Strategic decisions for sustainable management at significant tourist sites

This research explores how tourist site management and human attitudes and behaviours can help decrease the pressure of tourism on the environment. Estimates show that, together with ancillary sectors, the tourism industry is expected to contribute approximately 6.5 gigatons of greenhouse gases by 2025. These emissions are primarily a result of tourists favouring air travel and luxury experiences that require more energy outputs. Additionally, tourism continues to grow and has become a routine activity for the middle class who travel more regularly on an annual basis. With growing middle classes in many developing countries, the number of tourists who will be able to afford recreational travel is estimated to increase exponentially. The pressures and demands of increasing tourist numbers can strain vulnerable natural sites. These predictions show that changes within the tourism industry fabric are necessary. Against this backdrop, this research employs a combined methodology. A survey methodology was employed to explore tourist attitudes towards tourism sites and their behaviours and decision making with a top-down and bottom-up approach. Additionally, an interview methodology of tourism field experts was employed to investigate the attitudes of the industry and how consumer behaviours may be influenced. Findings from the survey and interview discussions were employed to inform four managerial aspects. First, the ticket price of the tourist experience should be proportional to the value proposition of the experience. Second, a government-led framework could guide businesses towards sustainable management and educate their tourists on greener practices. Third, businesses could integrate sustainability issues into their marketing and advertising to create awareness and ensure the longevity of the site. Lastly, tourism bodies and businesses could increase their partnerships with local custodians to add cultural value and understand the visitor experience

Emotions and dog bites: Could predatory attacks be triggered by emotional states?

Dog biting events pose severe public health and animal welfare concerns. They result in several consequences for both humans (including physical and psychological trauma) and the dog involved in the biting episode (abandonment, relocation to shelter and euthanasia). Although numerous epidemiological studies have analyzed the different factors influencing the occurrence of such events, to date the role of emotions in the expression of predatory attacks toward humans has been scarcely investigated. This paper focuses on the influence of emotional states on triggering predatory attacks in dogs, particularly in some breeds whose aggression causes severe consequences to human victims. We suggest that a comprehensive analysis of the dog bite phenomenon should consider the emotional state of biting dogs in order to collect reliable and realistic data about bite episodes

Interconnections : an analysis of disassemblable building connection systems towards a circular economy

This study investigates the interconnection methods used to create a circular economy building featuring modularity and designed for disassembly and relocation. Designing modular buildings for disassembly and reuse can decrease waste production and material depletion, in line with the circular economy framework. Disassemblable buildings require connections to be easily accessible. Visible connections may be unpopular features; however, concealing these, yet leaving these accessible, presents a substantial design challenge. This study demonstrates solutions to this challenge by analyzing a purposely designed case study: the Legacy Living Lab. The challenges of disguising and sealing, such as by waterproofing, two types of connections are analysed: structural and non-structural. This study details the materials and connections used across the two analyzed connection types and compares the weights and reusability of components. Thus, a necessary case study is provided for practitioners to advance circular economy theory in the building industry. Notably, all connections in the Legacy Living Lab can be easily accessed with standard building tools, facilitating its disassembly and fostering component reusability

Strategies for the adoption of hydrogen-based energy storage systems : an exploratory study in Australia

A significant contribution to the reduction of carbon emissions will be enabled through the transition from a centralised fossil fuel system to a decentralised, renewable electricity system. However, due to the intermittent nature of renewable energy, storage is required to provide a suitable response to dynamic loads and manage the excess generated electricity with utilisation during periods of low generation. This paper investigates the use of stationary hydrogen-based energy storage systems for microgrids and distributed energy resource systems. An exploratory study was conducted in Australia based on a mixed methodology. Ten Australian industry experts were interviewed to determine use cases for hydrogen-based energy storage systems’ requirements, barriers, methods, and recommendations. This study suggests that the current cost of the electrolyser, fuel cell, and storage medium, and the current low round-trip efficiency, are the main elements inhibiting hydrogen-based energy storage systems. Limited industry and practical experience are barriers to the implementation of hydrogen storage systems. Government support could help scale hydrogen-based energy storage systems among early adopters and enablers. Furthermore, collaboration and knowledge sharing could reduce risks, allowing the involvement of more stakeholders. Competition and innovation could ultimately reduce the costs, increasing the uptake of hydrogen storage systems

Bayesian calibration, comparison and averaging of six forest models, using data from Scots pine stands across Europe

Forest management requires prediction of forest growth, but there is no general agreement about which models best predict growth, how to quantify model parameters, and how to assess the uncertainty of model predictions. In this paper, we show how Bayesian calibration (BC), Bayesian model comparison (BMC) and Bayesian model averaging (BMA) can help address these issues. We used six models, ranging from simple parameter-sparse models to complex process-based models: 3PG, 4C, ANAFORE, BASFOR, BRIDGING and FORMIND. For each model, the initial degree of uncertainty about parameter values was expressed in a prior probability distribution. Inventory data for Scots pine on tree height and diameter, with estimates of measurement uncertainty, were assembled for twelve sites, from four countries: Austria, Belgium, Estonia and Finland. From each country, we used data from two sites of the National Forest Inventories (NFIs), and one Permanent Sample Plot (PSP). The models were calibrated using the NFI-data and tested against the PSP-data. Calibration was done both per country and for all countries simultaneously, thus yielding country-specific and generic parameter distributions. We assessed model performance by sampling from prior and posterior distributions and comparing the growth predictions of these samples to the observations at the PSPs. We found that BC reduced uncertainties strongly in all but the most complex model. Surprisingly, country-specific BC did not lead to clearly better within-country predictions than generic BC. BMC identified the BRIDGING model, which is of intermediate complexity, as the most plausible model before calibration, with 4C taking its place after calibration. In this BMC, model plausibility was quantified as the relative probability of a model being correct given the information in the PSP-data. We discuss how the method of model initialisation affects model performance. Finally, we show how BMA affords a robust way of predicting forest growth that accounts for both parametric and model structural uncertainty

