Achieving effective, sustainable environmental governance requires a better understanding of the causes and consequences of the complex patterns of interdependencies connecting people and ecosystems within and across scales. Network approaches for conceptualizing and analyzing these interdependencies offer one promising solution. Here, we present two advances we argue are needed to further this area of research: (i) a typology of causal assumptions explicating the causal aims of any given network-centric study of social-ecological interdependencies; (ii) unifying research design considerations that facilitate conceptualizing exactly what is interdependent, through what types of relationships, and in relation to what kinds of environmental problems. The latter builds on the appreciation that many environmental problems draw from a set of core challenges that re-occur across contexts. We demonstrate how these advances combine into a comparative heuristic that facilitates leveraging case-specific findings of social-ecological

This paper uses firm‐level data to examine how supply chain networks affected the recovery of firms from the Great East Japan Earthquake. Extensive supply chains can negatively affect recovery through higher vulnerability to network disruption and positively through support from trading partners, easier search for new partners, and general benefits of agglomeration. Our results indicate that networks with firms outside of the impacted area contributed to the earlier resumption of production, whereas networks within the region contributed to sales recovery in the medium term. The results suggest that the positive effects of supply chains typically exceed the negative effects.

This article analyses roles of social and extension networks in adoption of resource-conserving practices among Ethiopian farmers. We gathered data from 297 randomly sampled households on their agricultural practices, social networks, access to the extension, and geographical location. After examining general determinants of practising resource-conserving agriculture, we employ a two-stage regression with full-maximum likelihood correction for selection bias to establish the roles of general social networks and external professionals in acceptance of conservation techniques. In accordance with previous research, probit regression in the first stage shows that the access to extension increases with farmers' wealth and the size of their personal networks, and decreases with the distance of their households from village centres. However, after accounting for this unequal access to extension, the second-stage linear regression shows that regardless of education, wealth or geographical location, those whose religion and ethnicity match with their agent, report learning more about conservation from extension sources. Furthermore, farmers who are socially well connected within the community tend to be less receptive to agents' recommendations regarding resource conservation. Dissemination policy of conservation agriculture should consider the ethnic and religious affinity between farmers and their extension agents. It also needs to pay more attention to socially and geographically isolated individuals.

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Negative economic shocks have been found to propagate through input-output linkages to both upstream and downstream firms, leading to a substantial effect on the entire economy (Acemoglu et al.

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ABSTRACT. The importance of networks for social-ecological processes has been recognized in the literature; however, existing studies have not sufficiently addressed the dynamic nature of networks. Using data on the social learning networks of 265 farmers in Ethiopia for 2011 and 2012 and stochastic actor-oriented modeling, we explain the mechanisms of network evolution and soil conservation. The farmers' preferences for information exchange within the same social groups support the creation of interactive, clustered, nonhierarchical structures within the evolving learning networks, which contributed to the diffusion of the practice of composting. The introduced methods can be applied to determine whether and how social networks can be used to facilitate environmental interventions in various contexts.