Reducing Carbon Emissions from Transport Sector: Experience and Policy Design Considerations
Bibliographic record
Abstract
Countries aim to reduce fossil fuel usage and related environmental issues through various demand- and supply-side policies. Numerous studies have assessed the policies’ overview. However, analysis of the impacts and effectiveness of these policies in addressing transport-related CO2 emissions is limited globally and in countries like New Zealand, which have a lower CO2 emissions energy intensity compared to Europe, Asia, and Oceania averages. Therefore, this study first analyses the trends in energy consumption and CO2 emissions within the transport sector across the ten largest total CO2-emitting countries, as well as the ten largest transport CO2-emitting OECD countries. It then provides a systematic review of the relevant policies and, finally, estimates two econometric models to explore the effects of these policies on the energy market, aimed at reducing GHG emissions globally from the transport sector, with New Zealand as a case study. The study findings indicate that the transport sector remains a significant contributor to global fossil fuel consumption and CO2 emissions, accounting for 40.4% and 23.3%, respectively, in 2024. The ten largest CO2-emitting countries—China, the United States, India, Russia, Japan, Germany, South Korea, Iran, Canada, and Saudi Arabia—are responsible for 68% of global emissions. Additionally, the ten OECD countries, except the US, with the highest transport CO2 emissions—Japan, Germany, South Korea, Canada, Mexico, the UK, Italy, France, Spain, and Australia—accounted for 15.7% of the world’s total transport CO2 emissions. Although the share of renewable energy and electricity consumption in the transport sector has steadily risen to 3.54% and 1.4%, respectively, in 2022, further adoption of these sources can considerably lower greenhouse gas emissions in this sector. Results also indicate that both demand- and supply-side policies effectively reduce greenhouse gas emissions, with their impact amplified when implemented together. In New Zealand, demand-side policies have proven to be more effective in reducing emissions than supply-side strategies alone, though combining them is the most efficient approach. This study emphasizes the importance of strategic policy implementation to guide the world toward sustainable development.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".