Quantifying the Synergistic Benefits of Air Quality Improvement and Carbon Emissions Reduction: A Case Study of Henan, China

Abstract

Sustainable development goals link policies addressing air quality and energy efficiency to synergistic benefits for climate mitigation. However, the coal-dominated energy system poses major challenges for Henan Province in mitigating air pollution and climate change. While the government has issued a series of clean air policies and low-carbon energy targets, the simultaneous achievements of low-carbon transition and air quality goals at the sub-national level remain unclear. This study evaluates the effectiveness of policy implementation in Henan’s energy system using an integrated assessment framework that combines emission scenarios, air quality simulations, and health impact assessments. The results indicated that, by 2030, without system-wide energy transformation driven by carbon mitigation policies, air quality improvements in Henan Province will be limited, even under stringent end-of-pipe emission control measures. In contrast, low-carbon policies would yield significant co-benefits for both air quality improvement and climate mitigation. Beyond stringent end-of-pipe controls, the implementation of carbon mitigation policies aligned with China’s enhanced climate targets could further reduce Henan’s average PM_2.5 concentration by up to 4.1 µg/m³. The monetized health co-benefits in Henan Province would reach 4.57 billion RMB under the stringent carbon mitigation scenario. These results highlight the critical role of effectively implementing existing air pollution and energy policies in simultaneously achieving air quality, public health, and carbon mitigation goals in Henan.

1. Introduction

1.1. Motivation

China is the world’s largest emitter of carbon dioxide and one of the countries facing the most severe challenges related to energy consumption and air pollution [1,2,3]. According to the IPCC Sixth Assessment Report (AR6, 2022), human-induced climate change has led to more frequent and intense extreme events, such as river overflows, heavy rainfall, glacier outbursts, and urban flooding [4]. Climate change and air pollution are two pressing challenges for global sustainability, particularly in developing countries [5]. China has implemented a series of policies and action plans to address these issues, aiming to identify better coordinated solutions [6]. Sustainable development remains a global priority of the times. This approach not only meets the needs of contemporary people but also does not compromise the long-term interests of future generations. As emphasized by the Intergovernmental Panel on Climate Change (IPCC) [7], a comprehensive analysis of co-benefits and adverse effects is essential for estimating the actual costs of mitigation policies. With the introduction of a series of policies and action plans, emissions of primary pollutants and the severity of PM_2.5 pollution have both decreased substantially [8,9,10,11,12]. Building on these achievements, China has set more ambitious targets to pursue deep climate mitigation and improved air quality [13]. In 2018, and more recently, the country has updated its Nationally Determined Contribution (NDC) target, stressing the need to reach a carbon emission peak before 2030 and aiming for carbon neutrality before 2060 [14,15].

Since 1991, the overall carbon emissions in Henan Province have been on the rise [16]. In 2018, Henan’s CO₂ emissions reached 484 Mt, accounting for 5.62% of China’s total CO₂ emissions [17]. Additionally, located in east-central China, Henan has long suffered from severe air pollution, which results from the combined effects of high air pollutant emissions and its unique geographical conditions [18,19,20,21]. To understand the local air pollutant emissions reductions and the co-benefits of China’s energy policies, this study conducts a case study on Henan using a newly developed evaluation framework that couples integrated assessment and air quality models. The framework is designed to explore the policies’ effects on emissions reductions in SO₂, PM_2.5, PM₁₀, and other air pollutants as well as their co-benefit in curbing the growth of CO₂ emissions.

1.2. Literature Review

Early studies on the co-benefits and ancillary effects of greenhouse gas (GHG) mitigation policies were mostly conducted in developed countries, particularly the United States and Europe [22,23]. However, pursuing co-benefits is probably more significant in developing countries, as many of them are experiencing rapid development often associated with high fossil fuel consumption [24,25]. The relevant literature has revealed the potential pathways, opportunities, and challenges in China’s transition toward green development. For example, Jiang et al. [26] used the IPAC-AIM technology model, a sub-model of IPAC, to analyze the 1.5 °C scenario in China. They found that, to achieve the deep mitigation target, China’s fossil fuel-based energy system needs to undergo a rapid transition to zero-emission energy, and Carbon Capture and Storage (CCS) technology needs to play an important role. Zhou et al. [27], in their research on an assessment model based on global change, found that China’s CO₂ emissions should peak as early as 2020 or 2025 to meet the 1.5 °C or 2 °C targets, respectively. Tong et al. [28] developed different scenarios based on the global development models and social conditions of the Coupled Model Intercomparison Project Phase 6 (CMIP6). They identified effective sectoral emission control policies to mitigate anthropogenic pollutant emissions in China under different socioeconomic and climate change scenarios, pointing out that proactive air pollution control policies are critical for achieving short-term pollution reductions. Thompson et al.’s [29] systematic approach to evaluating the air quality co-benefits of US carbon policies has provided valuable insights for our research direction.

The homology of air pollutants and GHGs indicates that air pollutants and GHGs should be addressed synergistically rather than in isolation. He et al. [30] developed an integrated approach to assess the co-benefits of two distinct sets of China’s energy policies by combining an energy projection model, an emission estimation model, an air quality simulation model, and a health benefit evaluation model. Cheng et al. [31] investigated pathways for air quality improvement in China under current and future mitigation efforts towards carbon neutrality, highlighting the significance of ambitious climate mitigation strategies for China’s post-2030 air quality enhancement. These combined positive effects are referred to as co-benefits. Mao et al. [32] examined CO₂ emission reduction and the associated local air pollutant co-benefits in China’s transportation sector by focusing on policy instruments such as carbon taxes, energy taxes, fuel taxes, clean energy vehicle subsidies, and fare reductions. Geng et al. [33] evaluated the co-benefits in Shenyang’s public transportation sector resulting from the introduction of vehicles meeting new emission standards and alternative fuel vehicles in bus and taxi fleets. Li et al. [34] revealed that the industrial and power sectors will be the key focus areas for air pollution control and that the increased air pollution control costs arising from stricter control strategies can be alleviate by low-carbon policies in the long term. In addition, studies [35,36,37,38,39,40,41] have evaluated the co-benefits of climate change mitigation for air quality and human health at the provincial or municipal level, particularly in developed eastern regions (e.g., Beijing, Shanghai, Tianjin, and Shandong). Air pollutant emissions also exert a negative impact on industries such as tourism and construction. Previous literature [42,43] has analyzed the influence of air pollution on urban tourism development, finding that the PM_2.5 index has a negative effect on domestic and international tourists’ choice of urban tourism destinations and has become one of the key reference factors for tourists when selecting travel locations.

