Skip to main content

Advertisement

Log in

The driving effect of technological innovation on green development: dynamic efficiency spatial variation

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

In order to further explore the internal transmission mechanism between technological innovation and green development in manufacturing industry under the background of obvious development characteristics in the new era, this paper constructed an integrated methodology system to evaluate the internal impact mechanism of technological innovation value chain efficiency on green development efficiency based on spatial perspective. First, the Network Slack-based model and Global Malmquist-Luenberger model are constructed to reveal the internal development law of technological innovation and green development of manufacturing industry. Secondly, the spatial Dubin model is employed to analyze the impact of current development characteristics and technological innovation on green development. The results show that innovation value chain efficiency is higher than technological innovation efficiency, and economic transformation efficiency is lower than that of technological innovation value chain. During the study period, the efficiency of technological innovation value chain in the four economic regions present fluctuant growth trend, and the eastern region has the highest value. The green development efficiency in the east, central, west, and northeast regions of manufacturing industry is higher than 1, and it shows an obvious spatial agglomeration effect. Besides, the efficiency of technological innovation, information and communication technology, urbanization, and the advanced industrial structure are all conducive to the improvement of green development in manufacturing industry. This paper studies the influence mechanism of technological innovation value chain efficiency on green development based on spatial perspective and puts forward relevant countermeasures and suggestions to effectively promote green development of manufacturing industry, providing relevant theoretical research for green and high-quality development.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

Datasets used and analyzed during the current study are available from the corresponding author upon request.

References

  • Aigner D, Lovell C, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Econ 6(1):21–37

    Article  Google Scholar 

  • Chung YH, Fare R, Grosskopf S (1997) Productivity and undesirable outputs: a directional distance function approach[J]. J Eviron Manag 51(5):229–240

    Google Scholar 

  • Chen J, Wang L, Li Y (2021) Research on the impact of multi-dimensional urbanization on China’s carbon emissions under the background of COP21. J Environ Manag 273:111123

    Article  Google Scholar 

  • Chen X, Liu X, Zhu Q (2022) Comparative analysis of total factor productivity in China’s high-tech industries. Technol Forecast Soc Chang 175:121332

    Article  Google Scholar 

  • Cheng M (2020) Energy conservation potential analysis of Chinese manufacturing industry: the case of Jiangsu province. Environ Sci Pollut Res 27(14):16694–16706

    Article  Google Scholar 

  • Dong F, Li Y, Qin C, Sun J (2021) How industrial convergence affects regional green development efficiency: a spatial conditional process analysis. J Environ Manag 300:113738

    Article  Google Scholar 

  • Dong H, Xue M, Xiao Y, Liu Y (2021) Do carbon emissions impact the health of residents? Considering China’s industrialization and urbanization. Sci Total Environ 758:143688

    Article  CAS  Google Scholar 

  • Duan Y, Liu S, Cheng H, Chin T, Luo X (2021) The moderating effect of absorptive capacity on transnational knowledge spillover and the innovation quality of high-tech industries in host countries: evidence from the Chinese manufacturing industry. Int J Prod Econ 233:108019

    Article  Google Scholar 

  • Emrouznejad A, Yang GL (2016) A framework for measuring global Malmquist-Luenberger productivity index with CO2 emissions on Chinese manufacturing industries. Energy 115:840–856

    Article  CAS  Google Scholar 

  • Fu LH (2010) An empirical study on the relationship between my country’s industrial structure advancement and economic growth. Stat Res 27(8):79–81

    CAS  Google Scholar 

  • Fare R, Grosskopf S, Lindgren B (1992) Productivity change in Swedish pharmacies 1980–1989: a non-parametric Malmquist approach. J Prod Anal 3:85–102

    Article  Google Scholar 

  • Fang C, Cheng J, Zhu Y, Chen J, Peng X (2021) Green total factor productivity of extractive industries in China: an explanation from technology heterogeneity. Resour Policy 70:101933

    Article  Google Scholar 

  • Carayannis EG, Grigoroudis E, Goletsis Y (2016) A multilevel and multistage efficiency evaluation of innovation systems: a multiobjective DEA approach. Expert Syst Appl 62:63–80

    Article  Google Scholar 

  • Han J (2012) Research on China’s regional green innovation efficiency. Res Financ Econ Issues 348(11):130–137

    Google Scholar 

  • Huang H, Mo R, Chen X (2021) New patterns in China’s regional green development: an interval Malmquist-Luenberger productivity analysis. Struct Chang Econ Dyn 58:161–173

    Article  Google Scholar 

  • Lee H (2021) Is carbon neutrality feasible for Korean manufacturing firms?: The CO2 emissions performance of the Metafrontier Malmquist-Luenberger index. J Environ Manag 297:113235

    Article  CAS  Google Scholar 

  • Luo Y, Lu Z, Muhammad S, Yang H (2021) The heterogenous effects of different technological innovations on eco-efficiency: evidence from 30 China’s provinces. Ecol Indic 17:107802

    Article  Google Scholar 

  • Li B, Liu B, Liu W, Chiu Y (2017) Efficiency evaluation of the regional high-tech industry in China: a new framework based on meta-frontier dynamic DEA analysis. Socio-Econ Plan Sci 60:24–33

    Article  CAS  Google Scholar 

  • Liu C, Gao X, Ma W, Chen X (2020) Research on regional differences and influencing factors of green technology innovation efficiency of China’s high-tech industry. J Comput Math 369:112597

