Empirical Evidence from OECD and BRICS Countries on the Impact of Investor Behavior on Renewable Energy Deployment

doi:10.21203/rs.3.rs-1599984/v1

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Empirical Evidence from OECD and BRICS Countries on the Impact of Investor Behavior on Renewable Energy Deployment

https://doi.org/10.21203/rs.3.rs-1599984/v1

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The importance of employing Renewable Energy (RE) sources has recently expanded significantly, particularly in light of growing worries about climate change issues and rising fossil-fuel prices constituting a substantial threat to the country's economies. However, there has been very little examination of RE deployment from the perspective of investors to date. For this reason, determining the most successful investor behavior in the deployment of RE is critical. In terms of the establishment of a conceptual framework, the current paper provides a systematic and quantitative measure to compare the implications of variables in OECD and BRICS countries. This study employs a variety of panel regression procedures such as panel unit test, fixed and random effects model, generalized method of moments (GMM), and robustness check to investigate the determinants of RE investment in OECD and BRICS nations through a panel data study from 2000 to 2020. Economic growth, RE policy, and R&D spending all have a statistically significant and positive association with RE capacity, according to the GMM data. RE investment, on the other hand, is inversely related to energy, electricity use, and carbon emissions (CO2). Strong regulatory (policy, R&D) perception is significant in RE investment.

Renewable energy

RE investment

RE policies

R&D

OECD and BRICS countries

Investments in RE sources are gaining popularity as a viable way for governments to achieve energy independence while also stimulating economic growth. Masini and Menichetti (2012) look into the decision-making process that goes into investing in RE sources. They investigated behavioral factors influencing RE investment decisions and the relationship between RE investments and portfolio performance using a conceptual model and empirical study. They argue that additional policies should be chosen to stimulate the investment of renewable resources since policy instruments, particularly those relevant to investment decisions, have a significant impact on investment decisions. The findings also show that some investors have very distinct investment strategies. One sort of investor likes short-term incentives and is more driven to invest based on short-term policy incentives that have a higher possibility for instant profit. Other investors take a longer-term approach. They prefer policy incentives that yield a lower return on investment over a longer period of time, as long as the policy ensures the necessary long-term support (Masini and Menichetti, 2012; Bushee, 1998; Hirshleifer, 1993).

In order for RE investment policy to be successful, it must satisfy its main stakeholders (Bryson, 2004). Potential investors are identified as relevant stakeholders who make a crucial difference in the public policy efficacy of RE investment targets, using the logic of stakeholder identification given by Mitchell et al. (1997) and adapting it to the context of RE. However, concerning the relationship between investor behavior and RE investment, there is a lack of a comprehensive theoretical and empirical framework. Based on the literature, this research creates an investor perspective framework, which is then put to the test through an empirical investigation. To put it another way, the current study draws on existing knowledge of RE investment, develops a new conceptual framework to guide policy, and tests the generated conceptual model using quantitative methodologies. The goal of this paper is to provide major insights into the establishment of successful RE policies, with a specific focus on RE investment in OECD and BRICS countries, in order to shed light on the relationship between investor behavior and RE outcomes. The following is the research question addressed in this paper: What impact does investor behavior have on RE investments?

The paper makes manifold contributions to the literature. First, by offering a better knowledge of investor behavior toward RE resources, the study aids RE investors by proposing a conceptual model for designing effective policy instruments that should be used to overcome impediments in their way. Second, the current research makes a methodological addition by identifying the characteristics that are beneficial in RE investments using empirical analysis in a wide range of countries. Finally, this is the first attempt in the literature to include the OECD and BRICS countries in the empirical study, with data that is current and collected over a long period of time, as well as RE deployment from the investor's perspective. As a result, the publication contributes to the legitimacy of the research. Therefore, the paper contributes to the validity of the findings in this regard being extended to a broader and more comprehensive setting.

The following is how the rest of the paper is organized: The second part is a review of the literature. The current paper's methodology and data are described in the third part. This entails presenting the conceptual foundation as well as doing quantitative research. The empirical findings and debate are presented in the next part, followed by concluding remarks in the form of policy recommendations.

An emerging body of literature has looked into how policies should be crafted to efficiently mobilize investments in the RE sector (Menichetti, 2010). Despite this enormous effort, knowledge of RE investment and the variables linked with RE policy is still inadequate. While various studies have presented policy efficacy measures (Masini and Menichetti, 2012; Musango and Brent, 2011; Wüstenhagen and Menichetti, 2012), they only provide a restricted view of investors' opinions. As noted in the political economics literature, a key flaw in current research is the lack of attention on investors' perspectives (Lipp, 2007; Masini and Menichetti, 2012; Musango and Brent, 2011; Wüstenhagen and Menichetti, 2012).

