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A multi-criteria GIS-based model for wind farm site selection with the least impact on environmental pollution using the OWA-ANP method

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Abstract

Wind energy is considered one of the most efficient and cost-effective ways to generate electricity, since it has a low environmental impact. So, it is essential to identify the best places to build wind farms that have the lowest impact on human health and the highest performance. In order to determine the appropriate locations for the construction of wind power plants, in the study first, the interpolation maps of the most important parameters for the construction of wind power plants were created. Then, using the analytic network process (ANP) method due to higher accuracy than other weighting methods (the two-by-two comparison of external and internal data), the weight of each criterion was determined by establishing the external and internal relationships between the criteria and sub-criteria. In this study, since the objective was to prepare land suitability maps with different levels of risk in order to further manage the area, the OWA method was used to prepare land suitability maps. Based on the results of the ANP method for weighing each parameter, wind speed and protected areas were the most and least important parameters to build the power plant. According to the results of the OWA method, 0.78 and 0.1% of the area were suitable for building power plants at high and low risk levels, respectively. The study also found that the number of wind turbines that can be built in the region at both high and low risk levels was 422 and 75, respectively. Using the buffer function, the number of turbines for the construction of high-risk power plants was reduced to 284 by using the appropriate distance from residential areas. The ANP and OWA methods were used to prepare several maps for the evaluation of land suitability with different levels of risk, one of which could be used for the construction of a power plant.

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Availability of data and materials

The data used in this research are available by the corresponding author upon reasonable request.

Abbreviations

ANP:

Analytic network process

OWA:

Ordered weighted averaging

GIS:

Geographic information systems

MCDA:

Multi-criteria decision analysis

AHP:

Analytic hierarchical process

DEMATEL:

Decision-Making Trial And Evaluation Laboratory

WHO:

World Health Organization

GDP:

Gross domestic product

MCDM:

Multiple-criteria decision-making

CI:

Consistency index

RI:

Random index

vmin:

[1,0,… 0] For the AND operator

vn:

Sequential rank

vmax:

[0,0,…, 1] For the OR operator

vmean:

[1/N, 1/n,…, 1/n] for the arithmetic mean (WLC operator)

ANDness:

Measures the degree to which the OWA operator resembles logical AND

ORness:

Measures the degree to which the OWA operator is similar to the logical OR

Ctotal:

Total investment cost of the wind farm

Cturbine:

The investment cost for a turbine

N:

Number of wind farm turbines

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Funding

Shiraz University provided financial support (grant number: 240001–111) for this study.

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Authors and Affiliations

  1. Department of Geography, Faculty of Economics, Management and Social Sciences, Shiraz University, Shiraz, Iran

    Marzieh Mokarram

  2. Department of Range and Watershed Management, College of Agriculture and Natural Resources of Darab, Shiraz University, 71946-84471, Shiraz, Iran

    Marzieh Mokarram

  3. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

  4. Department of Electrical and Electronic Engineering, Shiraz University of Technology, 71555-313, Shiraz, Iran

    Mohammad Jafar Mokarram

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  1. Marzieh Mokarram
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  2. Hamid Reza Pourghasemi
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The participation of Marzieh Mokarram, Hamid Reza Pourghasemi, and Mohammad Jafar Mokarram includes the data collection, analyzing the results, and writing the article.

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Correspondence to Marzieh Mokarram.

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Mokarram, M., Pourghasemi, H.R. & Mokarram, M.J. A multi-criteria GIS-based model for wind farm site selection with the least impact on environmental pollution using the OWA-ANP method. Environ Sci Pollut Res 29, 43891–43912 (2022). https://doi.org/10.1007/s11356-022-18839-2

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