ENVIRONMENTAL TAX REFORM IN 

CALIFORNIA: ECONOMIC AND CLIMATE 

IMPACT OF A CARBON TAX SWAP

PREPARED BY

Regional Economic Models, Inc (REMI)

PREPARED FOR

Citizens Climate Lobby (CCL)

SCOTT NYSTROM, M.A.

Senior Economic Associate

ALI ZAIDI, B.A.

Assistant Economist

1776 I St. NW

Suite 750

Washington, DC 20006

(202) 716-1397

<scott.nystrom@remi.com>

 

MONDAY, MARCH 3, 2014
Regional Economic Models, Inc.

p. 1

ACKNOWLEDGEMENTS

Citizens Climate Lobby (CCL),1 Environmental Tax Reform-US (ETR-US),2 and the two 

authors would like to thank a group of individuals for helping to make this research 

possible. These people include Peter Fiekowsky, Thanh Huynh, Rebecca Claassen, Cathy 

Carruthers, Jessica Langerman, Alan Langerman, Rebecca Morris, John D. Kelley, Jerry 

Hinkle, Peg Mitchell, Carol Schywzer, Zaurie Zimmerman, Lise Olney, Joe Jordan, Liz 

Fischer, Danny Richter, Barbara Wilson, and Dr. Peter Joseph. We also give thanks to

the institutional support of groups like Environmental Tax Reform-Washington (ETRWA), Environmental Tax Reform-Massachusetts (ETR-MA), the Committee for a Green 

Economy (CGE),3 Zaurie Zimmerman Associates (ZZA),4 and the Climate Action Liaison 

Coalition (CALC).5 For editing, we give added thanks to Dr. Frederick Treyz, CEO of 

REMI.

6 All of their individual contributions aided in the completeness and quality of the

report and its eventual reporting and results with the potential economic, demographic, 

fiscal, and climate impacts of implementing a system of environmental tax reform in 

California. These results do not reflect the institutional views of REMI but rather the 

professional opinions of the authors and findings of the models.

 

1 <http://citizensclimatelobby.org/>

2 <http://etr-us.org/>

3 <http://www.committeeforagreeneconomy.com/>

4 <http://www.zza-inc.com/#!/CompanyProfile>

5 <http://climateactioncoalition.org/>

6 <http://www.remi.com/>
Regional Economic Models, Inc.

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EXECUTIVE SUMMARY

This report examines the economic, demographic, and climate impact of environmental 

tax reform in California. The primary policy levers behind this investigation are a carbon 

tax and revenue-neutrality. The carbon tax supposes the state begins to assess retail or 

wholesale sales taxes on energy (electricity, natural gas, and petroleum products) based 

on the underlying carbon content of the fuel to discourage their use and help to cut state 

emissions (in addition to AB32). The levels of pricing included here are $50/metric ton, 

$100/ton, and $200/ton. The first $4 billion/year in revenue is always for a fund meant 

to grow renewable investments. From there, there are two ideas here for returning the 

revenue without increasing spending: an “across-the-board” tax cut to income, sales, 

and corporation taxes (ATB) or a “fee-and-dividend” paid out to households modeled on 

the Alaska Permanent Fund (FAD). Running these scenarios in REMI PI+, an economic 

and demographic model of the state in use in its policymaking circles and CTAM, which 

forecasts anticipated emission and revenues, reveals California might be able to prosper

while reducing carbon emissions. Higher energy prices may have a negative effect, but 

tax relief can help to restore competitiveness of firms in California, and more income 

encourages spending for local businesses. In contrast, reduced energy demand has little

impact on jobs and gross domestic product (GDP). The “tax swap” could mean 300,000 

more jobs in the state by 2035, an extra $18 billion in GDP, an additional $16 billion in 

annual income, and carbon emissions less than 75% of 1990 levels.

0

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$200/ton (ATB)

$200/ton (FAD)

65%

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20142016

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Percentage of 1990 Carbon Emissions

No tax

$50/ton

$100/ton

$200/ton
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TABLE OF CONTENTS

 Acknowledgements p. 1

 Executive Summary p. 2

 Table of Contents pp. 3-4

 Word Cloud p. 5

 Introduction pp. 6-8

 How Does a Carbon Tax Work? pp 9-10

o Figure 1.1 – Calculating Carbon Content p. 9

 Policy Scenarios pp. 11-13

o Figure 2.1 – Carbon Tax Levels p. 111

o Figure 2.2 – Across-the-Board (ATB) p. 12

o Figure 2.3 – Fee-and-Dividend (FAD) p. 13

 Simulation Results pp. 14-46

o Figure 3.1 – Total Employment p. 15

o Figure 3.2 – Gross Domestic Product p. 15

o Figure 3.3 – GDP (Cumulative) p. 17

o Figure 3.4 – Output by Industry (ATB) p. 18

o Figure 3.5 – Percentage Change (ATB) p. 19

o Figure 3.6 – Output by Industry (FAD) p. 20

o Figure 3.7 – Percentage Change (FAD) p. 21

o Figure 3.8 – Jobs by Industry (ATB) p. 23

o Figure 3.9 – Percentage Change (ATB) p. 24

o Figure 3.10 – Jobs by Industry (FAD) p. 25

o Figure 3.11 – Percentage Change (FAD) p. 26

o Figure 3.12 – Jobs by Occupation (ATB) pp. 28-29

o Figure 3.13 - Percentage Change (ATB) p. 30

o Figure 3.14 – Jobs by Occupation (FAD) pp. 31-32

o Figure 3.15 - Percentage Change (FAD) p. 33

o Figure 3.16 – PCE-Price Index p. 34

o Figure 3.17 – PCE by Quintile p. 34

o Figure 3.18 – Energy Prices p. 35

o Figure 3.19 – Real disposable income p. 36

o Figure 3.20 – RDPI (Cumulative) p. 37

o Figure 3.21 – Income by Quintile p. 38

o Figure 3.22 – Population p. 39

o Figure 3.23 – ATB v. FAD p. 40

o Figure 3.24 – Carbon Tax Revenues p. 41

o Figure 3.25 – Revenues (Cumulative) p. 41

o Figure 3.26 – Revenues by Sector p. 42

o Figure 3.27 – Forecasted Dividend Check p. 43
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p. 4

o Figure 3.28 – Carbon Emissions p. 44

o Figure 3.29 – Savings (Cumulative) p. 45

o Figure 3.30 – 1990 Benchmark p. 46

 Assembly Bill 32 (AB32) pp. 47-48

o Figure 4.1 – Supply and Demand p. 46

o Figure 4.2 – Hybridization p. 48

 Regional Economic Models, Inc. (REMI) p. 50

 PI+ pp. 51-56

o Figure 5.1 – Model Structure p. 54

o Figure 5.2 – Model Framework p. 56

 Carbon Tax Analysis Model pp. 57-58

o Figure 6.1 – CTAM Structure p. 57

o Figure 6.2 – Policy Variables p. 58

 Author Biographies and Contact Information p. 60

 Notes p. 61
Regional Economic Models, Inc.

p. 5

WORD CLOUD
Regional Economic Models, Inc.

p. 6

INTRODUCTION

This white paper examines the series of interactions within the state of California amid 

its economy, demographics, the demand for energy, carbon dioxide emission, and the 

state budget in Sacramento. Specifically, it considers the impacts of implementation for

several levels of carbon taxes upon the same. A carbon tax (alternatively known as an 

“emissions fee” or “pollution tax”) is a tax charged by some level of government on the 

households and businesses within their jurisdiction for emitting a certain quantity of 

carbon into the atmosphere.7 By chemical default and due to the economics of buying 

and selling, all carbon dioxide emissions subject to the tax come from the combustion of 

tradable hydrocarbons—things such as coal (as electricity), natural gas (as electricity or 

in other forms), and petroleum (through various refined products). In essence, this

makes a carbon tax another form of a sales tax when applied at some point upstream or 

downstream in the energy supply-chain. Consequently, a carbon tax is an appropriate 

subject for assessment via the standard, traditional tools of fiscal analysis such as 

economic impact modeling. A carbon tax is a fiscal issue as much as an environmental 

one because the new revenues collected may go towards allocations on other priorities 

(education, transportation, energy efficiency) or replacing other revenue streams and 

providing tax relief in a revenue-neutral swap. The potential for a significant quantity of 

revenues from carbon taxes exists. For instance, in 2011, the United States emitted 

approximately 5.75 billion metric tons of carbon.8 At a $50/metric ton tax, this is about 

$280 billion or 8% of the federal budget.

9 This invites the consideration of a carbon tax 

as an economic, fiscal, and environmental issue.

Citizens Climate Lobby (CCL), a group of private citizens based in Coronado, California, 

contracted Regional Economic Models, Inc. (REMI) to examine these issues and their 

interrelationships through the lens of economic modeling. This study uses two tools:

REMI PI+, a proprietary economic and demographic model of sub-national units of the 

United States’ economy (to county geographies) and the Carbon Tax Analysis Model (or 

CTAM),10 an open-source, Microsoft Excel-based model of state-level carbon emission 

and tax revenues derived from the National Energy Modeling System (NEMS) of the 

U.S. Energy Information Administration (EIA).11 Integration among PI+ and CTAM and 

 

7 For a discussion of other alternative names, please see, Kate Galbraith, “A Carbon Tax by Any Other 

Name,” New York Times, July 24, 2013, <http://www.nytimes.com/2013/07/25/business/global/acarbon-tax-by-any-other-name.html>

8 “Overview of Greenhouse Gases: Carbon Dioxide Emissions,” U.S. Environmental Protection Agency, 

<http://www.epa.gov/climatechange/ghgemissions/gases/co2.html>

9 “The U.S. Federal Budget,” Congressional Budget Office, <http://www.cbo.gov/publication/42636>

10 Keibun Mori, Roel Hammerschlag, and Greg Nothstein, “Carbon Tax Modeling for Washington State,” 

Western Energy Policy Research Conference, September 5, 2013, 

<http://epi.boisestate.edu/media/21329/keibun%20mori,%20nothstein%20and%20hammerschlag%20-

%20carbon%20tax%20modeling%20for%20washington%20state.pdf>

11 “The National Energy Modeling System: An Overview,” U.S. Energy Information Administration, 

<http://www.eia.gov/oiaf/aeo/overview/>
Regional Economic Models, Inc.

p. 7

calibration of their baselines to the NEMS outlook then creates a regular framework for 

assessing impacts to the economy and carbon emissions inside of states. The results 

include the impact to jobs, gross domestic product (GDP), and incomes as well as to the 

quantity of anticipated emissions in the future (the total amount or by some benchmark,

such as 1990 levels). REMI does not advocate specific courses of action or policies; the 

intention of this study is to inform California on climate policy through modeling the 

direct and implied upshot of a carbon tax. We do not comment for or against the

dangers posed from concentration of carbon dioxide in the atmosphere in 

terms of climate variability. Additionally, there are further dimensions this study 

“leaves on the table,” such as the contrasting impacts in regions within California (the 

Bay Area, Central Valley, High Sierras, Inland Empire, Los Angeles Basin, San Diego, 

etc.). Another factor would include the respiratory health, quality of life, and the overall 

wellness benefits of reduced emissions of pollutants like mono-nitrogen oxides (NOX), 

sulfur dioxide (SOX), and particulate matters (PM); these emissions can correlate with 

carbon dioxide.

12 These are all interesting points from a policy vista, though they are “in 

addition” to economic and fiscal impacts seen from economic modeling in PI+ and 

CTAM for the state of California and potential policies.

13

 

12 Mark Z. Jacobson, “On the causal link between carbon dioxide and air pollution mortality,” Geophysical 

Research Letters, Vol. 35, 2008, L03809, 

<https://www.stanford.edu/group/efmh/jacobson/Articles/V/2007GL031101.pdf>

13 All images are open-source from Wikimedia
Regional Economic Models, Inc.

p. 8

The rest of this study covers many areas, including the precise scenarios modeled in PI+

and CTAM, the results (in economic and climate terms) of the simulations, a discussion 

the potential relationship of a tax and AB32,14 and background on the methodology of 

the models. A carbon tax and AB32, which created California’s nascent cap-and-trade 

system,15 would interact. On the other hand, it is perfectly possible for the two to coexist 

and reinforce the same objectives of reducing carbon emissions (both policies).

16 Both 

influence the economy (the carbon tax in particular, though revenues from a cap-andtrade can have budget impacts in the same manner as tax revenues). The descriptions in

the appendix go into greater depth on the structure, data, and methodologies for PI+ and 

CTAM as well as the figures and variables used to bridge the gap between the carbon 

emissions and revenues in the latter with the economics and demographics in the 

former. For those with additional interest in the topic of regional carbon taxes, studies 

like this exist for three other states and one province in Canada, including Oregon,17

Massachusetts,18 the state of Washington and King County, Washington,

19 and British 

Columbia (who first implemented carbon taxes in 2008).

20

 

14 “Assembly Bill 32: Global Warming Solutions Act,” California Air Resources Board, 

<http://www.arb.ca.gov/cc/ab32/ab32.htm>

15 “Cap-and-Trade,” U.S. Environmental Protection Agency, <http://www.epa.gov/captrade/>

16 “Mapping Carbon Pricing Initiatives: Developments and Prospects,” World Bank, May 2013, p. 55, 

<http://tinyurl.com/worldbankct>

17 Jenny Liu and Jeff Renfro, “Carbon Tax Shift: How to make it work for Oregon’s economy,” March 1, 

2013, <http://www.pdx.edu/nerc/sites/www.pdx.edu.nerc/files/carbontax2013.pdf>

18 Scott Nystrom and Ali Zaidi, “Modeling the Economic, Demographic, and Climate Impact of a Carbon 

Tax in Massachusetts,” REMI, July 11, 2013, <http://www.committeeforagreeneconomy.com/>

19 Scott Nystrom and Ali Zaidi, “The Economic, Demographic, and Climate Impact of Environmental Tax 

Reform in Washington and King County,” REMI, December 13, 2013, <http://tinyurl.com/REMI-WA>; 

Erin Ailworth, “Environmentalists call for a MA carbon tax,” Boston Globe, June 24, 2013, 

<http://www.bostonglobe.com/business/2013/06/23/group-seeks-carbon-tax-combat-climatechange/EGvlBc9ltLUCskJPgad0fL/story.html>

20 Stewart Elgie and Jessica McClay, “BC’s Carbon Tax after Five-Years: An Environmental (and 

Economic) Success,” University of Ottawa, <http://www.sustainableprosperity.ca/dl1026&display>
Regional Economic Models, Inc.

p. 9

HOW DOES A CARBON TAX WORK?

The fundamental goal of a carbon tax is to incentivize economic agents (individuals, the 

household, and the firm) to “internalize” external cost of carbon dioxide in their day-today purchasing decisions. Carbon, while harmless at dilute concentrations and to single 

organisms, may produce an “externality” or a “social cost” when spread across the globe. 

There is the potential that—in enough quantities—it disrupts existing economic 

activities by changing climate and raising sea levels. A carbon tax is “Pigouvian” for 

applying the externality to the cost of goods or services.

21 Carbon taxes have advantages 

in their simplicity and reliance on preexisting economic and social practices—namely, 

sales taxes and the current markets for energy. There are many places to apply a final 

price on carbon in the energy supply-chain, such as the point of extraction, refinement, 

or final sale. In this case, due to the architecture of CTAM, the hypothetical carbon tax 

in California is a retail sales tax on energy based on the carbon content inherent in 

electricity or fuel. Calculating the carbon content is a matter 0f stoichiometry. For 

example, one gallon of motor gasoline (depending on the particular blend) weighs 6.3 

pounds.22 Those 6.3 pounds produce 19.6 pounds of carbon dioxide when combusted 

with the oxygen in the air.23 Converting this into metric tons implies a carbon tax of 

$0.009/gallon for each $1/ton of carbon tax.24 The exercise is equivalent for all fuel 

types based on their typical unit for retail purchases, the average amount of carbon 

emissions in that unit, and the excise tax derived from the carbon content. This pricing 

of emissions means consumers (both individuals and businesses) have an incentive to 

purchase less of the fuel or electricity—satisfying the design of reducing emissions with 

the added benefit of the subsequent revenue is now available for many other purposes 

throughout the state, federal, or civic budget.

Figure 1.1 – This example shows the calculation of carbon content and the application 

of the carbon tax inherent in the CTAM model and this policy’s design.

 

21 Robert H. Frank, “Heads, You Win, Tails, You Win, Too,” New York Times, January 5, 2013, 

<http://www.nytimes.com/2013/01/06/business/pigovian-taxes-may-offer-economic-hope.html>

22 “Fact #519,” U.S. Department of Energy, May 19, 2008, 

<http://www1.eere.energy.gov/vehiclesandfuels/facts/2008_fotw519.html>

23 “How much carbon dioxide is produced by burning gasoline,” U.S. Energy Information 

Administration, <http://www.eia.gov/tools/faqs/faq.cfm?id=307&t=11>

24 1 pound = 0.00045359237 metric tons; 1 metric ton = 2,204.62 pounds

1 regular gallon of gasoline weighs about 6.3 pounds

Oxidation (combustion) yields 19.6 pounds of carbon

The 19.6 pounds is 0.009 metric tons of carbon dioxide

1 gallon of gasoline costs $0.009 to emit at $1/metric ton
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p. 10

Another long-term feature with a carbon tax is predictability and its relationship to the 

confidence of investors. “Investors” are a huge group, as well, which include traditional 

investment banks, venture capital, energy companies financing future projects off their 

own balance sheets, pension funds, and even individuals making personal choices about 

their pick of vehicles, appliances, heating, and retirements. Applying set prices on fuels 

and electricity based on the internal carbon content—as outlined with Figure 1.1—and 

increasing the rate over time (such a $10/year) sends signals to investors regarding the 

potential for higher fossil energy prices in the future. For example, gasoline prices in the 

United States have increased from $1.54/gallon to $3.58/gallon (in 2014 dollars) from 

1998 to 2013.25 That is a 132% increase in real terms in fifteen years. Recently, on the 

other hand, prices have hovered between $3.00/gallon and $4.00/gallon due to the 

weak national economy, additional supply in the western United States, and a host of 

other factors.26 Knowing the future trajectories of these prices is extremely complicated; 

uncertainty about the future often leads investors to “stay the course” into the mists on 

the horizon. Having guaranteed price changes under a carbon tax might help to modify 

this mentality. If the rate rises at $10/year, the formula within Figure 1.1 says prices for 

gasoline fifteen years hence will be at least $1.35/gallon more if the growth in global 

demand and fundamentals dictate no real change to prices. This changes the mindset of 

investors on the market to look for energy efficiency and less carbon-intensive business 

practice and capital projects. Households are more likely to buy efficient cars, windows, 

or homes if they think they can save money on them over a decade or more, and firms 

might feel more comfortable they will realize 8% to 10% return-on-investment (ROI) on 

renewable energy and the related. These processes help create “tipping points” within 

markets where low-carbon business setups and lifestyles become more popular, and PI+

and CTAM illustrates these gradually with their elasticity concepts.

 

25 “U.S. All Grades All Formulations Retail Gasoline Prices,” U.S. Energy Information Administration, 

<http://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=EMM_EPM0_PTE_NUS_DPG&f=A>

26 For a discussion of some of the factors in California, please see Mark Glover, “Unlike past years, 

California gas prices remaining flat,” Sacramento Bee, February 11, 2014, 

<http://www.sacbee.com/2014/02/11/6148057/aaa-unlike-past-years-california.html>
Regional Economic Models, Inc.

p. 11

POLICY SCENARIOS

The simulations here consider six discreet policy scenarios in two principle dimensions. 

The primary consideration of a carbon tax is the actual level of carbon pricing—this is 

part of the energy prices on the market, and therefore the level helps to determine the 

incentives to cut back on emissions, the revenues coming into the state budget, and the 

dynamic response of the economy to the net effect of these factors. For this study, there 

are three graduations for the carbon tax: $50/metric ton, $100/ton, and $200/ton. The 

three are “test cases” with a basis at $50/ton and a short sequence (on powers of two) up 

to $200/ton. These are not the only options for the state, but they do give a good sense 

of the sensitivity over an expansive range. All carbon taxes begin at $10/ton in 2015 and 

accelerate at $10/year until reaching their maximum level ($50/ton in 2019, $100/ton 

in 2024, $200/ton in 2034). The imbedded simplicity and predictability of this system

allows households and businesses to make purchasing decisions in anticipation of the 

carbon taxes in the future. In contrast, cap-and-trade does not ensure any firm prices, 

which makes anticipation a more difficult affair. The preference is for a stability of the 

impact on the economy and budget while allowing the market to choose a new level of 

emissions—not a certain one, but certainly a lower one.

Figure 2.1 – These are the fees applied to carbon dioxide emissions in the six scenarios. 

All taxes begin at $10/metric ton in 2015 and phase-in at $10/year until reaching the 

maximum rates of $50/ton, $100/ton, and $200/ton. For the sake of consistency, the 

coloration of the lines in the remainder of the report will remain the same where 

possible (though with three more for alternative recycling of the revenues).

The other principal concern with a carbon tax is how to allocate the revenue. There are 

an infinite number of ways to use the funds once a carbon tax becomes a part of the state 

budget—financing the traditional government expenditures on infrastructure, setting up 

$0

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p. 12

novel programs, or revenue-neutrality. The simulations herein take two approaches: an 

“across-the-board” (ATB) cut to California’s state income tax, sales tax, and corporation 

taxes, and a “fee-and-dividend” (FAD) system of holding funds and redistributing them 

throughout the economy in an annual, per capita check to households. Before returning 

the revenue, each plan for the recycling of the revenue allocates $4 billion/year to a 

renewable development fund to encourage the expansion of wind and solar capacity and 

energy efficiency. ATB means to lower taxes while making no overarching changes to the 

preexisting California tax code—the idea being the state’s politics has already settled on 

an acceptable system for itself (a “least-disruption” directive). On the other hand, FAD 

derives from the Alaska Permanent Fund, which pays dividends to state residents from 

royalties and earned interest,

27 and a CCL proposal for a similar system at the federallevel.

28 The approach is to apply ideas regarding federal policy to a state and examine its 

implications. The three tax graduations and options for the recycling of the revenues

arrive at six scenarios modeled and described in this report.

ACROSS-THE-BOARD (ATB)

Figure 2.2 – This flowchart shows the destination of the revenues from the carbon tax. 

After the first $4 billion/year goes towards replenishing a fund for the advancement of 

wind and solar power, the rest goes back into the economy via changes to existing 

taxes. The 50:25:25 ratios above come from the current mixture of revenues paid to 

Sacramento from the state income, state sales, and state corporation tax.29 The ratios 

are—roughly—the proportion already paid by revenue source, and therefore this 

represents a minimal disruption to the way California already does its taxes.

 

27 “About the Fund,” Alaska Permanent Fund,

<http://www.apfc.org/home/Content/aboutFund/aboutPermFund.cfm>

28 Todd J. Smith and Danny Richter, “Carbon Fee and Dividend FAQ,” Citizens Climate Lobby, 

<http://citizensclimatelobby.org/about-us/faq/>

29 “California Budget,” California Department of Finance, <www.ebudget.ca.gov>

Carbon tax 

revenues

Initial $4 

billion/year

Rewenable 

fund

Investment 

activities

Balance of 

the funds

50% State income 

tax cut

25% State sales 

tax cut

25% Corporation 

tax cut
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p. 13

FEE-AND-DIVIDEND (FAD)

Figure 2.3 – This shows the process of recycling of the revenue for fee-and-dividend. It 

is similar to across-the-board because the first $4 billion/year always goes towards 

the state renewable fund but, from there, this system takes its cues from the Alaska 

Permanent Fund and its “oil check” methodology to return the money. The state cuts a 

check, its size determined by revenues and eligible population, and sends it to qualified

Californian households based on the number of people in each individual family.

Total sum of the 

carbon taxes 

paid in the state 

of California

Deduct the first 

$4 billion/year 

for the state 

renewable fund

Divide the 

balance by the 

total state 

population

Send an annual, 

per capita check 

to eligible state 

households
Regional Economic Models, Inc.

p. 14

SIMULATION RESULTS

Results of the simulations cover the economic, demographic, fiscal, and climate impacts

of implementing a theoretical carbon tax in California. They cover six scenarios: a 3x2 

matrix of three tax levels ($50/metric ton, $100/ton, and $200/ton) and then two 

systems for revenue-neutrality. One relies on across-the-board (ATB) tax cuts within the 

context of current state fiscal policy. The other utilizes a model sourced from the Alaska 

Permanent Fund fee-and-dividend (FAD) scheme where the money collects in a fund 

and the state sends annual checks of it back to households. All of the results below are 

against a “do-nothing” baseline; this “null hypothesis” supposes and illustrates the drift 

of the California economy into the future and models how it would respond to the net 

tax changes of adding the carbon tax and undertaking ATB or FAD recycling. In essence, 

the results show the net implications of these policies, et ceteris paribus to any other 

developments in the regional, national, or global economies. Results include the impact 

on jobs, GDP, jobs by industry, jobs by occupational category, output by industry, the 

impact to the cost of living, prices for energy categories, household incomes, revenues 

paid in carbon taxes, and the size of the annual check under FAD. It also includes the 

impact on the household-level metrics by quintiles to give a sense of the stratification of 

impacts over the income distribution ladder.
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p. 15

ADDITIONAL TOTAL EMPLOYMENT (OVER BASELINE)

Figures 3.1 – All scenarios for implementing a carbon tax with revenue-neutrality 

generate a net increase in employment over the baseline. In essence, whatever 

“job destruction” of higher energy cost is less than the “job creation” inherent in the 

lower taxes or an increase in direct consumer spending out of the dividend. 

ADDITIONAL GROSS DOMESTIC PRODUCT (ANNUAL)

Figure 3.2 – Depending on the scenario, the state’s level of economic activity (as 

measured by GDP) stands to be higher under environmental tax reform.

One case shows this as much as $18 billion/year and even the negative cases represent 

diminutive loses of less than $3 billion/year within the tax swaps described.

0

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2033

2034

2035

Jobs (over baseline)

$50/ton (ATB)

$50/ton (FAD)

$100/ton (ATB)

$100/ton (FAD)

$200/ton (ATB)

$200/ton (FAD)

-$5,000

$0

$5,000

$10,000

$15,000

$20,000

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

Millions of 2014 dollars

$50/ton (ATB)

$50/ton (FAD)

$100/ton (ATB)

$100/ton (FAD)

$200/ton (ATB)

$200/ton (FAD)
Regional Economic Models, Inc.

p. 16

Job growth and GDP are interrelated, though not always in a one-to-one manner, as 

Figure 3.1 and Figure 3.2 demonstrate on ATB, FAD, and their respective merits for 

economic impacts. By design, a carbon tax raises the price of energy in a jurisdiction to 

incentivize energy consumers to use less of it. This would include raising the price of 

electricity, natural gas, and petroleum products for commercial and industrial firms—

the price of energy by type and sector. The former pairing and prices for the residential 

sector are variables in PI+. These costs form elements of cost of doing business in the 

state, which PI+ would then use to determine the competitiveness of California in terms 

of attracting firms, business retention, and how fast exiting ones will grow. Increasing 

the cost of energy with a carbon tax without offset, such as tax relief in the 

ATB scenarios, means reducing the competitiveness of Californian firms.

This means less business for those firms and a smaller economy. Therefore, in the ATB 

scenarios, which create an improvement in the cost of doing business, California has a 

larger economy, but one that looks essentially the same as the current one—just larger. 

The FAD scenarios, which do not offset costs, imply a smaller economy, but one more 

focused on consumer spending (due to an increase in incomes from the dividend) and 

with an increased share of personal income and less a locus on exports and business 

investments. Each has its relative merits for leaders to consider when designing policies. 

Additionally, industries associated with the former (retail, wholesale, services) tend to 

generate more jobs than those associated with the latter (manufacturing, power, and 

extraction). These adjustments move at differing speeds, which is why jobs and GDP do 

not always march in time with each other in the results.
Regional Economic Models, Inc.

p. 17

ADDITIONAL GROSS DOMESTIC PRODUCT (CUMULATIVE)

Figure 3.3 – This repackages the information to show the cumulative (horizontal sum) 

impact to GDP in the six scenarios. Only the $50/ton FAD simulation has a negative 

impact to GDP over the next twenty years, though it is nearly indistinguishable from 

the baseline Californian economy of the future. Others are appreciably positive.

The next subsection adds detail in breaking out the above macroeconomic indicators on 

the impact to jobs and GDP for portions of the economy by industry and by occupation. 

This illustrates the standing of each of the industries under a carbon tax swap as well as 

the socioeconomics when dividing jobs between industry and employment. In terms of 

industries, PI+ utilizes the North American Industrial Classification System (NAICS); 

NAICS is the standardized categorization of the U.S. Census on what constitutes a group 

of firms engaged in an industry and market competition.30 Dow and DuPont each might 

operate a plant of some sort in the same county for access to natural gas mainlines—to 

the NAICS, however, the chemical plants, the jobs, and the production are all “325.”31

On the occupation side, an industry may be in one part of the supply-chain, but 

individual firms hire a broad swath of differing sorts of workers. For instance, those 

chemical plants would hire engineers, managers, mechanics, accountants, IT, security, 

sales representatives, maintenance personnel, and a number of additional trades. PI+

uses the Standard Occupational Classification (SOC) from the Bureau of Labor Statistics 

(BLS) to describe the actual job that workers do.

