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

Different from existing studies that only analyze the emission reduction efficiency or mitigation of a single pollutant by the air pollutant control policy, this study focuses on synergies and efficiency of reducing CO2 and air pollutants. We select the panel data from 30 provinces and cities in China from 2013 to 2020, and apply the difference-in-differences model to assess China’s three-year action plan to fight air pollution (referred to as the three-year action plan). To distinguish the method of measuring single pollution emission efficiency, we calculate synergistic efficiency of reducing CO2 and air pollutants emissions through the super-efficiency slack-based measure model which considering various pollutants as undesirable outputs. Furthermore, this study conducted regional heterogeneity analysis of resource endowments by incorporating interactive items of regional dummy variables. The results of study demonstrate the three-year action plan achieve synergies and efficiency of reducing CO2 and air pollutants. And the policy effects are also more pronounced in non-resource-based regions. Furthermore, the three-year action plan enhances management level and the potential for synergistic mitigation, as well as the beneficial effects on resource allocation efficiency and capacity improvement in key regions.


Introduction
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 CO 2 and air pollutant emissions.In 2021, China's CO 2 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 CO 2 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 CO 2 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 SO 2 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 Timelimited 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 CO 2 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 (SO 2 , 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 CO 2 as an undesirable output, but also considers the total factor productivity of CO 2 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 CO 2 and synergistic efficiency achieved by the three-year action plan.The latest air pollutant control policy-three-year action plan also generates CO 2 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.

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 CO 2 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).
In the equations ( 1) and ( 2), (lnCO 2 ) it represents the logarithmically transformed CO 2 emissions, which is the dependent variable, represents the impact of synergistic mitigation of CO 2 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 CO 2 reduction of the three-year action plan as synergistic mitigation of CO 2 emissions, which is the cobenefit of the three-year action plan to CO 2 emission reduction.(multi − aspect 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.Action 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).γ i represents individual fixed effects.λ t represents time fixed effects.ε it is a random error term.The model has been tested by parallel trend.The methods and results are shown in supplementary materials S4.

Resource-based regional heterogeneity analysis framework
In order to assess the different effects of threeyear action plan in resource-based areas and nonresource-based regions, this study introduces regional dummy variables resource it as the interaction term of Action plan it .As shown in equations ( 3) and (4).See supplementary material S1 for the definition of resource-based areas.Group (Hammer et al 2020).Missing data were imputed using interpolation methods.This study measures multi-aspect pollutant synergistic emission reduction efficiency and its decomposition using an output-oriented super-efficiency slack-based measure (SBM) model.These indicators are all innovative forms of total factor productivity.
(1) Multi-aspect pollutant synergistic emission reduction efficiency (SERE): In this study, a contribution is made by considering CO 2 and air pollutants (PM 2.5 and SO 2 ) no single pollutants as undesirable outputs to measure emissions reduction efficiency.When measuring SERE, labor, capital, and energy are selected as input indicators, and actual regional GDP is chosen as the output indicator.In terms of selecting undesirable output indicators, CO 2 , SO 2 , and PM 2.5 are considered as undesirable outputs.Through the above data, we construct the super-efficiency SBM model with undesirable outputs under the data envelopment analysis framework, and use the frontier constructed by the effective decision-making unit in each period as the reference frontier to establish the globe Malmquist-Luenberger (GML) index, further decompose it into efficiency changes and technical changes, and finally, multiply the GML productivity index and its decomposition terms to obtain SERE.The detailed methodological description and discussion can be found in supplementary material S2.
(2) Multi-aspect pollutant synergistic emission reduction efficiency index (SEREEC): SEREEC measures the relative efficiency changes of multi-aspect pollutant synergistic emission reduction.It represents the relative distance between a region and the production frontier, reflecting the positive role of regional management in resource allocation.
(3) Multi-aspect pollutant synergistic emission reduction technological progress index (SERETC): SERETC measures the changes in frontier productivity, reflecting the growth effect of regional technological progress on the potential for multi-aspect pollutant synergistic emission reduction.
Characteristics of multi-aspect pollutant synergistic emission reduction efficiency and its decomposition index is showed in supplementary material S3.

Limitations
In these indicators, the CO 2 emission data are obtained from the Carbon Emission Accounts & Datasets, and according to the study of Shan et al (2018Shan et al ( , 2020)), it can be seen that there are some limitations in the accounting process.These include: (1) The use of national average emission factors when calculating fossil fuel-related emissions for each province in China is limited by the potential for additional uncertainty.(2) Waste treatment, the emission of other industrial processes and other industrial process components account for a small proportion are not considered in the inclusion of the first type of direct discharge.(3) Assuming that the economic structure of the nine provinces has remained unchanged over the past 10 years, the use of China Economic Census Yearbook 2008 makes the lack of up-to-date survey data that will improve in the future.

Numerical distribution and geographical representation of CO 2 and SERE
Figure 1 indicates that the distribution volatility of CO 2 emissions in the 11 provinces focused on by the three-year action plan is high.Among them, Shandong, Jiangsu, and Hebei have the highest CO 2 emissions compared to the other provinces.The peak CO 2 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 CO 2 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.
To illustrate the distribution characteristics of SERE in the provinces focused on by the threeyear 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.
In addition, figure 3 compares the trend of CO 2 emissions in the experimental group and control group more clearly, and before the implementation of the three-year action plan, the trend of CO 2 emissions in the experimental group and the control group is basically the same, and the growth trend of CO 2 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.
The left graph is a stacked bar chart showing the numerical variations of CO 2 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.

Analysis results of multi-aspect pollutant synergistic emission reduction efficiency
Table 1 shows the synergistic mitigation and of CO 2 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 LnCO 2 is significantly reduced.This air pollution reduction policy has also led to a decrease in CO 2 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).
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.

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   Note: The values in parentheses are standard errors, * , * * and * * * respectively indicate that they are significant at the 10%, 5%, and 1% levels.
Table 2. Results of resource-based regional heterogeneity analysis.
(1) 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.

Discussions
The three-year action plan focuses on controlling pollution at the source by closing high-energyconsuming enterprises, thereby achieving source control.Due to the inherent similarities between CO 2 and air pollutants, the efforts made in pollution control under the three-year action plan have also led to synergistic mitigation in CO 2 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 CO 2 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 CO 2 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-resourcebased regions are further developed.For resourcebased 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 CO 2 and air pollutants in non-resource-based regions.

Conclusions
The CO 2 emissions in the regions targeted by the three-year action plan are higher than those in nontargeted 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 CO 2 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 nonresource-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 CO 2 and air pollutants.The promotion of cobenefits 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 resourcebased 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 CO 2 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.
The values in parentheses are standard errors, * , * * and * * * respectively indicate that they are significant at the 10%, 5%, and 1% levels. Note: