Clean fuels, lower emissions in red meat processing transport
This project will develop, test via in-field trials, and deliver a data-integrated Visualisation and Analytics (DiVA) platform based on Internet of Things technologies for heavy transport emissions, efficiency, and sustainability.
The delivered solution will include:
An Internet of Things (IoT) solution equipped with GPS module, onboard diagnostics tools, and sensors to monitor the heavy vehicle telematics and GHG emissions;
DiVA, a cloud-based dashboard for data visualisation and analytics; and
Cost benefit analysis for clean alternative fuels / technology options
The Australian red meat processor sector is a thriving value chain which employs 30,900 people with nearly 80% of meat processing employment located in regional areas. As expectations for decarbonisation of industrial sectors grow, the red meat industry seeks to remain a leader in sustainable operations and responsible business growth.
As in other industrial sectors, the red meat industry is also exploring clean or low emissions sources of energy and various operational upgrades to minimise its impact on the environment. According to the 2021 Australia’s National Greenhouse Gas inventory, the largest non-energy sector contribution to Australia’s greenhouse gas (GHG) emission is transport at 17.5%.
Since the red meat industry is one of the largest contributors to Australia’s GDP and export activity, it is critical to ensure while economic growth is sustained, environmental impacts are minimised. In this context, the transport activity associated with livestock inbound and processed goods outbound by heavy vehicles is a source of various types of emission.
In order to achieve carbon neutral by 2030 (CN30), the GHG emission associated with livestock and meat products logistics needs to be managed and reduced. To help achieve this objective, a preliminary step is to understand the representative baseline environmental footprint for red meat processor heavy transport tasks.
Currently, most techniques to estimate carbon footprint of heavy vehicle transport are based on mathematical estimation, using simplistic assumptions related to fuel consumption and truck productivity. In other words, limited studies have captured and analysed real world data from such operations.
As reported by the Australia National Greenhouse Accounts, such mathematical estimation remains inaccurate and only suitable to provide rough estimates, since the actual emission produced by each vehicle depends on many factors such as make/model, age, driving behaviour, carrying weight, and even environmental factors.
Therefore, an intelligent and accurate model for monitoring GHG emission by transport is in high demand by industrial sectors that rely on heavy vehicle operations. The Australian red meat industry is not an exception in this case, given its diverse range of operations and geographical dispersion.
This project aims to:
Develop an accurate representative baseline environmental footprint for red meat processor-owned heavy transport tasks
Identify and assess opportunities to improve environmental, social and economic outcomes for red meat processor-owned heavy transport tasks, including recent technological advancement in vehicle and fuel systems
Identify costs, benefits and suggest alternate use cases and pilot studies for future actions.
Please note …
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