1. Introduction
The global environmental crisis has pushed global policymakers to make political and legally binding agreements among participants during the period of the Kyoto agreement in 1995, the Copenhagen accord in 2009, and the Paris agreement in 2015 [
1,
2]. After Copenhagen, Chinese policymakers decided to establish their own carbon trading markets in 2010, and by 2013 they launched their initial trading markets in seven pilot provinces [
3,
4,
5,
6].
Studies have shown that regulation is one of the most important factors influencing a company’s environmental policy [
7,
8,
9], and it is also the case in China [
2,
10,
11]. As the market response to an environmental regulation may vary with the timing of the market and the regulation [
2,
4], studying the initial market reaction to China’s regulatory initiative related to climate change may be worth exploring. Moreover, China’s institutional uniqueness can provide a more pronounced effect than other countries on the influence of regulations [
12,
13,
14,
15]. Therefore, a study on the impact of the Chinese government’s launch of its carbon markets in 2013 can offer an important implication given that the markets for carbon in many countries are still in their infancy.
Although information disclosure and the cost of equity has been studied widely [
16,
17,
18,
19], relatively few studies have focused on the relationship between carbon disclosures and the cost of capital and generally showed a relationship where carbon disclosure reduces the cost of capital, i.e., a positive market response [
20,
21,
22,
23]. However, as stringent environmental regulation can be perceived as an increase in corporate burden and therefore may lead to an adverse market reaction [
24,
25,
26,
27,
28], the relationship between carbon disclosure and cost of capital may change after the government’s regulatory actions.
This study investigated how the effect of carbon emission disclosure on the cost of capital appeared under the influence of the introduction of the Emissions Trading Scheme (ETS) regulation in China. We also examined whether this relationship could vary depending on the company’s ownership structure and industry characteristics. Based on multiple resources available, including annual reports and sustainability reports, we performed content analysis to construct a carbon disclosure index similar to the Carbon Disclosure Project (CDP) index. We analyzed the 2011-2016 six-year period of Chinese firms listed on the Shanghai and Shenzhen Exchanges, which are part of the pilot ETS regions.
Our empirical test results are as follows. First, we discovered an inverted-U-shaped relationship between carbon disclosure and the cost of capital for our sample companies located in regions where pilot ETS has been announced since 2011 and implemented since 2013. It appears that, below a certain disclosure level, the market predicted that the coercive pressure caused by the initial ETS implementation would increase the cost burden on companies and increase potential short-term risks. However, the non-linear result of this study showed that when the disclosure exceeds a certain level, the negative effect of regulatory pressure is overcome, and the traditional positive effect of disclosure appears.
Second, such a non-linear relationship for the ETS-regulated companies appeared only in non-state-owned enterprises (non-SOEs). For state-owned enterprises (SOEs), we found a linear relationship consistent with the previous studies [
20,
21,
22,
23], indicating that the market is not concerned about the regulatory effects on SOEs. Unlike developed countries, in China, social pressure is not enough, and governmental influence is relatively strong [
12,
15], making political legitimacy more critical for businesses. SOEs which have already secured political legitimacy may be less likely to be affected by regulatory pressures [
13].
Our final tests revealed that the inverted-U relationship between carbon disclosure and the cost of capital appears only in non-heavy pollution industries (non-HPI). The test result for the heavy pollution industries (HPI) is statistically insignificant. Since the polluting industry’s active disclosure efforts partially offset the negative impact from regulations, the various responses of information users may have been the cause of failure to obtain a significant result. Unlike SOEs, which showed a significantly negative coefficient, the market reaction for HPI might be interpreted as relatively mixed.
This study is one of the rare empirical studies investigating the effect of the mandatory ETS implementation of the Chinese government on the relationship between carbon disclosures and the cost of equity capital. So far, there have been only a few studies on carbon disclosure-cost of capital using samples from the United States, China, and South Africa [
20,
21,
22,
23]. They are all common in that they rely on content analysis of CDP questionnaires to measure carbon disclosure. If we ignore the comparability issue essential in content analysis, the difference between studies can be regarded due to the differences of test samples and study designs. Among them, only Li et al. [
21,
22] studied Chinese data. However, their research has a limitation in that the number of evaluated items measuring carbon disclosure is relatively simplified, and their research interests, such as the effect of media reporting or marketization, are different from ours. Considering the relatively massive size of the ETS markets in China, studying Chinese’s early experiences is meaningful in carrying out the ETS-related policies in other countries, including countries in the Third World. We believe that our study contributes to the literature in that it found complex relationships among variables, which is worth further research.
The remaining sections are organized as follows. The following section covers literature review and hypotheses developments.
Section 3 presents the research design and samples selection. The results of descriptive statistics and regression analyses are provided in
Section 4. Finally,
Section 5 summarizes this paper and discusses implications of the findings of this study.
