Clean and Affordable Energy within Sustainable Development Goals: The Role of Governance Digitalization

Abstract

Achieving sustainable development goals depends on governance effectiveness within the penetration of digital technologies in all spheres and levels. Furthermore, the energy sector is a crucial inhibitor of sustainable development that depends on the efficacy of digital public services. In this case, the paper aims at analyzing the impact of e-governance on achieving sustainable development goals, particularly on affordable and clean energy. The object of research is EU countries in the period 2013–2019. The data are compiled from the databases of Eurostat, the World Data Bank, and the Sustainable Development Report. The study applies the following methods: the taxonomy method to measure the e-governance index, the fixed effect, the random effect, and a pooled OLS regression model to check the impact of e-governance on affordable and clean energy. The results outlined the leaders (Estonia, Denmark, Finland, Sweden, Latvia, and Malta) and the outsiders (Romania, Croatia, Greece, Bulgaria, and Poland) in e-governance in 2019. The findings confirm the statistically positive impact of e-governance on extending affordable and clean energy. Thus, improving e-governance by 1 point causes the growth of renewable energy on average by 4.4 points. Furthermore, the industrial structures’ improvement allows increasing renewable energy on average by 0.4 and the trade activization (economic openness) contributes to an increase of renewable energy on average by 0.1. The democracy level does not have a statistically significant impact on achieving sustainable development goals for EU countries. The empirical results show that the countries with high values of e-governance (Sweden, Latvia, Finland, Denmark, and Austria) achieve the highest values of the SDG7 Index Score. Thus, within EU countries, governance digitalization is the strategic inhibitor of SDG achievement.

1. Introduction

The new economic development paradigm (based on principles of sustainable development) aims at harmonizing the economic, ecological, and social goals of global development. The resolution “Transforming our world: the 2030 Agenda for Sustainable Development” [1] contains 17 goals (SDGs) that cover 196 targets and a vast range of indicators for assessing achievement access. The 2030 SDGs require the relevant transformation and reorientation of all sectors and levels. It should be noted that the scientists [1,2,3,4,5,6,7] emphasize that the achievement of SDGs depends on the following:

• The efficiency of economic sectors: increasing the countries’ competitiveness without increasing the negative pressure on the environment by extending innovations [8,9,10,11,12,13,14,15,16,17];
• Social inclusion: well-being, inequalities, income gaps, inclusion in making decisions [3,12,18,19,20,21];
• Ecological: combating the climate changes and anthropogenic impact on the environment through providing and extending innovation and digital technologies [8,17,22,23,24,25,26];
• Political: strengthening the democracy; cooperation, coopetition, and communication with all stakeholders (businesses, local authorities, society, investors, banks, etc.); providing the relevant policies and the mechanism to support the accomplishment of SDGs [3,27,28,29,30,31,32,33,34,35,36,37,38,39].

At the same time, achieving SDGs requires transparency and accountability of governance [1,2,3,4,5,6,7]. This could be realized through implementing the concept of digital governance [40,41]. Digital technologies provide a unique way to conduce to economic growth and to reduce the negative environmental effects [8,17,22,23,24,25,26,42]. Digital technologies provide communication among governments, society, and businesses with minimum resources (time, natural resources, capital) and environment degradation [8,17,22,23,24,25,26,42]. However, scholars [43,44,45,46,47] justify that digitalization provokes the overconsumption of energy resources, which contradicts the sustainable development principles. Thus, in this case, the extension of renewable energies and smart technologies allows resolving the issues of the overconsumption of energy resources. Digitalizing governance requires an affordable level of digital skills, knowledge, literacy, and infrastructure [48,49,50,51,52,53]. In addition, considering the analytic report [42], 2.7 billion people are still without Internet access, which limits the efficiency of governance digitalization. Thus, it requires a coherent policy supporting the digitalization of the global development within the SDG concept.

Experts and researchers worldwide [54,55,56,57,58] aims at providing affordable and clean energy. All countries have relevant targets for] emphasize the priority role of the energy sector in providing a country’s competitiveness (as a core production/input resource) considering the principles of sustainable development. Thus, SDG7 [1,54,55,56,57,58] aims at providing affordable and clean energy. All countries have relevant targets for the share of renewable energies in total energy consumption. Moreover, most EU countries have already achieved the targeted indicators. However, many developing countries are still on the way to succeeding in the declared goals. Past studies [59,60,61,62] show that digitalization and world transformation cause changes in the energy sector’s landscape. Thus, the focus of energy development is shifted from intensification to rational and effective use by spreading clean energy, renewable resources, smart technologies, etc.

The analysis of the theoretical framework shows that most studies focus on justifying the links among energy consumption and environmental degradation, green production, information technologies (IT), economic growth, SDGs, and digitalization. However, linking digital governance impact and achieving SDGs, particularly providing affordable and clean energy, requires empirical justification to develop the relevant instruments and inhibitors to boost sustainable development in the countries. In this case, the study aims to bridge gaps by developing an approach to empirically justify the impact of e-governance on affordable and clean energy.

The paper has the following structure: the literature review (analysis of the theoretical framework on the relationship among governance, digitalization, and affordable and clean energy to justify the hypothesis of the research); materials and methods (describing the methodology that is applied to test the research hypothesis); results (explanation of empirical findings); conclusion and discussion (analysis of the core results, comparison of the results obtained with the previous studies, limitations, and further direction for research).

