Urbanization, Democracy, Bureaucratic Quality, and Environmental Degradation

Abstract

The study examines the relationship between urbanization and environment degradation while

controlling for political environment in 38 African countries over the period 1970-2011. Using

panel cointegration and causality analyses; the findings of the study show that urbanization,

environmental degradation and political economy variables (democracy and bureaucratic quality)

are cointegrated. Second, democracy and bureaucratic quality are effective in reducing

environmental degradation in the long-run. Third, there are positive bi-directional relationships

between CO2 emissions and Affluence and CO2 emissions and population as shown by panel

vector autoregressive and impulse response functions. However, a negative unidirectional

relationship runs from CO2 to bureaucratic quality. These results suggest that political economy

variables (democracy and bureaucratic quality) are important in explaining the relationship

between urbanization and environmental degradation.

Keywords: Urbanization, Environmental degradation, CO2 emissions, Democracy, Bureaucratic quality, Panel Cointegration

1

1.0 Introduction

The rate of urbanization has increased rapidly around the world and it has become one of the

most prominent features of economic development in the twenty first century. Urbanization

shifts production activities formerly undertaken in the home with little or no energy to outside

producers who do use energy (Jones 1989). In 2014, more than 54 percent of the world’s

population was urbanized and is expected to increase to 66% by 2050, compared with the 1950

rate of 30 percent (United Nations Department of Economic and Social Affairs [UNDESA]

2014). In 2007, for the first time, the global urban population exceeded rural population and this

has continued to date. Urbanization in Africa is increasing at a very fast rate though it is the least

urbanized by 2014 at 40% compared to the most urbanized region (North America) at 82%. By

2050, however, this figure is expected to reach 56%, which represents an annual growth rate of

1.1, surpassed only by that of the Asian region of 1.5%, which far exceeds the developed world

urbanization rate of only 0.4%. It can therefore be said that urbanization is a major demographic

trend in the world especially in Asia and Africa as it relates to its effect on energy transition,

environment and consequently overall development (Ghosh and Kanjilal 2014). It is important to

note that in 2014, however, there were six countries in SSA which had urbanization levels of

20%, including Burundi, Ethiopia, Malawi, Niger, South Sudan and Uganda.

The rapid rate of urbanization is attributed mainly to the movement of people from the

rural areas to urban centers to seek jobs in both the formal and informal sectors and a better

standard of living (Todaro 1997). With all the challenges associated with urbanization, the

United Nations Fund Population Agency [UNFPA] (2007) report suggests that no country in the

modern age has achieved sustained economic growth without contemporaneous urbanization. On

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the one hand, rapid urbanization has been shown to promote the formation of new cities,

infrastructural growth, poverty reduction, health services, and quality migration if well planned

(UNDESA 2014). On the other hand, it is usually associated with increased manufacturing and

economic activity resulting in high energy demand and consumption which accelerate the

emission of carbon dioxide the main cause of climate change (Zhao and Wang 2015; Shahbaz et

al. 2014; Sadorsky 2014a).

The adverse effect of urbanization on climate change is more severe on human health,

livelihoods, and agriculture especially in the tropics because of the lax environmental regulations

(Intergovernmental Panel on Climate Change [IPCC] 2001; Temurshoev 2006; Kurane 2010;

Dhillon and von Wuehlisch 2013). No wonder, Goldstone (2010) describes urbanization as a

demographic ‘megatrend’ that will have major social, economic and political impact. According

to the United Nations Environment Programme [UNEP] (2012) report, urban areas, which

currently occupy around three per cent of the world’s surface area, were estimated to consume

approximately 75 per cent of the natural resources and account for 60-80 per cent of all

greenhouse gas emissions. It is not surprising therefore that when the world came together in

2014 at UNEP’s headquarters in Nairobi, it added fresh impetus to efforts to chart a global

course forward: one that recognizes environmental sustainability as a fundamental element of the

post-2015 sustainable development agenda (UNEP 2015). This shows how environmental

sustainability issues have become important both locally and globally in the daily lives of

ordinary people (Elliott et al. 2015).

Reducing the intensity of energy use in developed and developing countries is considered

an important element in the world’s ability to grow sustainably. Likewise, reducing energy

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intensity is considered a practical solution to many of today’s common challenges including

global energy shortages; mitigating against further changes in the climate; and health impacts of

local air and water pollution. Understanding the factors that influence fluctuations in energy

intensity are of first-order importance for academics and policymakers, given the rise of rapidly

growing populations and energy demand (Elliott et al. 2015).

Consequently, many studies have been conducted with varying results based on different

estimation techniques, country (ies), and time periods. For example, Zhang and Lin (2012)

demonstrate a positive effect of urbanization on energy consumption and carbon dioxide

emissions while Al-Mulalli et al. (2012) find no relationship between urbanization, energy

consumption and carbon emissions, and Li and Lin (2015) and Poumanyvong and Kaneko

(2010) show that the relationship is determined by the level of development.

Many of these studies however, ignore the political economy dynamics in the

urbanization – carbon emissions relationship though some studies have looked at the direct

relationship between democracy and environmental quality (Torras and Boyce 1998; Deacon

1999; McGuire and Olson 1996). Raleigh and Urdal (2007), for instance, declare that political

factors, particularly regime type matters in determining environmental outcomes. Previous

research has shown that the inconsistent results also reflect different influences of urbanization

and industrialization on energy consumption/emissions at different development stages (Li and

Lin 2015; Poumanyvong and Kaneko 2010). Our focus on SSA with similar sociocultural and

economic conditions therefore helps to reduce or eliminate any inconsistencies attributable to the

level of economic development.

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We contribute to the literature in three main ways. First, we investigate whether the

democratization process in the region is having any impact or moderating role in the

urbanization, growth – carbon dioxide emissions relationship. This is a region that has undergone

massive political reforms of their economies and therefore it is appropriate to examine how this

is impacting the urbanization, growth, emissions relationship. Dryzek (1987), for example, aver

that political structure is a critical factor in dealing with the environmental degradation problem.

