Coordination Relationship of Carbon Emissions and Air Pollutants under Governance Measures in a Typical Industrial City in China

Abstract

Coordinating and controlling carbon and atmospheric pollutant emissions in industrial cities poses challenges, making it difficult to formulate effective environmental governance strategies in China. This study used the Community Multiscale Air Quality (CMAQ) and Long-range Energy Alternatives Planning (LEAP) models, with a typical industrial city in the Sichuan Basin as the case study. Five emission reduction scenarios, one integration scenario, and one baseline scenario were set to quantitatively analyze the synergistic effect between carbon emissions and atmospheric pollutant emissions. The results indicate a high synergy between sulfur dioxide and greenhouse gases. For every one-point decrease in the Air Quality Composite Index (AQCI), the Industrial Restructuring Scenario (IR), Other Source Management Scenario (OSM), Transportation Energy Efficiency Improvement Scenario (TEEI), Industrial Energy Efficiency Improvement Scenario (IEEI), and Transportation Restructuring (TR) scenarios would require a reduction in carbon emissions by 56,492.79 kilotons, 39,850.45 kilotons, 34,027.5 kilotons, 22,356.58 kilotons, and 3243.33 kilotons, respectively. The results indicate that governance measures, such as improving transportation structure and upgrading industrial technologies, provide stronger support for simultaneous carbon emissions reductions and air quality improvement.

1. Introduction

Atmospheric pollution and carbon emissions are two of the most pressing environmental issues facing the world today [1,2]. They have far-reaching impacts on global climate and health, affecting the quality of human life and causing serious harm to ecosystems [2,3]. With the acceleration of global industrialization and urbanization, the problems of air pollution and carbon emissions have become increasingly prominent [4,5]. Although countries around the world have taken a series of measures to address these issues, they remain a long-term and daunting challenge [6,7]. Carbon peaking is a crucial measure to mitigate climate change. Global temperatures have brought significant challenges worldwide, including extreme weather events, rising sea levels, and ecosystem collapse. By achieving carbon peaking, there is a way to slow the pace of climate change and reduce the risks associated with rising temperatures [8,9]. Carbon peaking refers to a historical turning point in a specific region or industry where the annual carbon emissions reach their highest recorded level. It signifies a shift in the trajectory of carbon emissions from increasing to decreasing, marking a transition in economic development from a high-energy, high-emission model to a cleaner and more sustainable one [8,9]. In 2021, China made a commitment to achieve carbon peaking by 2030 [10].

Yibin City is located in the economic zone of southern Sichuan, China. This city is an important industrial base for chemical, petroleum, and building materials. Yibin City is home to 902 large-scale industrial enterprises and 12 key industrial parks [11]. In 2021, the regional gross domestic product (GDP) exceeded 300 billion, reaching 314.808 billion yuan [11]. Yibin’s GDP ranked third in the Sichuan Basin, with a growth rate of 8.9%, which was the highest in the region. The industrial output value accounted for 48.15% of the total GDP, surpassing cities in northeast China like Shenyang City (32.8%) and Dalian City (44.0%) [11,12]. Yibin is emerging as a new industrial city in the inland region of China [10,12]. As Yibin is located at the edge of the Sichuan basin, the wind speed is light, coupled with the Yangtze River passing through the city, the humidity in the main urban area is enormous, resulting in weak diffusion capacity in the urban area and low atmospheric environmental capacity, making it difficult to absorb the large amount of pollutants emitted in the city [13,14]. With the rapid development of industry and increasing urbanization, Yibin City is facing serious carbon emission and air pollution problems [14,15]. In 2021, the annual average PM_2.5 concentration in Yibin City reached 44 μg/m³, representing a growth of 10.8% compared to the previous year. This makes it the city with the highest annual average PM_2.5 concentration in Sichuan province. Simultaneously, the carbon emissions in Yibin City also reached 17,220 million tons of CO₂e in 2021, representing a 1.5% increase compared to the previous year [11,16].

Regarding carbon emissions and air pollution, there is currently a lack of corresponding research in the academic community at the urban scale, and a lack of consideration for the simultaneous impact of control measures on both atmospheric pollutants and carbon emissions. For example, Shabbir Alam et al. [17] looked at carbon emissions and air pollution in India from the perspective of government restrictions, finding an approximately linear relationship between regional carbon emissions and air pollution, and a non-linear U-shaped relationship with renewable energy, but did not elaborate on the advantages and disadvantages of control measures. Wang et al. [18] confirmed from the perspective of spatial spillover that there is a certain symbiosis and aggregation between carbon emissions and air pollution but did not point out the synergistic impact of control measures on both. Tawalbeh et al. [19] used Python database multimodal analysis to deduce the possibility of CO, CO₂, and O₃ synergistic growth in a given area, but did not provide specific control measure recommendations. The environmental control strategies of cities at all levels in the Sichuan Basin also lack overall planning considerations, usually dealing with carbon emissions and air pollution separately, with significant differences in aspects like inventory compilation, emission source identification, department divisions, and accounting methods [20]. This makes it difficult to conduct quantitative analysis on the controls of carbon and air pollutant emissions. It also makes it hard to further quantify the synergistic effects of various emission reduction measures.

