In the format provided by the authors and unedited.ARTICLES

PUBLISHED ONLINE: XXMONTH XXXX | DOI: 10.1038/NCLIMATE3373

Urban cross-sector actions for carbon mitigationwith local health co-benefits in ChinaAnu Ramaswami1*, Kangkang Tong1, Andrew Fang1, Raj M. Lal2, Ajay Singh Nagpure1, Yang Li3,Huajun Yu3, Daqian Jiang4, Armistead G. Russell2, Lei Shi3, Marian Chertow4, YangjunWang5

and ShuxiaoWang3

Cities o�er unique strategies to reduce fossil fuel use through the exchange of energy and materials across homes,businesses, infrastructure and industries co-located in urban areas. However, the large-scale impact of such strategies hasnot been quantified. Using new models and data sets representing 637 Chinese cities, we find that such cross-sectoralstrategies—enabled by compact urban design and circular economy policies—contribute an additional 15%–36% to nationalCO2 mitigation, compared to conventional single-sector strategies. As a co-benefit, ∼25,500 to ∼57,500 deaths annuallyare avoided from air pollution reduction. The benefits are highly variable across cities, ranging from <1%–37% for CO2emission reduction and <1%–47% for avoided premature deaths. These results, using multi-scale, multi-sector physicalsystems modelling, identify cities with high carbon and health co-benefit potential and show that urban–industrial symbiosisis a significant carbon mitigation strategy, achievable with a combination of existing and advanced technologies in diversecity types.

Energy supply to support urban homes, businesses, industries,transportation, construction, water and waste infrastructuresectors is associated with >70% of global carbon (CO2)

emissions1. Further, fossil-fuel-related outdoor/ambient airpollution by fine particulate matter (<2.5 µm: PM2.5) contributesto more than 4.7 million premature deaths worldwide2, a majorityoccurring in populous urban areas. Therefore, city strategies thatreduce fossil fuel use have great potential to achieve both globalcarbon mitigation and local health protection goals. However,quantifying carbon and health co-benefits potential has beenchallenging for cities due to three main reasons, summarized below.

First, to quantify contributions toward global CO2 emissions,individual cities are now including both territorial (direct) fueluse as well as transboundary CO2 emissions embodied in supplychains that support their residential, commercial, and industrialactivities through different footprinting perspectives3. AlthoughCO2 emissions footprints of several individual cities and a few city-typologies worldwide have been reported4–6, quantifying total urbancontributions toward national or global CO2 emissions has beenchallenging6, because we do not yet have energy-use and energy-supply data for all city activity sectors, for all cities in a nation, withattention to local specificity.

Second, the unique opportunities that cities provide for CO2mitigation through co-location of residential–commercial andindustrial activities have not been fully assessed. Most CO2mitigation strategies modelled in global scenario models7,8 andin individual cities9,10 focus on energy efficiency within singlesectors (for example, industry, power plants, transportation orbuilding energy use)8. Although the role of urban co-locationin reducing travel demand is recognized7, uniquely urban cross-sectoral actions that advance system-wide energy efficiency byreutilizing ‘waste’ energy or materials across co-located homes,

commercial buildings, industries and the various infrastructuresectors in cities, have not been quantified to date in energy futuresmodels. These strategies, described by the term urban–industrialsymbiosis11,12 show much promise, particularly enhanced by recenttechnological innovations. Examples include advanced fourth-generation district heating/cooling systems13,14 that enable efficientconveyance and use of presently unutilized low-grade industrialwaste heat up to 30 km fence line distances15 to heat and coolbuildings in cities, energy exchanges across co-located industries16,and strategic material exchanges such as substituting fly ash andsteel slag for energy-intensive production of Portland cement usedin construction17–19. Compact urban design and spatial urbaninfrastructure planning that supports district energy15,20, alongwith circular economy policies21, are essential for implementingsuch cross-sectoral symbiosis in cities, highlighting the uniqueurban nature of these strategies. Estimating fuel savings and CO2reductions from widespread adoption of these actions requiresco-location data on homes, businesses and industry, as well asnew energy- and material-exchange algorithms to estimate cross-sectoral symbiotic exchange potential in diverse cities of a nation,based on each city’s unique mix of residential, commercial andindustrial activities.

Third, CO2 and PM2.5 co-benefits experienced by individualcities due to fuel-use reductions both within and outside theboundary, arising from city actions, are of interest. However, fewmodels are available to address CO2 and PM2.5 co-benefits atthe city scale. The emerging co-benefits literature largely focuseson national-scale aggregate co-benefit estimation22, with a fewmodels evaluating fuel use, CO2 and air pollution interactionsat regional scales23. Of interest to cities is the distribution ofCO2 and health co-benefits across different cities in a nation;the exploration of transboundary CO2 interactions from energy

1University of Minnesota, Twin Cities Campus, Minneapolis, Minnesota 55455, USA. 2Georgia Institute of Technology, Atlanta, Georgia 30332, USA.3Tsinghua University, 100084 Beijing, China. 4Yale University, New Haven, Connecticut 06520, USA. 5Shanghai University, 200444 Shanghai, China.*e-mail: anu@umn.edu

NATURE CLIMATE CHANGE | ADVANCE ONLINE PUBLICATION | www.nature.com/natureclimatechange 1

ARTICLESPUBLISHED ONLINE: XXMONTH XXXX | DOI: 10.1038/NCLIMATE3373

Urban cross-sector actions for carbon mitigationwith local health co-benefits in ChinaAnu Ramaswami1*, Kangkang Tong1, Andrew Fang1, Raj M. Lal2, Ajay Singh Nagpure1, Yang Li3,Huajun Yu3, Daqian Jiang4, Armistead G. Russell2, Lei Shi3, Marian Chertow4, YangjunWang5

and ShuxiaoWang3

Cities o�er unique strategies to reduce fossil fuel use through the exchange of energy and materials across homes,businesses, infrastructure and industries co-located in urban areas. However, the large-scale impact of such strategies hasnot been quantified. Using new models and data sets representing 637 Chinese cities, we find that such cross-sectoralstrategies—enabled by compact urban design and circular economy policies—contribute an additional 15%–36% to nationalCO2 mitigation, compared to conventional single-sector strategies. As a co-benefit, ∼25,500 to ∼57,500 deaths annuallyare avoided from air pollution reduction. The benefits are highly variable across cities, ranging from <1%–37% for CO2emission reduction and <1%–47% for avoided premature deaths. These results, using multi-scale, multi-sector physicalsystems modelling, identify cities with high carbon and health co-benefit potential and show that urban–industrial symbiosisis a significant carbon mitigation strategy, achievable with a combination of existing and advanced technologies in diversecity types.

Energy supply to support urban homes, businesses, industries,transportation, construction, water and waste infrastructuresectors is associated with >70% of global carbon (CO2)

emissions1. Further, fossil-fuel-related outdoor/ambient airpollution by fine particulate matter (<2.5 µm: PM2.5) contributesto more than 4.7 million premature deaths worldwide2, a majorityoccurring in populous urban areas. Therefore, city strategies thatreduce fossil fuel use have great potential to achieve both globalcarbon mitigation and local health protection goals. However,quantifying carbon and health co-benefits potential has beenchallenging for cities due to three main reasons, summarized below.

First, to quantify contributions toward global CO2 emissions,individual cities are now including both territorial (direct) fueluse as well as transboundary CO2 emissions embodied in supplychains that support their residential, commercial, and industrialactivities through different footprinting perspectives3. AlthoughCO2 emissions footprints of several individual cities and a few city-typologies worldwide have been reported4–6, quantifying total urbancontributions toward national or global CO2 emissions has beenchallenging6, because we do not yet have energy-use and energy-supply data for all city activity sectors, for all cities in a nation, withattention to local specificity.

Second, the unique opportunities that cities provide for CO2mitigation through co-location of residential–commercial andindustrial activities have not been fully assessed. Most CO2mitigation strategies modelled in global scenario models7,8 andin individual cities9,10 focus on energy efficiency within singlesectors (for example, industry, power plants, transportation orbuilding energy use)8. Although the role of urban co-locationin reducing travel demand is recognized7, uniquely urban cross-sectoral actions that advance system-wide energy efficiency byreutilizing ‘waste’ energy or materials across co-located homes,

commercial buildings, industries and the various infrastructuresectors in cities, have not been quantified to date in energy futuresmodels. These strategies, described by the term urban–industrialsymbiosis11,12 show much promise, particularly enhanced by recenttechnological innovations. Examples include advanced fourth-generation district heating/cooling systems13,14 that enable efficientconveyance and use of presently unutilized low-grade industrialwaste heat up to 30 km fence line distances15 to heat and coolbuildings in cities, energy exchanges across co-located industries16,and strategic material exchanges such as substituting fly ash andsteel slag for energy-intensive production of Portland cement usedin construction17–19. Compact urban design and spatial urbaninfrastructure planning that supports district energy15,20, alongwith circular economy policies21, are essential for implementingsuch cross-sectoral symbiosis in cities, highlighting the uniqueurban nature of these strategies. Estimating fuel savings and CO2reductions from widespread adoption of these actions requiresco-location data on homes, businesses and industry, as well asnew energy- and material-exchange algorithms to estimate cross-sectoral symbiotic exchange potential in diverse cities of a nation,based on each city’s unique mix of residential, commercial andindustrial activities.

