Is renewable energy a model for powering Eastern African countries ...

Renewable and Sustainable Energy Reviews (xxxx) xxxx–xxxx

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Is renewable energy a model for powering Eastern African countries transition to industrialization and urbanization? ⁎

Presley K. Wesseh Jr., , , Boqiang Lin

Collaborative Innovation Center for Energy Economics and Energy Policy, China Institute for Studies in Energy Policy, School of Management, Xiamen University, Fujian, 361005, China

A R T I C L E I N F O

A BS T RAC T

Keywords: Renewable energy Nonrenewable energy Energy poverty Mitigation Economic growth Eastern Africa

This study profiles a conversation on the appropriateness of renewable energy as a model for powering development in East African countries. Estimated output elasticities of nonrenewable energy (0.29 – 0.48) are by far larger than those of renewable energy (−0.12 – 0.08); and as such, highlights the relative importance of the former. Also, the biased component of technical change shows higher technological progress for nonrenewable energy. Furthermore, substitution elasticities are positive (0.77 – 0.92); suggesting huge potential for a transition towards renewable energy. However, inherent limitations in renewables, documented in Wesseh and Lin **[52,53], undermine the usefulness of East African countries reliance on renewable energy.

1. Introduction According to the United Nations geographic scheme, the Eastern African region constitutes 20 territories. As is the case for most SubSaharan African countries, this region suffers one of the most devastating electricity gaps in the world thus threatening the region's industrialization and urbanization. In line with achieving the United Nations Millennium Development Goal (MDG) which calls for providing energy access to all, ensuring supply security and mitigating greenhouse gas emissions, policies limiting the use of fossil fuels are gradually gaining grounds in most African countries. In fact it is popularly argued that, with their low capital and adaptive capacity, African countries and developing economies in general would be more vulnerable to extreme weather and that climate change would negatively impact their energy systems. For instance, unlike richer nations, it would be difficult for these countries to pay for air conditioning, import food from far away regions, and build out of the rage of rising waters. To substantiate these claims, the African Development Bank (AfDB) has marked green growth as the top priority of its development strategy, i.e., between 2013 and 2020. Wind and solar which are among the fastest growing renewable energy technologies would be at the forefront of power generation. The point is climate change would still persist if African countries reduce emissions and other countries don’t. Furthermore, this study argues that developing countries would

be able to reduce the effects of climate change if accelerated wealth creation coming at the hands of increased investment and policy support for cheaper and more stable production recipe (like fossil energy) is allowed. Moreover, developing renewable energy would sound much reasonable if such opportunities are able to end the energy poverty stimulate economic growth and ensure environmental sustainability. In fact, it is now becoming a general consensus among policy makers that large-scale deployment of renewable energy technologies, due to recent technological advancement and cost reductions, provide a cost-effective recipe for sustainable growth in African countries.1Although more abundant solar and wind resources are located in the North and West and in the North and East respectively (Table 1), the present study further argues that reducing cost cannot increase the competitiveness of renewables, at least not anytime soon, since renewable technologies are still in their early stage of commercialization. Therefore, relying on review work of [54] and [55], the objectives of the present study will be to: (i) develop reliable estimates of the economic impact of both renewable and nonrenewable energy, classical factors and technological progress for a group of East African countries using country-level panel data. (ii) Estimate the efficiency with which energy and other production inputs are used. (iii) Produce estimates of the substitution potential between renewable and non-renewable energy. (iv) Discuss the possibilities of deploying renewable energy technologies to alleviate energy poverty, ensure supply security and

⁎ Corresponding author at: Collaborative Innovation Center for Energy Economics and Energy Policy, China Institute for Studies in Energy Policy, School of Management, Xiamen University, Fujian, 361005, China E-mail addresses: [email protected] (P.K. Wesseh), [email protected], [email protected] (B. Lin). 1 A more comprehensive discussion of these issues is left as valuable opportunity for future research and model improvement.

http://dx.doi.org/10.1016/j.rser.2016.11.071 Received 5 February 2015; Received in revised form 6 January 2016; Accepted 4 November 2016 1364-0321/ © 2016 Elsevier Ltd. All rights reserved.

Please cite this article as: wesseh, Jr., P.K., Renewable and Sustainable Energy Reviews (2016), http://dx.doi.org/10.1016/j.rser.2016.11.071


