Key SDG7 Factors Shaping the Future of Clean Coal Technologies: Analysis of Trends and Prospects in Poland

Abstract

This article presents the results of an analysis aimed at verifying the relationship between the implementation of SDG Goal 7 and the use of clean coal technologies in Poland. Clean coal technologies in the United Nations plans will constitute a crucial element of the strategy for sustainable development in the energy context. They are intended to be one of the tools for building an energy system based on renewable energy sources, constituting a bridge that enables the transition of Poland’s energy system from coal to renewable energy sources. To identify whether this relationship exists, the Autoregressive Moving Average with Exogenous Input (ARMAX) model was used. The structure of the model, its correctness, and its accuracy were confirmed using information criteria; statistical tests such as Dickey-Fuller, Doornik-Hansen, Durbin-Watson, and Breusch-Pagan; and measures of prediction accuracy such as MAPE, MAE, and RMSE. The explanatory variables were the Objective 7 indicators adopted by Eurostat. Before being introduced to the ARMAX model, they were standardized using the Compound Annual Growth Rate (CAGR) indicator. The analysis made it possible to indicate which of the explanatory variables has the greatest impact on the development of clean coal technologies in Poland, to determine a synthetic CAGR measure for all the explanatory variables, and to compare the results obtained with the indicator determined by the United Nations.

1. Introduction

In 2015, the United Nations established 17 goals whose implementation aims to ensure sustainable development of the member countries of the United Nations [1]. They are an extension of the Millennium Development Goals [2]. Sustainable development goals (SDGs) must be ensured by implementing a global plan to protect the planet and its inhabitants. They aim to prevent climate change, poverty, inequality, discrimination, lack of access to natural resources, and wars [3]. Sustainable development is a state in which it is possible to meet the needs of modern society but also future generations [4]. The main goals are the following:

• Eliminating poverty
• Eliminating hunger
• Health and quality of life
• Education
• Gender equality
• Clean water and sanitation
• Clean and accessible energy
• Economic growth and decent work
• Innovation, industry, and infrastructure
• Reducing inequality
• Sustainable cities and communities
• Responsible production and consumption
• Climate action
• Life below water
• Life on land
• Peace, justice, and strong institutions
• Partnerships for goals [5].

The three areas affected by sustainable development have been integrated in the implementation of SDG7. These are environmental, economic, and social aspects.

In the environmental aspect, achieving goal 7 will allow consumers to access clean energy, without the emission of harmful substances that negatively affect the natural environment. This economic aspect is, above all, ensuring uninterrupted energy supplies in the required quantity, time, and place and at an affordable price. Without it, economic development is impossible. In the social aspect, achieving the SDG7 goal is to ensure the elimination of energy poverty. Access to energy affects the quality of life of society and access to education and health care.

The European Union is also working on the implementation of the Sustainable Development Goals in its member states, which is reflected in the directives it adopts [6]. In the research presented, the authors focused on Goal 7, that is, clean and accessible energy. In this case, the goal is to be achieved mainly due to the European Green Deal [7], the Clean Energy for All Europeans package [8], the RED Directives [9], and the EED Directives [10].

For each goal, a set of measures was adopted to allow evaluation of their degree of implementation. For Goal 7, the European Union has built a set of eight metrics included in the Eurostat database. These are the following:

Primary energy consumption

Final energy consumption

Final energy consumption in households per capita

Energy productivity

Share of renewable energy in gross final energy consumption

Energy import dependency

Population unable to keep home adequately warm by the poverty status

Greenhouse gas emissions intensity of energy consumption [11,12].

