Abstract
Environmental quality and economic growth are important factors that need to be balanced for sustainable development, especially in developing countries where technology is relatively backward. Many studies have shown that technology imports may be beneficial to economic growth, but once the resources and environment are taken into consideration, the role of technology imports becomes blurred. Based on provincial panel data of China from 2004 to 2019, this paper investigates the influence mechanism of domestic and foreign technology imports on the green economic efficiency (GEE) of 30 provinces in China. There are two main conclusions: First, GEE is spatially related and the impact of technology imports on GEE has a significant spillover effect. Besides, the relationship between technology imports and GEE is non-linear, both in terms of direct and indirect effects. Second, independent innovation plays an important role in the influence mechanism of technology imports on GEE. As the level of independent innovation increased, the impact of technology imports on GEE turns from negative to positive, and it is strengthened through the channel of “transfer-absorption-diffusion-re-innovation.” In this regard, some measures should be taken to enhance the role of technology imports in improving GEE.
Similar content being viewed by others
Data availability
The datasets can be obtained from the National Bureau of Statistic of China.
References
Abdel-Latif H (2019) FDI response to political shocks: what can the Arab Spring tell us? J Behav Exp Econ 24:100233. https://doi.org/10.1016/j.jbef.2019.07.005
Aggarwal A (2000) Deregulation, technology imports and in-house R&D efforts: an analysis of the Indian experience. Res Policy 29:1081–1093. https://doi.org/10.1016/S0048-7333(99)00074-8
Al-Faraj TN, Alidi AS, Bu-Bshait KA (1993) Evaluation of bank branches by means of data envelopment Analysis. Int J Oper Prod Man 13:45–52. https://doi.org/10.1108/01443579310043628
Amigues JP, Moreaux M (2019) Competing land uses and fossil fuel, and optimal energy conversion rates during the transition toward a green economy under a pollution stock constraint. J Environ Manage 97:92–115. https://doi.org/10.1016/j.jeem.2019.03.006
Amoako S, Insaidoo M (2021) Symmetric impact of FDI on energy consumption: evidence from Ghana. Energy 223:120005. https://doi.org/10.1016/j.energy.2021.120005
Anselin L (1988) Spatial econometrics: methods and models. Springer Netherlands, Berlin
Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage Sci 30:1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
Barbier E (2011) The policy challenges for green economy and sustainable economic development. Nat Resour Forum 35:233–245. https://doi.org/10.1111/j.1477-8947.2011.01397.x
Baron RM, Kenny DA (1986) The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 51:1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
Belmonte-Ureña LJ, Plaza-Úbeda JA, Vazquez-Brust D, Yakovleva N (2021) Circular economy, degrowth and green growth as pathways for research on sustainable development goals: a global analysis and future agenda. Ecol Econ 185:107050. https://doi.org/10.1016/j.ecolecon.2021.107050
Chang C, Robin S (2006) Doing R&D and/or importing technologies: the critical importance of firm size in Taiwan’s manufacturing industries. Rev Ind Organ 29:253–278. https://doi.org/10.1007/s11151-006-9114-8
Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Chen H-B, Dong K, Wang F-F, Ayamba EC (2020) The spatial effect of tourism economic development on regional ecological efficiency. Environ Sci Pollut Res 27:38241–38258. https://doi.org/10.1007/s11356-020-09004-8
Chen S-Y, Golley J (2014) ‘Green’ productivity growth in China’s industrial economy. Energy Econ 44:89–98. https://doi.org/10.1016/j.eneco.2014.04.002
Chung YH, Färe R, Grosskopf S (1997) Productivity and undesirable outputs: a directional distance function approach. J Environ Manage 51:229–240. https://doi.org/10.1006/jema.1997.0146
