Driving factors of NOX emission reduction in China’s power industry: based on LMDI decomposition model

Under the policy background of “joint prevention and control” of global greenhouse gas emission reduction and regional air pollutants, the power industry, as an important target industry of energy conservation and emission reduction policies, has become an effective choice to release dual pressures. In this paper, the “bottom-up” emission factor method was used to measure the emission of CO2 and NOX from 2011 to 2019. Then, the contributions of six factors to NOX emission reduction in China’s power industry were identified using the Kaya identity and logarithmic mean divisia index (LMDI) decomposition methods. The research results show that (1) there is a significant synergistic emission reduction effect between CO2 emission reduction and NOX emission reduction; (2) the factor that inhibits the growth of NOX emissions reduction in the power industry is economic development factor; and (3) the main factors that promote the reduction of NOX emission from the power industry are synergy effect, energy intensity, power generation intensity, and power production structure factors. Several suggestions are proposed, which are the power industry should adjust its structure, improve energy intensity, focus on applying low-nitrogen combustion technology, and improve the air pollutant emission information disclosure system to reduce NOX emissions.


Introduction
Currently, vigorously promoting the reduction of pollutant emissions has become a long-term strategy related to national development in China. As a kind of pollutant that has an essential impact on regional air pollution, nitrogen oxide (NO X ) is not only a toxic and harmful gas but also a necessary precursor of tropospheric ozone and atmospheric aerosols. Meanwhile, it is also an essential contributor to the formation of acid rain, which has great harm to human health and ecological environment. During the "Twelfth Five-Year Plan" period, the environmental protection work has emphasized controlling total NO X emission. The environmental and ecological protection goals during the "Fourteenth Five-Year Plan" period clearly state that the NO X emissions and volatile organic compounds will be reduced by more than 10%, and the total emissions of chemical oxygen demand and ammonia oxide will be reduced by 8% (CEC 2020).
Since greenhouse gas emissions and air pollutants have the relatively same source , reducing greenhouse gas emissions and controlling air pollutants have a specific synergistic effect in the formulation of standards and implementation measures. With unified planning and joint deployment of greenhouse gas emissions and air pollutant emissions, the reduction goal of these two kinds of emissions can be achieved with half the effort. The coordinated control of greenhouse gases and air pollutants emissions is an important technology and policy choice for emission reduction and pollution prevention in developing countries in the period of industrialization. Under the dual pressures of greenhouse gas emissions and air pollution, China's "Thirteenth Five-Year Plan for Controlling Greenhouse Gas Emissions" and the "Thirteenth Five-Year Plan for Ecological Environment Protection" issued by the State Council have clearly stated that strengthening the coordinated control of carbon emissions and air pollutant Responsible Editor: Ilhan Ozturk emissions is taken as an essential way of low-carbon transition (SCIO of China 2020).
Coordinated emission reduction includes two directions. One is the synergy of regional pollutant emission reduction leading to greenhouse gas reduction; the other is the synergy of regional pollutant emission reduction caused by the reduction of greenhouse gas. Burning of fossil fuels is an important cause of air pollution and a primary source of greenhouse gases. Air pollutants mainly include fine particulate matter (PM 2.5 ), ozone (O 3 ), sulfur dioxide (SO 2 ), and nitrogen oxides (NOx). Greenhouse gases mainly include carbon dioxide (CO 2 ), methane (CH 4 ), and other non-carbon dioxide greenhouse gases. As an essential industry, the power industry is not only the industry with the most carbon dioxide emissions but also one of the industries with the most NO X emissions in China (IEA 2020). From 2011 to 2013, NO X emissions of China's power industry accounted for more than 40% of the national NO X emissions. Although the proportion decreased significantly from 2014 to 2017, it still accounted for a large proportion, namely respectively 34.33%, 26.88%, 38.22%, and 33.75%. Therefore, the effectiveness of NO X emission reduction in the power industry is the key to fulfilling China's NO X emission reduction mission.
The current researches on China's NO X emissions usually complete the calculation of NO X emissions (Jiang et al. 2016;Tong et al. 2017). A simple regional NO X emissions difference analysis was carried out based on the division of provinces or eastern, central, and western regions (Pan et al. 2020;Yu and Liu 2020). However, although this method based on a large regional scale can reflect the overall characteristics of China's NO X emissions to a certain extent, it has not studied the reduction of NO X emissions in specific industries. For example, in the power industry, where NO X emissions are heavy, the results cannot reflect the effects of particular industries' emissions reduction and driving factors. It cannot directly reflect the root cause of NO X emission reduction, so it is difficult to put forward targeted emission reduction recommendations from the emission source. Therefore, this paper takes the main pollutants in the power industry: NOx as the research object, quantifies the coordinated emission reduction of NOx from CO 2 emission reduction activities in the power industry, analyzes the driving factors that affect NOx emission reduction, and attempts to answer the following questions: How much NOx emissions has the power industry reduced while reducing carbon? What are the main factors affecting NOx emission in the power industry? The answers to the above questions can accelerate the process of coordinated treatment of environmental problems in the power sector, provide theoretical support for the "ultra-low emission coordinated control" of the power industry, and have important significance for achieving the goal of integrated comprehensive treatment of pollution sources. Meanwhile, the methodology in this paper can also be employed to other industries for pollution prevention and emission reduction.
The study's contribution to the literature is threefold: First, this paper focuses on the synergistic emission reduction effects of greenhouse gases produced by the power industry on air pollutants. Second, the analysis comprehensively considers the relationships among factors such as the level of economic development, energy structure, energy intensity, power production structure, and power generation intensity. Third, this paper uses the "bottomup" emission factor method to measure the emission of nitrogen oxides and carbon dioxide generated by the power production activities of the Chinese power industry from 2011 to 2019.
Studies investigating the drivers of NO X emission generated by the power production activities are rare. The few studies that look at the power industry focus primarily on the greenhouse gases (Liao et al. 2019;Wang et al. 2019a, b, c) while only briefly mentioning NO X . In addition, most studies on the decomposition of NO X emission reduction do not consider the drivers that synergies caused by carbon emission reduction (Ding et al. 2017;Wang et al. 2019aWang et al. , b, c, 2020. The rest of the article is as follows: the "Literature review" section reviews and summarizes the relevant studies. The "Model" section presents the logarithmic mean divisia index (LMDI) model and decomposes the influencing factors of NO X emission reduction in the power industry based on the LMDI model. The "Empirical analysis" section decomposes the NO X emission reduction year by year from 2011 to 2019 and analyzes the trend of influencing factors. The "Conclusions and policy recommendations" section summarizes the article and offers policy recommendations.

