Can Technological Advancement Empower the Future of Renewable Energy? A Panel Autoregressive Distributed Lag Approach

Abstract

Energy is pivotal in achieving sustainable development’s economic, social, and environmental objectives. However, to attain this crucial goal, it is essential to focus on the type of energy we generate and the methods by which we use them. The availability, accessibility, and use of green technologies have improved significantly since the Fourth Industrial Revolution (4IR). This paper applies the pooled mean group Autoregressive Distributed Lag (PMG ARDL) model from 2000 to 2021 to 11 countries that, according to the Climate Council, are most affected by environmental degradation issues and are taking new initiatives to reduce their emissions. The results indicate a significant relationship between renewable energy consumption and technological advancements in the short and long term. However, there needs to be more of the literature about the negative impact of research and development on renewable energy consumption. The findings of this paper can assist policymakers in determining effective strategies in the renewable energy sector, as any technological advancement is an innovative way to transform the renewable energy industry completely. By optimizing energy production and reducing costs, technological advancement can help a country achieve its renewable energy goals.

1. Introduction

Energy use is fundamental to society’s growth. The need for energy for daily life will undoubtedly increase as the world population grows and the economy and society develop [1]. Fossil fuels are substantially depleted due to their heavy use in this process [2]. It has also led to global warming and climate change due to high greenhouse gas emissions [3]. This could lead to problems such as melting glaciers, rising sea levels, and ecological damage, which would be extremely dangerous for human society [4].

Reducing greenhouse gas emissions and combating climate change to prevent global temperatures from rising are essential priorities for the world [5]. Chien-Chiang et al. (2024) [6] confirms that achieving net-zero emissions is a key move in reducing the impacts of environmental change. The ecosystem and public health remain seriously threatened by CO₂ emissions. Nations must rapidly reduce emissions, shift to energy sources with lower carbon footprints, and increase spending on renewable energy technologies [7,8].

Energy innovation is essential for mitigating climate change within ambitious policy objectives and an evolving technological environment. Bousrih (2023) [9] confirms that technology, due to its widespread use, can be found in almost all industries worldwide, including energy systems, healthcare, agriculture, business, and engineering. Technological advancement is expected to have short- and long-term impacts on global productivity, equity and inclusion, environmental outcomes, and several other areas. Combining renewable energy and new technologies represents the future of energy development and sustainable development [10].

Although renewable energy technologies have advanced significantly in recent years, innovation and efficiency are still driven by ongoing research and development. Renewable energy sources will primarily meet future energy demands, lowering our carbon footprint through emerging technologies, according to Goodwin et al. (2023) [11]. Emerging renewable energy technology may assist with intermittent power, grid integration, and other clean energy issues. These technologies accelerate the adoption and impact of renewable energy projects by enhancing their cost-effectiveness, scalability, and reliability. The critical areas of current and future focus in the energy sector are developing new technologies to optimize energy production and consumption from available natural resources along with enhanced system management and distribution.

Solar and wind energy as essential renewable energy sources have been a significant development and a scientific focus area in recent decades [12]. Furthermore, Nazir et al. (2020) [13] emphasize that using sustainable renewable energy technologies is critical to the energy revolution. Implementing essential components such as reducing electricity costs, improving the flexibility and stability of energy systems, replacing aging infrastructure, reducing carbon emissions, providing reliable power to remote areas, and avoiding environmental changes is vital [14]. Likewise, fair access to inexpensive, safe, and sustainable clean power is one of the Sustainable Development Goals (SDGs). However, this will only ever be a goal if innovative and creative solutions can solve the many energy-related issues facing the markets, such as insufficient power generation, poor transmission and distribution infrastructure, sustainability issues, and environmental difficulties.

For several reasons, AI has shown itself to be an effective tool in the renewable energy industry. For instance, the amount of electricity produced by solar power plants may be accurately predicted using machine learning algorithms, according to Alfalahi et al. (2017) [15]. Prognostic and health management (PHM) methods for renewable energy systems, with a focus on wind turbine generators, may also benefit from the application of AI [16,17]. The shift toward attaining carbon neutrality can be facilitated by integrating renewable energy sources into large-scale multi-energy systems using advanced management systems [18]. Additionally, it has been demonstrated that deep learning methods work well for incorporating fluctuating renewable energy into contemporary power systems [19,20].

Wang et al. (2023) [21] examine the link between robot advances, carbon emissions, and energy use. They claim that developing renewable energy technologies stimulated by new smart grids and management systems may contribute to reducing greenhouse gas emissions, Yu et al. (2023) [22] concentrated on using artificial intelligence (AI) as a technological advancement in China’s low-carbon city. The authors confirmed that applying technology in energy efficiency and green energy fields reduces carbon emissions. These findings are confirmed by Yao et al. (2023) [14], who recognize that the energy sector’s artificial intelligence (AI) boosts energy efficiency. On the same path, Olabi et al. (2023) [23] and Li et al. (2022) [24] identify that the application of artificial intelligence (AI) promotes a decrease in carbon intensity in particular sectors such as manufacturing, electricity, and gas.

