Forty years have elapsed since China implemented reform and opening up. During this period, urbanization rates have increased rapidly, contributing to social and economic growth. With the advance of urbanization, construction land has expanded and occupied cultivated land and woodland and some cities have expanded outward in the form of a ‘spreading the pie’ (Deng et al., 2017; Zhao et al., 2018; Yue and Xue, 2020). At the current stage of development, urban construction land is increasing year by year in China. However, unreasonable land use structures and wastage of land resources have led to low urban land use efficiency (ULUE) (Aziz et al., 2012). The conflict between regional urbanization development, farmland protection, and ecological protection is becoming increasingly serious. Therefore, improving ULUE has become essential for promoting regional sustainable development and the construction of ecological civilization (Meng et al., 2021). In this context, China proposed the concept of new-type urbanization (NTU) to guide intensive land use during urbanization (Bai et al., 2014; Dadashpoor et al., 2019; Zhu et al., 2020).

Compared with the traditional development model of urbanization, NTU presents innovative ways of thinking, regional layouts, and development modes (Deng et al., 2020). By improving technological externality and adjusting urban industrial structures, NTU uses urban land efficiently (Chen et al., 2019b; Deng, 2020; Yang et al., 2022). However, rapid urbanization motivated by ‘land finance’ and industrialisation has led to the decline of cultivable land and rise of environmental pollution, putting significant pressure on sustainable urban development (Wang et al., 2016; He et al., 2016b; Wu et al., 2017). On the other hand, NTU promotes intensive, intelligent, green development, and improves the urbanization quality (Cheshmehzangi, 2016). This requires the minimisation of trade-offs and maximisation of synergy during urbanization to achieve rational and sustainable land development under urban construction (Yang et al., 2017).

The core concept of ULUE is to obtain the maximum economic output with the minimum resource input (Chen et al., 2019a). Existing studies on ULUE emphasize the intensive and effective use of existing land to realize the rational expansion of urban boundaries, putting forward the concepts of ‘compact city’ and ‘smart growth’ (Bonafoni et al., 2017; Yue and Xue, 2020). Research on this topic has focused on the following aspects: 1) the measurement of ULUE, regional differences, and dynamic evolution characteristics under the background of environmental constraints and urbanization. Due to different selections of input-output indicators and research time ranges, the conclusions of past studies vary (Lu et al., 2018). Hu et al. (2018) considered that ULUE in China showed an increasing trend year by year and showed regional differences, being in the order of Eastern, Western, and Central China from highest to lowest. 2) Spatio-temporal coupling of demographic-landscape urbanization and its driving forces. It is generally believed that the development of demographic-landscape urbanization is not coordinated, with population urbanization (PU) lagging behind the development of land urbanization (He et al., 2016a; Hou et al., 2016; Feng et al., 2019; Ma et al., 2020). Li et al. (2013) found that the rural land system and residence registration were the main reasons for PU lagging behind land urbanization. 3) Analysis of the influencing factors of ULUE and focus on the influence of industrial structure, urbanization, urban accessibility, financial expenditure, and other factors on ULUE (Wu et al., 2017; Zhu et al., 2019). Fan et al. (2018) explored the driving factors of ULUE and found that they varied with different urban functions. The research scopes of past studies on this topic include urban agglomerations, economic belts, provinces, and cities with different spatial scales and analytical methods include slack-based measurements, stochastic frontier method, data envelopment analysis, and impulse response function.

The above studies provide a good reference for analysing urbanization and ULUE but there are also some limitations. First, there are few studies on the combination of NTU and ULUE, with most research analysing the coordinated development of PU and land urbanization or the impact of PU on ULUE (Wu et al., 2018; Wu et al., 2019; Shang et al., 2022). NTU has new characteristics that promote ideas, spatial layout, and development modes and has different effects on ULUE (Peng et al., 2014; Zhao et al., 2018). Second, when measuring ULUE, one-sided emphasis is put on the input-output level of urban land in terms of economic efficiency while ignoring the balance between social and eco-environment benefits (Jiao et al., 2020). Urban land is a complex ecosystem based on the interaction between the ‘human economy, society, and ecological environment’. Therefore, when calculating the output level, the desirable and undesirable output levels of the economy, society, and environment should be considered comprehensively (Sun et al., 2015; Chen et al., 2019c; Sulemana et al., 2019).