For China, many existing studies focus either on the national level, specific industries, or economically developed regions, providing valuable insights for the country’s mitigation strategies. However, research on relatively underdeveloped regions remains relatively scarce and superficial. Furthermore, while most studies emphasize co-benefits related to emission reduction or health improvement, few have explored those associated with economic effects. Consequently, this study aims to address this research gap by forecasting Henan’s future CO₂ and air pollutant emissions. Using an integrated modeling framework, it evaluates both the emissions reduction co-benefits and the economic cost co-benefits of CO₂ mitigation policies on air pollutants.

The core source of carbon dioxide emissions in Henan Province is coal combustion, which contributes approximately 80% of the province’s total emissions. Industrial coal combustion is the primary source of sulfur dioxide emissions [44], while motor vehicles are the main source of nitrogen oxide emissions. The sources of PM_2.5 exhibit diverse characteristics and significant seasonal variations. From a provincial perspective, the primary sources include coal combustion, motor vehicles, industrial processes, and dust. Henan Province has formed a power energy structure characterized by ‘renewable energy and thermal power receiving equal emphasis’: renewable energy accounts for 51.4% of the total installed capacity, with thermal power (predominantly coal-fired power generation) accounting for 48.6%. In terms of power generation volume, however, renewable energy contributes 31.0% of the total, whereas coal-fired power generation remains the core pillar, accounting for approximately 68–69% [45].

1.3. Organization of This Paper

In this study, regarding Greenhouse Gases (GHGs), we focus primarily on carbon dioxide emissions and define the accounting boundary as encompassing Scope-1 emissions (direct emissions occurring within the province) and Scope-2 emissions (indirect emissions from energy imports generated outside the province). For air pollution, we concentrate on PM_2.5, as it is widely recognized as the primary culprit behind severe polluted weather in Chinese cities [46]. We began with an emission inventory analysis to examine the contribution of various sources to CO₂ emissions and PM_2.5 pollution in the baseline year 2017, for which the most recent emissions inventories are available. Specifically, this study proceeds as follows: (1) we assess the impacts of the immediate implementation of measures in seven distinct sectors (industry, agriculture, construction, transportation, services, residential life, electricity, and heating) in Henan Province on energy consumption, carbon emissions, and air quality as well as the impacts when all measures are implemented simultaneously across these seven sectors; (2) on this basis, we identify the critical areas for synergistic governance in Henan and develop a Long-range Energy Alternatives Planning (LEAP) model to predict energy demand, CO₂ emissions, and air pollutant emissions by 2030 under different governance scenarios; (3) we quantitatively evaluate the emissions reduction effects of different policies through a synergistic emissions reduction benefit analysis; (4) we apply the WRF-CMAQ (Weather Research and Forecast-Community Multiscale Air Quality) modeling system to examine the impacts of two-tier synergistic governance on urban air quality; (5) we assess residents’ health benefits under different scenarios using PRM models, and furthermore, we propose several priority areas and measures for implementing synergistic governance, along with important policy implications for Henan.

2. Materials and Methods

2.1. Study Domain

Henan Province is located in east-central China between longitudes 110°21′ E and 116°39′ E and latitudes 31°23′ N and 36°22′ N. In 2020, it achieved a Gross Domestic Product (GDP) of 5.5 trillion RMB, ranking fifth nationwide [47]. The province comprises 18 prefecture-level cities, covering 155 districts and counties. Its permanent resident population stands at 99 million, an increase of 5.3 million compared with 2010, representing a growth rate of 5.68% [48]. Figure 1 provides a brief overview of the region.

Figure 1. A brief introduction of Henan. (a) Geographical location of Henan in China, in the middle and lower reaches of the Yellow River. (b) Basic socioeconomic development situation of Henan. (c) Annual (CO₂) emissions and air quality, measured by annual average PM_2.5 concentrations, of Henan.

2.2. Overview of the Integrated Modeling Framework

Here, we project future air quality attainment in Henan through 2030, assess the CO₂ reduction co-benefits associated with meeting ambient PM_2.5 standards, and evaluate the health and climate impacts of air attainment-oriented energy policies. We integrate a comprehensive evaluation model framework that employs the Low Emissions Analysis Platform (LEAP) model to simulate energy consumption as well as CO₂ and pollutant emissions under different future scenarios, based on baseline year data, thereby generating target year datasets. Subsequently, we apply the WRF-CMAQ (Weather Research and Forecasting-Community Multiscale Air Quality) model to assess air quality and CO₂ concentrations in the target year. Additionally, we utilize an Elastic Carbon Emissions Reduction (ECER) model to analyze the synergistic reduction benefits of CO₂ and air pollutants and employ a Poisson Regression Model (PRM) for health impact analysis to evaluate the effects of ambient and household PM_2.5 exposure on public health in Henan Province. This integrated approach captures the nonlinear relationships between energy, emissions, concentrations, and health, enabling us to assess the co-benefits of air quality attainment for protecting public health and mitigating CO₂ within an internally consistent framework. The overall framework is shown in Figure 2.

Figure 2. Integrated modeling framework for studying mitigation co-benefits.

The comprehensive assessment model framework comprises the Low Emissions Analysis Platform (LEAP) model, the WRF-CMAQ (Weather Research and Forecasting-Community Multiscale Air Quality) air quality model, the Elastic Carbon Emissions Reduction (ECER) model, and the Poisson Regression Model (PRM). The LEAP model projects energy consumption, CO₂ emissions, and other atmospheric pollutant emissions for the target year. The WRF-CMAQ air quality model primarily simulates the atmospheric environmental quality in the target year: initially, the model’s credibility is verified by comparing observed and simulated PM_2.5 concentrations in the baseline year; subsequently, PM_2.5 concentration projections for the target year are generated. Afterwards, we use the ECER model to analyze the synergistic reduction benefits of CO₂ and air pollutants under different policy scenarios. Finally, the PRM is applied for health impact analysis to assess the effects of ambient and household PM_2.5 exposure on public health in Henan Province as well as to quantify the associated economic benefits.

2.3. LEAP Model (CO₂ and Air Pollutant Emissions Prediction and Emissions Reduction Analysis)

The Low Emissions Analysis Platform (LEAP) (2020.1.40 version) is an integrated, scenario-based modelling tool that allows the development of economic, energy, and environmental analysis [49]. Developed by the Stockholm Environmental Institute and the University of Boston, it aims to contribute to climate change mitigation [50]. For the construction of a model, at least one baseline year and one or more future years must be considered. According to this information, the projection of the emissions can be determined by means of a growth rate of the activity to be analyzed [51].

Energy-related activities account for over 95% of Henan Province’s CO₂ emissions and represent the largest source of air pollutant emissions in the province. The LEAP model has been widely applied to simulate future energy production and consumption as well as the associated CO₂ emissions. However, due to inconsistencies in accounting systems and the unavailability of pollutant emissions factors, this model is rarely used in air pollutant emissions research. In this study, based on a comparative analysis of CO₂ and air pollutant inventory compilation guidelines and the localized emissions factors, we developed a customized LEAP model to track energy-related emissions of both CO₂ and major air pollutants (SO₂, NO_X, VOC_S, CO, NH₃, PM_2.5, and PM₁₀) and examine the synergic effect between their emissions reductions under alternative policy scenarios.

