EN LA COLONIA PENITENCIARIA [25]

There are quite a few theoretical studies that formally model a direct link between the environment and growth, energy and growth, and energy and environment. The empirical literature appears to be richer. Initially we underpin some the theoretical concerns. Then, we introduce the empirical surveys that relate the transmission mechanisms within the energy– environment–economy (E-E-E) nexus. The theoretical work on economic growth mostly relies on the Solow growth model. More recently growth models depend heavily on the endogenous growth theory. There are a significant number of studies that model the relationship between the natural resource management, environment and economic growth (for review see Xepapadeas, 2005). Whereas Jorgenson and Wilcoxen (1993) selectively cover the theoretical work that models intertemporal general equilibrium framework to develop the interrelationships between energy, the environment, and economic growth. As claimed by Xepapadeas (2005) early works on the growth failed to take into account of the environmental issues of growth. Reviewing the recent literature, he argues that, “ (there is a)…necessity for growth theory to delve deeply into the analysis of the interrelationships between environmental pollution, capital accumulations and the growth of variables which are of central importance in growth theory.” p. 1221).

Kolstad and Krautkraemer (1993) point out that the resource use (particularly energy) cede instant economic benefits, its negative blow on the environment may be observed in the long run. Since, most of the theoretical work is dynamic; the empirical studies are mostly static in nature, entailing the need for dynamic empirical analysis. Jorgenson and Wilcoxen (1993) find out that the common feature of the models is relying on the effect

of policies on capital accumulation in modeling the relationships between the economy, energy and environment. Theoretically, there may be several transmission mechanisms through which environmental policy and economic growth may relate; partly due to some models considering pollution as an input to production; and partly, as a negative by-product (Ricci, 2007). Generally, environmental policies are considered to have negative impact on growth, due to their role as additional constraints in the models. Certainly, Dudek et al. (2003) show that the additional benefits from reduction of emissions will exceed the average cost. Hence, the methodology for empirical analysis should base on the dynamic effects in the energy– environment–economy nexus. Theoretical studies mainly believe that any effective policy should take the dynamic nature of the relationships and sight for a long run perspective.

The mismatch between theoretical work and empirical studies about the relationship between growth, energy and environment is pointed by Brock and Taylor (2005) and argue that the key is the so-called Environment-Kuznets-Curve (EKC) literature. Brock and Taylor (2005) find a tighter connection between theory and data. The focuses of many empirical studies has been on the relationship between the environment and economic growth (see Dinda, 2004; Stern, 2004 to review). The EKC studies that analyze linear (Shafik and Bandyopadhyay, 1992; de Bruyn et al., 1998), plus quadratic and cubic (Canas et al., 2003; de Bruyn et al., 1998; Heil and Selden, 1999; Roberts and Grimes, 1997) connection between GDP per capita and CO2 emissions, could not explore agreed-upon findings. Dinda (2004) find a dynamic link between CO2 emissions and income and CO2 emissions may lead economic growth from a production perspective. It may still be

possible to observe the emissions to lead energy use if the energy production process of a county is responsible for a major portion of emissions.

In another line of empirical research, there are a sizeable number of studies that examine the bond between energy use and economic growth. Since Kraft and Kraft (1978), the literature has tested the Granger (non) causality between energy and income with miscellaneous results (Akarca and Long, 1980; Yu and Hwang, 1984; Erol and Yu, 1987;

Hwang and Gum, 1992; Glasure and Lee, 1997). Most of these studies faced a numeral of methodological setbacks; particularly the omitted variables bias. In this regard the first significant study is Stern (1993) who supports using a multivariate analysis. Following Stern (1993), many studies employed recent and powerful time series techniques, (see for example, Stern, 2000; Asafu-Adjaye, 2000; Yang, 2000; Sari and Soytas, 2004; Ghali and El-Sakka, 2004; Lee, 2006). Nevertheless, this line of research also failed to accomplish common results. For instance, Soytas et al. (2007) study the long run Granger causality between emissions, energy use, and growth for US economy, with additional considerations for labor and capital. Though they do not find any evidence of causality between carbon emissions and income; and energy consumption and income, but verify that energy use is the foremost source of emissions.

