Distributional effects of a carbon tax on Chinese households: A case of Shanghai

Energy Policy 73 (2014) 269–277

Contents lists available at ScienceDirect

Energy Policy journal homepage: www.elsevier.com/locate/enpol

Distributional effects of a carbon tax on Chinese households: A case of Shanghai Zhujun Jiang a,b,n,1, Shuai Shao c,nn,1 a

Institute for Advanced Research, Shanghai University of Finance and Economics, Shanghai 200433, China Key Laboratory of Mathematical Economics, Shanghai University of Finance and Economics, Ministry of Education, Shanghai 200433, China c Institute of Finance and Economics Research, School of Urban and Regional Science, Shanghai University of Finance and Economics, Shanghai 200434, China b

H I G H L I G H T S








The direct distributional effect of carbon tax presents a weak progressivity. The indirect distributional effect of carbon tax is significantly regressive. The comprehensive distributional effect of carbon tax is regressive. The Suits index of carbon tax is  0.078. Imposing carbon tax on fossil fuels can intensify income inequality.

art ic l e i nf o

a b s t r a c t

Article history: Received 8 February 2014 Received in revised form 2 May 2014 Accepted 4 June 2014 Available online 7 July 2014

As an effective policy instrument to reduce CO2 emissions, the effects of a carbon tax on distribution have been the critical factor in determining whether a carbon tax will be acceptable in China. Taking Shanghai as an example, which is the economic center and front-runner of China, this paper estimates the distributional effect of a carbon tax on households in various income groups by using the input– output model and the Suits index. The results indicate that the comprehensive distributional effect of the carbon tax is regressive. The expenditure of the low-income group caused by the carbon tax accounts for 0.853% of the total expenditure, while that of the high-income group 0.712%. The direct distributional effect presents a weak progressivity, while the indirect one is significantly regressive, and the latter is much larger than the former. Moreover, the Suits index of the carbon tax is  0.078, implying that the carbon tax burden on the low-income group is the highest and thus that a carbon tax can intensify income inequality. Therefore, when introducing a carbon tax, some rational associated redistribution or compensation measures, such as purposive transfer payments, should be implemented to restrict or even eliminate the regressivity of the carbon tax. & 2014 Elsevier Ltd. All rights reserved.

Keywords: Carbon tax Distributional effect Suits index

1. Introduction As a major issue of human survival and development, global climate warming has paid extensive attention all over the world. Among the various climate-change mitigation policies, the carbon tax is extensively advocated since it is regarded as the lowest cost

n Corresponding author at: Institute for Advanced Research, Shanghai University of Finance and Economics, Shanghai 200433, China. Tel.: þ 86 2165902562. nn Corresponding author. Tel.: þ 86 2165902562. E-mail addresses: [email protected] (Z. Jiang), [email protected] (S. Shao). 1 Authors contributed equally to this work and are listed in alphabetical order by surname.

http://dx.doi.org/10.1016/j.enpol.2014.06.005 0301-4215/& 2014 Elsevier Ltd. All rights reserved.

emission-reduction measure (Baranzini et al., 2000). As an environmental tax imposed on CO2 emissions, the carbon tax can reduce CO2 emissions and thus mitigate climate warming by increasing tax burden. Theoretically, the essential role of a carbon tax is to correct distorted price signals and optimize the resource allocation by internalizing the environmental externality caused by anthropogenic CO2 emissions. Therefore, the carbon tax is considered as one of the most market-efficient economic measures in reducing CO2 emissions. Supporters of the carbon tax argue that it has three main advantages. First, a carbon tax can enhance the competitive power of renewable energy in cost and thus promote the utilization of renewable energy. Second, the revenue from a carbon tax can be used to subsidize environmental protection project and energy-saving and emission-reducing

270

Z. Jiang, S. Shao / Energy Policy 73 (2014) 269–277

technology. Finally, compared with carbon emissions trading, a carbon tax has lower administrative cost, more convenient implementation process, and more predictable effects. A carbon tax has been carried out in some countries and has achieved some desired results. However, until now, there are only a few developed countries actually imposing carbon taxes, such as Finland, Sweden, Denmark, Norway, Netherlands, and Italy. Most countries are cautious about the carbon tax considering that it may produce some negative impacts on national economy by affecting energy price and supply–demand relationship, increasing enterprises' costs, and generating the regressive effect of taxation. Discussions on mechanisms and channels of a carbon tax affecting national economy have been academic focus issue as well as the crucial basis for governments to determine whether to levy a carbon tax. Among various potential impacts of a carbon tax on economy, the influential direction and degree of a carbon tax on distribution are the critical factor in determining whether a carbon tax will be accepted by the public. Hence, it is also a key issue focused on by both governments and academic circles. Related studies mainly focus on whether a carbon tax will become a regressive tax or not. Whereas, the consensus on this issue is not reached since different conclusions are drawn by using different samples. Empirical evidences from most developed countries indicate that a carbon tax has a regressive effect on distribution (Poterba, 1991; Jacobsen et al., 2003; Kerkhof et al., 2008; Shammin and Bullard, 2009), while some researchers argue that the distributional effect of a carbon tax is proportional or progressive (Symons et al., 2000; Tiezzi, 2005; Dissou and Siddiqui, 2014). Bureau (2011) found that a carbon tax was regressive before revenue recycling. However, Gonzalez (2012) argued that carbon taxes were not necessarily regressive and whether the way revenue was recycled became a major determinant of how the carbon tax costs were distributed. Most existing studies focus on developed countries and there are fewer studies on developing countries. In developing countries, many factors, such as market forces, price regulation, and import restriction, are likely to generate the distributional effect of environmental policy. The results of Brenner et al. (2007) and Yusuf and Resosudarmo (2007) on China and Indonesia, respectively, indicate that a carbon tax has the progressive impact on distribution. In 2009, the Chinese government announced its emission– reduction target that CO2 emissions per unit of GDP would be reduced by 40–45% in 2020 than 2005 level. As a part of this target, China's 12th “Five-Year Plan” explicitly proposed an obligatory indicator that CO2 emissions per unit of GDP should decline by 17% compared with 2010 level. As an effective policy instrument to reduce CO2 emissions, the pleas to implement a carbon tax are becoming louder in China, but the understanding of the Chinese government in the potential effects of a carbon tax is not clear, especially in the distributional effect of the carbon tax. As a developing great power characterized by obvious imbalance among regions in natural resource endowment and economic development, the distributional effect of a carbon tax is particularly complex in China. The issue has become one of the vital references in determining whether to implement a carbon tax in China. Hence, the distributional effect of the carbon tax should be paid more attention to. In recent years, although studies on China's economic effect of a carbon tax have become plentiful, most existing literature focus on the impact of a carbon tax on the macro-economy or the competitive power of various industries, while the potential effect of a carbon tax on distribution is paid little attention to. Based on the CGE model, Cao (2009) carried out a comparative analysis on transfer payment effects of different allocation modes of the carbon tax revenue in various proportional taxation scenarios, but did not directly discuss the distribution effect of a carbon tax.