Raleigh and Urdal (2007) proclaim that democratic countries are more capable both to adapt to

urban problems and mitigate conflict. Second, there is an ongoing debate that suggests that

democracy by itself is not enough to ensure economic growth or a reduction of environmental

degradation (Torras and Boyce 1998). Some authors reject the democracy – dictatorship

dichotomy and suggest that market-oriented democracy and autocracy cannot solve

environmental issues in a satisfactory manner (Pellegrini and Gerlagh 2006). Barnett (2003), for

example, claims that rapid urban growth is likely to be a greater challenge to states that have low

functional capacity. Critical in this regard, such states may be unable to provide basic services to

a burgeoning population. In support of this view, Buhaug and Urdal (2013) indicate that the

negative effects of urbanization are pronounced in the contexts of economic shocks, low state

capacity, and absence of democracy. Accordingly, we examine how the interaction of democracy

and bureaucratic capacity affects the urbanization and environmental degradation link. Thirdly,

we employ robust panel data techniques to identify the long-run relationship and causal dynamics

in the urbanization, growth, and carbon dioxide relationship for 38 African countries over the

period 1970-2011.

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2.0 Literature Review

Urbanization is a key demographic indicator that basically increases urban density and in the

process transforms not just the physical space but also human behavior (Sadorsky 2014a). Many

theoretical perspectives are used to explain the urbanization-environment link, but the three most

popular are the urban transition, ecological modernization and the compact theories (Kasarda and

Crenshaw 1991; McGranahan et al. 2001; Poumanyvong and Kaneko 2010). The urban

transition theory, which is associated with McGranahan et al. (2001) builds on research claiming

an association between urban environmental burdens and growing affluence. In the process of

wealth accumulation environmental challenges become more dispersed, delayed and shift in

type. Marcotullio and Lee (2003) note that for low income cities environmental challenges

associated with urbanization are localized, immediate and health threatening, while for wealthy

cities environmental burdens are global, delayed (intergenerational) and ecosystem threatening.

The authors, however, observe that these tendencies are predispositions rather than

predetermined outcomes. Thus, as cities become more urbanized and industrial activity increases

environmental degradation could occur because of the increased energy use and emissions.

However, this negative effects could be reduced or eliminated by putting in place the appropriate

environmental regulations and technological innovations that are energy efficient. Accordingly,

the net effect of urbanization cannot be determined apriori (Sadorsky 2014a). Overall, the

importance of the “urban environmental transition” theory is at least threefold. First, it defines a

relationship between development (wealth) and the urban environment (in the fullest meaning).

Second, it points out that cities undergo a series of environmental challenges (which shift in

focus of impact and timing), some of which are missing in the global “sustainable development”

6

agenda (McGranahan et al. 1996). Third, the theory places the scale of environmental impact at

center stage of the policy engagement.

The ecological modernization theory states that at low stages of development societies

give priority to economic growth over environmental sustainability. As the societies become

more affluent they become more concerned with environmental damage and try to find out ways

to reduce environmental degradation. As a result, transformation within an economy and society

takes place through technological innovation, urbanization, and move from secondary sector to

tertiary sector (see, for instance, Crenshaw and Jenkins 1996; Gouldson and Murphy 1997; Mol

and Spaargaren 2000; Ahmed and Long 2013; Ahmed 2014). The globalization forces driving

the urbanization process also has the potential to bring in investment and knowhow to enhance

the productivity of local firms. Apparently, by bringing in new knowledge and investments in

physical infrastructure like roads and factories, foreign investors may help to reduce what Romer

(1993) referred to as “ideas and object gaps”. Dependency theorists, however, criticize the

modernization theory on the basis that an economy dominated by external agents will grow in a

disarticulated manner or does not allow for organic growth (Bornschier and Chase-Dunn 1985;

Ajayi 2006).

The main principle of the compact city theory is high-density development close to or

within the city core with a mixture of housing, workplaces and shops. The concentration of

production and consumption in a relatively small geographical area should provide opportunities

for economies of scale that can improve overall energy consumption (Elliott et al. 2015). Under

this theory, development of residential housing areas on (or beyond) the urban fringe, and single-

family housing in particular, are banned. Furthermore, central, high-density development

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supports a number of other attributes that are favorable to sustainable energy use: low energy use

for housing and everyday travel, efficient remote heating systems, low carbon emissions,

proximity to a variety of workplaces and public and private services, as well as a highly

developed public transport system (Holden and Norland 2005). The supporters of the compact

city theory (for example, Jacobs 1961; Newman and Kenworthy 1989; Commission of the

European Communities [CEC] 1990; Elkin et al. 1991; Sherlock 1991; Enwicht 1992; McLaren

1992) believe that the compact city has environmental and energy advantages, as well as social

benefits, including a better environment, affordable public transport, the potential for improving

the social mix and a higher quality of life. However, the supporters of the dispersed city argue

against the compact city because of its adverse effects on environmental quality (Næss 1997).

The compact city theory rejects suburban and semi-rural living, neglects rural communities,

affords less green and open space, increases congestion and segregation, reduces environmental

quality and lessens the power for making local decisions (Frey 1999; Holden and Norland 2005).

The literature reviewed shows that the theoretical discussions do not fully settle the issue of

whether urbanization results in lower or higher economic growth, energy consumption and

carbon dioxide emissions.

The empirical literature has also not given consistent results about the effects of

urbanization. For example, Jiang and Lin (2012) show that trends in industrialization and

urbanization in China are expected to increase China’s energy demand, which is predicted to

keep rising until 2020. In a study of developing countries for the 1967-1985, Parikh and Shukla

(1995) find that urbanization is more significantly related to carbon dioxide than GDP per capita.

A provincial level panel estimation using data from 1995 and 2010 by Zhang and Lin (2012)

show that urbanization has a positive effect on both energy consumption and CO2 emissions. 8

Sadorsky (2014a) examines the case for emerging economies and report that while

industrialization has a positive impact on energy consumption, urbanization has a negative effect

on energy consumption. In a cross country analysis, Liddle (2014) find that urban density has

negative relationship with carbon emissions. In an earlier study, Liddle (2004) reports that

densely populated countries tend to have a lower personal vehicle demand which implies less

transport related energy use per capita. Elliott et al. (2015) investigate the urbanization and

energy intensity relationship of 29 Provinces of China for the period 1997- 2010 and demonstrate

that the results are sensitive to economic modeling. In contrast to earlier studies, the AMG

results indicate that urbanization appears to have little or no short or long run impact on energy

intensity.

