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Abstract

Emission trading policies can provide environmental incentives for businesses, leading to a reduction in pollution emissions and promoting sustainable environmental development. Previous research indicated the significant impact of market-based environmental regulations on emission reduction by businesses, however, there is a lack of in-depth examination from the perspective of overall corporate management efficiency. In this study, we conducted research using the 2007 SO₂ emission trading pilot program as a quasi-natural experiment to investigate the mechanisms and effects of emission trading systems on corporate resource allocation efficiency. The study found that the implementation of emission trading systems significantly improves corporate resource allocation efficiency. Furthermore, through market regulation and administrative supervision mechanisms, corporate resource allocation efficiency can be further enhanced. However, emission trading systems have heterogeneous effects on resource allocation efficiency, with a stronger promotion effect on optimizing resource allocation in cases of greater financing constraints and higher levels of corporate governance. This study provides important policy insights for further promoting market-based environmental regulation reforms and improving corporate resource allocation efficiency.

Keywords

emissions trading enterprise resource allocation corporate governance business law and business ethics management social sciences economic development economic science environmental regulation reform

Introduction

In recent years, the global greenhouse gas emissions trading market has experienced rapid expansion, emerging as a prominent feature of global trade. Recognizing the importance of emissions trading, countries should promptly establish tailored emission trading markets that align with their national conditions. Since the turn of the century, China has surpassed other developed nations to become the world’s largest emitter of SO₂. The “2018 Global Environmental Performance Index,” jointly released by global economic institutions, ranks China 120th and India 177th among emerging market economies, highlighting the environmental pressures arising from rapid economic growth. China’s air quality ranks fourth from the bottom, reflecting the severe environmental pollution and gaps in environmental laws and regulations.

China’s economic reforms have brought transformative changes, but they have also led to environmental pollution and ecological imbalances. To address this, China should swiftly improve its emission trading markets, drawing from other countries’ experiences and pilot projects. The government has selected 11 local units, including Henan, Jiangsu, and Hunan, as pilot areas for implementing the SO₂ emission trading system. This emphasizes the crucial role of emissions trading as an institutional innovation for ecological construction. Market mechanisms should drive ecological development, with a focus on market-based allocation of water rights, carbon rights, and emissions trading. The emission trading system is a vital tool for promoting ecological construction and supporting high-quality economic development in China.

In the existing research, enterprise-level studies related to market-based environmental regulation primarily focus on whether market environmental regulation policies themselves can effectively reduce environmental pollution and achieve the fundamental goal of protecting the ecological environment for sustainable development (Hong et al., 2022; Shi et al., 2022). From the perspective of enterprise management, the associated research on market-based environmental regulation policies primarily explores the effects and mechanisms from a singular perspective, such as the impact on green technological innovation (H. Zhang et al., 2022; W. Zhang et al., 2022), green total factor productivity (Pan et al., 2022), and return on net assets (Zha et al., 2022). However, there has been a lack of research that directly extends the research perspective to the comprehensive evaluation model of enterprise resource allocation efficiency, which is influenced by various internal and external factors such as industrial structure optimization, enterprise competition, and innovation competition (Gong & Hu, 2013). Further optimizing the structure and efficiency of enterprise resource allocation is a crucial path for promoting high-quality economic development in China (Cai, 2013). The pilot policy of emission trading in China provides a good background for exploring the relationship between the comprehensive resource allocation evaluation model and market-based environmental regulation. Therefore, our study holds significant theoretical value and practical significance.

This paper examines the impact of the implementation of the pollution rights trading pilot policy announced by China in 2007 on firm resource allocation efficiency through a quasi-natural experiment. The study utilizes a difference-in-differences model, with a sample of companies from the China Industrial Enterprise Database for the period between 2005 and 2017.

Compared to previous studies, this paper’s marginal contributions lie primarily in the following aspects:

Enriching the theoretical research on firm resource allocation efficiency and providing insights into how environmental regulations can promote sustainable firm development: By using the implementation of the SO₂ emission trading system as a quasi-natural experiment and examining its impact on firm resource allocation efficiency, we found that the implementation of emission trading significantly improves firm resource allocation efficiency. Furthermore, through market regulation and administrative supervision mechanisms, firm resource allocation efficiency can be further enhanced. This expansion of the theoretical boundaries and the impact of market-based environmental regulations on firm development effectively addresses Allen’s questioning of the effectiveness of market-based environmental policy reforms in China (Allen et al., 2005).

Enriching the theoretical research on market-based environmental regulations and providing empirical evidence for the implementation of the pilot system of SO₂ emission trading in optimizing firm resource allocation efficiency and promoting high-quality economic development: By combining the macro and micro impact mechanisms of market-based environmental regulations, we analyze the economic effects of the implementation of the pilot system of SO₂ emission trading. Previous studies, such as those by Tu and Chen (2015), primarily focused on the impact of the pilot system on economic development, such as the Porter effect (Jin et al., 2022; Qi et al., 2018). This paper analyzes the impact of the pilot system on different heterogeneity characteristics at the firm level, extending its effects to the industry and macroeconomic dimensions.

The subsequent sections of this paper are structured as follows: Section 2 provides a comprehensive review of the relevant literature; Section 3 presents the research hypotheses; Section 4 discusses the sample data and research design; Section 5 presents the empirical process and analyzes the results; Section 6 offers a detailed discussion; and lastly, the Conclusion and Future Research section provides insights and recommendations.

Literature Review

Given that the main focus of this study lies in the pollution rights trading policy and the efficiency of firm resource allocation, the literature review section primarily examines relevant research on pollution rights trading policies and other similar environmental policies’ impact on enterprise total factor productivity, as it is the primary determinant of measuring firm resource allocation efficiency in this paper.

Research on Emission Trading

Some scholars have found that the emission trading policy has significant policy effects through research. Baker took the SO₂ emissions of listed companies in the United States as the research object (Patrick, 2021) and tested that the SO₂ emission trading policy can effectively reduce the total SO₂ emissions of the pilot state. However, the quality of emission reduction depends on the proportion of manufacturing companies in the state. Coleman, referring to the impact of SO₂ emission trading policies of large and medium-sized enterprises in major EU member countries, such as Britain, France, Germany, and Italy (Coleman et al., 2019), verified that there is significant industry heterogeneity in SO₂ emission trading policies among which the manufacturing industry is significantly affected by SO₂ emission trading policies. Another part of scholars believes that the policy effect of SO₂ emission trading policy needs to be revised. Stefan found that the main reason for Sweden’s significant emission reduction effect is the imbalance between supply and demand of inflation and deflation caused by the economic cycle (Åström et al., 2017), which reduces supply and demand and thus objectively reduces SO₂ emissions. In contrast, the impact of the SO₂ emission trading policy does not have a noticeable policy effect. Based on the EU-ETS emission trading system (Wei et al., 2021), Wei proved that the SO₂ emission trading policy has a limited driving effect on regional SO₂ emissions reduction and needs more momentum to become a critical influencing factor. It can be seen that in the developed countries that have implemented the emission trading policy, whether the policy objectives of emission trading can achieve the expected effect is still controversial.

