Multiplicative Structural Decomposition Analysis of Spatial Differences in Energy Intensity among G20 Countries
Bibliographic record
Abstract
Comparing the spatial differences in the energy intensity of the Group of Twenty (G20) countries and identifying the factors that influence these differences can help the G20 countries formulate targeted policies to achieve energy conservation goals. This study analyzes the spatial differences in the G20 countries’ energy intensity at the aggregate and sectoral levels based on an input–output framework and reveals its driving factors by employing multiplicative structural decomposition analysis, obtaining the sectoral energy intensity, input structure, and final demand structure effects. The results show that: (1) the gap in aggregate energy intensity among the G20 countries tended to converge from 2000 to 2014 with the reducing energy intensity in Russia, India, China, and South Korea having great potential to reduce global energy consumption and improve global energy efficiency; (2) in 2014, the main driving forces for above-average energy intensity was the sectoral energy intensity effect in India, South Korea, and Canada, the input structure effect in Russia and China, and the final demand structure effect in Indonesia; (3) using the average of the G20 countries as a reference, the energy reduction potential of China, Russia, India, South Korea, Indonesia, and Canada is 62.75, 31.94, 21.24, 7.67, 1.47, and 0.81 exajoules (EJ), respectively. The embodied energy consumption decline in these countries was equivalent to 21.78% of the G20’s total energy consumption in 2014; and (4) the most important factor of the high embodied energy intensity of key sectors in India and South Korea is the sectoral energy intensity effect, while for Russia and China, it is the input structure effect.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".