The majority of the research focuses on investor behavior as a barrier to RE investments (Niesten et al. 2018; Salm et al. 2016; Nasirov et al. 2015; Leete et al. 2013; Masini and Menichetti 2012; Masini and Menichetti, 2010; Wüstenhagen et al. 2007). Nasirov et al. (2015) explore the barriers to the adoption of RE sources from the investor's perspective. Grid connection constraints and a lack of grid capacity, long permit processing times, certainty of land and/or water leases, and limited access to finance are among the most significant barriers to RE projects, according to the research. Leete et al. (2013) intend to uncover common hurdles and incentives to investing in RE through a series of in-depth interviews with people in the field. Due to their inability to predict costs and the length of time required to develop RE technologies, investors/stakeholders do not have the option to re-invest in RE, according to the findings. Masini and Menichetti (2012) look at the decision-making process that goes into investing in RE solutions.

The lack of government/state-based subsidies is another impediment to RE projects (Tura et al. 2019, Jones, 2015, Lilliestam and Patt, 2015, Kostka et al. 2013, Shill et al. 2012). De Jongh et al. (2014) point out that South Africa has a lack of clear regulations and government assistance. According to several studies, the most significant RE is cost. Malik et al. (2019) for GCC countries, Hu et al. (2018), Fashina et al. (2018) for Uganda, Yuosoff and Kardooni (2012) for Malaysia, and Mostert (2009) for Nicaragua are just a few examples. Furthermore, according to Painuly (2001), RE energy companies face a number of challenges, including a lack of pre-financing, credit facilities, and technical competence.

RE investors are often treated in one of two ways in the literature. One way is as a homogeneous group of utility-type actors investing with profit maximization in mind (Bergek et al. 2013; Gross et al. 2010; Koo et al. 2011) and investors in RE often base their judgments on comparisons of various electricity producing methods. Other research defines RE investors as a varied set of participants that includes small and medium-sized private investors, independent power producers, and cooperatives (Agterbosch et al., 2004; Loock, 2012). Because of the hazards associated with fossil fuels, such as price volatility, import availability, and the cost of domestic economic exposure, investing in RE sources may be more appealing than investing in fossil fuels. RE sources are essentially homegrown energy supplies that are not dependent to the availability and pricing of imported energy based on global markets. Uncertainties in RE policies, prices, and regulations, on the other hand, can raise degrees of investment risk and uncertainty, making renewable investments less appealing than uncertain fossil-based sources (Finon and Perez, 2007; Popp et al. 2011).

OECD countries rank first in terms of CO₂ emissions compared to other regions. In 2018, OECD countries emitted an average of 8.7 tons of CO₂ per capita, while the rest of the world released 4.3 tons of CO₂ (OECD, 2020). Therefore, increasing the proportion of RE investments is required to create a low-carbon society (Polzin et al. 2015). A greater investment in the RE industry has been seen across the OECD countries. Germany, the United States, Japan, and the United Kingdom, for example, were the top investors in 2013. OECD countries have a number of priorities for attaining global CO2 targets, including reducing energy imports, expanding RE technologies, and lowering CO2 emissions. Belgium, Denmark, and Germany have projected a 100 percent RE share by 2050 to reach these targets (Sisodia et al. 2015; Klaus et al. 2010).

BRICS countries have abundant RE sources, however they largely employ carbon-intensive energy sources (Zeng et al. 2017). China, for example, has abundant wind resources (Meisen and Hawkins, 2009), whereas India and South Africa have ideal conditions for solar energy development (Nautiyal, 2012; Mulaudzi and Bull, 2016). There are several water resources in Brazil that are ideal for the construction of hydroelectric power facilities (Meisen and Hubert, 2010). Russia exports and uses a lot of fossil fuels every year, but it also possesses a lot of RE sources with a lot of promise (Kirsanova et al. 2018; Cherepovitsyn and Tcvetkov, 2017; Pristupa and Mol, 2015). However, information on the impact of investor behavior on RE implementation is limited. To our knowledge, no research has been done on the development of a conceptual model for the structural and behavioral factors that influence RE investor decisions, as well as the empirical testing of this model for OECD and BRICS nations.

