TX_1~AT/TX_2~AT International Journal of Energy Economics and Policy | Vol 12 • Issue 5 • 2022332 International Journal of Energy Economics and Policy ISSN: 2146-4553 available at http: www.econjournals.com International Journal of Energy Economics and Policy, 2022, 12(5), 332-341. Climate Change, Poverty and Income Inequality Linkage: Empirical Evidence from Nigeria Evelyn Nwamaka Ogbeide-Osaretin1*, Bright Orhewere2, Oseremen Ebhote3, Sadiq Oshoke Akhor4, Israel O. Imide5 1Department of Economics, Faculty of Arts, Mgt. a Social Sciences, Edo State University Uzairue, Edo State, Nigeria, 2Department of Economics, Western Delta University, Oghara, Delta State, Nigeria, 3Department of Business Administration, Faculty of Arts, Mgt. a Social Sciences, Edo State University Uzairue, Edo State, Nigeria, 4Department of Accounting, Faculty of Arts, Mgt. & Social Sciences, Edo State University Uzairue, Edo State, Nigeria, 5Department of Economics, University of Delta, Agbor, Delta State Nigeria. *Email: osaretin.evelyn@edouniversity.edu.ng Received: 02 July 2022 Accepted: 05 September 2022 DOI: https://doi.org/10.32479/ijeep.13556 ABSTRACT There seems to be a vicious cycle between climate change and income inequality. Hence, this study examined the existence of a feedback relationship between climate change and income inequality in Nigeria. The study employed an annual data series for the period from 1980 to 2020 which was estimated with the Dynamic Ordinary Least Square. Income inequality was measured by Gini while climate change was captured by temperature. The upshot of the study revealed that there is a feedback substantial connectivity between climate change and income inequality. The impact of climate change on income inequality conformed to the U-shaped hypothesis. Other factors of climate change were population growth, economic development, and emission of carbon dioxide. Hence, the study pertinently advocates and recommends effective population control, reduction of income inequality through the provision of employment and education, and the supply of modern and efficient energy in the purse of economic growth and development. Keywords: Climatic Change, Economic Development, Gini Coefficient, Poverty; Nigeria JEL Classifications: C32, I32, O15, Q0 1. INTRODUCTION In the last decades, the growth in global output has increased the welfare of many, lifting millions out of poverty. However, this drive is being threatened by global and regional poverty, and inequality beginning to rise again. An understanding of the causes of these is crucial for effective policy implications and achieving global equitable economic development. Suspected among these causes is climate change. World Bank reported that about 132 million people will transition into poverty by 2030 due to the rising climate change (Internal Displacement Monitoring Center, 2018; World Bank, 2020). This is also expected to increase the inequality between and within countries. In a report by United Nations, an estimate of US$ 383 million/day was recorded for global economic loss resulting from the disaster of climate change between 2010 and 2019 which is almost seven times the record of 1970-1979, US$ 49 million (World Meteorological Organization, 2021). It is of recent decades becoming clear that climate change, poverty, and income are inextricably linked and not independent. Unmitigated climate change is suspected to exacerbate the existing inequality between and within countries’ inequalities and poverty rates. Higher temperatures reduce productivity, income, and health. Hurricanes from climate change also destroy homes and hamper employment opportunities, making the economic situation of the poor more precarious. On the other hand, poor people and countries do not have enough resources to meet up with the requirement of clean energy to mitigate climate change hence, contributing This Journal is licensed under a Creative Commons Attribution 4.0 International License Ogbeide-Osaretin, et al.: Climate Change, Poverty and Income Inequality Linkage: Empirical Evidence from Nigeria International Journal of Energy Economics and Policy | Vol 12 • Issue 5 • 2022 333 to rising climate change (Albu and Albu, 2020). It has been suggested that the total damages from natural disasters and higher temperatures are higher in developing countries. As confirmed by Sarkodie and Strezov (2019) in a study on 192 United Nations, Africa has been noted to be among the most venerable to climate change. For instance, near-surface air temperature in 2020 was between 0.5°C and 0.88°C more than what was recorded between 1981 and 2010, and Africa was found to be warmer than the global average temperature in the combination of overland and Ocean (World Meteorological Organization, 2020). For the period 2015-2019, each year was warmer than all the years before 2014 (World Meteorological Organization, 2020). Sub-Saharan Africa has also been found to be among the regions with the highest level of poverty and inequality. About 41% of the population is still living below the $1.90 poverty line, while it was estimated that about 87% of the world’s poor will be in SSA by 2030. Africa is also the second most unequal continent in the world (Seery et al., 2019). Nigeria in Sub-Saharan Africa has been of particular interest in terms of the level of climate change, poverty, and inequality. Temperature as a measure of climate change was found by data to have risen from 26.85°C to in 1970 to 27.37°C in 2020. This is an average of 0.03°C per decade and in the last 30 years, it increased by 0.19°C per decade. Average rainfall increased from 1295 to 2018 (World Meteorological Organization, 2020). It