Institutional quality, green innovation and energy efficiency

Energy Policy 135 (2019) 111002

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Institutional quality, green innovation and energy efficiency a,b

Huaping Sun

a

c,d,∗

, Bless Kofi Edziah , Chuanwang Sun

a

, Anthony Kwaku Kporsu

T

a

Institute of Industrial Economics, Jiangsu University, Zhenjiang, 212013, PR China School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, PR China China Center for Energy Economics Research, School of Economics, Xiamen University, Xiamen, 361005, PR China d MOE Key Laboratory of Econometrics, School of Economics, Xiamen University, Xiamen, 361005, PR China b c

ARTICLE INFO

ABSTRACT:

Keywords: Energy efficiency Energy use Energy consumption Institutional quality Green innovation Green technology

This paper examines the energy efficiency performance of a sample of 71 developed and developing countries between 1990 and 2014. In most current energy literature, the transition to green technology is seen as a sustainable way to achieve a low-carbon or carbon-free environment. Bearing this in mind, we argue further that adopting green technology needs a strong backing and funding of reliable government institutions to shift the country's paradigm. Considering this issue, we adopt the parametric stochastic frontier approach built on the shepherd distance function to evaluate the effects of both governmental institutions and green technologies on energy efficiency. We find evidence of a significant positive influence of both green innovation and institutional quality on energy efficiency enhancement having controlled for some variables. Regarding energy efficiency levels of the individual countries- USA, Japan, Germany and Australia lead the chart while Belize, Panama, Singapore, Malta, Sierra Leone, Iceland, Jamaica, Bahrain and Ghana are the least energy efficient countries. Policy implications are further discussed.

1. Introduction

variations in relative factor prices, forms of specialization, the quality of government institutions and the level and direction of scientific and technological development. In this paper, we study what factors influence energy efficiency and in particular we ask whether institutional quality and green innovation promotes energy efficiency. In recent years, green innovation has become a very popular concept and the call for green growth has been more pressing than ever. One major potential benefits of green technological innovation is that it could considerably cut the cost of mitigation of carbon by developing more affordable and energy efficient technologies (Popp, 2012). Therefore, several countries have agreed and signed (i.e. during the Paris Agreement) to progress towards a green growth path. In spite of this noble act, several challenges arise in adoption of green technology at both the domestic and international level (Biresselioglu et al., 2018; Maskus, 2010). Countries or businesses will only make the decision to invest in green technologies if it will reduce inefficiency and increase productivity (Stucki, 2018). The question is whether investing in green technology can improve energy efficiency and increase economic development. Negative opinions, like those articulated by Palmer et al. (1995) indicate that green technology might lead to increased inefficiencies and productivity losses. Lin and Moubarak (2014) and Wurlod and Noailly (2018), on the contrary reported that green technology improves energy efficiency. The results in connection to this

Consumption of energy is commonly linked to modernization and economic growth, but energy consumption also generates environmental challenges in countries throughout the world (Chang et al., 2018). Many empirical studies have been carried out on the connection between energy use and economic development and in most of these studies, the issue of environmental pollution like Greenhouse Gas (GHG) emissions often emerge (Odhiambo, 2009; Yildirim et al., 2012; Tang et al., 2018). It is believed that the best and cost effective way to address these negative externalities is by efficient use of energy (Filippini and Zhang, 2016). Energy efficiency is therefore regarded as an important policy strategy to tackle energy security, promote economic progress and reduce GHG emissions (IEA, 2012). As such, many governments have in recent years strategically incorporate energy efficiency policies and targets into their national agendas (IEA, 2014). To achieve these energy goals and targets, there have been heavy investments to develop green technology in recent years (Wurlod and Noailly, 2018). Institutions have also been empowered to see to the implementation of these energy policies (Chang et al., 2018). Despite all these efforts, energy efficiency differences between countries remain largely significant (Zhou et al., 2012). Differences in energy efficiency levels across countries are largely determined by factors such as: ∗

Corresponding author. China Center for Energy Economics Research, School of Economics, Xiamen University, Xiamen, 361005, PR China. E-mail addresses: [email protected] (H. Sun), [email protected] (B.K. Edziah), [email protected] (C. Sun), [email protected] (A.K. Kporsu).

https://doi.org/10.1016/j.enpol.2019.111002 Received 16 February 2019; Received in revised form 15 September 2019; Accepted 17 September 2019 0301-4215/ © 2019 Elsevier Ltd. All rights reserved.

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question are still inadequate and our paper looks again at this matter and seeks to answer whether green innovation can enhance energy efficiency. In addition, a major drawback to the energy efficiency literature is failure to account for the role of government institutions in energy efficiency promotion. Government institutions are known to have strong governmental, constitutional and political power, so they tend to be more resourceful in enforcing effective policies (Vowles, 2008). Energy policies successfully implemented by government to enhance energy efficiency may subsequently determine the energy consumption attitude of citizens (Chang et al., 2018). Nonetheless this depends on how efficient these institutions are in enforcing such policies (Bhattacharya et al., 2017; Chang et al., 2018). Prior researchers have focused on the effects of government policies on energy (Cirone and Urpelainen, 2013; Sovacool, 2015; Sheikh et al., 2016; Bhattacharya et al., 2017; Moncada et al., 2017). For instance, government policy to commercialize use of energy technologies (Roessner, 1984), policies to drive energy transition (Kucharski and Unesaki, 2018), policies to respond to high-energy intensities (Cirone and Urpelainen, 2013), etc. Nevertheless, efficiency of these governmental institutions in improving energy efficiency has not been addressed in literature. In order to fill this gap, we concentrate on the efficiency of government institutions by accounting for the effects of institutional quality on energy efficiency.1 Based on the fact that these two topics under discussion (i.e. green innovation and institutional quality) have not been tackled in the energy efficiency literature, we therefore attempt to provide empirical assessment of these two issues in the light of the energy efficiency literature. It is also worth emphasizing that this study centers on estimating correlations among variables. The paper is made of five parts. Section 1 introduces the study by highlighting the relevant literature on institutional quality and green technology. Section 2 presents an overview of the literature relating to energy efficiency measurement and suggests the study's possible contribution. Section 3 introduces the stochastic frontier model and describes the data. Section 4 provides details of estimated results and interprets estimated energy efficiency score of sample countries. Section 5 concludes and provides policy implications to the analysis with limitations and future direction to the work.

