Study on the Synergistic Path of Pollution and Carbon Emissions Reduction in Anhui Province Electricity Industry

doi:10.21203/rs.3.rs-5288553/v1

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Research Article

Study on the Synergistic Path of Pollution and Carbon Emissions Reduction in Anhui Province Electricity Industry

https://doi.org/10.21203/rs.3.rs-5288553/v1

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Background

As a significant contributor to secondary energy production, the power generation industry has a considerable reliance on fossil fuels. It is a significant source of atmospheric pollutants and greenhouse gas emissions, making it a key target for emission reduction in environmental protection. This study is focused on the power generation industry in Anhui Province, China. The LMDI decomposition model and the LEAP model are employed to establish five emission reduction scenarios, which were used to forecast future trends in carbon dioxide and air pollutant emissions in the power generation sector from 2022 to 2035.

Results

The results indicate that the implementation of comprehensive measures for the promotion of washed coal technology, improvements in thermal power efficiency, and the substitution of clean energy, carbon dioxide, and atmospheric pollutants will reach their peak in 2027, achieving the most optimal emission reduction effects. The analysis of the synergistic emission reduction effects demonstrates that the implementation of comprehensive measures yields the most optimal reduction in carbon dioxide and atmospheric pollutants. Furthermore, the comprehensive scenario exhibits a considerable emission reduction contribution rate.

Conclusions

For the power generation industry in Anhui Province, the simultaneous implementation of three emission reduction measures represents the optimal path for synergistic improvement in pollution reduction and carbon reduction. These measures provide policy recommendations for the power generation industry in other regions seeking to achieve pollution and carbon reduction.

electricity industry

reduce pollution and carbon emissions

LEAP model

LMDI decomposition model

policy recommendations

The use of fossil fuels has propelled human society forward rapidly. Still, it has also generated a significant amount of greenhouse gases and atmospheric pollutants, leading to environmental issues that cannot be ignored. China has proposed the "2030" and "2060" plans to achieve its carbon peak and carbon neutrality goals [1]. China's carbon emission intensity has significantly decreased over the past two decades, with remarkable results in energy conservation and carbon reduction. In 2022, China's carbon emission intensity decreased by more than 50% compared to the year 2000. The power generation industry plays a role as a driving force in social development while also bearing significant pressure to reduce pollution and carbon emissions. Thermal power generation is the main method of electricity generation in China, accounting for over 60% of the country's total electricity output while also contributing to over 40% of China's carbon emissions [2]. Anhui is a major province for thermal power generation in the central-eastern region of China. In 2022, Anhui's electricity generation reached 329,877 GWh, with thermal power accounting for about 88% of the total. Additionally, the main source of carbon dioxide and air pollutants in the power industry is thermal power generation [3]. Therefore, Anhui Province's thermal power generation needs to explore pathways that can collaboratively reduce carbon emissions and pollutant discharges, better supporting its power generation industry in achieving its carbon peak and carbon neutrality goals.

Many scholars have currently researched the potential for reducing carbon dioxide and atmospheric pollutants in national or urban areas. Du and Li applied the mediation effect model to study the possibility of achieving synergistic benefits between pollutants and carbon emissions in China's industrial production [4]. Yang and others used an energy-economic model, an air quality model, and a concentration-response model to measure the synergistic effects of carbon dioxide and gas pollutants on the environment during the production process [5]. Jiang and others used the LEAP model and the WRF-SMOKE-CMAQ platform to simulate the impact of emission reduction measures in Shenzhen on the future trends of greenhouse gases [6]. He and Zeng analyzed the driving factors behind the carbon emissions from interprovincial transportation and the coordinated reduction of pollutants in China using the KAYA and LMDI models [7]. Thai researchers are exploring low-carbon reform methods for Thailand's transportation sector using the AIM model [8]. Some scholars have conducted research on energy use and emissions in the thermal power generation industry. Mao and others explored the opportunities for synergistic control of local air pollutants and carbon dioxide within the context of China's coal-fired power generation industry through a comprehensive decision-making framework for multi-pollutant collaborative control strategies [9].

The LDMI decomposition model is widely used in the analysis of factors influencing carbon emissions. RG Alajmi analyzed the impact of social factors on carbon emissions in Saudi Arabia using the LMDI model [10]. Koval explored the driving factors of carbon emissions in the EU's electricity sector using the LMDI model [11]. Chang used the LMDI model to analyze the factors contributing to high carbon emissions in APEC countries [12]. The Long-term Energy Alternatives Planning (LEAP) model, as a relatively intuitive model among bottom-up models, is a typical tool for analyzing carbon emissions. Chinese scholars are exploring the pathways for sustainable energy development and industrial transformation in the post-industrial city of Shenzhen using the LEAP model [13]. Turkish researchers analyzed the carbon dioxide reduction potential in the steel industry based on the LEAP industrial model [14]. Korean scholars used a LEAP-based transportation energy utilization model to analyze the contribution of the transportation sector to the country's long-term goals of achieving carbon peak and carbon neutrality [15].

