Valuing the visual impact of wind farms: A calculus method for synthesizing choice experiments studies.

Valuing the visual impact of wind farms: A calculus method for synthesizing choice experiments studies.
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评估风电场的视觉影响:综合选择实验研究的微积分方法。

DOI:
10.1016/j.scitotenv.2018.04.430
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发表时间:
2018
期刊:
The Science of the total environment
影响因子:
--
通讯作者:
Wen C
Wen C
中科院分区:
--
文献类型:
--
作者:
Wen C

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尽管减少碳排放的潜力巨大,但由于对景观的视觉影响,风力发电场的发展经常受到当地社区的反对。越来越多的研究应用了非市场评估方法,如选择实验(CE),通过调查问题来激发受访者对假设的风电场的支付意愿(WTP)或接受意愿(WTA),从而评估视觉影响。文献中已经发现了几种元分析来综合不同估值研究的结果,但它们在使用流行的多元元回归分析方面存在各种局限性。在本文中,我们提出了一种新的元分析方法来建立估计WTP或WTA与三个风电场属性(即与住宅/沿海地区的距离、涡轮机数量和涡轮机高度)之间关系的一般函数。该方法包括为单个研究建立WTA或WTP函数,拟合平均导数函数,并根据风电场属性推导WTP或WTA的一般积分函数。结果表明,不同研究中的受访者一致表明,将风电场移动到更远的距离会增加WTP,这可以通过非线性(自然对数)函数进行拟合。然而,在不同的研究中发现了对涡轮机数量和涡轮机高度的不同偏好。我们认为,本文提出的新分析方法是综合CE研究的主流多元元回归分析的替代方法,并且WTP或WTA对风电场属性的一般积分函数对未来的空间建模和效益转移研究很有用。我们还建议未来的多元元分析应该在回归函数中包含非线性成分。
Despite the great potential of mitigating carbon emission, development of wind farms is often opposed by local communities due to the visual impact on landscape. A growing number of studies have applied nonmarket valuation methods like Choice Experiments (CE) to value the visual impact by eliciting respondents' willingness to pay (WTP) or willingness to accept (WTA) for hypothetical wind farms through survey questions. Several meta-analyses have been found in the literature to synthesize results from different valuation studies, but they have various limitations related to the use of the prevailing multivariate meta-regression analysis. In this paper, we propose a new meta-analysis method to establish general functions for the relationships between the estimated WTP or WTA and three wind farm attributes, namely the distance to residential/coastal areas, the number of turbines and turbine height. This method involves establishing WTA or WTP functions for individual studies, fitting the average derivative functions and deriving the general integral functions of WTP or WTA against wind farm attributes. Results indicate that respondents in different studies consistently showed increasing WTP for moving wind farms to greater distances, which can be fitted by non-linear (natural logarithm) functions. However, divergent preferences for the number of turbines and turbine height were found in different studies. We argue that the new analysis method proposed in this paper is an alternative to the mainstream multivariate meta-regression analysis for synthesizing CE studies and the general integral functions of WTP or WTA against wind farm attributes are useful for future spatial modelling and benefit transfer studies. We also suggest that future multivariate meta-analyses should include non-linear components in the regression functions.
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