Research on Evolutionary Path of Land Development System Towards Carbon Neutrality

Abstract

Based on complex system theory and multi-dimensional coupling analysis paradigm, this study constructs a dynamic model covering land use, real estate development, and carbon emissions, and deeply explores the internal mechanism and evolution law of land development system in the process of moving toward a low-carbon path. Firstly, through nonlinear dynamics and bifurcation analysis, this study identifies three typical transformation paths that the system may experience: gradual, transitional, and hybrid, emphasizing the nonlinear, phased, and highly context-dependent characteristics of the transformation process. On this basis, early warning indicators and robustness analysis methods are introduced, which provide operational tools for identifying critical turning points in the system and improving the effectiveness and resilience of regulatory strategies. Furthermore, this paper proposes a multi-level regulation mechanism design framework, which combines the immediate feedback with the historical cumulative effect to achieve the refined guidance of land development patterns and carbon emission paths. The results provide a scientific basis and practical enlightenment for land use optimization, green infrastructure construction, and industrial structure adjustment under the background of realizing the “3060” dual carbon goal and the reform of territorial spatial planning in China. In the future, it is necessary to strengthen the empirical calibration of parameters, data-driven optimization, and collaborative research of multiple policy tools to further improve the applicability and decision-making reference value of the model.

1. Introduction

Global climate change is driving a profound transformation of human society to a low-carbon development model. With the proposal of China’s “3060” dual carbon target, the problem of carbon emissions from urban land development activities has become increasingly prominent. Studies have shown that land development and its induced changes in urban spatial structure not only directly contribute to about 15–20% of global carbon emissions [1], but will also have a profound impact on the carbon emission path in the next decades through the lock-in effect of spatial form [2]. Studied under this background, the land development system to the inner mechanism of the low carbon model transformation and evolution regularity is of theoretical and practical significance to achieving carbon neutrality.

In recent years, the relationship between land development and carbon emissions has become the focus of academic attention. Studies based on complex system theory have revealed a significant nonlinear association between urban spatial expansion and carbon emissions [3]. Studies on the coupling mechanism of land development intensity, spatial form, and carbon emissions show that different development modes will lead to significant differences in carbon emission intensity [4]. At the same time, important progress has been made in low-carbon-oriented land management policy innovation, including carbon quota trading based on market mechanisms and optimization of planning and control means [5]. However, the existing research still has obvious limitations: first of all, the greater the carbon effect of land development, the more important it is, from a static perspective, to reveal system dynamic evolution characteristics; second, there is a lack of systematic understanding of the coupling interaction between land development, real estate development and carbon emissions and other multi-dimensional elements. Thirdly, the exploration of the identification of key turning points and the design of intervention mechanisms is still insufficient.

Previous research has established the critical role of urban land development in global carbon emissions, with studies highlighting both direct emissions from construction activities and indirect emissions through spatial lock-in effects [6,7]. While early work focused primarily on quantifying emission levels, recent scholarship has increasingly emphasized the complex dynamics between land use patterns and carbon trajectories. Notable contributions have examined how urban form influences emission intensities [8], how real estate development cycles affect carbon outcomes [9], and how planning policies can guide low-carbon transitions [10]. Building on these foundations and drawing from the system critical transition theory proposed by Scheffer et al. (2019) [11], this study advances a novel theoretical framework that conceptualizes land development activities as a complex coupled system. This system integrates multiple dimensions, including land use patterns, real estate development dynamics, and carbon emission processes. Through the construction of a dynamic model, we investigate the nonlinear coupling relationships between system elements, analyze bifurcation characteristics during system evolution, and identify critical pathways toward low-carbon transformation.

The primary theoretical contribution of this research lies in its pioneering application of complex system dynamics methods to study carbon neutrality in land development, developing a multi-dimensional coupled evolutionary analysis paradigm. Through rigorous mathematical derivation and systematic analysis, we reveal key mechanisms governing the transition process and propose dynamic intervention strategies based on the system’s evolutionary characteristics. These findings not only deepen theoretical understanding of land development’s carbon emissions but also provide scientific guidance for promoting green transformation of land development patterns during China’s 14th Five-Year Plan period.

2. Multidimensional Coupling and Evolutionary Mechanisms of Land Development Systems

In the context of achieving China’s “3060” dual carbon goals, the transformation of land development patterns plays a critical role in guiding cities toward low-carbon and sustainable trajectories. Over the past decades, China’s urbanization has continued to expand rapidly, with the scale of urban construction land, the activity level of real estate development, and carbon emissions levels often rising simultaneously under traditional extensive growth models. However, current policy orientations emphasize a structural transformation toward low carbonization through the optimization of land planning, regulation of the real estate market, and carbon pricing mechanisms. This complex process involves the dynamic interaction of land, capital, and environmental resources and displays the nonlinear characteristics of urban systems under multidimensional coupling. To unveil the intrinsic mechanisms of such transformations, this study builds on previous work to establish a multidimensional coupled dynamical framework.

2.1. Construction of Multidimensional State Representation and Dynamical Equations

This research abstracts the land development system as a dynamical system composed of three-dimensional state variables: (1) Land Development Intensity, $L\left(t\right)$, which describes the gradual expansion and repeated upgrading of the scale and structure of construction land under planning controls and land use indicators; (2) Real Estate Development Activity, $R\left(t\right)$, reflecting market expectations that shift from capital-intensive construction cycles to stable growth phases, influenced by credit policy adjustments and industrial restructuring; (3) Carbon Emission Level, $C\left(t\right)$, serving as a composite carbon effect indicator measuring the energy consumption induced by land development and real estate construction, building operations, transportation, and industrial metabolism. The introduction of state vector can be represented as follows:

$$S\left(t\right)=[L\left(t\right),R\left(t\right),C\left(t\right){]}^{\mathrm{\top }}$$

(1)

This description method is based on the theoretical framework of human–land coupling system [12], and combined with recent studies on the association between urban carbon emissions and spatial patterns [13,14]. On this basis, the system is represented as a system of nonlinear stochastic partial differential equations to describe its multi-dimensional coupled evolutionary dynamics under spatial heterogeneity and random disturbances.

Firstly, the basic dynamic equations of the system are given as follows:

$${\displaystyle \frac{dL}{dt}}={\alpha }_{1}L\left(1-{\displaystyle \frac{L}{K_1}}\right)-{\beta }_{1}LR+D_1{\nabla }^{2}L+{\sigma }_{1}d{W}_1$$

(2)

$${\displaystyle \frac{dR}{dt}}={\alpha }_{2}R\left(1-{\displaystyle \frac{R}{K_2}}\right)-{\beta }_{2}RC+D_2{\nabla }^{2}R+{\sigma }_{2}d{W}_2$$

(3)

$${\displaystyle \frac{dC}{dt}}={\gamma }_{1}L+{\gamma }_{2}R-\delta C+D_3{\nabla }^{2}C+{\sigma }_{3}d{W}_3$$

(4)

Here, ${\alpha }_i$ and $K_i$ describe the endogenous growth trends and capacity limits under resource and institutional constraints; ${\beta }_i$ reflects the nonlinear coupling effects between different dimensions (such as the facilitation and constraint of real estate market changes on land development progress, and the feedback of carbon emissions on economic decisions and resource reallocation); ${\gamma }_1$ and ${\gamma }_2$ describe the contributions of land and real estate to carbon emissions; $\delta$ represents the carbon sequestration rate or carbon emission decay coefficient, which may be influenced by carbon taxes, carbon trading, clean energy deployment, and green building standards; $D_i{\nabla }^{2}X$ is the diffusion term, showing the impact of spatial heterogeneity on system evolution at the regional scale [15,16]; ${\sigma }_{i}d{W}_i$ is the stochastic disturbance term, capturing uncertainties such as macroeconomic cycle fluctuations, natural disaster impacts, and changes in international climate negotiations. The detailed theoretical derivation of these equations and rigorous mathematical analysis of system dynamics are provided in Appendix A.1. This includes the complete stability analysis and parameter selection framework that underpins the model construction.

From the practical context, given the land resource scarcity as well as the accelerated stock planning and renewal in China’s eastern coastal regions, along with the still significant development potential in the central and western areas under the new urbanization process, the diffusion term $D_i{\nabla }^{2}X$ can reflect the interlinked relationship between land development and real estate markets across different urban clusters and regions. This allows for an analogy at the macro level with China’s regional coordinated development, functional zoning, and spatial restructuring under green new urbanization policies [17]. Meanwhile, the stochastic disturbance term allows the model to respond to the uncertain experimental effects of policy pilots and institutional innovations, such as the early implementation of stricter building energy efficiency standards or carbon emission controls in certain cities, and then observe the dynamic impacts on the national overall pattern.

The phase space analysis shown in Figure 1 reveals the complex dynamics of interactions between land development, real estate activity, and carbon emissions in urban systems. The L-R phase portrait (left panel) demonstrates how land development intensity and real estate market activity exhibit multiple equilibrium states, with a clear saddle point indicating potential regime shifts in development patterns. This reflects the reality of urban land markets where development can stabilize at either high-intensity or low-intensity states depending on policy interventions and market conditions. The R-C phase portrait (middle panel) illustrates the intricate relationship between real estate activity and carbon emissions, showing how the system naturally evolves toward either high-carbon or low-carbon attractors. Of particular interest is the presence of separatrices that delineate the basins of attraction, suggesting that the timing and magnitude of policy interventions are crucial for achieving successful low-carbon transitions. The three-dimensional phase space (right panel) provides a comprehensive view of the system’s evolution, with trajectories (shown in different colors) representing different transition pathways. These trajectories demonstrate that while the system tends to converge toward stable states, the path taken depends significantly on initial conditions and policy parameters. This has important implications for policymakers: strategic interventions must consider not only the desired end state but also the feasible transition pathways given current market conditions and institutional constraints. This explanation aligns with the paper’s broader focus on understanding how land development systems can transition toward low-carbon equilibria while maintaining economic stability. The visualization and analysis support our argument that successful transitions require careful consideration of both market dynamics and environmental objectives within China’s spatial planning framework.

2.2. Stability and Bifurcation Analysis: From Critical Transitions to Policy Implications

To understand the stability of the system under parameter perturbations and policy interventions and the possible critical transitions that may occur, it is necessary to linearize and perform eigenvalue analysis of the a forementioned equation system at equilibrium points. Let the equilibrium point be $S^{\mathrm{*}}={\left[L^{\mathrm{*}},R^{\mathrm{*}},C^{\mathrm{*}}\right]}^T$, and perform a first-order Taylor approximation of the nonlinear system there, with its Jacobian matrix being:

$$J\left(S^{\mathrm{*}}\right)=\left[\begin{array}{ccc}{\alpha }_1\left(1-{\displaystyle \frac{2L^{\mathrm{*}}}{K_1}}\right)-{\beta }_1{R}^{\mathrm{*}}& -{\beta }_1{L}^{\mathrm{*}}& 0\\ 0& {\alpha }_2\left(1-{\displaystyle \frac{2R^{\mathrm{*}}}{K_2}}\right)-{\beta }_2{C}^{\mathrm{*}}& -{\beta }_2{R}^{\mathrm{*}}\\ {\gamma }_1& {\gamma }_2& -\delta \end{array}\right]$$

(5)

By solving the following characteristic equation, we can conduct further analysis:

$$\det \left(J-\lambda I\right)=0$$

(6)

If the real parts of all eigenvalues are negative, then the equilibrium point is a stable state, and land development, real estate market, and carbon emissions eventually return to equilibrium after small disturbances.

As shown in Figure 2, the bifurcation analysis reveals the complex dynamics of land development system transitions under varying policy intensities (β₁). The upper panel demonstrates how land development patterns respond to strengthening policy interventions, with two critical points (CP1 and CP2) marking significant structural changes. As policy intensity increases beyond CP1 (β₁ ≈ 0.3), the high-carbon development regime becomes increasingly unstable, while CP2 (β₁ ≈ 0.6) marks the threshold beyond which low-carbon development patterns become dominant. The middle panel illustrates corresponding transitions in real estate market activity, showing how market restructuring occurs through three distinct phases: initial resistance, rapid transformation, and stabilization in a new low-carbon equilibrium. This pattern reflects the real estate sector’s adaptation to environmental regulations through green building adoption and value chain transformation. The lower panel captures the resulting carbon emission trajectories, highlighting the presence of multiple stable states and demonstrating how policy thresholds can trigger transitions between high- and low-carbon regimes. Of particular significance is the hysteresis effect evident between CP1 and CP2, indicating that once the system transitions to a low-carbon state, it becomes increasingly resilient against reverting to high-carbon patterns. This analysis provides crucial insights for policymakers in China’s spatial planning system: successful transitions require policy intensities to exceed critical thresholds, but rapid changes near these thresholds must be carefully managed to avoid market disruptions. The existence of alternative stable states suggests that targeted interventions during transition periods can help guide the system toward optimal low-carbon equilibria while maintaining economic stability.