The need to achieve Goal 7 and improve the indicators selected to measure the level and effectiveness of its implementation should encourage the EU member states, especially Poland, to invest in clean coal technologies (CCT). These are one of the tools for optimizing the energy production and use processes. Undoubtedly, the need to reduce pollutant emissions from the coal combustion process requires the use of CCT, such as, for example CCS [13] or CCU [14], which will first capture pollutants and second make it possible to collect them and use them in other industrial processes. One of the exhaust gas purification technologies is based on membrane techniques developed by the authors [15]. They have several advantages that allow them to eliminate the main problems associated with the use of CCT during the energy production process. Membranes are key components in low energy technologies, and membrane modules can be used in existing energy installations without affecting or reducing the efficiency of the electricity or heat production process [16]. The authors constructed and used hybrid membranes with a polymer matrix with many fillers, e.g., zeolite 4A. These membranes have proven to be effective during the process of separating CO₂ from the mixture of gases generated during coal combustion. Membrane techniques can also be used to recover rare earth metals from fly ash [17]. Selective adsorption membranes were created using modified chitosan with the addition of an ion-imprinted polymer [18].

The implementation of Goal 7 will be closely related to the transformation of energy in the coming years, and both factors will influence the future of CCT. Therefore, the authors decided to check whether the measures adopted to achieve Goal 7 can be treated as variables influencing the development of CCT. The purpose of the research was to identify the factors that influence the development of CCT and to determine whether this influence is stimulating or negative. Knowledge of these factors is important in terms of shaping energy strategies, supporting innovation, and investing in the transition to a sustainable economy. This will allow for proper allocation of resources and optimization of the benefits derived from investing in the implemented technologies. It will also enable the detection of possible obstacles in the process of implementing CCT. It will make it possible to determine whether a sustainable development implementation strategy is being introduced.

In the presented research, the Autoregressive Moving Average with Exogenous Input (ARMAX) model was used to verify the impact of Goal 7 metrics on the development of CCT. Explanatory variables were the measures of goal 7. The ARMAX model also made it possible to create a forecast of this development until 2025. Before the explanatory and dependent variables were introduced into the ARMAX model, they were transformed using the compound annual growth rate indicator (CAGR) [19]. Due to this, the variables were standardized. It also made it possible to analyze the variables according to the methodology used by the European Union. Determining the CAGR indicator allowed for the verification of the indicators of the development rate, determination of the direction of their development trend [20], and the nature of variables. Because of this, it was possible to identify not only the variables that influence the development of CCT but also whether this influence is beneficial or hinders the development of CCT. A synthetic CAGR measure was also determined for all explanatory variables. The methods used during the research are described in the next section.

2. Materials and Methods

In order to carry out the analyses necessary to achieve the research objective, the CAGR indicator was used, which was also applied to standardize the explanatory variables introduced into the model. The CAGR value was calculated for each variable. This is an indicator that allows for tracking the average annual growth rate of the analyzed variable. This indicator is used, among others, by Eurostat, and the methodology for its determination is described in the document Sustainable Development in the European Union. The CAGR indicator is calculated according to the following formula [21]:

$$CAGR={\left({\displaystyle \frac{{\mathit{y}}_{\mathit{t}}}{{\mathit{y}}_{{\mathit{t}}_{\mathbf{0}}}}}\right)}^{{\displaystyle \frac{1}{\mathit{t}-{\mathit{t}}_{\mathbf{0}}}}}-1$$

(1)

where:

t—most recent year,

${\mathit{y}}_{\mathit{t}}$—value of the analyzed variable in base year,

${\mathit{y}}_{{\mathit{t}}_{\mathbf{0}}}$—value of the analyzed variable in most recent year,

${\mathit{t}}_{\mathbf{0}}$—base year.

Table 1. Interpretation of the CAGR indicator values.

CAGR	Interpretation
≥1	Dynamic variable growth
0 < CAGR < 1	Moderate increase
−1 < CAGR < 0	Decrease of the variable over time
<−1	Dynamic decrease of the variable

presents the value of the interpretation of the CAGR indicator. An indicator greater than 1 seems to be the best option among all, which is true for the original use of this indicator, that is, the average annual investment growth. However, until the nature of this variable is unknown, it is impossible to determine whether dynamic growth is advisable, as in the case of energy productivity, or unfavorable, as is the case with CO₂ emissions.

Before constructing a model, the time series must be examined for stationarity. Like many time series models, ARMAX also requires the time series to be stationary. A series is stationary if its mean and variance are constant and do not change over time [22]. To verify the stationarity of the time series, the enhanced Dickey-Fuller (ADF) test was used [23]. The test requires determining a statistical value and comparing it with a critical value. Two hypotheses are put forward, the null hypothesis H0, there is a unit root in the time series and the series is nonstationary, and hypothesis H1, there is no unit root in the series, and it is stationary. Summarizing the ADF test allows verification of whether the analyzed process is stable and unchanging over time [24].

The ADF test statistics are determined according to the following equation [25]:

$$\mathsf{\tau }={\displaystyle \frac{\widehat{\gamma }}{SE\left(\widehat{\gamma }\right)}}$$

(2)

where:

$\widehat{\gamma }$—estimated value of the regression coefficient.

This facilitates the process of proper model construction and forecasts using it. If the ADF test shows non-stationarity of the time series, it can be reduced to a stationary form, e.g., by differentiation, i.e., determining the differences between the variable y_t and the same variable shifted in time [26]. Due to this procedure, the trend and seasonal component are removed from the series.

The explanatory and explained variable in the next step should be entered into the ARMAX model [27]. The ARMAX model enables the construction of an extended ARIMA model using a moving average and autoregression with many explanatory variables that influence the explained variable y. Thanks to the ARMAX model, it was possible to take into account the influence of both previous terms of the time series y_t and exogenous variables on the explanatory variable. To build the model, it is necessary to adopt the following three parameters: p autoregressive conditions, q moving average terms, b—exogenous input.

The ARMAX model is described by the following equation [28,29]:

$$y\left(t\right)=\sum _{i=1}^{n_a}{a}_{i}y\left(t-i\right)+\sum _{j=1}^{n_b}{b}_{j}u\left(t-j\right)+\sum _{k=1}^{n_c}{c}_{k}e\left(t-k\right)+e\left(t\right)$$

(3)

where:

u(t)—input signal sequence,

y(t)—output signal sequence,

e(t)—white noise,

$a_i,b_j,c_k$—prediction coefficients,

n—order of the predictor.

The ARMAX model was selected based on the following information criteria [30,31]:

$$AIC=-2lnL\left(\widehat{\theta }\right)+2K$$

(4)

$$BIC=-2lnL\left(\widehat{\theta }\right)+Kln\left(n\right)$$

(5)

$$HQ=-2lnL\left(\widehat{\theta }\right)+2Kln\left(lnn\right)$$

(6)

where:

n—number of observations,

$L\left(\widehat{\theta }\right)$—model credibility function corrected by the penalty function of the number of K parameters of the model,

AIC—Akaike Information Criteria,

BIC—Schwarz information criteria

HQ—Hannan-Quinn information criteria.

The information criteria allow assessment of the accuracy of the model’s fit to empirical data. This in turn makes it possible to select the appropriate model from those created. Information criteria also allow minimizing the risk of model overfitting or underestimation.

After the model was developed, its accuracy and correctness had to be verified. For this purpose, an error analysis—model residuals—was performed. The residuals should be characterized by no autocorrelation and normal distribution, and the variance of the residuals should be constant over time. An appropriate statistical test was used, such as Ljung-Box [32], Doornik-Hansen [33], or Breusch-Pagan [34] test.

The Ljung-Box test was used to verify the phenomenon of autocorrelation in the residuals of the model. Autocorrelation of the residuals indicated an incorrect selection of the model because the residuals retained data regularities that the model was unable to describe. Predictions based on such a model could produce incorrect results. The test requires two hypotheses: H0, the residuals of the model show autocorrelation, and H1, the residuals of the model are correlated [35]. The test statistics were determined according to the following formula:

$$Q=n(n+2)\sum _{k=1}^h{\displaystyle \frac{{\widehat{\rho }}_k^2}{n-k}}$$

(7)

where:

${\widehat{\rho }}_k^2$—sample autocorrelation at lag k,

h—number of lags tested.

