Using triple bottom line metrics and multi-criteria methodology in corporate settings

With the growing importance of environmental sustainability in the corporate sector, businesses are compelled to progress from assessing and benchmarking their environmental impact to making decisions on how to prioritize impact reduction alternatives. Most often, business decisions are driven by financial metrics, but with sustainability improvements becoming a business goal, it is also important to assess metrics from environmental and social spheres; nevertheless, practically and systematically performing such an assessment is challenging. We present an application of a multi-criteria decision analysis (MCDA) method that addresses the aforementioned challenges in a corporate setting. Our case study company – one of the largest inland marine freight carriers in the United States – promotes a business culture focused on financially viable, yet socially and environmentally responsible solutions. Thus, we combine life cycle analysis (LCA), financial calculation methods, and corporate surveys to quantify environmental, economic, and social performance measures, respectively. Multiattribute utility theory is integrated with analytic hierarchy processes (AHPs) and fuzzy analysis to create a carefully designed framework for corporations with diverse groups of stakeholders. With company leadership, implementation is feasible and successful at prioritizing alternatives among diverse stakeholders. The process provides a platform for negotiation and promotes discussions on decision drivers. The use of MCDA methodologies promoted the inclusion of a suite of metrics that aligned with the company's sense of social and environmental responsibility, generating an in-depth analysis of the alternatives that factored in other things besides economics. Return-on-investments (ROI) calculations, the typical approach used in the corporate setting, would have required significantly less time and effort from the company, but the results of our MCDA application indicated that inclusion of triple bottom line metrics delve deeper into stakeholder preferences. Thus, our case study company gained a holistic view of the candidate alternatives, in addition to creating a platform for structured discussions about company goals and priorities.     

日喀则市农业生态足迹研究

采用水资源足迹和化石能源供给地概念,对日喀则市2005年农业活动的生态足迹进行实证分析.结果表明:日喀则市农业综合足迹为334 153.27 hm2,占日喀则市总足迹的77.6%,其中,化石能源地足迹仅占农业综合足迹的13.3%,表明农业现代化程度不高.对农业综合足迹贡献最大的是耕地和牧草地,二者合占65.6%,主要原因是大量使用农用化学品和牧草地严重超载;生产足迹占农业综合足迹的82.8%,说明农村占用的大部分资源主要用来进行资源再生产.对日喀则市农业综合足迹的实证研究表明,资源产量法可以有效减小土地面积法引起的生态赤字扩大化现象,将肉、蛋、奶产品作为次级产品更能反映牧草地利用现状.      

海南省自然生态承载力研究

自然生态承载力是当前国际可持续发展定量研究的前沿问题之一。提出按照各类土地的实际生产能力和实际生态服务功能计算各类土地的生态承载力,将消纳污染和水资源的供给功能纳入区域自然生态承载力计算模型,并简称该方法为"实际供给法"。依据海南省统计年鉴资料和生态调查资料,应用"实际供给法"计算和分析了海南省1952~2004年自然生态承载力的动态变化特征:①海南省自然生态承载力长期持续增长,由1952年的7.0804hm²/人上升到2004年的8.5984hm²/人,增加了1.5180hm²/人,年均增长0.4%;②可再生资源生态承载力占总生态承载力的绝大部分,1952年和2004年分别占98.6%和92.3%,而不可再生资源生态承载力仅分别占1.4%和7.7%;③水资源生态承载力呈跳跃式下降,从1952年的6.8165hm²/人下降到2004年的0.4473hm²/人;耕地、海域和淡水水域的承载力呈上升趋势,分别从1952年的0.1277、0.0057和0.0001hm2/人上升到2004年的2.6066、3.1192和1.1468hm²/人,其它土地类型生态承载力变化不明显;④在生态承载力组成中,水资源比例由大到小变化显著,从96.3%下降到5.2%,耕地、海域的比例由小到大变化显著,分别从1.8%、0.1%上升到30.3%、36.3%,其它土地都由小到大,但所占比例较小;海域、耕地和水资源是海南生态承载力的主要贡献因素,三者之和占到总量的71.8%~98.2%。并分析了海南省自然生态承载力动态变化的原因。     

Integrating selected ecological effects of mixed European beech–Norway spruce stands in bioeconomic modelling

