Study on the Impact of Climate Change on China’s Import Trade of Major Agricultural Products and Adaptation Strategies
Bibliographic record
Abstract
With global warming, China's agricultural products are facing severe production conditions and a complex international trade situation. In order to clarify the relationship between climate change and China's agricultural trade, this paper uses the GTAP model to explore the impact of climate change on China's agricultural trade from the perspectives of agricultural production and supply, energy substitution and trade policy. The results show that: (1) From the overall effect, the production supply risk and energy substitution risk caused by climate change have a positive impact on China's import trade, among which the energy substitution risk has brought about an import trade growth of 38.050%, the production supply risk has brought about an import trade growth of 12.635%, and the trade policy risk has a negative impact, bringing about an import trade decline of 12.589%. (2) Under the impact of production and supply risks caused by climate change, the import volume of different industrial sectors has increased by varying degrees, including livestock products (16.521%) > food crops (14.162%) > cash crops (7.220%). The increase in import trade mainly comes from the United States (10.731%), Canada (10.650%) and Australia (9.455%). (3) Under the impact of energy substitution risk caused by climate change, the increase in import trade was concentrated in food crops (48.144%) and livestock products (42.834%), mainly from the United States (57.098%), the European Union (55.014%) and Canada (53.508%). (4) Under the impact of trade policy risks caused by climate change, the import trade of different industrial sectors showed a downward trend, with cash crops (13.039%) > livestock products (12.588%) > cash crops (12.140%). The countries and regions with significant decline in import trade were ASEAN (-46.131%) and the United States (-28.028%). The trade deficit shifted to surplus, and the terms of trade were improved. Therefore, this paper suggests that we should deal with the impact of climate change on agricultural trade by developing "climate smart" agriculture, actively responding to low-carbon trade measures, and establishing an agricultural trade promotion mechanism to address the risk of climate change.
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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 | 0.003 | 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".