Jacqueline C. K. Lam

Ecological modernisation, environmental innovation and competitiveness: the case of public transport in Hong Kong

This paper focuses on the role of environmental innovation in the context of the development of ecological modernisation theory and as a driver for firms to gain competitive advantage in the market. While ecological modernisation theory offers a variety of theoretical and prescriptive viewpoints on the mechanisms through which modern societies respond to the environmental risks of industrialism, only limited attention has been given to issues of technological innovation and their implications for company competitiveness. Using the public transport sector in Hong Kong as a case study, this paper explores how transport operators have deployed environmental innovation as a means of enhancing their competitive position in the market thereby helping also to address significant local environmental concerns.

CrowdTravel: scenic spot profiling by using heterogeneous crowdsourced data

Traveling is one of the most important entertainments in the modern society. In general, before traveling to an unfamiliar city, one of the possible ways of travel planning is to search travel information from travel-related websites. With the advances of mobile social networks, increasingly more people are willing to record and share their travel experience via social media, which provides abundant information for people who are going to make travel plans. Textual reviews and travelogues with large scale of photos are two kinds of popular social travel sharing. They are complementary to each other in terms of structure and content, forming a large amount of fragmented travel knowledge. Moreover, the ever-increasing reviews and travelogues may impose a huge burden on gaining and reorganizing travel knowledge. To address these issues, this paper proposes CrowdTravel, a multi-source social media data fusion approach for multi-aspect tourism information perception, which can provide travelling assistance for tourists by crowd intelligence mining. We first study the problem of discovering popular scenic spots over crowd contributed data. Second, we propose a cross-media multi-aspect correlation method to connect fragmented travel information. Then we mine popular travel routes from travelogues based on Sequential Pattern Mining Algorithm. Finally, we achieve cross-media information relevance based on the similarity between the reviews and image contexts. We conduct experiments over a dataset of several popular scenic spots in Beijing and Xi’an, which is collected from two major online travel websites, namely Dazhongdianping and Mafengwo. The results indicate that our approach attains fine-grained characterization for the scenic spots and delivers excellent performance.

Video-based Emotion Recognition Using Deeply-Supervised Neural Networks

Emotion recognition (ER) based on natural facial images/videos has been studied for some years and considered a comparatively hot topic in the field of affective computing. However, it remains a challenge to perform ER in the wild, given the noises generated from head pose, face deformation, and illumination variation. To address this challenge, motivated by recent progress in Convolutional Neural Network (CNN), we develop a novel deeply supervised CNN (DSN) architecture, taking the multi-level and multi-scale features extracted from different convolutional layers to provide a more advanced representation of ER. By embedding a series of side-output layers, our DSN model provides class-wise supervision and integrates predictions from multiple layers. Finally, our team ranked 3rd at the EmotiW 2018 challenge with our model achieving an accuracy of 61.1%.

A review on health cost accounting of air pollution in China

Over the last three decades, rapid industrialization in China has generated an unprecedentedly high level of air pollution and associated health problems. Given that China accounts for one-fifth of the world population and suffers from severe air pollution, a comprehensive review of the indicators accounting for the health costs in relation to air pollution will benefit evidence-based and health-related environmental policy-making. This paper reviews the conventional static and the new dynamic approach adopted for air pollution-related health cost accounting in China and analyzes the difference between the two in estimating GDP loss. The advantages of adopting the dynamic approach for health cost accounting in China, with conditions guaranteeing its optimal performance are highlighted. Guidelines on how one can identify an appropriate approach for health cost accounting in China are put forward. Further, we outline and compare the globally-applicable and China-specific indicators adopted by different accounting methodologies, with their pros and cons being discussed. A comprehensive account of the available databases and methodologies for health cost accounting in China are outlined. Future directions to guide health cost accounting in China are provided. Our work provides valuable insights into future health cost accounting research in China. Our study has strengthen the view that the dynamic approach is comparatively more preferred than the static approach for health cost accounting in China, if more data is available to train the dynamic models and improve the robustness of the parameters employed. In addition, future dynamic model should address the socio-economic impacts, including benefits or losses of air pollution polices, to provide a more robust policy picture. Our work has laid the key principles and guidelines for selecting proper econometric approaches and parameters. We have also identified a proper estimation method for the Value of Life in China, and proposed the integration of engineering approaches, such as the use of deep learning and big data analysis for health cost accounting at the fine-grained level (city-district or sub-regional level). Our work has also identified the gap for more accurate health cost accounting at the fine-grained level in China, which will subsequently affect the quality of health-related air pollution policy decision-making at such levels, and the health-related quality of life of the citizens in China.

