Greening the environment: do climate-related development finances and renewable energy consumption matter? An African tale

Abstract

With the rise in global food insecurity, pollution, and wildlife extinction caused by climate change, development policies are now tailored toward addressing this quagmire. However, the unresolved question is, are climate-related development finances effective in greening the environment in developing countries? In this milieu, this study assessed the effectiveness of climate-related development finances and renewable energy consumption on CO₂ emissions in Africa by applying the system Generalized Method of Moments (GMM) estimation technique on data from 2000 to 2020 for 41 selected countries. The findings show that the overall climate-related development finances, adaptation-related development finances, and mitigation-related development finances have short-run carbon-enhancing and long-run carbon-reducing effects in Africa. Similarly, renewable energy consumption and the net inflows of foreign direct investment have short-run worsening and long-run carbon-abatement effects in Africa. In contrast, higher GDP per capita, urbanization, and higher energy intensity are effective in reducing CO₂ emissions in Africa only in the short run, however, they exacerbate CO₂ emissions in Africa over the long run. In this light, the study underscores the need to invest heavily in climate-related development projects, and green technology innovation and production in Africa. The results also suggest the need to upgrade the current energy structure in Africa to renewable energy sources for a greener, cleaner, and brighter Africa. However, these policy perspectives require enough funds for effective implementation. Hence, the study calls on the developed countries (the polluters) to support Africa with the required funds to pay off climate debt by 2030 and build climate-resilient practices.

Keywords:

• Climate-related development finance
• adaptation finance
• mitigation finance
• renewable energy consumption
• system GMM
• and africa

Introduction

Climate change is arguably one of the most challenging and pressing issues facing today’s world, caused by various anthropogenic and natural factors. Thus, its impacts include glaciation, flooding, land degradation, biodiversity loss, pollution, and loss of human life among others. These effects are more pronounced in developing countries, particularly in Africa where resources are woefully inadequate for effective adaptation and mitigation of climate change effects. CO₂ is the primary greenhouse gas, which accounts for about 70% of yearly emissions causing climate change [1–3]. The over-reliance on non-renewable energy consumption to foster economic growth and industrialization has led to rising CO₂ emissions in developing countries, particularly in Africa, re-echoing the heavy need to question the effectiveness of climate-related development finances such as mitigation and adaptation-related development finances in reducing CO₂ emissions in Africa. Besides, the United Nations (UN) Sustainable Development Goals (SDGs 7,8, 9, 12, and 13) set out to be achieved by the year 2030, emphasize the need for affordable clean energy, sustainable economic growth, technological innovation, sustainable consumption, and production as immediate solutions to combat climate change.

Similarly, several initiatives such as the Kyoto Protocol, the Paris Agreement, and the famous United Nations Framework Convention on Climate Change (UNFCCC), have been rolled out to reduce the global average temperature below 2 °C and attain Zero CO₂ emissions by the year 2050 [4,5]. However, such initiatives surrounding climate change negotiations, particularly “Who finances what?” are quite political and contentious. It will sadden one to know that developing countries particularly Africa are the least emitters of CO₂, yet they are the sufferers. Africa’s global emission rate averaged 3%, with an approximated 1.1 tons per person annually. In contrast, North America emits 14 tons per head annually, while China and Europe emit 7 tons per head annually. Despite these illuminating statistics, Africa has been disproportionately affected by climate change and frequently lacks the resources to adapt and mitigate it [6]. Against this backdrop, this study aims to investigate whether climate-related development finances from developed countries to Africa can help mitigate and adapt to the continent’s current climatic crisis.

Nevertheless, one way to lessen or prevent climate change effects on Africa or the world at large is through adaptation or mitigation measures. Climate-related development finances constitute adaptation and mitigation climate-related development finances. The Africa Centre for Economic Transformation (ACET) reported that, for the period 2020–2030, Africa needs an estimated amount of $140 billion annually from developed countries to be able to adapt and mitigate climate change effects. Out of this amount, roughly $30 billion is estimated for adaptation, $70 billion for mitigation, and $40 billion for loss and damage [7]. Regrettably, current flows are only $20 billion annually that goes to Africa [8]. Based on the current stream of climate funds, it is unlikely that Africa will be able to pay off its climate debt by 2030 despite being the less emitter. The amount of climate funds raised for African nations still need to catch up with what is required to mitigate the worst effects of climate change, support adaptation, and promote resilience. Worse still, much of the funds spent is geared toward mitigation, and some of the funds raised displace traditional development aid.

Climate change is imposing serious challenges on the African continent from food insecurity, pollution, biodiversity loss, and sustainable development. For instance, the IPCC [9] estimated that developing nations and Sub-Saharan Africa suffer a median loss of 1.5% of annual GDP due to global warming. At the same time, there is an increase in the frequency of catastrophic events like pandemics, floods, and droughts that wipe out years’ worth of progress all at once [10]. Most of these shocks fall on low-income earners, women, and children who are unable to protect themselves. Consequently, further empirical work needs to be conducted to assess the effectiveness of funds allocated to tackling climate change effects.

Greening the environment brings to light more solutions to the problem at hand, and a means to achieve sustainable economic growth and development. The economic theories which shed light on CO₂ emission and environmental sustainability are the environmental Kuznets Curve (EKC) hypothesis and the Jevons’ Paradox. The EKC hypothesis predicts that economic growth will eventually limit the environmental damage caused during the initial stages of development due to the assumption that as people become richer, they tend to care more about the environment and therefore shift to using more renewable energies and demand government protection of the environment. As such, more money will correlate with the service economy which displaces or offshores emissions. Likewise, Jevons & Flux [11] observed that as technology becomes more efficient, the price of that technology declines and two competing things happen: (1) Pollution per unit goes down, and (2) Number of units used goes up because they are cheaper. This increase in demand may increase fuel consumption and Carbon emissions (CO₂) as prices become cheaper. According to York & McGee [12], we might expect people to shift to renewables, but it actually lowers the overall energy prices and may result in a rebound effect.

