Search results for: carbon credit

Authors: Nasir Mehmood, Muhammad Yar Khan, Anam Javeed

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This study aims to scrutinize the dynamics which tend to impact customer’s purchasing intention of Islamic credit card and nexus of product’s knowledge and religiosity with the attitude of potential Islamic credit card’s customer. The theory of reasoned action strengthened the idea that intentions due to its proven predictive power are most likely to instigate intended consumer behavior. Particularly, the study examines the relationships of perceived financial cost (PFC), subjective norms (SN), and attitude (ATT) with the intention to purchase Islamic credit cards. Using a convenience sampling approach, data have been collected from 450 customers of banks located in Rawalpindi and Islamabad. A five-point Likert scale self-administered questionnaire was used to collect the data. The data were analyzed using the Statistical Package of Social Sciences (SPSS) through the procedures of principal component and multiple regression analysis. The results suggested that customer’s religiosity and product knowledge are strong indicators of attitude towards buying Islamic credit cards. Likewise, subjective norms, attitude, and perceived financial cost have a significant positive impact on customers’ purchase intent of Islamic bank’s credit cards. This study models a useful path for future researchers to further investigate the underlined phenomenon along with a variety of psychodynamic factors which are still in its infancy, at least in the Pakistani banking sector. The study also provides an insight to the practitioners and Islamic bank managers for directing their efforts toward educating customers regarding the use of Islamic credit cards and other financial products.

Keywords: attitude, Islamic credit card, religiosity, subjective norms

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Authors: Niloofar Yousefi, Marie Alaghband, Ivan Garibay

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With the increase of credit card usage, the volume of credit card misuse also has significantly increased, which may cause appreciable financial losses for both credit card holders and financial organizations issuing credit cards. As a result, financial organizations are working hard on developing and deploying credit card fraud detection methods, in order to adapt to ever-evolving, increasingly sophisticated defrauding strategies and identifying illicit transactions as quickly as possible to protect themselves and their customers. Compounding on the complex nature of such adverse strategies, credit card fraudulent activities are rare events compared to the number of legitimate transactions. Hence, the challenge to develop fraud detection that are accurate and efficient is substantially intensified and, as a consequence, credit card fraud detection has lately become a very active area of research. In this work, we provide a survey of current techniques most relevant to the problem of credit card fraud detection. We carry out our survey in two main parts. In the first part, we focus on studies utilizing classical machine learning models, which mostly employ traditional transnational features to make fraud predictions. These models typically rely on some static physical characteristics, such as what the user knows (knowledge-based method), or what he/she has access to (object-based method). In the second part of our survey, we review more advanced techniques of user authentication, which use behavioral biometrics to identify an individual based on his/her unique behavior while he/she is interacting with his/her electronic devices. These approaches rely on how people behave (instead of what they do), which cannot be easily forged. By providing an overview of current approaches and the results reported in the literature, this survey aims to drive the future research agenda for the community in order to develop more accurate, reliable and scalable models of credit card fraud detection.

Keywords: Credit Card Fraud Detection, User Authentication, Behavioral Biometrics, Machine Learning, Literature Survey

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Authors: Hai Chien Pham, Thi Phuong Anh Vo, Chansik Park

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Risks management in construction projects is important to ensure the positive feasibility of the projects in which financial risks are most concerned while construction projects always run on a credit basis. Credit risks, therefore, require unique and technical tools to be well managed. Underwriting technique in credit risks, in its most basic sense, refers to the process of evaluating the risks and the potential exposure of losses. Risks analysis and underwriting are applied as a must in banks and financial institutions who are supporters for constructions projects when required. Recently, construction organizations, especially contractors, have recognized the significant increasing of credit risks which caused negative impacts to project performance and profit of construction firms. Despite the successful application of underwriting in banks and financial institutions for many years, there are few contractors who are applying this technique to analyze and mitigate the credit risks of their potential owners before signing contracts with them for delivering their performed services. Thus, contractors have taken credit risks during project implementation which might be not materialized due to the bankruptcy and/or protracted default made by their owners. With this regard, this study proposes a model using the underwriting technique for contractors to analyze and assess credit risks of their owners before making final decisions for the potential construction contracts. Contractor’s underwriters are able to analyze and evaluate the subjects such as owner, country, sector, payment terms, financial figures and their related concerns of the credit limit requests in details based on reliable information sources, and then input into the proposed model to have the Overall Assessment Score (OAS). The OAS is as a benchmark for the decision makers to grant the proper limits for the project. The proposed underwriting model is validated by 30 subjects in Asia Pacific region within 5 years to achieve their OAS, and then compare output OAS with their own practical performance in order to evaluate the potential of underwriting model for analyzing and assessing credit risks. The results revealed that the underwriting would be a powerful method to assist contractors in making precise decisions. The contribution of this research is to allow the contractors firstly to develop their own credit risk management model for proactively preventing the credit risks of construction projects and continuously improve and enhance the performance of this function during project implementation.

