HQD encompasses a broad and evolving set of concepts reflected in the diverse definitions offered by scholars in the field. Guo et al. (2024) developed a comprehensive indicator system for China’s HQD, categorizing it into five critical areas, namely, innovation-driven growth, coordinated development, economic progress, environmental sustainability, and social wellbeing, with 24 sub-indicators. Du (2023) proposed a model focused on three dimensions—environmental sustainability, economic structure optimization, and social welfare enhancement—to assess the structural optimization of economic growth. Similarly, Ma et al. (2019) introduced an evaluation framework comprising 28 indicators across five dimensions, namely, high-quality supply, demand, development efficiency, economic operation, and international openness.

Despite these innovative contributions, the existing frameworks of indicators exhibit certain limitations that warrant further refinement. First, these systems tend to overlap process and result indicators. Since HQD epitomizes the outcomes of socio-economic progress, it is essential that the chosen indicators predominantly reflect results rather than processes. However, several existing indicators pertain more to ongoing processes. Second, the issue of redundancy in indicators persists; for instance, the overlapping measures of capital output efficiency, labor productivity, and TFP in previous systems exhibit strong correlations and similarities, which may impede the precise measurement of HQD. To overcome these challenges, future research should focus on devising more targeted and innovative indicators that differentiate between processes and outcomes and minimize redundancy within the evaluation system.

A blend of external and internal factors drives HQD. Among the internal factors, upgrading the industrial structure is the core driver of achieving HQD. Yang and Tian (2023) demonstrated that industrial intelligence upgrading exerts a substantial spatial spillover effect on regional economic quality. Furthermore, digital financial inclusion, often called the “catfish effect,” mitigates enterprise financing constraints by reducing information asymmetry and addressing the misallocation prevalent in traditional monetary systems. This facilitates substantial capital inflow for expansive development, catalyzing the transition to HQD (Lee et al., 2023; Li Z. et al., 2024). Investment-led growth, mainly through government infrastructure investments, also fosters economic advancement. Yang et al. (2022) identified a significant positive impact of government infrastructure investments on HQD, while Wang and Liu (2023) distinguished the varying effects of different types of infrastructure investments on economic enhancement.

Among the external drivers of HQD, the interplay between foreign direct investment and the host country’s growth has garnered significant scholarly interest. The pollution haven hypothesis argues that foreign investment and financial openness can catalyze the development of environmentally low-carbon industries, thereby facilitating HQD (Singhania and Saini, 2021). In addition, environmental regulation is a critical metric of governmental environmental governance and is pivotal in fostering sustainable development (Wu, 2025). Current literature predominantly assesses the impact of environmental regulation on HQD through its effects on TFP (Lian et al., 2024; Tong et al., 2022; Wen S. et al., 2022). The prevalent method for measuring environmental regulation involves quantifying the frequency of environment-related terms in government work reports (Du, 2023). Although this method offers a means to quantify the intensity of environmental regulation, it does not provide a thorough analysis of the effects resulting from the implementation of specific environmental policies. Therefore, it is essential to redirect research efforts toward more detailed environmental policies. This shift will enable a deeper understanding of how environmental regulation influences HQD.

Innovation-driven development and breakthroughs in core technologies constitute the foundational pillars of HQD. As postulated by the endogenous growth theory, endogenous factors such as technological innovation, characterized by positive externalities, are essential for sustaining long-term economic growth (Romer, 1986). This theory is robustly supported by empirical evidence indicating that green innovations catalyze significant advancements in economic development and environmental protection (Zhou et al., 2023). Regarding policy implementation, on one hand, the LCCP encourages enterprises to pursue independent innovation through proactive policy guidance, fiscal incentives, tax exemptions, and innovation funds. These measures effectively drive the development and adoption of low-carbon technologies, thereby reducing energy consumption and pollution emissions while enhancing the ecological capital. Furthermore, advancements in technological innovation contribute to higher labor productivity, expand the potential scope of economic activities, and enable the high-quality growth of the economy (Jiang et al., 2024). On the other hand, local governments often utilize command-and-control policy tools to enforce strict environmental standards for enterprises’ technology and production processes. This approach pressures pollution-intensive industries to increase innovation investments, facilitating green transformation and promoting HQD (Qu et al., 2023). In addition, existing empirical studies consistently demonstrate that technological innovation can reduce reliance on fossil fuels, lower greenhouse gas emissions, and foster sustainable economic growth (Du et al., 2025).