macro level. The first and commonly used indicator is energy-GDP ratio, also known in literature as energy intensity. This estimation method of efficiency relies on a single input, i.e. energy consumption and neglect other important inputs like capital and labour (Jebali et al., 2017). Such indicators are probably too simplistic and naive in the analysis of overall energy efficiency (Filippini and Hunt, 2015) and thus considered to be a less accurate measure for national or global energy policy to be based on (IEA, 2009). In this study, we employed the second indicator which combines inputs such as labour, capital and energy to lower production cost. In cases, where countries produce outputs without reducing quantity of inputs used or use an outdated tool that prevents reduction of inputs to the minimum is considered to be inefficient in production. In such state, energy input as well the other inputs are considered to be inefficiently used and the energy wasted is noted. Methodologically, this approach can be quantified as the proportion of intended energy input to the observed energy inputs (Hu and Wang, 2006). It has been considerably utilized to benchmark energy performance in a number of studies (Du and Lin, 2017; Hu and Wang, 2006; Jiang et al., 2017; Lin and Long, 2015; Zhou et al., 2012). Two principal methods exist in this energy efficiency measurement, i.e. the parametric Stochastic Frontier Analysis (SFA) and non-parametric Data Envelopment Analysis (DEA): DEA estimation is built on mathematical programming method, while the SFA is based on econometric techniques. Several number of studies have used the DEA to measure energy intensive countries or sectors efficiency (Chang, 2015; Guo et al., 2017; Honma and Hu, 2014; San, 2011). San (2011) analyzed efficiency of thirteen renewable energy technologies using the DEA approach. Honma and Hu (2014) compared Japan's performance in industrial energy efficiency to that of 14 industrial countries. Chang (2015) used the directional distance function to estimate total energy and environmental efficiency for G7 and BRICS countries. Makridou et al. (2016) analyzed the energy efficiency performance of energy-intensive industries in 23 European countries. Jebali et al. (2017) studied the Mediterranean countries' energy efficiency levels using the double bootstrap DEA method. Gökgöz and Erkul (2019) compared efficiency performance of European countries using slack-based model. Though DEA approach is useful in estimating energy efficiency levels, it considers all stochastic disturbances as part of the efficient factors and thus results from the efficiency estimates may be biased. Furthermore, in modelling of unobserved heterogeneity in the production of energy service, the parametric SFA techniques are more appealing to analyze energy efficiency (Filippini and Hunt, 2015). For these reasons, we utilize the SFA method because we consider the stochastic noise relevant and unobserved heterogeneity important to be addressed. The SFA approach was suggested by Aigner el at. (1977) and Meeusen and Broeck (1977) and since then, its use has so far been extensive in the field of energy for measuring efficiency at the micro and macro levels. From econometric standpoint, the SFA frontier functions, according to Filippini and Hunt (2015) can be categorized under three specifications: (1) Energy requirement function by Lin and Wang (2014); Boyd (2015), etc. (2) Shepherd Energy Distance function by Zhou et al. (2012); Lin and Long (2015); Du et al. (2018), etc. (3) Energy Demand Function by Filippini and Hunt (2011); Marin and Palma (2017); Song and Yu (2018), etc. Despite increasing interest in energy efficiency estimation, there is little empirical research into whole-economy aggregate energy efficiency. One of the few studies that estimated whole-economy aggregate energy efficiency is Filippini and Hunt (2011) who proposed the energy demand functions to assess energy efficiency for 29 OECD countries for the period 1978–2006. They estimated the “underlying energy efficiency trend” having controlled for income, price effects, climatic conditions, area, industrial structure, technical development and other exogenous factors by making use of the pooled model of Aigner et al. (1977) and Greene (2005a,b) True Random Effect model. The study

2. Literature review Energy efficiency has gained worldwide attention following the oil crisis of the 1970's (Geller et al., 2006; Wang and Nie, 2018) and since then a rich body of literature has developed to assess energy efficiency for countries and in various end-use sectors. Like other energy efficiency literature, there are two main aspects to the current study. The first strand concentrates on energy efficiency measurements. The second strand is on determinants of energy efficiencies. For the first strand, there has not been any consensus on the definition of energy efficiency (Filippini and Hunt, 2015) but several definitions are built on simple ratio of valuable output of production process in relation to energy input of production (Bhattacharyya, 2011). According to Patterson (1996), to monitor improvements in energy efficiency, diverse quantitative indicators are mostly considered and these include thermodynamic indicator, physical thermodynamic indicator, economic indicator, etc. 2.1. Energy efficiency measurements Focusing on economic indicator, two types of indicators exist at the 1 Chang et al. (2018) also studied efficiency of government and its institutional systems. However, unlike Chang et al. (2018) we highlight the significance of green technology and also adopt a different approach in evaluating energy efficiency across the sample countries.

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concludes that energy intensity is an inappropriate indicator for measuring energy efficiency. Given that the research focuses on a long sample period for several economies, an important factor to consider is the issue of heterogeneity. However, the estimation method used in this study fails to consider the issue of heterogeneity among countries, hence the estimation technique may suffer from heterogeneity bias. Besides, due to the number of variables considered in this study, issue of endogeneity may arise when strong correlation exists between explanatory variables. Thus, it is imperative that only important variables are considered to reduce endogeneity problems. In an effort to improve on the previous model, Filippini and Hunt (2012) and Filippini and Hunt (2016) estimated aggregate energy efficiency in the United States using the Mundlak (1978) model.2 Marin and Palma (2017) also adopted the energy demand function and SFA to investigate the energy efficiency in 10 EU countries with a household data for the period 1995–2013. Carvalho (2018) estimated aggregate energy demand function for electricity usage using the Bayesian Generalized True Random Effects stochastic frontier model which considers both persistent and transient inefficiency for 27 transitional economies and 6 OECD countries in the period 1994–2007. They observed convergence between Western European economies and most transition country groups. Contrary to aforementioned works, this study addresses the issue of economy-wide energy efficiency by means of the Shephard energy distance function. The empirical study closest to ours is Zhou et al. (2012) who proposed the Shephard energy distance function. One characteristic of their evaluation is that the reciprocal of energy consumption is seen as an output which is produced by labour, capital stocks, and GDP as inputs. Their estimations suggest that energy efficiency varies significantly among the investigated 21 OECD countries. They also used a non-parametric DEA method to compute the level of energy efficiency and compared those measurements with estimates from parametric approach. They suggested that the parametric frontier technique had greater discriminating power for assessing energy efficiency output relative to the non-parametric frontier counterpart. However, their econometric model is crosssectional in nature. Stern (2012) examined the trends in energy efficiency in 85 countries for a 37-year span using energy distance function. Energy efficiency differences were modeled as a stochastic feature of explanatory factors and estimated using the cross-section of time-averaged data. The fixed and random effect estimates vary in results and the energy efficiency trends for developed countries are higher than developing countries. Apart from China and India, energy efficiency trends in developing countries have been relatively flat or declining. For energy efficiency convergence, it was evidenced that convergence occurs over time across countries except for certain African countries. Lin and Du (2013) extended Zhou et al. (2012) cross-sectional data model into a panel model to estimate Chinese regional energy efficiency using Battese et al. (1992) metafrontier technique. However, in their model, they did not take into account environmental variables. Hu and Honma (2014) included environmental variables to estimate the total factor energy efficiency estimates for 47 regions across Japan. However, their limitation had to do with failure to consider unobserved heterogeneity in the data employed. As a departure from the aforementioned studies, the current study extends the energy distance stochastic frontier model by including both desirable and undesirable outputs within the production framework (Ouyang et al., 2019;Sun et al., 2018; Wu et al., 2019). Addition of undesirable outputs to the energy efficiency modelling has several benefits as outlined in Mandal (2010) and Shi et al. (2010). Hence, one of the novelties in this study lies in the use of both desirable and

