This research portrays the spatial and temporal progression of super-aging in regions throughout South Korea. Using a single-year population projection considering gross domestic migration, this research identifies which regions will shortly become a super-aged society. A cohort-component method with a migrant pool model is applied. The county-level national population registration data (2000–2018) are aggregated into 37 regions for the model run. In 2020, 16 rural regions will become super-aged societies. By 2029, all 37 regions, including the metropolitan areas, will join the group, with Sejong, the administrative capital, being the last to enter. In brief, the rural areas become super-aged earlier than the metropolitan areas, and within a decade, those 65 years old or older will make up the majority of the national population. Among all the metropolitan areas, Busan, the largest harbor city, will be the first to be super-aged in 2023. Sejong will experience the most radical change between 2020 and 2050. The research outcomes demonstrate that demographic changes in the rural and metropolitan areas are different; hence, the recent population policies, such as promoting fertility, may not work in the rural areas as they have already lost their population momentum due to the extreme and on-going urbanization throughout the nation. The unstoppable aging will pose adverse effects on future citizens (who are mostly senior) both financially and medically. An increase in health care expenditure and a nationwide blood shortage for transfusion are anticipated, for example.

With the increasing concerns in developing methodologies for Reducing Emissions from Deforestation and forest Degradation (REDD) projects, there is a need to understand the characteristics of existing Land-Use/Cover Change (LUCC) modules. This research presents a modular framework for assessing predictive accuracy of business-as-usual deforestation in the future by comparing two existing approaches: GEOMOD Modeling (GM) and Land Change Modeler (LCM). The comparison uses data from a case study in Chiquitanía, Bolivia. Data from 1986 and 1994 are used to simulate land-cover of 2000; the resulting maps are compared with an observed land-cover map of 2000. GM and LCM simulate business-as-usual deforestations at the pixel level. The model structures of GM's linear extrapolation and LCM's Markov Chain are compared to review quantity of LUCC; and the model structures of GM's empirical frequency, LCM's logistic regression, and LCM's multilayer perceptron are compared to review (spatial) allocation of LUCC. Relative operating characteristics, figure of merit, and multiple resolution analysis are employed to assess predictive accuracy of multiple transition modeling. By design, GM lacks the potential to model multiple transitions, and the LCM's multilayer perceptron may produce different results for each simulation due to its stochastic element. Based on the model structure and predictive accuracy comparisons, the LCM seems more suitable than the GM for a REDD application. When a project is to employ a predictive method for its spatially explicit baseline setting, then it is highly recommended to use the proposed framework to assess accuracy of the baseline as part of a project design document.t gis_1227 631..654

This paper proposes a new land-change model, the Geographic Emission Benchmark (GEB), as an approach to quantify land-cover changes associated with deforestation and forest degradation. The GEB is designed to determine ‘baseline’ activity data for reference levels. Unlike other models that forecast business-as-usual future deforestation, the GEB internally (1) characterizes ‘forest’ and ‘deforestation’ with minimal processing and ground-truthing and (2) identifies ‘deforestation hotspots’ using open-source spatial methods to estimate regional rates of deforestation. The GEB also characterizes forest degradation and identifies leakage belts. This paper compares the accuracy of GEB with GEOMOD, a popular land-change model used in the UN-REDD (Reducing Emissions from Deforestation and Forest Degradation) Program. Using a case study of the Chinese tropics for comparison, GEB’s projection is more accurate than GEOMOD’s, as measured by Figure of Merit. Thus, the GEB produces baseline activity data that are moderately accurate for the setting of reference levels.

This study is aimed to compare the strengths and weaknesses of three approaches—analytic hierarchy process analysis, sentiment analysis, and floating population analysis—in estimating the social demands for local forest ecosystem services (ES) in South Korea: Gariwangsan and Yeoninsan. The results were as follows: First, the survey respondents of Gariwangsan and Yeoninsan believed that the cultural ES category was the most fundamental one that should be maintained, whereas they thought the supporting ES category needed the least maintenance. Second, both forests had a high frequency of sentiment words related to the cultural ES category, followed by the regulating ES category, such as air and water quality improvement. Third, the spatiotemporal distribution of the floating populations in both forests was concentrated in their valleys and mountainous areas, indicating the finer-scale demands for the cultural and regulating ES category. Fourth, the research shows the areas that are high in demand and those that are not; this result helps forest management. In conclusion, none of the three methodologies was superior to the other two, as they each captured distinct ES demands. To investigate ES demands in a multifaceted way, we suggest applying the three approaches in tandem.

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