Indicators of perceived naturalness as drivers of landscape preference

The concept of naturalness is one of the more studied concepts in landscape preference research and describes how close a landscape is to a perceived natural state. In this study we explored the relationship between landscape preference and three landscape indicators of naturalness (level of succession, number of woodland patches and shape index of edges). We used computer-generated visualisations of a hypothetical landscape containing pasture and broadleaved woodland. In the landscape simulations we altered the values of the naturalness indicators between the levels of low, medium and high, creating 27 different visualisations. The survey was distributed as an on-line survey in seven different languages and obtained 703 respondents. The study showed a strong relationship with preference for both the level of succession and number of woodland patches, and a weaker relationship with shape index of edges. The two demographic factors which were shown to contribute most to the formation of preference were gender and having a landscape related profession. The results suggest that the selected indicators are more important drivers of preference than demographic factors.     

Externalities of Fugitive Dust

It is known that fugitive dust can cause human health and environmental problems, alone or in combination with other air pollutants. These problems are referred to as ‘external costs’ that have been traditionally ignored. However, there is a growing interest towards quantifying externalities to assist policy and decision-making. With this in mind, the present study aimed at discussing the environmental regulations that deal with fugitive dust, the impact of fugitive dust on human health and global climate system, and the available methods for calculating fugitive dust externalities. The damage cost associated with human health and global environmental problems was predicted based on the environmental strategy priority model. The damage cost estimated by the model ranged from 40 to 374 EUR/kg of emitted fugitive dust with a mean value of 120 EUR/kg of emitted fugitive dust. It was also found that PM_2.5 and PM₁₀ have contributed to about 60% and 36% of the estimated damage cost, respectively. The remaining 4% was attributed to both nitrate and sulfate aerosols.     

Predicting the conservation status of data‐deficient species

There is little appreciation of the level of extinction risk faced by one‐sixth of the over 65,000 species assessed by the International Union for Conservation of Nature. Determining the status of these data‐deficient (DD) species is essential to developing an accurate picture of global biodiversity and identifying potentially threatened DD species. To address this knowledge gap, we used predictive models incorporating species’ life history, geography, and threat information to predict the conservation status of DD terrestrial mammals. We constructed the models with 7 machine learning (ML) tools trained on species of known status. The resultant models showed very high species classification accuracy (up to 92%) and ability to correctly identify centers of threatened species richness. Applying the best model to DD species, we predicted 313 of 493 DD species (64%) to be at risk of extinction, which increases the estimated proportion of threatened terrestrial mammals from 22% to 27%. Regions predicted to contain large numbers of threatened DD species are already conservation priorities, but species in these areas show considerably higher levels of risk than previously recognized. We conclude that unless directly targeted for monitoring, species classified as DD are likely to go extinct without notice. Taking into account information on DD species may therefore help alleviate data gaps in biodiversity indicators and conserve poorly known biodiversity. Predección del Estado de Conservación de Especies con Deficiencia de Datos     

Contrasting Global Trends in Marine Fishery Status Obtained from Catches and from Stock Assessments

Abstract: There are differences in perception of the status of fisheries around the world that may partly stem from how data on trends in catches over time have been used. On the basis of catch trends, it has been suggested that about 70% of all stocks are overexploited due to unsustainable harvesting and 30% of all stocks have collapsed to <10% of unfished levels. Catch trends also suggest that over time an increasing number of stocks will be overexploited and collapsed. We evaluated how use of catch data affects assessment of fisheries stock status. We analyzed simulated random catch data with no trend. We examined well‐studied stocks classified as collapsed on the basis of catch data to determine whether these stocks actually were collapsed. We also used stock assessments to compare stock status derived from catch data with status derived from biomass data. Status of stocks derived from catch trends was almost identical to what one would expect if catches were randomly generated with no trend. Most classifications of collapse assigned on the basis of catch data were due to taxonomic reclassification, regulatory changes in fisheries, and market changes. In our comparison of biomass data with catch trends, catch trends overestimated the percentage of overexploited and collapsed stocks. Although our biomass data were primarily from industrial fisheries in developed countries, the status of these stocks estimated from catch data was similar to the status of stocks in the rest of the world estimated from catch data. We conclude that at present 28–33% of all stocks are overexploited and 7–13% of all stocks are collapsed. Additionally, the proportion of fished stocks that are overexploited or collapsed has been fairly stable in recent years.     

