*6.1. The Impact of Firm Ownership*

It is generally believed that non-state-owned firms are more flexible in their production, operation, and investment decisions than state-owned firms [70]. Therefore, non-stateowned firms are likely to enhance their market value by implementing a CET policy [14,71]. To this end, we conduct a heterogeneity analysis of the impact of firm ownership on the relationship between CET policy and companies' market value. The regression results are listed in Table 9. The results show that the implementation of the CET policy has a positive and significant impact on the market value of non-state-owned enterprises, while it has an insignificant positive impact on the market value of state-owned enterprises. This is because companies face greater pressure on emission reduction costs, incentivizing them to invest in more advanced cleaner technologies or low-carbon technologies. Non-state-owned companies that are encouraged to invest in clean technologies often convey information to investors that they are more productive and competitive, increasing investors' expectations of the company's future profits, leading to an increase in their market value. By contrast, state-owned enterprises enjoy more government subsidies, financial support, and more free carbon permits, making them lose their motivation to innovate. Thus, the effect of the CET policy on the market value of state-owned enterprises is insignificant.


**Table 9.** Heterogeneous analysis of the impact of firm ownership.

Note: Standard errors are in parentheses, and they are clustered at firm level. \*, \*\*, \*\*\* indicate statistical significance at the 10%, 5%, and 1% levels, respectively. Column (1) shows the regression results for SOCs. Column (2) shows the regression results for non-state-owned firms.

## *6.2. Heterogeneity Analysis of Different Regions*

Chinese provinces are categorized into three regions according to their locations and economic development: eastern, central, and western. The eastern region includes the provinces of Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. The central region encompasses the provinces of Jilin, Heilongjiang, Shanxi, Henan, Anhui, Hubei, Jiangxi, and Hunan. The western region includes the remaining provinces. Therefore, we conduct a heterogeneity analysis of different regions by dividing the entire sample into eastern, central, and western regions according to the companies' locations. The results are presented in Table 10. The results indicate that the implementation of the CET policy has the most significant effect on enhancing the market value of companies in the eastern and central regions of China, while this effect is not significant in the western region of China. This may be because the economic development, technological level, and marketization degree of the eastern and central regions are higher than those of the western region. Thus, companies in the eastern and central regions are more inclined to conduct innovative activities to achieve carbon emission reduction targets than those in the western region, enhancing their market value. Conversely, companies in the western region are more dependent on natural resources and produce more carbon emissions, thus the impact of the CET policy on companies' market value is negative.


**Table 10.** Heterogeneous analysis of the different regions.

Note: Standard errors are in parentheses, and they are clustered at firm level. \*, \*\*, \*\*\* indicate statistical significance at the 10%, 5%, and 1% levels, respectively. Column (1) shows the regression results for the eastern region. Column (2) shows the regression results for the central region. Column (3) shows the regression results for the western region.

#### *6.3. The Impact of Different Industries*

Companies in the manufacturing industries are mainly regulated by the CET policy, which makes it necessary to invest a large number of funds to purchase carbon permits or install emissions abatement equipment. This could squeeze out companies' original production investments, reducing their market value. Accordingly, we identify the impact of the CET policy on the market value of companies in manufacturing and non-manufacturing industries by using the difference-in-difference-in-difference (DDD) model. We multiply *did* by *industry* to obtain the dummy variable *ddd* of the DDD model for the manufacturing and non-manufacturing industries. The dummy variable *industry* is 1 if the company belongs to high-carbon industries (including the eight industries, C25, C26, C30, C31, C32, C22, D44, D45, and G56), and 0 otherwise. The regression results are listed in Table 11. The results indicate that the estimated coefficients of both the manufacturing and non-manufacturing companies are significantly negative, and the suppression effect of the manufacturing companies is greater than that of the non-manufacturing companies; the CET policy has a significantly negative impact on companies' market value for high-carbon industries. China's ETS pilot policy mainly involves manufacturing and supply industries, including transportation. Manufacturing companies face greater carbon constraints, and the implementation of the CET policy brings about a great cost effect for them, thereby reducing their market value even more.