However, the changes in parameter $\lambda$, such as the increase of carbon pricing policy, the decrease in total land supply due to strict planning, or the change in relative weight due to the tightening of real estate credit, may cause the change in eigenvalue distribution. Once an eigenvalue changes from negative to positive, system bifurcation will occur [18,19], namely the critical turning point [20]: at this time, the system may shift from the traditional high-carbon lock-in path to a new track of low-carbon development [13]. The characteristic equation $\mathrm{d}\mathrm{e}\mathrm{t}(J-\lambda I)=0$ reveals the system’s stability properties, with detailed eigenvalue analysis and stability criteria presented in Appendix A.2.

As land resource management policies become more stringent, carbon quota and tax systems are progressively refined, and green finance and technological innovations continue to emerge, the system parameters ($\delta ,{\beta }_i,{\gamma }_i$) may cross a critical threshold, thereby prompting the land development intensity and real estate growth patterns to shift away from the previous “high-density-high-emission” path toward a low-carbon and efficient, innovative equilibrium state. This process can be understood as a structural transformation under the combined effects of policy interventions and market forces. The dynamics of multi-agent interactions and reflexivity [21,22] are evident here: as the government implements stricter planning controls and carbon constraints, developers and investors will adjust their expectations and increase their investments in green building technologies and low-energy industries; land use decisions will gradually lean toward compact and functionally mixed spatial patterns, and the real estate market will gradually reconstruct its pricing models and profit mechanisms under the augmentation of the low-carbon industry chain.

The above theoretical analysis and evaluation-related policies for the government to provide a revelation. First of all, identifying the critical points of the system can help policymakers take intervention actions in advance. Through parameter sensitivity analysis, we investigate the optimal strength of carbon taxes or land transfer system adjustments that effectively align the system with desired low-carbon outcomes, thereby guiding land use and the real estate market toward a reduced carbon footprint. Secondly, the multi-dimensional coupling framework emphasizes the importance of coordinated action of policy instruments. In the actual policy, only the single-dimension constraints (such as a simple cut in the supply of land) can lead to short-term market movements, and the land planning, real estate financial regulation, and organic combination of the carbon market policy, can be expected only on the overall smooth to achieve the dynamic optimization and the sustainable evolution of the system.

For future development, on the one hand, researchers can introduce time-varying parameters and the nonlinear model policy response function, to simulate the policy tools in different stages of development and planning cycle of dynamic adaptation and adjustment strategy; On the other hand, they can use big data and remote sensing information spatial diffusion parameters, which are estimated to more accurately describe the urban agglomeration between internal and regional land use and carbon emissions conduction path. At the same time, the introduction of the non-stationary random process and multi-scale distribution pattern is helpful to identify the non-equilibrium steady state under the rapid urbanization and industrialization process, so that the study is closer to the real case of urban and regional development in China [23,24].

2.3. Dynamics of Low-Carbon Transition

The internal mechanism of the transformation of the land development system to the low-carbon mode can be revealed by bifurcation analysis and stability research. For the land use structure, when the real estate market expectations interact with carbon emissions constraints, the system parameter change may cause the macro pattern of evolution. For China, this analysis framework has practical value under the “dual carbon” target. Current national spatial planning and land management policy is derived from cultivated land protection and the incremental expansion of traditional logic, gradually to the whole domain; all elements of the comprehensive improvement of the management idea transformation are disseminated to the whole cycle. As a practice-driven innovation form in the new era, comprehensive land consolidation not only involves the optimal allocation of resources and scientific adjustment of land use mode, but it also has a profound impact on carbon emissions. In the past, studies have focused more on the local carbon footprint, land use structure change, and carbon metabolism characteristics during the implementation period of traditional land consolidation projects, but not enough attention has been paid to the carbon effect of the whole life cycle—from planning and preparation, construction implementation, restoration, and adjustment—to continuous income and final stagnation. This makes the transition of the system’s overall carbon locking mechanism and dynamics analysis a particularly critical turning point for the key.

Based on the nonlinear dynamic model mentioned above, the control parameter vector can be represented as follows:

$$\mu =\left(\alpha ,\beta ,\gamma \right)$$

(7)

It includes a series of parameters related to land resource allocation, policy constraints, technological progress, market expectation, and carbon emission reduction strategy in the system. Here, α represents the endogenous growth of land development, the real estate market, and the maximum size (such as cultivated land, the increment of urban construction control area of upper and lower limits, and real estate development timing and strength). $\beta$ reflects the intensity of the nonlinear interaction between elements (such as the constraint of the carbon tax and the carbon emission trading system on the behavior of real estate and land use subjects); $\gamma$ represents the contribution rate of land use and real estate development to carbon emission path and the sensitivity degree to clean technology and ecological restoration measures.

When the system parameters change, the eigenvalue spectrum of Jacobian matrix also changes. The characteristic equation is as follows:

$$\det \left(J\left(\mu \right)-\lambda I\right)=0$$

(8)

At the critical parameter, the eigenvalue structure changes, and the system bifurcates. Three typical low-carbon transition modes can be summarized according to different eigenvalue real parts and bifurcation types:

(1)

Gradual transition (Hopf bifurcation):

When the parameter changes cause a dominant eigenvalue to satisfy the condition at $\mu ={\mu }^{\mathrm{*}}$

$$\mathrm{Re}\left({\lambda }_1\left({\mu }^{\mathrm{*}}\right)\right)=0,\mathrm{Re}\left({\lambda }_i\left({\mu }^{\mathrm{*}}\right)\right)<0,fori=2,3,...,$$

(9)

Figure 3 illustrates the temporal evolution of land development (L), real estate activity (R), and carbon emissions (C) under three distinct transition scenarios. The gradual transition pathway (left panel) demonstrates how incremental policy implementation leads to smooth adaptation of the system, characterized by a steady decline in real estate market intensity and a controlled increase in land development efficiency, ultimately achieving sustained carbon emission reductions. This pattern typically emerges when policymakers implement progressive reforms that allow market participants sufficient time to adjust their strategies and investment decisions.

The abrupt transition scenario (middle panel) reveals more dramatic system behavior, where rapid policy changes trigger sharp adjustments in real estate market activity and sudden shifts in development patterns. This scenario might occur when stringent regulations or market shocks force rapid adaptation, such as the immediate implementation of strict carbon pricing or dramatic changes in land supply policies. While this pathway achieves faster emission reductions, it also shows more volatile market behavior that could pose risks to economic stability.

