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4th Edition of Global Conference on Biofuels and Bioenergy

September 09-11, 2027 | Valencia, Spain

September 09 -11, 2027 | Valencia, Spain
Biofuels 2027

Economic indicators and bioenergy supply in developed economies: QROF-DEMATEL and random forest models

Speaker at Biofuels and Bioenergy 2027 - Alexey Mikhaylov
Financial university under the Government of Russian Federation, Russian Federation
Title : Economic indicators and bioenergy supply in developed economies: QROF-DEMATEL and random forest models

Abstract:

Bioenergy is a renewable energy source that saves from fossil fuel dependence. Therefore, it is important to increase the efficiency of bioenergy investments to create an environmentally sustainable energy supply. This paper aims to identify economic indicators significant in forecasting the supply of bioenergy. Considering this goal, an integrated evaluation has been performed for 17 developed economies using the Random Forest method and the Fuzzy Decision-Making Trial and Evaluation Laboratory (QROF-DEMATEL) method. The main contribution of this study is conducting analysis by using both quantitative and qualitative data. Additionally, the coherence of the results made with the QROF-DEMATEL method is also verified by implementing a sensitivity analysis. The results of both approaches are quite similar and provide information about the reliability of the findings. This situation demonstrates that for the development of bioenergy investments, firstly, countries’ macroeconomic conditions should be improved. Consequently, economic growth and unemployment (weighting results - 0.159 and 0.155) should be primarily considered for the bioenergy supply forecast.

One of the most important conditions for increasing the efficiency of biomass energy investments is estimating the supply. The main reason for this is that the produced energy must fully meet the country’s demand (Ishaq and Dincer, 2020). In a case of under-produced biomass energy, the country’s demand will not be fully met. In this case, the country will either resort to fossil resources or have to import energy from other countries. This issue harms the country both economically and environmentally. On the contrary, if more biomass energy is produced than needed, the efficiency of energy investments will decrease (Colantoni et al., 2021). As a result, it will not be possible to ensure the sustainability of biomass energy investments.

Biomass energy is a type of energy obtained by burning organic materials. The materials burned in this process are not fossil fuels. Therefore, no carbon emission occurs in the process of securing biomass energy. Consequentially, it is possible to reduce the dependence on fossil fuels by producing environmentally sustainable energy (Irfan et al., 2020). Biomass energy also has some advantages compared to other types of renewable energy. For example, biomass energy is much easier to store. This situation brings a significant cost advantage to this type of energy. Furthermore, biomass energy sources can be found in abundance in nature. This results in increased efficiency of biomass energy investments (Konuk et al., 2021, Paraschiv and Paraschiv, 2020)

Bioenergy is a renewable energy source that saves from fossil fuel dependence. Therefore, it is important to increase the efficiency of bioenergy investments to create an environmentally sustainable energy supply. This paper aims to identify economic indicators significant in forecasting the supply of bioenergy. Considering this goal, an integrated evaluation has been performed for 17 developed economies using the Random Forest method and the Fuzzy Decision-Making Trial and Evaluation Laboratory (QROF-DEMATEL) method. The main contribution of this study is conducting analysis by using both quantitative and qualitative data. Additionally, the coherence of the results made with the QROF-DEMATEL method is also verified by implementing a sensitivity analysis. The results of both approaches are quite similar and provide information about the reliability of the findings. This situation demonstrates that for the development of bioenergy investments, firstly, countries’ macroeconomic conditions should be improved. Consequently, economic growth and unemployment (weighting results - 0.159 and 0.155) should be primarily considered for the bioenergy supply forecast.

One of the most important conditions for increasing the efficiency of biomass energy investments is estimating the supply. The main reason for this is that the produced energy must fully meet the country’s demand (Ishaq and Dincer, 2020). In a case of under-produced biomass energy, the country’s demand will not be fully met. In this case, the country will either resort to fossil resources or have to import energy from other countries. This issue harms the country both economically and environmentally. On the contrary, if more biomass energy is produced than needed, the efficiency of energy investments will decrease (Colantoni et al., 2021). As a result, it will not be possible to ensure the sustainability of biomass energy investments.

Biomass energy is a type of energy obtained by burning organic materials. The materials burned in this process are not fossil fuels. Therefore, no carbon emission occurs in the process of securing biomass energy. Consequentially, it is possible to reduce the dependence on fossil fuels by producing environmentally sustainable energy (Irfan et al., 2020). Biomass energy also has some advantages compared to other types of renewable energy. For example, biomass energy is much easier to store. This situation brings a significant cost advantage to this type of energy. Furthermore, biomass energy sources can be found in abundance in nature. This results in increased efficiency of biomass energy investments (Konuk et al., 2021, Paraschiv and Paraschiv, 2020)

Biography:

A research leader with 15+ years of academic and applied research management. Lead large-scale, multi-stakeholder initiatives — Head of 10+ funded research projects, managing teams of up to 15 researchers, budgets exceeding ₽15M per project, and crossborder collaborations (Russia, China, Brunei, UAE).

Authored 200+ peer-reviewed publications on financial forecasting, AI, and energy markets; consistently ranked in Stanford's World's Top 2% Researchers (2020–now).  Led a 3 year international research team (Russian Science Fund & BRFFR) - project "Mathematical models and computer technologies of production and energy scheduling in conditions of economic uncertainty" (2023–2025).

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