The Impact of Chinese Carbon Emissions Trading System on Efficiency of Enterprise Capital Allocation: Effect Identification and Mechanism Test

Abstract

The carbon emission trading system, as a significant policy instrument to ensure the Chinese economy achieves a green and low carbon transition, will also affect economic enterprise efficiency. This paper takes listed enterprises in a Chinese carbon trading pilot from 2011 to 2020 as research samples, constructs a multi-period differential model, and explores the impact of Chinese the carbon emission trading system on enterprise capital allocation efficiency. We find that the Chinese carbon emission trading system effectively improves the capital allocation efficiency of enterprises, which is more significant in enterprises with light pollution intensity and strong regional environmental regulation. Further analysis shows that the carbon emission trading system can improve the efficiency of enterprise capital allocation by improving the efficiency of working capital management and asset operation efficiency, while the path of human capital value is not established. Carbon trading market activity and government efficiency play a positive moderating role in the impact of the carbon emission trading system on enterprise capital allocation efficiency. The higher carbon trading market activity and government efficiency, the stronger the relationship between them. The above conclusions provide empirical evidence for the microeconomic effects of the Chinese carbon emission trading system, and also provide a useful reference for the government to implement carbon trading according to local conditions and improve the efficiency of enterprise capital allocation.

1. Introduction

On 1 November 2021, president Xi Jinping stressed at the World Leaders Summit of the 26th Conference of the Parties to the United Nations Framework Convention on Climate Change (UNFCCC): “China adheres to the concept of a community of life between man and nature, adheres to the path of ecological priority, green and low-carbon development, accelerates the construction of a green, low-carbon and circular economic system, continues to promote industrial restructuring, resolutely curb the blind development of projects with high energy consumption and high emissions, and accelerates the green and low-carbon transformation of energy.” In order to promote the green and low carbon transition of energy in an orderly manner, the national carbon trading market was officially launched on the basis of the carbon emission trading pilot in eight provinces and cities. The national carbon trading market aims to effectively allocate carbon emission rights through market mechanism and implement the responsibility of control and emission to enterprises. The system limits the amount of emissions companies can emit through a limited allocation of allowances. It also serves as an incentive for companies to sell their surplus carbon allowances in an effort to maximize emissions reduction.

Can the carbon emission trading system produce a positive environmental governance effect in China, reducing environmental pollution while achieving economic growth? In this regard, Grossman and Krueger [1] proposed a model of economic development and environmental quality, and found that the relationship between per capita GDP and environmental pollutant content is an inverted U-shaped relationship, which is called the Environmental Kuznets Curve (EKC). Some scholars also tested the applicability of EKC in China, believing that institutional factors such as environmental regulation, ownership of property rights and pollution punishment will have an impact on the shape of EKC [2,3]. Effective environmental policies can reduce the information asymmetry between government and enterprises, and reasonably allocate environmental resources to a certain extent, thus facilitating the upgrading of environmental governance technology and achieving the “win-win” goal of economic growth and pollution reduction.

The implementation of the carbon emission trading system is intended to force enterprises to improve efficiency to reduce carbon emissions in the production process. Based on this, exploring whether the efficiency of emission reduction enterprises is improved becomes the main basis for evaluating the implementation effect of the system [4]. As one of the important indicators to measure enterprise efficiency, capital allocation efficiency is the ability of enterprises to maximize output by comprehensively utilizing manpower, finance and assets [5]. In order to obtain more benefits from emission trading, enterprises will take the initiative to improve the production process, so as to improve the efficiency of asset output and have a positive impact on the efficiency of capital allocation [6]. However, the carbon emission trading system increases the cost of pollution control for enterprises, which will occupy their daily working capital, leading to the insufficient ability of enterprises to improve the efficiency of capital allocation [7]. Therefore, can the carbon emission trading system improve the efficiency of enterprise capital allocation? What is the path of influence? The above questions have yet to be tested.

In addition, as a system for the government to force enterprises to reduce carbon emission through market mechanism, the effect of carbon emission trading also depends on perfect market mechanism and strict government control. In the pilot area of the system, the relatively mature carbon market provides a trading platform with low cost for the trading subjects, which makes the transaction willingness of the control and emission enterprises strong, and injects impetus into the effect of carbon trading [8]. Therefore, carbon trading market activity, as an important representation of the perfection of the carbon trading market mechanism, is an important basis to ensure the economic function of carbon trading system. As Emission reduction is an environmental cost for enterprises, if only the market mechanism regulates the emission control behavior of enterprises, the carbon market is likely to suffer from an insufficient compliance rate and low trading volume. Therefore, the government’s handling efficiency of public affairs such as legislation and enforcement, administrative examination and approval, market regulation and so on in the carbon trading market will form a constraint on the carbon market, thus ensuring the smooth operation of the system [9]. In view of this, this paper combines the activity of the carbon trading market and government efficiency into the investigation of the impact mechanism of carbon emission trading, in order to improve the theoretical framework and empirical research of the impact effect of this system.

We contribute to the existing literature in the following aspects. First, this paper investigates the impact of the carbon emission trading system on the efficiency of capital allocation of enterprises, which provides empirical evidence for the microeconomic effects of this system. Secondly, carbon trading market activity and government efficiency are included in the study of the impact mechanism of the carbon emission trading system, which expands the perspective of the economic effects of the system from both the market and the government. Thirdly, through the investigation of the impact path of the carbon emission trading system on capital allocation efficiency, the relationship between them is clarified. Fourthly, the research conclusions of this paper are not only conducive to improving the economic development efficiency of enterprises through the carbon emission trading system, but also conducive to the realization of “double carbon” goals from the aspects of establishing competent governments and building effective markets.

2. Literature Review

The idea of carbon emission trading originated from Dales’ thinking on environmental issues. He proposed to design emission rights as government-owned rights that can be traded in the market, so that enterprises can discharge emissions under the government’s regulation and market rules, thereby internalizing external environmental costs [10]. In subsequent practice, EU ETS has attracted many scholars to study its economic effects because of its characteristics of largest scale, most complete regulations and strongest influence. Abrell et al. [11] used European listed companies as a research sample to explore the impact of EU ETS on their profit margins and enterprise value, but found that EU ETS had no significant impact on either; Chan et al. [12] selected listed companies in the cement, energy and steel industries as samples, and found that EU ETS can improve corporate profit margins and corporate value, but this only exists in the energy industry. In recent years, with the development of Chinese carbon trading pilots, Chinese scholars have begun to explore the impact of the carbon emissions trading system on enterprises. For example, from the perspective of corporate financial performance [13], concluded that carbon trading pilots improved corporate asset returns and corporate value, but did not prompt companies to increase R&D investment. Liu and Zhang [14] believed that pilot carbon trading can improve the R&D and innovation intensity of Chinese enterprises, and this promotion effect is more significant in larger enterprises. In addition to exploring the impact of carbon trading on financial performance and corporate innovation, some scholars have also explored the impact of carbon trading on corporate labor demand, and concluded that there is a significant positive correlation between the two [15].

