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Impacts of ISO 14001 adoption on firm performance:

Evidence from China

ABSTRACT: Based on a firm-level data set of Chinese firms for the period of 2004

to 2007, we estimate the impact of ISO 14001 adoption on firms’ performance. The

results show that ISO 14001 adoption has insignificant effect on firms’ financial

performance (e.g. ROA, ROE, ROS) largely due to simultaneous increase of sales and

cost. Despite the insignificant impact on financial performance, ISO 14001 adoption

may bring some non-financial benefits to firms, such as better access to the global

market and less inspections by the government, which may explain why Chinese

firms actively pursue ISO 14001 adoption.

Keywords: ISO 14001, firm performance, China, non-financial benefits

JEL Classification: D22, L51, Q56

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1. Introduction

With the urgent need on environmental protection, governments in many countries

have established many command-and-control policies, such as emission performance

standards and abatement technology mandates, to regulate pollution. However, such

command-and-control policies are often criticized as inflexible, heavy-handed,

cost-ineffective, and less incentive on induced dynamic technology progress. In the

face of the limitations on these rigid regulations, governments have started to

encourage more voluntary approaches, which are typically viewed as more flexible,

acceptable to the private sectors, and cost-effective (Arimura, Darnall and Katayama,

2011). Since mid-1990s, a variety of voluntary actions in environmental management

have been adopted by firms around the world, among which the most important

regime is the introduction of environmental management systems (EMS).

In recent years, the ISO 14001, a type of EMS and a certified standard designed

by the International Organization for Standardization (ISO), has been implemented

and spread quickly across the world (Ambec and Lanoie, 2008). With its fast growing

popularity researchers have begun to examine two questions: (1) What motivate firms

to adopt ISO 14001 standard? (2) What are the impacts of adopting ISO 14001 on

firms’ performance, in other words, does it “pays to be green”? Since the ISO 14001

standard was firstly adopted by firms in developed countries, literature is mainly

concentrated on the developed world. For instance, Hamilton (1995) and Khanna,

Quimio and Bojilova (1998) examined ISO 14001 adoption in the United States;

Canon-de-Francia and Garces-Ayerbe (2009) and Ziegler, Schröder and Rennings

(2007) studied the issue on European firms; while Arimura, Darnall and Katayama

(2011) was mainly focused on Japanese firms.

With the surge of enterprises in recent decades, environmental protection issues

in developing countries have become quite important to the world. Virtually, an

increasing amount of firms from developing countries, especially China, have

implemented EMS like ISO 14001. According to official statistics of ISO, at the end

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of 2005, the number of Chinese firms adopted ISO 14001 reached the top second in

the world. This number further surpassed Japan and became the top one at the end of

2008. However, studies on ISO 14001 are still quite limited among developing

countries, including China. Therefore, this paper intends to shed some light on the

consequences of ISO 14001 adoption to firms in developing countries based on a

survey sample of Chinese manufacturing enterprises.

Although scholars have thoroughly discussed the subject of ISO 14001 standards

and firm behaviors from both economics and management aspects, research findings

of how ISO 14001 adoption affect firms’ financial performance were widely diverged.

The first strand of studies showed that ISO 14001 adoption would encourage firms’

proactive behaviors, thus improved firm’s performance in terms of Tobin's Q, return

on assets (ROA), return on sales (ROS), return on equity (ROE), market share and so

on (e.g., Arimura et al. 2008; Nishitani 2011; Dowell et al., 2000; Jacobs et al., 2010

etc.). These literature argued that by introducing new environmentally responsible

products, green firms (signaling by ISO 14001 adoption), can gain differentiated

advantages which lead to higher profitability and larger market share. What’s more,

green firms may involve more efficient production processes, bringing cost

advantages by reducing input and waste-disposal costs (Hart and Ahuja, 1996). This

strand of conclusion has been further supported by a meta-analysis study by Orlytzky

et al. (2003), that corporate social responsibilities usually has a positive impact on

corporate financial performance. The second group of paper (e.g., Hamilton, 1995;

Khanna et. al, 1998), however, indicated negative effect of ISO 14001 adoption on

financial performance. The reason was that consumers and investors did not boost

sales revenue or stock market valuation to offset the increased costs of environmental

proactive behaviors, especially in the short term (Hamilton, 1995). The last collection

of studies claimed that, there was no clear relationship between the ISO 14001

adoption and firm’s performance (e.g., Hart and Ahuja, 1996; Gilley, Worrell,

Davidson and EI-Jelly, 2000; King and Lenox, 2001).

The diverse results may partially due to different estimation methods, or firms

from different sectors, as well as countries’ special background (King and Lenox,

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2001). For example, ISO14001 may bring some positive effects on certain sectors,

while the opposite impacts on the other sectors, so the aggregate net effect would

depend on the combination of the two, thereby little or no effects on the whole

economy may arise in some literature when samples were scattered across many

industrial sectors (Elsayed and Paton, 2005).

Comparing the recent surge of ISO 14001 adoptions in developing countries like

China, studies on how they affect firms’ financial performance in developing

countries are relatively rare and lagged. For example, Yang, Hong, and Modi (2011)

used a cross-country sample and also showed that environment management practices

affect performance of firms in developed countries and developing countries

differently. Firms from developed countries like Europe show a bigger and

statistically significant impact of lean manufacturing on financial performance, while

firms from developing countries like China do not. Besides, firms from developing

countries, like China, Argentina, and Turkey, predict inconclusive results to the

relationship between environmental management practice and financial performance.

