The Driving Forces of Energy-Related CO2 Emissions in the United States: A Decomposition Analysis
Bibliographic record
Abstract
This paper uses the logarithmic mean Divisia index (LMDI) approach to decomposition analysis to identify the factors that influence changes in carbon dioxide (CO2) emissions in the United States.  The LMDI approach decomposes CO2 emissions into specific determinants.  The data set includes the 50 states plus the District of Columbia from 1998 – 2018.  The five factors that influence the change in CO2 emissions include the emissions per unit of fossil fuel consumption, share of fossil fuels in total energy consumption, energy intensity, GDP per capita, and population.  The results indicate that, during the 20-year period, CO2 emissions declined in 36 states plus the District of Columbia.  The reduction in energy intensity served as the most important factor in the change of CO2 emissions, offsetting 63 percent of the effects of per capita GDP and population.  From the perspective of climate change, the importance of a change in energy intensity demonstrates the effectiveness of a decrease in primary energy consumption per unit of GDP.   
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".