Cross-sectional dependence in financial openness and its influence on renewable energy consumption in Asia

Abstract

Renewable energy is known for its lower environmental impact compared to conventional fossil fuels. The integration of financial openness plays a pivotal role in driving renewable energy consumption. The existing literature has predominantly overlooked the nonlinear impact of financial openness on renewable energy consumption. This study examines the nonlinear impact of financial openness on renewable energy consumption across different regions in Asia, such as Central Asia, East Asia, South Asia, Southeast Asia, and West Asia, by employing linear and nonlinear cross-sectionally augmented autoregressive distributed lag techniques. The findings of the linear analysis confirm that financial openness favorably impacted renewable energy consumption in the long run in all regions. On the other side, the nonlinear analysis highlights that an increase in financial openness promotes renewable energy consumption in all regions except West Asia. In contrast, the fall in financial openness hurt renewable energy consumption in Asia, Central Asia, and West Asia. Greenhouse gas emissions and GDP promote renewable energy consumption in almost all regions in both linear and nonlinear analysis, and ICT and trade help the consumption of renewable energy to rise in some regions. Policymakers in Asia and its subregions should focus on increasing the collaboration between the financial sectors of the Asian economies and increasing the flow of funds to renewable energy sources in Asia.

Keywords

Renewable energy consumption financial openness cross-sectional dependence

Introduction

This study analyses how financial openness has affected the growth of renewable energy consumption across Asia. The generation and use of energy are critical factors in national progress. One measure of a country's rate of progress is its energy consumption per person.¹ The affordability and availability of energy heavily impact lifestyle and living conditions in every nation. Energy deficits, on the other hand, may halt a nation's industrial and economic development.² Coal and oil, two nonrenewable commodities utilized for decades, were formerly considered essential to sustain economic expansion. Nevertheless, environmental costs are associated with using these resources intensively, including contributing to global warming.³ Reliable, low-cost, and economically feasible renewable energy services are essential to achieving sustainable growth without sacrificing the environment.⁴ Financial openness is essential because of the need for financial resources to achieve this goal.

Sustainable growth without environmental degradation is possible with the growth of renewable energy technology. In line with the sustainable development goals (SDGs) of the United Nations, renewable energy is essential for improving sustainable development and environmental sustainability.⁵ Contributing to the prevention of climate change is one of the main advantages of renewable energy. Renewable energy considerably lowers greenhouse gas emissions by substituting fossil fuel-based energy sources, assisting in accomplishing SDG 13 (Climate Action).⁶ Renewable energy sources complement SDG 15 (Life on Land) by preserving ecosystems and reducing habitat degradation during operation.⁶ The encouragement of energy availability and affordability is a further important factor.⁷ By extending energy access and lowering energy poverty, renewable energy technology can provide safe, dependable power to rural and underserved places, advancing SDG 7 (Affordable and Clean Energy). In line with SDG 3 (Good Health and Well-Being), renewable energy also lowers air pollution, improving public health outcomes.⁸ As a result, energy security is improved, the use of fossil fuels is decreased, and carbon footprints are reduced thanks to the growth of renewable energy sources.^9,10

Over the past four decades, the world has undergone significant economic growth, financial liberalization, and capital accumulation driven by numerous economic changes and ongoing political shifts, as noted by Koengkan et al.¹¹ Successfully enforcing renewable energy policy is hampered by several economic and technological issues. The high upfront costs associated with developing renewable energy facilities in comparison to traditional sources of energy derived from fossil fuels are a major barrier.¹² Due to this pricing disparity, governments and private businesses may find it difficult to make significant investments in renewable energy.¹³ By facilitating the availability of investment globally, financial openness may assist in addressing the economic obstacles that impede the successful enforcement of renewable energy regulations.¹⁴ This lessens the strain on public agencies and commercial businesses that might require assistance in financing such initiatives. Financial openness also makes it easier to share technical expertise and best practices across nations to remove technical obstacles to the growth of renewable energy sources.¹⁵ Cross-border investments and partnerships may hasten the adoption of renewable energy technology, resulting in more environmentally friendly economic development. As a result, financial openness impacts the advancement of renewable energy consumption in Asian economies is an important research question that has arisen in recent times. What is the course of that effect, if any?

This study focuses on Asia for a variety of reasons. Asia is the world's largest continent, with the largest global greenhouse gas emissions share. Policymakers across Asia have increased their reliance on renewable energy sources for protecting the environment and increasing reliable and affordable energy sources. According to a study by the IRENA, several Asian nations have made substantial progress toward boosting their use of renewable energy sources.¹⁶ As the region's major renewable energy user, China has invested heavily in building its infrastructure, mainly hydropower, wind power, and solar energy.¹⁷ India's solar and wind power usage has increased significantly in recent years due to the country's aggressive renewable energy objectives.¹⁸ After the nuclear tragedy at Fukushima, Japan made a concerted effort to increase its use of renewable energy sources, including solar, wind, and biomass. Thailand, Vietnam, and the Philippines are just a few Southeast Asian nations that have made major investments in solar, wind, and biomass projects to increase their renewable energy use.¹⁹ These nations have established laws, subsidies, and renewable energy programs to diversify their energy mix and lessen their dependency on fossil fuels to promote cleaner energy sources. The trend toward greater use of renewable energy sources in Asian nations indicates progress toward a more environmentally friendly and sustainable future. Given the continent's efforts to promote sustainability and the availability of dependable and clean energy sources, installing renewable energy throughout Asia is crucial and will need even more support from the financial sector. One of the most critical research problems is examining the long-term viability of using renewable energy when there is financial openness. It is crucial to determine if financial openness encourages a long-term switch to renewable energy sources or whether it creates problems like dependency on outside funding or unstable capital flows.⁶

Studies already published often concentrate on either renewable energy usage or financial openness without considering how the two are related.^20,21 Some empirical works on renewable energy consumption have focused on its relationship with financial development^22,23; although, none of the empirical studies have investigated the link between financial openness and renewable energy consumption. Specifically, there has been no prior study of the influence of cross-sectional dependence in financial openness on renewable energy consumption within Asian economies. The topic of financial openness and its economic effects has been studied extensively, but the issue of cross-sectional reliance across Asian countries in the context of the use of renewable energy seems to be less investigated. The role of Asia in global energy consumption becomes crucial as the globe works together to combat climate change and meet renewable energy goals. This research may help us better grasp if, in the Asian context, financial openness serves to advance or obstruct these global objectives. It is necessary to investigate how financial openness affects renewable energy consumption in Asian nations by considering the distinctive economic, political, and institutional settings of Asian economies.

