Carbon dioxide mitigation co-effect analysis of clean air policies: lessons and perspectives in China's Beijing–Tianjin–Hebei region

The GAINS Asia model, which was developed by the International Institute of Applied Systems Analysis, is a comprehensive assessment system that evaluates the interactions and the effectiveness of different atmospheric management policies. This model takes into account activities (such as power, industry, transportation, etc) and air pollution controls for different pollutants from different source sectors at 5 year intervals (Amann et al 2011, Zhang et al 2016). We employed this model to assess the CO₂ emission reductions due to the co-effects of implementing the BSPC. Details regarding the emission calculation principle of the model are provided in section 1 of the supporting information.

In this study, the base year is 2015 and the evaluated period covers 2015–2030 in 5 year intervals. Two scenarios are proposed to study the policy effectiveness: the baseline scenario and the policy scenario. Based on the World Energy Outlook 2018 Current Policy Scenario (WEO-2018-CPS) projected by the GAINS Asia model, the baseline scenario in our study assumed that the BSPC is not implemented in the BTH region. We recalibrated the activities of energy, agricultural, and industrial processes for 2020 based on the trend from the 'WEO-2018-CPS' and the 13th FYP targets published by the BTH regional government and China's renewable report (NDRC 2016, NEA 2017a). The APPCAP is not part of our baseline scenario due to the 5 year interval evaluation and that the 2020 targets set for BSPC are in comparison with the year of 2015. Then, we made the changes in 2025 and 2030 link to the 2020 values based on the growth rate of demands between 2020 and 2030 from the 'WEO-2018-CPS'. The trends in both scenarios (2025 and 2030) are keep consistent with 'WEO-2018-CPS'.

The policy scenario assumes that the BSPC is implemented in the BTH region in order to predict the effects of this control policy from 2015 to 2020. The policy packages of the policy scenario are shown in table S1 (available online at stacks.iop.org/ERL/16/015006/mmedia) in section 2 of the supporting information. Specifically, the policy packages are divided into four categories: the power and industry sector (with core content such as improving industrial structure and the associated distribution for steel, cement, coke, glass, and coal power plants); the transport sector (with core content such as increasing the number of new energy vehicles and implementing national vi (B) standard gasoline and diesel for traffic vehicles); the building sector (with core content such as replacing untreated coal of heating by households); and the cross sector (such as the pollution controls for stationary sources). The policy packages of the BSPC and its corresponding emission control measures in the GAINS Asia model are summarized in table S2 in section 3 of the supporting information. Within the policy scenario setting step, various air pollution sources (e.g. power, industry, transportation, residential building, and agriculture) and associated activities will be considered. More specifically, parameters (e.g. energy consumption by different fuels and sectors, industrial process activities, and utilization rate of atmospheric pollution control technology) are forecasted for 2020 using the consumption trends from the yearbooks (NBS 2016, 2017, 2018, 2019). For example, a reduction in coal consumption by urban residents and commercial combustion sectors is attributed to the following factors: (a) in the urban residential heating sector, we assumed that 80% of the heat demand was derived from the district heating system (Xiong et al 2015); (b) hard coal grade 2 is applied instead of hard coal grade 3, and the usage of natural gas and electricity increased. These assumptions are consistent with the BSPC regulations. From 2020 to 2030, it is assumed that the same levels of policy strictness will be maintained in the BTH region and the governments will continue to implement specific policy measures at the same rate. The detailed ways to determine the key parameters and default parameters of the GAINS Asia model used in our study are present in section 4 of the supporting information.

Two indicators are introduced to evaluate the synergistic effects of CO₂ and air pollutants under the BSPC. The indicator of ${S_1}$, which is named the 'degree of co-effect', represents the ratio of CO₂ reduction to one pollutant reduction (equation (1)).

$$\begin{equation}{S_1} = \frac{{\Delta {E_{{\text{C}}{{\text{O}}_{\text{2}}}}}}}{{\Delta {E_{\text{p}}}}}.\end{equation} \tag{ 1 }$$

$\Delta {E_{{\text{C}}{{\text{O}}_{\text{2}}}}}$ equals the CO₂ emission reduction (Mt). ΔE_P represents the pollutant reduction (Kt).

