Online-only Table 1.
Literature review of sector development studies using spatial data and published within last ten years.
| Source of Analysis | Year | Sector | Spatial Analysis | Spatial Constraints (exclusions) | Notes* | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Type | Extent | Resolution | Biophysical* | Land Use | Administrative | (Other spatial factors used – or – spatial ranking descriptions) | |||||
| Resource | Topography | Landcover | |||||||||
| Bosch et al.25 | 2017 | Wind | YP | Global | 1-km | capacity factor (CF) < 15% | slope > ~11° (20%) elevation > 2000 m | irrigated croplands, wetlands, artificial surfaces, water, snow and ice | protected areas (PAs) all identified by World Database of Protected Areas (WDPA) | Land suitability refined by land cover types in Table 4. | |
| Hoes et al.18 | 2017 | Hydro | YP | Global | 1-km | river discharge (Q) < 0.1 m3/s | <1-meter difference between two adjacent river cells (~255 meters at equator) | Gross theoretical potential based on global head and stream discharge calculations. | |||
| Dai et al.22 | 2016 | Wind | YP $$ | Global | 1-km | NA (based on relative price of wind production in relation to other energy sources) | slope > ~31° (60%) elevation > 2000 m | water, wetlands, snow and ice | urban | PAs (no definition) | Land cover suitability scores listed in Table 2. Distance from urban areas used to measure energy loss and cost of transmission. |
| Eurek et al.27 | 2016 | Wind | YP | Global | 1-km | net CF < 26% | slope >20° (~36%) elevation > 2500 m | permafrost areas, snow and ice, water | urban | PAs (WDPA: IUCN Cats. I-III) | Landcover suitability scores listed Table 1. Distance from large power plants and large cities (proxy for transmission lines): 0-80 km – near, 80-161 – mid, >161 – far |
| Silva Herran et al.23 | 2016 | Wind | YP $$ | Global | 10-km | NA | slope > 20° (~36%) elevation > 2000 m | water, wetlands, snow and ice | urban | PAs (no definition) | Identified wind potential within 3 ranges of urban areas 10 km, 20 km, 30 km. |
| Deng et al.26 | 2015 | CSP | YP | Global | 1-km | Direct normal irradiance (DNI) < 1900 kWh/m2/yr (~217 W/m2) | slope > 2° (~4%) | all forest and mix-forest, coast, cliffs, dunes, water, rock and ice | urban | Pas (Natura 2000 and WDPA: IUCN Cats. I–VI) | Land availability refined by land cover types identified in Table 2. Distance from infrastructure (defined in Fig. 1) used for three distance categories of very near, near, and far. |
| see above | PV | YP | Global | 1-km | none | slope >15° (~27%) | see above | urban | see above | see above | |
| see above | Wind | YP | Global | 1-km | wind speed < 6 m/s | slope >15° (~27%) elevation > 2000 m | rain forest, tropical forest, coast, cliffs, dunes, water, rock and ice | urban | see above | see above | |
| Eitelberg et al.8 | 2015 | Crop | LS | Global | na | Literature review of constraints used in modeling potentially available croplands identified in Table 3 | Only identifies suitable areas for agriculture without prioritization. | ||||
| Köberle et al.21 | 2015 | CSP | YP $$ | Global | 50-km | DNI < 1095 kWh/m2/yr (~125 W/m2) | none | forests, tundra, and wooded tundra | urban | bio-reserves (no definition) | Land availability refined by land cover types identified in Table 1. Applied cost based on distance from load centers (US$2,390,00/km). |
| see above | PV | YP $$ | Global | 50-km | none | none | see above | urban | see above | see above | |
| Oakleaf et al.20 | 2015 | Solar | LS | Global | 50-km | Global horizontal irradiance (GHI)< ~ 1595 kWh/m2/yr (182 W/m2) | slope >3° (~5%) | water, wetlands, rock and ice, and artificial areas | urban and land > 80 km from existing roads | none | Solar and Wind LS produced by multiplying feasibility by suitability by resource raster datasets and summed multiplication within 50-km cell. Feasibility raster dataset produced by equal weighting distance to demand centers (1 closest -0.001 furthest) and distance to power plants (1 closest -0.001 furthest) all values within 5-km cells were averaged and then multiplied by 2 for countries with wind development. Suitability raster dataset produced from constraints placed in binary raster (1-suitable, 0 – excluded) summed per 5-km cell. Solar resource raster dataset produced from global horizontal irradiation values (1 highest – 0.001 lowest suitable i.e. 182 W/m2) |
