Cellular Automata Model Research Articles

Computation Models for Tsunami Wave Propagation using Extended Cellular Automata

Using traditional models to simulate the propagation of large-scale tsunami waves leads to low accuracy and low efficiency. To ensure higher timing accuracy of tsunami wave spread, built an enhanced model that connects cellular automata with an existing model of tsunami waves. To determine the ideal time step value, our approach considers how time steps affect simulation accuracy. The spread of tsunami waves using a two-dimensional cellular automata model was used to test the model. Extending cellular automata (CA) models for tsunami wave propagation involves incorporating additional complexity and realism to better simulate the dynamics of these natural phenomena. The findings indicate that 1/4 of the time required for all of the cellular material to be traversed is the ideal time step for the tsunami wave spread global cellular automata simulation program. With a mean accuracy of 86.78% and a mean Kappa coefficient of 0.6443, this model demonstrated strong temporal and spatial consistency when compared to historical tsunami wave data from NOAA. Combining the Kappa coefficient with extended cellular automata (ECA) can be beneficial in various applications, particularly those involving classification tasks or spatial modeling. The earliest arrival tsunami wave spread can be predicted and simulated using this approach.

Predicting land cover changes in Xuan Lien Nature Reserve, Thanh Hoa province, Vietnam

The research was carried out in Xuan Lien Nature Reserve, which is a specially protected natural area and one of the five main centers of biodiversity in Vietnam. The work is aimed at analyzing changes and predicting the state of land cover in Xuan Lien Reserve. For these purposes, Landsat-7, Landsat-5 and Landsat-8 images obtained in 2000, 2010 and 2020 were studied. Normalized Difference Vegetation Index (NDVI) method was used to isolate the vegetation cover on the land areas, and Normalized Difference Water Index (NDWI) method was used. The results of the study indicate that the vegetation on the land is transforming in Xuan Lien Reserve. The area of broadleaf forests decreased by 6.2% from 2000 to 2020, and the area of wetlands increased by 4.9% due to the construction of Cua Dat hydroelectric complex in Reserve. The model of Markov chains and cellular automata was used to predict the state of the allocated lands in the study area. The predicted results of the model have been successfully confirmed using the agreement indices. According to the results of the predicted, by 2035 the area of broadleaf forests and wetlands will increase to 86.3% and 7.4%. The change in the structure of the vegetation cover of Reserve's land is explained by the measures taken by the Vietnamese government to regulate the water level in Cua Dat reservoir and expand the forest area.

Growth Simulation of Agriculturally Dominant Cities by Incorporating Multiple Drivers into a CA-based patch-generating Land Use Simulation Model: A Case Study in Siracusa, Italy

Abstract. Understanding the historical trajectory of land use and land cover (LULC) and predicting future alterations is essential to advancing the SDGs. Current researches on LULC in agriculture-dominated cities primarily focuses on the spatiotemporal pattern analysis of carbon storage changes, with relatively little attention given to LULC simulation studies. Moreover, most studies employing cellular automata (CA) models concentrate on urban centers, overlooking the specific sustainable development of agricultural cities. This paper selects Siracusa, Italy, as a case study, incorporating multiple drivers and constructing four development scenarios. The patch-generating land use simulation (PLUS) model is employed to predict the LULC for Siracusa by 2030 and to examine potential transformations under each scenario. Our findings show that 60% of the land in Siracusa is designated as cropland, predominantly situated in flat regions. Our simulation results reveal that urbanization and demographic growth are the main factors driving cropland conversion to urban areas. Different development scenarios exhibit significant variations in future land use structures; the cropland protection scenario emphasizes the stability and sustainability of agricultural development, whereas the economic development scenario highlights the substantial impact of urban and industrial expansion on agricultural land. These insights are instrumental for land use planning and policy-making in Siracusa and other agriculture-centric cities, providing a framework to guide urbanization while promoting agricultural sustainability.

