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Advocacy Coalitions’ Diffuse and Discrete Emotion-Belief Expressions in a Transmission Line Siting Controversy

Emma Scheetz, Allegra H. Fullerton, Anna M. Crawford, Christopher M. Weible and Tanya Heikkila

Abstract

This paper builds on the burgeoning literature of emotion analyses in the policy process by examining how emotions are used and portrayed in locating a contentious renewable transmission line in Southern California, US. Using the Advocacy Coalition Framework (ACF) as the theoretical guide, this paper finds evidence that actors experience similar emotions about beliefs to those within their coalition and dissimilar emotions about beliefs to actors in rival coalitions. This trend holds for diffuse (positive/negative) and most, but not all, discrete (e.g. anger, dismay, affinity) emotions expressions. Negative emotions also tend to define coalitions more strongly while positive emotions have varied patterns. The conclusion lays out future research directions to further our understanding of emotions and beliefs in policy and politics.

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Introduction

1In political systems, emotions are one conduit through which individuals and groups are likely to voice their support or opposition around salient and controversial policy ideas. These expressions of support and opposition have been studied for decades, particularly with the Advocacy Coalition Framework (ACF) (Sabatier & Jenkins-Smith, 1993; Nohrstedt et al., 2023). The ACF directs attention to forming and maintaining coalitions of people and organizations from inside and outside of government, to ensure policy decisions reflect their beliefs. The challenge with studying such coalitions is determining what characteristics of the coalitions should be highlighted. This paper approaches beliefs of coalitions through an emotion’s lens. The way beliefs are conceptualized through emotions changes how we determine coalitions methodologically and theoretically. This is part of a growing literature that seeks to understand coalitional beliefs in conjunction with emotions (Fullerton et al., 2023; Gabehart et al., 2023; Yordy et al., 2023).

2Past and current research displays two principal approaches to collecting and analyzing advocacy coalitions’ public expressions, as found in the news or social media, legislative testimonies, and more. The first and more traditional approach entails analyzing position-belief dyads (e.g., Zafonte & Sabatier, 2004; Heikkila et al., 2019; Heinmiller et. al., 2021; Ohno, 2022). The second and more recent approach entails analyzing emotion-belief dyads (e.g., Fullerton et al., 2023; Gabehart et al., 2023; Yordy et al., 2023). This paper builds on and contributes to the more recent approach by collecting and analyzing the emotion-belief dyads of two coalitions in a transmission line siting controversy. It examines diffuse (positive and negative valence) and discrete (e.g., anger, fear, compassion, etc.) emotional expressions to characterize advocacy coalitions. Diffuse and discrete lenses are used to assess the coalitions at two levels of magnification.

3The central question addressed is: What are the characteristics of the advocacy coalitions’ diffuse and discrete emotional expressions tied to their belief system expressions in a controversial siting of a transmission line in California, US? Answering this question using burgeoning methods in a new context will provide insight into the identification of coalitions using emotions and how emotions function in coalition building. This study contributes to the existing ACF literature on emotions (e.g., Fullerton & Weible, 2024; Fullerton et al., 2023; Gabehart et al., 2023) by analyzing coalitions using diffuse and discrete emotional levels to further understand how different magnification in emotion measures affect the identification of coalitions and the theoretical interpretation.

4This case study features the Tehachapi Renewable Transmission line. The transmission line provides a useful context to study emotions because it is a high conflict case about developing infrastructure in support of renewable energies (You et al., 2022). Fullerton et al. (2023) conducted a similar emotional analysis about the siting of a natural gas pipeline. However, compared to conflicts over a natural gas pipeline, the siting of renewable energy lines creates different fissures of conflict, particularly in Southern California, where renewable energy is highly supported. In this case, most people favored the transmission line as an infrastructure supporting renewable energy development but disagreed about the location, mainly because of potential adverse impacts on property values, views, and safety. In studying a case where disagreements focus on infrastructure siting but where there is agreement on the importance of the infrastructure, we expect to uncover distinct patterns of emotion-belief dyads, and therefore coalition formation and activation, compared to previous research.

5In studying the characteristics of advocacy coalitions via their expressed diffuse and discrete emotional expressions and belief systems, this paper finds that advocacy coalitions have distinct emotional expressions (both diffuse and discrete) tied to their beliefs about the transmission line siting. This paper confirms its hypothesis and contributes to the ongoing theoretical development of emotional expressions under the ACF. It also contributes to the broader policy process literature, namely that negative emotions contribute more strongly to coalition formation than positive emotions. The conclusion outlines the next steps in advancing such theory.

Theoretical Foundations

  • 1 This paper provides a cursory overview of the ACF; see Nohrstedt et al. (2023) for a more detailed (...)

6Since its creation in the 1980s and early 1990s (Sabatier & Jenkins-Smith, 1993), the ACF has operated on two basic premises.1 First, public policies are translations of belief systems of politically engaged people and organizations. Second, people and organizations tend to mobilize and coordinate their political behavior into advocacy coalitions around common belief systems to influence public policies (Nohrstedt et al., 2023).

7The belief system model in the ACF is categorized in three hierarchical tiers. At the broadest and most abstract tier, deep core beliefs refer to actors’ normative assessments of how the world is or ought to be, sociocultural identities, and fundamental values. Deep core beliefs constrain policy core beliefs, which are more pertinent to a policy issue (i.e., a policy subsystem), including policy preferences, perceptions of problem severity and causes, and relative priorities of different people. Secondary beliefs refer to mechanistic beliefs about how policies are supposed to achieve their ultimate goal (Sabatier, 1993).

8However, more than shared beliefs are needed to drive coalition formation and overcome threats to collective action. To address this, Paul Sabatier created the "devil shift" concept, the tendency for people in high-conflict situations to exaggerate the power and maliciousness of opponents – that is, feelings of fear through demonization of opponents is another driving force for coalitions (Sabatier et al., 1987). Later, William Leach and Paul Sabatier created the "angel shift," where coalitions can coalesce around their members' feelings of compassion or confidence in their allies (Leach & Sabatier, 2005).

  • 2 The study of coalitions has evolved and advanced since the ACF’s inception. Early research focused (...)

