Innovation-Led FDI Sustainability: Clarifying the Nexus between Financial Innovation, Technological Innovation, Environmental Innovation, and FDI in the BRIC Nations

Abstract

Spatial adequacy and capital formation are impactful macro factors in achieving economic sustainability; therefore, offering a conducive ambiance for foreign investors entices them to the technology and capital mobilization in the host economy. The purpose of the study is to highlight the nexus of the innovation-led FDI in BRIC nations from 1990–2019. The study has implemented several econometric techniques to establish the empirical nexus, including a unit root test with a structural break, a combined cointegration test, an augmented autoregressive distributed lagged, a nonlinear autoregressive distributed lagged and the Fourier Toda–Yamamoto causality test. The structural break test divulged one break year in the data set, and the study incorporated the structural break effects in line with the explained variable. The long run association between the explanatory explained and the control variables has been unveiled with the test statistics of the combined cointegration. Furthermore, the long run cointegration in the empirical equation has been found in the linear and nonlinear assessment. In terms of the symmetric investigation, the coefficient of innovation, that is, TI, FI, and EI on FDI, were revealed to be positive and statistically significant at a 1% level, suggesting the innovation culture boosts the inflows of the FDI in the economy, both in the long run and short run. Furthermore, the asymmetric association has been confirmed by implementing the standard Wald test with the null of symmetry in the long and short runs. Inferring to the asymmetric coefficients, it is apparent that the positive and negative shocks of TI, FI and EI have established a positive tie to FDI, which is significant at a 1% level. According to the elasticities of the asymmetric shocks, the positive innovation disclosed a more prominent impact than the negative innovation on the FDI inflows. Thus this study advocated for ensuring a conducive innovation environment by mobilizing economic resources. Finally, the causality test documented the feedback hypothesis to explain the causal association between technological innovation and FDI and environmental innovation and FDI.

1. Introduction

Due to foreign direct investment (FDI) advantages, emerging economies, transitional countries, and developing countries have all liberalized their FDI policies and implemented best practices to attract investors [1]. The host nation may benefit from FDI in several ways, including technology spillovers, support with human capital creation, a better business environment, a higher contribution to international trade integration, and a boost to firm growth. Beyond the apparent financial benefits [2,3], FDI may also improve the host country’s social and environmental conditions by promoting “cleaner” technologies and more socially conscious corporate practices. FDI is an integral aspect of globalization and the global economy because it fosters the production of new goods and services, it improves existing ones, and expands economies worldwide [4,5]. Moreover, it allows low-income countries to make up for their lack of resources, such as money for development, trade, investment, and taxes [6,7]. FDI ignited economic development through human capital development [8,9], technological advancement [10,11], domestic capital formation [12,13,14,15,16], trade liberalization, and financial efficiency [17,18]. FDI supports nations in transforming agro-based economies into modern economies through industrialization. FD, additionally, expands the economic cash flow accumulation by capitalizing the investment opportunities, economic resources optimization and mitigation of trade imbalance [1]. Furthermore, FDI has a disproportionately large direct effect on both the potential benefits of FDI and the pace of economic growth [9]. Foreign investment is useful for emerging nations because it hastens the pace of agricultural modernization and rejuvenates it. The nation, as a whole, benefits via more trade and a higher ability to transform raw resources into finished goods.

Referring to the determinants of the FDI inflows, in the literature, a growing number of researchers have implemented and revealed a list of micro and macro fundamentals that are responsible for encouraging foreign investors to mobilize foreign capital [6,19]. For example, Mahbub, et al. [20] highlight that institutional quality, clean energy, and access to financial services prompt the inflows of FDI in Bangladesh. Additionally, environmental regulation and access to established industrial facilities entice foreign investors for capital transfer, in the long run. Daude and Stein [21] examine the institutional quality’s impact on FDI and expose a positive tie between them. The study suggests that governmental effectiveness and the protection of investor rights motivate foreign capital flows.

In this study, we considered the technological innovation (hereafter TI), financial innovation (hereafter FI), and environmental innovation (hereafter EI) in the equation of FDI. Increasing the capacity of technological developments is essential to the acquisition of renewable energy sources, the improvement of energy efficiency, and the decrease of carbon dioxide emissions. All of these factors can contribute to environmental sustainability, which eventually leads to the persistence in inflows of FDI [22,23]. Financial efficiency and efficient intermediation picturized the overall financial system performance, indicating access to financial institutions in the host economy where foreign investors seem to mobilize capital and technology; they prefer efficient financial channels with effective participation from FIs. The absorbing capacity in financial institutions is characterized by the firms’ adaption and diffusion of innovative financial products and services. Financial innovation in the financial system expands the financial institutions’ capacity and scope regarding the service offering in the economy [24,25]. Therefore, a prompt and efficient mode encourages foreign investors to make favorable decisions regarding their capital investment and technology transfer.

Our contribution, first, regarding the nexus between the FDI-led technological innovations, the existing literature posted that a growing number of studies have been executed in exploring the effects of FDI on technological innovation [3,8,10,26]. It is assumed that the host country’s capacity for technological innovation may benefit greatly from the technology spillover effect brought about by foreign direct investment [27,28]. Nonetheless, very few studies have been initiated in the literature exploring the nexus of technological innovation-led FDI inflows in the economy [29]. The country’s capability to adopt the technology transfer through FDI critically depends on the economic environment in fostering technological innovation, and such conducive ambiance plays a beneficiary role in enticing the inflows of FDI. This argument supports the hypothesis that emerging nations would exhibit a leapfrogging pattern of technological development. To continue their march toward progress, emerging economies are conscious that they must bridge the technology divide and increase their capacity for innovation. Increasing resilience in the face of unique risks provided by the modern business environment is closely connected with the increased ability to innovate and exploit new technology [30]. Second, referring to the existing literature focusing on the impact of financial innovation on the aggregated economy, a growing number of studies have documented a positive linkage with economic growth [31,32], financial development [33], financial inclusion [25], trade openness [34,35,36]. Moreover, the selected macro fundamentals, such as financial development, trade openness, and financial inclusion, have significant effects on the behavior of FDI inflows in the economy [37,38,39,40]. The study by Qamruzzaman and Wei postulated that access to financial services from financial institutions acts as a catalyst in thriving the trend of the FDI inflows in the economy. Furthermore, the study suggested that financial innovation, adaptation, and diffusion in the financial system established a conducive ambiance in progressing the easy access to FIs to enjoy financial benefits. So it is implied that financial innovation indirectly affects FDI through the financial channel. However, as far as the present literature is concerned, the direct nexus between financial innovation and FDI has yet to be highlighted, thus managing the existing literature gap with the fresh evidence, the study has investigated the effects of financial innovation on the FDI inflows.

The purpose of the study is to evaluate the effects of the innovations, which is categorized by the technological (TI), financial (FI) and environmental (EI) innovations, on the FDI inflows in BRIC nations for the period 1990–2019.

In documenting the association and the explanatory variable’s coefficients magnitudes on FDI, we implemented several econometrical techniques, including the Bayer–Hanck combined cointegration test, the augmented ARDL, the nonlinear ARDL and the Fourier TY casualty test. In terms of the stationary test, the test statistics have revealed that all of the variables are stationary after the first difference. Moreover, the structural break test divulged one break year in the data set, and the study incorporated the structural break effects in line with the explained variable. The long run association between the explanatory explained and control variables has been unveiled with the test statistics of the combined cointegration. Furthermore, the long run cointegration in the empirical equation has been found in the linear and nonlinear assessments. In terms of the symmetric investigation, the coefficient of innovation, that is, TI, FI, and EI on FDI, were revealed to be positive and statistically significant at a 1% level, suggesting that the innovation culture in the economic boost of the inflows of FDI, both in the long run and short run. Furthermore, the asymmetric association was confirmed by implementing the standard Wald test with the null symmetry hypothesis in the long and short runs. Referring to the asymmetric coefficients, it is apparent that the positive and negative shocks of TI, FI, and EI positively tie to FDI, which is significant at a 1% level. According to the elasticities of the asymmetric shocks, the positive innovation disclosed a more prominent impact than the negative innovation. Thus it is advocated to ensure a conducive innovation environment by mobilizing economic resources.

The rest of the section is as follows. The related literature survey and hypothesis construction are based on the literature reported in Section 2. Variable proxies, theoretical development and estimation strategies are exhibited in Section 3. The empirical model estimation by employing econometrical techniques and their interpretation available in Section 4. Discussion of the study findings reported in Section 6. Conclusions and policy suggestions in Section 7.

2. Literature Review and Hypothesis Development

2.1. Effects of Technological Innovation

In the case of China, Cheung and Lin [29] assessed the impact of FDI on innovation for 1955–2000 by employing the pooled regression. The study established that the inflows of FDI positively influenced the measures of TI. Furthermore, the study revealed the spillover effects on technological innovation through the channel of human capital development, domestic trade internationalization and capacity building in the economy. Furthermore, foreign direct investment increases as a country’s capacity to absorb new technology and to innovate expands. At the same time, good company governance procedures and cultural aspects, such as power distance and individualism, attract inbound foreign direct investment (FDI). The interaction research sheds additional light on the notion that the quality of a country’s governance directly impacts a country’s ability to attract foreign direct investment via technology innovation, corporate governance, and cultural norms (FDI). As a determinant of technological innovation, Bibi, Khan, Sumaira, Zhang, Farah, Le thi Kim and Zhangyan [22] investigated the role of energy accessibility, FDI, and CO₂ emissions in promoting TI in the economy, by considering a panel of 139 countries for 1980–2019, through the application of the PQR. According to the coefficient of FDI, there is an adverse association between TI and FDI, indicating that the FDI inflows discourage and decrease the patent application by the non-residence in the lower quantile but at a higher quantile, it is exposed as statistically insignificant.

