This study explores the impact of the Carbon Inclusion Pilot Scheme in Guangdong on carbon emission efficiency in cities within the Greater Bay Area (GBA) utilizing panel data from 2011 to 2022 in a staggered Difference-in-Difference setting. The analysis reveals that the policy has a significant positive effect on carbon emission efficiency in GBA cities, leading to spatial spillover effects that benefit neighboring areas. Mechanism testing indicates that the policy achieves these results by reducing per capita electricity consumption and promoting the upgrading of industrial structures within the GBA. These findings offer valuable insights for policymakers and stakeholders interested in promoting low-carbon city development and aligning carbon emission reduction strategies with economic growth objectives.

Central Bank Digital Currency (CBDC) is an emerging field with strong global interest. According to the Atlantic Council’s Central Bank Digital Currency Tracker, by February 2025, 134 countries & currency unions, representing 98% of global GDP, are exploring a CBDC. There are 3 countries which have fully launched a CBDC, another 44 countries, including China, are running their CBDC pilot tests. As for Macau, by the end of 2024, the Macau SAR Government has completed the development of the prototype system of e-MOP, and the sandbox testing and public testing are going to initiate shortly. It is then justified to conduct a comprehensive analysis on e-MOP for its impacts on the Macau economy and community. Therefore, this research is organized to review the current stage of development of e-MOP, to address the possible impacts of e-MOP to the Macau economy and community, to justify the adequacy of the retail type CBDC to Macau, as well as to estimate the potential contributions of e-MOP to the economic diversification of Macau. Finally, it is also our intention to provide policy suggestions to reduce the challenges and risk of the e-MOP.

Government efficiency is a critical measure of public sector management capacity and service delivery, directly shaping policy implementation, public trust, and societal well-being. Despite its importance, the factors influencing government efficiency and the mechanisms through which it can be optimized remain underexplored. This project investigates the intersection of environmental governance and administrative performance, specifically focusing on the impact of air pollution on government efficiency. Leveraging data from the People’s Daily Online Leaders’ Message Board, this study employs natural language processing (NLP) techniques and an instrumental variable approach to conduct a rigorous empirical analysis. Preliminary findings indicate that air pollution significantly hampers government efficiency, with a 100% increase in the air pollution index resulting in an extension of 3.769 working days in government response time. Furthermore, air pollution intensifies negative sentiment in public messages, which further undermines government responsiveness. By identifying this transmission channel, the project advances theoretical understanding of the determinants of government efficiency and offers actionable policy insights to enhance governance performance in environments affected by air pollution. This research will provide a robust evidence base for policymakers to design interventions that mitigate the adverse effects of environmental factors on administrative effectiveness.

The project aims to develop a general equilibrium model to quantify the welfare implications of tourism, a rapidly growing service sector in developing countries. We will begin by combining global data on bilateral arrivals, sectoral expenditures, travel regulations, and spatial datasets to estimate the effects of tourism on domestic economic outcomes through reduced-form econometric analyses. The estimated effects will then inform the structure and calibration of the quantitative model. Building on the work of Faber and Gaubert (2019) and Caliendo et al. (2018), we will develop a multi-sector, cross-country quantitative spatial model that incorporates tourism as a service trade channel, along with input-output linkages and spillover effects.

Armed with the calibrated model, the analysis will proceed in two parts. First, we will conduct a counterfactual analysis to derive the welfare implications of tourism, including the impact of Chinese outbound tourism on foreign income and the consequences of COVID-19 lockdowns or restrictive Chinese visa policies on global welfare. Second, we will implement a detailed, Macao-specific analysis that incorporates elements consistent with its stylized facts and relevant policy implications.

The Hengqin Cooperation Zone (HCZ) facilitates cross-border investment by enabling investors from mainland China, Macao residents, and foreign investors to trade and invest in assets across both markets. Launched as part of broader economic integration in 2021 (https://www.hengqin.gov.cn/macao_zh_hans/hzqgl/index.html), this initiative marks a significant advancement in promoting the internationalization of Macao’s investors, allowing them to engage in both mainland and foreign assets.

We analyze the impact of HCZ on Macao’s residents. Given that Macao SAR is a capital-abundant economy, we anticipate that the announcement of HCZ will accelerate cross-border outflows into foreign economies, including bank loans and deposits, equity investments, bond investments, and foreign direct investments. Additionally, we observe that investments in Portuguese-speaking economies have been growing faster than those in non-Portuguese countries in the West. Eastern economies are also experiencing rapid growth, and these trends are not solely driven by mainland China. HCZ plays a crucial role in connecting Macao with both Eastern and Western markets.

