*3.1. Spearman's Correlation*

The study aimed to determine whether there is a correlation between the variables (HPV, RDD, PAT) and the indices (POP, GDP, TFC, TFC\_T, TFC\_I, RES, CO2, CO2\_T, CO2\_I) and to see which variables influence hydrogen indicators. The study used Spearman's correlation test for nine countries.

In Spearman's correlations of the matrices, it is clear that correlations occur in all the analyzed dependencies (Figures 2 and 3; Supplementary Data, Figures S1–S18). The reason for their association strength is different. The following thresholds were adopted in the analyses [85]: very strong (the correlation coefficient ≥0.80), strong (the correlation coefficient ≥0.60; <0.80), moderate (the correlation coefficient ≥0.40; <0.60), weak (the correlation coefficient ≥0.20; <0.40), and very weak (the correlation coefficient >0.0; <0.20). The relationships between the two variables can be positive or negative.

In 2008 (Figure 2), HPV was most strongly correlated with POP (0.60). The remaining correlation values are at a similar level in the range (0.45–0.57). These are moderate dependencies. In the case of RDD, the strongest correlation is seen for TFC\_I, and CO2\_I (0.90). The other correlations are also stronger compared to the relationship between the chosen indicators and HPV.

**Figure 2.** Spearman's correlation and compilation of data for all analyzed countries in 2008. \*, \*\*, \*\*\*—the more symbols, the stronger the correlation. Abbreviations: HPV—hydrogen production volume; RDD—energy RD&D in the hydrogen production and fuel cells category; POP—population; GPD—gross domestic product; TFC—total final energy consumption; TFC\_T—total final energy consumption in transport; TFC\_I—total final energy consumption in industry; RES—share of renewable energy of primary energy supply; CO2—total carbon dioxide emissions; CO2\_T—total carbon dioxide emissions in transport; CO2\_I—total carbon dioxide emissions in industry.

**Figure 3.** Spearman's correlation and compilation of data for all analyzed countries in 2018. \*, \*\*, \*\*\*—the more symbols, the stronger the correlation. Abbreviations: HPV—hydrogen production volume; RDD—energy RD&D in the hydrogen production and fuel cells category; PAT—number of patents; POP—population; GPD—gross domestic product; TFC—total final energy consumption; TFC\_T—total final energy consumption in transport; TFC\_I—total final energy consumption in industry; RES—share of renewable energy of primary energy supply; CO2—total carbon dioxide emissions; CO2\_T—total carbon dioxide emissions in transport; CO2\_I—total carbon dioxide emissions in industry.

In 2018 (Figure 3), HPV was most strongly correlated with TFC\_I and CO2\_I (0.65). The relationship between HPV and POP remained at the same level.

The very strong correlations in 2018 for RDD were maintained with TFC\_I and CO2\_I (0.90), as well as with TFC (0.88). Patents are most significantly correlated with CO2 (0.98). Significant relationships are also seen for the PAT relationship with TFC\_I, CO2\_I, TFC, GDP, and POP.

The research has shown that throughout the period analyzed (2008–2018) in addition to a correlation with economic indicators, HPV also strongly correlates with environmental and energy indicators (Supplementary Data, Figures S1–S18). The strongest correlations ≥0.90 are seen in three non-European countries. HPV-CO2 correlations, including in transport (CO2\_T) and industry (CO2\_I) sectors, lead to the conclusion that to achieve environmental sustainability, hydrogen production is a key factor and cannot be ignored. At the same time, hydrogen should be produced using renewable energy sources and should be completely free of CO2 emissions. It was also observed that there is a correlation regarding HPV-TFC including TFC\_I and TFC\_T. There are very strong correlations in all Asian countries, the United Kingdom, and Australia. In addition, it is strong in the Netherlands, Germany, and the United States. This is evidence that the production of hydrogen not only has an environmental impact but is also oriented to final energy consumption

(Supplementary Data, Figures S1–S18). Secondly, most of the correlations between RDD and the selected indicators were very strong or moderate negative correlations. The same is true for the correlation between patents (which are often the result of research and development works) and the analyzed indicators. Among the very strong and strong correlations (≥0.60), negative correlations for RDD occur with final energy consumption (KOR, UK,), CO2 emission (KOR, UK, JPN), and renewable energy sources (KOR, FRA, USA). Patents strongly correlate with CO2\_T (KOR, GER, AUS), RES (USA, AUS), and TFC (USA, AUS). It might be suggested that public funding is still very important, however, the level of funding in some countries is declining and the share of private funding is increasing to achieve a sustainable environment.

Attention should be given to the increase in hydrogen production, where many strong correlations with selected indicators have been demonstrated. This may be a major factor in achieving environmental goals that will accelerate the process of decarbonization of the economies of sustainable countries. However, in order to achieve this, hydrogen must be produced from renewable energy sources.

#### *3.2. Regression Models*

A total of 92 × 3 parameter combinations for HPV were analyzed, and separately, a total of 92 × 3 parameter combinations for RDD were also analyzed. Table 1 shows the results for HPV in the form of pairs for which the mean R<sup>2</sup> values were the highest. Similarly, the RDD results are shown in Table 2.

**Table 1.** The values of the coefficient of determination for the linear regression (R2) for selected combinations of HPV and parameters xd1 and xd2—four pairs with the highest mean R<sup>2</sup> for individual countries were presented. Color agenda: red—the highest value, green—the lower value; the best combination is bold.


Abbreviations: POP—population; GPD—gross domestic product; TFC\_T—total final energy consumption in transport; RES—share of renewable energy of primary energy supply; CO2—total carbon dioxide emissions; CO2\_T—total carbon dioxide emissions in transport; CHN—China; USA—United States; JPN—Japan; KOR—Republic of Korea; NLD—Netherlands; FRA—France; UK—United Kingdom; GER—Germany; AUS—Australia.

**Table 2.** The values of the coefficient of determination for the linear regression R2 for selected combinations of RDD and parameters xd1 and xd2—four pairs with the highest mean R<sup>2</sup> for individual countries were presented. Color agenda: red—the highest value, green—the lower value; the best combination is bold.


Abbreviations: POP—population; GPD—gross domestic product; TFC\_T—total final energy consumption in transport; RES—share of renewable energy of primary energy supply; CO2\_T—total carbon dioxide emissions in transport; USA—United States; JPN—Japan; KOR—Republic of Korea; NLD—Netherlands; FRA—France; UK—United Kingdom; GER—Germany; AUS—Australia.

In two-parameter linear regression, the highest value of the mean R<sup>2</sup> was achieved for HPV and the following parameter pair: TFC\_T/GDP and CO2\_T/GDP. In the category of countries, the highest value of R<sup>2</sup> for this pair of parameters was seen for China and the USA, and the lowest value was seen for France (0.664).

In two-parameter linear regression, the highest value of the mean R<sup>2</sup> was achieved for RDD and the following parameter pair: POP/GDP and RES. In the category of countries, the highest value of R2 for this pair of parameters applies to the USA and France, and the lowest value was seen for the United Kingdom (0.501). However, for the second pair (bold pair) in Table 2, the minimum value is four percentage points higher than for the first pair, therefore the following pair of parameters were used to present the results: RES and CO2\_T/GDP. In this case, the maximum value of the R<sup>2</sup> coefficient is also observed for the USA and France, and the lowest for Germany (0.544).

The results for the values of the c1, c2, c3 coefficients for the chosen pairs from Tables 1 and 2 are presented in Table 3.

**Table 3.** The values of the regression coefficients c1, c2, c3 (according to Formula (4)) for pairs xd1 and xd2 were selected based on Tables 1 and 2.


Abbreviations: HPV—hydrogen production volume; RDD—energy RD&D in the hydrogen production and fuel cells category; GPD—gross domestic product; TFC\_T—total final energy consumption in transport; RES—share of renewable energy of primary energy supply; CO2\_T total carbon dioxide emissions in transport; CHN—China; USA—United States; JPN—Japan; KOR—Republic of Korea; NLD—Netherlands; FRA—France; UK—United Kingdom; GER—Germany; AUS—Australia.

> For all countries except South Korea, an increase in energy consumption in transport has a positive effect on hydrogen production. However, for all countries except the Netherlands and the UK, CO2 emissions from transport had a negative influence on hydrogen production. In the USA, Japan, the Netherlands, and Australia, the positive effect of renewable energy sources is reflected in the proportion of hydrogen and fuel cells included in the energy technology budget. With the exception of the UK, Germany, and Australia, subsidies decrease as a result of the negative impact of CO2 emissions.

> Despite the good fit of the model to empirical data (according to the mean coefficient of determination in the above approach), the *p*-value is too high. For this reason, it was decided to calculate the best-fit regression model for each country. The results are presented in Table 4 and show the highest R2 coefficients for one-parametric regression, with a *p*-value < 0.05. The multiple regression models presented show that different characteristics in each country influence the level of hydrogen production or RD&D budget (Table 4; Supplementary Data, Table S2).


**Table 4.** Selected parameters xd0, xd1, and xd2 for which R2 is the highest under the condition *p*-value < 0.05.

Abbreviations: HPV—hydrogen production volume; RDD—energy RD&D in the hydrogen production and fuel cells category; POP population; GPD—gross domestic product; TFC—total final energy consumption; TFC\_I—total final energy consumption in industry; RES—share of renewable energy of primary energy supply; CO2—total carbon dioxide emissions; CO2\_T—total carbon dioxide emissions in transport; CO2\_I—total carbon dioxide emissions in industry; CHN—China; USA—United States; JPN—Japan; KOR—Republic of Korea; NLD—Netherlands; FRA—France; UK—United Kingdom; GER—Germany; AUS—Australia.

> For three countries, the HPV regression analysis gives the highest coefficient of determination (R2) for POP, whilst for two of the countries, it is a GDP indicator. For the data observed in NLD and FRA, no regression equation with an appropriate degree of significance was determined. In the case of two-parameter regression, the minimum R<sup>2</sup> value equals 0.89. However, similar to univariate regression for NLD and FRA, the results were not statistically significant.

> In the univariate regression for the explanatory variable RDD, the dependencies on CO2 and CO2\_T showed the greatest R2 factor with a *p*-value < 0.05. For NLD the *p*-value achieved is too high. For other countries, the coefficient of determination was reached at the level of 0.36 (for JPN) and 0.86 (for FRA).

> In the case of two-parameter regression, the minimum fit factor was 0.62 for JPN, and the highest was achieved for the GDP/POP and TFC/POP model for the USA. Causal relationships were established between the dependent and explanatory variables, which indicates that most of the variables can be used to predict each other.

> In the case of China, industrial CO2 emissions positively affected the level of hydrogen production. The main hydrogen production process in the industry, i.e., steam methane reforming, has a significant carbon footprint. The high level of CO2 emissions (almost 7 kg CO2/kg of H2), comes from fuel consumption and the process reactions [86,87]. According to Soltani et al. [86], it is estimated that about 3% of global industrial CO2 emissions come from this process. In China, on average 33% of CO2 emissions came from this process over the period analyzed. The indicator of total CO2 emissions from all sectors is less significant but has a negative impact on hydrogen production.

> The final energy consumption in the USA industry sector has a positive influence on hydrogen production. In 2018, record-breaking energy consumption was observed in the end-user sector (industry) in the USA. In recent years, fluctuations have been noticeable, inter alia, with the Great Recession in 2008 and a gradual return to average energy consumption levels seen before 2008. The increasing energy demand in the growing USA economy is not compatible with the ambitions for climate [88]. Today, 95% of hydrogen in the USA is produced via endothermic processes involving natural gas reforming, more specifically steam reforming. As heat must be supplied for the production of hydrogen, energy consumption in these processes and related industrial sectors is increasing. Si

multaneously, the production of hydrogen is negatively correlated with CO2 emissions in industry. The main drawback of hydrogen production processes is the fact that carbon dioxide is released into the atmosphere. Carbon dioxide capture and sequestration (CCS), as well as the modernization of systems for capturing CO2 from large industrial SMR installations [89–91], will significantly reduce emissions in the industrial sector. The reduction of emissions from the SMR in the future together with the development of other green methods of hydrogen production, is compatible with the climate neutrality goal.

Further analysis found that the GDP had a significant negative effect on the USA RD&D budget for hydrogen technologies. Currently, energy efficiency and renewables are the top priorities in the energy technology RD&D budget (according to the IEA). This is in contrast with the decreasing total RD&D spending on hydrogen and fuel cells. Solutions involving cheap and effective energy sources are required. It should be noted that expenditure on fossil fuel technologies is still nearly five times greater than that spent on hydrogen technologies (USD 581.4 million in 2018). In 2019, it was estimated that hydrogen and fuel cell research was allocated only 1.5% of energy technology RD&D budgets. Despite the constantly growing budget for energy technologies and the increase in gross domestic spending on R&D [92] current subsidies for hydrogen technologies negatively correlate with GDP growth. However, there are plans for significant investment in these solutions in the coming years.