There are few existing cases that integrate the Community Multiscale Air Quality (CMAQ) and the Long-range Energy Alternatives Planning (LEAP) models, specifically in the context of discussing carbon emissions and atmospheric pollutants. For instance, Yang et al. [21] pointed out that the management of general atmospheric pollutants, such as carbon dioxide and particulate matter, should be conducted simultaneously to achieve optimized results. However, their study solely utilized the CMAQ without incorporating the LEAP model for predictions. Bei et al. [22] indicated that under stringent carbon control measures, the improvement in air quality due to carbon dioxide reduction outweighs the impact on climate change. And they did not attempt to simultaneously utilize both the CMAQ and LEAP models. The CMAQ model has gained significant recognition in the academic community for its ability to predict air quality in the medium to long term. It is frequently used in atmospheric research. However, as a numerical model, CMAQ lacks compatibility with governance measures and applications in greenhouse gas predictions [20,21,22]. On the other hand, the LEAP model effectively integrates governance measures and energy changes to reflect carbon emission trends at the medium to long-term scale. However, it can only account for pollutant emissions in the atmospheric domain without predicting specific concentrations [23,24]. Combining both models can effectively address their respective shortcomings, and combine their strengths, making them suitable for analyzing carbon emissions and air quality based on governance measures in this study.

Therefore, this study is based on the LEAP and CMAQ models, using the typical industrial city of Yibin in the Chinese Basin region as an example. It aims to investigate the regulatory measures that can be implemented in energy, industrial, and transportation structures to achieve the carbon peak target. Additionally, it aims to analyze the carbon emissions and air quality improvement after implementing these measures and explore the synergistic relationship between carbon emissions and atmospheric pollutant emissions. Finally, the study aims to assess the pros and cons of different emission reduction measures in terms of their synergistic support for both carbon emissions and atmospheric pollutants.

The innovation and contributions of this study can be divided into the following points: (1) The innovative use of the CMAQ and LEAP models for long-term prediction of air quality and carbon emissions. (2) The innovative quantification of the impact of different control measures on air quality, using carbon peak as a constraint condition. (3) Under the condition of achieving coordinated reduction of carbon emissions and atmospheric pollutants, this study has implemented control measures that optimize the energy structure, industrial structure, and transportation structure. This provides a target basis for future policy formulation in the relevant sectors. (4) This study used an industrial city in the basin as an example and identified key pollutant species related to carbon emissions and atmospheric pollutant emissions. It provided generalizable results for carbon emissions and atmospheric pollutant control in other industrial cities within the basin.

2. Methodology

In this study, industrial structure adjustment, clean energy utilization, industrial pollution control, surface source pollution control, motor vehicle pollution control, and regulatory capacity building are included in the scope of the study. Based on the CMAQ and LEAP models, a quantitative analysis of the relevance of urban carbon emissions and major air pollutant emissions was conducted, and the synergistic effect and degree of emission reduction of cities implementing pollution reduction measures and carbon emission control were analyzed from the perspective of regional joint prevention of air pollution. Based on the changes in carbon emissions and air quality in Yibin from 2017 to 2020, and then the constrained scenario of the 2030 peak attainment target through the LEAP model, the change of energy consumption generated by the carbon emission reduction simulation scenario from 2021 to 2030 was converted into pollutant emission reduction. The improvement of air quality through 2030 was simulated by CMAQ, and, finally, the synergistic effect of atmospheric conventional pollutants and carbon emission constraint was derived.

2.1. Carbon Emission Model

The LEAP model is an effective tool for energy policy analysis and climate change mitigation assessment developed by the Stockholm Environment Institute (SEI) in Sweden [23,24]. The model is equipped with modules for energy demand forecasting and environmental impact prediction analysis, and can be used to predict the future short-, medium-, and long-term energy demand and construct energy supply and demand scenarios based on key scenarios of economic, industrial, or technological development, and can rely on a compiled environmental database to forecast the environmental impact of a given energy scenario, and to calculate greenhouse gas emissions from the perspective of resources, conversion, and utilization [24,25]. As shown in Figure 1, the LEAP model construction in this study is divided into six major modules: industrial sources, non-energy, mobile sources, energy conversion, energy loss, and impact values, and extended to several secondary and tertiary modules on top of these six basic modules. Direct and indirect energy consumption, including heat, electricity, and additional facilities such as desulfurization and denitrification were inserted while the sub-production lines were built. Non-energy modules include dust, solvent use, agriculture, storage and transportation, open burning of biomass, and other confounding sources [26,27]. The transportation framework covers passenger and freight transportation, with market flows through vehicle and vessel sales and retirements, kilometers and age as operating conditions. The energy interaction framework consists of energy losses (electricity, natural gas, etc., have some losses in transmission and storage) and power generation modules [28]. All fossil energy inputs are imported from the outer border. At the same time, the outward transfer of electricity is removed from the border (the outward transfer of electricity is counted in the energy consumption of other provinces).

