Previous studies have focused on the fact that government policies are the key factors in promoting the development of intelligent construction in construction enterprises. However, how to select different forms of policy support and quantify the intensity of policy support, as well as the impact on the behavioral strategies of construction enterprises and the government, still needs in-depth exploration. This paper constructs an evolutionary game model between construction companies and the government, using the system dynamics simulation software Vensim to analyze the model under three different government policy support scenarios. The study explores how varying levels of policy support and key factors influence the strategic choices of the game participants, providing valuable insights for promoting the development of intelligent construction. The key findings are as follows: (1) The willingness to adopt intelligent construction is heavily dependent on policy incentives. The incentive effect of the three single policies is much lower than that of the combined policies, and only high-intensity special fund support (more than 8 CNY/m²) significantly promotes widespread adoption. Among combinations of policies, tax incentives coupled with special funds prove most effective. (2) The government’s decision to actively promote intelligent construction hinges on a cost–benefit analysis. Under medium to high levels of special fund support, medium to low levels of service support are more beneficial for reaching a stable state of intelligent construction implementation. (3) Reducing the incremental costs of intelligent construction transformation is the primary key factor in promoting construction. The findings contribute to a deeper understanding of how both the government and construction companies can adjust their strategies in response to policy changes, ultimately leading to more effective policy implementation and strategic decision-making.

Amid intensifying global climate anomalies and accelerating urban expansion, land use systems have become increasingly dynamic, complex, and uncertain. Accurately predicting and scientifically evaluating the evolution of land use patterns is essential to advancing territorial spatial governance and achieving ecological security goals. However, most existing land use models emphasize quantity forecasting and spatial allocation, while overlooking the third critical dimension—structural complexity, which is essential for understanding the nonlinear, fragmented evolution of urban systems, thus limiting their ability to fully capture the evolutionary characteristics of urban land systems. To address this gap, this study proposes an integrated SD-PLUS-FD model, which combines System Dynamics, Patch-based Land Use Simulation, and Fractal Dimension analysis to construct a comprehensive three-dimensional framework for simulating and evaluating land use patterns in terms of quantity, spatial distribution, and structural complexity. Wuhan is selected as the case study area, with simulations conducted under three IPCC-aligned climate scenarios—SSP1-2.6, SSP2-4.5, and SSP5-8.5—to project land use changes by 2030. The SD model demonstrates robust predictive performance, with an overall error of less than ±5%, while the PLUS model achieves high spatial accuracy (average Kappa >0.7996; average overall accuracy >0.8856). Fractal dimension analysis further reveals that since 2000, the spatial boundary complexity of all land use types—except forest land—has generally shown an upward trend across multiple scenarios, highlighting the increasingly nonlinear and fragmented nature of urban expansion. The FD values for construction land and cultivated land declined to their historical low in 2005, then gradually increased, reaching their peak under the SSP1-2.6 scenario. Notably, the increase in FD for construction land was significantly greater than that for cultivated land, indicating a stronger dynamic response in spatial structural evolution. In contrast, forest land exhibited pronounced scenario-dependent variations in FD. Its structural complexity remained generally stable under all scenarios except SSP5-8.5, reflecting higher structural resilience and boundary adaptability under diverse socioclimatic conditions. The SD-PLUS-FD model effectively reveals how land systems respond to different socioclimatic drivers in both spatial and structural dimensions. This three-dimensional framework reveals how land systems respond to socioclimatic drivers across temporal, spatial, and structural scales, offering strategic insights for climate-resilient planning and optimized land resource management in rapidly urbanizing regions.

The decision-making regarding the development of intelligent construction in construction enterprises is crucial for the transformation and upgrading of the construction industry. This paper constructs an evolutionary game model among construction enterprises and applies system dynamics for simulation analysis of the game model. It explores the impact of key factors on the strategy choices of the game participants. The research findings indicate that the initial state of construction enterprises’ willingness to transition to intelligent construction in the evolutionary game model influences the final stable strategy. Direct benefits, the strength of government incentives, penalty intensity, and reduced costs through joint transition positively affect the probability of construction enterprises implementing intelligent construction, while incremental transition costs and positive spillover effect are negatively correlated. When the direct benefit rate exceeds 5%, costs are jointly reduced by more than 2%, and transition costs are below 35 CNY/m², it can significantly motivate enterprises to adopt intelligent construction. A certain level of government incentives (at least greater than 5 CNY/m²) has a positive effect on the transformation process; however, once the incentives exceed 10 CNY/m², their impact stabilizes. Penalties only affect the speed at which the system evolves toward a stable point. Current policy incentives do not require further enhancement. Meanwhile, reducing incremental transition costs is more effective than increasing the intensity of government incentives. The research conclusions contribute to the quantitative analysis of how changes in different key factors affect the dynamic evolution of strategy adjustments by construction enterprises over time, thereby providing corresponding recommendations for transformation and upgrading.