Impacts of Energy Efficiency on Hawai‘i’s Economy: A CGE-Modeling Approach

Iman Nasseri* Denise E. Konan†

This version: April 2012

AbstractThis 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

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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 intra-regional 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

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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 tourism- or 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 multi-sectoral 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,

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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

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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 bi-product 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 (CO 2), 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

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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, 2005Fuel Activity Consumption

('000 mmBTU)Emission(mtCO2e)

Aviation Gasoline Aviation Intra-State 181.80 12.66Aviation Overseas, Non-Bonded Fuel 2.10 0.14

Coal Electricity Generation 15,577.80 1,495.34Diesel Federal Government 121.90 8.96

Highway 6,193.30 455.29Electricity Generation 15,108.20 1,110.64Non Highway 9,207.00 676.84Small Boat 5.70 0.42Vessel Bunkering Intra-State 1,783.50 131.11Vessel Bunkering Overseas, Bonded Fuel 7,565.50 556.16Vessel Bunkering Overseas, Non-Bonded Fuel 33.60 2.47

Gasoline Agriculture 125.70 9.01Federal Government 158.44 11.36Highway 57,491.16 4,121.47Small Boat 1.75 0.13

Jet Fuel Aviation Intra-State 9,245.45 657.66Aviation Overseas, Bonded Fuel 39,864.24 2,835.69Aviation Overseas, Non-Bonded Fuel 34,267.99 2,437.61Federal Government 10,542.14 749.90State and Local Government 0.33 0.02Other End Users 95.69 6.81

Natural Gas (SNG and LPG) Highway 13.73 0.78Residential 893.51 50.56Commercial 4,615.94 261.20Industrial 58.84 3.33State and Local Government 172.79 9.78Federal Government 153.88 8.71

Residual Fuel Oil Commercial 17.58 1.39Industrial 1,485.72 117.45Electricity Generation 71,106.75 5,621.26Vessel Bunkering Intra-State 1,209.85 95.64Vessel Bunkering Overseas, Bonded Fuel 11,191.59 884.74Vessel Bunkering Overseas, Non-Bonded Fuel 80.36 6.35Federal Government 81.85 6.47

Other fuels* Industrial 8,423.54 562.56Total 307,079.20 22,909.92*Other fuels include: Naphtha, distillate fuel oil, waste oil, and fuel gas.

Source: Hawai‘i Department of Business, Economic Development, and Tourism

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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, 2005Output Employee

Compensation Value Added Job Count

$ million $ million $ millionCrops production 208 92 161 4,315Fruits, vegetables, and flowers 377 132 234 8,038Animal production 68 19 34 1,022Aquaculture, forestry, and logging 31 7 13 339Commercial fishing 43 10 18 1,970Mining 129 32 36 569Construction 7,178 2,284 3,157 44,332Petroleum manufacturing 2,426 78 196 423Clothing manufacturing 81 20 37 1,808Food processing 1,305 266 274 7,510Other manufacturing 1,353 337 425 8,772Air transportation 2,148 563 623 10,198Water transportation 1,677 171 262 3,550Ground transportation 163 51 68 4,420Trucking 362 154 209 3,589Warehousing and storage 59 30 41 912Scenic and support activities for transportation 685 320 466 7,360Information 2,338 706 1,334 14,260Electric 1,928 285 718 2,873Natural gas 85 12 31 126Wholesale trade 2,809 923 1,311 21,856Retail trade 6,222 2,132 2,875 88,747Rental, leasing, and others 936 148 470 5,391Accommodations 13,074 458 8,437 36,980Hotels 4,891 1,729 2,653 40,112Restaurants 3,473 1,142 1,469 59,147Fin., bus., prof. services 11,352 4,377 6,612 120,309Travel reservations 703 262 345 8,696Waste management services 250 80 121 1,682Education 935 480 525 17,147Hospitals 2,756 815 877 14,220Other health services 3,471 1,894 2,544 54,787Arts and entertainment 820 344 536 21,903Personal and laundry services 883 266 360 22,462Repair and maintenance 657 200 271 11,565Organizations 1,119 470 636 14,671State and local government 5,693 4,421 5,097 88,128Federal government 7,608 6,803 7,406 84,400Total 90,296 32,512 50,883 838,588Source: DBEDT (Anon. 2008)

