Impacts of Energy Efficiency on Hawai‘i’s Economy:
A CGE-Modeling Approach
Denise E. Konan†
Iman Nasseri*
This version: April 2012
Abstract
This paper models Hawai‘i’s economy in a CGE framework in order to analyze the economic
impacts of energy (electricity) efficiency. By tracing fuel use and energy flows in Hawai‘i’s
economy, this paper demonstrates the economic and environmental impacts from marginal energy
efficiency across various sectors of the Hawai‘i economy. Results include the comprehensive
(direct and indirect) effects of sector-level shocks including potential of energy savings, costs
imposed on the overall economy, and the associated greenhouse gas emission reduction. This
analysis sheds light on which sectors to optimally target with incentives and/or punishment policies
to achieve a high rate of energy efficiency and conservation in the State. Comprehensive economic,
energy, and emissions data are compiled for 68 sectors of Hawai‘i’s economy.
The study develops energy and GHG emissions intensity measures for output, value added, and
jobs. Greenhouse gas emissions elasticities are also developed for energy conservation and
efficiency scenarios. Based on direct fuel combustion and indirect GHG emissions associated with
intermediate good fuel combustion, the top GHG intensive sectors per dollar of output are
electricity, utility gas, and air transportation followed by commercial fishing and ground
transportation.
CGE analysis determines that the most significant economic impact, in terms of increase in the
sectoral output and job count, occurs in sectors associated with tourism industry such as
accommodations (real estate and rentals), services, trade, hotels, and restaurants. Likewise, the
analysis predicts that the largest saving potential in energy and GHG emissions lies in electricity
efficiency of the same sectors, with slightly different order in significance. This shows the high
GHG emissions elasticity of technological changes in the tourism industry. Especially, considering
the ratio of residents’ versus visitors’ population, it implies that the visitor expenditures are more
energy and carbon intensive than that of Hawai‘i households on a per person basis. The study
indicates the sensitivity of sectors to energy policy in a service-oriented economy.
Keywords: CGE modeling, energy efficiency, energy saving, greenhouse gas emissions
University of Hawai‘i at Mānoa, PhD student at the Department of Economics, email: iman@hawaii.edu
University of Hawai‘i at Mānoa, Dean of the College of Social Sciences, Professor of the Department of
Economics, email: konan@hawaii.edu
*
†
1
1 Introduction
1.1 Background and Motivation
For decades, rapidly rising greenhouse gas emissions and its potential outcomes including global
warming and other climate change impacts have triggered policy responses aimed at mitigating
the environmental externality. Scientists and engineers seek energy solutions that would generate
renewable power while curbing use of fossil fuels. Less emphasis has been placed on energy
conservation and efficiency measures that involve a focus on demand side measures.
Yet various technologies can lower the energy load of the consumer, whether residential,
industrial or commercial. Preferences can also be adapted to build tolerance for low energy
lifestyles. For example, when the 11 March 2011 earthquake and nuclear disaster in Sendai, Japan
generated power shortages the Environment Ministry ‘super cool biz’ campaign of foregoing
neckties and wearing aloha shirts to reduce electricity consumption by 15%. Other energy
efficiency measures include adoption of Energy Star appliances and lighting, solar water heating,
and sea water air conditioning.
In recognition of the significant local impacts from global climate changes, Hawai‘i became the
second state within the U.S. to adopt regulatory legislation similar to the Kyoto protocol, to
reduce greenhouse gas (GHG) emissions to 1990 levels by 2020. The way forward for Hawai‘i is
not well guided by the national policy dialogue. The U.S. climate change debate tends to focus on
reducing emissions of large emitters, particularly in stationary energy combustion through radical
technological solutions, such as carbon capture and storage and more widespread adoption of
nuclear power. Many energy options that are attractive on the U.S. mainland offer little prospect
of adoption in Hawai‘i for economic and environmental reasons.
Hawai‘i’s path to a low-carbon economy is more likely to be achieved through energy efficiency,
demand-side management, and renewable energy technologies. In 2008, the State of Hawai‘i
signed an MOU with the U.S. Department of Energy for the Hawai‘i Clean Energy Initiative
(HCEI) with the goal of decreasing energy consumption by means of increased energy efficiency
(up to 30%) and increased share of renewable energies (up to 40%) in Hawai‘i’s energy supply in
order to meet 70% of Hawai‘i’s clean energy demand by 2030.
Since then, many energy bills were introduced in the past legislation sessions suggesting effective
policies regarding either the fossil fuel consumption or greenhouse gas emissions. One of the
steps toward energy efficiency was signing the Act 155 of 2009 into law, which calls for creating
an Energy Efficiency Portfolio Standards (EEPS), with a goal of 4,300 gigawatt-hour (GWh)
reduction in electricity use by 2030. This act directs the Hawai‘i Public Utilities Commission
(PUC) to establish incentives and penalties that promote compliance.
Hawai‘i provides an attractive case study for energy efficiency and demand-side management.
Due to the geographic isolation of its islands, Hawai‘i’s power grids are contained, with no intraregional imports or exports. This allows for excellent data on power generation and end use.
Transportation fuels are readily assignable to economic activity. Data on air and maritime fuel
use are available and represent significant components of the energy profile. Hawai‘i supports
very little industrial activity. Tourism, military, government, and health services are among the
most important drivers of economic activity in the islands. Hawai‘i provides detailed and
tractable data and a simple energy infrastructure. Energy conservation and demand-side
management are likely to be the key to carbon reduction strategies.
In this paper, an integrated database of energy use and economic activity provides the basis to
analyze demand driven components of greenhouse gas emissions. A CGE model is developed
2
along with measures of industry demand carbon intensity, or more precisely, the carbon dioxide
equivalent (CO2e) global warming potential of three major greenhouse gas emissions.
The study develops energy and GHG emissions intensity measures for output, value added, and
jobs. Greenhouse gas emissions elasticities are also developed for energy conservation and
efficiency scenarios.
The carbon accounting methodology parallels that of the World Resources Institute’s (Anon.
2004) standard of measuring corporate emissions across the value chain. In computing carbon
intensity of sectors, we include scope 1 emissions (direct combustion of fossil fuel), scope 2
emissions (electricity use), and scope 3 energy emissions (other indirect emissions including air,
maritime, and ground transportation). This approach provides a comprehensive analysis of
energy-related carbon emissions. To avoid double counting, we allocate emissions based on the
final demand for Hawai‘i’s output.
This paper models Hawai‘i’s economy in a computable general equilibrium (CGE) framework in
order to analyze the scenario of energy (electricity) efficiency across economic sectors. Although
many previous studies have setup CGE models for Hawai‘i’s economy, none of the studies have
looked at the implications of energy efficiency for the State. Most of the existing literature has
used CGE modeling to look at the impact of different scenarios or policies on Hawai‘i’s economy
focusing on the tourism industry. The most common shocks in the previous studies are tourismor labor-based shocks.
Looking at the energy flows in Hawai‘i’s economy, this paper tries to follow the footprint of an
efficiency improvement in the economy, finding the impacts of a marginal measure on Hawai‘i’s
energy savings across economic sectors, possible costs imposed on the overall economy, and the
associated greenhouse gas emission reduction. This will shed some light on how to target
incentives or punitive policies to achieve a high rate of energy efficiency and conservation in the
State. While there are already some energy efficiency rules and incentives in place, this paper
performs a sensitivity analysis for alternative scenarios that could help to achieve the appropriate
energy efficiency goals.
The paper is organized as follows. Section 2 surveys the data used in the study and provides a
descriptive analysis of fuel use and associated emissions by economic activity source. The
methodology is set forth in section 3. Sections 4 and 5 provide results and concluding remarks.
1.2 Previous studies
CGE models have been extensively used in environmental studies of the economy. The multisectoral nature of CGE models along with their detailed supply side specification has made them
a good fit for both environmental and economic policy analysis. However, it has been typically
used at the national or international level. Bergman was among the first group who applied CGE
modeling for simulating environmental policy impacts. Using a static CGE model of an open
economy, Bergman (1991) included emissions and emission control activities in his model to
estimate the general equilibrium impacts of an emission control on the Swedish economy. Several
years later, he reviewed and discussed in specific the CGE modeling as a tool for analysis of
environmental policy and natural resource management issues, dividing this branch of CGE
models into two major groups: “Externality CGE Models” and “Resource Management CGE
Models” (Bergman and Henrekson 2005). He also extensively goes over the strengths and
weaknesses of environmental CGE model.
