# M ODELING THE 

# E CONOMIC



# , D EMOGRAPHIC



# , AND 

# C

LIMATE 

# I

MPACT OF

A 

# C ARBON 

# T

AX IN 

# M

ASSACHUSETTS

P REPARED BY

Regional Economic Models, Inc. (REMI)



# P REPARED FOR

Committee for a Green Economy (CGE)

Supported by



## S COTT 

## N YSTROM



## , M.A

.

Senior Economic Associate



## A LI 

## Z AIDI

Assistant Economist

1776 I St. NW

Suite 750

Washington, DC 20006

(202) 469

- 7159

< scott.nystrom@remi.com >



# T HURSDAY



# , J ULY 

# 11

, 2013

### Regional Economic Models, Inc.

p. 1 

# T ABLE OF 

# C ONTENTS

Table of Contents p. 1

Acknowledgements from CGE p. 2

Summary of Findings pp. 3

- 4

Introduction and Background pp. 5

- 7

Policy Scenarios pp. 8

- 9

Figure 1.1

– Carbon Tax Levels p. 8

Figure 1.2

– Revenue Recycling p. 9

Simulation Results pp. 10

- 23

Figure 2.1

– Additional GDP (Annual) p. 10

Figure 2.2

– Additional GDP (Cumulative) p. 11

Figure 2.3

– Total Employment p. 11

Figure 2.4

– Change in Population p. 12

Figure 2.5

– Carbon Tax Revenues (Annual) p. 13

Figure 2.6

– Carbon Tax Revenues (Cum ulative) p. 13

Figure 2.7

– Output by Industry p. 14

Figure 2.8

– Employment by Industry p. 16

Figure 2.9

– Employment by Occupation pp. 18

- 19

Figure 2.10

– Consumer Prices p. 20

- Fuel oil and other fuels

- Motor vehicle fuels, lubricants, and fluids

- Natural gas

- Electricity

Figure 2.11

– Consumer Price Index (CPI) p. 21

Figure 2.12

– Price Indices by Income Groups pp. 21

- 22

Figure 2.13

– Forecasted Carbon Emissions p. 23

Figure 2.14

– Difference in Emissions p. 23

Figure 2.15

– The 19 90 Benchmark p. 24

Figure 2.16

– Carbon Tax Options p. 25

Figure 2.17

– Average Annual Tax Revenue p. 25

Regional E conomic Models, Inc. p. 26

The PI + Model pp. 27

- 29

Figure 3.1

– Model Structure p. 29

Figure 3.2

– Simulation Methodology p. 29

Carbon Ta x Analysis Model (CTAM) pp. 29

- 31

Figure 4.1

– Carbon Calculations p p. 30

- 31

Integrating PI + and CTAM p. 32

Figure 5.1

– Policy Variables from CTAM p. 32

Hypothetical Tax Cases pp. 33

- 35

Figure 6.1

– Gross Domestic Product p. 33

Figure 6.2

– Total Employment p. 33

Figure 6.3

– Household Income p. 34

Figure 6.4

– Strengths and Weaknesses p. 34

Authors’ Biographies and Contact Information p. 36

This report does not reflect the institutional views of REMI. The words above reflect the professional findings of the

authors. There is a ©2013 by Regional Economic Models, Inc. and respective parties.

### Regional Economic Models, Inc.

p. 2 Photographs credited to Dira Cannistraro, Sara Gotschewski , and Jessica Langerman.



# A CKNOWLEDGEMENTS FROM 

# CGE

The Committee for a Green Economy would like to thank everyone who contributed to the initial support

of this research project . We deeply appreciate the gracious and thoughtful feedback from State Senator

Michael Barrett, State Representative Tom Conroy, and Sam And erson, who is the Committee Counsel

for the Massachusetts Joint Committee on Labor and Workforce Development. Their individual and

collective contributions to the final report were invaluable.

CGE would also like to thank Sarah Lehnert and Cassidy Jemo for their exceptional hard work on the

part of our organization . Our nation will benefit from the competence, self

- discipline, and imagination of

young leaders like you. Finally, CGE would like to recognize our economic advisor, Cathy Carruthers

. Her extraordinary vision, courage, and determination to make a difference in the fight against climate

change inspired us all. Without Cathy's generous and unstinting efforts to put a price on carbon

emissions, this report would not exist

.

Your grandchildren and ours will thank you for all you have done, Cathy

.



## C OMMITTEE FOR A 

## G REEN 

## E CONOMY

Gary Rucinski

Cofounder and Chairman Jessica Langerman

Cofounder and Treasurer Cathy Carruthers

Economic Advisor Zach Agush

Political Advisor

Michael Clarke

Campaign Manager Sarah Lehnert

Media Consultant Cassidy Jemo

Political Opinion Poll Assistant Analyst

### Regional Economic Models, Inc.

p. 3 

# E XECUTIVE 

# S UMMARY

This report explores whether or not a carbon tax in Massachusetts can improve the state economy. A

carbon tax levies a fee on carbon dioxide released into the atmosphere from the use of fossil fuels. The

state can reinvest this revenue or reduce other taxes. The purpose was to evaluate whether a carbon tax

and the recycling of its revenue can create jobs, grow the state’s economy , and protect l ow

- income

households while also lower ing the Massachusetts carbon footprint . This analysis found that it is possible

to meet the se objectives if the state allocates the carbon tax revenue appropriately

. However, n ot every

reinvest ment strategy met all obje ctives . The optimal method of meet ing the se goals was a 50

- 50 split in

subsequent tax reductions , shared betw een households and corporations:

 50% of the revenue split towards lowering state corporate income taxes

 50% split to households

— 25% to lower income taxes and 25% to a lower general sales tax

We ran three scenarios : a $15, $30, and $45 tax on each metric ton of carbon emitted by various fuel types,

including electricity. In broadest terms with the Conroy

- Barrett bill in the Massachusetts State House

, w e

were close to a revenue

- neutral analysis, keeping the fir st $100 million of revenue for an infrastructure

and research fund. The remainder of the tax revenue went to general tax cuts throughout the rest of the

economy. This included a reduction in corpo rate income, personal income, and general sales t axes to

compensate households ( p articularly low

- income groups) from paying too much after the initial, direct

impact of the new carbon tax and ensuing change in prices.

With a 50

- 50 split between households and firms, the impact on the state was positive

, and t he level of

carbon emissions fell . For the lowest scenario of $15 tax per metric ton, total state gross domestic product

(GDP) was nearly $ 2 billion higher from 2013 to 2035, and was close to $10 billio n higher for the case of

$45 per metric ton . Depending on the scenario, the economy saw an additional 2,000 to 11 ,000 jobs over

the baseline. The sectors of the economy that benefited the most were those with a high labor

- intensity, a

strong localization t o Massachusetts, and low overall fossil fuel usage.

$0 $100 $200 $300 $400 $500 2013 $ (millions) Additional GDP (annual)

$0 $2,000 $4,000 $6,000 $8,000 $10,000 Additional GDP (cumulative)

$15 tax

$30 tax

$45 tax

0 2,000 4,000 6,000 8,000 10,000 12,000

Jobs Additional employment (annual)

0 2,000 4,000 6,000 8,000 10,000 Additional jobs (annual average)

$15 tax

$30 tax

$45 tax

### Regional Economic Models, Inc.

p. 4 A carbon tax can generate revenue for the state and reduce its emissions below current levels. At $45 per

metric ton, tax revenue could total up to $2.5 billion per year . Simultaneously, c arbon emissions can fall

by 2 million to 8 million metric tons a year. This is enough to reduce carbon emissions in Massachusetts

to between 88 % and 80 % of 1990 emissions levels. C urrent ly, Mass achusetts emits approximately 89 % of

its 1990 emissions levels

. These state tax revenues and reductions in emiss ions increase with a higher tax.

Conversely , also, due to diminishing marginal returns, the rate s of revenu e generation and emissions

slow with a higher tax. Ultimately, a carbon tax can be consistent with the stated goals of bettering the

state economy, raising new revenues, and reducing emissions.



## M ETHODOLOGY

The analysis evaluated both the economic impact and the effect on carbon dioxide emissions of a

hypothetical carbon tax in the state of Massachusetts. We used REMI PI + , a dynamic equilibrium model of

the state economy based on public data and peer

- reviewed methodology used throughout the United

States. We also used the Carbon Tax Analysis Model (CTAM), an open

- source model of carbon tax

revenues and emissions savings used in other states for similar studies. We employed CTAM for forecast

generation of the influence of a tax on consumer behavior and emissions, and subsequently PI + to observe

how these changes would affect the total economy of the state. This included the GDP outlook, job

creation, demographic shifts , and cost of living for households. - 10

- 8

- 6

- 4

- 2

0 Metric tons (millions) Saved carbon emissions (annual)

- 140

- 120

- 100

- 80

- 60

- 40

- 20

0 Saved carbon emissions (cumulative)

$15 tax

$30 tax

$45 tax

78% 80% 82% 84% 86% 88% 90% 92% Carbon emissions as a percentage of 1990 levels

$15 tax

$30 tax

$45 tax

No tax

### Regional Economic Models, Inc.

p. 5 

# I NTRODUCTION AND 

# B ACKGROUND

This study examines the complex interactions between a state’s carbon dioxide emissions and its

economy, demographics and fiscal policy

. Like any state

, for Massachusetts the issue lies in finding an

optimal means of maximiz ing the area’s economi c growth, creating jobs, minimizing living costs and

mitigating the cost of doi ng business, while also reducing greenhouse gas (GHG) emissions as much as

possible . These targets do not always have to compete with each other . Stricter emissions standards, for

example, can lead to a greater degree of energy efficiency in a state, thus adding to the competitiveness of

firms in the area vying for domestic and international market share in key industries. We will use two

models, REMI PI + and the Carbon Tax Analysis Model (CTAM), to examine these factors of economic

performance and the emis sions of GHG into the Earth’s atmosphere in a consistent f ramework, and

report a possible yet positive way for the state to balance these concerns.