Regarding TI proxied by research and development, foreign direct investment (FDI) hurts technological innovation and international trade. Moreover, financial development and energy consumption have a positive and statistically significant impact on TI. Quantile-wise, financial growth has a negative significant and insignificant negative influence, whereas the highest quantile yields a positive coefficient, indicating that it has contributed to a rise in technological advancements, as proxied by R&D spending. This is because the highest quantile is the one that yields the positive coefficient [41,42].

A recent study by Abid, et al. [43] stated that, globally, foreign direct investment, financial development, and technological innovation have a statistically significant long run negative association with CO₂, but the study could not identify any relationship between technological innovation and FDI. However, the G8 countries need quality foreign direct investment to develop industries, technology, and finance. Hoang, et al. [10] performed a firm aspect study where the authors mentioned that FDI still limits Vietnamese enterprises’ technological innovation. Vietnamese enterprises’ technology innovation is influenced negatively and positively by FDI, and breakthrough solutions are available to promote both outcomes. Wu, et al. [44] confirmed that international high-tech exports and inward foreign direct investment significantly contribute to the emerging countries’ ability to produce cutting-edge technologies, but not the leading innovator countries. In the case of Brazil, based on Costa and de Queiroz [45], foreign and domestic companies have accumulated substantial competencies for using existing technologies but only shallow competencies for developing local technologies. Although foreign affiliates score higher than their local counterparts in more complex capabilities, this confirms their centrality to the Brazilian learning system. The study also suggested that a strategic foreign direct investment (FDI) policy that strengthens and deepens local TCs should emerge from this. Wang, et al. [46] conducted a study, based on OECD countries; technological innovation has a significant positive impact on GTFP when FDI is below the threshold value and a slightly significant positive impact when FDI exceeds the threshold value. However, Loukil [47] pointed out that under certain thresholds of technological development, FDI hurts innovation, while above those thresholds, FDI has a significant positive impact. Although it has been shown, via empirical research, that technology is a magnet for FDI, there is no evidence to suggest that the degree of preparation for technology affects the amount of FDI brought in. As a result, there is sufficient justification for emphasizing the capacity of developing economies to adjust to and incorporate new technologies. Furthermore, the power to use technology may conceivably boost the capacity to innovate in developing countries, increasing the flow of foreign direct investment (FDI) [48,49].

H1. 

Technological innovation positively encourages the inflows of FDI.

2.2. Effect of Financial Innovation

Innovation is essential for economic growth because it fuels the creative destruction upon which the theory is based. However, without a solid economic system, it is impossible to dedicate resources successfully to creative innovation. When the costs of learning about and dealing with financial goods, markets, and intermediaries have dropped, financial progress has been accomplished. Therefore, credit can be spread effectively due to the sophistication of the financial markets, which enables it to be used where it will have the most impact. Better resource management may be required to optimize the economic advantages of research and development (R&D) expenditures. Using the resources made accessible by external finance, businesses may increase their market competitiveness and meet the growing need for innovation [8,50,51,52]. For instance, Aghion, et al. [53] explored the role of financial innovation in promoting financial development; the study found that the adaptation and diffusion of financial and technological innovations in the financial system, enhance efficiency and process improvement that are essential for financial development and other macro fundamentals. Moreover, the literature provided by Blackburn and Hung [54], Buera, et al. [55], and Dabla-Norris, et al. [56], the growth of financial innovation in the financial system improves financial efficiency by lowering the associated cost and ensuring equitable financial development. Based on the empirical findings of Li, et al. [57], the new theoretical trade models confirm that, in contrast to the traditional theory of trade, which considered foreign direct investment and trade as substitutes, trade and FDI complement each other and contribute to an increase in insurance services trade. According to Nistor [58], BRICS economies seem to benefit from FDI because it contributes to their development.

One of the key engines driving economic growth has been a financially stable and effectively performing banking sector [59]. A sector, such as this one, helps to build local savings, which leads to the successful construction of local corporate investments. Consequently, the financial sector has significantly contributed to rising earnings and employment development. Tamara, et al. [60] discovered a complicated relationship between regulation and innovation. This is because regulation is the major engine of financial innovation, yet innovation is often at the core of the demand for new laws. More specifically, technological advances altering financial products and services and manufacturing processes are the key drivers of innovation in the financial sector [61]. Furthermore, financial innovation and promotion in the financial sector gain a competitive edge and foster financial intermediation and efficiency at large [41,48,52].

In terms of financial innovation effects on the economy, a growing number of studies have documented the important role of FI in bolstering economic progress, through financial development, human capital development, trade augmentation, environmental protection, –among others [33,62,63,64]. For example, for BRIC nations, Chishti and Sinha [65] conceptualize the influences of TI and FI on environmental sustainability and document the catalyst role in improving the environmental quality through the reduction of carbon emissions. Moreover, the asymmetric assessment established positive (negative) shocks in innovations that were positively tied with CO₂, suggesting that negative innovations in FI and TI decompose the environmental degradation. On the ground of financial innovation promotion, several economic forces have been documented in an empirical investigation, such as financial development [50],

H2. 

Financial innovation positively encourages the inflows of FDI.

2.3. Effects of Environmental Innovation

In recent years, environmental hazards have become a major focus of policy debates in developing economies. This is because more people are becoming aware of the risks they pose. However, lacking the technological know-how in these areas makes it harder for businesses to develop new ways to help the environment. So, thanks to foreign investment, most developing countries now have better access to information from other places. This is a big part of their efforts to improve the environment. Domestic businesses that are not as efficient, are forced to use new ways of making things better for the environment if they want to stay in business [61]. Some researchers have shown anxiety that foreign investment could hurt the environment and make it harder to develop these countries’ economies sustainably [8,35,41,66,67]. Businesses that pollute can go around their country’s strict environmental laws by outsourcing the dirty parts of their production process or moving to a country with a less strict economy, such as a developing nation, some of which are resource-rich. How much knowledge is transferred to the domestic economy depends on the labor market state, the number of qualified people available, how easy it is for people to learn new skills, and how willing the government is to participate in R&D [29]. To reduce the negative effects of industrial production on the natural world, innovators have turned to innovative or enhanced production methods, technologies, and products; this process is known as “environmental innovation [16,52,68,69,70,71]

As the developing world’s population has grown more aware of the dangers posed by pollution, talks on government remedies to this issue have been the major focus of policy debates. However, the relevant economies lack the technology resources required to support businesses innovating in ways that lessen their environmental impact. Due to the influx of foreign investment, most developing nations’ environmental innovation projects depend significantly on information from outside of the country. As a result, less efficient domestic enterprises are forced to adopt new environmentally friendly manufacturing procedures. Awodumi [72] assessed the role of FDI in environmental innovation for African economics for 1990–2019, by implementing the seemingly uncorrelated regression. The study highlights that the emission of GHG has been substantially reduced, and environmental quality has meaningfully improved with the inflows of FDI in the economy. Additionally, they advocated the economic resources efficient reallocation through FDI.

An empirical study by Awodumi [72] found that the FDI inflow improves resource efficiency outcomes through environmental innovation practices. According to Qin, et al. [73], FDI substantially encourages green innovation in Chinese cities, which is nonlinear. Namely, such effects only make sense when the absorptive capacity is above the threshold values. Based on China, Song, et al. [74] conducted an empirical study and concluded that to foster domestic innovation and technological advancement, China must not only provide the necessary platform for the FDI technology spillover, but must also take active and strong measures regarding the FDI technology spillover inflows to foster green innovation that is widely used in China without further harming the environment, such as infrastructure construction. The study arrived at this conclusion after realizing that, to promote local innovation and technical growth, China must offer the required platform for foreign direct investment.

Additionally, Feng, et al. [75] postulated that environmental restrictions and the relationship between environmental regulations and foreign direct investment (FDI) have a significant negative impact on the effectiveness of environmentally friendly innovation, but FDI from other countries has a discernibly beneficial impact. Although environmental regulation and the relationship between environmental regulation and foreign direct investment (FDI) have significant negative effects on the efficiency of green innovation for patent-intensive manufacturing, the inbound FDI has significant positive effects when considering the wide range of industries. Furthermore, Bi, et al. [76] showed that foreign direct investment (FDI), in the form of data, has a negative influence on the human resources that are committed to green innovation in manufacturing, but that it has a positive impact on the green innovation resources of the MGIS outputs. In OECD, Zamir and Mujahid [77] indicated that FDI, green energy consumption, and green innovation technology impact selected sample countries, positively. Economic growth, however, is adversely affected by environmental degradation.

H3. 

Environmental innovation positively encourages the inflows of FDI.

2.4. Limitations in the Existing Literature

1. Considering the literature focusing on the nexus targeting innovation and macro fundamentals. A growing number of studies have been executed in a very scattered manner, indicating the innovation, in terms of TI, FI, and EI on FDI in a single equation, has yet to be explored. With the present study, we tried to examine the innovation-led FDI nexus to mitigate the existing literature research gap with fresh insight.