Keywords: underwriting technique, credit risk, risk management, construction project

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Authors: Margarita Gáfaro, Karelys Guzmán, Paola Poveda

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This paper establishes the theoretical aspects that explain the barriers to accessing credit for small agricultural producers in developing countries and identifies successful policy experiences to mitigate them. We will test two hypotheses. The first one is that information asymmetries, high transaction costs and high-risk exposure limit the supply of credit to small agricultural producers in developing countries. The second hypothesis is that low levels of financial education and productivity and high uncertainty about the returns of agricultural activity limit the demand for credit. To test these hypotheses, a review of the theoretical and empirical literature on access to rural credit in developing countries will be carried out. The first part of this review focuses on theoretical models that incorporate information asymmetries in the credit market and analyzes the interaction between these asymmetries and the characteristics of the agricultural sector in developing countries. Some of the characteristics we will focus on are the absence of collateral, the underdevelopment of the judicial systems and insurance markets, and the high dependence on climatic factors of production technologies. The second part of this review focuses on the determinants of credit demand by small agricultural producers, including the profitability of productive projects, security conditions, risk aversion or loss, financial education, and cognitive biases, among others. There are policies that focus on resolving these supply and demand constraints and managing to improve credit access. Therefore, another objective of this paper is to present a review of effective policies that have promoted access to credit for smallholders in the world. For this, information available in policy documents will be collected. This information will be complemented by interviews with officials in charge of the design and execution of these policies in a subset of selected countries. The information collected will be analyzed in light of the conceptual framework proposed in the first two parts of this section. The barriers to access to credit that each policy attempts to resolve and the factors that could explain its effectiveness will be identified.

Keywords: agricultural economics, credit access, smallholder, developing countries

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Authors: Adewale Oladapo, C. A. Afolami

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The study analyzed the effect of micro credit access on poverty reduction among small scale women entrepreneurs in Ondo state, Nigeria. Primary data were collected in a cross-sectional survey of 100 randomly selected woman entrepreneurs. These were drawn in multistage sampling process covering four local government areas (LGAS). Data collected include socio economics characteristics of respondents, access to micro credit, sources of micro credit, and constraints faced by the entrepreneur in sourcing for micro credit. Data were analyzed using descriptive statistics, Foster, Greer and Thorbecke (FGT) index of poverty measure, Gini coefficients and probit regression analysis. The study found that respondents sampled for the survey were within the age range of 31-40 years with mean age 38.6%. Mostly (56.0%) of the respondents were educated to the tune of primary school. Majority (87.0%) of the respondents were married with fairly large household size of (4-5). The poverty index analysis revealed that most (67%) of the sample respondents were poor. The result of the Probit regression analyzed showed that income was a significant variable in micro credit access, while the result of the Gini coefficient revealed a very high income inequality among the respondents. The study concluded that most of the respondents were poor and return on investment (income) was an important variable that increased the chance of respondents in sourcing for micro-credit loan and recommended that income realized by entrepreneur should be properly documented to facilitate loan accessibility.

Keywords: entrepreneurs, income, micro-credit, poverty

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Authors: Grace Rachid, Mutasem El Fadel, Mahmoud Al Hindi, Ibrahim Jamali, Daniel Abdel Nour

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This study examines the feasibility of indirect solar desalination in oil producing countries in the Middle East and North Africa (MENA) region. It relies on value engineering (VE) and cost-benefit with sensitivity analyses to identify optimal coupling configurations of desalination and solar energy technologies. A comparative return on investment was assessed as a function of water costs for varied plant capacities (25,000 to 75,000 m3/day), project lifetimes (15 to 25 years), and discount rates (5 to 15%) taking into consideration water and energy subsidies, land cost as well as environmental externalities in the form of carbon credit related to greenhouse gas (GHG) emissions reduction. The results showed reverse osmosis (RO) coupled with photovoltaic technologies (PVs) as the most promising configuration, robust across different prices for Brent oil, discount rates, as well as different project lifetimes. Environmental externalities and subsidies analysis revealed that a 16% reduction in existing subsidy on water tariffs would ensure economic viability. Additionally, while land costs affect investment attractiveness, the viability of RO coupled with PV remains possible for a land purchase cost < $ 80/m2 or a lease rate < $1/m2/yr. Beyond those rates, further subsidy lifting is required.