Under the bottom-up framework, energy demand was calculated based on Equation (1):

$$E{C}_{j,l}={\displaystyle \sum _j{\displaystyle \sum _k{\displaystyle \sum _{l}A{C}_{j,k,l}\times E{I}_{j,k,l}}}}$$

(1)

where EC refers to the final total energy consumption, AC is the activity level (for example, product output, value added, etc.), EI is the final energy intensity, and subscript j,k,l represents departments, energy consumption terminal equipment, and energy varieties, respectively.

The CO₂ emissions were calculated using Equation (2):

$$E_{C{O}_2}={\displaystyle \sum _j{\displaystyle \sum _{l}E{F}_{j,l}}\times E{C}_{j,l}}$$

(2)

where E_CO2 refers to the carbon dioxide emissions from the j sector, l refers to energy varieties, and EF_j,l refers to the carbon dioxide emissions factor generated by l energy consumption in sector j.

Air pollutant emissions factors are closely related to usage and fuel activity data. The air pollutant emissions and emissions factors were calculated using the Equations in Table 1.

Table 1. Calculation formula for air pollutant emissions.

Formula	Symbol	Mean
$E_i={\displaystyle \sum _k{\displaystyle \sum _m{A}_k\times E{F}_{i,k,m}\times (1-\eta )}}$	Ei	the total amount of pollutants discharged
	i	types of pollutants
	k	fuel or product type
	m	technology type
	Ak	activity level data (fuel consumption or product production)
	EFi,k,m	emissions factor
	$\eta$	removal efficiency of control technology
$\begin{array}{l}E{F}_{S{O}_2}=2\times S\times (1-sr)\\ E{F}_{PM}=Aar\times (1-ar)\times f_{PM}\end{array}$	S	average sulfur content received from coal combustion
	sr	the proportion of sulfur into bottom ash
	Aar	the average coal ash received
	ar	the proportion of ash into bottom ash
	fPM	the proportion of particulate matter (such as PM2.5 and PM10) in the total particulate matter produced by the emissions source in a certain particle size range

Note: Other pollutant emissions factors come from previous studies [52,53,54]. S and Aar in coal are calculated according to the data reported by the enterprise survey, and sr, ar, f_PM refers to the proportion provided in the technical guide for the preparation of national air pollutant emissions source inventory.

In this study, seven air pollutants, SO₂, NO_X, PM_2.5, PM₁₀, VOC_S, NH₃, and CO, are selected for analysis, and the emissions are calculated by the formulas in Table 1. In this study, PM_2.5, PM₁₀, and SO₂ are calculated by a material balance algorithm, and other pollutants are calculated by an emissions factor method.

Using 2017 as the baseline year, we compared the CO₂ and air pollutant emissions results calculated by our developed LEAP model with the official emissions inventory of Henan Province, as shown in Supplementary Table S1. The deviations between the LEAP-calculated results and the official inventory data for PM_2.5 and PM₁₀ were all less than 4%, while those for CO₂ were less than 8%. These results verify the accuracy and reliability of our developed model in emissions accounting.

2.4. Analysis of Synergistic Effect of Emissions Reduction

Assuming p represents a climate policy, the formulas presented [55] in Table 2 quantify three key impacts of policy p: its effect on carbon dioxide (CO₂) emissions reduction, its effect on PM_2.5 emissions reduction, and the synergistic effect between CO₂ and PM_2.5 emissions reductions.

Table 2. The synergistic effect between carbon dioxide and PM_2.5 emissions.

Formula	Symbol	Mean
$\Delta E_{C{O}_2}^{\mathrm{p}}={\displaystyle \sum _j{\displaystyle \sum _l({\gamma }_{j,l}^p\times E{C}_l}})\times E{F}_{j,l}$	p	climate policy
	γpj,l	represents its ability to reduce l energy consumption in the j sector
	ΔECO2	CO2 brought by end-of-pipe governance and carbon reduction measures emissions reduction
$\Delta E_{P{M}_{2.5}}^{\mathrm{p}}={\displaystyle \sum _j{\displaystyle \sum _l({\delta }_{j,l}^p\times }}{A}_{j,l}^{P{M}_{2.5}})\times E{F}_{j,l}^{P{M}_{2.5}}\times (1-{\eta }_{j,l}^{P{M}_{2.5}})$	ΔEPM2.5	PM2.5 brought by end-of-pipe governance and carbon reduction measures emissions reduction
	δpj,l	represents the ability to reduce l energy consumption in the j sector that causes PM2.5 emissions
$S={\displaystyle \frac{\Delta E_{C{O}_2}/E_{C{O}_2}}{\Delta E_{P{M}_{2.5}}/E_{P{M}_{2.5}}}}$	S	elastic emissions reduction coefficient
	ECO2	CO2 before end-of-pipe governance and carbon reduction measures are taken emissions
	EPM2.5	PM2.5 before end-of-pipe governance and carbon reduction measures are taken emissions

Note: The elasticity coefficients of emissions reduction have two attributes: direction and size. If the p policy not only abates CO₂ emissions but also reduces PM_2.5 emissions, S is a positive number, and under this case, the larger the value of S, the stronger is the co-benefit of climate policy p on the PM_2.5 emissions reduction.

To more intuitively assess the degree of emissions reduction synergy among various measures, this study further employs the synergistic control effect coordinate system method to analyze the synergistic control effects of different measures on carbon dioxide and air pollutants of in Henan Province, as shown in Figure 3 [55].

Figure 3. Schematic diagram of benefit coordinate system of co-control of carbon dioxide and air pollutants.

Each point in the two-dimensional coordinate system corresponds to a specific end-of-pipe governance and carbon reduction measure. The vertical and horizontal coordinates of the point intuitively reflect the emissions reduction effect of the measure on CO₂ and other air pollutants, respectively. For example, the first quadrant indicates that this measure can reduce CO₂ and air pollutants at the same time. Conversely, the negative synergistic effect (reducing PM_2.5 emission but increasing CO₂, or reducing PM_2.5 emission but increasing CO₂, or increasing both PM_2.5 and CO₂ emission) is shown in the other quadrants.

2.5. Air Quality Simulation

The WRF-CMAQ simulation platform is used to predict PM_2.5 concentrations under different governance scenarios. We adopt a three-layer nested region to represent the simulated region of the WRF-CMAQ platform, where the resolution of the inner region is higher than that of the outer region. The simulated area adopts a three-layer nested grid scheme: the first layer is 36 km in China and the surrounding area, the second layer is 12 km in the central and eastern regions of China, and the third layer is in Henan Province and the surrounding area, with a grid spacing of 4 km. Vertically, 43 air pressure layers are set vertically. The input meteorological data is derived from the National Centers for Environmental Prediction (NCEP), with a temporal resolution of 6 h and a spatial resolution of 1° × 1° FNL global analysis data. The terrain data adopts 30″ spatial resolution data from MODIS (Moderate Resolution Imaging Spectroradiometer)-equipped satellites. Detailed parameters are shown in Table 3.

