SD Model Research Articles

New downscaling prediction models for spring drought in China

AbstractAccurate prediction of spring drought in China is helpful toward reducing associated agricultural losses. In this study, spring drought is defined as the three‐month Standardized Precipitation Evapotranspiration Index (SPEI) ending in May. Based on the year‐to‐year increment and downscaling method, three single‐predictor prediction models (P1 model, P2 model, and P3 model) and two multi‐predictor models for spring drought at 677 stations in China are developed for the period 1983–2020. As physical processes affecting spring drought in the China region, the tropical Pacific–Indian Ocean sea surface temperature (SST) in winter, the Davis Strait–Barents sea‐ice concentration (SIC) in winter, and the 500‐hPa spring vertical velocity, predicted by the Climate Forecast System, version 2 (CFSv2), are considered in the prediction models. The prediction skill of the downscaling models for the spring SPEI is measured by cross‐validation for the period 1983–2020. The CFSv2 model only shows convincing prediction skill for the spring SPEI at 35 of 677 stations. However, among the 677 stations, the temporal correlation coefficient between the observed and predicted spring SPEI at 621 stations for P1 model, 598 stations for P2 model, 545 stations for P3 model, 674 stations for the statistical downscaling model (SD model), and 675 stations for the hybrid downscaling dynamical–statistical prediction model (HD model) exceeds the 95% confidence level. Therefore, compared to the CFSv2 model, the prediction skill for spring drought is improved by the single‐predictor and multi‐predictor models. The prediction skill of HD model for spring drought, which combines preceding observational predictors and the simultaneous predictor of the CFSv2 model, is higher than that of SD model. In addition, the severe drought that occurred in Northeast and North China in spring 2017 can be successfully predicted by HD model.

Pattern‐orientedanalysis of system dynamics models via random forests

AbstractSystem dynamics (SD) modeling studies aim to reveal the causes of problematic dynamic behaviors and eliminate them through policy design and analysis. The analyst conducts sensitivity/scenario analyses and what‐if experiments to reveal the input–output relationships during modeling. However, during these analyses and investigations, the identification of input‐parameter spaces that cause the generation of different SD model behavior patterns is time consuming and susceptible to human bias. Therefore, we propose a metamodel‐based procedure for SD models that considers the necessity for unbiased and automated analysis and insight generation. The approach uses the random forest algorithm for metamodel generation and extracts interpretable IF–THEN rules from the metamodel, thereby identifying input subspaces that generate different qualitative or numerical SD model outputs. We illustrate the proposed approach using two well‐established SD models. These case studies reveal how the model analyst can utilize the proposed method to capture input–output relationships. © 2022 System Dynamics Society.

The policy effect on automobile industry considering the relationship between technology,market and production: the dual-credit policy as an example

ABSTRACT Comprehensively understanding how the policy influences the sustainability transition of the automotive industry is a critical focus in recent years. This paper focuses on the dual-credit policy, an underexplored but critical policy in China, and examines how it would influence the transition of automotive industry. To achieve this goal, an SD model is developed to simulate the effect of policy parameters on the policy objectives by considering the interaction of production, technology and market. The findings reveal that this policy can in general promote the sustainable development of the automotive industry by greatly decreasing its overall energy consumption. However, its impact on the market share of new energy vehicles is limited. Based on the results, some policy suggestions are proposed so as to improve the effectiveness of the dual-credit policy.