Promoting technological environmental innovations: the role of environmental regulation

This paper reviews and discusses the debate over the effectiveness of environmental regulation in promoting industrial Technological Environmental Innovation (TEI). Using the innovation-friendly regulatory principles adapted from Porter and van der Linde (1995a, 1995b), this paper demonstrates how properly designed and implemented environmental regulation (TEI promoting regulation) has played a critical role in promoting TEI in the transport industry in California and Hong Kong. In both cases, it has shown that stringent environmental regulations that send clear and strong signals for future environmental performance requirements are critical in promoting TEIs in the public transport industries. Unlike traditional command-and-control regulations, TEI promoting regulations are strongly supported by incentive and capability-enhancing measures.

Multi-region Ensemble Convolutional Neural Network for Facial Expression Recognition

Facial expressions play an important role in conveying the emotional states of human beings. Recently, deep learning approaches have been applied to image recognition field due to the discriminative power of Convolutional Neural Network (CNN). In this paper, we first propose a novel Multi-Region Ensemble CNN (MRE-CNN) framework for facial expression recognition, which aims to enhance the learning power of CNN models by capturing both the global and the local features from multiple human face sub-regions. Second, the weighted prediction scores from each sub-network are aggregated to produce the final prediction of high accuracy. Third, we investigate the effects of different sub-regions of the whole face on facial expression recognition. Our proposed method is evaluated based on two well-known publicly available facial expression databases: AFEW 7.0 and RAF-DB, and has been shown to achieve the state-of-the-art recognition accuracy.

FreeSense: A Robust Approach for Indoor Human Detection Using Wi-Fi Signals

Human detection aims to monitor how people are moving in an area of interest. There are many potential applications such as asset security monitoring, emergency management, and elderly care, etc. With the development of wireless sensing technique, Wi-Fi-based human detection method carries great potential due to advantages of pervasive accessibility and coverage flexibility. Previous studies have investigated the detection of human movements via signal variations. However, affected by noises, such as multi-path effect and device difference, existing approaches cannot achieve high accuracy and low false alarm rate at the same time. In this paper, we propose FreeSense, a novel Wi-Fi-based approach for human detection. Different from previous studies that characterize the variation of temporal wireless signals or calculate the deviation of Channel State Information (CSIs) from a normal profile, we will detect human movements by identifying whether there is any phase difference between the amplitude waveforms of multiple receiving antennas. In addition, we also model the sensing coverage for movements of different granularities in open space and propose a method to estimate the coverage range. Extensive experiments demonstrate that FreeSense can achieve an average false positive rate (FP) of 0.53% and an average false negative rate (FN) of 1.40%. The coverage range estimation method can achieve an average accuracy of 1.36 m, sufficient to guide the deployment of devices for human detection indoors.

CrowdTracker: Optimized Urban Moving Object Tracking Using Mobile Crowd Sensing

This paper proposes CrowdTracker, a novel object tracking system based on mobile crowd sensing (MCS). Different from traditional video-based object tracking approaches, CrowdTracker recruits people to collaboratively take photographs of the object to achieve object movement prediction and tracking. The optimization objective of CrowdTracker is to effectively track the moving object in real time and minimize the cost on user incentives. Specifically, the incentive is determined by the number of workers assigned and the total distance that workers move to complete the task. In order to achieve the objective, we propose the movement prediction (MPRE) model for object movement prediction and two other algorithms for task allocation, namely, T-centric and P-centric. T-centric selects workers in a task-centric way, while P-centric allocates tasks in a people-centric manner. By analyzing a large number of historical vehicle trajectories, MPRE builds a model to predict the object’s next position. In the predicted regions, CrowdTracker selects workers by utilizing T-centric or P-centric. We evaluate the algorithms over a large-scale real-world dataset. Experimental results indicate that CrowdTracker can effectively track the object with a low incentive cost.

Transitioning Towards a Low-Carbon Hydrogen Economy in the United States: Role of Transition Management

This paper describes the process of transitioning to a low-carbon hydrogen economy in the United States and the role of transition management (TM) in this process. Focusing on the transition process for hydrogen-based energy and transport systems in the United States, especially California, this study outlines the key characteristics of TM that have been employed in managing the transition. Several characteristics of TM have been noted in the United States’ hydrogen transition, including: (a) the complementarity of the long-term vision with incremental targets, (b) the integration of top-down and bottom-up planning, (c) system innovations and gradualism, (d) multi-level approaches and interconnectedness, and (e) reflexivity by learning and experimenting. These characteristics are instrumental in bringing about the development and initial commercialization of hydrogen fuel cell vehicles and related energy infrastructure in the United States.