THE ECONOMIC, DEMOGRAPHIC, AND CLIMATE 

IMPACT OF ENVIRONMENTAL TAX REFORM IN

WASHINGTON AND KING COUNTY

PREPARED BY

Regional Economic Models, Inc. (REMI)

SCOTT NYSTROM, M.A.

Senior Economic Associate

ALI ZAIDI, B.S.

Assistant Economist

1776 I St. NW

Suite 750

Washington, DC 20006

(202) 716-1397

<scott.nystrom@remi.com>

 

 

 

FRIDAY, DECEMBER 13, 2013
Regional Economic Models, Inc.

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TABLE OF CONTENTS

 Table of Contents pp. 1-2

 Acknowledgements p. 3

 Executive Summary p. 4

o Washington Summary p. 5

o King County Summary p. 6

 Word Cloud p. 7

 Background pp. 8-9

 Introduction pp. 10-11

 Scenarios pp. 11-14

 Figure 1.1 – Carbon pricing levels p. 12

 Figure 1.2 – Revenue recycling for Washington p. 13

 Simulation Results pp. 14-59

o Statewide Tax Reform pp. 15-38

 Figure 2.1 – Total employment, Washington p. 15

 Figure 2.2 – Total employment, King County p. 15

 Figure 2.3 – GDP (annual), Washington p. 16

 Figure 2.4 – GDP (annual), King County p. 16

 Figure 2.5 – GDP (cumulative), Washington p. 17

 Figure 2.6 – GDP (cumulative), King County p. 17

 Figure 2.7 – Output by industry, Washington p. 19

 Figure 2.8 – Output by industry, King County p. 20

 Figure 2.9 – Jobs by industry, Washington p. 22

 Figure 2.10 – Jobs by industry, King County p. 23

 Figure 2.11 – Jobs by occupation, Washington pp. 25-26

 Figure 2.12 – Jobs by occupation, King County pp. 26-27

 Figure 2.13 – PCE-Price Index p. 28

 Figure 2.14 – PCE-Price Index by quintile p. 28

 Figure 2.15 – Price impact to energy commodities p. 29

 Figure 2.16 – Personal income, Washington p. 31

 Figure 2.17 – Personal income, King County p. 31

 Figure 2.18 – Personal income by quintile p. 32

 Figure 2.19 – Population, Washington p. 33

 Figure 2.20 – Population, King County p. 33

 Figure 2.21 – Carbon tax revenues (annual) p. 34

 Figure 2.22 – Carbon tax revenues (cumulative) p. 34

 Figure 2.23 – Carbon emissions (annual) p. 35

 Figure 2.24 – Carbon emissions (difference) p. 36
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 Figure 2.25 – Carbon emission (cumulative) p. 36

 Figure 2.26 – Carbon emissions (1990 benchmark) p. 37

o County Tax Reform pp. 38-52

 Figure 3.1 – Revenue recycling for King County p. 38

 Figure 3.2 – Total employment p. 39

 Figure 3.3 – GDP (annual) p. 40

 Figure 3.4 – GDP (cumulative) p. 40

 Figure 3.5 – Output by industry p. 41

 Figure 3.6 – Jobs by industry p. 42

 Figure 3.7 – Jobs by occupation pp. 43-44

 Figure 3.8 – PCE-Price Index p. 45

 Figure 3.9 – PCE-Price Index by quintile p. 46

 Figure 3.10 – Price impact to energy commodities p. 47

 Figure 3.11 – Personal income p. 48

 Figure 3.12 – Personal income by quintile p. 48

 Figure 3.13 – Population p. 49

 Figure 3.14 – Carbon tax revenues (annual) p. 50

 Figure 3.15 – Carbon tax revenues (cumulative) p. 50

 Figure 3.16 – Carbon emissions (annual) p. 51

 Figure 3.17 – Carbon emissions (difference) p. 51

 Figure 3.18 – Carbon emissions (cumulative) p. 52

 Literature p. 53

 Regional Economic Models, Inc. (REMI) pp. 54-57

o PI+ pp. 54-57

 Figure 4.1 – Model structure p. 56

 Figure 4.2 – Modeling framework p. 57

 Carbon Tax Analysis Model (CTAM) p. 58

 Figure 5.1 – CTAM model workings p. 58

o Integrating PI+ and CTAM p. 59

 Figure 5.2 – Data and policy variables p. 59

 Authors’ Biographies and Contact Information p. 61
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ACKNOWLEDGEMENTS

Washington Environmental Tax Reform (ETR-WA) would like to thank everybody who supported this 

research project. Their individual and collective contributions to the report were invaluable. We would 

like to thank the researchers at the Northwest Economic Research Council (NERC) at Portland State 

University (PSU) for the inspiration in the original Oregon study, all those involved in the writing and 

funding of the Massachusetts study, and everyone in the economics and policy literature devoted to the 

analysis of the nexus between energy, economics, and the environment. We would like to thank the 

following for their editorial contributions. We are grateful for your generosity and time in making this 

study clearer, fairer, and a better representation of the economic and emissions impacts:

 Dr. Yoram Bauman, Proprietor, The Stand-Up Economist

 Dr. Jenny Liu, Assistant Director, Northwest Economic Research Center (NERC) at 

Portland State University (PSU)

 Dr. James Peach, Regents Professor, Arrowhead Center at New Mexico State 

University (NMSU)

 Dr. Thomas Potiowsky, Director, Northwest Economic Research Center (NERC) at 

Portland State University (PSU)

 Jeff Renfro, Senior Economist, Northwest Economic Research Center (NERC) at 

Portland State University (PSU)

 Dr. Frederick Treyz, CEO, Regional Economic Models, Inc. (REMI)

 Dr. Dan Wei, Research Assistant Professor, Price School of Public Policy at the 

University of Southern California (USC)

We would like to thank the analysts at the Washington Office of Financial Management (OFM) 

for pointing us in the correct direction for data on the budget for Washington and King County. 

In addition, ETR-WA thanks the following organizations for their support:
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EXECUTIVE SUMMARY

This white paper explores the economic, demographic, distributional, fiscal, and climate 

implications of remaking portions of the Washington and King County budgets through 

carbon pricing and environmental tax reform. The main principles of the policy design 

include Pigouvian taxes on carbon emissions, revenue-neutrality of the collected funds, 

and their redistribution by lowering state business and ordinance (B&O) taxes, state and 

local sales taxes, and King County property levies while adding rebates to low-income 

individuals and households via the Working Family credit. Using REMI PI+, a dynamic, 

multiregional model of Washington and the Carbon Tax Analysis Model (or CTAM), we 

assessed if environmental tax reform could generate revenue, create jobs, help grow the 

economy, and reduce Washington’s carbon footprint:

Key findings for Washington:

 Washington could add a net of 

between 5,000 and 40,000 jobs 

from this tax reform by 2035, 

depending on scenario

 Annual GDP, when measured in 

2013 dollars, is between $250 

million and $2.75 billion higher 

than baseline by 2035

 Net impacts to costs of living are 

minimal, and the impact to the

distribution of income in the state is 

relatively negligible

 Pricing carbon at $100/metric ton 

by 2033 will reduce state emissions 

to 75% of 1990 levels by 2035, 

complying with current state 

legislative goals

 Washington may even benefit if 

King County acts alone

Key findings for King County:

 King County could chose to “go it 

alone” with environmental tax 

reform and provide relief to local 

sales and property taxes

 Reform in King County could add 

between 1,000 and 8,000 jobs to 

the county by 2035

 Annual GDP in 2013 dollars could 

increase somewhere between $100 

million and $600 million

 “Core” industries in Seattle and 

Redmond such as professional 

services, software, finance, 

construction, and wholesale might 

benefit the most

 Environmental tax reform saves 

emissions between 8 million metric 

tons and 34 million metric tons 

from 2015 out to 2035

Environmental tax reform through a carbon tax offers an attractive balance between the 

state's multifaceted priorities of improving the economy, creating jobs, preventing the 

emission of (potentially) harmful carbon dioxide into the atmosphere, and offering tax 

relief. Furthermore, a carbon tax under these designs—the ones for revenue-neutrality 

and revenue recycling—does not have major influence on the distribution of income or 

costs of living in the state. Environmental tax reform has the potential to allow the state 

to increase its competiveness through lower taxes, improve its attractiveness to migrants 

with more jobs and lower sales taxes, and reduce the quantity of carbon emissions. The 

“give-and-take” here represents the net effects of the simulations for Washington and 

King County with summaries for both on the next two pages.
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WASHINGTON SUMMARY

This report examines the economic, demographic, fiscal, and climate impacts of several 

environmental tax reform scenarios for the state. We looked at introducing a price on 

carbon emissions with a tax on carbon-emitting fossil fuels and electricity based on how 

much carbon dioxide they release into the atmosphere. Scenarios included a fee at $10 

per metric ton, $30/ton, $50/ton, and $100/ton phased in at $5/year. This analysis was 

“revenue-neutral.” For every dollar in revenue, we reduced the B&O tax, sales tax, and 

increased the Working Family rebate in total by the same amount as revenues. The 

subsequent division of “recycled” dollars did not redistribute funds, as the business and 

household sectors received tax cuts equal to their environmental tax payments. Major 

results include the impacts on jobs, GDP, and state emissions:

Each increase in the carbon tax’s level has gradually less of an impact (about 60% of the 

$100/ton benefits coming in the initial $50/ton). The effect of the “tax swap” between 

taxing energy and the B&O and state sales taxes creates more jobs and GDP, helps to 

incentivize reductions in carbon emissions to 75% of 1990 levels by 2035, and brings 

Washington in line with its legislative goals to do the same.

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

20142016

2018

2020

2022

20242026

2028

2030

2032

2034

Jobs over baseline

Additional employment

$0

$500

$1,000

$1,500

$2,000

$2,500

$3,000

20142016

2018

2020

2022

20242026

2028

2030

2032

2034

Millions of 2013 dollars

Additional annual GDP

$10/ton

$30/ton

$50/ton

$100/ton

-160

-140

-120

-100

-80

-60

-40

-20

0

$10/ton $30/ton $50/ton $100/ton

Millions of metric tons of carbon

Total saved emissions (2015-2035)

70%

75%

80%

85%

90%

95%

100%

20142016

2018

2020

2022

20242026

2028

2030

2032

2034

Percentage of 1990 emissions

$10/ton

$30/ton

$50/ton

$100/ton

No tax
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KING COUNTY SUMMARY

This report also examines the economic, demographic, and climate impact of several 

environmental tax reforms for King County on its own. We looked at introducing a 

price on carbon in King County with fees on carbon-emitting fuels and electricity based 

on how much carbon dioxide they release into the atmosphere. Scenarios include a fee 

at $10 per metric ton, $30/ton, and $50/ton phased in at $5/year. This analysis was 

“revenue-neutral.” For every dollar in revenues, we used 50% of the money to reduce 

local sales taxes and the other 50% to reduce local property taxes. This “recycling” will 

help reduce the cost of living and strengthen businesses’ competitiveness. Major results 

include the impacts to jobs, GDP, the county’s estimated level of carbon emissions, and 

the performance of key sectors in King County:

Top eight industries benefiting:

 Construction

 Hospitals and ambulatory healthcare

 Monetary authorities – central bank; 

Credit intermediation and related

 Professional, scientific, and technical 

services

 Real estate

 Securities, commodity contracts, 

investments

 Real estate

 Wholesale and retail trade

Environmental tax reform in King County alone has potential to improve the economy 

while saving carbon emissions. The net effect of the “tax swap” between carbon and local 

property and sales taxes creates more jobs, higher GDP, and incentivizes reductions in 

carbon releases. Key industries benefiting include the heart of the regional economy, 

such as construction, professional services, and real estate.

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

20142016

2018

2020

2022

20242026

2028

2030

2032

2034

Jobs over baseline

Additional employment

$0

$100

$200

$300

$400

$500

$600

$700

20142016

2018

2020

2022

20242026

2028

2030

2032

2034

Millions of 2013 dollars

Additional annual GDP

$10/ton

$30/ton

$50/ton

-35

-30

-25

-20

-15

-10

-5

0

$10/ton $30/ton $50/ton

Millions of metric tons of carbon

Total saved emissions (2015-2035)
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WORD CLOUD
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BACKGROUND

Sometime in 1920, Arthur Cecil Pigou sat in his office at the University of Cambridge, where he worked as 

an economist and lecturer.1 At some point, his thoughts turned to the local railway. Steel roads and the 

iron horse were not new technologies by the beginning of the Roaring Twenties—the first rail linkage amid 

Cambridge and London opened in 1845,2 and massive industrial change was on the horizon with the rise 

of the personal automobile. To Pigou, they provided a useful example. A passenger bought a ticket and, 

for a given price, the railroad provided passage from Point A to Point B. On the surface, this is a simple 

case of “supply” (the company operating the trains and selling tickets to cover their costs in search of a 

profit) and “demand” (the eager passenger willing to pay the price to move to a new place in search of 

their own happiness). An earlier economist, Alfred Marshall, Pigou’s advisor for his doctoral education, 

first created supply and demand curves to illustrate how want for the railroad, “demand,” came together 

with the cost to provide it, “supply,” to eventually settle at the market price. Both sides found the overall 

situation agreeable enough to do business, and the passengers bought the tickets and the railroad ran the 

trains. In theory, everybody was happier. Pigou took exception.

He agreed with the basics of Marshall’s supply and demand concepts but felt they were limited in their 

scope. Yes, the railroad must be in a better position for having sold the ticket, else it would not sell them 

nor run their trains. Yes, the passengers must value their travel more than the money it took to acquire it. 

If not, they would simply never have parted with their pounds to purchase the tickets in the first place. 

Pigou asked, however, “What of those not directly involved in the transaction?” The wood- and coal-fired

steam engines of the day tended to shed a significant amount of fuel and burnt waste when rolling over 

the English countryside. These operations, Pigou said, imposed significant hardships on, “people not 

directly concerned, through, say, uncompensated damage done to surrounding woods by sparks from 

railway engines.”

3 Charred embers and potential forest fires lowered the value of the wood along the line, 

which was a lucrative sale item as lumber or firewood in the growing cities of London, Manchester, 

Birmingham, and Liverpool. If a significant quantity of fires harmed the commercial prospects of local 

landowners, their losses may outweigh the benefits of the direct operations of the railroad. On the other 

hand, local farmers often gleaned the coal and wood shed from the tracks to take home and burn in their 

own hearths, adding an additional economic and benefit-cost consideration unaccounted for by the 

passengers and railroads meeting at the marketplace of the ticket office.

The formal description, nomenclature, and recommendations on ways to resolve these sorts of situations 

made Pigou a seminal figure in welfare economics and one of the most important economists of the early 

twentieth century. In some cases, Pigou proposed what is now know as a “Pigouvian tax”—a fee or a tax, 

imposed by some level or entity of government to make buyers and sellers internalize the “externality” of 

their actions. Pigou defined an externality as a cost missing in market transactions where a policy lever

might correct it. To return to the previous example, Parliament could impose an annual fee on every 

meter of track operated in woodland areas and then contribute the money to a system designed to insure 

losses against forest fires. This would compensate landowners in the case of a disaster. Moreover, the tax 

would change the behavior of the railroad and its riders. The company might attempt to reroute lines into 

open terrain or pass the cost along to passengers with more expensive tickets. Both of these correct, or 

“internalize,” the externality because fewer trains run near forests due to avoidance of trouble or reduced 

demand. Pigouvian taxes engender fuller accounting of costs before taking action.

 

1 Adapted from John Cassidy, “An Economist’s Invisible Hand: Arthur Cecil Pigou, overlooked for 

decades, provides a guide to the financial crisis,” Wall Street Journal, November 28, 2009, 

<http://online.wsj.com/news/articles/SB10001424052748704204304574545671352424680>

2 Date from Gordon Biddle and O.S. Nock, The Railway Heritage of Britain, first printed in London by 

Michael Joseph, 1983, ISBN: 978-0-7181-2355-0

3 Quoted in Cassidy, “An Economist’s Invisible Hand,” Wall Street Journal
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Pigouvian ideas have seen application to a number of problems, but some of the most significant ones are 

those involving energy and the environment. Perhaps the most notable instance of this is the emission of 

carbon dioxide into the open atmosphere and its potential to induce climate disruptions on a planetary 

scale. Carbon dioxide is harmless to an individual. However, amplifying an isolated effect over a long 

timeline and global setting can have unintended consequences. Karl Benz, Ransom Olds, and Henry Ford 

revolutionized transportation with the production and marketing of the automobile in the early 1900s. It 

is unlikely they could have anticipated a billion or more cars and their exhaust potentially changing the 

composition of the atmosphere.

4 When the planetary scale of the climate renders localized, voluntary 

agreements impractical,

5 a Pigouvian tax is one way to correct for this situation. This study does not

argue for or against the dangers of higher concentrations of carbon in the atmosphere. 

