Abstract
In light of China’s escalating fossil CO\(_2\) emissions, fostering Enterprise–University-Institute (E–U–I) collaborative innovation in climate change mitigation technologies (CCMTs) is increasingly vital. This study employs patent analysis and social network analysis (SNA) to explore the E–U–I collaborative innovation networks (ECINs) in China’s CCMTs, focusing on four key sectors: building (BUILD), capturing, storage, sequestration, or disposal of greenhouse gases (CSSD), energy generation, transmission, or distribution (EGTD), and transportation (TRANS). Our findings highlight the distinct yet synergistic contributions of enterprises, universities, and institutes within these networks. This study observes a prevailing trend of Enterprise–Institute (E–I) collaboration, superseding Enterprise–University (E–U) collaboration post-2010. State-owned enterprises like State Grid and Sinopec have emerged as dominant forces in ECINs, primarily through intense R&D collaboration with their affiliated research institutes. Technologies such as smart grid, N\(_2\)O capture, and energy storage are pivotal in their respective sectors. Additionally, ECINs vary by sector, showing a general size reduction over time. The transition from E–U to E–I collaboration, alongside a shift from external to internal and interdisciplinary to intradisciplinary collaboration, appears to contribute to the downsizing of ECINs. The study concludes with discussions and insights aimed at enhancing the E–U–I collaborative innovation landscape and promoting high-quality development in CCMTs.
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The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Barthélemy, M. (2004). Betweenness centrality in large complex networks. The European Physical Journal B - Condensed Matter, 38, 163–168. https://doi.org/10.1140/epjb/e2004-00111-4
Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks. Proceedings of the International AAAI Conference on Web and Social Media, 3(1), 361–362. https://doi.org/10.1609/icwsm.v3i1.13937
Baumann, M., Domnik, T., Haase, M., et al. (2021). Comparative patent analysis for the identification of global research trends for the case of battery storage, hydrogen and bioenergy. Technological Forecasting and Social Change, 165, 120505. https://doi.org/10.1016/j.techfore.2020.120505
Benassi, M., Grinza, E., & Rentocchini, F. (2020). The rush for patents in the Fourth Industrial Revolution. Journal of Industrial and Business Economics, 47(4), 559–588. https://doi.org/10.1007/s40812-020-00159-6
Blondel, V. D., Guillaume, J. L., Lambiotte, R., et al. (2008). (2008) Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 10, P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008
Bolívar-Ramos, M. T. (2017). The relation between R&D spending and patents: The moderating effect of collaboration networks. Journal of Engineering and Technology Management, 46, 26–38. https://doi.org/10.1016/j.jengtecman.2017.11.001
Breschi, S., & Catalini, C. (2010). Tracing the links between science and technology: An exploratory analysis of scientists and inventors networks. Research Policy, 39(1), 14–26. https://doi.org/10.1016/j.respol.2009.11.004
Cantner, U., & Rake, B. (2014). International research networks in pharmaceuticals: Structure and dynamics. Research Policy, 43(2), 333–348. https://doi.org/10.1016/j.respol.2013.10.016
Cao, X., Li, C., Li, J., et al. (2022). Modeling and simulation of knowledge creation and diffusion in an industry-university-research cooperative innovation network: A case study of China’s new energy vehicles. Scientometrics, 127(7), 3935–3957. https://doi.org/10.1007/s11192-022-04416-4
Chen, Y., Rong, K., Xue, L., et al. (2014). Evolution of collaborative innovation network in China’s wind turbine manufacturing industry. International Journal of Technology Management, 65(1–4), 262–299. https://doi.org/10.1504/IJTM.2014.060954
Christensen, C. M., Baumann, H., Ruggles, R., et al. (2006). Disruptive innovation for social change. Harvard Business Review, 84(12), 94.
