Advertisement

Natural Hazards

, Volume 99, Issue 3, pp 1277–1293 | Cite as

Energy transition, CO2 mitigation, and air pollutant emission reduction: scenario analysis from IPAC model

  • Kejun Jiang
  • Sha ChenEmail author
  • Chenmin He
  • Jia Liu
  • Sun Kuo
  • Li Hong
  • Songli Zhu
  • Xiang Pianpian
Original Paper

Abstract

In China, Energy transition was proposed in the “12th Five-Year Plan” and gained resilient support by “Energy Revolution” announced by President Xi Jinping in 2014. In Paris Agreement, there are targets set up for 2100 to be well below 2 °C, with ambitious target on 1.5 °C. China signed the agreement and will support the global target. In the meantime, large-scale actions were initiated in 2013 by the national action plan on air pollution control for the period from 2013 to 2017. None of these strategies has clear long-term target. In our studies, energy transition will be decided by the long-term target of CO2 emission reduction, air pollutant reduction, and energy security. This paper will present the analysis from IPAC model, by setting up reduction target for CO2 emission under the global 2 °C and 1.5 °C target. Energy transition, CO2 emission, and air pollutant reduction will be discussed based on these targets. For air pollutants, SO2, NOx, PM2.5, black carbon, and mercury will be included. From the results, there will be a significant energy transition by large-scale use of renewable energy, nuclear and the share of coal will be reduced to less than 20% in 2050 from 66% in 2015. Energy transition will also contribute to a drastic reduction in air pollutants.

Keywords

Energy transition CO2 mitigation Air pollutant Scenario China 

1 Background

In China, there are already multiple national strategies set up for energy transition, GHG mitigation, and air pollutant reduction. Energy transition was proposed in the 12th Five-Year Plan (NEA 2011) and gained strong support by “Energy Revolution” announced by President Xi Jinping in 2014. In Paris Agreement, there are targets set up for 2100 to be well below 2 °C, with ambitious target on 1.5 °C. China signed the agreement and will support the global target. In the meantime, large-scale actions were initiated in 2013 by the national action plan on air pollution control for the period from 2013 to 2017. None of these strategies has clear long-term target.

Energy transition was firstly proposed in the 12th Five-Year Plan for China by identifying key areas such as developing renewable energy and nuclear, clean heat supply, and promoting natural gas development. In 2014, energy revolution strategy proposed by President Xi Jinping is the mile stone for energy transition, which focused on four aspects, including energy consumption revolution, energy supply revolution, energy technology revolution, and energy regime revolution. Controlling energy demand, enhancing energy efficiency, promoting clean energy including renewable energy, nuclear, and natural gas, promoting clean energy technology, and establishing energy management regime in order to support the revolution are key components in the energy revolution strategy. These are the fundamental framework to promote energy transition in China.

In 2015, Paris Agreement was adopted in COP21 (UNFCCC 2015), and China signed the agreement. In the Paris Agreement, there are targets set up for 2100 to be well below 2 °C. 1.5 °C is getting to be the ambitious target appeared in the agreement. China proposed NDC by setting up target to peak CO2 emission by around 2030, and make effort to peak earlier, carbon intensity reduction that will be 60% to 65% by 2030 compared with that in 2005, share of non-fossil energy will be 20% while it was 11% in 2015.

Supporting the global 2 °C emission reduction would be China’s emission reduction target, even though there is not yet a specified reduction target regarding this target. Studies about 2 °C emission pathway for China show that China could peak CO2 emission before 2025 and will be 65% reduction by 2050 compared with the peaking year (Jiang et al. 2013; Jiang 2014). As for 1.5 °C target, there is quite lack of studies about 1.5 °C target’s emission pathway, and it is hard to get people convinced that this target is reasonable. In order to answer the question whether this target is achievable or not in UNFCCC. IPCC launched the process to prepare special report on 1.5 °C Target, and this report was scheduled to be published by 2018. Recently, there are few researches about global emission pathway on 1.5 °C target presenting modelling results; it is suggested that the global emission will go to zero emission in between 2050 and 2060 and start negative emission afterwards (van Vuuren et al. 2016; Kriegler et al. 2018; and Rogelj et al. 2016, 2018). The study for China under the 1.5 °C target presents similar emission pathway to be near zero by 2050 (Jiang et al. 2018).

