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Record W2509491359

아프리카 건설·플랜트 시장특성 분석 및 한국의 진출방안 (Trends in the African Construction and Plant Building Market and Its Implication for Korea)

2012· article· ko· W2509491359 on OpenAlexaboutno aff
Youngho Park, Sungil Kwak, Hyelin Jeon, Jong-Moon Jang

Bibliographic record

VenueSSRN Electronic Journal · 2012
Typearticle
Languageko
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationOrder (exchange)Port (circuit theory)BusinessQuarter (Canadian coin)PoliticsGeographyDevelopment economicsEconomic growthEconomyEconomicsFinancePolitical scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

Korean Abstract: 아프리카의 인프라는 세계에서 가장 열악하다. 도로는 화물과 승객 수송의 80~90%를 담당하는 주요 국가 기간망이지만 포장비율은 20% 정도에 불과하며, 철도 역시 대부분이 식민시대에 건설된 것으로 노후화 정도가 심각하여 제 기능을 다하지 못하고 있다. 항만 및 공항 시설 역시 크게 낙후되어 있어 경제발전의 발목을 잡고 있다. 전력 인프라 또한 그 사정이 크게 다르지 않은데, 사하라이남 지역의 경우 인구는 8억 명에 달하고 있지만 발전능력은 인구 4, 500만 명의 스페인보다도 적으며, 이것도 남아공을 제외하면 아르헨티나와 비슷해진다. 더욱이 전력설비가 크게 노후화되어 1/4 정도는 제대로 가동되지 못하고 있는 실정이다. 그 결과 30여 개국에서는 매일 정전이 발생하고 있으며 이로 인해 막대한 규모의 경제적 손실을 겪고 있다. 그런데 최근 들어 정치적 안정과 괄목할 만한 경제성장에다가 자원개발 붐과 국제사회의 인프라 개발자금 지원이 결부되면서 아프리카에서 새로운 건설수요가 창출되고 있다. 많은 아프리카 국가의 건설부문 성장률이 경제성장률은 물론 다른 산업의 성장률을 크게 상회하고 있는데, 이러한 현상은 앙골라, 수단, 알제리, 콩고(DRC) 등 자원부국을 중심으로 더욱 두드러지게 나타나고 있다. 국가별로 정도의 차이는 있으나 많은 아프리카 국가들의 공공투자 계획을 보면 교통 및 전력 인프라 등 사회간접자본(SOC) 확충에 초점이 맞추어져 있으며, 여기에 더하여 산유국들은 국제유가 상승으로 축적한 오일머니 재원을 바탕으로 산업기반시설 확충에 나서고 있어 고부가가치 산업인 플랜트 건설수요도 빠르게 증가하고 있다. (후략) English Abstract: Africa is the poorest continent in the world in terms of public infrastructure. In any country with functioning public infrastructure, roads form the backbone of transportation, responsible for the 80-90% of movements of people and goods. In Africa, however, only 20% or so of existing roads have been paved. The vast majority of existing railways was laid during the colonial era and is now obsolete, unable to function properly. Much of its port and airport facilities are similarly outdated becoming, in effect, major obstacles to the continent’s economic development. Particularly conspicuous as well is the absence of proper electricity infrastructure. Almost 800 million Africans live in the sub-Saharan region, but the aggregate power generation capacity of the region lags behind the capacity of Spain (with a population of 45 million). If South Africa is not counted in with Sub-Saharan Africa, the region’s power capacity is lowered to the level of Argentina. Nearly a quarter of the existing power facilities are out of order and obsolete. Thirty or so African countries, therefore, experience power outages on a daily basis, and serious economic losses as a consequence. The increasing measure of political stability and signs of significant economic growth in Africa, coupled with activities in resource extraction market and the increasing influx of infrastructure development funds from the international community, have led to the explosive growth of the construction industry on the continent. The growth rate of the construction sector in numerous African countries hovers well above the average economic growth rate. This pattern is especially evident in such resource-rich countries as Angola, Sudan, Algeria, and the Democratic Republic of Congo. The public investment plans of these African states, notwithstanding differences in detail, mostly focus on expanding the social overhead capital (SOC), such as the infrastructure for transportation and electricity. Oil-producing countries now actively channel the wealth they have gained from the rise in international oil prices toward expanding their industrial infrastructure, fueling the growing demand for the development of various plants. (The rest omitted)

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.539
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.221
Teacher spread0.209 · 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 teacher head, 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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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