${S_1}\,$and ${S_2}$ has been suggested in some studies to assess the synergistic level (Nathan and Kristin 2010, Liu et al 2013, Lu et al 2019). The higher ${S_1}\,$is, the stronger the co-effect is. However, ${S_1}$ could be high when both $\Delta {E_{{\text{C}}{{\text{O}}_{\text{2}}}}}$ and ΔE_P are low in certain cases, indicating low reduction potentials. And also ${S_1}$ could be low when there are large changes in both air pollutant and CO₂ emissions. Therefore, the 'relative degree of co-effect' indicator ${S_2}$ has been used as the indicator for assessing the synergies in our study.

$$\begin{equation}{S_2} = \frac{{\frac{{{\Delta E_{{\text{C}}{{\text{O}}_{\text{2}}}}}}}{{{E_{{\text{C}}{{\text{O}}_{\text{2}}}}}}}}}{{\frac{{{\Delta E_{\text{p}}}}}{{{E_{\text{p}}}}}}}.\end{equation} \tag{ 2 }$$

${E_{{\text{C}}{{\text{O}}_{\text{2}}}}}$ is the CO₂ emissions (Mt). ${E_{\text{p}}}$ represents the pollutant p emissions (Kt). When we implement the BSPC, assuming the same effect of pollutant reduction level, the greater the indirect emission reduction ratio of CO₂, the higher the co-effects. This means the higher is ${S_2}$, the greater is the CO₂ mitigation potential of decreasing one-unit pollutant.

Data for this study was obtained from the China Guidebook for Air Pollution Emission Inventory (MEEC 2015), the Provincial Economic Yearbooks (BSB 2016, HSB 2016, TSB 2016), the China Statistical Yearbook (NBS 2016), the 13th FYP of Energy Development (NEA 2017a), the Clean Heating Plan for Winter in North China (2017–2021) (NEA 2017b), the 13th FYP of Renewable Energy Development (NDRC 2016), the 13th FYP of Industrial Transformation and Upgrading (GOHB 2016), the 13th FYP for the Comprehensive Development of Transportation Systems (GOBJ 2016, GOHB 2016, GOTJ 2016), the 13th FYP of Power Sector Development (PGC 2016), and several state-of-the-art studies (Xiong et al 2015, Zhang et al 2015, Su et al 2018).

All analyses conduced for this study are based on the meteorology of 2015. It is known that the interannual variability in meteorological conditions causes variations for individual years. In addition, in absence of local information from each province, the baseline projection of economic activities for each province up to 2030 has been developed from international data sources. Activity projections, especially the future development of the most polluting activities in Hebei (production levels of the various sectors in heavy industry, agricultural activities, etc) have important impacts on future emission projections and the mitigation potential. Further work that incorporates local information will be required to enhance the robustness of the findings.

Table 1 presents the projected major pollutants (SO₂, NO_x, and PM_2.5) emissions under different scenarios, which have been concluded in our forthcoming published studies (Xu et al 2020). Details regarding the emissions of these air pollutants in different sectors and scenarios of each region are shown in figures S1–S3 of the supporting information. Compared to the emissions in the 2020 baseline scenario, the BSPC would reduce emissions of SO₂ by 62.7 kt (equivalent to a 49% reduction of the emissions in the corresponding baseline scenario), 24 kt (13%), and 246.3 kt (23.9%), respectively, in Beijing, Tianjin, and Hebei by 2020. NO_x emissions reduce by 22 kt (10%) in Beijing, 11 kt (4.1%) in Tianjin, and 213 kt (12%) in Hebei by 2020. PM_2.5 emissions reduce by 50.4 kt (52%), 22 kt (23%), and 349 kt (40%) for Beijing, Tianjin, and Hebei by 2020, respectively. In addition, the emissions of SO₂, NO_x, and PM_2.5 are also lower in the policy scenarios than in the baseline scenarios for 2025 and 2030. The BSCP can significantly reduce air pollutant emissions in the BTH region.

