Skip to main content
Log in

Scientific relatedness in solar energy: a comparative study between the USA and China

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

In this paper, we make use of keywords in scientific articles in solar energy during the period 2000–2013 to investigate scientific relatedness at the topic level (i.e. relatedness between topic and topic) and the country level (i.e. relatedness between topic and country). The bibliometric analyses show that both publications and knowledge topics exhibit significant rise, and China has exceeded the USA and developed into the largest scientific producer after 2010. We determine the degree of relatedness by means of the topics co-occurrence network and explore the evolving dynamic processes of scientific relatedness which indicates decreasing patterns in the two countries. The results also highlight differences between the research directions in the USA and China: in the USA “energy efficiency and environment” prove more developed, while in China “solar power” shows more central. This study assesses the extent to which the scientific relatedness exerts influence on the literature productivity at the country level. We find negative relationships between scientific relatedness and publications in both of countries. Our work has potential implications for the future policies with respect to the innovative research in the solar energy field.

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Boschma, R., Balland, P.-A., & Kogler, D. F. (2013). Relatedness and technological change in cities: The rise and fall of technological knowledge in US metropolitan areas from 1981 to 2010: Paper Presented at the Annual Meeting of the Association of American Geographers, Los Angeles.

  • Boschma, R., Heimeriks, G., & Balland, P.-A. (2014). Scientific knowledge dynamics and relatedness in biotech cities. Research Policy, 43(1), 107–114.

    Article  Google Scholar 

  • Bradsher, K. (2010). On clean energy, China skirts rules. New York Times, 9, A1.

    Google Scholar 

  • Burt, R. S. (1992). The social structure of competition. Networks and organizations: Structure, form, and action. Boston: Harvard Business School Press.

  • Cameron, A. C., & Trivedi, P. K. (2013). Regression analysis of count data. Cambridge: Cambridge University Press.

    Book  MATH  Google Scholar 

  • Carnabuci, G., & Bruggeman, J. (2009). Knowledge specialization, knowledge brokerage and the uneven growth of technology domains. Social Forces, 88(2), 607–641.

    Article  Google Scholar 

  • Cataldi, M., Di Caro, L., & Schifanella, C. (2010). Emerging topic detection on twitter based on temporal and social terms evaluation. Proceedings of the Tenth International Workshop on Multimedia Data Mining. July 25, 2010. Washington, D.C., USA, pp. 1–10.

  • Chi, R., & Young, J. (2013). The interdisciplinary structure of research on intercultural relations: A co-citation network analysis study. Scientometrics, 96(1), 147–171.

    Article  Google Scholar 

  • Chu, S., & Majumdar, A. (2012). Opportunities and challenges for a sustainable energy future. Nature, 488(7411), 294–303.

    Article  Google Scholar 

  • Coser, R. L. (1975). The complexity of roles as a seedbed of individual autonomy. The idea of social structure: Papers in honor of Robert K. Merton, pp. 237–263.

  • Fei, L., Dong, S., Xue, L., Liang, Q., & Yang, W. (2011). Energy consumption-economic growth relationship and carbon dioxide emissions in China. Energy Policy, 39(2), 568–574.

    Article  Google Scholar 

  • Fthenakis, V., Mason, J. E., & Zweibel, K. (2009). The technical, geographical, and economic feasibility for solar energy to supply the energy needs of the US. Energy Policy, 37(2), 387–399.

    Article  Google Scholar 

  • Garud, R., & Karnoe, P. (2013). Path dependence and creation. New York: Psychology Press.

    Google Scholar 

  • Geum, Y., Kim, C., Lee, S., & Kim, M.-S. (2012). Technological convergence of IT and BT: Evidence from patent analysis. ETRI Journal, 34(3), 439–449.

    Article  Google Scholar 

  • Glänzel, W., & Thijs, B. (2012). Using ‘core documents’ for detecting and labelling new emerging topics. Scientometrics, 91(2), 399–416.

    Article  Google Scholar 

  • Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78(6), l360–1380.

    Article  Google Scholar 

  • Granovetter, M. (1983). The strength of weak ties: A network theory revisited. Sociological theory, 1(1), 201–233.

    Article  Google Scholar 

  • Granstrand, O., Patel, P., & Pavitt, K. (1997). Multi-technology corporations: Why they have ‘distributed’ rather than ‘distinctive core’ competences. California Management Review, 39(4), 8.

    Article  Google Scholar 

  • Guan, J., & Ma, N. (2007). China’s emerging presence in nanoscience and nanotechnology: A comparative bibliometric study of several nanoscience ‘giants’. Research Policy, 36(6), 880–886.

    Article  Google Scholar 

  • Guan, J., Yan, Y., & Zhang, J. (2014). How do collaborative features affect scientific output? Evidences from wind power field. Scientometrics,. doi:10.1007/s11192-014-1311-x.

    Google Scholar 

  • Gulati, R., Sytch, M., & Tatarynowicz, A. (2012). The rise and fall of small worlds: Exploring the dynamics of social structure. Organization Science, 23(2), 449–471.

