ULGUE is a core indicator of urban sustainable development. Its improvement requires efficient resource allocation, ecological environmental protection, and social equity. Enhancing ULGUE can significantly improve urban ecological environments, meet residents’ increasing demands for higher living standards, strengthen urban competitiveness, and facilitate sustainable urban development (Su and Yang, 2022).

Previous studies have identified multiple factors that influence ULGUE. Economic development, industrial structure optimization, environmental regulation, and land urbanization have generally been found to promote efficiency, whereas higher population density, excessive investment in science and technology, and financial development may exert negative effects (Ma et al., 2024). Larger cities, benefiting from advanced economic conditions and infrastructure, tend to achieve higher ULGUE (Wang et al., 2021). The role of technological innovation, however, remains contested. Whereas some studies suggest that it may generate short-term inefficiencies, others emphasize the positive contributions of innovation investment, internet penetration, and digital applications in land management (Wang et al., 2021; Cardone et al., 2023). In addition, industrial structure upgrading has been shown to improve land-use allocation, whereas stringent environmental regulations have been shown to foster low-carbon practices and stimulate technological advancement, thereby improving ULGUE (Zhang et al., 2025; Fu et al., 2022).

In terms of measurement methods, in existing research, ULGUE is divided into three dimensions—inputs, desirable outputs, and undesirable outputs—aiming to maximize resource efficiency while minimizing environmental costs. Approaches that are commonly employed include GIS-based models for evaluating ecological benefits in densely populated cities (Cardone et al., 2023) and super-efficiency SBM models for assessing the industrial output, emissions, and land use (Wang et al., 2021). In addition, some studies incorporate financial support systems to enrich the measurement framework (Fu et al., 2022).

In summary, the improvement of ULGUE is a complex, multifactorial process that requires the integrated consideration of economic, social, and environmental dimensions. Continuous optimization and adjustment of these factors are essential to effectively enhance ULGUE and advance the broader goals of urban sustainable development.

In this paper, we focus on two key urban pilot policies: the low-carbon city pilot policy and the smart city pilot policy.

The low-carbon city pilot policy aims to embed carbon reduction targets into local governance and planning, with the objective of building clean, energy-efficient, and environmentally friendly cities. It establishes explicit carbon emission constraints and green land-use standards, compelling local governments and enterprises to restructure development patterns. Existing research has examined its impacts from multiple perspectives. Studies show that the policy has significantly improved ULGUE (Liu and Xu, 2022), reduced enterprise pollution emissions (Yang et al., 2023), fostered urban green innovation by increasing green patent applications (Chen et al., 2022), and enhanced intelligent manufacturing capabilities (Wu et al., 2023). Positive spillover effects have also been observed, with green development diffusing from technology-intensive and coastal cities to inland regions (Chen et al., 2023). Nonetheless, limitations remain, including vague policy definitions and insufficient support (Khanna et al., 2014), neglect of social dimensions (Hunter et al., 2019), and constraints from traditional urbanization patterns and energy pressures (Sarker et al., 2018), which hinder the scalability and effectiveness.

The smart city pilot policy, in contrast, seeks to improve urban governance and efficiency through digital technologies, focusing on information infrastructure, data-driven decision-making, public service optimization, innovation ecosystems, and resource allocation. Existing studies emphasize three aspects: first is the conceptualization and implementation of the smart city pilot policy, with theoretical frameworks developed from governance, social, and economic perspectives, and comparative analyses highlighting different international approaches (Mora et al., 2023). Second, their economic and environmental impacts, as smart city construction has been found to enhance urban innovation and green total factor productivity, particularly in high-tech sectors, large cities, and the eastern coastal regions of China (Caragliu and Del Bo, 2019; Chen et al., 2024), while improving urban land-use efficiency through innovation investment, informatization, and optimized land allocation (Zhuo et al., 2025). Third, debates persist regarding carbon emissions. Although smart city initiatives generally promote energy conservation and efficiency through technological innovation and industrial upgrading (Pasolini et al., 2018; Shu et al., 2023), concerns over possible adverse effects still remain, such as intensified data monopolies (Yigitcanlar and Kamruzzaman, 2018).