9While this paper does not test the angel or devil shifts, it builds on their theoretical raison d être that affect or feelings are a driving force in coalition behavior. This paper, thus, unpacks categories of emotions and links them to the ACF's model of a belief system.2 Moreover, Sabatier’s theoretical logic underpinning the devil shift also mirrors contemporary arguments about the importance of emotions in political behavior. For example, Barrett (2017) argued that emotions have individual and collective aspects, meaning that emotions can serve as one of the potential glues that form and bind people into advocacy coalitions.

10This paper continues the efforts under the ACF to provide theoretical and empirical direction and momentum in using emotion-belief dyads to study advocacy coalitions (see, for example, Fullerton & Weible, 2024; Fullerton et al., 2023; Gabehart et al., 2023). One of the foundational assumptions for bringing emotions into the ACF is distinguishing between affect and emotions. Affect is defined as the biological and physiological feelings that are ambivalent and amorphous (Mercer, 2010). Emotions are defined as affects expressed through language (Ahmed, 2014; Durnová, 2018; Solomon, 2012). Thus, this analysis follows an "outside in" approach (Brader & Marcus, 2013) and does not aim to capture how people actually feel but rather measures only the emotions they express out loud. This study uses "emotions" and "emotional expressions" interchangeably, and expressed emotions are the fundamental concept of interests (Leavitt & Power, 1989; Robinson et al., 1994; Thoits, 1996).

11Another foundational assumption is that emotions have an intentionality: emotions relate to something (Leys, 2019). Translated into the ACF, emotions relate to beliefs, and reciprocally, beliefs relate to emotions. This combination then creates the emotion-belief dyads that also draw on the psychology literature that confirms the interdependent relationship between emotions and beliefs (Anderson, 2002; Frijda et al., 2000; Talisse, 2021).

12An emotion-belief dyad is the connection that a policy actor expresses between an emotion and a belief when discussing a policy issue. The incorporation of emotional expressions as a component of the ACF builds on well-established research outside the ACF that studying emotions is essential for understanding political ideas and behaviors (Clarke et al., 2006; Neuman et al., 2007; Stets & Turner, 2014; Wahl-Jorgensen, 2019; Durnová, 2019). Emotions play a crucial role, especially in communication through the media, where they serve as a vital tool for conveying the scientific and technical dimensions of policy matters, such as climate change, to the general populace. When emotions are embedded into the media's portrayal of these issues, they can bolster public engagement and foster the development of collective identities (Hoijer, 2012). Past studies of emotional responses to renewable energy projects show how they play a significant role in oppositional activism and how emotions can shape public perceptions of renewable projects (Cass & Walker, 2009). Moreover, while measuring emotions has been debated throughout various branches of policy and sociology scholarship, a growing consensus affirms that emotions can be measured accurately and studied reliably (Jasper, 2011; Roger & Robinson, 2014; Liu, 2020; Bustinza & Witkowski, 2022).

13Additionally, this paper analyzes two different classifications of emotions: diffuse and discrete. Diffuse emotions are often associated with valence, general positive or negative expressions of belief (Cacioppo et al., 1997; Watson, 1985). Discrete emotional expressions refer to the specific categories of emotional expressions (e.g., fear, anger, or compassion) following the logic that if an explicit emotional expression fits into one category, it cannot fit into another. Both diffuse and discrete emotions anchor themselves to expressed belief-system components (Goldie, 2012), resulting in emotion-belief dyads as this paper’s units of observation.

14Thus, this paper argues that coalitions will coalesce not just around common beliefs, but those beliefs will also be expressed in conjunction with diffuse positive or negative emotions (e.g., positive and negative valence) or discrete emotions (e.g., anger, fear, etc.). Using two aggregations of emotions in tandem with beliefs amplifies the ACF’s traditional hypothesis, traditionally measured with a position (e.g., pro or con) with a belief expression. The more diffuse aggregation gives a general indicator of sentiment and general support or opposition of beliefs, and the more discrete aggregation offers a more specific emotional interactor with beliefs. While we expect that diffuse and discrete emotional expressions will be consistent in coalition dynamics, diffuse and discrete emotions with beliefs provide a more holistic portrayal of advocacy coalitions. This leads us to test a single two-part hypothesis involving the tendency for both emotions and beliefs as indicators of coalitions.

Coalition Emotion-Belief Hypothesis. In a policy subsystem with disputes over a policy decision, two or more coalitions will be differentiated by (a) their diffuse emotion-belief dyads and (b) their discrete emotion-belief dyads.

Case Study

  • 3 It should be recognized that You et al. (2022) also found that other infrastructure sitings (i.e., (...)

15This analysis focuses on the discourse surrounding Southern California’s Tehachapi Renewable Transmission line. This case study was chosen because it represents a high conflict transmission line discourse using the measure developed by You et al. (2022).3 This transmission line is also unique because it transports renewable energy which may generate more support than non-renewable transmission lines (Cass & Walker, 2009), especially in Southern California, where renewable energy is generally favored.

16The Tehachapi transmission line project is over 250 miles long and was built between 2009 and 2018 (Southern California Edison, 2016). While most of the transmission line construction produced little to no conflict, a particular three-mile segment through the city of Chino Hills generated most of the discourse about the line. This segment resided particularly close to residential homes. While the Tehachapi project was using existing power lines through the city, they added an extra 100 feet to the height of the towers. Residents reported concerns about safety if the towers were to topple, along with worries about property values and obstructed views (City of Chino Hills, 2011). After years of mobilization and the local government filing petitions to change the route, the California Public Utilities Commission, in a 3-2 vote, approved the Chino Hills residents’ requests to reroute underground even though they had originally approved the above-ground route (City of Chino Hills, 2011). This change in approval came after commissioners went to visit the transmission line construction site. After the vote in July 2013, the energy company Southern California Edison dismantled the above-ground towers and built the underground route. This change ultimately cost the company $224 million, which was transferred to ratepayers across California (Southern California Edison, 2016). We identify two coalitions in this case, one that supports the original route (original route coalition) and one that supports the underground route (alternative route coalition).

