The Digital Economy’s Contribution to Advancing Sustainable Economic Development

Abstract

The expansion of the digital economy motivated researchers to precisely identify its effects and impact on all factors that support sustainable development in nations. This study examines the role of the digital economy in promoting sustainable economic development and interplays with renewable energy development in European countries over the period 1996–2020. The first aim is to provide a theoretical approach by reviewing the most relevant studies on the issue. The second aim is to develop a linear regression model for evaluating the effects of the digital economy on sustainable economic development and the interplays with the environment. The fixed-effect estimation technique for panel data models will be chosen to control for heterogeneity. The results confirm the hypothesis of a positive impact of the digital economy on economic development and environmental performance. This paper can be considered a useful viewpoint in understanding the complex relationship of the digital economy’s contribution to advancing sustainable economic development and promoting environmental performance, thus adding to the existing literature.

1. Introduction

Sustainable development requires harmonious cohesion among economic factors, resource utilization, and social equity (An et al., 2024), which justifies its three essential components: economic, social, and environmental sustainability. Many variables stimulate economic sustainability, and the digital economy is one of the most important ones, according to An et al. (2024). The expansion of the digital economy, notably in domains like artificial intelligence and blockchain, has emerged as a new driver for national economic growth (Horoshko et al., 2021; Pang et al., 2024), a trend in the international context. Organizations can enhance their sustainability efforts by digitizing processes and adopting data-driven solutions, along with social and environmental considerations. Innovation, research, and the development of new technologies are fundamentally connected to the digital economy, which can enhance efficiency in resource utilization. E-commerce, e-banking, e-government, e-health, and online education provide opportunities that support economic development and improve quality of life. A clear legislation that defines relevant regulatory principles aims to stimulate the vitality of the digital economy and promote its healthy and sustainable growth development (S. Chen et al., 2022), which means that governance should be effective. The development of the digital economy promotes the rationalization and optimization of the industrial structure (S. Chen et al., 2022). The world economy is shifting toward a digital economy promoting sustainable development (Hosan et al., 2022), and the digital economy has a significant role in increasing sustainable economic development (Aniqoh, 2020).

The digital economy requires ongoing adaptability to new technologies and work methods, emphasizing the need for a well-informed and educated society (Javaid et al., 2024). To thrive in a competitive digital landscape, individuals must possess digital skills, which indicates that education at every level should focus on cultivating these capabilities across various specialized fields, such as economy, medicine, engineering, and industry. The knowledge society ensures that education and training stay current to meet these changing needs and demands (Nyhan, 2002). Various factors, such as national economic structure, policies, institutions, and governance, shape digitalization’s economic impact. While European nations have seen significant growth in the digital economy, the level of digitalization differs across countries in the Euro area and Europe, highlighting variations in infrastructure, policy frameworks, technology investment, and overall readiness for digital solutions. Some nations thrive in internet connectivity, e-governance, and digital services, whereas others lag behind (Anderton et al., 2020). This disparity emphasizes the necessity for targeted strategies to enhance regional digital capabilities.

This study examines the role of the digital economy in promoting advanced sustainable economic development and improving environmental performance in European countries over the period 1996–2020, testing the following two hypotheses:

Hypothesis 1.

A positive correlation exists between the digital economy and economic development.

Hypothesis 2.

The digital economy has a positive significant impact on renewable energy generation.

The countries of Europe have been selected as the sample to examine the status of the digital economy and its impact on advancing sustainable economic development and environmental performance.

European countries’ experience in developing digital economy and renewable energy aligns with the 2030 Agenda for Sustainable Development (United Nations, 2015), who seek to succeed in 17 Sustainable Development Goals. The 2030 Agenda and the Paris Agreement on Climate Change provide the global framework for international cooperation on sustainable development and its economic, social, environmental, and governance dimensions (European Commission, 2025). This paper advances the literature exploring how the digital economy interacts with economic development and renewable energy development, considering government intervention, which complements the current literature on the status of European countries.

This paper’s structure is as follows: Section 2 examines the literature on the digital economy’s roles in promoting sustainable economic development and environmental sustainability. Section 3 describes the method, variables, and data sources. Section 4 summarizes the results of the empirical study conducted on 29 European countries from 1996 to 2020, including the main findings, their implications, and potential policy implications. Section 5 outlines the conclusions.

2. Theoretical Framework

Sustainable development was defined by the United Nations (2015) as development that meets the needs of the present without compromising the ability of future generations to meet their own needs. Sustainable economic development is only one of the major variables of sustainable development, along with sustainable environmental and social development. Sustainable economic development cannot function independently without correlation with the other variables (environmental and social) because the risk of economic development through massive industrialization (which has the strongest impact on GDP growth) is the damage to the environment and the population’s disease. Therefore, even in the context of sustainable economic development, sustainable environmental and social development must be considered. The impact of digitalization on the economy is a function, inter alia, of national economic structure and economic policies, institutions, and governance (Anderton et al., 2020; An et al., 2024). In Pang et al.’s (2024) opinion, the globalization of the digital economy has been resilient despite the heavy influence of the COVID-19 pandemic.

