Greenhouse gas emission widens income inequality in Africa

Over the past couple of decades, the world has witnessed a rise in greenhouse gas (GHG) emissions and rising income inequality that threatens human well-being. Addressing these challenges and ensuring sustainable development become a pressing issue for policymakers. This paper investigates the impact of GHG emissions on income inequality in Africa. The study uses a panel data set from 49 countries from 1981 to 2015 and shows that GHG emission widens income inequality. The result is robust for alternative emission indicators. A direct implication is that climate change policy should be designed to narrow income inequality. It is emphasized that mitigation actions should focus on the agriculture sector. Hence, intervention towards energy-smart agriculture, land conservation practices, exploiting the job creation potential, and strengthening value addition in the agricultural sector is decisive. Reforming agriculture reduces emission, narrows income inequality, and realizes sustainable development on the continent.


Introduction
Climate change and income inequality are the leading global challenges facing world citizens. These situations cause a heavy burden on the poor people in Africa. Although the continent accounts for only 2-3% of the world's carbon dioxide (CO 2 ) emission (UNCCC 2006), it is the second most unequal society in the world (Robilliard 2020). Both phenomena result in heavy burdens on the agriculture-based economy, and combating them must involve reducing vulnerabilities of the poor (Rao and Min 2018). Designing a prudent policy that unlocks the development potentials, maintaining environmental quality, and reducing income inequality has remained the pressing policy agenda of the time.
The nexus between GHG and income inequality is complex. For a bulk of literature, the relationship is shaped by the level of economic development (Chen et al. 2020) and exhibits a non-linear relationship (Baležentis et al. 2020;Uddin et al. 2020;Huang and Duan 2020). Pertinent to the mitigation mechanism, existing evidence is inconclusive. Heil and Wodon (2000) point out that few countries are historically responsible for creating and aggravating emissions; thus, addressing emissions inequality through distribution policies is appealing. Wang et al. (2016), on the other hand, present CO 2 taxation in developed economies affects lowerincome households; however, the result does not imply developing countries. As part of the mitigation mechanism, increased renewable energy consumption is associated with climate change concerns (Nyiwul 2017), which creates a challenge in approaching the problem. Besides, due to lack of enforcement mechanisms in high polluting countries, increasing inequality tends to reduce mitigation and adaptation actions (Nyiwul 2021). This paper is motivated by two observations (Fig. 1). A rise in CO 2 leads to a substantial increase in Gini disposable. To get a sense of the relationship, countries with higher CO 2 emissions recorded a higher income inequality and vice versa. On the other hand, the impact of CO 2 emission is more pronounced in income distribution than GDP per capita growth. Given the relationship, we ask two questions. Can GHG emissions cause income inequality? If so, can we draw a policy that mitigates both problems and guarantees sustainable development? In this regard, current efforts fall short of proposing a comprehensive and realistic mitigation mechanism. The paper argues that policy actions controlling GHG emissions do not necessarily reduce inequality (Hussein et al. 2013). Those people whose livelihood depends on agriculture could be affected. Besides, Nyiwul (2021) stated that polluting countries are less likely tacking climate actions, which makes the policy's effectiveness doubtful. This paper advances reassessing the mitigation mechanisms following Nyiwul (2019) through bringing sustainable development in the agriculture sector as the primary policy direction. In this way, the paper is not limited to addressing climate change or income inequality problems in isolation. Particular emphasis is paid to the African economies' architecture and the feasibility of development policies. Hence, the paper extends the policy discussion pertinent to GHG-inequality problems by offering an alternative development strategy that unlocks the growth potential. This paper departs from a bulk of literature by treating inequality as an outcome.
The paper used a panel data set from 1981 to 2015 for a panel of 49 African countries from the World Bank, World Development Indicators (WDI) database, and the Standardized World Income Inequality Database (SWIID) (Solt, 2020). We employed a panel data fixed effect, two-stage least squared estimation (2sls) method, and spatial correlation analysis. A key takeaway from the empirical analysis is that GHG emissions represented by CO 2 , agricultural methane gas, and fossil fuel energy consumption widen income inequality. Different specification tests confirm that the estimators are robust. Based on the findings, the paper suggests that Agriculture Lead Industrialization Development (ADLI) policy allows the economy relying on agriculture to transform phase by phase. Its central theme in agriculture transformation is improving rural people's per capita income. Realizing agricultural transformation through energy-smart-agriculture production and employment creation can reduce emissions, narrow inequality, and achieve sustainable development on the continent. Critical interventions related to renewable energy consumption, agriculture conservation practices, and value addition are highlighted in unlocking the continent's growth potential.