2. Literature Review

2.1. Governance and Digitalization

Industrial revolutions and the expansion of digital technologies evoke the rapid development and penetration of information technologies (IT) in all economic sectors and levels (local, national, multinational). Consequently, this boosts the development of an information society. Scholars [63,64,65,66,67,68,69,70,71] justify that IT and artificial intelligence enhance the penetration of digital technologies among society. At the same time, the penetration of IT activates transformation from traditional to digital communications between governments and businesses. It requires the openness and transparency of all communication channels. Thus, e-governance is defined as the provision of public services via the Internet and IT in all processes carried out by public sector organizations [72,73,74,75,76]. Past studies [77,78,79] show that e-governance applies a vast range of IT instruments at different levels in external and internal activities among government and local authorities, businesses, and society. This simplifies communications at all levels and reduces resource use (capital, labor, natural resources, etc.). Furthermore, e-governance requires the involvement of all stakeholders and an affordable level of digital literacy.

It should be noted that the prior studies [3,7,8,12,15,17,80,81,82] confirm the nonlinear effect of IT on the social, economic, political, and ecological development of a country. Furthermore, the intensity of the IT impact depends on the intermediate indicators: trade openness, industrial structure, productivity, corruption, financial development, trust in government, etc. [8,9,10,11,12,15,24,25,26,27,28,29,30,31,32,33]. However, previous studies [75,76] prove that digitalization negatively affects economic growth and government efficiency in developing countries. Thus, digitalization could increase unemployment and, consequently, aggravate the population’s well-being, inequalities, and income gaps in the society. In this case, the government should provide effective and proactive policies on life-long education, training subsidies for government staff, and programs on digital literacy [48,49,50,51,52,53]. At the same time, Dada D. [83] highlights that a lack of Internet access is the core reason for the insufficient impact of e-government on the country’s growth.

Extending e-governance is one of the ways for developing countries to increase the trust of the government authorities by transitioning from an inefficient to an effective governance system [84,85]. Thus, Dhaoui I. [86] proves that e-governance allows decreasing corruption due to the country’s increasing transparency of all public services. Using the FGLS (Feasible Generalized Least Squares) and PTR (Panel Threshold Regression) models, the researchers [87] prove that e-government and corruption have a u-shaped relationship. It means that e-governance could curb corruption for a certain point after the increase in e-governance causing increasing corruption. Al-Refai M. S [88] analyzed the GCC (Gulf Cooperation Council) countries and confirmed the statistically significant impact of the e-government index on the country’s economic growth. However, separately, the e-government index’s component positively affects the economic growth of Bahrain, Kuwait, and Saudi Arabia. At the same time, such an effect is not confirmed for Oman and Qatar [88].

The scholars [89] confirm that the digitalization of governance services promotes achieving sustainable development goals and increasing the country’s environmental performance. At the same time, based on the literature review, the students [90] prove that most research is focused on analyzing the efficiency of monitoring systems to achieve sustainable development goals within the spread of IT.

IT boosts trade openness in the country due to the activation of international and digital trade in the countries (selling more products to a vast range of markets using digital technologies) [91,92]. The researchers [92] underline the positive correlation between the internet penetration and trade openness. The coherent concussion is outlined by the studies [93,94,95,96], which empirically justify that digitalization has a positive statistically significant impact not only on economic growth but also on trade openness. Digital technologies induce the transformation and modernization of the industrial sector. Furthermore, they lead to changes in the industrial structure [97,98,99]. A prior study [100] concludes that IT influences energy consumption through the industrial structure and investment in research and development (R&D). Furthermore, the papers [100,101,102,103] empirically justify that Industry 4.0 and artificial intelligence enhance the energy security due to the development of renewable energy. Furthermore, it allows reinforcing the energy security of the country [104,105,106,107,108,109].

The scholars [110] justify that the industrial structure has a mediation role in the relationship between digitalization and the green growth of the country. Using the panel data for Chinese cities from 2011 to 2019, the researchers apply the [111] mediating effect model, the benchmark regression model, and the spatial Durbin model to check the relationship between digitalization and green innovation development. Based on findings [111], they empirically justify that digitalization positively affects green innovation development through the intermediaries: trade openness and industrial structure. Furthermore, the scholars [112] point out that investment in R&D has a mediating role in the chain of “digital economy and carbon dioxide emissions”. It is conducive to the relationship between digitalization and ecological development.

2.2. E-Governance and Affordable and Clean Energy

The analysis of the theoretical landscape on linking digitalization and energy development shows that experts have not achieved a consensus on the characteristics and direction of the relationship between digitalization and energy development. The concept of an open energy market (which is the core driver of SDG achievement) is based on the digitalization of governance and aims to provide affordable and clean energy [113]. Murugan D. and Shalini G. [114] emphasize that the efficacy of governance plays a crucial role in achieving SDGs within the era of Big Data. The scholars highlight that in India, digitalization allows the decline of the gaps in accessing electricity among households. Okudolo I. and Ojakorotu V. [115] analyze Nigerian success on the way to achieving the SDG in the context of the digitalization of local governance. The researchers confirm that e-local governance boosts SDG 2030, including combating climate change by spreading renewable energies. Furthermore, based on findings of the Italian case textual analysis, scholars [116] justify the similar conclusion that digitalization enhances the achievement of SDGs.