The hybrid transition (right panel) combines elements of both patterns, showing how carefully timed policy interventions can manage the trade-off between transition speed and market stability. This scenario demonstrates the potential for achieving significant emissions reductions while maintaining relatively stable land development patterns through strategic policy sequencing. The gradual rise in land development efficiency coupled with a managed decline in high-carbon real estate activity suggests a successful balance between environmental goals and market adaptation capabilities. These distinct transition pathways provide crucial insights for China’s spatial planning and carbon reduction policies, highlighting how the timing and intensity of policy interventions can significantly influence the system’s evolution toward low-carbon development patterns.

If there is no multiple eigenvalue fusion, the system will experience Hopf bifurcation. This means that the system shifts from a stable equilibrium to a slow oscillatory dynamic behavior. For Chinese cities, this gradual transformation corresponds to gradually reducing the driving effect of urban expansion on carbon emissions by improving the comprehensive land consolidation level, optimizing land use structure, and steadily promoting the renewal of low-carbon buildings and infrastructure over a long period of time. At the macro level, this is consistent with the carbon intensity constraint emphasized in the 14th Five-Year Plan: the policy intensity is moderate and increasing, the behavior adaptation period of market players is long, and the carbon emission reduction path shows the characteristics of a smooth transition.

(2)

Real transformation (Fold bifurcation):

When the system has a fold bifurcation, it satisfies the following equation:

$$\det \left(J\left({\mu }^{\mathrm{*}}\right)\right)=0,\mathrm{tr}\left(J\left({\mu }^{\mathrm{*}}\right)\right)<0$$

(10)

That is, multiple eigenvalues coincide at critical points, triggering a sudden transition from one attractor to another completely different attractor. This corresponds to the introduction of strong interventions at the policy or market level (such as sudden and large increases in carbon emission costs, rapid reductions in construction land quotas, and mandatory promotion of high-standard and low-carbon technologies). In this scenario, the urban system of land use pattern and the real estate investment logic will experience rapid reconstruction, like in the short term through carbon trading, the strict land total amount control, and the ecological restoration project of radical force system “jump” to the low carbon attractor. In practice, although this kind of abrupt transformation can quickly reduce carbon emissions, it may also cause market shocks, industrial chain fluctuations, and social adaptation difficulties. Therefore, in the promotion of comprehensive land consolidation in the whole region, if policymakers do not consider the ratio between policy intensity and market bearing capacity in advance, it may cause drastic fluctuations in low-carbon transformation.

(3)

Hybrid transformation (multiple bifurcation and interweaving) when:

$$P\left(\mu ,\lambda \right)={\lambda }^3+a\left(\mu \right){\lambda }^2+b\left(\mu \right)\lambda +c\left(\mu \right)=0$$

(11)

Multiple bifurcations may occur when the coefficients $a\left(\mu \right),b\left(\mu \right)$, and $c\left(\mu \right)$ are highly sensitive to the parameters. In such cases, the system exhibits complex nonlinear characteristics, including the coexistence of multiple stable states. For global land reclamation, this suggests that in specific regions (e.g., metropolitan areas, urban agglomerations, and internal functional areas), different types of intervention measures such as engineering, land resource distribution, industrial structure optimization, and agricultural and grassland management can produce a superposition effect, allowing the system to transition between multiple paths. Policymakers can leverage these nonlinear dynamics by introducing differentiated regulatory strategies at various stages to guide the system toward more efficient low-carbon land use patterns. The system displays three typical bifurcation types, each corresponding to different transition paths. The mathematical characterization of these bifurcations and their physical interpretations are detailed in Appendix A.3.

In order to further characterize these transition behaviors and provide early warning signals for decision-making, the following Lyapunov function can be introduced to analyze them:

$$V\left(S\right)=\int \left[{\displaystyle \frac{L^2}{2K_1}}+{\displaystyle \frac{R^2}{2K_2}}+{\displaystyle \frac{C^2}{2}}\right]d\mathsf{\Omega }$$

(12)

This function describes the levels of comparable “potential energy” in phase space for the system’s state variables. The time derivative of $V\left(S\right)$ is:

$${\displaystyle \frac{dV}{dt}}=\int \left[{\displaystyle \frac{{\alpha }_1{L}^2}{K_1}}-{\displaystyle \frac{{\beta }_1{L}^{2}R}{K_1}}+{\displaystyle \frac{{\alpha }_2{R}^2}{K_2}}-{\displaystyle \frac{{\beta }_2{R}^{2}C}{K_2}}+{\gamma }_{1}L(-\delta C)+{\gamma }_{2}R(-\delta C)\right]d\mathsf{\Omega }$$

(13)

When the system approaches the critical point, the “potential energy function” may flatten under parameter perturbation. This is characterized by a decrease in the recovery rate of key slow variables [20], which provides an early warning for the imminent transition of the system. The critical point, denoted as ${\lambda }_c$, is of great significance for China’s practice: through the whole life cycle of carbon accounting in land reclamation projects, dynamic monitoring of land use change and carbon emissions, and the efficiency index of land use under carbon pricing, time series analysis, policymakers can capture when the system is still in the weak signal stage and identify potential critical transitions for early intervention.

In conclusion, this section’s analysis of China’s current implementation of the whole domain of land comprehensive improvement and low-carbon transformation strategy provides a theoretical framework. Different from traditional land management, which focuses on the cultivated land area, project implementation, and the limitations of the short-term carbon method, through the nonlinear dynamics and bifurcation analysis, this study extends from the planning and preparation to the whole life cycle of stagnation, multi-factor coupling under low carbon evolution mechanism of cognition. Policy implications include:

(1)

In the context of “gradual transformation”, the behavior changes in market players can be gradually guided through mild policy iteration and technology promotion to reduce the risk of drastic fluctuations.

(2)

In the “abrupt transition” scenario, it is necessary to evaluate the implementation conditions and timing of strong policies to prevent the side effects of excessive social and economic costs while pursuing rapid emission reduction.

(3)

In the transformation of the “hybrid” situation, the flexible use of the diversified policy tool, which is based on the regional differentiation strategy, realizes a more sophisticated and long cycle of land use and industrial structure optimization; thus, in the multiple steady states, it chooses the most advantageous path to the low carbon target.

In practice, the theoretical insights, as well as the contemporary national spatial planning, carbon cap-and-trade system, green finance tools, land and real estate market regulation, and organic link, not only help to improve the precision of the policy and elasticity, but they also, in order to realize smooth transition from high-carbon lock to a low-carbon transition, provide a scientific basis. Future research can further use big data and high-resolution remote sensing information to quantitatively identify bifurcation points, monitor spatial-temporal heterogeneity, and random disturbance effects, so as to provide a generalized experience and paradigm for China and other economies undergoing rapid transformation.

