China's emissions embodied in exports: How regional and trade heterogeneity matter

Journal Pre-proof China's emissions embodied in exports: How regional and trade heterogeneity matter

Bingqian Yan, Yuwan Duan, Shouyang Wang PII:

S0140-9883(19)30260-9

DOI:

https://doi.org/10.1016/j.eneco.2019.104479

Reference:

ENEECO 104479

To appear in:

Energy Economics

Received date:

27 December 2018

Revised date:

28 May 2019

Accepted date:

6 August 2019

Please cite this article as: B. Yan, Y. Duan and S. Wang, China's emissions embodied in exports: How regional and trade heterogeneity matter, Energy Economics(2019), https://doi.org/10.1016/j.eneco.2019.104479

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China's emissions embodied in exports: how regional and trade heterogeneity matter Bingqian Yana,c, Yuwan Duanb,c†, Shouyang Wangd,e a

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National Academy of Economic Strategy, Chinese Academy of Social Sciences, Beijing 100836, China b School of International Trade and Economics, Central University of Finance and Economics, Beijing, 100081, China c Faculty of Economics and Business, University of Groningen, 9700 AV, Groningen, The Netherlands d School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China e Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing, 100190, China

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ABSTRACT

Trade facilitates the shifts of emissions from one place to another. Although

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studies have shown that regionally disaggregated model and model that distinguishes processing exports at national level are necessary to estimate the embodied emissions

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in China’s exports, no study evaluates this issue by simultaneously taking both regional and trade heterogeneity into account. To fill this gap, we re-estimate the CO2

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emissions embodied in China’s exports at both regional and industrial level, by using the newly-developed inter-regional input-output (IRIOP) model that distinguishes

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processing trade from other trade at regional level. Results show that comparing to the IRIOP model, the traditional multi-regional input-output (MRIO) model has overestimated the environmental loss from exports by 14%-25% in 2002 and 7%-20% in 2012 for different regions. The largest bias is found in regions and industries with the highest processing export shares. Therefore, the IRIOP model gives more accurate accounting on the regional environmental loss due to national exports and thus is important for establishing effective emission mitigation policies. JEL: F18, F640 Keywords: Emissions embodied in exports Processing production Bipartite Inter-regional Input-Output table Environmental loss †

Corresponding author. E-mail: [email protected] *Acknowledgement: The authors acknowledge the financial support by the National Natural Science of China (NO. 71704195).

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China's emissions embodied in exports: how regional and trade heterogeneity matter

ABSTRACT

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Trade facilitates the shifts of emissions from one place to another. Although studies have shown that regionally disaggregated model and model that distinguishes processing exports at national level are necessary to estimate the embodied emissions in China’s exports, no study evaluates this issue by simultaneously taking both regional and trade heterogeneity into account. To fill this gap, we re-estimate the CO2 emissions embodied in China’s exports at both regional and industrial level, by using the newly-developed inter-regional input-output (IRIOP) model that distinguishes processing trade from other trade at regional level. Results show that comparing to the IRIOP model, the traditional multi-regional input-output (MRIO) model has overestimated the environmental loss from exports by 14%-25% in 2002 and 7%-20% in 2012 for different regions. The largest bias is found in regions and industries with the highest processing export shares. Therefore, the IRIOP model gives more accurate accounting on the regional environmental loss due to national exports and thus is important for establishing effective emission mitigation policies.

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Keywords Emissions embodied in exports Processing production Bipartite Inter-regional Input-Output table Environmental loss

1. Introduction With production fragmentation, the production process of a final product is split into separate activities and dispersed across countries (Timmer et al., 2013; Los et al., 2015). The ongoing change also facilitates the shift of emissions from consumer countries to producer countries (Peters and Hertwich, 2006). As the world’s largest exporter, China bears huge environmental burden from international trade. For instance, Weber et al. (2008) showed that in 2005, one third of Chinese emissions (1700Mt) were due to production of exports. To establish effective environmental policies, it is of great importance to understand the environmental impact of exports for China. In particular, China is a vast country with large regional disparity in export distribution, thus the environmental effect of exports differs among regions. Under this background, this paper aims to evaluate the effect of international trade on China’s regional emissions, taking into account both regional and trade heterogeneity. One typical feature of China’s exports is the prevalence of processing trade, which comprises 40% of China’s total exports in 20121. Processing trade refers to the finished products 1

This figure is calculated by authors based on the newly developed inter-regional input-output table (Duan et al., 2014).

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that are re-exported after being assembled in China, a large part of whose raw materials, parts, and components are imported from abroad free of duty (Dietzenbacher et al., 2012; Pei et al., 2012; Su et al., 2013;Duan et al., 2014; Weitzel and Ma, 2014). Hence, processing exports have totally different intermediate input structure from the ordinary exports, with the former generating much less domestic activities (Pei et al., 2012). Consequently, the traditional input-output model that uses the same intermediate input structure assumption for processing and ordinary production, will thus give misleading conclusions on economic and environmental consequence of China’s international trade. For instance, studies have proved that input-output tables without separating processing trade from ordinary exports will exaggerate the contribution of China’s exports to its economic growth (Pei et al., 2012) and also overestimate the emissions due to exports (Dietzenbacher et al., 2012). However, the existing literature only distinguished the trade heterogeneity at national level and used the national input-output model to calculate the emissions embodied in Chinese exports, without considering China’s vast regional disparity in both economic and environmental terms. As shown in Su and Ang (2010), the estimates of embodied emissions in exports are highly dependent on spatial aggregation. They compared the estimated results by using Chinese IO tables that are aggregated at different region levels (1 region, 3 regions and 8 regions). The results show that the estimated embodied emissions in exports decrease with the increasing number of regions. Therefore, for a large country with regional diversity like China, it is necessary and meaningful to study the embodied emissions in trade at disaggregated region level. From the perspective of estimation methodology, there are two general approaches to estimate the emissions embodied in a region’s exports: emissions embodied in bilateral trade (EEBT) approach and multi-regional input-output (MRIO) approach. The former applied the single-region input-output (SRIO) model to each region, while the latter applies the full MRIO model to all regions. The difference between these two methods is called feedback effect. To study the mechanism of feedback effect, Su and Ang (2011) put forward the method of stepwise distribution of emissions embodied in trade (SED-EET) to distribute the emissions embodied in each country’s imports for intermediate use to each country’s final demand. Su and Ang (2014) further proposed the hybrid emissions embodied in trade (HEET) approach by combining the MRIO approach at China’s regional level and EEBT approach at national level for regional emission studies. Considering that this study aims to quantify the regional emissions embodied in China’s regional exports, the MRIO model is more suitable and thus is applied in the analysis. More importantly, according to Duan et al. (2014), there is a significant heterogeneity of export composition across regions. For instance, in 2012, processing exports accounted for 51% of exports in South Coast, while the share was only 7% in Northwest. Therefore, it is insightful to distinguish the trade heterogeneity at regional level and use the MRIO model to evaluate the emission effects of exports in different regions. As a consequence, the Bipartite inter-regional input-output (IRIOP) model, which separates the production of processing exports from non-processing exports at regional level, is necessary to accurately account for the embodied emissions in regional exports. For the above-mentioned reasons, this study employs the newly constructed IRIOP model to revisit the emissions embodied in Chinese exports at both regional and industrial level. This paper is an important complementary to the existing literature. Our results show that MRIO model overestimates the export-related emissions by 21% in 2002 and 16% in 2012 at national level, comparing to IRIOP model. The corresponding bias at

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regional level is in the range of 14%-25% in 2002 and 7%-20% in 2012, with the highest in South Coast and the lowest in Northern Municipalities. The estimation bias for the national environmental loss per unit of regional exports by using MRIO model also varies across regions and industries. The largest bias is found in regions and industries with high processing exports shares, i.e., South Coast and Other Manufacturing. We conclude that IRIOP model is more accurate and appropriate to address the related issues, such as allocating emission responsibilities and evaluating the migrating emission effects at regional level. The rest of the paper is organized as follows. Section 2 reviews the related literature. Section 3 and Section 4 introduce the methodology and data used in this paper, respectively. Section 5 describes the results. Section 6 concludes. 2. Literature review With the ongoing globalization and production fragmentation, the question “who produces/emits for whom” has gained more importance and input-output analysis has been widely used in this aspect. In the aspect of embodied emissions, the first strand of literature focuses on the environmental consequences of international trade. In this domain, both national input-output model and inter-regional input-output model are widely adopted to investigate the embodied emissions in international trade, or estimate the emissions from a consumption base (Munksgaard and Pedersen, 2001; Ahmad and Wyckoff, 2003; Ferng, 2003; Peters and Hertwich, 2008; Meng et al., 2018; Wang et al., 2018). The empirical studies always show that developed countries generally displace final-demand-related emissions to developing countries and developing countries become the net exporter of emissions. For instance, Ahmad and Wyckoff (2003) showed that the emissions emitted for satisfying the final demands in OECD countries is 5% higher than emissions from production in these countries in 1995. This reveals the phenomenon that developed countries outsourced emissions to developing countries. Later on, Peters and Hertwich (2006) found that a large amount of emissions embodied in Norwegian household consumption is from developing countries. Furthermore, Liddle (2018) compared the consumption-based carbon emissions with the territory-based carbon emissions for 117 countries over 1990-2013 and found that the consumption-based emissions are higher than territory-based emissions for most countries. China alone is responsible for over half of the carbon outflow. For China specific, Weber et al. (2008) showed that the one-third of Chinese emissions in 2005 were generated for export production. For more recent studies, Meng et al. (2018) evaluated both value added and emissions at country, sector and bilateral levels through various routes in global value chain from 1995 to 2009. Besides high domestic emissions embodied in exports, the exports by China and RoW and their relatively high imported contents in exports also lead to more foreign CO2 emissions. For other emissions such as SO2, NO𝑥 , and PM2.5, a similar phenomenon is also found in Xu et al. (2019), which documented that air pollutants embodied in China's exports to the U.S. were much greater than those embodied in imports from the U.S. The second strand of literature evaluates the environmental effect of interregional trade within China. Most studies found that developed regions outsource emissions from own region to less developed regions. Feng et al. (2013), for example, found that 80% of emissions associated with the goods consumed in developed regions were from less developed regions in 2007. Meng et al. (2013) further showed that increasing emissions in less developed inland regions were caused not only by their increasing direct exports, but also indirectly by joining the domestic

