Effect of climate change and global warming on longevity: Empirical evidence from Nigeria

The effects of climate change and global warming are having a significant impact on the environment, cultures, and economies all across the world. These issues are now among the most pressing of our time. This study investigates the impact of climate change and global warming on longevity in Nigeria from 1970 to 2022. The data was sourced from the World Development Indicators (WDI) and the Autoregressive Distributed Lags (ARDL) estimation method was used. The results show that all independent variables significantly reduce longevity in Nigeria, consistent with previous studies. The Wald test and p-value were used to evaluate the five hypotheses and find to reduce longevity in Nigeria significantly. If policymakers intervene by 1% in the independent variables, they can improve longevity by 23.3% in one year. This suggests that policymakers must consider future climate change and global warming patterns in Nigeria to develop longevity policies that ensure a sustainable long life for citizens.


Concept of Life Expectancy
A key indicator of population health is life expectancy since it measures mortality throughout a population's life.Life expectancy in a pre-modern, underdeveloped world was about 30 years.Life expectancy has risen quickly since the Age of Enlightenment, which has resulted in significant health disparities.The average life expectancy in the world has more than doubled since 1900 and is now around 70 years.The Central African Republic has the lowest life expectancy at 53 years, while Japan has a 30-year life expectancy (Roser et al., 2013).Nevertheless, there is still a significant discrepancy between and within nations.Life expectancy nevertheless offers a good indicator of average life durations.

Concept of Climate Change
The average temperature of the Earth's atmosphere, oceans, and land masses consistently rising above the ambient temperature is referred to as global climate change (Climate Change).According to some scientists, the planet's present rapid warming is being caused by an increase in greenhouse gases, or "heat-trapping gases".This is brought on by an excessive buildup of carbon dioxide (CO2) in the atmosphere of the Earth, which serves as a blanket to trap heat and warm the globe.Burning fossil fuels, destroying forests, as well as specific agriculture and waste management techniques, are the causes of this.The mechanism through which greenhouse gases retain radiant energy (heat) that is delivered from the Sun to the Earth, warming the atmosphere, is known as the greenhouse effect (Eneji, 2017).

A. Greenhouse Gas Emissions
Greenhouse gases, which trap heat in the Earth's atmosphere and cause global warming, are the main causes of climate change.
These gases are produced by a variety of human endeavors and have a major effect on the planet's climate system.
1. Carbon Dioxide (CO2) from Burning Fossil Fuels: Another strong greenhouse gas is methane, which can trap heat for longer periods than CO2 does.The digestion of animals (enteric fermentation), the management of manure, Climate Change, and Global Warming 5, and the decomposition of organic waste in landfills are some of the sources that emit it.For short-term climatic effects to be reduced, methane emissions must be addressed.
2. Methane (CH4) from Livestock and Landfills: Another potent greenhouse gas is methane, which can trap more heat over a shorter period than CO2 does.It is released by several processes, such as manure management, livestock digestion (enteric fermentation), Climate Change and Global Warming 5, and the breakdown of organic waste in landfills.The reduction of methane emissions is essential for reducing the effects of the climate now.
3. Nitrous Oxide (N2O) from Agricultural Practices: Nitrous oxide (N2O) is released into the atmosphere as a result of agricultural activities, particularly the use of synthetic fertilizers and specific land management techniques.An important greenhouse gas that weakens the ozone layer and contributes to global warming is nitrous oxide.Optimizing fertilizer use, enhancing soil management, and implementing more environmentally friendly agriculture methods are all strategies to lower N2O emissions.

B. Deforestation and Land-Use Changes
1. Reduced Carbon Sequestration Capacity: Deforestation occurs when forests are cleared for activities like logging, agriculture, and urban development.Forests are essential for carbon sequestration because they take CO2 from the air and store it in the soil and trees.The ability of the Earth to absorb CO2 is reduced by deforestation, worsening the greenhouse effect.
2. Altered Regional Climates: Natural weather patterns can be disturbed by extensive deforestation and changes in land use, which can shift regional temperatures.Climate control, atmospheric circulation, and precipitation patterns are all impacted locally and globally by forests.When forests are lost, weather patterns may change, influencing where rain falls and causing droughts or floods in some areas.

C. Industrial Processes and Other Anthropogenic Activities
Cement production, mining, and other industrial processes all produce greenhouse gases and other pollutants.These emissions help to worsen air quality and contribute to global warming.Other anthropogenic influences, such as the release of fluorinated gases used in refrigeration and electronics manufacture, such as hydrofluorocarbons and perfluorocarbons, also have a significant warming potential.In conclusion, human activities that emit greenhouse gases and affect land use are the primary causes of climate change.To create successful measures to mitigate climate change and create sustainable solutions for a world that is changing quickly, it is crucial to understand these causal elements.

Theoretical Framework
Three theoretical perspectives, namely the Gaia theory, the theory of metabolism, and the life history theory served as the foundation for our research on the connection between climate change and life expectancy (longevity).

The Gaia Theory
According to James Lovelock's 1960s Gaia hypothesis of metabolism, living things interact with their surroundings and adapt to shifts in the natural world.The capacity of an organism to adapt to environmental changes is essential to its survival.A change in the composition of the atmosphere caused by excessive greenhouse gas emissions from agriculture and forest removal may have negative effects on human health.Some contend that higher carbon emissions improve longevity because they increase life expectancy in nations with high carbon dioxide emissions.Additionally, a higher air CO2 concentration might result in healthier food production, enhancing longevity and life quality.

Theory of Metabolism
According to the principle of metabolism, every living thing relies heavily on its ability to burn food per unit of body weight each day, which has an impact on how healthy an organism is.An organism's metabolic rate and body temperature are correlated, and organisms that dwell in cold environments typically have lower metabolic rates than warm-blooded species.Even though body size affects the metabolic rate, warm-blooded animals that are exposed to cooler temperatures tend to speed up their metallic process to maintain a regular body temperature.This hypothesis holds that every temperature increase, whether brought on by humans or by natural forces, tends to speed up chemical reactions.This theory's justification is that the lower the metabolic rate, the higher the life expectancy or longevity.

The Life History
Theory According to the life history theory, which was created in the 1950s, an organism's variations in the course of its life are significantly influenced by its ecological and physical surroundings.According to this idea, an unstable environment has an impact on an organism's ability to reproduce and its population dynamics.Even if the environment in this place is related to resource availability, risks, and rivals, the climate has an impact on each of the aspects.For instance, a bad climate might influence the resources available in a place, which can make it more difficult for the organisms living there to get food.Survival of the fittest results from competition for limited resources, and those who lack the strength to win a significant share may not live to their full potential.emissions have a negative and significant impact on life expectancy, but that, depending on the source of the emissions, CO2

Empirical Review
emissions from petroleum have a positive impact on life expectancy while those from natural gas and coal have a negative impact.
The results are consistent with a 2007 study by Deschenes and Greenstone that found that, given current greenhouse gas emissions, the death rate in America will increase by the end of the twenty-first century.
However, some research indicates that CO2 emissions may lengthen life.They are (Monsef and Mehrjardi, 2015;Delavari et al., 2008).Monsef and Mehrjardi, (2015) found that life expectancy rises when CO2 emission rises in panel research of 136 nations.
Although CO2 emission was not considerable, its positive impact on life expectancy implies that it is not hazardous to human health.This is in line with research by Delavari et al., (2008) in Iran, which found that CO2 emissions have a small but positive impact on life expectancy there.On the flip side of climate change, the impact of global warming and the rise in the average temperature of the environment on human health has been studied.According to Wuebbles and Edmonds, (1988), the earth's surface warms up more when it is exposed to the sun's direct radiation, and the temperature rise has both immediate and long-term consequences on people's health.Extreme heat and stress are exacerbated by high temperatures, which can be problematic for those who have respiratory health issues.
For instance, a study conducted in Mozambique revealed a correlation between an increase in the average environmental temperature and a rise in the frequency of stroke cases (Gomes et al., 2015).Agwu and Okhimamhe, (2009) found that an increase in the average environmental temperature caused changes in Nigerians' health patterns.Agwu and Okhimamhe, (2009) found that residents of Zumba and Augie communities in Niger and Kebbi state in the north, as well as Enugwu Nanka and Akama Amankwo Ngwo communities in Anambra and Enugu, had higher rates of asthma, hypertension, ulcer, malaria, diarrhea, diabetes, and typhoid.These findings came from a cross-section study of communities in North-Central and South-Eastern Nigeria.Similarly, to this, WHO, (2015) found that Nigeria's average temperature will rise by 4.9°C between 1990 and 2100, which will likely 1 lead to an increase in diarrhea-related diseases.
Additionally, studies by Davies et al., (2004)  According to the research, life expectancy grows as the global mean temperature rises.In a parallel cross-country study in Europe, Bardi and Perini, (2013) likewise found that life expectancy is rising in the countries they looked at, despite the continent's rising temperature.However, their research showed that when temperatures rise, healthy life expectancy declines.The indicator of a person's change in health condition is their healthy life expectancy.The indicator of a person's change in health condition is their healthy life expectancy.The occurrence or absence of chronic diseases and the length of a disease determine changes in people's health status.In Bolivia, Winters, (2012) found that poor people's quality of life had decreased similarly as a result of climate change and bad weather.
After reviewing pertinent literature, the current paper can contribute to our understanding of climate change and public health, particularly in Nigeria.First, public health and industrial pollution were the subjects of earlier research in Nigeria.Since Nigeria is not an industrialized nation, just a small portion of the nation's CO2 emissions come from the industrial sector (World Bank, 2016).
In 2008, the industrial sector's CO2 emissions accounted for less than 1% of all greenhouse gas emissions.Second, the current study examined more variables that have an impact on life expectancy.Thirdly, and most importantly, the results varied, which is a caution that using the wrong determining variables and data might lead to misleading results in an empirical study.

