Does ICT diffusion exacerbate or mitigate the environmental impacts of renewable energy projects in Sub-Saharan Africa?

doi:10.21203/rs.3.rs-4959123/v1

The interplay between ICT, renewable energy, and CO2 emissions is a critical area of research, especially in the context of Sub-Saharan Africa, which faces significant challenges in terms of energy access, environmental sustainability, and technological development. This study examines the direct impact of information and communication technology (ICT) on environmental quality and its indirect impact through its interaction with renewable energy consumption. It also controls for other important macroeconomic variables across 48 Sub-Saharan African (SSA) countries from 2005 to 2020. The research employs various econometric panel data methods, including standard linear regression pooled-OLS, fixed-effects, random-effects models, and a more robust system-GMM approach. The results indicate that increased energy consumption, urbanization, and education negatively affect environmental quality, whereas trade openness has a positive impact. These findings are significant for policymakers, especially in the context of globalization and Africa's growing integration into the world economy through the African Continental Free Trade Area (AfCFTA) Agreement and the accompanying urbanization challenges. The study suggests an urgent need for a sustainable environmental strategy. It also highlights that the interaction between renewable energy and ICT diffusion can enhance environmental quality in SSA, implying that investments in R&D for renewable energy technologies are crucial for achieving environmental sustainability.

JEL Classification :C23, Q43, Q53

ICT diffusion

CO2 emissions

Renewable energy

Panel data econometrics

Sub-Saharan Africa

Just as there is increasing interest in the relationship between economic activity (income) and the environment, there is also growing interest in the connection between ICT and environmental quality [1]. Expanding access to ICT and its services has created significant economic opportunities for the poor in Sub-Saharan African (SSA) countries, promoting inclusive economic growth [2]. However, the rise in economic opportunities due to ICT comes with a cost, particularly for SSA countries, where it leads to a deterioration in environmental quality due to increased CO2 emissions. The exponential growth of ICT devices in recent decades has driven higher energy demand and, consequently, a larger share of global CO2 emissions, especially in emerging countries [3]. Similarly, the adoption and use of ICT devices in SSA consume substantial energy, much of which is sourced from fossil fuels.

However, renewable energy can be developed and deployed to mitigate the negative effects of ICT on CO2 emissions. According to [4] renewable energy can make the ICT industry greener and more sustainable. [5] also found that developing countries have the potential to achieve sustainable economic growth while reducing carbon emissions through ICT. In light of this, the present study aims to analyse the direct effects of ICT on the environment and its indirect effects through interaction with renewable energy consumption, while controlling for economic activity, total energy use, trade openness, education, and urbanization.

Several studies have explored the link between ICT and environmental quality, yielding mixed conclusions. Some studies ([6]; [7]; [8]; [9]) suggest that ICT diffusion improves environmental quality, while others ([10]; [1}; [11]) argue that it worsens environmental conditions. Another group of studies indicates that the level of a country's development determines how ICT affects environmental quality ([12]; [13]; [14]). Additionally, some research suggests a nonlinear interaction between ICT and environmental pollution ([15]; [16]; [17]). These mixed and inconsistent conclusions are likely due to variations in econometric methods, sample periods, and country samples. Few studies have examined the role of moderators on the impact of ICT on carbon emissions ([18]; [19]), particularly the mediating role of renewable energy consumption [20]. Given that ICT significantly contributes to carbon emissions, renewable energy could mitigate this adverse effect on environmental health. This study examines the direct and indirect effects of ICT diffusion on environmental quality, moderated by renewable energy, in Sub-Saharan Africa. It seeks to answer: Does ICT diffusion contribute to carbon emissions in SSA? Does the interaction between ICT diffusion and renewable energy improve environmental quality? Are there policy implications for environmental sustainability?

Currently, SSA does not significantly contribute to global emissions, but this is expected to change due to ongoing institutional and economic reforms aimed at higher growth, energy security, a diversified economy, modern industrialization, and transportation infrastructure. The ICT diffusion driven by digitization and automation necessary to achieve these objectives could undermine environmental quality. The region is vulnerable to the devastating impacts of climate change, and its reliance on revenues from natural resource extraction exacerbates this vulnerability [1]. SSA has implemented robust policy frameworks and measures to mitigate climate change effects, responding positively to international calls for action [21]. Investigating the determinants of environmental pollution is fundamental for effective action against global warming. This study focuses on the interaction of ICT diffusion with renewable energy as a determinant of environmental quality.

Despite growing attention to the nexus between ICT diffusion and environmental quality, several research gaps remain. First, most previous studies have focused on developed and emerging countries, while SSA countries, which are lagging in ICT development, have been rapidly advancing in this sector in recent years. The rapid growth of the ICT sector in SSA poses significant environmental challenges, warranting investigation and documentation of relevant evidence in this region. Second, few studies have examined the effect of ICT diffusion moderated by renewable energy consumption (see [20]). These studies typically focus on a narrow range of ICT indicators. By using principal component analysis, we construct a multiple-indicator ICT index to address this limitation, capturing the broad effect of ICT diffusion. Third, improving on previous literature, this study employs a more robust Generalized Method of Moments (GMM) approach to carefully address the relevance of instruments in system-GMM models. It is crucial to assess the strength of instruments in instrumental variables regressions.