In the meantime, China launched national action plan to improve air quality (State Council of the People 's Republic of China 2013). The policies of air pollution control which started since 2013 have put strong impact on energy use in China. Many of the actions in the plan are energy related. There is coal consumption cap and reduction specified in the plan, together with energy efficiency, promotion of renewable energy, natural gas, etc. By 2017, the targets set up in the plan were well-reached (MOE 2018). There is significant improvement of air quality in the past 5 years. The energy related policies in the plan are very well matching with CO2 emission reduction policies. Driven by these air pollution control action policies, after 2012, China started to control coal use and promote clean energy use, together with economic structure change by peaking most of energy-intensive products now, coal consumption peaked in 2013 in physical unit and in 2014 based on standard coal equivalent and then continue to decline. There were 4.7% reduction in 2015 and 3.7% in 2016. With the increase in natural gas and petroleum products, CO2 emission reduced after 2014. Based on the energy used data in 2016, there are around 450 million tons of CO2 emission reduced, which accounts for 5% of total CO2 emission from energy activities. Even though considering small increase in CO2 emission from clinker manufacture, the quantities of CO2 emission reduction are still 430 million tons.

Linking multiple development targets is getting attractive to the modelling studies, especially IAMs. The IPCC special report on 1.5 °C warming summarized the recent studies about energy transition, GHG emission, and SDGs (IPCC 2014). Major IAMs in the world are involved in these studies (van Vuuren et al. 2017; McCollum et al. 2017). Improving air quality is one of key SDGs and key part for IAMs to link GHG emission pathways and air pollutant emission reduction. There are studies on GHG mitigation with air pollutant emission reduction focusing on China (Liu et al. 2017; Zhang et al. 2016, Zhou et al. 2017), but there is still lack of detail analysis about China’s energy transition, CO2 emission reduction, and air pollutant emission reduction with Paris Agreement targets in China.

In our studies, energy transition will be decided by the long-term target of CO2 emission reduction, air pollutant reduction, and energy security. This paper will present the analysis from IPAC model, by setting up reduction target for CO2 emission under the global 2 °C and 1.5 °C target. The energy transition, CO2 emission, and air pollutant reduction will be given based on these targets. For air pollutants, SO2, NOx, PM2.5, black carbon, mercury will be encompassed.

2 Methodology

2.1 Methodology framework

This study was conducted based on the previous analysis about energy transition and 2 °C emission scenarios in China (Jiang et al. 2013, 2016). IPAC-AIM/technology model (a sub-model of Integrated Policy Assessment Model for China, IPAC) was used for emission scenario analysis (Jiang et al. 1998; Jiang and Hu 2006). This study analyses CO2, PM2.5, SO2, NOx, black carbon, and mercury emission from energy and industrial processes from 2010 to 2050 in China.

Air pollutant emissions are calculated in the model based on technologies. Emissions of these air pollutants and reduction technologies are referred and compared with other studies (Zhang et al. 2013; Wang et al. 2011, 2015, 2016; Yang et al. 2013; Tian et al. 2011; Streets et al. 2001).

2.2 Models

In 1992, the group of Energy Research Institute began to build Integrated Policy Assessment Model for China (IPAC). After more than 20 years of research and development, the current IPAC has become a comprehensive policy evaluation model, with a variety of model approaches (http://www.ipac-model.org.cn). The currently used models and methods are somewhat reflected in the IPAC group, such as computable general equilibrium model, the dynamic economic model, the partial equilibrium model, the minimum cost optimization model based on linear programming techniques described in detail and industry simulation models. The framework of IPAC is shown in Fig. 1. So far, IPAC model has been widely applied in policy evaluation of energy and climate change in China. The research outputs of IPAC have been used in the planning research relevant to the “10th Five-Year Plan”, “11th Five-Year Plan”, and “12th Five-Year Plan” in China; meanwhile, they have supported energy planning and polices in some provinces and municipalities. This research will provide quantitative analysis depended on the long-time model data and scenario research provided by IPAC model and issues some major future technologies, especially in low-carbon technology investment demands (Jiang et al. 1998; Jiang 2014).
Fig. 1