To begin, a conceptual model was proposed within the context of the relevant literature in order to construct a conceptual framework, and hypotheses were formed based on the models' goals. Then, for the OECD and BRICS countries, a country-level panel data analysis of RE investments was done for the period 2000–2020.

3.1. Conceptual Framework Analysis from Investors’ Perspectives

Academic and political debates have centered on what should be done to deploy renewable investments (RE projects) (Polzin et al. 2015; Bergek et al. 2013; Wüstenhagen and Menichetti, 2012). High upfront costs, risks and long-term viability of technology, long payback periods, high regulatory and infrastructure dependency, and uncertainty about public acceptance are all factors influencing investor investments in RE deployment (Rodrguez et al. 2014; Haley and Schuler, 2011; Muller et al. 2011). These characteristics are important drivers in real estate investments, and they have a direct impact on RE investors' risk and return.

The ultimate goal of a sustainable RE strategy is to lower the capital costs of RE technology by government subsidies, creating a level playing field for both fossil fuel-based and RE technologies (Polzin et al. 2015). As a result, policymakers should base their decisions on the issues that affect RE investors. According to Bergek et al. (2013), policymakers make judgments about risk regulation and administrative processes based on the insights of RE project developers (Friebe et al. 2014). Similarly, Chassot et al. (2014) place a premium on the perceived risk posed by policies, which is one of the most significant factors influencing RE investment decisions.

The essential parts of RE investor decision-making are traced in a conceptual analysis based on prior work on renewable investment, which develops the theoretical framework of renewable investment. The conceptual model (Zahra, 1993) is more sophisticated for assessing strategic options for RE investment, and it serves as a framework for comprehending this research as well as a starting point for identifying relevant study topics (Ata, 2015). Arguments such as property rights protection and the capacity to import materials are expected to play a role here. It also includes a diagram of the suggested conceptual framework for the linkages between RE market investment and variables. There is a dependent variable, installed cumulative RE capacity, and an econometric model has been suggested to solve the major research question. The conceptual foundation for this investigation is depicted in Fig. 1.

Installed cumulative RE capacity is affected by a variety of factors, including technological efficacy, RE regulations, climate policies/problems, market conditions, and country economic conditions, all of which influence investor behavior.

The availability of financial expenditures on renewable technologies for sustainable RE project encouragement is referred to as R&D capacity risk. The budget for R&D must be raised in order for RE investments to increase (Kul et al. 2020; Chu and Majumdar, 2012). Renewable and climate policy are one of the most significant factors in expanding RE investments. As a result, politicians must establish clear and suitable long-term RE policies to assist investors in incorporating climate change concerns and allocating money to low-carbon technology (He et al. 2019; Masini and Menichetti, 2012; Reuter et al. 2012). In the energy industry, rising power and energy consumption has prompted many countries, particularly those that import energy, to look for alternate energy sources (Kahia et al. 2017). Finally, rising economic growth leads to more investment in RE sources. According to Chen et al. (2021), in nations where people's democratic rights are better safeguarded, there is a positive association between economic growth and RE capability, whereas in less democratic countries, there is a negative relationship.

The three primary hypothesis proposals to be empirically investigated are as follows, based on the literature review described above.

H1: The perceived importance of implemented policies has a big impact on investor behavior and RE investments.

H2: The higher the proportion of RE investments in total investments, the higher the level of R&D and technological efficiency.

H3: RE investments are influenced by environmental and economic issues.

3.2. Data and Variables Selection

Data from the World Bank, the International RE Agency (IRENA), and the International Energy Agency (IEA) are analyzed using quantitative methodologies (IEA). Installed cumulative RE capacity is used as a dependent variable in this paper, and it is quantified in megawatts (MW). Table 1 summarizes all variables, and all variables are expressed in natural logs.

Table 1

Summary of the Variables Used for Analysis
Variables	Unit of Measurement	Sources
Installed Cumulative RE Capacity (REC)	Cumulative, in MW	IRENA
GDP per capita (GDP)	Constant 2017 international $	World Bank
Energy consumption (EC)	Percentage %	IRENA
Electricity Power Consumption (EPC)	kWh per capita	IEA
Renewable Policies (REP)	US Dollar	IEA
R&D	Percentage %	World Bank
Carbon Emission (CO₂)	Tons per capita	World Bank

GDP per capita is calculated using a constant 2017 international US dollar as an indicator of economic development. The economic variable of GDP per capita is commonly utilized in the literature to examine its impact on RE deployment (Dogan et al. 2021; Nyiwul 2017; Lucas et al. 2016; Wu and Broadstock, 2015). The impact of economic development on the deployment of RE has been studied in many ways. Marques and Fuinhas (2011), for example, discover that GPD has a detrimental impact on RE deployment. Bamati and Raoofi (2020) argue, on the other hand, that GDP plays a role in RE deployment.

