The impact of carbon emission trading pilot policy on China's energy eco-efficiency.

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Learn more: PMC Disclaimer | PMC Copyright Notice Carbon Balance Manag . 2026 May 7;21:81. doi: 10.1186/s13021-026-00451-3 The impact of carbon emission trading pilot policy on China’s energy eco-efficiency Liandi Zhang 1 , Shenglin Ma 2 , Yiyao Dai 1 , Wenguang Tang 1, ✉ Author information Article notes Copyright and License information 1 College of Science, Tianjin University of Commerce, Tianjin, 300134 China 2 School of Economics and Management, North University of China, Taiyuan, 030051 China ✉ Corresponding author. Received 2025 Jul 19; Accepted 2026 Apr 21; Collection date 2026 Dec. © The Author(s) 2026, modified publication 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13235055  PMID: 42098413 Abstract Under the “Dual Carbon” goals (carbon peaking and carbon neutrality), China’s Carbon Emissions Trading System (CETS) represents a key policy tool in addressing climate change, significantly contributing to carbon reduction and the enhancement of Energy Eco-Efficiency (EEE). As a comprehensive measure of coordination within the “energy-economy-environment” system, EEE effectively captures a country’s or region’s ability to harmonize energy consumption, green and sustainable development, and environmental protection. This study computes the EEE index using the SBM model and examines how a carbon emission trading pilot policy (CETPP) affects EEE in China and its spatial spillover by employing a difference-in-differences (DID) model and spatial econometric model. The results indicate that CETPP implementation significantly enhances regional EEE and advances China’s green and low-carbon transition process by improving the balance between economic and environmental goals, increasing employment, diversifying the energy supply, and strengthening economic resilience. Mechanistic research reveals that the CETPP promotes the improvement of EEE by reducing pollution emission intensity and that regional innovation ability can enhance the positive impact of the CETPP on regional EEE. Further analysis revealed significant endogenous spatial interactions in EEE across Chinese regions. However, EEE in the eastern and western regions can create a “siphon” effect on production factors that hinders development in neighbouring areas. Implementing a CETPP in a region not only advances local EEE but also stimulates EEE improvements in adjacent areas, with the strongest spillover effect observed in eastern China. To this end, it is essential to enhance the management of carbon dioxide emissions, actively advance the establishment of carbon trading markets. Moreover, region-specific measures should be implemented to promote the coordinated improvement of regional EEE. Keywords: Carbon emission trading pilot policy, Energy eco-efficiency, Difference-in-differences model, Spatial econometric model Introduction Since the Industrial Era, emissions of greenhouse gases (chiefly carbon dioxide) have risen sharply, intensifying the greenhouse effect. Although the growth rate of global carbon emissions has slowed in recent years, carbon dioxide emissions have not yet peaked [ 1 ]. According to the World Energy Outlook 2023 , global energy demand peaked in 2023 and will decline gradually thereafter [ 2 ]. Despite this decline, energy-related greenhouse gas emissions are projected to increase by 30%. In this context, the coordination of carbon reduction with economic and social development through policy measures has emerged as a critical issue that requires urgent attention [ 3 ]. The effectiveness of implementing carbon reduction policies not only directly influences the achievement of the United Nations Sustainable Development Goals (SDGs) but also significantly impacts the trajectory of the global carbon cycle [ 4 ]. As a leading global emitter of greenhouse gases, China faces the dual challenges of aligning economic and social development with the need to control greenhouse gas emissions while promoting sustainable development [ 5 , 6 ]. In September 2020, at the 75th session of the United Nations General Assembly, President Xi Jinping announced that China aims to peak carbon dioxide emissions before 2030 and to achieve carbon neutrality before 2060. Green development constitutes a defining feature of China’s modernization pathway. It is essential to firmly establish and embrace the notion that green mountains and clear waters hold value equivalent to mountains of gold and silver. Guided by the “Dual Carbon” goals of carbon peaking and carbon neutrality, China has adopted a coordinated strategy that integrates carbon reduction, pollution control, ecological restoration, and economic growth, thereby strengthening ecological security and fostering green development momentum [ 7 , 8 ]. To this end, China has established its carbon emissions trading market as a central policy instrument for combating climate change and achieving its “Dual Carbon” goals [ 9 , 10 ]. In fact, China began preparing for a carbon emissions trading market in 2011. The National Development and Reform Commission identified the first batch of six trading pilot areas, including Beijing, Tianjin, Shanghai, Chongqing, Guangdong, Shenzhen, and Hubei. After years of preparation and planning, on July 16, 2021, China’s carbon emission trading market, which specifically targets the power generation industry, officially opened for trading, marking the rollout of the carbon emission trading pilot policy (CETPP). The carbon market has the potential to improve EEE and optimize industrial and energy structures, ultimately reducing regional carbon emissions [ 11 ]. The standardized functioning of the carbon market is crucial for realizing the “Dual Carbon” goals and safeguarding the ecological environment [ 12 ]. In striving to meet global climate objectives and advance green sustainable development, EEE encompasses more than just the efficient use of energy; it also embodies various objectives related to economic, social, and environmental sustainability [ 13 – 15 ]. Reducing carbon emissions and improving EEE are central challenges for meeting global climate targets and advancing sustainable development. Nevertheless, existing indicator systems lack a robust framework to rigorously quantify and evaluate the effectiveness of reducing carbon emissions. Traditional indicators emphasize either economic growth or aggregate emission reductions, obscuring the complex interaction between efficiency and equity. As a comprehensive metric, “Energy Eco-Efficiency (EEE)” quantifies the economic or social welfare created per unit of energy consumption and its environmental impact and thus represents the coordinated optimization of “energy-economy-environment” systems. Therefore, enhancing EEE enables higher levels of development with lower resource use and reduced ecological costs [ 16 , 17 ]. This approach is crucial for alleviating the strain on the carbon cycle and fostering a harmonious integration of the carbon cycle with human development. Accordingly, this paper adopts a holistic perspective on the coordinated development of energy use, the economy, society, and the environment, and establishes EEE as the core observation indicator. The rationale for selecting EEE is its capacity to integrate the three analytical dimensions of “goals, processes, and results” into a cohesive framework. First, at the goal level, improvements in EEE directly advance the shift of the economic growth model towards resource-saving and environmentally friendly development [ 18 ]. Second, at the process level, industrial policies and environmental governance, whether through phasing out outdated production capacity, developing renewable energy, or promoting industrial energy-saving transformation, are directly manifested in improved regional EEE. Third, at the outcome level, spatial disparities in EEE reveal regional inequalities in transformative development. Regions with persistently low EEE and slow improvement are often the key areas facing the greatest transformation pressure and are prone to social conflict. Underlying these patterns are structural constraints, such as a single industrial structure, insufficient technological innovation, and weak institutional safeguards [ 19 ]. Therefore, by measuring, decomposing, and analysing the factors that influence EEE, we can identify the stages in which different regions are on the path to achieving balanced and sustainable development of energy, ecology, and environment in China, the specific constraints they face, and the differences in their dynamic mechanisms. Has the implementation of the CETPP in China’s pilot cities promoted the increase in EEE? In which types of regions can the CETPP play a more significant role? Is there a spatial correlation between the policy’s effect on EEE across regions? This paper investigates the impact of the CETPP on EEE in Chinese regions using a DID model and spatial econometric model. First, EEE is measured and analysed using the Super-SBM model. Second, the DID model is used to study the effect of the CETPP on EEE. In addition, given the existence of spatial correlation between provinces and the difficulty of capturing this spatial effect in the traditional DID methodology, the existence and impact of the spatial econometric model are again investigated. Our research provides three principal innovations beyond the current literature. (1) In terms of research perspectives, existing studies on EEE focus mostly on traditional factors such as industrial structure, energy consumption structure, and the level of technological development. Empirical work that integrates the CETPP into the analytical framework and systematically evaluates its policy effects, however, remains underexplored. This paper examines the impact of CETPP on EEE, clarifying the link between policy implementation and EEE while offering theoretical support and policy guidance for strengthening the national carbon market and improving regional EEE. (2) In terms of research methodology, the Super-SBM model is employed to measure EEE; this methodology provides a more comprehensive and fundamental depiction of the comprehensive efficiency of the “energy-economy-environment” system. Building on this, an empirical analysis is conducted using the DID model and spatial econometric model. The findings reveal the