• Journal of Climate Change Research
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• v.7 no.1
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• pp.85-93
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• 2016

This study the efficiency of greenhouse gas reduction of 'low carbon car collaboration fund' and its alternative 'control of average fuel efficiency and greenhouse gas', and 'improving driving behavior' were analyzed by using LEAP, long term energy analysis model. Total 4 scenarios were set, baseline scenario, without energy-saving activity, 'low carbon car collaboration fund' scenario, 'fuel efficiency improving scenario', and 'improving driving behavior' scenario. The contents of analysis were forecast of energy demand by scenario and application as well as reduction of greenhouse gas emission volume, and the period taken for analysis was every 1 year during 2015~2030. Baseline scenario, greenhouse gas emission volume in 2015 would be 7,935,697 M/T and 13,081,986 M/T in 2030, increased 64.8%. The analysis result was average annual increase rate of 3.4%. The expected average annual increase rate of other scenarios was, 'low carbon car collaboration fund' scenario 1.7%, 'fuel efficiency improving' scenario 3.0%. and 'improving driving behavior' scenario 3.4%. and these were each 1.7%, 0.3%. 0.3% reduce from baseline scenario. The largest reduction was 'low carbon car collaboration fund' scenario, and there after were 'fuel efficiency improving scenario', and 'improving driving behavior' scenario.

• Journal of Environmental Health Sciences
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• v.34 no.5
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• pp.343-350
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• 2008

Quantifying greenhouse gas (GHG) emissions in the waste sector is important to evaluating measures for reduction of GHG emissions. To forecast GHG emissions and identify potential emission reduction for GHG emissions, scenarios applied with environmental policy such as waste reduction and structural change of waste treatment were developed. Scenario I estimated GHG emissions under the business as usual (BAU) baseline. Scenario II estimated GHG emissions with the application of the waste reduction policy while scenario III was based on the policy of structural change of waste treatment. Scenario IV was based on both the policies of waste reduction and structural change of waste treatment. As for the different scenarios, GHG emissions were highest under scenarios III, followed by scenarios IV, I, and II. In particular, GHG emissions increased under scenario III due to the increased GHG emissions from the enhanced waste incineration due to the structural change of waste treatment. This result indicated that the waste reduction is the primary policy for GHG reduction from waste. GHG emission from landfill was higher compared to those from incineration. However, the contribution of GHG emission from incineration increased under scenario III and IV. This indicated that more attention should be paid to the waste treatment for incineration to reduce GHG emissions.

• Journal of Climate Change Research
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• v.3 no.3
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• pp.183-193
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• 2012

In this study, the S University's energy usage, greenhouse gas emissions situation and potential reduction amount were analyzed using a long-term energy analysis model, LEAP. In accordance with the VISION 2020 and university's own improvement plans, S University plans to complete a second campus through expansion constructions by 2020 and by allocating the needed land. Accordingly, increases in energy usage and greenhouse gas emissions seem inevitable. Hence, in this study, the calculations of potential reduction amount by 2020 were attempted through the use of LEAP model by categorizing the energy used based on usage types and by proposing usage typebased reduction methods. There were a total of 4 scenarios: a standard scenario that predicted the energy usage without any additional energy reduction activity; energy reduction scenario using LED light replacement; energy reduction scenario using high efficiency building equipment; and a scenario that combines these two energy reduction scenarios. As scenario-based results, it was ascertained that, through the scenario that had two other energy reduction scenarios combined, the 2020 greenhouse gas emissions amount would be 14,916 tons of $CO_2eq$, an increase of 43.7% compared to the 2010 greenhouse gas emissions amount. Put differently, it was possible to derive a result of about 23.7% reduction of the greenhouse gas emissions amount for S University's greenhouse gas emissions amount through energy reduction activities. In terms of energy reduction methods, changing into ultra-high efficiency building equipment would deliver the most amount of reduction.

• Journal of Korean Society on Water Environment
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• v.37 no.6
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• pp.469-482
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• 2021

The recent climate change and urbanization have seen an increase in runoff and pollutant loads, and consequently significant negative water pollution. The characteristics of the pollutant loads vary among the different flow regime depending on their source and transport mechanism, However, pollutant load reduction based on flow regime perspectives has not been investigated thoroughly. Therefore, it is necessary to analyze the effects of concentration on pollutant load characteristics and reductions from each flow regime to develop efficient pollution management. As non-point pollutants continuously increase due to the increase in impervious area, efficient management is necessary. Therefore, in this study, 1) the characteristics of pollutant sources were analyzed at the Dalcheon Basin, 2) reduction of nonpoint pollution, and 3) reduction efficiency for flow regimes were analyzed. By analyzing the characteristics of the Dalcheon Basin, a reduction efficiency scenario for each pollutant source was constructed. The efficiency analysis showed 0.06% to 5.62% for the living scenario, 0.09 to 24.62% for the livestock scenario, 0.17% to 12.81% for the industry scenario, 9.45% to 38.45% for the land scenario, and 9.8% to 39.2% for the composite scenario. Therefore, various pollution reduction scenarios, taking into account the characteristics of pollutants and flow regime characteristics, can contribute to the development of efficient measurements to improve water quality at various flow regime perspectives in the Dalcheon Basin.