Methods

17This paper uses news media articles to identify coalitions, a common practice in the ACF (Henry et al., 2022; Schaub, 2021; Naziz, 2020; Heikkila et al., 2019; Kukkonen et al., 2017; Shanahan et al., 2008; Martin & Rose, 2003). Although a common practice, news media does present some data limitations. News media captures only a portion of the discourse on this issue (Heinmiller et. al. 2021; Paterson & Scala, 2021). This data source can have a selection bias in what is covered, typically preferring to cover conflict, fear, and negative events (Powers & Fico, 1994, Zoch & Turk, 1998; Lawrence 2000; Dunaway 2011). Media will also meter whose voice is allowed in the discourse. The sample of actors from the media analysis is likely not expansive of the whole conflict and probably favors those with media training. Thus, we are only capturing part of the conflict and emotional expressions around beliefs. While our data sources are limited, we do find merit in using news media analysis because journalists often ask how people “feel” (Wahl-Jorgenson, 2019), and past studies have also confirmed the appropriateness of such a data source for studying emotions in the public discourse (Verhoeven & Metze, 2022; Yordy et al., 2023; Paterson & Scala, 2021). Although not establishing the full picture of emotion-belief expressions, we can use this data to understand in part how coalitions choose to express their emotion-belief dyads in news media.

18We identified the news articles using keyword searches on NewsBank, a news media database, for “Tehachapi” and “Transmission Line”. Articles are found between the years 2007 and 2016, the years when the route for the high conflict segment of the transmission line was announced (2007) and then when the construction of the segment was completed (2016). We manually searched the list and eliminated any duplicate articles or articles not about the Tehachapi Transmission Line. We eliminated any editorials or opinion pieces due to their normative nature. Editorials and opinion pieces are written to reveal opinions of the media or an individual, and because we are focusing on policy actors involved in the conflict, we do not find it appropriate to include these narratives. We did not exclude any specific newspapers; any newspaper that published a news article about the transmission line was included. In total, 154 articles were found.

  • 4 Yordy et al.’s (2023) thesaurus originally had twelve categories of emotions. This paper combines s (...)

19The 154 articles were compiled on the Discourse Network Analyzer Software v3 Leifeld (2013). This software allows the creation of a code structure and attaches statements to different coding categories lending itself to the emotion belief coding in this project. Other papers using similar codebooks have utilized this software as well (Fullerton and Weible, 2024; Gabehart et al., 2023; Yordy et al., 2023). Within each article, statements were recorded if they contained an emotional expression by a policy actor. To find explicit emotions, we captured statements that used specific emotion words that were categorized in a thesaurus. We used Yordy et al.’s (2023) thesaurus during the coding. The word was coded as one of the nine emotional categories from the emotional thesaurus4. No use of carelessness was found in the statements; thus, this emotion category was dropped, leaving eight emotion groupings for analysis. For example, the statement “Residents have voiced several concerns about the power-line project” would be coded as an explicit emotional statement as the word “concern” is in the thesaurus and falls under the fear category. When a new word was identified that was not included in the thesaurus, the coder placed the word alongside the most similar meaning other words (using a dictionary and the thesaurus), and it was discussed with the co-authors. If multiple co-authors agreed with the category, the word was added to the thesaurus and coded.

20For each statement, we also coded the narrator (who is speaking), the actor (who is feeling the emotion), the emotion being expressed, and the belief (what the emotion was about). In addition, the actor and narrators’ affiliations, such as local government or Southern California Edison, were recorded. If the actor or narrator did not have a specified affiliation, they were coded as ‘public.’ Lastly, we recorded the narrators’ and actors’ positions (original route or alternative routes) about the transmission line. Actor and narrator positions on the issue were determined by their statements either supporting the original route for the transmission line or the alternative route. These positions are determined by the culmination of an actor’s or narrators’ statements about the line. We used the position code to confirm and help identify the coalitions through cluster analysis.

21By coding for the narrator and the actor, we can distinguish between emotional expressions that are self-narrated (I feel angry) and other-narrated (they feel angry), as these two narrations can present very different displays of emotion. For this analysis, we have chosen to focus only on the self-narrated statements as other narrated statements lead to a different, more complicated interpretation of emotions and fall outside our research question. We have also limited the statements to only those about the transmission line itself. In total, we analyzed 506 statements.

22Every article was coded by the same author. A second author independently coded 20% of the articles. The emotion codes of the two authors were then compared at an 80% reliability rate of coding the same statements, emotions, and objects.

  • 5 We also recognize ambiguity in distinguishing belief system tiers in the ACF (see also Nohrstedt et (...)

23After the emotion coding, a second round of coding was conducted to further categorize the beliefs associated with each statement. These beliefs were organized around the specific topic, i.e., obstructed views, safety of the line, location of the line, etc. They were also divided based on statements made about the original route and about the alternative route. For example, the statement “City Officials said the power lines would pose a safety hazard” would be coded as a belief about the safety of the original route. Another statement, “Burying the towers will…benefit all residents of California,” would be coded as a belief about the location of the alternative route (see Appendix Table 3). We classified every belief captured as a policy core belief. These policy core beliefs included policy preferences about the location of the route and the relative priority of renewable energy and problem perceptions such as about the impacts of the views of the route (Sabatier & Jenkins-Smith, 1999, pg. 133)5.

Results

24To identify the advocacy coalition, we conducted a Girvan-Newman algorithm (Girvan & Newman, 2002). The approach divided the people making statements and the expressed emotion-belief dyads into three clusters, one favoring the alternate route (the coalition for the alternate route) and two favoring the original route (the coalition for the original route). The two clusters that make up the coalition for the original route were combined into one coalition because the algorithm identified a sub-discourse within the coalition. One cluster emphasized the job and energy benefits of the original route, while the other emphasized the safety and location benefits of the original route, but both favored the original route over the alternate route. For this analysis, we focus on the two coalitions and their combined discourses therein and leave the analysis of the sub-discourses for the future. Finally, the two clusters found in the Girvan-Newman algorithm were confirmed by manual coding, as described in the methods section.

25Table 1 presents the percentage of statements made by each member affiliation for each coalition and the E-I Index for each member. The top half of the table displays the coalition members for the alternative route (the alternative route coalition), while the bottom half shows the coalition members for the original route (the original route coalition). The percentages indicate the proportion of statements made per coalition member out of all statements made for a coalition. For example, the local government category for the coalition for the alternative route made 157 statements out of the 412 for its coalition.

26Table 1 also includes the Krackhardt E-I Index for each coalition member (Krackhardt & Stern, 1988). This index reports the ratio of statements made by each coalition member to statements clustered within the coalition versus those clustered within the opposing coalition as calculated with the Girven-Neuman Algorithm. An E-I Index of -1 indicates that all statements made by a coalition member are aligned with statements made by their coalition. An E-I Index of +1 indicates that all statements made by a coalition member are aligned with statements made by opposing coalitions or complete heterophily. As these statements were used to create the coalitions, E-I Index will be less than zero as anything above 0 would flip the actor to a different coalition. An actor with a rating of 0 indicates that half of their statements were made internal to the coalition and half were made external. Table 1 also lists the average E-I Index per coalition.

