Flexible Control of Urban Development Intensity in Response to Population Shrinkage: A Case Study of Shantou City

Abstract

This study proposes replacing traditional single-value urban development intensity control with an elastic interval-based approach to address urban development challenges under population shrinkage. It constructs a Floor Area Ratio (FAR) assignment framework guided by “ideal value determination—interval value demarcation—specific value agreement”. The northern central urban area of Shantou City serves as an empirical case. The study, focusing on the conflict between inefficient expansion and population loss, delineates elastic development intensity intervals through multi-dimensional factor analysis: a baseline FAR is determined based on master plan objectives and resource carrying capacity; upper limits are calculated considering transportation and ecological constraints; and lower limits are set according to economic feasibility and social demands, forming a gradient-based control framework. Practically, the study area is divided into differentiated density units, with optimized pathways designed for newly developed, under-construction, and existing plots across multiple scenarios. A multi-stakeholder negotiation mechanism is established to dynamically adapt elastic intervals. Results demonstrate that this method maintains the regulatory authority of master plans while significantly enhancing the adaptability of spatial governance. It provides a theoretical and practical paradigm for balancing regulatory rigidity and flexibility in shrinking cities, offering actionable solutions for vacancy risk mitigation and land-use intensification.

1. Introduction

Since the 1990s, 25% of cities worldwide with populations exceeding 100,000 have been experiencing population decline [1]. In China, academic attention to population shrinkage emerged around 2010, when the rapid pace of land urbanization outpaced population urbanization, leading to urban development intensity far exceeding population growth rates and resulting in large-scale “vacant cities” or “ghost cities” phenomena [2]. Over the past decade, the number of shrinking cities has continued to rise rather than decline. While slow population growth persists in some emerging economic industrial belts or metropolitan areas [3], most prefecture-level cities, particularly small and medium-sized cities, have witnessed significant population decreases [4]. The same is true in rural areas, where large-scale rural depopulation has improved the rural environment, led to the restoration of vegetation and increased plant diversity [5], and dramatically impacted health expenditures among rural populations [6]. As China’s urbanization has entered its middle to late stages, urban population shrinkage has increasingly become a focal point in academic research [7].

Urban development intensity, a critical metric regulating construction density and capacity, serves as a cornerstone of urban master planning and zoning regulations. Its determination primarily relies on projected population growth. Historically, however, inflated population forecasts and the economic incentives of land development have driven planners to set development intensity thresholds far exceeding actual demographic growth rates, resulting in escalating vacancy rates in residential and office buildings. Although the adjustment of administrative divisions has improved the land use efficiency to some extent, the effect on small cities is not obvious [8]. The existing studies pay more attention to the formulation method of the most reasonable value of the urban Floor Area Ratio (FAR) but do not pay enough attention to the “compensable and adjustable” characteristics of the FAR, which leads to the fact that the urban development intensity formulated in the planning is only applicable to the current period and does not take full and flexible consideration when facing the possible population growth or decline in the future. Once population shrinkage occurs on a large scale, the current “growth” urban development strategy will lead to the oversupply of urban space in the future, resulting in a series of economic, social, ecological, and other risks. Under these circumstances, the determination of urban development intensity metrics must comprehensively account for historical trends of population shrinkage. This necessitates the adoption of scientifically grounded assignment methods to implement elastic control over urban construction.

Shantou City, situated on the southeastern coast of China, is a vital port city and one of the earliest Special Economic Zones (SEZs) established in the country. Between 2000 and 2025, the urban core area of Shantou expanded from 95 km² to 290 km², representing a 3.05-fold increase [9]. According to the latest Shantou Territorial Space Master Plan (2021–2035), the city aims to achieve a built-up area of approximately 400 km² by 2035 [10]. In stark contrast to this extensive urban expansion, its permanent population grew from 4.58 million to 5.54 million over the same 25-year period, only 21% growth [11]. Currently, Shantou’s urbanization rate exceeds 70%, placing it in the later stages of urbanization. The city now faces severe population aging and mounting pressures of urban population shrinkage. Against this backdrop, this paper focuses on the issue of “excessive rigidity and insufficient flexibility” in urban development and construction in Shantou (a city). It reevaluates the rationality of key indicators concerning urban development intensity and proposes more scientific strategies for setting FAR and more flexible adjustment ranges for FAR tailored to different types, zones, and development stages of urban land to better cope with uncertainties regarding the city’s future population.