The mechanism of interest in this study is a carbon tax

. A carbon tax is a Pigovian tax imposed by a

governm ent or regulatory body that introduces a fee for releasing carbon dioxide into the atmosphere,

typically at the point of combustion of fuels. 1 The idea behind a carbon tax is to discourage the usage of

such fuels, which therefore reduces the total carbon e mitted into the air. In economic terms, a carbon tax

can lead to a reduction in “externalities.” 2 Externality is an individual or firm’s actions harming the

general economy, but that cost does not harm the individual or firm in an acute manner. Carbon dioxide

is innocuous to individuals in small quantities; on a global scale, however, more emissions may have

serious consequences. Making an argument for or against the danger posed by higher concentration s of

carbon in the atmosphere is not the purpose of this paper. The goal is reducing emissions and to evaluate

a way to combine it with improvements in the general economy.

A carbon tax is indeed a tax, but it does not solely imply a net increase in taxes levied on households and

the business community. Namely, a carbon tax can be “revenue

- neutral,” where the revenues from the

tax go to reducing personal income, sales, or corporate taxes (or any other taxes) in an area. In a situation

of revenue

- neutrality, a government’s share of income out of the total economy does not change, only the

allocation of collection. This may also be a more efficient means of collecting taxes. By making the emitter

feel some of the pain the cost of emissions , the tax will, in th eory, correct for the externality of releasing

the GHG. Policymakers can also “recycle” the revenue from a carbon tax by putting it into general tax

relief or investment programs including energy efficiency, weatherization, infrastructure, or scientific

re search that could generate both growth and productivity for the economy in the future. We will use

elements of both in our analysis of the economic implications.

The Committee for a Green Economy (CGE) contracted REMI to look into the issue of a carbon tax in the

state of Massachusetts. The Bay State, if it were to proceed with such a policy as the carbon tax, would

join a number of states and Canadian provinces in introducing and implementing novel carbon emissions

legislation. 3 The last major federal legi slation to address GHG emissions was the American Clean Energy

and Security Act of 2009. This bill usually went by its more common name as the Waxman

- Markey bill

1 There are many descriptions of carbon taxes in everything from academic papers, to periodical articles,

to online sources. For examples of these, please see, < http://www.carb ontax.net.au/category/what

- is

the

- carbon

- tax/ >, or < http://tinyurl.com/worldbankct >.

2 See, < http://www.investopedia.com/terms/e/externality.asp >

.

3 For a list as of May 9, 2013, see, < http://www.carbontax.org/progress/states/ >.

### Regional Economic Models, Inc.

p. 6 after its sponsors, Henry Waxman (D

- CA) and Ed Markey (D

- MA). 4 It designed a federal “cap

- and

trade ,” which works somewhat like a carbon tax in imposing a price on emitting gases . With little action

in Washington on this issue, states have begun to take matters into their own hands. California currently

has its own cap

- and

- trade under AB32. 5 Britis h Columbia first enacted a revenue

- neutral carbon tax in

2008. Oregon, New York, and Washington have looked into measures in recent history. Massachusetts

already participates in the Regional Greenhouse Gas Initiative (RGGI), a cap

- and

- trade system between

states in the Northeast. 6 A carbon tax in Massachusetts would be an enforcement mechanism for RGGI

with a price on emissions supplementing an auction price

. According to a World Bank report , “domestic

emissions trading schemes and economy

- wide instruments such as carbon tax or reform of fuel pricing”

can support each other as systems to curb emissions

. 7

Much of this study takes inspiration from a similar study for a carbon tax in Oregon, though there are a

few major differences. Earlier this year, the No rthwest Economic Research Center (NERC) at Portland

State University (PSU) released Carbon Tax Shift: How to Make It Work for Oregon’s Economy

. 8 Using CTAM,

the researchers there found the amount of tax revenue drawn out of the economy by the tax, and hence

the amount of revenue to recycle back into the economy by additional investments and tax cuts. CTAM is

an open

- source, Excel

- based model developed by Keibun Mori for Washington state

. 9 NERC replaced the

Washington data to make the model reflect Oregon, and we did the same for CTAM to represent the

4 This bill passed the House before stalling in the Senate. For its text or some of its history, see,

< http://www.opencongress.org/bill/111

- h2454/show >.

5 For more information on cap

- and

- trade in California, see the California Air Resources Board (CARB)

page for AB32 here, < http://www.arb.ca.gov/cc/ab32/ab32.htm >.

6 See a REMI analysis on RGGI and its economic implications for 12

- regions in the Northeast, here,

< http://www.rggi.org/docs/ProgramReview/November28/12_11_28_REMI_Presentation.pdf >

7 See p. 31, < http://tinyurl.com/worldbankct >.

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

9 For some discussion of how CTAM works, information on how Mori developed and calibrated it, and

th e actual Excel document file for download, see, < http://daily.sightline.org/2011/08/10/washington

carbon

- tax

- new

- model

- and

- analysis/ >. Our analysis used this base model with data updates for New

England and Massachusetts where appropriate to recalibrate it.

### Regional Economic Models, Inc.

p. 7 emissions from Massachusetts. Carbon Tax Shift found a generally net positive impact on the Oregon

economy from the tax when accounting for revenue recycling and neutrality under various levels of taxes

and scenarios for the latter. The researchers at NERC , however, used a “static” model of the economy,

which did not adjust over time for the incentives and costs created by the tax.

Using REMI ’s dynamic regional economic model, PI + , and CTAM, we modeled the economic impact of

various carbon tax scenarios for Massachusetts under the guidelines given by CGE. The use of a dynamic

model

— and not a static one

— makes this a more robust analysis. A dyna mic model will adjust for

regional competitiveness for business activity and household locations, which is a vital consideration

when introducing new taxes, spending, or any other incentives into the economy. The results of our

simulations will look very d ifferent in 2025 or 2035 than in 2015 for the same reason s . Our scenarios

include a $15, $30, and $45 tax per metric ton of carbon dioxide emissions, with a few options for

spreading the revenue back into the economy. This can be through infrastructure spe nding and cuts to

corporate, income, or sales taxes. 10 Economic m odeling allows policymakers and citizens to compare

alternatives and therefore make an informed decision.

REMI’s task and position here is to advise on the economic implications of a carbon tax. We do not seek

to advocate any specific policy or action, but rather to provide information and data to aid in the

decision

- making process with criteria related to macroeconomics. The remainder of this report contains

background on REMI and our model, PI + , and information on CTAM. It also has how we integrated

CTAM data into our simulations. After that, we describe our scenarios, their results in terms of how the

economy does (with indicators like total employment and GDP), and how much the state might emit in

carbon dioxide in alternative cases. There will be a wealth of information on the conclusions of the

simulations, which will outline a projected difference to the economy from having a carbon tax, or

different sorts of carbon taxes, versus doing n othing at all.

10 While our simulations are not an exact representation of any specific bill, our simulations are in

keeping with its structure and principles of having the tax, making some investments, and returning the

revenue to the state’s taxpayers in an efficient manner while reducing carbon dioxide emissions,

< http://lexington.patch.com/blog_posts/support

- builds

- for

- conroy

- barrett

- anti

- climate

- change

- bill >.

### Regional Economic Models, Inc.

p. 8 

# P OLICY 

# S CENARIOS

For this study, we have run three overarching scenarios . All scenarios start with a $5 carbon tax on each

metric ton of carbon emissions, enforced by fuel type, starting in late 2013. We told CTAM to exempt jet

fuel and marine fuel, given those industries’ unique nature in trading both inside and outside of the

jurisdiction of the state. After that, we have three cases for a peak carbon tax: $15/ton, $30/ton, and

$45/ton. Each one of these happens “gently,” phased in at t he rate of $10/year until the peak rate comes

into effect. We follow the same scheme with the revenue in every case. The first $100 million goes

towards a state fund for research and infrastructure development. Hence, this study is not quite revenue

neutra l, but close to it, given the $100 million is not a significant portion of the anticipated revenue. We

return the rest of the money to households and businesses in an even manner.

Figure 1 .1

– This shows the various levels of carbon tax under the three s cenarios. All start at $5/ton, and all use the

same phase in rate of $10/year. The eventual result is a low, medium, and high scenario to test the sensitivity of the

Massachusetts state economy to the impact of a carbon tax and associated state tax revenue s to these degrees. $0 $5 $10 $15 $20 $25 $30 $35 $40 $45 $50

2013 2014 2015 2016 2017 2018

- 2035 Carbon tax (2013 dollars/MT)

$15 tax

$30 tax

$45 tax

### Regional Economic Models, Inc.

p. 9 After the first $100 million, we distributed the remaining revenue back to the economy in a revenue

neutral manner. We split the money between the business sector variables and the household policy

variables to give 50% of the revenue to each. An important aspect of a carbon tax is its simplicity and

fairness. Everybody pays the tax in proportion to their usage of fuels and therefore their emissions of

carbon dioxide into the air, and e verybody receives some relief from the state funds available from the

carbon tax. It is difficult at this level of modeling to ensure that every household or firm receives the exact

money back it pays in the tax . H owever, broadly, major sectors of the economy see benefit s back roughly

in kind with their c arbon tax payments. At the same time, the tax will introduce incentive s to reduce the

use of carbon

- emit ting fuels, which will decrease GHG emissions.

We used these principles to design the tax cuts. The 50% to firms went to reducing state corporate income

taxes through the production cost variable in PI + . The half to households went 50% (for a share of the

overall of 25%) to lowering the personal income tax. The other 50% of that half went to lowering the state

sales tax, which we intend to help reduce som e of the “regressive” nature of the carbon tax. A regressive

tax is one that falls disproportionally on low

- income households or small businesses. 11 A carbon tax will

increase the real cost of fuels, gasoline, and electricity more than anything else. A low

- income household

spends more of its income on these necessities (along with housing, clothing, and food) than a high

income household, which has much more real disposable income. A general sales tax is oftentimes

regressive , so reducing the sales tax can c orrect for some of the regression in the carbon tax. In this way,

both sides of labor and capital see relief from tax es , in an even 50

- 50 manner

, and we protected low

income households by giving th em a break in their sales tax.

Figure 1.2

– This flow chart shows the path of the state revenue after the collection of the carbon tax. We do not aim

for a 100% revenue

- neutral situation (which is like Conroy

- Barrett) ; we followed this by sending the first $100

million in annual revenue to the st ate ’s investments . The balance goes back to taxpayers in the economy.

