Working Paper Series
The Roosevelt Project Special Series

Distributed Effects of
Climate Policy: A Machine
Learning Approach
Tomas W. Green and CHristopher R. Knittel

Roosevelt Project Report Sponsor
The Roosevelt Project participants thank the Emerson Collective forsponsoring this report and for their continued leadership on issues
at the intersection of social justice and environmental stewardship.

Distributed Effects of Climate Policy:
A Machine Learning Approach
Tomas Green and Christopher R. Knittel∗
September 2020
Abstract
We employ machine learning techniques to estimate household carbon footprints (HCFs)
for the average household in each Census tract—geographic areas that represent roughly
4,000 people. We find that there is significant variation in carbon footprints acrossincome and geography; income effects are driven by higher footprints related to transportation and consumer products and services, while geographic effects are primarilya result of the variable carbon intensity of the electricity grid. Using these footprints,
we assess the net effects of various climate policies on households in the United States
paying particular attention to the distribution across geography, urbanity, and income
groups. Our objective is to improve the understanding of the potential for regressivity,
geographic transfers, and rural-urban transfers among climate policy options and test
for ways to control for transfers—preserving transfers from high-income households to
low-income households, but mitigating transfers from rural areas to urban areas andfrom the Midwest and South to the Coasts. Our focus is on the net increase or decreaseof annual household expenses under 12 different policy scenarios, which included bothcarbon pricing schemes and regulatory standards. We find regulatory standards tend
to be regressive and, on average, are a net cost to low-income households—especiallythose in rural areas. Carbon pricing, when accompanied with a dividend, is progressive
for urban, rural, and suburban households, with the average low-income household receiving a larger dividend check than they spend in carbon taxes. However, there aretransfers from the Midwest and Plains to the Coasts when the dividend is evenly divided. We show that this can be mitigated through adjusting the dividend slightly(<8% increase or decrease). Increasing the progressive structure of a policy benefits
rural households more on average, but increases the overall heterogeneity of impacts
within each income group. Reducing the transfers between geographic regions andurban-rural households increases the average benefit to low-income households and reduces the heterogeneity of impacts within income groups. We encourage policy makers
to assess and control for unwanted transfers between households.
∗

Green: Roosevelt Project and Center for Energy and Environmental Policy Research, MIT, tomasgreen@alumn.mit.edu. Knittel: George P. Shultz Professor Sloan School of Management, Director Center
for Energy and Environmental Policy Research, Co-Director, Electric Power Systems Low Carbon Energy
Center, MIT and NBER, knittel@mit.edu. This paper has benefited from conversations with David Hsu,
Michael Kearney, Ernie Moniz, and the rest of the Roosevelt Project research team.

1

1

Introduction

How do policy makers address climate change aggressively enough to meet climate goals
without harming communities and households historically reliant on fossil fuels? Whileeconomic literature has determined that a carbon tax is the most economically efficient way to
fix the carbon “externality,” depending on what policy makers do with the tax revenues, thetax can be regressive. The potential regressive nature arises because low-income households
spend a larger share of their income on energy, and energy-consuming, products. Therefore,
ignoring how revenue is recycled, a carbon tax is likely to be regressive.1 Furthermore,households in the industrial heartland and the Midwest are reliant on a carbon-intensive
electrical grid; they also generally lack adequate public transit options and use fossil fuels toheat their homes during cold winters. Consequently, these households tend to spend a larger
share of their income on energy relative to households that live in coastal areas.
These stylized facts of carbon consumption across incomes and geography imply thatthe details of a carbon tax policy are of upmost importance. Whether a specific policy that
establishes a price on carbon is regressive or harms certain regions of the country depends
on how the carbon revenue is recycled. An additional stylized fact with respect to energy
consumption implies that a number of recycling methods will be progressive. Specifically,
although low-income households spend a larger share of their income on energy, high-income
households spend a larger amount on energy. Therefore, the high-income households add
a larger amount to the pool of revenues than they receive back as a dividend, while lowincome households, on average at least, receive a larger dividend than they pay in taxes.
This implies that a simple policy that rebates revenues equally to all households, a so-called
tax-and-dividend plan, will be progressive; low-income households will, on average, receive alarger dividend check than they spend in carbon taxes. However, a simple tax-and-dividend
plan that does not differentiate based on geography runs the risk of benefiting the industrial
heartland and the Midwest less than the Coasts.
These same concerns for geographic distribution and regressivity hold for alternative
climate change policies. Historically, instead of pricing carbon, policy makers have relied on
instruments such as a Corporate Average Fuel Economy (CAFE) standards and subsidies forelectric vehicles in transportation and Renewable Portfolio Standards (RPS) and subsidies
for wind and solar in electricity. While these policies tend to keep prices for the regulated
products lower, compared to a price on carbon, they, too, can be regressive (see, for example,
1. Recent work by Goulder et al. (2019) notes that once one considers the impact of a carbon tax on
inflation-adjusted transfers, wages, and capital, a carbon tax can be progressive even if one were to ignore
the revenues.

1Burger et al. (2020) and Davis and Knittel (2019)). Furthermore, these alternatives do not
generate revenues that can provide transfers to vulnerable groups.
In this paper, we bring attention, and perhaps more importantly data, to these issues
for policy making. Using data on energy consumption, transportation habits, and consumer
behavior from representative samples of US households to generate predictions of those same
households across all US households, we use machine learning techniques to better predict
consumption of households.2
Given the estimation of energy, products, and services consumption for 72,538 of the74,134 Census tracts in the US, we then model various policy designs to estimate the annual
cost and benefit of each policy on the average household. We analyze not only carbon pricing,with a variety of different revenue recycling plans, but also regulatory policies such as CAFE
and a Clean Energy Standard (CES). We add to our understanding of the importance ofgeography in policy outcomes, the leverage policy makers have to correct for the urbanrural divide, and the progressive outcome for a carbon price and dividend scheme comparedto other regulatory approaches. We calculate the incidence in these policies across income
quintiles and generate maps of the incidence across the geography. We also aggregate theseeffects across Congressional districts and correlate the impacts across political party.
Our results suggest that while a simple tax-and-dividend plan does a good job protecting low-income households, the distributional impacts across rural and urban households
may concern policy makers. As such, we analyze alternative revenue recycling plans thatvary across urbanity, income, Census regions, and electricity reliability regions. We showthat allowing household dividends to depend on certain readily-observable features of thehousehold allows policy makers to protect certain vulnerable populations.
Our analysis also points out that while all of the carbon tax-and-dividend plans weanalyze are progressive, alternatives to carbon pricing are regressive. The negative effects of
CAFE standards, as a share of income, are monotonically decreasing across income quintiles
implying CAFE standards are regressive.3 We find that the same is true for a clean energy
standard. Our modeling of the Obama Administration’s Clean Power Plan also suggeststhat it would have been a regressive policy. The results with respect to a CES and the
CPP are not surprising. These policies increase electricity prices, but do not generate any
revenues that can be used to overcome the regressivity of higher energy prices.4 The result
2. Our work extends past work that uses regression-based techniques. See, for example, Jihoon Min,
Hausfather, and Qi Feng Lin (2010) and C. Jones and Kammen (2014).
3. This replicates the results in Davis and Knittel (2019).
4. Our analysis ignores the climate benefits and co-benefits of such policy as they cannot be properly
estimated at the household level. Such costs are necessary to consider and are well covered in existing2with respect to CAFE standards is more nuanced. CAFE standards are an implicit tax-andsubsidy program, incentivizing car-markers to produce more vehicles that are more efficientand fewer cars that are less efficient. Thus, in equilibrium the car-makers implicitly tax
vehicles that are worse than the standard and subsidize vehicles that are better than thestandard.5 Therefore, the costs of CAFE to a household fall with the fuel economy ofthe vehicles in the household and can be negative. Because high-income households are
more likely to purchase vehicles with more technology (e.g., hybrids, EVs, etc.) and middle
America is more likely to own larger vehicles, high-income households are more likely to gainfrom CAFE standards, while low-income and Midwest households lose.
With respect to climate policy, these results underscore an important lesson: policies
that generate revenue, within the policy itself, afford policy makers with more flexibility toprotect certain groups. Carbon pricing affords policy makers with a direct source of revenue
to shape the incidence of a policy; policies such as CAFE Standards and CESs generate
regressive outcomes and large geographic differences in incidence, but not such revenues.
Therefore, for policy makers to undo these negative outcomes, additional revenue sources
will need to be found. This may be politically challenging, leaving the negative outcomesamong low-income and Midwest households intact. To be clear, all policy creates winners
and losers. Our focus is on understanding the impact on vulnerable groups and using our
results to understand how climate policies can be shaped to support these vulnerable groups.
Spurring the change necessary to steeply cut carbon emissions will pose significant costs andif these costs are distributed through regressive policy, the transition to a sustainable future
will not be equitable or sustainable.

2

BackgroundIn this section we discuss the existing literature pertaining to the impact of climate change
policies on households and how this varies by household types. We begin discussing carbon
pricing and then move to alternatives.

2.1

Carbon Taxes with and without Revenue Recycling

Ignoring the revenues generated, carbon taxation is generally thought of to be regressive toincome and expenditures (Metcalf et al. (2008); Mathur and Morris (2014)). However, theliterature.
5. See Davis and Knittel (2019) and Holland, Hughes, and Knittel (2009).

3

revenues from carbon taxes can be used to offset regressive effects. The Energy Modeling
Forum Model Inter-comparison Project Number 32 (EMF 32) convened 11 groups of academics who compared different models for the impact of a carbon tax with various revenue
recycling mechanisms (Mcfarland et al. (2018)). Papers varied in terms of the underlying
model assumptions, the structure of the carbon tax, and the types of recycling methods
employed. They examined both the impact to individual economic actors and to the overall
economy; taken together, this gives insight into the trade-off between equity and efficiency.
The first general conclusion from this work is that emission outcomes are largely insensitive to revenue recycling methods, but welfare and distributional outcomes can vary widely(Jorgenson et al. (2018)). Compared between using revenue for capital income tax reductions, labor income tax reductions, and lump sum transfers, capital tax cuts are the mostefficient and the most regressive recycling method, while lump sum transfers are the least
efficient and most progressive (Mcfarland et al. (2018), Goulder et al. (2019), Woollacott(2018), Jorgenson et al. (2018)). The second general result is that the use of the revenue
is important in determining the incidence of a given tax, but it is also important for efficiency considerations. The best use of the revenue, from an economic efficiency standpoint,
is to use it to reduce other, distortionary, taxes that exist. However, using the revenues toreduce other taxes can have important implications for incidence. For example, US capitaland labor-income taxes are designed to be progressive. Therefore, if you were to replacethese progressively-generated tax revenues with a regressively-generated carbon tax source,
the carbon tax will necessarily be regressive. These themes are included in (Mcfarland etal. (2018), Goulder et al. (2019), Woollacott (2018), Jorgenson et al. (2018).
A consensus within EMF 32 formed on the progressive benefits of a carbon fee-anddivided structure and the overall economic efficiency of reducing capital taxes. Issuing payments to households through a price-and-dividend scheme, while progressive, reduces economic efficiency and increases the heterogeneity of impact within an income group (Cronin,
Fullerton, and Sexton (2019)). Adopting a hybrid policy6 was found to have greater progressive effects and lower welfare loss compared to a pure-recycling method (Goulder etal. (2019)).
More recent work has found that the incidence of carbon taxes alone (ignoring uses ofrevenue) depends on the scope of the economic effects studied. A combined source- (e.g.,
production and transfers) and use-side (e.g., consumption) analysis can change conclusions
6. The hybrid policy consisted of implementing both tax reductions and lump-sum transfer, designed suchthat rebates were targeted to avoid welfare loss in the bottom two or three quintiles and the remainder ofthe revenue was used to reduce taxes.

4compared to only a use-side study (Goulder et al. (2019)). While the use-side effects ofcarbon taxes is regressive and reduces welfare for each recycling option examined (in keepingwith our findings and other literature), the source-side is progressive and positive for mostrecycling options. On net, source-side impacts outweighed the use-side.
However, the heterogeneity within an income group (whether quintile or decile) weremore heterogeneous than the effects across groups. The variability of impacts within an income group can be explained, in part, by a household’s geographic region. Regional impacts
tend to follow economic structures (e.g. impacts to Northeast and West Coast are similar),however capital ownership may be uncorrelated to regional impact depending on the presence of pass-through business entities (Ross (2018)). Regions also vary in terms of sourcesof energy used for the electric grid and for home heating. For example, many homes in New
England heat with fuel oil (a relatively dirty fuel), while homes in the Midwest tend to heatwith natural gas (a fuel with lower-emissions). Geography also accounts for significant variation in the co-benefits of climate policy (the benefits from reducing non-GHG contaminants
such as sulfates), as well in the welfare effects of policy (Woollacott (2018)).
The heterogeneity within an income group is a critical focus of our research. Indeed,
we show that where you live is nearly as strong a determinant of absolute policy impact as
your income.

2.2

Previous Estimates of the Costs of Climate Policies

Regulation
A long literature in economics seeks to understand the social cost of different climatechange policies per ton of CO2 reduced from the policy. Gillingham and Stock (2018) reviews
this literature and summarizes the empirical estimates of the cost per ton for a variety ofpolicies and technologies. We will refer to these as the implicit carbon cost. These arecalculated by dividing the cost of implementing the policy by the carbon emissions reducedby the policy. In many cases, the estimate range is wide because of high uncertainties in bothcosts and emissions avoided. Implicit carbon costs vary from negative prices for behavioral
energy efficiency programs (-$190/ton), to moderate prices ($11/ton) for EPA regulation
though the Clean Power Plan, to high prices for the Weatherization Assistance Program($350/ton) (Gillingham and Stock (2018)). A separate, but related question is what is thelevel of a carbon tax that would replace a given policy. Knittel (2019) finds that replacing
all current federal regulations with a carbon tax (in 2020) would require roughly $7 per ton
to achieve equivalent emissions reductions. This price would increase to $30 as regulations
5

ramp up by 2030.
Clean Energy Investments
Clean energy innovation as a method of reducing emission is widely embraced by prominent Republicans, Conservative-leaning organizations, and moderate Democrats. An optimal
path for inducing clean energy innovation is a combination of research subsidies and a carbon tax (Acemoglu et al. 2014). Compared to the optimal path, implementing only carbon
taxes is inefficient and would result in a welfare loss, although current US policy deviates
significantly from optimal policy and under current policies, climate change dynamics will
be significantly worse (Acemoglu et al. 2014).
Renewable Portfolio Standard/ Clean Energy Standard
A Renewable Portfolio Standard (RPS) has a range of implicit cost between $0 and$190 per ton of CO2 (Gillingham and Stock (2018)). Other estimates of the implicit cost of
an RPS is between $130 and $460 per ton (Greenstone and Nath 2019).
A research team at Resources for the Future modeled the effects of a Clean EnergyStandard (CES) introduced in Congress. Through a cost benefit analysis, they found net
benefits of $579 billion over the 2020 - 2035 time period. However, the study did not examine
welfare effects or an implicit carbon cost of a CES.

2.3

Previous Estimates of a Household Carbon Footprints

According to a multi-regional input output model, the largest aggregate contributors to HCF
are private transport (26%), home energy (23%), miscellaneous goods and services (10%),
health services (8%), and home food and beverages (6%). The remaining 30% of emissions are
distributed across the other 7 categories (Weber and Matthews (2008)). The largest fractionsof energy requirements of households (residential energy, transportation, and food)—and arealso the most energy intense per dollar (Wiedenhofer, Lenzen, and Steinberger (2013)).
Previous work has found that carbon footprints vary widely by income, urbanity, andgeography. With increasing income, direct energy (energy used to heat homes or drive cars)requirements raise weakly and indirect energy (energy involved in goods and services) raisestrongly (Wiedenhofer, Lenzen, and Steinberger (2013); Lenzen, Dey, and Foran (2004)).
As a result, emissions have a strong correlation with income (Sovacool and Brown (2010)),
but the emissions intensity per dollar declines with increasing income, as necessities are
more energy intense than luxuries (Lenzen (1998)). This distinction between total carbon
footprints and carbon intensity per dollar is important to consider when studying the effects
6

of urbanization on HCFs.
Total energy use and indirect energy is higher in urban households, but direct energy
use is lower than rural households (Wiedenhofer, Lenzen, and Steinberger (2013); Lenzen,
Dey, and Foran (2004)). Per dollar, direct energy has higher carbon content than indirectenergy consumption, leading rural households to have higher carbon intensity because moreof their budget is spent on energy intensive commodities, namely private transportation andresidential energy (Wiedenhofer, Lenzen, and Steinberger (2013); Munksgaard et al. (2005)).
While rural households have larger footprints than urban households (Baiocchi, Minx, andHubacek (2010)), it is not true that increasing urbanization decreases carbon footprints. As
population density increases, total carbon footprints weakly increase until a threshold is met,
at which point HFCs decline sharply (C. Jones and Kammen (2014); Ummel (2014)). This
trend is driven by the higher incomes and the greater vehicle miles traveled in the areas
outside of metropolitan centers. In result, the suburbs account for 50% of household carbon
emissions (C. Jones and Kammen (2014)), despite accounting for approximately one thirdof the U.S. Population.
While urban areas have lower emissions than suburbs, particularly in older areas suchas NYC, the differences within metropolitan areas (between city and suburbs) are smaller
than across metropolitan areas (e.g. between New York and San Francisco) (C. Jones andKammen (2014); Glaeser and Kahn (2008)). Variability in emissions depends, in part, oncity age. Older cities tend to have lower transit emissions but higher heating emissions
compared to newer cities (Glaeser and Kahn (2008)). Globally, urban density is not always
related to small carbon footprints, as there is greater dependence on the wealth of those
occupying city (Sovacool and Brown (2010)).

