Developing an indicator system for local governments to evaluate transport sustainability strategies

This study proposed an indicator system for measuring and monitoring transport sustainability at the county (or city) level. Twenty-one indicators were grouped into economy, environment, society, and energy aspects. A committee comprised of government officials from Taipei City and New Taipei City proposed transport solutions to improve the transport sustainability of the Taipei metropolitan area. Ten key indicators were selected to measure the sustainable transport strategies. This study applied Fuzzy Cognitive Maps (FCMs) and the Analytic Hierarchy Process (AHP) to construct the cause–effect relationships between these key indicators and to evaluate sustainable transport strategies. The evaluation results showed that the strategy of expanding mass rapid transit (MRT) lines was predicted to produce the most significant improvements; the strategy of integrating bus exclusive lanes would provide the least improvement; and the strategies of promoting cleaner vehicles and integrating Fu-Kang bus resources would perform similarly to each other in improving transport sustainability.     

Evaluating outcomes in planning: Indicators and reference values for Swiss landscapes

Evaluation of achievement of set targets is a necessary step in landscape planning in order to learn from the past, reassess implemented measures and enhance trust in public managers and institutions. Though it is commonly accepted that indicators play a major role in such evaluations, so far no accepted framework for evaluating planning outcomes exists. Furthermore, the selection of appropriate indicators and reference values to effectively assess conditions of landscapes and determine whether observed developments can be considered positive or negative remains challenging. Our study contributes to much-needed research on this topic with a proposed evaluation framework built on goals, indicators and reference values. We analyzed the landscape section of eight Swiss cantonal comprehensive plans to specifically address (1) whether currently tracked indicators suffice to evaluate landscape-planning goals; (2) what a minimal set of landscape indicators for regional planning might look like; and (3) how the ratified value approach could be operationalized to develop reference values for landscape indicators. All eight plans have a similar hierarchical goal system with six major landscape goals, up to 18 themes and 21–33 subordinate goals. The studied cantons track from 29 to 84 indicators. We found a considerable imbalance in the ratio between subordinate goals and indicators, with comparatively few indicators being tracked to assess visual and recreational landscape quality. Our proposed minimal indicator set is well balanced since it lists 5–7 indicators for each theme. The general procedure for modeling reference values is based on the assumption that the protection status of a landscape is a proxy for high societal appreciation of a place. Consequently, indicator values for these areas would reflect reference values (ratified values). We illustrate the procedure with the exemplary indicator impervious surface. The proposed indicators and maps are powerful tools for outcome evaluation and also facilitate benchmarking, i.e. interregional comparisons of landscape qualities, which could be very useful for landscape planning in Europe.     

Suitability of presence vs. absence indicator species to characterize stress gradients: Lessons from zooplankton species of boreal lakes

Indicator species are species that readily reflect some measure of habitat characteristics and have become an increasingly appealing tool in environmental monitoring. Traditionally, habitat conditions were derived based on the presence of specific indicator species, but the absence of indicator species may be just as informative. To evaluate the importance of presence vs. absence of indicator species for characterizing habitats across environmental gradients, we evaluated the interactions of zooplankton and acid-stress in 244 boreal lakes. We adopted the statistical methods proposed by Dufrene and Legendre (1997) to identify presence and absence indicator species to characterize high, intermediate and low acid-stress lake categories. Presence indicator species (identified by the statistical analysis) for highly stressed lakes were not entirely appropriate because further evaluation identified them as ubiquitous generalists. In contrast, absence indicators for highly stressed lakes were more appropriate as these habitat specialists were specifically absent from this category of lakes. On the other hand, presence indicators for the low acid-stress category were largely habitat specialists and therefore appropriate indicators. However there were no presence or absence indicators for lakes at intermediate acid-stress level. Thus the combined use of both presence and absence indicators is recommended to characterize habitats across a stress gradient. To evaluate if the successful application of this combined approach is dependent on a stress gradient, we applied the same analyses to a sub-set of uninfluenced (non-sensitive) lakes representing three different environmental conditions. This approach identified statistically significant presence and absence indicator species for all three different reference conditions. Yet, the absence-based approach was not essential under unstressed conditions, as presence indicator species were habitat specialists for all reference categories. Finally, this study also emphasizes the importance of meaningful ecological traits of species in order to ensure the appropriateness of statistically selected indicator species.     