This study addresses existing research gaps by examining the impact of ICT diffusion and renewable energy on CO2 emissions in 48 Sub-Saharan African (SSA) countries from 2005 to 2020 using a modified system-GMM model. Our research makes a significant contribution with a robust methodological approach to the empirical literature on this topic. Our GMM estimations ensure the significance of the instruments, which is crucial because biased estimates can result from weak links between endogenous variables and instruments. Testing for weak instruments in a system GMM is challenging. [22] assessed the strength of instruments for each equation based on level and first difference, but this method does not evaluate the joint instrument strength for the entire system. We use a recent technique by [23] to assess the joint instrument strength in system GMM, obtaining standard diagnostics and modifying moment conditions for one level equation based on both level and first difference equations.

The traditional approach using the instrumental variable (IV) method evaluates instrument strength with the F-statistic, testing the null hypothesis that the instruments have zero impact on endogenous variables. Our Kleibergen Paap F-statistic results across our system GMM models are largely adequate. According to [24] criteria, our models do not suffer from significant weak instrument bias, despite Stock & Yogo’s critical values assuming i.i.d errors.

Our study improves on previous literature by ensuring our system GMM models include relevant instruments, addressing a major limitation in earlier studies that failed to handle instrument significance systematically, such as those by [1], [25], and [2]. Empirical studies use difference and system GMM to address Nickell bias in dynamic models with fixed effects and endogeneity issues. Relevant instruments are necessary to avoid biases, though system GMM may not effectively treat omitted variable bias. We include time fixed effects to make our estimates robust against this bias.

ICT diffusion is essential for achieving environmental sustainability and leveraging renewable energy to accelerate the race to net-zero carbon emissions in SSA and globally. The remainder of the paper is organized as follows: Section 2 reviews the theoretical background and empirical literature, Section 3 details the data and methodology, Section 4 presents and discusses the empirical results in context with previous studies, and Section 5 concludes with the policy implications of the findings.

2.1 Theoretical background

According to [26], the interaction between ICT and environmental quality can be understood in three primary ways: These are the immediate environmental impacts resulting from the production, use, disposal, and recycling of ICT, which often have negative consequences. ICT indirectly influences environmental quality through applications that enhance energy efficiency and smart technologies. These include smart grids, smart meters, smart logistics, and decentralized energy production. By improving industrial production, building efficiency, and transportation, ICT can either increase or decrease emission levels. This also encompasses the use of ICT for renewable energy and innovation.

These long-term effects result from changes in consumption habits and economic structures due to the stable adaptation of ICT and related services. Increased efficiency leads to reduced prices and greater use of ICT, which can ultimately increase carbon emissions due to higher energy consumption. Advances in software technology can make hardware obsolete, driving consumer demand for new ICT devices and increasing energy consumption and emissions. This phenomenon is influenced by Moore's law, which states that hardware becomes cheaper and smaller, thus increasing its ecological footprint and disposal challenges. Since 35 percent of global greenhouse gas (GHG) emissions are directly linked to consumer behaviour, changing consumption patterns is critical for reducing emissions [27].

To achieve significant reductions in global GHG emissions, consumer usage patterns should be a key focus of sustainability policies targeting the ICT sector. The direct and indirect effects of ICT on green growth are illustrated in Fig. 2, which outlines the analytical framework for this study. The figure shows the theoretical basis, where the arrows represent the direct effect of ICT and its interaction with renewable energy (indirect effect) on CO2 emission levels. The lighter arrows indicate the direct effects of renewable energy and ICT diffusion, while the thicker arrow represents the combined impact of both factors on CO2 emissions. Based on this framework, the study tests the hypothesis that the interaction of renewable energy use and ICT diffusion reduces CO2 emissions.

2.2. A brief empirical review

The empirical relationship between renewable energy, ICT diffusion, and CO2 emission levels has garnered significant attention in the energy and environmental economics literature [27]. As a result, economies are increasingly shifting towards renewable energy sources for ecological reasons. It is projected that renewables will surpass coal in power generation by 2025 and will account for 50% of global electricity production by 2050 [28]. The expansion of renewable energy supply can lower energy prices, improve environmental quality, enhance human health, and create jobs [29]. Over the past two decades, the proliferation of ICT user devices has led to greater energy consumption and higher Greenhouse Gas (GHG) emissions, particularly in emerging economies ([3]; [30]). If the energy efficiency gains from ICT are not effectively managed, the increased energy demand can lead to continued environmental degradation by raising the ICT sector's CO2 emissions.

