Description

There is an increasing body of research on the role of socio-institutional networks in climate adaptation. The varying definitions of the term 'social network' reflect its conceptual and methodological development initially in mathematics (graph theory) and sociology, and more recently in environmental sustainability and related interdisciplinary areas, particularly climate change adaptation and resilience of social-ecological systems.

This research all focuses on human or organizational actors and their social relationships, and connections among units and between actors. For the purposes here, 'social network' is used to refer to institutional actors and the linkages among these, as well as other actors (individuals, organizations, interest groups etc.). It relates to the analysis of governance and decision-making networks, which are close to the concepts of policy and governance networks (e.g. Blanco et al. 2011). By including multiple types of actors it recognizes that informal ties as well as formal ones are deeply involved in 'governance' (e.g. see Pelling et al. 2008).

Berkhout et al., (2006) found that many of the resources required for carrying out the process of adaptation lie outside the boundary of a particular organization. As a result, inter-relationships between organisations are influential in determining how (and if) adaptation processes will occur. Following from this, it is important to identify the existing socio-institutional landscape and feedback processes in climate adaptation research, to speed up the necessary 'climate-adapted routines and capability to be developed' (Berkhout et al., 2006).

Against this background, a number of methods are emerging that can identify the various stakeholders involved in adaptation decisions, and map out their linkages. These can be represented (visually) and analyzed with network maps. These can be further analysed, in qualitative or quantitative terms using social network analysis to provide additional information The background and key benefits of the approach are provided in Box 1.

Participatory social network mapping and analysis is able to reveal insights about the substance of these relationships by making explicit the types of flows between actors (e.g. information, money, advice, policy, etc) and the perceptions of influence and power in the network. Quantitative SNA provides a variety of measures/indicators to help describe the overall relational structure of a social system, as well as the roles of individuals within it.

It can provide insights which can then be explored further with other methods - follow-up interviews, statistical analysis, agent-based modelling, etc.

The main difference between qualitative and quantitative social network analysis are that quantitative SNA graphs are 'whole' networks rather than ego-centric networks based on the perception of (usually) just one actor.

They are also much more comprehensive (i.e. with more nodes and links) and can be quantitatively analysed with SNA software using standard statistical tests.

Quantitative Social Network Analysis

Quantitative SNA aims at capturing the entire relevant network. The steps for quantitative social network analysis are:

• Clarifying objectives and defining the scope of analysis (e.g. mapping a knowledge domain).
• Developing a survey methodology and designing the questionnaire.
• Identifying the participants (network) and providing justification for boundaries (if appropriate).
• Collecting survey data and gathering further information from other resources.
• Analyzing the data through formal methods.
• Reviewing process and outcomes to identify problems/opportunities.
• Designing and implementing actions to bring about desired changes.
• Mapping the network again after an appropriate period of time.

This is a resource intensive task, and field research requires very high response rates, and high resources, as any missing data can weaken the analysis. Other approaches using existing data (e.g. co-citation networks, online databases, householder surveys) can also be considered, though it is not always easy to extract relational information or perform suitable data transformations.

Qualitative Social Network Analysis

Qualitative social network analysis or social network mapping (SNM) takes advantage of the early steps above - the interviews, surveys or focus group discussions - to elicit information on the relevant networks.

It can facilitate rich discussions, shared understanding and increased awareness between different stakeholders. This can be part of a rapid appraisal before detailed analysis begins. It can also identify entry points for policy influence (Turnpenny et al., 2005) and other 'flows' of resources which can include 'informal capital'.

A number of approaches and tools can be used for network analysis. Following Schiffer (2010), the NetMap guidance is a useful example for applying the approach in a participatory way. The method is usually applied using flipcharts, post-it notes and flat counters with a group of stakeholders who are split into homogeneous groups related to the type of institution they belong to e.g. Government level representatives, NGOs, farmers, etc.

Once the adaptation research question is well defined, participants go through the mapping exercise including an analysis of the network, and then come back into plenary for a discussion of the different networks from the different stakeholder perspectives. This enables a better 'shared understanding' of differing world views. The steps for participatory social network analysis (Schiffer, 2010) are:

• Identifying the question for the analysis.
• Define goals for each actor and note these on each post-it. Allow for multiple goals where appropriate, by noting more than one goal next to the actor (to understand conflicts and synergies).
• The resulting maps allow the participants to discuss the following questions in their groups and produce an in-depth analysis of the decision-making landscape.
• Come back together as a group to discuss the analysis of the results and compare perceptions of strengths, weaknesses, areas of influence and so on. This can promote a shared understanding of the issue and consensus on areas for action.

