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Fighting Fire with Fire(fighting Foam): The Long Run Effects of PFAS Use at U.S. Military Installations
December 2024
Working Paper Number:
CES-24-72
Tens of millions of people in the U.S. may be exposed to drinking water contaminated with perand poly-fluoroalkyl chemicals (PFAS). We provide the first estimates of long-run economic costs from a major, early PFAS source: fire-fighting foam. We combine the timing of its adoption with variation in the presence of fire training areas at U.S. military installations in the 1970s to estimate exposure effects for millions of individuals using natality records and restricted administrative data. We document diminished birthweights, college attendance, and earnings, illustrating a pollution externality from military training and unregulated chemicals as a determinant of economic opportunity.
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Empirical Distribution of the Plant-Level Components of Energy and Carbon Intensity at the Six-digit NAICS Level Using a Modified KAYA Identity
September 2024
Working Paper Number:
CES-24-46
Three basic pillars of industry-level decarbonization are energy efficiency, decarbonization of energy sources, and electrification. This paper provides estimates of a decomposition of these three components of carbon emissions by industry: energy intensity, carbon intensity of energy, and energy (fuel) mix. These estimates are constructed at the six-digit NAICS level from non-public, plant-level data collected by the Census Bureau. Four quintiles of the distribution of each of the three components are constructed, using multiple imputation (MI) to deal with non-reported energy variables in the Census data. MI allows the estimates to avoid non-reporting bias. MI also allows more six-digit NAICS to be estimated under Census non-disclosure rules, since dropping non-reported observations may have reduced the sample sizes unnecessarily. The estimates show wide variation in each of these three components of emissions (intensity) and provide a first empirical look into the plant-level variation that underlies carbon emissions.
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Technology Lock-In and Costs of Delayed Climate Policy
July 2023
Working Paper Number:
CES-23-33
This paper studies the implications of current energy prices for future energy efficiency and climate policy. Using U.S. Census microdata and quasi-experimental variation in energy prices, we first show that manufacturing plants that open when electricity prices are low consume more energy throughout their lifetime, regardless of current electricity prices. We then estimate that a persistent bias of technological change toward energy can explain the long-term effects of entry-year electricity prices on energy intensity. Overall, this 'technology lock-in' implies that increasing entry-year electricity prices by 10% would decrease a plant's energy intensity of production by 3% throughout its lifetime.
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Is Air Pollution Regulation Too Lenient? Evidence from US Offset Markets
June 2023
Working Paper Number:
CES-23-27R
This paper describes a framework to estimate the marginal cost of air pollution regulation, then applies it to assess whether a large set of existing U.S. air pollution regulations have marginal benefits exceeding their marginal costs. The approach utilizes an important yet under-explored provision of the Clean Air Act requiring new or expanding plants to pay incumbents in the same or neighboring counties to reduce their pollution emissions. These "offset" regulations create several hundred decentralized, local markets for pollution that differ by pollutant and location. Economic theory and empirical tests suggest these market prices reveal information about the marginal cost of abatement for new or expanding firms. We compare estimates of the marginal benefit of abatement from leading air quality models to offset prices. We find that, for most regions and pollutants, the marginal benefits of pollution abatement exceed mean offset prices more than ten-fold. In at least one market, however, estimated marginal benefits are below offset prices.
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Exploring New Ways to Classify Industries for Energy Analysis and Modeling
November 2022
Working Paper Number:
CES-22-49
Combustion, other emitting processes and fossil energy use outside the power sector have become urgent concerns given the United States' commitment to achieving net-zero greenhouse gas emissions by 2050. Industry is an important end user of energy and relies on fossil fuels used directly for process heating and as feedstocks for a diverse range of applications. Fuel and energy use by industry is heterogeneous, meaning even a single product group can vary broadly in its production routes and associated energy use. In the United States, the North American Industry Classification System (NAICS) serves as the standard for statistical data collection and reporting. In turn, data based on NAICS are the foundation of most United States energy modeling. Thus, the effectiveness of NAICS at representing energy use is a limiting condition for current
expansive planning to improve energy efficiency and alternatives to fossil fuels in industry. Facility-level data could be used to build more detail into heterogeneous sectors and thus supplement data from Bureau of the Census and U.S Energy Information Administration reporting at NAICS code levels but are scarce. This work explores alternative classification schemes for industry based on energy use characteristics and validates an approach to estimate facility-level energy use from publicly available greenhouse gas emissions data from the U.S. Environmental Protection Agency (EPA). The approaches in this study can facilitate understanding of current, as well as possible future, energy demand.
