Human-caused wildfire risk rating for prevention planning in Spain

This paper identifies human factors associated with high forest fire risk in Spain and analyses the spatial distribution of fire occurrence in the country. The spatial units were 6,066 municipalities of the Spanish peninsular territory and Balearic Islands. The study covered a 13-year series of fire occurrence data. One hundred and eight variables were generated and input to a dedicated Geographic Information System (GIS) to model different factors related to fire ignition. After exploratory analysis, 29 were selected to build a predictive model of human fire ignition using logistic regression analysis. The binary model estimated the probability of high or low occurrence of forest fires, as defined by an ignition danger index that is currently used by the Spanish forest service (number of fires divided by forest area in each municipality). Thirteen explanatory variables were identified by the model. They were related to agricultural landscape fragmentation, agricultural abandonment and development processes. The prediction agreement found between the model binary outputs and the historical fire data was 85.3% for the model building dataset (60% of municipalities). A slightly lower predictive power (76.2%) was found for the validation data (the remaining 40%). The probabilistic output of the logistic was significantly related to the raw ignition index (Spearman correlation of 0.710) used by the Spanish Forest Service. Therefore, the model can be considered a good predictor of human-caused fire risk, aiding spatial decisions related to prevention planning in Spanish municipalities.     

Modelling the risk of Pb and PAH intervention value exceedance in allotment soils by robust logistic regression

Soils of allotments are often contaminated by heavy metals and persistent organic pollutants. In particular, lead (Pb) and polycyclic aromatic hydrocarbons (PAHs) frequently exceed legal intervention values (IVs). Allotments are popular in European countries; cities may own and let several thousand allotment plots. Assessing soil contamination for all the plots would be very costly. Soil contamination in allotments is often linked to gardening practice and historic land use. Hence, we predict the risk of IV exceedance from attributes that characterize the history and management of allotment areas (age, nearby presence of pollutant sources, prior land use). Robust logistic regression analyses of data of Swiss allotments demonstrate that the risk of IV exceedance can be predicted quite precisely without costly soil analyses. Thus, the new method allows screening many allotments at small costs, and it helps to deploy the resources available for soil contamination surveying more efficiently.     

Factors influencing wastewater management performance: case study of housing estates in suburban Bangkok, Thailand

As population densities have risen and settlements become increasingly crowded, wastewater effluent volumes have, not unexpectedly, also risen. The adverse impacts of effluent from housing estates (HEs) in suburban areas of Thailand, served by local wastewater management (WWM) systems, are of particular concern. Because of this problem, which reflects the poor performance of WWM systems, it has become increasingly important that the pivotal factors that hinder WWM improvements be identified and effective solutions be proposed and implemented. The goal of the research described in this paper was to determine the critical factors influencing WWM performance at HEs in suburban Bangkok, using multiple regression analysis. Three significant factors encompassing financial, social, institutional and general aspects were identified for each type of WWM system (community centralized and onsite). For the community centralized system, the key factors were house price, type of organization managing the HE, and the attitudes of the organization. For onsite systems, the three factors were total number of house units, direct experience with water pollution and percentage of occupied houses. These findings reflect the importance of having a WWM that employs an integrated approach rather than focusing on specific aspects in isolation from other factors. WWM performance could be improved if significant factors indicating high priority concerns were to be identified and properly applied. Addressing the priority concerns could, consequently, contribute to the development of appropriate environmental management measures, plans, and policies related to HE WWM.     

我国能源消费需求的时变弹性分析

为了科学分析相关经济因素变化对我国能源需求的影响效应,本文在提取能源消费需求主要影响因素的基础上,建立了基于Bayesian理论的时变系数回归模型,利用MCMC方法获得了各时变回归系数的估计.同时,利用HP滤波方法,得到了能源消费需求相对于各相关因素弹性系数的趋势及波动情况,并就能源需求对各相关经济因素弹性系数的趋势变动原因进行了分析,结果表明:①在不同的时间段内,能源价格、产业结构、经济增长、全国总人口以及能源消耗结构对能源消费需求的影响强度具有时变效应;②电力价格变动对降低能源消费的作用最为显著,但边际效应在逐年下降;煤炭价格的提高对能源消费的增加具有很大推进作用,但"煤电联动"、能源价格市场化的建设可能进一步抑制能源需求;③城镇化建设进程可进一步优化人口结构、产业结构以及能源消费结构,这将在很大程度上限制能源需求的加速增长.     

Developing Peak Discharges for Future Flood Risk Studies Using IPCC's CMIP5 Climate Model Results and USGS WREG Program

Extreme climate events, floods, and drought, cause huge impact on daily lives. In order to produce society resilient to extreme events, it is necessary to assess the impact of frequent and high intensity storm events on design parameters. This article describes a methodology to develop future peak “design discharges” throughout the United States that can be used as a guidance to map future floodplains. In order to develop a lower and upper limit for anticipated peak flow discharges, two future growth scenarios — Representative Concentration Pathways (RCPs)‐RCP 2.6 and 8.5 were identified as the weak and strong climate scenario respectively based on the output from the global climate models. The Generalized Least Square technique in United States Geological Survey's Weighted Multiple Regression (WREG) program was used to develop regression equations that relate peak discharges to basin and climate parameters of the contributing watershed. The design discharges reflect the most recent climate model results. Number of frost days, heavy rainfall days, high temperature days, and snow depth were found to be the common extreme climate parameters influencing the regression equations. This methodology can be extended to other flood frequency events if rainfall data is available. The future discharges can be utilized in hydraulics models to estimate floodplains that can assist in resilient infrastructure planning and outline climate change adaptation strategies.     

