Progress and challenges in consolidating the management of Amazonian protected areas and indigenous territories

Effective management refers to the ability of a protected area or indigenous territory to meet its objectives, particularly as they relate to the protection of biodiversity and forest cover. Effective management is achieved through a process of consolidation, which among other things requires legally protecting sites, integrating sites into land‐use planning, developing and implementing management and resource‐use plans, and securing long‐term funding to pay for recurrent costs. Effectively managing all protected areas and indigenous territories in the Amazon may be needed to avoid a deforestation tipping point beyond which regional climatic feedbacks and global climate change interact to catalyze irreversible drying and savannization of large areas. At present, protected areas and indigenous territories cover 45.5% (3.55 million km²) of the Amazon, most of the 60–70% forest cover required to maintain hydrologic and climatic function. Three independent evaluations of a long‐term large‐scale philanthropic initiative in the Amazon yielded insights into the challenges and advances toward achieving effective management of protected areas and indigenous territories. Over the life of the initiative, management of sites has improved considerably, particularly with respect to management planning and capacity building, but few sites are effectively managed and many lack sufficient long‐term financing, adequate governance, support of nongovernmental organizations, and the means to withstand economic pressures. The time and money required to complete consolidation is still poorly understood, but it is clear that philanthropic funding is critical so long as essential funding needs are not met by governments and other sources, which could be on the order of decades. Despite challenges, it is encouraging that legal protection has expanded greatly and management of sites is improving steadily. Management of protected areas in other developing countries could be informed by improvements that have occurred in Amazonian countries.     

模糊综合评价法在钢铁企业安全投资合理性评价中的应用

为了对钢铁企业安全投资进行模糊综合评价,建立了钢铁企业安全投资的评价体系,确定各因素的权重。得出模糊综合评价矩阵,可对各因素进行评价分析;得出某钢铁企业安全投资现状分数为83.9分,等级为较好,需着重加强安全培训、劳保品、工业卫生等人因素方面的投资;在环境方面的投资较合理。     

Performance assessment system of health,safety and environment based on experts' weights and fuzzy comprehensive evaluation

Performance evaluation of Health, Safety and Environment (HSE) is the measurement of a company's achievement in HSE management. In order to receive a comprehensive and objective evaluation result, it is necessary to consider all evaluation factors and experts at different levels when HSE performance assessment is conducted. To improve conventional HSE performance evaluation, where weighted average method was used, a Fuzzy Comprehensive Evaluation (FCE) method is used in this study by taking experts' weights into account. Further, an HSE operating performance assessment system is designed to simplify manual and complex assessment process and generate charts and analysis reports automatically. Finally, a case of petrochemical enterprise is used to illustrate the effectiveness of the method and system.     

Development and evaluation of RAMP I – a practitioner’s tool for screening of musculoskeletal disorder risk factors in manual handling

RAMP I is a screening tool developed to support practitioners in screening for work-related musculoskeletal disorder risk factors related to manual handling. RAMP I, which is part of the RAMP tool, is based on research-based studies combined with expert group judgments. More than 80 practitioners participated in the development of RAMP I. The tool consists of dichotomous assessment items grouped into seven categories. Acceptable reliability was found for a majority of the assessment items for 15 practitioners who were given 1?h of training. The usability evaluation points to RAMP I being usable for screening for musculoskeletal disorder risk factors, i.e., usable for assessing risks, being usable as a decision base, having clear results and that the time needed for an assessment is acceptable. It is concluded that RAMP I is a usable tool for practitioners.     

A hybrid model for human factor analysis in process accidents: FBN-HFACS

Human factors are the largest contributing factors to unsafe operation of the chemical process systems. Conventional methods of human factor assessment are often static, unable to deal with data and model uncertainty, and to consider independencies among failure modes. To overcome the above limitations, this paper presents a hybrid dynamic human factor model considering Human Factor Analysis and Classification System (HFACS), intuitionistic fuzzy set theory, and Bayesian network. The model is tested on accident scenarios which have occurred in a hot tapping operation of a natural gas pipeline. The results demonstrate that poor occupational safety training, failure to implement risk management principles, and ignoring reporting unsafe conditions were the factors that contributed most failures causing accident. The potential risk-based safety measures for preventing similar accidents are discussed. The application of the model confirms its robustness in estimating impact rate (degree) of human factor induced failures, consideration of the conditional dependency, and a dynamic and flexible modelling structure.     