Framework of IPAC models

IPAC-AIM/technology model is a major component of the IPAC model, which aims to simulate energy consumption process by giving a detailed description of energy services and technologies to provide these services with different levels of energy efficiency, cost, and emission factors. IPAC-AIM/technology model is the minimum cost optimization model based on linear programming. Through doing this, it is easier for policy makers to understand the results of modelling simulation with telling the selection of technologies by various policies.

In the IPAC-AIM/technology model, technical parameters include the amount of service output, energy used by  other types non-energy inputs, technology fixed investment, and technical pollutant emissions factors. Technical fixed investment is given by year, including both the technical learning curve and the description of future technology cost.

The model covers more than 700 technologies in 55 sectors, of which more than 150 kinds of important technologies in low-carbon and energy-saving fields are selected as the focus of this analysis.

In calculation of primary energy, 100% efficiency for renewable including hydro, wind, solar, 33% efficiency for nuclear and biomass are used here.

2.3 Scenario

In this study, we use two scenarios, which are 2 °C scenario and 1.5 °C scenario. In the 2 °C scenario, we analyse the feasibility and roadmap for China to reduce CO2 emission to match the carbon budget under 2 °C target (Jiang et al. 2013) by enhanced energy efficiency improvement, low-carbon power technologies including renewable energy, nuclear, fossil fuel-fired power generation with CCS. Here, the 1.5 °C scenario’s feasibility analysis will be given based on the modelling analysis. The fundamental idea is to make power generation to have low emission or negative emission; furthermore, much more electricity consumption will be adopted in the end-use sector. Because energy efficiency is already fully adopted in the 2 °C scenario, there is no more analysis for further energy efficiency options in the 1.5 °C scenario. However, there are energy efficiency effects by electrification in end-use sectors, such as electric car in transport sector, even by considering power generation efficiency.

3 Scenario setting

This session presents key parameters in the modelling analysis for the two scenarios. Economy, population assumption will keep the same for both scenarios. Other parameters were documented in the previous publications (Jiang et al. 1998, 2010, 2016; Jiang 2014). Population and urbanization remain the same with the previous studies (Jiang et al. 2013); the population scenario is shown in Table 1.
Table 1

Population scenario in IPAC

 

Unit

2005

2010

2020

2030

2040

2050

Population

million

1308

1360

1440

1470

1470

1440

Urbanization rate

 

43%

49%

63%

70%

74%

79%

Urban population

million

562

666

907

1029

1088

1138

Person per household

 

2.96

2.88

2.80

2.75

2.70

2.65

Urban household

million

190

231

324

374

403

429

Rural population

million

745

694

533

441

382

302

Person per household

 

4.08

3.80

3.50

3.40

3.20

3.00

Rural household

million

183

183

152

130

119

101

The GDP growth used here was revised based on the previous IPAC studies and recent development trend in China. The GDP growth and structure changed were calculated based on IPAC-SGM model, which is a CGE model. The GDP assumption and the structure change in IPAC model are shown in Figs. 2 and 3, respectively (Jiang et al. 2010; Zhou et al. 2017).
Fig. 2