Energy consumption is the percentage of total energy use in industry. According to Rahman et al. (2019), there is a strong and positive association between RE usage and energy consumption. Qi et al. (2014) discover that economic growth resulted in high-energy demand, which has made it easier to accept renewable electricity.

Electricity consumption is measured in megawatt hours (MWh) per capita. Bednarczyk et al. (2021) emphasize that non-household electricity consumption has a negative influence on RE resources in gross final energy consumption. In other words, the increase in non-household electricity usage has an impact on the decline in RE.

Renewable policies, as well as solar and wind energy feed-in tariff data, are employed. In this policy, the feed-in tariff is calculated in US dollars. Many studies have found that policies have a positive impact on RE development and deployment (Bourcet 2020; Liu et al. 2019; Kilinc-Ata, 2016; Shrimali and Kniefel, 2011; Adelaja et al. 2010). Adedoyin et al. (2020) also stress the importance of policy in the development of renewable technology.

Clean energy R&D is expected to assist RE deployment as a fraction of overall government R&D for RE energy sources. According to Adedoyin et al. (2020), there is a bidirectional relationship between R&D and RE, hence investment R&D should focus more on long-term success in sustainable energy sources. Wang et al. (2020) indicate that R&D and policy considerations both contribute to the promotion of RE.

As an environmental variable, carbon emissions are measured in tons per capita. CO2 emissions, according to Nyiwul (2017), have resulted in an increase in RE development. According to Hao and Shao (2021), nations with lower carbon-intensive economies use higher proportions of RE in their total energy consumption. Similarly, Sharif et al. (2021) demonstrate that reducing CO2 emissions encourages the usage of RE sources. Zafar et al. (2019) highlight that renewable RE helps to improve environmental quality.

3.3. Model

A quantitative method was used to test the hypotheses from the conceptual framework. Hypotheses were tested using empirical models to answer the research issue. From 2000 to 2020, the method would use panel data analysis to compile a country-level panel data set for OECD and BRICS countries. The RE capacity is modeled using explanatory variables as a function.

(1)

$${REC}_{i,t}=f({GDP}_{i,t},{EC}_{i,t},{EPC}_{i,t},{ REP}_{i,t}, {R\&D}_{i,t},{CO2}_{i,t})$$

where i = 1, …, and t = 2000, …, 2020. In Eq. 1 relates RE capacity (${REC}_{i,t}),$GDP per capita (${GDP}_{i,t})$, energy consumption (${EC}_{i,t})$, electric power consumption (${EPC}_{i,t})$, RE policies (${ REP}_{i,t})$, research and development (${R\&D}_{i,t})$, and carbon emission (${CO2}_{i,t})$. Eq. 1 depicts the relationship between RE capacity and the explanatory variables as a linear relationship. The RE capacity model's panel data fixed effects regression could be represented as Eq. (2).

(2)

$${Y}_{i,t}={\beta }_{0}+{\beta }_{1}{GDP}_{i,t}+{\beta }_{2}{EC}_{i,t}+{\beta }_{3}{EPC}_{i,t}+{\beta }_{4}{REP}_{i,t}+{\beta }_{5}{R\&D}_{i,t}+{\beta }_{6}{CO2}_{i,t}+{\epsilon }_{i,t}$$

The installed cumulative RE capacity is represented by ${\text{Y}}_{\text{i},\text{t}}$ the coefficient of explanatory factors is β, the country fixed effect index is ${\text{u}}_{\text{i}},$and the random error term is ${{\omega }}_{\text{i}\text{t}}$ applied to each country at each year. Because Shrimali and Kneifel (2011) propose that a country's fixed effects are critical for controlling for unobserved heterogeneity to impact RE investment, the article uses a fixed effects model. The dynamic features of the variables are avoided by transforming all data into a natural logarithmic form. The equation's logarithmic form is shown below.