spatial spillover and differentiated driving effects of the CETPP across regions, offering systematic insights into the comprehensive effects of the policy. (3) In terms of research content, this study provides a detailed analysis of the structural, differential regional, and heterogeneous effects of the CETPP on EEE to elucidate the mechanisms and structural variations through which the CETPP influences EEE. It offers both theoretical and empirical support for advancing China’s “dual carbon” goals and achieving high-quality green development. Literature review Energy eco-efficiency Energy Eco-Efficiency (EEE) refers to the promotion of economic development under the guidance of the green development concept while achieving maximum economic output with minimal energy inputs and small environmental impacts; it is a multifaceted systemic balance that is pursued today and reflects the efficiency of the entire “energy-economy-environment” system. The construction of EEE indicators involves two main aspects: input and output. For the selection of input indicators, most studies were based on the setting of the Cobb-Douglas production function, where selected labour and capital factors served as input factors, and energy consumption was treated separately as an input factor; that is, certain labour, capital, and energy factors were selected as input indicators, and only the variables being measured differed. Significant variations exist in the selection of output indicators because the research objectives differ. With respect to the selection of desired output indicators, Liu et al. (2023) [ 20 ] evaluated EEE with economic development as the ultimate goal and selected GDP as the desired output, while Rashidi and Saen (2015) [ 21 ] added some indicators involving public services directly to the desired output. When selecting undesirable output indicators, some scholars chose industrial “three wastes” as indicators of undesirable output [ 22 , 23 ], and some scholars directly used carbon dioxide emissions as indicators of undesirable environmental output [ 24 ]. In the literature, scholars mostly use SFA, DEA, and their improved forms to calculate EEE. Ding et al. (2025) [ 15 ] used SFA to measure and evaluate energy efficiency. DEA is a more typical nonparametric method, but the traditional DEA model has some limitations, and Tone (2002) [ 25 ] proposed an improved model, the Super-SBM model. On this basis, Tan and Wang (2021) and Ma et al. (2018) [ 26 , 27 ] analysed the factors affecting EEE and their levels from different perspectives. Many studies have examined factors that influence EEE from multiple dimensions, including industrial structure, energy consumption structure, and the level of scientific and technological development [ 28 , 29 ]. However, research from a policy perspective remains scarce, and studies that investigate the impact of the CETPP on EEE are particularly limited. The methods used are primarily the Tobit model, the fixed-effects model, and related approaches [ 30 , 31 ]. Most studies assume no spatial interdependence and thus ignore spatial spillover effects, which biases empirical results. Because EEE is closely tied to urban policies and interregional development, econometric methods that incorporate spatial factors have increasingly become important for analysing its determinants [ 32 ]. Carbon emission trading pilot policy As a major emitter of greenhouse gases (GHGs) in China’s annual total emissions, carbon trading represents a market-based mechanism and pivotal instrument for addressing climate change mitigation [ 33 , 34 ]. Since 2013, China has established carbon emission trading pilot programs in seven administrative regions, each featuring distinct levels of economic development and industrial composition, prompting both domestic and international scholars to embark on research regarding this policy. Most of the related studies focus on the macro level, such as carbon emissions, industrial structure, and high-quality development, and the micro level, which is dominated by enterprises. At the macro level, Dai et al. (2022) [ 35 ] reported that the CETPP had a significant and sustained effect on reducing carbon emission intensity in pilot cities. Dong et al. (2022) [ 36 ] reported that the CETPP significantly impacted the combined advantages of overall carbon emission reduction and air quality enhancement, but increased carbon emissions in neighbouring cities. Liu et al. (2023) [ 37 ] elucidated the spatial heterogeneity characteristics of provincial carbon budgets in China, while Feng et al. (2025) [ 38 ] assessed the effects of urban carbon governance policies within the country. Their study indicated that the pilot carbon emission trading policy led to a reduction in total carbon emissions in pilot cities by approximately 1%−5%. However, the influence of carbon market policies on energy efficiency and emissions (EEE) remains to be systematically investigated. At the micro level, Zhang and Wang (2021) [ 39 ] concluded that the policy caused enterprises’ investment expenditures to decrease and significantly improved the sustainability performance of pilot enterprises. In summary, from the perspective of existing evaluation indicators, most of the literature employs single indicators such as carbon emissions or energy consumption to measure policy effects. However, research assessing the CETPP from the aspect of comprehensive efficiency remains insufficient. In terms of effect analysis, existing studies generally discuss economic, emission-reduction, and energy-saving effects separately and fail to systematically examine the synergistic effects of policies on development and emission reduction. From the research perspective, relevant evaluations predominantly focus on the local areas where policies are implemented, while giving limited consideration to spatial spillover effects. This can easily lead to biased conclusions. In terms of the research framework, studies that incorporate the CETPP and regional energy-economy-environment efficiency into the same analytical system while exploring their transmission mechanisms and heterogeneities are lacking. Theoretical analysis The CETPP represents a pivotal institutional arrangement for implementing the green development concept and facilitating the achievement of carbon peaking and carbon neutrality targets. As a typical market-oriented environmental regulation instrument, this policy imposes market-based pricing on the negative externalities of carbon emissions generated during production and business operations. Consequently, carbon emission allowances are endowed with well-defined economic attributes and circulation and trading functions, thereby providing a market-driven incentive mechanism for low-carbon transformation. On the one hand, the CETPP establishes a binding environmental regulation framework that requires payment for pollution emissions. This strengthens the low-carbon threshold for industrial access, forcing the exit of high-carbon and backwards production capacities and resolving overcapacity. It guides production factors to aggregate in low-carbon and efficient industries [ 40 ]. Consequently, regional EEE is enhanced through industrial structure upgrading. On the other hand, the innovation incentive mechanism formed by this policy can effectively stimulate the motivation of microeconomic entities to engage in green technology research and development. It accelerates the substitution of clean energy and the application of low-carbon technologies [ 41 , 42 ]. The optimization of resource allocation promotes technological iteration and upgrading at the regional level through industrial linkage effects and technology diffusion effects, thereby further enhancing regional EEE. On this basis, the first research hypothesis of this paper is proposed: Hypothesis 1 The implementation of a CETPP can improve regional EEE. The CETPP has transformed the use of carbon emissions from free to paid allocation, directly increasing the cost of using high-carbon energy and imposing rigid constraints on production entities within the region. To address this regulatory pressure, production entities must adjust their production decisions to achieve the carbon emission reduction goals by, on the one hand, optimizing the energy consumption structure, reducing the input scale of high-carbon fossil energy, and increasing the substitution ratio of renewable energy [ 43 ]. On the other hand, increasing investment in and application of low-carbon technologies, improving energy conversion efficiency and the greening level of production processes, and reducing carbon emissions per unit of output. The synergistic effect of the above dual decision-making adjustments will directly promote a systematic decline in the intensity of regional pollution emissions [ 44 ]. Optimizing the energy structure and improving efficiency enhance the degree of match between energy input and economic output, and reducing carbon emissions alleviates the carrying pressure on the ecological environment, thus forming a positive interaction between energy utilization and ecological protection [ 45 ]. On the basis of the above analysis, the second research hypothesis is proposed: Hypothesis 2 The CETPP enhances regional EEE by reducing the intensity of pollution emissions. According to absorptive capacity theory, a region’s ability to identify, absorb external resources, and transform them into actual productivity depends on its existing knowledge base and technological level [ 46 ]. The innovation capacity of a region serves as a concentrated manifestation of this ability. Regions with high innovation capacity typically boast well-established scientific infrastructures, a wealth of high-quality human capital, vibrant knowledge spillover networks, and efficient capabilities for transforming research findings into practical applications [ 47 ]. These advantages enable them to accurately identify the low-carbon technology demands and market opportunities underlying policies [ 48 ]. With a reserve of professional talent, they can swiftly absorb advanced external low-carbon knowledge, accelerate technology sharing and collaborative innovation through knowledge networks, and rely on the outcome-transformation system to convert technological principles into energy-saving and carbon-reduction solutions in actual production [ 49 ]. This, in turn, amplifies the promoting effect of the CETPP on regional EEE. On this basis, the third research hypothesis is proposed: Hypothesis 3 Regional innovation capacity can strengthen the positive effect of the CETPP on regional EEE. Model setting and data description Econometric model construction Baseline regression model To investigate the effects of the CETPP on EEE, this paper selects a comprehensive indicator, EEE, which can reflect the degree of effective energy utilization while considering the requirements of economic, social, and environmental sustainability. The first carbon emission trading pilot was viewed here as a quasi-natural experiment, and two-way fixed effects model was constructed using DID method, as shown in Eqs. (1). 