**Table 11.** Heterogeneous analysis of the different industries.

Note: Standard errors are in parentheses, and they are clustered at firm level. \*\*, \*\*\* indicate statistical significance at the 5% and 1% levels, respectively. Column (1) shows the regression results for manufacturing companies. Column (2) shows the regression results for non-manufacturing companies.

#### *6.4. Heterogeneity Analysis of the Marketization Degree*

The enhancement of companies' market value by the market-oriented mechanism is affected by the perfection of the market system. When market transaction costs, market power, and information asymmetry exist, the role of market-oriented mechanisms is weakened [72]. Accordingly, we evaluate the heterogeneity analysis of the marketization degree using the DDD model. We use data from the "Marketization Index for China's Provinces of Gang Fan for 2000–2017" to measure the degree of marketization in a certain region [73]. We divide the sample into two groups depending on whether the marketization index score in the region where the company is located is higher or lower than the median of all regions, then conduct a heterogeneity analysis. The regression results are listed in Table 12. These results indicate that the CET policy significantly affects companies' market value when the marketization degree is high. Conversely, the CET policy has a negative and insignificant impact on companies' market value when the marketization degree is low. These findings are in line with those of Jaraite–Kažukausk ˙ e and Kazukauskas (2015), Hu et al. (2020), ˙ and Ren et al. (2020) [74–76]. Companies in a region with a high degree of marketization demonstrate increased flexibility in response to market changes and the ability to profit from the carbon emission trading market. When the market system is not perfect, it affects the price of carbon emissions trading and the company's costs, benefits, and expectations. Hence, the degree of marketization influences companies' investment decisions on carbon emission reduction and innovative activities, and in turn, their market value.


**Table 12.** Heterogeneous analysis of marketization degree.

Note: Standard errors are in parentheses, and they are clustered at firm level. \*, \*\*, \*\*\* indicate statistical significance at the 10%, 5%, and 1% levels, respectively. Column (1) shows the regression results for companies with a high degree of marketization. Column (2) shows the regression results for companies with a low degree of marketization.

#### *6.5. Heterogeneity Analysis of Financial Constraints*

Under the carbon emission reduction pressure, companies have had to install carbon abatement equipment or upgrade their production progress by investing in low-carbon technologies, which undoubtedly aggravates their financial constraints [77]. Companies suffering from tight financial constraints cannot obtain sufficient financial resources to respond flexibly to the requirements of the CET policy. At the same time, this also affects the carbon emission reduction, production, and innovative activities of companies, thereby affecting their market value. We use the size-age (SA) index to measure the financing constraints of companies and divide the sample into two groups according to whether the SA index is higher than or lower than the median value for heterogeneity analysis. Companies with an SA index greater than the median value face loose financial constraints, while those with an SA index less than the median value face tight financial constraints. The SA index is calculated according to the formula: SA = 0.043 × size × size − 0.737 × size − 0.04 × age, where size is the logarithm of companies' total assets [74]. The regression results are listed in Table 13. The results indicate that the CET policy has a significantly positive impact on the market value of companies with an SA index greater than the median. Conversely, the CET policy has no significant effect on the market value of companies with an SA index less than the median. Companies with loose financial constraints have more flexibility and better financial resources, they can optimize their decisions and strategies to enhance their market value in the CET market.


**Table 13.** Heterogeneous analysis of financial constraints.

Note: Standard errors are in parentheses, and they are clustered at firm level. \*, \*\*\* indicate statistical significance at the 10% and 1% levels, respectively. Column (1) shows the regression results for companies with loose financial constraints (companies with an SA index greater than the median). Column (2) shows the regression results for companies with tight financial constraints (companies with an SA index less than the median).
