A multi-criteria geospatial evaluation of wind energy siting suitability in the Southern African Power Pool: A preliminary framework

Abstract

This study conducted a comprehensive geospatial analysis of wind energy siting suitability within the Southern African Power Pool region, employing Geographic Information Systems and multi-criteria decision analysis. By integrating key biophysical and infrastructural factors, including wind speed, proximity to power lines and roads, elevation, slope, and land cover this study evaluated potential locations for utility-scale wind farm development across 12 SAPP member countries. Comprehensive suitability maps were produced, categorizing areas from very low to very high potential based on a hybrid GIS-AHP framework. The analysis revealed significant spatial variations in suitability, identifying priority zones characterized by high mean wind speeds (exceeding 7–8 m/s), gentle slopes (0°–0.75°), and proximity to existing infrastructure. This study quantified suitable land areas, defining highly suitable parcels as contiguous clusters of high-potential pixels with a minimum mapping unit of 1 km² to ensure technical and economic feasibility. Mathematical modeling using Weibull parameters yielded shape factors ranging from 3.10 to 3.80 and scale factors of 11.06 to 12.27 m/s at 50 m heights in high-potential zones. These findings provide a data-driven tool for policymakers and investors to identify priority zones for sustainable wind energy development, fostering regional energy security and cross-border cooperation within the SAPP.

Keywords

wind energy siting Southern African Power Pool geographic information systems multi-criteria decision analysis Analytic Hierarchy Process (AHP)wind speed distribution infrastructure proximity elevation and slope land cover suitability

Introduction

The transition toward sustainable energy systems is a critical priority for the Southern African Power Pool, a regional body established to enhance electricity cooperation and security across 12 member states. While Southern Africa possesses vast, underutilized wind resources, the deployment of utility-scale wind farms is often hindered by fragmented planning and the lack of a unified regional suitability framework. Global reports from the International Renewable Energy Agency indicate that Africa's wind energy potential is immense, yet it remains the least exploited renewable resource on the continent due to infrastructural and technical barriers (IRENA, 2024). Furthermore, the Global Wind Energy Council emphasizes that regional power pools like SAPP require data-driven spatial planning to attract the investment-grade projects necessary for reaching net-zero targets (GWEC, 2024)

Despite the growing body of literature on wind energy in Africa, existing studies typically focus on individual nations such as South Africa (Szewczuk and Prinsloo, Wind Atlas for South Africa (WASA): Project overview and current status, 2010), Namibia (Kamati et al., 2022) often neglecting the cross-border infrastructure and topographic synergies that define the SAPP's integrated grid. Previous assessments frequently rely on local-scale modeling that does not account for the regional complexities of the Southern African plateau or the proximity to the shared SAPP transmission backbone (SAPP, 2021). Furthermore, many regional studies lack a standardized multi-criteria approach that balances technical feasibility with socio-economic and environmental constraints.

This study seeks to answer: To what extent can integrated GIS-AHP modeling identify cross-border wind energy priority zones within the SAPP that balance topographic feasibility with existing infrastructure proximity?

To address this question, we employ a multi-criteria decision analysis framework using the Analytic Hierarchy Process to weigh five critical siting factors: wind speed, elevation, slope, and proximity to both roads and power lines. By integrating these layers into a Geographic Information System, this study provides a harmonized suitability index across the SAPP domain. Unlike previous localized assessments, this research offers a preliminary spatial consistency check by qualitatively comparing identified high-suitability zones against existing and proposed wind farm locations. While recognizing the limitations of regional-scale data, this study provides a foundational tool for policymakers and investors to identify priority development corridors, thereby fostering regional energy security and sustainable infrastructure growth in Southern Africa.

Related work

The selection of optimal sites for wind energy development is a multi-dimensional challenge that requires balancing technical potential with economic constraints and environmental protection (Benti et al., 2023). In the context of the Southern African Power Pool (SAPP), a region characterized by vast geographic distances and a centralized transmission backbone (SAPP, 2021)., the integration of Geographic Information Systems (GIS) with the Analytic Hierarchy Process (AHP) has emerged as a worthwhile technique for managing large and often conflicting spatial criteria (Benti et al., 2023). This study synthesizes the existing literature by categorizing key determinants of wind energy feasibility into thematic constraints that influence the development of cross-border wind energy corridors.

Topographic and technical constraints

Technical suitability is primarily determined by the physical characteristics of the land and the quality of the wind resource (Metegam et al., 2024). Among these, wind speed is consistently identified as the most critical factor in suitability models. In some weighted analyses, wind speed accounts for as much as 42.7% of the total importance (Elmahmoudi et al., 2020). Wind turbines require a minimum cut-in speed to generate electricity, and areas with wind speeds below 4 m/s are typically considered unsuitable for utility-scale projects (Elmahmoudi et al., 2020). High-suitability zones are therefore those with stable and strong wind regimes, often exceeding 7 m/s and in some cases reaching above 8.5 m/s, ensuring optimal energy production (Elmahmoudi et al., 2020; Vale et al., 2020).

Topography further influences technical feasibility, particularly through slope and elevation. Flat or gently undulating terrain is preferred, as it reduces construction complexity and associated costs (Elmahmoudi et al., 2020). Slopes below 7% are generally considered optimal, while suitability declines significantly beyond this threshold (Shehab and Faisal, 2025).Many studies classify slopes above 10% as unsuitable due to the challenges of constructing turbine foundations and transporting heavy equipment (Ajaj et al., 2025). Elevation also affects wind performance, with certain altitude ranges such as 462–632 meters being associated with higher suitability in regional analyses equipment (Ajaj et al., 2025). Together, these topographic factors define the physical constraints within which wind energy projects can be efficiently developed.

Infrastructural and economic barriers

Beyond technical considerations, infrastructural and economic factors play a crucial role in determining project viability within the SAPP. Proximity to the transmission grid is particularly important, as it directly affects connection costs and energy losses. This factor is often ranked among the top criteria in suitability analyses, with weights such as 0.175 reported in some studies (Elmahmoudi et al., 2020). Areas located within 5 km of high-voltage transmission lines or substations are typically assigned the highest suitability scores, while suitability decreases progressively with increasing distance, often becoming unfeasible beyond 40 km (Vale et al., 2020). Additionally, safety considerations require maintaining buffer distances commonly around 500 meters from existing power lines (Ajaj et al., 2025).

Similarly, access to road infrastructure is essential for both the construction and maintenance of wind farms. The transportation of turbine components and regular site visits depend heavily on the availability of nearby roads, making this factor a key determinant of project cost efficiency (Shehab and Faisal, 2025). In multi-criteria decision models, road proximity may carry a weighting of approximately 6.8% (Elmahmoudi et al., 2020). Areas located within 5 to 10 km of road networks are generally considered highly suitable, whereas distances exceeding 40 km significantly reduce feasibility due to increased logistical challenges (Vale et al., 2020).

Justification for the integrated five-factor model

The selection of five core factors wind speed, elevation, slope, proximity to roads, and proximity to power lines is well supported by the literature as representing the primary determinants of wind energy suitability (Benti et al., 2023; Metegam et al., 2024). Although additional criteria such as land use and proximity to settlements are important for detailed site-level assessments, these factors mainly refine rather than define the initial suitability analysis. For example, settlement buffers ranging from 300 meters to 3 km are often applied to address social and environmental concerns (Ajaj et al., 2025). However, the five selected criteria capture the fundamental technical and geographic potential necessary for regional-scale planning (Metegam et al., 2024).

