Resources

Explore our collection of articles, technical research papers and service sheets showcasing our expertise across environmental and sustainability services.

Our latest articles

Article

NGER Scheme 2026 Updates: Why This Consultation Matters for Your Business

Australia is proposing significant updates to the NGER Scheme from 1 July 2026 that could materially affect how businesses measure and report emissions, particularly under the Safeguard Mechanism. With consultation open until 8 May 2026, this process offers a critical opportunity for organisations to understand the impacts and influence the final rules before they take effect.

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Our latest research

Technical Paper

Acoustic Effect of Platform Screen Doors: A Case Study of IMES Metro Station, Istanbul

This case study demonstrates how Platform Screen Doors (PSDs) at IMES Station significantly improved the acoustic environment by providing both safety and effective noise isolation, reducing reverberation and enhancing speech intelligibility. The findings show that integrating PSDs enables compliant, cost-efficient acoustic design by lowering noise transmission and minimizing the need for extensive additional treatments.

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Technical Paper

Modeling the Effect of Railpad Stiffness on Railway Ground-Borne Vibration Levels

This study develops a theoretical model to assess how railpad stiffness affects ground-borne vibrations under varying train speeds and axle loads, highlighting its critical role in vibration transmission. It finds that while soft pads reduce high-frequency vibration and stiff pads limit deflection but increase transmission, an optimal intermediate stiffness provides the best balance between vibration control and structural performance.

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Technical Paper

Correlation of Traffic Noise Parameters in Queensland

The current available approaches for predicting road traffic noise level indicators rely solely on Linear Regression models that use either LA10(18H) or LA10(1H) as an input variable. This paper extends the prediction scope of regression models to include important indicators and additional road traffic factors as input variables and compares the performance of several machine learning regression methods.

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