The ICT revolution has become a catalyst and a key player in the global economy's energy sector [31]. ICT contributes to energy efficiency by reducing primary energy usage in other sectors, such as modern industry, smart buildings, logistics, and transportation. The energy sector plays a crucial role in addressing climate change and environmental issues, with fossil fuel combustion accounting for about two-thirds of GHG emissions from CO2 [32]. ICT has the potential to reduce fossil fuel usage by promoting renewable energy adoption and enhancing the efficiency of technological devices and innovations [33].

Furthermore, developing alternative cleaner energy resources like renewables and biofuels can lower the cost of importing oil and gas [34]. Although ICT generally increases CO2 emissions in low-income economies (see Fig. 1, panel A), an increase in mobile phone and internet users can reduce CO2 emissions [12]. Conversely, [35] found an inverted U-shape relationship between CO2 and ICT in emerging economies, suggesting that ICT diffusion can both increase energy demand and reduce CO2 emissions [20].

Therefore, the effects of ICT on environment quality are contingent on the nature of the link between ICT diffusion, alternative cleaner energy sources and CO₂ emission levels ([4]; [12]; [20]; [36]. This link between renewable energy, ICT diffusion, and CO2 emission is not yet established conclusively in the extant literature, particularly in the SSA region, where such studies are scanty but rarely consider the interaction terms of ICT*renewable energy in investigating the subject matter. Overall, it is imperative to fill these research gaps by critically examining the environmental impact of ICT diffusion, renewable energy use, and their interaction term on CO₂ emissions in SSA countries.

3.1 Data and Variables

This research employs cross-panel macro datasets for 48 Sub-Saharan African (SSA) nations spanning from 2005 to 2020. The starting year of 2005 was chosen to ensure the inclusion of more SSA countries, as data for some ICT indicators were unavailable before 2005. The study primarily investigates the impact of renewable energy use on carbon emission levels, considering the moderating role of ICT.

The key variables in the model include carbon emissions measured in metric tonnes per capita, the share of total energy consumption attributed to renewable energy (RE), and the ICT index, which serve as proxies for environmental quality, renewable energy use, and ICT diffusion, respectively. The model also controls for GDP, energy use, trade openness, education, and urbanization. Trade openness is measured as the sum of total imports and exports of goods and services as a percentage of GDP, while urbanization is the percentage of the population living in urban areas. Education is represented by secondary school enrolment (% gross). All data are sourced from the World Bank's development indicators database [37]. Table 1 provides descriptions of all the variables used in this study.

Based on supporting literature, GDP, energy use, trade openness, and urbanization are expected to negatively impact environmental sustainability, while education is expected to have a positive effect. An increase in GDP generally leads to higher pollution levels, as individuals consume more energy and polluting goods ([38]; [39]). Higher energy use increases carbon emissions, thus harming the environment ([40]; [41]; [42]). International trade can shift pollution-intensive activities to different countries, worsening the host country's environmental quality ([43]; [44]). Urban growth may increase incomes, leading to the purchase of more energy-intensive goods, which can negatively impact the environment ([45]; [46]). Conversely, educated individuals are likely to be more environmentally conscious and prefer eco-friendly goods and services ([40]; [47]; [48]).

Table 1

Variables and unit of measurements
Variables	Unit of measurement	Sources
CO₂ emissions	Metric tons per capita	WDI
Energy use	Kg of oil equivalent per capita	WDI
GDP	constant 2015 US$	WDI
Renewable energy	% of total final energy consumption	WDI
ICD Index	ICT index constructed using PCA	Author
Education	School enrolment, secondary (% gross)	WDI
Trade Openness	% of GDP	WDI
Urbanisation	Urban population% of the total population	WDI
Note: WDI means World Development Indicators, while PCA is the principal component analysis

The ICT index being a latent variable as it is commonly treated in the extant literature, we use multiple ICT indicators as described by the Information Telecommunication Union (ITU) and adopted by several studies to capture ICT diffusion via index constructed using principal component analysis. These indicators are presented in Table 2.

Table 2

Index system for ICT
ICT Indicators	Source
Secured internet servers (per 1 million people)	WDI
Secure internet servers	WDI
individuals using the internet (% of the population)	WDI
Mobile cellular subscriptions	WDI
Mobile cellular subscriptions (per 100 people)	WDI
Tertiary school enrolment (Gender parity)	WDI
Fixed broadband subscriptions (per 100 people)	WDI
Fixed telephone subscriptions	WDI
Fixed broadband subscriptions	WDI
Fixed telephone subscriptions (per 100 people)	WDI
Mean secondary school enrolment in years	WDI
Tertiary school enrolment (Gross)	WDI
Secondary school education (Gender parity)	WDI

The extant literature establishes that education, urbanization and trade openness are significant positive determinants of environmental degradation ([40]; [49]; [19]). GDP and energy use stimulate carbon emissions [25].

3.2 Model Specifications

Following the underpinning theories and adopted econometric model, we specify the baseline carbon emission model to cover renewable energy use and ICT diffusion, which Eq. (1) represents:

Where i = 1, 2…, N where N represents the countries while t and t-1 are the current and previous years; lnCO_2, RE and ICT are natural logarithm of carbon emission, renewable energy and ICT diffusion. X is a vector of control variables: GDP, energy use, education, trade openness, and urbanization. µ_iare the country fixed effects; λ_t are the time effects; ε is an error term.

