Cost-Benefit Analysis of Kaptai Dam in Rangamati District, Chittagong, Bangladesh

This study aims to assess the net benefit of the kaptai dam on the Karnafuli river in Kaptai, Chittagong, Bangladesh. Kaptai Dam, the only hydroelectricity power source in Bangladesh, provides only 5% electricity demand of Bangladesh. The Dam is located on the Karnafuli River at Kaptai in Rangamati District, 65 km upstream from Chittagong. It is an earth-fill or embankment dam with a reservoir with a water storage capacity of 11,000 skm. Though the Dam's primary purpose is to generate electricity, it became a reservoir of water used for fishing and tourism. To find the net benefit value and estimate the environmental costs and benefits, we considered the environmental net benefit from 1962 to 1997. We identify the costs of Kaptai Dam, including its establishment cost, operational costs, the costs of lives that have been lost due to conflicts, and environmental costs, including loss of biodiversity, loss of land uses, and loss of human displacements. Also, we assess the benefits of electricity production, earnings from fisheries production, and gain from tourism to Kaptai Lake. The findings show that the Dam contributes tremendous value to Bangladesh. As a source of hydroelectricity, the Kaptai Dam is a source of clean energy, and its value might have been worthy of this Dam produced a significant portion of the electricity. However, providing less than 5% of the national demand for electricity followed by various external and sensitive costs, the Dam hardly contributes to the Bangladesh economy. This study thus recommends that Bangladesh should look for other sources of clean energy that have no chances of eco-political conflicts.Comment: 10 page

Coastal and Marine Tourism in the Future

Having the world\u27s largest unbroken sea-beach and vast coastline, Bangladesh has an immense potentiality to develop sustainable coastal and marine tourism. In Bangladesh, coastal and marine tourism is already in operation, though on a limited scale. But the growth of tourism in this country is lagging behind compared to the world as a whole. The contribution of this sector in the economy of Bangladesh is still below the mark. Therefore, the economy can be benefited by harnessing opportunities pertinent to the country’s coastal and marine tourism. To attract the local and foreign tourists, the country can improve the existing tourist sites. It can also develop new tourist spots in the coastal and marine areas. Introduction of new tourism products such as cruise to Swatch of no ground from Chittagong and Khulna, exclusive tourist zones for foreigners, surfing zones, community-based ecotourism, underwater tourism, and sports tourism in the coastal and marine areas can be thought of. In the process of developing tourism, proper planning, budgetary allocation, community participation, awareness building, coordination between agencies and proper marketing strategies are among the important factors. By developing the proposed tourism in marine and coastal areas, Bangladesh can increase GDP, generate more jobs, reduce poverty, earn foreign currencies, gain socio-cultural benefits, conserve environment, and protect coastal areas. In addition, development of coastal and marine tourism can create the opportunity to promote local culture and heritage by integrating local communities into the development process. Finally, government can play a vital role in promoting coastal and marine tourism by providing some special services including on arrival visa and one stop service to the foreign tourists

Estimating carbon sequestration cost function for developing countries

Carbon sequestration is considered as the cost effective way of reducing carbon dioxide emission and other greenhouse gases. This study estimated the carbon sequestration cost function considering land use change in developing countries. Using the instrumental variable method, this study found that the percentage change in the cost due to the percentage change in carbon sequestration is almost unitary. The result also shows that the presence of Clean Development Mechanism (CDM) project in a country and per capita GDP in purchasing power parity (GDP, ppp) have positive influence on the carbon sequestration cost function. The marginal cost calculated from the total cost function representing the unit price of carbon dioxide is very low compared with the marginal abatement cost of the developed countries but equals the price at the European CO2 emission trading market

The Effects of Mothersa Profession on their Childrenas Academic Performance: An Econometric Analysis

This study focused on school going children s educational performances of working nonworking mothers Factors including parents highest level of education and their profession family income family size and the number of school-going siblings in the family were considered as the explanatory variables of educational performances Based on the primary data collected through a random sample survey of the students from two schools in Chittagong University campus and applying the regression analysis of the ANCOVA model this study found that mothers level of education and family income have a significantly positive impact on students academic performances though the mothers employment status has a negative impact except those who are employed in teaching profession Thus this study suggests that the ideal profession for mothers is teaching which plays a vital role on their children s educational performances than the other professional mother

Estimating the Environmental Cost of Shrimp Farming in Coastal Areas of Chittagong and Coxs bazaar in Bangladesh

During the last three decades, shrimp has remained one of the major export items in Bangladesh. It contributes to the development of this country by enhancing export earnings and promoting employment. However, coastal wetlands and agricultural lands are used for shrimp culture, which reduces agricultural opportunity and peasants income, and destroys the mangroves and coastal eco-system. These are the external environmental costs that are not reflected in farmers price and output decisions. This study has aimed to estimate those external environmental costs through the contingent valuation method. The calculated environmental cost of shrimp farming is USD 13.66 per acre per year. Findings suggest that current shrimp production and shrimp price will no longer be optimal once the external costs are internalized. Thus alternative policy recommendations have been proposed so that shrimp farming becomes a sustainable and equitable means of aquaculture.Comment: 28 page

Deepfake Detection: A Systematic Literature Review

Over the last few decades, rapid progress in AI, machine learning, and deep learning has resulted in new techniques and various tools for manipulating multimedia. Though the technology has been mostly used in legitimate applications such as for entertainment and education, etc., malicious users have also exploited them for unlawful or nefarious purposes. For example, high-quality and realistic fake videos, images, or audios have been created to spread misinformation and propaganda, foment political discord and hate, or even harass and blackmail people. The manipulated, high-quality and realistic videos have become known recently as Deepfake. Various approaches have since been described in the literature to deal with the problems raised by Deepfake. To provide an updated overview of the research works in Deepfake detection, we conduct a systematic literature review (SLR) in this paper, summarizing 112 relevant articles from 2018 to 2020 that presented a variety of methodologies. We analyze them by grouping them into four different categories: deep learning-based techniques, classical machine learning-based methods, statistical techniques, and blockchain-based techniques. We also evaluate the performance of the detection capability of the various methods with respect to different datasets and conclude that the deep learning-based methods outperform other methods in Deepfake detection

Machine Learning in Access Control: A Taxonomy and Survey

An increasing body of work has recognized the importance of exploiting machine learning (ML) advancements to address the need for efficient automation in extracting access control attributes, policy mining, policy verification, access decisions, etc. In this work, we survey and summarize various ML approaches to solve different access control problems. We propose a novel taxonomy of the ML model's application in the access control domain. We highlight current limitations and open challenges such as lack of public real-world datasets, administration of ML-based access control systems, understanding a black-box ML model's decision, etc., and enumerate future research directions.Comment: Submitted to ACM Computing Surve

Applying Multi-Temporal Landsat Satellite Data and Markov-Cellular Automata to Predict Forest Cover Change and Forest Degradation of Sundarban Reserve Forest, Bangladesh

Overdependence on and exploitation of forest resources have significantly transformed the natural reserve forest of Sundarban, which shares the largest mangrove territory in the world, into a great degradation status. By observing these, a most pressing concern is how much degradation occurred in the past, and what will be the scenarios in the future if they continue? To confirm the degradation status in the past decades and reveal the future trend, we took Sundarban Reserve Forest (SRF) as an example, and used satellite Earth observation historical Landsat imagery between 1989 and 2019 as existing data and primary data. Moreover, a geographic information system model was considered to estimate land cover (LC) change and spatial health quality of the SRF from 1989 to 2029 based on the large and small tree categories. The maximum likelihood classifier (MLC) technique was employed to classify the historical images with five different LC types, which were further considered for future projection (2029) including trends based on 2019 simulation results from 1989 and 2019 LC maps using the Markov-cellular automata model. The overall accuracy achieved was 82.30%~90.49% with a kappa value of 0.75~0.87. The historical result showed forest degradation in the past (1989–2019) of 4773.02 ha yr−1, considered as great forest degradation (GFD) and showed a declining status when moving with the projection (2019–2029) of 1508.53 ha yr−1 and overall there was a decline of 3956.90 ha yr−1 in the 1989–2029 time period. Moreover, the study also observed that dense forest was gradually degraded (good to bad) but, conversely, light forest was enhanced, which will continue in the future even to 2029 if no effective management is carried out. Therefore, by observing the GFD, through spatial forest health quality and forest degradation mapping and assessment, the study suggests a few policies that require the immediate attention of forest policy-makers to implement them immediately and ensure sustainable development in the SRF