11 See, < http://www.investopedia.com/terms/r/regressivetax.asp >. 

### Revenue from

carbon tax First $100

million/year 50% to

infrastructure Construction

industries

50% to scientific

research Research

industries

Balance of the

revenue 50% to

businesses Reduction in

corporate

income taxes

50% to

households Half to lower

income taxes



### Half to lower

sales taxes

### Regional Economic Models, Inc.

p. 10 

# S IMULATION 

# R ESULTS

The results for the scenarios cover the economic and carbon impacts. On the economic side, this includes

the impact to state GDP, total employment across all industrie s and in the government sector, and

granular impacts to industry

- level employment, output, and occupation categories. Demographic results

include information on how the state’s overall population growth changes due to the changes and

incentives with a diff erent labor market and costs of living. Results from CTAM on the carbon side will

include anticipated impacts to emissions levels because of carbon tax levels, the benchmarked difference

from 1990 data in percentage terms, and a detailed table of several f actors. Those factors include the level

of tax, in $15 increments (starting at $10) to $100, anticipated state revenue from the same, the saved

emissions, and the difference from 1990 levels. We do not seek to have absolute answers or any

recommendations f or Massachusetts as REMI. Instead, we provide information here to isolate causes

from effect, and allow policymakers and the public to decide from there.



## A DDITIONAL 

## G ROSS 

## D OMESTIC 

## P RODUCT 

## (A NNUAL 

## ) TO 

## B ASELINE

Figure 2.1

– This shows the various levels of carbon tax under the three scenarios. All start at $5/ton, and all use the

same phase in rate of $10/year. The eventual result is a low, medium, and high scenario to test the sensitivity of the

Massachusetts state economy to the impact of a carbon tax a nd associated state tax revenues to these degrees. $0 $50 $100 $150 $200 $250 $300 $350 $400 $450 $500

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Millions of 2013 dollars

$15 tax

$30 tax

$45 tax

### Regional Economic Models, Inc.

p. 11 

## A DDITIONAL 

## G ROSS 

## D OMESTIC 

## P RODUCT 

## (C UMULATIVE 

## ) TO 

## B ASELINE

Figure 2.2

– The economy reproduces GDP annually as goods and services go through production, transportation,

and consumption. Hence, it is appropriate to sum it over years to give a total GDP impact over a period. For these

simulations, the total GDP difference fro m 2013 to 2035 is between $1 billion to $10 billion in real dollars. For the

high case, this is an average of approximately $ 450 million additional state GDP in any given year.

GDP symbolizes additional economic activity in the state. There are several rea sons this rises due to the

carbon tax’s introduction and ensuing tax relief in other areas. Discouraging fuel consumption in the state

of Massachusetts means fewer fuel imports

— New England has very little oil or natural gas extraction in

its borders and a small amount of the associated supply chain from drilling, transportation, and refining.

Reducing fuel imports from the Delaware River refineries or Texas keeps money in the state. Additional

consumer spending means more activity in sectors like retail, wh ich are inherently local, which also takes

activity away from other states and centralizes them in Massachusetts.



## T OTAL 

## C HANGE IN 

## E MPLOYMENT FROM 

## B ASELINE

Figure 2.3

– In keeping with a higher GDP, the carbon tax means more jobs in the Massachusetts econ omy. Do

note

— these are essentially “job

- years,” or a unit of labor demand equivalent to a job for twelve months. They are

not a measure of additional , new job creation but additionally available jobs over a “no tax” baseline case. $0 $1,000 $2,000 $3,000 $4,000 $5,000 $6,000 $7,000 $8,000 $9,000 $10,000

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Millions of 2013 dollars

$15 tax

$30 tax

$45 tax

0 2,000 4,000 6,000 8,000 10,000 12,000 14,000

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Jobs over the baseline

$15 tax

$30 tax

$45 tax

### Regional Economic Models, Inc.

p. 12 There is a more positive impact to employment than GDP in a proportional sense. There are a couple of

changes to economic incentives in the state to make this the case. A carbon tax will discourage the use of

emissions

- generating fuels in production processes, and this encourages firms to switch towards either

machines (capital) or labor inputs. A relatively high cost of fuel also gives an advantage to industries that

tend to rely on labor, such as healthcare or business services. Those industries tend to generate a high

level of j obs for their output. At the same time, energy

- intensive sectors like manufacturers, refineries, or

resource extraction

— the least likely sorts of business to grow more under a carbon tax

— generate a

compar atively smaller job count . The balance between GDP a nd employment would change with any

modifications to the 50

- 50 split between firms and households from the revenue. More of a split towards

businesses would increase GDP, but jobs would suffer, and vice versa. This is a rather precarious balance,

given tha t a 60

- 40 or 40

- 60 would drive at least one indicator below the baseline.



## P OPULATION 

## C HANGE FROM 

## B ASELINE

Figure 2.4

– The state’s population changes due to the carbon tax. The greater availability of jobs means a stronger

pull “in” to the state from the labor market. However, higher energy prices mean a higher cost of living, which

encourages households to move elsewhere and additional net commuting from the rest of New England to take work

inside Massachusetts. The net is close to zero , depending on scenario, to the tune of a few thousand people. - 3,000 - 2,000 - 1,000 0 1,000 2,000 3,000 4,000

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Population over the baseline

$15 tax

$30 tax

$45 tax

### Regional Economic Models, Inc.

p. 13 The next section has data about the projections of the expected carbon tax revenue and changes for

emissions. This information comes from C TAM, not REMI, with background data from EIA, some

economic/demographic forecast points from PI + , and the original research and methodology inside the

carbon model. The calculations are internal , and we will not give it an outstanding endorsement of the

ca pabilities it has because it is not a REMI model. On the other hand, it is very transparent in the way it

works, and all of the data used to calibrate it is public and open. Results include the anticipated revenue

from the carbon tax under the three scenar ios, the level of emissions, how those levels relate to historical

data for emissions in 1990, and a “sliding scale” of how the tax level relates to revenue, emissions, and

1990 levels of carbon, and economic performance.



## R EVENUES FROM 

## C ARBON 

## T AX 

## (A NNUAL 

## )

Figure 2.5

– This shows the anticipated carbon tax revenue from CTAM for the three levels. Note these do not scale

in a proportional manner, and higher levels of tax rates will eventua lly lead to a stagnate level of added revenue.



## R EVENUES FROM 

## C ARBON 

## T AX 

## (C UMULATIVE 

## )

Figure 2.6

– This adds the annual revenues over time. Over the period, from CTAM, Massachusetts could collect

between $20 billion and $50 billion in additional tax revenue from taxing carbon. This goes to general tax relief in

this simulation, besides the $100 million in each year reserved for a state infrastructure and research fund. $0 $500 $1,000 $1,500 $2,000 $2,500 $3,000

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Millions of 2013 dollars

$15 tax

$30 tax

$45 tax

$0 $10,000 $20,000 $30,000 $40,000 $50,000 $60,000

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Millions of 2013 dollars

$15 tax

$30 tax

$45 tax

### Regional Economic Models, Inc.

p. 14 Industry output by the U.S. Census’ NAICS definitions, m illions of 2013 dollars 2015 2020 2025 2030 2035

Forestry and logging; Fishing, hunting, and trapping

- $1.8

- $3.5

- $3.6

- $3.3

- $2.9

Agriculture and forestry support activities $0.0 $0.0 $0.0 $0.0 $0.0

Oil and gas extraction

- $0.6

- $0.8

- $0.8

- $0.6

- $0.3

Mining (except oil and gas)

- $0.5

- $1.1

- $1.3

- $1.4

- $1.4

Support activities for mining $0.0

- $0.1

- $0.1

- $0.1

- $0.1

Utilities

- $112.7

- $127.4

- $133.1

- $137.0

- $142.5

Construction $29.5 $33.0 $40.8 $36.0 $22.7

Wood product manufacturing $0.2 $0.2 $0.2 $0.1 $0.0

Nonmetallic mineral product manufacturing $0.2

- $0.9

- $1.1

- $1.2

- $1.3

Primary metal manufacturing

- $5.0

- $11.1

- $12.4

- $12.6

- $12.2

Fabricated metal product manufacturing $4.3 $5.1 $5.1 $4.9 $4.4

Machinery manufacturing $1.0 $1.3 $1.2 $1.2 $1.2

Computer and electronic product manufacturing $45.3 $102.5 $129.5 $149.6 $163.8

Electrical equipment and appliance manufacturing $0.9 $0.5

- $0.2

- $0.8

- $1.3

Motor vehicles, bodies and trailers, and parts manufacturing $1.3 $1.7 $1.8 $1.8 $1.8

Other transportation equipment manufacturing $2.5 $3.9 $3.9 $3.6 $3.1

Furniture and related product manufacturing $1.0 $1.3 $1.2 $1.1 $0.9

Miscellaneous manufacturing $4.1 $4.7 $4.0 $3.4 $3.2

Food manufacturing $3.9 $4.4 $4.1 $3.6 $3.0

Beverage and tobacco product manufacturing $2.8 $3.1 $3.0 $2.7 $2.3

Textile mills; Textile product mills

- $0.3

- $0.7

- $0.8

- $0.7

- $0.7

Apparel manufacturing; Leather and allied product manufacturing $0.7 $0.8 $0.8 $0.7 $0.6

Paper manufacturing

- $0.7

- $2.5

- $2.9

- $3.0

- $3.0

Printing and related support activities $1.9 $2.3 $2.2 $2.0 $1.7

Petroleum and coal products manufacturing

- $106.8

- $171.4

- $180.5

- $177.6

- $172.7

Chemical manufacturing

- $65.3

- $140.4

- $161.8

- $163.5

- $157.6

Plastics and rubber product manufacturing

- $1.1

- $4.6

- $5.5

- $5.8

- $5.8

Wholesale trade

- $3.6 $8.3 $15.1 $19.4 $22.1

Retail trade

- $31.3

- $14.1

- $3.0 $3.6 $8.0

Air transportation

- $2.3

- $4.8

- $6.0

- $6.5

- $6.7

Rail transportation

- $0.2

- $0.4

- $0.5

- $0.5

- $0.6

Water transportation

- $0.2

- $0.4

- $0.5

- $0.5

- $0.5

Truck transportation

- $0.9

- $1.4

- $1.5

- $1.6

- $1.5

Couriers and messengers

- $0.1

- $0.4

- $0.6

- $0.7

- $0.8

Transit and ground passenger transportation $0.2

- $0.7

- $1.1

- $1.3

- $1.5

Pipeline transportation

- $0.1

- $0.1

- $0.1

- $0.1

- $0.1

Scenic transportation; Support activities for transportation

- $0.5

- $0.6

- $0.8

- $0.9

- $1.1

Warehousing and storage $0.3 $0.4 $0.3 $0.2 $0.1

Publishing industries, except Internet $18.2 $28.6 $31.8 $32.4 $31.7

Motion picture and sound recording industries $0.7 $0.9 $0.9 $0.9 $0.9

Internet publishing and broadcasting; ISPs, search portals, and data $7.7 $10.2 $10.2 $9.5 $8.5

