We calculated the productivity growth for each sample industry and period by using the two emission yardsticks. As discussed above, the research focus is to evaluate the impact of the CO2 reduction policy scheme. For this, we divided the empirical analysis into two parts in which CO2 emissions were dealt with differently. The two analyses use the same value-added, labor force, energy consumption and capital stock variables, while they use different CO2 emissions variables, i.e., total and direct emissions. To examine how indirect emissions affect each industry and the values of the DDF (inefficiency), we compared the productivity growth and its decomposed components.
5.1. The Measurement of DDF
The measurement of DDF represents how far an observation is from the frontier of PPS at a specific period. As discussed in the Methodology section, the DDF , is a measure of the inefficiency of production when desirable and undesirable outputs are jointly produced. This measure tells us the percentage by which desirable outputs are increased or undesirable outputs are reduced in time period . For example, if equals 0.03, it is maximally possible to increase desirable outputs by 3% and reduce undesirable outputs by 3%. Therefore, for the non-zero values of , the inversed value of () measures the efficiency of joint production. If the value of is 0, it is unnecessary to change the current level of the desirable and undesirable outputs since the observation is on the frontier. Under this condition, efficiency of the joint-production is unity.
The DDF measurement results are listed in
Table 3. The oil, steel, and semiconductor industries have
values of 0 for the two types of emissions, implying that the joint-production is fully efficient for both types of emissions. Some industries, such as ceramic and cement, have low values in efficiency for the two types of CO
2 emission yardstick.
It is notable that the values for most industries using undesirable outputs as direct emissions are larger than those using total emissions, implying that measuring efficiency based on total emissions may be underestimated. The exception for this characterization is the shipbuilding industry, which shows lower inefficiency in total emissions than in direct emissions.
5.2. ML Index by Industrial Sector
We calculated the averages of green productivity growth, efficiency change, and technical change indexes for each industry, and results are listed in
Table 4. For comparative purposes, the conventional Malmquist productivity index (
index) is also listed in
Table 4.
Based on total emissions, Korea’s green productivity increased with an annual average growth rate of 0.7% during the study period. The result of this study is slightly lower than that of the previous studies. For example, Chung and Heshmati [
14] found that Korea’s industry green productivity increases by an annual average of 2.01% from 2001 to 2010. Green productivity increases in most sectors except for oil, cement, machinery, shipbuilding, and food/tobacco industries. Among the sample industries, the glass industry (7.9%) is the highest in green productivity growth, followed by semiconductor (4.3%) and display (2.8%). Shipbuilding (−2.5%) shows the lowest green productivity growth.
Based on direct emissions, green productivity increased by the annual average growth rate of 3.1%. Green productivity of direct emissions is higher than that of total emissions by about 2.4%p, originating from the fact that total emissions include indirect emissions. Under the hypothetical situation where all inputs and value-added are fixed, the level of carbon emissions is quite dissimilar across the two emission yardsticks. This dissimilarity is the main cause of the difference in green productivity growth. Also, this study’s green productivity growth measurement result of direct emission is slightly higher than the result of Chung and Heshmati [
14]. This result implies that only the total emission yardstick is likely to yield underestimated green productivity growth, which might guide policymakers to biased policy tools.
The display industry (15.9%) is the most productive industry, and the least productive industry is the cement industry (−1.5%). For most industries, the direct emission results show higher productivity growth than the total emission results. However, textile, steel, nonferrous metals, ceramic and shipbuilding showed the reverse result, i.e., the direction emissions show lower productivity growth than the total emissions. The fact that total emissions are higher than direct emissions signifies that the total emissions of the ML index should be lower than the direct emissions of the ML index. However, the five industries described above (textile, steel, nonferrous, ceramic and shipbuilding) are exceptions of this a priori conjecture mainly because these industries do not show significant differences between total emissions and direct emissions.
The average annual growth rate of efficiency is −1.1%, indicating a decline in efficiency. This finding means that the gap between the technology level of an average industry and the frontier technology has widened. This kind of efficiency decline arises in two possibilities: (i) The catching-up rate of an average industry is slower than the rate of the technological advancement of frontier industries, and (ii) when the rate of the technological deterioration of the frontier industries is slower than that of an average industry. Since the technical change indexes for the two emission yardsticks are positive, the second possibility could be excluded from the main reason for the average efficiency decline. Ceramic, display, and electronic/electricity caught up to the frontier, whereas oil, steel, and semiconductor do not show an efficiency change. The remaining industries have an efficiency change smaller than unity, meaning that they lagged behind the benchmark frontier. The direct emissions result shows that the average annual growth rate of efficiency is −2.2%, which is less than 1.1%p than the total emission yardstick. This difference in efficiency change between the two emission yardsticks suggests that the catching-up effect in total emissions is slightly faster than that in direct emissions.
The average annual rate of technical change in total emissions is 1.9%, indicating technical progress. This finding suggests that recently increasing concerns and policies about energy saving and environmental conservation have encouraged technology to be advanced. The glass industry (10.2%) has the highest positive technical change during the study period. Most industries increased technical change, whereas oil and shipbuilding industries are the exception. In direct emissions, the average annual growth rate of technical change is 5.5%. Also, technical progress has emerged in all industries except the oil industry during the study period.
Generally, these results show that Korea’s green productivity has been significantly influenced by technical progress rather than technical catching-up effects. These results are similar to the results of the analysis on green productivity in Korea [
8,
14]. In addition, we see dramatic differences in green productivity growth when using different emission yardsticks, especially for the industries such as paper and lumber, petrochemicals, glass, cement, machinery, semiconductors, display, electronic/electricity, automotive, food and tobacco industries. This signifies that when we consider the emission data in green productivity measurement, only the usage of the total emissions data set is likely to yield biased results. Also, it is necessary to consider the characteristics of the industry and the effect of indirect emissions because it is likely to result in the duplication of emissions.
For comparative purposes, we measured the conventional M index which does not consider the effect of undesirable outputs. The average annual rate of the M index is 2.1%, indicating a productivity gain. The ML index is 1.4%p lower than the M index, with especially large differences in the display and semiconductor industries. This result indicates that when policy target is related with CO2 emissions, only the M index results might result in overestimation and biasedness.