Keywords: solar energy, desalination, value engineering, CBA, carbon credit, subsidies

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Authors: Titus Wuyah Yunana

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This study analyzed the impact of commercial agricultural credit scheme on income of beneficiary farmers in Kaduna State using the Net farm income and double difference method. A questionnaire was used to source the data from 306 farmers comprising of 153 beneficiaries and 153 non-beneficiaries. The results indicated that the net farm income of the commercial agricultural credit scheme beneficiaries increases from N15,006,352.00 before scheme to N24,862,585.00 after the first and the second phases of the scheme. There was also an increase in the net farm income of the non-beneficiaries from N9, 670,385.40 to N14, 391,469.00 during the scheme. The double difference method analysis indicated a positive mean income difference value between beneficiaries and nonbeneficiaries after the first and the second phases of the scheme. The study recommends expansion in the number of beneficiaries and efficient allocation and utilization of the resources. The government should also introduce more programs that will assist the farmers to increase their productivity, income and the economy as a whole.

Keywords: agriculture, credit scheme, farmers, income, beneficiary

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Authors: Soon Ok Jo, Han Kyu Jeung, Si Woo Park

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With the process of consistent expansion of carbon fiber in width (Carbon Fiber Tow Spreading Technique), it can be expected that such process can enhance the production of carbon fiber reinforced composite material and quality of the product. In this research, the method of mechanically expanding carbon fiber and increasing its width was investigated by using various geometric rolls. In addition, experimental type of carbon fiber expansion device was developed and tested using 12K carbon fiber. As a result, the effects of expansion of such fiber under optimized operating conditions and geometric structure of an elliptical roll, were analyzed.

Keywords: carbon fiber, tow spreading fiber, pre-preg, roll structure

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Authors: Abdulmagid A. Khattabi, Ahmed A. Hablous, Mofied M. Elnemry

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The carbon steel is of one of the most common mineral materials used in engineering and industrial applications in order to have access to the required mechanical properties, especially after the change of carbon ratio, but this may lead to stimulate corrosion. It has been used in models of solids with different carbon ratios such as 0.05% C, 0.2% C, 0.35% C, 0.5% C, and 0.65% C and have been studied using three testing durations which are 4 weeks, 6 weeks, and 8 weeks and among different corrosion environments such as atmosphere, fresh water, and salt water. This research is for the purpose of finding the effect of the carbon content on the corrosion resistance of steels in different corrosion medium by using the weight loss technique as a function of the corrosion resistance. The results that have been obtained through this research shows that a correlation can be made between corrosion rates and steel's carbon content, and the corrosion resistance decreases with the increase in carbon content.

Keywords: proportion of carbon in the steel, corrosion rate, erosion, corrosion resistance in carbon-steel

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Authors: Adewale Oladapo, Carolyn A. Afolami

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Agricultural production should be the major financial contributor to the Nigerian economy; however, the petroleum sector had taken the importance attached to this sector. The situation tends to be more worsening unless necessary attention is given to adequate credit supply among food crop farmers. This research analyses the effect of credit use on the technical efficiency of cassava farmers in Ondo State, Nigeria. Primary data were collected from two hundred randomly selected cassava farmers through a multistage sampling procedure in the study area. Data were analysed using descriptive statistics and stochastic frontier analysis (SFA). Findings revealed that 95.0% of the farmers were male while 56.0% had no formal education and were married. The SFA showed that cassava farmer’s efficiency increased with farm size, herbicide and planting material at 5%,10% and 1% respectively but decreased with fertilizer application at 1% level while farmers’ age, education, household size, experience and access to credit increased technical inefficiency at 10%. The study concluded that cassava farmers are technically inefficient in the use of farm resources and recommended that adequate and workable agricultural policy measures that will ensure availability and efficient fertilizer distribution should be put in place to increase efficiency. Furthermore, the government should encourage youth participation in cassava production and ensure improvement in farmer’s access to credit to increase farmer’s technical efficiency.