P.K. Wesseh, B. Lin

Cameroon; [25] on Angola; [27] on Algeria and Egypt and [30] on Algeria. In contrast, some studies on Africa have supported the growth hypothesis. This hypothesis asserts that energy consumption complements capital and labor as important factors in the production process. This means that energy is crucial for growth as the economy is energy dependent. As a result, energy conservation policies may have negative effects on real GDP. Studies in this category are: [10] on Tanzania; [11] on Nigeria; [12] on South Africa; [24] on Cameroon; [29] on South Africa and Kenya and [1] on South Africa. On the conservation hypothesis, [56] examined the causal relationship between the logarithm of per capita energy consumption (LPCEC) and the logarithm of per capita GDP (LPCGDP) during the period 1965–2008 using the threshold cointegration and Granger causality tests. Results from the study indicated that the LPCEC and LPCGDP for Algeria are non-cointegrated and that there is a unidirectional causality running from LPCGDP to LPCEC, but not vice versa. There also exist multi-country studies in which all four hypotheses including feedback hypothesis, growth hypothesis, conservation hypothesis and neutrality hypothesis are supported. These studies include: [5–8,13,14,16,18,19,22,23,26,28,31,32]. The next strand of literature we discuss is the one dealing with inter-fuel/inter-factor substitution possibilities in Africa. There are studies for European countries, Asian countries as well as North and South American countries. A comprehensive review of these studies is given in [38]. [33,34] also cite a number of studies. Surprisingly, very little research on energy and resource substitution possibilities have been conducted for Africa despite the general consensus that African countries need to reduce their consumption of environmentally harmful resources and switch towards cleaner fuels like renewable energy. Moreover, given AfDB ambitious targets for green growth, research along these lines is necessary to explore the possibilities of replacing fossil energy with cleaner energy. To the authors’ best knowledge; there are only three studies for Africa currently. [2] investigated the potential for inter-factor and inter-fuel substitution between capital, labor, petroleum and electricity in Liberia. The authors employed Ridge regression to estimate the translog production function and reached conclusions that all inputs considered are substitutes. Notwithstanding, the study pointed out that opportunities to substitute petroleum for electricity; or labor and capital for electricity are limited in practice because of Liberia's current low scale electricity generation. Following similar line of research, [57], employed Ridge regression to examined the possibilities of substituting between fuels and factors in Ghana. Similar to results obtained in [2], these authors found that all the input pairs are substitutes. In a more recent study, [39] developed a translog production function for a group of 24 African countries. The authors applied random effects estimation to the model and found that African output is driven by a more intensive use of petroleum and electricity and to a lesser extent capital, labor and coal; relative to technological progress. The study also documented that petroleum, coal and electricity are substitutes. However, the authors pointed out that the extent to which substituting coal (or petroleum) for electricity will be successful in mitigating greenhouse gas emissions will largely depend on the extent to which these fuels are used in generating electricity. A summary of these studies is presented in Table 3. As may be observed, the inherent shortcomings of the above two studies is that they focus exclusively on the substitution between fossil fuels and factors while neglecting the possibilities of substituting renewable energy for fossil fuels in Africa. The present study would therefore add value to the literature by attempting to fill this gap.

Table 1 Renewable energy potential across Africa. Source: AfDB (2012). Region

Wind (TWh/yr)

Solar (TWh/yr)

Biomass (EJ/yr)

Geothermal (TWh/yr)

Hydro (TWh/yr)

East

30,000

20–74

1–16

578

– –

1,057 78

3–101

–

26

West

0–7

– 50,000– 60,000 25,000– 30,000 50,000

49–86 8–15

South

2,000– 3,000 – 3,000– 4,000 16

2–96

–

105

Central North

mitigate greenhouse gas emissions in the East African region. (v) Advance relevant policy implication for East Africa's industrial transition. Indeed, the originality and scientific contribution of this study adds value to the literature. First, this study is the first of its kind approach to renewable energy – economic growth potential for the East African region. Second, substitution elasticities between renewable energy and non-renewable energy have never been estimated for East African countries before despite the significant implications these may have on the region's economic development [1,2]. Third, the applied methodology is novel in the energy consumption – economic growth literature; although the approach has been recently applied by very few studies2 to estimate energy substitution effects. For we use the translog production model, which in applied production analysis, is considered to be the most flexible functional form. Finally, this study brings innovation into the literature not only in terms of the kind of data used but also the method of calculating the difference in technological progress to determine which energy type, weather renewable energy or nonrenewable energy, would be more significant for powering the region's transition to industrialization and urbanization. The remainder of the paper proceeds as follows: Section 2 reviews the relevant literature for Africa. Section 3 presents the data and documents technical details of the applied methodology. Section 4 reports the estimated results. Section 5 discusses East Africa's clean development possibilities in the context of the empirical findings and Section 6 draws the conclusions advancing relevant policy suggestions.

2. Review of relevant studies for Africa This section reports two strands of the empirical literature. First, a review of studies which examine the causal links between energy consumption and economic activities in African countries are presented. Next, studies that have attempted to investigate energy substitution effects in Africa are reviewed. Different methods and proxy variables for energy consumption have been applied in the literature for African countries. It is not surprising that this literature also produces mixed results. A summary of these studies is reported in Table 2. For a comprehensive review of the energy consumption – economic growth nexus literature, interested readers are referred to [58] and [59]. There are a number of country-specific studies which provide support for the feedback hypothesis. In other words, these studies suggest bidirectional Granger causality between energy consumption and economic growth. The vast majority of these studies have employed cointegration and error correction techniques. These include: [3] on Nigeria and Tanzania; [4] on Malawi; [9] on South Africa; [15] on Burkina-Faso; [17] on Cote d′Ivoire; [20] on Liberia; [21] on

3. Data and methods To the best of the authors’ knowledge, only five studies have applied the translog production model to the investigation of problems in the energy economics literature namely: [2,33–37] 2

