Title
Reducing greenhouse gas emissions of Amazon hydropower with strategic dam planning
Date Issued
01 December 2019
Access level
open access
Resource Type
journal article
Author(s)
Almeida R.M.
Shi Q.
Gomes-Selman J.M.
Wu X.
Xue Y.
Angarita H.
Barros N.
Forsberg B.R.
Hamilton S.K.
Melack J.M.
Montoya M.
Perez G.
Sethi S.A.
Gomes C.P.
Flecker A.S.
Cornell University
Publisher(s)
Nature Publishing Group
Abstract
Hundreds of dams have been proposed throughout the Amazon basin, one of the world’s largest untapped hydropower frontiers. While hydropower is a potentially clean source of renewable energy, some projects produce high greenhouse gas (GHG) emissions per unit electricity generated (carbon intensity). Here we show how carbon intensities of proposed Amazon upland dams (median = 39 kg CO2eq MWh−1, 100-year horizon) are often comparable with solar and wind energy, whereas some lowland dams (median = 133 kg CO2eq MWh−1) may exceed carbon intensities of fossil-fuel power plants. Based on 158 existing and 351 proposed dams, we present a multi-objective optimization framework showing that low-carbon expansion of Amazon hydropower relies on strategic planning, which is generally linked to placing dams in higher elevations and smaller streams. Ultimately, basin-scale dam planning that considers GHG emissions along with social and ecological externalities will be decisive for sustainable energy development where new hydropower is contemplated.
Volume
10
Issue
1
Language
English
OCDE Knowledge area
Ingeniería ambiental y geológica
Oceanografía, Hidrología, Recursos hídricos
Scopus EID
2-s2.0-85072391134
PubMed ID
Source
Nature Communications
ISSN of the container
20411723
Sponsor(s)
This work was supported by Cornell University’s Atkinson Center through a Postdoctoral Fellowship in Sustainability to R.M.A. and the Atkinson Academic Venture Fund. The computational work was supported in part by an NSF Expeditions in Computing award (CCF-1522054) and a Future of Life Institute grant. The computations for the Pareto frontier were performed using the AI for Discovery Avatar (AIDA) computer cluster funded by an Army Research Office (ARO), Defense University Research Instrumentation Program (DURIP) award (W911NF-17-1-0187). We are grateful to Bridget Deemer and Henriette Jager for providing valuable comments and suggestions. We thank Scott Steinschneider and all participants of the Amazon Dams Computational Sustainability Working Group, and appreciate helpful comments from Robert Howarth, Peter McIntyre, Fábio Roland, and the Cornell Limnology Group.
Sources of information:
Directorio de Producción Científica
Scopus