GDP in China with constant price in 2010

Fig. 3

Structure change in second industry

The share of GDP from energy-intensive industry (middle part in Fig. 3) will reduce due to the demand change. China’s GDP will surpass US in between 2020 and 2030; such a huge amount of GDP could not rely on existing economic pattern which is mainly driven by heavy industry development and raw material production. Figure 3 shows that the future GDP growth will mainly come from tertiary sector and non-energy-intensive industry, such as electronic products manufacture, light industry manufactures, etc. Based on the bottom study on demand of energy-intensive products, it is found that many energy-intensive products will peak between 2020 and 2025 with an assumption that in future the export of energy-intensive products will not increase much, when it is already major part of global output. Table 2 presents the product outputs covering major energy-intensive sector products, which was given based on demand analysis for these energy-intensive products by physical unit I/O table, with consideration of future infrastructure and consumption development. For example, in the scenario, building floor space was set-up to be 89 billion m2 in 2050 when China is well developed and personal income increases, with per capita floor space 64 m2. Besides in this case, the newly built edifice per annum will reach peak before 2020 and then start to decrease. Because there are more than 55% of steel, 70% of cement are used in building construction, and many other energy-intensive products are also closely linked with building construction; if newly built edifice will reach the peak, then it is easy to understand that the demand of many energy-intensive products will also peak. Moreover, outputs of many consumption goods in China are more than half of global output, which is shown in Table 3. There is no more space for much increase in future. Share of value added of energy-intensive industry in GDP will decrease from 11% in 2010, thus less than 6% in 2050.
Table 2

Energy-intensive product outputs scenario in IPAC

 

Unit

2005

2010

2014

2020

2030

2040

2050

Crude steel

million ton

355

627

813

710

570

440

360

Cement

million ton

1060

1868

2490

2950

1600

1200

900

Glass

million cases

399

580

831

740

690

670

580

Copper

million ton

2.6

4.79

7.95

7.6

7

6.5

4.6

Aluminium

million ton

8.51

16.95

24.38

25

17

15

12

Lead and zinc

million ton

5.1

8.9-

10.05

10

7

6.5

5.5

Sodium carbonate

million ton

14.67

20.34

25.25

25

24.5

23.5

22

Caustic soda

million ton

12.6

22..28

30.63

30

25

25

24

Paper and paperboard

million ton

62.05

92.7

117.85

110

110

105

100

Chemical fertilizer

million ton

52.2

63.38

68.76

64

59

56

53

Ethylene

million ton

7.56

14.12

16.96

24

23

23

23

Ammonia

million ton

46.3

49.65

56.99

52

50

50

45

Calcium carbide

million ton

8.5

14.7

25.2

22

16

11

7

Table 3

Share of outputs of selected goods from China in the world in 2015

Products

Output

Share of the world (%)

Crude steel

804 million ton

49.6

Steel products

1123 million ton

70.5

Cement

2359 million ton

51.3

Aluminium

31.41 million ton

56.6

Copper

7.96 million ton

34.4

Zinc

6.15 million ton

43.8

Vehicles

24.5 million

27

computer

314 million

90.6

TV

145 million

48.8

Refrigerator

79.9 million

71.8

Air conditioner

142 million

92.1

Washing machine

72.7 million

64.7

Microwave oven

77.5 million

75

Energy-intensive products are consuming nearly 50% of energy in China; so if there is no significant increase in energy-intensive products production, with a much lower growth than the GDP, the energy use in these energy-intensive products will also be limited. This will be a big contribution on energy intensity per GDP decrease and then contribution on CO2 intensity.

The key parameters in urban household in China are presented in Table 4. The data are based on population growth, size of household and personal income. By 2030, due to high income, urban household in China will have similar living quality with developed countries in the aspects of electronic appliance, space heating, space cooling, etc.
Table 4

Urban household parameters in IPAC

Service

Unit

Service

2020

2030

2050

Household

million

288

336

380

Share of HH with space heating

 

42%

44%

48%

Index of space heating intensity

2000 = 1

1.35

1.5

1.6

Index of space heating time

2000 = 1

1.33

1.36

1.4

Share of building with 50% efficiency standard

%

20

45

65

Ownership of air conditioner

Per 100 households (HH)

