• Journal of Korean Society for Atmospheric Environment
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• v.33 no.6
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• pp.570-582
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• 2017

In recent years, national and local government's air quality management and climate change adaptation policy has been significantly strengthened. The measures in the two policies may be in a relationship of trade-off or synergy to each other. Greenhouse gases and air pollutants are mostly emitted from the same sources of using considerable amounts of fossil fuels. Co-benefits, in which either measure has a positive effect on the other, may be maximized by reducing the social costs and by consolidating the objectives of the various policies. In this study, the co-benefits were examined by empirically analyzing the effects of air pollutants and greenhouse gas emission reduction, social cost, and cost effectiveness between the two policies. Of the total 80 projects, the next 12 projects generated co-benefits. They are 1) extend restriction area of solid fuel use, 2) expand subsidy of low-$NO_x$ burner, 3) supply hybrid-vehicles, 4) supply electric-vehicles, 5) supply hydrogen fuel cell vehicles, 6) engine retrofit, 7) scrappage of old car, 8) low emission zone, 9) transportation demand management, 10) supply land-based electric of ship, 11) switching anthracite to clean fuel in private sector, 12) expand regional combined-energy supply. The benefits of air pollutants and greenhouse gas-related measures were an annual average of KRW 2,705.4 billion. The social benefits of the transportation demand management were the highest at an annual average of KRW 890.7 billion, and followed by scrappage of old cars and expand regional combined-energy supply. When the social benefits and the annual investment budgets are compared, the cost effectiveness ratio is estimated to be about 3.8. Overall, the reduction of air pollutants caused by the air quality management policy of Gyeonggi-do resulted in an annual average of KRW 4,790.2 billion. In the point sources management sector, the added value of $CO_2$ reduction increased by 4.8% to KRW 1,062.8 billion, while the mobile sources management sector increased by 3.6% to KRW 3,414.1 billion. If social benefits from $CO_2$ reduction are added, the annual average will increase by 7.2% to KRW 5,135.4 billion. The urban and energy management sectors have shown that social benefits increase more than twice as much as the benefits of $CO_2$ reduction. This result implies that more intensive promotion of these measures are needed. This study has significance in that it presents the results of the empirical analysis of the co-benefits generated between the similar policies in the air quality management and the climate change policy which are currently being promoted in Gyeonggi-do. This study suggested that the method of analyzing the policy effect among the main policies in the climate atmospheric policy is established and the effectiveness and priority of the major policies can be evaluated through the policy correlation analysis based on the co-benefits. It is expected that it could be a basis for evaluation the efficiency of the climate change adaptation and air quality management policies implemented by the national and local governments in the future.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.55-62
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• 2022

The perovskite solar cell is an active part of research in renewable energy fields such as solar energy, wind, hydroelectric power, marine energy, bioenergy, and hydrogen energy to replace fossil fuels such as oil, coal, and natural gas, which will gradually disappear as power demand increases due to the increase in use of the Internet of Things and Virtual environments due to the 4th industrial revolution. The perovskite solar cell is a solar cell device using an organic-inorganic hybrid material having a perovskite structure, and has advantages of replacing existing silicon solar cells with high efficiency, low cost solutions, and low temperature processes. In order to optimize the light absorption layer thin film predicted by the existing empirical method, reliability must be verified through device characteristics evaluation. However, since it costs a lot to evaluate the characteristics of the light-absorbing layer thin film device, the number of tests is limited. In order to solve this problem, the development and applicability of a clear and valid model using machine learning or artificial intelligence model as an auxiliary means for optimizing the light absorption layer thin film are considered infinite. In this study, to estimate the light absorption layer thin-film optimization of perovskite solar cells, the regression models of the support vector machine's linear kernel, R.B.F kernel, polynomial kernel, and sigmoid kernel were compared to verify the accuracy difference for each kernel function.