Analysis of the Trend and Seasonal Cycle of Carbon Monoxide Concentrations in an Urban Area (4 pp)

Background, Aim and Scope Air quality is an field of major concern in large cities. This problem has led administrations to introduce plans and regulations to reduce pollutant emissions. The analysis of variations in the concentration of pollutants is useful when evaluating the effectiveness of these plans. However, such an analysis cannot be undertaken using standard statistical techniques, due to the fact that concentrations of atmospheric pollutants often exhibit a lack of normality and are autocorrelated. On the other hand, if long-term trends of any pollutant’s emissions are to be detected, meteorological effects must be removed from the time series analysed, due to their strong masking effects. Materials and Methods The application of statistical methods to analyse temporal variations is illustrated using monthly carbon monoxide (CO) concentrations observed at an urban site. The sampling site is located at a street intersection in central Valencia (Spain) with a high traffic density. Valencia is the third largest city in Spain. It is a typical Mediterranean city in terms of its urban structure and climatology. The sampling site started operation in January 1994 and monitored CO ground level concentrations until February 2002. Its geographic coordinates are W0°22′52″ N39°28′05″ and its altitude is 11 m. Two nonparametric trend tests are applied. One of these is robust against serial correlation with regards to the false rejection rate, when observations have a strong persistence or when the sample size per month is small. A nonparametric analysis of the homogeneity of trends between seasons is also discussed. A multiple linear regression model is used with the transformed data, including the effect of meteorological variables. The method of generalized least squares is applied to estimate the model parameters to take into account the serial dependence of the residuals of this model. This study also assesses temporal changes using the Kolmogorov-Zurbenko (KZ) filter. The KZ filter has been shown to be an effective way to remove the influence of meteorological conditions on O₃ and PM to examine underlying trends. Results The nonparametric tests indicate a decreasing, significant trend in the sampled site. The application of the linear model yields a significant decrease every twelve months of 15.8% for the average monthly CO concentration. The 95% confidence interval for the trend ranges from 13.9% to 17.7%. The seasonal cycle also provides significant results. There are no differences in trends throughout the months. The percentage of CO variance explained by the linear model is 90.3%. The KZ filter separates out long, short-term and seasonal variations in the CO series. The estimated, significant, long-term trend every year results in 10.3% with this method. The 95% confidence interval ranges from 8.8% to 11.9%. This approach explains 89.9% of the CO temporal variations. Discussion The differences between the linear model and KZ filter trend estimations are due to the fact that the KZ filter performs the analysis on the smoothed data rather than the original data. In the KZ filter trend estimation, the effect of meteorological conditions has been removed. The CO short-term componentis attributable to weather and short-term fluctuations in emissions. There is a significant seasonal cycle. This component is a result of changes in the traffic, the yearly meteorological cycle and the interactions between these two factors. There are peaks during the autumn and winter months, which have more traffic density in the sampled site. There is a minimum during the month of August, reflecting the very low level of vehicle emissions which is a direct consequence of the holiday period. Conclusions The significant, decreasing trend implies to a certain extent that the urban environment in the area is improving. This trend results from changes in overall emissions, pollutant transport, climate, policy and economics. It is also due to the effect of introducing reformulated gasoline. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of CO. The KZ filter has been the most effective method to separate the CO series components and to obtain an estimate of the long-term trend due to changes in emissions, removing the effect of meteorological conditions. Recommendations and Perspectives Air quality managers and policy-makers must understand the link between climate and pollutants to select optimal pollutant reduction strategies and avoid exceeding emission directives. This paper analyses eight years of ambient CO data at a site with a high traffic density, and provides results that are useful for decision-making. The assessment of long-term changes in air pollutants to evaluate reduction strategies has to be done while taking into account meteorological variability     

Risk-evaluation of Hi-tech Enterprise in the Process of Approaching Global Market

In the high technology industry, small and medium sized technology enterprises (SMSTEs) play a pivotal role in advancing the whole industry. To achieve sustainable development, they need to extend their scope of business activities beyond a national view and exploit international market actively to meet international competitions that increase quickly in the form of allocating resources within the scope of the world. However, the SMSTEs are also facing risks associated with themselves during the process of exploiting international market owing to their own restrictions, so what they should do is to consider risk evaluations in exploiting the international market.     

中国可持续社会养老保险的综合评价体系和实证分析

我国可持续社会养老保险的综合评价指标体系包括四个层次和三级子系统，内容涉及养老保险人数、机构设置、基金管理和代际平衡四个子目标。应用因子分析模糊综合评价方法对我国1990-2001年养老保险建设状况进行评价，本文认为基金管理，行政管理和代际失衡是影响我国可持续养老保险发展的主要因素。     

湖北省土地资源遥感调查与评价

根据1999年TM影像解译了湖北省土地利用现状，然后以土地利用现状图为基础，采用地理信息系统技术，结合湖北省的实际情况，选择土壤、地形、坡度等评价因子，对湖北省土地资源进行了适宜性评价，简要地将全省土地资源划分为九等。评价结果表明：湖北省水热条件好，土地适宜性广，宜农林牧业同时利用的土地广泛分布；但是该省的基本农田面积小，土地垦殖率高，低产田面积大，后备耕地资源严重不足；土地利用结构与土地资源的供给条件、利用要求不相适宜，利用效益末得到充分发挥。