Complex climate change adaptation network in Bhutan; how actor type impacts actor inclusion and clustering

Master’s ThesisInstitute of Political ScienceUniversity of Bern, Switzerland

Presented by:Jessica Russell17 September 2015

Thesis supervisor:Prof. Dr. Karin IngoldInstitute of Political ScienceUniversity of Bern, Switzerland

Background

> Policy processes and governance as networks: Actors participate in collective decision-making process’ called an “institution” Repeated transactions between actors become institutionalized in a “network” Actors collaborate within the network on a defined project, creating a “tie”

Stakeholder inclusion, scales and multi-level approaches: Climate change adaptation is local in scope, with the structure of institutional

arrangements being critical, local actor inclusion is important The multilevel governance framework involves two levels of action:

1) Vertical = crossing multiple government scales - local to national

2) Horizontal = across government departments, line ministries Vertical/horizontal integration through top-down/bottom-up institutional designs A “polycentric approach” to governance

Case study: Bhutan

Research Question 1

> Aim: Examine how actor inclusion in climate change adaptation institutions is shaped in a complex network in Bhutan, by applying a multilevel governance framework to explore links between actors and institutions within networks

> Research Question: Which type of actors are most included within complex networks and projects in climate change adaptation policy in Bhutan via the embededness of national and local actors in top-down and bottom-up institutional designs

> Hypothesis: National actors are well embedded in top-down institutional designs, and local actors are well embedded in bottom-up institutional designs Independent Variable: Actor type Dependent Variable: Actors’ inclusion

Data and Methodology – Research Questions 1, 2, 3

> 130 key actors were identified from a literature review and defined as nodes, categorised into 7 actor types: international organisation, national government, foreign government, local government, NGO, corporation or community

> 73 climate change adaptation-related policies/projects defined and categorised into: Institutional types: climate change adaptation, sustainable forestry, disaster

management, communication (14) Institutional designs: top-down (69) or bottom-up (4)

Social Network Analysis (SNA). Software- UCINET: Ties between actors and institutions coded as a dummy variable, with 1: tie

being present, and 0: no tie present Binary ties make up the 2 mode (actor x institution) asymmetric network matrix 2 mode data transformed into a 1 mode (actor x actor) symmetric network

matrix to run analysis Ties are undirected

Research Question 1: Methodology

Networks Analysed: Total network: All actors as nodes, with ties (both top-down and bottom-up),

in a transformed 2 mode to 1 mode network Sub-networks: Either top-down or bottom-up institutional designs, are also

made up of actors as nodes, with ties, in a transformed 2 mode to 1 mode network

> Method- Research Question 1 Social Network Analysis (SNA): three measures of centrality to assess

embededness: Degree centrality: the number of ties an actor directly shares with others in

the network Betweeness centrality: the number of times an actor connects two

disconnected actors Eigenvector centrality: higher when an actor is connected to institutions that

are also well connected

Research Question 1: Methodology

> Analysis: T-test: compare the mean centrality: degree, betweeness and eigenvector of

national government actors and all other groups (total network and top-down designs), and local government actors and all other groups (bottom-up designs)

Two-way ANOVA: tests the level of significance of differences in normed centrality: degree, betweeness, and eigenvector means between the 7 actor types, with the p-value

Regression model: to fit the data and estimate the significance and strength of the relationship between independent variable actor type and dependent variable actors’ inclusion (dependent variable: centrality)

If an actor has a high degree centrality, betweeness centrality and eigenvector centrality, relative to others = actor is well embedded in the network

Table 3. Normed betweeness centrality means for the total network and sub-networks

Research Question 1: Results

> Hypothesis: National actors are well embedded in top-down institutional designs, and local actors are well embedded in bottom-up institutional designs

National governments do not have a high degree centrality (Table 2- Annex), but high betweeness centrality (Table 3) and eigenvector centrality (Table 4- Annex) in top-down designs

Local actors have a slightly higher degree centrality, no betweeness centrality, but a high eigenvector centrality, in bottom-up institutional designs

No significant effect of actor type on degree, betweeness, eigenvector centrality

Conclude: National actors are relatively better embedded in top-down institutional designs, and local actors are relatively better embedded in bottom-up institutional designs

The Relevance of Clustering Research Question 2: Part A and B

> Clusters of actors collaborate inside closed groups, having repeated transactions leading to trust, thus supporting cooperation

> Core/Periphery- Actors in the core are more able to coordinate their activities

> Aim: Examine whether clustering occurs according to actor type in climate change adaptation institutions in a complex network in Bhutan, by applying a multilevel governance framework to explore links between actors and institutions

> Research Question 2. Part A: Do actors of the same type cluster together within networks? Hypothesis: Actors of the same type collaborate by clustering; the density

between within-group ties and outside-group ties are significantly different Independent Variable: Actor type Dependent Variable: Clustering

> Research Question 2. Part B: Which actors make up the core and periphery of the network?

