Biased innovation and network evolution: digital driver for green innovation of manufacturing in China

References

• Audretsch, D. B., Belitski, M., & Guerrero, M. (2022). The dynamic contribution of innovation ecosystems to schumpeterian firms: A multi-level analysis. Journal of Business Research, 144, 975–31. https://doi.org/10.1016/j.jbusres.2022.02.037
   Web of Science ®Google Scholar
• Baloch, M. A., Ozturk, I., Bekun, F. V., & Khan, D. (2021). Modeling the dynamic linkage between financial development, energy innovation, and environmental quality: Does globalization matter? Business Strategy and the Environment, 30(1), 176–184. https://doi.org/10.1002/bse.2615
   Web of Science ®Google Scholar
• Basile, R., Capello, R., & Caragliu, A. (2012). Technological interdependence and regional growth in Europe: Proximity and synergy in knowledge spillovers. Papers in Regional Science, 91(4), 697–722. https://doi.org/10.1111/j.1435-5957.2012.00438.x
   Web of Science ®Google Scholar
• Borgatti, S. P., & Foster, P. C. (2003). The network paradigm in organizational research: A review and typology. Journal of Management, 29(6), 991–1013. https://doi.org/10.1016/S0149-2063(03)00087-4
   Web of Science ®Google Scholar
• Cassi, L., Morrison, A., & Terwai, A. L. J. (2012). The evolution of trade and scientific collaboration networks in the global wine sector: A longitudinal study using network analysis. Economic Geography, 88(3), 311–334. https://doi.org/10.1111/j.1944-8287.2012.01154.x
   Web of Science ®Google Scholar
• Castells, M., & Cardoso, G. (2006). The network society: From knowledge to policy. Johns Hopkins Center for Transatlantic Relations.
   Google Scholar
• Chen, Y. (2015). Director’s ‘structural hole’ position and firm’s policy efficiency(in Chinese). Accounting Research, 01, 48–55+97.
   Google Scholar
• Chen, Z., Sarkar, A., Rahman, A., Li, X., & Xia, X. (2022). Exploring the drivers of green agricultural development (GAD) in China: A spatial association network structure approaches. Land Use Policy, 112, 105827. https://doi.org/10.1016/j.landusepol.2021.105827
   Web of Science ®Google Scholar
• Chen, W., Wu, F., Geng, W., & Yu, G. (2017). Carbon emissions in China’s industrial sectors. Resources, Conservation and Recycling, 117, 264–273. https://doi.org/10.1016/j.resconrec.2016.10.008
   Web of Science ®Google Scholar
• Chen, P.-C., & Yu, M.-M. (2014). Total factor productivity growth and directions of technical change bias: Evidence from 99 OECD and non-OECD countries. Annals of Operations Research, 214(1), 143–165. https://doi.org/10.1007/s10479-012-1087-4
   Web of Science ®Google Scholar
• CHONG, Z., & QIN, C. (2017). The trade network structure of “one belt one road” and its influence factors: A study based on analytic network process. International Economics and Trade Research, 05, 16–28. https://doi.org/10.13687/j.cnki.gjjmts.2017.05.002
   Google Scholar
• David, P. A., & van de Klundert, T. (1965). Biased efficiency growth and capital-labor substitution in the U.S., 1899-1960. The American Economic Review, 55(3), 357–394.