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EndogenousVariables

Residents (r)

Visitors (v)

State & Local Government (SL )

Foreign Countries

Industry (i,j)

Federal Military

Gov’t (FM)

Federal Civilian

Gov’t (FC)

ExogenousVariables Federal

CivilianInflow (IFC)

VisitorsIncome (Iv)

FederalMilitary

Inflow (IFM)

Labor (L), Prop Income (R), Capital (K)

Lump -Sum Tax (Tr)

SL Consumption (piGSLi)Sales Tax (piYiτi)

Intermediate Inputs (Zji)

Returns to Factors of Production (pLL, pRR, pKK)Residents Consumption (piCri)

Visitors Imports (pmCvm)

Residents Imports (pmCrm)Balance of Payment Deficit (pfxBP)

FC Consumption(piGFCi)

FM Consumption(piGFMi)

Exports (pxiXi)

Imports (pmM)

SL Imports(pmGSLm)

FC Imports(pmGFCm)

FM Imports(pmGFMm)

Visitors Consumption

(piCvi)

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.

Figure 1. General Equilibrium Model Of Hawai‘i’s Economy (Konan, et al. 2007)

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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 Figure2. 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.

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:

Y s=min [(1−ηs)SE s

α Es,

SNE1 s

β1s, …,

SNE(J−1) s

β (J −1 ) s,

ST1 s

γ 1 s,…,

ST Ks

γ Ks,V s

α Vs]

(4)

where α Es, β js, γks, and α Vs are unit input coefficients for intermediates (electricity, non-electricity 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.

Intermediate Inputs

CapitalProp. Income

Labor

Export, Xs

Tradable Goods, STks (10)

Non-electricityNon-tradable

Sectors, SNEjs (27)

Electricity Sector, SEs (1)

Value Added, VAs

Sector S’s Output, Ys

Domestic, Ds

Tradable Sectors

Domestic, Dks

Import, Ms

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ST ks=[θDks Dks

(ε ksm−1)εksm +θMs M s

(ε ksm−1)εksm ]

εksm

(ε ksm−1) (5)

where ε ksm 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:

V s=[α Ls Ls

( σs−1)σ s +α Ks K s

( σ s−1)σ s +α Rs Rs

( σs−1)σ s ]

σ s

(σ s−1) (6)

where σ s is the CES among value added variables and α Ls, α Ks, and α Rs 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).

Y s=[ βDs Ds

( εs−1 )εs + βXs X s

( εs−1 )εs ]

εs

(ε s−1) (7)

where ε s is the elasticity of transformation; and βDs, and β Xs 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.

U h=∏i=1

n

C hibhi , ∑

i=1

n

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).

Chi=[θDhi Dhi

(εhim−1)εhim +θMh M h

(ε him−1)εhim ]

ε him

(εhim−1) (9)

where ε him 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:

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∑i

pi C ri=pL L+PR R+PK K+ p fx BP−T r (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), 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 ( pfx BP) from a balance of payment deficit, described below in equation (14).

A representative visitor’s budget constraint is expressed as:

I v=∑i

pi C vi (11)

where I v 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

piY iτ i+T r (12)

A primary source of revenue is the State’s goods and services tax (τ i) 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=I FG (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 ( pfx), where pfx 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.

pfx BP=pm M−I v−I FG−∑j

pxj X j (14)

3.6 Supply Demand Balance

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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 (τ j) 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.

p jY j (1+τ j )=∑l=1

n

pl Z lj+ pL L j+PR R j+PK K j+Pm M Yj (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.

D j=∑h

Chj+∑g

G gj+∑l

Z lj (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=∑k=1

n

d ik+∑y

diy (1)

where i = type of energy,n = number of industry sectors,d ik = demand for energy type i by the kth industry sector, and d iy = demand for energy type i by the the final sector, y = residents, visitors, gov., etc.

The total estimated energy demand (d ik and d iy) are then calculated as follows:

d ik=Di × ρik and d iy=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

∑k =1

n

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:

12

ρgasoline ,k=ek , pet

∑k=1

n

ek , pet(3)

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.

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

Sector

(1)Final Energy

Demand

(2)Energy intensity(per $m output)

(3)Energy intensity

(per job)

(4)Energy intensity

(per $m value added)mmBTU Rank mmBTU Rank mmBTU Rank mmBTU Rank

Crops production 178,166 34 856 24 41 31 1,108 30Fruits, vegetables, and flowers 654,327 26 1,738 11 81 24 2,799 15Animal production 67,761 37 995 22 66 26 1,996 21Aquaculture, forestry, and logging 41,709 38 1,325 15 123 16 3,176 14Commercial fishing 426,175 28 10,009 4 216 9 23,687 5Mining 145,474 35 1,128 17 256 7 4,041 12Construction 3,651,537 9 509 29 82 23 1,157 29Petroleum manufacturing 4,804,254 6 1,981 10 11,358 3 24,511 4Clothing manufacturing 192,601 33 2,374 8 107 19 5,206 10Food processing 1,487,715 15 1,140 16 198 11 5,429 9Other manufacturing 869,399 21 643 27 99 20 2,046 20Air transportation 43,920,890 2 20,450 3 4,307 4 70,499 2Water transportation 4,417,048 7 2,633 7 1,244 5 16,859 6Ground transportation 727,006 24 4,456 5 164 12 10,691 7Trucking 848,069 22 2,341 9 236 8 4,052 11Warehousing and storage 84,146 36 1,437 14 92 21 2,067 19Scenic and support activities for transp. 1,000,261 18 1,461 13 136 14 2,147 17Information 420,227 29 180 38 29 33 315 38Electric 108,824,673 1 56,448 1 37,878 1 151,665 1Natural gas 2,101,805 14 24,862 2 16,683 2 66,799 3Wholesale trade 962,115 19 343 35 44 27 734 31Retail trade 3,679,074 8 591 28 41 30 1,280 27Rental, leasing, and others 612,021 27 654 26 114 18 1,301 26Accommodations 5,980,690 4 457 33 162 13 709 33Hotels 4,876,428 5 997 21 122 17 1,838 22Restaurants 2,531,920 12 729 25 43 28 1,724 23Fin., bus., prof. services 2,289,564 13 202 37 19 36 346 37

13

Travel reservations 731,813 23 1,041 20 84 22 2,121 18Waste management services 667,913 25 2,671 6 397 6 5,520 8Education 266,592 32 285 36 16 38 508 35Hospitals 2,903,116 10 1,053 19 204 10 3,309 13Other health services 1,189,054 16 343 34 22 35 467 36Arts and entertainment 383,050 30 467 32 17 37 715 32Personal and laundry services 937,972 20 1,062 18 42 29 2,602 16Repair and maintenance 330,027 31 502 31 29 34 1,220 28Organizations 1,082,608 17 967 23 74 25 1,702 24State and local government 2,892,221 11 508 30 33 32 567 34Federal government 11,205,004 3 1,473 12 133 15 1,513 25Total indirect 218,384,425 + Final direct 124,655,018Total demand 343,039,443 - Total electricity consumption 35,960,254Total energy consumption 307,079,189Source: 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 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