was estimated that about 83 million of the total population of Nigeria’s population are still absolutely poor. Inequality measured by the Gini index was found to be 44% in 2019 which grew marginally from 43% in 2009 and is the lowest among other countries in SSA and the world. Nigeria ranked the least of the 45 countries in Africa and had 157 positions in the global ranking on the assessment of the government’s commitment to reducing inequality (Seery et al., 2019; World Bank, 2020). An overview of Figure 1 showed that changes in poverty and inequality seem to be moving in the same direction as climate change captured by temperature in Figure 2. Although, the temperature seems to be more dynamic. Thus, we may argue that climate changes are a foremost contributor to the wider inequality gap given the high negative effect on agricultural productivity, health, and income thereby increasing the poverty rate (poverty tends to be highest in the agricultural sector). On the other hand, it may also be argued that the high level of income inequality and poverty are contributing to the effect of climate change as the unequal income distribution and poverty reduces the ability to mitigate climate change as well as engage in clean energy uses that reduces the degree of climate change. For instance, in 2016, about 74% of the country’s population relied on firewood for cooking (Monyei et al., 2018), while only about 55.4% have access to electricity as of 2019 (World Bank, 2021). In the same period, poverty increased from 48.2% in 2015 to 72% in 2016. Temperature also increased from 27.32°C to 27.77°C. Hence, climate change may be a root or a corollary of some levels of inequality and poverty. Hence, it has become paramount to analyze this nexus concerning Nigeria and the outcome may be extended to other countries for effectiveness in the policy formulation for poverty and income inequality reduction as well as climate change mitigation. Analysis of the impacts and causes of climate change has substantially increased over the decades with controversial findings. Some empirical evidence concluded that countries with lower income inequality tend to contribute less to climate change, hence suggesting across countries lower inequality for the mitigation of climate change and adoption of a green economy (Albu and Albu, 2020). Climate change has also been found to increase inequality both within counties and across countries (Diffenbaugh and Burke, 2019; Hsiang et al., 2019; Dasgupta et al., 2020). Others noted that climate change negatively impacts welfare and falls heavily on the poor increasing the poverty level (Skoufias, 2012). In Sub-Saharan Africa and Nigeria in particular, there are very few studies (Skoufias, 2012) that found that the impact of climate change varies with the pattern of income inequality on the impact of climate change on inequality. However, rather than just focusing solely on climate-specific policies given their impact on the global economy, inequality, and poverty, it is also imperative to ask how efforts of the global economy and developing countries to improve economic opportunity and reduce poverty and inequality can increase climate change and its vulnerability. It is also crucial to ask if the level of poverty and income inequality is increasing the risk of climate change. This is based on the assumption that with poverty and a wide income gap, the poor tend to carry out activities that cause harm to the climate (deforestation for wood fuel, burning of charcoal, dumping of refuse in rivers, among others). Hence, it can be argued that while climate change can impact inequality and poverty, poverty and inequality can impact climate change. This is a gap that has not been covered particularly in Nigeria. Hence, the current study is out to fill this gap. Therefore, the objective of this study is to determine if there exists a feedback impact between climate change, poverty, and inequality in Nigeria. This study, therefore, contributes to current literature in the following ways: first, it evaluates the possibility of a feedback effect between climate change and income inequality. Second, it made use of the efficient measures of climate change (temperature) which has not been considered in Nigeria Studies. Third, it explored the existence of a non-linear relationship between income inequality and climate. It is expected that there will be feedback connectivity between climate change and income inequality. Figure 1: Trend of temperature in Nigeria Source: Authors’ chart Ogbeide-Osaretin, et al.: Climate Change, Poverty and Income Inequality Linkage: Empirical Evidence from Nigeria International Journal of Energy Economics and Policy | Vol 12 • Issue 5 • 2022334 2. REVIEW OF LITERATURE The impact of climate change on inequality and poverty is a particular area of active research and policy interest, as a result of the inconclusive outcome on the nature and causes of observed inequality. This is a result of the relevance of climate change in achieving sustainable development. Climate change according to Yue and Gao (2018) is the increasing patterns of temperatures and weather that bring about environmental degradation and impact economic and social lives. Climate change is mainly caused by the emission of greenhouse gas which causes heat to be trapped by the atmosphere earth’s atmosphere resulting in global warming. Poverty is often defined with various measures. Defining poverty in terms of income, we have income poverty which is the lack of enough income to live up to the acceptable standard of living or pleasurable well-being. In terms of lack of basic needs of life, we have basic needs poverty which