However, some scholars have questioned the actual role of carbon trading in China. Chen [16] pointed out that, at present, the trading subjects in China’s carbon trading market are mainly large central enterprises and state-owned enterprises, which regard carbon assets as state-owned assets. In order to avoid the loss of state-owned assets, they will not easily sell carbon quotas, which reduces the liquidity of the carbon market and inhibits the trading willingness of trading subjects in the carbon market. Lo [17] believed that under the current institutional background of China, excessive government intervention made it difficult for the carbon market price to reflect the actual price regulated by the market mechanism, resulting in market failure. The unique economic background of China makes it difficult for carbon emissions trading to play a positive role, which is further reflected in the impact on micro entities. Hu and Ding [7] found that China’s carbon emissions trading negatively affects the total factor productivity and green total factor productivity of enterprises. At present, the regulatory role of carbon trading on micro enterprises is mainly realized through the “cost following” effect in the Porter hypothesis. Lyu et al. [18] explored the role of China’s carbon trading in low-carbon technology innovation, and found that carbon trading will inhibit the development of low-carbon technology in the short term because it is difficult to obtain economic benefits in the short term.

Another type of literature that is closely related to this study revolves around the definition and influencing factors of corporate capital allocation efficiency. The existing literature generally defines the efficiency of corporate capital allocation as the degree to which internal financial capital is allocated to projects with the highest marginal efficiency [19,20,21]. However, there are also a few scholars who include various production factors of enterprises into the category of capital, and believe that the efficiency of capital allocation can be measured by the output efficiency of production factors of enterprises [5,22]. Regarding the influencing factors of corporate capital allocation efficiency, existing research believes that R&D investment, labor force and policy environment will affect corporate capital allocation efficiency. Based on the perspective of R&D investment, Wu et al. [23] believed that increasing R&D investment promotes enterprises to cultivate market competitiveness while improving the level of technological innovation, so as to obtain more output from limited investment, which is ultimately reflected in the efficiency of capital allocation; from the perspective of labor force, Belo et al. [24] conducted an empirical study using the data of U.S. listed companies and found that corporate employment rate has a negative impact on capital allocation efficiency by reducing stock returns; based on the perspective of policy environment, Ma et al. [25] explored the relationship between industrial policy and the efficiency of corporate capital allocation, and found that industrial policy hindered the free flow of capital, thus reducing the efficiency of corporate capital allocation. From the review of the above literature, it can be seen that the carbon emission trading system will affect the basic activities of enterprises in the daily operation of enterprises, such as technological innovation, labor demand and business decision-making, while encouraging enterprises to reduce emissions. It can further reveal the impact of the carbon emission trading system on the capital allocation efficiency of enterprises through the above activities. In addition, when defining capital allocation efficiency, the existing literature generally equates capital allocation efficiency with fund allocation efficiency or investment efficiency. However, in addition to using financial capital, enterprises can also use human capital and asset capital to create value for themselves [26,27]. Therefore, this paper will integrate various types of capital of enterprises and analyze the impact of the carbon emission trading system on the efficiency of capital allocation.

3. Research Hypothesis

3.1. Impact of Carbon Emission Trading System on Capital Allocation Efficiency of Enterprises

Before analyzing the relationship between carbon trading and enterprise capital allocation efficiency, the first thing to be determined is the definition of enterprise capital allocation efficiency. Marx [28] believes that capital refers to the value that can bring surplus value, which is formally expressed as real money and means of production. Fisher [29] points out in Interest Theory that the value of capital can be measured as the discounted value of a series of future income streams discounted according to the rate of return on investment, and the income stream is the actual output of various production factors. Then, capital is not simply equivalent to fund, but all the elements that can create value in an enterprise, which includes labor, fund, assets etc. After determining the definition of capital, what needs to be further analyzed is the concept of capital allocation activities. In essence, an enterprise invests capital into its daily investment and business activities, which is the process of using capital as the cost of activities to ultimately obtain income. Then, capital allocation activities refer to the activities in which an enterprise invests all kinds of raised capital into various production factors to obtain actual output. To sum up, this paper defines the efficiency of capital allocation as the ratio of the actual output and the theoretical maximum output of an enterprise investing human capital, financial capital and asset capital into various production factors. Therefore, to explore the impact of the carbon emission trading system on the efficiency of capital allocation, the enterprise capital can be mainly divided into human capital, financial capital and asset capital for analysis.

From the perspective of human capital, the carbon emission trading system mainly plays a role by optimizing the human capital structure of enterprises and improving labor production efficiency. The fundamental purpose of the carbon emission trading system is to promote carbon emission reduction. Existing studies have also confirmed the emission reduction effect of the system [30]. The improvement of the natural environment will attract a foreign population to move in, among which there are many talents with a high education level and work skills, thus improving the quality of human capital in the local labor market [31]. Not only that, the improvement of environmental quality also helps to improve the learning ability, cognitive ability and attention of enterprise employees, thus improving their labor productivity [32]. On the one hand, the above-mentioned impact reduces the cost of searching for a high-quality labor force, so that the talent demand of technological innovation, performance improvement and market share expansion of enterprises can be optimally matched with high-quality labor force, thus guiding human capital to promote the maximization of enterprise output [33]; on the other hand, with the input of the same means of production, the improvement of the production efficiency of the existing human resources in the enterprise is more likely to make the enterprise achieve the goal of optimal output, and then improve the efficiency of capital allocation.

From the perspective of financial capital, the carbon emission trading system plays a role by improving the efficiency of enterprise operating capital and improving the rate of return on investment. Enterprises included in the carbon emission trading list have high emissions, which means that enterprises fail to make full use of internal production resources to maximize production efficiency [4]. Therefore, at the production level, carbon trading enables enterprises to rethink the low production efficiency of their products, redesign or adjust the product portfolio so as to reduce product costs or improve production efficiency and make full use of daily operating funds [34]. In addition, the carbon emission trading system encourages enterprises to disclose more complete environmental information [35], which is conducive to alleviating the information asymmetry between enterprises and market, reducing the uncertainty of enterprise investment, and thus improving the return rate of enterprise investment projects [36]. In conclusion, the carbon emission trading system has improved the efficiency of the use of enterprise working capital and investment funds, thus improving the efficiency of financial capital allocation.

From the perspective of asset capital, the carbon emission trading system plays a role in promoting enterprises to update assets and strengthen asset management. Under the constraint of limited carbon emissions, enterprises will reduce production scale or use a production mode with fewer emissions. The former will weaken the ability of enterprises to expand sales scale and create profits, which is unfavorable for enhancing the competitive position of enterprises; the latter is realized through the purchase of advanced production equipment or the technical transformation of existing equipment, so as to relatively reduce the output scale and update the existing production equipment to make it easier for enterprises to obtain a dominant position [37]. Therefore, for managers, the best choice is to introduce new production equipment, so that enterprises can produce with lower emissions and higher production efficiency to improve the efficiency of productive asset allocation. However, some enterprises may not be able to afford the funds needed to renew their assets. In order to cope with the constraints of the carbon emission trading system, the managers will strengthen the management of existing assets, so as to fully control the use status of assets, eliminate the assets with low use efficiency, improve asset operation efficiency [38,39], and finally improve the efficiency of enterprise asset capital allocation.