undesirable outputs in modelling energy efficiency in a parametric Shephard energy distance model. 2.2. Determinants of energy efficiency The second strand of related number of empirical studies is centered on determinants of energy efficiencies. For instance, Stern (2012) examined the drivers behind energy efficiency changes and observed that while technological change decreases energy intensity over time, substitution of energy for human capital increases energy intensity. Lin and Long (2015) further showed that energy price and the scale of an enterprise provide a support for improving energy efficiency. Marin and Palma (2015) noted that technology spillovers from abroad could increase domestic energy efficiency. Otsuka and Goto (2015) observed that an increase in population density increases energy efficiency. Using the spatial Durbin error model, Jiang et al. (2017) showed that FDI and population density has positive effects in its own provinces and in neighboring provinces' energy efficiency. Jebali et al. (2017) discovered that countries in Mediterranean regions with high economic growth and population density experiences high-energy efficiency levels. After series of robustness tests, Du et al. (2018) validated the significant positive effects of government funded research programmes on china's energy efficiency improvement. Song and Yu (2018) observed that technological progress and developed market instrument are crucial drivers of energy efficiency improvement. All of these researchers analyzed a number of energy efficiency determinants, while ignoring the impact on energy efficiency of institutional quality and green technology. Therefore, this paper addresses these deficiencies by asking two important questions: How does institutions quality affect energy efficiency levels? What is the impact of green technology on energy efficiency enhancement? To provide answers to these crucial questions, our aim therefore is to examine the effects of institutional quality and green technology on the energy efficiency. The contributions of this study are mainly as follows: Firstly, we focused on the significance of green innovation and the quality of government institutions in promoting energy efficiency. In doing so, we made use of energy distance SFA model suggested by Zhou et al. (2012), expanded it from its cross-section form to a panel model of 71 countries from 1990 to 2014. Secondly, we extended the energy distance stochastic frontier model by including both desirable and undesirable outputs within the production framework. Thirdly, with the econometric specification of the energy distance frontier model, we accounted for potential unobserved heterogeneity bias across the sample countries using the Greene's (2005a) True Fixed Effects model and the Pairwise Difference Estimator (PDE) of Belotti and Ilardi (2014). 3. Model and data 3.1. Shepherd Energy Distance function Taking into account a production process where all countries use inputs such as capital (K), labour (L) and energy (E) to produce a desirable output- GDP (Y) and undesirable output - CO2. Theoretically, the production technology is described as follows: T = {(K, L, E, Y, C): (K, L, E) can produce (Y, C)}

(1)

Where, T must satisfy the following production economic theory, that is: inputs, are necessary to produce outputs; both inputs and outputs are disposable; and inactivity is always possible. To estimate the energy efficiency from production efficiency framework, Zhou et al. (2012) redefined the Shepherd distance function using energy input. Equation (2) defined the shepherd distance, bearing in mind the input, output vector, and current technology:

2 The Mundlak (1978) model was considered to address the issue of unobserved heterogeneity.

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DE (K , L , E , Y , C ) = sup

E

: K,L,

, Y, C

(2)

Equation (5) is expanded and substituted into equation (4) and with simple transformation, we have:

Equation (2) tries to estimate the maximum possible reduction of E while keeping the given input and output vectors within the production technology constant. As such, E / DE (K , L , E , Y , C ) denotes the potential hypothetical optimum energy usage in a country. The Energy distance function is expressed as the proportion of optimal energy use to actual energy used (Zhou et al., 2012), which can be estimated using the following formula:

E / DE (K , L , E , Y , C ) 1 EE = = E DE (K , L, E , Y , C )

LnEct = 1 2 1 + 2

(3)

+

t l LnLc

+

t e LnEc

+

t y LnYc

+

t t ke (LnK c )(LnEc )

(LnK ct )(LnCct ) le (LnLct )(LnEct ) (LnLct )(LnCct ) +

LnEct = 1 2 1 + 2 1 + 2 +

t t lc (LnLc )(LnCc ) +

t tl (T)(LnLc )

+

0

+

t ll (LnLc )

t k LnK c

t t ye (LnYc )(LnEc )

+

(T)(LnYct ) +

t t tc (T)(LnCc ) + vc

t

T+

tt

T2

+

t t ky (LnK c )(LnY c )

+

+

t t ec (LnEc )(LnCc )

t tk (T)(LnK c )

+

+

t yy (LnYc ) t cc (LnCc )

+

+

1 2

t ll (LnLc )

2

t t cy (LnCc )(LnYc ) +

t ty (T)(LnYc )

+

+

+

ly

T+

t

t tc (T)(LnCc )

+

tt T

2

+

tk

vct

(6)

+

Lct ,

t l LnLc

Ect ,

Yct ,

Cct )

t y LnYc

+

+

uct

t c LnCc

+

1 2

t kk (LnK c )

2

2

+

+

t t ky (LnK c )(LnYc )

t t lc (LnLc )(LnCc )

t tk (T)(LnK c )

+

(T)(LnCct ) + Dc + vct

+

t tl (T)(LnLc )

+

t t kc (LnK c )(LnCc )

t t cy (LnCc )(LnYc ) t ty (T)(LnYc )

+

+

+

t

+

T+

ly tt

tc

(7)

uct

Where, Dc is the set of country dummies which controls for countryspecific heterogeneity of geographical, socio-economic, climatic and environmental factors of sample countries; uct is a non-negative onesided error random variable denoting the energy inefficiency in a specific country c, considering the present production technology at time t. After estimating equation (7), the energy inefficiency component uct of country (c) can be derived and the corresponding energy efficiency for each country at time t can be calculated with the formula: (8)

Following Battese and Coelli (1995), we made the inefficiency element uct to be explained by set of explanatory variables (referred here as determinants of energy efficiency) in a single-stage technique. Therefore, the inefficiency effect model is made up of the two main variables understudied (i.e. green innovation and institutional quality) along with control variables like trade openness, human capital and level of urbanization of the country. We can therefore specify the inefficiency function uct as:

kc

uct =

o

+ Patct + Instict + Conct +

t c

(9)

Where Patct is the green technology; Instict denotes the institutional quality; Conct represents control variables. ct is an error term; and is

cy

t tl (T)(LnLc )

2

2

t t kl (LnK c )(LnLc )

T2 +

lc

+

t kk (LnK c )

2

(LnLct )(LnYct )

c

t t ly (LnLc )(LnY c )

+

(LnCct )(LnYct )

+

1 2

t t kc (LnK c )(LnCc )

+

EEct = exp( uct )

t t kl (LnK c )(LnLc )

+

= Finally, setting (like in Battese and Coelli, 1992) and including country dummies into the model, we obtain:

2 1 LnCct + (LnK ct ) 2 kk 2 1 + (LnLct ) 2 ll 2 1 + (LnEct ) 2 ee 2 1 + (LnYct ) 2 yy 2 1 + (LnCct ) 2 cc

+

t t ky (LnK c )(LnYc )

+

LnDEt (K ct ,

+

+

t c LnCc

+

LnDEt (K ct , Lct , Ect , Yct , Cct )

LnDEt (K ct , Lct , Ect , Yct , t k LnK c

t y LnY c

+

2

t t kl (LnK c )(LnLc )

(T)(LnK ct )

t l LnLc

+

2

(LnLct )(LnYct ) +

Having adopted the energy distance function, one important step of stochastic frontier function is picking the right production function for analysis. Instead of the famous Cobb-Douglas production function, we choose the trans-log SFA production function following Lin and Du (2013); and Du et al. (2018) due to its flexible functional form. Besides, trans-log functions do not impose prior restrictions on technology (Christensen et al., 1973). The trans-log stochastic frontier production function with three inputs of capital (K), labour (L) and energy (E) and two outputs GDP (Y) and CO2 (C) therefore reads:

+

t cc (LnCc )

+

3.2. A stochastic energy distance frontier method

0

t k LnK c

+

0

t yy (LnY c )

+

From Equation (3), the Energy Efficiency (EE) is equal to reciprocal of the Shepherd energy distance function. EE denotes the degree to which actual energy used differs from the required optimal level of energy for production in a country. The definition of EE implies that it is between zero and one; a low score means poor energy efficiency performance denoting a pareto improvement in those countries. A higher score of one indicates that the country is at its optimal energy consumption, and so its energy efficiency lies along the frontier curve. However, an EE score, which is less than one, means a country's production is stationary within the frontier curve, signifying energy inefficiency in the production process.

Cct ) =

(5)

LnDEt (K ct , Lct , Ect , Yct , Cct ) = LnEct + LnDE (K ct , Lct , 1, Yct , Cct )

T

parameters to be estimated. Since the variables in equation (9) is inefficiency, a negative sign of the covariates means that energy inefficiency decreases. For instance, if both green technology (Patct ) and institutional quality (Instict )support energy efficiency, we expect the coefficients to be negative, that means, green technology and institutional quality decreases the distance from the frontier. However, when the variable coefficients sign is positive, it implies that the energy inefficiency increases and therefore increases the distance from the frontier. Given the econometric model specification, it is imperative that we discuss the literature on the estimation of SFA within the panel data model. In this empirical analysis, we identify at least three models. The

ty

(4)

Where is a normally distributed error component accounting for statistical noises and random external shocks beyond the production process. The s are the parameters to be estimated. The subscript T represents technological change, and means domestic technological progress. One property of input distance function, according to Lovel et al. (1994) is the linear homogenous property. The function of Shepherd energy distance is also linearly homogenous to the degree of one in the energy input. Therefore, we have:

vct

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first approach is Pitt and Lee (1981) half-normal panel data version of Aigner et al. (1977) model. The weakness of this approach is that it fails to control for the unobserved, time-invariant, country-specific heterogeneity. Therefore, estimates in this model can be biased by unobserved heteroteneity. The second approach, which Pitt and Lee (1981) also proposed, introduces the individual random effects as part of the inefficiency estimates and consider the inefficiency effects to remain the same over time. The main weakness here is that all unobserved, timeinvariant variables are captured by the country-specific effects and regarded as inefficiency. As the level of inefficiency do not change over time, this model is not ideal for analysis that takes a relatively long period like ours. To resolve these issues, Greene (2005a; 2005b) presents the third approach by adding a fixed or random individual country effects to the SFA panel data version of Aigner et al. (1977). The random or fixed effects allow the inefficiency to be time-varying in nature while controlling for country-specific unobserved heterogeneity which is not related to inefficiency. In this respect, the model generally introduces a complete set of country dummy variables to the SFA model which induces country-specific neutral function shifts (Greene, 2005b) when used in a log-linear production function. One of the limitations of this model is that the country-specific fixed effects absorb any time-invariant inefficiency. Consequently, the model estimates unreliable inefficiencies for short panels which are expected to be consistent over time (Saal et al., 2007). However, as our set of panels extends over a comparatively lengthy 24-year period, we assume time-variant inefficiency. In this case, country-specific fixed effects capture time-invariant country-specific features not controlled for in the model instead of time-invariant inefficiency. In addition, the ‘incidental parameter’ problem should be addressed by the sample size of 71 countries observed over 24 years leading to a consistent energy efficiency estimate.3 But to test the stability and robustness of the results in equation (9), we also adopt the Pairwise Difference Estimator (PDE) model of Belotti and Ilardi (2014) .4

5. Carbon emission is extracted from World Development Indicators (WDI) and transformed into a natural log. In terms of efficiency in energy contributing factors, three variables are selected in this paper, including the key variables of green technology and institutional quality. The purpose and source of this selection are described below: 3.3.1. Green technology The transition to green technology is considered one of the key solutions to address climate change and energy intensity (Acemoglu et al., 2012), making green technology a key determinant of energy efficiency. While there are many studies on determinants of energy efficiency in SFA literature, the question of how green technologies affect energy efficiency is limited. Previous studies measured technological change and innovation in several ways, particularly through Research and Development (R&D) in area of energy technologies (Garrone and Grilli, 2010). While R&D of energy technology measures added value of an innovation process, patent of energy technology directly reveals the results of R&D and innovation performance (Griliches, 1990). Patents therefore have recently been considered useful in measuring the rate of innovation in countries, despite certain limitations (Jaffe et al., 2000, 1993; Popp, 2005; Wurlod and Noailly, 2018). In spite of the limitations, patent statistics are still regarded useful and used widely.5 Therefore, we use patent data as a measurement of green innovation. The extraction of green technology patents was from OECD environmental statistics database (OECD, 2016). This database offers a country-level data on environment-related technology. Moreover, since environment-related technologies play an indispensable role in climate change mitigation and energy efficiency, we use it as proxy of green technology. Empirically, there are studies showing that consumption of green technologies can enhance energy efficiency (Lin and Moubarak, 2014; Wurlod and Noailly, 2018). Therefore, we expect green technology to have a positive impact on energy efficiency.

3.3. Data

3.3.2. Institutional efficiency Our institutional quality indicator comes from the World Economic Freedom Index (EFW). These data are widely used to measure the quality of institutions (e.g. Manca, 2010; Young and Sheehan, 2014). It is jointly published by Fraser and the Cato Institutes. EFW has one of the most complete sets of measures to assess the institutional quality of a country, and its policies and level of economic freedom starts as far back as 1970 to present. To measure the quality of the institutions, we combine five different categories or sub-categories: (i) size of government (ii) legal system and property rights (iii) sound money (iv) free trade internationally, and (v) regulation of credit, labor, and business. There are 24 components in these five areas, and these components consist of sub-components, which result in a total index of 42 variables. Fraser Institute makes use of a scale from 0 to 10 for each category, which calculates the average of these five indices to obtain a complete index for each country. In this index, zero represents the smallest institutional value and ten represent the highest institutional quality. Data are available every five years from 1970 to 1995, but available every year from 2000 to 2016. Since our sample starts in 1990, we calculate the missing data with real values in 1990 and 1995. Institutions are known to play a vital role in promoting energy efficiency, so we expect it to have a positive impact on energy efficiency. The sign of the coefficient should therefore be negative.

