Comprehensive Outlook on Macroeconomic Determinants for Renewable Energy in Malaysia

Abstract

Mitigating global warming has been a challenge, and policymakers are responding to this issue by strengthening the commitment to enhance the renewable energy target from 20 to 31 percent in 2025 for Malaysia. However, adopting renewable energy in stages based solely on microeconomic factors, such as the price of energy, is insufficient. Thus, it is essential to investigate the macroeconomic variables that influence the share of renewable energy in Malaysia. In detail, this study introduces selected macroeconomic indicators, including gross domestic investment, domestic investment, foreign direct investment, trade openness, urbanization, financial development, and carbon emissions level, and their impact on renewable energy in Malaysia. The study utilized ARDL (Auto-Regressive-Distributed Lag) estimation based on annual time series data spanning 50 years of observations, beginning in 1971 and ending in 2020. Long-run elasticities show that greater economic development and urbanization increase the proportion of renewable energy. In contrast, increased foreign investment, trade liberalization, and carbon emissions could reduce the use of these clean energies. This paper concludes with a policy recommendation that could assist the country in achieving its goal of implementing a low-carbon, renewable energy-focused state policy.

1. Introduction

Tackling global warming has been a challenge as it directly impacts the future of humanity. Global warming has been recognized to cause the permanent loss of critical resources, droughts and floods, ecosystem imbalances, and human life threats [1,2]. In addition, the global warming issue is the anxiety of low non-renewable resources not being easily replaced by inexpensive and efficient energy sources [3]. Most countries in the world are still heavily dependent on fossil fuels and coal because these resources are cheaper [4]. However, it contributes towards higher carbon emissions releases [5,6,7,8]. A recent study also remarked that global warming and climate changes have impacted the tourism sector [9]. Dealing with this matter, businesses and governments around the world have responded to this issue by providing low-carbon policies and promoting the transition to renewable energy resources.

The involvement of renewable energy has been increasingly demanded in recent years due to the lack of conventional energy sources. Renewable energy is a source of energy available by nature that can be used continuously, i.e., hydropower, solar, wind, bioenergy, wave, geothermal, and tidal [10]. Some active scholars believe that the use of renewable energy can reduce carbon emissions and environmental pollution [11,12,13,14]. Thus, promoting renewable energy is expected to tackle carbon emissions and environmental pollution. Meanwhile, other big steps have already been taken, i.e., to use more efficient non-renewable energy [15,16]. Examples are more efficient internal combustion engines, lower-friction tires, and higher-quality fuel to produce lower emissions.

Malaysia is attempting to fully explore the potential of renewable energy sources, including solar power, hydropower, biogas, biomass, geothermal energy, and waste materials. According to the Sustainable Energy Development Authority (SEDA) report, the Ministry of Energy and Natural Resources of Malaysia (KeTSA) set a goal in 2021 to achieve a 31 percent share of renewable energy in the national installed capacity mix by 2025. This goal is in line with Malaysia’s global climate commitment to reduce its carbon intensity (as a percentage of GDP) by 45 percent between 2005 and 2030. Realizing the government’s vision is essential for motivating the nation to meet its Nationally Determined Contributions (NDC) objectives. The Malaysia Renewable Energy Roadmap Report [17] is mandated to promote further decarbonization of the electricity sector in Malaysia through 2035. This action is anticipated to reduce GHG emissions in the power sector, helping Malaysia meet its NDC 2030 goal of a 45% reduction in GHG emission intensity per unit of GDP in 2030 compared to 2005 levels and a further 60% reduction in 2035.

Utilizing renewable energy has helped to prevent environmental pollution and does not deplete natural resources [18]. Malaysia is a developing nation, and as a result, both the need for energy and the future of the energy supply are expanding. The switch from dirty to green energy is inevitable due to decreased costs because demand from the public and businesses has increased. In addition to MyRER, the nation has also implemented several policies, such as Net Energy Metering (NEM) 3.0, Smart Automation Grants (SAG), and the Green Investment Tax (GIT), that enable the clean energy industry in Malaysia to thrive. These policies recognize the significance of renewable energy exploration. The Malaysian Central Bank also provided RM 1 billion to help small and medium-sized businesses (SMEs) implement low-carbon and sustainable practices. Given that numerous measures have been introduced to expedite the usage of renewable energy, the future of renewable energy in this nation appears promising. Therefore, it is important to pay close attention to what is motivating the country’s increased use of renewable energy.

Despite the heightened interest in diminishing carbon emissions using renewable energy, extant research has overlooked the role of macroeconomic variables to support this relationship. The existing studies are focused on building energy alternatives and their measurements [19,20]. Furthermore, scholars are taking a debate on the theme of climate change on tourism which can be a motor of economic growth [9,21]. The majority of studies and economic literature have investigated the role of renewable energy on economic growth, which is linked with energy efficiency and energy demand [22,23,24], while other variables, such as domestic investment, foreign direct investment, trade openness, urban population, and financial development are missing in the prior studies. A preliminary in the nexus between macroeconomic indicators and renewable energy has focused on particular sectors, such as the agriculture sector [25].