2. Research Review

As one of the core metrics for controlling urban development intensity, Floor Area Ratio (FAR) emerged during China’s transition from a planned economy to a market economy. Consequently, it inevitably embodied top-down planning characteristics, emphasizing the decomposition and implementation of total construction volumes and planning objectives from master plans to regulatory detailed plans. Historically, FAR served merely as a rigid tool for controlling construction capacity through uniform fixed values applied indiscriminately to diverse plots, failing to address site-specific needs. The current research on FAR primarily focuses on three areas:

• Precision optimization: Leveraging new technologies (e.g., spatial simulation models, big data analytics tools) to derive more accurate FAR values [12,13];
• Hierarchical allocation: Top-down approaches for total development intensity calculation and stratified/zonal distribution [14,15,16];
• Flexible control mechanisms: These are explored through two perspectives: Horizontal Elasticity, which defines FAR intervals (e.g., baseline ± thresholds) to accommodate spatial diversity [17,18]; Vertical Phasing, which introduces temporal flexibility (e.g., phased adjustments based on demographic shifts) [19,20]; and alternatively, determining standard FAR through intersectional analysis of multi-criteria constraints [21].

Despite significant academic advancements in urban development intensity research, two persistent issues remain: (a) Excessive rigidity: Current approaches predominantly rely on the single ceiling or optimal values [22], lacking elasticity to adapt to demographic shifts and resource constraint fluctuations [23]. (b) Questionable scientific basis: Arbitrary FAR determinations frequently lead to regulatory breaches during actual development processes [24], undermining effective planning guidance.

In response, scholars have proposed the FAR range-based control methodology, advocating for “interval values” instead of fixed numbers. This approach constructs mathematical models by analyzing multi-scale factors (plot-level to district-level) influencing FAR assignments, thereby deriving dynamic FAR ranges [25]. Emerging research has yielded several key innovations: cross-district FAR adjustment mechanisms [26]; vacancy rate-responsive FAR elastic control models [27]; power dynamics underlying flexible FAR governance [28,29]; and community participation frameworks for FAR modifications [30].

In summary, previous studies have significantly enhanced the flexibility of FAR assignments. However, research gaps persist: (1) limited implementation strategies for macro-level elastic control of development intensity [31]; and (2) insufficient empirical analysis on FAR flexibility range mechanisms in Chinese cities under population shrinkage [32]. This study adopts Shantou City as a case study to address these gaps and explore FAR elastic control methods. By establishing numerical intervals for plot ratios, it aims to regulate urban development intensity dynamically and provide flexible planning solutions for future urban construction.

3. Methods

3.1. Research Object

The research object is the northern shore area of the central urban area of Shantou, covering a total area of 212.3 square kilometers (including 108.7 square kilometers in Jinping District and 103.6 square kilometers in Longhu District). The resident population is 1.43 million (7th census data for 2020). In recent years, the formulation of floor area ratio in Shantou City only stipulates the upper limit of FAR in the controlled detailed planning, and the existing investigation shows that there are both “too high construction densities in the central area” and “too low construction densities in the peripheral area” in Shantou city (Figure 1). Therefore, the implementation of a unified indicator control standard in the city is too harsh for the central area and too lenient for the peripheral area.