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supply chains of developed coastal regions. Furthermore, Zhang et al. (2014) documented that regional carbon spillover was mainly concentrated in coastal regions and it led to the increase of emissions in central and western regions. Wang et al. (2017) quantified that the trade-related environmental damage from four major pollutants for each province in China in 2007. They found that environmental damage was transferred from Northern Municipalities, East Coast, and South coast to developing Northwest and Central Regions, resulting in inequality in regional development. Similarly, Duan et al. (2018) used ecological network analysis to show that most emissions in the rest of China are induced by the final demand in east regions. Similar results are found in Guo et al. (2012), Zhang and Tang (2015), Meng et al. (2017), Liu and Wang (2017), and Zhang (2017). Pei et al. (2018) went one step further and embedded the China’s domestic interregional input-output table for 2007 into the World Input-Output Table (WIOT). Results show that China’s regions are located in the upstream of global value chains and are net CO2 emission exporters. The researchers conclude that accounting the environmental damage from a consumer perspective is important and should be considered when establishing emission reduction targets for each region. This paper is also closely related with the studies which specially focus on China’s processing trade. In this domain, Chen et al. (2012), Koopman et al. (2012), Pei et al. (2012), and Su et al. (2013) have separated the processing exports from other productions in China’s national input-output tables by using various methods. They consistently find that processing exports can only generate limited domestic activities and therefore less domestic value added in comparison with other productions. Some studies analyzed the effect of different export assumption on emissions embodied in trade for China (including Dietzenbacher et al., 2012; Su et al., 2013; Weitzel and Ma, 2014; Jiang et al., 2016; Su and Thomson, 2016). Specifically, Dietzenbacher et al. (2012) documented that in 2002, the damage of international trade to China’s environment would be overestimated by more than 60% if one uses the uniform intermediate input assumption2. For 1997, the estimated emissions embodied in exports (based on the model that distinguishes processing exports) accounted for 12.6% of total domestic emissions, also lower than the 18.4% in the traditional model (Su et al., 2013). Weitzel and Ma (2014) further compared the emissions embodied in Chinese exports in 2007 using three different IO models: the standard national model, the regionally disaggregated model, and the model with export processing at national level. They found that both regionally disaggregated model and model that distinguishes processing exports yielded lower domestic emissions embodied in exports than the traditional model. Furthermore, by using the method of minimizing the information loss function, Su and Thomson (2016) constructed a time-series of national input-output tables (2006-2012) that distinguish processing trade (hereafter referred to as extended input-output model) to analyze the emissions embodied in both processing and ordinary exports. Results show that the traditional input-output model overestimated the emissions embodied in exports by 12% in 2007, which confirms the importance of using extended input-output model to study the related issues. Specifically, the emissions embodied in processing exports are over-estimated by 73%, while the emissions embodied in ordinary exports are under-estimated. Some recent literature also indicates the importance of integrating firm ownership and trade mode into traditional input-output model when estimating embodied emissions (such as Jiang et al., 2015; Liu et al., 2

They found that emissions embodied in exports accounted for 12.6% of total domestic emissions when using the new model, while the percentage is 20.3% when the traditional model is used.

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2016; Meng et al., 2018). For instance, Liu et al. (2016) found that ignoring firm ownership and trade mode in the national input-output table in 2007 would overestimate the embodied CO2 emissions in Chinese exports by 20%. The estimation differences vary significantly at the sectoral level. Recently, Jiang et al. (2016) revisited the global net CO2 emission transfer by applying a new world-wide MRIO table which separates China’s production into domestic use, processing exports, and non-processing exports. Comparing to the results obtained from the new MRIO table, they found that the results obtained from traditional MRIO table overestimated the net emissions from China to other regions by 15%. To the best of our knowledge, Jiang et al. (2017) is the only study that distinguishes trade heterogeneity at regional level. However, they only evaluated the regional disparity of energy intensity across regions and did not consider the emissions let alone the embodied emissions in exports. As Weitzel and Ma (2014) clearly pointed out that both regionally disaggregated model and model with exports processing generate different estimations on embodied emissions in exports from those based on the traditional single country model, it is necessary to take full consideration of both regional and export heterogeneity when measuring the domestic environmental effect of Chinese exports. Therefore, to fill this gap in the literature and form effective policies for reducing emissions, we will apply the IRIOP table to reevaluate the domestic emissions embodied in exports at regional level in China and analyze how the consideration of regional and trade heterogeneity matter for the embodied emissions estimation. 3. Methodology 3.1 Traditional inter-regional Input-output model Table 1 illustrates a simplified traditional MRIO table with 𝑛 regions and each region has 𝑚 industries3. Each row in the input-output table indicates the use of outputs, including 𝑟𝑠 intermediate use (in the blocks labeled 𝐙, whose element 𝑧𝑖𝑗 indicates the intermediate inputs

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from industry 𝑖 in region 𝑟 to industry 𝑗 in region 𝑠), domestic final use (in the blocks labeled 𝑓, whose element 𝑓𝑖𝑟𝑠 indicates the final products from industry 𝑖 in region 𝑟 to satisfy the final demand in region 𝑠)4 and exports (in the blocks labeled 𝑒, whose element 𝑒𝑖𝑟 indicates the exports from industry 𝑖 in region 𝑟 to foreign countries). And each column indicates the inputs of production, including intermediate inputs (in the blocks labeled 𝐙) and value added (in the blocks labeled 𝐰, whose element 𝑤𝑗𝑠 indicates the value added generated in industry 𝑗 in region 𝑠) 5. The data on CO2emissions is provided in the satellite account, whose element 𝑝𝑗𝑠 indicates the emissions generated by industry 𝑗 in region 𝑠. 3

To be clear, we use IRIO table(/model) to indicate the traditional inter-regional input-output table(/model) without distinguishing the production of processing exports, and IRIOP table to represent our special table that explicitly illustrates the different production technology between processing exports and other productions. 4 For the sake of clarity, the convention we use is that matrices are indicated by bold capital letters; vectors are columns by definition and are indicated by bold lowercase letters; scalars by italicized lowercase letters. A prime indicates transposition and a hat (or a circumflex) indicates a diagonal matrix with the elements of a vector on its main diagonal and all other entries equal to zero. 5 To be clear, the IRIO model here is based on the non-competitive import assumption, as indicated by the row of imported intermediate inputs. Specifically, the competitive import assumption fails to distinguish between imported products and domestic ones, while the non-competitive import assumption treats the imported products differently from those produced domestically. Su and Ang (2013) used five years’ Chinese data to show that the approach with competitive import assumption gives larger estimation than that with non-competitive import assumption. This is because that under the first assumption, the emissions embodied in China’s intermediate imports are accounted into those embodied in China’s exports, while it is not under non-competitive imports assumption. Since the emissions for producing the imported inputs are generated in other countries, they should not be considered as China’s environmental loss. Therefore, we choose the non-competitive import assumption in our IRIO model.

Journal Pre-proof Insert Table 1 here According to Table 1, the MRIO model can be expressed as 𝐙11 [ ⋮ 𝐙 𝑛1

⋯ 𝐙1𝑛 𝐟11 ⋱ ⋮ ]𝐮 + [ ⋮ ⋯ 𝐙 𝑛𝑛 𝐟 𝑛1

⋯ 𝐟1𝑛 𝐞1 𝐱1 ⋱ ⋮ ]𝐮 + [ ⋮ ] = [ ⋮ ] ⋯ 𝐟 𝑛𝑛 𝐞𝑛 𝐱𝑛

(1)

where 𝐱 𝑠 is the output vector in region 𝑠 with 𝑚 × 1 dimension; 𝐮 is a vector of ones of appropriate size used for summation. The input-output coefficient matrix is 𝐀11 𝐀 =[ ⋮ 𝐀𝑛1 ∗

⋯ 𝐀1𝑛 ⋱ ⋮ ], where 𝐀𝑟𝑠 is the industry-wise output in region 𝑟directly required to 𝑛𝑛 ⋯ 𝐀

where 𝐋 = (𝐈 − 𝐀)

−1

𝐋11 =[ ⋮ 𝐋𝑛1

⋯ 𝐋1𝑛 𝑟𝑠 ⋱ ⋮ ]is the Leontief inverse, whose element 𝑙𝑖𝑗 gives the 𝑛𝑛 ⋯ 𝐋

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(2)

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∑𝑛𝑟 𝐟1𝑟 + 𝐞1 𝐱1 ∗ [ ⋮ ]=𝐋 [ ] ⋮ ∑𝑛𝑟 𝐟 𝑛𝑟 + 𝐞𝑛 𝐱𝑛

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produce per unit industry-wise output in region 𝑠, obtained as 𝐀𝑟𝑠 = 𝐙 𝑟𝑠 (𝐱̂ 𝑠 )−1. A hat here indicates the diagonal matrix of a vector. Thus, equation (1) can be solved as

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input of good 𝑖 in region 𝑟 that is necessary to satisfy per unit output by industry 𝑗 in region 𝑠 (Miller and Blair, 2009); 𝐈 is the 𝑚𝑛 × 𝑚𝑛 identity matrix. Using the assumption of fixed input coefficients, the amount of output Δ𝐱 ∗ needed to satisfy the additional final demand vector Δ𝐲 ∗ can be obtained as Δ𝐱 ∗ = (𝐈 − 𝐀∗ )−1 Δ𝐲 ∗ = 𝐋∗ Δ𝐲∗ . Then the national CO2 emissions that are required for the export vector 𝐞∗ can be obtained as 𝑏 = 𝛒∗ ′(𝐈 − 𝐀∗ )−1 𝐞∗ = 𝛒∗ ′𝐋∗ 𝐞∗ (3) ∗ where 𝛒 denotes the CO2 emission coefficient vector with 𝑚𝑛 × 1 dimensions, and its element 𝜌𝑗𝑟 is defined as 𝜌𝑗𝑟 = 𝑝𝑗𝑟 /𝑥𝑗𝑟 , indicating the amount of CO2 emissions that are emitted

𝐁11 [ ⋮ 𝐁 𝑛1

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into air per unit of output by industry 𝑗 in region 𝑟. When diagonalizing 𝛒∗ and 𝐞∗ in equation (3), we can obtain the region-industry-specific CO2 emissions induced by the region-industry-specific exports: ̂1 𝛒 ⋯ 𝐁1𝑛 ⋱ ⋮ ]=[ ⋮ 0 ⋯ 𝐁 𝑛𝑛

⋯ 0 𝐋11 ⋱ ⋮ ][ ⋮ ̂𝑛 𝐋𝑛1 ⋯ 𝛒

⋯ 𝐋1𝑛 𝐞̂1 ⋱ ⋮ ][ ⋮ 𝑛𝑛 ⋯ 𝐋 0

⋯ 0 ⋱ ⋮] ⋯ 𝐞̂𝑛

(4)

𝑟𝑠 The element of 𝐁 𝑟𝑠 , 𝑏𝑖𝑗 indicates the emissions generated in industry 𝑖 in region 𝑟 due

to the exports by industry 𝑗 in regions 𝑠. Accordingly, the emissions generated in region 𝑟 due to the national exports can be obtained by summing up elements in 𝐁 𝑟𝑠 over all region 𝑠, that is: 𝑚 𝑟𝑠 𝑚 𝑚 𝑟 𝑟𝑠 𝑠 𝑛 𝑏𝑟∙ = 𝐮′ (∑𝑛𝑠=1 𝐁 𝑟𝑠 )𝐮 = ∑𝑛𝑠=1 ∑𝑚 (5) 𝑖=1 ∑𝑗=1 𝑏𝑖𝑗 = ∑𝑠=1 ∑𝑖=1 ∑𝑗=1 𝜌𝑖 𝑙𝑖𝑗 𝑒𝑗 𝑏𝑟∙ represents the environmental loss in region 𝑟 due to China’s export production. Furthermore, the emissions generated in industry 𝑖 due to the national exports are calculated by summing up all the elements correspondent to industry 𝑗 across all regions: 𝑟𝑠 𝑚 𝑟 𝑟𝑠 𝑠 𝑛 𝑛 𝑏𝑖∙ = ∑𝑛𝑟=1 ∑𝑛𝑠=1 ∑𝑚 (6) 𝑗=1 𝑏𝑖𝑗 = ∑𝑟=1 ∑𝑠=1 ∑𝑗=1 𝜌𝑖 𝑙𝑖𝑗 𝑒𝑗 which shows the pollution degree of each industry due to national exports. Meanwhile, the total domestic environmental loss for regional export production can be

Journal Pre-proof obtained by summing up all the elements in 𝐁 𝑟𝑠 over all region 𝑟. 𝑚 𝑟𝑠 𝑚 𝑚 𝑟 𝑟𝑠 𝑠 𝑛 𝑏∙𝑠 = 𝐮′ (∑𝑛𝑟=1 𝐁 𝑟𝑠 )𝐮 = ∑𝑛𝑟=1 ∑𝑚 𝑖=1 ∑𝑗=1 𝑏𝑖𝑗 = ∑𝑟=1 ∑𝑖=1 ∑𝑗=1 𝜌𝑖 𝑙𝑖𝑗 𝑒𝑗

(7)

∙s

Further, dividing 𝑏 by regions s’s total exports yields domestic environmental loss of one unit of regional export in regions s: 𝑏 ∙𝑠

𝜃 𝑠 = ∑𝑚

(8)

𝑠 𝑖=1 𝑒𝑖

In a similar way, we can also evaluate the domestic environmental loss per unit of regional export at industry level, 𝜃𝑗𝑠 =

𝑟𝑠 {𝐮′ (∑𝑛 𝑟=1 𝐁 )}𝑗

(9)

𝑒𝑗𝑠

where {}𝑗 give the 𝑗th element of the matrix in bracket.