In the digital economy era, the digital economy’s significance for regional green development has been validated in the related literature (Aniqoh, 2020; Gao et al., 2022; Hosan et al., 2022; Cai, 2023; Zheng & Wong, 2024; An et al., 2024). Technologies like the Internet of Things (IoT) and artificial intelligence (AI) help optimize resource use, decrease waste output, and support intelligent energy management.

The digital economy is fundamentally based on the widespread adoption of information and communication technology (ICT) across various industries to boost productivity (Uddin, 2024). The digital economy can serve as a catalyst for accelerating development in many ways and fields of activities. The literature predominantly uses empirical methods to identify the digital economy’s contribution to sustainable development, but there are also studies that use qualitative methods to establish correlations between the digital economy and various variables of sustainable development.

Uddin (2024) developed his study using qualitative methods based on a systematic process of categorization, thematic coding, and interpretation to identify key patterns, trends, and insights relevant to the digital economy sectors in Bangladesh to ascertain the contribution to the country’s economic growth. Aniqoh (2020) conducted his research to assist the government in determining the appropriate policy for implementing the digital economy and its impact on sustainable economic development in Indonesia using an interview methodology with government agencies, including the Ministry of Finance. Both studies concluded that the digital economy plays a significant role in enhancing sustainable economic growth development.

The empirical studies also identify the digital economy’s positive role based on econometric models. They evaluate the interplay between the digital economy and sustainable development (An et al., 2024; Long et al., 2022; Zhao et al., 2022; David et al., 2025) or the correlation between the digital economy and various environmental dimensions (Sadorsky, 2012; Cai, 2023; S. Chen et al., 2022; Gao et al., 2022; Ma & Lin, 2023; Shobande & Ogbeifun, 2022; Awijen et al., 2022).

The digital economy has significantly transformed the global business environment (Bukht & Heeks, 2017), impacting multiple areas, including digital infrastructure and e-commerce (Barefoot et al., 2018). At the same time, organizational resilience has emerged as crucial for sustained success in unstable business contexts (Linnenluecke, 2017; Williams et al., 2017). Regarding our interest in research based on the impact on sustainable development, An et al. (2024) evaluate the level of digital economy development and investigate the digital economy’s influence on sustainable development in China’s 268 cities, along with the interplay with green innovation, based on the use of fixed panel models, difference-in-differences models, mediating effect models, and threshold models. According to Y. F. Chen and Xu (2023), the digital economy has become a key force driving sustainable economic development. The digital economy boosts economic returns and promotes sustainable growth (An et al., 2024).

Zhao et al. (2022) evaluated the effects of the digital economy on high-quality urban development and its mechanism for the 222 Chinese cities at and above the prefecture level during 2011–2016 based on the threshold model and the spatial model that reflect the positive effect of the digital economy which has the characteristics of nonlinear increment and spatial spillover of the “marginal effects”. Evangelista et al. (2014) concluded that digitalization has major economic effects driving productivity and employment growth under inclusive public policies.

David et al. (2025) investigated the influence of internet and mobile penetration on economic growth and human development indices in 30 emerging economies from 2008 to 2023 using structural equation modeling (SEM) and data envelopment analysis (DEA) and found a strong correlation between digital penetration and GDP growth, contingent on effective governance structures. Long et al. (2022) concluded that the current technological transformation in China needs to move from the second stage, which is the transition from the introduced technological progress mode to the original technological progress mode, to the third stage, which is mainly based on the original technological progress. This third stage is the key to the successful transformation of the Chinese economy from the high-speed growth phase to the high-quality development phase.

The first studies on the impact of information and communication technology, like the study by Sadorsky (2012), highlighted the link between information and communication technology use and electricity consumption in emerging countries and found that Internet development increased electricity consumption. Cai (2023) evaluated the influence of information and communication technology on green economic recovery in China’s 216 prefecture-level cities from 2010 to 2019 based on a new dynamic threshold model illustrating that digitalization has a beneficial effect on technological development and that tech development has a favorable impact on green innovation performance. The author (Cai, 2023) concludes that technical innovation bridges digitalization and green innovation. S. Chen et al. (2022) evaluated the relationship between the digital economy, industrial structure, and carbon emission based on the mediation effect and a spatial panel model using panel data from 30 provinces in China from 2011 to 2019, revealing that the development of the digital economy can effectively promote the reduction of carbon emission and has a significant role in promoting the rationalization of the industrial structure.

