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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 sur OpenAlexaboutno aff
Youngho Park, Sungil Kwak, Hyelin Jeon, Jong-Moon Jang

Notice bibliographique

RevueSSRN Electronic Journal · 2012
Typearticle
Langueko
DomaineBusiness, Management and Accounting
ThématiqueTransport and Economic Policies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPopulationOrder (exchange)Port (circuit theory)BusinessQuarter (Canadian coin)PoliticsGeographyDevelopment economicsEconomic growthEconomyEconomicsFinancePolitical scienceEngineering
DOInon disponible

Résumé

récupéré en direct d'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 Much of its port and airport facilities are similarly outdated becoming, in effect, major obstacles to the continent’s economic development.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)

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,539
Score d'incertitude au seuil0,796

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,011
Tête enseignante GPT0,221
Écart entre enseignants0,209 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2012
Routes d'admission1
Résumé présentoui

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