Revealing Environmental Inequality Hidden in China s Inter-regional Trade

Transcription

1 Supporting Information for Revealing Environmental Inequality Hidden in China s Inter-regional Trade Wei Zhang 1,2, Yu Liu 3,4, Kuishuang Feng 5, Klaus Hubacek 5,6, Jinnan Wang 1,2*, Miaomiao Liu 1, Ling Jiang 7, Hongqiang Jiang 2, Nianlei Liu 2, Pengyan Zhang 8, Ying Zhou 2 and Jun Bi 1* 1 State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing , China 2 State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy for Environmental Planning, Beijing , China 3 Institutes of Science and Development, Chinese Academy of Sciences, Beijing , China 4 School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing , China 5 Department of Geographical Sciences, University of Maryland, College Park, Maryland 20742, United States 6 Department of Environmental Studies, Masaryk University, Brno 60200, Czech Republic 7 School of Government, Central University of Finance and Economics, Beijing , China 8 College of Environment and Planning, Henan University, Kaifeng , China *Corresponding to: wangjn1962@126.com(j. W.), jbi@nju.edu.cn(j. B.). This supporting information is organized as follows: Section 1 presents the methodology of China s Multi- Regional Input Output table in 2012, the compilation of China s sectoral air pollutant emission inventory of 2012, and the comparison between our emission inventory with other published inventories. Section 2 Compares previous studies with this paper. Finally, Section 3 shows the detailed methodology on the calculation of Atmospheric Pollutant Equivalent (APE). Section 4 shows the provincial emission accounting of three pollutants. Section 5 displays economic development and industrial structure in 30 provinces. Section 6 chooses Guangdong, Beijing, Hebei and Guizhou as the representations of Group I, Group II, Group III and Group IV and then analyze each province s sectoral net APE flows between itself and other provinces. Section 7 shows the detailed interregional transfer matrix of pollutants and value added. Number of pages: 27 Number of figures: 10 Number of tables: 16 S1

2 Contents 1. Data Resources and Uncertainties China s Multi-Regional Input Output table in Sectoral Air Pollutant Emission Inventory Uncertainty analysis Comparison of previous studies and this paper Atmospheric Pollutant Equivalents (APE) Provincial Emission Accounting of three pollutants Economic Development and Industrial Structure in 30 Provinces Sectoral Features of APE Transfer in Representative Provinces Interregional Transfer Matrix of pollutants and value added...21 References...26 S2

3 1 1. Data Resources and Uncertainties China s Multi-Regional Input Output table in The Multi-Regional Input Output Table (MRIOT) used in this paper was compiled based on 30 provincial input-output table (IOT) for 2012, which are newly released from China s National Bureau of Statistics 1. Additionally, 42 sectors in MRIOT are reclassified into 30 sectors for each province due to easy calculation. There are four steps to compile a MRIOT: Step 1, the goods in original provincial monetary input-output table are from domestic and imports together 2. So each province s IOT was firstly processed to be noncompetitive IOT 3, 4 by separating each sector s imported goods from domestic goods for both final use and inter-sector input according to the proportion of sectoral imports accounting for total output. Moreover, we double-check these proportions using national s proportions from Chinese IOT. Step 2, each province s import and export data were used to construct interregional trade coefficient matrix of MRIOT through a hybrid technique based on maximum entropy model and dual-constrained gravity model. The combination of those two models takes advantage of both minimization idea from the former and the distance philosophy from the latter 5. In addition, the iterative optimization algorithm is used to calculate interregional trade coefficient matrix. Step 3, making up MRIOT using Chenery-Moses model 6 based on provincial noncompetitive IOTs from step 1 and interregional trade coefficient matrix from step 2. Basic equation is as follows: (S1) d d T xij T F E X Here, F d and E represent final demand vector and export vector in each provinces, coefficient matrix, consisting of diagonal matrix rs T and its elements consumed by region s from sector i in region r by all products of sector i in region r, d x ij, T is the interregional trade rs t i represent the proportion of the products 20 t rs i T rs i rs Ti r (S2) Step 4, calibrate MRIOT using China s national IOT in Theoretically, the sum of each sector s value from 31 provinces in MRIOT should be equal to the value of the same sector in China s national IOT. However, they are not equal actually. Assuming China s national IOT is more accurate, RAS method 7, a diversified non-survey techniques, is used to calibrate MRIOT based on China s national IOT. Specifically, the original MRIO is firstly used as the initial matrix for calibration. Considering that different block matrixes present particular structures and characteristics, the calibration is processed respectively at the block matrix level. Then, as for the criteria of the calibration, we take the value from the nation IOT as the total amount, and take the provincial information from the MRIO as the data structure. In this way, both the rows and columns are calibrated in every block matrix. S3