Instead, we will examine the economic impact of addressing the assumed externality of 

carbon with environmental tax reform on Pigouvian principles. This concept is a familiar one

and has champions throughout the political spectrum. Gregory Mankiw, Harvard professor and former 

chair of the Council of Economic Advisors (CEA) under the George W. Bush administration, advocated for 

Pigouvian taxes as being, “the most direct and least invasive policy to address environmental concerns.”6

Against this backdrop, we begin our account of how such tax reform proposals would affect the economy 

and carbon emissions in the state of Washington.7

 

4 John Sousanis, “World Vehicle Population Tops 1 Billion Units,” Ward’s Auto, August 15, 2011, 

<http://wardsauto.com/ar/world_vehicle_population_110815>

5 This solution to externalities is the Coase theorem, named for Ronald H. Coase, who died earlier this 

year. Back to the railroad, landowners could pay the company to move, and each side could be better for it 

(the railway for receiving the payment, and landowners for saving more damage than costs in payments) 

without Parliament. Impracticality comes from nobody “owning” the whole of Earth’s atmosphere and the 

transaction costs of coordination amid 7 billion humans. Please see, “Ronald H. Coase,” The Concise 

Encyclopedia of Economics, <http://www.econlib.org/library/Enc/bios/Coase.html>

6 Gregory Mankiw, “The Pigou Club Manifesto,” Greg Mankiw’s Blog, October 20, 2006, 

<http://gregmankiw.blogspot.com/2006/10/pigou-club-manifesto.html>

7 All images credited to Wikimedia and used within permitted allowances
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INTRODUCTION

This study examines the series of interactions between a state’s economy, energy usage, carbon dioxide 

emissions, demographics, and fiscal policy. At current, Washington is considering a number of policy 

options for reducing carbon emissions. According to “Evaluation of Comprehensive GHG Emissions 

Reduction Programs Outside of Washington: Final Report,” a study produced by the Science Applications 

International Corporation (SAIC) for the Washington State Climate Legislative Executive Workgroup 

(CLEW), there are several ways of pursuing this goal.

8 They list a cap-and-trade program,9 carbon taxes,10

reduction of vehicle miles travel (VMT), low carbon fuel standards, zero emissions vehicles, renewable 

portfolio standards (RPS), and many others. Only three of these—cap-and-trade, carbon tax, and low 

carbon fuel standard—have a “high” chance of reducing carbon emissions by a large degree; the low 

carbon fuel standard has “negative” impacts on the overall welfare of Washington’s consumers and 

businesses.

11 The remaining two, carbon tax and a cap-and-trade, have “uncertain” welfare implications, 

according to the SAIC report. This uncertainty invites further study of an imperative issue for the state, 

which is where this study and ensuing white paper begins.

This “next step” in inquiring about environmental tax reform in Washington quantifies this uncertainty 

with a number of techniques. They include, chiefly, Pigouvian taxes, energy modeling, carbon dioxide 

emissions calculation, and dynamic regional modeling. The goal is to provide more information on the 

topic for policymakers and the public to strike the proper balance between legitimate concerns about job 

creation, economic growth, costs of energy, conditions for low-income households, competitiveness for 

businesses, and reducing carbon dioxide and associated pollutant emissions. Specifically, we will focus on 

a carbon tax as the simplest option for accomplishing these goals as well as how it differs from a cap-andtrade in its implementation. A carbon tax is a Pigouvian tax on fuels based on the internal carbon dioxide 

content associated with their combustion. The revenue from a carbon tax aids in balancing concerns 

about these reforms. Revenue can go towards lowering other taxes—called “revenue-neutrality,” or a “tax 

swap,” to reducing income, sales, or business taxes in exchange for the carbon tax covering the cash needs 

of the same partition of the budget. It can also go towards rebates or spending on other priorities like 

infrastructure, education, or research and development. This study considers these balances, and it will 

shed light on the uncertainties regarding environmental tax reform for Washington. 

Washington Environmental Tax Reform (ETR-WA), a group of private citizens, contracted Regional 

Economic Models, Inc. (REMI) to carry out this study. It relies on two tools: the open-source Carbon Tax 

Analysis Model (CTAM) built by Keibun Mori for the Washington Department of Commerce,12 and PI+, 

REMI’s proprietary economic and demographic model of sub-national breakouts of the United States.13

CTAM draws most of its underlying assumptions and data from the National Energy Modeling System 

(NEMS), a series of models on resource extraction, power generation and distribution, and energy 

 

8 Please see table of contents of the report, “Evaluation of Comprehensive GHG Emissions Reductions 

Programs Outside of Washington: Final Report,” Science Applications International Corporation (SAIC), 

September 20, 2013, <http://tinyurl.com/SAICwa>

9 For a basic introduction to how a cap-and-trade works, please see, “How cap-and-trade works,” 

Environmental Defense Fund, <http://www.edf.org/climate/how-cap-and-trade-works>

10 For a basic introduction to how a carbon tax works, please see, “Considering a Carbon Tax: Frequently 

Asked Questions,” Resources for the Future: Center for Climate and Energy Policy, 

<http://www.rff.org/centers/climate_and_electricity_policy/pages/carbon_tax_faqs.aspx>

11 SAIC, pp. 4-5

12 Keibun Mori, “Washington State Carbon Tax: Fiscal and Environmental Impacts,” Evans School of 

Public Affairs at the University of Washington, July 2011, 

<http://www.commerce.wa.gov/Documents/Washington-State-Carbon-Tax.pdf>

13 “PI+,” REMI, <http://www.remi.com/products/pi>
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consumption developed by the Energy Information Administration (EIA).

14 Using these tools in concert

provides a more comprehensive and robust illustration of energy, environmental, and economic impacts

of introducing a carbon tax in Washington. The showpiece of this paper describes the scenarios examined; 

it then presents their results in terms of economic impacts, demographics, and carbon emissions. The end 

covers a literature review of similar policies and studies, background on methodologies for CTAM and PI+, 

and information on the techniques and assumptions used to integrate them.

SCENARIOS

There are several dimensions of analytical inquiry in this study. The first of these is the level of carbon 

taxes implemented or auction prices for emission realized via cap-and-trade. The “prices” of emitting one 

metric ton of carbon into the atmosphere in this study are $10/ton, $30/ton, or $50/ton.15 These levels 

come from a sensitivity analysis in the SAIC study, which described these prices as, “not a forecasted 

expectation of price, but simply a range of possible scenarios.”16 For the statewide scenario, we also added 

a $100/ton tax case, which goes towards achieving Washington’s existing legislative benchmark of a 25% 

emission reduction from 1990 levels by 2035 in the state.17 These tax scenarios will serve as the fee 

associated with the carbon tax. A second consideration is regions—this study makes use of a 2-region 

REMI PI+ model, with the state of Washington divided into King County and the rest of the state. King 

County has about the area of Rhode Island and contains Seattle, Bellevue, Redmond, and their suburbs. 

In 2010, it had a population of over 1.9 million and an economy of approximately $170 billion; this is 

29.7% of the people in the state and 47.3% of economic activity. Therefore, these results do two things: 

 

14 For background on NEMS, please see, “The National Energy Modeling System: An Overview,” Energy 

Information Administration, <http://www.eia.gov/oiaf/aeo/overview/>

15 1 metric ton = 1,000 kg = 2,204.62 pounds = 1.102 short-tons

16 SAIC, p. 32

17 “Greenhouse gas emissions reductions – reporting requirements,” Washington State Legislature, 

<http://apps.leg.wa.gov/rcw/default.aspx?cite=70.235.020>
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they break the impact on King County out from the whole state for statewide reforms, and they examine 

the economic impact if King County went it alone in implementing a county-level carbon tax.

18 Lastly, this 

study compares the implementation of a carbon tax to that of a cap-and-trade, including the experiences 

of British Columbia (with a carbon tax) and cap-and-trade policies in other places (such as in Europe or in 

California with AB32 and its allowance trading system).

Figure 1.1 – This figure shows the fee applied to carbon emissions under the scenarios. This fee 

represents a carbon tax because the steady carbon pricing implies deliberate action on the behalf of

policymakers to set the rate. Cap-and-trade can approximate this effect if auction prices were steady 

like the above; in reality, however, auctions would have a great deal of variability due to technology, 

volatility in world petroleum markets, and macroeconomic cycles. A carbon tax is the clearest, most 

predictable, and simplest incentive for households and business making decisions about future 

investments and decisions, which is the majority of the modeling approach here. The colors are 

consistent throughout the rest of the report—the $10/ton tax is always crimson and so forth.

One of the most vital facets of environmental tax reform is its provision of tax relief for those taxpayers 

supporting other parts of the federal, state, or local budget. Washington is in unison with almost every 

other state of the union for having a balanced budget amendment in its constitution, which means any 

money gained from a carbon tax will not upset Olympia’s medium- and long-term needs to balance its 

revenues and expenditures. Theoretically, the carbon tax’s revenue could go towards paying for state 

spending programs on education, infrastructure, efficiency, weatherization, or a myriad of other or newer

ideas. This would mean, however, the state has permanently increased its spending over a baseline due to 

the new tax; the conversation over the optimal level of revenues/expenditures relative to the 

size of the state economy, or expectations on government spending, is separate from the 

level of carbon pricing. To avoid this issue and to concentrate strictly on tax reform, the carbon tax 

here is strictly “revenue-neutral”—the dollars in carbon tax revenue go towards a tax break of the same

portions from somewhere else in the budget. The nature of this “tax swap” implies the taxation of energy

usage and emissions replaces the taxing of consumption (the sales tax) and that of business operations 

(through the state business and occupation tax, “B&O,” in Washington). The system here for revenue 

recycling used PI+ to test different schemes in order to settle at the optimal balance in terms of jobs, 

competitiveness and growth, prices, and fairness to low-income families.

 

18 This requires the legislature passing laws to allow local jurisdictions to enact environmental tax reform

$0

$10

$20

$30

$40

$50

$60

$70

$80

$90

$100

$110

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

Emissiosn fee (2013 dollars per 

metric ton of carbon dioxide)

$10 tax

$30 tax

$50 tax

$100 tax
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Tax reform always has the potential to modify the redistribution system, which is inherent in any budget, 

and disturb its balance between different levels of income, types of businesses, and between labor and 

capital. The revenue recycling in Washington can avoid upsetting this situation in a direct manner by 

returning the carbon tax’s money to sections of the economy (such as individuals and households, and 

businesses) in a 1:1 manner to taxes initially paid. That is, if the household sector pays 60% of the carbon 

tax, then it should enjoy 60% of the subsequent tax cuts elsewhere. The chart below establishes the 

algorithm for revenue recycling in these results. Carbon taxes paid by businesses mean the reduction of 

B&O taxes, and carbon taxes paid by individuals mean a reduction in the state sales tax and refunds to 

low-income households (by means of the Working Family credit, rebates totaling 10% of total carbon tax 

revenues). Environmental taxes run the risk of being “regressive”—overpowering low-income households 

past their ability to afford higher energy costs.19 Direct transfers, rebates to correct for higher prices on the 

market, and lower sales taxes on other commodities are several means correcting for this matter.

20 This 

helps the lower rungs of the ladder feel less pain and adjusts for one of the drawbacks of cap-and-trade 

and carbon taxes cited in the economic and policy literature.

Figure 1.2 – This figure shows the revenue recycling for modeling. There are no direct additions to 

spending, and thus this study relies on pure revenue-neutrality. Broadly speaking, the different sectors 

of the economy receive what they paid in the first place, though this will not be true firm-to-firm or 

household-to-household given so many variations on their energy usage, preferences, incomes, and 

their preexisting tax payments. This provides a strong balance of priorities in the results.

Revenue-neutrality remains in the simulations for King County and King County alone, but the system for 

recycling has to change due to differences between the state budget and that of King County. King County, 

like many local governments, receives most of its money from sales and property taxes.21 Under these 

 

19 See, for example, Corbett A. Grainger and Charles D. Kolstad, “Who Pays a Price on Carbon,” National 

Bureau of Economic Research (NBER), working paper #15239, August 2009, 

<http://www.nber.org/papers/w15239>

20 See, for example, Terry Dinan, “Offsetting a Carbon Tax’s Costs on Low-Income Households,” 

Congressional Budget Office, working paper #2012-16, November 2012, 

<http://www.cbo.gov/sites/default/files/cbofiles/attachments/11-13LowIncomeOptions.pdf>

21 “King County Budget,” King County, 

<http://www.kingcounty.gov/exec/PSB/Budget/2014Budget.aspx>

Revenue 

recycling

Static impacts

Direct taxpayers

Tax reforms Pigouvian 

carbon tax

Business 

sector

Revenue 

collected

Cut in the 

B&O tax

Household 

sector

Revenue 

collected

Cut in the 

sales tax

Rebate (10% 

of total CT)
Regional Economic Models, Inc.

p. 14

circumstances, a balanced and simple approach of 50% of revenues reducing the sales tax and 50% 

reducing the property tax produced the strongest outcome. These cuts would mean lower retail prices for 

consumers and businesses as well as a lower ownership cost for housing, commercial space, and industrial 

equipment and facilities. REMI PI+ approximates the value of nonresidential and residential capital stock 

in a county, and this provided means for sharing the property tax cut between residential areas and 

commercial ownership. The sales tax falls by an amount equal to 50% of total carbon tax revenue—not an 

explicit rate cut in percentage terms, but rather a reduction in the total amount of sales tax collected in 

King County (or in Washington within other simulations) each year.

SIMULATION RESULTS

These results cover the economic, demographic, fiscal, and climate impact of seven scenarios: four (4) 

carbon taxes at $10/ton, $30/ton, $50/ton, and $100/ton for Washington, three more carbon taxes (3) 

for King County at $10/ton, $30/ton, and $50/ton (from SAIC). All results are against a “do-nothing” 

baseline, a “null hypothesis” that means no other changes happen in the state with the exception of the 

direct impacts to energy prices and taxes occurring because of environmental tax reform. There is an et

ceteris paribus condition for everything else. Economic results include the impact to employment, the 

GDP for state or county, production by industry, consumer prices, jobs by industry, change in the mixture 

of occupations, and distribution of income. Demographic impacts include the change in state population. 

Fiscal impacts include the total amount of carbon tax revenue anticipated, either in a given year or over 

the cumulative horizon of 2015 to 2035. Results on carbon emissions include the baseline, how tax reform 

changes it, and a comparison to various benchmarks and goals.

Increasing the carbon tax to higher levels than those modeled, or even infinitely, will not 

necessarily mean an improvement to the economic impact or higher revenue forecasts for 

the state or county. However, for the levels studied, it does generate a progressively higher level of 

carbon emissions reductions, tax revenue, and therefore revenue for recycling up to the price of $100/ton. 

This trend begins to reverse itself somewhere between $140/ton and $160/ton as the region runs out of 

ways to reduce carbon emissions via demand cutbacks. The modeling below shows the beginning of this 

process, where 60% of the gains from the $100/ton scenario comes in the first $50/ton of tax, with only 

another 40% of gains added in the second $50/ton of carbon price.
Regional Economic Models, Inc.

p. 15

STATEWIDE TAX REFORM

ADDITIONAL TOTAL EMPLOYMENT

Figure 2.1 – Upon simulation, the tax reform measures discussed here create a net increase in 

employment over the baseline. There are several reasons for this, including a decrease in energy 

imports making more dollars stay local, an increase in the growth of localized, labor-intensive 

industries, and additional competitiveness for the service sector from lower B&O taxes. The above 

represents jobs over a baseline—the total number of jobs available in the state economy in a year, which 

differs from a “rolling” concept of monthly or annual job creation reported by the national media.

Figure 2.2 – King County sees a similar effect in increased employment due to a carbon tax. The high 

concentration of service and technology firms in the area help it enjoy a local benefit to job numbers, but 

the proportional impact to King County is approximately the same as that in the rest of the state in 

percentage terms. There is potential for broad benefits to such tax reform in Washington. This is 

because of new incentives creating a more “efficient” tax system—one that relies on taxing energy and 

carbon output instead of discouraging consumer spending and business activities.

0

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King County, Statewide Reform

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Regional Economic Models, Inc.

p. 16

ADDITIONAL GROSS DOMESTIC PRODUCT (ANNUAL)

Figure 2.3 – The state’s overall level of economic activity increases because of the carbon 

tax. This point comes through measuring gross domestic product (GDP). GDP is the sum of new 

economic activity in an area during a timeframe. It includes consumption, investments, net exports, and 

government spending. The carbon tax means more local dollars stay local instead of spinning away to 

imports. Adding $2.7 billion to GDP in 2035 is about a 0.38% increase in total production, while adding 

$1.1 billion in 2025 is about 0.19% more in aggregate output for the state than in the baseline.

Figure 2.4 – King County sees growth here, too. King County is around 40% of the GDP of Washington. 

This is nearly the proportion of the impact here to the above change to total GDP in the state. King 

County’s consumption of items like gasoline drift downwards compared to the baseline because of 

consumers’ response to higher prices. Reduction in fuel demand means a reduction in demand for 

refineries (which Washington has a number of in the state), but also the ensuing supply-chain of 

pipelines, drilling, and extraction. These activities are typically outside of Washington and King County; 

in essence, this “exports” some of the impact of environmental tax reform to other states and nations. 

The $100/ton tax maximum adds about 0.33% to King County’s GDP in 2035.

$0

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King County, Statewide Reform

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Regional Economic Models, Inc.

p. 17

ADDITIONAL GROSS DOMESTIC PRODUCT (CUMULATIVE)

Figure 2.5 – GDP reproduces annually, which makes it appropriate to sum across years, “horizontally,” 

like the above. For example, a 737 produced in 2013 counts towards GDP in that year but, since 

production on the same plane does not take place again in future years, it only counts for one year. To 

add to GDP in that year Boeing or another aircraft manufacturer would need to make another one. This 

makes the total amount of production over time cumulative, which the above graph stacks. These are the 

same figure from the past page presented in a different manner. Environmental tax reform in the state 

of Washington has the potential to expand the state’s economy in the long-term, including adding $4 

billion more in the $10/ton scenario and over $25 billion more in the $100/ton scenario.

Figure 2.6 – King County is similar. It adds between $2 billion to $14 billion in economic activity from 

2015 to 2035. The above proportions hold, too, where around 40% to 45% of all statewide benefits take 

place in King County and Seattle. This growth continues the pattern towards additional employment 

seen on previous graphs, increases the area’s population, and expands the local tax base, which creates 

the potential for an increase in the number and quality of public services provided there.