Technological advancement in renewable energy research and applications can accelerate the shift to sustainable energy sources by improving efficiency, cost-effectiveness, and reliability. Reducing dependency on fossil fuels and mitigating climate change requires utilizing hydro, solar, wind, and geothermal energy as renewable energy sources [25,26]. Nevertheless, grid integration and energy management are challenging because intermittent renewable energy sources exist. Technologies have the potential to completely transform the renewable energy industry by improving the reliability and efficiency of renewable energy systems [26].

This study adds significantly in several ways. First, the labor market, economic development, and carbon emissions have been the main subjects of prior economic studies on technological advancement. Research on this matter has not been conducted significantly on how technologies affect the use of renewable energy. To close this knowledge gap, this study expands on prior research by conducting an in-depth empirical test to integrate technological advancements into renewable energy use. Furthermore, this article tackles a significant issue regarding energy management in the context of continuous technological advancement for a specific sample of emerging and developed countries ranked by the Climate Council organization as leading countries in energy policy innovation.

The paper’s remaining sections are organized as follows: Section 2 reviews the literature. Section 3 presents the empirical method. Section 4 presents the results. The last section covers the conclusions and implications of the research.

2. Literature Review

Clean, renewable, and sustainable energy is inevitably required to enhance social, economic, and environmental well-being and promote economic growth and productivity. Severe global warming and climate change are attributed to increasing CO₂ emissions, especially carbon emissions [27,28,29,30,31]. These events negatively impact human well-being and ecosystem sustainability [32,33,34,35]. Additionally, the decline in environmental issues and the frequency of climate-related catastrophes have caused several obstacles to sustainable development [36,37].

A shift to renewable energy is vital, and it will bring significant socio-economic changes that go far beyond a simple industrial revolution [38,39]. According to Zhao et al. (2024) [40], the transition to renewable energy is critical to reducing carbon emissions and supporting long-term social and economic growth. Gyimah et al. (2022) [41] and Su & Yang (2023) [42] reported these results.

Global technological advancement is occurring in parallel with climate change’s continuing consequences [43,44]. The nexus between renewable energy and new technologies can assist in forecasting weather and energy usage to determine the most favorable periods and locations for energy production. In addition, technologies are intended to estimate renewable energy production and determine the amount of energy stored so that companies can adapt their operations accordingly [45]. The advent of Industry 4.0, which has further boosted the development of artificial intelligence (AI), is an important technological development [46]. Integrating digital technology into manufacturing and related industries fundamentally changes industrial processes [23].

Innovation is essential for technological development and implementation. According to Chang et al. (2022) [47], Makeeva et al. (2024) [48], and Yao et al. (2023) [14], several indicators throughout the literature were used to assess an economy’s technological level. For example, information and communication technology (ICT), patents, and research and development (R&D) expenditures can be indicators of technological advancement.

2.1. ICT and Renewable Energy Consumption

As globalization accelerates, ICT could boost productivity, reduce inequality, and promote inclusive education. Additionally, it is an important driver behind the growth of the low-carbon economy. Concurrently, there is a developing belief that ICT may significantly lessen the environmental effects of industries, particularly by boosting their energy efficiency. All economic sectors can benefit from information and communication technology’s ability to optimize current processes or enable the creation of entirely new, more energy-efficient ones. ICT may promote environmental quality through several means, including improved productivity, increased energy efficiency, and enhanced renewable energy utilization and production [47]. According to Lee et al. (2023) [49], the rising use of ICT has improved environmental quality since it can reduce resource and energy consumption by replacing conventional goods and services with electric ones.

Moreover, ICT improves navigation, traffic management software, and transport information systems more efficiently, reducing energy consumption and emissions. Lin et al. (2021) [50] demonstrate that new communication technology positively impacts export levels, increasing labor productivity and human capital accumulation. In this regard, Lee et al. (2022) [51] demonstrate that industrial robot development positively impacts green technology innovation, for 34-country global panel data between 1993 and 2019. The author concludes that environmental regulation can strengthen the energy sector’s information and technology advancement effect.

On the other hand, it is also important to highlight that the increasing use of ICT is closely linked to the reduction in the cost of several goods and services, which boosts industrialization and other economic expansion. Yang and Wang (2023) [52] use a dataset of 61 developing countries from 1993 to 2019 to confirm that the use of industrial robots has positive impacts that help countries overcome the middle-income trap. In addition, according to the authors, the main factors facilitating this transition are foreign direct investment, industry structure, and technical innovation. Thus, encouraging ICT trade for some countries may also result in higher energy use and emissions, according to Lee et al. (2023) [49]. Moreover, the availability of ICT equipment and developed ICT infrastructure may use much energy during production and operation, according to Chang et al. (2022) [47]. Consequently, given that ICT can also result in increased energy consumption, a wealthier financial system, and increased industrial production, its beneficial effects on the environment may only last temporarily.

Based on the findings above, we can formulate Hypothesis 1.

H1: 

There is a positive relationship between ICT and renewable energy consumption.

2.2. Patents and Renewable Energy Consumption

Renewable energy constitutes a primary area of interest within green patents. These patents, often called environmentally friendly, cover a broad range of inventions, such as renewable energy-related technology, waste reduction strategies, pollution control strategies, and several other sustainable goods and procedures. The International Classification List of Green Patents, which is issued by the World Intellectual Property Organization (WIPO), is one well-known international classification scheme that is typically used to classify green patents. A systematic method for identifying and classifying green technologies is thus provided by this classification framework, which makes it easier to identify patents that support renewable energy use and production. Innovation in sustainable energy is the main driver behind the shift to renewable energy. According to recent studies, these “green” patents, which grant exclusive rights to inventions, promote investment in green innovation and support research and development in sustainable technology.

