Deconstructing the driving factors of land development intensity from multi-scale in differentiated functional zones

Spatio-temporal characteristics of LDI in the YRD region

Spatio-temporal characteristics at the provincial level

From 2011 to 2016, the LDI in the YRD showed a steady increase, rising from 14.98% to 16.06% at a rate of 1.40%. It basically matched the land urbanization rate of 1.91% and the population urbanization rate of 1.72% across the region in the same period, indicating the coordinated development of land construction in the YRD region and the gathering of the population into cities and towns. LDI had a significant provincial divergence, with a development gradient of Shanghai > Jiangsu Province > Anhui Province > Zhejiang Province (Fig. 2). Jiangsu, Anhui, and Shanghai have remained relatively flat over the five years, while Zhejiang was in a period of significant acceleration. Zhejiang has a large proportion of gardens and forests, accounting for 59.17% of the total land. Due to the natural state, the LDI in Zhejiang was relatively low, but it was still in the process of rapid land development. Anhui is mostly hilly and mountainous with undulating terrain, which causes its LDI to be at a relatively low level and grow slowly. In contrast, Shanghai and Jiangsu have a large base of land construction and both have been in a slow-growth phase over the five years.

Spatio-temporal characteristics at the municipal level

The average LDI in the municipalities increased from 16.98% in 2011 to 18.16% in 2016, with an average annual increase of 1.36%. As shown in Fig. 3, the LDI in the central area of the city cluster raised from 18.38% to 19.81%, with an average annual increase of 1.51%, which was remarkably higher than the average LDI across the region. Of these, the LDI of Shanghai metropolitan area, Su-Xi-Chang metropolitan area, Nanjing metropolitan area, Hefei metropolitan area, Ningbo metropolitan area, and Hangzhou metropolitan area were 36.49%, 29.08%, 24.17%, 19.95%, 15.87% and 15.84% in 2016 (Fig. 4). It shows that the main urban agglomeration in the YRD, especially the inner circle (i.e., one core and five sub-cores), is an important driving force for land construction activities, and the development pattern and the land construction agglomeration effect within each metropolitan area is driven by the core cities (e.g., Nanjing, Jiaxing, Hefei, Wuxi, and Zhoushan).

Spatio-temporal characteristics at the county level

The average LDI in the counties of the YRD increased from 25.10% in 2011 to 26.25% in 2016, with an average increase of 5.83% over the five years. The LDI at the county level in the YRD was generally on the rise and shows significant spatial variation. It can be seen from Fig. 5 that the high-value counties of LDI were mainly concentrated in central areas of the central city and driven by the radiation of the province and city cluster, the integrated development of construction land in adjacent counties is remarkable, especially in Xuzhou and Suzhou. In contrast, the LDI of the outer suburban counties was relatively low, especially in the KEFZ, showing a core-periphery structure and reflecting the role of ecological function positioning in guiding the LDI in the YRD.

Spatio-temporal characteristics at the functional zone level

The LDI decreased region by region according to a gradient of UZ > MAPZ > KEFZ (Table 2). The UZ is mainly located in Shanghai-Nanjing-Hangzhou-Ningbo along the river counties, where the overall LDI is high and has increased rapidly from 33.39% in 2011 to 34.91% in 2016. The economic effects of the Shanghai center radiate to the Su-Xi-Chang and Ningbo metropolitan areas, and the large concentration of population, capital, technology, and strong intrinsic links keep the sub-region in a state of high-intensity development. However, the counties within the UZ also show a clear geographical divergence. Relying on the advantages of water transport and economic activity, counties along the river, such as Tongling, Wuhu, Nanjing, Shanghai, Ningbo, and Jiaxing, develop at a higher rate than inland areas such as northern Jiangsu, southern Zhejiang, and Anhui. The MAPZ is mainly located in northern Jiangsu and northern Anhui, where the LDI has developed most steadily, with an average growth rate of 0.78%. 65% of the counties have growth rates concentrated between 0.00 and 1.00%, which is related to the long-term stability of agricultural development under the demands of natural conditions. The KEFZ is located in the south of Anhui and northwest of Zhejiang. Constrained by natural conditions and environmental policies, the regional development intensity base is small, but the growth rate is rapid, with an average annual growth rate of up to 1.57%.

