Enhancing the Synergistic Pathways of Industrial Pollution and Carbon Reduction (PCR) in China: An Energy Efficiency Perspective

Abstract

Industry is a major contributor to air pollution and CO₂ emissions, and a major force for reducing them. Based on the idea of improving the performance of the synergy effect “1 + 1 > 2”, this paper constructs a method that evaluates the synergistic effect of pollution and carbon reduction (PCR) in terms of energy efficiency under the framework of Data Envelopment Analysis (DEA) and analyzes the basic path of China’s synergistic effect of PCR from the viewpoint of energy efficiency. Based on the construction of a global non-radial directional distance function, we develop an emission performance measurement index for output factors. Furthermore, by comparing performance changes under individual and collaborative emission reduction scenarios, we establish an evaluation model for assessing the synergistic effects of PCR. The results show the following: (1) Collaboration between PCR enhances both the air pollution emission performance and carbon dioxide emission performance in China. (2) The synergistic effect of collaborative emission reduction is universal in different regions and provinces. However, the synergistic effect of PCR found in the east, central, and west has strong heterogeneity. (3) Different improvement pathways, such as prioritizing energy conservation or carbon mitigation, were designed to account for regional development disparities. Although these policy orientations can effectively enhance the synergy between pollution control and carbon mitigation, the extent of improvement varies considerably across regions.

1. Introduction

The Chinese government has set goals of peaking carbon dioxide emissions by 2030 and neutralizing carbon by 2060 as part of its national plan to reduce emissions [1,2]. The government also emphasizes the need to promote concurrent efforts in carbon mitigation, pollution management, green expansion, and economic development, advocating for ecological priority, resource conservation, and sustainable development [3]. The industry sector is the primary source of carbon dioxide and air pollutant emissions [4]. According to statistics from the China Carbon Accounting Database, in 2022, total Chinese carbon dioxide emissions reached 11.5 billion tons, equivalent to approximately 28.87% of the world’s emissions. Among these, industrial carbon emissions reached 4.2 billion tons, representing 38.18% of the national total, while industrial emissions of SO₂, NOx, and PM reached 79.6%, 40.9%, and 65.6% of national emissions, respectively. Therefore, the industrial sector bears the dual responsibility of reducing air pollutants and cutting CO₂ emissions [5]. It is critical to reconcile the relationship between these two tasks and clarify the mechanisms of their synergy in order to effectively achieve lower levels of pollution and carbon emissions [6,7]. Given that air pollutants and carbon emissions share the same root, source, and process, this paper focuses on the synergistic effect between industrial air pollution emissions and carbon emissions. It assesses the combined effects of industrial pollution reduction and carbon emission reduction (PCR) in China from the perspective of performance indices, which is crucial for achieving the effective control of industrial PCR.

The existing literature on evaluating the synergistic effects of PCR consists of four main categories: numerical simulation, correlation analysis, regression analysis, and marginal abatement cost evaluation. (1) Numerical simulation methods focus on predicting the synergistic benefits of policy implementation. Relevant studies use models such as the Computable General Equilibrium (CGE) model, Greenhouse Gas and Air Pollution Interactions and Synergies (GIANS) model, and Community Multiscale Air Quality (CMAQ) model. For instance, Chi et al. [8] combined a dynamic recursive CGE model with an air pollution and greenhouse gas interaction and synergy model; Chen et al. [9] used the Weather Research and Forecasting (WRF)–CMAQ model to assess the impact of air pollutants in the UK. (2) Correlation analysis focuses on identifying the best strategies for achieving synergistic effects using methods like coupling coordination models. For example, Zhang et al. [10] utilized data collected from 30 Chinese provinces from 2011 to 2020 to analyze the impact of digital inclusive finance on the coupling coordination of pollution abatement and low-carbon development; Chen et al. [11] built a coupling coordination model to quantitatively calculate the coordinated development of PCR from 2009 to 2018 along the Yellow River basin. Furthermore, other scholars have used this model to measure PCR [12,13,14]. (3) Regression analysis methods use historical data to verify the existence of the synergistic effects of PCR and explore their realization mechanisms. These studies construct econometric models to estimate the regression coefficients for the effect of reducing pollution on reducing carbon or vice versa and assess the synergistic effects. They also analyze the mechanisms through which these effects are achieved. For example, Zhu et al. [15] investigated the effect of digital technology innovation on PR in the Yangtze River Delta from 2015 to 2021. Other scholars have explored the impact of mandatory clean production audits [16], green technology innovation [17], carbon emission trading policies [18], and environmental taxes [19] on PCR. (4) How can the synergy between PCR be measured from the perspectives of the environment, climate, and economy? In recent years, some scholars have measured the efficiency of PCR from an input–output perspective. For example, Yan et al. [20] quantitatively assessed the spatiotemporal relationship between PM2.5 and CO₂ emissions across 360 cities in China from 2005 to 2020, as well as the benefits of sustained synergistic control. Liu et al. [17] utilized provincial-level industrial panel data from 2008 to 2021 in China and applied structural equation modeling to empirically investigate how green technologies influence the synergy effect of PCR through industrial structural adjustments. Wen et al. [21] employed panel data from 1997 to 2020 between China and RCEP countries to comprehensively evaluate the synergy effects of bilateral trade on “pollution reduction (PR)”, “carbon reduction (CR)”, and “economic growth”. Duan et al. [22] employed an integrated input–output framework for CO₂ emissions and atmospheric contaminants, using the shadow prices of undesirable outputs to calculate the marginal abatement costs and evaluate the synergistic effects in China. Other studies have researched marginal costs in CR [23], pollutant marginal costs [24,25], and waste market marginal costs [26]. While these studies contribute to predicting the effects of PCR policies, they have limitations, such as relying on simulated values based on engineering technical parameters and lacking historical retrospective analysis.

Existing research has extensively explored PCR, examining aspects such as synergy indicators and emission reduction strategies. These investigations have yielded a rich array of insights, elucidating the present status, influential mechanisms, and socio-economic interrelations of PCR. Furthermore, they have provided pivotal information for policy development aimed at amplifying synergistic outcomes. Nevertheless, there are discernible limitations in the current body of work. Firstly, models that center on greenhouse gas–air pollutant synergies predominantly assess the synergistic impacts of diverse policies but often lack a comprehensive portrayal of the actual coordination between pollution and carbon emissions. Secondly, most methodologies for evaluating synergy effects chiefly probe the correlation between air pollutants and greenhouse gases, failing to integrate multifaceted considerations such as economic, social, and environmental dimensions into the analytical framework of PCR synergies. In light of the existing literature, this paper proposes a novel method for quantifying the synergistic effects of PCR in terms of energy efficiency. This method is employed to assess the synergistic effects of PCR in China and explore pathways for improving these effects through energy-saving scenarios. The marginal academic contributions of this paper are as follows: (1) Within a multi-input, multi-output DEA framework, this study measures the PCR performance of China’s industrial sector. By comparing changes in China’s emission performance under separate and joint abatement scenarios, it constructs a quantitative evaluation method for assessing the synergy between pollution control and carbon mitigation from a performance perspective, providing methodological support for evaluating industrial synergy effects. (2) Utilizing the developed performance-based evaluation method, this study examines the synergy effect of PCR across multiple dimensions in China’s industrial sector, offering scientific evidence for understanding the current status of synergistic governance. (3) By assigning different weight vectors to input–output variables in the evaluation model, the study simulates the changes in synergy effects under scenarios such as the energy transition pathway, resource conservation and intensive utilization pathway, and CR priority pathway. Through a comparative analysis across scenarios, it explores strategies that could enhance the synergy effect in China’s industrial sector, providing practical insights for tapping the potential of synergistic improvements in PCR.