De Paulo, A. F., & Porto, G. S. (2023). Unveiling the cooperation dynamics in the photovoltaic technologies’ development. Renewable and Sustainable Energy Reviews, 187, 113694. https://doi.org/10.1016/j.rser.2023.113694
De Paulo, A. F., Ribeiro, E. M. S., & Porto, G. S. (2018). Mapping countries cooperation networks in photovoltaic technology development based on patent analysis. Scientometrics, 117(2), 667–686. https://doi.org/10.1007/s11192-018-2892-6
Ernst, H. (2003). Patent information for strategic technology management. World Patent Information, 25(3), 233–242. https://doi.org/10.1016/S0172-2190(03)00077-2
Ferreira, J. J., Fernandes, C. I., & Ferreira, F. A. (2020). Technology transfer, climate change mitigation, and environmental patent impact on sustainability and economic growth: A comparison of european countries. Technological Forecasting and Social Change, 150, 119770. https://doi.org/10.1016/j.techfore.2019.119770
Freeman, L. (1977). A set of measures of centrality based on betweenness. Sociometry, 40, 35–41. https://doi.org/10.2307/3033543
Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.
Friedlingstein, P., O’Sullivan, M., Jones, M. W., et al. (2023). Global carbon budget 2023. Earth System Science Data, 15(12), 5301–5369. https://doi.org/10.5194/essd-15-5301-2023
Giannopoulou, E., Barlatier, P. J., & Pénin, J. (2019). Same but different? Research and technology organizations, universities and the innovation activities of firms. Research Policy, 48(1), 223–233. https://doi.org/10.1016/j.respol.2018.08.008
Giannousakis, A., Hilaire, J., Nemet, G. F., et al. (2021). How uncertainty in technology costs and carbon dioxide removal availability affect climate mitigation pathways. Energy, 216, 119253. https://doi.org/10.1016/j.energy.2020.119253
Guan, J., & Zhao, Q. (2013). The impact of university-industry collaboration networks on innovation in nanobiopharmaceuticals. Technological Forecasting and Social Change, 80(7), 1271–1286. https://doi.org/10.1016/j.techfore.2012.11.013
Haščič, I., & Migotto, M. (2015). Measuring environmental innovation using patent data. OECD Publishing. https://doi.org/10.1787/5js009kf48xw-en
Hötte, K., & Jee, S. J. (2022). Knowledge for a warmer world: A patent analysis of climate change adaptation technologies. Technological Forecasting and Social Change, 183, 121879. https://doi.org/10.1016/j.techfore.2022.121879
IEA. (2023). Energy Technology Perspectives 2023. OECD Publishing. https://doi.org/10.1787/7c6b23db-en
Jaffe, A. M., Myslikova, Z., Qi, Q., et al. (2023). Green innovation of state-owned oil and gas enterprises in BRICS countries: A review of performance. Climate Policy, 23(9), 1167–1181. https://doi.org/10.1080/14693062.2022.2145261
Jung, Y., Kim, E., & Kim, W. (2021). The scientific and technological interdisciplinary research of government research institutes: Network analysis of the innovation cluster in South Korea. Policy Studies, 42(2), 132–151. https://doi.org/10.1080/01442872.2019.1593343
Kalemli-Ozcan, S., Sorensen, B., & Villegas-Sanchez, C., et al. (2015). How to construct nationally representative firm level data from the Orbis global database: New facts and aggregate implications. Tech. rep., National Bureau of Economic Research, https://doi.org/10.3386/w21558
Karimi, F., & Khalilpour, R. (2015). Evolution of carbon capture and storage research: Trends of international collaborations and knowledge maps. International Journal of Greenhouse Gas Control, 37, 362–376. https://doi.org/10.1016/j.ijggc.2015.04.002
Kwon, S. (2022). Interdisciplinary knowledge integration as a unique knowledge source for technology development and the role of funding allocation. Technological Forecasting and Social Change, 181, 121767. https://doi.org/10.1016/j.techfore.2022.121767
Kyle, P., Davies, E. G., Dooley, J. J., et al. (2013). Influence of climate change mitigation technology on global demands of water for electricity generation. International Journal of Greenhouse Gas Control, 13, 112–123. https://doi.org/10.1016/j.ijggc.2012.12.006
Lai, X., Ye, Z., Xu, Z., et al. (2012). Carbon capture and sequestration (CCS) technological innovation system in China: Structure, function evaluation and policy implication. Energy Policy, 50, 635–646. https://doi.org/10.1016/j.enpol.2012.08.004
