Month: April 2025

Learning in Polycentric Governance: Insights from the California Delta Science Enterprise

by Tara Pozzi, Mark Lubell, Tanya Heikkila, Andrea K. Gerlak, & Pamela Rittelmeyer

Science enterprises play an increasingly important role in shaping the policy process. While existing literature explores the nexus of science and decision-making, research is limited by a lack of empirical institutional analysis—specifically how science is shaped by and a feature of governance institutions. To address this gap, we integrate the ecology of games framework (EGF) and collective learning framework (CLF) to examine how polycentric systems of science actors and forums influence policy-relevant learning. This exploration is guided by three types of hypotheses to account for diverse actors:

  1.  Individual-level hypotheses consider how organizational affiliation, professional involvement, forum participation, and expertise on diverse issues of individual actors participating in a science enterprise may shape perceived learning.
  2. Forum-level hypotheses consider how variance in forum social dynamics, institutional structure, and functional domain characteristics may shape perceived learning.
  3. The learning stage hypothesis suggests that the perceived level of learning will be lower at later stages of the adaptive management cycle.

In 2021, we conducted a survey of science actors involved in managing and governing the California Delta. The survey participants were individuals who produce, interpret, or use science for Delta policymaking, including academics, government agency officials, and nonprofit and community representatives. Respondents were identified through a purposive sampling, using the Delta Science Program to disseminate the survey electronically to numerous listservs. The survey measured core perceptions of the regional science forums, such as extent of professional involvement and participation, expertise of diverse issues, leadership effectiveness, representative engagement, coordination, resources, and forum purpose.

To analyze the data, we estimated four generalized linear multi-level models using Bayesian methods. The models analyze the effect of individual- and forum-level variables on perceived learning across different science forums, with a separate model for a composite scale and each stage of the adaptive management cycle.

As illustrated in Figure 5, the social and institutional attributes of science forums are the most important drivers of learning, relative to the human and financial capital attributes of the forums or the level of individual actor engagement. For example, the variables of leadership, trust, and coordination consistently have the largest positive influence on all learning stages of adaptive management, whereas the resources variable is consistently less positive. This finding suggests that administrative and financial resource limitations are less important for learning than social drivers.

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Figure 5. Bayesian plot for learning models associated with “plan,” “do,” and “evaluate and respond” stages of adaptive management, and combined stages.

Through integrating two policy process frameworks, we have created a new theoretical basis for analyzing policy-related learning within polycentric governance systems. Our Bayesian approach allowed us to visualize the changing importance of social dynamics versus administrative resources across developmental stages of scientific forums. As polycentric systems grow over time, resources pose less limitations on their effectiveness. Our forum-level results also reaffirm findings in a comparative case study that social capital plays an important role in policy-related learning. The findings shed light on how science shapes and is shaped by the policy process, providing valuable insights into how policy-relevant learning occurs in polycentric governance systems.

You can read the original article in Policy Studies Journal at

Lubell, Mark, Tara Pozzi, Tanya Heikkila, Andrea K. Gerlak and Pamela Rittelmeyer. 2025. “ Learning in Polycentric Governance: Insights From the California Delta Science Enterprise.” Policy Studies Journal 53(1): 7–28. https://doi.org/10.1111/psj.12581.

About the Authors


Tara Pozzi is a PhD candidate in the Graduate Group in Ecology at the University of California, Davis. Her research focuses on how governance networks influence climate adaptation policy and planning.



Mark Lubell is a Professor in the Department of Environmental Science and Policy at University of California Davis. His research focuses on human behavior and the role of governance institutions in solving collective action problems and facilitating cooperation.


Tanya Heikkila is a Professor in the School of Public Affairs at University of Colorado Denver. Her work investigates how conflict and collaboration arise in policy processes, and what types of institutions support collaboration, learning, and conflict resolution.


Andrea K. Gerlak is a Professor in the School of Geography and Development and Director of the Udall Center for Studies in Public Policy at the University of Arizona. Her work addresses institutions, learning,  and governance of environmental challenges.


