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# Internal finance, financial constraint and pollution emissions: evidence from China

Thomas Pernet, Mathilde Maurel, Zhao Ruili

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Thomas Pernet, Mathilde Maurel, Zhao Ruili. Internal finance, financial constraint and pollution emissions: evidence from China. 2023. halshs-04318505

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CES

Centre d'Économie de la Sorbonne
UMR 8174

Internal finance, financial constraint, and pollution
emissions: evidence from China

Thomas PERNET, Mathilde MAUREL, Zhao RUILI

2023.15
# Internal finance, financial constraint, and pollution emissions: evidence from China*

Thomas Pernet†

Mathilde Maurel‡

Zhao Ruili§

## Abstract

This study explores the role of internal finance on firms' environmental behavior, focusing specifically on sulfur dioxide ($SO_2$) emissions in China's rapidly growing industrial sector. Using a rich and unique dataset provided by the Ministry of Environmental Protection (MEP), our baseline results find a statistically significant positive relationship between asset tangibility and $SO_2$ emissions intensity, revealing that credit-constrained firms with higher tangible assets contribute to elevated pollution levels. Additionally, we observe that firms with stronger internal finances experience a significant reduction in $SO_2$ emissions. Our empirical analysis uncovers two key mechanisms through which internal finance influences firm behavior. First, firms with stronger internal financial health, as measured by metrics like cash flow, current ratio, and coverage ratio, are more inclined to invest in Research & Development and Total Factor Productivity, especially in credit-constrained sectors. Second, these financially robust firms are more proactive in adopting $SO_2$ abatement technologies, an effect that becomes more pronounced in the context of credit-constrained firms. Our findings offer

<footnote>*The authors are grateful to Patricia Augier, Pascale Matel Lecombe, Sandra Poncet and Chen Zhao for providing precious comments. We would like to extend our sincere thanks to the participants of the NECC workshop on the climate policy in China organized by the *LabEx DYNAMITE* for their advice and feedback. We would like to thank as well the organizers (Etienne Le Rossignol and Candice Yandam) and participants of the Development Economics Sorbonne Informal Research Seminar (DESIR), but also the organizers of the JDD (Journées Doctorales du Développement) hosted at Université Paris Est Marne La Vallée and Neha B Upadhayay and Sébastien Marchand for their helpful comments</footnote>

<footnote>†Centre d'Economie de la Sorbonne, Université Paris 1 Panthéon-Sorbonne, France, email: t.pernetcoudrier@gmail.com</footnote>

<footnote>‡CNRS, France and Centre d'Economie de la Sorbonne, Université Paris 1 Panthéon-Sorbonne, France</footnote>

<footnote>§Shanghai University of International Business and Economics, China</footnote>
a nuanced understanding of how internal financial resources can serve as a dual lever for both innovation and sustainability, particularly in settings where external financing is limited.

**Keywords:** China, Pollution emissions, Financial constraints, Internal financing, TFP

**JEL Codes:** G2, G32, L25, L6, Q53
world, according to the WHO.<fnref n="3" /> In 2017, it ranked first in the world in terms of the number of natural disasters, surpassing the United States and India.<fnref n="4" /> China's contribution to the climate crisis is emphasized by the OECD.<fnref n="5" />

Our research offers a novel investigation into the intricate relationship between financial constraints and sulfur dioxide ($SO_2$) emissions in the context of China's rapid industrial growth. Using a comprehensive data set from China's Ministry of Environmental Protection (MEP), we focus specifically on $SO_2$ emissions at the firm level, capturing data for nine consecutive years (1999–2007). To the best of our knowledge, only a handful of studies have delved into this level of detail in the Chinese context (Fan et al. 2021; He et al. 2020; Wu et al. 2017; Zhang and Zheng 2019, among others).

We complement our environmental data with financial metrics from the annual surveys of Chinese manufacturing firms conducted by the National Bureau of Statistics of China (NBS). These surveys provide valuable proxies for internal finance, such as cash flow, current ratio, and coverage ratio, enabling us to explore how variations in internal financial resources correlate with changes in $SO_2$ emissions.

The focus of our empirical analysis is to establish a link between a firm's asset tangibility and its $SO_2$ emissions. We find a statistically significant positive relationship, consistent with our theoretical hypothesis that firms with higher asset tangibility tend to emit more $SO_2$. Our findings assert that firms with a higher degree of asset tangibility (firms often credit-constrained) are not only more likely to invest in emission-intensive technologies but are also contributing to an exacerbation of pollution levels. For each 10% increase in asset tangibility, $SO_2$ emission intensity increases in a range from 38.8% to 47.6%. Furthermore, our study explores the mitigating role of internal financial health on emissions. Using metrics

<footnote n="3">³In 2013, Shijiazhuang had only 47 days with good air quality.</footnote>

<footnote n="4">⁴In 2017, the province of Hunan suffered a direct economic loss of about 59 billion RMB due to natural disasters (National Bureau of Statistics of China; Ministry of Ecology and Environment) More statistics can be found at the following link https://drive.google.com/open?id=1nq-njkZ-TaQyzH30Ncq1R8hlpFE1InSG.</footnote>

<footnote n="5">⁵https://oecd-development-matters.org/2019/06/20/the-global-souths-contribution-to-the-climate-crisis-and-its-potential-solutions/.</footnote>
such as cash flow, current ratio, and coverage ratio as indicators, we find a significant negative association with SO₂ emissions.

We further explore how internal financing's effect on SO₂ emissions varies across sectors and regulatory contexts. Our analysis shows that credit constraints, bank regulations, and local innovation notably influence the impact of internal finances on emissions. Specifically, financially dependent industries and cities with stringent bank regulations benefit more from internal financing in reducing emissions. Additionally, cities characterized by high levels of innovation are more effective in utilizing internal financial resources to achieve environmental improvements.

In this study, we analyze two distinct mechanisms through which internal finance, primarily in the form of cash flow, influences firm behavior. Firstly, our empirical evidence shows that firms with higher internal cash flows are likely to allocate fewer resources to asset tangibility and instead favor investments in Research & Development (R&D) and Total Factor Productivity (TFP). This reallocation is especially marked in sectors where access to external financing is constrained, implying that internal finance offers firms the flexibility to shift from traditional asset investments to those that enhance innovation and productivity.

Secondly, our investigation extends to the role of internal finance in environmental responsibility, particularly in the acquisition of SO₂ abatement technologies. We find that companies with robust internal finances are more proactive in investing in pollution control measures. The significance of this investment is magnified in the context of credit-constrained firms, substantiating the notion that liquidity is a crucial factor in facilitating a firm's sustainable practices.

Collectively, our findings show that strong internal finances help firms both innovate and invest in cleaner technologies, ultimately reducing SO₂ emissions. This is especially true for firms limited by external financing options. Our results make it clear that good financial health has a direct role in a firm's ability to be both innovative and environmentally
responsible.

The rest of this paper is organized as follows: The role of internal finance in growth and environmental performance is described in Section 2. Our empirical strategy, data and preliminary evidence are presented in Section 3. Section 4 presents the baseline findings and sources of heterogeneity. Section 5 presents the mechanisms, and finally, the paper concludes in Section 6.

## 2 Internal/external finance, growth and environmental performance

The link between economic performance and finance is well-established, particularly in developed countries.<fnref n="6" /> Cross-country evidence shows that external finance is linked with better economic performances, financed through the credit market or the equity market (Hsu et al. 2014). In China, this link is operating in a specific way. The economy grew at an astonishing rate over the past few decades, fueled mainly by private firms,<fnref n="7" /> while it is well-established that the financial market was not allocating resources efficiently during that period.<fnref n="8" /> Prior works have also demonstrated a negative link between the credit market and the firms’ performance due to lending bias (Chen et al. 2016; Guariglia and Poncet 2008; Hasan et al. 2009).<fnref n="9" /> Indeed, state-owned enterprises (SOEs hereafter) are the most prominent loan recip-

<footnote n="6">⁶For the general case, see the recent meta-analysis in Bijlsma et al. (2018). For the specific case of China, see the recent analysis of Xu and Gui (2021). Besides, the literature has used different proxies to evaluate Chinese economic performance, such as the growth level (Guariglia and Poncet 2008; Hasan et al. 2009), GDP per capita (Boyreau-Debray 2003), total factor productivity (Ayyagari et al. 2010; Chen and Guariglia 2013; Li et al. 2018), assets growth (Guariglia et al. 2011).</footnote>

<footnote n="7">⁷Chinese manufacturing enterprises during 1999-2005 accounted for over 90% of China’s industrial output (Demetriades et al. 2008).</footnote>

<footnote n="8">⁸In regards to this poor functioning, China is not an exception. Most developing economies have poor financial markets, and their equity market is almost nonexistent.</footnote>

<footnote n="9">⁹The primary way to get funding in China is through a loan (Hale and Long 2011). By the end of 2002, the banking sector’s total assets stood close to 85% of the total assets of the entire financial sector (Ping</footnote>
ients in China, despite their poor economic performance (Hale and Long 2011). The Chinese government runs the banking system through four banks, which provide 60% of the loans to the economy; among them is a large share of non-performing loans (Allen et al. 2005). To secure a loan, private firms need to rely on their political connections (Cull et al. 2015) or provide much more collateral than their SOEs peers (Brandt and Li 2003). For them, internal finance is key.

Internal finance is also particularly important in driving the pollution emissions, as em-
phasized by the flourishing literature on this topic. <fnref n="10" /> Andersen (2017) designs a model
where private firms suffering from credit constraints invest relatively more in tangible assets,
increasing pollution emissions. Ghisetti et al. (2017) assesses the existence of direct negative
effects of financial barriers on environmental innovation investment decisions by analyzing
small and medium-sized manufacturing firms in Europe. Noailly and Smeets (2021) focus
on European patent data and show that innovative clean energy firms may be particularly
vulnerable to financing constraints. Using Chinese firm's level data and in the context of
the Chinese green credit policy in 2012, Fan et al. (2021) shows that firms with a record
of noncompliance with environmental regulations saw a larger increase in the interest rate,
decrease in loans, and more difficulty in access to loans. These effects are more pronounced
for small and private firms, which calls for a specific policy better adapted to the risk of
falling production. De Haas and Popov (2018) uses cross-country level data to evaluate
the impact of external finance on the emissions of CO₂. They find that the emissions of
CO₂ are dampened by the equity market's development, while the credit market's impact
on aggregated CO₂ emissions is positive and strong. Their conclusions echo the extensive
literature on external finance and growth. The equity market is more suited to bear the
investment risk related to innovation. At the same time, banks have a lower capacity to

<footnote n="2003">2003).</footnote>

<footnote n="10">¹⁰The empirical literature uses cash flow as a proxy for internal finance (Guariglia et al. 2011). External finance refers to the use of a third party to obtain financing. There are two broad parties: equity market and credit market.</footnote>
assess a project's innovative pertinence and support it.<fnref n="11" />

R&D projects and environmental innovation are often denied external financing in developing countries because of the lack of collateral, which is inherent to them. Tangible assets are more likely to be financed for two main reasons. The first reason is that a tangible asset has a finite monetary value and a physical form. Its liquidity might vary, but it can always be transacted with a monetary value, so in the event of default, the bank can convert the assets to cash. Secondly, banks lack the skills to judge innovative projects (Ueda 2004). By facing difficulties securing a loan from the banks to finance growth, private firms use their internal finance extensively to invest in research and development, innovative projects, and other forms of growth-oriented projects (Carpenter and Petersen 2002; Chow and Fung 2000; Guariglia et al. 2011; Poncet et al. 2010). Using firm-level data, Li et al. (2018) shows that internal finance significantly increases productivity through innovation, but this effect is limited to private firms. This paper examines whether intangible investments financed by internal funding are directed towards environmentally friendly innovations in the context of public policies to regulate pollutant emissions and the government's desire to reduce them. It is based on an extremely rich database and on the empirical strategy defined in the next section.