Literature review
With the increasing attention paid to the ecological environment, scholars at home and abroad have carried out active research and discussion on the prevention and control of air pollution. Most research focus on carbon emissions, while some target at sulfur oxides, PM 2.5 or PM 10 (She et al. 2021;Tian et al. 2022;Zhang et al. 2019). Many scholars have also conducted extensive research on NO X from the perspectives of chemistry, meteorology, and human health, but their synergistic relationship with carbon emission reduction and internal driving factors are almost ignored.
Several studies have analyzed the factors that contributed to carbon dioxide emission. Yirong (2022) got the asymmetric effects of environmental policy stringency on CO 2 emissions in top emitter's economies named China, USA, India, Russia, and Japan by using the non-linear panel ARDL approach. The study suggests that the high polluted economies need to revisit green environmental regulations policies. Saidi and Omri (2020) examined the short-and long-run impacts of renewable and nuclear energy consumption on CO 2 emissions in the case of 15 OECD countries over the period 1990-2018 using both the fully modified OLS (FMOLS) and the vector error correction model approach (VECM) estimation methods. Xu and Tan (2022) quantified the reduction potential for CO 2 emissions in the pharmaceutical industry and found that three main factors influencing the changes are technical efficiency, technological advancement, and technological leadership. Zhao et al. (2018) used the EBM DEA model to analyze China's transportation sector carbon dioxide emissions efficiency and its influencing factors. Liu et al. (2022a, b) decomposed innovation factors into three variables, namely the innovation input scale, innovation input carbon intensity, and innovation input efficiency, then compared the impact of different factors on carbon emissions and decoupling of high-emission subsectors. Sun and Huang (2022) applied stochastic frontier analysis to screen the factors influencing carbon intensity and construct a model for predicting carbon emission intensity based on factor analysis and an extreme learning machine. Zhu et al. (2022) used the STIRPAT model to analyze the factors influencing embodied carbon emissions of China's building sector. Zhang et al. (2022a, b) established a decomposition model of the factors influencing carbon emissions from power generation by utilizing the Kaya identity and LMDI decomposition approach. Tang et al. (2017) proposed a factor decomposition model for analyzing carbon emissions in energy consumption of the tourism industry from the perspective of the tourism life cycle.
Several studies have determined the factors that influence air-pollutant emissions. Bai et al. (2021) evaluated the effect of both the emission and meteorological changes on the winter PM 2.5 from 2015 to 2019, especially that of regional transport and local emissions on the PM 2.5 variations in China. Yi et al. (2021) carried out a comprehensive analysis of the temporal and spatial characteristics of SO 2 , NO X , and PM emission concentrations, and quantified the standardreaching rate and air pollutant emissions factors. Yang and Song (2021) developed an integrated input-output model to explore an optimal combination of clean coal-fired power generation technologies subject to emission constraints and maximization of gross regional production. Chen et al. (2022) analyzed emission characteristics and impact factors of air pollutants from municipal solid waste incineration in Shanghai. Wang et al. (2022) constructed a difference-indifferences model to study the impact of high-speed rail construction on air pollutant emissions. Wu et al. (2023) established different vehicle pollution control scenarios and projected the emissions of four pollutants from vehicles in Beijing. Du et al. (2021) provided firsthand data on air pollutant emissions from biomass porcelain kilns. Gao et al. (2022) analyzed the influencing factors of the pollutant emissions of passenger vehicles.
Many studies have analyzed synergistic emission reduction effect of air pollutants and greenhouse gases. Fu and Yuan (2017) analyzed the power industry's synergistic effect of carbon dioxide and sulfur dioxide by using empirical means to quantify synergistic emission reduction effects between two negative environmental externalities. Li et al. (2022) analyzed the spatial differences of synergy among CO 2 , PM2.5 and O 3 were quantitatively by using spatial autocorrelation analysis and geographically weighted regression model. And found the cities achieving the three synergistic emissions reduction were mainly in the southeast of China. Lu et al. (2019) quantitatively analyzed the impacts of the different measures in the Action Plan on the emissions of major air pollutants of sulfur dioxide (SO 2 ), nitrogen oxides (NO X ), particulate matter (PM), and CO 2 in the Jing-Jin-Ji (JJJ, i.e., Beijing-Tianjin-Hebei) region using the Greenhouse Gas and Air Pollution Interactions and Synergies model (GAINS)-China model. Zeng and He (2023) analyzed the synergistic reduction emission impact and indirect drivers between air pollution and carbon emission of the transportation sector by using the LMDI decomposition model.
Among the decomposition models to quantify the impacts of various factors, LMDI has gained popularity for a series of advantages. Hasan and Liu (2022) explored the factors influencing natural gas consumption in Bangladesh from 1994 to 2018 using the LMDI. Liu et al. (2021) developed the decomposition and decoupling technology based on the LMDI approach and quantified seven effects in China's transportation industry. He et al. (2022) used the combination of LMDI and K-means clustering to analyze the factors influencing carbon emissions from China's electricity industry. Quan et al. (2020) analyzed the influencing factors of carbon emission in China's logistics industry based on the LMDI method. Jiang et al. (2020a, b) used the Kaya-LMDI methods to analyze the factors influencing China's non-residential power consumption. Alajmi (2021) used the LMDI model to analyze the factors that impact greenhouse gas emissions in Saudi Arabia.  Zhang et al. (2021) used the LMDI method to decompose the driving factors of the production water use and domestic water use from 2003 to 2017.
The above literature has expanded the field of research on the driving factors of greenhouse gas or air pollutant emission changes and provided powerful empirical analysis conclusions for formulating targeted emission reduction policies. However, the existing literature has not yet addressed the impact of changes in CO 2 emissions from the power industry on NOx emissions. The power industry is the main industrial pollution source of CO 2 and NO X emissions, but there is no empirical research on the synergistic effect of emission reductions between CO 2 and NOx. Therefore, this paper considers the synergistic effect of carbon dioxide on nitrogen oxides in the power industry and employs the Kaya identity and LMDI methods to decompose and quantify the driving factors of NO X emissions reduction in the power industry. The innovation and contribution of this paper compared with other references mainly lie in the following aspect. Firstly, this is the first study provide the detailed analyses aiming to identify the impact factors of NO X emission reduction in China's power industry. These impact factors will provide government with a scientific basis for effective and targeted policy-making, which is a contribution to the national NO X emission reduction. Secondly, this paper quantifies the synergistic effect of carbon dioxide emission reduction on nitrogen oxide emission reduction, which supplements the theoretical basis of double control of greenhouse gas and air pollution in the power industry. Thirdly, this paper selects coal, coke, crude oil, kerosene, gasoline, fuel oil, diesel, and natural gas for emission estimation, covering almost all thermal power generation energy types in the "China Energy Statistical Yearbook," which can improve the accuracy of the calculation results.