Furthermore, the PH articulates that stringent environmental regulations incrementally elevate production costs for enterprises characterized by high pollution, high energy consumption, and low production efficiency. This regulatory pressure compels firms to enhance resource utilization efficiency through technological innovation (Porter and Van der Linde, 1995). Extensive research confirms that in the context of severe environmental regulations, pilot cities facilitate the integration of digital technologies with energy-efficient and low-carbon solutions, thereby expediting the supply and diffusion of green technological innovations. This process promotes the transformation of traditional industries toward more sustainable and intelligent operational frameworks, thereby enhancing both technical efficiency and labor productivity within the traditional manufacturing sectors (Yu et al., 2023; Zhao et al., 2023). Hence, the role of technological innovation in moderating the effect of LCCP on enhancing HQD is crucial as it can guide economic growth toward a greener, more inclusive, and more sustainable path. Based on this, we advance the following hypothesis:

Hypothesis 2

Technological innovation plays a positive moderating role in the relationship between LCCP and HQD.

According to the PH, stringent environmental regulations can mandate a constructive “cleansing” within industrial clusters by instigating a process of natural selection. This can enhance the quality and competitiveness of industries and ultimately facilitate an upgrade in the industrial structure (Porter and Van der Linde, 1995). Empirical studies substantiate that the LCCP effectively stimulates the upgradation of urban industrial structures (Wang and Chu, 2024; Xie and Teo, 2022). Specifically, at the metropolitan level, the LCCP has facilitated enhanced resource efficiency and catalyzed the transformation and upgrading of low-carbon industries, leading to reduced carbon emissions and improved environmental quality. At the industry level, the constraints imposed by low-carbon governance have curbed the expansion of pollution-intensive sectors and fostered the growth of clean industries, emerging manufacturing sectors, and services, thereby contributing to the optimization of industrial structures. At the enterprise level, the enforcement of LCCP has increased the cost of emissions, compelling firms to optimize their supply chains and reduce energy consumption to meet carbon reduction targets. In the long term, this has promoted organizational transformation and technological upgrading. Moreover, the policy has eliminated obsolete production capacity in high-energy-consuming and high-emission industries, imposed more stringent entry standards for these sectors, and incentivized the rapid development of the green industry sectors.

Upgrading the industrial structure influences the pollution intensity of production activities and the quality of the environment, thus serving as a dependable indicator for fostering HQD (Feng et al., 2024). Drawing from the theory of new structural economics, it is argued that endowment-driven structural transformations and industrial upgrades amplify an economy’s overall productivity and efficacy (Lin, 2021). Consequently, the optimization of industrial structures yields a “structural dividend” that not only enhances resource distribution but also improves the functionality of ecosystems. Additionally, numerous studies have corroborated that industrial upgrades under the impetus of the LCCP typically embody high technological sophistication and low carbon footprints (Wei Q. et al., 2022), thus critically contributing to the optimization of economic structures and the promotion of sustainable development (Wang Z. et al., 2023; Zheng et al., 2021). From the analysis presented, we propose the following hypothesis:

Hypothesis 3

Industrial upgrading plays a positive moderating role in the relationship between LCCP and HQD.

Green infrastructure represents a concept counterposed to “gray infrastructure” (such as roads, utility networks, and other municipal support systems). This concept underscores the necessity of integrating green spaces and natural areas within urban planning as indispensable resources for sustainable development (Taczanowska et al., 2024). From the landscape ecology perspective, green infrastructure is vital for sustaining biodiversity within urban environments, contributing to ecological balance, and enhancing environmental services (Vilanova et al., 2024). The LCCP advocates for expanding urban green infrastructure, recognizing it as a crucial catalyst for sustainable development. This initiative gives rise to the “green magnet effect,” a positive cyclical feedback mechanism in which urban green infrastructure actively contributes to urban development (Jiao et al., 2023).