Broadcasting, except Internet $1.6 $2.4 $2.4 $2.1 $1.8

Telecommunications $15.0 $15.9 $15.1 $13.8 $12.0

Credit intermediation; Funds, trusts, & other financial $43.7 $44.4 $42.9 $40.2 $37.2

Securities, commodity contracts, investments $33.3 $47.6 $47.4 $43.3 $38.2

Insurance carriers and related activities $13.9 $15.6 $14.5 $12.5 $10.5

Real estate $50.0 $13.6

- $0.5

- $13.0

- $26.6

Rental and leasing services; Leasers of nonfinancial assets $8.7 $12.0 $13.3 $13.9 $13.7

Professional, scientific, and technical services $95.9 $108.2 $103.3 $93.8 $82.7

Management of companies and enterprises $7.0 $10.6 $9.3 $6.8 $3.8

Administrative and support services $7.6

- $0.8

- $5.2

- $9.4

- $14.4

Waste management and remediation services $1.0 $0.1

- $0.3

- $0.7

- $1.2

Educational services $6.9 $2.2 $0.6

- $0.4

- $1.8

Ambulatory health care services $103.2 $104.4 $105.0 $105.3 $105.5

Hospitals $30.0 $21.1 $13.5 $8.0 $2.7

Nursing and residential care facilities $9.4 $8.0 $7.3 $6.5 $5.3

Social assistance $4.1 $4.1 $3.9 $3.5 $3.0

Performing arts and spectator sports $4.1 $4.7 $4.7 $4.5 $4.1

Museums, historical sites, zoos, and parks $0.5 $0.2 $0.0

- $0.1

- $0.3

Amusement, gambling, and recreation $2.9 $2.1 $1.8 $1.7 $1.5

Accommodation

- $0.5

- $8.8

- $12.2

- $14.5

- $16.8

Food services and drinking places $22.1 $21.0 $20.0 $17.9 $13.9

Repair and maintenance $7.3 $6.9 $6.5 $5.9 $5.0

Personal and laundry services $14.8 $14.6 $14.1 $13.6 $13.2

Membership associations and organizations $11.2 $12.8 $12.5 $11.5 $9.9

Private household services $1.2 $1.3 $1.4 $1.4 $1.3

Figure 2.7

– This chart shows the sensitivity and scale of the overall reaction, by NAICS industry, to the carbon tax

and revenue recycling. This is for the $30 tax scenario, and the units are in millions of 2013 dollars.

### Regional Economic Models, Inc.

p. 15 A number of key industries undergo disproportionate relative impac t. These include utilities , electronics,

petroleum products, chemical manufacturing, retail, professi onal services , and food services

. Such

impacts are due to the nature of the sectors’ economic inputs and outputs, their dependence on direct

consumer spending , and their position in the general economy . One should note, however, that these are

small, marginal changes. In percentages , the output of any one of these industries does not change by

more than a f raction of a percent in a ny given year (with the exception of petroleum and coal pr oducts,

which change by slightly over 2% in the $30 ta x run ). These industries have their respective reasons for

their behavior inside the model based on historical data

. The BLS compiles data and tables on these

through the North American Industrial Class ification System (NAICS), which PI + uses

. 12

Utilities include electronic power generation and distribution, water and sewers, and natural gas

distribution, and they are a special instance

. The Conroy

- Barrett legislation exempts power generation

and distribution in order to avoid imposing a double hit on a se ctor already subject to special treatment

under the current RGGI framework. Unfortunately, the CTAM model employed in this report cannot

reflect these kinds of exemptions. As a result, the data in Figure 2.7 for utilities attributed to the carbon tax

might be due to RGGI. An auction price for carbon in a cap

- and

- trade market is functionally similar to

that of a statutory carbon tax, which means that this carbon tax and RGGI could achieve the same goals of

incentivizing efficiency through other means.

Petrol eum products and chemical manufacturing show the most negative impact

, chiefly because of

higher fuel costs. Petroleum products involve the refining of crude oil

, while chemical manufacturing

involve s transforming organic or inorganic raw materials into se llable products. This includes basic

compounds, resins, rubber, pesticides, fertilizers, pharmaceuticals, photographic and film equipment,

paint, soap, and other home and industrial cleaning materials. The tax affects only end

- use consumers of

carbon

- emitting fluids, but nonetheless, these industries are both large and final consumers of liquid fuels

and electricity. Higher costs mean a reduction in their market shares (relative to baseline) because of

increasing imports and slower rate of in vestment and growth

. These industries are globally very

competitive and receive little direct consumption spending , so an increase in consumer demand from

lower income and sales taxes would not bring them as much relief.

The majority of economic sectors wo uld see positive effects from the carbon tax. The two sectors showing

the biggest gains are the computers and electronics and professional services sectors . Both are growth

sectors for Massachusetts. The e lectronics sector focuses on hardware for electroni cs, and professional

service s includes law , resea rch, accounting, and consulting

. Boston is a global leader in many of these.

Neither of these industries uses much fuel, relying instead on labor

. L owering corporate taxes in the state

offers them an advantage and allows them to grow quicker. On the other hand, retail and food service

places display divergent patterns. Retail involves selling goods without any further transformation while

food service requires meal preparation for on

- or off

- premise co nsumption. Each sees small changes

given their large size, and each suffers initially due to a decline in real incomes from elevated fuel prices.

However, food service has the bigger jump from revenue recycling

— restaurants are more of a luxury

than grocery stores, and general tax reductions lead to slightly more luxury spending from high

- income

households than spending on bare necessities from low

- income households

.

12 The NAICS is the U.S. Census Bureau’s standard accounting for what makes up an industry. The PI +

model in this case has 70

- sectors, which then approximates a 3

- digit NAICS configuration

. Please s ee,

< http://www.c ensus.gov/eos/www/naics/index.html >.

### Regional Economic Models, Inc.

p. 16 Industry employment by the U.S. Census’ NAICS definitions, number of jobs 2015 2020 2025 2030 2035

Forestry and logging; Fishing, hunting, and trapping

- 3

- 3

- 1 0 0

Agriculture and forestry support activities 0 0 0 0 0

Oil and gas extraction

- 10

- 10

- 8

- 5

- 3

Mining (except oil and gas) 0 2 5 7 8

Support activities for mining 0 0 0 0 0

Utilities

- 120

- 112

- 101

- 91

- 84

Construction 469 931 1,244 1,346 1,322

Wood product manufacturing 2 5 7 7 7

Nonmetallic mineral product manufacturing 5 11 15 17 16

Primary metal manufacturing

- 3

- 1 2 4 5

Fabricated metal product manufacturing 24 43 50 51 49

Machinery manufacturing 7 13 15 14 13

Computer and electronic product manufacturing 98 164 161 145 125

Electrical equipment and appliance manufacturing 5 8 7 6 4

Motor vehicles, bodies and trailers, and parts manufacturing 2 3 3 2 2

Other transportation equipment manufacturing 6 11 11 10 9

Furniture and related product manufacturing 5 8 8 7 6

Miscellaneous manufacturing 15 18 15 12 11

Food manufacturing 14 25 29 29 27

Beverage and tobacco product manufacturing 3 4 4 4 3

Textile mills; Textile product mills 2 5 6 6 6

Apparel manufacturing; Leather and allied product manufacturing 8 11 10 9 7

Paper manufacturing 7 16 20 21 19

Printing and related support activities 13 19 19 17 14

Petroleum and coal products manufacturing

- 14

- 17

- 13

- 10

- 7

Chemical manufacturing

- 34

- 44

- 29

- 14

- 4

Plastics and rubber product manufacturing 5 13 19 22 23

Wholesale trade 15 109 152 172 180

Retail trade

- 315 66 258 339 368

Air transportation

- 3

- 4

- 3

- 1 0

Rail transportation 0 2 2 3 2

Water transportation 1 2 3 4 5

Truck transportation 3 18 28 34 38

Couriers and messengers 7 18 25 30 33

Transit and ground passenger transportation 11 13 17 19 20

Pipeline transportation 0 0 0 0 0

Scenic transportation; Support activities for transportation

- 3

- 2

- 1

- 1

- 1

Warehousing and storage 4 8 8 7 6

Publishing industries, except Internet 34 47 45 40 33

Motion picture and sound recording industries 5 7 7 6 5

Internet publishing and broadcasting; ISPs, search portals, and data 17 19 16 12 9

Broadcasting, except Internet 6 9 10 9 8

Telecommunications 28 35 35 33 29

Credit intermediation; Funds, trusts, & other financial 105 103 92 77 64

Securities, commodity contracts, investments 158 217 210 185 157

Insurance carriers and related activities 49 57 54 46 37

Real estate 161 167 178 170 149

Rental and leasing services; Leasers of nonfinancial assets 16 21 22 21 19

Professional, scientific, and technical services 548 663 652 597 531

Management of companies and enterprises 35 51 46 38 30

Administrative and support services 231 336 424 479 510

Waste management and remediation services 8 12 16 18 19

Educational services 195 334 440 497 515

Ambulatory health care services 887 928 963 980 992

Hospitals 238 268 281 284 274

Nursing and residential care facilities 156 187 214 229 232

Social assistance 87 119 137 143 141

Performing arts and spectator sports 75 90 91 86 77

Museums, historical sites, zoos, and parks 7 11 14 16 17

Amusement, gambling, and recreation 75 96 114 123 124

Accommodation 14

- 5

- 3 0 0

Food services and drinking places 447 610 696 714 682

Repair and maintenance 68 77 81 78 71

Personal and laundry services 214 217 209 194 178

Membership associations and organizations 148 184 190 181 163

Private household services 154 148 142 131 119

Figure 2.8

– This is a similar graph to the last one on industry sales , though by employment instead. Employment

gains concentrate in the labor

- intensive commercial and service industries towards the bottom of the list.

