Analyzing synergies and efficiency of reducing CO₂ and air pollutants in the case of China's three-year action plan to fight air pollution

Rapid economic growth is accompanied by increased industrialization and total energy consumption. Although China has made many efforts to build a cleaner production mechanism and adjust the sustainable energy structure, China's energy consumption structure will still be dominated by fossil fuels in 2020 (NBS 2021). The use of fossil fuels adds significant CO₂ and air pollutant emissions. In 2021, China's CO₂ emissions exceeded 200 million tons, accounting for 11% of the global total (IEA 2022). China's PM_2.5 is characterized by a high level of regional air pollutant emissions (Wang et al 2021). China's CO₂ and air pollutant emissions are still a serious problem, and more policy effects will be produced if we do not only focus on the single pollutant reduction but consider the synergistic mitigation of various pollutants of pollutant control policies (Williams 2007, Muller 2012, Rafaj et al 2013, Rao et al 2013, Xu et al 2021).

China has pledged to peak carbon emissions before 2030 and achieve carbon neutrality before 2060. For China, considering synergistic mitigation of CO₂ and other co-benefits in the evaluation of air pollutant control policies is an important issue. To be clear, China's air pollution control policies have had a significant reduction effect on air pollutants. Yu et al (2022) found that the 'Air Pollution Prevention and Control Action Plan' significantly reduce PM_2.5 concentrations and SO₂ emissions in key implementation areas. Song et al (2020) used the RDD method to demonstrate that the expansion of the core areas for joint prevention and control of air pollution has improved regional air quality. Zhang et al (2020) argued that the 'Notice on the Time-limited Achievement of Air Pollution Control Targets in Key Cities' significantly reduced PM_2.5 emission concentrations. Therefore, when evaluating China's air pollutant control policy, we do not consider the direct emission reduction effect on air pollutants but evaluate its synergistic mitigation of CO₂ and other co-benefits.

Most of the studies on the synergistic mitigation and other co-benefits of air pollutant control policies are analyzed through simulation and prediction under air pollution emission reduction scenarios (Muller 2012, Li et al 2019, Ahmad et al 2023). More deeply, in order to assess the synergistic mitigation and co-benefits that have been achieved by existing air pollutant control policies, policy evaluation studies based on cost-effectiveness hypothesis were born. For high-emission and high-energy-consuming enterprises, excessive compliance costs can force them to exit the market competition, thereby enabling adjustments and upgrades in the industry's internal structure. This adjustment of production resource factors and the upgrading of industrial structure have increased total factor productivity (Rexhäuser et al 2014).

Existing literature assessing China's air pollution control policies mainly focuses on their effects on pollutant emissions (Li et al 2023) and energy efficiency (Wang et al 2018) or the enhancement of pollutant reduction efficiency (Zhang et al 2021). Building upon the cost-effectiveness hypothesis and existing research, this study continues to evaluate the policy effects of the air pollutants control policy on pollutant reduction and efficiency improvement. In addition, the air pollution control policy we selected is the latest 'Three-year Action Plan to Fight Air Pollution' (hereafter referred to as the three-year action plan) (State Council General Office 2018) currently implemented in China, which further strengthened the governance of air pollutants in key implementation areas. However, unlike previous studies on air pollutant control policies, which most focus only on the assessment of air pollutant emission reductions (direct policy effects). This study not only stops at the assessment of emission reduction but analyzes whether China's current air pollutant control policies have synergistically reduced carbon emissions and achieved multi-aspect pollutant synergistic emission reduction efficiency and decomposed the index from two aspects: technical efficiency and technological progress. In addition, there is very little research on the latest air pollutant control policy, the three-year action plan, and most of it is only for individual cities and regions. Our research scale provides an in-depth policy effects analysis of three-year action plan, and the research scale is selected as a national and regional comparative perspective, rather than a single region.

Compared to previous literature, this study makes three contributions. First, most studies have calculated total factor productivity using single pollutant emissions (SO₂, NO_x , etc) as undesirable outputs when assessing the impact of environmental policies on total factor productivity (D'Amato et al 2021). This study is not limited to the analysis of the total factor productivity of single air pollutant emissions or CO₂ as an undesirable output, but also considers the total factor productivity of CO₂ and air pollutants as undesirable outputs, that is, the multi-aspect pollutant synergistic emission reduction efficiency, and realizes the evaluation of the synergistic emission reduction efficiency and potential of the three-year action plan in a more comprehensive and innovative way.

