Red versus blue states: Inequality in energy-related CO2 emissions in the United States (1997–2021)
Bibliographic record
Abstract
The partisan divide over climate change issues in the U.S. has been increasing over time, particularly after the signing of the Kyoto protocol in 1997. This divide has posed a substantial hurdle to implementing a unified climate policy in the U.S. In this paper, I employ the Shapley value decomposition approach to measure the partisan gap in state-level per capita energy-related CO2 emissions. Despite a decrease in overall carbon emissions and stable per capita carbon inequality between states (measured by the Gini index), the inequality between the so called ‘red’ and ‘blue’ states widened over the period between 1997 and 2021. This is true regardless of whether each state is treated as a unit of analysis or assigned a weight equal to its population. Throughout the period, the divide by political party color was more pronounced than that by many other dimensions, such as income, climatic condition, and geographic location. These results also hold true when Mean Log Deviation is used as the measure of inequality. Driven primarily by disparity in transportation sector and coal use for electricity generation, the partisan gap was worse in 2021 than in 2020. While the partisan gap intensified, the carbon distribution changed from being top-concentrated to bottom-concentrated between 1997 and 2019, only to be reversed in the aftermath of the coronavirus pandemic in 2020 and subsequent economic recovery in 2021.
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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 | 0.003 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".