Do natural resources and economic components exhibit differential quantile environmental effects?

In view of the resource curse assumption, the environmental aspects of resource utilization are arguably posing more dangers to human existence. In the Middle East and North Africa (MENA) region, the region that holds more than 60% and 50% of the world's oil and gas reserves respectively, the need to examine the contribution of natural resources to environmental quality among other factors cannot be overemphasized. By leveraging on the novelty of observing the differential impact of natural resources and other economic components such as income and primary energy utilizations across the quantiles of carbon emission, this study implements the quantile regression approach alongside other relevant techniques to analyze data between 1990 and 2018 for selected countries in the MENA region including Saudi Arabia, Iran, Kuwait, Qatar, Algeria, Morocco, Oman, Egypt, and the United Arab Emirates (UAE). The result posits that natural resource utilization generally hampers the environment across the quantiles. However, this negative effect decreases until the 50th quantile before starting to rise again toward the upper quantiles. Additionally, primary energy utilization and globalization respectively worsen and improve environmental quantile, especially toward the upper quantiles while income affirms the inverted U-shaped hypothesis across the entire quantiles. Moreover, there is a statistically significant one-way directional causality from natural resources, economic expansion, primary energy use, and globalization to carbon emission levels. Hence, the study offers environmentally friendly resource utilization policies to the MENA economies and other resource-rich states by extension.     

Quantifying Habitat Requirements of Tree-Living Species in Fragmented Boreal Forests with Bayesian Methods

Abstract:  Quantitative conservation objectives require detailed consideration of the habitat requirements of target species. Tree-living bryophytes, lichens, and fungi are a critical and declining biodiversity component of boreal forests. To understand their requirements, Bayesian methods were used to analyze the relationships between the occurrence of individual species and habitat factors at the tree and the stand scale in a naturally fragmented boreal forest landscape. The importance of unexplained between-stand variation in occurrence of species was estimated, and the ability of derived models to predict species' occurrence was tested. The occurrence of species was affected by quality of individual trees. Furthermore, the relationships between occurrence of species at the tree level and size and shape of stands indicated edge effects, implying that some species were restricted to interior habitats of large, regular stands. Yet for the habitat factors studied, requirements of many species appeared similar. Species occurrence also varied between stands; most of the seemingly suitable trees in some stands were unoccupied. The models captured most variation in species occurrence at tree level. They also successfully accounted for between-stand variation in species occurrence, thus providing realistic simulations of stand-level occupancy of species. Important unexplained between-stand variation in species occurrence warns against a simplified view that only local habitat factors influence species' occurrence. Apparently, similar stands will host populations of different sizes due to historical, spatial, and stochastic factors. Thus, habitat suitability cannot be assessed simply by population sizes, and stands lacking a species may still provide suitable habitat and merit protection.     

Sample size requirements for multiple regression interval estimation

Sample size planning is one of the most important issues in the design of a study. Simple and accurate sample size formulas for a desired confidence interval width have been developed for many statistical procedures, but a simple and accurate sample size formula for the squared multiple correlation has been a notable exception. Several rule‐of‐thumb sample size recommendations for a multiple regression analysis have been proposed over the years but none are satisfactory. Other approaches have focused on the construction of elaborate tables of sample size requirements, but these tables are both unwieldy and inadequate. We present a simple, accurate, and general method of approximating the sample size requirement for obtaining a squared multiple correlation confidence interval with desired precision. We also present a simple method for approximating the sample size needed to estimate unstandardized regression coefficients with desired precision. Copyright © 2010 John Wiley & Sons, Ltd.     

企业碳信息披露质量评价及影响因素研究

科学有效地评价企业的碳信息披露质量并探寻影响碳信息披露的主要因素，有助于进一步提升企业的碳信息披露质量水平、发挥企业在我国如期实现碳达峰及碳中和进程中的积极作用。基于碳信息披露的四个维度构建制造业企业碳信息披露质量的评价体系，综合主成分分析法和熵权法测度我国制造业企业2018—2020年真实的碳信息披露质量水平及变化趋势，有效降低评价指标的维数，避免单一熵权法权重失真情况，并通过随机森林回归识别各个指标对企业碳信息披露质量的重要性。研究发现：（1）主成分和熵权法组合可有效降低信息量少的“干扰”维度对企业碳信息披露的影响，能客观真实地评价我国制造业企业的碳信息披露质量，从而获得比传统单独采用单一方法更有效的评价结果。（2）2018—2020年，我国制造业企业的碳信息披露质量整体呈现上升趋势且2020年的碳信息披露质量水平提升最大。（3）我国制造业企业的碳信息披露质量整体偏低，不同企业间的碳信息披露质量存在较大差异。（4）企业的研发投入、总资产、总负债等因素对企业碳信息披露质量有较大影响。     

Factors Affecting Take-up of and Drop-out from Home Composting Schemes

This paper reports survey results from Scotland and north-west England into home composting attitudes and behaviours. The results concentrate on: the take-up of home composting through promotional campaigns; and the reasons for drop-out. Motivations for take-up were balanced between environmental and gardening reasons, although capital cost was an important issue for some. Drop-outs occurred mainly through participants moving house or because of lack of success in producing compost. Few of those experiencing problems sought help. Those that did favoured official or professional sources. Neighbourhood social pressures to compost were relatively weak. The results are discussed in terms of the sustainability of home composting behaviour and the manage ment interventions that might be required in sustaining that behaviour.     

