Industrialization and carbon emission nexus in Sub-Saharan Africa. The moderating role of trade openness

Abstract

This study investigated how trade openness influenced the connection between industrialization and emissions of carbon in Sub-Saharan Africa (SSA). We utilized purposive sampling technique to select 28 SSA countries from 2003 to 2021. The study used Generalised Methods of Moments as the main estimator and Pooled Mean Group as the robustness estimator for the empirical analysis. The findings revealed that industrialization positively impacts carbon emissions in SSA nations. Additionally, the link between industrialization and carbon emissions in SSA nations is positively moderated by trade openness. The findings validate the necessity for governments in Sub-Saharan African nations to restructure their industrialization initiatives in order to lower emissions of carbon. Additionally, strict measures should be implemented to regulate free trade in the SSA region.

Impact statement

Global CO₂ emissions present a major policy challenge requiring government intervention. With businesses prioritizing expansion, governments in Sub-Saharan Africa need stringent regulations to manage industrial pollution, curb carbon emissions, and control urban development. As environmental issues gain social significance, the demand for stricter pollution controls increases, necessitating comprehensive laws and policies to reduce CO₂ emissions in the region.

Keywords:

• Industrialization
• carbon emission
• Sub-Saharan Africa
• trade openness

Reviewing Editor:

• David McMillan, University of Stirling, Stirling, United Kingdom

Subjects:

• Industrial Economics
• International Economics
• Environmental Economics
• Finance
• Urban Economics

1. Introduction

Recently, industrialization has become an inevitable trend toward social development and technological advancement, especially since the world is about to enter a new stage of the emergence of industries on a huge scale (Agyemang et al., 2023). Given the many advantages of industrialization, it is critical that all nations, particularly those in emerging nations like Sub-Saharan Africa (SSA), comprehend the factors that propel this process. The creation of new industries might strengthen the Country’s capacity to protect the environment and its resource guarantees, which is crucial for advancing sustainable development and spurring economic expansion (Khan & Ozturk, 2021).

The enormous growth in industrial activity has led to a rise in the emission of carbon. Emissions of carbon have grown dramatically since industrialization began, contributing to global warming (Ali et al., 2023). The process of industrialization has led to changes in the growth of the economy and a rise in emissions of carbon, resulting in pollution and climate change. As economies emphasize sustainability and environmental protection, concerns about emissions of carbon and ecological degradation have increased (Afriyie et al., 2023).

Trade openness serves as a catalyst to increase emissions of carbon (Baba Ali et al., 2023). This is because most countries, especially SSA, have opened up their trade for other foreign nations to invest, which has risen emissions of carbon (Amoah et al., 2023). Trade openness has increased industrialization in Africa since most advanced countries invest in Africa to capture the market and yield maximum gains. Moreover, since trade liberalization began in Africa, most advanced companies are relocating to Africa since there are low regulations on industrialization actions. As a result, Africa has become the pollution heaven in the world today. Therefore, there must be a study to examine the influence of trade openness, industrialization and emission of carbon in the Sub-Saharan Africa regions for possible policy implications and regulations.

Since SSA countries have prioritized economic growth, industrialization has increased (Gnangoin et al., 2022) and increased emissions of carbon (Aslam et al., 2021). In some instances, the goal of guaranteeing growth and development in SSA countries overlooks ecological conservation. As a consequence, growing economies like SSA have begun to emit carbon. According to the United Nations reports on carbon emission, seven countries from Sub-Saharan Africa are included among Africa’s top nine pollutants, demonstrating the degree of pollution (Azam et al., 2022). In order to decrease emissions of carbon, industrialized nations have been the focus of most earlier studies on the connection between emissions of carbon and industrialization (Chikezie Ekwueme et al., 2023; Doğan et al., 2023). Few research looked and the link among industrialization and emissions of carbon for a subset of African nations (Ogede & Tiamiyu, 2023; Rahman et al., 2023; Wang et al., 2022). Since industrialization as well as emissions of carbon are linked, neither prior research considered trade openness’s moderating effect. In order to achieve sustainable development objectives, governments in Sub-Saharan Africa (SSA) must thus work to combat global warming while promoting economic growth. This means empirical research is required to inform policy decisions in the SSA area.

In the realm of motivation and innovation, the study introduces innovative viewpoint to the current body of literature in various ways. To begin with, it presents unique empirical insights into the interplay among trade openness, industrialization, and carbon emissions. This sets it apart from prior studies that predominantly concentrated on the direct relationship between these factors, raising questions about the applicability of their empirical findings. Furthermore, unlike earlier research efforts, this study incorporates additional tests for reliability and robustness, ensuring the relevance of its results for policymaking. These methodological distinctions make the current research stand out as distinctive compared to the majority of previous studies. Lastly, in a departure from past research, the current study employs a substantial sample size encompassing 28 countries in Sub-Saharan Africa over an extended period of 19 years. It also incorporates a detailed control variable in examining the link among industrialization and carbon emissions in SSA nations.

Moreover, the research fills the literature gap by considering trade openness when analyzing how industrialization affects carbon emissions. The study aims to address the following objectives: (1) To investigate the relationship among industrialization and carbon emissions in SSA. (2) To examine how trade openness affects industrialization and carbon emissions in Sub-Saharan Africa in a moderating manner. Secondary data was used from the WDI database in order to accomplish the goals of this research. For the empirical study, the research takes into account the years 2003–2021. Purposive sampling was utilized to choose the nations having adequate and reliable data for the empirical analysis in order to produce panel data that was highly balanced. Of the 46 nations in the SSA, a total of 28 were selected. Additionally, GMM and PMG estimators were used in the study for the empirical analysis. The results imply that trade openness, industrialization, and carbon emission reduction laws and regulations must be strengthened and enforced.

Three key contributions are made by the study: First, in this era of industrialization, the study provides policymakers and authorities with information on measures to limit the number of emissions of carbon in SSA nations. Second, the study demonstrates how industrialization and emissions of carbon in SSA nations are impacted by trade openness. To boost industrialization and lower CO2 emission, the research also explores measures to promote and improve trade openness.