4 29 Table S1. Framework of MRIO Table. input output Intermediate use region 1 region m sector 1 sector n sector 1 sector n final use region 1 region m export total output sector 1 region 1 sector n sector 1 region m sector n import added value total input Table S2. Classification of economic sectors and Chinese regions in MRIO Table Regions Sectors for each region NO. Names Abb. NO. Names Abb. 1 Beijing BJ 1 Agriculture Agriculture 2 Tianjin TJ 2 Coal mining CoalMining 3 Hebei HE 3 Mining of petroleum and natural gas CrudeOilGas 4 Shanxi SX 4 Metal ores mining MetalOre 5 Inner Mongolia NM 5 Non-metallic minerals mining NonmetalOre 6 Liaoning LN 6 Production of food and tobacco FoodTobacco 7 Jilin JL 7 Textiles Textiles 8 Heilongjiang HL 8 Wearing apparel, leather, fur, etc. ClothingLeather 9 Shanghai SH 9 Wood processing and furniture WoodFurniture 10 Jiangsu JS 10 Papermaking, printing, stationery, etc. PaperPrinting 11 Zhejiang ZJ 11 Fossil fuel refining PetrolCoking 12 Anhui AH 12 Chemical industry Chemical 13 Fujian FJ 13 Production of non-metallic mineral products NonmetalProducts 14 Jiangxi JX 14 Smelting and processing of metals MetalSmelt 15 Shandong SD 15 Metal products MetalProducts 16 Henan HA 16 General equipment GeneralEquip 17 Hubei HB 17 special equipment SpecialEquip 18 Hunan HN 18 Transport equipment TransportEquip 19 Guangdong GD 19 Electrical equipment ElectricalEquip 20 Guangxi GX 20 Electronic equipment ElectronicEquip S4

5 Regions Sectors for each region NO. Names Abb. NO. Names Abb. 21 Hainan HI 21 Measuring instrument and meter MeasureInstru 22 Chongqing CQ 22 Other manufacturing products OtherManuf 23 Sichuan SC 23 Scrap and waste ScrapWaste 24 Guizhou GZ 24 Electricity and heat power ElectricHeatpower 25 Yunnan YN 25 Gas supply GasSupply 26 Shaanxi SN 26 Water supply WaterSupply 27 Gansu GS 27 Construction Construction 28 Qinghai QH 28 Transport and warehousing TranspWarehouse 29 Ningxia NX 29 Wholesale, retail, hotels and catering WholeRetailHotel 30 Xinjiang XJ 30 Other services OtherServices Table S3. Region classifications of 30 provinces in China. Region Beijing-Tianjin east Coast Coast west west Central Provinces in each region Beijing and Tianjin Hebei and Shandong Liaoning, Jilin and Heilongjiang Jiangsu, Shanghai and Zhejiang Fujian, Guangdong and Hainan Xinjiang, Qinghai, Gansu, Ningxia, Shaanxi and Inner Mongolia Sichuan, Chongqing, Guizhou, Yunnan and Guangxi Shanxi, Henan, Hubei, Anhui, Hunan and Jiangxi Sectoral Air Pollutant Emission Inventory The air pollution emission inventory we used contain three main air pollutants, SO 2, NO x and PM, which were aggregated into 30 sectors for each province to match MRIO table s sectors. These pollutants are mainly from four sources: industry, traffic, agriculture and services. Pollutant emissions from industries (sector 2~26 in table S2) and traffic (sector 28 in table S2) are derived from China Environmental Statistics (CES) database; pollutant emissions from agriculture (sector 1 in table S2) and construction (sector 27 in table S2), Wholesale, retail, hotels and catering (sector 29 in table S2) and other services (sector 30 in table S2) are estimated according their energy consumption owing to the absence in CES database Pollution Emissions of Industries and Traffic Based on CES Database CES database contain basic information and corresponding air pollution emissions data from 147,996 industrial sources and different types of vehicles in all 337 cities of China in In China, CES database is regarded as the most authoritative survey data for pollution. Industrial sources are further divided into Major Industrial Pollution Sources (MIPS) and General Industrial Pollution Sources (GIPS). In CES database, all industrial sources are MIPS, accounting for about 90% of total industrial air pollution emissions according to First China Pollution Source (FCPSC) database for the year of 2007, which almost cover all officially registered enterprises in China ( about 5.93 million individual emitting sources, including 1.58 million industrial sources, 2.90 million agricultural sources, 1.45 million household S5