$0

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King County, Statewide Reform

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Regional Economic Models, Inc.

p. 18

This next section discusses the distributional implications of the macroeconomic impacts. There are 

several ways to examine this in REMI PI+ and its results: output by industrial sector, employment by 

industrial sector, occupational employment, prices, prices of specific commodities (such as fuels), and real 

income and income distribution. PI+ uses the North American Industrial Classification System (NAICS) to 

define its industries.

22 NAICS, developed by the U.S. Census, defines “industries” as groups of like firms 

competing with each other in the same market to sell similar products. Hyundai and General Motors both 

make cars. They may make different models and brands for different segments of the North American or

Asian markets but, to NAICS, they are both “Motor Vehicle Manufacturing,” which is 3361 when coded. 

All industries, from agriculture, to resources, to manufacturing, to transportation, retail, services, and 

finance behave differently in the REMI model owing to the different ways they use energy, pay taxes, 

trade in local or international markets, and interact together. The sum of their hiring and production 

make up total employment and GDP. Occupations take account of the type of job, not just its industry. For 

example, banks hire employees of all types, such as executives, analysts, accountants, account managers, 

receptionists, tellers, security guards, and grounds personnel. PI+ defines these according to the Standard 

Occupational Classification (SOC) system of the Bureau of Labor Statistics (BLS).

23 The SOC sorts jobs 

into hierarchical occupations in order to make comparisons of wage levels and skills. The other analytical 

considerations of distribution involve costs of living by Consumer Price Index (CPI), prices for specific 

fuel types, and the distribution of income by quintiles. This provides depth to the give-and-take that takes 

place in the economy when adjusting to changes in energy prices.

The results below consider King County and Washington under a state-level environmental tax reform. All 

output is in millions of 2013 dollars (USD). All jobs and occupations are by individual jobs available on 

the labor market in the given year. The results are for the highest $100/ton tax case to give a total sense of 

the scale and magnitude of the impacts across the industries and occupations. There are 70-sectors, which 

approximates 3-digit NAICS, and 95 occupations. This offers an idea of the sensitivity, comparatively, of 

different industries and occupations to environmental tax reform.

 

22 “Introduction to NAICS,” United States Census Bureau, <http://www.census.gov/eos/www/naics/>

23 “Standard Occupational Classification,” Bureau of Labor Statistics, <http://www.bls.gov/soc/>
Regional Economic Models, Inc.

p. 19

FIGURE 2.7 – ANNUAL OUTPUT BY INDUSTRY (WASHINGTON, STATEWIDE, $100/TON)

NAICS Industries 2015 2020 2025 2030 2035

Forestry and logging; Fishing, hunting, and trapping -$1.6 -$17.8 -$37.6 -$53.2 -$63.3

Agriculture and forestry support activities $0.0 -$0.5 -$1.1 -$1.4 -$1.6

Oil and gas extraction -$1.6 -$10.5 -$18.9 -$22.2 -$18.5

Mining (except oil and gas) -$0.1 -$1.4 -$3.1 -$4.7 -$6.1

Support activities for mining -$0.1 -$0.6 -$1.3 -$1.8 -$2.1

Utilities -$15.0 -$45.3 -$66.4 -$78.9 -$78.5

Construction $11.6 $88.9 $201.5 $344.7 $450.9

Wood product manufacturing $0.8 $0.8 $0.2 $0.5 -$0.4

Nonmetallic mineral product manufacturing $1.8 $6.0 $9.9 $14.4 $16.2

Primary metal manufacturing -$0.8 -$10.6 -$22.9 -$33.5 -$41.7

Fabricated metal product manufacturing $2.5 $9.8 $16.9 $25.2 $30.8

Machinery manufacturing $0.7 $2.8 $4.4 $6.3 $7.3

Computer and electronic product manufacturing $4.5 $17.2 $29.1 $45.5 $56.1

Electrical equipment and appliance manufacturing $0.6 $1.5 $1.6 $1.4 $0.0

Motor vehicles, bodies and trailers, and parts manufacturing $3.6 $14.1 $23.8 $33.3 $38.8

Other transportation equipment manufacturing $1.8 -$0.8 -$10.3 -$21.8 -$37.6

Furniture and related product manufacturing $1.6 $5.6 $8.8 $11.6 $12.4

Miscellaneous manufacturing $2.0 $6.5 $8.3 $10.3 $12.0

Food manufacturing $1.8 $3.9 $5.5 $8.6 $10.2

Beverage and tobacco product manufacturing $1.9 $7.1 $12.3 $17.4 $20.0

Textile mills; Textile product mills $0.1 $0.2 $0.1 $0.0 -$0.1

Apparel manufacturing; Leather and allied product manufacturing $0.3 $1.3 $1.8 $2.8 $3.5

Paper manufacturing $0.8 $1.2 $1.0 $1.1 $0.3

Printing and related support activities $0.6 $2.0 $3.3 $4.8 $5.8

Petroleum and coal products manufacturing -$73.8 -$522.6 -$1,038.8 -$1,508.8 -$1,888.1

Chemical manufacturing $0.1 -$15.1 -$36.5 -$55.3 -$71.7

Plastics and rubber product manufacturing $2.0 $5.7 $8.4 $10.9 $11.0

Wholesale trade $25.0 $107.8 $208.3 $331.0 $433.9

Retail trade $40.2 $191.4 $377.0 $600.7 $792.5

Air transportation -$0.2 -$4.4 -$11.8 -$20.5 -$29.6

Rail transportation $0.0 -$0.4 -$1.0 -$1.6 -$2.2

Water transportation $0.0 -$0.7 -$1.8 -$3.1 -$4.3

Truck transportation $1.6 $6.5 $11.9 $18.4 $23.6

Couriers and messengers $0.4 $1.1 $1.7 $2.5 $2.9

Transit and ground passenger transportation $0.2 $0.9 $1.6 $2.6 $3.5

Pipeline transportation $0.0 -$0.1 -$0.1 -$0.2 -$0.2

Scenic and sightseeing transportation; Support activities for transportation $0.0 -$0.6 -$2.1 -$4.7 -$8.8

Warehousing and storage $1.9 $17.3 $35.0 $52.1 $63.1

Publishing industries, except Internet $7.6 $18.0 $23.4 $35.9 $47.7

Motion picture and sound recording industries $0.4 $1.6 $3.0 $4.7 $6.0

Internet publishing and broadcasting; ISPs, search portals, and data $1.8 $7.9 $15.1 $24.1 $31.8

Broadcasting, except Internet $0.6 $2.0 $3.2 $4.8 $6.1

Telecommunications $13.5 $48.3 $86.8 $133.9 $173.8

Monetary authorities - central bank; Credit intermediation and related $15.3 $52.2 $87.3 $123.9 $146.6

Securities, commodity contracts, investments $8.2 $35.1 $61.4 $85.3 $97.8

Insurance carriers and related activities $3.3 $12.6 $21.7 $30.8 $35.9

Real estate $10.0 $23.4 $55.9 $123.9 $182.9

Rental and leasing services; Leasers of nonfinancial intangible assets $3.8 $12.8 $22.2 $33.7 $42.4

Professional, scientific, and technical services $19.6 $90.5 $168.5 $258.4 $327.2

Management of companies and enterprises $2.4 $12.9 $23.1 $33.1 $37.9

Administrative and support services $7.5 $24.6 $42.7 $66.1 $85.4

Waste management and remediation services $0.7 $1.8 $3.5 $6.3 $8.5

Educational services $2.1 $10.3 $21.5 $34.9 $46.2

Ambulatory health care services $36.3 $132.3 $223.4 $318.0 $382.0

Hospitals $9.5 $50.3 $102.3 $164.6 $220.9

Nursing and residential care facilities $2.3 $11.2 $23.8 $40.0 $55.8

Social assistance $1.1 $5.4 $11.5 $19.3 $26.8

Performing arts and spectator sports $1.8 $7.6 $14.4 $22.7 $29.8

Museums, historical sites, zoos, and parks $0.3 $1.3 $2.7 $4.5 $6.1

Amusement, gambling, and recreation $1.9 $7.1 $13.0 $19.9 $25.5

Accommodation $3.5 $8.6 $14.3 $23.8 $32.9

Food services and drinking places $21.6 $88.0 $167.2 $260.5 $337.8

Repair and maintenance $6.3 $24.3 $44.7 $68.5 $87.0

Personal and laundry services $12.6 $44.0 $73.0 $101.3 $118.7

Membership associations and organizations $2.6 $11.9 $24.7 $40.8 $54.8

Private households $1.5 $5.4 $9.3 $13.2 $15.7

TOTAL OF ALL INDUSTRIES = $213.2 $619.3 $1,082.3 $1,831.8 $2,410.0
Regional Economic Models, Inc.

p. 20

FIGURE 2.8 – ANNUAL OUTPUT BY INDUSTRY (KING COUNTY, STATEWIDE, $100/TON)

NAICS Industries 2015 2020 2025 2030 2035

Forestry and logging; Fishing, hunting, and trapping -$0.5 -$6.2 -$13.4 -$18.6 -$21.7

Agriculture and forestry support activities $0.0 -$0.1 -$0.1 -$0.2 -$0.2

Oil and gas extraction $0.0 $0.0 $0.0 $0.0 $0.0

Mining (except oil and gas) $0.0 -$0.4 -$0.9 -$1.4 -$1.9

Support activities for mining $0.0 -$0.1 -$0.2 -$0.3 -$0.3

Utilities -$4.2 -$12.5 -$18.2 -$21.3 -$21.1

Construction $3.2 $23.5 $55.1 $94.4 $116.8

Wood product manufacturing $0.1 $0.3 $0.5 $0.7 $0.7

Nonmetallic mineral product manufacturing $0.7 $2.4 $3.9 $5.4 $5.8

Primary metal manufacturing -$0.1 -$1.8 -$4.0 -$6.1 -$8.0

Fabricated metal product manufacturing $1.0 $3.6 $6.0 $8.7 $10.3

Machinery manufacturing $0.4 $1.6 $2.5 $3.6 $4.1

Computer and electronic product manufacturing $2.4 $8.9 $14.9 $23.0 $28.1

Electrical equipment and appliance manufacturing $0.2 $0.6 $0.6 $0.6 $0.0

Motor vehicles, bodies and trailers, and parts manufacturing $2.5 $10.0 $16.7 $23.2 $27.0

Other transportation equipment manufacturing $0.9 -$0.9 -$6.7 -$13.7 -$23.2

Furniture and related product manufacturing $0.5 $1.9 $2.9 $3.9 $4.2

Miscellaneous manufacturing $1.5 $4.6 $5.8 $7.2 $8.4

Food manufacturing $1.1 $2.9 $4.4 $6.7 $8.0

Beverage and tobacco product manufacturing $1.2 $4.5 $7.7 $10.8 $12.3

Textile mills; Textile product mills $0.1 $0.1 $0.1 $0.0 -$0.1

Apparel manufacturing; Leather and allied product manufacturing $0.2 $0.9 $1.3 $2.0 $2.4

Paper manufacturing $0.2 $0.4 $0.4 $0.5 $0.4

Printing and related support activities $0.3 $0.9 $1.4 $2.0 $2.4

Petroleum and coal products manufacturing -$2.2 -$11.8 -$22.1 -$31.7 -$39.6

Chemical manufacturing $0.3 -$3.8 -$10.1 -$16.1 -$22.0

Plastics and rubber product manufacturing $0.7 $1.9 $2.8 $3.5 $3.4

Wholesale trade $12.3 $52.4 $100.9 $160.9 $211.4

Retail trade $10.7 $51.6 $103.9 $168.8 $224.2

Air transportation -$0.2 -$4.2 -$11.3 -$19.6 -$28.3

Rail transportation $0.0 -$0.1 -$0.3 -$0.5 -$0.7

Water transportation $0.0 -$0.6 -$1.6 -$2.8 -$4.0

Truck transportation $0.7 $2.6 $4.8 $7.5 $9.5

Couriers and messengers $0.2 $0.7 $1.1 $1.6 $1.9

Transit and ground passenger transportation $0.1 $0.5 $0.9 $1.4 $1.8

Pipeline transportation $0.0 $0.0 -$0.1 -$0.1 -$0.1

Scenic and sightseeing transportation; Support activities for transportation $0.0 -$0.4 -$1.5 -$3.1 -$5.6

Warehousing and storage $0.7 $6.6 $13.2 $19.7 $23.9

Publishing industries, except Internet $7.2 $16.5 $20.9 $32.1 $42.8

Motion picture and sound recording industries $0.3 $1.1 $2.1 $3.3 $4.2

Internet publishing and broadcasting; ISPs, search portals, and data $1.5 $6.3 $12.0 $18.9 $24.9

Broadcasting, except Internet $0.5 $1.4 $2.3 $3.4 $4.3

Telecommunications $8.2 $29.0 $51.7 $79.8 $103.7

Monetary authorities - central bank; Credit intermediation and related $7.0 $23.5 $39.1 $55.7 $66.3

Securities, commodity contracts, investments $6.4 $26.2 $45.1 $62.4 $71.4

Insurance carriers and related activities $2.2 $8.1 $13.8 $19.5 $22.6

Real estate $5.8 $18.7 $46.9 $97.6 $142.8

Rental and leasing services; Leasers of nonfinancial intangible assets $1.8 $5.7 $9.4 $13.8 $16.7

Professional, scientific, and technical services $12.2 $50.8 $92.8 $142.7 $181.2

Management of companies and enterprises $1.8 $8.8 $15.3 $21.9 $24.9

Administrative and support services $3.1 $8.1 $12.6 $19.8 $26.2

Waste management and remediation services $0.2 $0.9 $1.7 $2.8 $3.9

Educational services $1.1 $5.2 $10.5 $16.8 $21.8

Ambulatory health care services $13.3 $47.2 $78.1 $109.1 $127.8

Hospitals $3.9 $18.6 $36.2 $56.8 $74.2

Nursing and residential care facilities $0.8 $3.4 $6.8 $11.1 $14.9

Social assistance $0.6 $2.7 $5.7 $9.4 $12.9

Performing arts and spectator sports $1.4 $5.9 $11.0 $17.3 $22.6

Museums, historical sites, zoos, and parks $0.2 $0.8 $1.6 $2.7 $3.7

Amusement, gambling, and recreation $0.7 $2.6 $4.6 $7.0 $8.9

Accommodation $2.0 $5.2 $8.7 $14.1 $19.1

Food services and drinking places $8.6 $33.4 $61.5 $94.0 $119.8

Repair and maintenance $2.2 $7.8 $14.0 $21.0 $26.1

Personal and laundry services $6.1 $21.0 $34.4 $47.3 $55.3

Membership associations and organizations $1.1 $4.8 $9.5 $15.3 $20.0

Private households $0.5 $1.8 $3.1 $4.3 $5.1

TOTAL OF ALL INDUSTRIES = $135.9 $505.7 $912.7 $1,420.5 $1,798.6
Regional Economic Models, Inc.

p. 21

Washington stands to gain from environmental tax reform and these Pigouvian adjustments. Some of the 

greatest benefits come from vital drivers of the state’s economy in the classically Seattle- or Redmondbased sectors like technology, research, professional services, distribution, and healthcare, which see little 

increase in their costs yet could see a net reduction in their net tax burdens.

Each industry has a different profile of energy consumption, B&O taxes paid, and other inputs. Some of 

the “losers” in this scenario include heavy manufacturers such as primary metals, transportation 

equipment,

24 chemical manufacture,25 and from petroleum and coal products.26 Of these, only 

petroleum and coal products face sizable impacts—to the tune of about $1.9 billion less in output, each 

year, by 2035. This industry is the producer for petroleum products (such as gasoline and diesel) and coal 

for electricity generation. Assuming carbon emissions create externalities and a Pigouvian tax is the best 

way to adjust for them, the ensuing higher prices for energy reduces the demand for these goods. Lower 

demand will bring a reduction in output, which lowers the anticipated production for NAICS 324 in the 

future after the start of this scenario. On the other hand, petroleum and its supply-chain play an

important role in the United States’ economy. Washington still has a significant amount of output tied to 

it (even if refinery output in Washington is approximately 8% of that on the Gulf Coast). In 2012, for one 

instance, petroleum and coal products had an output in Washington of over $21 billion. By 2035, a 

reduction of $1.5 billion/year is a 4.5% decline (against baseline) in PI+. This industry does have losses

compared to the baseline, but the losses are proportionally small. Many other sectors in manufacturing, 

like computers and electronics and even leather, in fact see a slight increase, which means some of 

the industries in the region could benefit back from these policies.

Most of the industries have a mixed or positive effect. Some of the mixed ones are in the supply-chain for 

petroleum refining, including the aforementioned chemicals (feedstock) and primary metals (steel and 

equipment, particularly pipes). Resource extraction sees some slight losses, including those in forestry 

and fishing, utilities,

27 and mining. These industries concentrate on the state’s perimeter, while the 

technology and service clusters in Seattle and King County stand to gain the most to drive the economy 

forward. Gains come from wholesale and retail, food services, and drinking establishments, 

telecommunications, banking or other finance, healthcare, and the assortment of professional 

services.

28 The main factor here is these industries do not require a lot of power or fuel. Comparatively, 

they use little energy to stack pallets, cook food, or run ordinary office equipment. This means they pay 

very little in the direct carbon tax, but the reduced B&O tax rates create a strong response in terms of 

added output and job creation. The lower sales tax and rebates to low-income households engender more 

real purchasing power for consumers, and their direct spending goings to stores, restaurants, and other 

entertainment sectors. Construction is a broad industry with ties to every part of the economy. The 

growth in the economy requires commercial space, industrial footage, and housing for additional workers 

as well as their families. The construction industry will supply all of this over time.