As energy consumption in Japan increases, the level of hydrogen production also increases. However, CO2 emissions have a negative effect on hydrogen production. Japan has set a goal to reduce current hydrogen production emissions by 60% by 2030. Net-zero CO2 emissions across the production-to-final-use hydrogen cycle should be achieved in the long term [93]. Emissions from the hydrogen production process will be reduced further thanks to CO2 capture and storage technologies. The impact of total energy consumption is related to the fact that hydrogen is an energy carrier. It must be produced from another substance before it can be used as a source of energy or fuel. In Japan, the development of the supply chain and the diversification of uses (mobility, electric energy production, industry, and the housing sector), mean that hydrogen will become a key energy source.

This study also found that the total energy consumption in Japan negatively correlates with the number of subsidies allocated to hydrogen technologies. In contrast, CO2 emissions from transport positively correlate with the RD&D budget for hydrogen technologies. Japan has identified hydrogen as the solution to the problem of clean energy for transport. Fuel cell vehicles are recognized as one of the key drivers of the hydrogen economy, especially in the future of transport where green hydrogen could completely replace traditional fossil fuels. As with other new developments, making hydrogen fuel cell technology as efficient and profitable as possible requires investment. Moreover, Japanese automakers Toyota, Nissan, and Honda are global leaders in the development of fuel cell vehicles. In addition, they work with Air Liquide to strengthen the national refueling network [94]. The potential of hydrogen to create emission-free transport, industry, and energy generation is recognized, which will initiate a new phase of growing economy in Japan [95].

The population growth in Korea has had a positive impact on hydrogen production. The population increase is correlated with the need to meet the current energy demand. The total energy consumption in industry also has a positive effect on hydrogen production, but to a lesser extent. In Korea, several interrelations can be identified between the production of hydrogen and energy consumption in industry. Firstly, most of the hydrogen produced is still intended as feedstock for petrochemical plants [96]. In the future, hydrogen could play a significant role as a cleaner feedstock for chemicals [44]. It is predicted that hydrogen will play a significant role in transforming the energy power system. Korea, a hydrogen frontrunner, has set leadership priorities not only with respect to fuel cell cars, but also in the field of large-scale stationary fuel cells for power generation. Driven by population growth together with economic growth and industrial competition, hydrogen will play a particular role in contributing to the total final energy consumption.

Moreover, with energy consumption in the Korean industry allowing for the impact of GDP, an increase in RD&D subsidies for hydrogen and fuel cells can be seen. In contrast, total energy consumption by all sectors has a smaller but negative impact on subsidies for hydrogen technologies. South Korea is known for its innovative prowess. The hydrogen industry was worth USD 12.54 billion in 2020 according to the Korea Energy Economics Institute [97]. The hydrogen economy is seen as a key contributor to economic growth (USD 38.54 billion) as well as providing many thousands of new jobs (420,000 jobs) by 2040. It is estimated that hydrogen could account for 5% of the projected energy consumption in 2040, provided that roadmap targets are met [44]. South Korea plans to strengthen its energy base in all sectors. The effect that energy consumption in industry has on the number of subsidies for hydrogen technology is taken into consideration and is the most significant factor in the regression model presented here.

In the Netherlands, total energy consumption has a significantly positive impact on the energy technology budget in the hydrogen technologies category. The share of RES in the total primary energy supply is less important since the value of subsidies decreases as it grows. Research work in the field of energy efficiency is carried out in a multidirectional and multifaceted manner. In the last two years of the period under consideration, a significant increase in expenditure on hydrogen and fuel cells can be seen. The share of the budget increased almost 38 times from 2016 to 2018 (USD 0.344 million in 2016, USD 13.225 million in 2018). However, subsidies for the development of RES were even higher and in 2018 the share of the renewables budget was over eight times higher than the expenditure on hydrogen. Research expenditure on hydrogen technologies is justified by the need to increase energy efficiency. The amount of energy obtained from the combustion of hydrogen (about 118 MJ/kg at 298 K) is much higher than that obtained from gasoline (about 44 MJ/kg) [98]. Hydrogen energy is converted directly into electricity with high efficiency and low power losses. Hence, industry sees potential for adopting hydrogen as an energy source for heating. Currently, the production of hydrogen in the industry is estimated to be 180 PJ per year. The industrial hydrogen system has a significant impact on the energy system in the Netherlands [99].

