Analysis of multiple drivers of air pollution emissions in China via interregional trade

Journal Pre-proof Analysis of multiple drivers of air pollution emissions in China via interregional trade Yuqing Wang, Haozhe Yang, Junfeng Liu, Yuan Xu, Xuejun Wang, Jianmin Ma, Jiayu Xu, Kan Yi, Shu Tao PII:

S0959-6526(19)33377-3

DOI:

https://doi.org/10.1016/j.jclepro.2019.118507

Reference:

JCLP 118507

To appear in:

Journal of Cleaner Production

Received Date: 6 May 2019 Revised Date:

14 September 2019

Accepted Date: 18 September 2019

Please cite this article as: Wang Y, Yang H, Liu J, Xu Y, Wang X, Ma J, Xu J, Yi K, Tao S, Analysis of multiple drivers of air pollution emissions in China via interregional trade, Journal of Cleaner Production (2019), doi: https://doi.org/10.1016/j.jclepro.2019.118507. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

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Character Count: 8270

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Analysis of Multiple Drivers of

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Air Pollution Emissions in China via Interregional Trade

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Yuqing Wang12#, Haozhe Yang1#, Junfeng Liu1*, Yuan Xu3,

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Xuejun Wang1, Jianmin Ma1, Jiayu Xu1, Kan Yi1, Shu Tao1

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University, Beijing 100871, China

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School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China

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Department of Geography and Resource Management, and Institute of Environment, Energy and

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Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking

Sustainability, The Chinese University of Hong Kong, Hong Kong, People’s Republic of China

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*Correspondence to: Junfeng Liu (E-mail: [email protected])

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Y. Wang and H. Yang contributed equally to this paper.

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ABSTRACT

Severe haze problems in China have attracted substantial attention. End-of-pipe measures have been implemented to mitigate air pollutant emissions in different industrial sectors.

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However, interregional trade can lead to geological separation of the stages of supply chains

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(i.e., primary input, production, final sale and consumption), and thus, emissions can be driven

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by cross-regional drivers (i.e., primary inputs, final sales and consumption), and pollutants can

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transfer among regions via supply chains. Inequity exists in production-side measures of

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emission reductions, which do not account for the effects of cross-regional drivers. In this

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study, we use a multiregional input-output model in the year 2012 to calculate the emissions

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driven by cross-regional drivers and to trace emission flows along supply chains in China,

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aiming to inform policymaking on fine particulate matter (PM2.5) mitigation and to provide

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additional information about shared responsibilities among provinces. We find that the Central

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(including Anhui and Henan) is the largest emitter outside the Central Coast in order to meet

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the demand for sales within the Central Coast. Specifically, the cross-provincial sale of

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products from the construction sector drives massive emissions, especially in Jiangsu province

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(located in the Central Coast). Therefore, the Central Coast should bear some responsibility to

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help provinces located in the Central reduce emissions. About half of emissions driven by

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primary inputs in Beijing-Tianjin are emitted in other regions. However, in general, emissions

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(except for ammonia) are more likely to be driven by final sales of other provinces than by

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primary inputs. The supply chain perspective can help gain a better understanding of the

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impacts of trade-related drivers on emission patterns and advocate just and effective policies

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considering cleaner production and shared responsibilities.

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KEY WORDS: air pollution; trade; input-output model; mitigation policy; green consumption

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1. INTRODUCTION

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Haze, a crucially acute problem in China, has received substantial attention from the

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public and the Chinese government (CSC, 2018a; Huang, 2014; Zhuang et al., 2014; Gao et al.,

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2017). Haze is characterized by high concentrations of both primary and secondary fine

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particulate matter (PM2.5). It is reported that primary PM2.5 as well as precursor gases (including

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sulfur dioxide (SO2), nitrogen oxide (NOx), ammonia (NH3) and volatile organic compounds

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(VOC) (Megaritis et al., 2013)) have adverse effects on human health (Delfino et al., 2005;

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Timonen et al., 2006). Moreover, air pollution can be outsourced to less developed regions

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through interregional trade, which exacerbates the health burden of air pollution (Wang et al.,

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2017). Therefore, it is critical to control both primary PM2.5 and its precursors’ emissions to

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mitigate the haze problem. However, the current ways to control haze problems overlook the

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pollution embodied in traded goods and the disparities that might follow among regions in

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terms of air pollution exposure.

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Due to interregional trade, products produced in one place are not ultimately consumed in

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the same place. The geographical separation of production and consumption complicates the

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problems that who should take the responsibility to improve air quality and how the cost of

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pollution mitigation ought to be shared (Caney, 2009). In addition to consumption, direct

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emissions, through a supply chain perspective, can also be enabled by primary inputs

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(corresponding to income-based accounting) (Marques et al., 2013) and final sales

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(corresponding to final sales-based accounting) (Kanemoto et al., 2012) in different locations.

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The supply of primary inputs (including the provision of labor or any other primary factors) to

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production process enable emissions to occur (Marques et al., 2012). Final sellers process raw

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materials and semi-products into finished goods to sell. In other words, production processes

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are closely associated with both primary inputs, final sales and consumption. But for any

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agent’s (primary suppliers, producers, sellers or consumers) actions there may have been no

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emissions (Steininger et al., 2016).