• Journal of Energy Engineering
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• v.29 no.2
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• pp.10-22
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• 2020

As the national demand for solving the fine dust problem has increased, the government has announced intensive measures to deal with fine dust. So recently, selective catalytic reduction(SCR) has attracted attention as a technology for removing nitrogen oxides from precursors of fine dust. In this study, the government's policies related to fine dust and the current status of market and R&D were investigated, and economic analysis by scenarios was conducted by dividing cases where SCR technology was applied to industries. The results of economic analysis for each scenario were calculated using NPV, and companies with no denitrification facilities(Case 1) introduced general SCR technologies(Scenario 1-1) and low-temperature SCR technologies(Scenario 1-2). In addition, companies that have already installed denitrification facilities(Case 2) analyzed the two categories, using the general SCR technology as it is(Scenario 2-1) and replacing it with low-temperature SCR technology(Scenario 2-2). Comparative analysis was performed based on the results of each NPV.

• Journal of Environmental Impact Assessment
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• v.8 no.4
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• pp.59-72
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• 1999

The purpose of the study is to propose the concrete and realistic alternative measures for $CO_2$ emission reduction on commercial sector. To achieve the purpose, this study adopted AIM/KOREA simulation model modified from AIM(Asia-Pacific Integrated Model) originally developed by Japan National Environmental Research Institute. The results of simulation demonstrate that the $CO_2$ emission from the commercial sector in 1995 was estimated 864 million TC(tons of carbon); however, according to the base scenario, $CO_2$ emission in 2020 is expected to be increased to 1,872 million TC, which is 2.17 times greater than that in 1995. In order to mitigate the ever-increasing $CO_2$ emission, the results of AIM/KOREA simulations under various scenarios showed that the 30-thousand-won carbon tax scenario does not successfully motivate the selection of advanced technology; however, with the 300-thousand-won carbon tax, a substantial amount of $CO_2$ emission reduction by 1.69 million TC from the BaU((Business-as-Usual)scenario is expected to be achieved by year 2020. Such substantial reduction of $CO_2$ emission under the 300-thoudsand-won carbon tax scenario is due to the introduction of advanced technology, such as use of condensing boilers, forced by heavier carbon tax. Under the scenario that presumes the maximum introduction of gas-burning industrial appliances, an 2.66 million TC of $CO_2$ reduction was expected. The results of this study suggest that the $CO_2$ emission reduction measures can be interpreted in many different views. However, if people and industries are fully aware of the economic benefit of energy saving, a certain level of $CO_2$ reduction by a successful introduction of advanced energy saving technology appears to be achieved without carbon tax or subsidies.

• Journal of Climate Change Research
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• v.7 no.3
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• pp.341-349
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• 2016

This study is intended to set a greenhouse gas emission scenario based on green remodeling pilot project (Annex building of Seoul Customs Office) using LEAP model, a long-term energy plan analysis model, to calculate the energy saving and greenhouse gas emission till year 2035 as well as to analyze the effect of electric power saving cost. Total 4 scenarios were made, Baseline scenario, assuming the past trend is to be maintained in the future, green remodeling scenario, reflecting actual green remodeling project of Seoul Customs Office, behavior improvement and renewable energy supply, and Total scenario. According to the analysis result, the energy demand in 2035 of Baseline scenario was 6.1% decreased from base year 2013, that of green remodeling scenario was 17.5%, that of behavior improvement and renewable energy supply scenario was 21.1% and that of total scenario was 27.3%. The greenhouse emission of base year 2013 was $878.2tCO_2eq$, and it was expected $826.3tCO_2eq$, approx. 5.9% reduced, in 2035 by Baseline scenario. the cumulative greenhouse gas emission saving of the analyzing period were $-26.5tCO_2eq$ by green remodeling scenario, $2.8k\;tCO_2eq$ by behavior improvement and renewable energy supply scenario, and $2.0k\;tCO_2eq$ by total scenario. In addition the effect of electricity saving cost through energy saving has been estimated, and it was approx. 634 million won by green remodeling scenario and appro. 726 million won by behavior improvement and renewable energy supply scenario. So it is analyzed that of behavior improvement and renewable energy supply scenario would be approx. 12.7% higher than that of green remodeling scenario.