Su et al. (2009) analyzed the effects of a carbon tax on China's macro-economy, industrial development, and income distribution in various scenarios of tax rate. However, Su et al. (2009) focused on the effect of a carbon tax on the income inequality of urban and rural dwellers and did not analyze the influential mechanism of the carbon tax on distribution. In view of limitations above, taking Shanghai as an example, which is an international metropolis and China's economic center and whose economic growth and energy consumption are all on the top in China, this paper estimates the distributional effect of a carbon tax on different income groups and discusses the influential mechanism and channels by using the input–output model to provide some theoretical basis and decision-making reference for the implementation of a carbon tax in China. As the economic center and front-runner of China, Shanghai is expected to play a leading role in climate change mitigation. With the rapid development, the dependence of Shanghai's economy on energy consumption is increasing. According to Shanghai Statistical Yearbook, the total energy consumption in Shanghai has increased from 394.67 million tons of coal equivalent (tce) in 1993 to 1292.61 million tce in 2011, with a average annual growth rate of over 10%. Moreover, with the deepening of energy dependence, Shanghai's CO2 emissions also present an increasing trend. The total CO2 emissions from production sectors in Shanghai have increased from 525.02 million tons in 1993 to 1292.61 in 2011, with an average annual growth rate of above 8% (Yang and Shao, 2013). Obviously, Shanghai is encountering the great pressure to realize the target of energy saving and emission reduction (Shao et al., 2011). Therefore, Shanghai is a representative sample in China, and the study on Shanghai can attain some necessary decisionmaking references to the policy design of a carbon tax in China. The remainder of the paper is structured as follows. Section 2 addresses the fossil energy consumption of households in various income groups in Shanghai. Section 3 estimates direct and indirect effects of a carbon tax on various income groups. In Section 4, the progressivity of a carbon tax is measured and discussed. Conclusions and policy implications are provided in Section 5.

2. Energy consumption in various income groups As reported in Shanghai Statistical Yearbook, in 2010, the primary energy consumption in Shanghai reached 111.61 million tce, in which coal, oil, and natural gas consumptions were 58.76 million tons, 32.98 million tons, and 4.50 billion cubic meters, respectively. As the main source of CO2 emissions, the share of fossil fuels in the total primary energy consumption is about 85% in Shanghai. According to the Word Bank Report, above 70% of the total CO2 emissions are from fossil fuels in the world. Moreover, it is a consensus that imposing the carbon tax upstream is more effective than downstream. On one hand, it is more practicable to impose on fossil fuels than on CO2 emissions per se. On the other hand, the administrative costs of a carbon tax could be lower through its upstream implementation (Metcalf, 2009). Hence, like some countries where a carbon tax has been imposed (e.g., Sweden, Norway, Denmark), we treat fossil fuels as the carbon tax base. In addition, it is necessary to classify the households to discuss the distributional effect of a carbon tax. Similar to most related studies, we employ the classification by income levels of households. According to the recent classification standard of the National Bureau of Statistics of China, the Tabulation on the 2010 Population Census of Shanghai Municipality shows that in 2010, the urban population of Shanghai is 20.56 million, accounting for 89.3% of the total population of Shanghai, while the rural population only accounts for 10.7%. Furthermore, due to the limitation of

Z. Jiang, S. Shao / Energy Policy 73 (2014) 269–277

2 Since electricity is a secondary energy source and does not emit CO2 in its utilization process, it is not been taken into account in this paper. 3 The evidence from the micro-level data, such as the survey data of China's urban residents, may be more accurate and credible, but the energy data in China, especially the micro-level energy data, are very limited. Hence, we mainly use the data released by the related statistical yearbook.

Table 1 Per capita energy consumptions of various income groups in 2010 (Unit: kgtce). Source: Authors calculate by using related statistical data. Group

Transport fuela

Residential fuelb

Low Mid-low Mid Mid-high High

20.67 20.87 67.27 105.93 233.47

56.37 67.44 69.86 69.86 85.08

a b

Transport fuels contain petrol and diesel. Residential fuels contain coal, natural gas, pipeline gas, and LPG.