Makeeva et al. (2024) [48] focus on green patents—also called environmentally friendly patents—covering a wide range of inventions, such as technology for pollution management, waste reduction, renewable energy, and other ecologically friendly products and procedures. According to Guo et al. (2018) [53], during the last decade, patents have been oriented toward investment in green innovation and offer exclusive rights to researchers’ inventions, thus promoting sustainable technological development. Furthermore, patents are essential for knowledge transfer and technology transfer as they promote interaction in sustainable innovation and the adoption of green innovations [54]. The existence of green patents demonstrates a company’s commitment to environmental sustainability and corporate social responsibility. A company’s efforts to develop and implement green technology are demonstrated with tangible evidence, demonstrating a proactive approach to resolving environmental matters and minimizing its ecological footprint [55]. Green patents will ensure a knowledge transfer about how well green technologies may be employed to efficiently influence the production and consumption of energy from renewable sources. According to the above findings, we can formulate the following hypothesis.

H2: 

There is a positive relationship between patents and renewable energy consumption.

2.3. Research and Development (R&D) and Renewable Energy Consumption

R&D may affect energy consumption in several ways. On the one hand, R&D may reduce energy consumption by promoting energy conservation developments [14]. As anticipated by the endogenous growth theory, technological advancement or innovation brought about by research and development spending may encourage energy production and consumption efficiency. Therefore, by enabling improved technologies that minimize gas emissions, investments in research and development tend to decrease reliance on natural resources and increase efficient renewable energy consumption [56,57,58]. Conversely, research and development may result in higher energy consumption due to increased output linked to trade openness and economic growth by boosting economic growth and opening additional trade opportunities. This is especially likely to happen in economies where the marginal returns to R&D and innovation gradually decline. As per Newell (2010) [59], there is a decrease in returns on research and development as information accumulates and discoveries become more complex over time.

From another perspective, Chmielewsk et al. (2023) [60] demonstrate that the timing of novel technologies employed in the energy field, the adoption of laws allowing the introduction of solutions into the market, or the costs of technological changes in businesses enabling the beginning of production of an innovative product are some of the possible causes of the delay in the effects of R&D expenditure. Research and development expenditures directly impact company returns, as demonstrated by the findings of Busru et al. (2017) [61]. A delayed effect of research and development inputs has been observed in other research, according to Ayaydin et al. (2014) [62], Kiraci et al. (2016) [63], and Xie et al. (2020) [64], and it has been demonstrated that for high-tech companies, In China and Taiwan, this effect becomes apparent after two years, and in Japan, after about a year, according to Hsu (2013) [65]. Consistent with the methodology outlined in the existing literature, the authors endeavored to confirm the correlation between research and development expenditures and specific energy-related variables as soon as they were incurred, considering the impact of one- and two-year time shifts. Following this debate, we can formulate the third hypothesis.

H3: 

There is a positive impact of R&D on renewable energy consumption.

2.4. Research Gap

The literature review raised an essential concern about the current study’s context. Prior studies have focused on how technical advancements and renewable energy might improve energy efficiency and facilitate sustainable development. Nevertheless, the effect of such technological advances has received comparatively little attention. Focusing on a group of countries pursuing innovative renewable energy initiatives, this paper adds to the body of the current literature by analyzing the impact of technological advancements on renewable energy consumption in the short and long term, using different proxies of green innovation.

3. Data and Methods

3.1. Data

In this section, we present the details of the data used in the empirical part. According to the Climate Council organization, eleven countries (Sweden, Costa Rica, the United Kingdom, Iceland, Germany, Uruguay, Kenya, China, Morocco, New Zealand, and Norway; Council Climate (2024); https://www.climatecouncil.org.au/11-countries-leading-the-charge-on-renewable-energy/, accessed on 30 April 2024) are considered the leading economies inspiring nations to reduce their emissions through the wise application of efficient, targeted regulations combined with renewable resources. Because of data availability, only nine of these eleven countries were selected for this study: Sweden, Costa Rica, the United Kingdom, Iceland, Germany, Uruguay, China, New Zealand, and Norway, covering the period 2000–2021. The data were collected from the World Development Indicators (WDIs).

Previous research serves as a framework for variable selection. This type of selection aims to choose a set of variables that provide the best model fit, allowing accurate predictions of the short and long-term links between technological progress and renewable energy consumption. We used a proxy of renewable energy consumption (REN) for the dependent variable, as stated in the studies of Hassan^b et al. (2023) [8] and Taghizadeh-Hesary et al. (2023) [39]. Technological development can be approximated by three main proxies for the independent variables. First, the ICT sector is represented by two facets: the ICT trade for a country (ICT), as stated by Wang et al. (2024) [66], and the technological infrastructure (COMM), as stated by Gulnara et al. (2018) [67]. Second, we use patent applications (Patent), as stated by Triulzi et al. (2020) [68]. Third, we apply research and development expenditure (RD), as stated by Weiyu et al. (2022) [69]. The control variable chosen is urban population (Upop) according to Hong et al. (2022) [70] and Liu et al. (2022) [71]. The details are presented in

Table 1. Variables and descriptions.

