Coalition Cascades: The Politics of Tipping Points in Clean Energy Transitions

by Jonas Meckling & Nicholas Goedeking

Recent scholarship on policy change has devoted increased attention to change across subsystems – the passage of new healthcare legislation, for example, will change not only health policy, but will also impact labor policy and tax policy (among other domains). The means by which trans-subsystem policy change occurs, however, are not clearly defined. 

We look at the role of policy feedback in bringing about trans-subsystem policy change. Specifically, we argue that trans-subsystem policy feedback can result in what we call “coalition cascades”. Coalition cascades represent a kind of domino effect, whereby a policy change that happens within one subsystem can alter coalitions across subsystems, by bringing together actors already inside a subsystem, introducing new actors into a subsystem, and/or moving actors into other subsystems. If policy feedback is positive, these coalition cascades can bring about trans-subsystem policy change by solving coordination challenges that are often part and parcel of making policy change that cuts across policy subsystems. Conversely, negative policy feedback may only partially fix these coordination challenges, if at all.

Figure 1. Coalition cascades

We apply our model to California’s clean energy transition. In 2002, California adopted a renewable portfolio standard (RPS) that required investor-owned utilities (IOUs) to derive a specific percentage of their energy from renewable sources. The next decade and a half saw positive policy feedback as the RPS was expanded upon and gained greater popular support. Even IOUs, which had initially opposed an RPS, gradually turned in favor, resulting in a coalition that was able to repel efforts to roll back California’s renewable energy initiatives, such as Proposition 23 in 2010.

The adoption and subsequent expansion of the RPS, however, also challenged the capacity of the state’s energy grid. Energy storage eventually crystallized as the solution. New storage startups emerged to meet this demand, and a coalition of storage companies formed the California Energy Storage Alliance in 2009 to advocate on behalf of energy storage interests. Storage companies, in league with environmental groups, lobbied state lawmakers for rules requiring energy companies to store a percentage of their load. This effort initially met with opposition from utilities, but by 2013 the state had adopted a storage mandate. 

Figure 2. Storage mandate

In addition to transforming the state’s energy grid, California’s renewables initiatives also had implications for passenger vehicles. In 2009, state legislators and regulators began exploring how to roll out charging infrastructure to support widespread electric vehicle ownership. One issue that emerged was whether utilities would be involved in setting up this infrastructure. A coalition of utilities and environmental groups were in favor, while charging companies and ratepayers organizations were opposed, fearing the market power of utilities. Eventually, however, it became clear that utilities were essential for a state-wide build-out of charging infrastructure, and as a result both ratepayer groups and charging companies shifted their stance. Beginning in 2014-15, utilities became major players in the electric vehicle subsystem.

Figure 3. Utility charging programs.

The evolution of California’s renewable energy policies offers an excellent illustration of the relationship between policy feedback and trans-subsystem policy change. First, we see how policy feedback surrounding the adoption of an RPS changed the coalition supporting renewables by bringing utilities on board. Then we identify spillover effects as more ambitious renewables policies triggered changes in both grid policy (through the adoption of storage technology to manage load intermittency) and transportation policy (through the creation of a charging infrastructure to encourage electric vehicles). In both cases, changes in policy – and the ways those changes were received – shifted coalitional makeups. 

We studied coalition cascades in the context of California’s energy policies, but expect that the dynamics we observed are present across a wide array of policy domains. Our model offers greater insight into how policy change can have ripple effects across multiple domains. Specifically, it shows that how a policy change is received – whether positively or negatively, and by whom – can shift the coalitions for or against specific sets of policies, thus either encouraging or inhibiting additional policy change. In our case study, policy feedback was largely positive, resulting in policy change across multiple subsystems. Additional work is needed to look at examples of negative feedback. 

You can read the original article in Policy Studies Journal at

Meckling, Jonas and Nicholas Goedeking. 2023. “ Coalition Cascades: The Politics of Tipping Points in Clean Energy Transitions.” Policy Studies Journal 51(4): 715–739. https://doi.org/10.1111/psj.12507.

About the Authors

Jonas Meckling is Associate Professor of Energy and Environmental Policy at the University of California, Berkeley, and Climate Fellow at Harvard Business School. He studies the politics of climate policy and the energy transition. He received multiple awards for his research, including the American Political Science Association’s Emerging Young Scholar Award in the field of science, technology, and environmental politics. At Berkeley, he leads the Energy and Environment Policy Lab and the Climate Program of the Berkeley Economy and Society Initiative. Previously, he was a visiting professor at Yale University, served as Senior Advisor to the German Minister for the Environment and Renewable Energy, was a Research Fellow at Harvard University, and worked at the European Commission. 