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3.2 Bipartite inter-regional input-output model The IRIOP model is an extension of MRIO model, with a typical feature of distinguishing the production of processing exports from the other productions. Table 2 illustrates the basic framework of the IRIOP model. Compared with MRIO model, the IRIOP model split each production into processing trade and ordinary trade at both regional and industry level. For instance, the intermediate input matrix from 𝑟 to region 𝑠 𝐙𝑟𝑠 (as expressed in Table 1) is split 𝑂𝑃 𝑂𝑂 into 𝐙𝑟𝑠 and 𝐙𝑟𝑠 , which represents the intermediate input matrix from ordinary products in region 𝑟 to region 𝑠 for the production of processing goods and ordinary goods, respectively. Insert Table 2 here In the IRIOP table, the domestic output vector and final demand vector in each region now

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become partitioned 2𝑚𝑛 × 1 vector 𝐱 =

𝐱𝑃 1 𝐱𝑂 1 ⋮ 𝐱𝑃 𝑛 𝑂 (𝐱𝑛 )

and 𝐞 =

𝐞𝑃 1 𝐞𝑂 1 ⋮ , 𝐞𝑃 𝑛 𝑂 (𝐞𝑛 )

while domestic input matrix

the Leontief inverse now become partitioned 2𝑚𝑛 × 2𝑚𝑛 matrix 0 0 0 0 𝐈 0 0 0 𝐀𝑂𝑃 𝐀𝑂𝑂 𝐀𝑂𝑃 𝐀𝑂𝑂 𝐋𝑂𝑃 𝐋𝑂𝑂 𝐋𝑂𝑃 𝐋𝑂𝑂 𝑟𝑟 𝑟𝑟 𝑟𝑠 𝑟𝑠 𝑟𝑟 𝑟𝑟 𝑟𝑠 𝑟𝑠 𝐀= ⋮ and 𝐋 = ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ . 0 0 0 0 0 0 𝐈 0 𝐀𝑂𝑂 𝐀𝑂𝑃 𝐀𝑂𝑂 𝐋𝑂𝑂 𝐋𝑂𝑃 𝐋𝑂𝑂 [𝐀𝑂𝑃 [𝐋𝑂𝑃 𝑠𝑟 𝑠𝑟 𝑠𝑠 𝑠𝑠 ] 𝑠𝑟 𝑠𝑟 𝑠𝑠 𝑠𝑠 ] 𝐵𝐶 𝐵𝐶 For example, the element 𝑎𝑟𝑠𝑖𝑗 (of 𝐀 𝑟𝑠 ) indicates products 𝑖 from production type 𝐵 in

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region 𝑟 that are directly required to produce per unit of product 𝑗 in production type 𝐶 in region 𝑠. Note that processing products are never used for domestic use by definition, and 𝑃𝑂 therefore the partitions of their intermediate use are zero (i.e. 𝐀𝑃𝑃 𝑟𝑟 = 𝐀 𝑟𝑟 = 0). Accordingly, the region-industry-specific CO2 emissions induced by the region-industry-specific exports in a multi-region IRIOP model can be expressed as 𝑃𝑃 𝐐11 0 𝑂𝑃 𝑂𝑂 𝐐11 𝐐11 ⋮ ⋮ 0 0 𝑂𝑃 𝑂𝑂 [𝐐𝑠𝑟 𝐐𝑠𝑟

where

𝛒𝑃𝑟

(/𝛒𝑂𝑟 )

⋯ ̂𝑃𝑟 𝛒 0 0 𝑂𝑃 𝑂𝑂 ⋯ 𝐐𝑟𝑠 𝐐𝑟𝑠 0 = ⋮ ⋱ ⋮ ⋮ ⋯ 𝐐𝑃𝑃 0 0 𝑠𝑠 𝑂𝑂 ⋯ 𝐐𝑂𝑃 0 [ 𝐐 ] 𝑠𝑠 𝑠𝑠

0 ̂ 𝛒𝑂𝑟 ⋮ 0 0

⋯ 0 ⋯ 0 ⋱ ⋮ ⋯ 𝛒 ̂𝑃𝑠 ⋯ 0

𝐞̂𝑃𝑟 0 0 0 ⋮ 𝐋 ⋮ 0 0 ̂𝑂𝑠 ] [ 0 𝛒

0 ̂𝐞𝑂𝑟 ⋮ 0 0

⋯ 0 ⋯ 0 ⋱ ⋮ ⋯ 𝐞̂𝑃𝑠 ⋯ 0

0 0 ⋮ 0 𝐞̂𝑂𝑠 ]

(10) indicate the emission coefficient vector for processing (/ordinary) products in

Journal Pre-proof region 𝑟. Corresponding to equation (5), the emissions generated in region 𝑟 due to the national exports in IRIOP model can be obtained by summing up all the elements in 𝐐𝑟𝑠 over all region 𝑠 𝑂𝑃 ′ 𝑂𝑂 ′ 𝑛 𝑂𝑃 ′ 𝑛 𝑂𝑂 𝑞𝑟∙ = 𝐮′ (𝐐𝑃𝑃 𝑟𝑟 + 𝐐𝑟𝑟 )𝐮 + 𝐮 𝐐𝑟𝑟 𝐮 + 𝐮 (∑𝑠≠𝑟 𝐐𝑟𝑠 )𝐮 + 𝐮 (∑𝑠≠𝑟 𝐐𝑟𝑠 )𝐮 ′

′

′

𝑃 𝑂 𝑂𝑂 𝑂 𝑛 𝑂′ 𝑂𝑃 𝑃 𝒏 𝑂′ 𝑂𝑂 𝑂 = (𝛒𝑃𝑟 𝐞𝑃𝑟 + 𝛒𝑂𝑟 𝐋𝑂𝑃 𝑟𝑟 𝐞𝑟 ) + 𝛒𝑟 𝐋𝑟𝑟 𝐞𝑟 + ∑𝑠≠𝑟 𝛒𝑟 𝐋𝑟𝑠 𝐞𝑠 + ∑𝑠≠𝑟 𝛒𝑟 𝐋𝑟𝑠 𝐞𝑠 𝑃 𝑃 𝑚 𝑚 𝑂 𝑂𝑃 𝑃 = ∑𝑚 (11-a) 𝑖=1 𝜌𝑟𝑖 𝑒𝑟𝑖 + ∑𝑖=1 ∑𝑗=1 𝜌𝑟𝑖 𝑙𝑟𝑟𝑖𝑗 𝑒𝑟𝑗 𝑚 𝑚 𝑂 𝑂𝑂 𝑂 + ∑𝑖=1 ∑𝑗=1 𝜌𝑟𝑖 𝑙𝑟𝑟𝑖𝑗 𝑒𝑟𝑗 (11-b) 𝑂 𝑂𝑃 𝑂 𝑛 ∑𝑚 ∑𝑚 ∑ + 𝑠≠𝑟 𝑖=1 𝑗=1 𝜌𝑟𝑖 𝑙𝑟𝑠𝑖𝑗 𝑒𝑠𝑗 (11-c) 𝑚 𝑂 𝑂𝑂 𝑂 ∑ + ∑𝑛𝑠≠𝑟 ∑𝑚 𝜌 𝑙 𝑒 (11-d) 𝑖=1 𝑗=1 𝑟𝑖 𝑟𝑠𝑖𝑗 𝑠𝑗

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As shown in the above equation, the total emissions in region 𝑟 induced by the national exports are comprised by four parts: the emissions generated 1) for producing local processing exports (𝐞𝑃𝑟 ) (11-a). 2) for producing local ordinary exports (𝐞𝑂𝑟 ) (11-b); 3) for producing intermediate inputs that are further used to produce processing exports in other regions (∑𝑛𝑠≠𝑟 𝐞𝑃𝑠 ) (11-c); 4) for producing intermediate inputs that are further used to produce ordinary exports in other regions (∑𝑛𝑠≠𝑟 𝐞𝑂𝑠 ) (11-d). Similarly, the national environmental effect of regional exports can be obtained by summing up summing up all the elements in 𝐐𝑟𝑠 over all region 𝑟 𝑂𝑃 ′ 𝑂𝑂 ′ 𝑛 𝑂𝑃 ′ 𝑛 𝑂𝑂 𝑞∙𝑠 = 𝐮′ (𝐐𝑃𝑃 𝑠𝑠 + 𝐐𝑠𝑠 )𝐮 + 𝐮 𝑄𝑠𝑠 𝑢 + 𝑢 (∑𝑟≠𝑠 𝑄𝑟𝑠 )𝑢 + 𝑢 (∑𝑟≠𝑠 𝑄𝑟𝑠 )𝑢 ′

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𝑃 𝑂 𝑂𝑂 𝑂 𝑛 𝑂′ 𝑂𝑃 𝑃 𝑛 𝑂′ 𝑂𝑂 𝑂 = (𝜌𝑠𝑃 𝑒𝑠𝑃 + 𝜌𝑠𝑂 𝐿𝑂𝑃 𝑠𝑠 𝑒𝑠 ) + 𝜌𝑟 𝐿𝑟𝑟 𝑒𝑟 + ∑𝑟≠𝑠 𝜌𝑟 𝐿𝑟𝑠 𝑒𝑠 + ∑𝑟≠𝑠 𝜌𝑟 𝐿𝑟𝑠 𝑒𝑠 𝑚 𝑃 𝑃 𝑚 𝑚 𝑂 𝑂𝑃 𝑃 = ∑𝑖=1 𝜌𝑠𝑖 𝑒𝑠𝑖 + ∑𝑖=1 ∑𝑗=1 𝜌𝑠𝑖 𝑙𝑠𝑠𝑖𝑗 𝑒𝑠𝑗 (12-a) 𝑚 𝑚 𝑂 𝑂𝑂 𝑂 = ∑𝑖=1 ∑𝑗=1 𝜌𝑠𝑖 𝑙𝑠𝑠𝑖𝑗 𝑒𝑠𝑗 (12-b) 𝑚 𝑚 𝑂 𝑂𝑃 𝑂 𝑛 = ∑𝑟≠𝑠 ∑𝑖=1 ∑𝑗=1 𝜌𝑟𝑖 𝑙𝑟𝑠𝑖𝑗 𝑒𝑠𝑗 (12-c) 𝑚 𝑚 𝑂 𝑂𝑂 𝑂 𝑛 = ∑𝑟≠𝑠 ∑𝑖=1 ∑𝑗=1 𝜌𝑟𝑖 𝑙𝑟𝑠𝑖𝑗 𝑒𝑠𝑗 (12-d)