2.4

Policy and Policy Proposals

Carbon Pricing
Carbon pricing has gained prominence in the policy landscape over the last several years.
The Baker-Schultz carbon pricing framework has provided a road-map for a market-based
solution to climate change. It includes four main components: a carbon price (starting at$40 per ton and increasing at 2% above inflation), a dividend, a border adjustment for goods
traded into and out of the United States, and regulatory roll back (Baker et al. (2017)).
Seven bills introduced in the 116th Congress would implement a carbon price ranging
from $15 to $52 per metric ton of CO2 . The legislation varies in how revenue would be used;

7

five of the bills propose direct payments to consumers, either as a dividend or an increase
to social security; four bills include tax reform, either through a payroll tax cut or a tax
credit scheme; and most bills include ancillary uses of revenue such a research funding, block
grants, and infrastructure spending.7
Regulatory Policy
The Corporate Average Fuel Economy (CAFE) standard is a regulation that is a legacyof the 1970s Arab Oil Embargo. It has since been used with the goal of reducing carbon
emissions and was the subject of a protracted battle between the Trump Administrationand that State of California. CAFE mandates a certain fuel economy for the productionweighted average vehicle fleet. As noted above, this incents car-markers to produce more
vehicles that are more efficient, such as hybrids, and fewer cars that are less efficient, such
as SUVs, which creates an implicit subsidy and tax (respectively).
A Clean Energy Standard (CES) is a regulatory framework that mandates electricity
providers to acquire a certain percentage of their energy from clean or low-carbon sources.
There were two CES proposals in the 116th Congress, which both used on a credit trading
system to create a subsidy for clean electricity and an implicit tax on more carbon intensive
electricity. The way we model CES and CAFE is described in the following section.

3

Methodology

When estimating the effects of policy, the literature so far has applied economy-wide, sectoral models. Other bodies of research have quantified the size of household carbon footprints
according to sources of consumption. We seek to bridge these bodies of work by estimating the impacts of climate policy on households and examining the resulting geographic anddemographic variability. We seek to fill the important gap of identifying how the heterogeneity within an income group varies across the United States and its implications for policy
design. We also attempt to compare household impacts of tax policies versus regulatory
policies, showing that the impact to the public can be obscured and that the cost of regulatory compliance is passed on in regressive ways. This entails generating carbon footprints
at a fine-geographic resolution. To do this, we will use data on representative samples of US
households and build machine learning models to project the relationships in these data on
the entire US.
7. Resources for the Future compiles information on carbon pricing bills here: RFF Carbon Pricing Bill
Tracker.

8

The general process is as follows: we start with a data set that reports consumption of
a commodity along with household demographics and physical characteristics of housing fora representative sample of US consumers. Next, we use machine learning techniques thattrain a prediction model relating household demographics, geographic characteristics, andweather data to energy consumption. Finally, we apply the model to Census data with theequivalent variables at the smallest geographic unit of analysis. For most of our analysis thisis a Census tract. We developed 17 models from three federal surveys. The models predict
consumption of a utility, product, or service for each household. We then use emissions
factors to convert consumption to HCF.

3.1

Data Sources

The 2015 Regional Energy Consumption Survey (RECS) is used to estimate the electricity
and heating fuel consumed by each household (U.S. Energy Information Administration(2018)). RECS, conducted by the Energy Information Administration is comprised of two
surveys: one to households and one to energy suppliers. Together, it characterizes the energy
use and expenditure across a range of physical characteristics of housing and demographic
characteristics and the corresponding sample weight. Due to the lower response rate on the2015 survey, estimates could only be characterized at the Census Division level, of which
there are 9, though RECS separated the Mountain Division into North and South. The
2017 National Household Transportation Survey (NHTS) was used to estimate the vehicle
miles traveled per household (Federaal Highway Administration (2019)). NHTS characterizes non-commercial travel at the household level and associated demographics. The 2018
Consumer Expenditure Survey (CEX) was used to estimate household spending on products and services (U.S. Bureau of Labor Statistics (2020)). CEX is conducted quarterly
and annual estimates were made by aggregating across the five quarters for which there aredata for 2018, following the guidelines published by the Bureau of Labor Statistics. Whilea number of these data sets report demographic information at a coarser resolution than a
Census tract, we are able to use Census tract level data to “fit” carbon footprints at a finer
resolution.

3.2

Model

We model electricity, heating fuel use (methane, propane, and fuel oil), vehicle miles traveled,and consumer products and services (divided into the following groups: food, alcoholic beverages, housing, apparel, health, entertainment, personal care, education, tobacco products,
9

life insurance, cash contributions, and miscellaneous).
We use the Least Absolute Shrinkage and Selection Operator (Lasso) algorithm, whichallows us to increase the predictive power of the model and improve the accuracy of ourestimates by identifying which variables among all potentials are worth including.8 The
general equation for Lasso is given by:

n
m
X
X
X
2
L(λ, β) =(yi −(xij βj )) + λ|βj |,
i=1

j

(1)

j=1

where n is the number of observances, m is the number of covariates in the model, β is thecoefficient for variable, j, and lambda is a penalty term. The penalty term, λ, will adjust
how restrictive the model will be. If λ = 0, then all variables will be selected and we have
the Ordinary Least Squares result; the greater is λ fewer variables will be selected.
There are thousands of covariates among the variables and interaction terms. For example, the age of a house and source of home heating may both be predictive of energy
consumption (older homes might have less insulation and tend to use more energy to heat
their house, and people who heat their homes with natural gas will consume more naturalgas). However, the interaction between the age of the home and home heating could also be
important if, perhaps, older homes tend to have less efficient natural gas furnaces. Indeed,in our estimate for natural gas consumption, Lasso selected to include the interaction term
between age of home and whether the home heats with natural gas.9
We tested various values of lambda using a k-fold cross-validation method. Cross validation allows us to determine the optimal trade-off between including too few variables (witha large lambda) and too many variables (with a small lambda). RECS and CEX data were
given five folds (k=5) and NHTS data were given 10 (k=10), which are divided randomly.
After first determining a sequence of potential lambda values, the model is “trained”
with one of the folds omitted and used for testing. The error and standard deviation are
averaged over each fold. The value for lambda that minimizes cross-validation error (thedifference between the predicted value using the trained model and the actual value in thekth fold) is identified. For models relating to energy and transportation use, the crossvalidation error decreased monotonically with lambda, reflecting the diminishing ability of
8. The R package glmnet (Friedman, Hastie, and Robert Tibshirani (2010)) was used to conduct the Lasso
regression
9. The R package glinternet (Lim and Hastie (2019)) was used to find interaction pairs.

10including new variables to improve predictive power.10
The model is selected with the “1SE rule,” using the largest value of lambda that is one
standard deviation above the minimum lambda. This rule increases the regularization andtherefore improves the generalization of the model.11
With the exception of the model for electricity use, which does not have null values in
the outcome variable, a Probit model was used to transform the y and improve non-linear
prediction.12 A Probit model models the probability, p, for a variable being zero or one:pi = Φ

j=m!