### Regional Economic Models, Inc.

p. 17 The standard economic concept of labor productivity is essential to understanding the above table . Labor

productivity measures how much production a unit of labor can make in a given unit of time. For

example, if an artisan can make two chairs a day at a price of $200/chair, then his or her productivity for

that day is $400. Taken over the course of a year, that artisan would hav e an outp ut of $96,000/year for

240 days’ worth of work. This applies throughout the general economy, and different industries have

varying levels of labor productivity. An engineer or mechanic working for a chemical plant or factory line

may have an avera ge labor productivity of over $1,50 0,000/year

, given that industry ’s milieu as capital

intensive and highly productive. On the other hand , a constructio n worker might only generate

$30 ,000/year in output given the labor

- intensive characteristics of project s. The relationship to equate

these concepts together is the following:



### Output Units = Labor Units * Labor Productivity

In the scenarios we have constructed , those industries showing the greatest decline in output have high

level s of productivity. They do not lose much in employment despite their drop in output. Meanwhile,

the industries showing the greatest gains in output, such as professional services o r healthcare, are

generally in need of much more labor to accomplish their production

. A legal partnership needs far mo re

attorneys , clerks, secretaries, and administrators to operate as an enterprise than a woodcutting shop

needs operators and maintenance personnel

, because of the nature of the ir technology and production

. Thus, while a carbon tax may slightly decrease overall productivity in the state economy, it also directs

more of the state’s resources into labor

- intensive sectors, which yields the additional net employment

gains overall shown in the previous tables.

There is a socioeconomic asp ect, based on occupations

, to these employment numbers , as well . REMI uses

the Standard Occupation Classification (SOC) from the BLS and an industry

- occupation matrix to bridge

between NAICS and SOC

. 13 SOC is an equivalent to NAICS for type of work instead of industry

— what

does the employee actually do by profess ion instead of where they work by industrial category. Certain

types of firms hire certain patterns of workers, which leads to the data on impacts below. Occupations are

generally a better measure of wages and skill s sets than the industries alone. A nalysis of occupations

allows for a better understanding of how a given policy could influence the labor market from the

household perspective

. This should give a good idea of some of the distributional aspects of this policy.

This is not an absolute measure or a forward projection of how these occupations will see changes , but it

does supply some numbers for comparison.

There are two lessons to l earn from a carbon tax impact on occupations

. O ne, the tax increases demand

for nearly all occupations

. T wo, there is a broad, positive net impact across all levels of education and

professional standing in the state economy. This means that workers will b e able to use their skills sets

and training to shift from waning to waxing industries. A graduate from a state school with an

engineering degree might now be more likely to work for an architectural design firm instead of a

chemical plant, but they will s till be able to find a fit with their occupation between some industries. The

same could happen with a high school graduate moving from truck transportation into grounds and

maintenance. The broad impact across all occupations

, from top executives to the c onstruction trades,

means that no socioeconomic class absorbs a disproportionate impact of the carbon tax. All levels of

education and all lifestyles see some impacts and nearly all of them are positive.

13 See, < http://www.bls.gov/SOC/ >.

### Regional Economic Models, Inc.

p. 18 Occupational employment by the BLS’ SOC definitions, number of jobs 2015 2020 2025 2030 2035

Top executives 71 102 114 113 105

Advertising, marketing, promotions, public relations, and sales managers 19 29 32 31 29

Operations specialties managers 53 73 77 75 69

Other management occupations 91 123 141 144 138

Business operations specialists 134 194 215 215 204

Financial specialists 128 167 169 157 140

Computer occupations 157 213 220 208 191

Mathematical science occupations 4 5 5 5 4

Architects, surveyors, and cartographers 10 13 13 12 11

Engineers 49 75 79 76 70

Drafters, engineering technicians, and mapping technicians 25 36 37 35 31

Life scientists 7 9 10 10 9

Physical scientists 6 8 9 9 8

Social scientists and related workers 9 10 11 10 10

Life, physical, and social science technicians 6 8 9 9 8

Counselors and Social workers 51 59 65 66 65

Miscellaneous community and social service specialists 26 31 34 34 33

Religious workers 2 2 3 3 3

Lawyers, judges, and related workers 31 34 32 28 24

Legal support workers 18 21 20 19 16

Postsecondary teachers 61 97 124 138 141

Preschool, primary, secondary, and special education school teachers 64 77 85 85 77

Other teachers and instructors 26 38 46 50 50

Librarians, curators, and archivists 7 9 10 10 10

Other education, training, and library occupations 30 40 47 48 47

Art and design workers 17 25 26 25 23

Entertainers and performers, sports and related workers 31 42 48 49 48

Media and communication workers 31 41 43 41 38

Media and communication equipment workers 8 10 10 9 9

Health diagnosing and treating practitioners 313 346 366 373 371

Health technologists and technicians 184 206 219 224 222

Other healthcare practitioners and technical occupations 6 7 8 8 8

Nursing, psychiatric, and home health aides 170 204 228 243 251

Occupational therapy and physical therapist assistants and aides 16 18 19 20 21

Other healthcare support occupations 141 145 148 147 144

Supervisors of protective service workers 4 4 4 4 4

Fire fighting and prevention workers 3 3 3 3 2

Law enforcement workers 12 11 10 9 7

Other protective service workers 49 67 80 86 88

Supervisors of food preparation and serving workers 35 48 55 57 55

Cooks and food preparation workers 110 151 173 179 171

Food and beverage serving workers 267 368 424 438 422

Other food preparation and serving related workers 55 71 81 82 78

Supervisors of building and grounds cleaning and maintenance workers 8 10 12 12 12

Building cleaning and pest control workers 145 152 160 157 147

Grounds maintenance workers 42 57 69 75 78

Supervisors of personal care and service workers 9 10 11 11 10

Animal care and service workers 16 19 20 20 19

Entertainment attendants and related workers 27 32 36 36 35

Funeral service workers 11 11 11 10 10

Personal appearance workers 76 82 82 79 75

Baggage porters, bellhops, and concierges; Tour and travel guides 3 4 4 5 5

Other personal care and service workers 182 213 231 237 235

Supervisors of sales workers

- 12 21 37 43 44

Retail sales workers

- 82 112 210 248 257

Sales representatives, services 78 103 105 98 88

Sales representatives, wholesale and manufacturing 21 54 69 74 75

Other sales and related workers 43 56 63 64 60

Supervisors of office and administrative support workers 55 72 79 79 76

Communications equipment operators 5 5 5 5 4

Financial clerks 149 189 205 203 193

### Regional Economic Models, Inc.

p. 19 Information and record clerks 213 265 286 284 273

Material recording, scheduling, dispatching, and distributing workers 0 56 82 91 91

Secretaries and administrative assistants 214 261 281 278 264

Other office and administrative support workers 157 206 227 226 215

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

Agricultural workers 1 3 3 4 4

Fishing and hunting workers 0 0 0 0 0

Forest, conservation, and logging workers

- 1

- 1 0 0 0

Supervisors of construction and extraction workers 30 60 80 86 85

Construction trades workers 252 498 665 719 706

Helpers, construction trades 20 42 57 62 62

Other construction and related workers 11 17 21 22 21

Extraction workers 0 2 4 5 5

Supervisors of installation, maintenance, and repair workers 7 14 19 20 20

Electrical and electronic equipment mechanics, installers, and repairers 11 21 27 28 28

Vehicle and mobile equipment mechanics, installers, and repairers 20 43 55 59 58

Other installation, maintenance, and repair occupations 72 123 158 170 166

Supervisors of production workers 6 12 15 16 15

Assemblers and fabricators 38 65 72 71 68

Food processing workers 1 11 15 17 16

Metal workers and plastic workers 27 50 61 64 62

Printing workers 8 12 12 11 9

Textile, apparel, and furnishings workers 35 37 36 33 29

Woodworkers 3 5 6 6 6

Plant and system operators

- 18

- 19

- 16

- 13

- 12

Other production occupations 34 60 76 82 82

Supervisors of transportation and material moving workers 5 10 13 14 15

Air transportation workers 0 0 0 1 1

Motor vehicle operators 50 97 127 140 143

Rail transportation workers 0 1 1 1 1

Water transportation workers 1 2 3 3 4

Other transportation workers 16 18 18 18 16

Material moving workers 48 109 144 158 160

Military personnel 0 0 0 0 0

Figure 2.9

– This shows the employment impact by occupation instead of by industry. Any given industry employs

a wide variety of skill sets and educational backgrounds. For instance, a large bank will have executives, analysts,

administration professionals, accounta nts, sales representatives, customer service agents, all the way down to

maintenance crews for buildings. The categories above are from the SOC from the BLS; it is the standard, federal

data way of classifying types of j obs across different industries.

### Regional Economic Models, Inc.

p. 20 

## C HANGE IN 

## C ONSUMER 

## P RICES FROM 

## B ASELINE

Introducing the carbon tax would ordinarily affect the price for consumers’ goods in four categories: fuel

oil and other fuels, motor vehicle fuels, natural gas, and electricity. The higher the tax, the greater the

impact will be on these price indices. On the other hand, these higher costs will see some offset because

carbon tax revenue will return to the sta te’s households in the form of lower sales taxes and lower

personal income taxes. One should also note that market price changes shown might very well trend

lower overall

— even after the carbon tax. The energy industry has undergone tremendous changes in the

past five years, including the natural gas hydraulic fracturing revolution, switching of electrical power

generation towards natural gas, and a drop in demand due to a weak economy. Prices are constantly

fluctuating, and these changes are against a wavering baseline.

Figure 2.10

– Emissions from this fuel usage means their prices must go somewhat upwards. However, these prices

are against a baseline of falling fuel prices since 2007 due to reduced demand, the sha le gas revolution, and

additional crude oil drilling throughout North America. Pric es might be higher relative to baseline, but the realized,

sticker consumer prices might still be lower than the high prices experience d in the 2000s decade. 0.00% 10.00% 20.00% 30.00%

2015 2020 2025 2030 2035 Fuel oil and other fuels (change relative to baseline)

$15 tax

$30 tax

$45 tax

0.00% 5.00% 10.00% 15.00%

2015 2020 2025 2030 2035

Motor vehicle fuels, lubricants, and fluids (change relative to baseline)

$15 tax

$30 tax

$45 tax

0.00% 5.00% 10.00% 15.00%

2015 2020 2025 2030 2035 Natural gas (change relative to baseline)

$15 tax

$30 tax

$45 tax

0.00% 2.00% 4.00% 6.00%

2015 2020 2025 2030 2035 Electricity (change relative to baseline)

$15 tax

$30 tax

$45 tax

### Regional Economic Models, Inc.

p. 21 

## C HANGE IN 

## C ONSU MER 

## P RICES FROM 

## B ASELINE

Figure 2.11

– REMI does not have a 1:1 version of the consumer price index (CPI) inside of it, though we do

estimate it with something called the PCE

- Index ( Personal C onsumption Expenditure I ndex), which produces

similar results. This shows the average change in the total cost of living for a household in Massachusetts. Note this

is essentially a “one

- time” adjustment upwards over a baseline and not a change in the long

- term growth rate.



