Perspectives of Ukrainian bioenergy development: estimation by means of cluster analysis and marketing approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it