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中国大豆进口风险分散及进口来源结构优化——基于替代性与依赖性视角Risk dispersion and optimization of soybean imports for China: Based on substitution and dependence risks

2025· article· en· W6963334271 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsnot available
Fundersnot available
KeywordsDiversification (marketing strategy)ChinaDispersion (optics)Substitution (logic)Order (exchange)Agriculture

Abstract

fetched live from OpenAlex

为分散我国大豆进口风险,促进大豆进口多元化水平,以大豆进口风险理论分析为基础,对进口风险进行分类评价,运用非线性规划方法对大豆进口风险分散和进口来源结构优化进行模拟分析。实证分析表明:中国与RCEP成员国、南美洲国家间的大豆替代弹性较高,分别为2.13和1.84,由于RCEP成员国大豆产能不足,且RCEP成员国与南美洲国家的大豆替代弹性(2.29)较高,中国可从南美洲国家如阿根廷、乌拉圭等国进口大豆以减少替代风险;对于依赖性风险而言,中国对阿根廷、加拿大和俄罗斯大豆的依赖性风险值小于1,这3国属于进口风险低的机遇型国家,中国对美国和巴西大豆的依赖性风险值大于1,这2国为风险型国家,中国大豆依赖性风险主要来源于美国和巴西;中国大豆进口来源仍需优化,在满足进口风险最小化的前提下中国依然有增加进口的空间,可减少巴西大豆的进口,增加其他国家大豆的进口。今后我国可从提高国内大豆产量、积极开展进口合作、鼓励企业转移农业投资国度等方面减少大豆的进口依赖风险,保障我国大豆供应安全。 In order to disperse the risk of soybean imports for China and promote the diversification level of soybean imports, based on the theoretical analysis of soybean import risk, the import risk was classified and evaluated, and the nonlinear planning method was used to simulate and analyze the dispersion of the risk of soybean imports and the optimization of the structure of import sources. The empirical results showed that the soybean substitution elasticities between China and RCEP members and South American countries were 2.13 and 1.84 (relatively high), respectively. Due to insufficient soybean production capacity in RCEP members and soybeans from South American countries highly substitutes to soybeans sourced from RCEP members with a substitution elasticity of 2.29, importing soybeans from South American countries, such as Argentina and Uruguay, could help reduce the risks of substitution. In terms of dependence risk, China′s dependence values on soybeans from Argentina, Canada and Russia were less than 1, therefore, the three countries were opportunistic countries with low import risks for China; the dependence values on soybeans from the United States and Brazil were greater than 1, therefore, they were risky countries; China′s dependence risk on soybeans mainly came from the United States and Brazil. China′s import sources of soybean still needed to be optimized. By minimizing import risks, China still had room to increase its soybean imports. Reducing soybean imports from Brazil and increasing soybean imports from other countries were relatively optimal. In order to ensure the security of soybean supply in China, it is optional to reduce the risks of soybean imports by increasing soybean production in China, actively engaging in import cooperation, encouraging enterprises to transfer agricultural investment to other countries.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.116
GPT teacher head0.464
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations2
Published2025
Admission routes1
Has abstractyes

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