6 sources and 4.8 thousand centralized pollution treatment facilities) 8, 9.Air pollution emissions of GIPS are estimated according to emission coefficient, rather than statistics data of enterprises and they also cannot be allocated back to industrial sectors. Fortunately, the FCPSC database in 2007 include both MIPS and GIPS and they are also allocated to industrial sectors. Assuming the proportion of sectoral emissions of MIPS and GIPS were constant during 2007 and 2012, sectoral emissions of GIPS in 2012 are estimated as follows r Gi r r G Gi 2007 r (S3) G 2012 r Here, G i represents emissions of GIPS of sector i from region r in 2012, G r represents emissions of GIPS of region r in 2012 based on CES database. G r i and G r represent emissions of GIPS of sector i from region r and total emissions of GIPS of region r in 2007 based on FCPSC database The air pollution emission inventory of traffic was compiled based on CES database, which contained each province emissions of SO 2, NO x and PM from all types vehicles according to quantity of each vehicle and its pollutant emission coefficients. Here, the sector of traffic refers to for-profit transportation containing trucks, buses and taxis, do not include private cars Here, E, E, E k traffic k truck k bus and E E E E (S4) k k k k traffic truck bus taxi k E taxi represent the emissions of pollutant k from traffic, truck, bus and taxi. The emissions from truck and bus can obtain from CES database. The emissions from taxi, E k taxi, can be got as following: 68 E E k k taxi taxi taxi smallcar Ntaxi ttaxi N private t private N t (S5) Here, k E smallcar represents emissions of pollutant k from small car derived from CES database; N taxi and N private represent the quantity of taxi and private car respectively derived from China Statistical Yearbook 10 and China Transport Statistical Yearbook 11 ; China Transportation Statistical Yearbook 11. t taxi and t private represent mileage per year of taxi and private car respectively, derived from Estimated Air Pollution Emissions of Agriculture, Construction and Service Based on Energy Consumption Air pollution emissions of agriculture, construction and service are estimated based on energy consumption and emission factors as follows. Here, E C P (S6) k k s se se e C se represents each province s consumption of energy type e in sector s, which are obtained from China Energy Statistical Yearbook 12, k P se represents emission factors of energy type e in sector s for pollutants k, which are derived from technical report of FCPSC 9 and previous literature 13. Energy types include coal, natural gas and petroleum; sectors include agriculture, construction, wholesale, retail, hotels and catering and other services (table S2); pollutants include SO 2, NO x and PM Comparison of China s Air Pollutants Emission Inventory from Different Sources According to our emission inventory (Fig. S1a), China s emissions of SO 2, NO x and PM are approximate 25.5 Tg, 25.4 Tg and 14.8 Tg respectively in Industry is the main source of pollutants emissions, accounting for 75%, 65% and S6