We specify the carbon emission model with the interaction term for renewable energy and ICT diffusion as follows:

We use a Generalized Method of Moments (GMM) technique to estimate the specified equations, specifically employing system GMM as our primary estimation method. To ensure the robustness of our model, we also estimate using difference GMM for comparison. All GMM estimations are conducted using the `xtdpdgmm` command in the STATA statistical package. The number of instruments in system GMM increases sharply with the number of regressors and periods [50]. To address data loss, we use the orthogonal option, as first differences can exacerbate gaps in unbalanced panel data.

We conduct two specification tests for GMM models: higher-order serial correlation and Hansen tests. The second-order serial correlation test is performed on first-differenced error terms. The absence of second-order serial correlation indicates that the error term is serially uncorrelated. Hansen's test, also known as the overidentification test, complements the second-order serial correlation test by assessing the validity of the instruments based on endogenous lagged variables.

To detect weak instruments, we use the Kleibergen-Paap LM test and the Kleibergen-Paap F test. These diagnostic statistics are computed using the `ivreg2` command with the `gmm2s` option. The goal is to test whether the level equation and the first-differenced equations have a zero effect on the instruments. It's important to note that the `ivreg2` command produces the same coefficients as the two-step GMM `xtdpdgmm` command.

4.1 Pre-estimation Diagnostics

This study significantly differs from previous research by constructing a composite index for ICT diffusion based on the International Communication Union (ICU) classification, considering twelve indicators. Principal component analysis (PCA) is used to generate this index. Several preliminary tests are necessary for PCA, and our study passes all of them. These tests include checking for: adequate sample size from the ICT covariates, the strength of correlations between these covariates, and the strength of partial and overall intercorrelations between these indicators.

Appendix 2 provides evidence for the selection of these indicators by showing highly correlated covariates. The Bartlett χ² Statistic of 1061.52 with a p-value of 0.0000 confirms significant intercorrelations between these indicators. Table 3 presents the Kaiser-Meyer-Olkin (KMO) statistics, which indicate the overall and partial intercorrelations between the indicators. KMO statistics greater than 0.5 imply that the sample used to generate the ICT diffusion index is adequate.

Table 3

Principal components and eigenvalues (ICT diffusion)
Component	Eigenvalue	Difference	Proportion	Cumulative	KMO Statistic
PC1	4.0264	1.8227	0.3355	0.3355	0.7652
PC2	2.2037	1.0421	0.1836	0.5192	0.5483
PC3	1.1615	0.2680	0.0968	0.6160	0.7832
PC4	0.8934	0.0434	0.0745	0.6904	0.7856
PC5	0.8500	0.0895	0.0708	0.7613	0.8247
PC6	0.7605	0.0657	0.0634	0.8247	0.6873
PC7	0.6947	0.2510	0.0579	0.8825	0.7130
PC8	0.4436	0.0752	0.0370	0.9195	0.6576
PC9	0.3684	0.0990	0.0307	0.9502	0.6937
PC10	0.2694	0.0932	0.0225	0.9727	0.7022
PC11	0.1762	0.0245	0.0147	0.9874	0.8867
PC12	0.1516		0.0126	1.0000	0.7622
Overall	-	-	-	-	0.7415

After successfully completing the preliminary tests, we proceed to create a composite ICT index by normalizing the indicators. This normalization results in a mean of zero (0) and a standard deviation of one (1), accommodating the different scales of these indicators [51]. Table 4 illustrates the differences, proportion, cumulative values, and eigenvalues of these components. Following the Kaiser rule of eigenvalues, as employed in [2] and [52], we retain three components with eigenvalues greater than one (1) to form the composite ICT index. These three components collectively capture a significant portion (61.6%) of the information in our data. Both Table 4 and Fig. 2 confirm these findings.

Table 5 highlights the summary statistic of all the variables of interest. The data shows 5.05, 64.62 and 0.023 as average values of emissions, renewable energy and ICT diffusion index on the key variables. Similarly, the control variables such as GDP, energy use, education, trade openness and urbanization take 23.01, 6.14, 46.94, 70.89 and 40.55 as average values. Trade openness and renewable energy are the most dispersed variables, while trade openness and education have the highest maximum values. The correlation matrix of these variables is contained in appendix 1a. This appendix shows significant positive and negative links between the explanatory variables and the explained.