Identifying Node Role in Social Network Based on Multiple Indicators

It is a classic topic of social network analysis to evaluate the importance of nodes and identify the node that takes on the role of core or bridge in a network. Because a single indicator is not sufficient to analyze multiple characteristics of a node, it is a natural solution to apply multiple indicators that should be selected carefully. An intuitive idea is to select some indicators with weak correlations to efficiently assess different characteristics of a node. However, this paper shows that it is much better to select the indicators with strong correlations. Because indicator correlation is based on the statistical analysis of a large number of nodes, the particularity of an important node will be outlined if its indicator relationship doesn''t comply with the statistical correlation. Therefore, the paper selects the multiple indicators including degree, ego-betweenness centrality and eigenvector centrality to evaluate the importance and the role of a node. The importance of a node is equal to the normalized sum of its three indicators. A candidate for core or bridge is selected from the great degree nodes or the nodes with great ego-betweenness centrality respectively. Then, the role of a candidate is determined according to the difference between its indicators'' relationship with the statistical correlation of the overall network. Based on 18 real networks and 3 kinds of model networks, the experimental results show that the proposed methods perform quite well in evaluating the importance of nodes and in identifying the node role.     

Fuzzy knowledge- and data-based models of damage to reeds by grazing of Greylag Geese

This paper describes a fuzzy and neuro-fuzzy approach to modelling feeding intensity of Greylag Geese on reed. As a consequence of the presence of some non-measurable or random factors and the heterogeneity of reed and goose behaviour, the relationships between the model variables are often not well known and the data collected have a high degree of uncertainty. A fuzzy approach was selected which can be applied with vague knowledge and data of high uncertainty. Fuzzy logic can be used to handle inexact reasoning in knowledge-based models with fuzzy rules and fuzzy sets to handle uncertainty in data. The neural network technique was applied to develop the fuzzy data-based models. For training, several dataset combinations of three lakes in North Germany were used. The generalisation capability of these models was checked for other lakes. The performance of these models was compared with the results of the fuzzy knowledge-based model developed in the next step. The knowledge base of this model contains the Mamdani-type rules formulated by a domain expert. All models were implemented using the Fuzzy Logic Toolbox of MATLAB^®.     

Fuzzy Multi-Objective Optimization of a Green Supply Chain Network with Risk Management that Includes Environmental Hazards

Among the leading environmental risks, global climate alteration has become one of the most important controversial issues. Greenhouse gas emissions (CO₂, methane, etc.) and air pollution have motivated a need to develop and improve environmental management strategies. As a consequence, environmental sanctions are forcing commercial enterprises to re-consider and re-design supply chain processes in a green way. This article provides a multi-objective model to design a closed-loop supply chain (CLSC) network in a green framework. Our first and second objectives are to minimize all the transportation costs for the supply chain's forward and reverse logistics; the third objective is to minimize total CO₂ emissions; the fourth objective is to encourage customers to use recyclable materials as an environmental practice. To provide more realistic modeling by treating the uncertainty in decision-makers’ objectives, fuzzy modeling is used in this study. The model is explained and tested via fulfilling a numerical example. In scenario analyses, analytic hierarchy process (AHP), fuzzy AHP (F-AHP), and fuzzy TOPSIS (F-TOPSIS) approaches were applied and compared to evaluate different objectives to guide decision-makers.     