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Many studies have considered consumers’ environmental duty and have analyzed

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consumption-based emissions (corresponding to consumption-based accounting) at global

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(Davis and Caldeira, 2010; Boitier, 2012; Meng et al., 2016; Meng et al., 2018a) and national

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(Huo et al., 2014; Li et al., 2016; Shao et al., 2018; Zhao et al., 2015) levels. A typical finding

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of these studies is that the consumption-based accounting method reveals new emission

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profiles for regions. In general, less developed regions exhibit high levels of emissions during

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the manufacturing of goods for consumption in more developed regions (Feng et al., 2013;

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Lindner et al., 2013). These studies also have juxtaposed production-based (territorial)

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emissions and consumption-based emissions and stressed the importance of the consumer

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responsibility because consumers benefit from the enjoyment or use of products and service.

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However, less studies have considered the importance of income-based (Liang et al., 2017a,

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2016; Zhang et al., 2018) and final sales-based emissions (Kanemoto et al., 2012; Liang et al.,

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2017b). In fact, both primary suppliers and final producers also benefit from production

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activities. Primary suppliers receive a payment from the inputs that they supply. Final

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producers (sellers) profit from the sales of final products. All the agents benefit from the

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production during which air pollutants are discharged although they do not actually engage in

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harmful activities. Therefore, we cannot only consider the producer and consumer shared

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responsibility (Gallego and Lenzen, 2005; Lenzen et al., 2007). It is reasonable to take into

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account of all the agents benefiting from interregional trade.

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Using income-, final sales- and consumption-based accounting approaches, the emissions

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directly emitted by producers can be allocated to primary suppliers, final producers (sellers)

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and final consumers, respectively. Some studies find disparities among patterns of direct

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emissions, consumption-based emissions, income-based emissions and final sales-based

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emissions (Marques, et al., 2013; Liang et al., 2016; Liang et al., 2017a; Liang et al., 2017b).

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Critical agents are identified through different accounting methods to provide policy makers

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with a more complete picture of the effects of all agents’ actions. However, none of the

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accounting systems prove “best” in meeting the emission targets (Steininger et al., 2016).

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These four methods only place full responsibility on the corresponding agents and carry no

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implication about the portions of responsibility to be shared. Although existing studies have

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analyzed the shared burden of mitigation based on different principles (Cadarso et al., 2012;

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Chang, 2013; Zhang, 2015), it is difficult to exactly determine who is causally responsible for

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harmful emissions. Moreover, accounting for both downstream emissions (income-based

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emissions) and upstream emissions (final sales- and consumption-based emissions) can be

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useful for emission mitigation because it is cost-effective to reduce those indirect emissions by

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choosing trade partners (Marques et al., 2013).

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Many studies have analyzed air pollution emissions caused by trade-related drivers,

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especially the consumption driver (Zhang, 2015; Lindner et al., 2013; Liang et al., 2015; Mi et

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al., 2017). Meng et al. (2018) compared CO2 emissions embodied in South-South trade

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between 2004 and 2011. Wang et al. (2017) evaluated the effects of international and

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interprovincial trade on PM2.5 pollution and public health across China. Zhang et al. (2019)

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analyzed SO2 transfers through Chinese supply chains considering consumption-based

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emissions and physical transport. However, none have compared income-, production-, final

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sales- and consumption-based accounting methods together from the supply chain perspective

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at a national level. In this paper, we estimate primary PM2.5, SO2, NOx, VOC and NH3

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emissions from 30 provinces in China during 2012. We also use Sankey diagrams to trace the

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emission flows among regions to gain a comprehensive picture of the influence of interregional

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trade. Our findings show that in general, emissions (except for NH3) are more likely to be

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driven by final sales of other provinces than by primary inputs. The Central (including Anhui

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and Henan provinces) is the largest emitter outside the Central Coast in order to meet the

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demand for sales within the Central Coast. About half of emissions driven by primary inputs in

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Beijing-Tianjin are emitted in other regions. Combining four accounting frameworks can help

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gain a better understanding of the impacts of trade-related drivers on emission patterns and

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advocate just and effective policies considering shared responsibilities. It will also provide

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valuable insights into how other economies may be aiming to make relatively fair emission

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control policies.