The study is organized in five sections: the first section provides an overview of the chapter; the second covers concept definitions, theoretical and empirical reviews, and hypothesis development; the third section discusses methods and econometric models; the fourth section discusses the results and their implications; and the fifth section concludes with recommendations and policy implications.

2. Literature review

2.1. Africa’s sustainability policies, industrialization and carbon emissions

Industrialization is important for Africa’s economic growth and advancement. The industrialization patterns seen throughout the continent, however, significantly impact greenhouse gas emissions and climate change (Zhou et al., 2022). In order to encourage sustainable development in Africa, it is essential to comprehend these patterns and how they affect the environment. Furthermore, the implementation of robust environmental regulations and enforcement mechanisms is crucial for the effective reduction of emissions of carbon. In countries with stringent regulations and incentives for sustainable operations, the adoption of emission-minimization measures is more likely. They may develop carbon pricing schemes, set emission limits for businesses, and promote environmentally friendly technologies. For example, South Africa has implemented a carbon pricing policy to stimulate the reduction of emissions in various businesses (Wen et al., 2023).

International alliances and partnerships are crucial to promoting Africa’s sustained economic growth. African nations may obtain financing, information, and technology transfer to embrace cleaner and more sustainable industrial practices by collaborating with international organizations, governments, and enterprises. In order to advance low-carbon solutions, international alliances may strengthen capacity-building efforts, training programs, and research collaborations (Elfaki et al., 2022). Africa must overcome a number of obstacles to control industrialization and cut emissions of carbon. A major obstacle to investing in sustainable industrial growth is limited financial resources. African nations often have trouble getting the money and resources needed to promote eco-friendly technology. International cooperation and creative finance methods are essential to solve this issue and close the financial gap (Haibo et al., 2019).

Moreover, to promote sustainable industrialization and the reduction of emissions of carbon, comprehensive policies must be implemented by African governments. In order to promote sustainable industrial practices, public-private partnerships may be very important (Edziah et al., 2022).

In conclusion, Africa’s industrialization trends significantly impact Carbon emissions and climate change. While service-oriented and green development patterns have prospects for reduced emissions, resource-driven and manufacturing-led industrialization often produces significant emissions of carbon. It will be essential for Africa to overcome obstacles such as inadequate financial resources, technological transfer, and legislative frameworks if it is to achieve sustainable industrial growth and successfully reduce emissions of carbon (Khan et al., 2021). To promote Africa’s transition to low-carbon and climate-resilient economies, cooperative efforts between governments, international organizations, enterprises, and local communities are required.

2.2. Theoretical review

2.2.1. Neoclassical economic theory

Neoclassical theory recognizes the inherent connection between industrialization and economic growth. As an illustration, the economies of Sub-Saharan African countries experience growth due to industrialization, which subsequently results in increased production and consumption.

Additionally, the neoclassical theory advocates for the utilization of market-based incentives as a means of tackling environmental concerns. Carbon pricing instruments, such emissions trading programs and carbon taxes, offer economic incentives to businesses to reduce their carbon footprint. The promotion of cost-effective alternatives and the reliance on market forces to drive carbon reductions align with neoclassical concepts. The importance of financing study and development for technological advancement is highlighted by neoclassical economics, as stated by Kongkuah et al. (2021). Various industries have the potential to secure financial resources for developing and implementing sustainable technologies, such as eco-friendly industrial techniques, energy-efficient infrastructure, and renewable energy sources. These expenses aid in reducing the carbon emissions associated with industrial operations.

The theory posits a correlation between the transition from unsustainable to sustainable industrialization and the potential for economic structural changes and reduction of carbon emissions. The transition from carbon-intensive to low-carbon industries constitutes an integral aspect of this phenomenon. According to Kwakwa (2020), neoclassical economists contend that technical innovation may be used to decouple economic expansion from environmental deterioration, hence enabling sustainable industrialization with lower carbon emissions.

2.2.2. Economic integration theory

The significance of acknowledging initiatives like the European Union and the North American Free Trade Agreement (NAFTA) has been widely recognized due to their role in shedding light on collaborative economic efforts at both regional and international levels (Amoah et al., 2019; Long et al., 2020). Economic integration theory underscores the potential for knowledge and technology transfer diffusion within integrated regional markets. Within the framework of sub-Saharan African regional integration initiatives, foreign enterprises contribute advanced technology, managerial expertise, and best practices, aiming to enhance industrialization and reduce carbon emissions. These spill-over effects can enhance local businesses’ capabilities, facilitate the dissemination of innovative technologies, and stimulate industrial growth, which may, in turn, impact emissions of carbon (Yang et al., 2022).

To formulate effective policies that can leverage the benefits of industrialization while mitigating carbon emissions in this region, policymakers and relevant stakeholders must comprehensively grasp these dynamics within the economic integration theory.

2.3. Empirical study and hypothesis development

Examining industrialization’s impact on sub-Saharan Africa’s carbon emissions can be illuminated by leveraging insights from neoclassical theory and economic integration theory. The process of industrialization, often characterized by increased productivity and economic growth, frequently results in heightened carbon emissions (Wang et al. 2023). Nevertheless, implementing effective environmental management frameworks can mitigate the adverse environmental consequences of industrialization. Nations in sub-Saharan Africa must strike a harmonious balance between industrialization and environmental sustainability by enacting legislation pertaining to the environment, establishing emission constraints, and adopting sustainable development strategies (Mentel et al., 2022). Policymakers and scholars can formulate strategies aimed at promoting cleaner and environmentally conscious industrial practices in the region by delving into the intricate interplay between industrialization, environmental governance, and emissions of carbon.

The relationship between sub-Saharan Africa’s industrialization and emissions of carbon becomes more apparent when we consider the concept of economic integration. Efforts to foster economic cooperation, like customs unions and regional trade agreements, can significantly influence industrial activities and the associated emissions. The inclusion of sub-Saharan African nations in regional economic alliances may result in a rise in industrialization and, in turn, emissions of carbon. However, there is a chance to use economic cooperation as a means of encouraging long-term industrial development and cutting carbon emissions in the area. This can be accomplished by including environmental considerations in economic integration frameworks, for example, by establishing clean technology initiatives or emission reduction goals (Musah et al., 2023).