A novel spatio-temporal prediction model of epidemic spread integrating cellular automata with agent-based modeling

Since 2019, major infectious disease outbreaks have placed tremendous pressure on global public health systems, triggering extensive research on the predictive modeling of infectious diseases. Cellular Automaton (CA) is primarily used in the spatial prediction of infectious diseases to establish a model to for simulating the interaction between different regions and the infection risk to simulate the transmission process of the disease and predict its development trend. However, CA models are governed by initial fixed rules and local interactions, and often fail to capture the complex dynamics of epidemic transmission, which are influenced by factors such as public behavior and government intervention. In view of these limitations, we propose a factorial simulation model for the spatial spread of epidemics, the CA-ABM, which divides agents into three categories–public, government, and hospital agents–to comprehensively express the macro factors that affect the development of epidemics. Agent-Based Modeling (ABM) influences the transition rules of the CA through agent choices, constraints and supporting behaviors. Focusing on the COVID-19 pandemic in mainland China from February 6 to March 20, 2020, we simulate its spread. The results showed an average improvement of 8.4 % in prediction accuracy, with few errors, RMSE under 200, and R2 values over 0.9 in most provinces, demonstrating strong macro-scale stability. This approach helps regions to understand influencing factors and enables targeted infection risk assessment and prevention. In addition, scenario analysis based on CA-ABM model changes epidemic decision-making from “prediction-response” to “scenario-response” and provides theoretical reference for future epidemic management.

Travel Times of a Descending Melting Probe on Europa.

In this study, we calculated the travel times of a thermal probe that descends through Europa's ice shell. The ice column is simplified to a conductive layer. Using a cellular automaton model, the descent of the probe was simulated by tracking temperature changes, with cell interaction dictated by heat conduction and cell state transition rules determined by cell temperatures. Validation tests, including a soil column simulation, and comparison with experimental data, support the reliability of the model. Simulations were performed with 2 different cell sizes, 19 constant probe temperatures, and 5 ice thermal conductivities. A smaller cell size (mm) produced shorter travel times (between 22 days for a probe temperature and ∼4 years for ) than a larger cell size (m), which produced travel times between 27 years ( 600K) and ∼103 years ( 280K). The ice shell's thermal conductivity has a modest impact on descent times. The results are generally consistent with previous approaches that used more detailed probe engineering considerations. These results suggest that a probe relying solely on heat production may traverse Europa's conductive ice shell within a mission's timeframe.

Comparison of gap-based and flow-based control strategies using a new controlled stochastic cellular automaton model for traffic flow

ABSTRACT Autonomous vehicles are essential to future transportation systems, potentially reducing traffic congestion. This study examines the impact of different vehicle control strategies on traffic flow through simulations. We propose a novel stochastic cellular automaton model, the controlled stochastic optimal velocity (CSOV) model, which incorporates vehicle control effects. Within the CSOV model, two control strategies are implemented: gap-based control (GC), which adjusts vehicle velocity to balance the gaps between adjacent vehicles, and flow-based control (FC), which aims to maintain a consistent local flow between the front and rear vehicles. Results show that both control strategies improve traffic flow. However, under weaker control, the GC sometimes resulted in lower flow compared to no control. In contrast, the FC consistently enhanced flow across control strengths, yielding more robust outcomes. Furthermore, when both strategies achieved comparable flow rates, the FC provided a more stable velocity distribution under varying traffic densities than the GC.

A Coupled CA Urban Development Model (UDM) and Urban Fabric Generator (UFG) for the Assessment of Climate Hazards on Future Urban Development

Abstract. Cellular Automata (CA) spatial models have become a de facto means by which to simulate future scenarios of land use activity, particularly urban development. Increasingly, such models are being employed within large scale climate impact, adaption and resilience studies to provide future scenarios of land use change and urban development, facilitating the calculation of the economic impact of future climate hazards and development land use adaption options. However, many spatial CA models only provide information on whether land has undergone a transition from one use/activity to a new one. In the case of urban development, they are unable to provide information on the spatial pattern of new development at an intra-cell level of spatial fidelity. However, in many cases such information is important as the spatial impacts of hazards and the adaption and resilience of new urban development will be dependent on the precise spatial configuration of buildings, roads and urban green space. In this paper an Urban Fabric Generator (UFG) is presented that takes the outputs of an Urban Development Model (UDM) and simulates plausible spatial configurations of buildings, roads and urban green space. The utility of the UFG is demonstrated via two flooding case studies, where generated spatial patterns of urban form are used to parameterise a hydrodynamic pluvial flood model that evaluates the impact of future climate driven rainfall on property damage and the utility of infrastructure investment with regards to improving surface water flood resilience.