4. Empirical Results
Table 4 presents the descriptive statistics of the variables used in this study. The table shows that the mean of
COE and
CID are 0.114 and 0.22. The average size of companies (23.416) is slightly larger than that seen in the general samples, suggesting that the company disclosing carbon issues is likely to be more successful than the average company. The means of
HPI and
SOE are 0.561 and 0.130, respectively, indicating that more than half of our total sample firms are from high pollution industries, and the government controls only thirteen percent of our sample firms. Among the companies of carbon emission disclosure in the ETS regions, it can be said that the proportion of SOEs is tiny compared to the Chinese average (Li and Zhang [
60], about 60%).
Table 5 presents the correlation matrix of the test variables. The table shows no statistically significant relationship between
CID and
COE. This is contrary to the prediction from the literature that the two will deliver a negative relationship [
20,
21,
22,
23,
42,
43,
44,
45] and suggests the possibility that the relationships in both directions overlaps in our data.
The second point to look at is the relationship between CID and SOE. CID has a statistically insignificant but negative relationship with SOE, which supports the possibility that SOEs are negligent in disclosure and therefore did not have responded preemptively and proactively to regulatory initiatives. However, we do not know how the market will respond because SOE’s delayed response to regulations can result in a more significant regulatory impact, or conversely, it may mean that SOEs do not need to be proactive, and the market does not need to worry about them.
Finally,
CID showed a statistically insignificant negative relationship with
HPI. Contrary to the observations of some previous studies [
86,
87,
88,
89,
90], the test result in
Table 5 can be interpreted that the polluting industries have less disclosure, and if less disclosure is understood as a lack of preparation for the regulation, we can predict that the regulatory impact and adverse market reaction will more substantial in the pollution industries.
However, correlation analysis can show a superficial relationship between the two variables because other factors that affect them are not considered. Therefore, to investigate the causal relationship, in the next section we will attempt regression analyses controlling influencing factors.
Regression test results for Hypothesis 1 are in
Table 6 model 1. Since the coefficient of
CID2 is statistically significant at the 1% level,
CID forms an inverted-U relationship with
COE. This may be because the market is concerned about cost increase to respond to new regulations. However, if the level of environmental disclosure exceeds a certain level, it indicates that the cost effect will be dispersed into the past because sufficient responses from the company are already in place. Thus, the traditional negative relationship—more disclosure lowers the cost of capital—appears.
LEV is positive at a significance level of 1%, suggesting that high-leverage firms are perceived to have more risk. The negative relationship between
OWN and
COE implies that concentrated firms are regarded as having lower risks.
Table 6 model 2 and 3 report our test results for Hypothesis 2. The estimated coefficients of
CID and
CID2 are statistically significant at the 1% level only with non-SOEs samples. For SOEs,
CID shows a traditional linear relationship to COE, although the significance level is just 10%. Our interpretation is that the group most affected by the introduction of the ETS regulation is non-SOEs, and the market is not concerned about the regulatory impact on SOEs. This may be because the market did not worry about the possibility of short-term performance decrease due to regulatory shocks because SOEs had already acquired legitimacy and resource accessibility through establishing a connection with the government [
13,
63].
The test results for Hypothesis 3 are presented in Models 4 and 5. The estimated coefficients of CID and CID2 are statistically significant at the 5% and 10% levels only with Non-HPI firms. One possible interpretation is that the market was relatively more mixed with concerns about cost increases for which HPIs has no reason to be less, and assurances from HPI’s proactive response to the regulations, resulting in no significant results.
Next, to control for any potential endogeneity around a firm’s disclosure decision in our topic [
106], we employed a two-stage least squares (2SLS) model [
107]. In this model, we used two instrument variables: The Pollution Information Transparency Index (
PITI) variable and the industry—year mean of the carbon information disclosure (
CID_IND) variable based on the researches [
38,
107,
108]. The Pollution Transparency Information Disclosure Index (
PITI) evaluates pollution levels, violation records, environmental audits, and overall corporate environmental behaviors of the major cities of China, provided by the China Institute of Public and Environmental Affairs (IPE) and the Natural Resources Defense Council (NRDC).
Table 7 shows the second stage results, which showed increased coefficient values in all models and almost consistent results with
Table 6, except model 5 where the significance of
CID disappeared. Therefore, the 2SLS results show that most of the conclusions from
Table 6 are maintained even after endogeneity is controlled.
5. Conclusions and Discussion
In this study, we investigated the effect of corporate carbon disclosure under the new regulatory environment, the mandatory Emissions Trading Scheme (ETS) implementation, on the cost of capital of Chinese companies from 2011 to 2016. Specifically, we first examined the impact of a company’s CIP disclosure on the cost of capital. Next, we tested how this relationship between carbon disclosure and cost of capital differed between state-owned and non-state-owned firms and, finally, between high pollution–non-high pollution industries.
