Green economic growth in BRIC and CIVETS countries: The effects of trade openness and sustainable development goals

Abstract

This study aims to analyze the impact of trade openness and Sustainable Development Goals, Financial Development, and Technology on the economic growth of Brazil, Russia, India, China and Colombia, Indonesia, Vietnam, Egypt, Turkey, South Africa countries. The present analysis employs a balanced panel data set from 1996 to 2022. This study also uses various tests, such as the Johansen–Fisher cointegration and Granger causality test. The study's findings suggest that economic growth, trade openness, Sustainable Development Goals, financial development, inflation, technology, labor forces, and financial openness have a long-term relationship among them. In the long run, a positive relationship exists between economic growth, trade openness, and the sustainable development goals index in (BRIC) and (CIVETS) countries. Based on the heterogeneous panel non-causality tests, the findings demonstrate that trade openness and Sustainable Development Goals are a unidirectional causality between trade openness, Sustainable Development Goals, and economic growth.

1. Introduction

Trade openness and Financial development are a crucial factor in fostering sustainable economic growth in any country, as they serve as a vital means of ensuring long-term viability for nations [1]. Economic growth, as indicated by the rise in per capita national output, enables an enhanced material standard of living and reduces poverty. Sustainable development refers to the practice of satisfying the current generation's demands while ensuring that future generations may also meet their requirements. It specifically focuses on the responsible use of natural resources, the condition of the environment, and fairness between different generations [2]. In the current era of globalization, the interconnectedness of worldwide trade networks leads to the rapid deterioration of natural habitats, even in locations far away from where the goods are ultimately consumed [3].

In comparison, the negative consequences of economic success and economic inequality have been verified. The significance of worldwide trade as a catalyst for endangering species is inadequately comprehended [4]. Zaman, Pinglu [1] Found that trade openness has a negative impact on economic growth, but financial development has a positive effect on economic growth. Renewable energy acts as a universal remedy for achieving sustainable development within the path of economic progress [5].

There is a beneficial transfer of carbon emissions between different geographical areas in China. Concurrently, a study observe that semi-urbanization not only enables the reduction of emissions in a specific area, but also substantially decreases carbon emissions in nearby areas in China, finally indicating a substantial and adverse overall impact. In addition, the process of semi-urbanization has resulted in a reduction in carbon emissions. Specifically, for every 1 % rise in semi-urbanization, there is a corresponding decrease of 0.803 % in carbon emissions [6].

Trade openness (TO) indicate a country's involvement in the global commercial system. TO is one approach to measure a country's participation in the worldwide trading system. It has been stated that increased TO results in numerous economic benefits, such as enhanced technology transfer, labor, productivity, economic growth, and sustainable development. Economic growth, job creation, and poverty reduction can only be achieved if the economy is open to new ideas and opportunities [7]. The Millennium Development Goals (MDGs) were replaced with the Sustainable Development Goals (SDGs) in 2015. While the MDGs focused on enhancing well-being in emerging nations, the SDGs attempt to reconcile global economic, social, and environmental objectives [8].

International trade has been the primary driver of economic expansion and overall progress. Trade openness has been recognized as a factor contributing to the process of structural transformation in the economy [9]. SDGs have become an essential mission, and this research targets several countries worldwide, especially those that belong to the BRIC (i. Brazil, ii. Russia, iii. India, and iv. China) and CIVETS (v. Colombia, vi. Indonesia, vii. Vietnam, viii. Egypt, ix. Turkey and x. South Africa) groups. Emerging market economies (like BRIC and CIVETS) are the worlds most important for foreign trade. This economic expansion would be made possible due to the lower labor and production costs in the BRIC countries [10,11]. In 2009, the Economist Intelligence Unit (EIU) came up with the term as a reference to the CIVETS countries, which were thought to be the next rising stars of emerging market countries [12].

Developing economies have met their SDGs. To do this, countries in the developing world must shift their focus away from short-term gains and toward building a solid infrastructure that will promote long-term economic growth. In addition, it possesses the following two benefits: To begin, the implementation of new technological innovations will lead to a rise in productivity. As a result of this, there will be an increase in the demand for outputs, which is the fundamental force responsible for economic growth over the long run. Second, both classical and neoclassical economists agree that international trade is an important “growth engine” that contributes significantly to the expansion of the global economy but also plays a vital role in developing economic growth.

Additionally, trade openness motivates more production, which is why it is generally acknowledged that an open economy has a more significant influence on a quicker rate of economic growth when compared to a closed economy. Socioeconomic performance is treated in light of economic growth's environmental and social costs [13]. SDGs became a way to make progress on social and environmental issues. SDGs are a big idea with many parts. Its main goal is to improve things in the economic, social, and ecological realms [14]. Houssam, Ibrahiem [15] Demonstrated a positive and statistically significant correlation between the green economy (GE) and GDP per capita, as well as the level of total unemployment. Conversely, there is a negative and statistically significant correlation between the GE and the poverty rate in emerging nations.

The previous studies focus on domestic innovations and trade openness [16]. Arif, Sadiq [17] Focus on financial development, Economic growth, and trade openness. Hossain, Roy [18] Focus on foreign direct investment, trade openness and economic growth. Akayleh [19] Focuses on trade openness and economic growth. Khan, Hossain [20] Financial development, economic development, and trade openness. Ridzuan, Ismail [21] Focus on Foreign direct investment and trade openness. Le [22] Focuses on energy growth, financial development, and trade openness. Houssam, Ibrahiem [15] Focus on green economy and sustainable development in developing countries. Adebayo, Samour [23] Focus on Natural resources, trade globalization, and ecological sustainability. Adebayo, Bekun [24] Focus on Economic growth and emissions. Bekun, Adekunle [25] Focus on Sustainable electricity consumption in South Africa. When we have focused on “Green Economic Growth in BRIC and CIVETS Countries: The Effects of Trade Openness and Sustainable Development Goals”.

