Assessing the Environmental Sustainability Corridor: Carbon Emissions in Relation to Gold Price, Economic Growth, Foreign Direct Investment, and Renewable Energy Consumption

Abstract

The growing concerns about global warming and its perceived influence on economic sustainability require a reassessment of the environmental consequences of gold mining, with a special focus on BRICS countries: Brazil, Russia, India, China, and South Africa. This paper examines the environmental sustainability corridor, carbon emission, gold price, economic growth, foreign direct investment (FDI), and renewable energy use between 1989 and 2020. The long-run association among the variables is checked by us through the PMG technique. Our findings indicate that while the gold price, FDI, and renewable energy use decrease carbon emission, economic growth adds to the increase in carbon emissions in the long run. These findings bring out the dual challenge of promoting economic growth while managing environmental impact. The study underlines how policymakers need to provide regulatory frameworks which will encourage renewable energy and responsible foreign investment, as a means of trying to mitigate the environmental impacts of gold mining and achieve sustainable development. Our research adds to the continuing debate about how economic expansion can be balanced with environmental preservation for resource-rich countries.

1. Introduction

The implications of energy use from gold mining are immense: business opportunities created, less costly operations, and the modernization of the economy. Still, its environmental impact, especially on carbon emission, has been very worrying in recent times as the global community has shown more interest in climate change. Global warming is one of the critical indicators of climate change and has become one of the most vital topics in political and scientific discussion. It is attributed to a cluster of variables involving extreme weather conditions, increased sea level, a distortion in the water cycles, and ecosystem loss [1]. These are exacerbated by the mounting carbon dioxide (CO₂) emissions that have equally been identified as one of the significant drivers of greenhouse gasses and global warming. The primary sources of CO₂ emission include the use of fossil fuels for transport, heating, power, and in some industrial processes such as mining [2].

This puts gold in a very special and critical position in the world’s economy, while its mining, on the other hand, has a number of environmental costs. The relevance of BRICS countries in the ranking of major exporters and producers—that is, Brazil, Russia, India, China, and South Africa—makes them particularly relevant for any study focused on gold price, carbon emissions, and the overall environmental impact [3,4]. With the centrality of gold mining in such economies, coupled with its environmental impacts, this paper focuses on the BRICS countries in order to investigate the environmental sustainability corridor between carbon emission, gold price, economic growth, FDI, and renewable energy consumption.

The choice of BRICS countries is thus based on a dual role: they are both fast-growing economies with large gold mining activities and also regions under high pressure to balance economic growth with environmental sustainability [5]. The mining of gold comprises a series of processes: exploration, extraction, processing, and reclamation. Each of these stages involves huge energy inputs in the form of electricity and fuel used for machinery, transport, and processing equipment. For instance, extraction methods such as open-pit or underground mining require a great deal of energy in drilling, blasting, and hauling materials. In addition to this, the processing of gold ore, separating the metal from its surrounding material, involves various energy-intensive techniques like crushing, grinding, and chemical leaching. Understanding the nexus that exists between gold mining and energy consumption is very critical for a number of reasons. Firstly, high energy consumption in gold mining operations can lead to significant carbon emissions when the base fuel supply is of fossil fuel origin. This relationship is rather important in the context of BRICS countries, where gold mining represents an important economic activity feeding into the national productive systems in terms of value added to GDP and energy demand. Knowing this connection allows us to gauge the environmental impact of gold mining with more precision; it also shows a direction toward more sustainable sources of energy.

The BRICS countries together form an important stake in global output and resource consumption, and they face a different set of challenges in making international climate goals a reality [6]. These countries are, therefore, trying to balance the trade-off between economic development and a reduction in carbon emissions and, therefore, represent an ideal case study concerning the understanding of the intricate relationships among economic activities, energy use, and environmental sustainability. Further, as the BRICS countries try to balance economic growth with environmental sustainability, looking at energy consumption in gold mining also shows the bigger implications of resource extraction on carbon emissions. Given the necessity to explore how these countries can meet their economic objectives without compromising international climate goals, this study on the interactions of gold mining, energy use, and environmental impacts is timely and relevant.

It is estimated that the energy demand for rapidly growing economies like those of BRICS countries will considerably rise with time, hence making the goal of carbon emission reduction even more difficult. According to the projections made by the U.S. Energy Information Administration, energy demand worldwide will rise by 48% from 2012 to 2040 [7]. The rapid growth in demand, as juxtaposed with the global climate crisis, presents the nexus of economic growth and carbon emissions as a critical focus for scholars and policymakers [8]. BRICS countries are very concerned about the dynamics of energy use, economic growth, and carbon emissions, since these countries try to keep their economies running while addressing environmental responsibilities.

