Using Investments in Solar Photovoltaics as Inflation Hedges
Bibliographic record
Abstract
Mainstream strategies for protecting wealth from inflation involve diversification into traditional assets like common stocks, gold, fixed-income securities, and real estate. However, a significant contributor to inflation has been the rising energy prices, which have been the main underlying cause of several past recessions and high inflation periods. Investments in distributed generation with solar photovoltaics (PV) present a promising opportunity to hedge against inflation, considering non-taxed profits from PV energy generation. To investigate that potential, this study quantifies the return on investment (ROI), internal rate of return (IRR), payback period, net present cost, and levelized cost of energy of PV by running Solar Alone Multi-Objective Advisor (SAMA) simulations on grid-connected PV systems across different regions with varying inflation scenarios. The case studies are San Diego, California; Boston, Massachusetts; Santiago, Chile; and Buenos Aires, Argentina. Historical inflation data are also imposed on San Diego to assess PV system potential in dynamic inflammatory conditions, while Boston and Santiago additionally analyze hybrid PV-battery systems to understand battery impacts under increasing inflation rates. Net metering credits vary by location. The results showed that PV could be used as an effective inflation hedge in any region where PV started economically and provided increasingly attractive returns as inflation increased, particularly when taxes were considered. The varying values of the ROI and IRR underscore the importance of region-specific financial planning and the need to consider inflation when evaluating the long-term viability of PV systems. Finally, more capital-intensive PV systems with battery storage can become profitable in an inflationary economy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".