Gao et al. (2022) studied the direct impact, mediating effect, nonlinear relationship, and regional and development differences in information and communication technology development on green total factor energy efficiency based on the dynamic panel model for China’s 213 prefecture-level cities over the period 2011-2018, finding a positive relationship

Ma and Lin (2023) empirically explored the impact of digitalization on emission reduction performance for 271 Chinese cities from 2003 to 2019 using a panel two-way fixed-effect model, finding a positive impact. Wu et al. (2021) demonstrated that Internet development enhanced energy saving and reduced emissions based on panel data from 30 provinces in China from 2006 to 2017. Using a progressive difference-in-differences method, Guo et al. (2022) found that smart city construction could reduce per capita CO₂ emissions.

Shobande and Ogbeifun (2022) highlighted the importance of using ICT to promote environmental sustainability based on the standard fixed-effect panel and the Arellano–Bover/Blundell–Bond dynamic panel approach for 24 Organization for Economic Cooperation and Development (OECD) countries. Nepal et al. (2024) demonstrated the moderating influence of the digital economy on various energy sources, the transition to hydro-and wind energy, for 32 developing countries globally. Chowdhury et al. (2021) highlighted the significantly positive impact of information and communication technology (ICT) on renewable energy consumption while the negative and insignificant impact on the economic progress in India and China using the generalized method of moments (GMM) and pooled ordinary least square (OLS), fixed-effect, and random effect models. The literature shows that the interplay between variables under analysis is positive, but the relationship can also be negative in special contexts. Awijen et al. (2022) demonstrated that renewable energy consumption increases when innovation performance increases based on the ICT and percentage of Internet users.

3. Materials and Methods

This study examines the role of the digital economy in promoting advanced sustainable economic development. Another aim is to examine if implementing digital technologies improves environmental performance. Our analysis includes the European countries over the period 1996–2020. The European counties under analysis are as follows: Austria, Belgium, Bulgaria, Czechia, Cyprus, Croatia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxemburg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, and Switzerland. The chosen period (1996–2020) is justified by the availability of official databases of the World Bank (2025) and the European Commission (2025).

For empirical evidence, the fixed-effect model was chosen. This model is particularly useful in longitudinal studies, as it controls for individual heterogeneity that can bias results, enabling researchers to examine the effects of predictors on outcomes more accurately within entities over time (Baum, 2006; Gujarati, 2003). Due to the high number of cross-sectional units and the issue of heterogeneity, the fixed-effect estimation technique for panel data models is recommended for this study.

This research paper will run multiple regressions based on the fixed-effect model, log-linear model, where the dependent variable will be GDP per capita (lnGDP_PC), as the most representative indicator for economic growth and an indicator of Sustainable Development Goals adopted by the United Nations in 2015 (United Nations, 2025), associated with goal eight, respectively, decent work and economic growth. The literature generally uses this indicator to measure economic development (An et al., 2024; Zheng & Wong, 2024). Using the logarithm of GDP per capita is common in economic modeling because it helps linearize exponential growth patterns.

$$Log\left(Y_{i,t}\right)={\alpha }_i+{\beta }_1{DigiEcon}_{i,t}+{{\beta }_{2}Renewenerg}_{i,t}+{{\beta }_{3}Inves}_{i,t}+{{\beta }_{4}Urban}_{i,t}+{{\beta }_{5}Infl}_{i,t}+{{\beta }_{6}COemissions}_{i,t}+u_i+{\epsilon }_{i,t}$$

(1)

With the following: i refers to the country ($i=\overline{1,29}$); t refers to the year ($t=\overline{1,24}$); Y is the dependent variable; α_i are entity-specific intercepts that capture heterogeneities across entities; ${\beta }_1,{\beta }_2,\dots {\beta }_6$ are the coefficients of the explanatory variables; $u_i$ are the country-specific intercepts; and ${\epsilon }_{i,t}$ are the observation-specific errors.

To test the interplay between the digital economy and the environment, the dependent variable will be renewable energy consumption as % of total final energy consumption (lnRenewenerg).

$${Log(Y}_{i,t})={\alpha }_i+{\beta }_1{DigiEcon}_{i,t}+{{\beta }_{2}GDP\_PC}_{i,t}+{{\beta }_{3}Inves}_{i,t}+{{\beta }_{4}Urban}_{i,t}+{{\beta }_{5}Infl}_{i,t}+{{\beta }_{6}COemissions}_{i,t}+u_i+{\epsilon }_{i,t}$$

(2)

With the following: i refers to the country ($i=\overline{1,29}$); t refers to the year ($t=\overline{1,24}$); Y is the dependent variable; α_i are entity-specific intercepts that capture heterogeneities across entities; ${\beta }_1,{\beta }_2,\dots {\beta }_6$ are the coefficients of the explanatory variables; $u_i$ are the country-specific intercepts; and ${\epsilon }_{i,t}$ are the observation-specific errors.

In this context, we test two hypotheses abovementioned.

