2.4 Driving forces of emissions
2.4.2 Economic growth and development
Economic growth is another major driver of GHG emissions and is commonly measured as GDP per capita, which is defined as the value of all goods and services produced in a country over a specific period of time (Fisher et al., 2007; Nakicenovic et al, 2000a). Whilst GDP is not viewed as the best measure of economic development, it is universally used to allow for international comparisons (Nakicenovic et al., 2000a; Winkler, 2009). GDP growth is an important driver for GHG emissions because high GDP growth rates implies an increase in the production of goods and services, which results in an increase in energy consumption and waste (Haw and Hughes, 2007; Karakaya and Ozcag, 2005). Furthermore, an increase in economic growth results in industrial growth, which increases the demand for freight transport which increase emissions in the transport sector (EIA, 2009). Growth in GDP per capita also promotes household energy consumption, through a higher demand for private vehicles, electrification and domestic appliances (Haw and Hughes, 2007). Thus, changes in GDP are important for projecting emissions.
The existing literature suggests varied views on the relationship between GDP and energy usage. One may assume that an increase in GDP will automatically increase the energy usage in an area. According to Neumayer (2004), studies from 1960-1999 across approximately 160 countries have shown there is a strong relationship between CO2 emissions per person and GDP per person(0.9). As a result, a 1%
increase in GDP will result in a 0.9% increase in CO2 emissions. However research has shown that this is not always the case (Grubb et al., 2006). The first hypothesis that emerged to portray the relationship between the energy and income growth was portrayed in the Environmental Kuznets curve. The curve is
an inverted U shaped curve, which illustrates that the level of pollution or environmental degradation increases with development up to a certain point and thereafter declines as GDP continues to growth. The Kuznets curve can be used to portray different pollutants and environmental variables (Stern, 1998, 2004).
The curve in this case therefore depicts the energy intensity (energy/GDP) or the carbon intensity (GHG emissions/GDP) of an economy (Martin and Cerda, 2003). This curve is an inverted U, depicting that as a country‘s income increases initially, so too do the GHG emissions at an equivalent rate. This continues until a certain level of income is reached. At this stage the economy has the ability to produce more output with a similar level of energy usage and therefore the GDP increases, with no effect on the energy consumption (Grubb et al., 2006 and Martin and Cerda, 2003). Thereafter, the economy continues to grow, whilst GHG emissions gradually begin to decline (Stern, 1998). The Kuznets curve therefore suggests that high levels of economic growth will in the long-run benefit the environment (Stern, 1998).
This is explained by the fact that as an economy gets wealthier, there is a shift from energy-intensive heavy industries to growth in the service sector of the economy (Grubb et al., 2006). Other explanations include improved energy efficiency and a change in consumption patterns and structural changes, which are changes in the types of industries within an economy (Ma and Stern, 2008 and Martin and Cerda, 2003).
However, more recent studies have shifted away from the Environmental Kuznets approach to determining the relationship between income growth and energy usage. An example of where the Kuznets curve theory does not apply is in the case of the UK and the USA. Both countries are developed countries and are two of the earliest countries to industrialise (Grubb et al., 2006), therefore they should be in the downward slope of the inverted U shaped Kuznets curve. Figure 2.5 illustrates the GDP and the carbon emissions for both the countries from 1950 to 2000. The graph indicates that emissions closely follow GDP until around 1965 and then remained relatively constant, whilst GDP per capita continued to grow.
This indicates a delinking of the relationship between GDP and emissions (Martin and Cerda, 2003).
However, if the Environmental Kuznets curve theory were to apply, there should have been a decrease in emissions.
Figure 2.5: Relationship between emissions per capita and GDP in the UK and USA: 1950 – 2000 Source: Grubb et al., 2006
Another important observation from Fig. 2.5 is the peak in emissions in 1974 followed by a decline in emissions after the 1980s. This corresponds with the oil prices shocks, indicating that emissions are also impacted by external shocks and not always income (Grubb et al., 2006).
Delinking is only possible when an economy has reached a certain level of income, which is very high for most of the world‘s economies and almost impossible to reach. Also many developed countries today are using new resources, where the extent of the pollution is unknown and not correctly reflected in the Kuznets curve. Developed countries, whose emissions have declined, sustain their consumerist, energy intensive lifestyle by importing goods from developing countries that are energy intensive (Martin and Cerda, 2003). Therefore this does not truly reflect a decrease in emissions in developed countries.
Similar studies conducted in other countries showed variable results that did not display a strong relationship between income and emissions. Examples are studies conducted in India, Malaysia and China (Grubb et al., 2006), as well as studies by Martin and Cerda (2003) in Brazil, Spain and other countries.
The studies, by Grubb et al. (2006) and Martin Cerda (2003), indicate that the relationship between GDP and emissions is unpredictable and variable according to the specific country. In India, China and Malaysia, the study revealed contrasting results. India reflected a similar relationship between GDP per capita and emissions per capita, whilst China revealed a decrease in emissions, corresponding with an increase in GDP. Malaysia indicated a small growth in GDP and a larger growth in emissions (Grubb et al., 2006). These studies indicate that the relationship between income growth and emissions is a weak one and is affected by many unknown external factors. The relationship between these two factors appears to be country specific, where different countries respond differently to economic growth. This is due to other factors, besides GDP growth, that also have an impact on emissions (Martin and Cerda, 2003).
Structural change is also an economic development driving force behind GHG emissions (Fisher et al., 2007). It is one of the main reasons for a decline in emissions, whilst GDP continues to increase. Shifts
from energy intensive industries to low carbon service economies can significantly reduce GHG emissions. For example, in the UK, emissions peaked in 1973 and decreased by 20% by 1984 (Stern, 2007). Therefore according to McKibbin (2004), it is not only economic growth that impacts on emissions, but the composition of that growth. For example, GDP increases due to growth in the iron and steel industry will result in higher GHG emissions in comparison to an equal GDP growth in the Information Technology sector of the economy.
South Africa is an energy intensive country, where high energy consuming industries influence GDP growth. As a result, it is expected that an increase in GDP will in effect result in an increase in emissions (Haw and Hughes, 2007). According to Winkler (2007, 2009), if the other parameters affecting GHG emissions remain fixed, emissions closely follow GDP growth. Therefore a 2% growth in GDP will result in a 2% growth in GHG emissions. In the past, South Africa‘s GDP has generally increased, with slight dips in the 1980s and 1990s. Therefore, it can be assumed that South Africa‘s GDP is likely to continue increasing, with slumps in certain years (Winkler, 2009).