The applied dataset, model and methods of estimation form the domain of this section. 2


P.K. Wesseh, B. Lin

Table 2 Energy consumption and growth studies for Africa. Study

Country

Period

[3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [1] [32] [56]

Nigeria and Tanzania Malawi 19 African countries 17 African countries 11 African Countries 17 African countries South Africa Tanzania Nigeria South Africa

1960 1970 1971 1971

–1984 –1999 –2001 –2001

1971 1971 1980 1965

–2006 –2006 –2006 –2006

1971 1970 1980 1980 1975

12 African countries Cameroon Angola 23 African countries Algeria and Egypt ECOWAS countries South Africa and Kenya Algeria 6 emerging African economies South Africa 11 African countries Algeria

Granger causality Error Correction Toda and Yamamoto Toda and Yamamoto ARDL Toda and Yamamoto

Feedback hypothesis Feedback hypothesis All four hypothesis All four hypothesis All four hypothesis Growth hypothesis Feedback hypothesis Growth hypothesis Growth hypothesis Growth hypothesis

ARDL

– 2008 – 2006 –2008 –2008 –2008

Error Correction Panel Cointegration Panel Model Bootstrap tests Error Correction

1971 –2008 1971 1985 1965 1980

Conclusions

ARDL Cointegration ARDL

1968 –2003

Burkina Faso 20 African countries Cote d′Ivoire 21 African countries 30 African countries Liberia Cameroon

Method

Hidden cointegration Error Correction ARDL Panel causality

–2009 –2011 –2010 –2008

Panel Cointegration ARDL Error Correction ARDL Bootstrap tests Panel Error Correction Granger causality

1971 –2010 1971 –2010 1980 –2008 1965 –2008

Feedback hypothesis Growth hypothesis Feedback hypothesis Feedback hypothesis Growth hypothesis Feedback hypothesis Feedback hypothesis Conser. & growth Growth & Neutrality Feedback hypothesis Feedback hypothesis Feedback hypothesis Growth hypothesis Growth hypothesis Feedback hypothesis Growth hypothesis Growth hypothesis Growth hypothesis Conservation hypothesis

Table 3 Energy and resource substitution studies for Africa. Study

Country

Period

Method

Conclusions

[2]

Liberia

1980 –2010

Translog Production Model with time series Ridge regression estimations

[39]

24 African countries

1980 –2011

Translog production Model with panel data random effects estimations

[57]

Ghana

Translog production Model

*Output is driven mainly by labor and to a lesser extent by capital, petroleum and electricity *Capital, labor, petroleum and electricity are substitutes *No convergence in relative technological progress *Output is driven mainly by petroleum and electricity and to a lesser extent by capital, labor and coal *Increasing returns to scale *Petroleum, electricity and coal are Substitutes *Petroleum, electricity and coal are Substitutes

to examine convergence issues inherent in the literature while the choice of country allows for a wide geographical spread over the entire continent. Several transformations of the dataset were performed [20,40–43]. For instance, the point of approximation has been defined by normalizing all samples around the mean before taking logs. This was done by dividing all observations by their sample mean. The point of approximation simplifies a number of economic calculations because the translog production function is a Taylor series approximation. In our case, such a point would correspond to a hypothetical country whose level of technology, inputs and generic outputs represent those of the sample mean. If the data are not normalized around the sample mean, it casts doubts as to whether the production model would be able to satisfy all regularity conditions [44]. Output and capital stock have been calculated at constant prices (2005=100) in order to eliminate any impact of inflation. Data on output or real GDP in our case, gross capital formation (formerly gross domestic investment) and labor are published by the World Development Indicators (WDI). Labor in this study is calculated as a product of the active population and the employment to population

3.1. The data Variables used for analysis in this study include output, represented by real GDP; renewable energy, represented by total renewable electric power consumption; nonrenewable energy, represented by total nonrenewable electric power consumption (calculated as the difference between total electric power and total renewable electric power); labor; capital and the state of technological progress, represented by a time trend. Using electricity as a representation of energy is appropriate since, order than the transportation sector; it serves as the major fuel for households, industries, agriculture and service. Thus, power generation accounts for the vast majority of secondary energy. Electricity and real GDP data are available for almost all countries in the East African region, but long time series for labor and capital are available for a relatively small number of East African countries. Only countries that have data available for at least 5 observations for each variable are considered. The study collects data from 12 East African countries covering the period 1980–2011, resulting in an unbalanced panel database with a total of 384 observations. The selected time domain makes it possible 3