Model
The decomposition method has been extensively leveraged in the field of air pollution (i.e., CO 2 , SO 2 , and PM 2.5 emissions) to investigate the driving forces of emission change during a period. Although there is a considerable body of decomposition methods, Ang (2004) concluded that the LMDI method is optimal. The LMDI method can decompose multiple factors with zero residual errors and part of incomplete data sets (Mousavi et al. 2017;Xu et al. 2014). Therefore, this paper attempts to build an LMDI decomposition model based on the extended Kaya identity to analyze the influencing factors and annual characteristics that drive changes in NO X emission reduction in China's power industry.

Kaya identity
In 1989, Japanese scholar Yoichi Kaya put forward the Kaya identity for the changes in carbon emissions, decomposing carbon emissions into factors, and the basic expression is: where CO 2 , PE , GDP , and POP , respectively, represent carbon emissions, energy consumption, gross domestic product, and population.
where f is the carbon emission intensity of energy, that is, the carbon emission coefficient of energy; q is the unit energy consumption, that is, energy intensity; and g is the per capita income. It can be clearly seen from Eq.
(2) that carbon emissions are affected by energy carbon emission coefficient, energy intensity, per capita income, and population. The Kaya identity is simple in structure and easy to operate. It is suitable for the overall analysis of a country or a region. However, the carbon emission driving factors on the right side of the identity (Ang 2005) is limited and only reflect the macroscopic relationship among carbon emissions, energy, economy, and population. Therefore, this paper expands the Kaya identity and adds the synergistic emission reduction effect of CO 2 to NOx, as follows: where NE i is the NO X emission from the i th energy type in thermal power generation; CE i is the CO 2 emission from the i th energy type in thermal power generation; Q i is the consumption of the i th energy type in thermal power generation; TQ is the total energy consumption in thermal power generation; FG is the thermal power generation amount; G is the total power production; Y is the total GDP of China. Let is the quantified relationship of coordinated emission reduction between CO 2 and NOx in the power industry, that is, the amount of NOx emission per unit CO 2 emission. i is carbon emission coefficient of the i th energy type. i reflects the energy consumption structure in thermal power generation, expressed as the proportion of coal consumption in the power industry. reflects the energy intensity in thermal power generation. reflects the power production structure of the power industry, which is the proportion of thermal power generation in the total power generation. The value of total power generation in the gross national product reflects the power generation intensity, which is represented by to measure development of the power industry.

LMDI model
As mentioned in the introduction, among the decomposition models to quantify the impacts of various factors, LMDI gains popularity for a series of advantages (Jeong and Kim 2013;Pardo Martínez and Silveira 2012;Wang et al. 2023). Indeed, it has been widely employed at multiple levels and in different industries. Commonly used decomposition analysis methods for the influencing factors of pollutant emission include the index decomposition analysis (IDA) and structural decomposition analysis (SDA) methods. The IDA is a method widely used to formulate policies on energy and environmental issues in the world. The basic principle is to express the carbon emission calculation formula as the product of several factor indicators, and decompose it according to different methods of weights to determine the incremental share of each indicator (Yang et al. 2016a, b). Ang et al. (1998) proposed the LMDI, a factor decomposition IDA method for energy demand or gas emissions within a period. The core idea is to decompose the change of a dependent variable in the system into the sum of various changes related to the independent variables to measure the contribution of each variable to the change of the dependent variable. It is believed that the larger residual term in the Laspeyres exponential decomposition will significantly impact the final result, so the residual term cannot be ignored. The LMDI method has the advantages of reversible factors and can eliminate the residual term, which can make up for the shortcomings of the residual term after decomposition of other methods or improper decomposition of the residual term. Therefore, this paper uses the LMDI method to decompose the basic model of the Kaya identity in this paper.
Referring to the decomposition method of Ang (2004) on the influencing factors of carbon emissions, here the full differential processing of Eq. (3) can be obtained as: The right side of Eq. (4) is written in the form of rate of change (Ang 2015): where r , r , r , r , r , r , and r Y respectively indicate the synergy effect, the carbon emission coefficient of the fuel, the energy structure, the energy consumption rate for power generation, the power production structure, the power generation intensity, and the rate of change of economic development.
By calculating the definite integral in the time interval through the equation, we can get: According to the LMDI method (Ang and Liu 2007), the power industry emissions change can be expressed as: where ΔNE is the synergistic emission reduction effect of CO 2 on NOx, ΔNE is the carbon emission coefficient effect of fuel, ΔNE is the energy structure effect, ΔNE is the energy intensity effect, ΔNE is the power production structure effect, ΔNE is the power generation intensity effect, and ΔNE Y is the economic development effect. In addition, the contribution rate of each factor is defined as the ratio of this factor's contribution to the total change of NOx emissions in the electricity industry in the same period.
The expression of each decomposition factor is:

Data collation
Since China does not release specific data on greenhouse gas and NOx emissions in various industries, this paper adopts the "Method 1" of IPCC to estimate the CO 2 and NOx emissions of China's power industry. Since hydropower, wind power, photovoltaics, and nuclear power hardly emit CO 2 and NOx, this paper mainly considers thermal power generation as an emission source of power industry. Fossil fuels are still the main energy input for thermal power generation in various regions. According to the energy data available from the National Bureau of Statistics, this paper selects coal, coke, crude oil, kerosene, gasoline, fuel oil, diesel oil, and natural gas to estimate the emissions.
In summary, based on the energy balance sheet of the National Bureau of Statistics, this paper uses the emission factor method to calculate the CO 2 and NOx emissions generated by the energy consumption of power industry from 2011 to 2019. The specific steps are as follows: (1) combining the consumption of a certain fuel in the thermal power industry with the NOx emission factor to obtain NOx emissions produced by the consumption of a certain fuel in the power industry; (2) considering the selected eight fossil fuels, the total amount of NOx emissions in the power industry can be obtained by summarizing the emissions from the selected eight fossil fuels; (3) similarly, the total amount of CO 2 emissions in the power industry can be calculated and obtained. The energy consumption of the thermal power industry is equal to the sum of the input for processing and conversion of thermal power generation and the input for processing and conversion of heat supply. The NOx emission factors of China's power industry are shown in Table 1. According to Yang et al. (2023), the removal rate of nitrogen oxides in the power industry is set to be 60% from 2011 to 2014, and 80% from 2015 to 2019.
The CO 2 emission coefficient and related index of various energy are shown as follows ( Table 2).
The panel data observation includes the power industry in 30 provinces except for Hong Kong, Macau, Taiwan, and Tibet Autonomous Region due to the data availability. The time span is from 2011 to 2019. The energy consumption data of different types in the electricity production process comes from the "Energy Balance Sheet" standard quantity in the Chinese Energy Statistical Yearbook, and other data such as electricity production and GDP are from the Chinese Statistical Yearbook.