This effect has been validated across various dimensions, including economic growth, environmental sustainability, and social and mental wellbeing (Hong et al., 2020). Economically, green infrastructure under the LCCP enhances a city’s attractiveness, increases real estate values, and attracts tourists and investors, stimulating economic growth. LCCP-driven green spaces also encourage the development of new economic models, such as eco-tourism, green commerce districts, and sustainable urban development initiatives (Shan et al., 2024). Environmentally, the LCCP’s focus on expanding green spaces significantly improves urban environmental quality. By mitigating heat island effects, improving air quality, enhancing biodiversity, and regulating the urban water cycle, green spaces help create a healthier, more sustainable urban environment. Socially, these spaces offer recreational opportunities that elevate life quality and communal wellbeing. From a psychological perspective, the biophilia hypothesis posits that green spaces act as a crucial psychological refuge in contemporary urban life’s rapid pace. These areas facilitate stress reduction, mood improvement, and enhanced psychological resilience, thus supporting mental health in urban populations (Lin et al., 2019; Yang et al., 2019). Thus, green infrastructure is essential for promoting HQD and providing far-reaching benefits across multiple fronts.

Hypothesis 4

Infrastructure development plays a positive moderating role in the relationship between LCCP and HQD.

Neoclassical economics identifies that energy consumption frequently entails negative externalities, including air pollution and greenhouse gas emissions, which precipitate market failures because the associated costs are not fully integrated into market prices (Spanjer, 2009). Accordingly, the neoclassical economics theory advocates for internalizing these external costs through policy mechanisms such as carbon taxes and emission trading systems. Consumers and producers are incentivized to adopt more energy-efficient practices and technologies by factoring these costs into energy prices, thus enhancing overall energy efficiency (Illge and Schwarze, 2009). Additionally, drawing on the PH, the LCCP is anticipated to generate an “innovation compensation effect.” This effect posits that improvements in energy efficiency will enable economic growth at reduced energy consumption levels or potentially allow for continued economic expansion with a decrease in total energy use, thus facilitating sustainable development (Porter and Van der Linde, 1995).

With the promotion and implementation of LCCP, local governments can exert pressure on enterprises by encouraging the decarbonization of energy consumption structures and utilizing mechanisms such as emission trading. These actions help reduce the use of non-renewable, finite energy sources while promoting low-carbon and zero-carbon production models, thereby improving overall energy efficiency (Malinauskaite et al., 2020). Second, local governments support the development of low-carbon and renewable energy industries, such as solar, wind, and hydro energy. This strategy aims to foster the coordinated development of ecological civilization and economic growth, ultimately contributing to green economic development. Furthermore, the policy emphasizes the importance of establishing a robust energy management system, which includes energy audits, real-time monitoring, and efficiency evaluations. These measures are essential for identifying and addressing issues related to energy waste, thus optimizing energy utilization. Promoting greater energy efficiency and reducing environmental impacts contribute to sustainable development and play a pivotal role in fostering high-quality economic growth. Based on these premises, we propose the following hypothesis.

Hypothesis 5

The transformation of the energy system plays a positive moderating role in the relationship between LCCP and HQD.

The pursuit of HQD is a necessary consequence of adhering to the inherent laws of economic progression and is essential for societal advancement toward modernization (Bei, 2018). The 19th National Congress report of the Communist Party of China notes the transition of the Chinese economy into a new era, moving from a period of high-speed growth to a stage characterized by growth focused on HQD (Wang, 2020). HQD aims to achieve efficient, sustainable, inclusive, and balanced economic growth (Luo Y. et al., 2024; Zhao et al., 2019). Therefore, based on the relevant literature (Chao and Ren, 2011; Yi et al., 2019; Zhao et al., 2022) and the inherent principles of HQD, we establish a multidimensional evaluation system comprising five secondary indicators, namely, industrial structure, inclusive TFP, technological innovation, ecological environment, and residents’ living standards (see Table 1).