Total GHG emissions

GHG intensityper $m output

GHG intensityper job

GHG intensityper $m value added

mtCO2e mtCO2e Rank mtCO2e Rank mtCO2e RankCrops production 10,220 49 18 2 24 64 23Fruits, vegetables, and flowers 35,434 94 13 4 21 152 14Animal production 3,571 52 17 3 23 105 19Aquaculture, forestry, and logging 2,335 74 15 7 17 178 12Commercial fishing 31,489 740 4 16 8 1,750 4Mining 5,360 42 19 9 13 149 15Construction 156,227 22 27 4 22 49 25Petroleum manufacturing 253,476 105 12 599 3 1,293 5Clothing manufacturing 12,940 160 8 7 14 350 8Food processing 87,065 67 16 12 10 318 10Other manufacturing 40,140 30 23 5 20 94 20Air transportation 3,235,795 1,507 3 317 4 5,194 2Water transportation 229,224 137 9 65 5 875 6Ground transportation 51,909 318 5 12 9 763 7Trucking 61,784 171 6 17 7 295 11Warehousing and storage 4,805 82 14 5 19 118 16Scenic and support activities for transp. 71,908 105 11 10 11 154 13Information 19,849 8 34 1 30 15 34Electric 6,834,605 3,545 1 2,379 1 9,525 1Natural gas 139,757 1,653 2 1,109 2 4,442 3Wholesale trade 45,489 16 30 2 26 35 28Retail trade 139,833 22 26 2 28 49 26Rental, leasing, and others 34,203 37 20 6 18 73 22Accommodations 262,143 20 28 7 15 31 29Hotels 78,718 16 31 2 27 30 30

14

Restaurants 41,837 12 33 1 34 28 31Fin., bus., prof. services 76,528 7 36 1 35 12 36Travel reservations 19,287 27 24 2 25 56 24Waste management services 40,453 162 7 24 6 334 9Education 4,571 5 37 0 37 9 37Hospitals 99,788 36 21 7 16 114 17Other health services 43,233 12 32 1 33 17 33Arts and entertainment 3,834 5 38 0 38 7 38Personal and laundry services 29,991 34 22 1 31 83 21Repair and maintenance 11,250 17 29 1 32 42 27Organizations 9,152 8 35 1 36 14 35State and local government 132,692 23 25 2 29 26 32Federal government 805,956 106 10 10 12 109 18Source: 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. 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 Scenarioa) Economy-wide Impacts b) Sectoral Change In Output And Employment

Output Level Change Employment

15

Macroeconomic Indicators % Change Sector $ Million* % New Jobs’ CountDomestic Output, nominal -0.03 Agriculture 2.74 0.38 51Gross State Product, nominal -0.03 Manufacturing 3.90 0.14 24Exports, nominal +0.06 Air Transportation 0.74 0.03 3Consumer Price Index -0.00 Other Transportation 8.83 0.28 14Wages, real +0.25 Entertainment 1.07 0.13 25Proprietors Income, real +0.25 Hotel 0.64 0.22 77Capital Cost, real +0.17 Real Estate Rental 22.20 0.17 38Visitor Expenditures, real +0.08 Restaurants 8.45 0.24 135Visitor Price Index -0.08 Trade 10.89 0.12 133Lump Sum Transfer +0.01 Services 11.76 0.04 160Value Added +0.23 Waste 0.07 0.03 0Resident Welfare +0.29 Government 1.18 0.01 4

Natural Gas 0.09 0.11 0Petroleum Manufacturing -22.74 -0.94 -4Electricity -102.56 -5.32 -154

Source: Author’s estimation. * 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.