    Article  Google Scholar 

  • Hansen, M. T. (1999). The search-transfer problem: The role of weak ties in sharing knowledge across organization subunits. Administrative Science Quarterly, 44(1), 82–111.

    Article  Google Scholar 

  • Hassan, S.-U., & Haddawy, P. (2013). Measuring international knowledge flows and scholarly impact of scientific research. Scientometrics, 94(1), 163–179.

    Article  Google Scholar 

  • Hassan, S.-U., Haddawy, P., & Zhu, J. (2014). A bibliometric study of the world’s research activity in sustainable development and its sub-areas using scientific literature. Scientometrics, 99(2), 549–579.

    Article  Google Scholar 

  • Hausman, J. A. (1978). Specification tests in econometrics. Econometrica: Journal of the Econometric Society, 46(6), 1251–1271.

    Article  MATH  MathSciNet  Google Scholar 

  • Howarth, R. B., Haddad, B. M., & Paton, B. (2000). The economics of energy efficiency: Insights from voluntary participation programs. Energy Policy, 28(6), 477–486.

    Article  Google Scholar 

  • Hussinger, K. (2010). On the importance of technological relatedness: SMEs versus large acquisition targets. Technovation, 30(1), 57–64.

    Article  Google Scholar 

  • Jerome, L. W. (2013). Innovation in social networks: Knowledge spillover is not enough. Knowledge Management Research & Practice, 11(4), 422–431.

    Article  Google Scholar 

  • Joo, S. H., & Kim, Y. (2010). Measuring relatedness between technological fields. Scientometrics, 83(2), 435–454.

    Article  MathSciNet  Google Scholar 

  • Kim, D.-J., & Kogut, B. (1996). Technological platforms and diversification. Organization Science, 7(3), 283–301.

    Article  Google Scholar 

  • Kodama, M. (2005). Knowledge creation through networked strategic communities: Case studies on new product development in Japanese companies. Long Range Planning, 38(1), 27–49.

    Article  Google Scholar 

  • Kumar, S., & Jan, J. (2013). Mapping research collaborations in the business and management field in Malaysia, 1980–2010. Scientometrics, 97(3), 491–517. doi:10.1007/s11192-013-0994-8.

    Article  Google Scholar 

  • Kumar, S., & Jan, J. (2014). Research collaboration networks of two OIC nations: Comparative study between Turkey and Malaysia in the field of ‘Energy Fuels’, 2009–2011. Scientometrics, 98(1), 387–414. doi:10.1007/s11192-013-1059-8.

    Article  Google Scholar 

  • Lee, J. J. (2010). Heterogeneity, brokerage, and innovative performance: Endogenous formation of collaborative inventor networks. Organization Science, 21(4), 804–822.

    Article  Google Scholar 

  • Leydesdorff, L. (2008). On the normalization and visualization of author co-citation data: Salton’s Cosine versus the Jaccard index. Journal of the American Society for Information Science and Technology, 59(1), 77–85.

    Article  Google Scholar 

  • Leydesdorff, L., Kushnir, D., & Rafols, I. (2014). Interactive overlay maps for US patent (USPTO) data based on International Patent Classification (IPC). Scientometrics, 98(3), 1583–1599. doi:10.1007/s11192-012-0923-2.

  • Li, Z.-S., Zhang, G.-Q., Li, D.-M., Zhou, J., Li, L.-J., & Li, L.-X. (2007). Application and development of solar energy in building industry and its prospects in China. Energy Policy, 35(8), 4121–4127.

    Article  Google Scholar 

  • Livingstone, D. N. (2010). Putting science in its place: Geographies of scientific knowledge. Chicago: University of Chicago Press.

    Google Scholar 

  • Luan, C., Liu, Z., & Wang, X. (2013). Divergence and convergence: Technology-relatedness evolution in solar energy industry. Scientometrics, 97(2), 461–475.

    Article  Google Scholar 

  • Ma, F.-C., Lyu, P.-H., Yao, Q., Yao, L., & Zhang, S.-J. (2014). Publication trends and knowledge maps of global translational medicine research. Scientometrics, 98(1), 221–246.

    Article  Google Scholar 

  • Makri, M., Hitt, M. A., & Lane, P. J. (2010). Complementary technologies, knowledge relatedness, and invention outcomes in high technology mergers and acquisitions. Strategic Management Journal, 31(6), 602–628.

    Google Scholar 

  • Mallik, A., & Mandal, N. (2013). Bibliometric analysis of global publication output and collaboration structure study in microRNA research. Scientometrics, 98(3), 2011–2037.

    Article  Google Scholar 

  • Matthiessen, C. W., & Schwarz, A. W. (2010). World cities of scientific knowledge: Systems, networks and potential dynamics. An analysis based on bibliometric indicators. Urban Studies, 47(9), 1879–1897.

    Article  Google Scholar 

  • Milojević, S., Sugimoto, C. R., Yan, E., & Ding, Y. (2011). The cognitive structure of library and information science: Analysis of article title words. Journal of the American Society for Information Science and Technology, 62(10), 1933–1953.

