Analysis process

The growth of an economy’s output depends on capital formation as well as efficiency and productivity improvement. Labor and capital are two major inputs in production. When measuring an economy’s overall output, gross domestic product (GDP) is commonly used. While GDP (income) preferred to increase more, consumption of energy is preferred by an economy to be less and efficient. The question between change of GDP and consumption of energy is in an output and input relation: First, the increasing of GDP would be closely related to input consumption of energy directly because these resources are generally key input for production. In reverse, the supply of energy in an economy is at certain level and impossibly supply unlimited for GDP growth. An important point emerges upon this relation: How the energy is consumed in an economy and is the consumption efficient? The GDP growth goal and energy consumption level should be put together in order to set energy policy appropriately, the improvement and concerns to efficiency of energy consumption are key subjects to study and understand.

With respect to CO2 emissions for an economy, while GDP (income) is desirable, emissions (pollutions) are undesirable. The change in income and pollutions is a two-way relation: First, increasing income deteriorates the

environmental condition directly, because pollutions are generally by-products of a production process and are costly to dispose. Conversely, the growth of income is accompanied by the public increasing the demand for better environmental quality through driving forces such as control measures, technological progress, and the structure change of consumption. Desirable GDP and undesirable pollutions should be both taken into account in order to correct a nation’s output. This concept is called ‘green GDP.’ Green GDP is derived from GDP through a deduction of negative environmental and social impacts.

As mentioned above, many studies criticize the commonly-used indicator of energy inefficiency - the energy intensity as a direct ratio of the energy input to GDP for measuring energy efficiency (e.g., Patterson, 1996; Renshaw, 1981). The ratio is only a partial-factor energy efficiency indicator since energy input is the only input-considered factor. Another argument is that this partial-factor ratio is inappropriate to analyze the impact of changing energy use over time (APERC, 2002). We compute the energy efficiency by a total-factor framework including labor and capital inputs. A total-factor efficiency indicator can provide more information and a more realistic comparative base to examine the de facto situation across economies. We then calculate IRT, IRTR, and TFIE through Equation (6) to (8) from the results of the CRS DEA model for energy input. The IRT, IRTR, and TFIE for energy are called energy-saving target (EST), energy-saving target ratio (ESTR), and total-factor energy efficiency (TFEE), respectively. We use the software DEAP 2.1, kindly provided by Coelli (1996), to solve the linear programming problems as specified in Equation (5) for computing the target inputs and outputs of each economy in each year.

An inefficient economy can reduce or save EST in energy use without reducing the real economic growth. ESTR represents each economy’s inefficiency level of energy consumption. Since the minimal value of EST is zero, the value of ESTR is between zero and unit. A zero ESTR value indicates an economy on the frontier

with the best total-factor energy efficiency up to one among the observed economies and means that no redundant or over-consumed energy use exists (the amount of target zero) in this economy. An inefficient economy with the value of ESTR larger than zero means otherwise that the energy should and could be saved at the same economic growth level. A higher ESTR implies higher energy inefficiency and a higher energy-saving amount.

With respect to CO2 emissions, we use CO2 emissions instead of energy consumption into the same framework including capital and labor inputs to calculate IRT, IRTR, and TFIE through Equation (6) to (8) for CO2 abatement. The IRT, IRTR, and TFIE for energy are called CO2 abatement target (CAT), CO2 abatement target ration (CATR), and total-factor CO2 abatement efficiency (TFCE), respectively. CAT represents that an inefficient economy can reduce the amount of CAT in CO2 emissions in the same real economic growth. Each economy’s inefficiency level of energy consumption is CATR. The greater CATR whose value is between zero and unit is, the more inefficiency and amount of CO2 abatement are.

A zero CATR value indicates an economy on the frontier with the best total-factor CO2 abatement efficiency up to one among the observed economies. An inefficient economy with the value of CATR larger than zero means otherwise should and could reduce CO2 emissions without reducing economic growth level.

Then we use Wilcoxon signed rank test to compare the total-factor indicator, which is constructed in this study, with the traditional partial-factor indicator.

Following the rising income, the center of weight in production and consumption shifts from primary to secondary and then to tertiary industry. In the process of a shift from primary to secondary industry with larger energy consumption, environment condition deteriorate, while the shift from secondary to tertiary industry causes alleviation of the negative impact on the environment with higher energy efficiency and less energy waste. With higher income, citizens become more aware of environmental quality and induce their governments to

introduce stricter regulations. Moreover, the investments necessary for environmental protection are only feasible with the financial resources made available by a certain level of income. Industrialization causes wastes of toxic chemical substances and heavy metals, on the one hand, and leads to larger energy consumption that results in increased emissions of air-pollutants and GHG, on the other hand. For finding out the relation between input-reducing target and income level and the relation between industrial structure and input-reducing efficiency, this study use panel data regression models to analyze. The results will show the relation among input reducing, income level, and industrial structure in APEC economies.