As a major energy province in China, Anhui has, under the arrangement of national policies, for many years supplied its surplus electricity to the developed eastern provinces, providing power support for their economic development. However, this has also increased the pressure to reduce emissions in Anhui due to electricity production. From the perspective of the power generation structure in 2022, coal-fired power generation accounted for about 88% of the total electricity output. This structure is quite singular, leading to significant environmental pollution, and there is considerable potential for synergistic effects in reducing pollution and carbon emissions.

This study employs the LMDI average logarithmic decomposition model to investigate the key influencing factors of carbon emissions in the power generation industry of Anhui Province, with the objective of predicting future electricity generation. This is combined with the Low Emission Analysis Platform (LEAP) to explore the potential for synergistic reductions in carbon emissions and air pollutants.

KAYA-LMDI model

The KAYA carbon emission equation has a simple structure and direct, effective calculation characteristics, making it widely used in carbon emission research. The LMDI average logarithmic decomposition model is a mathematical model commonly used to analyze the primary influencing factors of an object under the combined effects of multiple variables. The model has a wide range of applicability and does not have residuals, often being used in conjunction with the KAYA model in carbon emission research [16].

In this study, the effects of carbon emissions are broken down into four main influencing factors: population effect, economic effect, energy effect, and emission intensity effect. The KAYA carbon emission equation is shown in formula (1).

$$\:\begin{array}{c}C=\frac{C}{E}\times\:\frac{E}{GDP}\times\:\frac{GDP}{POP}\times\:POP\#\left(1\right)\end{array}$$

In the formula, C represents the total greenhouse gas emissions, E represents energy consumption, GDP stands for Gross Domestic Product, and POP represents the population. Therefore, C/E represents the greenhouse gas emission intensity, indicating the number of emissions produced per unit of energy consumed. E/GDP represents energy consumption intensity, indicating the amount of energy consumed per unit of output, while GDP/POP represents per capita output.

This study aims to examine greenhouse gas (GHG) emissions from the power sector. To this end, the KAYA model has been adapted to align with the specific characteristics of the power sector. Please refer to formula (2).

$$\:\begin{array}{c}C=\frac{C}{P}\times\:\frac{P}{GDP}\times\:\frac{GDP}{POP}\times\:POP=F\times\:Q\times\:H\times\:POP\#\left(2\right)\end{array}$$

In the formula, P represents the amount of electricity generated, C/P represents the energy emission factor for electricity generation, that is to say, the electricity emission factor, which is represented by the symbol F; P/GDP represents the efficiency of electricity production, that is to say, the amount of electricity generated per unit of GDP, which is represented by the symbol Q; and GDP/POP is the per capita output value, which is represented by the symbol H.

The carbon emission effect of the power sector was decomposed into four parameters and calculated by applying the LMDI decomposition model, as follows:

$$\:\begin{array}{c}\varDelta\:{C}^{T}=\varDelta\:{C}_{F}^{T}+\varDelta\:{C}_{Q}^{T}+\varDelta\:{C}_{H}^{T}+\varDelta\:{C}_{POP}^{T}\#\left(3\right)\end{array}$$

where $\:\varDelta\:{C}^{T}$ represents the total effect of GHG emissions from electricity from the base year to year T, $\:\varDelta\:{C}_{F}^{T}$ represents the total effect of energy emissions from electricity generation from the base year to year T, $\:\varDelta\:{C}_{Q}^{T}$ represents the total effect of efficiency of electricity production from the base year to year T, $\:\varDelta\:{C}_{H}^{T}$ represents the total effect of GDP per capita from the base year to year T, and $\:\varDelta\:{C}_{POP}^{T}$ represents the total effect of population from the base year to year T. Each effect is calculated as follows.

$$\:\begin{array}{c}\varDelta\:{C}_{F}^{T}=\frac{{C}^{T}-{C}^{0}}{ln{C}^{T}-ln{C}^{0}}\text{ln}\left(\frac{{F}^{T}}{{F}^{0}}\right)\#\left(4\right)\end{array}$$

$$\:\begin{array}{c}\varDelta\:{C}_{Q}^{T}=\frac{{C}^{T}-{C}^{0}}{ln{C}^{T}-ln{C}^{0}}\text{ln}\left(\frac{{Q}^{T}}{{Q}^{0}}\right)\#\left(5\right)\end{array}$$

$$\:\begin{array}{c}\varDelta\:{C}_{H}^{T}=\frac{{C}^{T}-{C}^{0}}{ln{C}^{T}-ln{C}^{0}}\text{ln}\left(\frac{{H}^{T}}{{H}^{0}}\right)\#\left(6\right)\end{array}$$

$$\:\begin{array}{c}\varDelta\:{C}_{POP}^{T}=\frac{{C}^{T}-{C}^{0}}{ln{C}^{T}-ln{C}^{0}}\text{ln}\left(\frac{{POP}^{T}}{{POP}^{0}}\right)\#\left(7\right)\end{array}$$

In this context, the variables T and 0 represent, respectively, the year T and the base year.

