• Journal of Environmental Policy
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• v.8 no.1
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• pp.1-30
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• 2009

Emission trading schemes, exemplified by the EU Emission Trading Scheme, have been playing active roles in mitigating greenhouse gas emissions since the Kyoto Protocol employed an emission trading as one of the cost-effective mechanisms. The objective of this study is to investigate potential integration of forestry offsets in designing an emission trading scheme in South Korea. First, the study found feasible scopes in which forestry sectors can take part by analyzing five emission trading schemes: EU Emission Trading Scheme, Chicago Climate Exchange, New South Wales Greenhouse Gas Abatement Scheme, New Zealand Emission Trading Scheme, and Regional Greenhouse Gas Initiative. The rationale of including forestry offsets in a domestic emission trading scheme was derived from the fact that forestry offset credits can provide cost-effective ways for market participants to commit their emission targets and expand abatement activities through reducing greenhouse gases in other geographical locations as well as other industrial sectors. Even though forestry offset credits have risks induced by their technical complexities in terms of accounting, additionality, and leakage, the integration of forestry offset credits into an emission trading scheme would be able to provide positive opportunities both to forestry sectors and other industrial sectors. In addition, there are technical questions which need to be answered in order to maintain these opportunities.

• Journal of Environmental Policy
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• v.11 no.3
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• pp.149-170
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• 2012

The United Nations Climate Change Conference, Durban 2011, ended on December 12, 2011, 36 hours over its schedule, delivering the Durban Package, which consisted of, inter alia, the extension of the period for Kyoto Protocol term and the launch of Ad-hoc working Group on the Durban Platform for Enhanced Action. Despite the positive progress made in Durban, the future of post-2012 climate regime still seems cloudy. Before the Durban conference, some of Annex I countries with emissions reduction commitment under the Kyoto Protocol's first commitment period openly declared their intention not to participate in the second one, reducing the effectiveness of Durban agreement. Parties to the conference have a long list of difficult issues disturbing the materialization of the new legal agreement in 2020 such as level of mitigation targets of individual countries and legal nature of their commitment. Given this uncertainty, the Korean government should reinforce its domestic climate policies rather than settling in the fact that it remains as a non-Annex I county party under the Durban Agreement due to the extension of the Kyoto Protocol period. Domestically, it needs to continue to raise the public awareness for rigorous climate policies to transit its economy to low carbon pathway which reduces the country's dependency on fossil fuel in the long term. It is also important to implement cost effective climate policies to cope with domestic resistance and international competitiveness. Internationally, its priority would be working for trust-building in the on-going negotiation meetings to encourage meaningful participation of all parties.

• Journal of Energy Engineering
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• v.23 no.2
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• pp.74-82
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• 2014

The government is planning to expand the bio-gas supply as a method for mitigating greenhouse gas emissions to deal with climate change. By means of a policy instrument, the government is considering an introduction of the Renewable Fuel Standard (RFS) whose targets include bio-gas. This paper attempts to look into the economic effects of expanding the bio-gas supply by applying an input-output (I-O) analysis using a 2011 I-O table. The bio-gas supply sector consists of liquefied petroleum gas supply sector and city gas supply sector, based on the tenets of introducing the RFS. The production-inducing effect, value-added creation effect, and employment-inducing effect of the bio-gas sector are analyzed. The supply shortage effect and the price pervasive effect are also investigated. The results show that the production or investment of 1.0 won in the bio-gas supply sector induces the production of 1.0539 won and the value-added of 0.1998 won in the national economy. Moreover, the production or investment of 1.0 billion won, supply shortage of 1.0 won, and a price increase of 10.0% in the bio-gas supply sector touch off the employment of 0.5279 person, 1.6229 won, and an increase in overall price level by 0.0183%, respectively.

• Korean Journal of Environmental Agriculture
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• v.40 no.3
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• pp.211-218
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• 2021

BACKGROUND: Fine particulate matter (PM_2.5) is produced by chemical reactions between various precursors. PM_2.5 has been found to create greater human risk than particulate matter (PM₁₀), with diameters that are generally 10 micrometers and smaller. Ammonia (NH₃) and nitrogen oxides (NOx) are the sources of secondary generation of PM_2.5. These substances generate PM_2.5 through some chemical reactions in the atmosphere. Through chemical reactions in the atmosphere, NH₃ generates PM_2.5. It is the causative agent of PM_2.5. In 2017 the annual ammonia emission recorded from the agricultural sector was 244,335 tons, which accounted for about 79.3% of the total ammonia emission in Korea in that year. To address this issue, the agricultural sector announced the inclusion of reducing fine particulate matter and ammonia emissions by 30% in its targets for the year 2022. This may be achieved through analyses of its emission characteristics by monitoring the PM_2.5 and NH₃. METHODS AND RESULTS: In this study, the PM_2.5 concentration was measured real-time (every 1 hour) by using beta radiation from the particle dust measuring device (Spirant BAM). NH₃ concentration was analyzed real-time by Cavity Ring-Down Spectroscopy (CRDS). The concentrations of ozone (O₃) and nitrogen dioxide (NO₂) were continuously measured and analyzed for the masses collected on filter papers by ultraviolet photometry and chemiluminescence. CONCLUSION: This study established air pollutant monitoring system in agricultural areas to analyze the NH₃ emission characteristics. The amount of PM_2.5 and NH₃ emission in agriculture was measured. Scientific evidence in agricultural areas was obtained by identifying the emission concentration and characteristics per season (monthly) and per hour.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.