Successful Implementation of Environmental Policy and Governance: Understanding and Modeling Micro Governance

ABSTRACT Successful implementation of environmental policy and governance highly depends on the capacity to know about, build new and utilize existing governance structures. However, the theoretical tools and available methods to assess the complexity of emerging or existing structures are rather limited. This paper responds to this knowledge gap through the development of a theoretical framework, called the MiGov model, that enables us to understand and analyze the structure and functioning of governance processes on the micro level. Studying governance approaches on the micro level means assessing the complex interplay of involved social, institutional and environmental factors that continuously affect individual decisions and finally aggregate into (non‐)success of implementation. The micro governance framework therefore includes a variety of actor‐based (e.g., individual character, financial capacity), societal (e.g., launch of a policy, peer group influence) and ecological factors (e.g., local ecological characteristics) that typically influence the overall success of implementation. To facilitate the huge amount of data input demanded by research on the micro‐level of governance, we present agent‐based modeling (ABM) as a suited tool for operationalization. The use of agent‐based modeling enables the combination of fragmented and non‐compatible data with proxy data or theoretical assumptions from existing literature. ABM therefore also is an efficient research strategy in the face of lacking data, which is a severe and persistent problem in assessments of environmental policy implementation success. Once an agent‐based model has been established it is possible to experiment with different scenarios or threshold values, enabling a reality check to (partially) known micro structures. To exemplify the use of ABM for micro governance we present the case of ecosystem‐based soil management (EBM) in the Dutch agricultural sector as a typical case for micro governance. This case resembles the micro structure and dynamic interaction of a high variety of decision‐makers with dynamic, multi‐level interactions activated to increase the implementation of organic residues in the agricultural sector. The results of our ABM reveal that the agent‐based model is suited to identify case‐specific mechanisms that increase governance success. In particular, activities that decrease individually perceived uncertainty about implementation practices and effects increase implementation success whereas financial support like subsidies is, to our surprise, less effective.