Simulation of Somatic Evolution Through the Introduction of Random Mutation to the Rules of Conway’s Game of Life

Abstract Introduction Conway’s Game of Life (GOL), and related cellular automata (CA) models, have served as interesting simulations of complex behaviors resulting from simple rules of interactions between neighboring cells, that sometime resemble the growth and reproduction of living things. Thus, CA has been applied towards understanding the interaction and reproduction of single-cell organisms, and the growth of larger, disorganized tissues such as tumors. Surprisingly, however, there have been few attempts to adapt simple CA models to recreate the evolution of either new species, or subclones within a multicellular, tumor-like tissue. Methods In this article, I present a modified form of the classic Conway’s GOL simulation, in which the three integer thresholds that define GOL (number of neighboring cells, below which a cell will “die of loneliness”; number of neighboring cells, above which a cell will die of overcrowding; and number of neighboring cells that will result in spontaneous birth of a new cell within an empty lattice location) are occasionally altered with a randomized mutation of fractional magnitude during new “cell birth” events. Newly born cells “inherit” the current mutation state of a neighboring parent cell, and over the course of 10,000 generations these mutations tend to accumulate until they impact the behaviors of individual cells, causing them to transition from the sparse, small patterns of live cells characteristic of GOL into a more dense, unregulated growth resembling a connected tumor tissue. Results The mutation rate and mutation magnitude were systematically varied in repeated randomized simulation runs, and it was determined that the most important mutated rule for the transition to unregulated, tumor-like growth was the overcrowding threshold, with the spontaneous birth and loneliness thresholds being of secondary importance. Spatial maps of the different “subclones” of cells that spontaneously develop during a typical simulation trial reveal that cells with greater fitness will overgrow the lattice and proliferate while the less fit, “wildtype” GOL cells die out and are replaced with mutant cells. Conclusions This simple modeling approach can be easily modified to add complexity and more realistic biological details, and may yield new understanding of cancer and somatic evolution.

Analysis of mixed traffic flow characteristics based on cellular automata model under lane management measures

In the future, mixed traffic flow comprising Human-Driven Vehicles (HDVs), Connected and Autonomous Vehicles (CAVs), and CAV platoons will coexist for an extended period. Exploring the operational characteristics of mixed traffic flow under different lane management measures is essential for effective management and control. Initially, we analyze the motion characteristics of HDVs, CAVs, and CAV platoons and identify the car-following types within mixed traffic. Based on vehicle motion characteristics and the variance in maximum desired speeds among HDV drivers, we establish longitudinal motion rules for HDVs using a safety distance model. For CAVs and their platoons, we develop longitudinal motion rules by considering platoon merging and splitting behaviors, as well as speed and spacing error requirements for platoon driving. Subsequently, we formulate lateral lane-change rules based on the Symmetric Two-lane Cellular Automata (STCA) model, considering differences in reaction times and following distances between HDVs and CAVs. Finally, we conducted simulation experiments on a unidirectional three-lane highway using the multi-lane mixed traffic flow cellular automata model, analyzing the characteristics of mixed traffic under various lane management measures, such as mixed lane, CAV-dedicated lane, and CAV-priority lane. The results show that as the proportion of CAV (denoted as p) increases, the traffic flow capacity, optimal density, and jam density also increase. When p≤0.6, the k−q diagram exhibits a triangular shape, whereas for p>0.6, it assumes a trapezoidal shape. Implementing CAV-dedicated lane can reduce Cooperative Adaptive Cruise Control (CACC) degradation rates but only enhances traffic flow capacity when the CAV proportion reaches a certain threshold. Compared to the mixed lane scheme, when p≤0.3 and k≤0.3, the CAV-priority lane schemes not only meet traffic demands but also reduce CACC degradation rates. The vehicle speed in CAV-priority lane surpasses that in HDV lanes, facilitating improved traffic efficiency for CAVs. The distribution of maximum speeds among HDV drivers affects the fundamental diagram of mixed traffic flow and the performance of CAV-priority lane, with greater impacts observed as the standard deviation of the HDVs' maximum speed increases.

