Page 340 - Solid Waste Analysis and Minimization a Systems Approach

318 MODEL SUMMARY AND RECOMMENDATIONS FOR FUTURE RESEARCH

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.

20.4 Research Limitations

Several limitations of the research were identified. The first 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 findings, 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 accu-
racy is dependent on these organizations and people. To combat these issues, the survey
was also reviewed by a waste management expert, pretested, and simplified to ensure
accurate and consistent responses. A final drawback is the appropriateness of business
groupings. More business groupings would allow for a stronger analysis of waste gener-
ation, 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 sta-
tistical relationships may not have been established for smaller group sizes.

20.5 Recommendations for Future

Research

This research has led to the identification of several opportunities for additional
research. The list below summarizes these opportunities:

■ Collect additional data and recalculate the regression models to strengthen the rela-
tionships between the variables. Examine the correlations between variables in
greater detail.
■ Consider the further use of artificial intelligence (AI) methods to predict and eval-
uate solid waste (compare results to regression modeling). Test a variety of AI net-
work 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 3σ limits.
■ Improve the model by incorporating an economic benefit module. This would serve
as an incentive to industry to increase waste reduction activities for the financial
benefits (as demonstrated in Chap. 10).
■ Extend regression models for each waste group by collecting and considering addi-
tional independent variables (such as use of returnable containers, employment of
recycling coordinator, or level of automation).