While the existing literature such as [10,13–15] throws light on the role of green financing or technological innovation on CO₂ emissions around the globe particularly in China, little or no empirical works focus on such analysis or discussions on Africa. Additionally, to the best of our knowledge, no existing empirical literature throw light on how the disaggregated components of climate-related-development finances affect carbon emissions, most especially in Africa. Therefore, this study contributes to the existing literature by analyzing the effects of climate-related development finances, both aggregate and disaggregated components, and renewable energy consumption on CO₂ emissions in Africa. Closely related to the current study is the study by Ajong Aquilas & Tabi Atemnkeng [16] in which they used the panel estimation approach to assess the impact of climate-related development finances (mitigation finances only) and renewable energy consumption on greenhouse gas emissions in the Congo Basin. However, their analysis was only based on the short run and also limited in terms of geographic scope and the dimensions of climate-related development finances. The current study expands the status quo by incorporating both the aggregate and disaggregated components of climate-related development finances to ensure the robustness of the results. The study also offers novel analysis through the application of the system Generalized Method of Moments (system GMM) which allows us to account for both the short and long-run relationships among the variables.

The remaining sections of the paper are organized as follows: section two presents a general review of micro-and macroeconomics studies on climate-related development finances, renewable energy consumption, and CO₂ emissions; section three provides the methods and materials employed in the study; section four outlines the empirical results and discussions, and section five highlights the conclusion and recommendation for policy purposes.

Literature review

This section presents the theoretical foundation of the study and the review of previous empirical literature in the domain of climate-related development finances (green financing), renewable energy consumption, and CO₂ emissions.

Theoretical review

This study primarily summarizes the Key characteristics of the Environmental Kuznets Curve (EKC) hypothesis and the Jevons’ Paradox. These theories are presumed vital to the study in one way or another and for which they help in explaining the linkages among the concepts under study. A brief highlight of each theory is presented in the subsequent paragraphs.

The EKC theory predicts that economic growth will eventually limit the environmental damage caused during the initial stages of development. According to Kuznets [17], further economic growth is expected to overcome the environmental degradation that occurred in earlier phases of development and suggests that the relationship between income (economic growth) and environmental degradation takes the form of an inverted U-shape [18]. From the early 1990s, it was argued that each economy should concentrate on its growth and that the process of economic growth would eventually resolve any environmental issues due to the EKC concept. However, since environmental degradation (pollution) is a by-product of economic activity, recent empirical literature focuses on investigating the potential relationship between CO₂ emissions and income (economic growth) based on the EKC hypothesis.

On the other hand, the focus of Jevons’ Paradox is motivated by scientific evidence that cumulative emissions are a close proxy for the long-run impact of emissions on climate. This concept highlighted that 20th-century economic growth theory also sees technological change as the leading cause of increased production and consumption. In contrast, some ecologically-oriented economists and practically all governments, green political parties, and NGOs believe that efficiency gains lower consumption and negative environmental impact. Others doubt this “efficiency strategy” towards sustainability, holding that efficiency gains “rebound” or even “backfire” in pursuing this goal, causing higher production and consumption [19]. Jevons & Flux [11] observed that an improvement in energy efficiency involves the rebound effect means the demand for output will increase as fuel cost reduces. In terms of the overall impact, this increase in demand may increase the overflow of fuel consumption and Carbon emissions [20].

Empirical review

In this section, we present a review of past studies showing the relationship between green financing or climate-related development finances, renewable energy, and CO₂ emissions.

Climate-related development finances and CO₂ emissions

This section elaborates on the existing literature or empirical works to show the necessity of green financing, green technology and or climate-related development finances in promoting environmental sustainability. Based on the existing studies, there is little or no literature that tries to justify the important role of climate-related development finances in carbon reduction, particularly in Africa. Only a few works of literature highlight the need for green finance, and green technology innovation in enhancing environmental quality. However, these works fail to demonstrate how adaptation and mitigation finance affect carbon dioxide emissions, most especially in Africa and those studies were focused on developed and emerging economies like China, America, Asia, and Europe, with little or no study on Africa. This study underscores the necessity to understand the extent to which climate finance and renewable energy consumption matter for greening the environment and to provide important lessons and guidelines for policy perspectives.

Countries across the globe are experiencing adversity related to climate change. The main reason is the failure to limit greenhouse gas emissions into the atmosphere. Ahmed et al. [10] reiterated that mitigating these emissions is pivotal to raising the share of green energy in the total energy supply. The authors posited that public sector investment in renewable energy is vital in enhancing the technological level required for substantially increasing green energy production and supply. The study calls for investment in technological innovation to boost green energy supply and enhance environmental sustainability.

Li et al. [21] carried out an environmental assessment study in emerging states, for instance, Mexico, Indonesia, Nigeria, and Turkey, also known as MINT, which sought to scrutinise the relationship between economic growth, green finance renewable energy consumption, natural resource rent, energy innovation, urbanisation and environmental pollution. The study employed the cross-sectional-augmented autoregressive distributed lag (CS-ARDL) approach on data from 1990 to 2020 to examine the series’ short- and long-term relationships. The findings revealed that environmental pollution could be curb through effective mechanisms such as green finance, renewable energy consumption, and the promotion of energy innovation.

Sampene et al. [22] conducted a systematic analysis study to examine the impact of economic growth, biocapacity, renewable energy consumption, natural resource rent, agricultural value-added, green finance and information and communication technology on the ecological footprint in South Asian economies. The study adopted the Environmental Kuznets Curve (EKC) hypothesis to model the relationship among the variables with the dataset spanning 1990–2017. The cross-sectional-augmented autoregressive distributed lag (CS-ARDL) approach was applied to estimate the variable’s short and long-term relationship. The results showed the existence of the EKC hypothesis in the region. Additionally, renewable energy consumption, green finance, and ICT was found to be an effective catalyst to mitigate the ecological footprint.

Additionally, Li and Shao [14] investigated the relationship between green finance and CO₂ emissions and employed the Autoregressive-Distributed Lag–Error Correction Model (ARDL–ECM) on data collected from 2000 to 2019. The study used different financing options to construct a green finance index, including green credit, insurance, securities, and investment. The study found that green finance is significantly and negatively related to emissions per capita in the short and long term.

Similarly, Ren et al. [23] carried out a study to examine the effects of green finance on carbon mitigation in China using the vector error correction model on data from 2000 to 2018. The study constructed a green finance development index based on four indicators: green credit, green securities, green insurance, and green investment. They found that China’s green finance industry has developed rapidly over the years and that improvements in the green finance development index and the increasing consumption of non-fossil energy contributed to a decrease in carbon intensity.