First, current approaches to the construction of industrial taxonomies are summarized along with their usefulness for industrial energy modeling. Unsupervised machine learning techniques are then used to detect clusters in data reported from the U.S. Department of Energy's Industrial Assessment Center program. Clusters of Industrial Assessment Center data show similar levels of correlation between energy use and explanatory variables as three-digit NAICS codes. Interestingly, the clusters each include a large cross section of NAICS codes, which lends additional support to the idea that NAICS may not be particularly suited for correlation between energy use and the variables studied. Fewer clusters are needed for the same level of correlation as shown in NAICS codes. Initial assessment shows a reasonable level of separation using support vector machines with higher than 80% accuracy, so machine learning approaches may be promising for further analysis. The IAC data is focused on smaller and medium-sized facilities and is biased toward higher energy users for a given facility type. Cladistics, an approach for classification developed in biology, is adapted to energy and process characteristics of industries. Cladistics applied to industrial systems seeks to understand the progression of organizations and technology as a type of evolution, wherein traits are inherited from previous systems but evolve due to the emergence of inventions and variations and a selection process driven by adaptation to pressures and favorable outcomes. A cladogram is presented for evolutionary directions in the iron and steel sector. Cladograms are a promising tool for constructing scenarios and summarizing directions of sectoral innovation.
The cladogram of iron and steel is based on the drivers of energy use in the sector. Phylogenetic inference is similar to machine learning approaches as it is based on a machine-led search of the solution space, therefore avoiding some of the subjectivity of other classification systems. Our prototype approach for constructing an industry cladogram is based on process characteristics according to the innovation framework derived from Schumpeter to capture evolution in a given sector. The resulting cladogram represents a snapshot in time based on detailed study of process characteristics. This work could be an important tool for the design of scenarios for more detailed modeling. Cladograms reveal groupings of emerging or dominant processes and their implications in a way that may be helpful for policymakers and entrepreneurs, allowing them to see the larger picture, other good ideas, or competitors. Constructing a cladogram could be a good first step to analysis of many industries (e.g. nitrogenous fertilizer production, ethyl alcohol manufacturing), to understand their heterogeneity, emerging trends, and coherent groupings of related innovations.
Finally, validation is performed for facility-level energy estimates from the EPA Greenhouse Gas Reporting Program. Facility-level data availability continues to be a major challenge for industrial modeling. The method outlined by (McMillan et al. 2016; McMillan and Ruth 2019) allows estimating of facility level energy use based on mandatory greenhouse gas reporting. The validation provided here is an important step for further use of this data for industrial energy modeling.
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The U.S. Manufacturing Sector's Response to Higher Electricity Prices: Evidence from State-Level Renewable Portfolio Standards
October 2022
Working Paper Number:
CES-22-47
While several papers examine the effects of renewable portfolio standards (RPS) on electricity prices, they mainly rely on state-level data and there has been little research on how RPS policies affect manufacturing activity via their effect on electricity prices. Using plant-level data for the entire U.S. manufacturing sector and all electric utilities from 1992 ' 2015, we jointly estimate the effect of RPS adoption and stringency on plant-level electricity prices and production decisions. To ensure that our results are not sensitive to possible pre-existing differences across manufacturing plants in RPS and non-RPS states, we implement coarsened exact covariate matching. Our results suggest that electricity prices for plants in RPS states averaged about 2% higher than in non-RPS states, notably lower than prior estimates based on state-level data. In response to these higher electricity prices, we estimate that plant electricity usage declined by 1.2% for all plants and 1.8% for energy-intensive plants, broadly consistent with published estimates of the elasticity of electricity demand for industrial users. We find smaller declines in output, employment, and hours worked (relative to the decline in electricity use). Finally, several key RPS policy design features that vary substantially from state-to-state produce heterogeneous effects on plant-level electricity prices.