Therefore, there is a need for a greater understanding of whether macroeconomic indicators and renewable energy are related. As highlighted by Chung [26], the involvement of macroeconomic variables can potentially influence the potential of rising renewable energy consumption. In addition, some scholars remarked that capturing macroeconomic perspectives will diminish the mismatch between the distribution of energy sources and consumers, which causes the need for infrastructure for energy, increasing investment and costs [27,28]. However, the energy price structure has not supported energy diversification and conservation [29]. Likewise, market instability and the price of fossil energy or fuel oil result in price differences in the international and domestic markets, where people’s ability or purchasing power is still relatively low [30].

The contributions of this paper are twofold. First, it contributes to the literature by filling the gap in knowledge by investigating the interconnectedness between macroeconomic indicators and renewable energy in Malaysia for the period between 1970 and 2020. The unique study in this country is because Malaysia has a more settled policy but experiences the lowest renewable consumption compared to the neighboring countries (e.g., Indonesia, Philippines, and Thailand) [31]. In addition, Malaysia possesses rich natural resources that can promote more clean energy sources for the generation of renewable energy. Second, this is the first study to integrate macroeconomics indicators (e.g., GDP, Domestic Investment, Foreign Direct Investment, Trade Openness, Urban Population, and Financial Development) to predict renewable consumption in Malaysia that further can assist the government and policy researchers in dealing with global warming and renewable energy issues.

The research paper is presented as follows. Section 1 focuses on the main issue of global warming and renewable energy. Section 2 provides the literature and preliminary research on macroeconomic indicators and renewable energy. Section 3 concerns the methodology used in this research. Section 4 summarizes the results, followed by a discussion in Section 5. Lastly, the conclusion, implications, limitations, and suggestions are presented in Section 6.

2. Literature Review

Many studies have investigated the macroeconomic determinants for renewable energy, including GDP, domestic investment, foreign direct investment (FDI), trade openness, urban population, financial development, and pollution of CO₂. However, few past works concluded that FDI consists of two aspects, which can be positive and negative. From a positive view, it can be a source of reducing energy consumption. However, in comparison, FDI can also threaten the environment [26,32,33]. In addition, Doytch and Narayan [34] explained the results of net inflows of foreign direct investment into the environment supported by empirical proof for the “halo effect” of FDI, which included the explanation that foreign investment has expanded internal environmental performance corresponding to “green derivatives”. The “halo effect” reduces production costs and makes local companies more competitive. Therefore, domestic industries have benefited from the reproduction of foreign technologies. On the other hand, FDI is crucial to explain the increasing renewable energy consumption in upper-middle-income countries. In contrast, the impact in lower-middle-income countries is not so good when examining the impact of FDI.

A considerable favorable influence of increases in GDP on the consumption of renewable energy was also discovered by Chen [35]. Import and export trade in China is another important aspect that will influence energy usage. The increase in export volume will increase demand for renewable energy, which will increase renewable energy output. In contrast, a number of studies, including those by Omri and Nguyen [36], Akar [37], and Cadoret and Padovano [38], discovered a negative correlation between GDP and energy use. In a study of 64 countries from 1990 to 2011, Omri and Nguyen [36] found little correlation between GDP and renewable energy consumption in low-income countries. In income groups other than upper-income groups, trade openness was found to have a statistically significant effect on the usage of renewable energy. People can use their income to create environmentally friendly technologies that will aid in boosting the use of renewable energy sources. Additionally, it was discovered that GDP had a negative and significant impact on renewable energy consumption from 1998 to 2011 in the Balkan nations examined using the unit root test of the IPS panel and estimation of the GMM system [37]. Furthermore, according to Cadoret and Padovano [38], GDP has a detrimental effect on the use of renewable energy in EU nations. It is presumed that nations have met their goal for renewable energy. Market power, however, is insufficient, making it challenging to encourage investment in and consumption of renewable energy. A number of scholars have used various econometric methodologies to examine various aspects affecting the consumption of renewable energy in various nations, in addition to focusing merely on economic growth as measured by GDP.

According to Pao and Fu [39], non-hydroelectric renewable energy consumption is positively correlated with economic growth. In a study that investigated the relationship between different types of energy consumption, including renewable energy, non-renewable energy, non-hydroelectric renewable energy, and primary energy consumption, and economic growth in Brazil from 1980 to 2010, total renewable energy consumption had a significant impact on real GDP. Bekht and Othman [40] added that FDI could have both positive and negative externalities. From the perspective of the production function of foreign direct investment in the hospitality industry, this outcome is characterized by consistent returns to scale. However, the outcome depends on the economy’s absorption capacity. Therefore, foreign direct investment has a direct impact on economic growth and production. This affects energy usage and is known as “Economies of Scale.” The effects of scale aim to maintain constant energy intensity, which is viewed as an indirect positive effect of FDI on energy consumption. Therefore, economic progress at this stage has a favorable effect on energy usage.