Wang et al. [15] highlighted that green finance and environmental regulation could reduce CO₂ emissions and promote the sustainability of economic development. In the context of green finance development, the authors empirically analyzed the impact of green finance credits and green venture investments on the carbon emissions of resource-based cities in China. They found that green finance credit and green venture capital based on green finance development significantly inhibit CO₂ emissions. Therefore, they concluded that green finance plays a positive role in reducing carbon emissions in China.

Halimanjaya [13] assessed the relationship between the features of developing economies and the amount of official climate mitigation finance inflow. The results showed that developing nations with higher CO₂ emissions intensity, larger carbon sinks, lower per capita gross domestic product (GDP), and good governance tend to be selected as recipients of climate mitigation finance and receive more of it. CO₂ emissions is not a determinant of mitigation finance until the actual financial disbursement. Poverty aid tends to be allocated to nations with low CO₂ emissions, possibly to avoid diverting aid from poorer developing countries.

Abbasi and Riaz [24] investigated the influence of financial and economic development on CO₂ emissions in an emerging economy. The study found that per capita CO₂ emissions were cointegrated with financial development indicators and GDP. To this end, financial variables significantly impacted CO₂ emissions mitigation. The study underscored the need to adopt other mitigation policies to reduce CO₂ emissions footprints.

Zhang [25] explored the impact of financial development on China’s CO₂ emissions. The results of this study showed that China’s fiscal development is an important factor in the growth of carbon emissions and should be considered when forecasting carbon emissions demand. Additionally, the effects of financial intermediation on carbon emissions outweigh that of other financial development indicators; However, statistically, it can cause changes in carbon emissions, and its efficiency impact is far more significant. It seems weak. Subsequently, while the size of China’s stock market has a relatively significant impact on carbon emissions, its efficiency has a minimal impact. This partly reflects the relatively low liquidity of China’s stock market al.so, Shahbaz et al. [26] analyzes the effects of financial development, coal consumption, economic growth, and trade openness on environmental performance in South Africa. The study found that increased economic growth increases energy emissions, while financial development decreases energy emissions. Coal usage or consumption contributes significantly to the environmental degradation of the South African economy. Trade openness improves environmental quality by reducing the growth of energy pollutants.

Shahbaz et al. [27] further studied the question of whether financial development, in Malaysia’s case, reduces CO₂ emissions. The authors found that important long-term relationships exist between carbon emissions, financial development, energy use, and economic growth. Empirical evidence also shows that financial development reduces carbon emissions. Shahbaz et al. [28] examined links between economic growth, energy consumption, financial development, trade openness, and carbon emissions across the Indonesian case. Empirical findings show that economic growth and energy consumption increase CO₂ emissions, while financial development and trade opening up increase it. Trade openness and financial development can also play a role in improving the quality of the environment.

Ajong Aquilas & Tabi Atemnkeng [16] examined how the Congo Basin’s greenhouse gas emissions are impacted by the use of renewable energy and financing for climate-related development mitigation. Using panel data from 2002 to 2020, panel regression estimates empirically show that an increase in climate-related development mitigation finance significantly promotes the consumption of renewable energy; an increase in renewable energy consumption reduces greenhouse gas emissions; and an increase in renewable energy consumption reduces the efficacy of climate change mitigation measures.

Renewable energy consumption and CO₂ emissions

Wiredu et al. [29] employed the Cross-Sectional Augmented autoregressive Distributed Lag (CS-ARDL) model on data from 1990 to 2020 to explore how renewable energy source development is influenced by CO₂ emissions, financial and economic developments, human capital and other country’s risk factors, for instance, institutional quality and political risk. The choice of countries was mainly emerging African economies. The study established that renewable energy source development could be facilitated through all the variables of interest, including CO₂ emissions, financial and economic developments, and human capital. However, the country’s risk factors, such as political risks and institutional quality, had an inverse connection with the development of renewable energy sources. The study concluded that most economies’ current state of energy structure must be upgraded to a renewable energy source to help mitigate ecological dilapidation and achieve sustainable development goals.

In an attempt to investigate the effects of nuclear and renewable energy on the ecological footprint, CO₂ emissions, and load capacity factor in France, Pata and Samour [30] employed the co-integration and causality tests approach on annual time series data from 1977 to 2017. Their findings revealed no inverted U-shaped relationship between CO₂ emissions and income but rather the EKC hypothesis for the load capacity factor. In contrast, they also showed that nuclear energy reduces CO₂ emissions and increases the load capacity factor. Similarly, In light of the above concern, Ehigiamusoe and Dogan [31] assessed the impact of real income, renewable energy consumption, and their interaction effect on CO₂ emissions by using an estimation technique that controls different econometric and economic issues for instance, cross-sectional dependence and heterogeneity in low-income economies. The study found that renewable energy consumption mitigates CO₂ emissions and the interaction effect showed a positive sign. However, the marginal effect of renewable energy on emissions varies with the real income levels.

Using data from 1965 to 2019, Adebayo and Rjoub [32] provided a novel perspective on the impact of non-renewable and renewable energy consumption on CO₂ emission in the Argentinean economy. The study used wavelet tools to evaluate the connections among the variables. The study found that trade openness reduces CO₂ over the medium term, but there was no real relationship over the long term. Economic expansion and non-renewable energy sources were found to have both short- and long-term adverse environmental effects. The frequency domain causality results also show that non-renewable energy, trade openness, and economic growth can all be used to predict CO₂ emissions over the long term.

Kirikaleli et al. [33] explored the impact of economic growth, electricity consumption, and renewable energy consumption on consumption-based CO₂ emissions in Chile. The results showed that, while economic expansion and using renewable energy sources in Chile reduce consumption-based carbon emissions, these factors also increase consumption-based emissions in other countries. In the same vein, Shafiei and Salim [34] carried out a comparative analysis of the relationship between Non-renewable and renewable energy consumption and CO₂ emissions for OECD countries. The empirical results revealed that non-renewable energy consumption increases CO₂ emissions, whereas renewable energy consumption decreases CO₂ emissions. Additionally, the findings support the existence of an environmental Kuznets curve between urbanization and CO₂ emissions, implying that the environmental impact decreases at higher levels of urbanization.

