For the carbon productivity variable, firm-level emission and ETS participation data is retrieved from Emission Data Regulatory System and Offset Registry System. Annual emission of CO2 and ETS participation information is reported in those national carbon management systems. The ETRS updates its list of companies participating in ETS and their emissions annually. Pre- and post-ETS firms are matched with codes from the Korean Exchange (KRX).
Some control variables are added to deal with possible unobserved heterogeneity, and these were downloaded from the COMPUSTAT database. The variables are as follows: log-transformed total assets, Tobin’s Q, log-transformed sales, tangibility, cash-to-assets ratio, and cash. Then, emissions data and other firm-level control variables are merged into one database.
On the other hand, the data only contains manufacturing firms because it is hard to compare the carbon productivity of manufacturing industries to other industries (agriculture or service industry) where the running of factories does not produce most of the revenue stream. The final dataset spanned from 2011 to 2019 with 1,547 panel observations matched with the fiscal year.
Moreover, to test whether the carbon productivity is affected by CEOs who have environment-related experiences, board characteristics (work experience and educational background) are gathered from the TS2000, JOINS database. Furthermore, firm-level patent information is collected from the KIPRIS database to identify the effect of firm’s innovation.
Table 9. Description and source of the variables analyzed in this study.
Variable name Description Source
Dependent variable Carbon
Sales scaled by tons of CO2 equivalent emitted by ETS- participating firms. Values were multiplied by 1 million for clarity.
Ministry of Environment/
ETS Indicator variable equaling 1 if the firm participated in ETS. Ministry of Environment Subsample variables
ROA Return on assets COMPUSTAT
Patent Indicator variable equaling 1 if the firm registered more than one patent.
Korea Intellectual Property Rights Information Service Green CEOs Indicator variable equaling 1 if the CEO of a firm has
educational or job experiences in environment-related fields.
Log(asset) Log of a firm’s total assets. COMPUSTAT
Tobin’s q Tobin’s q calculated as: (the market value of common stock + book value of preferred stock + (current liabilities – current assets) + long-term debts) / book value of total assets.
Log(sales) Log of a firm’s sales. COMPUSTAT
Tangibility Net tangible assets are calculated as follows: total assets – intangible assets – total liabilities.
Cash Firm’s net cash. COMPUSTAT
Cash flow Free cash flow calculated as: net income + (depreciation/amortization) – change in working capital – capital expenditure.
3.2.1 Measuring carbon productivity
𝐶𝑃𝑖,𝑡 = 𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑖,𝑡
where 𝐶𝑃𝑖,𝑡 is the carbon productivity of firm 𝑖 in year 𝑡, 𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑖,𝑡 is the annual sales generated by firm 𝑖 in year 𝑡, and 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑖,𝑡 is the amounts of greenhouse gases emitted by firm 𝑖 in year 𝑡. For clarity, all the carbon productivity values are multiplied by one million.
3.2.2 Baseline regression
This analysis used the difference-in-difference method to identify the exogenous effects of emission trading scheme (ETS) participation specifically for its effect on firms in high-emitting industries, and employed pooled ordinary least squares (OLS) to analyze the effects:
= 𝛽0+ 𝛽1𝐸𝑇𝑆𝑗,𝑡∗ 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑗,𝑡+ 𝛽2𝐸𝑇𝑆𝑗,𝑡+ 𝛽3𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑗,𝑡+ ∑𝑛𝑗=1𝛽𝑗+3𝐶𝑂𝑁𝑇𝑅𝑂𝐿𝑆𝑗,𝑡+ 𝜀𝑗,𝑡 (2)
where 𝐶𝑎𝑟𝑏𝑜𝑛 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑖𝑡𝑦𝑗,𝑡 indicates firm-level carbon productivity, 𝐸𝑇𝑆𝑗,𝑡∗ 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑗,𝑡 indicates the main variable of interest where the exogenous entry of the ETS dummy variable interacts with the industry emission dummy variable, 𝐸𝑇𝑆𝑗,𝑡 indicates an indicator variable that equals 1 if the firm participates in ETS and 0 if the firm does not participate in ETS, 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑗,𝑡 is an indicator variable that equals 1 if the industry to which the firm belonged to emitted > 10,000 t CO2-eq annually on average and 0 if below, and 𝐶𝑂𝑁𝑇𝑅𝑂𝐿𝑆𝑗,𝑡 indicates the set of control variables used in the main regression.
