Food Miles, Carbon Footprinting and Their Potential Impact on Trade, presentation at the Australian Agricultural and Resource Economics annual conference
Bibliographic record
Abstract
To obtain market access for NZ food exports to high value developed country markets exporters are having to comply and consider environmental factors such as carbon footprinting. This growth in demand for environmental attributes is shown in the rise of the food miles debate or concept. Food miles is a concept which has gained traction with the popular press arguing that the further food travels the more energy is used and therefore carbons emissions are greater. This paper assesses, using the same methodology, whether this is the case by comparing NZ production shipped to the UK with a UK source. The study found that due to the different production systems even when shipping was accounted for NZ dairy products used half the energy of their UK counterpart and in the case of lamb a quarter of the energy. In the case of apples the NZ source was 10 per cent more energy efficient. In case of onions whilst NZ used slightly more energy in production the energy cost of shipping was less than the cost of storage in the UK making NZ onions more energy efficient overall. The paper then explores other developments in market access to developed markets especially the rise in demand for products to be carbon footprinted and the introduction of carbon labelling. A review of latest methodology in carbon footprinting the PAS from the UK is reviewed and implications for trade assessed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".