First, the current industrial structure is a key indicator for assessing whether an economy has achieved HQD. In this dynamic process, it is crucial to evaluate the proportional relationship among the three sectors of the economy and the degree of coordination in their development. This includes not only the upgradation of the industrial structure but also its rationalization. Furthermore, the production-oriented service sector, as a central field in the ongoing technological revolution and industrial transformation, should be incorporated into the evaluation of the HQD of the industrial structure. Thus, the advancement and rationalization of the industrial structure, alongside the share of productive services, form a three-tiered measure of high-quality industrial development. Second, the extent to which HQD embodies the inclusive characteristics of both efficiency and fairness constitutes another fundamental dimension for evaluating the quality of economic growth. The inclusive TFP index serves as a robust tool for assessing this dimension as it holistically measures the degree to which the benefits of economic growth are equitably distributed, thereby providing an indicator of the extent to which HQD has been realized. Third, technological innovation plays a pivotal and direct role in enhancing the quality of economic development. It improves productivity and optimizes industrial structures, simultaneously stimulating the emergence of new industries and facilitating the transformation of traditional sectors. Consequently, incorporating technological innovation as a fundamental dimension in the assessment of HQD aligns with current economic trends and offers a precise gauge of both the quality and future potential of economic growth. Fourth, HQD emphasizes the coordinated integration of economic, social, and environmental objectives. Specifically, ecological environmental protection, efficient resource utilization, and the concept of green development have progressively become core drivers of economic growth. This paper employs secondary indicators of the ecological environment index, such as the sulfur dioxide removal rate, industrial solid waste recycling rate, and PM_2.5 concentration, to assess the city’s primary investments and outcomes in environmental governance. Fifth, the wellbeing of the populace is the ultimate goal of economic development. Therefore, the evaluation of HQD must place people at its core. Beyond economic output, the quantity and quality of social public goods, such as education and healthcare, directly influence residents’ quality of life and happiness. In this regard, per capita GDP, per capita education expenditure, and per capita hospital bed availability are incorporated as tertiary indicators under the dimension of living standards in the framework for evaluating HQD.

Drawing on the calculation methods of Zhao et al. (2022), we employed the entropy weight method to measure the HQD of 351 prefecture-level cities in China from 2000 to 2021. The specific measurement steps for HQD are as follows.

In the first step, the raw indicators were standardized, as shown in Equation 2, 3:

(1) Positive variables (property: +):

In the second step, the proportional value P i j t for the j th indicator of city i in year t is calculated, as shown in Equation 4: P i j t = X i j t ∑ i = 1 m   X i j t . (4)

In the third step, the entropy value E j t is calculated for the j th indicator in year t , as illustrated in Equation 5: E j t = − 1 ln ⁡ m ∑ i = 1 m   P i j t ⁡ ln ⁡ P i j t . (5)

In the fourth step, the weight W j t of the j th indicator in year t is calculated, as outlined in Equation 6: W j t = Y j t ∑ j = 1 n   Y j t , Y j t = 1 − E j t . (6)

In the fifth step, the HQD index is calculated, as presented in Equation 7: H Q D i t = ∑ j = 1 n   W j t × X i j t . (7)

This paper assesses urban low-carbon governance by incorporating the LCCP as a DID variable. A city designated as a low-carbon pilot city is assigned a value of 1 from the time of its establishment and 0 otherwise. China introduced the LCCP in three distinct waves—2010, 2012, and 2017—choosing cities based on their foundational achievements in environmental management and the characteristics of their resource endowments (Dong et al., 2023).

To rigorously assess the broader impacts of the LCCP, it is crucial to incorporate control variables that might influence the results. Drawing from the research of Wang and Liu (2023) and Weng et al. (2022), we select the following control variables: foreign investment (FI), measured as the logarithm of annual utilized foreign capital (USD in ten thousand); education development (ED) level, quantified by the logarithm of per capita educational spending (yuan); information infrastructure development (IID), represented by the logarithm of internet broadband user numbers (thousands of households); regional total population (RTP), indicated by the logarithm of the area’s total population (ten thousand people); regional development (RD) level, calculated as the logarithm of the regional GDP (billions of yuan); and per capita development (PCD) level, gauged by the logarithm of GDP per capita (yuan).