Table 1: Coalition Membership and E-I Indexes for Actors

 

 

 

Percent of statements by coalition members (n=412)

Krackhardt E-I Index

Coalition for the Alternative Route

Local Government

38%

-0.76

Local Residents (people who live near the transmission line)

28%

-0.789

State Government

12%

-0.714

Hope for the Hills

9%

-0.818

California Public Utilities Commission

6%

-0.75

Chino Hills City Council Candidates

2%

-1

Chino Valley Fire District

1%

-1

Non-Residents (people who live far from the transmission line)

1%

-1

Save Archibald Ranch

1%

-1

Clipper Wind Power

0.50%

-1

Utility Reform Network

0.50%

-1

Coalition Average

 

-0.89

 

 

 

 

 

 

Percent of statements by coalition members (n=141)

Krackhardt E-I Index

Coalition for the Original Route

Southern California Edison

72%

-0.613

Kern County Government

9%

-0.714

Kern County Economic Development Corp.

4%

-1

Kern Wind Energy Association

4%

-0.333

Champlin Windpower

2%

-1

Sierra Club: Los Angeles Chapter

2%

-1

The Bakersfield Californian

2%

-1

California Independent System Operator

1%

0

California Fish and Wildlife

1%

-1

Strata Equity

1%

-1

University of California

1%

-1

 

Coalition Average

 

-0.78

Source: the Authors

  • 6 Hope for the Hills is a resident group that started as an organization to advocate against the tran (...)

27The alternate route coalition expressed seventy-four percent of all statements (412 of 553 statements), nearly three times the number of statements made by the original route coalition. Within the original route coalition, Southern California Edison, the company building the line, was responsible for seventy-two percent of all self-narrated statements. Most of the other coalition members are private wind power companies or governments supporting the use of renewable energy. Within the alternate route coalition, the majority of statements are made by several different actors. Most of the actors represent the interests of local citizens.6

28Only one actor in our data received an E-I score of zero, the California Independent System Operator. This actor only made statements about the benefits of renewable energy, which was a belief held by both coalitions. Many other actors received a score of negative one or close to negative one. Nearly all of the actors had an E-I Index over negative one-half which indicates that over seventy-five percent of statements made by each actor were internal. This provides one indicator of polarization between coalitions – that is, the extent that coalitions share similar discourse (Nam et al, 2022). The closer the coalitions’ E-I scores are to 1 or -1, the more polarized they are. Additionally, more polarization is seen within the coalition for the alternative route than the coalition for the original route since the coalition for the alternative route has a lower E-I Index average. However, a t-test conducted did not show a significant difference between the E-I indexes of each coalition.

  • 7 The full table of dyads expressed by each coalition can be found in the appendix.

29Each coalition is also assigned a set of emotion-belief dyads by the Girvan-Newman algorithm. The emotion belief dyads indicate what emotions about certain beliefs were expressed by each coalition. Table 2 displays the five most common dyads expressed by each coalition as well as the E-I Index associate with each dyad.7

Table 2: Coalition Membership and E-I Indexes for Dyads 

 

 

Percent of statements by dyad (n=419)

Krackhardt E-I Index

Coalition for the Alternative Route

1. Fear about the Safety of the Original Route

17%

-0.8

2. Dismay about the Location of the Original Route

11%

-1

3. Affinity for the Location of the Alternative Route

10%

-1

4. Affinity for the Renewable Energy of the Original Route

10%

-0.2

5. Fear about Views being blocked by the Original Route

8%

-0.333

Overall Coalition Average

 

-0.89

 

 

 

 

 

 

Percent of statements by dyad (n=134)

Krackhardt E-I Index

Coalition for the Original Route

1. Affinity for the Location of the Original Route

11%

-0.333

2. Affinity for the Electrical Power generated by the Original Route

8%

-0.667

3. Trust in the Safety of the Original Route

7%

-1

4. Dismay about the Cost of the Alternative Route

7%

-1

5. Compassion about the Renewable Energy of the Original Route

7%

0

Overall Coalition Average

 

-0.75

Source: the Authors

30The most common dyad stated by the coalition for the alternative route is expressing fear about the safety of the original route. This is contrasted by the third most common dyad stated by the coalition for the original route expressing trust in the safety of the original route. The dyads of each coalition are interrelated with the dyads made by the opposing coalition. Actors from the coalition for the alternative route expressed concerns about safety, the coalition for the original route answered with expressions of trust in safety in an attempt to assuage fears.

31Dyads about renewable energy tend to be shared across coalitions as indicated by their E-I Indices being close to zero. This aligns with the political landscape of Southern California and the general support for renewable energy. Most other dyads about the location of the line and the effects on views, property values, and safety are not shared strongly across coalitions. The overall E-I Index average for dyads for each coalition is close to negative one which indicates that dyads are generally not shared and are unique to each coalition.

32The findings thus far confirm that coalitions exist and are identifiable through their emotion-belief dyads. The focus now will shift to 4w identifying coalitions via diffuse emotional expressions.

33Figure 1 demonstrates the diffuse emotions expressed by each coalition with the discourse about the original and the alternative routes. Expressed emotions of anger, dismay, and fear, are categorized as negative diffuse emotions. Affinity, trust, content, obligation, and compassion are categorized as positive diffuse emotions.

34The first two columns represent the discourse about the original route specifically. We can see that the alternate route coalition expressed primarily negative emotions about the original route while the original route coalition expressed primarily positive emotions about the original route. Negative and positive dyads for the original route represent over eighty percent of the statements for each coalition, respectively. In the discourse about the alternative route, the expressions switch. The alternate route coalition stated over eighty percent positive emotions for the alternate route. While the original route coalition expressed over eighty percent negative emotions about the alternative route.

Figure : Diffuse Emotions Expressed by Each Coalition for Each Route

Figure : Diffuse Emotions Expressed by Each Coalition for Each Route

Source: the Authors

35We confirmed the patterns from Figure 1 using a Logarithmic Regression Quadratic Assignment Procedure regressing coalition membership by shared expressions of positive and negative diffuse emotions on belief dyads. The results of this regression are displayed in Table 3.

