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Rozenský, L., Hájek, M., Vrba, Z., Pokorný, R., Hansen, J., and Lípa, J. (2020). "An analysis of renewable energy consumption efficiency in terms of greenhouse gas production in selected European countries," BioRes. 15(4), 7714-7729.

Abstract

The consumption of renewable energy sources results in the minimal production of greenhouse gases. However, the issue of environmentally efficient use of renewable energy sources remains a key concern. The primary aim of this article was to assess whether the energy production from renewable energy sources was environmentally efficient in four selected European countries: Germany, Austria, Poland, and the Czech Republic. In order to achieve the primary research goal, a regression analysis method was used for several variables. The results of the analysis suggested that with an increase in the consumption rate of renewable energy sources and biofuels equivalent to one thousand tons of oil, the volume of emissions from all sectors would increase by 0.0048 thousand tons (4.8 tons) on average. The system of emission allowances was rather environmentally inefficient at the lower allowance levels; in the monitored period of 2007 to 2016, the dependence of greenhouse gas production on the consumption of fossil fuels did not statistically manifest itself. Based on the analysis, the land use, land-use change, and forestry production activities do not contribute to increasing total greenhouse gas emissions.


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An Analysis of Renewable Energy Consumption Efficiency in Terms of Greenhouse Gas Production in Selected European Countries

Ladislav Rozenský,* Miroslav Hájek, Zdeněk Vrba,* Richard Pokorný, Justin Michael Hansen, and Jan Lípa

The consumption of renewable energy sources results in the minimal production of greenhouse gases. However, the issue of environmentally efficient use of renewable energy sources remains a key concern. The primary aim of this article was to assess whether the energy production from renewable energy sources was environmentally efficient in four selected European countries: Germany, Austria, Poland, and the Czech Republic. In order to achieve the primary research goal, a regression analysis method was used for several variables. The results of the analysis suggested that with an increase in the consumption rate of renewable energy sources and biofuels equivalent to one thousand tons of oil, the volume of emissions from all sectors would increase by 0.0048 thousand tons (4.8 tons) on average. The system of emission allowances was rather environmentally inefficient at the lower allowance levels; in the monitored period of 2007 to 2016, the dependence of greenhouse gas production on the consumption of fossil fuels did not statistically manifest itself. Based on the analysis, the land use, land-use change, and forestry production activities do not contribute to increasing total greenhouse gas emissions.

Keywords: Environmental policy; Renewable energy consumption; Woodchips; Land use, land-use change, and forestry (LULUCF); EU ETS

Contact information: Department of Humanities in Medicine, 1st Faculty of Medicine, Charles University, Karlovo náměstí 40, Praha 2 128 00 Czech Republic; *Corresponding author: Rladislav@seznam.cz

INTRODUCTION

The consumption of wood chips can be counted as an important contribution to renewable energy resources (Jiang et al. 2017). According to the latest Eurostat statistics (European Statistical System), this segment (wood chip consumption) is growing faster than any other renewable energy type (Eurostat 2019). Presently, when there is a surplus of pulpwood on the market, usage as a fuel is one of the possible uses of this energy source (Hájek et al. 2019). This can help reduce the economic losses of forest owners, e.g., after the calamity from the overpopulation of Ips typographus in Europe (Vakula et al. 2015). It should be noted that the combustion of wood chips is not an entirely pure renewable energy. During its combustion, the sequestered carbon is released back into the atmosphere. However, there is a so-called substitution effect in which the combustion of wood chips can replace the combustion of solid fuels that have a higher proportion of carbon and other pollutants (Hájek et al. 2019).

The main objective of this work was to assess whether the consumption of renewable energy sources is environmentally efficient. The secondary goal was to evaluate other factors and how they affect greenhouse gas emissions in the monitored countries.

To better understand the context above, the following research questions have been raised: (1) whether the consumption of renewable energy sources (RES) contributes to the reduction of CO2 emissions; (2) whether the European Union Emission Trade Market (EU ETS) contributes to reducing CO2 emissions in the selected countries; (3) whether land use, land-use change, and forestry (LULUCF) production activities contributes to increasing greenhouse gas emissions; and (4) whether the consumption of solid fuels contributes to an increase in CO2 emissions.

Environmental policy uses several tools in order to meet the needs of environmental protection. Institutional instruments are utilized along with other instruments; the individual components of which are chosen by states according to the priorities of their environmental policy (Hájek et al. 2019). Emission tradable allowances, which are also dealt with in the analysis put forth in this paper, can be classified together with carbon taxes and excise duties among indirect, economic instruments (Yegorova 2013). However, the European Union’s LULUCF program and the promotion of the consumption of renewable energy sources, as well as their substitution for the consumption of fossil fuels, falls within the framework of institutional instruments for environmental protection (Hájek et al. 2019).

