Avoiding Greenwash in Reporting Life Cycle Greenhouse Gas Emissions of Space Solar Power: Environmentally-Extended Input-Output Versus Process-Based Approaches
Bibliographic record
Abstract
As the global energy landscape seeks sustainable alternatives to fossil fuels, the concept of space-based solar power (SBSP) is beginning to be considered on an international level. In the context of SBSP’s emergence as a viable and sustainable energy solution, accurate and transparent assessment of its environmental footprint is essential. This paper outlines challenges concerning some of the common narrative around SBSP and presents some guidance to the SBSP community on how to avoid falling victim to greenwash when discussing the technology. Through case studies and methodological considerations, it will then go on to explore the advantages and limitations of the two main modelling approaches in the reporting of its life cycle greenhouse gas (GHG) emissions, emphasising their applicability to the unique characteristics of developing Solar Power Satellites (SPS). In this regard, the paper will present a comparative Life Cycle Assessment (LCA) of the Innovative Heliostat Swarm and the Mature Planar Array SPS
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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 | 0.069 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".