On the other hand, Al-Mulali et al. (2013) studied the urbanization - carbon dioxide

emissions link for MENA countries over the period 1980-2009 and report that there is a long run

bi-directional positive relationship between urbanization, energy consumption, and CO2

emission. However, the significance of the long run relationship between urbanization, energy

consumption, and CO2 emission varied across the countries based on their level of development.

Consistent with these findings, Poumanyvong and Kaneko (2010) employed regression based on

the STIRPAT model for 99 countries over the period 1975-2005 to show that the impact of

urbanization on energy use and emissions varies across the stages of development. The results of

the study show that urbanization decreased energy use in the low-income group, while it

increases energy use in the middle- and high-income groups. The impact of urbanization on

emissions is positive for all the income groups, but it is more pronounced in the middle-income

group than in the other income groups.

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Additionally, Li and Lin (2015) find that urbanization decreases energy consumption in

low income countries and increases carbon dioxide emissions, while it decreases both energy

consumption and carbon dioxide emissions in middle and high income countries. However, Al-

Mulali et al. (2012) examine the case for seven different regions and report that there is no

relationship between urbanization, energy consumption and carbon emissions in low income

countries. A different result is obtained by Shahbaz et al. (2016) who investigate the relationship

between urbanization and carbon dioxide emissions for Malaysia over the period 1970Q1-

2011Q4 and find that the relationship is U-shaped i.e. urbanization initially reduces CO2

emissions, but after a threshold level, it increases CO2 emissions. The causality analysis suggests

that the urbanization Granger causes CO2 emissions.

In a related study, Al-Mulali and Ozturk (2015) used FMOLS to show that energy

consumption, urbanization, trade openness and industrial development increase environmental

damage while the political stability lessens it in the long run for MENA countries over the period

1996-2012. Similarly, Khanna et al. (2013) examine the local enforcement of two of China’s

recent energy efficiency policies based on household appliances across several pilot locations

between 2006 and 2009. They generally find high compliance but with a large variation.

Insufficient organizational coordination between government agencies and the low priority given

to energy efficiency in national quality testing are the main challenges for the implementation of

such policies.

Per the inconsistencies in the results of the various studies on the urbanization and

carbon emissions nexus, we contribute to the discussion by controlling for political economy

dynamics to show that politics and for that matter the role of government or the bureaucracy

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matters in explaining the urbanization and environmental degradation relationship. This is so

especially in the context of SSA, where many analysts show the net political benefits define

policies. The methodology is described next.

3.0 Methodology

Following Sadorsky (2014b), Martinez-Zarzoso and Maruotti (2011) and Liddle and Lung

(2010), we employ the Stochastic Impacts by Regression on Population, Affluence and

Technology (STIRPAT) framework in analyzing the relationship between environmental

degradation and urbanization. Fundamentally, the STIRPAT model relates environmental impact

to population, affluence and technology taking into consideration the stochastic process; it does

not assume a rigid structure to regression coefficients making it a desired model for hypothesis

testing. To account for urbanization and political economy variables in our model we augment

the STIPAT model as follows;

(1)

where , , , , and denote impact, population, affluence, technology,

urbanization and political economy variables respectively; , , and represent country,

time period, country specific effects and error term correspondingly whereas , , , and

denote their respective coefficients.

To estimate equation (1) the study uses a three-step approach which involves panel

unit root test to determine the order of integration among the variables. Second, panel

cointegration techniques (Pedroni 1999) are used to determine the long-run relationship among

the variables. Third, a dynamic ordinary least square is estimated to obtain the long-run 11

estimates. Finally, GMM dynamic panel vector autoregressive model, variance decompositions

(VD) and impulse response functions (IRFs) are used to determine the causal dynamics and the

reaction of the variables to changes of another variable.

3.1. Panel unit root tests

To examine the unit root in our panel data, we first employ the panel unit root test of Levin et al.

(LLC) (2002), Im et al. (IPS) (2003) and Hadri (2000). The LLC test, the most widely used is

based on the following ADF-type equation:

∆ x¿=z¿γ i+ ρ x¿−1+∑j=1

li

φij ∆ x¿−1+ε¿ , i=1,2 ,…,N , t=1,2 ,…,T (2)

Where k is the lag length, z¿ is a vector of deterministic terms and γ¿ is the corresponding vector

of coefficients. As seen in equation (2) the LLC test accounts for heterogeneity of autoregressive

coefficients which makes it preferred than earlier test developed by Breitung (2000) and Levin

and Lin (1993). Thus, the first-order autoregressive parameters are the same for all countries (i.e.

ρi=ρ ). The null hypothesis is that time series have unit root ( H 0 : ρ=0 ) and the alternative

hypothesis implies that no series contains a unit root, that is, all are (trend) stationary.

Conventionally, the t-statistic for the autoregressive coefficient ρ has a standard normal limiting

distribution if the underlying model does not include individual time trends (z¿) and fixed

effects. The IPS test extends the LLC test and also allows for heterogeneity in ρ under the

alternate hypothesis, however, tends to have low power in panels with small T. Contrary to the

LLC and IPS, Hadri (2000) is a residual-based Lagrange multiplier (LM) with the null

hypothesis that all the series in the panel are stationary (i.e. have no unit root) which performs

well in panels with small T. Our preference to panel unit root test (LLC, IPS and Hadri) root tests

12

as opposed to traditional unit root tests (DF, ADF, PP) is to increase the power of the test

through available information provided by cross-sections.

3.2. Cointegration tests on panel data

We test for cointegration using Pedroni’s (1999) Engle-Granger approach, which is based on

seven different statistics under a null of no cointegration in a heterogeneous panel. These test

statistics are characterized into panel based (within dimension) and group based cointegration

(between dimension) tests; panel-v, panel-rho, group-rho, panel-pp (non-parametric), group-pp

(non-parametric), panel-adf (parametric t), and group-adf (parametric t) normalized to be

distributed under N (0, 1). All of the statistics diverge to negative infinity as the p-value

converges to 0 with the exception of panel v-statistic which is a one-sided test and rejects the

null hypothesis of no cointegration for large positive numbers. The following equation represents

the Pedroni’s cointegration test:

log Y ¿=ni+δi t+bln∑i=1

n

X ¿−1+ε¿ ……………………… (5)

Where ni and δ i are effects of country and time fixed effects. The estimated residuals are

represented in the following equation.

