Effects of a carbon price in the U.S. on economic sectors, resource use, and emissions: An input–output approach

ARTICLE IN PRESS Energy Policy 38 (2010) 3527–3536

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Effects of a carbon price in the U.S. on economic sectors, resource use, and emissions: An input–output approach Jun-Ki Choi a,n, Bhavik R. Bakshi b,1, Timothy Haab c,2 a

Energy Sciences and Technology Department, Brookhaven National Laboratory, Upton, NY 11973, USA William G. Lowrie Department of Chemical & Biomolecular Engineering, The Ohio State University, 140 West 19th Ave., Columbus, OH 43210, USA c Department of Agricultural, Environmental and Development Economics, The Ohio State University, 2120 Fyffe Rd., Columbus, OH 43210, USA b

a r t i c l e in fo

abstract

Article history: Received 17 June 2009 Accepted 11 February 2010 Available online 4 March 2010

Despite differences in their implementation, most carbon policies aim to have similar outcomes: effectively raising the price of carbon-intensive products relative to non-carbon-intensive products. While it is possible to predict the simple broad-scale economic impacts of raising the price of carbonintensive products—the demand for non-carbon-intensive products will increase—understanding the economic and environmental impacts of carbon policies throughout the life cycle of both types of products is more difficult. Using the example of a carbon tax, this study proposes a methodology that integrates short-term policy-induced consumer demand changes into the input–output framework to analyze the environmental and economic repercussions of a policy. Environmental repercussions include the direct and the indirect impacts on emissions, materials flow in the economy, and the reliance on various ecosystem goods and services. The approach combines economic data with data about physical flow of fossil fuels between sectors, consumption of natural resources and emissions from each sector. It applies several input–output modeling equations sequentially and uses various levels of aggregation/disaggregation. It is illustrated with the data for the 2002 U.S. economy and physical flows. The framework provides insight into the short-term complex interactions between carbon price and its economic and environmental effects. Published by Elsevier Ltd.

Keywords: Carbon price Input–output analysis Resource consumption and emission

1. Introduction There has been extensive research on modeling the relationship between economic and ecological systems, many specifically focused on economic and energy systems (EIA, 2003; Goldstein, 1995; Hertel, 1997). These models simulate future economic performance or optimize energy systems under environmental and energy constraints for energy planning, climate policy analysis, and environmental taxation using scenario analysis. However, attempts to analyze the external social costs of resource consumption and sustainability effects are limited and few studies focus on the specific effects of carbon policy at the sectoral level. While some researchers have tried to compare different economic modeling schemes for analyzing the effect of carbon mitigation policies (Krause, 1996; Shrestha and Marpaung, 1999), and many have settled on the use of general equilibrium

n

Corresponding author: Tel.: + 1 631 344 2723; fax: + 1 631 344 3957. E-mail addresses: [email protected] (J.-K. Choi), [email protected] (B.R. Bakshi), [email protected] (T. Haab). 1 Tel.: + 1 614 292 4904; fax: + 1 614 292 3769. 2 Tel.: + 1 614 292 6237; fax: + 1 614 247 7066. 0301-4215/$ - see front matter Published by Elsevier Ltd. doi:10.1016/j.enpol.2010.02.029

models, this study is adopting input–output models for the framework. Input–output analysis (IOA) has been used to quantify the relations among various economic sectors to estimate the direct and the indirect interrelationships and the impact of changes in final demand and value added to the national economy (Leontief, 1936). For economic analysis, IOA is relatively straightforward compared to other economic tools due to fixed substitution between factors or commodities and no economies of scale. It has been popular since it reduces the gap between factual observation and deductive theoretical reasoning that raises concern about economics as an empirical science among non-economists (Duchin, 1998). Furthermore, economic input–output (EIO) models have been combined with physical flow information such as resource use and emissions for economic sectors, and has been popular for life cycle assessment and thermodynamic analysis of economic goods and services (Hannon, 2001; Lave et al., 1995; Ukidwe and Bakshi, 2007; Zhang et al., 2010). These features are not readily available in more sophisticated economic models, making EIO based methods unique and attractive for holistic econo-physical analysis. EIO models have been widely used in the fields of energy and environmental modeling in recent decades (Chang et al., 2008;