7 % of total emissions. Owing to the absence of emissions in 2012 from GAINS ( EDGAR ( REAS ( and MEIC ( we compare emissions in this study with other existing estimations 14, 15. The result (Fig. S1b) shows that emissions of SO 2 in our inventory are almost equal to Xia s estimations while emissions of NO x in our inventory are almost equal to Shi s estimations and about 86%~89% of Xia s estimations. Therefore, we believe that our emission inventory is reliable Figure S1. China s air pollutants emissions in 2012 (a) and the comparison with other estimations 14, 15 (b). Household direct emissions (including private vehicle) are from CES database 16 but not used in calculation of trade-embodied emissions in our article because they don t participate in territorial production activity. Table S4. The sectoral APE emission inventory of 30 provinces in China. Units: Gg Regions Provinces Agriculture Chemical Nonmetal- Products Metal- Smelt Electric- Heatpower Transp- Warehouse Other sectors Household Total Beijing- Tianjin Coast Coast Central Beijing Tianjin Hebei ,000 1, ,981 Shandong , , ,613 Liaoning , ,670 Jilin ,293 Heilongjiang ,008 Shanghai Jiangsu , ,937 Zhejiang ,829 Fujian ,114 Guangdong ,550 Hainan Anhui ,771 Shanxi , ,313 Jiangxi ,378 Henan , ,356 Hubei ,003 S7

8 Regions Provinces Agriculture Chemical Nonmetal- Products Metal- Smelt Electric- Heatpower Transp- Warehouse Other sectors Household Total Hunan ,774 Guangxi ,243 Chongqing ,300 Sichuan ,961 Guizhou , ,473 Yunnan ,433 Inner Mongolia , ,989 Shaanxi ,081 Gansu ,240 Qinghai Ningxia ,010 Xinjiang ,116 Total 2,227 2,742 6,271 6,074 21,297 5,719 10,581 3,229 58,141 Table S5. The sectoral SO2 emission inventory of 30 provinces in China. Units: Gg Regions Provinces Agriculture Chemical Nonmetal- Products Metal- Smelt Electric- Heatpower Transp- Warehouse Other sectors Household Total Beijing- Tianjin Coast Coast Central Beijing Tianjin Hebei ,531 Shandong ,215 Liaoning ,189 Jilin Heilongjiang Shanghai Jiangsu ,094 Zhejiang Fujian Guangdong Hainan Anhui Shanxi ,440 Jiangxi Henan ,364 Hubei Hunan Guangxi Chongqing Sichuan ,005 Guizhou ,512 Yunnan Inner Mongolia ,772 Shaanxi Gansu Qinghai Ningxia S8

9 Regions Provinces Agriculture Chemical Nonmetal- Products Metal- Smelt Electric- Heatpower Transp- Warehouse Other sectors Household Total Xinjiang Total 1,089 1,682 2,155 3,798 8, ,146 2,054 25, Table S6. The sectoral NOx emission inventory of 30 provinces in China. Units: Gg Regions Provinces Agriculture Chemical Nonmetal- Products Metal- Smelt Electric- Heatpower Transp- Warehouse Other sectors Household Total Beijing- Tianjin Coast Coast Central Beijing Tianjin Hebei ,772 Shandong ,780 Liaoning ,057 Jilin Heilongjiang Shanghai Jiangsu ,531 Zhejiang Fujian Guangdong ,322 Hainan Anhui Shanxi ,270 Jiangxi Henan ,587 Hubei Hunan Guangxi Chongqing Sichuan Guizhou Yunnan Inner Mongolia ,511 Shaanxi Gansu Qinghai Ningxia Xinjiang Total ,856 1,259 10,640 5,185 2, ,096 Table S7. The sectoral PM emission inventory of 30 provinces in China. Units: Gg Regions Provinces Agriculture Chemical Nonmetal- Products Metal- Smelt Electric- Heatpower Transp- Warehouse Other sectors Household Total Beijing- Tianjin Beijing Tianjin Hebei ,364 Shandong ,081 S9

10 Regions Provinces Agriculture Chemical Nonmetal- Products Metal- Smelt Electric- Heatpower Transp- Warehouse Other sectors Household Total 106 Coast Coast Central Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Fujian Guangdong Hainan Anhui Shanxi ,103 Jiangxi Henan Hubei Hunan Guangxi Chongqing Sichuan Guizhou Yunnan Inner Mongolia ,240 Shaanxi Gansu Qinghai Ningxia Xinjiang Total ,727 2,301 2, ,327 1,426 14, Uncertainty analysis Uncertainties from emission inventory Bottom-up emission inventories are usually uncertain due to incomplete knowledge about activity levels, technology distributions and emission factors 17. Previous study 18 showed the uncertainties in China s emission inventory were estimated to be 14%~13%, 13%~37%, 17%~54%, 25%~136% and 40%~121% for SO 2, NO x, PM 2.5, black carbon (BC), and organic carbon (OC), respectively. The inventory used in our study is mainly based on official statistics data, and scientific estimation according to energy consumption, which is different from other inventories totally based on estimation (GAINS, EDGAR, REAS and other studies 14, 19 ). The CES database is provided by China National Environmental Monitoring Centre(CNEMC), a MEP s official institute for emission statistic and environmental quality monitoring. Every year, CNEMC conduct the bottom-up emission survey of Major Industrial Pollution Sources (MIPS, accounting for 90% of total air pollutants emission) and estimate other emissions from vehicles and small industrial sources according to energy consumption, scientific emission factor and other reasonable manners. Then, CNEMC S10