3.2.3 Propensity score matching (PSM) model construction
Even though the analysis applied year- and industry-fixed effects, the results may still have included endogeneity problems, therefore PSM is used for dealing with these problems. Two treatments were presumed, firms participating and firms not participating in ETS, with N independent and identically distributed random variables. Each subject 𝑖 responded to treatment 𝑟1𝑖 and to control 𝑟0𝑖. Then, the average treatment effect was computed (the quantity to be estimated) as 𝐸[𝑟1] - 𝐸[𝑟0]. This created variable 𝑍𝑖 , where 𝑍 = 1 if subject 𝑖 was treated and 𝑍 = 0 if subject 𝑖 was controlled. 𝑋𝑖 was defined as a vector with observed pretreatment covariates for the 𝑖𝑡ℎ subject. Although observations of
𝑋𝑖 were pre-determined before the treatment assignment, its features could not include any of the variables used to determine the treatment assignment. The numbering was assumed not to contain any information beyond what was contained in 𝑋𝑖.
For a sample firm with a vector of covariates 𝑋, and some potential outcomes 𝑟0 and 𝑟1 under the control and treatment, treatment assignment was readily ignorable if the potential outcomes were independent of the treatment conditional on 𝑋:
𝑟0, 𝑟1 ⊥ 𝑍|𝑋. (3)
Then, the balancing score function 𝑏(𝑋) was formulated, a function of covariates such that the conditional distribution of 𝑋 given 𝑏(𝑋) was identical for treated and controlled samples:
𝑍 ⊥ 𝑋|𝑏(𝑋). (4)
The most trivial function would be 𝑏(𝑋) = 𝑋. The propensity score was finally measured, which is defined as the probability of a firm being assigned to ETS participation given a set of other control variables (observed covariates). These were used to reduce selection bias by equating groups based on control variables. Given a binary treatment indicator 𝑍 with the control variables 𝑋, the propensity score was defined as the conditional probability of treatment given 𝑋:
𝑒(𝑥) ≡ 𝑃𝑟 (𝑍 = 1|𝑋 = 𝑥). (5)
The following variables as the set of covariates were used: assets, sales, Tobin’s q, asset tangibility, leverage, cash, and cash flow.
Level Industry Classification
Name Before ETS ETS Phase 1 ETS Phase 2
Basic metals 7.938 9.490 10.405
Chemicals and chemical
products 4.695 6.249 8.763
Other non-metallic mineral
products 1.814 6.046 6.238
Bottom 3 Textiles 6.487 4.973 5.204
Wood and wood/cork products 3.791 5.207 5.678
Beverages 9.652 8.154 7.150
Table 10. Average carbon productivity by industry. Industries were classified using the 2-digit Korean Standard Statistical Classification (KSIC) codes.
Changes in carbon productivity by industries (Table 10) displays that the intensity of emission management might vary depending on industry classification, even among the manufacturing industry. Carbon productivity drastically increased after the ETS participation for the three highest- emitting industries. This is a positive conclusion because these industries are responsible for most of South Korea's emissions. Industries that were less sensitive to emissions, on the other hand, did not respond consistently to the adoption of the ETS. After participating in the ETS, some industries saw a rise in carbon productivity, while others saw a decline. Such varied results were mostly since such industries were generally unregulated by the government and hence less likely to be penalized for their emissions. This also points to the fact that the ETS has a heterogeneous effect on different industries. Changes in carbon productivity became steadier as the ETS matured, which was consistent with previous findings.