“CGE models obviously rest upon strong assumptions about optimizing behavior,
competitive markets, and flexible relative prices. In addition lack of data usually prohibits
econometric estimation of key supply and demand parameters. In view of this the validity and
usefulness for policy evaluation of the results generated by CGE models might be, and often is,
3
seriously questioned. However, there is no general answer to the question about what CGE models
are good for. The usefulness of a carefully designed and implemented CGE model depends on
what it is intended for and what the alternatives are.” (Bergman and Henrekson 2005)
There are also an increasing number of regional CGE models that have tried to address
environmental issues. Since Hawai‘i is truly a small open economy with highly-detailed data
available on the economic sectors’ activity levels and output, it is an ideal island example for
modeling and so, several studies have used CGE modeling framework to analyze the impacts of
various policies on this economy, more focusing on the tourism industry, which is the main
component of Hawai‘i’s economy.
Zhou et al. (1997) created both an I-O model and a CGE model of Hawai‘i’s economy to compare
the effects of a 10% reduction in nominal visitor expenditures through the lens of both modeling
tools. Their static CGE model is based on the U.S. Department of Agriculture CGE model
structure and the 60-sector 1982 IO table for the State of Hawai‘i.
Konan and Kim (2003) studied the economic impact of the transportation sector in Hawai‘i under
a number of alternative scenarios. They developed a CGE model of the economy with a special
focus on transportation and modeled the effects of both an increase and a decrease in visitor
expenditures due to the leading role of tourism in Hawai‘i’s economy. In another paper (Kim and
Konan 2004), they estimated direct and indirect demand for urban infrastructure (water,
wastewater, electricity, propane, and solid waste, etc.) under alternative scenarios for population
growth and visitor spending in Hawai‘i, using a CGE model. This paper uses their methodology
of estimating direct and indirect demand for energy.
Konan et al. (2007) also traced the visitor economic activity through Hawai‘i’s economy using a
CGE model. They simulated the changing sector-level economic activity, infrastructure demand,
and greenhouse gas emissions resulting from a million dollar increase in nominal visitor
expenditures, taking into account both direct and indirect visitors’ expenditures.
Konan (2011) used a regional CGE model for Hawai‘i’s economy to examine the impacts of
visitor expenditure growth and labor migration on Hawai‘i’s economy. The purpose was to show
how regional welfare, price levels, and production responded to alternative labor market rigidity
scenarios.
In her doctoral dissertation, Coffman (2007) developed a CGE model of Hawai‘i’s economy,
based on the 131-sector 1997 Hawai‘i State Input-Output Study, and analyzed the impacts of
different scenarios; a 10% reduction in nominal visitor expenditures (Essay 1); a sudden upward
jump in world oil prices (Essay 2); and a set of nine scenarios, including a 10% fuel tax on
petroleum manufacturing output, a 10% fuel tax on both petroleum manufacturing and electric
sector outputs, and a 50% increase in the world price of oil, each under three different cases of
market competition (Essay 3).
CGE modeling, however, has not been much used for energy efficiency analysis in the U.S. in
general and Hawai‘i in specific. In contrast, there have been several studies done mainly in the
Europe, looking at the economy-wide effects of energy efficiency improvements. (Turner and
Hanley 2011; N. D. Hanley et al. 2006; Barker, Ekins, and Foxon 2007; Allan, Hanley, et al.
2007)
Allan et al. (Allan, Gilmartin, et al. 2007) identified and reviewed a series of eight CGE modeling
studies (Semboja 1994; Dufournaud, Quinn, and Harrington 1994; Vikström 2004; Washida
2004; Grepperud and Rasmussen 2004; Glomsrød and Taoyuan 2005; N. D. Hanley et al. 2006;
Allan et al. 2006) that simulate energy efficiency improvements. The existing literature focused
on the possibility and magnitude of “rebound,” which is when energy consumption decreases less
than the improvement in energy efficiency, both in percentage term (Greening, Greene, and
4
Difiglio 2000). Although the models in the reviewed studies differed widely in many aspects
(such as the region of study, model nesting structure and parameters, or even the way they
introduced energy and energy efficiency into their model), all of them found the economy-wide
rebound effects to be larger than a minimum of 37%, with some of them finding very large
rebounds (greater than 50%) or even backfire, which is when energy consumption actually
increases following the energy efficiency improvement (Greening, Greene, and Difiglio 2000). It
is important to note that, similar to this study, the results of all reviewed CGE models in their
paper relied upon energy efficiency improvements by producers only. So, the results neither
count for efficiency improvements by consumers nor take into account the impact of inputs’
productivity improvement on energy efficiency. It is also worth mentioning that none of these
studies primarily focused on emission reduction potential of energy efficiency measures in the
economy, which is what has been done in this study.
2 Data: Energy, Emission, and the Economy
Hawai‘i’s mild tropical climate provides favorable conditions for energy conservation, as many
Hawai‘i homes are not designed with home heating or cooling. In 2009, Hawai‘i’s per capita
electricity consumption ranked third lowest, however, having the most expensive electricity in the
nation, it is ranked number two in the U.S. in total electricity expenditures per capita (EIA 2011).
That said, the main driver for electricity efficiency in the state of Hawai‘i is not saving energy per
se, but more importantly to reduce electricity expenditures due to kilowatt costs of over $0.33
(versus $0.10 nationally).
The primary concern of this study is in obtaining estimates of carbon emissions savings to be had
from improving electricity efficiency. To facilitate this, baseline data is assembled on Hawai‘i’s
economy, energy infrastructure, and greenhouse gas emissions. The baseline year of 2005 is
selected as the latest comprehensive input-output data and energy profile were developed. Efforts
are underway to update this data as the next input-output data for 2007 gets published. Yet, the
fundamental features of the economy have been relatively consistent over the past few years.
The U.S. Department of Energy (2011) estimated that nearly nine-tenths of Hawai‘i’s energy
derives from petroleum products. This heavy reliance on petroleum is related to an energy
infrastructure that has developed historically to provide capacity for air transportation. Two local
refineries, located on the island of Oahu, have the facility to process 147,500 barrels of oil per
day, obtaining crude oil imported largely from Asia and the Middle East and converting it into jet
and aviation fuel, motor gasoline, diesel fuel, and other petroleum products. An important biproduct of the refineries’ cracking process is a low-grade residual fuel oil, which has become the
primary fuel for the generation of electricity.
This analysis entails the compilation of data on fossil fuel use, greenhouse gas emissions, and
economic activity. Table 1 summarizes the fuels and activities covered in this study as well as the
greenhouse gas emissions attributed to the combustion of fuels. Data were obtained directly from
the Hawai‘i Department of Business, Economic Development, and Tourism. Given Hawai‘i’s
geographic isolation, jet fuel and aviation gasoline are significant components of the fossil fuel
profile. Residual fuel oil is primarily used for electricity generation and, to a lesser extent, for
maritime transportation. Gasoline is primarily used for highway purposes. Diesel fuel uses
include electricity generation, maritime travel, and commercial and industrial activities. At only
15.5 trillion BTUs per year, coal as an energy fuel is relatively insignificant.
Hawai‘i’s emissions were determined for three major greenhouse gases: carbon dioxide (CO2),
methane (CH4), and nitrous oxide (N2O). The Intergovernmental Panel on Climate Change
(Eggleston et al. 2006) established reporting tiers for the computation of greenhouse gas
emissions, with tier 1 reflecting ‘default’ calculations. Emissions estimates are derived from fuel
combustion based on national and regional energy statistics and Hawai‘i specific emissions
5
factors determined by fuel characteristics. Greenhouse gas emissions factors were obtained from
the U.S. Energy Information Administration (2007) and reconciled with previous inventory
estimates. A complete GHG profile for Hawai‘i is available online (UHERO 2009).
It is important to note that military fuel use data are incomplete and do not cover fuel purchased
for naval vessels. Military aviation and jet fuel uses are included in the study along with fuel
used in fixed military boilers and generators. Military purchases of electricity are captured in the
economic data. This study does not include emissions associated with landfills or agricultural
and forestry processes that are not fuel related.
Transportation leads Hawai‘i’s energy use due largely to high consumption of jet fuel for military
installations and commercial airlines. Vehicle fuel consumption rates on a per capita basis are
among the lowest in the nation (EIA 2011). The geography of the islands and population density
results in relatively short commuting distances.
Petroleum-fired power plants supply around seventy five percent of Hawai‘i’s electricity
generation. Coal and a suite of renewable energy sources including hydroelectricity, geothermal,
landfill gas, and other biomass round out Hawai‘i’s electricity generation. Hawai‘i is one of few
places in the U.S. that produces synthetic natural gas (SNG) and consumption of it is largely
commercial (hotels, restaurants, laundry).