As mentioned above, we propose the following assumptions:

Hypothesis (H1)

: Carbon emission trading system can improve the efficiency of enterprise capital allocation.

3.2. Analysis of Intermediary Mechanism

As a significant policy instrument for the administration to use market adjustment to regulate carbon emissions and urge firms to reduce pollution in the production process, the carbon emission trading system mainly relies on the enterprises to adjust the allocation of human, financial and other capital involved in daily production and management, which will be further reflected in the improvement of actual benefits generated by various types of capital. Therefore, from the perspective of human capital value, working capital management efficiency and asset operation efficiency, we discuss the role of carbon emission rights trading on the efficiency of enterprise capital allocation.

(1)

The human capital value path. The carbon emission trading system forces firms to realize clean technology innovation to reduce carbon emissions. The complexity of technology innovation makes it difficult for low-quality human capital to match it. Therefore, it will increase the demand of enterprises for high-quality human capital, induce human capital premium and improve human capital value [33]. This will encourage enterprises to expand human capital investment, enhance the ability of employees to collect, mine and absorb knowledge, so as to strengthen the internal knowledge spillover effect and improve the level of enterprise innovation [40], which will help enterprises improve product quality, expand market share and cultivate competitive advantages, and ensure that enterprises further improve capital allocation efficiency.

(2)

The working capital management efficiency path. When included in the carbon emission rights trading list, it means that enterprises do not sufficiently utilize their energy, which shows their characteristics of large pollution emissions, high resource consumption and low operating efficiency [41]. In order to reduce environmental costs, enterprises seeking to maximize profits will take the initiative to improve the links with insufficient operational efficiency, scientifically formulate production plans, monitor production processes and optimize cost control in the daily production and operation process [42], and ultimately improve the efficiency of working capital management. Meanwhile, inclusion in the carbon emission rights trading list will encourage the external media to pay attention to the enterprise, so as to reduce the degree of information asymmetry inside and outside the enterprise, so as to improve the efficiency of enterprise project investment [35]. This will further optimize the working capital allocation of enterprises and improve capital allocation efficiency.

(3)

The asset operation benefit path. Under the pressure of carbon emission trading, enterprises will strengthen the technological transformation of existing assets to eliminate production units with high energy consumption, high pollution and massive discharge, and meet the environmental protection objectives of the environmental supervision department. Technological transformation makes the enterprise bear a certain production cost, which will reduce the level of equipment stock in the production line. However, the production efficiency of old equipment is low and it is sensitive to cost changes, so the stock of old equipment will decrease more [43]. This will increase the stock ratio of new equipment in the enterprise, improve the asset operation efficiency on the whole, and then make the enterprise assets reach the optimal output state, so as to improve the efficiency of enterprise capital allocation.

As mentioned above, we propose the following assumptions:

Hypothesis (H2a):

The carbon emission trading system improves the efficiency of enterprise capital allocation by enhancing the value of enterprise human capital.

Hypothesis (H2b):

The carbon emission trading system improves the efficiency of enterprise capital allocation by improving enterprise working capital management efficiency.

Hypothesis (H2c):

The carbon emission trading system improves the efficiency of enterprise capital allocation by improving the operating efficiency of enterprise assets.

3.3. Analysis of Moderation Mechanism

Carbon trading is an emission right trading mechanism led by the government and operated under certain market rules. Its role needs to be played by an effective market and promising government. An active market stimulates investors’ investment sentiment by releasing good news, and attracts participants to actively participate in the transaction, in order to stimulate the actual effects of the carbon trading market mechanism. The quality of the system also depends on the strong supervision and resolute implementation of the policy-making body, so government efficiency also provides a guarantee for the effective operation of carbon trading. Next, this paper analyzes the moderating effects of the two.

(1)

Moderating effect of carbon market trading activity. The trading activity of the carbon market promotes market mechanism to play a positive role. Trading activity is the premise of the price discovery function and market effectiveness of the trading market, and provides an important basis for ensuring the pricing efficiency of the trading market [44]. Whether the trading entity actively participates in carbon trading depends largely on whether the market pricing is reasonable. The higher the trading activity, the more the carbon trading price can contain the real information of the current carbon emission rights trading, which reduces the transaction cost and return uncertainty of the emission control entity [8], so that the emission control enterprises have more power to take part in carbon trading. First, this strengthens the supervision effect of carbon trading on the production and operation of emission control enterprises, and promotes them to produce with equipment with lower energy consumption, less pollution and higher output efficiency, thus ameliorating the efficiency of enterprise asset and capital allocation; secondly, in the active carbon market, emission control enterprises can obtain relatively high income from carbon trading, which improves the output efficiency of financial assets of enterprises and strengthens the promotion of the efficiency of financial capital allocation; finally, the active participation of emission control subjects will enhance the improvement of carbon trading on regional environmental quality, which further enhances the attraction of the province to high-quality labor, and enables enterprises to improve their human capital allocation efficiency at a lower cost.

(2)

Moderating effect of government efficiency. Government with high efficiency provides the foundation for the standardized and orderly operation of the carbon market. During the actual operation of carbon trading, the government plays a role by strengthening carbon quota management, accelerating the speed of administrative examination and approval, and strengthening market supervision, so as to stabilize the carbon price and guarantee the quota performance of emission control enterprises, and make the carbon market operate smoothly to play the role of promoting emission reduction of emission control entities. The above effect relies on the efficiency of the government. The higher the efficiency of government, the more capable the government will be to stabilize the price of carbon emission rights within a reasonable range [45], thus greatly reducing the transaction cost of emission control enterprises, making them more willing to participate in market transactions, ultimately enhancing the role of carbon trading in forcing enterprises to improve technology, and further improving the efficiency of enterprise capital allocation. Meanwhile, the administration can improve relevant laws and regulations to strengthen the supervision and control of emission control enterprises, and formulate relevant laws and regulations to form a collaborative mechanism with carbon trading to enhance the implementation effect of carbon emission trading. In this regard, a government with higher administrative efficiency will formulate more targeted carbon emission rights trading regulations to enable enterprises to enhance the disclosure of carbon emission-related information [16], which is conducive to alleviating the information asymmetry between the market and enterprises, improving the efficiency of enterprise capital use and significantly improving capital allocation efficiency.

As mentioned above, we propose the following assumptions:

Hypothesis (H3a):

The higher the trading activity of the carbon market, the stronger the role of the carbon emission trading system in improving the efficiency of enterprise capital allocation.

Hypothesis (H3b):

The higher the government efficiency, the stronger the role of the carbon emission trading system in improving the efficiency of enterprise capital allocation.

The theoretical framework of relevant assumptions in this paper is shown in Figure 1.