The remaining content of this paper is as follows: part two is model construction and data processing, part three is empirical analysis, and part four is conclusions and recommendations.

2. Model Construction and Data Processing

2.1. Model Construction

This project focuses on evaluating the environmental efficiency of China’s industrial sector and constructs a low-carbon transition cost accounting system that leverages the global reference non-radial directional distance function (GNR-DDF). In response to the typical characteristics of China’s industry, including “high energy consumption, high emissions, and strong heterogeneity”, the method overcomes the dimensional limitations of traditional DEA models in complex production systems. This approach innovatively integrates duality theory and the shadow price mechanism, decoupling the technical substitution elasticity of the production possibility boundary, and builds a marginal cost accounting system with three-dimensional constraints: energy, economy, and environment. It accurately quantifies the joint management costs of air pollutants and CO₂ emissions. Unlike static analysis paradigms, the GNR-DDF model characterizes heterogeneity through dynamic reference set optimization techniques, evaluating the potential synergy and co-benefits of PCR within a unified frontier. It also reveals the technical compensation effect of the factor configuration efficiency on emission reduction costs.

2.1.1. Non-Radial Directional Distance Function

This study constructs production decision units (DMUs) based on DEA, with provinces as the research object. In the input–output indicator system, the fixed capital stock ($K$), labor input ($L$), and energy consumption ($E$) are selected as the input factors. Industrial value added ($Y$) is set as the expected output, while CO₂ emissions ($C$), industrial SO₂ emissions ($P_1$), and industrial smoke (dust) emissions ($P_2$) are considered undesirable outputs. $t$ indicates the time period (measured in years) and $n$ represents the decision-making unit (DMU), which in this study corresponds to provinces. Environmental production technology and global benchmark technology are employed to construct the global production frontier under the assumption of constant returns to scale (CRS). By introducing the non-radial directional distance function (DDF) optimization model, a production possibility set based on global benchmark technology, $PP{S}^G$ (see Formula (1)), is further constructed to achieve a comprehensive efficiency evaluation.

$$PP{S}^G=\left\{\begin{array}{r}\left(K,L,E,Y,C,P_1,P_2\right):{\displaystyle \sum _{t=1}^T{\displaystyle \sum _{n=1}^N{\lambda }_n^t{K}_n^t\le K,}{\displaystyle \sum _{t=1}^T{\displaystyle \sum _{n=1}^N{\lambda }_n^t{L}_n^t\le L,}{\displaystyle \sum _{t=1}^T{\displaystyle \sum _{n=1}^N{\lambda }_n^t{E}_n^t\le E,}}}}\\ {\displaystyle \sum _{t=1}^T{\displaystyle \sum _{n=1}^N{\lambda }_n^t{Y}_n^t\ge Y,{\displaystyle \sum _{t=1}^T{\displaystyle \sum _{n=1}^N{\lambda }_n^t{C}_n^t=C,{\displaystyle \sum _{t=1}^T{\displaystyle \sum _{n=1}^N{\lambda }_n^t{P}_{in}^t=P_i,{\lambda }_n^t\ge 0}}}}}}\end{array}\right\}$$

(1)

Under the global benchmark technology framework, the production possibility set $PP{S}^G$ is a closed and bounded set constructed by the linear combination of the decision unit (DMU) observation values (with combination coefficients denoted as $\lambda$). The technical characteristics of this set are expressed through the following axiomatic constraints: First, the input variables and expected outputs satisfy strong disposability, meaning that any increase in input redundancy or reduction in expected output will not disrupt the technological feasibility (if $K^{\prime }\ge K$, $L^{\prime }\ge L$, $E^{\prime }\ge E$, then $(Y,C,P)\in PP{S}^G(K^{\prime },L^{\prime },E^{\prime })$). Second, undesirable outputs ($C$, $P_i$) are subject to weak disposability constraints, indicating that the reduction in undesirable outputs requires a proportional sacrifice of expected outputs (if $\left(Y,C,P\right)\in PP{S}^G$ and $\theta \in \left[0,1\right]$, then $\left(\theta Y,\theta C,\theta P\right)\in PP{S}^G$). At the same time, the zero-combination axiom further strengthens the symbiosis of production activities: if undesirable outputs are completely eliminated ($C=0andP=0$), all production activities must be halted ($Y=0$). The constructed function model is as follows:

$$\begin{array}{l}\overline{D}\left(K,L,E,Y,C,P_1,P_2,G\right)=\max w_K{\beta }_K+w_L{\beta }_L+w_E{\beta }_E+w_Y{\beta }_Y+w_C{\beta }_C+{\displaystyle \sum _{i=1}^2{w}_{P_i}{\beta }_{P_i}}\\ s.t.{\displaystyle \sum _{t=1}^T{\displaystyle \sum _{n=1}^N{\lambda }_n^t{K}_n^t}}\le K-{\beta }_K{g}_K,{\displaystyle \sum _{t=1}^T{\displaystyle \sum _{n=1}^N{\lambda }_n^t{L}_n^t}}\le L-{\beta }_L{g}_L,{\displaystyle \sum _{t=1}^T{\displaystyle \sum _{n=1}^N{\lambda }_n^t{E}_n^t}}\le E-{\beta }_E{g}_E\\ {\displaystyle \sum _{t=1}^T{\displaystyle \sum _{n=1}^N{\lambda }_n^t{Y}_n^t}}\ge Y-{\beta }_Y{g}_Y,{\displaystyle \sum _{t=1}^T{\displaystyle \sum _{n=1}^N{\lambda }_n^t{C}_n^t}}=C-{\beta }_C{g}_C,{\displaystyle \sum _{t=1}^T{\displaystyle \sum _{n=1}^N{\lambda }_n^t{P}_{in}^t}}=P_i-{\beta }_{P_i}{g}_{P_i}\\ {\lambda }_n^t\ge 0,n=1,2,\dots ,N,i=1,2,{\beta }_K,{\beta }_L,{\beta }_E,{\beta }_Y,{\beta }_C,{\beta }_{P_i}\ge 0\end{array}$$

(2)

In Formula (2), $W^T=\left(w_K,w_L,w_E,w_Y,w_C,w_{P_1},w_{P_2}\right)$ represents the weight vector, with each element reflecting the importance of the respective variables in the evaluation. The weights can be flexibly set based on the specific research objectives and context. $B={\left({\beta }_K,{\beta }_L,{\beta }_E,{\beta }_Y,{\beta }_C,{\beta }_{P_1},{\beta }_{P_2}\right)}^T$ is the slack vector used to adjust the performance of the decision unit, reflecting the proportional change in each factor. $G=\left(g_K,g_L,g_E,g_Y,g_C,g_{P_1},g_{P_2}\right)$ is the direction vector, indicating the direction in which each factor should be adjusted to bring the decision unit to an efficient state.