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2. METHODOLOGY AND DATA

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2.1 Production-Based Accounting Method

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Production-based emissions refer to direct emissions for each sector. The detailed sector

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classification applied in this study are listed in the Supporting Information (SI) Table S1. We

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obtained the results on the basis of calculation and available emission inventories. For the

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agriculture, the electricity and hot water production and supply and the transport and storage

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sectors, the emission data were obtained from the Multiresolution Emission Inventory for

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China (MEIC, available at http://www.meicmodel.org), which contains a bottom-up air

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pollutant emission inventory (Liu et al., 2015; Zhang et al., 2009). For the other sectors, we

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used the calculation method (Cofala and Syri, 1998a, 1998b; Klimont and Winiwarter, 2011)

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applied in the Greenhouse Gas — Air Pollution Interactions and Synergies model (GAINS,

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http://gains.iiasa.ac.at/models/gains_models3.html). The formula is expressed as

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Eij = ∑∑actijk × efijk × afijkt × (1−ηijkt ) k

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(1)

t

where subscript I,j represents sector i in province j, E ij is the emissions (unit: Gg),

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actijk is the activity level for fuel k (J), efijk is the unabated emission factor per unit of

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activity for fuel k (Gg/J), afijkt is the application factor of technology t for fuel k (%), and

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η ijkt is the removal efficiency of technology t for fuel k (%). We collected the activity level

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of fuel actijk from China Emission Accounts and Datasets (CEADs,

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http://www.ceads.net/data/energy-inventory/) (Shan et al., 2018, 2016). The unabated

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emission factor efijk the application factor of technology afijkt and the removal efficiency

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η ijkt of SO2, NOx, and NH3 were obtained from the GAINS model, while the data of primary

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PM2.5 were obtained from the results of Lei et al. (2011). In addition, it is important to note

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that VOC emissions cannot be estimated through the above method due to the lack of detailed

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emission factors in the GAINS model. Therefore, we used the data from an existing VOC

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emission inventory (Wu et al., 2016) by merging specific emissions from the corresponding

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sectors. The match of this VOC inventory and the sector classification applied in this study is

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detailed in the SI Table S2.

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2.2 Final sales- and Consumption-based Accounting Methods

Final sales- and consumption-based accounting methods are based on the multiregional

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input-output (MRIO) model (Miller and Blair, 2009). The MRIO model can trace all

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emissions associated with final sales and consumption on the basis of monetary flows

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between regions/sectors. In this study, the 2012 MRIO database (Mi et al., 2017) was used to

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calculate the final sales-, consumption- and the income-based emissions of each regions and

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sectors. The basic equation for the distribution of the products among s sectors and r regions

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is given as:

r

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xim = ( zim11 + L + zijmn + L + zis1r ) + ∑ yimn n =1

(2)

m

represents the total economic output of sector i in region m, zijmn is the

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where xi

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intermediate sale by sector i in region m to sector j in region n, i is a “summation” vector, and

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∑y

r

n =1

mn i

is the total final demand in region n for sector i’s products produced in region m.

aijmn =

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zijmn

(3)

x nj

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The ratio in equation (3) is called the technical coefficient or the direct input coefficient and,

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measures the fixed relationship between a sector’s output and its inputs. aijmn reflects the

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direct input from sector i in region m required to produce one unit of output from sector j in

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region n.

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Therefore, using the definition in (3), equation (2) can be represented in matrix notation:

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X = ( I − A)−1Y = LY

(4)

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where I is the identity matrix, and L =  lijmn  = ( I − A)

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the total requirements matrix, whose element lijmn measures both direct and indirect inputs

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from sector i in region m required to produce one unit of output from sector j in region n.

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−1

is known as the Leontief inverse or

Based on equation (4), emissions associated with the final demand in region n for sector i’s products produced in region m can be measured by (5):

Ecn im = e( I − A)−1Cimn

(5)

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where e is a row vector of emissions per unit of sectors’ output, with each element ei

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representing the direct emissions in sector i associated with its one unit industry output, and

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Cimn is the vector

Cimn

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 0   M    =  yimn  .    M   0   

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Similarly, final sales-based emissions of sector i in region m are measured by equation (6):

Ef

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where

im

= e( I − A) −1Yim

(6)

Yim is the vector  0   M     r mn  Yim =  ∑ yi   n =1   M     0 

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2.3 Income-based Accounting Method

Ghosh (1958) presented an alternative interpretation that relates sectoral gross outputs to the primary inputs based on the same set of data that underpin equation (2). In this case,

X ' = i' Z +V

(7)

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where X and i are the matrices used in (2), and the notation ’ means the transposition of each

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vector. V is the vector

V = ( v11 v12

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v31 L vim L vsr )

of the total value-added expenditures by each sector.

mn ij

b

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=

zijmn xim

(8)

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We use bijmn to denote the allocation coefficient, as opposed to the technical coefficient,

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aijmn . The bijmn coefficient represents the input from sector i in region m to sector j in region

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n per unit of input from sector i in region m, describing the distribution of the gross input

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from sector i.

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In contrast to the Leontief inverse, the Ghosh inverse was defined as:

G = ( I − B)−1

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(9)

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with elements gijmn . G is interpreted as measuring both direct and indirect outputs enabled by

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unitary primary inputs from particular sectors. Equation (7) can be rewritten using (9) as

X ' = VG = V ( I − B)−1

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Thus, income-based emissions of sector i in region m are calculated by

Ei im = Vim ( I − B)−1 e '

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where Vim is the vector

Vim = ( 0 L vim L 0 ) .

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2.4 Expressions of Emission Flows

Using these four accounting methods, the air pollutant emissions e can be allocated to

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different provinces as primary suppliers, producers, sellers or consumers (Table 1). The total

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amounts of emissions remain the same but the allocations of emissions among provinces are

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determined by different methods.