Al-Mulali et al. [41] determined that roughly 79% of the nations have a correlation between economic growth and renewable energy usage, while 19% of the countries exhibited no long-term correlation between the variables. This study examines the relationship between renewable energy use and GDP growth in nations with high, upper-middle, lower-middle, and high incomes. Using the VECM-Granger causality approach, Tang [42] discovered a long-run one-way causality between foreign direct investment and electricity consumption in Malaysia, as well as a short-run feedback impact. In a study examining the relationship between carbon emissions, energy consumption, and economic growth in India from 1971 to 2007, Ozturk and Uddin [43] demonstrated a bidirectional causality between economic growth and energy consumption. Acaravci and Ozturk [44], on the other hand, investigated the relationship between energy consumption, CO₂ emissions, and economic growth in 19 European nations. In Denmark, Germany, Greece, Italy, and Portugal, the estimated long-term emissions elasticity in relation to energy use is positive. Ozturk and Acaravci [45] analyzed the long-term relationship between Turkey’s economic development, CO₂ emissions, energy consumption, and employment rate from 1968 to 2005 in a separate study.

Makhdum et al. [46] concluded that there was a long-run cointegration presence between the variables of institutional quality, natural resources, financial development, and renewable energy on economic growth in China from 1996 to 2020. In addition, their study also found that financial expansion and natural resources significantly affect ecological footprint levels in the short-run and long-run.

Chen et al. [47] investigates the determinant of renewable energy consumption by highlighting the role of democratic institutions. The authors used the panel threshold model in order to capture the indirect effects of democratic institutions on renewable energy consumption for developed and developing countries. The findings showed that there is an increased use of renewable energy in countries that provide better democratic rights, while there is a negative relationship between economic growth and renewable energy consumption in less democratic countries.

From 1974 to 2010, Satti et al. [48] investigated the causal relationship between coal usage and economic growth in Pakistan. The findings revealed a causal relationship between economic growth and coal usage. From 1980 to 2009, Saboori and Sulaiman [49] examined the correlation between economic growth, CO₂ emissions, and energy consumption in Malaysia. The results of the investigation are consistent with an inverted U-shaped association. The Granger Causality Test examines the feedback link between economic expansion and CO₂ emissions.

On the other hand, the studies by Usman et al. [50] explained that financial development and renewable energy consumption strongly improve the quality of the environment, while other factors, such as globalization, economic growth, and non-renewable energy lead to increasing environmental degradation. In addition, financial development, natural resources, globalization, non-renewable and renewable energy boosted economic growth in eight Arctic countries from 1990 to 2017. Shabaz and Lean [51] examined the relationship between energy use and economic growth in the urban and industrial regions of Tunisia from 1971 to 2008. The findings support the hypothesis of a feedback relationship between financial development and energy consumption, financial development and industrialization, and industrialization and energy consumption.

More recently, the use of renewable energies, such as solar panels, also known as photovoltaic systems, has been spread in various fields, including architecture of buildings and landscapes [52]. Thus, stakeholders play a vital role in outlining policies and channeling funds to encourage renewable energy use in buildings, corroborating the aims of sustainable development.

Policies to reduce energy use and CO₂ emissions must be maintained to preserve the environment for future generations without negatively impacting the economy. A few studies on the determinants of electricity demand consumption patterns have inspired more researchers to study the causes of growing electricity demand and its effect on the desire for renewable energy. Numerous earlier studies have examined only a few variables, resulting in skewed estimations and the omission of potential determinants. This study investigates the greatest number of probable macroeconomic factors that could affect Malaysia’s energy usage. The study’s findings provide significant insights and fill the knowledge gap.

3. Research Methodology

The general functional form of the renewable energy model for Malaysia is inspired by Kumaran et al. [14] model. Therefore, the model is derived as follows:

$$R{E}_t=f(GD{P}_t,D{I}_t,FD{I}_t,T{O}_t,UR{B}_t,FD,C{O}_2)$$

(1)

where:

RE_t represents renewable energy,

GDP_t represents economic growth,

DI_t represents domestic investment,

FDI_t represents foreign direct investments inflows,

TO_t represents trade openness,

URB_t represents urbanization,

FD_t represents financial development,

CO_2t represents environmental quality,

Malaysia’s GDP is predicted to exhibit a positive correlation with RE. With significant economic growth and expansion, the country may invest more in developing and promoting greener energy sources that reduce emissions, such as solar panels and wind energy. According to Omri and Nguyen [36], discretionary income can be used to develop green technologies that can help boost the utilization of renewable energy. It is anticipated that DI will likewise have a good effect on RE. The increased domestic investment might enhance the infrastructure that supports the development of renewable-energy-based industries or facilities. It is anticipated that FDI will be favorable as a greater amount of foreign investment will be poured into cleaner technology [53]. When cleaner items are imported from the nation’s trading partners, the TO is likely to receive a positive signal. According to Chung [14], most ASEAN nations, including Malaysia, gained sophisticated cleaner technologies from their developed country trading partners. Increased urbanization may potentially increase RE usage if renewable-energy-based products are easily accessible. High urbanization, according to Md Razak et al. [54] and Yassin and Aralas [55], may drive society to become more environmentally conscious. In addition, the increase in RE is associated with the emergence of financial institutions that actively lend loans to green technology-focused enterprises. Increasing CO₂ levels may increase the demand for renewable energy in the country. Due to the high concentration of carbon emissions emitted by the country, the nation will take preventative steps by increasing its use of renewable energy if global warming issues intensify.

To describe short- and long-term elasticity, Equation (1) is translated into log-linear forms (LN). According to Shahbaz et al. [51], the log form of the equation can yield consistent and reliable estimations. The logarithmic form of Equation (1) is as follows:

$$LNR{E}_t={\alpha }_0+{\beta }_{1}LNGD{P}_t+{\beta }_{2}LND{I}_t+{\beta }_{3}LNFD{I}_t+{\beta }_{4}LNT{O}_t+{\beta }_{7}LNUR{B}_t+{\beta }_{8}LNFD+{\beta }_{9}LNC{O}_2{\mu }_t$$

(2)

To conclude, all β are expected to have a positive sign with LNRE. However, the expected sign could be negative, and it is the responsibility of the authors to justify it. Next, we transform Equation (2) above in ARDL (Auto-Regressive-Distributed Lag) form. The ARDL model based on the Unrestricted Error Correction Model (UECM), as invented by Pesaran et al. [56], is stated below:

$$\begin{array}{l}\Delta LNR{E}_t={\beta }_1+{\theta }_{0}LNR{E}_{t-1}+{\theta }_{1}LNGD{P}_{t-1}+{\theta }_{2}LND{I}_{t-1}+{\theta }_{3}LNFD{I}_{t-1}+{\theta }_{4}LNT{O}_{t-1}+{\theta }_{5}LNUR{B}_{t-1}+{\theta }_{6}LNF{D}_{t-1}+{\theta }_{7}LNC{O}_2{}_{t-1}+\\ +{\displaystyle \sum _{i=1}^a{\beta }_i\Delta LNR{E}_{t-i}}+{\displaystyle \sum _{i=0}^b{\gamma }_i}\Delta LNGD{P}_{t-i}+{\displaystyle \sum _{i=0}^c{\delta }_i\Delta }LND{I}_{t-i}+{\displaystyle \sum _{i=0}^d{\lambda }_i}\Delta LNFD{I}_{t-i}+{\displaystyle \sum _{i=0}^e{\vartheta }_i}\Delta LNT{O}_{t-i}+{\displaystyle \sum _{i=0}^f{\psi }_i\Delta LNUR{B}_{t-i}}+{\displaystyle \sum _{i=0}^g{o}_i\Delta LNF{D}_{t-i}+}{\displaystyle \sum _{i=0}^h{\Omega }_i\Delta LNC{O}_2{}_{t-i}+}{\upsilon }_t\end{array}$$

(3)

where ∆ is the first difference operator, and u_t is the white-noise disturbance term. There are series of diagnostic tests, such as serial correlation, model stability, etc., to ensure the reliability of the model. The final version of the model comprising the long-run and short-run estimation is listed in Equation (3). We label each of the lag order for the short-run estimation from a to h. The level of renewable energy used (LNRE) is also added in Equation (3) for both short-run and long-run as it can be influenced and explained by its past values indicating disturbances or shocks.

The long-run elasticity is the coefficient of the one-lagged explanatory variable (multiplied by a negative sign) divided by the coefficient of the one-lagged dependent variable. The coefficients of the first differenced variables describe the short-run effects. The null of no cointegration in the long-run relationship is specified by the following hypothesis:

H0. 

θ0 = θ1 = θ2 = θ3 = θ4 = θ5 = θ6 = θ7 = 0 (there is no long-run relationship),

H1. 

θ0 ≠ θ1 ≠ θ2 ≠ θ3 ≠ θ4 ≠ θ5 ≠ θ6 ≠ θ7 ≠ 0 (there is a long-run relationship exists),

The probability of the F statistic value must be greater than the upper bound value of either 1,5 or 10% significant level in order to prove the existence of the long-run cointegrating relationship.

This study uses a comprehensive annual data set ranging from 1971 up to 2020 (50 years) as a sample period. A summary of the data and their sources is shown in

Table 1. Sources of data.