Table 3. Specific parameter settings for WRF simulation.

Parameter Name	Set Standards
Vertical layering	43
Horizontal resolution	4 km, 12 km, 36 km
Microphysics scheme	Thompson graupel scheme
Longwave radiation scheme	RRTM scheme
Shortwave radiation scheme	Goddard shortwave scheme
Near subsurface plan	Monin–Obukhov scheme
Land surface process plan	Unified Noah land-surface model
Boundary layer scheme	YSU scheme
Cumulus convection scheme	Grell–Devenyi ensemble scheme
Four-dimensional assimilation	Assimilation of the first layer grid

The chemical mechanism of the Community Multiscale Air Quality (CMAQ) is the saprc99 (statewide air pollution research center mechanism 99) gas phase chemical reaction mechanism and Aero6 aerosol reaction mechanism. In order to reflect the changes in meteorological conditions in different seasons of the year, we selected four representative months (this study adopts the meteorological division standard for seasonal categorization: specifically, March to May is defined as spring, June to August is defined as summer, September to November is defined as autumn, and December to February of the subsequent year is defined as winter). Correspondingly, January was chosen as the representative month for winter, April was chosen for spring, July was chosen for summer, and October was chosen for autumn [21]. Based on these meteorological and emission input data, the CMAQ model was employed to simulate the physical and chemical processes of various pollutants in the atmosphere. The configuration of model parameters refers to previous relevant studies [56].

In this study, we selected the 2017 annual average daily observation data of PM_2.5 from 100 national-controlled automatic air quality monitoring stations distributed across 18 cities in Henan Province. We then compared these data with the daily average PM_2.5 concentration from the simulation results to evaluate the correlation between the two and verify the reliability of the simulation outcomes. The key indicators used for comparison and verification in this study include Normalized Mean Bias (NMB), Normalized Mean Error (NME), Mean Fractional Bias (MFB), Mean Fractional Error (MFE), and correlation coefficient (R) [57].

The annual average concentration of PM_2.5 in Henan Province in 2017 simulated by the CMAQ model in this study is 47.46 µg/m³, while the observed average annual average concentration is 64.15 µg/m³. The Normalized Mean Bias (NMB), Normalized Mean Error (NME), normalized Mean Fractional Bias (MFB), and normalized Mean Fractional Error (MFE) were calculated according to the method provided by the U.S. Environmental Protection Agency [58,59,60,61]. Each index meets the accuracy evaluation standard of PM_2.5 simulation given in the literature, indicating that the simulation results are in good agreement with the observation results and the model is highly reliable.

This confirms that the emissions factors selected in this study and the corresponding base-year emissions can accurately reflect the actual situation in Henan Province, and the emissions inventory estimation results are reliable. The scatter plot of simulated values versus monitored values is shown in Figure 4.

Figure 4. Scatter chart of annual PM_2.5 analog and observed values in Henan Province in 2017.

The spatial distribution of the simulated annual average PM_2.5 concentration in Henan Province in 2017 is shown in Figure 5. Owing to the dense distribution of industrial cities in central and northern Henan Province, the region exhibits higher energy consumption and emissions intensity compared to other prefecture-level cities in the province, leading to relatively higher PM_2.5 concentrations in these areas.

Figure 5. Annual mean concentration distribution of simulated PM_2.5 in 2017.

2.6. Health Risk and Economic Benefit Assessment

According to the relevant research on air pollutants and health risks, Huang et al. conducted an analysis of the health benefits of PM_2.5 pollution control in the Beijing–Tianjin–Hebei region, and it was found that the health benefits are equivalent to 4.68% of the regional GDP [60,62,63]. In this study, the Poisson Regression Model (PRM) was used to evaluate the impact of air pollutants and improvement of treatment measures on residents’ health. The health risk assessment is calculated using Equation (3):

$$E=\exp [\beta \times (C-C_0)]\times E_0$$

(3)

where β is the PM_2.5 exposure response relationship coefficient with a health endpoint, C is the actual concentration of PM_2.5 per mesh, C₀ is the baseline reference concentration of PM_2.5, and E and E₀, respectively, correspond to the baseline mortality and incidence rate of the population health terminal under the level of C and C₀. In this study, C₀ is the standard concentration value of PM_2.5 recommended by the World Health Organization (WHO) in 2006 (10 µg m⁻³) [64].

Population exposure to health hazards caused by PM_2.5 is calculated using Equation (4):

$$\Delta E=P\times [1-{\displaystyle \frac{1}{\exp [\beta (C-C_0)]}}]\times E$$

(4)

where ΔE is the PM_2.5 health risk value of population exposure, and P is the number of people exposed in each grid; the population of Henan Province in the target year 2030 refers to the population development plan of Henan Province.

Based on the research findings of scholars at home and abroad regarding the health risks of air pollution on human populations as well as data from China’s Health and Family Planning Statistical Yearbook, six health endpoints were selected for analysis: all-cause mortality, respiratory diseases, cardiovascular diseases, acute bronchitis, chronic bronchitis, and asthma. Baseline mortality or morbidity and exposure response relationship coefficients for six health endpoints β values are shown in Supplementary Table S2.

The Willingness-To-Pay (WTP) method was employed to assess the total economic loss of health in the population caused by PM_2.5 exposure due to the lack of localized Value of a Statistical Life (VSL) results (refer to the results of the study of Beijing by Huang et al. [65]). Unit health economic losses caused by hospitalization and onset health endpoints refer to the results of the study by Huang Desheng et al. [60] in the Beijing–Tianjin–Hebei region, considering the per capita disposable income and GDP growth rate of residents in Henan Province; the value of life and the unit health economic loss of each health point are revised.

The economic benefit assessment is calculated using Equation (5):

$$H{C}^t={\displaystyle \frac{H{C}_{BeiJing}^{2010}\times G^t}{G_{BeiJing}^{2010}}}$$

(5)

where HC (yuan/case) is the unit health economic value of each health endpoint in Henan Province, G is the per capita disposable income of Henan Province, and t is the study year.

2.7. Key Parameters and Scenario Settings

According to the forecast results of domestic and foreign economic development trends, after the impact of the COVID-19 has gradually declined, the economic development of Henan Province will still maintain a rapid growth rate during the 14th five-year plan period. GDP is calculated at constant prices in 2017. Detailed information about the scenario settings is summarized in Table 4. The main parameters of urban socioeconomic development are listed in Supplementary Table S3.