X(βj xij ) ,

(2)

j=0

where Φ is the function for a normal distribution on the linear model. Variables that wereselected by Lasso were fed to the Probit model. The Probit model estimate was multiplied by
the Lasso model estimate, which was trained on the data with positive values. The equation
followed a Tobit-Type II estimate:

E[y|x] = E[y|x, z > 0] × (pz>0 ) + E[y|x, z ≤ 0] × (pz≤0 )= E[y|x, z > 0] × (pz>0 ),(3)where z is the dummy variable indicating whether y is positive or negative. In our case,there cannot be negative values of consumption as we did not account for cases such ashome generation of energy through solar power.

3.3

Footprint Estimation

The American Community Survey (ACS) was used to gather average household characteristics per Census tract for each variable (U.S. Census Bureau (2019)).13 A Census tract isa subdivision of a County, consisting of approximately 4,000 residents. We chose to conduct
our analysis at the Census tract level because tracts are drawn to have similar demographics,
and when variables are standardized, they will be more reflective of the average household
10. See Fig 24 and 25 in the Appendix for the cross-validation with respect to lambda for each model.
11. See, for example, Friedman, Hastie, and Rob Tibshirani (2010) and Krstajic et al. (2014).
12. The R package sampleSelection (Toomet and Henningsen (2008)) was used to fit the Probit model
13. We used the five-year estimate for the year 2015 using the Census API and the R package tidycensus(Walker (2019))

11

in that tract. However, for some variables (such as number of vehicles per household), thedata were sparsely populated and the county estimates were used instead.
The ACS does not have all the variables present in the other surveys, so the data were
supplemented with other sources. Vehicle fuel economy per Census tract were averaged
from the registry of motor vehicles (an IHS Markit report). Climate Normals (30-year
averages) were provided by the National Oceanic and Atmospheric Administration (Arguez
et al. (2012)),14 and the International Energy Conservation Code (IECC) climate region were
provided by county from Pacific Northwest National Laboratory. NOAA Climate data werematched to counties by spacial analysis; for counties without sufficient weather data, theheating degree days and cooling degree days in each IECC climate region were averaged andapplied. The residential prices of fuel oils were provided by the State Energy Data System(SEDS) (U.S. Energy Information Administration (2019)), and the residential electricity
prices were collected from the Utility Rate Database and aggregated at the county level(National Renewable Energy Laboratory (2020)).
Carbon footprints were then calculated by applying an emissions factor to the estimatesfor consumption. The Department of Energy’s emissions & Generation Resource Integrated
Database (eGRID) were used for the carbon intensity of the grid (U.S. Environmental Protection Agency (2020)). Each tract was assigned to a NERC subregion through geospatial
analysis (U.S. Department of Homeland Security (2019)); if a tract fit within one or more
subregion, the average emissions factor was used. The Complication of Air Pollutant Emissions Factors, published and updated by the EPA, was used to determine emission factors
for various fuel types (U.S. Environmental Protection Agency (2016)). Lifecycle emissions
were also factored into the emissions factor calculations using the GREET model (Argonne
National Laboratory (2019)). The emissions intensity per dollar of spending on goods andservices were used from Ummel (2014) after adjusting the dollars for inflation relative to
2015.

3.4

Policy Modeled

We model the effects for a given policy on households by estimating the cost to households
due to increased prices of energy and commodities with a carbon tax or regulatory cost;
we assess the household benefits of a policy by estimating savings related to a decrease inenergy and commodity prices due to a subsidy or a lump sum transfer, in the case of carbon
dividend schemes.
14. We used the 1981-2010 Normals for Heating Degree Days and Cooling Degree Days.

12

We make a number of assumptions. First, our approach only accounts for use-side
effects (not source-side effects) which are important to determine regressivity. Second, while
there are direct benefits to climate policy such avoiding social costs and co-benefits such as
reduced emissions of nitrogen oxides, they are outside the scope of this paper. That is, weare not crediting the households for any reductions in local pollutants or climate benefits
from the policies we model. Third, to model costs and benefits, we assume that consumers
are inelastic in their spending, there is complete pass through of tax and policy costs, and acarbon dividend scheme would be revenue neutral. Finally, we model the average household
footprint for each Census tract; therefore, we do not capture the full variance of household
effects in the United States, rather, we capture the expected variance for a representative
household across variables of geography, urbanity, and income. This is meant to give policy
makers an idea for the distributed effects of policy, rather than a perfect calculation of such
effects. We describe the distributional impacts of policy, not to assess the relative efficacy
of a policy to reduce emissions or to model the distributional impacts of climate change on
the United States.
There are 11 policy designs that we model listed in Table 1. We describe them in moredetail below. Each of the policies assume that the shadow value of the policy is $50 per tonof CO2 . For the carbon tax scenarios, this is the level of the tax. For the remaining policies
it is the shadow value of the constraint.
Table 1: Net Impact of Policy Scenarios by Urbanity (USD/Household Annually)
Scenario 1
Scenario 2
Scenario 3
Scenario 4
Scenario 5
Scenario 6
Scenario 7
Scenario 8
Scenario 9
Scenario 10
Scenario 11
Scenario 12

Carbon tax with no revenue recycling
Carbon price and dividend (CPD)
CPD with an adjustment for urban and rural households
CPD with an adjustment for geography
CPD with an adjustment for both urbanity and geography
CPD with an adjustment for NERC regions
CPD with an adjustment for household income
CPD with an adjustment for household income and geography
Corporate Average Fuel Economy (CAFE) standard
Clean Energy Standard (CES)
Obama-era Clean Power Plan
Combination of a CAFE standard, CES, and carbon pricing

A carbon tax with no revenue recycling is an edge case scenario where policy makers
decide to fix the externality, but commit all revenue to paying down the deficit. We consider
this the least likely policy to be enacted, but it nonetheless provides a baseline for consideration. CPD policy scenarios use this baseline to calculate total revenue and resulting dividend13per household.
For scenarios 2-6, the carbon footprints for households in each category (e.g., urban orrural) were averaged and the dividend was adjusted so that the average household would
break even. For scenarios 7 and 8, the dividend was calculated such that the average household in each quintile would break even, then increased by 75% for the bottom quintile, 25%
for the second quintile, reduced by 25% for the fourth quintile, and reduced for the fifth
quintile by approximately 40% (adjusted such that the policy remained revenue neutral).
Scenarios 7 and 8 were calculated differently from 2-6 because the latter were structured
such that transfers between each group (e.g., urban and rural) were eliminated; if 7 and 8
followed the same procedure, we would reduce dividends to low-income households because
they have a smaller absolute contribution. When adjusting for income, we are considering
the scenario where policy makers want to increase the progressive outcome of the policy.
Urban and Rural designations were determined following the method described in Isserman(2005) and income quintile ranges were followed the 2015 data from the Tax Policy Center.
In Scenario 9, the CAFE standard calculates the implicit tax/subsidy on each vehicle owned
according to the shadow price of the regulation, following Davis and Knittel (2019). Cars
that get worse fuel economy than the standard get taxed, cars that get better than thestandard get subsidized according to the model:
Price of a Car = MC + λ(GPM of car–Standard in GPM),
where MC is the observed price of the vehicle, λ is the shadow value of the constraint,
GPM is the gallons of gasoline-equivalent consumed by the vehicle, and Standard in GPM
is the assumed CAFE standard.15 We then multiplied the average tax by the vehicles perhousehold. We used the estimate for vehicles per household at the county level because ofpoor availability in the ACS data at the finer scale.
Scenario 10 is a CES. CES policy is still a developing area of research and does nothave universal characteristics, but such a standard is generally a technology-neutral portfolio standard that requires utilities to sell a give percentage of energy that is determined
to be from a ”clean” source. Qualifying clean energy producers are awarded credits thatcan be traded in a market, similar to a the operation of a renewable portfolio standard.
We model this such that the policy sets an emissions standard for the electricity grid andenergy providers with more carbon-intensive energy would buy credits (which would cost
$50/ton) from providers with less carbon-intense energy. Thus, areas with cleaner electricity
15. The details of this exercise are in Davis and Knittel (2019). We use the same footprint-based 2012targets as laid out by CAFE for the targets that go into the implicit tax/subsidy calculation.