A major linkage that this study identifies is FDI being a driver in economic growth and a driver in environmental change. FDI provides a very important avenue through which technology transfer, employment opportunities, and increased productivity can be ensured; however, it also poses a serious environmental risk, especially as regards resource-intensive industries like mining [9]. The literature on environmental economics has dealt with two opposing notions: the pollution haven hypothesis, which states that FDI could increase carbon emissions by shifting polluting industries to less regulated economies, and, on the other hand, the pollution halo hypothesis, which postulates that due to the generation of cleaner technologies and practices, FDI will enhance environmental standards [10]. These two competing theories take on real significance in the BRICS countries, which remain both recipients of considerable FDI and key contributors to global carbon emissions [11,12].

The present study, therefore, focuses on the impact of gold price, FDI inflows, renewable energy use, and economic growth on emissions in BRICS economies. In this respect, the current study is unique and addresses the lacunas in the existing literature with regard to the environmental impact of gold mining, especially in fast-growing economies where gold mining is one of the major economic activities.

We expect this study, in the end, to give guidelines to the authorities on how to foster economic growth along an environmentally sustainable path, keeping in mind the commitments of global climate goals. The rest of the paper is organized as follows: Section 2 reviews the relevant literature and conceptual framework; Section 3 describes the model specification, data description, and research methodology; Section 4 presents the empirical results; Section 5 gives the conclusion and policy recommendations.

2. Literature Review

During recent decades, global warming and climate change, primarily caused by GHG emissions, in particular, carbon emissions, have been the most pressing environmental problems [13]. The relationship between climate change, energy consumption, and economic growth has been increasingly considered by academics and policymakers. Based on [14], there has been an argument that environmental regulation can hinder production and ultimately decrease productivity growth in a long-run manner.

Price of Gold and Carbon Emissions

In the absence of any carbon pricing system, GHG emissions from the gold industry are economic externalities. The rise in investor interest in risks from climate change has led to a step up in demand for reporting on GHG emissions [15]. In fact, according to the World Gold Council, most of the emissions within the gold industry are mining-related, with the majority coming from electricity generation. There is obviously great potential for these to be switched over to renewable energy, which would greatly reduce CO₂ emissions. The World Gold Council has said it believes net-zero emissions in gold mining can be achieved by 2050, in line with the commitments of the Paris Agreement.

Though the contribution by the sector is relatively small at about 0.4%, the impact is concentrated; hence, it is significant. On the environmental impact of gold mining [16], it should be noted that most prior studies have focused little on energy use and emission at particular mining sites.

Additionally, some studies [17,18] tend to provide a general overview of the environmental effects of gold mining; specific information on the mechanisms through which energy consumption at mines creates varying levels of carbon emissions remains rather scant. Recent research from [19] has suggested that because of mineral extraction energy use, greenhouse gas emissions might be grossly heightened, though this is not well explored in the context of gold mining.

Economic Growth and Carbon Emissions

In the quest by countries to attain their development objectives, they must embrace growth strategies that do minimal damage to the environment. While the relationship between renewable energy consumption and economic growth has been the subject of wide research, empirical results generally present mixed results. For example, Ref. [20] found that renewable energy positively influences economic growth across Eurasia, while [21] documented that in some of those same countries, no significant relationship existed between renewable energy and economic growth. Furthermore, such a relationship has been shown to vary across regions by [22,23] in the specific regions of South America and Sub-Saharan Africa, respectively.

These inconclusive results mean that the effect of renewable energy on economic growth is related to some country-specific factors such as institutional quality, resource endowment, and energy infrastructure. In this connection, it has been found that in some regions, renewable energy and growth are positively linked, whereas in other regions, this is not the case [24,25].

FDI and Carbon Emissions

FDI has been researched at large with respect to environmental degradation. Some researchers argue that FDI inflows encourage the host country to be energy-efficient and hence lower its CO₂ emissions [23]. For example, Ref. [26] indicated that FDI leads to a decrease in CO₂ emissions due to transferred clean technologies. On the other hand, however, some studies indicate that FDI increases environmental degradation, especially when there are weak environmental regulations in a country [16,27].

Ref. [28] again reiterates that FDI has the potential to exert both positive and negative impacts on carbon emissions, based on the host country’s regulatory framework that guides FDI activities. For instance, Ref. [16] showed that in the case of China, FDI has degraded environmental performance by emitting more carbon, while in other regions, FDI has initiated innovation and efficiency that resulted in lower levels of emission.

2.1. Theoretical Framework

Several discussions related to the linkage of economic growth and environmental degradation have taken the hypothesis of the Environmental Kuznets Curve as a starting point. Ref. [13] opines that EKC stipulates that there is increasing environmental degradation during initial economic growth; however, beyond a threshold of income, higher incomes are related to improvement in environmental quality. Ref. [25] tested the hypothesis of the EKC in Malaysia, which proved that evidence was particularly valid for countries under rapid industrialization processes. However, different states around the world apply differently to EKC since many countries may not face the “turning point” against economic growth, which eventually minimizes environmental degradation caused by economic activities [22].