3. Analysis of Low-Carbon Transition Path of Land Development System

The above theoretical construction and stability analysis provide a basis for understanding the complex mechanism of the evolution of the land development system to the low-carbon mode. Based on this framework, starting from the actual regulatory environment and policy objectives, this section analyzes the possible low-carbon transition path of the system, and discusses how to ensure the smooth progress of the transition through the identification of early warning signals and the robustness test under uncertain conditions. The conclusions of this study have practical guiding significance for China’s land spatial planning and land consolidation policy making under the “3060” dual carbon target.

3.1. Identification and Classification of Transition Paths

Based on the following Hamilton–Jacobi equation:

$${\displaystyle \frac{\partial V}{\partial t}}+H\left(S,{\displaystyle \frac{\partial V}{\partial S}},t\right)=0$$

(14)

where

$$H=\sum _i p_i{f}_i\left(S\right)+{\displaystyle \frac{1}{2}}\sum _i {\sigma }_i^2{\displaystyle \frac{{\partial }^{2}V}{\partial S_i^2}}$$

(15)

Three typical transition paths can be identified through the numerical solution and eigenvalue analysis. These paths correspond to different policy intervention intensities, market response speeds, and exogenous shock types, and have corresponding comparisons in the actual urban planning and land use control scenarios in China.

(1)

The gradual transition path

This kind of path occurs under the condition of slow changes in parameters and can be expressed as:

$${\displaystyle \frac{dS}{dt}}=\epsilon f\left(S,\mu \left(\epsilon t\right)\right),\epsilon \to 0$$

(16)

The state of the system evolves along a quasi-stable manifold:

$$M\left(\epsilon \right)=\{S:f(S,\mu \left(t\right))=0\}$$

(17)

This class path reflects the gradual influence of policy adjustments on the system, such as the partitioned differentiation of land use control scales, the mild intensity of carbon taxes, or the rise in carbon trading prices. Under the background of the current reform of national spatial planning (such as “rules” and “three line” of three designated), policymakers are gradually improving the accuracy of spatial allocations of resources and carbon emission constraints and strengths. However, enterprise and market main bodies may struggle to adapt to the new system, since it has a certain lag. This kind of gradual transition path helps to reduce the social turbulence and economic impact, as the main body in the market expected adjustment and technology upgrades create buffer space. This is in line with China’s policy logic of promoting green development and carbon reduction transition in a “steady and orderly” manner.

(2)

The transition pathways of transformation

When the system crosses a certain crossover threshold, the dynamics exhibit fast jumps:

$$∥S\left(t\right)-S^{\mathrm{*}}∥\sim \exp \left(\lambda t\right)$$

(18)

where $\lambda$ is the unstable model eigenvalue. Such abrupt transitions often occur in the context of strong policy intervention and short-term strict control measures. For example, when the state suddenly strictly restricts the approval of construction land, rapidly increases the cost of carbon emissions, or rapidly implements compulsory industrial transformation policies in key regions, the system jumps from the original high-carbon development attractor to the low-carbon attractor. However, this path can quickly reduce carbon emissions associated with significant social costs and risks, such as higher volatility and land asset prices from quickly adjusting the real estate market, and the industrial chain of rapid shuffle. Therefore, such transition strategies should be carefully used, mostly in local pilot and short-term policy experiments, to avoid the spread of large-scale uncertainty.

(3)

The hybrid transition path

In reality, the land development system rarely presents a purely gradual or abrupt process, but it shows complex evolution characteristics under the subtraction and addition of multiple influencing factors. In this case, the system equation can be expressed as:

$${\displaystyle \frac{dS}{dt}}=f\left(S\right)+g\left(S\right)\xi \left(t\right)$$

(19)

Among these, random disturbances reflect exogenous factors such as the international economic environment, climate events, asymmetric policies, and other external influences. The path $\xi \left(t\right)$ embodies both the gradual optimization, which serves as the main theme in the long-term evolution, as well as local-scale mutations in both time and space. China’s promotion of comprehensive improvements to global land in the process of facing regional difference, economic structure transformation, and the international climate policy change is a typical scenario. This mixed path means that policymakers need to have flexible policy reserves and dynamic regulation strategies to cope with the nonlinear responses of different regions, time periods, and industrial sectors. Through the Hamilton–Jacobi equation framework (see Appendix A.4 for complete derivation), we can identify three typical transition paths that characterize the system’s evolution. For more detailed derivations and parameter analysis, please refer to Appendix A.

3.2. The Key Turning Point of the Early Warning Mechanism

In the process of low-carbon transition, the ability to timely capture early warning signals that the system is approaching a critical turning point directly affects the effectiveness and timeliness of policy intervention. To this end, a comprehensive set of indicators can be introduced as follows:

(1)

Recovery rate index:

$$\lambda (t)=-{\left[{\displaystyle \frac{{\partial }^{2}V}{\partial S^2}}\right]}^{-1}{\displaystyle \frac{\partial V}{\partial S}}$$

(20)

As the system approaches the bifurcation point, the recovery rate of key slow variables decreases [20], which means that the ability of the system to return to the original equilibrium is weakened after a small disturbance.

(2)

Volatility indicator:

$${\sigma }^2\left(t\right)=⟨\left(S\right(t)-⟨S⟩{)}^2⟩$$

(21)

The increase in the variance of state variables indicates that the sensitivity of the system increases when it approaches the turning point, and the response to external shocks is more intense.

(3)

Autocorrelation index:

$$AC\left(\tau \right)={\displaystyle \frac{⟨S\left(t\right)S\left(t+\tau \right)⟩}{⟨S^2⟩}}$$

(22)

Since the correlation increases, the system exhibits an enhanced memory effect near the critical point, making it difficult to return to the original state. In realistic policy scenarios, policymakers can use these early warning indicators to dynamically analyze urban land use data, real estate market prices, and carbon emissions monitoring data. When it is found that some of the indicators in a specific area or industry sector, in significant exceptions, such as the abnormal construction land intensity fluctuation, the real estate price, and the high carbon cost present since the correlation, the government can intervene in advance. For example, when formulating differentiated land use control and carbon market regulation policies for key urban agglomerations (such as Beijing-Tianjin-Hebei, Yangtze River Delta, and the Greater Bay Area), these early warning signals can be used to flexibly adjust the intensity and timing of regulation, so as to avoid sudden transition. The construction of early warning indicators is based on rigorous Lyapunov stability analysis, with full mathematical derivation provided in Appendix A.5.