Table 1: Fuel Use and Emissions By Activity In Hawai‘i, 2005
Fuel
Activity
Aviation Gasoline
Aviation Intra-State
Aviation Overseas, Non-Bonded Fuel
Electricity Generation
Federal Government
Highway
Electricity Generation
Non Highway
Small Boat
Vessel Bunkering Intra-State
Vessel Bunkering Overseas, Bonded Fuel
Vessel Bunkering Overseas, Non-Bonded Fuel
Agriculture
Federal Government
Highway
Small Boat
Aviation Intra-State
Aviation Overseas, Bonded Fuel
Aviation Overseas, Non-Bonded Fuel
Federal Government
State and Local Government
Other End Users
Highway
Residential
Commercial
Industrial
State and Local Government
Federal Government
Commercial
Industrial
Electricity Generation
Vessel Bunkering Intra-State
Vessel Bunkering Overseas, Bonded Fuel
Vessel Bunkering Overseas, Non-Bonded Fuel
Federal Government
Industrial
Coal
Diesel
Gasoline
Jet Fuel
Natural Gas (SNG and LPG)
Residual Fuel Oil
Other fuels*
Total
*Other fuels include: Naphtha, distillate fuel oil, waste oil, and fuel gas.
Source: Hawai‘i Department of Business, Economic Development, and Tourism
6
Consumption
('000 mmBTU)
181.80
2.10
15,577.80
121.90
6,193.30
15,108.20
9,207.00
5.70
1,783.50
7,565.50
33.60
125.70
158.44
57,491.16
1.75
9,245.45
39,864.24
34,267.99
10,542.14
0.33
95.69
13.73
893.51
4,615.94
58.84
172.79
153.88
17.58
1,485.72
71,106.75
1,209.85
11,191.59
80.36
81.85
8,423.54
307,079.20
Emission
(mtCO2e)
12.66
0.14
1,495.34
8.96
455.29
1,110.64
676.84
0.42
131.11
556.16
2.47
9.01
11.36
4,121.47
0.13
657.66
2,835.69
2,437.61
749.90
0.02
6.81
0.78
50.56
261.20
3.33
9.78
8.71
1.39
117.45
5,621.26
95.64
884.74
6.35
6.47
562.56
22,909.92
The primary economic data used in this study come from the State of Hawai‘i Input-Output Study
(DBEDT 2008), which is an update of the 2002 benchmark report of input-output (I-O) studies,
by including the latest available data on jobs, earnings, final demand, state taxes, components of
value added, and outputs of a few industries. The 2002 benchmark report was compiled from the
1997 Economic Census and organized according to the North American Industry Classification
System (NAICS). Intermediate and final demand values are provided for Hawai‘i’s economy at a
disaggregation of 68 sectors, thus providing a detailed description of agricultural, manufacturing,
and services production in Hawai‘i. While the model calibration uses disaggregated data, for
purposes of reporting, we present the findings at an aggregated 38-sector level.
Table 2 decomposes production costs into total output costs, employee compensation and total
value added. Hawai‘i is a services-oriented economy, with very little manufacturing activity.
Real estate (accommodations), government, business and professional services, trade, and health
are key economic sectors in terms of output and employment. The visitor industry is a major
employer, as reflected in job counts in hotels, restaurants, retail trade, and various entertainment
services. Government employment accounts for 20% of jobs and 34% of employee
compensation.
Table 2: Output, Employment, Value Added, and Job Count, 2005
Output
Crops production
Fruits, vegetables, and flowers
Animal production
Aquaculture, forestry, and logging
Commercial fishing
Mining
Construction
Petroleum manufacturing
Clothing manufacturing
Food processing
Other manufacturing
Air transportation
Water transportation
Ground transportation
Trucking
Warehousing and storage
Scenic and support activities for transportation
Information
Electric
Natural gas
Wholesale trade
Retail trade
Rental, leasing, and others
Accommodations
Hotels
Restaurants
Fin., bus., prof. services
Travel reservations
Waste management services
Education
Hospitals
Other health services
Arts and entertainment
Personal and laundry services
Repair and maintenance
Organizations
State and local government
Federal government
Total
$ million
208
377
68
31
43
129
7,178
2,426
81
1,305
1,353
2,148
1,677
163
362
59
685
2,338
1,928
85
2,809
6,222
936
13,074
4,891
3,473
11,352
703
250
935
2,756
3,471
820
883
657
1,119
5,693
7,608
90,296
Source: DBEDT (Anon. 2008)
7
Employee
Compensation
$ million
92
132
19
7
10
32
2,284
78
20
266
337
563
171
51
154
30
320
706
285
12
923
2,132
148
458
1,729
1,142
4,377
262
80
480
815
1,894
344
266
200
470
4,421
6,803
32,512
Value Added
$ million
161
234
34
13
18
36
3,157
196
37
274
425
623
262
68
209
41
466
1,334
718
31
1,311
2,875
470
8,437
2,653
1,469
6,612
345
121
525
877
2,544
536
360
271
636
5,097
7,406
50,883
Job Count
4,315
8,038
1,022
339
1,970
569
44,332
423
1,808
7,510
8,772
10,198
3,550
4,420
3,589
912
7,360
14,260
2,873
126
21,856
88,747
5,391
36,980
40,112
59,147
120,309
8,696
1,682
17,147
14,220
54,787
21,903
22,462
11,565
14,671
88,128
84,400
838,588
3 Model
Using the same I-O data, described in section 2, a Computable General Equilibrium (CGE) Model
is developed. CGE models solve for the equilibrium in the Arrow-Debreu Equilibrium framework
(Arrow and Debreu 1954), based on the Walrasian general equilibrium structure. Hence, the
convexity of the production and expenditure sets implies existence and uniqueness of the
equilibrium price vector, which clears the market.
In this model specifically, Hawai`i is assumed as a small and open economy, in which visitor
expenditures generate a significant share of foreign exchange. Visitors’ consumption bundle
consists of goods and services, most of which are not importable such as transportation, hotel, and
restaurant services. Production is assumed to be perfectly competitive using constant returns to
scale technologies. Households, visitors, various government entities, and exports are sources of
final demand, and prices are calibrated to clear markets.
A schematic representation of Hawai‘i’s general equilibrium is shown in Figure 1.
Endogenous
Variables
Federal
Military
Inflow (IFM)
Exogenous
Variables
Federal
Civilian
Inflow (IFC)
State & Local
Government (SL )
Federal
Military
Gov’t (FM)
Federal
Civilian
Gov’t (FC)
SL Consumption (piGSLi)
Sales Tax (piYiτi)
Lump -Sum Tax (Tr)
FC Consumption
(piGFCi)
Labor (L), Prop Income (R), Capital (K)
FM Consumption
(piGFMi)
SL Imports
(pmGSLm)
Intermediate Inputs (Zji)
Residents (r)
Industry (i,j)
Returns to Factors of Production (p LL, pRR, pKK)
Residents Consumption (piCri)
FM Imports
(pmGFMm)
FC Imports
(pmGFCm)
Residents Imports (pmCrm)
Balance of Payment Deficit (pfxBP)
Visitors
Consumption
(piCvi)
Exports (pxiXi)
Imports (pmM)
Visitors
Income (Iv)
Visitors (v)
Visitors Imports (pmCvm)
Foreign Countries
Figure 1. General Equilibrium Model Of Hawai‘i’s Economy (Konan, et al. 2007)
8
The approach assumes that standard equilibrium conditions are satisfied such as no excess
demand for all goods and services and that all agents’ expenditure equals income and in overall,
the economy is in balance. The model is estimated numerically using the GAMS (General
Algebraic Modeling Systems) software and the MPSGE platform (Rosenthal 2012; Rutherford
1999).
3.1 Production
Final output in sector i (Yi) is produced according to a nested Leontief function, shown in Figure
2. In this setup, value added (Vi) is composed of capital (Ks), labor (Ls), and proprietor income
(Rs) and intermediate input is made up of tradable inputs (STki) and non-tradable inputs (SNji),
which include energy inputs.
For the purpose of modeling efficiency in using electricity in different production technologies,
electricity sector (SE) is separated from other non-tradable sectors. The remaining non-electricity
non-tradable sectors (SNEji) include other energy sectors (petroleum manufacturing, and gas
production and distribution) and key service sectors (hotels, restaurants, health and other
services). All other sectors in the economy (agriculture, commercial fishing, clothing
manufacturing, food processing, etc.) are categorized as tradable sectors.