Analysis of NOX emission scale of China's power industry
Based on the above analysis methods and data, the NO X emissions produced by China's power industry from 2011  This phenomenon is because the rapid development of industry in those years increased the electricity demand, which caused the consumption of fossil fuels to increase more than that in previous years, resulting in the increase in NO X emissions in the power industry. In addition, as shown in Fig. 2, in those years, the domestic product had also increased significantly compared with previous years (Liao et al. 2019). With the expansion of the economic scale, the demand for electricity of economic activities had increased, and at the same time, more and more pollutant emissions were generated.

Identification of driving factors for NOX emission reduction in China's power industry
According to the model listed above, NO X emission reduction from 2011 to 2019 is exponentially decomposed, and the annual decomposition results are shown in Table 3 and Fig. 3. During 2011-2019, the contribution of the CO 2 emission reduction to NO X emission reduction mostly exceeded 100%, and only the contributions between 2013-2014 and 2014-2015 were 42.73% and 81.10%, respectively. The average decomposition result in the nine years was 115.36%. It indicates a significant synergistic emission reduction effect between CO 2 and NO X , namely NO X emissions amount will decrease with the increase of the CO 2 emission reduction.
During 2011-2012, NO X emissions were reduced by 881,000 t. The driving factors that negatively affected the changes in NO X emission reduction from the power industry during this period were energy structure, energy intensity, and economic development. Their contribution rates were respectively − 0.62%, − 50.68%, and − 119.67%. The driving factors that play a positive role are synergy effect, energy Electricity production GDP year GDP (10 billion yuan) Electricity production (10 billion kwh) intensity, power production structure, and power generation intensity, and their contributions are respectively 169.84%, 50.07%, and 51.05%. During 2012-2013, NO X emissions were reduced by 1.218 million tons. The driving factors that negatively affected the changes in NO X emission reduction from the power industry during this period were power production structure and economic development, with contributions of − 0.95% and − 50.85%. The driving factors that play a positive role are synergy, energy structure, energy intensity, and power generation intensity, and their contributions are respectively 124.97%, 0.05%, 21.02%, and 5.76%.
During 2013-2014, NO X emissions were reduced by 1.835 million tons. Economic development was the only driving factor that negatively affected the changes in NO X emissions reduction from the power industry during this period, which contributed by − 35.69%. The driving factors that play a positive role include synergy, energy structure, energy intensity, power production structure, and power generation intensity, and their contributions are respectively 42.73%, 0.30%, 72.41%, 12.75%, and 7.51%.
During 2014-2015, NO X emissions were reduced by 2.158 million tons. Economic development was the driving factor that negatively affected the changes in NOx emissions reduction from the power industry during this period, with the contribution of − 18.85%. The driving factors that play a positive role are synergy, energy structure, energy intensity, power production structure, and power generation intensity, and their contributions are respectively 81.10%, 0.30%, 11.20%, 8.36%, and 17.89%.
The US debt and European debt crises that occurred in 2011 have led to a slowdown in China's economic growth. The slowdown in economic growth has a relatively positive effect on the efficiency of electricity production and leads to an increase in the efficiency of electricity use. Studies have shown that the slowdown in economic growth is conducive to optimizing the power production structure in the short term. Electricity is one of the most critical energy sources at the moment. The change between economic development and power consumption positively correlates (Zhou et al. 2017). The slowdown in economic growth leads to a slowdown in power consumption growth, and power consumption determines the economic growth of power production. The net result of the slowdown is a reduction in power generation. According to the principles of China's power generation scheduling, hydropower, nuclear