 

24 NAICS 3364 through 3369, which includes the production of aircraft by Washington stalwarts like 

Boeing, as well as the production of railroad equipment, ships, and watercraft, 

<http://www.census.gov/cgi-bin/sssd/naics/naicsrch?code=3364&search=2012%20NAICS%20Search>

25 NAICS 325, which includes the production of all organic or inorganic materials for the formulation of 

non-fuel products, <http://www.census.gov/cgibin/sssd/naics/naicsrch?code=325&search=2012%20NAICS%20Search>

26 NAICS 324, which is primarily the refining of raw petroleum into sellable products, 

<http://www.census.gov/cgi-bin/sssd/naics/naicsrch?code=324&search=2012%20NAICS%20Search>

27 NAICS 22, including NAICS 2211 for power generation, <http://www.census.gov/cgibin/sssd/naics/naicsrch?code=2211&search=2012%20NAICS%20Search>

28 NAICS 54, a collection of white-collar professional trades, including legal services, accounting, 

consulting, architectural and engineering, design, advertising, and software development, 

<http://www.census.gov/cgi-bin/sssd/naics/naicsrch?code=54&search=2012%20NAICS%20Search>
Regional Economic Models, Inc.

p. 22

FIGURE 2.9 – EMPLOYMENT BY INDUSTRY (WASHINGTON, STATEWIDE, $100/TON)

NAICS Industries 2015 2020 2025 2030 2035

Forestry and logging; Fishing, hunting, and trapping -4 -34 -60 -72 -76

Agriculture and forestry support activities 0 -11 -20 -21 -18

Oil and gas extraction -4 -24 -38 -43 -38

Mining (except oil and gas) 0 6 16 29 41

Support activities for mining 0 -1 -2 -1 -1

Utilities -21 -56 -74 -79 -70

Construction 119 1,123 2,567 4,122 5,279

Wood product manufacturing 4 20 44 71 92

Nonmetallic mineral product manufacturing 7 35 68 101 122

Primary metal manufacturing 0 -3 -3 -2 1

Fabricated metal product manufacturing 10 46 82 119 144

Machinery manufacturing 3 13 22 30 34

Computer and electronic product manufacturing 11 36 53 67 70

Electrical equipment and appliance manufacturing 2 5 7 7 5

Motor vehicles, bodies and trailers, and parts manufacturing 6 20 29 36 36

Other transportation equipment manufacturing 5 19 30 41 46

Furniture and related product manufacturing 9 30 47 59 62

Miscellaneous manufacturing 9 26 32 36 38

Food manufacturing 5 22 43 64 80

Beverage and tobacco product manufacturing 3 12 21 29 34

Textile mills; Textile product mills 1 3 5 6 7

Apparel manufacturing; Leather and allied product manufacturing 5 17 22 29 30

Paper manufacturing 2 13 25 34 38

Printing and related support activities 4 14 23 28 30

Petroleum and coal products manufacturing -6 -30 -43 -47 -44

Chemical manufacturing 1 -2 -2 0 2

Plastics and rubber product manufacturing 7 28 51 72 84

Wholesale trade 116 488 883 1,277 1,527

Retail trade 500 2,176 3,857 5,437 6,358

Air transportation 0 -6 -14 -21 -27

Rail transportation 0 1 1 3 4

Water transportation 0 1 4 8 13

Truck transportation 12 53 102 160 207

Couriers and messengers 3 14 26 40 53

Transit and ground passenger transportation 4 17 34 54 71

Pipeline transportation 0 0 0 0 0

Scenic and sightseeing transportation; Support activities for transportation 0 1 -2 -6 -15

Warehousing and storage 22 192 374 531 615

Publishing industries, except Internet 11 31 44 56 60

Motion picture and sound recording industries 3 11 17 22 25

Internet publishing and broadcasting; ISPs, search portals, and data 4 17 26 32 34

Broadcasting, except Internet 2 7 12 17 20

Telecommunications 21 72 120 163 186

Monetary authorities - central bank; Credit intermediation and related 42 138 218 284 310

Securities, commodity contracts, investments 74 287 457 573 598

Insurance carriers and related activities 12 48 83 112 127

Real estate 39 168 377 652 862

Rental and leasing services; Leasers of nonfinancial intangible assets 10 34 56 78 90

Professional, scientific, and technical services 142 672 1,232 1,806 2,195

Management of companies and enterprises 11 53 82 100 98

Administrative and support services 128 520 943 1,384 1,691

Waste management and remediation services 3 16 34 57 77

Educational services 41 228 476 745 957

Ambulatory health care services 317 1,167 2,000 2,844 3,419

Hospitals 63 342 685 1,064 1,375

Nursing and residential care facilities 35 188 402 664 910

Social assistance 22 124 266 433 578

Performing arts and spectator sports 36 150 271 394 480

Museums, historical sites, zoos, and parks 2 11 22 34 42

Amusement, gambling, and recreation 39 166 317 476 596

Accommodation 32 97 170 258 325

Food services and drinking places 335 1,370 2,485 3,589 4,306

Repair and maintenance 55 212 383 561 682

Personal and laundry services 175 585 929 1,216 1,343

Membership associations and organizations 33 157 318 498 637

Private households 191 611 977 1,275 1,390

TOTAL OF ALL INDUSTRIES = 2,709 11,745 21,611 31,584 38,248
Regional Economic Models, Inc.

p. 23

FIGURE 2.10 – EMPLOYMENT BY INDUSTRY (KING COUNTY, STATEWIDE, $100/TON)

NAICS Industries 2015 2020 2025 2030 2035

Forestry and logging; Fishing, hunting, and trapping 0 -4 -7 -8 -8

Agriculture and forestry support activities 0 -2 -5 -5 -5

Oil and gas extraction 0 0 0 0 0

Mining (except oil and gas) 0 4 10 18 26

Support activities for mining 0 0 0 0 0

Utilities -5 -14 -17 -16 -13

Construction 32 312 732 1,170 1,477

Wood product manufacturing 0 2 4 6 7

Nonmetallic mineral product manufacturing 3 11 19 26 29

Primary metal manufacturing 0 -1 -1 -1 -1

Fabricated metal product manufacturing 4 16 27 38 44

Machinery manufacturing 2 6 10 13 14

Computer and electronic product manufacturing 6 17 25 30 31

Electrical equipment and appliance manufacturing 1 2 2 2 2

Motor vehicles, bodies and trailers, and parts manufacturing 4 13 20 23 23

Other transportation equipment manufacturing 3 9 13 18 19

Furniture and related product manufacturing 3 9 14 18 20

Miscellaneous manufacturing 6 18 21 24 25

Food manufacturing 2 9 17 25 31

Beverage and tobacco product manufacturing 1 6 10 14 16

Textile mills; Textile product mills 0 1 2 2 2

Apparel manufacturing; Leather and allied product manufacturing 3 12 15 19 20

Paper manufacturing 0 1 3 4 4

Printing and related support activities 2 6 10 12 13

Petroleum and coal products manufacturing 0 0 -1 -1 -1

Chemical manufacturing 0 0 -1 -1 -1

Plastics and rubber product manufacturing 2 7 11 15 17

Wholesale trade 51 212 381 550 657

Retail trade 116 516 933 1,334 1,565

Air transportation 0 -5 -13 -20 -26

Rail transportation 0 0 0 1 1

Water transportation 0 1 4 7 11

Truck transportation 4 19 36 55 70

Couriers and messengers 2 8 14 22 28

Transit and ground passenger transportation 2 9 17 27 35

Pipeline transportation 0 0 0 0 0

Scenic and sightseeing transportation; Support activities for transportation 0 0 -2 -5 -10

Warehousing and storage 8 71 137 195 227

Publishing industries, except Internet 10 27 40 51 54

Motion picture and sound recording industries 2 6 9 12 14

Internet publishing and broadcasting; ISPs, search portals, and data 3 11 18 22 22

Broadcasting, except Internet 1 4 7 10 12

Telecommunications 10 32 51 68 77

Monetary authorities - central bank; Credit intermediation and related 14 43 65 83 88

Securities, commodity contracts, investments 51 190 298 373 389

Insurance carriers and related activities 7 26 43 58 65

Real estate 16 90 205 344 452

Rental and leasing services; Leasers of nonfinancial intangible assets 2 7 12 15 17

Professional, scientific, and technical services 80 340 612 897 1,090

Management of companies and enterprises 8 34 51 62 60

Administrative and support services 47 179 326 490 616

Waste management and remediation services 1 4 8 14 18

Educational services 21 114 234 360 454

Ambulatory health care services 112 400 672 937 1,098

Hospitals 25 122 236 358 450

Nursing and residential care facilities 12 56 115 184 243

Social assistance 11 59 124 199 262

Performing arts and spectator sports 24 99 179 260 318

Museums, historical sites, zoos, and parks 1 6 11 17 22

Amusement, gambling, and recreation 10 39 72 106 131

Accommodation 17 53 91 136 169

Food services and drinking places 122 481 851 1,209 1,426

Repair and maintenance 17 63 111 159 189

Personal and laundry services 76 251 394 513 564

Membership associations and organizations 13 57 111 169 211

Private households 64 202 320 416 452

TOTAL OF ALL INDUSTRIES = 1,025 4,264 7,709 11,135 13,316
Regional Economic Models, Inc.

p. 24

Labor productivity is the concept for understanding the above table and its relationship to the earlier ones 

on output by industry. Labor productivity relates the amount of production by a firm to the number of 

workers it employs. For example, if a production team of 120 at a factory (including machinists, software 

technicians, and managers) can produce $36 million of batteries in a year, then output per unit of labor 

(one of the 120 workers) is $300,000/year—mathematically $36 million over 120. This concept is present 

throughout the whole economy, and different workers in different industries tend to have higher or lower 

levels of productivity because of the nature of their business or trades, technology, and the amount of 

capital used in their production process. The example of batteries above is an instance of a high-level of 

labor productivity, given the degree of automation and the utilization of machines in most assembly lines. 

Conversely, a “labor-intensive” industry such as retail, construction, or healthcare, uses relatively more 

labor to attain its needed quantities of output. Those industries need more labor to perform tasks, which 

makes their labor productivity somewhat less than the highly productive $300,000/worker for batteries. 

The general equation to relate these concepts together is the following:

Output = Labor * Labor Productivity

In terms of jobs, not output, one of the benefits seen in the carbon tax simulations is growth in total level 

of employment in labor-intensive service sectors. In the same instance, the output losses in some forms of 

heavy manufacturing and resource extraction are not particularly damaging to the total amount of 

employment, given their productivity. To give one example, in 2012, petroleum refining and coal 

products employed one worker for each $8.7 million of output, while food service employed one for

each $63,000 in output in Washington. Hence, the large output losses in manufacturing industries do not 

have a strong effect on employment, while the gains in the services and retail sectors (due to lower B&O 

taxes and a bump in consumer spending from lower sales taxes) mean a proportionally higher number of 

net adds to employment. Some manufacturing sectors add jobs, as well, including computers and 

electronic equipment. Gains are modest, but a lower cost curve and substitution away from the more 

expensive fuels and towards labor might lead to increased employment. 

Out of the service sector, there are two patterns: employment growth in sectors tied directly to consumer 

spending and growth in those competing nationally or internationally for market share. The largest ones 

in the former are retail, wholesale, and food service, which primarily serve households. These industries 

experience a surge in demand due to higher disposable incomes. Other focus sectors are some of the 

centerpieces of the Washington economy. These are banking and finance, professional services, 

management and administration, and healthcare services. These sectors are “services” and on 

the labor-intensive side. Nevertheless, they compete on a global and international scale with similar firms 

for business. An engineering consultancy in Seattle will fight for contracts with similar companies in 

Oakland, Los Angeles, Raleigh, and all the way to Sydney, Tokyo, and Hong Kong. Their low carbon-usage 

and the low B&O tax shifts their cost profiles and allows them to compete efficiently on these markets, 

grab business, expand the state’s GDP, and hire additional workers. This is true of the “export-oriented” 

industries above with ties to international markets and business-to-business services and transactions, as 

opposed to the level of direct consumer spending. Washington houses eight Fortune 500 companies’ 

headquarters: Costco, Microsoft, Amazon, Paccar, Starbucks, Nordstrom, Weyerhaeuser, and Expeditors 

International of Washington.29 The strength of King County and the region in retail, logistics, software, 

and resources is self-evident from this list, and the economic impacts on employment by industry from 

environmental tax reform are in keeping with the same. Impacts on occupations in King County and the 

rest of the state are similar. There is a concentration in services and technology driving most of the 

implications for firms’ labor demand in the state.

 

29 “Fortune 500, States, Washington,” CNN, 2012, 

<http://money.cnn.com/magazines/fortune/fortune500/2012/states/WA.html>
Regional Economic Models, Inc.

p. 25

FIGURE 2.11 – EMPLOYMENT BY OCCUPATION (WASHINGTON, STATEWIDE, $100/TON)

SOC Occupations 2015 2020 2025 2030 2035

Top executives 39 169 307 442 523

Advertising, marketing, promotions, public relations, and sales managers 11 48 86 123 147

Operations specialties managers 23 98 174 249 296

Other management occupations 33 156 306 470 587

Business operations specialists 55 261 495 741 916

Financial specialists 51 213 373 521 603

Computer occupations 54 239 432 629 760

Mathematical science occupations 1 6 10 15 18

Architects, surveyors, and cartographers 3 14 26 38 47

Engineers 15 71 132 197 242

Drafters, engineering technicians, and mapping technicians 8 36 66 96 114

Life scientists 3 12 23 35 44

Physical scientists 2 11 20 31 38

Social scientists and related workers 3 14 25 37 45

Life, physical, and social science technicians 3 12 23 35 43

Counselors and Social workers 18 79 150 228 289

Miscellaneous community and social service specialists 9 40 77 119 153

Religious workers 1 2 5 7 9

Lawyers, judges, and related workers 9 39 69 96 111

Legal support workers 5 23 42 60 72

Postsecondary teachers 21 99 196 303 382

Preschool, primary, secondary, and special education school teachers 41 169 317 479 588

Other teachers and instructors 11 47 92 139 174

Librarians, curators, and archivists 3 13 25 37 44

Other education, training, and library occupations 15 66 126 191 236

Art and design workers 10 41 73 103 122

Entertainers and performers, sports and related workers 13 57 106 158 196

Media and communication workers 10 43 80 118 146

Media and communication equipment workers 3 13 22 31 37

Health diagnosing and treating practitioners 114 479 877 1,299 1,613

Health technologists and technicians 76 312 565 828 1,016

Other healthcare practitioners and technical occupations 2 9 18 26 33

Nursing, psychiatric, and home health aides 53 247 482 753 987

Occupational therapy and physical therapist assistants and aides 5 23 41 61 76

Other healthcare support occupations 55 201 343 482 571

Supervisors of protective service workers 3 10 19 28 33

Fire fighting and prevention workers 3 12 21 32 38

Law enforcement workers 12 44 80 118 142

Other protective service workers 26 110 203 301 370

Supervisors of food preparation and serving workers 27 111 201 291 349

Cooks and food preparation workers 85 347 631 915 1,101

Food and beverage serving workers 202 834 1,524 2,218 2,682

Other food preparation and serving related workers 40 160 288 413 493

Supervisors of building and grounds cleaning and maintenance workers 4 17 32 48 59

Building cleaning and pest control workers 133 453 762 1,048 1,204

Grounds maintenance workers 24 99 183 273 336

Supervisors of personal care and service workers 6 20 34 49 58

Animal care and service workers 11 43 75 106 126

Entertainment attendants and related workers 14 59 108 161 199

Funeral service workers 9 31 50 66 73

Personal appearance workers 62 223 366 494 562

Baggage porters, bellhops, and concierges; Tour and travel guides 2 7 13 20 25

Other personal care and service workers 122 454 796 1,137 1,362

Supervisors of sales workers 45 193 344 489 576

Retail sales workers 279 1,196 2,124 3,007 3,532

Sales representatives, services 34 136 231 315 359

Sales representatives, wholesale and manufacturing 35 155 285 417 504

Other sales and related workers 18 80 157 243 305

Supervisors of office and administrative support workers 30 125 224 322 385

Communications equipment operators 3 9 15 18 18

Financial clerks 71 293 529 764 914

Information and record clerks 115 454 798 1,137 1,349

Material recording, scheduling, dispatching, and distributing workers 73 316 557 776 894

Secretaries and administrative assistants 83 348 635 924 1,112

Other office and administrative support workers 75 320 587 858 1,035

Supervisors of farming, fishing, and forestry workers 0 -1 -2 -2 -2

Agricultural workers 2 4 8 14 20

Fishing and hunting workers -1 -5 -8 -9 -10

Forest, conservation, and logging workers -1 -11 -20 -24 -25
Regional Economic Models, Inc.

p. 26

Supervisors of construction and extraction workers 8 74 169 271 347

Construction trades workers 72 623 1,408 2,253 2,878

Helpers, construction trades 5 51 118 192 249

Other construction and related workers 5 27 55 86 107

Extraction workers 0 0 4 11 18

Supervisors of installation, maintenance, and repair workers 8 37 73 111 137

Electrical and electronic equipment mechanics, installers, and repairers 11 49 92 136 164

Vehicle and mobile equipment mechanics, installers, and repairers 41 175 320 467 566