The independent variables best highlight the level of digital development and the environment, as well as how the government supports the implementation of research and development, being recognized by the literature (Apergis & Payne, 2010; Zheng & Wong, 2024; Ehigiamusoe & Dogan, 2022; Uddin, 2024; An et al., 2024; Long et al., 2022; Zhao et al., 2022; David et al., 2025). The digital economy variable (Digi_Econ) results from a composite variable based on Internet penetration and Mobile cellular subscriptions (per 100 people). The two indicators explain the digital economy variable because they are quantitative indicators developed through a unitary methodology. The digital economy is inherently linked to the level of digitalization, where higher levels of digital tools and processes lead to transformative economic activities and innovations. In this context, the economy is becoming more digital. As businesses and regions enhance their digital capabilities, they can seize new opportunities for growth, efficiency, and competitiveness in the global market. The sustainable environment can be expressed by a composite variable (EnvSus) that includes renewable energy consumption (% of total final energy consumption), the carbon intensity of GDP, and CO₂ emissions (kt). The model will consider independent variables such as renewable energy consumption as % of total final energy consumption (Renewenerg) as an indicator that plays a vital role in combating climate change and CO₂ emissions expressed in kt (CO2emissions). CO₂ emissions are from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production, which justify the pollution. The urban population as % of the total population (Urban) expresses the social dimension. The government’s involvement is justified from an economic point of view with the size of research and development expenditures (R&D_Exp). Also, the model includes investment (Invest), meaning gross capital formation (formerly gross domestic investment), which consists of outlays in addition to the fixed assets of the economy plus net changes in the level of inventories. Another variable will be inflation (Infl), which reflects the erosion of the actual income factor.

The variables are detailed in

Table 1. The variables included in the analysis.

Variable	Definition	Data Source
GDP_PC	GDP per capita (current USD)	World Bank (2025)
Renewenerg	Renewable energy consumption (% of total final energy consumption)	World Bank (2025)
Digi_Econ	A composite variable based on the Internet penetration rate as individuals using the Internet (% of population) and mobile cellular subscriptions (per 100 people)	World Bank (2025)
Invest	Gross capital formation (% of GDP)	World Bank (2025)
CO2emissions	CO2 emissions expressed in kt	World Bank (2025)
EnvSus	A composite variable-based renewable energy consumption (% of total final energy consumption), carbon intensity of GDP, and CO2 emissions (kt)	World Bank (2025)
Urban	Urban population as % of total population	World Bank (2025)
R&D_Exp	Research and development expenditure (% of GDP)	World Bank (2025)
Infl	Inflation, GDP deflator (annual %)	World Bank (2025)

Source: computed by author.

.

As part of Internet infrastructure, the Internet penetration rate is the foundation for advancing the digital economy. An increase in internet penetration signifies greater access for people to the Internet, enabling them to enjoy the conveniences brought by digital information technology (An et al., 2024). The evolution of the digital economy can be seen in Figure 1.

The dynamics of the digital economy reflect a significant growth rate for all the European countries analyzed from 1996 to 2020. Slight fluctuations are registered in most European countries, but the trend of countries implementing and using information technology is evident. Such dynamics are rarely encountered, leading us to validate the importance of the digital economy in the knowledge society, with a significant direct or indirect impact on all fields of activity.

Over the last two decades, internet access has become more widespread across Europe. The rise in e-commerce has been one of the most striking developments in the digital economy. European countries have seen a substantial increase in the volume of online sales, with many businesses transitioning to digital platforms. European countries have invested heavily in digital infrastructure, including broadband networks, data centers, and cloud services. These investments have enhanced the capacity and speed of Internet services, facilitating the growth of digital businesses and services. European countries have become increasingly involved in global digital trade, using digital platforms to expand their markets.

The COVID-19 pandemic accelerated digital transformation across European countries, resulting in a surge in remote work, online education, and increased demand for digital services. Businesses that were already digitally engaged adapted more quickly to the challenges posed by the pandemic, further highlighting the importance of a robust digital economy. As awareness about climate change and sustainability grows, there has been a push to integrate sustainability into the digital economy.

The dynamics observed during this period indicate a promising future for the digital economy in Europe, with ongoing potential for innovation and economic development.

By comparison, we can highlight the evolutionary status of the GDP per capita growth with the dynamic of Internet penetration, as seen in Figure 2.

The growth rate of Internet penetration is evident. It is not only a result of technological advancement but can also be considered a determining factor in economic, social, and educational evolution.

By identifying and applying quadratic regression by country (Figure 3), the relationship between a dependent variable, GDP per capita (lnGDP_PC), as the most representative indicator for economic growth, and the independent variable, digital economy (Digi_Econ), can be better seen. The use of log GDP per capita as the dependent variable and quadratic modeling effectively captures the complexities of the relationship between economic development and digital economy, illustrating not only direct contributions but also the amplifying effects of a robust digital economy on overall economic growth. This understanding is vital for policymakers and businesses utilizing digital transformation for sustainable economic growth.