This paper identifies the LCCP’s promotive effect on HQD in China in the baseline analysis. One crucial assumption of the DID model is that the treatment and control groups should exhibit parallel trends before implementing the LCCP. Given that meeting the parallel trends assumption is a fundamental prerequisite for utilizing DID, this study employs the event study approach (Jacobson et al., 1992; Wu et al., 2023) to structure the econometric model accordingly as follows: l n H Q D i t = ∝ + β 1 t r e a t i t − 5 + β 2 t r e a t i t − 4 + β 3 t r e a t i t − 3 + … + β 15 t r e a t i t 9 + X i t + Year fixed effects + Province fixed effects + ε i t , (8) where the dummy variable t r e a t i t q denotes the LCCP events; q represents periods before and after the LCCP; X i t signifies the control variables; Year FE refers to year fixed effects; Province FE refers to province fixed effects; and ε i t represents the random disturbance term.

Figure 3 presents the results of the event study based on Equation 8. Before the policy’s implementation, the parameter β k fails to reject the null hypothesis, which indicates no significant differences in HQD between pilot and non-pilot cities, thus affirming the parallel trends assumption. After the policy’s enactment, the regression coefficient exhibits fluctuations, consistent with the cyclical nature of the policy. The phased implementation of the policy in 2010, 2012, and 2017 shows significant effects in the first to third years, the sixth year, and the ninth year, demonstrating that the LCCP significantly influences HQD during the early and mid-phases of the policy cycle. This cyclical manifestation is likely influenced by variations in resource allocation, implementation intensity, and external environmental factors during policy execution (Heimberger, 2023). Notably, the event study confirms the absence of pre-trends, validating the identification strategy.

The selection of pilot cities in the LCCP is not entirely random, and the heterogeneity among these cities introduces additional complexities that may impact the validity of conclusions drawn from DID analyses. PSM provides a viable solution for addressing sample selection bias and city-specific heterogeneity within the DID model (Luo et al., 2023). Based on counterfactual reasoning, PSM effectively minimizes estimation errors caused by selection bias by matching treated and control units with similar propensity scores (Yu et al., 2022). The matching variables included RD, PCD, ED, RTP, IID, and FI. The balance test results of PSM matching (kernel matching) are reported in Table 4. After balance testing, the bias in all matched covariates between the two groups was reduced by at least 60%, and all t-tests confirmed the null hypothesis, indicating no systematic differences between the treatment and control groups. The p-values for the matched samples suggest a consistent distribution of covariates between the groups. This consistency validates the choice of covariates and the effectiveness of the matching process. Thus, we can confidently state that the matched sample fulfills the requirements for further regression analysis.

Increasingly, studies are integrating PSM with DID models to assess the effectiveness of policy implementations (Feng et al., 2021; Heckman et al., 1997; Hu and Wang, 2023; Xu et al., 2024). The PSM-DID analysis addresses individual heterogeneity and potential selection biases to some extent by matching treated and control groups (Hu and Ahmad, 2024). Accordingly, this paper employs the PSM-DID methodology to evaluate the effects of LCCP on HQD. To ascertain whether different matching methods yield varying results, kernel matching and nearest-neighbor matching were applied to validate the conclusions, with results presented in Table 5. Regardless of the matching method used, the impact of LCCP on HQD is consistently positive. This finding corroborates the previously discussed baseline tests and enhances the causal inference’s robustness.

The initial dataset for this investigation spanned from 2000 to 2018. This selection was based on the premise that the advent of the COVID-19 pandemic in 2019 could introduce statistical anomalies that could compromise the validity of our findings. However, to rigorously assess the persistent effects of the LCCP on HQD and validate our insights’ broad applicability, we extended the study period to include data up to the latest available statistics in 2021. Appendix Table A1 demonstrates descriptive statistical analysis. The regression results from 2000 to 2021 are depicted in Table 6. The regression analysis reveals a coefficient of 0.32 in column (1), achieving statistical significance at 5%. Upon integrating control variables, as displayed in column (2), the significance of the results strengthens, maintaining the 1% level. These consistent findings robustly support the hypothesis that the LCCP positively influences HQD.