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

10% electricity efficiency; all sectors 10% electricity efficiency; by sector i(1)

Electricitydemand reduction

in sector i

(2)Energy

demand reductionin sector i

(3)Total energy

demand reduction(intermed. and

final)

(4)Total GHG

emission reduction(direct and indirect)

mmBTU Rank

mmBTU Rank mmBTU Rank mtCO2e Rank

Crops production 3,275 29 2,966 29 13,390 26 753 26Fruits, vegetables, and flowers 13,824 23 11,103 26 56,635 20 3,224 19Animal production 1,587 33 1,444 32 6,543 30 370 30Aquaculture, forestry, and logging 823 36 716 35 3,096 34 173 34Commercial fishing - 38 27 36 0 35 0 36Mining 8,407 28 12,753 24 12,196 27 634 27Construction 128,585 5 128,770 7 500,318 5 27,457 5Petroleum manufacturing 124,487 6 158,767 4 N/A N/A N/A N/AClothing manufacturing 1,459 34 1,391 33 4,635 32 244 32Food processing 24,528 19 20,458 19 98,129 17 5,468 17Other manufacturing 27,406 16 27,595 16 105,906 15 5,856 15Air transportation 10,680 26 -4,466 38 29,247 25 1,355 25Water transportation 103,683 8 86,016 11 361,019 8 18,993 9Ground transportation 2,043 31 1,762 30 7,780 29 421 29Trucking 1,000 35 1,336 34 3,777 33 205 33Warehousing and storage 1,603 32 1,612 31 5,032 31 257 31Scenic and support activities for transp.

2,283 30 3,013 28 8,745 28 478 28

16

Information 12,757 24 12,899 23 52,583 21 2,945 21Electric 102,905 9 5,026,353 1 N/A N/A N/A N/

ANatural gas 140 37 -2,145 37 N/A N/A N/A N/AWholesale trade 27,003 17 26,786 17 102,169 16 5,550 16Retail trade 137,564 4 133,894 6 563,478 4 31,192 4Rental, leasing, and others 11,813 25 11,801 25 47,134 23 2,595 23Accommodations 192,961 2 186,132 3 797,597 2 44,195 2Hotels 295,281 1 291,226 2 1,203,541 1 66,773 1Restaurants 151,800 3 149,415 5 614,453 3 33,918 3Fin., bus., prof. services 99,010 10 99,910 9 393,244 7 21,846 7Travel reservations 36,445 15 35,572 15 141,835 13 7,723 13Waste management services 9,518 27 9,367 27 29,674 24 1,493 24Education 16,103 20 16,019 20 64,217 18 3,535 18Hospitals 118,127 7 110,966 8 489,711 6 27,079 6Other health services 47,131 13 45,946 13 198,557 11 11,046 11Arts and entertainment 25,929 18 25,771 18 107,175 14 5,961 14Personal and laundry services 40,789 14 38,929 14 166,057 12 9,149 12Repair and maintenance 13,955 22 13,761 22 52,292 22 2,821 22Organizations 73,637 12 72,538 12 302,412 10 16,718 10State and local government 91,260 11 90,855 10 353,040 9 19,323 8Federal government 14,348 21 14,319 21 57,740 19 3,173 20Total 1,974,15

1 6,865,57

76,405,810 331,693

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 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.

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.

17

0

10,000

20,000

30,000

40,000

50,000

60,000 Total GHG Emission Reduction (by sector) due to 10% Electricity Efficiency in all

sectorsG

HG

Em

issi

on R

educ

tion

(mtC

O2e

)

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 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.

18

Hotels

Restau

rants

Constructi

on

Fin., Bus.,

Prof.

Service

s

State a

nd loca

l govern

ment

Other hea

lth se

rvice

s

Travel

reserv

ations

Other man

ufacturin

g

Food proce

ssing

Federa

l govern

ment

Info

rmati

on

Rental

& le

asing an

d others

Air tra

nsporta

tion

Mining

Ground tr

ansp

ortatio

n

Ware

housing an

d storag

e

Truck

ing

Commercial

fish

ing0

200

400

600

800

1,000

1,200

1,400

Total Energy Demand Reduction due to 10% Electricity Efficiency in sector i, by sector

GasPetroleumElectricity

('000

mm

BT

U)

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.

a. Total Energy Reduction (‘000 mmBTU)