The simplicity of many bioeconomic models has been criticised several times, due to their lack of realism resulting from a deterministic nature and a single-species focus. In this context it was interesting to test the financial sensitivity of bioeconomic modelling against fairly well documented ecological effects in mixed forests. For this purpose our study linked existing results of ecological research with bioeconomic modelling. The presented methodological approach could not only show the importance of considering ecological effects in bioeconomic models; it in fact enabled prioritising ecological research from a financial point of view.In a first step, the possible influence of the tree species mixture on forest stand resistance, productivity and timber quality was derived from existing studies. In a second step, the available Monte Carlo simulations for Norway spruce (Picea abies [L.] Karst.) and European beech (Fagus sylvatica L.), simulated under site conditions and risks typical of southern Germany, were extended by the mentioned ecological effects and then evaluated from a financial perspective.The results showed a clear influence of all tested ecological effects on the financial indicators, financial risk and return. While testing each ecological effect separately, an increased resistance against wind, snow and insect attacks had the greatest influence on financial risk and return. It over-proportionally enhanced the financial return while simultaneously the financial risk was reduced. In contrast, a degraded timber quality could eliminate the positive effect of risk compensation in mixed forests almost completely. The least influence on the financial indicators finally showed a changed volume growth in mixed forests.A combination of the separately tested ecological effects (increased resistance, changed volume growth and decreased timber quality), between both tree species, underlined the dominating importance of the stand resistance. The integration of ecological effects, induced by interdependent tree species, in our bioeconomic model resulted in significantly lower financial risk than ignoring these effects. Moreover, the financial return of mixed stand variants with a proportion of Norway spruce greater than 60% even exceeded that of the most profitable pure stand.In conclusion this paper clearly confirmed that ignoring ecological effects in bioeconomic models could lead to seriously biased financial results. While a changed volume growth proved rather to be of minor importance for European beech/Norway spruce stands, tree resistance and timber quality may change the financial results significantly.     

改良生态足迹法在珠海的应用

改良生态足迹法从环境、水资源两方面入手,对传统生态足迹法进行了改进.在地理信息系统的支持下,利用改良生态足迹法对珠海2002年的生态足迹需求、供给和生态赢余进行了计算和分析,评估了珠海的可持续发展状况.结果表明:2002年珠海的生态足迹需求为3.492　8 hm2/人,供给为3.908　7 hm2/人,生态赢余为0.415　9 hm2/人,珠海的发展属于可持续性;其中水资源的生态足迹供给为0.635　8 hm2/人,需求为0.013　7 hm2/人,生态赢余为0.622　1 hm2/人;环境容量的生态足迹供给为3.489　5 hm2/人,需求为1.526　8 hm2/人,生态赢余为1.962　7 hm2/人.使用传统生态足迹法的计算结果却为:生态足迹需求为1.952　3 hm2/人,供给为0.316　4 hm2/人,生态赤字为1.635　9 hm2/人,珠海的发展属于不可持续性.而改良生态足迹法的计算结果更符合珠海的实际,对于其他城市的生态规划和建设也具有借鉴和参考价值.      

How deep is the footprint? A 3D representation

Depletion of natural capital stocks and use of natural capital flows are the central issues in the sustainability debate. Differentiation of these two components, considering natural capital and its limits, is important for planning and management of land use. This paper offers insights into this issue and proposes a new perspective of the Ecological Footprint (EF) in three dimensions, considering not only its size but also its depth: according to this viewpoint the footprint is not an area but a volume, although maintaining the same value as the one in two dimensions. Use of annual flows provided by the Earth is represented by the footprint size, expressed in global hectares (gha) and plotted in the (x,y) plane. Footprint depth represents the demand for extra land to meet human needs through depletion of stocks of natural capital. It is plotted on the z-axis. It can be seen as the number of years necessary to regenerate resources liquidated in 1 year (and to absorb the respective wastes) or as the number of planets necessary to support the inhabitants of the planet Earth. The evolution of these two components in the last five decades is studied and discussed.     

Analyzing the tradeoffs among ecological and fishing effects on an example fish community: A multispecies (fisheries) production model

Species interactions such as predation or competition can have notable implications for fish population dynamics. Stock status and associated biological reference points can be misrepresented if these interactions are not considered. Here we present a multispecies, biomass production model (MS-PROD) developed by incorporating modifications to the Schaefer production model. The modifications explicitly emphasize a functional group approach, highlighting the effects of species interactions that include competition between species within a group, competition between groups, and predation. The mathematical formulation also includes constraints on carrying capacity for both the entire system and for individual groups. We applied the model as initialized to the Northeast U.S. Large Marine Ecosystem finfish community. We then executed simulations designed to explore the behavior of the model with respect to fishery exploitation and ecological interactions. Collectively, the results demonstrated the utility, and the need, for incorporating ecological effects into fisheries models.     