(3)

$${Y}_{i,t}={\beta }_{0}+{\beta }_{1}{InGDP}_{i,t}+{\beta }_{2}{InEC}_{i,t}+{\beta }_{3}{InEPC}_{i,t}+{\beta }_{4}{InREP}_{i,t}+{\beta }_{5}In{R\&D}_{i,t}+{\beta }_{6}In{CO2}_{i,t}+{\epsilon }_{i,t}$$

Besides the fixed effect model, the random effect method is used to check whether some differences between the variables affect the renewable capacity because this method includes variables that do not change over time (Olanrewaju et al. 2019). Menegaki (2011) also highlights random effects models with homogeneity assumptions. The random effect model is expressed using the following equation:

(4)

$${Y}_{i,t}=\alpha +{\beta }_{1}{InGDP}_{i,t}+{\beta }_{2}{InEC}_{i,t}+{\beta }_{3}{InEPC}_{i,t}+{\beta }_{4}{InREP}_{i,t}+{\beta }_{5}In{R\&D}_{i,t}+{\beta }_{6}In{CO2}_{i,t}+{\mu }_{i,t}+{\epsilon }_{i,t}$$

i denotes the subscript of an entity (i = 1,…10), t shows time (t = 2000,….2020), $\alpha$ is an unknown intercept and $\beta$is a coefficient of explanatory variables, ${\epsilon }_{i,t}$ is the error term, and ${\mu }_{i,t}$ is the random heterogeneity specific to the observation. The Hausman Test is then used to determine whether the unique errors are linked to regression. The findings of the Hausman test are used to determine which model is more appropriate (Hausman, 1978). The Hausman test is written like this:

(5)

$$p=\left({\beta }_{RE}-{\beta }_{FE}\right) x (\sum FE-\sum RE {)}^{-1}x \left({\beta }_{RE}-{\beta }_{FE}\right)$$

${\beta }_{RE}$ denotes coefficient estimates from random effect, while ${\beta }_{FE}$ is coefficient estimates from fixed effects.$\sum FE$ shows the co-variance matrix of a fixed effect and $\sum RE$ is the co-variance matrix of a random effect. The H0 hypothesis is accepted and the random model is selected if the Hausman test result is significant (p > 0.05). The H1 hypothesis, indicating that the fixed model is adequate, is accepted if the Hausman test results are statistically insignificant (p 0.05) (Hausman and Taylor, 1981).

Finally, the dynamic GMM (Generalized Moments Method) model developed by Arrellano and Bond (1991) is used. GMM estimators, which do not require many assumptions, start from the moment relations that exist in the model. There are descriptive data for all factors on each measure of installed cumulative RE capacity, as well as a correlation matrix, in the appendix section. The correlation coefficient indicates that the explanatory variables are highly multicollinearity.

All variables were verified for stationarity using the Levin, Lin, and Chu (LLC) (Levin et al. 2002), Im, Pesaran, Shin (IPS) (Im et al. 2003), ADF-Fisher Chi square (ADF) (Dickey and Fuller, 1979), and PP-Fisher Chi square (PP) (Phillips and Perron, 1988) techniques. At both the I(0) and I(1) levels, Table 2 displays the unit root results for all of the data.

Table 2

Panel Unit Root Results
Tests	Results	Variables
		I (0) Unit Root
		REC	GDP		Energy Con.		Electricity Power Con.	RE Policies	R&D	CO₂
LLC	Statistics	25.0561	1.07355		-5.95230		-2.24346	-2.22841	-8.50488	3.95355
LLC	Prob.	1.0000	0.1415		0.0000		0.0124	0.0129	0.0000	1.0000
IPS	Statistics	32.7107	1.16024		-2.75445		-2.28630	-2.15436	-4.35891	9.21060
IPS	Prob.	1.0000	0.8770		0.0029		0.0111	0.0156	0.0000	1.0000
ADF	Statistics	2.38740	65.5851		120.205		339.717	84.3336	134.732	22.4472
ADF	Prob.	1.0000	0.8409		0.0015		0.0000	0.0312	0.0000	1.0000
PP	Statistics	2.62659	84.5302		128.863		345.394	167.810	130.504	20.7120
PP	Prob.	1.0000	0.2871		0.0003		0.0000	0.0000	0.0000	1.0000
Tests	Results	I (1) Unit Root
LLC	Statistics	-1.70975		-3.12788		-23.5704	-26.0043	-21.8347	-20.7843	-15.0819
LLC	Prob.	0.0437		0.0000		0.0000	0.0000	0.0000	0.0000	0.0000
IPS	Statistics	-2.12585		-6.53014		-20.0169	-22.1564	-16.6522	-22.0332	-16.4365
IPS	Prob.	0.0168		0.0000		0.0000	0.0000	0.0000	0.0000	0.0000
ADF	Statistics	132.182		173.605		469.457	628.988	423.198	493.745	391.733
ADF	Prob.	0.0001		0.0000		0.0000	0.0000	0.0000	0.0000	0.0000
PP	Statistics	128.754		157.807		878.791	1153.25	540.303	843.394	413.461
PP	Prob.	0.0003		0.0000		0.0000	0.0000	0.0000	0.0000	0.0000
* p value < the significance level of 0.1
**p value < the significance level of 0.05 and 0.1
*** p value < the significance level of 0.01, 0.05, 0.1