The Doornik-Hansen test was used to verify the normality of the distribution of model residuals [36]. For this purpose, a test statistic was determined, which allowed verification of whether the tested residuals deviated from the normal distribution. The test requires hypothesis H0, the data come from a normal distribution, or H1, the examined residuals differ from the normal distribution. Test statistics were determined according to the following equation [37]:

$$DH={\left[Z(\sqrt{b_1}\right]}^2+{\left[z_2\right]}^2$$

(8)

where:

$Z(\sqrt{b_1}$)—transformed sample skewness,

$z_2$—Wilson-Hilferty transformation.

If the distribution of residuals is not normal, it may mean that the model has been incorrectly selected, it does not consider all variables, and the predictions obtained using the model may be unreliable.

The heteroskedasticity of the residuals of the model was verified using the Breusch-Pagan test [38]. Heteroskedasticity occurs when the variance of the model residuals varies. The test requires hypothesis H0, the residuals are homoscedastic, or H1, where the residuals show heteroscedasticity. To determine the test statistics, the following formula was used:

$$LM={\displaystyle \frac{1}{2}}ESS$$

(9)

where:

ESS—Explained sum of squares in auxiliary regression.

The heteroskedasticity of the model residuals means that they show certain regularities and patterns, so they are not randomly scattered, and the model does not take into account all regularities occurring in the time series.

After verifying the accuracy of the model residuals, additional accuracy indicators of the expired forecast and the forecasts until 2025 were determined. For this purpose, indicators were used, such as the average prediction error (ME), root mean square error (RMSE) [39], average absolute error (MAE), and the mean absolute percentage error (MAPE) [40]. These errors made it possible to verify the accuracy of the built model.

3. Results

The presented research began with the transformation of exogenous variables and the endogenous variable. The transformation of variables using the CAGR indicator enabled the identification and determination of long-term trends that shape the explanatory variables and the dependent variable. It made it possible to estimate the values of the variables in the selected time horizon, assuming that the variables would change at the same pace as before (since 2005). Additionally, CAGR makes it possible to unify and compare the rates of return of all variables regardless of the unit and magnitude order of the original variable. Unifying variables using CAGR involves standardizing the growth rate on an annual basis. This allows multiple variables to be compared in terms of growth rate over time. This does not mean full standardization with respect to the scale of variables. This allowed analysis of the energy sector and identification of those factors that changed the slowest and fastest. Variables become standardized, but the CAGR carries additional information. It is easy to interpret because it clearly indicates how the variable increased or decreased on average per year over a given period of time. CAGR is used to analyze long-term trends and eliminates short-term fluctuations, which makes it useful in strategic analyses. Due to this, it excludes temporary and accidental changes to which the analyzed factors have been subjected. Its use simplifies the modelling and interpretation of the results obtained. Explanatory variables are primary energy consumption, final energy consumption, final energy consumption in households per capita, energy productivity, share of renewable energy in gross final energy consumption, dependency on energy imports, and population unable to keep the house adequately warm by poverty status. Furthermore, a dependence indicator on coal imports was distinguished for Poland because, considering the energy system in Poland, the increase in this indicator was of significant importance for the sustainable development and the future of CCT. This ratio represents technological progress that aims to reduce greenhouse gas emissions in the mining and energy industries, such as CCS, CCU, or membrane techniques. It also represents the efficiency and energy productivity of the coal combustion process, which can also be achieved by using the appropriate CCT.

Table 2. Values of the determined CAGR indicator.