Multi-scale deconstruction of driving factors

Analysis of global regression estimation

The OLS test (Table 3) showed significant spatial lag and spatial error effects with the great likelihood LM-Lag and LM-Error tests being significant. The Robust LM-Lag and Robust LM-Error tests showed that the Robust LM-Lag was only significant through 10% in 2011 and the Robust LM-Lag was significant in 2016, in summary, the SEM model fitted better than the SLM model during 2011–2016. Therefore, we chose the SEM to estimate the global spatial correlation quantitively.

The results of the SEM based on the GeoDa platform showed that natural-human factors such as SLP, PCL, EID, PGDP, FAI, and FD all passed the 1% significance test, except for the PUR in 2016 (Table 4). The parameter estimation results showed that the PUR and PGDP had a stable and significant positive drive on LDI during 2011–2016, while SLP, PCL, EID, FAI, and FD had significant negative effects. The positive effect of PUR was the most evident, with the elasticity coefficients of lnPUR being 0.326 and 0.155 for 2011 and 2016. It reflects that the concentration of population in towns and cities is the dominant factor influencing land expansion and construction activities. PGDP conducted the LDI to a lower extent, with coefficients of 0.156 and 0.292. The coefficients of lnSLP and lnEID were both negative in 2011 and 2016, indicating that land development activities are constrained by natural topographic conditions and the importance of ecosystems. The coefficients of lnPCL on LDI were − 0.242 and − 0.197, which reflects that the smaller PCL, the greater the pressure on the county’s cultivated land resources, and the higher the LDI. The coefficient of lnFAI decreased from − 0.151 to − 0.223, indicating a further dampening effect of fixed asset investment efforts on land development. The coefficient of the lnFD was all in the range from − 0.245 to − 0.255, reflecting a stable inhibitory effect of local fiscal regulation on LDI.

Analysis of local regression estimation

We used the goodness of fit to measure how well the regression values fit the observations. From Table 5, it is easy to find that the model-adjusted goodness of fit was > 0.89 for both 2011 and 2016, and it can be judged that the regression model performs better and the study has scientific validity. The PGDP and FAI were global variables in 2011 (i.e., bandwidth/number of samples > 90%), and the rest were local variables. While in 2016, only the PGDP and EID were global variables.

The natural factors, including SLP, PCL, and EID, generally reflect the natural condition of the land that hosts human activities. It can be found from Table 5 that all-natural factors negatively affected LDI, which reflects the constraints on land development imposed by topographical conditions, and the importance of protecting arable land and the ecological environment under the national territorial spatial control. By further decomposing the effects of different factors in different geographical locations, we can see that the impact of SLP on LDI in 2011 and 2016 showed significant geographical divergence in space, with the areas of greater inhibition being distributed in Zhejiang Province and the southern Anhui, and the intensity of the effects gradually decreasing from the hilly areas of Zhejiang and Anhui to the plains of Jiangsu (Fig. 6a,b). It reflects that under the ecological civilization, land development is being carried out in a restrained manner based on nature conservation, and the development and construction activities in the YRD region should uphold the principle of ecological sustainability and rational use of land based on topographic conditions. The inhibiting effect of the PCL on LDI is stronger in the MAPZ such as northern Jiangsu and northern Anhui. The PCL is proportional to the LDI in the peripheral areas of the urban agglomeration of southern Anhui and Zhejiang, which are in the KEFZ. This reflects the fact that to a certain extent the pressure on arable land resources increases and construction activities are restricted (Fig. 6c,d). Thus, while providing land resources for construction activities, the focus should also be on protecting cropland through appropriate development and limiting excessive land construction. The areas where EID inhibited LDI with greater intensity are the KEFZs and MAPZs in northern Jiangsu and northern Anhui (Fig. 6e,f), and the global inhibitory effect gradually appeared by 2016. Due to the spatial control of the national territory, the awareness of the protection of ecologically important areas during land development has increased.

Human factors include PUR, PGDP, FAI, and FD. Among them, the PUR and PGDP had significantly positive impacts on LDI in YRD counties, while FAI and FD had a negative inhibitory effect which was deepening over time. As shown in Fig. 6g,h, the regression coefficient of the PUR was significantly positive but decreased from the central areas of urban agglomerations, especially the Shanghai and Nanjing metropolitan areas, the Taihu, and Hangzhou-Jiaxing areas, to the peripheral urban agglomerations. That is to say, the urban agglomeration is facing greater pressure for land development and construction due to a large population. In 2011, the positive effect of PGDP on LDI was more significant in coastal regions such as southern Jiangsu and Zhejiang than that in Anhui and northern Jiangsu (Fig. 6i); while in 2016, that across counties tended to be averaged (Fig. 6j), indicating that the global contribution of economic development to land development is further accentuated. At this stage, the government is no longer just carrying out construction activity under the concentration of economic investment, but more focusing on the development and utilization of its functional positioning, moving towards a development pattern of high quality and efficient utilization. The inhibitory effect of FAI on LDI decreased from the central areas of the eastern coastal urban agglomeration to the peripheral non-urban agglomeration regions (Fig. 6k). Combined with the industry structure of investment, it is easy to see that urban agglomerations always have obvious advantages in terms of investment attraction and policies, with fixed asset investments mainly in manufacturing and real estate industries flowing to these areas to obtain a higher rate of return on investment and construction. In contrast, non-central counties in northern Anhui, northern Jiangsu, and southern Zhejiang, based on the positioning of the MAPZ or KEFZ, have relatively concentrated public welfare investment, mainly in the water conservancy, environment and public facilities management industries and agriculture, which are usually not conducive to absorbing land development factors (Fig. 6l), thus leading to inhibiting effects and spatial variability. The effect of FD on LDI depended on the actual situation of the counties. In Hangzhou, Nanjing, and Su-Xi-Chang metropolitan areas, the impact of FD on LDI was positive (Fig. 6m), while in contrast, the suppression effect was significant in the highly UZ of the Nanjing metropolitan area and the KEFZ of southern Zhejiang (Fig. 6n). It shows that local governments can attract more environmental industries through financial regulation and policy control, raise the threshold of access to land construction industries, and reasonably restrict excessive development to achieve the intensive use of national land space.

Differences in driving factors between functional zones

As shown in Table 6 and Fig. 7, the drivers of FDI within different functional zones, i.e., UZ, MAPZ, and KEFZ, have been estimated separately. The UZ includes Shanghai, Nanjing, Hangzhou, and Ningbo, the Su-Xi-Chang metropolitan areas, the counties along the rivers, and the main urban counties of each city. The overall average LDI of the region was over 33%, much higher than the others, making it a key area for land development across the YRD. The region has a flat topography, a high level of economic development, a high degree of openness based on the advantages of the coast, a gradually improving town system, and a certain degree of radiation drive from the cities. The regression coefficients of the drivers of each sub-model show that the LDI in UZ was determined by both natural-human factors, with the PUR and PGDP contributing more. It is also closely related to the larger economic scale and more robust town system in UZ. The inhibiting effect of natural factors on land development fully reflects those natural endowments are the basis for human activities. The PCL coefficient has the highest absolute value and the strongest negative effect, indicating the scarcity of arable land resources in UZ. By 2016, the negative impact of FAI and FD in the sample of UZ had deepened significantly, reflecting that the region is at a more mature stage of urban integration development. Promote a shift in industrial structure towards high value-added, high-end industries through changes in fixed asset input industries and increased fiscal freedom. There has been a fundamental shift in optimizing economic development to reduce energy consumption and environmental pollution.

The MAPZ is dominated by non-main urban counties such as northern Jiangsu and northern Anhui, where the LDI was 15.60% in 2011 and 16.22% in 2016. The terrain is suitable for cultivation and has good conditions for agricultural production. The LDI in the region is mainly driven positively by the PCL, with prominent disincentives from EID, FAI, and FD. The objectives of the region are to restrict large-scale, high-intensity industrialization and urbanization in the development of land space, and the important ecological farming requirements limit the occupation of land by construction activities. The region is subject to less pressure on arable land resources, and fixed asset investment is tilted from real estate and new district construction inputs to farming industries such as agriculture, forestry, animal husbandry, and fishing. By 2016, finance is gradually playing a role in the development of land in the MAPZ, and the guidance of agricultural development inputs in the fiscal plan based on functional positioning may limit the construction of regional urbanization to ensure the development capacity of agricultural products.

The KEFZ includes the low hill counties of southern Anhui and western Zhejiang. The area has a high average slope, with some natural beauty of the landscape, and the lowest overall LDI, at 5.56% and 6.01% in 2011 and 2016 respectively. The influence of the drivers reflects the fact that the region is mainly inhabited by SLP, and EID and driven by PGDP. Due to the topography and the requirements of ecological construction, the LDI in the area should be strictly limited, while the PGDP rises to a certain level, the requirements of the government or residents for the quality of the ecological environment prompt strong investment in green industries and promote the development and upgrading of environmental protection technology projects to protect and restore the ecological environment.