1 where i and t represent the province and year, respectively; represents the value of region i in year t ; is a regional dummy variable that represents whether region i is a pilot province or city for carbon emissions trading; and if it is a pilot province or city, then du = 1; if it is a nonpilot province or city, then du = 0. is a temporal dummy variable; this study takes 2014 as the cut-off point in time, meaning that after 2014, dt = 1; before 2014, dt = 0; reflects the effect of CETPP on EEE; and is a series of control variables. is the time fixed effect, is the individual fixed effect, and is the random perturbation term. Spatial econometric model Given that the provincial and municipal regions are interconnected, there is a certain spatial correlation. The CETPP affects EEE within a region and may also affect EEE in neighbouring regions. Therefore, this paper further considers spatial factors to construct a spatial measurement model with EEE as the explained variable. 2 where W is the spatial weight matrix, is the spatial lag of the explained variable, represents the spatial autocorrelation regression coefficient, and represents the coefficient of each spatial lag explanatory variable. When , it is the SDM; when , the spatial Durbin model (SDM) degenerates into the spatial error model (SEM); and when , the SDM degenerates into the spatial lag model (SLM). Selection and description of variables Explained variable and its characteristics In this paper, EEE is taken as the explained variable. Compared with the DEA model, the Super-SBM model not only considers slack variable factors to overcome the problem of input–output slack but also considers the impact of unexpected outputs in the production process, which can better and more comprehensively reflect the essence of efficiency. Therefore, in this paper, the Super-SBM model is constructed to measure EEE [ 16 , 50 ]. The model is shown in Eqs. ( 3 ). 3 where represents EEE ; represents the number of decision units; , and represent the inputs, expected outputs, and unexpected outputs, respectively, of a single decision unit; represents the input or output factor matrix; and , , , , and, are all greater than 0. Considering the comprehensiveness, systematicity, and data availability of the indicators, we select energy, labour, and capital inputs as the input indicators and expected output and unexpected output as the output indicators (Deng and Zhang 2022) [ 51 ], as shown in Table 1 . Table 1. EEE index system Target Guideline Index Input Energy input Total energy consumption (-) Labour input Year-end employment (-) Capital input Total fixed assets investment (-) Expected output Economic benefits GDP (+) Unexpected output Environmental pollution Industrial sulfur dioxide emissions (-) Industrial wastewater discharge volume (-) Industrial solid waste discharge volume (-) Environmental impact Carbon dioxide emission (-) Open in a new tab Specifically, energy input, labour input, and capital input, including total energy consumption, year-end employment, and total fixed assets investment, are selected as measurement indicators. The expected output considers mainly the economic benefits associated with EEE, that is, the total GDP of each province. Additionally, in this paper, the indicators for unexpected output are selected from two dimensions: environmental pollution and environmental impact. Most of today’s environmental pollution comes from industrial production waste, so the “three wastes” of industry are used as indicators to measure environmental pollution. The environmental impact mainly considers the greenhouse effect caused by the large amount of carbon dioxide emissions during energy consumption, which is measured by carbon dioxide emissions. From the perspective of indicator selection, these metrics integrate the core economic, social, and environmental dimensions and constitute composite economic indicators of sustainable development. High EEE values indicate greater wealth generated with fewer resources and less pollution, providing the material basis for realizing justice in other dimensions (such as ensuring employment quality and funding for environmental protection). The data for the EEE indicator system come from the China Statistical Yearbook, China Environmental Statistics Yearbook, China Energy Statistics Yearbook, and CEC.com. Among them, the data on total investment in fixed assets for 2006–2017 were obtained from the statistical yearbooks, and since the data for 2018–2022 of some provinces were not given, they were obtained on the basis of the increase in investment in fixed assets over the previous year [ 52 , 53 ]. Carbon dioxide emissions are calculated using the carbon emissions measurement methodology under the boundary definition guidelines [ 54 ]. In this paper, MATLAB is used to measure the EEE of China’s regions from 2006 to 2022, and the specific results are shown in the Appendix (Table 12 ). The Super-SBM model estimates the maximum economic efficiency attainable under specified environmental constraints and thus characterizes the interaction between “ecology” and “efficiency”. Using this approach, the composite indicators for the economy, society, and environment yield precise rankings and benchmarks that provide a consistent measurement scale for cross-regional comparison and policy evaluation. Table 12. Measurements of EEE province 2006 2007 2008 2009 2010 2011 2012 2013 2014 Beijing 1.172 1.180 1.179 1.174 1.172 1.183 1.184 1.182 1.182 Tianjin 0.461 0.393 0.371 0.380 0.359 0.344 0.324 0.309 0.291 Hebei 0.328 0.297 0.298 0.279 0.279 0.277 0.261 0.243 0.222 Shanxi 0.297 0.287 0.300 0.231 0.233 0.251 0.243 0.221 0.179 Inner Mongolia 1.001 1.018 0.342 1.027 0.343 0.315 0.338 0.309 0.285 Liaoning 0.344 0.321 0.314 0.264 0.241 0.264 0.254 0.231 0.225 Jilin 0.247 0.231 0.210 0.219 0.209 0.230 0.217 0.213 0.206 Heilongjiang 0.367 0.322 0.283 0.249 0.228 0.229 0.209 0.200 0.202 Shanghai 1.034 1.036 1.033 1.042 1.043 1.049 1.053 1.052 1.044 Jiangsu 1.003 1.028 1.009 1.029 1.023 1.044 1.033 1.038 1.038 Zhejiang 0.685 0.678 0.687 0.678 0.708 0.709 0.693 0.685 0.645 Anhui 0.387 0.351 0.330 0.347 0.378 0.404 0.390 0.397 0.403 Fujian 0.525 0.498 0.475 0.464 0.463 0.497 0.477 0.431 0.439 Jiangxi 0.368 0.357 0.339 0.322 0.324 0.346 0.347 0.341 0.331 Shandong 1.022 1.007 0.457 1.010 0.437 0.483 0.423 0.443 0.437 Henan 1.023 1.006 0.387 0.428 0.439 0.504 0.401 0.379 0.444 Hubei 0.371 0.362 0.357 0.334 0.365 0.370 0.362 0.394 0.386 Hunan 0.373 0.351 0.338 0.337 0.341 0.336 0.336 0.351 0.343 Guangdong 1.085 1.073 1.065 1.065 1.073 1.061 1.069 1.067 1.076 Guangxi 0.321 0.296 0.262 0.246 0.236 0.236 0.222 0.211 0.200 Hainan 1.006 0.434 0.364 0.343 0.345 0.303 0.289 0.267 0.249 Chongqing 0.367 0.328 0.325 0.331 0.330 0.339 0.345 0.317 0.315 Sichuan 0.341 0.341 0.350 0.317 0.358 0.390 1.007 1.004 0.404 Guizhou 0.221 0.217 0.201 0.200 0.188 0.175 0.171 0.171 0.167 Yunnan 0.345 0.344 1.001 0.381 0.332 1.006 1.004 0.356 1.003 Shaanxi 0.320 0.290 0.274 0.271 0.275 0.292 0.279 0.269 0.257 Gansu 0.272 0.250 0.211 0.193 0.188 0.178 0.166 0.154 0.144 Qinghai 0.195 0.187 0.182 0.165 0.160 0.144 0.137 0.134 0.123 Ningxia 0.184 0.187 0.187 0.175 0.169 0.170 0.169 0.160 0.147 Xinjiang 0.399 0.328 0.344 0.322 0.366 0.292 0.336 0.321 0.276 province 2015 2016 2017 2018 2019 2020 2021 2022 Beijing 1.188 1.206 1.207 1.218 1.210 1.189 1.220 1.221 Tianjin 0.297 0.261 0.273 0.264 0.251 0.279 0.270 0.289 Hebei 0.236 0.234 0.228 0.226 0.209 0.214 0.199 0.218 Shanxi 0.168 0.146 0.274 0.229 0.230 0.214 0.320 0.371 Inner Mongolia 1.001 1.002 0.323 0.387 0.341 1.003 0.436 0.495 Liaoning 0.237 0.322 0.319 0.304 0.296 0.280 0.272 0.288 Jilin 0.200 0.193 0.191 0.172 0.175 0.179 0.164 0.164 Heilongjiang 0.188 0.177 0.166 0.156 0.155 0.156 0.148 0.160 Shanghai 1.046 1.048 1.045 1.023 1.012 1.010 0.553 1.002 Jiangsu 1.059 1.054 1.054 1.057 1.058 1.051 1.066 1.056 Zhejiang 0.672 1.005 0.732 0.729 0.713 1.005 0.678 1.004 Anhui 0.388 0.376 0.396 0.447 0.444 0.451 0.392 0.415 Fujian 0.445 0.425 0.499 0.468 0.483 0.554 0.479 0.517 Jiangxi 0.304 0.298 0.339 0.307 0.329 0.324 0.325 0.345 Shandong 0.423 0.415 0.417 0.437 0.434 0.459 0.445 0.440 Henan 0.379 1.001 1.019 1.022 1.063 1.040 1.010 1.016 Hubei 0.401 0.413 0.445 0.475 0.481 0.458 0.376 0.415 Hunan 0.367 0.378 0.400 0.388 0.396 0.430 0.361 0.398 Guangdong 1.060 1.056 1.053 1.053 1.055 1.045 1.045 1.044 Guangxi 0.197 0.194 0.189 0.181 0.175 0.171 0.164 0.162 Hainan 0.240 0.237 0.226 0.224 0.229 0.221 0.220 0.231 Chongqing 0.308 0.317 0.319 0.301 0.306 0.311 0.287 0.296 Sichuan 0.411 1.003 0.438 1.012 0.596 0.607 1.006 1.004 Guizhou 0.174 0.172 0.176 0.173 0.174 0.175 0.167 0.173 Yunnan 0.387 0.377 1.026 0.615 0.497 1.003 1.019 1.024 Shaanxi 0.259 0.231 0.251 0.231 0.242 0.235 0.210 0.214 Gansu 0.133 0.129 0.155 0.152 0.148 0.148 0.144 0.149 Qinghai 0.121 0.129 0.127 0.119 0.118 0.131 0.129 0.143 Ningxia 0.138 0.136 0.147 0.147 0.153 0.155 0.159 0.167 Xinjiang 0.273 0.215 0.262 0.397 0.355 1.002 0.238 0.293 Open in a new tab The EEE of all provinces as a whole shows a polarized trend, and to visualize the differences in EEE among regions, Fig. 1 shows the average EEE values of 30 provinces during the period of 2006–2022. Fig. 1. Open in a new tab Average EEE values of 30 provinces (cities) for 2006 to 2022 There is a significant gap in EEE values among various provinces in China. Beijing has the highest value among all the regions, at 1.192, while Qinghai has the lowest value, at 0.144. In addition, the average EEE values of Beijing, Shanghai, Jiangsu, Zhejiang, and Guangdong are exceptionally high and far greater than China’s EEE (0.461). Beijing and Shanghai are driven primarily by low-carbon service sectors, including