4. Research Design

4.1. Sample Selection and Data Source

We selected the data from 2011 to 2020 of the Shanghai and Shenzhen A-share listed enterprises in eight provinces and cities as the research samples. The financial data used in the article were all from the CSMAR database, the data related to environmental regulation and government efficiency were from the China Statistical Yearbook, the data related to carbon emission trading were from China carbon emission trading network, and the list of firms included in the carbon emission trading system was from the official websites of local environmental protection bureaus and the Development and Reform Commission. The initial samples were screened as follows: (1) The samples of listed enterprises in agriculture, forestry, fishery, animal husbandry and finance were excluded; (2) the enterprises whose shares are marked as ST or ST * were excluded; (3) all variables were tailed up and down by 1%.

4.2. Model Setting and Variable Descriptions

In order to effectively measure the capital allocation efficiency composed of human capital, financial capital, and asset capital proposed in this paper, this paper introduces the stochastic frontier analysis model put forward by Kumbhakar et al. [46] to construct variables. The use of the above model requires the determination of input and output indicators. This paper builds relevant indicators based on the practices of Qin and Shao [22]. The output indicator is measured by the total operating income and is recorded as Y. The investment indicators are divided into three parts: human capital investment, working capital investment and asset capital investment. Human capital investment is equal to the number of employees of the company and is recorded as L; financial capital investment is equal to the total operating cost + selling expenses + administrative expenses − employee compensation payable − cash paid to employees − depreciation of fixed assets − depletion of oil and gas assets − a depreciation of productive biological assets + cash paid for investment + other cash related to investment activities, which is recorded as M; asset capital investment is equal to the cash paid for the purchase and construction of fixed assets, intangible assets and other long-term assets, and is recorded as A. After determining the mentioned indicators, we use the following model to determine the efficiency of capital allocation:

$$\begin{array}{l}\ln Y_{i,t}={\beta }_0+{\displaystyle \sum _n{\beta }_n\ln X_{n,i,t}}+{\beta }_{t}t+\frac{1}{2}{\displaystyle \sum _n{\displaystyle \sum _k{\beta }_{n,k}\ln X_{n,i,t}}}\ln X_{k,i,t}+\frac{1}{2}{\beta }_{\mathrm{u}}{t}^2\\ +{\displaystyle \sum _n{\beta }_n\ln X_{n,i,t}}t+v_{i,t}-u_{i,t}\end{array}$$

(1)

Among them, Y is on behalf of the output variable, X is the input, subscript i represents the enterprise i, n and k represent the elements n and k, which can be equal in value. T is the time trend, t = 1, 2,…, T, T is the number of sample coverage times, v_i,t and u_i,t represent random noise and inefficiency terms respectively.

For verifying H1, we use the methods of Zhang and Zhang [47] for reference and use the following double difference model for testing:

$$EC{A}_{\mathrm{i},\mathrm{t}}={\beta }_0+{\beta }_1{\mathit{Interact}}_{i,t}+{\beta }_2{\mathit{Control}}_{i,t}+Year+Firm+{\epsilon }_{i,t}$$

(2)

Among them, i and t represent enterprise i and time t, respectively, ECA represents the efficiency of enterprise capital allocation, Interact_i,t is the virtual variable of whether the enterprise enters the carbon emission rights trading system. If the enterprise enters the system list, the value of the year and the subsequent years is 1, otherwise, it is 0, and control is some control variables. Year and Firm represent the fixed effects of the year and the company, respectively. The control variables refer to the practices of Qin and Shao [22] and are selected according to research needs. The details are as follows: ① Enterprise size (Size_i,t), measured by natural logarithm of the total assets of the enterprise; enterprise size is related to the operating efficiency of enterprises. Large enterprises are difficult to reasonably allocate various resources in their organization due to their jumbled organizational levels, which leads to low economic development efficiency. ② Return on assets (Roa_i,t), measured by the ratio of the current year’s profit before interest and tax to the total assets at the end of the year; the higher the enterprises’ rate of return, the more it can fully mobilize its internal resources to obtain benefits for itself, which is positively related to the efficiency of capital allocation. ③ Cash ratio (CashRatio_i,t), measured by the ratio of cash and cash equivalents held by enterprises to current liabilities; the more cash an enterprise has within a reasonable range, the higher the daily operating efficiency of the enterprise. ④ Enterprise value (TobinQ_i,t), measured by the ratio of enterprise market value to total asset value; the higher the value, the stronger the capital operation ability of the enterprise. ⑤ Book to market ratio (BM_i,t), measured by the ratio of the book value of the enterprise to the market value. When the enterprise value is overestimated, the enterprise tends to over-invest to obtain excess returns, which will reduce the efficiency of enterprise capital allocation. ⑥ Earnings per share (EPS_i,t), measured by the ratio of the enterprise’s profit before interest and tax in the current year to the total number of shares; the higher the earnings per share of an enterprise, the higher the enterprise gains from its financial capital. ⑦ The sales growth rate (SalesGrowth_i,t) is the ratio of the difference between the total operating revenue of the enterprise in the current year minus the total operating revenue of the previous year and the total operating revenue of the previous year. There is a positive correlation between the growth rate of enterprise sales revenue and the output efficiency of enterprise working capital. The main variables are briefly defined in

Table 1. Main variable Definition.

Variable Name	Variable Symbol	Definition
Efficiency of capital allocation	ECAi,t	Construction of stochastic frontier analysis model
Virtual variable of carbon emission trading system	Interacti,t	If the enterprise enters the list of carbon emission trading system in that year, it is 1; otherwise, it is 0
Company size	Sizei,t	Natural logarithm of total assets
The beginning performance	Roai,t	The ratio of corporates’ annual EBIT to the total assets at the end of the year
Cash ratio	CashRatioi,t	Ratio of cash and cash equivalents held by enterprises to current liabilities
Tobin’s Q	TobinQi,t	Ratio of market value to total assets
Book to market ratio	BMi,t	Ratio of book value to market value
Earnings per share	EPSi,t	Ratio of the enterprise’s current year’s EBIT to the number of share capital
Sales growth rate	SalesGrowthi,t	Ratio of the difference between the total operating income of the enterprise in the current year and the total operating income of the previous year to the total operating income of the previous year

:

In order to verify H2a and H2b, the following settings are made with reference to the moderating effect model of Wen et al. [48]:

$$\begin{array}{l}EC{A}_{i,t}={\beta }_3+{\beta }_4{\mathit{Interact}}_{i,t}+{\beta }_5{\mathit{Interact}}_{i,t}\times {\mathit{Moderator}}_{i,t}+{\beta }_6{\mathit{Moderator}}_{i,t}\\ +{\beta }_7{\mathrm{Controls}}_{i,t}+Year+Firm+{\epsilon }_{i,t}\end{array}$$

(3)

Among them, Moderator_i,t represents the moderator, which needs to be measured by introducing the agency variables of carbon trading market activity and government efficiency. The turnover rate of carbon trading reflects the trading activity of carbon quotas in a certain region. The higher the turnover rate of carbon trading, the higher the trading investment frequency of carbon quota in the region, and the higher the trading activity of the carbon market. Therefore, we introduce the turnover rate of carbon trading (i.e., 100% × annual turnover of carbon quota/annual allocation of carbon quota) as the proxy variable of carbon trading market activity. Government efficiency is measured by the DEA model, in which the input exponent is the general budget fiscal expenditure, and the output index is the teacher–student ratio of colleges and universities, the density of the highway network, the number of beds per 10,000 people, the per capita agricultural machinery power, and the per capita GDP, covering the multi-dimensional government performance of education, transportation, medical care, agriculture and economic development. According to the determined input–output indicators, this paper uses DEAP2.1 software to calculate corresponding government efficiency.