2. The literature has posited the beneficial role of innovation in fostering the inflows of FDI. The conclusion has been avoided mostly by implementing the linear assessment. However, the cost associated with the innovation culture might negatively affect the innovation output. The present study has incorporated the asymmetric framework in exploring the asymmetric effects of innovation on FDI. It is anticipated that the output with the asymmetric assessment will open an alternative avenue for policy formulation.

2.5. Conceptual Development for a Causal Association

With the study, we do not intended to explore the key macro determinants that are critically important in ensuring the continual inflows of FDI in the economy; rather, the study has focused on dragging out the potential nexus -innovation-led FDI where innovation depicts technological innovation, financial innovation, and environmental innovation. Inferring the existing findings, it is obvious that there is no direct causal assessment targeting the innovation-led FDI nexus; precisely, scanty studies have considered the effects of innovation in the case of FDI through the indirect channel. Thus, detecting the direct causal effects, we constructed the following casual framework (see Figure 1) and developed the following six hypotheses to be addressed with the implementation of the Fourier TY causality test [16,78,79,80].

The following hypothesis is to be tested in evaluating the directional causalities.

$H_{A,B}^1$: Financial innovation granger causes FDI and vice-versa

$H_{A,B}^2$: FDI granger causes environmental innovation and vice-versa

$H_{A,B}^3$: Financial innovation granger causes technological innovation and vice-versa

$H_{A,B}^4$: Financial innovation causes eenvironmental innovation and vice-versa

$H_{A,B}^5$: Environmental innovation granger causes technological innovation and vice-versa

$H_{A,B}^6$: Technological innovation granger causes FDI and vice-versa

3. Methodology and Data of the Study

3.1. Variables Definition and Data Sources

A growing number of studies have revealed the potential effects of FDI on socioeconomic development and economic sustainability. The purpose of the study is to document the impact of innovation, specifically financial innovation, technological innovation, and environmental innovation, on the FDI sustainability in BRIC nations for 1990–2019. Considering the explained and explanatory variables of the study, the generalized empirical model is as follows:

$$FDI\int Fi{n}_{Innov}, Tec{h}_{Innov}, and En{v}_{Innov}$$

(1)

In Equation (1), FDI is explained with the proxy of the inflows of FDI in BRIC nations, financial innovation, technological innovation, and environmental innovation stand with FI, TI, and EI, respectively. In terms of the existing literature, the economy’s state of financial development and institutional quality has played an important role in fostering innovation, especially technological innovation. Thus, we considered as control variables, financial development and institutional quality (IQ) considered, and Equation (1) was reproduced in the following ways:

$$FDI\int Fi{n}_{Innov}, Tec{h}_{Innov}, and En{v}_{Innov}, In{s}_{quality}, Fi{n}_{Deve}$$

(2)

The above equation (2) can be rewritten after transforming all of the variables into natural logarithms in the following ways:

$$FD{I}_t={\alpha }_0+{\beta }_{1}T{I}_t+{\beta }_{2}F{I}_t+{\beta }_{3}E{I}_t+{\beta }_{4}F{D}_t+{\beta }_{4}I{Q}_t+{\epsilon }_t$$

(3)

The variables delimitation and data sources displayed in

Table 1. Variable definition and data sources.

Variables	Notation	Definition		Sources
Foreign Direct Investment	FDI	FDI inflows as a % of the GDP		WDI
Technological Innovation	TI	A patent application by the resident population		WDI
Financial Innovation	FI	Research and development expenditure (% of GDP)	[25,33,81,82]	WDI
Environmental Innovation	EI	Environmental-related technological innovation	[83]	OECD
Financial Development	FD	Financial development index		IMF
Institutional Quality	IQ	Governmental effectiveness		WGI

3.2. Theoretical Development and the Model Specifications

The contribution and determinants of FDI have been extensively investigated in the literature and have yet to establish a irrefutable conclusion. The effects of the FDI inflows on the economy immensely rely on the other macro fundamentals, economic structure, and socioeconomic profiles. The motivation of the study is to investigate the impact of technological innovation (TI), financial innovation (FI), and environmental innovation on the FDI inflows in BRIC nations for 1991–2021. Following the existing nexus between technological innovation and FDI, technological innovation exhibited the economic environment characterized by technical advancement and application, especially in the industrial process [84]. Conversely, the inflows of FDI not only ensure the long term capital adequacy for industrialization and infrastructural development but bring the technological know-how and advancement [85,86]; thus, foreign investors have felt discomfort in channeling their capital to those economies that are not ready to accept technology and many citizens have admitted their reluctance in embracing the innovation in the industrial process [16]. Hence, it is anticipated that innovation practices and output concentrating on the aggregated output that is operational efficiency have a positive connection with FDI, implying that technological innovation confirms the economic adaptability and absorbs capacity, which induces the foreign investors for investment decisions, alternatively, ${\beta }_1=\frac{{\theta }_{FDI}}{{\gamma }_{TI}}>1$. According to the existing literature, the inflows of FDI are immensely influenced by financial development, implying that a well-developed financial system contributed as a catalyst in the domestic capital accumulation with foreign investor contributions. Furthermore, when the financial system of the receiving nation is advanced enough to give a greater benefit to the companies, they absorb the backward linkage spillover effects, and focus on spending more on R&D to overcome the competition, the money supply via FDI may optimize the pace of economic growth. The destination country’s financial system must be sufficiently developed to provide additional benefits to the businesses. Another line of evidence advocated that foreign direct investment (FDI) increases competition, encouraging local firms to engage in R&D and new product development. This pattern will persist as long as confidence in the financial system is high. Therefore, it is anticipated that a positive linkage between financial innovating and FDI, that is, financial innovation in the financial system increases financial efficiency and efficient intermediation, which eventually lead to the development of FDI inflows, i.e., ${\beta }_1=\frac{{\theta }_{FDI}}{{\gamma }_{FI}}>1$. As a result of the rising levels of public knowledge of the dangers posed by environmental hazards in recent years, these types of issues have been the primary focus of policy deliberations. However, these areas suffer from a severe lack of technical know-how, which hinders the development of economically sustainable innovations that leave less of an impact on the environment. In the study by Long, et al. [87], it was established that sustainable economic growth is significantly influenced by environmental innovation, indicating the environmental sustainability by lowering the degree of carbon emissions and an ecological balance can be achieved through green technology. Creating ecologically responsible and sustainable business and economic systems has emerged as a critical objective. Increasingly, businesses are realizing the environmental costs of their manufacturing and shipping processes and are adopting greener methods to lessen their effects [88]. Firms see sustainability as a way to secure their company’s future success; therefore, they are taking steps in that direction. Thus, the reduction of environmental pollution and hazards, increase the environmental sustainability and prompts economic growth, most importantly the lower cost associated for environmental protection encourages investors for fund mobilization, that is ${\beta }_1=\frac{{\theta }_{FDI}}{{\gamma }_{EI}}>1$.

3.3. Estimation Strategy

The time series data analysis with robust econometric tools is immensely influenced by the variables’ order of integration [89]. We applied several unit root tests to explore the variables’ properties following [90,91,92,93]. Moreover, the structural break issue has been highlighted through Zivot and Andrews’ [94] unit root test. In exploring the stationary test statistics, the following equation has been executed

$$∆Y={\gamma }_0+{\gamma }_1{Y}_{t-1}+y{2}^t+{\sum }_{i=1}^w{\alpha }_i∆Y_{t-1}+{\mu }_t$$

(4)

$$∆y_t^d=\alpha y_{t-1}^d+{\sum }_{i=1}^p{\vartheta }_j∆y_{t-i}^d+{\sigma }_t$$

(5)

$$y_t={\beta }_0+{\beta }_{1}t+{\gamma }_t+{ϵ}_t$$

(6)

$${\gamma }_t={\gamma }_{t-1}+{\theta }_t$$

(7)

$$LM=\frac{1}{T^2}\frac{{\sum }_{t-1}^T{M}_t^2}{\widehat{{\delta }^2}}$$

The novel combined cointegration test offered by Bayer and Hanck [95], the r test statistics have been exported in the inclusion of several conventional cointegration tests introduced by Banerjee, et al. [96], Peter Boswijk [97], Johansen [98], and Engle and Granger [99]. The following Fisher equation is to be executed for the cointegration test.

$$EG-JOH=-2\left[ln\left(PEG\right)+LN\left(PJOH\right)\right]$$

$$EG-JOH-BO-BD=-2\left[LN\left(PEG\right)-\ln \left(PJPH\right)+\ln \left(PBO\right)+\ln \left(PBDM\right)\right]$$

Following the conceptual framework offered by [100], the study established the following empirical equation for exporting the long run and short run coefficients.

$$\begin{gathered} ∆lnFD{I}_t={\alpha }_0+{\displaystyle \sum }_{i=1}^n{\mu }_1∆lnFD{I}_{t-i}+{\displaystyle \sum }_{i=0}^n{\mu }_2∆lnT{I}_{t-i}+{\displaystyle \sum }_{i=0}^n{\mu }_3∆lnF{I}_{t-i}+{\displaystyle \sum }_{i=0}^n{\mu }_4∆lnE{I}_t+{\displaystyle \sum }_{i=0}^n{\mu }_5∆lnF{D}_{t-i} \\ +{\displaystyle \sum }_{i=0}^n{\mu }_6∆lnI{Q}_{t-i}+{\pi }_{1}lnFD{I}_{t-1}+{\pi }_{2}lnT{I}_{t-1}+{\pi }_{3}lnF{I}_{t-1}+{\pi }_{4}lnE{I}_{t-1}+{\pi }_{5}lnF{D}_{t-1} \\ +{\pi }_{6}lnI{Q}_{t-1}+{\omega }_{1t} \end{gathered}$$

(8)

The null and alternative hypothesis for explaining the long run association to be tested through F_bound: $Ho: {\pi }_1={\pi }_2={\pi }_3={\pi }_4={\pi }_5={\pi }_6=0; H1: {\pi }_1,{\pi }_2,{\pi }_3,{\pi }_4,{\pi }_5,{\pi }_6\ne 0$, t _(DIV-1): ${\pi }_1=0; {\pi }_1\ne 0$, and F _INV-1: $Ho: {\pi }_2={\pi }_3={\pi }_4={\pi }_5={\pi }_6=0; H1: {\pi }_2={\pi }_3={\pi }_4={\pi }_5={\pi }_6\ne 0$. The long run association is to be established once the test statistics are revealed to be greater than the critical value offered by [100] McNown, et al. [101].