METHODOLOGY
This study intends to shed light on how global warming and climate change impact life expectancy in Nigeria.The ex-post factor analysis is looking at the years 1991 through 2021.The data's source also included the World Development Indicators (WDI).The impact of climate change and global warming longevity in Nigeria was estimated using the Autoregressive Distributed Lags (ARDL) estimation method.Carbon dioxide (C02) emissions (metric tons per person) serve as a proxy for global warming and the environment, whereas temperature and rainfall do so for climate change.The addition of unemployment (UN) and the infant mortality rate as control variables was also supported by the theoretical premise that a person with a job may live longer than one without one, while a lower baby mortality rate increases longevity.

The Model
Variables considered are: Life expectancy rate (LE) This can be specifically expressed in explicit econometric (linear equation) form as: Where U -stochastic or random error term (with usual properties of zero mean and non-serial correlation).

Apriori Expectation
The a priori expectations are that all the βis, δis, and φis < 0. That is, lagged values of explanatory variables in the short-run as well as estimated values of the same variables in the long-run are expected to have negative effects on IFMR.Thus, the expected signs of the coefficients of the explanatory variables are β1< 0, β2< 0, β3< 0, β4< 0, and β5< 0; this implies that all the explanatory variables are expected to have negative effects on the infant mortality rate.This study provides empirical evidence on how climate change and global warming explain the longevity in Nigeria.

Estimation Techniques
The relationship between the variables in our model will be examined for analysis using the autoregressive distributed lag (ARDL) technique.If any of the explanatory factors and the dependent variable are non-stationary, estimating equation 1 by the ordinary least squares (OLS) method may produce erroneous findings and inferences.To identify the features of the data, i.e., whether it is stationary and the order of integration, the Augmented Dickey-Fuller unit root test is employed to ascertain whether the variables have unit roots.Next, the Autoregressive Distributed Lag (ARDL) method created by used to determine whether or not the variables in the equation have a long-term relationship.It primarily serves to determine whether the independent variables can accurately forecast the dependent variable in the short-and long-term.Short-run equilibrium may not happen even though the regression model's variables may have a long-run equilibrium relationship.
The error correction mechanism (ECM), which is used to correct or eliminate the disparity that happens in the short-run, is used to simulate the short-run dynamic adjustment.The percentage of the disagreement between the variables that can be removed in the following period is given by the coefficient of the error correction variable.It blends short-run dynamics with long-run equilibrium relationships between the variables while simultaneously adjusting for short-term disequilibrium.This methodology is used because it provides richness, flexibility, and versatility to econometric modeling.This makes it easier to anticipate with accuracy how the variables' economic linkages will play out.This methodology is employed due to its adaptability and capacity to handle variables with various degrees of stationarity, such as I (0) and I (1), as well as to enable forecasting, mean and median lag analysis, multiplier analysis, policy analysis, and multiplier analysis.

Descriptive Analysis
The summary statistics and correlation are labeled Tables 2 and 3 for descriptive statistics and correlation coefficient respectively.Skewness is a measure of the probability distribution of a real-valued random variable about its mean.
A normal distribution is symmetrical at point 0. If the value is greater than zero it is positively skewed but if it is less than zero, it is negatively skewed.From Table 2, it is observed that all the variables have positive skewness.Kurtosis measures the peakness or flatness of the distribution of the series.If the kurtosis is above 3, the distribution is peaked or leptokurtic relative to the normal and if the kurtosis is less than three, the distribution is flat or platykurtic relative to normal.From Table 2, all the variables are less than 3. Therefore, they are not leptokurtic relative to normal distribution.Jarque-bera is a test statistic to test for the normal distribution of the series.From Table 2, the Jarque-bera for LE, C02, RN, TP, UN, and IM are 0.34 years, 1.41 metric tons per capita, 4.06 mm, 1.650C, 5.90%, and 0.90 per 1,000 live births respectively.The probability value of the Jarque bera statistic of all the variables was found to be more than 5% level of significance which implies acceptance of the null hypothesis which states that the residual of the variables is normally distributed with zero means and constant variance.