However, China’s research on SO₂ emission trading policies mainly focuses on its trading market, each trading link itself, and the legality and compliance of policy application. S. Ren et al. (2019) found that market-oriented environmental policies can effectively reduce SO₂ emissions in the region, and the policy effect is increasing yearly. By measuring the liquidity of the market in the pilot area of SO₂ emissions trading, H. Zhang et al. (2022) believed that the impact on SO₂ emissions also needs to be further verified due to the insufficient liquidity of the transaction at the initial stage of establishment (H. Zhang et al., 2022). J. Chen et al. (2022) and W. Chen et al. (2022) contend that although emissions trading is considered one of the effective measures to address the externalities of environmental pollution from industries, its policy effectiveness varies across regions. Apart from Tianjin, other provinces and cities have not achieved the anticipated outcomes (J. Chen et al., 2022). Therefore, the research on emission trading policy in China is more qualitative research, but less quantitative research; At the same time, the policy effect of emission trading in China is also controversial, and more quantitative research is needed to demonstrate the policy effect of emission trading.

Environmental Regulation and Enterprise Total Factor Productivity

The relationship between environmental regulation and the efficiency of enterprise resource allocation is still controversial in academic circles. Among them, especially the view of the neo-classical school of economics, is that when the government implements environmental policies, it will first form an invisible pressure on the daily production and operation of enterprises, that is, increase the production costs of enterprises. The increased costs are mainly used for technological upgrading to optimize SO₂ emissions to meet government industry standards. The resulting technological investment will enter the daily accounting system of enterprises, causing changes in the structure of enterprise resource allocation (Guo, 2020). Therefore, the efficiency of enterprise resource investment allocation may change at the initial policy implementation stage (X. Ren et al., 2022). Christina et al. found that implementing the administrative order-based environmental policy will change the level of resource allocation efficiency of enterprises, especially compared with the manufacturing industry. The overall change of resource allocation in its industry is more significant than in other industries (Wong et al., 2020) after investigating the data of the primary asset allocation efficiency of thousands of small and medium-sized enterprises. However, scholars based on the theoretical basis of the relationship between environmental policy and economic development by Porter put forward opposite opinions (Porter & Linde, 1995). Kasper believes that as the effectiveness of environmental regulation continues to strengthen, most of the technological resources of enterprises are allocated to R&D and innovation activities. The efficiency improvement brought by innovation investment in the future can bring more than 30 years of long-term investment returns to seven manufacturing sectors in Germany (Kasper, 2022). Carlos also further confirmed that appropriate environmental policies in the EU manufacturing industry could improve enterprise resource allocation efficiency, change enterprise resource allocation structure, and improve total factor productivity (Soria-Rodríguez, 2020). Sangho uses the 15-year panel data of Japan’s construction and manufacturing industries and finds that environmental policies can effectively improve the overall resource allocation efficiency of the whole industry (Kim, 2022). Therefore, for developed countries, the implementation of environmental policies may change the efficiency of enterprise resource allocation at the initial stage, but with the extension of environmental policies, enterprises begin to attach importance to green technology innovation, which may have a positive impact on the efficiency of enterprise resource allocation in the long run. However, emission trading is a kind of market-based environmental regulation policy, and whether it can get the same effect needs further study.

With the increasing urgency of the Chinese government to promote high-quality economic development, some scholars have begun to pay attention to the relationship between China’s environmental policies and enterprise development. As a kind of government order, environmental policy treats new enterprises and century-old stores equally, thus affecting the development of enterprises in each life cycle stage and having an important impact on the development of enterprises (Peng & Jiang, 2021). X. Ren et al. (2022) found that China’s climate policy will hinder the improvement of total factor productivity of enterprises, but there are differences with the heterogeneity of property rights. Tang et al. (2020) also believes that command-and-control environmental regulation hassignarily hindered the growth of enterprise total factor production. However, H. Chen et al. (2021) found that China’s low-carbon city policy can effectively improve the total factor productivity of enterprises. Therefore, China’s environmental policy has the same uncertain impact on the efficiency of enterprise resource allocation. In addition, previous studies only considered the perspective of total factor productivity of enterprises, but did not discuss it from the perspective of enterprise resource allocation efficiency.

To sum up, the existing research still has the following shortcomings: 1. There are fierce debates on the policy effects of emissions trading in both developed countries and China, and the quantitative research on emissions trading in China is insufficient, so further quantitative research is needed to fill this deficiency; Secondly, the research on the influence of environmental policy on the efficiency of enterprise resource allocation is more inclined to promote the efficiency of resource allocation from the research of developed countries, but it is not known whether the market-based environmental regulation policy of emission trading can have the same effect; However, there is still a lot of controversy about the impact of China’s environmental policies on the total factor productivity of enterprises. In addition, the literature on the efficiency of emission trading policies on enterprise resource allocation from the micro-enterprise and industry levels needs to be filled.

Research Hypotheses

Under the complete market theory, unit entities can reasonably allocate resources to each link of the production process, making it close to the Pareto optimal structure level. However, due to the influence of environmental regulation policies under government intervention, enterprises may have a distorted resource allocation structure when allocating factor production resources (Elizabeth & Audia, 2020). From the perspective of market-oriented environmental regulation policy, the external emission trading system can affect the resource allocation structure and efficiency by changing the internal factor investment of enterprises (Xu et al., 2022). A perfect market-oriented environmental regulation system can effectively mobilize enterprises to participate in transactions, strengthen the allocation of factors, improve the decision-making mechanism, and expand financing channels, to optimize the efficiency of enterprise resource allocation, accelerate the frequency of capital turnover and promote economic growth.

Whether implementing a market-oriented environmental regulation policy can produce the same policy effect on all domestic manufacturing enterprises and further exert the market allocation effect will play a decisive role in optimizing the efficiency of enterprise resource allocation. From the perspective of the “environmental performance hypothesis,” the market-based environmental regulation policy can effectively play the dominant role of the market in allocating resources and encouraging the flow of trading rights to better operate efficiently in the implementation area (Fan & Wang, 2013). According to the “effective supervision hypothesis,” the government has the inherent advantages of information collection, screening, and screening on the transaction information of the market-based environmental regulation policy. After the pilot policy of emissions trading is started, the enterprises that participate in the market trading mechanism first have the first-mover advantage. The enterprises of other policy implementation targets are urged to participate in it, thus improving the efficiency of resource allocation in the entire industry. Therefore, implementing market-oriented environmental regulation policies may improve the structure of enterprise resource allocation and promote the efficiency of enterprise resource allocation.

However, the existing research has yet to reach a consistent conclusion on the impact of implementing market-based environmental regulation policies on the resource allocation efficiency of different types of enterprises. Martin found in his research on the impact of market-based environmental regulation policies that trading emission permits can effectively enhance the growth of EU enterprises’ assets and sales and improve the allocation of resources by reducing enterprises’ illegal costs. However, the impact on small and medium-sized enterprises is small (Martin et al., 2016). Sheng et al.’s (2023) research results show that the market-based environmental regulation policy has not significantly affected the domestic enterprise resource allocation efficiency after it is implemented in emerging developing countries. Dong and Wang (2021) pointed out that with the in-depth development of market-oriented environmental regulation policies, the efficiency of enterprise resource allocation in China will experience a situation of first decline and then increase. As the environmental regulation policy will cause compliance costs for enterprises with higher emissions, it may affect the matching efficiency of production factors at the initial stage of implementing the market-based environmental regulation policy. In addition, market-oriented environmental regulation policies are closely related to the country’s economic and financial development level. As an emerging economy, China has had a relatively perfect market mechanism system since the reform and opening up. However, facing the market-oriented environmental regulation policies, it still needs regulatory experience, safeguard mechanisms, trading environment, and other elements. Therefore, it needs to be clarified whether the pilot emission trading policy can promote the efficiency of enterprise resource allocation in China (Si & Cao, 2021; H. Zhang et al., 2022). However, in general, as an essential market-based environmental regulation tool and based on market pricing, emission trading pilot policy can help enterprises provide flexible emission reduction measures in resource allocation to minimize their compliance costs. Based on this, this paper proposes the following:

Hypothesis 1. Emission trading pilot policy can effectively optimize the efficiency of resource allocation among enterprises.