Moreover, Bilgili et al. [35] re-examined the Environmental Kuznets Curve (EKC) hypothesis by considering the potential effects of consuming renewable energy on the environment. As such, the paper examined the reliability of the EKC hypothesis using CO₂ emissions as the dependent variable and renewable energy consumption as the predictor variable. The study revealed that renewable energy consumption negatively impacts CO₂ emissions. Furthermore, the study shows that GDP per capita and GDP per capita squared impact CO₂ emissions positively and negatively, respectively.

Dong et al. [36] work looked at the Significant difference in the emission–renewables nexus across countries with different income levels. The study, thus, empirically investigated whether the effect of renewable energy consumption on CO₂ emissions differs across countries with different income levels, the emission–growth–renewables nexus for a global panel of 120 countries. The results showed that renewable energy consumption influences CO₂ emissions negatively. However, its effect is insignificant; the mitigation effect may be obscured by higher economic growth and increasing non-renewable energy consumption. Likewise, Sahoo and Sahoo [37] explored the nexus between renewable and non-renewable energy consumption on CO₂ emissions in India. The results showed that over the long-run, water energy consumption has a positive and insignificant impact on CO₂ emissions, while the consumption of nuclear energy negatively impacts CO₂ emissions. Thus, all non-renewable energy sources have a significant positive impact on CO₂ emissions in India.

Methods, variables, and sources of data

The two-step system Generalized Method of Moments estimation strategy has been adopted in this study to examine the effects of climate-related development finances and renewable energy consumption on environmental sustainability through carbon reduction. The data spans the period 2000 to 2020 for a sample of 41 African countries including, Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African Republic, Chad, Congo, Côte d‘Ivoire, Democratic Republic of the Congo, Egypt, Equatorial Guinea, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea Bissau, Kenya, Lesotho, Madagascar, Malawi, Mali, Mauritania, Morocco, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, South Africa, Tanzania, Tunisia, Uganda, Zambia, and Zimbabwe. The dependent variable in this study is carbon dioxide (CO₂) emissions which is the primary greenhouse gas responsible for over 70% of yearly global emissions and drives climate change [2,3]. Hence, the reduction of CO₂ amount is a safer way towards greening the environment and enhancing environmental sustainability. Therefore, we used CO₂ emission as the dependent variable. In the same vein, renewable energy consumption (REC) and climate-related development finances comprising adaptation-related development finance (ARDF), mitigation-related development finance (MRDF), as well as aggregate climate-related development finance (CRDF) are the primary independent variables in this study. Renewable energy consumption is considered an effective antidote to CO₂ emissions and greening the atmosphere [34,35], hence its inclusion in this study. Similarly, all finances aimed at either mitigating the adverse effects of climate change (preventing feature occurrences) or those rollouts to enable people to live harmoniously with climate change effects (adaptive measures) should in actual sense help to reduce CO₂ emissions, if such finances were to achieve the targeted aims or objectives. Sequel to this, we consider all climate-related development finances, be it adaption or mitigation or both to be an effective catalyst for carbon reduction and greening the environment, especially over the long run. Besides, energy intensity which measures the ratio between energy supply and gross domestic product measured at purchasing power parity, gross domestic product (GDP) per capita, urbanization (urban population growth), and foreign direct investment (net inflows) are used in this study as control variables. Energy intensity is included in this study to highlight the need for energy-efficient production. Higher energy intensity means that more units of energy are used to produce a given amount of output and this will by far increase CO₂ emissions, particularly in the long run. Using energy-efficient machines in production requires fewer units of energy and this will correlate with less CO₂ emission. Additionally, higher GDP per capita will correlate with higher production, from the agriculture, industry, and services sectors. This means that the more there is production, the more there is emission especially if increased production is accompanied by the usage of nonrenewable energy sources and less energy-efficient technologies as in the case of many African countries [36,37]. Moreover, foreign direct investment is included in the model specification to observe its effect on environmental sustainability as Multinational Enterprises (MNEs) are often accused of causing serve havoc to the environment where they operate, especially those involve in mining activities. Last but not least, urbanization is expected to cause an increase in the amount of CO₂ levels in the atmosphere. This is because, as the urban population increases, congestion, slums, choked gutters, environmental degradation, and deforestation, among others become eminent. All these serve as a breeding ground for CO₂ emissions. Table 1 presents a summary of all variables included in this study and their data sources.

Table 1. Variables description and data sources.

Variable	Description	Source
CRDF	Climate-Related Development Finance (thousands of dollars)	https://oe.cd/development-climate
ARDF	Adaptation Related Development Finance (thousands of dollars)
MRDF	Mitigation Related Development Finance (thousands of dollars)
CO2	Carbon dioxide emissions (metric tons per capita)	https://data.worldbank.org/indicator (accessed on January 10, 2023)
REC	Renewable energy consumption (% of total energy consumed)
ENI	Energy intensity level of primary energy (MJ/$2017 PPP GDP)
GDPP	Gross Domestic Product per capita (current USD)
FDI	Foreign Direct Investment, net inflows (% of GDP)
UPOP	Urban Population Growth (annual %)

Source: Authors’ Construct.

Estimation technique

Primarily, this study examines the effects of Climate-Related development finances and Renewable energy consumption on CO₂ emissions in the short and long run. To achieve this objective, the study utilizes the two-step system Generalized Method of Moments (GMM) on data from 2000 to 2020 for 41 African countries. The advantage of using the two-step system GMM over the static panel models of fixed and random effects lies in its ability to solve the endogeneity problem, omitted variable biases, measurement errors in data, and unobserved panel heterogeneity issues [38–40]. Besides, the two-step system GMM is preferred over the difference GMM and the one-step system GMM because it is more robust than the one-step system GMM, and it is more robust and efficient in handling autocorrelation and heteroskedasticity [38].

In this study, the Arellano-Bond test for second-order autocorrelation (AR2) in first-difference order is used to check for autocorrelation while the “robust” routine in Stata is used to account for any possible heteroskedasticity in the specified model(s). Furthermore, the Hansen J-test for over-identifying restrictions is used to determine the validity of the instruments. Roodman [38] argued that instrument proliferation can compromise the strength of the Hansen J statistics using the system-GMM. Hence, to handle the instrument proliferation trap, this study used the "Collapse" option in Stata as Roodman [38] recommended to collapse all the internally generated instruments.

