Keywords

1 Introduction

Transition to energy sustainability is a major goal of the pots-2015 development agenda adopted by countries in the sub-Saharan Africa region. Considering their level of gross domestic product (GDP) per capita, these countries are classified at different scales of development. Still, the concept of sustainability associated with that of development requires additional indicators beyond per capita GDP. Indicators of sustainable development are largely documented in the scientific literature (Eustachio et al., 2019). These indicators include energy access, primary energy use, and greenhouse gas (GHG) emissions. GHG emissions in the energy sector are of particular concern because energy is the primary contributor to these emissions, with a share estimated at 75% (Wang et al., 2019). GHG emissions contribute to global warming and have detrimental effects on the environment and on economic sectors such as agriculture and breeding in the sub-Saharan Africa region (Esso & Keho, 2016).

According to the International Energy Agency (2020a, b, c), the reduction of global emissions worldwide anticipated at 30.6 gigatonnes (−8% compared to 2019) is mainly due to the decline of emissions from coal (−8%), oil (−4.5%), and natural gas (−2.3%) following the lockdown measures in large economies such as China and the USA. This IEA scenario shows that, in addition to the level of demand, the primary energy mix causes different countries to have differentiated levels of contributions to greenhouse gas emissions, and therefore to mitigation efforts. In its 2018 report, the United Nations’ Intergovernmental Panel on Climate Change (IPCC, 2018) estimated that limiting global warming to 1.5 °C will require increasing the share of renewables in the electricity supply mix to 97% by 2050; the International Energy Agency estimated the share of renewables in the global electricity generation at 26% in 2018 (IEA, 2020a, b, c).

Different methods have been experimented with in studying the relationships between economic growth, energy use, and greenhouse gas emissions. These methods include statistical regression models, vector errors correlation models, Granger causality, multivariate statistical methods and bounds test. Using the Granger causality and the bounds test, Zaman and Abd-el.Moemen (2017) studied the correlation between economic growth, energy consumption, and CO2 emissions in a sample of sub-Saharan Africa countries, using data series of the World Bank recorded for the period 1970–2010. Their results show that economic growth is positively correlated with CO2 emissions in Benin, Cote d’Ivoire, Ghana, Nigeria, Senegal, South Africa, and Togo. Adom et al. (2012) also used the bounds test to establish a correlation between economic growth, industrialization process and greenhouse gas emissions in Ghana, Morocco, and Senegal. Their results show that industrialization supports economic growth, which is positively correlated with greenhouse gas emissions. Eustachio et al. (2019) proposed a methodology to monitor the process of sustainable development in different countries, which is based on system theory and uses data from the World Bank data series.

However, these studies are limited by the absence of recommendations on how the correlation between economic growth, energy use, and greenhouse gas emissions could inform the planning of a tailored energy agenda specific to each country. This study aims to identifying levers of action on the energy systems of eight (8) sample countries based on the structure of their economies and levels of greenhouse gas emissions associated with different sectors, including the energy sector. The results should contribute to countries’ efforts in mitigating the energy sector’s ecological footprint taking into account the cross-sectoral dimension of the energy sector.

2 Methodological Approach

The study approach consists in analyzing the correlation between the energy demand per capita of a country and the indicators of economic development exemplified here with the gross domestic product (GDP) per capita and the indicators of ecological footprint exemplified with the equivalent carbon dioxide emissions (CO2e) per capita. We start by collecting the data to compute these correlation factors using the online-accessible Global Change Data Lab platform. The computation integrates the following country indicators (the year 2016):

  • Primary energy use per capita in MWh

  • Gross domestic product per capita in US Dollars of 2011 purchasing power parity (PPP)

  • Greenhouse gas emissions in equivalent CO2 emissions per capita (CO2e)

  • Energy intensity of the economy (kWh per unit GDP)

  • Carbon intensity of the energy sector (kg CO2e per kWh)

  • Carbon intensity of the economy (CO2e emissions per unit GDP

The study exemplifies the following countries located in the sub-Saharan Africa region: Cameroon, Gabon, Kenya, Mozambique, Nigeria, Senegal, South Africa, and Uganda, which are representative of the four regional economic communities (RECs) in sub-Saharan Africa. Inside these countries, the study also looks at the energy and GHG emissions’ figures associated with the sectors of agriculture, building, electricity and heat generation, land-use change and forestry, manufacturing and construction, industry, and transport.

