Road Transport and Urban Mobility Greenhouse Gas Emissions Factor for Air Pollution Modeling in Burkina Faso
Bibliographic record
Abstract
The road transportation sector is one of the largest contributors to fossil fuel consumption, while the energy sector being the greatest consumer overall. Urban growth increases transport and mobility demands to meet human needs. Few studies exist on greenhouse gas emission factors in developing countries. This study assessed and modeled greenhouse gas emissions factors with fossil fuel implications in road transport including urban mobility in Burkina Faso. The methodology entails the development of a bottom-up model to estimate fuel demand and emission factors under the IPCC 2006 guideline. It assesses greenhouse gases by establishing the specific emission factors using Ouagadougou City as a site of emission data processing. The analysis has included satellite NO 2 emission data. The city suffers from significant gas emissions and air pollutants resulting from the high vehicle fleet growth and fuel consumption. Indeed, the transport sector consumes 89 % of fossil fuels sold in Burkina Faso. There is an average carbon dioxide (CO2) emission factor of 3.7623 kg/l and 3.270 kg/l for diesel and gasoline vehicles, respectively. Thus, in 2019, gasoline and diesel accounted for 71 % and 21 % of total fuel consumption respectively, and produced a total amount of 1034 513.84 tons of CO2 (1034.5 GgCO2). In the business-as-usual condition, an average annual CO 2 production of 213.71 thousand tons is simulated from 2019 to 2040. A total emission of 4 486 559.34 tons (4486.64 GgCO2) by 2040 is expected with a share of 62 % for gasoline and 38 % for diesel. With an average emission of 1.89 mol/m2, the satellite tropospheric nitrogen dioxide concentration is mostly affecting the Central Business Division (CBD) of Ouagadougou City. It corresponds to 56 µg.m-3 which is beyond the WHO standard of annual average exposure. Thus, these findings alert the need for urgent environmental regulations and climate change mitigation actions for sustainable mobility.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".