By employing the aforementioned formula to calculate the carbon emission effects of the four principal influencing factors, it is possible to ascertain which of these exerts the most significant influence on carbon emissions in the power generation industry in Anhui Province.

LEAP model

The Low Emission Analysis Platform (LEAP) is a research platform developed by the Stockholm International Environmental Research Institute (SIERI) for the purpose of greenhouse gas (GHG) emission projections and policy development. In order to achieve these goals, the platform covers a range of interrelated aspects, including economic, social, energy, and industrial considerations. The LEAP model is a flexible, adaptable structure that can be configured to align with the specific requirements of researchers. It is a prime exemplar of bottom-up greenhouse gas emission models and has gained considerable traction among scholars globally, particularly within the domain of environmental prediction.

The LEAP model comprises three principal modules: the Key Assumptions Module, the Energy Demand Module, and the Energy Conversion Module. The functions of these modules are distinct. The Key Assumptions module is responsible for the storage of the requisite fundamental data, including gross domestic product (GDP), population, urbanization rate, industrial output, electricity generation, and other socio-economic data, which serve as the basis for the calculation of greenhouse gas (GHG) emissions in the LEAP model. The Demand module is responsible for the storage and input of energy-related data, including the activity level of fuels, energy consumption intensity, type of energy source, emission factors, and the setting of energy consumption sectors. The Demand module reflects the specifics of energy use, which is one of the most important carbon calculations in the LEAP model. The Energy Conversion module deals with the production and processing of secondary energy sources such as electricity and heat. This module simulates the operation of the electricity and heat sectors to make the model more realistic. The LEAP model has an integrated Technology and Environment Database (TED database) to analyze pollutant emissions from different energy sources and the types of emissions, such as the physicochemical properties of different fuels and greenhouse gas emission factors. All the modules and the necessary data work together to form the comprehensive analyses of the LEAP model. The energy calculation rules of the model are as follows.

(1) Energy consumption calculations

On the final demand side, the calculation of energy consumption by the LEAP model is shown in Eq. (8):

$$\:\begin{array}{c}E{U}_{s,q,f}={\sum\:}_{q}\:{\sum\:}_{f}A{L}_{s,q,f}\times\:{EI}_{s,q,f}\#\left(8\right)\end{array}$$

Where $\:E{U}_{s,q,f}$ denotes the energy consumption of energy-using unit q on fuel f in scenario s, $\:A{L}_{s,q,f}$ denotes the activity level of energy-using unit q on fuel f in scenario s, and $\:{EI}_{s,q,f}$ denotes the energy intensity of energy-using unit q on fuel f in scenario s; In the energy processing and conversion module, the LEAP model calculates the energy consumption as shown in Eq. (9).

$$\:\begin{array}{c}E{T}_{s,q,f}={\sum\:}_{q}\:{\sum\:}_{f}E{P}_{s,q,f}\times\:\left(\frac{1}{{\eta\:}_{s,q,f}}-1\right)\#\left(9\right)\end{array}$$

where $\:E{T}_{s,q,f}$ denotes the net energy consumed by the energy conversion unit q in the s-scenario when using the primary energy source f for the process conversion of the secondary energy source, and $\:E{P}_{s,q,f}$ denotes the energy consumed by the primary energy source f in the s-scenario when the energy conversion unit q uses the primary energy source f for the process conversion of the secondary energy source. $\:{\eta\:}_{s,q,f}$ denotes the process efficiency of the energy conversion unit q in the s-scenario when using the primary energy source f for the process conversion of the secondary energy source;

(2) Emissions calculations

The LEAP model calculates carbon emissions using the Carbon Emission Factor (CEF) methodology prescribed by the IPCC, see Eq. (10).

$$\:\begin{array}{c}C{E}_{s,q,f}={\sum\:}_{q}\:{\sum\:}_{f}E{U}_{s,q,f}\times\:{F}_{f}\#\left(10\right)\end{array}$$

where $\:C{E}_{s,q,f}$ denotes the carbon emissions resulting from the consumption of fuel f by energy unit q in scenario s, $\:E{U}_{s,q,f}$ denotes the carbon emissions resulting from the consumption of fuel f by energy unit q in scenario s, and $\:{F}_{f}$ denotes the carbon emission factor for fuel f.

In this study, the LEAP model is used to forecast the greenhouse gas emissions from the power sector in Anhui Province.

Scenario analysis

The original data is considered in conjunction with the current status of the power generation industry and the implementation of future policies and other influencing factors in order to create different scenarios and set up different data to simulate possible future outcomes. The implementation of different policies allows for the exploration of ways to satisfy the outcomes of future development and the identification of an appropriate means of achieving the goal of carbon reduction.

