With energy demands set to rise and non-renewable methods of energy production under heavy scrutiny from depleting resources and climate change, there is a push to use sustainable and environmentally friendly alternatives. Both government agencies and the power industry agree that wind energy has the potential to be one of the most dominant renewable energy systems and is expected to have a vital role in supplying future energy (Schallenberg-Rodriguez, 2013; Messac 2012). Governmental agencies worldwide, such as the European Union, and the Inter-Governmental Panel on Climate Change have a target goal to reduce greenhouse gas emissions by 20%, reduce primary energy use by 20%, and increase the use of renewable energy by 20% by the year 2020 (European Commission, 2014). Since governing bodies are proposing renewable energy goals, the importance of determining suitable locations for wind farm sites is becoming a major consideration. (Kaldellis et al., 2016).
There are complex economical, social and ecological implications present when determining suitable locations for the potential siting areas of wind development, which can be overcome through the use of spatial planning techniques. Wind energy is limited to the amount of suitable land area and has the tendency to create land-use impacts, such as deforested areas, which impact wildlife populations and can lead to habitat fragmentation (Felber and Stoeglehner, 2014). Studies show that it is imperative to find suitable areas to site wind farms where avian species are located because proper placement is critical for their survival (Walsh-Thomas et al., 2012). Social opposition is also a large problem when implementing wind farms due to the associated noise pollution and negative visual impacts that the turbines inherently have on landscapes (Groth and Vogt, 2014).
Although one of the technical challenges to wind energy development is determining the most suitable locations, the lack of a sufficient and trusted model for estimating suitability that accounts for the differencing constraints such as land types/uses, economic feasibility and social consideration such as set back distances from habitable buildings (Miller and Li, 2014). Recent policies and regulations have supported the implementation of large-scale wind farms but there is heavy debate by stakeholders, which makes finding suitable locations challenging (Starrat, 2015; Calvert et al., 2016). A range of environmental, technical and biophysical factors influence the total quantity of land that may be available for wind energy production (Grassi, 2012). For example, the technical parameter could include how much wind energy is present, while an environmental factor could affect the placement of the wind farm to exclude siting in wetland areas (Hopkins et al., 2013). Since there is a lack of research in this area of study, the data analysis and collection procedures tend to be inconsistent, which can lead to confusion when comparing models with one another (European Commission, 2014).
Geographic Information Systems are powerful tools that can be integrated in planning and management applications to incorporate a plethora of spatial data (Latinopoulos and Kechagia, 2015). GIS functions allow a cohesive assessment of variables to be conducted in a cost-effective, timely manner on a large geographical scale (Szkliniarz and Vogt, 2011). In a study conducted by Baban and Parry (2001), the researchers looked to determine suitable locations for wind farms within the United Kingdom. Based on information collected from relevant literature in their study area and questionnaires distributed to those in the public and private sector to identify criteria, polices and factors attempting to find suitable locations for wind farms were developed (Baban and Parry, 2001). Baban and Parry (2001), identified 14 core criteria such as slope, historic sites and land use types which where then placed in a weighted overlay analysis to determine the suitable locations for wind farm development in the United Kingdom. In a more recent study conducted by van Haaren and Fthenakis (2011), the addition of an ecological and economical consideration integrated a more holistic approach. Researchers in this study condition the impact that wind turbines can have on migratory bird and thus gave special consideration to this area in analysis along with excluding certain ecological features such as wetlands, lakes and federal lands deeming them as not suitable for wind farm development (van Haaren and Fthenakis, 2011). An economical consideration was analyzed through the distance from transmission lines , distance from roads and land clearing costs associated with wind farm development (van Haaren and Fthenakis, 2011). This information was then analyzed in a multi-criteria approach which first excluded areas that were unsuitable, then identified the best feasible sites based on economic variables such as cost of access roads and power lines, and lastly assessed the ecological impacts on migratory birds in New York City (van Haaren and Fthenakis, 2011). While these studies used the same GIS approach to site wind farms which was a multi-criteria analysis, the variables and criteria differed, which demonstrates the lack of a trusted approach for determining wind farm suitability. The use of GIS as a tool to determine suitable locations for wind farm siting is critical to fully capture the scope of the factors that are under examination. The proper siting of wind farms is predicted to have an effect on the nearby urban communities and the environment, which results in a need to consider appropriate legislative restrictions to mitigate the social and environmental effects that will consequently occur. These advantages make GIS an essential asset in determining the spatial suitability analysis for wind farm siting (Al-Yahyai et al., 2012). A multi-criteria analysis (MCE) would include appropriate legislative constraints and create a standardized weighting scheme, which would designate a suitability score to each relevant factor. This would prove to be an effective option to identify potential land that is suitable for the siting of wind farms.
The purpose of the project is to develop a GIS-based multi-criteria evaluation (MCE) model to determine the most suitable locations for wind farm siting for Annapolis, Kings and West Hants Counties in Nova Scotia.
1. To identify a set of criteria that incorporates the biophysical, ecological and social aspects related to wind farm site suitability.
2. To develop a sequence of MCE model for wind farm siting.
3. To apply the MCE model to identify the areas for wind farm implementation in the study sites located in Annapolis, Kings and West Hants Counties.
4. To evaluate and compare the differences in criteria to determine how each factor influences land suitability for wind farm siting within the study sites in Nova Scotia.
5. To assess the strengths and limitations of the MCE models.