Other installation, maintenance, and repair occupations 42 220 455 717 912

Supervisors of production workers 7 26 45 64 76

Assemblers and fabricators 19 77 133 187 219

Food processing workers 10 45 80 114 134

Metal workers and plastic workers 15 66 120 176 213

Printing workers 3 11 18 24 26

Textile, apparel, and furnishings workers 27 89 136 176 191

Woodworkers 4 17 31 45 54

Plant and system operators -3 -9 -11 -7 -1

Other production occupations 29 120 213 304 360

Supervisors of transportation and material moving workers 7 36 67 98 119

Air transportation workers 0 -1 -3 -5 -7

Motor vehicle operators 58 259 484 718 879

Rail transportation workers 0 1 1 2 3

Water transportation workers 1 3 6 9 12

Other transportation workers 15 52 86 117 133

Material moving workers 81 387 717 1,039 1,241

TOTAL OF ALL OCCUPATIONS = 2,892 12,443 22,871 33,447 40,486

FIGURE 2.12 – EMPLOYMENT BY OCCUPATION (KING COUNTY, STATEWIDE, $100/TON)

SOC Occupations 2015 2020 2025 2030 2035

Top executives 16 65 116 164 191

Advertising, marketing, promotions, public relations, and sales managers 5 21 36 51 60

Operations specialties managers 11 45 78 109 127

Other management occupations 13 60 117 176 216

Business operations specialists 26 112 206 303 369

Financial specialists 28 110 188 257 292

Computer occupations 29 117 207 298 357

Mathematical science occupations 1 3 5 7 8

Architects, surveyors, and cartographers 1 7 12 18 22

Engineers 8 35 63 92 110

Drafters, engineering technicians, and mapping technicians 4 17 30 42 50

Life scientists 1 6 10 15 19

Physical scientists 1 6 10 15 18

Social scientists and related workers 1 5 9 14 17

Life, physical, and social science technicians 1 6 11 16 19

Counselors and Social workers 7 28 53 79 98

Miscellaneous community and social service specialists 3 14 27 42 52

Religious workers 0 1 2 3 3

Lawyers, judges, and related workers 5 19 32 45 51

Legal support workers 3 11 20 29 34

Postsecondary teachers 9 42 82 125 155

Preschool, primary, secondary, and special education school teachers 14 57 105 155 186

Other teachers and instructors 4 19 36 54 66

Librarians, curators, and archivists 1 5 9 14 16

Other education, training, and library occupations 6 24 45 67 82

Art and design workers 5 18 32 46 54

Entertainers and performers, sports and related workers 7 31 57 84 103

Media and communication workers 6 22 39 56 67

Media and communication equipment workers 2 7 11 16 19

Health diagnosing and treating practitioners 41 163 292 424 515

Health technologists and technicians 26 103 182 263 317

Other healthcare practitioners and technical occupations 1 4 6 9 11

Nursing, psychiatric, and home health aides 19 83 157 240 305

Occupational therapy and physical therapist assistants and aides 2 8 14 20 24

Other healthcare support occupations 20 71 118 163 189

Supervisors of protective service workers 1 3 6 9 10

Fire fighting and prevention workers 1 4 6 9 10

Law enforcement workers 4 13 23 33 39

Other protective service workers 10 40 74 111 138

Supervisors of food preparation and serving workers 10 38 68 97 115

Cooks and food preparation workers 30 119 212 303 359

Food and beverage serving workers 73 291 519 744 886
Regional Economic Models, Inc.

p. 27

Other food preparation and serving related workers 15 57 100 141 165

Supervisors of building and grounds cleaning and maintenance workers 2 6 12 18 22

Building cleaning and pest control workers 47 160 269 370 425

Grounds maintenance workers 9 35 64 96 119

Supervisors of personal care and service workers 2 8 13 18 21

Animal care and service workers 5 19 32 46 54

Entertainment attendants and related workers 6 24 43 64 78

Funeral service workers 4 13 21 28 31

Personal appearance workers 27 95 154 207 235

Baggage porters, bellhops, and concierges; Tour and travel guides 1 3 6 9 11

Other personal care and service workers 42 156 271 385 457

Supervisors of sales workers 12 52 93 133 157

Retail sales workers 72 308 555 794 934

Sales representatives, services 19 71 118 157 175

Sales representatives, wholesale and manufacturing 15 65 117 170 205

Other sales and related workers 7 34 67 104 131

Supervisors of office and administrative support workers 12 46 81 116 137

Communications equipment operators 1 3 5 6 6

Financial clerks 27 109 193 275 326

Information and record clerks 47 179 307 432 505

Material recording, scheduling, dispatching, and distributing workers 22 95 167 234 270

Secretaries and administrative assistants 35 140 250 358 423

Other office and administrative support workers 31 125 225 324 385

Supervisors of farming, fishing, and forestry workers 0 0 0 0 0

Agricultural workers 1 2 4 7 9

Fishing and hunting workers 0 0 -1 -1 -1

Forest, conservation, and logging workers 0 -1 -2 -2 -2

Supervisors of construction and extraction workers 2 21 49 78 99

Construction trades workers 20 177 408 649 817

Helpers, construction trades 1 14 34 55 70

Other construction and related workers 2 8 17 26 32

Extraction workers 0 2 4 7 10

Supervisors of installation, maintenance, and repair workers 3 12 24 36 44

Electrical and electronic equipment mechanics, installers, and repairers 4 17 32 46 55

Vehicle and mobile equipment mechanics, installers, and repairers 12 50 91 132 158

Other installation, maintenance, and repair occupations 16 81 165 256 321

Supervisors of production workers 3 11 18 24 27

Assemblers and fabricators 8 31 51 70 80

Food processing workers 3 13 23 33 39

Metal workers and plastic workers 6 25 43 60 70

Printing workers 1 5 8 10 11

Textile, apparel, and furnishings workers 12 39 58 73 78

Woodworkers 1 4 7 10 11

Plant and system operators 0 0 1 3 4

Other production occupations 12 44 75 104 121

Supervisors of transportation and material moving workers 3 13 24 35 42

Air transportation workers 0 -1 -4 -7 -8

Motor vehicle operators 22 93 172 252 305

Rail transportation workers 0 0 0 1 1

Water transportation workers 0 1 3 6 8

Other transportation workers 6 20 32 43 48

Material moving workers 28 132 242 350 416

TOTAL OF ALL OCCUPATIONS = 1,082 4,469 8,066 11,649 13,915

Occupational impacts are consistent with those to industries, which follow from industries hiring based 

on their level of demand, competitiveness, and output in PI+. Notably, few occupational categories see a 

negative impact. While petroleum and coal or utilities may not need quite as many engineers as they 

did before, those same educational backgrounds and skill sets would have applications elsewhere, such as

within fabricated metals, electronics, and automobiles. These are relatively small changes, so a 

mechanism for the “transfer” of labor from one industry to another is a change in the rate of new hires. 

For instance, environmental tax reform might make engineering majors at the state schools more likely to 

find a job in a different industry than before but not necessarily a new, different occupation. This means 

employment by industry may have negative impacts while employment by occupations remains neutral or 

goes positive relative to the baseline. The numbers above show this effect occurring in high-skilled 

professional and skilled trades. Those jobs will go on to support a large quantity of core spending and 

other jobs tied to retail and personal services.
Regional Economic Models, Inc.

p. 28

PERSONAL CONSUMPTION EXPENDITURE (PCE)-PRICE INDEX

Figure 2.13 – This shows the change in the PCE-Price Index in PI+ under the tax scenarios. The results 

for King County and Washington are similar, so this only includes statewide figures. The 

PCE is REMI’s version of the consumer price index (CPI),30 which is a weighted basket of goods used to 

study inflation, purchasing power, and the real value of nominal wages over time. For example, $1 in 

1987 would need to be $2 in 2013 to have the same real value when accounting for the CPI. This tax 

reform increases the cost of living in Washington to only a small degree. The above is a percent 

adjustment against a baseline—that is, inflation would be slightly higher from 2015 to 2025 and then 

steady against underlying inflation rate. This is not a compounding factor but rather a change against 

the baseline. This makes it something of a “one-time” adjustment of less than 1% over the course of a 

decade. The calculation of the PCE accounts for seventy-five consumption categories, local tax rates, and 

costs passed onto consumers by businesses. Hence, the results above are the net of higher energy prices 

from Pigouvian taxes, lower sales taxes rates, and whatever firms try to pass on to customers.

PCE-PRICE INDEX BY INCOME QUINTILE

Quintile 2015 2020 2025 2030 2035

Lowest 20% +0.06% +0.18% +0.25% +0.30% +0.33%

Low-Middle 20% +0.06% +0.18% +0.25% +0.29% +0.33%

Middle 20% +0.06% +0.17% +0.24% +0.29% +0.32%

High-Middle 20% +0.06% +0.17% +0.23% +0.28% +0.31%

Highest 20% +0.06% +0.17% +0.23% +0.27% +0.31%

Figure 2.14 – These results are for the $100/ton scenario. One concern with carbon taxes or a cap-andtrade is its potential to be disproportionately harmful to the welfare of low-income families. Fuel for 

transportation and heating can make up a large share of low-income purchases than it does for higherincome groups. The “first dollar” in any home goes towards necessities, such as housing, food, gasoline, 

clothing, or heating. When incomes rise, more “last dollars” come about, and consumers allocate them to 

luxury goods like extra vehicles, entertainment, or electronics. If a family has only “first dollars” then a 

carbon tax on fuel and energy might hurt those most. Conversely, this chart shows this is not the case. 

The lower sales tax helps make up the difference through other means. Higher-income earners also use 

more energy with larger homes and extra vehicles, which keeps the proportions here even.

 

30 “Consumer Price Index,” Bureau of Labor Statistics, <http://www.bls.gov/cpi/>

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

Percent change from baseline

Washington, Statewide Reform

$10 tax

$30 tax

$50 tax

$100 tax
Regional Economic Models, Inc.

p. 29

CHANGES IN ENERGY PRICES FROM BASELINE

The effect of the carbon tax is the strongest within four PI+ consumption categories: natural gas, motor 

vehicle fuels (including gasoline and diesel), petroleum-based distillates and fuel oils, and electricity. The 

cost impact will depend on three factors, including carbon-intensity, underlying consumption rates, and 

the level of carbon tax or cap-and-trade auction. For the same number of BTUs,31 natural gas tends to 

emit a smaller amount of carbon than petroleum. This makes gas relatively more immune from large

changes in retail prices. However, the Pacific Northwest already utilizes a significant quantity of natural 

gas for heating and cooking. This give-and-take and the level of tax produce a cost impact. Just as with the 

PCE, these are “one-time” adjustments against a moving baseline. They do not imply higher fuel prices in 

the future—fossil energy prices in the United States have fallen in the aftermath of unconventional shale, 

and they may continue to do so in the future. These “increased prices” below may come in a world of 

falling or, at least historically low, energy prices.

Figure 2.15 – This shows the cost difference to energy categories from implementing a carbon tax in 

Washington. While a higher cost, this is a “feature” and not a “bug” of policy. The higher costs generate 

revenue for other tax cuts and discourage the use of these fuels, which reduces the overall emissions level 

for the state. To emphasize, these changes are against a preexisting baseline, and one that might 

already include already falling or lower prices (depending on another series of assumptions).

 

31 A unit of heat defined as the amount of thermal energy needed to raise the temperature of one pound of 

water by one degree Fahrenheit, or approximately 1,055 joules or 252 calories, 

<http://www.britannica.com/EBchecked/topic/80372/British-thermal-unit-BTU>

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p. 30

The next section discusses the impact of 

this tax reform on personal income 

when accounting for the cost of living. 

“Real disposable income” depends on a 

number of factors, and the balance to 

the right lays them out. The higher 

number of jobs in the state increases 

aggregate wages, and, after dividing by 

the changes in the price index due to 

higher energy costs but low taxes, the 

result is “real income.” REMI PI+

includes a methodology for calculating 

these by quintiles. It relies on the 

changes in the mixture of jobs from one 

of the industries to the next and, given 

the fact some industries pay better than

the median and some pay worse, how 

the whole distribution of wages would 

change between the groups during an 

economic simulation.

32

 

32 “Decomposing Policy Effects on Employment, Wages, and Prices by Income Groups,” REMI, 

<http://www.remi.com/download/documentation/pi+/pi+_version_1.3/Income_Distribution.pdf>
Regional Economic Models, Inc.

p. 31

ADDITIONAL REAL DISPOSABLE PERSONAL INCOME

Figure 2.16 – The impact to real income is positive in all the scenarios. Higher energy prices and the 

elevated PCE would drag real incomes down on their own. On the other hand, boosted economic 

performance overall, additional employment, and higher nominal wages to go with it means real 

incomes in the state increase on net. The surge in income generates other responses in the model, such as 

net migration into the state, which would make these results muted in per capita terms compared to the 

aggregate concepts reported here for total quantities of income earned in the area.

Figure 2.17 – King County has a smaller impact to incomes than the rest the state, and one much smaller 

than a strict proportion of GDP or population. King County is about half of Washington’s GDP, but the 

above is between 10% and 20% of the impact to income. This is due to income mobility through 

commuting in PI+ and its modeling of the same. Like many urban areas, King County experiences a net 

outflow of people in the evening, working at King County jobs but living elsewhere for lifestyle reasons 

or for a lower cost of living. This takes their paychecks with them. In 2012, PI+ estimated this outflow as 

$20 billion/year compared to $5 billion/year inflow for a net loss of -$15 billion/year. The pattern 

continues in the results above where income flow leaks out of King County.

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Regional Economic Models, Inc.

p. 32

INCOME BY QUINTILE

Figure 2.18 – The above figure is for the $100/ton case. This tax reform would not have a tremendous 

impact on income distribution from this data. Households in the lowest 20% of income actually see close 

to the most benefit when adjusting for the Working Family rebate—the amount equal to 10% of total 

carbon tax revenue. Without this, the impact on their nominal income would be close to 0%, and the 

higher PCE-Price Index would mean their real incomes are lower. This adjustment brings the impact to 

their incomes in line with the middle 60% and keeps the overall distributional impact neutral.

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Washington, Statewide Reform

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Regional Economic Models, Inc.

p. 33

ADDITIONAL POPULATION

Figure 2.19 – PI+ includes a demographic response to tax reform, as well. This is certainly the case in 

Washington, where better job opportunities, higher wages and minimal impacts to the cost of living, 

and quality of life mean a population increase of up to 70,000 over the null in the $100/ton scenario. 

Note, this only accounts for improvements in labor market conditions—it makes no 

assumptions about directly increased quality of life from introduction carbon pricing, 

such as lowered qualifying air pollutants in King County and Washington.

Figure 2.20 – King County has a similar population response, though the bounce here is not as large as 

in the rest of the state. King County has high land and costs compared to many of the areas in the 

remainder of Washington—this makes people moving into the state likely to behave as those in the rest 

of it in living elsewhere and enjoying the strong job market in Seattle via commuting. There is still a 

positive demographic shift in Seattle and its suburbs from the carbon tax policy, where the higher job 

opportunity draws people in from other parts of the state and the United States to make a living. This 

effect continues despite the higher PCE given the raw number of jobs available.

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Regional Economic Models, Inc.

p. 34

CARBON TAX REVENUES (ANNUAL)

Figure 2.21 – These results are from CTAM, not PI+. They cover the eventual revenues gained from the 

carbon tax at varying levels. This is how much money recycles into the state economy with lower B&O 

taxes, sales taxes, and the rebate to low-income households. Introducing the tax generates progressively 

more revenue until it hits its peak; this causes a fall in carbon emissions. However, the NEMS baseline 

forecasts an increase in energy demand in Washington and the Pacific region in the future, which 

means the total amount of revenue “balances” against this demand growth. In essence, revenues are 

steady between the “push” of growing fuel demand and the “pull” of the price response away from 

emitting due to the carbon tax. This holds emissions down but keeps revenues up for the state.

CARBON TAX REVENUES (CUMULATIVE)

Figure 2.22 – This graph displays the total amount of cumulative revenue from the carbon tax over 

time. It is the same annual information above presented in a different manner. For context, 

Washington’s GDP in 2012 was about $375 billion. The revenue here could easily be over 0.5% of GDP in 

a given year and replace much of the taxes covering other portions of Washington’s state budget.

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Regional Economic Models, Inc.

p. 35

CARBON DIOXIDE EMISSIONS (ANNUAL FORECAST)

Figure 2.23 – This shows the baseline forecast for emissions from the EIA Extended Outlook forecast 

and the impact of environmental tax reform on statewide emissions. Each higher level of tax drives 

emissions lower with higher prices; for instance, a $50/ton carbon tax would mean about 61 million 

metric tons emitted in 2035 instead of the baseline of approximately 68 million metric tons.

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Washington, Statewide Reform

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Regional Economic Models, Inc.

p. 36

CARBON DIOXIDE EMISSIONS REDUCTIONS (ANNUAL DIFFERENCE)

Figure 2.24 – This figure makes the previous baseline of no tax into the x-axis and compares the 

difference in emissions saved between the four cases. Washington responds to the carbon tax; the tax 

induces enough of a behavioral change in the $100/ton scenario to reduce emissions by approximately 

14 million metric tons per year by 2035. According to the Environmental Protection Agency (EPA), in 

2011, the state of Delaware emitted 13.11 million metric tons of carbon dioxide and equivalent from fuel 

and power generation. 33 The $100/ton case represents a decline of the same amount in 2035.

CARBON DIOXIDE EMISSIONS (CUMULATIVE DIFFERENCE)

Figure 2.25 – This is the long-term emissions savings, cumulative from the baseline, from the figure 

above. For context, 140 million metric tons of carbon dioxide was about the annual carbon emissions of 

states such as Alabama, Georgia, Kentucky, and Missouri in 2011, according to the EPA.