To verify H3a, H3b and H3c, the regression model needs to be set first. Referring to the intermediary effect model of Wen et al. [49], the regression model is set as follows:

$${VAIC}_{\mathrm{i},\mathrm{t} = }{\alpha }_1+{\alpha }_2{\mathit{Interact}}_{i,t}+{\alpha }_3{\mathit{Controls}}_{i,t}+Year+Firm+{\epsilon }_{i,t}$$

(4)

$${ECA}_{\mathrm{i},\mathrm{t} = }{\alpha }_4+{\alpha }_5{Interact}_{i,t}+{\alpha }_6{VAIC}_{i,t}+{\alpha }_7{Controls}_{i,t}+Year+Firm+{\epsilon }_{i,t}$$

(5)

$${WCME}_{\mathrm{i},\mathrm{t}} = {\gamma }_1+{\gamma }_2{Interact}_{i,t}+{\gamma }_3{Controls}_{i,t}+Year+Firm+{\epsilon }_{i,t}$$

(6)

$${ECA}_{\mathrm{i},\mathrm{t}} = {\gamma }_4+{\gamma }_5{Interact}_{i,t}+{\gamma }_6{WCME}_{i,t}+{\gamma }_7{Controls}_{i,t}+Year+Firm+{\epsilon }_{i,t}$$

(7)

$${ACR}_{\mathrm{i},\mathrm{t}} = {\theta }_1+{\theta }_2{Interact}_{i,t}+{\theta }_3{Controls}_{i,t}+Year+Firm+{\epsilon }_{i,t}$$

(8)

$${ECA}_{\mathrm{i},\mathrm{t}} = {\theta }_4+{\theta }_5{Interact}_{i,t}+{\theta }_6{ACR}_{i,t}+{\theta }_7{Controls}_{i,t}+Year+Firm+{\epsilon }_{i,t}$$

(9)

Among them, VAIC_i,t represents the value of human capital. Based on the research of Zhu and Li [50], this variable is composed of human capital efficiency (HCE) and structural capital efficiency (SCE). The calculation methods are: HCE = (net profit + income tax + cash paid to and for employees)/(cash paid to employees + employee compensation payable), SCE = (net profit + income tax + cash paid to and for employees)/management expenses. The larger the VAIC, the greater the value of the enterprise’s human capital; WCME_i,t represents the efficiency of working capital management. According to the measurement method of the existing literature [51], the working capital turnover period is adopted as the proxy variable. The smaller the value, the higher the efficiency of working capital management; ACR_i,t represents the operating efficiency of assets, which is measured by the asset contribution rate based on the research of Jin and Gong [52]. We use the sequential coefficient test to testify the existence of intermediary effect. Since the equations are multivariate linear regression, we use the least squares regression method for simultaneous equations.

5. Empirical Results

5.1. Descriptive Statistics and Multicollinearity Test

The descriptive statistics of each variable are shown in

Table 2. Descriptive Statistics.

Variables	Observations	Mean Value	Median	Variance	Minimum	Maximum
ECAi,t	5601	0.828	0.836	0.062	0.594	0.953
Interacti,t	5601	0.008	0.000	0.091	0.000	1.000
Sizei,t	5601	22.390	22.150	1.466	19.850	27.070
Roai,t	5601	0.067	0.057	0.054	−0.077	0.272
CashRatioi,t	5601	1.118	0.433	1.993	0.035	12.670
TobinQi,t	5601	2.079	1.641	1.302	0.872	8.087
BMi,t	5601	0.551	0.519	0.262	0.099	1.174
EPSi,t	5601	0.440	0.307	0.512	−0.590	2.896
SalesGrowthi,t	5601	0.195	0.118	0.412	−0.419	2.796

. From the perspective of the explained variable enterprise capital allocation efficiency (ECA_i,t), and minimum values are 0.953 and 0.594). This shows that there are big differences in the capital allocation ability of enterprises. The average value of the virtual variable (Interact_i,t) of the carbon emission trading system is 0.008, indicating that only a few enterprises have entered the carbon trading list. The mean, median and variance of other control variables are within the normal range.

In order to avoid the appearance of multicollinearity, we also conduct variance expansion factor (VIF) analysis. We find that the range of each variable is between 1.00 and 3.61, and the mean VIF is 1.95, indicating that there is no serious multicollinearity problem in this model.

5.2. Influence of Carbon Emission Trading System on Capital Allocation Efficiency of Enterprises

Columns (1)–(4) of

Table 3. Influence of Carbon Emission Trading System on Capital Allocation Efficiency of Enterprises.

Variables	(1)	(2)	(3)	(4)
	ECAi,t	ECAi,t	ECAi,t	ECAi,t
Interacti,t	0.0097 *	0.0121 **	0.0082 *	0.0098 *
	(1.88)	(2.19)	(1.68)	(1.71)
Sizei,t	−0.0023	−0.0034 *	−0.0025	−0.0081 ***
	(−1.56)	(−1.75)	(−1.49)	(−2.93)
Roai,t	0.3831 ***	0.3744 ***	0.3784 ***	0.3721 ***
	(12.30)	(11.37)	(11.91)	(11.07)
CashRatioi,t	0.0042 ***	0.0042 ***	0.0044 ***	0.0045 ***
	(5.65)	(5.40)	(5.92)	(5.82)
TobinQi,t	−0.0025 **	−0.0026 **	−0.0027 **	−0.0032 ***
	(−2.32)	(−2.33)	(−2.46)	(−2.86)
BMi,t	−0.0023	−0.0047	−0.0247 ***	−0.0314 ***
	(−0.38)	(−0.76)	(−3.20)	(−3.80)
EPSi,t	0.0080 **	0.0095 **	0.0067 *	0.0079 **
	(2.18)	(2.39)	(1.83)	(1.97)
SalesGrowthi,t	0.0017	0.0015	0.0031	0.0037 *
	(0.82)	(0.74)	(1.59)	(1.91)
Constant	0.8513 ***	0.8788 ***	0.8683 ***	0.9947 ***
	(27.26)	(20.42)	(25.05)	(16.78)
Firm	NO	YES	NO	YES
Year	NO	NO	YES	YES
Observations	5601	5601	5601	5601
R2	0.231	0.231	0.266	0.267

Note: the values in parentheses are t values, *, **, *** indicate significant at the statistical level of 1%, 5%, and 10% respectively.

show the main regression results without controlling for fixed effects, controlling for firm fixed effects only, controlling for time fixed effects only and controlling for dual fixed effects, respectively. From the corresponding regression results, the coefficients representing the virtual variables Interact_i,t representing the positive relationship of the carbon emission trading system on capital allocation efficiency of enterprises is significant; that is, the carbon emission trading system significantly improves the efficiency of enterprise capital allocation. This shows that carbon emission rights trading has fully exerted the institutional effect and achieved the effect of forcing the emission control enterprises to improve their efficiency. Therefore, H1 is verified. From the results of column (4), the control variables Roa_i,t, CashRatio_i,t, EPS_i,t, SalesGrowth_i,t which represent the profitability of enterprises, are significantly positively related to the efficiency of capital allocation of enterprises, indicating that the stronger the profitability of enterprises, the higher the efficiency of capital allocation, which is consistent with the cognition of the mainstream literature. However, the scale of enterprises is negatively related to the efficiency of capital allocation; that is, the larger the scale of enterprises, the lower the efficiency of capital allocation. The possible reason is that the larger the scale of the enterprise, the more complex the organizational structure of the enterprise, resulting in low efficiency in making capital decisions such as production and operation, asset purchase and financial investment.

