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Energy is a resource of strategic importance for high density cities. International trade reshapes the urban economy and industrial structure of a city, which will indirectly affect energy use. As an international trade hub, Hong Kong relies on the import and export of services. Energy performance in the international trading of these services needs to be properly understood and assessed for Hong Kong’s urban renewal efforts. The paper aims to discuss these issues.

This study evaluates Hong Kong’s embodied energy in service trades based on an input-output analysis. The three criteria used for assessment include trading areas, industry sector and trade balance.

Analyzed by region, results show that Mainland China and the USA are the two largest sources of embodied energy in imports of services, while Mainland China and Japan are the two largest destinations of exports. In terms of net embodied energy transfer, Hong Kong mainly receives net energy import from Mainland China and the USA and supplies net energy export to Japan, the UK and Taiwan. Among industry sectors, manufacturing services, transport and travel contribute most significantly to the embodied energy in Hong Kong’s imported services, while transport and travel contribute most to the energy embodied in exported services.

This study identifies the characteristics of energy consumption of service trading and establishes a feasible approach to analyze energy performance of service trade in energy-deficient Hong Kong for the first time. It provides necessary understanding and foundation for developing energy strategies in a service-based, high density urban economy.

This study proposes an approach to solve the fundamental problem in using query-based methods (i.e. searching engines and patent retrieval tools) to screen patents of information and communication technology in construction (ICTC). The fundamental problem is that ICTC incorporates various techniques and thus cannot be simply represented by man-made queries. To investigate this concern, this study develops a binary classifier by utilizing deep learning and NLP techniques to automatically identify whether a patent is relevant to ICTC, thus accurately screening a corpus of ICTC patents.

This study employs NLP techniques to convert the textual data of patents into numerical vectors. Then, a supervised deep learning model is developed to learn the relations between the input vectors and outputs.

The validation results indicate that (1) the proposed approach has a better performance in screening ICTC patents than traditional machine learning methods; (2) besides the United States Patent and Trademark Office (USPTO) that provides structured and well-written patents, the approach could also accurately screen patents form Derwent Innovations Index (DIX), in which patents are written in different genres.

This study contributes a specific collection for ICTC patents, which is not provided by the patent offices.

The proposed approach contributes an alternative manner in gathering a corpus of patents for domains like ICTC that neither exists as a searchable classification in patent offices, nor is accurately represented by man-made queries.

A deep learning model with two layers of neurons is developed to learn the non-linear relations between the input features and outputs providing better performance than traditional machine learning models. This study uses advanced NLP techniques lemmatization and part-of-speech POS to process textual data of ICTC patents. This study contributes specific collection for ICTC patents which is not provided by the patent offices.