Hierarchical spatial point process analysis for a plant community with high biodiversity

A complex multivariate spatial point pattern of a plant community with high biodiversity is modelled using a hierarchical multivariate point process model. In the model, interactions between plants with different post-fire regeneration strategies are of key interest. We consider initially a maximum likelihood approach to inference where problems arise due to unknown interaction radii for the plants. We next demonstrate that a Bayesian approach provides a flexible framework for incorporating prior information concerning the interaction radii. From an ecological perspective, we are able both to confirm existing knowledge on species’ interactions and to generate new biological questions and hypotheses on species’ interactions.

An improved approach for identifying suitable habitat of Sambar Deer (Cervus unicolor Kerr) using ecological niche analysis and environmental categorization: Case study at Phu-Khieo Wildlife Sanctuary,Thailand

To assess habitat suitability (HS) has become an increasingly important component of species/ecosystem management. HS assessment is usually based on presence/absence data related to environmental variables. An exception is Ecological Niche Factor Analysis (ENFA), which uses only presence data and which does not require absence data. Most HS modelling is based on input of all environmental parameters (EnvPs) without environmental categorization, and does not take into account species interaction and human intervention for an assessment of HS. In this study, the EnvPs are arranged into four features: geographical features, consumable features, human-factor features, and species–human interaction features. These features affect species with respect to movement, behavior and activity. The research presented here has used an already existing dataset of wildlife species and human activities/visitations, which was compiled during 2004–2006 in Phu-Khieo Wildlife Sanctuary (PKWS). Data from 2004 to 2005 were used to produce HS maps, while the data of 2006 were used for evaluating these maps. Sambar Deer (SD) was chosen to predict its own HS. Six HS maps of SD were analyzed using ENFA in the following manner: (1) inputting all EnvPs together, (2) inputting each feature, separately and (3) integrating the four resulting HS maps by model averaging. It was found that model averaging was capable of predicting the HS of SD more reliably than the model with all EnvPs put in together. Multiple linear regressions were computed between the HS map with all EnvPs and the HS maps with each feature. The results show that the HS map with only geographical features has the highest coefficient value (0.516) while the coefficient values of other HS maps with the above features are 0.296, 0.53 and −0.006, respectively. This indicates that the geographical features have an influence on the other features and that the predicting power is lower when all EnvPs are computed in the ENFA model. Therefore, in order to generate HS, each feature should at first be put into the model separately. Following that, the average of all features will be combined.     

The effects of aggregation on the performance of the inverse method and indicators of network analysis

Food webs are usually aggregated into a manageable size for their interpretation and analysis. The aggregation of food web components in trophic or other guilds is often at the choice of the modeler as there is little guidance in the literature as to what biases might be introduced by aggregation decisions. We examined the impacts of the choice of the a priori model on the subsequent estimation of missing flows using the inverse method and on the indices derived from ecological network analysis of both inverse method-derived flows and on the actual values of flows, using the fully determined Sylt-Rømø Bight food web model. We used the inverse method, with the least squares minimization goal function, to estimate ‘missing’ values in the food web flows on 14 aggregation schemes varying in number of compartments and in methods of aggregation. The resultant flows were compared to known values; the performance of the inverse method improved with increasing number of compartments and with aggregation based on both habitat and feeding habits rather than diet similarity. Comparison of network analysis indices of inverse method-derived flows with that of actual flows and the original value for the unaggregated food web showed that the use of both the inverse method and the aggregation scheme affected indices derived from ecological network analysis. The inverse method tended to underestimate the size and complexity of food webs, while an aggregation scheme explained as much variability in some network indices as the difference between inverse-derived and actual flows. However, topological network indices tended to be most robust to both the method of determining flows and to the inverse method. These results suggest that a goal function other than minimization of flows should be used when applying the inverse method to food web models. Comparison of food web models should be done with extreme care when different methodologies are used to estimate unknown flows and to aggregate system components. However, we propose that indices such as relative ascendency and relative redundancy are most valuable for comparing ecosystem models constructed using different methodologies for determining missing flows or for aggregating system components.