The study starts with a presentation of the levels of CO2 and other greenhouse gas emissions in sample countries and in key economic sectors of these countries. Secondly, the study computes the correlation factors between energy demand per capita and the equivalent carbon dioxide emissions per capita, and between GDP per capita and equivalent carbon dioxide emissions per capita for each of the sample countries. From these assessments, we derive recommendations on the selection of tailored sustainable energy agendas that integrate renewable energy resources and other efficient measures to improve the carbon intensity of the countries’ economies.

3 Results

Fig. 15.1 displays the levels of carbon dioxide and greenhouse gas (GHG) emissions of sub-Saharan Africa countries in the sample.

Fig. 15.1
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CO2 and GHG emissions in 2016 (Global Change Data Lab, 2020a, b, c, d, e)

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Fig. 15.2
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GHG emissions per sector (Global Change Data Lab, 2020a, b, c, d, e)

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Legend: CO2 emissions are emissions from the energy and cement industries. GHG emissions include, in addition to CO2, five gases with greenhouse effect, including methane (CH4) and nitrous oxides (NOx) emitted in the energy sector and other key economic sectors.

Estimates of CO2 emissions range between 5.4 million tonnes in Uganda and 474.98 million tonnes in South Africa. Estimates of GHG emissions range between −84.96 million tonnes in Gabon and 497.39 million tonnes in South Africa. South Africa has the highest level of carbon dioxide emissions. However, its level of other GHG emissions is lower than in Nigeria. These other GHG emissions include emissions from land-use change and forestry and emissions from transformative industries with relatively high radiative forcing such as chlorofluorocarbons (CFCs), hydrofluorocarbons (HFCs). Their amounts are related to the number of transformative industries requiring heat or cold production. The total greenhouse gas emissions in Gabon are negative due to carbon capture by the vast forest resources of the country. GHG emissions in Kenya (47.77 million tonnes) are relatively low due to the same phenomenon of carbon capture accounted in the inventories. Figure 15.2 provides details of greenhouse gases emissions per sector.

Legend: L and F stands for land-use change and forestry. M and C is the manufacturing and construction sector. Other sectors include fugitive emissions, defined as the accidental leakages of gases such as methane in the oil and gas industry.

Agriculture is the first contributor to GHG emissions in Kenya, Senegal, and Uganda, and the second contributor to emissions in Nigeria, Mozambique, and South Africa. The sector predominantly produces methane (CH4) and nitrous oxides (NOx), whose levels are higher in composting and burning of crop and grass residues.

Emissions from power plants recorded in the electricity and heat sector are relatively high in South Africa due to this country’s installed capacity of 51,305 MW of which 91.2% is made of thermal units (USAID, 2020a, b).

The transport sector is an important contributor to greenhouse gas emissions in Nigeria and South Africa, where they contribute, respectively, with 50.8 and 55.4 million tonnes. The relatively high share of the sector in these countries’ GHG emissions is related to the scarcity of mass transport facilities that makes private cars necessary. Traffic congestion in sub-Saharan Africa has dramatic consequences on both the environment and the macroeconomic indicators of the countries. The sector also represents a relatively high proportion of emissions in Kenya, with 18.6%. In all other countries in the sample, its contribution is below 10% of greenhouse gas emissions.

Apart from South Africa, emissions from the building sector and the manufacturing and construction sectors are relatively low, compared to emissions from other sectors. The preponderance of traditional housing, with materials that are less polluting than concrete, especially in rural areas, may explain these levels of emissions. Figure 15.3 displays a comparison of greenhouse gas emissions per capita and CO2 emissions per capita in sample countries.