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Kulisic et al., 2007; Nguyen, 2008). For example, in the context of a carbon price, detailed working level data has been used to provide a comprehensive look at energy costs focusing on the manufacturing sector (Morgenstern et al., 2004). Other studies investigate the order of magnitude of a carbon tax required for meeting an emissions reduction goal such as the Toronto target (Gay and Proops, 1993; Pearce, 1991). However, these approaches do not properly connect changes in commodity prices caused by economic policy with the resulting changes in the demand of physical quantities. It is quite a demanding task to obtain the physical flow that satisfies both the level of comprehensiveness and accuracy in the decision making process through IOA. The use of physical I/O models has also been popular. Numerical examples for physical input–output approaches with aggregated input–output tables for Germany are described in Giljum and Hubacek (2004). Weisz and Duchin (2006) have shown that a monetary input–output model is equivalent to a physical input–output model as long as all sectors pay the same price for products from a sector (i.e. price homogeneity satisfied). Regional physical input–output models are developed for particular countries such as Germany, Finland, and Denmark ¨ ¨ 2002; Stahmer et al., 1998). In Japan, there has been (Maenp a¨ a, significant work on waste input–output models in physical units (Nakamura and Kondo, 2002). In the U.S., a physical input–output model for heavy metals has been developed (Hawkins et al., 2007). The use of physical IO models is limited due to the difficulties in developing them and lack of relevant data. This study proposes an integrated approach based on an economic input–output model to connect the change in price, consumer demand, environmental emission, and resource consumption in the presence of policies that influence physical flows, such as a carbon tax. The resulting model is not a full physical input–output model, but a physically augmented economic input–output model. It is similar to models developed for life cycle applications (Lave et al., 1995; Ukidwe and Bakshi, 2007; Zhang et al., 2010), but uses more physical data to overcome implicit assumptions of price homogeneity. Although the focus of this article is on the effect of a carbon price, the proposed framework is a step toward a more general framework that can simultaneously address the environmental, economic and technological impacts of economic perturbations due to factors such as energy policy, depletion of natural capital, and changes in technologies or consumer preferences. This general framework is expected to also provide insight into the biocomplexity of materials use, that is, the effect of changes in the use of a material on the direct and indirect flows of other materials and resources. Additional contributions of this work are as follows: when I/O modelers deal with the available economic benchmark data for tailored modeling, they often face the challenge of aggregating or disaggregating data to the appropriate level. In this work, we accomplish this goal by disaggregating selected sectors from the ‘‘use’’ and ‘‘make’’ tables. The common approach of directly disaggregating the direct requirement or total requirement matrix may not provide useful information about the consumption and production mixes of sectors, which include various technologies, for example, the power generation sector includes electricity from coal, natural gas, wind, and hyrdopotential. Second, we introduce the concept of price elasticity of demand into input–output analysis to capture the effect of a price change on consumer demand. Third, since not every industry/ household pays the same price for energy goods, we have calculated the physical flow of some fossil fuels by using nonhomogeneous price information in order to account for carbon tax that will be levied in proportion to CO2 emission. In addition, we address methods of adjusting the effect of changes in price and quantity in order to calculate the resulting changes in resource

consumption and emissions after carbon tax is levied on the economy. The rest of this paper is organized as follows: Section 2 presents the proposed methodology and summarizes the construction of a general framework. Section 3 presents issues related to modeling for the specific task; disaggregation and aggregation of industry sectors for the customized input–output table, integration of non-homogeneous price information to achieve physical flow of fossil fuels in the economy, and the way of levying a carbon price to all industry sectors. Finally, Section 4 illustrates the results of a case study and discusses the limitations of this study.

2. Methodology Using input–output models, this study considers the case of a policy-maker imposing the external social costs of production (or consumption) on producers (consumers) in the form of a tax. In micro-level economic analyses, the effects of this exogenous tax are analyzed in the market of interest. But, for large changes in sectoral level prices, the imposition of a tax may change the relative prices of commodities in all sectors, thereby changing their relative demand from all sectors. In order to integrate the effect of changes in consumer behavior, this study integrates traditional input–output models to allow for changes in the quantity demanded in reaction to an exogenous price shock. IOA is a well-established method and detailed description of traditional models is available elsewhere (Miller and Blair, 1985). We integrate these traditional models while considering price and quantity as separate entities. Fig. 1 summarizes the methodology used for this study. We start with the exogenous economic shock generated by policy changes but the starting point could be anywhere in this flow chart for application of the general framework. The cost push system (S2 in the figure) relies on the Leontief price model to capture direct and indirect changes in commodity prices that result from the imposition of a tax on industry sectors (S1). Change in prices causes the short-term consumer demand to change, which is estimated through the price elasticity of demand (S3). These changes in the quantity of final demand result in the change of each sector’s total production, which is captured by the Leontief demand model (S4). Finally, the changes in the flow of various materials and ecosystem resources and emissions are estimated. These steps are discussed in more detail in the rest of this section. 2.1. Cost push system In standard form, the Leontief price model (LPM) can be represented in either physical or monetary units depending on the data availability. Eq. (1) considers changes in the value added as exogenous, and computes the total change in prices for each sector. p ¼ AT p þv or p ¼ ðIAT Þ1 v

ð1Þ

Here, p is the price change vector of an economy while A is the direct requirements matrix, and v is the original value added vector in monetary terms. One of the merits of using the LPM is its ability to capture the direct and indirect portions of a price change separately. The total percentage price change can be expressed as T 1 Dp% Dk1 ¼ IDk1 þAT0 Dk1 þ ðAT0 Þ2 Dk1 þ ðAT0 Þ3 Dk1 þ UUU 1 ¼ ðIA0 Þ

ð2Þ %

where Dp is the percentage price change vector of an economy and Dk is the percentage change in value added in normalized 4 4 4 form, calculated as, Dk ¼ v ðx Þ1 Dv% , where x is the diagonal total

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Original Economic / Environmental System S1

Construct customized I/O table

Exogenous V.A Value added change (e.g. tax, subsidy, labor, capital, etc)

S2

Intermediate System Cost Push System System after price change in shortterm.