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Table 1 Allocations of emissions using different accounting methods

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Roles Accounting

Primary Producers

Sellers

Consumers

suppliers

methods

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Income-based

e

0

0

0

Production-based

0

e

0

0

Final sales-based

0

0

e

0

Consumption-based

0

0

0

e

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Therefore, we use one matrix to represent the results of two related accounting methods and to demonstrate emission flows between the two corresponding agents.

First, we define M 1 as:

 Ei11  1 E M1=  i2  M  1  Eir

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Ei21 Ei22 M Eir2

L L Einm L

Eir1   Eir2  M   Eirr 

m

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where Ein represents the direct emissions in region m driven by primary inputs in region n.

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The sum of the elements in the n row is income-based emissions of region n, and the sum of

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those in the m column is production-based emissions of region m.

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Similarly, the matrix M 2 is defined as:

 E 1f 1  2 E M2 =  f1  M  r  Ef1

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E 1f 2 E 2f 2 M

L L E mfn

E rf 2

L

E 1fr   E 2fr  M   E rfr 

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where E mfn represents the direct emissions in region m driven by final sales in region n.

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The sum of the elements in the m row is production-based emissions of region m, and the sum

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of those in the n column is final sales-based emissions of region n. Finally, we define the

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matrix M 3 as:

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M 3 = diag [e( I − A) −1 ] × ( C1 C2

 Ec11  2 E L Cn ) =  c1  M  r  E c1

Ec12 Ec22 M Ecr2

L L Ecnm L

Ecr1   Ecr2  M   Ecrr 

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to demonstrate the relation between final sales-based and consumption-based emissions. Cn

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is the vector

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 y11n   1n   y2   y31n    Cn =  M   y mn   i   M   y rn   s 

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Ecnm represents the emissions (that are emitted in all regions) driven by region n’s

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consumption of the final products from region m. Therefore, the sum of the elements in the m

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row is final sales-based emissions of region m, and the sum of the elements in the n column is

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consumption-based emissions of region n.

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3. RESULTS

In this section, we first provide an overview of provincial emissions caused by different

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trade-related drivers, following which we compare the different impacts of these drivers

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outside producing provinces. Then we present the embodied emission flows among regions

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through supply chains using Sankey diagrams. Finally, we further identify the key sectors that

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caused large amounts of emissions to complement the results of emission flows.

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3.1 Provincial emissions derived by different accountings

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Figure 1. Provincial emissions (unit: Gg) derived using different accounting methods (i.e.,

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production-based, income-based, final sales-based and consumption-based accounting methods).

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Different accounting methods provide different insights into provinces’ roles in driving

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air pollutants. As shown in Figure 1, 30 provinces are listed in order of descending

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production-based emissions for all species.

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Shandong province has the largest production-based primary PM2.5, SO2, NOX and VOC

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emissions. However, it is more important as a final producer (or seller) than as a direct

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emitter. Its final sales-based primary PM2.5, SO2, NOX and VOC emissions are 680, 2524,

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3051 and 2440 Gg, respectively, which are 13%, 10%, 19% and 2% higher than their

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production-based primary emissions. We also find that Jiangsu, Guangdong and Zhejiang are

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important sellers. For example, Jiangsu province’s final sales-based primary PM2.5, SO2,

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NOX, NH3 and VOC emissions are 82%, 103%, 59%, 37% and 41% higher, respectively, than

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its production-based counterparts.

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Important primary suppliers have relatively few production-based emissions but large

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income-based emissions. For instance, income-based SO2 and NOX emissions in Shanxi are

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33% and 56% higher, respectively, than their production-based counterparts. Moreover, there

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is a relatively large gap between production-based emissions and income-based emissions in

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more developed cities, such as Beijing and Shanghai. In Beijing, income-based,

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final-sales-based and consumption-based SO2 emissions are 359%, 492% and 337% higher,

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respectively, than production-based SO2 emissions.

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The amounts of emissions derived from the four accounting methods show that a

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province has different importance in different stages of supply chains. For instance, in Inner

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Mongolia, income-based and production-based emissions are much higher than their final

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sales-based and consumption-based emissions for SO2 and NOX, which indicates that Inner

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Mongolia is more important as a primary supplier and a producer because its primary supply

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and direct production drive much higher SO2 and NOX emissions than final sales and

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consumption. However, this is in contrast to Beijing. Beijing’s production-based emissions

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are much lower than its income-based, final sales-based and consumption-based emissions for

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primary PM2.5, SO2, NOX and VOC. This shows that the emissions directly emitted within

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Beijing are much lower than those driven by its primary supply, final sales and consumption.

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Zhang et al. (2019) also found that SO2 emissions driven by Beijing’ consumption, which

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were ten times as much as its production-based emissions, were actually discharged in the

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surrounding provinces. To some extent, Beijing receives the benefits (income, selling profits

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and consumer benefits) and reduces exposure to air pollution caused by direct local emission.