SIMULATION OF ACETYLCHOLINESTERASE INHIBITORS’ IMPACTS ON EMERGING AND PROGRESSING ALZHEIMER’S DISEASE BY CELLULAR AUTOMATA MODELING

Due to the complex nature of Alzheimer’s disease (AD), it begins insidiously in the brain many years before the appearance of the first symptoms and is usually diagnosed, when the disease is progressive and symptoms have appeared. It can be said that AD’s emergence is almost untrackable and the straightforward mechanism of AD is still unclear for the most part. Therefore, any kind of research on the emergence of AD can lead to valuable results for predicting and treating AD. Previously, we proposed a novel mathematical model based on cellular automata for the study of Alzheimer’s disease progress. In this paper, cellular automata (CA) mathematical modeling is used as a tool for simulation and experimenting with drugs’ effects in this far-fetched situation. For this goal, immune system interactions and neuron-to-neuron communications are assumed to have prominent roles. We have used the CA network, pathophysiological process, and physiological realities to get a clear insight into neuron-to-neuron communications. Our simple model investigates the influence of three Acetylcholinesterase Inhibitor injections, including Donepezil, Rivastigmine, and Galantamine on the reduction of defined factors such as Alzheimer’s Rate (AR), Critical Rate (CR), and Warning Rate (WR). It is shown that using drugs has different effects on the betterment of neurons and can decrease AR and CR factors and enhance WR, many years before the appearance of symptoms.

Cellular automaton model of self-healing

We propose a simple cellular automaton model of a self-healing system and investigate its properties. In the model, the substrate is a two-dimensional checkerboard configuration which can be damaged by changing values of a finite number of sites. The cellular automaton we consider is a checkerboard voting rule, a binary rule with Moore neighborhood which is topologically conjugate to the majority voting rule. For a single-color damage (when only cells in the same state are modified), the rule always fixes the damage. For a general damage, when it is localized inside a 3 × 3 square, the rule also fixes it always. When the damage is inside of a larger n × n square, the efficiency of the rule in fixing the damage becomes smaller than 100 % , but it remains better than 98 % for n ≤ 5 and better than 75 % for n ≤ 7 . We show that in the limit of infinite n the efficiency tends to zero.

Multi-lane cellular automata model considering bus-HOV lane

To quantify the impacts of high occupancy vehicles (HOV) entering the exclusive bus lane on road traffic efficiency, the car following and lane changing characteristics of bus, HOV and non-HOV were analyzed when setting the bus-HOV lane. Using the theory of cellular automata (CA) and combining the control rules of the bus-HOV lane, the lane occupied by a vehicle and the type of a vehicle regarded as constraints were introduced into the asymmetric lane changing rules, and then a multi-lane CA model was established for the car following and lane changing of multi-type vehicles considering the bus-HOV lane. MATLAB software was used to accomplish numerical simulation to analyze the specific effects of the proportion of HOV, bus departure frequency and bus stop spacing on the traffic flow parameters and their relationships. The results show that: when the proportion of HOV is less than 0.5 on a three-lane road segment, setting the bus-HOV lane can not only improve road traffic efficiency but also ensure the speed of bus operation under the condition of high initial space occupancy; when the proportion of HOV exceeds 0.5 , it is difficult to improve road traffic efficiency by restricting non-HOV from entering the bus-HOV lane; when the initial space occupancy is greater than 0.2 , the higher the bus departure frequency is, the greater the decline of traffic volume of the bus-HOV lane is; when there are 5 bus routes to dispatch a bus every 3 minutes, compared with the low initial space occupancy, the traffic volume of the bus-HOV lane under the condition of the high initial space occupancy reduces by about 30%; the bus stop spacing more significantly affects the traffic efficiency of the bus-HOV lane, the longer the bus stop spacing is, the higher the frequency of HOV lane changing is, the more the increase of the congestion segments on the bus-HOV lane and the adjacent normal lane is, thus the traffic efficiency of the whole road is influenced.

Simulation of container discharge dynamics using modified cellular automata

In the field of physical logistics, efficient and precise control of material flow during the emptying of containers such as silos and wagons is crucial. This study introduces a not conventional cellular automaton model to simulate spillage from containers. The model is implemented using Excel macros, providing a user-friendly and accessible platform for logistics professionals. By modifying the traditional cellular automaton framework, the simulation can accurately represents the dynamics of material flow during container emptying processes. The proposed method allows for fine-tuned control over the spillage by integrating specific constraints and parameters, enhancing operational efficiency and reducing material loss. The versatility of the model makes it applicable to a wide range of logistical scenarios, demonstrating its potential as a valuable tool in physical logistics management.