defines a person to be poor when he/she lacks needed food, education, health care, and other necessities of life. Poverty can also be defined in comparison to a universally acceptable income level which is absolute poverty. One is called poor if they are living below this level called the poverty line. Poverty can also be defined as relative poverty, chronic poverty, and transitory poverty (Todaro and Smith, 2011). Climate change is theoretically linked with poverty and inequality through the pursuit of development and resulting in a vicious cycle. Climate change can be exogenous to inequality or endogenous to inequality, hence suggesting a feedback relationship. Given the existence of income inequality, this will make some people poor. Climate change is exogenous and three ways have been identified by which climate change can affect poverty and inequality. Poverty and inequality increase the possibility of exposure of disadvantaged groups to the adverse effect of climate change. A major outcome of climate change is flooding. Given that poor and disadvantaged groups can only afford to live in slums, these areas are often flooded. Hence the flooding effect of climate change affects the poor group more. Climate change also aggravates the susceptibility of the poor group to the effect of climate change as a result of the poor quality of life. Finally, the poor and disadvantaged have a lower ability to manage and come out of the effect of climate change. They do not have enough resources to protect their health status or take care of health effects, easily get a new job/start a new investment if their current job/investment is negatively affected by climate change, or afford an insurance policy to compensate for the damage from climate change. All these aggravate the inequality gap and poverty status of the group. Climate change is also endogenous, the poor and disadvantaged groups are forced to engage in activities that cause harm to the climate resulting in climate change. As observed by Islam and Winkel (2017), and evidenced by studies on OECD, inequality and poverty aggregate environmental degradation contributing substantially to climate change. Countries with higher inequality tend to have higher levels of per capita waste generation. In line with the above, it may be expected that countries with higher inequality will tend to have higher levels of per capita GHG emissions change in climate in turn relatively affect the poor and the unequally treated group of the society. Inequality thus aggravates climate change (Islam and Winkel, 2017). Thus, given this possible endogeneity as presented in Figure 3a and 3b, it has become important and urgent to tackle the task of breaking the vicious cycle between climate change and inequality. Some earlier studies have been carried out to investigate this analytical framework. However, the outcome of these studies has been mixed results. Analyzing the existence of a feedback relationship between climate change and income inequality, the diverse impact of income inequality was found on climate change. Farmers are often believed to be the most vulnerable to climate change as a result of their direct and indirect dependency on climatic variables. Hence, Alam et al. (2017) analyzed the socioeconomic impacts of climatic changes on the farmers in Malaysia they employed a primary data analysis method on a survey of 198 paddy farmers in the Integrated Agricultural Development Area in North- West Selangor of Malaysia in 2009. The outcome showed that climate change adversely affects agricultural productivity, health, and profitability thereby increasing income inequality. Government spending through subsidies was found not to be adequate to support the farmers and reduce the effects of climate change on the farmers. This was contrary to Boyce (2007) who found that inequality brings about a reduction in carbon emission and hence climate change. Abaje and Oladipo (2019) investigated the impact of the recent changes in temperature and rainfall in the Kaduna State of Nigeria for the period 1971-2016. Linear regression, second- order polynomial, standard deviation, and Cramer’s test were employed in the analysis. The result showed an increasing trend in temperature which was on an average of 1.03°C and a mean increase of rainfall of 303.32 mm. This increase was found to be associated with the increase in greenhouse gases emission. Uzar and Eyuboglu (2019) examined the effect of CO2 emissions on income distribution in Turkey for the period 1984-2014. The Autoregressive Distributed Lag Model (ARDL) bound testing was employed to determine the existence of long-run connectivity among the variables. The study found that there is a positive impact of income inequality on the emission of CO2. Income inequality Granger causes CO2 emission using the Toda-Yamamoto causality test. Dasgupta et al. (2020) carried out a quantitative study on climate change’s impacts on inequality and poverty on a South African sub-national panel study. In conformity to Alam et al. (2017), the outcome revealed that a substantial relationship exists between inequality/poverty and mean temperature which was a measure of climate change. Climate change was found to reduce average growth, hence increasing inequality and poverty. In a similar study to that of Uzar and Eyuboglu (2019), Kusumawardani and Dewi (2020) investigated the effect of income inequality on climate change captured by carbon dioxide emissions in Indonesia. They employed an Autoregressive Distributed Lag (ARDL) model for the period 1975-2017. Income inequality was found to harm carbon dioxide which was found to be a function of the level of GDP per