Our empirical results were as follows. First, we discovered that around the implementation of ETS Chinese market’s response to carbon disclosure was non-linear. Instead of the uniformly diminishing effect of corporate carbon disclosures on the cost of capital, we found an increasing effect for the disclosures of less than a certain quality, and from disclosure over a certain quality, carbon disclosure diminished the cost of capital. We conjecture that at a time of high uncertainty, such as the early stage of a strengthened regulation, the market may show concerns about cost increase from corporate carbon information revealed in carbon disclosures. A net effect due to disclosure may appear in a decreasing direction from disclosing a certain quality or higher.
This may be because faithful disclosure is understood as a signal to the company’s regulatory readiness. The market may judge high carbon disclosure quality as a signal that a company responds sufficiently to carbon regulations. In such a company, the cost increases due to the introduction of regulations related to carbon emission may not be sudden, and the cost can be distributed in the past, present, and future, thereby reducing the side effects of short-term cost increase. In that case, the long-term cost reduction effect such as securing legitimacy and reducing future regulatory implementation costs and related costs such as fines and litigation will be dominant.
It can be theoretically explained that the initial period of regulation has a different impact from the rest. Companies initially experience fluctuations such as cost increases but soon respond by changing how resources are used through innovation and efficiency [
49,
50]. In an event study of EU ETS by Brouwers et al. [
48], the market showed a significant response only in 2006 and 2009, the first years of each regulatory phase. A time of transition like our study’s period, when external conditions become stringent and many companies are unprepared, can be when the difference between prepared and not becomes clear. The nonlinearity of this study may be a differential result between the prepared and the unprepared.
Regulation induces companies to create innovations, thus gaining a competitive advantage [
109,
110]. Conversely, in other conditions, regulation can have side effects contrary to the regulator’s intent [
111,
112]. Some studies of the financial impact of social performance have reported non-linearities in which financial benefits only occurred when large social investments were made, and the cost effect was greater in the middle investment group, implying that companies that achieve a comparative advantage through successful innovation are limited to those prepared through sufficient investment [
113,
114]. In short, regulation can bless those who are prepared and curse the unprepared in the name of cost and risk.
Our second test result is that the inverted-U relationship appears in the non-SOEs, whereas the traditional negative linear relationship appears in the SOEs. Our interpretation is that for SOEs, the market was less concerned about regulatory shocks. Since Chinese SOEs are directly affected by the government’s policies due to their high share and direct dispatch of government personnel, it is likely that the Chinese government’s environmental policy-related will is also being implemented in the SOEs. In this case, the effect of sudden cost increases from regulatory shocks may be low. The opposite interpretation is also possible. Since Chinese SOEs have already formed networks with the government and secured legitimacy, they can pay less for regulatory non-compliance. Under any interpretation that reflects China’s unique characteristics, the market’s less concern about SOEs in our data seems to be explainable.
Our final analysis is that the main findings of this study did not appear in the high-pollution industries, but rather the test result was statistically significant only in the non-pollution industries. Although the polluting industries are those that suffer the most from environmental regulations, some of them may have a higher level of disclosure, so the factor of an increase in the cost of capital due to regulatory shock and the factor of a decrease due to the partial increase in disclosure quality can be intertwined. On the other hand, the non-polluting industries were shocked by the regulation because there was no reason to prepare in advance, unlike the polluting industries, where regulation can become a fatal problem. Therefore, a proactive response is required.
This study looked at the market response of China’s carbon emission-related disclosures through the cost of capital in the early stages of the ETS regulation. Considering the importance of Chinese experience of the ETS implementation, especially in terms of the size of their carbon market, which is the largest outside the EU and the US, and the accumulation of experience leading among third world countries, our study has a contribution in terms of how companies will be impacted when the ETS system is implemented.
Moreover, as regulations tighten, ETS systems are in place, and carbon emissions become a major strategic choice for companies, all stakeholders, including investors, financial analysts, regulators, and even researchers, will be responding to management decisions related to carbon emission-related issues because the information usefulness of related information will increase. This study, which showed that carbon emission disclosures can affect corporate financing in a complex way, will be helpful to all stakeholders who need corporate information. The results of this study will have certain implications for information users because it shows that a disclosure does not mean that everyone gets the same fruit, and that a strategically well-designed approach is needed to obtain it. However, our study could not sample all the listed companies in the ETS regions due to limitations in the cost of capital data, and although our disclosure variable through content analysis is richer than in the previous studies, the problem of comparability may be pointed out. Therefore, there are certain limitations in terms of generalization, and we ask the reader’s attention.