<footnote n="11">¹¹The literature on external finance and investment decisions shows that asymmetric information, moral hazard, and tax considerations are the main mechanisms, which explains why the credit market is not suited to finance innovation. The equity market development helps circumvent the problems inherent to the credit market (Hall and Lerner 2010).</footnote>
# 3 Empirical strategy and Data, Preliminary Evidence

## 3.1 Empirical strategy and Data

Our empirical strategy aims to assess how asset tangibility, internal finance, and TFP affect firm-level pollution emissions from 1999 to 2007. While our analysis aligns with prior work in the field (Andersen (2017)), it diverges in key aspects, particularly by incorporating internal finance as a crucial variable. In particular, we include a variable for internal finance alongside asset tangibility to better capture the distinct ways financial structures affect pollution emissions, especially in the context of China's national pollution control policies since the early 2000s. By doing so, we acknowledge that these internally financed investments often target intangible assets, which deviate from the collateral requirements of most bank loans, thereby affecting the firm's emission behavior differently than externally financed, tangible assets would. Thus, we include both asset tangibility and internal finance variables to capture the full impact of financial decision-making on pollution emissions.

$$ 
\begin{aligned}
SO_2 \text{ intensity}_{fct} = & \alpha_1 \text{ asset tangibility}_{fct} + \alpha_2 \text{internal finance}_{fct} \\ 
& + \alpha_3 \text{TFP}_{fct} + \alpha_4 X_{fct} \\ 
& + \zeta_f + \gamma_c + \gamma_t + \epsilon_{fct}
\end{aligned} 
\quad (1) 
$$

$SO_2$ intensity$_{fct}$ refers to the intensity of sulfur dioxide emission ($SO_2$ divided by the firm's output). Sulfure dioxide is a colorless, dense, and toxic gas that is highly irritating and dangerous to health when inhaled. $SO_2$ intensity$_{fct}$ is available at the firms' level, located in $c$ at year $t$. The Ministry of Environmental Protection (MEP) has collected the primary data source of pollutants and waste in China since 1980. Firms considered heavy polluters are asked to report basic information such as company name, address, and output.
They also answer a very detailed questionnaire about their major pollutants' emissions (e.g., wastewater, COD, SO₂, industrial smoke, and dust). As reported in Jiang et al. (2014) and Wu et al. (2017), the dataset contains information about 85% of pollution emissions from major pollutants in China. The MEP has implemented strict procedures, including unforeseen visits from experts to ensure that these firms have not misreported their emissions. Our analysis is focused on the SO₂ statistics, a primary air pollutant for 419 four digits industries spread across 286 cities from 1999 to 2007. Furthermore, firms are required to report their investment related to pollution abatement equipment (such as sewage treatment devices and air cleaning devices) and their efficiency in terms of pollution removal.

The National Bureau of Statistics of China's (NBS) mission is to collect and analyze information on China's economy and society throughout its territory, to which end it conducts surveys among all non-state-owned enterprises with sales above 5 million RMB and state-owned enterprises. The survey is detailed and thorough insofar as it contains detailed information about the name, address, four-digit CIC industry classification, ownership, and financial variables (including output, sales, and fixed assets). The temptation to cheat and not provide true figures is minimized by the fact that, according to Chen et al. (2018), the NBS is not allowed to share information with other agencies (e.g., tax agencies, government). As a result, the survey is regarded as being trustworthy and widely used, e.g., since 1995, to compute statistics such as GDP.

We follow Manova (2013) to construct *asset tangibility* $ft_t$ at the firm level by summing the fixed assets, adjusted for depreciation, and divided by total assets.

Our next main variable, *internal finance* $f_{ct}$, is proxied by cash flow, current ratio and coverage ratio. Cash flow is defined as the net income<fnref n="12" /> plus depreciation, adjusted to tangible assets. Cash flow is commonly used as a proxy for internal finance in the literature

<footnote n="12">¹²Net income is defined as the difference between profit before tax but after extraordinary income and income tax.</footnote>
(Chen and Guariglia 2013; Chow and Fung 1998, 2000; Guariglia et al. 2011). The current ratio is a liquidity ratio that measures a company's ability to pay short-term obligations or those due within one year. Larger values suggest that the company has more liquidity and is less dependent on the credit market. The coverage ratio is a financial metric used to evaluate a company's ability to meet its debt obligations, typically calculated by dividing a firm's income or cash flow by the interest or principal payments due within a given period. With more internal finance, a firm can optimize its capital efficiency by investing in productive growth-enhancing activities, e.g. proprietary technology or R&D (Li et al. 2018). In China, private firms which generated a large amount of cash could grow at a relatively higher rate, even though they face external credit constraints (Guariglia et al. 2011).

Another coefficient of interest is total factor productivity, $TFP_{fct}$. We resort to the Olley and Pakes (1996) approach to compute it at the firm-level.<fnref n="13" /> We use a Cobb-Douglas function with three factors: labor, capital, and intermediate input. $TFP_{fct}$ is a significant component of growth efficiency in China (Brandt et al. 2012). The change in pollution induced by TFP can be positive or negative, depending on whether TFP leads to adopting new technologies stemming from environmentally friendly innovation (Grossman and Krueger 1995; Panayotou 1995; Kahn and Zheng 2016).<fnref n="14" />

We control for variables commonly used in the literature ($X_{fct}$): sales to total asset, total asset, employment, age and SOE, a dummy variable capturing the ownership of the firm. When the dummy is equal to 1, it implies the firms is a State-Owned Enterprise. When a firm needs to remove pollution, one option is to invest in pollution abatement equipment. Our

<footnote n="13">¹³We have tested the robustness of all results with the alternative Levinsohn-Petrin algorithm, see Levinsohn and Petrin (2003). The results are the same and available on request.</footnote>

<footnote n="14">¹⁴In our dataset, the sectors with the largest TFP in 2007 are Processing of Petroleum, Coking, Processing of Nuclear Fuel, Smelting and Pressing of Ferrous Metals and Smelting and Pressing of Non-ferrous Metals while the bottom sectors are Articles For Culture, Education and Sport Activity, Processing of Timber, Manufacture of Wood, Bamboo, Rattan, Palm, and Straw Products and Printing, Reproduction of Recording Media. TFP grew at an average rate of 1.14% over the period covered, the largest percentage changes being recorded in 2005 at 2%.</footnote>
dataset includes a variable that reports the theoretical capacity of an industry to remove $SO_2$ by kilogram/hour. We use this variable to compute $SO_2$ removing capacity$_{fct}$, set equal to the $SO_2$ removing capacity divided by the firm's total sales.

*Sales to total assets* measures the efficiency of company in using its assets to generate sales, e.g. revenue.<fnref n="15" /> All variables are calculated from firm-level data and in log.

Lastly, the model includes a set of fixed effect. $\zeta_f$ signifies firm-level fixed effects, accounting for constant, firm-specific traits; $\gamma_t$ indicates year-level fixed effects that adjust for annual, country-wide events affecting all cities; and $\gamma_c$ stands for city-level fixed effects. Standard errors are clustered at the city level.

We anticipate that the coefficient of *asset tangibility*$_{fct}$ will be positive, and the coefficient of *TFP*$_{fct}$ will be negative, while the coefficient of *internal finance*$_{fct}$ could be either positive or negative. The availability of internal finance allows firms to choose the type of assets they want to finance, such as R&D or productivity-enhancing assets, potentially including energy-cost saving ones. Investing in intangible assets does not necessarily lead to a decrease in pollution but is possible in the context of China's national policies of incentivizing pollution abatement. The question, therefore, remains an empirical one.

## 3.2 Preliminary Evidence

The primary air pollutant $SO_2$ reached peak emissions in 2005 at 32.41 million tons (see Figure 1). Among the 522 cities monitored by the Chinese Ministry of Environment, about 400 had annual average $SO_2$ levels that meet the Grade II national standard (0.06mg/m$^3$),<fnref n="16" />

<footnote n="15">¹⁵The higher the sales over assets ratio, the more efficient a company is at generating revenue from its assets.</footnote>

<footnote n="16">¹⁶China uses its own air quality standard, which is less stringent than the WHO's standard. China's National Environmental Monitoring Center (CNEMC) records real-time, hourly air quality data for major cities in China. The real-time data is available at http://www.cnemc.cn/. Major air pollutants are moni-</footnote>
and 33 cities met the worst grade (0.10mg/m3). Two years after the 11th Five-Year Plan (FYP) was launched, the situation had slightly changed, according to the Ministry of Environment's annual report on the environment's state.<fnref n="17" /> It states that 79% of the audited cities reached Grade II, which is two percentage points higher than in 2005. A towering achievement concerned the Grade III criteria, where less than 1.2% of the cities were above the threshold and represented four percentage points less than in 2005. The most polluted cities are located in Shanxi, Guizhou, Inner Mongolia, and Yunnan provinces.

[Figure 1 about here.]

Chinese policymakers decided to take the environmental issue seriously after the sulfur dioxide ($SO_2$) peak hurt the country in 1995. In no less than 3 years, the officials in Beijing proposed and ratified a law regulating $SO_2$ emissions. In 1998, the Acid Rain Control Zone and Sulfur Dioxide Pollution Control Zone policy, referred to as the Two Control Zone (TCZ) policy, was implemented by the central government to limit the emissions of $SO_2$. While the regulation of $SO_2$ emissions was initially designed to be implemented at the national level, the State Council subsequently chose 175 TCZ cities with very poor environmental records to engage with more effort. Three selection criteria were chosen according to pre-regulation environmental performance. A city was placed under scrutiny if the average annual ambient $SO_2$ concentration exceeded the national class 2 standard (0.06mg/m3), if the daily average ambient $SO_2$ concentration exceeded the national class 3 standard (0.25mg/m3), or if the city

<footnote>tored, including $SO_2$, NO2, and PM10. To evaluate air quality, the Chinese government applies three classes. Class 1 is for yearly $SO_2$ levels less than 0.02 mg/m3, or a daily average of less than 0.05mg/m3. Class 2 is less restrictive. The yearly average should not exceed 0.06 and a daily average of about 0.15. Class 3 is complacent with bad air quality. The yearly average can exceed 0.10 mg/m3, and the daily average is 0.25. By contrast, the WHO recommends a daily average of less than 0.02mg/m3. For the record, exposure to high $SO_2$ levels dangerously affects health. According to the WHO, "$SO_2$ can affect the respiratory system and the functions of the lungs and causes irritation of the eyes. Inflammation of the respiratory tract causes coughing, mucus secretion, aggravation of asthma and chronic bronchitis. It makes people more prone to respiratory tract infections".</footnote>

<footnote n="17">¹⁷The report is available at http://english.mee.gov.cn/Resources/Reports/soe/soe2007/201.</footnote>
experienced significant SO₂ emissions.<fnref n="18" /> Figure 2 shows the aggregated percentage change of SO₂ emissions by the status of the city (TCZ vs no TCZ). On average, cities targeted by the policy managed to maintain or reduce their emissions, especially after 2005.

[Figure 2 about here.]

In this context of a very strong political will to reduce emissions, the question of how companies respond to the incentives they receive is noteworthy. We find at a purely descriptive level that, on the one hand, tangible investments are correlated with the level of pollution (reflecting, in particular, that capital-intensive industries are more polluting), and that, on the other hand, internal finance is negatively correlated with pollution.

We plot the relationship between asset tangibility and SO₂ emissions in figure 3. The axes are both in logarithmic form, and each point on the graph represents the SO₂ emissions of a company in relation to the ratio of tangible assets to total assets. The positive slope indicates that SO₂ emissions is growing positively with the share of tangible over total assets.

[Figure 3 about here.]

In figure 4, we plot the three proxies for internal finance against SO₂ emissions. The negative slopes for each of our proxy for internal finance are negative, implying that a larger availability of internal finance leads to lower emissions of SO₂ (figure 4).

[Figure 4 about here.]