The manuscript is highly important for BRIC and CIVETS economies due to its economic significance. The concept of trade openness presents domestic enterprises with opportunities to access untapped markets, hence fostering enhanced productivity and the generation of novel ideas. Engaging in economic endeavors plays a crucial role in fostering global prosperity and expanding opportunities for individuals while concurrently promoting peace and ensuring the safety of all individuals [7,26,27]. In conclusion, this study reveals a research gap in countries that belong to the BRIC and CIVETS countries. The following research questions will address the research gap and contribute to the sustainable economic growth of the BRIC and CIVETS emerging markets.

• 1)How does trade openness affect the economic growth in BRIC and CIVETS countries?
• 2)What is the impact of the SDGs index on the economic growth of BRIC and CIVETS countries?
• 3)What is the relationship between financial development and economic growth in BRIC and CIVETS countries?
• 4)What is the impact of technology and economic growth in the BRIC and CIVETS countries?

This study aims to analyze the impact of trade openness and Sustainable Development Goals (SDGs), Financial Development, and Technology on the economic growth of Brazil, Russia, India, China (BRIC) and Colombia, Indonesia, Vietnam, Egypt, Turkey, South Africa (CIVETS) countries. The Levin-Lin-Chu (LLC) unit root test (URT) is employed to determine the order of integration for all variables. The present study used the ordinary least squares (OLS) technique for conducting short-term regression analysis. The findings show a positive relationship between economic growth, trade openness, and the SDGs index in BRIC and CIVETS countries. This study suggests a crucial guideline for policymakers. When policymakers make any decisions about trade openness, they must keep in mind that trade openness can influence SDGs in both positive and negative ways. When trade grows, it can cause pollution and the depletion of natural resources. Both of these things can directly affect the environment.

The rest of this study is divided into the following sections: 2) literature review; 3) methodology; 4) analysis and result in discussion; and finally, 5) conclusion and the research implication, respectively.

2. A comprehensive literature review

2.1. Theoretical literature

This essay does not extensively analyze the theoretical literature regarding the relationship between trade openness and economic growth. This section summarizes fundamental theoretical principles that have led and influenced a significant portion of the related empirical research.

Trade openness plays a crucial role in attaining more significant economic growth, as supported by previous research. Posner [28] Observed that technological advancements and progress can impact trade. Fagerberg [29] Demonstrated that international competitiveness is contingent upon technological competitiveness and the capacity to compete in delivery across nations. Theoretical studies suggest that technological development can have a more significant influence on trade patterns between countries than the comparative advantage theory. Trade inside developed economies is characterized by intra-industry transactions, which are influenced by the increasing innovations in these economies [16].

The Environmental Kuznets Curve (EKC) hypothesis posits that pollution levels increase with economic expansion until a certain threshold is reached. Beyond this threshold, further economic growth decreases environmental pollution [30,31]. This concept has been supported by studies conducted by Refs. [[32], [33], [34], [35]].

Tiwari, Shahbaz [33] Discovered a connection between economic growth and environmental performance over the long term. Akadiri, Bekun [32] Found that economic expansion has a favorable and substantial impact on carbon emissions, both in the short term and the long term.

Cetin, Ecevit [36] Conducted an empirical investigation on the trade openness of Turkey from 1960 to 2013, to examine the Environmental Kuznets Curve (EKC) theory. The results of the study provide empirical evidence of the positive effect of the Environmental Kuznets Curve (EKC) view, indicating that heightened levels of trade openness have a positive impact on carbon emissions within the context of Turkey. There exists a positive correlation between carbon emissions and economic growth, trade, as well as financial openness [37]. Koengkan, Fuinhas [38] Used data from 1980 to 2014 to investigate the correlation between MERCOSUR countries' financial openness and their levels of carbon emissions. According to the findings, more financial openness led to higher carbon emissions.

In contrast, A study conducted by Ayesha, Tariq [39] aimed to evaluate the plausibility of the Environmental Kuznets Curve (EKC) hypothesis by analyzing trade openness data spanning from 1995 to 2017. The results provide support for the validity of the Environmental Kuznets Curve (EKC) theory, which posits that there is no statistically significant correlation between trade openness and environmental degradation. EKC theory explains a relationship between environmental pollution and economic growth that follows an inverted U-shaped pattern [40].

Daly [41] argues that production-driven economies are inherently unsustainable due to their reliance on a steady-state economic model. Dogan and Inglesi-Lotz [42] Demonstrate that the Brundtland curve is present in Europe when the proportion of industrial activity represents the economic status. The research explores the role of the economic structure of European nations in the environmental Kuznets curve between 1980 and 2014 using the completely modified ordinary least squares technique. The authors affirm the presence of the environmental Kuznets curve when economic growth is employed as the indicator of economic structure. However, they identify the Brundtland curve when the industrial share is utilized instead.

Nevertheless, in the neoclassical context of the Solow [43] model, economic growth relies on accumulating the productive factors labour and capital. Capital income taxes can have a detrimental impact on both economic growth and per capita income in a steady state. The adjustment between equilibrium levels may require a significant period, years or even decades. The concept of endogenous growth has linked various economic decisions, such as education and research and development (R&D) expenditure, to overall economic development. Financial regulation can influence these decisions.

Adebayo, Samour [23] Referred that economic development and the exploitation of natural resources have a negative impact on ecological quality in the BRICS nations. In contrast, the use of renewable energy and increased trade globalization have a positive effect on environmental quality.

2.2. Trade openness (TO) and sustainable development goals

Greater trade openness contributes to higher economic growth [44]. Adopting financial transparency is a viable strategy for contemporary industrialized nations [45].