## C HANGES IN 

## C ONSUMER 

## P RICES FOR 

## I NCOME 

## G ROUPS

Year PCE

-

2013$

(<$5,9 00) PCE

-

2013$

($5,900

- $11,700 ) PCE

-

2013$

($11,700

- $17,600 ) PCE

-

2013 $

( $17,600

- $23,400 ) PCE

-

2013$

($23,400

- $35,100 ) PCE

-

2013$

($35,100

- $46,800 ) PCE

-

2013$

($4 6 ,800

- $58,500 ) PCE

-

2013$

($58,5 00+)

2012 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

2013 0.11% 0.11% 0.12% 0.12% 0.12% 0.12% 0.12% 0.11%

2014 0.16% 0.17% 0.19% 0.18% 0.18% 0.18% 0.18% 0.17%

2015 0.19% 0.20% 0.22% 0.21% 0.21% 0.21% 0.21% 0.19%

2016 0.19% 0.20% 0.22% 0.21% 0.21% 0.21% 0.21% 0.20%

2017 0.19% 0.20% 0.22% 0.21% 0.21% 0.21% 0.21% 0.20%

2018 0.19% 0.20% 0.22% 0.21% 0.21% 0.21% 0.21% 0.20%

2019 0.19% 0.20% 0.22% 0.21% 0.21% 0.21% 0.21% 0.20%

2020 0.19% 0.20% 0.22% 0.21% 0.21% 0.21% 0.21% 0.20%

2021 0.19% 0.20% 0.22% 0.21% 0.21% 0.21% 0.21% 0.20%

2022 0.19% 0.20% 0.21% 0.21% 0.21% 0.21% 0.21% 0.20%

2023 0.19% 0.20% 0.21% 0.21% 0.21% 0.20% 0.21% 0.20%

2024 0.19% 0.20% 0.21% 0.21% 0.21% 0.20% 0.21% 0.20%

2025 0.19% 0.20% 0.21% 0.21% 0.21% 0.21% 0.21% 0.20%

2026 0.19% 0.20% 0.21% 0.21% 0.21% 0.20% 0.21% 0.20%

2027 0.19% 0.20% 0.21% 0.21% 0.21% 0.20% 0.21% 0.20%

2028 0.19% 0.20% 0.21% 0.21% 0.21% 0.20% 0.21% 0.20%

2029 0.19% 0.20% 0.21% 0.21% 0.21% 0.20% 0.21% 0.20%

2030 0.19% 0.20% 0.21% 0.21% 0.21% 0.20% 0.21% 0.20%

2031 0.19% 0.20% 0.21% 0.21% 0.21% 0.20% 0.21% 0.20% 0.00% 0.05% 0.10% 0.15% 0.20% 0.25% 0.30%

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 $15 tax

$30 tax

$45 tax

### Regional Economic Models, Inc.

p. 22 2032 0.19% 0.20% 0.21% 0.21% 0.21% 0.20% 0.21% 0.20%

2033 0.19% 0.20% 0.21% 0.21% 0.21% 0.20% 0.21% 0.20%

2034 0.19% 0.20% 0.21% 0.21% 0.21% 0.20% 0.21% 0.20%

2035 0.19% 0.20% 0.21% 0.21% 0.21% 0.20% 0.21% 0.20%

Figure 2.12

– This shows the change in CPI for income groups with the $30 tax (the middle “red” scenario ). The

REMI model includes, as an econometric estimation, different price indices for different sizes of household income.

The methodology for the above calculations is online. 14 The theory is different homes spend differently based on their

income. For example, a low

- income home will spend a larger proportion of total income on food, fuel, and rent (all

necessities ) co mpared to an upper

- income family, which will have more disp osable income for luxury goods such as

electron ics, travel, and entertainment. The above calculations project a somewhat larger impact from a carbon tax on

working homes with incomes from $20,000/y ear to $40,000/year. However, this is not a large differentiation amid

the groups. Judging from above, the carbon tax will not impose much of a disproportionate effect on the working

poor or other low

- income people, especially given the sales and income ta x offsets included in Conroy

- Barrett.

14 See our document with the methodology for decomposing the impact by different income groups,

< http://www.remi.com/download/documentation/pi+/pi+_version_1.3/Income_Distribution.pdf >

### Regional Economic Models, Inc.

p. 23 This section shows changes to carbon emissions from CTAM. We found data from the Environmental

Protection Service (EPA) on emissions in 1990, and the rest of the results come from CTAM and its

research on calculating emissions and the price elasticities to various fuel times. Like REMI, CTAM works

best as a difference engine for finding the hypothetical impact between different scenarios than finding

the absolute level of forecasted emissions in a ny given year.



## F ORECA STED 

## C ARBON 

## D IOXIDE 

## E MISSIONS

Figure 2.13

– According to CTAM and sans the tax, emissions of carbon dioxide from Massachusetts are stable

around 64 million MT per annum. Increasing the carbon tax reduces this by as much as 7 million MT per year.



## D IFFERENCES IN 

## C ARBON 

## E MISSIONS FROM 

## B ASELINE

Figure 2.14

– This is the same data as Figure 2.13 , only transformed to make the purple “Baseline” into the zero line

on this chart. The difference from zero is the anticipated emissions reduction, per year, in CTAM under the price

conditions of the three levels. This modeling suggests significant reductions in carbon dioxide emissions. 66 67 68 69 70 71 72 73 74 75 76

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Millions of metric tons of CO 2

$15 tax

$30 tax

$45 tax

Baseline

- 8

- 7

- 6

- 5

- 4

- 3

- 2

- 1

0 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Millions of metric tons of CO 2

$15 tax

$30 tax

$45 tax

### Regional Economic Models, Inc.

p. 24 The next part looks at these levels of emissions compared to 1990. For several reasons

— the important one

being the Kyoto Prot ocol

— 1990 is a goal year of levels for limiting carbon emissions. 15 Massachusetts is

already below this level for a few reasons. These include the slow rate of population growth in New

England compared to the rest of the United States, low macroeconomic gro wth, the recession of a few

years’ past, and historically low natural gas prices. Natural gas is, relatively, less carbon

- intensive than

other sorts of fossil fuels, and the switching from households, firms, and power companies away from

petroleum and coal and towards gas does save emission. Massachusetts is already below 1990 levels, 16

and a carbon tax would allow it to go beyond Kyoto or other requirements.



## T HE 

## 1990 B ENCHMARK

Figure 2.15

– This gives the range the state might achieve with these levels of carbon taxes in relationship to

emissions in 1990. The b aseline always stays below 92 % in CTAM, but the $45 ta x could bring the state below 85 %

of 1990 levels and downwards. This should give some idea of the range of the possible for these carbon taxes.

T he next table looks at a number of similar scenarios to those above, and it gives a “menu” of possible

options for the state in increasing the carbon tax beyond the three in blue, red, and green. We did not

complete an economic impact study, including reve nue

- neutrality and state investment, for each of these.

That would have required a geometric increase in the number of simulations in PI + . This is not an

onerous task, but it would have quickly created more data to consider and many more lines in the above

figures without complicating the general story of the carbon tax in the Massachusetts economy. For the

sake of simplicity, we left it to three and reported the results as you saw them in the earlier section. The

“XYZ” consideration below has the level of carbon tax, associated revenue estimations from CTAM, the

amount of emissions in millions of metric tons (both the sum from 2013 to 2035), and the difference from

1990 levels in 2035 in a final column. Higher taxes could lead to more state revenues, but al so a somewhat

higher c ost of living for households in Massachusetts.

15 See the United Nations page on it, here, at < http://un fccc.int/kyoto_protocol/items/2830.php >.

16 The 1990 levels were 83.04 million MT of carbon in the state of Massachusetts according to the data,

< http://www.epa.gov/statelocalc limate/documents/pdf/CO2FFC_2010.pdf >. 78% 80% 82% 84% 86% 88% 90% 92%

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 $15 tax

$30 tax

$45 tax

No tax

### Regional Economic Models, Inc.

p. 25 Carbon tax rate Carbon tax

revenue (annual

average) Total emissions,

2013

- 2035 Total emissions

savings, 2013

- 2035 Rate of 1990

emissions in 2035

No tax $0 1790.714 0.00% 89.75%

$10 tax $596 1766.845

- 1.33% 88.17%

$25 tax $1,426 1722.974

- 3.78% 84.98%

$40 tax $2,150 1680.003

- 6.18% 81.80%

$55 tax $2,771 1638.116

- 8.52% 78.62%

$70 tax $3,282 1598.022

- 10.76% 75.43%

$85 tax $3,701 1559.728

- 12.90% 72.25%

$100 tax $4,023 1524.193

- 14.88% 69.06%

Figure 2.16

– Reading from left to right, the units are millions of 2013 dollars, millions of metric tons of carbon

dioxide, percentage change, and percentage change. Specifically, the fourth column shows the saved emissions (in

percentage terms) from the baseline, and the fifth column is the 2035 quantity of emissions in comparison to the

1990 benchmark from the Kyoto protocol requirements

. A higher tax intuitively means more revenue and a greater

reduction in emissions. On the other hand, there is a diminishing of returns with higher taxes. The higher the tax,

the slower the growth rate to revenue coming into the state and emission saved. The figure below shows this trend

and finds a hypothetical limit of revenue and emissions savings for the state at some high tax level.



## A VERAGE 

## A NNUAL 

## C ARBON 

## T AX 

## R EVENUE

Figure 2.17

– This is the second column from the left in graphical form. A higher tax meant more revenue, but at a

decreasing rate because the “easiest” sa vings in CTAM already happened. This suggests a “tipping point” of

around a $120 tax after which you would not see any marginal gain to additional revenue for the state or any

reductions in total emissions. This suggests a “range of the possible” for a carbon tax in the state. 