Fraundorfer M. [117] outlines that the efficiency of governance in the energy sector could be increased by extending digital technologies among households and spreading the concept of smart cities. Furthermore, previous studies [118,119] confirm that the characters and direction of the relationship depend on governance efficiency within the expanding digital technologies. The paper [120] maintains that e-governance provokes the growth of energy efficiency (increasing the share of renewable energy; declining the gaps in accessing electricity; increasing affordability for clean energy for households) through the development of smart cities.

Thus, the research [87] points out that digital technologies are conducive to increasing investment in R&D and industrial structural energy intensity decline. However, the studies [121,122] show that digital technologies stimulate energy consumption growth due to the “rebound effect”. Bhatt J. and Jani O. [123] highlight that digital technologies allow increasing the share of renewable energy in the total primary energy supply through developing the e-governance system for smart cities. Sharma M. [124] shows that implementing the e-governance project leads to increasing the number of populations who have access to electricity in the rural areas in Nepal. Furthermore, e-governance allows spreading renewable energies among households.

Considering the results mentioned above, the study aims at testing the following hypothesis: e-governance has a statistically significant impact on affordable and clean energy.

3. Materials and Methods

Considering the sustainable development concept [1,9,17,27,32,33,34,35,36,37,38], the study measures SDG7 affordable and clean energy through the following indicators: $\mathrm{SDG}{7}_{\mathrm{elecac}}$ (the percentage of the population having access to electricity); $\mathrm{SDG}{7}_{\mathrm{cleanfuel}}$ (the percentage of the population primarily using clean fuels and technologies for cooking); $\mathrm{SDG}{7}_{\mathrm{co}2\mathrm{twh}}$ (a measure of the carbon intensity of energy production); $\mathrm{SDG}{7}_{\mathrm{ren}}$ (the share of renewable energy in the total primary energy supply). In the framework of the research, the core variables explained are $\mathrm{SDG}{7}_{\mathrm{ren}}$ and $\mathrm{SDG}{7}_{\mathrm{score}}$ (the SDG7 Index Score), since the values of $\mathrm{SDG}{7}_{\mathrm{elecac}}$ and $\mathrm{SDG}{7}_{\mathrm{cleanfuel}}$ are constant throughout the period analyzed for the selected sample of countries. The decline in $\mathrm{SDG}{7}_{\mathrm{cleanfuel}}$ mostly depends on the increase in the $\mathrm{SDG}{7}_{\mathrm{ren}}$. The study bases on the panel data for the European Union (EU) countries, which, compared with the time series of the separate countries, have greater variability, less collinearity among variables, more degrees of freedom, and are able to eliminate the endogeneity [21,29]. The study period covers 2013–2019, which is limited by the data available for evaluation of the e-governance in EU countries. Considering the e-governance benchmarking report, the data are available for 2013–2015 and 2017–2019 [125]. The data for 2016 are calculated by the linear interpolation method, since these variables have a linear trend (increasing or decreasing). Thus, based on the previous studies [3,8,27,28,39,72], the following indicators are selected for evaluating e-government: ${\mathrm{eGov}}_{\mathrm{Ind}}$ (individuals who used the Internet for interaction with public authorities); ${\mathrm{eGov}}_{\mathrm{ke}}$ (key enablers). The composite indicator that measured the quality level of the services to businesses and citizens: eID, eDocuments, Authentic Sources and Digital Post; ${\mathrm{eGov}}_{\mathrm{buss}}$ (digital public services for businesses, the extent to which a service or information concerning a service for businesses is provided online and via a portal); ${\mathrm{eGov}}_{\mathrm{cit}}$ (digital public services for citizens, the extent to which a service or information concerning service for citizens is provided online and via a portal). The synthetic index of evaluating e-government services’ development in the EU countries is calculated based on the taxonomic method [12,17,111,112]:

$${\mathrm{eGov}}_{\mathrm{i},\mathrm{t}}=\frac{1}{\mathrm{n}}{\displaystyle \sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{eGov}}_{\mathrm{ij}}^{\mathrm{std}}$$

(1)

where ${\mathrm{eGov}}_{\mathrm{i},\mathrm{t}}$ is the synthetic index of the e-government services’ development in i-country in t-time; n is numbers of components of the synthetic index of the e-government services’ development; ${\mathrm{eGov}}_{\mathrm{ij}}^{\mathrm{std}}$ is the normalized value of j components of the synthetic index of the e-government services’ development in i-country in t-time:

$${\mathrm{eGov}}_{\mathrm{ij}}^{\mathrm{std}}=\frac{{\mathrm{eGov}}_{\mathrm{ij}}-\overline{{\mathrm{eGov}}_{\mathrm{j}}}}{{\mathrm{S}}_{\mathrm{j}}}$$

(2)

where ${\mathrm{eGov}}_{\mathrm{ij}}$ is the actual value of j components of the synthetic index of the e-government services’ development in i-country in t-time; $\overline{{\mathrm{eGov}}_{\mathrm{j}}}$ is the arithmetic mean value of j components of the synthetic index of the e-government services’ development; ${\mathrm{S}}_{\mathrm{j}}$ is a standard deviation of j components of a synthetic index of e-government services’ development.