3.3. Robustness Analysis and Conclusion Derivation of Transition Path

Under the highly uncertain external environment, even if a policy plan is managed with market expectations, the system may still face random shocks or path dependence problems. To this end, the probability density evolution equation is introduced as follows:

$${\displaystyle \frac{\partial P}{\partial t}}=-\nabla \cdot \left[f\left(S\right)P\right]+D{\nabla }^{2}P$$

(23)

Here, $P(S,t)$ represents the probability density of the system at state $S$. For real-world scenarios, this can incorporate various policy options, changes in the international carbon market, and domestic economic cycle fluctuations in a parametric form to evaluate the distribution of transition paths under different conditions. By analyzing the steady-state probability distribution and the corresponding Lyapunov potential energy function, a robust low-carbon transition path should satisfy the following condition:

$$\int P\left(S,t\right)V\left(S\right)dS\le V_0$$

(24)

That is, under the condition of disturbance and uncertainty, the overall “potential energy level” of the system is still within the predetermined threshold. This compatibility can be understood as a policy and social and economic adaptability of quantitative constraints; even having an impact, such as the international energy price volatility, sudden changes in regional industry structure), the system takes a relatively short time to return to low carbon development channels. The spatial diffusion effects introduce additional complexities through pattern formation mechanisms, analyzed in detail in Appendix A.6.

Based on the above analysis, the stable promotion of the low-carbon transition path requires the following: (1) an in-depth understanding of the nonlinear evolution characteristics and a bifurcation mechanism of the system, avoiding a single linear logic derivation; (2) providing customized strategies for land spatial planning and land consolidation policies in different regions and stages through the analysis of three typical paths: progressive, transitional and hybrid; (3) the use of early warning indicators to dynamically monitor and evaluate potential critical points, so as to timely intervene in the policy window period; (4) finding a transition plan with both flexibility and stability under highly uncertain conditions with the help of probability distribution and robustness analysis. For China, in “difference” planning, national spatial planning, and the whole field under the background of the practice of the comprehensive improvement of land, these results emphasize the importance of the construction of the policymakers of prospective and toughness. By accurately identifying the critical path and effectively managing the uncertainties and exogenous shocks in the early stage of transformation, China can smoothly realize the profound transformation from the traditional high-carbon expansion mode to the low-carbon sustainable development track while maintaining economic and social stability, laying a solid foundation for achieving the “3060” dual carbon goal as scheduled.

4. Regulation Mechanism Design

Based on the previous theoretical and empirical analysis, this section attempts to translate the research results of complex system dynamics into operational policy tools and regulatory schemes. By using the feedback control, the optimal control, and the governance mechanism together into a unified framework, we not only theoretically perfect the control of land development system, but also on a more practical level for China in the “3060” national spatial governance under the double carbon targets and land comprehensive improvement path can be implemented. Both the designed control system embodies the economics thinking under the attention to the allocation of resources efficiency and the social welfare maximization, and considering the reality of policy implementation, market adaptability and multi-sectoral coordination complex requirements.

4.1. Construction of Multi-Level Regulatory Framework

Based on the above analysis, we can try to establish a multi-level regulation framework with adaptive characteristics to cope with the nonlinear evolution and uncertain impact of land development system. Setting a desired trajectory for the policy target state (e.g., in line with the planning red line constraint, meet established carbon intensity decline index, remain reasonably stable real estate market), we can construct the following comprehensive feedback control law:

$$u\left(t\right)=K\left(S\left(t\right)-S^{\mathrm{*}}\left(t\right)\right)+{\int }_0^t G(t-\tau )\left(S\left(\tau \right)-S^{\mathrm{*}}\left(\tau \right)\right)d\tau$$

(25)

where $S^{\mathrm{*}}\left(t\right)$ is the feedback gain matrix, which determines the sensitivity of the regulation intensity; $KG\left(t\right)$ is the integral kernel function, which reflects the cumulative consideration of historical deviation. In terms of economic interpretation, the proportional control item corresponds to the immediate policy response, such as the rapid correction of the deviation by moderately increasing the carbon tax or strengthening the control of land transfer indicators $K\left(S\left(t\right)-S^{\mathrm{*}}\left(t\right)\right)$. When land development intensity or real estate market activity deviate from the set track, policymakers can quickly weaken the deviation by raising the threshold of construction land transfer, reducing the supply of inefficient land, or increasing the carbon emission fee. The integral control item reflects the long-term policy signals (such as the national spatial planning and assessment of target) incremental impact on the market. When the system deviates from the ideal state over a long period of time, the accumulated deviation signals will force policymakers to strengthen spatial constraints, increase subsidies for low-carbon technologies, or encourage green building and infrastructure investment in the long term. This dual regulation structure (the combination of short-term regulation and long-term constraints) helps not only to ensure the current equilibrium does not deviate, but also to achieve the optimization of resource allocation and carbon constraints in the long-term dimension.

4.2. Differentiated Regulation Strategies and Phased Policy Instruments

According to the different evolution stages of the system, differentiated regulation strategies can be flexibly applied:

(1)

Far away from the critical point region: flexible regulation

In the region where the system is still far from the intersection point, the macro pattern is relatively stable. In this case, a flexible control strategy can be adopted:

$$u_1\left(t\right)=-k_1\nabla V\left(S\right)$$

(26)

where $V\left(S\right)\nabla V\left(S\right)$ is the Lyapunov potential function or the planning objective function (such as the carbon emissions–economic cost trade-off index) and is its gradient. The economics implication is to gradually reduce the total cost of the social direction of mobile, such as through appropriate adjustments of the pace of land transfer, and green building materials are encouraged to use or provide appropriate low carbon technology subsidies, to stabilize the market expectations and continue to reduce the overall carbon intensity.

(2)

Close to a tipping point area: preventive strengthening regulation

When early warning indicators (such as recovery rate decline, since the correlation enhancement) indicate that the system is near the bifurcation point, more rigorous regulation should be implemented:

$$u_2\left(t\right)=-k_2\left(S-S^{\mathrm{*}}\right)-k_3\mathrm{s}\mathrm{i}\mathrm{g}\mathrm{n} \left({\lambda }_c\right)$$

(27)

${\lambda }_c$ is the recovery rate of key slow variables. When it is reduced to the critical value, the system can be stabilized in the safe range by strengthening the real-time limit on the total amount of construction land, strictly implementing the carbon quota management, and setting the upper limit of land use intensity in different regions. At the economic level, such regulation measures are equivalent to increasing the marginal cost of carbon emissions, thus forcing market players (such as real estate developers and industrial investors) to adjust their behaviors quickly to avoid entering a high carbon lock-in state.