An integrated approach for environmental assessments

LCA is a system-wide assessment, and the LCIA phase is confronted with the difficulties of local and regional effects in a number of impact categories. We integrate three different environmental techniques to demonstrate how these effects can be addressed in an environmental assessment. The techniques are life cycle inventory, environmental fate models, and an ecological impact assessment using fuzzy expert systems. Results of the LCI are mass and energy flows. In the environmental fate modelling step these mass flows are transformed into concentration and immission values by dispersion-reaction models. A generalised fuzzy expert system for the environmental mechanisms compares calculated exposure with site specific buffering capacities and formulates a generalised dose-response relationship. This generalised fuzzy expert system is used as a template for the assessment of local and regional environmental impacts. An application of this integrated approach is shown for a practical problem: production of magnesium car components. The environmental fate of nitrogen oxides which are released due to the major combustion source within that production system is simulated. Fuzzy expert models for crop damage, soil acidification and eutrophication determine the possible environmental impact of the immited nitrogen oxides. The important methodological extension of this integrated approach is a regionalised impact assessment depending on the spatial distribution of environmental characteristics.     

The selection, testing and application of terrestrial insects as bioindicators

Although the uses and merits of terrestrial insects as indicators have been extensively discussed, there is a lack of clear definition, goal directedness and hypothesis testing in studies in the field. In an attempt to redress some of these issues and outline an approach for further studies, three categories of terrestrial insect indicators, corresponding to differences in their application, are proposed, i.e. environmental, ecological and biodiversity indicators. The procedures in terrestrial insect bioindicator studies should start with a clear definition of the study objectives and proposed use of the bioindicator, as well as with a consideration of the scale at which the study is to be carried out. Bioindication studies are conducted at a variety of spatial and temporal scales within the context of earth-system processes, but the objectives of the study will largely determine the scale at which it would be optimally conducted. There is a tendency for studies to be conducted below their space-time scaling functions, giving them apparent predictability. The selection of potential indicator taxa or groups is then based on a priori suitability criteria, the identification of predictive relationships between the indicator and environmental variables and, most importantly, the development and testing of hypotheses according to the correlative patterns found. Finally, recommendations for the use of the indicator in monitoring should be made. Although advocating rigorous, long-term protocols to identify indicators may presently be questionable in the face of the urgency with which conservation decisions have to be made, this approach is critical if bioindicators are to be used with any measurable degree of confidence.     

Development of agri-environmental indicators to assess dairy farm sustainability in Quebec,Eastern Canada

Agri-environmental indicators are commonly used to assess agricultural sustainability. In North America, few are designed to be easily used by dairy farmers. As farmers assume more responsibility for managing natural resources, they play an increasingly important role in assessing sustainable agriculture. This paper describes the development of an indicator-based self-assessment tool for use at the farm level to evaluate dairy farm sustainability from an environmental standpoint. The agri-environmental indicator set was selected by using two participatory processes: the Delphi method and a focus group. The framework for developing the assessment tool consisted of six steps: (1) Defining the concept of environmental sustainability at the farm level; (2) setting up goals and principles for the assessment; (3) identifying potential indicators and selecting candidate indicators; (4) defining reference values, aggregating indicators into components, and establishing relative weights for indicators; (5) testing the candidate indicators on dairy farms, and (6) selecting the final indicator set. Six criteria were used to evaluate either the practicality or usefulness of indicators. Thirteen indicators were chosen, one of which was divided into four sub-indicators. These indicators were aggregated into four components: soil quality, cropping practices, fertilization management, and farmland management. Expert participation was the first validation of the indicator set. A compromise between feasibility, practicality, and relevance of measurement was found among indicators. When applied to 40 farms over two contrasting regions, the indicator set identified scores that differed according to production context. The tool is user-friendly and well-adapted to dairy farming systems.     

