Mineral Extraction in Bangladesh: Some Fundamental Reform Suggestions

This paper proposes some fundamental reforms of the way the extraction of minerals are managed in Bangladesh. It suggests a Mineral Management Initiative that consists of three components: (i) the creation of Mineral Oversight Committee, (ii) the establishment of a Mineral Revenue Program, and (iii) a comprehensive Mineral Capacity Building Program. The purpose of the Mineral Oversight Committee (MOC) would be to oversee all decisions of Petrobangla’s Board of Directors as well as to monitor all major transactions of Petrobangla, including especially the use of mineral revenues and royalties. The establishment of a Mineral Revenue Program (MRP) is proposed to ensure that the revenues and royalties from the extraction of Bangladesh’s minerals will accrue to all the people of Bangladesh, while a comprehensive Mineral Capacity Building Program (MCBP) would provide specialized resource and management training.Bangladesh, mineral extraction, natural gas, oil, management

The Impact of Development on CO2 Emissions: A Case Study for Bangladesh until 2050

Bangladesh, a country with a population of 160 million, is currently contributing 0.14 percent to the world’s emission of carbon dioxide (CO2). However, mostly due to a growing population and economic growth (which both lead to an increase in energy consumption), Bangladesh’s share in CO2 emissions is—despite the increasing use of alternative energy—expected to rise sharply. This study uses the example of Bangladesh to illustrate the impact of low-income countries’ energy neutral development on global CO2 emissions in 2050 by using a set of alternative assumptions for population growth and GDP growth. It also shows how complex the determinants for (a) gains in energy efficiency and (b) changes in carbon intensity are in low-income countries.climate change, carbon dioxide emission, Bangladesh, Copenhagen Accord

Does the HIPC Initiative Achieve its Goal of Debt Sustainability?

Debt sustainability, Structural change, Growth

Parallel algorithms and concentration bounds for the Lovasz Local Lemma via witness DAGs

The Lov\'{a}sz Local Lemma (LLL) is a cornerstone principle in the probabilistic method of combinatorics, and a seminal algorithm of Moser & Tardos (2010) provides an efficient randomized algorithm to implement it. This can be parallelized to give an algorithm that uses polynomially many processors and runs in $O(\log^3 n)$ time on an EREW PRAM, stemming from $O(\log n)$ adaptive computations of a maximal independent set (MIS). Chung et al. (2014) developed faster local and parallel algorithms, potentially running in time $O(\log^2 n)$, but these algorithms require more stringent conditions than the LLL. We give a new parallel algorithm that works under essentially the same conditions as the original algorithm of Moser & Tardos but uses only a single MIS computation, thus running in $O(\log^2 n)$ time on an EREW PRAM. This can be derandomized to give an NC algorithm running in time $O(\log^2 n)$ as well, speeding up a previous NC LLL algorithm of Chandrasekaran et al. (2013). We also provide improved and tighter bounds on the run-times of the sequential and parallel resampling-based algorithms originally developed by Moser & Tardos. These apply to any problem instance in which the tighter Shearer LLL criterion is satisfied

Stable phase retrieval with low-redundancy frames

We investigate the recovery of vectors from magnitudes of frame coefficients when the frames have a low redundancy, meaning a small number of frame vectors compared to the dimension of the Hilbert space. We first show that for vectors in d dimensions, 4d-4 suitably chosen frame vectors are sufficient to uniquely determine each signal, up to an overall unimodular constant, from the magnitudes of its frame coefficients. Then we discuss the effect of noise and show that 8d-4 frame vectors provide a stable recovery if part of the frame coefficients is bounded away from zero. In this regime, perturbing the magnitudes of the frame coefficients by noise that is sufficiently small results in a recovery error that is at most proportional to the noise level.Comment: 12 pages AMSLaTeX, 1 figur