Determining the Relationship between Residential Electricity Consumption and Factors: Case of Seoul

Kim, Minjeong

doi:10.3390/su12208590

Cited by 8 publications

(9 citation statements)

References 14 publications

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“…Fundamental cause theory explains that differences in health outcomes arise due to relative differences in access to resources according to the social hierarchy (69,70). (71) found that residences of Seoul's power elites were clustered in the city's southeastern area. Considering the evident health inequality according to the SEP in Korea (72, 73), Seoul's geographical health inequalities seem to appear through the high SEP population's affinity for a better environment sorted by housing price.…”

Section: Discussionmentioning

confidence: 99%

Spatial Distribution of Neighborhood-level Housing Prices and Its Association With All-cause Mortality in Seoul, Korea (2013–2018): a Spatial Panel Data Analysis

Kim

2020

Preprint

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Background: Housing prices are known to be a relevant indicator of the socioeconomic position of the neighborhood. In a society where the market system mainly drives housing prices, residents' spatial patterning is formulated according to their socioeconomic position. Methods: Dividing the 2013–2018 entire study period into three periods, we explored the geographical distribution of housing prices and all-cause mortality and their association in Seoul, where the country's capital city. The government authorities' data and 2015 census data were used for the study. We mapped the geographical distribution of housing prices and all-cause mortality and investigated the changes in distribution. We conducted a pooled OLS and spatial panel regression analysis to estimate housing prices elasticity of all-cause mortality. We also explored the possible mediating role of housing prices on the educational composition's effect on all-cause mortality. Results: We found the common trends of increasing spatial patterning of housing prices and all-cause mortality. The magnitude of spatial patterning was far greater in housing prices than all-cause mortality. In a pooled OLS regression analysis, we found that a 1% increase in housing price was associated with a 0.11% reduction in all-cause mortality after controlling the explanatory variables. Attenuation in the regression coefficient's magnitude was found after adding the neighborhood's educational composition in the model. As a result of spatial panel analysis, we found a direction and scale similar to the housing price elasticity of all-cause mortality in the final pooled OLS model. Conclusions: The results suggested that geographical health inequality in Korea's urban space mainly stemmed from the affinity of high socioeconomic position and the more developed spaces.

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Section: Discussionmentioning

confidence: 99%

Spatial Distribution of Neighborhood-level Housing Prices and Its Association With All-cause Mortality in Seoul, Korea (2013–2018): a Spatial Panel Data Analysis

Kim

2020

Preprint

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“…Several studies were conducted to validate the relationship between income and electricity consumption [8][9][10]. The primary analysis was conducted on the data of HVS 2013 to show the relationship between electricity bills per month and income.…”

Section: Relationship Between Income and Electricity Consumptionmentioning

confidence: 99%

Developing Trip Generation and Attraction Models Using High-Frequency Proxy Data

Amalan,

Wickramasuriya

2024

J. S. Asian Logistics Transport

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The attraction and generation of a trip play a crucial role in transport planning and forecasting of trips. Multiple linear regression (MLR) is the most popular method of calculating trip attractions (TA) and trip generations (TG) to produce a distribution that can be used to forecast trips with updated values of independent variables such as electricity consumption, no of households, area of the land use etc. Literature shows that the surveys require independent variables to be updated; making them expensive and time consuming. This study aims to develop an MLR model for TA and TG based on available survey data from 2013 for Western Province, Sri Lanka and to update those independent variables using High Frequency (HF) proxy data for the predicted year (2019). Data from HF proxy sources such as electricity consumption, GPS customer points, and Landsat satellite imagery data have been used to update the independent variables of TG and TA for 2019. In the initial stage of this research, data from a home visit survey conducted in 2013 and land use data from the Western Province of Sri Lanka were used to develop the MLR model for TG and TA. A correlation and a regression analysis were performed using these surveyed data. According to the study, the MLR model for TG for home-based work trips has an r2 value of 0.79 and TA for general purposes (including shops and businesses) and industrial has an r2 value of 0.74 and 0.79, respectively, indicates the strong relationship between the considered variables to predict TG and TA. The model is validated with 2013 survey data and would be helpful for real-time estimation of the TG and TA of each zone.

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“…Overall, their study depicts the changes in gross, agricultural, industrial and services value additions due to the contribution of energy consumption. In South Korea, Kim (2020) considered the relationship between electricity consumption and other factors; income, number of household members, and age, using correlation and multiple regression. A significant correlation is demonstrated in the study while confirming that only income and the number of household members affecting on electricity consumption.…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing the Impact of Causal Factors on Political Management to Determine Sustainability Policy under Environmental Law: Enriching the Covariance-based SEMxi Model

Sutthichaimethee

Jittawiriyanukoon

2022

IJEEP

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This research aims to develop a causal relationship model on political management for sustainability policy formation under Thai environmental law by applying the best and valid model with a non-spurious property called the Covariance-based on Structural Equation Model with exogenous variables (Covariance-based SEMxi Model). This newly-developed model is in distinction with any past models as it is made effectively applicable to any sectors across areas. The model can also be utilized to design a long-term forecasting model with the ability to determine appropriate future scenarios. When assessing the covariance-based SEMxi model performance, the mean absolute percentage error (MAPE) and the root mean square error (RMSE) are estimated at 1.19% and 1.30%, respectively, in comparison of other models, including Gray-Autoregressive Integrated Moving Average Model (GM-ARIMA), Gray Model (GM), Back Propagation Neural Network (BP), Artificial Neural Natural Model (ANN), and Multiple Regression Model (MR). As for the results, this research reveals a direct impact of economic factors on environmental and social factors. In the meanwhile, social factors have a direct impact on environmental and economic factors. The research also indicates a direct effect on the environment with a maximal magnitude of 67%. Whereas a direct effect of social factors on the environment is detected at the magnitude of 55%. These effects are perceived to exceed the specified carrying capacity set by Thailand. In addition, a causal relationship is observed between economic and social factors, where the environment is found with the lowest error correction capability of only 5 percent. At the same time, economic and social factors are noticed with greater correction capability of 59% and 31%, respectively. This finding implies that the ecosystem will experience slow recovery whenever it deteriorates. Hence, the government must place a higher concentration on the environment, while different measures on environmental legislation should be closely controlled to contain any future damage. Besides, energy consumption must be managed not to exceed the established carrying capacity by simultaneously implementing both proactive and reactive measures. This process can be strengthened by optimizing the newly-introduced model produced by this work for a scenario design in policy management to attain sustainability.

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Determining the Relationship between Residential Electricity Consumption and Factors: Case of Seoul

Cited by 8 publications

References 14 publications

Spatial Distribution of Neighborhood-level Housing Prices and Its Association With All-cause Mortality in Seoul, Korea (2013–2018): a Spatial Panel Data Analysis

Spatial Distribution of Neighborhood-level Housing Prices and Its Association With All-cause Mortality in Seoul, Korea (2013–2018): a Spatial Panel Data Analysis

Developing Trip Generation and Attraction Models Using High-Frequency Proxy Data

Analyzing the Impact of Causal Factors on Political Management to Determine Sustainability Policy under Environmental Law: Enriching the Covariance-based SEMxi Model

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