• Journal of Korean Society of Forest Science
• /
• v.111 no.2
• /
• pp.330-344
• /
• 2022

While deforestation and forest degradation has continued globally, global society has been making efforts to prevent deforestation towards sustainable development. Reforestation in developing countries is linked to Sustainable Development Goals (SDGs) such as climate change mitigation, conservation of biodiversity, eradication of poverty and upholding of human rights. Forest official development assistance (ODA) restores the global forest land, and increases the public benefit. Bilateral forest ODA projects of the Republic of Korea have gradually increased and most of those projects have currently been concentrated in Asian countries. Selecting recipient countries for forest ODA requires more comprehensive approach since the global goals for sustainable development has been widely adapted to ODA strategic plans. We proposed potentially promising countries that are eligible for receiving 'Green ODA' in perspective of economic, social and environment to implement reducing emissions from deforestation and degradation (REDD+), conserving biodiversity, and combating desertification. As a result, the study suggests that forestry cooperation could be expanded from Asian countries more toward South America and African countries. In addition, we emphasized the need to promote convergence and integration with green technology to fundamentally solve the negative impacts of deforestation such as food, energy, water resource shortages, and forest fires. We advocated expanding bilateral ODA in the forestry sector through diversification of project activities, financial sources, and participants. Our study can contribute to the provision of basic information for establishing long-term strategies to expand bilateral cooperation in the forestry sector.

• Journal of Korean Society of Forest Science
• /
• v.111 no.4
• /
• pp.603-612
• /
• 2022

The study aims were to evaluate a machine-learning, algorithm-based, forest biomass-estimation model to estimate subnational forest biomass and to comparatively analyze REDD+ forest reference emission levels. Time-series Landsat satellite imagery and ESA Biomass Climate Change Initiative information were used to build a machine-learning-based biomass estimation model. The k-nearest neighbors algorithm (kNN), which is a non-parametric learning model, and the tree-based random forest (RF) model were applied to the machine-learning algorithm, and the estimated biomasses were compared with the forest reference emission levels (FREL) data, which was provided by the Paraguayan government. The root mean square error (RMSE), which was the optimum parameter of the kNN model, was 35.9, and the RMSE of the RF model was lower at 34.41, showing that the RF model was superior. As a result of separately using the FREL, kNN, and RF methods to set the reference emission levels, the gradient was set to approximately -33,000 tons, -253,000 tons, and -92,000 tons, respectively. These results showed that the machine learning-based estimation model was more suitable than the existing methods for setting reference emission levels.

• Current Research on Agriculture and Life Sciences
• /
• v.31 no.2
• /
• pp.113-123
• /
• 2013

The carbon-offset mechanism based on forest management has been recognized as a meaningful tool to sequestrate carbons already existing in the atmosphere. Thus, with an emphasis on the forest-originated carbon-offset mechanism, the accurate measurement of the carbon stock in forests has become important, as carbon credits should be issued proportionally with forest carbon stocks. Various remote sensing techniques have already been developed for measuring forest carbon stocks. Yet, despite the efficiency of remote sensing techniques, the final accuracy of their carbon stock estimations is disputable. Therefore, minimizing the uncertainty embedded in the application of remote sensing techniques is important to prevent questions over the carbon stock evaluation for issuing carbon credits. Accordingly, this study reviews the overall procedures of carbon stock evaluation-related remote sensing techniques and identifies the problematic technical issues when measuring the carbon stock. The procedures are sub-divided into four stages: the characteristics of the remote sensing sensor, data preparation, data analysis, and evaluation. Depending on the choice of technique, there are many disputable issues in each stage, resulting in quite different results for the final carbon stock evaluation. Thus, the establishment of detailed standards for each stageis urgently needed. From a policy-making perspective, the top priority should be given to establishinga standard sampling technique and enhancing the statistical analysis tools.

• Smart Media Journal
• /
• v.8 no.4
• /
• pp.72-79
• /
• 2019

The automated demand response (DR) program encourages consumers to participate in grid operation by reducing power consumption or deferring electricity usage at peak time automatically. However, successful deployment of the automated DR program sphere needs careful assessment of appliances load profile (ALP). To this end, the recent method estimates frequency, consistency, and peak time consumption parameters of the daily ALP to compute their potential score to be involved in the DR event. Nonetheless, as the daily ALP is subject to varying with respect to the DR time ALP, the existing method could lead to an inappropriate estimation; in such a case, inappropriate appliances would be selected at the automated DR operation that effected a consumer comfort level. To address this challenge, we propose a more proper method, in which all the three parameters are calculated using ALP that overlaps with DR time, not the total daily profile. Furthermore, evaluation of our method using two public residential electricity consumption data sets, i.e., REDD and REFIT, shows that our energy management systems (EMS) could properly match a DR target. A more optimal selection of appliances for the DR event achieves a power consumption decreasing target with minimum comfort level reduction. We believe that our approach could prevent the loss of both utility and consumers. It helps the successful automated DR deployment by maintaining the consumers' willingness to participate in the program.

