Does foreign direct investments impair the ecological footprint? New evidence from the panel quantile regression

This study examines the impact of foreign direct investments on ecological footprint along with other explanatory variables of 92 countries from the year 2001 to 2016. Here, we applied the panel quantile regression model to meet the purpose of our study as it considers unobserved country heterogeneity, unlike other statistical methods. The study reveals that foreign direct investment has a positive relationship with the ecological footprint in each quantile except one, which proves the constancy of the pollution haven hypothesis. Moreover, we also tried to detect the impact of economic growth, manufacturing value-added, the percentage of world exports, and institutional quality on the ecological footprint in this study. The findings of this study also reveal that economic growth and manufacturing value-added are negatively associated with the ecological footprint. With respect to the percentage of world exports and institutional quality, we found a positive relationship with the ecological footprint. From the result of our study, different policy implications have been proposed for host countries and foreign investors on improving the economy through foreign direct investment with minimal ecological footprint.

     

Handling data uncertainties in event tree analysis

Event tree analysis (ETA) is an established risk analysis technique to assess likelihood (in a probabilistic context) of an accident. The objective data available to estimate the likelihood is often missing (or sparse), and even if available, is subject to incompleteness (partial ignorance) and imprecision (vagueness). Without addressing incompleteness and imprecision in the available data, ETA and subsequent risk analysis give a false impression of precision and correctness that undermines the overall credibility of the process. This paper explores two approaches to address data uncertainties, namely, fuzzy sets and evidence theory, and compares the results with Monte Carlo simulations. A fuzzy-based approach is used for handling imprecision and subjectivity, whereas evidence theory is used for handling inconsistent, incomplete and conflicting data. Application of these approaches in ETA is demonstrated using the example of an LPG release near a processing facility.     

Differential livelihood adaptation to social-ecological change in coastal Bangladesh

Regional Environmental Change - Social-ecological changes, brought about by the rapid growth of the aquaculture industry and the increased occurrence of climatic stressors, have significantly...     

Cadmium contamination in agricultural soils of Bangladesh and management by application of organic amendments: evaluation of field assessment and pot experiments

Environmental Geochemistry and Health - In recent years, cadmium (Cd) contamination in agricultural soils and its subsequent transfer to crops is one of the high-priority environmental and public...     

Analyzing system safety and risks under uncertainty using a bow-tie diagram: An innovative approach

A bow-tie diagram combines a fault tree and an event tree to represent the risk control parameters on a common platform for mitigating an accident. Quantitative analysis of a bow-tie is still a major challenge since it follows the traditional assumptions of fault and event tree analyses. The assumptions consider the crisp probabilities and “independent” relationships for the input events. The crisp probabilities for the input events are often missing or hard to come by, which introduces data uncertainty. The assumption of “independence” introduces model uncertainty. Elicitation of expert's knowledge for the missing data may provide an alternative; however, such knowledge incorporates uncertainties and may undermine the credibility of risk analysis.This paper attempts to accommodate the expert's knowledge to overcome missing data and incorporate fuzzy set and evidence theory to assess the uncertainties. Further, dependency coefficient-based fuzzy and evidence theory approaches have been developed to address the model uncertainty for bow-tie analysis. In addition, a method of sensitivity analysis is proposed to predict the most contributing input events in the bow-tie analysis. To demonstrate the utility of the approaches in industrial application, a bow-tie diagram of the BP Texas City accident is developed and analyzed.     

Handling and updating uncertain information in bow-tie analysis

Bow-tie analysis is a fairly new concept in risk assessment that can describe the relationships among different risk control parameters, such as causes, hazards and consequences to mitigate the likelihood of occurrence of unwanted events in an industrial system. It also facilitates the performance of quantitative risk analysis for an unwanted event providing a detailed investigation starting from basic causes to final consequences. The credibility of quantitative evaluation of the bow-tie is still a major concern since uncertainty, due to limited or missing data, often restricts the performance of analysis. The utilization of expert knowledge often provides an alternative for such a situation. However, it comes at the cost of possible uncertainties related to incompleteness (partial ignorance), imprecision (subjectivity), and lack of consensus (if multiple expert judgments are used). Further, if the bow-tie analysis is not flexible enough to incorporate new knowledge or evidence, it may undermine the purpose of risk assessment.Fuzzy set and evidence theory are capable of characterizing the uncertainty associated with expert knowledge. To minimize the overall uncertainty, fusing the knowledge of multiple experts and updating prior knowledge with new evidence are equally important in addition to addressing the uncertainties in the knowledge. This paper proposes a methodology to characterize the uncertainties, aggregate knowledge and update prior knowledge or evidence, if new data become available for the bow-tie analysis. A case study comprising a bow-tie for a typical offshore process facility has also been developed to describe the utility of this methodology in an industrial environment.     