This study considers five scenarios, which are defined as follows: the Business as Usual (BAU) scenario, the Washed Coal Promotion (WCP) scenario, the Generation Efficiency Improvement (GEI) scenario, the Clean Energy Substitution (CES) scenario, and the Integrated Measures (IMS) scenario. The aforementioned scenarios are delineated in Table 1.

Table 1

Scenario Setting Sheet
Scenario	Specific measures
BAU	Business as usual scenario, continuation of status quo data.
WCP	Scenario of promotion of washed coal. Gradual increase in the proportion of washed coal used and the level of activity due to the reduction in the cost of coal washing technology and its diffusion, with a corresponding reduction in the level of activity and the proportion of bituminous coal used.
GEI	Thermal power generation efficiency improvement scenario. As a result of technological iterations, thermal generating units are operated with improved efficiency, energy losses are reduced, and energy use intensity decreases.
CES	Clean energy substitution scenario. The use of technologies such as hydropower, wind power, photovoltaics, and biomass power generation to replace traditional coal-fired power generation reduces the level of activity in coal power generation, and the level of activity in power generation from other new energy sources is significantly increased.
IMS	Integrated Measures Scenarios. Scenarios that combine all measures from the WCP, GEI, and CES scenarios.

The parameter settings for the growth rate of energy use intensity for each scenario from 2023 to 2035 are presented in Table 2.

Table 2

Energy intensity growth rate settings
Fuel	Bituminous Coal	Washed Coal	Diesel	Gasoline	Natural Gas	Biomass
Unit	Tons/GWh	Tons/GWh	Tons/GWh	Tons/GWh	Cubic meters/GWh	Tons of Equivalent Coal/GWh
2022	390.329	7.502	0.048	0.027	133.056	189.431
Year	BAU
2023 ~ 2035	0.50%	0.20%	0.50%	0.50%	0.50%	0.20%
Year	WCP
2023 ~ 2026	0.10%	26.00%	0.50%	0.50%	0.50%	0.20%
2027 ~ 2029	-0.50%	22.00%
2030 ~ 2032	-1.00%	20.00%
2033 ~ 2035	-2.50%	16.00%
Year	GEI
2023 ~ 2026	0.50%	-2.00%	-2.00%	-2.00%	-2.50%	0.50%
2027 ~ 2029	-2.00%
2030 ~ 2032	-1.50%
2033 ~ 2035	-2.50%
Year	CES
2023 ~ 2026	2.00%	0.20%	-0.50%	-0.50%	-0.50%	1.50%
2027 ~ 2029	-1.00%
2030 ~ 2032	-1.50%
2033 ~ 2035	-2.00%
Year	IMS
2023 ~ 2026	2.00%	-2.00%	-2.00%	-2.00%	-2.50%	2.00%
2027 ~ 2029	-2.00%
2030 ~ 2032	-2.50%
2033 ~ 2035	-3.50%

In the study, the generation structure is configured according to a variety of scenarios in order to align with the requirements of future planning. It should be noted that the WCP and GEI scenarios do not involve the optimization of the generation structure or activity level. Consequently, the generation structure in these scenarios is consistent with that of the BAU scenario. In contrast, the CES and IMS scenarios require adjustment to account for the change in activity level of the generation type. The percentages depicted in Fig. 3 illustrate the changes in electricity generation before and after the restructuring. In the CES and IMS scenarios, the proportion of coal-fired generation declines from 88% in 2022 to 60% in 2035. Concurrently, the shares of wind, PV, hydropower, and biomass all exhibit an increase relative to the BAU, WCP, and GEI scenarios.

Synergy elasticity analysis

Elasticity analysis of emission reduction synergies is commonly used to evaluate the synergistic emission reduction effects between GHGs and gaseous pollutants. The calculation of synergistic emission reductions of air pollutants and CO₂ under different policy scenarios is presented in Eq. (11)^[16].

$$\:\begin{array}{c}{\epsilon\:}_{s,k}=\frac{\frac{{E}_{BAU,g}-{E}_{s,g}}{{E}_{BAU,g}}}{\frac{{E}_{BAU,k}-{E}_{s,k}}{{E}_{BAU,k}}}=\frac{\frac{\varDelta\:{E}_{s,g}}{{E}_{BAU,g}}}{\frac{\varDelta\:{E}_{s,k}}{{E}_{s,k}}}=\frac{\varDelta\:{E}_{s,g}}{{E}_{s,k}}*\frac{{E}_{BAU,k}}{{E}_{BAU,g}}\#\left(11\right)\end{array}$$

where $\:{E}_{BAU,g}$ represents GHG emissions under the baseline scenario, $\:{E}_{s,g}$ represents GHG emissions under scenario s, and $\:\varDelta\:{E}_{s,g}$ represents the change in GHG emissions in scenario s relative to the baseline scenario. $\:{E}_{BAU,k}$ represents the emissions of air pollutant k under the baseline scenario; $\:{E}_{s,k}$ represents the emissions of air pollutant k under scenario s; and $\:\varDelta\:{E}_{s,k}$ represents the change in the emissions of air pollutant k in scenario s relative to the baseline scenario. If $\:{\epsilon\:}_{s,k}$ > 1, it indicates that for scenario s, GHG emissions are sensitive to emission reductions of air pollutants and synergistic emission reductions are better; if $\:{\epsilon\:}_{s,k}$ < 1, it indicates insensitivity, and if $\:{\epsilon\:}_{s,k}$ = 1, it indicates unit elasticity.