350 Transport fuels

Residential fuels

300

Per capita energy consumption/kgtce

data availability, we cannot attain the detailed data about the energy consumption of various income levels of rural households. Therefore, we focus on the urban households which contain almost 90% of the total population of Shanghai. According to Shanghai Statistical Yearbook, the urban households can be divided into five groups: low, mid-low, mid, mid-high, and high income groups. The energy consumption types of households include coal, petrol, diesel, liquefied petroleum gas (LPG), fuel gas, and electricity.2 We estimate various types of energy consumptions of different income groups in Shanghai by using the related data because it is unavailable directly from the statistical data. The estimation process is as follows. With respect to the consumption of petrol and diesel, we can first obtain the per capita traffic expenditure of each group from Shanghai Statistical Yearbook, which contains four aspects: family vehicle, transport fuels, the services of vehicle, and traffic fees according to China Urban Life and Price Yearbook. Compared the expenditure of each group of Shanghai with the corresponding data of China, we can estimate the share of transport fuel expenditure of each group in the total traffic expenditure and then get the per capita expenditure of each group on transport fuels in Shanghai. Considering that the majority of transport fuels in China are petrol and diesel, the expenditure on transport fuels can be treated as that on petrol and diesel. Multiplying the per capita expenditure of each group on transport fuels by the corresponding population of each group, the share of the expenditure of each group on petrol and diesel in the total expenditure of five groups on them can be calculated. The share is equal to the share of real petrol and diesel consumption of each group in the total petrol and diesel consumption since all the households in Shanghai face the unified oil prices. Furthermore, China Energy Statistical Yearbook reports the various energy consumption of urban households in Shanghai, including the petrol and diesel consumption. At last, multiplying the total petrol and diesel consumption in Shanghai by the share above and then divided by the population of each group, we can attain the per capita petrol and diesel consumption of each group. With respect to residential fuels like coal and gas, the related statistical data do not report their detailed categorical data. However, we can obtain the per capita expenditure of each group on residential fuels from Shanghai Residents' Life and Price Yearbook, which can be regarded as a whole of all kinds of residential fuels. Multiplying the per capita expenditure of each group on residential fuels by the corresponding population of each group, the share of the expenditure of each group on them in the total expenditure of five groups can be estimated. Since all the households in Shanghai face the unified fuel prices, the share can also be treated as the share of real residential fuels consumption of each group. Based on the related data from China Energy Statistical Yearbook, summing up the consumptions of coal, natural gas, pipeline gas, and LPG in the unit of coal equivalent, we can obtain the total residential fuels consumption of urban households in Shanghai. Furthermore, multiplying the total residential fuels consumption by the share above and then divided by the population of each group, the per capita residential fuels consumption of each group in Shanghai can be calculated.3 Estimated per capita energy consumptions of various groups in 2010 are reported in Table 1 and Fig. 1. According to “energy

271

250

200

150

100

50

0

Low

Mid-low

Mid

Mid-high

High

Fig. 1. Per capita energy consumption of various income groups in 2010.

ladder” theory, the households with a low income usually rely on more traditional fuels like coal, while those with a high income mainly use more modern fuels, such as natural gas and transport fuels (petrol and diesel) (Barnes et al., 1997; Masera et al., 2000). Our results are consistent with this theory (see Fig. 1). The transport fuels consumption raises rapidly with the rise of households' income. The per capita transport fuels consumption of the low-income group is only 20.67 kgtce, while that of the highincome group reaches 233.47 kgtce, which is ten times more than the former, resulting from the more possession of private vehicles of the high-income group. In the low-income group, the consumption of transport fuels is obviously less than that of residential fuels, while in the high-income group, the corresponding situation is reverse. The results indicate that the greater the income gap, the more obvious the energy ladder phenomenon.

3. Distributional effects of carbon tax Since the households in different income groups present different lifestyles and consumption patterns, they are likely to substantially differ from the carbon tax payments. A carbon tax can affect prices of fossil fuels and consumers' expenditure in two channels. On one hand, due to the rise of fuel prices caused by a carbon tax, households' expenditure on fuels as basic means of subsistence (e.g., coal, petrol, and gas) will directly increase. On the other hand, since a carbon tax will push up the prices of products and services, especially energy-intensive ones, by increasing the cost of production factor and raw material, households' expenditure on those products and services will indirectly increase. Such a channel can be regarded as the indirect effect of

272

Z. Jiang, S. Shao / Energy Policy 73 (2014) 269–277

a carbon tax on households' expenditure (Symons et al., 1994; Rapanos, 1995; Cornwell and Creedy, 1996; Labandeira and Labeaga, 1999). Therefore, the distributional effect of a carbon tax can be divided into the direct effect and the indirect effect. Wier et al. (2005) pointed out that using the total households' expenditure may be more preferable than using the income to analyze the regressivity of the carbon tax. First, the tax base is expenditure, making it relevant to measure the regressivity relative to expenditure. Second, since households seek to smooth consumption over their life cycle according to permanent income hypothesis, actual income may be inappropriate to measure the regressivity. Thus, we adopt the impact of a carbon tax on the households' expenditure to estimate its distributional effect, which is measured by the households' tax payments relative to expenditure for the deciles.

Table 2 Direct effect of a carbon tax. Group

Per capita tax payment (RMB)

Share of the tax payment in expenditure (%)

Low Mid-low Mid Mid-high High

3.062 3.508 5.472 7.029 12.767

0.024 0.022 0.025 0.026 0.031

15

0.035

Per capita tax payment Proportion of carbon tax

12

3.1. Direct effect of carbon tax

j

ð1Þ

j

where subscript i denotes various groups of households, subscript j refers to various fuels, TAX direct denotes the direct effect of a i carbon tax of the ith group, TAX direct is the direct effect of a carbon ij tax of the ith group based on the jth fuel, Q j and CEF j are the consumption and CO2 emissions coefficient of the jth fuel, respectively, and T is the carbon tax per unit of CO2 emissions, i.e., the carbon tax rate. We estimate CEF j by using the reference method and parameters in 2006 IPCC Guidelines for National Greenhouse Gas Inventories together with China's released relevant parameters (see the detailed below). With respect to the CO2 emissions coefficient of transport fuels, according to China Energy Statistical Yearbook, we can first calculate the shares of petrol and diesel in the total transport fuels consumption of urban residents in Shanghai, respectively. Then, multiplying their shares by the CO2 emissions coefficients of petrol and diesel, respectively, and summing them up, we can obtain the weighted CO2 emissions coefficient of transport fuels. With respect to the CO2 emissions coefficient of residential fuels, because their consumptions are measured in the standard unit of coal equivalent, we first convert the CO2 emissions coefficient measured in the physical quantity into that in the standard unit of coal equivalent. Then, multiplying their CO2 emissions coefficients by their shares in the total residential fuels consumption, respectively, and summing them up, we can get the weighted CO2 emissions coefficient of residential fuels. The Research Group of Ministry of Finance of China suggested that China's carbon tax rate should be 10 RMB per ton of CO2 at the beginning of its implementation, while the Planning Institute of Ministry of Environmental Protection of China proposed 20 RMB per ton of CO2. Chen (2011) argued that the rational range of the 4 The fact may not be so. However, we focus on households’ consumption of conventional fuels, which are difficult to be substituted by new fuels in the short term. Moreover, considering that the change of fuels demand depends on their price elasticity per se and cross-price elasticity, levying a carbon tax will push up the prices of fuels with high carbon emissions and thus lead to more demand for fuels with low carbon emissions. Such an increase in demand may in turn cause the rise of prices of low-carbon fuels. Undoubtedly, it is very difficult to precisely forecast the change of various fuels consumption of various groups. Hence, we make the simplifying assumptions above.

