Grantee Research Project Results
Final Report: Environmental Management Systems: Do Formalized Management Systems Produce Superior Performance?
EPA Grant Number: R829440Title: Environmental Management Systems: Do Formalized Management Systems Produce Superior Performance?
Investigators: Andrews, Richard N.
Institution: University of North Carolina at Chapel Hill
EPA Project Officer: Hahn, Intaek
Project Period: November 19, 2001 through November 18, 2003 (Extended to May 14, 2005)
Project Amount: $340,000
RFA: Decision-Making and Valuation for Environmental Policy (2001) RFA Text | Recipients Lists
Research Category: Environmental Justice
Objective:
Many major businesses have recently mandated introduction of formalized environmental management systems (EMSs) by their subsidiaries and suppliers, frequently using the ISO 14001 international voluntary standard for EMSs as a template. Government agencies also have begun to promote such systems with public recognition and incentives. A key unanswered question is what differences in actual environmental performance are associated with the introduction of such systems, and particularly, whether such systems produce positive changes in performance and other benefits when they are mandated or encouraged by external incentives.
The objective of this project was to determine whether improved environmental performance results from the implementation of formalized EMSs and what differences in organizational characteristics, motivations, and decision making were associated with implementation of such systems and with environmental performance improvement. Specifically, we sought to determine whether there are systematic differences between businesses that adopt EMSs for their own organizational reasons (“self-initiated”) or with government assistance or other incentives, or under coercion from enforcement actions or corporate or customer mandates. We also sought to determine whether changes in environmental performance are associated with adoption of a formal EMS itself or with other actions with which EMS adoption might be confounded. A key example is the adoption of specific environmental performance improvement objectives that typically are included in EMSs and are derived from separate discretionary decisions both by adopting and by many non-adopting facilities.
Summary/Accomplishments (Outputs/Outcomes):
Data and Methodology
The primary data for this study were collected through a survey in 2003 of plant managers at manufacturing facilities in four U.S. industrial sectors: Motor Vehicle Parts and Accessories (SIC 3714), Chemicals and Chemical Preparations (SIC 2899), Plastic Products (SIC 3089), and Coating, Engraving, and Allied Services (SIC 3479). Within each sector the survey was sent to facilities that reported to U.S. Environmental Protection Agency’s (EPA) Toxics Release Inventory (TRI)in 2000. We distributed 3,198 surveys and received 617 responses, a response rate of approximately 20 percent, well distributed across the industries sampled. Secondary data from the EPA’s TRI and EPA’s Integrated Data for Enforcement Analysis database also were included in the analysis to assess regulatory pressures, prior environmental performance, and environmental performance outcomes.
Respondents were asked whether their facility had specific written objectives for each of nine categories of possible environmental performance improvements, and if so, the priority (on a scale of 0 to 4) assigned to that objective (Figure 1). Improvements in environmental performance were measured by reductions in total reported TRI emissions (1999-2001 vs. 1996-98) and by changes (2000-2002) reported by the respondents for 17 other environmental performance indicators and then coded as improved or not improved. We also used control variables for industrial sector, ownership, size of facility, parent corporation, prior environmental management experience, prior experience with other management innovations, attitude of the plant manager toward the business value of environmental management activities, and extent of involvement by employees and other stakeholders in the facility’s environmental management activities. We used descriptive statistics and chi-square analysis to determine variability in the formalization of EMSs and in priorities among environmental objectives. We then used a two-stage binominal regression model to investigate the factors associated with the probability of EMS adoption, and then the influence of the probability of EMS adoption, performance objectives, and other factors on environmental outcomes.
Results. Ninety-eight percent of the facilities reported having explicit written objectives for their environmental management activities, even though only 45 percent had adopted a formal or ISO-equivalent EMS. Of those who had adopted a formal EMS, 91 percent reported business or government pressures to do so.