Li, T. C., & Zhou, X. Y. (2022). Research on the mechanism of government-industry-university-institute collaborative innovation in green technology based on game-based cellular automata. International Journal of Environmental Research and Public Health. https://doi.org/10.3390/ijerph19053046
Li, W., & Zheng, X. (2024). Development mechanism and technological innovation of hydrogen energy: Evaluating collaborative innovation based on hydrogen patent data. International Journal of Hydrogen Energy, 52, 415–427. https://doi.org/10.1016/j.ijhydene.2023.05.310
Liu, W., Song, Y., & Bi, K. (2021). Exploring the patent collaboration network of China’s wind energy industry: A study based on patent data from CNIPA. Renewable and Sustainable Energy Reviews, 144, 110989. https://doi.org/10.1016/j.rser.2021.110989
Liu, W., Tao, Y., & Bi, K. (2022). Capturing information on global knowledge flows from patent transfers: An empirical study using USPTO patents. Research Policy, 51(5), 104509. https://doi.org/10.1016/j.respol.2022.104509
Liu, Z., Li, X., Peng, X., et al. (2020). Green or nongreen innovation? Different strategic preferences among subsidized enterprises with different ownership types. Journal of Cleaner Production, 245, 118786. https://doi.org/10.1016/j.jclepro.2019.118786
Ma, D., Li, Y., Zhu, K., et al. (2022). Who innovates with whom and why? A comparative analysis of the global research networks supporting climate change mitigation. Energy Research & Social Science. https://doi.org/10.1016/j.erss.2022.102523
Milani, S. (2020). Who innovates with whom and why? Evidence from international collaboration in energy patenting. Economics of Innovation and New Technology, 29(4), 369–393. https://doi.org/10.1080/10438599.2019.1629531
Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review, 45(2), 167–256. https://doi.org/10.1137/S003614450342480
Nylund, P. A., Brem, A., & Agarwal, N. (2022). Enabling technologies mitigating climate change: The role of dominant designs in environmental innovation ecosystems. Technovation, 117, 102271. https://doi.org/10.1016/j.technovation.2021.102271
Ockwell, D., Sagar, A., & De Coninck, H. (2015). Collaborative research and development for climate technology transfer and uptake in developing countries: Towards a needs driven approach. Climatic Change, 131(3), 401–415. https://doi.org/10.1007/s10584-014-1123-2
Pigola, A., Rezende, P., Carvalho, L., et al. (2022). High analytics information technologies: A patent-based cooperation network analysis. International Journal of Business, Economics and Management, 9, 121–138. https://doi.org/10.18488/62.v9i4.3163
Pingkuo, L., & Xue, H. (2022). Comparative analysis on similarities and differences of hydrogen energy development in the world’s top 4 largest economies: A novel framework. International Journal of Hydrogen Energy, 47(16), 9485–9503. https://doi.org/10.1016/j.ijhydene.2022.01.038
Probst, B., Touboul, S., Glachant, M., et al. (2021). Global trends in the invention and diffusion of climate change mitigation technologies. Nature Energy, 6(11), 1077–1086. https://doi.org/10.1038/s41560-021-00931-5
Pu, G., Zhu, X., Dai, J., et al. (2022). Understand technological innovation investment performance: Evolution of industry-university-research cooperation for technological innovation of lithium-ion storage battery in China. Journal of Energy Storage, 46, 103607. https://doi.org/10.1016/j.est.2021.103607
Rogelj, J., McCollum, D. L., Reisinger, A., et al. (2013). Probabilistic cost estimates for climate change mitigation. Nature, 493(7430), 79–83. https://doi.org/10.1038/nature11787
Song, Y., Zhang, J., Song, Y., et al. (2020). Can industry-university-research collaborative innovation efficiency reduce carbon emissions? Technological Forecasting and Social Change, 157, 120094. https://doi.org/10.1016/j.techfore.2020.120094
Sternitzke, C., Bartkowski, A., & Schramm, R. (2008). Visualizing patent statistics by means of social network analysis tools. World Patent Information, 30(2), 115–131. https://doi.org/10.1016/j.wpi.2007.08.003
Su, Y., & Li, D. (2023). Interaction effects of government subsidies, R&D input and innovation performance of Chinese energy industry: A panel vector autoregressive (PVAR) analysis. Technology Analysis & Strategic Management, 35(5), 493–507. https://doi.org/10.1080/09537325.2021.1979205
