The biophysical economic structure of four developed countries: Lessons for decarbonization
Bibliographic record
Abstract
Abstract This study provides a comparative analysis of the biophysical economic structures of four developed economies—Sweden, the United States, the United Kingdom, and Canada. We draw upon results from input–output models to map and analyze capital, energy, and carbon relationships in each economy. The capital stock of each country is divided into four sectors: (i) energy production and distribution, (ii) production of goods and services for consumption, (iii) residential, and (iv) construction and manufacture of capital. The findings reveal diverse strengths and challenges in decarbonization efforts across the countries. Sweden excels in “energy production and distribution” and “residential” sectors, owing to its commitment to renewable energy and energy efficiency, but requires attention in goods and services production where carbon intensity is high. The United Kingdom stands out for its high greenhouse gas intensity in residential capital stock and in the consumption of goods and services, underscoring the need for targeted low‐carbon investments. The United States and Canada display high GHG intensities across all sectors, necessitating a more robust transition toward renewable and low‐carbon energy solutions. In particular, the United States’ carbon intensity in its energy sector and Canada's industry dominated by fossil fuel extraction offer specific areas for intervention. The study concludes that focusing on sectors with the highest carbon intensity and adopting both sector‐specific and broader strategies can significantly accelerate each country's decarbonization efforts, thereby contributing to global climate change mitigation. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges .
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".