To further enhance the robustness and reliability of the research findings, we revisited the explained variable and incorporated additional indicators that more comprehensively reflect the wellbeing of residents. Specifically, we introduced several key variables, including per capita consumption expenditure (yuan/person), average years of education (years/person), the number of practicing (assistant) physicians per 10,000 people (persons), and the proportion of social security expenditure to GDP (%). These indicators enable a more holistic evaluation of residents’ welfare across economic, educational, healthcare, and social security dimensions, thus providing precise and multidimensional data support for research on HQD. After substituting the explained variable, we re-estimated the model. The results show that the conclusions remain robust, without any significant deviations, as shown in Table 7.

In this study, we adopt the innovation index defined by Kou and Liu (2017) as a measure of technological innovation. This index is calculated by estimating the mean value of patents of varying ages and weighting them according to the urban dimension. The results in Table 8 show a significant positive regression coefficient of 0.068 at the 1% significance level in column 1. This indicates that technological innovation is a critical mechanism through which the LCCP enhances HQD, thereby corroborating the PH. Although previous studies (Wang and Chu, 2024; Yu et al., 2023) primarily consider technological innovation as a dependent variable in exploring the relationship between LCCP and technological innovation, our analysis positions it as an influence mechanism, thus broadening the scope of existing research. We recognize that innovation is vital in shifting the development paradigm from resource-driven to efficiency-driven models (Tian et al., 2024). In practical terms, low-carbon urban governance has effectively reduced pollution emissions by fostering technological innovation and expediting the research, development, and widespread adoption of cleaner production technologies. Furthermore, low-carbon technological innovations stimulate a green transformation of consumption across various sectors, thereby driving sustainable economic growth. In essence, the LCCP fosters HQD by enhancing technological innovation. Consequently, Hypothesis 2 is substantiated.

In our analysis, we derive an index of industrial sophistication using the ratio of tertiary to secondary industry output values, a method described by Gan et al. (2011), to assess the degree of industrial upgrading. According to the results presented in Table 8, the regression coefficient for IU × LCCP in column 2 is 0.011, significantly positive at the 1% level, which supports that the level of IU positively moderates the relationship between LCCP and HQD. In other words, as the level of IU increases, the positive impact of LCCP on HQD strengthens, thus supporting the moderating effect proposed in Hypothesis 3. Previous research indicates that intensified LCCP efforts drive a more rational allocation of the industrial structure, attracting higher-value-added production elements through improved environmental quality, thus accelerating industrial modernization (LOISEL, 2010; Pan et al., 2023). Our findings extend this narrative, illustrating that LCCP-induced industrial upgrading contributes notably to HQD. Under low-carbon governance, the digital economy catalyzes changes in the industrial structure, which is crucial for advancing the development of a modern, eco-friendly industrial system (Zhong et al., 2022). Furthermore, this industrial upgrading fosters positive interplay among the primary, secondary, and tertiary sectors, steering the economic framework toward a service-driven economy and facilitating a leapfrog approach to sustainable economic growth (Hu et al., 2023). Overall, our study demonstrates how urban low-carbon governance promotes the green upgrading of industrial structures, effectively driving HQD.

To assess the impact of urban green space infrastructure on HQD, we constructed a measure of infrastructure development based on the green space area of parks per capita using data from the China Bureau of Statistics. In column 3 of Table 8, the regression coefficient of ID × LCCP is 0.001, significantly positive at the 10% level. This finding suggests that enhancements in infrastructure development substantially contribute to HQD. Our results corroborate the “green magnet effect,” where the construction of urban green spaces positively influences the economic development of cities (Hong et al., 2020), aligning with the conclusions drawn by He et al. (2024). The efficient use of urban land for green purposes is pivotal for optimizing economic value while minimizing resource consumption and environmental impact (Song et al., 2022). Given the scarcity of urban land resources, the strategic utilization of limited green space in community parks for maximal ecological benefits represents a critical challenge for current infrastructure research in China (Ma and Wang, 2023). Consequently, urban low-carbon governance in China emphasizes landscape gardening and urban greening initiatives to enhance urban livability and ecological networks, thereby fostering a green, low-carbon transformation and sustainable urban development. In conclusion, the LCCP is instrumental in driving HQD by enhancing infrastructure development, and Hypothesis 4 is verified.