Since the primary objective of the current analysis is to examine the effect of financial openness on renewable energy consumption in Asian economies; this study is an effort to close the above-stated gaps in the given literature. The novelty of the current analysis is as follows: First, it is the initial effort to evaluate the effect of cross-sectional dependence in financial openness on renewable energy consumption in selected Asian economies and regions. The degree of economic integration and globalization across nations is often reflected by cross-sectional dependency on financial openness. As nations become increasingly integrated via financial markets, highly globalized areas or eras may display increased cross-sectional reliance. It is crucial to investigate the relationship between cross-sectional dependence in financial openness and renewable energy consumption; Second, this analysis has selected Asian economies to investigate the link between cross-sectional dependence in financial openness and renewable energy consumption. Asian economies are ideal for such type of empirical inquiry as they are financially intertwined. Most of them are emerging economies and their energy demand is on the rise as well as their renewable energy consumption. Third, Asia is a large continent with different cultures and organizational structures in different parts of Asia; so, to account for heterogeneity it is imperative to estimate the relationship between financial openness and renewable energy consumption in different sub-regions of Asia, which is another novel point of the study; Fourth, this analysis relies on the asymmetric assumption because variables such as financial openness move asymmetrically, due to vulnerability to common external shocks related to business cycles which develops cross-sectional dependence in the effects of financial openness; it is crucial to estimate the asymmetric in the context of this relationship; Fifth; the application of novel asymmetric cross-sectionally augmented autoregressive distributed lag (CS-ARDL) is another significant contribution of the analysis because this technique can not only account for the cross-sectional dependence in the model but also focus on providing the asymmetric short and long run estimates simultaneously, ignored by the most past studies; Lastly; the results of analysis assist in the development of effective strategies for encouraging renewable energy consumption and fostering a more thorough knowledge of the elements influencing sustainable energy transitions in Asia. The findings of this study can have direct policy implications for Asian governments and international organizations.

Literature review

Empirical research has not sufficiently explored the relationship between financial openness and renewable energy consumption growth, posing a challenge in understanding their connection due to the limited academic study in this area. In the current empirical studies, variables such as renewable energy consumption, investment, and production were used to represent renewable energy, while financial development, foreign direct investment, financial streams, and, financial efficiency were used as determinants of renewable energy.^24,25

It is required to apply the closest analysis to analyze the relevant literature and the relationship between the two variables since there is a shortage of research investigating financial openness as a possible indicator of renewable energy usage. For instance, Kim and Park²⁶ examined the impact of financial expansion on renewable energy sources. The researchers examined a sample of data from thirty countries between 2000 and 2013 using OLS. By reducing financing costs and overcoming issues with “moral hazard and adverse selection,” they got to the idea that financial growth promotes investments in renewable energy. This impact is especially important for energy sources that need more capital and are more dependent on outside funding. Using panel data from 137 countries, Best²⁷ supported his assertion that liquidity helps in the switch to a more costly kind of energy by using several financial factors. The study's empirical findings show that in industrialized nations, a high level of financial stability makes it easier to move from energy sources fueled by carbon-based fuels to modern clean energy sources, notably wind. Additional funding for wind energy comes from domestic private debt instruments and bank private loans. Similarly, Ji and Zhang²⁸ looked at data from the Chinese stock market for the years 1990 to 2014. Their analysis indicates that the expansion of the financial sector has a big influence and is responsible for more than 42% of the variation in the proportion of alternative energy.

Anton and Nucu²⁹ extensively analyzed how the demand for renewable energy in 28 EU member states was impacted by financial advancement. The analysis of the fixed effect panel data revealed that the demand for renewable energy was positively impacted by economic expansion. They also found that the new European nations’ utilization of renewable energy was unaffected by the expansion of the financial markets. Mazzucato and Semieniuk³⁰ looked at how governmental and private funding affected renewable energy efforts in China, Kenya, Spain, the US, and Kenya from 2004 to 2014. Notwithstanding the reality that both sources seemed to be significant, the authors suggested that to accurately understand their importance, a more detailed separation between finance providers would be required. The study also found that public investor financing had grown into a more significant element in the development of renewable energy sources in the countries it assessed and that this aspect exclusively was to blame for the growth of asset finance in that framework. Compared to private investors, public actors often adopt riskier technologies.

According to Kartal et al.,³¹ government assistance supported renewable energy projects that were unable to get private finance, whereas governmental decisions appeared to have had little impact on the private sector's capacity to generate money. Sbia et al.³² conducted research on the impact of FDI, clean energy, trade liberalization, carbon footprints, and economic growth on energy use in the UAE. The methodologies utilized were “ARDL bounds testing and Granger causality in the vector error correction model (VECM)” and the study covered the years 1975 through 2011. The results show that FDI has a positive impact on the utilization of renewable energy as a result of financial expansion. The latter raised the inventories of both public and private capital, reduced financing costs, ignited economic development, and enhanced the usage of renewable energy. The results here corroborate those from Kim and Park.²⁶ Other writers, such as Fotio et al.,³³ Majeed et al.,³⁴ Gyimah et al.,³⁵ encourage the claim that reduced financing expenses for consumers and enterprises arise from more financial openness, which in turn stimulates economic activity and energy usage. As energy demand rises, homes and businesses become more interested in finding dependable, green alternatives, which leads to an increase in their usage of renewable energy.^36,37 As a result, financial openness subsequently promotes the use of renewable energy sources.

After going through the relevant literature, we can infer that there are still a lot of unsolved problems and gaps in this field of knowledge. The most important gap in the body of literature is the dearth of studies investigating the impact of financial openness on the consumption of renewable energy. Except for a few studies, most studies estimated the effects of financial development on energy consumption. So, there is presently no research on any possible links between renewable energy consumption and financial openness. Another shortcoming of the present body of research is its limited methodological scope. For instance, using the CS-ARDL technique can resolve the issues of cross-sectional dependence and provide the short and long-run estimates ignored by most past studies. Last but not least, this group of Asian economies has not previously been investigated in the context of financial openness-renewable energy consumption nexus. The hypothesis of this study is as follows: Enhanced financial openness is associated with a positive upswing in renewable energy consumption within Asian economies.

