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Social-Network Analysis of local governance models for China's eco-compensation program
Ecosystem Services ( IF 6.1 ) Pub Date : 2020-09-12 , DOI: 10.1016/j.ecoser.2020.101191
Cheng Chen , Bettina Matzdorf , Lin Zhen , Barbara Schröter

Payments for ecosystem services (PES) have played an increasingly important role in environmental conservation. In line with international trends, a growing number of governmental programs in China are embracing these policy innovations, which were labelled under the broad notion of “eco-compensation”. However, recent research indicates that the understanding of state–civil society and central–local relationships are knowledge gaps in the institutional arrangement of such policies. As one of the most important eco-compensation schemes, the Sloping Land Conversion Program (SLCP) is confronting many challenges to incentivise the local actors. In this study we examine the network of actors of the SLCP by focusing on the collaboration of state actor and civil-society actors and develop a typology of local governance models. To find out how different stakeholders perceive the implementation of the program and how they influence each other, we rely on Social Network Analysis (SNA) and a household survey. The results of our case studies in three townships show that local governance models derive and shape the program implementation. A new actor, which never appeared in the official document and previous research, was discovered. We found that local actors and civil-society actors could potentially bring together government and local farmers for up-front negotiations. In order to mediate conflicts, local government, village committees, forest field stations and farmers may be forced to coordinate and ideally deliberate the terms and conditions of implementation and management options. Therefore, for developing further such eco-compensation schemes in rural China, particular attention should be paid to the local actors and their cooperation for the participatory governance.

更新日期:2020-09-12
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