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Expressions (12-a) and (12-b) show the emissions generated at local region for producing local processing and ordinary exports, respectively; (12-c) and (12-d) indicate the emissions generated in other domestic regions to produce the intermediate input that are necessary for production of processing and ordinary exports in region 𝑠. Similar to equation (8), the domestic environmental loss per unit of export in region s in IRIOP model can be immediately estimated as: 𝜃𝑠 = ∑𝑚

𝑞 .𝑠

𝑂 𝑃 𝑗=1(𝑒𝑠𝑗 +𝑒𝑠𝑗 )

(13)

At the disaggregate level, we can evaluate the national environmental loss per unit of regional processing export and ordinary export: 𝜃𝑠𝑃 = 𝜃𝑠𝑂 =

𝑛 𝑂𝑃 𝑂𝑃 𝐮′ (𝐐𝑃𝑃 𝑠𝑠 +𝐐𝑠𝑠 +∑𝑟≠𝑠 𝐐𝑟𝑠 )𝐮

(14)

𝑃 ∑𝑚 𝑗=1(𝑒𝑠𝑗 ) 𝑛 𝑂𝑂 𝐮′ (𝐐𝑂𝑂 𝑠𝑠 +∑𝑟≠𝑠 𝐐𝑟𝑠 )𝐮 𝑂 ∑𝑚 𝑗=1(𝑒𝑠𝑗 )

(15)

as well as the for each industry (similar as equation 9): 𝑃 𝜃𝑠𝑗 = 𝑂 𝜃𝑠𝑗 =

𝑛 𝑂𝑃 𝑂𝑃 {𝐮′ (𝐐𝑃𝑃 𝑠𝑠 +𝐐𝑠𝑠 +∑𝑟≠𝑠 𝐐𝑟𝑠 )}𝑗

(16)

𝑃 𝑒𝑠𝑗 𝑛 𝑂𝑂 {𝐮′ (𝐐𝑂𝑂 𝑠𝑠 +∑𝑟≠𝑠 𝐐𝑟𝑠 )}𝑗 𝑂 𝑒𝑠𝑗

(17)

Similar with equation (6), the emissions generated in industry i due to the national processing exports and national ordinary exports are respectively calculated as:

Journal Pre-proof 𝑃𝑃 𝑂𝑃 𝑃 𝑃 𝑚 𝑂 𝑂𝑃 𝑃 𝑛 𝑛 𝑛 𝑞𝑖∙𝑃 = ∑𝑛𝑟=1 𝑞𝑟𝑖 + ∑𝑛𝑟=1 ∑𝑛𝑠=1 ∑𝑚 𝑗=1 𝑞𝑟𝑠𝑖𝑗 = ∑𝑟=1 𝜌𝑟𝑖 𝑒𝑟𝑖 + ∑𝑟=1 ∑𝑠=1 ∑𝑗=1 𝜌𝑟𝑖 𝑙𝑠𝑠𝑖𝑗 𝑒𝑠𝑗

(18-a) 𝑞𝑖∙𝑂

=

𝑂𝑃 ∑𝑛𝑟=1 ∑𝑛𝑠=1 ∑𝑚 𝑗=1 𝑞𝑟𝑠𝑖𝑗

=

𝑂 𝑂𝑂 𝑂 ∑𝑛𝑟=1 ∑𝑛𝑠=1 ∑𝑚 𝑗=1 𝜌𝑟𝑖 𝑙𝑟𝑠𝑖𝑗 𝑒𝑠𝑗

(18-b)

3.3 The mathematical derivation of estimation bias by traditional models To distinguish these two model, we use letters with (/without) a star to indicate the variables in MRIO (/IRIOP) model. For instance, 𝐱 ∗ = (𝑥𝑗 )𝑚𝑛×1 indicates the output vector in MRIO model. We define 𝐂 = 𝐂 ∗ ⨂𝐈 = (𝑐𝑖𝑗 )𝑚𝑛×2𝑚𝑛 as the concordance matrix between these ∗ two models, where 𝐂 ∗ = (𝑐𝑖𝑗 )𝑚×2𝑚 is the simple aggregation matrix, whose column sum equals 1. For instance, for the multi-regional model with 2 regions (𝑛 = 2),𝐂 = [

1 1 0 0 ]. The 0 0 1 1

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relationships between these two models are 𝐱 ∗ = 𝐂𝐱, 𝐞∗ = 𝐂𝐞, 𝐞̂∗ = 𝐂𝐞̂𝐂′, 𝐙 ∗ = 𝐂𝐙𝐂′, 𝐀∗ = 𝐙 ∗ (𝐱̂ ∗ )−1 = 𝐂𝐙𝐂′(𝐱̂ ∗ )−1 = 𝐂𝐀(𝐱̂)𝐂′(𝐱̂ ∗ )−1 = 𝐂𝐀𝐖′ , where 𝐖 = (𝐱̂ ∗ )−1𝐂(𝐱̂) ̂∗ = 𝐖𝛒 ̂𝐂′ 𝛒∗ = 𝐖𝛒, 𝛒 ∗ ̅𝑟 and 𝛒 ̅𝑟 as the emission coefficient vector in region 𝑟 in MRIO and IRIOP Denote 𝛒 model respectively, where the elements corresponding to region 𝑟 are nonzero while other elements equal to 0. Then the bias of estimating emissions generated in region 𝑟 due to exports can be expressed as ̅′𝑟 (𝐈 − 𝐀)−1 𝐞 − 𝛒 ̅∗𝑟 ′ (𝐈 − 𝐀∗ )−1 𝐞∗ 𝑞𝑟. − 𝑏𝑟. = 𝛒 ̅′𝑟 (𝐈 − 𝐀)−1𝐞 − 𝛒 ̅′𝑟 𝐖 ′ (𝐈 − 𝐂𝐀𝐖 ′ )−1𝐂𝐞 =𝛒 ′ −1 ′ ̅𝑟 [(𝐈 − 𝐀) − 𝐖 𝐂(𝐈 − 𝐀𝐖′ 𝐂)−1]𝐞 =𝛒 (19) Please refer to Appendix C for the detailed derivation of equation (19). In general, the MRIO model overestimates the embodied emissions, since it treats the production structure of processing exports the same as ordinary exports. Equation (19) shows that the estimation biases by the two models are caused by three factors in the local region: emission coefficients, its intermediate inputs structure, and processing export share. The higher emission intensity gap between processing exports and ordinary exports and also the higher processing export share in one region, the more serious estimation bias in embodied emissions of exports is caused by the traditional model. 4. Data To study the emissions embodied in Chinese exports at both regional and industrial level, we take full advantage of the new IRIOP table compiled by Duan et al. (2014). The IRIOP tables include eight regions in China, cover 17 industries for each region for three years 2002, 2007, and 2012 (please see Appendix A for the region and industry definition). The compilation of IRIOP tables are based on two types of widely used IO tables, that is, the bipartite tables compiled by Chinese Academy of Sciences (CAS) and China’s National Bureau of Statistics (NBS), and the interregional input-output (MRIO) tables, compiled by State Information Center (SIC) and NBS. The bipartite tables separate the production of processing exports from other productions in national input-output tables. Differently, the MRIO tables provide the inter-regional and inter-industry production linkages across different regions but without distinguishing processing exports from other productions. Duan et al. (2014) further constructed the IRIOP tables by nesting the MRIO within the bipartite tables6. In the process, other statistics, such as China’s 6

Processing exports in our tables includes two types of export regimes: ‘Processing & Assembly’ (P&A) exports and ‘Processing with Imported Materials’ (PIM) exports. P&A imports are materials that are owned by foreign

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Customs statistics and Regional Economic Accounts from the NBS, are thoroughly used to ensure IRIOP tables balanced and consistent with the official statistics7. The CO2 emission data by region, by industry and by production types is another necessary data for our estimation. However, such data is not available for now, and therefore is required to be estimated first. To this end, three steps are successively conducted. First, we estimate the province-industry-wise energy consumption data. In this respect, the data from China Energy Statistical Yearbook (CESY, 2013) and Province Statistical Yearbook (PSY) are thoroughly adopted. The CESY released sectoral energy consumption data in physical units based on the coal equivalent calculation method by 30 energy types8 at both national level and provincial level. However, the provincial energy use data only distinguishes 6 industry groups (1 agriculture industry, 1 manufacturing industry group, 1 construction, 2 service industry groups, and 1 others). To be consistent with the industry classification in our IRIOP tables, the energy use data for manufacturing requires a further disaggregation into 39 detailed sub-industries. This aligns with the “Data Treatment Scheme 2” suggested by Su et al. (2010) and Lenzen (2011). They have shown that when estimating the CO2 emission at sector level, disaggregating the energy data into a more detailed level to match the I-O data is superior to aggregating the I-O data to match the energy data. This is due to that the latter approach would lead to large information loss in the I-O tables. The literature about disaggregating energy data across sectors or production types can be generally classified into two strands. The first strand of literature realized it by distributing energy use among sub-industries according to their relative sizes in each industry group (Su and Ang, 2010; Su et al., 2010; Su et al., 2013). The second strand of literature, however, is based on the energy consumption shares of sub-industries in each industry group (for instance, Jiang et al., 2016). Since the energy consumption is always the mail culprit for the emissions, the second approach can provide more accurate estimation on sub-industries’ emission and thus is adopted in this study. To this end, the data from the PSY is used, which provides the energy use data of different fuel type for 39 manufacturing industries in each province. We employed them to proportionally disaggregate the energy use from 1 board industry group into 39 manufacturing sub-industries. companies and that are supplied to Chinese enterprises to produce P&A exports. The national IO tables record only the processing fees and not the P&A imports. Based on China Customs’ statistics, the trade flows in IRIOP tables include both P&A imports and processing fee. By doing this, all imports (including P&A imports) used to produce processing exports are recorded as intermediate inputs, and this can clearly reflect the underlying technology of the processing sector. 7 In more detail, processing exports in our tables include two types of export regimes: ‘Processing & Assembly’ (P&A) exports and ‘Processing with Imported Materials’ (PIM) exports. P&A imports are materials that are owned by foreign companies and that are supplied to Chinese enterprises to produce P&A exports. The P&A trade involves processing and assembly activities for which the Chinese enterprises receive a processing fee. The national IO tables record just these processing fees and not the P&A imports. Fortunately, China Customs’ statistics provide us with both P&A imports, and P&A exports at both regional level and HS_8 level. This allows us to adjust the trade flows and also the output in our IRIOP tables to include both P&A imports and processing fee. We also remove the processing fee from the service exports. As a result, the trade data in our IRIOP tables are consistent with the trade statistics from China’s customs. By doing this, all imports (including P&A imports) used to produce processing exports are recorded as intermediate inputs, and this can clearly reflect the underlying technology of the processing sector. For a full exposition of the estimation procedure the IRIOP tables, please refer to Duan et al. (2014). 8 The CESY publishes the total energy use of 30 different fuel types. They are raw coal, cleaned coal, other washed coal, briquettes, gangue, coke, coke oven gas, blast furnace gas, converter gas, other gas, other coking products, crude oil, gasoline, kerosene, diesel oil, fuel oil, naphtha, lubricants, paraffin waxes, white spirit, bitumen asphalt, petroleum coke, LPG, refinery gas, petroleum products, natural gas, LNG, heat, electricity, and other energy.

Journal Pre-proof ℎ That is, if we let 𝜀̅𝑟𝑚 indicate the ℎ-th (ℎ = 1, ⋯ ,20)energy use by the broad manufacturing ℎ industry in province 𝑟 from the CESY and 𝜀̃𝑟𝑗 indicate the ℎ-th energy use by the detailed

manufacturing sub-industry 𝑗 in province 𝑟 from the PSY, then our 39 sub-industry energy use ℎ in each province is formulated as 𝜀𝑟𝑗 =∑

ℎ 𝜀̃𝑟𝑗 𝑗

ℎ ℎ 𝜀̅𝑟𝑗 . 𝜀̃𝑟𝑗

Finally, we obtain the energy use data on 20

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different fuel types9 by 30 provinces and by 43 industries for the years of 2002, 2007, and 2012. (Please refer to Appendix B for more details). Second, we estimate the CO2 emissions data for 43 industries in each province. To do this, we follow the estimation procedure in Intergovernmental Panel on Climate Change guidelines (IPCC, 2006), Peters et al. (2006) and Guan et al. (2012), in which the emission includes three main parts, emissions from the energy combustion, processing emission, and emissions from international bunkers. The first part, that is emissions from the energy combustion, can be obtained from the following formula for each industry. ℎ ℎ ℎ ℎ ℎ {𝑐𝑜𝑛𝑏𝑢𝑠𝑡𝑖𝑜𝑛𝐶𝑂2 }𝑖 = ∑20 (20) ℎ=1 𝐸𝑖 × 𝑉𝑖 × 𝐹𝑖 × 𝑀𝑖 × 𝑂𝑖 where 𝐸 is the total physical amount of energy use from different fuel types (obtained from step 1 adjusted by energy loss, non-energy use, and energy transformation); 𝑉 is the Chinese specific Low-calorific Value of different fuel types (Unit: MJ/(tn,m3,kwh,tce)); 𝐹 is the emission factors of different fuel types, i.e., the carbon content in one unit of calorific value; 𝑀 is the molecular weight ratio, which is constant and equals 44/12=3.66 mt of 𝐶𝑂2 per mt of C; 𝑂 is the Chinese specific Oxidization rate, i.e. the fraction of carbon that is oxidized. The values of 𝑉 and 𝐹 are the same across different industries, but different across different fuel types, while the values of 𝑂 are different across different industries and fuel types. The values for 𝑉, 𝐹, and 𝑂 are obtained from the Liu et al. (2015) and Shan et al. (2017), which report the updated China’s specific emission factors according to the survey on China’s fossil fuel quality and cement process. These new emission factors are more accurate than the historical default values from other official reports (such as IPCC). The second part is the process emissions10. For example, the raw materials used in the production process of cement may decompose and generate CO2 emissions. The process emissions are obtained by multiplying the process emission factor (IPCC, 2006) and volume of productions (Chinese Statistical Yearbook, CSY, 2013) that generate emissions. The third part is the CO2 emissions from international bunkers. According to Peters (2006), we made adjustment of transport industry by adding the fuel purchased by Chinese airplanes and ships in refueling abroad and deducting the fuels by foreign airplanes and ships in refueling in 9

Comparing previous CEY, the CEY in 2012 has a more detailed energy classification and adds 10 new energy types. They are gangue, blast furnace gas, converter gas, naphtha, lubricants, paraffin waxes, white spirit, bitumen asphalt, petroleum coke and LNG. However, since the data on the Chinese specific Low-calorific Value (V) and emission factors (F) are only available for the old classification (20 energy types), we transform the 30 energy types from CEY into 20 energy types by aggregating the new energy type into the existing similar energy type. aggregate gangue into briquettes; aggregate blast furnace gas and converter gas into other gas; aggregate naphtha, lubricants, paraffin waxes, white spirit, bitumen asphalt, petroleum coke into other petroleum products; LNG into natural gas. 10 The process emissions are generated during the production process for Raw chemicals (Ammonia, Carbides, Petrol checmicals, Soda Ash), Nonmetal Mineral Products (Cement, Lime, Road Paving (Asphalt)), Smelting and Pressing of Ferrous Metals (Iron, Ferrochromium, Ferrochromium-silicon, Silicon metal, Ferro-unclassified, Coke as a reducing agent) and Smelting and Pressing of Nonferrous Metals (Coke as a reducing agent).

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China (CESY, 2013). Finally, we aggregated the emissions data (43 industries and 30 provinces in total) into 17 industries and 8 regions, to be consistent with the classifications in the IRIOP tables (See table A2). Next, we still require to split the region-industry-specific emissions into the emissions for different production types, that is, the processing production and the ordinary production. To this end, three different methods are used in the literature. The first one (as used in Su et al., 2013; Su and Thomson, 2016) takes the sectoral CO2 intensity of three presentative processing trade provinces (Guangdong, Fujian, and Jiangsu Provinces) as the emission intensity of processing exports. However, they ignored the heterogeneity of emission intensity for processing exports across regions. The second method (as used in Dietzenbacher et al., 2012) estimates the emission use of different production types according to their different reliance on local intermediate inputs. Differently, Jiang et al, (2015, 2016)(the third method) uses the energy intermediate inputs by different production types to allocate the energy use across them. Then the emissions by ownership and trade mode are further estimated by multiplying the energy use by the corresponding emission factors. In order to take full advantage of the available information and the information of energy intermediate inputs by trade mode, we combine the second and third method. In more detail, the emission coefficients are estimated according to the degree of each production type relying on the energy intermediate inputs (the energy-related industries included in IRIOP table are industry 2, mining; industry 7, chemical products, and industry 14, electricity, gas, and water supply). This is reasonable since fewer energy inputs imply fewer emissions during the production process. Based on the IRIOP table, the energy intermediate inputs for processing exports and ordinary production for each industry in region s are MP OO MO calculated as τPs = ∑r u′AOP and τO rs + u′As s = ∑r u′Ars + u′As , respectively (where u is a summation vector, with the elements corresponding to energy-related industries as 1 and other elements as 0, AMP and AMO are the imported intermediate input coefficient matrix for s s processing and ordinary production in region s, respectively). Meanwhile, the total energy intermediate inputs in industry j in region s can be obtained as τs = ∑r u′Ars + u′AMs, based on the IRIO table. Then the emission coefficient for industry j for each production type can be estimated as ρPs,j =

τP s,j τsj

ρsj , ρO s,j =

τO s,j τsj

ρsj

(21)

Note that these type-specific emission coefficients still yield the correct total emissions in each industry, that is, P O s s ρPsj xsj + ρO (22) sj xsj = ρj xj 5. Results 5.1 Regional emissions due to national exports We start this section with the stylized facts of regional emissions induced by China’s national exports. More specifically, equation (11) allows us to calculate the total emissions generated in each region for China’s national export production by production types (processing exports, and ordinary exports) based on the IRIOP tables. Tables 3-a and 3-b present the results respectively for 2002 and 2012. The bottom row of table 3 provides the national total emissions generated by national exports, which are arrived by summing all regional results together. As a comparison, the right panel of table 3 also displays the estimation of the national as well as the regional emissions based on MRIO model (equation 5). Several interesting findings are observed.

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Insert Table 3a here Insert Table 3b here First, the ordinary exports generate much more emissions than that of equivalent value of processing exports. For example, in 2012, the national exports generate1581Mt CO2 in total, 81% of which are emitted for production of ordinary exports. This is consistent with Su and Thomson (2016), in which the estimated China’s CO2 emissions embodied in exports are 1555Mt and 80% of them are emitted for ordinary exports. Recall that ordinary exports account for only 60% of the total exports, the results imply a much smaller emission intensity of processing exports compared with that of ordinary exports, which is consistent with the findings of Dietzenbacher et al. (2012). Second, three coastal regions suffer most from Chinese exports’ production in terms of environmental loss. In 2012, total domestic emissions for the export production are 1581Mt, 55% of which are generated in three coastal regions. In relative terms, the share of regional emission generated by national exports in total regional emissions varies considerably across regions. The largest share is found in South Coast, which is followed by East Coast, Northern Municipalities and North Coast. The smallest ratio is found in Southwest. For example, in 2012, almost one third emissions generated in South Coast and East Coast are due to national exports, while this ratio is only 11% for Southwest. This result is closely related with the regional export volumes. As calculated from the IRIOP table, more than 70% of processing exports and more than 60% of ordinary exports are concentrated in South Coast and East Coast, followed by Northern Municipalities and North Coast. Therefore, emission for exports is one of their major emission sources. Considering that the emissions for export production are generated for satisfying the final demand in other countries, the mitigation responsibility of this part of emissions should not be totally born by the emitting regions under the consumption-based principle. By production types, emissions generated by ordinary exports are mainly concentrated in the three coastal regions (South Coast, East Coast and North Coast). For example, among the 1275Mt CO2 emissions for ordinary exports in 2012, 20% are generated in East coast, followed by North Coast (17%) and South Coast (16%), while emissions generated in Northern Municipalities are the least (2%). This is driven by the fact that coastal regions account for around 70% of the total ordinary exports. Due to the geographical advantage, coastal regions have actively participated in the global fragmentation since the reform and open policy in 1978. For the production of processing exports, the same distribution pattern of emissions is found as for the production of ordinary exports: East Coast is also the most influenced in terms of environmental loss, followed by North Coast, while Northern Municipalities suffer the least. Furthermore, another interesting finding is that North Coast surpassed South Coast as the second largest emitter of CO2 emissions for both processing and ordinary exports, although South Coast exports much more goods than North Coast. The possible reasons are as follows. As seen from Figure 1, the emission intensity in North Coast is almost twice as that in South Coast. Meanwhile, although North Coast is a coastal region, its main products are concentrated in primary industry and require fewer intermediates from other regions (Meng et al, 2013). Therefore, emissions generated by exports of North Coast are mainly occurred in local region. Although Northern Municipalities is ranked in the third place according to the scale of ordinary exports, it generated the fewest emissions. This is highly related to the fact that the emission intensity in Northern Municipalities is the lowest. At the same time, with relatively small economy size, Northern Municipalities import most of the intermediate inputs from outside. This

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can be revealed from Figure 1 that the difference between the direct emission intensity and embodied emission intensity for regional exports in Northern Municipalities is the highest in 2012. According to equation (11), emissions generated in each region for export production depend on several factors such as (1) economy size; (2) region’s position in domestic supply chain; (3) regional industrial structure; (4) CO2 emission efficiency (Meng, et al, 2013). Therefore, except for improving the energy efficiency, regions could also reduce emissions via economic restructuring or upgrading the domestic value chain. On the other side of the coin, it is also interesting to evaluate the national emissions generated by per unit of exports in each region, to remove the effect of export scale. The results can reveal the insights about how green is the each region’s production and thus indicate the key region and sector that have the high emission reduction potentials. We will analyze the results in detail in section 5.3. Insert Figure 1 here 5.2 The bias in estimating environmental loss in each region due to national exports using different models Dietzenbacher et al. (2012) documented that the environmental loss due to exports would be overestimated when processing exports were not distinguished at national level. We move one step further and evaluate how the bias will be when processing exports are not distinguished at regional level. Based on the estimation bias expressed in mathematical forms in Section 3.3, this section discusses in detail about the empirical results which are summarized in the last column of Table 3. The results in table 3 show that MRIO model overestimates the export-related emissions by 21% (/16%), compared with the IRIOP model at national level in 2002 (/2012). The reason behind this is that processing exports only account for a very limited proportion in total outputs, and therefore when combining processing exports, ordinary exports, and domestic sales together, the average emission coefficients are very similar to those of ordinary productions (including both ordinary exports and domestic sales). In the end, the MRIO tables overestimate the emission related with that of processing exports. Su et al. (2013) found a drop of 32% in embodied emissions in 1997 once the processing trade is distinguished in the single-region input-output (SRIO) model; while Dietzenbacher et al. (2012) showed a drop of 40% in embodied emissions in 2002. This shows that the overestimation of ordinary model is higher in the context of SRIO than MRIO, which reveals the fact that regions with pollution-intensive production technology has lower share of processing exports (Figure 2). When the production structure is not distinguished among regions, the overestimation of emissions in coastal regions is higher than the underestimation of emissions in inland regions. Insert Figure 2 here Table 3 shows that the relative bias at regional level is in the range of 14%-25% in 2002 (7%-20% in 2012), with the highest in South Coast and the lowest in Northern Municipalities. It seems that the processing export share (as shown in Figure 2) is the main explanation for the bias in 2002, as the highest estimation bias is found in regions with the highest processing export shares (South Coast, East Coast, Northeast, and North Coast). Second, when the share of processing exports in coastal regions decreased in 2012, the estimation bias in these regions also decreased. In this case, the estimation bias in Central region and Southwest ranked second and third, due to their high emission intensity (as shown in Figure 1) and similar processing exports

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share as that in other regions (except to South Coast). Third, the relative estimation bias in Northern Municipalities is the smallest, because of its low emission intensity. Figure 1 illustrates that the emission intensity in Northwest is 7 times as that in Northern Municipalities. The results reveal that due to interregional dependence, trade heterogeneity not only has an impact on the estimation of embodied emissions in coastal regions where the processing export share is extremely high, but also influence those in inland regions. In this way, the IRIOP model can reveal the true picture of environmental consequence of trade. This is especially important for allocating the emissions reduction responsibilities under the consumption-based principle. Besides that, IRIOP model is also necessary to accurately evaluate the emission reduction performance of each region: to find out whether the emissions reductions are due to the production efficiency improvement or due to the increase share in processing exports. At sectoral level (Figure 3), several interesting findings are observed. First of all, for the production of ordinary exports, the emissions emitted by electricity, gas and water supply are the largest, accounting for more than half of the total domestic emissions induced by ordinary exports in 2012. The Trade and Transport industry ranked the second, followed by Non-metallic mineral products and Chemical products. The emissions emitted by these top four industries account for 83% for the emissions due to ordinary exports. This indicates that improving the emission efficiency in these four industries is essential for China’s emission reduction. Moreover, the top two industries that emitted most for the production of processing exports are Electricity, gas, and water supply and Chemical products, and they can explain more than half of the total emissions due to processing exports. This reveals again the high emission reduction potential in these two industries. Finally, when we compare the results obtained from the two models, large estimation biases are found in industries such as Electricity, gas and water supply, Metal products, Non-metallic mineral products and Chemical products in 2002 (Electricity, gas and water supply, Chemical products, Non-metallic mineral products, and Mining products in 2012). The reason behind this is the much higher emission coefficients in ordinary exports than those in their processing exports. Thus, when processing exports are treated as ordinary exports in MRIO model, the emissions generated in these industries for exports are overestimated the most. Chinese Central government announced to establish the national emission trading scheme in 2017 and include the eight emission-intensive industries (including Electricity, gas, and water supply, Chemical products, Non-metallic mineral products, Mining products, etc.) in the first round. The emission permits in these industries will be allocated according to their historical emissions. Under this background, the IRIOP model is of great importance for accurately accounting of emissions inventories. Insert Figure 3 here 5.3 Bias in estimating carbon emissions per unit of exports In Table 3, one of the findings is that the volume of processing exports is 2/3 of ordinary exports, while the emissions associated with processing exports is only 20% of those associated with ordinary exports. In this section, we investigate in detail the export volume and national environmental loss associated with each export type at industrial and regional level and at the same time, compare the different results obtained from these two models. The results can generate insights about how green is each region’s production and exports and then reveal the key region and industry that have high emission reduction potential.

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According to equations (8) and (13), we calculated the national environmental loss per unit of regional exports for each region, using MRIO and IRIOP model, respectively. The results are displayed in Figure 4. Furthermore, the environmental loss per unit of exports for each region-industry combination is estimated based on equations (16) and (17) (Table 4). As a comparison, the environmental loss per unit of exports for each region-industry combination (by using MRIO model, equations 8 and 9) is also summarized in Table 5. Insert Figure 4 here Insert Table 4 here Insert Table 5 here There are several interesting findings in Figure 4 and Tables 4-5. For example, per unit of export production in inland regions basically generates much more national emissions than that in Coastal regions (with North Coast as an exception, Figure 4). The largest environmental loss per unit of products is found in Northwest (0.25), which is followed by Southwest (0.19), North Coast (0.17), Central Region (0.15) and Northeast (0.15). It reveals that the export production is generally dirtier in inland regions than in coastal regions and thus the potential to reduce emissions in these regions is relatively high. This is not only due to the high emission-intensive export composition, but also because of the high emission coefficient in each sector in inland regions (Table 4). The national environmental loss per unit of exports in Northern Municipalities is the smallest, which is around 1/4 as that of Northwest. The export production in East Coast and South Coast are also relatively clean. However, though as coastal region, North Coast’s export production generates more emissions than other coastal regions. This can be explained by the fact that the exports by North Coast are mainly primary products, which require less imported inputs. Central government should establish relevant policies to help inland regions to improve the energy efficiency and promote the technology transfer from coastal regions to inland regions. At the same time, inland regions should also launch initiatives to defer the displacement of emission-intensive industries from coastal regions. Another interesting finding is that when comparing the national environmental loss per unit of ordinary exports and processing exports across regions (Figure 4), we found that the national emissions generated per yuan of processing exports are smaller than ordinary exports. In general, the environmental loss per unit of processing exports is less than half of that by ordinary exports in each region. The environmental loss of processing exports in Southwest is even 0, implying all the inputs for processing exports in Southwest are imported and thus generating zero emissions at home. Therefore, from the perspective of reducing emissions, processing exports are preferred to ordinary ones. Figure 4 also shows that the emissions per unit of regional exports is overestimated by MRIO model, ranging from 2%-25%. This is caused by the fact that MRIO model overestimates the domestic emissions associated with each region’s exports. The biggest relative bias is found for South Coast (MRIO overestimates the environmental loss by 25%), where the emissions generated per unit of exports obtained from IRIOP model is 0.08 ton/1000yuan, whereas the number is 0.10 according to the MRIO model. This is mainly caused by the high share of processing exports in South Coast, which amounts to 51% of the total export in South Coast (Figure 2). The relative estimation bias in Central Region follows next, due to the larger difference of environmental loss between processing export and ordinary export (0.04 vs. 0.21 ton/1000yuan) and relatively large processing export share in total exports in Central region

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(31%). Because of its low processing export share in Northwest, the estimation bias in Northwest is the second lowest, despite of the large difference of environmental loss between processing exports and ordinary exports. Therefore, the higher processing exports share and higher difference in environmental loss between processing and ordinary exports in one region, the larger estimation bias will be when the processing exports are not distinguished in MRIO tables. Evaluating the environmental loss per unit of product is crucial for Chinese policy orientation nowadays. Since China is still at the stage of highly developing, it would like to achieve the dual goal of both “economic growth” and “emission reduction”. For instance, Chinese government promised in Paris Agreement to reduce CO2 emission per unit of GDP in 2030 by 60-65% comparing to the level in 2005. IRIOP model is a more appropriate tool to address the relevant issues. At the sectoral level (Table 4), the environmental loss for exports of electricity, gas and water supply is extremely large (larger than 1, except for Northern Municipalities with 0.85), while the value for most industries are smaller than 0.5. This indicates that emissions generated for each yuan of exports in electricity, gas and water supply is at least twice as those due to exports of other industries. The result for Non-metallic mineral products, Mining, Construction, and Metal Products follow next. And the rankings are similar across regions. When comparing the environmental loss obtained by IRIOP and MRIO table at sectoral level (Table 4 and Table 5), we found that MRIO model generally overestimates the environmental loss in most industries, except for Agriculture, Mining, Construction, Trade and Transport, and Other Services. This is because that the processing exports in these industries are close to zero, the estimation biases by two models are negligible. The largest estimation bias is found in Other Manufacturing (the MRIO model overestimate the environmental loss of Other Manufacturing by 49% across regions), followed by Electronic Products (44%) and Paper and Printing (36%). The results are consistent with intuition that these manufacturing industries are more fragmented with high processing export share. 6. Conclusion By taking full consideration of China’s vast regional disparity and trade regime heterogeneity, we analyzed the regional CO2 emissions due to national exports as well as the national environmental loss per regional exports by using the newly developed Bipartite Inter-Regional Input-Output (IRIOP) table as well as the traditional Inter-Regional Input-Output (MRIO) table. Three interesting findings are concluded. First, we found that traditional MRIO model overestimates the export-related emissions by 21% (/16%), compared with the IRIOP model at national level in 2002 (/2012). The relative bias at regional level is in the range of 14%-25% in 2002 (7%-20% in 2012), with the highest bias found in South Coast and the lowest in Northern Municipalities. Accurate accounting for the emissions embodied in trade is important to allocate the emission reduction responsibilities and emission permit under the emissions trading scheme. Our empirical results prove that the IRIOP model can provide a more accurate accounting on the regional environmental loss due to the exports, which is important to address relevant issues. Furthermore, the estimated CO2 emissions in other regions caused by the final demand in one region (or inter-regional carbon leakage) in IRIOP model are higher than those in IRIO model. This reveals that the introduction of processing trade in IRIO model enhances the feedback effects among regions. Meanwhile, the largest bias of estimated inter-regional carbon leakage is

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found for coastal regions (including South Coast, East Coast, and North Coast). Both the bias of carbon leakage from coastal regions to other regions and the bias of carbon leakage from other regions to coastal regions are the largest. This confirms that the estimation biases are larger for regions with higher processing exports share and it is of great importance to determine the emission responsibility across regions by using the IRIOP model. Second, in general, emissions generated per unit of processing exports are much lower than those of ordinary exports (0.06 versus 0.15 ton/1000yuan). This is due to the fact that a majority of the inputs for processing exports are imported, thus less emissions are generated at home as comparing to the production of ordinary exports. At regional level, coastal regions suffer the most from national exports in terms of emissions (more than 55% of the emissions for producing exports are generated in coastal regions), followed by Central Region. The regional emission disparity due to national exports narrows down when the processing exports are distinguished. Third, for the environmental loss per unit of ordinary export, more emissions are generated in inland regions for each yuan of ordinary exports. The exports in most industries by Northwest are relatively “dirtier” than exports by other regions. In contrast, the exports by Northern Municipalities are the “cleanest”. When comparing the results obtained from two models, the MRIO model will overestimate the environmental loss per unit of exports in South Coast and Other Manufacturing industry the most. Results indicate that the higher the processing export share in one region (or industry), the higher estimation bias will be when MRIO model is used. Finally, our results point to three type of policy recommendations. First, it is necessary to distinguish the processing trade from the ordinary trade at regional level when making relevant policies. Figure 4 shows that the environmental loss per unit of processing exports is less than half of that by ordinary exports. The difference between the environmental losses of different production types varies across regions, with the highest difference found in inland regions (Southwest, Northwest, and Central Region). From the perspective of emission reduction, processing exports could be a priority in these regions. The second policy recommendation would be to stimulate the technological transfer from coastal regions to inland regions. As the carbon leakages from coastal regions to inland regions are higher than the other way around, inland regions pay environmental cost for coastal regions’ production. Since inland regions are at the early stage of development, their production technology is not as advanced as coastal regions and their relative comparative advantage lies in emission-intensive industries. In this aspect, coastal regions should provide subsidies and technological supports to inland regions to promote their clean technology and renewable energy development. Lastly, policies should be differentiated across regions. Our results show that inland regions generally have higher environmental loss per unit of exports, comparing to that in coastal regions. This on one hand is related with the energy-intensive export structure in inland regions, on the other hand, is caused by the dirty production technology in these regions. Thus, when the central government sets emission reduction target for each region, the principle of “common but differentiate responsibility” should be considered. The analysis is not limited to China and the same method can be generalized to other countries with high share of processing exports and vast regional disparity, such as Mexico. The large estimation biases of embodied emissions across regions in China by using the traditional MRIO model might be found in other countries as well. The analysis could contribute to accurately accounting for the embodied emissions and providing support to establish effective

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emission reduction policies.