In this paper, we measure energy system transition using the energy consumption elasticity coefficient, which is calculated as the ratio of the energy consumption growth rate to the economic growth rate. This coefficient effectively illustrates shifts in energy dependency throughout economic development. A coefficient greater than 1 suggests that energy consumption growth outpaces economic growth, potentially indicating inefficiencies in energy use. Conversely, a coefficient less than 1 implies that enhancements in energy efficiency largely fuel economic expansion. With technological advancements and improved efficiency, a higher economic output is expected to be achieved with reduced energy consumption, which is typically reflected by a lower elasticity coefficient (Ghadaksaz and Saboohi, 2024).

Regression analysis presented in Table 8, column 4, reveals that the coefficient TES × LCCP is −0.001, which is statistically significant at the 5% level. This indicates an inverse relationship between the energy consumption elasticity coefficient and HQD. The decline in energy consumption elasticity coefficient signals an enhancement in energy utilization and signifies a successful transformation within the energy sector. This supports the effectiveness of energy system transformations in fostering HQD. The LCCP has been instrumental in continuously optimizing the energy structure, enhancing energy conservation, and advancing renewable energy and the servitization of manufacturing. These efforts contribute to the robust development of the circular economy (Cheng et al., 2023). Collectively, these strategies confirm that the low-carbon energy transition positively moderates the enhancing effect of the LCCP on HQD. Therefore, Hypothesis 5 is valid.

The empirical results indicate that the regression coefficients for general cities in column (2) of Table 9 pass the significance test, suggesting that the LCCP significantly impacts the HQD of general cities more than provincial capitals. This disparity can be attributed to several factors. First, the stages of urbanization across different cities are highly heterogeneous. Some urban areas are in the acceleration stage, while others are in the late urbanization stage (Qiao et al., 2024). Provincial capital cities are generally in the middle to late stages of industrial transformation and upgrading. New green industries are developing rapidly, and environmental governance has already achieved a certain success. As a result, the impact of the LCCP is less significant in these areas. In contrast, many general cities are in the early stages of urbanization, where the effect of building scale on carbon emissions is relatively moderate, and economic development levels are lower. The LCCP allows these cities to implement sustainable building practices that can yield long-term benefits in reducing carbon footprints, making them more sensitive and responsive to the LCCP. Furthermore, some provincial capital cities rely heavily on resource-based industries such as coal and oil, resulting in an imbalanced industrial structure where economic growth is predominantly driven by resource extraction and related energy sectors. These cities face significant challenges, including high energy and water consumption, low industrial waste recycling rates, and persistently high carbon emissions. To overcome these obstacles and effectively leverage the LCCP to promote HQD, additional time is needed to address the “carbon curse” issue (Lee et al., 2024). Therefore, this paper argues that the LCCP is more conducive to promoting HQD in general cities, thereby contributing to the achievement of commonwealth among cities.

China, a nation with diverse ethnic groups, exhibits significant developmental disparities among its various ethnic provinces (YangLiu and Li, 2020). Our analysis, specifically the results shown in column (4) of Table 9, indicates that the impact of the LCCP is more pronounced in Han Chinese provinces than in ethnic autonomous regions, with a regression coefficient of 0.007, significant at the 1% level. The possible reasons are as follows: ethnic minority regions often depend heavily on traditional industries and agriculture, sectors that are typically less adaptable to rapid transformation into low-carbon or green industries. Furthermore, historical and geographical factors have contributed to the persistent economic underdevelopment of ethnic autonomous regions compared to the national average (Shi et al., 2022). These regions generally possess a weaker industrial base and lack the necessary technological and capital support to effectively implement the LCCP. Additionally, issues such as the limited awareness of low-carbon governance and lagging technological innovation in ethnic autonomous regions undermine the efficacy of the LCCP. The geographical remoteness of these regions often complicates policy implementation and oversight. Local governments in these regions may struggle with inadequate implementation power and resource allocation, which are crucial for meeting the sophisticated demands of the LCCP. Given these challenges, this paper argues that the LCCP should emphasize the unique conditions in ethnic autonomous regions.