Assessing landowners’ preferences to inform voluntary private land conservation: The role of non-monetary incentives

Land Use Policy(2021)

引用 16|浏览11
暂无评分
摘要
Private land conservation (PLC) is an increasingly recognized strategy to help address the global biodiversity crisis. Understanding landowners’ context-dependent preferences for different PLC policies is key to designing and implementing successful voluntary strategies aiming to foster participation and long-term engagement. However, funding shortfalls and diverse cultural values mean that traditional approaches such as land acquisition or payment for ecosystem services policies may not be the best approaches to increase landowners’ participation in PLC. In this study, we examine landowners’ preferences for monetary and non-monetary incentives and how these might increase participation in PLC. We also address a geographical gap in PLC literature by assessing landowners’ preferences for voluntary PLC policies in Uruguay, a country located in the Río de la Plata Grasslands ecoregion (South America), one of the most endangered and least protected biomes worldwide. This case study provides a useful test-bed of non-monetary incentives, since 96% of the land is privately owned and no voluntary PLC strategies are in place yet. Using a choice experiment, we found that landowners were more willing to engage in voluntary PLC if policies align with their values and needs. Non-monetary incentives, such as access to training and technical support, were preferred over monetary payments, highlighting opportunities to develop context-specific policies that would foster environmental stewardship and long-term engagement. Designing policies by including a diverse set of instruments, flexible contract lengths, and integrating the context-specific social and cultural characteristics underlying landowners’ identities and values, are crucial aspects for increasing participation.
更多
查看译文
关键词
Choice experiment,Policy instruments,Sustainability,Cultural landscapes,Biodiversity conservation,Stewardship
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要