This study focuses on the industrial production sector and constructs a framework for measuring the synergistic effects of PCR from the perspective of energy consumption performance improvement. Given the rigid characteristics of factor inputs in industrial production, an output-oriented model is adopted (with input weights set to 0), focusing on examining the synergistic relationship between the potential for industrial economic output growth and the space for pollution emission reduction, under the condition of constant factor inputs such as capital and labor. Meanwhile, China’s industrial sector is currently undergoing a critical phase of transformation toward “greening, low-carbonization, and intelligence”, requiring the coordination of dual goals: industrial output growth (expected output) and pollution emission control (undesirable output). In the absence of industry-specific prior weight information, equalized weights (each 1/2) are assigned to the two output systems to avoid subjective bias in the evaluation results. For the end-of-pipe treatment of industrial exhaust gases, the dual equalization principle is applied for weight distribution. To reflect the deep integration of industrial clean production and low-carbon transformation, equal weights are assigned to the two types of undesirable outputs. Consequently, the direction vector and weight matrix in this study are set as follows:

$$\left\{\begin{array}{l}G=(-K,-L,-E,-Y,0,-P_1,-P_2)\mathrm{and}{W}^T=\left(0,0,0,{\displaystyle \frac{1}{2}},0,{\displaystyle \frac{1}{4}},{\displaystyle \frac{1}{4}}\right)pollutionreductionalone\\ G=(-K,-L,-E,-Y,-C,0,0)\mathrm{and}{W}^T=\left(0,0,0,{\displaystyle \frac{1}{2}},{\displaystyle \frac{1}{2}},0,0\right)carbonreductionalone\\ G=(-K,-L,-E,-Y,-C,-P_1,-P_2)\mathrm{and}{W}^T=\left(0,0,0,{\displaystyle \frac{1}{2}},{\displaystyle \frac{1}{4}},{\displaystyle \frac{1}{8}},{\displaystyle \frac{1}{8}}\right)synergisticemissionreduction\end{array}\right.$$

(3)

2.1.2. Performance Index

Based on the existing literature [27], this study constructs the Pollution Emission Performance Index ($PEP{I}_i$) and the Carbon Emission Performance Index ($CEPI$) based on the efficiency improvement ratio measured by DEA. The formulas are as follows:

$$PEP{I}_i={\displaystyle \frac{Y/P_i}{\left(Y+{\beta }_{Y}Y\right)/\left(P_i-{\beta }_{p_i}{P}_i\right)}}={\displaystyle \frac{1-{\beta }_{P_i}}{1+{\beta }_Y}}$$

(4)

$$CEPI={\displaystyle \frac{Y/C}{\left(Y+{\beta }_{Y}Y\right)/\left(C-{\beta }_{C}C\right)}}={\displaystyle \frac{1-{\beta }_C}{1+{\beta }_Y}}$$

(5)

In Formula (4), $i$ represents the types of air pollutants, and $PEP{I}_i$ is the ratio between the expected output under actual unit air pollutant emissions and the expected output under potential unit air pollutant emissions. The value of $PEP{I}_i$ ranges from 0 to 1, with a larger $PEP{I}_i$ indicating that the DMU is closer to the frontier, producing more expected output for a given level of actual pollutant emissions. ${\beta }_Y$ represents the expansion ratio of industrial value added $Y$ in the region when the DMU undergoes efficiency improvement and ${\beta }_{P_i}$ indicates the reduction ratio of air pollution $P_i$ when the DMU undergoes efficiency improvement. Similarly, in Formula (5), $CEPI$ represents the ratio between the expected output under the actual unit CO₂ emissions and the expected output under potential unit CO₂ emissions. The value of $CEPI$ also ranges from 0 to 1, with a larger $CEPI$ indicating that the DMU is closer to the frontier, producing more expected output for a given level of actual carbon emissions. ${\beta }_C$ represents the reduction ratio of CO₂ ($C$) when the DMU undergoes efficiency improvement.

2.1.3. Synergistic Effects of PCR

Based on the explanation of the synergistic effects of PCR in this paper, the synergistic effect is further defined as the performance change ratio of synergistic emission reduction compared to individual emission reduction. The formula is as follows:

$$\Delta P_{P_i}={\displaystyle \frac{PEPI{T}_i-PEPI{B}_i}{PEPI{B}_i}}$$

(6)

$$\Delta P_{C{O}_2}={\displaystyle \frac{CEPIT-CEPIB}{CEPIB}}$$

(7)

In Formula (6), $\Delta P_{P_i}$ represents the ratio of the change in the pollution emission performance when implementing synergistic emission reduction compared to individual PR, which reflects the PR effect; $PEPI{T}_i$ is the pollution emission performance during synergistic emission reduction; and $PEPI{B}_i$ is the pollution emission performance during individual PR. In Formula (7), $\Delta P_{C{O}_2}$ represents the ratio of the change in the carbon emission performance when implementing synergistic emission reduction compared to individual CR, which reflects the CR effect; $CEPIT$ is the carbon emission performance during synergistic emission reduction; and $CEPIB$ is the carbon emission performance during individual CR. Considering the equal importance of PCR, this paper defines the synergistic effect of PCR as follows:

$$T=0.5\Delta P_{C{O}_2}+0.5\Delta P_P$$

(8)

Here, $\Delta P_P=\left(\Delta P_{P_1}+\Delta P_{P_2}\right)/2$ represents the PR effect, $\Delta P_{C{O}_2}$ represents the CR effect, and $T$ represents the synergistic effect of PCR.

2.2. Data Selection and Processing

This study focuses on the 30 provincial-level administrative regions of mainland China (excluding Tibet and Hong Kong, Macao, and Taiwan), with sample selection strictly following the three principles of data availability, temporal continuity, and policy relevance. The research period is set from 2011 to 2023, as this time window holds significant policy observation value: First, 2011 marks the beginning of the Twelfth Five-Year Plan (2011–2015), during which environmental protection authorities first included sulfur dioxide (SO₂) and nitrogen oxides (NOx) in the national total emission control indicator system, signaling the entry of environmental governance into the phase of quantitative target management. Second, 2023 is the midpoint of the “14th Five-Year Plan” (2021–2025), allowing for a systematic evaluation of the upgraded national air pollution control action plan.