5.3. Robustness Test

5.3.1. Parallel Trend Test

The precondition for the implementation of the double difference method is that the experimental group and the control group should meet the assumption of parallel trend. Specifically, in this paper, the condition that needs to be met is that before the implementation of the carbon emission trading system, the changing trend of enterprise capital allocation efficiency in the experimental group and the control group is the same. Therefore, we construct the following dynamic effect model to examine whether there are differences in the capital allocation efficiency of enterprises before the implementation of the carbon emission trading system:

$$\begin{array}{l}EC{A}_{i,t}={\lambda }_0+{\lambda }_{1}Interac{t}_{i,t}^{-3}+{\lambda }_{2}Interac{t}_{i,t}^{-2}+{\lambda }_{3}Interac{t}_{i,t}^{-1}+{\lambda }_{4}Interac{t}_{i,t}+\\ {\lambda }_{5}Control{s}_{i,t}+Year+Firm+{\epsilon }_{i,t}\end{array}$$

(10)

If the enterprise is in the list of carbon emission trading systems and the sample time is in the kth year of being included in the list, then ${\mathrm{Interact}}_{\mathrm{i},\mathrm{t}}^{\mathrm{k}}$ = 1; otherwise, ${\mathrm{Interact}}_{\mathrm{i},\mathrm{t}}^{\mathrm{k}}$ = 0 (k = −3,−2,−1). The results in

Table 4. Robustness Test.

Variables	Parallel Trend Test	Placebo Test	PSM + DID	Variables Replacement
	(1)	(2)	(3)	(4)
${\mathrm{Interact}}_{\mathrm{i},\mathrm{t}}^{-3}$	0.0034
	(0.53)
${\mathrm{Interact}}_{\mathrm{i},\mathrm{t}}^{-2}$	0.0085
	(1.56)
${\mathrm{Interact}}_{\mathrm{i},\mathrm{t}}^{-1}$	0.0008
	(0.13)
Interacti,t	0.0157 *	0.0172	0.0182 ***	−0.0258 *
	(1.73)	(1.61)	(3.02)	(−1.91)
Controlsi,t	YES	YES	YES	YES
Firm	YES	YES	YES	YES
Year	YES	YES	YES	YES
Constant	0.9947 ***	0.9950 ***	1.1042 ***	−0.2054 ***
	(16.77)	(16.79)	(14.63)	(−2.93)
Observations	5601	5601	3489	5164
R2	0.267	0.267	0.317	0.114

Note: the values in parentheses are t values, *, *** indicate significant at the statistical level of 1%, 5%, and 10% respectively.

show that before the formal implementation of the carbon emission trading system, the coefficients of ${\mathrm{Interact}}_{\mathrm{i},\mathrm{t}}^{-3}$-${\mathrm{Interact}}_{\mathrm{i},\mathrm{t}}^{-1}$ are not significant. Therefore, there is no significant difference in the capital allocation efficiency between the enterprises included in the carbon trading list and those not included. Therefore, the parallel trend hypothesis is verified.

5.3.2. Placebo Test

In this paper, the placebo test method was used to exclude the influence of the inherent characteristics of the experimental and control sample companies on the study results. Specifically, the implementation year of the carbon emission trading system is pushed forward by three years and the main regression experiment is conducted again. In Table 4, the coefficient of Interact_i,t is not significant, indicating that the main regression is not caused by the difference in the inherent characteristics of the company, so the experimental results are stable.

5.3.3. PSM-DID Test

According to the policy documents of each pilot province, whether an enterprise is included in the carbon trading list is determined according to certain industrial pollution emission standards, so the selection of enterprises entering carbon trading is non-random. This may lead to sample self-selection bias. In this paper, the PSM method was used to screen samples to exclude the above possibilities, and then DID test was conducted. Specifically, this paper selects enterprise size, return on assets, cash ratio, enterprise value, book-to-market, and earnings per share as covariates for kernel matching. According to the balance test in

Table 5. Balance Test.

Variables	Sample	Difference Test of Means		Drop (%)	t Test
		Treated	Untreated		t	p > t
Sizei,t	Matched	22.487	22.409	−54.4	0.37	0.715
	Unmatched	22.487	22.366		0.53	0.597
Roai,t	Matched	0.068	0.065	−212.2	0.37	0.713
	Unmatched	0.068	0.059		0.82	0.413
CashRatioi,t	Matched	0.624	1.067	93.5	−1.6	0.11
	Unmatched	0.624	0.5957		0.21	0.83
TobinQi,t	Matched	2.074	2.090	19.5	−0.08	0.933
	Unmatched	2.074	2.061		0.07	0.948
BMi,t	Matched	0.521	0.553	66.3	−0.81	0.416
	Unmatched	0.521	0.532		−0.23	0.822
EPSi,t	Matched	0.280	0.438	90.8	−2.09	0.037
	Unmatched	0.280	0.266		0.23	0.822
SalesGrowthi,t	Matched	0.148	0.196	93.4	−0.78	0.433
	Unmatched	0.148	0.152		−0.06	0.955

, the difference of each variable has significantly decreased after matching, so the matching effect is good. From the DID test results in Table 4, the coefficient of Interact_i,t is positive and significant at the statistical level of 1%, indicating that the experimental results are robust.

5.3.4. Replace the Measurement Method of Efficiency of Enterprise Capital Allocation

We use the alternative capital allocation efficiency measurement to test. Referring to the practice of Xiong and Ye et al. [53], the expected investment model proposed by Richardson [54] is used to measure the efficiency of capital allocation. The specific model setting is as follows:

$$\begin{array}{l}{Investment}_{i,t}={\eta }_0+{\eta }_1{Growth}_{i,t-1}+{\eta }_2{Lev}_{i,t-1}+{\eta }_3{Cash}_{i,t-1}+{\eta }_4{Size}_{i,t-1}+\\ {\eta }_5{Ret}_{i,t-1}+{\eta }_6{Investment}_{i,t-1}+{\epsilon }_{i,t}\end{array}$$

(11)

Among them, Investment_i,t is the investment scale of the company i in time t, which is equal to the sum of net value of fixed assets, long-term investment and intangible assets in time t divided by the average total assets of the current year. Growth_i,t−1, Lev_i,t−1, Cash_i,t−1, Size_i,t−1, Ret_i,t−1, Investment_i,t−1, respectively, represent the operating income growth rate, asset-liability ratio, cash held/initial assets, company size, stock return and investment scale of the company i in time t−1. The absolute value of the residual value of the model (recorded as InvestEff) reflects the difference between the expected investment scale and the actual investment scale of the enterprise. Therefore, the larger the value, the greater the deviation between the actual investment and the expected investment of the enterprise; that is, the lower the efficiency of capital allocation.

Column (4) in Table 4 is the result of resetting the replacement capital allocation efficiency variable. Among them, the independent variable Interact_i,t coefficient is −0.0258 and is significant at the statistical level of 10%, which indicates that the carbon emission trading system significantly suppresses the inefficient behavior of capital allocation; that is, improves the efficiency of enterprise capital allocation. This is consistent with the previous results. The results show that the regression is robust.

5.4. Mechanism Analysis

5.4.1. Analysis of Intermediary Mechanism

Table 6. Action Path Test.

Variables	Human Capital Value		Working Capital Management Efficiency		Operating Efficiency of Assets
	VAICi,t	ECAi,t	WCMEi,t	ECAi,t	ACRi,t	ECAi,t
	(1)	(2)	(3)	(4)	(5)	(6)
Interacti,t	−0.1375 (−0.85)	0.0107 ** (2.14)	−19.9268 ** (−2.02)	0.0091 * (1.76)	0.0052 ** (2.45)	0.0066 * (1.88)
VAICi,t		0.0070 *** (5.10)
WCMEi,t				−0.0000 *** (−2.66)
ACRi,t						0.6176 *** (5.97)
Controlsi,t	YES	YES	YES	YES	YES	YES
Constant	−2.6166 * (−1.73)	1.0106 *** (18.15)	−94.8626 (−0.54)	0.9846 *** (16.13)	0.1579 *** (9.08)	0.8972 *** (14.53)
Firm	YES	YES	YES	YES	YES	YES
Year	YES	YES	YES	YES	YES	YES
Observations	5591	5591	5557	5557	5601	5601
R2	0.296	0.290	0.278	0.102	0.929	0.292
Sobel Test	α2 is insignificant, |Z| = 0.7514 <0.97, there is no intermediary effect		γ2 and γ6 is significant, there is no need for Sobel Test		θ2 and θ6 is significant, there is no need for Sobel Test

Note: the values in parentheses are t values, *, **, *** indicate significant at the statistical level of 1%, 5%, and 10% respectively.

shows the mediation mechanism test results, the test results of the mediating effect of human capital value, where the coefficient of α₂ is not significant, but the coefficient of α₆ is significant at the statistical level of 1%. Therefore, it is necessary to use the Sobel test to judge the intermediary effect. The result shows that the Sobel test is not significant, so H3a is not valid. The failure of this path may be due to the time lag of carbon emission trading in improving air quality. It is difficult to quickly improve regional environmental quality to attract talents in the short term, which makes it difficult to facilitate high-quality talent migration, which leads to the overall quality of the local labor market not being improved and makes it difficult for enterprises to obtain high-quality talent to improve the efficiency of capital allocation. The results of columns (3), (4), (5) and (6) are analyzed for working capital management efficiency and asset operation efficiency; γ₂ and γ₆ and θ₂ and θ₆ are significant, indicating that the intermediary path of working capital management efficiency and asset operation efficiency is established; that is, H3b and H3c are established.

5.4.2. Analysis of Moderating Mechanism

Table 7. Moderating Effect Test.

Variables	(1)	(2)
	ECAi,t	ECAi,t
Interacti,t	−0.0093	−0.0209
	(−1.11)	(−1.10)
Activityi,t	0.0008
	(1.11)
Activityi,t × Interacti,t	0.0023 ***
	(2.85)
GEi,t		0.0022
		(0.10)
GEi,t × Interacti,t		0.0607 *
		(1.78)
Sizei,t	−0.0079 ***	−0.0081 ***
	(−2.79)	(−2.94)
Roai,t	0.3682 ***	0.3727 ***
	(10.62)	(11.05)
CashRatioi,t	0.0045 ***	0.0045 ***
	(5.69)	(5.81)
TobinQi,t	−0.0035 ***	−0.0032 ***
	(−2.93)	(−2.86)
BMi,t	−0.0321 ***	−0.0314 ***
	(−3.74)	(−3.80)
EPSi,t	0.0084 **	0.0079 *
	(1.97)	(1.96)
SalesGrowthi,t	0.0043 **	0.0037 *
	(2.10)	(1.91)
Constant	0.9828 ***	0.9935 ***
	(16.39)	(16.28)
Firm	YES	YES
Year	YES	YES
Observations	5601	5601
R2	0.262	0.267

Note: the values in parentheses are t values, *, **, *** indicate significant at the statistical level of 1%, 5%, and 10% respectively.

reports the test results of the regulation mechanism, in which column (1) and column (2), respectively, report the regulation effect of carbon trading market activity and government efficiency. In column (1), the coefficient of the interaction term Activity_i,t × Interact_i,t has a significant coefficient of 0.0023. It indicates that carbon market trading activity has a positive effect on promoting carbon trading. The willingness of participants to trade constitutes the basic element of carbon market trading. An active trading market can reduce transaction costs, improve pricing efficiency and ease the degree of information asymmetry, so as to effectively promote carbon trading in a wider range and a wider range of participants. In column (2), the coefficient of the interaction term GE_i,t × Interact_i,t is 0.0607 and is significant at the statistical level of 10%, indicating that the government can promote the smooth operation of carbon trading, and has an important relationship between its own work efficiency and the economic benefits of the carbon market. The government ensures the compliance rate of emission control enterprises and reduces the transaction costs of enterprises by strengthening the enforcement of laws and regulations related to carbon trading, accelerating the speed of administrative approval, and implementing environmental responsibility assessment. The higher its working efficiency, the more it can provide a solid foundation for the effective operation of carbon trading. The above empirical results show that carbon trading market activity and government efficiency play a positive role in regulating the relationship between the carbon emission trading system and enterprise capital allocation efficiency; that is, H2a and H2b are verified.

5.5. Heterogeneity Test

Due to different industrial attributes and location conditions of different enterprises, the role of the carbon emission trading system may have heterogeneous effects. From the industry level, different pollution intensities of industries make enterprises different in environmental supervision, industry access standards, resource dependence, etc; from the regional level, the effective operation of the carbon emission trading system depends on the executive power of the government, such as the punishment of enterprises that cheat and conceal carbon emissions, the management of the carbon emission trading process and the punishment of acts that undermine fair trading. Differences in regional environmental regulation will have an impact on the effectiveness of carbon trading. Therefore, this paper conducts a heterogeneity test based on the degree of industrial pollution of enterprises and regional environmental regulation.