Data sources

The data utilized in this study were obtained from a variety of sources, including the Anhui government's annual statistical documents, policy documents, development plans, government work reports, and news reports. For a comprehensive list of these sources, please refer to Table 3.

Table 3

Table of data sources
Sources	Data and policy requirements
Anhui Province Statistical Yearbook 2023	Annual electricity generation Generation structure
Greenhouse gas inventory of energy activities in Anhui Province, 2023 Energy Balance Sheet	Carbon emission factors for power generation Level of fossil fuel combustion activity in the power generation sector
Anhui Electric Power Development Plan	Power restructuring requirements and transition planning Requirements for reduction of energy consumption intensity per unit of GDP CO₂ emission reduction requirements per unit of GDP Planning for new photovoltaic and wind turbines New energy vehicle charging pile construction planning

Utilizing data from 2015 to 2022 as a historical reference, this study employs the LMDI model to identify the underlying factors influencing carbon emissions in the power generation industry within Anhui Province. These factors are categorized into four distinct domains: demographic, economic, energy, and carbon intensity. The study then proceeds to analyze the factor with the most pronounced carbon emission effect among the four, establishing a correlation between this parameter and power generation. This relationship is subsequently modeled using a curve fit, enabling the projection of future trends in power generation.

Analysis of factors affecting carbon emissions

The LMDI decomposition model was employed to decompose the demographic effect, the economic effect, the energy effect, and the carbon intensity effect for the period between 2015 and 2022. This resulted in the calculations of the four effects, which are presented in Table 4.

Table 4

LMDI decomposition calculation results
T	$\:\varDelta\:{C}_{F}^{T}$	$\:\varDelta\:{C}_{Q}^{T}$	$\:\varDelta\:{C}_{H}^{T}$	$\:\varDelta\:{C}_{POP}^{T}$	$\:\sum\:{C}^{T}$
2015 ~ 2016	-7.18	-7.37	67.66	2.60	55.71
2016 ~ 2017	-8.74	-24.22	99.76	3.40	70.20
2017 ~ 2018	-5.46	-34.27	141.54	3.33	105.14
2018 ~ 2019	-29.47	-35.61	97.23	3.30	35.46
2019 ~ 2020	-41.97	-77.94	39.57	2.78	-77.55
2020 ~ 2021	-51.69	-39.12	168.30	1.84	79.33
2021 ~ 2022	-168.41	30.87	69.28	3.51	-64.74
2015 ~ 2022	-312.91	-187.66	683.35	20.76	--

The results of the LDMI analysis indicate that the absolute value of the cumulative effect of $\:\varDelta\:{C}_{H}^{T}$ between 2015 and 2022 is the largest and most positive. Among the four factors, the influence effect between the economic effect and the carbon emissions of the power industry in Anhui is the most significant. In contrast, the absolute value of $\:\varDelta\:{C}_{POP}^{T}$ is the smallest, indicating that the population effect has caused relatively minor impacts on the power generation industry in Anhui.

Electricity generation forecasts

The generation and GDP parameters for 2015–2022 are shown in Table 5.

Table 5

Historical data on GDP and electricity generation
Year	GDP(Billion yuan)	Electricity generation(GWh)
2015	2383.118	206189
2016	2630.770	225269
2017	2967.622	247025
2018	3401.091	274122
2019	3684.549	288667
2020	3806.151	280897
2021	4295.918	308339
2022	4504.500	329877

Given the divergence from the prevailing trend in electricity generation values in 2020, we have excluded the influence of these anomalous values and established a mathematical relationship between electricity generation and GDP. This has enabled us to derive the functional relationship expressed by Eq. (12).

$$\:\begin{array}{c}Q=57.993GDP+72687\#\left(12\right)\end{array}$$

$$\:{R}^{2}=0.9935$$

Where Q represents the annual power generation and GDP represents the gross domestic product of Anhui Province in the corresponding year.

In accordance with the projected economic growth trajectory for Anhui Province, the anticipated GDP growth rate is set at 5.5%. This figure is incorporated into a functional equation that also includes projected power generation in Anhui Province until 2035. The resulting predicted values will then be applied to the LEAP model, and the outcomes of these calculations are presented in Table 6.

Table 6

Forecasts of GDP and electricity generation
Year	GDP(Billion yuan)	Electricity generation(GWh)
2023	4752.248	348317
2024	5013.621	363477
2025	5289.370	379470
2026	5580.286	396344
2027	5887.201	414145
2028	6210.997	432925
2029	6552.602	452738
2030	6912.995	473641
2031	7293.210	495693
2032	7694.337	518959
2033	8117.525	543503
2034	8563.989	569398
2035	9035.009	596717

Due to the change in generation composition, there is a difference between the BAU, WCP, and GEI scenarios (left) and the CES and IMS scenarios (right), as shown in Fig. 2.