In China, a research by Naeem et al. (2023) revealed a strong favorable association between industrialization and emissions of carbon. Similarly, research conducted in India Nwani et al. (2023) indicated that industrialization was the Country’s primary driver of emissions of carbon. These findings align with the results reported by Opoku and Boachie (2020), who observed industrialization’s significant role in increasing emissions of carbon in Turkey. Furthermore, a study carried out in Pakistan by Osei et al. (2023) revealed a favorable relationship between industrialization and emissions of carbon, providing additional support for this association.

On the other hand, a research investigation by Ostic et al. (2022) discovered that the escalation of industrial activity lead to increased emissions of carbon. A study conducted in Tunisia Rahman et al. (2022) also indicated that there was no definitive correlation between industrialization and emissions of carbon. In a parallel vein, the study conducted by Mentel et al. (2022) suggested that industrialization had a limited effect on emissions of carbon. In contrast to these unfavorable outcomes, a study undertaken Awad and Mallek (2023) in South Korea identified a link between the expansion of industrialization and the upsurge in carbon emissions. This observation was in line with Iranian research by Bekun et al. (2021), which highlighted the substantial role of industrialization in the increase of carbon emissions in that context. However, a study Ibrahim and Ajide (2021) conducted in Pakistan presented no substantial link between industrialization and emissions of carbon.

As per recent research findings, trade openness was favorable in influencing the association between industrialization and emissions of carbon, as indicated by Rasool et al. (2023). Similary, Rehman et al. (2021) revealed that trade openness played a crucial role in mitigating the adverse environmental effects associated with industrialization. This finding is consistent with a study conducted by Sarkodie et al. (2020), which emphasized that trade openness significantly moderated the connection among industrialization and CO₂ emission.

In contrast, a research investigation carried out in a certain East Asian country by Sejati et al. (2020) revealed an intriguing finding. It indicated that the extent of international trade had an adverse influence on the association between the process of industrialization and the release of carbon into the environment. Remarkably, this finding aligns with another research endeavor conducted in a South Asian nation, as undertaken by Shijian and Agyemang (2022). Their study unearthed that the degree of international trade did not exert a statistically significant moderating influence on the link between industrialization and emissions of carbon. Additionally, there is further support for this concept from research conducted in a Southeast Asian country Sikder et al. (2022), indicating that the interplay between industrialization and emissions of carbon remained largely unaffected by moderating influences in their study context.

Sultana et al. (2023) observed that contrary to prior research with unfavorable outcomes, trade openness exhibited a positive moderating effect on the relationship between industrialization and environmental quality. Similarly, Tian et al. (2019) affirmed these earlier observations as their findings indicated that trade openness serves a substantial moderating role in influencing the impact of industrialization on carbon emission in Oman.

As per Voumik and Sultana (2022), the degree to which a country engages in global trade and the presence of trade obstacles can have a dual effect on the ecological consequences of industrialization. On the one hand, it may stimulate the embracing of greener technologies and knowledge, resulting in more efficient and environmentally conscious manufacturing methods that lower carb emissions (Wang et al., 2022). Consequently, formulating effective policies to promote sustainable development and global carbon emission reduction necessitates a comprehensive understanding of these intricate relationships (Zhang et al., 2020). Based on the majority of prior studies, we hypothesize the following:

H1: Industrialization positively impact carbon emission in SSA countries.

H2: The link between industrialization and carbon emission is positively influenced by trade openness.

3. Methodology

3.1. Study design

The study considered the SSA region due to its association with high emissions (Tawiah et al., 2021). Sub-Saharan Africa is divided into four regions: West, Central, East, and Southern Africa. Purposive sampling was utilized to choose nations with complete datasets for all the variables used in the study from 2003 to 2021 in order to create well-balanced panel data that would support all reliability tests. Essentially, 28 of the 46 countries in SSA were considered for this research. A few nations were excluded from the final sample as there was not enough data available on certain of the study’s variables. The following countries are considered in the research: Rwanda, Senegal, Sierra Leone, Angola, Malawi, Namibia, Niger, Nigeria, Uganda, United Republic of Tanzania, Zambia, and Zimbabwe Benin, Botswana, Burkina Faso, Cameroon, Chad, Gambia, Ghana, Kenya, Lesotho, Liberia, and Madagascar are among the countries that make up the African continent.

The study employed a quantitative approach. Secondary data from WDI covering the years 2003–2021 were used in the study. Stata 17.0 was used for the empirical investigation.

3.2. Model construction

The study formulated the model below for the empirical analysis. Additionally, the investigation incorporated Urban population growth, Energy Consumption, Gross Fixed Capital Formation, and Economic Growth as control variables, as commonly employed in related studies (Jijian et al., 2021; Sare et al., 2023). Since the variables were not of the same measurement units, we used the natural logarithm of the values for the study. (1) $${\mathit{\text{InCO}}2M}_{\mathit{\text{it}}}={\beta }_0+\mathrm{\lambda }{\mathit{\text{InCO}}2M}_{\mathit{\text{it}}-1}+{\beta }_1{\mathit{\text{InIDT}}}_{\mathit{\text{it}}}+{\beta }_2{\mathit{\text{InUPG}}}_{\mathit{\text{it}}}+{\beta }_3{\mathit{\text{InEC}}}_{\mathit{\text{it}}}+{\beta }_4{\mathit{\text{InGF}}}_{\mathit{\text{it}}}+{\beta }_5{\mathit{\text{InEG}}}_{\mathit{\text{it}}}+{\epsilon }_{\mathit{\text{it}}}\mathrm{ }$$(1) (2) $${\mathit{\text{InC}}02M}_{\mathit{\text{it}}}={\beta }_0+\mathrm{\lambda }{\mathit{\text{InCO}}2M}_{\mathit{\text{it}}-1}+{\beta }_1\mathit{\text{In}}{\mathit{\text{IDT}}}_{\mathit{\text{it}}}+{\beta }_2{(\mathit{\text{InIDT}}}_{\mathit{\text{it}}}\times {\mathit{\text{InTOP}}}_{\mathit{\text{it}}})+{\beta }_3{\mathit{\text{InTOP}}}_{\mathit{\text{it}}}+{\beta }_4\mathit{\text{InU}}{\mathit{\text{PG}}}_{\mathit{\text{it}}}+{\beta }_5{\mathit{\text{InEC}}}_{\mathit{\text{it}}}+{\beta }_6{\mathit{\text{InGF}}}_{\mathit{\text{it}}}+{\beta }_7{\mathit{\text{InEG}}}_{\mathit{\text{it}}}+{\epsilon }_{\mathit{\text{it}}}$$(2)