Economy wide commodigy price change Leontief prIce model Supply chain effect

S3 Price elasticity Final demand change with respect to price change

S4 Demand Pull System

New Economic / Environmental System

Change of total production of sectors Leontief demand model Supply chain effect

Change of resource consumption Change of emission

Fig. 1. Flow chart for the proposed methodology.

economic output matrix and Dv% is the percentage value added change vector. The subscript in Eq. stands for each time period. In the expanded form, the first term is the cost caused by additional value added directly, the second term is the cost caused by the direct use of the inter-industry resource inputs whose prices are affected by the value added simultaneously, the third and consecutive terms are the additional costs caused by the indirect use of the inter-industry inputs and capture the round-by-round supply chain effect. The way of accounting for the amount of carbon tax on each sector will be illustrated in Section 3. A0 is a modified industry-by-industry technical coefficient matrix, which is derived via Eq. (3), 4

4

A0 ¼ m½o1 u½x 1

normalized by the percentage change in price as in Eq. (4).

ed;i ¼ 

2.2. Relationship between price change and quantity demanded In most existing input–output analyses, price and quantity are usually treated as mutually exclusive accounting quantities. In order to partially remedy this assumption, this study utilizes partially available information about price elasticities of demand to model consumer behavior in the short-run. Price elasticities of demand are unit-free measures of responsiveness of quantity demanded (Q) to changes in market prices (p)—measured as the percentage change in quantity demanded for a given commodity,

ð4Þ

Price elasticity data are difficult to obtain for all commodities, but such information about some specific energy commodities is available from previous studies and historical data (Espey, 1996; Goodwin et al., 2004; Hughes et al., 2006; Spees and Lave, 2007). Given the percentage price change obtained from the LPM and the vector of price elasticities of demand for commodity i (ed;i ), the vector of change in final demand can be estimated via Eq. (5). Detailed derivation of this equation is in the Supplementary Information.

ð3Þ

where, u is the commodity-by-industry ‘‘use’’ matrix, m is the industry-by-commodity ‘‘make’’ matrix, o is the total commodity output vector, and x is the total industry output vector in the initial period (Miller and Blair, 1985). Aggregation and disaggregation of sectors can be performed on the original make and use tables to construct this tailored technology matrix in the initial period. Details are in Section 3 and the Supplementary Information.

dQi =Qi dpi =pi

4

4

Dy ¼ y1 y0 ¼ Dp% ed;i y0

ð5Þ

Here also, subscripts stand for the time periods. The final demand y is related to the price and physical quantity as y ¼ pQ

ð6Þ

Without separation of price and quantity, which are two unique features of monetary valuation, the change in combined monetary value alone may not be useful because it cannot provide insight into which of these two contributed to the total change. For example, if the final demand (monetary value) of a commodity decreases relatively little compared to other commodities, it could be the effect of a relatively small elasticity in the presence of a significant price increase or it could be the effect of small change in price even if the elasticity is significant. 2.3. Demand pull system After price elasticity is integrated into the framework, the traditional input–output model is applied to account for the total

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production change accrued by the change in final demand via Eq. (7), x ¼ A1 x þy or x ¼ ðIA1 Þ1 y

ð7Þ

where, x is the total output of an economy and A1 stands for the technology matrix after the price of commodities has changed. This matrix, A1, also combines information about price and quantity of each inter-industry flow as in Eq. (6) for final demand. It is crucial to note that the change of values in the direct requirement matrix (i.e., from A0 to A1) should not be considered as a structural change in the economy since the change is purely due to the change in commodity price. Structural change is the change in the physical recipe of production and not the change in commodity prices. As in the LPM, total production change can be expanded as in Eq. (8)

Dx ¼ ðIA1 Þ1 Dy ¼ IDy þ A1 Dy þ A21 Dy þA31 Dy þ   

ð8Þ

This equation captures the direct and indirect supply chain effect as in Eq. (2) for price change. 2.4. Change in resource consumption and emission The effect of changes in monetary flows calculated in previous steps can be readily converted into effects on the physical flow of multiple resources, as done in existing life cycle assessment methods based on input–output models (EIO-LCA, 2008; ECOLCA, 2009). These efforts estimate the change in resource consumption or emissions in various categories caused by a change in the final demand of the economic and ecological goods and services. It utilizes the environmental coefficient matrix, R, which illustrates the environmental loads used by each sector in the model, normalized by the sector’s economic output vector x (i.e. emission/$ or resource consumption/$). After a market-based policy is introduced in an economy, the monetary outputs change over the short term. Those changes are accrued both by changes in price and corresponding change in physical amount. However, original resource consumption and emission per physical units should not be affected in the short term since the technology recipes being used are not affected. Therefore, the original R (environmental loads per dollar output) should be adjusted to account for the price change as in Eq. (9). 4

Radj ¼ ðI þ Dp% Þ1 R

ð9Þ

With this adjusted coefficient matrix, change in the emission and resource consumption can be estimated via Eq. (10),

De ¼ Radj ðIA1 Þ1 Dy

ð10Þ

where, De is a vector of the change in resource consumption and emission. Derivation of these equations can be found in the Supplementary Information.