A range of software existing for both quantitative and qualitative SNA. This includes software for visualisation and analysis e.g. GEPHI, UCInet, ORA, Netdraw, ORA, etc.

Toolbox tags

This toolbox entry has been labelled with the following tags:

Sector:		independent
Spatial scale:		independent
Temporal focus:		present; future
Onset:		independent
Role in decision process:		diagnostic
Level of skills required:		modest
Data requirements:		limited
Adaptation tasks:		Organizational self assessment; Organizational self assessment (in: Implementing adaptation actions / Getting started); Governance description

Strengths and Weaknesses

Socio-institutional network mapping (Quantitative approaches)

Strengths:

• can provide measures
• A range of software is available for visualization and analysis (e.g. GEPHI, UCInet, ORA)

Weaknesses:

• Large sample size needed, or ego-centric partial networks
• Tends to focus on methodology and technical issues rather than on hypotheses and theories
• Over-interpretation of results
• Some authors have questioned an assumed confidence in the measures to characterize the networks
• Data are often difficult and expensive to obtain, and empirical studies are often quite small. This means it is hard to use data for exploration of alternative measurement strategies

Socio-institutional network mapping (Qualitative Approaches):

Strengths:

• Can be done in a day
• Encourages participation across diverse viewpoints and actors
• Does not prescribe a particular classification of jargon
• Yields insights that would be difficult to get any other way
• Range of software available for visualization and analysis e.g. Netdraw, UCInet, ORA, but can also remain hand drawn maps

Weaknesses:

• Can be difficult to integrate different perspectives to produce cohesive maps of whole networks, especially where multiple scales are involved
• Some links are less reliably attributed - information is incomplete
• Can be difficult to bring together actors that have different perspectives; this can cause tensions, which in turn can bias the results
• Results are highly dependent on which actors are involved in the exercise and which actors are not (high subjectivity). Therefore, it is important to ensure actor type representativeness when implementing SNM.
• One full day can be too long for some actors to participate. Poor participation of key actors can bias the results.

Socio-institutuional network mapping (both approaches):

Strengths:

• Can generate an understanding of prevailing socio-institutional structures (based on how the actors themselves report them), relating a characterization of the individual actors connectivity, to its local network context, and to the overall whole-network features
  
  

Weaknesses:

• Subjective bias and can be difficult to generalize
• Time-consuming, intensive process
• Do not have a temporal or spatial dimension
• Networks have artificial boundaries (often necessarily)
• Design of process is critical to get as many differing viewpoints as possible

Applicability

The recognition of social network analysis, and the role of socio-institutional networks in climate adaptation is increasing, reflecting the growing viewpoint that adaptation is a socio-institutional process. The IPCC Special Report on Extreme Events (SREX) (2012) recognises this, in defining adaptation as a process of adjustment to the actual or expected climate and its effects, in order to moderate harm or exploit beneficial opportunities.

This process-based understanding requires a 'mapping' of the problem framing and actors and thus SNA has a high relevance for adaptation.

To understand the basic elements that constitute a network analysis (both qualitative and quantitative) and how these characteristics can relate specifically to the issue of climate change adaptation it is important to consider network topologies, outlined in the box.

An added complexity with the application of SNA to adaptation is the dynamic nature of climate change. The socio-institutional networks and relationships between the actors (and with their actions) will evolve over time. It is also necessary to consider the differences in decision framing and the links to uncertainty. This includes four common levels of decision framing:

• The architecture of stakeholders and knowledge, and the boundaries involved;
• The defined decision boundaries, i.e. what is in scope;
• Decision making, i.e. the methods, tools and metrics.
• Implementation, and the link to responsibility towards action.

Information on these aspects allows analysis of the value of information in making a decision.

Accessibility

The review and case studies provide a number of practical lessons on the application of social network analysis to adaptation. They provide useful information on the types of adaptation problem types where SNA might be appropriate, as well as data needs, resource requirements and good practice.