In view of the problems in Shantou’s urban development intensity, the study starts from three levels of “determination of ideal value—demarcation of interval value—agreement of specific value” to provide a flexible implementation plan for Shantou’s central urban construction. The first step is to determine the ideal value of FAR for each plot through the top-down allocation method of FAR and the analysis of relevant influencing factors, which can be used as a direct reference in the implementation process of urban development projects. The second step is to determine the upper limit of the FAR through the carrying capacity of transportation, environment, and other facilities, and the lower limit of the FAR through the development cost and other factors, which together constitute the interval value of the FAR. The third step is to determine the specific value of implementation within the flexibility interval through consultation with stakeholders (Figure 2).

3.2. Density Division and Status Floor Area Ratio

The first step of the research involves partitioning the built-up area of Shantou City into multiple “Density Elements”. Based on the dominant land use types of urban districts and anchored in existing neighborhood boundaries, the division integrates arterial roads and expressways as key spatial frameworks while fully accounting for natural topography’s influence on urban spatial segmentation. Ultimately, the study area is divided into 426 density elements, with an average area of 0.27 km². By utilizing Google Maps to collect updated data on newly constructed buildings (e.g., floor counts and floor areas), combined with current density element maps, a GIS-based analysis is conducted to generate the current FAR distribution map of Shantou’s northern central urban area (Figure 3).

3.3. Ideal Value Determination

The ideal value represents the optimal state of urban development intensity. This metric not only ensures the translation of total development volume defined in upper-level plans to specific plots but also guarantees that the economic efficiency of individual plots reaches desired levels. Therefore, the process should first forecast total development volume based on the urban master plan, followed by analyzing models of diverse influencing factors, and finally spatially allocate the total volume through FAR assignments (Figure 4).

3.3.1. Aggregate Forecast

Based on the Shantou Territorial Space Master Plan (2021–2035) and considering current population demands and growth trends in total construction volume, the supply scale of urban construction land is projected. The initial FAR for future urban development is predicted to be 0.90, utilizing an environmental standard-oriented model and peer city analogies. Through the population-residential building model and population-public service facility model, the total urban building demand is calculated as 126 million square meters, with residential buildings accounting for 71.55 million square meters.

3.3.2. Analysis of Influencing Factors

(1) Current Development Context: Given the need for clear control objectives and anchored in the core element of density elements, namely locational conditions, this study analyzes the impacts of transportation facilities, service facilities, and environmental amenities on development intensity to determine their respective influence coefficients.

• Step 1: A regression analysis was conducted across all 426 density elements, using transportation, service, and environmental factors as independent variables and current FAR as the dependent variable. The resulting formula is as follows:

FAR = 0.427 × Service Locational Index + 0.339 × Transportation Locational Index + 0.077 × Environmental Locational Index.

• Step 2: Drawing on empirical adjustments from cities like Shenzhen and aligning with Shantou’s future development trends, the coefficients were recalibrated to:

Service Locational Index = 0.45, Transportation Locational Index = 0.40, and Environmental Locational Index = 0.15.

A baseline FAR model for density elements in Shantou’s northern central urban area was established through weighted overlay analysis of transportation, service, and environmental parameters. The study area was subsequently categorized into four typologies: high-density zones, medium-high-density zones, medium-low-density zones, and low-density zones.

(2) Requirements of Upper-Level Plans: Urban development intensity must adhere primarily to the urban master plan. This study selects three primary factors—namely, transportation facilities, service facilities, and environmental amenities—for analysis. A development potential model for FAR was generated by conducting a weighted overlay analysis of these three factors. The model was then refined by incorporating ecological constraints, urban design aesthetics, and additional parameters, ultimately yielding the ideal FAR values for spatial allocation (Figure 5).

3.3.3. Ideal Value Distribution

To cultivate a distinctive urban character and align with the specific developmental features of Shantou’s northern central urban area, we subdivided the density elements into four districts—the Central, Western, Northern, and Eastern Districts—based on differentiated FAR control requirements.