By focusing on these key constraints, this study addresses the complex and often contradictory requirements of wind energy development within SAPP. It balances the need for high-quality wind resources with the economic necessity of proximity to shared infrastructure across member states. This integrated and thematic approach moves beyond localized analyses, offering a comprehensive framework for identifying priority zones for wind energy development across the Southern African plateau.

Table 1 summarizes previous studies on wind energy potential and site selection methods across Southern African Power Pool (SAPP) countries, detailing the study areas, methodologies, evaluation criteria, and publication years.

Table 1.

Related studies.

Ref	Study area	Type of site selection	Applied method	Evaluation criteria	Year
(Garcia and Atanda, 2018) (Elsner, 2019)	Angola (offshore)	Wind power generation and offshore wind energy.	HOMER Pro software, GIS-based analysis, and the BSM model.	EEZ data, annual wind speed, and BSW data set.	2021 2019
(Afolayan et al., 2021) (Mentis, 2013)	DRC (West side)	Offshore wind energy	CMIP6 models, ERA5 reanalysis, BCCCSM2-MR model and GIS mapping analysis, ArcGIS software.	Wind speed and EEZ data set.	2013 2021
(Erfort, 2020) (Ali Mostafaeipour, 2020) (Szewczuk and Prinsloo, 2010)	South Africa (Richards Bay, KwaDukuza, Durban, and Struis Bay)	Wind energy potential.	GIS-based multi-criteria evaluation, HOMER software, WAsP models, ARAS, WSM, and WASPAS techniques.	Energy production potential, power density, the wind flowing at speed, wind statistics and atlas data.	2020 2020 2010
(Samikannu et al., 2022) (Jain et al., 2002)	Botswana (Jamataka Village, Maun and Gabone)	Wind System	HOMER, hybrid system configurations and simulations.	Wind speed and wind power.	2022 2002
(Michael et al., 2021) (Kibona, 2020) (Kainkwa, 2000)	Tanzania (Njombe, Arusha, Tanga, Basotu, Dar es Salaam and Mtwara)	Wind energy potential and Wind energy.	Weibull distribution, numerical methods, statistical analysis and WRF mesoscale model.	Wind speed, Final Analysis data and average monthly wind power.	2021 2020 2000
(Mainza, 2020) (Banda et al., 2019)	Zambia (Choma, Mwinilunga, Lusaka, Mpika and Samfya)	Wind	DNV GL Wind Mapping System software, uncertainity index, and Anemometer.	Wind power and wind speeds.	2020 2019
(Mungwena, 2002)	Zimbabwe (Kariba and Bulawayo)	Wind energy potential	Wind energy conversion systems (WECS) and numerical analysis.	Wind energy resource average and wind speed ranges.	2002
(Mashwama and Thokozani ., Proposing the best combination of Renewable Energy resources available to the country of Eswatin, 2020)	Eswatini (Luyengo area)	Alternative energy sources	Methodology of modelling and forecasting.	Wind density w/m^2 and wind speed m/s	2020
(Pasanisi et al., 2021) (Sanusi et al., 2022)	Lesotho (Senqu/Orange river valley)	Energy resources of Southern Africa	WebGIS and wind atlas.	Wind speed m/s and power generation.	2021 2022
(Roque et al., 2021) (Cristóvão et al., 2021) (Hankins, 2009)	Mozambique (District of Namaacha, the coastal areas of Ponta de Ouro, Mavelane and Tofinho)	Wind energy and wind turbine performance.	HOMER and system advisor model (SAM).	Average wind speed, wind power and climate change.	2021 2021 2009
(Lima et al., 2021) (Davidson, 2014) (Le Fol, 2012)	Namibia (Luderitz and Walvis Bay)	Wind offshore resource	ROM climate simulation, RCP4.5, RCP8.5, CORDEX-Africa, WAsP and WindPro Programme.	Wind energy density, wind power and average wind speed.	2021 2014 2012
(SgurrEnergy, 2015)	Malawi (Mzimba met)	Wind farm	Meteodyn CFD, MERRA reanalysis and Shuttle Radar Topology Mission (SRTM).	Mean wind speeds.	2015

Methodology

Analytical framework

The siting assessment was conducted using a hybrid spatial decision-support framework that combined geoprocessing, criteria weighting, and suitability modeling. The approach integrates the following:

spatial factor modeling in ArcGIS Pro,

criteria prioritization using the Analytic Hierarchy Process (AHP), and

a weighted overlay procedure to generate a composite wind-suitability index.

This framework allows both the physical and infrastructural characteristics of the SAPP region to be expressed numerically and compared on a common decision scale.

Selection of siting factors

Wind farm development is influenced by the terrain, accessibility, and grid connectivity. Based on the literature, technical standards, and regional infrastructure conditions, five siting factors were adopted:

Distance to power lines reflects the cost and feasibility of grid integration.

Distance to roads indicates accessibility for turbine transport and maintenance.

Elevation influences wind availability and turbine efficiency.

Slope: relates to constructability and turbine foundation stability.

Land cover determines compatibility and environmental suitability.

Data processing and thematic layer development

Each factor was transformed into a spatial raster layer representing its influence on the wind farm potential. All layers were resampled to a uniform spatial resolution of 500 m using the bilinear interpolation method to preserve the continuity of wind speed gradients (Kiberet et al., 2025).

Proximity-Based layers

The Euclidean Distance was used to generate continuous surfaces for proximity to roads and transmission lines. Closer distances were assumed to be more suitable, whereas areas farther away incurred higher infrastructure costs.

Vector to raster conversion considerations

The conversion of vector data (such as power lines and roads) to raster format, necessary for weighted overlay analysis in GIS, may introduce several challenges. These include loss of spatial precision due to generalization of linear features into grid cells, edge effects where feature boundaries become less distinct, and potential misrepresentation of narrow or small features when raster cell size is coarsely relative to vector detail (Longley et al., 2015). Such effects can impact the accuracy of proximity measurements and suitability scores, especially near infrastructure edges. To mitigate these issues, an appropriate raster resolution was selected to balance computational efficiency and spatial detail, and preprocessing steps were applied to preserve feature integrity as much as possible. Nevertheless, residual uncertainties from rasterization should be acknowledged when interpreting the spatial suitability results.

Topographic layers

A 30 m SRTM DEM was used to derive the elevation and slope.

Land cover layer

The land cover was reclassified to emphasize areas with minimal ecological conflict. Natural vegetation and open land were considered more suitable, whereas wetlands, settlements, and dense forests were penalized.

All layers were resampled to a uniform resolution and spatially aligned before integration.

Criteria standardization

Because the variables have different units and measurement scales, each was transformed to a cell-based suitability score on a standardized scale (1–9), consistent with the ArcGIS Weighted Overlay requirements.

Three transformation types were applied.

Decreasing function (distance to roads, distance to power lines)

Increasing function (elevation ranges optimal for wind speed)

Discrete class function (land cover)

Normalization ensured that all factors could be logically aggregated in the final suitability model.

Reclassification Table.