Table 4

Summary statistics
Variable	Observation	Mean	Std. Dev.	Minimum	Maximum
CO2 emissions (log)	665	5.0553	1.3760	2.8292	11.4575
Energy use (log)	665	6.1471	0.8991	2.2563	8.0484
GDP (log)	743	23.0060	1.4802	19.0625	26.9378
Renewable energy	672	64.6228	26.9731	0.0000	97.4222
ICT index	750	0.0229	2.0017	-2.6628	10.8445
Education	711	46.9497	23.1782	5.9323	109.444
Trade Openness	685	70.8998	34.2066	0.7846	225.023
Urbanisation	739	40.5500	16.7782	9.375	90.092

4.2 Estimated Results of the impact of ICT and renewable energy on CO₂

We conduct the standard linear regression of the pooled-OLS, fixed-effects and random-effects models as a preliminary investigation. Table 5 contains estimates of these models. Overall, renewable energy use negatively affects emissions, while ICT diffusion positively influences it. Except for pooled-OLS, the interaction between renewable energy use and ICT diffusion negatively affects carbon emissions. While GDP and total energy usage have a statistically significant positive effect in all the three models, trade openness has a statistically significant negative impact in the first model. Education has a statistically significant positive impact in all three models, while urbanization is not statistically significant in any estimated three models.

Table 5

OLS estimates (Dependent variable: emissions)
Variable	Pooled-OLS	Fixed effects	Random effects
Renewable energy	-0.0318*** (0.0042)	-0.0218*** (0.0045)	-0.0294*** (0.0070)
ICT diffusion	0.0414*** (0.0536)	0.0487*** (0.0271)	0.0292*** (0.0007)
Renewable energy*ICT diffusion	0.0092 (0.0053)	-0.0116*** (0.0032)	-0.0072*** (0.0023)
GDP	0.1230** (0.0666)	0.0607** (0.1738)	0.0789*** (0.0189)
Energy use	0.2895*** (0.0839)	0.8839*** (0.1973)	0.5126*** (0.0423)
Trade openness	-0.0016 * (0.0032)	-0.0025 (0.0010)	-0.0037 (0.0008)
Education	0.0017** (0.0039	0.0046*** (0.0036)	0.0012*** (0.0005)
Urbanisation	0.0115 0.0052	0.0333 (0.0149)	0.0422 (0.0109)
Observation	667	667	667
R-squared	0.873	0.787	-
Note: the parenthesis contains the robust standard errors. p < 0.01, p < 0.05, p < 0.1**

Any strong correlation between the explanatory variable and the disturbance terms amidst complicated causal links between independent and dependent variables may result in biased estimates. Thus, we must appropriately deal with the potential endogeneity problem to obtain more reasonable and robust estimates.

Table 6 contains estimates of the model specified in Eqs. (1 & 2). Columns (1) and (3) show one-step GMM estimates, while columns (2) and (4) present estimates of two-step GMM. The underlined distinction between the two is that for their moment conditions, one-step estimators employ a random weighting matrix and two-step employ an optimal weighting matrix. The latter is more robust and efficient based on its inherent strength to handle heteroskedasticity and autocorrelation issues.

Table 6

Dynamic panel model estimation results
Variable	One-step GMM Diff-GMM (1)	Two-step GMM Diff-GMM (2)	One-step GMM System GMM (3)	Two-step GMM System GMM (4)	System GMM (5)	Long-run System GMM (6)
$\:ln{CO}_{2}$ emissions (lag)	0.436 (0.080)	0.435^** (0.171)	0.888^*** (0.104)	0.909^*** (0.135)	0.626^*** (0.093)
Renewable energy	-0.022^*** (0.005)	-0.020^*** (0.008)	-0.008^*** (0.005)	-0.007^*** (0.005)	-0.011^*** (0.002)	-0.029^*** (0.008)
^{ICT diffusion}	^0.009*** ^(0.008)	^−0.004*** ^0.017	^0.026*** ^(0.016)	^0.020** ^(0.019)	^0.005*** ^(0.001)	^0.013*** ^(0.004)
Renewable energy*ICT					-0.002^*** (0.0007)	-0.004^*** (0.001)
Energy use	0.658^*** (0.182)	0.783** (0.316)	0.157 0.115	0.1692^*** (0.138)	0.014^* (0.044)	0.039* (0.013)
GDP	0.066 (0.104)	0.294^* (0.316)	0.065 (0.064)	0.053^*** (0.067)	0.021^*** (0.006)	0.057^*** (0.017)
GDP²	-0.039 0.008	-0.159^*** (0.036)	-0.029^** (0.007)	-0.029^**. (0.006)	-0.004^*** (0.001)	-0.028^*** (0.005)
Trade openness	0.002 (0.000)	0.001. (0.001)	0.001^*** (0.001)	-0.001^** (0.002)	-0.008^*** (0.008)	-0.022^*** (0.004)
Education	0.001^*** (0.002)	0.00^4*** (0.004)	0.002^*** (0.001)	0.002^*** (0.002)	0.005^*** (0.001)	0.014^*** (0.004)
Urbanisation	0.008 (0.012)	0.009^*** (0.022)	-0.001^* (0.008)	0.002 (0.007)	0.001 (0.000)	0.005 (0.001)
Constant	-2.714 (3.022)	2.300 (6.448)	-0.360 (1.496)	-0.033 (1.432)	1.673 (0.365)	0.965 (0.075)
Observations	346	346	346	346	346	346
Hansen-Sargan test	0.021	0.006	0.175	0.747	0.483	0.537
AR(2)	0.216	0.037	0.268	0.367	0.625	0.293
AR(1)	0.000	0.000	0.092	0.017	0.007	0.002
Kleibergen-Paap LM stat.	0.079	0.140	0.051	0.008	0.000
Kleibergen-Paap Wald stat	5.30.	21.392	4.127	23.532	27.734
Stock-Yogo critical values:
10% maximal IV relative bias	13.911	13.317	12.639	13.511	12.963
20% maximal IV relative bias	7.683	7.303	7.514	7.640	7.715
30% maximal IV relative bias	5.512	5.257	5.798	5.834	5.990
Note: the parenthesis contains the robust standard errors. p < 0.01, p < 0.05, p < 0.1**