The generic form of the GMM econometric specification is shown in Model 1. (1) $$Y_{it}={\phi Y}_{it-1}+{\beta }^{\prime }{X}_{it}+({\delta }_i+{\epsilon }_{it})$$(1)

Where $Y_{it}$= dependent variable, assumed to be persistent; $Y_{it-1}$= lag value of the dependent variable; $X_{it}$= $k\times 1$ vector of the explanatory variable; ${\beta }^{\prime }$= $k\times 1$ vector of coefficients of the explanatory variables; $\phi$= coefficient of the lag value of the dependent variable; ${\epsilon }_{it}$= idiosyncratic error term, and ${\delta }_i$= country-specific effects.

Following model 1, the operational form of the econometric specifications is shown for the following objectives:

1. Modelling the effect of climate-related development finance (CRDF) on CO2 emissions (2) $${CO}_{2it}={\phi CO}_{2it-1}+{\alpha }_1{\mathit{lnCRDF}}_{it}+{\beta }_1{\mathit{REC}}_{it}+{\beta }_2{\mathit{ENI}}_{it}+{\beta }_3{\mathit{UPOP}}_{it}+{\beta }_4{\mathit{FDI}}_{it}+{\beta }_5{\mathit{lnGDPP}}_{it}+({\delta }_i+{\mathrm{\epsilon }}_{it})$$(2)

2. Modelling the effect of Mitigation Related Development Finance (MRDF) on CO2 emissions (3) $${CO}_{2it}={\phi CO}_{2it-1}+{\alpha }_2{\mathit{lnMRDF}}_{it}+{\beta }_1{\mathit{REC}}_{it}+{\beta }_2{\mathit{ENI}}_{it}+{\beta }_3{\mathit{UPOP}}_{it}+{\beta }_4{\mathit{FDI}}_{it}+{\beta }_5{\mathit{lnGDPP}}_{it}+({\delta }_i+{\epsilon }_{it})$$(3)

3. Modelling the effect of adaptation-related development finance (ARDF) on CO2 emissions (4) $${CO}_{2it}={\phi CO}_{2it-1}+{\alpha }_3{\mathit{lnARDF}}_{it}+{\beta }_1{\mathit{REC}}_{it}+{\beta }_2{\mathit{ENI}}_{it}+{\beta }_3{\mathit{UPOP}}_{it}+{\beta }_4{\mathit{FDI}}_{it}+{\beta }_5{\mathit{lnGDPP}}_{it}+({\delta }_i+{\epsilon }_{it})$$(4)

Where ${CO}_{2it}$= CO₂ emissions; ${CO}_{2it-1}$= lag value of CO₂ emissions; ${\mathit{lnCRDF}}_{it}$= log of climate-related development finances; ${\mathit{lnMRDF}}_{it}$= log of mitigation-related development finances; ${\mathit{lnARDF}}_{it}$= log of adaptation-related development finances; ${\mathit{REC}}_{it}$= renewable energy consumption; ${\mathit{ENI}}_{it}$= Energy intensity level of primary energy; ${\mathit{UPOP}}_{it}$= urbanization; ${\mathit{FDI}}_{it}$= foreign direct investment (net inflows); ${\mathit{lnGDPP}}_{it}$= log of GDP per capita; $\phi$= coefficient of the lag value of CO₂ emissions; ${\alpha }_1,$ ${\alpha }_2,$ and ${\alpha }_3$= coefficients of ${\mathit{lnCRDF}}_{it},$ ${\mathit{lnMRDF}}_{it},$ and ${\mathit{lnARDF}}_{it}$ respectively; while ${\beta }_1,$ ${\beta }_2,$ ${\beta }_3,$ ${\beta }_4,$ and ${\beta }_5$= coefficients of ${\mathit{REC}}_{it},$ ${\mathit{ENI}}_{it},$ ${\mathit{UPOP}}_{it},$ ${\mathit{FDI}}_{it},$ and ${\mathit{lnGDPP}}_{it}$ respectively.

To estimate the long-run effects of all variables specified in models 2, 3, and 4 respectively on CO₂ emissions, the current study employed the delta method developed by Papke et al. [41] to generate the coefficients and the asymptotic standard errors of only significant variables in the short run estimates. Algebraically, the long-run coefficient of a variable is generated as: $\mathit{LRk}=\beta \_k/([1-\alpha ]);$ where $\mathit{LRk}=$ the long-run coefficient for each explanatory variable, $\beta \_k=$ the coefficient of the Kth independent variable, and $\alpha =$ the coefficient of the lagged dependent variable.

Empirical results and discussions

In this section, we present the trend analysis of the key variables of interest, the scatter plots, the correlation analysis, descriptive statistics, and finally the two-step system GMM estimates of the relationship between climate-related development finances, renewable energy consumption, and CO₂ emissions in Africa.

Trends and scatter plots of the key variables in the study

Figure 1 presents the trends of the key variables of interest in the study which comprises of the overall climate-related development finances, mitigation-related development finances, adaptation-related development finances, renewable energy consumption, and CO₂ emissions in Africa. The figure portrays that between 2000 and 2009, the amount of funds coming into Africa from the developed nations to support climate-related development projects was mainly for mitigation purpose. However, from 2010 thereof, Africa begun to receive funds for both mitigation and adaptation purposes and this has been on the increase over the years, though the increment has been marginal until 2020 when the continent witness a slight decline for both mitigation and adaptation-related development finances.

Figure 1. Trends of climate related-development finances, mitigation related-development finances, CO₂ emissions, and renewable energy consumption in Africa. Source: Author’s construct using STATA 17

Furthermore, the trend of renewable energy consumption from 2000 to 2020 in Africa depicts that the rate of renewable energy consumption as a share of the total energy consumption in Africa has been on a decline since 2000 except in 2005 when Africa experienced a marginal increase. The continuous decline in renewable energy consumption as a share of the total energy consumption in Africa is quite worrisome, given that the rate of CO₂ emissions in Africa has been on the increase over the years accompanied by acute food insecurity, rising income inequality, and climate change [42,43]. Given the current trajectory, it is unlikely that Africa will be able to meet the UN SDG goal (s), 7 of clean and affordable energy, 11 of sustainable cities and communities, and 12 of responsible consumption and production. Therefore, it is imperative for African countries to upgrade their current energy structures to renewable energies for the attainment of these goals.