Figure 10. Difference-in-difference results from the baseline estimation model. Beta values are coefficients from Column 3 of Table 2.
Figure 10 depicts a scatter plot describing how emission reductions influenced increases in carbon productivity. The x-axis depicts the average log-transformed emission change of a firm before and after ETS, and the y-axis depicts the average log-transformed carbon productivity change before and after ETS. Firms in high-emitting industries (colored dots) generally reduced significant carbon emissions and enhanced carbon productivity after the ETS. The ETS adoption induced certain manufacturing firms to reduce emissions and enhance productivity in high-emitting industries. However, the emissions of many firms increased, and carbon productivity declined. This suggests that the ETS has not successfully engaged all firms in reducing emissions as intended.
Figure 11. Heatmaps of sample firms’ carbon productivity change by emission level. a. Before ETS to ETS Phase 1. b ETS Phase 1 to ETS Phase 2. Color scale indicates the number of firms per bin.
Since the results of the univariate analysis had several endogeneity problems, pooled ordinary least squares (OLS) estimation is employed with control variables and fixed effects to test our hypothesis.
Two heatmaps are shown in Before ETS to ETS Phase 1 (Fig. 11a) and ETS Phase 1 to Phase 2 (Fig.
11b). As the ETS was getting matured, two heatmaps of sample firms’ carbon productivity change by emission level display that overall carbon productivity enhanced while firm-level emissions declined.
During the second ETS phase, certain firms increase carbon productivity significantly or reduce carbon emissions, however, the magnitude of the changes differed across firms, reflecting heterogeneity across ETS participants. In addition, Fig. 11b (R-squared value: 0.2732) describes a more linear trend than Fig.
11a (R-squared value: 0.1023). This implies that the relationship between emission reductions and carbon productivity became more linear. Moreover, while some companies increased their carbon productivity by decreasing the emissions, several firms also went through the reversed phenomenon that increased the emissions by declining their carbon productivity. Namely, many firms are starting to consider two efforts at the same time, emission reduction and carbon productivity enhancement. The findings that the ETS has come to maturity are in line with previous studies on the EU ETS (Guo et al, 2020).
Table 11. Estimated effects of ETS participation on carbon productivity as determined by regression analyses.
Figure 12. Difference-in-difference results from the baseline estimation model. Beta values are coefficients from Column 3 of Table x.
Original Sample Matched Sample
(1) (2) (3) (4) (5) (6)
productivity Carbon productivity
ETS*Industry 0.038* 0.036* 0.036* 0.058*** 0.055*** 0.058***
(1.763) (1.656) (1.758) (2.862) (2.751) (3.116)
ETS 0.006 0.003 -0.004 -0.029* -0.016 -0.014
(0.350) (0.128) (-0.154) (-1.948) (-0.651) (-0.601)
Industry -0.017 -0.036 -0.040 -0.028 -0.043 -0.051
(-0.389) (-0.773) (-0.908) (-0.649) (-0.954) (-1.222)
Constant 0.076*** 0.072** -0.035 0.107*** 0.088*** -0.032
(2.973) (2.285) (-1.075) (4.341) (2.891) (-1.030)
Observations 995 995 995 1,155 1,155 1,155
squared 0.118 0.121 0.211 0.142 0.144 0.269
Controls No No Yes No No Yes
Year FE No Yes Yes No Yes Yes
Industry FE No Yes Yes No Yes Yes
For Table 11, Columns 1–3 show results for non-matched firms, and columns 4–6 reports estimation results for matched firms by propensity score matching (PSM). Columns 1 and 4 results without control variables and fixed effects (FE), columns 2 and 5 exhibit results with year- and industry-fixed effects, and columns 3 and 6 show results including control variables. Parentheses mean t-values based on standard errors clustered by firm. *, **, and *** indicate significance at the 10%, 5%, and 1% level, respectively.