• Journal of Climate Change Research
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• v.4 no.3
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• pp.211-219
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• 2013

This study uses the LEAP model that is a long-term energy analysis model to analyze reduction potential on S city residential sector energy usage for greenhouse gas emission. Energy consumption of S-si in 2009 is consumed most in residential and commerce sector by 39.1%. Also, energy and greenhouse gas emission of residential sector is expected to increase due to increase of households. Therefore, greenhouse gas reduction measures are desperately required in residential sector. For this study recognizes energy consumption of S-si residential sector and has established reduction measure of S-si residential sector greenhouse gas through literature search on domestic and foreign climate change correspondence policies. Also, construction of greenhouse gas reduction potential by reduction measures through LEAP model. There were a total of 5 reduction measures scenarios is Reference Scenario, LED Lighting, Energy Alternative, Green Life Practice, and Total Reduction Measure. As a result, greenhouse gas emission of Light Emitting Diode Lightings by 2020 was $1,181.0thousand\;tonCO_2eq$, decrease of 6.1% compared to the Reference Scenario and Greenhouse gas emission of Energy Alternative by 2020 was $1,171.6thousand\;tonCO_2eq$, decrease of 6.8% compared to the Reference Scenario. Greenhouse gas emission of Green Life Practice by 2020 was $1,128.7thousand\;tonCO_2eq$, decrease of 10.2% compared to the Reference Scenario. For Total Reduction Measures by 2020 emission was $966.9thousand\;tonCO_2eq$, decrease 23.1% compared to Reference Scenario.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.11
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• pp.1520-1527
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• 2013

The purpose of this study is to derive COE reduction targets of offshore wind power in Korea. In addition, innovation factors for achieving the COE reduction targets were derived. Also the COE reduction targets of offshore wind power was to improve that national policy, technology, industry and improving regulations would like to help. The results of this study has been created based on the various assumptions, scenarios and experts' discussions. Currently, offshore wind power generation price is 229.72won/kWh in 2012. According to the study, COE of offshore wind power has been proposed 88.8won/kWh at third scenario by 2030. This result has shown competitiveness with fossil fuel power generation.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.70-70
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• 2011

기후변화 연구는 불확실한 미래를 전망하는 과정이므로 '불확실성'은 모든 기후변화 영향평가의 키워드임에 분명하다. 하지만 불확실성 평가를 위해 IPCC에서 제공되고 있는 수많은 GCM 시나리오를 모두 활용하기에는 많은 시간과 노력이 필요하기 때문에 이를 효율적으로 수행할 수 있는 방법이 필요하다 본 연구에서는 시나리오 저감(scenario reduction)방법을 이용하여, 수많은 GCM 시나리오 대신 몇 개의 대표적 GCM 시나리오로도 충분히 불확실성을 유지할 수 있는 시나리오 저감(scenario reduction)방법을 수립하고 제시하였다. IPCC 기후시나리오 중 20C3M과 A & B 배출시나리오를 바탕으로 생산되는 71개의 GCM 시나리오를 다운로드 받아 월평균 기온과 강수량에 대하여 한반도를 대상으로 분석하였다. 비교결과, 기온 전망은 실측과 비슷한 경향성을 보였으나 강수량은 홍수기를 모의하지 못하는 것으로 나타났다. 시나리오 저감방법은 시나리오 분류(scenario cluster)방법과 시나리오 선정(scenario selection) 방법으로 구성된다. 시나리오 분류방법에서는 k-mean방법을 이용하여 5개의 cluster로 나누었으며, 시나리오 선정방법에서는 GCM 시나리오 선정기법을 조사 분석하여 연구방향과 목적에 따라 GCM 시나리오 선정기법을 선택할 수 있는 표를 제시하고, 이 중 시나리오의 확률밀도함수를 이용하는 PDF method를 적용하였다. 본 연구에서는 불확실성 정량화를 위해 maximum entropy를 이용하였다. 또한 시나리오 저감방법이 불확실성을 유지하는지 비교하기 위해 PDF method를 이용하여 정확성이 높은 순으로 5개의 GCM 시나리오를 선정(best 시나리오)하여 불확실성을 정량화하였다. GCM 시나리오의 분산을 이용하여 maximum entropy를 산정한 결과, 20C3M 배출시나리오에서는 모든 시나리오의 entropy는 3.08, 시나리오 저감방법은 2.75, best 시나리오는 2.28이었으며, 이는 시나리오 저감방법은 모든 시나리오의 89.3%의 불확실성을 설명하고 있으나 best 시나리오는 74.0%밖에 설명하지 못한다는 것을 나타낸다. A & B 배출시나리오에서도 시나리오 저감 방법을 사용한 GCM 시나리오가 best 시나리오보다 모든 시나리오의 불확실성을 더 잘 설명하는 것으로 나타났다. 이와 같이 수많은 GCM 시나리오를 사용하는 것보다 몇 개의 대표 시나리오를 이용하여 기후 변화 불확실성을 유지하면서 미래전망을 할 수 있다면, 매우 효율적으로 기후변화 연구를 수행할 수 있을 것으로 사료된다.