The calculation of carbon emissions in the LEAP model is provided in the intergovernmental panel on climate change (IPCC)’s sixth report [23,24,25,26,27,28]. The calculation of carbon emissions from energy consumption covers three main greenhouse gases including carbon dioxide, methane, and nitrous oxide [29]. Each greenhouse gas responds to its contribution to global warming based on its global warming potential (GWP) [30,31], which is measured on a 100-year GWP scale of 1 for carbon dioxide, 30 for methane, and 265 for nitrous oxide, and the calculated carbon emissions are expressed in carbon dioxide equivalent (CO₂e) [32]. The formula for calculating carbon emissions from energy consumption is as follows:

$${\mathrm{GHG}}_{\mathrm{Fuel}}={\displaystyle \sum }_i{\displaystyle \sum }_{j}A{C}_{i,j}\times NC{V}_j\times \left(E{F}_{{\mathrm{CO}}_2}+E{F}_{{\mathrm{CH}}_4}\times 30+E{F}_{{\mathrm{N}}_2\mathrm{O}}\times 265\right)$$

(1)

where ${\mathrm{GHG}}_{\mathrm{Fuel}}$ refers to the carbon emissions from energy consumption in the model, in t CO₂e; $A$ refers to the activity level of the fuel in t; $NCV$ refers to the calorific value of the fuel, in KJ/kg or KJ/Nm³; $EF$ refers to the carbon emission factor in kg/T and the calculation formula of $E{F}_{{\mathrm{CO}}_2}$ is as follows:

$$E{F}_{{\mathrm{CO}}_2}=CC\times O\times \raisebox{1ex}{$44$}\!\left/ \!\raisebox{-1ex}{$12$}\right.$$

(2)

where $E{F}_{{\mathrm{CO}}_2}$ is the CO₂ emission factor in kg/TJ; $CC$ refers to the carbon content per unit calorific value in tC/TJ; $O$ is the oxidation rate; 44/12 is the conversion factor of carbon atoms to CO₂ and the relative molecular mass. The methodology was used to account for carbon emissions in the LEAP model and to predict the year of peak carbon through activity level analysis.

2.2. Air Quality Model

In this study, the Weather Research and Forecasting (WRF) model (version 3.9) and Community Multiscale Air Quality (CMAQ) model (version 5.0.2) were integrated into a modeling system to predict changes in air quality. The WRF was developed by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) in the United States [33,34]. The CMAQ is the third-generation Chemical Mass Balance model (CMB3) developed by the United States Environmental Protection Agency (US EPA) [35,36].

The CMAQ utilized the AERO6 module for aerosol simulation, SOAP for organic aerosols, the CB05 mechanism for gas-phase chemistry, and the ACM2 scheme for vertical diffusion. The emission processing was performed using the Sparse Matrix Operator Kernel Emissions (SMOKE) model (version 5.3), which accounted for biogenic emissions based on the Model of Emissions of Gases and Aerosols from Nature (MEGAN) model (version 2.1). As shown in Figure 2, the WRF model employed the Lambert projection coordinate system, with a central longitude and latitude of (103° E, 45° N), and two standard latitudes at 25° N and 45° N. The meteorological simulations were conducted by a three-level and one-way nested modeling domain, with resolutions of 27 km × 27 km, 9 km × 9 km, and 3 km × 3 km, respectively. The first domain encompassed regions of China, East Asia, and South Asia. The second domain covered all areas of Sichuan Basin and surrounding cities. The third domain included cities such as Chengdu Plain, southern Sichuan, northeastern Sichuan, and parts of Chongqing. The CMAQ model shared the same resolution and center points as the WRF model, with a slightly smaller domain to reduce the influence of meteorological boundary conditions on air quality model simulations.

The emission inventory for sulfur dioxide (SO₂), nitrogen oxides (NO_x), carbon monoxide (CO), inhalable particulate matter (PM₁₀), fine particulate matter (PM_2.5), volatile organic compounds (VOCs), and ammonia (NH₃) in Sichuan Basin is derived from the local atmospheric source emission inventory of Sichuan Basin [37]. The emission inventory outside Sichuan Basin in this study utilized the 2018 MEIC inventory developed by Tsinghua University (http://meicmodel.org, accessed on 15 May 2023), which includes baseline and mitigation inventories specifically developed for the Sichuan Basin, along with other regional data. Based on the LEAP model, these inventories were processed to obtain mitigation inventories that reflect the changes in pollutant emissions in target region by 2030 (see Section 3.4). One baseline simulation scenario (driven by 2020 meteorological data and the baseline inventory) and five mitigation simulation scenarios (driven by 2020 meteorological data and corresponding mitigation inventories) were set up. The simulation period in this study focused on four representative months (January, April, July, and October), representing winter, spring, summer, and autumn, respectively. The average values for these four months represent the annual average.

2.3. Model Scenarios and Settings

This study simulates the carbon emissions and air quality of Yibin City from 2021 to 2030 based on the data released by Yibin City Yearbook Statistics [37], combining the actual consumption and pollutant emissions from 2017 to 2020 (source from the emission inventory of air pollution sources in Sichuan Basin) [20]. The scope of the LEAP and CMAQ models was specified, with industrial sources, mobile sources, and other sources (biomass open combustion sources, agricultural sources, and restaurant fume sources) as system boundaries. A baseline scenario was set up to assess the medium-term carbon emissions in Yibin City by combining the city’s energy conversion and energy loss. Then, a set of carbon emission reduction scenarios (i.e., five reduction scenarios and one integration scenario) was developed based on the reduction space. Based on this scenario, the air quality improvement was simulated to assess the synergistic effect of carbon and air pollution control. As shown in

Table 1. Model setup and governance measures.

