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Record W7124763178 · doi:10.65315/mjss.v35i98.4857

The Geoeconomic Case for U.S. Investment in Mongolian Copper

2025· article· W7124763178 on OpenAlexaboutno aff
Andres Loretdemola

Bibliographic record

VenueMongolian Journal of Strategic Studies · 2025
Typearticle
Language
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsExpropriationChinaPosition (finance)Copper miningInvestment (military)GeopoliticsForeign direct investmentDominance (genetics)

Abstract

fetched live from OpenAlex

China has a chokehold on global supply chains of critical minerals that presents a national security threat to the United States. This was a deliberate strategy to cultivate dominance since the 1990s. Following the example of Japan, the United States should strongly consider investing in Mongolian copper to regain strategic autonomy in this area.Mongolia has a difficult geographic position as a buffer state between Russia and China. As a hedge, it has pursued a multi-vector foreign policy for strategic balancing, with Washington being an important partner. Although the United States has no vital interests in Inner Asia, it should still pursue commercial relations in the region, since it is strategically important as the confluence of the Chinese and Russian spheres of influence. Mongolia’s most productive economic sector is mining, having one of the world’s largest copper mines. Copper will take on increasing economic importance in coming decades due to the high demand generated by the AI revolution. The United States is a net importer of refined copper.Mongolian copper has lower extraction costs, and the ore is of higher quality, than that of Chile and Canada, currently the main sources of foreign copper for the United States. But there are geopolitical risks to investing in Mongolia because of its rough neighborhood. The case of the expropriation of a Canadian uranium mine underscores them.There are reasons to remain optimistic. Mongolia is a healthy democracy. If paired with investment in domestic refining capacity, investments in Mongolian copper could be insulated from Chinese influence and could open up a viable, second transportation route through Russia. Монгол Улсын зэсийн салбарт АНУ-ын хөрөнгө оруулалт хийх геоэдийн засгийн үндэслэл Хураангуй: Хятад стратегийн чухал ашигт малтмалын дэлхийн нийлүүлэлтийн сүлжээнд ноёрхлоо тогтоосон нь АНУ-ын үндэсний аюулгүй байдалд заналхийлэл учруулж байна. Энэ нь давамгайлал бий болгох зорилготойгоор 1990-ээд оноос хэрэгжүүлж эхэлсэн стратегийн үр дүн юм. Японы жишгээр, АНУ энэ салбарт стратегийн бие даасан байдлаа сэргээхийн тулд Монгол Улсын зэсийн салбарт хөрөнгө оруулах талаар нухацтай авч үзэх ёстой. Орос, Хятадын дунд буфер байдлаар орших Монгол Улс стратегийн тэнцвэрт байдлаа хадгалахын тулд олон тулгуурт гадаад бодлого явуулж ирсэн. Энэ хүрээнд Вашингтон чухал түншийн байр суурь эзэлдэг. АНУ-ын хувьд Хятад, Оросын нөлөөллийн огтлолцол болсон стратегийн энэ чухал бүс нутаг болох Азид худалдаа эдийн засгийн харилцааг хөгжүүлэх нь зүйтэй. Монгол Улс дэлхийн хамгийн том зэсийн уурхайнуудын нэгийг эзэмшдэг. Харин АНУ боловсруулсан зэсийн цэвэр импортлогч. Хиймэл оюуны хувьсгал ирэх арван жилд зэсийн эрэлтийг эрс өсгөх тул эдийн засгийн ач холбогдол нь улам нэмэгдэх юм. Монголын зэс олборлолтын зардал бага, хүдрийн чанар АНУ-ын импортын гол эх үүсвэр болох Чили болон Канадынхаас өндөр байдал нь сонирхол татна. Монголд хөрөнгө оруулахад газарзүйн нөхцөлөөс шалтгаалан геополитикийн эрсдэл бий. Гэвч дотоодын боловсруулах хүчин чадалд давхар анхаарснаар Монгол дахь зэсийн хөрөнгө оруулалтыг Хятадын нөлөөнөөс тусгаарлаж, Оросоор дамжих тээврийн маршрутыг нээх боломжтой. Түлхүүр үг: Чухал ашигт малтмал, Зэсийн олборлолт, Нийлүүлэлтийн сүлжээний аюулгүй байдал, Гуравдагч хөршийн бодлого

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.132
Threshold uncertainty score0.261

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0050.002
Open science0.0000.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.051
GPT teacher head0.302
Teacher spread0.250 · 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 designNot applicable
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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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