| see above | Wind | LS | Global | 50-km | wind speed < 6.4 m/s | slope > 20° (~36%) | water, wetlands, rock and ice, and artificial areas | urban and land > 80 km from existing roads | none | See notes above with wind resource raster dataset produced from wind speed map (1 highest - 0.001 lowest suitable i.e. 6.4 m/s) | |
| see above | Coal | LS | Global | 50-km | outside of coal-bearing areas | none | none | none | none | LS based on coal reserve estimates (i.e. million short tons) per 50-km. | |
| see above | CO, CG | LS | Global | 50-km | any geological province without either CO or CG estimated undiscovered resources | none | none | none | none | LS based on undiscovered COG reserve estimates of billion BOEs per geological province | |
| see above | UO, UG | LS | Global | 50-km | any shale/sediment formations without recoverable UO or UG | none | none | none | none | LS based on undiscovered UOG reserve estimates of billion BOEs per assessment area | |
| see above | Mining | LS | Global | 50-km | any 50 km2 area without an identified mineral deposit | none | none | none | none | LS based on mineral deposit counts per 50-km | |
| see above | Ag | LS | Global | 50-km | estimated agricultural expansion <= 0 | none | none | urban 100% agriculture | none | LS based on mean agricultural expansion rate per 50-km | |
| see above | Bio | LS | Global | 50-km | estimated crop expansion = 0 | none | none | urban 100% cropped | none | LS based on gallons of gasoline equivalent (GGE) per 50-km | |
| see above | Urban | LS | Global | 50-km | urban expansion probability <= 0 | none | none | urban | none | LS based mean urban expansion probabilities per 50-km | |
| Zhou et al.19 | 2015 | Hydro | YP $$ | Global | 50-km | none | none | none | urban | PAs (WDPA identified) | Gross theoretical potential based on global head and stream discharge calculations. |
| Butt et al.32 | 2013 | CO, CG | LS | Global | NA | any geological province without either CO or CG estimated undiscovered resources | none | none | none | none | CO and CG ranking based on total amount of future petroleum available per geological province. Used original geological province polygons. Identified coal basins for additional references of other fossil fuel development potential but didn't use in analysis. |
| Zhou et al.24 | 2012 | Wind | YP $$ | Global | 1-km | none (due to goal of analysis) | elevation > 2000 m | wetland, water | urban | PAs (WDPA identified) | Three categories of land suitability refined by land cover types identified in SI Table 3. Calculated cost of building transmission based on Euclidian distance from transmission lines |
| Lu et al.17 | 2009 | Wind | YP | Global | 60 km × 50 km | CF < 20% | slope > ~11° (20%) elevation > 2000 m | forest, water, snow and ice | urban | none | Produced a global capacity factor map. |
| Hermann et al.55 | 2014 | CSP | YP LS | Africa | 28-km | DNI < 1800 kWh/m2/yr (~206 W/m2) | slope > 2° (~4%) | all forest and mix-forest, coast, cliffs, dunes, water, rock and ice | urban, cites, agricultural lands | PAs (WDPA: IUCN Cat. I–VI) | Suitability ranking based on DNI values (kWh/m2/yr): Suitable (1800 – 2000), Highly suitable (2000 – 2500), Excellent (2500 – 3000) |
| see above | PV | YP LS | Africa | 28-km | GHI < 1000 kWh/m2/yr (~114 W/m2) | slope > 45° (~100%) | same as above | urban, cites | PAs (WDPA: IUCN Cat. I–VI) | Suitability ranking based on GHI values (kWh/m2/yr): Suitable (1000 – 1500), Highly suitable (1500 – 2500), Excellent (2500 – 3000) | |
| see above | Wind | YP LS | Africa | 9-km | Wind Speed < 4 m/s | slope > 45° (~100%) | rain forest, tropical forest, coast, cliffs, dunes, water, rock and ice | urban, cites | PAs (WDPA: IUCN Cat. I–VI) | Suitability ranking based on annual average wind speeds at 80 m (m/s): Limited (4-5), Suitable (5-7), Highly suitable (7-9), Excellent (>9) | |