(3)

Crossing the critical point area: precise focus on regulation

When the system has undergone a transition or is about to undergo a rapid transition, precise governance should be implemented quickly:

$$u_3\left(t\right)=u^{\mathrm{*}}\left(t\right)+K\left(t\right)\delta S\left(t\right)$$

(28)

where $u^{\mathrm{*}}\left(t\right)$ is the nominal optimal trajectory control (similar to the idealized policy scheme), $K\left(t\right)$ is the time-varying gain matrix, $\delta S\left(t\right)$ is the deviation between the actual state $S\left(t\right)$ and the nominal trajectory $S^{\mathrm{*}}\left(t\right)$. In this scenario, policymakers can implement special rectification measures for key areas, such as launching land function reallocation schemes for areas overly dependent on high-emission industries, strictly controlling new projects in areas with overheated real estate markets, or upgrading high-carbon emission buildings, in order to bring the system back to the controllable track in the minimum time.

4.3. Collaborative Governance and Optimal Decision-Making Mechanism

In order to achieve the effective integration of multi-level regulation, a comprehensive objective functional can be constructed:

$$L\left(u,S\right)=\int \left[w_1{∥S-S^{\mathrm{*}}∥}^2+w_2\parallel u{\parallel }^2+w_{3}C\left(S\right)\right]dt$$

(29)

where $w_1,w_2,w_3$ are the weight coefficients and $C\left(S\right)$ is the carbon emission cost function. By solving the minimum value of this functional through optimal control theory, the balance can be obtained between the carbon emission reduction target (representing the demand for social and environmental benefits), the policy implementation cost (reflecting the intensity and cost of regulation), and the economic deviation cost (representing the loss of economic structure and spatial resource allocation from the ideal state).

The setting of weight coefficients is equivalent to the relative importance that policymakers assign to different targets in the social welfare function. For example, in the early stage of rapid urbanization, policies focused more on economic growth and housing accessibility, and thus the weight coefficients $w_1,w_2$, and $w_3$, were relatively smaller. However, under the “dual carbon” target, with the passage of time and technological progress, the weight of $w_3$ will gradually increase, guiding the market to gradually shift to a low-carbon path. In terms of spatial differentiation policy design, different regions can choose different weight allocations based on their own resource distribution, industrial structure, and environmental capacity, enabling classified policy implementation tailored to local conditions.

4.4. Institutional Guarantee and Coordination Mechanism for Policy Implementation

To ensure the effective operation of the above regulation mechanism in reality, it is necessary to build a multi-agent coordination system and a monitoring and early warning mechanism from the perspective of institutional construction and governance structure optimization.

(1)

Dynamic monitoring and data support

Establish a nationwide database of land use, real estate, and carbon emissions; monitor key state variables (such as construction land supply, land price, real estate investment growth rate, carbon emissions per unit building area, etc.) in real time; and dynamically update regulatory parameters through data analysis and model fitting. Remote sensing monitoring, GIS technology, and big data analysis can enhance the accurate grasp of land use change and carbon emission distribution patterns.

(2)

Policy tools that combine incentives and constraints

Carbon emission performance should be linked to land transfer fees, construction indicators, and financial support policies. Tax incentives and carbon quota subsidies will be given to enterprises that use land efficiently and adopt low-carbon technologies within the requirements of the plan; additional costs will be levied and restricted for development activities that exceed the emission standards or make extensive use of land. This kind of economic leverage design encourages market players to take the initiative to choose the low-carbon path.

(3)

Multi-sector and multi-level vertical and horizontal coordination mechanism

Vertically, the central government sets macro carbon constraint targets and red lines for land and space control, and local governments refine implementation plans. Horizontally, different functional departments (natural resources, housing, ecological environment, and fiscal and financial regulators) need to strengthen information sharing and policy coordination, and achieve complementarity in land resource allocation, real estate market regulation, and carbon emission reduction incentives through linkage decision-making.

The proposed regulatory mechanisms, while theoretically sound, may encounter several practical challenges during implementation. First, the dynamic monitoring system requires substantial technological infrastructure and cross-departmental coordination, particularly in less developed regions where data collection capabilities are limited. To address this, a phased implementation approach can be adopted, starting with key indicators in pilot regions while gradually expanding the monitoring scope. Second, market-based instruments like carbon quota trading face initial resistance due to information asymmetry and varying capacity levels among market participants. This can be mitigated through capacity building programs and transitional support policies, such as providing technical assistance to enterprises during the early stages. Third, the vertical and horizontal coordination mechanism may experience friction due to conflicting departmental interests and regional competition. To overcome this, we suggest establishing a dedicated coordination office at the provincial level, equipped with clear authority and accountability measures, to facilitate seamless policy integration and resolve potential conflicts. Additionally, the effectiveness of incentive–constraint policies might be compromised by local protectionism and uneven enforcement standards. This can be addressed through a combination of strengthened supervision mechanisms and performance-based resource allocation systems that reward consistent policy implementation. A comprehensive mathematical derivation and parameter sensitivity analysis of these regulatory mechanisms is provided in Appendix B.

In summary, the above regulation mechanism design highlights the idea of internalizing the external cost of carbon emissions in terms of economic connotation, and introduces policy intervention into the system dynamics framework through optimal control and bifurcation analysis. When policy parameters (such as carbon tax rate, land supply strategy, and technology subsidy intensity) are accurately calibrated, they can not only guide the allocation of social resources to a low-carbon and high-efficiency development track, but also help to reduce the risk of the system near the critical turning point. At the same time, through the establishment of long-term incentive–constraint institutional arrangements, dynamic monitoring, and early warning mechanisms, the robustness and flexibility of the regulation strategies can be maintained under the multi-dimensional space–time scale and uncertain conditions, thus providing solid support for China’s transformation from traditional high-carbon expansion to low-carbon and high-quality development mode.

4.5. Examples from China

In this subsection, we briefly present a few Chinese examples related to the findings of the previous research to better capture their practical significance.