3. Modeling the short-term effect of a carbon tax 3.1. Construction of the customized input–output table When modelers wish to use economic benchmark data for tailored modeling, they often need to address issues related to the level of aggregation/disaggregation. The U.S. Bureau of Economic Analysis (BEA) publishes data at three levels of aggregation; Type I (detailed level), Type II (summary level), and Type III (sector level), consisting of around 500, 100, and 15 sectors, respectively. This study combines one Type I, twelve Type II and thirteen Type III levels of aggregated sectors to balance out issues related to accuracy and comprehensiveness (see Supplementary Information for details). In this study, a combination of these three levels of data

is used because of two reasons. First, utilizing only the Type III model may provide crude insight easily, but the level of aggregation is too coarse. Second, focusing only Type I data may provide the most realistic relationship between the price and physical quantity, but the need to find price and elasticity of demand information for all flows in the specified year for this detailed level is difficult and demanding. As a compromise between having details about relevant sectors and difficulty in finding price and elasticity information about flows between sectors, we have constructed an intermediate level, 31  31 sector model. Relatively accurate data for various levels of producer price for energy commodities in a specific period are available from the Energy Information Administration (EIA). We use this information to calculate the physical flow information for energy sectors such as coal mining, different types of electricity generation, petroleum refineries, and natural gas distribution sectors. For highly aggregated sectors (Type III and some of Type I), it is not reasonable to assume any average price since many different commodities are aggregated in sectors. With regards to disaggregation, none of the three levels of the original BEA data include details of the power generation sector. This sector is available in a highly aggregated form (NAICS 2211). In general, this sector is among the most significant emitters of CO2, but some electricity generation technologies are less carbon intensive than others. In this work, we disaggregate the power generation sector via the make and the use tables into six subsectors representing different types of electricity generation: hydro powered (HPE), coal fired (CFE), natural gas fired (NGFE), petroleum fired (PFE), nuclear powered (NPE), and others (OTE). This study uses 2002 electricity production data when CFE, NGFE, PFE, NPE, HPE, and OTE had shares of 49.7, 18.7, 3, 19.3, 6.5, and 2.9%, respectively (EIA, 2002). Additional details about disaggregation are in the Supplementary Information. Marriott (2007) disaggregated the power generation sector based on information of geographic location or purchasing choices at the state level for the analysis of the interstate trading optimization. This study is similar to our allocation in the sense that it uses pricebased default allocation of 1997 while we use U.S. market share of electricity generation information of 2002. It is assumed that every other allocation follows generic disaggregation rules similar to schemes discussed in (Joshi, 2000; Wolsky, 1984) except for the special allocation of the commodities from sectors such as the oil and gas extraction, coal mining, natural gas distribution, and petroleum refineries. Some sector specific disaggregation rules are as follows: 90% of the coal input to power generation sector is allocated to CFE and all fossil fuel inputs from petroleum refineries and natural gas distribution into power generation sector are allocated to PFE and NGFE, respectively. Generally, most physical and monetary transactions of oil and gas extraction sectors goes into two main sectors: petroleum refineries and natural gas distribution. The petroleum refinery sector, which is originally embedded in the aggregated manufacturing sector is subtracted from both make and use tables where it is originally placed in the Type III model in order to allocate the petroleum input to economic sectors. Without this allocation, the petroleum flow may be accounted incorrectly since it is usually aggregated as monetary value in the manufacturing sector. This allocation is especially critical for our analysis due to its focus on levying a carbon tax on the petroleum refineries, which have a significant impact on direct and indirect changes in all other commodity prices.

3.2. Physical flow of fossil fuels calculated from non-homogeneous prices In order to estimate the additional cost accrued by a carbon tax, it is necessary to have inter-industry physical flows of fossil

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Table 1 Average price of energy commodities for year 2002. Power generation Coal ($/metric ton) Steam coal Coking coal Natural gas ($/103 m3)b Electricity (cent/kWh) Crude oil ($/barrel) Petroleum (cent/gallon) a b

28.7 a

131.0 4.8 a

53.5

Other industry

Residential use

37.0 56.5 142.0 4.8 26.2 72.3

a a

303.0 8.4 a

67.9

Energy commodities do not directly used by the indicated demands. Converting original data ($/thousand ft3) by using 1 ft3 = 0.0283 m3.

Table 2 Carbon emission coefficients used in the model.

Coal Oil NG

Energy content

C Emission/kcal

Emission/physical unit

7000 kcal/kg 10,300 kcal/kg 9800 kcal/Nm3

0.1 g C/kcal 0.078 g C/kcal 0.057 g C/kcal

704 kg C/ton 112 kg C/barrela 558 kg C/103m3b

a Crude oil density= 873 kg/m3 1 m3 = 264.172 gallons. 1 barrel of oil= 42 gallons, 42 gallonsn(1 m3/264.172 gallons) =15898 m3/barrel. b 1 ft3) = 0.0283 m3 is used for interpreting data in Table 1.

fuels. Consideration of the variation in the price paid for a given economic commodity by different customers can provide more accurate physical material flow information. For example, coal from the coal mining industry is sold to power generation industries at a much cheaper price, and electricity from power generation industry is sold cheaper to the manufacturing industries than to household consumers. Based on the 2002 U.S. BEA data, 61.23% of the monetary value of coal is used by the electricity generation sector; however, 80% of the physical amount of coal is actually used for electricity generation (EIA, 2002). In the data from BEA, price non-homogeneity is included in the beginning of the establishment survey but there is no way for researchers to know such details in the aggregated level of BEA’s benchmark model. Although it is not possible to consider nonhomogeneous prices of all commodities at the micro-sectoral level, those of the energy related commodities can be integrated relatively easily since the data are readily available (EIA, 2006), as in Table 1. Prices shown in the table are utilized to estimate the physical flow of each energy commodity from the ‘‘use table’’ (i.e., monetary transactions of electricity used by three different demands are divided by each corresponding price).