The Correlation Result
The correlation result is shown in (Table 3).The correlation coefficients in Table 3 above show that the independent variable is not highly correlated with each other.This will ease the problem of serial correlation.However, it is observed that rainfall (RN) is highly and positively correlated with infant mortality (IM) while temperature (TP) is highly and negatively correlated with infant mortality (IM).In addition, rainfall (RN) is highly and negatively correlated with unemployment (UN).A high correlation between the independent variable with the dependent variable is good but between independent variables is bad (Gujarati).Nevertheless, with ARDL, the issue of serial correlation will be automatically corrected as per and therefore is expected not to distort the model during estimation.

The Graphs
Before formal pretest (unit root tests), the study plots the time series of the variables under study as it may help reveal the integrating nature of the variables life expectancy (LE), temperature (TP), rainfall (RN), carbon dioxide (C02) emissions (metric tons per capita), unemployment (UN) and infant mortality (IM) rate are examined graphically as depicted below in Figure 1 which shows clear trend spanned 1970 to 2022.

Unit Root Test
The unit root results presented in Table 4 is the augmented dickey fuller test (ADF) which was chosen because it is widely used and its output is said to be robust.The stationarity of the variables is concluded based on the outcome of both ADF at constant only or constant and trend techniques.The results show that all the variables are stationary at either level or first difference.The summary result is posted in Table 4, therefore, based on the result of this stationarity test, the adoption of the ARDL technique could be given a pass since none of the variables is stationary beyond order one.

RESULTS AND DISCUSSION
Error Correction Regression (Short-Run) in percentages just as they appear since the variables are either in percentages, index, or logged.In the short run, all the estimated coefficients are significant at a 5% level of significance and while some are also negatively significant, others are positively significant.The results also show that rainfall and temperature which both proxy climate change respectively reduce and increase longevity.In other words, a unit increase in rainfall reduces longevity by 0.14% while a unit increase in temperature reduces longevity by 0.05% in the first year in Nigeria.
However, rainfall increases longevity in a year after as a unit change in precipitation reduces longevity by 0.11%.Therefore, climate change is a good predictor of longevity in Nigeria.Similarly, results show that in the short run, unemployment reduces longevity both at levels a year and two years later.Therefore, unemployment is a good predictor of longevity in Nigeria.Similarly, the infant mortality rate significantly reduces longevity at levels and a year after, indicating that this variable is also a good predictor of longevity in Nigeria.In the same way, since the coefficient of Cointeq (-1) is negative (0.23), significant (P= 0.0000), and in between -1 and 0, it ensures that the equilibrium formed is converging and stable.It means that if policymakers intervene by 1% change in the independent variables (C02, RN, TP, UN, and IM), there is a likelihood of improved longevity (increase in life expectancy).
Twenty-three (23) percent of the policy target will be achieved in one year.This means that the policy lag of this model is 4.33 years (Policy lag = 1 / (conteq (-1), that is 1/0.23=4.329).Hence, policymakers must consider future patterns of climate change and global warming in Nigeria while devising longevity (life expectancy) policy.By so doing it will ensure the sustainable long life of the citizens in Nigeria.As pointed out above, the coefficient of the error correction variable (ECM (-1)) is, as expected, negatively signed, statistically significant at a 5 percent level and its absolute value lies between zero and unity.Consequently, it will act to correct any deviations from long-run equilibrium.However, the size of the absolute value of the error-correction coefficient shows that the speed of restoration to equilibrium in the event of any temporary displacement is too slow (23%).

Long-Run Regression
The results in the long-run estimates in Table 6 are interpreted in percentages just as they appear since the variables are either in percentages, index, or logged.

Statistical Test of Hypotheses
This study is designed to answer how climate change and global warming composition explain the longevity opportunities in Nigeria.Thus, are hypotheses stated in null becomes.The Wald test and associated p-value were used to evaluate the five hypotheses put forth in this study.The probability value served as the basis for deciding whether to accept or reject the null hypothesis (PV).The regressor in question is implied to be statistically significant at the 5% level if the PV is less than 5% or 0.05 (i.e., PV 0.05); otherwise, it is not significant at that level.