Following the nonlinear framework proposed by Shin, the present study reproduces the linear ARDL equation in the following ways.

$$FD{I}_t=({\beta }^{+}T{I}_{1,t}^{+}+{\beta }^{-}T{I}_{1,t}^{-})+({\gamma }^{+}F{I}_{1,t}^{+}+{\gamma }^{-}F{I}_{1,t}^{-})+({\pi }^{+}E{I}_{1,t}^{+}+{\pi }^{-}E{I}_{1,t}^{-})+{\epsilon }_t$$

(9)

The asymmetric shocks in TI, FI and EI represent in the equation by $T{I}_{1,t}^{+}; TI$,$F{I}_{1,t}^{+}; F{I}_{1,t}^{-}; E{I}_{1,t}^{+}; E{I}_{1,t; }^{;-}$ It can be exported by employing the following equations.

$$\left\{\begin{array}{c}POS{\left(TI\right)}_{1,t}={\displaystyle {\displaystyle \sum }_{M=1}^t}lnT{I}_k^{+}={\displaystyle {\displaystyle \sum }_{M=1}^T}MAX\left(∆lnT{I}_k,0\right)\\ NEG{\left(TI\right)}_t={\displaystyle {\displaystyle \sum }_{M=1}^t}lnT{I}_k^{-}={\displaystyle {\displaystyle \sum }_{M=1}^T}MIN\left(∆lnT{I}_k,0\right)\end{array}\right.$$

(10)

$$\left\{\begin{array}{c}POS{\left(FI\right)}_{1,t}={\displaystyle {\displaystyle \sum }_{M=1}^t}lnF{I}_k^{+}={\displaystyle {\displaystyle \sum }_{M=1}^T}MAX\left(∆lnF{I}_k,0\right)\\ NEG{\left(FI\right)}_t={\displaystyle {\displaystyle \sum }_{k=1}^t}lnF{I}_k^{-}={\displaystyle {\displaystyle \sum }_{M=1}^T}MIN\left(∆lnF{I}_k,0\right)\end{array}\right.$$

(11)

$$\left\{\begin{array}{c}POS{\left(EI\right)}_{1,t}={\displaystyle {\displaystyle \sum }_{M=1}^t}lnE{I}_k^{+}={\displaystyle {\displaystyle \sum }_{M=1}^T}MAX\left(∆lnE{I}_k,0\right)\\ NEG{\left(EI\right)}_t={\displaystyle {\displaystyle \sum }_{M=1}^t}lnE{I}_k^{-}={\displaystyle {\displaystyle \sum }_{M=1}^T}MIN\left(∆lnE{I}_k,0\right)\end{array}\right.$$

(12)

By incorporating the decomposition series of the explanatory variables, the above equation (13) can be reproduced in the following manner for documenting the elasticities in the long run and short run horizons,

$$\begin{gathered} ∆{\mathrm{FDI}}_t=\partial {\mathrm{U}}_{t-1}+({\gamma }^{+}T{I}_{1,t-1}^{+}+{\gamma }^{-}T{I}_{1,t-1}^{-})+({\rho }^{+}F{I}_{1,t-1}^{+}+{\rho }^{-}F{I}_{1,t-1}^{-})+({\pi }^{+}E{I}_{1,t-1}^{+} \\ +{\pi }^{-}E{I}_{1,t-1}^{-})+{{\displaystyle \sum }_{j=1}^{q-1}{\lambda }_j∆FD{I}_{t-j}}_0+{\displaystyle \sum }_{j=1}^{r-1}({\pi }^{+}T{I}_{1,t-1}^{+}+{\pi }^{-}T{I}_{1,t-1}^{-})+{\displaystyle \sum }_{j=1}^{s-1}({\pi }^{+}F{I}_{1,t-1}^{+}+{\pi }^{-}F{I}_{1,t-1}^{-}) \\ +{\displaystyle \sum }_{j=1}^{y-1}({\pi }^{+}E{I}_{1,t-1}^{+}+{\pi }^{-}E{I}_{1,t-1}^{-})+{\epsilon }_t \end{gathered}$$

(13)

We executed the causality test with the Fourier function introduced by Nazlioglu, Gormus and Soytas [78]; it is today’s extended version of the previously familiarized casualty test. The following econometric framework is to be established in extracting the test statistics for the causality test.

$$y_t=\alpha \left(t\right)+{\beta }_1{y}_{t-1}+\cdots +{\beta }_{p+d}{y}_{t-\left(p+d\right)}+{\epsilon }_t$$

(14)

$$y_t=\alpha \left(t\right)+{\beta }_1{y}_{t-1}+\cdots +{\beta }_{p+d}{y}_{t-\left(p+d\right)}+{\vartheta }_{1}sin\frac{2k\pi t}{T}+{\vartheta }_{2}cos\frac{2k\pi t}{T}+{\epsilon }_t$$

(15)

4. Estimation and Interpretation

• Unit root test

Stationary properties of the research units have a critical impact on the robust econometrical tool selection. We thus have implemented several unit root tests in assessing the variables’ order of integration, by following the unit root framework familiarized by [90], Phillips and Perron [91,92,93], and the study performed by the unit root test and the unknown structural break unit root test [94] (

Table 2. Results of the conventional unit root test.

At Level					After the First Difference
	ADF	GF-DLS	PP	KPSS	ADF	GF-DLS	PP	KPSS
For Brazil
FDI	−0.7488	−0.6669	−1.9532	0.6741 ***	−8.3194 ***	−8.1245 ***	−6.2996 ***	0.0205
DEBT	−0.9833	−2.1793	−0.5675	0.8879 ***	−8.0734 ***	−5.351 ***	−6.9136 ***	0.0195
EPU	−1.5151	−1.7525	−1.5794	0.8463 ***	−8.8777 ***	−7.327 ***	−7.4118 ***	0.0195
GS	−1.0107	−1.2738	−1.2292	0.7815 ***	−9.4688 ***	−8.8025 ***	−8.0706 ***	0.0204
FD	−1.0267	−1.8994	−0.5755	0.6737 ***	−9.1871 ***	−5.3918 ***	−7.6811 ***	0.0207
FDI	−1.8368	−2.0009	−1.8205	0.937 ***	−7.678 ***	−5.9905 ***	−7.1345 ***	0.0188
For Russia
IQ	−0.5594	−2.1245	−1.4801	0.9352 ***	−7.9863 ***	−7.3349 ***	−5.5808 ***	0.0188
DEBT	−0.5356	−1.0835	−0.3704	0.9122 ***	−8.4582 ***	−8.0342 ***	−6.5719 ***	0.0191
EPU	−0.5998	−0.9074	−0.8814	0.8957 ***	−9.2356 ***	−5.6144 ***	−8.7009 ***	0.0194
GS	−1.9004	−1.9878	−0.3064	0.6789 ***	−6.1458 ***	−7.3246 ***	−8.5336 ***	0.0208
FD	−2.0514	−2.0985	−0.345	0.8979 ***	−7.7381 ***	−8.0296 ***	−8.0432 ***	0.0217
FDI	−2.4167	−0.5244	−0.5154	0.7538 ***	−5.5858 ***	−8.9416 ***	−6.9357 ***	0.0191
For India
IQ	−1.6315	−1.393	−2.2791	0.7099 ***	−8.7112 ***	−5.6486 ***	−7.3019 ***	0.0193
DEBT	−1.8923	−1.792	−2.0658	0.8298 ***	−6.8922 ***	−6.8115 ***	−7.0341 ***	0.0204
EPU	−1.974	−0.5619	−0.6138	0.5635 ***	−9.5571 ***	−5.7827 ***	−5.8899 ***	0.0207
GS	−0.6549	−1.2892	−0.5233	0.7879 ***	−7.9351 ***	−7.9349 ***	−9.5478 ***	0.0212
FD	−1.3922	−1.0563	−2.1668	0.7491 ***	−6.6772 ***	−7.1966 ***	−5.4428 ***	0.019
FDI	−1.6913	−1.3775	−1.9026	0.6921 ***	−5.9576 ***	−6.1984 ***	−7.6011 ***	0.0205
For China
IQ	−0.3786	−2.3999	−1.2085	0.8567 ***	−5.5527 ***	−5.5273 ***	−8.765 ***	0.021
DEBT	−1.3356	−1.727	−2.1657	0.6295 ***	−8.7676 ***	−8.1629 ***	−7.2992 ***	0.0191
EPU	−0.5988	−0.8767	−1.9112	0.6539 ***	−8.0145 ***	−8.3016 ***	−5.8106 ***	0.0194
GS	−1.7031	−1.9811	−0.965	0.5722 ***	−7.3175 ***	−9.5487 ***	−7.7143 ***	0.0209
FD	−1.7399	−1.921	−1.7078	0.8559 ***	−8.6457 ***	−7.2525 ***	−8.5882 ***	0.0198
FDI	−2.23	−2.3462	−0.6953	0.8221 ***	−7.4646 ***	−6.8531 ***	−5.3593 ***	0.0197

Note: *** denote level of significant at a 1%.