This study selects sulfur dioxide (SO₂) and industrial smoke dust as the indicators for undesirable outputs, based on the following criteria: First, both are core control targets of the “Air Pollution Prevention and Control Action Plan (2013–2017)” and subsequent revisions; second, based on the national environmental quality monitoring network, the data have undergone multi-dimensional validation by the “three lines and one list” system managed by the ecological and environmental regulatory authorities, ensuring full regional coverage and data authority.

In this study, capital, energy, and labor are used as input factors, industrial value added is used as the expected output, and carbon emissions and air pollution are used as undesirable outputs. The data processing methodology is constructed as follows:

(1) Capital: Represented by fixed capital stock. To estimate the capital stock of each province, researchers employ the Perpetual Inventory Method (PIM). Here, 2011 is taken as the base year, with the total fixed capital formation in that year used as the initial investment indicator. Using a 9.6% average depreciation rate and the geometric mean of the investment growth rate between 2012 and 2016, the initial capital stock for each province is retroactively calculated. For subsequent years, the capital stock is calculated using the following formula: $K_{\mathrm{t}}=K_{t-1}(1-\delta )+I_t$; here, the depreciation rate δ is set at 10.96% to reflect capital loss and renewal needs.

(2) Energy: This study uses the total industrial energy consumption (in ten thousand tons of standard coal) as the core indicator. The base data are directly sourced from the China Energy Statistical Yearbook and China Statistical Yearbook, constructing a panel database of energy consumption at the provincial and prefectural administrative levels from 2011 to 2023, ensuring the data’s authority and consistency in statistical standards.

(3) Labor: The study focuses on the number of employees in manufacturing (in persons) as a proxy variable for labor input. Data on the number of manufacturing employees by region from 2011 to 2023 are systematically organized from the China Labor Statistical Yearbook and China Statistical Yearbook, with data cleaning and matching performed in strict accordance with the “local statistics” principle.

(4) Industrial Value Added: Industrial value added (in 100 million yuan) is used to measure economic output. The raw data are sourced from the China Statistical Yearbook, and price fluctuations are eliminated using the GDP deflator (with 2011 as the base year), ultimately generating a comparable industrial value added series for each region.

(5) Carbon Emissions: Total carbon emissions (in ten thousand tons) are derived through a multi-source data fusion approach. Priority is given to official carbon emission inventory data published by provincial governments. For regions where these data are not available, emissions data are extracted from the EDGAR global emission database, and after verification using the IPCC standard methods, a complete emission account is formed. This method effectively addresses the statistical discrepancies associated with traditional energy consumption-based calculation methods.

(6) Air Pollution: A multi-dimensional pollution indicator evaluation system is constructed, including industrial sulfur dioxide emissions (in ten thousand tons) and industrial smoke (dust) emissions (in ten thousand tons). All data are directly sourced from official values published in the China Environmental Statistical Yearbook and China Statistical Yearbook, ensuring alignment with international IPCC standards. The summary statistics for the input–output variables are presented in

Table 1. Descriptive statistics of input–output variables.

	Primary Indicator	Secondary Indicator	Unit	Max	Min	SD
Input	Labor	Manufacturing Employment	10,000 people	272,537	5841.09	58,720.3
	Capital	Fixed Capital Stock	10,000 yuan	31,458.3	1494.49	6174.02
	Energy	Total Industrial Energy Consumption	10,000 tons of standard coal	1020.25	7.1867	176.15
Expected Output	Revenue	Industrial Value Added	100 million yuan	45,142.9	475.04	8465.77
Undesirable Output	Carbon Emissions	CO2 Emissions	10,000 tons	117,809	4420.45	24,708.8
	Air Pollution	Industrial SO2 Emissions	10,000 tons	1.82746	0.0014	0.38359
		Industrial Smoke (Dust) Emissions	10,000 tons	179.77	0.302031	30.1479

.

3. Empirical Analysis

3.1. Overall Characteristics

As shown in Figure 1, from 2011 to 2023, China’s industrial sector experienced significant changes in PR, CR, and synergistic effects. As depicted in the figure, the PR effect fluctuated from −9.231 in 2011 to 9.012 in 2023. Despite some fluctuations, the overall trend was upward. Between 2011 and 2015, the value fluctuated within the negative range, reaching its lowest point of −5.648 in 2015, a 38.8% decline compared to 2011. From 2016 to 2023, the value gradually increased from 4.397 in 2016 to 9.614 in 2020, a 118.6% increase compared to 2016. After some fluctuations, it reached 9.012 in 2023, a 6.3% decrease from 2020.

The CR effect overall declined from 12.464 in 2011 to 8.296 in 2023, showing a downward trend, though the rate of decline gradually slowed. From 2011 to 2014, the value increased from 12.464 to 14.448, a 15.9% rise. From 2015 to 2023, it gradually decreased from 11.582 in 2015, reaching 6.174 in 2020, a 46.7% decrease compared to 2015. It then slightly rebounded, reaching 8.296 in 2023.

The synergistic effect consistently increased from 1.617 in 2011 to 8.654 in 2023, demonstrating steady growth. From 2011 to 2016, the value increased from 1.617 to 7.423, a growth of 359.1%. Between 2017 and 2023, it fluctuated between 6.930 and 8.755, reaching 8.654 in 2023, a 16.6% increase compared to 2016.

Overall, although the individual indicators for PCR experienced fluctuations, the synergistic effect continued to strengthen, indicating a certain degree of synergy in improving PCR. The coordinated advancement of PCR not only helps improve environmental quality but also aids in addressing climate change, driving the industrial sector toward a green, low-carbon transformation.

As shown in

Table 2. National-level trends in PCR performance.

Year	Individual Reduction			Joint Reduction			PR Effect	CR Effect	Synergistic Effect
	BCO2	BP1	BP2	TCO2	TP1	TP2
2011	0.106	0.150	0.375	0.094	0.139	0.422	−9.231	12.464	1.617
2012	0.089	0.142	0.387	0.080	0.131	0.439	−8.847	13.500	2.327
2013	0.075	0.129	0.386	0.069	0.124	0.438	−5.889	13.335	3.723
2014	0.068	0.098	0.396	0.063	0.094	0.454	−5.266	14.448	4.591
2015	0.063	0.111	0.409	0.057	0.109	0.457	−5.648	11.582	2.967
2016	0.132	0.151	0.430	0.136	0.160	0.475	4.397	10.449	7.423
2017	0.174	0.184	0.444	0.176	0.189	0.497	1.875	11.985	6.930
2018	0.224	0.203	0.432	0.234	0.219	0.481	6.118	11.393	8.755
2019	0.282	0.154	0.454	0.285	0.144	0.502	−2.522	10.714	4.096
2020	0.286	0.322	0.456	0.310	0.356	0.484	9.614	6.174	7.894
2021	0.414	0.368	0.550	0.451	0.389	0.581	7.287	5.757	6.522
2022	0.580	0.605	0.641	0.598	0.626	0.651	3.299	1.632	2.466
2023	0.109	0.511	0.599	0.108	0.610	0.649	9.012	8.296	8.654

Note: The performance index is dimensionless, and the unit for the synergistic effect is expressed as a percentage (%).