Keywords: agriculture, access to credit, cassava farmers, technical efficiency

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Authors: Eneken Titov, Ly Hobe

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The collaboration between the universities and companies has been a challenging topic for many years, and although we have many good experiences, those seem to be single examples between one university and company. In Ülemiste Smart City in Estonia, the new initiative was started in 2020 fall, when five Estonian universities cooperated, led by the Ülemiste City developing company Mainor, intending to provide charge-free university courses for the Ülemiste City companies and their employees to encourage university-company wider collaboration. Every Ülemiste City company gets a certain number of free educational credit hours per year to participate in university courses. A functional and simple web platform was developed to mediate university courses for the companies. From January 2021, the education credit platform is open for all Ülemiste City companies and their employees to join, and universities offer more than 9000 hours of courses (appr 150 ECTS). Just two months later, more than 20% of Ülemiste City companies (82 out of 400) have joined the project, and their employees have registered for more than in total 3000 hours courses. The first results already show that the project supports the university marketing and the continuous education mindset in general, whether 1/4 of the courses are paid courses (e.g., when the company is out of free credit).

Keywords: education, educational credit, smart city, university-industry collaboration

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Authors: Aida Alemany, Laura Ballester, Ana González-Urteaga

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From 2007 subprime crisis to the recent Eurozone debt crisis the European banking industry has experienced a terrible financial instability situation with increasing levels of CDS spreads (used as a proxy of credit risk). This paper investigates whether volatility transmission channels in European banking markets have changed after three significant crises’ events during the period January 2006 to March 2013. The global financial crisis is characterized by a unidirectional volatility shocks spillovers effect in credit risk from inside to outside the Eurozone. By contrast, the Eurozone debt crisis is revealed to be local in nature with the euro as the key element suggesting a market fragmentation between distressed peripheral and non-distressed core Eurozone countries, whereas retaining the local currency have acted as a firewall. With these findings we are able to shed light on the impact of the different crises on the European banking credit risk dynamics.

Keywords: CDS spreads, credit risk, volatility spillovers, financial crisis

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Authors: M. Chumburidze, N. Salia, T. Namchevadze

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This work deal with case processing problems in business. The task of strategic credit requirements management of cases portfolio is discussed. The information model of credit requirements in a binary tree diagram is considered. The algorithms to solve issues of prioritizing clusters of cases in business have been investigated. An implementation of priority queues to support case management operations has been presented. The corresponding pseudo codes for the programming application have been constructed. The tools applied in this development are based on binary tree ordering algorithms, optimization theory, and business management methods.

Keywords: credit network, case portfolio, binary tree, priority queue, stack

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Authors: Sarbjit Singh

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This framework development explored the status of GSCM in manufacturing SMEs and concluded that there was a significant gap w.r.t carbon emissions measurement in the supply chain activities. The measurement of carbon emissions within supply chains is important green initiative toward its reduction. The majority of the SMEs were facing the problem to quantify the green house gas emissions in its supply chain & to make it a low carbon supply chain or GSCM. Thus, the carbon management initiatives were amalgamated with the supply chain activities in order to measure and reduce the carbon emissions, confirming the GHG protocol scopes. Henceforth, it covers the development of carbon management software (CMS) tool to quantify carbon emissions for effective carbon management. This tool is cheap and easy to use for the industries for the management of their carbon emissions within the supply chain.

Keywords: w.r.t carbon emissions, carbon management software, supply chain management, Indian Industry

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Authors: A. Bolaji Adesoye, Alimi Olorunfemi

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This paper examines the impact of small and medium-scale enterprises (SMEs) credit financing and financial market development and their shocks on the output growth of Nigeria. The study estimated a VAR model for Nigeria using 1970-2013 annual data series. Unit root tests and cointegration are carried out. The study also explores IRFs and FEVDs in a system that includes output, commercial bank loan to SMEs, domestic credit to private sector by banks, money supply, lending rate and investment. Findings suggest that shocks in commercial bank credit to SMEs has a major impact on the output changes of Nigeria. Money supply shocks also have a sizeable impact on output growth variations amidst other financial instruments. Lastly, neutrality of investment does not hold in Nigeria as it also has impact on output fluctuations.