32

 

30 “North American Industrial Classification System,” U.S. Census, 

<http://www.census.gov/eos/www/naics/>

31 “325 Chemical Manufacturing,” U.S. Census, <http://www.census.gov/cgibin/sssd/naics/naicsrch?code=325&search=2012%20NAICS%20Search> 

32 “Standard Occupational Classification,” Bureau of Labor Statistics, <http://www.bls.gov/soc/>

-$50,000

$0

$50,000

$100,000

$150,000

$200,000

$250,000

$50/ton 

(ATB)

$50/ton 

(FAD)

$100/ton 

(ATB)

$100/ton 

(FAD)

$200/ton 

(ATB)

$200/ton 

(FAD)

Millions of 2014 dollars
Regional Economic Models, Inc.

p. 18

FIGURE 3.4 – ANNUAL OUTPUT BY INDUSTRY ($200/TON, ATB)

NAICS Industries 2015 2020 2025 2030 2035

Forestry and logging; Fishing, hunting, and trapping -$1.0 -$17.9 -$41.4 -$62.8 -$80.7

Agriculture and forestry support activities $0.0 -$1.8 -$4.7 -$7.0 -$8.4

Oil and gas extraction -$50.6 -$410.3 -$721.9 -$854.1 -$781.6

Mining (except oil and gas) $0.0 -$6.8 -$17.5 -$28.8 -$39.6

Support activities for mining -$5.6 -$79.2 -$167.2 -$233.9 -$278.7

Utilities -$212.1 -$1,225.3 -$2,140.5 -$2,751.8 -$3,012.3

Construction $345.4 $2,169.4 $3,773.9 $4,884.5 $5,479.5

Wood product manufacturing $6.3 $35.7 $56.0 $65.3 $65.6

Nonmetallic mineral product manufacturing $12.6 $64.0 $93.6 $103.8 $98.8

Primary metal manufacturing $0.5 -$49.6 -$133.2 -$209.8 -$266.5

Fabricated metal product manufacturing $31.1 $168.8 $253.3 $303.9 $326.1

Machinery manufacturing $6.4 $27.4 $27.8 $24.8 $20.7

Computer and electronic product manufacturing $67.7 $348.8 $505.0 $646.6 $773.7

Electrical equipment and appliance manufacturing $7.6 $25.4 $9.4 -$24.2 -$67.6

Motor vehicles, bodies and trailers, and parts manufacturing $15.4 $92.3 $146.7 $182.5 $200.0

Other transportation equipment manufacturing $7.2 $25.5 $20.6 $5.7 -$13.2

Furniture and related product manufacturing $16.9 $87.7 $124.4 $134.2 $124.4

Miscellaneous manufacturing $19.2 $86.9 $109.9 $125.4 $134.2

Food manufacturing $16.9 $68.2 $95.8 $116.4 $127.9

Beverage and tobacco product manufacturing $8.2 $44.6 $75.3 $98.8 $110.2

Textile mills; Textile product mills $2.2 $5.8 $1.6 -$7.6 -$14.1

Apparel manufacturing; Leather and allied product manufacturing $3.0 $14.7 $14.4 $17.4 $16.8

Paper manufacturing $5.7 $25.4 $33.8 $35.4 $32.2

Printing and related support activities $10.3 $61.0 $97.8 $121.4 $133.4

Petroleum and coal products manufacturing -$482.0 -$4,211.3 -$8,056.2 -$11,420.3 -$14,113.5

Chemical manufacturing $50.8 -$17.0 -$316.4 -$687.3 -$1,055.5

Plastics and rubber product manufacturing $15.7 $62.4 $67.9 $51.1 $21.5

Wholesale trade $198.8 $1,286.3 $2,286.0 $3,117.6 $3,749.7

Retail trade $286.3 $2,061.1 $3,802.7 $5,253.5 $6,361.9

Air transportation $3.1 $3.9 -$22.9 -$64.0 -$109.7

Rail transportation $0.0 -$2.2 -$6.1 -$10.7 -$15.1

Water transportation $0.5 $1.1 -$0.4 -$3.1 -$5.9

Truck transportation $19.4 $121.6 $209.3 $276.1 $323.9

Couriers and messengers $5.8 $33.3 $55.9 $72.9 $85.2

Transit and ground passenger transportation $2.6 $13.5 $22.7 $30.4 $36.2

Pipeline transportation -$1.3 -$9.6 -$17.3 -$21.0 -$21.0

Scenic and sightseeing transportation -$2.2 -$26.8 -$58.9 -$94.4 -$131.1

Warehousing and storage $2.0 $7.5 $5.7 $0.0 -$7.3

Publishing industries, except Internet $57.6 $375.6 $645.7 $868.3 $1,047.9

Motion picture and sound recording industries $32.2 $149.1 $244.7 $321.8 $380.5

Internet publishing and broadcasting $11.8 $58.6 $90.0 $113.7 $133.2

Broadcasting, except Internet $10.1 $51.9 $78.7 $96.9 $111.7

Telecommunications $51.1 $296.1 $517.9 $722.2 $908.2

Monetary authorities $127.7 $660.5 $1,054.7 $1,326.8 $1,509.8

Securities, commodity contracts, investments $54.2 $265.3 $390.3 $443.9 $454.7

Insurance carriers and related activities $36.7 $193.1 $301.4 $359.0 $375.1

Real estate $121.4 $594.0 $964.8 $1,221.9 $1,349.1

Rental and leasing services $27.1 $122.4 $176.7 $210.0 $242.1

Professional, scientific, and technical services $129.0 $564.4 $753.3 $828.4 $891.1

Management of companies and enterprises $15.0 $51.1 $39.9 $10.8 -$18.8

Administrative and support services $56.1 $293.3 $458.7 $584.6 $703.7

Waste management and remediation services $5.9 $30.0 $48.6 $63.2 $74.1

Educational services $21.7 $144.8 $271.1 $381.4 $461.2

Ambulatory health care services $210.7 $1,174.8 $1,950.3 $2,575.6 $3,100.0

Hospitals $48.8 $272.3 $483.5 $695.0 $906.1

Nursing and residential care facilities $15.0 $87.9 $161.8 $234.0 $299.2

Social assistance $12.9 $86.1 $163.9 $236.8 $295.2

Performing arts and spectator sports $8.9 $51.0 $87.4 $118.4 $142.7

Museums, historical sites, zoos, and parks $2.3 $16.0 $30.4 $43.5 $53.9

Amusement, gambling, and recreation $14.0 $77.8 $131.0 $175.3 $213.2

Accommodation $12.3 $19.3 -$10.4 -$42.2 -$44.4

Food services and drinking places $47.0 $301.2 $567.0 $820.5 $1,038.5

Repair and maintenance $20.8 $114.8 $195.8 $263.6 $314.8

Personal and laundry services $26.4 $136.7 $204.1 $246.5 $278.9

Membership associations and organizations $21.3 $136.4 $252.4 $354.1 $426.9

Private households $4.0 $22.5 $35.4 $44.8 $54.4

TOTAL FOR ALL INDUSTRIES = $1,614.8 $7,231.4 $10,474.1 $12,505.4 $13,903.5
Regional Economic Models, Inc.

p. 19

FIGURE 3.5 – PERCENTAGE CHANGE ($200/TON, ATB, 2015-2035)

This reorganizes the table of change in industry outputs into their percentage changes 

by industry. As per the intuition, most sectors can grow faster under a carbon tax (sans

the oil and gas supply-chain, which shrink slightly).

-5.0% -4.0% -3.0% -2.0% -1.0% 0.0% 1.0% 2.0%

Oil and gas extraction

Petroleum and coal products manufacturing

Utilities

Support activities for mining

Forestry and logging; Fishing, hunting, and trapping

Pipeline transportation

Primary metal manufacturing

Chemical manufacturing

Mining (except oil and gas)

Scenic and sightseeing transportation

Air transportation

Rail transportation

Agriculture and forestry support activities

Textile mills; Textile product mills

Electrical equipment and appliance manufacturing

Accommodation

Water transportation

Other transportation equipment manufacturing

Warehousing and storage

Management of companies and enterprises

Machinery manufacturing

Food manufacturing

Internet publishing and broadcasting

Professional, scientific, and technical services

Rental and leasing services

Apparel manufacturing; Leather and allied product manufacturing

Beverage and tobacco product manufacturing

Real estate

Computer and electronic product manufacturing

Broadcasting, except Internet

Performing arts and spectator sports

Plastics and rubber product manufacturing

Motion picture and sound recording industries

Couriers and messengers

Paper manufacturing

Miscellaneous manufacturing

Waste management and remediation services

Insurance carriers and related activities

Administrative and support services

Securities, commodity contracts, investments

Transit and ground passenger transportation

Truck transportation

Telecommunications

Fabricated metal product manufacturing

Repair and maintenance

Food services and drinking places

Monetary authorities

Hospitals

Social assistance

Wood product manufacturing

Publishing industries, except Internet

Nonmetallic mineral product manufacturing

Nursing and residential care facilities

Educational services

Membership associations and organizations

Museums, historical sites, zoos, and parks

Personal and laundry services

Printing and related support activities

Private households

Amusement, gambling, and recreation

Wholesale trade

Motor vehicles, bodies and trailers, and parts manufacturing

Furniture and related product manufacturing

Ambulatory health care services

Retail trade

Construction
Regional Economic Models, Inc.

p. 20

FIGURE 3.6 – ANNUAL OUTPUT BY INDUSTRY ($200/TON, FAD)

NAICS Industries 2015 2020 2025 2030 2035

Forestry and logging; Fishing, hunting, and trapping -$1.3 -$24.3 -$55.9 -$83.5 -$104.6

Agriculture and forestry support activities $0.0 -$2.7 -$7.0 -$10.1 -$11.6

Oil and gas extraction -$52.3 -$431.7 -$763.8 -$906.2 -$833.5

Mining (except oil and gas) -$0.1 -$9.1 -$22.9 -$36.9 -$49.4

Support activities for mining -$6.7 -$99.6 -$212.1 -$297.2 -$352.4

Utilities -$208.3 -$1,254.1 -$2,225.4 -$2,880.3 -$3,162.1

Construction $352.1 $2,059.7 $3,416.3 $4,375.3 $5,005.6

Wood product manufacturing $4.5 $16.9 $17.4 $12.4 $6.6

Nonmetallic mineral product manufacturing $10.3 $37.7 $40.4 $32.4 $21.4

Primary metal manufacturing -$3.0 -$91.7 -$213.8 -$316.9 -$385.4

Fabricated metal product manufacturing $22.4 $71.8 $59.0 $30.2 $2.2

Machinery manufacturing $5.1 $3.4 -$25.4 -$55.6 -$79.1

Computer and electronic product manufacturing -$11.7 -$575.9 -$1,524.5 -$2,492.9 -$3,290.0

Electrical equipment and appliance manufacturing $2.4 -$27.0 -$88.8 -$153.2 -$209.6

Motor vehicles, bodies and trailers, and parts manufacturing $14.6 $76.2 $111.4 $131.9 $141.9

Other transportation equipment manufacturing $2.0 -$35.0 -$110.6 -$190.3 -$257.1

Furniture and related product manufacturing $9.1 $31.0 $25.4 $9.5 -$8.4

Miscellaneous manufacturing $13.8 $35.1 $5.0 -$24.8 -$46.0

Food manufacturing $19.0 $29.5 -$9.1 -$45.5 -$67.6

Beverage and tobacco product manufacturing $11.8 $50.4 $71.2 $83.9 $88.4

Textile mills; Textile product mills $1.0 -$2.1 -$10.2 -$19.5 -$26.5

Apparel manufacturing; Leather and allied product manufacturing $1.0 -$3.5 -$19.8 -$30.1 -$37.6

Paper manufacturing $3.9 $8.0 $1.7 -$5.4 -$11.1

Printing and related support activities $6.3 $24.5 $29.4 $30.8 $32.4

Petroleum and coal products manufacturing -$493.5 -$4,336.4 -$8,306.4 -$11,758.9 -$14,492.4

Chemical manufacturing $21.5 -$262.1 -$795.5 -$1,338.4 -$1,797.6

Plastics and rubber product manufacturing $9.1 $1.7 -$47.7 -$103.1 -$151.4

Wholesale trade $98.9 $479.7 $745.7 $927.2 $1,064.9

Retail trade $123.4 $860.0 $1,598.0 $2,246.6 $2,837.2

Air transportation $2.5 -$20.9 -$87.1 -$166.7 -$242.8

Rail transportation -$0.3 -$6.9 -$16.5 -$26.1 -$34.0

Water transportation $0.7 $0.2 -$3.6 -$8.7 -$13.4

Truck transportation $10.9 $46.4 $61.3 $66.0 $71.1

Couriers and messengers $3.6 $10.8 $8.7 $2.7 -$3.1

Transit and ground passenger transportation $3.4 $14.0 $20.2 $25.0 $29.3

Pipeline transportation -$1.4 -$11.0 -$20.2 -$24.6 -$24.8

Scenic and sightseeing transportation -$6.4 -$66.4 -$140.5 -$217.9 -$290.9

Warehousing and storage $0.2 -$13.9 -$39.1 -$66.0 -$89.7

Publishing industries, except Internet $24.3 $75.8 $35.1 -$22.8 -$48.3

Motion picture and sound recording industries $7.9 $4.1 -$20.3 -$50.2 -$77.7

Internet publishing and broadcasting $10.3 $7.6 -$26.4 -$50.8 -$54.0

Broadcasting, except Internet $7.2 $8.5 -$15.8 -$45.0 -$67.1

Telecommunications $64.8 $303.9 $472.4 $620.8 $769.6

Monetary authorities $167.8 $712.6 $995.6 $1,130.3 $1,195.5

Securities, commodity contracts, investments $68.3 $211.8 $174.5 $60.5 -$58.8

Insurance carriers and related activities $49.5 $213.2 $290.1 $315.0 $310.8

Real estate $200.7 $690.8 $794.5 $737.6 $631.4

Rental and leasing services $18.4 -$14.6 -$152.3 -$324.6 -$476.2

Professional, scientific, and technical services $108.9 $40.1 -$497.9 -$1,100.6 -$1,545.4

Management of companies and enterprises $5.1 -$95.9 -$298.4 -$527.6 -$737.2

Administrative and support services $56.7 $184.6 $175.9 $126.8 $102.0

Waste management and remediation services $7.2 $26.3 $33.1 $35.0 $35.7

Educational services $35.9 $214.0 $373.6 $504.2 $597.0

Ambulatory health care services $304.3 $1,565.4 $2,503.6 $3,238.2 $3,845.6

Hospitals $77.5 $379.0 $612.6 $831.9 $1,056.6

Nursing and residential care facilities $23.7 $124.2 $211.4 $290.6 $361.2

Social assistance $19.7 $119.5 $212.3 $294.1 $358.5

Performing arts and spectator sports $10.7 $48.1 $68.2 $79.9 $87.0

Museums, historical sites, zoos, and parks $3.0 $19.1 $33.9 $46.4 $55.7

Amusement, gambling, and recreation $22.6 $112.4 $179.2 $232.0 $276.4

Accommodation $20.1 $20.7 -$46.0 -$118.6 -$152.1

Food services and drinking places $73.3 $401.8 $684.3 $931.2 $1,142.9

Repair and maintenance $26.4 $120.4 $184.8 $236.4 $278.5

Personal and laundry services $42.2 $195.8 $277.8 $325.3 $359.4

Membership associations and organizations $29.9 $163.0 $279.0 $376.5 $448.3

Private households $7.5 $37.9 $58.2 $72.6 $85.3

TOTAL FOR ALL INDUSTRIES = $1,462.2 $2,472.5 -$941.6 -$5,039.8 -$7,990.0
Regional Economic Models, Inc.

p. 21

FIGURE 3.7 – PERCENTAGE CHANGE ($200/TON, FAD, 2015-2035)

This is the same illustration as Figure 3.5 only for the FAD program instead of ATB. The 

energy sector itself sees a contraction of between 2.5% and 5.0% and industries related 

to consumption (such as retail or services) improve over ATB’s results.

-5.0% -4.0% -3.0% -2.0% -1.0% 0.0% 1.0% 2.0%

Oil and gas extraction

Petroleum and coal products manufacturing

Utilities

Support activities for mining

Forestry and logging; Fishing, hunting, and trapping

Primary metal manufacturing

Pipeline transportation

Chemical manufacturing

Electrical equipment and appliance manufacturing

Scenic and sightseeing transportation

Computer and electronic product manufacturing

Mining (except oil and gas)

Air transportation

Management of companies and enterprises

Warehousing and storage

Textile mills; Textile product mills

Plastics and rubber product manufacturing

Rail transportation

Apparel manufacturing; Leather and allied product manufacturing

Other transportation equipment manufacturing

Rental and leasing services

Professional, scientific, and technical services

Accommodation

Agriculture and forestry support activities

Water transportation

Machinery manufacturing

Broadcasting, except Internet

Internet publishing and broadcasting

Motion picture and sound recording industries

Food manufacturing

Miscellaneous manufacturing

Paper manufacturing

Publishing industries, except Internet

Couriers and messengers

Fabricated metal product manufacturing

Administrative and support services

Truck transportation

Securities, commodity contracts, investments

Real estate

Wood product manufacturing

Performing arts and spectator sports

Beverage and tobacco product manufacturing

Furniture and related product manufacturing

Waste management and remediation services

Printing and related support activities

Nonmetallic mineral product manufacturing

Wholesale trade

Insurance carriers and related activities

Transit and ground passenger transportation

Telecommunications

Monetary authorities

Repair and maintenance

Food services and drinking places

Hospitals

Retail trade

Social assistance

Motor vehicles, bodies and trailers, and parts manufacturing

Membership associations and organizations

Nursing and residential care facilities

Museums, historical sites, zoos, and parks

Educational services

Personal and laundry services

Amusement, gambling, and recreation

Private households

Construction

Ambulatory health care services
Regional Economic Models, Inc.

p. 22

The general economy might stand to gain from environmental tax reform and a carbon 

tax. In particular, it can gain in certain industries that create large numbers of jobs while 

most lost output comes from industries without much labor-intensity that share a close 

tie with energy production. Every industry’s “profile” in terms of the carbon tax differs 

depending on its preexisting haul of output, its competitiveness, its current utilization of 

energy resources, its current tax apportionments, how close it is to direct consumer 

spending, and how it fits into other B2B transactions.

