Scholar] [CrossRef] [Green Version]
- Wu, L.; Jia, X.Y.; Wu, C.; Peng, J.C. The impact of heterogeneous environmental regulations on China’s green total factor productivity. China Popul. Resour. Environ. 2020, 30, 82–92. [Google Scholar]
- Koenker, R.; Bassett, J.G. Regression quantiles. Econom. J. Econom. Soc. 1978, 46, 33–50. [Google Scholar] [CrossRef]
- Holtz-Eakin, D.; Newey, W.; Rosen, H.S. Estimating vector autoregressions with panel data. Econom. J. Econom. Soc. 1988, 56, 1371–1395. [Google Scholar] [CrossRef]
- Mccoskey, S.; Kao, C. A residual-based test of the null of cointegration in panel data. Econom. Rev. 1998, 17, 57–84. [Google Scholar] [CrossRef]
- Love, I.; Zicchino, L. Financial development and dynamic investment behavior: Evidence from panel VAR. Q. Rev. Econ. Financ. 2006, 46, 190–210. [Google Scholar] [CrossRef]
- Baron, R.M.; Kenny, D.A. The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J. Personal. Soc. Psychol. 1986, 51, 1173. [Google Scholar] [CrossRef]
- Bonfadelli, H. The Internet and knowledge gaps: A theoretical and empirical investigation. Eur. J. Commun. 2002, 17, 65–84. [Google Scholar] [CrossRef]
- Qiu, Z.Q.; Zhang, S.Q.; Liu, S.D.; Xu, Y.K. From the digital divide to the dividend difference: The perspective of Internet capital. Soc. Sci. China 2016, 10, 93–115. [Google Scholar]
- Wang, X.H.; Zhao, Y.X. Is there a Matthew effect in the development of digital finance: The experience comparison of poor households and non-poor households. J. Financ. Res. 2020, 7, 114–133. [Google Scholar]
- Yang, W.P. Digital economy and regional economic growth: Late-comer advantage or late-comer disadvantage? J. Shanghai Univ. Financ. Econ. 2021, 23, 19–31. [Google Scholar]
- Hawash, R.; Lang, G. Does the digital gap matter: Estimating the impact of ICT on productivity in developing countries. Eurasian Econ. Rev. 2020, 10, 189–209. [Google Scholar] [CrossRef]
- Mitrovic, D. Measuring the efficiency of digital convergence. Econ. Lett. 2020, 188, 108982. [Google Scholar] [CrossRef]
- Huang, Q.H.; Yu, Y.Z.; Zhang, S.L. Internet development and manufacturing productivity improvement: Internal mechanism and China’s experience. China Ind. Econ. 2019, 8, 5–23. [Google Scholar]
- Nunn, N.; Qian, N. US food aid and civil conflict. Am. Econ. Rev. 2014, 104, 1630–1666. [Google Scholar] [CrossRef] [Green Version]
Variables Type | Variables | Mean | Standard Deviation | Minimum Value | Maximum Value |
---|---|---|---|---|---|
Explained variable | Green total factor productivity (GTFP) | 0.7239 | 0.1219 | 0.3584 | 1.4050 |
Explanatory variable | Digital economy (digital) | 0.0511 | 0.0490 | 0.0067 | 0.5568 |
Mediating variable | Industrial structure (indus) | 2.0149 | 0.0464 | 1.8574 | 2.1788 |
Environmental regulation (env) | 0.0035 | 0.0015 | 0 | 0.0123 | |
Innovation and entrepreneurship (ie) | 3.7627 | 0.7610 | 0.8609 | 4.6151 | |
Marketization level (mar) | 0.7452 | 0.2991 | 0.0506 | 2.8982 | |
Control variables | Foreign direct investment (fdi) | 0.0163 | 0.0171 | 0 | 0.1907 |
Human capital investment (hci) | 0.1632 | 0.0340 | 0.0000 | 0.3047 | |
Financial development (fin) | 0.6518 | 0.2485 | 0.1115 | 2.3629 | |
Government financial expenditure on science and technology (gov) | 0.0161 | 0.0159 | 0.0006 | 0.1880 | |