In both directions of literature, and particularly in the EKC literature, the large size of the work is on developed economies. There is still very limited literature that studies the link between energy use, economic development and environmental degradation in Pakistan, yet alone the dynamic link between CO2 emissions and income. Siddiqui (2004) in this regards is one of the pioneer studies that analyze the link between energy and economic growth. According to the results of her model, energy is a major source of economic

growth and indicates the possibility of inter-fuel substitution which may be result of changes in price structure.

The work on environmental Kuznets curve started in 1990’s when the world started to realize that earth average temperature increased dramatically and in the same period the Earth summit in Reo-de-Janeiro, Brazil was held to discuss the Global issue of climate change . In the same period researchers of environmental economics hypothesized Environmental Kuznets curve which got alarming attention rapidly. At the time it is found that the industrialization has cause economies to emit greenhouse gases especially CO2, therefore the first relationship was made between economic growth and CO2 emission.

The work was first started by the Grossman and Krueger 1991 to study the effect on NAFTA but, EKC got more attention and importance when Shafik and Bandyopadhyay’s (1992) contribute in the background study for the 1992 World Development Report stating the environmental quality improvement is essential for the sustainable development. Further this study was followed by Shukla and Parikh (1992); Grossman and Krueger (1995); Shafik (1994); Selden and Song (1995); Jaeger et al. (1995); Tucker (1995); Jha (1996); Horvath (1997); Barbier (1997); Matyas et al. (1998); Ansuategi et al.

(1998); Heil and Selden (1999); List and Gallet (1999); Brandoford et al. (2000); Stern and Common (2001); Roca (2003); Friedl and Getzner (2003), Dinda and Coondoo (2006); Managi and Jena (2008); Coondoo and Dinda (2008), jalil and Mahmud (2009) and Akbostanci et al. (2009).

The economic growth engaged several industries and firms and it tends to increase the demand for energy consumption, subsequently energy consumption was also included in the hypothesis of environmental Kuznets curve. The energy consumption contribute

highest to the environmental degradation, therefore the energy-economic growth nexus is included as the important determinant of carbon emission. Some of the studies in this regard include Hwang and Gum (1991); Stern (1993; 2000); Masih and Masih (1996);

Yang (2000); Glasure (2002); Hondroyiannis et al. (2002); Ghali and El- Sakka (2004);

Wolde-Rufael (2006, 2009); Narayan and Singh (2007); Narayan et al. (2008), and Jalil and Mahmud (2009).

After determining the energy and economic growth the trade openness is considered to be the next critical contributor to the environmental degradation. The relationship between environment and international trade has been empirically investigated but this effect depends mainly on the policies implemented within the economy. On trade determinant of environmental degradation there are two types of studies, one is for and other is against. The studies which shown trade openness influence negatively include; Suri and Chapman (1998); Schmalensee et al (1998); Beghin et al (1999); Abler et al (1999);

Lopez (1994); Cole et al (2000) and Antweiler et al., (2001); Copeland and Taylor (2001); Chaudhuri and Pfaff, (2002); Ozturk and Acaravci (2010) Nasir and Rehman (2011) but it is also believed that trade openness also help to counter the negative effect in helping the economy seek technology to attain the efficiency and after certain level of growth the environmental degradation is also decline and trade play vital role. Therefore, the mix results are found in literature regarding the role of international trade. The studies whose results favor environment because of trade openness are Lucas et al. (1992);

Shafik and Bandyopadhyay (1992); Birdsall and Wheeler (1993); Runge (1994);

Helpman (1998); Ferrantino (1997), and Grether et al. (2007.

The other variables which are also considered beside economic growth, energy consumption and trade openness are technological movement and population growth and density. The studies which include population as the determinant of environmental degradation is Dina (2004), Shahbaz (2011) and prior to this Panayotou (1997) indicated population is one the factor contributing to the environmental degradation. The role of the economic growth rate and population density is also an essential factor. Booming economic growth and increasing population do increase moderately the environmental price. Therefore in our study we have included the population with three other major factors of environmental degradation in order to sketch the comprehensive view of economy.