Journal Pre-proof Appendix B

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The basic idea is that we proportionally disaggregate the energy use (of 20 different fuel types) of one broad manufacturing sector (obtained from CESY) into 39 manufacturing industries based on the energy consumption share of each manufacturing industries in the broad manufacturing sector, which is obtained from PSY. However, for some provinces, the energy uses for the manufacturing industries only include data for primary consumed fuels or total energy consumption. For instance, the Statistical Yearbook of Hubei province only includes data for raw coal, gasoline, Diesel Oil and electricity. Besides, the energy consumption data of different fuel types for industrial sectors of some provinces are lacking. This is case (in year 2012) for Heibei, Jiangsu, Zhejiang, Shanghai, Shangdong, Guangxi, Hainan, Chongqing, Sichuan, Qinghai and Guizhou provinces. For these provinces, we use the intermediate input (from Coal mining and washing; Oil and natural gas extraction; Petroleum processing, coking and nuclear fuel processing; Electricity, heat production and supply; Gas production and supply) share of each manufacturing industry in the broad manufacturing sector (using each province’s provincial input-output table from National Statistics Bureau) to proportionally disaggregate the corresponding energy use of the broad manufacturing sector into sub-sectors.

Journal Pre-proof Appendix C

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To derive equation (19), we follow a similar method used in Su et al. (2010) and use the power series expansion of matrix inverse (Miller and Blair, 2009): ̅∗𝑟 ′ (𝐈 − 𝐀∗ )−1 𝐞∗ − 𝛒 ̅𝒓 ′(𝐈 − 𝐀)−1 𝐞 = 𝛒 ̅∗𝑟 ′ (𝐈 − 𝐀∗ )−1𝒆∗ − 𝛒 ̅∗𝑟 ′ 𝐖′ (𝐈 − 𝐂𝐀∗ 𝐖′ )−1 𝐂𝐞∗ 𝐷𝐶 = 𝛒 ̅∗𝑟 ′ (𝐈 − 𝐀∗ )−1 𝐞∗ − 𝛒 ̅∗𝑟 ′ 𝐖′ (𝐈 + 𝐂𝐀∗ 𝐖′ + (𝐂𝐀∗ 𝐖′ )2 + ⋯ )𝐂𝐞∗ =𝛒 ̅∗𝑟 ′ (𝐈 − 𝐀∗ )−1 𝐞∗ − 𝛒 ̅∗𝑟 ′ 𝐖′ (𝐂 + 𝐂𝐀∗ 𝐖′ 𝐂 + (𝐂𝐀∗ 𝐖′ )2 𝐂 + ⋯ )𝐞∗ =𝛒 ′ ̅∗𝑟 (𝐈 − 𝐀∗ )−1 𝐞∗ − 𝛒 ̅∗𝑟 ′ 𝐖′ 𝐂(𝐈 + 𝐀∗ 𝐖′ 𝐂 + (𝐀∗ 𝐖′ 𝐂)2 + ⋯ )𝐞∗ =𝛒 ̅∗𝑟 ′ [(𝐈 − 𝐀∗ )−1 − 𝐖 ′ 𝐂(𝐈 − 𝐀∗ 𝐖′ 𝐂)−1 ]𝐞∗ =𝛒

Journal Pre-proof Appendix D Proof of Equation (22) From Table 1 and Figure 2, we have 𝑂𝑃 𝑂𝑂 ∑ 𝐙 𝑟𝑠 + 𝐙 𝑀𝑠 = (∑ 𝐙𝑟𝑠 + 𝐙𝑠𝑀𝑃 ) + (∑ 𝐙𝑟𝑠 + 𝐙𝑠𝑀𝑂 ) 𝑟

𝑟

𝑟

and 𝑂𝑃 𝑂𝑂 〈𝐮′(∑ 𝐙 𝑟𝑠 + 𝐙 𝑀𝑠 )〉 = 〈𝐮′(∑ 𝐙𝑟𝑠 + 𝐙𝑠𝑀𝑃 )〉 + 〈𝐮′(∑ 𝐙𝑟𝑠 + 𝐙𝑠𝑀𝑂 )〉 𝑟

𝑟

𝑟

where 𝐮 is a summation vector, with the elements corresponding to energy-related industries as 1 and other elements as 0, 〈𝐠〉 indicates the diagonal matrix obtained from the vector 𝐠. Since 𝑂 𝑂𝑃 𝑂𝑂 𝑂𝑂 ̂ 𝑀𝑠 𝑀𝑃 𝑀𝑂 ̂𝑃 ̂𝑃 ̂𝑠 , 𝐙𝑟𝑠 ̂𝑠 , 𝐙𝑟𝑠 𝐙 𝑟𝑠 = 𝐀𝑟𝑠 𝐱 = 𝐀𝑂𝑃 = 𝐀𝑀𝑠 𝐱 = 𝐀𝑀𝑃 𝑟𝑠 𝐱 𝑠 , 𝐙𝑟𝑠 = 𝐀 𝑟𝑠 𝐱 𝑠 , and 𝐙 𝑟𝑠 𝐱 𝑠 , 𝐙𝑟𝑠 = 𝑂 𝐀𝑀𝑂 𝐱̂ we have 𝑠

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𝑟𝑠

𝑂 𝑀𝑃 ̂ 𝑂𝑂 𝑀𝑂 ̂ 𝑃 ̂𝑠 = 〈𝐮′(∑ 𝐀𝑂𝑃 〈𝐮′(∑ 𝐀𝑟𝑠 + 𝐀𝑀𝑠 )〉 𝐱 𝑟𝑠 + 𝐀 𝑠 )〉 𝐱 𝑠 + 〈𝐮′(∑ 𝐀 𝑟𝑠 + 𝐀 𝑠 )〉 𝐱 𝑠 𝑟

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𝑟

𝑟

𝑀𝐵 −1 𝑟𝑠 𝑀𝑠 According to equation (21), = 𝛒̂𝐵𝑟 〈𝐮′(∑𝑟 𝐀𝑂𝐵 𝑟𝑠 + 𝐀 𝑠 )〉 〈𝐮′(∑𝑟 𝐀 + 𝐀 )〉 , with ̂𝑟 〈𝐮′(∑𝑟 𝐀𝑟𝑠 + 𝐀𝑀𝑠 )〉−1 and 𝐵 = 𝑃, 𝑂. Pre-multiplying both side of the above equation with 𝛒 𝑀𝐵 −1 ̂𝑟 〈𝐮′(∑𝑟 𝐀𝑟𝑠 + 𝐀𝑀𝑠 )〉−1 = 𝛒̂𝐵𝑟 〈𝐮′(∑𝑟 𝐀𝑂𝐵 using 𝛒 𝑟𝑠 + 𝐀 𝑠 )〉 , we can get ̂𝑠 𝐱 ̂𝑠 = 𝛒̂𝑃 𝐱̂𝑃 + 𝛒̂𝑂 𝐱̂𝑂 𝛒 𝑠

𝑠

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which completes the proof.

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̂𝑟 𝛒

𝑠

𝑠

Journal Pre-proof References: Ahmad, N., Wyckoff, A., 2003. Carbon dioxide emissions embodied in international trade. OECD Sti Working Paper Dsti/doc, 25(4), 1-22. Chen, X., Cheng, L. K., Fung, K. C., Lau, L. J., Sung, Y. W., Zhu, K., Yang, C., Pei, J., Duan, Y., 2012. Domestic value added and employment generated by Chinese exports: a quantitative estimation. China Economic Review 23(4), 850-864. Dietzenbacher, E., Pei, J., Yang, C., 2012. Trade, production fragmentation, and china's carbon dioxide emissions. Journal of Environmental Economics Management 64(1),

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88-101. Duan, C., Chen, B., Feng, K., Liu, Z., Hayat, T., Alsaedi, A., Ahmad, B., 2018. Interregional

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carbon flows of China. Applied Energy 227, 342-352.

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Duan, Y., Dietzenbacher, E., Los, B., Yang, C., 2014. A new interregional input output table for China: construction and application. 22th International Input-Output Conference,

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Outsourcing CO2 within China. Proceedings of the National Academy of Sciences of the United States of America 110(28), 11654-11659.

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Journal Pre-proof Jiang, X., Duan, Y., Green, C., 2017. Regional disparity in energy intensity of china and the role of industrial and export structure. Resources Conservation Recycling 120, 209-218. Koopman, R., Wang, Z., Wei, S. J., 2012. Estimating domestic content in exports when processing trade is pervasive. Journal of Development Economics 99(1), 178-189. Lenzen, M., 2011. Aggregation versus disaggregation in input–output analysis of the environment. Economic Systems Research 23(1), 73-89. Liddle, B., 2018. Consumption-based accounting and the trade-carbon emissions nexus. Energy Economics 69, 71-78.