Variables	Symbol	Description
Renewable energy consumption	REN	Percentage of renewable energy consumption to total final energy consumption.
Information and communication technology	ICT	Percentage of ICT goods exports to total goods exports. It represents the effect of ICT trade on renewable energy.
Patent applications	Patent	Number of patent applications, residents.
Research and development	RD	Research and development expenditure as a percentage of GDP.
Communication and technology	COMM	Communications, computers, etc., as a percentage of service exports. It represents the ICT infrastructure of a country.
Urban population	Upop	Proportion of the total population living in cities

below.

After reviewing the details of the sample and data chosen,

Table 2. Descriptive analysis.

Variable	Obs	Mean	Std. Dev.	Min	Max
lnREN	198	3.2538	1.0093	−0.1625	4.4313
lnICT	198	0.7950	1.9242	−3.0932	3.4250
lnPatent	198	7.2163	3.2713	2.0794	14.1708
lnRD	198	0.3397	0.7718	−1.5642	1.3542
lnCOMM	198	3.5684	0.5605	1.6169	4.3288
lnUpop	198	4.3472	0.2035	3.5801	4.5611

Source: Authors’ estimates.

and

Table 3. Correlation matrix.

Variable	lnREN	lnICT	lnPatent	lnRD	lnCOMM	lnUpop
lnREN	1.0000
lnICT	−0.5296 *** (0.000)	1.0000
lnPatent	−0.6423 *** (0.000)	0.6504 *** (0.000)	1.0000
lnRD	−0.1791 ** (0.012)	0.2695 *** (0.000)	0.5977 *** (0.000)	1.0000
lnCOMM	−0.3108 *** (0.000)	0.6358 *** (0.000)	0.4711 *** (0.000)	0.2482 *** (0.000)	1.0000
lnUpop	0.4631 *** (0.000)	−0.7426 *** (0.000)	−0.5889 *** (0.000)	−0.0103 (0.885)	−0.4420 *** (0.000)	1.0000

Source: Authors’ estimates. Note: 1%, and 5% significant levels are indicated by the symbols *** and **, respectively. Numbers within parentheses are significance levels.

show the outcomes of a descriptive statistical analysis and a correlation analysis.

The results show that the standard deviations are large enough to investigate the variation in the data series. The correlation coefficients in Table 3 show the absence of a high correlation between the selected variables, which allows the exclusion of the multicollinearity possibility.

3.2. Methodology

To investigate the long and short-term effects of technological advancement on renewable energy consumption, following Javed et al. (2024) [72], this study adopted the Cobb–Douglas production function to the environmental quality function; mainly, this study estimated the logarithmic form of the following econometric model:

$${REN}_{it}=f\left({ICT}_{it},{Patent}_{it},{RD}_{it},{COMM}_{it},{Upop}_{it}\right)$$

(1)

where i = country and t time (year).

Equation (1) demonstrates how ICT, Patent, RD, COMM, and UPOP affect REN.

REN is renewable energy consumption (% of final total energy consumption); ICT is ICT export (% of total good export); Patent is patent applications (residents); RD is research and development expenditure (% of GDP); COMM is communications, computer, etc. (% of service exports); and Upop is urban population (% of total populations). The functional form of the main model is specified as follows:

$${REN}_{it}={\beta }_0+{\beta }_1{ICT}_{it}+{\beta }_2{Patent}_{it}+{\beta }_3{RD}_{it}+{\beta }_4{COMM}_{it}{+\beta }_5{Upop}_{it}+{\epsilon }_{it}$$

(2)

We utilize the logarithm form for all variables to accommodate linear relationships, stabilizing variance, and enhancing interpretability.

As we use panel data from nine countries covering the period (2000–2022), this study applies the pooled mean group (PMG) approach. One advantage of the pooled mean group (PMG) methodology is that it identifies long- and short-term relationships between the targeted variables. Moreover, the PMG-ARDL methodology considers cross-sectional dependency. Another advantage of the panel ARDL approach for this study is that it investigates the cointegration relationship between the variables, despite the degree of cointegration I(0) or I(1) [73,74]. In addition, this approach is helpful for small samples. The mathematical model of the ARDL approach includes the derivation of empirical estimates for both short- and long-run impact, as well as the lagged error correction term (ECT), which demonstrates the convergence rate to the long-run equilibrium.

Before executing the panel ARDL, this study performed some diagnostic tests, including the cross-section dependence and unit root tests.

3.2.1. Cross-Section Dependence Tests

First, this study examined the cross-sectional dependence of the data using various models: Pesaran (2004) [75] scaled LM, and Pesaran et al. (2008) [76] bias-corrected scaled LM tests.