ε ¿=ρiε ¿−1+μ¿………………………………….(6)

Although, Pedroni’s (1999) tests allow us to check the presence of cointegration between the

variables in the study, it does not provide us with long-run coefficient estimates. Thus, the

subsequent section employs Dynamic OLS to estimate long-run coefficients.

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3.3 The Dynamic OLS (DOLS) estimator

The long-run effect of urbanization-CO2 emissions nexus is calculated using Pedroni's group

mean Panel Dynamic OLS (DOLS) as mentioned earlier. This involves estimating separate

DOLS for each country and averaging the individual coefficients, b̂=N−1∑i=1

N i

b̂i ,; the

corresponding t-statistic is calculated as t b̂=N−1∑i=1

N i

t b̂ i ,/√ N . The DOLS regression in our case is

given by

log Y ¿=a i+blog( X¿)+∑j=1

li

φ ij log ( X ¿¿¿−1)+ε ¿…………………………(7)¿

Where φ ij are coefficients of current lead and lag differences which account for possible serial

correlation and endogeneity of the regressors, thus resulting in unbiased estimates.

3.4 Panel Vector Autoregressive Model (PVAR)

Cointegration implies the presence of long run relationship between time series, but does not

indicate the direction of causality. As a result, our final step involves the use of panel vector

autoregressive models (PVAR) in a generalized method of moments (GMM), variance

decompositions and impulse response functions to investigate the causal dynamics between the

variables in the study. PVAR models combine the traditional VAR approach for time series but

with panel data approach allowing for country specific effects or unobserved individual

heterogeneity. We specify our econometric model as follows:

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y¿=u0+B1 y¿−1+…+Bk y¿−1+α i+γt +μ¿

i=1 , …, N ;t=1 ,…,T

where y¿ is a matrix of variables of interest B j ' s are coefficients; α i denote unobserved country

effects; γt denote time effect; μ¿ is the idiosyncratic errors.

Series of econometric issues arise when estimating fixed effect panel models. For

instance, the presence of lagged dependent variables is likely to induce correlation between fixed

effects and regressors causing biasedness in regression estimates. A strategy implemented is the

use of forward mean-differencing to remove the mean of all the future observations available for

each individual time period (i.e. fixed effects). This transformation preserves the orthogonality

between mean-differenced variables and lagged regressors, with lagged regressors acting as

instruments for system GMM estimation. This procedure is achieved by using a (PVAR) in a

generalized method of moments (GMM) framework following Abrigo and Love (2015). Next,

the optimal number of lags to use in the PVAR model is determined to avoid specification

problem and satisfy moment condition. We employ moment and model selection criteria

(MMSC) for GMM models based on Hansen’s (1982) J statistic of over-identifying restrictions

proposed by Andrews and Lu (2001).

It is important to note that even though the GMM PVAR helps in investigating the causal

links it also enables us compute a panel VAR-Granger causality Wald test. The Wald test for the

joint significance is exploited to examine the direction of causal relationship among the

variables. Afterwards, impulse response functions are used to analyze the impact of changes in

one variable on another, they do not display the degree of importance of shocks on one variable

15

in explaining fluctuations in other variables. To account for the importance of changes in one

variable in explaining changes in other variables, a variance decomposition is performed.

3.5 Data

The data set constitutes an unbalanced panel of 38 African countries over the period of 1970-

2011. The definitions of the variables used in the empirical analysis are as follows; CO2

represents the natural logarithm of CO2 emissions (metric tons of carbon dioxide), Affluence

denote the logarithm of real per capita GDP (GDP per capita, in constant 2005 US dollars),

Technology is the natural logarithm of Share of industry the share in GDP (measured as industry

value added as a share of GDP), Urbanization is the natural logarithm of urbanization (measured

as a percent of the population of people residing in urban areas), political economy variables

made up of two democracy indicators namely; Democracy and Polity2. Democracy (Democ) and

Polity2 represent institutional democracy and revised polity score respectively. Institutional

democracy (Democ) is an additive eleven-point scale (0-1) derived from the coding of the

competitiveness of political participation, the openness and competitiveness of executive

recruitment and constraints on the chief executive. The higher the value of Democ the more

democratic a political system is, on the contrary, lower values indicate low democracy. On the

other hand, Polity2 varies from -10 to 10 depending on the autocratic or democratic nature of the

government. Negative scores are associated with autocracy, while a positive score indicates a

relatively democratic government which allows for fair elections and political freedoms for its

citizens. Bureaucratic quality1 (bur) captures the strength and quality of institutions. Strong

bureaucratic quality is evidenced in countries where revisions of policies and interruptions in

public services tend to be subtle when there is a change of government. Essentially, countries

1 Bureaucratic quality was only available for 30 countries from 1985 to 201116

that lack the “cushioning effect of strong bureaucracy” are assigned low scores, while countries

with strong bureaucracy and expertise to govern without radical changes in policy obtain high

score. It is scaled from 0 to 1. While the data for CO2, Affluence, Technology and Urbanization

were obtained from the World Development Indicators (WDI), political economy variables were

obtained from ICRG (bur) and polityIV database2 (democ, polity2).

Table 1 shows the annual average growth rate of the variables. The annual average

growth rate in CO2 emissions ranges from a high of 1.505 in Togo to a low of -3.116 in

Comoros. Algeria, Botswana, Cote D’Ivoire, Egypt, Gabon, Ghana, Madagascar, Mozambique,

Niger, Senegal, Sierra Leone, South Africa, Togo, Tunisia, Zambia, Mauritius and Zimbabwe

each have averages greater than the full sample’s average (-1.198). Senegal experienced the

highest average annual population growth of 18.262 followed by Gabon which recorded 17.891.