The trend of CO₂ emissions between 2000 and 2020 revealed that Africa’s rate of emissions has been undulating. For instance, between 2000 and 2006, Africa’s rate of emissions rises sharply and observed a slight decline in 2007, it then rises again in 2008 and observed a slight fall in 2009 and rise thereafter reaching its maximum in 2015 when the rate of renewable energy consumption and climate finances witness a decline. From 2015 upfront, the rate of CO₂ emissions has been on a decline except in 2019 when it observed a slight rise and fall marginally again in 2020.

Also, Figure 2 denotes the scatter plots showing the relationship between the overall climate related development finances, mitigation related-development finance, adaptation related-development finance, renewable energy consumption and CO₂ emissions in Africa. Based on the scatter plots, it can be observed that the rate of emissions of most African countries are clustered around zero with only two countries (South Africa and Equatorial Guinee) having an average emissions rate above 6 metric tons per capita. This suggest that African countries are the least emitters of CO₂ emissions despite been the most vulnerable to climate change. This argument supports the findings of Baatz [6] in which he opined that Africa’s global emission rate averaged 3%, with an approximated 1.1 tons per person annually while North America emits 14 tons per head annually, with China and Europe 7 tons per head annually. Additionally, from the scatter plots, it can be seen clearly that there is an inverse relationship between the overall climate-related development finances, mitigation and adaptation related development finances, renewable energy consumption and CO₂ emissions in Africa. However, despite these negative relationships, the nature of the slope depicts that adaptation-related development finances and renewable energy consumption appeared to be inelastic relative to the overall climate-related development finances and mitigation-related development finances which seem to be more elastic. Arguably, this implies that adaptation-related development finances and renewable energy consumption will be more effective compared to the overall climate-related development finances and mitigation-related development finances in reducing CO₂ emissions in Africa.

Figure 2. Scatter plots of the relationship between the overall climate-related development finances, mitigation finance, adaptation finance, renewable energy consumption and CO₂ emissions in Africa. Source: Author’s construct using STATA 17

Descriptive statistics

Table 2 presents the results of the descriptive statistics covering the mean, standard deviation, and minimum and maximum values of the variables employed in the study. The results show that the mean values of lnCRDF, ln MRDF, lnARDF, REC, lnGDPP, and ENI respectively are 9.198, 8.713, 5.591, 62.693, 7.014, and 6.508, while their standard deviations are respectively 3.579, 3.42, 5.561, 29.222, 1.031, and 3.843. observing these values, it becomes clear that the standard deviations of lnCRDF, lnMRDF, lnARDF, REC, lnGDPP, and ENI respectively are lower than their means, implying that African countries are less heterogeneous in terms of access to climate-related development finances received over the years, be it adaptation or mitigation or both, renewable energy consumption, economic performance, and energy intensity used in production. However, the standard deviation of adaptation-related development finance is almost to its mean denoting that there is a wide disparity in the amount of money received for adaptation purposes among African countries. Moreover, the average values of CO₂ and FDI are 1.019, and 3.638 respectively well below their standard deviations of 1.707, and 5.64 respectively. This suggests that African countries are highly heterogeneous in incoming FDI and the rate of CO₂ emissions over the years.

Table 2. Summary of descriptive statistics.

Variable	Obs	Mean	Std. Dev	Min	Max
lnCRDF	861	9.198	3.579	0	14.694
lnMRDF	861	8.713	3.42	0	14.658
lnARDF	861	5.591	5.561	0	14.187
CO2	820	1.019	1.707	0.02	10.349
REC	820	62.693	29.222	0.06	98.34
FDI	861	3.638	5.64	−18.918	64.384
lnGDPP	861	7.014	1.031	4.705	9.896
UPOP	861	3.739	1.37	−0.038	8.368
ENI	820	6.508	3.843	1.03	26.91

Source: Authors’ calculations through Stata 17.

Correlation analysis

The results of the matrix of correlations as presented in Table 3 show that there is a negative relationship between CO₂ emissions and lnCRDF, lnMRDF, lnARDF, REC, UPOP, and ENI. This implies that a reduction in CO₂ emissions is associated with higher levels of all climate-related development finances (adaptation or mitigation finances or both), renewable energy consumption, urbanization, and energy intensity. In contrast, the results show that there is a positive relationship between CO₂ emissions, FDI, and GDP per capita growth. This suggests that higher levels of CO₂ emissions are associated with foreign direct investment (net inflows) and GDP per capita growth. The results further show that reduction in CO₂ emissions is more correlated with renewable energy consumption amplifying the need for greater use of renewable energy in both domestic and industrial production in Africa. On the other hand, higher levels of CO₂ emissions are correlated with higher GDP per capita growth suggesting the need to adopt innovative means of production to increase economic growth and at the same time reduce CO₂ emissions. Besides, lnCRDF is highly correlated with lnMRDF and lnARDF with correlation coefficients of 0.953 and 0.687 respectively. The higher correlation coefficient of above 0.8 (0.953) between lnCRDF and lnMRDF means that including these variables in the same model may result in multicollinearity problems which can undermine the efficiency and consistency of our estimated parameters. Hence, the study adopted the stepwise regression approach to examine the relationship between these variables and CO₂ emissions.

Table 3. Matrix of correlations.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
(1) CO2	1.000
(2) lnCRDF	−0.077	1.000
(3) lnMRDF	−0.085	0.953	1.000
(4) lnARDF	−0.042	0.687	0.575	1.000
(5) REC	−0.647	−0.061	−0.061	−0.035	1.000
(6) FDI	0.039	−0.032	−0.043	0.009	−0.012	1.000
(7) lnGDPP	0.702	0.102	0.059	0.204	−0.705	0.020	1.000
(8) UPOP	−0.109	−0.015	−0.012	−0.010	0.342	0.157	−0.260	1.000
(9) ENI	−0.210	−0.009	0.014	−0.104	0.520	−0.001	−0.488	0.014	1.000

Source: Authors’ calculations through Stata 17.