Theoretical framework, econometric techniques, and data

Theoretical framework

Financial constraints, technical limitations, and poor infrastructure make increasing renewable energy production and consumption worldwide difficult while still assuring sustainable development.³⁸ Financial liberalization in these situations aids in bringing in more money from overseas and is projected to loosen financial constraints while reducing other obstacles to investments in renewable energy and sustainable development.³⁷ Since several foreign economies accepted the Paris Agreement, the reduction of CO2 and other pollutants has attracted interest on a global scale.³⁹ As a result, it is essential to significantly boost the consumption of renewable energy sources while decreasing the use of carbon-intensive fuels.^40,41

Although financial resources are necessary to expand technology and renewable energy innovation, Li et al.³⁹ discovered that the financial market is sometimes optimal or effective at allocating limited money to creative firms. This is especially true in developing countries. Due to the dearth of financial resources locally, looking for foreign resources is an apparent way to get finance.⁴² Financial openness, which links domestic and foreign financial markets, allows for greater economies of scale for foreign businesses and their investors and provides regional markets with important capital, innovative technology, and managerial skills.⁴³ Financial liberalization could potentially have a positive impact on national technological and renewable energy innovation. It enhances an economy in various ways, promoting more capital distribution.⁴⁴ Financial openness plays a pivotal role in driving renewable energy consumption by attracting foreign investment and capital inflows. These financial resources support the expansion of renewable energy infrastructure, fostering increased capacity and usage. Financial openness facilitates technology transfer and knowledge exchange, promoting collaboration and the adoption of advanced practices. This, in turn, enhances the efficiency of renewable energy consumption. It provides access to international capital markets, aiding in securing funding for renewable energy projects, and overcoming financial barriers. Also, competition within the sector, encouraged by financial openness, drives innovation and cost reductions, making renewable energy more competitive and affordable, ultimately spurring greater consumption.

The “absorptive capacity theory”⁴⁵ states that foreign capital inflows, a crucial result of financial openness, may have a positive ripple impact that promotes extensive use of renewable energy inside the host nations. As a result, foreign inflows may impact renewable energy usage by reducing the consumption of traditional carbon fuels in the host nations.⁴⁶ Incoming foreign direct investment may also be expected to promote the growth of the host country's renewable energy sector by upgrading the underdeveloped energy infrastructure.⁴⁷ Foreign investment inflows, especially unethical ones, may hurt renewable energy. This is due to the fact that nations that significantly depend on carbon fuels are often the target of especially nefarious foreign direct investments. In such circumstances, foreign direct investment inflows may raise the demand for non-renewable energy consumption, which negatively impacts renewable energy consumption.¹³ In consequence, luring clean foreign direct investment helps developing nations, especially in terms of the expansion of their renewable energy industries. Our study works under the framework of financial liberalization theory,⁴⁸ which suggests that opening up financial markets to international flows of capital leads to increased investment, access to finance, and economic growth. This theory also argued that financial openness can attract foreign investment, stimulate access to capital, and promote the development and consumption of renewable energy sources. The study's conceptual framework is visualized in Figure 1.

Figure 1.

Conceptual framework.

Model and econometric techniques

This analysis examines the link between financial openness and renewable energy consumption. Based on the above theoretical framework, we have developed the following long-run model to estimate the influence of financial openness on renewable energy consumption.

RE C_{it} = η_{0} + η_{1} FO ._{it} + η_{2} GD P_{it} + η_{3} GH G_{it} + η_{4} IC T_{it} + η_{5} Trad e_{it} + ε_{it}

(1)

where

RE C_{it}

is the renewable energy consumption which is determined by financial openness (FO), GDP per capita (GDP), greenhouse gas (GHG), ICT diffusion (ICT), trade openness (Trade), and

ε_{it}

is the error term. This research uses the CS-ARDL approach, a dependable and innovative technique originally developed by Chudik & Pesaran.⁴⁹ This method is more reliable in contrast to various panel cointegration strategies. The CS-ARDL is better due to its power to overcome the problems of heterogeneous slope coefficients, endogeneity, and cross-sectional dependency.

Over the last several years, cross-sectional dependency (CSD) has received much consideration. The expanding commercial, financial, and trade links make it impossible to deny the presence of CSD in the contemporary economy. The residuals may have cross-section dependency as we commonly do not include all of these parameters in our regression setups. It is claimed that CSD is a serious problem with panel data and that neglecting to consider it when estimating might cause the estimator to lose efficiency and provide test statistics with errors. Nevertheless, the most widely used panel data estimation methods are based on the erroneous premise that all data are CS-independent and cannot address this serious issue. Yet, the belief in CS independence may not be appropriate for panel data investigation.⁵⁰ To determine if CSD exists in the data, various CSD tests have been developed. Pesaran⁵¹ is performed in our study to check the presence of CSD. To confirm that our model is free of CSD, we need to reject the null hypothesis of no CSD by Pesaran et al.⁵¹

Like time series analysis, the unit root tests are also gaining popularity in panel investigation. To avoid erroneous results checking the integrating properties of the variables by employing the panel unit root tests are crucial. To ascertain the stationarity of variables at the level or first-difference, the literature presents various panel unit root tests, including “LLC, IPS, ADF.” We have employed the above-mentioned famous panel unit root tests to explore the integrating properties of the variables. The null of all the tests suggests the stationary series against the alternative hypothesis of the non-stationary series.⁵² Further, getting short- and long-run estimates by regressing the model only once is another significant advantage of the CS-ARDL model. The CSD is removed using mean cross-section values. The basic structure of the CS-ARDL is outlined below:

\begin{aligned} l Δ RE C_{it} & = C_{i} + λ_{i} (RE C_{it - 1} - β_{i} FO ._{it - 1} - Υ_{i} X_{it - 1} - ϕ_{1 i} {\bar{REC}}_{t - 1} - δ_{2} {\bar{FO .}}_{t - 1} - π_{2} {\bar{X}}_{t - 1}) \\ + \sum_{j = 1}^{p - 1} θ_{ij} Δ RE C_{it - j} + \sum_{j = 0}^{q - 1} η_{ij} Δ FO ._{it - j} + \sum_{j = 0}^{q - 1} τ_{ij} Δ X_{it - j} + η_{1 i} Δ {\bar{REC}}_{t} \\ + η_{2 i} Δ {\bar{FO .}}_{t} + η_{3 i} Δ {\bar{X}}_{t} + ε_{it} \end{aligned}