The synthetic index allows comparing countries on the e-government services’ development (the higher value of the synthetic index $\mathrm{eGov}$ means a more attractive environment within the e-government services’ development):

Group I comprises the countries with the highest level of e-governance services’ development:

$${\mathrm{eGov}}_{\mathrm{i},\mathrm{t}}\ge \overline{{\mathrm{eGov}}_{\mathrm{i}}}+{\mathrm{S}}_{{\mathrm{eGov}}_{\mathrm{i}}}$$

(3)

where $\overline{{\mathrm{eGov}}_{\mathrm{i}}}$ is the arithmetic mean value of the synthetic index of the e-governance services’ development; ${\mathrm{S}}_{{\mathrm{eGov}}_{\mathrm{i}}}$ is the standard deviation of the synthetic index of e-government services’ development.

Group II covers the countries with a high level of e-governance services’ development:

$$\overline{{\mathrm{eGov}}_{\mathrm{i}}}\le {\mathrm{eGov}}_{\mathrm{i},\mathrm{t}}<\overline{{\mathrm{eGov}}_{\mathrm{i}}}+{\mathrm{S}}_{{\mathrm{eGov}}_{\mathrm{i}}}$$

(4)

Group III encompasses the countries with a low level of e-governance services’ development:

$$\overline{{\mathrm{eGov}}_{\mathrm{i}}}-{\mathrm{S}}_{{\mathrm{eGov}}_{\mathrm{i}}}\le {\mathrm{eGov}}_{\mathrm{i},\mathrm{t}}<\overline{{\mathrm{eGov}}_{\mathrm{i}}}$$

(5)

Group IV includes the countries with the lowest level of e-governance services’ development:

$${\mathrm{eGov}}_{\mathrm{i},\mathrm{t}}<\overline{{\mathrm{eGov}}_{\mathrm{i}}}-{\mathrm{S}}_{{\mathrm{eGov}}_{\mathrm{i}}}$$

(6)

The achievement of SDG7 affordable and clean energy depends on various determinants that justify considering the control variables: the openness of the economy ($\mathrm{Trade}$); industrial structures ($\mathrm{Inds}$); research and development ($\mathrm{R}\&\mathrm{D}$), and democracy level ($\mathrm{Dem}$).

The energy consumption in the country relates to their industrial structures. The findings [17,26,126] empirically justify that those changes in industrial structures are conducive to the green economic growth, including the growth of renewable energy. Providing public quality services and equal access to society to electricity is due to the countries’ transition to the innovative development model based on the production of high-tech products [127,128]. The core inhibitor of the high-tech product development is the efficiency of R&D [51,52,81,97,129], which is measured by the number of patents in the country. The openness of the economy is the key concept of boosting the countries’ economic, financial, and innovative activities. Furthermore, the results of previous studies [8,28,32,33,40,130,131] show a positive correlation between trade openness and renewable energies. Furthermore, democracy allows eliminating the administrative and bureaucratic issues in trade and simplifying participation in the international production networks of innovative technologies, products, and services [3,39,91]. In the framework of the research, based on the studies [3,24,27,28,39,91], democracy is measured by the voice and accountability indicators, which experts estimate within the Worldwide Governance Indicators (WGI) project.

The results of the descriptive statistics of the explained, explanatory, and control variables are shown in

Table 1. The descriptive statistics of the selected variables.

Variables	Measure	Source	Mean	SD	CV	Min	Max	Sk.	Kurt	J.–B.
Explained variables
$\mathrm{SDG}{7}_{\mathrm{ren}}$	Share of renewable energy in total primary energy supply (%)	SDG [132]	28.74	21.10	0.74	0.00	85.30	0.84	2.70	22.23
$\mathrm{SDG}{7}_{\mathrm{score}}$	The SDG Index Score		75.37	7.81	0.10	50.5	90	−0.48	4.62	26.88
Components of explanatory variable $\mathrm{eGov}$
${\mathrm{eGov}}_{\mathrm{Ind}}$	Percentage of individuals	Eurostat [133]	29.05	18.34	0.63	2.00	77.00	0.84	3.00	21.22
${\mathrm{eGov}}_{\mathrm{ke}}$	The country’s score is from a minimum of 0 to a maximum of 100.		54.92	25.42	0.46	7.00	100.00	−0.32	2.17	8.40
${\mathrm{eGov}}_{\mathrm{buss}}$	The country’s score is from a minimum of 0 to a maximum of 100.		60.01	19.14	0.32	16.00	97.50	−0.55	2.50	2.03
${\mathrm{eGov}}_{\mathrm{cit}}$	The score of the country, from a minimum of 0 to a maximum of 100		45.02	20.71	0.46	12.00	87.25	0.30	11.07	9.94
Control variables
$\mathrm{Trade}$	Trade (% of GDP)	World Data Bank [134]	132.57	67.62	0.51	54.87	380.10	1.56	5.44	119.08
$\mathrm{Inds}$	Industry (including construction), value added (% of GDP)		14.40	5.69	0.40	3.89	34.90	0.77	4.51	35.03
$\mathrm{R}\&\mathrm{D}$	Patent applications		3462.82	9254.31	2.67	2.00	48,480.0	4.08	19.15	2482.44
$\mathrm{Dem}$	Estimate of governance (ranges from approximately −2.5 (weak) to 2.5 (strong) governance performance)		1.07	0.35	0.33	0.31	1.67	−0.29	2.29	6.30

Note: Mean—the average value; SD—standard deviation; CV—coefficient of variation; Min—minimum value; Max—maximum value; Sk.—skewness; Kurt.—kurtosis; J.–B.—Jarque–Bera.

.