Danish, Wang B, Wang Z-H (2018) Imported technology and CO2 emission in China: collecting evidence through bound testing and VECM approach. Renew Sust Energ Rev 82:4204–4214. https://doi.org/10.1016/j.rser.2017.11.002
Dong J-R, Fan J-Q, Zhang W-Q, Tong M-Y (2020) the impact of different technology sources on the technological innovation of China’s equipment manufacturing industry: based on the perspective of the type of corporate ownership. Sci Technol Prog Policy 37:72–80. https://doi.org/10.6049/kjjbydc.2019050718 (in Chinese)
Duffy J, Ralston J (2020) Innovate versus imitate: theory and experimental evidence. J Econ Behav Organ 177:727–751. https://doi.org/10.1016/j.jebo.2020.06.014
Dutz AM, Sharma S (2012) Green growth, technology and innovation. World Bank Policy Research Working Paper No. 5932. https://www.researchgate.net/publication/228166667. Accessed 21 Jan 2021
Feng Y-G, Jiang Y-T (2021) Comparison and improvement of estimating methods of elasticity of factor substitution. J Quant Tech Econ 38:139–158. https://doi.org/10.13653/j.cnki.jqte.2021.04.008 (in Chinese)
Ferreira JJM, Fernandes CI, Ferreira FA (2020) Technology transfer, climate change mitigation, and environmental patent impact on sustainability and economic growth: a comparison of European countries. Technol Forecast Soc 150:119770. https://doi.org/10.1016/j.techfore.2019.119770
Fukuyama H, Weber WL (2009) A directional slacks-based measure of technical efficiency. Socio-Econ Plan Sci 43:274–287. https://doi.org/10.1016/j.seps.2008.12.001
Fu X-L, Pietrobelli C, Soete L (2011) The role of foreign technology and indigenous innovation in the emerging economies: technological change and catching-up. World Dev 39:1204–1212. https://doi.org/10.1016/j.worlddev.2010.05.009
Gan T, Liang W, Yang H-C, Liao X-C (2020) The effect of economic development on haze pollution (PM2.5) based on a spatial perspective: urbanization as a mediating variable. J Clean Prod 266:121880. https://doi.org/10.1016/j.jclepro.2020.121880
Grossman GM, Krueger AB (1992) Environmental impacts of a North American Free Trade Agreement. CEPR Discussion Papers 8:223–250. http://cepr.org/active/publications/discussion_papers/dp.php?dpno=644. Accessed 4 Feb 2021
Gu G-X, Wang Z, Wu L-Y (2021) Carbon emission reductions under global low-carbon technology transfer and its policy mix with R&D improvement. Energy 216:119300. https://doi.org/10.1016/j.energy.2020.119300
Hair JFJr, Anderson RE, Tatham RL, Black WC (1995) Multivariate data analysis, 3rd edn. Macmillan, New York
Hansen EB (1999) Threshold effects in non-dynamic panels: estimation, testing, and inference. J Econometrics 93:345–368. https://doi.org/10.1016/S0304-4076(99)00025-1
Huang J-B, Xiang S-Q, Wang Y-J, Chen X (2021) Energy-saving R&D and carbon intensity in China. Energy Econ 98:105240. https://doi.org/10.1016/j.eneco.2021.105240
Jiao J-L, Yang Y-F, Bai Y (2018) The impact of inter-industry R&D technology spillover on carbon emission in China. Nat Hazards 91:913–929. https://doi.org/10.1007/s11069-017-3161-3
Katrak H (1997) Developing countries’ imports of technology, in-house technological capabilities and efforts: an analysis of the Indian experience. J Dev Econ 53:67–83. https://doi.org/10.1016/S0304-3878(97)00011-4
Li Q-F, Wen B-J, Wang G-S, Cheng J-H, Zhong W-Q, Dai T, Liang L, Han Z-K (2018) Study on calculation of carbon emission factors and embodied carbon emissions of iron-containing commodities in international trade of China. J Clean Prod 191:119–126. https://doi.org/10.1016/j.jclepro.2018.04.224
Li G-D, Fang C-L, He S-W (2020a) The influence of environmental efficiency on PM2.5 pollution: evidence from 283 Chinese prefecture-level cities. Sci Total Environ 748:141549. https://doi.org/10.1016/j.scitotenv.2020.141549
Li C-X, Jia Q, Li G-Z (2020b) China’s energy consumption and green economy efficiency: an empirical research based on the threshold effect. Environ Sci Pollut Res 27:36621–36629. https://doi.org/10.1007/s11356-020-09536-z
