A framework for allocating conservation resources among multiple threats and actions

Land managers decide how to allocate resources among multiple threats that can be addressed through multiple possible actions. Additionally, these actions vary in feasibility, effectiveness, and cost. We sought to provide a way to optimize resource allocation to address multiple threats when multiple management options are available, including mutually exclusive options. Formulating the decision as a combinatorial optimization problem, our framework takes as inputs the expected impact and cost of each threat for each action (including do nothing) and for each overall budget identifies the optimal action to take for each threat. We compared the optimal solution to an easy to calculate greedy algorithm approximation and a variety of plausible ranking schemes. We applied the framework to management of multiple introduced plant species in Australian alpine areas. We developed a model of invasion to predict the expected impact in 50 years for each species-action combination that accounted for each species’ current invasion state (absent, localized, widespread); arrival probability; spread rate; impact, if present, of each species; and management effectiveness of each species-action combination. We found that the recommended action for a threat changed with budget; there was no single optimal management action for each species; and considering more than one candidate action can substantially increase the management plan's overall efficiency. The approximate solution (solution ranked by marginal cost-effectiveness) performed well when the budget matched the cost of the prioritized actions, indicating that this approach would be effective if the budget was set as part of the prioritization process. The ranking schemes varied in performance, and achieving a close to optimal solution was not guaranteed. Global sensitivity analysis revealed a threat's expected impact and, to a lesser extent, management effectiveness were the most influential parameters, emphasizing the need to focus research and monitoring efforts on their quantification.     

Microtargeting for conservation

Widespread human action and behavior change is needed to achieve many conservation goals. Doing so at the requisite scale and pace will require the efficient delivery of outreach campaigns. Conservation gains will be greatest when efforts are directed toward places of high conservation value (or need) and tailored to critical actors. Recent strategic conservation planning has relied primarily on spatial assessments of biophysical attributes, largely ignoring the human dimensions. Elsewhere, marketers, political campaigns, and others use microtargeting—predictive analytics of big data—to identify people most likely to respond positively to particular messages or interventions. Conservationists have not yet widely capitalized on these techniques. To investigate the effectiveness of microtargeting to improve conservation, we developed a propensity model to predict restoration behavior among 203,645 private landowners in a 5,200,000 ha study area in the Chesapeake Bay Watershed (U.S.A.). To isolate the additional value microtargeting may offer beyond geospatial prioritization, we analyzed a new high-resolution land-cover data set and cadastral data to identify private owners of riparian areas needing restoration. Subsequently, we developed and evaluated a restoration propensity model based on a database of landowners who had conducted restoration in the past and those who had not (n = 4978). Model validation in a parallel database (n = 4989) showed owners with the highest scorers for propensity to conduct restoration (i.e., top decile) were over twice as likely as average landowners to have conducted restoration (135%). These results demonstrate that microtargeting techniques can dramatically increase the efficiency and efficacy of conservation programs, above and beyond the advances offered by biophysical prioritizations alone, as well as facilitate more robust research of many social–ecological systems.     

HUD状态下飞行员视觉注意资源分配机制研究

为了解决平视显示器（Head Up Display,HUD）飞行中的注意资源分配问题，基于飞行模拟器，设计双作业飞行任务开展相关研究。基于事件相关电位（Event Related Potential，ERP）技术，选取行为绩效和P300成分为评价指标，采用oddball模式，在双作业飞行任务条件下开展实验研究。结果表明:随主任务难度的增加，主、辅任务的作业绩效均显著下降，被试者对HUD上异常信息的正确操作率降低，且反应时间延长；随主任务难度的增加，由辅任务所诱发的P300的波幅显著降低。在实验结果的基础上，最终提出了HUD状态下飞行员视觉注意资源分配机制。     