).

In the following, the present study has extended the stationary test with the implementation of Ng and Perron’s [102] unit root test, and their results are displayed in

Table 3. Unit root test results: Ng-Perron.

	At Level				∆
	MZa	MZt	MSB	MPT	MZa	MZt	MSB	MPT
For Brazil
FDI	−2.1276	−1.6611	0.2554	7.6084	−18.8759	−4.7499	0.1313	5.1012
FI	−2.7255	−1.7275	0.2305	7.3255	−22.1305	−3.8603	0.1464	4.1037
TI	−2.2167	−1.3783	0.2483	8.6775	−23.7656	−5.2645	0.1427	3.855
EI	−2.3269	−1.2995	0.2895	8.469	−23.741	−5.2579	0.1402	3.1742
FD	−2.4996	−1.3892	0.2602	8.1447	−24.8683	−4.9875	0.1679	4.5418
IQ	−2.0116	−1.6975	0.348	7.5247	−18.7768	−5.4711	0.1656	4.6833
For Russia
FDI	−2.5374	−1.5656	0.3256	7.4165	−19.1285	−5.4095	0.1262	4.6136
FI	−2.1771	−1.2342	0.3142	8.2959	−21.6942	−4.8348	0.1256	3.6256
TI	−1.7973	−0.876	0.2284	7.2982	−20.4093	−4.3306	0.1631	3.1598
EI	−2.2654	−0.8124	0.2785	7.537	−21.4393	−5.5774	0.1618	4.8133
FD	−2.0305	−1.0232	0.3243	7.7295	−17.3789	−5.249	0.1702	4.7837
IQ	−1.8588	−0.7075	0.226	9.061	−18.1555	−4.298	0.1591	4.4135
For India
FDI	−2.7141	−1.0193	0.275	7.2054	−21.5648	−5.2447	0.1346	3.8703
FI	−1.892	−1.0308	0.3276	7.366	−19.5657	−4.8024	0.1631	4.3113
TI	−2.0217	−1.1214	0.344	8.5998	−23.1505	−4.9551	0.1624	3.362
EI	−1.9448	−1.6573	0.2326	8.2949	−18.2604	−4.9858	0.1265	3.5019
FD	−2.3778	−1.6243	0.297	8.7717	−23.4992	−3.975	0.1624	4.1949
IQ	−1.9132	−1.3028	0.2811	7.6679	−25.261	−4.3249	0.1394	3.2857
For China
FDI	−2.0899	−0.8448	0.2563	7.4068	−20.3399	−3.7198	0.1341	4.4937
FI	−2.6551	−1.6981	0.3587	9.0708	−19.0122	−4.377	0.1771	4.017
TI	−2.7207	−0.7892	0.3562	7.5954	−21.5977	−4.3531	0.163	4.0752
EI	−2.0506	−0.8361	0.2796	7.5502	−24.1224	−3.8939	0.1683	3.4093
FD	−2.0907	−1.7101	0.3361	7.3525	−24.9861	−5.7163	0.1316	5.1521
IQ	−1.717	−0.8276	0.2331	8.0114	−22.774	−5.4142	0.1485	4.8353
Asymptotic critical values: Ng and Perron (2001) [102], Table 1				1%	−23.8	−3.42	0.143	4.03
				5%	−17.3	−2.91	0.168	5.48
				10%	−14.2	−2.62	0.185	6.67

. Inferring the test statistics, i.e., MZa, MZt, MSB, and MPT, the study institutes that all of the test statistics are higher than the critical values at a 1% level, indicating the cancellation of the null hypothesis. Instead, it confirms the order of integration after the first difference I (1).

Following the unit root test introduced by Zivot, we assess the variables’ order of integration with the possible break year; the results of the unit root with a structural break are displayed in

Table 4. Results structural break unit root.

	At Level		After the First Difference
	T-Statistic	Time Break	T-Statistic	Time Break
Pane-A: for Brazil
FDI	1.8355(3)	1997	5.6051(1) ***	2007
FI	3.4496(3)	2000	5.2656(3) ***	2005
TI	3.1948(1)	2006	6.3947(1) ***	2012
EI	1.9118(2)	2006	5.2538(1) ***	2019
FD	3.4129(1)	1997	8.1332(1) ***	2016
IQ	1.7578(3)	2008	9.4873(3) ***	2014
Panel-B: for Russia
FDI	3.195(2)	1995	5.1569(2) ***	2012
FI	2.1139(3)	1996	7.1741(3) ***	2012
TI	3.0494(2)	2001	7.3156(1) ***	2004
EI	3.3463(2)	1998	7.2301(1) ***	2009
FD	1.1989(3)	2007	8.1339(3) ***	2000
IQ	3.1543(2)	2003	5.6564(2) ***	2007
Pane-C: for India
FDI	2.9063(3)	1998	8.4561(3) ***	2003
FI	2.3096(3)	2005	8.4941(1) ***	2003
TI	2.3516(1)	1995	5.4392(2) ***	2007
EI	2.6606(3)	2009	8.7844(1) ***	2014
FD	1.9331(2)	1999	8.0342(3) ***	2010
IQ	3.1088(1)	2009	9.3692(3) ***	2007
Panel-D: for China
FDI	3.4025(1)	1996	4.8959(1) ***	2005
FI	2.4398(2)	2000	8.6577(3) ***	2003
TI	2.2283(1)	1996	4.602(1) ***	2010
EI	2.2793(3)	2000	4.6597(3) ***	2011
FD	1.6069(3)	2005	5.2141(3) ***	2010
IQ	3.4301(1)	2000	5.391(3) ***	2010

Note: *** denote level of significant at a 1%.

. In terms of the test statistics, all of the variables have exposed stationary after the first difference with one break year. Especially, the break year for FDI in Brazil is 5.6051(2007), in Russia is 5.1569(2012), in India is 8.4561(2003), and in China 4.8959(2005), respectively.

The novel combined cointegration test [95] assessed the long run association between FDI, TI, FI, EI, FD and IQ in the BRIC nations.

Table 5. Results of the Bayer–Hanck combined counteraction test.

		Brazil	Russia	India	China	CV
EG-JOH	1	14.698	11.664	11.75	11.937	11.229
	2	11.106	11.182	11.168	11.076	10.895
	3	11.404	10.844	11.246	11.195	10.637
	4	10.801	10.743	10.745	10.765	10.576
	5	10.627	10.654	10.722	10.608	10.419
EG-JOH-BO-BDM	1	34.808	30.898	33.585	35.73	21.931
	2	28.069	26.294	26.691	29.311	21.106
	3	23.291	23.21	22.573	23.374	20.486
	4	22.082	22.232	21.082	22.436	20.143
	5	20.904	20.866	20.85	20.956	19.888

exhibited the results of the cointegration test. In terms of the test statistics derived from the cointegration test, i.e., EG-JOH & EG-JOH-BO-BDM, they were revealed to be statistically significant at a 5% level, suggesting the long run cointegration in the empirical model.

• Long-run cointegration: AARDL

Next, the study implemented the augmented ARDL framework in depicting the long run linkage between the explanatory and explained variables by executing the Equation (19).

Table 6. AARDL cointegration test.

Empirical Model		Test Statistics	(1)	(2)	(3)	(4)
FDI|TI, FI, EI, FD, IQ		Foverall	10.55 ***	7.468 ***	14.209 ***	13.561 ***
		tDV	−6.179 ***	−7.045 ***	−6.669 ***	−5.522 ***
		FIDV	10.802 ***	6.685 ***	7.304 ***	9.586 ***
Critical value: K = 5	1%		5%		10%
	I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
Pesaran, Shin and Smith [100]	5.095	6.77	3.673	5.002	3.087	4.277
Narayan [103]	−3.96	−5.13	−3.41	−4.52	−3.13	−4.21
Sam, et al. [104]	3.58	5.91	2.46	4.18	2.00	3.47

Note: *** denote level of significant at a 1%.

reported the results of the test statistics derived for cointegration assessment. Referring to the test statistics, i.e., F_overall, t_DV, and F_IDV, it is apparent that all of the test statistics are statistically significant at a 1% significance, suggesting the long run cointegration between the research units in all four sample countries.

4.1. Long Run and Short Run Coefficients: AARDL Estimation

Table 7. Results of the long run and short run coefficients with the AARDL.

