Global assessment of onshore wind power resources considering the distance to urban areas

Herran, Diego Silva; Dai, Hancheng; Fujimori, Shinichiro; Masui, Toshihiko

doi:10.1016/j.enpol.2015.12.024

Cited by 48 publications

(27 citation statements)

References 14 publications

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“…To construct energy supply cost curves, we implemented multiple sources of information. Solar and wind supply curves are from a study considering urban distance 49 . Biomass potential and supply curve data is from a land-use allocation model 50 .…”

Section: Methodsmentioning

confidence: 99%

Energy transformation cost for the Japanese mid-century strategy

et al. 2019

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The costs of climate change mitigation policy are one of the main concerns in decarbonizing the economy. The macroeconomic and sectoral implications of policy interventions are typically estimated by economic models, which tend be higher than the additional energy system costs projected by energy system models. Here, we show the extent to which policy costs can be lower than those from conventional economic models by integrating an energy system and an economic model, applying Japan’s mid-century climate mitigation target. The GDP losses estimated with the integrated model were significantly lower than those in the conventional economic model by more than 50% in 2050. The representation of industry and service sector energy consumption is the main factor causing these differences. Our findings suggest that this type of integrated approach would contribute new insights by providing improved estimates of GDP losses, which can be critical information for setting national climate policies.

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Section: Methodsmentioning

confidence: 99%

Energy transformation cost for the Japanese mid-century strategy

et al. 2019

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“…13,16 ), but none globally map feasibility of land conversion based on factors of yield potential and access to infrastructure to distinguish relative conversion pressure 8 . Global mapping of renewable energy potential maps have incorporated only simple land constraints 17–19 or select few spatial development feasibility factors (e.g., market accessibility that considers distance to urban areas, load centers, and transmission lines 20–24 , or site construction and operational costs 21,24,25 ), at times doing so only post-hoc to categorize potential energy production 26,27 or to compare implementation costs 23,24 . Global fossil fuels and mining sectors maps have been limited to one fuel or mineral type 28–31 , do not include spatial siting factors 20,32 , or rely on proprietary industry data that limits public distribution 33 .…”

Section: Background and Summarymentioning

confidence: 99%

“…Distance from large power plants and large cities (proxy for transmission lines): 0-80 km – near, 80-161 – mid, >161 – farSilva Herran et al . 23 2016WindYP $$Global10-kmNAslope > 20° (~36%) elevation > 2000 mwater, wetlands, snow and iceurbanPAs (no definition)Identified wind potential within 3 ranges of urban areas 10 km, 20 km, 30 km.Deng et al . 26 2015CSPYPGlobal1-kmDirect normal irradiance (DNI) < 1900 kWh/m 2 /yr (~217 W/m 2 )slope > 2° (~4%)all forest and mix-forest, coast, cliffs, dunes, water, rock and iceurbanPas (Natura 2000 and WDPA: IUCN Cats.…”

Section: Online-only Tablesmentioning

confidence: 99%

“…26 ‡ Elevation 146 (m; 1 km)>3,000 m based on ref. 60 Landcover 143 (1 km)wetlands, rock/ice, artificial areas based on refs 23,26 Wind turbine locations ** ( n = 90,106) § any cell with ≥3 turbines based on refs. 59,147,148 indications of wind developmentHydro Hydropower potential locations 18 ( n = 11,839,398; kWh/y)cells with <1 MW potential to accommodate utility-scale hydropower development 130 i.→ Limited hydropower potential locations to those generating ≥8,760,000 kWh (i.e., 1 MW) and removed any locations ≤1 km of existing hydroelectric dams, consistent with average distance of existing dams (1.14 km; this study based on GRanD data).ii.→ Hydropower potential locations spanned fully inundated, or partially inundated cells; attribute values of fully inundated cells to the closest terrestrial cell ( n = 935).iii.→ Divided kWh by 1000 to calculate MWh and rasterize locations.Existing hydropower dams 149 ( n = 2,134) § any cell within 1km of a dam identified as hydroelectric for main, major, or secondary useCoalGlobal coal basins maps 20 ( n = 2,053)NAi.→ For each jurisdiction (country or state):a.→ Clipped basins by jurisdiction and calculate basin area withinb.→ Divided estimates of technically recoverable coal by the area of the coal basins within the jurisdiction to obtain million short ton/km 2 c.→ Rasterized basins and attribute million short ton/km 2 to every coal basin cell within the jurisdiction.ii.→ Merged jurisdictional results into a single raster of coal yield.iii.→ Limited analysis cells by constraints including removal of any cells with existing active coal mine.Country or state-level estimates of technically recoverable coal 20 ( n = 305; million short tons)NAExisting coal mines from 8 datasets 150–157 ( n = 2,301) § any cell with active coal mineCOGlobal 158,159 , U.S. 160 and Australia 161 assessment units (AUs) of median recoverable oil reserve ( n = 778; barrels of oil or bbl), and dry gas (n = 705; ft 3 ), and liquid natural gas (NGL) ( n = 823; bbl)NAi.→ Geo-referenced and digitized all world shale prospective area (PA) maps and assigned technically recoverable values to each PA.ii.→ For dry gas, converted reserve estimates from ft 3 of dry gas to BOE using a conversion factor of 6000 ft 3 to 1 BOE, then summed for each PA or AU the total gas estimates in BOE from the converted dry gas and NGL.iii.→ For each sector AU or PA:a.→ Divided the technically recoverable oil or gas reserve estimates by the area of the AU/PA to obtain BOE/km 2 .b.→ Rasterized AU/PA and attribute BOE/km 2 to every cell.iv.→ Combined all AUs/...…”

Section: Online-only Tablesmentioning

confidence: 99%

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Mapping global development potential for renewable energy, fossil fuels, mining and agriculture sectors

et al. 2019

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Mapping suitable land for development is essential to land use planning efforts that aim to model, anticipate, and manage trade-offs between economic development and the environment. Previous land suitability assessments have generally focused on a few development sectors or lack consistent methodologies, thereby limiting our ability to plan for cumulative development pressures across geographic regions. Here, we generated 1-km spatially-explicit global land suitability maps, referred to as “development potential indices” (DPIs), for 13 sectors related to renewable energy (concentrated solar power, photovoltaic solar, wind, hydropower), fossil fuels (coal, conventional and unconventional oil and gas), mining (metallic, non-metallic), and agriculture (crop, biofuels expansion). To do so, we applied spatial multi-criteria decision analysis techniques that accounted for both resource potential and development feasibility. For each DPI, we examined both uncertainty and sensitivity, and spatially validated the map using locations of planned development. We illustrate how these DPIs can be used to elucidate potential individual sector expansion and cumulative development patterns.

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“…Finally, a project's incremental transmission needs have to be weighed against locations with the best VRE resources. For example, siting wind turbines in distant, windy locations that require larger transmission investments presents economic tradeoffs versus siting them closer to load where wind resources are poorer (Hoppock and Patiño-Echeverri 2010;Lamy et al 2016;Silva Herran et al 2016;Fischlein et al 2013). Furthermore, liberalized electricity markets frequently present a coordination problem between investments in the regulated electrical grid (e.g., transmission network) and investments in new power generation (Wagner 2019).…”

Section: Introductionmentioning

confidence: 99%