11 establishes an emission database (CES) and officially publishes a related book named Annual Statistic Report on Environment in China. We compare CES with FCPSC database (Table S8) and find that in 2007, the total emissions of SO 2 from CES are more than FCPSC (6.4%), emissions of NO x and PM from CES are less than FCPSC (8.5% and 15.3% respectively). Chinese government obtained a detailed sectoral emission factors based on FCPSC after The emission factors combined with statistical experience are used to improve the following CES s data quality after 12th Five-Year Plan periods ( ) to evaluate local government s performance of pollution control. Particularly after 2012, the emission data has been taken as the basis of Action Plan of Air Pollution Control ( ). Table S8. Comparison of China s official statistical emission inventories between CES and FCPSC database Sectors Sources Emissions in 2007 (Tg) SO2 NOx PM CES Industrial FCPSC CES/FCPSC 101% 106% 84% CES Household&vehicle FCPSC CES/FCPSC 165% 63% 89% CES The total emission FCPSC CES/FCPSC 106% 91% 85% Data quality control technology and regulations are used in the FCPSC as well as CES after 2009 to ensure the reliability of survey data collected from pollution sources. There are 9 technical regulations and 5 procedure regulations regarding each step of data collection, including field survey, field monitoring, data checking, data entry to the system, data aggregation, etc. Field survey means data collection and reporting in site (industrial factories etc.) by staff of local environmental protection agencies; Field monitoring is deployed with online monitoring system, such as CEMS (Continuous Emissions Monitoring System) on stack, and site environmental monitoring; Data checking mainly refers to checking the original data by local environmental protection agencies, such including magnitude and units checking; Data entry refers to the entry of the original data (paper based tables) into the system; Data aggregation refers to aggregating the dataset bottom up to national level, namely from dataset in local environmental protection bureau to city environmental protection agencies, then to provincial environmental protection agencies and ultimately to MEP. The FCPSC and CES are widely used in existing studies 8, 20-23, which indicate that the emission inventories based on FCPSC and CES are reliable. The high spatial resolution carbon dioxide emission map for China based on FCPSC are indirectly validated by using in chemical transport models 20 and satellite observations Uncertainties from MRIOT There are additional uncertainties from MRIO analysis which include but is not limited to linking trade through supply chain among different regions. Uncertainties mostly come from the source (survey) data and data manipulation of MRIO S11