| Wu et al.56; Wu et al.57 | 2017, 2015 | CSP | YP $$ LS | East Africa | 5-km | DNI < ~ 2191 kWh/m2/yr (250 W/m2) | slope > ~3° (5%) elevation > 1500 m | forest, cropland, wetland, snow/ice, water (see Table 2 in Wu et al., 2015) | urban, population (pop.) density > 100/km2, railways and waterbodies and land up to 500 m from these features | PAs (WPDA identified) and lands within 500 m of PAs | 2 km2 minimum developable area and 5 km2 project opportunity areas (POAs). For cost estimates see Table 7 in in Wu et al., 2015 Criteria maps at a resolution of 500 m and averaged criteria scores within POAs Criteria Values: See Table 8 in in Wu et al., 2015 Criteria Weights: Varies per country see online tables at http://mapre.lbl.gov/spatial-data/ |
| see above | PV | YP $$ LS | East Africa | 5-km | GHI < ~ 2453 kWh/m2/yr (280 W/m2) | see above | see above | see above | see above | see above | |
| see above | Wind | YP $$ LS | East Africa | 5-km | wind speed < ~ 6.2 m/s (300 W/m2) | slope > ~11° (20%) elevation > 1500 m | forest, wetland, snow/ice, water (see Table 2 in Wu et al., 2015) | see above | see above | see above | |
| He & Kammen58 | 2016 | CSP | YP | China | 1-km | GHI < 1400 kWh/m2/yr (160 W/m2) | slope > ~2° (3%) elevation > 3000 m | forest, cropland, wetland, shurblands, savannas, grasslands, snow and ice | urban | PAs (WDPA identified) | Assessed YP based on two different grouping of constraints; upper (i.e. most available land for solar development or least restrictive constrains) and lower (i.e. least available land for solar development or most restrictive constrains), identified in Table 2. Capacity factors identified by province. |
| see above | PV | YP | China | 1-km | see above | see above | see above | none | see above | ||
| He & Kammen60 | 2014 | Wind | YP | China | 1-km | wind speed <= 6 m/s | slope > ~11° (20%) elevation > 3000 m | forest, cropland, wetland, water, snow and ice. | urban | PAs (WDPA identified) | Land availability refined by land cover types (Table 1 in ref) Slope % (0-2,2-3,3-4,4-20) varied power density (Table 1 in ref) Assessed YP based on two different grouping of constraints; upper (i.e. most available land for wind development or least restrictive constrains) and lower (i.e. least available land for solar development or most restrictive constrains), see Table 1 in ref |
| Lambin et al.16 | 2013 | Crop | LS | Six regions /countries | varies | See Table 1 in ref. for listing of constraints. Constraint values dependent on regional location as described by 6 case studies. | |||||
| Lopez et al.59 | 2012 | CSP | YP | United States | 1-km | DNI < 1825 kWh/m2/yr (~208 W/m2) | slope > ~2° (3%) | water, wetlands | urban | PAs see Table A-4 in Ref. for PA list | Capacity factors for CSP based on DNI ranges (Table A-4) |
| see above | PV | YP | United States | 1-km | none | see above | see above | urban | see above | State specific capacity factors for PV (Table A-2) | |
| see above | Wind | YP | United States | 1-km | wind speed < 6.4 m/s | slope > ~11° (20%) | water, wetlands plus land within 3km of wetlands | urban plus land within 3 km of urban | PAs plus land within 3 km of PAs see Table A-5 in Ref. for PA list. | ||
| Mohammed & Alshayef48 | 2017 | CO, CG | LS | Ayad, Yemen | NA | none applied | LS based on GIS, multi-criteria decision analysis (GIS-MCDA) using Analytical Hierarchy Process (AHP) for criteria weights and Weighted Linear Combination (WLC) to derive final LS used to prioritize COG development locations. Spatial criteria placed in three categories high, moderate, and low. Criteria and weights identified in Table VI. Validated spatially with existing oil and gas fields. | ||||
| Jangid et al.44 | 2016 | Wind | LS | Jodhpur District, India | not listed | average wind speed variation over months < 1.6 m/s at 20 m height | none | forested lands | including and within 500 m of residential land, > 1 km from a road | none | LS based on GIS-MCDA using AHP/WLC methodology to locate wind farms. Spatial criteria classified into low, medium, and high. Criteria (weight, highest category description): wind speed (0.4, highest), land use/cover (0.3, least and shortest vegetation), slope (0.15, flat), distance from roads (0.12, closest), distance from residential areas (0.03, furthest). |