TO has created more opportunities for developing countries, allowing them to access resources and technology from abroad that would have otherwise been out of reach [46,47]. Capital, labour, financial development, TO, and the government's involvement in public expenditure and institutional quality are all critical components in modern economic growth models, which consider this when predicting future economic growth. A proper financial system has the potential to encourage technical improvement and efficiently allocate resources to the manufacturing sector, both of which are seen as essential components of a foundation for long-term SDGs [48]. According to prior research, TO promotes economic, social, and environmental prosperity [49,50]. On the other hand, the consumption of fossil fuel energy, and agricultural activities in South Africa is detrimental to environmental sustainability, highlighting a compromise between economic progress and environmental well-being [51]. The movement of trade across different economic regions, especially the growth of the financial sector, mitigates the negative impact of environmental degradation [52].

2.3. Economic growth and trade openness

The degrowth concept usually posits that economic growth and environmental protection are incompatible. A study conducted by Lee, Olasehinde-Williams [53] demonstrates that both green trade and economic complexity have positive individual impacts on the environment. An increase in the stringency of environmental policies by a certain percentage results in a corresponding rise of 0.005 % in the positive effect of green trade on sustainable development. There is a positive correlation between the level of sustainable development and the influence of strict environmental policies on the connection between green commerce and sustainable development [54]. Sustainable investment is a crucial market-oriented strategy for attaining inclusive green growth. To accomplish the goal of inclusive green growth, it is essential for enterprises that offer sustainable products to be financially viable to attract private investment [55].

The positive impact of TO on economic growth has faced scrutiny because of the significant energy requirements associated with it, leading to environmental damage [56]. There is a great deal of controversy and debate around economic growth, which has been the subject of extensive research and investigation in published works. For example, Al-Jafari [57] showed that more economic growth is bad for the environment since it will increase pollution and the rate of global warming. However, the EKC theory states that environmental pressures increase with rising income at the beginning of economic development but decrease as wealth continues to climb [58]. Trade openness has a positive effect on the economy's growth over the long term [59] [Raghutla [27,60]. showed a positive impact on TO and economic growth in BRICS countries.

H1

There is a positive relationship between economic growth and trade openness.

2.4. Economic growth and sustainable development goals

The environmental Kuznets Curve has dominated the empirical discussion on the link between the economy and ecology for decades [61]. EKC argues that economic growth should be seen as an enabler of better environmental quality rather than a source of environmental degradation. There are three things to remember here: To begin with, technological advancements, replacement processes, and shifts in demand all have the potential to lessen growth's resource intensity. An open political system that responds to public concerns about environmental quality is especially advantageous. Second, the association between EKC and a small subset of medications has only been established. For a third reason, the EKC's “turning point” may be a level of income that is far too high for global realization since the ecosystems would collapse before such a turning point could be reached. According to the EKC theory, there is no reason to expect an “automatic” separation of economic activity and environmental concerns in the future [2]. The better material quality of living and the reduction of poverty are outcomes made possible as a direct result of economic growth, measured by improvements in national output per capita. This notion of SDGs focuses on addressing today's demands without sacrificing tomorrow's [2].

Sheikh, Malik [62] Examined BRICS countries from 1990 to 2016. They found a positive association between economic growth and SDGs in the BRICS countries. It is predicted that the regression coefficient is 0.170, which indicates that a change of one percentage point in economic growth will induce a shift of approximately 0.170 percentage points in the sustainability of the BRICS countries. This study found that economic growth is increased by 0.125 % when one (1 %) percentage increase in sustainable development goals. The GDP showed a negative effect on both economic and environmental sustainability [63].

H2

There is a positive relationship between economic growth and sustainable development.

Our study findings and empirical analysis indicate that trade can have both positive and negative effects on sustainable development. There is an ongoing and persistent requirement to assist emerging nations as they strive to integrate into the multilateral trade system and reap the benefits of their involvement. The increase in trade activities presents a substantial threat to the viability of numerous species, primarily due to habitat degradation or contamination.

The conceptual framework of this study is illustrated in Fig. 1, delineating the trajectory of this specific research undertaking. Here, economic growth is the dependent variable, while trade openness and SDGs are the leading independent variables. Financial development, technology, labor force, financial openness, and inflation are control and macroeconomic variables, respectively. All the factors influence economic growth.

Fig. 1.

Intellectual framework of this study.

In summary,

Arif, Sadiq [17] Assert that financial development exerts a beneficial and substantial influence on the long-term and short-term dynamics of economic growth in South Asian economies. Trade openness has a beneficial impact on economic growth. Le [22] Indicates that energy consumption, investment in fixed assets, government spending, financial development, and trade openness have a positive and significant effect on economic growth in the examined emerging market and developing economies (EMDEs). Oloyede, Osabuohien [64] Demonstrated a favorable correlation between economic growth rate and trade openness in both the combined simulated Economic Community of West African States (ECOWAS) and Southern African Development Community regions, as well as in the individual Africa's regional economic communities (RECs) region. However, this correlation was found to be statistically insignificant. The findings highlight the importance of the government and other relevant stakeholders implementing and enforcing policies to translate the observed economic growth into significant trade benefits and increased trade openness in ECOWAS and SADC. Elfaki, Handoyo [65] Utilized the autoregressive distributed lag (ARDL) model to calculate the long-term and short-term relationship between the variables. The results obtained from the autoregressive distributed lag (ARDL) analysis demonstrate that industrialization, energy consumption, and financial development have a beneficial impact on long-term economic growth.