12 model Moreover, the uncertainties may also come from MRIO s sector detail, region coverage, and the number of environmental extensions 27. A study from Lin et al (2014) found that the uncertainties in Chinese input-output model are relative small, which contribute about 10% of total errors in export-related pollutant emissions 28. The MRIOT we used is based on each province s single IOT in 2012 released by National Statistics Bureau(NSB). The method to compile the MRIOT we used (see Section 1.1) is also scientific and reasonable, which is widely used to the compilation of China s MRIOT in 2002 and 2007 in published paper or book Comparison of previous studies and this paper Table S9. Comparison of previous studies and this paper. Related studies Year Subject Object Scope Indicator Lin et al trade-related air quality international trade China vs. US daily mean surface air (2014) 32 pollutant concentrations Zhao et al trade-embodied domestic consumption 30 provinces in emissions of primary PM2.5, (2015) 33 emissions and international China SO2, NOx and NMVOC export Jiang et al trade-related health international export 30 provinces in the mortality attributable to (2015) 34 impact China PM2.5-related air pollution Zhao et al trade-related health domestic consumption 7 regions in China premature deaths related to (2017) 17 impact PM2.5 pollution Zhang et al trade-related health international trade 13 world regions premature deaths related to (2017) 35 impact PM2.5 pollution This study 2012 environmental injustice domestic consumption 30 provinces in value added and air hidden in tradeembodied China pollutants emissions (SO2, emissions and NOx, PM) economic gains 3. Atmospheric Pollutant Equivalents (APE) China s Ministry of Environmental Protection (MEP) established a system of imposing discharge fee on various aquatic, atmospheric and solid pollutants in To simplify the discharge fee of various pollutants, MEP designed a new measure named pollutant equivalent, which allows aggregating different types of pollutants according to their environmental and health impacts by assigning a specific coefficient representing their respective damage to each pollutant. pollutant equivalent also was used in Chinese first tax law of environmental protection to levy the taxation on various pollutants 36. The conversion coefficient can be calculated according to MEP s scientific method, which comprehensively considers each pollutant s impact on ecological system, toxicity on organism and technical feasibility to remove 37. Above methodology gives us a chance to comprehensively represent the severity of air pollution by using three major air pollutants. Similar as carbon dioxide equivalent is used to measure all greenhouse gases uniformly, in this study, SO 2, S12

13 NO x, soot and dust are converted into a new measure named atmospheric pollutant equivalents (APE) as follows. APE E i i (S7) Ri Here, i indicates the type of pollutants, including SO 2, NO x, and PM (i.e. soot and dust); E i and R i represent emissions and conversion coefficient of pollutants i, respectively. The conversion coefficients of each air pollutants are shown in Table S6, which are released in China s official documents concerning pollution charge schedule 38. In other words, per kg APE equal to 0.95, 0.95, 2.18 and 4 kg of SO 2, NO x, soot and dust based on the impacts of individual pollutant on air quality or public health. Previous research also indicated that sulfate, nitrate, and ammonia constituted 40~57% of PM 2.5 in eastern China 39, and sulfur dioxide is also proved to be the main component of PM 2.5 and significantly impact ischemic heart disease mortality 40. Table S10. Conversion coefficients to APE of four air pollutants. air pollutants conversion coefficient SO NO x 0.95 Soot 2.18 PM Dust 4 S13

14 Provincial Emission Accounting of three pollutants Figure S2. The consumption- and production-based emissions of China s 30 provinces. The bars outlined in black represent the SO2 (a), NOx (b) and PM(c) generated by all products and services produced in each province, and the coloured bars represent the respective pollutants produced along the entire production chain allocated to the consuming region. The numbers at the right side of each bar represent the ratios of consumption-to-production of emissions. If the ratio is greater than 1, that province has net outsourced its emissions to other provinces; if the ratio is less than 1, other provinces have net outsourced their emissions to this province Figure S3. Transfer matrix of SO2, NOx and PM among provinces in China. The horizontal axes of a, b and c show the emissions of three pollutants transferred from other provinces to the focal province; the vertical axes show the emissions of three pollutants transferred from the focal province to other provinces. S14