Fig. 15.3
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GHG and CO2 emissions per capita (Global Change Data Lab, 2020a, b, c, d, e)

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The level of greenhouse gas emissions per capita is highest in South Africa (7.48 tonnes) and in Cameroon (5.19 tonnes). The population of South Africa (about 57 million in 2016) was more than twice that of Cameroon. In South Africa, the emissions from the electricity and heat sector explain the high levels of both CO2 and greenhouse gas emissions per capita. In Cameroon, emissions from land-use change and forestry represent an important share of per capita figures; the sector represented 57.6% of total greenhouse gas emissions of the country. CO2 emissions per capita in Gabon (2.64 tonnes) is relatively high driven by the industry sector, but these emissions are largely compensated by the capture and storage functions of the large forestry sector that represents 110% of total greenhouse gas emissions of the country. Kenya also has overall a negative level of greenhouse gas emissions per capita (−0.27 tonnes). In Kenya, as in Gabon, the forestry sector contributes to capture a large share of greenhouse emissions (93.3 million tonnes), but this contribution is lowered by the emissions from the agriculture and transport sectors.

Figure 15.4 displays a comparison between the level of primary energy use per capita (in MWh) and the levels of CO2 and GHG emissions (CO2e) per capita for each of the sample countries.

Fig. 15.4
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Primary energy use, CO2, and GHG emissions per capita in 2016 (Global Change Data Lab, 2018)

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The regularity of the GHG emissions’ curve is interrupted in Gabon due to reasons mentioned in previous paragraphs. Gabon has a relatively high per capita energy use explained by its status as oil producer and a relatively low CO2 emissions per capita from the sector (0.49 tonnes). This is because Gabon also consumes a large proportion of its biomass and hydropower resources. South Africa has a relatively high energy use per capita, which is due to its available capacity for electricity and heat production, and a high level of CO2 and greenhouse gas emissions per capita due to the share of thermal energy in this production capacity.

The correlation is statistically significant between primary energy use per capita and CO2 emissions per capita with a factor of 0.99. The CO2 emissions in the study are those from the energy and cement industries, which explains the high correlation factor. The correlation is not statistically significant between the primary energy use per capita and the greenhouse gas emissions per capita. This confirms energy use is not the primary driver of greenhouse emissions in our sample countries.

Figure 15.5 displays a comparison between:

  • The energy intensity of the economy measured in kWh per 2011 PPP USD of GDP.

  • The carbon intensity of the energy sector measured in kg of greenhouse gas emissions per kWh of primary energy use, and.

  • The carbon intensity of the economy measured in kg of greenhouse gas emissions per 2011 PPP USD of GDP.

Fig. 15.5
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Energy intensity and carbon intensity of countries (Global Change Data Lab, 2020a, b, c, d, e)

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Legend: Data on energy intensity (energy use per unit GDP) and carbon intensity of the economy (GHG emissions per unit GDP) are figures of 2016. Data on the carbon intensity of the energy sector are figures of 2014, which are the most recent in the database. Figures of carbon intensity in the energy sector are relatively stable unless there is a major change in the primary energy use mix of countries, such as massive penetration of renewable energy technologies in the electricity supply mix. Therefore, we include the 2014 figures in this comparison.

South Africa is the most carbon-intensive economy of the sample, followed by Senegal (0.26 kg CO2e per unit GDP), Mozambique (0.24 kg CO2e per unit GDP), and Gabon (0.21 kg CO2e per unit GDP). With the exceptions of Gabon and Mozambique that have relatively high shares of hydropower in their electricity mix, these countries highly rely on thermal energy production powered by coal (South Africa) and diesel fuel (Senegal). Senegal still imports these fossil fuels, despite recent confirmation of oil and gas reserves, which affects both its macroeconomic and environmental indicators (energy bill and emission factor). South Africa produces coal and imports oil. The use of carbon-intensive fuels in the oil industry can explain the situation in Gabon. In Mozambique, besides hydropower units, thermal power plants installed in the country have relatively high capacities (643 MW) (USAID, 2020a, b). Despite these power capacities, access to electricity is on average 29% in the country, the majority of electricity produced being exported to South Africa. Uganda is the least carbon-intensive economy of the sample (0.07 kg CO2e per unit GDP).

Mozambique, with 2.99 kWh per unit GDP, is the most energy-intensive economy of the sample, followed by South Africa (2.27 kWh per unit GDP). The situation may be explained by the fact the database includes figures of energy production in the country and not figures of energy consumed in the economic activities of the country. Gabon (0.87 kWh per unit GDP) and Senegal (0.86 kWh per unit GDP) have relatively high carbon-intensive economies compared to other countries in the sample due to reasons explained in the previous paragraphs. Uganda (0.37 kWh per unit GDP) is the least energy-intensive economy of the sample.