• Central District: As the urban core is dominated by built-up areas, redevelopment focuses on localized regeneration. The optimal FAR for existing built-up zones is maintained at current levels to preserve urban continuity.
• Western District: This area, characterized by mountainous and aquatic natural landscapes, serves as the city’s ecological barrier. FAR allocations are strictly limited to prioritize environmental conservation.
• Northern District: Primarily designated for new industrial and logistics land uses, FAR standards are appropriately relaxed to accommodate functional demands.
• Eastern District: As a newly developing zone, FAR controls are further relaxed compared to the Northern District, with development intensity intentionally set higher to foster economic agglomeration (Figure 6).

Assuming the ratio of residential land area to road land area remains constant across residential neighborhoods within each district, and adhering to relevant legal regulations while referencing practices from other cities, the total planned residential building volume can be calculated using the following formula: The total Planned Residential Building Volume = (High-rise FAR × Residential Land Area in Fourth-Density Elements) + (Mid-rise FAR × Residential Land Area in Third-Density Elements) + (Low-rise FAR × Residential Land Area in Second-Density Elements) + (Single-story FAR × Residential Land Area in First-Density Elements).

Using this formula enables the determination of optimal FAR metrics for residential buildings within each density element (

Table 1. Ideal value of FAR Allocation Results for Residential Land.

Density Partition	High Density	Medium and High Density	Medium and Low Density	Low Density
Middle Area	2.0 (3)	1.4 (1.5)	1.0 (0.7)	0.7 (0.4)
East Area	2.4 (3.6)	1.7 (1.8)	1.2 (0.8)	0.8 (0.5)
Western Area	1.6 (2.4)	1.1 (1.2)	0.8 (0.6)	0.6 (0.3)
North Area	2.2 (3.3)	1.5 (1.7)	1.1 (0.8)	0.8 (0.4)

The figure in parentheses is the current status of this density partition FAR.

).

3.4. Interval Value Demarcation

The control interval is flexible in form, providing an adjustable spatial range, yet rigid in content, as it establishes inviolable boundaries that represent the extreme thresholds of urban development. These urban development control intervals are defined by upper and lower limits. Since the factors influencing the upper and lower bounds of land development intensity typically differ, separate analytical frameworks are required (Figure 7). When delineating the elastic development intensity interval, two main aspects and six influencing factors are considered. They are the “Service level constraint” that determines the upper limit of FAR, which includes three aspects: traffic carrying capacity, environmental bearing capacity, and facility carrying capacity. Additionally, “economic feasibility constraint” is considered for the lower limit of FAR, encompassing factors such as land price, floor price, and current construction degree.

3.4.1. Determination of Limit Values

The determination of upper FAR limits begins with an analysis of transportation, environmental, and infrastructural carrying capacities. Due to its low latitude, Shantou’s humid climate, high building and road network density, and narrow roadways significantly constrain urban transportation capacity. Transportation capacity encompasses road network capacity and public transit capacity. The actual population these systems can accommodate must remain below theoretical maxima to ensure service quality. Thus, this study sets the upper limit of the road network saturation level at 0.8 and the upper limit of the public transit system saturation level at 0.75, meaning the ratio of actual traffic volume to maximum theoretical capacity must not exceed these thresholds. Based on this framework, the average maximum FAR is calculated. By benchmarking against upper development intensity limits in peer Chinese cities such as Shenzhen, Guangzhou, and Xiamen, and under the constraints of maintaining desired service levels for both road networks and public transit systems, the average maximum FAR is allocated using the following formula. This allocation process ultimately yields the land development intensity limits for distinct density elements (

Table 2. Limit the values of FAR for each area.

Density Partition	High Density	Medium and High Density	Medium and Low Density	Low Density
Middle Area	4.5	4.0	2.5	1.5
East Area	4.5	4.5	3.0	2.0
Western Area	4.0	4.0	2.0	1.5
North Area	4.0	3.5	3.0	2.0

).

$$Max\overline{F}=(F_R\times S_R+F_C\times S_C+\dots )/S$$

(1)

In the formula, F is the average FAR; FR is the average FAR of residential land; SR is the residential area; FC is the commercial average land use FAR; SC is the commercial land area; S is the total area.