15 NO. 1 Table S11. The regional and sectoral characteristics of top 25-pair APE transfer inter-provinces. Outflow regions sectors regions sectors Jiangsu Construction, OtherServices, TransportEquip Inner Mongolia Inflow ElectricHeatpower 2 Jiangsu Construction, OtherServices Shanxi ElectricHeatpower, NonmetalProducts 3 Beijing Construction Hebei MetalSmelt, NonmetalProducts, ElectricHeatpower Inner Liaoning Construction, OtherServices, TransportEquip ElectricHeatpower 4 Mongolia 5 Jiangsu Construction Hebei ElectricHeatpower, MetalSmelt, NonmetalProducts Inner Zhejiang Construction, OtherServices ElectricHeatpower, MetalSmelt 6 Mongolia 7 Liaoning Construction, OtherServices Shanxi ElectricHeatpower, MetalSmelt 8 Zhejiang Construction, OtherServices, TransportEquip Shanxi ElectricHeatpower, MetalSmelt 9 Beijing Construction, OtherServices, TransportEquip Shanxi ElectricHeatpower, MetalSmelt, NonmetalProducts 10 Guangdong Construction, OtherServices, TransportEquip Shandong ElectricHeatpower, MetalSmelt, NonmetalProducts Inner Guangdong Construction, OtherServices, TransportEquip ElectricHeatpower, MetalSmelt 11 Mongolia 12 Guangdong Construction, OtherServices, TransportEquip Henan ElectricHeatpower, MetalSmelt, NonmetalProducts Inner Beijing Construction, OtherServices, TransportEquip ElectricHeatpower, MetalSmelt, NonmetalProducts 13 Mongolia 14 Guangdong Construction, OtherServices, TransportEquip Shanxi ElectricHeatpower, MetalSmelt 15 Guangdong Construction, OtherServices, TransportEquip Guizhou ElectricHeatpower, NonmetalProducts Inner Shanghai Construction, OtherServices, TransportEquip ElectricHeatpower, MetalSmelt 16 Mongolia 17 Guangdong Construction, TransportEquip, ElectricalEquip Hebei MetalSmelt, ElectricHeatpower 18 Zhejiang Construction Hebei ElectricHeatpower, MetalSmelt, NonmetalProducts 19 Shanghai Construction, OtherServices, TransportEquip Hebei MetalSmelt, ElectricHeatpower, NonmetalProducts 20 Shanghai Construction, OtherServices, TransportEquip Shanxi ElectricHeatpower, MetalSmelt, NonmetalProducts Construction, OtherServices, TransportEquip, Inner Shandong ElectricHeatpower, MetalSmelt 21 SpecialEquip, FoodTobacco Mongolia 22 Tianjin Construction, OtherServices, TransportEquip Hebei ElectricHeatpower, MetalSmelt, NonmetalProducts 23 Jiangsu Construction, OtherServices Shandong ElectricHeatpower, NonmetalProducts 24 Jiangsu Construction, OtherServices Henan ElectricHeatpower, NonmetalProducts 25 Shandong Construction, OtherServices, SpecialEquip Shanxi ElectricHeatpower, MetalSmelt S15

16 5. Economic Development and Industrial Structure in 30 Provinces Guangdong Jiangsu Shandong Zhejiang Henan Hebei Liaoning Sichuan Hubei Hunan Shanghai Fujian Beijing Anhui Inner Mongolia Shaanxi Heilongjiang Guangxi Jiangxi Tianjin Shanxi Jilin Chongqing Yunnan Xinjiang Guizhou Gansu Hainan Ningxia Qinghai 5,707 5,406 5,001 3,467 2,960 2,658 2,485 2,387 2,225 2,215 2,018 1,970 1,788 1,721 1,588 1,445 1,369 1,304 1,295 1,289 1,211 1,194 1,141 1, ,000 2,000 3,000 4,000 5,000 6,000 GDP (billion RMB) Tianjin Beijing Shanghai Jiangsu Inner Mongolia Zhejiang Liaoning Guangdong Fujian Shandong Jilin Chongqing Hubei Shaanxi Hebei Ningxia Heilongjiang Xinjiang Shanxi Hunan Qinghai Hainan Henan Sichuan Jiangxi Anhui Guangxi Yunnan Gansu Guizhou GDP per capita (1000 RMB) Figure S4. GDP and GDP per capita in 30 provinces of China in S16

17 Beijing (a) Primary Industry Secondary Industry Tertiary Industry Coast Central Coast - Tianjin Inner Mongolia Xinjiang Ningxia Qinghai Gansu Shaanxi Yunnan Guizhou Sichuan Chongqing Guangxi Hunan Hubei Henan Shanxi Jiangxi Anhui Hainan Guangdong Fujian Zhejiang Jiangsu Shanghai Heilongjiang Jilin Liaoning Shandong Hebei Tianjin Beijing 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Perchentage of each sector (b) Coast Central Coast Beijin g- Tianjin Inner Mongolia Xinjiang Ningxia Qinghai Gansu Shaanxi Yunnan Guizhou Sichuan Chongqing Guangxi Hunan Hubei Henan Shanxi Jiangxi Anhui Hainan Guangdong Fujian Zhejiang Jiangsu Shanghai Heilongjiang Jilin Liaoning Shandong Hebei Tianjin Beijing 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Perchentage of each sector Agriculture Coal mining Mining of petroleum and natural gas Metal ores mining Non-metallic minerals mining Production of food and tobacco Textiles Wearing apparel, leather, fur, etc. Wood processing and furniture Papermaking, printing, stationery, etc. Fossil fuel refining Chemical industry Production of non-metallic mineral products Smelting and processing of metals Metal products General and special equipment Transport equipment Electrical equipment Electronic equipment Measuring instrument and meter Other manufacturing products Scrap and waste Electricity and heat power Gas supply Water supply Construction Transport and warehousing Wholesale,retail,Hotels and catering Other services Figure S5. Sectoral industrial structures of China s 30 provinces in (a) The industrial structures of three major economic sectors; (b) The industrial structures of 30 economic sectors. S17