Scenarios	Sources of Control	Measures	Issues and Remarks
Baseline	/	No artificial intervention	This scenario is the zero scenario, and all scenarios are extended on the baseline scenario.
Industrial restructuring (IR)	Industrial Sources	By 2030, the share of total energy will be reduced by more than 20% for bituminous coal, 5% for crude oil, 6% for natural gas, and 15% for electricity.	Environmental protection continues to upgrade, ultra-low emission transformation and deep treatment continue to promote the industrial energy to rise
Industrial energy efficiency improvement (IEEI)		Until 2030 Yibin industrial coal, crude oil type energy effect by 10%, with natural gas energy effect by 8%, with electricity part of the energy effect by 5% or more. Control energy loss, electricity to less than 6%, natural gas to less than 2%.
Transportation restructuring (TR)	Mobile Sources	By 2030, the proportion of new energy private cars in Yibin should reach 15%, the proportion of new energy buses should reach 12% or more, and new energy freight vehicles should reach 10% or more.	Orderly recovery of traffic, with passenger and freight turnover returning to previous levels after the end of the epidemic in 2023
Transportation Energy Efficiency improvement (TEEI)		By 2030, road transport will improve energy use efficiency by 15% and waterways by 10%. Construction machinery will improve energy use efficiency by 15% and agricultural machinery by 5%.
Other source management (OSM)	Biomass open combustion sources, agricultural sources, restaurant fume sources	Until 2030, a complete ban on straw burning, 20% of livestock manure recovery for biomass power generation, 15% of energy saving in restaurants.	/
Integration Scenario(IS)	Industrial sources, mobile sources, biomass open burning sources, agricultural sources, restaurant fume sources	Overlaying the control strategies of the above five scenarios	The scenario is an integration of all scenarios

, the industrial aspect reduces carbon emission equivalents by improving energy structure and kiln efficiency; the transportation aspect divides passenger and freight transportation into highways and waterways for comparison and analysis and considers new energy vehicle ratio and fuel economy, new and old vehicle replacements to achieve the purpose of reducing carbon emission. The same applies to other engineering and agricultural machinery. Other sources consider Yibin’s ability to implement environmental protection to create a series of carbon reduction strategies.

2.4. Air Quality Comprehensive Index

The air quality comprehensive index (AQCI) is a dimensionless index used to describe the overall condition of urban environmental air quality [38]. It comprehensively considers the pollution levels of six pollutants, namely SO₂, NO₂, PM10, PM_2.5, CO, and O₃, as specified in the “Technical regulation for ambient air quality assessment” [39]. A higher air quality comprehensive index value indicates a richer overall pollution [40]. The air quality comprehensive index in China is primarily used to evaluate and rank urban air quality [41,42]. In this study, the concentrations of conventional atmospheric pollutants in the air quality model were calculated to obtain the AQCI, facilitating the assessment of the correlation between air quality improvement and changes in carbon emissions. The calculation method for the AQCI is presented as follows:

$$I_{sum}={\displaystyle \sum }{I}_i$$

(3)

$$I_i=\mathrm{MAX}\left(\frac{C_{i,a}}{S_{i,a}}, \frac{C_{i,d}^{per}}{S_{i,d}}\right)$$

(4)

where $I_{sum}$ represents the AQCI, and $I_i$ represents the individual index for pollutant i. $C_{i,a}$ denotes the annual mean concentration of pollutant i, $S_{i,a}$ represents the secondary standard for the daily average value of pollutant i, and i includes SO₂, NO₂, PM₁₀, and PM_2.5. $C_{i,d}^{per}$ represents the specific percentile concentrations of 24 h average concentrations, $S_{i,d}$ represents secondary standard of 24 h average concentrations, and i includes SO₂, NO₂, PM₁₀, PM_2.5, CO, and O₃ (it corresponds to the 8 h average).

3. Results and Discussion

3.1. Model Verification

In this study, we compared the air pollutant concentrations measured at typical air quality monitoring stations in Yibin City in 2021 with the corresponding hourly average pollutant concentrations simulated by the CMAQ model to verify the reliability of the modeling results (

Table 2. Evaluation of six pollutants in Yibin City in 2021 (µg/m³, CO mg/m³).

Pollutant Type	Months	Observed Average	Simulated Mean	REMS	Bias
SO2	January	8.93	17.27	17.83	8.33
	April	9.26	12.38	16.61	3.13
	July	8.43	18.67	21.30	10.23
	October	9.51	18.04	19.92	8.53
NO2	January	35.17	30.72	18.85	−4.45
	April	28.26	21.95	22.87	−6.31
	July	21.07	21.13	17.77	0.06
	October	31.93	28.49	22.48	−3.44
CO	January	0.91	0.27	0.66	0.24
	April	0.60	0.21	0.42	−0.08
	July	0.56	0.18	0.39	0.13
	October	0.67	0.28	0.44	0.06
O3	January	25.84	45.40	30.68	19.56
	April	65.40	65.10	40.54	−0.30
	July	98.85	75.99	46.97	−22.86
	October	47.25	53.31	38.71	6.06
PM2.5	January	73.13	39.54	49.55	−33.60
	April	31.12	20.43	25.74	−10.69
	July	25.24	17.78	14.50	−7.46
	October	45.37	29.94	36.51	−15.43
PM10	January	88.17	63.47	53.66	−24.70
	April	50.78	35.10	41.43	−15.68
	July	44.21	32.91	26.74	−11.30
	October	65.67	52.18	46.79	−13.49