Nicholas Goedeking is Senior Researcher at the German Institute of Development and Sustainability (IDOS) and Visiting Fellow at the University of Sussex Business School. His work examines the political economy of climate policy and sustainability transitions. He is particularly interested in urban climate governance and the politics of low-carbon infrastructure systems. Before his doctorate, Nicholas worked on energy efficiency policy in Berlin and Brussels, including for the European Commission. He holds a Ph.D. in Environmental Science, Policy, and Management from the University of California, Berkeley. 

The Advocacy Coalition Index: A New Approach for Identifying Advocacy Coalitions

by Keiichi Satoh, Antti Gronow & Tuomas Ylä-Anttila

Often the first step to finding a solution is knowing what the problem is.

In April 2018, Antti Gronow, Tuomas Ylä-Anttila and Keiichi Satoh were attending the Joint Sessions of the European Consortium for Political Research (ECPR) in Nicosia, Cyprus. The session in question was organized by Chris Weible, Karin Ingold and Daniel Nohrstedt and it made Gronow and Ylä-Anttila think of how problematic it is to study advocacy coalitions in a comparative context. Coalitions among political actors are central to politics and policy, which is a fact long recognized within the Advocacy Coalition Framework (ACF).

In Cyprus, Gronow and Ylä-Anttila realized that previous research lacks a consistent way of identifying and measuring advocacy coalitions. During a break in the sessions, Gronow and Ylä-Anttila shared their concerns regarding the lack of a consistent method for identifying advocacy coalitions with Keiichi Satoh. Three months later, inspired by a figure explaining the fuzzy sets used in the qualitative comparative analysis, Satoh showed an initial sketch of a way to identify coalitions to Gronow and Ylä-Anttila. After intensive discussions, this sketch evolved into the Advocacy Coalition Index (ACI).

How does the ACI work?

The ACI is a combined measure of policy beliefs and coordination of action, based on techniques of social network analysis. It is a standardized method for identifying and analyzing advocacy coalitions that can be applied to comparative research and also to other research contexts involving attribute and relational data.

To use the index, researchers must first obtain information about policy actors’ beliefs and coordination relationships between these actors. Such data can be collected through a survey, public statements, or any reliable method of data collection. Next, the method focuses on identifying homophilous ties (in which like-minded actors coordinate with one another), cross-coalition ties (coordination between actors holding diverging beliefs), and missing ties (ties that do not exist between like-minded actors). The ACI can be expressed as a formula in the following way:

ACI= 1 – (Cross-coalition ties + Missing ties)

Political subsystems with typical, adversarial advocacy coalitions are likely to be closer to the value of one as a result of the calculation. In addition, to characterize different kinds of advocacy coalitions within subsystems, scholars can analyze variation in the homophilous ties score and in the ratio of cross-coalition ties and homophilous ties (the CCH ratio), as illustrated in the figure below. For example, in the case of adversarial coalitions (i.e. typical advocacy coalitions), there are many homophilous ties between like-minded actors (i.e., few “missing ties”), and almost no ties between actors with dissimilar beliefs.

The ACI can be applied in many different contexts in a consistent way. A standard way of measuring advocacy coalitions thus allows scholars to compare their results with studies conducted in other countries or other policy subsystems.

Our work also has implications outside academia. Policymakers and analysts now have a tool to reliably detect coalitions involved in policy processes, which helps in designing policy proposals that are politically feasible. Policy can be designed, implemented, and evaluated with a clearer understanding of the kinds of coalitions that are involved, as long as appropriate data exists. 

We are confident that our systematic, data-driven approach will be a useful contribution to the field of public policy research. We also hope that the ACI will be used as a tool in the policy process.

You can read the original article in Policy Studies Journal at

Satoh, K., Gronow, A. and Ylä-Anttila, T. 2023. “The Advocacy Coalition Index: A new approach for identifying advocacy coalitions.” Policy Studies Journal 51: 187–207. https://doi.org/10.1111/psj.12450

About the Authors

Keiichi Satoh is an Assistant Professor at the Faculty of Social Sciences, Hitotsubashi University, Japan. His research interests include climate and energy policy, social movements, and political processes using network theory and methods. His research has appeared in peer-reviewed journals such as Social Movement Studies, Urban Studies, and Journal of Comparative Policy Analysis.

Antti Gronow is a Senior Researcher at the Faculty of Social Sciences, University of Helsinki. His research interests include climate policy, advocacy coalitions, social network analysis, and political polarization. His research has been published in peer-reviewed journals such as Global Environmental Change, Governance, Policy Studies Journal, Public Administration, and JPART. Follow him on X: @AnttiGronow

Tuomas Ylä-Anttila is an Associate Professor of Political Science at the University of Helsinki. He currently leads four research projects on policy networks, communication networks and climate change politics, and chairs the 14-country comparative research effort Comparing Climate Change Policy Networks (see compon.org). His work has appeared in journals such as Global Environmental Change, Public Administration, Policy Studies Journal, Governance, and British Journal of Sociology.