As seen in Table 2, all variables are stationary at the first level, and to avoid spurious regression, a first-order unit root test was used in the study. As a result, for the investigated countries, all exogenous variables have become stationary, and a regression analysis is conceivable. Table 3 summarizes the results of numerous estimations of the fixed-effect and random-effect models (equations 2 and 4).

Table 3

Estimation Results from Fixed and Random Effects Models
Dependent variable	REC (Installed cumulative RE capacity)
Explanatory variables	Fixed Effects Model Estimation¹			Random Effects Model Estimation
Explanatory variables	Coefficient	Standard Error	p-value	Coefficient		Standard Error	p-value
GDP per capita	-0.019633	0.044664	0.6604	-0.055385		0.132484	0.6760
Energy consumption	0.074423	0.027066	0.0012	-0.143386		0.067211	0.0398
Electricity Power Consumption	-0.004263	0.001676	0.0519	-0.032617		0.013348	0.0046
Renewable Policies	-0.000935	0.003275	0.7754	-0.033690		0.008305	0.0008
R&D	0.007003	0.003403	0.0479	0.020400		0.007284	0.0052
Carbon Emission	-0.076127	0.030003	0.0114	-0.167991		0.076408	0.0338
R-squared		0.813648			0.693023
Probability (F-Statistic)		0.0000000			.129078
Notes: Standard errors are corrected for country/state-level serial correlation. The variance inflation factor (VIF) was used to check for colinearity between independent variables.
* p value < the significance level of 0.1
**p value < the significance level of 0.05 and 0.1
*** p value < the significance level of 0.01, 0.05, 0.1
¹ In addition, the study performed weighted least squares (WLS) statistical analysis to obtain more robust results. The WLS method overcomes the problems of autocorrelation and varying variance from panel data (Javeed et al., 2021). The results of the robustness test confirm the previous panel regression results. The robustness test panel enables data autocorrelation and varying variance to be overcome (Lu and White, 2014; Prokhorov and Schmidt, 2009).

The findings of the fixed effects panel data regression are shown in Table 3, which identify a few variables as significant drivers of RE investment. The R square value of 0.813 suggests a satisfactory fit for the fixed effects model, according to the panel regression findings for the fixed effects model. As a result, all exogenous variables combined could account for about 81 percent of the variation in REC. Even though all coefficients are nonzero, an F-statistic probability of 0.000 suggests that the overall panel regressions are significant (F < 0.05). A positive link between REC and energy usage and R&D was discovered using panel data fixed effects regression. In other words, a 1% increase in energy consumption and R&D spending results in a 0.007% rise in REC growth. This finding is supported by recent research by Khezri et al. (2021) and Wu et al (2020). The findings suggest that R&D spending has a favorable impact on RE sources such as solar, wind, bioenergy, and geothermal.

On the other hand, GDP, electricity power consumption, RE policies, and CO₂ are inversely correlated to REC. The negative relationship between CO₂ and REC was an astonishing outcome. However, recent studies by Ponce and Khan (2021) find that RE and energy efficiency is negatively related to CO₂ emissions. Similarly, Zaidi et al. (2018) show that REC has an insignificant effect on CO₂ emissions in Pakistan. Another conclusion drawn from the study is that there is no statistically significant link between REC and income or renewable policy. The findings obtained from the analysis are also supported by the literature. According to Hughes (2010), FITs fail in the UK because they prevent local promotion of RE capacity. Likewise, Delmas et al. (2007) established that the quota (RPS) policy system had no effect on RE production.

These findings mean that the current RE policies are insufficient to promote investment in RE. Furthermore, the world's three largest economies (the United States, China, and India) declared net-zero carbon goals, and the United Kingdom hosted the UN Climate Change Conference (UNCCC) of the Parties (COP-26) in October-November 2021, which resulted in new important agreements for UNFCCC implementation. However, it has been accepted that the steps planned will not be sufficient to prevent irreversible climate change. Governments should work harder in partnership with businesses, science, and civil society. Despite the fact that this result shows that countries should devote more resources (policy and R&D) to RE investments in order to attain net-zero ambitions (UNCC, 2022).