Liu X-R, Sun T, Feng Q, Zhang D (2020) Dynamic environmental regulation threshold effect of technical progress on China’s environmental pollution. J Clean Prod 272:122780. https://doi.org/10.1016/j.jclepro.2020.122780
Loiseau E, Saikku L, Antikainen R, Droste N, Hansjürgens B, Pitkänen K, Leskinen P, Kuikman P, Thomsen M (2016) Green economy and related concepts: an overview. J Clean Prod 139:361–371. https://doi.org/10.1016/j.jclepro.2016.08.024
Lin B-Q, Chen X (2020) How technological progress affects input substitution and energy efficiency in China: a case of the non-ferrous metals industry. Energy 206:118152. https://doi.org/10.1016/j.energy.2020.118152
Lin Y-Y, Chen P-Y, Chen C-C (2013) Measuring green productivity of country: a generlized metafrontier Malmquist productivity index approach. Energy 55:340–353. https://doi.org/10.1016/j.energy.2013.03.055
Liu Y-J, Dong F (2021) How technological innovation impacts urban green economy efficiency in emerging economies: a case study of 278 Chinese cities. Resour Conserv Recy 169:105534. https://doi.org/10.1016/j.resconrec.2021.105534
Lv Y-L, Chen W, Cheng J-Q (2019) Modelling dynamic impacts of urbanization on disaggregated energy consumption in China: a spatial Durbin modelling and decomposition approach. Energy Policy 133:110841. https://doi.org/10.1016/j.enpol.2019.06.049
Ma Y-J, Zhang Z-W, Zhao Z (2021) Introduction of technology, capacity for absorption and quality of innovation: empirical evidence from the high-tech industries in China. J Macro-quality Res 9:59–73. https://doi.org/10.13948/j.cnki.hgzlyj.2021.02.005 (in Chinese)
Maneejuk P, Yamaka W (2020) An analysis of the impacts of telecommunications technology and innovation on economic growth. Telecommun Policy 44:102038. https://doi.org/10.1016/j.telpol.2020.102038
Meng W-S, Shao F-Q (2020) Measurement of green economic growth efficiency in China’s provinces. Stat Decis 36:105–109. https://doi.org/10.13546/j.cnki.tjyjc.2020.16.023 (in Chinese)
Meng W-S, Zhang Y (2020) Natural resource endowment, path selection of technological progress, and green economic growth: an empirical research based on China’s provincial panel data. Resour Sci 42:2314–2327. https://doi.org/10.18402/resci.2020.12.05 (in Chinese)
Michali M, Emrouznejad A, Dehnokhalaji A, Clegg B (2021) Noise-pollution efficiency analysis of European railways: a network DEA model. Transport Res D-Tr E 98:102980. https://doi.org/10.1016/j.trd.2021.102980
Myovella G, Karacuka M, Haucap J (2021) Determinants of digitalization and digital divide in Sub-Saharan African economies: a spatial Durbin analysis. Telecommun Policy 102224. https://doi.org/10.1016/j.telpol.2021.102224
Parrado R, De Cian E (2014) Technology spillovers embodied in international trade: intertemporal, regional and sectoral effects in a global CGE framework. Energy Econ 41:76–89. https://doi.org/10.1016/j.eneco.2013.10.016
Peng W-B, Yin Y, Kuang C-G, Wen Z-Z, Kuang J-S (2021) Spatial spillover effect of green innovation on economic development quality in China: evidence from a panel data of 270 prefecture-level and above cities. Sustain Cities Soc 69:102863. https://doi.org/10.1016/j.scs.2021.102863
Pittman RW (1983) Multilateral productivity comparisons with undesirable outputs. Econ J 93:883–891. https://doi.org/10.2307/2232753
Qu C-Y, Shao J, Cheng Z-H (2020) Can embedding in global value chain drive green growth in China’s manufacturing industry? J Clean Prod 268:121962. https://doi.org/10.1016/j.jclepro.2020.121962
Sha R, Li J-Y, Ge T (2021) How do price distortions of fossil energy sources affect China’s green economic efficiency? Energy 232:121017. https://doi.org/10.1016/j.energy.2021.121017
Sheng B, Zhao W-T (2020) Regional global value chain, market fragmentation and industrial upgrading: an analysis from the perspective of spatial spillover. Finance Trade Econ 41:131–145. https://doi.org/10.19795/j.cnki.cn11-1166/f.20200906.005 (in Chinese)