Protective Effect of Galangin Methylation Modification Based on Cell Imaging on Inflammatory Lung Injury and Its Molecular Mechanism

Background Recently, inflammation has become a major threat to human health. Studies have confirmed that some Chinese traditional medicine ingredients may effectively interfere with the expression of inflammatory mediators through epigenetic modification, showing a great potential of the application. Objective To investigate the role of the PPAR/DNMT3A pathway in the reversal of galangin-mediated inflammatory lung injury, promote the development of new anti-inflammatory drugs, reduce the side effects of chemical synthetic drugs on the body, and prove the effectiveness and safety of galangin in inhibiting inflammatory response and injury. Methods 120 rats were randomly divided into 6 groups: (Group 1) LPS group; (Group 2) LPS + galangin group; (Group 3) LPS + galangin + GW9662 group; (Group 4) LPS + galangin + DNMT3A siRNA group; (Group 5) LPS + galangin + siRNA negative group; (Group 6) control group. The model of inflammatory lung injury was established by intrathecal instillation of LPS in the first five groups and NS in the control group. SD survival rate was recorded every 24 hours after modeling, lasting for 168 hours. The lung tissues were taken 168 hours after the establishment of the model. The pathological morphology of lung tissue was observed after the staining under the light microscope, and the lung dry/wet weight ratio was calculated after drying. After NS was perfused into lung tissue, the lavage fluid was collected and the levels of IL-6 and TNF-a were measured by ELISA. The contents of PPAR, DNMT3A, phosphorylated p65, and ERK in monocytes were detected by the WB method, and the binding contents of p65 and AP-1 in the promoter regions of IL-6 and TNF-a genes were detected by the Chip-qPCR method. Results Intraperitoneal injection of galangin could inhibit the synthesis of alveolar inflammatory factors (TFs) in the SD model of lung injury induced by LPS, reduce the degree of pathological injury of lung tissue, and improve the survival rate of the SD model. GW9662 can completely reverse the protective effect, while DNMT3A interference can only partially block its protective effect. In addition, galangin could significantly inhibit the LPS-induced expression of p65 and AP-1 in alveolar monocytes and their binding content in the promoter region of inflammatory genes by activating PPAR/DNMT3A pathway. GW9662 could completely reverse the inhibitory effect of galangin. DNMT3A interference could restore the binding content of transcription factors at the promoter of the inflammatory gene but had no significant effect on its synthesis. Conclusion Galangin can interfere with the binding of transcription factors to inflammatory gene promoters through the methylation modification induced by PPAR/DNMT3A pathway, so as to inhibit the synthesis of inflammatory molecules and reverse inflammatory lung injury.

Simulation Analysis of Xi'an Eco-City Construction

Based on reviewing the theoretical research and construction practice of eco-city, this paper analyzes the system constituent elements from five subsystems, including economic subsystem, social subsystem, population subsystem, resource subsystem and environmental subsystem. The SD model of Xi 'an urban ecosystem was established by system dynamics method, the six main variables selected are as follows: GDP, per capita water resources, energy consumption per 10000 yuan of GDP, per capita public green area, number of doctors per 10000 people and GDPSO2 emissions per ten thousand yuan, different simulation results under different strategies are analyzed. According to the simulation data of ten leading indicators of eco-city construction, the paper calculates the measurement of Xi'an eco-city construction level. It puts forward specific suggestions from economy, resources, environment and security of eco-city construction.

Exploring construction workers' attitudinal ambivalence: a system dynamics approach

PurposeThis study aimed to identify driving factors of safety attitudinal ambivalence (AA) and explore their influence. Construction workers' intention to act safely can be instable under conflicting information from safety management, co-workers and habitual unsafe behaviour. Existing research explained the mechanism of unsafe behaviours as individual decisions but failed to include AA, as the co-existence of both positive and negative attitude.Design/methodology/approachThis study applied system dynamics to explore factors of construction workers' AA and simulate the process of mitigating the ambivalence for less safety behaviour. Specifically, the group model building approach with eight experts was used to map the causal loop diagram and field questionnaire of 209 construction workers were used to collect empirical data for initiating parameters.FindingsThe group model building identified five direct factors of AA, namely the organisational safety support, important others' safety attitude, emotional arousal, safety production experience and work pressure, with seven feedback paths. The questionnaire survey obtained the initial values of the factors in the SD model, with the average ambivalence at 0.389. The ambivalence between cognitive and affective safety attitude was the highest. Model simulation results indicated that safety experience and work pressure had the most significant effects, and safety experience and positive attitude of co-workers could compensate the pressure from tight schedule and budget.Originality/valueThis study provided a new perspective of the dynamic safety attitude under the co-existence of positive and negative attitude, identified its driving factors and their influencing paths. The group model building approach and field questionnaire surveys were used to provide convincible suggestions for empirical safety management with least and most effective approaches and possible interventions to prevent unsafe behaviour with tight schedule and budget.

