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Record W4307370469 · doi:10.3389/frsus.2022.1021971

Life cycle impacts of concentrated solar power generation on land resources and soil carbon losses in the United States

2022· article· en· W4307370469 on OpenAlex
Shreya Rangarajan, Rebecca R. Hernandez, Sarah M. Jordaan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Sustainability · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsMcGill University
FundersAlfred P. Sloan Foundation
KeywordsEnvironmental scienceElectricity generationLand useRevegetationElectricitySolar powerEnvironmental economicsEnvironmental resource managementEngineeringCivil engineeringPower (physics)GeographyElectrical engineering

Abstract

fetched live from OpenAlex

Endpoint impacts related to the transformation of land—including that related to energy infrastructure—have yet to be fully quantified and understood in life cycle assessment (LCA). Concentrated solar power (CSP) which generates electricity by using mirrors to concentrate incoming shortwave radiation onto a receiver, may serve as an alternate source of reliable baseload power in the coming years. As of 2019 (baseline year of the study), the United States (U.S.) had 1.7 GW of installed capacity across a total of eight CSP sites. In this study, we (1) develop an empirical, spatially explicit methodology to categorize physical elements embodied in energy infrastructure using a LCA approach and manual image annotation, (2) use this categorization scheme to quantify land- and ecosystem service-related endpoint impacts, notably potential losses in soil carbon, owing to energy infrastructure development and as a function of electricity generated (i.e., megawatt-hour, MWh); and (3) validate and apply this method to CSP power plants within the U.S. In the Western U.S., CSP projects are sited in Arizona, California, and Nevada. Project infrastructure can be disaggregated into the following physical elements: mirrors (“heliostats”), generators, internal roads, external roads, substations, and water bodies. Of these elements, results reveal that mirrors are the most land intensive element of CSP infrastructure (>90%). Median land transformation and capacity-based land-use efficiency are 0.4 (range of 0.3–6.8) m 2 /MWh and 40 (range of 11–48) W/m 2 , respectively. Soil grading and other site preparation disturbances may result in the release of both organic and inorganic carbon—the latter representing the majority stocks in deeper caliche layers—thus leading to potentially significant losses of stored carbon. We estimate three scenarios of soil carbon loss into the atmosphere across 30 years, based on land transformation in m 2 per megawatt-hour (m 2 /MWh) and carbon stock in kilograms of carbon per megawatt-hour (kg C/MWh). Results reveal that potential belowground CO 2 released may range from 7 to 137% of total life cycle CO 2 emissions. While this study takes a simplistic approach to estimating loss of carbon, the broad methodology provides a valuable baseline for improving comparative analyses of land-related endpoint impacts across energy technologies and other product systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.205
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it