# E

NVIRONM

ENTAL 

# T

AX 

# R EFORM IN



# C ALIFORNIA 

# : E C

ONOMIC AND 

# C

LIMATE



# I MPACT OF A 

# C

ARBON 

# T

AX 

# S WAP

P REPARED 

# B Y

Regional Economic Models, Inc (REMI)



# P REPARED 

# F OR

Citizens Climate Lobby (CCL)



# S COTT 

# N YSTROM



# , M.A.

Senior Economic Associate



# A LI 

# Z AIDI



# , B.A.

Assistant Economist



# 1776 I St. NW

Suite 750

Washington, DC 20006

(202) 716

- 1397

< scott.nystrom@remi.com >



# M ONDAY



# , M ARCH 

# 3

, 2014

# Regional Economic Models, Inc.

p. 1 

# A CKNOWLEDGEMENTS

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 Cla assen, 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 thank s to

the institutional support of groups like Environmental Tax Reform

- Washington (ETR

WA), 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. Fred erick 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, demogra phic,

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

California

. These results do not reflect the institutional view s 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.

p. 2 

# E XECUTIVE 

# S UMMARY

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 tax es 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 incl uded 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 tax es (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 policy making 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

t ax 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 job s 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 50,000 100,000 150,000 200,000 250,000 300,000 350,000

2014 2017 2020 2023 2026 2029 2032 2035 Jobs (over baseline) 

### Additional Employment

$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 2017 2020 2023 2026 2029 2032 2035 Millions of 2014 dollars 

### Additional Annual GDP

$50/ton (ATB)

$50/ton (FAD)

$100/ton (ATB)

$100/ton (FAD)

$200/ton (ATB)

$200/ton (FAD)

- $2,000 $0 $2,000 $4,000 $6,000 $8,000 $10,000 $12,000 $14,000 $16,000 $18,000

2014 2017 2020 2023 2026 2029 2032 2035 Millions of 2014 dollars 

### Additional Anual Household Income

$50/ton (ATB)

$50/ton (FAD)

$100/ton (ATB)

$100/ton (FAD)

$200/ton (ATB)

$200/ton (FAD)

65% 70% 75% 80% 85% 90% 95% 100% 105% 110% 115%

2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 

### Percentage of 1990 Carbon Emissions

No tax

$50/ton

$100/ton

$200/ton

# Regional Economic Models, Inc.

p. 3 

# T ABLE OF 

# C ONTENTS

 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 Figur e 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

# Regional Economic Models, Inc.

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 Ec onomic Models, Inc. (REMI) p. 50

 PI + 

# pp. 51

- 5 6

o Figure 5.1

– Model Structure p. 54

o Figure 5.2

– Model Framework p. 56

 Car bon Tax Analysis Model pp. 57

- 58

o Figure 6.1

– CTAM Structure p. 57

o Figure 6.2

– Policy Variables p. 5 8

 Author Biograph ies and Contact Information p. 60

 Notes p. 61

# Regional Economic Models, Inc.

p. 5 

# W OR D 

# C LOUD

# Regional Economic Models, Inc.

p. 6 

# I NTRODUCTION

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 quan tity 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 form s ), and petroleum (thr ough 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 o n 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, t he 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/a

carbon

- 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%20was hington%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 calibr ation of their baselines to the NEMS outlook then creates a regular framework for

assessing impact s to the economy and carbon emissions inside of state s

. 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 tota l amount or by some benchmark

, such as 1990 level s ). REMI does not advocate specific course s 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, Cen tral 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 matter s (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 fro m economic modeling in PI + 

# and

CTAM for the state of California and potential policies

. 13

12 

## Mark Z. Jacobson, “On the causal link between c arbon dioxide and air pollution mortality,” Geophysical

Research Letters , Vol. 35, 2008, L03809,

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

13 

## All images are open

- source from Wikimedia

# Regional Economic Models, Inc.

p. 8 The rest of this study cover s 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 

# B oth

influence the economy (the carbon tax in particular , though revenues from a cap

- and

trade can have bud get 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

emission s and revenues in the latter wi th 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



# Massachus etts, 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.committeefo ragreeneconomy.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

- climate

change/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 H OW 

# D OES A 

# C ARBON 

# T AX 

# W ORK 

# ?

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

househol d, and the firm) to “internalize” external cost of carbon dioxide in their day

- to

day 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 good s or service s

. 21 

# Carbon taxes have advan tages

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 extrac tion, 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 s toichiometry