Table 4. Setting of key parameters of social and economic development in Henan Province.

Key Parameter		2020	2025	2030
Demographic parameters	Total population/billion	0.115	0.11	0.111
	Urbanization rate/%	54.2	61	62.7
Economic parameters	GDP/trillion	5.04	6.76	8.63
	GDP growth rate/%	1.3	6	5
	Industrial structure	9.7:41.6:48.7	7.3:39.7:53.0	6.5:36.9:56.6

Based on three main emissions reduction pathways (Figure 6), we establish a two-dimensional scenario framework consisting of four scenarios to comprehensively investigate the impacts of carbon mitigation policies and air pollution policies in Henan Province, with a focus on energy-related and industrial process-related air pollutant emissions and induced disease burden. This scenario framework is used to analyze the whole-system costs, benefits, and co-benefits of mitigation policies, with the aim of identifying cost-effective green transition pathways at the provincial level. The first dimension is carbon mitigation scenarios, including three scenarios: a Business-As-Usual (BAU) scenario, an Energy-Saving and Low-Carbon (ESLC) scenario, and a Reinforcement Scenario (RS). The BAU scenario describes China’s current climate governance path. The ESLC scenario describes the full achievement of China’s initial nationally determined contribution target, which aims to reduce CO₂ emissions intensity by 60–65% by 2030 compared to 2005 and to reduce total coal consumption in Henan Province to less than 60% by 2025 [66]. The RS mainly describes that the penetration rate of clean energy, such as electricity, heat, and natural gas, is further improved compared with the energy-saving and low-carbon scenarios. A more detailed introduction on sectoral emissions reduction can be found in Supplementary Table S4.

Figure 6. Three main emissions reduction paths.

The second dimension is the levels of end-of-pipe air pollution control measures. We consider two different scenarios, i.e., a reference case with emissions control levels fixed at the 2017 level (REF) and level of controls that realize Maximum Feasible Reduction (MFR). Based on these two dimensions, we draw up a total of four scenarios to investigate the effects of end-of-pipe control and the air quality co-benefits of carbon mitigation (Table 5).

Table 5. Scenario settings.

Scenarios		Carbon Mitigation Policies	Air Pollution Policies
Carbon mitigation policies	REF_BAU	Current climate governance path	End-of pipe control strategies fixed at 2017 level
	REF_ESLC	Carbon emissions reduction in 2017–2030 in line with China’s NDC targets
	REF_RS	Carbon emissions peak by 2030
Air pollution policies (end-of-pipe control)	MFR_ESLC		Adopt stronger end controls to achieve the maximum feasible emissions reductions
	MFR_RS

2.8. Data Resources

The parameters used to build the LEAP model are adjusted according to the input–output table of China and Henan in 2017, which is taken from the China Statistical Yearbook [67] and Henan Statistical Yearbook [68]. The energy balance table of Henan Province comes from China’s Energy Statistical Yearbook [69]. The emissions of carbon dioxide and air pollutants in the emissions inventory or revised base year are calculated based on the data of the second pollution census and research data.

3. Results and Discussion

3.1. Evolution of Energy Consumption Under Climate Change Mitigation Scenarios

The implementation of stronger carbon emissions reduction policies will largely reshape Henan Province’s energy system after 2020 (Figure 7a). To achieve the strategic goal of low-carbon development, it is necessary to integrate and promote the development of energy conservation and efficiency improvement and “Re-electrification” in the terminal sector. Enhancing the electrification rate can facilitate energy conservation and efficiency improvement in the terminal sector (Figure 7b) while generating carbon reduction effects and an amplification effect. According to the prediction results of the LEAP model, under the RS, Henan Province’s total social energy consumption will peak in 2025 at 233 Mtce and then drop to 209 Mtce in 2030, representing a 28.9% decrease compared to the BAU scenario (Figure 8a). Under the BAU scenario (Figure 8b), terminal energy consumption will increase from 169 Mtce in 2017 to 236 Mtce in 2030. Under the ESLC scenario, growth will be moderate, with terminal energy consumption rising from 169 Mtce in 2017 to 209 Mtce in 2030. Under the RS, coal consumption in 2030 will be 49.6% lower than in 2017, while the demand for natural gas and renewable energy will increase significantly. These two energy sources will dominate terminal energy demand (accounting for approximately 62.8%), with their consumption rising from 11.9 Mtce and 55.7 Mtce in 2017 to 16.1 Mtce and 99.6 Mtce in 2030, respectively.

Figure 7. Energy consumption in Henan Province under three different scenarios from 2017 to 2030: (a) overall energy consumption in Henan Province; (b) electrification rate of key energy consuming sectors for 2030.

Figure 8. Energy structure in Henan Province from 2017 to 2030: (a) primary energy consumption; (b) terminal energy consumption; (c) energy consumption by sector.

From the sectoral perspective (Figure 8c), the low-carbon transition toward carbon neutrality in the power sector will significantly reduce the share of fossil fuels in Henan’s primary energy mix, which is consistent with previous studies focusing on China [70,71]. Under the BAU scenario, the energy consumption of Henan Province’s power sector will increase to 96.8 Mtce in 2025 before decreasing to 84.7 Mtce in 2030. Under the ESLC scenario, the power sector’s energy consumption will peak at 91.5 Mtce in 2025, with coal, oil, and natural gas accounting for 90.5%, 0.15%, and 1.87% of the total, respectively. Under the RS, the power sector’s energy consumption will peak earlier, in 2021, at 91.4 Mtce; among this, coal accounts for 90.9%, while oil and natural gas account for 0.15% and 2.63%, respectively.

3.2. Carbon Dioxide and Air Pollutant Emissions

Due to the complex interaction between the energy structure and regional air pollution in Henan Province, we employed emissions inventory analysis to detect the contribution of electricity imports and local sources to Henan CO₂ emissions. In the 2017 base year, Henan Province emitted 575.2 Mt CO₂, and the average PM_2.5 concentration was 67.6 µg m⁻³. Anthropogenic emissions of primary PM_2.5, PM₁₀, SO₂, NO_X, NH₃, CO, and VOC_S in Henan Province reached 268.6 kt, 433.7 kt, 381.1 kt, 889.1 kt, 32.7 kt, 3417.7 kt, and 425.3 kt, respectively. To ensure consistency, the emissions estimated in this study were compared with those from other studies focusing on the Beijing–Tianjin–Hebei (BTH) region and its surrounding areas (Table S5). Emissions of primary PM_2.5, PM₁₀, SO₂, and NO_X (agricultural emissions and solvent use are not considered in this paper, except for NH₃ and VOC_S) are reasonably consistent with those in the MEIC database but higher than those in the MEE inventory. The end-of-pipe treatment efficiency of industrial enterprises in this study was determined based on corporate pollution control technologies and operational hours. Compared with the MEE results, this approach may be less conducive to emissions reduction.