130

180

260

Index of air conditioner intensity

2000 = 1

1.3

1.4

1.6

Index of air conditioner utilization time

2000 = 1

1.6

1.8

2.2

Ownership of refrigerator

per 100 HH

100

120

130

Average space of refrigerator

L

250

310

390

Efficiency of refrigerator

kWh/day

0.8

0.8

0.7

Ownership of washing machine

per 100 HH

100

100

100

Times to use washing machine per week

h

5.4

8

8

Ownership of TV

per 100 HH

180

220

290

Average capacity of TV

W

320

300

280

Hours per TV per day

h

3.5

3.2

2.9

Penetration rate of CFL

%

100%

100%

100%

Light

per HH

14

21

27

Ownership of water heater

per 100 HH

100

100

100

Ownership of solar heater

per 100 HH

18

25

33

Ownership of electric cooking

per 100 HH

130

140

260

Hours per day of electric cooking

min

12

30

50

Capacity of other electric appliance

W

1500

1800

2300

Hours of other electric appliance

min

50

80

100

Tables 5 and 6 present the key scenarios set for transport sector in China, which were used in the previous scenario analysis in IPAC modelling team (Jiang et al. 2013). The change of technology is the major factor in different scenarios. Table 7 illustrates the electric car data from IPAC team with collaboration technicians from 14 car manufacture companies in China (Zhuang and Jiang 2012). In the 1.5 scenario, transport sector will be totally electrified before 2050 except air transport (Jiang et al. 2018).
Table 5

Vehicle fleet (10,000 units)

 

2005

2010

2020

2030

2040

2050

Total

3160

6227

18,583

36,318

51,717

55,810

Passenger cars

2132

4299

15,504

32,323

46,083

48,922

Trucks

1027

1928

3079

3995

5634

6888

Cars

1919

3921

14,982

31,558

45,075

47,662

Family cars

1100

3145

14,032

30,454

43,675

46,062

Other cars

819

776

950

1104

1400

1600

Minibus

131

265

313

383

524

214

Large passenger coach

82.3

113.4

208.8

382.5

483.84

1045.8

Small passenger coach

214

378

522

765

1008

1260

Motorcycles

6582

9848

10,613

11,193

11,193

10,634

Table 6

Traffic turnover volume (billion passenger-km/billiontonne-km)

 

2005

2010

2020

2030

2040

2050

Passengers

3446

5100

8631

13,869

20,640

28,312

Freight

9394

14,429

23,832

36,035

57,379

79,970

Passenger in road transport

2628

3980

6699

10,634

14,866

17,405

Passenger in rail transport

606

752

1072

1385

1791

2315

Passenger in air transport

204.5

360.4

853.2

1841.9

3976.6

8585.1

Passenger in water transport

7

7

7

7

7

7

Freight in road transport

2251

3565

6853

10,713

19,345

22,637

Freight in rail transport

2073

2692

4003

5576

7769

10,824

Freight in air transport

8

12

29

70

182

477

Freight in air transport

4954

7949

12,296

18,136

26,758

39,490

Freight in pipeline transport

109

209

651

1540

3325

6541

Table 7

Roadmap for electric vehicle development technology in China

 

2006–2010

2011–2015

2016–2020

2021–2025

2026–2030

The amount of electric vehicle (10,000)

Few

125

800

5000

9400

Sale quantity of electric vehicle market per year (10,000)

Few

25

135

840

880

Cruising range (km)

112

130

200–600

350–800

400–1000

Power consumption measuring on 100 km

16–18

14

13

8

8

Battery energy density (Wh/kg, Wh/L)

90–125 Wh/kg

150 Wh/kg; 150 Wh/L

225 Wh/kg; 200 Wh/L

500 Wh/kg; 460 Wh/L

700 Wh/kg

Total battery energy

16

24

40–48

80–93

112–124

Battery pack lifespan

1000 times

1500 times

3000 times

3800 times

3800 times

Battery cost (Yuan/kWh)

5025

2513

717

503

200

From the analysis above, we can get the traffic turnover scenario, which is shown in Table 6.

4 Scenario results

The energy scenario and emission scenario were analysed based on the previous studies for 2 °C scenario for China. The results are defined in the following below.