South Africa however, had the least population growth (11.537). Botswana, Cape Verde,

Cameroon, Chad, Comoros, Congo, Dem. Rep, Congo Rep, Cote D’Ivoire, Ghana, Kenya,

Madagascar, Morocco, Mozambique, South Africa, Swaziland, Tunisia, Zambia documented

averages below the full sample’s average (15.553). Ghana recorded the highest affluence

(growth) value (8.931) and Mali the least (5.459). The sample had an annual affluence (growth)

average of 6.623. Algeria, Botswana, Cameroon, Cote D’Ivoire, Niger, South Africa, Togo,

Tunisia and Zimbabwe, however, had annual averages greater than 7.0 over the entire period

(1970-2011). Equally, Ghana recorded highest on technology (4.052) and Comoros the least

(2.635). About half of the countries in the study recorded an annual average of technology above

3. Urbanization ranges from a low of 2.062 (Burundi) to a high of 4.158 (Ghana). Apart from

Burundi, Burkina Faso, Liberia, Mali, Mauritania, Rwanda, and Seychelles, each recorded

2 Available at: http://www.systemicpeace.org/inscrdata.html.17

Urbanization values less than 3. The sample had an annual average urbanization of 3.374. South

Africa experienced the highest polity (7.0) with negative polity values recorded for about half of

the sample. Similarly, annual averages for democracy range from a high of 7 (South Africa) to a

low -7.049 (Zambia). More than half of the countries in the sample had negative averages for

democracy. Cameroon had the highest bureaucratic quality (0.750) and Morocco the least

(0.000) from 1985 to 2011. The average bureaucratic quality was 0.366 with Botswana,

Cameroon, Gabon, Ghana, Guinea-Bissau, Liberia, Togo and Tunisia documenting averages

more than 5.0.

[Table 1 here]

Correlations are presented in Table 2. CO2 is highly correlated with Affluence (0.912),

followed by Urbanization (0.700), Technology (0.632) and Bureaucratic quality (0.499).

However, CO2 has low correlations with Polity2 (0.028), Democracy (0.028) and Population

(0.171). This indicates a plausible association among the variables in the study.

[Table 2 here]

3.6 Empirical results

3.6.1 Panel unit root results

The unit root tests depicted in both levels and first differences are depicted in Table 3. The null

hypothesis of existence of a unit root cannot be rejected for LLC (in all instances) and IPS (all

but Population, Urbanization and Democracy). However, Hadri rejects the null hypothesis of

existence of unit root for all the series at 1% level for the series in level form. Under the first

difference condition, all the three tests (LLC, IPS and Hadri) reject the null hypothesis at the 1%

18

level of significance. As a result, there is strong evidence that all the series are integrated with

order one.

[Table 3 here]

3.6.2 Panel cointegration results

The relationships between the variables are investigated using a Pedroni cointegration technique.

Each cointegration test is distinguished by the political economy variables (Polity2 [1], demo [2]

and bur [3]) (Table 4). Majority of the test provide sufficient evidence of cointegration in the

panel data by rejecting the null hypothesis of no cointegration. Specifically, 6 out of 7 tests reject

the null hypothesis in [1] and [3] and all in [2].

[Table 4 here]

Table 5 reports DOLS estimates. Similarly, each DOLS estimates is distinguished by the

political economy variables (Polity2 [1], demo [2] and bur [3]). The coefficients of Affluence are

significant in all the three equations ([1], [2] and [3]). These results substantiate the short run

findings of Poumanyvong and Kaneko (2010), Sharma (2011), Leitao and Shahbaz (2013) and

Sadorsky (2014b). Precisely, affluence coefficient ranges between -12% and 2%. Consequently,

the negative coefficient of affluence (see [3]) is in line with Marcotullio and Lee’s (2003)

reasoning that negative effects of environmental degradation could be realized by appropriate

environmental regulations that are energy efficient. This suggests that the bureaucratic quality is

one of the most important channels for reducing environmental degradation in the long-run in

Africa. Whereas population coefficient is negative throughout, those of technology and

urbanization are mixed. The lack of access to energy by many countries in the region might

19

explain why the urbanization estimates are not robust. Also, the International Energy Agency

(IEA) report (2014) documents that more than 620 million people (two-thirds) in SSA remain

without access to electricity. The negative coefficients of democracy and bureaucratic quality

imply that democracy and bureaucratic quality tend to reduce environmental degradation in the

long-run.

[Table 5 here]

Afterwards, the causal link between variables is investigated in PVAR framework using

Granger causality test. Results are summarized in Table 6. The most striking results are; bi-

directional relationships between population and CO2 emissions, Affluence and CO2 emissions

and finally unidirectional causality from CO2 emissions to bureaucratic quality. The next step

involves assessing the strength and the impact of the causality using impulse response functions

(IRFs).

[Table 6 here]

Our results from the IRFs indicate that bidirectional relationships between population and CO2

emissions, Affluence and CO2 emissions are positive whereas the unidirectional causality from

CO2 emissions to bureaucratic quality after 10 years of initial shock [Figures 1-3]. Also,

controlling for democracy unravels a unidirectional causality from CO2 to urbanization at 1%

level of significance and a weak unidirectional causality from Democracy to CO2. Such results

lead us to investigate the importance of shocks (impulse) on one variable in explaining changes

in the other using variance decompositions (response). The 10-year horizon of Affluence remains

the one of the highest contributor to CO2 (24.91%), confirming a moderate causality from

energy and Affluence to CO2 in the long run. Additionally, the variance decomposition (VDs)

20

shows that CO2 explains approximately 54.25% of the variations in bureaucratic quality while

bureaucratic quality explains 1.41% of the variation in CO2 in the long run [Table 7 here]

This confirms very strong unidirectional causality from CO2 to bureaucratic quality. This means

environmental degradation give rise to effective policies in Africa.

4.0 Conclusion

The study examines the relationship between urbanization and environment degradation while

controlling for political environment in 38 African countries over the period 1970-2011. The

findings of the study show that environmental degradation, population, affluence, technology,

urbanization and political economy variables (democracy and bureaucratic quality) are

cointegrated. Second, democracy and bureaucratic quality are effective in reducing

environmental degradation in the long-run. Third, there are positive bi-directional relationships

between CO2 emissions and Affluence and CO2 and population. However, a negative

unidirectional relationship runs from CO2 to bureaucratic quality.