The study employs an unbalanced panel data consisting of 71 sample countries over the period 1990–2014. Our analysis span between the period 1990–2014, because of the availability of enough data during these periods. The study used five variables as input-output indicators to represent the production frontier. These are three inputs of capital stock (K), labour (L) and energy use (E), while GDP (Y) and Carbon emission (C) are considered as single desirable and undesirable outputs, respectively. The sources of data for input-out indicators are as follows: 1. Real GDP figures are obtained from the Penn World Tables (PWT) and are measured in the natural log at current Purchasing Power Parities (PPP) (millions of US dollar as at 2011) 2. Stock of Capital is derived from PWT, transformed into a natural log at current PPPs (millions of US dollar as at 2011). 3. Labour force is derived from PWT and measured in the natural log of number of people employed. 4. Energy consumption is extracted from Energy Information Administration, converted into natural logarithm of Quadrillion Btu (British thermal unit). 3 The problem of ‘incidental parameter’ in short panels occurs as the estimated number of parameters increase with increasing sample size resulting into inconsistent estimates for country-specific fixed effects and inefficiency component (Greene, 2005a; 2005b). 4 According to literature, the maximum simulated likelihood estimator (MMSLE) for homoscedastic normal–half and normal–exponential models, and the pairwise difference estimator (PDE) for heteroskedastic normal–exponential specifications are known to address the ‘incidental parameter’ problem (Belotti and Ilardi, 2014).

3.3.3. Degree of opening up The extent to which a country is open to the outside world determines the rate of technological spillover, which ultimately increases 5 See Marin and Palma (2015) for details on the limitations of the use of patent data.

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energy efficiency. To measure openness, we use the ratio of trade to GDP. Trade is an essential channel for the diffusion of technology (Keller, 2009; Nasreen and Anwar, 2014; Cai et al., 2017). The importation of advanced technologies, such as machinery, helps increase energy efficiency in the host country (Fisher-Vanden et al., 2004; Hübler, 2011; Sbia et al., 2014). Based on these studies, we assume the positive effects of this variable on energy efficiency. We obtain this data from the WDI.

Table 1 Descriptive statistics of variables.

3.3.4. Human capital Human capital is measured as the bedrock of any country's development. The innovative ability of a country rests on the shoulders of human capital (Romer, 1990, 1994) and the ability to “absorb knowledge” flowing from the neighboring countries also depends on the human capital (Cohen and Levinthal, 1990). However, it entirely depends on the education and experience of the workers. Human capital plays a vital role in energy efficiency (Danquah, 2017; Filippini and Hunt, 2011). So, enhancing energy efficiency of a country depends not only on efficiency of the machines but also technical know-how of labour (Filippini and Hunt, 2012, Filippini and Hunt, 2011). Thus, we expect a positive effect of human capital on energy efficiency. To measure human capital, we use the human capital index data from PWT (Feenstra et al., 2016).

Definition

Symbols

obs

Mean

Std. Dev.

Min

Max

GDP Labour Capital Energy Carbon dioxide Green Patent Institutional Quality Urbanization Opening up Human Capital

Y L K E CO2 Pat Inst Urb Open HC

1775 1775 1775 1773 1772 1448 1775 1775 1775 1775

25.957 15.769 26.935 −0.0287 10.867 3.176 1.884 4.0744 4.1623 0.8884

1.839 1.755 2.000 1.960 2.012 2.777 0.1707 0.4443 0.5464 0.2689

20.747 10.940 20.638 −5.423 5.730 −1.609 1.0812 2.1808 2.6213 0.1701

30.472 20.498 31.870 4.784 16.146 9.645 2.1943 4.605 6.0904 1.3176

Even after controlling for other independent variables in model (3), the coefficient of this variable remains negative and significant at 1% level of significance. When the production function of the energy distance function is changed from translog to the Cobb-Douglass function, the coefficient of green technology still remains similar to model (3). Also, even after changing the setting of the energy distance function, that is by estimating the PDE model, the results from model (5) remain completely consistent with that of model (3). These outcomes imply that green technologies enhance energy efficiency. The coefficients of this value range from −0.528 to −0.0820 and it indicates that as green technology increases, more improvement is likely to be evident in global energy efficiency. Efforts to limit the growth in energy demand has been a major initiative around the world and green innovation is seen to be play a significant role in this process. These results therefore confirm that of Lin and Moubarak (2014) who found that an increase in green technology improves energy efficiency. As for the second key variable-institutional quality, its coefficient is positive and statistically significant at 1% in model (2). This suggests that quality of institutions do not promote energy efficiency. However, in model (3), after controlling for other determinants of energy efficiency, as identified in the recent empirical literature, the coefficients of institutional quality change from positive to negative and statistically significant at 1% significant level, which is consistent with theory. The inclusion of other determinants helps to address omitted variables concerns. Therefore, this result is consistent with works of Bhattacharya et al. (2017); Chang et al. (2018); Sarkodie and Adams (2018) who identified institutional quality to play a critical role in stimulating energy efficiency. As for the control variables introduced in model (3) to explain the differences in the energy inefficiency function, the coefficient of trade openness has a positive sign suggesting a negative effect on energy efficiency. A similar result was presented by Jiang et al. (2017). A potential clarification for this counterintuitive outcome might be emanating from the measurement of trade, i.e. aggregating imports and exports. Positive results accruing from imports of high technologies may be totally stripped-off by export of primary resources and low tech (OECD, 2009). Moreover, some countries restrict importation of high technology products (for example, China) which could have improved energy efficiency. For human capital, the sign is negative which suggest a positive effect on energy efficiency. However, the result is either statistically significant at 10% (in model 4) or insignificant (in model 3 & 5). A similar result is obtained by Danquah (2017) and Stern (2012). Possibly, the positive human capital effect may come in the form of green technology, which has a strong positive correlation with energy efficiency. Urbanization also correlates positively with energy inefficiency but it is insignificant. This suggests that though urbanization reduces energy efficiency, but its impact is not significant enough. Similar result is presented in Lv et al. (2017).

