Life cycle impacts of concentrated solar power generation on land resources and soil carbon losses in the United States
Bibliographic record
Abstract
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) m2/MWh and 40 (range of 11–48) W/m2, 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 m2per megawatt-hour (m2/MWh) and carbon stock in kilograms of carbon per megawatt-hour (kg C/MWh). Results reveal that potential belowground CO2released may range from 7 to 137% of total life cycle CO2emissions. 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".