Impacts of Carbon Tax Policies on Low-Carbon Technology Investment in the Electricity Supply Chain Under Peak–Valley Pricing
Bibliographic record
Abstract
This article investigates low-carbon technology (LCT) investment strategies within the electricity supply chain, employing a hybrid decision-making framework that considers both decentralized and cooperative approaches. By integrating a peak–valley pricing mechanism, the article examines the impact of carbon tax policies (CTPs) on LCT investment. It analyzes the competitive and cooperative interactions between power generation and electricity retail (ER) enterprises, focusing on how the CTP influences investment decisions, electricity pricing, and the profitability of the electricity enterprise under a wholesale electricity pricing discount strategy. The findings are as follows: 1) Surprisingly, CTPs may not always incentivize LCT investment under peak–valley pricing. When unit carbon emissions are low, CTPs promote greater LCT investment, stimulate electricity demand during peak and valley periods, and enhance the profitability of the ER enterprise. However, when unit carbon emissions are high, the absence of CTPs more effectively drives LCT investment, increases electricity demand, and yields higher profits for the ER enterprise. 2) Under a wholesale electricity pricing discount strategy, compared to the case without CTPs, when unit carbon emissions are low, CTPs lead to lower initial wholesale electricity prices during peak and valley periods, thereby increasing marginal profits for the ER enterprise. Conversely, when unit carbon emissions are high, CTPs lead to higher initial wholesale electricity prices in both periods, reducing the ER enterprise’s marginal profits. 3) Under a CTP, higher unit carbon emissions increase retail electricity prices during peak and valley periods, which reduces electricity demand, LCT investment, and the profitability of electricity enterprises. Furthermore, an increase in the unit cost of electricity generation raises retail electricity prices during peak and valley periods, further exacerbating declines in demand, investment, and profits.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".