In this respect, therefore, such an analysis of the EKC in resource-dependent economies like the Nigerian one would indeed be highly instructive. Further, the impact of economic growth on the environment could also be gauged better with the inclusion of variables like trade openness and urbanization [29].

2.2. Gaps in the Literature and Contribution

There have been many studies related to economic growth, renewable energy, and carbon emissions; however, some gaps still remain, especially for specific industries like gold mining. While many of the research works really focus on overall energy consumption and, consequently, impacts on emissions, in most studies, not much detail is given to the sector-specific dynamics [16]. The role of FDI as a driver of sustainable practices in industries is also underexamined, especially in regions of varied institutional quality [28].

This calls for a response through filling these gaps with empirical evidence on how gold mining, FDI, and renewable energy interact in affecting carbon emissions within the BRICS countries. This research will be new because it considers the symmetric and asymmetric effects of exchange rate undervaluation to capture how economic factors influence environmental outcomes.

2.3. Hypothesis

The contribution of this paper lies in how comprehensively it investigates the interaction between economic factors and environmental impacts, and is hence useful for policymakers and relevant stakeholders in resource-rich countries. This research could be the way to carry across the nuances of the sustainability challenges faced by BRICS nations by bringing forward how fluctuations in the price of gold and FDI, together with the contribution of renewable energy, shape carbon emissions.

Hence, the hypotheses of this study are as follows:

H1. 

Due to a higher gold price, carbon emissions become higher because mining is more active in the BRICS country economies.

H2. 

Foreign direct investment in resource/commodity-intensive industries contributes to higher carbon emissions per dollar of output; that is, FDI serves pollution haven functions.

H3. 

The higher use of renewable energy contributes to lower carbon emissions in BRIC countries’ economies.

H4. 

Economic development in BRIC countries is positively related to higher carbon emissions, whereas the use of renewable energy weakens these positive effects.

3. Data and Methodology

The primary goal of this research is to access the environmental sustainability corridor in BRICS nations linking CO₂ emission, gold price, renewable energy, FDI, and economic growth, trying to check their long-run cointegration. Data used were on BRICS countries from 1989 to 2020. The proxy used to measure our variables can be explained in

Table 1. List of variables used.

Variable	Definition	Unit	Source
CO2	Carbon emission	Kiloton	World Bank Development Indicators (https://databank.worldbank.org/source/world-development-indicators) (accessed on 30 July 2024)
GP	Gold price	$	Word Bank Commodity Markets (https://www.worldbank.org/en/research/commodity-markets) (accessed on 30 July 2024)
GDP	Gross domestic product	$	World Bank Development Indicators (https://databank.worldbank.org/source/world-development-indicators) (accessed on 30 July 2024)
FDI	Foreign direct investment as a percentage of GDP	%	World Bank Development Indicators (https://databank.worldbank.org/source/world-development-indicators) (accessed on 30 July 2024)
RE	Renewable energy consumption (% of total final energy consumption)	%	Statistical Review of World Energy (https://www.energyinst.org/statistical-review/resources-and-data-downloads) (accessed on 30 July 2024)

below.

From the table above, CO₂ will be used as our dependent variable, and our main variable of interest is the reaction of gold price to carbon emission in BRICS nations in the long run, and GDP, FDI, and RE will serve as our control variables in our analysis [30,31]. All the above variables were extracted based on various studies and our EKC theoretical framework that helps to explain the impact of carbon emission in the long run. Carbon Emission (CO₂) refers to the level of CO₂ emitted by the burning of fossil fuels and industrial processes. Gold Price, GP, refers to a price at which gold is traded in a market: normally measured in US dollars per troy ounce. Gross Domestic Product, GDP, refers to the total market value of all final goods and services produced within a nation during a given time period. FDI, the net inflows of investment to acquire a lasting management interest—10 percent or more of voting stock—in an enterprise operating in an economy other that of the investor, is expressed here as a percentage of GDP. RE (Renewable Energy Consumption) shows the share of renewable energies concerned such as wind, solar, hydroelectric, and biomass in total final energy consumption.

The inclusion of the gold price as an independent variable is important for a number of reasons. First, changes in the price of gold might have direct impacts on the intensity of mining operations that consequently impact carbon emissions. Increasing prices of gold usually encourages higher mining activities that result in increased energy use and, subsequently, increased emissions. Furthermore, gold mining is mostly energy-intensive, relying highly on fossil fuels, which increases carbon output [32].