3.3.5. Urbanization Urbanization can foster economic growth and enhance living conditions, but it also has the tendency to either improve or reduce energy efficiency (Sadorsky, 2013). Many studies have found a positive link between energy efficiency and urbanization (Markandya et al., 2006), while some found a negative relationship (Rafiq et al., 2016; Li et al., 2018) and others revealing mixed results (Sadorsky, 2013). Therefore, the predicted effects of urbanization on energy efficiency is still not clear (Lv et al., 2017). Urbanization is measured as the urban population over the total population of the country and extracted from the WDI. Table 1 shows the descriptive statistics of all variables in their natural log form. 4. Empirical results 4.1. Efficiency estimates In SFA, the first step is to test for the statistical significance of the sample countries’ inefficiency. The next step is to test for a suitable functional form that fits the sample data.6 Finally, the test for the presence and nature of technical change is also needed. We use the Likelihood Ratio (LR) test to run these tests.7 Table 2 presents the maximum likelihood estimated results for the translog and Cobb-Douglass stochastic frontier production function. We placed the two key variables into the inefficiency function one by one, starting with green technology. Therefore, in model (1), we introduced the proxy for green technology to examine the effect of green innovation on energy efficiency. In model (2), we added institutional quality variable. In model (3), other control variables were added. For model (4), we used the Cobb-Douglas function following Zhou et al. (2012). For robustness of results, we used Pairwise Difference Estimator (PDE) of Belotti and Ilardi (2014) in model (5). Wurlod and Noailly (2018) found that a percentage rise in green patent is associated with 0.3 percent reduction in energy intensity. Upon investigating the impact of green technology on energy inefficiency, the estimated coefficient of green technology in the first and second model is negative and significant at 1% confidence level. 6 However, for robustness of the results, we show the results for both cobbDouglass function and translog production function. 7 See appendix for details.

6

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Table 2 Results of the maximum likelihood estimates of the parameters of the SFA with the efficiency component. Distance Function

Model (1)

Model (2)

Model (3)

Model (4)

Model (5)

LnL

−4.678*** (0.394) 1.017** (0.431) −0.215 (0.678) 0.907* (0.516) 0.194*** (0.0280) −0.193*** (0.0459) −0.115 (0.0890) −0.0201 (0.0309) 0.0559*** (0.0186) 0.0592** (0.0256) −0.155*** (0.0174) 0.0889 (0.0586) −0.0237 (0.0321) 0.0731*** (0.0242) −0.108*** (0.0240) 0.000498*** (5.22e-05) 0.00388** (0.00166) −0.00885*** (0.000969) 0.00604*** (0.00207) −0.00253** (0.00125) Yes

−4.563*** (0.388) 0.744* (0.425) −0.00733 (0.650) 0.866* (0.512) 0.179*** (0.0279) −0.235*** (0.0446) −0.194** (0.0860) −0.0298 (0.0305) 0.0559*** (0.0194) 0.0594** (0.0260) −0.143*** (0.0177) 0.151*** (0.0570) −0.00936 (0.0309) 0.0572** (0.0247) −0.1000*** (0.0236) 0.000451*** (5.20e-05) 0.00423*** (0.00161) −0.00866*** (0.000999) 0.00525** (0.00206) −0.00246* (0.00127) Yes

−2.267*** (0.393) 1.514*** (0.382) −1.658*** (0.617) 0.481 (0.479) 0.105*** (0.0253) −0.183*** (0.0426) −0.00114 (0.0828) −0.0469* (0.0277) −0.0264 (0.0178) 0.0579** (0.0255) −0.0327** (0.0164) 0.0847 (0.0549) −0.138*** (0.0290) 0.134*** (0.0224) −0.0713*** (0.0204) 0.000295*** (4.76e-05) 0.00473*** (0.00140) −0.00497*** (0.000913) 0.000784 (0.00193) −0.000875 (0.00112) Yes

−0.115*** (0.0275) −0.0908*** (0.0115) −0.0579*** (0.0186) −0.679*** (0.0144)

−1.764*** (0.546) 1.150*** (0.433) −0.179 (0.759) −0.797 (0.591) 0.0704* (0.0415) −0.136** (0.0529) −0.0788 (0.0958) −0.122*** (0.0309) −0.0335 (0.0216) 0.0425 (0.0315) 0.0239 (0.0230) 0.0738 (0.0669) −0.0487 (0.0426) 0.0934*** (0.0260) −0.0870*** (0.0304) 0.000219*** (7.44e-05) 0.00171 (0.00220) −0.00416*** (0.00139) 0.00461 (0.00311) −0.00200 (0.00163) No

−0.487*** (0.0301)

−0.528*** (0.0312) 1.329*** (0.338)

29.79*** (5.147) −2.660*** (0.0827) −6.764*** (0.166) 1564.0808 1445

29.81*** (4.979) −5.078*** (0.624) −6.807*** (0.156) 1565.7379 1436

−0.434*** (0.0426) −2.040*** (0.567) 2.115*** (0.141) −0.374 (0.419) 0.157 (0.225) 23.22*** (4.710) −8.719*** (1.074) −6.512*** (0.107) 1727.3455 1436

−0.373*** (0.0423) −1.759*** (0.482) 2.270*** (0.134) −0.730* (0.397) −0.0264 (0.185) 13.41*** (0.246) −8.824*** (0.891) −6.156*** (0.0970) 1551.5224 1436

LnK LnY LnCO2 LnL*LnL LnK*LnK LnY*LnY LnCO2*LnCO2 LnL*LnK LnL*LnY LnL*LnCO2 LnY*LnK LnY*LnCO2 LnCO2*LnK Time Time*Time Time*LnK Time*LnL Time*LnY Time*LnCO2 Country Dummies Green Technology Institutional Quality Open Human Capital Urbanization Constant sigma_u sigma_v Log-Likelihood Observations

Yes

−0.0820*** (0.0271) −0.721** (0.352) 0.731*** (0.110) −0.211 (0.434) 0.103 (0.171) −4.328*** (0.860) −4.507*** (0.330) 1436

Note: The figures (i.e. the standard error) in parentheses symbolises significance at the 1% (***), 5% (**), and 10% (*) level.

4.2. Energy efficiency analysis

results in Filippini and Hunt (2011); Stern (2012); and Zhou et al. (2012). The elevated average level of energy efficiency shows that countries, on average, make significant strides in catching-up in the short-term to the benchmark technology. Fig. 1 shows the time changes of the estimated trends in energy efficiency. The energy efficiency level is higher for all years (89.6%–93.5%) also suggesting a high degree of energy efficiency. Though efficiency trends have been erratic (i.e. full of ups and downs) during 1990–2002, the trend has been quite stable from 2002 onwards. From 1996 to 2004, there has been a gradual but consistent decrease in

Having estimated the stochastic frontier production functions, we utilize model (3) in Table 2 to estimate the average energy efficiency over the sample period for each country. The energy efficiency estimates range from 60.55% to 99.44%. For most countries, therefore, the estimated energy efficiency values are very high and always higher than 60%, with a mean value of 90.1%. The high levels of energy efficiency reflect that our attention is on transient energy efficiency (Filippini and Hunt, 2015) and are consistent with similar aggregate energy efficiency 7

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Fig. 1. The trend of energy efficiency score for the period 1990–2014.