The rest of the paper is organized as follows. Section 2 presents a brief literature review. In Section 3, data and methodology are presented. The main results are discussed in Section 4. In Section 5, the paper discusses why agriculture is still important in Africa. Finally, in Section 6, the paper concludes and forward future policy insights.

Literature review
The association between GHG emissions and income inequality is barely researched, and evidence in Africa is rare. The paper offers a condensed review that presents factors shaping the relationship between GHG emissions and inequality, the direction of the relationship, and the mitigation mechanisms that help unlock sustainable development ambition.
While establishing a relationship, one must understand the context of the analysis. Economic development highly shapes the GHG-inequality nexus (Grunewald et al. 2017;Chen et al. 2020); as for developing countries, higher income inequality is linked with lower emissions. That implies a threshold level of development is warranted for developing countries for the relationship prevailed (Demir et al. 2019). Liu et al. (2019) also show that time is crucial in understanding the relationship and that higher income inequality in the US surges CO 2 emission in the short run. Moreover, Uddin et al. (2020) echoed that the effect is conditional on the time frame and asserted that for an extended period, there is no significant relationship. Padilla and Serrano (2006), on the other hand, asserted that income inequality had been followed by an essential variation in the distribution of emissions.
The other bulk of literature is interested in testing the direction of the relationship. Two batches support a nonlinear relationship (Baležentis et al. 2020;Huang and Duan 2020), given the inverted U-shaped hypothesis of the environmental Kuznets curve (Kuznets 1955) is dominant. The first batch of literature establishes a positive relationship between GHG and inequality (Boyce 1994;Torras and Boyce 1998;Golley and Meng 2012;Baek and Gweisah 2013;Zhang and Zhao 2014;Jorgenson et al. 2017;Baloch et al. 2020;Hailemariam et al. 2020). In those pieces of literature, the main feature is that wealthy households generate more emissions per capita than poor households. Moreover, other factors such as urbanization rate, the share of agriculture income, literacy, and reliance on non-renewable energy sources moderate the relations.
The other side of the literature asserted that the relationship is negative (Heerink et al. 2001;Ravallion et al. 2000;Yang et al. 2011;Hübler 2017;Ali et al. 2016;Kusumawardani and Dewi 2020;Huang and Duan 2020). In this stream of literature, the role of income, globalization, trade openness, and emission reduction policies have been pointed out. The big picture disclosed that narrowing global inequality could improve climate outcomes (Rao and Min 2018). The broad literature presented in this part uses CO 2 as a dependent variable. This paper departs from the current literature by treating GHG emissions as a predictor.
However, this literature fails to provide a tailor-made policy action to the problem. A fundamental issue that remains scarcely answered is addressing these pressing problems of our time. In this regard, little attention has been paid to proposing sustainable development policies that underscore the GHG-inequality problems. Even the existing ones do not account for the differences in economic structure, resilience to climate change exposure, and the means of livelihood across countries. Some policies lack inclusiveness; others fail to fit under a specific context. For instance, Torras and Boyce (1998) stress the importance of evenly distributed income to alleviate environmental degradation. However, income inequality results from complex economic dynamism and variation in development policies across countries. Environmental degradation and emission reduction can be achieved through carbon taxation, economic globalization, emission trading, renewable fuel standard, and feed-in tariff mechanisms (Alton et al. 2014;Wang et al. 2016;Bae 2018;Yameogo et al. 2021). However, policy effectiveness is determined by countries' commitment and socio-economic conditions in implementing actions. Agostini and Jiménez (2015) also highlighted that gasoline tax is somewhat progressive to household income. It is documented that inequality weakens the effectiveness of specific climate change policies (Bae 2018). Limitations of these strands of literature are the lack of tailor-made action and systematic evidence evaluating the impact of policy actions in developing countries.