• Journal of Forest and Environmental Science
• /
• v.35 no.2
• /
• pp.102-114
• /
• 2019

The issue of sustainable forest management (SFM) continues to emerge as part of the REDD+ mechanism mitigation efforts. Especially for some developing countries, such as Indonesia, forest management is required to provide benefits to the welfare of local communities in addition to forest conservation efforts. This study aims to identify the economic, social, and environmental impacts of community-based forest management (CBFM) implementation activities, which is one of the implementations of SFM at field level. The primary objectives were to find out the impacts of CBFM activities based on local people's perceptions and to identify what factors need to be considered to increase local people's satisfaction on CBFM activities. The data from 6 sub-villages was derived through surveys with local people involved in CBFM activities, interviews with a key informant, and supported by secondary data. The results of the study state that CBFM activities have increased the local people's income as well as their welfare, strengthening the local institution, and help to resolve conflicts in the study area. CBFM has also been successful in protecting forests by rehabilitating unproductive lands and increase forest cover area. By using binary logistic regression analysis, it found that income, business development opportunities, access to forests, conflict resolution, institutional strengthening, and forest rehabilitation variable significantly affected the local people's satisfaction of CBFM activities.

• Journal of Forest and Environmental Science
• /
• v.36 no.2
• /
• pp.113-121
• /
• 2020

This study was carried out in degraded and non-degraded community forests (CF) in the Terai region of Kanchanpur district, Nepal. A total of 63 concentric sample plots each of 500 ㎡ was laid in the inventory for estimating above and below-ground biomass of forests by using systematic random sampling with a sampling intensity of 0.5%. Mallotus philippinensis and Shorea robusta were the most dominant species in degraded and non-degraded CF accounting Importance Value Index (I.V.I) of 97.16 and 178.49, respectively. Above-ground tree biomass carbon in degraded and non-degraded community forests was 74.64±16.34 t ha^-1 and 163.12±20.23 t ha^-1, respectively. Soil carbon sequestration in degraded and non-degraded community forests was 42.55±3.10 t ha^-1 and 54.21±3.59 t ha^-1, respectively. Hence, the estimated total carbon stock was 152.68±22.95 t ha^-1 and 301.08±27.07 t ha^-1 in degraded and non-degraded community forests, respectively. It was found that the carbon sequestration in the non-degraded community forest was 1.97 times higher than in the degraded community forest. CO₂ equivalent in degraded and non-degraded community forests was 553 t ha^-1 and 1105 t ha^-1, respectively. Statistical analysis showed a significant difference between degraded and non-degraded community forests in terms of its total biomass and carbon sequestration potential (p<0.05). Studies indicate that the community forest has huge potential and can reward economic benefits from carbon trading to benefit from the REDD⁺/CDM mechanism by promoting the sustainable conservation of community forests.

• Journal of Forest and Environmental Science
• /
• v.30 no.2
• /
• pp.233-242
• /
• 2014

Estimating above-ground biomass is important in establishing an applicable methodology of Measurement, Reporting and Verification (MRV) System for Reducing Emissions from Deforestation and Forest Degradation-Plus (REDD+). We developed an estimation model of diameter at breast height (DBH) from IKONOS-2 image that led to above-ground biomass estimation (AGB). The IKONOS image was preprocessed with dark object subtraction and topographic effect correction prior to watershed segmentation for tree crown delineation. Compared to the field observation, the overall segmentation accuracy was 64%. Crown detection percent had a strong negative correlation to tree density. In addition, satellite-based crown area had the highest correlation with the field measured DBH. We then developed the DBH allometric model that explained 74% of the data variance. In average, the estimated DBH was very similar to the measured DBH as well as for AGB. Overall, this method can potentially be applied to estimate AGB over a relatively large and remote tropical forest in Northern Borneo.