The discussion of the association between the price of gold and carbon emissions is important because it shows the mechanisms in which economic factors affect the outcomes in the environment. The most significant mechanism operating in this connection is the energy use implicated in extracting and processing gold. With increased prices, there is a high likelihood that the mining companies will increase their rate of operations; this is followed by increased energy consumption. This inevitably leads to an increase in energy consumption and, consequently, increased carbon emissions, especially for countries whose main source of energy is fossil fuel.

This means that the choice of independent variables in our model is informed by empirical evidence from the available literature. We cannot afford to miss out on how gold price and carbon emission, a proxy in this study, are intrinsically linked together; such would have ramifications for the generalization of the results obtained for the environmental sustainability of mining activities.

Our research will first consider the pre-condition test before analysis, that is, to check the unit root level of all the variables involved in our analysis. Our unit root will be based on this equation:

Zit = ψiZ_it-1 + W′_itZ_i + it

(1)

Equation (1) above explains the stationarity test in its most basic form, which is based on autoregressive first-order elements of a simple panel data model, where i = 1, 2, …, N; t = 1, 2, …, T_i; Zit is the tested variable; and its stationarity is represented by the error term. Based on the parameters relevant to the study, the Wit term can refer to nothing, panel-specific means (constant), or a constant for trends (time trend and panel-specific means). Wit is = 1 by default; hence, the term W′_itZ_i refers to panel-specific measures (fixed effects). Wʹ_it = (1; t) if a trend is provided, so W′_itZ_i signifies linear time trends and panel-specific means. Unbalanced panels are allowed in the Im-Pesaran-Shin (IPS), Hadri LM tests, and Fisher type. But balanced panels are required for the other tests so that the value Ti = T for all i. H₀ = ψ = 1 for all I, as against its alternative hypothesis, Ha: ψ < 1, where, for all I, panel unit root tests are utilized to test the null hypothesis. H₁ may hold for all i, a part of all i, or for just an individual part, depending on the approach used; the outcome of the respective test clearly specifies the alternative hypothesis. As a result, the equation is often written in this form:

∆Z_it = αZ_it−1+ µ_it

(2)

where H₀ = α and H₁ is <α.

Our model will be based on the theoretical framework EKC model. The model utilizes the theoretical background and numerous studies based on the hypothesis that CO₂ = f(GDPC), that is, that carbon dioxide emission is a function of GDP per capital. In our case, we will have it as a function of GDP; so, therefore, when we include our explanatory and control variables, our model will be presented in the form as in Equation (3) below.

$${{\mathrm{C}\mathrm{O}}_2}_{\mathrm{i}\mathrm{t}}={\mathsf{\beta }}_{\mathrm{o}}+{\mathsf{\beta }}_1{\mathrm{G}\mathrm{P}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_3{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_4{\mathrm{R}\mathrm{E}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\mu }}_{\mathrm{i}\mathrm{t}}$$

(3)

The parameters are the values of β, representing the slope and intercepts. The subscript represents t, the time of the series, i, the individual country (cross-section), and µ_it, the error term. All other variables are as explained before.

Estimation Procedures

The Panel Auto Regressive Distributed lagged (PARDL) technique has a variant called the pooled mean group (PMG). Mean group (MG) and pooled mean group are the other two techniques (PMG). These estimating techniques account for the long-term variation in variable adjustment dynamics in relationships. The characteristics of countries, their natural resources, and their responses to policies, financial crises, or external shocks vary greatly. Heterogeneity may cause a sizable bias in estimating a single value for each explanatory variable. Therefore, it is advisable to use an estimate that considers country heterogeneity while accounting for distinct long-term and short-term dynamics [33]. PMG is the most helpful tool in this situation since it produces results that are less resistant to outliers.

$$\begin{gathered} {{\mathrm{C}\mathrm{O}}_2}_{\mathrm{i}\mathrm{t}}={\mathsf{\beta }}_{\mathrm{o}\mathrm{i}}+{\sum }_{\mathrm{l}=1}^{\mathrm{p}}{\mathsf{\beta }}_1{{\mathrm{C}\mathrm{O}}_2}_{\mathrm{i},\mathrm{t}-1}+{\sum }_{\mathrm{l}=0}^{\mathrm{p}}{\mathsf{\beta }}_2{\mathrm{G}\mathrm{P}}_{\mathrm{i},\mathrm{t}-1}+{\sum }_{\mathrm{l}=0}^{\mathrm{p}}{\mathsf{\beta }}_3{\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i},\mathrm{t}-1}+ \\ {\sum }_{\mathrm{l}=0}^{\mathrm{p}}{\mathsf{\beta }}_4{\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{i},\mathrm{t}-1}+{\sum }_{\mathrm{l}=0}^{\mathrm{p}}{\mathsf{\beta }}_5{\mathrm{R}\mathrm{E}}_{\mathrm{i},\mathrm{t}-1}+{\mathrm{\mu }}_{\mathrm{i}\mathrm{t}} \end{gathered}$$