Hypothesis One (H01)
Ho1: Precipitation/rainfall does not reduce longevity in Nigeria.The Wald-test in Table 7 showed that the estimated F-value for and was determined to be -0.68 and its probability value is 0.0402.We rejected the first null hypothesis (H01) and concluded that Precipitation/rainfall reduced longevity in Nigeria between 1970 and 2022 since the probability value was less than 0.05 or 5 percent threshold of significance, which fell in the acceptance region.

Hypothesis Two (H02)
Ho2: Temperature does not reduce longevity in Nigeria.The Wald-test in Table 8 showed that the estimated F-value for TP was determined to be 2.10 and its probability value is 0.0156.
We rejected the second null hypothesis (H02) and concluded that Temperature reduced longevity in Nigeria between 1970 and 2022 since the probability value was less than 0.05 or 5 percent threshold of significance, which fell in the acceptance region.

Hypothesis Two (H03)
Ho3: Environment (C02 emissions) does not reduce longevity in Nigeria.The Wald-test in Table 9 showed that the estimated F-value for C02 was determined to be 2.18 and its probability value is 0.0430.We rejected the third null hypothesis (H03) and concluded that environment (C02 emissions) did not reduce longevity in Nigeria between 1970 and 2022 since the probability value was less than 0.05 or 5 percent threshold of significance, which fell in the acceptance region.

Hypothesis Two (H04)
Ho4: Unemployment does not reduce longevity in Nigeria.The Wald-test in Table 10 showed that the estimated F-value for UN was determined to be 5.03 and its probability value is 0.0315.We rejected the fourth null hypothesis (H04) and concluded that unemployment reduced longevity in Nigeria between 1970 and 2022 since the probability value was less than 0.05 or 5 percent threshold of significance, which fell in the acceptance region.

Hypothesis Two (H05)
Ho5: Infant mortality does not reduce longevity in Nigeria The Wald-test in Table 11 showed that the estimated F-value for IM was determined to be 1.72 and its probability value is 0.0198.We rejected the second null hypothesis (H05) and concluded that infant mortality reduces longevity in Nigeria between 1970 and 2022 self since the probability value was less than 0.05 or 5 percent threshold of significance, which fell in the acceptance region.By examining the overall fit and significance of the model, the probability F-statistic value of 0.000000 is less than 0.05 (Table 12), by inference, it could be observed that the model is fit.There is no autocorrelation among the variables as captured by the Durbin Watson (DW) statistic of 2.050152, which is at the threshold of 2. It shows an unbiased estimate and the model could be used for policy decisions.The coefficient of determination (R-square), used to measure the goodness of fit of the estimated model (Table 12),

Model Evaluation
indicates that the model is excellently fit in prediction, that is, 0.898 or 90 percent change in longevity/life expectancy (LE) was due temperature (TP), rainfall (RN), carbon dioxide (C02) emissions (metric tons per capita), unemployment (UN) and infant mortality (IM) rate collectively, while about 10 percent unaccounted variations was captured by the white noise error term such as the influence of governance, environment and so on.It showed that the independent variables have a strong significant impact on longevity/life expectancy in Nigeria.Since the post regression tests in Table 13 are insignificant at a 5% level of significance there is no issue in the model.Figure 2  The probability value served as the basis for deciding whether to accept or reject the null hypothesis (PV).The regressor in question is implied to be statistically significant at the 5% level if the PV is less than 5% or 0.05 (i.e., PV< 0.05); otherwise, it is not significant at that level.The Wald test results show that all the independent variables significantly reduce longevity in Nigeria.In the same way, since the coefficient of Cointeq (-1) is negative (0.23), significant (P= 0.0000), and in between -1 and 0, it ensures that the equilibrium formed is converging and stable.It means that if policymakers intervene by 1% change in the independent variables (C02, RN, TP, UN, and IM), there is a likelihood of improved longevity (increase in life expectancy).Twenty-three (23) percent of the policy target will be achieved in one year.This nbmeans that the policy lag of this model is 4.33 years (Policy lag = 1 / (conteq (-1), that is 1/0.23=4.329).Hence, policymakers must consider future patterns of climate change and global warming in Nigeria while devising longevity (life expectancy) policy.By so doing it will ensure the sustainable long life of the citizens in Nigeria.
All data associated with this study are present in the paper.Selected Model: ARDL(2, 0, 2, 0,