Nevertheless, the progress of the financial sector and the degree of trade openness adversely impact the economic growth rate. Houssam, Ibrahiem [15] Demonstrated a positive and statistically significant correlation between the green economy (GE) and GDP per capita, as well as the level of total unemployment. Conversely, there is a negative and statistically significant correlation between the GE and the poverty rate in emerging nations.

3. Methodology

The present study employs a balanced panel data set of ten emerging economies, covering the period from 1996 to 2022, to conduct the research. In order to show an empirical inquiry, a determination was made to gather data on the BRIC and CIVETS countries. These nations are widely acknowledged as developing economies. The entirety of the information contained within this research has been exclusively derived from secondary sources. Table 1 provides a complete overview of the data sources, including detailed descriptions, units of measurement, and the sources from which the information was obtained.

Table 1.

Descriptions of all the variables.

Elements	Sign	Measurement formula	Sources
Dependent Variable
Economic Growth	EG	GDP per capita	WDIa
Independent Variable
The ratio of exports + imports to GDP	EIGDP	The ratio of exports + imports to EIGDP.	WDI
Export-to-GDP ratio	EGDP	The export-to-GDP ratio (EGDP).	WDI
Import-to-GDP ratio	IGDP	The import-to-GDP ratio (IGDP).	WDI
Sustainable Development Goals	SDG	Sustainable development index (SDG Index) of each country each year.	SDG Indexb
Financial development	FD	Domestic credit to the private sector by banks (as a percentage of GDP) is used as a proxy for financial development (FD).	WDI
Consumer price index	IFR	Consumer price index.	WDI
Technology	Tech	Residents and non-residents alike utilize technology (Tech) as a metric of total patent applications.	WDI
Labour force	LBR	Total labour force (LBR)	WDI
Country-level Variable
Financial Openness	FOP	KAOPEN (The Chinn-Ito Index) The index ranges from 0 to 1, where a higher value indicates more financial openness.	KNOEMAc

Note: World Development Indicator (WDI); Sustainable development goals (SDGs) index.

^ahttps://databank.worldbank.org/source/world-development-indicators.

^bhttps://dashboards.sdgindex.org/profiles.

^chttps://knoema.com/NBERCIFOI2017/the-chinn-ito-financial-openness-index.

3.1. Econometric model

The authors employed a balanced panel data set of ten emerging economies covering 1996–2022 to conduct this research. This study uses the following equation (1) to analyze the results:

$${EG}_{\text{it}}=\mathrm{C}+{\mathrm{\beta }}_1{TO}_{it}+{\mathrm{\beta }}_2{SDG}_{it}+{\mathrm{\beta }}_3\sum _{\mathrm{c}=1}^{\mathrm{C}}{\mathrm{Y}}_{\text{it}}^c+{\in }_{\text{it}}$$ (1)

The country and year are denoted subscripts i and t, respectively. Here, EG indicates economic growth used as the dependent variable in this study. The independent variable is trade openness, which is proxied by the ratio of exports + imports to GDP (EIGDP), export-to-GDP ratio (EGDP), and import-to-GDP ratio (IGDP). The other independent variables are the Sustainable Development Goals (SDGs) index, Financial development (FD), Consumer Price Index (INF), Technology (Tech), and Labour force (LBR), Financial Openness (FOP). This study uses the consumer price index as inflation (INF). C stands for constant. ${{\mathrm{\beta }}_1,\mathrm{\beta }}_2{\text{and}\mathrm{\beta }}_{3}areestimatorparameters\text{.}$ $\in$ is the error term, and Y is with superscripts c are the vectors of control variables.

Three metrics of TO and the measurement of TO are used in this paper (see Equations (2b), (2c), (2d))).

$${\text{TO}}_{\text{it}}=\mathrm{C}+\lambda {EIGDP}_{it}+{\in }_{\text{it}}$$ (2a))

Here, EIGDP indicates exports plus (+) imports to GDP.

$${\text{TO}}_{\text{it}}=\mathrm{C}+\lambda {EGDP}_{it}+{\in }_{\text{it}}$$ (2b)

Here, The export/GDP ratio is denoted by the symbol (EGDP).

$${\text{TO}}_{\text{it}}=\mathrm{C}+\lambda {IGDP}_{it}+{\in }_{\text{it}}$$ (2c)

Here, The import/GDP ratio is denoted by the symbol (IGDP).

Finally, everything comes down to this:

$${TO}_{\text{it}}=\mathrm{C}+{\mathrm{\beta }}_1\sum _{\mathrm{h}=1}^{\mathrm{d}}{\text{TO}}_{\text{it}}^d+{\in }_{\text{it}}$$ (2d)

This experiment pooled all the independent factors to see how they affected the outcome. Last but not least, Equation (1) of the Econometric model expands to:

$${\text{EG}}_{\text{it}}=\mathrm{C}+{\mathrm{\beta }}_1\sum _{\mathrm{h}=1}^{\mathrm{d}}{\text{TO}}_{\text{it}}^d+{\mathrm{\beta }}_2{SDG}_{it}+{\mathrm{\beta }}_3\sum _{\mathrm{c}=1}^{\mathrm{C}}{\mathrm{Y}}_{\text{it}}^c+{\in }_{\text{it}}$$ (3)

The utilization of the term “TO” have used as a metric for quantifying both the extent of our exports and imports. Economic growth and financial development are variables expressed as a % of GDP in this paper. SDG is represented by the SDG index, the consumer price index (INF), and the FOP (KAOPEN index). Numerical values are assigned to variables like technology and labor. Furthermore, because each data series is a different unit, it is critical to normalize the data series that has been picked. As a result, we converted the data series into a single unit for the sake of this proposal. The following parameters can be used to parameterize equation (4):

$${EG}_{\text{it}}={TO}_{it}^{\beta 1i}+{SDG}_{it}^{\beta 2i}+{FD}_{it}^{\beta 3i}+{\mathit{INF}}_{it}^{\beta 4i}+{Tech}_{it}^{\beta 5i}+{LBR}_{it}^{\beta 6i}+{FOP}_{it}^{\beta 7i}$$ (4)