(4)

The variables in the equation in Equation (4) are represented in a regular order, and the equation uses a typical PARLD estimation. All other variables are as previously mentioned, with the exception of the lag order, which is designated by the subscript l, and the individual country represented by the subscript i. The PARDL framework is formally explained by Equation (5). The equations were enhanced through several phases to create the following equation with different lagged variables.

$$\begin{gathered} ∆{{\mathrm{C}\mathrm{O}}_2}_{\mathrm{i}\mathrm{t}}={\mathsf{\beta }}_{\mathrm{o}}+\mathsf{\alpha }1\left({{\mathrm{C}\mathrm{O}}_2}_{\mathrm{i}\mathrm{t}}-1-{\mathsf{\beta }}_1\mathrm{i}-{\mathsf{\beta }}_2\mathrm{i}{\mathrm{G}\mathrm{P}\mathrm{i}}_{\mathrm{t}-1}-{\mathsf{\beta }}_{3\mathrm{i}}{\mathrm{G}\mathrm{D}{\mathrm{P}}_{\mathrm{S}\mathrm{i}}}_{\mathrm{t}-1}-{\mathsf{\beta }}_4\mathrm{i}{\mathrm{F}\mathrm{D}\mathrm{I}\mathrm{i}}_{\mathrm{t}-1}- \\ {\mathsf{\beta }}_5{\mathrm{i}\mathrm{R}\mathrm{E}\mathrm{i}}_{\mathrm{t}-1}1\right)+{\sum }_{\mathrm{l}=1}^{\mathrm{p}-1}{\mathsf{\alpha }}_1\mathrm{l}\mathrm{i}∆{{\mathrm{C}\mathrm{O}}_2}_{\mathrm{i},\mathrm{t}-1}+{\sum }_{\mathrm{l}=0}^{\mathrm{p}-1}{\mathsf{\alpha }}_2\mathrm{l}\mathrm{i}∆\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{i}\mathrm{t}+{\sum }_{\mathrm{l}=0}^{\mathrm{p}-1}{\mathsf{\alpha }}_3\mathrm{l}\mathrm{i}∆\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{i}\mathrm{t} \\ +{\sum }_{\mathrm{l}=0}^{\mathrm{p}-1}{\mathsf{\alpha }}_4\mathrm{l}\mathrm{i}∆\mathrm{F}\mathrm{D}\mathrm{I}\mathrm{i}\mathrm{t}+{\sum }_{\mathrm{l}=0}^{\mathrm{p}-1}{\mathsf{\alpha }}_5\mathrm{li}∆\mathrm{REit} \end{gathered}$$

(5)

Any change in the aspect of the explanation variables will have an effect on the dependent variable, as is the case in the long-term, as shown by the final equation, which is composed of elements within parentheses showing the long-term impact and elements denoting the short-term impact. The short run is measured by the various parameters of α(s) and $\beta$_1 for the long run, which, based on the derivative, is accumulated by α, and corrected per annum by (1 − α) % in the case of any distortion in the equilibrium.

4. Results and Discussion

The data in

Table 2. Descriptive statistics.

	CO2	GP	GDP	FDI	RE
Mean	5.554	789.96	5176.697	1.9456	28.736
Median	4.266	494.242	5729.058	1.678	17.995
Maximum	17.127	1788.325	10,370.36	6.187	95.406
Minimum	0.635	345.903	515.411	−0.2030	0.084
Std. Dev.	4.131	441.046	2941.706	1.481	30.369
Skewness	0.486	0.611	−0.188	0.634	1.325
Kurtosis	2.092	1.875	1.82	2.487	3.14
Observations	160	160	160	160	160

show how our variables were statistically distributed. The standard deviation gives us more accurate and complete estimates of the variation for each variable by giving us more accurate and complete estimations of each variable. In contrast, the definition of the highest and lowest values in each series is based on the minimum and maximum values. Comparing the minimum and maximum values, then, reveals the range of variables that were examined for all observational data. A total of 160 observations were made. GDP has the highest maximum and minimum values, and FDI has the lowest maximum and minimum values.

General Insights Based on Descriptive Statistics

Skewness: Skewness values give more details about each variable’s distribution. Positive skewness of CO₂, GP, FDI, and RE indicates that these distributions have longer right tails and therefore include higher-than-average values. The slight negative value for GDP indicates there are some below-average values pulling this distribution left.

Kurtosis: According to the kurtosis values, all the variables are platykurtic—a kurtosis value less than 3—with the exception of RE, which is mesokurtic. A platykurtic distribution has thinner tails compared to a normal distribution; there are fewer extreme outliers in this type of distribution. The mesokurtic distribution would contain tails similar in size to that of a normal distribution, as in the case of RE.