4.1 Energy scenario

Because there are already high intensive energy efficiency policies in the 2 °C scenario, the total primary energy demand in the 1.5 °C scenario is not much different with that of the 2 °C scenario. From now on, energy demand in China will grow much more slowly compared with last 15 years. Economic structure changing is the major driving force for this. Based on the study, by using physical unit input–output table analysis form IPAC, output in energy-intensive sectors such as steel making, building material making, non-ferrous, chemical industry, petro-chemical, etc., already reached their peak, or will reach its peak soon, except some products related with daily consumption. These energy-intensive products contributed around 70% of newly increased energy demand in last 20 years and more in China. This means nearly 70% of driving force for energy increase in China disappeared hereafter (see Figs. 4, 5).
Fig. 4

Energy transition in the 2 °C scenario: primary energy demand

Fig. 5

Primary energy demand in China, 1.5 °C scenario

Another important aspect is energy conservation in China. From the “9th Five-Year Plan” (2006–2010), very strong policies on energy conservation were adopted in multiply level in China, from central government to local government, sector, and industries. And the achievement is significant (Jiang et al. 2010).

Focusing on energy mix, much more towards to low carbon in the modelling analysis, renewable energy including solar, wind, biomass, and hydro power could share 35%, nuclear will take 32% by 2050. After 2020, there is no significant increase in energy demand. Much higher efficiency comes from increase in electricity at end-use sector, and power from renewable energy is 100% generation efficiency (see Fig. 5). The final energy demands are presented in Figs. 6, 7, 8, and 9.
Fig. 6

Final energy demand by energy type in China, 2 °C scenario

Fig. 7

Final energy demand by sector, 2 °C scenario

Fig. 8

Final energy demand by energy type in China, 1.5 °C scenario

Fig. 9

Final energy demand by sector, 1.5 °C scenario

As shown in Fig. 6, the final energy demand in China will keep on increasing slowly until 2025 or 2030 and then start to decrease. Besides the factors discussed above, much higher electrification in end-use sectors is another key factor to lower final energy demand. Share of electricity in final energy demand increases from 22.6 in 2015 to 43.9% in 2050 in 2 °C scenario and 62.8% in 2050 in 1.5 °C scenario. Electricity use is much highly efficient than fossil fuels in transport, cooking, space heating, etc. Final energy demand by sectors shows that energy demand in industry will take large share in 2050, with not much shape changed from 2020 up to 2050. This mainly due to electrification in other end-use sectors including building and transport sectors, while there is still fossil fuel used in industry, and CCS has to be adopted in some energy-intensive sectors such as steel making, and cement sector.

4.2 Emission scenarios

Figure 10 presents the net CO2 emission from energy and industrial processes by sectors. After reaching peak around 2020, there would be a quick reduction by 2030 and then drop downt to negative emission in 2050. In 2030, the net CO2 emission would be 6.12 Gt, and it is − 0.59 Gt in 2050, while it is 10.13 Gt in 2020. After 2020, there would be 483 million tons of CO2 reduced per year, which means nearly 220 million tons of coal and oil reduction per year. Power generation sector is the key sector for leading emission reduction. Its CO2 emission reached its peak in 2015 and then will take rapid reduction after 2020, going to − 18.6 million ton CO2 in 2040, and − 1537 million ton CO2 in 2050. BECCS plays key role in the reduction. It will start departure after 2030 and will capture more than 1.5 billion ton CO2 per year in 2050 (see Fig. 11).
Fig. 10

CO2 emission in 2 °C and 1.5 °C scenarios

Fig. 11

CO2 emission reduction by CCS

In both scenarios, air pollutants are significantly reduced (see Figs. 12, 13, 14, 15, 16). For SO2, there will be 83% and 88% reduction in 2050 compared with 2015 in 2 °C and 1.5 °C scenarios, respectively. They are 82% and 89% for NOx, 90% and 94% for PM2.5, 83% and 92% for Mercury, 83% and 95% for black carbon, respectively. Based on the study for emission reduction with respect to major air pollutant by 2030 to reach national standard (Xue et al. 2014), this emission reduction could contribute to the air quality target to meet the national standard by 2030. And from the results, we find that the difference of air pollutant emission between the two scenarios is not large, that means the energy transition in both scenarios is going to much lower fossil fuel use, which is the major emission sources of air pollutants.
Fig. 12