P.K. Wesseh, B. Lin

relied mainly on the translog cost function. While the costs approach might seem very appealing especially from a market perspective, these may require data on input prices which are largely unavailable for a number of East African countries [45–47]. Hence, this study employs a notable alternative i.e., the log linear translog production function, thus featuring a very important innovation. This approach has used in the energy and production economics literature to investigate efficiency and technological change, productivity growth, separability and aggregation as well as input substitution possibilities. By definition, the translog production model is a representation of a second-order differential approximation (Taylor approximation) at any given point. It represents a rather simple and locally flexible functional form that places little or no restrictions on the underlying production technology. In other words, this model imposes no requirements on the value of the function, whether on its first or second derivatives, at the approximation point. While global flexibility might be of greater preference, local approximation has dominated the applied production economics literature due to the limited information gap surrounding the properties of global approximation. Given that the marginal rate of technical substitution in African countries may be affected over time, it is assumed here that technical change is non-neutral and scale-augmenting. Hence, we specify the general functional form of the translog production function in the contest of panel data as:

ratio. Because data on capital stock are not observed directly, this indicator was also calculated at constant prices (2005=100) for each country by means of the perpetual inventory method. This method to inventory accounting creates the possibility of continuous adjustment wherein inflows and outflows are brought to a balanced. Another advantage of applying the perpetual inventory technique is simply that the approach does not require such strong assumptions as the proportionality method. The mathematical expression used in the calculation is given by

Kit = Kit −1(1 − δ ) + Iit

(1) th

where Kit indicates the current capital stock for the i country at time t for i=1, 2…12 and t=1.2…32; Kit−1 represents capital stock in the previous year; δ is the depreciation rate of capital stock and is assumed to be 5%. The depreciation rate adopted in this study is a reflection of the total wealth estimates documented by the World Bank [2];Iit symbolizes the current year's capital investment. The following equation has been used to obtain the initial capital stock:

K0 = I0 /(g + δ )

(2)

K0 from the expression above represents the initial capital stock,I0 is the initial capital investment and g gives a measure of the average growth rate of capital investment. Data on electricity consumption are collected from the United States Energy Information Administration (EIA). In order to set the stage for a comprehensive analysis, it is necessary to begin with the use of tables and charts. Descriptive statistics of the data are calculated and presented in Table 4. The results produce evidence that all sample means are different from 0. On the other hand, sample standard deviations lie in the range of 0.79 and 1.84. All variables are slightly skewed to the left. The value of kurtosis, 3 for normal distribution, coupled with the Jacque-Bera statistics suggests that distributions of the output and labor series have thicker tails than normal. The results in Table 4 also show that, on average, slightly more electricity is generated from renewable sources across East African countries relative to nonrenewable sources.

J

ln Yit = β0 +

j =1

+

Mean Std. Dev. Skewness Kurtosis Jarque-Bera

J

Variable REa

NREb

22.2 0.79 −0.15 2.31 8.938*

20.4 1.08 −0.05 2.85 0.544

15.9 1.07 −0.93 4.23 79.69*

6.77 1.63 −0.48 3.22 15.72*

5.54 1.84 −0.24 3.00 3.63

j =1 k =1

1 *2 γt 2 2

∑ αj ln xjitt*

J

∑ ηjit = ∑ Labor

J

(3)

In the expression above, i = 1, 2,…,12 represents the cross-sectional units; t = 1, 2,…,32 indicates the time periods; j , k = 1, 2,…,J are the applied inputs; ln Yit is the logarithm of the output associated with the ith country in time t ; ln xjit is the logarithm of the jth input associated with the ith country in time t ;t* is the time-trend representation of technical change; β , γ and α are parameters to be estimated. In particular, βij ’s are the input parameters that provide information about input substitutability; γ1 and γ2 are the pure or autonomous component of technical change. These parameters represent a neutral shift effect of the production technology that cannot be attributed to a given input, and αij ’s are the biased component of technical change representing technical innovation that is influenced by the efficiency in the use of various inputs. To make the estimation of Eq. (3) possible, a number of conditions will have to be satisfied. In the first place, symmetry has to be met. In other words, Young's theorem must be satisfied and this requires that βjk = βkj for all j , k . This implies that the production structure in Eq. (3) has 1 neutral-scale parameter ( β0 ), j + 2 first-order parameters ( βj , γ1, γ2 ) and (j + 1)(j /2) + j second-order parameters (βjk , αj ). At this junction, one must not forget that the translog specification is so flexible to the extent that no priori restrictions are placed on the value of output elasticities, returns to scale, elasticities of substitution and technical change. In the first place, the output elasticity of the jth input from Eq. (3) is given by:

Table 4 Summary statistics for all logarithmic transformed variables.

Capital

J

∑ ∑ βjk ln xjit ln xkit + γ1t* +

j =1

Researchers have committed substantial amount of efforts trying to specify general forms of the cost and production function. A vast majority of these different functional forms makes econometric estimation not just possible but as well incorporates economic effects. Furthermore, they demonstrate some level of consistency regarding properties surrounding the input requirement set. For the most part, the choice of any given functional form must be based on the purpose of the specific study in question. Notwithstanding, many studies in the literature have looked to basic theoretical properties and production technologies consistency as the basis for selection [39]. As we have already mentioned, existing studies that have applied panel data techniques to examine issues of energy and economy have

Output

1 2

J

3.2. The model

Statistic

∑ βj ln xjit +

j =1

j =1

∂ ln yit ∂ ln xjit

=

J

⎛

J

⎞

j =1

⎝

k =1

⎠

∑ ⎜⎜βj + ∑ βjk ln xkit + αjt*⎟⎟

(4)

