The Relationship Between Information-Sharing and Resource-Sharing Networks in Environmental Policy Governance: Focusing on Germany and Japan

  • Lee, Junku (University of Tsukuba, Graduate School of Humanities and Social Sciences) ;
  • Tkach-Kawasaki, Leslie (University of Tsukuba, Faculty of School of Humanities and Social Sciences)
  • Published : 2018.12.31


Environmental issues are among the most critical issues nowadays. These issues are no longer confined to individual countries, and international society has been progressing in building global dialogues since the early 1970s. Within these international efforts, Germany and Japan have played essential roles in global environmental governance. However, there are major differences in nation-level environmental policies in both countries. Governance based on network structure is more efficient than that based on hierarchy for solving complex problems. The network structure is formed through horizontal cooperation among various autonomous actors, and the relationship intensity among actors is one of the key concepts in the governance. Using social network analysis as a framework to explain complicated societal structures explains how interaction among actors creates networks, and these networks further affect their interactions. The purpose of this study is to investigate the structure of environmental policy governance as collaborative governance in Germany and Japan. To address this goal, this paper analyzes the relationship between the informational dimension of governance networks and its complement resource-sharing networks in both countries. The results show that the information-sharing networks have lower-level network influence on the resource-sharing networks as higher-level networks even if not all of the information factors have singular influences. The results suggest that the information-sharing networks may be one of the pieces of the puzzle for explaining this phenomenon in environmental governance in Germany and Japan.

Table 1. Factors and Questions (Translated into English)

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Table 2. Dependent variables and questions (Translated into English)

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Table 3. Network structural effect of the factor (3) based on the information sharing network