power, and other priority power generation, thermal power is based on the principle of efficiency first. As shown in Fig. 4, in 2012, the proportion of thermal power generation dropped significantly, 2.9% less than the previous year. In the case of a slowdown in power demand growth, it will reduce thermal power units, especially thermal power units with a higher coal consumption rate, and the proportion of wind, hydropower, and natural gas power generation will be reduced increase accordingly. In the end, it not only optimizes the power production structure but also improves energy efficiency. The slowdown in economic growth is conducive to improving the industrial structure in the short term. In the economic downturn, most companies that consume more electricity will gradually withdraw from the market due to cost pressures, facilitating the adjustment of corporate structure and products in the same industry, promoting technological progress, and ultimately reducing the industry's power consumption intensity.
During 2015-2016, NO X emissions were reduced by 2.674 million tons. The driving factor that negatively affected the changes NO X emissions reduction from the   (Jiang et al. 2020a, b). The promotion of desulfurization and denitrification projects is one of China's five critical projects for energy conservation and emission reduction during the "Twelfth Five-Year Plan." The completion of the supporting construction of desulfurization facilities for 50.56 million kilowatts of coal-fired units has been installed, but the desulfurization facilities are not stable. Implementation of desulfurization transformation for 42.67 million kilowatts of coal-fired units that reached the standard, completion of the construction of denitration facilities for 400 million kilowatts of coal-fired units in service, and implementation of low-nitrogen combustion technology for 70 million kilowatts of coal-fired units (State Council of China 2012).
During 2016-2017, NO X emissions were reduced by 604,000 t. The driving factors that negatively affected the changes in NO X emissions reduction from the power industry during this period were energy intensity and economic development, with contributions of − 2.77% and − 35.69%. The driving factors that play a positive role are synergy, power production structure, and power generation intensity, and their contributions are respectively 125.49%, 1.62%, and 11.37%. Energy structure only played a small role, with only a contribution rate of − 0.01%.
During 2017-2018, NO X emissions were reduced by 274,000 t. Economic development was the driving factor that negatively affected the changes in NO X emission reduction from the power industry during this period, with a contribution of − 56.68%. The driving factors that play a positive role are synergy, energy intensity, power production structure, and power generation intensity, and their contributions are respectively 127.66%, 11.79%, 6.94%, and 10.28%. Energy structure only played a small role, with only a contribution rate of 0.0008%.
During 2018-2019, NO X emissions were reduced by 137,000 t. The driving factors that negatively affected the changes in NO X emission reduction from the power industry during this period were energy structure, energy intensity, and economic development, with contributions of − 0.15%, − 24.11%, and − 68.83%. The driving factors that play a positive role are synergy, power production structure, and power generation intensity, and their contributions are respectively 147.99%, 21.77%, and 24.32%.
The possible reason for the above phenomenon is that China's economic growth rate has generally increased during 2016-2019. At this time, large amount of power input is required to maintain rapid economic growth. In 2018, driven by industries including high-tech manufacturing, information transmission, software and information technology services, coupled with the impact of extreme temperatures in summer (Council China Electricity 2019), the growth rate of electricity consumption in both the secondary industry and the service industry rose significantly. Therefore, economic development factor promote the growth of NO X emissions in the power industry. As China is in the process of industrialization and urbanization, the carbon dioxide emissions produced have also increased significantly. The emissions of CO 2 and NO X are roughly simultaneously, and the economic development effect makes NO X emissions reduction decrease.