SO2 emissions in 2 °C and 1.5 °C scenarios

Fig. 13

NOx emissions in 2 °C and 1.5 °C scenarios

Fig. 14

PM2.5 emissions in 2 °C and 1.5 °C scenarios

Fig. 15

Black carbon emissions in 2 °C and 1.5 °C scenarios

Fig. 16

Mercury emissions in 2 °C and 1.5 °C scenarios

5 Conclusion

From this study, based on the observations and deductions in both scenarios, air pollutant emissions are reduced significantly, which will benefit both climate change target and local air pollution targets. Energy transition towards much less fossil fuel use by 2050, strong co-benefit effects could be observed from these scenarios results. There is still lack of studies on air quality target by 2050, but it is expected that air quality could reach WHO’s standard; in this case, emissions of air pollutant have to be drastically reduced to the very lowest level. Moreover, there is a strong demand on energy transition to be much less fossil fuel use. This quite matches with the 1.5 °C scenario results.

Notes

Funding

Funding was provided by National Key Basic R&D Plan (CN) (Grant No. 2014CB441301), National Key R&D Plan (Grant No. 2016YFC0207503).

References

  1. IPCC (2014) Climate change mitigation, AR5 of IPCC WGIII. http://www.ipcc.ch/report/ar5/wg3/
  2. Jiang K (2014) Secure low-carbon development in China. Carbon Manag 3(4):333–335Google Scholar
  3. Jiang K, Hu X (2006) Energy demand and emissions in 2030 in China: scenarios and policy options. Environ Econ Policy Stud 7(3):233–250CrossRefGoogle Scholar
  4. Jiang K, Xiulian H, Matsuoka Y, Morita T (1998) Energy technology changes and CO2 emission scenarios in China. Environ Econ Policy Stud 1(2):141–160CrossRefGoogle Scholar
  5. Jiang K, Liu Q, Zhuang X, Xiulian H (2010) Technology road map for low carbon society in China. J Renew Sustain Energy 2(3):031008CrossRefGoogle Scholar
  6. Jiang K, Zhuang X, Miao R, He C (2013) China’s role in attaining the global 2 °C target. Clim Policy 13(sup01):55–69CrossRefGoogle Scholar
  7. Jiang K-J, Zhuang X, He C-M, Liu J, Xiang-Yang X, Chen S (2016) China’s low-carbon investment pathway under the 2 °C scenario. Adv Clim Change Res 7(4):229–234CrossRefGoogle Scholar
  8. Jiang K, He C, Dai H, Liu J, Xu X (2018) Emission scenario analysis for China under the global 1.5°C target. Carbon Manag 9(5):481–491CrossRefGoogle Scholar
  9. Kriegler E, Bauer N, Baumstark L, Fujimori S, Luderer G, Rogelj J et al (2018) Pathways limiting warming to 1.5 °C: a tale of turning around in no time? Philos Trans 376(2119):20160457CrossRefGoogle Scholar
  10. Liu MM, Huang YN, Jin Z, Liu XY, Bi J, Jantunen MJ (2017) Estimating health co-benefits of greenhouse gas reduction strategies with a simplified energy balance based model: the Suzhou city case. J Clean Prod 142:3332–3342CrossRefGoogle Scholar
  11. McCollum DL et al (2017) Connecting the sustainable development goals by their energy inter-linkages. Environ Res Lett 13(3):033006CrossRefGoogle Scholar
  12. NEA (2011) National 11th five year plan on energy. National Administration of Energy, BeijingGoogle Scholar
  13. Rogelj J, Schaeffer M, Friedlingstein P, Gillett NP, van Vuuren DP, Riahi K, Allen M, Knutti R (2016) Differences between carbon budget estimates unravelled. Nat Clim Change 6(3):245–252CrossRefGoogle Scholar
  14. Rogelj J, Popp A, Calvin KV, Luderer G, Emmerling J, Gernaat D et al (2018) Scenarios towards limiting global mean temperature increase below 1.5 °C. Nat Clim Change 8(4):325–332CrossRefGoogle Scholar