9 0.025 6

Proportion of tax payment/%

¼ ∑ TAX direct ¼ ∑ Q j CEF j T TAX direct i ij

Per capita tax payment/CNY

0.030

The direct effect of a carbon tax refers to affecting the households' expenditure through the direct fuel consumption. Assuming that there is no energy substitution and households' energy demand keeps constant when introducing a carbon tax,4 the direct effect can be expressed as follows:

0.020 3

0 Group

Low

Low-mid

Mid

Mid-high

High

0.015

Fig. 2. Direct effect of a carbon tax.

carbon tax rate should be 19–46 RMB per ton of CO2 for China's industry in 2009–2020. Referring to the studies above, the middle level of their purposed carbon tax rates, i.e., 20 RMB per ton of CO2 is chosen in this paper. The estimated results of the direct effect of a carbon tax on various income groups are presented in Table 2 and Fig. 2. The direct distributional effect of a carbon tax mainly relies on households' fuels consumption and its structure, which are closely related to households' income level. It is easily seen that the direct carbon tax payment increases with the income level, and its level is relatively low. Even for the high-income group who bears the largest direct impact, the carbon tax payment is only 12.767 RMB, accounting for about 0.031% of the total household expenditure. Hence, the direct effect of a carbon tax presents a weak progressivity and the tax burdens on various income groups are all relatively small.

3.2. Indirect effect of carbon tax 3.2.1. Methods As mentioned above, the indirect effect of a carbon tax roots in the increasing production cost of commodity induced by the carbon tax. Since it involves industrial linkage, the input–output model reflecting industrial relationship is a prevalent method to simulate the indirect distributional effect of a carbon tax on households (Symons et al., 1994, 2000; Cornwell and Creedy, 1996; Labandeira and Labeaga, 1999; Wier et al., 2005; Ahmad and Stern, 2009; Grainger and Kolstad, 2010; Datta, 2010; Sun and Ueta, 2011; Fan and Zhang, 2013; Mathur and Morris, 2014). The existing research samples cover developed and developing countries, e.g., US, UK, France, Italy, Spain, Danish, Australia, India, Pakistan, and China.

Z. Jiang, S. Shao / Energy Policy 73 (2014) 269–277

This approach generally assumes that no energy substitution takes place in the production process when introducing a carbon tax, and the tax burdens on producers are fully transmitted into final commodity prices. Thus, those households that demand a commodity will eventually pay the carbon tax paid by the producer of this commodity (Labandeira and Labeaga, 1999; Wier et al., 2005; Ahmad and Stern, 2009; Sun and Ueta, 2011). We will employ this approach, although the degree of transmission will depend on technological development and substitution possibilities in industries as well as in households. Moreover, because the estimate of indirect carbon tax payments may suffer from some uncertainty, the tax payments are assumed to be linear in the production of each sector (Labandeira and Labeaga, 1999; Wier et al., 2005; Grainger and Kolstad, 2010; Fan and Zhang, 2013). Thus, the carbon tax payments from the indirect energy demand of households can be expressed as follows: TAX indirect ¼ TEðI  AÞ  1 Cei i

ð2Þ

where subscript i denotes various groups of households, TAX indirect is the total indirect carbon tax payments of households as a consequence of the consumption of goods and services, T is the carbon tax per unit of CO2 emissions, E is a vector of the CO2 emission intensity (i.e., CO2 emissions per unit of economic output) of each sector in the input–output table, ðI  AÞ  1 is the Leontief inverse matrix, I is a unit matrix and A is a coefficient matrix describing inter-sector commodity flows, C is a matrix of the composition of consumption commodity aggregates, i.e., the private consumption commodity aggregates apportioned by production sectors, and e is a vector including the households' expenditure of each commodity consumption. 3.2.2. Data To get the CO2 emission intensity of each industry, the CO2 emissions should be estimated. Since we assume the carbon tax levying upon fossil fuels, we consider the CO2 emissions of each industry from the fossil fuels combustion rather than those from the production process. We employ the method and parameters

273

used in our previous study (Shao et al., 2011) to estimate the CO2 emissions of each industry, i.e., the reference method and parameters in the 2nd Volume (Energy Volume) of 2006 IPCC Guidelines for National Greenhouse Gas Inventories together with China's released relevant parameters. To obtain more accurate results, we consider all types of final fossil fuels consumption reported in the statistical data except electricity and heat since CO2 is not directly emitted in the utilization of electricity and heat. The 15 kinds of involved energy source include raw coal, cleaned coal, coke, coke oven gas, other gases, crude oil, petrol, kerosene, diesel, fuel oil, liquefied petroleum gas, refinery gas, natural gas, other petroleum products, and other coking products. As we know, this paper takes into account the most types of energy source among the related studies on Shanghai. The estimation formula is as follows: C i ¼ ∑ C ij ¼ ∑ Eij  N j  CC j  Oj  M j

ð3Þ

j

where subscript i denotes the industrial sector, subscript j refers to various fuels, C i stands for the gross energy-related CO2 emissions of the ith sector, C ij is the CO2 emissions of the ith sector based on the jth fuel, Eij denotes the total energy consumption of the ith sector based on the jth fuel, N j , CC j , and Oj are the net calorific value, carbon content, and the carbon oxidation factor of the jth fuel, respectively, and M is the molecular weight ratio of CO2 to carbon (44/12). The use of special parameters of each country is encouraged by the IPCC (2006) based on its methods. Therefore, we adopt the principle of priority to select the related parameters announced officially in China and the second choice of the defaults provided by the IPCC (2006) to assure the accuracy of the results. The involved data are derived from Shanghai Statistical Yearbooks on Industry, Energy, and Transport, China Energy Statistical Yearbook, and The People's Republic of China National Greenhouse Gas Inventory. According to the classification of industrial sectors in the China Energy Statistical Yearbook, we re-classify and integrate 133 sectors in the 2007 Input–Output Tables of Shanghai into 42 sectors, whose output data can be derived from Shanghai Statistical Yearbooks on

Table 3 CO2 emission intensity of 42 sectors in 2007 (Unit: kg/RMB). Sector

CO2 emission intensity

Sector

CO2 emission intensity

Production and supply of electric power and heat power Processing of petroleum, coking, and nuclear fuel Smelting and pressing of ferrous metals Transport, storage, and post Manufacture of raw chemical materials and chemical products Agriculture, forestry, animal husbandry, and fishery Manufacture of non-metallic mineral products Manufacture of paper and paper products Manufacture of rubber Manufacture of textile Wholesale, retail trades, hotels, and catering services Manufacture of medicines Processing of food from agricultural products Manufacture of chemical fibers Manufacture of beverage

0.7369 0.1882 0.1641 0.1292 0.0558 0.0511 0.0503 0.0349 0.0291 0.0252 0.0202 0.0141 0.0129 0.0127 0.0120

0.0071 0.0065 0.0062 0.0053 0.0050 0.0049 0.0042 0.0032 0.0026 0.0021 0.0021 0.0019 0.0013 0.0011 0.0006

Extraction of petroleum and natural gas

0.0116

Manufacture of textile wearing apparel, footwear and caps Other sectors Manufacture of metal products Manufacture of articles for culture, education, and sport activities Manufacture of general purpose machinery Printing, reproduction of recording media Manufacture of artwork and the recycling and disposal of waste Manufacture of leather, fur, feather and related products Manufacture of transport equipment Manufacture of furniture Manufacture of special purpose machinery Manufacture of electrical machinery and equipment Production and supply of fuel gas Production and supply of water Manufacture of measuring instruments and machinery for cultural activity and office work Manufacture of communication equipment, computers and other electronic equipment Manufacture of tobacco Mining and washing of coal Mining and processing of ferrous metal ores Mining and processing of non-ferrous metal ores Mining and processing of non-metal ores

N.A.a N.A.a

Manufacture of foods 0.0109 Construction 0.0106 Processing of timber and manufacture of wood, bamboo, rattan, 0.0099 palm, and straw products Smelting and pressing of non-ferrous metals 0.0085 Manufacture of plastics 0.0082

0.0005 0.0003 N.A.a N.A.a

a Expect the extraction of petroleum and natural gas, mining and processing industries in Shanghai have no actual output and fuel consumption in most years due to the poor natural resources endowment in Shanghai.

Z. Jiang, S. Shao / Energy Policy 73 (2014) 269–277

3.2.3. Results and discussion According to Shanghai Statistical Yearbook, the expenditure of households in Shanghai includes eight categories: food, clothing, residence, household facilities and articles, health care and medical services, transport and communications, education, cultural and recreation, other miscellaneous goods and services. As shown in Fig. 3, there is an obvious difference in both scale and structure of expenditure among various groups in Shanghai. The lowincome group pays more for food, with the highest Engel coefficient of 42%. With the rise of income level, the share of food in the total expenditure decreases, while the transport and communications expenditure noticeably increases. Such results indicate that the expenditure of the low-income and high-income groups is mainly used for basic requirements and the high quality of life, respectively. Based on the input–output table released by the Shanghai Statistics Bureau, we merge the input–output table into 42 sectors. And then, we match the residential consumption categories to the corresponding sectors in the input–output table. According to Eq. (2), the indirect effects of a carbon tax on various income groups are estimated and presented in Table 4. The estimated results indicate that although the indirect carbon tax payment increases with the income level, its share in the total expenditure presents a downward trend from lowincome group to high-income group. Hence, the indirect effect of a carbon tax shows a significant regressivity. Compared with the high-income group, the low-income group bears heavier tax. The share of the carbon tax payment in the total expenditure of the low-income group is 0.829%, while this share of the high-income 45000

Food

Clothing

Residence

Health Care and Medical Services

40000

Household Facilities and Services Transport and Communications

Education, Cultural and Recreation Services Miscellaneous Goods and Services

35000

Expenditure/CNY

30000

Table 4 Indirect effect of a carbon tax. Group

Per capita tax payment (RMB)

Share of the tax payment in expenditure (%)

Low Mid-low Mid Mid-high High

104.08 130.46 160.43 195.90 277.29

0.829 0.817 0.742 0.732 0.681

Direct effect Indirect effect Proportion of tax payment

300

0.87

250

0.84

200

0.81

150

0.78

100

0.75

50

0 Group

Proportion of tax payment/%

Industry, Energy, and Transport. The calculated CO2 emission intensity of each sector in 2007 is shown in Table 3. Table 3 indicates that the production and supply sector of electric power and heat power in Shanghai presents the highest CO2 emission intensity as a result of 0.737 kg/RMB. This is mainly because that the coal-fired power plants are dominant in Shanghai, which consume a large number of coal as the highest emitting fossil fuel. As expected, some energy-intensive sectors, such as processing of petroleum, coking, nuclear fuel, smelting and pressing of ferrous metals, and transport, storage, and post also show a relatively high CO2 emission intensity, while the manufactures of tobacco, communication equipment, computers and other electronic equipment, and measuring instruments and machinery for cultural activity and office work have a remarkably low CO2 emission intensity.