Third, CO2 and PM2.5 co-benefits experienced by individualcities due to fuel-use reductions both within and outside theboundary, arising from city actions, are of interest. However, fewmodels are available to address CO2 and PM2.5 co-benefits atthe city scale. The emerging co-benefits literature largely focuseson national-scale aggregate co-benefit estimation22, with a fewmodels evaluating fuel use, CO2 and air pollution interactionsat regional scales23. Of interest to cities is the distribution ofCO2 and health co-benefits across different cities in a nation;the exploration of transboundary CO2 interactions from energy

1University of Minnesota, Twin Cities Campus, Minneapolis, Minnesota 55455, USA. 2Georgia Institute of Technology, Atlanta, Georgia 30332, USA.3Tsinghua University, 100084 Beijing, China. 4Yale University, New Haven, Connecticut 06520, USA. 5Shanghai University, 200444 Shanghai, China.*e-mail: anu@umn.edu

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SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE3373

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SI-1: Figure and Tables

Figure SI-1: Effectiveness of downscaling industrial energy use (electricity end-use is shown here) from national data to individual cities by employee, compared to at-scale data reported (for a subset of 280 cities) from the China City Statistical Handbook1. The correlation coefficient (excluding outliers) of 0.8 indicates effectiveness of this downscaling approach, which is applied to estimate industrial non-electricity fuel use in 13 smaller industry sub- in each city (excluding cement, steel, power and heat). Further details are provided in Tong et al (2017)2.

Figure SI-2: Effectiveness of downscaling household energy use (electricity end-use is shown here) from provincial data to individual cities by household numbers, compared to at-scale data reported (for a subset of 280 cities) from the China City Statistical Handbook 1. Correlation coefficient of 0.91 indicates the effectiveness of downscaling for the household sector. Further details are provided in Tong et al (2017)2.

y = 0.68x + 26631R² = 0.80

0

1000000

2000000

3000000

4000000

5000000

6000000

0 2000000 4000000 6000000 8000000 10000000

Dow

n-sc

aled

indu

stri

al e

lect

rici

ty u

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(10

MW

h)

At-scale (city-scale) industrial electricity use reported for cities(10 MWh)

y = 0.91x + 20529R² = 0.91

0

400000

800000

1200000

1600000

2000000

0 500000 1000000 1500000 2000000

Dow

n-sc

aled

hou

seho

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lect

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0 M

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At-scale household electricity use reported for cities(10 MWh)

Table SI-1: Comparison of bottom-up data utilized in the China City Industry and Infrastructure Database (developed in this paper) versus China’s National Statistical Yearbook Energy Balance Table (NBS, 20113, Top-Down analysis). Further details are provided in Tong et al (2017)2.

Sector/Item Our Bottom Up Data (10,000 ton coal-equivalent (tce))

Data Source of Chinese City and Infrastructure Database

National Energy Balance Sheet (10,000 ton coal-equivalent (tce))

Difference

Total coal use in powerplants 108,000 Tsinghua University

(individual plants >6MW) 110,000 2%

Thermal Input to Industrial production:

Industrial Sector

-12%

- Steel 32,437

Tsinghua University

- Fossil Inputs: 134,485

- Cement 14,845 - Heat supply fossil inputs (primary): 13,716

- Brick 8,161

- Lime 3,513

- Glass 778

- Coking 5,009

- Ammonia 5,859

- Ceramics 3,722

- Alumina 2,640

- Copper 276

- Other chemical 2,541

SUM OF Above sectors 79,781

- Other Industries 50,587

Downscaled from National Statistical Yearbook based on employment at county level (CIED database ) and industrial energy use statistics (NBS, 2011)

Total of Industrial sector, excluding powerplants

130,368 148,201

Residential and commercial Thermal Inputs (primary):

-Direct fossil fuel inputs: 24,317

Direct fossil fuel inputs down-scaled from

Tsinghua University Provincial data; District heating data from China

Urban Construction Yearbook(Ministry of

Housing and Urban-Rural Development, 2011) with energy conversion from Provincial Handbooks

- Direct fossil fuel inputs: 19,917 35%*

(see note below) - Primary Energy District

heating supply:16,039 - Heat Supply (in primary energy units): 6121

Transportation 38,373 34,770 9%

Agricultural and Other Thermal Inputs Not included in our work 8,065 NA

SUM OF ABOVE EXCLUDING AGRICULTURE

317,097 This dataset combining all the above sources 319,009 -0.6%

Footnote: Several studies note apparent differences in sub-sectors, e.g., between bottom-up data and statistical yearbook data caused by categorization differences. Zhou et al (2008, 2010)4,5 note that bottom up energy use in homes and commercial buildings appears larger compared to the national energy balance sheet, because of classification differences, and as the latter reports housing for industry workers within the industrial energy use category.

Table SI-2: Comparison of energy intensity between What-If FYP-Efficiency-plus-Symbiosis scenario and High-Efficiency-plus-Symbiosis scenario (international bests practice) in selected key industrial sectors

Sector Energy intensity: 2010 Base-Case to FYP-Efficiency-plus-symbiosis scenario

Energy intensity: High-Efficiency-plus-Symbiosis scenario (international best practicea)

Steel 0.572 to 0.527 tce/t steel 0.5 tce/t steel Power Plant 500 to 450 g coal/kwh 379 g coal/kwh

Cement 0.163 to 0.15 tce/t cement 0.1 tce/t cement Buildings Varies by Climate zone (36)

69 - 17 kwh/m2 25 kwh/m2

Industrial Boiler Efficiency

75% to 82.5% 85%

a For the other industries China’s 2010-2015 FYP targets 6-12 were close to the international best practices13.