Electricity Petroleum Gas-1,000

0

1,000

2,000

3,000

4,000

5,000

6,000

1,974

4,895

(3)(61) (405) (3)

Energy Demand Reduction due to 10% Elec-tricity Efficiency (all sectors) by source

Total Final demand

Total Intermediate DemandEn

ergy

Con

sum

ptio

n R

educ

tion

('0

00 m

mBT

U)

b. Total Energy Reduction (%)

Electricity Petroleum Gas(2.00%)

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00% 10.1%

2.7%

(0.1%)

(0.4%) (0.4%)

(0.3%)

1.6% 1.6%

(0.1%)

Energy Demand Reduction due to 10% Elec-tricity Efficiency (all sectors) by source

Total Intermediate DemandTotal Final demandTotal Demand

Figure 5. Total Energy Reduction Due To 10% Electricity Efficiency In All Sectors, By Source

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

19

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

Energy intensityper $m output

GHG intensityper $m output

Energy saving potential

GHG saving potential

mmBTU Rank mtCO2e Rank Rank RankElectric 56,448 1 3,545 1 13 14

Natural gas 24,862 2 1,653 2 36 36

Air transportation 20,450 3 1,507 3 26 26

Commercial fishing 10,009 4 740 4 37 37

Ground transportation 4,456 5 318 5 30 30

Waste management services 2,671 6 162 7 25 25

Water transportation 2,633 7 137 9 8 9

Clothing manufacturing 2,374 8 160 8 33 33

Trucking 2,341 9 171 6 34 34

Petroleum manufacturing 1,981 10 105 12 38 38

Fruits, vegetables, and flowers 1,738 11 94 13 21 20

Federal government 1,473 12 106 10 20 21

Scenic and support activities for transportation 1,461 13 105 11 29 29

Warehousing and storage 1,437 14 82 14 32 32

Aquaculture, forestry, and logging 1,325 15 74 15 35 35

Food processing 1,140 16 67 16 18 18

Mining 1,128 17 42 19 28 28

Personal and laundry services 1,062 18 34 22 12 12

Hospitals 1,053 19 36 21 6 6

Travel reservations 1,041 20 27 24 14 13

Hotels 997 21 16 31 1 1

Animal production 995 22 52 17 31 31

Organizations 967 23 8 35 10 10

Crops production 856 24 49 18 27 27

Restaurants 729 25 12 33 3 3

Rental, leasing, and others 654 26 37 20 24 24

Other manufacturing 643 27 30 23 16 16

Retail trade 591 28 22 26 4 4

Construction 509 29 22 27 5 5

State and local government 508 30 23 25 9 8

Repair and maintenance 502 31 17 29 23 23

Arts and entertainment 467 32 5 38 15 15

Accommodations 457 33 20 28 2 2

Other health services 343 34 12 32 11 11

Wholesale trade 343 35 16 30 17 17

Education 285 36 5 37 19 19

Fin., bus., prof. services 202 37 7 36 7 7

Information 180 38 8 34 22 22

Source: Author’s estimation based on direct and indirect emissions

20

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).

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 service-oriented 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 UseFuel Unit CO2 CH4 N2O CO2eSNG (propane) kg/mmBTU 56 0.0009 0.0001 56.587Refinery Gas kg/mmBTU 64 0.003 0.0006 64.454Residual kg/mmBTU 79 0.003 0.0006 79.054Diesel kg/mmBTU 73 0.007 0.0006 73.513Waste Oil (Assume: blended with Residual) kg/mmBTU 66 0.003 0.0006 66.784Aviation Gasoline kg/mmBTU 69 0.01 0.0006 69.619Gasoline kg/mmBTU 71 0.01 0.0006 71.689Jet Fuel Kerosene kg/mmBTU 71 0.003 0.0006 71.134Coal kg/mmBTU 95 0.001 0.0015 95.992GWP 1 25 298

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