Factor	High suitability (Score 8–9)	Low suitability (Score 1–2)
Distance to Grid	< 5 km	> 80 km
Slope	0° – 2°	> 15°
Wind Speed	> 8 m/s	< 4 m/s
Distance to Road	< 5 km	> 50 km

Weight derivation using AHP

The relative importance of each factor was determined through a pairwise comparison matrix using Saaty's fundamental scale (Mengstie et al., 2024). The weights were calculated to reflect the high priority of wind resource quality and proximity to the SAPP transmission backbone. Type equation here.

The weight vector was calculated using the normalized principal eigenvector method:

w_{i} = \frac{\sum_{j = 1}^{n} a_{i j}}{n}

To ensure the logical coherence of the decision-maker's judgments, the Consistency Index ($CI$) was calculated as:

C I = \frac{λ_{m a x} - n}{n - 1}, C R = \frac{C I}{R I}

The Consistency Ratio was strictly monitored to ensure it remained below the 0.1 threshold (Alamrew et al., 2024). In this study, the final model achieved a CR of 0.045, indicating a highly consistent and reliable weighting scheme.

These weights were later converted into percent influence values (summing to 100%), as required by ArcGIS.

Table: Pairwise Comparison Matrix for SAPP Wind siting Factors

Factor	Wind speed	Dist. to grid	Dist. to road	Elevation	Slope	Weight
Wind Speed	1	2	3	4	4	41.6%
Dist. to Grid	0.5	1	2	3	3	24.9%
Dist. to Road	0.33	0.5	1	2	2	14.8%
Elevation	0.25	0.33	0.5	1	1	9.3%
Slope	0.25	0.33	0.5	1	1	9.3%

Consistency Ratio = 0.045 (Matthew, 1982; Mengstie et al., 2024)

Weighted overlay suitability modeling

The final suitability index was generated using the Weighted Linear Combination method. This process involved multiplying each standardized factor layer by its respective AHP weight and summing them to produce a composite suitability score for every pixel in the study area. The mathematical model is defined as:

S = \sum_{i = 1}^{n} w_{i} \times x_{i}

Where:

$S$ is the final suitability scor e

$w_{i}$ is the relative weight of factor i derived from the AHP matrix.

$(x_{i})$ is the standardized score (1–9) of factor i.

This additive approach allows for the compensatory nature of siting factors, where high wind resource potential can offset moderate distances from infrastructure (Elmahmoudi et al., 2020; Metegam et al., 2024). Wind turbines require a minimum cut-in

Exclusion zones and constraints

To ensure the final map reflects only technically and environmentally viable land, a Boolean exclusion mask was applied. Areas falling within these “No-Go” zones were assigned a value of 0 and removed from the final calculation.

Data Sources: Primary exclusion data included the World Database on Protected Areas for ecological constraints and OpenStreetMap for human settlements, water bodies, and existing industrial zones.

Setback Buffers: Following established safety and environmental protocols, mandatory buffers were applied, including a 500-meter setback from major settlements to minimize noise impact and a 250-meter buffer from permanent water bodies (Ajaj et al., 2025; Vale et al., 2020).

Environmental Integrity: By applying these masks separately from the weighted layers, the model ensures that high-priority conservation areas are completely removed rather than simply receiving a low score, thereby maintaining the environmental rigor required for regional power pool planning (Benti et al., 2023; Metegam et al., 2024).

Classification and thresholding

The continuous suitability scores were classified into five qualitative levels Very Low, Low, Moderate, High, and Very High using the Jenks Natural Breaks optimization method. This method was chosen because it minimizes the variance within each class while maximizing the variance between classes, making it highly effective for identifying distinct suitability tiers in the non-uniform wind speed and topographic datasets of the SAPP region (Kiberet et al., 2025; Mankelkelot et al., 2026). This classification allows for a clear identification of “priority zones” that are most suitable for immediate investment-grade feasibility studies (Benti et al., 2023).

Model verification

To assess the credibility of the results, the locations of existing or proposed wind energy installations in the SAPP countries were cross-checked against the high-suitability zones. The agreement between the modeled suitability and real-world installations provided qualitative validation of the workflow.

Results and discussion

Table 2 presents the land area distributions and percentages within specified distance ranges from power lines for each SAPP member country, highlighting spatial accessibility to the electrical grid.

Table 2.

Distance from the power line.

Angola			DRC
Distance in Km	Areas	Percentage	Distance in Km	Areas	Percentage
0–5	32,826.06	2.49	0–5	54,666.63	2.31
5–20	91,609.99	6.95	5–20	131,360.17	5.56
20–40	125,643.27	9.53	20–40	157,208.94	6.66
40–80	249,314.26	18.90	40–80	310,696.94	13.15
80<	819,529.21	62.14	80<	1,707,916.21	72.31
	1,318,922.80	100.00		2,361,848.89	100.00

Namibia			South Africa
Distance in Km	Areas	Percentage	Distance in Km	Areas	Percentage
0–5	284,554.66	29.35	0–5	343,244.14	21.38
5–20	317,318.37	32.73	5–20	523,252.46	32.59
20–40	160,218.84	16.53	20–40	347,848.36	21.66
40–80	126,693.96	13.07	40–80	267,308.50	16.65
80<	80,647.71	8.32	80<	124,129.33	7.73
	969,433.54	100.00		1,605,782.79	100.00

Lesotho			Eswatini
Distance in Km	Areas	Percentage	Distance in Km	Areas	Percentage
0–5	8839.11	21.83	0–5	8537.50	39.76
5–20	16,555.87	40.89	5–20	11,062.91	51.52
20–40	9361.59	23.12	20–40	1869.59	8.71
40–80	5735.06	14.16	40–80	4.91	0.02
80<		0.00	80<		0.00
	40,491.63	100.00		21,474.92	100.00

Botswana			Zambia
Distance in Km	Areas	Percentage	Distance in Km	Areas	Percentage
0–5	60,321.48	8.89	0–5	72,204.07	9.01
5–20	133,827.48	19.72	5–20	178,461.16	22.28
20–40	128,444.06	18.92	20–40	171,095.87	21.36
40–80	191,915.10	28.27	40–80	193,125.08	24.11
80<	164,300.37	24.20	80<	186,186.14	23.24
	678,808.48	100.00		801,072.31	100.00

Zimbabwe			Malawi
Distance in Km	Areas	Percentage	Distance in Km	Areas	Percentage
0–5	34,970.35	7.97	0–5	25,206.88	20.02
5–20	92,032.36	20.97	5–20	54,791.95	43.51
20–40	108,568.21	24.74	20–40	35,599.71	28.27
40–80	151,430.36	34.51	40–80	10,321.90	8.20
80<	51,782.39	11.80	80<		0.00
	438,783.68	100.00		125,920.43	100.00

Mozambique			Tanzania
Distance in Km	Areas	Percentage	Distance in Km	Areas	Percentage
0–5	104,621.59	12.00	0–5	76,510.28	7.96
5–20	227,221.16	26.07	5–20	186,905.35	19.45
20–40	199,813.12	22.92	20–40	176,833.46	18.40
40–80	212,849.33	24.42	40–80	228,843.25	23.81
80<	127,194.67	14.59	80<	292,014.66	30.38
	871,699.88	100.00		961,107.00	100.00

Distance from road of all south Zone
Distance in Km	Areas	Percentage
0–5	1,106,502.71	10.85
5–20	1,964,399.09	19.27
20–40	1,622,504.84	15.91
40–80	1,948,238.55	19.11
80<	3,553,700.62	34.86
	10,195,345.82	100.00

Figure 1 illustrates the spatial distribution of distances from existing power lines across a region within the Southern African Power Pool (SAPP). Using a gradient color scheme, it categorizes areas by proximity to power infrastructure, which is a critical factor in evaluating wind energy-siting suitability. The distance classes ranged from 0–5 km (green) to over 80 km (dark purple), indicating areas with varying degrees of accessibility to the power grid. Proximity to transmission lines significantly influences the feasibility and cost-effectiveness of wind energy projects, as shorter distances typically reduce the transmission infrastructure costs and energy losses. This map serves as a key input in the multi-criteria geospatial analysis for identifying optimal wind energy sites in the SAPP region.