It is widely acknowledged that all data should be transformed into natural logarithms, and some research has done so [27]. The Empirical equation (5) incorporates all of the dynamic qualities of the sample data, allowing for a thorough examination of the effect of various factors and interactions. equation (5) is written as follows:

$${lnEG}_{\text{it}}={{\beta }_{1i}lnTO}_{it}+{{\beta }_{2i}lnSDG}_{it}+{{\beta }_{3i}lnFD}_{it}+{{\beta }_{4i}lnINF}_{it}+{{\beta }_{5i}lnTech}_{it}+{{\beta }_{6i}lnLBR}_{it}+{{\beta }_{7i}lnFOP}_{it}+{\mathit{ϵ}}_{\mathit{i}\mathit{t}}$$ (5)

Where ${\beta }_{1i},{\beta }_{2i},{\beta }_{3i},\dots \dots \dots \dots \dots {\beta }_{7i}$ which are the elasticity of economic growth concerning (i) trade openness, (ii) the Sustainable Development Goals index, (iii) financial development, (iv) inflation, (v) technology, (vi) the total labor force, and (vii) financial openness.

3.2. Summary statistics and correlation

Table 2 shows descriptive results, which also include the correlation matrix.

Table 2.

Summary statistics and correlation matrix.

Criteria	EG	TO	SDG	FD	INF	Tech	LBR	FOP
Mean	161.527	1347.352	62.781	91.469	7.106	217.441	438.508	61.249
Standard Deviation (SD)	16.521	244.354	8.523	11.412	3.123	9.347	49.471	5.233
Minimum	137.417	823.832	46.428	68.218	3.038	169.217	287.291	43.301
Maximum	224.634	2035.644	82.778	117.551	18.322	285.573	495.677	81.462
Obs.	270	270	270	270	270	270	270	270

Correlation Matrix
Variables	1	2	3	4	5	6	7	8
	EG	TO	SDG	FD	INF	Tech	LBR	FOP
1	1
2	0.468*	1
3	0.382**	0.425**	1
4	0.392**	0.185**	0.380**	1
5	0.137**	0.147**	0.317*	0.027*	1
6	0.261**	0.329*	0.461**	0.014**	0.202*	1
7	0.227**	0.270**	0.310**	0.143*	0.093*	0.135*	1
8	0.173*	0.129*	0.229*	0.107**	0.281*	0.204*	0.415*	1

**, * specifies 1 % and 5 % significance levels, respectively.

The mean of EG is 161.527, with a standard deviation of 16.521. Average EG values at a modest level imply that the BRIC & CIVETS countries are more likely than other countries to accomplish economic growth. TO, SDG, FD, INF, Tech, LBR, and FOP mean 1347.352, 62.781, 91.469, 7.106, 217.441, 438.508, and 61.249, respectively, with standard deviations of 244.354, 8.523, 11.412, 3.123, 9.347, 49.471 and 5.233. Furthermore, we may conclude that multicollinearity issues do not undermine our findings due to a maximum value of less than 0.80 [66,67].

4. Analysis and results discussion

4.1. The Levin–Lin–Chu (LLC) unit root test (URT)

This study uses the Levin–Lin–Chu URT test to determine the unit root test, as our data set is balanced panel data. Table 3 shows the Levin–Lin–Chu URT. First, identify the order of integration for all variables. Furthermore, selecting the appropriate econometric models is a fundamental step, and we were able to achieve our study objectives through the use of these strategies. When conducting an empirical investigation, the author ensured that no variables were integrated into a two-step sequence I (2), which is known to create inaccurate and misleading results. We employ three-unit root tests (Level, constant & trend, and none).

Table 3.

Levin–Lin–Chu (LLC) unit root test.

Variables	Intercept in test equation
	Statistic	P-value	Statistic	P-value
$\Delta$ EG	0.234	0.162	14.027	0.000
$\Delta$ TO	1.278	0.196	10.012	0.000
$\Delta$ SDG	0.837	0.172	1.902	0.000
$\Delta$ FD	−1.038	0.128	−3.417	0.000
$\Delta$ INF	−4.927	0.281	−3.073	0.000
$\Delta$ Tech	1.063	0.140	1.573	0.000
$\Delta$ LBR	0.821	0.216	0.114	0.000
$\Delta$ FOP	0.163	0.086	−2.731	0.000
	Level		First Difference I (1)

Variables	Trend and Intercept in test equation
	Level		First Difference I (1)
	Statistic	P-value	Statistic	P-value
$\Delta$ EG	3.204	0.131	3.178	0.000
$\Delta$ TO	1.362	0.102	5.032	0.000
$\Delta$ SDG	0.025	0.129	2.018	0.000
$\Delta$ FD	−1.038	0.164	−3.002	0.000
$\Delta$ INF	−2.476	0.134	−2.183	0.000
$\Delta$ Tech	1.748	0.217	1.038	0.000
$\Delta$ LBR	2.831	0.194	2.025	0.000
$\Delta$ FOP	0.341	0.157	−0.021	0.000

Table 3 shows only two unit root test results (URT) (Level, constant, and trend) conducted in this study. The LLC unit root test was used in this investigation. To ensure accurate results, it is imperative to determine the appropriate lag lengths for this test. Therefore, we have opted to select the lag length based on the criterion known as the “Schwartz” criterion. Table 3 findings indicate that the variables of economic growth, TO, SDG, INF, Tech, LBR, and FOP highly accepted the null hypothesis of non-stationary.