The findings provide three main policy implications. First, that urbanization as an

inevitable process has a significant impact on carbon emissions and therefore has to be managed.

With SSA’s urbanization rate at 40% and expected to increase to 60% by 2050 and population to

triple over the period (Freire et al. 2014), Africa does not have a choice but to put in the

necessary steps to reduce the dangers of environmental degradation. This is critical in light of the

fact that Africa’s economy is highly dependent on the primary sector and its abundant natural

resources, is particularly vulnerable to the effects of climate change. The big question for policy

makers is how to harness the positive effects of urbanization in terms of education, health,

manufacturing activity, and infrastructural development) while reducing its negative tendencies.

21

It is worth noting that with the high population growth and urbanization, the SSA region

recorded the lowest human development index (HDI) of 0.502 in 2013 (United Nations

Development Programme [UNDP] 2014).

Second, if indeed, urbanization is not just a subplot but the main policy narrative for SSA

(Freire et al. 2014), then it is indicative that the future of the region is dependent on not just

government policy but the capacity to implement the desired framework necessary for

sustainable development. Even as the countries have embarked on massive economic reforms

they must deepen the political reforms already underway and more importantly improve the

public administrative system to ensure the proper functioning of the bureaucracy to ensure

implementation success. This is consistent with the view that rapid urban growth is likely to be a

greater challenge to states that have low functional capacity because they will be unable to

provide basic services to a burgeoning population (Barnett 2003). A similar argument is made by

Parnell and Walawege (2011) who claim that environmental change is more likely to have

significant consequences for growing African cities with weak management capacity but fast

growing populations. The UN Habitat (2009) report also notes that weak urban management

structures and under capacitated local and regional states set up dynamic global environment

change urbanization dynamics across the developing world but more severe in Africa.

Third, is the problem of fossil fuels and traditional sources of energy other than

electricity which form the bulk of energy supply for many SSA and causes damage to the

environment. The SSA countries must therefore be proactive and invest more in less intensive

energy sources and improve electricity supply to reduce the emission of gases. The IEA (2014)

report notes that more than 620 million people live without electricity and over 730 million

22

people use hazardous, inefficient forms of cooking. The report further notes that SSA has 13% of

the world population, but only 4% of its energy demand. What is at the heart of the energy mix is

bioenergy use (mainly fuel wood and charcoal), which outweighs demand for all other forms of

energy combined. Four out of five people in sub-Saharan Africa rely on the traditional use of

solid biomass, mainly fuel wood, for cooking. No wonder it is described as the epicenter of the

global challenge to overcome the energy poverty. Castellano et al. (2015) have noted that if sub-

Saharan Africa aggressively promotes renewables, it could obtain a 27 percent reduction in CO2

emissions; this would result in a 35 percent higher installed capacity base and 31 percent higher

capital spending (or an additional $153 billion).

Finally, with its rich source of energy and yet low in demand, it is the argument of the

paper based on the review of literature and the findings of the study that urbanization could be a

vehicle to promote sustainable development if it is given the desired attention to harness its

positive effects while reducing the negative effects. This requires strong government support and

the political will to prioritize efforts, keep an eye on the long term, and focus on the regulations

and capabilities of its machinery to maximize the benefits of urbanization.

23

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27

Table 1: Annual average growth rate

Country CO2 Population Affluence Technology Urbanization Polity2 Democracy BureaucracyAlgeria 1.009 17.023 7.858 3.954 3.945 0.857 -4.976 0.442Benin -1.773 15.562 6.389 2.889 3.489 0.756 -0.366 NABotswana 0.014 14.317 7.663 3.789 3.376 6.805 6.805 0.586Burkina Faso -2.276 15.609 6.277 3.218 2.747 -0.195 -1.829 0.370Burundi -3.058 15.700 5.497 2.812 2.062 -7.195 -4.122 0.250Cape Verde -2.103 13.828 5.836 2.969 3.003 -5.778 1.500 NACameroon -1.127 14.770 7.193 3.108 3.717 4.000 -0.463 0.750Central African Republic -2.094 15.667 6.442 3.203 3.686 0.488 -5.537 0.375Chad -2.961 15.222 5.893 2.672 3.289 -14.098 -1.610 NAComoros -3.116 14.749 6.262 2.635 3.079 -1.806 -3.306 NACongo, Dem. Rep. -1.846 14.266 6.471 2.753 3.305 1.439 -0.537 NACongo, Rep. -2.221 16.900 6.045 3.302 3.576 -25.927 -2.512 0.110Cote d'Ivoire -0.824 14.959 7.457 3.826 3.948 -1.415 -4.951 0.250Egypt, Arab Rep. -0.709 16.489 6.969 3.082 3.740 -19.244 -4.537 0.396Gabon 0.389 17.891 6.817 3.477 3.767 -2.000 -5.475 0.500Ghana 1.228 13.775 8.931 4.052 4.158 -1.810 -5.929 0.555Guinea-Bissau -1.212 16.309 6.252 3.065 3.660 -1.610 -0.683 0.547Kenya -1.767 14.282 6.133 2.835 3.305 -2.216 -1.676 0.299Liberia -1.362 16.271 6.185 2.866 2.985 1.488 -3.366 0.597Madagascar -1.014 15.339 6.169 3.004 3.520 -11.098 -0.927 0.188Malawi -2.007 15.716 5.680 2.645 3.291 -5.732 -0.073 0.198Mali -2.369 16.147 5.459 2.923 2.716 2.512 -2.951 0.250Mauritania -2.802 16.059 5.555 2.764 2.805 2.902 -0.780 0.355Morocco -1.867 15.402 6.254 3.195 3.322 -0.585 0.341 0.000Mozambique -0.328 15.520 6.683 3.434 3.817 0.098 -6.122 NANiger -0.270 16.878 7.316 3.372 3.486 0.083 -6.778 0.521Nigeria -2.522 16.412 5.661 2.836 3.075 2.439 -1.195 0.331Rwanda -2.065 16.382 5.804 2.868 2.786 0.829 -0.927 0.393