Trend analysis of driving factors for reducing the NO x emissions in China's power industry
It can be observed from Table 3 that synergy effect, energy intensity, power production structure, and power generation year Electricity produced by hydropower Electricity produced by thermal power Electricity produced by nuclear power Electricity produced by wind power Proportion of thermal power generation intensity contribute to curbing NO X emissions. Economic development has always played a positive role in promoting the NO X emission growth of the power industry and is the most important driving factor for the emission growth of the power industry. Strict "Ultra-low emissions (ULE)" standards for innovating coal-fired power plants were put into effect on July 1, 2014, which enforced 580 million kilowatts of existing installed capacity (about 71% of the total) to meet the standards by 2020 (Tang et al. 2019).

Synergy effect
The synergy effect reflects the quantitative relationship between CO 2 and NO X coordinated emission reductions produced by the power industry, namely the NO X emissions per unit CO 2 emissions. In the annual decomposition, the synergistic effect has a noticeable positive driving effect on NOx emissions. As shown in Table 1 With the gradual implementation of energy-saving and emission-reduction measures during the "Twelfth Five-Year Plan," the NO X released per unit of coal used for power generation have been significantly reduced (Sui et al. 2016). As shown in Fig. 5, CO 2 emissions have increased year by year from 2015 to 2019, and at the same time, due to the large synergistic effect, the reduction of NOx has decreased.

Energy intensity
The decomposition results in Fig. 6 show that energy intensity hindered NO X emission reduction in 2011-2012, 2016-2017, and 2018-2019, and their contribution rates were − 50.68%, − 2.77%, and − 24.11%, respectively. Generally speaking, energy intensity promotes NO X emission reduction. The main reasons are in three aspects: First, with technological progress, the capacity of a single unit of thermal power continues to increase with the application of new technologies in thermal power generation, and the coal consumption rate is continuously decreasing; the second is the continuous improvement of thermal power unit operation technology and grid dispatching technology; the third is the optimization of the thermal power installed capacity structure (National Development and Reform Commission 2014). China has accelerated the speed of shutting down small thermal power units with high energy consumption and heavy pollution (G. Zhang et al. 2023), so that the thermal power production structure has been optimized. The coal consumption rate of power generation is greatly reduced, which improves the energy efficiency of thermal power generation (Zhao et al. 2008).