In contrast, when the results of this study were applied to the first difference I (1) data series, the results of EG, TO, SDG, INF, Tech, LBR, and FOP highly rejected the null hypothesis of non-stationary at the one percent significance level. Based on the findings of the conducted tests, it may be inferred that the selected variables exhibit stationary at their first differences, denoted as I (1). Furthermore, it is worth noting that all the variables chosen show an order of integration of I (1). The presence of a potential long-term association among EG, TO, SDG, INF, Tech, LBR, and FOP has been investigated and documented in Table 8.

Table 8.

The output elasticities in the long-term.

Variable	Coefficient
TO	0.08184** (2.28866)
SDG	0.12575*** (2.57164)
FD	0.02428 (1.46729)
INF	−0.12384*** (−4.81478)
TECH	0.010365** (2.503676)
LBR	0.002358*** (15.141)
FOP	0.000133 (0.014057)
C	10.84661*** (4.454056)
Schwarz criterion	4.232281
Prob(F-statistic)	0.0000
R-squared	0.566806
Adjusted R-squared	0.554275

Note: Dependent variables are referred to as EG. The OLS approach yielded these outcomes. The levels of significance indicated by *** and ** are, respectively, 1 and 5 % of the total.

4.2. Augmented-Dickey-Fuller test (ADFT)

Table 4 shows the error correction term (ECT) has a unit root test.

Table 4.

Error correction model.

			t-value	Prob.*
Augmented-Dickey-Fuller test (ADFT) statistic			−14.7612	0.0000
Critical values at level	1 %		−3.45651
	5 %		−2.87295
	10 %		−2.57293
Variable	Coefficient	Std. Error	t-value	Prob.
ECT(-1)	−0.93661	0.063451	−14.7612	0.0000
C	0.004932	0.116858	0.042208	0.9664

Note that the ECT model has a unit root, and that the exogenous variable has a constant value. The lag length is 0 (Automatic, based on SIC, maxlag = 10). * indicates the one-sided p-values calculated by MacKinnon (1996). Equation for the augmented Dickey-Fuller test in which the dependent variable is denoted by ECT and the results are shown using the OLS method.

The probability value of the ADFT is statistically significant at the 1 % level, suggesting strong evidence that the process being analyzed, specifically the ECT, is stable. Consequently, the null hypothesis is rejected. The error correction model pertains to models that exhibit long-term relationships, assuming that the error term is regular at order 1(0) at the level and that all variables in the model are stationary at order I (1) after differencing. This observation signifies the existence of cointegration or a long-term relationship over a prolonged period.

4.3. Short-term regression analysis

The short-term coefficient regression analysis is shown in the following Table 5.

Table 5.

Short-term regression analysis.

Variable	Coefficient
C	0.004092 (0.034607)
D(TO)	−0.00147*** (−2.64902)
D(SDG)	−0.03006** (−2.55168)
D(FD)	−0.02004 (−1.46382)
D(INF)	−0.22245*** (−6.40504)
D(TECH)	0.011142*** (3.81943)
D(LBR)	0.002287*** (20.59244)
D(FOP)	0.00236 (0.351309)
ECT(-1)	−0.93512*** (−14.4922)
Schwarz criterion	4.247757
Prob (F-statistic)	0.00000
R-squared	0.770954
Adjusted R-squared	0.763319

Note: Dependent variables are referred to as D(EG). The OLS approach yielded these outcomes. The levels of significance indicated by *** and ** are, respectively, 1 and 5 % of the total.

D(TO), D(SDG), D(FD), D(INF), D(TECH), D(LBR), and D(FOP) are all short-term coefficients with p-value 0.000, 0.004,0.135, 0.001,0.002,0.000,0.127 respectively. To say it another way: The ECT (−1) value is negative & significant at a 1 % level (P = 0.000). It means that long-term association is possible. It is an independent variable in the short-run model. For the short-term model, we employ lag ECT as an independent variable. In this case, the ECT (−1) coefficient is 93.53 %, and the p-value is 0.000, indicating a rapid return to equilibrium. In this case, the annual growth rate is 93.51 percent.

4.4. Johansen–Fisher panel cointegration test (J-FPCT)

Table 6 shows the Johansen–Fisher panel cointegration test.

Table 6.

Johansen–Fisher panel cointegration test.

Hypothesized		Trace	0.05
No. of CE(s)	Eigenvalue	Statistic	Critical Value	Prob.**
None *	0.284009	372.2973	187.4701	0.0000
At most 1 *	0.241809	290.4458	150.5585	0.0000
At most 2 *	0.194716	222.6249	117.7082	0.0000
At most 3 *	0.179811	169.5677	88.8038	0.0000
At most 4 *	0.167896	121.0036	63.8761	0.0000
At most 5 *	0.151062	75.97305	42.91525	0.0000
At most 6 *	0.101031	35.84958	25.87211	0.0021
At most 7	0.039035	9.755343	12.51798	0.1387

Hypothesized		Max-Eigen	0.05
No. of CE(s)	Eigenvalue	Statistic	Critical Value	Prob.**
None *	0.284009	81.85148	56.70519	0.0000
At most 1 *	0.241809	67.82088	50.59985	0.0004
At most 2 *	0.194716	53.05725	44.4972	0.0047
At most 3 *	0.179811	48.56406	38.33101	0.0024
At most 4 *	0.167896	45.03054	32.11832	0.0008
At most 5 *	0.151062	40.12347	25.82321	0.0003
At most 6 *	0.101031	26.09424	19.38704	0.0046
At most 7	0.039035	9.755343	12.51798	0.1387

The value displayed above is the result of an unrestricted cointegration rank test (trace).

The value displayed above is the result of an unrestricted cointegration rank test (Maximum Eigenvalue).