finance, technology, and information services. In contrast, Jiangsu, Zhejiang, and Guangdong have successfully transitioned to high-end manufacturing, the digital economy, and green technologies, characterized by low energy consumption and emission intensity per unit of output. High EEE regions have captured more transformation dividends (such as green employment and investment), whereas low EEE areas (e.g., Qinghai) bear heavier transformation burdens. The high efficiency of these five provincial-level administrative regions reflects both early achievements and a major driving force for the balanced and sustainable development of China’s energy, ecological environment. Moreover, the large gap between them and less-developed regions constitutes a central imbalance issue that the development must address. China’s regional coordinated development therefore requires these five regions not only to sustain their lead but also to convert their advantages into systematic forces that can propel a nationwide improvement. Doing so will narrow regional disparities and increase overall efficiency, thereby realizing genuine coordinated development. The dynamic trend graph of EEE over time for China as a whole and for East, Central, and West China is shown in Fig. 2 ; according to the current division method, China’s three macroregions encompass 12, 9, and 9 provincial-level units, respectively. As shown in Fig. 2 , the levels of EEE in China’s three macroregions differ greatly; the eastern region is consistently at the highest level from 2006 to 2022, while the EEE values in the central and western regions are lower than the national EEE average. There are significant spatial differences among the sample regions, and to address this significant feature, a spatial econometric model is constructed in Sect. " Extended analyses "" to investigate the underlying influence mechanisms in depth. Fig. 2. Open in a new tab Trend of average EEE changes in China and different regions The EEE level in the eastern region has always been the highest because the coastal provinces in the eastern region have relatively developed economies and a significant portion of the service sector, which has low energy consumption and low pollution emissions. The values of EEE in the central region are lower than the national average within the research range. This is because the central region has a larger population, high energy consumption and labour input, and mainly features coal mining and heavy chemical industry development, which increase pollution emissions. However, the EEE in the central region showed a steady upwards trend after 2008, and the development momentum was good. The EEE values in most years in the western region are at the lowest level, with the fastest growth rate of 45.14% occurring in 2019–2020. This may be due to the Western Development Strategy, which promotes the improvement of resource utilization efficiency and economic development acceleration in the western region. However, the natural resource scarcity and fragile ecological environment in the western region have not changed; thus, the EEE values in the study interval are still far from the national EEE level. Core explanatory variable The core explanatory variable in this paper is the CETPP; that is, . The six provinces (cities) that have implemented pilot projects are used as the treatment group, and the remaining provinces (cities) that have not implemented pilot projects are used as the control group. The year 2014 is taken as the cut-off point. Control variables Considering other factors that may affect EEE, the control variables in this paper are fiscal expenditure ( FE ), foreign investment ( FC ), industrial agglomeration ( ind ), urbanization level ( urb ), and R&D intensity ( inv ). Among them, fiscal expenditure ( FE ) is a macroeconomic regulation measure taken by the government to intervene in various aspects, such as economic development, environmental pollution, and carbon reduction. The government can impact EEE through fiscal expenditure by increasing enterprise admission standards and other measures. In this article, the level of fiscal expenditure ( FE ) is measured as the proportion of local general public budget expenditure to GDP in each province. Following, Hanif et al. (2019) [ 55 ], the ratio of actual foreign investment to GDP is used to measure the degree of foreign investment ( FC ) in various provinces and autonomous regions. The “pollution shelter” hypothesis suggests that if foreign-funded enterprises exploit the relatively low environmental regulation standards of the host country to relocate high-pollution industries on a large scale, a decrease in EEE is highly likely; in contrast, the successful introduction of green and low-carbon technologies will help improve EEE. Industrial agglomeration ( ind ) enables companies in related industries to be geographically concentrated, which helps promote collaborative cooperation among industries in the region, thereby promoting EEE improvement. Moreover, agglomeration contributes to the generation of innovative thinking and is a manifestation of knowledge and technology spillover. Existing research has shown that industrial spatial agglomeration can effectively increase the energy utilization efficiency of enterprises. For example, an empirical study by Qin et al. (2020) [ 56 ] revealed that the development of industrial clusters significantly promotes the total factor energy productivity of enterprises. The urbanization level ( urb ) has a dual effect on EEE. In recent years, with the acceleration of urbanization throughout the country, economic scale expansion has led not only to more energy consumption but also to a gradually generated economies of scale effect, which in turn has affected the transformation of EEE from quantity to quality accumulation. R&D intensity ( inv ) affects EEE by promoting technological innovation and reducing pollution emissions. On this basis, this study includes R&D investment in the control variable system and measures R&D intensity using the proportion of R&D expenditure to regional GDP. Descriptive statistics In this paper, 30 provinces and cities in China (excluding Tibet) are taken as the research sample. Since the CETPP was fully rolled out in China in 2021, the research interval for the subsequent empirical analyses is limited to the period from 2006 to 2020 and focuses mainly on the impact of the CETPP on EEE and its spatial spillover effects. Table 2 synthesizes the descriptive statistics for all the variables. Table 2. Descriptive statistics of each variable Variables Obs. Mean Std. Dev Min Max 450 0.460 0.321 0.118 1.218 450 0.093 0.291 0 1 450 0.242 0.108 0.075 0.758 450 0.024 0.021 0.001 0.121 450 0.939 0.202 0.324 1.372 450 0.558 0.137 0.275 0.896 450 0.019 0.015 0.002 0.070 Open in a new tab Empirical results and analysis Benchmark regression results To investigate the effect of the CETPP on EEE, a regression on Eqs. (1) is conducted, and the baseline regression results are shown in Table 3 . Table 3. Regression results of the benchmark model Variables EEE (1) (2) (3) (4) 0.327*** (0.054) 0.233***(0.049) 0.207***(0.050) 0.124** (0.050) −1.372***(0.130) −1.285***(0.134) −1.504*** (0.141) 1.637** (0.676) 1.329** (0.652) −0.094 (0.521) −0.604 (1.364) 3.670*** (0.923) Constant 0.429*** (0.016) 0.770***(0.035) 0.713***(0.042) 1.015*** (0.092) Regional fixed effects Yes Yes Yes Yes Time fixed effect Yes Yes Yes Yes N 450 450 450 450 R-squared 0.078 0.268 0.277 0.339 Open in a new tab ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively Column (1) of Table 3 presents the estimated results of the individual regression of the CETPP on EEE, while columns (2), (3), and (4) introduce fiscal expenditure, foreign investment, and other control variables, respectively. The CETPP has a positive effect on EEE at the 1% significance level, and this result is still significant after fiscal expenditure, foreign investment, and other control variables are added, indicating that the implementation of the CETPP helps to improve EEE and realize sustainable economic development. Thus, Hypothesis 1 is verified. Fiscal expenditure has a statistically significant negative effect on EEE at the 1% level. This likely reflects the lag between infrastructure investment and economic returns, as large upfront spending increases pollution emissions and yields limited short-term benefits, thereby impeding the improvement of EEE. Elevated levels of industrial agglomeration and urbanization have also constrained EEE. Society must further reduce pollution emissions and energy consumption in future production and daily life. When rapid industrial agglomeration occurs without concurrent green technology upgrades, it can become a concentrated source of pollution and energy consumption that exceeds the environmental carrying capacity; likewise, urbanization that prioritizes speed and scale can sharply increase per capita energy demand and ecological pressure through extensive expansion and delayed green infrastructure development. Together, these three factors both directly reduce EEE and impede the realization of the coordinated balance of the “energy-economy-environment” system by producing unevenly distributed benefits and costs. They thereby widen regional development gaps and make progress towards a green, low-carbon society more difficult. Foreign direct investment facilitates the diffusion of clean production technologies and efficient energy management models through technology spillover and green capital injection, directly improving regional EEE. R&D intensity has a significant positive effect on EEE at the 1% level, indicating that scientific research fosters technological breakthroughs and innovation. Local green technology development and industrial chain upgrading reduce energy use and emission intensity, thereby improving EEE. Together, these changes accelerate energy-structure optimization, low-carbon industrial transformation, and the green growth dividend from EEE gains. When inclusive measures such as skills retraining, ecological compensation, and transition social security specifically target workers in traditional sectors and vulnerable communities, technological progress can be converted into shared development that advances environmental quality, employment stability, and social resilience in tandem. Robustness test Parallel trend test A fundamental