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Journal Pre-proof Table 1 A simplified MRIO with 𝑛 regions and 𝑚 sectors Final use (1 columns per region)

Region Region 1 n

Total

Region 1

…

𝑚 industries, region 1

𝐙11

⋯

𝐙1𝑛

𝐟11

⋯

𝐟1𝑛

𝐞1

𝐱1

…

⋮

⋱

⋮

⋮

⋱

⋮

⋮

⋮

𝑚 industries, region 𝑛

𝐙𝑛1

⋯

𝐙𝑛𝑛

𝐟𝑛1

⋯

𝐟𝑛

𝐞𝑛

𝐱𝑛

Imported inputs

𝐙𝑀1

⋯

𝐙𝑀𝑛

𝐟𝑀1

⋯

𝐟𝑀𝑛

0

𝐌

Value added

(𝐰1 )

′

⋯

(𝐰𝑛 )′

Output

(𝐱1 )

′

⋯

(𝐱𝑛 )′

CO2 emissions

(𝐩1 )

′

⋯

-p

Intermediate use (𝑚 columns per region)

Regio exp n n orts

ro

of

…

lP

re

(𝐩𝑛 )′

Table 2 A simplified IRIOP with 𝑛 countries and 2𝑚 sector

na

Intermediate inputs

(2m columns per region) ⋯

Jo ur

Region 1

Outp

Final use

Region n

Regi

⋯

Regio

Export

P

O

P

O

P

O

on 1

P

0

0

0

0

0

0

0

0

0

𝐞𝑃1

𝐱𝑃1

O

𝐙𝑂𝑃 11

𝐙𝑂𝑂 11

⋯

⋯

𝐙𝑂𝑃 1𝑛

𝐙𝑂𝑂 1𝑛

𝐟𝑂1𝑛

⋯

𝐟𝑂1𝑛

𝐞𝑂1

𝐱𝑂1

P

0

0

0

0

0

0

0

0

0

⋮

⋮

O

⋮

⋮

⋱

⋱

⋮

⋮

⋮

⋱

⋮

⋮

⋮

P

0

0

0

0

0

0

0

0

0

𝐞𝑃𝑛

𝐱𝑃𝑛

O

𝐙𝑂𝑃 𝑛1

𝐙𝑂𝑂 𝑛1

⋯

⋯

𝐙𝑂𝑃 𝑛𝑛

𝐙𝑂𝑂 𝑛𝑛

𝐟𝑂𝑛𝑛

⋯

𝐟𝑂𝑛𝑛

𝐞𝑂𝑛

𝐱𝑂𝑛

𝐙𝑀𝑃 1

𝐙𝑀𝑂 1

⋯

⋯

𝐙𝑀𝑃 𝑛

𝐙𝑀𝑂 𝑛

𝐟𝑀 1

⋯

𝐟𝑀 𝑛

0

𝐌

Value added

(𝐰1𝑃 )′ (𝐰1𝑂 )′ ⋯

⋯

(𝐰𝑛𝑃 )′ (𝐰𝑛𝑂 )′

Total output

(𝐱1𝑃 )′ (𝐱1𝑂 )′

⋯

(𝐱 𝑃𝑛 )′ (𝐱)′

Region 1

nn

ut

⋮

Region n

Imported inputs

⋯

Journal Pre-proof CO2

𝑝

(𝐮1 )′ (𝐮1𝑂 )′

emissions

⋯

(𝐮𝑃𝑛 )′ (𝐮𝑂𝑛 )′

⋯

Table 3-a CO2 emissions (Mt) in each region due to national exports (2002) Regions

IRIOP

MRIO

Total

Total Processing

Ordinary

(2)

(3)

42.7 (9.9%)

8.4 (2.0%)

34.4 (8.0%)

53.3 (12.4%)

10.5 (24.6%)

45.1 (21.9%)

8.4 (4.1%)

36.7 (17.8%)

51.4 (24.9%)

6.3 (13.9%)

North Coast

70.0 (12.4%)

11.8 (2.1%)

58.2 (10.3%)

86.5 (15.3%)

16.5 (23.5%)

East Coast

113.6 (21.1%)

19.5 (3.6%)

94.1 (17.5%)

137.3 (25.5%)

23.8 (20.9%)

South Coast

93.0 (29.5%)

28.0 (8.9%)

64.9 (20.6%)

115.1 (36.5%)

22.1 (23.8%)

Central Region

71.2 (8.6%)

10.0 (1.2%)

61.2 (7.4%)

85.5 (10.4%)

14.3 (20.1%)

Northwest

30.2 (8.7%)

4.2 (1.2%)

26.0 (7.5%)

35.7 (10.3%)

5.5 (18.1%)

Southwest

40.1 (9.2%)

7.8 (1.8%)

32.3 (7.4%)

46.6 (10.8%)

6.5 (16.3%)

Total

505.8

98.1

407.7

611.3

105.5 (20.9%)

of

ro

Municipalities

-p

Northern

(5)=(4)-(1) [(5)/(1)*100%]

re

North East

(4)

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(1)

Difference

Table 3-b CO2 emissions (Mt) in each region due to national exports (2012) Regions

IRIOP

Difference

Total

Processing

Ordinary

(2)

(3)

129.4 (15.0%)

23.5 (2.7%)

105.9 (12.3%)

143.4 (16.6%)

13.9 (10.8%)

34.7 (21.6%)

5.2 (3.3%)

29.5 (18.3%)

37.3 (23.1%)

2.5 (7.3%)

272.2 (17.6%)

57.7 (3.7%)

214.5 (13.8%)

317.8 (20.5%)

45.6 (16.7%)

348.0 (27.6%)

86.9 (6.9%)

261.1 (20.8%)

388.1 (30.9%)

40.2 (11.5%)

245.1 (28.6%)

45.9 (5.4%)

199.2 (23.3%)

293.4 (34.3%)

48.3 (19.7%)

Central Region

239.7 (11.3%)

44.6 (2.1%)

195.1 (9.2%)

285.4 (13.5%)

45.7 (19.1%)

Northwest

180.7 (13.0%)

27.5 (2.0%)

153.1 (11.0%)

207.0 (14.9%)

26.3 (14.6%)

Southwest

131.8 (10.8%)

15.3 (1.3%)

116.5 (9.6%)

153.9 (12.6%)

22.2 (16.8%)

Total

1581.4

306.5

1274.9

1826.3

244.8 (15.5%)

North East Northern Municipalities North Coast East Coast South Coast

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(1)

na

Total

MRIO (4)

(5)=(4)-(1) [(5)/(1)*100%]

Note: the numbers in brackets in column (1)-(4) shows the ratio of emission generated by national exports to total regional emissions.

Table 4 National environmental loss per 1000 yuan of exports for each region-industry combination (unit: ton/1000yuan) using IRIOP model North

Northern

North

East

South

Central

North

South

east

Municipalities

coast

coast

coast

region

west

west

Journal Pre-proof Agriculture

0.10

0.13

0.10

0.07

0.06

0.08

0.14

0.07

Mining

0.24

0.11

0.45

0.25

0.15

0.25

0.22

0.38

Food products

0.09

0.07

0.07

0.07

0.07

0.09

0.12

0.07

0.12

0.08

0.11

0.11

0.08

0.11

0.17

0.11

Wooden products

0.16

0.07

0.17

0.11

0.13

0.14

0.12

0.15

Paper and printing

0.15

0.08

0.12

0.11

0.08

0.10

0.14

0.06

Chemical products

0.16

0.07

0.30

0.14

0.09

0.23

0.37

0.26

Non-metallic

0.42

0.36

0.59

0.53

0.46

0.57

0.97

0.87

Metal products

0.23

0.15

0.26

0.19

0.12

0.23

0.39

0.28

Machinery

0.15

0.08

0.17

0.13

0.07

0.14

0.23

0.14

Transport

0.11

0.08

0.17

0.11

0.06

0.13

0.15

0.07

0.10

0.03

0.13

0.09

0.03

0.08

0.15

0.10

0.10

0.03

0.13

0.07

0.05

0.17

0.05

1.79

0.85

0.20

0.22

0.14 0.09

Textile

and

wearing apparel

Electronic products manufacturing Electricity, gas and

re

products

1.61

1.20

1.09

1.45

2.59

1.12

0.26

0.21

0.19

0.25

--

0.29

0.11

0.13

0.10

0.11

0.14

0.23

0.15

0.06

0.09

0.06

0.06

0.09

0.14

0.08

0.07

0.17

0.11

0.08

0.15

0.25

0.19

and

transport Other services

0.15

na

Construction

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water supply Trade

0.08

-p

Other

ro

equipment

of

mineral products

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Note: the exports in Construction in Northwest are zero, thus the corresponding value is indicated by --.

Table 5 National environmental loss per 1000 yuan of exports for each region-industry combination (unit: ton/1000yuan) using MRIO model

Agriculture

North

Northern

North

East

South

Central

North

South

east

Municipality

coast

coast

coast

region

west

west

0.10

0.13

0.09

0.07

0.06

0.08

0.14

0.07

Mining

0.23

0.11

0.44

0.24

0.14

0.25

0.22

0.38

Food products

0.11

0.06

0.10

0.07

0.07

0.10

0.13

0.08

0.12

0.08

0.13

0.11

0.09

0.12

0.17

0.11

Wooden products

0.16

0.07

0.18

0.11

0.13

0.14

0.13

0.14

Paper and printing

0.15

0.09

0.17

0.12

0.10

0.15

0.16

0.13

Chemical products

0.21

0.08

0.36

0.16

0.13

0.26

0.38

0.28

Non-metallic

0.42

0.37

0.59

0.54

0.48

0.57

0.97

0.86

Metal products

0.24

0.15

0.29

0.20

0.17

0.27

0.40

0.29

Machinery

0.16

0.09

0.20

0.14

0.09

0.16

0.22

0.16

Textile

and

wearing apparel

mineral products

Journal Pre-proof Transport

0.12

0.09

0.20

0.13

0.08

0.15

0.17

0.14

0.11

0.02

0.21

0.12

0.07

0.15

0.21

0.14

0.10

0.05

0.17

0.09

0.08

0.14

0.18

0.11

1.87

0.85

1.61

1.21

1.17

1.45

2.59

1.12

0.20

0.21

0.25

0.21

0.18

0.24

--

0.29

0.14

0.11

0.13

0.10

0.11

0.14

0.23

0.14

0.09

0.06

0.09

0.06

0.05

0.09

0.13

0.08

0.16

0.07

0.20

0.13

0.10

0.19

0.25

0.21

equipment Electronic products Other manufacturing products Electricity, gas and water supply Construction Trade

and

Other services

of

transport

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na

lP

re

-p

ro

Note: the exports in Construction in Northwest are zero, thus the corresponding value is indicated by --.

Journal Pre-proof Appendix A

Table A1 Classification of China’s eight regions in IRIOP table Region Northeast Northern Municipalities North Coast East Coast South Coast Central Region Northwest

8

SW

Southwest

Province included Heilongjiang, Jilin, Liaoning Beijing, Tianjin Hebei, Shandong Shanghai, Jiangsu, Zhejiang Guangdong, Fujian, Hainan Shanxi, Henan, Hubei, Hunan, Anhui, Jiangxi Inner Mongolia, Shannxi, Ningxia, Gansu, Xinjiang Sichuan, Chongqing, Yunnan, Guizhou, Guangxi, Qinghai, Tibet

of

Code NE NM NC EC SC CR NW

re

-p

ro

Id 1 2 3 4 5 6 7

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na

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Table A2 Classification of industries in IRIOP table Id Industries 1 Agriculture 2 Mining 3 Food products 4 Textile and wearing apparel 5 Wooden products 6 Paper and printing 7 Chemical products 8 Non-metallic mineral products 9 Metal products 10 Machinery 11 Transport equipment 12 Electronic products 13 Other manufacturing products 14 Electricity, gas and water supply 15 Construction 16 Trade and transport 17 Other services

Journal Pre-proof

Research Highlights 1. This paper re-estimates the CO2 emissions embodied in China’s exports at both regional and industrial level, by using the newly-developed inter-regional input-output (IRIOP) model that distinguishes processing trade from other trade at regional level. 2. We find that comparing to the IRIOP model, the traditional multi-regional input-output (MRIO) model has overestimated the environmental loss from exports by 14%-25% in 2002 and 7%-20% in 2012 for different regions.

of

3. We find that the largest bias is found in regions and industries with the highest

ro

processing export shares.

4. Compared with the traditional MRIO model, the IRIOP model gives more

-p

accurate accounting on the regional environmental loss due to national exports

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na

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re

and thus is important for establishing effective emission mitigation policies.

Figure 1

Figure 2

Figure 3

Figure 4