The uppers and downers in the industry list offer interesting cases. Some of the “losers” 

(though percentage changes are fairly miniature in simulations, most less than 2% and 

all less than 5%) include petroleum and coal products (which includes petroleum 

refining), utilities, chemical manufacturing (energy-intensive and a feedstock to 

refining), oil and gas extraction, and primary metal manufacturing (for steel 

products such as pipelines). FAD adds computers and electronic products and 

professional and technical services (which are big industries within California and 

competitive on the national and international market, with San Jose and Los Angeles 

competing with firms in Seoul, Shanghai, and London). Conversely, the “winners” are a 

numerous collection. They involve localized, labor-intensive industries with direct ties to 

households and their spending such as construction, retail trade, food service 

and drinking places, financial services, and healthcare. Some specialized sectors 

in manufacturing, such as furniture, wood products, paper, and motion pictures 

and sound recording—this being California—see benefits, too, given the reduction in 

cost available to them under the ATB case and their light use of electrical power and 

fossil energy compared to heavy manufacturing.

The difference in impacts on industry output between ATB and FAD lies in the natures 

of the various industries’ customers. Under FAD, industries such as healthcare and 

education services, entertainment and gaming, and personal services would 

do better because their customers are the individuals and families receiving checks from 

the carbon tax dividend. ATB delivers benefits or neutral impacts to more industries. 

Computers and electronics is perhaps a representative case for the difference 

between the two. Computers and electronics gain under ATB because of their 

relatively low usage of energy resources and the highly competitive nature of the market 

shares in question—any change in their cost of doing business, cost of capital, and taxes 

can lead to bigger swings. However, with FAD, computers see a decline in output for 

equal (though opposite) reasons. The industry itself is far enough back in the production 

supply-chain and exports too much of its product out of California to feel much of a 

demand surge from the dividends, and its responsiveness to business costs and the 

competitive nature of the industry between regions leads to a decline in output. The 

general effect is still positive, and the job results in the next sections show what begins 

to happen on the labor market within California.
Regional Economic Models, Inc.

p. 23

FIGURE 3.8 – EMPLOYMENT BY INDUSTRY ($200/TON, ATB)

NAICS Industries 2015 2020 2025 2030 2035

Forestry and logging; Fishing, hunting, and trapping -4 -66 -136 -187 -224

Agriculture and forestry support activities 7 43 91 139 173

Oil and gas extraction -109 -787 -1,322 -1,544 -1,486

Mining (except oil and gas) 3 35 88 148 208

Support activities for mining -13 -145 -239 -257 -230

Utilities -177 -808 -1,153 -1,216 -1,092

Construction 3,051 21,106 38,761 51,975 60,612

Wood product manufacturing 29 185 316 398 436

Nonmetallic mineral product manufacturing 47 289 496 638 716

Primary metal manufacturing 5 3 -9 -12 -8

Fabricated metal product manufacturing 117 687 1,106 1,398 1,580

Machinery manufacturing 22 116 163 195 210

Computer and electronic product manufacturing 97 426 544 613 648

Electrical equipment and appliance manufacturing 24 94 84 41 -13

Motor vehicles, bodies and trailers, and parts manufacturing 36 195 276 301 289

Other transportation equipment manufacturing 15 80 130 174 213

Furniture and related product manufacturing 89 455 640 681 627

Miscellaneous manufacturing 68 295 374 423 450

Food manufacturing 42 269 502 695 822

Beverage and tobacco product manufacturing 15 110 213 296 340

Textile mills; Textile product mills 13 62 78 54 54

Apparel manufacturing; Leather and allied product manufacturing 35 207 255 314 328

Paper manufacturing 17 92 147 175 182

Printing and related support activities 63 359 533 593 576

Petroleum and coal products manufacturing -33 -182 -243 -248 -227

Chemical manufacturing 62 164 164 128 78

Plastics and rubber product manufacturing 53 277 428 515 555

Wholesale trade 903 5,541 9,288 11,815 13,211

Retail trade 3,499 22,793 38,119 47,227 51,221

Air transportation 9 30 6 -31 -63

Rail transportation 0 5 11 19 26

Water transportation 1 9 20 34 51

Truck transportation 141 918 1,642 2,228 2,684

Couriers and messengers 45 254 425 554 651

Transit and ground passenger transportation 41 229 408 566 693

Pipeline transportation -2 -11 -14 -12 -9

Scenic and sightseeing transportation -15 -153 -289 -392 -458

Warehousing and storage 24 102 122 116 107

Publishing industries, except Internet 114 598 848 929 906

Motion picture and sound recording industries 87 453 741 957 1,108

Internet publishing and broadcasting 20 96 137 152 148

Broadcasting, except Internet 25 137 225 294 349

Telecommunications 82 448 729 922 1,031

Monetary authorities 271 1,223 1,754 1,976 2,022

Securities, commodity contracts, investments 369 1,764 2,591 2,956 3,064

Insurance carriers and related activities 133 715 1,145 1,386 1,476

Real estate 394 2,797 5,448 7,721 9,349

Rental and leasing services 127 676 1,071 1,321 1,460

Professional, scientific, and technical services 865 4,248 6,531 8,224 9,707

Management of companies and enterprises 71 241 244 221 212

Administrative and support services 975 6,510 11,957 17,026 21,244

Waste management and remediation services 26 162 304 440 559

Educational services 339 2,645 5,361 7,858 9,685

Ambulatory health care services 1,620 9,167 15,566 20,781 25,184

Hospitals 312 1,854 3,416 4,923 6,270

Nursing and residential care facilities 225 1,438 2,795 4,148 5,356

Social assistance 273 1,952 3,889 5,744 7,253

Performing arts and spectator sports 114 757 1,416 2,012 2,496

Museums, historical sites, zoos, and parks 16 126 247 354 429

Amusement, gambling, and recreation 246 1,515 2,733 3,784 4,627

Accommodation 118 494 790 1,128 1,561

Food services and drinking places 799 5,704 10,976 15,578 18,799

Repair and maintenance 211 1,278 2,322 3,222 3,883

Personal and laundry services 405 2,118 3,174 3,805 4,234

Membership associations and organizations 264 1,736 3,260 4,568 5,471

Private households 481 2,494 3,600 4,165 4,661

TOTAL FOR ALL INDUSTRIES = 17,202 106,624 185,295 245,149 286,475
Regional Economic Models, Inc.

p. 24

FIGURE 3.9 – PERCENTAGE CHANGE ($200/TON, ATB, 2015-2035)

Most industries see gains in employment with a carbon tax under ATB, though some see 

more than others do. Construction and the services-related sectors, in particular, see 

more jobs than direct and indirect relations to extraction do.

-4.0% -3.0% -2.0% -1.0% 0.0% 1.0% 2.0% 3.0%

Oil and gas extraction

Utilities

Petroleum and coal products manufacturing

Support activities for mining

Forestry and logging; Fishing, hunting, and trapping

Pipeline transportation

Scenic and sightseeing transportation

Primary metal manufacturing

Air transportation

Agriculture and forestry support activities

Management of companies and enterprises

Warehousing and storage

Rail transportation

Other transportation equipment manufacturing

Internet publishing and broadcasting

Electrical equipment and appliance manufacturing

Chemical manufacturing

Machinery manufacturing

Computer and electronic product manufacturing

Water transportation

Professional, scientific, and technical services

Accommodation

Food manufacturing

Couriers and messengers

Textile mills; Textile product mills

Broadcasting, except Internet

Performing arts and spectator sports

Insurance carriers and related activities

Beverage and tobacco product manufacturing

Apparel manufacturing; Leather and allied product manufacturing

Miscellaneous manufacturing

Motion picture and sound recording industries

Monetary authorities

Securities, commodity contracts, investments

Real estate

Transit and ground passenger transportation

Truck transportation

Telecommunications

Waste management and remediation services

Mining (except oil and gas)

Paper manufacturing

Hospitals

Publishing industries, except Internet

Fabricated metal product manufacturing

Plastics and rubber product manufacturing

Social assistance

Repair and maintenance

Administrative and support services

Food services and drinking places

Nursing and residential care facilities

Private households

Membership associations and organizations

Rental and leasing services

Wood product manufacturing

Educational services

Wholesale trade

Printing and related support activities

Personal and laundry services

Museums, historical sites, zoos, and parks

Amusement, gambling, and recreation

Nonmetallic mineral product manufacturing

Motor vehicles, bodies and trailers, and parts manufacturing

Ambulatory health care services

Furniture and related product manufacturing

Retail trade

Construction
Regional Economic Models, Inc.

p. 25

FIGURE 3.10 – EMPLOYMENT BY INDUSTRY ($200/TON, FAD)

NAICS Industries 2015 2020 2025 2030 2035

Forestry and logging; Fishing, hunting, and trapping -6 -96 -197 -266 -307

Agriculture and forestry support activities 7 20 46 91 135

Oil and gas extraction -113 -832 -1,410 -1,652 -1,599

Mining (except oil and gas) 3 27 72 129 190

Support activities for mining -17 -194 -334 -372 -345

Utilities -171 -818 -1,189 -1,260 -1,129

Construction 3,104 20,389 36,523 49,068 58,284

Wood product manufacturing 21 111 174 216 246

Nonmetallic mineral product manufacturing 39 213 356 468 553

Primary metal manufacturing -1 -57 -113 -134 -130

Fabricated metal product manufacturing 86 373 531 662 787

Machinery manufacturing 19 64 68 79 95

Computer and electronic product manufacturing -24 -614 -1,142 -1,342 -1,289

Electrical equipment and appliance manufacturing 9 -34 -120 -184 -221

Motor vehicles, bodies and trailers, and parts manufacturing 30 145 197 214 210

Other transportation equipment manufacturing 5 -23 -66 -79 -61

Furniture and related product manufacturing 49 181 187 145 90

Miscellaneous manufacturing 50 153 138 150 186

Food manufacturing 49 227 385 533 651

Beverage and tobacco product manufacturing 21 126 227 309 356

Textile mills; Textile product mills 7 24 23 1 2

Apparel manufacturing; Leather and allied product manufacturing 15 51 10 34 64

Paper manufacturing 12 57 89 113 127

Printing and related support activities 40 188 267 302 308

Petroleum and coal products manufacturing -34 -190 -256 -262 -238

Chemical manufacturing 32 -48 -190 -279 -313

Plastics and rubber product manufacturing 33 107 125 138 159

Wholesale trade 463 2,376 3,860 4,964 5,768

Retail trade 1,544 10,201 17,701 22,836 26,146

Air transportation 8 -26 -126 -219 -281

Rail transportation 0 -3 -1 5 13

Water transportation 1 8 16 29 45

Truck transportation 82 437 748 1,048 1,366

Couriers and messengers 29 99 126 148 183

Transit and ground passenger transportation 55 243 391 526 651

Pipeline transportation -2 -13 -17 -15 -10

Scenic and sightseeing transportation -45 -407 -755 -1,012 -1,164

Warehousing and storage 4 -115 -306 -471 -577

Publishing industries, except Internet 56 200 222 227 243

Motion picture and sound recording industries 30 185 342 508 673

Internet publishing and broadcasting 18 46 52 65 80

Broadcasting, except Internet 18 53 62 78 108

Telecommunications 103 468 705 870 977

Monetary authorities 347 1,259 1,578 1,629 1,596

Securities, commodity contracts, investments 464 1,483 1,522 1,254 1,038

Insurance carriers and related activities 176 787 1,147 1,338 1,420

Real estate 612 3,271 5,687 7,759 9,408

Rental and leasing services 111 492 706 832 915

Professional, scientific, and technical services 760 1,424 199 -809 -839

Management of companies and enterprises 26 -303 -790 -1,125 -1,261

Administrative and support services 1,008 5,167 8,608 12,011 15,195

Waste management and remediation services 31 154 262 367 468

Educational services 540 3,644 6,889 9,733 11,806

Ambulatory health care services 2,334 12,140 19,836 25,939 31,058

Hospitals 489 2,490 4,179 5,725 7,145

Nursing and residential care facilities 350 1,956 3,513 4,977 6,277

Social assistance 413 2,641 4,923 7,006 8,694

Performing arts and spectator sports 132 756 1,342 1,888 2,369

Museums, historical sites, zoos, and parks 21 146 271 375 447

Amusement, gambling, and recreation 387 2,091 3,556 4,765 5,732

Accommodation 182 532 620 776 1,122

Food services and drinking places 1,208 7,215 12,753 17,322 20,528

Repair and maintenance 272 1,426 2,462 3,376 4,103

Personal and laundry services 646 2,997 4,245 4,905 5,305

Membership associations and organizations 370 2,079 3,642 4,940 5,862

Private households 912 4,202 5,946 6,794 7,355

TOTAL FOR ALL INDUSTRIES = 17,420 91,351 150,517 198,186 236,775
Regional Economic Models, Inc.

p. 26

FIGURE 3.11 – PERCENTAGE CHANGE ($200/TON, FAD, 2015-2035)

The results for FAD are similar, although a couple manufacturing sectors (in chemicals, 

electrical capital, primary metal, and computers) have slightly more negative impacts to 

their employment compared to ATB and the baseline.

-4.0% -3.0% -2.0% -1.0% 0.0% 1.0% 2.0% 3.0%

Oil and gas extraction

Utilities

Petroleum and coal products manufacturing

Forestry and logging; Fishing, hunting, and trapping

Support activities for mining

Pipeline transportation

Scenic and sightseeing transportation

Primary metal manufacturing

Electrical equipment and appliance manufacturing

Computer and electronic product manufacturing

Air transportation

Management of companies and enterprises

Chemical manufacturing

Warehousing and storage

Other transportation equipment manufacturing

Professional, scientific, and technical services

Rail transportation

Agriculture and forestry support activities

Apparel manufacturing; Leather and allied product manufacturing

Textile mills; Textile product mills

Internet publishing and broadcasting

Couriers and messengers

Machinery manufacturing

Broadcasting, except Internet

Miscellaneous manufacturing

Publishing industries, except Internet

Plastics and rubber product manufacturing

Motion picture and sound recording industries

Water transportation

Accommodation

Food manufacturing

Truck transportation

Securities, commodity contracts, investments

Fabricated metal product manufacturing

Performing arts and spectator sports

Furniture and related product manufacturing

Insurance carriers and related activities

Paper manufacturing

Wholesale trade

Beverage and tobacco product manufacturing

Monetary authorities

Mining (except oil and gas)

Wood product manufacturing

Waste management and remediation services

Printing and related support activities

Transit and ground passenger transportation

Rental and leasing services

Administrative and support services

Telecommunications

Real estate

Retail trade

Repair and maintenance

Hospitals

Motor vehicles, bodies and trailers, and parts manufacturing

Nonmetallic mineral product manufacturing

Social assistance

Food services and drinking places

Membership associations and organizations

Nursing and residential care facilities

Museums, historical sites, zoos, and parks

Educational services

Private households

Personal and laundry services

Amusement, gambling, and recreation

Ambulatory health care services

Construction
Regional Economic Models, Inc.

p. 27

There are two concepts that help in reading the impacts to jobs in contrast to impacts on 

industry output. One is labor productivity, and the other is “factor substitution.” Labor 

productivity is the amount of production associated with a unit of labor. For example, if 

an aircraft production line produces 100 units per year and each one of the planes sells 

for $200 million, the output of the line is $2 billion/year. In 2012, California’s output in 

aerospace products approached $35 billion, so a line like this would be about 6% of 

the industry. Suppose the line employs 5,000 workers—this implies a labor productivity 

of $400,000, which is the $2 billion in output over 5,000 labor units. Technology and 

manufacturing firms tend to have high labor productivity. They rely on automation and 

capital, and some enterprises (such as petroleum refining and related activities) have 

millions of dollars in output for one worker. Other industries are more “labor-intensive” 

in the sense their production processes, their nature in the service sector, and their 

technology mean they require more workers to create the same amount of output. 