	(5)	(6)	(7)	(8)
Panel-A: Long Run coefficients
TI	0.1574(0.0113) [−13.9292]	0.0542(0.002) [−27.1]	0.0515(0.0063) [8.1746]	0.1157(0.0118) [−9.805]
FI	0.1461(0.0111) [13.1621]	0.1551(0.0104) [14.9134]	0.1704(0.0027) [63.1111]	0.0814(0.0097) [8.3917]
EI	0.1134(0.0033) [34.3636]	0.0417(0.0071) [5.8732]	0.0842(0.002) [42.1]	0.1277(0.0075) [17.0266]
FD	0.0684(0.0041) [16.6829]	0.0328(0.0054) [6.074]	0.1568(0.0037) [42.3783]	0.0669(0.0085) [7.8705]
IQ	−0.0852(0.0083) [−10.265]	−0.0783(0.0056) [−13.9821]	−0.18(0.0118) [−15.2542]	−0.1508(0.0059) [−25.5593]
DMU	0.0166(0.0051) [3.2549]	0.0326(0.0093) [3.5053]	0.0326(0.0093) [3.5053]	0.0579(0.0114) [5.0789]
C	3.6667(0.533) [6.8792]	3.3994(0.4512) [7.5341]	3.427(0.533) [6.4295]	1.9288(7.9084) [0.2438]
∆TI	0.0748(0.0025) [29.92]	0.0902(0.0035) [25.7714]	0.0714(0.0097) [7.3608]	0.1093(0.0104) [10.5096]
∆FI	0.0329(0.0057) [5.7719]	0.0132(0.0046) [2.8695]	0.0318(0.0097) [3.2783]	0.0237(0.0042) [8.3095]
∆EI	0.0565(0.0062) [9.1129]	0.0338(0.0036) [9.3888]	0.0413(0.0054) [7.6481]	0.0112(0.0105) [1.0666]
∆FD	−0.084(0.0039) [−21.5384]	−0.0683(0.01) [−6.83]	−0.0611(0.0067) [−9.1194]	−0.0774(0.0087) [−8.8965]
∆IQ	0.0584(0.0089) [6.5617]	0.0887(0.0035) [25.3428]	0.0764(0.0042) [18.1904]	0.0674(0.0034) [19.8235]
DMU	0.0597(0.0045) [13.2666]	0.0563(0.0036) [15.6388]	0.0563(0.0036) [15.6388]	0.0735(0.0039) [18.8461]
ECT(−1)	−0.3307(0.0044) [−75.1713]	−0.359(0.0808) [−4.4435]	−0.4518(0.0101) [−44.7424]	−0.3008(0.0201) [−14.9674]
Panel-C: Residual Diagnostic test
$x_{Auto }^2$	0.801	0.569	0.599	0.821
$x_{ Het }^2$	0.729	0.555	0.72	0.533
$x_{ Nor}^2$	0.533	0.584	0.87	0.815
$x_{RESET }^2$	0.618	0.494	0.504	0.761

displayed the long run and short run coefficients and the test statistics from the residual diagnostic tests in the panel A, B, and C, respectively.

The BRIC thesis has established a positive and statistically significant linkage to the coefficient of the technological innovation on FDI inflows. Specifically, a 1% increase of TI in the economy will result in an acceleration of the FDI inflows in Brazil by 0.1574% (0.0748), Russia by 0.0542% (0.0902%), India by 0.0515% (0.0714%), and China by 0.1157% (0.1093%), respectively. Taking into account the magnitudes of technological innovation on FDI, the study depicts that in the long run, the inflows of FDI will be more responsive due to TI in Brazil and China, whereas the inflows of FDI in Russia and India have been significantly responsive in the short run due to TI. Even though a positive tie was found between TI and FDI, the response to the FDI inflows varies due to the economic structure and other macro agents’ presence in the economy.

The coefficient of financial innovation displayed positive inflows of FDI in the BRIC nations in the long and short runs, and all of the coefficients are statistically significant at 1%. In the long run, a 1% improvement in FI will intensify the appearance of foreign contribution in the form of FDI in Brazil by 0.1461%, Russia by 0.1551%, India by 0.1704%, and China by 0.0814%, respectively. In terms of the short run investigation, the contributory effects from FI to FDI are detected, in particular, a 10% progress can accelerate the inflow of FDI in Brazil by 0.329%, Russia by 0.132%, India by 0.318%, and China by 0.237%. In terms of the elasticities intensity, even though the favorable impact is documented in the long run and short run, the inflows of FDI are more responsive in the long run.

The study found that the contributory effects of environmental innovation (EI) on the FDI accumulation in the long run and short run, suggesting environmental sustainability through efficient technology inclusion, especially for carbon emissions. In particular, a 10% increase in investment for R&D targeting green innovation which results in a positive momentum in receiving FDI in the host economy in the long run (short run), particularly in Brazil by 0.1134% (0.0565%), Russia by 0.0417% (0.0338%), India by 0.0842% (0.0413%), and China by 0.1277% (0.0112%), respectively. The study findings have established encouraging effects on FDI that environmental innovation has duel effects on the economy; that is, the increases of the FDI inflows, side by side, prompt environmental sustainability. Usman, et al. [105] advocated that environmental efficiency and ecological balance can be obtained through cultivating innovation focusing on environmental protection, meaning that investment in environmental protection improves energy efficiency, leading to environmental sustainability.

The empirical model estimation passes several diagnostic tests to ensure conceptual model conformity and estimation consistency. Referring to the test statistics derived from a residual diagnostic test, the study confirmed that the estimation models are free from serial correlation and residuals are normally distributed in all four target models. Moreover, the efficient estimation has been established through the Ramsey test.

4.2. Asymmetric Investigation: TI, FI, and EI on FDI

Next, the possible long run association between the asymmetric decomposition of explanatory variables and the explained variables has been exposed with the cointegration assessment procedure in AARDL.

Table 8. Asymmetric cointegration with the interactive term.

Empirical Model	Test Statistics	(9)	(10)	(11)	(12)
FDI|TI, FI, EI, FD, IQ,	Foverall	11.619 ***	12.217 ***	10.49 ***	15.268 ***
	tDV	−6.738 ***	−6.818 ***	−5.688 ***	−5.719 ***
	FIDV	9.313 ***	11.246 ***	6.933 ***	10.552 ***

Note: *** denote level of significant at a 1%.

displays the test statistics dealing with the long run associations, and it is apparent that all of the test statistics are revealed to be statistically significant at a 1% level. This confirms the empirical model’s asymmetric linkage between the explained and explanatory variables.

Panel–C in

Table 9. Asymmetric long run coefficients.

	(13)	(14)	(15)	(16)
Panel-A: Long Run coefficients
$T{I}^{+}$	0.0719(0.0044) [16.1319]	0.0986(0.0046) [21.3721]	0.1758(0.0067) [26.1325]	0.0949(0.0067) [14.0259]
$T{I}^{-}$	0.0928(0.0022) [41.7462]	0.0296(0.0075) [3.9128]	0.1127(0.0077) [14.4761]	0.036(0.0037) [9.6899]
$F{I}^{+}$	0.1398(0.0054) [27.2716]	0.1123(0.0053) [20.9893]	0.1273(0.0102) [12.478]	0.1262(0.004) [31.0713]
$F{I}^{-}$	0.0334(0.0063) [5.2427]	0.0546(0.0045) [12.137]	0.0412(0.0019) [4.0312]	0.0592(0.0048) [14.809]
$E{I}^{+}$	0.1191(0.0091) [13.0822]	0.1375(0.0019) [69.3855]	0.0832(0.0059) [13.9971]	0.0751(0.0064) [11.5983]
$E{I}^{-}$	0.1089(0.0039) [27.9441]	0.0355(0.0052) [6.7666]	0.0266(0.0046) [5.7713]	0.0154(0.0096) [1.5973]
FD	0.0975(0.0072) [13.3977]	0.1017(0.0094) [10.7467]	0.0295(0.0072) [4.0936]	0.0656(0.0065) [10.0192]
IQ	0.0501(0.0087) [5.7384]	0.0704(0.0063) [11.1578]	0.0498(0.0047) [10.5556]	0.0332(0.0022) [14.9495]
DMU	0.0361(0.0087) [4.1494]	0.0185(0.0026) [6.9856]	0.0185(0.0056) [3.2903]	0.0208(0.0071) [2.9198]
Panel-B: Short Run coefficients
$T{I}^{+}$	0.0287(0.004) [7.175]	0.0369(0.0113) [3.2654]	0.0326(0.006) [5.4333]	0.0701(0.0092) [7.6195]
$T{I}^{-}$	0.0095(0.0092) [1.0326]	0.0175(0.0019) [9.2105]	0.0401(0.0088) [4.5568]	0.0361(0.0065) [5.5538]
$F{I}^{+}$	0.0091(0.0022) [4.1363]	0.0705(0.0025) [28.2]	0.0317(0.006) [5.2833]	0.048(0.0105) [4.5714]
$F{I}^{-}$	0.0595(0.0062) [9.5967]	0.0451(0.0107) [4.2149]	0.0085(0.0058) [1.4655]	0.0651(0.0074) [8.7972]
$E{I}^{+}$	0.0221(0.0035) [6.3142]	0.0445(0.0098) [4.5408]	0.0335(0.0091) [3.6813]	0.0615(0.003) [20.5]
$E{I}^{-}$	0.0168(0.0043) [3.9069]	0.0595(0.0107) [5.5607]	0.0421(0.0112) [3.7589]	0.0482(0.0036) [13.3888]
FD	0.0522(0.0039) [13.3846]	0.062(0.0087) [7.1264]	0.0684(0.0106) [6.4528]	0.0067(0.0072) [0.9305]
IQ	0.0377(0.0098) [3.8469]	0.0379(0.0068) [5.5735]	0.0099(0.0047) [2.1063]	0.0565(0.0119) [4.7478]
DMU	0.0052(0.0083) [0.6265]	0.0155(0.0099) [1.5656]	0.0128(0.0119) [1.0756]	0.0455(0.0049) [9.2857]
CointEq(−1) *	−0.2444(0.0076) [−31.8273]	−0.5631(0.0112) [−50.1294]	−0.3567(0.0093) [−38.3316]	−0.2447(0.0079) [−30.8035]
Panel-C: Long Run and Short Run symmetry
$W_{LR}^{TI}$	7.723	6.106	12.794	9.594
$W_{LR}^{FI}$	5.828	8.922	4.144	13.691
$W_{LR}^{EI}$	13.007	5.316	7.684	11.737
$W_{SR}^{TI}$	10.442	7.126	4.877	4.877
$W_{SR}^{FI}$	10.565	2.843	12.17	12.534
$W_{SR}^{EI}$	13.252	11.018	5.445	12.423
Panel-D: Residual Diagnostic test
$x_{Auto }^2$	0.846	0.564	0.553	0.736
$x_{ Het }^2$	0.65	0.751	0.726	0.663
$x_{ Nor}^2$	0.682	0.864	0.804	0.654
$x_{RESET }^2$	0.848	0.728	0.749	0.507