Domestic, Ds
Sector S’s
Output, Ys
Export, Xs
Intermediate
Inputs
Value Added,
VAs
Labor
Prop.
Income
Electricity
Sector, SEs (1)
Non-electricity
Non-tradable
Sectors, SNEjs (27)
Capital
Tradable Goods,
STks (10)
Import,
Ms
Domestic,
Dks
Tradable Sectors
Figure 2: Nesting Of The Production Function
At the first level, a Leontief production function (zero elasticity of substitution) represents final
output (Ys) in sector s:
𝑆𝑁𝐸(𝐽−1)𝑠 𝑆𝑇1𝑠
(1−𝜂𝑠 )𝑆𝐸𝑠 𝑆𝑁𝐸1𝑠
𝑆𝑇
𝑉
,
,
…
,
, 𝛾 , … , 𝛾 𝐾𝑠 , 𝛼 𝑠 ]
𝛼𝐸𝑠
𝛽1𝑠
𝛽(𝐽−1)𝑠
1𝑠
𝐾𝑠
𝑉𝑠
𝑌𝑠 = min⁡[
(4)
where 𝛼𝐸𝑠 , 𝛽𝑗𝑠 , 𝛾𝑘𝑠 , and 𝛼𝑉𝑠 are unit input coefficients for intermediates (electricity, nonelectricity non-tradable, and tradable) and value added respectively; and 𝜂𝑠 is the efficiency level
for sector s used as a shock parameter for the efficiency scenario analysis.
At the second level, tradable intermediate inputs are provided by flexible domestically produced
and importable commodities represented through an Armington‡ (1969) constant elasticity of
substitution (CES) production nest:
‡
Armington assumption implies goods are differentiated by country of origin.
9
(𝜀𝑘𝑠𝑚 −1)
𝜀𝑘𝑠𝑚
𝑆𝑇𝑘𝑠 = [𝜃𝐷𝑘𝑠 𝐷𝑘𝑠
(𝜀𝑘𝑠𝑚 −1)
𝜀𝑘𝑠𝑚
𝜀𝑘𝑠𝑚
(𝜀𝑘𝑠𝑚 −1)
+ 𝜃𝑀𝑠 𝑀𝑠
]
(5)
where 𝜀𝑘𝑠𝑚 is the CES substitution between domestically produced good k and imports by
producer s; Dks is sector k demand by producer s for domestically produced goods; Ms is imported
demand in sector s; and The parameter shares are represented by 𝜃𝐷𝑘𝑠 and 𝜃𝑀𝑠 respectively.
And, value added is formed through another CES nest:
(𝜎𝑠 −1)
(𝜎𝑠 −1)
𝜎𝑠
𝑉𝑠 = [𝛼𝐿𝑠 𝐿𝑠 𝜎𝑠 + 𝛼𝐾𝑠 𝐾𝑠
(𝜎𝑠 −1)
𝜎𝑠
𝜎𝑠
(𝜎𝑠 −1)
+ 𝛼𝑅𝑠 𝑅𝑠
]
(6)
where 𝜎𝑠 is the CES among value added variables and 𝛼𝐿𝑠 , 𝛼𝐾𝑠 , and 𝛼𝑅𝑠 are corresponding
parameter shares.
Output commodity Ys can either be consumed domestically or exported and, under the Armington
assumption, is differentiated for those markets using a constant elasticity of transformation (CET)
function between domestic (Ds) sales and exports (Xs).
(𝜀𝑠 −1)
𝜀𝑠
𝑌𝑠 = [𝛽𝐷𝑠 𝐷𝑠
(𝜀𝑠 −1)
𝜀𝑠
𝜀𝑠
(𝜀𝑠 −1)
+ 𝛽𝑋𝑠 𝑋𝑠
]
(7)
where 𝜀𝑠 is the elasticity of transformation; and 𝛽𝐷𝑠 , and 𝛽𝑋𝑠 are parameter shares.
3.2 Electricity Efficiency
Electricity consumption in the benchmark data is assumed to calibrate the model to status quo
production technologies, used as reference for efficiency scenario analysis. In the electricity
efficiency scenarios, the production functions are assumed to be more efficient by 𝜂𝑠 % compared
to benchmark. The scenarios considered in this paper are 1) 10% efficiency in electricity
consumption in the all sectors’ production, and 2) 10% efficiency in electricity consumption in
each sector (except for energy sectors), keeping the rest of economy the same in order to compare
the impacts of efficiency by sector.
3.3 Consumption
On the demand side, the model reflects the behavior of Hawai‘i residents (r) and visitors (v), both
following utility-maximizing behavior represented by a Cobb-Douglas function.
𝑏
𝑈ℎ = ∏𝑛𝑖=1 𝐶ℎ𝑖ℎ𝑖
∑𝑛𝑖=1 𝑏ℎ𝑖 = 1
,
(8)
where Chj and bhj are consumption and income expenditure share of good i, for consumer type h
(h = r, v).
In addition, they consume both domestically produced goods (i = 1,…,n) and an imported
composite good (m).
(𝜀ℎ𝑖𝑚 −1)
𝜀ℎ𝑖𝑚
𝐶ℎ𝑖 = [𝜃𝐷ℎ𝑖 𝐷ℎ𝑖
(𝜀ℎ𝑖𝑚 −1)
𝜀ℎ𝑖𝑚
𝜀ℎ𝑖𝑚
(𝜀ℎ𝑖𝑚 −1)
+ 𝜃𝑀ℎ 𝑀ℎ
]
(9)
where 𝜀ℎ𝑖𝑚 is the Armington CES between domestically produced good i and imports by
consumer h; Dhi is sector i demand for domestically produced goods; Mh is imported demand by
consumer h; and 𝜃𝐷ℎ𝑖 and 𝜃𝑀ℎ represent corresponding parameter shares.
A representative resident’s budget constraint can be written as:
∑𝑖 𝑝𝑖 𝐶𝑟𝑖 = 𝑝𝐿 𝐿 + 𝑃𝑅 𝑅 + 𝑃𝐾 𝐾 + 𝑝̅𝑓𝑥 𝐵𝑃 − 𝑇𝑟
(10)
where pi represent the market prices for imports and commodities i = 1,… ,n ,m respectively. The
resident derives income from factors of production including labor (L), proprietor income (R),
10
and capital (K), with pL, pR, pK being the market price of the respective factors. The resident also
pays a lump-sum tax (Tr), net of transfer payments, to the state and local government (and thus
household income is not necessarily equal to labor income because of transfers). The resident also
receives foreign exchange (𝑝̅𝑓𝑥 𝐵𝑃) from a balance of payment deficit, described below in
equation (14).
A representative visitor’s budget constraint is expressed as:
𝐼𝑣 = ∑𝑖 𝑝𝑖 𝐶𝑣𝑖
(11)
where 𝐼𝑣 represents visitor’s income, which is taken to be exogenous.
3.4 Government
The IO table represents government activity through three branches: the state and local
government (SL), the federal military government (FM), and the federal civilian government
(FC). Federal military and civilian governments are then aggregated to form the federal
government (FG) for the purpose of this analysis. Each government type purchases domestic
commodities (Ggi) and imports (Ggm) according to a Leontief utility function to assure a constant
level of public provision, where g = SL, FG.
The state and local government depends entirely on the economy for the tax base.
∑𝑖 𝑝𝑖 𝐺𝑆𝐿𝑖 + 𝑝𝑚 𝐺𝑆𝐿𝑚 = ∑𝑖 𝑝𝑖 𝑌𝑖 𝜏𝑖 + 𝑇𝑟
(12)
A primary source of revenue is the State’s goods and services tax (𝜏𝑖 ) on the sales (Yi) of
commodity i. The state and local government also impose a variety of taxes, such as property and
income taxes, on residents.
Federal government inflows are assumed to adjust endogenously to assure neutral levels of
federal government provision (i.e., unaffected by the shock). The federal public sector budget
constraint is given by:
∑𝑖 𝑝𝑖 𝐺𝐹𝐺𝑖 + 𝑝𝑚 𝐺𝐹𝐺𝑚 = 𝐼𝐹𝐺
(13)
where the sum on the left-hand side represents the cost of public expenditures; and IFG represents
federal revenue inflows into the State.
