Leslie A. Martin

Dynamic conservation for migratory species

Citizen science, big data, and a habitat marketplace enable dynamic habitat for migratory birds in California’s Central Valley. In an era of unprecedented and rapid global change, dynamic conservation strategies that tailor the delivery of habitat to when and where it is most needed can be critical for the persistence of species, especially those with diverse and dispersed habitat requirements. We demonstrate the effectiveness of such a strategy for migratory waterbirds. We analyzed citizen science and satellite data to develop predictive models of bird populations and the availability of wetlands, which we used to determine temporal and spatial gaps in habitat during a vital stage of the annual migration. We then filled those gaps using a reverse auction marketplace to incent qualifying landowners to create temporary wetlands on their properties. This approach is a cost-effective way of adaptively meeting habitat needs for migratory species, optimizes conservation outcomes relative to investment, and can be applied broadly to other conservation challenges.

When Do Firms Go Green? Comparing Price Incentives with Command and Control Regulations in India

There are two commonly accepted views about command-and-control (CAC) environmental regulation. First, CAC delivers environmental outcomes at very high cost. Second, in a developing country with weak regulatory institutions, CACs may not even yield environmental benefits: regulators can force firms to install pollution abatement equipment, but cannot ensure that they use it. We examine Indias experience and find evidence that CAC policies achieved substantial environmental benefits at a relatively low cost. Constructing an establishment-level panel from 1998 to 2009, we find that the CAC regulations imposed by Indias Supreme Court on 17 cities improved air quality with little effect on establishment productivity. We document a strong effect of deterred entry of high-polluting industries into regulated cities; however little effect on the overall level of manufacturing output, employment, or productivity in those cities. We also find sustained reductions in within-establishment coal use, with no evidence of leakage into other fuels. To benchmark our results, we use variation in coal prices to compare the CAC policies to price incentives. We show that CAC regulations were primarily effective at reducing coal consumption of large urban polluters, while a coal tax is likely to have a broader impact across all establishment types. Our estimated coal price elasticity suggests that a 15-30% excise tax would be needed to generate reductions in coal consumption equivalent to those produced by these CAC policies.

An Experimental Study of Monthly Electricity Demand (In)Elasticity

We document substantial rigidity in household electricity demand in response to large price shocks. We partnered with an electricity retailer to run a field experiment in which randomly-selected households received discounts of up to 50% on their total electricity bill or up to 95% off their per unit cost of electricity for a full month. We show that the quantity of electricity consumed was unaffected by these discounts. Exploiting rich billing, smart meter, and survey data, we document responses that are much more inelastic than previously observed in scenarios that raise prices for a few hours or raise or lower prices for indefinitely-long periods of time. Our results hold even among subgroups that we ex-ante believed were most likely to respond.

Can Road Charges Alleviate Congestion

We describe the results of a large road use pricing experiment that installed GPS responders in 1400 vehicles and implemented usage, time-of-day, and cordon charges via a system of virtual accounts. Using six-second location data collected over an eight to ten month period, we find a mean price elasticity of -0.13 to per kilometer charges, which is consistent with the literature on short-term demand response to fuel price increases. However constant charges do not reduce congestion; they lead primarily to reductions in high-speed driving and off-peak road use. We show that charges targeted at peak times or central areas are more successful in relieving congestion. We also document the role of public transit, providing evidence that time-of-day and cordon charges push out the distance between home and station at which households opt to walk to public transport. We see no increase in driving to commuter rail and no reduction in commutes to work. We conclude by showing that low-income drivers are the most responsive to these road use charges and benefit the most from replacing existing transport taxes with fees that reflect each drivers contribution to road use externalities.

Smart Meters, Personalized Feedback and Retailer Switching in Electricity Markets

This paper uses a randomized experiment and a structural model to study the extent to which consumers value receiving personalized feedback from smart electricity meters. Using data from a market with retail competition, we examine whether consumers “vote with their feet”, either by switching retailers or exhibiting increased loyalty for their existing retailer, in response to messages they receive based on their smart meter data. We find consumers value being informed: randomly offering information from smart meters to consumers reduces the overall switching rate by 8%. Among customers who engage with their smart meter data we find a 21% reduction in retailer switching.