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Heterogeneity in Preferences for Woody Biomass Energy in the US Mountain West

机译:美国西部山区木质生物质能源偏好的异质性

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摘要

Millions of acres of public forest in the US Mountain West are substantially degraded and are in need of restoration. Mechanized restoration treatments can improve forest health and reduce the likelihood of severe wildfire. These treatments produce some timber, and substantial amounts of forest residues that can be used to generate renewable energy and displace fossil fuels. Using the choice modeling method, this study investigates social preferences for generation of energy with woody biomass produced by restoration treatments on public forests in the Mountain West. Both multinomial logit and latent class logit (LCL) models are fit to the data and used to estimate marginal willingness to pay (MWTP) for increased amounts of woody biomass energy generation and important associated co-benefits and costs. Positive and statistically significant MWTP is found for the number of homes powered with wood, the extent of healthy forests, avoiding increases in the number of large wildfires, and local air quality. Significant heterogeneity was found in respondent preferences for the attributes. The heterogeneity can be explained in part by sociodemographic and attitudinal characteristics of respondents. The LCL revealed four classes of respondents with distinct preferences, revealing conflicting viewpoints toward forest management for woody biomass energy generation.
机译:美国西部山区的数百万英亩公共森林已经严重退化,需要恢复。机械化的恢复治疗可以改善森林健康,并减少发生严重野火的可能性。这些处理产生一些木材,以及大量的森林残留物,可用于产生可再生能源和替代化石燃料。使用选择建模方法,本研究调查了西部山区公共森林恢复处理产生的木质生物量能源产生的社会偏好。多项式logit模型和潜在类logit(LCL)模型都适合于数据,并用于估计增加的木质生物质能源产生量以及重要的相关联收益和成本的边际支付意愿(MWTP)。发现用木材驱动的房屋数量,健康森林的范围,避免了大面积野火数量的增加以及当地空气质量的MWTP呈正向且具有统计意义。在受访者对属性的偏好中发现了显着的异质性。异质性可以部分由受访者的社会人口统计学和态度特征来解释。 LCL揭示了四类具有不同偏好的受访者,揭示了对森林经营木质生物质能发电的观点不一致。

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