The R-square value was 0.69, and the Prob. (F-statistic) was 0.129, according to the random effects model result in Table 3. While REC and R&D have a positive link; GDP, energy consumption, electricity power consumption, RE policies, and CO₂ have a negative relationship.

In the regression model that looked at REC in OECD and BRICS nations between 2000 and 2020, the Hausman test was utilized for exogenous variables. Table 4 shows the results of the Hausman test.

Table 4

Hausman Test Results for Random Effects
Test Summary	Chi-Square Statistic	Chi-Square	Probability
Cross-Section Random	7.356853	6	0.0348

As seen in Table 4, H0 is rejected because the random effects correlated Hausman test result is p > 0.05, the fixed effect model is more appropriate to estimate the net effect of exogenous variables on REC, and the alternative H1 hypothesis is accepted. In this case, the fixed effect model's R2 score indicates that it is suitable for the GMM model. Table 5 shows the findings of the GMM model.

Table 5
**Estimation Results from GMM Model**
Dependent variable	REC (Installed cumulative RE capacity)
Explanatory variables	Coefficient	Standard Error	p-value
GDP per capita	0.067107*	0.009315	0.0000
Energy consumption	-0.036121*	0.004213	0.0000
Electricity Power Consumption	-0.026330*	0.011304	0.0201
Renewable Policies	0.026005*	0.001003	0.0000
R&D	0.023241*	0.000571	0.0000
Carbon Emission	-0.236776*	0.018699	0.0000
* p < 0.05

According to the panel regression results for the GMM model, all variables are statistically significant (p < 0.05), indicating that it is appropriate. The findings are consistent with the fixed effects model's results: there are positive correlations between RE capacity, GDP per capita, RE policies, and R&D. RE policies and R&D spending cause REC to expand by 0.06 percent, 0.02 percent, and 0.02 percent, respectively, with a 1% growth in GDP per capita. Recent research has discovered similar results (Gershon and Emekalam, 2021; Tudor and Sova, 2021). According to Sadorsky (2009), for G7 countries, a 1% increase in GDP per capita boosts RE consumption by 3.5 percent. Recent research has discovered similar results (Gershon and Emekalam, 2021; Tudor and Sova, 2021). According to Sadorsky (2009), for G7 countries, a 1% increase in GDP per capita boosts RE consumption by 3.5 percent. Similarly, Baye et al. (2021) find that a 1% rise in real GDP per capita results in a 0.32 percent increase in REC in African countries. Omri and Nguyen (2014) find that economic development is the key driver for REC growth using a two-stage GMM panel estimate regression technique for 64 nations. In high-income, middle-income, and low-income nations, a 1% rise in GDP per capita improves the REC by 0.199 percent, 0.169 percent, and 0.149 percent, respectively.

Conversely, there are negative correlations between REC, energy use, electricity consumption, and CO2. REC drops by 0.03 percent, 0.02 percent, and 0.23 percent, respectively, when energy consumption, electricity consumption, and CO2 consumption all rise by 1%. These findings are in line with some previous research. For example, Baye et al. (2021) show that CO2 emissions have a negative impact on REC per capita in Sub-Saharan African countries, and they attribute this to energy inefficiency and a lack of environmental awareness.

The impact of numerous variables on RE investment is demonstrated in this paper. This research aims to develop a conceptual framework for quantitatively assessing RE investment from the perspective of investors, and then a quantitative test provides useful insight into the investor's role in promoting the growth of RE technology. This study provides a systematic and quantitative method for comparing the implications of variables in OECD and BRICS countries, allowing for easy comparisons.

A statistically significant association was discovered between RE and energy consumption, electric power consumption, R&D and CO2 according to the fixed effect model results. However, although energy consumption and R&D and REC have a statistically positive link, electricity consumption, RE policies, and CO2 have a statistically negative relationship. For the OECD and BRICS countries, the GMM approach revealed a statistically significant link between all explanatory variables and RE capacity. It was concluded that while RE policies, R&D and economic growth promote RE investments, CO₂, energy and electricity consumption negatively affect RE investments. As a result of rising energy and electricity consumption, OECD and BRICS countries choose fossil-based energy sources over RE sources.

