Heterogeneity in carbon intensity patterns: A subsampling approach
Bibliographic record
Abstract
Carbon intensity, defined as carbon dioxide (CO2) emissions per unit of gross domestic product (GDP), is a critical metric for assessing the effectiveness of climate policy across nations. This paper presents an analysis of the persistence and stationarity of carbon intensity data across 176 countries and 44 regions from 1990 to 2014, employing subsampling confidence intervals. Subsampling is a robust statistical technique that performs well with finite samples and requires minimal assumptions about the data. Our findings categorize countries into three distinct groups based on their carbon intensity patterns: convergent, persistent, and divergent. We observe that climate mitigation policies in countries with a convergent pattern tend to have only temporary effectiveness, whereas in countries with a divergent pattern, such policies can lead to permanent changes. Additionally, using unsupervised learning methods, we delve into the underlying factors influencing these classifications. This study is particularly significant for understanding the long-term impacts of climate policies, offering valuable insights for policymakers and international bodies. By identifying and analyzing these distinct patterns, our research contributes to the strategic planning and implementation of more effective and sustainable climate policies globally, aligning with the goals of international agreements like the Paris Accord.
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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".