Economic Impacts of a Low Carbon Economy on Global Agriculture: The Bumpy Road to Paris
Bibliographic record
Abstract
Limiting climate change below a 2 °C temperature increase this century will require substantial reductions of greenhouse gas emissions and the transition to a climate-friendly, low carbon society. In this paper, the economic impact of a less carbon-intensive economy on agricultural markets is estimated by means of an integrated modelling framework. First, the macroeconomic impacts of moving into a global low carbon economy are analysed by applying different carbon taxes in a general equilibrium modelling framework. Second, the potential adoption of emission mitigation technologies is quantified and used in the Aglink-Cosimo model to assess the impacts on agricultural markets of emission mitigation scenarios compatible with the 2.0 °C target prescribed in the Paris Agreement. Results for 2030 show reductions in global non-CO2 GHG emissions from agriculture (i.e., methane and nitrous oxide) by 10, 16 and 19% in 50, 100 and 150 USD/t CO2eq global carbon tax scenarios, respectively (Least Developed Countries excluded). Only between 0.6% and 1.3% of the global reduction is caused by indirect macroeconomic effects, although at the regional level they can cause up to 5.8% of the reduction in agricultural emissions. Results suggest that ambitious mitigation targets can provoke significant negative impacts on agricultural production and underline the importance of integrating GHG emission developments and impacts of related policies into agricultural market projections.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".