An Analytical Impact Assessment Framework for Wildlife to Inform the Siting and Permitting of Wind Energy Facilities

Report

Title: An Analytical Impact Assessment Framework for Wildlife to Inform the Siting and Permitting of Wind Energy Facilities
Authors: Schwartz, J.
Publication Date:
January 01, 2013
Pages: 44
Affiliation:
Sponsoring Organization:
Stressor:

Document Access

Website: External Link
Attachment: Access File
(1 MB)

Citation

Schwartz, J. (2013). An Analytical Impact Assessment Framework for Wildlife to Inform the Siting and Permitting of Wind Energy Facilities. Report by IFC International. pp 44.
Abstract: 

In the United States overall electrical generation capacity is expected to increase by 10-25 gigawatts (GW) per year to meet increases in demand. Wind energy is a key component of state and federal renewable energy standards, and central to the Department of Energy’s 20% by 2030 wind production goals. Increased wind energy development may present increased resource conflict with avian wildlife, and environmental permitting has been identified as a potential obstacle to expansion in the sector.

 

ICF developed an analytical framework to help applicants and agencies examine potential impacts in support of facility siting and permitting. A key objective of our work was to develop a framework that is scalable from the local to the national level, and one that is generalizable across the different scales at which biological communities operate – from local influences to meta-populations. The intent was to allow natural resource managers to estimate the cumulative impacts of turbine strikes and habitat changes on long-term population performance in the context of a species demography, genetic potential, and life history.

 

We developed three types of models based on our literature review and participation in the scientific review processes. First, the conceptual model was developed as a general description of the analytical framework. Second, we developed the analytical framework based on the relationships between concepts, and the functions presented in the scientific literature. Third, we constructed an application of the model by parameterizing the framework using data from and relevant to the Altamont Pass Wind Resource Area (APWRA), and an existing golden eagle population model. We developed managed source code, database create statements, and written documentation to allow for the reproduction of each phase of the analysis.

 

ICF identified a potential template adaptive management system in the form of the US Fish & Wildlife Service (USFWS) Adaptive Harvest Management (AHM) program, and developed recommendations for the structure and function of a similar wind-facility related program. We provided a straw-man implementation of the analytical framework based on assumptions for APWRA-wide golden eagle fatalities, and presented a statistical examination of the model performance.

 

APWRA-wide fatality rates appear substantial at all scales examined from the local APWRA population to the Bird Conservation Region. Documented fatality rates significantly influenced population performance in terms of non-territorial non-breeding birds. Breeder, Juvenile, Subadult, and Adult abundance were mostly unaffected by Baseline APWRA-wide fatality rates. However, increased variability in fatality rates would likely have impacts on long-term population performance, and would result in a substantially larger REA estimate.

 

We developed four recommendations for future study. First, we recommend establishment of concept experts through the existing system of non-profits, regulatory agencies, academia, and industry in the wind energy sector. Second, we recommend the development of a central or distributed shared data repository, and establish guidelines for data sharing and transparency. Third, we recommend development a forum and process for model selection at the local and national level. Last, we recommend experimental implementation of the prescribed system at broader scales, and refinement the expectations for modeling and adaptive management.

Find Tethys on InstagramFind Tethys on FacebookFind Tethys on Twitter
 
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.