Variables	Description	Sources
LNRE	Share of primary energy from renewable sources *	WDI
LNGDP	GDP per capita (constant 2015 US$)	WDI
LNDI	Gross fixed capital formation (% of GDP)	WDI
LNFDI	Foreign direct investment, net inflows (% of GDP)	WDI
LNTO	Trade (% of GDP)	WDI
LNURB	Urban population growth (annual%)	WDI
LNFD	Broad money (% of GDP)	WDI
LNCO2	CO2 emissions (metric tons per capita)	WDI

Note: WDI stands for World Development Indicator [57].

.

4. Result and Discussion

The first step in the analysis procedure is determining whether the data are stationary. Before pursuing short-run and long-run estimations, it is crucial to use this technique to evaluate the model’s suitability and state. A few variables, including LNDI, LNFDI, LN TO, and LNURB, are even stationary at a level under the ADF and PP unit root tests, as shown in

Table 2. Testing ADF and PP Unit Root.

Level I(0)	ADF Unit Root		PP Unit Root
	Intercept	Intercept and Trend	Intercept	Intercept and Trend
LNRE	−2.352 (1)	−2.072 (1)	−1.894 (1)	−1.560 (2)
LNGDP	−1.129 (0)	−2.732 (1)	−1.129 (0)	−2.307 (1)
LNDI	−4.637 (0) ***	−4.865 (0) ***	−4.378 (5) ***	−4.541 (6) ***
LNFDI	−2.846 (0) *	−2.897 (0)	−2.995 (3) **	−3.009 (3)
LNTO	−2.902 (0) *	−2.677 (0)	−2.825 (3) *	−2.425 (2)
LNURB	0.770 (0)	−3.714 (0) **	0.770 (0)	−3.643 (1) **
LNFD	−1.483 (1)	−1.094 (1)	−2.133 (3)	−1.142 (3)
LNCO2	−1.971 (0)	−2.887 (0)	−3.079 (14) **	−2.574 (6)
First difference I(1)	ADF Unit Root		PP Unit Root
	Intercept	Intercept and Trend	Intercept	Intercept and Trend
LNRE	−4.993 (0) ***	−5.602 (1) ***	−4.824 (8) ***	−4.909 (10) ***
LNGDP	−4.951 (0) ***	−4.962 (0) ***	−4.906 (2) ***	−4.921 (2) ***
LNDI	−7.651 (1) ***	−7.554 (1) ***	−16.551 (47) ***	−17.714 (47) ***
LNFDI	−7.732 (0) ***	−7.696 (0) ***	−7.727 (2) ***	−7.693 (1) ***
LNTO	−8.887 (0) ***	−9.300 (0) ***	−9.269 (3) ***	−11.934 (7) ***
LNURB	−7.091 (0) ***	−7.204 (0) ***	−7.145 (1) ***	−7.332 (3) ***
LNFD	−4.639 (0) ***	−4.644 (0) ***	−4.639 (0) ***	−4.644 (0) ***
LNCO2	−6.184 (0) ***	−6.345 (0) ***	−6.253 (10) ***	−8.177 (15) ***

Note: 1. ***, ** and * 1%, 5%, and 10% of significant levels, respectively. 2. The optimal lag length is selected automatically using the Schwarz Info Criteria (SIC) for the ADF test, and the bandwidth was selected using the Newey–West method for PP.

. For both the ADF and PP unit root tests, all variables are stationary at the first difference at a 1% significant level. The analysis’s mixed stationarity results show that ARDL estimation is a better fit for the model than the Vector Error Correction Model (VECM).

Using the ARDL technique for the cointegration test, it is important to investigate the existence of long-run cointegration for this model. The value of 5.88 is found for the F statistic.

Table 3. Detecting the presence of long-run cointegration based on F stat.

Model	Lag Order	F Statistics
RE = f(GDP,DI,FDI,TO,URB,FD,CO2)	(1, 4, 1, 4, 4, 4, 2, 4)	5.888 ***
Critical Values for F stat	Lower I(0)	Upper (1)
10%	2.03	3.13
5%	2.32	3.5
1%	2.96	4.26

Note: The critical values are based on Pesaran et al. [56]. k is the number of variables, and it is equivalent to 6. *** represent 1% levels of significance, respectively. Estimation is based on Schwarz Criterion (SC).

reveals that the model’s F statistic score is greater than the upper bound value for a significance level of 1.5 and 10%, according to Narayan’s [58] critical value table. This result confirmed the existence of long-run cointegration in the model by accepting the alternative cointegration hypothesis, which can explain the short-run and long-run elasticities.

To ensure the accuracy of the model’s output, it is crucial to carry out all relevant diagnostic tests before continuing with the estimation. Serial correlation, functional form, normality test, and heteroscedasticity were the tests used for diagnosis. The results are shown in

Table 4. Diagnostic Tests.