As the digital economy matures and expands, the advantages for European economies may increase, resulting in exponential wealth growth rather than just linear growth. If European countries significantly adopt digital technologies, they could experience rapid economic growth due to improved efficiencies and productivity. Initially, as more businesses transition online, GDP per capita may rise considerably. However, as the digital economy continues to develop, those benefits may accelerate if the digital ecosystem evolves further, contributing to exponential growth in economic productivity.

By identifying and applying quadratic regression by country (Figure 4), the relationship between the dependent variable, renewable energy consumption (lnRenewenerg), and the independent variable, digital economy (Digi_Econ), can be better seen.

Using quadratic regression allows for capturing complex relationships where increases in the digital economy may have proportionally larger impacts on renewable energy consumption as the economy grows and matures in the European countries. Figure 4 suggests that further growth in the digital economy results in even more significant increases in renewable energy consumption due to enhanced infrastructure and demand dynamics. By facilitating efficiency, supporting investments, and aligning with sustainability trends, the digital economy plays a crucial role in promoting the growth and utilization of renewable energy resources in European countries. The quadratic regression illustrates this nuanced relationship, emphasizing how advancements in one area can positively impact the other in intricate and meaningful ways in all European countries.

Bellow,

Table 2. Pairwise correlations.

a. Model 1
Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
(1) lnGDP_PC	1.000
(2) Digi_Econ	0.602 *	1.000
(3) Renewenerg	0.144 *	0.294 *	1.000
(4) Invest	−0.149 *	−0.154 *	0.092 *	1.000
(5) Urban	0.457 *	0.241 *	−0.075 *	−0.166 *	1.000
(6) CO2emissions	0.153 *	−0.033	−0.267 *	−0.167 *	0.059	1.000
(7) Infl	−0.198 *	−0.141 *	−0.046	−0.101 *	−0.059	−0.026	1.000
(8) R&D_Exp	0.519 *	0.355 *	0.283 *	−0.088 *	0.516 *	0.241 *	−0.088 *	1.000
b. Models 5 and 6
Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
(1) lnRenewenerg	1.000
(2) Digi_Econ	0.308 *	1.000
(3) GDP_PC	0.063	0.502 *	1.000
(4) Invest	0.081 *	−0.154 *	−0.146 *	1.000
(5) Urban	−0.222 *	0.241 *	0.459 *	−0.166 *	1.000
(6) CO2emissions	−0.180 *	−0.033	0.066	−0.167 *	0.059	1.000
(7) Infl	−0.050	−0.141 *	−0.090 *	−0.101 *	−0.059	−0.026	1.000
(8) R&D_Exp	0.237 *	0.355 *	0.440 *	−0.088 *	0.516 *	0.241 *	−0.088 *	1.000
c. Models 2 to 4
Variables	(1)	(2)	(3)	(4)	(5)		(6)
(1) lnGDP_PC	1.000
(2) Digi_Econ	0.602 *	1.000
(3) EnvSus	−0.278 *	−0.175 *	1.000
(4) Invest	−0.149 *	−0.154 *	0.120 *	1.000
(5) Urban	0.457 *	0.241 *	−0.210*	−0.166 *	1.000
(6) R&D_Exp	0.519 *	0.355 *	0.136*	−0.088 *	0.516 *		1.000

* shows significance at the 0.05 level. Source: computed by the author using Stata 15.1.

presents pairwise correlations, and

Table 3. Descriptive statistics.

a. Model 1
		N	Std.Dev	min	max	kurtosis		skewness	t-value
(1) lnGDP_PC		725	0.906	7.216	11.725	3.06		−0.56	295.866
(2) Digi_Econ		725	0.959	−1.983	1.503	2.247		−0.75	0
(3) Renewenerg		725	13.361	0	61.29	4.423		1.239	35.149
(4) Invest		725	4.327	1.157	43.666	5.646		0.427	144.943
(5) Urban		725	12.386	50.649	98.079	2.123		0.127	157.355
(6) CO2emissions		725	166,000	1352.3	904,000	10.417		2.648	18.347
(7) Infl		725	34.572	−9.899	913.213	660.029		25.18	3.635
(8) R&D_Exp		684	0.883	0.203	3.874	2.608		0.801	42.307
b. Models 5 and 6
Variables	Obs	Mean	Std.Dev.	Min	Max	p1	p99	Skew.	Kurt.
(1) lnRenewenerg	719	2.522	0.968	−2.408	4.116	−1.347	4.085	−1.334	6.641
(2) Digi_Econ	725	0	0.959	−1.983	1.503	−1.957	1.223	−0.75	2.247
(3) lnGDP_PC	725	29,861.93	23,946.11	1361.409	124,000	1659.929	113,000	1.41	5.03
(4) Invest	725	23.293	4.327	1.157	43.666	12.261	35.832	0.427	5.646
(5) Urban	725	72.383	12.386	50.649	98.079	51.082	97.789	0.127	2.123
(6) CO2emissions	725	113,000	166,000	1352.3	904,000	2318.4	830,000	2.648	10.417
(7) Infl	725	4.667	34.572	−9.899	913.213	−2.517	37.956	25.18	660.029
(8) R&D_Exp	684	1.429	0.883	0.203	3.874	0.28	3.579	0.801	2.608
c. Models 2 to 4
		N	Std.Dev	min	max	kurtosis		skewness	t-value
(1) lnGDP_PC		725	0.906	7.216	11.725	3.06		−0.56	295.866
(2) Digi_Econ		725	0.959	−1.983	1.503	2.247		−0.75	0
(3) EnvSus		725	0.438	−0.917	2.132	4.487		0.92	0
(4) Invest		725	4.327	1.157	43.666	5.646		0.427	144.943
(5) Urban		725	12.386	50.649	98.079	2.123		0.127	157.355
(6) R&D_Exp		684	0.883	0.203	3.874	2.608		0.801	42.307