3.4.2. Determination of Lower Limit Value

The existing FAR regulations primarily focus on controlling maximum development intensity in urban cores. However, in Shantou, low-density elements currently account for 60.26% by quantity and 75.10% by area, reflecting a pronounced trend of low-density sprawl. Without minimum FAR controls, land resource wastage, inefficient utilization, and elevated public service costs would be exacerbated.

• High-FAR Built/New Development Zones (Urban Core):
  For areas with high existing or planned FAR, where modifying ratios incurs prohibitive costs, the strategy prioritizes maintaining current levels. Excess FAR from future developments should be offset through compensatory measures (e.g., green infrastructure investments) aligned with optimal value benchmarks.
• Unbuilt/Planned Zones (Core or Peripheral Clusters):
  Economic feasibility assessments of proposed projects serve as the basis for setting lower FAR limits.
• Redevelopment Zones (Periphery or Urban Villages):
  Compare the minimum FAR under economic feasibility with the current FAR, adopting the higher value as the lower limit to balance viability and intensification (Table 3).

Table 3. Determination of the FAR lower limit under different scenarios.

Realistic Situation	Spatial Distribution	Lower Limit	Strategies
Fideal > Feconomy > Fstatus	Undeveloped areas or areas to be developed	Feconomy	Encourage an increase in FAR
Fideal > Fstatus > Feconomy	Between the periphery and the central area, or to be reconstructed	Fstatus	Encourage an increase in FAR
Fideal > Fvintage > Feconomy	Built areas, such as the central area of the city	Fideal (Equal to the lower limit)	Appropriate controls, maintaining the current status, and the implementation of FAR compensation for newly constructed land in the density partition zone

Table 3. Determination of the FAR lower limit under different scenarios.

Realistic Situation	Spatial Distribution	Lower Limit	Strategies
Fideal > Feconomy > Fstatus	Undeveloped areas or areas to be developed	Feconomy	Encourage an increase in FAR
Fideal > Fstatus > Feconomy	Between the periphery and the central area, or to be reconstructed	Fstatus	Encourage an increase in FAR
Fideal > Fvintage > Feconomy	Built areas, such as the central area of the city	Fideal (Equal to the lower limit)	Appropriate controls, maintaining the current status, and the implementation of FAR compensation for newly constructed land in the density partition zone

Based on the above classification principles, the lower limit of FAR for residential land with different density zones can be obtained (

Table 4. Lower limit values of FAR for each area.

Density Partition	High Density	Medium and High Density	Medium and Low Density	Low Density
Middle Area	2 (ideal)	1.4 (ideal)	0.7 (status)	0.5 (economy)
East Area	2.4 (ideal)	1.7 (ideal)	0.8 (status)	0.5 (economy)
Western Area	1.6 (ideal)	1.1 (ideal)	0.6 (status)	0.5 (economy)
North Area	2.2 (ideal)	1.5 (ideal)	0.8 (status)	0.5 (economy)

).

3.5. Specific Value Agreement

Through the above analysis, the elastic control interval for urban development intensity is ultimately derived (

Table 5. FAR Development Standard for land in the north shore area of the Central City, Shantou.

	High Density		Medium and High Density		Medium and Low Density		Low Density
Types	Interval	Ideal value	Interval	Ideal value	Interval	Ideal value	Interval	Ideal value
Middle Area	[2.0, 4.5]	2.0	[1.4, 4.0]	1.4	[0.7, 2.5]	1.0	[0.5, 1.5]	0.7
East Area	[2.4, 4.5]	2.4	[1.7, 4.5]	1.7	[0.8, 3.0]	1.2	[0.5, 2.0]	0.8
Western Area	[1.6, 4.0]	1.6	[1.1, 4.0]	1.1	[0.6, 2.0]	0.8	[0.5, 1.5]	0.6
North Area	[2.2, 4.0]	2.2	[1.5, 3.5]	1.5	[0.8, 3.0]	1.1	[0.5, 2.0]	0.8