Figure 9 shows the prefectural emissions in 2017. As expected, the total emissions of air pollutants and CO₂ vary widely across the cities due to differences in demographic status and economic development level, in addition to urban and industrial structures. The cities with the most serious emissions are mainly Anyang, Jiaozuo, and Zhengzhou.

Figure 9. Prefectural emissions in baseline 2017 scenario. (a) Geographical location of the cities, (b) CO₂, (c) NH₃, (d) primary PM_2.5, (e) primary PM₁₀, (f) SO₂, (g) NO_X, (h) CO, (i) VOC_S.

Combined with the analysis of carbon dioxide and air pollutant emissions inventories, indirect emissions from imported electricity account for 8.4% of the total carbon dioxide emissions in Henan Province. Additionally, the contribution of interregional air pollutant transport to PM_2.5 concentration in Henan Province varies by season [72], with an average contribution rate of 36.2% (Figure 10). It can be seen that the transmission outside the province has a significant impact on the air pollution in Henan Province.

Figure 10. Contribution of local and external sources of carbon dioxide emissions and PM_2.5 pollution in Henan Province in 2017.

Under the RS, strict carbon emissions controls will lead to a large and earlier reduction in carbon dioxide emissions (Figure 11a). Under the BAU scenario, Henan’s carbon emissions will peak in 2028, while under the ESLC scenario, they will peak earlier, around 2025. Under the BAU scenario, compared with 2017, carbon emissions in 2025 and 2030 will increase by about 71.66 Mt (+12.51%) and 72.02 Mt (+12.53%), respectively. Under the ESLC scenario, Henan’s carbon emissions will peak around 2025, and carbon dioxide emissions will reach 593 Mt. Under the RS, in 2025, carbon emissions will be significantly reduced by about 33.71 Mt (−5.89%) from 2017 levels and by 198.11 Mt (−34.80%) in 2030. As shown in (Figure 11b), the main air pollutants come from industrial sectors. The implementation of deep carbon emissions reduction policies and end-of-pipe control measures can reduce pollutant emissions by varying degrees (Figure 11c). In 2030, under the MFR_ESLC scenario, emissions of SO₂, PM₁₀, PM_2.5, and NO_X will decrease by 147.46 kt (−54.13%), 164.03 kt (−48.64%), 100.37 kt (−46.48%), and 158.23 kt (−16.13%) compared with the REF_ESLC scenario. Compared with efficient end-of-pipe control, the reinforcement scenario achieves more significant reductions in NO_X, CO, VOC_S, and NH₃ emissions, mainly due to the substitution of coal with fuel oil and biomass. In road traffic, increasing the proportion of electric vehicles and clean energy vehicles can reduce NO_X emissions more than strengthening end-of-pipe control measures. Under the scenario’s carbon constraints, the REF_RS scenario would help reduce emissions of all air pollutants in the next 10 years. Meanwhile, the emissions of SO₂, PM₁₀, PM_2.5, NO_X, CO, VOC_S, and NH₃ would be reduced by 163.82 kt (−42.66%), 163.14 kt (−35.47%), 93.52 kt (−32.70%), 322.15 kt (−31.68%), 944.62 kt (−24.96%), 124.10 kt (−26.40%), and 12.26 kt (−31.63%) in 2030 compared with the 2020 level.

Figure 11. Prediction of carbon dioxide and air pollutant emissions under three different scenarios in Henan Province: (a) carbon dioxide emissions; (b) emissions of air pollutants from major energy consuming sectors in 2017; (c) sector air pollutant emissions under different scenarios; (d) carbon dioxide and air pollutant emissions in 2025 and 2030.

Different driving forces of air pollutant emissions and intensive policies lead to differences in future emissions reduction rates. Enhanced end-of-pipe control measures could significantly reduce PM_2.5, PM₁₀, SO₂, and VOC_S emissions from the industrial sector by 30–80% by 2030. However, according to the emissions inventory of Henan Province (Figure 11c), the significant increase in NOx emissions mainly stems from vehicle exhaust emissions. From 2017 to 2030, the growth in power demand driven by economic development in Henan Province will result in a slight increase in SO₂ emissions: under the REF_BAU scenario, emissions will increase by 80.23 kt (21.11%).

Under the reinforcement scenario, the application of efficient end-of-pipe control measures (Table 6) achieves a more significant effect on the emissions reduction in air pollutants (Figure 11d). In 2025, under the MFR_RS scenario, the emissions of SO₂, NO_X, CO, PM₁₀, PM_2.5, VOC_S, and NH₃ would be reduced by 172.99 kt (−57.86%), 134.02 kt (−15.33%), 634.58 kt (−16.76%), 238.71 kt (−56.92%), 152.15 kt (−57.07%), 96.16 kt (−21.06%), and 8.84 kt (−24.66%). Meanwhile, in 2030, the emissions of SO₂, NO_X, CO, PM₁₀, PM_2.5, VOC_S, and NH₃ would be reduced by 204.67 kt (−68.46%), 289.77 kt (−33.15%), 1062.60 kt (−28.07%), 273.42 kt (−65.19%), 168.84 kt (−63.33%), 138.02 kt (−30.23%), and 11.05 kt (−30.83%) compared with the 2020 level. The emerging clean air measures can bring significant common benefits of carbon dioxide and air pollutant emissions reduction by affecting the local energy system.

Table 6. End-of-pipe control measures.

Technologies or Measures of Air Pollution Prevention	Reduction Ratio								Reference
	SO2	NOX	CO	PM10	PM2.5	VOCS	NH3	CO2
Membrane-based absorption method	95%	--	--	--	--	--	--	--	[73]
Wet flue gas desulfurization technology	95–99%	--	--	--	--	--	--	--	[74]
Cu-SAPO-34/SiC catalytic membrane technology	--	90–92%	--	99.99%	99.99%	--	--	--	[75]
Fe3O4NPs/CeO2 NPs/GO catalytic oxidation	--	92–96%	--	--	--	--	--	--	[76]
Two-stage electrostatic precipitators	--	--	--	--	>95%	--	--	--	[77]
Manganese-based catalyst synergetic plasma	--	--	--	--	--	56–96%	--	--	[78]
Ammonia removal	--	--	--	--	--	--	--	>90%	[79]
Dust collector technology	--	--	--	95%	90%	--	>80%	--	[80]
CeO2 and load Pd	--	>50%	>50%	--	--	>50%	--	--	[81]

3.3. Co-Benefit Between CO₂ and Air Pollutant Emissions Reduction

Figure 12 shows that carbon dioxide emissions reduction will have a significant synergistic effect on reducing local energy-related air pollutant emissions. In 2030, under the REF_ESLC scenario, the elasticity of CO₂ emissions reductions to PM_2.5, PM₁₀, VOC_S, SO₂, NO_X, CO, and NH₃ emissions reductions (which is used to measure the synergic effect between their emissions reductions) is 0.60, 0.53, 0.92, 0.41, −1.13, 1.58, and 1.92, respectively. Under this energy-saving and low-carbon scenario, there is no synergy between CO₂ and NO_X. This synergistic effect on VOC_S and CO will be very strong, while the emissions reduction effect on SO₂ and NH₃ is not obvious, but there is basically no emissions reduction effect on NO_X at the same time. Under the REF_RS scenario, there is good synergy: the elasticity of CO₂ emissions reductions to PM_2.5, PM₁₀, VOC_S, SO₂, NO_X, CO, and NH₃ emissions reductions is 1.24, 1.12, 1.89, 0.83, 1.61, 2.08, and 1.85. Overall, under the MFR_RS scenario, the emissions reduction rate of carbon dioxide and other air pollutants is the largest: respectively, 70.43%, 63.62%, 66.35%, 25.09%, 75.26%, 34.28%, 20.36%, and 24.27%. By industry, industrial processes have the strongest synergistic effect with CO₂ emissions reduction, followed by heat supply. However, CO₂ reduction in local power generation may actually increase emissions of certain pollutants; for example, replacing coal with natural gas and fuel oil in this sector increases nitrogen oxide (NOx) emissions (note: natural gas/fuel oil combustion typically reduces CO₂ compared to coal; this part may require verification of technical logic). From a policy perspective, developing urban public transport, optimizing freight structures, switching freight vehicles from oil to natural gas or other clean fuels, and decarbonizing boiler fuels yield the most significant synergistic effects for reducing both CO₂ and air pollutant emissions.

Figure 12. Synergic control of emissions reduction benefits.

3.4. Air Quality in Henan Province Under Four Alternative Scenarios

Henan’s future air quality, represented by the concentration of PM_2.5 (currently the primary pollutant causing most Chinese cities to fail to meet national ambient air quality standards), was projected under four alternative governance scenarios: a solo energy adjustment scenario considers only efforts to optimize the energy structure; the air–climate synergistic governance scenario accounts for the interactions between carbon dioxide and air pollutant emissions reductions, assuming the province would adopt a synergistic governance strategy to improve air quality and mitigate climate change. The reduction ratio of the primary emissions of major air pollutants in 2030 relative to their 2017 levels under four alternative scenarios is summarized in Table 7 (available technologies or measures used for air pollution and carbon dioxide prevention are summarized in Supplementary Table S6). By comparison, from the solo energy adjustment scenario to the air–climate synergistic governance scenario, the emissions reduction ratios of major air pollutants in Henan would increase due to the co-benefits of synergistic reductions in urban CO₂ and air pollutant emissions.

Table 7. Reduction ratio of the primary emissions of major air pollutants in 2030 relative to their 2017 levels under four different scenarios.

Scenario	SO2	NOX	CO	PM10	PM2.5	VOCS	NH3
REF_ESLC	28.53%	−10.33%	7.44%	22.25%	19.62%	12.74%	6.09%
REF_RS	42.22%	21.87%	16.91%	31.37%	28.34%	18.63%	19.02%
MFR_ESLC	67.22%	7.47%	9.44%	60.06%	56.98%	17.43%	13.74%
MFR_RS	75.26%	34.28%	20.36%	66.35%	63.62%	25.09%	24.27%

Under the mitigation policy, PM_2.5 concentrations in Henan Province will decline significantly from 2017 to 2030, especially in more polluted areas (Figure 13). In these scenarios, REF_BAU is a counterfactual scenario where we assume no strengthened end-of-pipe control measures are implemented after 2017. PM_2.5 concentration in the central and northern part would still be above China’s air quality standard (Figure 13). The implementation of pollutant control measures will lead to varying degrees of improvement in PM_2.5 concentrations, while strengthened carbon mitigation policies could bring more significant air quality improvements across a wider region (Figure 13). When a deeper carbon mitigation policy (MFR_RS) is implemented, PM_2.5 concentrations will be further reduced by 2.35 µg/m³ in 2025 and 4.10 µg/m³ in 2030, reflecting the increasingly limited marginal effects of energy structure adjustment measures. Compared with the baseline scenario, the annual average concentration improvement effect of PM_2.5 under the 2030 MFR_RS scenario is shown in Figure 14. PM_2.5 concentration in central and northern regions of Henan Province have decreased significantly because atmospheric pollutant sources in this region are mainly influenced by the industrial structure, where industrial emissions play a dominant role. The implementation of pollution reduction measures under the low-carbon scenario can, therefore, effectively reduce PM_2.5 concentrations.

Figure 13. PM_2.5 concentration: annual PM_2.5 concentration and differences between mitigation scenarios and REF_BAU scenarios in 2025 and 2030.

Figure 14. Compared with the REF_BAU, improvement effect of PM_2.5 concentration under MFR_RS.

3.5. Assessment of the Health Impact of PM_2.5 Emissions Reductions on Residents

Air pollution is associated with numerous health impacts, including Chronic Obstructive Pulmonary Disease (COPD) linked to enhanced ozone (O₃) and Acute Lower Respiratory Illness (ALRI), stroke, Ischemic Heart Disease (IHD), COPD, and Lung Cancer (LC) linked to PM_2.5. With the aging of China’s population structure, the impact of air pollution on the elderly will be further amplified. Given the significant co-benefits of improved air quality, after China achieves the 2030 carbon peaking target, Henan’s low-carbon transition can substantially reduce the disease burden caused by air pollution. Based on the PRM model, we estimated the premature death toll of PM_2.5 exposure in Henan Province under different scenarios. The results show that a large number of premature deaths related to PM_2.5 can be avoided by implementing stricter end control and long-term deep carbon emissions reduction (Figure 15). In the baseline scenario (REF_BAU), premature deaths related to PM_2.5 would be approximately 10.56 (95% confidence interval, CI95: 8.60–12.53) thousand in 2025 and would be reduced to 10.22 (CI95: 8.32–12.12) thousand in 2030. Under the MFR_ESLC scenarios, 1972 premature deaths could be avoided in 2030; when deep climate change mitigation is achieved (MFR_RS), the avoided excess mortality could be as many as 2093 in 2030, accounting for 20.48% of the total mortality in the baseline scenario. This indicates that efforts to reduce air pollution are indispensable and crucial to protect public health, particularly in the context of the future rapidly aging population and increased population vulnerabilities to air pollution.

Figure 15. PM_2.5-related disease burden and premature deaths.

3.6. Evaluate the Economic Benefits of PM_2.5 Emissions Reduction

The monetized health benefits of both mitigation policies could be substantial. The avoided outpatient and hospitalization expenditure from PM_2.5-related disease will increase over time under mitigation scenarios (Figure 16). From 2017 to 2030, the expenditure on diseases caused by PM_2.5 will generally rise first and then decline, which is consistent with the results of major pollutant emissions and PM_2.5 concentrations. Under the MFR_ES scenarios, compared with the REF_BAU scenario, avoided disease expenditure would amount to 4.35 billion RMB in 2025 and 4.57 billion RMB in 2030, respectively. This indicates that carbon mitigation policies would avoid substantial disease-related costs and reduce the household expenditure burden.