-

Breusch and Pagan (1980) [77] test statistics (LM):

$$LM=\sum _{i=1}^{N-1}\sum _{j=i+1}^N{T}_{ij{{\widehat{P}}^2}_{ij}}\to X^2{\displaystyle \frac{N(N-1)}{2}}$$

(3)

-

Pesaran (2004) [75] scaled LM test statistics:

$${LM}_s=\sqrt{{\displaystyle \frac{1}{N(N-1)}}}\sum _{i=1}^{N-1}\sum _{j=i+1}^N{T}_{ij{{\widehat{P}}^2}_{ij}}\to N\left(0,1\right)$$

(4)

-

Pesaran (2004) [75] CD test Statistics:

$${CD}_P=\sqrt{{\displaystyle \frac{2}{N(N-1)}}}\sum _{i=1}^{N-1}\sum _{j=i+1}^N{T}_{ij{{\widehat{P}}^2}_{ij}}\to N\left(0,1\right)$$

(5)

-

Pesaran et al. (2008) [76] bias-corrected scaled LM test statistics:

$${LM}_{BC}=\sqrt{{\displaystyle \frac{1}{N(N-1)}}}\sum _{i=1}^{N-1}\sum _{j=i+1}^N{T}_{ij{{\widehat{P}}^2}_{ij}}-{\displaystyle \frac{N}{2\left(T-1\right)}}\to N\left(0,1\right)$$

(6)

where ${{\widehat{P}}^2}_{ij}$ is the correlation coefficient between the residuals.

3.2.2. Unit Root Tests

Then, panel unit root tests were performed to check the variables’ stationarity. Following Pesaran (2007) [78], this study employed a second-generation panel unit root test. The cross-section augmented Dickey–Fuller (CADF) equation is expressed as follows:

$$∆Y_{it}={\alpha }_i+{\beta }_i{Y}_{it-1}+\sum _{j=0}^{pj}{\gamma }_{ij}{∆Y}_{it-\mathrm{j}}+{\delta }_i{\overline{Y}}_{t-1}+\sum _{j=0}^{pj}{{\theta }_{ij}∆\overline{Y}}_{t-j}+{\epsilon }_{it}$$

(7)

where ${\overline{Y}}_t$ is the cross-sectional mean of $Y_{it}$.

The CIPS test statistic is estimated using the mean of CADF individual statistics associated with cross-sectional units:

$$CIPS={\displaystyle \frac{1}{n}}{\sum }_{i=1}^n{CADF}_i$$

(8)

3.2.3. Estimation Methods

For estimating the primary model described in Equation (2), we employ the pooled mean group ARDL (PMG-ARDL) technique. In addition to addressing concerns with slope heterogeneity, and endogeneity, the PMG-ARDL method allows us to handle cross-sectional dependence between countries (Shahbaz et al., 2018 [79]; Wang et al., 2021 [80]; Nyeadi, 2023 [81]) and produces accurate estimates. Furthermore, the PMG-ARDL is designed to capture dynamic connections by including lagged dependent and explanatory factors (Ebaidalla, 2024) [82]. The basic PMG model is presented as follows:

$$∆{REN}_{it}=\vartheta \left({REN}_{it-1}+{{\theta }_{ij}X}_{it}\right)+{\sum }_{j=0}^{q-1}{\beta }_{ij}∆X_{it-j}+{\sum }_{j=1}^{p-1}{\gamma }_{ij}{∆REN}_{it-\mathrm{j}}+{\epsilon }_{it}$$

(9)

where $\vartheta$ denotes the error correction term, ${\beta }_{ij}$ and ${\gamma }_{ij}$ denotes the short-run parameters, ${\theta }_{ij}$ denotes the long-run parameters, $X_{it}$ denotes explanatory and control variables, and ${ \epsilon }_{it}$ denotes the error terms.

3.2.4. Robustness Methods

Additionally, this study uses the panel Fully Modified OLS (FMOLS) model to validate the PMG-ARDL model’s long-run outcomes. The literature frequently uses this technique to estimate the long-run coefficients. The FMOLS model can reduce endogeneity, autocorrelation, and small-sample bias issues related to the OLS estimator.

3.2.5. Causality Test

Lastly, to investigate how the selected variables are causally related, we apply the Dumitrescu and Hurlin Panel Causality Test as another robust method to detect the possible casual relationships among the selected variables. A causality test is desired to determine the directions of the causal relations. The test proposed by Dumitrescu and Hurlin (2012) [83] extended the Granger causality test for panel data, with a bootstrap panel causality test. The test statistics and probability values of the Dumitrescu and Hurlin tests are derived using the Monte Carlo simulation. Dumitrescu and Hurlin test statistics are the sum of individual Wald statistics. This study employs the Dumitrescu and Hurlin causality test because it suits scenarios with cross-sectional dependence. The following equations are Wald statistics for panel causality tests.

$$Y_{it}={\alpha }_i+\sum _{j-1}^p{\gamma }_{ij}{Y}_{it-\mathrm{j}}+\sum _{j-1}^p{{\theta }_{ij}\mathrm{Z}}_{i,t-j}+u_{it}$$

(10)

where P denotes the optimal lag length. In this test, the null hypothesis implies no causal influence from Z to Y, whereas the alternative hypothesis implies causality from Z to Y.

$$W_{NT}^{Hnc}={\mathrm{N}}^{-1}\sum _{i-1}^N{W}_{it}$$

(11)

$$Z=\sqrt{({\displaystyle \frac{N}{2P}} ){\displaystyle \frac{T-2P-5}{T-P-3}} }\times \left({\displaystyle \frac{T-2p-3}{T-2P-1}}\overline{W}-P\right)$$

(12)

4. Results

4.1. Cross-Section Dependence Test Results

The cross-section dependence test results in

Table 4. Cross-sectional dependence tests.

Test	Statistic	Prob.
Breusch–Pagan LM	283.8156	0.0000
Pesaran scaled LM	29.2054	0.0000
Pesaran CD	−1.8338	0.067

Source: Authors’ estimates.

show that all three tests’ statistics are statistically significant at 1% and 10%, confirming the rejection of the null hypothesis. This suggests that the countries being studied are interconnected, and economic shocks in one may have an impact on others.

Due to the appearance of cross-section dependence, this study ran the second-generation unit root test; cross-sectional augmented Dickey–Fuller (CADF), and the cross-sectional Im, Pesaran, Shin (CIPS) unit root tests proposed by Pesaran (2007) [78].

4.2. Unit Root Test Results

The results of CIPS and CADF tests in

Table 5. Cross-sectional dependence unit root tests.

	CIPS		CADF
	I(0)	I(1)	I(0)	I(1)
lnREN	−2.172	−3.964 ***	−2.269 * (0.058)	−2.691 *** (0.002)
lnICT	−2.225 *	−4.106 ***	−1.990 (0.238)	−3.146 *** (0.000)
lnPatent	−2.036	−4.700 ***	−1.762 (0.498)	−2.492 ** (0.012)
lnRD	−1.591	−3.869 ***	−1.671 (0.608)	−2.742 *** (0.001)
lnCOMM	−1.854	−4.211 ***	−1.948 * (0.280)	−2.491 ** (0.012)
lnUpop	−1.947	−2.317 *	−2.273 * (0.056)	−2.045 (0.189)

Source: Authors’ estimates. Note: 1%, 5%, and 10% significant levels are indicated by the symbols ***, **, and *, respectively. Numbers within parentheses are significance levels.

reveal that all selected variables are I(1) at the 1% and 5% significance level, except Upop, which is I(0) for the CADF test and I(1) for the CIPS test at the 10% significance level. The findings indicate that the selected variables are stationary at different levels, I(0) and I(1). As a result, the panel ARDL approach can be utilized.

4.3. Panel ARDL (PMG) Test Results

The PMG model’s results are presented in

Table 6. PMG ARDL test.

	Coefficient	z-Statistic (p Value)
Long-Run Equation
lnICT	0.0414 **	2.35 (0.019)
lnPatent	0.0498	0.93 (0.350)
lnRD	−0.3950 ***	−5.06 (0.000)
lnCOMM	0.1395 **	2.25 (0.025)
lnUpop	6.0980 ***	5.90 (0.000)
Short-Run Equation
COINTEQ01	−0.3050 ***	−3.67 (0.000)
ΔlnICT	0.0564	0.76 (0.450)
ΔlnPatent	−0.1134	−1.36 (0.174)
ΔlnRD	−0.0010	−0.01 (0.993)
ΔlnCOMM	0.0974	1.50 (0.134)
ΔlnUpop	−1.4226	−0.10 (0.917)
C	−7.4411 ***	−3.57 (0.000)

Source: Authors’ estimates. Note: 1% and 5% significant levels are indicated by the symbols *** and **, respectively.

below. The PMG model’s results show a positive long-run relationship between ICT, COMM, and renewable energy consumption. Smart grids, smart buildings, smart logistics, and smart industrial processes are examples of information and communication technologies contributing to the global transition to a more energy-efficient and sustainable future. A 1% increase in ICT exports will increase renewable energy consumption by 0.04%. This positive impact is confirmed by Adebayo et al. (2022) [16]. According to Chang et al. (2022) [47], ICT may improve the sustainability of the environment through higher productivity, increased energy efficiency, and improved use and production of renewable energy sources. The increasing usage of ICT has been shown to improve environmental quality, according to Lee et al. (2023) [49], since it can lower non-renewable resources and energy consumption by substituting electronic goods and services for conventional ones.

Furthermore, a 1% increase in communications will result in a 0.14% increase in renewable energy consumption. Lin et al. (2021) [50] demonstrate that using new technology in communication also positively impacts export levels, increasing labor productivity. This, in turn, impacts economic growth and increases investment and innovation in the renewable energy sector. Lee et al. (2022) [52] confirm these findings.

In this regard, we can confirm the hypothesis below.

H1: 

There is a positive relationship between ICT and renewable energy consumption.

Regarding the PATENT variable, the results show that new patents positively impact renewable energy consumption, but this effect is insignificant. The issue of intellectual property rights via patents and the transfer of climate change technologies have become increasingly sensitive. According to Harvey (2008) [84] and Johnstone et al. (2010) [85], patents are crucial for drawing in financial resources, creativity, and communication required to bring about a “clean energy revolution”. According to Guo et al. (2018) [53], during the last decade, patents have been oriented toward investment in green innovation and offer exclusive rights to researchers’ inventions, thus promoting sustainable technology development. Furthermore, patents are essential for knowledge transfer and technology transfer as they promote interaction in sustainable innovation and the adoption of green innovations [54]. So, the results did not show enough evidence to accept the hypothesis below.

H2: 

There is a positive relationship between patents and renewable energy consumption.