  15. State Council of the People's Republic of China (2013) Action plan for the prevention and control of air pollutionGoogle Scholar
  16. State Council MOE (2018) Environment report of China 2018. Ministry of Environment Protection, BeijingGoogle Scholar
  17. Streets DG, Gupta S, Waldhoff ST, Wang MQ, Bond TC, Yiyun B (2001) Black carbon emissions in China. Atmos Environ 35(25):4281–4296CrossRefGoogle Scholar
  18. Tian H, Zhao D, Wang Y (2011) Air pollutant emission inventory from biomass burning in China. Acta Sci Circumst 31(2):349–357Google Scholar
  19. UNFCCC (2015) Paris agreement. UNFCCC, BonnGoogle Scholar
  20. Vuuren DP, van Soest H, Riahi K, Clarke L, Krey V, Kriegler E, Rogelj J, Schaeffer M, Tavoni M (2016) Carbon budgets and energy transition pathways. Environ Res Lett 11(7):075002CrossRefGoogle Scholar
  21. Vuuren DP et al (2017) Energy, land-use and greenhouse gas emissions trajectories under a green growth paradigm. Global Environ Change 42:237–250CrossRefGoogle Scholar
  22. Wang S, Wang HM, Zhu FH, Chen H, Sun XL, Zou Y, Liu G (2011) Mercury emission characteristics from coal-fired power plants based on actual measurement. Environ Sci 32(1):34–38Google Scholar
  23. Wang Y, Ji J, Yi H, Chen W, Hao C, Teng Q (2015) Study on characteristic of black carbon emission from diesel vehicles in China in 2013. Environ Sustain Dev 40(2):45–47Google Scholar
  24. Wang X, Yan L, Lei Y, He K, He J (2016) Estimation of primary particulate emission from steel industry in China. Acta Sci Circumst 36(8):3033–3039Google Scholar
  25. Xue W, Fu F, Wang J, He K, Lei Y et al (2014) Modeling study on atmospheric environmental capacity of major pollutants constrained by PM2.5 compliance of Chinese cities. China Envrion Sci 34(10):2490–2496Google Scholar
  26. Yang W, Li J, Zhu L, Wang Z (2013) Comparison of anthropogenic emission inventories of China mainland. Res Environ Sci 26(7):703–711Google Scholar
  27. Zhang N, Qin Y, Xie S (2013) Spatial distribution of black carbon emission in China. Chin Sci Bull 58(31):1855–1864Google Scholar
  28. Zhang SH, Worrell E, Crijns-Graus W, Krol M, de Bruine M, Geng GP, Wagner F, Cofala J (2016) Modeling energy efficiency to improve air quality and health effects of China’s cement industry. Appl Energy 184:574–593CrossRefGoogle Scholar
  29. Zhou Q, Yabar H, Mizunoya T, Higano Y (2017) Evaluation of integrated air pollution and climate change policies: case study in the thermal power sector in Chongqing city, China. Sustainability 9(10):1–17Google Scholar
  30. Zhuang X, Jiang K (2012) A study on the roadmap of electric vehicle development in China. Automot Eng 34(2):91–97Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Kejun Jiang
    • 1
  • Sha Chen
    • 2
    Email author
  • Chenmin He
    • 3
  • Jia Liu
    • 4
  • Sun Kuo
    • 4
  • Li Hong
    • 4
  • Songli Zhu
    • 5
  • Xiang Pianpian
    • 6
  1. 1.Energy Research InstituteChinese Academy of Macroeconomic ResearchBeijingChina
  2. 2.Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental and Energy EngineeringBeijing University of TechnologyBeijingChina
  3. 3.College of Environmental Science and EngineeringPeking UniversityBeijingChina
  4. 4.Renm ConsultingBeijingChina
  5. 5.Energy Research InstituteBeijingChina
  6. 6.School of EnvironmentTsinghua UniversityBeijingChina

Personalised recommendations