On average, for panel data, the share of renewable energy in the total primary energy supply accounted for 28.74%. Considering the EU energy strategy in 2030, this indicator should be increased to 32%. The empirical results of skewness confirm that the data are higher than the average. This indicates the positive tendency to achieve EU targets. However, the right side of the distribution shows the existence of the countries that are far from the indicated energy targets.

Based on the studies [57,59,61], the basic econometric model for analyzing the impact of e-governance on SDG7 affordable and clean energy could be written as follows:

$$\mathrm{SDG}{7}_{\mathrm{it}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{eGov}}_{\mathrm{it}}+{\mathsf{\alpha }}_2{\mathrm{Trade}}_{\mathrm{it}}+{\mathsf{\alpha }}_3{\mathrm{Inds}}_{\mathrm{it}}+{\mathsf{\alpha }}_4\mathrm{R}\&{\mathrm{D}}_{\mathrm{it}}+{\mathsf{\alpha }}_5{\mathrm{Dem}}_{\mathrm{it}}+{\mathrm{e}}_{\mathrm{it}}$$

(7)

where $\mathrm{SDG}{7}_{\mathrm{it}}$ is an explained variable that is measured by $\mathrm{SDG}{7}_{\mathrm{ren}}$ and $\mathrm{SDG}{7}_{\mathrm{score}}$ i-country in t-time; ${\mathsf{\alpha }}_0$…${\mathsf{\alpha }}_5$ are the searching parameters of the model; ${\mathrm{e}}_{\mathrm{it}}$ are errors.

The study applies the panel models to evaluate the searching parameters ${\mathsf{\alpha }}_0$…${\mathsf{\alpha }}_5$. The panel models allow fixing the features of the unobserved effects in the countries, which could be constant over time (in the fixed effect model FE (8)) or random changes (in the random effect model RE (9)):

$$\mathrm{SDG}{7}_{\mathrm{it}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{eGov}}_{\mathrm{it}}+{\mathsf{\alpha }}_2{\mathrm{Trade}}_{\mathrm{it}}+{\mathsf{\alpha }}_3{\mathrm{Inds}}_{\mathrm{it}}+{\mathsf{\alpha }}_4\mathrm{R}\&{\mathrm{D}}_{\mathrm{it}}+{\mathsf{\alpha }}_5{\mathrm{Dem}}_{\mathrm{it}}+{\mathrm{e}}_{\mathrm{it}}$$

(8)

$$\mathrm{SDG}{7}_{\mathrm{it}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{eGov}}_{\mathrm{it}}+{\mathsf{\alpha }}_2{\mathrm{Trade}}_{\mathrm{it}}+{\mathsf{\alpha }}_3{\mathrm{Inds}}_{\mathrm{it}}+{\mathsf{\alpha }}_4\mathrm{R}\&{\mathrm{D}}_{\mathrm{it}}+{\mathsf{\alpha }}_5{\mathrm{Dem}}_{\mathrm{it}}+{\mathrm{u}}_{\mathrm{it}}+{\mathrm{e}}_{\mathrm{it}}$$

(9)

where ${\mathrm{u}}_{\mathrm{it}}$ is a between-entity error; ${\mathrm{e}}_{\mathrm{it}}$ is a within-entity error.

The Hausman test is applied to select the valid parameters of the model (7) within comparing the results of fixed and random effects models [135,136]. The absence of reasons for rejecting the null hypothesis of the Hausman test means that the calculated parameters obtained using the FE and RE models are similar. In this case, the results of the RE model can be accepted. At the same time, rejecting the null hypothesis is the basis for recognizing FE as the effect due to failing to meet the assumptions necessary to estimate RE. Furthermore, the Breusch–Pagan Lagrange multiplier test (LM) is applied to compare the findings of random effects regression and a pooled OLS regression [137,138]. The rejection of the null hypothesis of the LM test (no panel effect) confirms the necessity to interpret the findings on the RE model.

4. Results

In the first stage, the study measures the synthetic index of evaluating e-government services’ development. Considering the findings (Figure 1) during the whole time analyzed, Estonia, Denmark, Finland, Malta, and Sweden have the highest values 1.89 (in 2019), 1.51 (in 2019), 1.46 (in 2019), 1.32 (in 2019), and 1.37 (in 2019), respectively. The lowest values are in Romania (−1.67 in 2013), Bulgaria (−0.498 in 2019), Croatia (−0.54 in 2019), and Greece (−0.53 in 2019). Furthermore, thirteen countries are below zero on e-government services’ development: Belgium, Bulgaria, Croatia, Czech Republic, France, Germany, Greece, Hungary, Italy, Poland, Romania, Slovak Republic, and Slovenia.

A comparative analysis of the synthetic index of e-government services’ development in 2013 and 2019 among selected countries allows allocating four countries’ groups (

Table 2. The findings of countries’ grouping on the synthetic index of e-government services’ development for 2013 and 2019.