School garden as a learning alternative in the era of COVID-19 pandemic

Indonesia as an agrarian country faces several obstacles to achieving food sovereignty, due to the limited number of the younger generation involved in the agricultural sector. Most of the farmers are elderly with limited capacity. Therefore, as early as possible, it is necessary to make efforts to increase interest in farming among elementary school students through School Garden activities. The introduction of the School Garden becomes an interesting lesson because students can carry out farming activities independently. This community service program was carried out at SD Model located in Ngemplak, Sleman, Yogyakarta. This program aims to introduce the School Garden through urban farming so that it can foster interest in agriculture. The methods used include presentations with video tutorials and giving farming modules. The results of the implementation of the community service program showed that 97.7% of students liked the introduction of the School Garden activity and 81.4% of students felt that these activities foster their interest in agriculture.

Electromagnetic multipoles of positive parity states in 27Al by elastic and inelastic electron scattering

Nuclear structure of 27Al nucleus has been investigated using shell model with self-consistent Hartree–Fock calculations. Shell model calculations are performed with full sd model space for positive parity states. For all selected excited states, Skyrme interactions are adopted to generate from them a one-body potential in Hartrre-Fock theory to calculate the single-particle matrix elements and compared with those of the harmonic oscillator. The deduced results are discussed for the longitudinal and transverse form factors and compared with the available experimental data. It has been found that the core-polarization effects are essential in obtaining a reasonable description of the data.

Simulation and Analysis of Renewable and Nonrenewable Capacity Scenarios under Hybrid Modeling: A Case Study

This work analyzes the response of the electricity market to varied renewable and nonrenewable installed capacity scenarios while taking into account the variability of renewables due to seasonality and El Niño-Southern Oscillation (ENSO) episodes. A hybrid system dynamics/dynamic systems (SD/DS) model was developed by first deriving an SD hypothesis and stock-flow structure from the Colombian electricity supply and demand dynamics. The model’s dynamic behavior was then transformed into a Simulink model and analyzed using the DS tools of bifurcation and control theory to provide deeper insights into the system, both from a Colombian perspective and from the perspective of other market scenarios. Applying the developed hybrid model to the Colombian electricity market provided a detailed description of its dynamics under a broad range of permanent (fossil fuel) and variable (renewable) installed capacity scenarios, including a number of counterintuitive insights. Greater shares of permanent capacity were found to guarantee the security of supply and system robustness in the short-term (2021–2029), whereas greater shares of variable capacity make the system more vulnerable to increased prices and blackouts, especially in the long-term (2040–2050). These critical situations can be avoided only if additional capacity from either conventional or non-conventional generation is quickly installed. Overall, the methodology proposed for building the hybrid SD/DS model was found to provide deeper insights and a broader spectrum of analysis than traditional SD model analysis, and thus can be exploited by policy makers to suggest improvements in their respective market structures.