Keywords: SMEs financing, financial development, investment, output, Nigeria

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Authors: Noelia Collado-Rodriguez

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The Mortgage Credit Directive 2014/17/UE should have been transposed the 21st of March of 2016. However, in Spain not only we did not meet the deadline, but currently we just have a preliminary draft of the so-called Mortgage Credit Act. Before we analyze the preliminary draft from the standpoint of the responsible lending principle, we should point out that this preliminary draft is not a consumer law statute. Through the text of the preliminary draft we cannot see any reference to the consumer, but we see references to the borrower. Furthermore, and more important, the application of this statute would not be, according to its text, circumscribed to borrowers who address the credit to a personal purpose. Instead, it seems that the preliminary draft aims to be one more of the rules of banking transparency that already exists in the Spanish legislation. In this sense, we can also mention that the sanctions contained in the preliminary draft are referred to these laws of banking ordination and oversight – where the rules of banking transparency belong –. This might be against the spirit of the Mortgage Credit Directive, which allows the extension of its scope to credits aimed to acquire other immovable property beyond the residential one. However, the borrower has to be a consumer accordingly with the Directive. It is quite relevant that the prospective Spanish Mortgage Credit Act might not be a consumer protection statute; specially, from the perspective of the responsible lending principle. The responsible lending principle is a consumer law principle, which is based on the structural weakness of the consumer’s position in the relationship with the creditor. Therefore, it cannot surprise that the Spanish preliminary draft does not state any of the pre contractual conducts that express the responsible lending principle. We are referring to the lender’s duty to provide adequate explanations; the consumer’s suitability test; the lender’s duty to assess consumer’s creditworthiness; the consultation of databases to perform the creditworthiness assessment; and the most important, the lender’s prohibition to grant credit in case of a negative creditworthiness assessment. The preliminary draft just entitles the Economy Ministry to enact provisions related to those topics. Thus, the duties and rules derived from the responsible lending principle included in the EU Directive will not have legal character in Spain, being mere administrative regulations. To conclude, the two main questions that come up after reading the Spanish Mortgage Credit Act preliminary draft are, in the first place, what kind of consequences might arise from the Mortgage Credit Act if finally it is not a consumer law statute. And in the second place, what might be the consequences for the responsible lending principle of being developed by administrative regulations instead of by legislation.

Keywords: consumer credit, consumer protection, creditworthiness assessment, responsible lending

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Authors: Parisa Golbayani, Dan Wang, Ionut¸ Florescu

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In this work we implement machine learning techniques to financial statement reports in order to asses company’s credit rating. Specifically, the work analyzes the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor’s. The paper focuses on companies from the energy, financial, and healthcare sectors in the US. The goal of this analysis is to improve application of machine learning algorithms to credit assessment. To accomplish this, the study investigates three questions. First, we investigate if the algorithms perform better when using a selected subset of important features or whether better performance is obtained by allowing the algorithms to select features themselves. Second, we address the temporal aspect inherent in financial data and study whether it is important for the results obtained by a machine learning algorithm. Third, we aim to answer if one of the four particular neural network architectures considered consistently outperforms the others, and if so under which conditions. This work frames the problem as several case studies to answer these questions and analyze the results using ANOVA and multiple comparison testing procedures.

Keywords: convolutional neural network, long short term memory, multilayer perceptron, credit rating

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Authors: Sami Mestiri, Abdeljelil Farhat

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The aim of this current paper is to predict the credit risk of banks in Tunisia, over the period (2000-2005). For this purpose, two methods for the estimation of the logistic regression model with random effects: Penalized Quasi Likelihood (PQL) method and Gibbs Sampler algorithm are applied. By using the information on a sample of 528 Tunisian firms and 26 financial ratios, we show that Bayesian approach improves the quality of model predictions in terms of good classification as well as by the ROC curve result.

Keywords: forecasting, credit risk, Penalized Quasi Likelihood, Gibbs Sampler, logistic regression with random effects, curve ROC

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Authors: Paramita Nur Kurniati, Stanislaus Riyanta

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In the midst of the heightened usage of electronic money (e-money), Indonesian government expenditure is yet governed through cash-basis transactions. This conventional system brings about a number of potential risks and obstacles to operational conduct, including state financial liquidity issue. Consequently, Ministry of Finance is currently establishing the cashless payment methods for State Budget (APBN). Included in those advance methods is credit card facility as a government expenditure payment scheme. This policy is one of the innovations within the public sector learned from other countries’ best practices. Moreover, this particular method is already prominent within the private-sector realm. Qualitative descriptive analysis technique is implemented to evaluate the contemporary innovation of using government credit card in the path towards cashless society. This approach is expected to generate several benefits for the government, particularly in minimizing corruption within the state financial management. Effective coordination among policy makers and policy implementers is essential for the success of this policy’s exercise, without neglecting prudence and public transparency aspects. Government credit card usage shall be the potent resolution for enhancing the government’s overall public service performance.