Copeland and Taylor (2003) point out, in the absence of change in the structure and

technology of the economy, increasing economic activity would result in an equiproportionate growth in pollution or other environmental impacts. This ‘scale’ effect suggests a monotonically increasing relationship between real GDP and pollution and makes economic growth and sustainable development two conflicting goals. However, economic growth generates technological progress; polluting inputs are used more efficiently in the production process or through abatment technologies. If the ‘technical’

effect is strong enough to offset the scale effect, economic growth is compatible with less pollution and the link may become locally decreasing. Three other mechanisms also lead to changes in the output composition of countries: unbalanced growth processes of pro-duction factors; biased technological progress between industries or variations in relative world prices. These specialisation patterns between unequally pollution-intensive sectors are usually referred to as ‘composition’ effects. The sources-of-growth explanation of the

EKC shape relies on that particular argument. If economic growth is first induced by ac-cumulation of a production factor (capital) used relatively more intensively in a polluting sector but then shifts toward accumulation of a factor (labor or human capital) more intensively used in a less or non polluting sector, a straightforward application of Rybczinsky’s theorem leads pollution to follow the same path as the production of the polluting good, an U-inverted pattern. A similar argument can be used to explain why capital abundant economies (rich countries) are expected to pollute more than labor-abundant ones (poor countries). All these supply side arguments have two major implications. Firstly, economic growth may not require any environmental policy measure to be compatible with a more efficient use of polluting inputs or natural resources. Secondly, as Copeland and Taylor (2003, Ch. 3.1) indicate, we can have a stable relationship between pollution and technology and primary factors, and between income and these same variables, without having a simple and stable relationship between pollution and income. In plain words, the same level of income may be linked to different levels of pollution, depending on the factor which generated this income level.

From a social point of view, the willingness to tolerate the inconveniences of pollution in order to increase income plays a major role in determining the strength of policy re-sponses to environmental damages. Consequently a pure scale effect generated by neutral growth could be overcome by environmental policy measures if, at some level of income, the relative willingness to pay for pollution reduction exceeds the relative growth in in-come5. The income-pollution relationship is also sensitive to the way pollution is measured (i.e. in levels, per capita or intensity terms), as well as to the level of spatial aggregation of the data. In this paper, we focus on per capita levels of pollution as it

represents the most common specification of the dependent variable in the IER literature on air pollutants.

Given the variety of theoretical foundations, no single functional form can be advocated a priori to link indicators of environmental degradation with measures of economic

activity. As the income-pollution relationship is a reduced form function, all the underlying forces which determine its shape for a particular geographical area are subsumed, i.e. they remain unexplained. The early empirical IER literature has addressed the functional uncertainty by retaining three main parametric flexible specifications:

quadratic and cubic functions which capture nonlinearities and spline linear

functions which gauge thresholds effects. More recently, researchers have turned to non-parametric and seminon-parametric regressions which leave the functional form unspecified and avoid the risk of choosing an inadequate parametric function. Moreover, the lack of long time series on pollutants at the country level has made authors favour cross-country/region panel data. The absence of a range of explanatory variables which consistently capture the differences between countries may lead to biased estimates. This heterogeneity issue has been neglected in most of the parametric and nonparametric analysis of IER panels. Moreover, when it has been investigated, the F-tests used were not robust to functional misspecification. Consequently, the estimated IER appears to be highly sensitive to the pollutant or environmental damage considered, to changes in the sample composition (size or/and time periods considered) and to differences in econometric specifications.