prerequisite for the DID method is the assumption of parallel trends, which means that without policy rollout, the trends of the two groups are parallel, and there is no notable difference between them. If the parallel trend hypothesis is not valid, the differences between the two groups cannot be attributed to policies or intervention measures, and the results of the DID model may be unreliable. The parallel trend test results are shown in Fig. 3 . Fig. 3. Open in a new tab Results of the parallel trend test The regression coefficients of each year before the implementation of the policy in the benchmark year 2013 fluctuated around the 0 axis, indicating that the difference between the two groups before the policy implementation and that the changes in EEE between them followed a parallel trend. Additionally, in the year in which the CETPP was implemented and in the two years thereafter, the estimated coefficient passed a significance test at the 10% level, suggesting that the policy’s influence on EEE showed a significant positive correlation in the short term. The reason can be inferred as follows: the introduction of the CETPP led various regions to focus on green reducing emissions. As the first batch of pilot cities are all economically developed areas with relatively complete industrial structures and a variety of leading industry types, many enterprises joined the carbon market during the pilot period. The operational framework of the carbon market was updated quickly, and carbon emission trading covers many industries. In the short term, the policy effect of the carbon market became increasingly obvious, leading to a rapid increase in EEE. However, the impact on EEE weakened from the second year onwards; although positive fluctuations were observed, the increase was no longer significant. This may be because other cities across the country also began to focus on reducing energy consumption and emissions and advocated for green production development under the influence of this policy. The carbon market in the first batch of pilot cities is becoming increasingly perfect, and the scope of updates is smaller than that of large-scale promotion when the policy was first introduced. On the other hand, with the continuous development of the Chinese economy, the marginal cost of reducing emissions is gradually increasing, which has also slowed the increase in EEE under the CETPP. Placebo test A placebo test was conducted to exclude the possibility that other variables not accounted for have the same effect on the EEE assessed in this study [ 11 ]. That is, the placebo test was designed to demonstrate that the observed effects stem primarily from the CETPP rather than other unobserved factors. Specifically, 6 regions from 30 research samples were randomly generated as the treatment group, and the benchmark model was used to estimate the coefficients; then, the experiment was repeated 500 times. The experimental findings are presented in Fig. 4 . Fig. 4. Open in a new tab Results of the placebo test The vertical red solid line in the figure represents the true coefficients of the benchmark regression results, the horizontal red solid line represents the significance level of 10%, the red curve represents the estimated p-value, and the blue curve represents the density distribution of the estimated coefficients. Drawing from the distribution and importance of the coefficient values depicted in the figure, the following conclusion can be inferred: most of the coefficients are clustered around 0, and the kernel density plot and p-value of the coefficients are mostly on one side of the two red solid lines, indicating that most of the coefficients deviate greatly from the estimated coefficients from real quasi natural experiments and have no statistical significance, suggesting that limited policy efficacy was not affected by random factors and that the CETPP has indeed improved EEE (rather than other factors), passing the placebo test. Propensity score matching method (PSM-DID) Considering the notable variations among samples, to alleviate sample selection bias and its endogeneity issues, we further selected samples with strong comparability. In this section, we use the propensity score matching method for robustness testing. This study employs the matching variables of fiscal expenditure, foreign investment, industrial agglomeration, urbanization level, and R&D intensity and adopts the adjacency 1:1 method for matching. For the selected variables, there should be no discrepancy between the experimentally equivalent policy-receiving and nonreceiving groups. After matching, the balance test is used to determine the presence of a notable disparity in the control variables between the two groups. The results of the balance test are listed in Table 4 . Table 4. Results of the balance test Variables U before matching Average value Standard deviation T-test M after matching Treatment group Control group t P > t U 0.190 0.255 −74.3 −5.22 0.000 M 0.185 0.180 5.8 0.53 0.594 U 0.039 0.020 83.2 8.46 0.000 M 0.029 0.026 10.3 0.82 0.413 U 0.890 0.952 −28.7 −2.62 0.009 M 0.980 0.981 −0.4 −0.03 0.978 U 0.338 0.361 −25.5 −2.29 0.022 M 0.372 0.373 −0.8 −0.06 0.952 U 0.029 0.016 92.6 8.10 0.000 M 0.022 0.022 −0.1 −0.01 0.995 Open in a new tab After PSM, the deviations between the variables in the two groups are significantly reduced. Moreover, the p-values before matching are statistically significant at the 1% level, whereas after matching, the p-values are not significant. This finding indicates that the variables in the two groups after processing through PSM exhibit no notable discrepancy, the deviation is small, the matching results are reliable, and more effective estimation results can be obtained. Propensity score matching also excludes some samples that do not fulfil the requirements. The PSM-DID regression outputs are systematically summarized in Table 5 . Table 5. PSM-DID regression results Variables EEE (1) (2) 0.128*(0.075) 0.121* (0.072) −3.294*** (0.680) 6.431*** (1.425) −0.322 (1.142) −0.445 (2.931) 2.106 (2.264) Constant 1.056***(0.11.) 1.083*** (0.288) Regional fixed effects Yes Yes Time fixed effects Yes Yes N 154 154 R-squared 0.295 0.303 Open in a new tab ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively The estimation results show that the effect of the CETPP on EEE is significant and positive regardless of the presence or absence of control variables, which suggests that the CETPP promotes the region’s EEE. Furthermore, the symbols representing the control variables align with those found in the benchmark regression outcomes. Thus, the PSM-DID model further improves the DID model, which also proves the robustness of the benchmark regression results. Replacing the explained variable Considering the diversity of measurement methods and indicator selection for EEE, this paper uses the proportion of regional energy consumption to GDP to represent single-factor EEE as the explained variable for regression [ 57 ]. The outcomes are presented in Table 6 . Table 6. Benchmark regression results after replacing the explained variable Variables EEE (1) (2) (3) (4) 0.392*** (0.082) 0.195**(0.066) 0.101*(0.056) 0.226*** (0.061) −2.875***(0.174) −2.558***(0.172) −3.149*** (0.172) 5.948*** (0.868) 5.630*** (0.797) −1.177* (0.637) −1.112 (1.666) 2.737** (1.127) constant 0.978*** (0.024) 0.236***(0.044) 0.473***(0.054) −0.348*** (0.112) Regional fixed effects Yes Yes Yes Yes Time fixed effect Yes Yes Yes Yes N 450 450 450 450 R-squared 0.050 0.417 0.474 0.565 Open in a new tab ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively Like the benchmark regression, column (1) presents the approximated outcomes of the CETPP’s separate regression on single-factor EEE, while columns (2), (3), and (4) introduce fiscal expenditure, foreign investment, and other control variables, respectively. We find that the effects of the CETPP and each control variable on the single-factor EEE align with the findings of the benchmark regression; that is, the policy can improve the single-factor EEE of each of the regions, which in turn promotes EEE, and these findings remain significant after the EEE measurement method is replaced, which indicates that the regression results of the benchmark model are robust. Mechanism analysis Pollution inhibition effect The preceding subsection offered a preliminary discussion of the relationship between the CETPP and EEE. To further examine the causal mechanism between the CETPP and EEE in the presence of other influences, this paper introduces interaction terms into the benchmark regression to test for more complex moderating effects among the relevant variables. When the effectiveness of the CETPP is evaluated, the implementation of the CETPP leads to changes in pollution intensity. Therefore, fully considering the mechanism of the impact of pollution emission intensity on EEE is necessary. On the basis of the benchmark regression model, the following model (4) is constructed: 4 where is the mechanism variable, specifically the proxy variable for pollution intensity (denoted as Pollution); the other symbols retain the meanings described in Eqs. (1). In this paper, annual provincial carbon emissions are used as the proxy variable for pollution emission intensity (Pollution). The larger the value of Pollution is, the higher the pollutant emissions. According to the regression results in column (1) of Table 7 , the estimated coefficient of the policy interaction term is −0.086, which is significantly negative at the 1% level. This finding indicates that the CETPP has reduced the regional pollution emission intensity. The CETPP has alleviated the carrying load of the ecological environment through the effective reduction of pollution emissions, establishing a positive development pattern where efficient energy utilization and ecological protection are promoted in tandem, consequently enhancing the regional EEE. Therefore, the reduction in pollution emission intensity is an important mechanism through which the CETPP promotes the improvement of EEE, and Hypothesis 2 is verified. Table 7. Mechanism analysis of the impact of the CETPP on EEE Variables Pollution EEE (1) (2) −0.086*** 0.341*** (0.033) (0.052) Innovation 0.107*** (0.015) 0.317*** (0.048) FE −1.067*** −0.392** (0.093) (0.168) FC −0.210 −2.303*** (0.489) (0.634) ind 0.415*** −0.271*** (0.045) (0.060) urb −0.160** 0.856*** (0.091) (0.122) inv −0.874 −1.417* (0.631) (0.861) Constant 0.286*** 0.554*** (0.079) (0.162) Year FE YES YES Region FE YES YES N 450 450 Adj_R 2 0.457 0.546 Open in a new tab ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively Innovation empowerment effect In different contexts, the intensity of the impact of the CETPP on EEE varies. To further examine the moderating effect of regional innovation level (Innovation) between the CETPP and EEE, the following model (5) is constructed: 5 where Innovation is the moderating variable and represents the coefficient of the interaction term between the CETPP and the innovation level. The meanings of the other symbols remain the same as those in Eqs. (1). In this paper, the natural logarithm of the number of accepted domestic invention patent applications is used as a proxy variable for regional innovation capacity. A larger value of this indicator indicates a higher level of regional innovation. According to the regression results in column (2) of Table 7 , the estimated coefficient of the interaction term between the CETPP and regional innovation capacity is significantly positive at the level, indicating that an improvement in regional innovation capacity can positively moderate the positive relationship between the CETPP and EEE. In general, the improvement in regional innovation capacity can enhance technology R&D and application by relying on the advantages of innovative basic resources, human capital, and knowledge network cooperation, further magnifying the promoting effect of the CETPP on EEE. Thus, Hypothesis 3 is verified. Extended analyses Provinces and municipalities are interdependent, and regional differences in economic base, energy supply, industrial structure, and ecological environment produce substantial variation in EEE levels. Therefore, to assess the magnitude of CETPP’s effect on EEE and to determine whether spatial spillovers occur, additional empirical testing is required. Selection of the spatial weight matrix and autocorrelation test In spatial econometric models, spatial weight matrices can reflect the degree of interdependence between different regions. Before the spatial spillover effects of EEE are examined, their respective spatial correlations need to be examined. On this basis, three spatial distance matrices from 2006 to 2020 are selected for calculation, namely, the spatial geographic distance matrix, spatial economic distance matrix, and nested economic-geographic weighting matrix. Specifically, the spatial geographic distance matrix mainly measures the spatial separation between regions and is calculated using the reciprocal of the latitudinal longitudinal distance ( ) between regions and is standardized as ; the spatial economic distance matrix varies with the stage of territorial development. After standardization, it is denoted as . The specific construction method is shown in the following formula: 6 where and are the average per capita GDP of region i and region j , respectively, from 2006 to 2020. The nested matrix of economic geography accounts for both economic and geographical factors, assuming that they are equally important to the results, with a weight of 0.5 and standardized as . Spatial autocorrelation is employed to quantify the spatial autocorrelation intensity among areal units’ characteristic distributions. In this paper, the global Moran ’ s I index is used to measure the correlation of spatial units. Its formula is as follows: 7 where is the observed value of region i , n is the number of observed values, and W is the weight matrix for each space. Table 8 shows the global Moran ’ s I index of EEE for each province under different weight matrices ( , , ) in the period from 2006 to 2020. Table 8. Moran’s I index of EEE in the period from 2006 to 2020 Global Moran’s I Index of EEE Year p p p 2006 0.023 1.563 0.118 0.285 2.523 0.012 0.155 2.988 0.003 2007 0.022 1.561 0.119 0.310 2.747 0.006 0.169 3.226 0.001 2008 0.006 1.154 0.248 0.227 2.133 0.033 0.129 2.655 0.008 2009 0.026 1.678 0.093 0.385 3.371 0.001 0.203 3.802 0.000 2010 0.045 2.270 0.023 0.327 2.997 0.003 0.182 3.576 0.000 2011 0.018 1.462 0.144 0.223 2.088 0.037 0.131 2.667 0.008 2012 0.006 0.803 0.422 0.152 1.499 0.134 0.087 1.940 0.052 2013 0.007 1.165 0.244 0.206 1.955 0.051 0.113 2.395 0.017 2014 0.011 1.268 0.205 0.188 1.806 0.071 0.114 2.398 0.016 2015 0.017 1.454 0.146 0.268 2.462 0.014 0.153 3.026 0.002 2016 0.001 0.943 0.346 0.150 1.464 0.143 0.081 1.818 0.069 2017 0.004 1.074 0.283 0.128 1.299 0.194 0.082 1.862 0.063 2018 0.004 0.835 0.404 0.108 1.139 0.255 0.067 1.616 0.106 2019 0.017 1.431 0.153 0.156 1.539 0.124 0.098 2.127 0.033 2020 0.001 0.908 0.364 0.105 1.098 0.272 0.060 1.473 0.141 Open in a new tab The results show that the global Moran ’ s I index of EEE is positive under different weight matrices, indicating that spatial autocorrelation is positively related to EEE. Taking the weight matrix as an example, the global Moran ’ s I value of EEE is between 0.060 and 0.203; except for 2018 and 2020, it passes a significance test of 10% or above in all other years, indicating that the EEE of the 30 provinces in China (except Tibet) exhibits a significant spatial correlation overall. In addition, the table indicates that the significance of the global Moran ’ s I index of EEE under is significantly greater than that under the and weight matrices. The economic geography nested matrix ( ) is chosen as the means through which to investigate the local regional spatial clustering patterns of EEE across China’s 30 provincial-level administrative units through local spatial autocorrelation analysis. Regression analysis of spatial econometric models The Lagrange multiplier (LM) test and Hausman test were carried out to achieve a better fit. Combined with the test results, the SLM under time fixed effects was chosen for the spatial econometric study. Additionally, owing to the impact of the epidemic in 2020, China’s industries shut down production, the economy turned down, and import and export trade was affected; to mitigate the influence of the epidemic on EEE, the sample data for 2020 are deleted here, and the regression is carried out by utilizing the data from 2006 to 2019 as a proof of the robustness of the results. It is evident from Table 9 that the regression coefficient of the CETPP has a statistically significant positive effect on EEE, suggesting that the pilot program has significantly increased the region’s EEE and that the control variables are consistent with the previous conclusions. The spatial autoregressive coefficient rho is 0.276, with high statistical significance and a positive value at the 1% level, meaning that interregional EEE demonstrates a notable endogenous spatial interaction effect; that is, the region’s EEE can lead neighbouring regions to transform together with EEE to exhibit a “high-high” agglomeration condition in spatial terms. After the data from 2020 are deleted, the robustness test results also reveal that the CETPP significantly promotes EEE and that spillover effects in EEE occur between regions, indicating the robustness of the SLM. The implementation of the CETPP promotes the concentration of capital, supportive policies, and technological infrastructure, resulting in scale effects that increase environmental quality, employment, and energy utilization; it can motivate and enable other regions to adopt green technologies, thereby facilitating their diffusion and helping to balance economic growth with environmental impacts. The policy can extend green industrial chains and increase labour mobility, creating additional job opportunities in neighbouring areas; it can also strengthen cooperation, interaction, and the sharing of benefits and risks with adjacent regions, thereby improving regional resilience and environmental awareness [ 58 ]. Table 9. SLM regression results and robustness test Variables SLM Robustness test (1) (2) 0.116** (0.048) 0.133*** (0.051) −1.421*** (0.140) −1.438*** (0.141) 0.969 (0.644) 0.848 (0.649) −0.096 (0.504) −0.165 (0.548) −0.586 (1.318) −0.425 (1.417) 3.555*** (0.892) 3.545*** (0.904) 0.276*** (0.101) 0.305*** (0.103) N 450 420 R-squared 0.350 0.358 Open in a new tab ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively Heterogeneity analysis Heterogeneity analysis in three macroregions To delve deeper into the spatial spillover effects of EEE across various regions of China, 30 provinces and cities (except Tibet) were initially categorized into three distinct regions and regression tests were conducted to investigate the effects of the CETPP on EEE under regional heterogeneity. Table 10 presents the results of the heterogeneity analysis of the SLM across the three macroregions. Table 10. Heterogeneity analysis results for the three microregions Variables SLM Eastern region Central region Western region (1) (2) (3) du x dt 0.198***(0.060) 0.090(0.076) −0.108*(0.063) FE −5.050***(0.499) −2.348***(0.378) −0.963***(0.149) FC 1.485*(0.850) 1.200(1.689) 4.625**(2.066) ind −0.485(0.509) −0.266(0.913) −2.795**(1.129) urb −0.430(1.331) 0.160(2.253) 5.311*(3.040) inv 8.318***(1.773) −29.83***(6.032) −0.269(0.892) rho −0.432***(0.111) 0.887***(0.235) −1.046***(0.223) N 180 135 135 R-squared 0.628 0.208 0.137 Open in a new tab ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively Between 2006 and 2020, the CETPP coefficients in the eastern and central regions are positive, verifying that policy orientation is conducive to the promotion of regional EEE. The policy orientation in the eastern region passes the significance test at the 1% level, indicating a direct effect. However, for the western region, the interconnection between the two is negative at the 10% level, indicating that the ecological environment is more fragile. It is difficult to substantially enhance the efficacy of EEE by relying solely on the implementation of the CETPP; other policies need to be implemented simultaneously to promote EEE in the region. In addition, the spatial autoregressive coefficients in the eastern and western regions are significantly negative, indicating that the EEE of a region may have a dampening effect on the EEE of neighbouring regions, with production factors clustering in places where EEE works well. This aggravates environmental pollution in neighbouring regions and leads to a “siphon” effect. Regarding the control variables, the effect of fiscal expenditure on EEE is significantly