, the marginal PCR performance in China showed an upward trend from 2011 to 2023. According to Table 2, under individual emission reduction, the marginal effectiveness of PR increased from 0.2625 to 0.555, with an annual average growth rate of 6.43%. In the joint emission reduction scenario, it increased from 0.2805 to 0.6295, with an annual average growth rate of 6.97%. Compared to individual emission reduction, the PR performance under joint emission reduction was higher, and the growth rate was faster.

Regarding CR performance, under individual emission reduction, it increased from 0.106 to 0.109, with an annual average growth rate of 0.23%. In the joint emission reduction scenario, it rose from 0.094 to 0.108, with an annual average growth rate of only 1.16%. Compared to individual emission reduction, the joint emission reduction also exhibited better CR performance.

These data suggest that from 2011 to 2023, in terms of PR, joint emission reduction exhibited more significant performance improvement and faster growth, indicating that coordinated governance had a more prominent scale effect on pollutant reduction. In terms of CR, while the growth rate was faster under joint emission reduction, the initial value was lower (0.094 vs. 0.106), and the final value was still slightly lower than under individual emission reduction. This shows that the synergy of CR technologies still needs to overcome bottlenecks. Overall, the synergistic emission reduction strategy demonstrates a clear advantage in the PR sector, while in the CR sector, further optimization of the technological synergy path is needed to achieve comprehensive efficiency improvements.

3.2. Regional Spatial Pattern

According to the data from 2011 to 2023 in

Table 3. Regional disparity in synergistic CR effects across different regions.

Year	Overall Gini	Intra-Regional Gini Coefficient			Inter-Regional Gini Coefficient			Contribution Rate
		East	Central	West	East-Central	East-West	Central-West	Inter-Regional	Intra-Regional	Overlap
2011	0.116	0.071	0.104	0.151	0.4168	0.4911	0.3645	12.79	31.65	55.56
2012	0.098	0.072	0.109	0.105	0.4749	0.4454	0.3072	15.76	31.83	52.41
2013	0.135	0.074	0.079	0.204	0.3861	0.4826	0.3862	8.71	32.08	59.21
2014	0.131	0.074	0.142	0.163	0.4033	0.4206	0.4241	12.1	32.29	55.62
2015	0.099	0.053	0.065	0.151	0.2526	0.4625	0.4744	8.456	32.2	59.35
2016	0.093	0.09	0.045	0.118	0.2839	0.3719	0.409	8.268	33.7	58.03
2017	0.102	0.084	0.103	0.11	0.3091	0.4785	0.4918	10.61	32.56	56.83
2018	0.139	0.113	0.149	0.13	0.3191	0.4585	0.4992	22.52	31.03	46.45
2019	0.13	0.094	0.18	0.089	0.4782	0.4606	0.4524	36.43	29.32	34.26
2020	0.142	0.109	0.19	0.12	0.3729	0.4065	0.3513	11	31.23	57.77
2021	0.138	0.15	0.125	0.117	0.5346	0.5346	0.399	16.38	32.92	50.71
2022	0.453	0.621	0.086	0.083	0.7516	0.7865	0.5387	61.1	34.23	4.671
2023	0.166	0.138	0.178	0.153	0.4955	0.5541	0.4385	30.36	31.11	38.52

, the regional synergistic effects of industrial PCR in China exhibit the following characteristics:

The overall Gini coefficient fluctuated and increased, indicating a widening regional disparity. The overall Gini coefficient (G) for the national PCR performance rose from 0.116 in 2011 to 0.166 in 2023. In 2022, it peaked at 0.453, suggesting that the regional synergistic gap generally expanded, especially after 2020, when the disparity between the eastern and western regions intensified.

As shown in Figure 2a, the contribution rate structure evolved to reflect a weakening of regional interactions. The inter-regional contribution rate surged from 12.79% in 2011 to 61.1% in 2022, making it the primary factor behind the disparity and indicating that the regional collaboration gap has worsened. The contribution rate of the super variability density decreased from 55.56% in 2011 to 38.52% in 2023, reflecting the weakening of cross-regional interactions and insufficient policy coordination effectiveness. The intra-regional contribution rate remained stable at around 30%, with continued attention needed to address internal disparities in the central and western regions.

Inter-regional disparities dominate the overall difference, with the East–West conflict being particularly prominent. The inter-regional Gini coefficient significantly exceeds the intra-regional one. As shown in Figure 2b, the East–West gap has long been the largest, reaching 0.7865 in 2022, far surpassing the East–Central and Central–West gaps. This highlights the core issue of the imbalance in CR capabilities between the East and West. Within-region disparities have also widened, with the gap in the East continuing to expand. The Gini coefficient rose from 0.071 to 0.138, the Central region increased from 0.104 to 0.178, and the West remained relatively stable, changing from 0.151 to 0.153. This reflects the growing problem of uneven technology and policy implementation in the East.

The regional imbalance in PCR performance primarily stems from the development gap between the East and West, as well as the weak regional collaboration mechanisms. It is necessary to strengthen the diffusion of technologies from the East to the Central and Western regions, improve cross-regional compensation policies, and optimize the synergistic management mechanisms reflected by the super variability density indicator to reduce the “carbon barrier” between regions.

From the dynamic characteristics of the national and East, Central, and West regional kernel density maps (Figure 3a–d), China’s industrial sector shows significant spatiotemporal differentiation and structural disparities in its synergistic PCR effects. The specific manifestations are as follows:

Regional Synergistic Effect Differentiation Intensifies, with Clear Gradient Characteristics Between East, Central, and West. The national kernel density curve shifts to the right, indicating that synergistic effects are gradually concentrating at higher levels. However, after 2020, the rate of this rightward shift slows, reflecting a weakening of the driving momentum. Regionally, the East has consistently led in synergistic effects, with its kernel density curve shifting steadily to the right and exhibiting higher density values (vertical height of 0–0.1), showing significant concentrated effects from policy implementation and corporate responses. The Central region has a relatively stable distribution (vertical height of 0–0.006), with a limited improvement in synergistic effects, indicating an urgent need for breakthroughs in technology diffusion efficiency. The West, although showing an overall rightward shift, has a wide horizontal axis range (−40% to 80%), with some provinces exhibiting negative synergistic effects, highlighting the dual contradiction of uneven high-carbon industry transfer and low-carbon technology application within the region.