). The statistical indices used include root-mean-square error (RMSE) and bias [43,44,45,46,47]. The RMSE statistical values range from approximately 0 µg/m³ to 50 µg/m³. Notably, the bias statistical values for NO₂ and CO are around ±6 µg/m³, indicating a high level of trustworthiness in the simulated values [47]. However, the bias statistical values for SO₂ and O₃ in January and April are relatively unstable. Combined with the underestimation of particulate matter, this is related to the depreciation of precursor emission source inventory and the overestimation of wind speed under static wind conditions, especially wind speed, which is the main parameter affecting the accuracy of simulation results [47,48,49]. Overall, the model is relatively reliable and can be used to evaluate the environmental benefits of emission reduction from subsequent measures.

3.2. Analysis of Carbon Peak Targets

The Chinese government has made carbon peaking an important strategic goal and proposed to achieve it by 2030 [10]. Therefore, Yibin needs to achieve the carbon peaking target before 2030. This will have a positive impact on Yibin’s social and economic development, promoting clean energy transition, and reducing environmental pressure.

Under the baseline scenario, the LEAP model analyzed the natural growth rate and increase curve of freight passenger turnover, industrial output, population size, livestock size, and straw volume in Yibin City from 2017 to 2020. At the same time, the model combines the outline of the “14th Five-Year Plan and 2035 Vision for National Economic and Social Development of Yibin City” [50], the “14th Five-Year Plan for Ecological and Environmental Protection of Yibin City” [51], and the “14th Five-Year Plan for Modern Comprehensive Transportation Development of Yibin City” [52]. According to the urban development strategy, it is simulated that the passenger and cargo turnover will return to the pre-epidemic level in 2023, and then grow slowly at a rate of 1.21% per year; the industrial output value will grow at a rate of 3.2% per year after 2021; the number of restaurants will return to the pre-epidemic level in 2024, and then grow slowly at a rate of 2% per year; and the number of straw product and livestock will also grow at a rate of 2.2% to 3.16% after 2021. Figure 3 illustrates the urban energy trends calculated by the LEAP model, showing that solid fuels account for the primary input, followed by hydropower, natural gas, oil fuels, heat, biomass, and renewable energy. After conversion, the energy ultimately flows into industrial sectors, transportation, and other sectors, with a portion of the energy being lost in the process [53]. Fossil fuels constitute the primary energy input in urban areas, with the industrial sector accounting for a significant portion of energy consumption. This aligns with the typical characteristics of an industrial city [39,40].

As shown in

Table 3. Carbon Emissions in Yibin from 2017 to 2030 (million tons of CO₂e).

Department	Industrial Source	Mobile Source	Other Sources	Total
2017	18.57	0.94	0.04	19.55
2018	17.2	0.94	0.04	18.18
2019	16.66	0.84	0.04	17.54
2020	16.09	0.84	0.04	16.97
2021	16.45	0.87	0.05	17.36
2022	16.81	0.94	0.05	17.8
2023	17.18	0.99	0.05	18.21
2024	17.56	0.99	0.05	18.6
2025	17.94	1	0.05	19
2026	18.34	1.01	0.05	19.4
2027	18.74	1.02	0.06	19.81
2028	19.15	1.03	0.06	20.24
2029	19.57	1.04	0.06	20.67
2030	20	1.04	0.06	21.11
Total	250.26	13.49	0.7	264.44

, in the LEAP simulation, from 2017 to 2030, Yibin generated a total of 264.44 Mt CO₂e of carbon emissions, with industrial carbon emissions about 18.5 times that of transportation and about 363 times that of other sources, with industrial emissions accounting for the major contribution. After 2020, Yibin’s carbon emissions are on a slow upward trend with an average annual increase of 2.19% until 2030, when they reach 21.11 Mt CO₂e, an increase of 4.13 Mt CO₂e or 24.38% compared to the beginning of the simulation. According to Yibin’s current carbon emission simulation, it will be in a state of growth around 2030 and unable to reach the carbon peak target. The results align with the projected carbon emission trends for China’s region until 2030, as simulated by Dong et al. in 2017 [27]. The reasons behind this situation in Yibin are primarily due to its energy structure being heavily reliant on coal, and its economic development still being highly dependent on high-energy-consuming and high-emission industries such as steel and cement continue to expand their production capacity, resulting in a continuous increase in carbon emissions.

3.3. Carbon Peak under the Integration Scenario

After conducting simulation calculations using the LEAP, the carbon emissions of Yibin from 2017 to 2030 under the coupling of the five scenarios have been obtained. As shown in Figure 4 and

Table 4. Carbon intensity of energy sources in Yibin City under the integration scenario (million tons of CO₂e).