Electricity production structure
As shown in Fig. 7, the power production structure has a weak hindering effect on NO X emission reduction in 2012-2013, the effect is only − 0.95%, and it has a promoting effect in the rest of the years, but the contribution rate is not high, only in 2011-2012, the contribution rate reached up 50.07%. It can be seen from Fig. 4 that the overall trend of the proportion of thermal power generation has declined year by year from 81.75% in 2011 to 72.90% in 2017. However, the decline in the proportion of thermal power generation has gradually weakened and even increased slightly in 2017 and 2019, which has hindered NO X emission reduction. China currently strongly supports the power generation with clean energy and has corresponding policy support to accelerate the development and construction of wind power and photovoltaic power generation projects (Ding et al. 2017). Wind power and photovoltaic power generation are required to account for about 12.2% of the total electricity consumption in 2022. Currently, in the transitional period of marketoriented reform, it is still necessary to maintain policy support for clean energy power generation, improve the structure of power production, and reduce NO X emissions in the power industry.

Power generation intensity
During 2011-2019, the power generation intensity factor has always contributed to reducing NO X emissions, but only in 2011-2012 was the main factor. According to Fig. 8, it is obvious that the contribution rate of power generation intensity factor in promoting emission reduction did not exceed 25% for the rest of the years. It can be seen from Fig. 9 that the proportion of industrial electricity consumption in electricity production has exceeded 69% over the years. Although this proportion has been declining year by year, from 73.61% in 2011 to 67.57% in 2019, industrial electricity consumption is still rising. Suppose the proportion of industrial added value in GDP continues to increase. In that case, it will increase industrial power consumption and thus increase the emission of air pollutants in the power industry (Yang et al. 2016a, b). China should actively promote the transformation of the energy  year Electricity Production Structure NOx emissions reduction structure of power generation enterprises, develop towards a green power generation model, and gradually increase the proportion of clean energy power generation such as wind energy, tidal energy and nuclear energy. At the same time, it is necessary to promote the intensive development of economy, realize the balanced development of power and ecological environmental protection, and promote the strategy of highquality development of China's power, which is conducive to reducing NOx emissions of the power industry.

Energy structure
The proportion of coal consumption in the power industry represents the energy factor. Figure 10 shows that during the period 2011-2012, 2016-2017, and 2018-2019, the effect of NO X reducing emissions was − 0.62%, − 0.01, and − 0.15%. In other years, the energy structure factor promoted NO X emission reduction with an average value of 0.14%. In general, it had a weak negative effect on NO X emission reduction. China's oil and natural gas resources are relatively scarce. The oil-fired power generation units built in the early days have been gradually replaced by coal-fired power generation units and withdrawn from the market. Gas-fired power has always been due to China's "lack of fuel and gas," and imported gas turbine equipment and spare parts are expensive. This leads to high costs, poor economy, and weak growth. It depends on financial subsidies, two-part electricity prices, or the transfer of power generation contracts to survive, and cannot compete with coal power. Although in recent years, the relevant state departments have decided to "bring natural gas into one of the main energy  sources of China's modern clean energy system" and build gasfired generator sets to cope with peak load demand and reduce pollutant emissions. However, the proportion of coal consumption in the actual electricity production process is still rising in fluctuations (Ding et al. 2017). It can be clearly seen from Fig. 10 that coal consumption in the power industry accounts for more than 82% of total coal energy consumption. Between 2015 and 2017, it rose linearly, reaching 91.78% in 2017, with an annual growth rate of 4.14% (Fig. 11).

Economic development
The environmental Kuznets curve hypothesis holds that as economic development continues to increase, environmental pollution will become more and more serious until a critical point begins to decline . It can be seen from Fig. 12 that economic development factor has been hindering the NO X emission reduction from 2011 to 2019. The effect gradually narrowed from − 119.67 to − 10.40% in 2012-2016 and rebounded to − 69.83% in 2016-2019. Figure 12 shows that China's GDP growth rate has increased significantly after 2015, which justifies the environmental Kuznets curve hypothesis. Electricity consumption is one of the important economic indicators, and the changes between economical operation and electricity consumption show a synchronous trend. Increasing economic growth has led to an increase in electricity consumption (Lyu et al. 2016), resulting in an increase in power generation. According to the principles of China's power generation dispatching, hydropower, nuclear power, and other priority on-grid power generation, thermal power is based on the principle of efficiency first (S.  Wang et al. 2019a, b, c). From 2015 to 2017, China's economic growth rate generally showed an increasing trend. At this time, a large amount of electric energy investment is needed to maintain, and the economy grows rapidly. Economic development factor inhibits the growth of NO X emission reduction in the power industry (Fig. 13).