At present, China is in an important period of improving the quality and efficiency of NTU construction. Thus, there is an urgent need for relevant research to explore the influencing mechanisms of NTU on ULUE to realize the intensive use of construction land and the stable development of NTU. Therefore, we summarize the influence mechanism of NTU on ULUE, and straighten out the interaction mechanism between the two systems. Then, the index evaluation system is constructed from the four aspects of population urbanization, economic urbanization, spatial urbanization and social urbanization to fully reflect the comprehensiveness of the new-type urbanization construction and highlight the people-oriented value concept. Finally, in the process of promoting the NTU, this study focuses on exploring its impact on the ULUE and analyses the interaction mechanism, which is conducive to the intensive use of urban land. Based on this, the study takes the Yangtze River Delta as a typical case to empirically explore the impact of NTU on ULUE, which will play a complementary role in solving the land use problem and achieving sustainable socio-economic development.

Yangtze River Delta (29°20′N–32°34′N, 115°46′E–123°25′E) is located on the east coast of China. It includes Shanghai Municipality and Jiangsu, Zhejiang, and Anhui provinces and comprises 26 closely linked cities, with Shanghai as the core. It is the core region of urbanization in China (Fig. 1). The Yangtze River Delta has a developed social economy, a large population and a rapid expansion of construction land, which leads to the problems of disorderly urban space spread, serious construction encroachment on green ecological land, and low degree of conservation and intensive utilization in the process of urban land development. The contradiction between human and land is acute. Therefore, by selecting Yangtze River Delta region as a typical case to explore the impact mechanism of NTU on ULUE, it is more conducive to promoting regional integration and providing reference for the management of urban land intensive use. In 2020, the Yangtze River Delta covered 211 700 km², with a GDP of 17.86 trillion yuan (RMB) and permanent population of 154 million, accounting for 2.21%, 19.84%, and 11.04% of the national totals, respectively (NBSC, 2020). As a result, there is a growing demand for construction land in the area, which may lead to increasing conflict between regional development and environmental protection.

Figure 1.  Location of the study area in China and its city distribution

Based on the slack-based measure (SBM) model proposed by Tone (2001), we chose non-radial and non-angle models. The models comprehensively consider the input and output of each decision-making unit (DMU) and put the relaxation variable directly into the objective function, which solves the problem of input-output slacks. Based on this, we developed the SBM-Undesirable model. This model also considers undesirable outputs, making ULUE more accurate in the context of increasing environmental restrictions.

The principle of the model is as follows: there are n homogeneous DMUs, each with three vectors (input, output, and undesirable output), expressed as x, y ^g and y ^b, respectively (Liu et al., 2017; Chen et al., 2020). Additionally, X, Y ^g and Y ^b are defined as $X=\left({x}_{i j}\right)$, ${Y}^{g}=\left({y}_{i j}^{g}\right)$and ${Y}^{b}=\left({y}_{i j}^{b}\right)$, respectively. Based on the actual input-output, assuming X > 0, Y ^g > 0 and Y ^b > 0, the set of production possibilities, that is, all combinations of desirable and undesirable outputs produced by input x of N factors, is P. The model is as follows:

According to the definition, the SBM model with undesirable outputs can be defined as:

where $ {s}_{l}^{-} $ is the input slack of lth input variable (l = 1, 2, 3, …, m); $ {s}_{r}^{g} $ and $ {s}_{r}^{b} $ are the desirable and undesirable output slacks, respectively. $x_{l0} $ indicates the investment amount of the r0-th decision-making unit. s₁ and s₂ represent the number of desirable output variables and undesirable output variables, respectively. ${y_{r{0}}^g} $ and ${y_{r{0}}^b} $ represent the desirable output and undesirable output of the r0-th decision-making unit, respectively. λ is the weight variable that determines the scale effect of each DMU; and ρ is the comprehensive efficiency of DMU. When ρ = 1, all slacks satisfy $ {s}_{l}^{-}={s}_{r}^{g}={s}_{r}^{b}=0 $ and the kth DMU is efficient. If ρ < 1, DMU is inefficient. The input and output need to be improved. The model is a nonlinear programming model but it can be transformed into a linear programming model using the Charnes-Cooper method (Lu et al., 2018; Yu et al., 2019).