Age at first marriage and its relation to fertility in Bangladesh

The 2007 Bangladesh Demographic and Health Survey(BDHS)data are exploited to examine the effects of socioeconomic and demographic factors on age at first marriage linkage to reproductive behavior of Bangladeshi women.The mean age at first marriage of women is found to be 15.48 years.Multivariate logistic regression technique shows that place of residence,religion,region,wealth index,education,and occupation are significantly important factors for determining age at first marriage.The relationship between marriage and fertility suggests that women who marry at a younger age produce more children than women who marry late.Findings of this study show that if the age at first marriage of adolescents is increased by 1 year,the age at first birth is postponed by 0.728 years.With the increase in age at first marriage,the fecundability of women sharply rises,whereas the proportion of temporary sterility decreases.Although there is a positive association between age at first marriage and age-specific marital fertility rates,the total parity per woman at the end of the reproductive period is expected to reduce by 0.196 for each 1-year delayed marriage.     

The Sensitivity of Population Viability Analysis to Uncertainty about Habitat Requirements: Implications for the Management of the Endangered Southern Brown Bandicoot

Abstract:  Whenever population viability analysis (PVA) models are built to help guide decisions about the management of rare and threatened species, an important component of model building is the specification of a habitat model describing how a species is related to landscape or bioclimatic variables. Model-selection uncertainty may arise because there is often a great deal of ambiguity about which habitat model structure best approximates the true underlying biological processes. The standard approach to incorporate habitat models into PVA is to assume the best habitat model is correct, ignoring habitat-model uncertainty and alternative model structures that may lead to quantitatively different conclusions and management recommendations. Here we provide the first detailed examination of the influence of habitat-model uncertainty on the ranking of management scenarios from a PVA model. We evaluated and ranked 6 management scenarios for the endangered southern brown bandicoot ( Isoodon obesulus ) with PVA models, each derived from plausible competing habitat models developed with logistic regression. The ranking of management scenarios was sensitive to the choice of the habitat model used in PVA predictions. Our results demonstrate the need to incorporate methods into PVA that better account for model uncertainty and highlight the sensitivity of PVA to decisions made during model building. We recommend that researchers search for and consider a range of habitat models when undertaking model-based decision making and suggest that routine sensitivity analyses should be expanded to include an analysis of the impact of habitat-model uncertainty and assumptions.     

基于RBF神经网络的城市建成区面积预测研究——兼与BP神经网络和线性回归对比分析

城市建成区面积预测是城市研究的一个核心问题,其与城市经济社会之间表现为一种复杂的非线性关系,传统的方法模型难以精确预测。作为一种较新的人工神经网络模型,RBF神经网络能以任意精度全局逼近任意非线性关系,表现出了极强的处理复杂非线性系统的能力。以合肥市建成区面积预测为例,构建了基于RBF网络的预测模型,作为对比,同时用BP神经网络、一元线性回归和多元线性回归模型进行了预测。预测结果的综合分析表明,在预测精度上,RBF网络＞BP网络＞多元线性回归模型＞一元线性回归模型。研究显示,RBF网络能为城市建成区面积预测提供一种新思路和新方法,进而可为城市土地利用及其规划制定提供科学的决策依据     

Supplier assessment based on corporate social responsibility criteria in Indian automotive and textile industry sectors

Mandatory disclosure of corporate social responsibility (CSR) reporting forces supply chain members to consider CSR issues in developed countries. Researchers have viewed CSR content within different themes, such as human (social), ethical and environmental perspectives. So far, in India, very little research has been done to study the impact of human issues in employment (social), ethical issues in business operations and pollution (environmental) issues at both regional and specific sector level. This paper emphasises the perspective view of supplier selection, considering CSR issues as opposed to traditional methods using conventional selection criteria such as cost, quality, delivery and service. It employs six CSR criteria such as discrimination, abuse of human rights, child labour, long working hours, unfair competition and pollution to assess small- and medium-sized suppliers who produce and deliver products to the automotive and textile industry sectors. The results of this study indicate that to prevent discrimination against pay variation, minimise cases of abuse of human rights and incidence of child labour, suppliers must put in place proper policies and procedures regarding employment contract, identification and verification records. Furthermore, to reduce unfair competition, suppliers of both automotive and textile sectors may introduce processes for investigating reports of unethical conducts and take severe disciplinary action. The suppliers of both automotive and textile sectors believe that the competitive advantage can be achieved through environmental oriented business practices. However, they differ in their approaches in attaining this objective.     

Predictive Models of Lumbar Loadings When Handling Boxes

Back problems resulting from the compression forces on the intervertebral disks during manual material-handling tasks are an important problem affecting workers in various industries. The quantification of these forces using intradiscal pressure or biomechanical modeling is complex, time consuming, and costly, and these methods cannot be readily used in the workplace to estimate loadings on the lower back. The objective of this study was to develop a predictive model that would allow the estimation of lumbar loadings for lifting and lowering boxes using easily measured anthropometric variables and variables related to the task. A dynamic and planar segmental model and a model of internal forces at L5/S1 were used to determine the compression forces on the lower back. Two predictive models, a field model and a laboratory model, were developed to estimate the compression forces when lifting or lowering 3.3 kg to 22.0 kg boxes between heights of 15 cm and 185 cm. Both models were validated by an examination of the residuals. Their predictive performance was also compared, with the laboratory model offering a slightly better prediction than the field model. Thus, these equations represent a practical tool for a better planning of handling tasks in the working environment with the purpose of reducing the back injuries of workers.