Su, Y., & Yan, Y. (2023). The influence of the two-tier network of a regional innovation system on knowledge emergence. Journal of Knowledge Management, 27(9), 2526–2547. https://doi.org/10.1108/JKM-12-2021-0959
Sun, H., Geng, Y., Hu, L., et al. (2018). Measuring China’s new energy vehicle patents: A social network analysis approach. Energy, 153, 685–693. https://doi.org/10.1016/j.energy.2018.04.077
Sun, Y. (2016). The structure and dynamics of intra- and inter-regional research collaborative networks: The case of China (1985–2008). Technological Forecasting and Social Change, 108, 70–82. https://doi.org/10.1016/j.techfore.2016.04.017
Tonurist, P., & Karo, E. (2016). State owned enterprises as instruments of innovation policy. Annals of Public and Cooperative Economics, 87(4), 623–648. https://doi.org/10.1111/apce.12126
Vinuales, J. E. (2015). The Paris Climate Agreement: An initial examination. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2704670
Vivona, R., Demircioglu, M. A., & Audretsch, D. B. (2023). The costs of collaborative innovation. The Journal of Technology Transfer, 48(3), 873–899. https://doi.org/10.1007/s10961-022-09933-1
Wang, G., Li, Y., Zuo, J., et al. (2021). Who drives green innovations? Characteristics and policy implications for green building collaborative innovation networks in China. Renewable & Sustainable Energy Reviews, 143, 110875. https://doi.org/10.1016/j.rser.2021.110875
Wang, L., Wang, Y., Lou, Y., et al. (2020). Impact of different patent cooperation network models on innovation performance of technology-based SMEs. Technology Analysis & Strategic Management, 32(6), 724–738. https://doi.org/10.1080/09537325.2019.1705275
Wennersten, R., Sun, Q., & Li, H. (2015). The future potential for carbon capture and storage in climate change mitigation—An overview from perspectives of technology, economy and risk. Journal of Cleaner Production, 103, 724–736. https://doi.org/10.1016/j.jclepro.2014.09.023
Yamashita, A. S., & Fujii, H. (2022). Trend and priority change of climate change mitigation technology in the global mining sector. Resources Policy, 78, 102870. https://doi.org/10.1016/j.resourpol.2022.102870
Yang, H. X., Wu, Z. X., & Wang, B. H. (2010). Role of aspiration-induced migration in cooperation. Physical Review E, 81, 065101. https://doi.org/10.1103/PhysRevE.81.065101
Yin, C., Gu, H., & Zhang, S. (2020). Measuring technological collaborations on carbon capture and storage based on patents: A social network analysis approach. Journal of Cleaner Production, 274, 122867. https://doi.org/10.1016/j.jclepro.2020.122867
Zahoor, A., Yu, Y., Zhang, H., et al. (2023). Can the new energy vehicles (NEVs) and power battery industry help China to meet the carbon neutrality goal before 2060? Journal of Environmental Management, 336, 117663. https://doi.org/10.1016/j.jenvman.2023.117663
Zhang, R., Tang, Y., Zhang, Y., et al. (2023). Collaborative relationship discovery in green building technology innovation: Evidence from patents in China’s construction industry. Journal of Cleaner Production, 391, 136041. https://doi.org/10.1016/j.jclepro.2023.136041
Zhou, H., Zhu, X., Dai, J., et al. (2023). Innovation evolution of industry-university-research cooperation under low-carbon development background: In case of 2 carbon neutrality technologies. Sustainable Energy Technologies and Assessments, 55, 102976. https://doi.org/10.1016/j.seta.2022.102976
Zhu, X., Woo, S. E., Porter, C., et al. (2013). Pathways to happiness: From personality to social networks and perceived support. Social Networks, 35(3), 382–393. https://doi.org/10.1016/j.socnet.2013.04.005
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This research was supported by Heilongjiang Province Higher Education Teaching Reform Research Project (SJGY20210173).
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QY: Conceptualization of this study, Methodology, Writing - original draft and editing. HC: Data curation, Writing - review, Software.
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Yuan, Q., Chen, H. Measuring Enterprise–University–Institute collaborative innovation network of climate change mitigation technologies in China: a social network analysis approach. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-04925-7
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DOI: https://doi.org/10.1007/s10668-024-04925-7