Abstract
Water storage is a driver for economic growth and often mentioned as a proxy for water security. Hydropower storage projects deliver multiple benefits contributing to water and energy security; however, the reservoir creation raises concerns about greenhouse gas (GHG) emissions and puts in doubt how clean hydropower generation is. As storage becomes more relevant under climate change, adequate assessment is necessary to ensure projects’ sustainability. This study quantifies hydropower global median lifecycle greenhouse emissions at 23 gCO2e/kWh using the G-res Tool to estimate the net emission for 480 hydropower storage projects. This result is aligned with the IPCC estimates.
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Acknowledgements
This study was based on the previous work carried out by ex IHA staff members, Mathis Rogner and Emma Smith, under the funded UNESCO/IHA research project on the GHG status of freshwater reservoirs and published in the IHA Hydropower Status Report 2018 (IHA 2018b).
Declaration of Interest Statement
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this chapter.
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Annex
Annex
Distribution of the installed capacity of study dataset and IHA Global Hydropower Station database and expected values to perform Chi-Square test
Installed capacity | Study dataset | IHA database | Study dataset expected values | |
|---|---|---|---|---|
# Stations | # Stations | Distribution | # Stations | |
100 | 268 | 10,357 | 0.841006902 | 404 |
200 | 72 | 810 | 0.065773447 | 32 |
300 | 35 | 351 | 0.028501827 | 14 |
400 | 21 | 180 | 0.014616322 | 7 |
500 | 12 | 117 | 0.009500609 | 5 |
600 | 10 | 87 | 0.007064555 | 3 |
700 | 2 | 49 | 0.003978888 | 2 |
800 | 5 | 46 | 0.003735282 | 2 |
900 | 4 | 37 | 0.003004466 | 1 |
1000 | 1 | 41 | 0.003329273 | 2 |
1100 | 9 | 37 | 0.003004466 | 1 |
1200 | 2 | 41 | 0.003329273 | 2 |
1300 | 5 | 28 | 0.00227365 | 1 |
1400 | 5 | 14 | 0.001136825 | 1 |
1500 | 3 | 14 | 0.001136825 | 1 |
1600 | 2 | 9 | 0.000730816 | 0 |
1700 | 2 | 8 | 0.000649614 | 0 |
1800 | 1 | 8 | 0.000649614 | 0 |
1900 | 0 | 9 | 0.000730816 | 0 |
2000 | 0 | 7 | 0.000568413 | 0 |
2100 | 2 | 5 | 0.000406009 | 0 |
2200 | 1 | 4 | 0.000324807 | 0 |
2300 | 0 | 1 | 8.12018E−05 | 0 |
2400 | 3 | 8 | 0.000649614 | 0 |
2500 | 2 | 7 | 0.000568413 | 0 |
2600 | 0 | 3 | 0.000243605 | 0 |
2700 | 0 | 1 | 8.12018E−05 | 0 |
2800 | 1 | 2 | 0.000162404 | 0 |
2900 | 2 | 2 | 0.000162404 | 0 |
3000 | 0 | 4 | 0.000324807 | 0 |
>3000 | 10 | 28 | 0.00227365 | 1 |
Total | 480 | 12,315 | ||
Chi-Square test | 7.89169E−87 | |||
Distribution of climate zones of study dataset and GRanD database and expected values to perform Chi-Square test
Study dataset | GRanD database | Study dataset expected values | ||
|---|---|---|---|---|
Climate | # Stations | # Stations | Distribution | # Stations |
Boreal | 69 | 291 | 0.190445 | 91.4136 |
Temperate | 338 | 1056 | 0.691099 | 331.728 |
Subtropical | 30 | 46 | 0.030105 | 14.4503 |
Tropical | 43 | 135 | 0.088351 | 42.4084 |
Total | 480 | 1528 | ||
Chi-Square test p value | 5.5E−05 | |||
Distribution of surface area of study dataset and GRanD database and expected values to perform Chi-Square test
Study dataset | GRanD database | Study dataset expected values | ||
|---|---|---|---|---|
Surface area | # Stations | # Stations | Distribution | # Stations |
20 | 249 | 997 | 0.652486911 | 313.1937173 |
40 | 64 | 156 | 0.102094241 | 49.0052356 |
60 | 31 | 82 | 0.053664921 | 25.7591623 |
80 | 17 | 37 | 0.02421466 | 11.62303665 |
100 | 14 | 25 | 0.016361257 | 7.853403141 |
120 | 10 | 19 | 0.012434555 | 5.968586387 |
140 | 10 | 20 | 0.013089005 | 6.282722513 |
160 | 4 | 12 | 0.007853403 | 3.769633508 |
180 | 7 | 13 | 0.008507853 | 4.083769634 |
200 | 5 | 15 | 0.009816754 | 4.712041885 |
220 | 5 | 9 | 0.005890052 | 2.827225131 |
240 | 5 | 11 | 0.007198953 | 3.455497382 |
260 | 6 | 8 | 0.005235602 | 2.513089005 |
280 | 2 | 6 | 0.003926702 | 1.884816754 |
300 | 2 | 7 | 0.004581152 | 2.19895288 |
320 | 2 | 4 | 0.002617801 | 1.256544503 |
340 | 1 | 4 | 0.002617801 | 1.256544503 |
360 | 1 | 2 | 0.001308901 | 0.628272251 |
380 | 3 | 3 | 0.001963351 | 0.942408377 |
400 | 2 | 3 | 0.001963351 | 0.942408377 |
420 | 2 | 3 | 0.001963351 | 0.942408377 |
440 | 0 | 3 | 0.001963351 | 0.942408377 |
460 | 1 | 1 | 0.00065445 | 0.314136126 |
480 | 0 | 2 | 0.001308901 | 0.628272251 |
500 | 1 | 2 | 0.001308901 | 0.628272251 |
520 | 3 | 4 | 0.002617801 | 1.256544503 |
540 | 0 | 2 | 0.001308901 | 0.628272251 |
560 | 0 | 2 | 0.001308901 | 0.628272251 |
580 | 0 | 2 | 0.001308901 | 0.628272251 |
600 | 0 | 2 | 0.001308901 | 0.628272251 |
>600 | 33 | 72 | 0.047120419 | 22.61780105 |
Total | 480 | 1528 | ||
Chi-Square test | 0.000708537 | |||
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Ubierna, M., Santos, C.D., Mercier-Blais, S. (2022). Water Security and Climate Change: Hydropower Reservoir Greenhouse Gas Emissions. In: Biswas, A.K., Tortajada, C. (eds) Water Security Under Climate Change. Water Resources Development and Management. Springer, Singapore. https://doi.org/10.1007/978-981-16-5493-0_5
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