Development and external validation of prognostic models to predict sudden and pump-failure death in patients with HFrEF from PARADIGM-HF and ATMOSPHERE

BackgroundSudden death (SD) and pump failure death (PFD) are the two leading causes of death in patients with heart failure and reduced ejection fraction (HFrEF).ObjectiveIdentifying patients at higher risk for mode-specific death would allow better targeting of individual patients for relevant device and other therapies.MethodsWe developed models in 7156 patients with HFrEF from the Prospective comparison of ARNI with ACEI to Determine Impact on Global Mortality and morbidity in Heart Failure (PARADIGM-HF) trial, using Fine-Gray regressions counting other deaths as competing risks. The derived models were externally validated in the Aliskiren Trial to Minimize Outcomes in Patients with Heart Failure (ATMOSPHERE) trial.ResultsNYHA class and NT-proBNP were independent predictors for both modes of death. The SD model additionally included male sex, Asian or Black race, prior CABG or PCI, cancer history, MI history, treatment with LCZ696 vs. enalapril, QRS duration and ECG left ventricular hypertrophy. While LVEF, ischemic etiology, systolic blood pressure, HF duration, ECG bundle branch block, and serum albumin, chloride and creatinine were included in the PFD model. Model discrimination was good for SD and excellent for PFD with Harrell’s C of 0.67 and 0.78 after correction for optimism, respectively. The observed and predicted incidences were similar in each quartile of risk scores at 3 years in each model. The performance of both models remained robust in ATMOSPHERE.ConclusionWe developed and validated models which separately predict SD and PFD in patients with HFrEF. These models may help clinicians and patients consider therapies targeted at these modes of death.Trial registration numberPARADIGM-HF: ClinicalTrials.gov NCT01035255, ATMOSPHERE: ClinicalTrials.gov NCT00853658.Graphics abstract

Research on the Scheme of Multi-objective Planning Water Resources Optimization System Based on Network Model

Water resource scheduling is a key way to efficiently allocate regional water resources and enhance water use efficiency. This study takes the optimal scheduling of water resources in a certain area as an example. Aims to enhance the ecological, social and economic benefits in the scheduling area, and establish SD-MOP water resources optimal dispatch model. The results show that compared with the traditional SD model, the SD-MOP model predicts that the dispatch result in 2025 is closer to the actual water demand of the city, and saves the cost of 2.053 million yuan, and the pollutant discharge is only 0.891t, which brings considerable economic and environmental benefits.

Modelling the Impact of Mobile Application Adoption on the Taxi Demand: An Application of a System Dynamics Approach

<p>The improvement of technology brings a significant impact on transportation industries. The taxi industry has undergone tremendous changes with the existent of e-hailing service in the industry. Due to the introduction of mobile applications, e-hailing service takes part to compete in the market. The government has given priority to overcome the problem by introducing travel demand strategies that focus on mitigating the demand competition between the taxi and e-hailing services. One of the strategies is the adoption of a mobile application in taxi service. This paper aims to develop a system dynamics model to analyse mobile application adoption’s impact on customers’ demand on the mode share of taxi and e-hailing services as a measured output. System dynamics is a decision-experimentation method that creates a learning environment in which policymakers gain a better understanding of how the system will respond to their decisions and the potential unintended consequences of decisions. With the developed SD model, the feedback relations between mobile application adoptions on the output of taxi demand can be observed. Furthermore, the demand competition between the taxi and e-hailing services can be minimised using this SD model. The result shows that, by implementing the usage of the mobile application in taxi services, more users will be attracted to use the taxi service. With that service option, users will shift their attraction from e-hailing to taxi service, which is then able to minimise the demand competition. This research can benefit policymakers and authorities in the department of transportation to serve as a planning tool so that the demand of taxi and e-hailing services in Malaysia with the adoption of mobile application in taxi service can be predicted.</p>

The COVID-19 epidemic and evaluating the corresponding responses to crisis management in refugees: a system dynamic approach

