Recent Working Papers

Machine Learning from Schools about Energy Efficiency

Fiona Burlig, Christopher R. Knittel, David Rapson, Mar Reguant, Catherine Wolfram

In the United States, consumers invest billions of dollars annually in energy efficiency, often on the assumption that these investments will pay for themselves via future energy cost reductions. We study energy efficiency upgrades in K-12 schools in California. We develop and implement a novel machine learning approach for estimating treatment effects using high-frequency panel data, and demonstrate that this method outperforms standard panel fixed effects approaches. We find that energy efficiency upgrades reduce electricity consumption by 3 percent, but that these reductions total only 24 percent of ex ante expected savings. HVAC and lighting upgrades perform better, but still deliver less than half of what was expected. Finally, beyond location, school characteristics that are readily available to policymakers do not appear to predict realization rates across schools, suggesting that improving realization rates via targeting may prove challenging.

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Attribute Substitution in Household Vehicle Portfolios

James Archsmith (UC Davis), Kenneth Gillingham (Yale), Christopher R. Knittel (MIT), and David Rapson (UC Davis)

Household preferences for goods with a bundle of attributes may have complex substitution patterns when one attribute is changed. For example, a household faced with an exogenous increase in the size of one television may choose to decrease the size of other televisions within the home. This paper quantifies the extent of attribute substitution in the context of multi-vehicle households. We deploy a novel identification strategy to examine how an exogenous change in the fuel economy of a kept vehicle affects a household's choice of a second vehicle. We find strong evidence of attribute substitution in the household vehicle portfolio. This effect operates through car attributes that are correlated with fuel economy, including vehicle footprint and weight. Our findings suggest that attribute substitution exerts a strong force that may erode a substantial portion of the expected future gasoline savings from fuel economy standards, particularly those that are attribute-based. Elements of our identification strategy are relevant to a broad class of settings in which consumers make sequential purchases of durable portfolio goods.

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Subsidizing Fuel Efficient Cars: Evidence from China's Automobile Industry

Chia-Wen Chen, Wei-Min Hu, Christopher R. Knittel

The Chinese automobile market is the largest in the world with annual sales exceeding 20 million vehicles. The tremendous growth in sales---over 200 percent from 2008 to 2015---and concerns over local air quality have prompted China's policy makers to incentivize the adoption of more fuel efficient vehicles. We examine the response of vehicle purchase behavior to China's largest national subsidy program for fuel efficient vehicles during 2010 and 2011. Using variation from the program's eligibility cutoffs, we find that the program boosted sales for subsidized vehicle models, but that the program also created a substitution effect within highly fuel efficient vehicles and most subsidies went to inframarginal consumers. This substitution effect greatly reduces the cost effectiveness of the program. We calculate that the average cost per ton of carbon dioxide saved is over 82 USD, well above the social cost of carbon used in U.S. regulatory filings. Using the framework in Boomhower and Davis (2014) and accounting for local pollution benefits, we show that ignoring the substitution effect would lead one to conclude that the program is welfare enhancing, whereas in fact the marginal cost of the program exceeds the marginal benefit by almost as much as 300 percent. We also show that the program was not well-targeted; the effect of the subsidy on sales of fuel efficient vehicles was smaller in areas where consumers were more likely to purchase fuel inefficient models or were lower educated.

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