Figure SI-3: Schematic illustrating the material and energy exchanges associated with waste reutilization modeled at different spatial scales (sub-city, city, province, grid-region), and their coherence with national data. The dark red represent high grade heat converted to electricity in the city; the pink arrows represent three pathways for medium-grade heat, including inter-industry exchange, to existing DES, and, to electric power plants; the purple arrow demonstrates low-grade heat exchange; and the dotted green arrow represents building energy efficiency that contributes to reduction in electricity generation across the grid. Material exchange among select industries and air pollution are modeled across each province.

Figure SI-4: Flow chart describing energy cascade algorithm to assign waste heat from industries to industrial/residential/commercial sectors. The dashed lines represent the primary energy use saved from heat exchange.

Table SI-3: Waste heat utilization potential estimated from more than 25 Case Studies from China, including current waste heat utilization rate that were not double-counted in our scenario. Table SI-3a: Industrial waste heat energy sources by temperate grades (high-, medium-, and low-grade)

Industrial product

Industrial process Waste heat (%) per unit input fuel

Distribution of High T (high grade) (> 400°C), Medium T (medium grade) (100-400°C) and Low T (low grade) (<100°C) (by percentage) waste heat

% of waste heat

Current waste heat use rate %

Sources

Steel

Sinter (sintering machine) 7.8% High T: Sinter - 800°C, pellet - 500°C 57.7% 17.2% 14,15

Pellet (pellet machine) Medium T: exhaust gas - 300°C 42.3%

Pig iron (blast furnace) 18.6%

High T: Molten iron - 1300°C; Blast furnace slag - 1500°C; hot blast stove exhaust gas - 500°C 55.9%

31.4%

14,15

Medium T: Blast furnace gas - 200°C 19.8% Low T: Blast furnace cooling water - 40°C 24.4%

Steel (basic oxygen furnace) and Ingot (casting) 4.6% High T: Steel slag - 1550°C; steel billet - 900°C; basic oxygen furnace gas - 1600°C 100% 33.3%

14,15

Hot rolled steel(hot rolling) 4.8% High T: heating furnace exhaust gas- 900°C 71.3% 26.7%

14,15 Medium T: heating furnace cooling water -200°C 28.7%

Aluminum Electrolytic aluminum production 50% Medium T: waste heat from cell sidewalls (270-400°C) exhaust gas from cell (110-

160°C) 100% 10.8% 16-18

Alumina Alumina production 12.2% High T: calcination product -1000°C 30.6% 47% 19-21

Medium T: Sinter(clinker) exhaust gas 230°C -250°C; Calciner exhaust gas - 250°C 69.4%

Copper Copper production 52.9%

Medium T: Exhaust gas from electric furnace - 270°C 1.2% n/a 9,22 High T: Slag from electric furnace -1320°C, exhaust gas from flash smelting furnace-1340°C, basic oxygen furnace/flash converting furnace -1150°C, and anode furnace (1350- 1450°C)

98.8% 62.5%

Clinker Shaft kiln clinker 30% High T: Hot air from cooler & preheater – 800-900°C n/a n/a 23,24 Rotary kiln clinker 30% High T: Hot air from cooler & preheater – 800-900°C n/a n/a 23,24 New dry-process clinker 30% Medium T: Hot air from cooler & preheater – 300-400°C n/a n/a 24-26

Cement NCMT (NSP) 42% Medium T: Roller mill - 200°C; ball mill -250°C 26% 80% 27,28

Glass Float process Exhaust gas from Glass-melting furnace 35% High T: exhaust gas from traditional air-fired furnace - 400-550°C; exhaust gas from

oxy-fuel fired furnace (950-1300°C) 16% 8% 27,29-32

Brick Brick production 40% Medium T: exhaust gas -200°C 100% 0% 23,33 Lime Lime production 30% Medium T: exhaust gas -250°C 100% 0% 23,34

Ceramic Building ceramic production 30% Medium T: exhaust gas from furnace - 400°C 25% 85% 35-37 Sanitary ceramic production 20% Medium T: exhaust gas from furnace - 400°C 58% 15% 38

Coke Machine coke oven 87% High T: red coke (950-1050°C), crude gas (500 - 900°C) 80.5% 80% 39-41

Medium T: exhaust gas – (180-250°C) 19.5%

7

Table SI-3b: Heat “sink” industrial sectors have processes that utilize medium grade waste heat below 200°C 42

Sector Temperature of steam input (°C)

Percentage of thermal input that can be substituted by steam/hot water from other industries

Sources

Beverage manufacturing

115°C 50% 43

Food manufacturing

<200°C 7% 42,44

Textiles 130°C -180°C 52%-55% 45 Paper mill 200°C 42% 46 Chemical <400°C 40% 47,48

Note: total direct input heat and direct electricity input data come from National Energy Statistical Yearbook (2011) 3, we assume that these industries generate the steam themselves and thus can replace boilers with medium grade waste heat (150-400 °C) from other industries Table SI-4 Current utilization rate of power plant fly ash in different Chinese provinces for the year 201049. The utilization rate computes the percentage of waste generated that is reused; thus one minus the reutilization rate is the percent assumed to be available for further material exchange.