Source: computed by the author using Stata 15.1.

presents descriptive statistics.

Robustness tests are essential for validating the results of a fixed-effect model. Given the large number of cross-sectional units and the issue of heterogeneity, the fixed-effect estimation technique for panel data models was chosen. The results of the fixed-effect model were compared with those of a random effect model using Hausman’s specification test to determine the suitability of the fixed-effect model. The Variance Inflation Factor (VIF) was checked to detect multicollinearity among explanatory variables in regression models, including fixed-effect models, being lower than 10, which means that the variable could be considered as a linear combination of other independent variables. The Wooldridge test for autocorrelation in panel data was conducted. The Modified Wald test for groupwise heteroskedasticity in the fixed-effect regression model was also performed.

4. Results

The results of the regression analysis based on the fixed-effect model are summarized in

Table 4. The results of the regression analysis.

	(1)	(2)	(3)	(4)	(5)	(6)
	lnGDP_PC	lnGDP_PC	lnGDP_PC	GDP_PC	lnRenewenerg	lnRenewenerg
Digi_Econ	0.473 ***	0.439 ***	0.445 ***	4073.5 ***	0.200 ***	0.216 ***
	(0.0131)	(0.0141)	(0.0137)	(794.9)	(0.0274)	(0.0277)
EnvSus		−0.553***	−0.515 ***	19,079.6 ***
		(0.0734)	(0.0715)	(2660.0)
Urban	−0.0121 **	−0.00630	−0.00437	917.6 ***	0.0249 **	0.0328 ***
	(0.00445)	(0.00449)	(0.00437)	(156.5)	(0.00875)	(0.00888)
R&D_Exp	−0.161 ***	−0.150***	−0.117 ***	−2102.6	0.158 *	0.213 **
	(0.0353)	(0.0346)	(0.0339)	(1226.1)	(0.0673)	(0.0682)
lnGDP_PC				11,948.6 ***
				(1404.0)
Invest	0.0156 ***		0.0140 ***	56.96	−0.0147 ***	−0.0163 ***
	(0.00230)		(0.00220)	(81.05)	(0.00441)	(0.00442)
GDP_PC					0.00000887 ***	0.00000939 ***
					(0.00000202)	(0.00000206)
Renewenerg	0.0119 ***
	(0.00255)
CO2emissions	0.00000145 ***				−0.00000429 ***
	(0.000000419)				(0.000000769)
Infl	−0.000486 *				−0.000893 *
	(0.000220)				(0.000426)
_cons	10.28 ***	10.60 ***	10.09 ***	−154,273.3 ***	1.075	−0.0606
	(0.342)	(0.326)	(0.327)	(18,364.0)	(0.664)	(0.652)
N	684	684	684	684	684	684
R2	0.823	0.817	0.828	0.595	0.503	0.476

Standard errors in parentheses, * p < 0.05, ** p < 0.01, *** p < 0.001.

.

This study estimates the fixed-effect model with panel data from 29 European countries and identifies a significant positive relationship for both hypotheses under analysis. The coefficient of Digi_Econ is significantly positive, with a p-value less than 0.001.

The models correlated the dependent variable with variations in the independent variables to better position the impact of the digital economy, thus highlighting that implementing digital technologies improves environmental performance and optimizes economic development. Therefore, the digital economy not only optimizes resource use efficiency but also contributes to reducing environmental impact through innovative technologies. A positive correlation exists between an economy’s digitalization degree and sustainable development indicators, such as GDP and environmental sustainability indices.