   Web of Science ®Google Scholar
• Deng, H., Bai, G., Shen, Z., & Xia, L. (2022). Digital economy and its spatial effect on green productivity gains in manufacturing: Evidence from China. Journal of Cleaner Production, 378, 134539. https://doi.org/10.1016/j.jclepro.2022.134539
   Web of Science ®Google Scholar
• Derudder, B., & Taylor, P. J. (2021). The GaWC perspective on global-scale urban networks. In Handbook of cities and networks (pp. 601–617). Edward Elgar Publishing. https://www.elgaronline.com/edcollchap/edcoll/9781788114707/9781788114707.00037.xml
   Google Scholar
• Doreian, P., & Conti, N. (2012). Social context, spatial structure and social network structure. Social Networks, 34(1), 32–46. https://doi.org/10.1016/j.socnet.2010.09.002
   Web of Science ®Google Scholar
• Egilmez, G., Kucukvar, M., & Tatari, O. (2013). Sustainability assessment of U.S. manufacturing sectors: An economic input output-based frontier approach. Journal of Cleaner Production, 53, 91–102. https://doi.org/10.1016/j.jclepro.2013.03.037
   Web of Science ®Google Scholar
• Färe, R., Grifell‐Tatjé, E., Grosskopf, S., & Knox Lovell, C. A. (1997). Biased technical change and the Malmquist productivity index. The Scandinavian Journal of Economics, 99(1), 119–127. https://doi.org/10.1111/1467-9442.00051
   Web of Science ®Google Scholar
• Feldman, M. (2016). Geography of innovation. https://doi.org/10.1057/978-1-349-94848-2_537-1
   Google Scholar
• Feng, Z., Cai, H., Chen, Z., & Zhou, W. (2022). Influence of an interurban innovation network on the innovation capacity of China: A multiplex network perspective. Technological Forecasting and Social Change, 180, 121651. https://doi.org/10.1016/j.techfore.2022.121651
   Web of Science ®Google Scholar
• Fritze, M. P., Urmetze, F., Khan, G. F., Sarstedt, M., Neely, A., & Schfers, T. (2018). From goods to services consumption: A social network analysis on sharing economy and servitization research. Journal of Service Management Research, 2(3), 3–16. https://doi.org/10.15358/2511-8676-2018-3-3
   Google Scholar
• Gai, M., Yang, Q., He, Y., & 1. Institute of Marine Sustainable Development, Liaoning Normal University, Dalian 116029, China 2. University Collaborative Innovation Center of Marine Economy High-Quality Development of Liaoning Province, Dalian 116029, China. (2022). Spatiotemporal changes and influencing factors of agricultural green development level in main grain-producing areas in Northeast China. 资源科学, 44(5), 927–942.
   Google Scholar
• Gao, J., Feng, Q., Guan, T., & Zhang, W. (2023). Unlocking paths for transforming green technological innovation in manufacturing industries. Journal of Innovation & Knowledge, 8(3), 100394. https://doi.org/10.1016/j.jik.2023.100394
   Web of Science ®Google Scholar
• Gao, K., & Yuan, Y. (2022). Spatiotemporal pattern assessment of China’s industrial green productivity and its spatial drivers: Evidence from city-level data over 2000–2017. Applied Energy, 307, 118248. https://doi.org/10.1016/j.apenergy.2021.118248
   Web of Science ®Google Scholar
• Guan, J., Zhang, J., & Yan, Y. (2015). The impact of multilevel networks on innovation. Research Policy, 44(3), 545–559. https://doi.org/10.1016/j.respol.2014.12.007
   Web of Science ®Google Scholar
• Herring, H., & Roy, R. (2007). Technological innovation, energy efficient design and the rebound effect. Technovation, 27(4), 194–203. https://doi.org/10.1016/j.technovation.2006.11.004
   Web of Science ®Google Scholar
• He, G., & Wang, Z. (2015). Energy biased technology progress and green growth transformation——an empirical analysis based on 33 industries of China. China’s Industrial Economy, 02, 50–62. https://doi.org/10.19581/j.cnki.ciejournal.2015.02.006
   Google Scholar
• Huggins, R., Johnston, A., Munday, M., & Xu, C. (2023). Competition, open innovation, and growth challenges in the semiconductor industry: The case of Europe’s clusters. Science and Public Policy, 50(3), 531–547. https://doi.org/10.1093/scipol/scad005
   Web of Science ®Google Scholar
• Kaneko, S., & Managi, S. (2004). Environmental productivity in China. Economics Bulletin, 17(2), 1–10.