Provinces Utilization rate Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, Fujian, Jiangxi, Guangdong, Guangxi, Hainan

>90%

Chongqing, Anhui, Gansu, Qinghai, Xinjiang 80~90% Hebei, Shandong, Henan, Hubei, Hunan, Shaanxi, Sichuan

70~80%

Inner Mongolia, Jilin 60~70% Shanxi, Heilongjiang, Liaoning 50~60% Guizhou, Yunnan 40~50% Ningxia <40%

8

Table SI-5a: PM2.5 Emission Factors (EF) for Industrial, Commercial, Residential, and Transportation Energy Use

Sector Combustion Technology EF Unit Ref. Sector Combustion Technology EF Unit Ref.

Cement, Steel, & Power Plants

Coal-fired Power Plant 0.551 g/kg-product TU

Surface Transpor

tation

Heavy Duty Truck - Diesel 0.849 g/kgce 50 Coal in cement plant 1.610 Heavy Duty Truck - Gasoline 0.150 g/kg 51 Coal in steel plant 25.78 Light Duty Truck - Diesel 0.782 g/kgce

Heat Supply Sector- Energy Use

Coal-Fired Pulverized Boiler 12.0

g/kg-fuel

52

Light Duty Truck - Gasoline 0.150 g/kg 50 Coal-Fired Grate Boiler 5.25 Heavy duty bus - Diesel 0.861

g/kgce

51 Coal-Fired Fluidized Bed Boiler 5.40 Heavy duty bus - Gasoline 0.700

Oil-Fired Boiler 0.670 Heavy duty bus - CNG 0.718 Gas-Fired Boiler 0.160 53 Heavy duty busy -LPG 0.734

Residential Energy Use

Heating Coal Stove 9.620 54 Light duty bus - diesel 2.340 51 Heating - Briquette Stove 6.940

53

Light duty bus gasoline 3.100 Heating - Gas Heater 0.160 Car-Diesel 0.339

55 Cooking Fuel (stove-coal) 9.680 Car-Gasoline 0.112 Cooking Fuel (stove-briquette) 6.940 Car-CNG 0.117

Natural Gas 0.150 Car-LPG 0.106 Manufactured cylinder 0.340 2-stroke motorcycle 0.412 51

LPG 0.520 4-stroke motorcycle 0.412 Rail-Inner combustion locomotive 1.440 56

Note: TU: Tsinghua University (TU) data yields PM2.5 emissions per unit ton coal equivalent determined for thermal power plants, steel, and cement plants, based on each individual plant’s fuel type and technology.

9

Table SI-5b: Abated PM2.5 Emission Factors

Production sector Abated PM2.5 Emission factor Unit Source

Cement 0.489

g/kg product Based on 52,57

Coking Plants 2.60 Sintering 0.347 Blast Furnace (iron) 0.384 Basic oxygen furnace (steel) 0.261 Electric Arc Furnace (steel) 1.120 Casting 3.08 Nonferrous Metal (Copper, Zinc, Lead) 38.7 Brick 0.232 Lime 0.491 Aluminum 16.5 Alumina 267 Steel Open Hearth Furnace 12.4 Float Glass 7.13 Sheet Glass 9.62 Other Glass 2.65 Refined Oil 0.090 Fertilizer 1.67

10

Figure SI-5: Comparison of primary PM2.5 emissions per area in our model compared with Yangtze River Delta cities reported by Fu et al 2013 58

y = 0.9446x + 585.33R² = 0.642

0

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4000

6000

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0 2000 4000 6000 8000 10000

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5 em

issi

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PM2.5 emissions per area reported by Fu et al 2013 (kg/sq.km)

11

SI-2: Air Pollution Modeling and Health Risk Assessments

The linked model development undertaken in this paper, revealed three key insights.

1. CO2 and PM2.5 Emissions per Area: In-boundary annual CO2 emissions per unit area (kg/km2) in the

base case are highly correlated (R2 = 0.84), as expected, with in-boundary PM2.5 annual emission flux

(kg/km2). Further, the computed reductions in the Base-Case PM2.5 annual emissions flux (kg/km2) are

also highly-correlated (R2 = 0.90) with the reductions in Scope 1 CO2 emissions flux, or, spatial intensity.

These results are as expected given both CO2 and PM2.5 emissions are largely derived from fuel

combustion.