One way to reconcile the limitations is to approach the problems by understanding the factors driving GHG emissions in Africa. The energy need explanation by Nyiwul (2017) highlighted that the increased consumption of renewable energy is associated with climate change concerns caused by pollutants. Given that climate change threatens sustainable development, the preferred mitigation and adaptation actions taken on the continent shall prioritize achieving development goals (L. M. Nyiwul 2019). The study suggests that replacing fossil fuel use with renewable energy and enhancing energy efficiency should gain enough attention. Renewable energy target setting, conservation efforts through forestry, reducing urban transport sector emission, waste management, and climate-smart agriculture practice are essential steps in achieving emission reduction goals.

3
Besides, to attain sustainable development, adaptation policies should focus on agriculture.
The paper emphasizes two feasible mechanisms for an agriculture-based economy. In one form, it appreciates the role of forestry as a dominant global ecosystem. Forest development is proven to serve as a sponge for carbon emission (Hussein et al. 2013). Given that Africa is the home for 17% of the world's forest (Pan et al. 2013), utilizing this resource in a way to ensure development is a viable option. Furthermore, we argue that climate change adaptation policies must focus on the agriculture sector, as the sector absorbs a vast share of the population. Nevertheless, GHG is also associated with land-use practices and changes in forest area coverage in the agriculture sector (Harris et al. 2012). Hence, analyzing the emission attributed to agricultural land use and forest area coverage allows identifying its corresponding implication on emission and agriculture income. A decrease in the share of the agricultural value added could be related to a decline in rainfall, which causes a significant change in the urbanization rate (Barrios et al. 2006;Brückner 2012). In Henderson et al. (2017), the urbanization process is an escape from the agriculture moisture shock that seriously threatens agricultural products in Africa. Placing agriculture sector-oriented emission reduction and production enhancement is thus the central theme of any intervention on the continent.
In a nutshell, the existing literature has limitations in linking the GHG-inequality problem with practicable development policies. This paper claims climate change policy implementation should come from the dominant economic activity on the continent. For the agriculturebased economy, understanding the emission-inequality nexus and addressing the problems from agriculture-based development policies can unlock alternative development policies.

Data and methodology
The data is obtained from the Standardized World Income Inequality Database (SWIID) and the World Bank Development Indicators (WDI) from 1981 to 2015 across 49 African countries. The income inequality data is collected and harmonized by Gini indices using the Luxemburg Income Study data as the standard (Solt 2019) version 9.1. The SWIID has comparable Gini indices of disposable and market income inequality for 198 countries from 1960 to the present. The primary predictor CO 2 emission per capita, alternative emission indicators, and control variables are from the World Bank World Development Indicators (WDI). The database contains a bulk of indicators for 217 economies since 1960. However, the main problem with the dataset is missing data points; some country's record is not complete and creates variation in the number of observation. The paper addresses the bias associated with missing values using an alternative estimation technique (Tables 1 and 2).
The summary statistics show that CO 2 is significantly correlated with income inequality. The impact of GHG emission on income inequality is estimated using the following econometric specification. Our baseline equation fits the data with the panel data fixed effect regression model using CO 2 as the primary predictor: Where: Gini it is the vector of outcome variables represented by Gini disposable (post-tax, post-transfer) and Gini market (pre-tax, pre-transfer), CO2 it is our variable of interest-it measures CO 2 emission (metric tons per capita), CV it is the vector of control variables such as GDP per capita growth, urban population growth, and school enrollment (average primary and secondary school enrollment), γ i , δ t , ε it capture country fixed effect, time fixed effect, and the idiosyncratic error term, respectively. β 1 captures the impact of emission on income inequality. However, relying on the baseline estimator could be misleading due to endogeneity problems. Endogeneity arises due to several reasons in our estimation. The first is measurement error (i.e., the dataset from SWIID is obtained via imputation) that suffered from computational errors. Second, reverse causality (i.e., GHG emission could result from income inequality-a bulk of literature presented in this regard) and the problem associated with omitted variable bias. We identify key instruments to address the problems associated with estimation using the two-stage least squared estimation (2sls) method. By identifying exogenous variations and exploiting these sources of variation related to agriculture value addition from forest and fishery, population growth, arable land, and fertilizer usage as an instrument, the paper addresses the challenge and ensures that estimators are robust. Furthermore, we cement our findings by using alternative emission indicators such as agricultural methane emissions and fossil fuel energy consumption.
Equations 2a and 2b are the 2sls specifications of the baseline model, whereas Eqs. 2c and 2d are part of the robustness test for the causal inference; Where: In the same manner, from Eqs. 2a and 2b Gini it and CO2 it measure Gini disposable and carbon emission. Thus, using IV it a set of instrumental variables such as ForAr it -forest area (percentage of land area),PopGrowth it -population growth rate, and AgriValueAdd it -agricultural value added from forestry and fishery, we specify the first-stage equation. CV it is the vector of other control variables, and ε it , μ it represent the corresponding error terms. Estimation results are presented in Tables 3 and 4.
From Eqs. 2c and 2d, OtherEmissions it is represented by agriculture methane gas emission and fossil fuel energy consumption. IV it is the set of instrumental variables, such as ArableLand it -share of total land suitable for agricultural practices and Fertilizers it -the KG amount of fertilizers applied in the squared area of land. CV it is the vector of other control variables, and ε it , μ it represent the corresponding error terms. Estimation results are presented in Tables 5 and 6.

Fixed effect regression results
The first empirical exercise shows a statistically significant positive effect of CO 2 emission on Gini indicators after controlling school enrollment and other covariates; a unit increase in CO 2 emission widens income inequality on average by 0.39-0.55 units. The finding is consistent with massive literature that identified a positive relation. However, oil rent, urbanization, and school enrollment significantly reduce inequality. Oil rent has implications for a few African countries that rely on oil export through a better redistribution of revenue through public services. Likewise, the urbanization process and its socio-economic consequences have narrowed inequality. Although urbanization shows an attempt to escape agriculture moisture shock (Henderson et al. 2017), it has a redistribution effect in balancing income. The implication of education can be understood from its potential in creating more job opportunities and complementing reform actions (Ozkok 2015;Njikam 2017;Berrill et al. 2018). Even though enrollment varies across the continent, it did an excellent job reducing inequality in Africa. However, the country-specific cases exhibit varying degrees of correlation. Countries ranked on top with their wealth and economic growth perform differently than the rest of the sample countries. For instance, in the wealthiest countries like South Africa, Libya, Tunisia, and Algeria, CO 2 emission is negatively correlated to Gini disposable ( Fig. 2, top). On the other hand, among the fastest-growing economies, we find a positive correlation between GDP per capita growth and CO 2 emission in Morocco, Kenya, Ethiopia, Ghana, and Tanzania (Fig. 2, bottom). The paper presents a mixed effect in line with the baseline relationship within Africa. The continent is home to rapidly  distribution. Change in carbon from a land-use pattern makes CO 2 vary in Africa. It is estimated that 60% of Africa's forests were lost before 1850, and a significant percentage vanishes each year (Houghton and Hackler 2006). These have changed the earthly ecosystem and the availability of cropland. The current variation across countries shapes the effect on income distribution. Besides, the tradeoff between obtaining energy from renewable and non-renewable sources is another defining factor for carbon emission (Nyiwul 2017;Awodumi and Adewuyi 2020). Positive growth in nonrenewable energy consumption enhances economic growth, but the effect on environmental quality is mixed. The level of policy commitment and effectiveness also matters in understanding the variability. A carbon tax per ton of CO 2 can achieve South Africa emission reduction targets by 2025 (Alton et al. 2014). However, its effectiveness is conditional on trading partners' response to a common goal, otherwise resulting in higher employment losses. The bottom line from these explanations is that related dynamics can explain variability apart from the baseline estimates. Therefore, there are several reasons for not interpreting the relationship as causal. As a result of the variability in natural vegetation and policy perused across countries, CO 2 could negatively correlate to inequality (Fig. 2, top). In this case, our baseline estimator may vanish or inflate the inequality widening hypothesis. Furthermore, we might encounter omitted variable bias as we naturally have other predictors that determine the level of CO 2 emission. These predictors vary across countries, and we need to capture these phenomena. From Table 1, some variables correlated to CO 2 but not with Gini, hitherto not included in our baseline model.
