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HKU Business School held its Inaugural ESG Conference

24 Nov 2023

On 24 November 2023, the HKU Business School held its Inaugural ESG Conference, setting the stage for impactful discussions on cutting-edge research findings and recommendations for pressing environmental and social challenges.

It was a remarkable gathering of 75 brilliant academics, including faculty members and PhD students from a range of disciplines such as Accounting, Economics, Finance, IMM, Management, and Marketing, making it a truly interdisciplinary event.

Some of the thought-provoking topics covered included the use of AI as a teaching tool to bridge the gender inequality gap, the impact of institutional investors’ selection of green stocks on portfolio risk and diversification, and the effectiveness of incorporating environmental factors in management compensation to encourage “greener” behaviour.

Thanks to the organizers of this event, Prof. Roni Michaely, Prof. Shipeng Yan, and Prof. Guojun He, as well as all the incredible speakers and attendees, who voluntarily dedicated a whole day to academic discussion and the exchange of knowledge, their contributions have played an integral role in the success of the ESG research and made the event truly inspiring and insightful.

Presentation Topics:

Family Friendly Workplace Policies

Julian Costas-Fernandez, Sebastian Findeisen, Anna Raute and Uta Scönberg

Government Procurement and Corporate Commitment to Climate Change

Omri Even-Tov, Guoman She, Lynn Wang, and Detian Yang

Can Publicizing Information on the Carbon Intensity of Insurers’ Investments Affect Their Carbon-Intensive Investments?

Jeffrey Ng and Xiao Zhang

Deceptive Environmental Disclosures during Conference Calls

Shuqing Luo, Detian Yang, Zhige Yu and Guochang Zhang

Physical Climate Change Exposure and Pollution Outsourcing along Supplier Chain

Bingkun Zhang

Corporate Social Responsibility and Product Price: Evidence from the 2013 Indian Companies Act

Qingwei Wang, Jiang Zhang and Hong Zou

How green is green? Anatomy of ESG funds’ selection

Dunhong Jin, Roni Michaely and Menghan Wang

A BIT less of ESG scandals: How does bilateral investment treaties termination affect foreign multinationals’ ESG practices abroad?

Xin Chen and Yifei Zhang

Beyond the Bench: The Role of Climate Litigation in Averting Climate Crisis

Roni Michaely, Zoey Yiyuan Zhou, Joe Hong Zou

ESG-based CEO Compensation: Theory and Empirical Evidence

Jing Li, Chuan Lin, Ke Na and Guochang Zhang

Can Artificial Intelligence Improve Gender Equality?Evidence from a Natural Experiment

Zhengyang Bao, Difang Huang and Chen Lin

Inflation and Household ESG Preference

Wenzhi Ding, Chen Lin and Mingzhu Tai

When Minority Entrepreneurs Talk about Problems: A Study of Problematization Narratives in Pitches and the Entrepreneur’s Ethnicity

Soojin Oh

Biodiversity Risk and Bank Loans

Mengdie Deng and Tse-Chun Lin

Greenwashing of the Green Building Certification

Sumit Agarwal, Eduardo Araral, Mingxuan Fan, Yu Qin and Huanhuan Zheng

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AI Image Generation Evaluation Results Released: ByteDance and Baidu Perform Well, DeepSeek Janus-Pro Falls Short
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AI Image Generation Evaluation Results Released: ByteDance and Baidu Perform Well, DeepSeek Janus-Pro Falls Short
The frontier of AI models has evolved beyond text processing to encompass the ability to understand and generate visual content. These models not only comprehend images but also generate visual content based on textual prompts. This study presents a systematic evaluation of the image generation capabilities of AI models, focusing on two core tasks: generating new images and revising existing images. Using carefully curated multidimensional test sets, we conducted a comprehensive evaluation of 22 AI models with image generation capabilities, including 15 text-to-image models and 7 multimodal large language models. The results show that ByteDance’s Dreamina and Doubao, as well as Baidu’s ERNIE Bot, demonstrate impressive performance in both new image generation and image revision tasks. Overall, multimodal large language models deliver superior performance compared to text-to-image models.