5. Discussion

Models 1 to 4 validate the first hypothesis, indicating a positive and significant relationship between the digital economy and economic development, which is in accordance with the literature (Uddin, 2024; An et al., 2024; Long et al., 2022; Zhao et al., 2022; David et al., 2025). The digital economy plays an increasingly important role in sustainable economic development, facilitating access to markets and services for a greater number of people, including those in rural or disadvantaged areas, through online platforms that allow entrepreneurs and small businesses to sell their products on global markets, thus contributing to reducing economic inequalities. Digitalization thus facilitates the transition to circular economies through platforms that support the reuse, recycling, and sharing of resources. This economic model reduces the resources needed for production and minimizes waste. Digitalization stimulates innovation, allowing companies to develop new products and services faster and more efficiently. This flexibility allows rapid adaptation to changes in consumer demand, which is essential for a sustainable economy. Digital tools can significantly reduce businesses’ operating costs, from streamlining inventory management to reducing marketing expenses using social networks and online advertising. This allows companies to invest more in sustainable development. Digital technologies can help optimize energy consumption through smart solutions, thus reducing companies’ carbon footprint and contributing to a cleaner environment. Digitalizing financial services, such as fintech, facilitates access to credit and payment solutions for small businesses and entrepreneurs that stimulate the local economy. From a human capital perspective, online educational resources improve access to vocational training and continuing education, generating a well-trained workforce essential for innovation and sustainable development. The digital economy facilitates rapid adaptation to crises, such as pandemics or economic changes, by allowing online commercial activities and remote collaboration. This resilience is essential for maintaining a sustainable economy. The digital economy offers opportunities for economic growth, contributing to creating a more sustainable future by promoting efficiency, innovation, and inclusion in various aspects of economic life. Development policies and strategies must support transitioning to a digital economy that respects sustainability principles.

The level of investment will always be a factor of economic growth because it contributes to the diversification of the economy by developing new sectors and industries, which determines the reduction in dependence on limited resources and ensures better economic stability in the long term. Investments create new jobs by reducing unemployment and increasing the population’s income, thus increasing consumption and stimulating the economy. From an environmental perspective, only investments in green technologies and renewable energies contribute to reducing carbon emissions and protecting natural resources. These initiatives are essential for combating climate change and for promoting a sustainable economic future. In conclusion, it is important to implement an effective strategy for attracting and managing investments because they are an essential engine for sustainable economic development by stimulating economic growth, creating jobs, promoting innovation, and diversifying economies.

The degree of urbanization in the short term positively impacts economic development. However, in the long term, the relationship becomes negative because a high degree of urbanization without a well-established plan can lead to the development of urban areas characterized by uncontrolled expansion. Rapid urbanization can lead to an overload of existing infrastructure, such as transport, water supply, sewage systems, and energy, and this overload can cause major problems that affect not only citizens’ quality of life but also economic efficiency. Excessive urbanization can also intensify social and economic inequalities, with a concentration of resources and opportunities among a minority, which causes social exclusion and conflicts, affecting economic and social stability. Furthermore, intense and uncontrolled urbanization can cause prices for housing, goods, and services to increase, reducing purchasing power and affecting consumption, an important engine of the economy. For these reasons, it is essential to implement sustainable urbanization policies that support balanced development between urban and rural areas, protect natural resources, and improve the quality of life for all population segments.

From an environmental perspective, we can see a positive relationship when we use renewable energy production (Sweidan, 2021; Zhang et al., 2021; Lee et al., 2022). In the short term, we can also identify a positive and significant relationship from the perspective of the composite indicator, but, in the long term, the relationship becomes negative. Environmental sustainability is, in general, an essential pillar of sustainable economic development. However, there are situations where environmental protection measures can have a negative impact on economic development, such as traditional industries that depend on environmentally unfriendly practices. Transitioning to more sustainable processes can involve high capital costs, affecting profitability. Sometimes, environmental protection measures determine construction restrictions to protect a natural habitat that can limit the development of housing or infrastructure, thus affecting access to services and quality of life. Environmental projects may require the relocation of communities or restrict access to certain areas, which can generate social and economic tensions, ultimately generating economic and social instability. Imposing strict environmental standards can lead to higher prices for certain goods and services, affecting demand and, consequently, the economy. While environmental sustainability is essential for long-term and healthy economic development, the best approach involves a balance of protecting the environment and stimulating economic growth. Environmental protection measures must be implemented carefully to minimize economic development’s negative impact and ensure a fair and efficient transition.

Research and development expenditures are usually considered essential for economic and technological progress, but research projects can be designed without taking into account real market needs. This can lead to developing technologies or products that have no practical application or are not attractive to consumers, resulting in unproductive investments. High spending on research and development can have long-term benefits. However, short-term effects can be negative, as the resources regarding economic growth or job creation are not immediately visible. Moreover, while public research can make important contributions, the private sector is often more agile and can react faster to market demands. Public investment that does not resonate with market dynamics can slow down the pace of innovations needed for rapid economic development. It is important for governments to plan and implement this spending carefully, ensuring that it is efficient, tailored to the needs of the economy, and supported by policies that encourage both public and private investment.

