• Clean Technology
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• v.20 no.4
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• pp.439-448
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• 2014

The potential mitigation of greenhouse gas (GHG) is studied in the Pohang steel industrial complex (PHSIC). The total GHG emission in 2010 is estimated to be in the range from 4,174,000 to 4,574,000 $tCO_2-eq$ in PHSIC. To meet the target proposed by the government, it is needed to reduce 552,000 $tCO_2-eq$ at minium by 2020. To estimate the potential amount of GHG reduction, the technologies used in the voluntary carbon reduction projects are applied to 51 companies which are subject to GHG target management. From the viewpoint of technological availability and payback period, the fuel conversion and waste heat recovery have an advantage in the short term with a possibility to reduce 160,000 $tCO_2-eq$. In the mid term, the thermal technologies in steel and iron industry have the potential to cut 229,000 $tCO_2-eq$, while the electrical technologies have the potential of 125,000 $tCO_2-eq$ reduction. The gap between the target GHG mitigation and potential reduction using the short and mid term technologies is about 38,000 $tCO_2-eq$, which should be compensated by the fundamental process innovation and the implementation of the most cutting-edge technologies including renewable energy.

• Journal of the Korean Society of Marine Environment & Safety
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• v.2 no.1
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• pp.137-150
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• 1996

Safety is built in to the activities of the prudent ship operator. Ant investment made towards this end is likely to have a measurable payback in positive terms. That there must be an investment is inevitable, because the industry at large has let things slip too far too long. Those who have not allowed it to slip too far and who are the first to recognize that safety, far from costing money, in the long term actually preserves it, will be wieners. Too many seem to have lost sight of the fact that every one hundred pennies saved is a full one hundred pennies profit. Every hundred pennies of additional revenue contributes no more then fifteen pence to profit. Environmental protection is not so simple, nor so financially attractive. Man needs the minerals of the Earth as well as the products of the soil and sea survive. We(the human race) are still not in the position, politically or financially to manage the Earth's assets without causing damage. The evidence of our damage is evident in many different parts of the Glove and will in some cases haunt several generations still to come. We have learned a lot, and continue to learn, but despite the best intentions some Government needs for their people will be at the expense of people in another region for the foreseeable future. We sailors ply the seas with the raw materials of commerce as well as the finished and part finished goods. It does not always sit well to consider too deeply what effect the ship and the cargo it carries is having, or may have, on some communities, or on the sea through which sail. None my generation can hold up his head and claim to be without blame in the pollution of the seas. Times are changing though, and Governments are turning their attention more to the protection of our planet and its precious resources. This will not be without cost. The investment will have to be made not for our benefit, but for the benefit of generations yet to come, however the cost will have to be borne by society as a whole, not by the shipping community alone. The debate surrounding the choice between engineering our way to a better tomorrow, or adapting our working practices will continue. Each method has the same goal as its target and as long as we attain the goal does it really matter how we get there?

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.245-256
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• 2021

Poor logistical infrastructure and agri-food supply chain management leads to significant food waste in logistic system. The concept of the sustainable value added agri-food chains requires defined approach to the analysis of the existing situation, possible improving strategies and also assessment of these changes impact on further development. The purpose of research is to provide scientific substantiation of theoretical and methodological principles and develop practical recommendations for the improvement of the agri-food logistics distribution system. A case study methodology is used in this article. The research framework is based on 4 steps: Value Stream Mapping (VSM), Gap and Process Analysis, Validation and Improvement Areas Definition and Imitation Modelling. This paper presents the appropriateness of LEAN logistics tools using, in particular, Value Stream Mapping (VSM) for minimizing logistic losses and Simulation Modeling of possible logistics distribution system improvement results. The algorithm of VSM analysis of the agri-food supply chain, which involves its optimization by implementing the principles of sustainable development at each stage, is proposed. The methodical approach to the analysis of possible ways for optimizing the operation of the logistics system of the agri-food distribution is developed. It involves the application of Value Stream Mapping, i.e. designing of stream maps of the creation of the added value in the agri-food supply chain for the current and future state based on the minimization of logistic losses. Simulation modeling of the investment project on time optimization in the agri-food supply chain and economic effect of proposed improvements in logistics product distribution system functioning at the level of the investigated agricultural enterprise has been determined. Improvement of logistics planning and coordination of operations in the supply chain and the innovative pre-cooling system proposed to be introduced have a 3-year payback period and almost 75-80% probability. Based on the conducted VSM analysis of losses in the agri-food supply chain, there have been determined the main points, where it is advisable to conduct optimization changes for the achievement of positive results and the significant economic effect from the proposed measures has been confirmed. In further studies, it is recommended to focus on identifying the synergistic effect of the agri-food supply chain optimization on the basis of sustainable development.

• Journal of Digital Convergence
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• v.17 no.6
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• pp.169-175
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• 2019

The aim of this study is to examine the relative economic benefits between the septic tank and exclusive sewage pipe line in designing the large building at combined sewer districts. With the case study of Lotte World Tower Building, we analyze a cost-benefit between two alternatives. The research results showed 2 years of payback period, about ￦6.17 billion of NPV, and 1.93 of B/C ratio for installing the exclusive sewage pipe line in comparison with septic tank. This results provide useful guidelines for policy establishment of the septic tank closure and for plausibility of installing exclusive sewage pipe line when constructing a large building. In the future, it will be necessary to consider additional cost-benefit analysis including burden charge borne by causers, the burden of management responsibility with a exclusive sewage pipe line, and the economic benefits of reducing odor.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.4
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• pp.45-55
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• 2022

Accelerator is a private investment institution that provides startups with comprehensive solutions to solve various difficulties such as startup facilities, funds, commercialization, securing a market etc. In addition to the role of an investor as a new startup support model, accelerators have contributed much to improvement of business ability of startups through intensive mentoring. Considering that previous studies gave weight to the determinants of investment from the perspective of investors, this study made a comparative analysis on the relative importance of determinants of investment in startups among accelerators, investors and entrepreneurs through the method of AHP. Results show that accelerators and investors regard "managerial characteristics" of startups as of the highest importance, whereas entrepreneurs think that "market characteristics" of startups are the most important. The result stems from an empirical judgment from the perspective of investors that success of startups depends on the ability of entrepreneur, and it is considered that investors evaluated marketability of startups as the most important factor in consideration of investment payback period. The result is similar to the result of previous studies on the determinants of investment determinants of angel investors and venture capitals. This paper is expected to make a contribution to the advancement of investment decision-making model for accelerators to discover startups with high possibility to grow and achieve more in incubation and investment.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.