   Google Scholar
• Karanfil, F., & Yeddir-Tamsamani, Y. (2010). Is technological change biased toward energy? A multi-sectoral analysis for the French economy. Energy Policy, 38(4), 1842–1850. https://doi.org/10.1016/j.enpol.2009.11.061
   Web of Science ®Google Scholar
• Klump, R., McAdam, P., & Willman, A. (2012). The normalized ces production function: Theory and empirics. Journal of Economic Surveys, 26(5), 769–799. https://doi.org/10.1111/j.1467-6419.2012.00730.x
   Web of Science ®Google Scholar
• Krackardt, D. (1987). QAP partialling as a test of spuriousness. Social Networks, 9(2), 171–186. https://doi.org/10.1016/0378-8733(87)90012-8
   Web of Science ®Google Scholar
• Liang, Z., Chen, J., Jiang, D., & Sun, Y. (2022). Assessment of the spatial association network of green innovation: Role of energy resources in green recovery. Resources Policy, 79, 103072. https://doi.org/10.1016/j.resourpol.2022.103072
   Web of Science ®Google Scholar
• Liu, S., Hou, P., Gao, Y., & Tan, Y. (2022). Innovation and green total factor productivity in China: A linear and nonlinear investigation. Environmental Science and Pollution Research, 29(9), 12810–12831. https://doi.org/10.1007/s11356-020-11436-1
   PubMed Web of Science ®Google Scholar
• Malecki, P. O., & Edward, J. (2008). The evolution of technologies in time and space: From national and regional to spatial innovation systems. In M. Ron (Ed.), Economy (1st ed., pp. 401–430). Routledge.
   Google Scholar
• Min, S., Kim, J., & Sawng, Y.-W. (2020). The effect of innovation network size and public R&D investment on regional innovation efficiency. Technological Forecasting and Social Change, 155, 119998. https://doi.org/10.1016/j.techfore.2020.119998
   Web of Science ®Google Scholar
• Peng, J., Xiao, J., Wen, L., & Zhang, L. (2019). Energy industry investment influences total factor productivity of energy exploitation: A biased technical change analysis. Journal of Cleaner Production, 237, 117847. https://doi.org/10.1016/j.jclepro.2019.117847
   Web of Science ®Google Scholar
• Qin, L., Kirikkaleli, D., Hou, Y., Miao, X., & Tufail, M. (2021). Carbon neutrality target for G7 economies: Examining the role of environmental policy, green innovation and composite risk index. Journal of Environmental Management, 295, 113119. https://doi.org/10.1016/j.jenvman.2021.113119
   PubMed Web of Science ®Google Scholar
• Radziwon, A., Rong, K., Lin, Y., Yu, J., & Zhang, Y. (2022). Exploring regional innovation ecosystems: An empirical study in China. In B. Catherine, B.-H. Thierry, & C. Patrick (Eds.), Innovation policies and practices within innovation ecosystems (pp. 10–34). Routledge. https://doi.org/10.4324/9781003279464 .
   Google Scholar
• Rogers, M. (2004). Networks, firm size and innovation. Small Business Economics, 22(2), 141–153. https://doi.org/10.1023/B:SBEJ.0000014451.99047.69
   Web of Science ®Google Scholar
• Sims, C. A., Lovell, M. C., & Solow, R. M. (1974). Output and labor input in manufacturing. Brookings Papers on Economic Activity, 1974(3), 695–735. https://doi.org/10.2307/2534251
   Google Scholar
• SUN, Z., FAN, J., SUN, Y., & H, L. I. U. (2022). Structural characteristics and influencing factors of spatial correlation network of green science and technology innovation efficiency in China. Economic Geography, 03, 33–43. https://doi.org/10.15957/j.cnki.jjdl.2022.03.004
   Google Scholar
• Wang, J., Wu, H., & Chen, Y. (2020). Made in China 2025 and manufacturing strategy decisions with reverse QFD. International Journal of Production Economics, 224, 107539. https://doi.org/10.1016/j.ijpe.2019.107539
   Web of Science ®Google Scholar
• Weber, W. L., & Domazlicky, B. R. (1999). Total factor productivity growth in manufacturing: A regional approach using linear programming. Regional Science and Urban Economics, 29(1), 105–122. https://doi.org/10.1016/S0166-0462(98)00013-1
   Web of Science ®Google Scholar
• Wübbeke, J., Meissner, M., Zenglein, M. J., Ives, J., & Conrad, B. (2016). Made in china 2025. Mercator Institute for China Studies Papers on China, 2(74), 4.