capita. Thus, the existence of the Environmental Kuznets Curve (EKC) was confirmed in Indonesia and the relationship between GDP per capita and CO2 emission was found to be an inverted “U” shape. Urbanization and dependency were found to negatively affect CO2 emissions. Ogbeide-Osaretin, et al.: Climate Change, Poverty and Income Inequality Linkage: Empirical Evidence from Nigeria International Journal of Energy Economics and Policy | Vol 12 • Issue 5 • 2022 335 Albu and Albu (2020) explored the connectivity between income inequality and climate change in European Union countries. They accounted for the consequences of the increase in carbon emissions on the increase in inequalities. The two-stage OLS estimation method was applied to two groups of European Union countries, (15 old member states and 13 new member states). The relationship between income inequality and carbon emission was different for the two groups. In the analysis of the effect of income inequality, poverty, and growth on the quality of the environment captured by carbon emission rate, Yameogo, and Dauda (2020), employed the ARDL model on data for Nigeria and Burkina Faso for the period 1980-2016. The result showed inverted U-Shaped connectivity between environmental degradation and growth of income for Nigeria while U-shaped connectivity was found for Burkina Faso. There was a positive relationship between income inequality and environmental degradation in both countries. Government expenditure and poverty were found to increase the level of carbon emission in Nigeria in the long run. In the short run, income inequality was found to reduce carbon emissions in Nigeria and it had an adverse effect in Burkina Faso. Following this is the study of Sam et al. (2021) who adopted the micro econometric empirical analysis to analyze the effect of climate change on household welfare through the rising prices of cereal. Data on five food groups were gathered from the 2009/2010 Swaziland Household Income Expenditure Survey and was analyzed by the Ideal Demand System (AIDS). Also, the food price projections of the International Food Policy Research Institute (IFPRI) were employed to estimate the proportional increase in income that is needed to keep the households on the required welfare level. Results showed that an increase in food prices as a result of climate change has led to an increase in the poverty rate of about 71-75 % as compared to 63% before the increase in prices. Hence, an income transfer of 17.5 and 25.4% of the former income level is needed to keep welfare at the level before the price increase. Hundie (2021) explored income inequality, economic growth, and carbon dioxide emission linkage in Ethiopia. The study made use of the ARDL bond testing and the Dynamic Ordinary Least Square method of estimation over the period 1979-2014. The result revealed that in the long run, the emission of CO2 increases with the increase in economic growth and the square of economic growth confirming the Kuznets U curve hypothesis of environment. Income inequality was found not to have a substantial effect on CO2and a positive relationship with it. Population size and urbanization were other factors accounting for the increase in the emission of CO2. Yang et al. (2022) examined the impact of the channel between income inequality and climate change (carbon emissions) to clarify the nonlinear relationship between income inequality, and the different degrees of carbon emissions in the United States and France from 1915 to 2019. They made use of wavelet decomposition and Quantile-on-Quantile regression and the results revealed that for France, income inequality impacts carbon emissions negatively when there is low-income inequality. However, when income inequality increases, its impact changes from negative to positive which is amplified by the increase in the emission of carbon emissions. On the other hand, as income inequality becomes deeper, the emission-enhancing effect is reversed gradually for the United States. However, the impact of carbon emissions on income inequality are same for both countries. In the short run, the income inequality and carbon emissions relationship in the two countries are randomly volatile while in the medium run, it is a three-dimensional inverted “V” shaped relationship for the US and a three-dimensional “V” shaped relationship for France. Also, in the long run, it exhibits a “V” shaped relationship with the US. In a more recent study by Cevik and Jalles (2022) on the linkage between climate change and income inequality, a panel of 158 countries was explored spanning the period 1955-2019. The researchers found that the increase in climate change vulnerability leads to an increase in income inequality. On segmentation of the sample size, it was revealed that there was no statistical impact of climate change vulnerability on income inequality for the developed countries while the reverse was the case for developing countries. This was accounted to the weak capacity of adaptation and mitigation by the developing countries. 2.1. Summary of Reviewed Literature and Contribution to Knowledge The analysis of connectivity between climate change and inequality has been examined by some studies. In summary, the studies tend to conclude that climate change increases income inequality. This was for within the countries and, across countries. Most of the studies investigated a one-way relationship between climate change and income inequity/poverty. The majority of the study found climate change increasing poverty rather than inequality d poverty increasing climate change. However, needed attention has not been drawn to the fact that there is a two-way relationship between climate change and inequality/poverty. While it is well recognized that climate change causes and aggravates inequality, it is also important to note that inequality can also aggravate climate change. This is the major contribution of this current study to existing literature. 