<footnote n="18">¹⁸A city was designated as an acid rain control zone if (1) its average pH value of precipitation was equal to or less than 4.5; (2) its sulfate deposition was above the critical load; (3) its SO₂ emissions were high.</footnote>
# 4 Empirical findings and analysis

## 4.1 The role of internal finance in SO₂ mitigation

Table 1 reports equation 1 estimation, which depicts the impact of the material nature of investments and the direct impact of the availability of internal finance (cash flow, current ratio and coverage ratio) on pollution. The table is organized with four different specifications, and all columns include controls for firm characteristics such as sales to asset, total asset, and employment, among others.

The coefficient on asset tangibility is consistently positive and statistically significant at the 1% level across all four columns. This supports our theoretical assumption that firms with higher asset tangibility tend to produce higher levels of SO₂ emissions. It aligns with the idea that such firms, often facing credit constraints, are more likely to invest in tangible, emission-intensive assets like heavy machinery. Specifically, for each 10% increase in asset tangibility, SO₂ emission intensity increases in a range from 38.8% to 47.6%. This is higher than previous estimations (Andersen (2017)), adding weight to our argument that the material nature of investments has an exacerbating effect on pollution. This underscores the detrimental environmental impact of the positive relationship between tangible assets and SO₂ emissions, an impact that becomes particularly severe in rapidly developing economies where industrial growth often takes precedence over environmental considerations.

The internal financial factors of cash flow, current ratio and coverage ratio have a negative and considerable influence on SO₂ emissions. The findings suggest that firms with stronger internal financial health are better equipped to invest in cleaner technologies, contributing to lower emissions. Specifically, for each 10% increase in cash flow, SO₂ emissions decrease by 10.7%. Similarly, for a 10% increase in the current ratio, emissions decrease by 6.0%
(6.3% for the coverage ratio). This empirical evidence underscores the potential of internal financing to play a role in lowering emissions.

[Table 1 about here.]

The coefficients on Total Factor Productivity (TFP) are stable and negative, significant at least at the 5% level. This reinforces the notion that efficiency gains, possibly facilitated by cleaner technologies, are associated with reduced pollution levels. A 10% increase in TFP correlates with an 8.8% to 10.8% reduction in SO₂ emissions.

Our additional controls, such as pollution removing capacity, employment, age, and SOE ownership, offer further validation. SO₂ removing capacity is significant and negative across all specifications, reinforcing the effectiveness of pollution control measures at the firm level. A 10% improvement in SO₂ removing capacity is correlated with a reduction in SO₂ emissions ranging from 7.8% to 11.1%. The age of the company has a non-linear relationship to SO₂ emissions, which provides us with a more detailed comprehension of the factors that cause pollution. In conclusion, the findings from this updated Table 1 reaffirm and deepen our initial results, revealing nuanced interactions between firm-level financial conditions and environmental outcomes.

## 4.2 Heterogeneous responses to internal financing in SO₂ emission reduction

The main goal of this section is to investigate how the effects of internal financing on SO₂ emission intensity differ across various sectors and cities. We investigate how the effect of internal financing on environmental outcomes is modified by local regulations, institutional
elements, and industry-specific. The results of the differential effect of local regulation on
emissions of pollution are presented in Table 2.

In column 1, we interact cash flow with credit constraint. Credit constraints is char-
acterized as a dummy variable, taking on the value of 1 for industries that are financially
dependent. The interaction term between cash flow and credit constraints confirms the
hypothesis that financially dependent industries benefit more from internal financing when
aiming to mitigate environmental pollution. The significance of this term at the 1% level pro-
vides empirical evidence that internal financial resources are especially effective in reducing
SO₂ emissions within these sectors.

Bank Regulation (column 2), defined as a dummy variable, is based on the 75th per-
centile of the inverse Herfindahl-Hirschman Index at the city level. Calculated from the
market shares of bank branches in 1998,<fnref n="19" /> this index serves as a gauge for credit allocation
inefficiencies in China’s regionally segmented financial markets. These inefficiencies dispro-
portionately affect private enterprises, subjecting them to discriminatory lending, especially
from state-owned banks (Boyreau-Debray and Wei 2005; Jarreau and Poncet 2014).

The interaction term with cash flow has a coefficient of -0.048, significant at the 1% level.
This highlights that stringent bank regulations have an important indirect environmental
effect by limiting the availability of credit for the private sector. As a result, private firms
are more reliant on internal financial resources for growth, which in turn leads to a more
substantial reduction in SO₂ emissions, especially in cities burdened by these regulations.
This is particularly salient in China's financial landscape where about 75% of the total credit
supply comes from bank loans and state-owned banks are the predominant issuers (Li et al.
2018). Therefore, our findings serve as empirical evidence that strict banking regulations,
by constraining credit for the private sector, have the unintended but positive consequence

<footnote n="19">¹⁹This data is sourced from the Almanac of China Finance and Banking, and we use 1998 to avoid endogeneity problem.</footnote>
of reducing SO₂ emissions through increased reliance on internal financing.

[Table 2 about here.]

The innovation dummy variable is defined as taking the value of 1 for cities characterized by high levels of innovation. This marker serves not merely as a categorical label but as an indicator of a city's capacity to adopt new technologies and sustainable practices—who can potentially impact the emission of pollution.

In this context, an interaction term with cash flow has a coefficient of -0.035**, significant at the 5% level. This suggests that cities marked by high innovation are more adept at leveraging internal finances for environmental improvements. Specifically, an increase in cash flow in these cities leads to a more substantial reduction in SO₂ emissions compared to less innovative areas. This could be due to factors like superior eco-efficient infrastructures or progressive environmental policies that incentivize sustainable practices.

In Column (4), the interaction term between cash flow and *Two Control Zone* (TCZ) policy yields a negative coefficient, but fails to reach statistical significance. The Two Control Zone variable is a policy indicator that takes the value of 1 if the company is located in a region that has been specifically designated for the control of acid rain or sulfur dioxide pollution. This policy was promulgated in 1998 with the explicit aim of limiting SO₂ emissions. The absence of statistical significance in the interaction term suggests that the TCZ policy does not substantially modulate the effect of cash flow on SO₂ emissions. In other words, the internal finances of firms located within these zones are neither more nor less effective in mitigating sulfur dioxide emissions compared to those situated outside the zones.

In column (5), the cash flow and *Special Policy Zone* (SPZ) interaction term has a coefficient of -0.040***, which is highly statistically significant. The *Special Policy Zone* is a
binary variable signifying whether a firm is located within a designated SPZ, a zone char-
acterized by growth-driven policies that make these locales particularly attractive to foreign
enterprises, exporters, or high-tech firms. Such firms typically enjoy numerous benefits such
as reduced tax rates and access to more affordable credit or subsidies (Hering and Poncet
2014; Wang and Wei 2008). An incremental increase in cash flow has a noticeably amplified
effect on reducing SO₂ emissions. This points to the inference that firms in these zones
are more inclined to allocate their internal finances towards eco-friendly technologies and
practices. The findings of this study demonstrate the potential of specific policy areas to
help utilize internal resources for the improvement of the environment.

In the last column, we include all interaction term and confirms the conditional nature
of cash flow's impact on SO2 emission intensity under varying settings and constraints.

5 Mechanisms

We now explore the depth of our dataset to investigate the mechanisms by which our variables of interest influence pollution. We are paying a special attention to the financial structure of firms, especially to the availability of internal financing and the intensity of financial constraints. Our theoretical approach is summarised in Table 3. Overall, both access to bank credit and availability of internal finance can be expected to favour R&D, technical progress (column 3) and investment in pollution abatement equipment (column 4). We emphasize the availability of internal finance, which is likely to reduce the share of tangible assets (column 2) and makes investment in intangible assets (including R&D and pollution abatement equipment) more likely. All of these mechanisms examined at the firm level have the potential to reduce the emission of pollutants, as shown in the previous section. They are tested one by one below.
[Table 3 about here.]

## 5.1 The differential impact of internal finance on asset tangibility, R&D, and TFP

A growing literature analyzes the extent to which the availability of internal sources of financing affects the firms’ investments in fixed assets, inventory, or R&D (Chen and Guariglia 2013; Fazzari et al. 2000; Rajan and Zingales 1998). According to this literature, firms with more internal finance are less likely to invest in tangible assets, and more likely to use their cash flow to fund technological innovations and R&D. From the supply side, banks are reluctant to finance intangible assets, because the latter are not considered as valuable collateral (Brown and Petersen 2009).<fnref n="20" /> The biases that stem from this preference of banks for tangible assets, and from investments in R&D and intangible assets to be handled by internal finance, result in more or less pollution, as shown in the previous section.

The environmental literature has documented the decrease in polluting emissions induced by TFP, which means that more productive firms are more resource efficient (Brown and Petersen 2009; Fazzari et al. 1988; Li et al. 2018). According to table 3 and the previous section results, this is also true for China, as higher TFP reduces pollutant emissions. This section examines to which extent internal funding plays a role by being used to fund technology improvements and higher TFP.

Equations 2, 3 and 4 embed this literature. Equation 2 hypothesizes that assets composition (tangible *versus* intangible) is affected by internal finance availability proxy by *cash flow*.

<footnote n="20">²⁰Collateral is behind the scarcity of loans to finance innovative activities, but is not the only reason. The literature mentions two additional factors: informational asymmetry and moral hazard (Brown et al. 2013; Hall and Lerner 2010).</footnote>
Equation 3 expresses that productivity-enhancing activities such as R&D, and the adoption of new technology, depend primarily on the firm's internal finance availability. Equation 4 entails the effect of internal finance of TFP.

We estimate the following equations at the firm-level:

$$ \text{asset tangibility}_{fct} = \beta_1 \text{internal finance}_{fct} + \beta_5 X_{fct} + \zeta_f + \gamma_c + \gamma_t + \epsilon_{fct} \quad (2) $$

$$ \text{R\&D}_{fct} = \beta_1 \text{internal finance}_{fct} + \beta_5 X_{fct} + \zeta_f + \gamma_c + \gamma_t + \epsilon_{fct} \quad (3) $$

$$ \text{TFP}_{fct} = \beta_1 \text{internal finance}_{fct} + \beta_5 X_{fct} + \zeta_f + \gamma_c + \gamma_t + \epsilon_{fct} \quad (4) $$

Where *asset tangibility* $fct$ in equation 2 refers to the share of asset tangibility over total assets, and $R\&D_{fct}$ in equation 3 represents the share of research expenditure over total asset. In equation 4, the dependent variable $TFP_{fct}$ is the productivity of firm $f$ computed with the Olley-Pakes algorithm at the firm-city-time level, as explained in the data section. We use $cash flow_{fct}$ as a proxy for internal finance following the methodology explained in section 3.2. Analyses can be done at firm-level, $f$, city $c$ and year $t$. $X_{fct}$ includes usual controls found in the literature, namely *liabilities to assets*, *total asset*, *employment*, *age* and *soe*, a dummy variable to indicate the firm's ownership type (state-owned vs. private). $\gamma_t$ represents fixed effects for each year, correcting for events that impact all cities across the country annually; while $\gamma_c$ denotes fixed effects specific to each city. Finally, entering firm fixed effects ($\zeta_f$) removes all unobserved factors contributing to a firm's assets accumulation within a city.
Table 4 presents our findings on the relationships among asset tangibility, R&D, and TFP, all in relation to internal finance, specifically cash flow. The table estimates the elasticity of each dependent variable with respect to various independent variables.

For asset tangibility, the elasticity with respect to cash flow stands at -0.074 and is statistically significant at the 1% level. The coefficient suggests a negative relationship, indicating that higher cash flow is associated with a decrease in asset tangibility.

[Table 4 about here.]

In the R&D equation, the elasticity of R&D with respect to cash flow is 0.0005, and is statistically significant at the 5% level. This indicates that an increase in cash flow leads to a slight increase in research and development expenditure within the firms in the sample. For TFP, the elasticity with respect to cash flow is 0.089 and is significant at the 1% level. This positive coefficient implies that higher internal finance in terms of cash flow correlates with increased Total Factor Productivity.