. 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 carbo n 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 cont ent. This pricing

of emission s 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

# Regional Economic Models, Inc.

p. 10 Another long

- term feature wi th 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

. Appl yin g set price s on fuels

and electricity based on the internal carbon content

— as outlined with Figure 1.1

— and

increasing the r ate over time ( such a $10/year) sends signal s to i nvestors 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/gal lon and $4.00/ gallon due to the

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

other factors. 26 

# Knowing the future trajectories of these prices is extremely complicated;

uncertainty about the future often leads investor s 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 dicta te 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 r elated. 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 Glov er, “ 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

- c alifornia.html >

# Regional Economic Models, Inc.

p. 11 

# P OLICY 

# S CENARIOS

The simulations here co nsider 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: $5 0/ 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 expansiv e 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 business es 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 re cycling 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 $50 $100 $150 $200 $250

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Carbon tax (by metric ton in 2014 dollars)



## $50/ton

$100/ton

$200/ton

# Regional Economic Models, Inc.

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 househ olds. Before returning

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

renewable d evelopment 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 a cceptable 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 federal

level

. 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.



# A CROSS



# - THE



# - B OARD 

# (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 econom y 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

— roughl y

— the proportion already paid by revenue source , an d 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

# Regional Economic Models, Inc.

p. 13 F EE



# - AND



# - D IVIDEND 

# (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 r evenues and eligible population, and sends it to qualified

California n 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 

# S IMULATION 

# R ESULTS

R esults of the simulations cover the economic, demographic, fiscal, and climate impact s

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 t he

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 bel ow 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 sh ow 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, ou tput 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 o f the stratification of

impac ts over the income distribution ladder.

# Regional Economic Models, Inc.

p. 15 A DDITIONAL 

# T OTAL 

# E MPLOYMENT 

# ( OVER 

# B ASELINE 

# )

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.



# A DDITIONAL 

# G ROSS 

# D OMESTIC 

# P RODUCT 

# (A NNUAL 

# )

Figure 3.2

– De

pending 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 50,000 100,000 150,000 200,000 250,000 300,000 350,000

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 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 raise s 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 co mmercial and industrial firms

—

the price of energy by type and se ctor. 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, bus iness 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 California n firms.

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

scenarios, which cre ate 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 lar ger

. The FAD scenarios, which do not offset costs

, imply a smaller economy, but one more

focused on consumer spending (due to an increase in income s 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 jo bs and GDP do

not always march in time with each other in the results.

# Regional Economic Models, Inc.

p. 17 A DDITIONAL 

# G ROSS 

# D OMESTIC 

# P RODUCT 

# (C UMULATIVE 

# )

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 California n economy of the future. Others a re 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 und er 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, manage rs, mechanics, accountants, IT, security,

sales representatives, maintenance p ersonnel, 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 wor kers 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/cgi

bin/sssd/naics/naicsrch?code=325&search=2 012%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 F IGURE 

# 3.4

– A NNUAL 

# O UTPUT BY 

# I NDUSTRY 

# ($200/ TON



# , ATB)

NAICS Industr ies 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 F IGURE 

# 3.5

– P

ERCENTAGE 

# C

HANGE 

# ($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 F IGURE 

# 3.6

– A NNUAL 

# O UTPUT BY 

# I NDUSTRY 

# ($200/ TON



# , FAD )

NAICS Industr ies 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 F IGURE 

# 3.7

– P

ERCENTAGE 

# C

HANGE 

# ($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 refo rm and a carbon

tax. In particular, it can gain in certai n industries that create large number s 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 t erms of the carbon tax differs

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

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

spending, and how it fits into other B2B transa ctions

.

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 ga s extraction

, and primary metal manufacturing ( for steel

products such as pipelines ). FAD adds computers and electronic product s 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” ar e a

numerous collection. They involve localized, labor

- intensive industrie s 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 ele ctrical 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

, ent ertainment 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 benefit s or neutral impact s to more industries.

Computers and electronics is p erhaps a representative case for the difference

between the two. Computer s 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 th e job resu lts in the next sections show what begin s

to happen on the labor market within California

.