The problems can be solved using key instruments (Daron Acemoglu et al. 2001;Autor et al. 2013;Angrist and Krueger 2001;Abadie 2003). It is documented that the factors that affect GHG emissions are complex and multidimensional. Aside from the land use pattern and energy consumption path in explaining emission, another way to approach the problem is by understanding the economy's fundamental drivers. Agriculture is the backbone of most African economies, and development is linked to the sector's performance (Senbet and Simbanegavi 2017). The sector has provided food and brought people out of poverty (Gassner et al. 2019). Hence, allied activities in the agriculture sector become unquestionably important in determining GHG emissions and inequality. We identified two channels related to agriculture production and demographic changes to link the issues and formulate policies. The first channel links the role of forest to emission. The part elaborates how forest development continued to play an essential role in reducing emissions and unlocking development challenges. The second channel underscores the role of population growth in line with its impact on energy use and rural-urban migration. Our estimators are robust for alternative emission indicators following the same identification assumption.

Does forest area coverage matter?
Africa is home to about 674 million hectares of forests that account for 17% of the world total (FAO 2010). The coverage offers more than one-third of the total CO 2 reductions required to keep global warming below 2 °C by 2030 (Griscom et al. 2017). However, climate change and land-use practices remain the main factors shaping forest function in recent years (Pan et al. 2013). High reliance on energy from wood is the primary driver of deforestation, which puts pressure on forest development and coverage (Lemenih and Kassa 2014). The sector's development comes on top when fighting global climate change, especially in countries where other reduction technologies are not cost-effective. Given moderately high forest coverage and low carbon emissions in developing countries, the carbon sponge function must be checked (Fig. 3). It is also puzzling why the continent continued suffering from climate change.
For countries in the tropical and sub-tropical developing countries, forest degradation is a crucial contributor to GHG emissions (Pearson et al. 2017). However, its impact is less known as more empirical research is leaning towards assessing the impact of deforestation. The infancy development stage of the industrial sector and capacity constraint symbolize the African economy. Under these circumstances, the impact of climate change suppressed the growth performance of the agriculture sector. One approach to building a resilient economy and enhancing agriculture productivity is by planting trees and increasing forest area coverage (Popkin 2019). It is argued that adding value to forest development can serve the dual purpose of reducing emissions and raising GDP per capita.
To support our argument, we used the agricultural value added from forestry and fishery as an instrument for CO 2 emission ( Table 3). The first stage regression result shows that agriculture forest and fish value added reduces CO 2 emission. However, the effect of CO 2 emission on income inequality remained virtually the same as the baseline fixed-effect model estimate. The finding signals that the agriculture sector is a possible entry point to mitigate the problem. If it has reduced carbon emission, the remaining task becomes how to use the sector in distributing income evenly? We answer this concern in the discussion section.
The specification further unfolds the relationship that has not been presented in the baseline estimation regarding urbanization. Urbanization tends to reduce CO 2 emission and has two implications. First, it is worth mentioning that the result is driven by the shift from agrarian energy use pattern to the urban (dominated by hydroelectric power). Second, partly it also explains that the agriculture sector emits more pollutants than the industry sector in Africa. Meaning the proliferation of manufacturing industries does not pull the urbanization in Africa. In understanding the drivers of urbanization, Henderson et al. (2017) indicate that urban migration provides an "escape" from adverse agricultural moisture shocks, which leads to reduced farm income. This finding unfolds that the impact of CO 2 emission in elucidating the growth of GDP per capita is not as high as the growth in Gini disposable (Fig. 1).

Population growth
Another factor affecting carbon emissions in developing countries is the growing population trend. Pimentel (1991) shows that the rapidly growing population puts pressure on the global environment and threatens its ability to supply a quality environment. Likewise, Alam et al. (2016) revealed that the relationship between CO 2 emission and population growth is statistically significant for India and Brazil, yet not for China and Indonesia. In this study, the percentage of people living in the urban area conveys the same story. Our data show that annual population growth in Africa over the past three decades mounted at 2.6%. A significant part of the development is attributed to the growth of the population living in urban centers. Urban dwellers reached 37% of the total population, and the share rose to 88% in some countries. However, the urbanization dynamics are not associated with the growth in CO 2 emitting industries (Fig. 4).
We provide empirical evidence showing that total population and urban population growth significantly reduce CO 2 emissions between 13 and 38 units. However, country-specific cases are still mixed. The spatial correlation shows a negative correlation between urban population and CO 2 in the Northern part. In contrast, Southern, Western, and Central African countries exhibit a positive correlation. Even if evidence varies across countries, the growing importance of literacy represented by school enrollment plays an essential role in moderating the adverse effect. Education shapes the relationship by creating awareness on deforestation, land degradation, and building a habit to use less carbon emission technologies.