Per capita tax payment/CNY

274

0.72

Low

Low-high

Mid

Mid-high

High

Fig. 4. Total distributional effect of a carbon tax.

group is 0.681%. Summing up the direct effect in Table 2 and the indirect effect in Table 4, we can get the total distributional effect of a carbon tax (see Fig. 4). Similar to the results of the indirect effect, Fig. 4 suggests that the total distributional effect of a carbon tax presents an evident regressivity. As income rises, the total carbon tax payment increases, but it constitutes a smaller and smaller share in the total expenditure, decreasing from 0.853% of the low-income group to 0.712% of the high-income group. Hence, the lowincome group bears more tax burden than other income groups. This finding is consistent with the conclusions of most current studies (Cornwell and Creedy, 1996; Brännlund and Nordström, 2004; Kerkhof et al., 2008; Shammin and Bullard, 2009; Mathur and Morris, 2014), i.e., the distributional effect of a carbon tax is regressive. In addition, we find that the indirect effect is much larger than the direct. This means that the indirect distributional effect of a carbon tax on households induced by pushing up the prices of products and services, especially energy-intensive ones, is far greater than its direct distributional effect caused by consuming fuels.

25000

4. Measurement of carbon tax progressivity

20000 15000 10000 5000 0 Group

Low

Low-mid

Mid

Mid-high

High

Fig. 3. Expenditure structure of various income groups in Shanghai in 2010.

The tax progressivity is the most commonly used concept to study the tax distribution in different income groups. The Suits index proposed by Suits (1977) is widely adopted to examine the progressivity and regressivity of taxes, which is based on the principle of the concentration curve and the Gini coefficient. Fig. 5 depicts an example of a concentration curve used to calculate the Suits index, where the accumulated percent of the tax burden is plotted vertically while the accumulated percent of the income on the horizontal axis.

Z. Jiang, S. Shao / Energy Policy 73 (2014) 269–277

Table 5 Suits index of a carbon tax.

B

50

Suits index

Direct Indirect Total

0.022  0.082  0.078

C L

0

0

50

A 100

Accumulated percent of income Fig. 5. An example of the concentration curve used to calculate the Suits index.

Following Suits (1977), the Suits index is specified as follows: S ¼ ðK  LÞ=K ¼ 1  ðL=KÞ

n 1 ∑ ½Tðyi Þ þTðyi  1 Þðyi  yi  1 Þ 10000 i ¼ 1

80

60

40

20

ð4Þ

where S denotes the Suits index (i.e., the index of tax progressivity), K refers to the area of the triangle OAB in Fig. 5, and L stands for the area OABC between the curve and the horizontal axis OA. The Suits index varies from  1 to 1. In the case of regressivity, L 4K and 1 rS o0, while a proportional tax implies L ¼K and S ¼0, and a progressive tax yields LoK and 0 oS r1. Suggested in Suits (1977): S ¼ 1

Effect

100

Accumulated percent of tax burden (direct)

Accumulated percent of tax burden

100

275

ð5Þ

where yi denotes the accumulated percent of the income, measured on the horizontal axis, which ranges from 0 to 100, Tðyi Þ is the corresponding accumulated percent of the tax burden from a given tax, and n stands for the number of households' income groups. Since the 1980s, many studies employed the Suits index to measure the distribution effect of taxation or public expenditure. For instance, Kienzle (1981) empirically demonstrated that the Suits measurement methodology applied in a broader and more useful sense in public taxation and expenditure analysis than the previous methods. Sarte (1997) combined a dynamic equilibrium model with the Suits index to analyze the effects of progressive taxation on the degree of income inequality in a competitive economy and asserted that the Suits index is a helpful indication of the tax burden borne by each agent type. Using the Suits index, Metcalf (1999) assessed the distributional impact of a shift toward greater reliance on various environmental tax policies. Furthermore, Price and Novak (1999) and Hansen et al. (2000) employed the Suits index to investigate the issue of the tax incidence of lotteries. The studies for China were conducted by Liu and Nie (2004, 2009), who used the Suits index to estimate the distributional impact of three major indirect taxes, including value added tax, consumption tax, and business tax. Recently, Sterner (2012) also adopted the Suits index to examine the distributional effects of transport fuels taxation in seven European countries and found some weak evidence of regressivity. However, the study using the Suits index to measure the distributional effect of a carbon tax in China is still absent. According to Eq. (5), the Suits index of a carbon tax in Shanghai is estimated and reported in Table 5. Once levying a carbon tax on

0

0

20

40

60

80

100

Accumulated percent of income Fig. 6. Concentration curve of the direct effect.

fossil fuels, the Suits index of the direct effect is 0.022, indicating that the direct effect of the carbon tax is progressive, but the progressivity is small, approximately proportional. The Suits indices of the indirect effect and the total effect are  0.082 and  0.078, respectively. Therefore, on the whole, levying a carbon tax on fossil fuels appears regressive in Shanghai since the tax burden on low-income households is greater than that of highincome ones. Furthermore, we present the concentration curves of distributional effects of a carbon tax in Figs. 6–8, which also consolidate the results in Table 5. Fig. 6 shows that the concentration curve is below the diagonal line but very close to it. This suggests that the progressivity of the direct effect of a carbon tax is weak in Shanghai. Figs. 7 and 8 show that the concentration curves are above the diagonal line, further indicating that the indirect effect and the total effect of a carbon tax in Shanghai are all regressive.

5. Conclusions and policy implications China has become the largest CO2 emissions country in the world, accounting for one-quarter of global CO2 emissions in 2011 and 80% of the world's rise in CO2 emissions since 2008 (Peters et al., 2012; Liu et al., 2013). With the deepening of industrialization and urbanization, China's economic development has to encounter the rigid energy demands and severe environmental constrains. Energy saving and emission reduction have become the necessary way to resolve the conflicts among economy, energy, and the environment, and to realize the sustainable development. As the economic center of China, Shanghai should play a leading role in energy saving and emission reduction. Although a carbon tax is generally regarded as an effective policy instrument to reduce CO2 emissions, it will inevitably generate the redistribution of

276

Z. Jiang, S. Shao / Energy Policy 73 (2014) 269–277

Accumulated percent of tax burden (indirect)

100

80

60

40

20

0

0

20

40

60

80

100

Accumulated percent of income Fig. 7. Concentration curve of the indirect effect.