South Africa (0.33 kg CO2e per kWh) and Senegal (0.2 kg CO2e per kWh) have the energy sectors with the highest carbon footprint. In both cases, the share of thermal units in the electricity production mix largely explains this situation. Kenya (0.05 kg CO2e per kWh) and Mozambique (0.06 kg CO2e per kWh) have the energy sector with the lowest ecological footprint due to the large share of hydropower and geothermal (Kenya) units in their electricity production mix.

The correlation between greenhouse gas emissions per capita and the energy intensity of the economy defined as primary energy use per unit of GDP (R = 0.178) is statistically not significant. The correlation between the energy use per capita and the energy intensity of the economy (R = 0.48) is statistically not significant. The correlation between greenhouse gas emissions per capita and the carbon intensity of the economy defined as per capita equivalent CO2 emissions per unit GDP (R = 0.157) is not statistically significant in sample countries.

4 Discussion of Findings

With the exception of South Africa, the levels of greenhouse gas emissions observed in sample countries are relatively low compared to other regions of the world. More concerning is the trend of emissions, which is constantly increasing, for example in South Africa, the greenhouse emissions increased by 44% between 2000 and 2016 to reach 497.39 million tonnes in 2016. Similar trends are observed in other countries of the sample during the same period: Uganda (+78%), Mozambique (+38%), Nigeria (+25%), and Senegal (+22%) (Global Change Data Lab, 2020a, b, c, d, e). Ritchie and Moser’s (Ritchie & Roser, 2020) claim that the level of CO2 emissions may reach 100 gigatonnes by 2050 in the absence of strong policies to mitigate climate change. However, there have also been positive trends recorded during the period, including in Gabon, where the carbon capture capacities were multiplied by a factor of 13, and in Kenya, where the compensation of greenhouse emissions by carbon capture increased by 51%.

South Africa combines a carbon-intensive and energy-intensive economy. The greenhouse gases are emitted predominantly by the country’s heat and electricity generation sector. Over 91% of South Africa’s electricity production is from thermal units such as coal power plants.

Another example is that of Nigeria, which combines a relatively low carbon-intensive and low energy-intensive economy, despite the fact the country is ranked second in our sample in terms of annual greenhouse gas emissions, with 481.02 million tonnes in 2016. Two factors can explain this observation: the primary energy mix and demography. In 2016, the Nigerian primary energy mix was made of 76% biomass and biofuel energy, 14% oil, and 9% natural gas (IEA, 2020a, b, c). Other energy sources are coal, hydropower, solar photovoltaic and wind energy. Biomass energy is considered carbon neutral, which explains the relatively low levels of greenhouse gas emissions from the energy sector. With regard to the demographic factor, the population was estimated at 186 million inhabitants in 2016, with 1.6 tonnes of equivalent CO2 (CO2e) emissions per capita, which contributed to lower the absolute figure of the economy carbon intensity.

Gabon combines negative greenhouse gas emissions per capita and an economy in which carbon intensity and energy intensity are higher than in Kenya and Nigeria. Two factors can explain this observation: renewable energy systems in the electricity supply mix and demography. Gabon consumes a large proportion of its biomass and hydropower resources. Hydropower represented 51% of the 720 MW installed electricity generation capacity (Ngari, 2020). The presence of an oil industry explains the carbon intensity of the economy and the relatively high level of GDP per capita in 2016 (14,334 PPP USD 2011). These figures divided by the country population estimated at 2.01 million inhabitants in 2016, return high levels of carbon and energy intensity of the economy.

Cameroon, Mozambique, and Nigeria emit the majority of greenhouse gases (GHG) in the sector of land-use change and forestry. In Cameroon, the sector represents 57.6% of greenhouse gas emissions, and in Nigeria, 38.5% of emissions. Energy initiatives to curb the trend of GHG emissions in these countries should primarily target measures to reduce deforestation, forest degradation, and enhance carbon stocks (REDD+), through diversification of the primary energy supply. The reduction of the share of biomass energy in Cameroon and Nigeria supply mixes requires investment in alternative fuels such as biofuels (e.g pellets for cooking) from waste recycling and hydropower for electricity generation. In Mozambique, an investment in decentralized renewable energy systems and network infrastructure should contribute to increase access to electricity and lower pressure on biomass resources.

