Resource frontiers and agglomeration economies: The varied logics of transnational land-based investing in Southern and Eastern Africa

Actor-level data on large-scale commercial agriculture in Sub-Saharan Africa are scarce. The peculiar choice of transnational investing in African land has, therefore, been subject to conjecture. Addressing this gap, we reconstructed the underlying logics of investment location choices in a Bayesian network, using firm- and actor-level interview and spatial data from 37 transnational agriculture and forestry investments across 121 sites in Mozambique, Zambia, Tanzania, and Ethiopia. We distinguish four investment locations across gradients of resource frontiers and agglomeration economies to derive the preferred locations of different investors with varied skillsets and market reach (i.e., track record). In contrast to newcomers, investors with extensive track records are more likely to expand the land use frontier, but they are also likely to survive the high transaction costs of the pre-commercial frontier. We highlight key comparative advantages of Southern and Eastern African frontiers and map the most probable categories of investment locations.Supplementary informationThe online version contains supplementary material available at 10.1007/s13280-021-01682-z.     

基于贝叶斯网络的消防协同治理影响因素研究

为有效提升消防安全协同治理的效能,在识别和量化政府部门、企业和公民社会消防协同治理的影响因素的基础上,分别构建政府部门、企业和公民社会影响因素的贝叶斯网络模型,辨识影响实现高效消防协同治理的关键因素。研究结果表明：部门的权力与利益、有效领导和组织激励3个因素对政府消防协同治理影响最为显著;交流沟通和参与渠道分别是影响企业和公民社会的关键因素,并且基于全局贝叶斯网络模型的敏感性分析可知,政府部门是整体消防协同治理的核心,调节相关部门的权力与利益和具备有效领导的能力能有效提高政府消防协同治理水平,明显改善整体消防协同治理效能。     

油气管道腐蚀可靠性的贝叶斯评价法

对油气管道腐蚀危害因素进行分析,建立其失效故障树。根据故障树分析原理,找出导致管道腐蚀穿孔破坏的23个因素。通过对故障树的定性分析,采用下行法求出油气管道腐蚀失效故障树的96个全部最小割集,并确定失效的主要影响因素。结合最小割集不相交化法和贝叶斯可靠性评定法对管道腐蚀失效进行定量分析,通过某油气管道事故统计数据,利用贝叶斯可靠性评定方法求出油气管道腐蚀可靠性的一阶矩和二阶矩。对一阶矩和二阶矩进行拟合,求出油气管道腐蚀可靠性的第一近似下限和第二近似下限。结果表明:得出的油气腐蚀管道贝叶斯可靠性评价结果可以指导管道系统的维护和维修,降低管道运行的风险。     

基于贝叶斯网络模型的船舶搁浅事故分析

根据海上交通安全事故统计数据显示,船舶搁浅事故是主要的海上交通安全事故之一。为了研究船舶搁浅事故的发生机理,收集了我国东部海域部分海事局辖区的船舶搁浅事故样本。首先从人为因素、环境因素和船舶因素三方面分析导致船舶搁浅事故的原因,确定网络节点。然后找出每起搁浅事故的事故链,并建立船舶搁浅事故贝叶斯网络模型。再对建立的贝叶斯网络模型进行仿真,选取21起船舶搁浅事故对建立的模型进行验证,确定模型的有效性。最后,利用HUGIN软件找出船舶搁浅事故各影响因素的致因概率,得到导致船舶搁浅事故的致因链。在人为因素方面,瞭望不当和不熟悉航道情况占据的比例最高;在船舶因素方面,舵机设备故障对船舶搁浅事故影响最大;在环境因素方面,风/浪/流对船舶搁浅事故影响最大。     

无定河流域地表水硝酸盐浓度的时空分布特征及来源解析

无定河流域作为黄河的一级支流,其水生态环境质量深刻影响着黄河流域生态保护与高质量发展.为识别无定河流域硝酸盐污染来源,对2019～2021年期间无定河的地表水样品进行了采集,探究了流域地表水体硝酸盐浓度的时空分布特征及影响因素,借助水化学方法、氮氧同位素示踪技术以及MixSIAR模型定性和定量地确定了地表水硝酸盐各来源及其贡献率.结果表明,无定河流域硝酸盐浓度存在显著时空差异.时间上,丰水期地表水NO^-₃-N浓度均值高于平水期;空间上,下游地表水NO^-₃-N浓度均值高于上游.地表水硝酸盐浓度的时空差异主要受降雨径流、土壤类型以及土地利用类型的影响.无定河流域地表水丰水期硝酸盐的主要来源是生活污水及粪肥、化学肥料和土壤有机氮,其贡献率分别为43.3%、 27.6%和22.1%,降水的贡献率仅占7.0%.不同河段地表水硝酸盐污染源贡献率存在差异,上游土壤氮贡献率明显高于下游,为26.5%;而下游生活污水及粪肥的贡献率明显高于上游,为48.9%.可为无定河乃至干旱及半干旱地区的河流硝酸盐来源解析和污染治理...     

基于贝叶斯网络分类的土壤盐渍化遥感监测

贝叶斯网络是一种将贝叶斯概率方法和有向无环图的网络拓扑结构有机结合的概率模型。采用贝叶斯网络分类对具有典型干旱特征的库车县土壤盐渍化情况进行监测，首先应用条件独立性测试原理建立贝叶斯网络结构，把研究区遥感数据进行离散化，然后应用贝叶斯定理作为分类原则，将每个像元分为像元最大概率的类别。研究结果表明该方法分类6种地类的整体分类精度达到96％，为该区盐渍地面积、空间分布等特征监测提供了较好的依据。     

Internal corrosion hazard assessment of oil & gas pipelines using Bayesian belief network model

A substantial amount of oil & gas products are transported and distributed via pipelines, which can stretch for thousands of kilometers. In British Columbia (BC), Canada, alone there are over 40,000 km of pipelines currently being operated. Because of the adverse environmental impact, public outrage and significant financial losses, the integrity of the pipelines is essential. More than 37 pipe failures per year occur in BC causing liquid spills and gas releases, damaging both property and environment. BC oil & gas commission (BCOGS) has indicated metal loss due to internal corrosion as one of the primary causes of these failures. Therefore, it is of a paramount importance to timely identify pipelines subjected to severe internal corrosion in order to improve corrosion mitigation and pipeline maintenance strategies, thus minimizing the likelihood of failure. To accomplish this task, this paper presents a Bayesian belief network (BBN)-based probabilistic internal corrosion hazard assessment approach for oil & gas pipelines. A cause-effect BBN model has been developed by considering various information, such as analytical corrosion models, expert knowledge and published literature. Multiple corrosion models and failure pressure models have been incorporated into a single flexible network to estimate corrosion defects and associated probability of failure (PoF). This paper also explores the influence of fluid composition and operating conditions on the corrosion rate and PoF. To demonstrate the application of the BBN model, a case study of the Northeastern BC oil & gas pipeline infrastructure is presented. Based on the pipeline's mechanical characteristics and operating conditions, spatial and probabilistic distributions of corrosion defect and PoF have been obtained and visualized with the aid of the Geographic Information System (GIS). The developed BBN model can identify vulnerable pipeline sections and rank them accordingly to enhance the informed decision-making process.     

Game theoretic analysis of environmental impact assessment system in China

Environmental impact assessment (EIA) system has been established in China since 1973. In present EIA cases, there are four participants in general: governments, enterprises, EIA organizations and the public. The public has held responsible for both social costs and social duties. The public supervises social costs produced by enterprises discharging pollutant in EIA. However public participation is mostly deputized by governments, which severely weaken the independence of the public as one participant in EIA. In this paper, EIA refers to the different attitudes of the participants whose optional strategies may be described by a proper game model. According to disfigurements in EIA, three sides (governments, enterprises, and EIA organizations) dynamic iterative game theory, dynamic game theory of incomplete information, and perfect Bayesian equilibrium theory to analyze the reciprocity relation among governments, EIA organizations and enterprises. The results show that in a short period, economic benefit is preponderant over social benefit. Governments and enterprises both do not want to take EIA to reveal social costs. EIA organizations’ income comes from enterprises and the collusions are built between them to vindicate economic benefit. In a long run, social benefit loss caused by environmental pollution must be recuperated sooner or later and environmental deterioration will influence the achievements of economic benefit, so both governments and enterprises are certain to pursue high social benefit and willing to take EIA, helpful to increase private benefit. EIA organizations will make fair assessment when their economic benefit are ensured. At present, the public as silent victims can not take actual part in EIA. The EIA system must be improved to break the present equilibrium of three sides, bringing the public to the equilibrium to exert public supervision.     

Bayesian estimation and consequence modelling of deliberately induced domino effects in process facilities

Process facilities handling hazardous chemicals in large quantities and elevated operating conditions of temperature/pressure are attractive targets to external attacks. The possibility of an external attack on a critical installation, performed with an intention of triggering escalation of primary incidents into secondary and tertiary incidents, thereby increasing the severity of consequences needs to be effectively analysed. A prominent Petrochemical Industry located in Kerala, India was identified for studying the possibility of a deliberately induced domino effect. In this study, a dedicated Bayesian network is developed to model the domino propagation sequence in the chemical storage area of the industry, and to estimate the domino probabilities at different levels. This method has the advantage of accurately quantifying domino occurrence probabilities and identifying possible higher levels of escalations. Moreover, the combined effect from multiple units can be modelled easily and new information can be added into the model as evidences to update the probabilities. Phast (Process hazard analysis) software is used for consequence modelling to determine the impact zones of the identified primary and secondary incidents. The results of the case study show that such analyses can greatly benefit green field and brown field projects in determining the appropriate safety and security measures to be implemented or strengthened so as to reduce its attractiveness to external threat agents.     

Probabilistic analysis of aluminium production explosion accidents based on a fuzzy Bayesian network

Explosion accidents of molten aluminium in contact with water during aluminium production often occur and may cause injury and death. In this paper, a fuzzy Bayesian network (BN) was employed to probabilistically analyse the explosion accident of molten aluminium in contact with water. A fault tree-Bayesian network (FT-BN) model was established in the cause-effect analysis of the explosion accident, including three processes: electrolysis, molten aluminium transportation and aluminium casting. Fifty-three nodes were proposed in the model to represent the evolution process of the explosion accident from failure causes to consequences. Furthermore, the occurrence probabilities of basic events (BEs) were determined by expert judgement with weighted treatments based on fuzzy theory. By giving certain occurrence probabilities of each BE, the probability of an explosion accident was estimated. Subsequently, importance measures were assessed for each BE, which could reflect the impact on the occurrence of the top event (TE), and the final ranks were provided. The results indicate that using wet ladles and tools, water on the ground, breakage of the tap hole, damage to the casting mould, and leakage of circulating water are five main problems that cause explosion accidents. Safety advice was provided based on the analysis results. This study can help decision makers improve the safety management of aluminium production.