The predicted CO₂ emissions of the BTH region in the baseline and policy scenarios are presented in figure 1. In 2015, Hebei produced substantially more CO₂ than Beijing and Tianjin (about 807 Mt, 92 Mt, and 169 Mt, respectively). Figure 1 shows the primary emission tendencies in the baseline and policy scenarios. CO₂ emissions of Beijing are expected to grow significantly if the BSPC is not implemented, and it is projected to increase by 14%, 39%, and 55% relative to 2015 by 2020, 2025 and 2030, respectively. For Tianjin and Hebei, the increases in the baseline scenarios for 2020, 2025, and 2030 are all within 11% when compared with 2015. The larger increases of relative change in CO₂ emissions in Beijing under the baseline scenarios are probably due to the large size and strong growth of the Beijing's economy driving further residential consumption (Wen and Wang 2019, Jiang et al 2019b, Wei et al 2020). If the BTH area achieves the policy targets by 2020, it is predicted that Beijing, Tianjin, and Hebei will reduce CO₂ emissions by 20.7 Mt (equivalent to a 19.7% reduction in the corresponding baseline scenario), 6.8 Mt (3.8%), and 80.2 Mt (9.2%), respectively. CO₂ emissions from the BTH area would be reduced by 107.7 Mt (9.3%), of which Hebei accounts for 74.5%. By 2030, it is expected that Beijing, Tianjin, and Hebei will reduce their emissions by 37.8 Mt (26.6%), 4.85 Mt (2.5%), and 69.9 Mt (8.6%) compared with the 2030 baseline scenario, respectively (figure 2). It is worth noting that Tianjin and Hebei presents smaller reductions by 2030 compared with 2020 and 2025. On the one hand, it is due to that the baseline scenario slightly decreases by 2030 in Hebei, generating smaller policy benefits. On the other hand, for both Tianjin and Hebei, it is mainly because by 2030, increased levels of activity will require more power plants to be built for the increasing power generation requirements, leading to a smaller reduction potential. CO₂ emissions from the BTH area in 2030 would be reduced by 112.6 Mt (9.8%), of which Hebei accounts for 62.0%. From 2020 to 2030, the absolute emission reductions and the relative emission reduction ratio of CO₂ will remain relatively stable in each of the BTH region. Specifically, Beijing: 21–35 Mt (20%–25%); Tianjin: 5–6.7 Mt (2.5% to 3.8%); Hebei: 70–92 Mt (9%–11%) (figure 2). This indicates that the indirect effects of BSPC on CO₂ emission reductions is stable and continuous. In each scenario, the emissions of Hebei would be several times higher than those of Beijing and Tianjin. The high emissions are mainly due to the fact that most of the industrial activity occurs in Hebei, and therefore, a greater amount of fossil fuels are consumed.

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In addition, it is worth noting that although significant reductions in CO₂ emissions have been projected in Beijing when compared with the corresponding baseline scenario of each year, the CO₂ emissions in the 2030 policy scenario only show a slight increase compared to those in 2015. This shows that after the implementation of the BSPC, Beijing's CO₂ emissions have remained at a relatively stable level from 2015 to 2030. Further, in the BTH region, the CO₂ emissions have also remained at a stable level since 2015, suggesting that the CO₂ emission in BTH area peaked in 2015. This is consistent with the conclusions drawn from Zhao et al (2018), indicating that the increasing CO₂ emission trends in the BTH region from 2000 to 2014 and Yuan (2019), which show that the CO₂ emissions presents a downward trend from 2014 to 2016 based on a 2 year interval study in the BTH region. And then the CO₂ emissions will keep relatively stable (decrease within 1% compared with 2015) in 2020 and 2025, and decrease slightly by 3% in 2030 under the BSPC compared with 2015 in the BTH region. The CO₂ emissions under the policy scenarios in each region will not change significantly from 2015 to 2030 under the BSPC, however, the APPCAP would significantly reduce 352.7 Mt (39.9 ± 5.3%) of CO₂ emissions from 2012 to 2017 (Lu et al 2019). This also indicates that the peak for CO₂ emissions would have come in the BTH region. Compared with the study of Tao et al (2019) which suggests that China's total CO₂ emissions will peak by 2030, it could be concluded that the relatively stable CO₂ emissions in the BTH region contribute to the peak of CO₂ emissions being achieved before 2030 in China and the CO₂ emissions from the BTH region have a positive effect on controlling China's total CO₂ emissions.