To calculate ULUE, the comprehensive benefits to the society, economy, and environment were considered. Additionally, negative environmental effects were included in the index system (Lu et al., 2018; He et al., 2020; Kuang et al., 2020) (Table 1). Following the Cobb-Douglas function, the input indicators used were construction land area, investment in fixed assets, and the number of employees in secondary and tertiary industries. Output indicators were divided into two groups: desirable and undesirable outputs. The desirable output indicators reflected the outcome of the city’s production and operation activities within a certain period. They included economic, social, and environmental benefits, which were the added value of secondary and tertiary industries, average wages of employees, and coverage of green land in built-up areas, respectively. Undesirable output indicators included wastewater discharge, industrial SO₂ emissions, and smoke discharge, which were used to characterise the negative environmental effects in the process of urban socio-economic development.

Referencing Peng et al. (2014) and Chen et al. (2016b), the index evaluation system for NTU was defined by four aspects: population, economy, space, and society (Table 2). To avoid the multicollinearity issues caused by the correlation between variables, we used the principal component analysis to calculate the comprehensive result. According to the Kaiser-Meyer-Olkin (KMO) test, the cumulative contribution rate of the first two principal components reaches 90.2%, which meets the general standard wherein the eigenvalue is greater than 1 and the cumulative contribution rate of variance is greater than 85%. Therefore, the selected principal component can represent all the information of the original index. The model is as follows:

where F_j is the score of principal component j (j = 1, 2), L_uj is the loading value of indicator r and principal component j (r = 1, 2, …, 11), and X_u is the normalized evaluation indicator. The comprehensive index was calculated by taking the variance contribution rate of the first two principal components as the weight of each principal component. The specific formula is as follows:

where Y is the NTU index and W_j is the contribution of variance to the principal component j. According to 3σ-principle, the index results can be revised using coordinate transformation.

The vector autoregressive (VAR) model, proposed by Sims (1980), was used by He and Peng (2017) to investigate the dynamic impact of external random interference on endogenous variables. However, the VAR model does not support panel data or consider unobservable individual heterogeneity. To overcome these limitations, Holtz-Eakin et al. (1988) extended the VAR model and developed a panel data vector autoregression (PVAR) model. The PVAR has the advantages of both time series and panel data, which is convenient for investigating the dynamic relationship of endogenous variables. The above theoretical framework shows that the relationship between NTU and ULUE is complex and there may be endogenous causality among evaluation units. To determine the influence degree of NTU on ULUE accurately, we established the PVAR model based on the research results of Grossmann et al. (2014) and Kuang et al. (2020). The model can be expressed as:

where i = 1, 2, …, N indicates the evaluation unit, that is, each city in the Yangtze River Delta; t = 1, 2, …, T represents the year; m represents the lag order of the model; y_it is the endogenous variable that varies with time and space, including ULUE, PU, EU, SU, and SCU; α_i is a vector of fixed effects and stands for the individual differences of the cross section; μ_t is a vector with time effect; and ε_it is a random interference term. To avoid heteroscedasticity among variables, all endogenous variables (ULUE, NTU, PU, EU, SU, and SCU) were logarithmically treated and expressed by lnULUE, lnNTU, lnPU, lnEU, LnSU, and lnSCU, respectively.

In addition, Test for stationarity of variables needs to be carried out. The stationarity of panel data is a prerequisite for the PVAR model, which ensures that it has no unit root (Lin and Zhu, 2017). After logarithmic processing of the original data, the unit root test of lnNTU and lnULUE was conducted. There are two types of unit root tests. The first type tests homogeneous unit roots, such as Levine-Lin-Chu (LLC) and Breitung. Another type includes Im, Pesaran and Shin (IPS), Fisher-Augmented Dickey-Fuller (Fisher-ADF), and Fisher-Phillips-Perron (Fisher-PP) tests for homogeneous unit roots. The above five methods were comprehensively adopted to avoid the possible defects of a single test method. The values of lnULUE, lnNTU, lnPU, lnEU, lnSU, and lnSCU were tested. The results in Table 3 show that all variables passed the significance test and have strong stationarity, which can provide a basis for the generalised method of moments (GMM) estimation, impulse response analysis, and prediction variance decomposition.