 

33 “State Energy CO2 Emissions,” Environmental Protection Agency, 

<http://epa.gov/statelocalclimate/resources/state_energyco2inv.html>

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Regional Economic Models, Inc.

p. 37

CARBON DIOXIDE EMISSIONS (1990 BENCHMARK)

Figure 2.26 – This benchmarks Washington emissions to 1990. In 1990, Washington emitted 72.18 

million metric tons of carbon into the air.34 The use of 1990 as a benchmark is arbitrary, but it derives 

from the Kyoto Protocol, a series of international treaties between industrialized nations and developing 

ones for emissions using 1990 levels as a significant signpost for further negotiations.35 Washington is 

already below its 1990 levels, though only slightly, and a carbon pricing system would help drive 

emissions below 90% and towards 85% or 75% depending on the scenario. The state’s emissions are 

slow to fall, however, given the previous embrace of natural gas for fuel and hydroelectric power; some 

obvious efficiency is already present in the NEMS baseline for Washington. Nonetheless, the state 

experiences a reduction in emission from carbon pricing, and a $30/ton tax or auction price (or 

anything above that) prevents noteworthy growth in the level of emissions in the future.

The environmental tax reform measures above aid the state towards meeting Governor Jay Inslee’s goals 

for emission reductions. In keeping with existing state reductions criteria, Washington endeavors to equal 

1990 emissions levels by 2020 and 75% of 1990 levels by 2035.36 The state has the further initiative of 

reaching 50% of 1990 emissions by 2050, though the NEMS Extended Outlook baseline only allow for 

analysis out to 2035. According to the most recent historical data from the EPA, Washington is already 

below its Kyoto allowance for 1990 levels (emitting 72.18 million metric tons of carbon dioxide in 1990 

and 70.81 million metric tons in 2011, a reduction of 1.9%).37 The growths in CTAM and NEMS baselines

of energy demand in Washington from 2020 to 2035 keep the state from seeing much more of a “natural 

drift” for reduction in emissions. Some form of a carbon price in the state would help to rectify these

situations. The $50/ton tax scenario comes close; it meets an 85% goal of 1990 levels by 2035, but the 

doubling in the event of the maximum—the $100/ton carbon tax—does meet 75% by 2033, 2034, and 

2035. This is beyond the hypothetical range of carbon pricing in the SAIC report,38 but we included the 

$100/ton case here to show the economic impact of meeting the goal.

 

34 EPA

35 For more information, please see, “Kyoto Protocol,” United Nations Framework Convention on 

Climate Change, <http://unfccc.int/kyoto_protocol/items/2830.php>

36 <http://apps.leg.wa.gov/rcw/default.aspx?cite=70.235.020>

37 EPA

38 SAIC, pp. 4-5

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Regional Economic Models, Inc.

p. 38

COUNTY TAX REFORM

The next study area examines a hypothetical environmental tax reform built on carbon taxation for King 

County by itself. King County has an open economy, much like Washington and many of the other states 

of the union. It has a large service sector and little in terms of natural resources and the supply-chain for 

oil or natural gas. Reducing the importation of these fuels may have positive impacts for keeping “local 

dollars” local. This is especially true when combined with relief to property and sales taxes in the county. 

The simulations are quite similar to the ones for Washington statewide, though differences exist within 

the revenue recycling owing to the discontinuities between a county-level budget and the one for Olympia. 

Without a B&O tax, a reduction in property taxes—which impacts both residential real estate and 

commercial/industry square footage—serves the same role. The “help” to households comes in the form of 

a lower local sales tax. For these scenarios, 50% of tax revenues go to reducing sales taxes and the other 

50% towards local property taxes:

Figure 3.1 – This is the recycling scheme for King County’s tax reform modeled here. After a few 

iterations to test sensitivities in PI+, this provided a strong balance between maintaining local 

competitiveness, a neutral impact on the cost of living, and protecting lower-income households from 

any potentially regressive impacts or changes from these policies. PI+ includes variables for the imputed 

or actual rent costs of housing, apartments, and commercial/industrial space, which, when modified, 

adjust accordingly in response to implementing these changes on the local tax regime.

The results for King County are the same as those for the statewide tax reform, though with some minor 

simplifications for displaying information for 1-region (King County) instead of 2-regions. This includes 

the major economic indicators, demographics, distributional impacts, tax revenue, and implications for 

emissions. Altogether, King County sees a modest impact from implementing a carbon tax. It is a positive 

impact in all the same aspects as the statewide ones. The scenarios are the same with $10/ton, $30/ton, 

and $50/ton (from SAIC) carbon rates to give an overview of local sensitivities.

Revenue 

recycling

Share 

collected

Tax 

reforms

Pigouvian 

carbon tax

50%

Local sales 

tax cuts

50%

Property 

tax cuts
Regional Economic Models, Inc.

p. 39

ADDITIONAL TOTAL EMPLOYMENT

Figure 3.2 – King County experiences a net positive impact to total employment under a 

county-level environmental tax reform. Narratives are much the same. Competitiveness for local, 

labor-intensive businesses increases, and imports of out-of-county and out-of-state fuel sources 

diminishes, leaving Seattle and the surrounding area to grow. This is a general case in state and local 

taxation, where taxing things made out of the area is typically a better way to find revenue in a less 

damaging setting than drawing from local income, consumption, or business activities.

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King County, County Reform

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Regional Economic Models, Inc.

p. 40

ADDITIONAL GROSS DOMESTIC PRODUCT (ANNUAL)

Figure 3.3 – As before, carbon taxes and revenue recycling add to the total level of economic 

activity in King County compared to the baseline. The impact of King County “going alone” is 

less than a statewide scenario because of the linkages between the Seattle metro and the rest—the 

economic expansion in the whole state creates a positive impact back into its largest metropolitan area 

and vice versa. The highest impact of $600 million/year is about 0.17% of King County’s GDP.

ADDITIONAL GROSS DOMESTIC PRODUCT (CUMULATIVE)

Figure 3.4 – This is the same exercise as the total amount of additional GDP over time. King County had 

a GDP of about $175 billion in 2012. These numbers could hit over 2% of county GDP, which would 

greatly reduce other tax burdens and cover other revenue categories. King County’s most recent budget 

totaled $7.6 billion. The above dollars could significantly rework how this influences the economy—

covering up to 30% of current expenditures and much of current local receipts.

39

 

39 “2013 Budget,” King County, <http://www.kingcounty.gov/council/budget/2013_budget.aspx>

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Regional Economic Models, Inc.

p. 41

FIGURE 3.5 – ANNUAL OUTPUT BY INDUSTRY (KING COUNTY, COUNTY REFORM, $50/TON)

NAICS Industries 2015 2020 2025 2030 2035

Forestry and logging; Fishing, hunting, and trapping -$0.6 -$7.7 -$15.5 -$17.0 -$15.7

Agriculture and forestry support activities $0.0 -$0.1 -$0.2 -$0.2 -$0.2

Oil and gas extraction $0.0 $0.0 $0.0 $0.0 $0.0

Mining (except oil and gas) $0.0 -$0.5 -$1.1 -$1.4 -$1.5

Support activities for mining $0.0 -$0.1 -$0.2 -$0.3 -$0.3

Utilities -$4.0 -$12.3 -$16.0 -$15.9 -$15.6

Construction $7.7 $45.1 $75.8 $77.4 $77.3

Wood product manufacturing $0.2 $0.4 $0.3 $0.1 $0.1

Nonmetallic mineral product manufacturing $0.8 $2.3 $2.6 $2.2 $2.0

Primary metal manufacturing -$0.1 -$2.0 -$4.1 -$4.8 -$4.9

Fabricated metal product manufacturing $0.9 $2.8 $3.3 $3.1 $3.3

Machinery manufacturing $0.3 $0.8 $0.6 $0.3 $0.3

Computer and electronic product manufacturing $1.7 $3.2 $0.8 -$0.7 $0.0

Electrical equipment and appliance manufacturing $0.2 $0.1 -$0.4 -$0.8 -$1.1

Motor vehicles, bodies and trailers, and parts manufacturing $2.5 $8.7 $11.5 $11.4 $11.5

Other transportation equipment manufacturing -$0.3 -$9.3 -$21.1 -$26.1 -$27.6

Furniture and related product manufacturing $0.5 $1.3 $1.4 $1.2 $1.1

Miscellaneous manufacturing $0.9 $2.2 $1.4 $0.8 $1.0

Food manufacturing $0.3 -$1.2 -$3.2 -$3.3 -$2.6

Beverage and tobacco product manufacturing $0.9 $2.9 $4.2 $4.6 $4.7

Textile mills; Textile product mills $0.0 $0.0 -$0.2 -$0.2 -$0.2

Apparel manufacturing; Leather and allied product manufacturing $0.2 $0.7 $0.6 $0.7 $0.8

Paper manufacturing $0.1 $0.1 -$0.1 -$0.1 -$0.2

Printing and related support activities $0.2 $0.5 $0.6 $0.6 $0.7

Petroleum and coal products manufacturing -$2.1 -$12.3 -$21.3 -$23.9 -$24.9

Chemical manufacturing $0.0 -$5.1 -$11.9 -$14.5 -$15.1

Plastics and rubber product manufacturing $0.6 $1.4 $1.1 $0.6 $0.4

Wholesale trade $10.9 $38.1 $56.6 $65.8 $75.7

Retail trade $14.3 $57.9 $91.0 $107.9 $123.8

Air transportation -$0.4 -$5.3 -$11.9 -$15.2 -$16.9

Rail transportation $0.0 -$0.2 -$0.5 -$0.6 -$0.6

Water transportation $0.0 -$0.5 -$1.2 -$1.5 -$1.7

Truck transportation $0.6 $1.8 $2.4 $2.6 $3.1

Couriers and messengers $0.1 $0.2 -$0.1 -$0.2 -$0.2

Transit and ground passenger transportation $0.0 $0.0 -$0.1 $0.0 $0.2

Pipeline transportation $0.0 $0.0 -$0.1 -$0.1 -$0.1

Scenic and sightseeing transportation; Support activities for transportation -$0.1 -$1.7 -$4.3 -$6.1 -$7.4

Warehousing and storage $0.0 $0.1 $0.0 -$0.1 -$0.1

Publishing industries, except Internet $3.3 $2.2 -$5.2 -$7.6 -$4.7

Motion picture and sound recording industries $0.2 $0.6 $0.9 $0.9 $1.1

Internet publishing and broadcasting; ISPs, search portals, and data $0.6 $1.6 $2.6 $3.7 $5.0

Broadcasting, except Internet $0.3 $0.4 $0.0 -$0.3 -$0.2

Telecommunications $6.0 $15.8 $19.5 $20.6 $23.7

Monetary authorities - central bank; Credit intermediation and related $6.9 $19.8 $25.0 $25.3 $27.5

Securities, commodity contracts, investments $4.7 $15.2 $20.3 $20.4 $20.8

Insurance carriers and related activities $1.4 $4.6 $6.6 $7.5 $8.3

Real estate $56.0 $197.2 $288.9 $317.7 $344.2

Rental and leasing services; Leasers of nonfinancial intangible assets $1.8 $4.4 $4.9 $4.2 $4.0

Professional, scientific, and technical services $7.1 $14.4 $12.3 $11.1 $15.2

Management of companies and enterprises $0.5 -$2.2 -$6.7 -$9.0 -$9.4

Administrative and support services $2.4 -$0.8 -$8.2 -$12.5 -$13.4

Waste management and remediation services $0.4 $1.1 $1.6 $1.8 $2.2

Educational services $0.7 $4.3 $8.7 $11.5 $13.2

Ambulatory health care services $4.2 $11.3 $15.7 $19.8 $26.7

Hospitals $1.6 $11.3 $25.1 $35.9 $44.0

Nursing and residential care facilities $0.2 $1.9 $4.7 $7.4 $9.6

Social assistance $0.2 $1.8 $4.0 $5.8 $7.1

Performing arts and spectator sports $1.0 $3.8 $6.2 $7.6 $8.9

Museums, historical sites, zoos, and parks $0.1 $0.8 $1.5 $2.0 $2.3

Amusement, gambling, and recreation $0.4 $1.4 $2.1 $2.6 $3.1

Accommodation $1.8 $3.1 $2.3 $1.9 $2.6

Food services and drinking places $10.9 $42.2 $66.3 $76.4 $82.8

Repair and maintenance $2.3 $7.3 $10.6 $11.9 $13.1

Personal and laundry services $6.3 $19.7 $25.4 $25.0 $25.3

Membership associations and organizations $0.9 $5.2 $10.2 $13.5 $15.5

Private households $0.6 $1.9 $2.5 $2.6 $2.6

TOTAL OF ALL INDUSTRIES = $159.4 $501.9 $688.6 $754.3 $850.1
Regional Economic Models, Inc.

p. 42

FIGURE 3.6 – EMPLOYMENT BY INDUSTRY (KING COUNTY, COUNTY REFORM, $50/TON)

NAICS Industries 2015 2020 2025 2030 2035

Forestry and logging; Fishing, hunting, and trapping 0 -5 -8 -7 -5

Agriculture and forestry support activities 0 -5 -9 -8 -5

Oil and gas extraction 0 0 0 0 0

Mining (except oil and gas) 0 3 9 14 18

Support activities for mining 0 0 0 0 0

Utilities -5 -13 -14 -12 -10

Construction 61 448 845 972 1,035

Wood product manufacturing 1 2 3 4 4

Nonmetallic mineral product manufacturing 3 10 15 16 16

Primary metal manufacturing 0 -1 -1 -1 0

Fabricated metal product manufacturing 4 13 18 20 22

Machinery manufacturing 1 4 6 6 7

Computer and electronic product manufacturing 4 9 8 8 9

Electrical equipment and appliance manufacturing 0 1 0 0 0

Motor vehicles, bodies and trailers, and parts manufacturing 4 12 14 12 11

Other transportation equipment manufacturing 0 -5 -7 -1 6

Furniture and related product manufacturing 2 7 8 7 7

Miscellaneous manufacturing 4 10 9 8 9

Food manufacturing 1 3 7 11 14

Beverage and tobacco product manufacturing 1 4 7 8 8

Textile mills; Textile product mills 0 0 1 1 1

Apparel manufacturing; Leather and allied product manufacturing 3 9 8 9 9

Paper manufacturing 0 1 2 2 2

Printing and related support activities 1 5 7 7 7

Petroleum and coal products manufacturing 0 0 -1 0 0

Chemical manufacturing 0 -1 -3 -2 -1

Plastics and rubber product manufacturing 2 5 7 8 8

Wholesale trade 45 166 249 285 313

Retail trade 154 585 854 922 952

Air transportation -1 -7 -14 -13 -11

Rail transportation 0 0 0 1 1

Water transportation 0 2 4 7 10

Truck transportation 4 15 23 29 34

Couriers and messengers 1 4 8 11 14

Transit and ground passenger transportation 1 1 3 8 13

Pipeline transportation 0 0 0 0 0

Scenic and sightseeing transportation; Support activities for transportation 0 -7 -15 -16 -14

Warehousing and storage 1 2 3 3 5

Publishing industries, except Internet 5 15 23 29 33

Motion picture and sound recording industries 1 4 5 6 6

Internet publishing and broadcasting; ISPs, search portals, and data 1 4 7 8 8

Broadcasting, except Internet 1 2 3 4 5

Telecommunications 7 21 29 32 34

Monetary authorities - central bank; Credit intermediation and related 14 39 49 50 51

Securities, commodity contracts, investments 38 115 146 141 136

Insurance carriers and related activities 5 17 28 33 35

Real estate 118 438 660 725 762

Rental and leasing services; Leasers of nonfinancial intangible assets 2 6 7 7 8

Professional, scientific, and technical services 48 135 189 235 298

Management of companies and enterprises 2 -3 -10 -8 -3

Administrative and support services 38 78 102 145 208

Waste management and remediation services 1 5 9 11 14

Educational services 15 101 206 270 306

Ambulatory health care services 36 110 172 226 300

Hospitals 11 81 177 244 288

Nursing and residential care facilities 4 37 90 136 173

Social assistance 5 44 99 139 166

Performing arts and spectator sports 17 71 118 144 161

Museums, historical sites, zoos, and parks 1 5 11 14 15

Amusement, gambling, and recreation 6 25 44 56 66

Accommodation 15 39 52 62 74

Food services and drinking places 153 597 919 1,017 1,048

Repair and maintenance 18 61 91 103 111

Personal and laundry services 79 238 301 287 279

Membership associations and organizations 11 62 120 153 169

Private households 74 214 269 250 237

TOTAL OF ALL INDUSTRIES = 1,020 3,837 5,962 6,834 7,473
Regional Economic Models, Inc.

p. 43

FIGURE 3.7 – EMPLOYMENT BY OCCUPATION (KING COUNTY, COUNTY REFORM, $50/TON)

SOC Occupations 2015 2020 2025 2030 2035

Top executives 16 58 88 99 105

Advertising, marketing, promotions, public relations, and sales managers 4 15 23 26 29

Operations specialties managers 9 30 44 51 57

Other management occupations 20 81 131 151 163

Business operations specialists 22 81 129 155 178

Financial specialists 25 83 118 129 138

Computer occupations 20 63 92 111 132

Mathematical science occupations 1 2 2 3 3

Architects, surveyors, and cartographers 1 3 5 6 7

Engineers 6 18 26 32 39

Drafters, engineering technicians, and mapping technicians 3 9 13 15 18

Life scientists 1 3 4 6 7

Physical scientists 1 3 4 5 6

Social scientists and related workers 1 3 5 6 7

Life, physical, and social science technicians 1 3 5 6 7

Counselors and Social workers 4 19 36 47 55

Miscellaneous community and social service specialists 2 11 22 28 33

Religious workers 0 1 1 1 2

Lawyers, judges, and related workers 3 10 13 15 18

Legal support workers 2 6 9 11 12

Postsecondary teachers 8 40 74 93 104

Preschool, primary, secondary, and special education school teachers 16 61 100 116 124

