Main Drivers of Carbon Dioxide Emissions in Vietnam Trajectory 2000-2011: An Input-Output Structural Decomposition Analysis
Bibliographic record
Abstract
Vietnam’s rapid economic growth has resulted in serious environmental concerns both at local and global scales. In-depth understanding of the key factors behind the rapid growth of CO2 emissions is of great significance in the development of local and global climate policies. Furthermore, this provides insight into how emerging economies can develop a low emission future. Recent works have demonstrated the effectiveness of the input–output model and structural decomposition analysis in analyzing how changes in different socio-economic factors affect energy-based CO2 emissions in the sectoral level using production and consumption-based perspectives. In the context of Vietnam’s economy, such aspects have not been fully explored in previous literature. This study thus analyzes the driving forces responsible for the increase in CO2 emissions in Vietnam from both production and consumption perspective during periods 2000 – 2007 and 2007 - 2011. The results using the production perspective indicate that during 2000 - 2011 the incremental change in CO2 emissions in Vietnam is driven mainly by the consumption structure (100.5%) and consumption volume (219.4%) which are offset by the decline in technology (-132.7%) and production structure (-22.5%). Population (24.1%) had a small effect on total CO2 emissions. Results using the consumption perspective show that even with large variations between the two periods, household, export and investment are the main drivers responsible for the sharp increase in CO2 emissions. This is offset by the decrease in import factor. Policy implications indicate that improving technology, adjusting production and consumption structure, and optimizing international trade are important factors for alleviating CO2 emissions in Vietnam.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".