The grouping basis of the heterogeneity test is as follows: For the degree of industrial pollution, this paper refers to the ideas of Jie et al. [55] and sets the 16 heavy pollution industrial enterprises specified in the classified management directory of environmental protection verification industry of Listed Companies in 2010 as the high environmental pollution group, and other industrial enterprises as the low environmental pollution group. With regard to the intensity of environmental regulation, this paper refers to the practices of Wang and Xu [56], and uses the proportion of provincial sewage charges to the total industrial output value to measure, and groups the enterprises according to the median of this index. The group with environmental regulation intensity is set as the group with higher intensity of environmental regulation, and the others are set as the group with lower environmental regulation intensity. The results of this heterogeneity test are reported in columns (1) to (4) in

Table 8. Heterogeneity Test.

Variables	Higher Level of Pollution (1)	Lower Level of Pollution (2)	Higher Level of Environmental Regulation (3)	Lower Level of Environmental Regulation (4)
Interacti,t	0.0081	0.0091 **	0.0140 ***	0.0113
	(1.29)	(2.47)	(4.28)	(1.59)
Sizei,t	−0.0116 *	−0.0063 **	−0.0166 ***	−0.0055
	(−1.93)	(−2.03)	(−4.21)	(−1.56)
Roai,t	0.2872 ***	0.4167 ***	0.3629 ***	0.3348 ***
	(5.31)	(10.16)	(6.58)	(7.68)
CashRatioi,t	0.0057 ***	0.0041 ***	0.0044 ***	0.0038 ***
	(3.54)	(4.80)	(3.20)	(3.95)
TobinQi,t	−0.0017	−0.0036 ***	−0.0014	−0.0011
	(−0.88)	(−2.71)	(−1.23)	(−0.48)
BMi,t	0.0005	−0.0457 ***	−0.0137	−0.0364 ***
	(0.03)	(−4.90)	(−1.36)	(−3.03)
EPSi,t	0.0154 ***	0.0040	0.0085	0.0102 **
	(2.60)	(0.82)	(1.51)	(2.15)
SalesGrowthi,t	0.0046	0.0033	0.0037	0.0047
	(1.44)	(1.40)	(1.53)	(1.28)
Constant	1.0498 ***	0.9669 ***	1.1506 ***	0.9415 ***
	(7.69)	(14.75)	(13.13)	(12.46)
Firm	YES	YES	YES	YES
Year	YES	YES	YES	YES
Observations	1689	3912	2472	3129
R2	0.284	0.272	0.254	0.245

Note: the values in parentheses are t values, *, **, *** indicate significant at the statistical level of 1%, 5%, and 10% respectively.

. Columns (1) and (2) show that the coefficient of the explanatory variable Interact_i,t of the low pollution group is significant at 0.0091, while that of the high pollution group is insignificant at 0.0081, indicating that the carbon emission trading system can better improve the capital allocation efficiency of enterprises with lower pollution. For heterogeneity of environmental regulation, columns (3) and (4) show that the coefficient of Interact_i,t in areas with high environmental regulation is 0.0140 and is significant at the statistical level of 1%. On the contrary, the coefficient of explanatory variable in the low environmental regulation group is not significant. The above results show that strict regional environmental regulation can promote carbon emission trading and improve the efficiency of enterprise capital allocation.

6. Research Conclusions and Policy Recommendations

In the study, we took a Chinese carbon emissions trading pilot as the natural experimental platform. We used the data of listed firms in China’s carbon trading pilot from 2011 to 2020, and investigated the impact of the carbon emissions trading system on enterprise capital allocation efficiency based on a multi-period DID model. We found that the carbon emission trading system was positively associated with enterprise capital allocation efficiency. After a series of robustness tests, the results still remained unchanged. Meanwhile, we further investigated three channels that linked the carbon emission trading system and enterprise capital allocation efficiency. We found that the carbon emissions trading system chiefly played a role in enhancing the efficiency of corporate capital allocation by improving the operational efficiency of working capital and asset operation, while the human capital value path did not hold and carbon trading market activity and government efficiency strengthened the positive association between the carbon emission trading system and enterprise capital allocation efficiency. Heterogeneity analysis showed that the effect was more pronounced for companies in industries with lower pollution intensity and areas with stronger environmental regulations. For the above conclusions, we put forward the following policy recommendations.

Firstly, as a pilot policy to open the national carbon emissions trading market, the pilot carbon emissions trading market would have a demonstration effect by forming a complete mechanism in quota allocation, market trading, and supervision and by focusing on efficiency and fairness to provide useful inspiration for the full implementation of carbon emissions trading. Specifically, government departments should accurately monitor the emission data of key emission departments and prevent companies from falsely reporting their own carbon emissions to ensure the initial distribution efficiency. In terms of market transactions, government departments must stick to the principles of government guidance and market leadership. It is necessary to respect the free trading of emission rights in the market, and also to supervise or impose heavy penalties on those who drive up the price of emission rights deliberately.

Secondly, push the effective operation of the carbon market by improving market mechanisms and improving government efficiency. Active market trading is the foundation for the operation of the carbon market. The carbon market needs to gradually expand the industry scope of participating entities and introduce institutional investors to make up for the lack of professional trading knowledge of the emission control companies and facilitate business transactions. At the same time, the carbon trading market should launch various carbon financial derivatives in time to expand the trading scope of trading entities and enhance market activity. Government supervision and enforcement is the key to the operation of the carbon market. When formulating carbon trading guidelines, the government should fully consider the actual situation of emission reduction of local enterprises, formulate carbon trading guidelines reasonably, improve the efficiency of trading approval, reduce the transaction costs of enterprises in carbon trading, and ensure that emission control companies complete carbon trading compliance on time.

Thirdly, implementing environmental regulation policies is not in conflict with improving enterprise efficiency. The key lies in adopting flexible policy and fully considering the characteristics of enterprises to provide continuous economic motivation for them to reduce emissions. This paper finds that the policy effect of the carbon emissions trading system varies among enterprises with different pollution levels, indicating that it is difficult for this environmental regulation method to balance efficiency and fairness. Therefore, the carbon emission trading system should fully take industry pollution levels into account and provide relatively loose conditions for heavily polluting enterprises in quota allocation, system design and supervision mechanism correspondingly to prevent them from being forced to withdraw from the market due to excessive economic pressure.

Fourth, strengthen environmental regulations to ensure the successful implementation of the carbon emissions trading system. The results of the heterogeneity test in this paper manifest that the effect of the carbon emission trading system is more pronounced for companies in areas with stronger environmental regulations, leading to higher capital allocation efficiency, which indicates that regional environmental fiscal expenditure guarantees the implementation of the carbon emissions trading system effectively. Therefore, in the first place, expenditure to curb environmental pollution should be regarded as the emphasis of public financial expenditure. The proportion of financial expenditure ought to be adjusted. Government needs to motivate companies to make an investment in environmental protection. In addition, they should adjust the investment caliber according to the actual environmental protection needs of the region, and strengthen evaluation of the actual benefits of environmental investment effectively. Last but not least, a long-term and stable environmental protection investment mechanism should be established by publishing relevant documents to ensure the continuous expenditure of environmental protection investment.