18 APE emissions per unit of output (kg / 10,000 yuan) 1,600 1,400 1,200 1, Agriculture Chemical NonmetalProducts MetalSmelt ElectricHeatpower TranspWarehouse OtherServices 0 Beijing Tianjin Hebei Shandong Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Fujian Guangdong Hainan Anhui Shanxi Jiangxi Henan Hubei Hunan Guangxi Chongqing Sichuan Guizhou Yunnan Inner Mongolia Shaanxi Gansu Qinghai Ningxia Xinjiang Beijing- Tianjin Coast Coast Central Figure S6. APE emissions intensity in major sectors of regions and provinces. 6. Sectoral Features of APE Transfer in Representative Provinces In this paper, we choose Guangdong, Beijing, Hebei and Guizhou as the representations of Group I, Group II, Group III and Group IV. Then we analyze each province s sectoral net APE flows between itself and other provinces. Figure S7. Net outflows of APE and value added between Beijing and other provinces. S18

19 Figure S8. Net outflows of APE and value added between Hebei and other provinces. Figure S9. Net outflows of APE and value added between Guangdong and other provinces. S19

20 Figure S10. Net outflows of APE and value added between Guizhou and other provinces. S20

21 7. Interregional Transfer Matrix of pollutants and value added Table S12. Inter-regional APE transfer within China at provincial level. Units: Gg regions BJ TJ HE SD LN JL HL SH JS ZJ FJ GD HI AH SX JX HA HB HN GX CQ SC GZ YN NM SN GS QH NX XJ Total BJ TJ HE SD LN JL HL SH JS ZJ FJ GD HI AH SX JX HA HB HN GX CQ SC GZ YN NM SN GS QH NX XJ Total S21

22 Table S13. Inter-regional SO2 emission transfer within China at provincial level. Units: Gg regions BJ TJ HE SD LN JL HL SH JS ZJ FJ GD HI AH SX JX HA HB HN GX CQ SC GZ YN NM SN GS QH NX XJ Total BJ TJ HE SD LN JL HL SH JS ZJ FJ GD HI AH SX JX HA HB HN GX CQ SC GZ YN NM SN GS QH NX XJ Total S22

23 Table S14. Inter-regional NOx emission transfer within China at provincial level. Units: Gg regions BJ TJ HE SD LN JL HL SH JS ZJ FJ GD HI AH SX JX HA HB HN GX CQ SC GZ YN NM SN GS QH NX XJ Total BJ TJ HE SD LN JL HL SH JS ZJ FJ GD HI AH SX JX HA HB HN GX CQ SC GZ YN NM SN GS QH NX XJ Total S23

24 Table S15. Inter-regional PM emission transfer within China at provincial level. Units: Gg regions BJ TJ HE SD LN JL HL SH JS ZJ FJ GD HI AH SX JX HA HB HN GX CQ SC GZ YN NM SN GS QH NX XJ Total BJ TJ HE SD LN JL HL SH JS ZJ FJ GD HI AH SX JX HA HB HN GX CQ SC GZ YN NM SN GS QH NX XJ Total S24

25 Table S16 Inter-regional value added transfer within China at provincial level. Units: billion RMB regions BJ TJ HE SD LN JL HL SH JS ZJ FJ GD HI AH SX JX HA HB HN GX CQ SC GZ YN NM SN GS QH NX XJ Total BJ TJ HE SD LN JL HL SH JS ZJ FJ GD HI AH SX JX HA HB HN GX CQ SC GZ YN NM SN GS QH NX XJ Total S25