## A probit model of energy efficiency technology decision making

The diffusion of energy efficient technologies, or lack thereof, has been the subject of numerous studies. The Industrial Assessment Center (IAC) program conducts assessments of plants in small and medium-sized companies and makes energy efficiency recommendations that will result in cost savings and high rates of return. This study uses a detailed database from the IAC program to examine the firm's decision to implement a recommendation. This study estimates two probit models; one captures the probability that a particular recommendation is made, and the other the probability that a recommendation is implemented. The second model, the decision to adopt, is the primary focus of the analysis. The paper interprets the results from the economic variables in terms of the speed of adoption (i.e., in the context of standard diffusion models). Many measures of technology performance have a statistically significant influence on the adoption decision. Higher implementation cost or energy prices reduce the chance of adoption for many technologies, but not all. This effect is in addition to the effect these variables have on payback, which also lowers the chance of adoption as payback rises. In general, many economic variables influencing the Cost and energy savings of the technology have a statistically significant effect on the decision to adopt, but the size of the effect is rather small. For example, a longer payback period lowers the probability of adoption by 2% per year. Other effects suggest resource constraints or risk aversion. Higher implementation costs lower the probability of adoption. For every thousand-dollar increase in implementation costs, the probability is lowered by 0.03%.

### Duke Scholars

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### Citation

*Proceedings Aceee Summer Study on Energy Efficiency in Industry*,

*1*, 521–533.

*Proceedings Aceee Summer Study on Energy Efficiency in Industry*1 (December 1, 2001): 521–33.

*Proceedings Aceee Summer Study on Energy Efficiency in Industry*, vol. 1, Dec. 2001, pp. 521–33.