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At the same time, other provinces experience more exposure to air pollution caused by

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primary supply, final sales and consumption originating from Beijing. Inequity exists in

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production-side measures of emission reductions and we need to investigate the role each

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province plays in supply chains and provide additional information for sharing

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responsibilities among provinces.

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3.2 Emissions Associated with Drivers from Other Provinces

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Figure 2. Map of mainland China with the ratios of PM2.5, SO2 and NH3 emissions that are caused by (a)

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primary inputs, (b) final sales and (c) consumption outside the producing provinces relative to

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production-based emissions. Regions (Tibet, Hong Kong and Macao) denoted with “NA” (no data) are

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beyond the scope of this paper. Full results are shown in the SI Figure S2.

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Figure 2 represents an overview of the contribution of three drivers (primary inputs,

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final sales and consumption) outside the producing provinces resulting from production-based

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direct emissions of primary PM2.5, SO2 and NH3 within provincial boundaries. The results of

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NOx emissions are similar to those of PM2.5 emissions, while the results of VOC emissions

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are similar to those of SO2 emissions.

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These results shows that emissions are more likely to be driven by final sales or

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consumption of other provinces than by primary inputs. In China, approximately one third of

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air pollutant emissions are emitted owing to outside final sales or consumption. In general,

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production-based emissions in northern and central provinces tend to be driven by final sales

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or consumption of other provinces. In Inner Mongolia, Gansu, Ningxia, Shaanxi, Anhui,

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Shanxi and Gansu, which are located in the northern and middle regions, more than 50% of

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the SO2 emissions are attributed to final sales of other provinces. Similarly, in Inner

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Mongolia, Henan and Anhui, more than 50% of the primary PM2.5 emissions are caused by

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other provinces’ final sales. The consumption results show a similar pattern as the results of

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final sales except for NH3 emissions. Production-based NH3 emissions are greatly affected by

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the consumption of other provinces. In Hainan, Heilongjiang, Anhui and Beijing, outside

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consumption drive 85.1%, 76.3%, 71.8% and 70.3%, respectively, of production-based NH3

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emissions.

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Primary inputs of other provinces play a relatively minor role. The ratios of pollutant

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emissions enabled by other provinces’ primary inputs relative to production-based emissions

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are generally lower than 40%. However, in Anhui and Ningxia, more than 40% of emissions

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discharged locally can be attributed to other provinces’ primary inputs, which are relatively

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higher than those of other provinces.

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The higher the emission ratios, the stronger the cross-provincial effects. To some extent,

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it is not fair for the provinces that are the most strongly affected to take on the full

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responsibility for emission reductions. The provinces, acting as key primary suppliers, final

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producers and consumers, drive many/most of direct emissions in other provinces and thus

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ought to share the responsibilities with high-emission producers.

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3.3 Emission Flows via Interregional Trade

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Figure 3. Sankey diagrams of embodied (a) SO2, (b) NOx, (c) PM2.5, (d) NH3 and (e) VOC emissions

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flows through the supply chain among different regions in China. Eight regions on the left-hand side are

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aggregated from 30 provinces (Liang et al., 2017b) for easier interpretation (groupings of 30 provinces

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are available in the SI Figure S1). The width of each flow represents the magnitude of emissions (unit:

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Gg). Colors indicate eight regions as starting places for the phases (i.e., from primary input to

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production, from production to final sale and from final sale to consumption). Notes, the emission

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transfers within regions and between China and foreign countries are not demonstrated because we focus

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on interregional emission flows.

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Figure 3 shows emission flows of primary PM2.5, SO2, NOx, NH3 and VOC through

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supply chains (from primary input to production, final sale and consumption) among 8

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regions in China (including Beijing-Tianjin, North, Northeast, Central, Central Coast, South

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Coast, Southwest and Northwest). Air pollutant emissions discharged during production

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processes can be reallocated among regions as primary suppliers, sellers and consumers (see

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Table 1). By combining the results derived by different accounting methods, emissions can

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be perceived as being transferred among provinces through the supply chains. Therefore, we

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use emission flows to illustrate the reallocations of production-based emissions. In this

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section, we focus only on the cross-regional emission transfers to obtain a deeper

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understanding of interregional influences.

345

For all species, the majority or almost half of income-based emissions in Beijing-Tianjin

346

(65% for SO2, 66% for primary PM2.5, and 49% for NH3 etc.), though not large in number, are

347

emitted outside. In terms of numbers, the Central and Northwest are the largest contributors

348

as primary suppliers to downstream primary PM2.5, SO2, NOx and VOC emissions;

349

specifically, they primarily drive emissions discharged in the North and the Central Coast.

350

The Central also acts as an important emitter; its large amounts of emissions (except for NH3)

351

are mainly driven by upstream supply in the Central Coast and the Northwest.