). However, a multi-stakeholder negotiation and adjustment mechanism must be introduced in the practical implementation to address case-specific variations. Public participation forums are conducted within the predefined elastic control interval to determine specific FAR assignments, using the ideal value as the baseline. In Shantou’s practice, four scenarios are analyzed based on the current development intensity of individual plots:

Scenario 1: For newly developed plots where the current FAR exceeds the maximum limit (Fstatus > Fmax), strict regulatory enforcement is mandated.

Scenario 2: For existing or planned plots where the current FAR exceeds the ideal value but remains below the maximum limit (Fmax > Fstatus > Fideal > Fmin), public discussions are required to absorb the surplus development capacity within their respective density elements, ensuring aggregate intensity balance.

Scenario 3: For plots where development intensity surpasses the lower limit but falls short of the ideal value (Fmax > Fideal > Fstatus > Fmin), prioritize increasing the current FAR where feasible. If infeasible, compensate for the deficit capacity within the density element through public negotiation, incentivizing future intensification to align with master plan targets.

Scenario 4: For plots where development intensity remains below the lower limit (Fstatus < Fmin), actively encourage development toward the ideal value.

4. Conclusions

Under the background of population shrinkage, the formulation of urban development intensity must not only meet the spatial demands of the current population but also provide elastic spatial provisions for future demographic decline. This paper proposes an elastic control mechanism for urban FAR based on external conditions. On the one hand, it ensures that the total development volume defined by the master plan remains unexceeded, thereby reinforcing planning control over development intensity. On the other hand, it introduces an interval control method for development intensity through the integration of “ideal FAR values” and “threshold limits”, with practical validation conducted in Shantou’s northern central urban area. This study reveals that the existing single-value FAR regulations fail to address spatial demand reductions caused by population shrinkage, while standalone FAR intervals lack direct applicability in practice. Only by combining ideal values with interval thresholds can urban development intensity be effectively governed.

Suggestions for urban development intensity in the north bank area of Shantou Central City are as follows:

• Middle Area: the FAR of high-density areas should be between 2.0 and 4.5, the ideal value is 2.0; the FAR of medium-high density areas should be between 1.4 and 4.0, the ideal value is 1.4; the FAR of medium-low density areas should be between 0.7 and 2.5, the ideal value is 1.0; the FAR of low-density areas should be between 0.5 and 1.5, the ideal value is 0.7;
• East Area: the FAR of high-density areas should be between 2.4 and 4.5, the ideal value is 2.4; the FAR of medium-high density areas should be between 1.7 and 4.5, the ideal value is 1.7; the FAR of medium-low density areas should be between 0.8 and 3.0, the ideal value is 1.2; the FAR of low-density areas should be between 0.5 and 2.0, the ideal value is 0.8;
• Western Area: the FAR of high-density areas should be between 1.6 and 4.0, the ideal value is 1.6; the FAR of medium-high density areas should be between 1.1 and 4.0, the ideal value is 1.1; the FAR of medium-low density areas should be between 0.6 and 2.0, the ideal value is 0.8; the FAR of low-density areas should be between 0.5 and 1.5, the ideal value is 0.6;
• North Area: the FAR of high-density areas should be between 2.2 and 4.0, the ideal value is 2.2; the FAR of medium-high density areas should be between 1.5 and 3.5, the ideal value is 1.5; the FAR of medium-low density areas should be between 0.8 and 3.0, the ideal value is 1.1; the FAR of low-density areas should be between 0.5 and 2.0, the ideal value is 0.8;

The FAR allocation framework proposed in this study has three main features: First, the FAR allocation framework demonstrates significant flexibility. By establishing three control parameters—upper limit, lower limit, and ideal value—for plot-level FAR, the framework defines a numerical interval (bounded by the upper and lower limits) to ensure a flexible yet constrained range for FAR determination. Second, the FAR allocation framework has practical applicability. According to the requirements of the higher-level planning and the current situation of the land, the framework provides an ideal value that can be directly referenced and used to provide specific reference for development and construction. Third, the FAR allocation framework is realistic and operable, which proposes to negotiate and adjust the FAR allocation according to the actual situation, so as to balance and take into account the different interests of the public.

