Department of Environmental Studies
This project is an extension of an internship I had with the New Hampshire Pollution Program (NHPP), located in the State’s Department of Environmental Services. This program’s staff, and numerous other regional and national level pollution prevention (P2) organizations are concerned with determining which approaches, methods, and organizational structure (which this report defines as program elements) have the greatest impact on program effectiveness. Reasons for the interest in program effectiveness include reporting requirements to government agencies to justify funding or to apply for grants, and a universal concern that one’s program is making the best use of its resources. The first step in determining which program elements contribute the most to program effectiveness, is to determine which programs are, or are not, effective. All the students of program effectiveness, consulted for this paper, appear to reach the same conclusion: It is difficult to determine program effectiveness. Even with the best estimates of program effectiveness available, it is even more difficult to establish a cause and effect relationship between specific program elements and their impact on effectiveness. Therefore, my purpose in developing this project was to devise an alternative method of determining how to P2 program can make the best, or at least a good, use of its resources. I decided to consider a program’s activity level as an indicator of good resource use. The more active a program is, the better use it is making of the resources at its disposal. I sought to identify which program elements are common to the most active programs. This may not be the most definitive approach, but I showed that it is at least measurable. Programs and organizations interested in assessing the efficiency of their resource use, can use this measurable data as a tool to allocate their resources. There were two major portions to determining the key program elements used by active P2 programs. This first was developing a method of measuring activity levels, and the second was determining which program elements to consider as possibly having the greatest impact. I narrowed things down to four program activities, that could be measured. They are: the number of requests an organization receives for assistance, the number on-site assessments of P2 performed for businesses, the number on-site reports developed, and the number of workshops and seminars a program participates in. The second step was identifying program elements with the potential to impact a programs activity level. Nine were identified as follows: P2 or TUR legislation in a state, mandating P2 planning for industry, the location of the technical assistance program inside or outside of a regulatory agency, the stability of the funding mechanism, size of budget, size of staff, the availability of grant money to industry for P2, and information clearinghouse, and an outreach program. Once a program was determined to be either highly active or one with low activity levels, based on responses to a nation wide survey of state level P2 programs, a correlation was made between the programs and program elements. If a program element was common among the highly active programs, but uncommon among programs with low activity levels, than it was considered be a key element to achieving high activity levels. Several program elements were identified as key elements. They are mandating P2 planning for industry, the location of a P2 program outside a regulatory agency, stability of funding, the size of a program’s budget, and the size of the staff. The last two were more strongly identified as influential factors in program activity level, but they are also the most difficult for a program to control, since they’re externally controlled. A program may be better off concentrating on the non-monetary program elements, since funding and staff positions paid for by funding are often hard to come by. The measurability of the approach is its strong point, since it attempts to eliminate subjective analysis, which can be heavily biased. Applying the conclusions drawn in this project are know guarantee that a program will achieve high activity levels, but is can be a place to start.