Accumulated percent of tax burden(total)

100

80

60

40

20

0

0

20

40

60

80

100

Accumulated percent of income Fig. 8. Concentration curve of the total effect.

households' income. Thus, this paper estimates and discusses the impacts of a carbon tax on the expenditure of households in different income groups in Shanghai. The main conclusions are summarized as follows. First, households' energy consumption accords with the “energy ladder” theory. The distributional effect of a carbon tax can be divided into two parts: the direct and indirect effects. The expenditure resulting from a carbon tax increases with the rise of households' income level. The expenditure from the indirect effect of a carbon tax is more than that from the direct effect. The direct distributional effect presents a weak progressivity, while the indirect distributional effect is significantly regressive. Thus, the total distributional effect is also regressive, indicating that lowincome households bear more tax burden. The direct effect largely depends on the quantity and structure of households' fuels consumption, while the decisive factors of the indirect effect are the CO2 emission intensity of various economic sectors and households' expenditure structure. Second, both the Suits index and the concentration curve of the carbon tax indicate that imposing a carbon tax on fossil fuels is

regressive and thus can intensify income inequality. The progressivity of the direct distributional effect is approximately proportional. However, the indirect and the total distributional effects are all significantly regressive. Therefore, the tax burden on the lowincome group is the highest. The distributional effect of a carbon tax is a key factor in determining whether it can be accepted by the public and be successfully implemented, especially under the special background of the widening income gap in China. This study indicates that the high-income group consumes more transport fuels than other groups. Barker and Köhler (1998) also argued that imposing a carbon tax on transport fuels will result in a progressive effect. Hence, at the beginning of implementing a carbon tax, it can be considered to impose it first on transport fuels and then to gradually expand the scope of taxation. Moreover, as pointed out by Hammar and Jagers (2007), different carbon tax rates should be applied to different energy products. For instance, petrol and diesel should be imposed on by higher tax rates while those essential energy sources for heating, which is generally used by low-income households, should be imposed on by lower tax rates. Alternatively, households whose income level is below the standard line can be exempted from the carbon tax. Undoubtedly, compared with other mitigation measures of climate change, it is one of important features of a carbon tax to increase tax revenue. Some researchers believe that a carbon tax can generate a result of “double dividend” (Pearce, 1991; Goulder, 1995). If such a “double dividend” hypothesis is tenable, the redistribution of the carbon tax revenue will play a key role in its distributional effect. This means that the regressivity of a carbon tax can be restricted or even eliminated by rationally designing the redistribution mode of the tax revenue. Barker and Köhler (1998) testified that different redistribution modes can lead to different distributional effects of a carbon tax. When the carbon tax revenue is used to reduce the payroll tax, a carbon tax will be weakly regressive. When the revenue is used for transfer payments, it will be strongly progressive. Therefore, when introducing a carbon tax, some associated redistribution or compensation measures should be implemented pertinently to the greatest extent, such as purposive transfer payments, reducing or exempting some distorting taxes, and subsidizing low-income groups, so as to restrain potential economic distortion, negative impacts, and the damage to the social welfare caused by a carbon tax and thus to assure optimal economic results of implementing a carbon tax. Moreover, the implementation of a carbon tax is more likely to be successful when wide economic reforms are carried out simultaneously, such as energy subsidies reform.

Acknowledgments This paper is supported by the National Natural Science Foundation of China (Nos. 71003068 and 71373153), the Ministry of Education Foundation of China (No. 12YJC790081), the Program for New Century Excellent Talents in University (No. NCET-13-0890), Shanghai “Chen Guang” Program (No. 12CG45), the Shanghai Soft Science Research Program (No. 14692103900), and the Key Project of Zhejiang Statistics Research Program (No. 201416).

References Ahmad, E., Stern, N., 2009. Effective Carbon Taxes and Public Policy Options: Insights from India and Pakistan. Asia Research Centre Working Paper no. 28, London School of Economics and Political Science, London, UK. Baranzini, A., Goldemberg, J., Speck, S., 2000. A future for carbon taxes. Ecol. Econ. 32 (3), 395–412.

