This study examines the impact of unexpected and expected socioeconomic changes on air pollution and carbon. For the impact of socioeconomic changes on air pollution, the analysis used the air quality, thermal power generation and macroeconomic index data to identify the effect of thermal power generation and COVID-19 on air quality. For the impact of socio-economic changes on carbon, the analysis used firm-level emissions and corporate variables to identify how ETS implementation affected carbon productivity (a firm-level revenue created per unit of carbon emission).
The findings are that the companies increased carbon productivity after participating in South Korea's National Emissions Trading System (ETS), especially for high-emitting industries, and for companies that are profitable, innovative and managed by CEOs with environment-related education levels. or work experience successfully increasing their carbon productivity. This article is reproduced with permission from “Effects of Emissions Trading Programs on Corporate Carbon Productivity and Implications for Firm-Level Responses,” copyright by Springer Nature. Estimated effects of firm size, R&D intensity and governance characteristics on carbon productivity, as determined by regression analyses.
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Introduction
For example, at the national level, China's carbon productivity was positively influenced by per capita gross domestic product (GDP), technology level, trade openness, and foreign direct investment, but negatively influenced by energy consumption structure, industrial share, and degree of urbanization. (Li and Wang, 2019). In contrast, foreign trade contributed most to China's increased carbon productivity in western China, but not in eastern and central China (Zhang et al., 2018). While regional and industry-level analyzes of carbon productivity are increasing, investigations of firm-level carbon productivity effects during the ETS have been limited to our knowledge.
In Chapter 3, the study tested the impact of industry-level emissions on the relationship between ETS participation and carbon productivity. A difference-in-difference model is used to investigate the effects of ETS on carbon productivity. Next, the study also tested the impact of firm-level emissions on the relationship between ETS participation and carbon productivity.
Socio-economic Changes on Air Pollutants
However, a drastic decline in both values is shown in both graphs since the onset of COVID-19. NO2 Monthly national average NO2 concentration SO2 Monthly national average SO2 concentration CO Monthly national average CO concentration COVID-19 Dummy variable for the outbreak of COVID-19. In this subsection, I identify the effects of thermal power generation on air quality for China and Korea.
The data period is from 2015 to 2019 to exclude the effect of COVID-19 on thermal energy production and air quality. The overall results show that thermal power generation worsened CO concentrations in Korea during 2015–2019. Similarly, as shown in Table 3, thermal energy production is positively related to CO levels.
The slope of the CO concentration is 0.127 (log change) due to the thermal power generation. In this subsection, I identify the effects of the COVID-19 outbreak on air quality using two methods. Second, I used the Pooled OLS regression model to test the impact of COVID-19 on air quality in China and Korea.
The results show that the COVID-19 has a negative association with the concentrations of all CAPs. Overall results indicate that the concentrations of PM10, PM2.5, NO2, SO2 and CO after COVID-19 are lower than the concentrations before COVID-19. The results imply that the COVID-19 improved the level of PM10, PM2.5, NO2 and CO concentrations after COVID-19 compared to the cases before COVID-19.
According to the results in Tables 5 and 6, the concentrations of PM10, PM2.5, NO2, and CO in Korea and China are jointly negatively associated with the outbreak of COVID-19. In addition, NO2 concentration decreases more in Korea. The overall results show that post-COVI-19 PM10, PM2.5, NO2, SO2 and CO concentration levels are lower than pre-COVID-19 concentrations in Korea and PM10, PM2.5, NO2, CO concentration levels are lower than pre-COVID concentrations 19 in China. In the case of SO2 in China, the association between COVID-19 and SO2 was not statistically significant.
Socio-economic Changes on Carbon
Changes in carbon productivity by industry (Table 10) show that the intensity of emissions management can vary depending on industry classification, even within manufacturing. Changes in carbon productivity became more stable as the ETS matured, which was consistent with previous findings. The x-axis represents a company's average log-transformed emissions change before and after ETS, and the y-axis represents the average log-transformed carbon productivity change before and after ETS.
Firms in high-emitting industries (colored dots) generally reduced significant carbon emissions and increased carbon productivity after the ETS. As the ETS was maturing, two heat maps of carbon productivity of sample firms varying by emission level show that overall carbon productivity increased while firm-level emissions fell. During the second phase of the ETS, some firms significantly increase carbon productivity or reduce carbon emissions, however, the magnitude of the changes varied among firms, reflecting the heterogeneity among ETS participants.
Furthermore, while some companies increased their carbon productivity by reducing emissions, several companies also went through the reverse phenomenon, increasing emissions by reducing their carbon productivity. Indeed, many companies are starting to think about two efforts at the same time, reducing emissions and increasing carbon productivity. In columns 1–3 of Table 11, the carbon productivity of firms in high-emission business sectors tended to be higher for ETS participating firms compared to non-participating firms, 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-emission industries compared to another group of firms. The government forced high-emitting companies to participate in the ETS, but these companies also tend to be large and can change their carbon emission strategy quickly, so 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 receipt (Rosenbaum and Rubin, 1983) is applied to solve this endogeneity problem. Then, the results of the re-estimated regression with the corresponding samples are shown in Columns 4–6 of Table 11.
In this analysis, the three criteria, profitability (return on assets, ROA), innovation and board characteristics assessed changes in carbon productivity for companies in Figure 13.
Discussion and Conclusions
The findings showed that the use of the market mechanism motivates companies to change their production processes to reduce their greenhouse gas emissions. The fact that carbon productivity has increased since the introduction of the ETS seems to be a promising sign that South Korean companies can continue to improve their environmental management capabilities in the future. Furthermore, a key finding is that increasing carbon productivity requires innovation and investment in research.
From the perspective of policymakers, it is essential to establish a strategy and appropriate regulations for high-emitting enterprises to support and encourage enterprises to find an optimized path of reduction, and to develop a plan to help and encourage innovative enterprises. In conclusion, the results of this study will help policy makers to formulate appropriate policies and plans, and business managers to make carbon-related decisions. The study shows that the production of thermal energy affects the deterioration of CO concentrations and that COVID-19 has had a positive effect on the improvement of air quality.
In the field of carbon management, profitability, intensity of innovation and availability of environmental professionals can articulate the mechanism of ETS impacts on carbon productivity. As the analysis of this study covers various topics such as air quality, COVID-19 and carbon productivity, numerous further studies are predicted. Among several possible topics, the impact of COVID-19 on global carbon productivity would be considered valuable enough to identify the change in carbon productivity or carbon emission levels following the COVID-19 outbreak.
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Acknowledgments
Appendix
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