Sectors like this include retail, construction, healthcare, education, and food 

service—all industries primarily benefiting under a carbon tax swap. This is why the 

employment results are “higher” than that of GDP.

Output = Labor Units * Labor Productivity

Another, lesser issue is factor substitution. To a limited degree, firms can substitute 

different input types for each other when designing an optimal way to produce a good or 

a service. For instance, imagine a wholesaler is relying on a software product to do its 

payroll. The firm finds the software, which is a type of capital requiring an investment, 

outdated and clunky, and requires a significant number of hours from HR staff to make 

it work. The company has an option to upgrade to a modern system; however, the cost of 

the new system (new capital) is prohibitive and potentially disruptive to other legacy 

products. Sticking with the old system and its implication for higher labor inputs is an 

implicit choice by the company to rely on labor before capital in this instance. The same 

process can happen in other industries between labor and capital, as well as between 

labor, capital, and fuel types (electricity, natural gas, and petroleum products). The PI+

model intrinsically handles the substitution amid factors in the regular architecture of 

its structure. This means labor productivity can change in the model, and therefore an 

industry could lose some output in the simulations while still gaining some amount of 

employment. An industry like construction, with its variety of production processes,

would be a chief candidate for factor substitution.

The greatest job gains in the simulations are in labor-intensive, service-based sectors 

like construction, retail, food, drinking places, education, and healthcare. The 

impact here is double owing to their labor-intensity and sensitivity to general tax cuts 

(ATB) and additions to consumer spending (FAD). The actual types of jobs arising out of 

environmental tax reform reflect this in the numbers above.
Regional Economic Models, Inc.

p. 28

FIGURE 3.12 – EMPLOYMENT BY OCCUPATION ($200/TON, ATB)

SOC Occupations 2015 2020 2025 2030 2035

Top executives 297 1,729 2,917 3,743 4,231

Advertising, marketing, promotions, public relations, and sales managers 81 470 777 988 1,115

Operations specialties managers 149 805 1,303 1,653 1,883

Other management occupations 270 1,712 3,099 4,215 5,002

Business operations specialists 406 2,422 4,152 5,481 6,431

Financial specialists 317 1,667 2,652 3,303 3,707

Computer occupations 286 1,501 2,343 2,910 3,303

Mathematical science occupations 9 43 67 82 91

Architects, surveyors, and cartographers 25 142 240 324 398

Engineers 99 501 799 1,079 1,354

Drafters, engineering technicians, and mapping technicians 61 319 510 664 789

Life scientists 19 90 137 174 204

Physical scientists 14 52 75 103 138

Social scientists and related workers 22 121 206 274 326

Life, physical, and social science technicians 16 70 108 147 188

Counselors and Social workers 114 709 1,303 1,829 2,239

Miscellaneous community and social service specialists 69 445 828 1,168 1,426

Religious workers 2 15 29 42 52

Lawyers, judges, and related workers 60 301 466 559 594

Legal support workers 35 181 286 350 380

Postsecondary teachers 148 1,005 1,910 2,695 3,242

Preschool, primary, secondary, and special education school teachers 273 1,581 2,743 3,628 4,178

Other teachers and instructors 72 456 837 1,156 1,374

Librarians, curators, and archivists 20 119 208 276 317

Other education, training, and library occupations 108 653 1,169 1,586 1,863

Art and design workers 82 460 741 934 1,055

Entertainers and performers, sports and related workers 70 452 836 1,176 1,440

Media and communication workers 82 476 814 1,082 1,276

Media and communication equipment workers 33 184 306 400 469

Health diagnosing and treating practitioners 643 3,751 6,487 8,762 10,641

Health technologists and technicians 421 2,521 4,386 5,917 7,144

Other healthcare practitioners and technical occupations 13 75 131 180 219

Nursing, psychiatric, and home health aides 199 1,278 2,447 3,604 4,647

Occupational therapy and physical therapist assistants and aides 34 204 357 487 604

Other healthcare support occupations 337 1,859 3,076 3,999 4,729

Supervisors of protective service workers 16 86 139 174 193

Fire fighting and prevention workers 19 97 155 191 207

Law enforcement workers 69 356 568 702 763

Other protective service workers 166 967 1,625 2,122 2,480

Supervisors of food preparation and serving workers 79 535 998 1,387 1,658

Cooks and food preparation workers 261 1,746 3,237 4,483 5,344

Food and beverage serving workers 572 3,933 7,413 10,409 12,542

Other food preparation and serving related workers 110 728 1,359 1,893 2,263

Supervisors of building and grounds cleaning and maintenance workers 46 346 683 1,014 1,289

Building cleaning and pest control workers 486 2,743 4,509 5,845 6,903

Grounds maintenance workers 326 2,626 5,317 8,055 10,397

Supervisors of personal care and service workers 25 139 236 313 371

Animal care and service workers 44 277 476 631 747

Entertainment attendants and related workers 78 459 810 1,104 1,334

Funeral service workers 4 21 35 44 48

Personal appearance workers 188 1,026 1,568 1,928 2,212

Baggage porters, bellhops, and concierges; Tour and travel guides 10 56 99 137 168

Other personal care and service workers 418 2,521 4,342 5,863 7,159

Supervisors of sales workers 312 2,013 3,377 4,223 4,634

Retail sales workers 1,865 12,082 20,318 25,410 27,849

Sales representatives, services 217 1,169 1,883 2,357 2,650

Sales representatives, wholesale and manufacturing 280 1,734 2,926 3,759 4,256

Other sales and related workers 154 992 1,784 2,412 2,847

Supervisors of office and administrative support workers 208 1,205 2,020 2,603 2,988

Communications equipment operators 18 84 124 137 131

Financial clerks 506 2,891 4,832 6,221 7,137

Information and record clerks 711 3,991 6,579 8,428 9,693

Material recording, scheduling, dispatching, and distributing workers 501 2,991 4,866 5,952 6,414

Secretaries and administrative assistants 595 3,442 5,873 7,696 8,953

Other office and administrative support workers 534 3,142 5,363 7,000 8,084

Supervisors of farming, fishing, and forestry workers 1 6 10 14 16

Agricultural workers 18 110 194 259 299

Fishing and hunting workers -1 -18 -37 -51 -61
Regional Economic Models, Inc.

p. 29

Forest, conservation, and logging workers 1 -1 -5 -7 -9

Supervisors of construction and extraction workers 195 1,339 2,460 3,308 3,871

Construction trades workers 1,640 11,288 20,688 27,737 32,355

Helpers, construction trades 132 945 1,762 2,399 2,841

Other construction and related workers 58 367 647 852 984

Extraction workers -14 -125 -198 -199 -147

Supervisors of installation, maintenance, and repair workers 60 395 709 952 1,116

Electrical and electronic equipment mechanics, installers, and repairers 79 489 838 1,086 1,243

Vehicle and mobile equipment mechanics, installers, and repairers 241 1,565 2,762 3,660 4,235

Other installation, maintenance, and repair occupations 404 2,709 4,964 6,775 8,067

Supervisors of production workers 45 244 379 467 515

Assemblers and fabricators 138 728 1,101 1,329 1,449

Food processing workers 67 427 727 920 1,015

Metal workers and plastic workers 132 745 1,191 1,505 1,704

Printing workers 35 192 286 324 325

Textile, apparel, and furnishings workers 89 464 653 751 790

Woodworkers 41 233 361 423 433

Plant and system operators -21 -117 -162 -155 -115

Other production occupations 206 1,109 1,755 2,178 2,412

Supervisors of transportation and material moving workers 43 269 464 611 712

Air transportation workers 5 19 16 7 -1

Motor vehicle operators 445 2,774 4,828 6,363 7,389

Rail transportation workers 1 3 7 11 15

Water transportation workers 2 11 20 31 43

Other transportation workers 52 295 487 620 704

Material moving workers 497 3,019 5,085 6,524 7,403

TOTAL FOR ALL OCCUPATI/ONS = 18,295 112,251 194,251 256,157 298,366
Regional Economic Models, Inc.

p. 30

FIGURE 3.13 – PERCENTAGE CHANGE ($200/TON, ATB, 2015-2035)

While industries might contract, occupations are more robust. Individuals are more able 

to shift and churn between industries with their same skill set to similar responsibilities 

that might be with another firm yet in a wholly dissimilar NAICS.

-2.0% -1.5% -1.0% -0.5% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0%

Fishing and hunting workers

Extraction workers

Plant and system operators

Forest, conservation, and logging workers

Air transportation workers

Supervisors of farming, fishing, and forestry workers

Agricultural workers

Rail transportation workers

Physical scientists

Water transportation workers

Life, physical, and social science technicians

Life scientists

Engineers

Computer occupations

Mathematical science occupations

Law enforcement workers

Fire fighting and prevention workers

Lawyers, judges, and related workers

Legal support workers

Drafters, engineering technicians, and mapping technicians

Supervisors of protective service workers

Media and communication equipment workers

Financial specialists

Art and design workers

Baggage porters, bellhops, and concierges; Tour and travel guides

Operations specialties managers

Media and communication workers

Preschool, primary, secondary, and special education school teachers

Entertainers and performers, sports and related workers

Librarians, curators, and archivists

Assemblers and fabricators

Supervisors of production workers

Social scientists and related workers

Sales representatives, services

Business operations specialists

Other protective service workers

Architects, surveyors, and cartographers

Other production occupations

Other education, training, and library occupations

Metal workers and plastic workers

Other teachers and instructors

Advertising, marketing, promotions, public relations, and sales managers

Textile, apparel, and furnishings workers

Animal care and service workers

Supervisors of transportation and material moving workers

Other sales and related workers

Communications equipment operators

Other management occupations

Other healthcare practitioners and technical occupations

Information and record clerks

Miscellaneous community and social service specialists

Counselors and Social workers

Funeral service workers

Building cleaning and pest control workers

Postsecondary teachers

Other office and administrative support workers

Financial clerks

Entertainment attendants and related workers

Material moving workers

Other food preparation and serving related workers

Nursing, psychiatric, and home health aides

Motor vehicle operators

Top executives

Religious workers

Supervisors of personal care and service workers

Supervisors of food preparation and serving workers

Secretaries and administrative assistants

Other personal care and service workers

Supervisors of office and administrative support workers

Food and beverage serving workers

Cooks and food preparation workers

Printing workers

Other transportation workers

Electrical and electronic equipment mechanics, installers, and repairers

Supervisors of installation, maintenance, and repair workers

Other installation, maintenance, and repair occupations

Material recording, scheduling, dispatching, and distributing workers

Sales representatives, wholesale and manufacturing

Food processing workers

Health diagnosing and treating practitioners

Vehicle and mobile equipment mechanics, installers, and repairers

Health technologists and technicians

Supervisors of building and grounds cleaning and maintenance workers

Other construction and related workers

Personal appearance workers

Grounds maintenance workers

Other healthcare support occupations

Occupational therapy and physical therapist assistants and aides

Woodworkers

Supervisors of sales workers

Retail sales workers

Supervisors of construction and extraction workers

Construction trades workers

Helpers, construction trades
Regional Economic Models, Inc.

p. 31

FIGURE 3.14 – EMPLOYMENT BY OCCUPATION ($200/TON, FAD)

SOC Occupations 2015 2020 2025 2030 2035

Top executives 277 1,314 2,071 2,643 3,075

Advertising, marketing, promotions, public relations, and sales managers 63 264 384 479 565

Operations specialties managers 139 500 656 784 934

Other management occupations 319 1,704 2,887 3,866 4,646

Business operations specialists 386 1,709 2,560 3,251 3,906

Financial specialists 347 1,226 1,507 1,639 1,807

Computer occupations 217 455 202 42 142

Mathematical science occupations 8 24 23 20 21

Architects, surveyors, and cartographers 23 98 137 167 204

Engineers 65 69 -49 -57 82

Drafters, engineering technicians, and mapping technicians 46 126 133 161 230

Life scientists 16 24 -2 -16 -7

Physical scientists 10 -12 -61 -86 -78

Social scientists and related workers 26 116 178 228 275

Life, physical, and social science technicians 13 5 -28 -39 -21

Counselors and Social workers 153 838 1,446 1,975 2,408

Miscellaneous community and social service specialists 94 521 909 1,246 1,513

Religious workers 3 19 35 48 59

Lawyers, judges, and related workers 67 238 300 322 331

Legal support workers 40 145 187 204 214

Postsecondary teachers 196 1,153 2,063 2,847 3,428

Preschool, primary, secondary, and special education school teachers 297 1,382 2,180 2,810 3,292

Other teachers and instructors 88 482 833 1,129 1,352

Librarians, curators, and archivists 22 108 176 229 267

Other education, training, and library occupations 125 637 1,062 1,414 1,684

Art and design workers 52 196 265 332 415

Entertainers and performers, sports and related workers 78 445 793 1,113 1,385

Media and communication workers 78 361 570 756 924

Media and communication equipment workers 27 121 190 254 318

Health diagnosing and treating practitioners 868 4,488 7,357 9,706 11,727

Health technologists and technicians 523 2,741 4,540 6,026 7,303

Other healthcare practitioners and technical occupations 16 80 131 175 214

Nursing, psychiatric, and home health aides 296 1,660 2,951 4,175 5,293

Occupational therapy and physical therapist assistants and aides 49 269 450 602 737

Other healthcare support occupations 470 2,346 3,716 4,730 5,543

Supervisors of protective service workers 16 60 80 92 102

Fire fighting and prevention workers 17 62 78 85 91

Law enforcement workers 64 225 285 314 338

Other protective service workers 171 761 1,120 1,387 1,626

Supervisors of food preparation and serving workers 107 614 1,068 1,440 1,707

Cooks and food preparation workers 343 1,964 3,413 4,601 5,460

Food and beverage serving workers 786 4,597 8,069 10,965 13,090

Other food preparation and serving related workers 161 896 1,538 2,055 2,420

Supervisors of building and grounds cleaning and maintenance workers 57 341 628 916 1,168

Building cleaning and pest control workers 750 3,512 5,303 6,553 7,553

Grounds maintenance workers 395 2,567 4,878 7,275 9,415

Supervisors of personal care and service workers 35 170 269 345 403

Animal care and service workers 58 309 497 642 757

Entertainment attendants and related workers 99 516 860 1,148 1,387

Funeral service workers 6 27 41 50 54

Personal appearance workers 299 1,443 2,075 2,440 2,702

Baggage porters, bellhops, and concierges; Tour and travel guides 14 60 93 120 147

Other personal care and service workers 697 3,655 5,907 7,661 9,104

Supervisors of sales workers 162 977 1,654 2,127 2,450

Retail sales workers 983 6,124 10,487 13,556 15,609

Sales representatives, services 242 948 1,274 1,462 1,626

Sales representatives, wholesale and manufacturing 163 815 1,305 1,692 2,007

Other sales and related workers 169 866 1,441 1,922 2,316

Supervisors of office and administrative support workers 211 1,001 1,557 1,973 2,309