Variable	CAGR	Trend	The Nature of the Trend	Trend Assessment
Primary energy consumption, Mtoe	1.24	dynamic growth	+	positive
Final energy consumption, Mtoe	1.71	dynamic growth	−	negative
Final energy consumption in households per capita, KGOE	0.89	moderate growth	+	positive
Energy productivity KGOE	3.15	dynamic growth	+	positive
Share of renewable energy in gross final energy consumption by sector, %	5.05	dynamic growth	−	negative
Energy import dependency, %	9.50	dynamic growth	−	negative
Energy import dependency—solid fossil fuels, %	57.68	dynamic growth	−	negative
Population unable to keep home adequately warm by poverty status, %	−14.12	dynamic decline	−	positive

presents the average CAGR value determined in the years 2005–2022, with 2005 taken as the base year. It should be noted that for most variables, the indicator has a value greater than 1 (Figure 1). The highest CAGR value occurs in the case of dependence on energy imports. This shows the increasing level of dependence on coal imported from abroad, until recently, mainly from Russia. The final energy consumption in households per capita is growing the slowest. In the case of the population unable to keep their homes adequately warm by the poverty status variable, the measure indicates a dynamic decline.

However, in order to properly interpret the obtained results, it is necessary to additionally recognize the nature of the variable’s development trend. For this purpose, the sign of the variable coefficients obtained in the ARMAX model was used. Taking into account the trend and the nature of the variable, the last column of Table 2 includes an assessment of the trend, that is, whether it affects the analyzed phenomenon positively or negatively.

Four explanatory variables received a positive rating, including primary energy consumption, final energy consumption in households, and energy productivity, which is growing dynamically, and its increase is a desirable phenomenon. An increase in energy efficiency means that the implemented technologies are effectively using the processed energy. The use of modern coal combustion systems would have a positive impact on both productivity and greenhouse gas emissions. Furthermore, the population unable to keep their homes adequately warm was positively rated because in this case the dynamic decline is beneficial and means that the percentage of extremely poor households in Poland is decreasing. This was possible thanks to the large-scale use of coal fuel in Poland, which has always been one of the cheapest sources of energy and heat. The remaining four indicators were rated negatively, including energy import dependency, share of renewable energy, and final energy consumption, because their growth was not desirable.

The next step of this research was to build the ARMAX model. The ratio of CO₂ emissions generated during energy production processes to a unit of energy produced using coal in a given year was adopted as the dependent variable that represented the use of CCT. To verify the correlation of independent variables, multicollinearity analysis was performed. If the variables are highly correlated with each other, this may result in incorrect determination of the impact of the explanatory variables on the independent variable. Multicollinearity analysis was performed using Belsley-Kuh-Welsch diagnostics (BKW, 1980). Cond (

Table 3. Collinearity Diagnostics According to Belsley-Kuh-Welsch (BKW, 1980).

Lambda	Cond	Phi_1	Phi_2	Theta_2	Primary Energy Consumption	Energy Productivity	Energy Import Dependency
2.57	1.00	0.006	0.011	0.00	0.01	0.01	0.01
1.67	1.24	0.11	0.04	0.00	0.00	0.00	0.02
1.00	1.60	0.00	0.00	1.00	0.00	0.00	0.00
0.52	2.22	0.18	0.01	0.00	0.02	0.00	0.17
0.22	3.38	3.38	0.51	0.00	0.30	0.00	0.027
0.014	3.58	0.14	0.43	0.00	0.95	0.99	0.78

) stands for condition index. According to the Belsley-Kuh-Welsch method, cond ≥ 30 means a strong, almost linear correlation between variables. Cond of 10–30 means low interdependence. In the research presented, the number of status indicators for cond ≥ 30 is 0, and for cond ≥ 10 is 1. This means that one variable shows low interdependence, but this did not affect the quality of the built model.

The time series was analyzed in terms of its stationarity. For this purpose, the Dickey-Fuller test was used.

The test, taking into account the intercept and the linear trend, showed that the series was non-stationary. After a single differentiation, the series was reduced to a stationary form (

Table 4. Results of the ADF test.

ADF	p-Value
Before differentiation	0.24
After differentiation	0.005

).

A p-value lower than the adopted significance level of 0.05 means that the null hypothesis about the occurrence of a unit root can be rejected. In other words, the time series is stationary.

After reducing the time series to a stationary form, the optimal model was selected. A total of 198 models were created. The models were compared in terms of information criterion. The lower this criterion is, the more accurate the model is. Ultimately, the ARMAX (2, 2, 2) model was chosen.