Peak Shape and Wave Structure Reveal Differences in Regional Internal Concentration. The national main peak height decreased from 0.06 in 2012 to 0.04 in 2022, with the peak width significantly expanding, indicating a shift from concentrated improvement to the more dispersed development of synergistic effects. The East’s main peak is sharp and stable (0.05–0.1), reflecting the high efficiency and consistency of emission reduction technology promotion. The Central and Western regions exhibit low-level characteristics, with the Western region’s main peak spanning negative values to 80%, and a peak height of less than 0.06, reflecting regional imbalances in technology dissemination and policy implementation. Additionally, the differences in the number of wave peaks further validate the regional stratification: the national and Eastern regions mostly feature a single peak, occasionally exhibiting a double peak (such as in the East in 2020), showing the coexistence of advanced and lagging enterprises; the West exhibits a prominent multi-peak phenomenon (such as in 2016 and 2022), while the Central region shows a “wide peak with small peaks”, highlighting the fragmentation of provincial policy effects.

Distribution Extensibility Maps Regional Disparities in Synergistic Effect Diffusion Ability. The distribution range of national synergistic effects expanded from 0–150% in 2012 to 0–300% in 2022, with a growing trend of decentralization. The East has the smallest extensibility (horizontal width of 0–250%), with high-value areas being densely distributed, indicating a convergence of technology paths and mature market mechanisms. The West exhibits the largest extensibility (horizontal width of −40–80%), with both efficient provinces and lagging areas coexisting, highlighting the significant difficulty in achieving regional collaboration. The Central region’s distribution range remains relatively stable, but low-density values expose its insufficient technological iteration momentum.

Overall, China’s synergistic PCR effects present a pattern of “East Leading, Central Progressing Slowly, and West Polarizing”. The East maintains an efficiency advantage through the intensive promotion of technologies, but the national decentralization trend may weaken the overall emission reduction potential. The Central and Western regions need to build differentiated policy systems—the Central region should strengthen the cross-regional transfer of technologies and industrial upgrading, while the West urgently needs to eliminate negative synergistic effects and curb the transfer of high-carbon industries. Notably, after 2020, the growth rate of national synergistic effects has slowed, suggesting that new momentum can be generated through market mechanisms such as expanding carbon markets and green finance innovation while strengthening East–West carbon compensation mechanisms to break regional collaboration barriers and promote the transition from “locally efficient” to “regionally balanced” PCR.

The underlying reasons are as follows. The first is differences in energy structure: the eastern region has a relatively optimized energy mix, with the lowest proportion of coal consumption and a higher share of clean energy use. Once the energy structure reaches a certain level of optimization, further reducing the coal share yields limited marginal improvements in the synergy between PCRs. The second is differences in industrial structure: the eastern region has a lower share of industry, with a more developed service sector and high-tech industries, leading to a lower industrial pollution intensity. As a result, further declines in the industrial share have an insignificant impact on the synergy between PCR effects. The third is differences in the R&D investment intensity: in both the eastern and central regions, R&D efforts significantly moderate the relationship, with the eastern region’s strong technological innovation capacity improving energy efficiency, reducing pollutant emissions, and promoting pollution–carbon synergies. In the central region, R&D investment also contributes to enhancing the synergy effect to a certain extent. However, in the western region, the R&D intensity does not exhibit a significant moderating effect, and a weaker technological innovation capacity limits improvements in the synergy between PCR effects.

3.3. Provincial Quantitative Evaluation

As shown in Figure 4, the upper quartile of PR effects for China’s provinces is 22.23, the lower quartile is −17.86, and the median is 4.64, indicating that the distribution of PR effects is relatively concentrated. For CR effects, the upper quartile is 18.92, the lower quartile is 2.32, and the median is 7.86. The PR effects at the provincial level in China’s industrial sector exhibit differentiated characteristics. The distribution of values for PR effects is more scattered, with significant positive values as well as some negative values, indicating considerable differences in the effects of PR across provinces. In contrast, the distribution of values for CR effects is more concentrated, predominantly in the positive range with smaller fluctuations, indicating greater overall stability. Comparatively, the performance of PR effects shows high variability, reflecting significant differences in pollution governance collaboration across provinces. On the other hand, the CR effects exhibit more convergent positive characteristics, with more equal carbon emission reduction collaboration between provinces.

As shown in Figure 5, the synergistic effects of PCR at the provincial level in China exhibit distinct differences before and after the “14th Five-Year Plan”. On the overall level, after the “14th Five-Year Plan”, the average synergistic effect increased to 15.0%, up from 9.3% before the plan, indicating an improvement in the overall synergistic efficiency.

At the provincial level, regional differences are significant: Some provinces saw substantial increases in their synergistic effects, such as Hainan, where the effect surged from 30.11% to 146.38% after the “14th Five-Year Plan”, achieving a leap forward. Shandong increased from 18.80% to 49.52%, and Anhui rose from 16.22% to 39.43%, reflecting outstanding synergistic results. However, there were provinces with a decline in performance, such as Shanghai, which fell from 11.48% to 0.25%, and Liaoning, which worsened from −4.17% to −9.97%. Additionally, some provinces exhibited noticeable fluctuations. For example, Hebei was at −15.88% before the “14th Five-Year Plan”, and while still negative afterward, it improved to −1.95%.

Overall, the synergistic effect of PCR in China’s provinces improved after the “14th Five-Year Plan”, but the issue of regional development imbalance remains, with some provinces still needing to further tap into their synergistic potential.

From 2011 to 2023 (as shown in Figure 6a–d), China’s industrial sector exhibited a significant trend of spatial optimization in synergistic PCR effects. By analyzing the different color changes on the map, it is possible to intuitively understand the changes and spatial distribution of synergistic PCR effects in each province.

Spatial Evolution Pattern. In 2011, the synergistic effects of PCR showed obvious regional differences. Eastern and Central provinces such as Beijing, Shanghai, Jiangsu, and Zhejiang had relatively small green areas, primarily showing lighter red and yellow areas, indicating limited CR effects with relatively stronger PR effects. Western provinces, such as Xinjiang and Qinghai, almost had no green areas, reflecting weak CR effects, with pollution control still being the main task. By 2015, CR effects had strengthened in the Eastern and Central regions. In particular, provinces like Jiangsu, Zhejiang, and Guangdong saw larger green areas, indicating significant progress in CR policies. However, the green areas in Western regions remained limited, with the overall situation dominated by red and orange areas, showing that PR remained the dominant task. By 2019, the CR effects had expanded across the country, especially in the East and Central regions, with deeper green areas, indicating significant progress in CR policies. Some Western provinces such as Sichuan and Shaanxi also saw increased green areas, reflecting improvements in CR effects. The dominance of PR effects across the country gradually shifted towards the parallel promotion of PCR. By 2023, the green areas representing CR effects covered most of the country, especially in the East and Central regions, where green areas were most prominent, indicating significant advancements in PCR in these regions. In the West, provinces such as Xinjiang and Gansu also showed increasing green areas, indicating that the push for CR effects was no longer limited to the East. Overall, the synergy between PR and CR gradually reached equilibrium across the country.