3.5 Balance of Payments
A balance of external payments (BP) is maintained under the assumption of a fixed (to the dollar)
exchange rate (𝑝̅𝑓𝑥 ), where 𝑝̅𝑓𝑥 is the exchange rate with the “rest of the world.” The quantity of
imports (M) are constrained by the inflow of dollars obtained from visitor expenditures (Iv),
federal government expenditures (IFG), and Hawai‘i exports (Xj). Because Hawai‘i is a small open
economy and thus a price taker, import and export prices are perfectly inelastic.
𝑝̅𝑓𝑥 𝐵𝑃 = 𝑝̅𝑚 𝑀 − 𝐼𝑣 − 𝐼𝐹𝐺 − ∑𝑗 𝑝̅𝑥𝑗 𝑋𝑗
(14)
3.6 Supply Demand Balance
Constant returns to scale and perfect competition ensure that the producer price (pj) equals the
marginal cost of output in each sector j. In addition, the state and local government collects a
general excise tax (𝜏𝑗 ) on sales. This implies that the value of total output (supply) equals
producer costs, where pL, pR, and pK equals the market price of labor, proprietor income, and
capital respectively.
𝑝𝑗 𝑌𝑗 (1 + 𝜏𝑗 ) = ∑𝑛𝑙=1 𝑝𝑙 𝑍𝑙𝑗 + 𝑝𝐿 𝐿𝑗 + 𝑃𝑅 𝑅𝑗 + 𝑃𝐾 𝐾𝑗 + 𝑃𝑚 𝑀𝑌𝑗
11
(15)
In addition, sector j output, which supplied to the domestic market (Dj), is demanded by
consumers ℎ ∈ {𝑟, 𝑣}, government agencies 𝑔 ∈ {𝑆𝐿, 𝐹𝐺}, and industries l = 1,…, n.
𝐷𝑗 = ∑ℎ 𝐶ℎ𝑗 + ∑𝑔 𝐺𝑔𝑗 + ∑𝑙 𝑍𝑙𝑗
(16)
In equilibrium, the value of output balances the value of inter-industry, consumer, represent
exogenous and government agencies demand.
3.7 Energy Consumption and GHG Emissions
Energy demand by various industries and by households and visitors (demand by final
consumers) are estimated by using standard techniques. The total estimated energy demand (Di)
can be expressed as follows:
𝐷𝑖 = ∑𝑛𝑘=1 𝑑𝑖𝑘 + ∑𝑦 𝑑𝑖𝑦
(1)
where
i = type of energy,
n = number of industry sectors,
𝑑𝑖𝑘 = demand for energy type i by the kth industry sector, and
𝑑𝑖𝑦 = demand for energy type i by the the final sector, y = residents, visitors, gov., etc.
The total estimated energy demand (𝑑𝑖𝑘 and 𝑑𝑖𝑦 ) are then calculated as follows:
𝑑𝑖𝑘 = 𝐷𝑖 × 𝜌𝑖𝑘 and
𝑑𝑖𝑦 = 𝐷𝑖 × 𝜌𝑖𝑦
(2)
where
𝜌𝑖𝑘 = share of kth industry sector in total consumption of energy type i,
𝜌𝑖𝑦 = share of the final sector y in total consumption of energy type i, and
∑𝑛𝑘=1 𝜌𝑖𝑘 + ∑𝑦 𝜌𝑖𝑦 = 1.
Shares are either assigned based on the energy source characteristic or are estimated based on the
sectors’ expenditure on three energy sectors (petroleum manufacturing, gas production and
distribution, and electricity). For example, as aviation gasoline is only consumed by the air
transportation sector, we will have:
𝜌𝑎𝑣𝑔𝑎𝑠,𝑎𝑖𝑟𝑡𝑟𝑛𝑠𝑝 = 1;
𝜌𝑎𝑣𝑔𝑎𝑠,𝑘 = 0;⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡∀𝑘 ≠ 𝑎𝑖𝑟𝑡𝑟𝑛𝑠𝑝;
However, for gasoline as another example, the shares have been estimated based on sectoral
expenditure on petroleum manufacturing:
𝑒𝑘,𝑝𝑒𝑡
𝜌𝑔𝑎𝑠𝑜𝑙𝑖𝑛𝑒,𝑘 = ∑𝑛
(3)
𝑘=1 𝑒𝑘,𝑝𝑒𝑡
where
𝑒𝑘,𝑝𝑒𝑡 = kth industry sector’s expenditure on petroleum manufacturing sector’s output.
Based on the above method, estimates for petroleum products (aggregating individual fuels),
natural gas, and electricity consumption are derived, allowing for both the estimate of overall
aggregate levels of demand for energy as well as estimates of per capita levels.
As described in section 2, the GHG emissions are then calculated by using standard methods
established by the Intergovernmental Panel on Climate Change (Eggleston et al. 2006) for the
computation of greenhouse gas emissions. This study achieves tier 2 accounting for stationary
and mobile energy sources.
12
4 Simulation and Results
This section presents the results of the analysis. The macroeconomic measures of Hawai‘i’s
economy are calculated and analyzed using a CGE model. The change in energy demand and
associated energy intensities are calculated. Also, the GHG emissions and associated GHG
intensity of sectors are computed as an aggregation of greenhouse gases in terms of carbon
dioxide equivalent global warming potential over a 100-year time horizon following the
methodologies established by the IPCC (Solomon et al. 2007).
Before looking at the results from the efficiency shock scenario, the levels and intensities of
energy demand and GHG emissions by sectors are presented in Table 3 and Table 4, providing a
pre-shock (or status quo) analysis of the Hawai‘i’s economy.
Table 3. Energy Intensity By Economic Activity, mmBTU and Rank
(1)
Final Energy
Demand
Sector
Crops production
Fruits, vegetables, and flowers
Animal production
Aquaculture, forestry, and logging
Commercial fishing
Mining
Construction
Petroleum manufacturing
Clothing manufacturing
Food processing
Other manufacturing
Air transportation
Water transportation
Ground transportation
Trucking
Warehousing and storage
Scenic and support activities for transp.
Information
Electric
Natural gas
Wholesale trade
Retail trade
Rental, leasing, and others
Accommodations
Hotels
Restaurants
Fin., bus., prof. services
Travel reservations
Waste management services
Education
Hospitals
Other health services
Arts and entertainment
Personal and laundry services
Repair and maintenance
Organizations
State and local government
Federal government
Total indirect
+ Final direct
Total demand
- Total electricity consumption
Total energy consumption
mmBTU
Rank
178,166
654,327
67,761
41,709
426,175
145,474
3,651,537
4,804,254
192,601
1,487,715
869,399
43,920,890
4,417,048
727,006
848,069
84,146
1,000,261
420,227
108,824,673
2,101,805
962,115
3,679,074
612,021
5,980,690
4,876,428
2,531,920
2,289,564
731,813
667,913
266,592
2,903,116
1,189,054
383,050
937,972
330,027
1,082,608
2,892,221
11,205,004
218,384,425
124,655,018
343,039,443
35,960,254
307,079,189
34
26
37
38
28
35
9
6
33
15
21
2
7
24
22
36
18
29
1
14
19
8
27
4
5
12
13
23
25
32
10
16
30
20
31
17
11
3
(2)
Energy intensity
(per $m output)
(3)
Energy intensity
(per job)
mmBTU
mmBTU
856
1,738
995
1,325
10,009
1,128
509
1,981
2,374
1,140
643
20,450
2,633
4,456
2,341
1,437
1,461
180
56,448
24,862
343
591
654
457
997
729
202
1,041
2,671
285
1,053
343
467
1,062
502
967
508
1,473
Rank
24
11
22
15
4
17
29
10
8
16
27
3
7
5
9
14
13
38
1
2
35
28
26
33
21
25
37
20
6
36
19
34
32
18
31
23
30
12
41
81
66
123
216
256
82
11,358
107
198
99
4,307
1,244
164
236
92
136
29
37,878
16,683
44
41
114
162
122
43
19
84
397
16
204
22
17
42
29
74
33
133
(4)
Energy intensity
(per $m value added)
Rank
mmBTU
Rank
31
24
26
16
9
7
23
3
19
11
20
4
5
12
8
21
14
33
1
2
27
30
18
13
17
28
36
22
6
38
10
35
37
29
34
25
32
15
1,108
2,799
1,996
3,176
23,687
4,041
1,157
24,511
5,206
5,429
2,046
70,499
16,859
10,691
4,052
2,067
2,147
315
151,665
66,799
734
1,280
1,301
709
1,838
1,724
346
2,121
5,520
508
3,309
467
715
2,602
1,220
1,702
567
1,513
30
15
21
14
5
12
29
4
10
9
20
2
6
7
11
19
17
38
1
3
31
27
26
33
22
23
37
18
8
35
13
36
32
16
28
24
34
25
Source: Author’s estimation.