Parameter identification of the STICS crop model, using an accelerated formal MCMC approach

This study presents a Bayesian approach for the parameters’ identification of the STICS crop model based on the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm. The posterior distributions of nine specific crop parameters of the STICS model were sampled with the aim to improve the growth simulations of a winter wheat (Triticum aestivum L.) culture. The results obtained with the DREAM algorithm were initially compared to those obtained with a Nelder-Mead Simplex algorithm embedded within the OptimiSTICS package. Then, three types of likelihood functions implemented within the DREAM algorithm were compared, namely the standard least square, the weighted least square, and a transformed likelihood function that makes explicit use of the coefficient of variation (CV). The results showed that the proposed CV likelihood function allowed taking into account both noise on measurements and heteroscedasticity which are regularly encountered in crop modellingPeer reviewe

Towards a common methodology for developing logistic tree mortality models based on ring-width data

Tree mortality is a key process shaping forest dynamics. Thus, there is a growing need for indicators of the likelihood of tree death. During the last decades, an increasing number of tree-ring based studies have aimed to derive growth-mortality functions, mostly using logistic models. The results of these studies, however, are difficult to compare and synthesize due to the diversity of approaches used for the sampling strategy (number and characteristics of ‘alive’ and ‘death’ observations), the type of explanatory growth variables included (level, trend, etc.), and the length of the time-window (number of years preceding the alive/death observation) that maximized the discrimination ability of each growth variable. Here, we assess the implications of key methodological decisions when developing tree-ring based growth-mortality relationships using logistic mixed-effects regression models. As examples we use published tree-ring datasets from Abies alba (13 different sites), Nothofagus dombeyi (one site) and Quercus petraea (one site). Our approach is based on a constant sampling size and aims at (1) assessing the dependency of growth-mortality relationships on the statistical sampling scheme used; (2) determining the type of explanatory growth variables that should be considered; and (3) identifying the best length of the time window used to calculate them. The performance of tree-ring based mortality models was reasonably high for all three species (Area Under the receiving operator characteristics Curve: AUC > 0.7). Growth level variables were the most important predictors of mortality probability for two species (A. alba, N. dombeyi), while growth-trend variables need to be considered for Q. petraea. In addition, the length of the time window used to calculate each growth variable was highly uncertain and depended on the sampling scheme, as some growth-mortality relationships varied with tree age. The present study accounts for the main sampling-related biases to determine reliable species-specific growth-mortality relationships. Our results highlight the importance of using a sampling strategy that is consistent with the research question. Moving towards a common methodology for developing reliable growth-mortality relationships is an important step towards improving our understanding of tree mortality across species and its representation in dynamic vegetation models

Selecting parameters for Bayesian calibration of a process-based model: a methodology based on canonical correlation analysis

Bayesian statistics is becoming increasingly common in the environmental sciences because of developments in computers and sampling-based techniques for parameter estimation. However, the use of the Bayesian approach is still limited in forest research, especially for models with many parameters. Some studies have used parameter screening to make the calibration of a computationally expensive model possible. In this paper we introduce a new methodology for parameter screening, based on canonical correlation analysis. Furthermore we show how parameter screening impacts the performance of a process-based model. The methodology presented here can be generally applied and is particularly suitable for complex process-based models because it is not computationally demanding and is easy to implement. It provides an overall ranking in relation to all outputs of the model, as opposed to common sensitivity methods that analyze one model output variable at a time. We found that parameter screening can be used to reduce the computational load of Bayesian calibration, but only the least important parameters should be excluded from the calibration if we do not want to affect model performance. In this exercise, 25% of the parameters of a process-based forest model could be excluded from the calibration without affecting model performance. When calibration was limited to a more restricted number of parameters, model performance significantly deteriorated

Exploring environmental benefits of reuse and recycle practices : a circular economy case study of a modular building

Recent research indicates that circular economy practices have the potential to provide significant environmental benefits. In particular, recycling has been associated with reductions of greenhouse gas emissions. However, in this study, the authors posit that in a building context, environmental benefits of reuse practices could far surpass recycling. To test this, we evaluated the environmental benefits of a prototype and purpose-built, modular building designed for disassembly and reuse through a life cycle assessment of its components. We then compared the results of our life cycle assessment with the results of a contemporary construction approach with a focus on the recyclability of materials. Our results indicate that, compared to recycling, designing and building for reuse components offsets greenhouse gas emissions by 88% while also benefiting several other tested environmental indicators. Our findings help guide the judicious adoption of practices to reduce buildings’ waste production and greenhouse gas emissions