Song M-L, Tao J, Wang S-H (2015) FDI, technology spillovers and green innovation in China: analysis based on Data. Envelopment Analysis. Ann Oper Res 228:47–64. https://doi.org/10.1007/s10479-013-1442-0
Sun H-P, Edziah BK, Sun C-W, Kporsu AK (2019) Institutional quality, green innovation and energy efficiency. Energy Policy 135:111002. https://doi.org/10.1016/j.enpol.2019.111002
Tone K (2001) A slacks-based measure of efficiency in data envelopment analysis. Eur J Oper Res 130:498–509. https://doi.org/10.1016/S0377-2217(99)00407-5
Teng Y-H (2012) Indigenous R&D, technology imports and energy consumption intensity: evidence from industrial sectors in China. Energy Procedia 16:2019–2026. https://doi.org/10.1016/j.egypro.2012.01.307
Twum FA, Long X-L, Salman M, Mensah CN, Kankam WA, Tachie AK (2021) The influence of technological innovation and human capital on environmental efficiency among different regions in Asia-Pacific. Environ Sci Pollut Res 28:17119–17131. https://doi.org/10.1007/s11356-020-12130-y
UN (2015) Transforming Our World: the 2030 Agenda for Sustainable Development. https://sustainabledevelopment.un.org/post2015/transformingourworld/publication/. Accessed 20 Aug 2021
UNEP (2011) Towards a green economy: pathways to sustainable development and poverty eradication. https://www.iisd.org/publications/towards-green-economy-pathways-sustainable-development-and-poverty-eradication. Accessed 13 Jan 2021
Walker NL, Williams AP, Styles D (2020) Key performance indicators to explain energy & economic efficiency across water utilities, and identifying suitable proxies. J Environ Manage 269:110810. https://doi.org/10.1016/j.jenvman.2020.110810
Wang X-W, Du X, Zhang H-M (2018) Analysis on the effect of “Manufacturing Return” in developed European countries and the U S from globalization to anti-globalization trend. Jilin Univ J Soc Sci Ed 58:76-86+205. https://doi.org/10.15939/j.jujsse.2018.04.jj4 (in Chinese)
Wang X-T, Luo Y (2020) Has technological innovation capability addressed environmental pollution from the dual perspective of FDI quantity and quality? Evidence from China. J Clean Prod 258:120941. https://doi.org/10.1016/j.jclepro.2020.120941
Wang H-R, Cui H-R, Zhao Q-Z (2021a) Effect of green technology innovation on green total factor productivity in China: evidence from spatial durbin model analysis. J Clean Prod 288:125624. https://doi.org/10.1016/j.jclepro.2020.125624
Wang S, Wang J-X, Fan F (2021b) The hidden mediating role of innovation efficiency in coordinating development of economy and ecological environment: evidence from 283 Chinese cities. Environ Sci Pollut Res 28:47668–47684. https://doi.org/10.1007/s11356-021-13808-7
Wang S-H, He Y-Q, Song M-L (2021c) Global value chains, technological progress, and environmental pollution: inequality towards developing countries. J Environ Manage 277:110999. https://doi.org/10.1016/j.jenvman.2020.110999
Waugh ME, Ravikumar B (2016) Measuring openness to trade. J Econ Dyn Control 72:29–41. https://doi.org/10.1016/j.jedc.2016.03.009
Wen Z-L, Ye B-J (2014) Analyses of mediating effects: the development of methods and models. Adv Psychol Sci 22:731–745. https://doi.org/10.3724/SP.J.1042.2014.00731 (in Chinese)
Xie R-H, Yuan Y-J, Huang J-J (2017) Different types of environmental regulations and heterogeneous influence on “Green” productivity: evidence from China. Ecol Econ 132:104–112. https://doi.org/10.1016/j.ecolecon.2016.10.019
Xu C, Zhao W-Q, Zhang M-Z, Chen B-D (2021a) Pollution haven or halo? The role of the energy transition in the impact of FDI on SO2 emissions. Sci Total Environ 763:143002. https://doi.org/10.1016/j.scitotenv.2020.143002
Xu Y-Y, Wang J, Zhang Y-F, Yin X-R (2021b) Different effects of acquisition of foreign or domestic technology on innovation performance of high-tech industry: a comparative analysis from the perspective of time lag. Sci Technol Prog Policy. 38:70–78. https://doi.org/10.6049/kjjbydc.2020070444 (in Chinese)
Yang Z-B, Shao S, Yang L-L, Liu J-H (2017) Differentiated effects of diversified technological sources on energy-saving technological progress: empirical evidence from China’s industrial sectors. Renew Sust Energ Rev 72:1379–1388. https://doi.org/10.1016/j.rser.2016.11.072