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Regulating Mismeasured Pollution: Implications of Firm Heterogeneity for Environmental Policy
August 2018
Working Paper Number:
CES-18-03R
This paper provides the first estimates of within-industry heterogeneity in energy and CO2 productivity for the entire U.S. manufacturing sector. We measure energy and CO2 productivity as output per dollar energy input or per ton CO2 emitted. Three findings emerge. First, within narrowly defined industries, heterogeneity in energy and CO2 productivity across plants is enormous. Second, heterogeneity in energy and CO2 productivity exceeds heterogeneity in most other productivity measures, like labor or total factor productivity. Third, heterogeneity in energy and CO2 productivity has important implications for environmental policies targeting industries rather than plants, including technology standards and carbon border adjustments.
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Relative Effectiveness of Energy Efficiency Programs versus Market Based Climate Policies in the Chemical Industry
April 2018
Working Paper Number:
CES-18-16
This paper addresses the relative effectiveness of market vs program based climate policies. We compute the carbon price resulting in an equivalent reduction in energy from programs that eliminate the efficiency gap. A reduced-form stochastic frontier energy demand analysis of plant level electricity and fuel data, from energy-intensive chemical sectors, jointly estimates the distribution of energy efficiency and underlying price elasticities. The analysis controls for plant level price endogeneity and heterogeneity to obtain a decomposition of efficiency into persistent (PE) and time-varying (TVE) components. Total inefficiency is relatively small and price elasticities are relatively high. If all plants performed at the 90th percentile of their efficiency distribution, the reduction in energy is between 4% and 13%. A modest carbon price of between $9.48/ton and $14.01/ton CO2 would achieve reductions in energy use equivalent to all manufacturing plants making improvements to close the efficiency gap.
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Longitudinal Environmental Inequality and Environmental Gentrification: Who Gains From Cleaner Air?
May 2017
Working Paper Number:
carra-2017-04
A vast empirical literature has convincingly shown that there is pervasive cross-sectional inequality in exposure to environmental hazards. However, less is known about how these inequalities have been evolving over time. I fill this gap by creating a new dataset, which combines satellite data on ground-level concentrations of fine particulate matter with linked administrative and survey data. This linked dataset allows me to measure individual pollution exposure for over 100 million individuals in each year between 2000 and 2014, a period of time has seen substantial improvements in average air quality. This rich dataset can then be used to analyze longitudinal dimensions of environmental inequality by examining the distribution of changes in individual pollution exposure that underlie these aggregate improvements. I confirm previous findings that cross-sectional environmental inequality has been on the decline, but I argue that this may miss longitudinal patterns in exposure that are consistent with environmental gentrification. I find that advantaged individuals at the beginning of the sample experience larger pollution exposure reductions than do initially disadvantaged individuals.
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Consequences of the Clean Water Act and the Demand for Water Quality
January 2017
Working Paper Number:
CES-17-07
Since the 1972 U.S. Clean Water Act, government and industry have invested over $1 trillion to abate water pollution, or $100 per person-year. Over half of U.S. stream and river miles, however, still violate pollution standards. We use the most comprehensive set of files ever compiled on water pollution and its determinants, including 50 million pollution readings from 170,000 monitoring sites, to study water pollution's trends, causes, and welfare consequences. We have three main findings. First, water pollution concentrations have fallen substantially since 1972, though were declining at faster rates before then. Second, the Clean Water Act's grants to municipal wastewater treatment plants caused some of these declines. Third, the grants' estimated effects on housing values are generally smaller than the grants' costs.
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