The main contribution of this study lies in three aspects: The first is the theoretical contribution. The existing method of using the numerical interval of FAR to control the intensity of urban development essentially discusses the rigidity and flexibility of the plot ratio. However, only using the numerical interval to define the elastic range of urban construction will lead developers to adopt a certain “limit value” that is favorable to them. Ignoring the “best value” for the optimal comprehensive effect of the city, therefore, this paper proposes the strategy of “numerical interval” and “optimal value” to guide urban development intensity, taking into account both “rigidity” and “flexibility”. The second is the practical contribution. Most existing FAR studies focus on specific plots in a small range, and few studies on urban large-scale areas lead to the phenomenon of the “fallacy of composition”. This paper discusses the concept of FAR elasticity in a larger area of a city for the first time. The third is the method contribution. Previous studies on the flexibility of FAR of micro-plots have focused on the use of restrictive conditions (building height, building density, etc.) and related norms (parking space index, per capita green area, etc.) to calculate the plot ratio. However, these methods are obviously not suitable for large-scale urban areas. The method adopted in this paper is to establish an urban development intensity guidance model to determine the best FAR, determine the interval value through indicators such as service level and economic feasibility, and then make dynamic and flexible adjustments around the optimal value within the interval.

5. Discussion

This study addresses the chaotic development intensity control in Shantou’s northern central urban area by proposing a flexible control methodology. Specifically, it utilizes the ideal FAR values to guide optimal urban development intensity, providing direct references for urban construction. The “elastic interval” defined by upper and lower limits constrains extreme development ranges: Urban Core: Upper limits enforce strict compliance to prevent exceeding the total development volume mandated by the master plan. Peripheral Areas: Lower limits ensure land-use efficiency, curbing low-density sprawl typical of suburban expansion.

In addition, the elastic method of urban development intensity control provided by this research corresponds to different application scenarios for different types of land plots. For new development plots, this method can comprehensively analyze the influencing factors of plots before construction, formulate the most scientific and reasonable FAR value, and reserve space for future adjustment of FAR. For the land under construction, this method can re-evaluate the rationality of the construction intensity and adjust the detailed design plan through legal procedures. For existing plots, this method provides adjustment plans for specific plots in the face of urban renewal. Detailed planning index adjustment procedures can also be initiated proactively for some plots with poor spatial quality, and consensus actions can be reached through interval control and democratic consultation.

Notably, FAR determination inherently involves researcher subjectivity. This study analyzes multiple influencing factors and assigns weighting coefficients to reflect their relative importance to enhance scientific rigor. While the selection of factors and weight assignments is not fully objective, this approach surpasses traditional single-factor FAR determination. Moreover, the elastic interval framework confines subjective biases within acceptable thresholds.

Historically, the decomposition of development intensity from macro to micro scales prioritized technical rationality while neglecting policy dynamics. With rising public awareness, diverse stakeholder demands for land and spatial resources have emerged, transforming FAR from a mere metric into an outcome of interest-driven negotiations. This study ultimately proposes making minor adjustments to the FAR through negotiation to address personalized needs in different scenarios. However, numerous issues may arise in practical operation, such as how to reconcile the differentiated demands of various stakeholders and what the specific steps should be. These will be among the key research directions in this field in the future. Additionally, urban development intensity is closely linked to urban population change trends, yet population change trends are predictive values. How to more accurately calculate future urban development demands through more scientific computational methods, thereby enhancing the scientific basis for determining the ideal FAR and narrowing the range of FAR flexibility, also constitutes an important research direction in this domain going forward.