At the 0.05 level, the trace test & the Max-eigenvalue test reveal that there are seven cointegrating equations. * means that the hypothesis was found to be rejected at the 0.05 significance level; **MacKinnon-Haug-Michelis (1999) p-values. Where the lags interval is used in the first difference (1–4) in this test.

We evaluate all variables to ensure that they are all in the same order as the previous combination order of one. The trace test identifies seven cointegrating equation(s) at the 0.05 significant level (where, None *p = 0.000, At most 1 *p = 0.000, At most 2 *p = 0.000, At most 3 *p = 0.000, At most 4 * = 0.000, At most 5 * = 0.000, At most 6 * = 0.002, At most 7 = 0.138), and the Max-eigenvalue test identifies seven cointegrating equation(s) at the 0.05 significant level (where, None *p = 0.000, At most 1 *p = 0.0004, At most 2 *p = 0.0047, At most 3 *p = 0.0024, At most 4 * = 0.0008, At most 5 * = 0.0003, At most 6 * = 0.0046, At most 7 = 0.1387). A maximum of 4 lag intervals can be analyzed using the Schwartz information criterion, which selects lag length criteria (in first differences). When the probability value of the J-FPCT is less than the 5 % significance level, it implies that the variables have a long-term relationship. The empirical findings support the hypothesis that the variables have long-term equilibrium relationships. In the long run, economic growth is cointegrated with TO, SDGs, inflation, technology, the labor force, and financial openness, among other factors. The results indicate a significant correlation between the variables for an extended duration.

4.5. Normalized cointegrating coefficients (NCC)

Table 7 shows the normalized cointegrating coefficients.

Table 7.

Normalized cointegrating coefficients.

EG	TO	SDG	FD	INF	TECH	LBR
1	−0.00761 (−0.00767)	−0.39297 (−0.09885)	−0.38006 (−0.1341)	0.10821 (0.41085)	−0.30183 (−0.03836)	−0.002262 (−0.00148)

Note: (standard error in parentheses).

When the EG value is one (1), it indicates that EG depends on the other variables. On the other hand, if TO is negative with a co-efficient of −0.00761 and a standard error of −0.00767. It indicates TO positively impacts economic growth. The result is similar to that [68]. SDGs co-efficient is negative −0.39297, the standard error is −0.09885, the FD co-efficient is negative −0.38006, the standard error is −0.1341, the TECH co-efficient is negative −0.30183, and the standard error is −0.03836. LBR co-efficient is negative −0.002262, and the standard error is −0.00148.

The negative coefficients associated with the variables SDG, FD, TECH, and LBR suggest a positive relationship with economic growth. Hence, it can be inferred that these variables, namely SDG, FD, TECH, and LBR, favorably influence economic growth. In contrast, the coefficient of inflation (INF) is positive 0.10821 with a standard error of 0.41085, suggesting a positive relationship between inflation and its detrimental impact on economic growth in study countries.

4.6. Long-term regression analysis

The investigation findings on long-term elasticities within economic growth are displayed in Table 8.

The OLS method was employed in this investigation to reach this goal. The relationship between EG and TO is positive. It indicates that EG is increased by 8.18 % for every 1 % increase in TO when the p-value is 0.027. The finding is similar to Ref. [64], but the opposite result was found by Ref. [69]. The relationship between EG and SDGs is positive. EG increases by 12.57 % for every 1 % increase in the SDGs index when the p-value is 0.000. The finding is similar to Ref. [15]. The correlation between FD & EG is positive but not statistically significant. It shows that a 1 % rise in financial development is a 2.42 % increase in economic growth when the p-value is 0.127.

On the other hand, the relationship between EG and inflation is negative. It indicates that EG is decreased by 12.38 % for every 1 % increase in inflation when the p-value is 0.001. The relationship between EG and technology is positive. If technology increases by 1 %, EG will be increased by 10.36 % when the p-value is 0.035. The relationship between EG and labor is positive. If the labor force increases by 1 %, EG will be increased by 0.23 % when the p-value is 0.000. The relationship between EG and financial openness is positive. If FOP increases by 1 %, EG will be increased by 0.01 % when the p-value is 0.149.

According to the empirical evidence, it can be inferred that the expansion of trade openness (TO), Sustainable Development Goals (SDGs), financial development, inflation, technology, the labor force, and financial openness have the potential to contribute to economic growth through various mechanisms. The results suggest that these variables have a positive effect on economic growth. The outcomes of this study indicate that the SDGs) have a notable and favorable impact on the development of economies. Moreover, the studies mentioned that TO exert a much more significant positive effect on EG. The study's outcomes suggest that inflation can impede economic progress due to its negative correlation. Based on our findings, it is recommended that both domestic and foreign investors augment their investments in the manufacturing sector.

The study proposes that it would benefit emerging market economies to augment their investment in the industrial sector, enabling them to enhance their output levels. Furthermore, when governments get involved in industrial development initiatives, other countries will have more faith in them and invest more money. It implies that the total percentage of trade openness in these countries is highly influenced by financial development, technology, labor force, and financial openness. Based on the findings of the study, it can be observed that countries that exhibit higher levels of trade openness and technological and economic progress are likely to have accelerated economic growth.

4.7. Granger causality tests (GCT)

The Pairwise GCT is shown in Table 9. The GCT is executed by employing a lag selection method based on the Schwarz Information Criterion (SIC) to ascertain the appropriate number of lags (specifically, 4) for the given dataset. To GCT, it is necessary to ensure that all variables are stationary. To do this, we have transformed all variables into a stationary form denoted as I(1).

Table 9.

Pairwise granger causality tests.

