ABSTRACT
This study utilizes a nonparametric DEA approach, to assess the energy efficiency of 135 selected countries across the globe during the period of 2000 – 2014 to account for environmental factors in energy efficiency analysis and to analyze the relationship between economic factors and energy efficiency within a two – stage framework. Three kinds of variables are used: input, desirable output, and undesirable output. The inputs are labor, capital, and energy consumption. The undesirable outputs (environmental factors) are Carbon dioxide (CO2), methane (CH4) and Nitrous oxide (N2O) emissions, the desirable output variable is gross domestic product (GDP). Energy efficiency is measured within a total factor framework by way of an SBM-Undesirable model. The second stage assesses the determinants of energy efficiency in countries by way of a bootstrapped truncated regression, FE, 2SLS and Systems Generalized Methods of Moments to control for possible heteroscedasticity, autocorrelation and endogeneity.
The results showed that the selected countries are on average 39% energy efficient within the study period, suggesting that increasing the levels of energy consumption in countries is not being used to produce the maximum GDP possible. The results also showed that incorporating the environmental factors improves the efficiency scores. Income per capita (GNIPC) and CAPLAB (Technological progress) are found to have significantly positive effects on energy efficiency of the countries, whilst higher debt stock and population growth leads to higher inefficiency given their negative significant relationship with energy efficiency.
CHAPTER ONE INTRODUCTION
Energy has become the wheels on which all economies drive as it is a key factor used in the production of almost all goods and services (Narayan & Smith, 2008; Odhiambo, 2009; Wolde- Rufael, 2009). According to the World Resources Institute, the use of energy is the major cause of greenhouse gas emissions and global warming and accounts for 61.4% of total greenhouse gas emissions (Sadorsky, 2010).
In the light of increasing globalization, environmental concerns have captured serious attention from both governments and international organizations as production processes are accompanied by certain bad outputs from production processes (Al-Tuwaijri, 2004; Cherchye, Rock, & Walheer, 2015; Chiu, Liou, Wu, & Fang, 2012; K. Wang, Lu, & Wei, 2013). There is therefore the need for environmentally efficient production processes.
Environmental efficiency is of interest to every nation, due to the increase in emissions associated with the environmentally unsustainable production processes in most economies (Zaim & Taskin, 2000). Though the term environmental efficiency might mean different things to different people, it is simply defined as using less inputs to produce outputs with minimal environmental concerns (Pittman, 1983). Therefore, Environmental efficiency is seen as a necessary condition for economic and social development (Bai-Chen, Ying, & Qian-Qian, 2012; Halkos & Petrou, 2019).
In the development and economic growth of most economies, energy is one key factor that plays an essential role and has therefore become a fundamental part of global economic life ( Barros & Assaf, 2009; Cleveland, 1997; Kashani, 2005; Murphy & Hall, 2011; Ramachandra, Loerincik, & Shruthi, 2006). Countries consume energy in their production processes which has both positive and negative impact on the environment such as greenhouse-gas emissions which is a threat to the world climate (Stern, 2007). This pollution growth has heightened global concern for climate change. This led to the September 2015 adoption of the 17 Sustainable Development Goals (SDGs) to be accomplished by 2030, among which is the crucial call to “take urgent action to combat climate change and its impacts” (SDG 13) (United Nations Environment Programme, 2016).
According to the Intergovernmental Panel on Climate Change (IPCC), the unprecedented increase in the global greenhouse gas (GHG) emissions in recent years is mainly driven by economic and population growth (IPCC, 2014). The IPCC (2014) reported that more than half of the average global temperature observed from 1951 to 2010 was caused by these gases; carbon dioxide (CO2), methane (CH4), nitrogen oxide (N2O) and the fluorinated gases (F-gases).With the strong growth of emerging economies such as China and India, reliance on energy is expected to further increase heavily (Barros & Assaf, 2009) leading to a rise in the concerns on global warming (Zhang, Cheng, Yuan, & Gao, 2011).
Fare, Grosskopf, Lovell, and Pasurka (1989) posit in their weak disposability assumption that production processes will always be accompanied by some undesirable output such as carbon dioxide (CO2), sulfur oxide (SOX) and nitrous oxide (N2O) that are detrimental to the natural environment. As economies rapidly expand and businesses increase their demand for energy, there is an increase in the green- house emissions, as the desirable output is accompanied by some bad outputs. Since these green-house emissions and other air pollutants derived from the consumption
of energy is a major contributor to global warming and regional atmospheric contamination, academic researchers, industry entrepreneurs and government officials, have recently recognized that sustainable development is one core solution to balance economic and social development with environmental protection and climate change mitigation (Zaim & Taskin, 2000).
Growing demands for environmental quality has forced policy-makers to consider the consequences of their actions in the formulation of economic policies. As environmental concerns are pronounced increasingly in relation to global warming, it is treated as an international matter (Zaim & Taskin, 2000) and therefore it is necessary to put in substantial effort to enhance environmental efficiency in this sector so as to cope with the massive demand and combat these green-house emissions.
Energy efficiency can be defined as the use of less energy to produce the same amount of some useful output (Ang, 2006). Therefore, an entity that is able to use less amount of energy inputs to produce the same amount of “useful outputs” is said to be energy efficient (Patterson, 1996). Environmental efficiency on is simply defined as using less inputs to produce outputs with minimal environmental concerns (Pittman, 1983).