In columns 1-3 of Table 11, firms’ carbon productivity in highly emitting business sectors tended to be higher for the ETS participating firms compared to those that did not, according to the baseline regression results, regardless of the specifications used. For column 3, specifically, carbon productivity was 3.6% greater for ETS participating firms in high-emitting industries compared to another group of firms. Figure 12 shows the graphical picture of the difference-in-difference model results provided in column 3 of Table 11.
The government has forced high-emitting companies for participating in the ETS, but these companies also tend to be large and can change their carbon emission strategy fast, therefore the results could have been induced due to unobserved firm-specific factors., the propensity score matching (PSM), a statistical matching technique that attempts to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that predict the treatment’s reception (Rosenbaum and Rubin, 1983) is applied to resolve this endogeneity problem. By simply comparing outcomes between units that applied the treatment rather than those that did not, the PSM methodology aims to weaken the bias from confounding variables that can be detected in an estimate of the treatment effect. As a consequence, the PSM was employed to match firms participating in ETS with non-participating firms by their characteristics. Then, the results of the re-estimated regression with the matched samples are shown in Columns 4–6 of Table 11. The findings were not driven by endogeneity, as evidenced by the regression analysis with matched samples, which was robust and consistent with our hypothesis.
Figure 13. Mechanisms for increasing carbon productivity before and during ETS. Firms are grouped by a. profitability (return on assets, ROA), b. innovation, and c. board characteristics. T- statistics compare carbon productivity values with statistical significance marked as *** for a p-value
<0.01, ** for a p-value <0.05, and * for a p-value < 0.1.
ROA > 0 ROA < 0 Patent > 0 Patent = 0 Green CEO Non-Green CEO
(1) (2) (3) (4) (5) (6)
productivity Carbon productivity
ETS*Industry 0.047** -0.072* 0.052* 0.022 0.047* -0.013
(2.472) (-1.665) (1.963) (0.623) (1.805) (-0.450)
ETS -0.031 0.152*** -0.010 0.009 0.001 0.019
(-1.374) (2.851) (-0.289) (0.216) (0.028) (0.499)
Industry -0.061 0.086 -0.028 -0.050 -0.044 0.012
(-1.521) (0.988) (-0.522) (-0.638) (-0.814) (0.186)
Constant 0.055** 0.021 0.063 0.088* 0.080** 0.021
(2.090) (0.324) (1.553) (1.884) (2.308) (0.406)
Observations 882 113 522 472 801 194
Adjusted R-squared 0.08 0.246 0.038 0.204 0.158 0.199
Controls Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes Yes Yes
Table 12. Estimated effects of firm size, R&D intensity, and board characteristics on carbon productivity as determined by regression analyses. Parentheses present t-values based on standard errors clustered by firm. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
In this analysis, the three criteria, profitability (return on assets, ROA), innovation, and board characteristics assessed changes in carbon productivity for firms in Figure 13. As the first action, the sample firms were classified into profitable (ROA > zero) and not profitable (ROA < zero) groups. As the second action, sample firms were classified into innovative (firm-year observations with > 1 registered patent) and non-innovative (no registered patents) groups. For board diversity, sample firms were classified into those managed by green CEOs and those not. After adopting the ETS, profitable, innovative, and green-CEO-managed firms considerably increased their carbon productivity, according
to the univariate analysis. Figure 13 shows that the firm-level carbon productivity was higher for the firms that are profitable, innovative, and managed by green CEOs on average and the findings were statistically significant.
Based on these findings, the same specification method for the baseline regression was utilized to investigate whether such differences could be observed using the difference-in-difference model (Table 12). The results are statistically significant that, after participating in the ETS, profitable, innovative, and green-CEO-managed firms in high-emission industries had carbon productivities, 4.7%, 5.2%, and 4.7% higher than other firms, respectively. percent, and 3.3 percent greater than other firms. The findings show that firms should be less financially difficult to engage in innovative projects to overcome the hardship of an ETS. Furthermore, green CEOs can be more effective in managing such projects.