The case of air pollutants is suggestive, particularly the one for CO2 emissions. Many authors make use of different versions of the database from the Carbon Dioxide

Informa-tion Analysis Center (CDIAC) to test the EKC hypothesis with a panel of world countries. Holtz-Eakin and Selden (1995) (HES95), Heil and Selden (2001) (HS01) and Schmalensee et al. (1998) (SSJ98) use similar countries’ panel data sets including over

120 countries and covering roughly 40 years6; they estimate time- and country-fixed effects quadratic functions (HES95 and HE01) and a spline-regression model with the same fixed effects (SSJ98). HES95 and HE01 find U-inverted shapes with very different turning points, ranging from US$35,000 to several millions depending on whether per capita income and emissions are measured in levels or in logarithms. SSJ98 get a within

sample maximum of US$10,000 with a 10-segment regression. A nonparametric pooled regression is used by Taskin and Zaim (2000) to investigate the link between a CO2 environmental efficiency index and GDP per capita for 52 countries over 1975-1990.

Their results point towards a third order polynomial specification. A semiparametric version of the time- and country- fixed effects models used by HES95, HS01, and SSJ98 is estimated by Bertinelli and Strobl (2005) for a panel7 of 122 countries over the 1950-1990 period. They find that the pooled regression are monotonically increasing.

Recently, Dijkgraaf and Vollebergh (2005) and Azomahou et al. (2006) tackle the fun-damental assumption of poolability for CO2-IER panels in parametric or nonparametric frameworks respectively. Focusing on the sample of 24 OECD countries mainly responsi-ble for the U-inverted shape found in HES95, HS01 and SSJ98, Dijkgraaf and Vollebergh (2005) compare directly different versions of fixed-effects models to country-specific time- series regressions (with and without trends) and conclude that less than half (11) of the OECD countries display the U-inverted shape depicted by the pooled fixed-effects estimates. Azomahou et al. (2006) check the structural stability of the per capita IER

with a nonparametric poolability test for a panel of 100 countries over the 1960-1996 period. They conclude that there is a stable cross-sectional relationship through time which allows the pooling of the data. The pooled country-fixed effects nonparametric regression displays a monotonically increasing pattern. In addition, nonparametric estimates are shown to be preferred to parametric ones.

Some authors have carried IER estimates with panels at low level of spatial aggregation.

List and Gallet (1999) use state levels of SO2 and NOx emissions for the US spanning

from 1929 to 1994. They estimate IERs with per capita data and a linear trend. The state-fixed effects models produce global EKCs for all states; quadratic and cubic state-specific regressions also yield a majority of respectively 79% and 98% hump-shaped functions for SO2 emissions and a rough 80% EKCs for NOx with both specifications. However, the vast majority of the state-specific turning points fall outside the confidence interval for the peak produced by the fixed-effects models. With the same data, Millimet et al. (2003) compare pooled time- and individual-fixed effects cubic models and spline regressions with time- and state-fixed effects semiparametric specifications8. They show that while the EKC obtained for per capita NOx emissions is robust to the estimation strategy, the functional forms for SO2 vary substantially. However, the null hypothesis of equality between the spline or cubic models and the partial linear models is rejected for both pollutants. These authors also compute specific semiparametric estimates for selected US states9 and they conclude that the EKC shape remains robust at the state level for NOx, but the results for SO2 are mixed. De Groot et al. (2004) utilise a panel dataset on Chinese provinces covering the period 1982-1997. They investigate the IER for wastewater, waste gas (aggregate emissions of CO2, NOx and SO2) and solid waste from

the industrial sector with the pooled region-fixed effects model. They contrast the results obtained when expressing the dependent variable in levels, per capita and intensity terms.

The relationship is shown as being monotonically decreasing for wastewater regardless of the dependent variable, increasing (respectively decreasing) for waste gas with the ex-plained variable in levels or per capita (respectively intensity) terms and very versatile for solid waste depending on the dependent variable used. More recently, Aldy (2005) tests the EKC hypothesis for production as well as consumption-based per capita CO2 emissions in the US at the state level. The author globally validates the EKC shape with the state- and year-fixed effects quadratic models as well as with the spline regressions.

He provides evidence of significant different peaks for both CO2 series. When state-specific quadratic models are fitted, the equality of the estimated functions and EKC peaks between states is rejected despite the fact that the vast majority of the states does depict EKC-type relationships. Since the data span over a long time period, Aldy (2005) also controls for common stochastic trends in the time-series and concludes that only about 20% of the state-specific relationships were cointegrated10.