Table 5. ARMAX (2, 2, 2) model information criteria.

Criterion	Value
AIC	0.96
BIC	5.92
HQ	0.91

presents the value of the information criterion for the selected model.

Some of the variables did not show statistical significance and therefore were not used to build the model.

Table 6. Measure used to build the ARMAX model.

Indicator	p-Value
Primary energy consumption	2.36 × 10−8 ***
Energy import dependency	1.17 × 10−7 ***
Energy productivity	0.0045 ***

presents the indicators that were identified as significantly influencing the endogenous variable with a p-value of less than 0.05.

In the case of these variables, statistical significance was confirmed by Student’s t-test, which showed that the null hypothesis that the variable was not statistically significant should be rejected. The p-value is less than α = 0.01, which is marked with the *** sign.

After building the model, expired forecasts were determined for the period 2005–2022. This made it possible to determine the residuals of the model, that is, the difference between the empirical value of the estimated explained variable and the value using the ARMAX model in the same period t. The adopted significance level for statistical tests was set at 0.05. The p-value allows interpretation of the statistical significance of the obtained test results.

The time series of residuals was also verified for the presence of the Autoregressive Conditional Heteroskedasticity (ARCH) effect. Heteroskedasticity means that the variance of the residuals shows trends and patterns. In other words, the variance of the residuals is not non-stationary. In the case analyzed, the test confirmed that the model residuals were homoscedastic. The p-value is 0.17 and is higher than a significance level of 0.05, which means that there is no reason to reject the null hypothesis about the lack of the ARCH effect in the residuals. This means that the model is correctly selected and forecasts based on it will be accurate and reliable. In the case of the Doornik-Hansen test, the p-value was 0.93, so it was also higher than the assumed significance level. This means that there are no grounds to reject the H0 hypothesis on the normality of the distribution of model residuals. In the Ljung-Box test, the p-value is 0.28, which also means that there are no grounds to reject the null hypothesis about the lack of autocorrelation of residuals.

Table 7. Results of statistical tests performed.

Test	Test Statistic	p-Value
Ljung-Box	1.14	0.28
ARCH	1.89	0.17
Doornik-Hansen	0.13	0.93

presents the results of the tests performed.

The ARMAX (2, 2, 2) model was considered properly constructed. Therefore, the model errors were determined and are presented in

Table 8. ARMAX (2, 2, 2) model errors.

Error	Value
ME	0.08
RMSE	0.11
MAE	0.08
MPE, %	−6.52
MAPE, %	6.52

.

The errors indicate that the model can be considered accurate. MAPE is 6.52%, which means that the predicted values differ by 6% from the actual values, which can be considered an acceptable level of precision.

Therefore, the model was successfully verified and validated and used for forecasting. Figure 2 shows the time series of actual and theoretical values with a forecast until 2025. The forecast indicates that in the next 3 years, the value of the explained variable will not undergo significant changes. The CAGR value for the explained variable still indicates a decline, which in 2025 compared to the last known observation (2022) changed by 3%.

Furthermore, the presented research compared the CAGR indicator with the results presented by the United Nations. The results included in the Sustainable Development Report are calculated on a linear basis of the annual growth rate. In this study, the indicator was calculated in accordance with the methodology adopted by the European Union. Each of the individual indicators for the explanatory variables is presented in Table 2. To compare the obtained result with the results presented by the United Nations, a common CAGR indicator was determined for all variables.

The ARMAX model was used to build the overall CAGR indicator for Poland. Indicators for explanatory variables determined by the ARMAX were used to estimate the weights of individual factors. A weighted average was determined, which is a synthetic CAGR indicator for all explanatory variables. Since the values were normalized, the sum of the weights was equal to 1. The value of the CAGR indicator taken into account in the calculations was the average CAGR in the years 2006–2022.