Other teachers and instructors 4 18 32 39 44

Librarians, curators, and archivists 1 5 8 10 11

Other education, training, and library occupations 6 24 41 50 54

Art and design workers 4 12 18 21 24

Entertainers and performers, sports and related workers 5 23 40 50 56

Media and communication workers 4 15 25 31 36

Media and communication equipment workers 1 4 6 8 9

Health diagnosing and treating practitioners 19 81 148 196 237

Health technologists and technicians 13 53 91 116 140

Other healthcare practitioners and technical occupations 0 2 4 5 6

Nursing, psychiatric, and home health aides 8 43 86 123 155

Occupational therapy and physical therapist assistants and aides 1 3 5 7 9

Other healthcare support occupations 9 30 46 55 66

Supervisors of protective service workers 1 3 5 6 6

Fire fighting and prevention workers 1 4 6 6 7

Law enforcement workers 5 15 22 24 25

Other protective service workers 10 33 51 62 73

Supervisors of food preparation and serving workers 12 46 71 79 82

Cooks and food preparation workers 37 142 220 246 256

Food and beverage serving workers 89 350 544 609 636

Other food preparation and serving related workers 18 68 103 114 117

Supervisors of building and grounds cleaning and maintenance workers 2 7 10 12 13

Building cleaning and pest control workers 58 180 248 257 264

Grounds maintenance workers 11 33 49 57 66

Supervisors of personal care and service workers 2 7 10 11 12

Animal care and service workers 5 16 23 25 27

Entertainment attendants and related workers 5 19 31 37 42

Funeral service workers 4 13 16 16 15

Personal appearance workers 28 90 118 116 116

Baggage porters, bellhops, and concierges; Tour and travel guides 1 4 6 7 8

Other personal care and service workers 41 139 204 226 243

Supervisors of sales workers 15 55 80 88 92

Retail sales workers 96 357 522 566 587

Sales representatives, services 15 46 63 68 73

Sales representatives, wholesale and manufacturing 13 50 76 87 97

Other sales and related workers 20 73 112 127 138

Supervisors of office and administrative support workers 10 36 55 63 71

Communications equipment operators 1 3 4 4 3

Financial clerks 26 91 137 157 174

Information and record clerks 41 134 195 222 248

Material recording, scheduling, dispatching, and distributing workers 23 78 112 122 129

Secretaries and administrative assistants 32 117 182 210 231

Other office and administrative support workers 32 115 177 203 223

Supervisors of farming, fishing, and forestry workers 0 0 0 0 0

Agricultural workers 1 1 1 2 3

Fishing and hunting workers 0 -1 -1 -1 -1

Forest, conservation, and logging workers 0 -2 -3 -2 -2
Regional Economic Models, Inc.

p. 44

Supervisors of construction and extraction workers 4 30 57 65 70

Construction trades workers 36 248 462 531 567

Helpers, construction trades 3 20 38 45 49

Other construction and related workers 2 10 16 19 20

Extraction workers 0 2 4 6 7

Supervisors of installation, maintenance, and repair workers 4 16 26 30 32

Electrical and electronic equipment mechanics, installers, and repairers 4 15 23 26 29

Vehicle and mobile equipment mechanics, installers, and repairers 13 49 73 83 91

Other installation, maintenance, and repair occupations 35 140 225 256 276

Supervisors of production workers 3 8 11 12 13

Assemblers and fabricators 7 19 25 28 32

Food processing workers 3 12 18 21 22

Metal workers and plastic workers 6 18 26 30 34

Printing workers 1 3 5 5 5

Textile, apparel, and furnishings workers 12 34 41 40 39

Woodworkers 1 4 5 5 5

Plant and system operators 0 0 1 2 3

Other production occupations 10 29 40 46 52

Supervisors of transportation and material moving workers 2 8 12 14 16

Air transportation workers 0 -3 -5 -5 -3

Motor vehicle operators 21 76 118 138 154

Rail transportation workers 0 0 0 0 1

Water transportation workers 0 1 3 5 7

Other transportation workers 6 19 25 25 25

Material moving workers 25 87 129 149 167

TOTAL OF ALL OCCUPATIONS = 1,094 4,083 6,323 7,226 7,876

Much of the discussion for King County alone remains the same as for Washington considering the major 

impacts by industry and the occupations. All results above are for the higher case of a $50/ton tax, as all 

previous reports formatted the same. King County, on the other hand, does not have much output in the 

petroleum and coal industry from the remainder of the state. Additionally, in King County, this tax 

reform has a significant impact on construction and real estate.

40 These industries respond to the 

change in the local property tax much as the service and manufacturing industries replied to the change in 

the state B&O tax in other simulations. A lower price for real estate and commercial space induces 

population movement into King County, a higher rate of rental transactions, and investments in the area’s 

capital stock as demand increases. Construction and real estate stand to gain from this, being within a 

position “in the middle” of these industries. Both are “local” industries by nature and labor-intensive, 

which yields an increases in employment in these industries.

The sales tax explains many of the other changes observed in King County. Another large set of impacts 

takes place in the retail and food service sectors, which enjoy a boost in real purchasing power out of 

households no longer paying quite as high of a rate for consumer goods. Secondary industries in this same 

basket include accommodation, amusements and gaming,

41 and even healthcare. The additional

real income of households goes somewhere, and these industries are the most likely to receive much of the 

extra “last dollars” in the economy. The effect of increased competitiveness and market shares are present 

as well in industries such as insurance, professional services, administration and support, 

education, investment securities, and publishing. There are broad implications for occupations in 

King County from the carbon tax—and almost all of them positive. Only forest, conservation, and 

logging workers and air transportation workers have negative numbers associated with them, and 

those are near zero for all years. This means workers in King County will have a chance to shift their skills 

between industries in the face of these economic changes, finding different industries with need of 

approximately the same skills as the economy continues to evolve.

 

40 NAICS 531, an industry usually concerned with the buying, renting, and leasing of real estate, 

<http://www.census.gov/cgi-bin/sssd/naics/naicsrch?code=531&search=2012%20NAICS%20Search>

41 NAICS 713, another broad industry including destinations for disposable income such as amusement 

parks, arcades, gambling, boating marinas, and various other activities, <http://www.census.gov/cgibin/sssd/naics/naicsrch?code=713&search=2012%20NAICS%20Search>
Regional Economic Models, Inc.

p. 45

PERSONAL CONSUMPTION EXPENDITURE (PCE)-PRICE INDEX

Figure 3.8 – King Country actually has a downward impact to its PCE in the simulations. That is, when 

accounting for the net difference between lower sales taxes, lower property taxes, and higher energy 

costs, the cost of living in the area actually drops slightly. All cases are less than 0.5%, but there is a 

slight, “one-time” adjustment in costs before the drift back towards the baseline in the later years. This 

will contribute to growth in real income and make King County a more attractive place to live, drive 

real estate investment, and tie back into the strong impact on construction and real estate seen in the 

previous distributional data. This cost concept is a large positive from this tax development.

-1.0%

-0.8%

-0.6%

-0.4%

-0.2%

0.0%

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King County, County Reform

$10 tax

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p. 46

PCE-PRICE INDEX BY INCOME QUINTILE

Quintile 2015 2020 2025 2030 2035

Lowest 20% -0.04% -0.12% -0.15% -0.13% -0.11%

Low-Middle 20% -0.04% -0.12% -0.15% -0.13% -0.11%

Middle 20% -0.04% -0.12% -0.15% -0.13% -0.10%

High-Middle 20% -0.04% -0.11% -0.14% -0.13% -0.10%

Highest 20% -0.04% -0.11% -0.14% -0.13% -0.10%

Figure 3.9 – As before, the price impact across different income quintiles (in the $50/ton scenario) are 

approximately the same. This gives the impact of this case a neutral profile in terms of income. At the 

very least, nobody feels harm, especially given even the lowest 20% still see a fall in the prices that they 

experience. The gist of the results remains positive, which continues here. This contributes to strong 

growth in real income in King County under its “individual” tax reform considering how the sales tax 

cut and property tax cut—which covers most of household spending—more than makes up for the 

difference in energy prices. The same ideas about low-income households spending more of their income 

on necessities like fuels applies here, though the share of energy consumption by income continues to 

stay rather constant across quintiles due to differences in house size, use of electronics by income level, 

and increases in travel budgets. Distributional impacts are still modest for King County, and the pattern 

on income paid to quintiles is similar with the growth in below-median wage industries above.
Regional Economic Models, Inc.

p. 47

CHANGES IN ENERGY PRICES FROM BASELINE

Figure 3.10 – Much of the impact in King County alone is similar to that of the whole state. There is one 

exception in a greater impact to motor vehicle fuel costs because of King County’s high usage of motor 

gasoline for commuting, its wealthier population, and its relatively inefficient road network from 

geographical constraints and monster traffic.

42 King County would experience an increase in fuel prices. 

Yet, this generates much of the benefits of the policy by reducing energy consumption, carbon emission, 

and generating revenue for recycling into other budget categories. Gasoline prices have routinely 

swung between $3/gallon and $4/gallon in the United States in the past five years, and the highest 

numbers would represent an increase of about $0.45/gallon for the $50/tax scenario.

 

42 Seattle ranks seventh-worst according to one survey, just ahead of infamous I-495 traffic snarls 

surrounding Washington, DC and behind the congestion in the Bay Area of California, please see, 

Alexander E.M. Hess and Samuel Wigley, “Ten cities with worst traffic,” USA Today, May 4, 2013, 

<http://www.usatoday.com/story/money/cars/2013/05/04/worst-traffic-cities/2127661/>

0%

5%

10%

15%

20%

2015 2020 2025 2030 2035

Residential natural gas

$10 tax

$30 tax

$50 tax

0%

5%

10%

15%

20%

2015 2020 2025 2030 2035

Motor vehicle fuels

$10 tax

$30 tax

$50 tax

0%

2%

4%

6%

8%

10%

12%

14%

16%

2015 2020 2025 2030 2035

Fuel oil and other fuels

$10 tax

$30 tax

$50 tax

0%

2%

4%

6%

8%

10%

12%

14%

2015 2020 2025 2030 2035

Residential electricty

$10 tax

$30 tax

$50 tax
Regional Economic Models, Inc.

p. 48

ADDITIONAL REAL DISPOSABLE PERSONAL INCOME

Figure 3.11 – This result differs from the statewide reform. This time, King County does much better 

than it does when the rest of the state undertakes a like carbon pricing system. The rest of the state 

economy does improve when King County acts alone (but not as much). The lower cost of living in the 

area and higher job opportunity draws a greater number of people and economic activity to the region. 

In the statewide scenario, the shares of GDP and income stay relatively the same between King County 

and the rest of the state—in these simulations, King County gains relative to the rest of Washington. The 

lower cost of living in the area also pushes these numbers higher from the lower PCE.

INCOME BY QUINTILE

Figure 3.12 – There is not a strong distributional pattern for King County in implementing a carbon tax 

when combined with the other tax relief measures. The lowest two quintiles see the most gain, mostly 

owing to the creation of below-median wage jobs in the service and hospitality sectors, and for their 

reliance on large parts of their income going towards taxable commodity groups (such as food and 

clothing) and housing or rent. This should alleviate some concerns about the distributional worries 

about a carbon tax when applied on something like the King County-level.

$0

$200

$400

$600

$800

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$1,200

2014

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Millions of 2013 dollars

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0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

0.30%

0.35%

0.40%

2014

2015

2016

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2018

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Percent change from baseline

King County, County Reform

Lowest 20%

Low-Middle 20%

Middle 20%

High-Middle 20%

Highest 20%
Regional Economic Models, Inc.

p. 49

ADDITIONAL POPULATION

Figure 3.13 – King County adds population in the event of a carbon tax due to the increased job 

opportunity in the area and a reduced cost of living. Many of these are people moving in from the rest of 

the state, though some are “new” from the rest of the United States. This growth adds to the economy

through investment in housing and square footage and new services, as seen in the simulations.

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

2014

2015

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Individuals over basline

King County, County Reform

$10 tax

$30 tax

$50 tax
Regional Economic Models, Inc.

p. 50

CARBON TAX REVENUES (ANNUAL)

Figure 3.14 – This shows the revenue from a King County carbon tax from CTAM. Economic activity 

and fuel consumption in this area is about half of the state’s total; hence, the expected revenues in the 

way CTAM models and shares dollars is similar. This is, again, well over 1% of the area’s economy in the 

$50/ton case, which would have much to do with how the local government allocates resources and 

influence economic growth with its budget. Other trends are similar to those from before: the early 

phasing-in of the carbon tax brings in more revenues, people respond, but the NEMS long-term forecast 

of increased energy consumption in Washington keeps total revenues flat out towards 2035.

CARBON TAX REVENUE (CUMULATIVE)

Figure 3.15 – This totals the above over time. Cumulative revenues to 2035 from this carbon pricing 

could total upwards of $25 billion for King County. This is, again, approximately half of the $50 billion 

modeled from the statewide tax, but this is a very significant amount of money, particularly if it 

remains in the King County region and goes towards improving its economy specifically. This positive 

displacement of imports and revenues can have a powerful result, as seen in the simulations.

$0

$200

$400

$600

$800

$1,000

$1,200

$1,400

$1,600

$1,800

2014

2015

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Millions of 2013 dollars

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$0

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

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Regional Economic Models, Inc.

p. 51

CARBON DIOXIDE EMISSIONS (ANNUAL FORECAST)

Figure 3.16 – This is the adjusted forecast from CTAM for carbon emission from King County with 

various levels of carbon pricing. CTAM is technically a state-level model, so the above represents a share 

of emissions for King County based on the equivalents in PI+ (to provide an example, PI+ has separate 

forecasts for motor gasoline purchases in King County and the rest of state, which then share gasolinesourced carbon emissions in these results). These are estimates, and the lack of an EPA data table for 

counties makes them more difficult to verify.43 They are likely to be high—CTAM and PI+ require 

carbon taxes paid by location, which is different from location of generation (mostly 

from power plants). CTAM better captures the impacts of demand shifts from price.

CARBON DIOXIDE EMISSIONS REDUCTIONS (ANNUAL DIFFERENCE)

Figure 3.17 – These amounts are, again, equivalent to the annual emissions of the District of Columbia.

 

43 The closest analog was the Vulcan Project at Purdue University, last updated in 2008 however, and now 

seemingly defunct, <http://www.purdue.edu/eas/carbon/vulcan/index.php>

20.0

20.5

21.0

21.5

22.0

22.5

23.0

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24.0

2014

2015

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Millions of metric tons per year

King County, County Reform

$10 tax

$30 tax

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No tax

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

2014

2015

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20172018

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Millions of metric tons per year

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$10 tax

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No tax
Regional Economic Models, Inc.

p. 52

CARBON DIOXIDE EMISSIONS (CUMULATIVE DIFFERENCE)

Figure 3.18 – This is the sum of reduced emissions by King County over time. A total of 33 million metric 

tons is approximately the current annual output of Alaska, Connecticut, Montana, or Nevada.44 These 

are small reductions in percentage terms, though they do pile-up over time. Without reliable data for 

King County on either present emissions, 2011 emissions (the most recent history year), or historical 

data to 1990, making a comparison to the 1990 benchmark is an uncertain proposition.

 

44 EPA

-35

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-25

-20

-15

-10

-5

0

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$10 tax

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p. 53

LITERATURE

The conclusions of this study are broadly consistent with the findings of other studies on the potential 

impact of state-level carbon pricing systems in American states or Canadian provinces. The most relevant 

examples include a study in Oregon by the Northwest Economic Research Center (NERC) at Portland 

State University,45 a study on the performance of the British Columbia carbon tax by the University of 

Ottawa,46 and a previous REMI study on a similar carbon tax effort in the state of Massachusetts.

47 State 

and provincial policies are leading many efforts at environmental tax reform after the stalling of WaxmanMarkey and the federal cap-and-trade bill in Washington, DC in 2009.48 The tax in British Columbia is 

unique for North America in being currently active, and its impacts may mirror the results of the 

economic and carbon modeling above.

Several major findings emerged in the study out of the University of Ottawa.49 British Columbian carbon 

emissions on a per capita basis fell at a much faster rate from 2008 to 2011 than the rest of Canada. Total 

rest of Canada emissions were down 1.1% over that period, but British Columbia saw a total fall of 10.0%. 

The British Columbian tax focused strongly on revenue-neutrality and its recycling. The tax began a 

$10/ton (CAD) in 2008, and gradually escalated at $5/year to a maximum $30/ton in 2012. Policymakers 

in Victoria used this revenue to provide tax relief through credits, personal income tax cuts, reductions in 

the tax burdens on small businesses, and some remittance to low-income households to protect them 

from the direct effect of higher energy prices. British Columbia’s economy, while struggling in the wake of 

the major recession, did not “crash” relative to the rest of Canada: per capita GDP numbers slipped by -

0.15% from 2008 to 2013 while the rest of Canada fell back -0.23%. This experience is an example of 

cutting carbon dioxide emissions while having minimal economic impacts.

NERC and REMI found similar results to those in Washington. The NERC study used a different model, 

and both studies examined a similar state to Washington. NERC used CTAM, calibrated for Oregon, and 

ran the inputs through IMPLAN—an input/output model that produces multipliers from the different 

types of economic activity in a region. Depending on the reinvestment of the funds, an Oregon carbon tax 

adds a net of 1,200 to 8,000 jobs to the economy. IMPLAN differs from PI+ in relying only on demand 

variables, which forces the assumption that a $1 increase in energy costs will mean a $1 decrease in 

output, which is a more complicated situation requiring cost variables and notions of competitiveness. 