Null Hypothesis:	Obs	F-Statistic	Prob.
TO doesn't Granger Cause EG	266	2.52868	0.1413
EG doesn't Granger Cause TO		1.58799	0.0182
SDG doesn't Granger Cause EG	266	0.77193	0.5444
EG doesn't Granger Cause SDG		1.34571	0.0237
FD doesn't Granger Cause EG	266	0.57007	0.6846
EG doesn't Granger Cause FD		0.55716	0.0442
INF doesn't Granger Cause EG	266	0.89636	0.0568
EG doesn't Granger Cause INF		1.5029	0.0321
TECH doesn't Granger Cause EG	266	0.51214	0.7269
EG doesn't Granger Cause TECH		0.03066	0.9982
LBR doesn't Granger Cause EG	266	3.59832	0.1372
EG doesn't Granger Cause LBR		0.30043	0.8775
FOP doesn't Granger Cause EG	266	0.16363	0.1566
EG doesn't Granger Cause FOP		0.75661	0.0245

The empirical findings suggest that there exists a unidirectional causal connection between economic growth and TO, as evidenced by the p-values of 0.1413 and 0.0182. It implies that an increase in TO will ultimately increase EG.

There exists a unidirectional causal connection between economic growth and SDGs, as evidenced by the p-values of 0.5444 and 0.0237. It implies that an increase in SDGs will ultimately increase EG.

There exists a unidirectional causal connection between economic growth and FD, as evidenced by the p-values of 0.6846 and 0.0442. It implies that an increase in FD will ultimately increase EG.

There exists a two-way causal connection between economic growth and inflation, as evidenced by the p-values of 0.0568 and 0.0321. It implies that an increase in inflation will ultimately increase EG and vice versa.

Therefore, this study could not provide a definitive answer as to the impact of technology or the labor force on economic growth when the p-value is 0.7269, 0.9982, 0.1372, and 0.8775, respectively.

There exists a unidirectional causal connection between economic growth and FOP, as evidenced by the p-values of 0.1566 and 0.0245. It implies that an increase in FOP will ultimately increase EG.

It is determined that economic growth can be attained by the utilization of many strategies, including trade openness (TO), sustainable development goals (SDGs), financial development (FD), and financial openness (FOP). The proposition posits that heightened levels of TO, SDGs, FD, and FOP will ultimately result in amplified economic growth.

5. Conclusion and implication

5.1. Conclusion

Trade openness creates new market opportunities for domestic companies, increasing productivity and innovation due to increased competition. In addition, it helps alleviate poverty, raises salaries, and generates geopolitical benefits from closer economic integration.

The present analysis employs a balanced panel data set from 1996 to 2022. The LLC unit root test is employed to determine the order of integration for all variables. This study also uses various tests, such as ADFT, J-FPCT, NCC, and GCT.

The study's findings show that EG, TO, SDG, financial development, inflation, technology, labor forces, and financial openness have a long-term relationship among them. In the long run, a positive relationship exists between economic growth, TO, SDGs, FD, and FOP in BRICS and CIVETS countries. The proposition posits that heightened levels of TO, SDGs, FD, and FOP will ultimately result in amplified economic growth.

5.2. Policy implication

These findings suggest that if ten emerging countries were more trade- and investment-friendly, technologically and economically progressive, their economies would expand faster. Further, this study suggests that economic growth in these countries is highly influenced by factors such as TO, SDGs, FD, Tech, labor force, and financial openness. It exhibits that higher levels of trade openness, SDGs, FD, and FOP will likely accelerate economic growth. As TO permits economies to increase production, the returns to scale and the economics of specialization improve in the short run. Therefore, the decision-makers in these countries that comprise the BRICS and CIVETS blocs should launch development strategies to boost the additional funds invested in the economic and financial sectors. In addition, trade makes it easier to integrate global trade with the sources of innovation and increases the gain from foreign direct investment (FDI). Overcoming the shortage of finances that plagues the economic and financial sectors can be accomplished with this method. In addition, enhanced TO can result in faster economic growth, advancements in technology and innovation, higher levels of productivity, higher average wages, and expanded employment prospects for the nation's populace.

Finally, it suggests that both the private and public sectors should continue supporting and implementing economic growth in the future to achieve Sustainable Development Goals (SDGs), create employment opportunities, and reduce poverty. But, when policymakers make any decisions about TO, they must keep in mind that TO can influence SDGs in both positive and negative ways. For example, when trade grows, it can cause pollution to rise and the depletion of natural resources. Both of these things can directly affect the environment.

5.3. Future research suggestions and limitations

This study has numerous research prospects. Future research endeavors could investigate the factors influencing carbon efficiency in countries with substantial populations and significant CO₂ emissions, such as the United States. Furthermore, researchers may be enhanced in the future by including digital financial inclusion, environmental, social, and government (ESG), as well as overcome the limitations of this study. One notable constraint of this study pertains to the reliance on economic growth, as measured by GDP per capita, as the dependent variable.

Funding

This research received no external funding.

Ethics approval statement

Not Applicable.

Patient consent statement

Not Applicable.

Available information

There is no further information available regarding this work.

Availability of data and material

SDG Index,1 KNOEMA,2 WDI,3 Sustainable Development Goals⁴, Export-to-GDP⁶, Import-to-GDP⁶, Financial development⁶, Consumer price index⁶, Technology⁶, Labour force⁶, Financial Openness⁵.

CRediT authorship contribution statement

Md. Abdul Halim: Writing – review & editing, Writing – original draft, Software, Methodology, Data curation, Conceptualization. Syed Moudud-Ul-Huq: Writing – review & editing, Software, Formal analysis, Data curation.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Md. Abdul Halim reports a relationship with Mawlana Bhashani Science and Technology University that includes: employment and non-financial support. Md. Abdul Halim has patent No with royalties paid to No. Nothing else that may be perceived to have influenced the submitted work. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.