A. Serial Correlation [p-Value]	B. Functional Form [p-Value]	C. Normality [p-Value]	D. Heteroscedasticity [p-Value]
0.902	0.437	6.063 **	0.893
[0.611]	[0.519]	[0.048]	[0.4347]

The diagnostic test performed as follows A. Lagrange multiplier test for residual serial correlation; B. Ramsey’s RESET test using the square of the fitted values; C. Based on a test of skewness kurtosis of residuals; D. Based on the Harvey. ** represent 5% level of significance.

. Given that the probability value for all tests is greater than the 10% significant threshold, with the exception of the normality test, which is significant at the 5% level, the model is free from econometric issues. This is standard for ARDL estimation and evaluates a different lag or disturbance of the past value towards the dependent variables. Thus, the model that is being proposed here still produces a robust outcome.

The model was further diagnosed for stability test using a cumulative sum of recursive residuals and a cumulative sum of squares of recursive residuals, as displayed in Figure 1. The CUSUM and CUSUMSQ test exhibits a blue line within two dotted lines, indicating the stability, consistency, and reliability of the model both in the short and long term.

Table 5. Short-run Elasticities.

Short-Run Elasticities
Variables	Coefficient
ΔLNGDP	4.738 ***
ΔLNGDP(−1)	0.548
ΔLNGDP(−2)	2.614
ΔLNGDP(−3)	−6.616 ***
ΔLNDI	0.002
ΔLNFDI	−0.059
ΔLNFDI(-1)	0.094
ΔLNFDI(-2)	0.046
ΔLNFDI(-3)	0.159 **
ΔLNTO	−0.403
ΔLNTO(-1)	0.816 ***
ΔLNTO(-2)	−0.425
ΔLNTO(-3)	0.315
ΔLNURB	3.647 **
ΔLNURB(-1)	−0.983
ΔLNURB(-2)	−0.518
ΔLNURB(-3)	0.562
ΔLNFD	−1.056 **
ΔLNFD(-1)	−0.730
ΔLNCO2	−2.686 ***
ΔLNCO2(-1)	−0.612
ΔLNCO2(-2)	1.074 *
ΔLNCO2(-3)	0.457
ECT	−0.904 ***

Note: 1. ***, ** and * are 1%, 5%, and 10% of significant levels, respectively. 2. Δ refer to difference.

reveals the result of short-run elasticity calculations. In this section, the outcomes are explained in detail. However, a greater emphasis will be placed on long-run elasticities, whose effects are more significant for policymakers. First, it is found that LNGDP has a positive and significant relationship with LNRE at present lag. In the short term, a 1% increase in LNGDP statistically increases the renewable energy share by 4.74 percent. However, the result is contradicted by the result of LNGDP at the three most recent lags, which indicates a significant and negative relationship with LNRE. On the basis of the previous three years, it is determined that LNFDI has a positive effect on LNRE. A 1% increase in LNFDI accelerates the share of renewable energy by 0.159%, according to statistical analysis. LNTO was discovered to have a positive and statistically significant relationship with LNRE based on the previous lag and LNURB based on the current lag. The LNRE is improved by 0.816% and 3.647% for every 1% increase in LNTO and LNURB, respectively. LNFD and LNCO₂ have a negative and statistically significant relationship with LNRE. The LNRE will decrease by 1.056% and 2.686%, respectively, for every 1% increase in both variables. However, based on the previous two lags, LNCO₂ has a significant and positive effect on LNRE.

A significant negative was revealed by the error correction model (ECT). However, the coefficient of the ECT is −0.904, confirming the long-term convergence of LNRE determinants. Consequently, any policy suggested by these studies is valid and implementable.

The long-run elasticities are shown in

Table 6. Long-run Elasticities.

Variables	Coefficient
LNGDP	5.636 ***
LNDI	−0.082
LNFDI	−0.227 *
LNTO	−1.659 ***
LNURB	1.878 ***
LNFD	−0.212
LNCO2	−3.977 ***
C	−34.961 ***

* represents significant at 10% level, *** represents significant at 1% level.

. First, it is found that LNGDP has a greatly positive and significant effect on LNRE in the country. Technically, a 1% increase in LNGDP results in a 5.636% increase in LNRE. These positive effects have the greatest magnitude relative to other variables. In addition to meeting expectations, this result demonstrates a significant impact on economic growth in Malaysia. This indicates that as the economy grows, the need for energy will also increase, which, in turn, facilitates the growth of the renewable energy sector. The findings are consistent with previous studies such as Pao and Li [59] for MIST economies and Pfeiffer and Mudlder [60] for developing countries, while they contradict Kumaran et al. [14] for ASEAN-4 countries.