In Kenya, Senegal, and Uganda, agriculture is the primary contributor to greenhouse gas emissions, with over one-third of emissions. Therefore, energy initiatives in these countries should target mechanisms to integrate renewable energy systems such as solar photovoltaic and wind pumping and irrigation in the sector. In addition to energy-based solutions, other levers of action to mitigate CO2 emissions in the sector include sustainable agriculture practices such as crop rotation and combination of food crops with other biomass species to increase the plant cover and prevent land erosion. Aside from CO2, agriculture is a source of relatively significant emissions of methane, which is another greenhouse gas with higher radiative forcing. Therefore, these countries could also invest in organic composting and have prohibitive legislation on post-harvest burning of crop residues. In addition to investment in sustainable land management and agriculture practices, rural electrification is another indispensable lever in all sample countries, considering that the majority of rural communities in these countries still rely on biomass energy, and kerosene, especially in Nigeria. According to Chirambo (2018), by focusing on rural electrification and linking access to energy to sustainable agriculture practices, Africa could achieve sustainable development goals by 2030.

Transport is another sector that requires attention in all countries of the sample, especially in Kenya, Nigeria and South Africa, where it represents a relatively important share of greenhouse gas emissions. Affordable access to transport is necessary for the key economic activities. However, the majority of vehicles, which are predominantly fossil fuel-powered vehicles, have a significant ecological footprint. Countries in Sub-Saharan Africa have, recently and at different degrees, invested in mass transport with buses and trains. However, this level of investment is still low compared to the demand in the sector. The recent tragedy in Cameroon (2016 Eseka train derailment) provides a reminder of the contrast between the demand for mass transportation and the options accessible to the populations. In addition to the limited availability of mass transport, countries face the issue of second-hand cars that are imported in the region with higher energy demand and higher levels of greenhouse gas emissions.

5 Conclusion

The chapter provides an interpretation of the figures pertaining to study the relationships between economic growth, primary energy use, and greenhouse gas emissions. The analyses covered eight countries in the sub-Saharan Africa region, including countries with the relatively high level of greenhouse gas emissions, such as Nigeria and South Africa and countries with relatively low levels of emissions, such as Senegal and Kenya. The findings show that the macroeconomic structure and distribution of activities between the various sectors have an impact on the level of primary energy use and on the level of greenhouse gas (CO2e) emissions. Therefore, any attempt to mitigate greenhouse gas emissions or to improve energy access should build from this macroeconomic structure.

So far, the debate on the transition to energy sustainability in Sub-Saharan Africa has repeatedly clashed with these countries’ quest for increased economic growth measured in terms of gross domestic product. However, the study shows that tailored actions in some key sectors that are specific to each country may return a high value in terms of transition to energy sustainability (SDG-7) and climate change mitigation (SDG-13) through reduction of greenhouse gas emissions without harming the economy. These actions could also impact SDG-11 on responsible production and consumption. For instance, in Senegal, investments in solar water pumping and irrigation may return better environmental (emissions reduction) and economic (productivity in the agriculture sector) values than promoting rooftop solar photovoltaic energy in buildings. In South Africa, recycling waste to energy in the manufacturing and construction sectors combined with investments in large hydropower systems for electricity generation is necessary to curb the growing CO2 emissions pattern.

Addressing CO2 emissions in the transport sector by investing in mass transportation, in both vehicles and fuels, is a key lever of action in all the countries in our sample. Alternative transport fuels such as jatropha oil have been experimented with in many countries of the region, including Senegal, with interesting results, especially on capacities to recover lands eroded by food crops (Dafrallah & Ackom, 2016).

These study findings should support the ambitions of countries in the Sub-Saharan Africa region to make a transition towards a sustainable energy future; ambitions stated in the post-2015 Development Agenda that include the Sustainable Development Goals. Beyond a quantitative review of the relationships between economic growth, energy use and environmental protection, the study’s findings provided a reminder of the need to have a more inclusive approach to addressing the collective action problems of energy poverty, economic deprivation, and climate change.

Future studies could refine these findings by focusing on groups of countries that have similar levels of development (GDP per capita), energy use (energy per capita), or level of greenhouse gas emissions (CO2e). This could support tailor-made recommendations to address one or more dimensions of the energy-economic-environment nexus.