The data were mainly obtained through public information sources, including China Urban Construction Statistical Yearbook (MHURC, 2000–2020) and China Urban Statistical Yearbook (NBSC, 2000–2020). The indicator of the annual average balance of net fixed assets was converted by consumer price index (CPI) to eliminate the effects of price factor. When processing foreign direct investment data, they were converted into RMB according to the exchange rate of USD/RMB in the current year. The moving average method was used to deal with the missing data of individual years. Based on the above, the city-level data of the Yangtze River Delta from 2000 to 2020 were obtained.

The super-efficiency SBM model was used to calculate the land use efficiency of 26 cities in the Yangtze River Delta, with an average value of 1.003 and maximum of 1.063 in 2009 (Fig. 2 and Fig. 3). During the entire study period, ULUE was constantly changing. Especially from 2000 to 2008, ULUE showed a fluctuating upward trend, and the efficiency value increased from 0.987 in 2000 to 1.001 in 2008. The Yangtze River Delta became a key area for attracting foreign investment and developing an export-oriented economy because of its superior geographical location and natural background advantages. The reason might be China’s accession to the World Trade Organisation (WTO) and the accelerated development of economic globalisation. As a result, various development zones with different characteristics, such as high-tech, economic development, and bonded zones, were formed. The total economic volume and development speed accelerated and urban construction land was efficiently utilised. Especially in Jiangsu, the urban land use efficiency (ULUE) increased to 1.034 in 2007, which was more significant than other areas. From 2009 to 2011, ULUE gradually decreased in the Yangtze River Delta; in 2011, it dropped dramatically and the efficiency value was only 0.975. Owing to the adverse effect of global economic crisis in 2008, part of the export-oriented economy of the Yangtze River Delta became unsustainable. Therefore, it is critical to change the mode of economic development and improve the industrial activity in the area. Moreover, under the current strict rules for basic farmland protection, an extensive construction mode for any city (county), development zone, or industrial area is difficult to implement. Urban development has changed from ‘incremental expansion’ to ‘stock tapping’ to improve land use intensity. The land use efficiency of each area decreased in varying degrees and was especially marked in Jiangsu and Anhui. However, some parts of Shanghai and Zhejiang were constrained by the available amount of land, and land use efficiency and development intensity remained at a high level. From 2012 to 2020, global trade protectionism rose and the downward pressure on domestic economic development increased, leading to a growth rate of about 6.0%. The intensity of urban land expansion was not significant and the state had increased its control of land for urban construction. Land use was concentrated around the urban built-up area, mostly to fill spaces in idle suburban land. As a result, ULUE of the Yangtze River Delta changed little and the average efficiency remained at 0.998.

Figure 2.  Temporal change of urban land use efficiency (ULUE) in Yangtze River Delta from 2000 to 2020

Figure 3.  Temporal change of urban land use efficiency (ULUE) in provinces and municipality of Yangtze River Delta from 2000 to 2020

Data on ULUE in 2000, 2005, 2010, 2015, and 2020 were selected for spatial visualisation (Fig. 4). From the perspective of spatial evolution, ULUE of the Yangtze River Delta varied greatly among different cities and the reasons were complex. From 2000 to 2020, ULUE of Shanghai was much higher than that of other cities, with an average of 1.312. One reason for this is the reuse of inefficient land under government guidance; for example, Shanghai renovated and redeveloped abandoned land in industrial parks to improve ULUE. The second reason is the government’s land-management level; Shanghai adopted an appropriate land use management mode to control disordered expansion to avoid the low efficiency. Nanjing, Hangzhou, Suzhou, and Hefei have high levels of land use efficiency, mainly due to rapid economic development, a strong ability to attract populations from other regions, activation of the amount of land in the built-up area, controlling the amount of new construction land, and improving the intensity of land development investment. All these measures effectively guarantee the rational use of urban land. Correspondingly, the peripheral cities locating in Yangtze River Delta, such as Yancheng, Tongling, Xuancheng, and Jiaxing, need to improve their land use efficiency. There are two reasons for the inefficiency of land use in these cities: first, the policies of sustainable land use in Tongling and Xuancheng are not perfect; second, the expansion mode of urban land ‘from core to edge’ will lead to differences between urban development and land use level, resulting in overall utilisation inefficiency. This situation is particularly prominent in Yancheng City. Since 2000, with the accelerated construction of new urban areas, the extent of the population and industrial suburbanization has been improved. However, land urbanization is proceeding much faster than PU and the ability to attract economic activities and a foreign population is not strong, which makes ULUE remain at a low level.