Normality: The descriptive statistics also indicate that the distributions of the data are not perfectly normal for all variables, as shown through the measures of skewness, kurtosis, and probability values less than 0.05 from Jarque–Bera test results. Actually, this deviation from normality needs to be remembered while interpreting these results and selecting appropriate econometric models.

The difference in the mean and median for CO₂, GP, and RE suggests that there are outliers in the data and asymmetric distributions, probably due to environmental performance differences, commodity market shocks, and energy policies varying across the BRICS countries. On the contrary, similar values of the mean and the median for GDP and FDI suggest more stable distributions and less variability and potentially fewer extreme values among these variables.

The results from

Table 3. Unit root table.

Variable	ADF				PP
	Intercept		Trend and Intercept		Intercept		Trend and Intercept
	Level	1st Diff.	Level	1st Diff.	Level	1st Diff.	Level	1st Diff.	Integration Order
C02	0.2581	0.000	0.122	0.001	0.601	0.000	0.817	0.000	I (1)
GP	1.0000	0.000	0.499	0.004	1.000	0.001	0.746	0.006	I (1)
GDP	0.2990	0.011	0.876	0.088	0.956	0.006	0.852	0.079	I (1)
FDI	0.0096	---------	0.050	0.000	0.012	------	0.010	------------	I (0)
REN	0.9045	0.000	0.975	0.000	0.924	0.000	0.976	0.000	I (1)

explain the panel unit root analysis summary of the Augmented Dickey–Fuller (ADF) [34] and Phillip Peron (PP) test [35]. All the variables mentioned above were presented in Table 3, which shows results from the Augmented Dickey–Fuller or ADF and Phillips Perron or PP test for unit root analysis. From the table, the integration order for each variable is clearly indicated, and we have tried to clearly indicate from which variable the first difference results. The results show that all other variables become stationary after taking their first difference, while FDI is stationary at this level. The fact that we have such a mixed stationarity of variables justifies the use of the PARDL model of analysis after the verification of the cointegration level in our model.

The result from

Table 4. Johansen Fisher panel cointegration result.

	Trace Test (p-Value)	Max Eigen Test (p-Value)
None	0.000 ***	0.000 ***
At most 1	0.000 ***	0.287
At most 2	0.003 **	0.049 *
At most 3	0.064 *	0.143
At most 4	0.072 *	0.072 *

Note: (***), (**), and (*) indicate that the estimated parameters are significant at the 1%, 5%, and 10% significance level, respectively.

and

Table 5. Kao (Engle granger based) panel cointegration result.

	Statistic	p-Value
ADF	−5.092	0.000 ***

Note: (***), indicate that the estimated parameter is significant at the 1% significance level respectively.

, which help explain the cointegration result of the panel analysis based on the Fisher [36] ad Kao [37] panel cointegration tests, shows that the probability results showing a p-value less than 0.05 explain the presence of the long run among the variables.

The estimation results in

Table 6. PMG result.

Dep. Var = CO2	Coefficient	Std. Error	p-Value
Long-run Coefficient
GP	−0.001	0.000	0.000 ***
GDP	0.000	0.004	0.000 ***
FDI	−0.300	0.028	0.000 ***
RE	−0.232	0.027	0.000 ***
Short-run Coefficient
ECM(-1)	−0.516	0.289	0.080 *
D(GP-1)	0.001	0.000	0.029 *
D(GDP-1)	0.001	0.000	0.070 *
D(FDI-1)	0.049	0.020	0.057 *
D(RE)	0.068	0.066	0.304
_cons	4.489	2.622	0.092 *
Pesaran’s CD test (p-value) 0.4090

Note: (***) and (*) indicate that the estimated parameters are significant at the 1%, and 10% significance level respectively.

show the long- and short-run effects of our model. The coefficient for ECM, which measures the adjustment speed back to equilibrium, was negative, with the range as predicted from 0 to 1, and the significant probability value was 0.51, that is, a 51% adjustment speed back to equilibrium in the short run. The probability value from Pesaran’s CD results are 0.409, which is greater than the 0.05 critical value and, as such, shows that we accept the null hypothesis, which states that there is no cross-sectional dependency among our variables. GP was significant in both the short and long run; the level of significance in the short run shows that the variable has a positive relationship with CO₂ emission, but the relationship, in the long run, is a negative relationship. This shows that a unit increase in the gold price in BRICS nations will reduce carbon emission by 0.001 units in the long run. Our findings align with the WGC’s assessment on achieving a net zero carbon emission for gold mining by 2050. The factor that accounts for gold’s resilience in the face of climate change, in contrast to the demand for the vast majority of other metals, is that the demand for copper is particularly varied and is not focused in any one industry or geographic region. In addition, the value movers of gold are not solely an expression of the equilibrium between supply and demand because gold is both a commodity and an important cultural luxury good in addition to being a monetary asset. Because of this, its worth as a store of value is exceptionally resilient, which is true even in the face of harsh conditions and stress in the economy and markets.