Currently, increasing scholarly attention has been devoted to examining the synergistic effects of dual-pilot policies. For example, studies have investigated the combined impacts of low-carbon city pilots and innovative city pilots on urban carbon emissions, along with the integrated effects of low-carbon city pilots and new energy demonstration cities on the carbon emission intensity (Li and Zhang, 2024). These investigations demonstrate that dual-policy frameworks exhibit significantly stronger emission reduction effects than single-policy approaches.

The synergy between the low-carbon city and smart city policies represents a significant innovation in China’s ongoing pursuit of new urbanization and sustainable development. As previously noted, scholars have conducted comprehensive assessments of the effectiveness of both the smart city pilot policy and low-carbon city pilot policy, encompassing multidimensional benefit analyses across economic, environmental, and social dimensions. Both policies have been demonstrated to influence ULGUE. However, research examining the synergistic effects between these two policies remains limited, particularly research on how the implementation sequence influences their differential impacts, despite methodological advances and accumulated policy implementation experience. Therefore, in this study, we analyze the differential impacts arising from the implementation order and intensity of these policies. The long-term coordination mechanisms of dual-pilot policies are further elucidated, exploring approaches to enhance policy synergies and clarifying the optimal implementation pathways and mechanisms for sustained impact.

In this study, we employ a multi-period DID approach to estimate the effects of the dual implementation of low-carbon and smart city pilot policies on ULGUE. By incorporating appropriate control variables, the multi-period DID framework enables a rigorous identification of whether significant differences in ULGUE arise between the pilot and non-pilot regions before and after the introduction of these policies. The empirical specification of the DID model is presented in Equation 1. ULGUE = β 0 + β 1 CarbonSmart + Control i , t + η i + γ t + ε i , t . (1)

Here, subscripts i and t denote the city and year, respectively; ULGUE represents urban land green use efficiency; CarbonSmart is the core explanatory variable, which is defined as a multi-period DID indicator capturing the joint policy intervention of simultaneously implementing the low-carbon city and smart city pilot policies; and Control_i,t denotes time-varying covariates influencing ULGUE. City-specific fixed effects (ηi), year-specific fixed effects (γ_t), and a stochastic error term (ε_i,t) are included in the equation. Specifically, CarbonSmart has the value of 1 for cities that are simultaneously designated as both low-carbon and smart city pilots from the year of policy implementation onward, and the value is 0 otherwise.

The dataset used in this study is primarily drawn from the China Urban Statistical Yearbook, China Urban Construction Statistical Yearbook, municipal statistical bulletins on national economic and social development, and carbon emission data obtained from the Emission Database for Global Atmospheric Research. To ensure data comparability, pilot cities designated as province-level units, municipalities directly under the central government, county-level districts, or those with substantial missing data were excluded. The final panel dataset comprises 170 prefecture-level cities in China over the period 2008 to 2019.

Table 2 presents the essential statistical characteristics of the main variables.

The baseline regression results are presented in Table 3. Columns (1)–(3) report the estimated effects of various pilot policies on ULGUE without controls, whereas columns (4)–(6) present the results with control variables. Specifically, columns (1) and (4) show the impacts of the low-carbon city pilot policy on ULGUE before and after incorporating control variables, respectively. Columns (2) and (5) report similar results for the smart city pilot policy, and columns (3) and (6) illustrate the outcomes for the dual-pilot policy.

As shown in columns (1) and (4), the low-carbon city pilot policy significantly improved ULGUE (coefficient = 0.049, p < 0.01) before incorporating control variables. After including the control variables, the effect remained positive (coefficient = 0.020), though at a reduced significance level (p < 0.10). Columns (2) and (5) indicate that the smart city pilot policy also significantly increases the ULGUE (coefficient = 0.034, p < 0.01) without controls, and it remained statistically significant (coefficient = 0.011, p < 0.05) after including controls. Furthermore, columns (3) and (6) reveal that the dual-pilot policy significantly enhances ULGUE both without (coefficient = 0.069, p < 0.01) and with (coefficient = 0.029, p < 0.01) controls.

After controlling for covariates, the dual-pilot policy (coefficient = 0.029) demonstrated a stronger positive impact on ULGUE than either the low-carbon city (coefficient = 0.020) or smart city (coefficient = 0.011) policies individually. This suggested that the dual-pilot policy generated a greater synergistic enhancement in ULGUE relative to single-policy approaches.