Note: * denote level of significant at a 10%.

displays the standard Wald test statistic results with the null hypothesis of symmetry in the long and short runs. Referring to the Wald test results, it is obvious that all of the test statistics are statistically significant at a 1% level, implying the rejection of the tested null hypothesis. Alternatively, it establishes the asymmetric linkage between the explained variables, i.e., FDI and explanatory variables. Thus, we conclude that there is an asymmetric association in the empirical equation, both in the long and short runs.

In Panel-A, the long run asymmetric coefficient is displayed. Referring to the asymmetric coefficients of technological innovation, that is $T{I}^{+}\& T{I}^{-}$ on the FDI inflows in the BRIC nations, a study publicized a positive and statistically significant linkage between them, in the long run (short run). More precisely, a 10% positive variation in TI, according to elasticity, will result in the improvisation of receiving FDI in Brazil by 0.719% (0.287%), Russia by 0.0986% (0.0369%), India by 0.1758% (0.0326%) and China by 0.0949% (0.0701), respectively. Additionally, a 1% contraction in promoting TI in the economy can shrinkage the present trend in the FDI inflows in Brazil by 0.0928% (0.0095%), Russia by 0.0296% (0.0175%), India by 0.1127% (0.0401%), and China by 0.0361% (0.0401%), respectively. Following the asymmetric elasticity, it is revealed that, especially in the long run, it is imperative to induce technological innovation by offering incentives and other privileges to attract foreign investment for economic progress. Nevertheless, the shrink in technological innovation has adversely hurt the FDI inflows in the BRIC nations, especially in India and Brazil, compared to Russia and China.

A positive connection between the asymmetric variables of financial innovation, i.e., $F{I}^{+}\& F{I}^{-}.$ Moreover, FDI has been disclosed in the long run and short run in the BRIC nations, suggesting that innovation in the financial system encourages foreign investors to mobilize their economic resources in the economy, exhibiting financial efficiency and efficient intermediation. In particular, with a 10% progress in innovation, the concentrating financial system expedites the foreign capital contribution in the form of FDI in Brazil by 1.398% (0091%), Russia by 1.123% (0.705%), India by 1.273% (0.317%), and China by 1.262% (0.481%). Moreover, the adverse shocks in financial innovation degraded the persistence inflows of FDI in Brazil by 0.334% (0.595%), Russia by 0.546% (0.451%), India by 0.412% (0.085%), and China by 0.592% (0.651%). In terms of the elasticities of the asymmetric variables, the positive innovation establishes a significant impact on the FDI inflows, in comparison to the negative shocks, implying that progressive changes in the financial system with the inclusion and diffusion of innovative financial products and services, portray the financial development with efficiency which entice foreign investors to transfer their capital and technological support to that economy.

The coefficients of positive (negative) shock in environmental innovation revealed a positive and statistically significant linkage with the BRIC nations FDI inflows in the long and short runs. More precisely, the inflows of FDI will accelerate (reduce) in Brazil by 1.191% (1.089%), Russia by 1.375% (0.355%), India by 0.832% (0.266%), and China by 0.751% (0.154%), due to a 10% improvement (degradation) in innovation focusing on environmental protection. Additionally, the positive connectivity in the short run asymmetric investigation has unleashed, precisely, a 10% variation with the positive (negative) trend in FI will result in the acceleration (shrink) of FDI inflows in Brazil by 0.221% (0.168%), and Russia by 0.445% (0.595%), 0.335% (0.421%), 0.615% (0.482%). In terms of the magnitudes of the asymmetric coefficients, it is apparent that positive increments in EI have significant effects on FDI, both in the long and short runs, compared to the negative trends in EI. Therefore, it is suggested that to attract foreign investors to mobilize their technology and capital in the host economy, it is imperative to ensure the continual inflows of investment in innovation, concentrating on environmental protection through green technology.

Next, the directional association in the empirical equation has been derived by implementing the novel Toda–Yamamoto causality test with the Fourier function, familiarized by Enders and Jones [106]. According to the directional association, the feedback hypothesis holds between technological innovation and FDI in Russia, India, and China, financial innovation and FDI in Brazil and India, environmental innovation and FDI in Brazil and China, and institutional quality and FDI in India, respectively. Moreover, the unidirectional causality revealed that FDI led technological innovation in Brazil, FDI led financial innovation in Russia and China, environmental innovation led FDI in Russia, and FDI led environmental innovation in India, financial development prompted by FDI in Brazil and China, and FDI improved governmental effectiveness in Brazil and Russia (

Table 10. Toda–Yamamoto Fourier causality test.

	(17)		(18)		(19)		(20)
TI $\bcancel{\to }$ FDI	2.459	FDI→TI	5.726	FDI←→TI	6.361	TI←→FDI	4.045	TI←→FDI
FDI $\bcancel{\to }$ TI	3.677 *		3.507		4.248		4.550
FI $\bcancel{\to }$ FDI	4.335 **	FI←→FDI	1.588	FDI→FI	6.338	FDI←→FI	0.959	FDI→FI
FDI $\bcancel{\to }$ FI	5.779 ***		6.266		3.035		3.368
EI $\bcancel{\to }$ FDI	5.506 ***	EI←→FDI	6.634	EI→FDI	2.511	FDI→EI	4.139	EI←→FDI
FDI $\bcancel{\to }$ EI	6.730 ***		0.929		3.879		5.667
FD $\bcancel{\to }$ FDI	0.735	FDI→FD	2.279		0.466		0.070	FDI→FD
FDI $\bcancel{\to }$ FD	5.060 ***		2.318		1.929		5.067
IQ $\bcancel{\to }$ FDI	0.245	FDI→IQ	1.978	FDI→IQ	6.679	FDI←→IQ	2.912
FDI $\bcancel{\to }$ IQ	4.822 **		4.409		3.567		0.544

Note: ***, **, * denote level of significant at a 1%, 5% and 10%.

).

5. Robustness Test: DOLS, FMOLS, and CCR

Next, we implemented the base regression model by following the framework offered by [107,108], for checking the empirical mode coefficient robustness. The results of the robustness test are displayed in

Table 11. Results of the robustness test.

	DOLS			FMOLS			CCR
Variables	Coefficient	std	t-stat	Coefficient	std	t-stat	Coefficient	std	t-stat
Panel-A: Brazil
FI	0.1795	0.0462	3.8852	0.1514	0.0416	3.6394	0.1283	0.0141	9.0992
TI	0.1708	0.0515	3.3165	0.1772	0.0223	7.9461	0.1731	0.033	5.2454
EI	0.1178	0.0516	2.2829	0.1669	0.0219	7.621	0.1347	0.077	1.7493
FD	0.0413	0.0846	0.4881	0.0424	0.0819	0.5177	0.0664	0.094	0.7063
IQ	0.0893	0.0167	5.3473	0.1005	0.0615	1.6341	0.0849	0.0521	1.6295
Adj.R	0.8883			0.8941			0.9033
R	0.9409			0.9279			0.9395
Panel-B: Russia
FI	0.1764	0.0798	2.2105	0.078	0.0698	1.1174	0.1201	0.0608	1.9753
TI	0.1881	0.0319	5.8965	0.1908	0.0269	7.0929	0.1429	0.037	3.8621
EI	0.0247	0.0627	0.3939	0.0951	0.0795	1.1962	0.0501	0.0541	0.926
FD	0.0393	0.0582	0.6752	0.1104	0.037	2.9837	0.1637	0.0901	1.8168
IQ	0.1073	0.0802	1.3379	0.0621	0.0726	0.8553	0.055	0.0913	0.6024
Adj.R	0.724			0.774			0.811
R	0.822			0.833			0.893
Panel-C: India
FI	0.1793	0.0271	6.6162	0.1414	0.0618	2.288	0.1397	0.0928	1.5053
TI	0.184	0.0821	2.2411	0.1792	0.0814	2.2014	0.183	0.0958	1.9102
EI	0.0917	0.0769	1.1924	0.1901	0.0282	6.7411	0.1697	0.0831	2.0421
FD	0.0365	0.0837	0.436	0.0511	0.0496	1.0302	0.0934	0.0864	1.081
IQ	0.081	0.0446	1.7937	0.0848	0.0671	1.2637	0.0799	0.0529	1.5103
Adj.R	0.8935			0.8927			0.8928
R	0.9399			0.949			0.9421
Panel-D: China
FI	0.1814	0.0737	2.4613	0.0848	0.085	0.9976	0.098	0.0663	1.4781
TI	−0.1706	0.0806	2.1166	0.1215	0.0266	4.5676	0.1655	0.0179	9.2458
EI	0.0252	0.0771	0.3268	0.0742	0.026	2.8538	0.0953	0.0867	1.0991
FD	0.0736	0.0852	0.8638	0.0727	0.0698	1.0415	0.0287	0.0672	0.427
IQ	0.1841	0.0405	4.5456	0.1754	0.0524	3.3473	0.1597	0.0624	2.5592
Adj.R	0.8851			0.9081			0.8948
R	0.9376			0.9313			0.9365

. Referring to the estimated coefficients, it is apparent that the sign of explanatory variables, FF, TI, and EI, have been positively connected to the inflows of FDI in the BRIC nations. The findings from the robustness test confirmed the empirical model construction efficiency and stability in explaining their connection.

