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Enregistrement W2396361064 · doi:10.55016/ojs/sppp.v9i1.42582

A Survey of the Literature on Local Content Policies in the Oil and Gas Industry in East Africa

2016· article· en· W2396361064 sur OpenAlexaff
Chilenye Nwapi

Notice bibliographique

RevueThe School of Public Policy Publications · 2016
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueNatural Resources and Economic Development
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésPetroleum industryFossil fuelBusinessPolitical scienceRegional scienceEngineeringGeographyWaste managementEnvironmental engineering

Résumé

récupéré en direct d'OpenAlex

Although oil and gas exploration has been going on in East Africa for decades, until recently exploration activities grew more slowly compared to other regions in Africa. Today, there has been a series of oil and gas discoveries in several East African countries, including Kenya, Madagascar, Mozambique, Tanzania and Uganda. Debate is however mounting over what effect the new oil and gas discoveries would have on East Africa, given the trajectory of older oil-producing countries in Africa, particularly Angola, Nigeria and Sudan. The challenge for East Africa is, therefore, how to maximize the potential benefits from the resources to avoid the under-developmental path that these other countries followed. There is general consensus that lack of specialized skills is a major obstacle to Africa’s realization of its resource potentials. One instrument currently being adopted by most oil and gas resource-rich countries (both in and outside Africa) to deal with the skills problem and to enhance linkages between the oil and gas sector and other sectors of the economy is the formulation of local content policies (LCPs). Typically, LCPs require companies to give preferential treatment to nationals of the country in which they operate in matters of employment and in the procurement of goods and services. It is believed that this would result in technology transfer and facilitate the ability of the country to take charge of its own development. But LCPs come with certain tradeoffs: Their potential incompatibility with international trade agreements threatens their sustenance; they can create unrealistic expectations capable of discouraging investment; and they are easily prone to corruption. However, there is a strong case for emerging oil and gas-producing East African countries to consider adopting the LCP. The nascent nature of the oil and gas industry in the region means that these countries would not have the technical and even managerial expertise to meet the demands of the industry. And training and education are essential for economic development. The LCP appears to be a potent tool to train local professionals. The question however is how to design the policy to reflect the particular needs and circumstances of each country. East African countries may consider adopting a localist approach to LCP by giving special consideration to the localities where the oil and gas and exploitation takes place. This approach may help them to address wider socio-economic problems associated with oil and gas development. They may also consider adopting a regional approach, which would enable all the countries to pull their resources together to jointly address the skills problem facing the region and thereby help one another. Given the enormous oil and gas skills gap in the region, it may help East Africa to avoid the imposition of stringent local content targets on oil and gas companies operating in the region and, instead, adopt an incremental and compartmentalized approach that takes stock of what skills are available, in what compartments at any given time and set their local content targets accordingly. Promotion of linkage development is essential to enhance the contribution of the oil and gas industry to the macro economy. Linkage development can facilitate technology transfer and economic diversification. But even these measures will have only minimal positive impact if the potential for elite capture and corruption is not addressed through the injection of transparency and accountability measures into the LCP design and implementation.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,016
Score d'incertitude au seuil0,044

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,009
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0080,022
Études des sciences et des technologies0,0020,003
Communication savante0,0050,006
Science ouverte0,0010,002
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0120,001

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 Opus0,104
Tête enseignante GPT0,256
Écart entre enseignants0,152 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

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

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

En bref

Citations20
Publié2016
Routes d'admission1
Résumé présentoui

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