Keywords: cashless basis, cashless society, government credit card, public sector innovation

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Authors: Liu Zhiyuan, Sun Zongdi

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In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network model. Finally, after the training of BP neural network, the prediction values of Shanghai carbon trading price and trading volume are obtained, and the model is tested.

Keywords: Carbon trading price, carbon trading volume, BP neural network model, Shanghai City

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Authors: R. H. Fattepur, Sameer R. Fattepur, D. K. Sreekantha

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Dairy Farming is one of the key industries in India. India is the leading producer and also the consumer of milk, milk-based products in the world. In this paper, we have attempted to the replace the human expert system and to develop an artificial expert system prototype to increase the speed and accuracy of decision making dairy farming credit risk evaluation. Fuzzy logic is used for dealing with uncertainty, vague and acquired knowledge, fuzzy rule base method is used for representing this knowledge for building an effective expert system.

Keywords: expert system, fuzzy logic, knowledge base, dairy farming, credit risk

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Authors: Hyunchul Ahn, William X. S. Wong

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Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: corporate credit rating prediction, Feature selection, genetic algorithms, instance selection, multiclass support vector machines

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Authors: Young Nam Chun, Mun Sup Lim

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Carbon dioxide and methane, the major components of biomass pyrolysis/gasification gas and biogas, top the list of substances that cause climate change, but they are also among the most important renewable energy sources in modern society. The purpose of this study is to convert carbon dioxide and methane into high-quality energy using char and commercial activated carbon obtained from biomass pyrolysis as a microwave receptor. The methane reforming process produces hydrogen and carbon. This carbon is deposited in the pores of the microwave receptor and lowers catalytic activity, thereby reducing the methane conversion rate. The deposited carbon was removed by carbon gasification due to the supply of carbon dioxide, which solved the problem of microwave receptor inactivity. In particular, the conversion rate remained stable at over 90% when the ratio of carbon dioxide to methane was 1:1. When the reforming results of carbon dioxide and methane were compared after fabricating nickel and iron catalysts using commercial activated carbon as a carrier, the conversion rate was higher in the iron catalyst than in the nickel catalyst and when no catalyst was used. 

Keywords: microwave, gas reforming, greenhouse gas, microwave receptor, catalyst

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Authors: Adel Elomri

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The notion of carbon footprinting is ever more widespread as companies are becoming increasingly aware that tackling carbon emissions and being seen to do so is a key issue to face governments, customers and other stakeholders’ pressures towards delivering environmentally friendly services and activities. In this contest, many firms are taking self-initiatives to reduce their own carbon emissions while some other are constrained to obey to different regulations/policies (e.g. carbon tax or carbon Cap) designed by higher authorities targeting a low-carbon environment. Using buyer-vendor framework, this paper provides some insights on how effective are these self-initiatives and regulatory policies when only concerning firms at the individual level and not the whole supply chain they are part of. We show that when firms individually engage in reducing their direct carbon emissions either under self-initiatives or regulatory policy, an opposite expected outcome resulting in a higher global supply chain emission can occur. This effect is referred to as the carbon seesaw effect. Moreover, we show that coordinating or centralizing the supply chain -contrary to what one may think at first- is not often the appropriate solution to get rid of this effect.

Keywords: carbon emissions, supply chain coordination, EOQ, sustainable operations

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Authors: Richard, Iyan Subiyanto, Chairul Hudaya

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Activated carbon has been playing a significant role for many applications, especially in energy storage devices. However, commercially activated carbons generally require complicated processes and high production costs. Therefore, in this study, an activated carbon originating from green coke waste, that is economically affordable will be used as a carbon source. To synthesize activated carbon, KOH as an activator was employed with variation of C:KOH in ratio of 1:2, 1:3, 1:4, and 1:5, respectively, with an activation temperature of 700°C. The characterizations of activated carbon are obtained from Scanning Electron Microscopy, Energy Dispersive X-Ray, Raman Spectroscopy, and Brunauer-Emmett-Teller. The optimal activated carbon sample with specific surface area of 2,024 m²/g with high carbon content ( > 80%) supported by the high porosity carbon image obtained by SEM was prepared at C:KOH ratio of 1:4. The result shows that the synthesized activated carbon would be an ideal choice for energy storage device applications. Therefore, this study is expected to reduce the costs of activated carbon production by expanding the utilization of petroleum waste.