36The odds ratio for the diffuse emotions expressed about the original route are both significant. If an actor expresses the same positive emotions about the original route as another actor, they are over two times more likely to be in the same coalition than an opposing coalition. If an actor expresses the same negative emotions about the original route as another actor, they are over five times more likely to be in the same coalition. This indicates that negative emotions may be more significant in determining coalitions. Beliefs associated with negative emotions are more likely to determine which coalition an actor belongs in than a belief associated with a positive emotion.

37The discourse about the alternative route does not yield significant results. This is likely because only one actor from the coalition for the original route made statements about the alternative route, Southern California Edison. Due to the limited variation in the dependent variable, the results were insignificant. The result for negative emotions expressed about the alternative route indicates that actors who share these emotions are less likely to be in the same coalition. This outcome does not confirm our hypothesis. However, since the odds ratio is insignificant, few conclusions can be drawn from the result.

38Overall, the results indicate that positive and negative diffuse emotions are shared within coalitions and not shared across coalitions with a few potential caveats.

Table 3: Diffuse Emotions Logistic Regression Results

Odds Ratio

T-value

Positive Emotions about the Original Route

2.655*

2.689

Negative Emotions about the Original Route

5.323*

5.967

Positive Emotions about the Alternative Route

1.65

1.836

Negative Emotions about the Alternative Route

0.596

-1.991

R2 = 0.1017

Source: the Authors

39As for discrete emotions expressed in the statements about the transmission line, similar patterns are found. A Quadratic Assignment Procedure was used to analyze the correlations between each discrete emotion and coalition membership.

40Both coalitions may express the same discrete emotions, but they will likely express these emotions about different beliefs. One coalition may express dismay about the location of the original route, the other may express dismay about the location of the alternative route. While both coalitions express dismay, they are expressing dismay about different beliefs. These are separate dyads that belong to different coalitions. If the emotions expressed across coalitions are about the same beliefs, then the E-I Index will move away from negative one and closer to zero. The results of the Quadratic Assignment Procedure and the E-I Index for each discrete emotion are displayed in Table 4.

Table 4: Discrete Emotion Correlation for the Original Route Discourse

Quadratic Assignment Procedure Correlation Coefficients

Average Krackhardt E-I Index for the Coalition for the Alternative Route

Average Krackhardt E-I Index for the Coalition for the Original Route

Anger

1#

-1

-

Fear

0.813*

-.73

-0.58

Dismay

0.63*

-1

-1

Affinity

0.235**

-0.80

-0.88

Obligation

0.404*

-0.75

-1

Content

1

-1

-1

Trust

0.439

-1

-0.83

Compassion

0.115

-0.9

-0.08

Note: Quadratic Assignment Procedure used to calculate correlations. p<0.05* and p<0.01 **. # Indicates a discrete emotion (i.e., anger) is used by only one coalition.

Source: the Authors

41Most of the discrete emotion categories render a significant correlation. For anger, fear, and dismay, the significant correlation indicates that actors who express these emotions about the same beliefs are more likely to be in the same coalition. Actors who express fear about the effects of the original route on views, property values, and safety are more likely to be in the coalition for the alternative route than the coalition for the original route. Actors who express fear about the safety and the extra labor needed to build the alternative route are more likely to be in the coalition for the original route than the coalition for the alternative route. This is shown by the correlation coefficient being closer to one than zero. Correlation coefficients of one mean that there is perfect correlation between the use of the emotion and coalition membership. For anger, only actors from the coalition for the alternative route expressed anger, thus its correlation coefficient is one. A correlation coefficient of zero indicates that there is no correlation between the use of the emotion and coalition membership. In addition, the E-I Indices for anger, fear, and dismay are negative one or close to negative one, which indicates that when these emotions are expressed, actors from opposing coalition generally do not express these emotions about the same beliefs. Thus, expressions of anger, fear, and dismay are shared within coalitions and not between coalitions, confirming the hypothesis. These are the only three discrete emotions that confirm the hypothesis. Because they are the only three negative discrete emotions, these results indicate that negative emotions may be more defining of coalitions. Actors generally coalesce around certain beliefs that are tied to negative emotions.

42For obligation and particularly affinity, the correlation coefficient results indicate that actors who express these emotions about the same beliefs are less likely to be in the same coalition. However, when considering the E-I Index, these results signify that actors in the same coalition do not express affinity or obligation for the same beliefs. Most affinity and obligation statements are made internally, indicated by their E-Index rating being close to negative one. Thus, expressions of affinity and obligation are not necessarily shared across coalitions but rather these emotions are expressed about different beliefs within coalitions.

43Three of the discrete emotions did not have significant correlations: trust, content, and compassion. Trust and content were likely not significant due to the low number of statements (n=19 and n=3) expressing these emotions and the small number of actors expressing both emotions. There is not enough variation in actor coalitions to induce a significant result. Both emotions did have low E-I Index scores indicating that generally emotions of trust and content about beliefs were shared within a coalition, not between coalitions.

44The results for compassion as a discrete emotion are different from the other emotions. Compassion not only has a small correlation coefficient but an E-I Index close to zero for the coalition for the original route, meaning that many of the expressions of compassion about beliefs were shared by both coalitions. Actors from both coalitions expressed compassion about the effects to natural spaces and views of the line, the renewable benefits of the line, and the extra labor needed to complete the alternative route. Not all compassion dyads were shared between coalitions, but compassion yields the most shared dyads as compared to the other discrete emotion categories. This evidence denotes that compassion may act differently from other emotions in coalition forming.

45Given the significant coefficients and low E-I Index scores of anger, fear, and dismay, it appears that negative emotions are more likely to determine coalition membership and are more likely to be shared by members of the same coalition. These results partially confirm the hypothesis. Three of the eight discrete emotions are shared within coalition members and not between coalitions.

46However, not all the discrete emotions are shared within coalitions. As discussed above, the results for affinity and obligation indicate that most statements expressing these emotions were not necessarily shared across coalitions but also were not necessarily shared within coalitions either. This indicates that expressions of affinity and obligation were less concentrated around a central dialogue unlike negative discrete emotions. In addition, one positive discrete emotion, compassion, was expressed about shared beliefs between coalitions. These dissonant results do not confirm the second part of the hypothesis. These results warrant further investigation into the patterns of discrete emotions and coalition forming. Finally, the findings suggest that negative emotions are a stronger force in creating opposing coalitions than positive emotions (Sabatier & McLaughlin, 1987; Fullerton et. al., 2023).