• Journal of Climate Change Research
• /
• v.7 no.4
• /
• pp.373-381
• /
• 2016

Before the Conference of Parties (COP) 21 of the United Nations Framework Convention on Climate Change (UNFCCC) in 2015, most parties of UNFCCC had submitted their intended nationally determined contributions (INDCs) and to achieve their voluntary targets, some parties consider using international market mechanisms. As one of such mechanisms, Japan promoted its own bilateral mechanism called Joint Crediting Mechanism (JCM). In this study, feasibility studies and projects under JCM have been analyzed by project type, sector, country and region, which could provide some implications in designing Korea's future climate policy to achieve Korea's targets of 11.7% using international market mechanism in INDC. Since 2010, JCM has promoted 542 projects and feasibility studies in 44 countries according to the Institute for Global Environmental Strategies (IGES) database. Among 542 projects, about 80% were feasibility studies implying that JCM was more focused on project identification. However, current trends of JCM show that more projects will be soon implemented based on these feasibility studies. For sectoral categorization, projects were categorized into seven sectors-energy technology, energy efficiency, renewable energy, waste management, city, strategic planning and projects related to the country's efforts to reduce emissions from deforestation and forest degradation (REDD+). JCM projects were mitigation focused with more than 70% of projects were related to energy efficiency, renewable energy and energy technology. At the regional and country level, JCM is highly focused on Asia and especially, more than 100 projects were developed in Indonesia. Based on the analysis of JCM, in order to develop bilateral international mechanism for Korea, it is worthwhile to emphasize that Korea considers Asian countries as her partner. In addition, Korea may consider the collaboration with Multilateral Development Banks (MDBs) to implement projects identified by Korea and Asian partner countries. Furthermore, strategically, it is recommendable to develop jointly with Japan who has already capacity and networks with other Asian countries to mitigate GHG emissions. Such financial resources from MDBs and Japan may contribute to meet the 11.3% of GHG reduction target from abroad according to INDC of Korea.

• Journal of Climate Change Research
• /
• v.8 no.4
• /
• pp.297-303
• /
• 2017

Considering Japan's Greenhouse Gas (GHG) emissions reduction target for Fiscal Year (FY) 2030, the Joint Crediting Mechanism (JCM) was analyzed in order to estimate its significant contribution to Japan's Nationally Determined Contribution (NDC) and check its availability as a new mechanism to achieve Korea's 2030 mitigation target of 11.3% using carbon credits from international market mechanisms. The total budget for JCM Model Projects (1.2 billion JPY/yr) and JCM REDD+ Model Projects (0.8 billion JPY/yr), which are expected to deliver at least 50% of issued credits to Japan, is estimated about 21.6 billion JPY by the year 2030. This budget is about one third of the purchase of carbon credits from international carbon markets. So far, JCM credits of $378tCO_2-eq$. have been allocated to Japan, which are about 77% of the total issued credit through five-JCM Model Projects implemented from the year 2014. It is expected that Japan will obtain about $0.5MtCO_2-eq$. credits more from 100-ongoing JCM Projects, which are only 1% of Japan's NDC target through JCM credits. With regard to regular issued credits from implemented projects, expected new issued credits from pipeline projects and the less budget for JCM implementation as compared to purchasing carbon credits, JCM credits can be reached a resonable level of Japan's NDC target of $50{\times}100MtCO_2-eq$. through JCM until FY 2030.

• Molecules and Cells
• /
• v.42 no.4
• /
• pp.292-300
• /
• 2019

Immunometabolism, defined as the interaction of metabolic pathways with the immune system, influences the pathogenesis of metabolic diseases. Metformin and carbon monoxide (CO) are two pharmacological agents known to ameliorate metabolic disorders. There are notable similarities and differences in the reported effects of metformin and CO on immunometabolism. Metformin, an anti-diabetes drug, has positive effects on metabolism and can exert anti-inflammatory and anti-cancer effects via adenosine monophosphate-activated protein kinase (AMPK)-dependent and AMPK-independent mechanisms. CO, an endogenous product of heme oxygenase-1 (HO-1), can exert anti-inflammatory and antioxidant effects at low concentration. CO can confer cytoprotection in metabolic disorders and cancer via selective activation of the protein kinase R-like endoplasmic reticulum (ER) kinase (PERK) pathway. Both metformin and CO can induce mitochondrial stress to produce a mild elevation of mitochondrial ROS (mtROS) by distinct mechanisms. Metformin inhibits complex I of the mitochondrial electron transport chain (ETC), while CO inhibits ETC complex IV. Both metformin and CO can differentially induce several protein factors, including fibroblast growth factor 21 (FGF21) and sestrin2 (SESN2), which maintain metabolic homeostasis; nuclear factor erythroid 2-related factor 2 (Nrf2), a master regulator of the antioxidant response; and REDD1, which exhibits an anticancer effect. However, metformin and CO regulate these effects via different pathways. Metformin stimulates p53- and AMPK-dependent pathways whereas CO can selectively trigger the PERK-dependent signaling pathway. Although further studies are needed to identify the mechanistic differences between metformin and CO, pharmacological application of these agents may represent useful strategies to ameliorate metabolic diseases associated with altered immunometabolism.