Fuel	2017	2018	2019	2020	2021	2022	2023	2024	2025	2026	2027	2028	2029	2030	Total
Difference	-	-	-	-	−142.58	−293.44	−452.34	−615.84	−786.04	−1128.49	−1356.89	−1594.52	−2482.33	−3499.04	−12,351.51
Natural Gas	758.79	1129.88	1586.69	1776.77	1797.75	1818.78	1839.86	1861	1882.16	1903.36	1941.05	1979.47	2291.89	2517.95	25,085.4
Gasoline	213.46	173.36	142.01	113.24	141.69	165.74	198.34	196.16	193.95	191.73	189.48	187.21	184.92	182.61	2473.91
Kerosene	0.11	0.09	0.1	0.08	0.08	0.08	0.08	0.08	0.08	0.09	0.09	0.09	0.09	0.09	1.23
Diesel	865.49	902.29	839.28	878.66	874.81	904.78	902.94	899.41	895.88	892.36	888.84	885.34	881.85	702.62	12,214.55
Fuel Oil	2.61	4.06	3.71	8.09	8.27	8.45	8.64	8.83	9.02	9.22	9.42	9.63	9.84	10.06	109.86
Liquefied petroleum gas	0.04	0.17	0.78	0.09	0.09	0.09	0.09	0.09	0.1	0.1	0.1	0.1	217.48	669.1	888.42
Anthracite	15,437.85	14,475.02	14,044.22	13,402.05	13,591.21	13,783.75	13,976.77	14,171.09	14,367	14,399.19	14,537.67	14,675.1	13,680.13	13,249.34	197,790.4
Anthracite	1808.36	735.38	527.73	420.54	429.79	439.24	448.91	458.78	468.88	479.19	489.73	500.51	511.52	124.65	7843.21
Lignite	0.09	137.03	41.79	21.08	21.55	22.02	22.51	23	23.51	24.02	24.55	25.09	25.64	26.21	438.09
Coke	89.95	73.65	63.73	19.33	19.76	20.19	20.64	21.09	21.55	22.03	22.51	23.01	23.51	24.03	464.97
Unspecified Coal	5.77	5.87	5.66	3.15	3.22	3.29	3.36	3.44	3.51	3.59	3.67	3.75	3.83	3.92	56.03
Biomass	34.3	36.48	38.66	40.84	37.93	34.79	31.42	27.79	23.9	19.73	15.27	10.51	5.42	-	357.06
Compressed Natural Gas	-	0.34	0.01	0.02	0.02	0.02	0.02	0.02	0.03	0.03	0.03	0.03	0.03	0.03	0.63
Coal gangue	328.52	506.45	248.75	287.58	293.9	300.37	306.98	313.73	320.63	327.69	334.9	342.26	349.79	99.72	4361.28
Total	19,545.36	18,180.08	17,543.11	16,971.51	17,220.05	17,501.6	17,760.56	17,984.51	18,210.22	18,272.32	18,457.32	18,642.11	18,185.96	17,610.33	252,085.04

(where the white area represents the difference from the baseline scenario), Yibin’s carbon emissions started to slowly rise in 2017, with an increase of approximately 1.1%. By 2028, it reached the peak of carbon emissions and began slowly declining, decreasing by approximately 2.8%. By 2030, the annual carbon emissions were 17.61 million tons of carbon dioxide equivalent, a reduction of 3.9 million tons compared to the simulation’s start in 2017. Compared to the baseline scenario, a total carbon emission reduction of 25.21 million tons of carbon dioxide was achieved from 2017 to 2030. Under the integrated scenario, Yibin can achieve its carbon peak emissions target before 2030 and steadily decrease emissions afterward.

3.4. Pollutant Reduction Ratio under Carbon Peak

An energy consumption analysis of five carbon reduction scenarios determined the changes in total energy consumption, and the proportional reduction of pollutants between 2021 and 2030 was derived. Figure 5 presents the proportion of energy demand before (a) and after (b), implementing a carbon reduction scenario. A significant decrease in the proportion of coal demand and an increase in electricity and natural gas demand is evident. From 2021 to 2030, electricity increased by approximately 98.2%, natural gas by 45%, and bituminous coal decreased by 66.5%. Regarding the proportion of energy demand, there was a decrease of 17% in anthracite coal, while electricity and natural gas increased by 14% and 5%. The source reduction driven by decarbonization measures is significantly evident, simultaneously leading to a decrease in air pollutants corresponding to the reduction levels of different fuels.

As shown in

Table 5. Proportion of emission reductions in different scenarios.

Scenarios	Emission Reduction Individuals	Pollutant Reduction Ratio	Pollutant Type
IR	Industry-Coal	−20.77%	SO2, NOx, CO, VOCs, NH3, TSP, BC, OC
	Industry-Oil	−74.71%
	Industry-Natural Gas	69.10%	NOx, CO, VOCs
IEEI	Industry-Coal	−10%	SO2, NOx, CO, VOCs, NH3, TSP, BC, OC
	Industry-Fuel Oil	−10%
	Industrial-Natural gas	−8%	NOx, CO, VOCs
TR	Minibuses-Gasoline	−12%	NOx, CO, VOCs, TSP, BC, OC
	Minibuses-gasoline	−12%
	Minibuses-Diesel	−2%
	Mini Van-All fuel	−8%
	Trucks-All fuel	−8%
TEEI	Road Vehicle, Waterborne Vessels, Agricultural Machinery	−15%	SO2, NOx, CO, VOCs, TSP, BC, OC
	Construction Machinery	−5%
OSM	Open Straw Burning	−100%	CO, VOCs, NH3, TSP, BC, OC
	Livestock and Poultry Breeding	−20%	NH3, VOCs
	Social Catering	−15%	CO, VOCs