Extant literature suggests that GMM takes account of unobserved heterogeneity and keeps away from biased estimates when the dependent variable contains a time lag [53]. Hence, columns (1) and (2) of Table 6 present estimates of difference GMM [54], while columns (3) to (5) give estimates of system GMM ([55]; [56]). We report the long-run coefficients of column (5) in column (6). Individual fixed effects banish indifference GMM as it uses the difference between t and t-1 to modify all the regressors. The bottom side of Table 6 shows a serial correlation of order (AR (1)) based on a p-value which is 0.000 in columns (1) and (2). Based on a p-value of 0.2163, there is no AR (2) serial correlation in column (1). But column (2) shows AR (2) serial correlation with a p-value less than 0.1. More so, the Hansen-Sargan test results establish that the lagged exogenous and endogenous variables are not sufficient instruments as both columns (1) and (2) have p-values less than 0.1 (0.0210 & 0.0062). Therefore, the results of difference GMM suggest a system GMM.

Columns (3), (4) and (5) all pass our model specifications and identification test. However, column (3), a one-step system GMM, suffers from weak instruments. Columns (4) and (5) depict valid and pertinent tools for the two-step system GMM model. The instruments are valid based on less than 0.01 p-value of the L.M. statistic of Kleibergen-Paap. Therefore, we accept a null hypothesis that the model is under-identified at a 1% significance level. Similarly, the Kleibergen-Paap F-statistic is more than the critical value proposed by Stock & Yogo (2005) based on the null hypothesis that the 10 per cent OLS bias is less than the bias that IV estimates embody.

We can observe a negative relationship between renewable energy consumption and carbon emission levels in column (4), while a positive relationship exists between ICT diffusion* renewable energy interaction term and the emission levels. Specifically, a 1% rise in renewable energy use results in a -0.008% reduction in carbon emission at 1% level of significance. The findings are in line with our a-priori expectations, and the extant literature underpins these (see [25]; [57]; [19]). The results suggest a rising level of renewable energy use in the SSA region, where most of the countries have begun to embrace the energy transition trend and respond to the devastating effect of environmental degradation in their respective countries. Although the region contributes less than 3% of the whole global pollution, it is very much vulnerable to its adverse effects.

The empirical evidence also shows that a 1% per cent rise in ICT diffusion induces carbon emissions by 0.02% at a 5% significance level. This adverse impact of ICT on environmental quality is supported by several studies, including that of [25], [58], [1], and [9], who find that ICT use significantly raises carbon emissions level. We attribute the ICT's detrimental effect on environmental quality to the growing penetration of ICT tools, a growing exposure of people (especially the urban dwellers) to ICT, and how these ICTs cause inefficient energy use in Sub-Saharan Africa.

Our findings support this expectation as we expect that energy consumption deteriorates environmental quality. The same column (4) shows that a 1% rise in energy use raises emission levels by 0.17%. Extant literature establishes that fossil fuels consumption degrades the environment. The perpetual and unregulated consumption of these fossil fuels undermines the environmental quality as it raises carbon emissions and other greenhouse gas emissions. Our findings are supported by related research works such as [25], [59] and [60]. The region's growing dependence and inefficient utilization of energy resources could further elucidate the positive effect of energy use on carbon emissions. Close to 89% of the total electricity used in the region comes from nonrenewable energy sources [33], while SSA scores poorly in energy efficiency.

The association between trade openness and carbon emissions is a negative one. Our results show that a 1% rise in trade openness is associated with a -0.008% decline in carbon emission at a 1% significance level. This finding is justified by the factor endowment theory, which proposes that the intensity of capital and labour in economies determines the link between trade openness and carbon emission. As it is well blessed with natural endowments and a huge workforce, SSA is likely to produce and trade environmentally friendly goods comparatively. In contrast, it is the reverse case for developed nations. Previous studies that underpin our findings include [1]; [61], and [62].