PurposeThe COVID-19 pandemic is a new crisis in the world that caused many restrictions, from personal life to social and business. In this situation, the most vulnerable groups such as refugees who are living in the camps are faced with more serious problems. Therefore, a system dynamic approach has been developed to evaluate the effect of applying different scenarios to find out the best response to COVID-19 to improve refugees’ health and education.Design/methodology/approachThe interaction of several health and education factors during an epidemic crisis among refugees leads to behavioral responses that consequently make the crisis control a complex problem. This research has developed an SD model based on the SIER model that responds to the public health and education system of Syrian refugees in Turkey affected by the COVID-19 virus and considered three policies of isolation, social distance/hygiene behavior and financial aid using the available data from various references.FindingsThe findings from the SD simulation results of applying three different policies identify that public health and education systems can increase much more by implementing the policy of social distance/hygiene behavior, and it has a significant impact on the control of the epidemic in comparison with the other two responses.Originality/valueThis paper contributes to humanitarian organizations, governments and refugees by discussing useful insights. Implementing the policy of social distance and hygiene behavior policies would help in a sharp reduction of death in refugees group. and public financial support has improved distance education during this pandemic.

System Dynamics Modeling and Simulation of Enterprise Patent Management Optimization

Taking the perspective of cybernetics theory, enterprise patent management can be described as a controllable information system which includes various activities and performances. This paper aims to analysis the relationship between enterprise patent activities and patent performance, and to help convert technological innovation achievements into patent rights more effectively and consolidate technology superiority. This paper uses System Dynamics method to construct a multiscale nonlinear enterprise patent management model. Validity of this model is tested, simulation is conducted in VENSIM, sensitivity analysis of this model shows that: Technology Disclosure Sufficiency does not have much influence on patent application quantity, but it has strongly positive effect on patent grant rate and patent portfolio diversity, it can also lower the risk of patent circumvention. Simulation shows that technology disclosure sufficiency is a key determinant to reduce information loss and improve output efficiency of enterprise patent management system. Collaboration of R&D personnel, patent engineers and patent agencies should be enhanced to improve TDS, so as to promote enterprise patent management efficiency. This SD model provides a foundation for future research in patent management optimization.

A neural system dynamics modeling platform and its applications in randomized controlled trial data analysis

Conventional statistical methods used in clinical trials lack the ability to predict patient specific risk and very often do not consider the effects of time varying interventions. The aim of this study is to test a novel artificial neural network based model for clinical trial analysis to represent the continuous time evolution of risk. The novel methodology tested utilizes system dynamics and artificial neural networks. This methodology was applied to analyze data from 2,221 patients with acute myocardial infarction enrolled in a well characterized randomized study, the HORIZONS-AMI trial. Outcomes analyzed included: (1) target lesion revascularization (TLR) and (2) stent thrombosis. The proposed neural system dynamics (NSD) model was compared against traditional Cox Proportional Hazards (Cox PH) and Cox neural network (NN) models. Model performance was evaluated using C-statistic at 1, 2 -and 3- years follow-up and model-based simulation studies were performed to examine the effect of different variables on the predicted risk of TLR and stent thrombosis. The NSD model achieved comparable performance to Cox models for TLR. For stent thrombosis, the NSD model outperformed Cox models (1-year C-statistic: Stent thrombosis – Cox PH: 0.60, Cox NN: 0.66, neural SD model: 0.69). The neural SD model identified clinically relevant variables such as stent count, stent type and multiple lesions treated as significant predictors for TLR; and stent count and peak platelet count for stent thrombosis. Simulations illustrated change in predicted TLR risk maximal for the neural SD model, compared to other models. For stent thrombosis, simulation scenario illustrated predicted event rate doubling when clopidogrel is discontinued at 6 months compared to extended use. We have demonstrated an alternative analytical methodology that combines system dynamics and artificial neural networks to analyze results from a randomized trial. This novel approach can incorporate patient-specific longitudinal data and provide personalized risk prediction. • Incorporation of non-linear interactions and time-varying data improves personalized risk prediction. • Neural System Dynamics (NSD) model combines neural networks with system dynamics. • NSD model represents continuous time evolution of risk. • NSD model captures clinically relevant risk predictors of Target Lesion Revascularization and Stent Thrombosis. • NSD model based simulations provide more personalized risk trajectories with actionable insights using follow-up intervention information.