The second hypothesis is validated by models 5 and 6, and it is in accordance with the literature (Zheng & Wong, 2024; Chowdhury et al., 2021; Lee et al., 2023; Awijen et al., 2022), where the relationship between the digital economy and renewable energy generation is positive and significant. Digital technologies like the Internet of Things (IoT) enable real-time energy consumption and the monitoring of renewable energy production. This helps to optimize energy use and reduce waste, facilitating more efficient integration of renewable sources such as solar and wind power into electricity grids. Predictive algorithms and models can help maximize the efficiency of renewable energy systems, thus improving their integration into the electricity grid. The digital economy facilitates the creation of digital platforms for energy exchange between consumers, which allow people who produce renewable energy (e.g., through solar panels) to sell their surplus energy to other consumers, stimulating the use of renewable sources. Renewable energy projects can be more easily promoted and financed using digital platforms, connecting investors with project developers. Artificial intelligence can optimize renewable energy generation and distribution operations by better managing electricity grids and energy flows, identifying the most efficient ways to integrate alternative energy sources into existing systems. Digital platforms facilitate international collaboration in renewable energy research and development, accelerating global innovation and investment in sustainable technologies. The digital economy facilitates the use and generation of renewable energy and promotes innovation, efficiency, and accessibility. This collaboration is crucial for moving towards a more sustainable energy system and for reaching global climate change objectives.

The degree of urbanization polarizes citizens in a given territory, and the impact on renewable energy is positive because cities become centers of innovation and sustainable development. Zheng and Wong (2024) utilized the same variable (urban) to account for the influence of various factors on renewable energy development and reached a similar conclusion in our model: the acceleration of urbanization plays a significant role in promoting the development of renewable energy. Cities often have more advanced infrastructure and access to technology, which allows for the easier implementation of renewable energy solutions, such as solar panels, wind turbines, and geothermal energy systems. Many cities adopt active policies to reduce greenhouse gas emissions and promote renewable energy sources. These policies may include incentives for using green energy, subsidies for installing solar panels, or favorable regulations for developing renewable energy infrastructure. Urbanization can promote the development of public transport infrastructure and sustainable mobility solutions (e.g., electric vehicles), which can be integrated with renewable energy sources. This helps to reduce dependence on fossil fuels and increase the use of green energy. Urbanization positively contributes to renewable energy generation by strengthening infrastructure, facilitating innovation and community collaboration, promoting enabling policies, and creating economies of scale. This correlation between urbanization and renewable energies is essential for the global transition to a more sustainable energy system.

The positive impact of GDP per capita on renewable energy consumption is identified by the literature (e.g., Apergis & Payne, 2010; Zheng & Wong, 2024; Ehigiamusoe & Dogan, 2022) and generally justified by the improvement in the purchasing power of the population, which allows consumers and businesses to invest more in renewable energy technologies and solutions, such as solar panels, wind turbines, and heating systems using renewable sources. Countries with higher GDP per capita generally have better access to advanced technologies, research, and development. This facilitates the rapid development and adoption of renewable energy technologies, contributing to a faster transition from fossil fuels to renewable sources. GDP per capita positively influences renewable energy consumption by increasing purchasing power, facilitating infrastructure investments, promoting environmental awareness, and supporting the development of advanced technologies.

According to the model, this study reveals that government intervention based on research and development expenditures can enhance the positive relationship between the digital economy and renewable energy development (Zheng & Wong, 2024). Public spending on research and development positively impacts renewable energy generation by stimulating innovation, reducing costs, attracting investment, and developing informed policies. This support is essential for accelerating the transition to a more sustainable energy system and addressing the challenges of climate change.

6. Conclusions

This study successfully addresses how the digital economy interplays with economic development and renewable energy development, considering government intervention using the fixed-effect model. This complements the current literature on the status of the 29 European countries. From the path analysis results, a positive relationship for both hypotheses was found. The first hypothesis is confirmed by models 1 to 4, where a positive correlation exists between the digital economy and economic growth development, as the literature confirms (Uddin, 2024; An et al., 2024; Long et al., 2022; Zhao et al., 2022; David et al., 2025). The second hypothesis is validated by models 5 and 6, meaning the digital economy positively impacts renewable energy generation, as the literature confirms (Zheng & Wong, 2024; Chowdhury et al., 2021; Lee et al., 2023; Awijen et al., 2022).

This study is not free of its weaknesses, which must be considered. As a study based on quantitative indicators, the primary limitation is the availability of indicators for only 29 European countries during the period from 1996 to 2020. The selected timeframe (1996–2020) is justified by the accessibility of official databases from the World Bank (2025) and the European Commission (2025). As this research is quantitative, the second limitation is the unavailability of data for a series of indicators that would have better justified the independent variables.

The digital economy’s contribution to promoting sustainable economic development is complex and multidimensional. Implementing digital solutions in energy management allows for better consumption monitoring, promotes the use of renewable energy sources, and reduces dependence on fossil fuels. The digital economy can provide valuable information for formulating more effective public policies, which can be based on dated analyses and impact assessments, contributing to sustainable development.

Future research directions include expanding the analysis by categories of countries (higher-income countries, lower-income countries) worldwide and including qualitative indicators in the analysis by evaluating the effects of the digital economy and analyzing the potential reverse causality—whether economic development (GDP) affects the digital economy.