Figure 1.

Distance from the power line map.

Table 3 shows the land area distributions and percentages within defined distance ranges from roads across SAPP countries, reflecting the accessibility for the construction and maintenance of wind energy infrastructure.

Table 3.

South-Distance from road.

Angola			DRC
Distance in Km	Areas	Percentage	Distance in Km	Areas	Percentage
0–5	551,696.33	42.10	0–5	1,268,035.82	54.05
5–20	620,001.93	47.31	5–20	943,635.17	40.22
20–40	119,160.49	9.09	20–40	126,283.46	5.38
40–80	19,660.18	1.50	40–80	8214.98	0.35
80<		0.00	80<		0.00
	1,310,518.93	100.00		2,346,169.42	100.00

Namibia			South Africa
Distance in Km	Areas	Percentage	Distance in Km	Areas	Percentage
0–5	358,974.10	37.24	0–5	1,004,842.20	62.90
5–20	460,283.05	47.75	5–20	575,671.11	36.03
20–40	103,725.72	10.76	20–40	16,824.59	1.05
40–80	35,415.98	3.67	40–80	235.60	0.01
80<	5481.52	0.57	80<		0.00
	963,880.36	100.00		1,597,573.51	100.00

Lesotho			Eswatini
Distance in Km	Areas	Percentage	Distance in Km	Areas	Percentage
0–5	12,292.34	30.51	0–5	13,390.19	62.69
5–20	17,155.01	42.58	5–20	7965.23	37.29
20–40	10,655.46	26.45	20–40	4.36	0.02
40–80	183.52	0.46	40–80		0.00
80<		0.00	80<		0.00
	40,286.32	100.00		21,359.78	100.00

Botswana			Zambia
Distance in Km	Areas	Percentage	Distance in Km	Areas	Percentage
0–5	183,212.29	27.15	0–5	319,381.75	40.12
5–20	246,101.75	36.46	5–20	373,514.78	46.92
20–40	129,662.32	19.21	20–40	95,476.90	11.99
40–80	100,933.35	14.95	40–80	7633.25	0.96
80<	15,004.73	2.22	80<		0.00
	674,914.44	100.00		796,006.68	100.00

Zimbabwe			Malawi
Distance in Km	Areas	Percentage	Distance in Km	Areas	Percentage
0–5	289,544.67	66.39	0–5	63,919.65	51.09
5–20	143,688.10	32.94	5–20	53,821.44	43.02
20–40	2924.31	0.67	20–40	7381.17	5.90
40–80		0.00	40–80		0.00
80<		0.00	80<		0.00
	436,157.08	100.00		125,122.26	100.00

Mozambique			Tanzania
Distance in Km	Areas	Percentage	Distance in Km	Areas	Percentage
0–5	424,516.24	49.00	0–5	433,193.77	45.37
5–20	403,805.06	46.61	5–20	403,844.44	42.30
20–40	37,878.47	4.37	20–40	98,439.71	10.31
40–80	204.57	0.02	40–80	18,427.37	1.93
80<		0.00	80<	857.84	0.09
	866,404.34	100.00		954,763.14	100.00

Distance from the road of all south Zone
Distance in Km	Areas	Percentage
0–5	4,922,999.07	48.58
5–20	4,249,486.84	41.94
20–40	748,416.94	7.39
40–80	190,908.80	1.88
80<	21,344.09	0.21
	10,133,155.73	100.00

Figure 2 presents the spatial distribution of distances from existing road networks within the Southern African Power Pool (SAPP) region, categorized into five classes ranging from 0–5 km (lightest shade) to > 80 km (darkest shade). Road accessibility plays a crucial role in the siting of wind energy projects, affecting the transportation of construction materials, turbine components, and maintenance logistics. Areas closer to roads are generally more favorable for wind energy development because of reduced infrastructure and transportation costs. This map is a vital layer in geospatial analysis for wind energy siting, contributing to a comprehensive assessment of logistical feasibility across the SAPP region.

Figure 2.

Distance from the road.

Table 4 distributions and percentages within defined distance ranges from roads across SAPP countries, reflecting accessibility for the construction and maintenance of wind energy infrastructure.

Table 4.

South elevation.

Angola			DRC
Elevation (m)	Areas (sq km)	Percentage	Elevation (m)	Areas (sq km)	Percentage
−33–419	109,673.97	8.38	−33–419	481,226.92	20.51
419–800	138,990.13	10.62	419–800	1,201,194.42	51.20
800–1142	418,023.84	31.94	800–1142	440,002.76	18.76
1142–1491	526,453.74	40.22	1142–1491	152,393.41	6.50
1491–5780	115,760.93	8.84	1491–5780	71,053.09	3.03
	1,308,902.61	100.00		2,345,870.59	100.00

Namibia			South Africa
Elevation (m)	Areas (sq km)	Percentage	Elevation (m)	Areas (sq km)	Percentage
−33–419	64,392.99	6.69	−33–419	200,203.44	12.54
419–800	98,214.97	10.20	419–800	210,729.98	13.20
800–1142	315,414.78	32.75	800–1142	492,450.68	30.85
1142–1491	395,196.61	41.04	1142–1491	473,934.12	29.69
1491–5780	89,808.71	9.33	1491–5780	218,852.50	13.71
	963,028.06	100.00		1,596,170.72	100.00

Lesotho			Eswatini
Elevation (m)	Areas (sq km)	Percentage	Elevation (m)	Areas (sq km)	Percentage
−33–419		0.00	−33–419	8674.11	40.61
419–800		0.00	419–800	5877.66	27.52
800–1142		0.00	800–1142	4241.67	19.86
1142–1491	551.04	1.37	1142–1491	2407.09	11.27
1491–5780	39,735.25	98.63	1491–5780	159.20	0.75
	40,286.28	100.00		21,359.72	100.00

Botswana			Zambia
Elevation (m)	Areas (sq km)	Percentage	Elevation (m)	Areas (sq km)	Percentage
−33–419		0.00	−33–419	2680.58	0.34
419–800	10,119.09	1.50	419–800	73,592.05	9.25
800–1142	563,624.22	83.51	800–1142	318,390.18	40.00
1142–1491	101,170.59	14.99	1142–1491	376,761.44	47.33
1491–5780		0.00	1491–5780	24,582.73	3.09
	674,913.90	100.00		796,006.97	100.00