Emission reduction pathway analysis

Carbon dioxide emission reduction analysis

This study uses 2022 as the base year and the LEAP model to project GHG emission trends through 2035.

From the simulation results in Fig. 3 of the LEAP model, it can be seen that the BAU scenario has the highest carbon dioxide emissions among all the scenarios. In 2035, the carbon dioxide emissions from the power generation industry in Anhui Province reached 553.122 million tons in the BAU scenario and 536.763 million tons in the WCP scenario, 422. 316 million tons in the GEI scenario, 364.465 million tons in the CES scenario, and 304.045 million tons in the IMS scenario. From the trend of change, the difference in carbon emissions between the BAU and WCP scenarios is not significant, and the WCP scenario is only slightly lower than the BAU scenario, indicating that the popularization and application of coal-washing technology cannot play a good role in carbon reduction. The carbon emission reductions from thermal power efficiency improvements in the GEI scenario are significantly better than those from promoting coal scrubbing technology, with total carbon emissions reduced by about 50% in 2022–2035 compared to the WCP scenario. Due to the improvement of power generation efficiency, the fuel intensity consumed to produce a unit of electrical energy is reduced, and the emission reduction effect is significant, but the carbon emission still shows a growing trend due to the large base of power generation. Carbon emissions from power generation in the CES scenario peak at 352.443 million metric tons in 2031 and then level off. In 2022–2035, the CES scenario reduces total carbon emissions by about 85% compared to the WCP scenario and by about 70% compared to the GEI scenario. Replacing traditional coal-fired power generation with clean energy is currently one of the most optimal solutions to reduce emissions, it does not produce large amounts of carbon dioxide in the power generation process and has a promising development prospect. The IMS scenario, combined with the comprehensive emission reduction measures of the above scenarios, has the strongest binding force for emission reduction, with carbon emissions peaking at 341.126 million tons in 2027 and then declining to 304.045 million tons in 2035, which is lower than the baseline year of 313.289 million tons, and the effect of emission reduction is remarkable.

Overall, carbon emissions are able to peak under both the CES and IMS scenarios, and the IMS scenario peaks earlier than the CES scenario, with greater carbon reduction intensity and better emission reduction effects.

Air pollutant emission reduction analysis

Coal-fired power plants in Anhui Province are primarily fueled by bituminous coal, with some use of washed coal and diesel fuel. The nitrogen in the fuel is burned to produce nitrogen oxides, the sulfur is converted to sulfur dioxide, and other fuels produce small amounts of methane and volatile organic compounds. The study of emission reduction of air pollutants is also of great significance. The study used the LEAP model to analyze the trend of pollutant emissions in the power generation industry in Anhui Province, and the air pollutants included in the study were SO₂, NO_X, N₂O, CH_4, and VOCs.

As shown in Fig. 4, the emission of NO_X is comparatively substantial in comparison to other airborne pollutants, with a total of 1017.82 thousand tons recorded in the base year. Furthermore, the projected trajectory of emissions in the various scenarios exhibits a parallel trend with that of CO₂. By 2035, the BAU scenario is projected to have the highest NO_X emissions of all scenarios, at 1,804.88 thousand tons. The WCP scenario is slightly lower than the BAU scenario, at 1,746.11 thousand tons. In contrast, the GEI scenario is projected to show a significant decrease in emissions compared to the BAU scenario, at 1,381.99 thousand tons. In the CES scenario, emissions reach a maximum of 1,163.61 thousand tons in 2032 and then level off to 1,155.73 thousand tons in 2035. In the IMS scenario, emissions exhibit a different trajectory, with a peak in 2027 and a subsequent decline, reaching 1,020.72 thousand tons in 2035. The generation process produces a significantly lower quantity of N₂O in comparison to NO_X, with a ratio of approximately 0.005. Furthermore, none of the scenarios exhibit a peak in emissions, with the BAU scenario emitting 8,922.84 tons and the IMS scenario demonstrating the lowest emissions, at 6,048.71 tons, by 2035. The results demonstrate that the promotion of coal-washing technology as a replacement for bituminous coal does not lead to a substantial reduction in nitrogen oxide emissions. Conversely, scenarios involving improvements in thermal power efficiency, the substitution of clean energy, and integrated measures exhibit greater efficacy in reducing nitrogen oxides emissions.