Conclusion
At present, China's power productivity is increasing year by year, and there are many factors affecting the changes in NO X emissions in the power industry. It is necessary to study the impact of these factors on NO X emission changes in the power industry, and then provide reference for achieving NO X emissions reduction target and formulating targeted policies. This paper makes appropriate changes in the basic model design of Kaya identities, adds the synergistic emission reduction effect of CO 2 to NO X , and uses the LMDI model to decompose the changes in NO X emissions in China's power industry from 2011 to 2019 into six driving factors. The main findings are as follows: 1. Among the influencing factors of China's NO X emissions, the synergy effect, economic development, power production structure effect, power generation intensity, and energy intensity are five key factors. Proper handling of these five key influencing factors will effectively  reduce NO X emissions from China's power industry. The research of Ding et al. (2017) and Lyu et al. ( 2016) also showed that economic growth is the most crucial factor for changes of NOx emissions. 2. The reduction of carbon dioxide emissions can significantly promote the emission reduction of nitrogen oxides, and the average synergistic emission reduction effect in 2011-2019 is about 115.36%. The existing literature also shows that NOx emission reduction has high co-benefit for CO 2 reduction (Yao et al. 2015;Q. Y. Zhang et al. 2022a, b). 3. Various driving factors affect NO X emission reduction in the power industry in different sizes and directions. Among them, the main positive driving factors are, in order, synergy effect, energy intensity, power generation intensity, and power production structure. Economic development factors hinder the emission reduction of nitrogen oxides in the power industry. At the same time, the role of energy structure is not obvious, and it only plays a weak positive driving role. Compared with existing studies, the studies showed that the thermal power generation efficiency effect, energy intensity, and structure of energy consumption contributed a lot to the drop of NO X emission while the effects of energy structure effect was relatively small, which is basically consistent with the research conclusion of this paper (Wang et al. , 2020.

Policy recommendations
China is expected to maintain a relatively high economic growth rate. As a basic industry supporting economic development, the power industry should focus on the balance of improving ecological environment and increasing productivity. A comprehensive approach to reducing NO X emissions from the power industry is required, starting from multiple drivers. Some policy recommendations are proposed as follows: 1. Adjust the energy structure and improve energy efficiency The control of NO X emissions in the power industry should be improved from the root cause, so it is recommended to rationally adjust the power production structure and energy structure to further improve energy efficiency. The empirical results also show that small changes in the power production structure, energy intensity and power generation intensity can achieve better emission reduction effects (Wei et al. 2018). In the power generation process, it is neceesary to reduce the utilization of high-coal thermal power units, optimize support policies for renewable energy such as wind power and photovoltaics to pormote their large-scale development (Liu et al. 2022a, b). Diversified development and utilization of low-pollution power generation such as hydropower, wind power, photovoltaics is required to reduce NO X emissions in China's power industry.

Promote the innovation of low-nitrogen combustion technology
The promotion of clean power generation technology in the power industry should be vigorously promoted. The application of clean power generation technology can promote the low emission and energy-saving of thermal power units (Jiang 2022;Ma and Takeuchi 2017). The improvement of production technology can reduce the intensity of pollution emissions. The CO 2 emission reduction per unit of output is a critical factor in promoting NO X emission reduction in the power industry. First, improve the adopted low-nitrogen combustion technologies and processes, including research on application conditions, energy consumption, stability, and adaptability. Secondly, develop new technologies for denitrification, focusing on improving the efficiency of the flue gas denitrification process and reducing the economic cost of denitrification. Then, further strengthen the coupling and matching of multiple environmental protection equipment and collaborative processing research. Finally, the technology to effectively monitor the production of NO X emissions is also needed to support NO X emissions reduction in power industry.

Transform the economic growth pattern
It has been widely verified that both emissions and concentrations of NOx are correlated with or influenced by several aspects of socio-economic development (J. Wang et al. 2017a, b;Xu et al. 2019). In order to realize the emission reduction of power industry and the sustainable development of economy, China needs to change the tendency of excessively pursuing economic growth rate and realize the transformation of China's economic development from extensive pattern to intensive pattern. The report of the 19th National Congress of the Communist Party of China pointed out that China's economy has shifted from a stage of rapid growth to a stage of high-quality development. Achieving high-quality development means that we must no longer sacrifice the environment and excessive consumption of resources in exchange for high-speed growth. It is necessary to focus on improving economic efficiency and improving the quality of economic growth. It is necessary to gradually get rid of factor dependence on input, promote the economy to enter the development track of innovation-driven and endogenous growth, and take a new path of industrialization. It is necessary to promote industrial optimization and upgrade with high-end implantation and technological innovation as the core, and effectively improve energy utilization efficiency. 4. Improve the information disclosure system of air pollutant emissions in the power industry Controlling the emission of nitrogen-containing pollutants in the power industry is crucial for the government to improve the atmospheric environment, so it is necessary to improve the information disclosure system. The implementation of the environmental information disclosure system effectively reduced local governments' concealment of and failure to report pollution in their jurisdictions . On the issue of information disclosure, the government should lead the improvement of the thermal power air pollutant emission information disclosure system , ensure the authenticity of information disclosure, and concentrate on sorting out relevant information about air pollutants in the power industry. At the same time, a unified standard is needed to established for the power industry's environmental protection data release mechanism (Zhu and Zhang 2012). With the continuous improvement of the thermal power air pollutant emission information disclosure system, legal forces from all sectors of society will participate in it. The government and thermal power companies accept legal supervision from all aspects of society and improve the management and supervision level, which can effectively improve the ability of the power industry to reduce emissions of nitrogen-containing pollutants.