Conclusion

47What are the characteristics of the advocacy coalitions’ diffuse and discrete emotional expressions tied to their belief system expressions in a controversial siting of a transmission line in South California, US? Guided by the ACF, this paper answers this question examining the emotion-belief expressions in the news media.

48The findings confirm most of the hypothesis that coalitions can be identified through their emotion-belief expressions. The most consistent results came from the analysis of diffuse emotional expressions. However, discrete emotions were only partially shared within coalitions, and coalitions often shared expressions of compassion. The findings confirm past studies of emotion-belief expressions under the ACF (Gabehart et al., 2023; Fullerton et al., 2023; Fullerton & Weible, 2024) and studies using different techniques of the same case (You et al., 2022). It adds to this growing literature by examining and comparing how coalition policy actors self-expressed diffuse or discrete emotions regarding their beliefs. In contrast, Fullerton et al. (2023) examined how coalition policy actors ascribed emotions and beliefs to their allies or opponents. Gabehart et al. (2023) analyzed self-expressed discrete but not diffuse emotions. The findings are also consistent with Fullerton & Weible (2024) in showing the strength of negative emotions with respect to positive emotions in characterizing coalitions. Thus, one of the primary contributions of this paper is broadening the interpretation of coalitions via different self-expressed abstractions of emotional expressions.

49However, any interpretation of these findings should be made cautiously, pointing to a future research agenda. First, while this paper shows how the study of emotion-belief dyads advances our knowledge of coalitions, it does not show the effects on other aspects of the policy process. That is, future research needs to analyze the impacts of emotional expressions on the political behavior of coalitions, coalition stability, conflict, learning, policy change, and outcomes (Ohno, 2022).

50Second, this is just one case of the siting of a transmission line for renewable energy. The extent to which this work might generalize to other cases, especially those outside of the siting of energy infrastructure, remains an empirical question to explore. We also expect the findings in this paper to depend on the theoretical approach applied; for example, a different interpretation of this case would emerge if this analysis were adapted and used under a different theoretical framework (e.g., Shanahan et al., 2018). However, we do have reason to believe that there is some generalizability of results. Other studies on emotion belief dyads have found that coalitions share emotion-belief expressions (Gabehart et al., 2023; Fullerton et al., 2023). While an extensive body of literature has confirmed that coalitions share beliefs (e.g., Zafonte & Sabatier, 2004; Heikkila et al., 2019; Heinmiller et. al., 2021; Ohno, 2022), we expect that the emotions expressed about those beliefs will also be shared in different contexts, particularly along diffuse (positive and negative) emotional distinctions.

51In closing, this paper fits into the broader literature about how people engage in policy and political issues. It builds on the idea and past findings that people do not engage emotionlessly in politics but rather with emotions, as studied herein via their verbal expressions in the public discourse. In public policy and public discourse, understanding the influence of emotions on individual behavior is a worthy pursuit. As policy processes involve human endeavors, it is crucial to consider the emotional underpinnings that affect public discourse and policy outcomes. Emotions not only interact the perceptions and give nuance to belief statements, they also have been found to influence policy acceptance (Huddy et al., 2005). This study explicitly studies emotions, perhaps building from energy infrastructure studies where negative emotions have been implicitly studied. For example, threats, or fear perceived by others’ policy positions are studied within policy conflict studies (Weible & Heikkila, 2017, Kagan et al., 2023) and policies that create anger and anxiety are more likely to be faced with resistance (Verhoeven & Duvyendajk, 2016). The findings in this study give further fodder for the study of negative discrete emotions and their role in policy processes. This paper advances knowledge about what we know about emotions, either as diffuse expressions or discrete expressions, that combine with beliefs to better understand polarization and conflicts.

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Appendix

Appendix

Coding Emotions in Public Discourse

Use DNA v3 (https://www.philipleifeld.com/​software/​software.html). Below is a table detailing the coding scheme used for the project.

When emotion words or statements were identified, it was coded to have one narrator, one actor, one emotion, and one object.

The unit of coding are statements

A statement is a complete idea about the topic or the context surrounding such a project that includes either a direct or indirect use or reference to emotion. A complete idea will generally include a subject, verb, and object. The statement can be either a single sentence or multiple related sentences. Each statement has one narrator, one actor, one object, and one emotion.  

Table 1. Coded units, names, and definitions and descriptions

Coding Unit #

Coded name (abbreviation)

Definitions and Definitions

1

narrator (nar)

The individual speaking. Use the full name (first and last) if known. Use the individual’s full name (if known) in the first statement attributable to the actor using “first” “last” format. In subsequent statements utilize DNA’s drop-down list function for the same actor to ensure consistency. 

If a journalist is paraphrasing for another person, the person being paraphrased is the actor. Look for words such as “said, cited, expressed” to find paraphrased phrases. 

“Who is speaking?”

2

narrator affiliation (nar_aff)

The affiliation of the narrators. Be as specific as possible and include the full name of the organization. If the coded statement is from a journalist, include the name of their publication as the affiliation. If a narrator is not clearly speaking on behalf of the organization they belong to or if they are identified by their profession rather than their organization, code their title or profession instead (e.g. “emergency physician”). If a narrator is a member of the public, or has no affiliation mentioned, list “public” as their affiliation. 

3

narrator position (nar_pos)

The narrator position (supportive (pro) against (anti) neutral (neutral)) concerning the central focal point of interest, such as a policy, project, process, or situation? The focal point of interest will vary by research project. Use available information from the article to inform this code, if within the narrative negative things about the object are brought up use anti. Positive things use pro, both negative and positive things neutral and if not brought up use unidentified. ** iterative

4

actor (actor)

The individual, entity, or organization that feels the emotion as expressed. When coding statements use the individual’s full name (if known) in the first statement attributable to the actor using ”first” “last” format. In subsequent statements utilize DNA’s drop-down list function for the same actor to ensure consistency. If a statement involves a group – code the statement once for each member of the group if every member is named. If a group is cited but every member is not named, code the statement once and list the group as the actor (e.g. protesters, State Assembly members, etc) 

“Who is feeling the emotion?”

5

actor_affiliation (actor_aff)

“Actor affiliation” The affiliation of the actor. Be as specific as possible with the name coded.  Use the full organization name (if known) in the first instance that organization is coded. In subsequent statements use DNA’s dropdown list function to ensure consistency. If an actor is not clearly speaking on behalf of the organization they belong to or if they are identified by their profession rather than their organization, code their title or profession instead (e.g. “emergency physician”). If an actor is quoted as a member of the public, or has no affiliation mentioned, list “public” as their affiliation. 

6

actor position (actor_pos)

Is the actor supportive, against, or neutral towards the situation? Use available information from the article to inform this code, if within the narrative negative things are brought up, code as anti. Positive things, pro, both positive and negative code as neutral. If not brought up, code as unidentified. Code as pro / anti / neutral / undisclosed 

This was a holistic approach, we took in the statements in their entirety  

If we found both, neutral  

7

actor narrator (actor_nar)

Is the actor self narrating (speaking on their own behalf?) Click yes if yes, leave blank if no

8

emotion (emot)

What is the emotion being expressed?  When coding emotions, we code both explicit and implicit emotions. Explicit emotions are emotions that are directly mentioned in the statement. “He was concerned about the pipeline.” Concerned is the explicit emotion.


  • Write the emotion word and then in parentheses write one of the 12 categories that this emotion fits into. 

  • The 12 categories are: desire, uncertainty, trust, approval, suffering, obligation, fear, disapproval, content, compassion, careless, and anger 

  • Review the thesaurus before coding and use the search feature within it while coding 

    • Many emotion words have previously been identified. The word in parentheses needs to be one of the 12 categories from the thesaurus e.g. “demand (anger), concern (fear), hurt (suffering), need (desire).”  

  • If there is not a word or phrase that directly implies an emotion, but the statement as a whole conveys emotion then code the implicit emotion (one of the 12 categories) in parentheses without any emotive words/phrases out of the parentheses.

    • Implicit emotions are not directly mentioned but the statement can still convey a feeling. “Who knows why they are building the pipeline” would be an implicit emotion where who knows is uncertainty. When we code implicit emotions, only write one of the 12 emotional categories (e.g. do not write who knows).

  • If there are multiple emotions, code each one as a separate line of code  

  • If the same emotion is used more than once, code it multiple times

“What emotion are they expressing?”

9

explicit or Implicit (exp) 

Is the emotion expressed explicit (check the box) or implicit (leave box unchecked). Explicit emotions are included in the thesaurus. If you find your emotion word in the thesaurus, code it as explicit. If it is not in the thesaurus, it is most likely an implicit emotion.

  • Explicit is checked if an emotion word in the statement is in the thesaurus, unchecked if an indicator word is used to convey emotion.

  • adverbs are often implicit

  • look for negative modifiers, these often signify an implicit disapproval or suffering emotion  

10

emotional tense: (emot_tense)

Is the subject of the emotion in the past, current, future or ongoing?  

Code only one category 

Code text as written (e.g. do not interpret the present tense to be ongoing because of your own outside information) 

  • Past: the subject of the emotion has already happened 

  • Future – The subject of the emotion is in the future – there is an explicit mention that the actor thinks something will or might happen (residents are concerned about a proposed…” 

  • Ongoing: the subject of the emotion is expressed as something in the present, or as a lasting feeling 

11

behavior (behave)

Is the emotion word a procedural word? Click yes if so, leave blank if not.

For example, an actor might “approve” an action

  • Proposing a bill or a rule

  • Court ordered

  • Ex. of words identified as behaviors: Approve, propose, order

12

object (obj)

What is the emotion about? For example, an actor may make an emotion statement that relates generally to the infrastructure sitting debate as a whole (e.g. feeling hopeless when the pipeline seems inevitable), an event/venue (e.g. town hall meeting perceived as unfair), an argument frame (e.g. being frustrated about expected environmental damage), another actor (e.g. being angry about perceived FERC bias).  

 

Select one of the following four categories: 

1. Focal Point of Interest: For example, in a study about a pipeline siting conflict tbs would be the pipeline itself, externalities of the pipeline (e.g. pollution, economic growth) details of the pipeline (e.g. disagreements over the route, disagreement about pipeline surveyors on property, or other infrastructure that comes with the pipeline).  

2.  Policy Processes – the emotion is about the formal and informal policy process (or the policy itself) in which the actors are engaged in. This includes actions or inactions from other actors/orgs about the policies. This includes any formal policies including court hearings, federal govt, investigations, local govts as well as informal policies such as grassroots activities, or activities taken on behalf of policy actors (e.g. protests, petitions, advocating, wanting FERC to take an action or inaction) (leaders spending time advocating)  

3.  People_Org – The emotion is about other people – including orgs?  

4. Other – the emotion is about something else  

13

context (ctx) 

 

Enter other relevant information about the context of this statement if it is not clear from the statement itself, particularly as it relates to the emotion being expressed (e.g. what/who caused the emotion, what/who is the target of the emotion) Include hat tipping (if the narrator acknowledges the emotions of one or more actors with a conflicting point of view while also stating their own point of view), or devil’s shift or inanimate for objects. 

Emotion Groups

When coding in DNA, we initially categorized emotions in 12 different groups. When we moved to the second round of excel coding, we reduced the categories to nine different groups combining similar emotion areas. Below the table lists the 12 emotion categories, in the second column it is listed what group of the nine emotion categories the 12 emotional categories were sorted into.

The process was hand coded. Every statement was read to ensure that the expressed emotion fit within the new category. A few statements changed categories based on interpretations of the expressed emotion as belonging more appropriately ina different group. The vast majority of the statements did not need to be changed. In summary, approval and desire became affinity, dissatisfaction and suffering became dismay, and fear and uncertainty combined to create the fear category.

Table 3

12 Emotion Categories

9 Emotion Categories

Approval

Affinity

Desire

Affinity

Dissatisfaction

Dismay

Suffering

Dismay

Fear

Fear

Uncertainty

Fear

Anger

Anger

Obligation

Obligation

Compassion

Compassion

Content

Content

Trust

Trust

Carelessness

Carelessness

Emotion-Belief Dyads

Table 4 displays all the emotions expressed about each belief for both coalitions along with the percentage of statements for each coalition and the average E-I Index for each emotion-belief dyad, grouped by belief.