Where ηjit are the output elasticities that vary with the levels of input and state of technology. As can be seen, these are simply the logarithmic marginal product of the translog function and measure the degree of responsiveness of output to a percentage change in the jth input. Economics of scale estimates from the translog function is given as the sum of the output elasticities. Next, the marginal elasticities of output make it possible to derive the substitution elasticities that vary with the quantity of inputs used. Where MP is the marginal product,

***Indicate significance at the 10% level. a Renewable electricity. b Nonrenewable electricity.

4


P.K. Wesseh, B. Lin

effects modeling techniques are preferable [48]. However, for nonexperimental data, nothing could ever be more misleading. According to [49], unobserved heterogeneity should be regarded as random variable. This therefore means that the distinguishing factor between fixed effects and random effects models concerns the nature of the relationship happening between the omitted variables and the observed variables. Unless these correlations are allowed for, one cannot control for the effects of omitted variables. This therefore makes the fixed effects methodology very attractive. At this junction, one has to be reminded that the interaction terms arising from our application of the translog model imply that there may be heterogeneity among countries, even in the instance of equal slope coefficients across countries. Moreover, it is likely that standard errors associated with estimates of the fixed effects model might be significantly greater than standard errors associated with random-effects estimates, which could lead to larger p-values and larger confidence intervals. This is due to the fact that estimates of the random effects model utilize both the within and between individuals information. On the other hand, fixed effects estimates utilize just the within-individual differences, and disregard all information concerning the differences existing between individuals. If, for each individual, explanatory variables vary across individuals with limited fluctuation across time, then estimates of the fixed effects model would be largely inaccurate. Rather than imposing uncommon coefficients from the outset, Eq. (7) was first estimated by OLS estimator which is equivalent to the fixed effects model. Next, the likelihood ratio test was used to check for common intercept and slope. However, the results of this analysis showed no evidence of fixed effects across countries. For this reason, the random effects model was also estimated using the standard GLS estimator.3 We then applied [50] tests to check which model was more suitable. The tests results, which are not reported for the sake of conserving space, suggested no evidence of correlation between the individual country effects and the other parameters in the model, implying that the random effects model is more appropriate. Hence, this study reports estimates of the random effects model. Subsequently, all calculations are based on these estimates.

the relevant symmetric substitution elasticity between inputs j and k for the ith country in time t is given as: −1 ⎡ −βjk + (ηjit / ηkit )βkk ⎤ (it ) (it ) ⎥ σjk(it ) = ⎢1 + ⎢⎣ ⎥⎦ −ηjit + ηkit

(5)

Inputs j and k are substitutes if σjk(it ) > 0; independent if σjk(it ) = 0 or compliments if σjk(it ) < 0 . Lastly, output elasticity with respect to the rate of technical change can be calculated as:

d ln yit dt*

J

= γ1 + γ2t* + dxj =0

∑ αj ln xjit j =1

(6)

Technical change in the above expression varies with the level of input and is both time and country specific. While this measure is usually nonnegative, negativity could arise in the instance where countries face tightened or new regulations. In the sense of Hicksian, technical change is input j using if αj > 0; neutral if αj = 0 or saving if αj < 0.[44] highlights that factor-augmenting technical change that has equal rates of augmentation is the same as Hicks-neutral technical change for constant returns to scale. Notwithstanding, if there are no constant returns to scale, then αj = 0 for all j is a necessary requirement for Hicks-neutral technical change. For simplicity and clarity of model, Eq. (3) is expanded in the form below:

ln Yit = β0 + βK ln Kit + βL ln Lit + βRE ln REit + βNRE ln NREit + β(RE )(K ) ln REit ln Kit +β(RE )(L ) ln REit ln Lit + β(RE )(NRE ) ln REit ln NREit + β(NRE )(K ) ln NREit ln Kit +β(NRE )(L ) ln NREit ln Lit + β(RE )(RE )(ln REit )2 + β(NRE )(NRE )(ln NREit )2 1

+γ1t* + 2 γ2t*2 + αK ln Kitt* + αL ln Litt* + αRE ln REitt* + αNRE ln NREitt* (7) In the above,K ,L ,RE and NRE are inputs of capital, labor, renewable energy and nonrenewable energy respectively. To reduce the number of parameters and facilitate estimation with random effects techniques (since number of cross section has to be greater than number of coefficients), the translog components for capital and labor and the output and substitution elasticities between these factors and energy inputs are excluded from the model [2,35].

4. Empirical results This section reports coefficient estimates of the translog production model including output elasticities and the rate of technical change and energy substitutability.