Research Question 2: Part A and B Methodology

> PART A & B: An MDS plot is used to initially locate clusters: Actors located in the centre of the cluster = core actors Actors located further away from the centre = peripheral actors Analysis: Total network (both top-down and bottom-up institutional designs)

> Part A: Clustering analysed via tie density: Structural Block model option of an ANOVA Density model: test whether within

and between group ties are significantly different across actor types

> Part B: Location via a simple Core/Periphery model: Core actors = highest density of ties amongst themselves, collaborating in

common institutions Peripheral actors = lower density of ties amongst themselves, fewer institutions

in common

Figure 5. An MDS plot shows the total network. Red circles: international organisations, orange: foreign government, yellow: national government, green: local government, blue: NGO, purple: corporation, pink: community

Table 5. Structural Block model option of an ANOVA Density model (a) with model fit (b), and a Simple Core/Periphery model with density matrix table (c) for the total network

Research Question 2: Results

> Part A: Do actors of the same type cluster together within networks? Hypothesis: Actors of the same type collaborate by clustering; the density between within-group ties and outside-group ties are significantly different

Results: There is a statistically significant effect of variable “actor type” on variable “clustering” in the network; accept that the density between within-group ties and outside-group ties are significantly different

> Part B: Which actors make up the core and periphery of the network? Results: Many national level actors in the core

> Conclude: Actors of the same type collaborate by clustering National actors cluster and occupy the core of the network, sharing many ties

and cooperating with other actor types

The Relevance of Fragmentation Research Question 3

> We define fragmentation where there is a high proportion of actors unable to reach each-other in the network: Vertical fragmentation: Between institutions at different scales such as from

national to local Horizontal fragmentation: Between institutions in different sectors such as

forestry or water

> Fragmentation in institutions: Could affect actors’ ability to implement projects effectively, through inhibiting

joint decision-making Creates barriers to cooperation

> Aim: Examine fragmentation in climate change adaptation institutions in a complex network in Bhutan, by applying a multilevel governance framework to explore links between actors and institutions within networks

> Research Question: Does the network display fragmentation?

Research Question 3 - Hypothesis and Methodology

> Hypothesis: Top-down institutional designs are less fragmented than bottom-up institutional designs

> Fragmentation analysis:

Networks: the total network (both top-down and bottom-up institutional designs), and sub-networks; top-down and bottom-up

Fragmentation score: the proportion of actors unable to reach each-other

Compare the fragmentation score of institutional design relative to each-other

> Density score:

Equal to 1 = all actors within the network are tied directly to each other

Equal to 0 = network is fully disconnected and therefore fragmented

Research Question 3: Results

Table 6. Fragmentation score for the total network, and sub-networks; top-down or bottom-up institutional designs

> Results: Low amount of fragmentation in top-down designs High amount of fragmentation in bottom-up designs Overall density scores (Table 1- Annex) are low for each institutional design

(top-down = 0.035, bottom-up = 0.052) Conclude: Few ties through which information can flow, both networks are

disconnected and therefore fragmented

Conclusion

Some difference between top-down and bottom-up institutional designs: National actors relatively better embedded in top-down, local actors relatively better embedded in bottom-up (Table 2- Annex, Table 3, Table 4- Annex)

Lack of inclusion of local actors (Table 2, 3, 4)

Some actors may work on climate change projects in isolation- UNFAO (Fig. 5)

Actors of the same type collaborate by clustering (Figure 5) (Table 5)

National actors cluster/core of the network, sharing many ties/cooperate with different actor types (Table 5)

Top-down institutional designs less fragmented than bottom-up, but with overall low density scores for each (Table 1-Annex). Conclude that both demonstrate fragmentation (Table 1- Annex) (Table 6)

The total network in Bhutan is fragmented (Table 1- Annex) (Table 6), but displays clustering (Table 5); conclude that information sharing between actors to plan, monitor and enforce climate change adaptation projects may be low

Recommendations

> General recommendations:

Increased involvement from national government actors: more influence, ability to collaborate and capacity to coordinate in the network

> Recommendations for a “polycentric” approach to governance:

A hybrid approach between top-down, and bottom-up designs, with national government providing a guiding framework, allowing local communities to make implementation decisions based on community knowledge

Increase both horizontal and especially vertical collaboration, to increase local actor inclusion

Support vertical and horizontal integration, to decrease fragmentation