(2)

where the variables with bar ( ${\bar{REC}}_{t}, {\bar{FO .}}_{t} {\bar{, X}}_{t}$ ) represent the cross-sectional mean values, while REC_it is our dependent variable, determined by FO_it and a set of other independent variables X_it(GDP_it, GHG_it, ICT_it, and Trade_it). The study's most significant accomplishment is investigating the asymmetric effect of financial openness on renewable energy consumption. For this purpose, Shin et al.⁵³ suggested utilizing the partial sum processes as indicated to divide the concerned variable, financial openness in this study, into its positive and negative components:

Pos . FO ._{it} = \sum_{n = 1}^{t} Δ Pos . FO ._{it} = \sum_{n = 1}^{t} \max (Δ Pos . F . O ._{it}, 0)

(3\rm a)

Neg . FO ._{it} = \sum_{n = 1}^{t} Δ Neg . FO ._{it} = \sum_{n = 1}^{t} \max (Δ Neg . F O_{it}, 0)

(3\rm b)

The

Pos . FO ._{it}

and

Neg . FO ._{it}

highlight the positive (rise) and negative (fall) changes in the original series of financial openness. In the next step, replacing

Pos . FO ._{it}

and

Neg . FO ._{it}

variables in place of FO in equation (2). We will convert equation (2) into a nonlinear CS-ARDL model (4) as shown below:

\begin{aligned} l Δ RE C_{it} = & C_{i} + λ_{i} (RE C_{it - 1} - α_{i} Pos . PO ._{it - 1} - β_{i} Neg . PO ._{it - 1} - ρ_{i} X_{it - 1} - ϕ_{1 i} {\bar{REC}}_{t - 1} \\ - ϕ_{i} {\bar{Pos . FO .}}_{t - 1} - η_{i} {\bar{Neg . FO .}}_{t - 1} - φ_{2} {\bar{X}}_{t - 1}) + \sum_{j = 1}^{p - 1} θ_{ij} Δ RE C_{it - j} \\ + \sum_{j = 0}^{q - 1} δ_{ij} Δ Pos . P . O ._{it - j} + \sum_{j = 0}^{q - 1} π_{ij} Δ Neg . P . O ._{it - j} + + \sum_{j = 0}^{q - 1} ω_{ij} Δ X_{it - j} \\ + η_{1 i} Δ {\bar{REC}}_{t} + η_{2 i} Δ {\bar{Pos . PO .}}_{t} + η_{3 i} Δ {\bar{Neg . PO .}}_{t} + η_{4 i} Δ {\bar{X}}_{t} + ε_{it} \end{aligned}

(4)

Model (4) is the nonlinear CS-ARDL model due to the inclusion of the partial sum variables. Shin et al.⁵³ treated partial sum variables as one because they are part of the same variable; henceforth, linear and nonlinear models are treated with the same procedure, and no extra effort is required in estimating the nonlinear model.

Data and descriptive analysis

In this study, we aim to examine the relationship between financial openness and renewable energy consumption in Asian economies. We collected data for thirty-two Asian economies from 1998 to 2021. The list of countries is documented in Table A1. Table 1 presents detailed information about the variables we selected, along with their descriptive statistics. Our dependent variable is renewable energy consumption (REC), which we measure using the total energy consumption from nuclear, renewables, and other sources (quad Btu). The REC data series is obtained from the EIA. This has been used previously in studies of Fu &Ullah⁵⁴ and Dong &Ullah.⁵⁵ The primary variable of interest is financial openness (FO), which we quantify using the financial globalization index provided by the KOF (see Figure A1). It is an index ranging from 1 to 100 and comprises several measures. This variable has been used by a variety of studies, such as Chen et al.⁵⁶ and Sahoo and Sethi.⁵⁷ Countries with more liberal financial policies have easier access to global financial markets. This may make it simpler for governments, companies, and people to invest in and implement renewable energy technology. It can also ease the financing of renewable energy projects. Higher levels of renewable energy usage may result from improved access to funding.

Table 1.

Variables and data description.

Variables	Definitions	Mean	Median	Max	Min	Std. Dev.	Skewness	Kurtosis	Sources
REC	Total energy consumption from nuclear, renewables, and other (quad Btu)	0.651	0.054	24.71	0.000	2.225	6.870	59.43	EIA
FO	Financial globalization index	3.971	4.106	4.590	2.624	0.463	−0.784	2.746	KOF
GDP	GDP per capita (constant 2015 US$)	8.358	8.253	11.10	5.967	1.246	0.393	2.278	WDI
GHG	Total greenhouse gas emissions (kt of CO2 equivalent)	11.57	11.71	16.43	6.745	1.909	0.085	2.930	WDI
ICT	Individuals using the Internet (% of the population)	2.506	3.076	4.605	−5.604	1.924	−1.288	4.268	WDI
Trade	Trade (% of GDP)	4.328	4.329	6.081	2.897	0.582	0.283	3.060	WDI

To investigate the impact of financial openness on REC, we incorporate several control variables into our model. These variables include GDP per capita, greenhouse gas emissions, ICT, and Trade. Previous research suggests that these control variables influence REC. For example, Wang et al.⁵⁸ emphasized the positive role of economic growth in promoting REC. Higher GDP per capita means more prosperity in the economy, which increases the purchasing power of the people and more renewable energy consumption. Mo et al.⁵⁹ found that highly polluted economies tend to invest more in REC. As the country becomes more polluted, the demand for a clean environment increases, and people consume more green energy resources. Li et al.⁶⁰ identified trade as a significant indicator of REC. Trade helps developing economies to import clean and green technological equipment from advanced economies and is also a good source of technology transfer, which boosts renewable energy consumption. Lv et al.⁶¹ established a positive relationship between ICT and REC. ICT significantly and favorably influences the use of renewable energy by facilitating effective energy management, grid integration, increased energy efficiency, and the broad acceptance of clean energy technology.GDP per capita is held constant and measured in 2015 US dollars to facilitate meaningful comparisons. We quantify GHG emissions in kilotons of CO2, representing the amount of carbon dioxide released. ICT is quantified as the percentage of the population that utilizes the internet. Trade is measured as a percentage of GDP, indicating a country's level of engagement in international trade activities. We collected the data series for all control variables from the WDI. The descriptive statistics in our analysis provide estimates of the mean, median, kurtosis, and skewness of the variables, offering valuable insights into their distribution and characteristics. The mean scores and S.D are reported as: for REC (M: 0.651; S.D 2.225); for FO (M: 3.971; S.D 0.463); for GDP (M: 8.358; S.D 1.246); for GHG (M: 11.57; S.D 1.909); for ICT (M: 2.506; S.D 1.924); and for Trade (M: 4.328; S.D: 0.582).