The results also show that research and development (RD) has a negative long-term effect on renewable energy consumption (REN) with a coefficient of −0.40. This result is in some way contradictory to the conventional literature that shows a positive relationship between research and development and the extent of renewable energy use, according to Khezri et al. (2021) [86] and Gaur et al. (2023) [19]. As predicted by endogenous growth theory, technological advancement or innovation brought about by R&D spending may encourage energy production and consumption efficiency. Therefore, by enabling more efficient technologies that minimize gas emissions, R&D investments tend to reduce the reliance on natural resources and increase the efficient consumption of renewable energy, according to Churchill et al. (2019,2021) [56,57] and Dinda (2004) [58]. The negative coefficient obtained for the variable RD can also be explained by the nature of the countries included in the sample regarding the research and development indicators. In fact, according to the World Bank report for 2022, Kenya, Costa Rica, Morocco, and Uruguay spend less than 1% of GDP on research and development expenditure. This result rejects the hypothesis below.

H3: 

There is a positive relationship between R&D and renewable energy consumption.

Regarding the short-term relationship, the results show a negative and statistically significant ECM coefficient at 1%, with an adjustment speed to long-run equilibrium equal to −0.3050. This indicates a monotonically convergent path to the long-run equilibrium path directly [87].

The cross-section short-run coefficients are shown in

Table 7. Cross-section short-run coefficient.

	Sweden	Costa Rica	United Kingdom	Iceland	Germany	Uruguay	China	New Zealand	Norway
COINTEQ01	−0.4687 *** (0.001)	−0.6631 *** (0.000)	−0.4913 *** (0.000)	−0.1938 *** (0.000)	−0.0655 *** (0.000)	−0.5963 *** (0.000)	−0.0118 *** (0.001)	−0.1860 *** (0.001)	−0.0729 *** (0.000)
lnICT	0.1177 *** (0.000)	−0.0275 *** (0.000)	−0.0424 *** (0.000)	0.0028 *** (0.000)	−0.2123 ** (0.008)	−0.1384 *** (0.000)	0.5176 *** (0.000)	0.2869 *** (0.000)	0.0037 ** (0.040)
lnPatent	0.1265 *** (0.003)	−0.0260 *** (0.000)	−0.7119 (0.164)	−0.0224 *** (0.000)	−0.2497 (0.189)	−0.1718 *** (0.000)	0.0274 ** (0.041)	0.0264 *** (0.000)	−0.0191 ** (0.015)
lnRD	0.3574 *** (0.003)	0.1119 *** (0.001)	0.4574 *** (0.001)	0.0430 *** (0.001)	−0.7496 * (0.096)	0.2095 *** (0.000)	−0.2359 ** (0.032)	−0.1668 (0.180)	−0.0357 * (0.055)
lnCOMM	0.2420 ** (0.035)	0.0925 *** (0.002)	0.4760 (0.264)	−0.0060 *** (0.000)	−0.1811 (0.204)	−0.0558 *** (0.000)	0.2304 *** (0.000)	0.0845 *** (0.000)	0.0061 * (0.062)
lnUpop	15.6010 (0.898)	80.0582 (0.898)	−47.7318 (0.860)	23.6259 (0.927)	5.4356 (0.987)	−59.8497 (0.943)	−8.7753 (0.864)	−19.2483 (0.918)	−1.9222 (0.980)
C	−11.2170 (0.674)	−16.8459 (0.561)	−47.7318 (0.188)	−4.5696 (0.526)	−1.5608 (0.201)	−14.6421 (0.326)	−0.0659 (0.935)	−4.5434 (0.681)	−1.6868 * (0.067)

Source: Authors’ estimates. Note: 1%, 5%, and 10% significant levels are indicated by the symbols ***, **, and *, respectively. Numbers within parentheses are significance levels.

. According to the research, all nine countries’ ECM coefficients are negative and significant at 1%. This suggests that the dependent and the chosen independent variables have a long-term relationship. Furthermore, the findings indicate that China has the lowest convergence rate to the long-run equilibrium at 1%, with Costa Rica having the highest rate at 66%, followed by Uruguay at 60%, the United Kingdom at 49%, Sweden at 47%, New Zealand and Iceland 19%, and Germany and Norway (7%). The coefficients of technological variables are significant for most of the selected sample countries, with positive or negative signs reflecting the diversity of their economic structures.

4.4. Robustness Test Results

The FMOLS model’s output in

Table 8. Robustness test FMOLS results.

	Coefficient	t-Statistic
lnICT	0.0353 *	0.083
lnPatent	−0.0175	0.684
lnRD	−0.1874 ***	0.004
lnCOMM	0.1878 ***	0.000
lnUpop	12.6749 ***	0.000

Source: Authors’ estimates. Note: 1%, and 10% significant levels are indicated by the symbols ***, and *, respectively.

reveals that all the estimated coefficients match the long-term results of the PMG-ARDL model reported in Table 6. Consequently, we conclude that our findings hold up well to various estimating techniques, supporting the significance of our primary explanatory variables.

4.5. Causality Test Results

In

Table 9. Dumitrescu and Hurlin Panel Causality Test.