Figure 16. The co-benefits on population health compared to the REF_BAU scenario.

4. Conclusions

4.1. Comparison with Other Studies

In addition to scenario design and policy assumptions, differences may also be attributed to modeling mechanisms, parameter settings, and end-of-pipe control measures. Among these factors, the uncertainty of health impact assessment, especially due to the selection of constantly updated end governance, will be crucial and has been widely discussed in previous studies [1,82,83]. Therefore, more detailed uncertainty analysis and reporting will be necessary for future research for a better comparison among studies. Previously, most provincial-level studies focused on NDC, 1.5, and 2 degree scenarios instead of the carbon peak carbon neutralization background. Still, we try to compare the physical and monetized health co-benefits of carbon mitigation with other studies (Table 8).

Table 8. Comparison of the health benefits of carbon mitigation in 2030 with other studies.

	National Level	Provincial Level
Avoidable premature deaths	102,730–167,425 [84]	Shanghai: 108,200 [85]
	19,962 [86]	Different provinces: 248–10,383 [87]
	18,402–162,217 [88]	Sichuan: 7690–14,170 [89]
	94,000–160,000 [87]	Xinjiang: 1175 [86]
		Henan: 2093 a
Monetized health co-benefits	90.0 billion USD [84]	Different provinces: 1.6–53.7 billion USD [87]
	2.72–9.45 billion USD [86]	Sichuan: 15.9 billion USD [89]
	619 billion RMB [88]	Northwest region: 1.03–4.87 billion USD [86]
	406 billion USD [90]	Henan: 4.57 billion RMB a

^a This study.

4.2. Limitations

It should be noted that our research has certain limitations in terms of data, models, and other aspects. For instance, when formulating carbon mitigation policies for Henan Province, we assumed that Henan would adopt the same carbon intensity or per capita emissions reduction rates as the national average in China, without considering potential individualized carbon reduction pathways. Given that current policies generally only cover a 5-year horizon, our study involves a certain degree of uncertainty in policy extrapolation. Additionally, the emissions reduction elasticity analysis does not provide a detailed assessment of energy-saving technologies, which also constitutes an area for improvement.

4.3. Policy Suggestion

Lu et al. [91] used the GAINS-China model, and their results suggest that implementing energy replacement in the industrial and residential sectors in Beijing–Tianjin–Hebei can not only generate large amounts of air pollutant emissions reduction but also can lead to significant CO₂ emissions reduction. Yu et al. [92] used an energy system optimization model, and a low-carbon emissions reduction path that can realize the synergistic control of CO₂ and traditional air pollutants in China’s power industry was obtained. Compared to other studies, this study reveals the substantial potential of implementing a two-tier synergistic governance framework for CO₂ emissions and air pollution in Henan Province. Under the medium-intensity emissions reduction scenario (REF_ESLC), end-of-pipe control measures are maintained at the current technological level and integrated with mature energy-saving and carbon-reduction technologies (e.g., new energy substitution). The required investment falls within the local fiscal capacity, enabling a balance between economic development and environmental governance needs. This scenario can be implemented in phases and features high operability.

Power generation, manufacturing, and road transportation are the main common sources of CO₂ emissions and PM_2.5 pollution in Henan Province, a pattern also observed in many other megacities in China, such as Beijing, Shanghai, Tianjin, and Chongqing [93,94]. Given this, Henan should prioritize these three sectors in promoting the synergistic governance of CO₂ emissions and air pollution locally. Regarding Henan’s large-scale power dispatch, all coal-fired power units have achieved ultra-low emissions: the proportion of units with a capacity of 600,000 kilowatts or above has reached 65%, and the average coal consumption of coal-fired power units in the Province has dropped to 300 g of coal equivalent per kilowatt-hour (gce/kWh), 6 g lower than the national average. Furthermore, industrial manufacturing and road transportation should be regarded as two key areas to advance regional collaborative governance.

The manufacturing industry is a key contributor to Henan’s local CO₂ emissions and locally sourced PM_2.5 pollution. In the future, the industrial sector should assume primary responsibility for addressing resource and environmental constraints and take the lead in pursuing green and low-carbon transformation. Promoting low-carbon development and deep decarbonization in the industrial sector not only controls carbon dioxide emissions but also delivers more far-reaching multiple values and synergistic benefits. According to existing plans, the general trend of Henan Province’s industrial development in the future is that high-tech industries and light industries, represented by equipment manufacturing; transportation vehicle manufacturing; and electronic information, food, clothing, and furniture manufacturing, will gradually replace heavy industries such as steelmaking to become the pillars of industrial and economic development. This study found that Henan’s future industrial development trend is basically consistent with the national development trend and also similar to the current structure of Japan’s industrial sector across most industries. Therefore, the future GDP share of various industrial sub-sectors in Henan Province has been established (see Supplementary Table S7).

Road traffic is another key factor contributing to local greenhouse gas emissions and PM_2.5 pollution in Henan Province. For road passenger transport, the development of public transport and travel demand that management hold the greatest potential for the collaborative reduction in greenhouse gas and air pollutant emissions. Therefore, this study suggests that Henan Province should accelerate the construction of its public transport system, particularly rail transit and bus rapid transit [95,96]. It is recommended to establish an energy-saving comprehensive transportation system centered on railways and lay the foundation for an urban layout dominated by public transport and non-motorized travel. Electric vehicles should account for 45% of road passenger transport. For road freight transport, efforts should be made to optimize the freight structure and promote the transition of freight vehicles from oil-fired to gas-fired and electric-powered models. Pure electric trucks are expected to account for 25% of road freight transport by 2030, which will make significant contributions to reducing local greenhouse gas and air pollutant emissions in Henan Province. This provides policy insights for Henan Province to continuously optimize its cargo distribution system and vigorously develop railway transport as a replacement for heavy-haul road transport, with 30% of road freight shifted to railways. Additionally, trucks using Liquefied Natural Gas (LNG) or other new fuels as viable alternatives to diesel trucks should be promoted through financial subsidies and market incentives [97,98].

Overall, there are still some uncertainties and limitations in this study. For example, emissions of carbon dioxide and air pollutants may deviate from actual conditions due to unexpected changes in emissions factors and coefficients; additionally, this study focuses on the synergistic governance of CO₂ and air pollutant emissions in the energy sector and does not conduct a cost–benefit analysis. Hence, the extension of the research to non-energy sectors and the cost-effectiveness of the two-tier synergistic governance model compared with traditional governance strategies require further exploration.