Yang X-H, Yang Z-M, Jia Z (2021) Effects of technology spillover on CO2 emissions in China: a threshold analysis. Energy Rep 7:2233–2244. https://doi.org/10.1016/j.egyr.2021.04.028
Ye C-S, Ye Q, Shi X-P, Sun Y-P (2020) Technology gap, global value chain and carbon intensity: evidence from global manufacturing industries. Energy Policy 137:111094. https://doi.org/10.1016/j.enpol.2019.111094
Yu L-P, Li H-Y, Wang Z-G, Duan Y-L (2019) Technology imports and self-innovation in the context of innovation quality. Int J Prod Econ 214:44–52. https://doi.org/10.1016/j.ijpe.2018.11.023
Zeng F-H, Li J (2000) Research on the system arrangement of “Market for Technology.” Manage World 5:191-192+203. https://doi.org/10.19744/j.cnki.11-1235/f.2000.05.027 (in Chinese)
Zhang L (2012) Do imports of technology facilitate technological progress? Evidence from China. Procedia Eng 29:2826–2831. https://doi.org/10.1016/j.proeng.2012.01.398
Zhang N, Choi Y-R (2013) Total-factor carbon emission performance of fossil fuel power plants in China: a meta frontier non-radial Malmquist index analysis. Energy Econ 40:549–559. https://doi.org/10.1016/j.eneco.2013.08.012
Zhang Z-B, Jin T-J, Meng X-H (2020) From race-to-the-bottom to strategic imitation: how does political competition impact the environmental enforcement of local governments in China? Environ Sci Pollut Res 27:25675–25688. https://doi.org/10.1007/s11356-020-09003-9
Zhao P-J, Zeng L-E, Lu H-Y, Zhou Y, Hu H-Y, Wei X-Y (2020) Green economic efficiency and its influencing factors in China from 2008 to 2017: based on the super-SBM model with undesirable outputs and spatial durbin model. Sci Total Environ 741:140026. https://doi.org/10.1016/j.scitotenv.2020.140026
Zhou P, Ang BW, Wang H (2012) Energy and CO2 emission performance in electricity generation: a non-radial directional distance function approach. Eur J Oper Res 221:625–635. https://doi.org/10.1016/j.ejor.2012.04.022
Zhuo C-F, Deng F (2020) How does China’s Western Development Strategy affect regional green economic efficiency? Sci Total Environ 707:135939. https://doi.org/10.1016/j.scitotenv.2019.135939
Zhu Y-F, Wang Z-L, Yang J, Zhu L-L (2020) Does renewable energy techno- logical innovation control China’s air pollution? A spatial analysis. J Clean Prod 250:119515. https://doi.org/10.1016/j.jclepro.2019.119515
Zou X, Lei C, Hu C (2019) Environmental decentralization and regional green development. China Popul Resour Environ 29:97–106 (in Chinese)
Zugravu-Soilita N (2017) How does foreign direct investment affect pollution? Toward a better understanding of the direct and conditional effects. Environ Resour Econ 66:293–338. https://doi.org/10.1007/s10640-015-9950-9
Author information
Authors and Affiliations
Contributions
ZX proposed the idea, conceived the framework, wrote the original, and revised manuscript. YH collected data. YL proposed the methodology and revised the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Eyup Dogan
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Before the empirical analysis, we performed the unit root test and multicollinearity test on panel data to avoid spurious regression. Table 11 shows that both the LLC test and the ADF-Fisher test reject the null hypothesis, indicating that the panel sequence is stable. And the variance inflation factor (VIF) test in Table 12 shows that the maximum VIF value is 2.87, and the VIF < 10 is acceptable (Hair et al. 1995). So, we can assume that there is no severe multicollinearity between variables.
Rights and permissions
About this article
Cite this article
Zheng, X., Yu, H. & Yang, L. Technology imports, independent innovation, and China’s green economic efficiency: an analysis based on spatial and mediating effect. Environ Sci Pollut Res 29, 36170–36188 (2022). https://doi.org/10.1007/s11356-021-17499-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-021-17499-y