Although many studies abound in energy efficiency (Adom, 2019; Alberini, 2018; Apergis, Aye, Barros, Gupta, & Wanke, 2015; Jebali, 2017; Keho, 2016; Moncef Krartia, 2018; Ohene-Asare & Turkson, 2018; Shen, 2017; Zhou, Ang, & Poh, 2008; Zhu & Chen, 2019), only a few (Ang, 2006; Chang, 2013; Chontanawat, Hunt, & Pierse, 2008; Hsieh, Lu , Li , & Xu, 2019; Oh & Lee, 2004; Ohene-Asare & Turkson, 2018; Pao & Tsai, 2010, 2011; Yang & Wei, 2019; Yongming Han 2018; Zhang et al., 2011) captured the negative externalities of energy use such as global warming. But none to the best of the authors knowledge has focused specifically and wholly on all continents
across the globe, although some have included some African countries in their analysis (Adom, 2019; Ohene-Asare & Turkson, 2018; Ramanathan, 2005; Zhang et al., 2011) , Jebali (2017) and Hsieh, et al. (2019) covered only the Mediterranean and European countries respectively, even though country-specific benchmarks are necessary since levels of energy efficiency vary considerably among countries (Song, Yang, Wu, & Lv (2013). The purpose of this study is to evaluate the environmental energy efficiency of countries by using the slacks-based measure of Tone (2001). Next, the study investigates the economic and risk factors that can affect the efficiencies of the countries using truncated bootstrapped regression.
Resources used in productive processes which promote economic growth usually have some level of tamper on the quality of the environment. A study by Stern (2008), reports that a 1% increase in scale (economic growth) results in a 1% increase in emissions. Hence there is the need for sound, efficient and sustainable management of the environment. Globally, CO2 emissions have been of major interest to environmentalists and environmental economists as compared to other emissions such as SO2, NOX and so on. Human beings produce CO2 by burning fossil fuels such as coal, oil and gas in their commercial and domestic activities. Given the growing usage of fossil fuels for the production of goods and services, (CO2) emissions have increased significantly in the past century (Boopen, 2010) and accounts for about 72% of emitted greenhouse gases (Sanglimsuwan, 2011). Globalization is likely to increase trade volumes, expand economic activities and affect environmental quality (Vutha, 2008). Su (2011) opined that emissions in a country ‘s globalization processes measured as a percentage of its total emissions are increasing overtime.
Despite the many energy efficiency related studies (Adom, 2019; Alberini, 2018; Halkos & Petrou, 2019; Hsieh, et al. 2019; Iftikhar, Wang, Zhang, & Wang, 2018; Keho, 2016; Kounetas &
Zervopoulos, 2019; Shen, 2017; Wang, Duan, Ma, & He, 2019; Chen, Shang, & Wu, 2018; Yang & Wei, 2019; Yongming Han 2018; Zhongshan Yang, 2019; Zhu & Chen, 2019), this study identifies some gaps in the recent literature.
First, although country and cross country level energy efficiency studies exist (Adom, 2019; Apergis et al., 2015; Chang, 2013; Gómez-Calvet, Conesa, Gómez-Calvet, & Tortosa-Ausina, 2014; He, Zhang, Lei, Fu, & Xu, 2013; Honma & Hu, 2014b; Hsieh, et al., 2019; Hu & Wang, 2006; Keho, 2016; Li & Hu, 2012; Moncef Krartia, 2018; Rao, Wu, Zhang, & Liu, 2012; Song et al., 2013; Zhao Xiaoli, Yang Rui, & Ma Qian, 2014; Zhang, Kong, & Yu, 2015; Zhang et al., 2011; Zhongshan Yang, 2019), these studies fail to account for the effects of the bad output in the fuel burning process on the quality of the environment hence the results may not prove effective when assessing environmental-friendly energy efficiency. To take into account the growing concern regarding environmental impact, undesirable output should be incorporated into the environmental DEA framework, but none of the known energy efficiency studies to the best of the author’s knowledge, has simultaneously incorporated undesirable outputs and applied bootstrapping (IEA, 2015; Li & Hu, 2012) though Jebali (2017); Song, Zhang, Liu, and Fisher (2013); Zuckerman, Welch, and Pope (1990) applied bootstrapping, Halkos and Petrou (2019); Hsieh, et al. (2019); Iftikhar et al. (2018); Kounetas and Zervopoulos (2019); Ohene-Asare and Turkson (2018); Wu et al. (2018) incorporated bad output in their study of countries. The bootstrap helps to purge the efficiency estimates of sampling variations resulting in reliable confidence interval for the estimates and produces estimates that mimic the true efficiency score (Simar & Wilson, 1998, 2000). Again, incorporating undesirable outputs gives a better efficiency assessment since energy production and use account for two-thirds of the world’s greenhouse gas (GHG) emissions (IEA, 2015; Li & Hu, 2012).
Second, the traditional DEA models applied by some studies (Eller, Hartley, & Medlock, 2011; Ike & Lee, 2014; Sueyoshi & Goto, 2012b; Thompson, Lee, & Thrall, 1992; Wolf, 2009) failed to incorporate non-radial slacks which in the evaluation of efficiencies of Decision Making units (DMUs) has a high discriminatory power and seem to be more effective in measuring environmental efficiency than the radial efficiency measure which often leads to the case where a lot of DMUs have the same efficiency score of 1 and hence difficulty in ranking the environmental performance of these DMUs. Although a number of non-radial DEA models under the traditional DEA framework for instance, (Banker, 1986; Chen, 2003; Ray, Seiford, & Zhu, 1998; Zhu, 1996) have been developed, few studies have investigated how it is being applied in environmental efficiency assessment to the best of the authors knowledge (Peng Zhou, Poh, & Ang, 2007). It is therefore important to extend the traditional non-radial DEA models into the case where bad outputs exist.