    Article  Google Scholar 

  • Mutschke, P., & Haase, A. Q. (2001). Collaboration and cognitive structures in social science research fields. Towards socio-cognitive analysis in information systems. Scientometrics, 52(3), 487–502.

    Article  Google Scholar 

  • Newman, M. E., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.

    Article  Google Scholar 

  • O’brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673–690.

    Article  Google Scholar 

  • Palepu, K. (1985). Diversification strategy, profit performance and the entropy measure. Strategic Management Journal, 6(3), 239–255.

    Article  Google Scholar 

  • Phelps, C., Heidl, R., & Wadhwa, A. (2012). Knowledge, networks, and knowledge networks a review and research agenda. Journal of Management, 38(4), 1115–1166.

    Article  Google Scholar 

  • Quintana-García, C., & Benavides-Velasco, C. A. (2008). Innovative competence, exploration and exploitation: The influence of technological diversification. Research Policy, 37(3), 492–507.

    Article  Google Scholar 

  • Rafols, I., & Meyer, M. (2010). Diversity and network coherence as indicators of interdisciplinarity: Case studies in bionanoscience. Scientometrics, 82(2), 263–287.

    Article  Google Scholar 

  • Rigby, D. L. (2013). Technological relatedness and knowledge space: Entry and exit of US cities from patent classes. Regional Studies (ahead-of-print), pp. 1–16.

  • Rip, A., & Courtial, J.-P. (1984). Co-word maps of biotechnology: An example of cognitive scientometrics. Scientometrics, 6(6), 381–400.

    Article  Google Scholar 

  • Sabir, R. I., & Sabir, R. M. (2010). Managing technological innovation: China’s strategy and challenges. Journal of Technology Management in China, 5(3), 213–226.

    Article  Google Scholar 

  • Sanz-Casado, E., Garcia-Zorita, J. C., Serrano-López, A. E., Larsen, B., & Ingwersen, P. (2013). Renewable energy research 1995–2009: A case study of wind power research in EU, Spain. Germany and Denmark. Scientometrics, 95(1), 197–224.

    Google Scholar 

  • Schumpeter, J. A. (1934). The theory of economic development: An inquiry into profits, capital, credit, interest, and the business cycle (Vol. 55). New Jersey: Transaction Publishers.

    Google Scholar 

  • Solow, R. M. (1956). A contribution to the theory of economic growth. The Quarterly Journal of Economics, 70(1), 65–94.

    Article  Google Scholar 

  • Su, H.-N., & Lee, P.-C. (2010). Mapping knowledge structure by keyword co-occurrence: A first look at journal papers in Technology Foresight. Scientometrics, 85(1), 65–79.

    Article  Google Scholar 

  • Tanriverdi, H., & Venkatraman, N. (2005). Knowledge relatedness and the performance of multibusiness firms. Strategic Management Journal, 26(2), 97–119.

    Article  Google Scholar 

  • Valente, T. W. (2012). Network interventions. Science, 337(6090), 49–53.

    Article  Google Scholar 

  • Virmani, A., (2005). A tripolar century: USA, China and India. Working Paper, No. 160. New Delhi: Indian Council for Research on International Economic Relations.

  • Wang, C., Rodan, S., Fruin, M., & Xu, X. (2014). Knowledge networks, collaboration networks, and exploratory innovation. Academy of Management Journal, 57(2), 484–514.

  • Zhang, G., Xie, S., & Ho, Y.-S. (2010). A bibliometric analysis of world volatile organic compounds research trends. Scientometrics, 83(2), 477–492.

    Article  Google Scholar 

  • Zhou, P., & Leydesdorff, L. (2006). The emergence of China as a leading nation in science. Research Policy, 35(1), 83–104.

    Article  Google Scholar 

  • Zhu, W., & Guan, J. (2013). A bibliometric study of service innovation research: Based on complex network analysis. Scientometrics, 94(3), 1195–1216.

    Article  Google Scholar 

Download references

Acknowledgments

This study is supported by a grant from National Natural Science Foundation of China (No. 71373254). The authors are very grateful for the valuable comments and suggestions from the anonymous reviewers and Editor-in-Chief of the journal, which significantly improved the quality and readability of the paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jiancheng Guan.

Appendix: Retrieval profiles for solar energy

Appendix: Retrieval profiles for solar energy

TS = (“solar energy*” OR “solar radiation” OR “solar cell*” OR “solar photovoltaic*” OR “solar power” OR “solar heat*” OR “solar plant*” OR “solar concentrate*” OR “solar thermal” OR “solar collect*” OR “solar technolog*”) AND PY = (2000–2013) Refined by: Document Type = (ARTICLE) AND [excluding] Web of Science Categories = (HORTICULTURE OR PLANT SCIENCES OR FORESTRY) Databases = SCIEXPANDED,SSCI, CPCI-S, CPCI-SSH Timespan = 2000–2013 Lemmatization = On.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Yan, Y. & Guan, J. Scientific relatedness in solar energy: a comparative study between the USA and China. Scientometrics 102, 1595–1613 (2015). https://doi.org/10.1007/s11192-014-1487-0

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-014-1487-0

Keywords

Navigation