# Regional Economic Models, Inc.

p. 23 F IGURE 

# 3.8

– E

MPLOYMENT BY 

# I

NDUSTRY 

# ($200/ TON



# , ATB)

NAICS Industr ies 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 F IGURE 

# 3.9

– P

ERCENTAGE 

# C

HANGE 

# ($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 F IGURE 

# 3.10

– E

MPLOYMENT BY 

# I

NDUSTRY 

# ($200/ TON



# , F AD )

NAICS Industr ies 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 F IGURE 

# 3.11

– P

ERCENTAGE 

# C

HANGE 

# ($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 impact s 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 a mount of production associated with a unit of labor. For example, if

an aircraft pro du ction 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

aeros pace 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

manu facturing 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

- intensiv e”

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 substitut e

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 compan y 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 inpu ts is an

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

. T he same

process can happen in other industries between labor and cap ital, 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 ch ange 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 simulation s 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 F IGURE 

# 3.12

– E

MPLOYMENT BY 

# O CCUPATION 

# ($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 OCCUPAT I/O NS = 18,295 112,251 194,251 256,157 298,366

# Regional Economic Models, Inc.

p. 30 F IGURE 

# 3.13

– P

ERCENTAGE 

# C

HANGE 

# ($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 F IGURE 

# 3.14

– E

MPLOYMENT BY 

# O CCUPATION 

# ($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 OCCUPAT I/O NS = 18,434 94,912 155,020 203,086 241,995

# Regional Economic Models, Inc.

p. 33 F IGURE 

# 3.15

– P

ERCENTAGE 

# C

HANGE 

# ($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 P ERSONAL 

# C ONSUMPTI ON 

# E XPENDITURE 

# (PCE)

- P RICE 

# I NDEX



# 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 comprehensiv e, specific figure that includes

major consumpt ion 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 relati ve to the

baseline, a vector “

one

- time ” adjustment upwards, and not a change in the rate.



# PCE

- P RICE 

# I NDEX BY 

# I NCOME 

# Q UINTILE

$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 h omes, 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% 0.4% 0.6% 0.8% 1.0% 1.2% 1.4% 1.6% 1.8%

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Change from baseline



## $50/ton (ATB)

$50/ton (FAD)

$100/ton (ATB)

$100/ton (FAD)

$200/ton (ATB)

$200/ton (FAD)

# Regional Economic Models, Inc.

p. 35 C HANGES IN 

# E NERGY 

# P RICES 

# ( FROM 

# B ASELINE 

# )

PI + 

# has seventy

- five consumption categories ranging from cars to nondurable goods, to

food and fuel s

, transportation, insurance of all descriptions , hospitals , personal services,

and tourism . Changing these (fuel prices upwards for the carbon tax, and item s 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 re sidential 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

nonc onventional 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% 5% 10% 15% 20% 25% 30% 35% 40%

2015 2020 2025 2030 2035 

# Fuel oil and other fuels

$50

$100

$200

0%

5% 10% 15% 20% 25%

2015 2020 2025 2030 2035



# Motor vehicle fuels

$50

$100

$200

0% 10% 20% 30% 40% 50%

2015 2020 2025 2030 2035



# Residential electricity

$50

$100

$200

0%

10% 20% 30% 40% 50% 60%

2015 2020 2025 2030 2035 

# Residential natural gas

$50

$100

$200

# Regional Economic Models, Inc.

p. 36 A DDITIONAL 

# R EAL 

# D ISPOSABLE 

# P ERSONAL 

# I NCOME 33 

# ( A NNUAL 

# )

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

- t ax 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 $2,000 $4,000 $6,000 $8,000 $10,000 $12,000 $14,000 $16,000 $18,000

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. 37 A DDITIONAL 

# R EAL 

# D ISPOSABLE 

# I NCOME 

# (C UMULATIVE 

# )

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

. Compli cating this issue

is the change in population and the potential distributional impacts of the policy.

$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. 38 I NCOME BY 

# Q UINTILE



# Figure 3.21

– The two graphs relate the $200/ton simulation (to show the maximum

impact) and by 20% increments. One surpris ing 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 st eady 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% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5%

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Change (from baseline) 

# $200/ton, ATB

Lowest 20%

Low

- Middle 20%

Middle 20%

High

- Middle 20%

Highest 20%

0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 5.0%

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Change (from baseline) 

# $200/ton, FAD

Lowest 20%

Low

- Middle 20%

Middle 20%

High

- Middle 20%

Highest 20%

# Regional Economic Models, Inc.

p. 39 C HANGE IN 

# P OPULATION 

# ( FROM 

# B ASELINE 

# )

Figure 3.22

– The state’s po pulation 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 50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000 450,000 500,000

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 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 A CROSS



# - THE



# - B OARD V



# . F EE



# - AND



# - D IVIDEND



# As demonstrated in the results, each of these options has strengths and weaknesses in

how to use the money co ming 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 inv estments and exports, while FAD

slightly increases the quantity of household income from 2015 out to 2035.