Using PI+ and relying on energy costs, the Massachusetts study found a generally positive impact from a 

carbon tax on the Bay State economy. Results included an average of between 2,000 and 9,000 additional 

annual jobs depending on the maximum tax. Washington and Massachusetts behaved in a comparable 

manner in these two studies because the displacement of energy imports and tax relief on the local level 

improved economies. This makes sense, too, given their similarities: they both have large metropolitan 

areas (Seattle and Boston) producing half of GDP and both rely on technology as their Hercules, have 

small amounts of resource extraction, and very little in terms of oil and gas.

 

45 Jenny H. Liu and Jeff Renfro, “Carbon Tax and Shift: How to Make it Work for Oregon’s Economy,” 

NERC, March 1, 2013, <http://www.pdx.edu/nerc/sites/www.pdx.edu.nerc/files/carbontax2013.pdf>

46 Stewart Elgie and Jennifer Wesanki, “BC’s Carbon Tax Shift After Five Years: An Environmental (and 

Economic) Success Story,” University of Ottawa, July 24, 2013, 

<http://www.sustainableprosperity.ca/article3685>

47 Scott Nystrom and Ali Zaidi, “Modeling the Economic, Demographic, and Climate Impacts of a Carbon 

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

48 “H.R. 2454 – American Clean Energy and Security Act of 2009,” Open Congress, June 26, 2009, 

<http://www.opencongress.org/bill/111-h2454/show>

49 Thanks to David Roberts for compiling them, please see, David Roberts, “The positive economic impact 

of a carbon tax,” Grist, <http://grist.org/climate-energy/the-positive-economic-impact-of-a-carbon-taxin-uh-hang-on-10-charts/>
Regional Economic Models, Inc.

p. 54

REGIONAL ECONOMIC MODELS, INC. (REMI)

REMI is a modeling firm specializing in services related to economic impacts and policy analysis. Its 

headquarters is in Amherst, Massachusetts, though this report originated in its office in Washington, DC. 

REMI was first an applied project at the University of Massachusetts-Amherst by a research professor, Dr. 

George Treyz, on the economic impact of expanding Interstate 90 from Boston, to Worcester, Springfield, 

and eventually Albany and Buffalo, New York. Dr. Treyz generalized the methodology and incorporated 

the present entity in 1980. REMI operates to provide software, support services, and issue-based expertise 

and consulting in almost every state, the District of Columbia, and other countries in North America, 

Europe, Latin America, the Middle East, and Asia. The typical REMI users or consulting client works for a 

federal agency, planning organization, regional/metropolitan authority, state government (such as a DOR 

or DOT), consulting firm, university, or a non-profit research group. Current REMI clients in Washington 

include the state Office of Financial Management (OFM)50 and non-profit Washington Research Council 

(WRC).51 There are peer users throughout the rest of the country, but one of note in the Pacific Northwest 

is the Legislative Revenue Office (LFO) in the state of Oregon.

52

PI+

REMI used a 2-region, 70-sector version of its PI+ modeling software configured to gently shatter and 

divide King County from Washington in the build software. PI+ is a computerized, multiregional, dynamic 

model of the states or other sub-national units of the United States economy underneath a Windows-

 

50 <http://www.ofm.wa.gov/>

51 <http://researchcouncil.org/>

52 For a complete list, please see, “Clients,” REMI, <http://www.remi.com/clients>
Regional Economic Models, Inc.

p. 55

based graphic user interface (GUI). PI+ relies on four quantitative methodologies to guide its approach to 

economic modeling. This allows their strengths to come through and their interactions to supplement any 

weaknesses they may have without the other portions:

1. Input/output tabulation (IO) – IO models, sometimes called “social accounting matrices”

(SAM), quantifies the interrelation of industries and households in a computational sense. It 

models the flow of goods between firms in supply-chains, wages paid to households, and final 

consumption by households, government, and the international market. These channels create

the “multiplier” effect of $1 going further than when accounting for its echoing. The make-anduse table needed to make an input-output matrix comes from the Bureau of Labor Statistics 

(BLS),53 and the work behind it won New York University and Harvard University professor 

Wassily Leontief the Nobel Prize in economic science in 1973.54

2. Computable general equilibrium (CGE) – This is a broad classification of models, and PI+

employs this modeling system. CGE modeling adds market concepts to the IO structure. This 

includes how those structures evolve over time and how they respond to alternative policies. CGE 

involves concepts on markets for labor, housing, consumer goods, imports, and the importance of 

competitiveness to fostering economic growth over time. Changing one of these will influence the 

others—for instance a new knife factory in Washington would improve the labor market, and then 

bring it to a head by increasing migration into the area, drive housing and rent prices higher, and 

induce the market to create a new subdivision to return to “market clearing” conditions. This 

accounting for long-term effects makes PI+ unique amongst regional models.

3. Econometrics – REMI uses statistical parameters and historical data to populate the numbers 

inside the IO and CGE portions. The estimation of the different parameters, elasticity terms, and 

figures gives the strength of various responses. It also gives the “time-lags” from the beginning of 

a policy to the point where markets have had a chance to clear. This contrasts with many of the 

“pure” CGE models, which only have a “before” and “after” component.

4. New Economic Geography – Economic geography provides REMI a sense of economies of 

scale and agglomeration. In literal terms, this is the quantification of the strength of clusters in an 

area and their influence on productivity. One example would include the technology and research 

industries in Seattle. The labor in the area specializes to serve firms like Amazon and Microsoft, 

and, thus, their long-term productivity grows more quickly than that of smaller regions with no 

proclivity towards software development (such as Helena, Montana). The same is true on the 

manufacturing side with physical inputs, such as with the supply-chain for Boeing and Paccar in 

Washington in the production of transportation equipment. Final assembly will have a close 

relationship and a high degree of proximity to its suppliers of parts, repairs, transportation, and 

other professional services, which show up in clusters in the state.

The methodologies and equations behind PI+ are publically available and peer-reviewed. The initial set of 

publications by Dr. Treyz and his research staff appeared in Journal of Regional Science, the American 

Economic Review, and Review of Economics and Statistics.

55 REMI relies on data from public sources. 

These include the Bureau of Economic Analysis (BEA), BLS, EIA, and the Census Bureau.56 Forecasts for 

 

53 “Inter-industry relationships (Input/output matrix),” Bureau of Labor Statistics, 

<http://www.bls.gov/emp/ep_data_input_output_matrix.htm>

54 “Wassily Leontief,” Library of Economics and Liberty, 

<http://www.econlib.org/library/Enc/bios/Leontief.html>

55 For a full listing of journal citations, please see p. 47, “Model Equations,” REMI, 

<www.remi.com/download/documentation/pi+/pi+_version_1.5/PI+_v1.5_Model_Equations.pdf>

56 “Data Sources and Estimations Procedures,” REMI, 

<http://www.remi.com/download/documentation/pi+/pi+_version_1.5/Data_Sources_and_Estimation

_Procedures.pdf>
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future macroeconomic conditions in REMI come from a combination of resources, including the Research 

Seminar in Quantitative Economics (RSQE),

57 the University of Michigan, and the BLS. This serves as the 

main framework for the software model needed to perform simulations.

Figure 4.1 – This diagram represents the structure and linkages of the regional economy in PI+. Each 

rectangle is a discreet, quantifiable concept or rate, and each arrow represents an equation linking the 

two of them. Some are complex econometric relationships, such as the one for migrant, while some are 

rather simple, such as the one for labor force, which is just the population times the participation rate. 

The change of one relationship causes a change throughout the rest of the structure because different 

parts move and react to incentives at different points. At the top, Block 1 represents the macroeconomic 

whole of a region with final demand and final production concepts behind GDP, such as consumption, 

investments, net exports, and government spending. Block 2 forms the “business perspective:” an 

amount of sales orders arrive from Block 1, and firms maximize profits by minimizing costs when 

making optimal decisions about hiring (labor) and investment (capital). Block 3 is a full demographic 

model. It has births and deaths, migration within the United States to labor market conditions, and 

international immigration. It interacts with Block 1 through consumer and government spending levels 

and Block 4 through labor supply. Block 4 is the CGE portion of the model, where markets for housing, 

consumer goods, labor, and business inputs interact. Block 5 is a quantification of competitiveness. It is 

literally regional purchase coefficients (RPCs) in modeling and proportional terms, which show the 

ability of a region to keep imports away while exporting its goods to other places and nations.

 

57 <http://rsqe.econ.lsa.umich.edu/>
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For the environmental tax reform work here, four of the above rectangles (and corresponding variables 

underneath them) formed the basis for the policy simulation. They included:

 Consumer prices – For higher consumer costs and prices for fuel oil, gasoline, diesel, natural 

gas, electricity, but offsets for lower prices in commodities eligible for the sales tax

 Housing prices – This was the variable for offsetting the reduction of property tax rates in King 

County in response to revenue recycling from the carbon tax

 Production costs – This variable adjusted the competitiveness of industry based on variables 

for the cost of natural gas, petroleum-products, electricity, the cost of capital (in the case of the 

property taxes), and the overhead costs associated with the B&O tax

 Real disposable income – The 10% rebate to low-income households fell into this category to 

directly increase their income and spending

The REMI model has two main purposes: forecasting and the analysis of alternatives. All models have a 

“baseline” forecast of the future of a regional economy at the county-level. Using the model involves the 

supplying of “exogenous” shocks, which implicates a response in local economic performance and the 

area’s demographics. These shocks, “policy variables” in REMI terminology, represent the direct effect of 

a carbon tax in King County and Washington. The structure handles the translation of the static effects 

from CTAM into the full economic, demographic, wage, price, and distributional impacts seen in this 

report. This makes PI+ a powerful tool for conveying the economic “story” behind policy. PI+ translates 

these considerations into understandable concepts for everyone like GDP and jobs.

Figure 4.2 – This shows the basic process of simulation in PI+. The control forecast, the red line above, is 

a “do-nothing” scenario, a null hypothesis of making no changes. The other lines represent a new 

forecast after introducing some exogenous considerations with the policy variables in the structure. The 

main point of the model is to compare the difference—the delta—between the lines to see the “impact.” 

Policy A has stronger long-term performance, though Policy B is better in the immediate, though both of 

them are certainly a superior option to doing nothing by the generic metric to the left.

500

750

1,000

1,250

1,500

1,750

2,000

2,250

2,500

2,750

2012

2015

2018

2021

2024

2027

2030

2033

20362039

2042

2045

2048

2051

2054

2057

2060

Economic/Demographic Indicator

Control Forecast

Policy A

Policy B
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CARBON TAX ANALYSIS MODEL (CTAM)

This study used CTAM, which first appeared for the state of Washington a few years previously. The 

results here include an updated CTAM for the most recent Annual Energy Outlook from the EIA.58 There 

are fuller descriptions of CTAM available elsewhere, including a detailed one on pp. 22-23 of the NERC 

study for Oregon. CTAM is open-source, Microsoft Excel-based and available online.59 CTAM takes the 

projections of anticipated consumption in different energy sorts (such as gasoline, distillates, natural gas, 

or electricity) from the EIA. The EIA and NEMS Extended Outlook forecast is at the U.S. Census regions 

level,60 and a combination of historical data and fixed shares leads to a state-level totals for consumption 

and emissions. The assumptions behind NEMS are the baseline for CTAM. From there, the user enters

levels of carbon taxes—the starting level, the annual increase, and the maximum rate implemented. CTAM 

includes parameters for the price response by major fuel types. That is, it includes an elasticity of how 

strongly consumers cut back on energy usage in the face of higher prices. Neither NERC nor REMI 

updated the default elasticity for fuel types in CTAM, which came from an analysis of their estimations by 

Keibun Mori. Using the default parameters from Mori was at the request of ETR-WA.

Figure 5.161 – This shows the processes inside of CTAM to develop an impact to carbon tax revenues and 

carbon dioxide emissions. It starts on the left with the level of tax, and then assigns a price change to a 

host of fuel categories from their internal carbon content in chemical terms. The elasticity in the middle 

determines the strength of the demand response, which will reduce total consumption of energy—and 

therefore carbon dioxide emissions—before leading to the final results of money raised and the 

emissions reduced. This produced the information needed to simulate these cases in PI+. CTAM and PI+

work well together for both being multiyear and having multiple layers of fuel price changes.

 

58 “Annual Energy Outlook 2013,” Energy Information Administration, 

<http://www.eia.gov/forecasts/aeo/>

59 Eric de Place, “Washington Carbon Tax: New Model and Analysis,” Sightline Daily, August 10, 2011, 

<http://daily.sightline.org/2011/08/10/washington-carbon-tax-new-model-and-analysis/>

60 Washington is in the “Pacific” region with Alaska, California, Hawaii, and Oregon

61 From p. 26 of the Oregon study
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INTEGRATING PI+ AND CTAM

The bridge between the two models follows from their dimensions. These dimensions are multiple years, 

the multiple fuel types by sectors, and how they relate to each other in CTAM and PI+ terminology, data, 

and variables. CTAM has four major sectors for fuel demand and emissions—residential, commercial, 

industrial, and transportation. These correspond to sectors and variables in PI+ between households and 

NAICS industries. CTAM provides more granularity than PI+ in terms of fuel types, so we agglomerated

up from CTAM categories to the REMI layers of electricity, natural gas, and petroleum-products. The 

following shows the exact factor out of CTAM and its process to become an input in REMI. In addition, 

because CTAM is state-level, there were several factors in REMI to divide between King County and the 

rest of the state by fuel source, which was different than the Massachusetts study, and this adds an extra 

column here. These factors came from the REMI baseline projections.

Sector CTAM PI+Regional Shares

Residential

Kerosene, Distillate Fuel 

Oil

Consumer price (fuel oil 

and other fuels)

Consumer spending (fuel 

oil and other fuels)

Natural Gas Consumer price (natural 

gas)

Consumer spending

(natural gas)

Electricity Consumer price 

(electricity)

Consumer spending 

(electricity)

Commercial

Liquefied Petroleum 

Gases, Motor Gasoline, 

Kerosene, Distillate Fuel 

Oil

Residual (commercial 

sectors) fuel costs

Output of all commercial 

sectors

Natural Gas Natural gas (commercial 

sectors) fuel costs

Output of all commercial 

sectors

Electricity Electricity (commercial 

sectors) fuel costs

Output of all commercial 

sectors

Industrial

Motor Gasoline, Distillate 

Fuel Oil

Residual (industrial 

sectors) fuel costs

Output of all industrial 

sectors

Natural Gas Natural gas (industrial 

sectors) fuel costs

Output of all industrial 

sectors

Electricity Electricity (industrial 

sectors) fuel costs

Output of all industrial 

sectors

Transportation

Motor Gasoline

Consumer price (motor 

vehicle fuels, lubricants, 

and fuels)

Consumer spending 

(motor vehicle fuels, 

lubricants, and fuels)

Distillate Fuel Oil Consumer prices (fuel oil 

and other fuels)

Consumer spending (fuel 

oil and other fuels)

Figure 5.2 – This shows the mapping of CTAM data into PI+ variables. The column on the right is the 

factor in the regional baselines of PI+ used to spread the impact of the carbon tax from emissions 

between King County and the rest of Washington based on the logical factor to use for correlation.

The spreading of tax and auction revenues between King County and the rest of the state was different. 

Using data graciously provided by OFM, we determined the share of B&O taxes paid by NAICS industry in 

each region. These shares became the share each industry received of the B&O tax cuts from revenue 

recycling. We used OFM data on the amount of sales tax collected by consumption category by region, 

which we used to adjust consumer price when recycling the revenue in that way. This meant taxable 

commodities (such as new furniture) saw reductions in prices, while items or services left untaxed by the 

state (such as a trip to the dentist’s office) saw no large changes.
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Regional Economic Models, Inc. (REMI)

1776 I St. NW Suite 750

Washington, DC 20006

(202) 469-7861

433 West Street

Amherst, MA 01003

(413) 549-1169

Scott Nystrom received his B.A. in history, B.S. in economics (summa cum laude with each), and his M.A. 

in economic history (with distinction) from Iowa State University in Ames, IA. He has worked at REMI 

since 2011. He is the chief point of contact in its Washington, DC office for software training, technical 

support, and related economic consulting projects. Mr. Nystrom works and on a daily basis with state 

governments on taxation and transportation research, local and regional authorities, federal agencies, 

consultants, universities, non-profits, and research institutions. Other responsibilities include research on 

integrating energy models into a regional framework, theoretical research on modeling intermodal 

transportation, and business development. His major projects have included impact analyses on energy, 

healthcare, labor, transportation, state taxes and budgets, and federal fiscal policy. Highlights include the 

“fiscal cliff,” the TransCanada Keystone XL pipeline, and the Southern California Association of 

Governments (SCAG) regional transportation plan (RTP) to 2035.

Ali Zaidi is an assistant economist and research assistant with REMI in Washington, DC. He holds a B.A. 

in economics from the University of Massachusetts-Amherst. He performed the calibration of the CTAM 

model for Washington, “WACTAM,” and assisted Mr. Nystrom in validating the results and developing 

this report. Mr. Zaidi has also assisted on analyses of a carbon tax in Massachusetts, federal immigration 

reform, and integrating regional models with agent-based systems to perform studies of national security 

scenarios, international trade stoppages, and capital flows.

This report does not reflect the institutional views of REMI. It is the result of the professional opinions of 

the authors and the findings of the models reported within this document.