Second, Existing literature only focuses on the direct emission reductions of the air pollution control policies, specifically its impact on air pollutant reduction. However, greenhouse gases and air pollutants share a common origin, we consider not only direct air pollutants but also the synergistic mitigation effects of CO₂ and synergistic efficiency achieved by the three-year action plan. The latest air pollutant control policy-three-year action plan also generates CO₂ reduction effects and synergistic efficiency, which are our key evaluation points. In addition, the research scale of this study is not only a single city or regional perspective of three-year action plan, but a national and inter-regional comparative perspective.

Third, unlike previous studies that only assess the policy effects at the regional level, we evaluate the differential effects of the three-year action plan on different resource regions by considering the heterogeneity of resource endowments. This includes incorporating heterogeneity analysis for key regions, particularly those with abundant resources.

2.1. Assessing model

2.1.1. Difference-in-differences (DD) method

To analyze the effects of the three-year action plan on the synergistic mitigation of CO₂ and various pollutants, we employ the DD method for evaluation. The treatment group consists of 11 key implementation provinces covered by the three-year action plan, namely the Beijing-Tianjin-Hebei and surrounding areas, the Yangtze River Delta region, and the Fenwei Plain. The DD model is defined as shown in equations (1) and (2).

$$\begin{align}{\left( {{\text{lnCO}_2}} \right)_{it}} & = \alpha + {\beta _1}\left( {\text{Action}\,{\text{plan}_{it}}} \right) \nonumber\\ & \quad + \rho {X_{it}} + {\gamma _i} + {\lambda _t} + {\varepsilon _{it}}\end{align} \tag{ 1 }$$

$$\begin{align}{\left(\text{multi} - \text{aspect}\,{\text{sere}}\right)_{it}} & = \alpha + {\beta _1}\left( {\text{Action}\,{\text{plan}_{it}}} \right) \nonumber\\ & \quad + \rho {X_{it}} + {\gamma _i} + {\lambda _t} + {\varepsilon _{it}}.\end{align} \tag{ 2 }$$

In the equations (1) and (2), ${\left( {{\text{lnC}}{{\text{O}}_{\text{2}}}} \right)_{it}}$ represents the logarithmically transformed CO₂ emissions, which is the dependent variable, represents the impact of synergistic mitigation of CO₂ emissions. Because the three-year action plan is a commanding policy tool for reducing air pollutants, the most immediate and accomplished effect is the reduction of air pollutants. Therefore, we define the CO₂ reduction of the three-year action plan as synergistic mitigation of CO₂ emissions, which is the co-benefit of the three-year action plan to CO₂ emission reduction. ${(\text{multi} - {\text{aspect}}\,{\text{sere}})_{it}}$ represents the multi-aspect pollutant synergistic emission reduction efficiency indicator, which is also the dependent variable. The detailed description of this indicator is provided in section 2.2. ${\text{Action}}\,{\text{plan}_{it}}$ is the interaction term between the treat dummy variable and the post dummy variable, indicating the effect of the three-year action plan. ${X_{it}}$ represents control variables, including per capita regional GDP (economic level), value added of the secondary industry (industrial structure), the number of large-scale industrial enterprises (industrial scale), green coverage area of built-up areas (greening level), urban road area at the end of the year (road construction level). ${\gamma _i}$ represents individual fixed effects. ${\lambda _t}$ represents time fixed effects. ${\varepsilon _{it}}\,$is a random error term. The model has been tested by parallel trend. The methods and results are shown in supplementary materials S4.