Year	2013	2019	2013	2019
Group	Group I		Group II
Thr.	$\mathrm{eGov}\ge$ 0.491	$\mathrm{eGov}\ge$ 1.293	$-0.256\le \mathrm{eGov}<0.491$	$0.515\le \mathrm{eGov}<1.293$
List of countries	Denmark (0.762), Estonia (0.869), Finland (0.898), Malta (1.048), Sweden (0.562)	Denmark (1.512), Estonia (1.888), Finland (1.455), Latvia (1.365), Malta (1.317), Sweden (1.372)	Austria (0.106), Belgium(−0.236), Cyprus (−0.129), Ireland (0.218), Latvia (0.119), Lithuania (−0.217), Luxembourg (−0.160), Netherlands (0.367), Portugal (0.261)	Austria (1.279), Belgium (0.607), Cyprus (0.635), France (0.557), Ireland (0.7070, Portugal (0.609), Spain (0.771), Luxembourg (1.044), Lithuania (0.520), Netherlands (1.082),
Group	Group III		Group IV
Thr.	$-1.004\le \mathrm{eGov}<-0.256$	$-0.263\le \mathrm{eGov}<0.515$	$\mathrm{eGov}<-1.004$	$\mathrm{eGov}<-0.263$
List of countries	Bulgaria (−0.959), Czech Republic (−0.897), France (−0.285), Germany (−0.391), Italy (−0.869), Poland (−0.704), Slovak Republic (−0.996), Slovenia (−0.475), Spain (−0.277)	Czech Republic (0.071), Germany (0.124), Hungary (−0.098), Italy (0.121), Slovak Republic (−0.066), Slovenia (0.213)	Croatia (−1.188), Greece (−1.455), Hungary (−1.226), Romania (−1.668)	Bulgaria (−0.498), Croatia (−0.538), Greece (−0.531), Poland (−0.300), Romania (−1.317)

Note: $\mathrm{eGov}$ is a synthetic index of e-government services; Thr. is thresholds for grouping.

): the highest, high, low, and lowest levels of e-governance services’ development. In general, the value of e-government services’ development increased in 2019 (the threshold 0.491) compared with 2013 (the threshold 1.293). Furthermore, the list of countries for the first group (Group 1, highest values) changed in 2019 compared with 2013 by adding one country, Latvia, to Denmark, Estonia, Finland, Malta, and Sweden. Latvia strengthened the e-government services’ development from 0.119 in 2013 to 1.365 in 2019. The members of Group II also changed in 2019 compared with 2013. Thus, France moved from Group III in 2013 to Group II in 2019, increasing the value from −0.285 to 0.557.

Furthermore, the positive tendency in the e-government services’ development of Spain, the value increased from −0.277 in 2013 (Group III) to 0.771 in 2019 (Group II). It means that countries improve their policies on spreading the concept of e-government. It should be noted that the list of countries with the low and lowest level (IV and III Groups) did not cardinally change. Poland and Bulgaria 2019 moved to Group IV (the lowest level) from Group III (the low level). However, Hungary had the opposite tendency: it significantly improved the e-government services (from −1.226 in 2013 to 0.098 in 2019), which moved the country from IV to III Group.

In the next stage, all data are checked on stationarity by the unit root test cross-sectional dependence tests. Considering the findings (

Table 3. The empirical results of a stationarity check using the unit root test cross-sectional dependence tests.

Variables	Level	Levin–Lin–Chu		Harris–Tzavalis
		Statistic	p-Value	Statistic	p-Value
$SDG7$	At level	−3.926	0.000	−3.612	0.000
	At first difference	−43.368	0.000	−14.496	0.000
$SDG{7}_{ren}$	At level	−3.949	0.000	−2.698	0.004
	At first difference	−210	0.000	−14.203	0.000
$eGov$	At level	4.728	1.000	6.252	1.000
	At first difference	−2.761	0.003	−6.829	0.000
$Trade$	At level	−8.063	0.000	0.407	0.658
	At first difference	−40.025	0.000	−8.155	0.000
$Inds$	At level	−24.086	0.000	−2.961	0.002
	At first difference	−17.910	0.000	−9.546	0.000
$R\&D$	At level	5.961	0.000	−1.617	0.053
	At first difference	−310	0.000	−11.892	0.000
$Dem$	At level	−13.201	0.000	−0.576	0.282
	At first difference	−12.462	0.000	−8.789	0.000

Note: eGov is a synthetic index of e-government services; SDG7_co2twh is CO₂ emissions from fuel combustion per total electricity output (MtCO₂/TWh); SDG7_ren is a share of renewable energy in total primary energy supply (%); Trade is the openness of the economy; Inds is industrial structures; R&D is research and development; Dem is a democracy level.

) at the level, not all data are stationary within both tests. However, all data become stationary at the first level.

The results of digitalization’s impact on SDG7 affordable and clean energy indicators are shown in

Table 4. The empirical results of e-governance impact on the share of renewable energy in total primary energy supply using the FE, RE, and POLS models.

Variables	FE Model				RE Model				POLS Model
	Model 1		Model 2		Model 1		Model 2		Model 1		Model 2
	Coef.	P > |t|	Coef.	P > |t|	Coef.	P > |t|	Coef.	P > |t|	Coef.	P > |t|	Coef.	P > |t|
	Explained Variable: $\mathrm{SDG}{7}_{\mathrm{ren}}$
$\mathrm{eGov}$	3.959	0.000 *	3.860	0.000 *	4.114	0.000 *	4.602	0.000 *	9.173	0.000 *	13.640	0.000 *
$\mathrm{Trade}$	–	–	0.044	0.318	–	–	0.099	0.003 *	–	–	0.187	0.000 *
$\mathrm{Inds}$	–	–	0.312	0.103	–	–	0.405	0.021 **	–	–	0.400	0.087 ***
$\mathrm{R}\&\mathrm{D}$	–	–	−0.001	0.667	–	–	0.001	0.235	–	–	0.001	0.002 *
$\mathrm{Dem}$	–	–	−1.833	0.686	–	–	−0.270	0.948	–	–	−4.439	0.452
Constant	29.044	0.000 *	34.659	0.000 *	29.057	0.000	38.511	0.000 *	29.491	0.000	55.425	0.000 *
sigma_u	20.293		19.235		20.164		17.151		R-sq. = 0.120 Adj R-sq. = 0.114		R-sq. = 0.462 Adj R-sq. = 0.444
sigma_e	2.787		2.811		2.787		2.811
rho	0.981		0.979		0.981		0.974