6. Discussion

The study revealed a positive statistically significant linkage between technological innovation and FDI in the BRIC nations in the long and short runs. Referring to the symmetric estimation, a 1% increase of TI in the economy will result in the acceleration of the FDI inflows in Brazil by [$FD{I}_{LR}=0.1574\%;FD{I}_{SR}=0.0748]$, Russia by [$FD{I}_{LR}=0.0542\%;FD{I}_{SR}=0.0902\%]$, India by [$FD{I}_{LR}=0.0515\%;FD{I}_{SR}=0.0714\%],$ and China by $FD{I}_{LR}=0.1157\%;FD{I}_{SR}=0.1093\%]$, respectively. The existing literature supports our study findings [29,109]. Taking into account the magnitudes of technological innovation on FDI, the study depicts that, in the long run, the inflows of FDI are more responsive, due to TI in Brazil and China, whereas the inflows of FDI in Russia and India are significantly responsive in the short run, due to TI. Even though a positive tie was found between TI and FDI, the response to the FDI inflows varies due to economic structure and other macro agents’ presence in the economy. Regarding the asymmetric nexus between TI and FDI, we found that the asymmetric coefficients of TI [] create a positive connection with FDI, which is statistically significant at a 1% level. In particular, a 1% positive (negative) shock in TI will generate catalyst (diminishing) effects on the inflows of FDI in the BRIC nations. Following the asymmetric elasticity, it is revealed that it is imperative to induce technological innovation by offering incentives and other privileges, especially in the long run, to attract foreign investment for economic progress. Nevertheless, the shrink of technological innovation has adversely hurt the FDI inflows in the BRIC nations, especially in India and Brazil, compared to Russia and China. The ability to easily maintain the advantages of FDI is becoming easier to achieve as a result of improvements in technology. According to Borensztein, et al. [110], rising nations gain from technology transfers brought about by foreign direct investment (FDI) inflows.

Consequently, technology may be the most significant factor driving inward FDI in these economies. According to Hsu and Tiao [111], a favorable investment climate that facilitates technology transfers benefits FDI. Moreover, the study of Cumming and Zhang [112] postulated that the economic capacity to adopt technological change is significantly influenced by technological innovation and the attractive ambiance in transferring technological know-how through FDI, alluring foreign investors.

Referring to symmetric and asymmetric empirical estimations, the study exposed a positive and statistically significant linkage between financial innovation and the inflows of FDI in the BRIC nations. These findings advocate the beneficiary role of FI in accelerating FDI in the economy by offering efficient financial intermediation and financial efficiency; particularly, the efficiency in the financial institutions has enticed foreign investors to channel economic resources into the host economy with an efficient financial system. In particular, according to the symmetric coefficients with a 10% positive change in FI, they can entice foreign investors and encourage them to mobilize economic resources in the form of FDI in Brazil by 0.1461% (0.329%), Russia by 0.1551% (0.132%), India by 0.1704% (0.318%), and China by 0.0814%, respectively. In terms of the elasticities intensity, even though the favorable impact is documented in the long run and short run, the inflows of FDI are more responsive in the long run. Moreover, the asymmetric assessment exposed the positive (negative) shocks in FI that are positively connected in the long and short runs. Precisely, a 10% positive (negative) change in FI, according to the magnitudes, exhibits that the FDI inflows will be augmented (degraded) in Brazil by 1.398% (0.334%), Russia by 1.123% (0.546%), India by 1.273% (0.412%), and China by 1.262% (0. 592%). In terms of the elasticities of asymmetric variables, the positive innovation establishes a significant impact on the FDI inflows in comparison to the negative shocks, implying that progressive changes in the financial system with the inclusion and diffusion of innovative financial products and services portray the financial development with efficiency, which entice foreign investors to transfer their capital and technological support to that economy. The literature advocated that the investment in research and development expand skills and human capital development and more innovative infrastructure, characterized by the protection of property rights, trade openness and national innovation [113,114,115]

The association between environmental innovation and FDI in the BRIC nations has explored positively the connections in a symmetric and asymmetric framework, signifying the beneficiary effects of innovation in managing the environment on the FDI inflows by enticing foreign investors to transfer technology and mobilizing capital. Our study findings are supported by [87]. In terms of the symmetric assessment, in the long run (short run), a 1% increase in investment for R&D targeting the green innovation which results in a positive momentum in receiving FDI in the host economy, in the long run (short run), particularly in Brazil by 0.1134% (0.0565%), Russia by 0.0417% (0.0338%), India by 0.0842% (0.0413%), and China by 0.1277% (0.0112%), respectively. Study findings established an encouraging effect on FDI. Environmental innovation has dual effects on the economy, increasing the FDI inflows and promoting environmental sustainability. Usman, Radulescu, Balsalobre-Lorente and Rehman [105] advocated that environmental efficiency and ecological balance can be obtained through cultivating innovation focusing on environmental protection, meaning that investment in environmental protection improves energy efficiency, leading to environmental sustainability. In terms of the magnitudes of the asymmetric coefficients, it is apparent that positive increments in EI have significant effects on FDI both in the long and short runs, compared to the negative trends in EI. Therefore, it is suggested that, for attracting the foreign investors in mobilizing their technology and capital in the host economy, it is imperative to ensure the continual inflow of investment in innovation concentrating on environmental protection through green technology. Precisely, the inflows of FDI will be accelerated (reduced) in Brazil by 1.191% (1.089%), Russia by 1.375% (0.355%), India by 0.832% (0.266%), and China by 0.751% (0.154%) due to a 10% improvement (dilapidation) in innovation focusing on environmental protection.

7. Conclusions

The purpose of the study is to evaluate the effects of innovation, which is categorized by the technological (TI), financial (FI), and environmental (EI), groups, on the FDI inflows in the BRIC nations for the period 1990–2019. In documenting the association and the explanatory variables’ magnitudes on FDI, we implemented several econometrical techniques, including the Bayer-Hanck combined cointegration test, the augmented ARDL, the nonlinear ARDL, and the Fourier TY casualty test. The key findings from the study are as follows.

First, in terms of the stationary test, the test statistics have revealed that all the variables are stationary after the first difference. Moreover, the structural break test divulged one break year in the data set, and the study incorporated the structural break effects in line with the explained variable.

Second, the long run association between the explanatory explained and control variables has been unveiled with the test statistics of the combined cointegration. Furthermore, the long run cointegration in the empirical equation has been found in the linear and nonlinear assessments.

Third, In terms of the symmetric investigation, the coefficients of innovation, that is, TI, FI, and EI on FDI, were revealed to be positive and statistically significant at a 1% level, suggesting that the innovation culture in the economic boost of the inflows of FDI, both in the long run and the short run.

Fourth, the asymmetric association was confirmed by implementing the standard Wald test with the null symmetry hypothesis in the long and short runs. Referring to the asymmetric coefficients, it is apparent that the positive and negative shocks of TI, FI, and EI, positively tie to FDI; according to the elasticities of the asymmetric shocks, the positive innovation disclosed a more prominent impact than the negative innovation. Thus it is advocated to ensure a conducive innovation environment through mobilizing the economic resources.

The following suggestions have been proposed from the study findings.

• The innovation-led FDI advocated the beneficial role in attracting foreign investors and mobilized their resources in the BRIC nations. Thus, taking note of the study findings, it is suggested that the innovation policies in the BRIC nations have to be aligned with the industrial, economic, and financial policies. That is, the industrial absorption capacity in accepting innovative technology in their process requires substantial capital investments, and in some instances, industries have exhibited a disinclination for technological improvement. Thus, the BRIC nations have to devise incentives for industrial development through technological advancement. This economic ambiance eventually entices the investors to make favorable investment decisions and transfer capital and technology, in the form of FDI.
• Governmental effectiveness was revealed to be positively connected with FDI. On the ground of further improvement in the inflows of FDI, the study advocated that the BRIC nations have to ensure the investors’ protection, good governance, and political stability in inducing foreign investors to mobilize the economic resources in the host economy from the home economy.
• Economic stability has been considered the key deterministic factor in encouraging the FDI inflows, suggesting that instability and uncertainty adversely impact foreign investor decisions, especially in the long run. The protection of investors’ rights and economic accountability was found to be a catalyst in the equation of FDI and was confirmed by the existing literature [15,16,116,117]. For instance, Chen, et al. [118] advocated for the favorable effects of good governance on FDI by highlighting the beneficiary role of good governance in mitigating economic risks, such as environmental and political risks and offering a conducive ambiance for progress. Therefore, we suggested that the BRIC nations ensure and depict an investment-oriented economy with an efficient institution that confirms a governmental effectiveness and the protection of investor rights.