2.1.2. Resource-based regional heterogeneity analysis framework

In order to assess the different effects of three-year action plan in resource-based areas and non-resource-based regions, this study introduces regional dummy variables ${\text{resource}_{it}}$ as the interaction term of ${\text{Action}}\,{\text{plan}_{it}}$. As shown in equations (3) and (4). See supplementary material S1 for the definition of resource-based areas.

$$\begin{align} & {\left( {{\text{lnCO}_2}} \right)_{it}} = \alpha + {\beta _1}\left( {\text{Action}\,{\text{plan}} \times {\text{resourc}}{\text{e}_{it}}} \right) \nonumber\\ & \qquad \quad \qquad + \rho {X_{it}} + {\gamma _i} + {\lambda _t} + {\varepsilon _{it}} \end{align} \tag{ 3 }$$

$$\begin{align} & {\left({\text{multi}} - {\text{aspect sere}}\right)_{it}}\nonumber\\ & \quad = \alpha + {\beta _1}\left( {\text{Action}\,{\text{plan}} \times {\text{resourc}}{\text{e}_{it}}} \right) \nonumber\\ & \qquad + \rho {X_{it}} + {\gamma _i} + {\lambda _t} + {\varepsilon _{it}}\end{align} \tag{ 4 }$$

2.2. Measurement of multi-aspect pollutant synergistic emission reduction efficiency indicators

3.1. Numerical distribution and geographical representation of CO₂ and SERE

Figure 1 indicates that the distribution volatility of CO₂ emissions in the 11 provinces focused on by the three-year action plan is high. Among them, Shandong, Jiangsu, and Hebei have the highest CO₂ emissions compared to the other provinces. The peak CO₂ emissions of the provinces focused on by the three-year action plan (Shandong and Hebei) are higher than those of the non-focus provinces. However, the focus provinces also show superior performance in terms of SERE. Most of the provinces focused on by the three-year action plan have higher SERE than the non-focus provinces. Particularly noteworthy are Shandong, Jiangsu, and Hebei, which have the highest CO₂ emissions. This is because these areas have a high level of economic development and strong geographical advantages. They are all located in the eastern coastal region and have good development resources to promote the improvement of SERE. It also demonstrates that the three-year action plan has generated indirect policy benefits in the focused implementation areas.

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To illustrate the distribution characteristics of SERE in the provinces focused on by the three-year action plan more clearly, we use figure 2 to represent the geographic distribution of SERE. The provinces focused on by the three-year action plan that are located in the eastern coastal region of China exhibit higher SERE values compared to the provinces located closer to the inland region. Among them, Beijing, Tianjin, and Shanghai are directly administered municipalities in China, with distinct locational advantages and economic and political dominance. Jiangsu and Zhejiang have strong industrial transformation capabilities and are economically developed, demonstrating significant advantages as well. Therefore, the SERE values of the aforementioned municipalities and provinces belong to the high-value range within the focused implementation areas of the three-year action plan. They have a greater capacity to contribute to the productivity of synergistic reduction of multiple pollutants and possess larger potential for resource allocation and element optimization. Henan and Shandong are provinces with high population density and a large population base in China. The significant development potential and demographic dividend of these provinces also lead to greater benefits from non-production factors in terms of synergistic emission reduction and economic growth within the region.

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In addition, figure 3 compares the trend of CO₂ emissions in the experimental group and control group more clearly, and before the implementation of the three-year action plan, the trend of CO₂ emissions in the experimental group and the control group is basically the same, and the growth trend of CO₂ emissions in the control group after the implementation of the three-year action plan is faster than that of the experimental group. We preliminarily judge three-year action plan has produced certain collaborate reduction benefits for key areas.

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The left graph is a stacked bar chart showing the numerical variations of CO₂ emissions in the 30 provinces of China. The right graph is a Cleveland's dot plot depicting the SERE values. Both graphs share the same vertical axis, which represents the 30 provinces of China. The 'Pilots' category represents the provinces focused on by the three-year action plan, while the 'Non-pilot' category represents the non-focused provinces. The gray horizontal lines in the right graph are the standard error bars.

3.2. Analysis results of multi-aspect pollutant synergistic emission reduction efficiency

Table 1 shows the synergistic mitigation and of CO₂ and air pollutants of Three-year action plan. The results in the column (1) of table 1 indicate that the policy effect of the three-year action plan on LnCO₂ is significantly reduced. This air pollution reduction policy has also led to a decrease in CO₂ emissions, which is favorable for achieving China's synergistic goal of 'reducing pollution and carbon emissions' as proposed by the Ministry of Ecology and Environment (MEE 2022).