Note: $\mathrm{eGov}$ is a synthetic index of e-government services; $\mathrm{SDG}{7}_{\mathrm{ren}}$ is a share of renewable energy in total primary energy supply; $\mathrm{Trade}$ is the openness of the economy; $\mathrm{Inds}$ means industrial structures; $\mathrm{R}\&\mathrm{D}$ is research and development; $\mathrm{Dem}$ is a democracy level; sigma_u is a standard deviation of residuals within groups u; sigma_e is a standard deviation of residuals (overall error term) e; rho is an intraclass correlation; FE, RE, and POLS are fixed effect, random effect, and pooled OLS regression models, respectively; R-sq. is R-squared; Adj R-sq. is Adj R-squared; Model 1 means a model without control variables; Model 2 is a model with control variables; *, **, and *** mean statistical significance at 1%, 5%, and 10%, respectively.

, and the validity of models is shown in Table 4. All variables from (8) and (9) models are taken in a one-year lag to consider the endogeneity caused by the simultaneous connection between e-government and energy.

The findings of the e-governance impact on the share of renewable energy in the total primary energy supply show that the growth of $\mathrm{eGov}$ by 1 point causes an increase in renewable energy in total primary energy supply by 3.959 points (Model 1 of FE model) and 3.86 (Model 1 of FE model). It should be noted that in the FE model, the impact of control variables on the share of renewable energy in the total primary energy supply is insignificant. At the same time, in the RE and POLS models, the impact of most variables on $\mathrm{SDG}{7}_{\mathrm{ren}}$ is statically significant. Furthermore, the Hausman and LM tests (Table 6) show that the RE model should be considered for analysis. Thus, increasing $\mathrm{eGov}$ by 1 point led to the growth of renewable energy in total primary energy supply by 4.114 (Model 1) and 4.602 (Model 2) points. The impact of R&D and Dem are not statistically significant in the RE model, and Dem in the POLS model. The industrial structures’ improvement allows increasing $\mathrm{SDG}{7}_{\mathrm{ren}}$ by 0.405 (RE model) and 0.4 (POLS model). However, the trade activization (economic openness) contributes to the increase in $\mathrm{SDG}{7}_{\mathrm{ren}}$ by 0.099 (RE model) and 0.187 (POLS model).

The empirical results of the e-governance impact on the SDG Index Score output using the FE, RE, and POLS models are shown in

Table 5. The empirical results of e-governance impact on SDG7 affordable and clean energy using the FE, RE, and POLS models.

Variables	FE Model				RE Model				POLS Model
	Model 1		Model 2		Model 1		Model 2		Model 1		Model 2
	Coef.	P > |t|	Coef.	P > |t|	Coef.	P > |t|	Coef.	P > |t|	Coef.	P > |t|	Coef.	P > |t|
	Explained Variable: $SDG7$
$\mathrm{eGov}$	0.729	0.027 **	0.584	0.019 **	0.780	0.015 **	1.116	0.002 *	2.589	0.001 *	5.218	0.000 *
$\mathrm{Trade}$	–	–	0.009	0.595	–	–	0.051	0.000 *	–	–	0.091	0.000 *
$\mathrm{Inds}$	–	–	0.130	0.072 ***	–	–	0.210	0.002 *	–	–	0.275	0.000 *
$\mathrm{R}\&\mathrm{D}$	–	–	0.000	0.569	–	–	0.000	0.360	–	–	0.000	0.006 *
$\mathrm{Dem}$	–	–	−1.454	0.396	–	–	−0.543	0.725	–	–	−2.799	0.129
Const.	76.045	0.000 *	78.025	0.000 *	76.050	0.000 *	80.857	0.000 *	76.205	0.000 *	88.076	0.000 *
sigma_u	7.986		8.100		7.972		5.173		R-sq. = 0.065 Adj R-sq. = 0.059		R-sq. = 0.645 Adj R-sq. = 0.6336
sigma_e	1.052		1.060		1.052		1.060
rho	0.983		0.983		0.983		0.960

Note: $\mathrm{eGov}$ is a synthetic index of e-government services; $\mathrm{SDG}{7}_{\mathrm{co}2\mathrm{twh}}$ means CO₂ emissions from fuel combustion per total electricity output; $\mathrm{Trade}$ means the openness of the economy; $\mathrm{Inds}$ means industrial structures; $\mathrm{R}\&\mathrm{D}$ is research and development; $\mathrm{Dem}$ is a democracy level; sigma_u is a standard deviation of residuals within groups u; sigma_e is a standard deviation of residuals (overall error term) e; rho is an intraclass correlation; FE, RE, and POLS are fixed effect, random effect, and pooled OLS regression models, respectively; R-sq. is R-squared; Adj R-sq. is Adj R-squared; Model 1 means a model without control variables; Model 2 is a model with control variables; *, **, and *** are statistical significance at 1%, 5% and 10%, respectively; Const.—constant.