Figure 4.  Spatial patterns of urban land use efficiency (ULUE) from 2000 to 2020 in Yangtze River Delta, China

The NTU level in the Yangtze River Delta rose continuously from 2000 to 2020, with an average annual growth rate of 3.47%. From the perspective of the spatial layout (Fig. 5), the spatial differentiation of the NTU level of each evaluation unit was large, showing a certain degree of spatial coupling characteristics with the layout of ULUE. Overall, the ‘core-periphery’ layout features were significant and the high-value areas of the NTU level were distributed along the ‘Huning-Huhang-Hangyong’ traffic trunk line and presented ‘Z’ structure layout characteristics, which were basically consistent with social and economic development levels. Meanwhile, the level of NTU in neighbouring counties was low. The NTU level of the municipality directly under the central government and sub-provincial cities was higher than that of the general prefecture-level cities. This was most obviously true for Shanghai, Nanjing, and Hangzhou. These cities had an efficient administration, a good development foundation, and a low cost of attracting capital, technology, high-end talents, and other advantageous production factors. Therefore, they could take the lead in realizing the requirements of NTU and achieving the goal of sustainable development during social and economic transformation. Correspondingly, Yancheng in the northern Jiangsu, Anqing in the western Anhui, and Jinhua in the northern Zhejiang were the low-level units of NTU. Restricted by the basic geographical conditions of mountains and hills, these areas were mostly designated as ecological conservation areas where industrial development was limited by a severe lack of urban land supply. Despite the constraints of land management policies, the urban extensive growth mode has not been changed, which made the level of NTU low.

Figure 5.  Temporal-spatial patterns of new-type urbanization (NTU) from 2000 to 2020 in Yangtze River Delta, China

Before running the PVAR model, this study selected the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Hannan and Quinn’s Information Criterion (HQIC) to determine the optimal number of lag periods. Decision results of lag period selection were obtained (Table 4). When the lag period was 4, the statistical result of AIC was the lowest. When the lag period was 2, the statistical values of BIC and HQIC were the lowest. Normally, when the results of three criteria were inconsistent, BIC and HQIC were correct. With the gradual increase of the sample size, AIC may mistakenly select an excessively high period, while the independent distribution of BIC and HQIC can select a more accurate lag period. Therefore, the study selected 2 as the lag period.

Considering the regression variables that may appear in the operation of the model, the forward-mean differencing was used to remove individual effects to realize the orthogonal of the lag variable and transpose variable. Since there may be endogenous problems among variables, the two lag periods of each variable were used as the tool variable and then GMM was used to estimate the PVAR model. In Table 5, when ULUE is an exogenous variable, the first two lag periods have a significant positive effect on the current period, with coefficients of 0.9465 and 0.5345, respectively, indicating that there are positive and progressive effects as well as self-enhancement mechanisms in the time scale of ULUE in Yangtze River Delta. The improvement of ULUE showed a gradual trend and it is necessary to realize intensive and efficient utilisation of urban land through the innovation of land management and utilisation mechanisms. The estimated results are shown in Table 5, where h_lnULUE, h_lnNTU, h_lnPU, h_lnEU, h_lnSU, and h_lnSCU are the sequence of lnULUE, lnPU, lnEU, lnSU, and lnSCU after Helmert transformation; L1 and L2 represent the lag variables of the first cycle and the second cycle, respectively.

