The purpose of this study is to identify the dynamics of hypertension adherence and casual relationships among variables affecting hypertension adherence using system thinking approach in the context of a ecological theory. And this is to develop effective policies for hypertension adherence.
Based on the previous studies, we have constructed a causal loop diagram explaining the relationship between hypertension adherence and factors affecting hypertension adherence. We have found 8 main feedback loops. The causal loop diagram was classified into individual, intra-individual and social levels.
Through this study, we should first understand that the problem of hypertension adherence is not simply a problem of the individual domain, but rather a phenomenon where various factors interact and appear. To increase hypertension adherence, it is necessary to review the various intervention methods and intervention point. Long-term and complex interventions are needed rather than short-term policy interventions to increase adherence.

Efforts to reduce carbon emissions are increasing worldwide, and the Korean government is implementing policies to cut carbon emissions across all industries, including the construction industry.
However, in this case, it faces many difficulties because of the characteristics of the construction industry, which are more varied than other industries. Most computational methods that have been used to study the carbon emissions of construction sites do not fully consider the diverse characteristics of the construction industry or take into account changes in the system over time.
In this study, a computational model of the carbon emissions of reinforced concrete works was developed using the system dynamics method, and the variables that have the greatest effect on carbon emissions were derived by simulating the model.
The results of the study are expected to be used as basic data to predict the carbon emissions of construction sites and to establish policies in the construction industry.

Future prospects of land demand can contribute to land use & management planning, urban planning, land & housing policies. Land Demand Prediction Model is applied as system dynamics method based on social factors(population, household, and housing) and economic factors (production activity population, employment rate, GDP) that affect land demand for the future.
In order to verify the validity of the model, historical trends have been analyzed since 1970 and forecasting future trends is limited to 2050. Land demand change factors were set as population, household, housing supply, GDP and so on. Scenarios for predicting future land demand were set to lower, middle and higher level by combining housing supply and GDP. As a result, land demand on mid-level is expected to increase steadily to 19,504 hectares per year by 2020, then sharply decline to 1,692 hectares by 2050.
From this study, it suggests that 'Land Demand Prediction Model' by System Dynamics would most effective way to forecast future land use damand and this study has confirmed a possibility in supporting decision-making on population policy.