Empirical results and discussion

The results of the cross-sectional dependency test proposed by Pesaran et al.⁵¹ are shown in Table 2. The most prevalent characteristic of panel data analysis is cross-sectional dependency since, when we collect data from a broad range of nations, all or some of them would likely be impacted by a single shock owing to the interconnectivity of the countries. Cross-sectional dependence across Asian nations in terms of both financial openness and renewable energy consumption relies on various factors, such as economic circumstances, policy frameworks, and regional dynamics, which might affect the degree of variability in financial openness and renewable energy consumption in Asia. Although it may provide chances for cooperation and coordinated efforts, it also brings hazards that must be controlled by sensible regulations and procedures. The dynamics of cross-sectional reliance in Asia may be significantly shaped by regional collaboration and common aspirations for sustainable development. For instance, the financial crisis of 2008 shook the world and Asia was no exception. Similarly, renewable energy and environment agreements also boost cooperation in renewable energy investment within the Asian economies. These crises and accords make the Asian countries interdependent in the context of financial cooperation and renewable energy development. As a consequence, if we do not manage the cross-sectional dependency, the findings can be misinterpreted. The Pesaran test outcomes showed that the model has a cross-sectional dependency since all statistics related to our variables are significant.

Table 2.

Results of cross-sectional dependence.

	REC	FO	GDP	GHG	ICT	Trade
Pesaran's test	17.89***	2.597***	6.707***	12.03***	31.78***	15.08***
Prob.	0.000	0.000	0.000	0.000	0.000	0.000
Off-diagonal elements	0.590	0.364	0.508	0.529	0.460	0.411

Once cross-sectional dependency has been confirmed, the second crucial stage in conducting a panel study is to investigate the integrating characteristics of the variables using panel unit root tests. IPS, ADF, and LLC tests are used to determine if the variables are I(0) or I(1). According to Table 3, the outcomes of panel unit root tests show that the variables REC and GHG are I(1), whereas FO, ICT, and Trade are I(1) irrespective of the unit root tests. The variable GDP is I(0) in the LLC test and I(1) in the IPS and ADF tests.

Table 3.

Results of unit root.

	LLC	I(1)	IPS	I(1)	ADF	I(1)
	I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
REC	2.550	−9.602***	−0.061	−4.488***	3.984	−19.37***
FO	−5.161***		−2.353***		−5.296***
GDP	−4.616***		−1.245	−4.038***	0.934	−16.74***
GHG	−1.255	−3.698***	−0.709	−4.836***	4.744	−21.856
ICT	−13.89***		−4.882***		−20.55***
Trade	−3.489***		−1.830***		−1.978**

Checking the validity of the long-term link between the variables (REC, FO, GDP, GHG, ICT, and Trade) is the third phase of the panel research. Cointegration tests are well known for this purpose, and we have chosen the Westerlund⁶² test, which provides accurate results even when the models incorporate cross-sectional dependence. This test consists of two panels (Pt and Pa) and two groups (Gt and Ga). The results of Westerlund⁶² are shown in Table 4, which demonstrates the significance of one-group and one-panel statistics, proving a valid long-run connection between the variables.

Table 4.

Westerlundcointegration test.

Statistic	Gt	Ga	Pt	Pa
Value	−3.288***	−5.031	−13.70***	−6.957
z-value	6.059	4.931	2.509	0.765
p-value	0.000	1.000	0.006	0.778

The empirical evaluation relied on both the linear and nonlinear CS-ARDL models to meet the goals of the research. A total of six models have been evaluated: Asia, Central Asia, East Asia, South Asia, Southeast Asia, and West Asia. The outcomes of the linear and nonlinear approaches are shown in Table 5. According to long-term linear forecasts, a 1% increase in the FO estimations will result in improvements to the REC of 0.930%, 1.020%, 2.187%, 1.092%, 0.882%, and 1.247% in Asia, Central Asia, East Asia, South Asia, Southeast Asia, and West Asia. These results validate our hypothesis. On the other side, for every 1% rise in FO_pos, we observe that long-run REC increases by 0.743%, 0.197%, 1.935%, 1.132%, and 0.596% in Asia, Central Asia, East Asia, South Asia, and Southeast Asia. In West Asia, FO_pos does not significantly affect REC. In contrast, FO_neg is significantly linked to REC only in Asia, Central Asia, and West Asia—a 1% improvement in FS_neg causes the REC to decrease by 0.381%, 0.940%, and 1.668%.

Table 5.

Linear and nonlinear CS-ARDL results.