Developing and validating models to predict sudden death and pump failure death in patients with heart failure and preserved ejection fraction

BackgroundSudden death (SD) and pump failure death (PFD) are leading modes of death in heart failure and preserved ejection fraction (HFpEF). Risk stratification for mode-specific death may aid in patient enrichment for new device trials in HFpEF.MethodsModels were derived in 4116 patients in the Irbesartan in Heart Failure with Preserved Ejection Fraction trial (I-Preserve), using competing risks regression analysis. A series of models were built in a stepwise manner, and were validated in the Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity (CHARM)-Preserved and Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trials.ResultsThe clinical model for SD included older age, men, lower LVEF, higher heart rate, history of diabetes or myocardial infarction, and HF hospitalization within previous 6 months, all of which were associated with a higher SD risk. The clinical model predicting PFD included older age, men, lower LVEF or diastolic blood pressure, higher heart rate, and history of diabetes or atrial fibrillation, all for a higher PFD risk, and dyslipidaemia for a lower risk of PFD. In each model, the observed and predicted incidences were similar in each risk subgroup, suggesting good calibration. Model discrimination was good for SD and excellent for PFD with Harrell’s C of 0.71 (95% CI 0.68–0.75) and 0.78 (95% CI 0.75–0.82), respectively. Both models were robust in external validation. Adding ECG and biochemical parameters, model performance improved little in the derivation cohort but decreased in validation. Including NT-proBNP substantially increased discrimination of the SD model, and simplified the PFD model with marginal increase in discrimination.ConclusionsThe clinical models can predict risks for SD and PFD separately with good discrimination and calibration in HFpEF and are robust in external validation. Adding NT-proBNP further improved model performance. These models may help to identify high-risk individuals for device intervention in future trials.Clinical trial registrationI-Preserve: ClinicalTrials.gov NCT00095238; TOPCAT: ClinicalTrials.gov NCT00094302; CHARM-Preserved: ClinicalTrials.gov NCT00634712.Graphic abstract

Toward software artifacts ecosystem

In the process of developing and maintaining a software product, many things are created and used that are called software artefacts. Software artifacts are created, changed, reused, and change relationships in the development and maintenance processes of a software product. The complexity and variety of software artifact relationships require adequate means of description and management. They may be a software artifacts ecosystem. In the article, for the first time, a concept of a software artifact ecosystem is proposed. The concept describes a generic model of the software artifacts ecosystem, which is the Cornerstone ecosystem type and consists of three actors — the platform, the software, and the artifact. Based on the generic model, the SD model of the software artifacts ecosystem is described. The roles of actors in the ecosystem are indicated, the relationships between actors are described. The developer's activities will be more efficient, the software is understandable, and the development and maintenance is cheaper when the styles (standards) are used. As case study, based on the generic model of the software artifacts ecosystem, a declarative model of the programming style ecosystem has been developed. Three-level model of programming style artifact is proposed. The tools and processes for creating and using a programming style artifact are developed and described.

Risk of primary liver cancer associated with gallstones and cholecystectomy: A competing risks analysis.

Previous research has revealed a positive relationship between GSD, cholecystectomy and primary liver cancer (PLC). However, previous studies had several limitations including the retrospective design, narrow assessment of potential confounders and lack of competing risk models in time-to-event analyses. We conducted a large prospective cohort study to explore the relationship between GSD, cholecystectomy and PLC. A total of 95,021 participants who had not been diagnosed with PLC previously were enrolled from the Kailuan Cohort study. Demographic characteristics and biochemical parameters were recorded at baseline for all participants. We used Cox regression models and competing risk regression models to evaluate the association of GSD and cholecystectomy with the risk PLC. A total of 306 incidental PLC cases were identified during a median follow-up of 9.05 (8.75–9.22) years per participant. Compared with the normal group, the multivariable HRs (95%CI) for the association of GSD and cholecystectomy with PLC were 1.77 (1.05–2.94), 5.25 (1.95–14.17). In the CS model, the multivariable HRs (95%CI) was 1.76 (1.05–2.94) for the association of GSD and cholecystectomy with PLC and 5.25 (1.95–14.17) for GSD and cholecystectomy. Similar results were also obtained in the SD model with corresponding multivariate HRs (95%CI) of 1.75 (1.01–3.00), 5.22 (1.90–14.07) in the GSD group and cholecystectomy group, respectively. GSD and cholecystectomy were associated with an elevated risk of PLC.Registration number: ChiCTR–TNRC–11001489.