3.3. Estimation technique

4.1. Renewable energy, nonrenewable energy, technological progress and economic growth in Eastern African countries

Statistically controlling for unobserved variables remains one of the most difficult problems when drawing inferences from nonexperimental data. In the panel/cross sectional data literature, the most commonly estimated models have been fixed effects and random effects models. A class of estimators which have been developed to analyze dynamic panel data (DPD) models constitutes an important highlight of the technique of first differencing to remove unobserved heterogeneity. On the one hand, if the researcher is convicted of no omitted variables in the model, or perhaps omitted variables exist but are not correlated with the dependent variables, then a random effects model is probably best. This model will not only use all the available data, but it will also produce unbiased estimates of the coefficients and yield the smallest standard errors. However, there is still likelihood that at least some bias in the estimates will be generated since omitted variables are not controlled for. On the other hand, if there are omitted variables that are correlated with the variables in the model, then a fixed effects model would provide a way to control for omitted variable bias. Each individual in the fixed-effects model is used as its own control. The idea here is that, the omitted variables will have similar effects at a later time that they would have on an individual at one time; thus making their effects fixed. The literature on experimental research has suggested that random

Given the high number of estimated parameters in our translog formulation, we begin with an investigation of multicollinearity in the data using the approach of [51]. According to Kmenta, a simple measure of the degree of multicollinearity is obtained by regressing each of the independent variables on the remaining independent 2 variables. The calculated coefficients of determination (Rˆ ) from the regression can then be used as a measure of the degree of multi2 collinearity in the sample. In this study, the values obtained for Rˆ corresponded to 0.35, 0.44, 0.47 and 0.48 for capital, labor, renewable energy and nonrenewable energy respectively; suggesting that multicollinearity is not severe and consequently not a problem in the estimated model. The estimated results of the parameters of the translog production model in Eq. (7) are reported in Table 5. The results show that labor and nonrenewable energy have a significant positive effect on economic 3 Results from GLS estimator when compared with those of GMM estimator looked quite similar. These results are not presented in this paper to conserve space but are certainly available upon request from the authors.

5


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Nonrenewable energy

Fig. 1. Output elasticities of renewable and nonrenewable energy in East African countries (1980 – 2011).

individual countries and regions are presented. Consistent with results reported in Table 5, the estimated output elasticities in Fig. 1 indicate that changes in nonrenewable energy would by far stimulate the level of economic activities in Eastern African countries as compared to renewable energy with output elasticities ranging between 0.29 – 0.48 and −0.12 – 0.08 for nonrenewable energy and renewable energy respectively. Output elasticities of renewable energy are positive for Burundi, Ethiopia, Kenya, Malawi and Rwanda; and negative for Madagascar, Mozambique, Mauritius, Uganda, Zambia, Zimbabwe and the Eastern African region in general (Fig. 1). Obviously, the results here reinforce those in Table 5 and demonstrate that fossil energy is very crucial for East Africa's battle against energy poverty and the transition to industrialization and urbanization.

Indicates significance at the 10% level.

growth in the East African region while renewable energy (despite its higher concentration in East Africa's electricity mix as demonstrated in Table 4) and capital have insignificant negative and positive effects on economic activities respectively. Furthermore, parameters associated with the model squared terms reveal that investing in nonrenewable energy is more economically attractive than investing in renewable energy. This highlights the significant role of traditional fossil energy in Africa's fight against energy poverty relative to the renewable energy type that would rather serve as economic burden to East Africa's development (as indicated by the negative coefficient value). As will be discussed in the next section, renewables are relatively costly with lower capacity factors. Consequently, a large portion of the society's resources goes into generating renewable energy thus causing a strain on the entire economy which in turn leads to reduced output. Significance of the alpha parameters associated with the energy variables suggests that technological progress in East African countries is scaled biased or driven mainly by the efficiency with which various energy inputs are used rather than a neutral shift effect. In other words, real technological progress is lacking in these countries and that casts doubts on the sustainability of growth. Another issue to consider on sustainable growth in the East African region is the fact that commodity exports account for a large part of GDP thus making these economies vulnerable to fluctuating international demand. Hence, economically attractive fuels like coal and natural gas will provide opportunities for domestic production which will in term generate local demand and enhance a more sustainable growth. To further strengthen these findings and given that the translog production model does not have a direct interpretation of the elasticities of output, elasticity of substitution and the rate of technical change, marginal products of renewable energy, nonrenewable energy and technical change are computed at each data point.

4.3. Energy efficiency and technical progress in East Africa

0.0% -0.4% -0.8% -1.2% -1.6% -2.0%

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Output elasticities of renewable and nonrenewable energy give a measure of how output changes due to a unit change in renewable and nonrenewable energy. The measures here are both time- and countryspecific and useful for drawing inferences on resources allocation over time. To preserve space, however, only average values over time for

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4.2. Output elasticities of renewable and nonrenewable energy in East Africa

0.4%

Ke

Rate of technical change and energy efficiency (1980 - 2011)

Technical change as defined previously measures the effects of technology accumulation. The non-neutrality of technical progress in East African countries is further confirmed by the negativity of the autonomous component of overall technical change in Fig. 2. Plots of the estimated rates of technical change and energy efficiency as reported in Fig. 2 shows that overall technological innovation in East African countries is rather negative and averages around −2.2. While

hi

***

Renewable energy

Et

R2

iu

−0.001 0.003 0.003*** 0.002*** 0.97

qu

−2.28

αK αL αRE αNRE

bi

0.014

γ2

am

0.026***

γ1

oz

0.007

β(NRE )(NRE )

i

β(NRE )(L )