14

will benefit through lower rates, while areas with dirtier electricity will pay higher rates.
The household cost or benefit was the implicit tax or subsidy multiplied by the electricity
consumed.16

Scenario 11, the Clean Power Plan (CPP), examined the change in electricity prices andthe resulting rise in costs of energy for each household. The rise in energy costs was assumed
to be only due to the associated permit price in each state. Using an analysis of projected
permit prices conducted by the Nicholas Institute (Ross, Hoppock, and Murray (2016)), we
estimated the policy cost as follows:
Policy Cost = (Permit Cost/MWH)(MWH Consumed).
where the Permit Cost in each state was based on the estimated permit costs in 2030,
as to capture the full effect of the proposed policy of the CPP.

Scenario 12 combined regulatory policy costs with a carbon price and dividend scheme.
Each sector of the HCF was affected by one type of climate policy to avoid compounding
costs or benefits in our model. We, therefore, assumed that areas of consumption thatwere covered by regulation policy would be exempt from the carbon price. This scenario
blended the effects of a Clean Energy Standard, CAFE Standard, and a carbon price withan evenly divided dividend. Therefore, the carbon price applied only to home heating fuels
and consumer goods (to avoid double taxation) and the dividend was equally divided to all
households, based on the revenue collected from heating fuels and consumer goods alone;
the net policy effects were the combined effects of CES, CAFE, and the scaled-down CPD.

4

Results

4.1

Household Footprint

Figure 2 shows the distribution of carbon footprints across geography. An expanded viewof New York City is included to highlight the effect of Urbanization—an average household
16. By assuming inelastic consumption of energy, we note that the associated costs and benefits will be
more accurate in the short-term. This assumption is not accurate in long-run, as consumers will adapt todifferentiated energy prices, for example by changing appliances. We also note that $50 per ton is a highestimate for permit prices.

15

on Long Island has a footprint nearly three times larger than that of an average householdin Manhattan. Across the map, there are similar trends visible; major metropolitan areas
have a “donut” trend, where the city center has low carbon emissions and the suburban
areas outside the city have high emissions. Some rural and suburban areas have lower than
average emissions, such as, through the Carolinas, the southern Mississippi Valley, and partsof the Pacific Coast. This is driven by two different factors: the Carolinas and the southern
Mississippi Valley have more households that are low income, which depresses their overall
footprint. The Coast has higher income, but operates on a grid with lower emissions intensity
and in a climate that does not necessitate the same level of energy required for cooling andheating.
In Figure 23 we show the contributions to total footprints by the six categories.

Figure 1: Total Household Carbon Footprints for the Continental United States
Our results highlight the importance of accounting for not only differences across incomes, but also differences across geography and urbanity. Across the US, the average carbon
footprint is 24.2 tons per household per year .17 The bottom 20% of footprints of households
generate 17.0 tons, or less, of carbon dioxide emissions, while the top 10% of households
generate 30.0 tons, or more. Meaningful differences exist across both income and geography,
however. The average carbon footprint of a household in the bottom 20% of income is 18.1;
the average is 29.1 for households in the top-20 percent of income. Average carbon emissions
17. This is the population-weighted average across all Census tracts.

16

Figure 2: Total Household Carbon Footprints for the Most Populous City in Each Census
Division17per dollar of income, in contrast, falls monotonically across income quintiles, with the bottom quintile producing 1.02 tons per every $1,000 of income and the top quintile producing
0.2 tons per every $1,000 of income.
Carbon footprints in rural communities exceed those of suburban and metropolitan areas. Average footprints are 27.7 tons per year in rural areas, but 26.0 and 21.1 tons insuburban and urban areas, respectively. There are also important differences across race.
While footprints are negatively correlated with the share of residence that are African American (correlation of -0.32), emissions per $1,000 of income is positively correlated with theshare of African American residents (0.26).

Figure 3: Subregions of the North American Electricity Reliability Corporation That Were
Used to Assign Emissions Factors to Electricity Consumption)
The footprint associated with electricity consumption is heavily influenced by the emissions intensity of the associated North American Electric Reliability Corporation (NERC)
subregion. Fuel oil- and methane-related carbon emissions are concentrated in regions that
rely on those fuels for home heating, most notably in the Northeast where fuel oil is heavily
relied upon. Transportation emissions are greater in the suburbs where households tend tohave longer commutes and multiple cars. The transportation footprint is generally larger
in the Midwest where fuel economy for private vehicles tends to be lower.18 Products and
18. We did not model the footprint related to public transit or air travel, which would likely increase theestimate in urban areas.

18

services account for a carbon footprint that was strongly correlated with income. Thesefindings are consistent with similar studies in the literature (C. Jones and Kammen (2014),
C. M. Jones and Kammen (2011), Ummel (2014), Jihoon Min, Hausfather, and Qi Feng Lin(2010)).

Figure 4: Total Household Carbon Footprints (in tons) Across Income quintiles and urbanity,
compared to U.S. average (represented by the dashed line)
Figure 4 shows the distribution of carbon footprints across two dimensions: urbanity
and income. This shows that as income increases, the footprint distribution moves right,
and for a given income group, increased urbanization generally shifts the distribution left.
It also shows that there can be wide and bi-modal distributions within each grouping.
Figure 5 shows the significant effect that geography and NERC Region have on footprints related to energy. While income and urbanity both influence the size of footprint,
the variances across geography reduce the absolute difference between income quintiles andbetween urban and rural populations. That is, there are often significant differences between the urban and rural areas of one city (as seen in Figure 2) but the variances acrossmetropolitan areas can exceed the differences within metropolitan areas.
Figure 7 shows the variation in HCFs across party affiliation, as determined by thepolitical party of the Census tract’s House Member in the 116th Congress. While households represented by Republicans tend to have slightly higher footprints than average andhouseholds represented by Democrats tend to have slightly lower footprints than average;
however, there is a wide distribution for both parties that largely overlap.
19

Figure 5: Total Household Carbon Footprints according to Census Region; Income Quintile;
NERC Region; and Urbanization, broken out by footprint contribution

20

To get a sense of how carbon footprints correlate with key demographics and regionalvariables, Table ?? reports the results of a regression of the natural log of carbon footprints
on these key variables. Column 1 omits state fixed effects while column 2 includes them.
While these estimates do not reflect causal relationships, the conditional correlations areinteresting and intuitive. We find that a one percent change in the number of both heating
and cooling degree days is associated with a roughly a 0.05% increase in footprints when state
effects are omitted; however, these elasticities fall by an order of magnitude when we include
fixed state effects. Income has a similarly-sized elasticity without fixed state effects andbecomes more strongly correlated when we look within states. Older households, measured
by the age of the head of household, are associated with lower carbon footprints. African
American and Asian households have lower footprints relative to white and multiple-racehouseholds; Hispanic households have, effectively, similar footprints. Footprints increase
with education, as the omitted group is households with less than a high school education.
Population density is correlated with lower footprints.
The elasticity of footprints with respect to the carbon content of the grid is nearly 0.14
without state effects and 0.06 with fixed state effects. The share of total carbon footprints
coming from the electricity is 25%. These elasticities suggests that those households whose
electricity has a higher carbon content also use more electricity. Homeowners have higher
carbon footprints, as do households living in older homes (homes built before 1960 are theomitted group). Those homes heated with electricity, propane, and heating oil have higher
carbon footprints.

21

ln(CO2 Total), No State FEsln(HDD)ln(CDD)ln(Household Income)ln(Household Age)
Percent African American
Percent Asian
Percent Hispanic
Percent with HS Education
Percent with Some College
Percent with BA
ln(Population Density)ln(Emissions Rate lb/mWh)
Percent Owner Occupied
Home Built 2010 to 2015
Home Built 2000 to 2009
Home Built 1980 to 1999
Home Built 1960 to 1979
Percent Heat with Propane
Percent Heat with Electricity
Percent Heat with Fuel Oil
Percent Heat with Other than Natural Gas
R2
N

ln(CO2 Total), State FEs

0.052

0.007

(0.001)**(0.001)**

0.031

0.007

(0.001)**(0.001)**

0.056

0.116

(0.002)**(0.001)**-0.203-0.183(0.003)**(0.002)**-0.146-0.137(0.002)**(0.001)**-0.230-0.087(0.005)**(0.004)**

0.008

0.038

(0.003)**(0.002)**

0.162

0.088

(0.007)**(0.005)**

0.281

0.235

(0.006)**(0.004)**

0.298

0.183

(0.006)**(0.004)**-0.014-0.011(0.000)**(0.000)**0.138-0.035(0.001)**(0.008)**

0.314

0.245

(0.003)**(0.002)**-0.081-0.043(0.008)**(0.006)**-0.111-0.123(0.004)**(0.003)**-0.077-0.060(0.003)**(0.002)**-0.025-0.024(0.003)**(0.002)**-0.011-0.024(0.005)*(0.004)**0.049-0.029(0.002)**(0.002)**

0.113

0.237

(0.003)**(0.003)**-0.219-0.174(0.008)**(0.005)**0.80
72,5370.91
72,537

Notes. These represent OLS regressions of Census tract household carbon footprints on key demographics, weather conditions, and building stock variables. The first column does not include
Fixed State Effects, while the second column does. Standard errors are clustered at the state level.