In light of the findings, it is vital to design a national policy on economic growth, research and development, and RE policies that are effective in RE investments. It illustrates that in order to grow RE investments, both the OECD and the BRICS countries must focus more on their policies. Increasing the share of R&D spending in the RE sector, in particular, will stimulate current technology while simultaneously reducing CO2. In addition, the OECD and the BRICS countries should collaborate to develop energy-efficient and efficient projects, as well as to support environmental and sustainable activities. As a result, it has the potential to attract green investments from both the public and private sectors. That is, governments should devote greater resources to increasing public understanding of ecofriendly topics. According to the findings, the OECD and BRICS countries should establish policies to encourage more RE investment in order to meet their net zero carbon commitments, and that increased country cohesion is advocated. Green development at the national level will be enabled by sustainable development, which is founded on the principles of ensuring energy security, safeguarding the environment from the detrimental effects of fossil fuels, and combating climate change. The study's findings indicate that harmonization of national and international energy standards is crucial.

Another significant policy recommendation from the study is that consumers should be able to access RE due to technological constraints. Although the Russian Federation of the BRICS countries has significant RE potential, the volume of commercial RE consumption other than hydro, such as wind and solar, is insufficient to attract the necessary investment. As a result, authorities must foster the right investment climate to encourage the commercialization of RE. Furthermore, RE strategies should emphasize education as a means of increasing RE consumption. The training exercises should emphasize RE's potential contribution to sustainable development and a clean environment, among other energy sources.

The paper only covers OECD and BRICS countries. Another point to consider is that the study is based on a few variables that have been classified. Future scholars may look at additional countries and utilize different tests and methods than those used in this work. In addition, the data sample size could be increased and transferred to other variables in the study.

Competing interests: The authors declare no competing interests.

Authors Contributions: Nurcan Kilinc-Ata performed material preparation, data collection and analysis. Ilya A. Dolmatov reviewed and supervised the paper.

Funding: There is no any funding for this paper.

Data availability: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate: Not Applicable.

Consent for publication: Not Applicable.

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Table A1: Variable Summary Statistics

	REC (Installed cumulative RE capacity)	Carbon Emission	Energy Consumption	Electricity Power Consumption	GDP per capita	R&D	Renewable Policies
Observations	798	798	798	798	798	798	798
Mean	3.900375	0.884710	1.420833	3.802345	4.498922	0.067673	-0.782985
Median	3.995414	0.919109	1.433140	3.828870	4.577434	0.261611	-1.000000
Maximum	5.951570	1.421597	1.749597	4.813041	5.060279	1.246940	-0.003795
Minimum	0.845098	-0.034657	0.975390	1.397940	3.411383	-1.524778	-2.187087
Std. Dev.	0.867208	0.238532	0.132296	0.333615	0.288492	0.582450	0.353748
Skewness	-0.772619	-0.912992	-0.118781	-0.686563	-1.242475	-0.838361	0.316284
Kurtosis	3.907878	4.874968	3.243383	7.701421	4.818040	2.594141	3.235723
Jarque-Bera	106.7991	227.7534	3.846062	797.6287	315.2182	98.95599	15.15230
Probability	0.000000	0.000000	0.146163	0.000000	0.000000	0.000000	0.000513
Sum	3112.500	705.9985	1133.825	3034.271	3590.140	54.00327	-624.8222
Sum Sq. Dev.	599.3832	45.34727	13.94937	88.70527	66.33256	270.3806	99.73466

Table A2: Variable Correlation

	REC	GDP per capita	Energy consumption	Electricity Power Consumption	Renewable Policies	R&D	Carbon Emission
REC (Installed cumulative RE capacity)	1.00	-0.11	0.31	-0.03	0.05	-0.20	-0.29
GDP per capita	-0.11	1.00	-0.37	0.59	0.17	0.48	0.57
Energy consumption	0.31	-0.37	1.00	0.03	-0.17	-0.29	-0.21
Electricity Power Consumption	-0.03	0.59	0.03	1.00	-0.01	0.36	0.62
Renewable Policies	0.05	0.17	-0.17	-0.01	1.00	0.18	0.02
R&D	-0.20	0.48	-0.29	0.36	0.18	1.00	0.42
Carbon Emission	-0.29	0.57	-0.21	0.62	0.02	0.42	1.00

Notes: Table A2 shows the correlation coefficient for all variables in the current paper and the variables are summarized in Table A1.

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Empirical Evidence from OECD and BRICS Countries on the Impact of Investor Behavior on Renewable Energy Deployment

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1. Introduction

2. Literature Review

3. Data And Methodology

3.1. Conceptual Framework Analysis from Investors’ Perspectives

3.2. Data and Variables Selection

3.3. Model

4. Results And Discussion

5. Conclusion And Policy Implications

Declarations

References

Appendix

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