Regression results

The results as presented in Table 4 show that the overall climate-related development finances (lnCRDF) including adaptation and mitigation-related finances (lnARDF & lnMRDF) aid CO₂ emissions in Africa in the short run. However, they are capable of serving as effective carbon disinfectants over the long run. This is evident by the negative and statistically significant impacts of lnCRDF, lnARDF, and lnMRDF on CO₂ emissions in the long run. Similarly, renewable energy consumption (REC) has a short-run significant positive and a long-run significant negative effect on CO₂ emissions in Africa. On the contrary, urbanization (UPOP), GDP per capita growth (lnGDPP), and energy intensity (ENI) levels significantly reduce CO₂ emissions only in the short run, however, they worsen CO₂ emissions over the long run. Additionally, net inflows of foreign direct investment (FDI) have a short-run carbon-enhancing and a long-run carbon-reducing effect in Africa. Lastly, the results show that previous levels of CO₂ emissions positively and statistically significantly influence future levels of emissions in Africa.

Table 4. GMM Estimates of the effects of climate-related development finances, and renewable energy consumption on CO₂ emissions.

	(1)	(2)	(3)
VARIABLES	CO2	CO2	CO2
Short run estimates
L.CO2	1.160***	1.140***	1.193***
	(0.00326)	(0.00336)	(0.00718)
REC	0.00310***	0.00261***	0.00359***
	(0.000237)	(0.000164)	(0.000371)
UPOP	−0.0239***	−0.0184***	−0.0300***
	(0.00488)	(0.00546)	(0.00644)
FDI	0.00339***	0.00334***	0.00381***
	(0.000521)	(0.000488)	(0.000598)
lnGDPP	−0.164***	−0.146***	−0.203***
	(0.00630)	(0.00642)	(0.00914)
ENI	−0.0231***	−0.0204***	−0.0273***
	(0.00194)	(0.00187)	(0.00212)
lnCRDF	0.0108***
	(0.000815)
lnMRDF		0.0104***
		(0.000678)
lnARDF			0.00691***
			(0.000590)
Constant	0.955***	0.841***	1.276***
	(0.0597)	(0.0653)	(0.0597)
Long run estimates
REC	−0.0195***	−0.0187***	−0.0186***
	(0.00148)	(0.00138)	(0.00150)
UPOP	0.1501***	0.132***	0.156***
	(0.0284)	(0.0362)	(0.0283)
FDI	−0.0213***	−0.0239***	−0.0198***
	(0.00329)	(0.00339)	(0.00320)
lnGDPP	1.0294***	1.0411***	1.0514***
	(0.0327)	(0.0303)	(0.0338)
ENI	0.145***	0.146***	0.141***
	(0.0129)	(0.0137)	(0.0133)
LnCRDF	−0.0678***
	(0.00490)
LnMRDF		−0.0743***
		(0.00573)
lnARDF			−0.0358***
			(0.00196)
System GMM diagnostic test^a
	(1)	(2)	(3)
	CO2	CO2	CO2
AR (2)	0.539	0.574	0.509
Hansen J	0.126	0.103	0.070
Number of Instruments	27	27	27
Number of Groups	41	41	41
Year Dummies	Yes	Yes	Yes
Prob > F	0.000	0.000	0.000
Observations	779	779	779

Standard errors in parentheses.

^aSource: Authors’ calculations through Stata 17.

Note: *** denotes statistical significance at 1% (p < 0.01); ** indicates statistical significance at 5% (p < 0.05), and * represents statistical significance at 10% (p < 0.1).

Discussion of findings

The study was carried out to determine the effects of climate-related development finances (adaptation and mitigation finances) and renewable energy consumption on CO₂ emissions in Africa. The findings acquiescingly reveal that climate-related development finances are critical for environmental sustainability. Specifically, the results show that the overall climate-related development finances, adaptation-related development finances, and mitigation-related development finances are important carbon disinfectants over the long run in Africa. However, they exacerbate CO₂ emissions in Africa in the short-to-medium term. Intuitively, with adaptation vis-à-vis mitigation-related development finances, African countries can be able to utilize their comparative advantage in terms of green hydrogen production, renewable energy production and consumption, credible carbon offsets, as well as building climate resilience projects among others, and this will help to reduce CO₂ emissions in Africa over the long-term. It is estimated that for the period 2020–2030, Africa needs on average more than $140 billion annually from the developed countries to pay off climate debt. Thus, around $30 billion for adaptation, $70 billion for mitigation, and 40 billion for loss and damage [8]. However, recent statistics indicate that only about $20 billion annually flows into Africa, a deficit of $120 billion per year [7]. The low inflows of funds from developed countries to Africa to support climate-related development projects posed a serious challenge to Africa’s ability to adapt and mitigate climate change effects. An estimated median loss of 1.5% of annual GDP is witnessed in developing countries and Sub-Saharan Africa mainly because of global warming [9]. Climate change has further resulted in rising catastrophic events such as floods, droughts, global warming, and pandemics across the continent and around the globe. Climate change is inimical to human and wildlife existence. Alas, it is the poor people, women and children, and the most vulnerable that bear the full brunt of changing climatic conditions. It is therefore imperative that developed countries (the polluters) do the needful to assist African countries with the required funds to ward off climate debt and combats its effects for a green and sustainable development in Africa. The findings of this study are in agreement with the results of previous empirical studies [10,14,16,21–23,42,44,45] in which it was found that green financing, and or climate-related development finances can serve as an effective catalyst for technological innovation, renewable energy production and consumption, and consequently in lowering carbon emissions and other greenhouse gases in the atmosphere. Additionally, the short-run positive effects of climate adaptation and mitigation-related development finances on CO₂ emissions in Africa is reasonably due to the fact that the effects of most government policies or macroeconomic policies are not felt immediately, they become noticeable over the long-term. For instance, planting of trees as an adaptative measure to lower CO₂ emissions may not necessarily result in reducing CO₂ emissions in the short-term, as the trees are still at the infant stage and will require a longer period to mature. Likewise, it takes a longer period for people to adopt to a new technology or an innovation, hence, shifting from non-renewable production and consumption of energy as a mitigative measure to lower CO₂ emissions will take a longer period to be fully materialized, all else being equal.