Pamela Rittelmeyer is a Senior Regulatory Analyst of energy efficiency programs at the California Public Utilities Commission.  Her work centers around better understanding various perspectives of environmental problems and supporting policy development.

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Interlocal learning mechanisms and policy diffusion: The case of new energy vehicles in Chinese Cities

by Weixing Liu, Liang Ma, Xuan Wang, & Hongtao Yi

While policy diffusion based on learning mechanisms has received extensive scholarly interest, this literature has at least two limitations. First, policy learning is usually identified through indirect evidence, such as geographical proximity or successful policy innovations adopted in other jurisdictions. Second, measures of policy learning are used without considering how they interact with other factors. 

To address these limitations, our study measures policy learning through field learning conducted by local government officials, using the case of Chinese local financial subsidy policy for new energy vehicles (NEVs). Site visits from local government officials offer a direct mechanism of policy learning by enabling policymakers to exchange strategies and information with peers about the “know-how” of policy implementation. 

H1: When city i initiates policy learning from city j, policy innovation is more likely to diffuse from city j to city i.

We also examine the moderating effect of top-down mandates on . While governments learn from each other, they are also embedded in a multi-level regime, in which higher level authorities mandate or incentivize subordinate governments to adopt policies they favor. If the top-down signals are strong, peer-to-peer learning may be weakened.

H2: The adoption of the focal policy by the superior government will attenuate the impact of policy learning on policy diffusion across jurisdictions.

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Figure 1. The theoretical framework and hypothesis

To test these hypotheses, we collected data on the adoption of NEV policies in 282 cities between 2014 and 2018. The key independent variable captures interlocal learning between cities. To identify site visits, we searched for the keywords of “visits and learning” and “learning and exchange” on official websites and official papers in the cities. To examine the role of top-down mandates, we included a variable indicating whether NEV policies had been adopted by the superior government. Finally, we controlled for other horizontal diffusion mechanisms.

Using a directed EHA method based on a logit model, we find that the direct information exchange between local governments can significantly promote the diffusion of the NEVs’ local financial subsidy policies between cities. However, the purpose for the site visit has different implications on the diffusion of the policy. Empirical results indicate that compared with policy learning within public services, urbanization, government management, and cooperative projects, learning in the field of economic development, which is more related to the NEVs financial subsidy policy, can significantly promote the diffusion of policy innovation.

Consistent with H2, the results also show that policy behavior signals from a superior government can replace or decrease the impact of interlocal policy learning on policy diffusion. Lastly, the research shows that the level of authority of the government official who conducts the site visit plays a role in the adoption of the policy. That is to say, the more authority that the visiting government official has, the higher the chance of adoption of that policy in the other city. 

In summary, the findings indicate that policy learning plays a crucial role in policy diffusion, and governments can leverage site visits and other learning approaches to facilitate policy adoption. While conventional information channels help policymakers to learn about emerging policy innovations, face-to-face interactions may be more influential in policy diffusion. Particularly for leading government officials with scarce attention to specific policies, their dedicated site visits could boost policy learning and then policy diffusion and spread.

You can read the original article in Policy Studies Journal at

Liu, Weixing, Liang Ma, Xuan Wang and Hongtao Yi. 2025. “ Interlocal Learning Mechanisms and Policy Diffusion: The Case of New Energy Vehicles Finance in Chinese Cities.” Policy Studies Journal 53(1): 49–68. https://doi.org/10.1111/psj.12576.

About the Authors

Weixing Liu is an assistant professor at the School of Government, University of International Business and Economics. His research focuses on policy process, environmental policy, and networks.

Liang Ma is a professor at the School of Government at Peking University. His research interests include digital governance and performance management.

Xuan Wang is an assistant professor at the National School of Development at Peking University. His research interests include tax policy, China’s economy, and policy innovations.

Hongtao Yi is a professor at Askew School of Public Administration and Policy at Florida State University. His research interest focuses on network governance and environmental policy.

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