3.3. Levying a carbon tax Since a carbon tax is designed to be levied in proportion to the amount of each sector’s carbon emission, calorific values of different fossil fuels in Table 2 are used to convert the mass of carbon-based fuel input into carbon emission (Kraines and Yoshida, 2004). In this study, the carbon tax is not levied to the extracted quantities of the crude oil, gas or coal but is levied to any economic sector whose carbon emission is generated by utilizing these fossil resources. Eq. (11) calculates the total carbon tax of each sector, Ci, Ci ¼ ti ðaFi;coal þ bFi;oil þ gFi;ng Þ

ð11Þ

where ti is the tax rate (dollars/ton-C) of sector i, a, b, and g stand for the carbon intensity (emission/physical unit) of each fossil fuel, F represents the physical amount of each fossil fuel used in

the production of sector i. Table 3 shows monetary and physical flows of fossil fuels, and the amount of carbon tax for selected sectors when a hypothetical carbon tax of $50/ton is levied (full sector information is available in Supplementary Information). Although some sectors paying a relatively large carbon tax are included in this table, it should be noted that this tax is levied on all economic sectors in this study. In Table 3, the monetary flow column shows the monetary transaction from the use table, while the physical flow column is based on the price information given in Table 1. Direct carbon tax is calculated by multiplying the physical flow information with information given in Table 2, and the last column is the total amount of carbon tax, which is estimated via Eq. (11). Based on the year 2002 economy, CFE uses the largest amount of coal (459 million metric tons/yr) for the production of electricity. Manufacturing and transportation sectors are the two dominant petroleum consuming industry sectors (around 1.2 billion barrels/yr each). A relatively large amount of natural gas is used by the manufacturing sector and via final demand (186 and 116 billion m3/yr, respectively). With regard to the direct additional costs, almost every sector is affected by the tax since fossil fuels are used directly or indirectly. CFE is the hardest hit: it pays a total of about 16 billion dollars/yr for using fossil fuel. PFE and PR are paying 625 million dollars/yr and 3.3 billion dollars/yr in this scenario. It should be noted that the amount paid by PR is significant considering that the single sector is actually pulled out from the aggregated manufacturing sector while all other manufacturing sectors are aggregated in a MFG sector.

4. Results 4.1. Price and production changes Fig. 2 illustrates the breakdown of the percentage price change. In terms of price increase by the direct carbon tax, the percentage prices of the CFE and the PFE are affected the most since they are the primary sources of carbon emission. Sectors with no price changes by ‘‘tax itself’’ imply no direct use of fossil fuel for the production. Although these sectors do not pay a direct carbon tax, their commodity prices are affected indirectly due to their indirect reliance on fossil resource. This is much like the Pigovian taxes since it is designed to correct the negative externalities of a market activity by imposing tax on producers who pollute the environment in the hope of encouraging them to reduce pollution. Generally, consumers reduce consumption with increase in price and the ratio of this change is usually expressed as the price elasticity of demand. Although the use of electricity for home appliances is relatively fixed, historical evidence shows that the residential electricity demand can change, even in the short term, especially when consumers face rapidly rising energy prices due to factors such as unstable oil prices, deregulation, and record cold

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Table 3 Total amount of carbon tax on each sector with tax of $50/ton C (see Supplementary Information for full sector).

Crop production (CP) Animal production (AP) Coal fired elect (CFE) Petroleum fired elect (PFE) Petroleum refineries (PR) Construction (CONST) Manufacturing (MFG) Transportation and warehousing (TW) Information (INFO) Finance, insurance, real estate (FIRRL) Professional and business service (PBS)

Monetary flow (million $)

Physical flow (million physical unit)

Direct carbon tax (million $)

Total CTax

Coal

Petro

N/G

Coala

Petrob

N/Gc

Coal

Petro

N/G

(Mil $)

0.0 118.4 13182.0 0.0 99.4 0.0 2656.9 12.3 5.4 48.3 54.2

4471.8 1934.3 0.0 2495.3 14213.2 12525.5 35664.2 33408.3 965.2 852.7 11630.5

683.4 352.0 0.0 0.0 3418.1 780.8 26408.2 877.3 1482.4 1269.1 1454.1

0.0 2.5 459.3 0.0 2.1 0.0 56.8 0.3 0.1 1.0 1.2

148.5 64.3 0.0 111.9 472.1 416.1 1184.7 1109.8 32.1 28.3 386.3

4.8 2.5 0.0 0.0 24.1 5.5 185.9 6.2 10.4 8.9 10.2

0.0 89.2 16175.3 0.0 74.9 0.0 2001.5 9.3 4.1 36.4 40.8

829.4 358.8 0.0 625.0 2636.2 2323.1 6614.7 6196.3 179.0 158.2 2157.1

134.1 69.1 0.0 0.0 670.8 153.2 5182.4 172.2 290.9 249.1 285.4

963.5 517.0 16175.3 625.0 3381.8 2476.4 13798.6 6377.8 474.0 443.6 2483.3

All taxes are passed to the final demand and price increase of commodities reflect this. a b c

Metric ton. Barrel. Thousand cubic meter.