Table 2
reveals the summary statistics of all the variables used in this research irrespective of the models they are included.Before going into the main regression analysis, it is important to show the relationship between longevity proxy by life expectancy (LE) and climate change proxy by temperature (TP) and rainfall (RN), global warming (environment) proxy by carbon dioxide (C02) emissions (metric tons per capita), unemployment (UN) and infant mortality (IM) rate.Mean is the average value of the series and from Table2, the mean for life expectancy (LE) is 47 years, 8 months, and 4 days approximately, while the mean for carbon dioxide (C02), rainfall (RN), and temperature (TP) is 0.63 metric tons per capita, 1324.41 mm, and 27.130C respectively.In addition, the mean for unemployment (UN) and infant mortality (IM) are respectively 5.50% and 110.79 per 1,000 live births.

Table 2
Summary StatisticsThe median is the middle value of the series when the values are arranged in an ascending or descending order.From Table2, the median for life expectancy (LE) is 46 years, 6 months, and 12 days approximately, while the median for carbon dioxide (C02), rainfall (RN), and temperature (TP) are 0.62 metric tons per capita, 1311.70 mm, and 27.130C respectively.In addition, the median for unemployment (UN) and infant mortality (IM) are respectively 5.94% and 119.9 per 1,000 live births.Maximum is the highest value of the series for the period under study.Table2indicates that the maximum value for LE is 55.44 years approximately, while the maximum values for C02, RN, and TP are 1.01 metric tons per capita, 1411mm, and 27.860C respectively.On the other hand, the minimum values for UN and IM are 7.9%, and 168.6 per 1,000 live births respectively.The minimum is the lowest value of the series for the period under study.Table 2 indicates that the minimum value for LE is 39.71 years approximately, while the minimum values for C02, RN, and TP are 0.33 metric tons per capita, 1272 mm, and 26.270C respectively.On the other hand, the minimum values for UN and IM are Source: Author's Compilations using Eviews 12 Edition (2023); (Ref: Annexure) Keynotes: LE= Life expectancy at birth, total (years), TP= Temperature TEMP= Temperature Increase (°C), RN = Precipitation/rainfall in mean annual (mm), C02= Carbon dioxide emissions (metric tons per capita), IM = Infant mortality rate, and UN = Unemployment (UN).1.6%,and56.68per 1,000 live births respectively.Standard Deviation is a measure of spread or dispersion in the series.From Table2the standard deviation for LE, C02, RN, TP, UN, and IM are 3.47 years, 0.179 metric tons per capita, 33.5 mm, 0.390C, 1.66%, and 26.91 per 1,000 live births respectively.This shows that climate change proxy by rainfall (RN) has the largest spread over the period under study while environment proxy by carbon dioxide emissions by metric ton per capita (C02) has a minimal spread over time.

Table 4
Unit Root Test

Table 5 ,
is the error correction model (ECM) (short-run) regression.The results in the short-run estimates in Tableare interpreted

Table 5
Error Correction Regression (Short-Run) Jerumeh et al., (2015)tation in Eviews 12 (2023); (Ref: Annexure)The findings show that in the short run, and a year after, global warming/environment proxy by carbon dioxide (C02) in metric tons per capita significantly reduced longevity proxy by life expectancy rate in Nigeria.In other words, a unit increase in global warming reduces longevity by 0.22% in Nigeria.Therefore, global warming is a good predictor of longevity in Nigeria.This finding is in agreement with Balan, (2016),Jerumeh et al., (2015), and Ali and Ahmad, (2014) who show that carbon dioxide (C02) hurts life expectancy (longevity).

Table 6
Long-Run EstimatesStill, in the long run, all the independent variables (C02, RN, TP, UN, and IM) significantly reduce longevity in Nigeria.Masozera et al., (2007), Miller et al., (2021), Orru et al., (2017), Wang et al., (2021), and Watts et al., (2015) who find adverse effects of climate change on longevity.The next section is the statistical test of the hypothesis.

Table 8
Results of Wald Test on Temperature in Nigeria

Table 9
Results of Wald Test on Environment (C02 emissions) in Nigeria

Table 11
Results of Wald Test on Infant Mortality in Nigeria

Table 12
also shows the ARDL bound test result, the F_PSS value is 3.680390, which is bigger than the 1%, 5%, and 10% upper bound values showing that there is cointegration among the proposed variables.

Table 12
Model Evaluation, ARDL Bound Test Result