As illustrated in Fig. 5, SO₂ emissions from power generation are the second most prevalent, trailing only nitrogen oxides. The majority of these emissions originate from the release of elemental sulfur during the combustion of coal. The base year emissions of SO₂ are 641.96 thousand tons. By 2035, the BAU scenario indicates an increase in emissions to 1,133.62 thousand tons. In contrast, the WCP scenario demonstrates a reduction in emissions compared to the BAU scenario, reaching 1,046.16 thousand tons. Furthermore, the GEI scenario exhibits a decline in emissions to 865.65 thousand tons. The CES scenario indicates a peak in emissions in 2030 at a high of 722.04 thousand tons, which subsequently levels off and decreases to 709.41 thousand tons in 2035. In contrast, the IMS scenario's emissions peak at 699.36 thousand tons in 2027 and then continue to decline, reaching 623.11 thousand tons in 2035. The projected results indicate that the promotion of fine coal washing technology is a more effective method for reducing SO₂ emissions than NO_X, thermal power efficiency improvement, and clean energy substitution. Furthermore, the combined scenario demonstrates the most effective control of SO₂ emissions.

The findings indicate that the emissions of methane and volatile organic pollutants (VOCs) are influenced to some extent by the quantity of biomass utilized. As the amount of biomass employed increases, the emissions of methane and VOCs also demonstrate an upward trajectory. On the one hand, biomass is a low-carbon fuel that is widely used in thermal power generation. However, at this stage, the emissions of the two are relatively low, and the impact on the reduction of emissions can be considered negligible. On the other hand, methane and VOCs can also be utilized to achieve complete combustion. Consequently, methane and VOCs will not be discussed in the subsequent synergistic emission reduction in this study.

Analysis of synergistic emission reduction effects

In this study, the synergistic abatement effect between CO₂ and air pollutants was evaluated using the abatement synergy elasticity calculation formula. The results of the elasticity coefficient calculation are presented in Table 7.

Table 7

The coefficient of elasticity for synergistic emission reductions
	WCP	GEI	CES	IMS
$\:{\epsilon\:}_{N{O}_{X}}$	0.908	1.009	1.039	1.036
$\:{\epsilon\:}_{{N}_{2}O}$	0.962	1.081	1.434	1.398
$\:{\epsilon\:}_{S{O}_{2}}$	0.383	1.000	0.998	1.000

The results of the calculations indicate that the synergistic emission reduction elasticity coefficients $\:\epsilon\:$ of NO_X and N₂O for the GEI, CES, and IMS scenarios are all greater than 1, whereas the synergistic emission reduction elasticity coefficients of NO_X and N₂O for the WCP scenario are all less than 1. The findings indicate that CO₂ emission reduction is more responsive than NO_X emission reduction when thermal power efficiency is enhanced, clean energy is substituted, and comprehensive measures are implemented. Conversely, NO_X emission reduction is more responsive than CO₂ emission reduction when measures to promote coal-washing technology are introduced.

Emission reduction contribution analysis

The analysis of the emission reduction of CO₂, NO_X, N₂O, and SO₂ in each scenario indicates that the emission reduction contribution of the policy on the promotion of fine coal washing technology is less than 10% for CO₂ and NO_X, which represents a relatively low emission reduction contribution. In contrast, the emission reduction contribution for SO₂ reaches 17.1%, which suggests that the fine washing of bituminous coal can reduce the emission of SO₂ to a certain extent. The contribution to emission reductions for both CO₂ and air pollutants under the implementation of thermal power efficiency improvement scenarios exceeds 50 percent, indicating that partial improvements in thermal power generation efficiency can result in more substantial emission reductions. In the context of the transition from traditional coal-fired power generation to clean energy sources, the reduction of carbon dioxide and other air pollutants is attributed to the contribution of these sources above 80%. Furthermore, the reduction of all gases on the surface is also evident. In general, the order of emission reduction contribution of CO₂ and air pollutants in different scenarios is as follows: IMS > CES > GEI > WCP > BAU.

This study explores the peak emissions of carbon dioxide and air pollutants and feasible emission reduction pathways, as well as methods to realize the synergistic effects of pollution reduction and carbon reduction, through the combined use of the LMDI and LEAP models and the scenario analysis methodology to analyze the power generation industry in Anhui Province, China. The highlight of the study is to identify strategies for simultaneously reducing the emission of carbon dioxide and air pollutants in the power generation industry. To this end, the study employs a synergistic emission reduction effect analysis system to evaluate the combined impact of carbon and pollution reduction.

At the present time, the power generation sector has significant potential for the reduction of pollution and carbon emissions [17–19]. This paper demonstrates that the use of washed coal in power generation does not result in significant emission reductions. Instead, it provides a reference point for examining other potential emission reduction pathways. From a practical standpoint, the efficiency of thermal power generation is typically around 40% at present. Through the continuous iterative updating of equipment and technology, the efficiency of power generation can realize a modest increase. However, the benefits of power generation are exponentially higher than the numerical increase in power generation efficiency. Consequently, improvements in power generation efficiency will have an impact on the amount of energy used and will reduce the energy consumption of the unit of power generation capacity [20–21]. The current focus of the transformation and upgrading of the power industry is the generation of clean energy. This represents a significant opportunity to reduce pollution and carbon emissions [22–23]. The generation of wind power, photovoltaic power, hydropower, and other forms of clean energy can reduce the intensity of fossil energy use at the source, thereby increasing the substitution of clean energy. This can serve as a significant solution to the problem of greenhouse gas emission reduction in the power generation industry [24–25]. At this juncture, there is a growing emphasis on clean energy among a multitude of individuals and a burgeoning body of scholarly research exploring the broader deployment of clean energy as a means of mitigating the social ramifications of the greenhouse effect and environmental contamination. Nevertheless, the utilization of clean energy still holds considerable promise and offers invaluable environmental benefits [26–27].