Whereas CO2M denotes carbon emissions, IDT donates industry, TOP donates trade openness, UPG donates urban population growth, EC donates energy consumption per capita, GF donates gross fixed capital formation, ε donates error term, t donates year, and I donate the nation.

3.3. Variables description

The dependent variable in this study is carbon emissions. Trade openness is the moderating variable, whereas industrialization is the explanatory variable. Lastly, the study’s control parameters include the rise in the urban population, energy consumption, economic growth, and gross fixed capital creation. Table 1 summarizes the measurements and signs of each variable.

Table 1. Description of variables.

Variables Categories	Variables	Symbol	Measurement	Sign
Dependent	Carbon Emission	lnCO2M	Natural logarithm of CO2 emissions (metrics tons per capita)
Independent	Industrialisation	lnIDT	Natural logarithm of the proportion of industry (such as construction, value added) to % of GDP	+
Moderating	Trade openness	lnTOP	Natural logarithm of the export-to-import ratio expressed as a % of GDP	+
Control	Urban population growth	lnUPG	Natural logarithm of the growth in urban population (yearly%)	+
	Energy Consumption	lnEC	Natural logarithm of energy usage per capital (measured in kilogrammes of oil equivalent per capital)	+
	Gross Fixed Capital Formation	lnGF	Natural logarithm of Gross Fixed Capital Formation (annual percentage growth)	−
	Economic Growth	lnEG	Natural logarithm of the Difference between current year growth from previous year’s growth rate.	+

4. Results and discussion

4.1. Descriptive statistics

This section presents a statistical overview of the dependent and independent variables of the sub-Saharan African countries and describes the characteristics of the model’s variables. The principal descriptive metrics are the average, standard deviation, lower value, and highest value of the variables for the time frame in consideration. Table 2 provides a descriptive and statistical overview of each variable employed to measure the impact of industrialization on carbon emission in sub-Saharan African nations.

Table 2. Descriptive statistics.

Variable	Obs	Mean	Std. Dev	Min	Max	Skewness	Kurtosis
InCO2M	532	4.5698	7.9096	0	7.9096	2.8687	6.057
InIDT	532	7.0412	13.096	0	13.096	1.1854	3.9945
InTOP	532	3.8952	6.2955	−18.917	6.2955	1.7564	3.0332
InUPG	532	3.2052	2.798	0	532.88	2.5643	11.851
InEC	532	3.9396	1.3306	0	1.3306	3.7773	12.45
InGF	532	3.0949	4.3647	−.01414	4.3647	4.5863	10.964
InEG	532	0.6604	1.5009	−14.144	1.4729	−1.5784	9.5761

The mean value of the dependent variable (carbon emission) value for SSA nations is 4.5698, with a minimum of 0.00 and a maximum of 7.9096 based on the descriptive statistics of the research variables in Table 2. This means that the average carbon emission in the studied nations for the twenty-one-year period was 71%, indicating that carbon emissions in Sub-Saharan African countries have grown over time.

The average industry contribution to carbon emission is 7.0412. Moreover, the greatest and lowest values are 13.096 and 0.00, respectively. This implies that for the studied period, most nations in SSA have been industrialized over time. Trade openness recorded a mean value of 3.8952 with minimum and maximum values of −18.917 and 6.2955, respectively, indicating that trade openness has increased in SSA nations for the studied years. The mean and standard deviation of population density for the tested nations are 3.8952 and 6.2955, respectively, with the lowest value of 0.00 and a high of 532.88. This suggests that the majority of the examined nations have dense populations. In addition, the lowest, highest, and standard deviation values of the sample SSA nations for energy consumption per capita are 0, 1.3306, and 1.3306, with an average of 3.9396. This indicates that energy consumption is considerable in these nations. Gross fixed capital formation has a mean of 0.6604, a standard deviation of 1.5009, and minimum and maximum values of -14.144 and 1.4729, indicating that gross fixed capital formation is higher in SSA nations.

According to Marshall and Jonker (2010), a vital role of descriptive statistics is determining how variables are distributed relative to a normal distribution. For this purpose, the evaluation of kurtosis and skewness is incorporated, where skewness measures the distribution’s lack of symmetry and kurtosis measures how much mass is in a distribution’s ends. It thus serves as a measure of how much of a random variable’s (Y) variance is due to extreme values. An outlier is an extreme value of a random variable Y, and the greater the kurtosis of a distribution, the greater the likelihood of outliers. Consequently, the standard measurement formula for the kurtosis of a normally distributed random variable Y is 3 Marshall and Jonker (2010), which indicates that a random variable (Y) with kurtosis greater than 3 has more mass in its tails than a normally distributed random variable. According to Jijian et al. (2021), can a sample be deemed to have a normal distribution if the skewness ranges from 2 to +2 and the kurtosis ranges from 7 to +7.

Assessing Table 2 descriptive statistics, the dependent and explanatory variables exhibit a mixture of negative and positive skewness values. The value of the dependent variable (carbon emission) was significantly distorted. Trade openness recorded highly negatively skewed values for the independent and control variables, while the other variables recorded highly positively skewed values. The data are either moderately or significantly distorted. This suggests that the tail region may serve as an aberration for the statistical model, which may have a negative effect on the model’s outcome. Therefore, the authors employed robust estimation models to ensure that the findings are objective and statistically accurate. The skewness of the distribution, however, indicates that there are values that warrant concern. Alternatively, certain variables (such as carbon emission, trade openness, population density, energy consumption per capita, and gross fixed capital formation) have a kurtosis greater than 7 to +7. According to the admissible kurtosis values, this indicates that our sample has a distribution with extreme values or outliers that is acceptable.

4.2. Cross-sectional dependency

Cross-sectional dependency (CD) must be taken into account when evaluating models with panel data (Twum et al., 2022; Wen et al., 2023). The influence of CD dependence on estimates is dependent on a number of factors, including the type of cross-sectional dependency and the degree of cross-sectional correlations. Table 3 presents Friedman’s test for CD analysis.

Table 3. Friedman’s CD Test.

Test	Statistics	p-values
Fixed Effect (FE) Analysis	19.584	.0014
Random Effect (RE) Analysis	28.341	.0028

H₀ is no cross-section dependency.

The p-values are in italic.

The CD test in Table 3 indicates the presence of CD in the study variables since the fixed effect and random effects results demonstrate significant values at a 1% level. Hence, the presence of CD in the research variables suggests that the Ho of the absence of CD is rejected and Ha of the presence of CD accepted. Therefore, the findings indicate that a shockwave in one nation would impact the other SSA countries.

4.3. Stationarity test

Testing the data to see whether they are stationary, which means maintaining consistent statistical qualities across time, is another essential aspect in a study by Ostic et al. (2022). To reduce the occurrence of false estimation techniques, the authors examined the stationarity of the panel data and the sequence of integration. We first performed the (Levin et al., 2002) test to determine whether our data series are stable at a level or at first differences. Table 4 presents the Levin Lin-Chu unit root results. From the results, all the study variables are stationary except CO2M, which was found to be non-stationary.

Table 4. LLC Unit root test results.

Variable	Adjusted	p-value
InCO2M	7.0533	.0000
InIDT	−8.0204	.0000
InTOP	−10.6725	.0000
InUPG	−7.7083	.0000
InEC	−3.5599	.0000
InGF	−9.8148	.0000
InEG	−5.9157	.0000

H₀ is no stationarity.

The p-values are in italic.

Since the unit root statistic of CO₂ in Table 4 is 7.0533, yet, statistically significant. We therefore did further tests for the unit root to bring finality to the unit root tests results. As a result, we employed the CIPS and ADF unit root tests as detailed in Table 5. From the findings of Table 5, all the variables were stationary at the level. Therefore, the alternative hypothesis of stationarity is accepted and the null hypothesis of no stationarity is rejected implying that all the variables were integrated at level.

Table 5. CIPS and ADF unit root tests.

	CIPS
	Level
Variable	Constant	Constant & Trend	ADF
InCO2M	−3.4863***	−5.4937***	−3.4748***
InIDT	−2.9856***	−4.4638***	−4.3483***
InTOP	−3.5729***	−5.482***	−6.3745***
InUPG	−2.9732***	−5.4732***	−4.3785***
InEC	−2.9482***	−6.3646***	−7.4743***
InGF	−3.4721***	−4.3802***	−9.5743***
InEG	−3.5792***	−5.9532***	−6.3968***

*** denote statistical significance at 1%.

4.4. Estimation techniques

The generalized method of moments (GMM) and the pooled mean group (PMG) were utilized by the author to demonstrate the diverse and comprehensive effects of industrialization on emissions of carbon in Sub-Saharan Africa (Kong et al., 2023). Panel estimates are performed via the dynamic model given in Equations. However, in this work, the GMM can solve this potential issue (Osei et al., 2019). Additionally, the pooled mean group (PMG) was used to compare and explore the relationship between several characteristics such as population, energy consumption, fixed gross capital creation, industrialization, urbanization, and carbon emissions across the SSA countries. Since the PMG-ARDL model makes use of the cointegration form of the normal (ordinary) ARDL model developed by Pesaran (2007), it is regarded as a useful substitute model for the GMM. PMGARDL provides both short- and long-term estimations as well as the necessary degree of adjustment during a condition of disequilibrium. The authors employed GMM as the main estimate (R1) and PMG estimator to test the robustness of the findings in (R2) for all panels. Panel A comprises the countries of Central Africa, Panel B comprises the countries of Southern Africa, Panel C comprises the countries of Eastern Africa, and Panel D comprises the countries of Western Africa. Panel E combines all of the chosen countries. Table 6 displays the outcomes of the estimation technique.

Table 6. Estimation results.

	Panel A		Panel B		Panel C		Panel D		Panel E
variables	(R1)	(R2)	(R1)	(R2)	(R1)	(R2)	(R1)	(R2)	(R1)	(R2)
L.InCO2M	0.3275***		0.2635***		0.4736***		0.3453***		0.5487***
InIDT	0.3745***	0.1047***	0.1073**	0.1498**	0.2174***	0.1352***	0.2534***	0.0653***	0.1432***	0.2354***
InUPG	0.1241*	0.1421	0.2039*	0.2135	0.2164**	0.1463	0.1682**	0.1746**	0.2465*	0.1635
InEC	0.2643***	0.2047***	0.1264***	0.2635***	0.1264***	0.2645***	0.1637***	0.2635***	0.2538***	0.3726***
InGF	−0.1736	−0.0857	0.0946	0.0146	0.2746	0.1374	0.12642	0.0948	0.14763	0.1463
InEG	−0.1327**	−0.0957	−0.6173*	−0.2634	−0.1236**	−0.0949**	−0.1362	−0.0954	−0.1635**	−0.2464**
Wald chi^2	374.56		647.57		573.56		684.57		957.83
Prob > chi^2/AR (2)	0.0000	0.5736	0.0000	0.6483	0.0000	0.7894	0.0000	0.6473	0.0000	.8261
Hansen Test	0.6741		0.5683		0.7832		0.5464		0.8242
Sargan Test	693.22		473.48		447.63		674.59		1265.11
Obs.	57	76	57	76	152	171	190	209	468	532

The coefficients with its significance level for each of the variables are presented in the multiple regression table.