Keywords: activated carbon, energy storage material, green coke, specific surface area

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Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

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Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

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Authors: Mousaab Alrhmoun, Magali Casellas, Michel Baudu, Christophe Dagot

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The aim of the study is to improve removal of trace organic contaminants dissolved in activated sludge by the process of filtration with membrane bioreactor combined with modified activated carbon, for a maximum removal of organic compounds characterized by low molecular weight. Special treatment was conducted in laboratory on activated carbon. Tow reaction parameters: The pH of aqueous middle and the type of granular activated carbon were very important to improve the removal and to motivate the electrostatic Interactions of organic compounds with modified activated carbon in addition to physical adsorption, ligand exchange or complexation on the surface activated carbon. The results indicate that modified activated carbon has a strong impact in removal 21 of organic contaminants and in percentage of 100% of the process.

Keywords: activated carbon, organic micropolluants, membrane bioreactor, carbon

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Authors: Ehsan Heidaryan

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Gas hydrate sediments are ice like permafrost in deep see and oceans. Methane production in sequestration process and reducing atmospheric carbon dioxide, a main source of greenhouse gas, has been accentuated recently. One focus is capture, separation, and sequestration of industrial carbon dioxide. As a hydrate former, carbon dioxide forms hydrates at moderate temperatures and pressures. This phenomenon could be utilized to capture and separate carbon dioxide from flue gases, and also has the potential to sequester carbon dioxide in the deep seabeds. This research investigated the effect of synthetic surfactants on carbon dioxide hydrate formation, catalysis and consequently, methane production from hydrate permafrosts in sediments. It investigated the sequestration potential of carbon dioxide hydrates in ocean sediments. Also, the catalytic effect of biosurfactants in these processes was investigated.

Keywords: carbon dioxide, hydrate, sequestration, surfactant

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Authors: Fuchun Li, Hong Xiao

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We propose a new early warning model for predicting financial stress events for a given future time. In this model, we examine whether credit conditions play an important role as a nonlinear propagator of shocks when predicting the likelihood of occurrence of financial stress events for a given future time. This propagation takes the form of a threshold regression in which a regime change occurs if credit conditions cross a critical threshold. Given the new early warning model for financial stress events, we evaluate the performance of this model and currently available alternatives, such as the model from signal extraction approach, and linear regression model. In-sample forecasting results indicate that the three types of models are useful tools for predicting financial stress events while none of them outperforms others across all criteria considered. The out-of-sample forecasting results suggest that the credit-regime switching model performs better than the two others across all criteria and all forecasting horizons considered.

Keywords: cut-off probability, early warning model, financial crisis, financial stress, regime-switching model, forecasting horizons

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Authors: Liyu Adhi Kasari Sulung

Abstract:

Poverty has been a major problem in Indonesia. Islamic micro finance (IMF) named Baitul Maal Wat Tamwil (Bmt) plays a prominent role to eradicate this. Indonesia as the biggest muslim country has many successful applied products such as worldwide adopt group-based lending approach, flexible financing for farmers, and gold pawning. The Problems related to these models are operation risk management and internal control system (ICS). A proper ICS will help an organization in preventing the occurrence of bad financing through detecting error and irregularities in its operation. This study aims to seek a proper risk management scheme of credit system in Bmt and internal control system’s rank for every stage. Risk management variables are obtained at the first In-Depth Interview (IDI) and Focus Group Discussion (FGD) with Shariah supervisory boards, boards of directors, and operational managers. Survey was conducted covering nationwide data; West Java, South Sulawesi, and West Nusa Tenggara. Moreover, Content analysis is employed to build the relationship among these variables. Research Findings shows that risk management Characteristics in Indonesia involves ex ante, credit process, and ex post strategies to deal with risk in credit system. Ex-ante control consists of Shariah compliance, survey, group leader reference, and islamic forming orientation. Then, credit process involves saving, collateral, joint liability, loan repayment, and credit installment controlling. Finally, ex-post control includes shariah evaluation, credit evaluation, grace period and low installment provisions. In addition, internal control order sort three stages by its priority; Credit process as first rank, then ex-post control as second, and ex ante control as the last rank.

Keywords: internal control system, islamic micro finance, poverty, risk management

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