, the types of pollutants affected include SO₂, NO_x, CO, VOCs, NH₃, total suspended particulate (TSP), black carbon (BC), and organic carbon (OC). Under the IR scenario, the industrial sector’s power plants and industrial boilers are successively reduced or increased based on different energy types. Specifically, pollutant reduction from coal combustion is 20.77%, pollutant reduction from oil combustion is 74.71%, and pollutant emissions from natural gas combustion increase by 69.1%. Under the IEEI scenario, the energy consumption of power plants and industrial boilers in the industrial sector is reduced, resulting in a 10% reduction in pollutants from coal and oil combustion, and an 8% reduction in pollutants from natural gas combustion. Under the TR scenario, pollutants generated by gasoline consumption from micro and small passenger cars are reduced by 12%, diesel consumption is reduced by 2%, and pollutants generated by gasoline and diesel consumption from trucks are reduced by 8%. Under the TEEI scenario, pollutants generated by all energy consumption from road vehicles, water vessels, and agricultural machinery are reduced by 15%, while pollutants from construction machinery are reduced by 5%. Under the OSM scenario, pollutants generated by open burning are reduced by 100%, pollutants from livestock farming are reduced by 20%, and pollutants from social catering are reduced by 15%.

3.5. Air Quality Improvement

Five control scenarios and one baseline scenario were established based on the emission reduction ratios brought by the carbon peaking control measures. Simulations were conducted for four typical months (January, April, July, and October) using the baseline scenario, which was based on the locally verified and validated 2020 inventory, with adjustments made for future emission reductions from carbon control measures on industrial sources, mobile sources, and other scattered sources. Thus, a corresponding list of pollutant emissions reflecting the 2030 situation under the five control scenarios in Yibin City was obtained. The evaluation value for CO was calculated as the 90th percentile of daily concentrations during the study period, while the max 8 h average (MDA8) was calculated as the 95th percentile and the arithmetic average was used for other pollutants. Figure 6 shows the evaluation values of six conventional pollutants for the baseline simulation 2021 and the five control simulations in 2030 in Yibin City, and

Table 6. Proportion of change in pollutant concentrations between 2021 and 2030 (%).

Scenarios	PM2.5	PM10	O3	SO2	NO2	CO
IR	−0.90%	−0.60%	−0.20%	−10.30%	−1.60%	−4.20%
IEEI	−0.70%	−0.60%	0.00%	−5.20%	−1.40%	−5.80%
TR	0.00%	0.00%	−0.10%	0.00%	0.00%	0.00%
TEEI	−0.30%	−0.20%	−0.20%	−0.10%	0.10%	−0.30%
OSM	−1.70%	−1.80%	−0.10%	0.10%	−0.30%	−0.10%

lists the corresponding values. Among the six conventional pollutants, the concentrations of CO and SO₂ showed the most significant reductions under the IEEI scenario (5.8%) and the IR scenario (10.3%), respectively. In addition, the IR scenario achieved the maximum decrease in SO₂ concentration. Among the carbon control measures related to mobile source emissions, the TR scenario had a weak impact on improving the city’s air quality, with only a 0.1% reduction in O₃ concentration. The TEEI scenario also had a relatively weak contribution to improving the city’s air quality. The reason is that Yibin City already had relatively high overall electrification of motor vehicles [37], which imposed certain limitations on the number of new energy sources that could be substituted due to scenarios such as TR and TEEI. Among the five carbon control measures, the measures related to industrial source emissions control significantly improved the city’s air quality. The OSM scenario had a relatively significant contribution to reducing particulate matter concentrations but had little impact on reducing the attention of other pollutants. In addition, there was a very weak increase in NO₂ concentration under the TEEI scenario and in SO₂ concentration under the OSM scenario, indicating that these measures had little impact on reducing the concentrations of NO₂ and SO₂, respectively.

3.6. Analysis of Coordination Relationship

The reduction intervals of different air pollutants were obtained under reduced carbon emissions by analyzing the changes in air pollutant concentrations under different carbon control scenarios and the baseline scenario. Based on this, the correlation strength of carbon emissions and air pollutants in the Yibin region was analyzed, as shown in

Table 7. Coordination relationship of carbon emissions with air pollutants.

Pollutants	Emission Reduction Intervals and Averages	Correlation Strength
SO2	−0.1%~10.3% (3.1%)	3
CO	0%~5.8% (2.08%)	3
PM2.5	0.3%~1.7% (0.72%)	2
NO2	−0.1%~0.6% (0.64%)	2
PM10	0%~1.8% (0.64%)	2
O3	0%~0.2% (0.12%)	1

Note: 3 is strong, 2 is moderate, 1 is associated, and 0 is not associated.