2. In-boundary PM2.5 Concentrations in the 2010 Base-Case are less correlated with the In-boundary

PM2.5 emission fluxes than are emissions of PM2.5 and CO2 ((R2) for a linear fit is 0.49 with a non-zero

intercept (21 ug/m3). The same is seen for the reductions of in-boundary PM2.5 concentrations in cities

versus the reduction in PM2.5 flux (annual emission per area) between the base- and scenario-case (R2=0.65

in Figure SI-6). Wind-blown air pollution transport from the rest of the province to cities is likely a factor,

found to be significant in the multivariable regressions shown in Table SI-6b. The equation explaining the

change of in-boundary PM2.5 concentration is emerged from this multi-variable regression as:

∆𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑃𝑃𝑃𝑃2.5 = (∆𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑃𝑃𝑃𝑃2.5)0.538 × (∆𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑅𝑅𝑅𝑅𝑃𝑃𝑃𝑃𝑃𝑃2.5)0.102 × (∆𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑃𝑃𝑃𝑃2.5

∆𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑅𝑅𝑅𝑅𝑃𝑃𝑃𝑃𝑃𝑃2.5)−0.0006 × 𝑊𝑊𝑊𝑊𝐶𝐶𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊−0.093

× 𝐹𝐹−2.583

These results demonstrate the role of the hinterland and the role of the city-hinterland relative reductions,

in impacting PM2.5 concentration reductions within the city.

3. Premature Mortality per 10,000 population attributable to air pollution in the different cities versus

PM2.5 Concentrations is the third key insight. This relationship for the Base-Case (Figure SI-7) shows a

logarithmic relationship consistent with the concentration response function of Burnett et al.59, wherein

reductions in PM2.5 concentration at lower Base-Case concentrations will yield larger mortality reduction,

compared to cities with a very high base case PM2.5 concentrations (>50 ug/m3). This is an expected

12

outcome, arising from the concentration response function and has been discussed by others60. Thus greater

% mortality reductions are expected in cities that are “cleaner” in terms of Base-Case PM2.5 concentrations.

Other factors, such as the population age distribution in the various cities, also play a role.

The combination of all the above factors results in certain groups of cities experiencing high CO2 and

high health benefits, while others may see high CO2 mitigation percentages but not a similar level of

health benefits, as illustrated in the main manuscript (Figure 4, main paper). The dispersion modeling

approach adopted in this paper is intended to assess these interactions across a large number of cities,

illustrating the “big-picture” differences between city types, and demonstrating the key interactions

discussed here. Models incorporating chemical reactions can capture secondary formation of PM2.5, but

given the down-scaling estimation differences across cities associated with the smaller industries (See SI-

1.), we have taken a first order approach in this paper, with the purpose of illustrating the complexity in

connecting CO2 reductions within cities to health benefits in those cities.

Figure SI-6. Reduction in annual average modeled PM2.5 concentration (ug/ m3) versus reduction of In-Boundary PM2.5 annual emission flux (kg/sq.km) across cities; reductions are the difference in the Base-Case minus the scenario Case.

13

Table SI-6: Regression analysis with Ln-Transformed PM2.5 Concentration within cities as the response variable. A) Analysis of correlates of Base-Case PM2.5 Concentrations

Single Variable Regression

Multivariable Regression

Predictor Variables Response: ln (PM2.5 Concentration in Base-Case (ug/m3))

ln (In-Boundary PM2.5 Emission Flux in Base-Case (kg/sq.km))

0.523 (***) 0.400 (***)

ln (RoP PM2.5 Emission Flux in Base-Case (kg/sq.km)) -- 0.363 (***) Ratio of City vs RoP PM2.5 Emission Flux -- 0.002 WindSpeed (m/second) -- -0.0267 Intercept -1.088 (***) -2.663 (***) R2 0.525 0.605

B) Analysis of correlates of PM2.5 Concentration reductions in Base-Case vs What-If FYP-Efficiency-

plus-Symbiosis scenario

Single Variable Regression

Multivariable Regression

Variables transformed Response: ln (Reduction of PM2.5 Concentration in What-If FYP-

Efficiency-plus-Symbiosis scenario compared to Base-Case

(ug/m3)) ln (Reduction In-Boundary PM2.5 Emission Flux (kg/sq.km)) 0.545 (***) 0.538 (***) ln (Reduction of RoP PM2.5 Emission Flux in Base-Case (kg/sq.km)) -- 0.102 (***) Ratio of Reduction in City's PM2.5 Emission Flux vs Reduction of RoP PM2.5 Emission Flux

-- - 0.0006 (***)

Wind Speed (m/second) -- - 0.093(*) Intercept -2.485 (***) -2.583 (***) R2 0.648 0.674

Significant level: ‘***’: 0.001; ‘**’: 0.01, ‘*’: 0.05.

14

Figure SI-7. Base-Case: PM2.5 related premature deaths per 10,000 population versus PM2.5

concentration.

15

Figure SI-8. Spatial distribution of co-benefits from efficiency and urban industrial-symbiosis in 637 cities in Mainland China. (a) Million metric tonnes direct CO2 emissions reduced; (b) Premature deaths avoided annually (in number of persons).