The level of foreign direct investment in Malaysia has a significant and negative relationship with the level of renewable energy sources. Although a 1% increase in LNFDI reduces the share of renewable energy by 0.227%, all other factors influencing LNFDI remain unchanged. LNTO, on the other hand, hypothesizes a significant and negative relationship with LNRE. Statistically, a 1% increase in LNTO results in a 1.659% decrease in LNRE. These findings indicate that foreign investors and trade activities continue to rely heavily on polluting forms of energy, such as fossil fuels and coal, because they are less expensive than renewable energy. Additionally, the result revealed that LNURB has a positive and substantial relationship with LNRE. This result is consistent with the ecological modernization theory, according to which the country’s inhabitants are more aware of climate change due to rapid urbanization. Consequently, as Yassin and Aralas [55] discuss, they may wish to switch from dirty energy to cleaner energy in order to reduce this phenomenon. Statistically, a 1% increase in LNURB results in a 1.878% increase in LNRE.

Lastly, increased carbon emissions, as measured by LNCO₂, reduce LNRE. Statistically, a 1% decrease in LNCO₂ results in a 3.977% increase in LNRE, which is the second largest effect size among the variables. A lower level of carbon emissions indicates that the country should prioritize the use of renewable energy sources, such as solar, biomass, and hydroelectricity, through the development of this sector to ensure that the country’s transition to a low-carbon state can be achieved. On no level do LNDI and LNFD become significant. Therefore, it is deemed incapable of influencing the level of LNRE in the long run.

5. Discussion

This paper investigates the country’s macroeconomic determinants for renewable energy based on short-run and long-run elasticities using ARDL estimation. Based on the current lag, the level of economic growth has a positive impact on renewable energy in both the short and long term. The long-term result reveals that foreign direct investment and trade liberalization reduce the share of renewable energy. Meanwhile, it has been discovered that expanding financial development raises the share of renewable energy in the short term, but not in the long term. Short-term and long-term carbon emissions may potentially reduce demand for renewable energy. The following is a detailed discussion of the key findings of the study.

Given that GDP development has a significant and favorable impact on Malaysia’s renewable energy sector, there are primarily two economic elements to consider. The first is that economic development is dependent on energy supplies. Thus, economic development cannot exist in the absence of energy. Second, economic development conditions impact the extent and scale of energy dependence and utilization. Economic growth can encourage large-scale energy expansion and utilization. This demonstrates the fact that the growth of the renewable energy industry and the scale of their energy use are strongly dependent on Malaysia’s economic development, which has significant policy implications. As a result, economic growth is a necessary condition and determining factor for renewable energy, as economic growth would be the key impetus for the development of Malaysia’s renewable energy industry. This suggests that when an economy’s productivity grows, it will create more output while requiring more input. Importantly, the use of renewable energy will grow, as will the adoption of its technology. However, economic growth is not a country’s main policy goal, and the deployment of renewable energy has been impeded by its unreliability and high costs.

The study’s findings revealed a negative relationship between FDI and renewable energy resources, which could represent the ineffectiveness of investment policies in encouraging green foreign investment. If a country’s level of renewable energy has a negative relationship with foreign direct investment (FDI), the level of FDI decreases as the level of renewable energy increases. There could be several reasons for this. For instance, it could imply that FDI is largely focused on or relies on traditional energy sources, such as fossil fuels, rather than renewable energy supplies. This could also be due to a lack of incentives for renewable energy. The government may not have policies and incentives in place to stimulate FDI in the renewable energy industry, making it less attractive to foreign investors. Furthermore, the country may lack the required technological capabilities and infrastructure to support the expansion of the renewable energy sector, making it less appealing and incapable of persuading foreign investors to use renewable energy sources. As a result, if FDI has a negative association with levels of renewable energy, it would suggest that the presence of renewable energy resources in a country can have a negative impact on foreign investment, thereby reducing economic growth and development.

Importantly, the study discovered a negative relationship between trade liberalization and renewable energy. This indicates that as trade openness improves, so the adoption and use of renewable energy may decrease. This could be caused by a number of reasons. First, there could be competition from traditional energy sources. As trade liberalization increases, renewable energy may face increases competition from fossil fuels. Trade liberalization may also increase reliance on imported energy, particularly non-renewable energy, reducing the motivation to invest in renewable energy. Second, increased trade liberalization may weaken incentives for renewable energy development because governments may prioritize trade and economic growth above renewable energy development. Finally, as trade openness grows, the country may lack the technological ability and facilities to maintain long-term energy growth. As a result, if trade openness has a negative association with renewable energy, it implies that as trade openness increases, renewable energy adoption and use may decline, thus impeding the transition to a more sustainable energy mix.

Another interesting finding is that a strong relationship between urbanization and renewable energy implies that as urbanization grows, so does the demand for renewable energy. As cities become more densely populated, infrastructure will improve, requiring more technological capabilities and making it easier to sustain the growth of the renewable energy sector. Urbanization can also raise the level of education, resulting in a more educated population as well as enhanced public awareness and political will to address environmental issues, such as the need to transition to a more sustainable energy mix. This is congruent with the idea of ecological modernization, which asserts that environmental conservation and economic growth may be achieved by implementing new environmentally friendly technologies and manufacturing processes, resulting in a more sustainable development path. As a result, the government may need to develop policies and incentives to encourage the adoption of renewable energy, particularly in high-energy-demanding urban regions.

