Study: How Should Backing for Clean Technology be Designed?

Governments often offer subsidies to consumers for clean-technology products, from home solar panels to electric vehicles. But what are the right levels of subsidy, and how should they be calculated? As a new paper co-authored by MIT researchers shows, governments can easily make subsidies too low when they ignore a basic problem: Consumer demand for these products is usually highly uncertain.

Indeed, the paper’s analysis suggests this has already happened in the case of the Chevy Volt, an electric car introduced in 2010 that suffered slow initial sales before gaining more traction in the marketplace.

“The government will miss their target by a lot when ignoring demand uncertainty,” says Georgia Perakis, the William F. Pounds Professor of Management at the MIT Sloan School of Management and a co-author of the paper.

While discussion of “demand uncertainty” might sound a bit abstract, it matters. Governments usually provide subsidies based on overall adoption targets, such as the number of cars or solar panels they would like to see adopted over a period of time. But green technologies are often new products, and no one really knows how many consumers are waiting to buy them.

Some models of subsidies assume a steady ratio between the dollar amount of the subsidy and the total number of cars or solar panels that will be sold. But as the new paper indicates, that’s not quite the right approach. Given uncertain markets, subsidy levels don’t correlate steadily with sales. Instead, it takes relatively high subsidy levels to kick-start a certain amount of business; then a more gradual increase can help achieve higher sales.

For clean technologies, the research project shows, these increased subsidies should still pay for themselves even at higher levels, when issues such as reductions in pollution, which lead to lower health-care costs, are factored in.

The paper, “The Impact of Demand Uncertainty on Consumer Subsidies for Green Technology Adoption,” has been published online by Management Science. The co-authors are Perakis; Maxime C. Cohen PhD ’15, an assistant professor at New York University; and Ruben Lobel PhD ’12, an assistant professor at the University of Pennsylvania.

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