Table 3 presents direct and indirect energy required to produce final demand output levels by
sector. Total energy used to produce, for example, health services would include direct fuel
13
combusted (for transportation or in generators) as well as indirect energy demand from
intermediate purchases of other goods and services. Thus, intermediate demand for energy is
attributed to the sector responsible for final demand as an indirect energy use. Total final demand
includes residential demand, visitor demand, state and local government demand, federal
government demand, and exports.
The most significant final demand for energy is in the form of direct demand, 124.6 trillion BTU,
which includes exports of fuel (mostly jet fuel for international air transport), residential
purchases of gasoline, and military fuel. Indirect final demand for electricity implies energy
demand of 108.8 trillion BTU. Domestic air transportation final demand uses 43.9 trillion BTUs.
Federal government (11.2 trillion), accommodations (6 trillion), hotels (4.9 trillion), petroleum
manufacturing (4.8 trillion), and water transportation (4.4 trillion) are among the highest sources
of final energy demand measured in BTUs.
Table 4. GHG Intensity By Sector, Metric Tons CO2 Equivalent (mtCO2e) And Rank
Sector
Crops production
Fruits, vegetables, and flowers
Animal production
Aquaculture, forestry, and logging
Commercial fishing
Mining
Construction
Petroleum manufacturing
Clothing manufacturing
Food processing
Other manufacturing
Air transportation
Water transportation
Ground transportation
Trucking
Warehousing and storage
Scenic and support activities for transp.
Information
Electric
Natural gas
Wholesale trade
Retail trade
Rental, leasing, and others
Accommodations
Hotels
Restaurants
Fin., bus., prof. services
Travel reservations
Waste management services
Education
Hospitals
Other health services
Arts and entertainment
Personal and laundry services
Repair and maintenance
Organizations
State and local government
Federal government
Total GHG
emissions
mtCO2e
10,220
35,434
3,571
2,335
31,489
5,360
156,227
253,476
12,940
87,065
40,140
3,235,795
229,224
51,909
61,784
4,805
71,908
19,849
6,834,605
139,757
45,489
139,833
34,203
262,143
78,718
41,837
76,528
19,287
40,453
4,571
99,788
43,233
3,834
29,991
11,250
9,152
132,692
805,956
GHG intensity
per $m output
mtCO2e
Rank
49
18
94
13
52
17
74
15
740
4
42
19
22
27
105
12
160
8
67
16
30
23
1,507
3
137
9
318
5
171
6
82
14
105
11
8
34
3,545
1
1,653
2
16
30
22
26
37
20
20
28
16
31
12
33
7
36
27
24
162
7
5
37
36
21
12
32
5
38
34
22
17
29
8
35
23
25
106
10
GHG intensity
per job
mtCO2e
Rank
2
24
4
21
3
23
7
17
16
8
9
13
4
22
599
3
7
14
12
10
5
20
317
4
65
5
12
9
17
7
5
19
10
11
1
30
2,379
1
1,109
2
2
26
2
28
6
18
7
15
2
27
1
34
1
35
2
25
24
6
0
37
7
16
1
33
0
38
1
31
1
32
1
36
2
29
10
12
GHG intensity
per $m value added
mtCO2e
Rank
64
23
152
14
105
19
178
12
1,750
4
149
15
49
25
1,293
5
350
8
318
10
94
20
5,194
2
875
6
763
7
295
11
118
16
154
13
15
34
9,525
1
4,442
3
35
28
49
26
73
22
31
29
30
30
28
31
12
36
56
24
334
9
9
37
114
17
17
33
7
38
83
21
42
27
14
35
26
32
109
18
Source: Author’s estimation based on direct and indirect emissions
Column (2) in Table 3 provides an estimate of energy intensity, which includes direct purchases
and indirect consumption of intermediates, required to produce $1 million of output for final
demand. The power generation sector requires the highest intensity, with one million dollars of
electricity requiring 56 billion BTU and utility gas stands next in line requiring 25 billion BTU.
14
Other highly energy intensive sectors include air transport (20 billion BTU) and commercial
fishing (10 billion BTU). In terms of jobs, the most energy intensive sectors are electricity,
natural gas, and petroleum manufacturing. Intensity per Hawai‘i value added and respective
rankings are also provided.
Table 4 provides similar information presented in Table 3, but for greenhouse gas emissions as
the variable of interest. Overall, major greenhouse gas emitting sectors include electricity at 6.8
million metric tons of CO2 equivalent (mmtCO2e), and air transport at 3.2 mmtCO2e. It is
important to note that air transport emissions exclude those generated for ‘export’ of jet fuel for
travel to foreign destinations.
Normalizing GHG emissions on a value basis provides quite a different ranking of relative
impact. Per million dollars of final demand, the top three emitters include electricity, utility gas,
and air transportation. Surprisingly, commercial fishing activity ranks fourth out of 38 economic
sectors in GHG intensity and tops petroleum refining. A million dollars of commercial fishing
demand requires CO2 equivalent emissions of 10 thousand metric tons. This is owing to high fuel
costs to power ships as well as the onboard and onshore (for their storages) refrigeration. Low
carbon intensity sectors include government services, performing arts, and finance and
professional services.
Table 4 also reports the carbon intensity of an average worker. Electricity production results in
2.4 thousand metric tons of CO2e per job. Utility gas generates 1.1 thousand metric tons of CO2e
per job. Other high emitting employment sectors include petroleum manufacturing (599 metric
tons), air transport (317 metric tons) and water transportation (65 metric tons). The education
sector together with art and entertainment sector share the position of lowest carbon intensity,
with less than a metric ton of CO2e per worker. The GHG intensity of value added is highest in
electricity, air transportation, natural gas, commercial fishing, and petroleum manufacturing.
Now, let us analyze the impacts of an efficiency shock on Hawai‘i’s economy. The efficiency
shock, as explained in section 3, assumes the production of local goods becoming more efficient
in using electricity as an input, a one-off step change in the production technology. It is worth to
mention that as the energy efficiency improvement is assumed at no cost in this analysis, the
results would only reflect the gains (benefits) that would come about from this improvement as
well as the distribution of the overall gain across economy.
Table 5. Macroeconomic Measures’ Changes, Under 10% Electricity Efficiency Scenario
a) Economy-wide Impacts
Macroeconomic Indicators
Domestic Output, nominal
Gross State Product, nominal
Exports, nominal
Consumer Price Index
Wages, real
Proprietors Income, real
Capital Cost, real
Visitor Expenditures, real
Visitor Price Index
Lump Sum Transfer
Value Added
Resident Welfare
Source: Author’s estimation.
% Change
-0.03
-0.03
+0.06
-0.00
+0.25
+0.25
+0.17
+0.08
-0.08
+0.01
+0.23
+0.29
b) Sectoral Change In Output And Employment
Employment
Output Level Change
Sector
$ Million*
%
New Jobs’ Count
2.74
0.38
51
Agriculture
3.90
0.14
24
Manufacturing
0.74
0.03
3
Air Transportation
8.83
0.28
14
Other Transportation
1.07
0.13
25
Entertainment
0.64
0.22
77
Hotel
22.20
0.17
38
Real Estate Rental
8.45
0.24
135
Restaurants
10.89
0.12
133
Trade
11.76
0.04
160
Services
0.07
0.03
0
Waste
1.18
0.01
4
Government
0.09
0.11
0
Natural Gas
-22.74
-0.94
-4
Petroleum Manufacturing
-102.56
-5.32
-154
Electricity
* In 2005 dollars.
Hence, as it would be a positive supply-side disturbance, the energy prices would be expected to
decline, therefore generally lowering the price of outputs, which supposedly stimulates economic
activity. Tables 5, 6, and 7 present the post-shock results.
15
Table 5 presents both economy-wide and sectoral macroeconomic impacts of 10% electricity
efficiency. As panel a of Table 5 suggests, the energy efficiency will have an overall positive
impact on almost all macro measures as expected, except for a small shrinkage in total output
(-0.03%), which is due to major declines in the electricity (-5.3%) and the petroleum
manufacturing (-0.9%) sectors’ output. All other sectors, however, would have their output
increases between 0.01% and 0.38%. Hawai‘i will see an increase of 500 in total jobs, 0.3% in
residents’ welfare, 0.25% in real wages and proprietors income and 0.23% in total value added.