Number of companies (com) | 6.5667 | 1.1025 | 0 | 9.3096 |
Variables | Nationwide | East and Central | West | |||
---|---|---|---|---|---|---|
Tobit | OLS | Tobit | OLS | Tobit | OLS | |
Digital | 0.3549 *** (0.0755) | 0.2497 *** (0.0595) | 0.4194 *** (0.0767) | 0.2940 *** (0.0593) | −0.1495 (0.2566) | 0.1955 (0.2540) |
Control variable | Yes | Yes | Yes | Yes | Yes | Yes |
_cons | 0.7126 *** (0.0323) | 0.6338 *** (0.0206) | 0.7709 *** (0.0381) | 0.7195 *** (0.0241) | 0.6039 *** (0.0681) | 0.4392 *** (0.0448) |
Observations | 2574 | 2574 | 1809 | 1809 | 765 | 765 |
Quantile | 0.1 | 0.25 | 0.5 | 0.75 | 0.9 |
---|---|---|---|---|---|
Digital | 0.2678 (0.2161) | 0.3225 * (0.1655) | 0.4138 *** (0.1388) | 0.5138 ** (0.2120) | 0.5929 ** (0.3016) |
Control variables | Yes | Yes | Yes | Yes | Yes |
Observations | 2574 | 2574 | 2574 | 2574 | 2574 |
Regions | Variables | Response Intensity | Responding Speed | Cumulative Effect |
---|---|---|---|---|
Nationwide | digital→GTFP | 0.0021 | 3 | 0.0104 |
Central city | digital→GTFP | 0.0075 | 1 | 0.0207 |
Peripheral city | digital→GTFP | 0.0015 | 4 | 0.0063 |
Variables | (1) | (2) |
---|---|---|
Digital | 0.7113 *** (0.2539) | |
Tele | 0.0118 *** (0.0010) | |
Control variables | Yes | Yes |
_cons | −0.0609 *** (0.0071) | 0.6462 *** (0.0218) |
Kleibergen-Paap rk LM Statistic | 95.217 [0.000] | |
Kleibergen-Paap rk Wald F Statistic | 113.960 {16.38} | |
Cragg-Donald Wald F Statistic | 152.137 {16.38} | |
Observations | 2574 | 2574 |
Variables | (1) | (2) |
---|---|---|
Digital | 0.3327 *** (0.0938) | 0.3049 *** (0.0938) |
Control variable | No | Yes |
_cons | 0.6932 *** (0.0347) | 1.2278 *** (0.0787) |
City fixed effect | Yes | Yes |
Year fixed effect | Yes | Yes |
Observations | 2574 | 2574 |
0.4922 | 0.5125 |
Variables | GTFP | Indus | GTFP |
---|---|---|---|
(1) | (2) | (3) | |
Digital | 0.3549 *** (0.0755) | 0.1399 *** (0.0133) | 0.3204 *** (0.0776) |
Indus | 0.1933 * (0.1032) | ||
Control variables | Yes | Yes | Yes |
_cons | 0.7126 *** (0.0323) | 1.9161 *** (0.0077) | 0.3463 * (0.1980) |
Observations | 2574 | 2574 | 2574 |
Observed Coef. | Z | P > |z| | Normal-Based [95% Conf. Interval] | ||
---|---|---|---|---|---|
ind_eff | 0.050 | 3.14 | 0.002 | 0.019 | 0.081 |
dir_eff | 0.200 | 3.95 | 0.000 | 0.100 | 0.299 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Liu, Y.; Yang, Y.; Li, H.; Zhong, K. Digital Economy Development, Industrial Structure Upgrading and Green Total Factor Productivity: Empirical Evidence from China’s Cities. Int. J. Environ. Res. Public Health 2022, 19, 2414. https://doi.org/10.3390/ijerph19042414
Liu Y, Yang Y, Li H, Zhong K. Digital Economy Development, Industrial Structure Upgrading and Green Total Factor Productivity: Empirical Evidence from China’s Cities. International Journal of Environmental Research and Public Health. 2022; 19(4):2414. https://doi.org/10.3390/ijerph19042414
Chicago/Turabian StyleLiu, Yang, Yanlin Yang, Huihui Li, and Kaiyang Zhong. 2022. "Digital Economy Development, Industrial Structure Upgrading and Green Total Factor Productivity: Empirical Evidence from China’s Cities" International Journal of Environmental Research and Public Health 19, no. 4: 2414. https://doi.org/10.3390/ijerph19042414