The proportion of the French energy technology budget allocated to hydrogen and fuel cells is negatively affected by increases in renewable energy sources, and to a lesser extent, by CO2 emissions in transport. The main goal of hydrogen production in the French strategy is to decarbonize industrial processes, for which the demand for hydrogen is currently the highest [6]. To produce carbon-neutral hydrogen, access to clean energy is essential. Due to the additional power demand and the departure from nuclear energy, renewable energy sources are being used. The decrease in the overall share of RES in the primary energy supply may increase RD&D expenditure, not only on renewables, but also on the development of hydrogen technologies.

Further analysis found that the increase in the proportion of renewable energy sources in the UK has a negative effect on hydrogen production, whilst the population growth is positively correlated with the amount of hydrogen generated. Currently, renewable energy sources account for less than 5% of hydrogen production. However, this situation is forecast to change in the future [100]. A decrease in CO2 emissions in transport positively influences the subsidies for hydrogen technologies. Simultaneously, with increasing total energy consumption, the RD&D budget for hydrogen and fuel cells increases. Currently, the level of carbon-free hydrogen production is insignificant. However, since over a third of industrial energy consumption is for high-temperature processes [4], the UK government needs to provide subsidies over the next decade to make hydrogen technology the lowemission energy solution ideal for generating this type of energy.

In Germany, the production of hydrogen is positively related to total CO2 emissions. However, the growth in CO2 emissions from transport is negatively related to the amount of hydrogen generated. Whilst transport emissions account for a significant proportion of total emissions, fossil fuels remain the main source. Germany has a very large industrial sector, consuming vast amounts of energy. Demand for hydrogen is expected to remain

particularly noticeable in industry, while its growth in transport will be driven by a smallerscale market growth impulse until 2030. The long-term goal of the German economy is for a gradual increase in the use of hydrogen in transport, especially green hydrogen; this will result in a decrease in CO2 emissions in this sector. However, it should be noted that the hydrogen strategy has to start with blue hydrogen (produced from natural gas with CO2 emission), due to insufficient volumes of green hydrogen in the near future [2]. The environmental policy focused on hydrogen technologies and cooperation of the government with individual sectors of the economy is necessary for the aspect of eliminating barriers to the hydrogen economy. In Germany, such challenges are visible, among others, in the transport sector, e.g., high costs of production and purchase of FCEVs, their limited availability and the lack of modern German models, low development of refuelling network infrastructure, and their profitability [101].

The last finding of this research was that the industrial CO2 emissions in Australia are positively correlated with hydrogen production, whilst total CO2 emissions have a negative and less significant influence on the level of hydrogen generated. Industry accounts for a significant proportion of hydrogen production compared with the volumes dedicated to other sectors. This is reflected in the lower impact of total CO2 emissions compared to industrial emissions. Mineral refining, chemical production, and steel manufacturing are currently emission-intensive industries in the Australian economy [12]. The use of hydrogen may allow low-carbon products to be obtained in these sectors. Australia has a great potential to produce low-emission hydrogen thanks to the opportunity of using large coal and natural gas resources in combination with the use of carbon capture and storage technologies. The possibility of producing clean hydrogen is also noticeable due to the intensely increasing share of renewable sources such as solar and wind in energy generation.

#### **4. Conclusions**

The growth rate of all socio-economic and environmental variables changes over time. There are numerous reasons for these fluctuations. The coming years will be decisive with respect to decarbonization, energy transformation, and the development of the hydrogen ecosystem.

The future of hydrogen will not only have environmental, energy, and economic dimensions, but will also be a cross-cutting topic with far-reaching consequences for foreign policy, security of supply, and geo-economic cooperation. Hydrogen is expected to play a key role in a future climate-neutral economy enabling emission-free transport and energy storage as well as energy-saving industry. All analyzed countries recognize the important role of hydrogen in their national energy and climate plans up to 2030. However, they still need instructions on what economic, social, and environmental factors are conducive to the development of hydrogen.