energy efficiency. Table 3 shows the energy efficiency scores from the individual country perspective. We observe that USA (99.44%), Japan (99.37%), Germany (98.25%) and Australia (98.01%) are by far the most energy efficient countries considering their respective efficiency scores (i.e. efficiency between 99% and 98%). The rest, China (97.97%), UK (97.95%), Brazil (97.88%), France (97.80%), Italy (97.63%), South Korea (97.52%), etc. all lie above the total average energy efficiency. These efficiency estimates for the sample countries seem to be consistent with estimates found in works like Filippini and Hunt (2011) and Zhou et al. (2012). The worst performing countries are Belize (60.55%), Panama (63.57%), Singapore (67.75%), Malta (69.46%), Sierra Leone (70.61%), Iceland (77.08%), Jamaica (78.61%), Bahrain (79.29%), and Ghana (79.72%) which lie below the overall energy efficiency estimates. The efficiency values of Stern (2012) and Adom et al. (2018) for countries like Ghana, Tanzania and Zimbabwe have been also lower. For these countries, they will continue to function with comparatively elevated inefficiencies in the short term unless their governments modify policies or change management. That is to say, in these economies energy security and carbon emissions will be better enhanced by short-term policies. The results for the estimated energy efficiency scores in Table 3 are further illustrated in Fig. 2 (developed economies) and Fig. 3 (developing economies). We also analyze energy efficiency estimates for these countries from the regional perspective by grouping them into seven regions: Latin America and the Caribbean, Middle East and North Africa, North America, South Asia, Sub-Saharan Africa, East Asia and Pacific and Europe and Central Asia (see Table 4). Among the regions, North America, on the average, leads the chart as the most energy efficient region,8 followed by Europe and Central Asia, South Asia and East Asia and Pacific. The region with the lowest energy efficiency score is SubSaharan Africa. To investigate the energy efficiency estimates further, we grouped the countries under developing and developed economies in order to track the changes in energy efficiency in each economy during the period 1990–2014. Fig. 4 displays the time evolution of total efficiency

Table 3 Energy efficiency score for sample countries grouped under the category of developed and developing economies.

8 However, this region contains only two countries: USA and Canada. So, these result may be bias to the other regions with more than two countries.

8

Developing Countries

EE Score (%)

Developed Countries

EE Score (%)

Algeria Bangladesh Belize Bolivia Brazil Cameroon China Colombia Costa Rica Ecuador Egypt El Salvador Gabon Ghana India Indonesia Iran Jamaica Jordan Kenya Malaysia Mexico Morocco Nepal Nigeria Pakistan Panama Peru Russia Senegal Sierra Leone South Africa Sri Lanka Tanzania Thailand Tunisia Turkey Venezuela Viet Nam Zimbabwe

88.50 94.40 60.54 89.23 97.88 91.14 97.97 95.03 92.10 91.71 92.72 91.95 83.51 79.72 97.33 88.47 92.30 78.61 85.25 89.86 88.35 95.69 91.77 86.32 82.27 92.39 63.57 93.11 96.90 87.98 70.61 95.34 89.95 89.33 86.91 82.85 95.45 88.15 82.90 87.53

Argentina Australia Austria Bahrain Canada Chile Cyprus Denmark Finland France Germany Greece Iceland Ireland Israel Italy Japan Korea, South Kuwait Malta Netherlands New Zealand Norway Portugal Singapore Spain Sweden Switzerland United Kingdom United States Uruguay

96.39 98.01 96.13 79.29 97.31 93.31 87.96 95.90 96.37 97.80 98.25 95.62 77.08 92.84 95.42 97.63 99.37 97.52 89.85 69.46 95.01 94.07 91.87 94.09 67.75 97.05 95.97 95.94 97.95 99.44 90.78

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Fig. 2. Energy efficiency score in developed economies.

Fig. 3. Energy efficiency score in developing economies.

paths for developing and developed economies. For developed economies, efficiency paths appear more flat and declines to meet with developing economies at a point before rising up slowly. However, for the developing countries, the efficiency paths, though appear to be undulating but rises to converge with the developed countries at a point before declining again. Furthermore, in Fig. 5, we pooled the developed countries together and in Fig. 6, we did the same for developing countries to track the changes in energy efficiency in each country during the period 1990–2014.9 From observation, the indication of energy efficiency convergence appears to be greater or stronger by pooling the sample countries together under the various economies. For instance, for the developed economy, most of the countries seem to converge at the top, except for the efficiency paths in Singapore and Iceland which seem to diverge from the other developed countries, although they start the period as one of the most energy-efficient countries, but with less progress over time. Similarly, for the developing economies, we notice the growth of energy efficiency over time in most countries especially in major economies like China, India, Pakistan, South Africa, Venezuela and Peru. As a result, there is a strong evidence of convergence among most developing countries. However, economies like Algeria, Ghana, Thailand and Iran seem to diverge from the other countries. According to Adom et al. (2018), Ghana and Algeria show a downward trend and diverge from the other Africa countries.

Table 4 Energy efficiency Score from regional perspectives. Regions

Scores (%)

North America Europe and Central Asia South Asia East Asia and Pacific Latin America and the Caribbean Middle East and North Africa Sub-Saharan Africa

98.3724 94.5161 92.0765 90.1338 87.8716 86.7421 85.7311

5. Conclusion and policy implications A green growth path is crucial for reducing emissions of greenhouse gases for sustainable global economic growth. This is especially relevant, when 70% of emissions come from excessive energy consumption, making energy efficiency a crucial concept. In recent years, therefore, governments have strongly invested in the development of green technology and innovation to enhance energy efficiency. Similarly, government institutions have been empowered to see to the implementation of energy policies that improve energy efficiency (Chang et al., 2018). But there appears to be uncertainty regarding the benefits of these investment in green technology (Palmer et al., 1995) as well as the potential role of government institutions in improving energy efficiency. In an effort to investigate the above issues, this paper assess the role of green technology and institutions in clarifying the cross-country disparities in national energy efficiency for 71 developed and developing economies around the world, using annual data from 1990-

9 Due to the unbalance nature of our data, our efficiency scores also appears to be unbalance for some countries, so we omitted countries with missing data in these diagrams.

9

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Fig. 4. Time evolution of total efficiency paths for the developing and developed economies.

Fig. 5. Time series plot of overall energy efficiency for developed countries (1990–2014).