Table 2: Conditional Correlations of Household Carbon Footprints and Demographics,
Weather Conditions, and Building Stock Variables
22

Figure 6: Carbon Footprint by Category23

Figure 7: Distribution of Household Carbon Footprints across political parties (according
to party affiliation of House Members in the 116th Congress), compared to U.S. average(represented by the dashed line)

24

4.2

Policy Impacts

Next we turn to analyzing the impacts of different policies on households. We begin with taxand-dividend plans where we vary how dividends are determined. Then, we study regulatory
standards. Our results highlight the large differences in incidence across policies, even withindifferent carbon tax designs, and across geographic regions within policies. However, eventhe most basic tax-and-dividend plan, one that has each household within the US receiving
the same dividend payment, is highly progressive; higher-income households receive lower
net payment, the dividend amount minus carbon tax payments, especially as a share of theirincome.
Under a simple tax-and-dividend plan, Scenario 2 above, 96% of Census tracts withan average income in the bottom 20% of income receive a larger dividend than they pay inincreased prices of energy and commodities, with an average net gain of $307 per household
per year. This is a substantial amount of income for these households. On average, ahousehold receives over $16.21 per year for every $1,000 of income. Among tracts in thesecond income quintile, 68% of households have net gains (average gain of $115 per year or$3.27 per $1,000 of income), 46% in the third quintile (-$16 or -$0.29 per year per $1,000
of income), 35% in the fourth quintile (-$115 or -$1.32 per year per $1,000 of income), andonly 17% in the fifth quintile (-$226 or -$1.65 per year per $1,000 of income).
This simple plan, however, leads to transfers from suburban and rural homes to metropolitan households. Rural households are less likely to benefit from this simple tax-and-dividend
plan. Across all income levels, 31% of rural households receive a net positive dividend withan average net gain of -$90 per household per year (-$1.90 per $1,000 of income). In contrast, 74.6% of metropolitan households receive net benefits, with an average net benefit of$163 per household per year ($2.72 per $1,000 of income). Suburban areas lose, on average,
under the simple tax-and-dividend plan. The average suburban household spends $56 morein taxes per household per year, with over 57% of households spending more in taxes thanthey receive in dividends ($1.92 per $1,000 of income).
In the second income quintile, 70% of households benefit, with an average net dividendof $43 per year ($3.27 per $1,000 of income). However, the difference among households inthe second quintile is dramatic. A large share of households receive a net positive dividend(68.5%) and the average net impact is $115 per year, but the average net impact is negative($-4.53 per household per year) among rural households in the second income quintile.
Households in the first quintile also see a wide distribution, although rural households
still see a net positive impact, it is less than half of the net benefit to the first quintile overall25

($122 per household per year , compared to $307). The share of suburban tracts in thebottom 20% of income that have net benefits is 90% with an average net dividend of $242
per household per year ($12.80 per $1,000 of income).19
Table 3: Net Impact of Policy Scenarios by Income Quintile (USD/Household Annually)
1st Q.
Scenario 1: Carbon Tax -907
Scenario 2: CPD 307
Scenario 3: CPD – adjusted for urbanity 273
Scenario 4: CPD – adjusted for geography 331
Scenario 5: CPD – adjusted for urb. + geo. 306
Scenario 6: CPD – adjusted for NERC Reg. 330
Scenario 7: CPD – adjusted for income 676
Scenario 8: CPD – adjusted for inc. + geo. 676
Scenario 9: CAFE -68.1
Scenario 10: CES -5.06
Scenario 11: CPP -161
Scenario 12: CPD & Regs -21.5

2nd Q.
-1088
115
116
141
136
142
273
273
-87.9
-5.49
-191
-42.2

3rd Q.
-1219
-16.2
-6.96
-10.9
-3.8
-13
0
0
-86.4
3.56
-177
-77.1

4th Q.
-1322
-115
-126
-151
-153
-150
-331
286
-53.4
27.2
-145
-99.7

5th Q.
-1457
-226
-249
-289
-299
-281
-587
571
-23.4
41.1
-130
-149

Table 4: Net Impact of Policy Scenarios by Urbanity (USD/Household Annually)

Tax
Baseline
CPD – adjusted for urbanity
CPD – adjusted for geography
CPD – adjusted for urb. + geo.
CPD – adjusted for NERC Reg.
CPD – adjusted for income
CPD – adjusted for inc. + geo.
CAFE
CES
CPP
CPD & Regs

Metropolitan Rural Suburban-1044-1297 -1264
163-89.9 -56.4
117
4.52
-93.2
111
-33.6 -34.7
82.2
5.23
-57.6
122
-56.6 -36.6
192-34.8 -67.4
147
95.9
-95.5
-48
-136
-74.4
20.9
-20.6 1.81
-127
-204
-186
-52.0
-93.4 -88.1

19. For additional information on the net impact per $1000 of income, percentage of households who havea net benefit, and average dividend amount in each CPD scenario, see the Appendix, section A.

26

4.2.1

Alternative Dividend Plans

Tables 9 and 10 display the average dividend amount per household for policy scenarios 2
through 8 according to income and urbanity, respectively. In each scenario, the revenue
collected is the same (approximately $101 Billion USD), but divided differently. In scenarios
3, 5, and 8, there is an explicit adjustment for urbanity of the household and in scenarios 7
and 8 there is an explicit adjustment for income. However, adjusting for urbanity and geography increases the dividend for the bottom three income quintiles and adjusting for income
increases the dividend for rural households. Adjusting for NERC regions also increases the
progressive structure of the dividend.
Figures 8, 10, and 12 show the geographic distribution of household impacts for policy
scenarios 2, 5, and 7, respectively. Each figure includes an expanded view of St. Louis,
Missouri to facilitate discussion on impacts to major Midwestern cities. Figures 9, 11,
and 13 show the income distribution of household impacts for policy scenarios 2, 5, and 7,
respectively. Each figure includes the overall effect and the cross-sectional impacts acrossurbanity for each income group.

Figure 8: Net Impact of $50 Carbon Price and Dividend
Scenario 5, a carbon price and dividend adjusted for urbanity and geography has themost homogeneous impact across the United States and the most narrow distribution of
impacts within each income group. Scenario 7, a carbon price and dividend adjusted forincome, has the greatest heterogeneity net effects within income groups. St. Louis is a good
example of the effects of urbanization across each of these policy scenarios. In each case, there27

Figure 9: Net Impact of $50 Carbon Price and Dividend - According to Income Quintile and
Urbanity
are greater net benefits in the city center, lower net benefits in the suburbs, and rural areas
tend to break even or lose on net. But, compared to the baseline CPD, the CPD adjusted forurbanity and geography has more homogeneous effects (a smaller absolute difference) andthe CPD adjusted for income has more heterogeneous effects (a larger absolute difference).
Comparing across geographic differences, when controlling for urbanity and geography, there
does not seem to be clear and strong advantage for any particular state. However, when thisis not controlled for, there are stronger advantages to California and New York and lower
advantages to the Midwest and Mountain North.

