ISO 14001 Certification and Environmental Performance in the Quebec Pulp and Paper Industry

Philippe Barla*

First version: May 2005 (Preliminary)

Abstract

In this paper, we test if the adoption of the international norm ISO 14001 has a significant impact on environmental performance in the Quebec pulp and paper industry. Using monthly data for a panel of 38 plants over the 1997-2003 period, we show that: i) the ISO certification does not lead to a reduction in total suspended solids (TSS) emissions; ii) discharges of biological oxygen demand (BOD) appears to be significantly lower in the first year following certification; iii) this last impact does not appear to be lasting beyond the one-year window. We also show that, contrary to the group of plants that do not adopt the ISO norm, the adopting plants do not experience a significantly trendily reduction in emissions over our sample period. Key words: Environmental Management Systems, ISO 14001, Environmental Performance. JEL Classifications: Q50, Q52, Q58. * GREEN, département d’économique, Université Laval, Québec, Canada, G1K 7P4, E-mail : philippe.barla@ecn.ulaval.ca.

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ISO 14001 Certification and Environmental Performance in the Quebec Pulp and Paper Industry

Philippe Barla

1. Introduction

Environmental policy has considerably evolved over the last decades. While in the seventies

command and control regulations were the norm, incentive based mechanisms such as tradable

permits have become increasingly popular in the eighties. More recently, governments and

industry lobbies have favoured the recourse to voluntary approaches in dealing with

environmental challenges. The basic idea is that business would adopt efficient pro-active

environmental initiatives as a result of raising public pressures and the threat of more stringent

governmental regulations. Besides lowering abatement costs, these voluntary measures would

also help reducing environmental policy monitoring and enforcement costs. One obvious

question that remains, at this stage, largely open is whether or not these voluntary actions actually

reduce pollution.

The growing interest for the international standard ISO 14001 relating to the

implementation of Environmental Management Systems (EMSs) illustrates very well the trend

toward voluntary self-regulation. As of December 2003, over 36,000 organizations worldwide

had voluntary established an EMS that was certified to respect the mandatory prescriptions of the

ISO 14001 norm. An EMS may be viewed as a set of management rules and procedures

designed at reducing the environmental impacts of an organization. It involves for example

reviewing and documenting the organization activities having a negative impact on the

environment, developing an environmental policy statement and a plan to achieve environmental

objectives. These targets are however, for the most part, decided internally by the organization.

In other words, the ISO norm does not prescribe any specific objective except for respecting the

existing regulations. Critics have therefore argued that ISO 14001 could be used a marketing

device for improving corporate image without having any significant impact on environmental

performance [17]. In this paper, we test the impact of ISO 14001 on the effluents of a sample of

38 Quebec pulp and paper plants using monthly data over the period 1997-2003. The

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environmental performances of plants that have been registered ISO 14001 are compared to their

performance before certification and to the environmental performance of plants that are not (yet)

certified. Our results suggest that while the ISO 14001 certification does not have a significant

impact on total suspended solids (TSS), it does reduce the plant discharges of biological oxygen

demand (BOD) in the first year following certification. This impact does not however appear to

be lasting beyond the one-year window. Furthermore, the groups of plants that do not adopt the

ISO norm over our sample period experience an overall significant trendily reduction in

emissions (TSS and BOD) contrary to the group of plant that adopt the norm.

The empirical literature on corporate environmentalism is still quite limited and has been

mostly studying the adoption determinants of self regulations (see for example [7], [8], [13]). A

few recent works have nevertheless examined the impact of voluntary initiatives and particularly

EMS on environmental performance. Using a cross section of 150 US companies, Anton et al.

[1] find that the level of comprehensiveness of an EMS has a significant negative impact on the

toxic release emission rate. The reduction is particularly important for firms having high

pollution intensity in the past. Using a survey conducted among 236 Mexican manufacturer

plants, Dasgupta et al. [3] shows that plants with a high degree of conformity with ISO guidelines

are more likely to report (self-assessed) compliance with environmental regulations. However,

these two studies do not directly test the impact of ISO certification since they use data preceding

the official publication of the norm. Our main contribution is therefore to specifically test if the

adoption of an ISO 14001 certified EMS has a significant impact on environmental performance.

Moreover, the panel structure of our dataset allows us to control for plant specific unobservable

factors that may be correlated with the adoption decision thereby reducing missing variable

biases.

This paper also relates to the literature on the determinants of environmental performance.

Several studies have examined the impacts of monitoring and enforcement activities (inspections,

legal proceedings, fines) on environmental performance ([4],[6],[10],[11],[12]). More recently,

[5] compares these traditional enforcement strategies with those based on information disclosure

(i.e. publication of “worst” polluters lists). Of particular relevance for our analysis is the study by

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Laplante and Rilstone [11] showing that inspections significantly reduces emissions in the

Quebec pulp and paper industry.

The rest of this paper is organized as follows. In Section 2, we provide a general

background on the ISO standard describing how it may affect environmental performance and we

briefly depict the Quebec pulp and paper industry. The data are described in Section 3 with some

preliminary evidence. The empirical model and the results are presented in Section 4 and 5

respectively. We conclude in Section 6.

2. Background

2.1 The ISO 14001 standard

The ISO 14001 requirements are contained in a short document entitled Environmental

management systems – Specification with guidance for use published by the International

Organization for Standardization (ISO). It was officially introduced in 1996 after having been

developed for a few years by a technical committee composed of “environmental experts”

representing over 50 national standards organizations. The ISO 14001 standard is part of a set of

guidelines that together constitute the ISO 14 000 series. ISO 14001 is however the only

mandatory guidelines that can be audited for certification (or registration) by an accredited third

party. These guidelines are applicable to any kind of organizations of various nationalities and

size.

We now briefly describe the main requirements contained in the ISO 14001 standard (for

a detailed description see [18]). To be certified ISO 14001, an EMS should to established and

operate using the following five steps. First, the high management of the organization must adopt

an environmental policy statement containing an explicit commitment to i) comply with all the

applicable regulations and other obligations and ii) continuously reduce and prevent pollution.

The statement should also provide a framework for establishing environmental targets and

objectives to be achieved by the organization. Second, in a planning stage, the organization

reviews the significant environmental impacts of its activities. It identifies all its legal,

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contractual and voluntary environmental obligations and establishes procedures for meeting

them. Using these reviews and on the basis of the policy statement, the organization then defines

objectives and targets to be achieved through an implementation plan. The plan must assign

responsibilities within the organization and describe the resources dedicated to the plan as well as

its implementation schedule. Third, the plan is executed by, for example, setting up workers

training programs or developing operational and communication procedures designed at

preventing accidental pollution. Procedures must also be set up to properly documents the EMS

activities. Fourth, procedures and routines must be designed for controlling and monitoring the

organization environmental impacts. Corrective procedures must be established to deal with

cases of non-conformity. The EMS should also elaborate detailed procedures (frequency,

methodology) for regular auditing. To be certified, an accredited third party must regularly audit

the organization EMS in order to access if it respects the ISO requirements. Fifth, top

management periodically re-evaluates the operations of the EMS modifying them to continuously

improve their effectiveness.

An ISO-14001 certified EMS may therefore improve environmental performance through

i) the requirement of respecting all applicable environmental regulations, ii) the investment in

documenting and analyzing the plant environmental impacts and iii) the development of

systematic, written and standardized checklist-type procedures to reduce and prevent pollution.

In our analysis, the first aspect is likely to be of little importance since as we will show below, the

compliance rate with regulation is particularly high in the Quebec pulp and paper industry.

Several potential benefits may justify the decision by businesses to adopt the ISO 14001

norm. First, it may help the firm improving its corporate image toward consumers, investors and

the surrounding communities affected by the environmental externalities. Empirical studies (see

[1] and [7]) and managers’ survey [20] suggest that this is a significant factor favouring the

implementation of an EMS and its ISO certification. Being certification may also help firms

reducing their liability risks by demonstrating “due diligence” thereby reducing insurance costs.

For example, [1] and [7] show that US companies with a large number of Superfund sites (a

proxy for liability costs awareness) are more likely to adopt comprehensive EMS. Evidences also

suggest that adoption may result from regulatory pressures through regulator inspections for

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example (see [3] and [7]). In some cases, regulations may provide direct incentives for the

adoption of an EMS and its ISO certification (see [9]). Moreover, the adoption of voluntary

measures by a significant portion of an industry could pre-empt more stringent environmental

regulations. The process required by the ISO standard may also lead to costs savings by

improving input productivity or by reducing waste disposal and pollution abatement costs (for a

case study see [2]). Finally, certified companies may pressure their suppliers to become

themselves certified.1

On the cost side, implementing and maintaining an ISO 14001 EMS involve internal costs

mostly administrative and external auditing and marketing expenditures. Szymanki and Tiwari

[17] reports costs of up to US$ 100,000 per year for US companies. For Canada, Yiridoe et al.

[20] find that, on average, organizations spend about 2% of total expenditures on obtaining and

maintaining their ISO 14001 EMS. Their estimates also suggest significant economies of scale in

adopting costs with the initial implementation cost varying from about CND$ 40,000 for small

organizations (less than 100 employees) to CND$ 75,000 for large organization (more than 500

employees). Scale is in fact a very significant factor for explaining the probability of adopting an

EMS. Other factors that increase the probability of adoption by reducing implementation costs

are: past experience with the ISO 9000 quality management standard and the average level of

worker education [13]. On the other hand, firm’s level of indebtedness reduces the likelihood of

adoption.

2.2 The Quebec pulp and paper industry

Over the last century, a vibrant pulp and paper industry has developed in Quebec based on

abundant forests and inexpensive energy. As of December 2002, 60 plants belonging to 28

different companies employed over 35,000 employees. Quebec produces about 30% of the

Canadian and 3% of the world paper and cardboard production. Its weight is event larger for

newsprint where it represents 10% of the world production. Close to 60% of its paper production

is exported to the US.

1 So far, empirical evidence suggests that this effect is not particularly strong (see [3], [13] and [20]). It may however become increasingly important as the number certifications grow. For example, Ford Motors is now requiring ISO 14001 3rd Party certification from its suppliers with manufacturing facilities.

5

Beside its economic importance, this sector is also a major source of pollution particularly

of effluent discharges. Given the availability of data (see Section 3), our analysis will focus on

two conventional indicators for water pollution in this industry namely Total Suspended Solids

(TSS) and Biological Oxygen Demand (BOD). TSS is a direct measure of the quantity of solid

waste (wood fibbers, ashes etc.) rejected in the production waters while BOD is an indirect

indicator of the environmental impacts of the effluent.2 Primary treatment using gravity is useful

to control TSS while a secondary treatment based on biological process is needed to reduce BOD.

Both aspects may also be controlled up streamed by reducing the effluents through the recycling

of the process water. The industry is also responsible for other water pollutants such as BPC,

dioxins and furans as well as air emissions (particles, SO2, sulphates, greenhouses gases) and

solid wastes.

Since the early seventies, the Quebec industry has been submitted to progressively more

stringent environmental regulations by both the federal and provincial governments leading to

significant abatement investments. For example over the 1992-1999 period, the industry spent

over 1.5 billions for improving its environmental performance (62% was directed toward water

pollution control). For the most part, regulations have taken the form of industry specific

performance standards. The last revision of the norms pertaining to BOD and TSS dates back to

1992. The norms are set in kilograms per ton of production and involve both a limit on the

average monthly and the daily maximal discharge. The limits are more severe for plants built

after 1992 and do not apply to plants rejecting their effluents in the municipal waste water system

(for an overview see [11]). Over our sample period, the compliance rate with regulations for

BOD and TSS has always exceeded 95%.

Interestingly, in 1993, the provincial government introduced a gradual process of plant

specific environmental certification that involves some of the steps required by ISO 14001. All

the plants have now received their first certification, which is valid for five years. This first

generation of attestations gathers in one document all the environmental regulations the plant has

2 BOD is an indicator of the total quantity of oxygen required over a five-day period for decomposing the organic matters contained in the water waste. High BOD may be damaging for the environment by asphyxiating the aquatics life.

6

to respect. It also describes a process for characterizing over a one-year period the plant effluent

using detailed indicators. This evaluation should eventually be used to define new plant specific

environmental targets that will be included in the second-generation attestation. These new

regulatory requirements have therefore provided strong incentives for plants to actually

implement an EMS. In fact, the Quebec Forest Industry Council [14] reports that in 2001 all the

Quebec plants were operating an EMS. Unfortunately, we do not dispose of any information on

the EMSs that are not ISO certified. In this paper, we are therefore not testing the impact of the

adoption of an EMS but rather examining if the ISO 14001 certification of an EMS is making a

difference. This precision is important to remember when interpreting our results.

3. Data and Preliminary Evidence

The main source of the data used in this paper is the Quebec Ministry of the Environment, which

collects information in order to access the industry conformity with existing environmental

regulations [15]. Under the Règlement sur les fabriques de pâtes et papiers, plants have to

continuously measure their effluent and report their daily discharges of TSS and BOD.

Obviously, the validity of these self-reported discharges could be questioned. However there are

a number of reasons to believe that these data are relatively accurate [11]. First, the Ministry

realizes each year five inspections for validating the accuracy of the reported data and five

inspections of the pollution monitoring equipments. For the two years for which we have the

information (2001 and 2002), the rate of conformity was 100%. The Ministry also realized each

year twenty tests of toxicity to validate the data reported by the plants (the conformity rate was

respectively 90% and 95% for 2001 and 2002). Second, the technologies used by these plants as

well as their productive capacity are relatively well known by the regulator making reporting

grossly inaccurate figures difficult. Third, reporting fraudulent information is a serious criminal

offence. Fourth, in the past, conformity rates with regulations have not always been as high as

those prevalent for our sample period suggesting that plants do report non-compliance. The

Ministry database also includes information on the production processes, the types of output

produced and the production for each plant. Unfortunately, this last information is confidential.

We were however able to recover from other variables the annual output, which combines with

the monthly water flow helps us construct a proxy for monthly production (see Section 4).

7

The date of ISO certification was obtained from the ISO 14001 Registered Company

Directory Database produced by QSU Publishing Company. The information was updated and

validated by directly contacting the plants.

We include in our sample all the Quebec plants that have been continuously operating

during the 1997-2003 period. In other words, we eliminated plants that have closed during our

sample period.3 We eliminate plants that produce paper based building materials as they operate

in a rather different market. We also exclude plants that reject their effluents into the municipal

water system for these plants are not in charge of pollution control of their effluent and are not

submitted to the same regulations. These criteria eliminate 21 plants leaving us with a sample of

38 mills observed over 84 months.4

Figure 1 presents the pattern of ISO 14001 certification over our sample period. The first

two plants were certified in 1998 and, as of December 2003, 18 plants were operating an ISO

14001 EMS. Overall, our sample includes 548 ISO 14001 observations from a total of 3192.

Figure 2 and 3 compare the average emission rates (i.e. emissions of TSS and BOD per unit of

output) for the group of plants that become ISO over our sample period (referred from now on as

the adopters) and the group that do not (the non-adopters).5 At this stage, it is worth noting that

i) adopters appear to have higher emission rates and ii) while for TSS, the adopters appear to be

catching up with the environmental performance of the non-adopters, the opposite seems to occur

for BOD. Obviously, several factors other than the ISO certification may determine the level of

emissions.

4. The Empirical Model

Our empirical specification is inspired by former empirical analysis of environmental

performance determinants especially [11] and [12]. It is also guided by the theoretical framework

3 We keep however plants that have experienced considerable output reduction as a result of strikes. Also, for nine observations, we used a regression on 12 months lags to complete the missing information. 4 We also use the data for 1996 when lags are required (see Section 4). 5 The average for the adopter group includes observations for plants that are not yet ISO but that will eventually obtain their certification over our sample period.

8

proposed by Dasgupta et al. [3] to explain variations in the environmental performance of plants

submitted to similar regulations. In this setting, the cost-minimizing level of a plant’s emissions

is determined by comparing the Marginal Abatement Cost (MAC) with the Expected Marginal

Penalty (EMP). Figure 4 illustrates the optimal level of pollution. The MAC is quite standard

and reflects the increasing marginal cost associated with reducing emissions. The EMP captures

the expected price of emissions, which increases with the level of emissions reflecting the raising

pressures on the plant as pollution augments even if it fully complies with formal regulations.

Indeed as emissions increases, not only regulatory scrutiny may intensify (e.g. marginal price

increases as a result of more frequent inspections by the regulator) but also informal pressures by

consumers, investors and local communities are likely to intensify. The equilibrium level of

emissions will therefore depend upon the various factors affecting the MAC and the EMP.

Based on this framework, our baseline reduced form emission equations take the following form:

)1(

___

)()()(

,12

1,

3

1,

8

1,,3,2,1

11

1,,3,2,112,0,

tir

irl

ill

jijjtititi

ktkktititititi

REGIONQTYPE

PROCESSSTRIKEADOPTERSTADOPTERSNONT

MONTHQLogINSPISOELogELog

ηθ

λγγγ

δβββφα

+++

++++

+++++=

∑∑

∑

∑

==

=

=−

The variable Ei,t measures the discharges of either BOD or TSS (in tons) by plant i over month t.

We also use in alternative specifications the emission rates (i.e. emissions per unit of production).

Following [11], [12], the emission level is regressed on its 12 months lag to capture the

underlying inertia associated with installing new equipments or adjusting the production process.

The variable ISO is a dichotomous variable taken the value 1 if plant i is certified ISO 14001 at

time t and 0 otherwise. As mentioned above, the implementation of an ISO certified EMS

implies reviewing the production process and developing systematic procedures, which may shift

the MAC curve down thereby reducing the equilibrium level of pollution.

Several researches have shown the significant impact of formal regulatory pressures on

environmental performance (see [5],[11],[12]). Increased regulatory scrutiny is likely to shift the

EMP up thereby leading to reduction in pollution. In line with these works, we introduce the

variables INSP that measures the number of toxicity inspections over the last six months at plant

9

i. Each year, the Ministry of the Environment carries out twenty toxicity inspections, which

consist in leaving 10 rainbow trout for 96 hours in a plant undiluted effluent. The test fails if

more than 50% of the trout die. These inspections may therefore trigger an overall improvement

in the plant performance including in terms of BOD and TSS.6 The absolute level of

discharge will also depend upon the plant production Qit. As already mentioned, the plant

production data are unfortunately confidential, however we were able to recover from the other

environmental data the annual production. To construct Qit, we combine the annual production

data with the monthly level of water flow, which should reflect variation in production.

Specifically for a specific year,

∑=

= 12

1,

,,

tti

tiiti

W

WYQ

with Yi the annual production of plant i and Wi,t the amount of water rejected by plant i during

month t.

Seasonal effects are captured by introducing month specific dummies (MONTH).

Temperature variations may affect the efficiency of the pollution control equipments (shifting the

MAC) as well as influencing public pressures (and thus EMP), as water pollution may be more

problematic during the warm seasons when rivers are used for leisurely activities. In view of the

preliminary evidence reported in figure 2 and 3, we allow the time trend to differ for the adopters

and the non-adopters. More precisely, we define the binary variable P_ISO, which takes the

value 1 (for all t) if plant i eventually becomes ISO during our sample period and zero otherwise.

T_ADOPTERS is equal to TREND x P_ISO while T_NON_ADOPTERS = TREND x (1-P_ISO).

Ignoring this possibility could bias our results since the variable ISO may be picking up trend

differences between the two groups (ISO takes the value 1 mostly at the end of our sample

period). If there is no difference, the coefficients on these two variables will not be statistically

different. The production of a few plants was disturbed by strikes that lasted several weeks and

even months. We control for these events by including a binary variable STRIKE. To control for

the type of production process, we include eight dummies (PROCESS) that are further defined in

6 Note that we tried several alternative specifications for capturing the impact of these inspections (e.g. current inspection plus lags). The results of these alternative specifications are very close to those reported here especially for the variable of interest in the paper namely ISO. We also tried specifications that include the other type of inspections (accuracy of the self-reporting emissions and inspections of the pollution monitoring equipments). These inspections had no significant impacts emissions.

10

table 1. We distinguish four types of outputs (QTYPE) namely newsprint (the reference group),

pulp, cardboard and other papers.7 Controlling for the technology and the nature of the output

produced is important as it may affect emissions by shifting the MAC curve. The variables

REGIONS indicates the plant area of operation. It is based on the administrative division of the

Quebec territory into 17 regions. The plants in our sample are present in 13 different regions.

These variables may capture variations in EMP. For example, plants located in highly populated

area are likely to experience more intense pressures to control emissions.

Finally, ti,η represents the error term. Given the panel structure of our data, we assume

that t,iiti, εµη += i.e. is composed of a plant specific component iµ and a plant-time varying term

ti,ε that is assumed, at this stage, IID Normal ti,ε ~N(0, ). 2εσ iµ accounts for any plant specific

unobservable factors. It also accounts for unobservables that vary little over our time period but

that may be quite different across plants. This may include for example the socio-economic

characteristics of the surrendering communities (education level, income), the importance of

R&D activities, the average age of the plant workers or their education level.8 These variables

may affect the plant environmental performance by shifting the EMP or the MAC. In the absence

of these plant effects, the model could be estimated by OLS. If they are present, the appropriate

estimation procedure depends upon whether these effects are correlated or not with the included

variables. In the latter case, they may be treated as random effects (RE) and the model may be

estimated by GLS. Otherwise, they should be treated as fixed effects (FE) (for details see [19]).9

In the FE specification, the coefficients on the variables that are time invariant such as REGION

cannot be identified. If we allow for the possibility that the plant effects are correlated with the

included explanatory variables, we do however maintain that the all the explanatory variables

including ISO may be treated as strictly exogenous.10

7 Note that most plants produce several output types implying several QTYPE being set to one. 8 See [1] and [3] for evidence on the impact of these variables using cross-section data. 9 In short panels, the introduction of lag endogenous variables (e.g. Log(Ei,t-12)) as an explanatory variable could lead to biased estimators (see [19]). This should however not be a major issue here as T is relatively large (T=84) (see [19]). Furthermore, in our case, the cure proposed in the literature could worse since it is based on first differencing the model in order to eliminate the plant specific effects and then proceed with an instrumental-variable procedure. First differencing may be problematic since our variable of interest (ISO) is a binary variable. Indeed, (ISOi,t-ISOi,t-1) will only be one when the plant becomes certified and zero otherwise. If the impact of ISO on emissions does not occur precisely at the certification time, we will be unable to detect its impact. 10 In other words, for all the included variables, we assume that stXE siti ,,0),( ,, ∀=ε . Clearly this could be problematic since past emission levels may affect the decision to adopt ISO (feedback effects). However, finding

11

Before turning to the estimation results, it is useful to examine some descriptive statistics.

Table 1 reports the variables mean and standard deviation both for the whole sample and for the

two groups of plants in our sample i.e. adopters and non-adopters. These figures confirm that

ISO adopters are polluting somewhat more than non-adopters. This could however be linked to

differences in the two groups characteristics. Adopters are larger (both in terms of production

and water flows), they also tend to be producing pulp and newsprints while non-adopters are

more often involve in cardboard and other paper production. Relating to these differences are

variations in the type of technology used. These observations strongly suggest that the variable

ISO is correlated with several of the included variables making the RE specification likely

inadequate.

5. The Results

5.1 The Base Case

Table 2 reports the estimation results for the BOD and TSS emission equations. Beside the RE

and FE estimates, we also report the OLS results. As expected, several coefficient estimates are

affected when plant specific effects are taken into account. For both equations in the RE

specification, the Breusch-Pagan test strongly rejects the hypothesis of . Similarly, in the

FE specification, the hypothesis of no plant specific effects is rejected for both cases.

Furthermore, for several variables including ISO, the RE and FE estimates are quite different

suggesting correlation between the plant effects and the included variables. Using a Hausman

type specification test, we strongly reject the null hypothesis of no correlation for both BOD and

TSS.

02 =ασ

11 We therefore focus our analysis on the FE results. As [11] and [12], the coefficients on

the 12 months lag emissions are positive and statistically significant. For BOD, ISO certification

is associated with a statistically significant reduction in emissions of about 8.6%. The ISO

certification does not however appear to have a significant impact on TSS discharges. The same

good instruments for ISO is difficult and there are mounting evidence that inference based on weak instruments may be very misleading [16]. 11 The test is based on the RE model augmented by the within transformation of the explanatory variables (see [19]). In the absence of correlation, these additional variables will not be jointly statistically significant. We obtain respectively for BOD and TSS F-values of 135.24 and 75.47 with (7, 3144) degrees of freedom clearly rejecting the null hypothesis of no correlation.

12

hold true for the impact of INSP: inspections significantly reduce BOD (by 7%) but not TSS.

This is very much in line with the results obtained by [11]. The elasticity of emissions with

respect to production is close to one particularly for BOD. However, when the between plant

variations is not completely swept out (as in the FE model), the coefficient on this variable is less

than one (OLS and RE estimates). Large plants appear to proportionally pollute less than smaller

plants. The coefficient on the trend variable differs whether the plant is among the ISO adopting

group or not. For the adopters, there is no significant trend while non-adopters have experienced

an annual decline of about 7.5% per year in both BOD and TSS emissions. Differences between

the two groups in technology and output mix could explain this major difference. Not

surprisingly, plants that experience a strike see their emissions drop considerably. Emissions

levels are also significantly lower during the warmer months.

5.2 Alternative Specification and Further Results

In this section, we explore our results robustness to changes in specifications and estimation

methods and provide complementary results. First, the nature of results is very similar if we use

the emission rates as endogenous variable instead of the absolute emission levels.12 Second, so

far our specification assumes that the ISO certification has a one-time effect. The impact of ISO

may be more gradual and may actually start before certification. We therefore estimate a model

where the variable ISO is replaced by the three binary variables PRE_ISO, YEAR1_ISO and

POST_ISO, which are set to one if, at time t, a plant is respectively within 12 months of obtaining

certification, in its first year following certification or has been ISO for more than one year. The

FE results for the main variables are reported in column (1) of tables 3 and 4. It appears that the

reduction in BOD emissions mostly takes place during the first year following the

implementation of the ISO EMS. This impact does not seem to last as the coefficient on

POST_ISO is close to zero and not statistically significant. Next, since it appears that the two

groups of plant (ISO adopters and non adaptors) may be quite different, we re-estimate the model

using only observations for plants that eventually get certified (column (2) of tables 3 and 4). The

results are very similar to those obtained using the two groups. So far we have assumed that the

variance of the residuals is similar across plants. This may not be the case as figures 2 and 3

12 Results available from the author.

13

suggest. Furthermore, serial correlation may be present. We re-estimate the model allowing the

variance of the residuals to differ across each plant (i.e. Var ) and allowing for an AR(1)

serial correlation structure (i.e.

2, )( iti σε =

tititi ,1,, ξρεε += − ). Our main conclusions are unaffected by these

changes (see column 3 tables 3 and 4).

6. Discussion and Conclusion

Despite growing interest in voluntary measures to address environmental externalities, there is, so

far, few empirical evidence of the effectiveness of these initiatives. One good example is the

international norm ISO 14001 that is becoming increasingly popular but whose environmental

impact has not yet been extensively evaluated. In this paper, we address this issue by testing if

the ISO certification of an EMS has a significant impact on two traditional indicators (BOD and

TSS) in the Quebec pulp and paper industry. Using a very rich panel dataset covering 38 plants

over the 1997-2003 period, we find rather mix evidence on the effectiveness of the ISO

certification. While it appears that BOD emission significantly decline as a result of the

certification, we find evidence that this decline is not permanent as emission returns to normal

after twelve months. ISO certification is not associated with any significant changes in TSS

emissions. Furthermore, we find that non-adopting plants have experienced overtime more

significant emission reductions than plants that eventually adopt ISO.

While one could conclude that the ISO norm is ineffective, several limitations of our

analysis could temper this conclusion. First, both BOD and TSS have long been regulated in the

pulp and paper industry and compliance has been relatively high. It may therefore well be that an

ISO – EMS put more emphases on newly regulated pollutants or even on contaminants that are

not yet legally controlled (for example green house gases). Unfortunately, data for other

pollutants are either unavailable or measured much less frequently making an econometric

analysis more perilous. Second, recall that our analysis specifically tests the impact of the ISO

certification of an EMS. The adoption of an EMS could therefore well have a significant

impact.13 Third, our maintain hypothesis of strict exogeneity of the regressors could bias our

results. Poor environmental performance could trigger the decision to adopt ISO thereby creating

feedbacks effects from past emissions to the ISO variable. In such circumstances, the FE

13 Note that our ISO variable should however be somewhat correlated with the adoption of an EMS.

14

estimators will be bias [19]. It is unclear however if the biases go against the effectiveness of

ISO in reducing emissions. These limitations suggest extensions for future research.

15

05

1015

20N

umbe

r of I

SO 1

4001

pla

nts

1996 1997 1998 1999 2000 2001 2002 2003

Figure 1. Number of ISO 14001 plants per year

.000

5.0

01.0

015

.002

.002

5N

on a

dopt

ers/

Ado

pter

s

Jan 1996 Jan 1998 Jan 2000 Jan 2002 Jan 2004date

Non adopters Adopters

Figure 2. Average BOD emission per unit of output (ton/ton)

.001

5.0

02.0

025

.003

.003

5.0

04N

on A

dopt

ers/

Ado

pter

s

Jan 1996 Jan 1998 Jan 2000 Jan 2002 Jan 2004date

Non Adopters Adopters

Figure 3. Average TSS emission per unit of output (ton/ton)

16

Figure 4. Determination of the Equilibrium Level of Emissions.

EMPMAC

Emissionse*

$

17

Table 1. Descriptive Statistics Variable Sample Adopters Non-Adopters

TSS emissions (tons) 52.20 (88.97)

66.44 (116.8)

39.38 (49.16)

TSS rate (emission/Q) 0.0021 (0.0024)

0.0024 (0.003)

0.0019 (0.0016)

BOD emissions (tons) 27.69 (56.80)

39.10 (77.58)

17.43 (22.20)

BOD rate (emission/Q) 0.0011 (0.0014)

0.0013 (0.0019)

0.0009 (0.0007)

Quantity of Water Rejected (W) (cube meter) 1252 (1103)

1590 (1134)

947 (974)

ISO 0.17 (0.37)

0.36 (0.48)

0 (0)

Number of Inspections in the last six months (INSP)

0.19 (0.40)

0.19 (0.40)

0.20 (0.40)

Production (Q) (ton) 21802 (13939)

25679 (12657)

18312 (14120)

Strike (STRIKE) 0.006 (0.078)

0.007 (0.088)

0.004 (0.068)

PROCESS 1 (1 = Mechanical Pulp) 0.10 (0.30)

0.05 (0.22)

0.14 (0.35)

PROCESS 2 (1 = Chemithermomechanical Pulp) 0.21 (0.40)

0.27 (0.44)

0.15 (0.35)

PROCESS 3 (1 = Kraft Pulp) 0.21 (0.40)

0.22 (0.41)

0.20 (0.40)

PROCESS 4 (1 = Other Chemical Pulp) 0.14 (0.35)

0.05 (0.22)

0.22 (0.41)

PROCESS 5 (1 = Recycled Pulp) 0.35 (0.47)

0.33 (0.47)

0.37 (0.48)

PROCESS 6 (1 = Pulp Bought) 0.38 (0.48)

0.33 (0.47)

0.42 (0.49)

PROCESS 7 (1 = De-inking) 0.21 (0.40)

0.27 (0.44)

0.15 (0.35)

PROCESS 8 (1 = Bleaching) 0.21 (0.40)

0.27 (0.44)

0.15 (0.35)

PROCESS 9 (Base: Thermomechanical Pulp) 0.38 (0.48)

0.50 (0.50)

0.27 (0.44)

QTYPE 1 (1 = Pulp) 0.26 (0.44)

0.33 (0.47)

0.20 (0.40)

QTYPE 2 (1 = Other Paper) 0.44 (0.49)

0.33 (0.47)

0.55 (0.49)

QTYPE 3 (1 = Cardboard) 0.21 0.11 0.30

18

(0.40) (0.31) (0.45) QTYPE 4 (Base: Newsprint) 0.39

(0.48)

0.44 (0.49)

0.35 (0.47)

Number of observations 3192 1512 1680

19

Table 2. Results for the Base Case. BOD Emissions TSS Emissions Explanatory Variables

OLS RE FE OLS RE FE

Constant -3.119*** ( 0.3478)

-4.107*** (0.3999)

-6.227*** (0.5555)

-3.794*** (0.3252)

-4.539*** (0.3788)

-5.071*** (0.5793)

Log( )12, −tiE 0.5244*** ( 0.0141)

0.2782*** (0.0149)

0.1222*** (0.0148)

0.4379*** (0.0149)

0.2992*** (0.0154)

0.2051*** (0.0158)

ISO -0.173*** ( 0.0416)

-0.0227 (0.0393)

-0.0861** (0.0448)

-0.1667*** (0.0385)

-0.0338 (0.0384)

-0.0236 (0.0467)

INSP -0.079*** ( 0.0290)

0.0786*** (0.0256)

-0.0772*** (0.0229)

-0.0194 (0.0269)

-0.0154 (0.0251)

-0.0117 (0.0239)

Q 0.3636*** ( 0.0238)

0.6161*** (0.0341)

1.0243*** (0.0573)

0.5220*** (0.0233)

0.6809*** (0.0321)

0.9371*** (0.0598)

T_NON_ADOPTERS 0.0004 ( 0.0005)

-0.0018*** (0.0004)

-0.0065*** (0.0005)

-0.0005 (0.0005)

-0.0018*** (0.0004)

-0.0046*** (0.0005

T_ADOPTERS 0.0005 ( 0.0005)

-0.0018*** (0.0005)

0.0007 (0.0008)

-0.0006 (0.0005)

-0.0021*** (0.0005)

-0.00110 (0.0008)

STRIKE -1.752*** ( 0.1536)

-1.4733*** (0.1435)

-0.8506*** (0.1534)

-0.8423*** (0.1426)

-0.6590*** (0.1399)

-0.2701** (0.1599)

MONTH 1 0.0243 ( 0.0556)

0.0579 (0.0488)

0.0690 (0.0437)

-0.0081 (0.0514)

-0.00015 (0.0479)

-0.00118 (0.0455)

MONTH 2 0.0280 ( 0.0555)

0.0504 (0.0487)

0.0743 (0.0436)

-0.0366 (0.0514)

-0.0337 (0.0479)

-0.02613 (0.0454)

MONTH 3 0.0266 ( 0.0555)

0.0347 (0.0487)

0.0254 (0.0436)

-0.0546 (0.0514)

-0.0647 (0.0479)

-0.0803* (0.0455)

MONTH 4 -0.0164 ( 0.0554)

-0.0240 (0.0487)

-0.0403 (0.0435)

-0.0498 (0.0513)

-0.0619 (0.0478)

-0.0769* (0.0454)

MONTH 5 -0.0403 ( 0.0555)

-0.0669 (0.0488)

-0.1105** (0.0439)

-0.0544 (0.0514)

-0.0737 (0.0479)

-0.1026** (0.0457)

MONTH 6 -0.0398 ( 0.0555)

-0.0915* (0.0489)

-0.1549*** (0.0440)

-0.0828 (0.0514)

-0.1081** (0.0480)

-0.1432*** (0.0459)

MONTH 7 -0.0558 ( 0.0556)

-0.1237** (0.0492)

-0.2197*** (0.0450)

-0.0845 (0.0515)

-0.1201** (0.0482)

-0.1756*** (0.0469)

MONTH 8 -0.0797 ( 0.0556)

-0.1481*** (0.0491)

-0.2439*** (0.0450)

-0.1040** (0.0515)

-0.1397*** (0.0482)

0.1949*** (0.0469)

MONTH 9 -0.0590 ( 0.0554)

-0.1120*** (0.0488)

-0.1752*** (0.0439)

-0.0934* (0.0513)

-0.1213** (0.0479)

-0.1577*** (0.0458)

MONTH 10 -0.0507 ( 0.0554)

-0.0918* (0.0487)

-0.1422*** (0.0437)

-0.0667 (0.0513)

-0.0830* (0.0478)

-0.1080** (0.0455)

MONTH 11 0.0049 ( 0.0553)

-0.0084 (0.0486)

-0.0272 (0.0434)

-0.0443 (0.0512)

-0.0551 (0.0477)

-0.0679 (0.0453)

PROCESS 1 -0.0091 ( 0.0595)

0.0248 (0.0829)

- 0.02018*** (0.0555)

0.1993** (0.0773)

-

PROCESS 2 0.3520*** ( 0.0545)

0.6087*** (0.0854)

- 0.2133*** (0.0498)

0.3666*** (0.0766)

-

20

PROCESS 3 0.1777 ( 0.1150)

0.3360* (0.1950)

- -0.6128*** (0.1064)

-0.6789*** (0.1743)

-

PROCESS 4 0.3291*** ( 0.0767)

0.4331*** (0.1101)

- -0.1805** (0.0710)

-0.0792 (0.1010)

-

PROCESS 5 -0.0074 ( 0.0555)

0.0505 (0.0892)

- -0.0391 (0.0515)

0.0208 (0.0804)

-

PROCESS 6 0.0491 ( 0.0413)

0.0837 (0.0605)

- 0.0618 (0.0382)

0.1430** (0.0552)

-

PROCESS 7 0.1412* ( 0.0612)

0.0667 (0.1035)

- -0.0471 (0.0567)

-0.0993 (0.0926)

-

PROCESS 8 -0.491*** ( 0.1810)

-0.9114*** (0.2958)

- -0.5967*** (0.1673)

-0.6297** (0.2653)

-

QTYPE 1 0.9917*** ( 0.1420)

1.531*** (0.2324)

- 1.6207*** (0.1340)

1.8752*** (0.2099)

-

QTYPE 2 0.0538 ( 0.0376)

0.0706 (0.0639)

- -0.0824** (0.0348)

-0.0882 (0.0571)

-

QTYPE 3 -0.193*** ( 0.0541)

-0.2646*** (0.090)

- -0.2452*** (0.0502)

-0.3355*** (0.0808)

-

R2: 0.81 ^2ασ =0.008

^2εσ =0.248

H0: =0 2ασ

2χ (1)=5386

R2: 0.88 H0: all iα =0 F(37,3136)= 77.78

R2: 0.84 ^2ασ =0.007

^2εσ =0.271

H0: =0 2ασ

2χ (1)=2646

R2: 0.88 H0: all iα =0 F(37,3136)= 55.05

21

Table 3. Alternative Specification Results – BOD emissions. Explanatory Variables

(1) (2) (3)

Constant -3.450*** (0.4174)

-8.314*** (0.8690)

-5.517*** (0.5846)

Log( )12, −tiE 0.1225*** (0.0148)

0.1085*** (0.0201)

0.0828** (0.0153)

ISO - -0.0746* (0.0411)

-0.1083** (0.0526)

PRE_ISO -0.0193 (0.0467)

- -

YEAR1_ISO -0.1403** (0.0566)

- -

POST_ISO -0.0204 (0.0703)

- -

INSP -0.0778*** (0.0229)

-0.0450 (0.0303)

-0.0322 (0.0239)

Q 1.031*** (0.0574)

1.255*** (0.0819)

1.157*** (0.0499)

T_NON_ADOPTERS -0.0065*** (0.0005)

-0.0059*** (0.0007)

T_ADOPTERS 0.0003 (0.0010)

0.00005 (0.0007)

0.00055 (0.0009)

R2 0.88 0.86 ^ρ =0.54

22

Table 3. Alternative Specification Results – TSS emissions. Explanatory Variables

(1) (2) (3)

Constant -3.758*** (0.4343)

-5.656*** (0.8861)

-7.501*** (0.5850)

Log( )12, −tiE 0.2046*** (0.0158)

0.1363*** (0.0225)

-0.1159*** (0.0163)

ISO - -0.0213 (0.0418)

-0.0442 (0.0528)

PRE_ISO 0.0347 (0.0486)

- -

YEAR1_ISO -0.0541 (0.0589)

- -

POST_ISO 0.1169 (0.0732)

- -

INSP -0.0119 (0.0238)

-0.0012 (0.0309)

-0.0142 (0.0241)

Q 0.9459*** (0.0598)

1.0832*** (0.0832)

1.0032*** (0.0515)

T_NON_ADOPTERS -0.0046*** (0.0005)

-0.0028*** (0.0006)

T_ADOPTERS -0.0024** (0.0010)

-0.0012 (0.0007)

-0.00028 (0.0009)

R2 0.88 0.79 ^ρ =0.51

23

References [1] W. R. Q. Anton, G. Deltas and M. Khanna, Incentives for environmental self-regulation and

implications for environmental performance, Journal of Environmental Economics and Management 48 (2004) 632-654.

[2] O. Boiral and J.-M. Sala, Environmental Management: Should Industry Adopt ISO 14001?,

Business Horizons / January-February 1998. [3] Dasgupta S., H. Hettige and D. Wheeler, What Improves Environmental Compliance?

Evidence from Mexican Industry, Journal of Environmental Economics and Management, 39, 39-66 (2000).

[4] C. Dion, P. Lanoie and B. Laplante, Monitoring of Pollution Regulation: Do Local

Conditions Matter?, Journal of Regulatory Economics; 13:5-18 (1998). [5] J. Foulon, P. Lanoie and B. Laplante, Incentives for Pollution Control : Regulation or

Information?, Journal of Environmental Economics and Management 44, 169-187 (2002). [6] E. Helland, The Enforcement of Pollution Control Laws: Inspections, Violations, and Self-

Reporting, The Review of Economics and Statistics, Vol. 80, No. 1 (Feb., 1998), 141-153. [7] M. Khanna and W.R. Q. Anton, Corporate Environmental Management, Land Economics,

Vol. 78 (4), 539-557. [8] M. Khanna and L. A. Damon, EPA’s Voluntary 33/50 Program: Impact on Toxic Releases

and Economic Performance of Firms, Journal of Environmental Economics and Management 37, 1-25 (1999).

[9] K. Kollman and A. Prakash, EMS-based Environmental Regimes as Club Goods: Examining

Variations in Firm-level Adoption of ISO 14001 and EMAS in U.K., U.S. and Germany, Policy Sciences; March 2002; 35: 43-67, 2002.

[10] P. Lanoie, M. Thomas and J. Fearnley, Firms Responses to Effluent Regulations: Pulp and

Paper in Ontario, 1985-1989, Journal of Regulatory Economics; 13:103-120 (1998). [11] B. Laplante and P. Rilstone, Environmental Inspections and Emissions of the Pulp and

Paper Industry in Quebec, Journal of Environmental Economics and Management, 31, 19-36 (1996).

[12] W. A. Magat and W. K. Viscusi, Effectiveness of the EPA’s Regulatory Enforcement: The

Case of Industrial Effluent Standards, Journal of Law and Economics, Vol. 33, No. 2 (Oct., 1990), 331-360.

24

25

[13] M. Nakamura, T. Takahashi and I. Vertinsky, Why Japanese Firms Choose to Certify: A Study of Managerial Responses to Environmental Issues, Journal of Environmental Economics and Management 42, 23-52 (2001).

[14] Quebec Forest Industry Council, Environnemental Performance, Quebec (2001). [15] Quebec Ministry of the Environment, Bilan de conformité environnementale – secteur des

pâtes et papiers, (1996-2002). [16] D. Staiger and J.H. Stock, Instrumental variables regressions with weak instruments,

Econometrica 65(3), 557-586. [17] M. Szymanski and P. Tiwari, ISO 14001 and the Reduction of Toxic Emissions, Policy

Reform, March 2004, Vol. 7(1), pp. 31-42. [18] E. Wall, A. Weersink and C. Swanton, Agriculture and ISO 14000, Food Policy 26 (2001)

35-48. [19] J.M. Wooldridge, Econometric Analysis of Cross Section and Panel Data, The MIT Press,

Cambridge MA, 2002. [20] E. K. Yiridoe, J. S. Clark, G. E. Marett, R. Gordon and P. Duinker, ISO 14001 EMS

Standard Registration Decisions Among Canadian Organizations, Agribusiness; Fall 2003; 19, 4; 439-457.