Wednesday, April 21, 2010

Innovation and Energy Efficiency

Changes in the energy/GDP ratio that are not related to changes in the relative price of energy are called changes in the autonomous energy efficiency index (AEEI, Kaufmann, 2004). These could be due to any of the determinants of the relationship between energy and output listed at the beginning of this section and not just technological change. Even A in (3) is just general TFP and, therefore, includes the effects of technological change on augmenting other inputs as well as energy. There are two related ways of measuring the level of technology that control for the other factors that we consider in this section of this paper. The first, distance function, approach asks: “What is the minimum energy requirement to produce a given level of output holding all other inputs constant?” The level of energy efficiency in period t relative to period 0, Bt , is given by:

where y is the vector of outputs and x the vector of non-energy inputs with subscripts indicating the periods and Ei() is a function indicating the minimum energy required in period i in order to achieve the given outputs given the level of inputs. Equation (4) can also be used to measure the relative level of energy efficiency of two countries. The functions in (4) can be estimated econometrically (e.g. Stern, 2010) or non-parametrically.

An alternative approach is an index of energy augmenting technical change. This involves a reformulation of the production function (3):


so that each input is multiplied by its own technology factor Ai that converts crude units of the input into “effective units”. AE is the index of energy augmenting technical change, which holds the use of all other inputs and their augmentation indices constant. In some but not all situations, AE = B.

Estimates of the trend in AEEI, energy efficiency, or the energy augmentation index are mixed. This is likely because the direction of change has not been constant and varies across different sectors of the economy and strong correlations between the state of technology and the levels of other inputs result in biased and inconsistent results (Stern, 2010). Jorgensen and Wilcoxen (1993) estimated that autonomous energy efficiency is declining. Berndt et al. (1993) use a model with linear time trends to estimate augmentation trends labor, electricity, fuels, machines, and structures in US manufacturing industry between 1965 and 1987. The rates of augmentation are -1.2%, 11.8%, -3.4%, 4.4%, and 8.7% respectively per annum. Patterns for Canada and France were entirely different. Stern (2010) uses a method intended to address the issue of biased estimation. He finds that energy efficiency (4) improved from 1971 to 2007 in most developed economies, former communist countries including China, and in India. But there was no improvement or a reduction in energy efficiency in many. Globally, such technological change resulted in 40% growth in energy use over the period than would otherwise have been the case.

Judson et al. (1999) estimate separate EKC relations for energy consumption in each of a number of energy-consuming sectors for a large panel of data. They estimate time effects that show rising energy consumption over time in the household and other sector but flat to declining time effects in industry and construction. This suggests that technical innovations tend to introduce more energy using appliances to households and energy saving techniques to industry (Stern, 2002).

When there is endogenous technological change, changes in prices may induce technological changes. As a result, an increase in energy prices does tend to accelerate the development of energy saving technologies, while periods of falling energy prices may result in energy-using technological change. There can also be an effect on the general rate of TFP growth (Berndt, 1990). Jorgenson (1984) found that technical change was biased and tended to be energy using. If this is the case, lower energy prices tend to accelerate TFP growth and vice versa. More recent results may contradict this conclusion (e.g. Judson et al., 1999). Newell et al. (1999) provide some information on the degree to which energy price increases induce improvements in the energy efficiency of consumer products. They decompose the changes in cost and energy efficiency of various energy using appliances using the concept of a transformation frontier of possible cost and efficiency combinations. For room air conditioners, large reductions in cost holding efficiency and cooling capacity occurred from 1960 to 1980 in the US. Also the cost of high efficiency air conditioners relative to inefficient ones was reduced. From 1980 to 1990 the former trend ended but the mix of air conditioners offered from those that were feasible to manufacture shifted sharply in favor of higher efficiency. Only about one quarter of the gain in energy efficiency since 1973 was induced by higher energy prices. Another quarter was found to be due to raised government standards and labeling. For gas water heaters the induced improvements were close to one half of the total, although much less cost reducing technical change occurred. Popp (2002) similarly finds that increased energy prices have a significant though quantitatively small effect on the rate of patenting in the energy sector.

Recent research investigates the factors that affect the adoption of energy efficiency policies or energy efficiency technology (Matisoff, 2008; Fredriksson et al., 2004; Gillingham et al., 2009; Wei et al., 2009; Stern, 2010). Differences across countries and states, over time, and among individuals can be due to differences in endowments and preferences but also due to market failures. Gillingham et al. (2009) provide a classification of various market and behavioral failures that affect energy efficiency. Market failures include environmental externalities, information problems, liquidity constraints in capital markets, and failures of innovation markets. Fredriksson et al. (2004) find that the greater the corruptibility of policy-makers the less stringent is energy policy and that the greater lobby group coordination costs are the more stringent energy policy is.

Matisoff (2008) finds that the most significant variable affecting the adoption of energy efficiency programs across U.S. states is citizen ideology. A broad band of states from Florida to Idaho has not adopted any policies. The initial level of criteria air pollutants was also a significant determinant of the number of programs adopted and the adoption of a renewable portfolio standard. Wei et al. (2009) compute an energy efficiency index based on the data envelopment analysis approach to examine energy efficiency in China. Using 1997–2006 panel data for 29 provinces, they find that energy efficiency is negatively associated with the secondary industry share in GDP, the state-owned economic share in GDP and the government expenditure share in GDP, and is positively associated with the technical level and non-coal share in energy consumption.

Stern (2010) uses a stochastic production frontier to model trends in energy efficiency (4) over time in a panel of 85 countries. He finds that energy efficiency rises with increasing general total factor productivity but is also higher in countries with more undervalued exchange rates in PPP terms. Higher fossil fuel reserves are associated with lower energy efficiency. Energy efficiency converges over time across countries and technological change was the most important factor mitigating the global increase in energy use and carbon emissions due to economic growth.

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