Communications equipment operators 20 74 101 109 105

Financial clerks 528 2,426 3,719 4,680 5,462

Information and record clerks 776 3,483 5,217 6,480 7,544

Material recording, scheduling, dispatching, and distributing workers 268 1,376 2,185 2,730 3,108

Secretaries and administrative assistants 699 3,392 5,384 6,914 8,130

Other office and administrative support workers 554 2,659 4,196 5,383 6,336

Supervisors of farming, fishing, and forestry workers 1 1 1 3 5

Agricultural workers 13 58 100 143 179

Fishing and hunting workers -2 -23 -49 -66 -78
Regional Economic Models, Inc.

p. 32

Forest, conservation, and logging workers 0 -11 -24 -32 -34

Supervisors of construction and extraction workers 198 1,277 2,284 3,078 3,673

Construction trades workers 1,661 10,787 19,260 25,866 30,745

Helpers, construction trades 134 909 1,653 2,255 2,720

Other construction and related workers 58 320 534 694 813

Extraction workers -16 -160 -268 -285 -233

Supervisors of installation, maintenance, and repair workers 56 312 541 734 892

Electrical and electronic equipment mechanics, installers, and repairers 69 353 572 743 884

Vehicle and mobile equipment mechanics, installers, and repairers 191 1,111 1,953 2,658 3,207

Other installation, maintenance, and repair occupations 428 2,410 4,186 5,704 6,969

Supervisors of production workers 29 99 127 160 202

Assemblers and fabricators 83 234 246 283 363

Food processing workers 41 235 401 522 604

Metal workers and plastic workers 92 352 488 630 790

Printing workers 23 95 128 142 148

Textile, apparel, and furnishings workers 90 370 466 521 564

Woodworkers 26 120 165 183 187

Plant and system operators -21 -141 -212 -219 -181

Other production occupations 150 566 784 977 1,165

Supervisors of transportation and material moving workers 32 153 245 324 395

Air transportation workers 5 -4 -41 -74 -95

Motor vehicle operators 344 1,781 2,953 3,918 4,733

Rail transportation workers 0 -2 -3 -2 2

Water transportation workers 2 2 0 2 10

Other transportation workers 63 304 472 591 675

Material moving workers 337 1,632 2,584 3,341 3,977

TOTAL FOR ALL OCCUPATI/ONS = 18,434 94,912 155,020 203,086 241,995
Regional Economic Models, Inc.

p. 33

FIGURE 3.15 – PERCENTAGE CHANGE ($200/TON, FAD, 2015-2035)

Both ATB and FAD display similar patterns where close to all occupations might have an 

increase over the baseline. Even the “worst” occupation for fishing and hunting loses 

less than 2.5% of its total jobs (against baseline) out to 2035.

-2.5% -2.0% -1.5% -1.0% -0.5% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5%

Fishing and hunting workers

Extraction workers

Plant and system operators

Forest, conservation, and logging workers

Air transportation workers

Physical scientists

Life, physical, and social science technicians

Rail transportation workers

Engineers

Life scientists

Supervisors of farming, fishing, and forestry workers

Water transportation workers

Computer occupations

Agricultural workers

Mathematical science occupations

Drafters, engineering technicians, and mapping technicians

Assemblers and fabricators

Art and design workers

Law enforcement workers

Fire fighting and prevention workers

Supervisors of production workers

Supervisors of protective service workers

Operations specialties managers

Financial specialists

Lawyers, judges, and related workers

Legal support workers

Other production occupations

Metal workers and plastic workers

Media and communication equipment workers

Architects, surveyors, and cartographers

Advertising, marketing, promotions, public relations, and sales managers

Business operations specialists

Media and communication workers

Supervisors of transportation and material moving workers

Printing workers

Sales representatives, services

Material moving workers

Other protective service workers

Preschool, primary, secondary, and special education school teachers

Material recording, scheduling, dispatching, and distributing workers

Sales representatives, wholesale and manufacturing

Baggage porters, bellhops, and concierges; Tour and travel guides

Librarians, curators, and archivists

Motor vehicle operators

Social scientists and related workers

Textile, apparel, and furnishings workers

Entertainers and performers, sports and related workers

Other education, training, and library occupations

Food processing workers

Top executives

Other sales and related workers

Information and record clerks

Communications equipment operators

Financial clerks

Other office and administrative support workers

Woodworkers

Electrical and electronic equipment mechanics, installers, and repairers

Supervisors of office and administrative support workers

Other teachers and instructors

Other management occupations

Animal care and service workers

Other healthcare practitioners and technical occupations

Supervisors of installation, maintenance, and repair workers

Supervisors of sales workers

Secretaries and administrative assistants

Vehicle and mobile equipment mechanics, installers, and repairers

Miscellaneous community and social service specialists

Other installation, maintenance, and repair occupations

Counselors and Social workers

Postsecondary teachers

Entertainment attendants and related workers

Retail sales workers

Other transportation workers

Supervisors of food preparation and serving workers

Funeral service workers

Other food preparation and serving related workers

Cooks and food preparation workers

Building cleaning and pest control workers

Food and beverage serving workers

Supervisors of personal care and service workers

Nursing, psychiatric, and home health aides

Other construction and related workers

Religious workers

Supervisors of building and grounds cleaning and maintenance workers

Other personal care and service workers

Health technologists and technicians

Grounds maintenance workers

Health diagnosing and treating practitioners

Other healthcare support occupations

Personal appearance workers

Occupational therapy and physical therapist assistants and aides

Supervisors of construction and extraction workers

Construction trades workers

Helpers, construction trades
Regional Economic Models, Inc.

p. 34

PERSONAL CONSUMPTION EXPENDITURE (PCE)-PRICE INDEX

Figure 3.16 – The PCE-Index is the PI+ measurement of the average cost of living in a 

region. It is similar to the Consumer Price Index (CPI), which attempts to measure 

monetary inflation, though it is a more comprehensive, specific figure that includes 

major consumption items like fuel and housing—unlike the CPI. The figures are not 

cumulative—they represent a less than 2% change in the cost of living relative to the 

baseline, a vector “one-time” adjustment upwards, and not a change in the rate.

PCE-PRICE INDEX BY INCOME QUINTILE

$200/ton, ATB 2015 2020 2025 2030 2035

Lowest 20% 0.11% 0.48% 0.71% 0.86% 0.98%

Low-Middle 20% 0.11% 0.49% 0.72% 0.88% 0.99%

Middle 20% 0.11% 0.48% 0.71% 0.87% 0.98%

High-Middle 20% 0.10% 0.47% 0.69% 0.84% 0.95%

Highest 20% 0.10% 0.45% 0.66% 0.81% 0.91%

$200/ton, FAD 2015 2020 2025 2030 2035

Lowest 20% 0.18% 0.85% 1.25% 1.47% 1.57%

Low-Middle 20% 0.18% 0.86% 1.26% 1.48% 1.58%

Middle 20% 0.18% 0.85% 1.25% 1.47% 1.57%

High-Middle 20% 0.18% 0.83% 1.22% 1.43% 1.53%

Highest 20% 0.17% 0.81% 1.19% 1.40% 1.49%

Figure 3.17 – One concern with carbon taxes is the potential to injure low-income 

families disproportionally because of their reliance on fossil fuels. When examining the 

simulations, however, the difference in the impact to the cost of living by quintile is 

negligible. Higher incomes mean larger homes, more vehicles, and more travel, all of 

which increases their energy consumption (and therefore their carbon tax). The sales 

tax reductions in the ATB scheme allow some relief to low-income earners, as well.

0.0%

0.2%

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$50/ton (ATB)

$50/ton (FAD)

$100/ton (ATB)

$100/ton (FAD)

$200/ton (ATB)

$200/ton (FAD)
Regional Economic Models, Inc.

p. 35

CHANGES IN ENERGY PRICES (FROM BASELINE)

PI+ has seventy-five consumption categories ranging from cars to nondurable goods, to 

food and fuels, transportation, insurance of all descriptions, hospitals, personal services, 

and tourism. Changing these (fuel prices upwards for the carbon tax, and items eligible 

for the California state sales tax downwards for the revenue-neutrality) is how the model 

simulates the direct impact of environmental tax reform on the residential sector of the 

economy. Importantly, these are adjustments from the baseline and one-time, 

not a forecast of anticipated growth rates for energy prices in California

sometime in the future. This shows the impact of the tax, which might actually be 

against a baseline of declining prices for energy due to the newfound development of 

nonconventional fossil sources and efficiency gains in the electricity markets from new 

renewable power, storage, or other future technologies.

Figure 3.18 – These four categories show the effect on residential prices for energy 

from carbon tax. The effect between the ATB and FAD approaches are very similar; 

therefore, the average between the two is what is above. PI+ and CTAM technically 

report separate retail and wholesale prices for energy in the commercial and 

industrial sectors, but these are, again, not too dissimilar from the impact to the 

residential sector, so the numbers above approximate impacts to businesses.

0%

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2015 2020 2025 2030 2035

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2015 2020 2025 2030 2035

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2015 2020 2025 2030 2035

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2015 2020 2025 2030 2035

Residential natural gas

$50 

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Regional Economic Models, Inc.

p. 36

ADDITIONAL REAL DISPOSABLE PERSONAL INCOME33 (ANNUAL)

Figure 3.19 – The above is the aggregate impact to after-tax income for California 

households because of the carbon tax and recycling. A few factors drive the trends: the 

quality of the labor market (changing wages and number of jobs), changes to the cost 

of living (the PCE-Index above), GDP growth, the size of annual dividend, and the 

population of the state. The impact is a net positive for most of the cases.

 

33 PI+ calculates real disposable personal income (RDPI) in a comprehensive manner as total, after-tax 

income received by households, including wages and salaries, investment returns, rents, transfer receipts 

from all levels of government, adjustments for costs of living, and minus all taxes

-$2,000

$0

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$50/ton (ATB)

$50/ton (FAD)

$100/ton (ATB)

$100/ton (FAD)

$200/ton (ATB)

$200/ton (FAD)
Regional Economic Models, Inc.

p. 37

ADDITIONAL REAL DISPOSABLE INCOME (CUMULATIVE)

Figure 3.20 – While ATB has an advantage in lowering business costs and taxes in a 

complementary manner with carbon tax, FAD has superiorities in creating a greater 

boost to real incomes. The state economy is larger in the former, but actual paid wages 

to Californian households are more momentous for the latter. Complicating this issue 

is the change in population and the potential distributional impacts of the policy.

$0

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(ATB)

$50/ton 

(FAD)

$100/ton 

(ATB)

$100/ton 

(FAD)

$200/ton 

(ATB)

$200/ton 

(FAD)

Millions of 2014 dollars
Regional Economic Models, Inc.

p. 38

INCOME BY QUINTILE

Figure 3.21 – The two graphs relate the $200/ton simulation (to show the maximum 

impact) and by 20% increments. One surprising aspect of carbon tax simulation is its 

propensity to generate jobs and income in the lowest 60% of households in the state. 

Many of the jobs created through environmental tax reform and revenue-neutrality 

are in the service sector, which tends to be steady employment for the middle class and 

dependable income. Many of these families spend a larger share of their money on 

sales tax-eligible goods and have children, which increases their potential annual 

dividend. In contrast, the top 40% tends to work in technology and skilled trades that 

have less of a change in ATB and FAD. They also spend more of their money on 

services and investments, which means a sales tax cut does them less aid.

0.0%

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Change (from baseline)

$200/ton, ATB

Lowest 20%

Low-Middle 20%

Middle 20%

High-Middle 20%

Highest 20%

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Change (from baseline)

$200/ton, FAD

Lowest 20%

Low-Middle 20%

Middle 20%

High-Middle 20%

Highest 20%
Regional Economic Models, Inc.

p. 39

CHANGE IN POPULATION (FROM BASELINE)

Figure 3.22 – The state’s population increases in all simulations due to a number of 

factors. Those include the improved availability of jobs on the labor market, lower 

taxes (under ATB), and future prospects for the annual dividend (under FAD). The 

difference in population under the two recycling options is due to their divergent 

impacts on these factors—ATB generates jobs and GDP and lowers taxes, but FAD has 

a similar amount of jobs and higher personal income. Households tend to look at the 

latter two when making location decisions, which thereby draws them into California 

at a slightly higher rate for the FAD scenario than inside of ATB.

0

50,000

100,000

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Population (over baseline)

$50/ton (ATB)

$50/ton (FAD)

$100/ton (ATB)

$100/ton (FAD)

$200/ton (ATB)

$200/ton (FAD)
Regional Economic Models, Inc.

p. 40

ACROSS-THE-BOARD V. FEE-AND-DIVIDEND

As demonstrated in the results, each of these options has strengths and weaknesses in 

how to use the money coming into the state budget from under the new carbon tax. This 

subsection discusses them and compares how they rate on various metrics—and against 

the baseline, to give goodly sense of sensibility and of proportion. All of the data below is 

for the $200/ton carbon tax to use the maximum case in making comparisons, though 

the aggregate results are similar for any other tax level.

Figure 3.23 – This shows the total GDP and real disposable personal income over the 

simulation period for the two options chosen here and the baseline. ATB generates a 

larger economy (as measured by GDP) via investments and exports, while FAD 

slightly increases the quantity of household income from 2015 out to 2035.

LABOR SHARE OF INCOME34

BASE = 70.87%; ATB = 70.93%; FAD = 71.13%

All portions of the economy would pay a robust, comprehensive carbon tax (including 

commercial and industrial enterprises), which means returning the revenues only 

to households would increase the labor share of GDP. However, with private 

industry less competitive under FAD than under ATB, the higher share is of a smaller 

economic “pie,” which means the difference in impacts to absolute household income 

between the two is minimal. Population increases in both, though more migrants come 

into the state with FAD than ATB. The impact to per capita GDP and income thus is

rather close to zero. Improvements in individual incentives to move to a region (such as 

lower unemployment rates, higher pay, cheaper cost of living) mean new migrants will 

“rush” into the area to balance the market. They fill jobs, take wages, and thereby return 

California closer to national averages. This keeps the per capita “return” on work and 

living in the state much the same between scenarios.

 

34 Calculated as real disposable personal income divided by gross domestic product

$63,200,000

$63,250,000

$63,300,000

$63,350,000

$63,400,000

$63,450,000

$63,500,000

$63,550,000

$63,600,000

$63,650,000

BASE ATB FAD

Millions of 2014 

dollars

GDP (cumulative, 2015-2035)

$44,750,000

$44,800,000

$44,850,000

$44,900,000

$44,950,000

$45,000,000

$45,050,000

$45,100,000

$45,150,000

BASE ATB FAD

Milloins of 2014 

dollars

RDPI (cumulative, 2015-2035)
Regional Economic Models, Inc.

p. 41

CARBON TAX REVENUES (ANNUAL)

Figure 3.24 –The revenues out of CTAM increase during the $10/year phase-in period, 

though they are actually rather stable once they achieve maximums. This is because of 

the “smoothness” of long-term macroeconomic forecasts (nobody tries predicting the 

business cycle in the 2020s or 2030s), growth in state population and GDP, and 

increased energy efficiency—the net of the two canceling one and other out.

CARBON TAX REVENUES (CUMULATIVE)

Figure 3.25 – This shows the total summed over time. Results for both ATB and FAD

are very similar. Hence, only the average of the two methodologies merits inclusion on 

the state revenue impacts and the impact on carbon dioxide emissions out of CTAM.