Development and Comparison of Multiple Regression Models to Predict Bankfull Channel Dimensions for Use in Hydrologic Models

Channel dimensions are important input variables for many hydrologic models. As measurements of channel geometry are not available in most watersheds, they are often predicted using bankfull hydraulic geometry relationships. This study aims at improving existing equations that relate bankfull width, depth, and cross‐sectional area to drainage area (DA) without limiting their use to well‐gauged watersheds. We included seven additional variables in the equations that can be derived from data that are generally required by hydrologic models anyway and conducted several multiple regression analyses to identify the ideal combination of additional variables for nationwide and regional models for each Physiographic Division of the United States (U.S.). Results indicate that including the additional variables in the regression equations generally improves predictions considerably. The selection of relevant variables varies by Physiographic Division, but average annual precipitation (PCP) and temperature (TMP) were generally found to improve the models the most. Therefore, we recommend using regression equations with three independent variables (DA, PCP, and TMP) to predict bankfull channel dimensions for hydrologic models. Furthermore, we recommend using the regional equations for watersheds within regions from which data were used for model development, whereas in all other parts of the U.S. and the rest of the world, the nationwide equations should be given preference.     

Analysis of willingness for relocation of the local communities living in the Critical Tiger Habitat of the Sariska Tiger Reserve,India

Many Indian Protected Areas (PAs) act as a support system for the communities living in and around them. Large-scale human interventions in these PAs have resulted in biodiversity loss, threat to wildlife and habitat fragmentation. The Sariska Tiger Reserve (STR) is no exception. In this reserve, tiger (Panthera tigris) became extinct in 2004.To create inviolate space for the reintroduced tigers, government has planned voluntary relocation of villages located inside Critical Tiger Habitat. The voluntary relocation plan will be more challenging if people are not willing to get relocated from PAs. Therefore, we have empirically analysed the identified factors influencing local communities’ willingness in getting relocated outside the STR using logit model. Results revealed that “restriction of access” and “market access” are the most influential factors and positively associated while forest dependency is negatively associated with local communities’ willingness. Based on these results, it was recommended that policy should be directed towards restriction on accessing forest resources along with reduction in forest dependency by nurturing and strengthening villagers’ livelihood to ensure successful relocation. Displacement of existed small markets around the reserve will also persuade them to relocate in areas more connected to market and other facilities.     

The states of public opinion on the environment

State-level public opinion on the environment within the US is examined, using data from the General Social Survey (GSS) from 2000 to 2010. A Multilevel Regression and Post-Stratification (MRP) approach is taken to estimate three different survey items on environmental issues at the US state level. This allows for a comparable understanding of state-level public opinion on environmental issues, which is then connected to economic, environmental, and political conditions, and representation in policy making. The findings indicate the MRP technique is a sophisticated way to make accurate estimations of state-level public opinion, and those estimates provide further insight into the shaping of public opinion and public policy.     

区域分异视角下乡村企业入城意愿分析——基于浙、豫、川三省193家企业的实证研究

乡村企业入城是实现我国城镇化发展的基本路径。利用浙江、河南、四川三省193家乡村企业的实地调查数据,运用多元有序Logistic回归模型进行相关验证,实证分析我国东中西部三个省份的乡村企业入城意愿及其影响因素。通过对调研数据的描述统计发现:有58.55%的乡村企业主有入城意愿,仅有21.24%的乡村企业主不愿意或非常不愿意搬迁,集群环境、土地价格、运输成本等是企业愿意搬迁的主要原因,土地价格政策、当地经济发展水平、产品产销信息、集群环境是影响企业搬迁方向的重要因素,当地的土地、税收和融资等政府政策对于企业是否入城产生重要影响。通过多元有序Logistic回归模型估计结果表明:企业总资产、土地价格、运输成本、集群环境、与政府关系对入城意愿有显著负向影响,税收政策有显著正向影响,这些影响因素主要是通过影响企业的显性成本(土地价格、运输成本),以及企业的交易成本、寻租成本等隐性成本(集群环境、与政府关系和税收政策),进而影响企业的迁移意愿,且浙江、河南与四川三地计量结果差异显著。在上述研究的基础上,本文提出相应的政策建议。     

影响食品行业社会组织参与食品安全风险治理能力的主要因素研究

事实再次证明,单纯由政府主导的食品安全监管模式已无法满足人们对于食品安全的消费需求。政府和市场在食品安全风险治理中出现的政府公权和市场私权的"双重失灵",迫切需要社会组织的参与。本文通过深度访问和问卷调查的形式,基于主成分分析方法将影响食品行业社会组织参与食品安全风险治理能力的影响因素指标融合为互不相关的综合指标,并由此构建了多元线性回归模型,研究了影响食品行业的社会组织参与食品安全风险治理能力的主要因素。研究结果表明,社会组织的公信力、与政府的关系、政府支持力度、社会组织的信息公开程度、专职人员的数量与质量等因素与其参与治理能力存在较强的正相关;社会组织法定代表人年龄、现职国家机关公务人员在组织内担任职务的比例与其参与治理的能力存在较高的负相关。因此,社会组织在食品安全的风险治理中是否能发挥自身独有的优势,弥补政府和市场在食品安全风险中的双重失灵,内在地取决于社会组织的外部环境、内部治理结构与内部管理水平。构建具有中国特色的食品安全社会共治体系亟需逐步完善社会组织的立法,明确社会组织的法律地位,确保社会组织在法律框架下按照各自的章程自主性地开展的各种活动,政府在法律框架下履行支持社会组织发展的职能力度;同时优化内部人员结构,建设具有数量充足、能力结构基本完备的工作人员队伍,并依据改革要求,优化法定代表人的结构,建立诚信信息服务平台,提升自身的社会公信度,由此保障社会组织在参与食品安全风险治理过程中的独立性,发挥自身专业性、自治性等优势。