Similarly, the findings convincingly show that renewable energy consumption has a significant short-run positive and a long-run negative impact on CO₂ emissions in Africa. This suggests that shifting from the usage of non-renewable energies such as coal, fossil fuel, natural gas, and other petroleum products, to the usage of renewable energies including solar panels, hydropower, wind power, geothermal, and biomass for both domestic and industrial production in Africa can play a significant role to creating a greener and brighter Africa, as CO₂ emissions will be minimal over the long-term. Africa has huge potential in terms of renewable energy production and usage; however, it lacks the resources for the effective utilization of these energy sources. As earlier demonstrated, Africa currently received only about $20 billion annually from the developed countries to finance climate-related development projects. However, the amount is $120 billion short of what is required annually to finance climate-related development projects for a greener, cleaner, and brighter Africa [7]. Africa needs and should be supported with enough funds and technical know-how from the developed countries (the polluters) to enable them to offset climate debt through green investment projects such as renewable energy production and consumption, and other adaptive measures. The findings of this study concord with previous empirical studies [29–37,46–49] in which it was shown that while renewable energy usage has a long-run carbon abatement effect, consumption of non-renewable energy exacerbates CO₂ emissions over the long run.

Furthermore, the results reveal that urbanization, economic expansion, and energy intensity have short-run negative and long-run positive impacts on CO₂ emissions in Africa. It can be deduced from the findings that a high rate of urbanization worsens carbon emissions in the long run. This is because as cities grow to certain levels, congestion, and slums which are breeding grounds for carbon emissions become self-evident, particularly in Africa. Also, due to heavy traffics in the cities as a result of urbanization, air pollution increases due to rising CO₂ emissions from the vehicles on the road which use more non-renewable energy such as fossil fuel and other petroleum products. For instance, [28,29,33] demonstrate that non-renewable energy usage worsens CO₂ emissions in both the short and long run. Likewise, economic expansion translates to higher economic activities, which means higher production. The environmental Kuznets Hypothesis predicts economic expansion to reduce CO₂ emissions over the long run, based on the assumption that higher economic growth means higher income, and as people become richer, they begin to care more about the environment and therefore demand more renewable energy rather than non-renewable energy. However, the reverse is true in Africa as the continent relies more on non-renewable energy sources for both domestic and industrial production. It is not surprising that higher GDP per capita is found to stimulate CO₂ emissions in Africa over the long run. These findings agree with the results of earlier empirical works [28,31] which found economic growth to be positively related to CO₂ emissions, particularly over the long term.

In the same vein, higher energy intensity reduces carbon emissions only in the short run, it however, promotes CO₂ emissions in Africa over the long term. These findings suggest that at lower levels of production, a higher amount of energy required to produce a given unit of output may not necessarily result in increased CO₂ emissions due to the relative efficiency of inputs at this stage of production. However, as production continues, the higher amount of energy required to produce a given unit of output will result in rising CO₂ emissions over the long run. Therefore, to abate CO₂ emissions in Africa, switching to using more energy-efficient machinery in production becomes essential. Thus, using inputs or machinery which requires less energy intensity to produce a given unit of output. This finding is in tandem with the results of a study by Ajong Aquilas and Tabi Atemnkeng [16] in which it was noted that higher energy intensity significantly stimulates greenhouse gas emissions in the Congo Basin. Lastly, the results suggest that the net inflows of foreign direct investment have a short-run carbon enhancing and a long-run carbon-reducing effect in Africa. This implies that the net inflows of foreign direct investment in the form of technology, knowledge, and capital transfer may not immediately result in carbon abatement, however, it can help in reducing carbon emissions in Africa over the long run. Hence, African governments should make good use of foreign investment in their respective countries and capitalized on the Build Operate Transfer (BOT) form of investment as a means to set up measures to curtail CO₂ emissions in the medium-to-the long term.

Conclusion and recommendations

This study examines the effects of climate-related development finances, both aggregate and disaggregated components including adaptation and mitigation-related development finances and renewable energy consumption on CO₂ emissions in Africa. The study employed the system Generalized Method of Moments (system GMM) on data from 2000 to 2020 for 41 selected African countries for the analysis. The findings convincingly reveal that climate-related development finances, adaptation or mitigation-related development finances, and renewable energy consumption are important catalysts for carbon abatement over the long run in Africa. Besides, the net inflows of foreign direct investment have a short-run positive and long-run carbon disinfecting effect in Africa. On the contrary, higher GDP per capita, higher energy intensity, and urbanization are only effective in reducing emissions in the short run, however, they worsen CO₂ emissions over the long run. Based on these findings, the current study contends that to reduce CO₂ emissions, which is the primary greenhouse gas responsible for over 70% of yearly global emissions that drives climate change [2,3], African countries need to invest heavily on green technology production and consumption as mitigative measures such as the production and usage of solar panels, biomass, geothermal, hydropower, wind power, among others, as well as other adaptative measures such as smart cities, trees planting, and climate-smart agriculture practices. However, embarking on such policy initiatives requires huge sums of capital for effective implementation, which the continent lacks. Therefore, it becomes imperative that the developed nations (the polluters) support Africa with the required funds to enable them ward-off climate debt over the long run. Also, African governments should make good use of foreign direct investment inflows and include climate resilience policies as part of contract negotiations and enforcement. Moreover, major industries in Africa should adopt energy-efficient machinery for production to reduce CO₂ emissions over the long run, as the findings suggest that machineries which requires higher energy intensity for production contributes significantly to CO₂ emissions in the long run in Africa. Likewise, both industries and households should shift away from the consumption of non-renewable energies to the usage of renewable energies for effective carbon abatement in Africa over the long run.

Limitations and suggestions for future studies

The findings, as reported in this study, is limited to Africa and may not be applicable to other developing or emerging economies. The study is also limited to only examining the nexus between climate related development finances and CO₂ emissions in Africa without regard to analyzing the moderation effects of institutional quality and or policies for environmental sustainability in the relationship between climate related development finances and carbon emission (CO₂ emissions) in Africa. Hence, future studies may attempt to explore this novel area of study.

Disclosure statement

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

Data availability statement

Data for the analysis can be accessed online at the following links: https://oe.cd/development-climate and https://data.worldbank.org/indicator (accessed on January 10, 2023).