# y =

- 37.331x 2 

# + 929.72x

- 988.02

R2 = 0.9979

$0 $500 $1,000 $1,500 $2,000 $2,500 $3,000 $3,500 $4,000 $4,500

$0 $10 $25 $40 $55 $70 $85 $100 Millions of 2013 dollars

### Regional Economic Models, Inc.

p. 26 

# R EGIONAL 

# E CONOMIC 

# M ODELS



# , I NC



# . (REMI)

REMI is from Amherst, Massachusetts, though this paper and its content originate in our office in

Washington, DC. REMI is an economic services firm, specializing in issues related to regional modeling,

policy analysis, and fore casting. REMI began as a research project by a professor at UMass

- Amherst, Dr.

George Treyz, in the 1970s when assessing the construction of the Mass Pike. Eventually, in 1980, Dr.

Treyz founded a business around his research, which has grown to the presen t firm. REMI provides

software, support services, as well as issue expertise in nearly every state, the District of Columbia, and in

several foreign nations around the globe. Model users are primarily in state governments, but also

include federal agencies , planning organizations, consulting firms, universities, and private industry

involved in policy and infrastructure development.

REMI is a Massachusetts firm, and we have an institutional presence in the state’s modeling and

policymaking processes. Curren tly, REMI works with such organizations as the Boston Redevelopment

Authority (BRA), the Massachusetts Department of Revenue, and the Massachusetts Institute of

Technology (MIT). 17 Our relationship with these groups includes providing a software package wit h a

model customized to the state, helping with the interface, vetting data, and interpreting results. In some

cases, REMI runs simulations and reports results, such as here.

17 For a full list, see, < http://www.remi.com/clients >. Other clients in Massachusetts include NERA

Economic Consulting, ICF International, the Economic Development Research Gr oup (EDRG), UMass

Amherst, and the Northeast States for Coordinated Air Use Management (NESCAUM).

### Regional Economic Models, Inc.

p. 27 

# T HE 

# PI + 

# M ODEL

REMI used a 1

- region, 70

- sector version of the PI + model configur ed to the state of Massachusetts for this

study. PI + is a fully dynamic, multiregional, computerized model of the state economy. The REMI model

relies on four different quantitative methodologies in its framework, which allows them to highlight each

other’ s strengths while compensating weaknesses. These methodologies include:

1. Input/output tabulation (IO)

– IO modeling is sometimes called “social accounting” because it

shows the interrelationships between different industries and households in the economy. T his

includes the flow of goods and services between firms in supply chains, final sales to households,

and wages paid to and spent by individuals. These interconnections create multipliers. The data

for the table comes from the Bureau of Labor Statistics ( BLS) 18 and the theoretical underpinnings

for IO modeling comes from the Nobel laureate Wassily Leontief.

2. Econometrics

– The REMI model includes statistical parameters for behavior of firms and

households based on historical data. In modeling terms, this is the source of our elasticities and

parameters. This includes how actors respond to changes in prices or wages and the “rate of

adjustment” from a shock until the economy returns to a new balance.

3. Computable General Equilibrium

– This is a broad class of mo dels. Computable general

equilibrium modeling adds market concepts and the principles of equilibrium economics to the

REMI algorithm. This includes markets for housing, labor, consumer goods, and importantly, a

concept of market shares and competitiveness for businesses. For example, consumers in the state

of Massachusetts may demand automobiles, but in all likelihood those cars come from plants in

Michigan or the Southeast, or even overseas. This flow of goods and services can change over

time, and with it the attractiveness of the state for labor and capital, given changes in economic

conditions. With a carbon tax, this is very crucial, given that changes in fuel and energy prices

may greatly influence a new general equilibrium for Massachusetts.

4. Economic Geography

– Geography gives the REMI model a sense of agglomeration, labor

pooling, and economies of scale. Labor

- intensive industries, such as healthcare or professional

services in Boston and Cambridge, tend to cluster in urban centers where specialized pools of

educated workers are easy to obtain. Manufacturers tend to do the same thing given their

tendency to locate near their input suppliers, customers, and transportation hubs. This allows

them to lower their costs and increase their productivity.

REMI began as a research inquiry, and the literature behind PI + is public and oftentimes appears in peer

reviewed journals. These include the Journal of Regional Science

, American Economic Review , and the Review

of Economics and Statistics

. 19 REMI only uses data from public sources. R eferences include the Bureau of

Economic Analysis (BEA), BLS, the U.S. Census Bureau, and the Energy Information Administration

(EIA) at the Department s of Commerce and Energy. 20 The forecast for future economic co nditions comes

from macroeconomic and industry trends identified by the Research Seminar in Quantitative Economics

18 For the most recent BLS make and use table, which we then transform into an IO table from there, see,

< http://www.bls.gov/emp/ep_data_input_output_matrix.htm >.

19 For journal citations from the above publications, see p. 46 of our equations document online,

< www.remi.com/download/documentation/pi+/pi+_version_1.4/PI+_v1.4_Model_Equations(2) .pdf >.

20 For a full listing of data sources and types, see our document online of data sources and procedures,

< www.remi.com/download/doc umentation/pi+/pi+_version_1.4/Data_Sources_and_Estimation_Proce

dures.pdf >.

### Regional Economic Models, Inc.

p. 28 (RSQE) at the University of Michigan in Ann Arbor, MI and the BLS. 21 The REMI model exists in a block

structure of simultaneous equations:

F igure 3.1

– This is the overall structure of REMI’s representation of the state economy. Each rectangle is a “stock,”

a finite concept such as population or the number of jobs. Each arrow shows an equation that links them together.

For example, the populat ion times the participation rate equals the labor force; government spending, plus capital

investment, plus net exports, plus consumption, and minus intermediates, then equals GDP.

Each of the five blocks above adds its own perspective on the economy. Bloc k 1 is the macroeconomy,

given the GDP components outlined in the text for Figure 1 above. Block 2 is the business perspective on

the economy; sales orders come in from Block 1, and industries have to make production decisions (in

terms of hiring workers a nd investing in capital) to eventually generate their needed output. Block 3 is

the demographic portion of the model, which includes natural births and deaths, migration within the

United States and from the rest of the world, and participation in the labo r market. Block 4 introduces

equilibrium concepts to the REMI model: households appraise the labor market, housing, and the cost of

living when making location decisions. For businesses, they make an analogous consideration about their

costs for labor, cap ital, intermediates, and fuel. Block 5 quantifies regional competitiveness, which means

how much an area will export and displace imports when competing on a domestic and international

21 See, < http://rsqe.econ.lsa.umich.edu/ >.

### Regional Economic Models, Inc.

p. 29 marketplace against other states and nations. For these simulations, we changed these variables to

illustrate the direct effects of a carbon tax in Massachusetts:

 Consumer prices

– For higher fuel oil, electricity, natural gas, gasoline

, and motor oil prices due

to the carbon tax, but also a lower general sales tax in the cas e of revenue recycling

 Production costs

– To illustrate the higher cost of input fuels to industries, but to also show the

positive shock of lower business and corporate income taxes in the state

 Real disposable income

– This concept is the amount of mone y left over to households to spend

after taxes, which goes up if the state lowers income tax rates

PI + has two purposes: forecasting and policy analysis by the examination of alternative worlds. The

model has an underlying forecast based on the government data, by county, and the macroeconomic

trends described on p. 8 near the top. To use the model, we introduced “exogenous” changes to the

variables in the structure in Figure 1 . We call these “policy variables,” and they represent the direct effect

of a car bon tax on the Massachusetts economy. From there, the model automatically passes the changes

through the rest of the structure until the model reaches a new equilibrium at some point in the future

after adjusting over time. We used CTAM to develop the diff erences in emissions from the carbon tax and

the amount of carbon tax revenue and recycling to introduce into PI +

.

Figure 3. 2

- This shows the basic methodology and comparisons inside of the REMI model. The blue line is the

control, the “do

- nothing” null hypothesis. From there, we looked at alternative policies, here represented by the

generic Policy A and Policy B. Both A and B are better than the control, and B generates short

- term gains while A

is the better idea in the long run. CTAM works in terms of tax revenue and carbon emissions, while the REMI

equilibrium model concentrates on economic and demographic outputs like GDP or population.



# C ARBON 

# T AX 

# A NALYSIS 

# M ODEL 

# (CTAM)

We updated CTAM for Massachusetts data, which we describe here. Longer, fuller descriptions of this

model are available in other places. A concise explanation is on pp. 22

- 23 of the aforementioned NERC 750 1,000 1,250 1,500 1,750 2,000 2,250 2,500 2,750

2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050 2052 2054 2056 2058 2060 Economic/demographic indicator

Control Forecast Policy A Policy B

### Regional Economic Models, Inc.

p. 30 study. In broad terms, CTAM uses projections from EIA about the anticipated consumption of different

fuel types (such as gasoline, kerosene, coal, natural gas, or petroleum distillates) by Census regions. EIA

generates these forecasts using the National Energy Modeling System (NEMS), their internal government

model used to generate energy forecasts into the future. 22 CTAM then shares these fuel consumption

quantities into the states underlying the Census regions. For Washington and Oregon, this was out of the

Pacific region; for Massachusetts, this was out of N ew England. The fuel quantities become emissions by

multiplying with the rates of emissions. For instance, one gallon of gasoline everywhere will generate

approximately the same amount of carbon dioxide upon its combustion, which CTAM assumes. This

exercis e, taken together, gives CTAM’s user the ability to see a base forecast of emissions in a state or

region, where one can then begin to ask it scenarios of carbon taxes.

CTAM relies on a meta

- analysis of price elasticities by fuel type to determine how sens itive the users of

various fuels are to changes in their prices. By extension, these elasticities determine how effective a

carbon tax is at discouraging economic actors from burning fossil fuels, and therefore reducing carbon

emissions. NERC did not updat e the elasticities for the Oregon study

— explicitly, per agreement with

CGE, REMI did not update these numbers either. These default elasticities represent a “most likely”

scenario of price and emissions sensitivities to a carbon tax. Alternative elasticiti es would generate a

different result, though not one substantially different from the information presented in this study’s

results. CTAM allows its user to pick an initial rate of carbon tax for 2014, a maximum rate at some point

in the future (such as $4 0 per carbon ton), and a rate of phasing. We adjusted these numbers to generate

the high

- level inputs destined for the macroeconomic simulations in PI +

.