Null Hypothesis:	W-Stat.	Zbar-Stat.	p Value	Decision
LOG(ICT) does not Granger cause LOG(REN) LOG(REN) does not Granger cause LOG(ICT)	4.4954 *** 2.8503	3.7430 1.2755	0.000 0.202	Unidirectional causality ICT → REN
LOG(PATENT) does not Granger cause LOG(REN) LOG(REN) does not Granger cause LOG(PATENT)	2.6518 3.7652 **	0.9777 2.6479	0.328 0.008	Unidirectional causality PAT← REN
LOG(RD) does not Granger cause LOG(REN) LOG(REN) does not Granger cause LOG(RD)	3.8092 ** 2.2571	2.7137 0.3857	0.007 0.700	Unidirectional causality RD → REN
LOG(COMM) does not Granger cause LOG(REN) LOG(REN) does not Granger cause LOG(COMM)	3.5393 ** 4.3374 ***	2.3090 3.5061	0.021 0.001	Bidirectional causality COM↔ REN
LOG(UPOP) does not Granger cause LOG(REN) LOG(REN) does not Granger cause LOG(UPOP)	6.4803 *** 3.6920 **	6.7205 2.5380	0.000 0.011	Bidirectional causality Upop↔ REN

Source: Authors’ estimates. Note: 1%, and 5% significant levels are indicated by the symbols ***, and **, respectively.

below, we present the results of the causality test. They show two bidirectional causalities between REN and Upop and REN and COMM. There are two unidirectional causalities from the specified independent variables to the dependent variable: from ICT and RD to REN. Moreover, the results show one more unidirectional causal relation from REN to Patent.

5. Conclusions, Recommendations, and Limitations

Using clean renewable energy instead of conventional fossil fuels has become the mainstream trend in response to the growing global climate and environmental concerns. However, the effects of the widespread use of renewable energy, such as those on stability, safety, and cost-effectiveness, are becoming more apparent. Adopting technological advancement in renewable energy systems can solve these issues. Future national energy strategy plans, reliable technical guidelines, and significant financial incentives are needed by governments worldwide to support the use of technological advancement in the energy field. This paper investigates the state of nine leading countries in the renewable energy adoption process from 2000 to 2021. The results of the PMG model demonstrate a long-term correlation between renewable energy use and indicators for technological development. Thus, encouraging the information and communication industry will increase the consumption of renewable energy sources, according to Yao et al. (2023) [14]. The findings for the PATENT variable indicate that, although not significantly, new patents favor using renewable energy. The transfer of climate change technologies and protecting intellectual property rights through patents have grown increasingly controversial. Patents are essential for attracting capital, innovation, and communication needed to bring about a clean energy revolution, as claimed by Harvey (2008) [83] and Johnstone et al. (2010) [84].

Results from the PMG ARDL model indicate a favorable long-term association between ICT, COMM, and renewable energy use. Indeed, the advancement of information and communication technologies is helping the globe move toward a more sustainable and energy-efficient future. According to Chang et al. (2022) [47], ICT may promote the sustainability of the environment through higher productivity, increased energy efficiency, and improved consumption and production of renewable energy sources.

The findings indicate that the long-term impact of research and development (RD) on renewable energy (REN) consumption is negative. The findings are at odds with the literature, which, according to Khezri et al. (2021) [85] and Gaur et al. (2023) [19], clearly demonstrates a positive correlation between research and development and the utilization of renewable energy. As the endogenous growth theory suggests, technical innovation or improvement brought about by R&D spending may promote energy production and consumption efficiency. Thus, R&D efforts typically decrease dependency on natural resources and boost the effective consumption of renewable energy sources by enabling more efficient systems that limit gas emissions, according to Dinda (2004) [58] and Churchill et al. (2019,2021) [56,57]. The negative impact of research and development (RD) on renewable energy consumption (REN) in our data may be explained by the fact that according to the World Bank estimates for 2022, Kenya, Costa Rica, Morocco, and Uruguay spend less than 1% of their GDP on R&D.

Regarding the short-term relationship, the results show a negative and statistically significant ECM coefficient at 5%. This indicates a monotonically convergent path to the long-run equilibrium path directly [87].

6. Recommendations

The main recommendation of this paper is underlined especially for countries such as Uruguay, Costa Rica, Kenya, and Morocco. More efforts must be made to invest in R&D to develop renewable energy technology, focusing on high-impact areas like smart grids, enhanced forecasting models, and next-generation renewable energy solutions. According to the recent UN report, to achieve net-zero emissions by 2050, at least USD 4 trillion needs to be invested annually in renewable energy R&D until 2030, including expenditures on infrastructure and technologies. This investment will pay off, though not nearly as much as annual subsidies for fossil fuels. By 2030, just reducing pollution and its effects on the environment may save the world up to USD 4.2 trillion annually.

Secondly, make the adoption of renewable energy technology a global priority. Intellectual property rights hurdles and other barriers to knowledge sharing and technological transfer have to be removed if renewable energy technology is to be a worldwide public good, meaning that it should be accessible to everyone, not just the wealthy. Thirdly, strong raw materials and component supplies for renewable energy are crucial. Access to all the essential parts and supplies on a larger scale will be vital. Countries require significant international coordination to increase and diversify their industrial capabilities worldwide. Furthermore, more funding is required to guarantee a fair energy transition. This funding should go toward training people in new skills and providing incentives for supply chains to be built using environmentally and culturally sensitive sustainable practices.

7. Limitations

This research addresses a gap in the current literature on the relationship between technological advancement and renewable energy consumption. However, it has a few limitations that set the stage for more investigation. While this study investigated the impact of four technological variables on renewable energy consumption in a single mode, future research should look at their effects separately. Furthermore, the current study encourages future research into potential characteristics influencing renewables, such as trade openness, growth, environmental levies, government efficacy, and industry. It is also important to shed more light on these potential channels of connection between technological advancement and renewable energy sources.