CP AP FL FHT SAAF OE CM MM NMMQ SAM HPE CFE NGFE PFE NPE OTE NGD WSOS PR CONST MFG WT RT TW INFO FIRRL PBS EHS AERAFS AS GS (Unit: %) 0

tax itself direct input use indirect input use

2

4

6

8

10

12

14

16

Fig. 2. Percentage price change calculated by Eq. (2) for the carbon tax of $50/ton C.

winter temperatures. Therefore, understanding the demand of electricity, especially in predicting the impact of price changes on consumption can help municipalities, utility companies, and policy makers in predicting future energy needs and design pricing and taxation policies (Espey and Espey, 2004). A study explaining the variation in elasticity of estimated gasoline demand in the United States examined 101 different studies and found that in the short-run (defined as 1 year or less), the average price elasticity of demand for gasoline is 0.26 (Espey, 1996). That is, a 10% hike in the price of gasoline lowers quantity demanded by 2.6%. In the long-run (defined as longer than 1 year),

the price elasticity of demand is  0.58. With respect to the price elasticity of electricity demand, Spees and Lave (2007) reported this to be 0.1 and  1 for the short- and long-run, respectively. Espey (1997) found that in the short-run the price elasticity of electricity demand is 0.25, with a standard deviation of 0.15, while the long-run price elasticity of 0.64 has a standard deviation of  0.44. While it is not possible to find an accurate relationship between the magnitude of a carbon tax and its effect on consumption, we can be reasonably sure that a rise in carbon taxes or prices will cause consumption to decrease, at least in the short-run. In this work, three different price elasticity of demand scenarios are considered. In the base scenario, SC1, a fixed elasticity of ed;i ¼ 0:3 is used for all commodities. Scenario SC2 uses a larger elasticity for electricity (ed;elect ¼ 0:5), and scenario SC3 considers larger elasticity of all commodities other than electricity (ed;i Z 0:3 for iaelectricity). Details about these scenarios are provided in the Supplementary Information. Results of the total production change with the variation in elasticity are shown in Fig. 3. It shows that for scenario SC2, the reduction in direct consumer demand for electricity causes the output of all sectors to drop more than the base scenario. This is because the total scale of the economy decreases due to lower electricity production, which more or less reduces production of all other economic activities even though the production recipes are not considered to change. Secondly, compared to the base scenario, production of CFE and PFE in scenario SC2 is affected much more than other sectors because of the decreased consumer demand in use of fossil based electricity and relatively large increase in prices due to carbon tax. Thirdly, reduction in total electricity production causes a significant direct reduction in coal because it is the primary resource used to generate electricity. In scenario SC3, additional reduction in the production is due to decreased consumption of all commodities except for the electricity. Although, consumers do not change their consumption pattern of electricity despite its higher price, the total production of electricity sectors does decrease more as compared to the base scenario because of the reduced production in other sectors that utilize electricity; however, the amount of reduction in the total production of electricity is less than in the scenario SC2. In scenario SC3, reduction in physical production of coal, electricity (total electricity from various technologies), natural gas, and petroleum compared to the base scenario are 6.8% (44.8 million metric tons), 8.5% (3.9 billion MWh), 1% (4.6 billion m3), and 3.2% (212 million barrels). Among the 3.9 billion MWh of electricity reduction, 49% comes from CFE.

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CP AP FL FHT SAAF OE CM MM NMMQ SAM HPE CFE NGFE PFE NPE OTE NGD WSOS PR CONST MFG WT RT TW INFO FIRRL PBS EHS AERAFS AS GS

SC3 SC2 SC1

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

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CP AP FL FHT SAAF OE CM MM NMMQ SAM HPE CFE NGFE PFE NPE OTE f NGD WSOS PR CONST MFG WT RT TW INFO FIRRL PBS final demand itself

EHS AERAFS

direct production indirect production -2.5

-2.0

-1.5

-1.0

-0.5

AS GS (Unit: %) 0.0

Fig. 4. Percentage change in the total production (monetary unit) for Scenario SC3.

0.0 (unit: %)

Fig. 3. Percentage reduction in the total production calculated by Eq. (8) in monetary units for different elasticity scenarios.

Sectoral breakdown of the decreased percentage production can be calculated by Eq. (8) and an example is shown in Fig. 4. Among many changes, decrease in the total production of CFE and PFE is relatively large as compared to other commodities. This is because most economic activities rely on those electricity sources. It also explains the relatively large decrease in the production of coal and oil/gas, which directly affect the production of CFE and PFE. Reduction in coal production is mostly due to reduced direct use of coal while reduction in oil/gas production is more due to indirect use of the product by other economic activities. Although, the total market share of the NGFE (18.7%) and NPE (19.3%) is relatively high, their total percentage production does not decrease as much as other fossil fuel based electricity since their percentage price change is very small (see Fig. 2), while price elasticity of demand for electricity ðed;elect Þ is set as the same for all six power generation technologies. Overall, responding to the decreased demand of commodities, the total production of all other commodities also decreases. Although this study has not set up cross price elasticities between commodities, it should be noted that the total production of sectors is mutually interdependent via the endogenous interindustrial production.