*** denote statistical significance at 1%, ** denote statistical significance at 5%, *denote statistical significance at 10%.

The Wald chi² values for the relationship between industrialization and CO₂ emission in R1 in all the panels are 374.56, 647.57, 573.56, 684.57, and 957.83. These values demonstrate that the technique is statistically appropriate for the empirical study. Further evidence of a significant variation in the factor interaction comes from the strong AR (2) figures of 0.5736, 0.6483, 0.7894, 0.6473, and 0.8261 in all the panels. The p-values provide additional evidence of the methodology’s exceptional effectiveness and efficiency in empirical research. As a result, the model explains a larger percentage of the range of effects that the different explanatory variables have on the dependent variable. In light of this, the five distinct panels’ interactions between the variables were accurately represented by the estimators that were used (Figure 1).

Figure 1. Average carbon dioxide emissions over the periods.

Also, industrialization was positively correlated with carbon emissions. According to the positive connection, CO₂ emission in all the panels by 0.1073, 0.1432, 0.3745, 0.2174, and 0.2534 respectively at 1% significant level, as industrialization grows. The findings imply that rising industrialization will cause carbon emissions in SSA nations to rise as well. The mechanism of the findings is due to the increase in industrial activities in the region with lax regulations to control these industries in reducing carbon emission. Moreover, due to the increase in trade worldwide most companies in the advance nations have relocated in emerging markets like SSA where there is a ready market for products and services. As a result, industrial activities have increased causing a positive effect on carbon emission in the region. The findings suggest the need to switch from conventional carbon-intensive industrial processes to more environmentally friendly practices. Thus, industries need to prioritize the sustainability agenda by implementing strategies like waste minimization, energy efficiency, adoption of cleaner technology, utilization of renewable resources, and sustainable industrial practices (Zhou et al., 2022). Moreover, policymakers should take a balanced stance, encouraging economic growth and incorporating adequate environmental laws and financial incentives for businesses to switch to sustainable practices. The findings support that of (Ali et al., 2023; Elfaki et al., 2022; Meng et al., 2021; Ostic et al., 2022) who found that industrialization increase carbon emission.

Population density and CO₂ emissions are positively and strongly associated across all panels, as expected. In particular, carbon emissions rose by 0.1241, 0.2039, 0.2164, 0.1682, and 0.2465 for a 1% increase in population density. In the panels, the association is statistically significant at 10%, 10%, 5%, 5%, and 10%. The findings indicate that when population density rises, the number of people living in poverty rises along with the burden on natural resources, which exacerbates environmental deterioration through the overuse of natural resources. The results thus imply that, in order to lower carbon emissions over the long run, population density in SSA nations should be guided by effective legislation. The results corroborate those of researchers who discovered a positive association between population density and carbon emissions (Liang et al., 2023; Uzair Ali et al., 2022).

Moreover, energy consumption positively impacts carbon emission in all the panels at a 1% significant level. The figures show that in all the panels a 1% increase in per capita energy consumption causes an increase in emissions of around 0.1637, 0.2643, 0.1264, 0.1264, and 0.2538 respectively. The mechanism of the results is due to the high level of energy consumption within the regions with less environmental control, which has increased carbon emissions. The direction of the findings suggests that governments and policymakers should strengthen environmental laws on energy consumption to decrease carbon emissions in the regions. Moreover, reducing total energy consumption and lowering related carbon emissions may be accomplished through implementing energy-efficient technology, enhancing industrial processes, and encouraging energy conservation measures. Thus, it is suggested that public and private sector organizations allocate resources towards sustainable infrastructure initiatives, such as constructing energy-efficient buildings, renewable energy facilities, and electric transportation networks. The findings support that of (Ali et al., 2022; Mukhtarov et al., 2022; Pan et al., 2023) who discovered a positive association between energy consumption and the emission of carbon.

Furthermore, except for panel A, which displayed a negative but statistically insignificant association with carbon emission, the gross fixed capital formation also suggested a positive and statistically insignificant link with carbon emission in panels B, C, D, and E. This illustrates the association between rising carbon emissions and the country’s gross fixed capital. Conversely, as panel A illustrates, a decrease in the countries’ carbon emissions would result from an increase in their gross fixed capital creation. The findings indicate how recent businesses such as resource extraction and heavy manufacturing companies produce higher carbon emissions and use more energy in countries that have increased carbon emissions. These industries also contribute considerably to gross fixed capital creation. Therefore, it’s critical to promote the adoption of greener technologies, increase energy efficiency, and allocate resources towards sustainable practices in these businesses. The findings suggest that infrastructure planning and building techniques must be re-evaluated to give low-carbon or carbon-neutral technology priority. This entails using green construction techniques, employing sustainable materials, and adopting energy-efficient designs. Moreover, sustainable, and resilient cities should be the main focus of urban development, with an emphasis on green areas, public transit, and energy-efficient structures. The results aligns with that of Kobayakawa (2022; Zhang et al., 2023) who discovered a positive connection between cross fixed capital formulation and emission.

Additionally, across all the panels, economic growth showed a statistically significant and a negative connection. According to the findings, SSA nations’ carbon emissions will decrease with every 1% increase in economic growth. A possible cause of the findings is that nations in SSA have pooled their resources, best practices, and innovations in green growth practices to address global concerns connected to climate change and environmental degradation. The results align with those from Sejati et al. (2020), Ali et al. (2023), Baba Ali et al. (2023) and Edziah et al. (2022) where economic growth have been recorded to have a negative impact on CO₂ emission levels with technological advancements playing a major contributory role in reducing emission levels.

In Panel E, there is a single regression with the total of all independent variables. The robustness estimator is PMG, and the main estimator is GMM. The statistics supported population expansion and industrialization, which resulted in a rise in CO₂ emission in every country in Sub-Saharan Africa. Nonetheless, there is a statistically significant and positive association between carbon emission and resilience. This demonstrates that the PMG findings corroborate the panel GMM estimate.