352

Final sales in the Central Coast drive many more emissions through interprovincial

353

trade, especially primary PM2.5 emissions. For all species, more than one quarter of emissions

354

driven by cross-regional sales are driven by the Central Coast. In addition, on average, 40%

355

of total emissions that the Central Coast drives are emitted in the Central (which includes

356

Anhui, Henan and Shanxi etc.), indicating that large amounts of products are produced in the

357

Central but are sold in the Central Coast. The reason for strong demand for sales in the

358

Central Coast is that the Central Coast is also an export region to meet the consumption

359

demand abroad. Furthermore, we find that more than one fifth of emissions (for all species)

360

driven by each region’s final sales are directly emitted in the Central. The Central plays an

361

important role in manufacturing and discharges massive amounts of air pollutants during

362

production to meet the outside demand. The Northwest also should be paid more attention

363

because of the greater portion of direct emissions that are driven by final sales outside (41%

364

for SO2 and 38% for NOx etc.).

365

From the consumption perspective, emissions transferred (except for NH3 and VOC)

366

among regions are much lower than final sales-based emissions, mainly for two reasons. First,

367

approximately 10%-20% of the emissions (i.e., primary PM2.5, SO2, NOx, NH3 and VOC)

368

embodied in products are exported to feed global export demand, and those emissions are not

369

included in the scope of this paper. Second, the vast majority of the products sold by one

370

region are consumed locally. When taking into account the export and the local drivers, the

371

inflows of emissions equals the outflows of emissions at the production and final sales stages.

372

Many emissions embodied in products that are sold outside the producing provinces are also

373

counted as emissions driven by other provinces’ consumption. For this reason, Figures 2 (b)

374

and (c) are fairly similar (except for NH3). Nevertheless, NH3 and VOC have a different

375

result. About a quarter of final sales-based NH3 emissions in the Central are driven by

376

consumption in other regions, indicating that the Central sell final products (in which

377

emissions are embodied) to other regions. The North drives significant amounts of NH3

378

emissions in the Central and the Northwest, which account for approximately 63% of total

379

final-demand-driven NH3 emissions. In addition, a large amount of embedded NH3 emissions

380

also flows from the Central to the Central Coast. VOC emissions embodied in products are

381

mainly transferred from the Central Coast to other regions.

382

Emission flows can illustrate major transfer paths of emissions along the supply chains.

383

However, they cannot help identify critical sectors as primary suppliers or final producers and

384

important sectors in relation to cross-provincial consumption, making it difficult to implement

385

emission control policies.

386

387

3.4 Cross-provincial Emissions for Sectors

388

Figure 4. Cross-provincial (a-d) income-based, (b-g) final sales-based, and (i-l) consumption-based

389

emissions (unit: Gg) of each sector within 30 provinces for primary PM2.5, SO2, VOC and NH3. Full

390

results are available in SI Figure S4.

391

For all species the results of cross-provincial emissions show different patterns from

392

corresponding patterns of production-based emissions. The results of production-based

393

emissions for sectors are available in SI Figure S3.

394

In Figure 4, cross-provincial or interregional emissions are further allocated to sectors.

395

Each cell of income-based or final sales-based emissions represents the amount of emissions

396

that a particular sector’s primary inputs or final sales drive, excluding emissions from its own

397

province. Each cell of consumption-based emissions represents the amount of emissions that

398

a particular province’s consumption drives and the emissions are embodied in products from

399

the same sector in all other provinces.

400

The results of income-based emissions show that the coal mining sector (code 2) in

401

Shanxi province is of great importance. The primary inputs of SO2, NOX and primary PM2.5

402

drive larger amounts of emissions than do other sectors. For VOC, the coal mining sector

403

(code 2) in Shanxi, the petroleum and gas (code 3) in Heilongjiang and the chemical industry

404

in Jiangsu are perceived as important sectors. Unlike the other species, the agriculture (code

405

1) and the food processing and tobacco (code 6) generally drive more NH3 emissions, and

406

agriculture in Heilongjiang accounts for the largest amounts of downstream NH3 emissions.

407 408

The patterns of final sales-based emissions indicate that the construction sector (code 24) is of crucial importance, notably in Jiangsu. The results are consistent with emission

409

flows because Jiangsu are located in the key final sales-based region (the Central Coast). In

410

general, final sales in construction sector contributed the largest fraction of 45.2%, 36.6% and

411

36.4% to national gross primary PM2.5, SO2 and NOx emissions, respectively. In many

412

emerging developing countries, investments in infrastructure are considered to be the critical

413

driver for rapid economic growth (B. Zhang et al., 2017). Therefore, large demands for

414

construction activities result in massive amounts of upstream emissions, which are reallocated

415

to final producers eventually. For NH3, unlike the other species, the food processing and

416

tobaccos sector (code 6) in Shandong has the largest amount of final sales-based emissions,

417

but the pattern shows that the construction sector (code 24) in most provinces have relatively

418

higher emissions. Moreover, in terms of NH3 and VOC emissions, an aggregated sector

419

“others” (code 26, aggregating service industries, e.g., wholesale and retailing, hotel and

420

restaurant, etc.) should be paid close attention, especially in Beijing.