Variables	Asia	(2)	Central Asia	(4)	East Asia	(6)	South Asia	(8)	Southeast Asia	(10)	West Asia	(12)
Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
Short-run
D(FO)	0.070		0.019		1.187		0.191		0.118		0.247
	(0.225)		(0.066)		(1.511)		(0.113)		(0.127)		(0.211)
D(FO-pos)		0.918***		0.260		1.386**		0.897		0.181		0.668
		(0.302)		(0.185)		(0.585)		(0.750)		(0.573)		(0.597)
D(FO-neg)		0.982		0.114		1.096		1.165		0.585		0.275
		(0.696)		(0.144)		(1.165)		(1.168)		(1.272)		(0.402)
D(GDP)	1.691***	2.021***	0.105	0.319	1.397***	1.258***	0.996	0.760	0.457	0.619	0.137	0.292
	(0.388)	(0.460)	(0.198)	(0.388)	(0.643)	(0.132)	(0.964)	(0.782)	(0.229)	(0.995)	(0.149)	(0.308)
D(GHG)	0.817*	1.733*	0.113	0.103	1.474	1.069	0.192*	0.358**	0.523***	0.688**	0.109	0.192
	(0.458)	(0.971)	(0.109)	(0.191)	(1.740)	(1.153)	(0.107)	(0.162)	(0.183)	(0.298)	(0.098)	(0.183)
D(ICT)	0.157	0.185	0.041	0.050	1.494	1.132	0.094	0.159	0.014	0.069	0.036	0.022
	(0.150)	(0.127)	(0.050)	(0.026)	(1.565)	(1.203)	(0.017)	(0.203)	(0.041)	(0.094)	(0.034)	(0.024)
D(Trade)	0.321*	0.568*	0.057**	0.067***	1.252*	1.382***	0.223	0.434	0.208**	0.261**	0.088	0.240
	(0.167)	(0.337)	(0.023)	(0.013)	(0.651)	(0.428)	(0.216)	(0.480)	(0.105)	(0.117)	(0.099)	(0.240)
Long-run
FO	0.930***		1.020***		2.187**		1.092***		0.882***		1.247***
	(0.225)		(0.066)		(1.111)		(0.113)		(0.127)		(0.211)
FO-pos		0.743*		0.197*		1.935*		1.132***		0.596**		0.140
		(0.418)		(0.106)		(1.040)		(0.341)		(0.300)		(0.095)
FO-neg		0.381***		0.940***		1.486		0.103		0.819		1.668***
		(0.102)		(0.085)		(1.585)		(0.750)		(0.573)		(0.597)
GDP	1.417***	1.350***	0.091	0.027	0.801***	0.665***	0.582**	0.567**	0.483*	0.526*	0.037	0.032
	(0.199)	(0.301)	(0.190)	(0.357)	(0.109)	(0.167)	(0.283)	(0.237)	(0.271)	(0.326)	(0.048)	(0.045)
GHG	1.303***	1.507***	0.401	0.578	2.116**	1.534**	0.625***	0.543*	0.788**	0.564*	0.392***	0.328*
	(0.248)	(0.192)	(0.306)	(0.480)	(0.959)	(0.678)	(0.148)	(0.275)	(0.346)	(0.310)	(0.125)	(0.179)
ICT	0.288***	0.371***	0.037	0.001	1.644**	1.483**	0.013	0.040	0.064	0.022	0.012	0.015
	(0.084)	(0.096)	(0.048)	(0.026)	(0.712)	(1.610)	(0.017)	(0.034)	(0.114)	(0.035)	(0.011)	(0.005)
Trade	0.297***	0.364***	0.097**	0.073***	2.089**	1.843**	1.130	1.054	1.005	1.031	0.022	0.033
	(0.102)	(0.051)	(0.048)	(0.017)	(0.939)	(0.776)	(0.123)	(0.209)	(1.218)	(0.058)	(0.032)	(0.034)
Observations	704	704	66	66	66	66	154	154	198	198	220	220
R-squared	0.416	0.291	0.391	0.311	0.627	0.294	0.506	0.186	0.428	0.163	0.648	0.279
Number of groups	32	32	3	3	3	3	7	7	9	9	10	10

Note: Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

These findings are backed by Paramati et al.,⁴⁴ who stated that financial openness allows countries to access international capital markets and attract FDI inflow. Increased capital flows provide funding for renewable energy projects, such as building wind farms or solar power plants. These investments help expand the capacity for renewable energy generation, leading to higher REC. The study of Qin and Ozturk⁶³ also supported our results and noted that foreign capital increases renewable energy consumption. The study of Hasan and Du⁶⁴ described that financial openness facilitates technology transfer by allowing the exchange of ideas, knowledge, and expertise across borders. Foreign investors and multinational corporations bring advanced renewable energy technologies and know-how to host countries. This transfer of technology enables the adoption and implementation of more efficient and cost-effective renewable energy solutions, boosting REC. In support of our findings, Qin et al.⁶⁵ argued that financial openness promotes trade and market integration, enabling countries to export and import renewable energy products and services. Expanded market access provides opportunities for renewable energy producers to sell their products in international markets, stimulating demand and incentivizing the expansion of renewable energy capacity to meet export needs. This increased market size leads to higher REC. Wang et al.⁶⁶ stated that financial openness exposes countries to international best practices and standards in renewable energy policies and regulations. Through global cooperation and collaboration, countries learn from each other's experiences and adopt effective policy measures to support the growth of renewable energy sectors. This also means that financial openness creates a conducive environment for renewable energy investments and drives REC. Rasoulinezhad & Saboori⁶⁷ reported the positive impact of financial openness on REC by offering renewable energy finance. Our result finds resonance in Kang et al.⁶⁸ research, emphasizing the beneficial impact of foreign capital on REC in South Asia. Similar outcomes are also found by Tiwari et al.⁶⁹ for the Asia region.

The main variable in the analysis is FO, but we have also included some control variables, such as GDP, GHG, ICT, and Trade. The estimated GDP and GHG coefficients promote REC in most regions, while ICT and Trade proliferate REC in a few regions only. For instance, for every 1% intensification in GDP, the long-run REC increased in Asia by 1.417%, East Asia by 0.801%, South Asia by 0.582%, and Southeast Asia by 0.483%, in the linear analysis. In the nonlinear analysis, the REC improves by 1.350% in Asia, 0.665% in East Asia, 0.567% in South Asia, and 0.526% in South East Asia. Similarly, apart from Central Asia, the estimates of GHG are significantly connected to REC in all regions. The REC rises by 1.303% (linear) and 1.507%(nonlinear) in Asia, 2.116% (linear) and 1.534%(nonlinear) in East Asia, 0.625% (linear) and 0.543%(nonlinear) in South Asia, 0.788% (linear) and 0.564%(nonlinear) in Southeast Asia, and 0.392% (linear) and 0.328(nonlinear) in West Asia for every 1% proliferation of GHG. A 1% escalation in Trade increases REC by 0.297% (linear) and 0.364%(nonlinear), 0.097%(linear) and 0.073%(nonlinear), 2.089% (linear) and 1.843%(nonlinear) in Asia, Central Asia, and East Asia. Likewise, a 1% improvement in ICT improves REC by 0.288% (linear) and 0.371% (nonlinear), and 1.644% (linear) and 1.483% (nonlinear) in Asia and East Asia. In the short run, the FO_pos, GDP, GHG, and Trade are significantly and positively connected to REC in one or two regions; the rest of the estimates attached to other variables in most regions do not significantly influence REC.