Table 1 shows the proportion of facilities that reported increases, decreases, or no change in environmental outcomes for each of 17 environmental performance indicators. The table compares facilities that had both a mandate from business partners and a formalized EMS to facilities that had neither. We used the chi-square test to determine if statistically significant differences were observed in the reports by these facilities. For instance, close to half (0.47) of facilities with both a mandate and a formal EMS reported that energy use had decreased during the study period whereas just over a quarter (0.27) of facilities with neither a business mandate nor a formal EMS reported the same outcome. Where statistically significant differences were observed in the facilities’ reported environmental performance during the study period, the p value is noted in the last column of the table.
Across virtually all indicators, facilities that both had mandates to introduce an EMS and had EMSs in place, were more likely to report environmental performance improvements than were those that had neither (Table 1). Most of these differences, however, were modest in their magnitude: decreases in energy use, increased use of recycled inputs and recycling of waste, decreases in hazardous and non-hazardous waste generation, and decreased incidence of severe leaks or spills, all outcomes that could be improved incrementally through facility-level management decisions, and may also be least costly to achieve (“low-hanging fruit”). The results showed no significant improvements for air and water pollution and material inputs to production; outcomes at least two of which are central concerns for environmental regulation but each of which might require more costly capital investments in changing products and production processes.
Figure 1. Discretionary Objectives of Environmental Management Activities
Model Results
EMS Adoption. Facilities in the chemicals and auto-supply industries, first-tier suppliers to the auto industry, facilities with corporate mandates, and facilities that also had quality-management systems all were far more likely than others to have adopted formal EMSs. Publicly traded facilities were also more likely than privately owned ones to have a formal EMS and larger facilities slightly more likely than smaller ones to have them. In short, facilities with more advanced prior management capabilities (having a total quality management system in place) or a corporate mandate, or in industries or supplier relationships experiencing customer pressures to do so, were the most likely to have all the environmental management practices in place conforming to a formal or ISO-equivalent EMS.
B2B Mandate/EMS in Place | No B2B mandate/No formal EMS |
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Environmental Activity | N = 192 | N = 79 |
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| Increased | Unchanged | Decreased | Increased | Unchanged | Decreased | sig. |
Water use | 0.22 | 0.31 | 0.47 | 0.23 | 0.38 | 0.38 | |
Energy use | 0.26 | 0.27 | 0.47 | 0.37 | 0.35 | 0.27 | ** |
Recycled inputs | 0.61 | 0.30 | 0.09 | 0.37 | 0.52 | 0.11 | ** |
Recycling of waste | 0.72 | 0.20 | 0.08 | 0.45 | 0.48 | 0.07 | *** |
Chemical inputs per unit output | 0.07 | 0.38 | 0.55 | 0.08 | 0.47 | 0.45 | |
Total material inputs | 0.29 | 0.37 | 0.34 | 0.30 | 0.36 | 0.34 | |
Hazardous waste generation | 0.09 | 0.17 | 0.74 | 0.12 | 0.38 | 0.50 | *** |
Non-hazardous waste generation | 0.19 | 0.24 | 0.57 | 0.22 | 0.45 | 0.33 | *** |
Wastewater effluent | 0.17 | 0.35 | 0.48 | 0.15 | 0.42 | 0.43 | |
Air pollution emissions | 0.08 | 0.34 | 0.58 | 0.12 | 0.43 | 0.45 | |
Greenhouse gas emissions | 0.08 | 0.50 | 0.42 | 0.07 | 0.57 | 0.36 | |
Noise generation | 0.07 | 0.60 | 0.33 | 0.05 | 0.74 | 0.21 | |
Smell generation | 0.07 | 0.53 | 0.40 | 0.03 | 0.67 | 0.30 | |
Disruption of the natural landscape | 0.04 | 0.79 | 0.16 | 0.10 | 0.88 | 0.02 | * |
Soil contamination | 0.02 | 0.63 | 0.35 | 0.03 | 0.81 | 0.16 | |
Severe leaks or spills | 0.02 | 0.39 | 0.59 | 0.05 | 0.61 | 0.34 | ** |
Violations or potential violations | 0.05 | 0.50 | 0.45 | 0.10 | 0.55 | 0.35 | |
p < 0.05, ** p < 0.01, ** *p < 0.001 |
Environmental Performance Change. In the second stage, we ran the model for each of the eighteen indicators of possible environmental performance change. The model results for each of the environmental performance indicators in the second-stage equation are shown in Table 2. No meaning is implied by the grouping of these indicators into separate tables. The most widely significant positive factor associated with environmental performance changes was emphasis on eco-efficiency objectives, followed closely by emphasis on pollution-prevention objectives, recent non-compliances, and high TRI emissions in the past as illustrated by the significance of these variables for multiple indicators.
We used a binomial logistic regression model to investigate the effects of the variables of interest on improved environmental performance outcomes. This model applies a maximum-likelihood technique to estimate the likelihood of a certain outcome. Although the parameter estimates are not intuitively meaningful, the regression coefficients can be transformed to show the change in the odds of the outcome’s occurrence per a one unit change in the independent variable. For improvements in each of the EPIs and in TRI emissions, the model took the following general form:
Log p(epi k) = α + β1(probability of EMS formalization) + β2(environmental objectives)i + β3(pollution prevention planning) + β4(regulatory pressure)i + β5 (prior performance) + β6(size) + β7(industry)i + ε
Table 2 shows the results for this analysis. The log-likelihood chi-square statistic is presented along with a pseudo R-Square statistic and the Hosmer and Lemeshow goodness-of-fit chi-square. The parameter coefficient is shown, along with a point estimate of its effect on the odds that improved environmental performance was reported, and the standard error of the parameter coefficient. The p value for estimates that were statistically significant is noted within the table.
Odds ratios that are greater than 1 indicate that the presence of the variable at the facilities increased the likelihood that improvement in the performance indicator was reported. Conversely, an odds ratio less than 1 indicate that the presence of the variable at the facility decreased the likelihood that improvement in the performance indicator was reported. For instance, facilities that reported the facility had developed a pollution prevention or waste minimization plan were nearly three times as likely to report an increase in recycled inputs (the odds ratio equals 2.99). In discussing these results, parameter estimates are reported in the text as well as in the table.
Influence of a Formal EMS. The presence of a formal EMS was significant and positive for waste recycling (1.24), reductions in hazardous wastes (0.98), and severe leaks and spills (1.18). However, it was not significant for any other environmental performance changes. These results suggest that implementation of a formal EMS may have its primary effects mainly on relatively simple, day-to-day but high-visibility practices and performance indicators rather than improving a facility’s environmental performance more systematically.
Environmental Performance Objectives. In contrast, significant differences in a wider range of environmental outcomes were associated with facilities’ choices of objectives and priorities for environmental performance improvement. Facilities that placed high priority on compliance objectives had no significant positive performance outcomes, probably because more than three quarters of all facilities reported placing high priority on compliance objectives. In effect, the important differences were between facilities that prioritized other objectives in addition to compliance and those that concentrated only on compliance. Facilities that placed a high priority on pollution prevention were significantly more likely to report improvements even in compliance-related indicators such as spills (1.02), violations (1.02), soil contamination (1.32), and smells (1.16), perhaps because many environmental regulatory agencies have emphasized pollution prevention in their compliance-assistance programs over the past two decades. Facilities that placed a high priority on eco-efficiency objectives had significant positive outcomes for a host of additional changes including hazardous wastes (1.15), energy use (1.25), recycled inputs (1.30), waste recycling (1.20), non-hazardous wastes (1.55), and material inputs (0.87).
Prior Environmental Management Experience. Facilities with experience in formal pollution prevention and/or waste minimization plans showed significant associations with positive performance changes for waste recycling (0.97), use of recycled inputs (1.09), and reductions in both hazardous and non-hazardous wastes (0.68 and 0.83, respectively).
Regulatory Pressures. Recent non-compliances were significantly and positively associated with improvements in air emissions (0.03), violations (0.03), non-hazardous wastes (0.03), soil contamination (0.04), and TRI emissions (0.03). Previous high TRI emissions also were highly significant and positive for subsequent reductions in TRI pollutant releases (0.24) and also were significant for improvements in air emissions (0.48), leaks and spills (0.43), and energy and chemical inputs (0.31 and 0.29, respectively).
Discussion
These results suggest implications for public policies intended to promote or reward either implementation of management procedures such as EMSs or superior environmental performance directly. One should not expect to see significant changes in environmental performance simply because a facility adopts a formal EMS, particularly in air emissions, wastewater discharges, and other regulated pollutants. Business-to-business EMS mandates may combine with other market forces to promote more environmentally efficient production, particularly for businesses facing export-market expectations and supply-chain pressures.
Environmental regulations also appear to play an important role in improving the environmental performance of manufacturing facilities, whether or not an EMS is present. Although inspections and fines did not appear to affect performance improvements, instances of noncompliance did, and facilities with higher TRI releases in the past also were more likely to report improvements in indicators related to toxics (e.g., chemical use) and to other regulated emissions (e.g., air pollutants, leaks and spills) as well as in TRI releases themselves.
These findings suggest that public policy promotion and rewards should focus on the specific environmental performance objectives targeted as priorities for improvement, and on the facility’s success in achieving them, not merely on the adoption of an EMS. To their credit, EPA’s National Performance Track Program and some similar state award and incentives programs already include specific performance requirements, but others offer benefits for EMS adoption without necessarily coupling rewards to actual performance improvements. Our findings also suggest that regulatory pressures remain essential to some important elements of environmental performance improvement and are probably an important contextual factor for others. Environmental policymakers should therefore use management-based strategies as complements, not substitutes, for government regulation and enforcement.
Related Research
A number of additional studies have utilized the intellectual foundations, instrument, and/or data of this study to develop related analyses and publications. Examples include a parallel survey of factory managers in comparable sectors in Thailand; a study of the use of EMSs in public-sector organizations, particularly municipal wastewater treatment utilities; a study of the use of EMSs by facilities located in minority and low-income neighborhoods; and a study currently underway of environmental mandates in electronics and automotive supply-chain relationships in the United States and Mexico.
Hazardous Waste | Air Emissions | Wastewtr Effluent | Spills and Leaks | Violations | ||||||||||||||||
Parameter | Estimate | Odds Ratio | Estimate | Odds Ratio | Estimate | Odds Ratio | Estimate | Odds Ratio | Estimate | Odds Ratio | ||||||||||
Intercept | -0.65 | -- | -0.95 | -- | -0.28 | -- | -0.94 | -- | -1.76 | -- | ||||||||||
(0.51) | (0.60) | (0.55) | (0.70) | (0.59) | * | |||||||||||||||
Probability of Formal EMS | 0.98 | 2.67 | 0.68 | 1.98 | 0.37 | 1.45 | 1.18 | 3.26 | 0.27 | 1.31 | ||||||||||
(0.50) | * | (0.57) | (0.50) | (0.58) | * | (0.54) | ||||||||||||||
Compliance Goals | -0.85 | 0.43 | -0.56 | 0.57 | -0.58 | 0.56 | 0.62 | 1.86 | 1.01 | 2.73 | ||||||||||
(0.52) | (0.59) | (0.52) | (0.64) | (0.55) | ||||||||||||||||
Pollution Prevention Goals | 0.41 | 1.51 | 0.64 | 1.90 | 0.3 | 1.34 | 1.02 | 2.78 | 1.02 | 2.76 | ||||||||||
(0.37) | (0.43) | (0.39) | (0.45) | * | (0.41) | * | ||||||||||||||
Eco-Efficiency Goals | 1.15 | 3.17 | 0.39 | 1.48 | 0.54 | 1.72 | 0.68 | 1.98 | -0.23 | 0.79 | ||||||||||
(0.40) | ** | (0.46) | (0.42) | (0.47) | (0.42) | |||||||||||||||
P2/Waste Minimization Plans | 0.68 | 1.97 | 0.03 | 1.04 | 0.26 | 1.29 | -0.39 | 0.67 | -0.04 | 0.97 | ||||||||||
(0.04) | * | (0.34) | (0.33) | (0.41) | (0.34) | |||||||||||||||
Recent Inspections | 0.00 | 1.00 | 0.01 | 1.01 | 0.01 | 1.01 | 0.00 | 1.00 | 0.00 | 1.00 | ||||||||||
(0.005) | (0.03) | (0.01) | (0.01) | (0.01) | ||||||||||||||||
Recent Non-Compliance | 0.02 | 1.02 | 0.03 | 1.03 | -0.02 | 0.99 | 0.00 | 1.00 | 0.03 | 1.03 | ||||||||||
(0.01) | (0.01) | * | (0.01) | (0.01) | (0.01) | * | ||||||||||||||
Recent Fines/10000 | -0.06 | 0.94 | 0.20 | 1.22 | 0.02 | 1.02 | -0.02 | 0.98 | 0.01 | 1.01 | ||||||||||
(0.07) | (0.15) | (0.01) | (0.07) | (0.01) | ||||||||||||||||
TRI EmissionX-5 | 0.22 | 1.24 | 0.48 | 1.61 | 0.1 | 1.11 | 0.43 | 1.53 | 0.26 | 1.30 | ||||||||||
(0.16) | (0.19) | ** | (0.14) | (0.18) | * | (0.15) | ||||||||||||||
Facility Size | -0.08 | 0.93 | -0.13 | 0.88 | -0.11 | 0.90 | -0.20 | 0.82 | -0.10 | 0.90 | ||||||||||
(0.10) | (0.11) | (0.10) | (0.12) | (0.10) | ||||||||||||||||
Auto Supply Sector | 0.21 | 1.24 | 1.03 | 2.79 | 0.3 | 1.35 | -0.42 | 0.66 | 0.01 | 1.01 | ||||||||||
(0.35) | (0.37) | ** | (0.33) | (0.40) | (0.36) | |||||||||||||||
Plastics Sector | -0.06 | 0.94 | 1.12 | 3.06 | -0.77 | 0.46 | -0.68 | 0.51 | 0.21 | 1.24 | ||||||||||
(0.32) | (0.36) | ** | (0.34) | * | (0.37) | (0.34) | ||||||||||||||
Coatings Sector | 0.08 | 1.08 | 0.72 | 2.04 | 0.18 | 1.20 | -0.92 | 0.40 | -0.07 | 0.94 | ||||||||||
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| (0.32) |
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| (0.37) | * |
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| (0.30) |
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| (0.37) | * |
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| (0.32) |
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n |
| 470 |
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| 389 |
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| 436 |
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| 340 |
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| 409 |
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Log Likelihood(χ) |
| 40.38 | *** |
| 43.91 | *** |
| 28.61 | ** |
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| 44.48 | *** |
| 31.19 | ** |
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Pseudo-R2 |
| 0.11 |
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| 0.14 |
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| 0.08 |
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| 0.16 |
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| 0.10 |
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Hosmer & Lemeshow(χ) |
| 4.15 |
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| 9.46 |
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| 9.43 |
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| 3.92 |
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| 10.85 |
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*** p < 0.0001, ** p < 0.01, * p < 0.05 |
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Water Use | Energy Use |
| Chemical Use |
| Material Use |
| TRI Emissions |
| Recycled Inputs | |||||||||||||||
Parameter | Estimate | Odds Ratio | Estimate | Odds Ratio | Estimate | Odds Ratio | Estimate | Odds Ratio | Estimate | Odds Ratio | Estimate | Odds Ratio | ||||||||||||
Intercept | -1.20 | -- | -1.83 | -- | -1.70 | -- | -0.26 | -- | -1.92 | -- | -1.75 | -- | ||||||||||||
(0.53) | * | (0.53) | *** | (0.57) | *** | (0.53) | (0.52) | ** | (0.61) | ** | ||||||||||||||
Probability of Formal EMS | 0.18 | 1.19 | 0.59 | 1.81 | 0.24 | 1.27 | 0.15 | 1.16 | 0.19 | 1.21 | 0.73 | 2.07 | ||||||||||||
(0.49) | (0.48) | (0.51) | (0.52) | (0.49) | (0.54) | |||||||||||||||||||
Compliance Goals | 0.01 | 1.01 | -0.20 | 0.82 | -0.18 | 0.83 | -0.71 | 0.49 | 0.20 | 1.23 | -0.86 | 0.42 | ||||||||||||
(0.50) | (0.50) | (0.52) | (0.54) | (0.48) | (0.59) | |||||||||||||||||||
Pollution Prevention Goals | -0.68 | 0.51 | 0.03 | 1.03 | 0.39 | 1.48 | -0.36 | 0.70 | 0.04 | 1.04 | -0.13 | 0.88 | ||||||||||||
(0.38) | (0.36) | (0.38) | (0.39) | (0.36) | (0.40) | |||||||||||||||||||
Eco-Efficiency Goals | 0.65 | 1.91 | 1.25 | 3.49 | 0.63 | 1.88 | 0.87 | 2.40 | -0.12 | 0.88 | 1.30 | 3.69 | ||||||||||||
(0.40) | (0.40) | ** | (0.42) | (0.43) | * | (0.38) | (0.46) | ** | ||||||||||||||||
P2/Waste Minimization Plans | 0.55 | 1.74 | -0.07 | 0.93 | 0.29 | 1.34 | 0.26 | 1.30 | 0.21 | 1.23 | 1.09 | 2.99 | ||||||||||||
(0.32) | (0.31) | (0.33) | (0.34) | (0.31) | (0.38) | ** | ||||||||||||||||||
Recent Inspections | 0.00 | 1.00 | 0.02 | 1.02 | 0.02 | 1.02 | 0.03 | 1.03 | -0.01 | 0.99 | 0.00 | 1.00 | ||||||||||||
(0.01) | (0.01) | (0.01) | (0.02) | (0.01) | (0.01) | |||||||||||||||||||
Recent Non-Compliance | 0.00 | 1.00 | 0.00 | 1.00 | -0.01 | 0.99 | -0.01 | 0.99 | 0.03 | 1.03 | -0.02 | 0.99 | ||||||||||||
(0.01) | (0.01) | (0.01) | (0.01) | (0.01) | * | (0.01) | ||||||||||||||||||
Recent Fines/10000 | 0.01 | 1.01 | 0.01 | 1.01 | 0.00 | 1.00 | 0.01 | 1.01 | 0.00 | 1.00 | 0.02 | 1.02 | ||||||||||||
(0.01) | (0.01) | (0.01) | (0.01) | (0.01) | (0.02) | |||||||||||||||||||
TRI EmissionX-5 | 0.20 | 1.23 | 0.31 | 1.36 | 0.29 | 1.33 | 0.28 | 1.32 | 0.24 | 1.27 | -0.28 | 0.76 | ||||||||||||
(0.15) | (0.14) | * | (0.14) | * | (0.16) | (0.06) | *** | (0.16) | ||||||||||||||||
Facility Size | -0.04 | 0.96 | -0.01 | 0.99 | -0.04 | 0.96 | -0.35 | 0.70 | -0.01 | 0.99 | 0.33 | 1.40 | ||||||||||||
(0.09) | (0.09) | (0.10) | (0.11) | ** | (0.10) | (0.10) | ** | |||||||||||||||||
Auto Supply Sector | 0.95 | 2.57 | 0.71 | 2.04 | 1.14 | 3.13 | 1.15 | 3.15 | 0.94 | 2.56 | -0.79 | 0.45 | ||||||||||||
(0.32) | ** | (0.32) | * | (0.34) | *** | (0.34) | ** | (0.33) | * | (0.35) | * | |||||||||||||
Plastics Sector | -0.47 | 0.62 | 0.12 | 1.13 | 0.51 | 1.67 | 0.17 | 1.19 | 0.26 | 1.29 | -0.68 | 0.51 | ||||||||||||
(0.32) | (0.31) | (0.32) | (0.32) | (0.33) | (0.34) | * | ||||||||||||||||||
Coatings Sector | 0.50 | 1.64 | 0.42 | 1.53 | 0.89 | 2.43 | 0.39 | 1.48 | 0.89 | 2.45 | -0.83 | 0.44 | ||||||||||||
(0.29) | (0.31) | (0.32) | ** | (0.33) | (0.32) | * | (0.33) | ** | ||||||||||||||||
n | 493 |
| 511 |
| 436 |
| 455 |
| 565 |
| 415 |
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Log Likelihood(χ) | 48.79 | *** | 48.33 | *** | 34.79 | ** | 30.98 | ** | 61.88 | *** | 55.18 | *** | ||||||||||||
Pseudo-R2 | 0.13 |
| 0.12 |
| 0.10 |
| 0.09 |
| 0.14 |
| 0.17 |
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Hosmer & Lemeshow(χ) | 4.72 |
| 10.04 |
| 8.74 |
| 10.47 |
| 7.38 |
| 7.84 |
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*** p < 0.0001, ** p < 0.01, * p < 0.05 |
Compliance Goals | -1.06 | 0.35 | -1.42 | 0.24 | -0.88 | 0.42 | -0.76 | 0.47 | 0.94 | 2.55 | 0.22 | 1.25 | ||||||||||||
(0.52) | * | (0.51) | ** | (0.70) | (0.81) | (0.78) | (0.67) | |||||||||||||||||
Pollution Prevention Goals | -0.13 | 0.88 | 0.08 | 1.08 | 0.70 | 2.02 | 1.32 | 3.75 | 1.16 | 3.19 | 1.05 | 2.85 | ||||||||||||
(0.37) | (0.36) | (0.48) | (0.58) | * | (0.48) | * | (0.46) | |||||||||||||||||
Eco-Efficiency Goals | 1.20 | 3.32 | 1.55 | 4.73 | 1.08 | 2.94 | 0.25 | 1.29 | 0.24 | 1.27 | -0.09 | 0.91 | ||||||||||||
(0.41) | ** | (0.40) | *** | (0.57) | (0.61) | (0.51) | (0.49) | |||||||||||||||||
P2/Waste Minimization Plans | 0.97 | 2.63 | 0.83 | 2.30 | 0.93 | 2.53 | 0.56 | 1.74 | 0.53 | 1.69 | 0.26 | 1.30 | ||||||||||||
(0.32) | ** | (0.33) | ** | (0.50) | (0.57) | (0.46) | (0.44) | |||||||||||||||||
Recent Inspections | 0.00 | 1.00 | 0.00 | 1.00 | -0.01 | 0.99 | 0.00 | 1.00 | 0.00 | 1.00 | -0.06 | 0.94 | ||||||||||||
(0.01) | (0.01) | (0.01) | (0.01) | (0.01) | (0.03) | |||||||||||||||||||
Recent Non-Compliance | -0.02 | 0.98 | 0.03 | ** | 1.03 | 0.02 | 1.02 | 0.04 | 1.04 | -0.02 | 0.98 | -0.03 | 0.97 | |||||||||||
(0.01) | (0.01) | (0.01) | (0.01) | ** | (0.02) | (0.02) | ||||||||||||||||||
Recent Fines/10000 | 0.01 | 1.01 | 0.00 | 1.00 | 0.02 | 1.02 | 0.01 | 1.01 | -0.01 | 0.99 | 0.01 | 1.01 | ||||||||||||
(0.01) | (0.01) | (0.04) | (0.01) | (0.01) | (0.01) | |||||||||||||||||||
TRI EmissionX-5 | -0.05 | 0.95 | 0.30 | 1.35 | 0.03 | 1.03 | -0.03 | 0.97 | 0.00 | 1.00 | 0.24 | 1.27 | ||||||||||||
(0.16) | (0.16) | (0.21) | (0.04) | (0.21) | (0.18) | |||||||||||||||||||
Facility Size | 0.12 | 1.13 | -0.12 | 0.89 | -0.07 | 0.94 | 0.15 | 1.16 | 0.33 | 1.39 | 0.07 | 1.08 | ||||||||||||
(0.10) | (0.10) | (0.13) | (0.14) | (0.13) | ** | (0.12) | ||||||||||||||||||
Auto Supply Sector | -0.13 | 0.87 | 0.48 | 1.61 | 1.21 | 3.36 | -0.38 | 0.68 | -0.97 | 0.38 | 0.23 | 1.26 | ||||||||||||
(0.33) | (0.32) | (0.43) | ** | (0.48) | (0.44) | * | (0.38) | |||||||||||||||||
Plastics Sector | -0.22 | 0.80 | -0.27 | 0.76 | 0.73 | 2.07 | -0.51 | 0.60 | 0.24 | 1.27 | 0.22 | 1.24 | ||||||||||||
(0.31) | (0.30) | (0.43) | (0.45) | (0.39) | (0.38) | |||||||||||||||||||
Coatings Sector | -0.07 | 0.93 | -0.65 | 0.52 | 0.82 | 2.27 | -0.18 | 0.84 | -0.45 | 0.64 | -0.63 | 0.53 | ||||||||||||
(0.30) | (0.31) | ** | (0.42) | * | (0.44) | (0.41) | (0.42) | |||||||||||||||||
n | 493 |
| 513 |
| 290 |
| 263 |
| 313 |
| 376 |
| ||||||||||||
Log Likelihood(χ) | 52.63 | *** | 72.18 | *** | 29.62 | ** | 24.10 | * | 36.18 | ** | 33.37 | ** | ||||||||||||
Pseudo-R2 | 0.14 |
| 0.18 |
| 0.13 |
| 0.12 |
| 0.15 |
| 0.12 |
| ||||||||||||
Hosmer & Lemeshow(χ) | 4.98 |
| 4.42 |
| 8.14 |
| 10.48 |
| 1.70 |
| 13.53 |
| ||||||||||||
*** p < 0.0001, ** p < 0.01, * p < 0.05 |
Journal Articles:
No journal articles submitted with this report: View all 14 publications for this projectSupplemental Keywords:
risk management, socioeconomic, social science, sustainable industry/business, corporate performance, economics and decision making, economics and business, environmental engineering, environmental statistics, market mechanisms, new/innovative technologies, cleaner production, enforcement impact, environmental decision making, environmental impact comparison, government regulatory costs, policy analysis, policy making, statistical methods, voluntary programs,, RFA, Scientific Discipline, Economic, Social, & Behavioral Science Research Program, Sustainable Industry/Business, cleaner production/pollution prevention, Sustainable Environment, Technology for Sustainable Environment, Economics and Business, Corporate Performance, decision-making, Environmental Statistics, New/Innovative technologies, Social Science, Economics & Decision Making, Market mechanisms, environmental performance, environmental management systems (EMS), policy analysis, voluntary regulations, environmental management systems, policy making, valuation, environmental decision making, risk management, decision making, environmental impact comparison, socioeconomics, environmental policy, government regulatory costs, environmental Compliance, EMS, enforcement impact, pollution prevention, social sciences, voluntary programsProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.