201410. With parametric Shepherd energy distance function, we use the Greene's (2005a) True-fixed effect model with collection of other environmental variables to examine the energy inefficiency factors. Our results are consistent with other studies that consider these two issues using other methodological approaches. Starting with green innovation, we find clear evidence that green technology increases energy efficiency, like in works of Lin and Moubarak (2014) and Wurlod and Noailly (2018). Likewise, the variable for institutional quality turns out to be negative and statistically significant, after controlling for some variables in the model. Like in Chang et al. (2018), this signify that a rise in institutional quality promotes energy efficiency. For the other variables, trade openness and urbanization, they are positively correlated with energy inefficiency but the latter is insignificant. This suggest that trade openness and urbanization reduce energy

efficiency. The result for trade openness is consistent with the work of Jiang et al. (2017) who found a reducing effect of trade openness on energy efficiency. For urbanization, the results are similar to the works of Lv et al. (2017) who had similar outcome. For human capital, we found that it is negatively related with energy efficiency but statistically insignificant like in Danquah (2017) and Stern (2012). Regarding the estimated energy efficiency scores, they are quite large for most countries, ranging from 60.55% to 99.44%.11 Comparing aggregate efficiency scores of developed and developing countries, our results show that there appear to be convergence of efficiency estimates of both economies. This effect could primarily be attributed to catch-up effects of the low performing countries especially those of the transition economies. Under each economy (i.e. developed and developing), we pooled the countries together, we realized that the countries for each economy

10 This era (i.e. 1990–2014) was chosen because of the availability of enough data and because during these periods, efforts by governments to develop green technology increased significantly.

11 This is in line with other studies, who estimated transient technical inefficiency, e.g. Filippini and Hunt (2011); Stern (2012); and Zhou et al. (2012).

10

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Fig. 6. Time series plot of overall energy efficiency for developing countries (1990–2014).

countries should consider establishing R&D fund (if not yet established) to explore more and new green innovations. In addition, government may increase innovation by adopting R&D subsidies. Likewise, government could also offer low interest loans to entrepreneurs who invest in R&D projects. With the right institutional environment and conditions, green technology can be profitable, which should increase potential investment opportunities in the energy infrastructure sector (Sovacool, 2015). As some developing countries restrict the import of high-tech products from developed countries, it is appropriate to revise these policies to encourage innovation, technology transfers and spillovers. This will help boost the proportion of clean energy sources while meeting the rising energy demand and replacing the conventional fossil fuels energy. So that, economic development and carbon emissions can be decoupled gradually. Finally, there are some limitations to our study for further study. First, it is important to note that we are not aware methodologically of how to deal with the issue of endogeneity in the SFA model since current literature does not offer clear guidance on this problem (Mutter et al., 2013). However, in our study we focus on variables which are vital to the study in order to reduce the endogeneity problem. Secondly, this study did not differentiate between transient and persistent energy efficiency. According to Filippini and Hunt (2016), True-fixed effect model estimates only the transient type of energy efficiency. In future studies, we will consider distinguishing between the two types of efficiencies. Therefore, we submit that our study raises issues for future research.

seem to converge at the top, except for countries like Singapore, Iceland, Algeria, Ghana, Thailand and Iran that seem to diverge from the other countries. These results are consistent with Adom et al. (2018), who found that countries like Ghana and Algeria show a downward trend and diverge from the other Africa countries. As for the mean energy efficiency of all the individual countries, the result shows that USA, Japan, Germany and Australia are the most energy efficient countries while Belize, Panama, Singapore, Malta, Sierra Leone, Iceland, Jamaica, Bahrain, and Ghana are the least efficient countries. Except for USA, these efficiency estimates for the sample countries seem to be consistent with estimates found in works like Filippini and Hunt (2011); Stern (2012); Zhou et al. (2012); and Adom et al. (2018). As a recap, the contribution of this study is in threefold: First, it examined the role of green innovation and institutional quality within the stochastic frontier model. Secondly, it extended the energy distance stochastic frontier model by taking into account both desirable and undesirable outputs in modelling energy efficiency. Thirdly, we accounted for potentially unobserved country heterogeneity bias using the TFE and PDE model. Following from the findings, it is clear that government institutions and green technology play a vital role in enhancing energy efficiency. A significant manifestation of quality institutions and the consumption of green energy will reduce energy intensity. Therefore, both institutional quality and green technology foster improvement in energy efficiency across the globe. Hence, some policy implications are as follows: the emphasis on strengthening institutional quality ought to be stressed across countries with the intent of achieving other related benefit like economic development and prosperity. For example, there are evidences that countries with better institutional quality focus primarily on achieving sustainable economic development of which climate change and energy efficiency is an integral part. In most cases, these countries focus on addressing carbon issues from the perspective of increasing the proportion of green innovation in their over-all energy mix. In so doing, they tend to implement policies that minimize the consumption of fossil fuel energy. Although the development of green technology has risen over the years and have proved useful in reducing energy intensity, the technological gap between the developed and developing economies is still significantly wide, though some developing countries like China and Russia are catching up. To minimize the gap, government of these

Acknowledgment The authors appreciate the valuable comments of anonymous reviewers. We are also grateful for the financial support provided by the National Natural Science Foundation of China (No. 71774071, 71810107001, 71834003, 71690241, 71673230), the Soft Science Project in Zhenjiang (YJ2018004), Young Academic Leader Project of Jiangsu University (5521380003), and the Fundamental Research Funds for the Central Universities at Xiamen University (Grand No. 20720191006). We would like to thank Mr. Zengguang Zhong of MOE Key Laboratory of Econometrics, School of Economics at Xiamen University who kindly provides data analysis support. 11

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Appendix Diagnostic Tests In stochastic frontier analysis, the first process is to test for the statistical significance of the sample countries’ inefficiency. To do this, we adopt the generalized Likelihood Ratio (LR) test to examine the joint significance (null hypothesis) of independent variables. At 1% sig level, the null hypothesis is decisively rejected, which denotes that the inefficiency model is valid and so we can go ahead to use the SFA model. The 1% level of significance, critical values can be found in Table 1 of Kodde and Palm (1986). The estimation of SFA requires the use a suitable functional form that is fit for the sample data. In most cases, either Cobb-Douglass production function or a more flexible translog production function is considered. The selection of appropriate functional form of the model requires us to use LR test with Cobb-Douglass production function as a null model to test whether translog production will fit the given data. Again, the hypothesis is rejected at the 1% significance level, denoting that an estimated translog method assumes to be better in explaining the production function compared to Cobb-Douglass function. Therefore, we find the evidence to use the translog production function.12 The test used here follows a chisquare distribution. To test for the presence and nature of technical change, time line was included in the production function. After, the LR test, the null hypothesis of no technical change is strongly rejected. The result therefore shows the presence of significant technical progress in the energy efficiency. However, to confirm if observed technical change is Hicks neutral requires us to test the null hypotheses of no Hicks neutral. After doing that, the null hypothesis is rejected; thus, the non-neutral type of technical progress is considered adequate for our model. The test used here also follows a chi-square distribution. The results of all the diagnostics tests are presented in Table 1 below: Table 1

Generalized Likelihood-Ratio (LR) Tests of Hypotheses Null Hypothesis

LR Test

Critical Value

Decision

No inefficiency Cobb-Douglas Function fits the data No technical change Technical Change is Non-neutral

913.14 192.48 78.75 80.16

8.27 24.05 12.48 8.27

Reject Reject Reject Reject

Ho Ho Ho Ho

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12 However, for robustness of the results, we also show the results for the cobb-Douglass function.

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