Primary tumor removal improves the prognosis in patients with stage IV breast cancer: A population-based study (cohort study).

Adjuvant therapy including chemotherapy, hormonal therapy, and radiotherapy were often used as a common stereotypy for female stage IV breast cancer rather than surgery. This study aimed to define the role of local surgery in metastatic breast cancer. Female metastatic breast cancer patients were identified in the Surveillance, Epidemiology, and End Results (SEER) program data (2010-2013). We compared survival time between patients who received primary tumor removal (PTR) versus those who did not. Multivariate Cox regression models and competitive risk models were built to adjust potential confounders. Of 7669 female stage IV breast cancer patients, 2704 (35.3%) had surgery on their breast tumor and 4965 (64.7%) did not. In the entire cohort, women who underwent PTR had a 45% reduced risk of breast cancer-related death (multi-adjusted hazard ratio [HR], 0.55; 95% CI, 0.50 to 0.60) compared with women who did not undergo PTR (P<0.001). In a cause-specific hazard model (CS model), the multivariable HRs (95% CI) for the association of PTR with breast cancer related-death were 0.54 (0.50-0.60) in the multivariate-adjusted analysis. Similar results were also observed in the sub-distribution hazard function model (SD model) with corresponding multivariate HRs (95%CI) of 0.57 (0.52-0.63). Our study suggested that PTR was associated with improved survival in female stage IV breast cancer patients. The role of PTR in these patients needs to be re-evaluated.

Urbanization and eco-environment coupling circle theory and coupler regulation

A near-distance, nonlinear coupling relationship objectively exists between urbanization and the eco-environment. The issue of how to coordinate the relationship between them has become a global strategic and scientific issue. This study reveals the nature, relationship and intensity of coupling between urbanization and the eco-environment from a theoretical perspective. Based on the strength of coupling, relationships can be characterized as having very-low, low, medium, high, very-high or full coupling intensity, which correspond to the categories of random coupling, indirect coupling, loose coupling, cooperative coupling, close coupling, and controlled coupling. Together, these make up an urbanization and eco-environment “coupling tower.” This study also develops an urbanization and eco-environment coupling circle theory and generates 45 coupling graphs (including linear, exponential-curve, logarithmic-curve, double exponential-curve and S-curve graphs) per 10° of rotation of the coupling circle, with different graphs corresponding to different urban development stages and development models. Of the various coupling graphs, the S-curve graph is considered the optimum, as it reflects the best interactivity scenario between urbanization and the eco-environment. Using an S-curve coupling graph, and with the help of an SD model and based on the complex one-to-one, one-to-many, and many-to-many relationships between the variables, this study develops the Urbanization and Eco-environment Coupler (UEC). The UEC is composed of 11 regulating elements and 201 variables. If one variable changes, it changes the whole, affecting the structure, function and regulation of the entire coupler. The UEC includes three spatio-temporal scales: static regulation between multiple urbanization areas and eco-environment areas at the same time, dynamic regulation between the same urbanization area and eco-environment area at different times, and dynamic regulation between multiple urbanization areas and eco-environment areas at different times. Regulation gradually promotes evolution from low-level coupling to high-level coupling between urbanization and the eco-environment.