| Baranzelli et al.49 | 2015 | UO, UG | LS | Northern Poland | 100-m | none (study area within shale gas basin) | none | caves and caverns, aquatic areas | urban and industrial areas, roads, railways, transmission lines, water wells, oil and gas wells | nature reserves, 100- year flood zones | LS based on GIS-MCDA using AHP/WLC methodology to site well pads. Spatial criteria continuous values identified in Table 4 and weights identified in Table 5. Analyzed two impact scenarios (high and low) for full development of resource. Used LS to place wells across landscape based on scenario definitions identified in Table 2. |
| Blachowski47 | 2015 | Coal | LS | Southwest Poland | 50-m | land outside coal deposits | none | none | none | none | LS based on GIS-MCDA using AHP/WLC methodology to rank highest conflict areas for coal mining. 15 spatial criteria and weighting identified in Table 4. |
| Brewer et al.66 | 2015 | PV | LS | Southwest US | 10-m | Global Tilted Irradiance (GTI) < 2373 kWh/m2/yr (~271 W/m2) | slope > 3.1° (~5%) | distances from rivers > 17.3 km | distances from roads > 0.56 km, distances from power lines > 32.7 km | none | Used constraints to restrict further analysis to two counties per state with highest area available for solar development. LS based on GIS-MCDA using WLC methodology to rank highest areas for utility PV development in selected Western US counties. Five spatial criteria withnine evenly distributed bins (i.e. 1–9); distance to roads (0–6 km), distance to rivers (0–45 km), distance to power lines (0–85 km), GTI (1095–2920 kWh/m2/yr), slope (0–90°) . Weights based on estimated cost differences identified Table 3. Created a public approval layer based on survey of acceptable distances (i.e. any, > 1 mile, > 6 miles, and >10 miles) from 5 features (i.e. residential areas, ag lands, cultural and historic areas, bird breeding and nesting sites, and recreation areas). Combined LS with public approval layer to identify suitable areas with the least public resistance. |
| Hernandez et al.61 | 2015 | CSP | LS | California, US | 30-m | DNI < 2190 kWh/m2/yr (~250 W/m2) | slope > ~2° (3%) | water, snow and ice | distances from roads >10 km, distances from transmission lines >20 km | areas where unlawful to build roads based on US and California statutes | LS based on compatibility index: used decision support tool, the Carnegie Energy and Environmental Compatibility (CEEC) model, to develop a three-tiered spatial environmental and technical compatibility index (i.e. Compatible, Potentially Compatible, and Incompatible). Land cover types impacted by PV and CSP solar identified in Table 1. Constraint listings for solar development base on older literature found in Table S4. |
| see above | PV | LS | California, US | 30-m | DNI < 1460 kWh/m2/yr (~166 W/m2) | slope > ~3° (5%) | see above | see above | see above | see above | |
| Zolekar & Bhagat51 | 2015 | Ag | LS | Upper Pravara and Mula River Basin, India | 5.8-m | NA | none – all slopes categorized in spatial criteria | water – all other land cover categorized in spatial criteria | none – land use categorized in spatial criteria | none | LS based on GIS-MCDA using AHP/WLC methodology to produce land suitability of agriculture in “hilly zones”. 12 spatial criteria categories and weights identified in Table 7. Criteria listings of various references for different types of land suitability identified Table 1. |
| Miller & Li62 | 2014 | Wind | LS | Northeast Nebraska, US | 200-m | wind speed < 5.6 m/s | slope > ~11° (20%) | wetlands, water | pop. density > ~58/km2 (150/mi2), >20 km from transmission line, >10 km from roads | none | LS based on GIS-MCDA using WLC with assigned criteria weights to produce land suitability for wind power development. Spatial criteria placed in 5 suitability categories (0/unsuitable – 5/high) Criteria and weights identified in Table 4: wind speed (0.25), distance to transmission lines (0.16), slope (0.16), land use (0.16), distance to roads (0.16), and pop. density (0.08). |