negative in all three regions, whereas foreign investment promotes EEE, and this effect is significant in the eastern and western regions. Industrial agglomeration has a significant inhibitory effect on EEE in the western region at the 1% level. This difference may be due to the incomplete industrial structure, enterprises with high energy consumption and significant pollution levels, and the slow popularization of green emission reduction production methods in the western region. R&D intensity significantly promotes EEE in the eastern region but inhibits EEE in the central region. This is because the east is a relatively developed region that can make full use of its economic and industrial advantages, respond positively to the policy orientation, and expedite research and development in science and technology. On the other hand, the central region has a weaker foundation and a longer cycle of R&D investment and output, and its development model has not yet fully shifted from “resource-factor-promoting” to “green-development-driven”, slowing the EEE process. (2) Heterogeneity analysis of EEE levels In this paper, the EEE levels in different regions are considered, and with reference to the EEE measurement results, the average EEE in each province and city is calculated, compared with the national average EEE, and then divided into two parts, high and low, to study the spatial diffusion impact of EEE. Specifically, when the national EEE average is taken as the boundary, among the 30 provinces in the study area, 11 provinces and cities have EEE values higher than the national average, including Beijing, Guangdong, Jiangsu, Shanghai, Zhejiang, Henan, Yunnan, Sichuan, Inner Mongolia, Shandong, and Fujian, and 19 provinces and cities in China have EEE values lower than the national average, including Hubei, Anhui, Hunan, Xinjiang, Jiangxi, Chongqing, Hainan, Tianjin, Liaoning, Shaanxi, Hebei, Shanxi, Guangxi, Heilongjiang, Jilin, Guizhou, Gansu, Ningxia, and Qinghai. Table 11 documents the heterogeneity analysis of the SLM across EEE levels. As shown in Table 11 , the CETPP can significantly contribute to the EEE of regions with low EEE values and has a positive effect on the EEE process in neighbouring regions. However, for high-efficiency regions, there is a negative correlation, which may be because high-efficiency regions already have high efficiency levels, so it would take a long time to increase this value. Additionally, because high-efficiency regions produce a “siphon” effect, the increase in EEE in neighbouring regions is likely to lead to a decrease in the efficiency value of the local region. The relationship between fiscal expenditure and EEE in regions with low efficiency values is negatively correlated at the 1% level, and foreign investment promotes EEE in all regions. Industrial agglomeration has a positive effect on the EEE of inefficient areas, which are generally characterized by high emissions and serious pollution, and the reduction in pollution emissions by industrial clusters is directly reflected in the improvement in efficiency values; the level of urbanization inhibits the EEE of inefficient areas. Regardless of whether the regional efficiency value is high or low, R&D investment contributes to an increase in factor productivity and reduces pollution while providing economic benefits. Table 11. Heterogeneity analysis results of EEE levels Variables SLM Regions with high EEE Regions with low EEE (1) (2) du x dt 0.004(0.084) 0.047**(0.021) FE −0.417(0.654) −0.443***(0.052) FC 4.735***(1.569) 0.768***(0.220) ind −0.074(0.732) 0.413*(0.217) urb 0.530(1.936) −1.234**(0.565) inv 13.32***(3.507) 0.677*(0.348) rho −0.223*(0.121) 0.477***(0.060) N 165 285 R-squared 0.374 0.184 Open in a new tab ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively Conclusions and recommendations This study employs panel data from 30 provinces (cities) in China and conducts a series of empirical analyses using the Super-SBM model, DID model, and spatial econometric model. It investigates the impact of the CETPP on EEE while also uncovering the spatial effects among different regions and the heterogeneous characteristics of policy influence across various areas. The main research conclusions are summarized below: The CETPP has significantly promoted regional EEE. The implementation of the CETPP has notably enhanced regional EEE by reducing pollution emission intensity and improving energy utilization efficiency. This improvement is reflected not only in the balance between economic growth and environmental protection but also in the increase in the employment rate, the diversity of the energy supply, and the resilience of the economic system, thus comprehensively advancing China’s green and low-carbon transition process. Regional innovation capacity strengthens the positive impact of the CETPP on EEE. The research reveals that regions with greater regional innovation capacity can more effectively absorb and apply low-carbon technologies, thereby strengthening the promoting effect of the CETPP on EEE. These findings indicate that regional innovation capacity plays a crucial moderating role in the promoting effect of CETPP on EEE. The CETPP has a significant spatial spillover effect, which promotes EEE in neighbouring regions. The implementation of the CETPP not only promotes EEE in the local region but also drives EEE in neighbouring regions through the spatial spillover effect. This effect is particularly evident in the eastern region, indicating that the coordinated action of the policy across regions is highly important for achieving overall improvement of EEE nationwide. Drawing on the research conclusions, this paper provides the following recommendations: Pilot projects should be strengthened and expanded. The top-level institutional design should be improved, the scope of pilots and the range of industry sectors should be broadened, and the data foundation for carbon emissions trading should be consolidated. The allocation and management of carbon allowances should be optimized, market vitality and financial functionality should be enhanced, and the role of pilot programs in advancing national integration should be clarified. The full utilization of regional innovation capacity can strengthen the positive impact of the CETPP on regional EEE. The research and development of breakthrough carbon-reduction technologies such as wind power and photovoltaics should be intensified, green upgrading technologies for traditional industries should be advanced, and negative-carbon and ecological-restoration technologies should be developed while increasing investment in green and low-carbon initiatives. In light of regional heterogeneity, tailored strategies to improve regional EEE based on local conditions should be developed. In traditional energy regions (for example, some cities in Shanxi, Heilongjiang, and northern Shaanxi), industrial renewal and diversification should be prioritized, livelihoods secured, and ecosystems restored; in traditional manufacturing bases (for example, the older industrial districts in the Yangtze River Delta and the Pearl River Delta), green upgrading across industrial chains should be promoted and carbon-reduction efforts coordinated; in regions rich in renewable resources (for example, the wind and solar hubs in northwest and north China and hydropower areas in the southwest), local value retention should be balanced with industrial extension; and in ecologically sensitive and agricultural regions (for example, the Qinghai-Tibet Plateau, the upper reaches of the Yangtze and Yellow Rivers, and key agricultural zones), realizing the value of ecological products and strengthening climate resilience should be the focus; and in megacities and urban agglomerations (for example, core cities in the Beijing-Tianjin-Hebei region, the Yangtze River Delta, and the Guangdong-Hong Kong-Macao Greater Bay Area), systematic emission reductions should be implemented, innovation spillovers fostered, and regional coordination enhanced. The purpose of this paper was to take a step towards understanding the effect of the CETPP on EEE. However, further research is needed. For example, aspects of interest include those where (1) refining the sample data to the municipal level is considered to provide a more refined perspective for the comprehensive study of the impact of the CETPP on EEE and (2) incorporating quantitative indicators such as regional historical and geographical factors is considered to assess the equity of the transformation process, thereby more comprehensively accounting for the complexity of EEE in the research. Appendix See Table 12 here, Author contributions Liandi Zhang and Yiyao Dai analysed the model, drafted and revised the manuscript. Wenguang Tang and Shenglin Ma designed the research. Funding This work was supported by National Social Science Foundation of China (Youth Program), Grant No. 20CTJ011, and National College Students Innovation and Entrepreneurship Training Program, Grant No. 202410069044. Data availability The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request. Declarations Ethics approval and consent to participate Not applicable. Competing interests The authors declare no competing interests. Footnotes The original online version of this article was revised: the corresponding author email address was corrected. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Change history 6/20/2026 The original online version of this article was revised: the corresponding author email address was corrected. References 1. Lanne M, Liski M. Trends and breaks in per-capita carbon dioxide emissions, 1870–2028. Energy J. 2004;25(4):41–65. [ Google Scholar ] 2. Zhou C, Zhang W, Richardson-Barlow C, Zhang Z. Navigating carbon neutrality: policy pathways and consistency on industrial decarbonization in China. Carbon Balance Manag. 2025;20:66. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. Sun D, Li Y. Carbon peaking pressure and corporate R&D investment. Econ Lett. 2025;251:112328. [ Google Scholar ] 4. Wen H, Yang W. Artificial intelligence applications and the improvement of carbon emission efficiency from the perspective of sustainable development: empirical evidence from China. Carbon Balance Manag. 2026;21:41. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Hu J, Wang D, Zeng HJ, Ma SL. From disclosure to decision: how climate risk shapes firms’ human capital strategies in China. Theor Appl Climatol. 