Also both consumer (i.e., resident) and visitor price index would decline. Panel b of Table 5
presents sectoral change in output and employment. The highlight in sectoral indicators is that
both output and employment will increase in all sectors except electricity and petroleum
manufacturing. However the change differs across sectors as the efficiency improvements
increase the competitiveness of electricity intensive sectors more than others through a reduction
in their relative price.
Table 6. Energy Demand And GHG Emission Reduction
Sector i
Crops production
Fruits, vegetables, and flowers
Animal production
Aquaculture, forestry, and logging
Commercial fishing
Mining
Construction
Petroleum manufacturing
Clothing manufacturing
Food processing
Other manufacturing
Air transportation
Water transportation
Ground transportation
Trucking
Warehousing and storage
Scenic and support activities for transp.
Information
Electric
Natural gas
Wholesale trade
Retail trade
Rental, leasing, and others
Accommodations
Hotels
Restaurants
Fin., bus., prof. services
Travel reservations
Waste management services
Education
Hospitals
Other health services
Arts and entertainment
Personal and laundry services
Repair and maintenance
Organizations
State and local government
Federal government
Total
10% electricity efficiency; all sectors
(1)
(2)
Electricity
Energy
demand reduction
demand reduction
in sector i
in sector i
mmBTU
Rank
mmBTU
Rank
3,275
29
2,966
29
13,824
23
11,103
26
1,587
33
1,444
32
823
36
716
35
38
27
36
8,407
28
12,753
24
128,585
5
128,770
7
124,487
6
158,767
4
1,459
34
1,391
33
24,528
19
20,458
19
27,406
16
27,595
16
10,680
26
-4,466
38
103,683
8
86,016
11
2,043
31
1,762
30
1,000
35
1,336
34
1,603
32
1,612
31
2,283
30
3,013
28
12,757
24
12,899
23
102,905
9
5,026,353
1
140
37
-2,145
37
27,003
17
26,786
17
137,564
4
133,894
6
11,813
25
11,801
25
192,961
2
186,132
3
295,281
1
291,226
2
151,800
3
149,415
5
99,010
10
99,910
9
36,445
15
35,572
15
9,518
27
9,367
27
16,103
20
16,019
20
118,127
7
110,966
8
47,131
13
45,946
13
25,929
18
25,771
18
40,789
14
38,929
14
13,955
22
13,761
22
73,637
12
72,538
12
91,260
11
90,855
10
14,348
21
14,319
21
1,974,151
6,865,577
10% electricity efficiency; by sector i
(3)
(4)
Total energy
Total GHG
demand reduction
emission reduction
(intermed. and final)
(direct and indirect)
mmBTU
Rank
mtCO2e
Rank
13,390
26
753
26
56,635
20
3,224
19
6,543
30
370
30
3,096
34
173
34
0
35
0
36
12,196
27
634
27
500,318
5
27,457
5
N/A
N/A
N/A
N/A
4,635
32
244
32
98,129
17
5,468
17
105,906
15
5,856
15
29,247
25
1,355
25
361,019
8
18,993
9
7,780
29
421
29
3,777
33
205
33
5,032
31
257
31
8,745
28
478
28
52,583
21
2,945
21
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
102,169
16
5,550
16
563,478
4
31,192
4
47,134
23
2,595
23
797,597
2
44,195
2
1,203,541
1
66,773
1
614,453
3
33,918
3
393,244
7
21,846
7
141,835
13
7,723
13
29,674
24
1,493
24
64,217
18
3,535
18
489,711
6
27,079
6
198,557
11
11,046
11
107,175
14
5,961
14
166,057
12
9,149
12
52,292
22
2,821
22
302,412
10
16,718
10
353,040
9
19,323
8
57,740
19
3,173
20
6,405,810
331,693
Source: Author’s estimation based on direct and indirect emissions
Comparing the energy demand after applying the efficiency shock with the benchmark demand,
we calculated the energy savings potential. Using the energy consumption reduction amounts and
16
applying the same methodology for estimating the baseline greenhouse gas emissions, greenhouse
gas emission reductions of those scenarios are calculated and reported in Table 6. Columns 1 and
2, provide electricity and energy (total of electricity, natural gas, and petroleum products) demand
reductions associated with 10% electricity efficiency in all sectors; and each row in columns 3
and 4 present the total economy-wide energy saving (in mmBTU) and greenhouse gas emission
reduction (in metric tons CO2e) associated with 10% electricity efficiency in the corresponding
sector.
Considering that column is 2 inclusive of column 1 implies that in most sectors, the total
electricity saving is slightly offset by a marginal increase in oil and gas demand. Among few
exceptions, energy sectors (i.e. electricity and petroleum manufacturing) save both electricity and
petroleum products as they use energy to produce energy and so lower output implies lower input.
60,000
Total GHG Emission Reduction (by sector) due to 10% Electricity Efficiency in all
sectors
50,000
40,000
30,000
20,000
10,000
0
Hotels
Accommodations
Restaurants
Retail Trade
Construction
Hospitals
Fin., Bus., Prof. Services
State and local government
Water transportation
Organizations
Other health services
Personal and laundry services
Travel reservations
Arts and entertainment
Other manufacturing
Wholesale Trade
Food processing
Education
Fruits, Vegatables and…
Federal government
Information
Repair and maintenance
Rental & leasing and others
Waste managementt services
Air transportation
Crops production
Mining
Scenic and support…
Ground transportation
Animal production
Warehousing and storage
Clothing manufacturing
Trucking
Aquaculture, Forestry &…
Commercial fishing
GHG Emission Reduction (mtCO2e)
Comparing Sectoral results in column 3 with those of column 2 basically reflects how the
petroleum product saving as a result of fewer electricity generation is distributed across sectors.
In other words, difference between column 3 and column 1 is approximately equal to the indirect
petroleum products’ demand reduction due to electricity efficiency in sector i. Note that energy
sectors are excluded from sectoral analysis, as they are unique in terms of energy consumption
behaviors that make them exclusive from other sectors in terms of electricity efficiency.
Figure 3. GHG Emission Reduction (By Sector), mtCO2e
The carbon emissions associated with 10% electricity efficiency are shown in Figure 3. By far,
the most significant opportunities for greenhouse gas emissions reductions from electricity
conservation are in four sectors: hotels (51 thousand metric tons), accommodations (34 thousand
metric tons), restaurants (26 thousand metric tons), and retail trade, followed by construction and
hospitals.
Figure 4 demonstrates total energy demand reduction potential by source, associated with 10%
electricity efficiency in each sector. Interestingly, 10% electricity efficiency will save a bigger
portion of petroleum products than electricity. Note that the petroleum products savings are
indirect saving, the majority of which coming from the reduction in electricity sector’s demand
for power generation. That would then makes the petroleum products savings inclusive of the
energy savings in electricity consumption and the difference in the electricity and oil demand
reduction bars in Figure 4, which shows much larger petroleum product saving compared with
electrical energy saving, equals the avoided energy conversion loss in the power generation
17
sector. In the hotels sector, for example, 10% electricity efficiency will save three times as much
petroleum (905 thousand mmBTU) as electricity (300 thousand mmBTU), implying a 33%
efficiency of Hawai‘i’s generation system, which corresponds to the typical efficiency of thermal
power generation systems. In case of 10% electricity efficiency in all sectors, a total of 6.4 trillion
BTUs energy will be saved, of which 4.5 trillion BTUs comes from petroleum and 1.9 trillion
BTUs from electricity.
Total Energy Demand Reduction due to 10% Electricity Efficiency in sector i, by sector
1,400
('000 mmBTU)
1,200
1,000
800
600
400
0
Gas
Hotels
Accommodations
Restaurants
Retail Trade
Construction
Hospitals
Fin., Bus., Prof. Services
Water transportation
State and local government
Organizations
Other health services
Personal and laundry…
Travel reservations
Arts and entertainment
Other manufacturing
Wholesale Trade
Food processing
Education
Federal government
Fruits, Vegatables and…
Information
Repair and maintenance
Rental & leasing and others
Waste managementt…
Air transportation
Crops production
Mining
Scenic and support…
Ground transportation
Animal production
Warehousing and storage
Clothing manufacturing
Trucking
Aquaculture, Forestry &…
Commercial fishing
200
Petroleum
Electricity
Figure 4. Total Energy Saving Potential Due To 10% Electricity Efficiency By Sector
Table 7 summarizes and provides a comparison of rankings for the energy and GHG intensity, as
well as the energy and GHG saving potential under the efficiency scenario. Sectors use fuels in
different ratios, and emissions factors differ across fuels, as reported in Appendix I.
Practicing energy efficiency by Hawai‘i’s consumers is an important tool in reducing greenhouse
gas emissions. Demand-side management incentives may be considered for users of energy
intensive sectors like electricity, utility gas, or transportation. Konan and Chan (2010) provided
detailed analysis of the energy and greenhouse gas intensity of Hawai‘i resident and visitor
expenditures.
Energy Consumption Reduction
('000 mmBTU)
a. Total Energy Reduction (‘000 mmBTU)
b. Total Energy Reduction (%)
Energy Demand Reduction due to 10%
Electricity Efficiency (all sectors) by source
Energy Demand Reduction due to 10%
Electricity Efficiency (all sectors) by source
12.00%
6,000
10.1%
Total Final
demand
5,000
4,895
4,000
Total Final demand
8.00%
Total Intermediate
Demand
Total Demand
6.00%
3,000
4.00%
2,000
1,974
2.00%
1,000
0
(61)
-1,000
Total Intermediate Demand
10.00%
Electricity
1.6%
0.00%
(3) (3)
(405)
Petroleum
2.7%
1.6%
(2.00%)
Gas
(0.4%)
(0.4%)
Electricity
Petroleum
(0.1%)(0.3%)(0.1%)
Gas
Figure 5. Total Energy Reduction Due To 10% Electricity Efficiency In All Sectors, By Source
18
Figure 5 summarizes the energy saving results in a different and interesting way. The 10%
electricity efficiency in all sectors saves some 6.4 trillion BTUs of energy demand in the State,
due to 6.9 trillion BTUs cut in intermediate demand (by producing sectors), being offset by 0.5
trillion BTUs increase in final demand (by consuming agents). As electricity efficiency is only
assumed as technological change for the producing sectors, the final demand for all energy
sources (i.e., electricity, petroleum and gas) increases with increase in residents’ demand for
energy as well as increase in jet fuel exports due to larger number of visitor arrivals
corresponding to the observed growth in sectors associated tourism industry.
Table 7. Summary Of Industry Ranking By Energy And GHG Intensity And Saving Potential Due
To 10% Economy-wide Electricity Efficiency
Sector
Electric
Natural gas
Air transportation
Commercial fishing
Ground transportation
Waste management services
Water transportation
Clothing manufacturing
Trucking
Petroleum manufacturing
Fruits, vegetables, and flowers
Federal government
Scenic and support activities for transportation
Warehousing and storage
Aquaculture, forestry, and logging
Food processing
Mining
Personal and laundry services
Hospitals
Travel reservations
Hotels
Animal production
Organizations
Crops production
Restaurants
Rental, leasing, and others
Other manufacturing
Retail trade
Construction
State and local government
Repair and maintenance
Arts and entertainment
Accommodations
Other health services
Wholesale trade
Education
Fin., bus., prof. services
Information
Energy intensity
per $m output
mmBTU
Rank
56,448
1
24,862
2
20,450
3
10,009
4
4,456
5
2,671
6
2,633
7
2,374
8
2,341
9
1,981
10
1,738
11
1,473
12
1,461
13
1,437
14
1,325
15
1,140
16
1,128
17
1,062
18
1,053
19
1,041
20
997
21
995
22
967
23
856
24
729
25
654
26
643
27
591
28
509
29
508
30
502
31
467
32
457
33
343
34
343
35
285
36
202
37
180
38
GHG intensity
per $m output
mtCO2e Rank
3,545
1
1,653
2
1,507
3
740
4
318
5
162
7
137
9
160
8
171
6
105
12
94
13
106
10
105
11
82
14
74
15
67
16
42
19
34
22
36
21
27
24
16
31
52
17
8
35
49
18
12
33
37
20
30
23
22
26
22
27
23
25
17
29
5
38
20
28
12
32
16
30
5
37
7
36
8
34
Energy saving
potential
Rank
13
36
26
37
30
25
8
33
34
38
21
20
29
32
35
18
28
12
6
14
1
31
10
27
3
24
16
4
5
9
23
15
2
11
17
19
7
22
GHG saving
potential
Rank
14
36
26
37
30
25
9
33
34
38
20
21
29
32
35
18
28
12
6
13
1
31
10
27
3
24
16
4
5
8
23
15
2
11
17
19
7
22
Source: Author’s estimation based on direct and indirect emissions
This analysis only takes into account the increased efficiency in using electricity as an input in
production functions. Hence, we cannot do a full rebound effect discussion here, but a similar
effect can be seen in the results presented in panel b of Figure 5. It highlights the role of final
demand for electricity (by consuming agents) in total demand conservation under this scenario. In
other words, although 10% electricity efficiency in each sector generates a 10.1% decline in
intermediate demand for electricity, but the final demand increase offsets part of that and brings
the total demand reduction ratio down to only 1.6% (due to the very large share of final demand
in total demand for electricity).
19
5 Conclusions
This study analyzes the economic impacts of an assumed 10% electricity efficiency in Hawai‘i’s
economic sectors. The efficiency is assumed as a one-off step change at no cost in the production
technology. Hence, the results would only reflect the gains (benefits) that would come about from
this improvement as well as the distribution of the overall gain across economy.
A CGE model is developed and a methodology is advanced to estimate greenhouse gas emissions
reduction potential of such technological change through the estimated energy demand
reductions, using data on the input-output structure of the economy, detailed fossil fuel use (in
BTUs). The results provide sector-level analysis for energy and greenhouse gas emissions saving
potential, as well as change in macroeconomic measures under the assumed electricity efficiency
scenario.
Energy and GHG intensity indices are also developed, based on which economic sectors are
ranked and analyzed. The energy intensity index measures total direct and indirect energy
measured in millions BTU (mmBTU) required to produce one million dollars in total output.
Electricity production is the most energy intensive, requiring 56.5 billion BTU to produce one
million dollars of output. Utility gas, air transportation, and commercial fishing follow, requiring
25 billion, 20.5 billion, and 10 billion BTUs per million dollars, respectively. As expected, the
rankings of sectors are almost the same in terms of greenhouse gas emissions intensity with a few
exceptions, though none of those exceptions are among top 10.
However, when it comes to energy and GHG emission saving potential, the rankings are totally
different, with hotels, accommodations, and restaurants standing in top three places, respectively,
followed by retail trade, construction, and hospitals. This clearly shows the high GHG emissions
elasticity of technological change in the tourism industry. Especially considering the ratio of
residents’ versus visitors’ population, this result implies that the visitor expenditures are more
energy and carbon intensive than that of Hawai‘i households on a per person basis, which verifies
the result obtained by Konan and Chan (2010).
These results indicate directions for greenhouse gas emissions reduction policies in serviceoriented economies like Hawai‘i. First, visitors are likely to experience the largest welfare impact
of any increase in the price of carbon, whether through a cap and trade, carbon tax, or other
policies. And second, the scope to use electricity demand-side management efforts to lower
carbon emissions is limited to resident use and a handful of economic industries (particularly
hotels and restaurants, retail trade, and health services).
As this analysis only analyzes the gain distribution of a free energy efficiency improvement
throughout the Hawai‘i’s economy, further research is needed to look into the costs of the energy
efficiency improvement in order to understand how benefits compare to costs in overall and by
sector. This study could provide a base CGE model for future research on total welfare analysis
of energy efficiency, as well as other policy tools, such as the role of gasoline taxes, carbon taxes,
fuel efficiency standards, and other greenhouse gas emissions reductions plans.
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Appendix I. Linking Petroleum Product Use and Carbon Dioxide Emissions
For GHG emission calculations, the quantity of petroleum products in terms of millions of BTU
is multiplied with the greenhouse gas emission factor of each petroleum product (Table I-1) (e.g.,
71.689 for highway gasoline and 69.619 for aviation gasoline).
Table I-1. Emission Factor for Petroleum Product Use
Fuel
SNG (propane)
Refinery Gas
Residual
Diesel
Waste Oil (Assume: blended with Residual)
Aviation Gasoline
Gasoline
Jet Fuel Kerosene
Coal
GWP
Unit
kg/mmBTU
kg/mmBTU
kg/mmBTU
kg/mmBTU
kg/mmBTU
kg/mmBTU
kg/mmBTU
kg/mmBTU
kg/mmBTU
23
CO2
56
64
79
73
66
69
71
71
95
1
CH4
0.0009
0.003
0.003
0.007
0.003
0.01
0.01
0.003
0.001
25
N2O
0.0001
0.0006
0.0006
0.0006
0.0006
0.0006
0.0006
0.0006
0.0015
298
CO2e
56.587
64.454
79.054
73.513
66.784
69.619
71.689
71.134
95.992