鄱阳湖流域排污权初始分配模式的比较研究

排污权初始分配是排污权交易的起点和基础,在分配模式和实证研究方面需要加强。采用地区人口法、经济总量法、历史排污量法、环境容量法和综合分配法5种模式研究了鄱阳湖流域11个地市水污染物化学需氧量(COD)与氨氮的初始排污权分配。结果表明:COD和氨氮初始排污权在地市之间的分配格局相似;南昌、新余与鹰潭3市按经济总量模式分配的排污量明显高于其它模式,上饶、吉安两市人口分配模式高于其它模式,景德镇、九江两市历史排污量模式高于其它模式,赣州市环境容量模式高于其它模式,宜春市综合模式高于其它模式。综合分配模式为赣州市排污权总量最高,南昌、九江、吉安、宜春、抚州和上饶6市次之,景德镇、萍乡、新余、鹰潭4市最低。总体来看,排污权配额因分配模式不同而有所差异,综合分配模式得到的初始排污权最为合理。建议鄱阳湖流域严格执行综合分配模式下的各地市初始排污权分配额度,确保区域经济社会与生态环境的协调发展。     

基于可靠性分配的整机加速因子计算方法研究

目的验证长寿命高可靠整机产品的可靠性。方法基于RPN数据的可靠性分配方法,得到子系统在各故障模式下的失效率,接着针对导致各故障模式发生的多种应力,研究温度循环、温度驻留、湿度、振动应力下的加速因子,推导出整机加速因子计算公式,并以某典型电子产品加速试验为例,验证该方法的有效性。结果整机加速因子为322.5,比传统方法计算得到的4.78×105更符合实际情况。结论基于可靠性分配的整机加速因子计算方法为整机可靠性验证提供了合理的理论依据。     

碳排放权初始分配方法与问题分析

为了有效减缓气候变化对人类社会可持续发展的威胁,积极有效控制二氧化碳的排放刻不容缓。排污权交易理论的提出与发展为低成本高效率地解决气候变化问题提供了可能。在深入研究排污权交易理论以及对比初始排污权分配的方法基础上,结合国内排污权交易市场与碳金融市场的发展情况,为国内构建成熟的排污权交易市场提出建议。     

Spatial allocation of material flow analysis in residential developments: a case study of Kildare County,Ireland

Studies of urban metabolism provide important insights for environmental management of cities, but are not widely used in planning practice due to a mismatch of data scale and coverage. This paper introduces the Spatial Allocation of Material Flow Analysis (SAMFA) model as a potential decision support tool aimed as a contribution to overcome some of these difficulties and describes its pilot use at the county level in the Republic of Ireland. The results suggest that SAMFA is capable of identifying hotspots of higher material and energy use to support targeted planning initiatives, while its ability to visualise different policy scenarios supports more effective multi-stakeholder engagement. The paper evaluates this pilot use and sets out how this model can act as an analytical platform for the industrial ecology–spatial planning nexus.     

湖库型水体总氮总量控制目标的区域分配研究

基于层次分析法原理,以资源禀赋、社会经济、农业生产生活、污染治理水平为主要考虑因素设计指标体系和层次结构,建立了湖库型水体总氮总量控制目标分配方法,并在石头口门水库流域进行了应用,为湖库型水体总氮总量控制目标分配及石头口门水库流域总氮总量控制提供借鉴。     

Irrigation Water Allocation Using an Inexact Two‐Stage Quadratic Programming with Fuzzy Input under Climate Change

Agricultural irrigation accounts for nearly 70% of the total water use around the world. Uncertainties and climate change together exacerbate the complexity of optimal allocation of water resources for irrigation. An interval‐fuzzy two‐stage stochastic quadratic programming model is developed for determining the plans for water allocation for irrigation with maximum benefits. The model is shown to be applicable when inputs are expressed as discrete, fuzzy or random. In order to reflect the effect of marginal utility on benefit and cost, the model can also deal with nonlinearities in the objective function. Results from applying the model to a case study in the middle reaches of the Heihe River basin, China, show schemes for water allocation for irrigation of different crops in every month of the crop growth period under various flow levels are effective for achieving high economic benefits. Different climate change scenarios are used to analyze the impact of changing water requirement and water availability on irrigation water allocation. The proposed model can aid the decision maker in formulating desired irrigation water management policies in the wake of uncertainties and changing environment.