Decoupling the growing population trend to the rural and urban areas also gives a better insight. Two-thirds of the continent population lives in rural areas, with low literacy and health service provision. Under these circumstances, managing population growth is more challenging in rural areas than in urban areas. It is thus customary to think that rural population growth puts pressure on the environment and other emission indicators. However, population growth in urban centers is relatively low, and its burden on emitting pollutants is relatively low. When this situation is accompanied by slow industrial development, the relationship between population growth and CO 2 emission becomes negative. Nevertheless, the relationship remained robust with the baseline estimates. Therefore, controlling demographic changes and using the growing youth labor force in a productive sector that emit less carbon are another entry point in tackling the challenges. In a nutshell, the paper shows the two channels through which the baseline association could be understood in widening income inequality. Furthermore, the empirical exercise suggests intervention on these exogenous indicators to reduce income inequality brings the desired objective.

Alternative emission indicator
The final empirical exercise for the income inequality widening hypothesis uses alternative emission indicators. It is well-known that methane is the second most important GHG emission contributor, following CO 2 . Bhatia et al. (2004) show that agricultural soils contribute to methane and nitrous oxide emission causing global warming. Moreover, from the experimental study by Biernat et al. (2020), arable organic farming showed the ability to produce agricultural commodities with low nitrous oxide emission per unit area. Besides, the rates of methane uptake by arable soils were less sensitive to crop type, field management practices, and fertilizer application rates (Koga et al. 2004), and the rate is strongly influenced by long-term tillage management. This part unfolds the emission related to agriculture practices and its effect on income inequality.
Africa has diverse soil characteristics, land-use types, and climate conditions. It is presented that the emission contributions from methane gas and fossil fuel energy take a significant share of GHG (see Appendix 3). These emission indicators can potentially represent the emission characteristics in developing countries. Given that more people rely on agriculture for daily livelihood, we cement the inequality widening findings using methane gas and fossil fuel energy consumption. These emission types are, in turn, determined by the size of arable land and the type of fertilizer applied per acre of land.
The first stage regression result shows that both arable land coverage and fertilizer usage per square meter of land reduce agriculture methane emission on average between 1.5 and 38 units. The result could be driven by organic agricultural practice to a great extent. The expansion of agriculture reduces emissions and has also proven to benefit the continent by providing the means of livelihood. The relationship also brings enormous benefits to unlock the climate change stress on production. Agriculture absorbs two-thirds of the people and absorbs 60% of the labor force.
The key implication from the finding is that best practices in this regard have to be expanded at the continental level. Some nations can also design a sustainable development strategy taking the agriculture sector as a pillar and linking policy to reducing inequality. However, the impact of agriculture methane gas emission on income inequality remained virtually the same as the baseline estimates. The other emission type that characterizes the African economy is fossil fuel energy consumption. In this regard, the continent has vast reserves of fossil fuels: oil, coal, and natural gas, yet its reserve is not appropriately utilized in reducing carbon emissions (UNECA 2011). The agriculture sector needs energy at each step of the production process. Available data shows that the agriculture system heavily relies on fossil fuel energy to operate, which leads to increasing GHG emissions and affects the sector's production (FAO 2016). Moreover, globally, fossil-fuel-related emissions account for about 65% of the excess mortality and 70% of the climate cooling by anthropogenic aerosols (Lelieveld et al. 2019). It is in part a result of approximately 337 billion metric tons of CO 2 emission to the atmosphere since 1751 from the consumption of fossil fuels (Boden et al. 2013). Interestingly, the problem worsens as energy consumption in the agriculture sector is expected to rise over the period ahead, and bearing in mind its related impact on climate is warranted.