-.2

rit

0.001

au

0.005

β(NRE )(K )

M

β(RE )(RE )

-.1

M

−0.058***

r

β(RE )(NRE )

.0

aw

0.014

al

0.018

β(RE )(L )

M

β(RE )(K )

.1

a

0.343***

ca

−0.321

ru

βRE βNRE

.2

ny

0.298***

as

0.037

βL

ag

βK

.3

Ke

14.62***

ad

β0

.4

op

Coefficient

nd

Variable

.5

i

Table 5 Parameter estimates of the translog model.

ia

Output elasticities of renewable and nonrenewable energy (1980 - 2011)

P.K. Wesseh, B. Lin

Autonomous technical change

Biased technical change

Total technical change

Fig. 2. Technical change and energy efficiency in East African countries.

6


P.K. Wesseh, B. Lin .96

T(NRE )(RE ) =

αNRE α − RE ηNRE ηRE

Elasticities of substitution

this measure is usually nonnegative, we argue that the negative value for East African countries is due to the immense pressure on these countries to limit their energy choices which becomes a serious economic burden. One may also see that the efficiency of energy or the biased component of technical change, although relatively low (5.1–7.5%), is positive and accounts for bulk of the overall state of technological innovation in the region. Hence, achieving energy efficiency in East African countries is not just crucial for energy security and mitigation, but also presents opportunities for technological advancement and sustainable development of the region. Since we have shown that technical change is nonneutral and depends on various energy inputs, attempt is also made to evaluate the difference in technological progress between renewable energy and nonrenewable energy. The expression we develop to calculate the technology gap is given by

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an

m

Ea

st

er

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Af

ric

Zi

R

ba

bw

bi

e

a

a m Za

ia

nd

an

ga

nz Ta

U

e

da an w

am

bi

qu

iu rit au

M

oz M

al M

R

i

s

r

aw

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Ke M

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i

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ba m

Ea

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Af

ric

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bw

a

a

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am

bi

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-0.5

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ia

This study estimates the translog production model to provide a comprehensive discussion on the effectiveness of renewable energy as a model for powering development in the East African region. The study utilizes country-level panel data for a group of East African countries over the period 1980 – 2011. Several findings have been documented from the estimation of a random-effects model using generalized least squares estimator. First, the results show that nonrenewable energy has driven economic growth in the East African region by far more than the renewable energy type and that increasing investment in nonrenewable energy will present greater opportunities for East Africa's economic transition than renewable energy. Second, there is evidence that technological innovation is scaled biased implying that achieving energy efficiency in East African countries is not just crucial for energy security and mitigation, but also presents opportunities for technological improvement and the sustainable development of the region. Technological progress in nonrenewable energy was found to be faster than renewable energy. Third, the study documents that East African countries have the potential of substituting between renewable and nonrenewable energy while boasting output and mitigating greenhouse gas emissions at the same time. However, the low energy content of most renewable energy sources, sitting problems and subsequent

0.0

op

op

nd ru

6. Conclusions

0.5

nd

hi

Energy is a driving force behind all socio-economic activities and as such, it is squarely in this sector that Africa's battle for long-term development and economic prosperity will be won or lost. Sustainable solutions focusing on supply security, energy poverty eradication and mitigation issues are therefore being placed at the forefront of most African energy policy discussions. Results of this study have demonstrated remarkable growth records driven largely by nonrenewable energy and the possibilities of energy conversion. Furthermore, the analysis in this study shows that investment in nonrenewable energy will yield greater potential benefits than investment in renewable energy (as indicated by the model's squared terms). In light of the empirical results and given the issues of scale, economics and sitting problems inherent in most renewable power generation, we argue in this study that developing renewable energy to replace nonrenewable energy while the East African region is still at its embryonic stage of development does not sound reasonable. Hence, this study challenges the effectiveness of relying on renewable energy while transitioning through industrialization and urbanization (see Wesseh and Lin [52,53]).

1.0

ru

Et

Bu

5. What do the results mean for East African countries?

1.5

hi

.80

sources. These issues are discussed in details in the next section.

Based on the parameter estimates in Table 5 and Fig. 1, substitution elasticities between renewable and nonrenewable energy have been computed (Fig. 4). It can be observed that the estimates are positive for all countries and range between 0.77 and 0.92 suggesting the possibilities of substituting between renewable and nonrenewable energy. Notwithstanding, opportunities of substituting nonrenewable energy for renewable energy in East African countries are limited in practice due to the inherent limitations of most renewable energy

Et

.84

Fig. 4. Elasticities of substitution between renewable energy and nonrenewable energy (1980–2011).