(1)

Gradual path and Tian-zi-fang renovation project of Shanghai

The Tian-zi-fang renovation project of Shanghai is a typical old district renovation project, which realizes the transformation and upgrading of land use by preserving historical buildings and introducing creative industries. The transformation process of Tian-zi-fang is gradual, and can be divided into the main phases of 1998–2003 (gathering of artists), 2004–2007 (gathering of cultural and creative industries), and after 2008 (becoming a showcase of Shanghai-style culture and a tourist hotspot). This project focuses on environmental protection and the integration of low-carbon concepts, such as the use of green building materials and the promotion of energy-saving technologies, making the renovated Tian-zi-fang not only a hotspot for cultural tourism, but also a highlight of the city’s low-carbon transformation.

(2)

Transitional path and the construction project of the Xiong’an New Area

The construction and development of the Xiong’an New Area began in 2017, aiming to ease Beijing’s non-capital functions and explore a new model for optimizing the development of densely populated and economically dense areas. As a newly established state-level new area in China, the Xiong’an New Area emphasized the concept of green, low-carbon, and smart development from the very beginning of its planning. The construction of the Xiong’an New Area focuses on efficient and intensive land use, promotion of green buildings and clean energy, and strict control of carbon emissions, aiming to create a new low-carbon eco-city model. In addition, it also focuses on low-carbon and environmental protection in industrial development, strengthens the transformation and upgrading of traditional industries, improves the efficiency of energy and resource utilization, and reduces carbon emissions. Under the guidance of the policy, Xiong’an New Area has rapidly completed structural construction in a short period of time, and achieved significant carbon emission reduction.

5. Conclusions and Prospects

Based on the analysis paradigm of complex system theory, this study considers land development activities as a nonlinear dynamic system which is jointly coupled with multi-dimensional elements such as land use, real estate market, and carbon emissions, and constructs a multi-dimensional dynamic model and regulation mechanism design framework that can describe the characteristics of low-carbon transition. Through mathematical derivation and parameter sensitivity analysis, this study deeply reveals the internal evolution mechanism and potential transformation path of the land development system, which provides scientific reference and decision-making enlightenment for China’s land space governance and industrial structure optimization under the constraints of the “3060” dual carbon target.

5.1. Main Conclusions and Contributions

Firstly, it is found that the low-carbon transformation of the land development system presents significant nonlinear and bifurcating characteristics. The model shows that when the system parameters cross the critical threshold, the coupling relationship between land development, real estate activity, and carbon emissions may change dramatically, which leads to the transition from a high carbon locking state to a low carbon attracting state. This means that the transition path is not linear and gradual, but it also shows a variety of possible trajectory choices with the change in policy intensity, resource endowment, and market expectation. Under the background of China’s 14th Five-Year Plan, the implementation of the “dual carbon” goal and the acceleration of the reform of territorial spatial planning, understanding and predicting this bifurcation feature is of practical significance for precise regulation and risk prevention.

Secondly, the study further identifies the typical types of land development system transformation paths: gradual, transitional, and mixed. This path classification is highly consistent with the realistic policy scenarios: the gradual path reflects the logic of moderate policy evolution and gradual market adaptation, which is suitable for most urban agglomerations and regions to steadily promote emission reduction in the short term; the transitional path reveals that under the guidance of strong policies or emergencies, the system may quickly complete structural reconstruction in a short period of time and achieve significant carbon emission reduction. The hybrid path emphasizes the high flexibility and uncertainty of the system under long-term time series and complex external disturbances. This phased and typed feature provides a solid theoretical basis for China’s differentiated policies in different development stages and regional types.

Thirdly, this study proposes a multi-level and dynamically adjustable regulatory mechanism design scheme from the perspective of policy regulation. The scheme combines the immediate feedback control with the historical integral control, which can not only cope with the market fluctuations and policy shocks in the short period, but can also ensure that the system gradually converges to the steady-state region in line with the low-carbon target in the long term. On this basis, the early warning mechanism, recovery rate index, and collaborative governance framework are introduced, which provide a forward-looking tool for policymakers to take timely intervention measures before the critical transition point, and so that they can ensure the evolution of the land development system on the optimal track. Through the combination of carbon emission performance assessment, land space planning bottom line control, green building, and ecological restoration incentives, as well as the coordination of financial and tax policies, these regulatory tools have strong operability and flexibility in practice.

5.2. Research Limitations and Future Prospects

Although this study provides an innovative framework and quantitative tool for understanding the low-carbon transition of the land development system, there are still some limitations that deserve further discussion. Firstly, the selection and calibration of model parameters need to be supported by more empirical data for different regions, industrial structures, and resource endowments in China, so as to improve the accuracy and situational applicability of policy simulation. Secondly, under the complex policy environment and the game of multiple stakeholders, the implementation difficulty and constraints of the regulation mechanism have not been fully discussed. Future research can introduce the perspective of behavioral economics and institutional economics to analyze the actual implementation path of policy instruments and the reaction mechanism of stakeholders. Finally, in the face of the uncertainty of global climate change and the adjustment of international economic patterns, the description of exogenous shocks under multiple scenarios is still limited in this study. In the future, quantitative uncertainty analysis and robust optimization techniques can be used to enhance the robustness of decision-making strategies.

Based on the above limitations and challenges, future research can be further carried out in the following directions. (1) Regional differentiation and multi-scale analysis: By introducing finer spatial heterogeneity factors, regional comparison and case studies are conducted to explore the differences in transformation paths and applicable regulation mechanisms of different types of urban agglomerations under the modes of resource endowment, industrial structure, technology foundation, and planning and control. (2) Data-driven dynamic regulation strategy optimization: Combined with big data and artificial intelligence methods, high-resolution remote sensing images, carbon emission inventory data, and market transaction data are used to improve the accuracy of system state identification and the accuracy of early warning signal extraction, so as to achieve a more refined and dynamic regulation strategy optimization. (3) Research on the synergy effect of multiple policy instruments and institutional innovation: Under the framework of a wider policy mix, the synergy effect and feedback mechanism of carbon tax, quota trading, land use regulation, ecological compensation, green finance, and new infrastructure construction should be studied to explore the path of institutional innovation, so as to reduce policy conflicts and resource waste, and promote the implementation of transition plans with more flexibility and anti-risk ability while taking into account economic efficiency and environmental protection.

In summary, based on the theory of complex systems, this study deepens the understanding of the low-carbon transformation of the land development system, and provides theoretical and policy references for promoting, high-quality sustainable development under the “3060” dual-carbon target in China. With the continuous improvement of theoretical research, the accumulation of empirical experience, and the continuous promotion and optimization of policy pilots, the regulatory framework of low-carbon transformation of land development is expected to be further mature and developed in practice, providing more powerful scientific support for the construction of resilient, low-carbon and sustainable urbanization process.