4.5. The moderating analysis

The study shows how trade openness moderates the relationship between industrialization and carbon emissions in Equation (2)(2) $${\mathit{\text{InC}}02M}_{\mathit{\text{it}}}={\beta }_0+\mathrm{\lambda }{\mathit{\text{InCO}}2M}_{\mathit{\text{it}}-1}+{\beta }_1\mathit{\text{In}}{\mathit{\text{IDT}}}_{\mathit{\text{it}}}+{\beta }_2{(\mathit{\text{InIDT}}}_{\mathit{\text{it}}}\times {\mathit{\text{InTOP}}}_{\mathit{\text{it}}})+{\beta }_3{\mathit{\text{InTOP}}}_{\mathit{\text{it}}}+{\beta }_4\mathit{\text{InU}}{\mathit{\text{PG}}}_{\mathit{\text{it}}}+{\beta }_5{\mathit{\text{InEC}}}_{\mathit{\text{it}}}+{\beta }_6{\mathit{\text{InGF}}}_{\mathit{\text{it}}}+{\beta }_7{\mathit{\text{InEG}}}_{\mathit{\text{it}}}+{\epsilon }_{\mathit{\text{it}}}$$(2) . Two tests were carried out, MI and M2, considering the panels. M1 was used for the GMM estimation, and M2 was used for the PMG robustness estimation. Table 7 presents the findings.

Table 7. Moderating analysis.

	Panel A		Panel B		Panel C		Panel D		Panel E
Variables	M1	M2	M1	M2	M1	M2	M1	M2	M1	M2
L.InCO2M	0.3645***		0.4726***		0.5736**		0.3746**		0.5115***
InIDT	0.4736***	0.3467***	0.4281**	0.4825**	0.3928**	0.3538***	0.5384***	0.4839***	0.5928***	0.3947***
InIDT × InTOP	0.2746***	0.1746***	0.2746**	0.2598**	0.2847**	0.1476***	0.4837***	0.2846***	0.3095***	0.2846***
InTOP	0.4736**	0.2847**	0.3846**	0.3948*	0.4837**	0.1473**	0.1364***	0.1463***	0.2846***	0.1374**
InUPG	0.2384	0.1276	0.3287***	0.1264**	0.2164	0.3281	0.2587**	0.3298*	0.3475**	0.4286*
InEC	−0.1847**	−0.2735**	−0.1836**	−0.3847*	−0.2837**	−0.2926**	−0.2112	−0.1211	−0.2711**	−0.1732*
InGF	0.2746**	0.1836**	−0.1487	−0.1364	0.1763	0.2036	0.1521**	0.3726**	0.2736	0.2631**
InEG	0.1273**	0.2735***	0.1746***	0.1827**	0.4642***	0.2614***	.2731***	0.1648**	0.3824**	0.2164***
Wald chi^2	684.58		568.74		483.18		607.46		1184.48
Prob > chi^2/AR (2)	0.0000	0.5731	0.0000	0.6743	0.0000	0.7836	0.0000	0.6782	0.0000	0.8746
Hansen Test	0.5673		0.6739		0.5847		0.4893		0.7858
Sargan Test	978.47		684.93		793.28		578.47		1382.49
Obs.	57	76	57	76	152	171	190	209	468	532

The coefficients with its significance level for each of the variables are presented in the multiple regression table.

*** denote statistical significance at 1%.

** denote statistical significance at 5%.

* denote statistical significance at 10%.

Table 7 demonstrates how the SSA-selected countries’ industrialization and carbon emission coefficient grew when trade openness was introduced, causing pollution and environmental harm in the majority of SSA countries. Despite the direct association findings in Table 6, the results indicate that trade openness has a moderating impact in all panels that has raised the coefficient of industrialization and CO₂ emission. This proves that the proportion of carbon emissions from SSA countries is larger. Governments in SSA countries are therefore recommended to enact laws and policies to regulate industrial activity and trade openness. As a result, the third hypothesis is refuted by the moderating function data. The robustness of the results is further supported by the primary estimator’s results.

Generally, as the findings indicated, trade openness has a strong relationship with carbon emissions in SSA countries. This is as a result of the increase in trade liberalization of most countries in SSA countries, which has caused an increase in carbon emissions. In addition, the positive relationship results from increased import of goods and negative industrial activities in SSA countries, which have increased carbon emissions.

4.6. Discussion

As the theory of economic integration suggests, industrialization is necessary for attaining transformational and sustained economic development in Africa (Mentel et al., 2022). Because of this, SSA’s development agencies and other regional governmental entities have been working hard in recent years to devise plans for greatly enhancing industrialization, which is the main tenet of economic integration theory. Industrialization, as defined by neo-classical economic theory, is characterized by the persistence, efficacy, and expansion of economic growth (Ogede & Tiamiyu, 2023). In light of this, several organizations have pushed the economies of the SSA countries to broaden industrialization since doing so would lessen the cyclical nature of economic growth and encourage confidence in investors (Appiah et al., 2022). Despite these advantages, industrialization has resulted in emissions of carbon (Elfaki et al., 2022). In some African cities, the rise in industries has caused in an increase in emissions of carbon (Naeem et al., 2023). Hence, in order for Sub-Saharan Africa to meet the SDGs on climate protection, economic growth must be balanced with environmental preservation. However, in the last decade, SSA has often been used as a pollution haven for developed countries.

Based on this premise, our first hypothesis supposed a positive link between industrialization and CO₂ emission in SSA. Therefore, the first hypothesis is accepted. According to our findings, with more companies in SSA and more fossil fuel consumption, emissions of carbon would likely increase. Our results align with those of Ostic et al. (2022), who argued that China’s industrialization raises carbon dioxide emissions because the industrial sector uses a lot of energy. Therefore, encouraging industrialization eventually results in high energy consumption and carbon dioxide emissions.

