Energy and the Environment
Paper Session
Saturday, Jan. 7, 2017 1:00 PM – 3:00 PM
Hyatt Regency Chicago, Acapulco
- Chair: Catherine Wolfram, University of California-Berkeley
I'm Sitting This One Out: What Non-Participants Reveal About Counterfactual Emissions
Abstract
In a voluntary emissions-reductions system, regulators must evaluate and sign off on firms' claims of what they would do absent credits. This paper uses the behavior of non-participants and rejected applicants to ex-post evaluate these claims. We focus on carbon offset projects in industrial energy efficiency, co-generation, input substitution, and fuel switching that are supplied by firms in India to the international emissions trading market through the Clean Development Mechanism (CDM). We identify the firms involved in over 600 CDM projects in a comprehensive dataset of Indian manufacturing firms. We first look for signs of strategic selection into program participation. After controlling for firm size and industry, there is no evidence that applicants are more likely to have decreasing emissions trends pre-application. We then evaluate behavior ex-post. We find that participants indeed reduce emissions relative to similar non-participant firms, but in a way that is moderated by a greater expansion of output. Looking across project types, the largest emission reductions come from projects that improve energy efficiency and export excess energy to the grid. Fuel switching and input blending projects are more questionable recipients of credits because non-participants engage in these activities at similar rates. JEL codes: Q56, Q52, O14.Knowledge Capital, Technology Adoption and Environmental Policies: Evidence From the United States Automobile Industry
Abstract
Technology plays a key role in reducing greenhouse gas emissions from the transportation sector. I estimate a structural model of the car industry that allows for endogenous product characteristics to investigate how gasoline taxes, R&D subsidies and competition affect fuel efficiency and vehicle prices in the medium-run, both through car-makers’ decisions to adopt technologies and through their investments in knowledge capital. I use technology adoption and automotive patents data for 1986-2006 to estimate this model. I show that 92% of fuel efficiency improvements between 1986 and 2006 were driven by technology adoption, while the role of knowledge capital is largely to reduce the marginal production costs of fuel-efficient cars. A counterfactual predicts that an additional $1/gallon gasoline tax in 2006 would have increased the technology adoption rate, and raised average fuel efficiency by 0.47 miles/gallon, twice the annual fuel efficiency improvement in 2003-2006. An R&D subsidy that would reduce the marginal cost of knowledge capital by 25% in 2006 would have raised investment in knowledge capital. This subsidy would have raised fuel efficiency only by 0.06 miles/gallon in 2006, but would have increased variable profits by $2.3 billion over all firms that year. Industry competitiveness also affects the two types of technology improvement choices. JEL Codes: L13, L62, O3, Q4, Q55.Climate Adaptation: Evidence From Extreme Weather
Abstract
As climate change progresses and the frequency of extreme weather events increases, will agents be able to adapt to reduce the associated damage? This paper develops a two- part strategy to quantify the existing level of extreme weather adaptation and its effects on damage across U.S. counties. First, we estimate the relationship between the frequency of extreme weather events and damage. Second, we use our empirical estimates to calibrate a simple dynamic model that relates frequency and damage to adaptation. From this calibrated model, we quantify the level of adaptation and its effects on damage. We find that even in the most event-prone areas, adaptation investments are relatively small and reduce the damage from extreme weather by less than ten percent. JEL Codes: Q50, Q54.Discussant(s)
Koichiro Ito
, University of Chicago
Rema Hanna
, Harvard University
Katie S. Whitefoot
, Carnegie Mellon University
Lint Barrage
, Brown University
JEL Classifications
- Q4 - Energy
- Q5 - Environmental Economics