28

Figure 10: Net Impact of $50 Carbon Price, Dividend adjusted for Urbanity and Geography

Figure 11: Net Impact of $50 Carbon Price, Dividend adjusted for Urbanity and Geography- According to Income Quintile and Urbanity

29

Figure 12: Net Impact of $50 Carbon Fee, Dividend adjusted for Income

Figure 13: Net Impact of $50 Carbon Fee, Dividend adjusted for Income - According to
Income Quintile and Urbanity30

4.2.2

Regulations

Figures 14, 16, and 18 show the geographic distributions for a CAFE Standard (Scenario9), Clean Energy Standard (Scenario 10), and the Clean Power Plan (Scenario 11), respectively. Figures 15, 17, and 19 show the distribution of net effects across income groups andurbanity for each regulatory approach. As shown, CAFE standards are regressive and tend
to disadvantage rural and Midwestern areas while benefiting coastal and urban areas. The
conclusions for regressivity are less clear in the case of a CES, as the distribution is bi-modal,
but there is a clear disadvantage to households in dirtier NERC regions, which are locatedin the Midwest and the Plains.
The regulatory policies we model all have net costs to the bottom two income quintiles.
The Clean Power plan has the largest absolute cost per household (-$161 per household forthe lowest 20%, or -$8.54 per $1000 of income; and -$191 for the second quintile, or -$5.44
per $1000 of income), followed by CAFE standards (-$68 per household for the lowest 20%,or -$3.61 per $1000 of income; and -$88 per household for the second quintile, or -$2.50
per $1000 of income). Although it followed a regressive trend, the costs associated with a
Clean Energy Standard were small in comparison (-$5.06 per household for the lowest 20%,or -$0.27 per $1000 of income; and -$5.49 for the second quintile, or -$0.16 per $1000 ofincome). It should be noted that the estimated carbon emissions reduction between eachregulatory policy and between the regulatory policies and the carbon pricing policies are not
the same. We seek to draw attention to the general trends in regional and income effects,
not only absolute impacts.
The estimation of costs and benefits for the Clean Energy Standard are based on theassumption of inelastic consumption of electricity for each household and 100% pass-throughof the permit prices. If a household is connected to a part of the grid with lower carbon
emissions than the national average, then they will have subsidized electricity rates and will
therefore have a net benefit, scaled by the amount of consumption for that household. In
areas with more carbon-intensive grids, especially in states such as Missouri, Wisconsin,
and Illinois, then households will have higher electricity rates and a resulting net cost. We
applied a price of $50 per ton of CO2 -equivalents, although the permit prices are likely to
be lower. Accounting for the benefits of a CES such as reduced nitrogen oxide emissions isimportant for policy-making but outside the scope of this paper.
The costs of the Clean Power Plan are based on the estimated permit prices for eachstate that would be necessary to be in compliance with the Obama-era policy by the year
2030. As with the CES, estimating the benefits of avoided emissions and the co-benefits31

of lowered pollutants and their respective distribution across the country is important but
outside our scope. In fact, any benefits to the CPP are not included in our framework.

Figure 14: Net Impact of CAFE Standard

Figure 15: Net Impact of CAFE Standard - According to Urbanity and Income Quintile32

Figure 16: Net Impact of Clean Energy Standard

Figure 17: Net Impact of Clean Energy Standard - According to Urbanity and Income
Quintile

33

Figure 18: Net Impact of the Clean Power Plan

Figure 19: Net Impact of the Clean Power Plan - According to Urbanity and Income Quintile

34

5

Discussion

All policy creates winners and losers. Climate change policies are not different. However, ourresults highlight that if policy makers are not careful, winners and losers can be concentrated
within certain income brackets and certain regions of the country. Given the controversial
nature of climate change policy in general, policies that do not rectify these discrepancies
are likely to be resisted.
We find that geography can play a role that is as significant as household income. This islargely due to the relative carbon intensity of the electricity grid. Other modeling has foundthat the majority of emissions reductions will come from the electric power sector (Goulder
et al. (2019)), therefore this regional difference will likely be mitigated with higher amounts ofrenewable penetration and the retirement of coal power in Appalachia and the Midwest. As
decarbonization occurs in the United States, there is potential to reduce the heterogeneous
impacts of climate policy within an income group—especially through programs that will
reduce consumption and emissions in rural areas. Such policy could include community solar
and weatherization assistance programs and the extension of the Production Tax Credit.
However, new wind and solar in the heartland will not erase the substantial advantage ofthe West Coast in renewable energy and efficiency measures. A carbon dividend can be aneffective tool for mitigating regional transfers, while still incentivizing households everywhere
to reduce emissions.
Based on the conclusions of Jorgenson et al. (2018), we can expect that all policy
scenarios that assess a similar price on carbon (scenarios 1 through 8) will have similar
emissions reductions. However, we cannot assume that there will be equivalent reductions
through regulatory control (scenarios 9 through 12). Indeed, as discussed in Knittel (2019),
existing regulations could be replaced with a $7 per ton price on carbon. We draw attention
instead to the trends in regressivity and regional transfers across policy scenarios. There is
likely to be a blend of regulatory approaches and pricing policy in a comprehensive climate
strategy and future work should focus on how policy can be efficiently combined, and which
sectors of the economy should be decarbonized through the instruments available.20
Figures 21 and 20 show that for a Carbon Price and Dividend policy, there are transfers
20. Goulder et al. (2019) showed that 64 to 68% of the emissions reduced from a carbon price come from theelectric power sector. Our work shows that the electric grid accounts for the large regional differences between
effects of climate policy, while higher costs to high-income households are a factor of greater transportation
emissions and more product and service consumption. If a carbon price is not economy wide, or properly
applied through border adjustment policy, our conclusions that a price and dividend scheme will be broadly
progressive may not hold.

35

that exist between income quintiles, between urban and rural households, and between regions.21 We believe that the former is desirable, as a progressive policy will yield a transition
that is equitable and resilient to change. The latter, however, is not necessary in achieving a
progressive outcome and should be avoided in the design of an national climate policy. Further, we believe that dividends are the clearest and most efficient way to correct for transfers
between urban and rural populations. Across incomes, the typical household in the upper
quintile of income transfers $289 per year, the fourth and third quntiles transfer $105 and$17 out to lower income quintiles, respectively. Across urbanity, the typical rural household
transfers $286 per year to urban households. Finally, households in the South and Midwest
transfer money to the Coasts under the plain CPD program. It is these last two transfer
that we seek to analyze and reduce.

Figure 20: Transfers between households in each Census region for a Carbon Price andDividend (the left side represents relative tax paid through a carbon price for the average
household in each region, the right side represents the dividend received; the flows between
regions reflect the transfers between those regions)
Policy makers can easily protect both rural populations and low-income households if
a carbon dividend scheme accounts for geography and/or urbanity. Creating a ladder forthe dividend, where low-income households are paid more and high-income households arepaid less, can indeed increase the progressive trend of the policy, but also increases the heterogeneity of outcomes within an income group. While income-adjusted policy design might
21. While the amount of money flowing out of a quintile is exact, the specific mix of where the dollars flow
is arbitrary. We show the results for one mix.

36

Figure 21: Transfers between income quintiles and urban-rural households for a Carbon
Price and Dividend
have more natural political support, mitigating the negative effects for some households in
low-income brackets (even while an income group as a whole benefits) is also likely a goalfor policy makers. That is, it is not enough to examine the effects to the average householdwithin an income group, the distribution of outcomes by geography must also be considered.
Therefore, policy makers must also consider heterogeneity of impacts and their geographic
distribution.
Accounting for both geography (determined by the Census divisions of the country) andurbanity (determined by population density and Census classifications) produce an outcomethat is significantly less heterogeneous and more progressive than an equal dividend given
to all households. Such a policy could be a good bipartisan “win-win” that will benefit
constituents in progressive districts and conservative alike.
There will be a trade-off between the simplicity of policy, and thus administratively
simple, and fixing regional transfers; and there is also a trade-off between protecting vulnerable populations who could be adversely affected by a policy and maintaining the incentive
to reduce emissions. For example, carbon tax revenues could be adjusted based on stateboundaries so that households in more carbon-intense states would receive higher dividends
than states that have lower emissions. However, such a policy could create an adverse incen-

37

tive for state policy makers to maintain a higher carbon footprint so that their constituentswould continue to receive a higher dividend.
There is more to do. The interaction between command-and-control regulation and acarbon price and dividend should be explored in further depth with a computational andgeneral equilibrium model. Other components of a carbon pricing proposals, such as borderadjustments, deserve attention and modeling. Finally, the distribution of source-side effects,
costs to industry and business, and costs to local governments also warrants exploration.