By enabling the import of better and more affordable technologies, higher-quality inputs, and managerial know-how, trade openness is a policy option that boosts local output (Sejati et al., 2020). Basing on the neoclassical economic theory, (Wang & Su, 2019) contended that, trade openness can help local consumers to boost economic efficiency. Thus, the larger market would allow domestic exporting companies to profit from economies of scale. This suggests that SSA nations need to greatly increase their trade openness in order to achieve reduced unemployment, higher living standards, lower poverty rates, and quicker economic growth. Consequently, Sub-Saharan Africa has opened up its trade to facilitate imports and enhance exports (Kongkuah et al., 2021). Furthermore, several trade agreements have been implemented to make it easier for nations in Sub-Saharan Africa to interchange commodities and services. Due to this, the amount of foreign commerce that has taken place in Africa over the last several years has increased dramatically. Promoting industrialization and trade openness lead to balanced economic growth (Zheng et al., 2021), however, the environment suffers due to emissions of carbon (Singha et al., 2021). Our findings suggest that industry and emissions of carbon in Sub-Saharan Africa will rise in proportion as trade opens up. This affirms that SSA nations’ industries and emissions of carbon have increased dramatically due to increased trade openness in recent years. As a result, the second theory is accepted. Our results are consistent with those of Wiredu et al. (2023), who argued that trade openness may potentially lead to the relocation of polluting industries to nations with less stringent environmental regulations, hence increasing emissions of carbon. Contrarily, Wang et al. (2022) disagree with our results since the authors concluded that, trade openness may stimulate the adoption of greener technologies and knowledge, resulting in more efficient and environmentally conscious manufacturing methods that lower carb emissions. The findings imply that in order to lower emissions of carbon, the government and policymakers in SSA should tighten regulations on industrial activity and trade openness policies.

5. Conclusion and policy implications

In recent years, trade liberalization has made industrialization and emissions of carbon a popular study issue in wealthy and developing nations. In particular, emerging economies like those in Sub-Saharan Africa have suffered from trade openness, which has impeded economic growth. This is because the world’s emerging economies have been destroyed by rising carbon emissions brought on by economic development, which includes increased industrialization, trade with other countries, urbanization, trade openness, and global commerce, among other things. With trade openness playing a moderating influence, the study’s goal is to investigate how industrialization affects carbon emissions.

The initial subject of the current research was SSA nations. 28 of the 46 nations in Sub-Saharan Africa were chosen because data for some of the available variables used in the research was unavailable. The World Development Indicator database, the source of the empirical analysis, was mined for secondary data. The study’s empirical analysis considered the years 2003–2021. After that, panel data were used in the study’s estimate methodologies analysis, using the GMM and PMG estimators.

The findings demonstrate a statistically significant and positive association between emissions of carbon and industry. Furthermore, trade openness has been shown to be positively associated with industrialization and emissions of carbon; this means that as SSA nations increase their trade with the rest of the world, their industries and emissions of carbon will also increase.

The recommendations derived from the findings suggest that to reduce emissions of carbon, authorities in SSA nations should fortify and execute industrialization-related laws and programs. Trade openness has a favorable effect on industrialization, but to reduce emissions of carbon over the long term, SSA nations need to impose stringent rules, and trade restrictions. Therefore, to reduce emissions of CO2 emission in SSA nations, laws and restrictions on the import and export of products and services should be put into place.

5.1. Policy implications

Global pollutants such as CO₂ emission offer a policy challenge since only government action can completely minimize negative externalities. Considering most businesses in the world now spend all their resources on expanding their operations, Africa has become a pollution refuge. As a result, governments across the continent must put rules in place to regulate and safeguard the environment concerning carbon emissions. As civilizations evolve, environmental issues become more socially significant, and the demand for pollution controls increases.

First, the governments of all SSA nations should create stringent policies to monitor and manage industry pollution and stop the flow of carbon emissions. Strict guidelines and regulations should be applied to the industries in the SSA nations to direct their actions toward the environment and minimize other negative externalities like carbon emissions arising from their activities. Additionally, to reduce carbon emissions and regulate the overpopulation characteristic of big areas in Sub-Saharan Africa, the environment protection authorities in these nations should enforce stringent regulations controlling urban settlement. As Africa has emerged as a hotspot for carbon emissions in recent years, stringent laws and policies governing trade openness are necessary to reduce CO₂ emissions.

5.2. Limitations and Future studies

Regarding the limitation of the study, the WDI does not have a complete dataset for the various countries in SSA, hence some of the countries were excluded. Additional nations in the SSA area may be included in future studies if data is available. Furthermore, because of the availability of data, the study only considered 22 years. Future studies can consider more years if data for developing nations is accessible. Future studies may consider incorporating supplementary control variables to offer a more comprehensive explanation of the association between industrialization and carbon emissions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Joseph Owusu Amoah

Joseph Owusu Amoah is faculty member at the Department of Accounting, School of Business, Simon Diedong Dombo University of Business and Integrated Development Studies (SDD UBIDS), Wa, Ghana. He holds Master in Accounting from University for Development Studies, Tamale and a BA Accounting from the University for Development Studies, Tamale, Ghana. His research focuses on Foreign Direct Investment, Environmental Economics and Corporate Governance.

Imhotep Paul Alagidede

Imhotep Paul Alagidede is a full-time and visiting professor of economics and finance at several institutions, including the University of the Witwatersrand, Simon Diedong Dombo University of Business and Integrated Development Studies, the African Economic Research Consortium, and the University of Stellenbosch. He has a strong interest in emerging market economies, indigenous knowledge systems, and consciousness exploration. Alagidede holds a Bachelor’s degree in Economics and History from Kwame Nkrumah University, a Master of Science in Economics and Finance, and a PhD in Economics from Loughborough University. He has published extensively in top journals and serves as the Grand Editor of the Journal of Indigenous and Ancestral Studies. Alagidede has mentored numerous fellows and consulted for governments, multilateral agencies, and corporations.

Yakubu Awudu Sare

Yakubu Awudu Sare holds PhD degree in Finance from University of Ghana. He is a Professor in Finance and currently the Dean for School of Business at the Simon Diedong Dombo University of Business and Integrated Development Studies. His research interest are Corporate Finance, Foreign Direct Investment, and International Trade.