During the two lag periods, the NTU level increased by 1% and ULUE increased by 0.3514% and 0.2543%, respectively, indicating that NTU has a promotional effect on ULUE. The reason is that the promotion of NTU, intensive space use, and a compact layout is conducive to the continuous improvement of ULUE. PU and EU had positive effects on ULUE in the two lag periods and both passed the significance test at a 1% level. SU and SCU had negative impacts on ULUE during the two lag cycles but these were not significant. The above results show that the main factors promoting ULUE are population agglomeration and economic growth. In NTU, more attention is paid to the quality of human urbanization and economic development, strictly controlling the disorderly spread of construction land, optimising the land use structure, and promoting the continuous improvement of ULUE.

To reveal the dynamic impact trajectory of a random impact with standard deviation, the Monte Carlo method was used to carry out impulse response analysis between NTU and ULUE in the Yangtze River Delta (Fig. 6). The solid red line represents the impulse response value, and the solid blue line represents the estimated values of 5% quantile and 95% quantile, respectively.

Figure 6.  Analysis results for impulse response function (IRF) in the Yangtze River Delta, China

Fig. 6a shows that ULUE was impacted by a standard deviation and has a significant positive response to itself. During the first three periods, it had a weak positive U-shape fluctuation. With the extension of time, the continuous positive response had a decreasing trend, which confirms the inertial effect and path dependence characteristics of ULUE in GMM estimation results. However, the cumulative effect intensity decreased continuously. In particular, facing the orthogonalization pulse of lnULUE, the responsivity of lnULUE was 0.1723, which gradually decreased with the increase in the number of subsequent periods. The response degree of lnULUE to itself in stages 5 and 10 was 0.0724 and 0.0162, respectively. Compared with initial stage, they were reduced by 0.0999 and 0.1561. Fig. 6b shows the response of ULUE to a standard deviation of NTU. The results show that ULUE has no apparent response to NTU in the current period. However, it then showed a relatively gentle positive effect trend, presenting an inverted U-shaped feature, which indicates that with the promotion of the NTU policy in the future, the efficient and intensive development of construction land will have a strong pulling effect on ULUE. It peaks in the third year and then the positive response intensity gradually weakens.

Figs. 6c–6f shows the responses of ULUE to the changes in PU, EU, SU, and SCU. There were significant differences in impulse response characteristics. First, the response of ULUE to PU was inconspicuous. In the face of the standard deviation pulse from lnPU, the responsivity of lnULUE in the first period was 0. Then, it dropped to a negative value and moved below 0. After the second period, it gradually rose and converged to 0. The phenomenon indicates that in the process of PU, the production and living of new residents requires a large amount of urban land. Moreover, the expansion of urban land has a negative impact on the improvement of ULUE but the impact is weak. Second, for a standard deviation impulse of lnEU, ULUE did not respond in the current period. From the beginning of the first stage, lnEU showed a positive reaction and the response value increased year by year. In the first period, the response value of lnULUE was 0.0212 peaked. After that, although it decreased year by year, the response remained positive, indicating that the development of EU improves ULUE. Third, the response of lnULUE in the current period was 0 after receiving a standard deviation impulse from the lnSU. After that, the responses of the 5th and 10th periods were –0.0053 and 0.0021, respectively. This indicates that the excessive development of land resources will cause the deterioration of the ecological environment, which is not conducive to the improvement of ULUE. Similar to PU, the impulse response of SCU to ULUE fluctuated during the first four periods. After that, the response tended to be negative but the impact was small. This shows that with the continuous evolution of SCU, the coordination between ULUE and social development will gradually increase.

To evaluate the importance of various structural impulses to specific variables, variance decomposition was applied to analyse the importance of different variables to the fluctuation of ULUE. Like the impulse response analysis, the Monte Carlo simulation was carried out 500 times. The results are in Table 6.