. In the Yibin region, carbon emissions had a strong coordination relationship with sulfur dioxide and carbon monoxide, a moderate relationship with particulate matter and nitrogen dioxide, and a weak relationship with ozone. The energy consumption in industrial cities like Yibin primarily relies on fossil fuels, most of which contain sulfur and carbon components [14]. It makes the generation of SO₂ and CO₂ more likely, and CO can be produced through incomplete combustion of carbon-containing fuels. These relationships exhibit notable variations in carbon control measures related to industrial sources. PM_2.5 largely depends on the secondary formation from emission sources, whereas PM₁₀ is influenced by primary emissions [54], resulting in an indirect and less sustainable response to carbon control measures. NO₂ shows more pronounced fluctuations in carbon control measures targeting motor vehicles, indicating certain limitations in its co-emission relationship with carbon. O₃ relies on photochemical reactions involving VOCs and nitrogen oxides (NO_x). Even a reduction in one precursor or an unequal reduction in both can increase O₃ formation [55]. Carbon control measures generally do not consider the relationship with ozone precursors, resulting in a weak synergistic relationship between O₃ and carbon emissions.

By calculating the various components of air pollutants under different carbon control scenarios, the AQCI for each scenario in 2030 was obtained. The ratio of the reduction in the AQCI to the corresponding carbon emissions reduction was then calculated, resulting in the synergy index (the smaller index, the smaller the deviation between carbon emissions reduction and air quality improvement, indicating a stronger correlation). As shown in

Table 8. Synergy index under different scenarios.

Scenarios	AQCI	AQCI Improvement	Carbon Reduction (Kilotons CO2e)	Synergy Index (Carbon Reduction/AQCI Improvement)
IR	2.63	0.043	2429.19	56,492.79
IEEI	2.59	0.085	1900.31	22,356.58
TR	2.67	0.003	9.73	3243.33
TEEI	2.67	0.004	136.11	34,027.50
OSM	2.65	0.022	876.71	39,850.45

, a decrease of one AQCI point in the IR scenario requires a reduction of 56,492.79 thousand metric tons of CO₂e. In the IEEI scenario, decreasing one AQCI point requires reducing 22,356.58 thousand metric tons of CO₂e. In the TR scenario, decreasing one AQCI point requires reducing 3243.33 thousand metric tons of CO₂e. In the TEEI scenario, decreasing one AQCI point requires a reduction of 34,027.5 thousand metric tons of CO₂e. In the OSM scenario, decreasing one AQCI point requires a reduction of 39,850.45 thousand metric tons of CO₂e.

As shown in Figure 7 and Figure 8, the scenarios with the most significant carbon emission reductions, ranked from strongest to weakest, are as follows: IR, IEEI, TEEI, OSM, and TR. The synergistic effects (impact of carbon reduction on air quality) of each scenario, from strongest to weakest, are as follows: TR, IEEI, TEEI, OSM, and IR. Thus, the measures with the best decarbonization effects do not necessarily correspond to significant improvements in air quality. The most notable difference between the IR and TR scenarios is that the effects are almost opposite. The IR scenario, being implemented in industrial cities, restrains the activity level of fossil fuel usage, thereby reducing carbon dioxide emissions at the source. However, as the essence of the IR scenario lies in replacing fossil fuels with new energy sources, the overall emissions from industrial sources remain the same. This limitation results in the inability to reduce air pollution at the endpoint effectively. Due to abundant hydroelectric power reserves in the Sichuan Basin, China, and a high level of electrification of vehicles in southern cities of Sichuan [8], the implementation of a scenario focused on replacing conventional vehicles with new energy sources for transportation improvement in Yibin City does not have significant implementation space [52,53], resulting in a greenhouse gas emission reduction of only 9.73 kilotons. However, the unexpected synergistic effect of the TR scenario exceeds that of any other scenario, indicating that, in industrial cities, the TR scenario and its demonstrated policy direction are the optimal solutions for simultaneously reducing atmospheric pollutants and carbon emissions. It is contingent upon many conventional fossil fuel-powered vehicles in the city. Although the IEEI scenario lacks the advantages of the TR scenario, as industrial cities primarily focus on industrialization for economic benefits, it can still positively impact urban emission reduction as a long-term policy.

4. Conclusions

Under governance measures, Yibin City can achieve its carbon peak target before 2030. Among the six conventional air pollutants, sulfur dioxide and carbon monoxide concentration changes exhibit high sensitivity to carbon emissions. The relationship between nitrogen dioxide and particulate matter concentration changes and carbon emissions depends more on the generation of greenhouse gases. When carbon control measures focus on addressing emissions from vehicle exhaust and banning open burning, the concentration changes of nitrogen dioxide and particulate matter become more sensitive. When the carbon reduction effect is the same, replacing traditional fuel vehicles with new energy vehicles significantly improves air quality, and using source substitution in the industrial sector for carbon control has a weaker impact on air quality. The synergistic effect between the two approaches differs by approximately 17.4 times. Considering the synergistic reduction of atmospheric environment and carbon emissions, governance strategies that do not solely focus on carbon reduction should be advocated.

In future research, a more detailed analysis can be conducted to assess the environmental benefits of urban-wide carbon reduction and the impact on pollutant emissions. By quantitatively analyzing measures that have proven carbon reduction benefits, such as coal-to-electricity conversion for industrial boilers, the substitution of upstream power plants with renewable energy sources, and the use of low-carbon materials, the transitional energy load and environmental costs can be linked, providing more scientific theoretical support for overall environmental improvement in industrial cities.