Hardware Design Strategies Oriented to Edge Computing and AgriFood Electronics for Image Processing using Cellular Automata

There has been a growing interest from academia and industry in developing circuits and systems for edge computing and quality control tasks in food production lines, where image-processing is frequently required. This paper outlines the required considerations for designing a fruit classification system based on image-processing using Cellular Automata (CA) models and integrating it into reconfigurable hardware (HW) such as Field Programmable Gate Arrays (FPGAs). Parallel processing in CA requires numerous processing elements to be implemented and mapping CA models to HW generally comes with limitations. Homogeneous CA arrays are easier to design and implement in HW but can be resource-demanding. To fill this gap, this study explores different alternatives for the HW implementation of CA models, particularly trading computational-parallelism for a more optimized use of the available HW resources. We conducted experimental tests of the designed HW system using the Digilent Nexys development board, and the operation was validated against software-based benchmarks for image-processing, particularly concerning edge-detection. The presented study provides a broader range of design solutions for the HW implementation of two-dimensional CA models and a better understanding of their advantages and disadvantages. The results show that solutions focusing on instruction-parallelism add some complexity to the conception and require more design effort, compared to homogeneous CA models composed of identical cells. However, the instruction-parallel design solutions can significantly improve the HW resource utilization, especially when implementing computationally intensive CA rules in FPGAs.

Spatiotemporal Eolution and Prediction of Ecosystem Service Value in Taihang Mountains Based on MCCA Land Use Scenario Simulation

Ecosystem service value （ESV） is an important indicator related to regional ecological well-being, and understanding its evolution can provide references for regional ecological civilization construction and sustainable development. The Taihang Mountains, an important ecological security barrier in North China, were taken as an example. Based on land use data from 2000 to 2020, this study revealed the changes in land use and ecosystem service value in the Taihang Mountains and conducted multi-scenario simulations of ESV in 2035. Additionally, the land use transfer matrix, improved equivalent factor method, and mixed-cell cellular automata model were used. The results showed that： ① From 2000 to 2020, the main land use types in the Taihang Mountains were cropland, forest land, and grassland. The area of cropland and grassland significantly decreased, whereas the area of forest land and construction land remained stable. The scale of land use transfer was 2.06×104 km2, with a high intensity of transfer from cropland and grassland to other areas, mainly guided by ecological construction. ② The ecosystem service functions in the Taihang Mountains were mainly dominated by regulation services, and the overall ESV showed a stable upward trend, increasing by 1.51×1010 RMB from 2000 to 2020. The spatial distribution of ESV was uneven, and the polarization phenomenon was evident, with high-value areas concentrated in the high-altitude areas in the northern and central-southern parts and low-value areas mostly located in the flat foothills and central plateaus on both sides. They were greatly influenced by human activities. ③ By 2035, compared with that in 2020, the ESV in the Taihang Mountains was projected to increase by 5.37 billion RMB in the natural growth scenario, 4.34 billion RMB in the urban development scenario, and 7.64 billion RMB in the ecological tourism scenario, mainly due to the conversion from cropland and grassland to forest land. The natural growth scenario showed a high intensity of spatial transformation of ESV, the urban development scenario experienced severe ESV loss in cropland, and the ecological tourism scenario showed a significant increase in ESV, which was an effective path for promoting green development in the Taihang Mountains. The research results can provide scientific references for the healthy management and sustainable development of the ecosystem in the Taihang Mountains.

Cellular automata modelling to simulate patterns of urban growth for Nusantara: Indonesia’s new capital

This paper uses cellular automata (CA) modelling to simulate possible patterns of urban growth for Nusantara–Indonesia’s new capital. The modelling uses criteria such as projected population growth and planned development stages and a range of relevant factors that influence urban development. Further the study simulates the possible impact of future urban growth on key biodiversity areas (KBAs). Two scenarios were modelled to simulate urban growth patterns–(1) the nature sensitive city and (2) the Indonesian government’s current plan. Results of the scenario-based CA modelling demonstrate that scenario 1 offers a more sustainable and liveable approach to urban growth, despite its larger land footprint. This is achieved by preserving protected and key biodiversity areas, which are essential for the long-term well-being and resilience of the environment. While scenario 2 is more land-efficient, it presents a possible risk to the overall ecological integrity and liveability of the metropolis by impinging into key biodiversity areas. The study’s cellular automata approach and methodology can be employed for urban planning and biodiversity impact assessment in similar contexts of new city development.