Figure 4.1

– This shows the main processes inside of CTAM, taken from p. 26 of the NERC study. A new tax

changes the price of fuels, which therefore leads to a difference in consumption after a response described in the

22 See the EIA page on NEMS, < http://www. eia.gov/oiaf/aeo/overview/ >.

### Regional Economic Models, Inc.

p. 31 elasticities. From there, the model multiplies the emissions by fuel type, and generates an output of how much the

economy emits (or save s in emissions) and generates in total revenue by area due to the new carbon tax.

We updated CTAM to be specific to Massachusetts for our runs. Where applicable, we substituted the

Pacific region (Alaska, Hawaii, Washington, Oregon, and California) data wi th equivalent data for New

England (from the most recent Annual Energy Outlook from the EIA for 2013). 23 Where necessary, we

shared this down to Massachusetts

- level out of the other states in New England. We updated the

macroeconomic parameters in CTAM for consistency with PI + , which were specifically growths in the

state’s total number of households (0.9%) and GDP (3.0%). Climate policies often set the achievement of

some level of emissions watermarked to 1990 a s a key goal. This can include matching 1 990 l evels, trying

to go below 8 5% of the same, or some other proportion. We updated the historical emissions in CTAM for

Massachusetts from data out of the Environmental Protection Agency (EPA). 24

23 See, < http://www.eia.gov/forecasts/aeo/ >.

24 Go here, < http://www.epa.gov/statelocalclimate/docum ents/pdf/CO2FFC_2010.pdf >. Interestingly,

Massachusetts is already well below 1990 levels of emissions owing to its small population growth, low

growth in GDP (relative to the nation), the weak national economy from 2008 to present, and the switch

from coa l to natural gas as a fuel source for electrical power generation. The state has already achieved

this important benchmark, but it could continue to improve from there. CTAM does not cover every type

of emission in the EPA document. Consequently, we made a n adjustment upwards at a level consistent

with the difference between CTAM and EPA in 2010 emissions for every subsequent year. This means the

level of emissions start at the same level, which dictates where the state is relative to 1990, and we can

conce ntrate on the change in emissions in the actual analysis.

### Regional Economic Models, Inc.

p. 32 

# I NTEGRATING 

# PI + 

## AND 

# CTAM

Next, we completed a bridge to bring the emissions and taxation simulations in CTAM into the economic

simulations of the regional economic model. CTAM has four major sectors of the state economy

— residential, commercial, industrial, and transportation. The majority of transportation GHG emissi ons

comes from motor gasoline, which is a consumption item for households, given the model reported

separate categories for motor gasoline purchased by commercial or industrial enterprises. CTAM has

more data than REMI in terms of fuel types. REMI only has three available energy types, which are

natural gas, elec tricity, and petroleum. CTAM breaks petroleum into further categories, including

gasoline, diesel, distillates, lubricants, and a few others . We had to agglomerate this detail from CTAM

up to genera l petroleum layer to make these categories consistent with each other. The following table

shows the exact results in CTAM and the way we bridge d them into PI + :



### CTAM PI +

Residential Kerosene, Distillate Fuel Oil Consumer Price (Fuel oil and other fuels)

Natural Gas Consumer Price (Natural gas)

Electricity Consumer Price (Electricity)

Commercial Liquefied Petroleum Gases, Motor Gasoline,

Kerosene, Distillate Fuel Oil Residual (Commercial Sectors) Fuel Costs

Natural Gas Natural Gas (Commercial Sectors) Fuel

Costs

Electricity Electricity (Commercial Sectors) Fuel Costs

Industrial Motor Gasoline, Distillate Fuel Oil Residual (Industrial Sectors) Fuel Costs

Natural Gas Natural Gas (Industrial Sectors) Fuel Costs

Electricity Electricity (Industrial Sectors) Fuel Costs

Transport Motor Gasoline Consumer Price (Motor vehicle fuels,

lubricants, and fluids)

Distillate Fuel Oil Consumer Price (Fuel oil and other fuels)

Figure 5.1

– This shows the revenue category in CTAM mapped into a price variable in PI + . We only included those

of a degree to influence the economy. These changes in prices will generate the dynamic adjustments to the overall

regional competitiveness for Massachusetts’ industries, commercial operations, industries, and households.

With the revenue raised, we faced finding some way of allocating the surplus to keep the state constraint

of a balanced budget. We sent the money to the NAICS industries that best proxy those for scientific

research and development and infrastructure development, which involve a good deal of professional

services and construction

, when the first $100 million of carbon tax revenue went specifically to these

sorts of programs. 25 We reduced production costs to show the impact of a reduction in corporate taxes,

spread by the amount each industry pays, which we spread based on output. Lower sales taxes meant a

general reduction in the prices for consumer ca tegories eligible for the sales tax. Lower income taxes

meant more disposable income for households to spend.

25 The model for this simulation contained 70

- sectors, which appropriates 3

- digit NAICS. REMI only goes

so deep in the NAICS to ensure the quality of the data in the model. The U.S. Census withholds the

deepest industry detail for reasons of individual and business privacy. For a listing of our sectors, see,

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

- Hierarchical_v1.4.pdf >

### Regional Economic Models, Inc.

p. 33 

# H YPOTHETICAL 

# T AX 

# C ASES

After implementing the carbon tax to reduce state emissions, the choice about how to return the revenue

to the state econom y is an important one . It is a complex balance between maintaining competitiveness

by lowering costs for business

, maintaining high returns to work and investment by lowering income

taxes , and protecting low

- income households with a reduced amount of regre ssive taxes, such as the sales

tax. This section provides a description of the relative advantages of the three versus each other in terms

of GDP, total employment, and real income as well as the reasons for their patterns as th ey echo in the

economy. We r an hypothetical tax cut s of $100 million in the three categories. We then made comparisons

between them about their performance in the tables below

. One should note these are not a general lesson

about the efficiency of certain taxes against each other

— far from it. These are small changes, on the

margin, and not typical or universal cases of designing a tax code for a state economy. The results would

look very different with a different sense of scale or emphasis.



### G ROSS 

### D OMESTIC 

### P RODUCT 

### I MPACT

Figure 6.1

– GDP growth comes from competitiveness in the REMI model as a representation of the way a real

economy works. Low er costs and high e r productivity drive market shares; this expands business activity and

economic growth. Here, lower corporate income taxes enable firms in Massachusetts to export more and displace

imports, which means more GDP in the state. The sales and income tax generate more demand and consumer

spending, but not a change t o long

- run competitiveness, which means they do not drive GDP quite as much.



### T OTAL 

### E MPLOYMENT 

### I MPACT

Figure 6.2

– There is a similar pattern to the GDP one , but with less of a deviation than the corporate income tax.

Consumer spending goes towards labor

- intensive indu stries, which can mean more jobs in the short

- term th an other

initiatives that might increase the state’s long

- term competitiveness through lowering costs and more productivity. $0 $50 $100 $150 $200 $250

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Millions of 2013 dollars

Corporate income tax

General sales tax

Personal income tax

0 500 1000 1500 2000

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Jobs over baseline

Corporate income tax

General sales tax

Personal income tax

### Regional Economic Models, Inc.

p. 34 H OUSEHOLD 

### I NCOME 

### I MPACT

Figure 6.3

– Lowering sales or income taxes, however, gives the biggest boost to household incomes. Intui tively, this

should make sense

— this means more money left in paychecks and lower prices at retail. Some of the money might

leave the state, given most states’ need to import many consumer staples, but this still increases the disposable

income of household s to a greater degree than waiting for competitiveness to stoke the labor market.

When using small examples on the margin, lower business costs do the most to further the state ’s health

in terms of its GDP. However, lower personal taxes do somewhat more in terms of increasing the real

incomes of households. Both approaches have their strengths and weaknesses:

Category Strengths Weaknesses

Corporate

income tax Improves state competitiveness, grows the

economy, generates more tax revenue for the

statehouse in Boston to work with Does not necessarily pass all of this

benefit along to households to the same

degree as the other tax categories

General

sales tax Reduces the cost of living for households,

which makes them richer in real terms and

helps low

- income families the most Additional consumer spending means

more net imports to the state and does

not improve competitiveness

Personal

income tax Attracts economic migrants looking for a high

return to work in terms of wages, can increase

the state’s overall level of productivity “Between” the other two in strengths,

and it does not help low

- income

households as much as sales taxes

Figure 6.4

– This chart shows the relative strengths and weaknesses of these three approaches. No one answer is

right

— it is a ma tter of the state’s priorities and balancing the costs of the carbon tax with the benefits here of

reducing other major sorts of taxes. There is not necessarily one answer between the three for the state.

There are good things and bad things about each way . The choice for Massachusetts is balancing the

multifaceted priorities of growing the state economy, keeping the returns to work high, and protecting

low

- income households from potential downsides or regressive taxes. The 50

- 50 revenue recycling option

in this report manages to maintain a positive impact to GDP, employment, and a minimal impact on the

cost of living. Alternative approaches might create different results, but this gives some guidelines for the

ups and the downs of making these small changes on the margin in the state. $0 $50 $100 $150 $200 $250

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 Millions of 2013 dollars

Corporate income tax

General sales tax

Personal income tax

### Regional Economic Models, Inc.

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### Regional Economic Models, Inc.

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1776 I St. NW

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Washington, DC 20006

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Scott Nystrom is a Senior Economic Associate in REMI’s DC office. He holds a B.A. in history, B.S. in

economics, and M.A. in economic history, all summa cum laude , from Iowa State University in Ames, IA.

He heads REMI’s software training and technical cons ulting services for the District of Columbia and the

surrounding area. Some of Mr. Nystrom’s previous analyses include the economic impact of federal fiscal

policies, such as the “fiscal cliff” or federal sequestration, energy projects like the Keystone XL pipeline

and LNG exports, tax incentives in Arkansas and Missouri, and the $500 billion regional transportation

plan for the Southern California Association of Governments (SCAG). Other projects include analyzing

state renewable portfolios and Medicaid ex pansion in North Carolina.

Ali Zaidi is an Assistant Economist and the research associate in REMI’s DC office. He holds a B.A. in

economics from the University of Massachusetts

- Amherst, and he is currently finishing his B.S. in

computer science from the s ame institution. He worked on calibrating the CTAM model used here for the

state of Massachusetts and assisting Mr. Nystrom in running and validating its results, the simulations in

PI + , and assuring the quality of this report and its results.

This repor t does not reflect t he institutional views of REMI. The words above reflect the professional findings of the

authors. There is a ©2013 by Regional Economic Models, Inc. and respective parties.