Zimbabwe			Malawi
Elevation (m)	Areas (sq km)	Percentage	Elevation (m)	Areas (sq km)	Percentage
−33–419	18,590.48	4.26	−33–419	7879.97	6.30
419–800	104,910.24	24.05	419–800	56,153.52	44.88
800–1142	173,993.71	39.89	800–1142	27,911.27	22.31
1142–1491	127,825.98	29.31	1142–1491	26,034.63	20.81
1491–5780	10,837.76	2.48	1491–5780	7142.34	5.71
	436,158.18	100.00		125,121.73	100.00

Mozambique			Tanzania
Elevation (m)	Areas (sq km)	Percentage	Elevation (m)	Areas (sq km)	Percentage
−33–419	558,802.49	64.71	−33–419	146,500.40	15.36
419–800	237,655.44	27.52	419–800	131,169.11	13.75
800–1142	51,131.63	5.92	800–1142	248,116.17	26.02
1142–1491	14,759.27	1.71	1142–1491	315,075.06	33.04
1491–5780	1209.41	0.14	1491–5780	112,763.15	11.82
	863,558.24	100.00		953,623.88	100.00

Elevation (m) of all South Zone
Slope (m)	Areas (sq km)	Percentage
−33–419	1,599,020.61	15.79
419–800	2,268,707.95	22.41
800–1142	3,053,340.46	30.15
1142–1491	2,512,655.72	24.81
1491–5780	691,936.26	6.83
	10,125,661.01	100.00

Figure 3 displays the topographic variation across the Southern African Power Pool (SAPP) region, classified into five elevation bands ranging from below sea level (−33 m) to 5780 m above sea level. Lower elevations (green) dominated the northern and eastern parts of the region, whereas higher elevations (brown to dark brown) were concentrated in the central and southern zones. Elevation influences wind energy siting because of its effect on wind speed and turbine performance, as wind tends to be stronger and more consistent at greater altitudes. Steep terrain can also pose challenges for construction and maintenance. This map is essential for the geospatial evaluation of wind energy potential, helping to identify locations where elevation supports efficient and sustainable wind farm development.

Figure 3.

Elevation.

Table 5 displays the land cover classifications with areas and percentages for various land use types within the SAPP countries, emphasizing environmental and land use suitability for wind farm development.

Table 5.

South land cover.

Angola			DRC
Classname	Areas	Percentage	Classname	Areas	Percentage
Water	4738.89	0.36	Water	41,619.75	1.76
Trees	763,902.16	57.93	Trees	1,778,280.79	75.31
Flooded Vegetation	6582.21	0.50	Flooded Vegetation	7330.58	0.31
Crops	14,192.08	1.08	Crops	8172.76	0.35
Built Area	2983.73	0.23	Built Area	7237.96	0.31
Bare Ground	10,233.07	0.78	Bare Ground	267.72	0.01
Snow/Ice		0.00	Snow/Ice		0.00
Clouds	92.58	0.01	Clouds	328.52	0.01
Rangeland	516,012.40	39.13	Rangeland	518,156.00	21.94
	1,318,737.11	100.00		2,361,394.09	100.00

Namibia			South Africa
Classname	Areas	Percentage	Classname	Areas	Percentage
Water	3188.69	0.33	Water	9850.82	0.61
Trees	191,489.06	19.76	Trees	178,216.59	11.10
Flooded Vegetation	1064.99	0.11	Flooded Vegetation	990.39	0.06
Crops	3265.19	0.34	Crops	181,507.46	11.31
Built Area	870.57	0.09	Built Area	37,415.46	2.33
Bare Ground	102,225.08	10.55	Bare Ground	11,394.73	0.71
Snow/Ice		0.00	Snow/Ice	4.68	0.00
Clouds		0.00	Clouds	1.80	0.00
Rangeland	667,169.06	68.83	Rangeland	1,186,041.19	73.88
	969,272.65	100.00		1,605,423.13	100.00

Lesotho			Eswatini
Classname	Areas	Percentage	Classname	Areas	Percentage
Water	200.96	0.50	Water	140.63	0.65
Trees	824.07	2.04	Trees	9568.09	44.55
Flooded Vegetation	6.18	0.02	Flooded Vegetation	2.64	0.01
Crops	4559.26	11.26	Crops	2568.83	11.96
Built Area	1082.90	2.67	Built Area	1129.58	5.26
Bare Ground	12.43	0.03	Bare Ground	2.00	0.01
Snow/Ice		0.00	Snow/Ice		0.00
Clouds		0.00	Clouds		0.00
Rangeland	33,805.80	83.49	Rangeland	8063.10	37.55
	40,491.61	100.00		21,474.89	100.00

Botswana			Zambia
Classname	Areas	Percentage	Classname	Areas	Percentage
Water	1843.79	0.27	Water	14,051.11	1.75
Trees	226,242.75	33.33	Trees	447,739.56	55.89
Flooded Vegetation	3506.37	0.52	Flooded Vegetation	9057.51	1.13
Crops	4880.68	0.72	Crops	35,434.98	4.42
Built Area	2154.84	0.32	Built Area	4400.30	0.55
Bare Ground	5660.36	0.83	Bare Ground	235.76	0.03
Snow/Ice		0.00	Snow/Ice		0.00
Clouds		0.00	Clouds		0.00
Rangeland	434,519.81	64.01	Rangeland	290,153.42	36.22
	678,808.59	100.00		801,072.64	100.00

Zimbabwe			Malawi
Classname	Areas	Percentage	Classname	Areas	Percentage
Water	4796.74	1.09	Water	24,746.99	19.65
Trees	158,129.32	36.04	Trees	19,160.90	15.22
Flooded Vegetation	47.46	0.01	Flooded Vegetation	1057.75	0.84
Crops	23,930.28	5.45	Crops	18,110.14	14.38
Built Area	3327.19	0.76	Built Area	5144.66	4.09
Bare Ground	190.71	0.04	Bare Ground	11.41	0.01
Snow/Ice		0.00	Snow/Ice		0.00
Clouds		0.00	Clouds	1.68	0.00
Rangeland	248,362.06	56.60	Rangeland	57,686.79	45.81
	438,783.77	100.00		125,920.32	100.00

Mozambique			Tanzania
Classname	Areas	Percentage	Classname	Areas	Percentage
Water	16,040.05	1.84	Water	63,870.55	6.65
Trees	424,711.70	48.74	Trees	340,106.96	35.40
Flooded Vegetation	4489.62	0.52	Flooded Vegetation	2682.45	0.28
Crops	13,637.90	1.57	Crops	65,863.21	6.85
Built Area	5892.75	0.68	Built Area	12,399.82	1.29
Bare Ground	731.51	0.08	Bare Ground	976.31	0.10
Snow/Ice	0.68	0.00	Snow/Ice	2.53	0.00
Clouds	16.69	0.00	Clouds	743.15	0.08
Rangeland	405,805.13	46.57	Rangeland	474,183.05	49.35
	871,326.03	100.00		960,828.04	100.00

Landcover of all South Zone
Classname	Areas	Percentage
Water	185,682.17	1.82
Trees	4,538,738.86	44.52
Flooded Vegetation	36,857.77	0.36
Crops	376,158.66	3.69
Built Area	84,097.95	0.82
Bare Ground	132,010.81	1.29
Snow/Ice	7.90	0.00
Clouds	1187.58	0.01
Rangeland	4,840,295.63	47.48
	10,195,037.33	100.00

Figure 4 illustrates the distribution of various land cover types within the Southern African Power Pool (SAPP) region using satellite-derived classifications, such as water, trees, flooded vegetation, crops, built areas, bare ground, snow/ice, clouds, and rangeland. Land cover significantly affects the suitability of sites for wind energy development in Brazil. For instance, areas with dense tree cover or protected ecosystems may present environmental constraints, whereas open rangelands and bare ground are generally more favorable for wind farm construction because of easier accessibility and minimal ecological disturbance. Built-up areas may limit turbine placement because of space restrictions and safety regulations. As such, this land cover map is a critical component of the geospatial multi-criteria decision analysis aimed at identifying optimal locations for wind energy siting across the SAPP region.