Table 1. Benchmark results.

	(1)	(2)	(3)	(4)
Variables	LnCO2	SERE	SEREEC	SERETC
${\text{Action}}\,{\text{plan}_{it}}$	−0.0410**	0.0712***	0.0303**	0.0284*
	(0.0189)	(0.0159)	(0.0144)	(0.0144)
Control variables	YES	YES	YES	YES
Time fixed effect	YES	YES	YES	YES
Individual fixed effect	YES	YES	YES	YES
Constant	1.302	−9.620***	−2.329	−6.309***
	(2.034)	(2.039)	(1.848)	(1.846)
Observations	240	240	240	240

Note: The values in parentheses are standard errors, *, ** and *** respectively indicate that they are significant at the 10%, 5%, and 1% levels.

According to the results in the columns (2) (3) and (4) of table 1, the three-year action plan has a significant positive effect on SERE and SEREEC, but its effect on SERETC is weakly significant. After controlling for a series of variables, the three-year action plan has a positive influence and enhancement on efficiency, indicating that the management level in the pilot areas has been effective in optimizing resource allocation and improving efficiency. However, the contribution of technological progress in the pilot areas to the promotion of synergistic reduction potential is limited. Therefore, SERETC will be excluded from subsequent analyses. In order to enhance the credibility and robustness of the benchmark results, we apply three method of sensitivity test, which refer to supplementary material S5.

3.3. Results of heterogeneity analysis of resource-based regions

Table 2 primarily presents the different policy effects of the three-year action plan on resource-based and non-resource-based regions. Significant policy effect differences are observed between the two types of regions. The three-year action plan leads to significant CO₂ reduction and substantial improvement in the multi-aspect pollutant synergistic emission reduction efficiency in non-resource-based regions, as evidenced by the heterogeneity analysis between resource-based and non-resource-based regions. As China's air pollution control policies are implemented in stages, the three-year action plan represents the second phase of air pollution control. For resource-based regions, the early-stage policy effects in air pollution control are evident, but in the later stage, additional financial investments and technical support are required. During the implementation of the three-year action plan, there is no significant enhancement in multi-aspect pollutant synergistic emission reduction efficiency in resource-based regions, which is an indicator of their resource allocation capacity and potential for multi-pollutant reduction. This indicates the need for policy adjustments in the three-year action plan specifically tailored to resource-based regions.

Table 2. Results of resource-based regional heterogeneity analysis.

	(1)	(2)	(3)	(4)
Variables	LnCO2	SERE	SEREEC	SERETC
${\text{Action}}\,{\text{plan}_{it}}$	−0.0628***	0.124***	0.0355**	0.0728***
	(0.0204)	(0.0187)	(0.0157)	(0.0162)
Control variables	YES	YES	YES	YES
Time fixed effect	YES	YES	YES	YES
Individual fixed effect	YES	YES	YES	YES
Constant	1.492	−1.566	−0.221	−0.751
	(2.011)	(1.850)	(1.551)	(1.605)
Observations	240	240	240	240

Note: The values in parentheses are standard errors, *, ** and *** respectively indicate that they are significant at the 10%, 5%, and 1% levels.

The three-year action plan focuses on controlling pollution at the source by closing high-energy-consuming enterprises, thereby achieving source control. Due to the inherent similarities between CO₂ and air pollutants, the efforts made in pollution control under the three-year action plan have also led to synergistic mitigation in CO₂ emissions. Additionally, measures such as optimizing heating methods, promoting new energy vehicles, and planning urban green landscapes have further adjusted the industrial structure and promoted green upgrades, resulting in reduced fossil fuel consumption in industries and the realization of co-benefits from the three-year action plan. This study differs from previous literature that only focused on a single air pollutant (Jiang et al 2021). Instead, it evaluates the synergistic mitigation of CO₂ emissions achieved by the three-year action plan, which is consistent with the findings of Shu et al (2022) and Zhang et al (2022). This study also supports the notion that the three-year action plan has achieved synergistic mitigation in different pollutants.