3. METHODOLOGY Two major determinants of climate change are rainfall and temperature. However, we focused only on temperature. 3.1. Conceptual Framework The study adopted the approach of Burke et al. (2015b), and Dasgupta et al. (2020) to determine the non-linear relationship between climate change mean temperatures and our economic outcome variables (yit). This current study made use of normal levels of dependent variables rather than the first difference as in Burke et al. (2015b) and Dasgupta et al. (2020). A country responds to changes in temperature based on the country’s current level of temperature at a particular time, Tt. taking the quadratic state can be given as: hTt = α1Tt + α2T 2t (1) We can then add the warming impact h(Tt) to the reference scenarios without the climate impacts of the variable yit. We look Ogbeide-Osaretin, et al.: Climate Change, Poverty and Income Inequality Linkage: Empirical Evidence from Nigeria International Journal of Energy Economics and Policy | Vol 12 • Issue 5 • 2022336 at the distribution within a country, and, we considered income inequality indices such as the Gini index or the Atkinson measure A(Ω) of inequality or the class of Generalized Entropy Indices. The poverty headcount ratio P0 can also be used which measures the proportion of the population that is counted as poor Dasgupta et al. (2020). However, this study made use of the Gini index as a measure of income inequality as a result of its simplicity and general acceptability. Thus, the impact of climate on income inequality can be computed and simulated using this formula; ( ) ( )( ) g GNIt 1 (1 gt h Tt h T0 | GNIt e − + + − = (2) Where eg is the growth factor including climate impacts or g is its growth rate. The equation 2 result shows the effect of temperature on GNI in a given country at a particular time t. 3.2. Econometric Model Based on the theoretical under pinning that there could be a feedback relationship between climate change and inequality given the poverty level, thus study adopts a two equation model. GNIt = α1Tt + α2T 2 t + α3POV+ α4Xt +µ1 (3) Tt =β1GNIt + β2POVt+ β3Zt + µ2 (4) We control for annual temperature Tit and its squared term to capture the potential non-linear effects of climate change on income inequality. This was to test if an inverted U-shaped relationship exists between climate change and income inequality, taking into account the possibility that these relations are not linear. Inequality may decrease due to initial increases in temperature, but, beyond a threshold, the incremental increases in temperature may lead to increased inequality. Thus, it is expected that for some set of coefficients of temperature, T1 < 0; T2 > 0. In this case, the results indicate a non-linear relationship. The term Xt and Xt are the matrix of other relevant control variables of the income inequality (unemployment rate and population growth) and relevant control variables of the climate change (carbon dioxide (metric tons per capita), Real GDP per capita, unemployment rate, population growth). From the above, equation 3 and 4, introducing the control variables is transformed to: 0 1 2 3 4 5 α α α α α α ε = + + + + + + tGINI T TSQ POV UNMPR POPG t (5) 0 1 2 3 4 5 6 β β β β β β β = + + + + + + + tT GINI POV UNMPR POPG CADIOX RGDPpc ut (6) Where GINI = Gini Index a measure of income inequality T = Temperature a measure of climate change POV = National poverty level captured by headcount UNMPR = Unemployment rate POPG = Population growth rate RGDPpc = Real Gross Domestic product per capita. This was used to captured the level of development CADIOX = Consumption of coal in a thousand short tons εt and ut are the error term for the income inequality and climate change equations respectively. εt and ut are the error term for the income inequality and climate change equations respectively. A Priori, 1 2 3 4 5 6 1, 2 3, 4, 5 6, , , , , 0; , 0 0α α α α α α β β β β β β> > < 3.3. Data and Estimation Method The study employed secondary data spanning from 1980 to 2020. The data for GINI, POV, and UNMPR were obtained from the World Bank (2021) and Sasu (2022). Data for temperature was acquired from Climate Change Knowledge Portal (2021), while the RGDPpc, POPG, and CADIOX were obtained from the World Development Indicators (2021). The variables were subjected to various pre-estimation tests to determine their diagnostic properties. The ARDL bounds testing was employed to determine the presence of a long-run relationship given that the variables were stationary at orders one and zero. From the outcome of the ARDL result, the dynamic ordinary least Square method of estimation was used in carrying out the long-run analysis. The E-views 9 econometric package was used for the analysis. 