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Enregistrement W4230150466 · doi:10.21003/ea.v187-06

Perspectives of Ukrainian bioenergy development: estimation by means of cluster analysis and marketing approach

2021· article· en· W4230150466 sur OpenAlex

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Notice bibliographique

RevueEconomic Annals-ХХI · 2021
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueAgriculture Market Analysis Ukraine
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBioenergyUkrainianEnergy securityPopulationBusinessEnergy mixEnvironmental economicsEconomicsEngineeringRenewable energyElectricity generationSociology

Résumé

récupéré en direct d'OpenAlex

Development of the world economy requires energy supply, which under stable growth must be based on alternative energy resources. Bioenergy is an integral part of energy security supply in volatile countries. It can satisfy a considerable part of energy demand of agribusinesses and other companies as well as facilitate problem-shooting in energy, ecological and social sectors in some regions. Enhancing bioenergy in Ukraine is one of the strategic ways in the development of the alternative energy sector, taking into account high volatility of the country and significant potential of biomass available for energy production. This research intends to determine conditions and mechanisms of development and functioning of bioenergy clusters based on preliminary specification of the bioenergy potential of the territories, taking into account modern marketing approaches. This article contains evaluation of the bioenergy production growth in countries such as China, Germany, France, the USA, Canada, Brazil and Ukraine. The feasibility of the cluster approach for Ukrainian bioenergy development has been proved. In order to combine Ukrainian regions according to all types of energy resources the authors applied the method of clustering analysis. The key point of the method implies that, based on the given set of indicators which are defined as the main characteristics of the object, every object of the population belongs to a similar class. Therefore, in order to study the efficiency of usage of bioenergy resources in a particular region, it is necessary to classify a set of indicators to identify standard forms. To systemize Ukrainian regions, the Isodata algorithm Isodata, based on the types of the economic and energy potential of biomass, is taken into account. To implement the analysis, the following indicators are considered: the biomass energy potential of primary cell waste, the biomass energy potential of trimming, the biomass energy potential of refining, the energy potential of wooden biomass, the mold biomass potential, the energy potential of bioenergy crops, the corn energy potential (biogas). Market players organize groups with regard to their industries, territories and other factors, namely clusters which are likely to become effective tools in while carrying out scale projects under tough competition. In the minor energy sector cooperation between research and manufacturing enterprises, which satisfies energy needs both of cities and individual customers, is growing. This approach perfectly meets all requirements of the regional development of Ukrainian bioenergy. The main goal of bioenergy clusters is to develop competitive advantages of regions by increasing all types of biomass and biofuel production. This implies the following priorities: creation of a database of agribusiness enterprises, which potentially are members of the cluster and corresponding infrastructure, establishment of marketing communications in order to inform members and potential investors about bioenergy advantages, introduction of regional databases by means of webpages, newsletters, public discussions etc., enhanced vocational training of bioenergy industry employees and investment attraction to finance bioenergy projects. As a result, the authors of the paper propose a classification of Ukrainian regions based on the indicators of the economic energy potential of wastes and energy crops in agribusinesses, which is the basis for cluster formation. Vinnytsia, Kyiv, Poltava, Sumy, Khmelnitsky and Chernihiv regions refer to the first type with the biggest bioenergy potential, which makes it possible to create 2 energy clusters by combining central-west and north-east regions. Such a methodology gives an opportunity to satisfy the needs of the regions and districts which need additional energy resources taken from own biomass. Priority tasks of the bioenergy cluster include: development of the database of agribusiness entities which potentially are the cluster members and corresponding infrastructure, informing members and investors about bioenergy benefits, creation of the regional information database identifying the resources, capacity and the transport system, vocational training, investment attraction in order to implement bioenergy projects. Based on clusters, economic relations build up a competitive and sound investment climate to support the economy, which, in turn, provides high living standards. The authors have defined the procedure for exercising the cluster initiative and determined the structure of marketing support for cluster projects.

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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)0,001
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 enseignants0,638
Score d'incertitude au seuil0,573

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
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 savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 GPT0,207
Écart entre enseignants0,196 · 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