Consistent with the Environmental Kuznets hypothesis (EKC) is the effect of economic growth on carbon emission in this study. GDP exerts a positive influence on carbon emissions. A 1% rise in GDP leads to a rise in carbon emission by 0.053% at a 1% significance level. The same column (4) shows a negative but statistically significant coefficient of GDP's square term. A 1% boost in GDP results in a -0.029 reduction in carbon emission. This suggests the EKC hypothesis is supported by an inverted U-shape curve relationship between the two variables. This finding is also fully supported by [63] and [1].

Education is positively related to carbon emission. A 1% improvement in education translates to a 0.002% rise in carbon emission. This implies that the educational curriculum in the region does not significantly capture the need for environmental sensitization. This is consistent with the finding of [40], [64], [65], and [66].

Column (5) shows the findings of a sub-model that considers the interaction of ICT diffusion and the use of renewable energy sources. The findings suggest that renewable energy and C.T. jointly reduce carbon emission levels. The coefficient of the interaction term is found to be negative and highly significant at a 1% level, implying that renewable energy and ICT diffusion could jointly reduce emissions by -0.002%. The results suggest that renewable energy use dampens the impact of ICT diffusion on the quality of the environment. This result is further explained by the fact that the ICT sector continues to introduce renewable energy in its own domain and promote smarter technologies to reduce environmental emissions. However, column (5) results are qualitatively the same as the results of column (4). But quantitatively, the coefficients in column (4) carry more magnitude than those in column (5).

Column (6) presents the long-run results. The results conform to the ones contained in column (5) qualitatively. Besides the coefficients having more magnitude in column (6), the long-run results indicate that trade openness has a long-term favourable impact on carbon emissions. A 1% rise in trade openness is expected to raise emissions by 0.02%. This is unsurprising given the prevailing economic and institutional measures aimed at boosting economic activities and diversifying the region away from fossil fuel-based economies to promote green growth and industrialization while decisively tackling the energy poverty and crisis in the region.

African economies are among the blocs with the environmental menace, thereby aggravating the detrimental effect of climate change on the bloc. Worthy of mention is that there is a paucity of empirical evidence lending credence to the ICT-diffusion-environmental sustainability nexus for the SSA region. This highlighted a gap in the extant literature for African environmental sustainability that is worthy of revisiting to inform decision-makers on policy blueprints. To this end, our study leverages a battery of panel econometric tools such as standard linear regression of the pooled-OLS, fixed-effects, and random-effects models complemented by a more robust system of GMM that accounts for unobserved heterogeneity and presents unbiased estimates.

The empirical findings credence to the popular EKC phenomenon, highlighting the detrimental effect of economic growth (GDP) to a certain threshold. A stage in environmental economics known as the scale stage prioritizes economic growth over environmental quality. The plausible explanation for these empirical results, as outlined by the system-GMM regression, is that most African economies, especially SSA, are concentrated on economic expansion from an anthropogenic channel that engenders environmental degradation. Furthermore, the present study alluded to the fact that ICT diffusion energy use and education worsen the quality of the environment. More specifically, we make a claim and join a strand of study that validates the ICT-induced pollution, i.e., ICT diffusion in the African blocs is still at the infancy stage where ICT penetration spill over to other sectors of the economy. However, the product is that it engenders environmental degradation. Thus, this suggests the need for government administrators in the investigated bloc to be more involved in green ICT strategies and policies. Similarly, our study also highlighted trade openness, education, urbanization, and energy use. These outcomes imply that energy use from nonrenewable energy comes with its impact on environmental quality. The African continent is characterized by heavy investment and nonrenewable energy, which is the main source of emissions. From a policy guide on energy portfolio, there is a need for African leaders to invest in alternative and clean energy technologies. Additionally, an increase in urban population, i.e., urbanization and education, dampen the environmental quality. This suggests that there is a gap in the level of education as its not sustainable for environmental sustainability in Africa, which is also reflected in the demographic of her urban population. Interestingly, renewable energy utilization and its interaction with ICT jointly shows strength to improve environmental quality, i.e., renewable energy enhances environmental quality by reducing the detrimental impact of CO₂ emissions. This is a desirable outcome as there are deliberate strides on most economies on the bloc to invest in renewable energy such as hydro energy, photovoltaic and solar, among other clean technologies for environmental sustainability. The interaction term of ICT*renewable energy is indicative to policymakers in SSA that both indicators are determinants for clean growth; as such, they need to promote clean growth technologies should be pursued in her environmental sustainability search.

From a policy lens, there is a need for concerted commitment on the part of decision-makers and related stakeholders on the need for a balanced ecosystem in the SSA space by intensifying commitment to environmental laws and regulation. This can be further achieved by transitioning away from oil, gas, and coal energy sources to a cleaner alternative energy source in the region's energy portfolio. Additionally, the SSA region will require investment in research and development (R&D) that fosters clean energy utilization in the bloc.

Although the present study explores the connection between ICT diffusion, renewable energy use and on CO₂ emissions in SSA. This study does not claim to complete exhaust the theme as there is room for advancement in the literature, especially on the determinant of other channels that contributes to environmental pollution in the bloc like FDI influx and foreign aid in terms of energy investment from nonrenewable. Additionally, future studies can also consider the role of institutional apparatus in moderating the environment on the African continent and, by extension arm policymakers with more insight for achieving the SDG-13 admits economic expansion.