L ABOR 

# S HARE OF 

# I NCOME 34 

# 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 impact s 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 un employment 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

l iving in the state much the same between scenarios.

34 

## Calculated as real disposable personal income divided by gros s 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 C ARBON 

# T AX 

# R EVENUES 

# (A NNUAL 

# )

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 maximum s . 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

.



# C ARBON 

# T AX 

# R EVENUES 

# (C UMULATIVE 

# )

Figure 3.25

– This shows the total summed over time. Results for bot h 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 $10,000 $15,000 $20,000 $25,000 $30,000 $35,000 $40,000 $45,000 $50,000

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

$100/ton

$200/ton

$0 $100,000 $200,000 $300,000 $400,000 $500,000 $600,000 $700,000

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

$100/ton

$200/ton

# Regional Economic Models, Inc.

p. 42 C ARBON 

# T AX 

# P AID BY 

# S ECTOR 

# (N ON



# - G OVERNMENT 

# )

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. Ga soline 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. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Percent of carbon tax paid



## Residential

Commercial

Industrial

# Regional Economic Models, Inc.

p. 43 F ORECASTED 

# A

NNUA L 

# D IVIDEND 

# C HECK 

# (P ER 

# C APITA 

# )

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 pe ople relocate in the United

States every year, 36 

# and more than 5. 7 5 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 o f 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 R ate: 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

- laws

disenfranchise

- 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 $200 $400 $600 $800 $1,000 $1,200

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2014 dollars



## $50/ton

$100/ton

$200/ton

# Regional Economic Models, Inc.

p. 44 C ARBON 

# D IOXIDE 

# E MISSIONS 

# (A NNUAL 

# F ORECAST 

# )

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. Cal ifornia 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 e missions 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 outpu t 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_energyco2i nv.html >

40 

## “Annual Energy Outlook 2013,” U.S. Energy Information Administration

,

< http://www.eia.gov/forecasts/aeo/IF_all.cfm > 230 250 270 290 310 330 350 370 390 410

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Millions of metric tons



## No tax

$50/ton

$100/ton

$200/ton

# Regional Economic Models, Inc.

p. 45 C ARBON 

# D IOXIDE 

# E MISSIONS 

# (C UMULATIVE 

# D IFFERENCE 

# )

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 m ost

obvious efficiencies first

— “low

- hanging fruit ”

— and decreasing emis sions 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 carb on taxes than the lower initial rates.

41 

## 2,204,622,62 0,000 pounds, the equivalent to 112,480,746,000 gallons o f gasoline, which is

approximately 80% of current annual consumption in the United States - 1,400 - 1,200 - 1,000 - 800 - 600 - 400 - 200 0 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Millions of metric tons



## $50/ton

$100/ton

$200/ton

# Regional Economic Models, Inc.

p. 46 C ARBON 

# D IOXIDE 

# E MISSIONS 

# (1990 B ENCHMARK 

# )

Figure 3.30

– This shows California’s “progress” in terms of its current and f orecasted

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 y ear 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 m eaning 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_pr otocol/items/2830.php > 65% 70% 75% 80% 85% 90% 95% 100% 105% 110% 115%

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Percent of 1990 emissions



## No tax

$50/ton

$100/ton

$200/ton

# Regional Economic Models, Inc.

p. 47 

# A SSEMBLY 

# B ILL 

# 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 . T he 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 mega watts in capacity

— AB32 will cover not only electrical power

generation (starting in 2013 and 2014), but also natural gas and vehicle fuels ( out to

2 020). 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 problem s from two different sides. 44



# In essence, a carbon tax chooses a price for emissions based o n 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 ” t he 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 betwe en 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 t ax 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 auct ion

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

Stanfor d University described this as, “If covered firms are cost

- minimizing, they will

reduce emissions up to the point where, at the margin, the cost s of emissions abatement

equals the emissions price. The two policies thus tend to bring about equality of

margi nal 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 national

level 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

an d 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 pri ce

( 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 

# R EGIONAL 

# E CONOMIC 

# M ODELS



# , I NC



# . (REMI)

REMI is an economic and policy analysis firm specializing in services related to regio nal

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, tra de associations,

labor unions, or non

- profit s. REMI’s list of clients in California is extensive. It includes

California Department of Finance (CalFinance), 49 

# California Department of R esources,

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 behin d 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 S cenarios,”