Prospect of castor oil biodiesel in Bangladesh: Process development and optimization study

In this study, castor oil (CO) has been investigated as a potential source for biodiesel production in Bangladesh. Castor oil has been extracted from the seeds by mechanical press and the Soxhlet extraction method. Maximum oil content of 55.7% has been found by the Soxhlet extraction method. The physicochemical properties such as free fatty acid (FFA) content, kinematic viscosity, saponification value, and density of the oil have been measured by different standard methods. The FFA content and viscosity have been found considerably higher such as 33.5% and 253 mm²/s, respectively. Biodiesel has been prepared using a three-step method comprising of saponification of oil followed by acidification of the soap and esterification of FFA. The overall yield of FFA from CO is found to be around 89.2%. The final step is esterification that produces fatty acid methyl ester (FAME) and a maximum 97.4% conversion of FFA to biodiesel has been observed. The effect of the oil to methanol molar ratio, catalyst concentration, reaction temperature, and time has been investigated for esterification reaction and optimized using the response surface methodology. ¹H NMR of crude castor oil and castor oil methyl ester (COME) was studied and analyzed that confirms the complete conversion of castor oil to biodiesel. Finally, the biodiesel, produced under optimum conditions, was characterized using the various standard method and found comparable with petro-diesel and biodiesel standard.     

Methodology for computer aided fuzzy fault tree analysis

Probabilistic risk assessment (PRA) is a comprehensive, structured and logical analysis method aimed at identifying and assessing risks of complex process systems. PRA uses fault tree analysis (FTA) as a tool to identify basic causes leading to an undesired event, to represent logical dependency of these basic causes in leading to the event, and finally to calculate the probability of occurrence of this event.To conduct a quantitative fault tree analysis, one needs a fault tree along with failure data of the basic events (components). Sometimes it is difficult to have an exact estimation of the failure rate of individual components or the probability of occurrence of undesired events due to a lack of sufficient data. Further, due to imprecision in basic failure data, the overall result may be questionable. To avoid such conditions, a fuzzy approach may be used with the FTA technique. This reduces the ambiguity and imprecision arising out of subjectivity of the data.This paper presents a methodology for a fuzzy based computer-aided fault tree analysis tool. The methodology is developed using a systematic approach of fault tree development, minimal cut sets determination and probability analysis. Further, it uses static and dynamic structuring and modeling, fuzzy based probability analysis and sensitivity analysis.This paper also illustrates with a case study the use of a fuzzy weighted index and cutsets importance measure in sensitivity analysis (for system probabilistic risk analysis) and design modification.     

Therapeutic potential of dopamine agonists in the treatment of type 2 diabetes mellitus

Environmental Science and Pollution Research - Diabetes is a global health concern that has affected almost 415 million people globally. Bromocriptine is a dopamine D2 agonist, which is a Food and...     

Improvement of Physico-mechanical Properties of Chitosan Films by Photocuring with Acrylic Monomers

Natural polymer, chitosan was obtained from dried prawn shell waste through the preparation of chitin and was characterized. Thin film of chitosan was prepared by casting method from its 2% chitosan solution. Mechanical properties like tensile strength (TS), elongation at break (Eb) of chitosan film were studied. Five formulations were developed with 2-ethyl-2-hydroxy methyl-1,3-propandiol trimethacrylate (EHMPTMA), a trifunctional monomer and 2-ethylhexyl acrylate (EHA), a monofunctional monomer in the presence of photoinitiator Darocur-1664 (2%). The film was soaked in those monomer formulations in dissimilar soaking times and irradiated under UV-radiation at different radiation intensities for the improvement of the properties of chitosan film. The cured films were then subjected to various characterization tests like TS, Eb, polymer loading (PL), water absorbency, gel content etc. The formulation, containing 25% EHMPTMA and 73% EHA showed the best performance at 10th UV passes of UV radiation for 4 min soaking time.