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Environmental Law: Final Project © 2013 South University Final Project Case Scenario: EPA Vs. A1 Uranium, Clinton Chemicals, and ChurchHill Unlimited Chemicals. In 1988, three companies sought to open plants in the state of South Carolina. These plants would produce hazardous chemicals along with 100% uranium for use in nuclear weapons. The three companies were A1 Uranium, Clinton Chemicals, and an international company called ChurchHill Unlimited Chemicals.

The three companies sought a certification from the state agency for full operation within a 20- mile radius of restaurants and office buildings. The companies were provided certification by the state to do so. When the construction of the plant was completed, the companies began moving the necessary equipment into the plant. During the process a truck that was transporting toxic material began leaking. This was noticed by a pedestrian, but the pedestrian never told anyone about the leak.

A child who was playing in the vicinity dropped the ball in the waste. The child’s mother unknowingly picked the ball from the waste and returned it to her child to play. Two days later both mother and child became sick along with others who had stepped on the waste. In addition, the fumes from the plants were nauseating and caused some of the employees to vomit and have dizzy spells. An important thing to note here is the fact that the staff at the plant noticed the leak each time the truck came into the area, but none took the initiative to correct the problem.

One of the citizens who got sick contacted the Environmental Protection Agency (EPA). The citizen asked the EPA to investigate the illnesses that were affecting the people. W14290 GETCLARITY INC. (A) Mathew MacFayden and Matthew Morden wrote this case under the supervision of Professor Xinghao (Shaun) Yan solely to provide material for class discussion. The authors do not intend to illustrate either effective or ineffective handling of a managerial situation. The authors may have disguised certain names and other identifying information to protect confidentiality.

This publication may not be transmitted, photocopied, digitized or otherwise reproduced in any form or by any means without the organization. To order copies or request permission to reproduce materials, contact Ivey Publishing, Ivey Business School, Western University, London, Ontario, Canada, N6G 0N1; (t) 519.661.3208; (e) [email protected] ; INTRODUCTION It was a late-December evening, and Mathew MacFayden, a second-year HBA student at the Richard Ivey School of Business, had just finished his final exam of the semester. He sat down at his desk and reviewed an e-mail he had received an hour before starting his exam. The message was from a startup called getClarity Inc. for whom MacFayden and his fellow classmate, Matthew Morden, had been working.

One of getClarity’s recently acquired clients was requesting a data-analysis report. After examining the report requirements, MacFayden got up to make a pot of coffee. It was going to be a long night. GETCLARITY INC. GetClarity Inc. was a start-up firm that specialized in the accumulation, analysis, and sale of various types of data, including its own data, which was obtained through an exclusive contract.

The firm launched its application and services in January 2012, at which point it began an intensive sales process that was directed at industry-leading clients. There were three main revenue-generating functions within getClarity. The first was the company’s proprietary software, which was licensed out and used to display integrated sets of data on a GIS mapping system. The second revenue stream derived from consulting services that utilized getClarity’s data to solve a variety of business issues. Finally, getClarity provided unique reports that varied from the simple application of getClarity’s own data to the complex integration of customer data.

Since getClarity was still in its start-up phase, only limited personnel was available, and thus the team included the two founders, as well as two current HBA students (MacFayden and Morden), and one contract salesman. The team’s small size meant that MacFayden and Morden were given plenty of opportunity to take on considerable responsibility in a variety of the business functions. Still, the majority of their work centred on data analysis and development of client facing reports. The data typically belonged to getClarity, but occasionally, clients would incorporate their own data into the report in order to gain further insight. For the exclusive use of W.

Danak, 2017. This document is authorized for use only by Wojtek Danak in Data Analysis and Decision Modeling Fall 2017 taught by Bala, California State University, East Bay. from September 2017 to December 2017. mailto: [email protected] MacFayden and Morden’s job involved figuring out how to extract value from getClarity’s data and apply it to each client. The main goal was to display the data and analysis in such a way that senior management could understand the implications and would then go on to ask intelligent questions of their analytics department. REPORTS The reports that MacFayden and Morden formulated involved various forms of data analysis, including everything from the creation of simple displays of the data to pivot tables to more sophisticated statistical analysis, all depending on the client and on the report requested.

The goal was to simplify and display any amount of data onto one page. CLIENT REPORT REQUEST The current report request came from an auto-manufacturing client that sought to analyze a potential dealership location. The basis of the initial analysis centred on population age, and the report would make use of getClarity’s population data for the requested area. MacFayden and Morden would study the demographic data for the selected area as percentages of the area population (see Exhibit 1), and this data would then to be compared to national population data to see the relative age of the area. See Exhibit 2 for national demographic data as percentages of total national population.

The need for this comparison arose when the client’s management determined that an older average age served as a strong indicator that the dealership would be successful. Furthermore, identifying the average age of the consumers in the area would provide some insight into which vehicles the dealership should stock. For these reasons, it was necessary to determine whether the area was above or below the national average for age. In order to accomplish this task, a simulation would have to be performed, followed by a t-test with varying hypotheses for the average age of the area. The overall goal of this portion of the analysis was to determine, on average, how much older or younger the selected area was, compared to the rest of the population.

DECISION After reviewing the report request, MacFayden and Morden started to discuss how best to go about their task to provide some useful feedback for getClarity’s client. What types of analysis would need to be performed? What would be the best way to display the data once that analysis had been performed? And how conclusive would their analysis prove to be? For the exclusive use of W.

Danak, 2017. This document is authorized for use only by Wojtek Danak in Data Analysis and Decision Modeling Fall 2017 taught by Bala, California State University, East Bay. from September 2017 to December 2017. EXHIBIT 1: DEMOGRAPHIC DATA Statistic Value Population 26,380 % Work at Home 5.30% Pop % .60% Pop % .30% Pop % .80% Pop % .10% Pop % .80% Pop % .70% Pop % .40% Pop % 80 + 5.50% Never legally married 33.60% Legally married 47.40% Separated 2.70% Divorced 8.10% Widowed 8.20% Not Common-law 94.70% Common-law 5.30% 2-person family 42.30% 3-person family 24.20% 4-person family 20.60% 5+-person family 13.00% New to FSA per year 18.90% No degree 15.00% Any degree 85.00% High school certificate 20.00% Trades degree 9.10% College degree 15.10% Any university degree 40.70% University degree below BA 7.50% University cert. or degree 33.20% Bachelor's degree 20.10% University degree above BA 3.70% Medical degree 0.70% Master's degree 7.50% Doctorate degree 1.20% Avg.

Vehicle Age 5 Avg. Vehicle Estimated Value 10,937 Avg. House Age 36 Avg. Building Value 400,559 Percentage Value Of Provincial Avg. 150.50% Older Smoker 25.00% Mid-Age Smoker 53.10% Young Smoker 21.90% For the exclusive use of W.

Danak, 2017. This document is authorized for use only by Wojtek Danak in Data Analysis and Decision Modeling Fall 2017 taught by Bala, California State University, East Bay. from September 2017 to December 2017. EXHIBIT 2: NATIONAL DEMOGRAPHICS Statistic Value Population 34,362,970 % Work at Home 7.30% Pop % .10% Pop % .60% Pop % .30% Pop % .70% Pop % .40% Pop % .10% Pop % .10% Pop % 80 + 3.60% Never legally married 35.00% Legally married 48.00% Separated 3.00% Divorced 8.00% Widowed 6.10% Not Common-law 88.90% Common-law 11.10% 2-person family 49.00% 3-person family 21.70% 4-person family 20.20% 5+-person family 8.60% New to FSA per year 13.80% No degree 15.80% Any degree 83.90% High school certificate 23.50% Trades degree 12.90% College degree 20.40% Any university degree 27.10% University degree below BA 4.80% University cert. or degree 22.30% Bachelor's degree 14.10% University degree above BA 2.30% Medical degree 0.70% Master's degree 4.30% Doctorate degree 0.80% Avg.

Vehicle Age 4 Avg. Vehicle Estimated Value 8,136 Avg. House Age 33 Avg. Building Value 323,935 Percentage Value Of Provincial Avg. 102.90% Older Smoker 23.50% Mid Age Smoker 55.10% Young Smoker 22.10% For the exclusive use of W.

Danak, 2017. This document is authorized for use only by Wojtek Danak in Data Analysis and Decision Modeling Fall 2017 taught by Bala, California State University, East Bay. from September 2017 to December 2017. Ex 1 & 2 Demo Data Exhibit 1 Exhibit 2 Area Demographic Data National Demographics Statistic Value Statistic Value Population 26,380 Population 34,362,970 % Work at Home 5.30% % Work at Home 7.30% Pop % .60% Pop % .10% Pop % .30% Pop % .60% Pop % .80% Pop % .30% Pop % .10% Pop % .70% Pop % .80% Pop % .40% Pop % .70% Pop % .10% Pop % .40% Pop % .10% Pop % 80 + 5.50% Pop % 80 + 3.60% Never legally married 33.60% Never legally married 35.00% Legally married 47.40% Legally married 48.00% Separated 2.70% Separated 3.00% Divorced 8.10% Divorced 8.00% Widowed 8.20% Widowed 6.10% Not Common-law 94.70% Not Common-law 88.90% Common-law 5.30% Common-law 11.10% 2 persons family 42.30% 2 persons family 49.00% 3 persons family 24.20% 3 persons family 21.70% 4 persons family 20.60% 4 persons family 20.20% 5+ persons family 13.00% 5+ persons family 8.60% New to FSA per year 18.90% New to FSA per year 13.80% No degree 15.00% No degree 15.80% Any degree 85.00% Any degree 83.90% High school certificate 20.00% High school certificate 23.50% Trades degree 9.10% Trades degree 12.90% College degree 15.10% College degree 20.40% Any university degree 40.70% Any university degree 27.10% University degree below BA 7.50% University degree below BA 4.80% University cert. or degree 33.20% University cert. or degree 22.30% Bachelor's degree 20.10% Bachelor's degree 14.10% University degree above BA 3.70% University degree above BA 2.30% Medical degree 0.70% Medical degree 0.70% Master's degree 7.50% Master's degree 4.30% Doctorate degree 1.20% Doctorate degree 0.80% AVG Vehicle Age 5 AVG Vehicle Age 4 AVG Vehicle Estimated Value 10,937 AVG Vehicle Estimated Value 8,136 AVG House Age 36 AVG House Age 33 AVG Building Value 400,559 AVG Building Value 323,935 Percentage Value Of Provincial AVG 150.50% Percentage Value Of Provincial AVG 102.90% Older Smoker 25.00% Older Smoker 23.50% Mid Age Smoker 53.10% Mid Age Smoker 55.10% Young Smoker 21.90% Young Smoker 22.10% Question 1 Regional Average Age National Average Age Pop % .60% 10 Pop % .10% 10 Pop % .30% 25 Pop % .60% 25 Pop % .80% 35 Pop % .30% 35 Pop % .10% 45 Pop % .70% 45 Pop % .80% 55 Pop % .40% 55 Pop % .70% 65 Pop % .10% 65 Pop % .40% 75 Pop % .10% 75 Pop % 80 + 5.50% 85 Pop % 80 + 3.60% .

Question 2 Sample Data S Percentages Population Data P Percentages t-Test: Two-Sample Assuming Equal Variances Pop % .246 Pop % .241 Pop % .133 Pop % .126 S Percentages P Percentages Pop % .178 Pop % .133 Mean 0..17292 Pop % .141 Pop % .167 Variance 0.. Pop % .098 Pop % .144 Observations Pop % .077 Pop % .091 Pooled Variance 0. Pop % .074 Pop % .061 Hypothesized Mean Difference 0 Pop % 80 + 0.055 Pop % 80 + 0.036 df persons family 0. persons family 0.49 t Stat 0. persons family 0. persons family 0.217 P(T<=t) one-tail 0. persons family 0. persons family 0.202 t Critical one-tail 1.+ persons family 0.+ persons family 0.086 P(T<=t) two-tail 0. There is no significant difference between the two means No degree 0.15 No degree 0.158 t Critical two-tail 2.

Any degree 0.85 Any degree 0.839 High school certificate 0.2 High school certificate 0.235 Trades degree 0.091 Trades degree 0.129 College degree 0.151 College degree 0.204 Any university degree 0.407 Any university degree 0.271 University degree below BA 0.075 University degree below BA 0.048 University cert. or degree 0.332 University cert. or degree 0.223 Bachelor's degree 0.201 Bachelor's degree 0.141 University degree above BA 0.037 University degree above BA 0.023 Medical degree 0.007 Medical degree 0.007 Master's degree 0.075 Master's degree 0.043 Doctorate degree 0.012 Doctorate degree 0.008 we used population age, because we were told that the higher the population age is, the more successful the dealership is likely to be.

For family size, we considered that the more people in a family, the more cars the need to have and that it helps determine what size cars the dealership needs to stock. We used the education demographics because they are usually a good indicator of family income Area Avg. Age 2 Groups .60% .30% .80% .10% .80% .70% .40% .30% Area Random Numbers 70 Avg. 42. National Avg.

Age .10% .60% .30% .70% .40% .10% .10% .70% National Random Numbers 15 Avg. 39. Question 4 After running the data, we got an average age for the Area of 42.19, and an average national age of 39.16. These numbers are not far off of the numbers we got in question 1, meaning that the methods used in question 1 are fairly accurate. The increase in the average age for the Area leads us to conclude that this is a good area for the automaker to conduct business in as the area age is higher than the national average.

Z-Test Raw Data Area National .. Question 5 z-Test: Two Sample for Means Area National Mean 42..605 Known Variance Observations Hypothesized Mean Difference 0 z 2. P(Z<=z) one-tail 0. z Critical one-tail 1. P(Z<=z) two-tail 0. The two data sets are significantly different, and the average age in the section is higher than in the population z Critical two-tail 1.

SUBPOENA DUCES TECUM (CIVIL) – Case No.:       ATTORNEY ISSUED VA. CODE §§ 8.01-413, 16.1-89, 16.1-265; Commonwealth of Virginia Supreme Court Rules 1:4, 4:9       /       HEARING DATE AND TIME       Court       ADDRESS OF COURT v./ In re:       TO THE PERSON AUTHORIZED BY LAW TO SERVE THIS PROCESS: You are commanded to summon       NAME       STREET ADDRESS                   CITY STATE ZIP TO the person summoned: You are commanded to make available the documents and tangible things designated and described below:       at (location) at (date) \ (time) m. to permit such party or someone acting in his or her behalf to inspect and copy, test or sample such tangible things in your possession, custody, or control.

This Subpoena Duces Tecum is issued by the attorney for and on behalf of       PARTY NAME             NAME OF ATTORNEY VIRGINIA STATE BAR NUMBER OFFICE ADDRESS TELEPHONE NUMBER OF ATTORNEY OFFICE ADDRESS FACSIMILE NUMBER OF ATTORNEY DATE ISSUED SIGNATURE OF ATTORNEY Notice to Recipient: See next page for further information. RETURN OF SERVICE (see next page of this form) TO the person summoned: If you are served with this subpoena less than 14 days prior to the date that compliance with this subpoena is required, you may object by notifying the party who issued the subpoena of your objection in writing and describing the basis of your objection in that writing. TO the person authorized to serve this process: Upon execution, the return of this process shall be made to the clerk of court.

NAME: ADDRESS: |_| PERSONAL SERVICE Tel. No. Being unable to make personal service, a copy was delivered in the following manner: |_| Delivered to family member (not temporary sojourner or guest) age 16 or older at usual place of abode of party named above after giving information of its purport. List name, age of recipient, and relation of recipient to party named above: |_| Posted on front door or such other door as appear to be the main entrance of usual place of abode, address listed above. (Other authorized recipient not found.) |_| not found , Sheriff DATE by , Deputy Sheriff CERTIFICATE OF COUNSEL I, , counsel for      , hereby certify that a copy of the foregoing subpoena duces tecum was (delivery method) to      , counsel of record for      , on the       day of      ,      . Environmental Law: Subpoena Duces Tecum Draft Template © 2013 South University

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Introduction

The case scenario presented involves several serious violations tied to environmental laws that govern the handling of hazardous materials. A1 Uranium, Clinton Chemicals, and ChurchHill Unlimited Chemicals engaged in activities that not only contributed to potential health hazards for the local population but also raise key questions regarding compliance with Environmental Protection Agency (EPA) regulations. This report examines the legal and ethical implications of the actions taken by the companies, the responsibilities of the local authorities and companies under environmental laws, and the eventual repercussions faced by the relevant parties.

Background

In the late 1980s, three companies were given the green light by local state agencies to establish plants within a 20-mile radius of populated areas in South Carolina. Their operations involved the production of hazardous chemicals and uranium intended for nuclear warfare. During the commissioning phase, incidents of hazardous material leaks went unreported, leading to severe health implications for the local community. This case exemplifies the complex interplay between industrial activity and environmental regulation, emphasizing the necessity of stringent adherence to safety protocols.

Legal Framework

The Role of the EPA

The Environmental Protection Agency plays a crucial role in safeguarding environmental health, primarily through the enforcement of the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) and the Resource Conservation and Recovery Act (RCRA). Under these statutes, companies are held liable for damages caused by hazardous waste, which includes the mishandling or mismanagement of toxic materials (EPA, 2022).

Negligence and Liability

In this context, the failure of the employees at the chemical plants to report observable leaks indicates negligence. The legal doctrines of negligence and strict liability apply, as affected citizens could claim harm due to the companies' failure to uphold safety practices. Local state agencies that issued operating certifications without thorough environmental impact assessments may also bear some responsibility for oversight failures.

Analysis of Events

Health Risks

The health risks stemming from hazardous waste exposure can lead to long-term consequences, as witnessed with the child who played with the contaminated ball and ultimately faced illness. Such public health crises underscore the urgency for companies to maintain diligence in hazardous materials management. Comprehensive health impact assessments should precede operational certifications (Barraza et al., 2020).

Community Impact

The fume emissions reported by employees leading to nausea and dizziness highlight broader environmental justice issues wherein marginalized communities are often subjected to greater health risks. Studies reveal that minority and economically disadvantaged communities bear the brunt of adverse environmental factors, making it imperative to address systemic inequities in environmental policies (Lerner & Hsu, 2018).

The Role of Employees

The negligence exhibited by employees at the chemical plants also sparks discussion regarding whistleblower protections and the responsibilities of workers within hazardous environments. The Occupational Safety and Health Administration (OSHA) mandates that employees should report unsafe practices, yet discouraging workplace cultures can lead to silence regarding critical safety issues (McGowan et al., 2020).

Corporate Accountability

Ethical Considerations

From an ethical standpoint, companies must prioritize the welfare of the public while maintaining operational efficiency. The decision not to address the leaking materials demonstrates an ethical breach, departing from corporate social responsibility principles that demand transparency and accountability in all business practices (Porter & Kramer, 2006).

Recommendations

1. Enhanced Training: Companies must institute rigorous training programs on environmental compliance and workplace safety for all employees, ensuring recognition of hazardous conditions.
2. Whistleblower Protection Schemes: Establishing channels for employees to report unsafe conditions without fear of reprisal fosters a culture of safety and accountability.
3. Regular Audits & Compliance Checks: Routine assessments by external agencies can bolster compliance with environmental laws, ensuring quick identification and rectification of potential hazards (Schiffman et al., 2019).
4. Community Engagement: Companies should actively engage with local communities, informing them of potential hazards and mitigation strategies to build trust and collaboration.

Conclusion

The interactions among A1 Uranium, Clinton Chemicals, ChurchHill Unlimited Chemicals, and their respective communities shed light on the complex relationship between industrial activity and environmental sustainability. The failure to manage hazardous materials responsibly has far-reaching consequences not only for public health but also for corporate reputations and legal obligations. This case reinforces the essential role of environmental laws in protecting communities from hazardous activities and the importance of aligning corporate practices with social responsibility.

References

1. Barraza, R., Dunn, J., & Esty, D. (2020). Environmental Health Impact Assessment: Tools for Public Engagement. Environmental Health Perspectives.
2. EPA. (2022). Overview of the Resource Conservation and Recovery Act (RCRA). Environmental Protection Agency.
3. Lerner, S., & Hsu, H. (2018). Environmental Justice: An Introduction. Journal of Environmental Studies and Sciences.
4. McGowan, A., Peterson, A., & Cheng, H. (2020). The Role of Workers in Environmental Compliance: Whistleblower Protections and Safety Protocols. Journal of Occupational Health Psychology.
5. Porter, M. E., & Kramer, M. R. (2006). Strategy and Society: The Link Between Competitive Advantage and Corporate Social Responsibility. Harvard Business Review.
6. Schiffman, J. B., Shapiro, S. T., & Leary, H. (2019). Corporate Compliance in Hazardous Environments: Lessons Learned From Environmental Case Studies. Environmental Management.
7. Toolis, E. (2021). Accountability in Environmental Governance: The Role of Major Corporations. Journal of Sustainable Business.
8. Magar, A. (2022). The Importance of Environmental Education in Preventing Industrial Accidents. International Journal of Environmental Science and Technology.
9. United States Government Accountability Office. (2021). Toxic Waste and Environmental Public Health: Lessons from the Past and Regulations in Place.
10. Sze, J., & London, J. K. (2020). Environmental Justice at the Intersection: The Role of Community Organizing. Urban Affairs Review.
This comprehensive examination highlights the various aspects surrounding the case of A1 Uranium, Clinton Chemicals, and ChurchHill Unlimited Chemicals, framing it within the broader context of environmental law and corporate accountability. The conclusions drawn advocate for systemic change while underscoring the importance of a proactive approach to environmental health and safety.