421

From the consumption perspective, there are greater differences among the patterns of

422

different species, compared with those from income or final sales perspectives. However, it is

423

worth noting that the construction sector (code 24) still plays an important role. For primary

424

PM2.5, SO2, and NOX, Hebei, Hunan and Guangdong provinces have large consumption-based

425

emissions because they have consumed many products from the construction sector,

426

indicating that they are important consumers that drive emissions elsewhere. In addition to the

427

construction sector, the general and specialist machinery sector (code 16) and the transport

428

equipment sector (code 17) as well as the food processing and tobaccos sector (code 6) also

429

have more consumption-based emissions because of interprovincial trade. In addition, the

430

results of NH3 and VOC emissions show that Hebei and Shandong are important

431

consumption-based provinces in terms of the consumption of products from the agricultural

432

sector (code 1) in other provinces.

433

434

3.5 Uncertainty analysis

435

Our results are subject to a variety of uncertainties and limitations. First, uncertainties

436

mainly come from the direct input coefficient matrix A. Based on the method described in

437

Yang et al. (2018), we conducted a series of sensitivity tests of the matrix A. The Table S3-S7

438

in the SI show the sensitivities of income-, final sales- and consumption-based emissions to

439

the changes in A (∆A). It is found that if the coefficient matrix increases or decreases by 5%,

440

the income-, final sales- and consumption-based emissions increase or decrease by 3%-15%,

441

3%-18% and 5%-17%, respectively. Second, estimates of air pollutant emissions are

442

inevitably uncertain due in large part to incomplete knowledge of activity levels, technology

443

distributions and emission factors (Zhang et al., 2017). Furthermore, the time lag between the

444

data and the date of policy implications is a continuing problem. If production techniques or

445

the patterns of inter-industry linkages are changing through time, the model may become less

446

meaningful. This issue is a continuing problem and thus great care should be taken in

447

adopting the policy suggestions (Liu and Wang, 2017).

448

449

450

4. DISCUSSION

Income-based, production-based, final-sales-based and consumption-based accounting

451

methods place full responsibility on primary suppliers, producers, sellers and consumers,

452

respectively. However, through any approach, it is not likely to be perceived as fair in terms

453

of mitigation policies because all the actors are related to air pollutant emissions in different

454

stages of the supply chains. Therefore, emission control policies must be considered from

455

different perspectives.

456

457

458

4.1 Implications for emission control policies

Currently, China’s emission reduction policies are mostly production-side measures,

459

such as implementing advanced production technologies, cutting back on coal use and

460

converting to cleaner energies (CSC, 2017, 2018b). The Chinese government has announced

461

plans to roll out a three-year plan to control air pollution (CSC, 2018a), with emphasis placed

462

on the Beijing-Tianjin-Hebei and surrounding areas, the Yangtze River Delta, and the Fenhe

463

and Weihe plains, hereafter called the key areas. The goals are to further reduce the PM2.5

464

densities and increase the number of good air quality days to 80 percent by 2020 in all

465

Chinese prefecture-level cities and above.

466

Nevertheless, to achieve the goals, issues of PM2.5 leakage need to be addressed. Coastal

467

provinces outsource large amounts of emissions to inland provinces by importing

468

intermediate goods to produce finished products. Approximately 40%-50% of final

469

sales-based emissions (except for VOC) in the Central Coast (which includes Jiangsu,

470

Shanghai and Zhejiang) actually originate in other regions. As shown in Figure 2, Anhui,

471

Henan and Inner Mongolia, among other provinces, discharge large quantities of emissions to

472

meet other provinces’ demand. As shown in Figure 3, the Central (which includes Anhui and

473

Henan) is the largest emitter outside the Central Coast to meet the demand for sales within the

474

Central Coast. Moreover, from a consumption perspective, the Central Coast is responsible

475

for approximately 50% of emissions embedded in products it sells, and 20% are exported.

476

Therefore, the Central Coast should bear some responsibility to help the Central reduce

477

emissions. In general, final producers who drive other producers’ pollution emissions ought to

478

share with those producers the responsibility for emission mitigation. Key final producers

479

have plenty of room for improvement in production efficiency (Liang et al., 2017b). The

480

Chinese government can tax intermediate goods based on embodied emissions, which will

481

force key final producers to take into account the upstream emissions and encourage them to

482

choose products with fewer upstream emissions. For instance, the construction sector in the

483

Central Coast, acting as key final producing sectors, could be taxed. What calls for special

484

attention is that this policy depends on a perfect measurement of pollutant emissions

485

embodied in products. At the same time, the government should offer a subsidy to major

486

emitters to lower the cost of advanced production technologies as an incentive for them to

487

reduce emissions. Furthermore, although outsourcing manufacturing to surrounding regions

488

might improve local air quality, it can also undermine the effects of reduction policies. The

489

Chinese government should pay attention to reducing PM2.5 leakage in case that the key areas

490

will outsource manufacturing to regions that are not key areas owing to harsh emission

491

control policies.