    Article  Google Scholar 

  • Li G, Zhou Y, Liu F, Tian A (2021) Regional difference and convergence analysis of marine science and technology innovation efficiency in China. Ocean Coast Manag 205:105581

    Article  Google Scholar 

  • Miao C, Duan M, Zuo Y, Wu X (2021) Spatial heterogeneity and evolution trend of regional green innovation efficiency–an empirical study based on panel data of industrial enterprises in China’s provinces. Energy Policy 156:112370

    Article  Google Scholar 

  • Oh D (2010) A global Malmquist-Luenberger productivity index. J Product Anal 34(3):183–197

    Article  Google Scholar 

  • Pittman R (1983) Multilateral productivity comparisons with undesirable outputs. Econ J 93:883–891

    Article  Google Scholar 

  • Pearce D, Markandya A, Barbier E (1989) Blueprint 1: for a green economy. Earthscan Ltd., Oxford

    Google Scholar 

  • Sun H, Kofi EB, Kwaku KA, Asumadu SS, Taghizadeh-Hesary F (2021) Energy efficiency: the role of technological innovation and knowledge spillover. Technol Forecast Soc Chang 167:120659

    Article  Google Scholar 

  • Song M, Zheng W, Wang S (2017) Measuring green technology progress in large-scale thermoelectric enterprises based on Malmquist-Luenberger life cycle assessment. Resour Conserv Recycl 122:261–269

    Article  Google Scholar 

  • Tu Y, Wu W (2020) How does green innovation improve enterprises’ competitive advantage? The role of organizational learning 26:504–516

    Google Scholar 

  • Tone K, Tsutsui M (2009) Network DEA: a slacks-based measure approach. Eur J Oper Res 197(1):243–252

    Article  Google Scholar 

  • Wang X, Wang Y, Lan Y (2021) Measuring the bias of technical change of industrial energy and environmental productivity in China: a global DEA-Malmquist productivity approach. Environ Sci Pollut Res 28:4189–41911

    Google Scholar 

  • Wang Y, Pan J, Pei R, Yi B, Yang G (2020) Assessing the technological innovation efficiency of China’s high-tech industries with a two-stage network DEA approach. Socio-Econ Plan Sci 71:100810

    Article  Google Scholar 

  • Yang B, Zhu S (2021) Public funds in high-tech industries: a blessing or a curse. Socio-Economic Planning Sciences.https://doi.org/10.1016/j.seps.2021.101037

  • Yang K, Lee L (2021) Estimation of dynamic panel spatial vector autoregression: stability and spatial multivariate cointegration. J Econ 221(2):337–367

    Article  Google Scholar 

  • Zhu L, He F (2022) A multi-stage Malmquist-Luenberger index to measure environment productivity in China’s iron and steel industry. Appl Math Model 103:162–175

    Article  Google Scholar 

  • Zuo Z, Guo H, Li Y, Cheng J (2022) A two-stage DEA evaluation of Chinese mining industry technological innovation efficiency and eco-efficiency. Environ Impact Assess Rev 94:106762

    Article  Google Scholar 

  • Zhang B, Luo Y, Chiu Y (2019) Efficiency evaluation of China’s high-tech industry with a multi-activity network data envelopment analysis approach. Socio-Econ Plan Sci 66:2–9

    Article  Google Scholar 

  • Zhu J, Sun Y (2020) Dynamic modeling and chaos control of sustainable integration of informatization and industrialization. Chaos, Solitons Fractals 135:109745

    Article  Google Scholar 

  • Zhang D, Vigne SA (2021) How does innovation efficiency contribute to green productivity? A financial constraint perspective. J Clean Prod 280:124000

    Article  Google Scholar 

  • Zhao Y, Shi X, Song F (2020) Has Chinese outward foreign direct investment in energy enhanced China’s energy security? Energy Policy 146:111803

    Article  Google Scholar 

  • Zhu B, Zhang M, Zhou Y, Wang P, Sheng J, He K, Wei YM, Xie R (2019) Exploring the effect of industrial structure adjustment on interprovincial green development efficiency in China: a novel integrated approach. Energy Policy 134:110946

    Article  Google Scholar 

Download references

Acknowledgements

The authors are also grateful to the editor and anonymous reviewers for their careful review and insightful comments.

Funding

This research received financial support from the National Science Fund for Distinguished Young Scholars of China (71825006).

Author information

Authors and Affiliations

Authors

Contributions

Manli Cheng: conceptualization, data curation, analysis, and writing. Zongguo Wen: writing, review, and supervision. Shanlin Yang: review and supervision.

Corresponding authors

Correspondence to Manli Cheng or Zongguo Wen.

Ethics declarations

Ethics approval and consent to participate

This research did not involve human participants, human data, or human tissues. This study was based on the published materials.

Consent for publication

This research does not contain any individual person’s data in the form of individual details, images, or videos. This work is based on the published literature.

Competing interests

The authors declare no competing interests.

Additional information

Responsible Editor: Philippe Garrigues

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheng, M., Wen, Z. & Yang, S. The driving effect of technological innovation on green development: dynamic efficiency spatial variation. Environ Sci Pollut Res 29, 84562–84580 (2022). https://doi.org/10.1007/s11356-022-21431-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-022-21431-3

Keywords

Navigation