The USA, China, Japan, South Korea, the Netherlands, France, the United Kingdom, Germany, and Australia are all strongly committed to decarbonization of the economy. National strategies, roadmaps, financial support, and targets for hydrogen have generated unprecedented momentum for the hydrogen industry. All activities will require continuation in the form of new partnerships and the creation of hydrogen communities.

The posed hypothesis *There is a correlation between selected economic, energy, and environmental indicators and the development of the hydrogen economy in countries involved in the implementation of hydrogen technologies* was proved by the presented dependencies. As this article shows, several factors influence the hydrogen economy. Five key indicators have been identified: population, GDP, CO2 emissions (including CO2 from industry and transport), RES, and TFC (including industrial and transport sectors). These should be considered when modeling and analyzing the future role of hydrogen. Hydrogen production volumes, the share of RD&D budget, and the number of patents filed were selected as indicators of selected aspects of the hydrogen economy.

The hydrogen and fuel cells category is a small proportion of the total RD&D budget (1.5–16%). However, hydrogen RD&D is growing in most countries. In 2018, growth was positive in all countries except Korea and France. In contrast, the dynamics of hydrogen production are negative. Only in selected years are the dynamics positive for a few countries. The exception is China, where the dynamics of hydrogen production are positive throughout the 2008–2019 period.

The multiple regression models and correlations presented here show that, to a great extent, the different characteristics in each country contribute to the development of the vision of the hydrogen economy. The increase in the share of renewable energy sources in a given country can significantly contribute to strengthening the country's future hydrogen production market, whilst at the same time limiting the harmful impact of CO2 emissions. Hydrogen production, driven by an increasing population and economic growth, will play a crucial role in contributing to total final energy consumption. The amount of energy obtained from hydrogen is much higher than that from fossil fuels and can be efficiently converted directly into electricity.

Important synergies exist between hydrogen production and CO2 emissions. Hydrogen, as an energy carrier, must be produced from another substance. Unfortunately, this still tends to involve fossil fuels. Reducing emissions along the entire value chain of hydrogen technologies may be achieved in the distant future. However, today, in certain industrial sectors such as steelmaking, blue hydrogen could be used to reduce carbon emissions. It is necessary to properly target environmental policies in order to reward low-emission and zero-emission technologies and hence the fuels produced in these processes.

The proportion of the energy technology RD&D budget allocated to hydrogen and fuel cells is strongly linked to CO2 emissions. Fuel cells are a promising technology and compare favorably with internal combustion engine technology. Subsidies for hydrogen technologies are necessary to develop energy-efficient solutions in many sectors of the economy. Subsidies are influenced by total energy consumption.

It can be seen that the research variables affect a selected group of hydrogen indicators. This study contributes to the further development of the hydrogen economy. The analysis could be extended using additional indicators, e.g., the size and value of hydrogen imports and exports.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/en14164811/s1, Figures S1–S18: Matrices for analyzed countries (Figure S1 Matrix for China part1, Figure S2 Matrix for China part2, Figure S3 Matrix for South Korea part1, Figure S4 Matrix for South Korea part2, Figure S5 Matrix for the United Kingdom part1, Figure S6 Matrix for the United Kingdom part2, Figure S7 Matrix for Germany part1, Figure S8 Matrix for Germany part2, Figure S9 Matrix for France part1, Figure S10 Matrix for France part2, Figure S11 Matrix for Japan part1, Figure S12 Matrix for Japan part2, Figure S13 Matrix for the USA part1, Figure S14 Matrix for the USA part2, Figure S15 Matrix for the Netherlands part1, Figure S16 Matrix for the Netherlands part2, Figure S17 Matrix for the Australia part1, Figure S18 Matrix for Australia part2, Table S1: Raw data for statistical analysis, Table S2: Multiple regression models in chosen countries.

**Author Contributions:** Conceptualization, J.C. and R.K.; methodology, P.O.; formal analysis, J.C. and P.O; validation, J.C, R.K. and P.O.; investigation, J.C., R.K. and P.O.; resources, J.C., R.K. and P.O.; writing—original draft preparation, J.C., R.K. and P.O.; writing—review and editing, J.C., R.K. and P.O.; visualization, J.C. and P.O.; project administration, J.C. and R.K.; funding acquisition, J.C., R.K. and P.O. All authors have read and agreed to the published version of the manuscript.

**Funding:** The research was funded within the frame of statutory works of the Mineral and Energy Economy Research Institute, Polish Academy of Sciences.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