In order to advance the power generation industry in Anhui Province, it is imperative to expedite the phase-out of obsolete coal power production facilities. Concurrently, it is essential to conduct comprehensive energy audits and implement technological enhancements within the coal power production process, with the objective of minimizing energy losses throughout the entire power generation cycle. It is imperative to accelerate the substitution of green power, continuously optimize the power generation structure, and simultaneously provide policy incentives to facilitate the transformation and upgrading process through subsidies or other means. This approach will enable renewable energy sources to fulfill their potential in maintaining supply and achieving the goal of carbon peaking with enhanced quality.

The LEAP model study on the synergistic emission reduction path of carbon dioxide and air pollutants in the power generation industry in Anhui Province indicates that the combined implementation of coal washing technology, thermal power efficiency improvement, and clean energy substitution represents the optimal strategy for reducing emissions of carbon dioxide and air pollutants. This approach is projected to achieve peak emissions of these pollutants by 2027.

The analysis of the synergistic emission reduction effect demonstrates that the implementation of thermal power efficiency improvement measures, clean energy substitution measures, and comprehensive measures results in a greater reduction in CO₂ emissions than in NO_X emissions. However, the sensitivity of CO₂ and SO₂ emission reduction is comparable. In the context of the implementation of measures to promote coal washing technology, it can be observed that the abatement of NO_X exhibits a slightly higher sensitivity than that of CO₂, while the abatement of sulfur dioxide demonstrates a higher sensitivity in comparison to CO₂. In general, the integration of measures for the reduction of carbon dioxide and air pollutants yields the most effective synergistic emission reduction.

In summary, the optimal strategy for reducing pollution and carbon emissions in the power generation industry in Anhui Province is to simultaneously implement three emission reduction measures: the promotion of coal washing technology, thermal power efficiency improvement, and clean energy substitution.

Authors' information

Corresponding Author

Wang Xiaohui--Anhui Provincial Academy of Eco-Environmental Science Research, Hefei 230061, China;

School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China;

Anhui Institute of Ecological Civilization, Hefei 230009, China.

Authors

Wu Lei—Anhui Institute of Ecological Civilization, Hefei 230009, China;

Institute of Atmospheric Environment Research, Anhui Provincial Academy of Eco-Environmental Science Research, Hefei 230061, China;

School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China.

Zhu Weiwei--School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China.

Qian Jing--Institute of Atmospheric Environment Research, Anhui Provincial Academy of Eco-Environmental Science Research, Hefei 230061, China;

Anhui Institute of Ecological Civilization, Hefei 230009, China.

Yang Manyi--School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China.

Authors' contributions

Wu: Methodology, Content checking and optimization.

Zhu: Writing – original draft, Formal analysis, Data collection, Content Optimization.

Qian: Methodology, Formal analysis

Yang: Data collection, Formal analysis.

Wang: Content checking and optimization.

Availability of data and materials

Data will be made available on request.

Competing interests

We have declared no financial interests/personal relationships which may be considered as potential competing interests.

Ethics approval and consent to participate

Not applicable. This study does not involve or relate to any animal, human, human data, human tissue, or plant material.

Consent for publication

Not applicable. This study does not contain any individual persons data.

Funding

This research was funded by the following programs:

(1) Anhui Institute of Ecological Civilization（NO. AHSWY-2022-03）

(2) Chinesisch-Deutsche Kooperation zu Klimawandel-NDC Umsetzung (NO. 83463768)

Acknowledgements

The authors would like to express their sincere gratitude to the reviewers for their constructive comments and useful suggestions that help us improve this study, and thanks to all the authors for their work.

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Study on the Synergistic Path of Pollution and Carbon Emissions Reduction in Anhui Province Electricity Industry

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Abstract

Background

Results

Conclusions

Figures

Background

Methods

KAYA-LMDI model

LEAP model

Scenario analysis

Synergy elasticity analysis

Data sources

Results

Analysis of factors affecting carbon emissions

Electricity generation forecasts

Emission reduction pathway analysis

Analysis of synergistic emission reduction effects

Emission reduction contribution analysis

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1

	WCP	GEI	CES	IMS
\(\:{\epsilon\:}_{N{O}_{X}}\)	0.908	1.009	1.039	1.036
\(\:{\epsilon\:}_{{N}_{2}O}\)	0.962	1.081	1.434	1.398
\(\:{\epsilon\:}_{S{O}_{2}}\)	0.383	1.000	0.998	1.000