(14)

4.4. Substitution of renewable and nonrenewable energy in East Africa

Technology gap

.88

.76

In the above expression,T(NRE )(RE ) is the difference in technology between nonrenewable energy and renewable energy, αNRE and αRE are coefficient estimates as provided in Table 5, ηNRE and ηRE are output elasticities of nonrenewable energy and renewable energy respectively as reported in Fig. 1. Positivity of T(NRE )(RE ) implies that nonrenewable energy has a faster technological progress than renewable energy. On the other hand, negativity means that the state of technological progress of renewable energy is faster than that of renewable energy. Better still, a zero value of T(NRE )(RE ) implies equality of technical progress between renewable and nonrenewable energy. Results from the analysis as documented in Fig. 3 points to both positive and negative values although this difference is modest between the two energy types. In general, difference in technical progress between nonrenewable energy and renewable energy for the Eastern African region is positive suggesting that technical progress is faster for nonrenewable energy in the region. This makes nonrenewable energy economically attractive and provides opportunities for economics of scale.

Bu

.92

Fig. 3. Difference in technical progress between renewable and nonrenewable energy (1980 – 2011).

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P.K. Wesseh, B. Lin

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transformation into a form relevant for present day applications calls for massive capital investment, thereby reducing the privileges of substituting renewable energy for nonrenewable energy. Hence, given the alarming rate of energy poverty, this study challenges the effectiveness of East African countries reliance on renewable energy while transitioning through industrialization and urbanization. Finally, the applied model reinforces the assertion that imposing restrictions like homogeneity, homotheticity or separability on the production technology is not realistic and should rather be a testable hypothesis within any applied analysis concerning production. While the use of a flexible functional form may generate valuable insights, there are also certain limitations that must be pointed out. In the first place, the range of underlying technologies that can be characterized is limited by a flexible functional form. In other words, if a technology is consistent with homotheticity, then that technology will be able to satisfy all properties of the production function. Drawing on insights from fundamental duality theory, this characteristic of flexible functional forms might not be very surprising since an explicit implication of the theory is that any specification of a production or cost function would certainly place some kind of restrictions on the technology. In additional, it is being increasingly recognized that most flexible functional forms, if not all, become very inflexible especially when considering separable technologies. Because of these reasons, the translog production function should not be considered or treated as a panacea for solving all possible model specification problems especially in applied production analysis. To enhance the completeness of work in this study and add a comprehensive value to the caption, future research will focus on cost analysis issues of developing renewable energy for the East African region relative to nonrenewable energy. Such an analysis will account for external costs on the one hand and the inherent limitations of renewable energy on the other hand. Therefore, results in this study should not be interpreted as sufficiently robust for drawing the conclusion that renewable energy is completely useless for powering the development of East African countries. Acknowledgements The paper is supported by Xiamen University - Newcastle University Joint Strategic Partnership Fund, the Grant for Collaborative Innovation Center for Energy Economics and Energy Policy (No: 1260-Z0210011), and Xiamen University Flourish Plan Special Funding (No:1260-Y07200). References [1] Lin B, Wesseh PK. Jr., Energy consumption and economic growth in South Africa reexamined: a nonparametric testing approach. Renew Sustain Energy Rev 2014;40:840–50. [2] Wesseh PK, Jr., Lin B, Owusu-Appiah M. Delving into Liberia's energy economy: technical change, inter-factor and inter-fuel substitution. Renew Sustain Energy Rev 2013;24:122–30. [3] Ebohon OJ. Energy, economic growth and causality in developing countries: a case study of Tanzania and Nigeria. Energy Policy 1996;24:447–53. [4] Jumbe CBL. Cointegration and causality between electricity consumption and GDP: empirical evidence from Malawi. Energy Econ 2004;26:61–8. [5] Wolde-Rufael Y. Energy demand and economic growth: the African experience. J Policy Model 2005;27:891–903. [6] Wolde-Rufael Y. Electricity consumption and economic growth: a time series experience for 17 African countries. Energy Policy 2006;34:1106–14. [7] Akinlo AE. Energy consumption and economic growth: evidence from 11 African countries. Energy Econ 2008;30:2391–400. [8] Wolde-Rufael Y. Energy consumption and economic growth: the experience of African countries revisited. Energy Econ 2009;31:217–24. [9] Odhiambo NM. Energy consumption and economic growth nexus in Tanzania: an ARDL bounds testing approach. Energy Policy 2009;37:617–22. [10] Akinlo AE. Electricity consumption and economic growth in Nigeria: evidence from cointegration and co-feature analysis. J Policy Model 2009;31:681–93. [11] Odhiambo NM. Electricity consumption and economic growth in South Africa: a trivariate causality test. Energy Econ 2009;31:635–40. [12] Menyah K, Wolde-Rufael Y. Energy consumption, pollutants emissions and

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implications for renewable energy expansion in the ECOWAS region. Energy Policy 2016;89:125–37. Wesseh PK, Jr., Lin B. Can African countries efficiently build their economies on renewable energy?. Renew Sustain Energy Rev 2016;54:161–73. Omri A. An international literature survey on energy-economic growth nexus: Evidence from country-specific studies. Renew Sustain Energy Rev 2014;38:951–9. Isa Z, Ahmed RM, Sayed A, Kun SS. Review paper on economic growth–aggregate energy consumption nexus. Int J Energy Econ Policy 2015;5:385–401. Eddrief-cherfi S, Kourbali B. Energy consumption and economic growth in Algeria:

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