MétaCan
Menu
Retour à la cohorte
Enregistrement W2605056900

Regional Assessment of Agricultural Residues for Bioenergy Production in Ghana

2014· article· en· W2605056900 sur OpenAlexaboutno aff
Francis Kemausuor, Evans Yakah, Andreas Kamp

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueEnergy and Environment Impacts
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPer capitaAgricultural economicsAgriculturePopulationGeographyConsumption (sociology)ElectricityLatin AmericansChinaStandard of livingDeveloping countryEnvironmental protectionBusinessEconomic growthEconomicsEngineeringPolitical science
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

1. INTRODUCTIONEnergy is an important component of socio-economic development (Carley et al., 2011). In every country of the world, energy is needed to provide a range of services including, but not limited to, lighting, heating, cooking, and ensuring mobility. Industrial development cannot happen without access to energy. It has been shown by the Human Development Index (HDI) that access to energy somewhat matches extent of economic growth of a country (Chontanawat et al., 2008). Per capita energy consumption is highest in those countries and regions that are regarded as most developed. These include the United States, Canada, Japan, Europe, China and other notable countries in Asia and Latin America. The countries/regions with the least development are also the regions with the lowest per capita energy consumption. Majority of countries in Sub-Saharan Africa and South East Asia fall into this category. For example, per capita electricity consumption in sub-Saharan Africa [minus South Africa] was 180 kWh in 2010 compared to a world average of 2500 kWh (Bazilian et al., 2012). Only about 31% of the population in sub-Saharan Africa has access to electricity (Suberu et al., 2013). Apart from poor access to electricity, there is also poor access to modern fuels for cooking and heating. Close to 80% of the population in sub-Saharan Africa still rely on woodfuel for cooking and heating (Prasad, 2011) due to a lack of access to modern fuels such as Liquefied Petroleum Gas (LGP) and Natural Gas. This has implications for forestry as rural households resort to the use of charcoal and firewood, which are sourced from or processed from forestry resources.Many of the countries that do not have high access to energy services often rely on expensive energy imports that weighs negatively on their trade balance and leave little for other infrastructural developmental. To curb this situation and to advance home grown fuels, renewable energy has emerged as an alternative source of energy that is promoted globally. Technologies for assessing renewable energy may have higher start-up costs but levelised costs keep decreasing as efficiencies improve. Among the more popular renewable energy sources are solar energy, wind energy and biomass energy. For many countries in Sub-Saharan Africa (SSA), biomass energy is already the most consumed energy source but comes in traditional forms, such as firewood and charcoal. Advancement in technology has provided an opportunity to modernize biomass into cleaner energy carriers, such as liquid biofuels or biogas for transportation, cooking and electricity generation. To meet increasing demand for energy as well as ensure a low carbon future, it is anticipated that renewable energy forms, including modern biomass energy will be integrated into the global energy mix. For most developing countries with high agricultural potentials, the idea of modernizing biomass, especially for application in rural communities, is one that sounds very appealing. Residues from crop production, which come at virtually no cost to rural communities, can be used for the generation of electricity to address rural lighting challenges or to produce biogas to decrease the reliance on traditional biomass. Decreasing traditional biomass use is especially desirable because it has the potential to reduce deforestation. Large-scale deployment of modern biomass energy (which could also mean a lot of small-scale deployment in several rural communities) may help diversify fuel supply in many situations, which in turn may lead to a more secure energy supply with important environmental benefits (Fernandes and Costa, 2010). Like many other developing countries, Ghana is also seeking to become more sustainable in its use of energy resources. In this regard, the country is planning to increase the use of renewable energy in its energy mix. Already biomass dominates the energy mix but it is used in unsustainable combustion routes with very low conversion efficiencies. …

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)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,039
Score d'incertitude au seuil0,077

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,003
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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 Opus0,016
Tête enseignante GPT0,243
Écart entre enseignants0,227 · 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'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
Publié2014
Routes d'admission1
Résumé présentoui

Explorer davantage

Même sujetEnergy and Environment ImpactsTravaux en français237 207