To ensure the accuracy of the DID basic results, the treatment and control groups should show no significant difference in ULGUE prior to the pilot implementation. The results of the parallel trend test are illustrated in Figure 1. Current (0) denoted the year of implementation of the dual-pilot policies; −4 to −1 denoted four to one years before policy implementation; and 1–4 denoted one to four years after policy implementation. As shown, the regression coefficients were not significant before current (0) became significantly positive, beginning in current (0). This indicated that there was no significant difference in ULGUE between the treatment and control groups before the implementation of the dual-pilot policies, whereas significant differences emerged thereafter, thereby satisfying the prerequisites of the DID model. Furthermore, after current (0), the regression coefficients of ULGUE were significantly different from 0 and exhibited a gradual decline, suggesting that the ULGUE of the treatment cities continued to increase.

A placebo test in DID was employed to validate the robustness and credibility of the DID estimation results, ensuring that these findings were attributable to the treatment effect rather than other confounding factors. The placebo test used fake treatment groups, in which some control units were randomly assigned as if they had received the treatment. Specifically, a pseudo-treatment group was generated by randomly selecting 79 observations from the full sample of 1,989 observations, matching the size of the actual treatment group. These observations were then assumed to be cities under the dual-pilot policies. This procedure was repeated 500 times to obtain 500 sets of regression results. The estimated coefficients and corresponding p-values from the pseudo-policy dummy variables were plotted to visualize the distribution.

As shown in Figure 2, the blue dots representing placebo test coefficients were concentrated around 0 and approximated a normal distribution. The red curve depicted the expected distribution under no true effect, whereas the red vertical dashed line at x = 0.03 indicated the actual treatment coefficient from the main study. The horizontal dashed line denoted the 1% significance threshold (p = 0.01). The placebo test results strongly confirm the robustness of our findings. Most placebo coefficients were distributed between −0.02 and 0.02, which is consistent with the “no effect” expectation. The actual treatment coefficient, located far from the placebo distribution center, suggested that the true treatment effect was unlikely to have been randomly generated. This placebo test supports the main results, indicating that the treatment effect is genuine and unlikely to be caused by chance or other confounding factors.

To further validate the superior effectiveness of dual-pilot policies on ULGUE, we conducted comprehensive subgroup analyses across different city classifications (Table 4).

Single-policy analysis: we first examined each pilot policy independently. Column (1) retains cities that implemented only the low-carbon city pilot policies (treatment group) and those without any pilot policies (control group), with Carbon serving as the DID dummy. Column (2) adopts the same framework for smart city pilots. Both coefficients were significantly positive, confirming that either policy alone effectively enhanced ULGUE.

Comparative policy effectiveness: to evaluate whether dual policies outperformed single approaches, we created a “single-pilot policy” group that encompassed cities with either the low-carbon or smart city designation. Results in column (3) show that the single policy improved ULGUE compared to non-pilot cities (significant at 10% level), although the effect magnitude was smaller than that of dual policies.

Intensity-based analysis: we further implemented an intensity DID model by assigning values of 1 for single-pilot cities and 2 for dual-pilot cities. Column (4) indicates that each additional pilot policy increased ULGUE by 0.012 units (significant at 5% level). This gradient effect supported the hypothesis that policy intensity positively correlated with improvements in ULGUE, providing robust evidence for synergistic benefits of coordinated policy implementation.

Comparison of the pilot policy implementation sequences: based on the approval timeline, cities with dual-pilot policies were divided into two groups: those that first implemented the low-carbon city pilot policy and later the smart city pilot policy, and those that adopted the reverse sequence. To examine whether the order of policy implementation affected ULGUE, we investigated the differential effects arising from the order of the dual-policy adoption.

We first compared cities that initially implemented the low-carbon city pilot policy (followed by the smart city pilot policy) with cities without any pilot policies to measure the net effect of adopting the low-carbon policy first. The results, reported in column (1) of Table 5, show that the policy had a coefficient of 0.021, which was significant at the 10% level. For cities that first implemented the smart city pilot policy followed by the low-carbon city pilot policy, the coefficient in column (2) was 0.016, which was also significant at the 10% level. These findings suggested that implementing the low-carbon city pilot policy prior to the smart city pilot policy generated a stronger positive effect on ULGUE than the opposite sequence.