Table 4: Coalition Membership and E-I Indexes for Dyads

 

 

 

 

Percent of statements about dyad (n=419)

Krackhardt E-I Index

Coalition for the Alternative Route

Fear, Dismay, Compassion, and Anger about the Safety of the Original Route

26.3%

-0.95

Anger, Compassion, Dismay, Fear about the Location of the Original Route

20.8%

-0.775

Fear, Dismay, and Anger about Views being blocked by the Original Route

15.3%

-0.778

Affinity and Obligation about the Renewable Energy generated by the Original Route

11.2%

-0.35

Affinity, Content, and Dismay about the Location of the Alternative Route

11.0%

-1

Dismay, Fear, and Compassion about the impacts to Property Values by the Original Route

8.6%

-1

Fear, Dismay, and Anger about the Noise generated by the Original Route

3.1%

-1

Affinity about Views not being blocked by the Alternative Route

1.2%

-1

Dismay about the impacts to Natural Areas of the Original Route

1.0%

-1

Affinity about the Cost of the Alternative Route

0.5%

-1

Obligation to the construction Timeline of the Original Route

0.5%

-1

Trust about the Safety of the Alternative Route

0.2%

-1

Compassion about the impacts to Natural Areas by the Alternative Route

0.0%

-1

 

 

 

 

 

 

Percent of statements about each dyad (n=134)

Krackhardt E-I Index

Coalition for the Original Route

Compassion, Content, Trust, and Fear about the Renewable Energy generated by the Original Route

13%

-0.583

Affinity, Obligation, and Trust about the Power generated by the Original Route

12%

-0.889

Compassion, Fear, and Affinity about the impacts to Natural Areas of the Original Route

11%

-0.555

Affinity and Trust about the Safety of the Original Route

10%

-1

Dismay, Fear, and Compassion about the extra labour needed to build the Alternative Route

8%

-0.667

Affinity and Trust about the Jobs created by the Original Route

7%

-1

Dismay about the Cost of the Alternative Route

7%

-1

Fear and Dismay about the Safety of the Alternative Route

6%

-0.500

Affinity and Dismay about the construction time needed for the Alternative Route

5%

-1

Affinity and Obligation about the Cost of the Original Route

4%

-1

Affinity and Obligation about the Noise generated by the Original Route

3%

-1

Compassion about the views block by the Original Route

1%

0

Affinity about the Location of the Original Route

0%

-0.333

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Notes

1 This paper provides a cursory overview of the ACF; see Nohrstedt et al. (2023) for a more detailed account and the meta-reviews for a summary of its findings worldwide (Jang et al., 2016; Nohrstedt & Olofsson, 2016; Pierce et al., 2017; Li & Weible, 2021; Osei-Kojo et al., 2022). Pertinent to the current analysis, this study examines a specific policy situation about implementing (i.e., the siting of a transmission line) in an energy policy subsystem in Southern California, US. Such specific policy situations tend to mobilize some policy actors from the broader policy subsystem and other people and organizations who care about the issue of debate.

2 The study of coalitions has evolved and advanced since the ACF’s inception. Early research focused on understanding coalitions through their belief systems, mainly through coding position-belief dyads of policy actors as expressed in public documents (e.g., Jenkins-Smith et al., 1991) or surveys (Sabatier et al., 1987). Most of these past studies tended to confirm the ACF’s argument that “beliefs” were the most important type of belief in identifying advocacy coalitions. The most important of these beliefs were policy core beliefs, which are topically and geographically central to the debate (i.e., germane to what the ACF calls the “policy subsystem”). Commonly identified belief components include general policy proposals or solutions and perceptions of problem severity and causes (Sabatier & Jenkins-Smith, 1999, p. 133).

3 It should be recognized that You et al. (2022) also found that other infrastructure sitings (i.e., gas pipelines) in the United States were as a population of higher conflict than transmission sitings.

4 Yordy et al.’s (2023) thesaurus originally had twelve categories of emotions. This paper combines some of the emotional categories to create nine groupings as a methodology for analyzing emotions has evolved with new findings (Appendix Table 4). Past analyses of emotion-belief dyads showed no statistically significant difference between several of the emotion categories; thus, they were combined into one category that encapsulates both emotions.

5 We also recognize ambiguity in distinguishing belief system tiers in the ACF (see also Nohrstedt et al., 2023, pg 144). For example, one of the significant issues is differentiating policy core from secondary beliefs. While this is an ongoing issue, it is less of a concern in this study as we are not interested in belief stability or the relative importance of ACF's belief tiers in identifying coalitions.

6 Hope for the Hills is a resident group that started as an organization to advocate against the transmission line being built above ground through the city of Chino Hills.

7 The full table of dyads expressed by each coalition can be found in the appendix.

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List of illustrations

Title Figure : Diffuse Emotions Expressed by Each Coalition for Each Route
Credits Source: the Authors
URL http://journals.openedition.org/irpp/docannexe/image/4583/img-1.png
File image/png, 56k
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References

Electronic reference

Emma Scheetz, Allegra H. Fullerton, Anna M. Crawford, Christopher M. Weible and Tanya Heikkila, Advocacy Coalitions’ Diffuse and Discrete Emotion-Belief Expressions in a Transmission Line Siting ControversyInternational Review of Public Policy [Online], 6:3 | 2024, Online since 30 December 2024, connection on 22 March 2025. URL: http://journals.openedition.org/irpp/4583; DOI: https://doi.org/10.4000/13gfu

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About the authors

Emma Scheetz

University of Colorado Denver School of Public Affairs, Center for Policy and Democracy, USA.
emma.scheetz@ucdenver.edu
https://orcid.org/0009-0004-3375-8042

Allegra H. Fullerton

University of Colorado Denver School of Public Affairs, Center for Policy and Democracy, USA.
allegra.fullerton@ucdenver.edu
https://orcid.org/0000-0003-2783-0824

By this author

Anna M. Crawford

University of Colorado Denver School of Public Affairs, Center for Policy and Democracy, USA.
anna.crawford@ucdenver.edu

Christopher M. Weible

University of Colorado Denver School of Public Affairs, Center for Policy and Democracy, USA.
chris.weible@ucdenver.edu
https://orcid.org/0000-0003-2241-5891

By this author

Tanya Heikkila

University of Colorado Denver School of Public Affairs, Center for Policy and Democracy, USA.
tanya.heikkila@ucdenver.edu
https://orcid.org/0000-0002-2619-4320

By this author

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Copyright

CC-BY-4.0

The text only may be used under licence CC BY 4.0. All other elements (illustrations, imported files) are “All rights reserved”, unless otherwise stated.

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