4. EMPIRICAL PRESENTATION AND INTERPRETATION OF RESULTS 4.1. Correlation Result The intensity of multi-collinearity among the variables was determined using the correction matrix. The result from Table 1 showed that there is no multi-collinearity among the variables used in the result. This is proved by the correction coefficients of less than 0.8 for the variables. However, the correlation coefficient between temperature and temperature square of 0.9999 is not surprising as the latter was derived from the former hence, they tend to move together. The result further revealed that there is a positive correlation between inequality and temperature. This tends to suggest that climate change leads to inequality and vice-versa. However, the correlation does not indicate causation hence a further empirical analysis was carried out. 4.2. Descriptive Statistics As presented in Table 2, the mean, maximum, minimum, and Jargue-Bera (J.B) of the variables showed good performance in the statistics of the variables. The result of the skewness showed that result that all of the variables are positively skewed. The Jargue-Bera test, on the other hand, confirmed distributional Ogbeide-Osaretin, et al.: Climate Change, Poverty and Income Inequality Linkage: Empirical Evidence from Nigeria International Journal of Energy Economics and Policy | Vol 12 • Issue 5 • 2022 337 normality in all the variables. This means that all of the variables are distributed regularly 4.3. Stationarity Test To determine the level of stationary of the variables, the Augmented Dickey-Fuller test was employed. As presented in Table 3 while income inequality and population growth were stationary at levels, other variables were stationary at first difference. Hence, we proceed to run a cointegration analysis using the ARDL bound testing techniques. 4.4. Cointegration Test From the result of the unit root where some of the variables were integrated of order one and zero. The bound testing method was thus employed to determine the existence of cointegration between climate change and income inequality. From the income inequality model, the result showed that there is the existence of cointegration between the variables at the lower bound only at a 5% level of significance. This is as shown from the F sat of 2.717687 which is higher than the tabulated value of 2.62 lower bound but lower than 3.79 upper bound. Hence, we conclude that there is cointegration between the variables (Table A1 of the appendix). Also, from the climate change model, the existence of cointegration was also found at a lower bound of 5% significance levels. The Fsat of 2.661207 which is more than the tabulated values of 2.45 but lower than 3.61 uppers bound respectively allowed us to reject the null hypothesis of no cointegration between the variables (Table A2 of the appendix). 4.5. Estimation of the Models 4.5.1. Estimation of income inequality model From the outcome of the cointegration test carried out where the null hypothesis of cointegration was rejected, we proceed to the estimation of the model using the dynamic OLS. Table 4 shows the DOLS o the inequality model. Examining the diagnostic statistics of the result the R2 of 0.675073 showed that about 68% of the variation in the dependent variable is explained by the independent variables which is not bad. On the performance of the variables of the model, the outcome of the estimation showed that there is a negative relationship between temperature (T) and income inequality (GINI) and a positive relationship Table 3: Summary of the unit-root tests output employing the ADF Variable Levels 5% critical 1st difference 5% critical Remark GINI −3.139398 −2.936942 I (0) T −1.948335 −2.941145 −8.101568 −2.941145 I (1) T2 −1.958416 −2.941145 −8.075350 −2.941145 I (1) POV −1.712944 −2.938987 −10.99401 −2.938987 I (1) POPG −5.311883 −2.960411 I (0) RGDPpc −0.580213 −2.938987 −4.569165 −2.938987 I (1) UNMPR −0.124458 −2.938987 −7.205141 −2.938987 I (1) CADIOX −2.303747 −2.936942 −6.876319 −2.938987 I (1) GINI: Gini Index a measure of income inequality, T: Temperature a measure of climate change, POV: National poverty level captured by headcount, UNMPR: Unemployment rate, POPG: Population growth rate, RGDPpc: Real Gross Domestic product per capita, CADIOX: Consumption of coal in a thousand short tons Table 1: Correlation matrix result Variables GINI T TSQ POV POPG RGDPpc UNMPR CADIOX GINI 1.000000 T 0.077048 1.000000 TSQ 0.077078 0.999965 1.000000 POV 0.638624 0.425834 0.424772 1.000000 POPG −0.226438 0.184657 0.185821 −0.216889 1.000000 RGDPpc 0.099989 0.543493 0.543516 0.315193 0.578165 1.000000 UNMPR 0.200912 0.401293 0.401862 0.422842 0.285371 0.711676 1.000000 CADIOX −0.597379 0.080869 0.079646 −0.449570 0.448070 0.095579 0.014362 1.000000 Source: Author’s computation. GINI: Gini Index a measure of income inequality, T: Temperature a measure of climate change, POV: National poverty level captured by headcount, UNMPR: Unemployment rate, POPG: Population growth rate, RGDPpc: Real Gross Domestic product per capita, CADIOX: Consumption of coal in a thousand short tons, TSQ: Temperature square Table 2: Descriptive statistics Statistics GINI T TSQ POV POPG RGDPpc UNMPR CADIOX Mean 43.06195 27.17659 738.6741 54.52902 2.587127 1799.386 11.43598 0.610519 Median 43.00000 27.21000 740.3841 59.30000 2.586546 1607.238 11.90000 0.610000 Maximum 56.00000 27.83000 774.5089 72.90000 2.849252 2563.900 33.28000 0.928241 Minimum 35.08000 26.32000 692.7424 35.20000 2.488785 1324.297 3.600000 0.325560 SD 4.470221 0.331577 18.00321 12.23253 0.078620 450.5880 6.328673 0.169989 Skewness 0.667670 −0.181138 −0.145911 −0.247856 0.823394 0.473706 1.021092 −0.075513 Kurtosis 3.623020 2.983925 2.954869 1.616939 4.077668 1.590788 4.690907 2.064996 Jarque-Bera 3.709285 0.224649 0.148962 3.687588 6.616846 4.925921 12.00904 1.532446 Probability 0.156509 0.893754 0.928225 0.158216 0.036574 0.085182 0.002468 0.464765 Sum 1765.540 1114.240 30285.64 2235.690 106.0722 73774.82 468.8750 25.03129 Sum square deviation 799.3150 4.397722 12964.62 5985.394 0.247245 8121182. 