It can be seen from Table 6 that the first period of lnULUE can explain 100% of its own change, which indicates that the change of ULUE in the Yangtze River Delta is mainly influenced by its own fluctuation. However, the degree of the impact has a downward trend. By the 10th stage, 54.1% of the lnULUE change could be explained by itself. The impulse response of NTU on ULUE was relatively large and showed an increasing trend by year, which means that the impact of NTU on ULUE is a long-term process. From the explanatory power of various types of urbanization on ULUE, the changes in PU, EU, and SU had relatively greater impacts on ULUE, whereas the effect of SCU was smaller. Among them, PU contributed the most to the variation in ULUE, with an average contribution rate of 14.6%. The average contribution rates of EU, SU, and SCU were 13.4%, 3.9%, and 0.11%, respectively. In the first period, lnPU, lnEU, lnSU, and lnSCU had a low degree of explanation for lnULUE, with an average of only 3.8%. However, as time went on, their explanatory power increased and, by the 10th period, their explanatory power on the change of lnULUE was 24.9%, 23.7%, 6.8% and 3.9%, respectively.

The study used a non-radial directional distance function that comprehensively considers economic benefits and environmental costs to evaluate ULUE, which is more in line with the connotation of intensive land use in the new normal period. The PVAR model can accurately reveal the impact of NTU on ULUE and effectively deal with the endogenous problem of variable data. However, the study only considered the impact mechanism of NTU on ULUE in the analysis framework and did not include current qualitative factors, such as coordinated development planning, urban structure transformation, and policy adjustment in the Yangtze River Delta. In addition, the study only considered the level of prefecture-level cities; hence further research should consider different scales to provide a deeper understanding of the influence mechanism of NTU on ULUE. The above research conclusions were confirmed in the existing research results (Xiao et al., 2022). Econometric model also showed that new-type urbanization has a significant positive impact on land use, which implied that the implementation of the policy has not reduced the sustainability of land use, but played a significant role in improving it. And these are in line with basic cognitive common sense (Deng, 2020; Koroso et al., 2021).

The orderly promotion of NTU is a critical driving force for the efficient use of urban land. The results of this study have important policy implications for the sustainable development of urbanization and construction land in China. First, decision-makers should take effective measures to improve the quality of NTU and coordinate the development of different types of urbanization. In March 2014, the State Council issued the National New-Type Urbanization Plan (2014–2020). Departing from traditional urbanization, which has economic development at the core, NTU emphasizes the protection of the ecological environment, the universal coverage of public services, and the balance between urban and rural development. This urbanization model is in line with the goal of coordinated development among PU, EU, SU, and SCU. It is a key policy tool for the local government to deal with the efficient use of urban land. The above situation has also been confirmed by the following research results (Wu et al., 2017; Yue and Xue, 2020; Koroso et al., 2021). Second, policymakers should use ULUE as a critical index for urban land management. To achieve the goal of ecological civilisation construction, they should integrate key technologies and optimize land use spatial allocation to improve ULUE. Finally, with the orderly advancement of the ‘Yangtze River Delta regional integration strategy’, the relationship between NTU and urban land use will gradually evolve toward high-quality coordination. From 2009 to 2018, driven by new-type urbanization strategy, ULUE of 280 prefecture-level cities in China increased by 3.25% annually (Zhang et al., 2022). While policymaking, we should fully consider the development stage and functional orientation of cities and promote the flow of production factors, which can improve ULUE in the Yangtze River Delta.

This main purpose of the study was to explore the spatiotemporal evolution characteristics of urban land use efficiency (ULUE) and new-type urbanization (NTU), and then analyse the impact of NTU on changes in ULUE in the Yangtze River Delta systematically. The results show that the ULUE in Yangtze River Delta fluctuates significantly, showing a trend of rising first and then falling. There are significant regional differences, among which the ULUE of Shanghai is significantly higher than that of other regions, and ULUE of cities in Anhui is the smallest. For the NTU, during the study period, the level of NTU in Yangtze River Delta continued to rise, with an annual growth rate of 3.47%. The high-value areas of the NTU level were distributed and presented ‘Z’ structure layout characteristics, which made the ‘core-periphery’ layout features significant. Based on the analysis of spatiotemporal characteristics of ULUE and NTU, the influence mechanism of NTU process on ULUE in the Yangtze River Delta is explored. The positive impact of NTU on ULUE was not decisive, and PU, EU, SU, and SCU can also explain the change of ULUE in the Yangtze River Delta to a certain extent. Nevertheless, the influence intensity of the four subsystems of NTU differed. In addition, from the perspective of variance decomposition, PU contributed the most to the changes in ULUE, followed by PU, SU, and SCU.