. The findings show that the impact of $\mathrm{eGov}$ on CO₂ emissions is statistically significant in all empirical models. Furthermore, in all models with the control variables, the e-governance has a positive value. The incentive effect on SDG achieving is observed for $\mathrm{Inds}$ (FE, RE, and POLS models), $\mathrm{Trade}$ (RE and POLS models), and $\mathrm{R}\&\mathrm{D}$ (POLS model).

Considering the findings of the Hausmann and LM tests (

Table 6. The findings of the Hausmann and LM tests.

Explained Variable	Hausman Test		LM Test		Selected Model
	chi2	Prob > chi2	chi2	Prob > chi2
Explained variable: $\mathrm{SDG}{7}_{\mathrm{ren}}$
Model 1	4.87	0.431	343.77	0.000 *	RE model
Model 2	1.28	0.258	384.67	0.000 *	RE model
Explained variable: $\mathrm{SDG}7$
Model 1	23.26	0.0003 *	305.92	0.000 *	FE model
Model 2	0.99	0.3196	386.82	0.000 *	RE model

Note: FE and RE are fixed effect and random effect models, respectively; Model 1 is a model without control variables; Model 2 is a model with control variables; * is statistical significance at 1%.

), the interpretation results of e-governance influence on the SDG Index Score should be conducted using the FE model (without explanatory indicators) and the RE model (with explanatory indicators). Thus, increasing e-governance by 1 point causes the growth of the SDG Index Score by 0.729 points in the FE model and 1.116 in the RE model. It confirms that, within EU countries, governance digitalization is the strategic inhibitor of SDG achievement. Rho for FE and RE models are 0.983 and 0.960, respectively.

The graphical interpretation of the positive relationship between the implementation of governance digitalization and SDG achievement is shown in Figure 2.

The countries with high values of e-governance (Sweden, Latvia, Finland, Denmark, and Austria) achieve the highest values of the SDG7 Index Score. However, the findings for Estonia, Malta, and the Netherlands confirm the incentive impacts of other factors (governance efficiency, the attraction of green investments, etc.), which could also significantly affect SDG achievement.

5. Discussion and Conclusions

The study aims at analyzing the impact of e-governance on affordable and clean energy for EU countries for the period 2013–2019. In the first stage, the synthetic index of the e-government services’ development is calculated for EU countries. The findings allow outlining the leaders and outsiders of e-governance in 2019. Thus, the following countries are the leaders: Estonia with a value of 1.888, Denmark with a value of 1.512, Finland with a value of 1.455, Sweden with a value of 1.372, Latvia with a value of 1.365, and Malta with a value of 1.317. The outsiders comprise Romania (−1.317), Croatia (−0.538), Greece (−0.531), Bulgaria (−0.498), and Poland (−0.300). The empirical results confirm the statistically significant impact of e-governance on the share of renewable energy in the total primary energy supply. Thus, the extension of e-governance allows increasing the share of renewable energy in the total primary energy supply by 4.114 (in the model without control variables) and 4.602 (in the model with control variables). Similar conclusions are obtained in the studies [72,73,76,77,84]. In this case, the government of EU countries should continue to provide the strategy for governance digitalization. Thus, the EU Digital Single Market [139] should actively promote among the country outsiders the synthetic index of the e-government services’ development. It should be noted that the EU Digital Single Market is built on the three core pillars (infrastructure, affordability, economics/society) that are coherent with the sustainable development principles.

Furthermore, the results show that industrial structure positively affects the share of renewable energy in the total primary energy supply and integrated index of SDGs7 affordable and clean energy. Thus, the modernization and the transformation of the industrial structure allow extending renewable energy, which boosts the SDGs7 achievement. Furthermore, trade openness provokes enlarging clean and affordable energy among the EU countries. In this case, the latter should promote a policy on trade support. However, trade openness requires more resources and capital. In this case, with trade activation, the EU countries should promote green production and awareness among all trade stakeholders. It should be noted that the impact of patent applications (the measure of R&D) on both SDGs7 affordable and clean energy and on the share of renewable energy is not considerable. However, prior studies [26,32,33,57] justify the opposite conclusions: patent applications are the core inhibitors of enlarging renewable energy. The findings show that the EU government has already accumulated a number of patent applications that require relevant implementation and commercialization.

Furthermore, the studies [140,141,142,143,144,145,146] confirm that transformation of the energy market requires new management philosophy (triangulate approach) [147,148] and instruments for its regulation to achieve the SDGs. Thus, based on the best practices, the following policies should be extended among all EU countries: green shipping, feed-in tariff, green taxes, green loans, etc.

Despite the valuable findings (which are coherent with previous studies [26,32,33,57,72,77,78], the study has a few limitations that could be the direction for further research. This study focuses only on the one SDG7 and does not analyze the other SDGs, which should be considered in future studies. Furthermore, spreading renewable energies depends on corruption, transparency, and efficiency of the legislation, which are left beyond the framework of this study. Furthermore, digitalization and extending affordable and clean energy require a powerful investment in green innovations, smart grids, and green technologies. In this case, the impact of green investment or/and foreign direct investment should be considered within the analysis of inhibitors to the achievement of SDGs. Furthermore, the future study should consider the policy recommendation under the EU energy transition goals in the context of the invasion of Ukraine and its implication on achieving the energy-related SDGs by EU countries.