   Google Scholar
• Wu, F., Hu, H., Lin, H., & Ren, X. (2021). Enterprise digital transformation and capital market performance: Empirical evidence from stock liquidity (in Chinese). Journal of Management World, 2021(7), 130–144+10.
   Google Scholar
• Xuhui, L. I., & Yitao, T. A. O. (2023). Measurement, regional differences, and causes of China’ s green and low?carbon innovation development under the “dual carbon” goals. China Population, Resources & Environment, 33(1), 124–136.
   Google Scholar
• Yang, X., Li, X., & Zhong, C. (2019). Study on the evolution trend and influencing factors of China’s industrial directed technical change. The Journal of Quantitative & Technical Economics, 04, 101–119. https://doi.org/10.13653/j.cnki.jqte.2019.04.006
   Google Scholar
• Yuan, B., & Xiang, Q. (2018). Environmental regulation, industrial innovation and green development of Chinese manufacturing: Based on an extended CDM model. JOURNAL of CLEANER PRODUCTION, 176, 895–908. https://doi.org/10.1016/j.jclepro.2017.12.034
   Web of Science ®Google Scholar
• Zenglein, M. J., & Holzmann, A. (2019). In W. Claudia (Ed.), Evolving made in China 2025 (Vol. 8, pp. 78). Germany: MERICS Papers on China.
   Google Scholar
• Zhang, Y., Wang, J., Xue, Y., & Yang, J. (2018). Impact of environmental regulations on green technological innovative behavior: An empirical study in China. Journal of Cleaner Production, 188, 763–773. https://doi.org/10.1016/j.jclepro.2018.04.013
   Web of Science ®Google Scholar
• Zhang, B., Yu, L., & Sun, C. (2022). How does urban environmental legislation guide the green transition of enterprises? Based on the perspective of enterprises’ green total factor productivity. Energy Economics, 110, 106032. https://doi.org/10.1016/j.eneco.2022.106032
   Web of Science ®Google Scholar
• Zhao, N., Liu, X., Pan, C., & Wang, C. (2021). The performance of green innovation: From an efficiency perspective. Socio-Economic Planning Sciences, 78, 101062. https://doi.org/10.1016/j.seps.2021.101062
   Web of Science ®Google Scholar
• Zhao, P., Lu, Z., Kou, J., & Du, J. (2023). Regional differences and convergence of green innovation efficiency in China. Journal of Environmental Management, 325, 116618. https://doi.org/10.1016/j.jenvman.2022.116618
   PubMed Web of Science ®Google Scholar
• Zheng, W.-L., Wang, J.-W., Jiang, A.-D., Rehman Khan, S. A., Yang, X.-Q., Zhang, X., & Zhang, Z.-Y. (2021). Study on environmental performance evaluation of different linkage development types of the logistics and manufacturing industries considering the unexpected output. Journal of the Air & Waste Management Association, 71(8), 1025–1038. https://doi.org/10.1080/10962247.2021.1920516
   PubMed Web of Science ®Google Scholar
• Zhou, X., Yu, Y., Yang, F., & Shi, Q. (2021). Spatial-temporal heterogeneity of green innovation in China. Journal of Cleaner Production, 282, 124464. https://doi.org/10.1016/j.jclepro.2020.124464
   Web of Science ®Google Scholar
• Кузнецов, С., Турдаков, Д., Назар, Б., & Валерий, А. (2014). Social network analysis: Methods and applications. https://xueshu.baidu.com/usercenter/paper/show?paperid=32e66bde64fbf74278db7569b85702dc&site=xueshu_se
   Google Scholar