The impact of heterogeneous accessibility to metro stations on land use changes in a bike-sharing context

The integration of urban rail transit and land use has been adopted as a crucial approach to fostering compact development in cities. Proximity to rail transit stations can increase the probability of land use changes, while few studies have analyzed the spatial heterogeneity of the impact of rail transit on land use changes. This study proposes a distance-decay function to delineate the spatial heterogeneity of metro station accessibility using bike-sharing data and examines the impact of metro stations accessibility on land use changes. Jiading New Town in Shanghai, China, is selected as our study case. By utilizing a non-linear distance-decay function to delineate metro station accessibility as a driving factor for training neural networks in a vector-based cellular automata model, an improvement in simulation accuracy is achieved compared to the model using a linear distance-decay function. This study could help establish more efficient strategies for promoting integrated development of rail transit and land use. The significance of this study lies in the generality of the optimized land use vector-based cellular automata model considering the spatial heterogeneity of metro station accessibility, which could also be applied to other situations, such as considering the accessibility to bus stops and road networks.

A Driving Warning System for Explosive Transport Vehicles Based on Object Detection Algorithm.

Due to the flammable and explosive nature of explosives, there are significant potential hazards and risks during transportation. During the operation of explosive transport vehicles, there are often situations where the vehicles around them approach or change lanes abnormally, resulting in insufficient avoidance and collision, leading to serious consequences such as explosions and fires. Therefore, in response to the above issues, this article has developed an explosive transport vehicle driving warning system based on object detection algorithms. Consumer-level cameras are flexibly arranged around the vehicle body to monitor surrounding vehicles. Using the YOLOv4 object detection algorithm to identify and distance surrounding vehicles, using a game theory-based cellular automaton model to simulate the actual operation of vehicles, simulating the driver's decision-making behavior when encountering other vehicles approaching or changing lanes abnormally during actual driving. The cellular automaton model was used to simulate two scenarios of explosive transport vehicles equipped with and without warning systems. The results show that when explosive transport vehicles encounter the above-mentioned dangerous situations, the warning system can timely issue warnings, remind drivers to make decisions, avoid risks, ensure the safety of vehicle operation, and verify the effectiveness of the warning system.

Cellular Automata Simulation of Pitting Corrosion of Stainless Steel in Marine Environments

Pitting corrosion poses a significant hidden risk to the reliable operation of stainless steel equipment. In this study, the evolution of pitting corrosion in stainless steel in marine environments was simulated by developing two-dimensional cellular automata (CA). The model demonstrated its capability to generate corrosion sectional views and top views that were consistent with experimental results. The simulation results obtained from the CA pitting corrosion model converged with the experimental data, yielding minimal errors of 4%. This convergence substantiated the effective ability of the model to simulate pitting corrosion evolution. When the CA model was used to predict pitting development in stainless steel, the obtained predicted values aligned within a 10% margin of error compared with experimental values. The developed CA model was used as a foundation to investigate the effect of chloride ion concentration on both pitting rate and pit morphology. The result showed that the impact of chloride ions concentration on pitting growth was more significant when concentrations were below 3.5wt.%. The establishment of the CA model makes it possible to simulate and predict pitting development patterns, thereby providing guidance for ensuring the safe operation of marine equipment.

Dynamic simulation of street-level carbon emissions in megacities: A case study of Wuhan City, China (2015–2030)

Dynamic simulation of carbon emissions (CE) in megacities is crucial for regional carbon reduction management, however, limited simulation accuracy hinders its application in carbon reduction policies. An integrated modeling framework was developed based on high-resolution multi-source data to analyze the street-level carbon emissions in Wuhan from 2015 to 2030. First, we conducted principal components analysis on the 5 driving factors of carbon emissions (including point of interests, electricity consumption, gross domestic product, population, and nighttime light) to delineate single carbon emission character region (CECR). Then, a machine learning method was used to simulating CE and explaining the contributions of different CECRs. Multi-scenarios CE accountings also were conducted with CECR simulations and an improved cellular automata model. Results shows that: (1) CE in Wuhan showed strong aggregation during the historical period. The five CECRs formed a near-concentric circle. The changes of CECR reflected the enhancement of human activity. (2) CE gradually shifted from the central urban areas to the surrounding regions during the scenario period, showing significant spatial spillover effects. Administrative districts with lower CE density exhibited greater scenario variation in total carbon emissions, indicating a higher potential for carbon reductions. (3) The total CE of Wuhan (148.11 Mt) in 2030 is projected to increase by 42.6 % compared to 2015 in the baseline scenario, representing 105 % of the low scenario and 91 % of the high scenario. The growth rate of total CE in Wuhan significantly slows down (<1 %) under all scenarios. The high-resolution dynamic simulation of CE will provide an important scientific basis for low-carbon city management in China.