Z. Jiang, S. Shao / Energy Policy 73 (2014) 269–277

Barker, T., Köhler, J., 1998. Equity and ecotax reform in the EU: achieving a 10 percent reduction in CO2 emissions using excise duties. Fisc. Stud. 19 (4), 375–402. Barnes, D.F., Plas, R.V.D., Floor, W.M., 1997. Tackling the rural energy problem in developing countries. Financ. Dev. 34 (2), 11–15. Brännlund, R., Nordström, J., 2004. Carbon tax simulations using a household demand model. Eur. Econ. Rev. 48 (1), 211–233. Brenner, M.D., Riddle, M., Boyce, J., 2007. A Chinese sky trust? Distributional impacts of carbon charges and revenue recycling in China. Energy Policy 35, 1771–1784. Bureau, B., 2011. Distributional effects of a carbon tax on car fuels in France. Energy Econ. 33 (1), 121–130. Cao, J., 2009. Development of low carbon: the design of Chinese carbon tax policy and the analysis of CGE model. J. Financ. Res. 12, 19–29. Chen, S.Y., 2011. Marginal abatement cost and environmental tax reform in China. Soc. Sci. China 3, 85–100. Cornwell, A., Creedy, J., 1996. Carbon taxation, prices and inequality in Australia. Fisc. Stud. 17 (3), 21–38. Datta, A., 2010. The incidence of fuel taxation in India. Energy Econ. 32 (S1), S26–S33. Dissou, Y., Siddiqui, M.S., 2014. Can carbon taxes be progressive? Energy Econ. 42 (3), 88–100. Fan, Y., Zhang, H., 2013. Income distribution impacts of carbon tax Chinese urban residents and the design of carbon subsidy scheme. Econ. Theory Bus. Manag. 7, 81–91. Gonzalez, F., 2012. Distributional effects of carbon taxes: the case of Mexico. Energy Econ. 34 (6), 2102–2115. Goulder, L.H., 1995. Effects of carbon taxes in an economy with prior tax distortions: an intertemporal general equilibrium analysis. J. Environ. Econ. Manag. 29 (3), 271–297. Grainger, C.A., Kolstad, C.D., 2010. Who pays a price on carbon? Environ. Resour. Econ. 46 (3), 359–376. Hammar, H., Jagers, S.C., 2007. What is a fair CO2 tax increase? On fair emission reductions in the transport sector. Ecol. Econ. 61 (2–3), 377–387. Hansen, A., Miyazaki, A.D., Sprott, D.E., 2000. The tax incidence of lotteries: evidence from five states. J. Consum. Aff. 34 (2), 182–203. IPCC, 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories, 〈http:// www.ipcc-nggip.iges.or.jp/public/2006gl/vol2.html〉. Jacobsen, H.K., Birr-Pedersen, K., Wier, M., 2003. Distributional implications of environmental taxation in Denmark. Fisc. Stud. 24 (4), 477–499. Kienzle, E.C., 1981. Measurement of the progressivity of public expenditures and net fiscal incidence. South. Econ. J. 48 (1), 197–203. Kerkhof, C.A., Moll, C.H., Drissen, H., Wilting, C.H., 2008. Taxation of multiple greenhouse gases and the effects on income distribution: a case study of the Netherlands. Ecol. Econ. 67 (2), 318–326. Liu, Z., Guan, D.B., Crawford-Brown, D., Zhang, Q., He, K., Liu, J., 2013. A low-carbon road map for China. Nature 500, 143–145. Liu, Y., Nie, H., 2004. Effect of indirect tax incidence on income distribution. Econ. Res. J. 5, 22–30. Liu, Y., Nie, H., 2009. Effects of value added tax and business tax on income distribution. Financ. Trade Econ. 6, 63–68. Labandeira, X., Labeaga, J., 1999. Combining input–output analysis and microsimulation to assess the effects of carbon taxation on Spanish households. Fisc. Stud. 20 (3), 305–320.

277

Metcalf, G.E., 1999. A distributional analysis of green tax reforms. Natl. Tax J. 52 (4), 655–682. Metcalf, G.E., 2009. Cost Containment in Climate Change Policy: Alternative Approaches to Mitigation Price Volatility. NBER (National Bureau of Economic Research) Working Paper no. 15125, 〈http://www.nber.org/papers/w15125〉. Mathur, A., Morris, A.C., 2014. Distributional effects of a carbon tax in broader U.S. fiscal reform. Energy Policy 66, 326–334. Masera, O.R., Saatkamp, B.D., Kammen, D.M., 2000. From linear fuel switching to multiple cooking strategies: a critique and alternative to the energy ladder model. World Dev. 28, 2083–2103. Pearce, D.W., 1991. The role of carbon taxes in adjusting to global warming. Econ. J. 101 (407), 938–948. Poterba, J.M., 1991. Is the gasoline tax regressive? Tax Policy Econ. 5, 145–164. Peters, G.P., Marland, G., Quéré, C.L., Boden, T., Canadell, J.G., Raupach, M.R., 2012. Rapid growth in CO2 emissions after the 2008–2009 global financial crisis. Nat. Clim. Chang. 2, 2–4. Price, D.I., Novak, E.S., 1999. The tax incidence of the three Texas lottery games: regressivity, race, and education. Natl. Tax J. 52 (4), 741–751. Rapanos, V.T., 1995. The effects of environmental taxes on income distribution. Eur. J. Polit. Econ. 11 (3), 487–501. Sarte, P.G., 1997. Progressive taxation and income inequality in dynamic competitive equilibrium. J. Public Econ. 66, 145–171. Sterner, T., 2012. Distributional effects of taxing transport fuel. Energy Policy 41, 75–83. Suits, D.B., 1977. Measurement of tax progressivity. Am. Econ. Rev. 67 (4), 747–752. Shammin, R.M., Bullard, W.C., 2009. Impact of cap-and-trade policies for reducing greenhouse gas emissions on U.S. households. Ecol. Econ. 68 (8–9), 2432–2438. Su, M., Fu, Z.H., Xu, W., Wang, Z.G., Li, X., Liang, Q., 2009. The research of levying carbon tax in China. Rev. Econ. Res. 72, 2–16. Symons, E.J., Proops, J., Gay, P., 1994. Carbon taxes, consumer demand and carbon dioxide emissions: a simulation analysis for the UK. Fisc. Stud. 15 (2), 19–43. Symons, E.J., Speck, S., Proops, J.L.R., 2000. The Effects of Pollution and Energy Taxes Across the European Income Distribution. Keele Department of Economics Discussion Papers, Department of Economics, Keele University. Sun, W., Ueta, K., 2011. The distributional effects of a China carbon tax: a rural– urban assessment. Kyoto Econ. Rev. 80 (2), 188–206. Shao, S., Yang, L.L., Yu, M.B., Yu, M.L., 2011. Estimation, characteristics, and determinants of energy-related industrial CO2 emissions in Shanghai (China), 1994–2009. Energy Policy 39 (10), 6476–6494. Tiezzi, S., 2005. The welfare effects and the distributive impact of carbon taxation on Italian households. Energy Policy 33 (12), 1597–1612. Wier, M., Birr-Pedersen, K., Jacobsen, H.K., Klok, J., 2005. Are CO2 taxes regressive? Evidence from the Danish experience. Ecol. Econ. 52 (2), 239–251. Yang, L.L., Shao, S., 2013. Techno-economic Drivers of Energy-related Industrial CO2 Emissions Growth in Shanghai (China) over 1994–2011. School of Urban and Regional Science Working Paper, Shanghai University of Finance and Economics. Yusuf, A.A., Resosudarmo, B., 2007. On the Distributional Effect of Carbon Tax in Developing Countries: The Case of Indonesia. Economics and Development Studies Working Paper no. 200705, Department of Economics, Padjadjaran University.