Figure 4.

Land cover.

Table 6 provides slope degree classifications alongside land areas and percentages across SAPP countries, indicating terrain steepness relevant to construction feasibility and turbine stability.

Table 6.

South slope.

Angola			DRC
Slope (in Degree)	Areas	Percentage	Slope (in Degree)	Areas	Percentage
0–0.91	795,440.59	60.85	0–0.91	1,598,575.13	68.25
0.91–2.75	429,862.66	32.88	0.91–2.75	608,504.91	25.98
2.75–5.77	60,362.20	4.62	2.75–5.77	99,789.38	4.26
5.77–10.62	17,101.17	1.31	5.77–10.62	28,274.31	1.21
10.62–33.45	4442.52	0.34	10.62–33.45	6942.96	0.30
	1,307,209.13	100.00		2,342,086.69	100.00

Namibia			South Africa
Slope (in Degree)	Areas	Percentage	Slope (in Degree)	Areas	Percentage
0–0.91	716,282.13	74.48	0–0.91	856,905.08	53.78
0.91–2.75	183,629.25	19.09	0.91–2.75	450,758.78	28.29
2.75–5.77	44,106.74	4.59	2.75–5.77	183,535.52	11.52
5.77–10.62	14,753.00	1.53	5.77–10.62	79,387.68	4.98
10.62–33.45	2890.93	0.30	10.62–33.45	22,625.37	1.42
	961,662.06	100.00		1,593,212.43	100.00

Lesotho			Eswatini
Slope (in Degree)	Areas	Percentage	Slope (in Degree)	Areas	Percentage
0–0.91	1760.10	4.37	0–0.91	4579.19	21.44
0.91–2.75	7954.55	19.75	0.91–2.75	8697.22	40.72
2.75–5.77	13,238.80	32.86	2.75–5.77	5457.70	25.55
5.77–10.62	13,397.70	33.26	5.77–10.62	2363.74	11.07
10.62–33.45	3935.14	9.77	10.62–33.45	261.88	1.23
	40,286.28	100.00		21,359.72	100.00

Botswana			Zambia
Slope (in Degree)	Areas	Percentage	Slope (in Degree)	Areas	Percentage
0–0.91	666,136.28	98.70	0–0.91	619,511.91	77.83
0.91–2.75	7347.54	1.09	0.91–2.75	144,370.75	18.14
2.75–5.77	1276.04	0.19	2.75–5.77	24,252.16	3.05
5.77–10.62	154.05	0.02	5.77–10.62	6582.74	0.83
10.62–33.45		0.00	10.62–33.45	1289.41	0.16
	674,913.90	100.00		796,006.97	100.00

Zimbabwe			Malawi
Slope (in Degree)	Areas	Percentage	Slope (in Degree)	Areas	Percentage
0–0.91	290,820.85	66.68	0–0.91	72,269.54	57.76
0.91–2.75	112,413.97	25.77	0.91–2.75	32,307.74	25.82
2.75–5.77	24,564.70	5.63	2.75–5.77	13,344.73	10.67
5.77–10.62	7168.61	1.64	5.77–10.62	5374.10	4.30
10.62–33.45	1190.05	0.27	10.62–33.45	1825.50	1.46
	436,158.18	100.00		125,121.60	100.00

Mozambique			Tanzania
Slope (in Degree)	Areas	Percentage	Slope (in Degree)	Areas	Percentage
0–0.91	650,018.67	75.58	0–0.91	561,150.07	59.02
0.91–2.75	164,038.77	19.07	0.91–2.75	273,613.80	28.78
2.75–5.77	30,925.11	3.60	2.75–5.77	76,353.41	8.03
5.77–10.62	11,953.10	1.39	5.77–10.62	29,508.78	3.10
10.62–33.45	3071.13	0.36	10.62–33.45	10,196.16	1.07
	860,006.78	100.00		950,822.21	100.00

Slope (in Degree) of all South Zone
Slope (in Degree)	Areas	Percentage
0–0.91	6,833,450.25	67.60
0.91–2.75	2,423,500.27	23.97
2.75–5.77	577,206.79	5.71
5.77–10.62	216,018.98	2.14
10.62–33.45	58,671.05	0.58
	10,108,847.33	100.00

Figure 5 shows the variation in terrain steepness across the Southern African Power Pool (SAPP) region, categorized by degree ranging from 0°–0.75° (flat, shown in light green) to 9.72°–32.21° (steep, shown in red). The slope is a critical factor in wind energy siting as it directly impacts construction feasibility, turbine stability, and access for maintenance. Flatter areas are generally preferred for wind farm development because of reduced construction complexity and cost. Steep slopes can increase foundation requirements, cause erosion risks, and limit the development of road and grid infrastructure. This map is an essential component of the multi-criteria analysis framework used to identify technically suitable and economically viable sites for wind energy installations in the SAPP region.

Figure 5.

Slope.

Table 7 Classifies wind energy suitability levels with associated land areas and percentages across SAPP member countries, derived from an integrated multi-criteria geospatial analysis.

Table 7.

South suitability.

Angola			DRC
Suitability level	Areas	Percentage	Suitability level	Areas	Percentage
Very Low	50,618.25	3.85	Very Low	153,053.75	6.50
Low	446,733.62	33.97	Low	1,389,950.11	59.06
Moderate	483,623.46	36.78	Moderate	560,085.32	23.80
High	264,732.29	20.13	High	241,387.56	10.26
Very High	69,296.78	5.27	Very High	9021.66	0.38
	1,315,004.41	100.00		2,353,498.40	100.00

Namibia			South Africa
Suitability level	Areas	Percentage	Suitability level	Areas	Percentage
Very Low	1498.06	0.16	Very Low	9532.75	0.60
Low	13,513.99	1.40	Low	45,907.43	2.87
Moderate	173,115.33	17.91	Moderate	194,247.65	12.15
High	309,834.48	32.06	High	344,796.34	21.57
Very High	468,503.68	48.48	Very High	1,004,212.73	62.81
	966,465.53	100.00		1,598,696.90	100.00

Lesotho			Eswatini
Suitability level	Areas	Percentage	Suitability level	Areas	Percentage
Very Low	819.25	2.02	Very Low		0.00
Low	9235.64	22.81	Low	2468.73	11.50
Moderate	11,490.82	28.38	Moderate	6433.14	29.96
High	9523.66	23.52	High	7684.21	35.78
Very High	9422.31	23.27	Very High	4888.82	22.77
	40,491.67	100.00		21,474.91	100.00