$0

$5,000

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Regional Economic Models, Inc.

p. 42

CARBON TAX PAID BY SECTOR (NON-GOVERNMENT)

Figure 3.26 – The area chart shows the share of carbon taxes paid into the state by 

broad sectors of the economy (counted here as residential households, commercial 

enterprises, and industrial operations). Households pay the lion’s share of the carbon 

fee due to their reliance on motor gasoline. Gasoline for cars is as much as 40% of the 

carbon tax (and carbon dioxide) in some years in the state. This graph shows that the 

ATB share of funds returned to business, which is somewhere between 25% and 50% 

with lowered corporation taxes and the $4 billion/year renewable fund, is roughly 

similar to the initial share paid by private industry in carbon pricing.

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Percent of carbon tax paid

Residential

Commercial

Industrial
Regional Economic Models, Inc.

p. 43

FORECASTED ANNUAL DIVIDEND CHECK (PER CAPITA)

Figure 3.27 – This shows the estimated size of the carbon dividend paid to Californian 

households based on a few parameters. It involves taking the annual tax revenues 

from CTAM and dividing them by the population forecast in the simulations of PI+. The 

Alaska Permanent Fund has a list of requirements for their dividend—this includes at 

least a year of state residency and no felony convictions.

35 Results assume an 85% 

eligibility rate in the state. Approximately 35 million people relocate in the United 

States every year,36 and more than 5.75 million Americans have a felony record.

37

Further data reveals California is not much different from national averages.

38 The 

two together give a rough estimate of 85% eligibility in California under the Alaska 

criteria, though Sacramento is free to pick its own. Quantities above represent an 

annual rebate that scales with family size. To give one example, in 2020, a 

family of four could receive $2,000 (in 2014 dollars) inside their annual check.

 

35 “Eligibility Requirements,” Alaska Department of Revenue, 

<https://pfd.alaska.gov/Eligibility/EligibilityRequirements>

36 “Figure A-1. Number of Movers and Mover Rate: 1948-2013,” U.S. Census, 

<http://www.census.gov/hhes/migration/data/cps/historical/Figure%20A-1.1.png>

37 Michael McLaughlin, “Felon Voting Laws Disenfranchise 5.85 Americans with Criminal Records,” 

Huffington Post, July 12, 2012, <http://www.huffingtonpost.com/2012/07/12/felon-voting-lawsdisenfranchise-sentencing-project_n_1665860.html>

38 “State-to-State Migration Flows,” U.S. Census, 

<https://www.census.gov/hhes/migration/data/acs/state-to-state.html>

$0

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$200/ton
Regional Economic Models, Inc.

p. 44

CARBON DIOXIDE EMISSIONS (ANNUAL FORECAST)

Figure 3.28 – This is the carbon portion and environmental impact of tax reform. The 

difference between ATB and FAD is minor—hence, only the average between the two is 

present. California emits between 350 million and 410 million metric tons per year

(depending on fluctuations in weather, GDP growth, plant shutdowns, and other 

factors).

39 The data and assumptions for this forecast come out of the Annual Energy 

Outlook (AEO) from EIA,40 which serves as the baseline for energy consumption and 

prices in CTAM and PI+. When removing AB32 and assuming no federal legislation, 

EIA has California’s emissions slowly increasing in the 2020s and early 2030s. The 

incentives behind a carbon tax would encourage this to decline. For context, by 2035 in 

the $200/ton case, a difference in emissions of 125 million metric tons each year is 

about the carbon output from a state the size of Alabama or North Carolina.

 

39 “State Energy CO2 Emissions,” U.S. Environmental Protection Agency, 

<http://epa.gov/statelocalclimate/resources/state_energyco2inv.html>

40 “Annual Energy Outlook 2013,” U.S. Energy Information Administration, 

<http://www.eia.gov/forecasts/aeo/IF_all.cfm>

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Millions of metric tons

No tax

$50/ton

$100/ton

$200/ton
Regional Economic Models, Inc.

p. 45

CARBON DIOXIDE EMISSIONS (CUMULATIVE DIFFERENCE)

Figure 3.29 – This is the same information as the previous figure now presented as the 

cumulative savings over time. California emits more carbon dioxide than any other 

state. These figures show reduced emissions from the Golden State by as much as a 

billion tons.41 Do notice, as well, the marginal improvement to savings declines at 

higher levels of taxation. The first $50/ton saves over 400 million tons. Multiplying 

that by four would give you 1.6 billion tons, though CTAM reports only 1.1 billion tons 

of savings for a $200/ton tax. This is because energy consumers will adopt the most 

obvious efficiencies first—“low-hanging fruit”—and decreasing emissions more and 

more becomes difficult the more you do it. This does not reduce the effectiveness of the 

policy; however, it does mean the response in demand from consumers is less under 

higher-and-higher carbon taxes than the lower initial rates.

 

41 2,204,622,620,000 pounds, the equivalent to 112,480,746,000 gallons of gasoline, which is 

approximately 80% of current annual consumption in the United States

-1,400

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$50/ton

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$200/ton
Regional Economic Models, Inc.

p. 46

CARBON DIOXIDE EMISSIONS (1990 BENCHMARK)

Figure 3.30 – This shows California’s “progress” in terms of its current and forecasted 

emissions relative to levels in 1990. The 1990 benchmark is arbitrary from an 

economic and environmental standpoint, but it is a common rule of thumb given its 

importance in the Kyoto Protocol and its goals for reducing emissions by keeping them 

at or below the amounts from 1990.42 In 1990, California emitted 363 million metric 

tons of carbon; in 2011, the most recent year of historical data from EPA, California 

emitted almost exactly the same amount at 360 million metric tons. The carbon tax 

helps to “bend the curve” downwards in the future, with $100/ton bringing 90% of 

1990 emissions by 2025 and $200/ton potentially meaning a dip below 75% of Kyoto 

Protocol permitted emissions by the end of the NEMS analysis period in 2035.

 

42 “Kyoto Protocol,” United Nations Framework Convention on Climate Change, 

<https://unfccc.int/kyoto_protocol/items/2830.php>

65%

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Percent of 1990 emissions

No tax

$50/ton

$100/ton

$200/ton
Regional Economic Models, Inc.

p. 47

ASSEMBLY BILL 32 (AB32)

In 2006, California became unique in the United States for passing the Global Warming 

Solutions Act, otherwise known as AB32 legislatively and in terms of its final programs. 

AB32 implements a “cap-and-trade” system in the state, which differs from a carbon tax 

principally in means—not goals. California is not the only state or region working on 

cap-and-trade. The Regional Greenhouse Gas Initiative (RGGI) is also a cap-and-trade, 

which covers nine Northeastern states (with Pennsylvania and three Canadian provinces 

as “observers”).

43 RGGI, however, covers only emissions from the generation of power 

from facilities over 25 megawatts in capacity—AB32 will cover not only electrical power 

generation (starting in 2013 and 2014), but also natural gas and vehicle fuels (out to 

2020). This covers 85% of emissions from California. Retail carbon taxes, as 

modeled, would not compete with the implementation or goals of AB32, but 

rather would enhance them by serving as an enforcement mechanism. A 

carbon tax and a cap-and-trade approach the same problems from two different sides.44

In essence, a carbon tax chooses a price for emissions based on carbon content and 

allows the price incentives of higher energy costs to “pick” a new quantity of emissions 

out on the market. Cap-and-trade allows the statehouse or Congress to choose a specific 

quantity of emissions and, in an auction, markets “pick” the allowance price necessary 

to incentivize consumers to the point they only would emit that amount.

45 Emissions 

decline either way; one selects a price and arrives at a quantity, and one selects quantity 

and then settles at a price. Consider simplified supply and demand curves for energy 

and the mutual interactions of these two policies:

Figure 4.1 – The supply and 

demand curves to the left show 

the microeconomic foundations 

of cap-and-trade against the 

carbon tax. Carbon taxes set the 

difference between P2 and P3 

and allow the market to find a 

new Q2; cap-and-trade sets Q2 

and allows the market to find 

the necessary auction price (still 

P2 minus P3) to incentivize 

consumers to cut back. The area 

of Q2 times P2 minus P3 is the 

total quantity of carbon tax or 

auction revenues paid.

 

43 “Welcome,” Regional Greenhouse Gas Initiative, <http://www.rggi.org/>

44 “Climate Policy Memo #1: Cap-and-Trade v. Taxes,” Pew Center on Global Climate Change, March

2009, <http://www.c2es.org/docUploads/Policy-Memo-1-CapTradevTax.pdf>

45 For more explanation, please see, “Carbon Tax v. Cap-and-Trade,” Environmental Economics, 

<http://www.env-econ.net/carbon_tax_vs_capandtrade.html>
Regional Economic Models, Inc.

p. 48

Figure 4.2 – This illustrates the 

situation of an “incomplete” 

carbon tax. The tax is enough to 

incentivize emissions of Q3. This 

is shy of the eventual goal of Q2, 

so the cap-and-trade auction 

price adds the difference. The

auction brings in the gold area 

(the auction price times Q2) 

while the carbon tax brings in 

the purple area in the middle of 

the block. Thus, final energy 

prices change by P2 minus P3 

and final emissions fall to Q2, 

which is the same as before.

Cap-and-trade and carbon taxes can be symbiotic, and, as shown above, both interact to 

further the goals of reducing emissions—which is anything less than Q1 in Figure 4.1

and Figure 4.2 (eventually Q2 in each). Lawrence H. Goulder and Andrew R. Schein of

Stanford University described this as, “If covered firms are cost-minimizing, they will 

reduce emissions up to the point where, at the margin, the costs of emissions abatement 

equals the emissions price. The two policies thus tend to bring about equality of 

marginal abatement.”46 They also advocated “hybrid” systems where a combination 

of carbon taxes, cap-and-trade, and price floors or caps for auction prices would give 

policymakers the most options for avoiding price volatility, adjusting to the business 

cycle and new technological developments, and interfacing with regional- or nationallevel pricing schemes.47 Imagining a situation where AB32 and a carbon tax are active at 

the same time is not hard. For instance, presume a wholesaler was looking to sell fuel 

this quarter, but AB32 required it to purchase permits along with the fuel. Prior to the 

auction, however, a retail carbon tax would reduce the demand at the pump for gasoline 

and other fuels in the first place, meaning the wholesaler would need a reduced quantity

of allowances. Many wholesalers across the state would face the same situation, which 

implies a reduction in demand at the auction and a correspondingly lower permit price 

(under AB32). Such a system would spread out the “direct” impact of policy meant to 

reduce emissions, and it would give options for policymakers to modify their plans. 

Furthermore, households and firms involved in this paradigm would receive tax relief

elsewhere, leaving the net impact, at the macro-level, on their cost of living and cost of 

doing business totality at a minimum.

 

46 Lawrence H. Goulder and Andrew R. Schein, “Carbon Taxes v. Cap-and-Trade: A Critical Review,” 

Stanford University, August, 2013, p. 4, 

<http://www.stanford.edu/~goulder/Papers/Published%20Papers/Carbon%20Taxes%20vs%20Cap%20

and%20Trade%20-%2015%20Aug%20%2713.pdf>

47 Ibid., p. 36, emphasis added
Regional Economic Models, Inc.

p. 49
Regional Economic Models, Inc.

p. 50

REGIONAL ECONOMIC MODELS, INC. (REMI)

REMI is an economic and policy analysis firm specializing in services related to regional 

modeling. REMI’s headquarters is in Amherst, Massachusetts, though its research and 

consulting practice reside in Washington, DC. It first began as a research project by a 

professor at the University of Massachusetts-Amherst named George Treyz. In the late 

1970s, Dr. Treyz developed an economic model to assess the impact of expanding the 

“MassPike” (I-90 through central Massachusetts from Boston to Worcester, Springfield, 

and connecting to the New York State Thruway in Albany out to Syracuse, Rochester,

and Buffalo). He generalized the methodology to all counties and incorporated the firm 

in 1980. REMI provides data, software, support, and issue-oriented consulting across 

the country and the globe. There are users of the REMI data, models, or studies in every 

state (and the District of Columbia) and foreign nations in North America, Europe, Asia, 

and the Middle East.48 Typical REMI clients work for state and local governments, the 

federal or regional agencies of an area, consulting firms, universities, trade associations, 

labor unions, or non-profits. REMI’s list of clients in California is extensive. It includes 

California Department of Finance (CalFinance),49 California Department of Resources,

Recycling, and Recovery (CalRecycle),50 South Coast Air Quality Management District 

(SCAQMD),51 Southern California Association of Governments (SCAG),52 Los Angeles 

County Metropolitan Transit Authority (LACMTA),53 and Los Angeles World Airports 

(counting LAX, the sixth-busiest airport in the world).54 Others include the Sol Price 

School of Public Policy (at the University of Southern California)

55 and the Office of the 

Comptroller of the city and county of San Francisco.56 By itself, California is one of the 

ten largest economies in the world (ahead of South Korea and behind Italy), and REMI 

has always held an important part in its policymaking discussion.

 

48 “Clients,” REMI, <http://www.remi.com/clients>

49 “Major Regulations,” California Department of Finance, 

<http://www.dof.ca.gov/research/economic_research_unit/SB617_regulation/view.php>

50 “Cost-Benefit Analysis and Distributional Impacts of Used Oil Management Policy Scenarios,” 

California Department of Resources, Recycling, and Recovery, July 30, 2013, 

<http://www.calrecycle.ca.gov/Publications/Documents/1466%5C20131466.pdf>

51 Sue Lieu, Shah Dabirian, and Greg Hunter, “Socioeconomic Report 2012,” South Coast Air Quality 

Management District, <http://www.aqmd.gov/aqmp/2012aqmp/Final/FinalSocioeconomicReport.pdf>

52 Marlon G. Boarnet, Wallace Walrod, and Scott Nystrom, “Economic Benefit from Accelerating 

Transportation Infrastructure Investment,” Southern California Association of Governments, 

<http://economy.scag.ca.gov/Economy%20site%20document%20library/2012EconomicSummit_Benefi

tsTransInvest.pdf>

53 “State Route 138 Avenue ‘T’ to Route 18,” Los Angeles County Metropolitan Transit Authority,

<http://media.metro.net/projects_studies/cmia/images/P%20138T%20Web.pdf>

54 “Economic Impacts of Los Angeles International Airport and the LAX Master Plan Alternatives on the 

Los Angeles Regional Economy,” Hamilton, Rabinovitz, & Alschuler, January 2001, 

<http://ourlax.org/docs/draft_eir_NE/T05_LR.pdf>

55 Cristy Lytal, “Going Green Good for the Economy,” University of Southern California, 

<http://priceschool.usc.edu/newsletter/march-2010/climate/>

56 Ted Egan, “The Economic Impact of San Francisco’s Nightlife Businesses,” San Francisco, March 5, 

2012, <http://sfcontroller.org/Modules/ShowDocument.aspx?documentid=2953>
Regional Economic Models, Inc.

p. 51

PI+

REMI used a 1-region, 70-sector PI+ model of the state of California to commence this 

analysis in concert with a California-level CTAM model (“CACTAM”). PI+ is the software 

branding of the “base” REMI model for economic and demographic modeling. It is the 

foundation for other products, which include such “expansion packs” as TranSight (for 

transportation analysis), Tax-PI (for deeper budgetary analysis), Metro-PI (for analysis 

of sub-county geographies), and eREMI (an Internet-based tool for data analytics and 

forecasting). California has fifty-eight counties, and they range in population from about 

10 million (Los Angeles County) to just under 2,000 (Alpine County, the county seat at 

the little town of Markleeville, California). PI+ can break the state down into any county 

or any set of counties, though this analysis concentrates on the statewide impact to all 

the counties—an agglomeration of 58-regions into a lonely 1-region. The 70-sectors in 

these simulations approximate 3-digit NAICS.57 This provides a strong balance between 

depth of study (a 23-sector model does not break out the manufacturing industries, for 

 

57 “Industries for PI+ v. 1.4 Models,” REMI, 

<http://www.remi.com/download/documentation/pi+/pi+_version_1.4/NAICS_Industries_for_PI+-

Hierarchical_v1.4.pdf>
Regional Economic Models, Inc.