4.2. Change in resource consumption and emissions Based on the total production change estimated in Section 4.1, it is possible to estimate sectoral and total economy-wide changes in emissions and dependence on various ecosystem goods and

services via the approach described in Section 2.4. Fig. 5 shows the total reduction in selected ecosystem goods and services (EGS) inputs for production in the presence of a $50/ton carbon tax. Basic information about EGS is available in the Millennium Ecosystem Assessment (2009) and the approach used in this work is based on Zhang et al. (2010). Full forms of the abbreviations used in Fig. 5 are provided in the Supplementary Information. Among provisioning services (water, fish and related services, grass, wood, non-metallic mineral, and metallic mineral), a relatively large reduction in water use, especially W-PP (water from power plants) is expected directly due to the reduced electricity consumption. The other provisioning services are mainly affected by the reduced activities of fishing, animal production, forestry/logging, non-metallic mining, and metallic mining. Among the selected regulating services, soil erosion is reduced because of the decreased economic activity by crop production, animal production, and construction sectors. Pollination services are directly related to the crop production sector. Reliance on supporting services such as land use of different types and flow of detrital material also decrease. Mostly, ecosystem services are directly related to a few dominating sectors. Like reduced reliance on ecosystem goods and services, emissions from various sectors also tend to decrease. Fig. 6 shows the total reduction of selected emissions after a carbon tax is levied. Some major sectors contributing to the direct emissions are shown in Table 4. Emissions of CO2, SO2, HFC and NOx that are related to the power generation sector are found to decrease the most. Major portion of emissions of styrene, trilchloroethane, methanol, and sulfur dioxide are directly related to the manufacturing sector’s activities. Major contributor of nitrogen oxide and carbon monoxide emissions is a transportation

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-1.8

-1.3

-0.8

-0.3

(Unit: %)

-2.0

-1.5

-1.0

0.0(Unit:%)

-0.5

POL

STY

SE (C) TCE SE (F) CO2 (R)

TOL

CO2 (F)

AMM

W (PS)

MET

W (PP) HFC W (AL) N2 O

FRS GR

CH4

WD (D)

CO2

DDS

PM10

L (UI) Lead

L (T) L (RP)

VOC

L (C)

Nox

NFM

CO

NM SO2

MM Fig. 6. Total percentage reduction in emissions. Fig. 5. Total percentage reduction in resource consumption.

sector and the supporting activities of agriculture and forestry. Detailed sectoral data can be found from Supplementary Information and ECO-LCA (2009). The economy-wide total reduction of each ecological resource and emission can be further analyzed to the sectoral level and it provides information about the sectors that dominate in reducing the flow of specific resources or emissions. As an example, Fig. 7 shows the economy-wide breakdown of CO2.reduction with a carbon tax of $50/ton C, not the breakdown of total carbon emission. A relatively large fraction of the reduction comes from the manufacturing and transportation sectors with around 15% each. Fifty two percent of the total reduction is from the power generation sectors with the main reduction coming from CFE (37 Mmt), PFE (1.6 Mmt), and NGFE (1.1 Mmt). This large percentage reduction in electricity sectors is due to the relatively large increase in the prices of fossil fuel based electricity. Therefore, it is crucial to look at the electricity sector carefully to design an appropriate amount of carbon tax for specific target reduction in CO2, as discussed next.

4.3. Carbon tax rate for a specific reduction target A reasonable amount of carbon tax can be designed based on the proposed framework for a certain target level of reduction in emissions and resource use. In the short term, theoretically, higher CO2 reduction can be achieved with a higher price elasticity of demand or higher percentage price increase. The former option corresponds to a more sensitive demand response to a fixed price change, while the latter is based on a larger carbon tax when the demand elasticity is fixed. Therefore, a policy for demand side management and design of reasonable taxation

Table 4 Major sectors for direct emissions (full forms of acronyms are in the Supplementary Information.). Emission abbreviation

Name

Contributing sectors

STY TCE TOL NH3 MET HFC N2O CH4 CO2 PM10 Lead VOC NOX CO SO2

Styrene Trilchloroethane Toluene Ammonia Methanol Hydrofluorocarbon Nitrous oxide Methane Carbon dioxide Particulate matter Lead Volatile organic compound Nitrogen oxide Carbon monoxide Sulfur dioxide

MFG, AS MFG PFE, PR MOM, PFE MOM,PR,MFG CFE, NGFE, PFE CP, AP, WSOS CM, WSOS CFE, PFE, NGFE SAAF, PFE CFE, NGFE, PFE SAAF SAFA, TW, PFE TW, SAAF CFE, PFE, MFG

scheme are crucial. As a way of verifying the results from our work, we compared our results with those from other I/O based carbon tax analyses. Our study represents the tax as $/ton C, but some existing studies represent it as $/ton CO2. These units may be compared based on the fact that the relative mass of carbon to carbon dioxide is approximately 0.273, so a carbon tax of $100 per ton of carbon corresponds to a tax of $27.30 per ton of carbon dioxide emission. Fig. 8 shows the result of a carbon tax for target amount of CO2 reduction from our study. Due to the linearity of the I/O model, a linear relationship between the reduction target and the corresponding amount of carbon tax is shown. The dotted line stands for the economy-wide tax scenario while the straight

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Table 5 Carbon tax range for 20% CO2 reduction in previous studies (Symons et al., 1994)

Rest of Sectors 13%

Transportation 15%

Barrett (1990) Ingham and Ulph (1989) Chandler and Nicholls (1990) Nordhaus and Yohe (1983) US congressional budget office (1991) Bye, Bye and Lorentsen (1989) Manne and Richels (1989) This study

Power generation 52%

Manufacturing 14%

Construction 3%

Pet refinery 3%

Fig. 7. Percentage reduction of CO2 by sectors with a carbon tax of $50/ton C.