This finding regarding GDP is supported by Alsagr,²³ who noted that higher GDP levels provide countries with increased financial resources, enabling them to invest in renewable energy technologies and projects. This means that an increased GDP leads to greater REC by fostering a favorable environment for renewable energy investments. This means also that GDP increases the demand for renewable energy by providing financial resources and green technology. The study by Solaun & Cerdá⁷⁰ stated that the need to reduce GHG emissions has driven the growth of green finance and sustainable investment. Financial institutions and investors are increasingly interested in funding projects that contribute to environmental sustainability, including renewable energy initiatives. The availability of green finance options, such as green bonds or sustainable investment funds, provides capital for renewable energy development and accelerates REC. The results infer that increasing levels of CO2 emissions often lead to greater environmental awareness and concern. This drives individuals, businesses, and governments to pursue cleaner and more sustainable energy sources, such as renewables. These empirical inferences are backed by the work of Shah et al.⁷¹ and Li & Ullah,⁷² who found similar results for Asia.

The result regarding ICT is supported by Li et al.,⁷³ who noted that ICT facilitates demand response programs and energy efficiency initiatives in China. With the help of smart grid technologies, consumers receive real-time information about electricity prices and adjust their energy consumption accordingly. This demand-side management encourages load shifting, where energy-intensive activities are scheduled during periods of high renewable energy availability. ICT-enabled smart home systems and devices optimize energy use and reduce wastage. By integrating ICT into energy management, consumers actively participate in the energy transition and contribute to increased REC. The study of Bellakhal et al.⁷⁴ supported our results and noted that trade allows countries to access larger markets for renewable energy products and services. Exporting renewable energy technologies, equipment, and expertise to other countries with a growing demand for clean energy contributes to increased REC. Trade enables renewable energy companies to scale up their production and take advantage of economies of scale, making renewable energy products more affordable and accessible. As trade expands, it stimulates the demand for renewable energy, driving the growth of REC. The study of Dong et al.⁷⁵ reported similar results for Southeast Asia.

Conclusion and implications

The transition toward renewable energy sources has become a global imperative due to concerns over climate change and the need to reduce greenhouse gas emissions. As countries seek to enhance their energy sustainability and reduce dependence on fossil fuels, understanding the factors that drive renewable energy consumption (REC) becomes crucial. One significant factor that can influence REC is financial openness, which encompasses the degree to which economies integrate with international financial markets and engage in cross-border financial transactions. This study focuses on exploring the impact of financial openness on REC in the context of Asian economies. Asia, with its diverse economic landscape and varying degrees of financial openness, presents a compelling region for examining the relationship between financial integration and renewable energy adoption. By analyzing the experiences of Asian economies, this research aims to shed light on the potential role of financial openness in promoting and facilitating the transition to renewable energy sources. Financial openness can influence REC through various economic channels. Firstly, access to capital and FDI can enable countries to fund renewable energy projects and expand their renewable energy infrastructure. Secondly, financial openness can facilitate the transfer of technology and knowledge, allowing countries to adopt more efficient and advanced renewable energy solutions. Financial openness fosters market expansion, trade opportunities, and policy learning. The existing body of literature has overlooked the financial openness and REC nexus in the case of Asia. This study examines the impact of financial openness on REC in Asian economies by employing linear and nonlinear CS-ARDL methods. The study's novelty lies in several aspects: It pioneers the investigation of cross-sectional dependence in financial openness and its impact on renewable energy consumption in Asian economies and regions, reflecting global economic integration. The study adopts an asymmetric assumption due to the nonlinear movement of variables like financial openness, enhancing understanding of its impact. This study employs the novel asymmetric CS-ARDL technique, providing both short and long-run asymmetric estimates and addressing a gap in previous research.

The findings of the linear model indicate that financial openness favorably impacted renewable energy consumption in the long run in all regions. Long-term linear forecasts suggest that a 1% increase in financial openness (FO) estimates will result in REC improvements of 0.930% in Asia, 1.020% in Central Asia, 2.187% in East Asia, 1.092% in South Asia, 0.882% in Southeast Asia, and 1.247% in West Asia. On the other side, in the nonlinear analysis, an increase in financial openness promotes renewable energy consumption in all regions except West Asia. This infers that a 1% increase in FO_pos yields long-term REC increases of 0.743% in Asia, 0.197% in Central Asia, 1.935% in East Asia, 1.132% in South Asia, and 0.596% in Southeast Asia. In contrast, the fall in financial openness hurt renewable energy consumption in Asia, Central Asia, and West Asia in the long run. This means that a 1% fall in FO_neg results in REC reductions of 0.381%, 0.940%, and 1.668% in Asia, Central Asia, and West Asia. Results also infer that greenhouse gas emissions and GDP promote renewable energy consumption in almost all regions in both linear and nonlinear analysis, and ICT and trade help the consumption of renewable energy to rise in some regions. In the short run, the positive change in financial openness, GDP, greenhouse gas emissions, and trade are significantly and positively connected to renewable energy consumption in one or two regions; the rest of the estimates attached to other variables in most regions do not significantly influence renewable energy consumption.