Botswana			Zambia
Suitability level	Areas	Percentage	Suitability level	Areas	Percentage
Very Low		0.00	Very Low	21,909.72	2.74
Low	5518.69	0.81	Low	180,351.96	22.51
Moderate	118,639.52	17.48	Moderate	267,017.66	33.33
High	223,799.48	32.97	High	186,977.08	23.34
Very High	330,850.92	48.74	Very High	144,815.91	18.08
	678,808.61	100.00		801,072.34	100.00

Zimbabwe			Malawi
Suitability level	Areas	Percentage	Suitability level	Areas	Percentage
Very Low	5231.19	1.19	Very Low	8504.46	6.75
Low	38,310.62	8.73	Low	20,880.58	16.58
Moderate	122,897.89	28.01	Moderate	22,997.43	18.26
High	160,146.53	36.50	High	39,086.40	31.04
Very High	112,196.48	25.57	Very High	34,451.58	27.36
	438,782.72	100.00		125,920.45	100.00

Mozambique			Tanzania
Suitability level	Areas	Percentage	Suitability level	Areas	Percentage
Very Low	6134.54	0.71	Very Low	98,545.63	10.31
Low	50,775.67	5.87	Low	191,984.41	20.09
Moderate	308,382.95	35.63	Moderate	278,769.18	29.17
High	246,280.02	28.46	High	232,878.59	24.37
Very High	253,829.33	29.33	Very High	153,513.23	16.06
	865,402.51	100.00		955,691.03	100.00

Suitability level of all south Zone
Suitability level	Areas	Percentage
Very Low	357,135.35	3.51
Low	2,399,271.68	23.57
Moderate	2,551,323.54	25.06
High	2,270,877.72	22.30
Very High	2,602,748.64	25.56
	10,181,356.93	100.00

Figure 6 displays the overall wind power suitability levels across the Southern African Power Pool (SAPP) region, which were synthesized from multiple geospatial criteria, including wind speed, distance to infrastructure (roads and power lines), elevation, slope, and land cover. Suitability was classified into five levels: Very Low (red), low (orange), moderate (yellow), high (light green), and Very High (dark green). Areas rated as “Very High” or “High” are considered most favorable for wind energy development because of optimal physical and infrastructural conditions. Conversely, “Very Low” and “Low” suitability zones present significant constraints, such as steep terrain, poor access, or environmental conflicts. This comprehensive suitability map supports informed decision-making by helping policymakers, investors, and planners identify priority zones for sustainable and economically viable wind energy projects in the SAPP region.

Figure 6.

Restriction factors influencing wind farm site selection across the Southern African power pool (SAPP) region. The map shows exclusion zones including protected areas, wetlands, water bodies, and urban settlements. These areas were masked out in the suitability modeling to avoid environmentally sensitive or socially restricted locations, ensuring sustainable wind energy development.

Table 8 shows the distribution of average wind speed ranges with corresponding land areas and percentages across SAPP countries, illustrating the spatial variation in wind resource potential within the region.

Table 8.

South wind speed.

Angola			DRC
Wind speed m/s	Areas/sq km	Percentage	Wind speed m/s	Areas/sq km	Percentage
0–4	311,654.96	23.63	0–4	1,937,776.19	82.05
4–6	727,676.04	55.17	4–6	372,845.95	15.79
6–7	234,624.10	17.79	6–7	41,751.71	1.77
7–8	44,881.85	3.40	7–8	8425.16	0.36
8–19	66.79	0.01	8–19	1009.09	0.04
	1,318,903.73	100.00		2,361,808.09	100.00

Namibia			South Africa
Wind speed m/s	Areas/sq km	Percentage	Wind speed m/s	Areas/sq km	Percentage
0–4	5430.06	0.56	0–4	38,301.49	2.38
4–6	126,408.62	13.04	4–6	316,858.65	19.73
6–7	440,240.45	45.41	6–7	447,344.73	27.85
7–8	347,346.60	35.83	7–8	578,967.21	36.04
8–19	49,989.15	5.16	8–19	224,877.49	14.00
	969,414.87	100.00		1,606,349.57	100.00

Lesotho			Eswatini
Wind speed m/s	Areas/sq km	Percentage	Wind speed m/s	Areas/sq km	Percentage
0–4	3528.01	8.71	0–4	702.69	3.27
4–6	14,695.35	36.29	4–6	9394.03	43.74
6–7	6952.30	17.17	6–7	8819.57	41.07
7–8	5501.56	13.59	7–8	2481.86	11.56
8–19	9814.41	24.24	8–19	76.75	0.36
	40,491.63	100.00		21,474.89	100.00

Botswana			Zambia
Wind speed m/s	Areas/sq km	Percentage	Wind speed m/s	Areas/sq km	Percentage
0–4		0.00	0–4	83,934.50	10.48
4–6	37,614.08	5.54	4–6	314,627.88	39.28
6–7	319,269.94	47.03	6–7	291,464.93	36.38
7–8	280,188.76	41.28	7–8	94,267.48	11.77
8–19	41,735.76	6.15	8–19	16,777.63	2.09
	678,808.54	100.00		801,072.43	100.00

Zimbabwe			Malawi
Wind speed m/s	Areas/sq km	Percentage	Wind speed m/s	Areas/sq km	Percentage
0–4	31,525.47	7.18	0–4	22,481.45	17.85
4–6	209,738.16	47.80	4–6	52,963.41	42.06
6–7	124,328.43	28.33	6–7	23,738.52	18.85
7–8	66,211.71	15.09	7–8	15,693.80	12.46
8–19	6979.99	1.59	8–19	11,043.32	8.77
	438,783.76	100.00		125,920.50	100.00

Mozambique			Tanzania
Wind speed m/s	Areas/sq km	Percentage	Wind speed m/s	Areas/sq km	Percentage
0–4	42,201.05	4.84	0–4	266,586.05	27.74
4–6	522,985.88	60.00	4–6	453,665.13	47.20
6–7	213,690.63	24.52	6–7	112,280.96	11.68
7–8	80,057.89	9.18	7–8	67,557.21	7.03
8–19	12,734.66	1.46	8–19	60,984.28	6.35
	871,670.11	100.00		961,073.63	100.00

Wind Speed of all South Zone
Wind speed m/s	Areas/sq km	Percentage
0–4	2,744,173.15	26.91
4–6	3,159,521.39	30.99
6–7	2,264,529.48	22.21
7–8	1,591,606.04	15.61
8–19	436,113.92	4.28
	10,195,943.97	100.00

Figure 7 illustrates the spatial distribution of average wind speeds across the Southern African Power Pool (SAPP) region, categorized into five classes ranging from 0–4 m/s (lightest shade) to 8–19 m/s (darkest shade). Wind speed is the most fundamental criterion for assessing the potential for wind energy generation, as the energy output is directly proportional to the cube of the wind velocity. Regions with higher wind speeds are particularly important. This map plays a crucial role in identifying zones with strong wind resources, guiding the prioritization of high-potential locations for further feasibility studies, and investing in renewable energy infrastructure.

Figure 7.

Wind speed map.