If the factor of Poland’s dependence on coal imports is not considered, the CAGR is 0.87, which indicates a moderate increase, similar to the official result, that is, a moderate improvement, insufficient to achieve the goal. If an additional category of coal fuel imports is added, the CAGR indicator takes the value −6.93, that is, a dynamic decline (

Table 9. Synthetic value of the CAGR indicator.

Indicator	CAGR	Weight	Product
Primary energy consumption, Mtoe	1.24	0.156	0.19
Final energy consumption, Mtoe	1.71	0.13	0.22
Final energy consumption in households per capita, KGOE	0.89	0.15	0.14
Energy productivity, KGOE	3.15	0.15	0.49
Share of renewable energy in gross final energy consumption by sector, %	5/05	0.12	0.63
Energy import dependency, %	9.50	0.13	1.26
Population unable to keep home adequately warm by poverty status, %	−14/12	0.15	−2.06
Sum			0.87

).

4. Discussion

CCT in the United Nations plans constitutes a crucial element of the strategy for sustainable development in the energy context. They are one of the tools for building an energy system based on renewable energy sources.

Clean coal technologies will play an important role in the achievement of SDG 7, especially in the case of countries with a strong energy connection to coal [41]. Furthermore, industrial production requires the supply of large amounts of energy at the right time, which also means that coal may be irreplaceable in these cases [42]. The literature also describes the impact of clean coal technologies on the achievement of the SDG7 target. For example, a positive relationship has been demonstrated between CCS and CCU and the ability to provide energy at an acceptable price [43]. CCS allows a reduction of CO₂ emissions and, at the same time, ensures energy security [44].

CCT can provide a bridge between a coal-based system and one based on renewable energy sources. This transition is not easy to implement, especially when considering the scale of necessary investments, the need to ensure stable operation of the energy system, and the need to provide access to renewable energy technology and critical raw materials crucial for its development. CCT can support the transition by stabilizing the operation of the energy system. A characteristic feature of RES is that energy production depends on time of day, weather conditions, and season. CCT will enable access to energy during periods of reduced RES efficiency. Additionally, the waste from the coal combustion process, such as fly ash, will provide rare earth elements, i.e., critical raw materials without which it is impossible to build energy storage facilities, electric vehicles, and, above all, wind turbines.

The determined CAGR indicator and the ARMAX model allowed analysis of the impact of Goal 7 implementation indicators on the development of CCT. Factors that had a significant impact on the explained variable are primary energy consumption, dependence on energy imports, and energy productivity. The final energy consumption is the most dynamically growing of the three energy consumption indicators. Hence, the nature of the trend is negative. In the case of primary energy consumption and final energy consumption in households, the trend has stabilized at a constant level in recent years. This indicates a reduction in the demand for, among other purposes, energy in households, which may mean an increase in energy efficiency. The energy productivity index also shows a dynamic increase, which, considering the dominant share of coal in the Polish energy mix, may indicate the use of CCT, which increases the productivity of the coal combustion process. The growing share of renewable energy in the mix was evaluated as negative. This may be misleading because it is generally a positive phenomenon, but from the point of view of the share of coal in the mix, it is not a favorable trend. The dynamic increase in import dependence has a negative impact on the dependent variable [45]. Dependence on coal imports may result in the imported fuel being of lower quality and unsuitable for combustion in Polish installations. Imported coal can be delivered directly to recipients, without coal enrichment, and beneficiation is one of the initial stages of fuel purification [46]. The significant impact of dependence on coal imports has an extremely negative impact on the synthetic CAGR indicator. There will be no sustainable energy development in Poland without access to a stable energy source, which in the case of this country is coal. The sudden withdrawal of coal from the energy mix will lead to a disruption of the energy system and energy security and may lead to an increase in the percentage of extremely poor households in Poland.

The dynamic decline in the population rate that cannot keep their homes adequately warm due to poverty status has a positive impact on the development of CCT. The increase in society’s wealth translates into investments in modern, effective, and clean heating systems. Eliminating energy poverty is also associated with increasing the effectiveness of support programs that promote the use of clean coal technologies.