The three-year action plan has stimulated the management capacity of key regions in enhancing synergistic reduction potential. The plan requires governments at all levels to establish air quality management institutions and further improve environmental protection agencies. By enhancing the level of management and combining with big data analysis in environmental monitoring, the decision-making process for pollution control has become more specialized. The three-year action plan combines measures for pollution control, such as coal-fired boiler renovation, industrial exhaust treatment, and source control of pollutants, directing government resources towards environmental protection and green industries. For enterprises, the three-year action plan increases the cost of pollutant emissions, which in turn compels companies to improve production efficiency, innovate emission reduction technologies, and promote upgrades in technology, infrastructure, and human capital, leading to the reallocation of resources. This policy has established a collaborative governance mechanism among various levels of government, breaking administrative barriers and improving the overall input-output model.

Moreover, the three-year action plan enables synergies and efficiency of reducing CO₂ and air pollutants in non-resource-based regions. Although the main policy objective of the three-year Action Plan is to reduce air pollutants, especially PM_2.5 emissions, the policy also takes into account different sectors such as transportation, agriculture and domestic pollution sources, so that the management potential and synergistic emission reduction in non-resource-based regions are further developed. For resource-based regions, although they have the advantages of resource intensive, because emission reduction technologies and equipment innovation require a certain period of operation and application, this also provides support for the realization of the policy effect of synergies and efficiency of reducing CO₂ and air pollutants in non-resource-based regions.

The CO₂ emissions in the regions targeted by the three-year action plan are higher than those in non-targeted regions. However, the multi-aspect pollutant synergistic emission reduction efficiency is higher in the targeted regions. The distribution of multi-aspect pollutant synergistic emission reduction efficiency in China exhibits distinct geographic characteristics. For example, developed regions and coastal areas have higher efficiency compared to other regions, influenced by their geographical advantages, population dividends, and economic development. Through further evaluation, this study concludes that the three-year action plan has achieved synergistic CO₂ reduction benefits and enhanced resource allocation capacity in the targeted regions, promoting the potential of regional management levels in technological advancements. Additionally, the study reveals the three-year action plan's impact on pollutant reduction and efficiency improvement in non-resource-based regions during the early stages of implementation.

Therefore, we provide the following recommendations for policymakers: (1) Establishing a coordinated mechanism through structural adjustments and management to achieve synergistic mitigation of CO₂ and air pollutants. The promotion of co-benefits of the three-year action plan requires attention to industrial chain adjustment and reorganization in the regions where pollution control policies are implemented, gradually reducing, or eliminating pollution-intensive production processes. It is also necessary to establish a coordinated governance system involving multiple departments, and reduce policy implementation barriers. (2) Considering heterogeneity factors such as resource endowment and regional development, and tailor the institutional design and implementation of air pollution control policies to different regions. In the resource-based regions, the governments should encourage the introduction of innovative and appropriate synergistic emission reduction technologies and improve allocation efficiency. (3) Expanding the technological boundaries of multi-pollutant synergistic reduction, introduce new emission reduction technologies, and innovate existing pollutant removal and reduction processes. It is necessary to focus on the role of technological progress in the targeted regions of the three-year action plan. This includes reducing the cost of technology transfer for emission reduction and applying new technologies to achieve both emission reduction and output efficiency improvement.

However, there are still some limitations and uncertainties in this study, which will be improved in future studies. Firstly, the sample data scale used in this study is provincial, because there is currently a lack of city-scale CO₂ emission data and energy data after 2019, and the implementation period of the three-year action plan is from 2018 to 2020. The lack of 2020 samples will not be able to accurately evaluate. In the future, the relevant data at the city scale can be further evaluated to the city level. Secondly, when we calculate SERE, air pollutants contained in undesirable outputs still need to be abundant, due to data limitations. Therefore, after obtaining more sufficient air pollutant data in future studies, more air pollutants (such as ozone) will be measured, and a comprehensive multi-aspect pollutant synergistic emission reduction efficiency measurement system can be established.

This work was supported by the Sino-German Center (Grant No. M-0049), the Shanghai Science and Technology Commission (Grant No. 20230742200), and Fudan Tyndall Centre of Fudan University (Grant No. IDH6286315)

The data cannot be made publicly available upon publication because no suitable repository exists for hosting data in this field of study. The data that support the findings of this study are available upon reasonable request from the authors.