Developing a systematic simulation-based approach for selecting indicators in strategic cumulative effects assessments with multiple environmental valued components

Indicator selection is a critical step in conducting effective strategic cumulative effects assessments. Selecting an appropriate set of indicators to represent multiple and sometimes disparate values is particularly challenging because the interpretation of impacts depends on indicator roles and relationships among indicators. However, systematic approaches for selection of indicators for strategic cumulative effects assessments (CEA) are unclear. For a 909,000 ha case study area involving 214 watersheds in coastal British Columbia, we defined a suite of twenty indicators linked to six Valued Components (VCs) that could be forecasted for forest, riparian and species at risk as three key values consistent with present land-use planning policies in British Columbia, Canada. We used spatio-temporal process-based models to project and integrate the stressor–response relationships between forest harvesting and run-of-river power resource management activities and the suite of selected indicators. For a likely development scenario, we assessed the correlative structure among projected indicator responses and, using a PCA-based analysis of outcomes, identified both patterns of potential redundancies and ecological processes linking indicators and dominant processes influencing VCs. Our results suggest that strategic CEAs will benefit if indicator selections are not made independently for each VC. Identifying the type of indicator, i.e., pressure or condition, and scale of its representation was important in determining if assessed impacts for individual indicators could be appropriately integrated to quantify overall impacts in the landscape. Consideration of indicator–indicator relationships both within and among VCs clarifies the influences of spatial scale, potential redundancies among indicators, and the role of underlying ecological processes in interpreting and aggregating indicator responses. Our case study demonstrates that relative scales of ecological processes, disturbances and management actions can limit how cohesive the interpretations of impacts may be across VCs in strategic CEA. Analysis of correlative structures among the twenty indicators suggested criteria-based statistical redundancies occurred between only two indicator pairs, however PCA suggested three ecological processes (road disturbance, Spotted Owl habitat state, retention and recruitment of old forest) were operating to relate behaviors of multiple indicators. Careful consideration of the interacting roles of ecological processes as they relate to values is required when determining appropriate indicators and designing how best to aggregate indicator results into an effective strategic CEA. A three step systematic and generalizable approach to forecasting present and future states of indicators will improve efficiencies and effectiveness of strategic CEA.     

Fuzzy clustering of fuzzy ecological data

Besides the problem of searching for effective methods for data analysis there are some additional problems with handling data of high uncertainty. Uncertainty problems often arise in an analysis of ecological data, e.g. in the cluster analysis of ecological data. Conventional clustering methods based on Boolean logic ignore the continuous nature of ecological variables and the uncertainty of ecological data. That can result in misclassification or misinterpretation of the data structure. Clusters with fuzzy boundaries reflect better the continuous character of ecological features. But the problem is, that the common clustering methods (like the fuzzy c-means method) are only designed for treating crisp data, that means they provide a fuzzy partition only for crisp data (e.g. exact measurement data). This paper presents the extension and implementation of the method of fuzzy clustering of fuzzy data proposed by Yang and Liu [Yang, M.-S. and Liu, H-H, 1999. Fuzzy clustering procedures for conical fuzzy vector data. Fuzzy Sets and Systems, 106, 189-200.]. The imprecise data can be defined as multidimensional fuzzy sets with not sharply formed boundaries (in the form of the so-called conical fuzzy vectors). They can then be used for the fuzzy clustering together with crisp data. That can be particularly useful when information is not available about the variances which describe the accuracy of the data and probabilistic approaches are impossible. The method proposed by Yang has been extended and implemented for the Fuzzy Clustering System EcoFucs developed at the University of Kiel. As an example, the paper presents the fuzzy cluster analysis of chemicals according to their ecotoxicological properties. The uncertainty and imprecision of ecotoxicological data are very high because of the use of various data sources, various investigation tests and the difficulty of comparing these data. The implemented method can be very helpful in searching for an adequate partition of ecological data into clusters with similar properties.     

Development of environmental performance indicators supported by an environmental information system: Application to the Norwegian defence sector