6

Conclusion

The results from our work underscore the high variability in household carbon footprints
across a number of dimensions. Two dimensions warrant focus. First, our results suggestthat largely driven by consumption of goods and services, low-income consumers are likely
to spend more on carbon taxes, as a share of their income. We are not the first to find thisresult. The regressivity of carbon taxes, ignoring the use of the revenues, is a well-known
argument against their use. While recent work suggests that after accounting for the impact
of carbon taxes on transfers, firms, and employment (known as source-side effects), carbon
taxes are no longer regressive, but the regressivity of carbon taxes on the consumption
dimension (use-side effects) is likely to be a major political obstacle.
Our work highlights a second dimension that is likely to be just as large, if not larger, ofa political obstacle: the wide range in carbon footprints across rural and urban communities.
Indeed, the geographic effect of carbon footprints is nearly as significant as the variability
across income levels.
These results highlight the importance of how revenues from a carbon tax are recycled
into the economy. From an economic efficiency perspective, the best use of the revenue
is to reduce existing taxes that are a drain on economic activity and efficiency, such as
income or sales taxes. The intuition here is that as we replace economically inefficient(e.g., income) taxes with efficiency-enhancing taxes (such as carbon taxes), we not only help
mitigate climate change, but we also improve the overall efficiency of the macro economy.
The drawback of such a carbon tax policy is that it requires jointly adopting a carbon tax
together with larger tax reforms, as well as the commitment of policy makers to not increase
the income or sales taxes in the future. We suspect that the political hurdles of such a system
are insurmountable.
A more simple policy design refunds the revenues collected by the carbon tax in the38

form of household “dividends,” so-called tax-and-dividend plans (Baker et al. (2017)). Most
tax-and-dividend plans that we are aware of do not differentiate across households; eachhousehold receives the same dividend amount each year. While a policy such as this has theadvantage of being simple and straightforward, this ignores the large geographic differences
in carbon footprints that we document, particularly across rural and urban settings.
We show that correcting for heterogeneity can also improve the progressive outcome ofpolicy. However, one important result to note is that when adjusting the dividend to increase
the amount for low-income households and reduce the amount for high-income households
benefits rural households more on average but increases the overall heterogeneity of impacts
within each income group. Adjusting the dividend for both geography and urbanity increases
the average benefit to low-income households and reduces the heterogeneity of impacts within
income groups.
We recommend a tax-and-dividend policy design that accounts for the rural-urban dividein carbon footprints. There are many ways to do this. The basic structure is to condition
the level of the dividend on some information about the type of the household. It is ofupmost importance that households have limited ability to alter their type themselves. If
a household can take strategic actions to affect their dividend level, then they will haveless of an incentive to reduce their carbon footprints. In addition, the dividends cannot be
state specific. Having them be based on the average carbon content of a given state will
reduce the incentives of state policy makers to adopt carbon-reducing policies. We leave
the details of such a plan for future policy discussions, but we hope that our results canaid policy makers in weighing the different trade-offs associated with designing a carbon
tax-and-dividend policy.

39

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45

A

Additional Tables
Table 5: Net Impact of Policy Scenarios per $1000 of Income by Income Quintile

Carbon Tax
CPD
CPD – adjusted for urbanity
CPD – adjusted for geography
CPD – adjusted for urb. + geo.
CPD – adjusted for NERC Reg.
CPD – adjusted for income
CPD – adjusted for inc. + geo.
CAFE
CES
CPP
CPD & Regs

1st
-48.10
16.30
14.50
17.60
16.20
17.50
35.90
35.90
-3.61
-0.27
-8.54
-1.14

2nd
-31.00
3.27
3.30
4.01
3.87
4.04
7.78
7.78
-2.50
-0.16
-5.44
-1.20

3rd
-21.70
-0.29
-0.12
-0.20
-0.07
-0.23
0.00
0.00
-1.54
0.06
-3.15
-1.38

4th
-15.10
-1.32
-1.44
-1.72
-1.74
-1.71
-3.78
3.26
-0.61
0.31
-1.66
-1.14

5th
-10.60
-1.65
-1.81
-2.10
-2.17
-2.04
-4.27
4.15
-0.17
0.30
-0.95
-1.09

Table 6: Net Impact of Policy Scenarios per $1000 of Income by Urbanity

Carbon Tax
CPD
CPD – adjusted for urbanity
CPD – adjusted for geography
CPD – adjusted for urb. + geo.
CPD – adjusted for NERC Reg.
CPD – adjusted for income
CPD – adjusted for inc. + geo.
CAFE
CES
CPP
CPD & Regs

Metropolitan Rural-18.10-26.10
2.72
-1.90
1.91
0.00
1.80
-0.76
1.30
0.00
2.00
-1.20
2.97
-0.83
2.22
1.76
-0.81
-2.73
0.39
-0.39
-2.27
-4.15
-0.9040

46

Suburban-19.50
-1.14
-1.73
-0.80
-1.17
-0.82
-1.56
-1.99
-1.15
0.04
-2.91
-1.36

Table 7: Fraction of Households with Positive Net Impact by Income Quintile

CPD
CPD – adjusted for urbanity
CPD – adjusted for geography
CPD – adjusted for urb. + geo.
CPD – adjusted for NERC Reg.
CPD – adjusted for income
CPD – adjusted for inc. + geo.
CAFE
CES
CPP
CPD & Regs

1st
0.962
0.95
0.989
0.986
0.98
1
1
0.0487
0.331
0
0.463

2nd
0.685
0.7
0.802
0.808
0.798
0.892
0.915
0.0854
0.356
0
0.450

3rd
0.459
0.494
0.479
0.509
0.467
0.486
0.509
0.114
0.411
0
0.366

4th
0.348
0.336
0.189
0.199
0.226
0.104
0.133
0.201
0.541
0
0.276

5th
0.172
0.15
0.08030.07890.09130.03330.0338
0.31
0.619
0
0.113

Table 8: Fraction of Households with Positive Net Impact by Urbanity

CPD
CPD – adjusted for urbanity
CPD – adjusted for geography
CPD – adjusted for urb. + geo.
CPD – adjusted for NERC Reg.
CPD – adjusted for income
CPD – adjusted for inc. + geo.
CAFE
CES
CPP
CPD & Regs

Metropolitan
0.746
0.689
0.709
0.669
0.73
0.711
0.681
0.161
0.496
0
0.415

Rural Suburban
0.313 0.434
0.501 0.373
0.427 0.456
0.529 0.419
0.366 0.461
height0.433 0.465
0.637 0.415
0.104 0.113
0.336 0.386
000.343 0.327 height

Table 9: Average Household Dividend by Income Quintile

CPD
CPD – adjusted for urbanity
CPD – adjusted for geography
CPD – adjusted for urb. + geo.
CPD – adjusted for NERC Reg.
CPD – adjusted for income
CPD – adjusted for inc. + geo.
CPD & Regs

1st
1208
1174
1232
1207
1231
1577
1577
523

47

2nd 3rd
4th
5th
1208 1208 1208 1208
1209 1217 1197 1185
1233 1213 1172 1145
1228 1220 1170 1135
1235 1211 1173 1153
1366 1224 992 847
1366 1224 1037 863
523 523 523 523

Table 10: Average Household Dividend by Urbanity

CPD
CPD – adjusted for urbanity
CPD – adjusted for geography
CPD – adjusted for urb. + geo.
CPD – adjusted for NERC Reg.
CPD – adjusted for income
CPD – adjusted for inc. + geo.
CPD & Regs

B

Metropolitan
1208
1161
1155
1126
1166
1222
1179
523

Additional Figures

48

Rural
1208
1302
1264
1302
1242
1261
1390
523

Suburban
1208
1169
1229
1205
1228
1180
1152
523

Figure 22: Policy Scenarios 1 - 6

49

Figure 23: Policy Scenarios 7 - 1250

C

Model Performance
Table 11: R-Squared Values for models
Lasso Only
Jones and Kammen (Reported)
Electricity 0.687
0.608
Natural Gas 0.674
0.471
Propane 0.646

Lasso & Probit
0.623
0.835
0.614

Fuel Oil 0.604
0.206
Vehicle Miles Traveled 0.324
0.324
Food Cons. 0.332

0.812

Alcoholic Bev. Cons.

0.07160.0503

Housing Costs

0.243

0.184

Apparel Cons.

00.002210.142

0.0849Healthcare Costs

0.277
0.248

Entertainment Costs 00.00349

Personal Care Cons.

0.115

0.0610

Education Costs

0.01310.00513

Tobacco Prod. Cons. 0.0195

0.0133

Life Insurance Costs 0

0.0111

Misc. Cons.

00.00239

Cash Contributions 0

0.0037351

D

Model Development

The cross validation errors for 12 models is shown below. Two dotted lines show the minimum
CV error for all tested values of lambda and the CV error that is one standard deviation
above the minimum.

52

Figure 24: Cross-Validation Errors for Lasso Models

53Figure 25: Cross-Validation Errors for Lasso Models (Continued)

54

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