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Hong Kong halves environmental impact review times with AI in efficiency drive

Hong Kong authorities have slashed the time required for environmental impact assessments by up to half using artificial intelligence, with an official saying that AI adoption reduced wait times for some public services by up to 90 per cent. The first batch of 30 projects aimed at enhancing efficiency was announced on Monday. It involves 13 government departments, covering transport and food…

Hong Kong halves environmental impact review times with AI in efficiency drive

Hong Kong's authorities have successfully reduced the duration of environmental impact assessments by up to half through the implementation of artificial intelligence (AI), according to an official statement. The adoption of AI has led to a significant improvement in processing and waiting times for various public services, with some experiencing a reduction of up to 90 percent. This efficiency boost forms part of a broader initiative to enhance administrative procedures across the city.

The first phase of this project, announced on Monday, involves 30 initiatives across 13 government departments. These sectors include transport, food business licensing, leisure and sports facilities, environmental hygiene, complaint handling, and environmental protection. The rollout of these projects is set to continue into the next year.

Warner Cheuk Wing-hing, Deputy Chief Secretary and head of the government's AI Efficacy Enhancement Team, highlighted that the integration of AI has notably decreased processing and waiting times for several public services.

Among the key developments, the Environmental Protection Department has embraced AI in the environmental impact assessment (EIA) process. This EIA regulation mandates the evaluation and mitigation of environmental impacts for major development projects before their approval. By combining AI with technologies such as light detection and ranging, the Internet of Things, and data analytics, the assessment timeline has been compressed from 36 to 48 months to a more manageable 15 to 24 months.

This technological advancement has also facilitated real-time monitoring for environmental and bird surveillance, alongside reducing the time taken for tree baseline surveys by 70 percent.

Cheuk emphasized the government's ambition to extend AI applications to at least 100 administrative procedures by the end of 2024 and to 200 procedures by the end of 2027. The task force has been working in collaboration with AI companies to tailor these initiatives to the specific operational needs of each department, ensuring that the technologies align with current developments and trends in AI.

If a pilot department successfully integrates a solution, the experience, technologies, and operational models can be scaled across other departments with necessary adjustments.

Other notable initiatives include the Leisure and Cultural Services Department's plan to test an AI drowning detection system at two main pools in Wong Tai Sin and Ma On Shan, which will offer real-time monitoring to assist lifeguards in swiftly identifying potential drowning incidents. Given the ongoing challenge of a shortage of lifeguards, this system could be expanded to additional public swimming pools in the future.

In related news, the government previously piloted an AI-based drowning detection system at the Kwun Tong Swimming Pool and the Sun Yat Sen Memorial Park Swimming Pool in Sai Ying Pun in 2023 and 2024, respectively.

Additionally, the first batch of projects aims to expedite enforcement against illegal dumping in designated areas, prioritize cases received through the 1823 public inquiry and complaints hotline, combat scalping in leisure facility bookings, and accelerate the processes for renewing driving licences and obtaining food business licences. These initiatives collectively underscore Hong Kong's commitment to leveraging AI to improve efficiency and service delivery across various sectors.

Written by urgent.news from South China Morning Post - Hong Kong's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

Read the original at scmp.com →

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