Definition of the analysis domain

In this study, the “South Zone” is defined as the total terrestrial area encompassing the 12 member states of the Southern African Power Pool, specifically excluding all offshore and maritime territories. This analysis domain covers approximately 10.18 million km², providing a comprehensive terrestrial evaluation of wind energy potential across Angola, Botswana, the Democratic Republic of Congo, Eswatini, Lesotho, Malawi, Mozambique, Namibia, South Africa, Tanzania, Zambia, and Zimbabwe (SAPP, 2021). By focusing strictly on land-based siting, the model prioritizes regions where grid integration and road accessibility are technically feasible within existing national infrastructures.

AHP consistency and methodological rigor

The weighting of the five primary siting factors wind speed, proximity to power lines, proximity to roads, elevation, and slope was validated using the Analytic Hierarchy Process to ensure decision-making reliability. Following the established standards in geospatial multi-criteria decision analysis, a Consistency Ratio of $\leq$ 0.1 was maintained throughout the weighting process (Matthew, 1982). This level of rigor aligns with recent regional studies, such as the landslide susceptibility assessment in Addi Arkay, Ethiopia, which similarly employed AHP to weight environmental and topographic factors while adhering to strict consistency thresholds (Mengstie et al., 2024). Maintaining a low CR ensures that the pairwise comparisons of infrastructure proximity and biophysical constraints are mathematically sound and free from logical bias.

Furthermore, the integration of these weights allows for a balanced assessment where technical wind potential is tempered by logistical feasibility. This approach is consistent with suitability analyses in other domains, such as sustainable ecotourism site selection in Bahir Dar, where AHP consistency was paramount in balancing ecological sensitivity with infrastructural (Mankelkelot et al., 2026).

Land-use exclusion and suitability distribution

To ensure the practical and ethical viability of the proposed wind energy sites, a binary exclusion mask was applied to the SAPP region. This process involved the strict removal of “Built Areas” (urban settlements), “Water Bodies,” “Flooded Vegetation,” and “Protected Areas” from the final suitability results. This urban and land-use exclusion rigor is a critical step in modern GIS-AHP frameworks to avoid socio-environmental (Mankelkelot et al., 2026; Mengstie et al., 2024).

The results of the suitability modeling indicate significant spatial variation across the South Zone:

Very High Suitability: 25.56% (approx. 2.60 million km²) of the terrestrial area is classified as highly favorable, primarily due to the overlap of high wind speeds and gentle slopes. High Suitability: 22.30% (approx. 2.27 million km²) shows strong potential with minor infrastructural or topographic constraints.

Marginal to Low Suitability: Areas categorized as “Low” (23.57%) or “Very Low” (3.51%) are typically restricted by steep terrain (slope > 10°) or extreme distance from existing power lines (Table 2, Table 6).

Wind speed analysis further corroborates these findings, with 4.28% of the region experiencing optimal speeds between 8–19 m/s, while the majority of the landmass (30.99%) falls within the 4–6 m/s range. The concentration of “Very High” suitability zones in countries like Namibia (48.48%) and South Africa (62.81%) highlights these nations as primary candidates for utility-scale wind farm expansion within the SAPP framework.

Conclusion

This study presents a comprehensive geospatial analysis of wind energy siting suitability across the Southern African Power Pool (SAPP) region by integrating critical biophysical and infrastructural factors using Geographic Information Systems (GIS) and multi-criteria decision analysis. The Analytic Hierarchy Process (AHP) was applied to derive weighted criteria, which, combined with spatial data on wind speed, proximity to roads and power lines, elevation, slope, and land cover, yielded detailed suitability maps. The results revealed considerable spatial variability in wind energy potential, with regions characterized by higher wind speeds, gentle slopes, favorable elevation, and proximity to existing infrastructure identified as the most suitable for wind farm development. The identification of extensive contiguous suitable land parcels averaging 36.810 km² underscores the feasibility of utility-scale wind projects in the region. Validation against existing installations further corroborated the effectiveness of the proposed suitability framework. These findings offer valuable, data-driven insights to support policymakers, investors, and energy planners in prioritizing zones for sustainable wind energy deployment, thereby contributing to the advancement of renewable energy integration and energy security in Southern Africa.

Footnotes

ORCID iDs

Samuel Bimenyimana

Chen Wang

Godwin Norense Osarumwense Asemota

Jean Marie Vianney Uwizerwa

Fidele Mwizerwa

Yiyi Mo

Martine Abiyese

Olivier Habihirwe

Institutional review board statement

The authors declare that the content of this study complies with ethical standards. The authors confirm that this paper has not been published previously, it is not under consideration for publication elsewhere and is not under consideration for publication elsewhere.

Informed consent statement

Not applicable.

Author contributions

All authors contributed equally to this manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

Data availability statement

The data for this study are available from the corresponding authors upon request.

Author Biographies

Samuel Bimenyimana is a PostDoctoral Researcher in Electrical Engineering. His area of research is energy buildings, energy economics, energy policy, energy efficiency, clean environment, solar energy, sustainability of energy systems, energy management, energy development, climate change, and affordable energy.

Chen Wang is a Distinguished Professor at the College of Civil Engineering, Huaqiao University, Xiamen, China, and Head of the Department of Construction Management. His area of research includes vertical greenery systems, mathematical modelling for civil engineering, swarm intelligence, Ant Colony Optimization, fuzzy-QFD, tensile membrane steel structures, sustainability in construction management, international BOT projects, energy conservation, and building-integrated solar applications.

Godwin Norense Osarumwense Asemota is a Professor in Electrical Engineering. His area of research includes power systems engineering, high voltage engineering, energy efficiency, optimisation, statistical inference, electromagnetics, fibre optics, renewable energy, finance and banking, convex mathematics, number theory, cryptography, and differential equations with their applications.

Jean Marie Vianney Uwizerwa is a Manager of Minigrid Projects at Rwanda Energy Group / Energy Development Corporation Limited (REG-EDCL). His area of research is renewable energy technologies focusing on rural electrification, floating solar PV systems, energy storage systems, grid stabilisation, energy security, and sustainable energy development.

Mucyo Ndera Tuyizere is an Environmental Planning Professional with a specialization in Geography. His area of research is climate adaptation, natural resource management, and spatial analysis using cutting-edge geospatial technologies.

Fidele Mwizerwa is a Geospatial Data Science Analyst and Lecturer at the University of Rwanda, College of Science and Technology, Department of Spatial Planning. His area of research focuses on GIS, remote sensing, environmental management, renewable energy, climate resilience, sustainable development, spatial modeling, earth observation, and geospatial approaches to energy planning and ecological systems in Africa.

Yiyi Mo is a Researcher affiliated with the College of Civil Engineering at Huaqiao University, Xiamen, China, focusing on sustainable building design, wind energy, and energy optimization. Key research includes wind energy siting, window optimization for energy efficiency, and building simulation.

Martine Abiyese holds a Bachelor's degree in Applied Economics and a Master of Science in Energy Economics. Her area of research focuses on energy economics, renewable energy systems, energy policy, and sustainable energy development.

Olivier Habihirwe is a Data Analyst. His area of expertise includes data science, geospatial analysis, database management, and renewable energy applications. His research interests focus on renewable energy, GIS-based modelling, hydro energy siting suitability, solar energy systems, and data-driven decision-making in energy and development projects. He also has experience in statistical analysis and machine learning with Python.

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