Apart from that, it has resulted in alteration in temperature and moisture shock, affecting crop production and making households suffer from production shortages. The paper argues that its effect goes beyond climate change and affects income distribution by reducing agricultural productivity. Our data shows that fossil fuel energy consumption is the second-largest emission contributing to GHG in Africa (see Appendix 3). On average, 97% of energy sources in Northern African countries such as Egypt, Morocco, and Algeria come from fossil fuels. Other countries in the Central and South-Eastern part also obtain the energy source from fossil fuel, which makes the correlation coefficient positive (Fig. 5). We advance the empirical exercise to link fossil fuel energy consumption with agriculture activities: landuse change. The continent's topography is characterized by desert, forest, arable land suitable for irrigation, rift valley, and mountainous region. These variations from West-to-East and North-to-South determine the fossil fuel energy consumption in the agriculture sector. Meanwhile, extraction of gas and oil from the existing environment needs to take place in such a way as to attain sustainable development and reduce carbon emissions. The last empirical exercise links fossil fuel energy consumption with agricultural land usage.
The first stage regression result shows that a rise in arable land reduces fossil fuel emission. Unlike other parts of the world, our result is highly driven by the effect coming from countries in the Southern and Eastern parts. The result makes sense because most of the Sahara desert in the North is not used for agriculture production. It also implies that the energy sources in the Southern part of Africa signal an encouraging result for the expansion of the agriculture sector that does not contribute to GHG emissions. As the consumption trend increases from time to time, it is crucial to shift to renewable energy sources to meet Africa's energy demand. In most rural parts, solar energy has been a common trend in recent years.
In general, the main takeaway from the paper is that GHG widens income inequality in Africa, and climate change policy action should be oriented towards tackling the problem in agriculture. Transforming the sector can answer the challenge posed by emission and inequality.

Discussion
Two insights regarding the nexus between greenhouse emission and income inequality are presented. Our finding supports the hypothesis that claims GHG emission widens income inequality. The baseline estimation result is further cemented by 2sls estimation using key instruments for emission indicators. The paper proposes a climate change policy in the agriculture sector that narrows income inequality in light of the main finding. Policy orientations should gear towards the agriculture sector for two reasons. The first reason is that agriculture development is proven to reduce emissions. The paper shows that agriculture, forest and fish value added, arable land, and fertilizer consumption have reduced emissions. On the other hand, it employs more than 60% of the labor force.
Pertinent to climate policy in the agriculture sector, the climate change mitigation mechanism should be geared towards energy-smart agriculture. Nyiwul (2019) shows that the African continent can advance the agriculture sector performance and ensure food security through irrigation systems, land and soil conservation practices, and livestock and fishery development. The agriculture sector needs energy at different steps of the value chain. The total GHG emissions from the agriculture food chain are estimated at 20% of global GHG emissions per year (FAO 2016). Hence, two candidates are economically feasible for developing countries to reduce emissions further. The continent can significantly reduce agriculture sector emissions by relying on renewable energy sources and scaling up conservation efforts by planting trees. Besides, agriculture can be energy smart by improving access to improved energy services and efficiency in the production value chain.
Given the great potential of the agriculture sector in alleviating poverty (Senbet and Simbanegavi 2017), the required energy demand can be met from renewable energy sources. Solar energy, wind, small hydropower, geothermal heating, fertilizer optimization, drip irrigation, and agriculture conservation are feasible options. Renewable energy usage can bring efficiency gain and additional income to the sector dwellers (Chien and Hu 2007) and improves GDP performance by promoting capital formation (Chien and Hu 2008). On the other side, the impact of climate change on agriculture can also be mitigated by soil and water conservation, improving pest control technologies, and applying crop rotations (Pimentel 1991). With the enormous benefits of reshaping the energy sources and conservation practices, the next question remained: Does it simultaneously reduce income inequality? To answer this question, we assess the GDP contribution across regions.
The sector's potential can be examined in line with its potential for employment creation for the growing youth population. Compared with other sectors, the agriculture sector can do an excellent job of redistributing income evenly. To understand the mechanism, we decompose the share of GDP and assess the employment creation potential. The agriculture sector employs more than 60% of the total labor force, followed by service and manufacturing (Fig. 6). For an extended period, the sector's productivity was hampered by various policy and nonpolicy factors such as drought, backward agriculture production techniques, land ownership problems, and political unrest. As a result, the sector is characterized by underperformance (Bjornlund et al. 2020). Hence, investing in agriculture value addition is a gateway to boost per capita income and realize income redistribution. However, it is not easy to realize these objectives and transform the sector's performance to achieve the desired objective with the existing traditional agricultural practices.