1602.084 1.155851 Observations 41 41 41 41 41 41 41 41 Source: Authors’ computation from Eviews 9. GINI: Gini Index a measure of income inequality, T: Temperature a measure of climate change, POV: National poverty level captured by headcount, UNMPR: Unemployment rate, POPG: Population growth rate, RGDPpc: Real Gross Domestic product per capita, CADIOX: Consumption of coal in a thousand short tons, SD: Standard deviation, TSQ: Temperature square Ogbeide-Osaretin, et al.: Climate Change, Poverty and Income Inequality Linkage: Empirical Evidence from Nigeria International Journal of Energy Economics and Policy | Vol 12 • Issue 5 • 2022338 The result also divulged that population growth and household size were found to have a positive relationship with GINI as expected which was however insignificant. The result revealed a 1% increase in population growth by 19% in GINI in Nigeria. This upshot is in agreement with the outcome of Onwuka (2006), and Ogbeide- Osaretin and Orehwereh (2020) who found that population is harmful to development and will increase the income gap. 4.5.2. Estimation of the climate change model Following the outcome of the cointegration test which confirmed the existence of cointegration among the variables, the DOLS was employed in the estimation of the model The upshot of the DOLS estimation as presented in Table 5 revealed that in conformity to expectation, income inequality had a substantial positive impact on climate change (T). A 1 unit increase in GINI leads to a 0.09 increase in temperature. This is in line with some studies (Yameogo and Dauda, 2020; Hundie, 2021). On the other hand, it was found by some other studies by Kusumawardani and Dewi (2020) that GINI has a negative relationship and impact on climate change. Contrary to our expectations, poverty and unemployment were found to have a negative relationship with climate change. While poverty had an insignificant impact on climate change, unemployment was found to have a significant impact on climate change. This is also contrary to the findings of Yameogo and Dauda (2020) who found that poverty increases climate change. The result further revealed that following some other studies, (Hundie, 2021), population growth was found to have a substantial positive impact on climate change. Population growth was also found to have the highest magnitude in terms of its impact on climate change. However, it is expected that population growth reduces the consumption of energy and the efficiency in the use of energy. Hence, the release of greenhouse gasses will increase climate change and temperature will reduce. Other important contributors to climate change are the emission of CO2 and the level of development. The result revealed that these had substantial positive impacts on climate change at a 5% level of significance in agreement with our expectations. 1 unit increase in CADIOX and RGDPpc results in the 2.471865 and 1.89616 unit increases in temperature in Nigeria respectively. This is in line with the findings of Kusumawardani and Dewi (2020) and Hundie (2021). between temperature square (TSQ) and income (GINI). This tends to confirm the existence of the non-linear relationship between climate change and income inequality which showed a U-shaped relationship. Climate change is found to substantially impact GINI in Nigeria at a 5% level of significance. One unit increase in T initially reduces GINI by 1279 units and later increases inequality by 23 units. This outcome conforms with the studies of Alam et al. (2017), Dasgupta et al. (2020), and Sam et al. (2021). In line with expectations, poverty was found to have a positive substantial impact on GINI. A 1% increment in poverty leads to a 37% increase in income inequality. As revealed by Ogbeide- Osaretin et al. (2016), poverty widens the income inequality gap. As the poor do not often have access to quality and higher levels of education which will create room for employment or increase their income-earning ability. The cycle continues, and the inequality gap widens unless it is broken by effective government policies such as increasing the welfare of the poor (increased access to education and health). However, contrary to expectation, the unemployment rate (UNMPR) was found to have a negative relationship with GINI which was however not significant. The results revealed that an increase in unemployment reduced income inequality. Nevertheless, the unemployment rate in Nigeria is more under-employment, and in most cases, the recorded data often underestimates the unemployment rate in Nigeria. Figure 2: Trends of inequality and poverty Source: Authors’ chart Table 4: Dynamic ordinary least square estimation of the income inequality model Dependent variable=Income inequality Method=DOLS Diagnostics: R2=0.675073 Independent variable Coefficient t-sat Probability T −1279.193 −2.519587 0.0220* TSQ 23.50179 2.504725 0.0227* POV 0.377314 4.543038 0.0003* UNMPR −0.318333 −1.556236 0.1381 POPG 19.06347 1.020289 0.3219 C 17380.76 2.525623 0.0218 *Source: Authors’ computation, **Significant at 5% and 10% level respectively. DOLS: Dynamic ordinary least square, TSQ: Temperature square, T: Temperature a measure of climate change, POV: National poverty level captured by headcount, UNMPR: Unemployment rate, POPG: Population growth rate Table 5: Dynamic ordinary least square estimation of climate change model Dependent variable=Income inequality Method=DOLS Diagnostics: R2=0.850496 Independent variable Coefficient t-sat Probability GINI 0.093341 2.829523 0.0142* POV −0.008594 −0.955033 0.3570 POPG 2.889689 2.201680 0.0464* UNMPR −0.052672 −2.155823 0.0504* CADIOX 2.471865 3.672075 0.0028* LOG (RGDPpc) 1.896167 3.417267 0.0046* C 16.07390 5.325664 0.0001 *Source: Author’s computation, **Significant at 5% and 10% level respectively. DOLS: Dynamic ordinary least square, GINI: Gini Index a measure of income inequality, POV: National poverty level