Search

Global concerns around climate change and the volatility of conventional fuel prices have prompted researchers and technologists to make significant efforts to identify and exploit alternative energy sources that are cleaner and more sustainable. Wind energy has seen considerable development among these alternative energy sources, mainly due to its abundance and global availability for extraction and the existing knowledge within the aviation and aerospace fields. Many nations, including European countries, already operate offshore wind farms (OWFs) and are progressively carrying out new projects and expanding on other projects. The Australian offshore environment provides unique opportunities for wind energy extraction, particularly along the southern coast of mainland Australia and the regions around Tasmania, where substantially strong winds blow most of the year. A significant challenge to establishing wind farms is the selection of site locations with optimal outputs. This can become a complex decision-making problem if there are numerous options and no information from previous projects. This paper aims to develop a decision-making framework to select the optimal location for installing OWFs while addressing financial, performance-related, and availability-related objectives. This paper adopts a game-theoretical approach to develop a decision-support tool to account for the interdependencies of influencing factors and possible conflicts amongst the parties. The game model is applied to an OWF development case study in the Bass Strait, known for its dominant and strong winds. • A framework to find the optimal location for installing Offshore Wind Farms (OWFs). • Game theory decision-making methodology for offshore renewable energy applications. • Define the influencing factors and conflicts amongst the interested parties. • Apply to OWFs development in Bass Strait, known for its dominant and strong winds. [ABSTRACT FROM AUTHOR]

This paper aims to assess the uniqueness of stakeholder engagement activities in air route development (ARD) and identify what leadership and governance attributes are more relevant in the four critical stages of ARD. Our context is the Australian aviation market, analysing the differences in this process when developing routes to airports of different sizes. In-depth semi-structured interviews were used as the primary data collection tool. Twenty-two ARD experts from airports, airlines, and tourism authorities (TAs) shared their views on ARD and stakeholder engagement. Our results are presented using the four stages of ARD (i.e., development of route objectives, market research, route development activities, and route implementation) and key leadership and governance attributes identified in each of these stages. In a tourism sector heavily dependent on air transport, where Australian airports operate under a light-handed regime with little competition among major airports, we have identified strong partnerships between the involved stakeholders. While the airport often leads the ARD process, airports of different sizes engage differently. TAs play a significant role, particularly for regional and emerging airports. The results of this study analyse the ARD procedures and strategies used by airports, airlines, and TAs. • Australian aviation market encourages unique stakeholder engagement in ARD. • One of a few qualitative ARD studies used in-depth interviews. • Airports of different size engage differently in the ARD process. • Tourism authorities play key role for regional and emerging airports. [ABSTRACT FROM AUTHOR]

Illegal firewood collection has a series of unintended environmental, economic, and public safety impacts associated with harvesting undermining the development of a sustainable firewood industry. In this paper, we present Tasmanian firewood consumers with a labelling system and explore their willingness to pay for labelled firewood. We employ a discrete choice experiment with 239 potential firewood consumers. We estimate consumers' willingness to pay for different sources of firewood, coming from different distances, and whether it is important for a share of the proceeds to be invested in social or environmental priorities. To account for preference heterogeneity, we estimate a two−class latent class model and mixed multinomial logit model. The results from the first class of the latent class model and mixed multinomial logit model suggest that consumers are willing to pay a premium for repurposed wood, firewood sourced from agroforestry−based operations, proceeds invested in wildlife conservation, or the firewood comes from sites closer to their residence. The second class from the latent class model is characterized by consumers who are concerned only with the cost. Our survey respondents prefer their firewood to be accredited and regulated by independent non − profit bodies such as the Firewood Association of Australia. [ABSTRACT FROM AUTHOR]

• Machine learning models using acoustic indices can distinguish time of day in audio. • Such models may misclassify samples from dawn and day. • Models may need to be trained (calibrated) for each observation site. • Signal-to-noise ratio, spectral entropy and spectral cover appear most informative. Environmental sounds, such as bird calls, insects, animal and human activities, support monitoring the health of an area being listened to. Historically these observations needed to be made in the field, but in recent decades non-intrusive acoustic recorders can be deployed for long periods instead, reducing time spent in the field and increasing the volume of raw data collected. The volume of data that can be collected makes human-based processing impractical, so automated analysis approaches are required. Based on the observation that different times of the day exhibit characteristically different soundscapes, this paper investigates predictive (i.e., machine learning) models that use acoustic indices (a calculated representation of some aspect of the recording) to learn and later identify the gross time of day (dawn, day, evening or night). The analysis was based on recordings from north-west Tasmania, Australia, captured in Spring 2017, with 1-min segments of audio sampled at regular intervals across each day. No attempt was made to eliminate unwanted noise (such as wind and rain) before the audio was processed. While audio recordings will typically have accompanying time stamps, this study can be used as the basis for future work in environmental acoustics on: the preferred machine learning classifier; portability of models across sites; the quantity of data required for training; and feature selection. [ABSTRACT FROM AUTHOR]