ENVIRONMENTAL POLICY ANALYSIS AND INSTRUMENTS FOR BIODIVERSITY CONSERVATION: A REVIEW OF RECENT ECONOMIC LITERATURE

This paper provides a synthesis of recent literature dealing with the institutional environment, policy framework, and economic instruments used in policy analysis related to the conservation and sustainable use of biodiversity resources. The paper analyzes the economic consequences of alternative policy options and summarizes the application of these economic issues in the formulation of biodiversity protection policy. The paper also concludes that the proper understanding of underlying institutions and, if needed, institutional reforming procedures are also required to provide appropriate incentive structures for conservation and sustainable use of biodiversity resources. Illustrations of these principles and examples are taken from published accounts of biodiversity policy debates and policy implications.biodiversity, conservation, resource management, Environmental Economics and Policy, Q2, Q3,

Large and realistic models of Amorphous Silicon

Amorphous silicon (a-Si) models are analyzed for structural, electronic and vibrational characteristics. Several models of various sizes have been computationally fabricated for this analysis. It is shown that a recently developed structural modeling algorithm known as force-enhanced atomic refinement (FEAR) provides results in agreement with experimental neutron and x-ray diffraction data while producing a total energy below conventional schemes. We also show that a large model (500 atoms) and a complete basis is necessary to properly describe vibrational and thermal properties. We compute the density for a-Si, and compare with experimental results

Realistic inversion of diffraction data for an amorphous solid: the case of amorphous silicon

We apply a new method "force enhanced atomic refinement" (FEAR) to create a computer model of amorphous silicon (a-Si), based upon the highly precise X-ray diffraction experiments of Laaziri et al. The logic underlying our calculation is to estimate the structure of a real sample a-Si using experimental data and chemical information included in a non-biased way, starting from random coordinates. The model is in close agreement with experiment and also sits at a suitable minimum energy according to density functional calculations. In agreement with experiments, we find a small concentration of coordination defects that we discuss, including their electronic consequences. The gap states in the FEAR model are delocalized compared to a continuous random network model. The method is more efficient and accurate, in the sense of fitting the diffraction data than conventional melt quench methods. We compute the vibrational density of states and the specific heat, and find that both compare favorably to experiments.Comment: 7 pages and 10 figure

AGRICULTURAL AND FOREST LANDUSE CHANGES, AND THE ENVIRONMENTAL KUZNETS CURVE FOR DEFORESTATION: AN INTERTEMPORAL DYNAMIC EQUILIBRIUM

This study is an investigation of the process of land conversion from forest to agricultural production, and assessing the factors affecting the process. We also analyze the conditions that would ultimately lead to the EKC type of relationship between deforestation and societal income in an economy.Land Economics/Use,

Advance care planning in 21st century Australia: a systematic review and appraisal of online advance care directive templates against national framework criteria

Objectives A drive to promote advance care planning at a population level has led to a proliferation of online advance care directive (ACD) templates but little information to guide consumer choice. The current study aimed to appraise the quality of online ACD templates promoted for use in Australia. Methods A systematic review of online Australian ACD templates was conducted in February 2014. ACD templates were identified via Google searches, and quality was independently appraised by two reviewers against criteria from the 2011 report A National Framework for Advance Care Directives. Bias either towards or against future medical treatment was assessed using criteria designed to limit subjectivity. Results Fourteen online ACD templates were included, all of which were available only in English. Templates developed by Southern Cross University best met the framework criteria. One ACD template was found to be biased against medical treatment – the Dying with Dignity Victoria Advance Healthcare Directive. Conclusions More research is needed to understand how online resources can optimally elicit and record consumers’ individual preferences for future care. Future iterations of the framework should address online availability and provide a simple rating system to inform choice and drive quality improvement

Managing interactions between household food security and preschooler health:

Food security does not assure good nutrition. The nutritional status of an individual is influenced not only by food but also by nonfood factors, such as clean water, sanitation, and health care. The effect of all of these factors must be considered in efforts to rid the world of malnutrition. Food security will result in good nutrition only if nonfood factors are effectively dealt with. In this paper, Lawrence Haddad, Saroj Bhattarai, Maarten Immink, and Shubh Kumar show how malnutrition among preschool children is determined by a complex interaction of illness and lack of food. The authors look at three countries —Ethiopia, Pakistan, and the Philippines — to study how food availability and diarrhea interact and what this interaction means for preschooler malnutrition. Their results show that the links between food consumption, diarrhea, and malnutrition are stronger than most economic studies have assumed. When diarrhea is prevalent, the effects of food shortages on child malnutrition are worse, and when food is scarce, the effects of diarrhea on child malnutrition are worse.Food security Ethiopia., Malnutrition in children Ethiopia., Food security Pakistan., Malnutrition in children Pakistan., Food security Philippines., Malnutrition in children Philippines.,

A shadow function model based on perspective projection and atmospheric effect for satellites in eclipse

Accurate Solar Radiation Pressure (SRP) modelling is critical for correctly describing the dynamics of satellites. A shadow function is a unitless quantity varying between 0 and 1 to scale the solar radiation flux at a satellite’s location during eclipses. Errors in modelling shadow function lead to inaccuracy in SRP that degrades the orbit quality. Shadow function modelling requires solutions to a geometrical problem (Earth’s oblateness) and a physical problem (atmospheric effects). This study presents a new shadow function model (PPM_atm) which uses a perspective projection based approach to solve the geometrical problem rigorously and a linear function to describe the reduction of solar radiation flux due to atmospheric effects. GRACE (Gravity Recovery And Climate Experiment) satellites carry accelerometers that record variations of non-conservative forces, which reveal the variations of shadow function during eclipses. In this study, the PPM_atm is validated using accelerometer observations of the GRACE-A satellite. Test results show that the PPM_atm is closer to the variations in accelerometer observations than the widely used SECM (Spherical Earth Conical Model). Taking the accelerometer observations derived shadow function as the “truth”, the relative error in PPM_atm is −0.79% while the SECM 11.07%. The influence of the PPM_atm is also shown in orbit prediction for Galileo satellites. Compared with the SECM, the PPM_atm can reduce the radial orbit error RMS by 5.6 cm over a 7-day prediction. The impacts of the errors in shadow function modelling on the orbit remain to be systematic and should be mitigated in long-term orbit prediction

MoBYv2AL: Self-supervised Active Learning for Image Classification

Active learning(AL) has recently gained popularity for deep learning(DL) models. This is due to efficient and informative sampling, especially when the learner requires large-scale labelled datasets. Commonly, the sampling and training happen in stages while more batches are added. One main bottleneck in this strategy is the narrow representation learned by the model that affects the overall AL selection. We present MoBYv2AL, a novel self-supervised active learning framework for image classification. Our contribution lies in lifting MoBY - one of the most successful self-supervised learning algorithms to the AL pipeline. Thus, we add the downstream task-aware objective function and optimize it jointly with contrastive loss. Further, we derive a data-distribution selection function from labelling the new examples. Finally, we test and study our pipeline robustness and performance for image classification tasks. We successfully achieved state-of-the-art results when compared to recent AL methods