captured by headcount, UNMPR: Unemployment rate, POPG: Population growth rate, RGDPpc: Real Gross Domestic product per capita, CADIOX: Consumption of coal in a thousand short tons Ogbeide-Osaretin, et al.: Climate Change, Poverty and Income Inequality Linkage: Empirical Evidence from Nigeria International Journal of Energy Economics and Policy | Vol 12 • Issue 5 • 2022 339 5. POLICY RECOMMENDATIONS AND CONCLUSION 5.1. Policy Implications The connectivity between climate change and income inequality was examined to determine if there is a feedback relationship between them. Time series annual data was employed where climate change was measured by temperature and income inequality by GINI. Based on the empirical estimates, the following policy colloraries were drawn and recommendations made: 1. Temperature was found to have a negative substantial impact on GINI while temperature square had a positive substantial impact on GINI. This implication of the above is that at the initial level of temperature, income inequality falls as everyone tends to be on the same level with the effect of temperature as a result of climate change. However, as temperature increases with the increases in climate change, the poor not being able to afford means of reducing the effect and are exposed more to climate change, and their sources of income are also affected thereby increasing the income inequality gap. This study thus advocates for control measures for reducing climate change such as reduction of greenhouse gas emissions and putting in place emission fees. 2. Income inequality was also found to have a positive significant impact on climate change. This reveals that the increase in income gap will lead to an increase in activities that are harmful to the environment thereby increasing climate change. Therefore, we advocate for the reduction of income inequality through a transfer of income from the rich to the poor is effective in reducing energy inequality. Also, there is the need to, provide access to commercialized energy to households, increase access to education by the low-income group, and the availability of efficient energy infrastructures to reduce income inequality which will lead to effective climate change adaptation. 3. Poverty was found to have a positive substantial impact on income inequality. Thus, as poverty increases, the gap between the poor and the rich increases. We, thus, counsel for the reduction in poverty through the provision of employment, and an increase in access to education and health. 4. As divulged by the result, population growth negatively and significantly impacts climate change. Hence, we recommend the zealous pursuit of a population growth reduction policy. This can be done by employing practically fertility reduction and birth control. 5. The emission of carbon dioxide substantially impacts climate change. As the emission of CO2 increases, the rate of climate change increases which is often seen with the increase in temperature and rainfall. We, therefore, advocate for the use of efficient sources and modern energy. This will help to mitigate climate change and hence. 6. Development captured by real GDP per capita was revealed to have a positive substantial impact on climate change. As the quest for development increases, industrialization and household usage of energy increase which is a significant contributor to climate change. Hence, this current study counsels that policy measures for modern sources of energy should be pursued. 5.2. Conclusion Climate change and income inequality are current priorities for the achievement of sustainable development. While there is a current pursuit of development by developing countries, which have increased economic growth and national income through advancements in technology, the increase in income has not been evenly distributed. Therefore, the objective of this study is to investigate the interaction between climate change and income inequality. The upshot of the result revealed that there is a significant feedback impact between climate change and income inequality in Nigeria. The impact of climate change on income inequality shows a U-shaped hypothesis. Other contributors to climate change were population growth, economic development, and the emission of carbon dioxide. Effective population control and reduction of income inequality through the provision of employment and education are pertinently recommended. Also, efficient and modern energy uses in the purse of development are strongly recommended to reduce climate change and reduction of income inequality. 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Ogbeide-Osaretin, et al.: Climate Change, Poverty and Income Inequality Linkage: Empirical Evidence from Nigeria International Journal of Energy Economics and Policy | Vol 12 • Issue 5 • 2022 341 APPENDIX Table A1: Autoregressive distributed lag model bounds test for income inequality equation ARDL bounds test Date: 04/29/22 Time: 01:14 Sample: 1981 2020 Included observations: 40 Null Hypothesis: No long-run relationships exist Test statistic Value k F-statistic 2.717687 5 Critical value bounds Significance (%) I0 Bound I1 Bound 10 2.26 3.35 5 2.62 3.79 2.5 2.96 4.18 1 3.41 4.68 ARDL: Autoregressive distributed lag model Table A2: Autoregressive distributed lag model bounds test for climate change equation ARDL bounds test Date: 04/29/22 Time: 01:03 Sample: 1981 2020 Included observations: 40 Null hypothesis: No long-run relationships exist Test statistic Value k F-statistic 2.661207 6 Critical value bounds Significance (%) I0 bound I1 bound 10 2.12 3.23 5 2.45 3.61 2.5 2.75 3.99 1 3.15 4.43 ARDL: Autoregressive distributed lag model