Renewable Energy Consumption

Outside of wood chip combustion, the consumption of renewable energy sources does not cause the release of greenhouse gases. The substitution of such gases for solid fossil fuels ultimately leads to a reduction in total greenhouse gas production (Rozenský et al. 2019). The consumption of renewable resources is supported by the policies defined by the country, which is based on the support from the state for their usage (Kharlamova et al. 2018). The overall consumption of renewable energy sources is growing, according to Eurostat (Statistical Agency of the European Union) statistics (Eurostat 2019). The largest increase in time horizons was achieved by the combustion of wood chips (Khattak et al. 2020). It should be noted that wood chips release greenhouse gases during combustion, especially CO2, and thus releases the carbon sequestered by the growth of wood into the air (Zeng et al. 2019). Nonetheless, its substitution for other fossil fuels with a higher carbon content ultimately reduces the production of greenhouse gases (Hájek et al. 2019). Additionally, the processing of degraded pulpwood and wood waste into wood chips appears to be one of the possibilities for processing the wood waste and residues generated as a byproduct during forestry and wood production (Ul Hai et al. 2019).

Land Use, Land-use Change, and Forestry

On 30 May 2018, the European Parliament and the EU Council adopted a regulation regarding the inclusion of greenhouse gas emissions and their absorption due to land use, land use change, and forestry in the 2030 climate and energy policy framework and amended Regulation (EU) No. 525/2013 and Decision No. 529/2013/EU. Thus, EP (European Parliament) and Council Regulation EP 2018/841, titled LULUCF measures, was implemented. In doing so, the European Parliament and EU Council considered the fact that the land use, land use change, and forestry (LULUCF) sectors could potentially deliver long-term climate benefits and therefore make a major contribution to achieving the Union’s greenhouse gas reduction target in addition to the long-term goals of the Paris Agreement (Pistorius et al. 2017). The principle of the LULUCF program is primarily based on reporting the amount of sequestered nitrogen (Ellison et al. 2014). However, this is only in the broader sense. In a narrower sense, this reporting must be understood as a whole set of measures and activities, often of a production nature, which leads to an increased degree of carbon sequestration and its preservation in biomass, e.g., wood. In the field of forestry, these are primarily educational interventions in young stands and afforestation and crop protection (Gonzales-Garcia et al. 2014). During these activities, greenhouse gases are also released through production or transport. Analyzing the relationship between carbon sequestration activities and its correlation with greenhouse gas emissions can lead to the knowledge necessary to reduce these emissions.

EU ETS (European Emission Trade System)

The EU ETS is a common instrument of the European Union designed to reduce greenhouse gases (Hájek et al. 2019). Some authors classify this instrument as an indirect economic instrument for air protection. In this model, it is represented as a variable in terms of greenhouse gas production. Due to the research goals of this work, the theory of this tool will not be given more space.

EXPERIMENTAL

Materials

The objective of this study was to assess the research on environmental effectiveness. For this purpose, the data were created in order to form a timeline, from which the charts were then compiled for a more comprehensive understanding of the problem. Next, a regression analysis was performed, which attempted to assess the remaining objectives. Another aim was to assess additional factors, as well as to identify how greenhouse gas emissions are affected in the countries under review. The data of four EU Member States (Germany, Austria, Poland, and the Czech Republic) were further analyzed in detail. These are developed EU countries that form a comprehensive territorial European region. All countries selected for this analysis have the same institutional environmental instruments and do not use the voluntary economic instrument, i.e., a carbon tax, outlined by the EU (Lin and Li 2011). The German-speaking countries (Germany and Austria) are developed countries with GDPs (gross domestic product) above 100% of the EU average. Poland and the Czech Republic are among the newest members of the EU and exhibit dynamic development and GDP growth. All these countries have tradable EU ETS emission allowances in their environmental policy tool mix, a common EU instrument for reducing emissions. These countries also use renewable energy sources, depending on the traditions, environmental policies, geographical location, and geothermal conditions of the country (Chen et al. 2020). All the above-mentioned EU member states also use LULUCF measures as another instrument part of the common EU environmental policies.

Methods

The effects of renewable energy sources on the amount of CO2 production were analyzed in detail. Since the used model affected other factors and tools, e.g., the EU ETS or the consumption of fossil fuels, the authors used a regression analysis, which concluded that it was a suitable research method to assess the synergistic effect of several factors on the research goals. Greenhouse gas emissions (expressed in tons per year of CO2 per capita) are the basic dependent variable. These data were pulled from the European Statistical Office, section “Statistic A-Z.” (Eurostat 2019). The emission allowance prices are an explanatory variable. The emission allowance price was chosen as a variable because the EU ETS is a fundamentally obligatory regulatory element. The data were obtained from the European Energy Exchange and from the Energy Regulatory Office (ERU 2020). A unit is the average annual emission allowance in the EUR per 1 allowance. The consumption of fossil fuel is an explanatory variable, which was chosen because the consumption of fossil fuels relates to greenhouse gas emissions. The consumption of renewable energy is an explanatory control variable in our model. Their substitution for energy from the combustion of fossil fuels containing carbon has the ultimate effect of reducing the total greenhouse gas production. The data were obtained from the Eurostat database for which the per capita amount and year for the population of the country was calculated as of the 31st of December of the respective year according to the Eurostat database in our calculation model (Eurostat 2019). These data were reported by converting the consumption of renewable energies into their tons of oil equivalent. In the case of this variable, the theoretical expectations were negative, i.e., with the increasing consumption of renewable energies, there is a decline in greenhouse gas production. The LULUCF program must be understood as a set of activities and production processes leading to the provision of measures resulting from this European Regulation, as well as production activities leading to its goal, i.e., the promotion of carbon sequestration and its balance. The Eurostat database “Land use, land use change and forestry” served as a source for this timeline (Eurostat 2019). This variable was identified as the primary goal of the research, assuming the volume of bound carbon, or related activities, does not have a major effect on the increase of greenhouse gases. For the analysis of the data from 2007 to 2016 (carbon sequestration within LULUCF, greenhouse gas emissions, emission allowance price, consumption of solid fuels, and the consumption of renewable energy sources), regression and correlation analyses were used.