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example) and quality (a 160-sector model has more issues with data suppression at the 

county- or state-level). The result is a computerized, multiregional, and multiyear model 

within a Microsoft Windows-based graphic user interface (GUI) of the state’s economy 

and demographics. The PI+ model relies on four methodologies, which highlights their 

strengths while complimenting for their weaknesses:

1. Input/output tabulation (I/O) – At the core of the PI+ model is an input-output 

table (sometimes called a Social Accounting Matrix, or SAM), which captures the 

structure of the regional or national economy in terms of business-to-business 

transactions, wages, consumption, and “multipliers.” To provide an example, an 

automobile assembly plant in Michigan will have a lengthy supply-chain behind 

it, with parts manufacturers in Wisconsin, steel mills in Indiana, railroads based 

in Nebraska, and Great Lakes boats from Ohio for ore from the Mesabi Range in 

northern Minnesota. I/O models are strong when following the path of a dollar 

through supply-chains in the computational sense, though they have several key 

weaknesses. These include their “before” and “after” nature (everything happens 

instantaneously), no scarcity of labor or capital, no concept of competitiveness, 

and no adjustments to the structure of the economy in response to incentives. 

REMI and PI+ include other modeling techniques to deepen the representation of 

the structure over time and transcend this transactional accounting.

2. Computable general equilibrium (CGE) – CGE models are a broad class of 

programs that rely on the principles of equilibrium economics. In essence, the 

utilization of CGE principles in PI+ adds market-level concepts to its economic 

and demographic structure. The standard supply-and-demand graph illustrates 

what is called a partial equilibrium—a point where demand and supply balance at 

a given price and quantity. A general equilibrium is when all markets “clear” in 

interrelation to each other. For example, say a new turbine parts manufacturer 

moved into Kings County, California (which has a population of 150,000 and its 

county seat in Hanford, California). If the plant employs 5,000 workers then, in 

all likelihood, the local labor market could not absorb it all alone. Demand for 

workers would bid-up the price of labor, and many workers with training for 

technical occupations would move to Kings County from other parts of California 

and the rest of the United States, which would increase the cost of housing and 

needs for government service. Still others would commute to the area from other 

metropolitan areas like Visalia, California in Tulare County and Fresno County. 

An I/O model would only see a multiplier effect from the plant, but the CGE 

model simulates the effects on all of the markets above—those for housing, labor, 

taxes, government spending, commuting, and others. CGE concepts allow PI+ to 

take account of long-term incentives when describing impacts.
Regional Economic Models, Inc.

p. 53

3. Econometrics – REMI uses historical data to determine those parameters 

necessary to make the model work mathematically. This includes the estimation 

of elasticity (the slope of supply and demand curves), strengths of responses, and 

“time-lags” on how long it takes individual markets to adjust. Some markets, such 

as those for labor, tend to work relatively quickly as people and firms look for 

work and labor. Other markets, like that for housing, tend to need more time as 

individuals and existing capital work to catch-up to the new set of incentives on 

the market. This allows the I/O and CGE components of the model to work 

together and maintain a truly dynamic structure.

4. New Economic Geography – Economic geography is the study of the idea of 

cities and concentrated industries as engineers of economic growth. PI+ uses this 

approach to illustrate how specialization in labor pools and industry clusters give 

a region a competitive advantage. For instance, on the labor-side, the selection of 

trained cardiologists in cities known for their medical centers and universities 

(some examples including metropolitan areas resembling Boston, Massachusetts 

or Minneapolis/St. Paul and Rochester, Minnesota) is high compared to that in 

smaller Mountain West cities (such as Missoula, Montana). All else the same, a 

hospital in Cleveland or Houston is able to find the productive, qualified worker it 

needs easier than a hospital in Las Cruces, New Mexico, which tends to make the 

industry more competitive in areas with labor specialized to its needs. The same 

would be true with other endeavors, such as scientific research in Raleigh, North 

Carolina or software in Silicon Valley. The same concept holds for manufacturing

when thinking about supply-chains instead of labor input. Appropriate examples 

of concentrated supply-chains would include the textiles and furniture industries 

in the Southeast, commercial aircraft in Washington, agribusiness in Nebraska 

and Iowa, and shipbuilding in Virginia Beach, Virginia and the Gulf Coast. The 

strength of these clusters is monumental to the growth of any regional economy. 

Different cities and parts of the United States tend to specialize in different things 

economically, which makes a handful of main industries the cornerstone of their 

economic wellbeing. PI+ constantly assesses the “health” of these clusters in light 

of new policies like environmental tax reform.

The methodology and underlying equations to PI+ are peer-reviewed and available to 

publically.

58 The initial publications by Dr. Treyz and the research team appeared in 

such publications as the Journal of Regional Science, the Review of Economics and 

Statistics (describing the econometrics of the migration equation for predicting labor 

mobility and household relocations in the United States),59 and the American Economic 

 

58 “PI+ v. 1.5 Model Equations,” REMI, <http://www.remi.com/resources/documentation>

59 George I. Treyz, Dan S. Rickman, Gary L. Hunt, and Michael J. Greenwood, “The Dynamics of U.S. 

Internal Migration,” Journal of Economics and Statistics, May 1993, <http://tinyurl.com/ltfhthc>
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p. 54

Review.

60 PI+ relies on public data from statistical agencies like the Bureau of Economic 

Analysis (BEA), Bureau of Labor Statistics (BLS), U.S. Census, EIA,61 the Department of 

Defense (DOD), and other bodies. Beyond the regions, trends in the macroeconomic and 

global economy come from the BLS forecast and the Research Seminar in Quantitative 

Economics (RSQE) at the University of Michigan-Ann Arbor.

62 This provides much of 

the background data for building the software harnessed for the simulations in this 

report—and all simulations in all REMI models.

Figure 5.1 – This shows the equation structure of the PI+ model. Current prices, 

preferences, income, and technology form the overall economy in Block 1, which then 

goes to firms making production decisions in Block 2. Households reside in Block 3; it 

includes demographics, consumption, participation rates, and labor supply. Block 4 

includes marketplaces for house, labor, and capital, costs of living, and the costs of 

doing business, which lead into regional imports and exports out of Block 5.

 

60 Note 58, p. 53

61 “Data Sources and Estimation Procedures,” REMI, <http://www.remi.com/resources/documentation>

62 “RSQE specializes in economic forecasting of the U.S. and Michigan economies,” Research Seminar in 

Quantitative Economics, <http://rsqe.econ.lsa.umich.edu/>
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p. 55

Simulating the net impact of carbon taxes and ATB or FAD in PI+ involved the usage of 

four variable sets of Figure 5.1 of the model’s structure. Under the four “rectangles” are 

variables to change costs or incentives in the economy, or simply to add more money to 

a portion, depending on the variable in question:

 Consumer prices – The consumer price variables include price indices for the 

seventy-five consumption categories in the model. Modeling the downside of the 

carbon tax on the residential sector requires increasing the cost of fuels by the 

categories, which increases household costs and reduces their purchasing power 

elsewhere. The upside of sales tax cuts goes into the model by lowering the cost of 

consumption categories eligible for the state sales tax.

 Output – This was the destination of the $4 billion/year fund for increasing the 

level of investments into renewable energy in California. PI+ involved this as an 

incremental increase in statewide construction activity. REMI did not trigger the 

generic construction industry but rather relied on research into how investments 

in wind and solar differ from the general industry.63

 Production costs – This increased the costs of electricity, natural gas, and that 

of petroleum products for commercial and industrial sectors in California. This, 

by itself, leads to a decline in market shares (an increase in the state’s imports 

and a decrease in its exports). It also includes the reduction in corporation taxes, 

which yields opposite effects. The exact impact on a specific industries depended 

on the nature of its marketplaces, how much fuel it burns, how much tax it pays, 

and how much it might feel a boost in direct consumption from low state income 

taxes, state sales taxes, or the annual carbon dividend.

 Real disposable personal income – RDPI is the end of a process for the PI+

model. The model calculates total wages paid based on the fundamentals of the 

labor market in terms of supply and demand, the number of jobs, and nominal 

wages. After that, the model takes out taxes and includes capital income and net 

transfer payments. Then, lastly, it uses the PCE-Index to determine what those 

dollars really mean in terms of actual purchasing power. ATB meant lowering the 

taxes taken out (and therefore increasing household savings and spending), and 

FAD meant adding another layer of “non-wage” income in the form of an annual 

transfer from the state government—the carbon tax dividend. Both lead to more 

consumer spending and growth in the related industries.

 

63 Scott Nystrom and Zilin Cui, “A Multiregional Macroeconomic Framework for Analyzing Energy 

Policies,” USAEE Dialogue, <http://dialogue.usaee.org/index.php/volume-20-number-1-2012/56-amultiregional-macroeconomic-framework-for-analyzing-energy-policies>
Regional Economic Models, Inc.

p. 56

Figure 5.2 – This shows the most basic process of simulation and analysis in REMI 

models. The control forecast, in crimson, is the “do-nothing” scenario of no impact to 

any variables. It is the “general drift” of the economy as forecasted by REMI out of 

regional and national trends and changing economic structures and relationships. 

From there, the analyst changes variables—like those on the previous page—and the 

model re-simulates the economy. Most of the actual work is in comparing the vertical 

difference between the lines, or the “impact,” as it was in much of this report.

500

750

1,000

1,250

1,500

1,750

2,000

2,250

2,500

2,750

2012

20142016

2018

2020

2022

2024

2026

2028

2030

2032

20342036

2038

2040

2042

20442046

2048

2050

2052

20542056

2058

2060

Economic, Demographic, or 

Fiscal Indicator

Control Forecast Policy A Policy B
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p. 57

CARBON TAX ANALYSIS MODEL (CTAM)

CTAM is an open-source, Microsoft Excel-based model designed to forecast state-level 

carbon dioxide emissions and potential revenues under different levels of carbon tax.64

The spreadsheet document is available online.65 CTAM uses projections from the AEO of 

the EIA (which, in turn, comes from NEMS) on the anticipated energy usage by type and 

sector for 9-regions of the United States out to 2035. The forecast in the AEO is usually

only in thermal quantities, such as quadrillions of BTUs, though sometimes they have 

volumetric measurements like gallons or short tons of coal used for power. CTAM uses 

parameters on the chemical relationships between heat, fuel, and carbon to build a 

“shell” on NEMS to forecast the carbon emissions implied by EIA’s outlook. From there, 

CTAM relates this data within energy to fiscal concepts such as the carbon tax. NEMS 

only has a “Pacific” region and not one for California. The Pacific region includes the 

Pacific Rim states of Alaska, California, Hawaii, Oregon, and Washington. Creating the 

shares down to California involves using historical data on fuel consumption levels by 

state and making allocations from there. Consumer responses in CTAM come from price 

elasticity by fuel type—Figure 6.1 illustrates the exact process in the model.

Figure 6.1 – This shows the calculations in CTAM to estimate how much emissions go 

down because of a carbon price. Mori estimated price elasticity from meta-studies, 

and this study uses the same figures as his originals in the CACTAM calibration.

 

64 Keibun Mori, “Washington State Carbon Tax: Fiscal and Environmental Impacts,” University of 

Washington, <http://www.commerce.wa.gov/Documents/Washington-State-Carbon-Tax.pdf>

65 Eric de Place, “Washington Carbon Tax: New Model and Analysis,” Sightline Daily, August 10, 2011, 

<http://daily.sightline.org/2011/08/10/washington-carbon-tax-new-model-and-analysis/>
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INTEGRATING PI+ AND CTAM

Bridging the two models involves lining their various dimensions up with one another. 

Both models are annual, so both can use the same set of years from 2015 to 203566 in 

the simulation of carbon policies. CTAM has four main sectors of fuel demand and of 

carbon emissions: residential, commercial, industrial, and transportation. These four 

groupings all have an analogous concept in PI+ as a consumer price or production cost. 

CTAM has some more granularities in terms of fuel than PI+. Agglomerating the CTAM 

fuel types upwards into the three fuel classifications in PI+, which are electricity, natural 

gas, and petroleum products, is the approximate way to deal with this. The table below 

shows how to associate the rows of carbon tax revenues from CTAM (by sector and fuel 

type) with the related policy variables in the PI+ model structure:

Sector CTAM PI+

Residential

Kerosene, Distillate Fuel Oil Consumer price (fuel oil and 

other fuels)

Natural Gas Consumer price (natural gas)

Electricity Consumer price (electricity)

Commercial

Liquefied Petroleum Gases, 

Motor Gasoline, Kerosene, 

Distillate Fuel Oil

Residual (commercial sectors) 

fuel costs

Natural Gas Natural gas (commercial 

sectors) fuel costs

Electricity Electricity (commercial 

sectors) fuel costs

Industrial

Motor Gasoline, Distillate Fuel 

Oil

Residual (industrial sectors) 

fuel costs

Natural Gas Natural gas (industrial 

sectors) fuel costs

Electricity Electricity (industrial sectors) 

fuel costs

Transportation

Motor Gasoline Consumer price (motor vehicle 

fuels, lubricants, and fluids)

Distillate Fuel Oil Consumer price (fuel oil and 

other fuels)

Figure 6.2 – Data in the second-column from CTAM, as sorted by sector in the first, 

corresponds to the explicit variables in PI+ on the right. The variables above are 

applicable for a state-level (or any other 1-region) simulations because adding more 

regions requires using data in PI+ to break CTAM into sub-state groupings. These 

models work in a symbiotic way; they are both inherently dynamic and regional.

 

66 The most recent last forecast year in the AEO, though it will soon change to 2040
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Regional Economic Models, Inc.

p. 60

Regional Economic Models, Inc. (REMI)

1776 I St. NW Suite 750

Washington, DC 20006

(202) 716-1397

433 West St.

Amherst, MA 01003

(413) 549-1169

Scott Nystrom67 received his B.A. history, B.S. economics (summa cum laude), and M.A. 

economic history from Iowa State University in Ames, Iowa. He has worked at REMI 

since 2011, and he is the head of software training, technical support, and economic 

consulting in its Washington, DC office. Mr. Nystrom works on a daily basis with clients 

all over the United States and the world in state and local governments, federal agencies, 

provincial authorities, regional councils, consulting firms, academic institutions, and 

non-profits research organizations. Major projects have included impact analyses of the 

federal “fiscal cliff” and sequestration, the TransCanada Keystone XL Pipeline, the $500 

billion Southern California Association of Governments (SCAG) transportation plan, 

Medicaid expansion in North Carolina, and studies on carbon taxes in Massachusetts, 

Washington, and King County, Washington (Seattle and its major suburbs). His other 

responsibilities include research on integrating energy models into a macroeconomic 

framework, modeling the impacts of intermodal transportation projects, and business 

development and traveling throughout North America.

Ali Zaidi68 is an assistant economist and research associate in REMI’s Washington, DC 

office. He holds his B.A. economics from the University of Massachusetts-Amherst. He 

performed the calibration of the California version of CTAM model this project (as well 

as MACTAM and WACTAM for Massachusetts and Washington, respectively). His other 

work has included aiding on analyses of federal immigration reform and integrating 

regional models with agent-based systems to look at national security scenarios, border 

closures, and international capital flows. Mr. Zaidi’s research plans include helping to 

develop tools for analyzing diverse infrastructure types as one system.

 

67 <http://www.linkedin.com/pub/scott-nystrom/5b/274/337>

68 <http://www.linkedin.com/in/lucrosmc>
Regional Economic Models, Inc.

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NOTES