OSTRBU_2018_v17n2_176_t0003.png 이미지

Table 4. Results of LR-QAP for resource sharing network

OSTRBU_2018_v17n2_176_t0004.png 이미지


  1. Ansell, C., & Gash, A. (2008). Collaborative governance in theory and practice. Journal of Public Administration Research and Theory, 18(4), 543-571.
  2. Barrett, S., & Konsynski, B. (1982). Inter-Organization Information Sharing Systems. Management Information Systems Quarterly, 6(4), 93-105.
  3. Betsill, M. M., & Bulkeley, H. (2004). Transnational networks and global environmental governance: The cities for climate protection program. International Studies Quarterly, 48(2), 471-493.
  4. Borgatti. (2002). UCINET 6 For Windows: Software for Social Network Analysis. Harvard, Massachusetts: Analytic Technologies.
  5. Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing social networks. Sage.
  6. Brink, E., & Wamsler, C. (2018). Collaborative Governance for Climate Change Adaptation: Mapping citizen-municipality interactions. Environmental Policy and Governance, 28(2), 82-97.
  7. Broekel, T., Balland, P. A., Burger, M., & van Oort, F. (2014). Modeling knowledge networks in economic geography: a discussion of four methods. Annals of Regional Science, 53(2), 423-452.
  8. Butts, C. (2008). A Relational Event Model for Social Action. Sociological Methodology, 38(1), 155-20.
  9. Chen, S., Ilany, A., White, B. J., Sanderson, M. W., & Lanzas, C. (2015). Spatial-temporal dynamics of high-resolution animal networks: What can we learn from domestic animals? PLoS ONE, 10(6), 1-11.
  10. Corbett, T., & Noyes, J. (2008). Human services systems integration: A conceptual framework. Institute for Research on Poverty.
  11. Currarini, S., Matheson, J., & Vega-Redondo, F. (2016). A simple model of homophily in social networks. European Economic Review, 90, 18-39.
  12. Dekker, D., Krackhardt, D., & Snijders, T. A. B. (2007). Sensitivity of MRQAP tests to collinearity and autocorrelation conditions. Psychometrika.
  13. Foljanty Jost, G., & Jacob, K. (2004). The climate change policy network in Germany. European Environment.
  14. Gould, R. V. (2002). The Origins of Status Hierarchies: A Formal Theory and Empirical Test. American Journal of Sociology, 107(5), 1143-1178.
  15. Holvoet, N., Dewachter, S., & Molenaers, N. (2016). Look Who's Talking. Explaining Water-Related Information Sharing and Demand for Action Among Ugandan Villagers. Environmental Management, 58(5), 780-796.
  16. Huisman, M., & Steglich, C. (2008). Treatment of non-response in longitudinal network studies. Social Networks, 30(4), 297-308.
  17. Intergovernmental Panel on Climate Change (IPCC). (2007). Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Published for the Intergovernmental Panel on Climate Change.
  18. Jones, C., Hesterly, W. S., & Borgatti, S. P. (1997). A General Theory of Network Governance: Exchange Conditions and Social Mechanisms. Academy of Management Review, 22(4), 911-945.
  19. Kanter, R. M. (1994). Collaborative advantage. Harvard Business Review, 72(4), 5-24.
  20. Kearns, A., & Forrest, R. (2000). Social Cohesion and Multilevel Urban Governance. Urban Studies, 37(5-6), 995-1017.
  21. Kim, K. W., & Song, M. (2015). Mitigating Hazards by Better Designing a Recycling Program: Lessons Learned from South Korea. Journal of Contemporary Eastern Asia, 14(2), 17-36.
  22. Kinne, B. J. (2014). Dependent Diplomacy: Signaling, Strategy, and Prestige in the Diplomatic Network1. International Studies Quarterly, 58(2), 247-259.
  23. Kossinets, G., & Duncun, J. W. (2009). Origins of Homophily in an Evolving Social Network. American Journal of Sociology, 115(2), 405-450.
  24. Krackhardt, D. (1987). QAP Partialling As A Test of Spuriousness. Social Networks, 9, 171-186.
  25. Lee, W. J., Lee, W. K., & Sohn, S. Y. (2016). Patent network analysis and quadratic assignment procedures to identify the convergence of robot technologies. PLoS ONE, 11(10), 1-16.
  26. Lewis, J. M. (2011). The future of network governance research: Strength in diversity and synthesis. Public Administration, 89(4), 1221-1234.
  27. Maciel, C. D. O. (2018). Social Network Analysis and Dyadic Identification in The Classroom. Revista de Administracao Mackenzie, 19(1), 1-27.
  28. Mathur, N., & Skelcher, C. (2007). Evaluating democratic performance: Methodologies for assessing the relationship between network governance and citizens. Public Administration Review, 67(2), 228-237.
  29. McDougall, C., Jiggins, J., Pandit, B. H., Thapa Magar Rana, S. K., & Leeuwis, C. (2013). Does Adaptive Collaborative Forest Governance Affect Poverty? Participatory Action Research in Nepal's Community Forests. Society and Natural Resources, 26(11), 1235-1251.
  30. Mcpherson, M., Smith-lovin, L., & Cook, J. M. (2001). BIRDS OF A FEATHER : Homophily in Social Networks. Annual Review of Sociology, 27, 415-444.
  31. Meadowcroft, J. (2009). Background Paper to the 2010 World Development Report. World Bank Policy Research Working Paper.
  32. Nariyama, H. (2015). Daishinsai ni manabu shakai kagaku dai 3-kan Fukushima genpatsu jiko to fukugo risuku gabanansu [Learning from the Great Earthquake Social Sciences Volume 3 Fukushima Nuclear Accident and Combined Risk . Governance]. TOYO KEIZAI INC.
  33. Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577 LP-8582. Retrieved from
  34. O'Riordan, T., & Jager, J. (1996). Politics of climate change: a European perspective. Psychology Press.
  35. Park, H. W., & Thelwall, M. (2008). Link analysis: Hyperlink patterns and social structure on politicians' Web sites in South Korea. Quality and Quantity, 42(5), 687-697.
  36. Perry-Smith, J. E., & Shalley, C. E. (2003). The social side of creativity: A static and dynamic social network perspective. Academy of Management Review, 28(1), 89-106.
  37. Provan, K. G., & Kenis, P. (2008). Modes of network governance: Structure, management, and effectiveness. Journal of Public Administration Research and Theory, 18(2), 229-252.
  38. Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7(2), 147-177.
  39. Sorensen, E., & Torfing, J. (2005). Network governance and post-liberal democracy. Administrative Theory & Praxis, 27(2), 197-237.
  40. Stork, D., & Richards, W. D. (1992). Nonrespondents in Communication Network Studies: Problems and Possibilities. Group & Organization Management, 17(2), 193. Retrieved from
  41. United Nations. (2018). National greenhouse gas inventory data for the period 1990-2016.
  42. Urban, M. C. (2015). Accelerating extinction risk from climate change. Science.
  43. Valero, J. N. (2015). Effective Leadership in Public Organizations: The Impact of Organizational Structure in Asian Countries. Journal of Contemporary Eastern Asia, 14(2), 69-79.
  44. van Duijn, M. A. J., & Huisman, M. (2011). Statistical models for ties and actors. In The SAGE handbook of social network analysis (pp. 459-483). Sage Thousand Oaks, CA.
  45. Wanna, J. (2008). Collaborative government: meanings, dimensions, drivers and outcomes. In Collaborative governance: a new era of public policy in Australia (pp. 3-12). The Australian National University E Press Canberra.
  46. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge university press.
  47. Wu, I. L., Chuang, C. H., & Hsu, C. H. (2014). Information sharing and collaborative behaviors in enabling supply chain performance: A social exchange perspective. International Journal of Production Economics, 148, 122-132.
  48. Wurzel, R. K. W. (2006). Environmental Policy-Making In Britain, Germany and the European Union. Manchester University Press. Retrieved from