Main Sources of Synergistic Effects and Temporal Evolution. From the four maps, it is evident that PR effects (red and orange areas) dominated in the early stages, especially in 2011 and 2015, with many provinces focusing on pollution control, achieving significant PR results. Over time, CR effects gradually strengthened, particularly in the East and Central regions, where green areas increasingly appeared, indicating a gradual improvement in the balance of synergistic PCR effects in these provinces. This is especially true in terms of energy structure optimization and the promotion of low-carbon technologies. The map of 2023 shows that the balance between PR and CR effects has been gradually achieved, and that the overall synergistic effects are presenting a more balanced pattern. This reflects China’s progress in implementing PCR policies, which not only effectively reduced pollutant emissions but also achieved significant results in controlling carbon emissions.

Overall, the dominance of PR effects in the Central and Western regions persists, while Eastern provinces have achieved significant emission reductions through CR measures. The synergistic advancement of PCR is gradually being realized. The synergistic effects of PCR have evolved from being PR-dominated to a balance between PR and CR, particularly in the Eastern and Central regions, where CR effects have significantly strengthened, and the synergistic effects have gradually balanced. In Western regions, efforts continue to focus on PR effects, gradually enhancing CR effects, and reflecting the positive results achieved in the nationwide synergistic promotion of PCR.

3.4. Comparison of Synergistic PCR Effects Under Different Scenarios

3.4.1. Weight Setting Logic and Policy Implications

The figure compares the weight settings and dynamic synergistic effects across four scenarios: the baseline scenario, energy revolution, resource conservation, and CR priority. These comparisons reveal differentiated pathways for synergistic PCR at the provincial level.

In the weight setting: Baseline scenario (0, 0, 0, 1/2, 1/8, 1/8, 1/4): Focuses on end-of-pipe treatment, with an emphasis on reducing undesirable outputs (carbon emission weight 1/4, pollution weight 1/8), without adjusting the weights for capital, labor, energy, and other input factors, reflecting a passive dependence on high-carbon capacity. Energy revolution scenario (0, 0, 1/3, 1/3, 1/12, 1/12, 1/6): Focuses on energy conservation, efficient utilization, and clean energy substitution to enhance the synergistic effects of PCR. Resource conservation scenario (1/9, 1/9, 1/9, 1/3, 1/12, 1/12, 1/6): Reduces capital, labor, and energy inputs (each 1/9) to improve the total factor efficiency through resource recycling. CR priority scenario (1/9, 1/9, 1/9, 1/3, 1/18, 1/18, 2/9): Highlights carbon emission control (weight 2/9), weakens short-term pollution control goals, and aligns with low-carbon transition needs in service-led regions.

3.4.2. Synergistic PCR Effects Under Different Scenarios

As shown in Figure 7, based on empirical data across three dimensions—synergistic effects, PR effects, and CR effects—this study systematically evaluates the implementation of the energy revolution path, resource conservation path, and CR priority path in different provinces and offers differentiated policy optimization suggestions.

PR effects: The analysis reveals the pollution control potential of different paths. The CR priority path in Henan Province exhibits an absolute advantage of 96.11%, reflecting the significant role of low-carbon transformation in high-energy industries (e.g., carbon capture technologies in the steel industry) in synergistically reducing pollutants. However, in Shaanxi Province, the resource conservation path achieves a 77.3% relative increase, indicating that industrial process optimization and waste recycling offer higher marginal benefits in PR, especially suitable for the gradual transformation of traditional industrial bases.

CR effects: The evaluation strengthens the priority of the energy revolution path. Gansu Province leads the country with an absolute effect increase of 178.18% and a relative growth of 126.97%, setting a benchmark for synchronized growth in the renewable energy capacity and grid absorption capability, providing a replicable model for high-carbon density regions. In contrast, Xinjiang, under the resource conservation path, achieves a 53.73% CR increase, but compared to the breakthrough effects of the energy revolution, its incremental improvement model may not meet the timeliness requirements of the “dual-carbon” goals. Shaanxi’s CR performance under the energy revolution path (106.09%) demonstrates that even non-resource-rich regions can still tap into significant emission reduction potential through technological innovations (e.g., coal chemical CCUS).

Synergistic effects: Both the energy revolution path and the resource conservation path show significant win–win benefits but with regional heterogeneity. Gansu Province, under the energy revolution path, achieved a 78.4% relative increase and an 87.7% absolute peak, highlighting its leading role in renewable energy transformation (e.g., large-scale wind and solar applications) and the clean transformation of traditional energy. In contrast, Sichuan Province, relying on the resource conservation path, achieved a 70.62% relative increase and an 82.51% performance peak, demonstrating the effectiveness of its circular economy system and industrial energy efficiency policies, though these are slightly less comprehensive than Gansu’s energy revolution model. This result confirms that the deep decarbonization of the energy system plays a crucial role in synergistic effects, especially in the western regions, with favorable resource endowments but ecological vulnerabilities.

The energy revolution path should be the core strategy for Western provinces (Gansu, Xinjiang, Shaanxi), focusing on the layout of renewable energy bases and supporting energy storage facilities to maximize both CR and synergistic effects. Eastern industrial provinces (Henan, Tianjin) should implement a “precise enhanced version” of the CR priority path, using differentiated carbon pricing mechanisms to drive technological innovation and avoid the economic impacts of “one-size-fits-all” policies. Southwestern regions (Sichuan, Shaanxi) can adopt a “dual-track” system of resource conservation and energy revolution, gradually replacing fossil fuels while improving industrial energy efficiency. Additionally, a “province-pairing” technology transfer platform should be established, where Gansu’s renewable energy integration experience can be shared with Xinjiang, and Henan’s industrial CR technologies can be extended to Tianjin. A regional ecological compensation fund should be set up to provide financial support for provinces with an outstanding CR performance (e.g., Gansu, Henan), incentivizing lagging provinces (e.g., Xinjiang) to break through path dependence.

From the perspective of top-performing regional provinces, developing a framework centered on “energy revolution leadership and multi-path complementarity” is key to achieving an optimal configuration. China’s environmental governance should establish this framework to achieve an optimal balance in synergistic PCR, through region-specific policies and dynamic performance monitoring.

3.4.3. Path Selection for Synergistic PCR

As shown in Figure 8, between 2011 and 2023, China’s synergistic PCR effects exhibited different trends under various pathways.