The efficiency of enterprise resource allocation results from the joint action of various departments and business projects within the enterprise. In addition to the internal role, the mutual influence of external industry resource allocation efficiency should also be considered. Implementing market-oriented environmental regulation policies will cause enterprises affected by financing constraints to be unable to maximize their production efficiency to obtain sufficient emission trading resources, resulting in a mismatch of enterprise resource structure (Wan et al., 2022). The market-oriented environmental regulation policy will affect the resource allocation efficiency of enterprises through market regulation of emission trading resources and government administrative supervision constraints. (1) Market regulation channels. The most direct impact of implementing the market-based environmental regulation policy is controlling the use and transaction of the enterprise’s sewage discharge rights. Quantitative management and control of enterprise emission rights are key factors affecting productivity and resource allocation efficiency. When an enterprise purchases emission rights, on the one hand, it will bring pressure to increase the cost; on the other hand, if the supply of emission rights in the market is short-term, the enterprise may bear the loss of insufficient production capacity to avoid the cost of environmental penalties, resulting in a decrease in the scale of human, financial and material investment (Yoo, 2019), thus reducing the overall investment efficiency of the enterprise and resulting in a mismatch of resource allocation efficiency. (2) The government regulatory constraints. The compliance cost brought by government regulatory constraints is essential for enterprise capacity allocation and resource adjustment (Z.-R. Wang et al., 2023). However, once an enterprise has mismatched its essential resources, it will reduce its investment in energy-saving and emission-reduction technologies and long-term asset allocation (Sun et al., 2021), thus affecting the overall efficiency of resource allocation. Therefore, implementing market-based environmental regulation policies may affect the efficiency of enterprise resource allocation through government regulatory constraints. However, whether it is the market regulation channel or government regulation constraint, its purpose is to directly or indirectly improve the inefficiency of resource mismatch between enterprises. Based on this, this paper proposes the following:

Hypothesis 2. Emission trading pilot policies can improve the efficiency of resource allocation among enterprises through market regulation channels and government regulatory constraints channels.

Sample Data and Research Design

Sample Data

This paper uses the enterprises in China Industrial Enterprise Database from 2005 to 2017 as the research sample and uses the methods of S. Ren et al. (2019) for reference to deal with them as follows: remove the missing samples of critical variables such as SO₂ emissions. At the same time, the samples of non-economic significant variables, namely gross output value and total assets of 0, gross profit margin of more than 99%, and long-term assets such as non-current assets exceeding total assets, are excluded. Because this article has carried on the grouping discussion to the enterprise property suitable nature in the follow-up research, therefore excludes the total assets of less than 3 million Yuan and the operating income of less than 5 million Yuan state-owned enterprises. After removing all abnormal samples and samples of little significance for this study, 79,664 sample data were obtained in 10 years. Considering there may be only a small number of enterprise samples in some industries, the data with several sample enterprises less than 10 in the industry were deleted. The sample extremum and abnormal value were shrunk by 1% before and after.

Significant Variables and Their Measures

1. The pilot policy of SO₂ emission trading. Considering the time and space effects of policy implementation, virtual variables are set to replace the pilot emission trading policy implementation. As all regional samples before 2007 did not involve pilot emission trading, the value is 0. The value is 1 for regions implementing the pilot after 2007 and 0 for non-pilot regions.

2. Total factor productivity (TFP). The so-called TFP is total factor productivity, which is the overall efficiency of transforming various investment factors into final output in the production process, that is, the concentration of productivity level. The measurement of total factor productivity must first set the form of the production function. Cobby-Douglas production function (C-D) is the most commonly used production function in current research due to its simple and intuitive structure and representativeness. Although it is more flexible than the logarithmic production function, it cannot provide more information in actual research than the former, so the text still uses the Cobby-Douglas production function. The production function is as follows:

$Produc e_{kt} = TF P_{kt} C_{Kt}^{α_{c}} L_{kt}^{α_{l}}$ (1)

Where $Produc e_{kt}$ represents the output of enterprise k in year t; $TF P_{kt}$ represents the total factor productivity of the enterprise k in year t; $C_{Kt}^{α_{c}}$ represents the capital factor stock of the enterprise k in year t; $L_{kt}^{α_{l}}$ represents the stock of labor factors in year t of enterprise k; $α_{c}$ and $α_{l}$ represent the production elasticity of capital factors and labor factors respectively. The natural logarithm of Equation 1 can be converted into linear Equation 2:

$\ln (Produc e_{kt}) = α_{0} + α_{c} c_{kt} + α_{l} l_{kt} + ε_{kt}$ (2)

Among them, $c_{kt}$ and $l_{kt}$ correspond to $\ln C_{Kt}$ and $\ln L_{kt}^{}$ respectively, and $\ln (TF P_{kt})$ is the sum of $ε_{kt}$ that can be observed by enterprises and affect the current factor selection and disturbance, so there is:

$\ln (TF P_{kt}) = α_{0} + ε_{kt} = \ln (Produc e_{kt}) - α_{c} c_{kt} + α_{l} l_{kt}$ (3)

Currently, the measurement methods of total factor productivity are mainly based on the semi-parametric model proposed by Olley and Pakes (1996) and Levinsohn and Petrin (2003). Considering the possible differences under different measurement results, this paper compares the measurement results of the two methods (referred to as OP (Olley & Pakes, 1996) and LP (Levinsohn & Petrin, 2003) methods). The OP and LP methods are used to estimate the capital elasticity, labor elasticity, and total factor productivity by year and industry, respectively, to alleviate the model’s estimated Simplicity Bias and Selectivity and Attrition Bias. When the OP method measures an excellent theoretical relationship between capital stock and corporate investment, the perpetual inventory method measures capital stock. In contrast, the LP method uses the total intermediate investment of enterprises to replace corporate investment. In addition, the industrial added value and capital stock are reduced according to the factory price index of industrial products and the fixed asset investment price index of the location of the enterprise, respectively, regarding the methods of Li Qingyuan, etc., to ensure the fitting of the total factor productivity measurement process (Li & Zhang, 2021).

3. Resource mismatch. According to the complete competition theory, when the marginal output of an enterprise is entirely equal to the marginal factor input, the total factor productivity of the enterprise forms a balanced Pareto optimal state (Xiao et al., 2022). On the other hand, if the factors such as policy intervention, market regulation, and so on cause the mismatch of factor resources, the overall distribution of total factor productivity of the enterprise will tend to be discrete. Referring to the method (Hsieh & Klenow, 2009) of Hsieh and Klenow (2009), taking into account the heterogeneity of the structure of the sample enterprises in China, this paper uses the difference value of 80% and 20% quantiles of each effect dimension subtracted as an alternative variable to reflect the dispersion degree of total factor productivity, to explain the actual situation of enterprise resource mismatch. In addition, to ensure the robustness of the empirical results, this paper uses the standard deviation at the regional-industry level as an alternative variable to test the robustness.

4. Selection of control variables. This paper is mainly based on the internal characteristics of the enterprise, Industry characteristics, and market environment. The internal characteristics are the total assets turnover rate, the proportion of fixed assets, the proportion of research and development expenses, the proportion of management expenses, and the asset-liability ratio, which reflect the company’s asset allocation; Industry characteristics are market concentration, the proportion of sales expenses, operating income and gross profit margin respectively; For the market environment, several variables are selected to reflect the market characteristics, such as the number of enterprises, the dispersion of enterprise size, the entry threshold rate and the exit threshold rate. See Table 1 for the specific definitions of each variable.

Table 1.

Variable Definition.

Type	Name	Definition
Dependent variable	ERML Enterprise Resource Mismatch Level	The dispersion degree of total factor productivity in enterprises
Independent variable	Emissions trading ET	The implementation year of the emission trading pilot shall prevail if the value of the provinces and cities that belong to the emission trading pilot after that is 1; If the previous value is 0
Control variable	Total assets turnover TAT	Operating income/total assets of the enterprise
	Fixed assets proportion FA	Total fixed assets/total assets of the enterprise
	Proportion of research and development expenses RD	Total Enterprise R&D Expenses/Total Revenue
	Share of administrative expenses AE	Total Enterprise Management Expenses/Total Revenue
	Asset-liability ratio ALR	Total corporate responsibility/total assets
	Market concentration MC	The weighted sum of squares of operating income of each enterprise
	Sales expenSEs as a percentage of se	Total Enterprise Sales Expenses/Total Revenue
	Operating income BI	The natural logarithm of the business income of the enterprise
	Gross profit margin GPM	Corporate gross profit/operating income
	Number of enterprises NOE	The number of enterprises takes the natural logarithm.
	ESD of enterprise scale dispersion	Standard deviation of total assets
	Admission threshold ATR	Number of newly established enterprises and proportion of total enterprises in the whole industry
	Exit threshold ETR	Number of enterprises canceled and proportion of total enterprises in the whole industry

Model Setting

We outline the following arguments based on Beck et al. (2010): First, compared with the traditional method, the double difference method can avoid the endogenous problem of policy as an explanatory variable, effectively controlling the interaction effect between the explained variable and the explanatory variable. If the sample is panel data, the double difference model can not only take advantage of the exogenous nature of the explanatory variables but also control the impact of unobservable individual heterogeneity on the explanatory variables. Therefore, the double difference method can control the unobservable individual heterogeneity between samples and the influence of the unobservable overall factors that change with time to obtain an unbiased estimate of the policy effect. In addition, using fixed effect estimation also alleviates the problem of missing variable bias to some extent. Second, the traditional method of evaluating the policy effect is mainly to set a dummy variable of whether the policy occurs and then conduct regression. In contrast, the model setting of the double difference method is more scientific. It can estimate the policy effect more accurately. Third, the principle and model setting of the double difference method is easy to understand and apply. Compared with the static comparison method, the double difference method does not directly compare the average changes of the sample before and after the policy. However, it uses individual data for regression to judge whether the impact of the policy has significant statistical significance. As implementing the pilot emission trading policy will not affect all industries and regions, the responses to the policy effects will differ among regions and industries. After fully considering the fixed effects of provinces, cities, industries, and years, this paper establishes the following measurement model:

$ERM L_{pit} = β_{0} + β_{1} E T_{pit} + X_{pit} + ϕ_{pt} + γ_{it} + ε_{pit}$ (4)

Through the establishment of formula (1), the regression analysis is carried out to analyze the impact of the emission rights pilot trading policy on the enterprise resource allocation efficiency in the pilot areas, where the subscript p represents the pilot provinces and cities and the non-pilot provinces and cities, and t represents the specific year time; i represents industries of pilot provinces and cities and non-pilot provinces and cities. $ERM L_{pit}$ indicates the dispersion degree of total factor productivity of enterprises located in industry I of province and city P in year T, $E T_{pit}$ indicates whether the city, industry and year where the enterprise is located has implemented the emission trading pilot policy. $X_{pit}$ is the aforementioned control variable, $ϕ_{pt}$ and $γ_{it}$ respectively represent the fixed effect of provinces, cities, years, industries, and years. This fixed effect fully takes into account the potential impact differences that do not change with provinces, cities and industries in terms of provinces, cities and industries. $ε_{pit}$ represents a random perturbation term. If the coefficient of $E T_{pit}$ in the formula is significantly negative, it can be judged that the pilot policy of emission trading can effectively improve the efficiency of enterprise resource allocation.

Empirical Process and Result Analysis

Descriptive Statistics

In Table 2, the dispersion of ERML_OP and ERML_LP, which reflect the level of enterprise resource mismatch, are significantly different. The standard deviation of dispersion calculated by either the OP or the LP method is 0.8817 and 0.9025, respectively. The average value is also greater than the median, indicating that the dispersion distribution of enterprise total factor productivity may show the trend of a thick tail on the right, resulting in a more significant overall standard deviation and higher dispersion. At the same time, other control variables are also showing fluctuations in varying degrees. It is this difference that provides the research basis and research conditions for the follow-up research.

Table 2.

Descriptive Statistical Results.

Variable name	N	Average/mean value	Standard deviation	Minimum value	P25	Median	P75	Maximum
ERML_OP	79,664	2.3023	0.8817	0.6934	1.6784	2.1704	2.7886	5.0859
ERML_LP	79,664	2.4895	0.9025	0.7389	1.8488	2.3839	3.0275	5.1381
TAT	79,664	0.0705	0.0812	0.0006	0.0198	0.0441	0.0889	0.4778
FA	79,664	0.3734	0.2152	0.0131	0.2030	0.3512	0.5241	0.8911
RD	79,664	0.0004	0.0021	0.0000	0.0000	0.0000	0.0000	0.0163
AE	79,664	0.0004	0.0024	0.0000	0.0000	0.0000	0.0000	0.0198
ALR	79,664	0.2500	0.1688	0.0221	0.1299	0.2146	0.3219	0.8364
MC	79,664	2.5317	0.8248	1.6094	1.9459	2.3026	2.9957	5.1591
SE	79,664	0.0123	0.0580	0.0000	0.0000	0.0000	0.0000	0.4000
BI	79,664	13.0899	1.3666	10.3620	12.0937	12.967	13.956	16.822
GPM	79,664	0.8379	0.1284	0.3581	0.7798	0.8659	0.9259	1.0875
NOE	79,664	0.2620	0.0746	0.0967	0.2129	0.2571	0.3054	0.4771
ESD	79,664	1.1970	0.3763	0.4075	0.9355	1.1702	1.4310	2.2289
ATR	79,664	0.0831	0.1883	0.0000	0.0000	0.0000	0.0000	0.8571
ETR	79,664	0.0418	0.1191	0.0000	0.0000	0.0000	0.0000	0.6154

Note. This table presents the summary statistics of the variables used in the analysis. All variables are defined in detail in Table 1; while P25 and P75 indicate the 25th and 75th empirical quartiles, respectively.

Univariate Impact Analysis

Before the main regression analysis, to further identify the correlation between the implementation effect of the pilot emission trading policy and the mismatch of enterprise resources. This paper first discusses the policy’s compelling from the perspective of univariate impact. According to the analysis of the average test results of the enterprise resource mismatch level before and after the implementation of the emission trading pilot policy in Table 3, the average and median of the resource mismatch water of the pilot area enterprises implementing the emission trading policy are significantly lower than the control group (non-pilot area enterprises) at the level of 1%. Therefore, implementing the emission trading policy can reduce the degree of enterprise resource mismatch to a certain extent and optimize the structure and efficiency of enterprise resource allocation.

Table 3.

Test Results of Mean and Median of Enterprise Resource Mismatch Level Before and After the Implementation of Emission Rights.

	Processing unit (25,249)		Control group (54,415)		Diff
Variable	Average/mean value	Median	Average/mean value	Median	Average/mean value	Median
ERML_OP	2.208	2.088	2.346	2.215	−0.138***	−0.127***
ERML_LP	2.433	2.339	2.515	2.409	−0.082***	−0.070***

Note.*** indicate the 1% statistical significance levels.

Benchmark Regression Analysis

Table 4 shows the results of the main regression analysis with the mismatch level of enterprise resources as the dependent variable, in which the total factor productivity dispersion calculated by two different methods, OP and LP, are presented separately. On this basis, the provincial-annual and industry-annual fixed effects are controlled to mitigate the possible impact of missing variables on the results. In contrast, the results without considering the fixed effects are presented in columns (1) and (3). At the same time, all the analyses are regressed by using the cluster robust standard error at the level of province-city-year and province-city-industry. According to the overall results in Table 2, the regression coefficient of the independent variable ET in all cases is significantly negative, indicating that emission trading can effectively reduce the dispersion of total factor productivity mismatch of enterprises, supporting the conclusion of Hypothesis 1 that the pilot policy of emission trading can improve the efficiency of resource allocation among enterprises. In addition, judging from the actual effect, the implementation of the pilot policy of emissions trading, whether by the OP method or the LP method, has reduced the dispersion of total factor productivity by 2.11% to 3.72% (Divide the regression coefficient in column 2 and column 4 by the mean sample value under OP and LP methods to get the overall decline level), it can be seen that it is enough to affect the improvement of enterprise resource allocation efficiency.

Table 4.

Benchmark Regression Analysis.

	(1)	(2)	(3)	(4)
Variable	ERML_OP	ERML_OP	ERML_LP	ERML_LP
ET	−0.1258*** (−5.3246)	−0.0485*** (−3.5238)	−0.1434*** (−7.5617)	−0.0925*** (−7.2511)
FA	1.0967*** (17.8369)	0.8900*** (18.1122)	1.0474*** (18.1164)	0.8027*** (17.4224)
TAT	−0.1096*** (−4.0755)	−0.0825*** (−5.3099)	−0.0422* (−1.8978)	−0.0047 (−0.3245)
AE	2.6003 (1.4486)	0.3619 (0.2289)	6.5782*** (4.0487)	0.6589 (0.4366)
RD	−1.5189 (−1.0262)	−3.4833*** (−2.6275)	−0.9805 (−0.7251)	−2.9352** (−2.3203)
MC	−0.0123 (−0.2156)	0.1294*** (3.4262)	0.9034*** (14.4863)	0.9316*** (20.9663)
BI	0.1109*** (6.7489)	0.0420*** (5.3748)	0.2527*** (18.0796)	0.2281*** (29.1017)
ESD	−0.2052*** (−7.0257)	−0.1132*** (−9.2349)	−0.2311*** (−9.2602)	−0.2071*** (-16.0380)
GPM	0.2305** (2.3170)	−0.0395 (−0.6463)	0.2873*** (3.4906)	0.0397 (0.6979)
SE	0.2081*** (6.0388)	0.2545*** (8.7199)	0.1092*** (3.3653)	0.1787*** (6.5669)
ALR	1.8381*** (15.3302)	1.1270*** (19.9859)	1.2803*** (14.4250)	0.8486*** (16.2850)
NOE	0.1325*** (5.1402)	0.0664*** (4.4277)	0.3102*** (10.8007)	0.2483*** (16.5705)
ATR	0.0870*** (3.3610)	0.0753*** (3.2856)	0.0498** (2.0616)	0.0604*** (2.6487)
ETR	0.0570* (1.7760)	0.0498* (1.7427)	0.0341 (1.1535)	0.0501* (1.8026)
_cons	0.5485*** (3.6995)		−1.2818*** (−10.2932)
Region/Year	NO	YES	NO	YES
Ind/Year	NO	YES	NO	YES
N	79,664	79,664	79,664	79,664
Adj.R-Square	.0672	.1591	.2272	.2850

Note.*, **, and *** indicate the 10%, 5%, and 1% statistical significance levels; the t value is in parentheses.

From the results of all control variables contained in Table 4, it can be seen that the coefficients of total asset turnover rate, the proportion of R&D expenses, and enterprise size dispersion are significantly negative, which means that if the enterprise has the advantages of moderate scale, strong R&D ability, and high asset liquidity, then the company usually has a strong resource allocation ability and means to deal with the potential risks in the future. A reasonable explanation for this finding is that companies with vital innovation and fast asset turnover are “star” enterprises in the Boston matrix. “star” enterprises are usually in the quadrant of high growth rate and high market share and can improve production efficiency even in uncertain environments (Choi et al., 2020). Similarly, “problem” enterprises with good development prospects may also meet the characteristics of solid innovation. On the premise that the investment projects meet the expectations of the capital market, their financing capacity will be significantly improved, which can positively impact alleviating the turnover pressure caused by insufficient cash flow and help reduce the level of enterprise resource mismatch (Guo, 2020; S. Ren et al., 2019; X. Ren et al., 2022). Under the influence of policies such as emission trading pilots, enterprises with strong asset liquidity will have a greater tolerance for internal resource mismatch when facing the impact of external environment uncertainty (Martin et al., 2016; Peng & Jiang, 2021).

Robustness Test

Considering that the double difference method used in this paper can avoid the endogenous problem of policy as an explanatory variable, at the same time, the fixed effect estimation also alleviates the error problem of missing variables to a certain extent. However, in order to further effectively alleviate the possible endogenous problems in this paper and verify the robustness of the main regression results, this paper has carried out the following robustness tests: (1) parallel trend test; (2) Placebo test; (3) Hazard test (4) Other robustness tests: specifically including 1. prolonging sample period 2. replacing explanatory variables 3. replacing explained variables 4. repeating random sampling 5. replacing fixed effect; Through the above tests, the endogenous stability of this paper has been alleviated to a certain extent, and the reliability of this research conclusion has been scientifically verified.

Parallel Trend Test

In order to eliminate the possible impact of events other than the pilot emission trading policy on the conclusion according to the sample within 2 years before and after the implementation of the pilot policy, the sample is divided into 2 years before the implementation of the pilot policy of emission trading; The first year before the implementation of the pilot emission trading policy, The year when the pilot emission trading policy was implemented; The first year after the implementation of the pilot emission trading policy; The second year after the implementation of the pilot emission trading policy. So as to construct five grouping variable indexes ET (−2); ET(−1); ET(0); ET(1); ET(2), the value is 1 when the sample belongs to the corresponding grouping; otherwise, the value is 0, from the parallel trend test results in Table 5, it can be seen that the coefficients of ET(−2) and ET(−1) are not significant, indicating that there is no significant difference in resource allocation efficiency among enterprises before the implementation of the pilot emission trading policy, with the continuous advancement of the pilot policy, the enterprise resource mismatch level measured by LP method has been significantly improved with continuity, in addition, although the results under the OP method are not significant, the coefficient in the first year after the implementation of the pilot policy has changed from positive to negative, and the statistical value of T has also increased significantly, indicating that the pilot policy of emissions trading can have a particular impact on the efficiency of enterprise resource allocation. The conclusions of both methods support the benchmark regression assumption.

Table 5.

Parallel Trend Test.

	(1)	(2)
	ERML_OP	ERML_LP
ET(−2)	0.0058 (0.1691)	−0.0324 (−1.1604)
ET(−1)	0.0176 (0.4348)	−0.0479 (−1.5234)
ET(0)	0.0214 (0.5594)	−0.0737** (−2.6507)
ET(1)	−0.0568 (−1.2275)	−0.1032** (−2.6408)
ET(2)	−0.0348 (−0.7012)	−0.1241** (−3.0783)
Controls	YES	YES
Region/Year	YES	YES
Ind/Year	YES	YES
N	79,664	79,664
Adj.R-Square	.1908	.2620

Note.** indicate the 5% statistical significance levels; the t value is in parentheses.

Placebo Test

After the parallel trend test, to further verify the possible impact of other factors on the results. We used the placebo test to judge the unobservable driving factors of the emission trading pilot policy on the efficiency of enterprise resource allocation. Based on the implementation time point of the randomized emission trading pilot policy, 1,000 returns were made. According to the distribution of the left (coefficient) and right (T value) of the placebo test results in Figure 1, both are near the value of 0. Therefore, the impact of the emission trading pilot policy on the efficiency of enterprise resource allocation is not caused by other unobservable factors.

Figure 1.

Placebo test results after 1,000 regressions based on the implementation time point of randomized emission right pilot trading policy: (a) coefficient of placebo test results and (b) T-value of placebo test result.

Hazard Inspection

Generally, market-based environmental regulation policies adopt a point-to-point strategy, that is, a “region-city” hierarchical strategy. The macro factors that affect the implementation of the pilot policies for emission trading in the pilot areas include the degree of economic development at the regional level and the degree of improvement of environmental protection policies (Coleman et al., 2019; Porter & Linde, 1995; Si & Cao, 2021; Wan et al., 2022; Wei & Zhenxing, 2021). Since the implementation of emission trading policies on regional industry resource allocation efficiency may be related to the previous economic and environmental development level of these areas, in this case, the implementation of the emission trading policy cannot be regarded entirely as an exogenous impact on the efficiency of resource allocation of local enterprises. In order to solve this problem, in addition to controlling the fixed effect of the industry, this paper further uses the Hazard model method (Kroszner & Strahan, 1999) to redefine the explanatory variable ET as ET (H) and uses region-year data for regression. The specific measures are as follows: before the formal implementation of the emission trading policy in the pilot area, the value will be assigned to 0, the value will be assigned to 1 in the year of implementation, and the observed value 1 year after the formal implementation of the emission trading policy will be eliminated. The dispersion of enterprise total factor productivity and other control variables are measured by the mean and median of the variables in a region, an industry, and a year in the benchmark regression, respectively, to directly compare the differences between regions. Regression controls the fixed effect of year-region and cluster standard error at the year-region level. The empirical results in Table 6 show that the formal implementation of the emission trading policy has no significant impact on industrial resource allocation, which indicates that other previous development factors in the region do not drive the effect of the emission trading policy.

Table 6.

Hazard Inspection.

	(1)	(2)	(3)	(4)
	ERML(OP)_mean	ERML(OP)_med	ERML(LP)_mean	ERML(LP)_med
ET(H)	0.0208 (0.8772)	0.0075 (0.3022)	0.0321 (1.3475)	0.0167 (0.6498)
Controls	YES	YES	YES	YES
Region/Year	YES	YES	YES	YES
N	2,855	2,855	2,855	2,855
Adj.R-Square	.3651	.3143	.4467	.3905

Note. ERML (OP)_ Mean and ERML (OP)_ Med is the mean and median of the total factor productivity dispersion measured by the OP method; ERML(LP)_ Mean and ERML (LP)_ Med is the mean and median of total factor productivity dispersion measured by LP method. The results of control variables and constant terms are omitted in the table, and the robust t value adjusted by city-year clustering is shown in brackets.

Other Robustness Tests

In addition to the robustness tests mentioned above, this paper also conducted the following robustness tests: 1. Extended the sample period: extended the sample period to 2017 (Table 7); 2. Substitution of the explained variable: by using the standard deviation of the total factor productivity of enterprises in the provincial, municipal and industrial dimensions as the substituted explained variable, the regression analysis was conducted again to clarify the impact of emissions trading on resource allocation efficiency (Table 8); 3. Replacement of explained variables: on the one hand, a substitutable variable TR was constructed by adding 1 and taking the logarithm of the panel data to construct the pilot policy implementation of emissions trading. On the other hand, this paper constructed the incremental index of the provincial and municipal emissions trading scale in the form of trading volume increments (TRI) to examine the impact of the increase in emissions trading scale on the dispersion of enterprise total factor productivity (Table 9); 4. Repeated random sampling: randomly sampled a total of 20,000 samples via bootstrap for 1,000 times (Table 10); 5. Substitution of fixed effects: the provincial, municipal, and industrial fixed effects were substituted for the provincial and municipal fixed effects in addition to the annual fixed effect (Table 11). The results of all the above robustness tests were consistent with the benchmark regression results, demonstrating the robustness of the conclusions.

Table 7.

Extended Sample Period.

	(1)	(2)
	ERML_OP	ERML_LP
ET	−0.1913*** (−15.1637)	−0.1920*** (−17.1056)
Controls	YES	YES
Region/Year	YES	YES
Ind/Year	YES	YES
N	123,091	123,091
Adj.R-Square	.1488	.2654

Note.*** indicate the 1% statistical significance levels; the t value is in parentheses.

Table 8.

Substitution of Explanatory Variables.

	(1)	(2)	(3)	(4)
	ERML_OP	ERML_OP	ERML_LP	ERML_LP
TR	−0.0773** (−2.0749)		−0.1234*** (−4.2792)
TRI		−0.0879* (−1.7526)		−0.1660*** (−4.2505)
Controls	YES	YES	YES	YES
Region/Year	YES	YES	YES	YES
Ind/Year	YES	YES	YES	YES
N	79,664	78,751	79,664	78,751
Adj.R-Square	.1588	.1593	.2812	.2812

Note.*** indicate the 1% statistical significance levels; the t value is in parentheses.

Table 9.

Replacement of Explained Variables.

	(1)	(2)
	ERMLSD_OP	ERMLSD_LP
ET	−0.0204*** (−3.8654)	−0.0349*** (−7.2577)
Controls	YES	YES
Region/Year	YES	YES
Ind/Year	YES	YES
N	79,664	79,664
Adj.R-Square	.1808	.3228

Note.*** indicate the 1% statistical significance levels; the t value is in parentheses.

Table 10.

Repeated Random Sampling.

	(1)	(2)
	ERML_OP	ERML_LP
ET	−0.0485*** (−6.6129)	−0.0925*** (−14.1077)
Controls	YES	YES
Region/Year	YES	YES
Ind/Year	YES	YES
N	79,664	79,664
Adj.R-Square	.1591	.2850

Note.*** indicate the 1% statistical significance levels; the t value is in parentheses.

Table 11.

Replace Fixed Effect.

	(1)	(2)	(3)	(4)
	ERML_OP	ERML_OP	ERML_LP	ERML_LP
ET	−0.0700*** (−5.1339)	−0.1315*** (−4.6420)	−0.1052*** (−8.3210)	−0.0954*** (−3.9740)
Controls	YES	YES	YES	YES
Region/Year/Ind	YES	NO	YES	NO
Ind/Year	NO	YES	NO	YES
N	79,664	79,664	79,664	79,664
Adj.R-Square	.1347	.1026	.2650	.2576

Note.*** indicate the 1% statistical significance levels; the t value is in parentheses.

Further Research

According to the above research conclusions, the emission trading pilot policy can significantly improve enterprise resource allocation efficiency. In further research, this paper intends to analyze the impact effect, impact mechanism, and heterogeneity of the pilot policy of emission trading on the efficiency of enterprise resource allocation.

Mechanism Testing

The impact of the implementation of the pilot emission trading policy on the efficiency of enterprise resource allocation mainly comes from two aspects: on the one hand, the market regulation channel enables enterprises to actively participate in the emission trading and reasonably allocate the factor resources from the profit maximization motive; on the other hand, the government department as a policy maker can urge enterprises to adjust the factor resources through supervision and inspection passively. Can the emission trading pilot policy affect enterprise resource allocation efficiency through market regulation and administrative supervision? In order to further test the effectiveness of this mechanism, because of the market regulation channel, this paper makes an empirical analysis by referring to the method of Bu (2010), taking the emission trading market efficiency (TME) as an alternative variable, in which the emission trading market efficiency is measured by the proportion of emission trading tax revenue to local fiscal revenue. The higher proportion of this indicator indicates that the more active the market transaction is, the larger the scale of emissions trading between enterprises is. Column (1) of Table 12 shows the impact of the emission trading pilot policy on market efficiency under the fixed effect of controlling provinces, cities, and industries. The results show that the pilot emission trading policy can significantly promote the improvement of market efficiency, play the fundamental role of the trading market in the allocation of resources through the market regulation mechanism, and provide an impact path for enterprises to take the initiative to participate in the emission trading to optimize the allocation of resources reasonably.

Table 12.

Mechanism Test Results.

	(1)	(2)
	TME	ASI
ET	0.0603*** (11.5161)	0.1660*** (7.6746)
Controls	YES	YES
Region/Year	YES	YES
Ind/Year	YES	YES
N	805,122	805,122
Adj.R-Square	.0376	.1003

Note.*** indicate the 1% statistical significance levels; the t value is in parentheses.

Implementing the pilot policy of emissions trading must be balanced with the vigorous enforcement of the law (Shi et al., 2022). The enforcement of the law is mainly reflected in the penalties for ignoring the rules of the emissions trading market. Generally, in the early stage of implementing environmental regulation policies, there will be objections and obstacles from local governments or obtained interest groups (Shen, 2022). In theory, the lower the implementation intensity of policies, the higher the probability that enterprises will ignore the policies and regulations, the lower the probability that they will actively participate in the emission trading mechanism, and the higher the probability that they will be punished when they encounter surprise inspections. In order to further explore the impact of emission trading policies under different policy implementation intensities on enterprise resource allocation efficiency, this paper takes whether the sample enterprises have been subject to administrative penalties related to environmental regulations as the dividing standard and takes the number of enterprises subject to penalties after the implementation of emission trading pilot policies as an indicator of administrative supervision intensity (ASI). Suppose the enterprises have been subject to more administrative penalties. In that case, the local administrative supervision intensity may be higher. Table 12 (2) lists the regression results of the intensity of administrative supervision. It can be seen that the implementation of the pilot policy of emissions trading has a promoting effect on the enhancement of the intensity of administrative supervision, which is beneficial to the “passive” transfer of enterprise resources from the enterprises with low operating efficiency to the enterprises with high operating efficiency, thus improving the efficiency of enterprise resource allocation in the whole industry.

Heterogeneity Research

According to Lin (2011)“Financing Constraint Hypothesis,” the looser the external credit conditions, the lower the financing constraint pressure on enterprises. Therefore, enterprises facing high financing constraints may need help with the phenomenon that the overall resource allocation efficiency of the enterprise will decrease due to the cash flow problem when carrying out emission trading projects. In order to measure the corporate financing constraint index, this paper uses the SA index proposed by Hadlock and Pierce (2010). To calculate the financing constraint degree at the enterprise level, construct a variable FC which reflects the financing constraint degree of the enterprise, sorting from low to high, taking the median as the grouping basis, and taking the enterprises exceeding the median as the high financing constraint group, with the value of 1; Otherwise, the value for the low financing constraint group is 0. As can be seen from the results in Table 13, the regression coefficient of its cross-product ET × FC is significantly negative regardless of whether OP or LP method is adopted, indicating that the implementation of the pilot policy of emission trading will improve the resource mismatch level of enterprises with high financing constraints more significantly. The possible reason is that when an enterprise faces high financing constraints, it can trade the emission rights formed by the difference in production capacity to obtain non-recurring revenue due to insufficient production capacity, thus stimulating its resource allocation efficiency.

Table 13.

Implementation of Pilot Emission Trading Policies, Financing Constraints and Enterprise Resource Allocation.

	(1)	(2)
	ERML_OP	ERML_LP
ET × FC	−0.0745*** (−4.1987)	−0.0718*** (−4.2117)
ET	−0.0356** (−2.4831)	−0.0792*** (−6.0208)
FC	−0.0071 (−0.7098)	0.0094 (0.9897)
Controls	YES	YES
Region/Year	YES	YES
Ind/Year	YES	YES
N	79,664	79,664
Adj.R-Square	.1595	.2852

Note.**, and *** indicate the 5%, and 1% statistical significance levels; the t value is in parentheses.

They implemented a pilot emission trading policy, corporate governance, and resource allocation. On the one hand, to deal with the management assessment such as performance evaluation, corporate strategic decisions are usually based on the maximization of profit target, thus affecting other stakeholders to varying degrees; On the other hand, the implementation of other relevant policies will also have an impact on corporate governance and its resource allocation. They were drawing on the corporate governance structure scoring index adopted by Zhong (2012). To construct a variable CG that reflects the level of corporate governance, ranking it from high to low. The value of samples larger than the median is 1, or 0 anyway. The empirical results are shown in Table 14, in which the estimation coefficients of cross-product ET × CG are all significantly negative, that is, implementing the pilot emission trading policy can promote the efficiency of enterprise resource allocation to a greater extent based on good corporate governance.

Table 14.

Implementation of Emission Trading Pilot Policies, Enterprise Governance and Enterprise Resource Allocation.

	(1)	(2)
	ERML_OP	ERML_LP
ET × CG	−0.0314* (−1.9388)	−0.0514*** (−3.4590)
ET	−0.0435*** (−3.0349)	−0.0816*** (−6.0802)
CG	0.0416*** (3.8140)	0.0384*** (3.7858)
Controls	YES	YES
Region/Year	YES	YES
Ind/Year	YES	YES
N	79,664	79,664
Adj.R-Square	.1604	.2837

Note.* and *** indicate the 10% and 1% statistical significance levels; the t value is in parentheses.

Discussion

Research Findings and Implications

This paper empirically tests the impact of the emission trading pilot policy on the efficiency of enterprise resource allocation through a quasi-natural experiment on the emission trading pilot policy published by China in 2007. A series of robustness tests, such as the parallel trend test and placebo test, have verified the effectiveness of the results. The empirical results show that the pilot emission trading policy can reduce the degree of enterprise resource mismatch and optimize the structure and efficiency of enterprise resource allocation.

This conclusion provides strong support for the impact of market-based environmental regulation policies on firm investment efficiency, as proposed by J. Chen et al. (2022) Despite being two different types of emission control policies, the targets and objectives of policy implementation are aligned (H. Chen et al., 2021; J. Chen et al., 2022; Lu & Li, 2023). Furthermore, it effectively corroborates the findings presented by Tang et al. (2020), which indicate that command-and-control environmental regulation significantly hinders the growth of total factor productivity. This negative impact primarily stems from increased costs and adverse effects on resource allocation efficiency within firms (Tang et al., 2020). By contrasting these perspectives, it once again underscores the importance of China fully utilizing market mechanisms to harness the effective role of markets in environmental regulation policies, thereby promoting further enhancement of firm resource allocation efficiency.

The reason for this result may be, on the one hand, theoretically speaking, the emission trading system may bring certain cost pressure to enterprises in the short term. Market oriented environmental regulation policies realizes the internalization of emission reduction costs of related enterprises, which may affect their investment and management decisions, but has seldom received attention (Y. J. Zhang & Wang, 2021). However, in the long term, the emission trading system can stimulate enterprises to improve pollution control technology and production technology through compliance pressure and economic compensation effect (Dong & Wang, 2021; Liu & Sun, 2021; Lv & Bai, 2021; Xu et al., 2022; H. Zhang et al., 2022) and compensate for pollution control costs by improving resource allocation efficiency (Qi et al., 2018). On the other hand, from the perspective of policy inspiration, the emission trading system is a system reform designed by the Chinese government at the top level, giving full play to the role of government guidance and market leadership (Allen et al., 2005; Huang et al., 2023). When faced with the dual challenges of environmental pollution and economic transformation, the emission trading system provides an essential direction for the Chinese government to explore the environmental regulation of realizing the dual dividend of economy and environment. The pilot policy should be further promoted and extended to more provinces and cities in China. Because this policy not only has a positive impact on corporate investment efficiency but also potentially generates positive effects on both single-factor and total-factor energy efficiency in cities (Hong et al., 2022). At the same time, other countries and regions can also refer to the case of China’s emission trading pilot policy, and formulate emission trading policies in line with their national conditions, to make the environmental regulation policy and economic development more integrated and coordinated (Åström et al., 2017).

In the mechanism test, the emission trading pilot policy can provide an impact path for enterprises to actively participate in emission trading and reasonably optimize the allocation of factor resources through market regulation mechanisms and administrative supervision. For instance, with the widespread adoption of digital technologies, the use of blockchain and big data technologies can establish a value transmission network for environmental resources between the government and polluters, thereby enabling effective administrative regulation of environmental pollution (Zhao et al., 2021). Therefore, the government also needs to mobilize market participation enthusiasm further (Pan et al., 2022). However, it must strengthen the adequate supervision of policy implementation while relaxing the entry restrictions. Finally, in the heterogeneity analysis, we found that when the enterprise is at a good governance level or has high financing constraints, the emission trading pilot policy improves the efficiency of resource allocation of enterprises more significantly. When the governance environment or financing conditions are more relaxed, the management may participate more actively in market-oriented environmental regulation policies and make effective decisions.

Policy Recommendations

(1) Based on the findings of our study, the implementation of pollution rights trading policy can effectively promote the improvement of resource allocation efficiency among enterprises in pilot provinces and cities. Market mechanisms play a crucial role in enhancing resource allocation efficiency. Therefore, it is necessary to further implement environmental regulatory policies that are more in line with market mechanisms. By fully harnessing the resource allocation function of the market and aligning the reform objectives with the economic interests of the stakeholders, a mutually beneficial relationship between policy incentives and market development can be achieved. On one hand, optimizing market design is essential. This involves continuously improving the mechanisms of pollution rights trading market to facilitate more efficient resource allocation. Measures can include increasing market transparency, reducing transaction costs, and providing more participation opportunities. On the other hand, establishing incentives for emission reduction is crucial. Adopting stricter emission targets and correspondingly increasing the cost of emission quotas can improve incentives for companies to reduce emissions. Additionally, incentive measures can be introduced to encourage the adoption of cleaner and more energy-efficient technologies, further improving resource allocation efficiency.

(2) Another pathway through which pollution rights trading policy impacts resource allocation efficiency is administrative supervision. Therefore, it is important to enhance the intensity of policy supervision to ensure the effective implementation and operation of pollution rights trading pilot policies. However, it is also crucial to avoid excessive supervision, as it may have negative effects. We suggest, firstly, strengthening supervision and law enforcement. Enhancing supervision and law enforcement on the pollution rights trading market will ensure its proper functioning and fair competition. Strict penalties should be imposed on companies that violate regulations to maintain market order and public interests. Secondly, providing policy support and guidance. More comprehensive policy support and guidance should be provided to companies to help them understand and effectively respond to the regulations of pollution rights trading. This can include providing accurate emission data and information, promoting best practices, conducting training and educational activities to enhance companies’ understanding and adaptability to the regulatory system.

(3) In order to promote high-quality economic development while implementing effective environmental regulatory policies, it is crucial to provide reliable and grounded market incentives while granting sufficient flexibility to enterprises. Although companies are economic entities pursuing profit maximization, our study reveals significant heterogeneity in resource allocation efficiency among firms under pollution rights trading policies, which is influenced by different financing constraints and governance levels. This heterogeneity suggests that efficiency-oriented market-based regulatory policies may face challenges in achieving fairness. Therefore, considering the consequences of this heterogeneity, it is necessary to continue deepening the reform and provide support for highly governed and financially constrained companies. On the other hand, it is important to actively promote mechanisms for the transformation and upgrading of high-performance enterprises, while providing them with greater compliance incentives in distribution mechanism design.

Conclusion and the Future Research

This paper conducts a quasi-natural experiment on the emission trading pilot policy published in 2007. The results show that the emission trading pilot policy can significantly optimize the efficiency of enterprise resource allocation in the pilot provinces and cities. However, further research found that the main path to optimizing enterprise resource allocation efficiency is through market regulation and administrative supervision mechanisms. However, financing constraints and corporate governance levels will have heterogeneity in the emission trading pilot policy. High financing constraints and corporate governance level can further promote the impact of the emission trading pilot policy on the efficiency of enterprise resource allocation.

The implications of our research should be interpreted in light of the study’s limitations. First, the indicators used to measure the efficiency of resource allocation in firms need further improvement. Second, the quasi-natural experiment of the pollution rights trading pilot policy still has several shortcomings, including the lack of random allocation, the difficulty in fully excluding other influencing factors in quasi-natural experiments, and the constraints of time and data availability. Lastly, this study only focuses on Chinese listed companies and does not consider the unique circumstances of other countries or regions.

Taking into account the limitations of the current study, future research should focus on the continuous improvement of the calculation method for enterprise total factor productivity. This includes the adoption of more scientifically precise algorithms and considering the variable allocation of resources in enterprise investment and financing. Additionally, it would be beneficial to reference data from other countries to compare the policy effects and impacts of pilot policies implemented after the comprehensive implementation of emission trading and the initial and intermediate stages of the comprehensive SO₂ emission reduction policy.

Footnotes

We are grateful to the editors for their initial reading of the manuscript and their suggestions for improvement. We also thank the reviewers for their helpful comments.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research,authorship,and/or publication of this article.

Ethical Approval

Ethical approval is not applicable to this study as no human participants or animals are used.

ORCID iDs

Bing Zhou

Gong Zhang

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