Environmental performance indicators (EPIs) can be an important tool for evaluating and reporting the integration of environmental practices and tools in the defence sector, ensuring that environmental issues are being consistently and clearly followed into sector activities on a local and national scale. However, proper use of indicators is dependent on a suitable conceptual framework to manage and assess the environmental performance. Furthermore, monitoring is fundamental to environmental efficiency evaluations both to assess adherence to standards and to support management options. The use of EPI assures that a monitoring system for a public sector addresses only the key variables associated with significant environmental impacts and also improves communication with stakeholders. The main objective of this paper is to develop EPIs supported by an environmental information system (EIS), as a tool for environmental efficiency evaluations in the defence sector. To put the proposed approach into practice, the Norwegian defence EIS is used as a data source – a system developed to increase environmental awareness and to promote environmental efficiency, in the reporting of environmental aspects like use of ammunition, generation of waste, energy consumption, fuel consumption, use of chemicals, and water consumption. Use of real data acquired from the EIS is used to test and evaluate the robustness of the public sector's environmental performance indicator conceptual framework (SEPIIS), used to support the developed indicators. The main findings show how an indicator framework can be effectively combined with the use of an EIS designed to process data from a sector's activities, stressing how environmental issues could be integrated into overall public services performance management. The EPI developed for the Norwegian defence allowed to trace the sector's environmental performance, by comparisons with commissions from the Norwegian Ministry of Defence and national goals for GHG emissions, identifying areas for priority response measures. This joined approach of indicators and information systems could be useful to increase and improve environmental ex post and ex ante assessments, reporting and communication of defence activities.     

应用改进模糊相似优先方法模拟林肯羊生态适宜区

自1955年Zcdch第一次从不相容现象出发提出模糊集概念以来,摸糊数学正迈进和渗透到自然科学和社会科学的各个领域。它在植物学、微生物学、海洋科学、生态学、气象学、抢质学、     

Indicators for assessing socioeconomic sustainability of bioenergy systems: A short list of practical measures

Indicators are needed to assess both socioeconomic and environmental sustainability of bioenergy systems. Effective indicators can help to identify and quantify the sustainability attributes of bioenergy options. We identify 16 socioeconomic indicators that fall into the categories of social well-being, energy security, trade, profitability, resource conservation, and social acceptability. The suite of indicators is predicated on the existence of basic institutional frameworks to provide governance, legal, regulatory and enforcement services. Indicators were selected to be practical, sensitive to stresses, unambiguous, anticipatory, predictive, estimable with known variability, and sufficient when considered collectively. The utility of each indicator, methods for its measurement, and applications appropriate for the context of particular bioenergy systems are described along with future research needs. Together, this suite of indicators is hypothesized to reflect major socioeconomic effects of the full supply chain for bioenergy, including feedstock production and logistics, conversion to biofuels, biofuel logistics and biofuel end uses. Ten indicators are highlighted as a minimum set of practical measures of socioeconomic aspects of bioenergy sustainability. Coupled with locally prioritized environmental indicators, we propose that these socioeconomic indicators can provide a basis to quantify and evaluate sustainability of bioenergy systems across many regions in which they will be deployed.     

基于PATH模型的远程医疗绩效评价体系构建

目的 探索构建适用于我国远程医疗发展的绩效评价体系。方法 采用文献法、头脑风暴法初步构建评价框架和指标库,运用德尔菲法和层次分析法确定指标及其权重。结果 基于PATH模型建立了远程医疗绩效评价体系,包括以病人为中心、医疗效果与效率、员工为导向、安全性、反应性管理5个一级指标、11个二级指标和32个三级指标。结论 PATH模型适用于远程医疗绩效评价体系的建立,此外,以病人为中心的指标权重最高,因此,应加强对远程医疗服务费用的控制,同时从各个角度入手,提高患者满意度。     

Ranking municipal solid waste treatment alternatives based on ecological footprint and multi-criteria analysis