| Effat & Effat46 | Solar | LS | Ismailia, Egypt | 100-m | None | none | Water, wetlands, and sabkahs (i.e. salt flats) | urban areas and land within 2 km of urban areas, cultivated lands | none | LS based on GIS-MCDA using AHP/WLC methodology to produce a prioritization map for solar development. Spatial criteria placed into ten categories identified in Tables 6-7. Criteria (weights, highest category description): solar radiation (0.47, highest), aspect (0.24, southern) distance to powerlines (0.12, closest), distance to roads (0.09, closest), and distance to cities (0.08, closest) | |
| Elsheikh et al.63 | 2013 | Ag | LS | Terengganu, West Malaysia | based on crop type selected by user within tool | LS based on GIS-MCDA using the Agriculture Land Suitability Evaluator (ALSE) specific for tropical and subtropical crops. Spatial criteria created for each crop in tool and placed into five suitability levels typical for ag suitability (i.e. S1, S2, S3, N1. and N2) | |||||
| Gorsevski et al.64 | 2013 | Wind | LS | Northwest Ohio, US | 30-m | none | none | wetlands, water | developed areas, airports | none | LS based on GIS-MCDA using WLC for combining spatial criteria and weights Borda ranking method for deriving weights Spatial criteria continuous from 0-1 identified Table 1. Weights identified in Table 2. Performed spatial sensitivity on weights. |
| Pazand et al.50 | 2011 | Mining | LS | Northwest Iran | 100-m | none applied | LS based on GIS-MCDA using AHP/WLC methodology to produce a prioritization map for copper porphyry exploration. Used five main spatial criteria; airborne magnetic, stream sediment geochemical data, geology, structural data and alteration zones. Criteria weights identified in Table 6. | ||||
| Clifton & Boruff65 | 2010 | CSP | LS | Western Australia | 90-m | DNI < 2000 kWh/m2/yr (~228 W/m2) | slope > ~2° (4%) | forest, wetland, snow/ice, water (specifics identified in Table S2) | none | PAs (no definition), cultural sites | Development potential classes based on CSP index standard deviations from the mean: high (>2), medium (1–2), low (0–1). Criteria Values to produce CSP index: Ag productivity (0 – highest yield to 1 lowest yield): 0.16 Distance to roads (1 – closest to 0 furthest, no threshold distance): 0.16 Distance to transmission lines and substations (same as roads): 0.16 DNI values (1 max to 0 lowest): 0.5 |
| Janke45 | 2010 | CSP | LS | Colorado, US | 1500-m | none | none | none | none | all US federally managed lands (due to goal of study) | LS based on GIS-MCDA using WLC with assigned weights. Spatial criteria and weights identified in Table 1. |
| see above | Wind | LS | Colorado, US | 1500-m | none | none | none | none | see above | see above | |
| Khoi & Murayama53 | 2010 | Crop | LS | Tam Dao National Park Region, Vietnam | 30-m | none (used fuzzy spatial criteria with 0 values but no exclusions related to overall suitability scoring) | LS based on GIS-MCDA using AHP/WLC methodology to produce a crop farming suitability map. Used method to derive 3 suitability maps relating to terrain and water, soil quality, and access to roads and park. These three suitability maps were then applied weights using AHP and combined using WLC to produce final suitability. Spatial criteria had continuous value ranging from 0-1 identified in Table 2. Weights produced from AHP identified in Table 3. | ||||
Three types of analysis were reviewed and can be classified as land suitability (LS), yield potential (YP) of a resource, or economic feasibility ($$) of siting. Studies ordered by spatial extent analyzed from global to local and sub-ordered by date of reference. Abbreviations of development sector are as follows: Ag – agricultural expansion (undefined definition or combination of crop and pasture expansion), Bio – crop expansion specific to biofuel crops, Coal – coal mining, CO – conventional oil, CG – conventional gas, Crop – crop expansion, CSP – concentrated solar power, Hydro – hydropower, Mining – mineral extraction, PV – photovoltaic solar power, Solar – solar power without specification of technology, UO – unconventional oil, UG – unconventional gas, and Wind – wind power. All values denoted with tilde symbol (~) indicate values were converted from the referenced value within the cited literature.
*All table and figure numbers identified in the Notes and Biophysical columns are found within the corresponding source document.