Our argument lends from Senbet and Simbanegavi (2017) that reducing poverty and fostering broader structural change by transforming the agricultural practice of smallholder farmers that account for 80% of the population should be the development policy orientation in Africa. One way is to detach its dependence on rainfall and use the water potential through irrigation. Producing all seasons supports the food demand, and the surplus, if traded, can improve the smallholder farm income. Food security can be realized using techniques that put little water stress (Pfister et al. 2011). Arable land expansion accompanied by irrigation and water-efficient production technique is necessary. Moreover, emphasis on agricultural products value addition should get utmost importance. It has been documented that paying attention to agriculture enhances farmers' income, achieves food security, and reduces poverty (Gassner et al. 2019). Furthermore, the industrialization narrative for sustainable economic development may fail to work in Africa for different reasons. Most importantly, timing and sequencing matter in policy implementation across countries. Our data shows that school enrollment is growing steadily, which may not respond to the industrial sector's skilled human resource needs. Besides, in recent years the urban population's growth has increased at a decreasing rate which backs the manufacturing sector job is not readily available (see Appendix 4). These pieces of evidence suggest that the component and the pull factor are not growing in tandem with population growth, energy demand, and capacity. Therefore, the two approaches can be framed within the Agriculture Development Led Industrialization (ADLI) policy. It is a holistic strategy that equally addresses climate change and income inequality and realizes sustainable development. The vein of the policy is that agriculture sector transformation should come first and pave the way for industrial development. The strategy is proposed given African countries' comparative advantages in the agriculture sector. Utilizing these potentials enables countries to break the vicious cycle and enhance the productivity of rural farmers through agriculture transformation. Production and productivity can be enhanced through producing all seasons using irrigation, optimal fertilizers usage, and improving the efficiency of the value chain in the sector. Similarly, those tasks should increase access to finance for rural farmers and create a market link. The production process should also be oriented to commercial crops, livestock, fishery, and urban agriculture. In the meantime, value addition on agricultural products improves the price of agricultural products, eventually raising the rural population's income and reducing inequality.
In the long run, the role of industrial development is not discarded. However, given the comparative advantage, African countries must utilize their advantages in light of global environmental degradation and its unprecedented effects on the rural poor. That is why the need to balance income inequality, improve environmental quality, and achieve rapid economic growth requires a shift in perspective

Conclusion
The paper unlocks the question surrounding sustainable development in Africa by addressing the bottlenecks on GHG emissions and income inequality. It establishes a causal relationship using key indicators and a sound econometric method. The findings show that GHG emission widens income inequality. To cement the causal mechanism, we used instruments representing the features of the agrarian economy which are correlated emission indicators. The alternative estimation method confirms baseline findings, and the agriculture channel has been presented. The main takeaway from our finding is that transforming the agriculture practices of smallholder farmers is a good candidate for realizing sustainable development and tackling both problems.
The conclusion leads to three implications. First, the continent needs to go a long way in transforming the agriculturebased economy into an industrial economy. Given the existing capacity constraints on literacy, infrastructure development, technology, and institutional capability, industrial development should focus on agriculture value addition. Agro-processing firms are vital in realizing this objective. If countries promote industrialization, shifting the available scarce resources to agriculture transformation and ensuring emission management practices should be in place. Besides, planting trees across countries can absorb a significant portion of a pollutant under capacity constraints. Capitalizing the agricultural value addition from forestry and fishery is another approach. Doing so achieves a higher GDP per capita growth, reduces emissions, and can distribute income through job creation. Second, population growth, especially a rise in urban population, reduces emissions. The shift to renewable energy sources in cities is highlighted. If accompanied by a growth in literacy, urban agriculture, and the use of energy-efficient products, both problems could be controlled. Third, from the alternative emission indicators, an alternative approach relies on organic fertilizers; although the data does not differentiate between organic and inorganic fertilizer, it should be highlighted that using the former is proven to reduce emissions. Given the massive potential for the agricultural sector, the result put forward an alternative means to develop the continent is through value addition in agricultural products.
In a nutshell, our result is robust for different specifications and alternative emission indicators. Moreover, spatiotemporal evidence has also been provided to enhance the discussion. There are other ways to extend this paper. Using micro-level data like household surveys, a comparative analysis with the developed nations, and incorporating other indicators that explain the continent economic characteristics will add to the existing literature in the area.