Developed vs developing countries

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Although GHG emissions have historically been overwhelmingly due to developed nations, changing population and economic growth dynamics in developing countries will result in them surpassing developed nations in terms of GHG emissions by around 2030 (Dowlatabadi and Oravetz, 1997). Thus the energy intensities of these developing economies will be crucial in determining the need for carbon-free power. Unfortunately, analysis of energy intensity trends has been concentrated on the developed world, and thus E/GDP as a measure of energy intensity is even less well defined for the developing world, due to difficulties in defining GDP, and poor data on energy use. This is one of the inputs into the large uncertainties in projections of future global energy intensities and hence carbon emissions.

Looking at energy intensity trends in developing countries, these have generally been rising over the past decade (Goldemberg, 1996), reflecting an earlier stage of industrialization where heavy industry and infrastructure expand. The fear of rising energy intensities in populous nations as they industrialize has been a part of the rationale behind the Clean Development Mechanism agreed in the Kyoto Protocol of the UN Framework Convention on Climate Change. However, analysis of technology transfer in developing countries suggests that recipients of such a mechanism are reluctant to use new energy efficient technologies unless they are also widely used in the donor country (Sagar and Kandlikar, 1997).

Figure 4.4 details changes in energy use and growth in GDP from 1950 through 1966 for six developing countries. This figure illustrates the lack of similar trends in energy efficiency for different developing nations.

As energy intensity is measured in terms of energy input per economic output, the recent efficiency gains in China may partially reflect how the world has historically underpriced Chinese economic production. If GDP based on market currency rates has been undervalued (as in the Chinese case), then falling energy intensity could be a reflection of this underestimation. If the historic GDP is given as power purchasing parity (relative to US $), the efficiency gains may be significantly smaller.

Periods of industrialization can be characterized not only by an increase in energy use but an increase in energy intensity. This is shown for the cases for India and Argentina. Therefore, even if developed countries steadily improve their energy efficiencies, an increasingly large proportion of energy consumption will be in developing countries, and thus their changing energy intensities are of great importance to global carbon emissions as their percentage of global energy use increases. Other commentators propose that developing countries will not necessarily have rising energy intensity trends as they industrialize. Several developing countries have experienced long run negative energy intensity trends, including Korea and Brazil (Nilsson, 1993). A later section will discuss how structural changes in the economy are linked to technological change with far reaching consequences for energy efficiency. This was certainly the case for Mexico, where energy intensity declined following its economic troubles of the 1980s.

Will the impact of energy efficiency on the need for carbon-free energy technologies be much reduced due to the importance of developing countries as they industrialize? Could global energy intensities actually rise in the next 100 years? For insights into these questions we need to examine the complex process of energy efficiency changes. However, in any predictions of energy efficiency there are great uncertainties, and this is especially true in developing

GDP and Carbon Emissions, Selected Developing Countries Argentina

Carbon Emissions

Carbon Emissions

China

2.000

500

1.800

u

00

1.600

S

400

1.400

g

o

1.200

1

o

5?00

1.000

s

s

LJ

800

200

600

o

PH

u

400

a u

o

India

0-1 1980

0-1 1980

Figure 4.4 Trends in GDP and energy intensities for selected developing countries (Source: Nancy Keat, WRI, and Mark Levine, in Levine et al., 1992).

Figure 4.4 Trends in GDP and energy intensities for selected developing countries (Source: Nancy Keat, WRI, and Mark Levine, in Levine et al., 1992).

Uncertainty in Primary Energy Model Predictions

Uncertainty in Primary Energy Model Predictions

Year

Figure 4.5 Uncertainties in global energy model predictions.

Year

Figure 4.5 Uncertainties in global energy model predictions.

countries where a lack of data, a lack of modeling experience and dynamically changing economies and societies make prediction difficult.

4.4.2 Uncertainty in predicting energy efficiency

So far we have been discussing the impact of changes in future energy use in terms of median values. However, very large uncertainties exist for the impact on the amount of carbon-free power needed for a given scenario of energy intensity change. This is illustrated in Figure 4.5 taken from the ICAM climate model developed by Dowlatabadi and fellow researchers at Carnegie Mellon University. This considerable uncertainty is due to the interacting factors affecting energy use as discussed in previous sections. This large uncertainty in future requirements for carbon-free power to meet specific climate targets has been used by some researchers (e.g., Wigley, Richels and Edmonds, 1996) to suggest a delay in abatement policies, including efforts to improve the energy efficiencies in technologies, and thus, by aggregation, reduce the energy intensity of an economy. However, any delay in instigating energy efficiency policies increases the risk of "lock in" to more energy-intensive technological paradigms. This danger is explored further in the section on path dependence.

4.4.3 Uncertainties in energy intensity trends of developing countries

Energy intensity trends in developing countries will be crucial in determining future global energy use in the long term. However, the largest uncertainty exists in the energy intensity paths of the developing countries. This is due in part to their development being modeled based on the experience of the development of industrialized countries under very different conditions. Predictions, by their nature, are only as strong as their weakest or most uncertain component. Identification of these uncertainties is critical to making better decisions.

There is significant heterogeneity in the extent of knowledge available about different parts of the world. For example, whereas commercial energy markets dominate energy supply in industrialized nations, non-commercial fuels, often collected communally, meet the needs of a sizable fraction of the world's population. Economic models of production are often specified for industrialized nations where capital is plentiful and labor is scarce (or needs to be made less important in order to limit union power). These models are often adopted as being representative of production patterns in less industrialized settings -even where there may be a desire to increase employment for social reasons and there is a dearth of capital. Therefore, insights about the distributional impacts of policies involving first world and other nations are highly uncertain if not fundamentally suspect.

Additionally, when modeling changes in energy use we adopt continuous mathematical forms and consider only small perturbations to the system. When considering long-term global change issues and sustainability, we may need to redefine our representations as discontinuous processes suffering significant changes. This change in how the issues are represented will also entail a different set of questions. This is very different from the more familiar question of "will the benefits of a small intervention outweigh the costs of its implementation?" This is particularly true when predicting future energy efficiencies of developing countries.

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