The selection of a municipal solid waste (MSW) treatment alternative is a complex task in which a widespread set of criteria must be taken into account. Additionally to economic or social aspects, the decision process should consider the environmental perspective. With the purpose of quantifying the environmental burdens, a wide variety of environmental and sustainability indicators have been developed in the last years. Furthermore, integrative frameworks have been highlighted as the best option to achieve more comprehensive assessments.In this work, four different options of MSW treatment were ranked from an environmental point of view applying two methods: (1) the ecological footprint (EF) as single composite indicator and (2) multi-criteria analysis (MCA) integrating the EF together with other material flow indicators related to water consumption, emissions to air and water and occupied landfill volume. The MCA methods selected were a combination of Analytic Hierarchy Process (AHP) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) aided by Geometrical Analysis for Interactive Aid (GAIA). The objective was twofold: on the one side, the identification of the most beneficial waste treatment alternative (including thermal plasma gasification which as yet has not been assessed systematically) from an environmental perspective and, on the other side, the comparison of the results yielded by the two ranking methods proposed.The ranking obtained in both cases was (from best to worst): thermal plasma gasification, biological treatment of organic fraction with energy recovery from refuse derived fuel, incineration with energy recovery and landfilling. Hence, the EF proved to be a good screening indicator although it did not provide a comprehensive measure of environmental impacts associated to the waste treatment options considered. Besides, the combined application of AHP and PROMETHEE/GAIA as MCA methodology was found to be a suitable way, not very complex at user level, to integrate the information provided by a set of environmental criteria and to aid decision making.     

森林资源可持续状况评价方法

为了准确、快速地评估森林经营单位的森林资源可持续状况,提出了森林资源可持续状况的评价指标、评价方法和评价结果分级。森林资源可持续状况评价指标包括森林资源质量状况、森林资源利用状况和森林受干扰状况3个方面共28个评价指标。通过参照技术规定、查阅专业用表和使用经验数值等3种方法确定评价指标的基准值;根据基准值把评价指标测定值分级为"好"、"中"、"差"3个等级,并分别赋值1.0、0.62和0.38。利用专家咨询法或层次分析法,根据森林经营单位的主要经营目标和森林主导功能,确定森林资源质量状况评价指标的权重。根据评价指标的实测值、赋值和权重,计算森林资源质量状况指数、森林资源利用状况指数和森林受干扰状况指数,进而计算森林资源可持续状况指数。根据森林资源可持续状况指数值的大小,将森林经营单位的森林资源可持续状况划分为优、良、中和差四个等级。     

Nonlinearity,fuzziness and incommensurability in indicator-based assessments of vulnerability to climate change: A new mathematical framework

The earth’s climate system is highly nonlinear and the vulnerability of a community to a climate hazard is no exception. While this fact is widely accepted, indicator-based vulnerability assessments (IBVA) hardly ever take such nonlinearities into account. This is mainly due to the fact that the majority of assessment studies use methods based on Multiple Attribute Utility Theory (MAUT) (e.g. simple additive weight or multiplicative aggregation) to aggregate indicators. These methods convert all indicators into a global utility function and produce only a linear, threshold-free scaling of the effects of an indicator on vulnerability. In a previous paper, we showed that outranking procedures developed in decision-making science offer a more theoretically-sound approach to aggregation because they allow the analyst to incorporate the incommensurability, fuzziness and uncertainty associated with indicators. In this paper, we develop a new mathematical framework for vulnerability in order to clearly identify various sources of nonlinearity and incommensurability in vulnerability assessments. We then propose a new outranking formulation which can accommodate both and can be used to conduct assessments at different scales. We do so by introducing the concept of harm criterion as a mediator between an indicator and the vulnerability it represents. The new assessment approach can aggregate a mix of indicators with various degrees of subjectivity and non-linearity, without converting them into a single utility function and without requiring them to be mutually compensating.We illustrate the proposed approach by applying it to a simplified model of urban vulnerability to heat, focusing on the non-linear relationship between mortality and temperature above a ‘comfort temperature’, long evidenced in the epidemiological literature. We compare vulnerability rankings yielded by linear and non-linear characterizations of the relationship between temperature and mortality and find that the incorporation of non-linearity can have a significant impact on the rankings.     

模糊C－均值聚类和TWINSPAN分类的比较研究

以英国威尔士北部Ｓｎｏｗｄｏｎｉａ山地Ａｂｅｒ山谷植被为例,对模糊ｃ－均值聚类和ＴＷＩＮＳＰＡＮ分类进行了应用和比较研究。两种方法的结果一致。模糊ｃ－均值聚类结果给出样地和植被类型间的隶属程度,在一定程度上优于ＴＷＩＮＳＰＡＮ。