It is positively correlated with GDP in both the short run and the long run; more specifically, a one-unit increase in GDP will make carbon emissions increase by 0.001 units in the long run. This also coincides with the estimation from [38,39]. This positive correlation serves to indicate that with increased economic activities, as mirrored by increased GDP, there is also a resultant increase in carbon emissions. This, to a great extent, may be related to increased energy use and industrial activity, accompanying economic growth, which in turn can lead to higher carbon emissions for those economies reliant on fossil fuels. Our assertion thus denotes the relevance of understanding economic growth versus environmental sustainability trade-offs and calls for policies promoting decoupling economic growth from carbon emissions.

FDI has a positive effect in the short run, but in the long run, a unit increase in foreign inflow investment in BRICS countries reduces carbon emissions by 0.30 units. FDI could, therefore, influence carbon emissions through a variety of channels that might constitute the reasons for such a unit increase in FDI causing carbon emissions to shrink by 0.30 units. Technology transfer, cleaner production processes, investment in renewable energy, compliance with environment legislation, and the diversification of the economy are some of the key mechanisms which may account for this. However, the contribution of FDI to carbon emissions varies across contexts, as it also depends on the sector in which the investment takes place, the host country’s environmental legislation, and how FDI is integrated into the local economy. In BRICS countries, which are simultaneously pursuing economic growth and environmentally sustainable development, it seems from the results that FDI helps to reduce carbon emissions by 0.30 units with the increase in investment. Our finding shows a negative relationship between FDI and CO₂, which is in line with [31] in their research on economic sustainability in Algeria; the findings show that FDI reduces carbon emissions in Algeria. RE was not significant in the short run but was significant in the long run with a negative relationship. A unit increase in RE in BRICS countries will reduce carbon emissions by 0.23 units in the long run. Our findings align with [40] in their investigation of the consequence of nuclear energy use on carbon dioxide emission in a subset of OECD member nations between 1990 and 2018, showing that nuclear energy may eventually cut carbon emissions.

The stability of model parameters over time, investigated through the CUSUM and CUSUM of squares tests, is depicted in Figure 1 and Figure 2. Looking at both figures, the blue line representing the residuals falls well within the critical boundaries represented by the red lines. It is thus concluded here that there is no parameter instability in this model, as the residuals are constant and fall within their acceptable range. The outcome from the results shows that, within the period of the sample, the estimated parameters are stable; hence, the model is reliable to use under the given analysis.

Discussion

In fact, our results denote a number of critical messages that denote the relationship between carbon emissions with our selected economic variables in BRICS countries. The estimated ECM coefficient of −0.51 denotes a large adjustment speed required to return to the equilibrium. This infers that 51 percent of the deviation from the long-run equilibrium is corrected in the short run. The finding agrees with earlier studies that noted carbon emission dynamically adjusts to changes in economic factors [41].

The statistical significance of the gold price (GP) in both the short and long run depicts exciting dynamics. Regarding this positive relationship between carbon emissions in the short run, it is something that [42] observes because increasing gold prices boosts mining activities, thereby upping emissions. Of particular interest in this analysis is our finding of a negative long-run relationship where for every unit increase in GP, it reduces carbon emissions by 0.001 units. This was in agreement with the projection by [43] to the effect that with advances in sustainable mining practices, there is a possibility of lower emissions associated with gold production by 2050. The relationship underpinning this is one wherein gold, as a commodity and cultural asset, is resilient; its demand, therefore, compared to metals like copper, is less vulnerable to economic fluctuations [44].

The positive correlation of GDP with carbon emissions in both the short and long run indicates that heightened economic activities translate into larger carbon outputs. The result is consistent with the literature, where it was stated that economic growth is normally manifested by increased energy use and heightened industrial activities, particularly in countries whose capital base depends on fossil fuels [45]. It, therefore, adds to the strength of arguing for urgency in the search for pathways through which policymakers could decouple economic growth from carbon emissions as the transition is made towards greener economic models [46].

Foreign Direct Investment or FDI demonstrates a dual impact in our estimations. It is seen that, in the short run, FDI positively contributes to carbon emission, while in the long run, it emerges as one of the important contributors to a reduction in emissions by −0.30 units per unit rise in investment. This result also receives support from the studies of [4] on the grounds that FDI may facilitate opportunities for technology transfers and the adoption of cleaner ways of production, which might lower the level of emissions. Another important element is that the context-dependent nature of the FDI impact brings into sharp focus host country regulations and sectoral dynamics, especially in emerging economies such as those that compose the BRICS grouping, in this case [47].