The built model and the forecast indicate that stagnation should be expected in the coming years in relation to the ratio of CO₂ emissions to the amount of energy produced using coal. The huge declines in CO₂ emissions that have occurred in Poland since the beginning of the 1990s were mainly caused by the liquidation of a significant part of heavy industry. This indicates the need to apply additional countermeasures that would allow further reduction of CO₂ emissions because those used so far are unable to provide a more effective impact on reducing emissions.

The results obtained using the CAGR indicator used by the European Union on a set of variables selected by Eurostat showed results comparable to the rate determined by the United Nations based on a similar but not identical set of indicators. In this case, the composition of the set of indicators is population with access to electricity, population with access to clean fuels and technology for cooking, CO₂ emissions from fuel combustion per total electricity output, and renewable energy share in total final energy consumption [47]. Both measures indicate slow improvement in the trends of the analyzed variables. However, it seems that both sets should be supplemented with additional items. The set of the United Nations could be enriched with the most important indicators according to the analysis, i.e., dependence on fuel and energy imports and energy productivity. The dependence on imports will be of key importance in the context of sustainable energy development and energy security, which cannot be ensured with a high degree of dependence on imports. Energy efficiency, in turn, will directly translate into sustainable development by allowing the economic management of fuel deposits that can be preserved for future generations and by reducing greenhouse gas emissions, which will also allow the planet to remain intact for future generations. The set of EU indicators could be supplemented with information on access to clean fuels and clean energy generated using fossil fuels, which would enable direct control of the deployment of clean coal technology implementation strategies in EU countries, such as Poland in particular.

5. Conclusions

SDG 7 assumes that by 2030, access to affordable clean energy will be ensured, both from renewable energy sources and fossil fuels. This energy is to be obtained in an efficient and sustainable manner. This condition, in the case of fossil fuels, can be met using CCT, especially in countries whose energy mixes are based on carbon fuels. In this case, the United Nations assumes that coal will constitute the fuel of the transition period, during which the potential of renewable energy sources will be built at a level capable of completely taking over the role of coal. To make this possible, coal and other fossil fuels must be burned in a way that eliminates the negative impact of this process on the environment.

The aim of this article was to identify the indicators of the implementation of SDG 7 that will influence the development of CCT. This knowledge will allow monitoring of the energy transition process and determining which factors may have a negative impact on the use of CCT, which will enable the implementation of possible remedial actions. This information is also valuable to decision makers, such as governments and state institutions, that are responsible for creating regulations and motivational measures to support the development of sustainable energy and invest effectively in the development of CCT. Identifying factors that influence the development of CCT will also facilitate the building of social acceptance and support for these types of solutions.

Identifying the factors that are most important for the development of clean technologies is crucial for several reasons. First, it will enable the proper allocation of resources because investment funds will be used most effectively, e.g., in areas where CCT will enable obtaining the best economic and ecological results. This will contribute to the reduction of greenhouse gas emissions, which is key to combating climate change. The development of CCT is also crucial for energy security, especially in the case of Poland, which is highly dependent on coal. Diversification of energy sources is beneficial; therefore, the ability to maintain the diversity of energy carriers in the energy mix is very desirable. It seems justified to conduct research on hybrid CCT and renewable energy systems. They can be concentrated within one energy factory, complementing each other, developing technologies and methodologies to manage the peaks and valleys of demand for energy from RES and CCT. It is also necessary to optimize the energy mix, especially in the context of local conditions in various regions of Poland. Knowledge of the factors that stimulate but also inhibit the development of CCT will allow the creation of accurate regulations and effective energy policies and strategies. It is easier to make decisions when it is known which of the factors analyzed should be worked on to intensify the pace of CCT development and thus effectively fight climate change. Poland should also place greater emphasis on the integration of clean coal technologies with the energy transition. Transformation should be carried out in two directions: the development of renewable energy sources and of clean coal technologies, the use of which also has a stimulating effect on the development of renewable energy sources. There should also be more CCT funding programs.