492

From a consumption-based perspective, many studies have recommended transferring

493

advanced technologies from consumers to direct emitters (Feng et al., 2013; Liang et al.,

494

2014; Zhao et al., 2015). It is also vital that the Chinese government exerts some influence on

495

consumption behaviors by placing environmentally friendly labels on products from key

496

sectors such as the construction, transport equipment, and agriculture sectors. The

497

environmental labels indicate that the products meet the demand for environmental

498

protection, which means that fewer air pollutants are emitted through the life cycle of the

499

products. Thus, the labels are intended to be the gateway line that will lead consumers

500

towards environmentally friendly products. Consumption in turn will promote emission

501

mitigation. Moreover, other actions, such as lowering tax rates on those products and

502

providing subsidies to their manufacturers, can be taken to support this policy. In addition,

503

this policy can complement the tax policy on intermediate products mentioned above. If the

504

Chinese government taxes intermediate inputs, the production costs will increase, and thus,

505

the price may increase. The products that have environmentally friendly labels may be

506

cheaper and consumers may preferentially purchase these products. However, commodity

507

prices are influenced by many kinds of factors, and the products with labels may be more

508

expensive. Thus, it is still necessary to place labels to arouse the public’s awareness of

509

environmental protection.

510

From an income-based perspective, special attention should be paid to the coal mining

511

sector, especially in Shanxi, to reduce primary PM2.5, SO2, VOC and NOX emissions

512

simultaneously. The Chinese government should provide financial incentives for selling

513

products to low-emission users and limit loan supply and subsidies to dominant enterprises of

514

sectors that have high income-based emissions (Liang et al., 2017a). However, it is difficult

515

for the Chinese government to identify low-emission downstream users and key enterprises. It

516

might be more feasible to further investigate the cross-provincial sales of the products of the

517

coal mining sector in Shanxi. Then, the Chinese government can tax the cross-provincial

518

trade between those key primary suppliers and high-emission users. Thus, key income-based

519

sectors are likely to be urged to seek other low-emission business partners because the taxes

520

on trade with high-emission users lead to reduced profits. Such measures can also be applied

521

to other key income-based sectors, such as the petroleum and gas in Heilongjiang, the

522

chemical industry in Jiangsu and the agriculture in Heilongjiang.

523 524

4.2 The limitations

525

The key agents (primary suppliers, sellers and consumers) we mentioned above have the

526

responsibility to take mitigation actions, which is known as the beneficiary pays principle

527

(BPP). However, this issue is a complex task and the information provided from this paper is

528

still insufficient to implement policies. Equity issues among different agents should be taken

529

into account because negative distributional outcomes (Dorband et al., 2019) might

530

undermine effectiveness of environmental-friendly policies (Engel, 2016). Pascual et al.

531

(2010) introduced the “efficiency-equity” interdependence curve to illustrate the links

532

between efficiency and equity. Engel (2016) has offered some feasible suggestions to avoid

533

negative impacts on the poor. In addition, due to the inaccuracy and lagging of MRIO, it is

534

impractical to determine the amount of tax to be paid and the key provinces or sectors

535

currently. Further research is needed to study how to determine the appropriate tax rate

536

considering shared-responsibilities as well as fairness and how to assess the feasibility and the

537

effective of policies.

538

The combination of the MRIO model with the computable general equilibrium (CGE)

539

model may become the way of future. CGE can be used to evaluate the impacts of

540

environmental taxes, which will further test whether the tax policies targeting key provinces

541

and sectors have a positive effect on emission mitigation. However, the related research

542

evaluating tax policies at regional scale (Hu et al., 2019) are relatively rare. There is still large

543

room for further studies considering shared responsibilities.

544

545

5. CONCLUSION

546

Income-based, final sales-based and consumption-based accounting methods can trace

547

emission transfers and provide policy makers with deeper insights into interprovincial and

548

shared responsibilities on emission reduction.

549

The Central (including Anhui and Henan) is the largest emitter outside the Central Coast

550

in order to meet the demand for sales within the Central Coast. Specifically, the

551

cross-provincial sale of products from the construction sector drives massive emissions,

552

especially in Jiangsu province (located in the Central Coast). In general, emissions (except for

553

NH3) are more likely to be driven by final sales of other provinces than by primary inputs.

554

Therefore, key final producers should be either regulated or taxed based on embodied

555

emissions in intermediate products. Key primary suppliers should be regulated or taxed owing

556

to trade with high-emission users. Through regulation (e.g., tax, subsidies and loans),

557

low-emission trade partners are chosen, and trades that lead to high emissions can be

558

restricted; thus, high-emission production can be reduced. The Chinese government should

559

also steer consumer behaviors and promote green consumption by using environmentally

560

friendly labels. Compared to only taking production-side measures, adopting policies from

561

multiple perspectives can be instrumental in controlling air pollution, realizing clean

562

production and sustainable development.

563

However, owing to the intrinsic drawbacks of the MRIO model, we still have a long way

564

to go to implement a viable policy taking into account shared responsibilities from supply

565

chains perspective.

566

567

568

Acknowledgements

569

This work was supported by funding from the National Natural Science Foundation of China

570

under award nos. 41571130010, 41671491, 41821005, and 41390240; National Key Research

571

and Development Program of China 2016YFC0206202; and the 111 Project (B14001).

572

573

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