16

SI-3: Results from What-If High-Efficiency-plus-Symbiosis scenario

The main text described the results of the What-If FYP-Efficiency-plus-Symbiosis scenario. Here we show the results of the What-If High-Efficiency-plus-Symbiosis scenario (See Figure SI-9), modeled by applying the energy cascade and material exchange algorithm to the cities after international best practice efficiencies are assumed to be achieved in the single sectors in the What If analysis.

Figure SI-9: Comparison of CO2 mitigation potential in What-If High-Efficiency-plus-Symbiosis scenario implemented nationwide and allocated spatially to urban areas (top two bars) assuming China achieves International Best Practices standards in the 2010-15 period, and the corresponding Urban Cross-Sectoral Symbiosis Actions modeled in this paper, showing also unused waste heat (bottom bar).

17

SI-4: Demonstrating the data in Shijiazhuang City, as an example

Data that are gathered at the city scale, which were collected from census data, China’s industrial

enterprise dataset, and statistical yearbook (Tables SI-7 and SI-8). In addition, the energy use data for

three pillar industrial sectors—power plants, cement plants, and steel plants—were collected at the city

level by Tsinghua’s research group. Table SI-8 demonstrates energy use data downscaled from provincial

level data to city level using the downscaling methods described in Table SI-1.

18

Table SI-7 At-scale data for Shijiazhuang Data Category Data Item Unit Data Data source Demographic Total Population

person 2831586

China 6th Census Data Urban population 2770344 Rural population 61242

Economy and Employment

Total GDP billion RMB

123.978

China City Statistical Yearbook Primary industry-GDP 0.769 Secondary industry-GDP 36.152 Tertiary industry- GDP 87.057 Total workers in industry sector

person

188,481

China Industrial Enterprise Dataset

3111- Cement Manufacturing 222 3210-Ironmaking 858

3230-Steel Rolling 5,351 44-Electricity and Heat supply 27,097

2520-Coking 1,121 2511-Pretroleum Refinery 2,022

2621-Nitrogen manufacturing 3,484 2612-Inorganic Alkali 620

2611-Inorganic Acid Manufacturing 285 2614-Organic Chemistry Raw Material

Manufacturing; 3220- Steelmaking; 3141-plate glass; 3131-Brick; 3112-Lime and

gypsum; 3132-Construction Ceramics; 3151-Sanitary ceramics; 3316- Alumni smelting;

and 3311- Copper smelting

0

14-Food Manufacturing 1,064 15- Beverage Manufacturing 1,170

17-Textiles manufacturing 18,679 22-Pulp and paper manufacturing 781 26-Chemical not listed in 4 digits 1,641

Floor area Urban Residential Living space per person sq.m/person 29.3 China Regional Economic Statistical Yearbook Rural Residential Living space per person 40

Heating & cooling degree days

Number of days below 18°C

264 Collected by the Authors Number of days above 26°C

129 Community wide electricity, fuel, and water use

Total Electricity Use GWh

13,166

China City Statistical Yearbook

Electricity use in Industry 8,521 Electricity use in residential sector 1,601 Total water use million ton 276.29 Water use in residential sector 64.16 Total LPG use ton 14106 LPG use in residential sector 8619 Total natural gas use million

cu.m 144.19

Natural gas use in residential sector 57.72 Existing district heating system (DHS)

Total DHS heat supply TJ

45780

China Urban Construction Statistical Yearbook

Total DHS-CHP heat supply 220 Total DHS-boiler heat supply 45560 Total Heating area million

sq.m 80.31

Total Residential DH-heating area 58 Primary energy use in industry

3 pillar industry (amount of production)

power plants 10,151 kilo tce (20,301 GWh electricity)

Tsinghua Dataset cement plants 19 kilo tce (80 kilo ton clinker and 273

kilo ton cement) steel plants 770 kilo tce (1,907 kilo ton crude

steel and 1,762 kilo ton pig iron)

19

Table SI-8 Down-scaled energy use data for Shijiazhuang. The “-“ indicates no employment in these sectors, and hence no energy use.

Data Category Data item Energy Use (kilo ton coal equivalent)

Data source

Primary energy use in industry

2520-Coking 204 Based on at-scale employment number from CIED and provincial energy use dataset from Tsinghua University

2511-Pretroleum Refinery 183 2614-Organic Chemistry Raw Material Manufacturing

-

2621-Nitrogen manufacturing 368 2612-Inorganic Alkali 35 2611-Inorganic Acid Manufacturing - 3141-plate glass manufacturing - 3131-Brick Manufacturing - 3112-Lime and gypsum Manufacturing - 3132-Construction Ceramics Manufacturing - 3151-Sanitary ceramics Manufacturing - 3316- Alumni smelting - 3311- Copper smelting - 14-Food Manufacturing 5 Based on at-scale employment

number from CIED and the national level industrial sectoral energy input table from National Energy Statistical Yearbook