Assessing the sustainability of water governance systems: the sustainability wheel

We present and test a conceptual and methodological approach for interdisciplinary sustainability assessments of water governance systems based on what we call the sustainability wheel. The approach combines transparent identification of sustainability principles, their regional contextualization through sub-principles (indicators), and the scoring of these indicators through deliberative dialogue within an interdisciplinary team of researchers, taking into account their various qualitative and quantitative research results. The approach was applied to a sustainability assessment of a complex water governance system in the Swiss Alps. We conclude that the applied approach is advantageous for structuring complex and heterogeneous knowledge, gaining a holistic and comprehensive perspective on water sustainability, and communicating this perspective to stakeholders.     

Creating local ecological footprints in a North American context

Many urban areas face conflicts between economic growth and environmental limitations. The ecological footprint is popular for communicating the global impacts of local activities, and if calculated at the local level, should be useful in emphasising the impacts of local planning, engaging community members, and allowing intra-community comparisons. We compare the two dominant methods for local ecological footprints: the compound and component methods. Both fail to adequately represent local footprints in North America. We propose a hybrid component method that takes advantage of the benefits of the component method but incorporates interpolation from the compound method to address data limitations. Grounded in Montreal, Canada, a city currently facing significant conflict between environment and growth, the study applies the hybrid method and discusses its potential benefits and limitations for community activists and planners.     

Local aspects of UK renewable energy development: exploring public beliefs and policy implications

Despite several recent UK initiatives to promote renewable energy development at the local level, little research has been carried out to investigate public beliefs about aspects of local renewable energy development. This research attempted to address this gap. Empirical data were collected at the Awel Aman Tawe development in South Wales immediately before and after a public participation process. The results indicate that support for specific aspects of local energy development (partnership with local communities, local use of generated energy and profits put back into the local community) was consistently high across time, with support for local ownership at a slightly lower level, yet still high. Secondly, socio-demographical analyses indicated that personal factors such as respondents' age, gender and employment status were important in shaping several of these beliefs. Since UK renewable energy development has often been controversial and subject to delay, results suggest that the adoption of a locally embedded development approach by public and private sector stakeholders will be strongly supported by local people. The implications of the results are discussed in relation to existing industry best practice guidelines and recent policy initiatives.     

Occupational Entropy and Mind Indicators for Sustainable Energy Development

Abstract

A concept of occupational entropy is developed and related to the efficient use of mental (cognitive, emotional, and moral) resources and capacities. The corresponding “mind” indicators and pertinent response actions have proven essential for monitoring the state and projecting the behavior changes toward energy sustainability, as well as sustainable development in general.     

Is the convenience yield a good indicator of a commodity's supply risk?

A strong increase in the demand for some commodities over the last decade will have a major impact on their future supply situation. Of increasing importance, therefore, is an assessment of a commodity's criticality, and especially its supply risk, by appropriate indicators. The literature has proposed numerous indicators of the supply risk. Here, we use the convenience yield of commodity futures as a supply risk indicator to address some of the major shortcomings of existing indicators, especially regarding their predictive power. This paper aims to test the applicability of the convenience yield as an indicator of a commodity's future supply risk. Therefore, we calculate historical convenience yields for 3-, 15-, and 27-month futures contracts for five major industrial metals (aluminum, copper, lead, nickel, and zinc) during the period 1999 to 2011. We compare the convenience yields at the beginning of the contract period to known indicators at maturity to find that the convenience yield has generally predictive power for the static stock lifetime (i.e., inventory volume/turnover) and future spot prices. Furthermore, we find that, with some restrictions, the convenience yield is an applicable indicator of a commodity's supply risk.