Regression analysis allows one to get information about the dependence of quantitative characteristics (Litschmannová 2011). The variable, whose behavior is explained in this research, is called a dependent variable (the explained variable). The X variable, whose behavior explains the behavior of the dependent variable, is called an independent variable (Hindls et al. 2002). Correlation analysis deals with interdepend-dencies, emphasizing the strength or intensity of the relationship (Bílková et al. 2009). In most cases, the linear regression equation used can be defined as Eq. 1,

 (1)

where η is unknown, β0 is a parameter (the intercept), and β1 is another parameter (the slope).

The intensity of the dependence was measured using a determination index (Budíková et al. 2010). If the dependency function is validated, the determination index is 1 (and vice versa if the value is 0). The Pearson correlation coefficient, for the two variables X and Y, was also calculated (Croissant and Millo 2008). Additional indicators were also analyzed via elementary statistical analysis with the selected characteristics as follows: position, variability, and concentration (median, variance, standard deviation, kurtosis, and skewness). The following hypotheses were verified in this paper: (1) H0: there was no linear relationship between the X (LULUCF) and (GHG emission levels) variables; (2) H0: there was no linear relationship between the X (emission allowance price) and Y (level of greenhouse gas emissions) variables; (3) H0: there was no linear relationship between the X (consumption of fossil fuels) and Y (GHG – green house gas emission level) variables; and (4) H0: there was no linear relationship between the X (consumption of RES) and Y (level of greenhouse gas emissions) variables.

For testing the hypotheses, the fixed probability error of the first type (so-called materiality level) was chosen to be 5% (Shmueli 2010). Significance tests of the regression parameters were performed in order to determine if the correlation between the sample variables was strong enough to be considered as proven for the base set.

RESULTS AND DISCUSSION

Graphic Analysis

The following graphs show the course of the explained emission variable and the individual explanatory variables.

Fig. 1. Relation of the explanatory variables to the explanatory (Czech Republic)

Fig. 2. Relation of the explanatory variables to the explanatory (Germany)

Fig. 3. Relation of the explanatory variables to the explanatory (Austria)

Fig. 4. Relation of the explanatory variables to the explanatory (Poland)

The values of the Pearson’s correlation coefficient are shown in Table 1. The value always expresses the correlation of the explained variable “emissions” (volume of emissions from all sectors in the territory of the given country) with the individual explanatory variables listed in the columns for individual countries listed in the rows.

Table 1. Pearson Correlation Coefficient

Table 2. Pearson Correlation Test

The values can be interpreted as follows: 100%, there is a 100% correlation between the emission variable and the given explanatory variable, i.e., as the explanatory variable increases, so do the emissions; 0%, there is a 0% correlation between the emission variable and the given explanatory variable, i.e., the variables are independent of each other; and -100%, there is a -100% correlation between the emission variable and the given explanatory variable, i.e., as the explanatory variable increases, the emissions decrease.

Table 2 shows the p-values for the Pearson correlation test. If a significance level of 5% was chosen and the value in Table 2 was less than 0.05, then a statistically significant correlation between the emission variable and the explanatory variable in the column for the country in the row could be made.

Panel regression

Due to the situation in which four units (four states) were found, along with four explanatory variables, one cannot use the random effects method. Therefore, 3 models of fixed effects were used. For the panel data models, the fixed effects represented the constants for individual units.

Model with fixed effects estimated using LSDV (least squares dummy variable) method

If the least squares dummy variable (LSDV) method is used, a dummy variable is assigned to each unit, as shown in Eq. 2,

yit = β0 + β1xit + αiDi + eit (2)

where yit is the dependent variable (DV) emission volume (i – country; t – time), β0 is the control parameter for zero ground (0), xit is the independent variable (IV) allowance price, β1 is the coefficient for IV, Di is the dummy variable for units (states), αi is the coefficient for units (states), and eit is the random component.

Table 3. Model with the Fixed Effects Estimated Using the LSDV Method