15- Beverage Manufacturing 6 17-Textiles manufacturing 61 22-Pulp and paper manufacturing 11 26-Chemical not listed in 4 digits 284 The remaining 2-digit industries 523

Energy Use in Residential Sector

District heating 1,043 Downscaled based on population data and provincial energy use dataset from Tsinghua University

Household stoves for heating 315 Cooking fuel 194 Heating hot water for shower (assuming the electricity cooking and electricity hot water shower is all for heating hot water)

133

Cooling-AC 13 Other electric equipment 112

Energy Use in Commercial Sector

District heating 224 Downscaled based on tertiary GDP and provincial energy use dataset from Tsinghua University

Non-DHS Heating (Coal) 31 Other primary energy use 62 Electricity Use 263

Energy Use in Transportation

Total fuel use 1,097 Downscaled based on population data and provincial energy use dataset from Tsinghua University

In Shijiazhuang, primary energy is mainly used in the power plants and steel plants. These two sectors accounted for 87% of total primary energy used in industrial sector (Fig SI-10a). The industrial waste heat structure (Fig SI-10b) demonstrates that low-grade waste heat is dominant.

20

Figure SI-10. (a) Primary energy use by sector in 2010 in the Base-Case (b) Industrial waste heat by different grades, after applying the recovery efficiency rate in Shijiazhuang, showing the dominance of low grade heat.

368 -

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Industrial sectorsgenerating waste heat

Industrial sectors canserve as heat sink

Residential+Commericalenergy use

Transportation fuel use

a.2010 Base-Case: Primary energy use in different sectors (kilo tce)

Transportation fuel useCooking & other fuel useNon DES for heatingCurrent DESIndustries can serve as heat sinkOther indusries (boilers)cement plantssteel plantspower plants

4.89

210.79

747.92

0

100

200

300

400

500

600

700

800

900

1000

1100

High grade (>400°C) Medium grade (100 ~ 400 °C) Low grade (<100 °C)

b. Available industrial waste heat by grades aggregating all heat-generating sectors shown in Fig (a) in Shijiazhuang (kilo tce)

21

Table SI-9 Data on waste heat exchange from industry to industrial/residential/commercial sectors for Shijiazhuang in 2010

Total available waste heat by grade shown in Figure SI-10a (1000 tce)

Usage of waste heat derived from algorithms shown in Fig. SI-4

Primary energy use for heat generation in potential waste heat sinks (1000 tce)

Waste Heat requirement for displacing Primary Energy (1000 tce)

Outcome of heat exchange

CO2 Reduction (million metric tonnes)

High-grade

4.89 Cascade A: Organic Rankine cycle (ORC) with 15% efficiency

Generates electricity: 39.79 GWh

0.041

Medium-grade

210.79 Cascade B: Apply to industrial "heat" sink (based on thermal input <200C from Table SI-2-1b)

Industrial sectors Primary energy – 368 Food – 5 Beverage – 6 Textiles – 61 Pulp and Paper – 11 Chemical - 284

40 a 40 kilo tce waste heat applied in industrial heat sinks; displaces 50 kilo tce primary energy use in the industrial sector.

0.122

Cascade C: Apply to existing DES

Current DES Primary Energy - 1,266

837 b 170 kilo tce waste heat replacing 290 kilo tce primary energy use (coal & gas) used in current DES.

0.653

Low-grade

747.92

Cascade D: Apply to the advanced DES (based on Equation 2 in SI-2)

Non DES Primary Energy - 408

179.5 c

278 kilo tce in non-DES are replaced.

0.884

Cascade E: Unutilized Low Grade Heat

529 kilo tce could potentially be used for future heating and cooling

1.535

1 Applies 450 g coal/kwh electricity generated and 2.2 kgCO2/kg coal combusted (500 g coal/kwh is calculated as the weighted average of minimum existing coal-fired power generation standards7, a 10% improvement in power sector efficiency as applied to the Scenario Case) 2 Applies 1.3 kg CO2/kg gas combusted and 2.2 kgCO2/kg coal combusted61 3 Applies 0.70 efficiency for residential/commercial DES and line loss of 30%62 based on authors’ calculation of current DES efficiency and 2.2 kgCO2/kg coal combusted from IPCC stationary combustion emission factors61 4 Applies 0.65 efficiency for residential/commercial non-DES boilers63 and 2.2 kgCO2/kg coal combusted61 5 Applies 80% coal/20% natural gas fuel mix that could be displaced using unutilized low grade heat a Based on Weighted average of percent thermal inputs below 200 °C that can be displaced with waste heat based on Table SI-2b, assuming 0% chemical industry primary energy inputs below 200 °C b Applies current DES boiler efficiency of 70% and line loss is 30%62 with a 5% improvement in existing building efficiency c Applies line loss of 5%64 with a 15% improvement in existing building efficiency using end use heating intensity of 68.8 kWh/sq.m

22

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