Finally, the inverse relationship between CO₂ emissions and renewable energy (RE) deployment indicates that as RE implementation drops, carbon emissions grow. This could imply that Malaysia’s economic sectors and industries continue to rely heavily on non-renewable energy sources, such as fossil fuels, reducing the motivation to invest in renewable energy. Another factor influencing the growth of the renewable energy sector is the fact that the government does not have policies in place to curb carbon emissions and promote the adoption of renewable energy. This may be because transitioning to a more sustainable energy mix is more challenging due to the country’s lack of technological know-how and infrastructure to support the growth of the renewable energy sector. Lastly, there is the cost consideration. Renewable energy sources may continue to be more expensive than traditional energy sources, reducing the incentives for renewable energy adoption.

6. Conclusions and Policy Recommendations

In the 12th Malaysia Plan (12MP) 2021–2025, Malaysia is committed to its goal of becoming a carbon-neutral nation as early as 2050, particularly to accelerate the growth of the green economy, improve energy sustainability, and ensure that the renewable sector remains at the center of the country’s energy sector development. Despite the fact that Malaysia accounts for less than 0.7% of global greenhouse gas emissions, the Malaysian Ministry of Energy and Natural Resources (KeTSA) established a target of 31% renewable energy participation in the national installed capacity mix by 2025. Malaysia’s worldwide climate commitment is to lower its economy-wide carbon intensity (as a percentage of GDP) by 45% by 2030 compared to 2005 levels. To meet this target, Malaysia’s government has established a number of policies, regulations, and incentives aimed at fostering the expansion of the renewable energy industry and stimulating investment in this area from both domestic and international investors. The Feed-in Tariff (FiT) plan is one of the most important incentives and regulations that have been implemented to attract investment in this industry and boost the expansion of renewable energy companies. For example, the FiT program encourages the development and deployment of renewable energy projects by providing a guaranteed rate for every watt of renewable energy produced. Aside from the FiT, the government provides a number of tax breaks to industries that participate in the production and development of renewable energy. In addition, the Malaysian government established the Renewable Energy Fund to provide financial assistance to renewable energy projects in the form of grants, subsidies, and tax breaks. This is due to the fact that the development of the renewable energy sector is dependent on access to capital and investment, whether from domestic or foreign investors. To stimulate the development of renewable energy projects, both the public and commercial sectors can provide financing options. Aside from that, the deployment of net energy metering is another incentive to increase renewable energy adoption in Malaysia (NEM). By allowing individuals and institutions to produce their own renewable energy and sell any excess energy back to the grid, the NEM plan provides a financial incentive for the use of renewable energy. It is clear that the growth of the economy can have a significant impact on the renewable energy sector growth via the growth in business and trade, which attracts both local and FDI.

The growth of renewable energy sectors would also have multiple effects on the energy supply chain, new investment, and driving the development of new technologies. As the renewable energy sector grows, the demand for components and materials used in renewable energy systems, such as solar panels and wind turbines, will increase. The growth of the renewable energy sector may also attract new FDI, new business opportunities, and job creation within the supply chain through suppliers and manufacturers, increasing competition and potentially reducing the cost of renewable energy components. As renewable energy systems become more efficient, it may result in a reduction of energy waste, reducing the demand for energy and potentially reducing the costs associated with energy waste management. Indeed, the growth of the renewable energy sector can improve the competitiveness of the renewable energy industry, sustainable energy and the environment.

The strong negative effects on renewable energy in Malaysia of both FDI and trade liberalization reveal that trade policy is ineffective in attracting renewable energy investment. An influx of FDI can harm the host country, resulting in detrimental environmental repercussions. Furthermore, these findings imply that the disadvantage of FDI inflows is due to a high level of non-renewable energy input and commodities, which would exacerbate pollution and the deterioration of air quality due to large emissions of dangerous gases like CO₂ and NO₂. This has serious environmental and human health consequences. Furthermore, the expansion of the renewable energy industry may result in a decrease in demand for traditional energy sources, potentially resulting in job losses and business closures in the traditional energy supply chain.

As a result of this finding, the study suggests that the Malaysian government should pay special attention to FDI policy, regulations, and restrictions in order to ensure that FDI inflows to Malaysia comply with environmental policy. The Environmental Quality Act of 1974 (EQA 1974) is Malaysia’s primary environmental protection and conservation law. The Environmental Quality Act of 1974 empowers those responsible for environmental protection to enact regulations governing acceptable conditions for the release of environmentally hazardous substances and pollutants, as well as regulations prohibiting the release of gaseous substances into the environment. Thus, to stimulate economic growth without negatively impacting the environment, Malaysia should encourage green FDI inflow with special green incentives focused on renewable energy (RE), which could help to reduce pollution and safeguard the production process with fewer negative effects on the environment, ultimately leading to a positive impact on human health.