Tax ($/t-CO2) 0

20

40

60

80

100

120

Country

Value ($/ton CO2)

U.K U.K US US US Norway US US

$34–59 $87–205 $82 $100 $113 $126 $300 $136

reduction in carbon dioxide emission from the power generation sector only with a $35 per CO2 emission tax (Newcomer et al., 2007) and our analysis shows that a tax of $49.5 per ton of CO2 emission is necessary to meet the same condition (straight line in Fig. 8). Although our study is not aiming to estimate carbon trading prices, there are a number of consultants who provide guidance about these prices. One of the examples is the point carbon, which assesses the price of EU allowance contracts for spot delivery and the price refers to one EU allowance, equivalent to one metric ton of carbon dioxide emission. As of the end of 2009, the price is around h17 ($26) per ton of CO2. The effect of this kind of carbon price on macro-economic aspects (price and quantity) and the change in environmental loads (ecosystem goods and services and emissions) can be analyzed via the framework presented in this paper.

140

0 5. Summary and discussion

-5

% CO2 reduction

-10

-15

-20

-25 Economy wide -30

Power generation only

-35 Fig. 8. Carbon tax rate vs. % CO2 reduction.

line shows a scenario with carbon tax levied on electricity sectors only. The base year is 2002 as we have used the most recent data from BEA. The policy-maker may use this information for designing the level of economy-wide carbon tax to be levied for a certain target reduction of CO2 in a relatively short period time. Previous studies have investigated the order of magnitude of a carbon tax required for economy-wide CO2 emissions reduction of 20% such that the Toronto target is met (Gay and Proops, 1993; Pearce, 1991) and the results range from $34 to $300 per ton of CO2, as shown in Table 5. Although, the temporal and spatial scales of each study are different, our result of an economy-wide tax of $136/ ton CO2 for a 20% reduction from the original state (dotted line in Fig. 8) is well within the range of these studies, which are also based on an I/O framework. We have not included studies using other economic models. Another study provides the result of a 10%

Understanding the effect of an external cost such as a carbon tax on prices, resource use and emissions to the environment requires a holistic evaluation of the interaction between economic sectors and physical flows. Such an evaluation may help both industries and policy makers for making economically feasible and environmentally viable decisions. Some existing studies focus on the economic and/or environmental repercussions of the change in the demand of various types of renewable and non-renewable energy technologies. However, studies that connect changes in prices caused by certain economic policy and the resulting changes in the demand of physical quantities such as emissions or dependence on ecosystem goods and services are limited or rare. One focus of this paper is to separately quantify the effect of energy policy on price and physical flows, which are usually aggregated as a single monetary value in energy/environmental modeling studies using input–output analysis. Since the methodology couples both economic elements (price and quantity) through the price elasticity of demand, it helps in understanding their interaction. In the modeling stage, information on non-homogeneous price of energy commodities and calorific value of fossil fuel are utilized in order to levy a consistent level of carbon price to the whole economy. Available sets of economic data from U.S. BEA are tailored for the specific purpose of this study by disaggregating sectors pertinent to carbon dioxide emissions and aggregating others. Since price and quantity are both changing in our model, we have devised a way of adjusting these effects to account for the physical amount of resource consumption and emission changes over a short time period where the production recipe is assumed to remain unchanged. The proposed methodology may provide a general framework to design a certain level of market-based policy tools, which simultaneously address the environmental, economic and technological impacts of economic perturbations with input–output modeling. Although, a full detailed model (i.e., 500 sectoral level) can be analyzed with the framework we proposed, actual data for price elasticity of demand of each commodity are very difficult (even impossible) to achieve. Therefore,

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our current model presents a compromise between detailed modeling and practical challenges posed by data availability. The major benefit of adopting input–output analysis is to employ its capability to capture the direct and the indirect changes in price and the total production of each economic sector systematically. While demonstrating the richness a comprehensive input–output model can generate, the proposed methodology does suffer from some restrictions that are inherent to input–output analysis. It is clear that techniques of production will change over time for a variety of reasons; introduction of new technologies in a sector, economies of scale, new product inventions, substitution of inputs in a production process because of change in relative prices, etc. Although this study focuses on the short-term analysis, these issues are crucial for addressing the long-term effect of the carbon price. Input–output analysis uses benchmark survey based tables, which for budgetary and administrative reasons and detailed sectoral level benchmark are only issued every 5 years in U.S. This lag between issuance of table renders long-term analysis difficult; the question is how quickly and how dramatically does an economy’s technical coefficient matrix will change. It may be possible to adopt the R.A.S techniques (Lahr and de Mesnard, 2004; Parikh, 1979) in the input–output framework, which is a partial-survey technique to develop reasonable estimates of the technical coefficient in the next period in the absence of information on the full set of transaction in the base year.

Acknowledgment Partial financial support from the National Science Foundation (ECS-0524924) is gratefully acknowledged. We acknowledge all reviewers for their great insight and comments for the paper.

Appendix A. Supplementary Information Supplementary data associated with this article can be found in the online version at doi:10.1016/j.enpol.2010.02.029.

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