Network structure and low-carbon governance performance: a qualitative comparative analysis

Abstract

Network effectiveness is an important topic in network research. While many studies have been conducted in the Western contexts, few scholars have examined this topic in the area of environmental governance in the context of China. Taking the low-carbon governance networks of 16 cities in China as the research context, this article adopts the method of fuzzy set Qualitative Comparative Analysis to examine the relationship between network structure, social capital and governance performance. The analyses reveal two causal paths leading to network effectiveness and two causal paths leading to network ineffectiveness. The results indicate that networks combining high bridging social capital with high bonding social capital or combining high bridging social capital with low network load are more likely to demonstrate effectiveness. Networks with the combination of low bonding social capital and high network load, or the combination of high bonding social capital, low bridging social capital and network load are more likely to be ineffective.

Notes

Notes

1 The definition of high and low network load will later be operationalized in the measurement and data section. Specifically, we define it a low network load when the number of network tasks does not exceed 1.5 times the number of actors, i.e., an actor undertakes no more than 1.5 tasks. We tested alternative operationalization of cutoff values, and the results are robust to a range of values.

2 In crisp sets, coverage is measured by the proportion of cases that display the overall solution to the total number of instances of the outcome. However, in fuzzy sets, the measure of coverage is simply the overlap expressed as a proportion of the sum of the membership scores in the outcome, that is the sum of the coverage membership scores in a causal condition or combination of causal conditions divided by the sum of all the membership scores in a cause or causal combination (Ragin 2006).

Additional information

Funding

This research was supported by National Natural Science Foundation of China (Project 71633004), Research Startup Fund of Renmin University of China (Project 311318000121), Renmin University of China: the special developing and guiding fund for building world-class universities (disciplines) (Project 2020030), and the Outstanding Innovative Talents Cultivation Funded Programs 2020 of Renmin University of China.

Notes on contributors

Hongtao Yi

Hongtao Yi ([email protected]) is an associate professor in the John Glenn College of Public Affairs at The Ohio State University. He is also an affiliated professor in School of Public Administration and Policy at Renmin University of China. His research interest focuses on network governance and environmental policy.

Weixing Liu

Weixing Liu ([email protected]) is a Ph.D. student in School of Public Administration and Policy at Renmin University of China. His research focuses on policy process, environmental policy and networks.

Fei Li

Fei Li ([email protected]) is an associate professor in the School of Information and Safety Engineering at Zhongnan University of Economics and Law. His research focuses on environmental monitoring and assessment.