28

Senegal -0.764 18.262 6.458 3.479 3.292 -1.780 -0.537 0.313Seychelles -2.697 15.752 5.575 2.806 2.227 0.000 -5.634 0.367Sierra Leone -0.829 15.837 6.587 3.125 3.633 3.214 0.333 NASouth Africa 0.919 11.537 8.820 2.946 3.874 7.000 7.000 NASwaziland -1.616 14.544 6.425 2.957 3.528 -10.222 -3.889 0.170Togo 1.505 16.911 7.987 3.388 3.873 2.800 5.800 0.622Tunisia 0.111 14.676 7.675 3.518 3.262 2.634 -4.000 0.500Zambia -1.139 14.581 6.685 3.363 3.191 -4.195 -7.049 0.250Mauritius -0.345 15.639 6.837 3.204 3.781 0.439 -5.439 0.194Zimbabwe 0.051 15.850 7.365 3.642 3.888 -5.878 -6.079 0.438Full Sample -1.198 15.553 6.623 3.147 3.374 -2.363 -2.397 0.366

NA-Data not available

29

Table 2: Correlation Matrix

CO2 PopulationAffluenc

e Technology UrbanizationPolity

2 Democracy BureaucracyCO2 1.000Population 0.171 1.000Affluence 0.912 -0.037 1.000Technology 0.632 0.092 0.711 1.000Urbanization 0.700 0.027 0.764 0.572 1.000Polity2 0.028 0.008 0.060 -0.103 0.103 1.000Democracy 0.135 -0.064 0.120 0.051 -0.040 0.084 1.000Bureaucracy 0.499 0.138 0.404 0.227 0.076 -0.116 0.207 1.000

30

Table 3: Panel unit root test

Deterministic Terms LLC statistics IPS HadriLevelsCO2 Constant, trend -1.105 0.804 8.298***Affluence Constant, trend 5.002 -0.670 10.234***Population Constant, trend 1.164 -3.363*** 11.270***Urbanization Constant, trend 29.569 -4.635*** 10.476***Polity2 Constant, trend 1.136 4.982 11.524***Democracy Constant, trend 69.087 -7.887*** -5.998***Bureaucracy Constant, trend 0.541 1.515 16.789First differences∆CO2 Constant -30.436*** -32.588*** 3.523***∆Affluence Constant -28.121*** -29.365*** 2.040***∆Population Constant -69.871*** -25.501*** 0.610∆Urbanization Constant 7.189 -22.893*** 1.344∆Polity2 Constant -28.199*** -29.218*** 2.299**∆Democracy Constant 97.646 -32.240*** 4.994***∆Bureaucracy Constant -9.509*** -9.237*** 1.108

*,**, and *** indicate significance at the 10%, 5% and 1% level respectively

Table 4: Pedroni (1999) panel cointegration test

Pedroni (1999) test[1]

Statisticsa[2]

Statisticsb[3]

Statisticsc

Panel testPanel v-statistics -4.134** -1.933** -1.387**Panel rho-statistics -0.620** -2.027** 0.935**Panel PP-statistcs -3.885*** -5.704*** -3.269***Panel ADF-statistcs -2.304*** -3.544*** -4.279***Group testGroup rho-statistic 1.366 -0.814** 2.605Group PP-statistics -6.605*** -7.270*** -3.231***Group ADF-statistics -3.593*** -5.973*** -3.837***

*,**, and *** indicate significance at the 10%, 5% and 1% level respectively; a-polity equation, b-democracy equation and c- bureaucratic quality equation

31

Table 5: Panel DOLS estimates

Dependent Variable: CO2

[1] [2] [3]Affluence 1.666 1.550 -11.666

(0.300)*** (0.391)*** (1.069)***Population -5.724 -1.788 -3.469

(1.084)*** (1.004)* (11.166)Technology 0.627 -0.446 -0.833

(0.240)*** (0.354) (0.530)Urbanization 5.074 -0.601 6.072

(1.562)*** (1.525) (21.060)Polity2 -0.012

(0.019)Democracy -0.098

(0.044)**Bureaucracy -3.202

(1.039)***N 1203 999 189

*,**, and *** indicate significance at the 10%, 5% and 1% level respectively

32

Table 6: GMM Panel VAR Estimates

pop→CO2 pop→CO2 Aff→CO2 CO2→Aff Tech→CO2 CO2→Tech Urb→CO2 CO2→Urb Demo→CO2 CO2→Demo[1] -0.403 0.013 0.473 0.012 0.156 -0.052 0.580 -0.004 0.005 -6.924χ2(1) [8.800]*** [9.554]*** [6.563]*** [1.044] [1.482] [1.297] [15.520] [2.749]* [0.567] [0.217]

pop→CO2 pop→CO2 Aff→CO2 CO2→Aff Tech→CO2 CO2→Tech Urb→CO2 CO2→UrbPolity2→CO

2 CO2→Polity2[2] -0.081 0.009 0.224 0.024 0.095 -0.003 0.028 -0.018 0.003 -0.337χ2(1) [2.831]* [3.975]** [23.999]*** [3.916]** [3.324]* [0.012] [0.253] [26.079]*** [3.384]* [0.804]

pop→CO2 pop→CO2 Aff→CO2 CO2→Aff Tech→CO2 CO2→Tech Urb→CO2 CO2→Urb Bur→CO2 CO2→Bur[3] 0.004 0.015 0.130 0.033 0.047 -0.094 0.001 -0.005 0.076 0.129χ2(1) [0.009] [7.782]*** [4.455]** [3.342]* [0.850] [5.737]** [0.001] [1.187] [2.037] [23.766]***

Chi square statistic from Granger causality test in parenthesis. *,**, and *** indicate significance at the 10%,5% and 1% level respectively.