The results of this research carry substantial policy implications for government officials, policymakers, and energy strategists. These implications can guide the formulation of effective policies and strategies aimed at accelerating the transition to renewable energy sources and promoting sustainable development. It is suggested that governments should prioritize efforts to enhance financial openness and integration with international financial markets. By creating a favorable environment for FDI, capital flows, and access to global financial resources, countries can attract the necessary funding to support renewable energy projects and infrastructure development. Policymakers should design policies that promote investment in the renewable energy sector, such as offering financial incentives, streamlining regulatory processes, and providing a stable and transparent investment environment. Secondly, it is crucial for governments to establish comprehensive and robust renewable energy policies. Policymakers should prioritize the establishment of transparent goals and regulations to encourage the adoption of renewable energy. This can be achieved through measures like renewable portfolio standards, feed-in tariffs, and tax incentives. Policies should actively support research, development, innovation, and technology transfer to strengthen the capabilities of renewable energy sources. A supportive policy framework will create a conducive environment for REC growth. Thirdly, governments should actively engage in international collaboration and knowledge-sharing initiatives to benefit from global expertise and best practices in renewable energy. Participation in international forums, partnerships, and platforms can facilitate the exchange of information, experiences, and technological advancements. Policymakers should foster international cooperation in renewable energy research, policy formulation, and capacity-building to accelerate the transition to renewable energy sources. Fourthly, governments and financial institutions should collaborate to develop innovative financial instruments and mechanisms to support renewable energy investments. Governments should also encourage the integration of renewable energy projects into existing financial systems, such as facilitating access to loans, providing guarantees, and reducing investment risks through appropriate risk-sharing mechanisms. Fifthly, alongside promoting renewable energy, policymakers should prioritize energy efficiency measures to maximize the impact of REC. Governments should implement energy efficiency standards, encourage energy audits and conservation programs, and provide incentives for energy-efficient technologies and practices. Energy efficiency measures can help reduce overall energy consumption, making it easier to meet renewable energy targets and contribute to sustainable REC growth. Lastly, policymakers need to identify and address barriers and risks associated with financial openness and renewable energy adoption. This includes managing exchange rate fluctuations, market volatility, and potential economic vulnerabilities. Governments should establish mechanisms to mitigate risks and provide support to renewable energy projects during economic downturns. Policies should be in place to ensure a fair and inclusive transition, considering the social and economic implications of renewable energy adoption, such as job creation, retraining programs, and equitable access to renewable energy benefits. To obtain more accurate empirical findings and enhance the effectiveness of economic policies, it is suggested that further research explores the relationship between financial openness and REC within a time-varying context, rather than assuming a stable parameter.

This study has made several important contributions to existing literature but indeed it has some limitations. The foremost limitation of the study is related to the availability of disaggregated data, that is, data on the solar, wind, hydel, etc. is unavailable separately which makes it difficult to estimate the impact of financial openness on each of these renewable energy factors separately. Another important limitation of the study is the unavailability of renewable energy data for all Asian economies and regions which makes the scope of study limited.

Highlights

The study investigates the influence of financial openness on renewable energy consumption across diverse Asian regions.

Regions under examination include Central Asia, East Asia, South Asia, Southeast Asia, and West Asia.

The study utilizes both linear and nonlinear CS-ARDL techniques.

Linear analysis affirms the long-term positive impact of financial openness on renewable energy consumption across all regions.

Nonlinear analysis shows that increased financial openness boosts renewable energy except in West Asia; reduced financial openness hampers renewable energy use in Asia, Central Asia, and West Asia

Policy recommendations advocate greater collaboration among Asian economies’ financial sectors and increased funding for renewable energy initiatives in Asia.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Muhammad Hafeez

Sana Ullah

Lei Wang is an Associate Professor at the School of Business, Guilin University of Electronic Technology, with a PhD in Management Science and Engineering and many years of experience in this field. His research interests mainly focus on service innovation, value co-creation, and green economy.

Muhammad Hafeez (PhD 2020, Beijing University of Posts and Telecommunications, Beijing, China) is currently an Assistant Professor and an Adjunct Research Faculty/ Non-Resident Researcher at Institute of Business Management Sciences, University of Agriculture, Faisalabad, Pakistan and Lebanese American University, Beirut, Lebanon, respectively. From July 2021 to February 2022, he was an Adjunct Research Faculty at Center for Network Economy and Information Management, BUPT, Beijing, China and Logistics Statistics Institute, Beijing Wuzi University, Beijing, China. From Sep 2020 to Feb 2022, he was an Assistant Professor at Faculty of Management and Administrative Sciences, University of Sialkot, Punjab, Pakistan. From Sep 2016 to Aug 2020, he was working as Research Assistant at The Centre of Industrial Economics and Green Development, Beijing University of Posts and Telecommunications, Beijing, China. He has 100+ publications in reputed journals. He conducts research on interlinked topics related to Sustainability/Environmental Economics, Finance/Financial Economics and Global Economy having keywords; innovation, digital infrastructure, ecoinnovation, ICT, financial globalization, financial integration, financial structure, financial indicators and green financing projects, environmental innovation, environmental entrepreneurship, and green growth. Recently, he is working on a project entitled “Research on Applications of Blockchain Technology in Green Supply Chain Management”. His work has been published in reputed journals such as Journal of Innovation and Knowledge, Journal of Cleaner Production, Resources Policy, Environmental Technology & Innovation, Environment, Development and Sustainability, International Journal of Emerging Markets, Journal of Environmental Planning and Management, Environmental Science and Pollution Research, among others. He is also an active member of the academic community, serving as an editorial members and reviewers of many journals and conferences.

Sana Ullah is completed his PhD in Economics at Quaid-i-Azam University Islamabad, Pakistan. With PhD in Economics, he works as a Research and Evaluation professional at ADB and visiting research fellow at Lebanese American University, Lebanon. He also published widely on these topics in internationally respected peer-reviewed journals such as Telematics and Informatics, Technology in Society, Journal of Cleaner Production, Economic Analysis and Policy, Resources, Conservation and Recycling, and Journal of Innovation & Knowledge.

Izzet Ulvi Yonter completed his PhD in Business Management programme at Istanbul Nisantasi University. He works on international trade, energy, and environment. Currently actively serving as a deputy in the Grand National Assembly of Türkiye.

Appendix

Table A1.

List of sample countries.

No	Country	No	Country
1	Armenia	17	Lebanon
2	Azerbaijan	18	Malaysia
3	Bahrain	19	Mongolia
4	Bangladesh	20	Nepal
5	Bhutan	21	Pakistan
6	Cambodia	22	Philippines
7	China	23	Russia
8	Cyprus	24	Singapore
9	Georgia	25	Sri Lanka
10	India	26	Syria
11	Indonesia	27	Tajikistan
12	Iran	28	Thailand
13	Japan	29	Turkey
14	Jordan	30	UAE
15	Kazakhstan	31	Uzbekistan
16	Kuwait	32	Vietnam

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