Acknowledgements

We want to acknowledge the help of Dr. Sebastian Kripfganz with his Stata package in computing the long-run effects with the nclom command after the xtdpdgmm estimation.

Funding

We want to acknowledge the receipt of funds from the National Natural Science Foundation of China under project number: 71874138.

Disclosure Statement

There was no potential conflict of interest among the authors

DATA ACCESSIBILITY

The data for this research can be accessible at: https://data.mendeley.com/datasets/fvp5b222tf/1

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No competing interests reported.

Appendix.docx

Does ICT diffusion exacerbate or mitigate the environmental impacts of renewable energy projects in Sub-Saharan Africa?

Status:

Version 1

Abstract

Figures

1. Introduction

2. Theoretical background and empirical literature

2.1 Theoretical background

2.2. A brief empirical review

3. Data and methodology

3.1 Data and Variables

3.2 Model Specifications

4. Results and Discussion

4.1 Pre-estimation Diagnostics

4.2 Estimated Results of the impact of ICT and renewable energy on CO₂

5. Conclusions and policy recommendations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

Variable	One-step GMM Diff-GMM (1)	Two-step GMM Diff-GMM (2)	One-step GMM System GMM (3)	Two-step GMM System GMM (4)	System GMM (5)	Long-run System GMM (6)
\(\:ln{CO}_{2}\) emissions (lag)	0.436 (0.080)	0.435^** (0.171)	0.888^*** (0.104)	0.909^*** (0.135)	0.626^*** (0.093)
Renewable energy	-0.022^*** (0.005)	-0.020^*** (0.008)	-0.008^*** (0.005)	-0.007^*** (0.005)	-0.011^*** (0.002)	-0.029^*** (0.008)
^{ICT diffusion}	^0.009*** ^(0.008)	^−0.004*** ^0.017	^0.026*** ^(0.016)	^0.020** ^(0.019)	^0.005*** ^(0.001)	^0.013*** ^(0.004)
Renewable energy*ICT					-0.002^*** (0.0007)	-0.004^*** (0.001)
Energy use	0.658^*** (0.182)	0.783** (0.316)	0.157 0.115	0.1692^*** (0.138)	0.014^* (0.044)	0.039* (0.013)
GDP	0.066 (0.104)	0.294^* (0.316)	0.065 (0.064)	0.053^*** (0.067)	0.021^*** (0.006)	0.057^*** (0.017)
GDP²	-0.039 0.008	-0.159^*** (0.036)	-0.029^** (0.007)	-0.029^**. (0.006)	-0.004^*** (0.001)	-0.028^*** (0.005)
Trade openness	0.002 (0.000)	0.001. (0.001)	0.001^*** (0.001)	-0.001^** (0.002)	-0.008^*** (0.008)	-0.022^*** (0.004)
Education	0.001^*** (0.002)	0.00^4*** (0.004)	0.002^*** (0.001)	0.002^*** (0.002)	0.005^*** (0.001)	0.014^*** (0.004)
Urbanisation	0.008 (0.012)	0.009^*** (0.022)	-0.001^* (0.008)	0.002 (0.007)	0.001 (0.000)	0.005 (0.001)
Constant	-2.714 (3.022)	2.300 (6.448)	-0.360 (1.496)	-0.033 (1.432)	1.673 (0.365)	0.965 (0.075)
Observations	346	346	346	346	346	346
Hansen-Sargan test	0.021	0.006	0.175	0.747	0.483	0.537
AR(2)	0.216	0.037	0.268	0.367	0.625	0.293
AR(1)	0.000	0.000	0.092	0.017	0.007	0.002
Kleibergen-Paap LM stat.	0.079	0.140	0.051	0.008	0.000
Kleibergen-Paap Wald stat	5.30.	21.392	4.127	23.532	27.734
Stock-Yogo critical values:
10% maximal IV relative bias	13.911	13.317	12.639	13.511	12.963
20% maximal IV relative bias	7.683	7.303	7.514	7.640	7.715
30% maximal IV relative bias	5.512	5.257	5.798	5.834	5.990
Note: the parenthesis contains the robust standard errors. p < 0.01, p < 0.05, p < 0.1**

Does ICT diffusion exacerbate or mitigate the environmental impacts of renewable energy projects in Sub-Saharan Africa?

Status:

Version 1

Abstract

Figures

1. Introduction

2. Theoretical background and empirical literature

2.1 Theoretical background

2.2. A brief empirical review

3. Data and methodology

3.1 Data and Variables

3.2 Model Specifications

4. Results and Discussion

4.1 Pre-estimation Diagnostics

4.2 Estimated Results of the impact of ICT and renewable energy on CO2

5. Conclusions and policy recommendations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

4.2 Estimated Results of the impact of ICT and renewable energy on CO₂