The table also includes control variables, such as liabilities-to-assets, total assets, employment, age, and a square of age, along with firm, year, and city fixed effects. These controls are included to account for firm-level heterogeneity and other unobserved effects that could potentially influence the dependent variables. Liabilities-to-assets, total assets, and employment also have significant coefficients across different dependent variables.

In summary, the empirical results support our theoretical framework that internal finance, particularly cash flow, has a differential impact on asset tangibility, R&D, and TFP. These findings suggest that firms strategically allocate their internal financial resources, opting for investments that enhance productivity and innovation over investments in tangible assets.

Table 5 extends the earlier analyses by incorporating the interaction of internal finance, represented by cash flow, with a credit constraint dummy. This addition allows us to ex-
plore the mechanism by which internal finance affects asset tangibility, R&D, and TFP,
particularly under varying conditions of credit constraint. Data regarding the financial sta-
tus of companies reliant on external financing is accessible only at the sector level, not at
the individual firm level, making it less granular. We employ the concept of industry-level
external finance dependency, which is understood as the sector's susceptibility to banking
influences. The industry's external finance dependency computation is straightforward—it
is the share of capital expenditure not financed with cash flow from operations. Previous
works have used US data to proxy for the exposure to external finance (Rajan and Zingales
1998; Claessens and Laeven 2003; Kroszner et al. 2007) and in the context of China (Jar-
reau and Poncet 2014; Manova et al. 2015; Fan et al. 2015). We use the Chinese data and
replicate the methodology proposed by Fan et al. (2015), who used the annual surveys of
Chinese manufacturing firms dataset during the years 2004–2006 to aggregate the capital
expenditure and cash flow at the two digits industrial level. Fan et al. (2015) argue that the
financial pattern between the US and China is almost similar.<fnref n="21" />

In the asset tangibility column, we observe that the interaction term between cash flow
and credit constraint has a statistically significant coefficient of -0.012 at the 1% level. This
suggests that for firms in financially dependent industries (where the credit constraint dummy
takes the value of 1), higher cash flow has an even more pronounced downward effect on asset
tangibility compared to firms in less financially constrained sectors.

[Table 5 about here.]

For R&D, the interaction term yields a coefficient of 0.001 and is significant at the 1%
level. This implies that in credit-constrained industries, firms with greater cash flow tend
to allocate more resources to R&D. It appears that the easing of credit constraints through

<footnote n="21">²¹Unlike the US methodology, which uses the median over time, the authors use the aggregate value from the Chinese data because about 68% of the observations have 0 capital expenditure Tobacco is the least vulnerable sector in the US, while it ranks second in China. The leather products industry is the second least vulnerable in the US and the fifth least vulnerable in China. Table ?? in the appendix gives the value of financial dependence for the 29 industries in China. The average value is -.57, and industries with a high technological requirement are also the most vulnerable. The *Petroleum* industry and *Processing of Nuclear Fuel* industries are at the bottom of the table, stressing their high reliance on credit.</footnote>
internal financing allows for increased expenditure in innovation activities, even more so than
in industries where credit is less of an issue.

Regarding TFP, the interaction term has a coefficient of 0.001 but is not statistically
significant. While the sign is positive, it does not provide strong evidence to support the
notion that internal finance differently affects TFP under credit constraints. Thus, the
relationship between cash flow and TFP appears to be fairly stable across different levels of
financial dependency.

The coefficients of other control variables remain relatively consistent with the earlier
model, reinforcing our previous interpretations. Liabilities-to-assets, total assets, and em-
ployment variables still present significant coefficients across different dependent variables.

The results continue to support the idea that internal finance plays a significant role in
the firm's investment behavior, and that its influence varies depending on the level of credit
constraint a firm faces. Specifically, firms in credit-constrained industries appear to make
more strategic use of their internal finances in making investment choices. It appears that
these companies are avoiding investing in physical assets, possibly to prioritize research and
development when they have a surplus of internal funds.

Overall, the empirical results corroborate our theoretical propositions. They suggest
that not only does internal finance affect asset allocation decisions, but its impact can differ
significantly depending on the industry's reliance on external credit.

## 5.2 Internal finance and SO₂ abatement equipment

In this section, we carefully examine if the firm's internal financing is used to purchase
pollution abatement equipment. In the same vein, and using the same MEP data, Wang
and Chen (1999) reported that the sources of investment in abatement gradually switched
from subsidies to the firm's profit, opening up more room for internal finance to acquire
pollution abatement equipment.
The MEP data now allows us to investigate three different but interconnected measures that indicate a firm's efforts in pollution abatement: Abatement Capacity, SO₂ Removed, and SO₂ Removal Per Hour. We use this enriched dataset to study the behavior of firms in the context of SO₂ emission reduction from 1998 to 2007. We estimate the following equations:

$$ 
\begin{aligned}
\text{Abatement Capacity}_{fct} = & \alpha_1 \text{internal finance}_{fct} \\
& + \alpha_2 X_{fct} \\
& + \zeta_f + \gamma_c + \gamma_t + \epsilon_{fct}
\end{aligned}
\qquad (5) $$

where Abatement Capacity$_{fct}$, SO₂ Removed$_{fct}$, and SO₂ Removal Per Hour$_{fct}$ are the three new measures of a firm's pollution abatement efforts for each year in our sample. The variable cash flow$_{fct}$ is employed as a key explanatory variable. X$_{fct}$ is a matrix of control variables comprising liabilities to asset$_{fct}$, and total asset$_{fct}$. Additionally, we include employment$_{fct}$, age$_{fct}$, and soe$_{fct}$. The model incorporates various fixed effects: ζ$_{f}$ embodies static characteristics unique to each firm; γ$_{t}$ symbolizes year-specific fixed effects to control for nationwide occurrences affecting all cities within a year; and γ$_{c}$ accounts for city-specific fixed effects. All variables are in logarithmic form, and standard errors are grouped at the city level.

[Table 6 about here.]

Results are presented in Table 6. The table includes three specifications that focus on different measures of pollution abatement performance, namely, "Abatement Capacity", "SO₂ Removed" and "SO₂ Removal Per Hour." The positive and statistically significant coefficient for cash flow in all three measures is consistent with the theoretical expectation that firms with greater liquidity are more inclined to invest in the scale and efficiency of
their pollution abatement activities. This is likely motivated by both regulatory pressures and corporate commitments to sustainable operations. Columns (2) and (3) extend this analysis by investigating the annual amount of SO₂ removed and the rate of SO₂ removal per hour. The significance of the coefficients in these columns supports the theoretical premise that liquidity-rich firms are more capable of allocating funds to environmentally beneficial technologies, likely in response to social pressures and potential regulatory incentives.

The correlation between liabilities to asset and total asset with all the dependent vari-
ables is significant and positive. On the other hand, employment and age have negative
coefficients. This implies that smaller and younger firms may be less efficient in managing
pollution abatement. However, the positive and significant coefficients for the squared term
of age indicate that as firms mature, they are likely to adopt more effective pollution control
strategies. Lastly, the negative coefficients for the state-owned enterprise indicator suggest
that state-owned enterprises are less effective in pollution abatement activities, holding other
factors constant.

These findings substantiate earlier insights about the role of internal finance in acquiring
pollution abatement equipment. Specifically, they reveal nuanced relationships between
various firm attributes and effective SO₂ management, offering further empirical evidence
that complements the existing theoretical foundations.

In the table 6, we focus on the effect of internal finance on the acquisition of pollution abatement equipment when a firm is credit constrained.

The interaction term cash flow × credit constraint is statistically significant across all three measures of pollution abatement: "Abatement Capacity", "SO₂ Removed", and "SO₂ Removal Per Hour". This suggests that the impact of liquidity on pollution abatement measures is conditioned by the firm's credit constraints. For firms facing credit constraints (i.e., where the credit constraint dummy takes the value of 1), an increase in cash flow has an additional positive effect on pollution abatement beyond the main effect of cash flow alone.
Specifically, the coefficients are 0.013, 0.060, and 0.032 for "Abatement Capacity", "SO<sub>2</sub> Removed", and "SO<sub>2</sub> Removal Per Hour", respectively, and they are statistically significant at the 5% level or lower.

[Table 7 about here.]

This implies that for firms operating in financially dependent industries, or otherwise fac-
ing limitations in external financing, internal cash flow is particularly crucial for investments
in pollution abatement technologies. Overall, these interaction effects reveal the nuanced
role that internal financing plays in influencing environmentally responsible behaviors, par-
ticularly for firms that are more credit-constrained. This provides evidence that financial
limitations can influence a company's ability to be environmentally responsible, suggesting
a more intricate relationship between financial structure and sustainability efforts.

6 Conclusion

Our study makes a significant contribution to understanding the relationship between inter-
nal finance and sulfur dioxide (SO2) emissions intensity in the context of China's industrial
landscape. Utilizing a comprehensive dataset that spans nine years, we have shown that
there is a statistically significant positive relationship between a firm's asset tangibility and
its SO2 emissions intensity, indicating that credit-constrained firms with higher asset tan-
gibility contribute to elevated pollution levels. Conversely, the presence of internal finance,
as indicated by cash flow, current ratio, and coverage ratio, has a mitigating effect on pol-
lution emission. Internal finance plays a dual role in influencing firm behavior. On one
hand, firms with stronger internal finances are more likely to invest in D and Total Factor
Productivity, particularly in sectors where external financing is constrained. On the other
hand, such firms are also more proactive in acquiring SO2 abatement technologies, an effect
that becomes more pronounced in the context of credit-constrained firms.
In the Chinese context, internal financing is essential, and it is sensitive to the injunctions of the national environmental regulatory policies. Addressing the tendency of bank lending to favour tangible assets, as well as encouraging pollution control investments, particularly within state-owned enterprises, whether through green innovation or emission reduction equipment, would further protect the environment.
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# 7 Appendix

[Table 8 about here.]

[Table 9 about here.]

[Table 10 about here.]

[Table 11 about here.]
List of Figures

<table><tr><td>1</td><td><strong>Note:</strong> The horizontal red line represents the launch of the 11th Five-Year Plan (FYP). From 2006 onward, China enforced more environmental severity with an optimistic target of a reduction in the emissions of SO<sub>2</sub> by 10% in 2010 as compared with the level in 2005. <strong>Source:</strong> The SO<sub>2</sub> emissions data are from the China Statistical Yearbook (2000, 2010)</td><td>36</td></tr><tr><td>2</td><td><strong>Note:</strong> The y-axis is the year-to-year percentage change of SO<sub>2</sub> emissions in different locations (TCZ vs No TCZ). <strong>Source:</strong> Authors' own computation.</td><td>37</td></tr><tr><td>3</td><td><strong>Note:</strong> The x-axis represents the log of tangible assets divided by the total assets, and the y-axis is the log of SO<sub>2</sub> emissions. <strong>Source:</strong> Authors' own computation.</td><td>38</td></tr><tr><td>4</td><td><strong>Note:</strong> The x-axis represents the log of cash flow (left side) and log of current ratio (right side) and the y-axis is the log of SO<sub>2</sub> emissions. <strong>Source:</strong> Authors' own computation.</td><td>39</td></tr></table>
<figure><img src="image_3.png" /><figcaption>Figure 1: SO₂ emissions in China from 2000 to 2010</figcaption></figure>

**Note:** The horizontal red line represents the launch of the 11th Five-Year Plan (FYP). From 2006 onward, China enforced more environmental severity with an optimistic target of a reduction in the emissions of SO₂ by 10% in 2010 as compared with the level in 2005.

**Source:** The SO₂ emissions data are from the China Statistical Yearbook (2000, 2010)
<figure><img src="image_4.png" /><figcaption>Figure 2: Aggregated percentage change of SO₂ emissions in TCZ and no TCZ cities</figcaption></figure>

**Note:** The y-axis is the year-to-year percentage change of SO₂ emissions in different locations (TCZ vs No TCZ). **Source:** Authors' own computation.
<figure><img src="image_5.png" /><figcaption>Figure 3: Log asset tangibility against log SO₂ emissions</figcaption></figure>

**Note:** The x-axis represents the log of tangible assets divided by the total assets, and the y-axis is the log of SO₂ emissions. **Source:** Authors' own computation.
<figure><img src="image_6.png" /><figcaption>Figure 4: Log internal finance against log SO₂ emissions</figcaption></figure>

**Note:** The x-axis represents the log of cash flow (left side) and log of current ratio (right side) and the y-axis is the log of SO₂ emissions. **Source:** Authors' own computation.
List of Tables

<table><tr><td>1</td><td>Table 1: Determinants of SO<sub>2</sub> emissions</td><td>41</td></tr><tr><td>2</td><td>Table 2: Heterogeneity effect</td><td>42</td></tr><tr><td>3</td><td>Table 3: Transmission channels</td><td>43</td></tr><tr><td>4</td><td>Table 4: Asset tangibility versus R&D and internal finance</td><td>44</td></tr><tr><td>5</td><td>Table 5: Asset tangibility versus R&D and internal finance</td><td>45</td></tr><tr><td>6</td><td>Table 6: Internal finance and SO<sub>2</sub> pollution abatement equipment</td><td>46</td></tr><tr><td>7</td><td>Table 7: Effects of cash flow on Pollution Abatement in Credit-Constrained Firms</td><td>47</td></tr><tr><td>8</td><td>Table 8: TCZ and SPZ cities in China</td><td>48</td></tr><tr><td>9</td><td>Table 9: TCZ and SPZ cities in China (continued)</td><td>49</td></tr><tr><td>10</td><td>Table 10: TCZ and SPZ cities in China (continued)</td><td>50</td></tr><tr><td>11</td><td>Table 11: Credit constraint from the annual surveys of Chinese manufacturing firms</td><td>51</td></tr></table>
<table><caption>Table 1: Determinants of SO₂ emissions</caption><thead><tr><th></th><th colspan="4">Dependent variable: SO<sub>2</sub> emission intensity</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>log asset tangibility</td><td>0.388***<br>(0.046)</td><td>0.472***<br>(0.041)</td><td>0.476***<br>(0.051)</td><td>0.400***<br>(0.055)</td></tr><tr><td>log(cash flow)</td><td>-0.107***<br>(0.012)</td><td></td><td></td><td>-0.095***<br>(0.015)</td></tr><tr><td>log(current ratio)</td><td></td><td>-0.060***<br>(0.012)</td><td></td><td>-0.061***<br>(0.017)</td></tr><tr><td>log(coverage ratio)</td><td></td><td></td><td>-0.063***<br>(0.009)</td><td>-0.039***<br>(0.009)</td></tr><tr><td>log(tfp)</td><td>-0.088**<br>(0.035)</td><td>-0.108***<br>(0.034)</td><td>-0.097**<br>(0.039)</td><td>-0.076*<br>(0.038)</td></tr><tr><td>log(sales to asset)</td><td>-0.263***<br>(0.028)</td><td>-0.282***<br>(0.032)</td><td>-0.267***<br>(0.033)</td><td>-0.255***<br>(0.033)</td></tr><tr><td>log(total asset)</td><td>-0.529***<br>(0.031)</td><td>-0.489***<br>(0.030)</td><td>-0.536***<br>(0.038)</td><td>-0.549***<br>(0.037)</td></tr><tr><td>log(employment)</td><td>0.065*<br>(0.033)</td><td>0.049<br>(0.034)</td><td>0.071**<br>(0.033)</td><td>0.082**<br>(0.033)</td></tr><tr><td>log(age)</td><td>0.776***<br>(0.167)</td><td>0.785***<br>(0.185)</td><td>0.803***<br>(0.197)</td><td>0.780***<br>(0.195)</td></tr><tr><td>age sqr</td><td>-0.245***<br>(0.036)</td><td>-0.245***<br>(0.042)</td><td>-0.257***<br>(0.046)</td><td>-0.251***<br>(0.045)</td></tr><tr><td>SO<sub>2</sub> removing capacity</td><td>-0.100***<br>(0.024)</td><td>-0.111***<br>(0.023)</td><td>-0.078***<br>(0.027)</td><td>-0.082***<br>(0.025)</td></tr><tr><td>soe</td><td>0.141***<br>(0.035)</td><td>0.186***<br>(0.025)</td><td>0.140***<br>(0.050)</td><td>0.126**<br>(0.052)</td></tr><tr><td>firm</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>industry-year</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>city</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>39,719</td><td>45,585</td><td>30,376</td><td>30,153</td></tr><tr><td>R<sup>2</sup></td><td>0.898</td><td>0.891</td><td>0.904</td><td>0.905</td></tr></tbody></table>

This table reports estimates of equation 1. *asset tangibility* denotes tangible assets over total assets. *cash flow* is defined as net income + depreciation over assets. *current ratio* is measured as current assets over current liabilities. *coverage ratio* is measured as the earning before interest and taxes over interest expenses. *TFP* stands for Total Factor Productivity and is estimated using the Olley and Pake algorithm. *SO₂ removing capacity* is the capacity to remove SO₂ emissions per hour divided by sales. All variables are in logs. Control variables are sales over asset, total asset, employment, age, age square, SO₂ removing capacity and SOE ownership. Heteroskedasticity-robust standard errors clustered at the city level appear in parentheses. * Significance at 10%, ** Significance at 5%, *** Significance at 1%.
<table><caption>Table 2: Heterogeneity effect</caption><thead><tr><th></th><th colspan="6">Dependent variable: SO2 emission intensity</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>log asset tangibility</td><td>0.385***<br>(0.045)</td><td>0.387***<br>(0.046)</td><td>0.387***<br>(0.045)</td><td>0.388***<br>(0.046)</td><td>0.386***<br>(0.046)</td><td>0.400***<br>(0.041)</td></tr><tr><td>log(cash flow)</td><td>-0.091***<br>(0.012)</td><td>-0.101***<br>(0.010)</td><td>-0.092***<br>(0.011)</td><td>-0.095***<br>(0.020)</td><td>-0.091***<br>(0.010)</td><td>-0.078***<br>(0.018)</td></tr><tr><td>log(tfp)</td><td>-0.088**<br>(0.035)</td><td>-0.088**<br>(0.035)</td><td>-0.088**<br>(0.035)</td><td>-0.088**<br>(0.035)</td><td>-0.088**<br>(0.035)</td><td>-0.082**<br>(0.036)</td></tr><tr><td>log(sales to asset)</td><td>-0.262***<br>(0.028)</td><td>-0.262***<br>(0.028)</td><td>-0.262***<br>(0.028)</td><td>-0.263***<br>(0.028)</td><td>-0.262***<br>(0.028)</td><td>-0.274***<br>(0.027)</td></tr><tr><td>log(total asset)</td><td>-0.528***<br>(0.031)</td><td>-0.530***<br>(0.031)</td><td>-0.529***<br>(0.031)</td><td>-0.529***<br>(0.031)</td><td>-0.529***<br>(0.031)</td><td>-0.548***<br>(0.032)</td></tr><tr><td>log(employment)</td><td>0.064*<br>(0.033)</td><td>0.065*<br>(0.033)</td><td>0.064*<br>(0.033)</td><td>0.065*<br>(0.033)</td><td>0.064*<br>(0.033)</td><td>0.081**<br>(0.035)</td></tr><tr><td>log(age)</td><td>0.773***<br>(0.167)</td><td>0.776***<br>(0.166)</td><td>0.774***<br>(0.166)</td><td>0.775***<br>(0.167)</td><td>0.774***<br>(0.166)</td><td>0.725***<br>(0.153)</td></tr><tr><td>age sqr</td><td>-0.244***<br>(0.036)</td><td>-0.245***<br>(0.036)</td><td>-0.244***<br>(0.036)</td><td>-0.245***<br>(0.036)</td><td>-0.244***<br>(0.036)</td><td>-0.236***<br>(0.033)</td></tr><tr><td>SO<sub>2</sub> removing capacity</td><td>-0.101***<br>(0.024)</td><td>-0.101***<br>(0.025)</td><td>-0.100***<br>(0.025)</td><td>-0.100***<br>(0.024)</td><td>-0.100***<br>(0.025)</td><td>-0.090***<br>(0.029)</td></tr><tr><td>soe</td><td>0.141***<br>(0.035)</td><td>0.141***<br>(0.034)</td><td>0.141***<br>(0.034)</td><td>0.142***<br>(0.035)</td><td>0.141***<br>(0.034)</td><td>0.140***<br>(0.032)</td></tr><tr><td>log(cash flow) × credit constraint</td><td>-0.037***<br>(0.013)</td><td></td><td></td><td></td><td></td><td>-0.028*<br>(0.014)</td></tr><tr><td>log(cash flow) × Bank regulation</td><td></td><td>-0.048***<br>(0.014)</td><td></td><td></td><td></td><td>-0.026<br>(0.017)</td></tr><tr><td>log(cash flow) × inn. capacity</td><td></td><td></td><td>-0.035**<br>(0.015)</td><td></td><td></td><td>-0.003<br>(0.029)</td></tr><tr><td>log(cash flow) × Two Control Zone</td><td></td><td></td><td></td><td>-0.018<br>(0.018)</td><td></td><td>-0.001<br>(0.020)</td></tr><tr><td>log(cash flow) × Special Policy Zone</td><td></td><td></td><td></td><td></td><td>-0.040***<br>(0.012)</td><td>-0.027<br>(0.027)</td></tr><tr><td>firm</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>industry-year</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>city</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>39,719</td><td>39,719</td><td>39,719</td><td>39,719</td><td>39,719</td><td>39,719</td></tr><tr><td>R<sup>2</sup></td><td>0.898</td><td>0.898</td><td>0.898</td><td>0.898</td><td>0.898</td><td>0.900</td></tr></tbody></table>

This table reports estimates of equation 1. *Asset tangibility* represents the proportion of tangible assets to total assets. *cash flow* is net income plus depreciation, scaled by assets. *TFP* is Total Factor Productivity, estimated using the Olley and Pake algorithm. *Credit constraints* is a dummy variable taking the value of 1 if the industry is financially dependent. *Bank regulation* is a dummy variable based on the 75th percentile of the Herfindahl-Hirschman Index calculated from the share of each bank branch relative to the total branches in that area in 1998. *inn.* *capacity* is a dummy variable based on the 75th percentile of a patent value-adjusted index at the city and 4-digit industry level in 2001. *Two Control Zone* and *Special Policy Zone* are policy variables indicating the firm's location. Heteroskedasticity-robust standard errors clustered at the city level are in parentheses. \* Significance at 10%, \*\* Significance at 5%, \*\*\* Significance at 1%.
<table><caption>Table 3: Transmission channels</caption><thead><tr><th></th><th>SO<sub>2</sub> emissions</th><th>Tangible assets</th><th>TFP</th><th>Equipment (to remove SO<sub>2</sub> emissions)</th></tr></thead><tbody><tr><td>Equations/Tables</td><td>1/1 and 2</td><td>2 and 3/ 4 and 5</td><td>4/4 and 5,</td><td>??/6 and 7</td></tr><tr><td>Asset tangibility</td><td>+</td><td></td><td></td><td></td></tr><tr><td>Internal finance</td><td></td><td></td><td></td><td></td></tr><tr><td>cash flow</td><td>+/-</td><td>-</td><td>+</td><td>+</td></tr><tr><td>SO<sub>2</sub> removing capacity (capacity to remove SO<sub>2</sub> emissions)</td><td>-</td><td></td><td></td><td></td></tr><tr><td>TFP</td><td>-</td><td></td><td></td><td></td></tr></tbody></table>

**Note:** Tangible assets is proxy by asset tangibility over total assets, or Research and Development over total assets. TFP stands for Total Factor Productivity, and is estimated using the Olley and Pake algorithm. Equipment is the number of SO₂ removal equipment installed. *SO₂ removing capacity* is the capacity to remove SO₂ emissions per hour divided by sales. *Asset tangibility* denotes tangible assets over total assets. Internal finance represents all flows of money generated by a firm. It is usually proxied by *cash flow* (net income + depreciation over assets) or *current ratio* (current assets divided by current liabilities). External finance represents all funds that firms obtain from outside banks or shareholders. It is proxied by *credit supply* (all credit or long-term credit to GDP ratio).
<table><caption>Table 4: Asset tangibility versus R&D and internal finance</caption><thead><tr><th></th><th colspan="3">Dependent variable:</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr><tr><th></th><th>Asset tangibility</th><th>RD</th><th>TFP</th></tr></thead><tbody><tr><td>log(cash flow)</td><td>-0.074***<br>(0.005)</td><td>0.0005**<br>(0.0002)</td><td>0.089***<br>(0.005)</td></tr><tr><td>log(liabilities to asset)</td><td>0.022***<br>(0.007)</td><td>0.001<br>(0.001)</td><td>0.025***<br>(0.007)</td></tr><tr><td>log(total asset)</td><td>-0.136***<br>(0.019)</td><td>-0.0003<br>(0.001)</td><td>0.083***<br>(0.018)</td></tr><tr><td>log(employment)</td><td>0.056***<br>(0.010)</td><td>0.001**<br>(0.001)</td><td>-0.109***<br>(0.014)</td></tr><tr><td>log(age)</td><td>-0.009<br>(0.025)</td><td>-0.002<br>(0.001)</td><td>0.011<br>(0.028)</td></tr><tr><td>age sqr</td><td>0.030***<br>(0.009)</td><td>0.002***<br>(0.0005)</td><td>0.038***<br>(0.007)</td></tr><tr><td>soe</td><td>-0.014<br>(0.012)</td><td>-0.001<br>(0.001)</td><td>-0.048***<br>(0.011)</td></tr><tr><td>firm</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>industry-year</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>city</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>69,761</td><td>32,464</td><td>69,761</td></tr><tr><td>R<sup>2</sup></td><td>0.909</td><td>0.626</td><td>0.766</td></tr></tbody></table>

This table reports estimates of equations 2 and 3. *Asset tangibility* represents the proportion of tangible assets to total assets. *RD* measures research and development expenditure. *cash flow* is net income plus depreciation, scaled by assets. *TFP* is Total Factor Productivity, estimated using the Olley and Pake algorithm. *Log(cash flow)* is the natural logarithm of cash flow. Heteroskedasticity-robust standard errors clustered at the city level are in parentheses. * Significance at 10%, ** Significance at 5%, *** Significance at 1%.
<table><caption>Table 5: Asset tangibility versus R&D and internal finance</caption><thead><tr><th rowspan="3"></th><th colspan="3">Dependent variable:</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr><tr><th>Asset tangibility</th><th>RD</th><th>TFP</th></tr></thead><tbody><tr><td>log(cash flow)</td><td>-0.069***<br>(0.005)</td><td>0.0002<br>(0.0002)</td><td>0.088***<br>(0.007)</td></tr><tr><td>log(liabilities to asset)</td><td>0.022***<br>(0.007)</td><td>0.001<br>(0.001)</td><td>0.025***<br>(0.007)</td></tr><tr><td>log(total asset)</td><td>-0.136***<br>(0.019)</td><td>-0.0004<br>(0.001)</td><td>0.083***<br>(0.018)</td></tr><tr><td>log(employment)</td><td>0.056***<br>(0.010)</td><td>0.001***<br>(0.001)</td><td>-0.109***<br>(0.014)</td></tr><tr><td>log(age)</td><td>-0.009<br>(0.025)</td><td>0.001<br>(0.001)</td><td>0.011<br>(0.028)</td></tr><tr><td>age sqr</td><td>0.030***<br>(0.009)</td><td>0.00001**<br>(0.00000)</td><td>0.038***<br>(0.007)</td></tr><tr><td>soe</td><td>-0.014<br>(0.012)</td><td>-0.001<br>(0.001)</td><td>-0.048***<br>(0.011)</td></tr><tr><td>log(cash flow) × credit constraint</td><td>-0.012***<br>(0.004)</td><td>0.001***<br>(0.0002)</td><td>0.001<br>(0.007)</td></tr><tr><td>firm</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>industry-year</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>city</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>69,761</td><td>32,464</td><td>69,761</td></tr><tr><td>R<sup>2</sup></td><td>0.909</td><td>0.626</td><td>0.766</td></tr></tbody></table>

This table reports estimates of equations 2 and 3. *Asset tangibility* represents the proportion of tangible assets to total assets. *RD* measures research and development expenditure. *cash flow* is net income plus depreciation, scaled by assets. *TFP* is Total Factor Productivity, estimated using the Olley and Pake algorithm. *Credit constraints* is a dummy variable taking the value of 1 if the industry is financially dependent. Heteroskedasticity-robust standard errors clustered at the city level are in parentheses. * Significance at 10%, ** Significance at 5%, *** Significance at 1%.
<table><caption>Table 6: Internal finance and SO₂ pollution abatement equipment</caption><thead><tr><th rowspan="3"></th><th colspan="3">Dependent variable:</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th></tr><tr><th>Abatement capacity</th><th>SO2 removed</th><th>SO2 removal per hour</th></tr></thead><tbody><tr><td>log(cash flow)</td><td>0.009*<br>(0.005)</td><td>0.104***<br>(0.017)</td><td>0.031**<br>(0.014)</td></tr><tr><td>log(liabilities to asset)</td><td>0.022***<br>(0.008)</td><td>0.117<br>(0.072)</td><td>0.082**<br>(0.032)</td></tr><tr><td>log(total asset)</td><td>0.087***<br>(0.011)</td><td>0.802***<br>(0.065)</td><td>0.303***<br>(0.042)</td></tr><tr><td>log(employment)</td><td>-0.029***<br>(0.010)</td><td>0.025<br>(0.049)</td><td>-0.096**<br>(0.036)</td></tr><tr><td>log(age)</td><td>-0.107***<br>(0.027)</td><td>-0.294<br>(0.215)</td><td>-0.303***<br>(0.093)</td></tr><tr><td>age sqr</td><td>0.048***<br>(0.008)</td><td>0.416***<br>(0.071)</td><td>0.142***<br>(0.028)</td></tr><tr><td>soe</td><td>-0.056**<br>(0.024)</td><td>-0.109<br>(0.097)</td><td>-0.215***<br>(0.066)</td></tr><tr><td>firm</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>industry-year</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>city</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>69,762</td><td>69,762</td><td>69,762</td></tr><tr><td>R²</td><td>0.732</td><td>0.782</td><td>0.703</td></tr></tbody></table>

This table reports estimates of equation (??). Abatement Capacity refers to the firm's equipment designed to mitigate SO2 emissions. SO2 Removed quantifies the annual amount of SO2 mitigated by the firm through end-of-pipe solutions following production. SO2 Removal Per Hour represents the rate at which SO2 is eliminated, measured in kilograms per hour. cash flow is net income plus depreciation, scaled by assets. Heteroskedasticity-robust standard errors clustered at the city level are in parentheses. * Significance at 10%, ** Significance at 5%, *** Significance at 1%.
<table><caption>Table 7: Effects of cash flow on Pollution Abatement in Credit-Constrained Firms</caption><thead><tr><th rowspan="2"></th><th colspan="3">Dependent variable:</th></tr><tr><th>(1)<br>Abatement capacity</th><th>(2)<br>SO2 removed</th><th>(3)<br>SO2 removal per hour</th></tr></thead><tbody><tr><td>log(cash flow)</td><td>0.015**<br>(0.007)</td><td>0.133***<br>(0.019)</td><td>0.046**<br>(0.020)</td></tr><tr><td>log(liabilities to asset)</td><td>0.022***<br>(0.008)</td><td>0.118<br>(0.072)</td><td>0.082**<br>(0.032)</td></tr><tr><td>log(total asset)</td><td>0.087***<br>(0.011)</td><td>0.804***<br>(0.065)</td><td>0.304***<br>(0.042)</td></tr><tr><td>log(employment)</td><td>-0.029***<br>(0.010)</td><td>0.023<br>(0.049)</td><td>-0.096**<br>(0.036)</td></tr><tr><td>log(age)</td><td>-0.107***<br>(0.027)</td><td>-0.297<br>(0.215)</td><td>-0.303***<br>(0.093)</td></tr><tr><td>age sqr</td><td>0.048***<br>(0.008)</td><td>0.416***<br>(0.071)</td><td>0.142***<br>(0.028)</td></tr><tr><td>soe</td><td>-0.057**<br>(0.024)</td><td>-0.111<br>(0.096)</td><td>-0.217***<br>(0.066)</td></tr><tr><td>log(cash flow) × credit constraint</td><td>0.013**<br>(0.006)</td><td>0.060***<br>(0.019)</td><td>0.032*<br>(0.017)</td></tr><tr><td>firm</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>industry-year</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>city</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>69,762</td><td>69,762</td><td>69,762</td></tr><tr><td>R<sup>2</sup></td><td>0.732</td><td>0.782</td><td>0.703</td></tr></tbody></table>

This table reports estimates of equations 2 and 3. *Abatement Capacity* refers to the firm's equipment designed to mitigate SO2 emissions. *SO2 Removed* quantifies the annual amount of SO2 mitigated by the firm through end-of-pipe solutions following production. *SO2 Removal Per Hour* represents the rate at which SO2 is eliminated, measured in kilograms per hour. *cash flow* is net income plus depreciation, scaled by assets. *credit constraints* is a dummy variable taking the value of 1 if the industry is financially dependent. Heteroskedasticity-robust standard errors clustered at the city level are in parentheses. \* Significance at 10%, \*\* Significance at 5%, \*\*\* Significance at 1%.
<table><caption>Table 8: TCZ and SPZ cities in China</caption><thead><tr><th>City</th><th>Code</th><th>TCZ</th><th>SPZ</th><th>City</th><th>Code</th><th>TCZ</th><th>SPZ</th></tr></thead><tbody><tr><td>Linyi</td><td>3713</td><td>1</td><td>0</td><td>Tongling</td><td>3407</td><td>0</td><td>0</td></tr><tr><td>Suihua</td><td>2312</td><td>1</td><td>0</td><td>Tianshui</td><td>6205</td><td>1</td><td>0</td></tr><tr><td>Wuzhong</td><td>6403</td><td>1</td><td>0</td><td>Lanzhou</td><td>6201</td><td>0</td><td>1</td></tr><tr><td>Chuzhou</td><td>3411</td><td>1</td><td>0</td><td>Wuwei</td><td>6206</td><td>1</td><td>0</td></tr><tr><td>Fuyang</td><td>3412</td><td>1</td><td>0</td><td>Pingliang</td><td>6208</td><td>1</td><td>0</td></tr><tr><td>Suzhou</td><td>4213</td><td>1</td><td>0</td><td>Qingyang</td><td>6210</td><td>1</td><td>0</td></tr><tr><td>Huaihua</td><td>4312</td><td>1</td><td>0</td><td>Zhangye</td><td>6207</td><td>0</td><td>0</td></tr><tr><td>Zhaotong</td><td>5306</td><td>1</td><td>0</td><td>Jiuquan</td><td>6209</td><td>1</td><td>0</td></tr><tr><td>Huaian</td><td>3208</td><td>1</td><td>0</td><td>Dingxi</td><td>6211</td><td>1</td><td>0</td></tr><tr><td>Heze</td><td>3717</td><td>1</td><td>0</td><td>Jinchang</td><td>6203</td><td>0</td><td>0</td></tr><tr><td>Hezhou</td><td>4511</td><td>1</td><td>0</td><td>Longnan</td><td>6212</td><td>1</td><td>0</td></tr><tr><td>Anshun</td><td>5204</td><td>1</td><td>0</td><td>Jiayuguan</td><td>6202</td><td>1</td><td>0</td></tr><tr><td>Tongliao</td><td>1505</td><td>1</td><td>0</td><td>Shijiazhuang</td><td>1301</td><td>0</td><td>1</td></tr><tr><td>Lu'an</td><td>3415</td><td>1</td><td>0</td><td>Zhangjiakou</td><td>1307</td><td>0</td><td>0</td></tr><tr><td>Jinzhong</td><td>1407</td><td>1</td><td>0</td><td>Baoding</td><td>1306</td><td>0</td><td>1</td></tr><tr><td>Baoshan</td><td>5305</td><td>1</td><td>0</td><td>Cangzhou</td><td>1309</td><td>1</td><td>0</td></tr><tr><td>Ziyang</td><td>5120</td><td>1</td><td>0</td><td>Handan</td><td>1304</td><td>0</td><td>0</td></tr><tr><td>Nanchong</td><td>5113</td><td>1</td><td>0</td><td>Qinhuangdao</td><td>1303</td><td>1</td><td>1</td></tr><tr><td>Guang'an</td><td>5116</td><td>1</td><td>0</td><td>Langfang</td><td>1310</td><td>1</td><td>0</td></tr><tr><td>Yunfu</td><td>4453</td><td>1</td><td>0</td><td>Hengshui</td><td>1311</td><td>0</td><td>0</td></tr><tr><td>Chaozhou</td><td>4451</td><td>1</td><td>0</td><td>Xingtai</td><td>1305</td><td>0</td><td>0</td></tr><tr><td>Xuancheng</td><td>3418</td><td>1</td><td>0</td><td>Chengde</td><td>1308</td><td>0</td><td>0</td></tr><tr><td>Baicheng</td><td>2208</td><td>1</td><td>0</td><td>Tangshan</td><td>1302</td><td>0</td><td>0</td></tr><tr><td>Suqian</td><td>3213</td><td>1</td><td>0</td><td>Pingdingshan</td><td>4104</td><td>1</td><td>0</td></tr><tr><td>Bazhong</td><td>5119</td><td>1</td><td>0</td><td>Xuchang</td><td>4110</td><td>1</td><td>0</td></tr><tr><td>Ya'an</td><td>5118</td><td>1</td><td>0</td><td>Nanyang</td><td>4113</td><td>1</td><td>0</td></tr><tr><td>Huludao</td><td>2114</td><td>1</td><td>0</td><td>Xinyang</td><td>4115</td><td>1</td><td>0</td></tr><tr><td>Baise</td><td>4510</td><td>1</td><td>0</td><td>Anyang</td><td>4105</td><td>0</td><td>0</td></tr><tr><td>Matsubara</td><td>2207</td><td>1</td><td>0</td><td>Luoyang</td><td>4103</td><td>0</td><td>1</td></tr><tr><td>Bayannaoer</td><td>1508</td><td>1</td><td>0</td><td>Zhengzhou</td><td>4101</td><td>0</td><td>1</td></tr><tr><td>Wulanchabu</td><td>1509</td><td>1</td><td>0</td><td>Kaifeng</td><td>4102</td><td>1</td><td>0</td></tr><tr><td>Zhangjiajie</td><td>4308</td><td>1</td><td>0</td><td>Xinxiang</td><td>4107</td><td>1</td><td>0</td></tr><tr><td>Chongzuo</td><td>4514</td><td>1</td><td>0</td><td>Luohe</td><td>4111</td><td>1</td><td>0</td></tr><tr><td>Zhongwei</td><td>6405</td><td>1</td><td>0</td><td>Zhoukou</td><td>4116</td><td>1</td><td>0</td></tr><tr><td>Erdos</td><td>1506</td><td>1</td><td>0</td><td>Hebi</td><td>4106</td><td>1</td><td>0</td></tr><tr><td>Jieyang</td><td>4452</td><td>1</td><td>0</td><td>Zhumadian</td><td>4117</td><td>1</td><td>0</td></tr><tr><td>Chizhou</td><td>3417</td><td>1</td><td>0</td><td>Shangqiu</td><td>4114</td><td>1</td><td>0</td></tr><tr><td>Laiwu</td><td>3712</td><td>1</td><td>0</td><td>Jiaozuo</td><td>4108</td><td>0</td><td>0</td></tr><tr><td>Simao</td><td>5308</td><td>1</td><td>0</td><td>Puyang</td><td>4109</td><td>1</td><td>0</td></tr><tr><td>Lijiang</td><td>5307</td><td>1</td><td>0</td><td>Sanmenxia</td><td>4112</td><td>0</td><td>0</td></tr><tr><td>Sanya</td><td>4602</td><td>1</td><td>0</td><td>Wuhan</td><td>4201</td><td>0</td><td>1</td></tr><tr><td>Bozhou</td><td>3416</td><td>1</td><td>0</td><td>Xianning</td><td>4212</td><td>0</td><td>0</td></tr><tr><td>Hefei</td><td>3401</td><td>1</td><td>1</td><td>Xiaogan</td><td>4209</td><td>1</td><td>0</td></tr><tr><td>Bengbu</td><td>3403</td><td>1</td><td>0</td><td>Yichang</td><td>4205</td><td>0</td><td>0</td></tr><tr><td>Wuhu</td><td>3402</td><td>0</td><td>1</td><td>Xiangfan</td><td>4206</td><td>1</td><td>1</td></tr><tr><td>Anqing</td><td>3408</td><td>1</td><td>0</td><td>Huanggang</td><td>4211</td><td>0</td><td>0</td></tr><tr><td>Huaibei</td><td>3406</td><td>1</td><td>0</td><td>Jingzhou</td><td>4210</td><td>1</td><td>0</td></tr><tr><td>Huainan</td><td>3404</td><td>1</td><td>0</td><td>Shiyan</td><td>4203</td><td>1</td><td>0</td></tr><tr><td>Ma'anshan</td><td>3405</td><td>0</td><td>0</td><td>Huangyou</td><td>4202</td><td>0</td><td>0</td></tr><tr><td>Huangshan</td><td>3410</td><td>1</td><td>0</td><td>Jingmen</td><td>4208</td><td>0</td><td>0</td></tr></tbody></table>
<table><caption>Table 9: TCZ and SPZ cities in China (continued)</caption><thead><tr><th>City</th><th>Code</th><th>TCZ</th><th>SPZ</th><th>City</th><th>Code</th><th>TCZ</th><th>SPZ</th></tr></thead><tbody><tr><td>Ezhou</td><td>4207</td><td>0</td><td>0</td><td>Zunyi</td><td>5203</td><td>0</td><td>0</td></tr><tr><td>Yongzhou</td><td>4311</td><td>0</td><td>0</td><td>Liupanshui</td><td>5202</td><td>1</td><td>0</td></tr><tr><td>Chenzhou</td><td>4310</td><td>0</td><td>0</td><td>Guiyang</td><td>5201</td><td>0</td><td>1</td></tr><tr><td>Changsha</td><td>4301</td><td>0</td><td>1</td><td>Nanjing</td><td>3201</td><td>0</td><td>1</td></tr><tr><td>Changde</td><td>4307</td><td>0</td><td>0</td><td>Xuzhou</td><td>3203</td><td>0</td><td>0</td></tr><tr><td>Xiangtan</td><td>4303</td><td>0</td><td>0</td><td>Yancheng</td><td>3209</td><td>1</td><td>0</td></tr><tr><td>Hengyang</td><td>4304</td><td>0</td><td>0</td><td>Lianyungang</td><td>3207</td><td>1</td><td>1</td></tr><tr><td>Yueyang</td><td>4306</td><td>0</td><td>0</td><td>Nantong</td><td>3206</td><td>0</td><td>1</td></tr><tr><td>Zhuzhou</td><td>4302</td><td>0</td><td>0</td><td>Yangzhou</td><td>3210</td><td>0</td><td>0</td></tr><tr><td>Yiyang</td><td>4309</td><td>0</td><td>0</td><td>Zhenjiang</td><td>3211</td><td>0</td><td>1</td></tr><tr><td>Shaoyang</td><td>4305</td><td>1</td><td>0</td><td>Changzhou</td><td>3204</td><td>0</td><td>1</td></tr><tr><td>Loudi</td><td>4313</td><td>0</td><td>0</td><td>Wuxi</td><td>3202</td><td>0</td><td>1</td></tr><tr><td>Changchun</td><td>2201</td><td>1</td><td>1</td><td>Shangrao</td><td>3611</td><td>0</td><td>1</td></tr><tr><td>Siping</td><td>2203</td><td>0</td><td>0</td><td>Ji'an</td><td>3608</td><td>1</td><td>0</td></tr><tr><td>Liaoyuan</td><td>2204</td><td>1</td><td>0</td><td>Nanchang</td><td>3601</td><td>0</td><td>1</td></tr><tr><td>Jilin</td><td>2202</td><td>0</td><td>1</td><td>Ganzhou</td><td>3607</td><td>0</td><td>0</td></tr><tr><td>Tonghua</td><td>2205</td><td>0</td><td>0</td><td>Xinyu</td><td>3605</td><td>1</td><td>0</td></tr><tr><td>Xiamen</td><td>3502</td><td>0</td><td>1</td><td>Jiujiang</td><td>3604</td><td>0</td><td>0</td></tr><tr><td>Longyan</td><td>3508</td><td>1</td><td>0</td><td>Pingxiang</td><td>3603</td><td>0</td><td>0</td></tr><tr><td>Ningde</td><td>3509</td><td>0</td><td>0</td><td>Jingdezhen</td><td>3602</td><td>1</td><td>0</td></tr><tr><td>Putian</td><td>3503</td><td>1</td><td>0</td><td>Yingtan</td><td>3606</td><td>0</td><td>0</td></tr><tr><td>Sanming</td><td>3504</td><td>0</td><td>0</td><td>Yinchuan</td><td>6401</td><td>0</td><td>0</td></tr><tr><td>Zhangzhou</td><td>3506</td><td>0</td><td>0</td><td>Shizuishan</td><td>6402</td><td>0</td><td>0</td></tr><tr><td>Nanping</td><td>3507</td><td>1</td><td>0</td><td>Xining</td><td>6301</td><td>1</td><td>0</td></tr><tr><td>Quanzhou</td><td>3505</td><td>0</td><td>0</td><td>Hanzhong</td><td>6107</td><td>1</td><td>0</td></tr><tr><td>Linfen</td><td>1410</td><td>0</td><td>0</td><td>Yan'an</td><td>6106</td><td>1</td><td>0</td></tr><tr><td>Taiyuan</td><td>1401</td><td>0</td><td>1</td><td>Baoji</td><td>6103</td><td>1</td><td>1</td></tr><tr><td>Datong</td><td>1402</td><td>0</td><td>0</td><td>Xianyang</td><td>6104</td><td>1</td><td>1</td></tr><tr><td>Yuncheng</td><td>1408</td><td>0</td><td>0</td><td>Weinan</td><td>6105</td><td>0</td><td>0</td></tr><tr><td>Changzhi</td><td>1404</td><td>1</td><td>0</td><td>Xi'an</td><td>6101</td><td>0</td><td>1</td></tr><tr><td>Xinzhou</td><td>1409</td><td>1</td><td>0</td><td>Shangluo</td><td>6110</td><td>1</td><td>0</td></tr><tr><td>Shuozhou</td><td>1406</td><td>0</td><td>0</td><td>Tongchuan</td><td>6102</td><td>0</td><td>0</td></tr><tr><td>Jincheng</td><td>1405</td><td>1</td><td>0</td><td>Mianyang</td><td>5107</td><td>0</td><td>1</td></tr><tr><td>Luliang</td><td>1411</td><td>0</td><td>0</td><td>Chengdu</td><td>5101</td><td>0</td><td>1</td></tr><tr><td>Yangquan</td><td>1403</td><td>0</td><td>0</td><td>Deyang</td><td>5106</td><td>0</td><td>0</td></tr><tr><td>Qujing</td><td>5303</td><td>0</td><td>0</td><td>Yibin</td><td>5115</td><td>0</td><td>0</td></tr><tr><td>Kunming</td><td>5301</td><td>0</td><td>1</td><td>Meishan</td><td>5114</td><td>0</td><td>0</td></tr><tr><td>Yuxi</td><td>5304</td><td>0</td><td>0</td><td>Dazhou</td><td>5117</td><td>1</td><td>0</td></tr><tr><td>Lincang</td><td>5309</td><td>1</td><td>0</td><td>Leshan</td><td>5111</td><td>0</td><td>0</td></tr><tr><td>Beijing</td><td>1101</td><td>0</td><td>1</td><td>Luzhou</td><td>5105</td><td>0</td><td>0</td></tr><tr><td>Nanning</td><td>4501</td><td>0</td><td>1</td><td>Zigong</td><td>5103</td><td>0</td><td>0</td></tr><tr><td>Liuzhou</td><td>4502</td><td>0</td><td>0</td><td>Guangyuan</td><td>5108</td><td>1</td><td>0</td></tr><tr><td>Guigang</td><td>4508</td><td>0</td><td>0</td><td>Neijiang</td><td>5110</td><td>0</td><td>0</td></tr><tr><td>Beihai</td><td>4505</td><td>1</td><td>1</td><td>Suining</td><td>5109</td><td>0</td><td>0</td></tr><tr><td>Guilin</td><td>4503</td><td>0</td><td>1</td><td>Panzhihua</td><td>5104</td><td>0</td><td>0</td></tr><tr><td>Qinzhou</td><td>4507</td><td>1</td><td>0</td><td>Tianjin</td><td>1201</td><td>0</td><td>1</td></tr><tr><td>Wuzhou</td><td>4504</td><td>0</td><td>0</td><td>Dandong</td><td>2106</td><td>1</td><td>0</td></tr><tr><td>Laibin</td><td>4513</td><td>0</td><td>0</td><td>Shenyang</td><td>2101</td><td>0</td><td>1</td></tr><tr><td>Hechi</td><td>4512</td><td>1</td><td>0</td><td>Yingkou</td><td>2108</td><td>1</td><td>0</td></tr><tr><td>Fangchenggang</td><td>4506</td><td>0</td><td>0</td><td>Jinzhou</td><td>2107</td><td>0</td><td>0</td></tr></tbody></table>
<table><caption>Table 10: TCZ and SPZ cities in China (continued)</caption><thead><tr><th>City</th><th>Code</th><th>TCZ</th><th>SPZ</th><th>City</th><th>Code</th><th>TCZ</th><th>SPZ</th></tr></thead><tbody><tr><td>Tieling</td><td>2112</td><td>1</td><td>0</td><td>Meizhou</td><td>4414</td><td>1</td><td>0</td></tr><tr><td>Fuxin</td><td>2109</td><td>0</td><td>0</td><td>Heyuan</td><td>4416</td><td>1</td><td>0</td></tr><tr><td>Chaoyang</td><td>2113</td><td>1</td><td>0</td><td>Shanwei</td><td>4415</td><td>0</td><td>0</td></tr><tr><td>Dalian</td><td>2102</td><td>0</td><td>1</td><td>Qingyuan</td><td>4418</td><td>0</td><td>0</td></tr><tr><td>Liaoyang</td><td>2110</td><td>0</td><td>0</td><td>Dongguan</td><td>4419</td><td>0</td><td>0</td></tr><tr><td>Anshan</td><td>2103</td><td>0</td><td>1</td><td>Harbin</td><td>2301</td><td>1</td><td>1</td></tr><tr><td>Panjin</td><td>2111</td><td>1</td><td>0</td><td>Mudanjiang</td><td>2310</td><td>1</td><td>0</td></tr><tr><td>Benxi</td><td>2105</td><td>0</td><td>0</td><td>Heihe</td><td>2311</td><td>1</td><td>0</td></tr><tr><td>Fushun</td><td>2104</td><td>0</td><td>0</td><td>Qiqihar</td><td>2302</td><td>1</td><td>0</td></tr><tr><td>Weifang</td><td>3707</td><td>0</td><td>1</td><td>Jiamusi</td><td>2308</td><td>1</td><td>0</td></tr><tr><td>Qingdao</td><td>3702</td><td>0</td><td>1</td><td>Jixi</td><td>2303</td><td>1</td><td>0</td></tr><tr><td>Yantai</td><td>3706</td><td>0</td><td>1</td><td>Daqing</td><td>2306</td><td>1</td><td>1</td></tr><tr><td>Zaozhuang</td><td>3704</td><td>0</td><td>0</td><td>Shuangyashan</td><td>2305</td><td>1</td><td>0</td></tr><tr><td>Jinan</td><td>3701</td><td>0</td><td>1</td><td>Hegang</td><td>2304</td><td>1</td><td>0</td></tr><tr><td>Dezhou</td><td>3714</td><td>1</td><td>0</td><td>Qitaihe</td><td>2309</td><td>1</td><td>0</td></tr><tr><td>Liaocheng</td><td>3715</td><td>1</td><td>0</td><td>Hohhot</td><td>1501</td><td>0</td><td>0</td></tr><tr><td>Binzhou</td><td>3716</td><td>1</td><td>0</td><td>Hulunbeier</td><td>1507</td><td>1</td><td>0</td></tr><tr><td>Jining</td><td>3708</td><td>0</td><td>0</td><td>Chifeng</td><td>1504</td><td>0</td><td>0</td></tr><tr><td>Tai'an</td><td>3709</td><td>0</td><td>0</td><td>Baotou</td><td>1502</td><td>0</td><td>1</td></tr><tr><td>Weihai</td><td>3710</td><td>1</td><td>1</td><td>Wuhai</td><td>1503</td><td>0</td><td>0</td></tr><tr><td>Zibo</td><td>3703</td><td>0</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>Dongying</td><td>3705</td><td>1</td><td>0</td><td></td><td></td><td></td><td></td></tr><tr><td>Rizhao</td><td>3711</td><td>1</td><td>0</td><td></td><td></td><td></td><td></td></tr><tr><td>Shanghai</td><td>3101</td><td>0</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>Urumqi</td><td>6501</td><td>0</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>Karamay</td><td>6502</td><td>1</td><td>0</td><td></td><td></td><td></td><td></td></tr><tr><td>Ningbo</td><td>3302</td><td>0</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>Hangzhou</td><td>3301</td><td>0</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>Huzhou</td><td>3305</td><td>0</td><td>0</td><td></td><td></td><td></td><td></td></tr><tr><td>Wenzhou</td><td>3303</td><td>0</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>Quzhou</td><td>3308</td><td>0</td><td>0</td><td></td><td></td><td></td><td></td></tr><tr><td>Shaoxing</td><td>3306</td><td>0</td><td>0</td><td></td><td></td><td></td><td></td></tr><tr><td>Zhoushan</td><td>3309</td><td>1</td><td>0</td><td></td><td></td><td></td><td></td></tr><tr><td>Jinhua</td><td>3307</td><td>0</td><td>0</td><td></td><td></td><td></td><td></td></tr><tr><td>Lishui</td><td>3311</td><td>0</td><td>0</td><td></td><td></td><td></td><td></td></tr><tr><td>Jiaxing</td><td>3304</td><td>0</td><td>0</td><td></td><td></td><td></td><td></td></tr><tr><td>Chongqing</td><td>5001</td><td>0</td><td>0</td><td></td><td></td><td></td><td></td></tr><tr><td>Shaoguan</td><td>4402</td><td>0</td><td>0</td><td></td><td></td><td></td><td></td></tr><tr><td>Foshan</td><td>4406</td><td>0</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>Zhanjiang</td><td>4408</td><td>0</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>Canton</td><td>4401</td><td>0</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>Shenzhen</td><td>4403</td><td>0</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>Shantou</td><td>4405</td><td>0</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>Maoming</td><td>4409</td><td>1</td><td>0</td><td></td><td></td><td></td><td></td></tr><tr><td>Jiangmen</td><td>4407</td><td>0</td><td>0</td><td></td><td></td><td></td><td></td></tr><tr><td>Zhuhai</td><td>4404</td><td>0</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>Zhongshan</td><td>4420</td><td>0</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>Zhaoqing</td><td>4412</td><td>0</td><td>0</td><td></td><td></td><td></td><td></td></tr><tr><td>Huizhou</td><td>4413</td><td>0</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>Yangjiang</td><td>4417</td><td>1</td><td>0</td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><caption>Table 11: Credit constraint from the annual surveys of Chinese manufacturing firms</caption><thead><tr><th></th><th>CIC</th><th>Value</th></tr></thead><tbody><tr><td>General Purpose Machinery</td><td>35</td><td>-2.59</td></tr><tr><td>Tobacco</td><td>16</td><td>-1.54</td></tr><tr><td>Measuring Instruments and Machinery for Cultural Activity and Office Work</td><td>41</td><td>-1.34</td></tr><tr><td>Textile Wearing Apparel, Footwear, and Caps</td><td>18</td><td>-1.32</td></tr><tr><td>Leather, Fur, Feather and Related Products</td><td>19</td><td>-1.11</td></tr><tr><td>Metal Products</td><td>34</td><td>-0.93</td></tr><tr><td>Printing, Reproduction of Recording Media</td><td>23</td><td>-0.8</td></tr><tr><td>Beverages</td><td>15</td><td>-0.72</td></tr><tr><td>Processing of Timber, Manufacture of Wood, Bamboo, Rattan, Palm, and Straw Products</td><td>20</td><td>-0.72</td></tr><tr><td>Transport Equipment</td><td>37</td><td>-0.72</td></tr><tr><td>Furniture</td><td>21</td><td>-0.65</td></tr><tr><td>Artwork and Other Manufacturing</td><td>42</td><td>-0.62</td></tr><tr><td>Textile</td><td>17</td><td>-0.48</td></tr><tr><td>Processing of Food from Agricultural Products</td><td>13</td><td>-0.47</td></tr><tr><td>Plastics</td><td>30</td><td>-0.47</td></tr><tr><td>Medicines</td><td>27</td><td>-0.44</td></tr><tr><td>Electrical Machinery and Equipment</td><td>39</td><td>-0.44</td></tr><tr><td>Chemical Fibers</td><td>28</td><td>-0.41</td></tr><tr><td>Articles For Culture, Education and Sport Activity</td><td>24</td><td>-0.4</td></tr><tr><td>Foods</td><td>14</td><td>-0.32</td></tr><tr><td>Non-metallic Mineral Products</td><td>31</td><td>-0.29</td></tr><tr><td>Special Purpose Machinery</td><td>36</td><td>-0.27</td></tr><tr><td>Rubber</td><td>29</td><td>-0.26</td></tr><tr><td>Raw Chemical Materials and Chemical Products</td><td>26</td><td>-0.23</td></tr><tr><td>Smelting and Pressing of Non-ferrous Metals</td><td>33</td><td>-0.1</td></tr><tr><td>Communication Equipment, Computers and Other Electronic Equipment</td><td>40</td><td>0.02</td></tr><tr><td>Paper and Paper Products</td><td>22</td><td>0.07</td></tr><tr><td>Smelting and Pressing of Ferrous Metals</td><td>32</td><td>0.33</td></tr><tr><td>Processing of Petroleum, Coking, Processing of Nuclear Fuel</td><td>25</td><td>0.62</td></tr></tbody></table>

Based on Chinese data is calculated at the 2-digit Chinese Industrial Classification (CIC) level. 29 Data available in the years 2004–2006 in the NBC Database. Computation used the aggregate rather than the median external finance dependence at the 2-digit industry level. One reason is the median firm in the annual surveys of Chinese manufacturing firms database often has no capital expenditure.