The high contribution of renewable energy consumption in the long run, depicting a negative association with carbon emissions at 0.23 units per unit increase, confirms the findings of [48]. Their study emphasizes the contribution of renewable sources towards reducing carbon emissions. Their study showed that the mere idea of shifting to cleaner energy can be a contributing factor in attaining various sustainable development goals. Although RE was insignificant in the short run, the long-run implications are vital for the BRICS nations to achieve a balanced economic growth with environmental sustainability.

In sum, these findings render the reviewed literature even more substantial as it reflects nuanced understanding in the interactions of the economic variables with carbon emissions in the BRICS countries. Future research should, therefore, focus on broader variables with longitudinal data so as to capture with higher precision such a complex relationship.

5. Conclusions and Recommendations

This research contributes to the extant literature by considering a detailed analysis of the relationship between carbon emission and some selected important economic variables, namely gold prices, economic growth, FDI, and renewable energy consumption, within a BRICS framework. Given the emphasis on these interlinked factors, the study closes significant knowledge gaps related to the degree to which economic activities related to gold mining impact environmental sustainability. This, therefore, calls for the framing of policies that would be conductive to economic growth while at the same time not jeopardizing environmental sustainability—a fact quite useful for policy framers, investors, and other actors in natural resource-rich countries.

This study employed panel data ranging from 1989 to 2020 for BRICS countries, which are the regions with huge experience in gold mining, to estimate the environmental corridor linking CO₂ emissions, the price of gold, economic growth, FDI, and renewable energy consumption. During the estimation process, the PMG technique, which has been developed to analyze this sort of relationship, was applied. The unit root results revealed mixed stationarity across the variables, and from there, the Kao and Fisher panel cointegration tests confirmed the presence of long-term relationships in our model.

From the results of the PMG long-run estimation, it can be observed that there is a negative relation of gold prices, FDI, and renewable energy consumption with CO₂ emissions, while economic growth, measured by GDP, is positively related to CO₂ emissions in the long run. These findings support the preliminary report of the WGC in 2018 that forecasts gold mining to reach carbon neutrality by 2050, driven by the transition into renewable energy and the low-carbon economy. As a matter of fact, our research goes on to prove that the gold industry is especially placed for resilience, with returns that should remain fairly flat across different climate change scenarios.

Considering the probably adverse impact on environmental degradation emanating from gold mining, specific and stringent policies to mitigate this aspect should be developed by policymakers for enhanced sustainability within a BRICS context. To this end, and based on the results obtained from the research hypothesis, we are able to recommend the following:

Incentivizing the use of renewable energy: The policy framers should provide fiscal incentives and support mechanisms for gold mining companies to utilize renewable energy sources. This could be in the form of tax breaks, grants available for renewable energy projects, or subsidies for investing in clean technology. By creating an environment that caters to renewable resources, mining operations will be in a position to reduce carbon footprints while remaining economically productive.

Regulatory framework for responsible FDI: There is a need to have in place those regulations that will encourage responsible FDI, ensuring that foreign investment aligns with sustainable mining. Policymakers can develop guidelines where companies must justify their commitment to environmental standards and carbon reduction strategies before permits are issued for operation.

Inclusion of EIA: The inclusion of EIA should be made mandatory during the approval process for gold mining. EIAs will ensure that there is better consideration of potential environmental risks and the formulation of necessary mitigation strategies so that such economic advancement in gold mining does not take place at the cost of environmental degradation.

Stakeholder engagement and education: There is a need for policymakers to engage local communities, industry players, and environmental bodies in best practices in the mining industry that are sustainable. Increased collaboration of this nature, as it also shows all parties the benefits of renewable energy, will achieve more in the quest for a balance between economic growth and environmental conservation.

Support for research and innovation: Investment into research and innovation dealing with cleaner mining technologies and renewable energy solutions is paramount. Policymakers are supposed to fund various research initiatives in developing new methodologies that ensure minimal carbon emission at the extractive and processing stages of gold.

Actually, our study focuses on the specific policy implications that will assist policymakers, investors, and other stakeholders in the gold-mining and energy industries within the BRICS countries. Recommendations go toward not only encouraging economic growth through gold mining but also ensuring that the growth is planned toward the goals of environmental sustainability. These policies can offset the negative factors associated with mining sector economic growth and ensure a more secure future.

Finally, there are various limitations that this study faces. The analysis is limited to five variables, significant as they may be but perhaps inadequate to capture the full complexity of the relationships between them. Other parameters that may influence the investment environment include regulatory frameworks, technological changes, and social dynamics. In addition, the research has focused only on BRICS countries, and different challenges and opportunities in each country may have implications for general applicability. Future research should also extend the range of variables considered and possibly include case studies in other emerging economies to further elaborate on the dynamics involved. These can further look at how the nature of the relationships evolves over time through longitudinal studies amidst changing conditions in the global economy and climate policies.