2025;156:556. [ Google Scholar ] 6. Sun D, Li J. Carbon peaking pressure and corporate earnings management. Int Rev Econ Finance. 2025;103:104503. [ Google Scholar ] 7. Yan Z, Liang J, Wu Y, Ma S. Analysis of the carbon emission reduction mechanism in the textile industry under the dual control of carbon emissions and carbon trading prices. Ind Textila. 2026;77:106–14. [ Google Scholar ] 8. Wang Z, Ma S. Research on the impact of digital inclusive finance development on carbon emissions—based on the double fixed effects model. Global NEST J. 2024;26(7):06227. [ Google Scholar ] 9. Sun D, Luo Q. Green transition under carbon peak pressure: beyond greenwashing. Appl Econ. 2026. 10.1080/00036846.2026.2635633. [ Google Scholar ] 10. Wen L, Yuan Y, Zeng H, Ma S. The impact of ESG performance on textile industry green exports under the dual carbon goals. Global NEST J. 2026;28(1):07977. [ Google Scholar ] 11. Yu D, Li J. Evaluating the employment effect of China’s carbon emission trading policy: based on the perspective of spatial spillover. J Clean Prod. 2021;292:126052. [ Google Scholar ] 12. Wang Y, Li M, Hou J. Can carbon emission trading policy promote enterprise green management innovation? Carbon Balance Manag. 2025;20:50. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 13. Wu Y, Wang X, Xie H, Ma S. How digital trade can reshape the trajectory of green and low-carbon development under the leadership of dual-control objectives. Global NEST J. 2025;27(7):07580. [ Google Scholar ] 14. Wang D, Hu J. From grid to factory floor: how ultra-high voltage transmission drives corporate automation. Energy Strategy Rev. 2026;65:102221. [ Google Scholar ] 15. Ding R, Sun F, Zhong T. Does technological innovation promote green development in the Yangtze River Economic Belt? Based on the spatial econometric analysis. Environ Dev Sustain. 2025;27(8):19319–53. [ Google Scholar ] 16. Cui S, Wang Y, Zhu Z, Zhu Z, Yu C. The impact of heterogeneous environmental regulation on the energy eco-efficiency of China’s energy-mineral cities. J Clean Prod. 2022;350:131553. [ Google Scholar ] 17. Feng S, Kong Y, Liu S, Zhou H. Spatiotemporal pattern and convergence test of energy eco-efficiency in the Yellow River Basin. Int J Environ Res Public Health. 2023;20(3):1888. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Khan U, Wang S. Balancing growth and decarbonization: political economy dynamics in China’s 2060 carbon neutrality strategy. Energy Sustain Dev. 2026;91:101897. [ Google Scholar ] 19. Gong W, Lewis J. The politics of China’s just transition and the shift away from coal. Energy Res Soc Sci. 2024;115:103643. [ Google Scholar ] 20. Liu P, Meng X, Wang Y, Guo J, Feng F. Study of the spatial divergence features and motivating factors of energy green consumption levels in “2 + 26” cities. Environ Sci Pollut Res. 2023;30(8):19776–89. [ DOI ] [ PubMed ] [ Google Scholar ] 21. Rashidi K, Saen R. Measuring eco-efficiency based on green indicators and potentials in energy saving and undesirable output abatement. Energy Econ. 2015;50:18–26. [ Google Scholar ] 22. Ren F, Yu X. Coupling analysis of urbanization and ecological total factor energy efficiency-a case study from Hebei Province in China. Sustain Cities Soc. 2021;74:103183. [ Google Scholar ] 23. He J, Hu S. Ecological efficiency and its determining factors in an urban agglomeration in China: the Chengdu-Chongqing urban agglomeration. Urban Clim. 2022;41:101071. [ Google Scholar ] 24. Li G, Zhou P, Wu R. Local government competition, fiscal decentralization, and environmental governance efficiency. Finance Res Lett. 2025;86:108537. [ Google Scholar ] 25. Tone K. A slacks-based measure of super-efficiency in data envelopment analysis. Eur J Oper Res. 2002;143(1):32–41. [ Google Scholar ] 26. Tan J, Wang R. Research on evaluation and influencing factors of regional ecological efficiency from the perspective of carbon neutrality. J Environ Manage. 2021;294:113030. [ DOI ] [ PubMed ] [ Google Scholar ] 27. Ma X, Li Y, Zhang X, Wang C, Dong B, Gu Y. Research on the ecological efficiency of the Yangtze River Delta region in China from the perspective of sustainable development of the economy-energy-environment (3E) system. Environ Sci Pollut Res. 2018;25:29192–207. [ DOI ] [ PubMed ] [ Google Scholar ] 28. Hao Y, Guo Y, Wu H. The role of information and communication technology on green total factor energy efficiency: does environmental regulation work? Bus Strategy Environ. 2022;31(1):403–24. [ Google Scholar ] 29. Wang F, Wu M, Du X. Does industrial upgrading improve eco-efficiency? Evidence from China’s industrial sector. Energy Econ. 2023;124:106774. [ Google Scholar ] 30. Zhang J, Li B, He H, Shen Y. Natural resource dependence and renewable energy development: does government policy support matter? J Clean Prod. 2024;436:140466. [ Google Scholar ] 31. Zhu W, Xu LX, Tang LN, Xiang XQ. Eco-efficiency of the Western Taiwan Straits Economic Zone: an evaluation based on a novel eco-efficiency model and empirical analysis of influencing factors. J Clean Prod. 2019;234:638–52. [ Google Scholar ] 32. Zhou Y, Kong Y, Sha J, Wang HK. The role of industrial structure upgrades in eco-efficiency evolution: spatial correlation and spillover effects. Sci Total Environ. 2019;687:1327–36. [ DOI ] [ PubMed ] [ Google Scholar ] 33. Zhang XL, Li GW, Wu R, Zeng HJ, Ma SL. Impact of carbon emissions, green energy, artificial intelligence and high-tech policy uncertainty on China’s financial market. Finance Res Lett. 2025;82:107599. [ Google Scholar ] 34. Liu LW, Chen CX, Zhao YF, Zhao E. China’s carbon-emissions trading: overview, challenges and future. Renewable and Sustainable Energy Reviews. 2015;49:254–66. [ Google Scholar ] 35. Dai S, Qian Y, He W, Wang C, Shi T. The spatial spillover effect of China’s carbon emissions trading policy on industrial carbon intensity: evidence from a spatial difference-in-difference method. Struct Change Econ Dyn. 2022;63:139–49. [ Google Scholar ] 36. Dong Z, Xia C, Fang K, Zhang W. Effect of the carbon emissions trading policy on the co-benefits of carbon emissions reduction and air pollution control. Energy Policy. 2022;165:112998. [ Google Scholar ] 37. Liu Z, Zhang J, Zhang P, Jiang L, Yang D, Rong T. Spatial heterogeneity and scenario simulation of carbon budget on provincial scale in China. Carbon Balance Manag. 2023;18:20. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 38. Feng L, Zeng S, Wang S. Engineering mayors and urban carbon governance: evidence from China. Carbon Balance Manag. 2025;20:43. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 39. Zhang Y, Wang W. How does China’s carbon emissions trading (CET) policy affect the investment of CET-covered enterprises? Energy Econ. 2021;98:105224. [ Google Scholar ] 40. Feng X, Zhao Y, Yan R. Does carbon emission trading policy has emission reduction effect?—An empirical study based on quasi-natural experiment method. J Environ Manage. 2024;351:119791. [ DOI ] [ PubMed ] [ Google Scholar ] 41. Cai W, Ye P. Does carbon emission trading improve low-carbon technical efficiency? evidence from China. Sustain Prod Consum. 2022;29:46–56. [ Google Scholar ] 42. Zhang Y, Zhang J. Estimating the impacts of emissions trading scheme on low-carbon development. J Clean Prod. 2019;238:117913. [ Google Scholar ] 43. Ma S, Benkraiem R, Abedin M, Zeng H. Climate anomalies and corporate environmental governance: empirical evidence from ENSO events. Financ Res Lett. 2025;85:107970. [ Google Scholar ] 44. Wang W, Xu X, Li M, Ma S. Do environmental regulations affect corporate investment preferences? institutional considerations based on fiscal decentralization. Int Rev Econ Finance. 2026;106:104939. [ Google Scholar ] 45. Ma S, Appolloni A. Can financial flexibility enhance corporate green innovation performance? evidence from an ESG approach in China. J Environ Manag. 2025;387:125869. [ DOI ] [ PubMed ] [ Google Scholar ] 46. Cohen WM, Levinthal DA. Absorptive capacity: a new perspective on learning and innovation. Adm Sci Q. 1990;35(1):128–52. [ Google Scholar ] 47. Chen Z, Wang H. Research on the impact of the Belt and Road Initiative on the sustainability of the resource-based economy of participating countries. Environ Sci Pollut Res. 2022;29(60):91139–54. [ DOI ] [ PubMed ] [ Google Scholar ] 48. Wang Z, Feng Y, Yao N, Lin O, Li Q, Liu B. Impact of policy interventions on low-carbon technology innovation diffusion in supply networks. Manag Decis Econ. 2024;45(7):5040–53. [ Google Scholar ] 49. Song J, Zhu L. The impact of digital innovation on carbon productivity: an empirical study based on spatial spillover effects. Sustainability. 2026;18(4):2084. [ Google Scholar ] 50. Zhang C, Zhou R, Hou J, Feng M. Spatial-temporal evolution and convergence characteristics of agricultural eco-efficiency in China from a low-carbon perspective. Sustainability. 2022;14(24):16509. [ Google Scholar ] 51. Deng X, Zhang L. Spatio-temporal disparity of water use efficiency and its influencing factors in energy production in China. Ecol Inform. 2022;71:101779. [ Google Scholar ] 52. Zhang J, Wu GY, Zhang JP. The estimation of China’s provincial capital stock: 1952–2000. Econ Res J. 2004;10:35–44. [ Google Scholar ] 53. Holz CA. New capital estimates for China. China Econ Rev. 2006;17(2):142–85. [ Google Scholar ] 54. Chen G, Shan Y, Hu Y, et al. Review on city-level carbon accounting. Environ Sci Technol. 2019;53(10):5545–58. [ DOI ] [ PubMed ] [ Google Scholar ] 55. Hanif I, Raza S, Gago P, Abbas Q. Fossil fuels, foreign direct investment, and economic growth have triggered CO2 emissions in emerging Asian economies: some empirical evidence. Energy. 2019;171:493–501. [ Google Scholar ] 56. Qin Q, Jiao Y, Gan X, Liu Y. Environmental efficiency and market segmentation: an empirical analysis of China’s thermal power industry. J Clean Prod. 2020;242:118560. [ Google Scholar ] 57. Ang B, Goh T. Bridging the gap between energy-to-GDP ratio and composite energy intensity index. Energy Policy. 2018;119:105–12. [ Google Scholar ] 58. Zeng H, Liu H, Wang C, Ma S. The impact of external uncertainty factors on the time-frequency and quantile effects of China’s clean energy stock market index. Energy Sources B Econ Plan Policy. 2025. 10.1080/15567249.2025.2591800. [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. 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