The application of the qualitative approach is very broad, and can be applied to most adaptation settings. The approach can be useful for adaptation planning, decision-framing, uncertainty and the links to choices of tools.

The quantitative approach provides important additional context for progressing towards adaptation implementation, though there is a need for balanced representation (i.e. of participants) to avoid subjectivity influencing results. The quantitative approach can provide a more detailed analysis, providing correlations, but there is a need for high sample sizes, thus the added time and resources limit the approach to more specific applications (as in the case of the Finnish case study, aligning to an existing survey).

Finally, the Mediation case studies provide some useful messages on the lessons from the application of the approach, outlined below.

1. Barriers to adaptation are part of socio-institutional processes and can potentially be revealed and negotiated through social network analysis.

2. Capacity to adapt is capacity to act in socio-institutional processes, i.e. flows alone are not an indicator of adaptive capacity per se since there can be an imbalance of power which diminishes capacity.

3. The drivers or determinants of adaptive capacity are far more than the availability of information and finance (flows).

4. Adaptive networks can be described formally and this can also help us to identify what outcomes different network configurations may produce.

5. Descriptions of both actors and networks can be related to qualitative metrics and used to benchmark progress towards outcomes.

6. Transformations in adaptive capacity are changes in actor-networks (e.g. new institutional arrangements, new entities or new roles and responsibilities).

Further Reading and References

King, A., 2000. Managing Without Institutions: The Role of Communication Networks in Governing Resource Access and Control. Department of BiologicalSciences, University of Warwick, Coventry.

Lonsdale, K. G. Gawith, M.J.; Johnstone, K. Street, R. B.;West, C. C.; Brown, A. D. (2010). Attributes of Well-Adapting Organisations, 1-89.

Mathur, V. and Downing, TE. 2012. Framing Adaptation to Climate Change: From prediction to a wicked problem and implications for vulnerability assessment. Oxford: Global Climate Adaptation Partnership, Oxford (submitted).

Moser, S.C. & Ekstrom, J. a, 2010. A framework to diagnose barriers to climate change adaptation. Proceedings of the National Academy of Sciences of the United States of America, 107(51), pp.22026-31. Available at: [Accessed July 5, 2011].

Oh, H., Chung, M.-H., Labianca, G., 2004. Group social capital and group effective- ness: the role of informal socializing ties. Academy of Management Journal 47, 860-875.

Olsson, P., Gunderson, L.H, Carpenter S.R. , Ryan, P., Lebel, L. Folke, C., Holling, C.S. (2006) Shooting the Rapids : Navigating Transitions to Adaptive Governance of Social-Ecological Systems. Ecology and Society, 2006, 11 (1), 18.

Orr, P., Eales, R., White, O., & Walljes, I. 2008. Annex 1 Overcoming Barriers to the Delivery of Climate Change Adaptation - ESPACE Summary Report. Part of the ESPACE (European Spatial Planning: Adapting to Climate Events) project.

Pelling, M. et al., 2008. Shadow spaces for social learning: a relational understanding of adaptive capacity to climate change within organisations. Environment and Planning A, 40(4), pp.867-884. Available at: [Accessed July 21, 2011].

R Core Team (2012) R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria (available online at: )

Renn, O. 2008 Risk Governance: Coping with Uncertainty in a Complex World (Earthscan, London).

Sandström, A., Rova, C. 2010. Adaptive co-management networks: a comparative analysis of two fishery conservation areas in Sweden, Ecology and Society 15(3): 14.

Schiffer, E., Hauck, J. 2010. Net-Map: Collecting Social Network Data and Facilitating Network Learning through Participatory Influence Network Mapping, Field Methods 22(3): 231-249.

Stein, C., Ernstson, H. & Barron, J., 2011. A social network approach to analyzing water governance: the case of the Mkindo catchment, Tanzania. Physics and Chemistry of the Earth, Parts A/B/C. Available at: [Accessed August 23, 2011].

Turnpenny, J., Haxeltine, A., Lorenzoni, I., O'Riordan., T and Jones., M., 2005: Mapping actors involved in climate change policy networks in the UK, Tyndall Centre for Climate Change Research. Working Paper 66, Norwich.

Toolbox category

This detail page belongs to toolbox category
Stakeholder engagement and analysis tools

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weADAPT case studies identified for this toolbox entry: