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MODEL SUMMARY AND RECOMMENDATIONS FOR FUTURE RESEARCH
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businesses or groups of businesses. A standardized statistical method was lacking in the past (a nonstandardized system of averages is used by regulators to estimate waste). This research also validated the use of SIC codes to categorize waste rates of businesses.
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20.4 Research Limitations
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Several limitations of the research were identi ed. The rst limitation was the nature of the data collection instrument and accuracy of the data. The research data was collected using a mailed survey. Nonrespondents may have skewed the research ndings, although a validation process using data collected from other sources did not indicate this. Also, since most data was collected from other organizations or respondents, the level of accuracy is dependent on these organizations and people. To combat these issues, the survey was also reviewed by a waste management expert, pretested, and simpli ed to ensure accurate and consistent responses. A nal drawback is the appropriateness of business groupings. More business groupings would allow for a stronger analysis of waste generation, but would have made data collection and analysis very time consuming and costly. More groupings would have extended the data collection beyond budget as well, and statistical relationships may not have been established for smaller group sizes.
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20.5 Recommendations for Future Research
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This research has led to the identi cation of several opportunities for additional research. The list below summarizes these opportunities:
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Collect additional data and recalculate the regression models to strengthen the rela-
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tionships between the variables. Examine the correlations between variables in greater detail. Consider the further use of arti cial intelligence (AI) methods to predict and evaluate solid waste (compare results to regression modeling). Test a variety of AI network types to identify the optimal type to evaluate solid waste generation. Benchmark the waste disposal and recycling practices of low waste generators in greater detail. Perform detailed case studies on these companies to document best practices to aid other companies in improving environmental performance. Conduct detailed case studies applying the performance parameters. Evaluate the effectiveness of the 3 limits. Improve the model by incorporating an economic bene t module. This would serve as an incentive to industry to increase waste reduction activities for the nancial bene ts (as demonstrated in Chap. 10). Extend regression models for each waste group by collecting and considering additional independent variables (such as use of returnable containers, employment of recycling coordinator, or level of automation).
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SUMMARY
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Apply the environmental model to other countries waste generation and compare
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the results to this research. This will aid in identifying global leaders in waste reduction and environmental performance. Establish a Web site to allow businesses to con dentially evaluate their solid waste generation performance and to collect additional data. Since this was the rst comprehensive analysis and model development of solid waste generation of U.S. companies, improvement upon the performance parameters can be achieved by collecting and analyzing more data. The more data included in the analysis, the more accurate the models and performance parameters will be. The business groupings used may also be revaluated to ensure they best segment businesses based on waste generation. Another area of potential research involves developing a data collection information system to record and store waste generation and recycling rates of various industries. This information could be easily accessible to government agencies to monitor and control solid waste generation rates. A software program could also be developed to allow company management to easily determine waste generation control limits for their company based on indicator variables. This program could also estimate potential economic bene ts from increasing the company s recycling level. This would serve as both a nancial and environmental incentive for businesses to reduce waste and promote a green image. This software program would provide the rst step in measuring and comparing waste generation to aid in waste reduction. The model developed for this research is applicable to Internet applications. This enhances the con dentiality and ease of use aspects. One trend observed from the models was that as land ll disposal costs increased, annual solid waste generation decreased for manufacturing waste groups. The methods, recycling procedures, and management tools used by companies that have lower waste generation due to higher disposal costs could be studied to aid other companies in solid waste reduction. Case studies could be conducted to apply the control limits and economic bene t analysis to various companies to rate the standards. Follow-up studies could be conducted to examine the variance in estimated cost-bene ts. Standard procedures could be developed to aid companies in reducing their annual solid waste generation and increase recycling based on the models developed for this research. The case studies could serve as guides and benchmark best practices to reduce solid waste, increase recycling, and economically bene t the company. The cost-bene t analysis discussed in Chap. 18 serves as a starting point to investigate these economical issues from increased recycling.
20.6 Summary
The major objective of this study was to build a model to better understand solid waste generation of U.S. businesses and government agencies. A review of the literature shows that predicting and evaluating solid waste generation is relevant and important
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