There Dinda and Coondoo (2006) explored the short-term dynamics

There is a rich empirical literature on the relationship between environmental quality and economic growth, including several studies of pollution convergence, thus testing whether there is a negative growth-level relationship in environmental pollution. Dinda and Coondoo (2006) explored the short-term dynamics of the income–emission relationship in 88 countries.

The results showed that cointegration exists between CO2 emission and GDP per capita in North America, South America, Asia, and Oceania. Soytas et al., (2007), found that energy consumption Granger eventually causes carbon emission. Warr and Ayres (2010) found that unidirectional causality existed from energy to GDP. An increase in supplied energy has both short-term and long-term effects to increase the output; however, output growth does not increase energy consumption. The researchers suggest that an increase in energy inputs can sufficiently stimulate the output growth in the short run. Meanwhile, over a period of several years, GDP positively responds to increased energy and useful work inputs by readjusting to the long-term equilibrium relationship. Recently, Borhan, et al.

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, (2013), applied the fixed and random effects model to examine the relation between pollution and economic growth.Oh and Lee (2004) found that unidirectional causality exists from GDP to energy in the long run, implying that enforcing an energy conservation policy is feasible without compromising economic growth in Korea. At the same time, Hung and Shaw (2004) used panel data model to test air pollution and income whether existed EKC relationship or not. They especially observed that making new regulation in 1990 and renovating air-quality monitoring stations in 1994 did not reduce air pollution. Halicioglu (2009) examined the dynamic causal relationships among carbon emission, energy consumption, income, and foreign trade in Turkey; he found that income was the best variable in clarifying the carbon emission in Turkey, and then found energy consumption and foreign trade could explain the carbon emission. However, Talebi et al.

, (2012), found that there is no relationship from GDP to energy consumption in Iran.Most attention has been devoted to convergence of carbon dioxide (CO2) emissions at the global or regional level (e.g., Strazicich and List 2003; NguyenVan 2005; Aldy 2006; Ezcurra 2007; Panopoulou and Pantelidis 2009; Westerlund and Basher 2008; Camarero et al. 2008, 2013; Brock and Taylor 2010; Kumar and Managi 2010; Orda ´s Criado et al.

2011). The economic theory recognizes that the per capita output in a country is determined by the amount of physical capital, human capital and technological advancement. Acemoglu and Robinson (2010) argue that human capital, physical capital and technology are the only determinants of growth. They further state that to find out why some countries grow faster than others, we need to look for more fundamental causes which may underlie the proximate differences across countries. Over the last three decades, the focus of thinking has shifted away from the so called ‘proximate causes’ to the more ‘fundamental causes’ of economic growth. In this context, the role of institutions in explaining the cross-country differences in the economic growth has received more attention.

The path breaking studies by North and Thomas (1973), North (1981), Olson (1982) and Jones (1987) inspired the researchers to explore the role of institutions in explaining the persistent differences in the economic development across countries (see e.g., Lau et al., 2014). The relevant literature suggests that institutions play a significant role in determining the growth performance of nations. The quality of institutions in any given country plays an important role in determining the growth process by influencing the incentive structure for investment in human and physical capital as well as technological advancement and innovations.It is generally believed that institutions, particularly the security of property rights play a key role in determining the long-run economic growth (Knack and Keefer, 1995; Rodrik et al., 2004).

North (1990) argues that secure property rights and better contract enforcement determine growth. He states that the failure of the developing countries to design institutional framework based on secure property rights and enforced contracts is the major reason for their underdevelopment. An enormous amount of empirical work examining the relationship between institutions and growth has developed over the last three decades. Knack and Keefer (1995) using data for 97 countries over 1974–1989, showed that the quality of institutions is important for growth and investment. They used two institutional variables in growth regressions capturing the security of property rights and enforcement of contract using five indicators: i) rule of law; ii) corruption; iii) bureaucratic quality; iv) protection against risk of expropriation and v) repudiation of contracts. These indicators were from the International Country Risk Guide (ICRG) dataset.

They also used four indicators: i) contract enforceability; ii) infrastructure quality; iii) nationalization potentials and iv) bureaucratic delays. These indicators were obtained from the Business Environmental Risk Intelligence (BERI) dataset. They found that the relationship between institutional variables and the economic growth is positive. Mauro (1995), using cross section data for 67 countries over 1980–1983, shows that corruption is negatively linked with investment which lowers the economic growth. On the other hand, he finds that bureaucracy has a positive impact on the investment.

Barro (1998), in a panel of 100 countries over the sample period 1960–1990, finds that ‘rule of law’ has a positive impact on growth. Rodrik, et al. (2004), using the index of ‘rule of law’ as proxy for institutions, estimated the contribution of institutions, geography, and trade in determining income levels of the countries. They found that institutions have a strong impact on income.

They also found that variables like geography and trade are insignificant once the institutions play their role effectively.Hall and Jones (1999), following Knack and Keefer (1995), used a weighted average measure of institutions from the International Country Risk Guide (ICRG) dataset for 127 countries. They showed that differences in social infrastructure across countries are caused by large differences in capital accumulation, educational attainment, and productivity.

This accounts for cross-country income differences. Acemoglu et al. (2001), using differences in European mortality rates as an instrument for contemporary institutions, found large effects of institutions on the income per capita. Acemoglu et al. (2006) estimate the role of institutions on economic growth.

They used ‘constraint on executive’ from Polity IV as a proxy for private property institutions. The authors showed that private property institutions exercise a major influence on long-run growth, investment and financial development. Valeriani and Peluso (2011) analyze the impact of institutional quality on the economic growth at different stages of development by employing a panel over 1950–2009 for 181 countries using a pooled regression and fixed effects.

They found a positive impact of institutions, measured by civil liberties, quality of government and number of veto players, on economic growth. They also showed that institutions are more effective in developed countries as compared to developing countries.Chauffour (2011), using data for more than 100 countries over 1975–2007, found that institutions, measured by economic freedom and civil and political liberties determine why some countries achieve and sustain better economic outcomes. This study shows that a one unit differential in the initial level of economic freedom between two countries (on a scale of 1 to 10) is associated with an almost 1 percentage point differential in their average long-run economic growth rates. For civil and political liberties, the long-term effect is also positive with a differential of 0.3 percentage point.