California Department of Resources, Recycling, and Recovery , July 30, 2013,

< http://www.calrecycle.ca.gov/Publications/Documents/1466%5C20131466.pdf >

51 

## Sue Lie u, Shah Dabirian, and Greg Hunter, “Socioeconomic Report 2012,” South Coast Air Quality

Management District , < http://www.aqmd.gov/aqmp/2012aqmp/Final/FinalSocioeconomicRepor t.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), Metr o

- 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 C ounty, 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 

## “I ndustries for PI + 

## v. 1.4 Models,” REMI

,

< http://www.remi.com/download/documentation/pi+/pi+_version_1.4/NAICS_Industries_for_PI+

- Hie rarchical_v1.4.pdf >

# Regional Economic Models, Inc.

p. 52 example) and quality (a 160

- sector model has mor e 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 capt ures 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 pla nt in Michigan will have a lengthy supply

- chain behind

it, with pa rts 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 thei r “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 suppl y

- 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 part s 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 th e markets above

— those for housing, labor,

taxes, government spending, commu ting, 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

a s 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. Th is 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 o r 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 endeavor s , 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 th e 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 publica tions as the Journal of Regional Science

, the Review of Economics and

Statisti cs (describing the econometrics of the migration equation for predicting labor

mobility and household relocation s 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 >

# Regional Economic Models, Inc.

p. 54 Review

. 60 

# PI + 

# relies on public data from statistical agencies like the Bureau of Economic

Analysis (BEA), Bureau of Labor Statis tics (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 

## “Da ta 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

Quanti tative Economics , < http://rsqe.econ.lsa.umich.edu/ >

# Regional Economic Models, Inc.

p. 55 Simulating the net impact of carbon taxes and ATB or FAD in PI + 

# involv ed 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 pri ces

– 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 ho usehold 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 cost s 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 indu stries depen ded

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 person al 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. T hen, lastly, it uses the PCE

- Index to determine what those

dollars really mean in terms of actual purchasing power. ATB meant lower ing the

taxes taken out (and therefore increasing household savings and spending), and

FAD meant adding a nother 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

- a

multiregional

- 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 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050 2052 2054 2056 2058 2060 Economic, Demographic, or Fiscal Indicator



## Control Forecast Policy A Policy B

# Regional Economic Models, Inc.

p. 57 

# C ARB ON 

# T AX 

# A

NALYSIS 

# M ODEL 

# (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 quadr illions 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 emiss ions 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 ill ustrates 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 ori ginals in the CACTAM calibration.

64 

## Keibun Mor i, “Washington State Carbon Tax: Fiscal and Environmental Impacts,” University of

Washington , < http://www.commerce.wa.gov/Documents/Washington

- State

- Carbon

- Tax.pdf >

65 

## Eric d e 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/ >

# Regional Economic Models, Inc.

p. 58 I NTEGRATING 

# 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 2035 66 

# 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 c ost.

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, natu ral

gas, and petroleum products

, is the approximate way to deal with this . The table below

shows how to associate the rows of carbon tax revenue s 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

sector s ) fuel costs

Industrial Motor Gasoline, Distillate Fuel

Oil Residual (industrial sectors)

fuel costs

Natural Gas Natural gas (industrial

sectors) fuel costs

Electricity Electricity (industrial sector s )

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 t he right . The variables above are

applicable for a state

- level (or any other 1

- region) simulation s 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

# Regional Economic Models, Inc.

p. 59

# 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 Nystrom 67 

# 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

si nce 2011, and h e 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 instit utions, and

non

- profits research organizations

. Major projects have included impact analyses of the

federal “fiscal cliff” and sequestration, the Tr ansCanada Keystone XL Pipeline, the $500

billion Southern California Association of Governments (SCAG) trans portation 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 mac roeconomic

framework, modeling the impacts of intermodal transportation projects, and business

development and traveling throughout North America.

Ali Zaidi 68 

# 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 o n analyses of federal immigration reform and integrating

regional models with agent

- based systems to look at national security scenarios, border

closures, a nd international capital flows. Mr. Zaidi’s research plans include helping to

develop tools for anal yzing diverse infrastructure types as one system.

67 

## < http://www.linked in.com/pub/scott

- nystrom/5b/274/337 >

68 

## < http://www.linkedin.com/in/lucrosmc >

# Regional Economic Models, Inc.

p. 61 

# N OTES