33

Table 7: Variance Decomposition Analysis (%)

Impulse

CO2Populatio

nAffluenc

eTechnolog

yUrba

nPolity

2Respons

eCO2

585.7

5 0.38 9.69 2.33 0.65 1.18

1066.6

0 8.64 24.91 3.53 1.55 2.55

CO2Populatio

nAffluenc

eTechnolog

yUrba

n demoCO2

594.1

1 0.01 3.50 1.668.95e

-6 0.72

1083.1

8 0.01 11.07 3.178.90e

-7 2.56

CO2Populatio

nAffluenc

eTechnolog

yUrba

n burCO2

597.2

4 0.10 1.80 0.351.06e

-7 0.52

1092.0

7 0.37 5.76 0.38 0.00 1.41

CO2Populatio

nAffluenc

eTechnolog

yUrba

n burbur

35.05 0.85 0.31 0.71 0.01 63.07

54.25 0.51 0.88 0.41 0.02 43.93

34

Figure 1: Impulse responses of Urbanization, Polity2 and CO2 emissions

0.51

1.52

-.10

.1

.2

-.20

.2

.4

.6

-.2

0

.2

-.3-.2-.1

0.1

-.6-.4-.2

0.2

-.015-.01

-.0050

.01.012.014.016

-.005

0

.005

-.01

-.005

0

-.0020

.002

.004

-.02-.015

-.01-.005

0

-.02-.01

0.01

-.006-.004-.002

0

0.05

.1.15

-.02-.01

0.01

0.005

.01.015

-.02-.01

0.01.02

0.01.02.03

-.01

-.005

0

-.01

0

.01

.02

.04

.06

0.005

.01.015

0.01.02.03.04

-.03-.02-.01

0

-.004-.002

0.002

-.015-.01

-.0050

.005

-.04-.03-.02-.01

0

.01.015

.02

.025

-.01-.005

0.005

.01

0.02.04.06

-.005

0

.005

0.02.04.06

0.02.04.06

-.01-.005

0.005

.01

0.05

.1.15

.2

0 5 10 0 5 10 0 5 10 0 5 10 0 5 10 0 5 10

pol ity2 : poli ty2

U rban : pol ity2

industry : poli ty2

gdppc : pol ity2

pop : pol ity2

CO2 : poli ty2

pol ity2 : Urban

Urban : U rban

industry : Urban

gdppc : U rban

pop : U rban

CO2 : Urban

pol ity2 : industry

Urban : industry

industry : industry

gdppc : industry

pop : industry

CO2 : industry

polity2 : gdppc

U rban : gdppc

industry : gdppc

gdppc : gdppc

pop : gdppc

CO2 : gdppc

pol ity2 : pop

U rban : pop

industry : pop

gdppc : pop

pop : pop

CO2 : pop

polity2 : CO2

U rban : CO2

industry : CO2

gdppc : CO2

pop : CO2

CO2 : CO2

95% CI Orthogonalized IRFstep

impulse : response

35

Figure 2: Impulse responses of Urbanization, Demo and CO2 emissions

-100

102030

-4-202

-15-10

-505

-15-10

-505

-10-505

-20-10

010

-.003-.002-.001

0.001

.01.012.014.016

-.0020

.002

.004

.006

-.004-.002

0.002

-.002

0

.002

-.01

-.005

0

-.020

.02

.04

-.0050

.005.01

.015

0.05

.1.15

-.020

.02

.04

-.01-.005

0.005

.01

-.05

0

.05

0.01.02.03

-.01

-.005

0

-.03-.02-.01

0.01

0.02.04.06

-.0050

.005.01

-.02-.01

0.01.02

0.002.004.006.008

0.002.004.006.008

0.005

.01.015

-.0050

.005.01

.005.01

.015.02

-.0050

.005.01

.015

-.050

.05.1

0.01.02.03.04

-.050

.05.1

0.05

.1.15

-.03-.02-.01

0

-.10

.1

.2

0 5 10 0 5 10 0 5 10 0 5 10 0 5 10 0 5 10

democ : democ

Urban : democ

industry : democ

gdppc : democ

pop : democ

CO2 : democ

democ : Urban

Urban : U rban

industry : Urban

gdppc : U rban

pop : Urban

CO2 : Urban

democ : industry

Urban : industry

industry : industry

gdppc : industry

pop : industry

CO2 : industry

democ : gdppc

Urban : gdppc

industry : gdppc

gdppc : gdppc

pop : gdppc

CO2 : gdppc

democ : pop

Urban : pop

industry : pop

gdppc : pop

pop : pop

CO2 : pop

democ : CO2

Urban : CO2

industry : CO2

gdppc : CO2

pop : CO2

CO2 : CO2

95% CI Orthogonalized IRFstep

impulse : response

36

Figure 3: Impulse responses of Urbanization, bureaucratic quality and CO2 emissions

0.02.04.06

-.005

0

.005

-.02-.01

0.01.02

-.020

.02

.04

-.0050

.005.01

.015

0

.05

.1

-.004-.002

0.002.004

.005

.01

.015

-.0020

.002

.004

.006

-.0020

.002

.004

.006

-.0020

.002

.004

-.01-.005

0.005

-.010

.01

.02

-.01

-.005

0

0.05

.1.15

-.03-.02-.01

0.01

-.03-.02-.01

0.01

-.04-.02

0.02

-.02-.01

0.01

0.002.004.006.008

-.03-.02-.01

0.01

.03

.04

.05

.06

.005.01

.015.02

0.02.04.06

-.006-.004-.002

0.002

0

.005

-.01

-.005

0

-.01-.005

0

.005

.005.01

.015.02

-.0050

.005.01

.015

-.010

.01

.02

.03

-.0020

.002

.004

-.020

.02

.04

0.02.04

.06

0.005

.01.015

0.05

.1.15

0 5 10 0 5 10 0 5 10 0 5 10 0 5 10 0 5 10

bureau_icrg : bureau_icrg

Urban : bureau_icrg

industry : bureau_ic rg

gdppc : bureau_icrg

pop : bureau_icrg

CO2 : bureau_ic rg

bureau_icrg : Urban

Urban : U rban

industry : Urban

gdppc : U rban

pop : U rban

CO2 : Urban

bureau_icrg : industry

Urban : indust ry

industry : industry

gdppc : industry

pop : indust ry

CO2 : industry

bureau_icrg : gdppc

Urban : gdppc

industry : gdppc

gdppc : gdppc

pop : gdppc

CO2 : gdppc

bureau_icrg : pop

Urban : pop

industry : pop

gdppc : pop

pop : pop

CO2 : pop

bureau_icrg : CO2

Urban : CO2

industry : CO2

gdppc : CO2

pop : CO2

CO2 : CO2

95% CI Orthogonalized IRFstep

impulse : response

37

38