In this paper, we combine a survey data of environmental management practices

of manufacturing firms with China’s annual industrial firm census data to explore the

impact of ISO 14001 adoption on firm performance. We first investigate whether ISO

14001 adoption affects firm performance, and do not find any significant effect of

ROA, ROS or ROE. To understand the insignificant effect, we further decompose the

impacts of ISO 14001 adoption on both revenue and cost side. Our finding predicts

that ISO 14001 adoption would increase sales and costs of a focal firm at about the

same magnitude, which suggests that increasing sales and costs may offset with each

other and the overall net effect is negligible. We also divide industries into three

sub-categories: heavy, middle and low pollution industries, to see if our conclusion

may differ regarding to different sectors. We find that ISO 14001 adoption

significantly affects sales and costs of firms in heavy and middle pollution industries,

but no effects on low pollution industries. However, for firms in heavy and middle

pollution industries, the net effects of ISO 14001 adoption on financial performance

(i.e. ROA, ROE, and ROS) are still marginally significant or insignificant.

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Given the overall insignificant impacts of ISO 14001 adoption on financial

performance, one might wonder what is the driving force that so many Chinese firms

actively adopt ISO 14001. One possible explanation is that, in developing countries

like China, ISO 14001 adoption may bring firms some implicit benefits rather than

directly boosting the financial performance, and the mechanisms might be different

from those in developed countries. Specifically , due to data availability we mainly

focus on two possible mechanisms: (1) ISO 14001 certification might help firms

expand global market through increased exports; (2) ISO 14001 certification may

relieve firms from government environmental inspection. These potential benefits

from ISO 14001 certification may not be reflected in the financial report immediately,

but may still explain why many firms in developing countries like China are so active

in adopting ISO 14001 .

The paper is organized as follows. In Section 2, we give a brief introduction of

data and empirical methodologies. Section 3 presents the main results and robustness

checks, and we conclude in section 4.

2. Data and Methods

2.1. Data

To examine the effect of ISO 14001 adoption on firms’ financial performance, we

combined a firm-level Corporation Social Responsibility (CSR) survey data set which

was conducted by China Center for Economic Research (CCER) with China’s

industrial firm census data constructed by National Bureau of Statistics annually. This

CSR survey represents 1,268 manufacturing firms randomly selected from 12 cities

based on China’s industrial firm census. The 12 sampled cities constitute a reasonable

representation of Chinese cities in terms of geographic locations and economic

development levels1. In each sampled city, the survey was designed to cover about

                                                              1The 12 sampled cities distribute among coastal, central and western regions. According to per-capita GDP of 2005, these 12 cities are grouped into three categories, i.e. high, middle and low income. The income gap is roughly 2 times between any two neighboring groups. 

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100 firms with annual sales over 5 million RMB in manufacturing industries. The

majority of our sample were domestic privately owned firms, with a percentage of

68.7%. The rest were about equally distributed among SOEs and foreign owned

firms2. In terms of export, 37.2% of sampled firms were engaged in exporting, and the

average share of export to total sales was 16.1%. In this CSR survey, respondents

were asked for two specific questions on ISO 14001 adoption: (1) whether the firm

has adopted ISO 14001? (2) In which year the firm adopted ISO 14001? Based on

these two questions, we created a dummy variable to indicate a firm whether adapted

ISO 14001 in a specific year. The overall percentage of firms adopted ISO 14001 was

14% in our sample.

As the CSR survey sampled firms from China’s industrial firm census data of

year 2004, we can combine the CSR survey with China’s industrial firm census data

of years 2004-2007 to get firms’ financial information after the year of ISO 14001

information. As a result, we created an unbalanced panel dataset with 4184 firm-year

observations. We get financial performance indicators, such as total sales, total output,

value added, and exported output from China’s annual industrial firm census data. The

summary statistics of variables are reported in Table 1.

---------------------------------

Insert Table 1 about here

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2.2.Panel Regression Method

The first purpose of this study is to examine the effects of firm’s ISO14001

adoption on firms’ financial performance. With our panel dataset, the basic model

specification can be expressed as follows:

0 1 itit it j p i t itY ISOAdoption X (1)

where i, j, p, t are subscripts representing firm, 2-digit industry, province and year,

respectively. itY is a series of firm’s financial performance measures, including, ROA

                                                              2Since firms of Hong Kong, Macau, and Taiwan (HMT) ownership only took up a small fraction, we categorized them into foreign owned.

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(i.e. return on assets, calculated by dividing net profit to total assets), ROE (return on

equity, calculated by dividing net profit to total equity), and ROS (return on sales,

calculated by dividing net profit to sales revenue). itISOAdoption is our key

explanatory variable which indicates whether firm i adopted ISO14001 in year t. itX is

a vector of control variables, including firm size, ownership type, financial risk,

advertising expenditure, and exporting ratio in total revenue. Firm size is measured by

the logarithm of firms’ total employment. We constructed three dummy variables -

state-owned firms, domestic private-owned firms, and foreign-owned firms - to

control firms’ ownership type. Financial risk is measured by the debt-to-asset ratio.

Advertising expenditure is the logarithm of firms’ advertising expenditure. Exporting

ratio is measured as firm’s exporting sales divided by total sales. We also control for

several industry-level factors that may affect firm’s financial performance, including

2-digit industry concentration ratio (Herfindahl index) and industry growth rate within

a city. We also control for 2-digit industry dummies, province dummies, and year

dummies.

Random-effect and fixed-effect models are alternative choices for our model

specifications. We conduct Hausman test to examine the consistency of the random

effect estimators. Since the Hausman test statistic is greater than the critical

Chi-squared value of 3.84, fixed-effect models are preferred. One econometric issue

we need to be cautious is that some of our control variables from the CSR survey is

time invariant, like pollution cost in 2004, or the dummy variable of whether firm has

an environmental logo, these variables are fully collinear with firm fixed-effect and

cannot be estimated. Therefore, following Hausman and Taylor (1981), we deploy the

Hausman-Taylor model which allows us to control for firm fixed effect as well as

time-invariable variables.

We further decomposed financial performance indicators into two components,

including market performance, represented by sales, value added, total output and

scale of assets; and operational efficiency, represented by total cost and environmental

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cost3. We choose the same specification of regression (1), except that the outcome itY

changes to market performance or operational efficiency.

2.3.Propensity Score Matching Method

Since parameter estimation is often criticized for its incapability of overcoming

endogenous problem (King and Lenox, 2001; Berchicci and King, 2010;

Gonzalez-Benito and Gonzalez-Benito, 2005), we also use the propensity score

matching method, a non-parametric estimation method developed by Rosenbaum and

Rubin (1983), as our alternative econometric strategy.

We first classify our sample into two groups, namely the treatment group and the

control group. The former is composed of firms that adopt ISO 14001, while the latter

is composed of the non-adoption counterparts. The second step is to calculate

propensity scores, which measure the extent of matching of two groups in

multi-dimensions. We controlled several factors that may affect the propensity of

firms to implement ISO 14001, including firm size, age, and etc. Specifically, the

propensity score is defined as “the conditional probability of receiving a treatment

given pre-treatment characteristics” by Rosenbaum and Rubin (1983), i.e.

p( )=Pr[D=1|X]=E[D|X]X . Where X is the multidimensional vector of characteristics

of the control group, and D is an indicator for ISO adoption. To estimate propensity

scores, we follow Dehejia and Wahba (2002) and Becker and Ichino (2002) to use a

logit model specified as below:

i i

exp( )p( )=Pr[D =1|X ]=

1+exp( )i

ii

XX

X

(2)

With calculated propensity scores, we then use the nearest-neighbor matching

methods to search the closest control sample, both backwards and forwards, from the

estimated propensity score values of the treatment group. Finally, we use bootstrap to

obtain robust variance for the average effect of treatment on the treated (ATT). ATT

                                                              3 Due to data availability, we only have pollution cost in 2004, and environmental operation cost in 2005.  

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can be estimated by the differences of the potential outcomes of the treatment group

and the control group, that is,

1i 0i i 1i 0i i

1i i i 0i i i i

ATT=E[Y -Y |D =1]=E{E[Y -Y |D =1,p( )]

=E{E[Y |D =1,p(X ]-E[Y |D =0,p(X |D =1]}iX

） ） (3)

where 1iY and 0iY represent the potential outcomes of the treatment group and the

control group, respectively.

3. Estimation Results

In this section, we first present the results of panel regression with the full

sample, investigating the impacts of ISO 14001 adoption on firm’s financial

performance, sales, and cost. Then we divide our sample into three sub-groups

according to pollution level to investigate how the impacts of ISO 14001 adoption

vary across industries with different pollution level. After reporting regression results,

we also show results by the propensity score matching method. Finally, we further

explore the implicit benefits for firms from ISO certification.

3.1. Financial Performance after ISO 14001 adoption

Table 2 reports regression results of the impacts of ISO 14001 adoption on firms’

financial performance. The dependent variables in Columns 1-3 are ROA, ROE and

ROS, respectively. Although the adoption of ISO 14001 has a positive effect on firms’

ROA in the current year, the coefficient is significant only at a marginal level of 10

percent. However, the effects of ISO 14001 adoption on firms’ ROE and ROS in the

current year are insignificant. Our findings are consistent with several prior studies.

The insignificant impact of ISO 14001 adoption on ROE could be attributed to the

fact that ROE does not include debt but reflects equity capital only (Iwata and Okada,

2011). For firms with debts, ROE is always larger than ROA. And a higher ROE is

not always an indicator of an impressive performance. In this regard, ROA is a better

indicator of the financial performance. Besides, the insignificant of current ROS may

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suggest that the cost savings will only be captured over time (Hart and Ahuja, 1996).

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Insert Table 2 about here

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3.2. Total revenue and total cost after ISO 14001 adoption

The insignificant results of financial performance indices do not provide

persuasive evidence on the benefits of the ISO 14001 adoption. Therefore, we further

decompose the financial indicators into firm’s revenue and cost. The former is

measured by the logarithm of annual sales revenue, while the latter is the logarithm of

annual total cost.

The results are listed in Columns 4-5 of Table 2. From Column 4, we find that

firms’ adoption of ISO 14001 can improve their sales revenue by 34.5% in the current

year, with the significance at 1% level. This coefficient is not only statistically

significant, but also has a large economic magnitude. This finding is straight forward

and consistent with prior studies (Galdeano-Gomez, 2008; Judge and Douglas, 1998;

Yang, Hong and Modi, 2011). Firm’s adoption of ISO 14001 can be a signal of either

firm’s environmental friendliness or, to a certain extent, better environmental

performance. This signal will help firms use a differentiation strategy so as to exploit

niches in environmentally conscious market segments (Ambec and Lanoie, 2008).

However, in Column 5 of Table 2, this regression shows that firms’ adoption of

ISO 14001 has a significantly positive impact on firms’ total cost to achieve their

revenue with a magnitude similar to that of revenue reported in Column 4 . The

results show that although ISO 14001 adoption may increase firms’ total revenue, it

also increases firms’ total cost to achieve the revenue. There are a few reasons to

expect ISO 14001 will increase firms’ total cost by 34.8%. First, a considerable

amount of expenditure is needed to process the application and compliance of ISO

14001 standard. Typically, the cost of ISO 14001 certification can range between

$10,000 and $128,000, depending on the size of the firm and the sophistication of the

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environmental management system. As well, an annual cost between $5,000 and

$10,000 is required to maintain the ISO 14001 status (Bansal and Hunter, 2003).

Second, the cost of organizational change in the process of certification may be quite

high (Hart and Ahuja, 1996).

In sum, the regression results show that ISO 14001 adoption has positive impacts

on firms’ total revenue and total costs of similar magnitude. The results help to

explain the insignificant impacts of ISO 14001 on firms’ financial performance (e.g.

ROA, ROE, and ROS), as the benefit of rising revenue from ISO 14001 adoption was

largely offset by the simultaneously increasing in costs.

3.3. ISO 14001 adoption in industries with different pollution intensities

Elsayed and Patan (2005) pointed out although the net aggregate impact maybe

very small or even negligible, the impacts of ISO 14001 adoption may be different

across industrial sectors. Hart and Ahuja (1996) also argues heavy polluters maybe

more prone to adopt better environmental management, since it is easier to achieve

pollution reduction (i.e. “there was still a great deal of ‘low-hanging fruit’ to be

picked”). Therefore, more thorough targeted sector analysis might be important and

instructive on the heterogeneous inner motivations why firms adopt ISO 14001

certification.

In order to investigate the effects of ISO 14001 adoption among industries with

different pollution intensities, we divide the sample into three sub-groups according to

textiles of waste gas emission intensity: ‘heavy-pollution’, ‘mid-pollution’, and

‘light-pollution’ industries.

The results are reported in Columns 2-4 in Table 3. For heavy-pollution

industries, such as cement, iron-making, and petroleum refining industries, ISO 14001

adoption has a positively significant effect on ROA, but not on ROE or ROS. By

contrast, for mid- and light-pollution industries, ISO 14001 adoption does not show

any positively significant effects on financial indicators. The coefficient on ROS is

even negatively significant in light-pollution industries. From the aspect of magnitude

effects on ROA, ROE and ROS, we find the coefficients are mostly no more than 5%,

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which are economically insignificant.

Next, we compare the impact of ISO 14001 adoption on total revenue and total

cost of firms in three sub-groups. Results reported in Table 3 demonstrate that ISO

14001 adoption has positive and significant impacts on total revenue and total cost of

firms in heavy-pollution and mid-pollution industries, rather than in light-pollution

industries. It worth noticed that the marginal effects on sales and cost in heavy

pollution industry are over 50% which is much larger than those for firms in

mid-pollution industries. This result could be understood that firms from high

pollution industries value ISO standard much more than other industries. In sum,

these sub-group analyses confirm that ISO 14001 adoption has different impacts

among industries with variant pollution degrees.

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Insert Table 3 about here

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3.4. Analyses using propensity score matching method

In this part, we use propensity score matching method to analyze how ISO 14001

adoption affects firms’ financial performance, total revenue and total costs. We use the

nearest neighbor matching approach. The upper panel of Figure 1 shows the kernel

density functions of the treatment group and the control group. Clearly, the kernel

density functions of the two groups are significantly different before matching. We

choose firms from the control group to match those in the treatment group, based on

propensity scores. The lower panel of Figure 1 shows the kernel density functions of

the two groups after matching. The improved kernel density functions reveal that

characteristics of the observations in the control and treatment groups are similar. We

report some basic summary statistics of variables used in propensity score calculation

in Table 4a. In line with the previous literature, we choose firm size, registration type,

advertising fee as the explain variables. And we also include some city-level and

city-industry-level variables to control for city specific features. It’s quite clear that all

the variables between two groups are indifferent after matching, which suggests that

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the average effect of treatment on the treated (ATT) is only driven by ISO 14001

adoption.

--------------------------------------------------

Insert Figure 1 and Table 4a about here

--------------------------------------------------

We report average effect of treatment on the treated (ATT) of outcome variables

(financial performance, total revenue and total costs) in the pre-matching sample and

the post-matching sample in Table 4b. Among the three financial indicators, only the

ATT for ROS in post-matching sample is marginal significant. The loss of

significance for the ATT on ROA and ROE shows the impact disappeared after

controlling other firm characteristics using the propensity score matching method.

Further, we report ATTs for ISO14001 adoption’s impact on total revenue and total

cost in Table 4. Results show that the impacts of ISO14001 adoption on total revenue

and total cost are still statistically significant in the post-matching sample, but with

much lower magnitudes compared to those in the pre-matching sample (0.320 vs.

1.349 for total revenue; 0.279 vs. 1.349 for total costs). If comparing these results of

PSM method with simply panel regressions (0.32 vs. 0.345 for sales; 0.27 vs.0.348 for

cost), we find that the estimation coefficients are a little bit smaller, we guess the

reason comes from mitigating the bias of endogeneity or measurement error problem.

These results also confirm that ISO 14001 adoption do have statistically significant

(with lower economic significance) impacts on firms’ total revenue and total costs

with other factors controlled.

------------------------------

Insert Table 4b about here

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We also utilize the propensity score matching method to test the impacts of

ISO14001 adoption among industries with different pollution levels. Results reported

in Table 5 show that ISO14001 adoption has positive and significant impacts on all

three financial performance indicators (ROA, ROE and ROS), total revenue and total

costs for firms in heavy-pollution industries. The economic magnitude here is slightly

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smaller than that in panel regression models, which also shows the robustness of our

results. However, the coefficients for firms in mid-pollution industries become

insignificant with propensity score matching method, we believe there could be two

reasons: (1) sample size is relatively small and it’s hard to find the matching

counterpart. (2) the endogeneity problem in panel regressions may raise estimation

bias .

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Insert Table 5 about here

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3.5. Implicit benefits of ISO 14001 adoption

Our previous analyses show that ISO 14001 adoption does not significantly

enhance firm’s financial performance. One may wonder why Chinese firms still

actively pursue better EMS and apply ISO 14001 standard. Are there any implicit

benefits other than financial achievement? In this part, we focus on three possible

implicit benefits: access to global market (proxy by Export Ratio), and Government

Environmental Inspection. Export ratio data comes from China’s annual industrial

firm census data and is defined as the ratio of a firm’s exporting sales divided by its

total sales in a given year. Government environmental inspection data comes from the

CSR survey and is defined as the average frequency of government environmental

inspections in one year. As the information for government environmental inspection

is only available for year 2005, we only use predictors before 2005 in the regressions.

Besides, we add some extra control variables in the regressions, such as IPO dummy,

firm’s pollution cost in 20044, environmental logo dummy, and environment report

dummy. IPO dummy takes the value of 1 if the focal firm is a listed firm. Pollution

cost in 2004 is defined as the logarithm of pollution discharging expenditure in 2004.

Environmental logo dummy indicates whether the focal firm has an environmental

friendly logo for its product. Environment report dummy takes the value of 1 if the

                                                              4  Information of pollution cost derives from China’s annual industrial firm census data, and is only available for the year of 2004. 

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focal firm publishes annual environment report.

Results reported in Column 1 of Table 6 show that ISO 14001 adoption may

increase export ratio by 4.5%. Given the mean of export ratio in whole sample is only

11%, this growth effect is remarkable. One might expect that firms with ISO

certification are easier to expand in global market, since in many cases ISO14001

certification is the permit to developed countries.

Besides of the benefits from boosted sales and increased export ratio, firms may

also enjoy some other implicit benefits. The increasing trend of ISO 14001 may also

reflect the strengthening environmental regulations. Thus we examined the

relationship between the ISO adoption and government environmental inspections.

Column 2 in Table 6 shows that here is a negative correlation between the two. Given

the average times of government inspection is 2.86, ISO adopted firms may get 4.3%

(0.123/2.86=0.043) less inspections than those without ISO 14001 standard. This

suggests that the ISO 14001 certification might be treated as a signal of green firms

by the government, and hence relieves the focal firm from frequent government

inspections.

------------------------------

Insert Table 6 about here

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3.6. Additional robustness checks

In this section, we also conduct some other robustness check to support our findings

above. Hart and Ahuja (1996) argue that there might be a lagged relationship between

ISO 14001 and financial performance. We also try the similar robustness check and

the results are listed in Table 7. We firstly conduct an analysis to investigate the

impact of ISO14001 adoption on the one year lagged performance. As the results

show, the previous marginal effects still hold, except that the coefficient for ROS is

positively significant. And these coefficients are relatively smaller as for the impact of

ISO may decline. For the sub-sample tests, there is not too much difference across

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industries of different pollution levels, only the effect of ISO14001 adoption on ROA

is no longer significant for firms in heavy-pollution industries.

Basically, the effects of ISO14001 adoption on financial performance as well as

sales and cost get weakened after one year implementation. Still, the coefficients for

sales and cost regressions are similar. Compared with firms in light-pollution

industries, firms in heavy-pollution industries benefit more from ISO 14001 adoption.

As for the operational cost, the coefficient shows that ISO14001 adoption still has a

positively significant effect in one year’s lag. It’s probably due to the annual

maintenance fee in environmental facilities and equipment as well as increased

management costs. This pattern exists in heavy-pollution industries, in which firms

have to invest more on pollution control or pollution prevention facilities to meet the

environmental standard.

------------------------------

Insert Table 7 about here

------------------------------

We also select several alternative indicators to measure market performance,

such as value added, total output and scale of asset. These data come from China’s

annual industrial firm census data. We adopt similar Hausman and Taylor regression

model, and get consistent patterns in Table 8.

In terms of value added, the result shows that firm’s adoption of ISO14001 has a

positively significant impact in the full sample, which may arise due to the boosted

demand triggered by the ISO 14001 adoption. At the industry level, the effect of

ISO14001 adoption on value added varies across industries with different degrees of

pollution. Only the coefficient in mid-pollution industries is positively significant. By

contrast, in light pollution industries, the increase in market demand of ISO 14001

firms may not be that prominent, while in heavy-pollution industries, increase in cost

of pollution control is so significant that offsets the increase in total revenue. Our

findings are consistent with prior studies (e.g. Yamaguchi, 2008). In terms of total

output and scale of asset, similar results are found as well. In sum, ISO14001

adoption increases firm’s performance through an increase in market demand or

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improved productivity.

Another issue we need to highlight here is firm’s environmental cost. In this

regression we tried to examine how the adoption of ISO 14001 affects the pollution

cost in 2004. Our explanatory variable ISOAdoptioni excludes those firms that adopt

ISO 14001 after 2004 (including 2004 current year). Moreover, all other control

variables in this regression take the value in year 2004. Similarly, for the regression of

the ISO14001’s effect on environmental operation cost in 2005, ISOAdoptioni

excludes those firms that adopt ISO 14001 after 2005 for time consistency. And

correspondingly, all other control variables take the value in year 2005.

------------------------------

Insert Table 8 about here

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4. Concluding Remarks

China has become the leading ISO14001 certification country in the world.

According to the ISO Survey of Certifications – 2009, out of total 34,334 ISO 14001

certificates issued in 2009, China alone acquired about 16,000 certificates, that is

almost half of the total certificates. In this study, we used a firm-level data of both

CSR and financial performance to investigate how environmental management

system (EMS) implementation (ISO 14001 adoption) affects Chinese firm’s

performance. Our panel data analysis shows that ISO 14001 adoption does not have a

significantly positive impact on Chinese firm’s financial performance, in terms of

ROA, ROE, and ROS. This finding has been further supported by our alternative

non-parametric test, the propensity score matching method.

Our study is in-line with some western literature on the relationship of “best

environmental management” and corporate financial performance. For instance,

Christmann (2000) also found “best practiced” environmental management do not

significantly contribute to cost advantages, thus casting doubt on the broad application

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of these environmental certifications, which were taken for granted to protect

environmental protection and increase competitiveness. Darnall et al. (2008) also

points out the voluntary environmental management practices may be driven by

institutional pressures, which are more viewed as symbolic actions to increase

external legitimacy without necessarily improving internal efficiencies. Even though

in some cases, the external legitimacy may lead to some small financial gains, it may

only work in short term or not at all, while lacking the internal capabilities to maintain

or support better environmental management overtime is the key. Besides,

environmental management system practices seem to be influenced by national

cultures (Darnall et al. 2008). For instance, U.S. and UK are more likely to be

successful then other countries. Very few studies include China in the cross-country

comparison. But given the transparencies of information disclosure, and many

Chinese firms are not listed on the financial market, it is not surprising such an effect

would be smaller than that in the western countries. We further investigate the

underlying mechanism of this insignificant relationship between ISO 14001 adoption

and financial performance. According to our empirical study, there is a simultaneous

increase of sales and cost these two effects share a similar magnitude so as to offset

with each other. That’s why firm’s EMS implementation, i.e. ISO 14001 adoption,

appears to be unrelated to financial performance when looking at the net effects.

Firm’s ISO 14001 adoption enhances sales, because it satisfies the customers’

preference on environmentally friendly product, bringing the firm a competitive

advantage of lean production and efficient operation process, and help expand

international market. However, adopting an environmental management system and

certifying it with the international standard of ISO 14001 also induce a large amount

of cost, such as abatement equipment purchasing, pollution control and prevention

costs, ISO 14001 initial adoption and maintenance fee, process reengineering, staff

environmental training, third-party auditing and so on. Since Chinese firms lag behind

in environmental management and other regulation compliance activities, the cost of

ISO 14001 adoption may be larger than that for firms in developed countries. That is

why the conventional cost-saving benefits brought by ISO 14001 adoption

19  

(Christmann, 2000; Hart and Ahuja, 1996; Klassen and McLaughlin, 1996) have not

been observed in the Chinese context.

Given ISO 14001 adoption does not bring financial gains to Chinese firms, then

why do they adopt it so actively? We find that adopting ISO 14001 may improve

firm’s exporting performance, since the environmental standards are relatively higher

in the international market. Besides, ISO 14001 certification can act as a positive

signal for environmental friendly, so that it helps firms relieve from frequent

environmental inspections. These unique characteristics and institutional background,

which are different from the developed countries and rarely discussed in the previous

literature, explains why Chinese firms are actively pursuing the ISO 14001

certification.

Our study has several managerial implications. First, adopting ISO 14001 may

cater to customers’ preference for green product and therefore enhance firm’s sales

revenue. Moreover, it helps to cope with Green Trade Barrier set by the developed

countries, which leads to an increase of export. Besides, the ISO14001 adopting firms

can be more qualified to establish a cooperative relationship with multinational firms

like GM and Ford, which often have requirements on green supply chain.

Second, both the central and local governments are putting more efforts on

environmental protection, and the frequent environmental routine inspections are

imposed on firms. ISO 14001 adoption gives the firm an opportunity to relieve from

the pressure of governmental inspection, as a strategic move responding to

strengthening government regulations. Therefore, managers in Chinese firms have

these underlying market and institutional incentives to adopt ISO 14001.

Finally, the popularity of ISO 14001 standard in China relies more on the local

institutional settings and government policy, which is quite different from the

voluntary or mimetic adoptions in developed countries. The local governments

themselves are also involved in the political competition, attracting more firms to

adopt ISO 14001 certification, are has become important criteria in environmental

aspect for local government as well. So ISO 14001 certification is not only a

hard-won opportunity for managers to expand their potential market, but also fits into

20  

the local propaganda regime so the local governments themselves are motivated to

provide with financial and technical aids for those ISO 14001 firms due to the

underlying institutional incentives.

Finally, from the viewpoint of social welfare, we are still not capable to evaluate

whether such the promotion of ISO 14001 is desirable or not. Our paper shows the

ISO 14001 has insignificant effects on firms’ financial performance, but may bring

some non-measured hidden benefits. From the perspective of the society, it is possible

the overall cost of ISO 14001 is greater than the social benefits. Although this is an

important policy question, our data is not sufficient to examine this issue, so more

detailed society-wide cost-benefit analysis is needed in the future to bridge the gap,

which will involve not only assessment on firms, but also environmental receptors,

and associated externality calculations.

21  

References:

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Arimura, T. H., Hibiki, A., & Katayama, H. (2008). Is a voluntary approach an effective environmental policy instrument? A case for environmental management systems. Journal of Environmental Economics and Management, 55(3), 281-295.

Bansal, P., & Hunter, T. (2003). Strategic explanations for the early adoption of ISO14001. Journal of Business Ethics, 46(3), 289-299.

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Dowell, G., Hart, S., & Yeung, B., (2000). Do corporate global environmental standards create or destroy market value? Management Science, 46(8), 1059-1074. Elasyed, K., & Paton, D. (2005). The impact of environmental performance on firm performance: Static and dynamic panel data evidence. Structural Change and Economic Dynamics, 16, 395-412.

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23  

Table 1: Summary Statistics Variable Mean S.D. Min. Max.

ROA 0.07 0.09 -0.04 0.32 ROE 0.18 0.22 -0.09 0.77 ROS 0.04 0.04 -0.04 0.13 Sales (logged) 10.99 1.46 6.14 17.98 Cost(logged) 10.94 1.46 5.58 17.88 Output(logged) 11.02 1.46 6.23 18.38 Value Added(logged) 9.64 1.52 0.69 17.22 Asset(logged) 10.69 1.58 6.19 17.29 Pollution cost of year 2004 (logged) 1.25 1.94 0 9.41 Environmental operation cost of year 2005 (logged)

1.73 1.48 0 8.52

Frequency of Government Inspection 2.86 4.32 0 60 Firm size 5.29 1.19 2.2 10.52 Financial risk 0.59 0.25 -0.24 1 Advertising expenditure (logged) 1.34 2.47 0 13.46 Export Ratio 0.11 0.27 0 1 HHI in 2-digit industry 0.13 0.17 0 1 Growth rate of sales within 2-digit industry in a city

1.35 0.47 0.09 11.69

ISO 14001 adoption 0.14 0.35 0 1 IPO 0.04 0.21 0 1 SOE 0.18 0.38 0 1 Private ownership 0.65 0.48 0 1 Foreign ownership 0.2 0.4 0 1 Environmental logo 0.12 0.33 0 1 Environment report 0.25 0.43 0 1

Notes:

1. Due to data constraints, information of pollution cost is only available for 2004. And the

variable is calculated by the logarithm of pollution cost.

2. Due to data constraints, information of environmental operation cost is only available for

2005. And the variable is calculated by the logarithm of environmental operation cost.

3. Financial risk is measured by debt-to-asset ratio.

4. Firm size is the logarithm of employment.

5. Environmental logo is a dummy which takes the value of 1 if the focal firm has an

environmental friendly logo on its product.

6. Environment report is a dummy which takes the value of 1 if the focal firm publishes

annual environment report.

24  

Table 2: The Effect of ISO14001 Adoption on Firm's Performance Panel regression

[1] [2] [3] [4] [5]

ROA ROE ROS Sales Total Cost

Main Effect

Firm's adoption of ISO14001 0.014* 0.005 -0.003 0.345** 0.348**

(0.008) (0.023) (0.004) (0.076) (0.074)

Controls

HHI in 2-digit industry of the city -0.010 0.010 0.002 -0.511** -0.514***

(0.017) (0.050) (0.009) (0.163) (0.160)

Growth rate of 2-digit industry in the city -0.001 0.000 0.000 -0.032** -0.028***

(0.001) (0.002) (0.000) (0.007) (0.007)

Firm size 0.018*** 0.049*** 0.005*** 0.623** 0.604**

(0.003) (0.008) (0.002) (0.027) (0.026)

Financial leverage -0.068*** 0.205*** -0.030*** -0.303** -0.231**

(0.007) (0.020) (0.004) (0.066) (0.064)

Advertising expenditure 0.001 0.002 0.001** 0.011* 0.009

(0.001) (0.002) (0.000) (0.006) (0.006)

Export ratio -0.017** -0.013 -0.001 -0.123* -0.090

(0.008) (0.023) (0.004) (0.075) (0.073)

State ownership -0.282 -1.261 -0.053 5.952 7.027

(0.618) (2.118) (0.182) (6.893) (8.166)

Private ownership -0.365 -1.227 -0.062 3.138 4.262

(0.778) (2.856) (0.232) (8.671) (10.27)

2-digit industry dummies Y Y Y Y Y

Province dummies Y Y Y Y Y

Constant 0.696 2.593 0.019 -7.351 -9.694

(1.518) (5.321) (0.422) (17.04) (20.29)

R-squared 0.269 0.143 0.232 0.675 0.682

Number of obs. 4184 4184 4184 4184 4184

Notes:

1. *** p<0.01, ** p<0.05, * p<0.1

2. Standard errors in parentheses

25  

Table 3: The Effect of ISO14001 Adoption by Different Polluting Levels Panel regression

D.V. Full Sample Sub-sample: the degree of pollution

[1] [2] [3] [4]

Heavy pollution Mid pollution Light pollution

ROA 0.014* 0.034** 0.015 -0.021

(0.008) (0.014) (0.016) (0.018)

ROE 0.005 0.032 0.019 -0.077

(0.023) (0.040) (0.053) (0.053)

ROS -0.003 0.000 0.005 -0.0270**

(0.004) (0.007) (0.009) (0.011)

Sales 0.345** 0.531*** 0.286*** 0.088

(0.076) (0.136) (0.098) (0.133)

Cost 0.348** 0.501*** 0.289*** 0.135

(0.074) (0.136) (0.093) (0.133)

Number of obs. 4184 1868 1560 756

Notes:

1. Control variables include HHI in 2-digit industries, growth rate of 2-digit industry in the

city, firm size, firm's financial leverage, annual advertising fee, firm's exporting ratio,

ownership type, as well as dummies for regions and 2-digit industries.

2. *** p<0.01, ** p<0.05, * p<0.1;

3. Standard errors in parentheses.

26  

Table 4a: Variables used for propensity score matching

Mean %deduct T-test

Variable Sample Treated Controlled %bias bias t P-value Size Pre-matching 6.391 5.220 98.700 16.830 0.000

Post-matching 6.391 6.407 -1.400 98.600 -0.160 0.872

State ownership Pre-matching 0.232 0.201 7.500 1.290 0.197

Post-matching 0.232 0.265 -7.900 -6.000 -0.930 0.350

Private ownership Pre-matching 0.448 0.644 -40.100 -6.870 0.000

Post-matching 0.448 0.418 6.000 85.000 0.730 0.464

HHI in 2-digit

industry of a city

Pre-matching 0.181 0.133 24.400 4.630 0.000

Post-matching 0.181 0.187 -3.200 86.800 -0.360 0.721

Growth rate of 2-digit

industry in a city

Pre-matching 1.279 1.422 -14.100 -1.830 0.067

Post-matching 1.279 1.312 -3.300 76.800 -1.060 0.290

Advertising fee Pre-matching 2.570 1.197 48.400 9.590 0.000

Post-matching 2.570 2.607 -1.300 97.300 -0.140 0.890

Table 4b: Impact of ISO14001 Adoption on Firm's Performance Propensity score matching

Variable Sample Treated group Controlled group ATT s.e. t-value

ROA Pre-matching 0.066 0.065 0.000 0.005 0.080

Post-matching 0.066 0.059 0.007 0.008 0.970

ROE Pre-matching 0.175 0.177 -0.002 0.013 -0.160

Post-matching 0.175 0.155 0.020 0.022 1.090

ROS Pre-matching 0.041 0.036 0.005 0.003 1.82*

Post-matching 0.041 0.039 0.003 0.005 0.640

Sales Pre-matching 12.236 10.886 1.349 0.084 15.98***

Post-matching 12.236 11.916 0.320 0.102 2.41**

Cost Pre-matching 12.188 10.838 1.349 0.084 15.97***

Post-matching 12.188 11.909 0.279 0.108 2.12**

Notes: 1. “Pre-matching” refers to the sample without matching the ISO14001 adopting

group with the non-adoption group, and “Post-matching” refers to the sample after matching.

2. “Treated group” and “Controlled group” refer to firms with and without ISO14001 certification, respectively.

3. *** p<0.01, ** p<0.05, * p<0.1; Standard errors are calculated using Bootstrap with 300 replications.

27  

Table 5: Impact of ISO14001 Adoption by Different Polluting Levels

Propensity score matching

A、Full sample B、Heavy pollution C、Mid pollution D、Light pollution

ATT t-value ATT t-value ATT t-value ATT t-value

ROA 0.007 0.970 0.041 2.89*** 0.004 0.400 0.006 0.460

ROE 0.020 1.090 0.057 1.76* 0.002 0.080 0.000 0.010

ROS 0.003 0.640 0.024 3.56*** -0.002 -0.310 -0.010 -1.100

Sales 0.320 2.41** 0.326 2.07** 0.185 0.720 0.108 0.490

Cost 0.279 2.12** 0.239 1.74* 0.190 0.750 0.108 0.500

Notes: *** p<0.01, ** p<0.05, * p<0.1;

28  

Table 6: Implicit Benefit from ISO14001 Adoption [1] [2]

Export Ratio

Government

Environmental

Inspection

Main Effect

Firm's adoption of ISO14001 0.045** -0.123*

(0.019) (0.065)

Controls

Firm size -0.019*** 0.134***

(0.006) (0.023)

Whether the firm is a listed firm 0.081 -0.319***

(0.168) (0.104)

State ownership -0.476 -0.249***

(0.894) (0.061)

Foreign ownership 1.409 -0.319***

(1.693) (0.063)

HHI in 2-digit industry of the city 0.005 -1.029***

(0.038) (0.184)

Growth rate of 2-digit industry in the city -0.001 -0.233**

(0.002) (0.098)

Financial leverage 0.018 0.020

(0.016) (0.087)

Pollution cost of year 2004 / 0.070***

(0.012)

Whether the firm has environmental logo / -0.0325

(0.065)

Whether the firm publishes formal

environmental report

/ 0.336***

(0.047)

Export ratio / 0.024

(0.102)

2-digit industry dummies Yes Yes

Province dummies Yes Yes

Constant 1.616 -0.198

(2.754) (0.455)

R-squared 0.277 0.2153

Number of obs. 3963 916

Notes: *** p<0.01, ** p<0.05, * p<0.1; Standard errors in parentheses.

29  

Table 7: The Effect of ISO14001 Adoption in Year t+1 Panel regression

D.V. Full Sample Sub-sample: the degree of pollution [1] [2] [3] [4]

Heavy Mid Light

ROA 0.010 0.010 0.020 -0.006

i,t+1 (0.008) (0.014) (0.018) (0.018)

ROE 0.025 0.002 0.055 -0.024

i,t+1 (0.027) (0.046) (0.130) (0.051)

ROS 0.008** 0.006 0.014 -0.005

i,t+1 (0.004) (0.007) (0.021) (0.010)

Sales 0.236*** 0.292*** 0.290*** -0.002

i,t+1 (0.065) (0.094) (0.097) (0.137)

Cost 0.242*** 0.321*** 0.272*** 0.008

i,t+1 (0.065) (0.095) (0.089) (0.137)

Number of obs. 3078 1373 1149 556

Notes: 1. Control variables include HHI in 2-digit industries, growth rate of 2-digit

industry in the city, firm size, firm's financial leverage, annual

advertising fee, firm's exporting ratio, ownership type, as well as

dummies for regions and 2-digit industries. 2. *** p<0.01, ** p<0.05, * p<0.1; Standard errors in parentheses.

30  

Table 8: The Effect of ISO14001 Adoption on Alternative Performance Indicators

D.V. Full Sample Sub-sample: the degree of pollution [1] [2] [3] [4]

Heavy Mid Light

Panel analyses

Value added 0.308*** 0.546 0.444*** -0.348

(0.102) (0.457) (0.127) (0.239)

Total output 0.332*** 0.540** 0.312*** 0.008

(0.070) (0.275) (0.082) (0.159)

Asset 0.264*** 0.334*** 0.217*** 0.126

(0.058) (0.100) (0.079) (0.109)

Number of obs. 4184 1868 1560 756

Cross-sectional analyses

Pollution cost of year 2004 0.193* / / /

(0.003) / / /

Environmental operation cost 0.392** / / /

(0.000) / / /

Number of obs. 971

Notes: 1. Control variables include HHI in 2-digit industries, growth rate of 2-digit industry in the

city, firm size, firm's financial leverage, annual advertising fee, firm's exporting ratio,

ownership type, as well as dummies for provinces and 2-digit industries. 2. Due to data availability, pollution cost and environmental operation cost are examined in

cross-sectional models. Therefore, the sample size reduces to 971, and is not large enough

to carry out sub-sample test with different polluting levels. 3. *** p<0.01, ** p<0.05, * p<0.1; Standard errors in parentheses.

31  

Figure 1: Kernel Density of the Treated and Controlled Groups

0

5

10

15D

ensi

ty

0.0 0.2 0.4 0.6 0.8 1.0

Propensity Score

TreatControl

Before Matching

0

2

4

6

Den

sity

0.0 0.2 0.4 0.6 0.8 1.0

Propensity Score

TreatControl

After Matching