The CO₂ emissions of the power and heating sector, which is a significant source of CO₂ emissions in the BTH region, accounts for 22.4%, 44.7%, and 32.5% of the total emissions in 2015 for Beijing, Tianjin, and Hebei, respectively. CO₂ emissions in the power and heating sector in Beijing would decline by 49% and 54% compared with the baseline scenario for 2020 and 2030, respectively, while the emissions in Tianjin and Hebei would undergo minimal change. In Beijing, the main reason for the reductions was the closure of the thermal power plants and coal-fired units in the power sector under the BSPC, which caused a dramatic decline in coal consumption. It is estimated that by 2025, Beijing's power demand will be 35 million kW, in which nearly 75% will depend on external sources (Liu 2020). The main source of external transmission is from Shanxi and Inner Mongolia, which are still largely powered by coal (Jiang et al 2019a). Therefore, it may have negative effects of indirect emissions transmission on Shanxi and Inner Mongolia based on the generation structures used in the two provinces. In Hebei and Tianjin, although some thermal power units have been renovated or shut down, the thermal power output has increased since 2017 (TSB 2019), and coal consumption in the power sector is also increasing (GCMR 2019). The increasing demand due to its own industrial structures and economic development status is consistent with the analysis report on the mid-term evaluation and future prospect of power coal control during the 13th FYP period (NCEPU 2019), which predicts that the coal consumption in China's power sector will peak in 2020 and then plateau. Current ultra-low emissions from power plants only target general air pollutants, such as SO₂, but do not target CO₂. Therefore, CO₂ emissions in the power and heating sector in Tianjin and Hebei would not decrease significantly by 2020 and 2030 compared with the baseline scenarios. In addition, the residential combustion sector in Beijing and the sector of industrial combustion in Tianjin and Hebei also play important roles in CO₂ emissions. Their emissions account for 27.5%, 28.7%, and 28.9%, respectively, of the total emissions in 2015.

As is shown in figure 1, significant CO₂ emission reductions was projected in the residential combustion sector, indicating that implementing energy replacement from coal to gas and electricity can achieve significant reductions to both CO₂ and air pollutants emissions (figures S1–S3). Specifically, the reductions in the residential combustion sector will account for 39%, 73%, and 57% of the total CO₂ emissions reductions by 2020 for Beijing, Tianjin, and Hebei, respectively. Further, by 2030, it is projected to accounts for 44%, 88%, and 43% for each region, respectively. In addition, the reductions in the power and heating plants of Beijing will account for 57% of the total reductions by 2020. And the reductions in the Industrial combustion sector for Tianjin and Hebei will account for 20% and 19% of the total reductions by 2020, respectively. Overall, the degrees of emission reduction potential vary in different sectors and regions.

Our results demonstrate that BSPC can reduce CO₂ emissions while reducing air pollutants. For controlling air pollutants (SO₂, NO_x, and PM_2.5), as shown in figures S1–S3 of the supporting information, reductions mainly occur due to the energy structure optimization and transformation (e.g. control coal consumption, coal to clean policy in residential combustion and thermal power plant sector), the transportation structure optimization and transformation (e.g. increasing new energy vehicles), and the industrial structure optimization and transformation (e.g. control of production capacity in high-pollution and high-emission industries). Therefore, the optimization and transformation of these major sectors has great impacts on CO₂ emission reductions.

3.2.1. Evaluation by pollutants

The metric ${S_2}$ is applied to evaluate the co-effects of CO₂ emission reductions and other atmospheric pollutants reductions (listed in table 1) under policy scenarios of different regions (figure 3). The ${S_2}$ values range from 0.38 to 2.94 in Beijing, from 0.07 to 0.99 in Tianjin, and from 0.23 to 1.20 in Hebei and BTH. The values of ${S_2}$ for BTH are close to those of Hebei because Hebei dominates the total emissions of the BTH area.

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High values of ${S_2}$ indicate high synergies due to the co-effects of implementing the BSPC. Our results show that NO_x has the highest co-effects with CO₂ in Beijing, Tianjin, Hebei, and BTH in each of the policy scenarios because of the higher reduction potential of CO₂ per unit of NO_x emission reductions. This finding is consistent with the results from Feng et al (2018), which indicated that a co-effect for CO₂ control exists when NO_x emission control measures are implemented and identified that the energy efficiency technologies have significant co-effects on reducing NO_x and CO₂ emission levels simultaneously in China's cement industry with the Integrated MARKAL-EFOM System (TIMES) model. In our study, the values of ${S_2}$ for NO_x in Beijing are significantly higher than those of Tianjin and Hebei, which indicates a stronger co-effect on the CO₂ emission reductions due to application of NO_x control measures in Beijing. Higher ${S_2}$ value of NO_x in Beijing means higher reduction potential of CO₂ emissions while reducing per unit of NO_x emission.

3.2.2. Evaluation by key sectors