The baseline scenario represents the natural development trend without additional policy interventions. The synergistic effect increased from 4.14% in 2011 to 19.93% in 2023, showing an overall upward trend. This suggests that, even without additional policy interventions, synergistic effects still showed some improvement, but the process was unstable. The improvement in this pathway mainly relied on natural economic growth and technological progress, lacking strong policy-driven momentum.

The resource conservation path emphasizes improving the total factor efficiency through resource recycling and improving resource utilization to achieve PCR goals. The synergistic effect increased from 8.95% in 2011 to 10.69% in 2023, showing a steady upward trend. The improvement in this path was more stable, indicating that resource-saving measures could continue to play a significant role over the long term, especially in the Central and Western regions, where the effects of resource-saving measures were more pronounced.

The energy revolution path focuses on optimizing the energy structure and promoting clean energy. The synergistic effect fluctuated significantly, decreasing from 18.15% in 2011 to 9.19% in 2015, peaking at 29.40% in 2017, and then dropping to 15.82% in 2023. This path exhibited significant volatility in synergistic effects, but overall contributed greatly, especially in the eastern coastal regions where the effects of energy revolution measures were more pronounced. Energy-saving strategies effectively decrease the overall consumption of fossil fuels by enhancing the energy utilization efficiency, thereby directly reducing greenhouse gas emissions such as carbon dioxide. Statistics reveal that over 80% of air pollution in our country stems from fossil fuel combustion. These measures not only curtail the emission of pollutants like dust, sulfur oxides, and nitrogen oxides but also demonstrate significant impacts, especially in high-energy-consuming industries. In these sectors, a production capacity below the baseline level of energy efficiency constitutes over 20%. Through technological advancements such as optimizing the thermal efficiency of heating furnaces and harnessing waste heat for recovery and utilization, both the energy consumption and carbon emission intensity per unit of output value can be substantially mitigated.

The CR priority path prioritizes CR, using strict carbon emission control measures to drive pollutant reduction. The synergistic effect increased from 6.78% in 2011 to 12.38% in 2023, showing an overall upward trend, with a notable rise from 30.97% to 33.06% between 2016 and 2017. This indicates that strengthening CR measures can effectively drive pollutant reduction.

The differences in the synergistic effects across various pathways suggest that proactive policy interventions (such as resource conservation, energy revolution, or CR prioritization) are more effective in enhancing the synergies between PR and carbon mitigation than natural development (baseline scenario). Among these, the CR priority pathway demonstrates the overall optimal effect. The synergy effect between PR and carbon mitigation has remained consistently present. From 2011 to 2016, China’s synergy effect exhibited a fluctuating upward trend, indicating that coordinated abatement, compared to separate emission reductions, effectively enhanced the overall efficiency of both pollution control and carbon mitigation systems. During this period, both PR and carbon mitigation effects were significant, suggesting that coordinated measures not only improved China’s air pollutant emission performance but also had an even more pronounced impact on its CO₂ emission performance. Although short-term fluctuations were observed, the improvement in synergistic effects was particularly notable during the policy intensification phase (2016–2017). However, with the progressive implementation of targeted measures for air pollution control and CO₂ emission reduction, the marginal potential for end-of-pipe mitigation has continued to diminish, making it increasingly difficult to rely solely on such approaches. As a result, from 2017 to 2023, the synergy effect has declined, underscoring the need for China to shift towards source-based governance to further enhance the synergy between PR and carbon mitigation.

4. Conclusions and Recommendations

Based on the systemic characteristics of PCR, this study proposes a novel method to quantify the synergy effect between pollution control and carbon mitigation. Within a total factor framework, it measures the marginal abatement costs of air pollutants and carbon dioxide and compares their variations under separate and joint abatement scenarios. From a performance evaluation perspective, this approach uncovers the synergy effects of PCR in China, offering a new theoretical framework for the quantitative assessment of pollution–carbon synergies. This study, through the construction of a low-carbon transition cost accounting system based on the global reference non-radial directional distance function (GNR-DDF), combined with panel data from 30 provincial-level administrative regions in mainland China from 2011 to 2023, systematically evaluated the synergistic effects of industrial sector PCR and their regional disparities, proposing pathways to enhance these synergistic effects. The research findings are as follows:

(1) Significant Progress in Synergistic PCR. From 2011 to 2023, the synergistic effect increased from 1.617% to 8.654%, showing a steady upward trend. The PR effect improved from −9.231 to 9.012, indicating significant progress in pollution control. However, the CR effect decreased from 12.464 to 8.296, although the decline slowed, reflecting ongoing efforts in carbon emission reduction. Despite fluctuations in the individual indicators for PCR, the continuous enhancement of the synergistic effect indicates good coordination between the two in policy implementation.

(2) Significant Spatial Differentiation in Regional Synergistic Effects. The Eastern region has consistently led in synergistic effects, while the Central and Western regions lag behind. The East–West gap remains prominent, and the inter-regional Gini coefficient is significantly higher than the intra-regional Gini coefficient. Internal disparities in the Eastern region have expanded, and the Central region’s technological diffusion efficiency urgently needs improvement. Some provinces in the West even showed negative synergistic effects, reflecting an imbalance in the transfer of high-carbon industries and the application of low-carbon technologies. The widening regional synergy gap suggests the need to strengthen technology diffusion and policy coordination, optimizing regional collaborative management mechanisms.

(3) Significant Differences in Synergistic Effects under Different Pathways. The energy revolution path shows an outstanding performance in CR, especially in resource-rich Western regions (e.g., Gansu, Xinjiang) where it has had a significant impact on CR. The resource conservation path has demonstrated stable synergistic effect improvement in the Central and Western regions. The CR priority path can effectively drive pollutant reduction in the short term but must avoid the economic impacts of a “one-size-fits-all” policy. This study shows that proactive policy interventions are more effective in enhancing synergistic effects than natural development. The synergistic effect improvement during the policy strengthening phases (e.g., 2016–2017) was particularly notable in the energy revolution and CR priority paths.

This study proposes the following policy recommendations:

First, a governance framework centered on ‘energy revolution leadership and multi-path complementarity’ should be established to achieve synergistic and efficient growth in PCR through regionally differentiated policy combinations. Second, it is essential to strengthen the diffusion of technologies from the eastern region to the central and western regions, improve cross-regional compensation policies, and address barriers to regional collaboration. Third, a ‘provincial pairing’ technology transfer platform should be established to promote the exchange of advanced experiences from high-carbon regions to low-carbon regions. Fourth, by prioritizing CR policies, expanding carbon markets, and fostering green financial innovations, new momentum can be generated to drive the evolution of PCR from ‘localized efficiency’ to ‘regional balance.’ Future research could further quantify the investment returns of each pathway to provide more refined cost–benefit analyses for policy formulation.