Volatility forecasting has traditionally been examined within univariate frameworks, focusing on individual stocks. However, multivariate settings are more empirically relevant, given the significant correlations observed among asset volatilities. Building on the success of univariate rough volatility models, this project investigates the advantages of a multivariate rough volatility approach. We propose the multivariate fractional Brownian motion (mfBm) as our core model—a natural extension of the univariate fractional Brownian motion (fBm)—which captures both the rough dynamics of individual asset volatilities and their interdependencies, closely aligning with observed market behaviors. Our objectives are threefold: first, to propose a novel estimation method for all model parameters, deriving the consistency and asymptotic normality of these estimators using in-fill asymptotics, given the absence of a drift term in fBm-type models; second, to provide theoretical insights into the forecasting benefits of the multivariate framework; and third, to validate these estimators through simulation studies and assess forecasting performance via empirical analysis. This approach aims to enhance volatility forecasting.

In this project, we plan to introduce a new staggered difference-in-differences (DiD) method for causal inference. Our model specification lies between the canonical DiD model and the most heterogeneous DiD model. In the canonical DiD model, all treatment effects are assumed to be the same. Whereas in the most heterogeneous DiD model, all treatment effects are assumed to be different. In our model, treatment effects are classified. The number of groups is a parameter, so is the treatment effect for each group. The K-means method is used to estimate the number of groups. Asymptotic theory is developed. Examples are illustrated to highlight the usefulness of the new approach.

This project aims to enhance the estimation and prediction accuracy of conditional factor models in high-dimensional financial data using transfer learning techniques. By utilizing auxiliary datasets alongside the primary target dataset, we propose a novel transfer learning approach combined with instrumented principal component analysis (IPCA), named Trans-IPCA. The method includes a data-driven procedure to identify informative auxiliary samples, ensuring the effective transfer of useful information while avoiding negative transfer effects. Monte Carlo simulations demonstrate the superior performance of the proposed Trans-IPCA method over existing classical and penalized IPCA methods in both in-sample fitting and out-of-sample forecasting scenarios. The outcomes of this research promise significant methodological advancements and improved empirical applications in asset pricing.

This study investigates the emerging use of livestreaming as an innovative marketing tool among Chinese mutual funds. In an increasingly digitalized financial landscape, mutual fund companies in China are leveraging livestream platforms to engage retail investors in real time—marking a significant shift from traditional marketing approaches. This research aims to document the stylized facts and patterns in Chinese mutual funds livestreaming, and assess the effectiveness of livestreaming in enhancing investor outreach, increasing fund subscriptions, and strengthening brand loyalty. It further seeks to uncover the underlying mechanisms driving this digital marketing transformation within the mutual fund sector.

Paying dividends is one of the most critical financial decisions made by a firm and motivates a large strand of corporate finance research. In this paper, we conduct a field experiment to test four prominent dividend theories and shed light on the dividend puzzle.

The four prominent theories in the dividend literature are agency theory, bird-in-hand theory, signaling theory, and tax clientele theory. These theories relax the frictionless market assumption of the MM theorem by recognizing various market frictions. However, research on these theories has produced mixed findings, partly due to the endogenous nature of dividend policy. Overlaps between theories further obscure distinct effects, and reverse causality complicates interpretations—for example, whether investors select dividend-paying stocks or firms cater to such investors. This unresolved debate underscores the importance of dividend policy in corporate finance, necessitating innovative approaches for deeper insights.

Our experimental design leverages shareholder engagement to influence managerial perceptions, providing robust causal evidence and enabling the evaluation of multiple dividend theories simultaneously (Bowley et al., 2023). This approach offers a replicable framework for addressing key questions in financial economics and inspires future research using randomized experiments.

This project investigates the economic and governance implications of Corporate Opportunity Waivers (COWs)—a regulatory mechanism that allows executives to pursue external business ventures without violating fiduciary duties. While COWs are designed to attract capital and promote entrepreneurial flexibility, they may carry unintended consequences—namely, the heightening of agency conflicts and a potential increase in firms’ cost of capital. This study explores whether the financial benefits of COWs ultimately outweigh the agency risks and governance challenges they inadvertently introduce.

Combining econometric methods with machine learning-based textual analysis, the project examines how market participants perceive and respond to agency risks associated with COW adoption. A difference-in-differences (DiD) design will identify causal effects on firm financing outcomes, while sentiment analysis and topic modeling will quantify evolving perceptions from corporate disclosures, earnings calls, and media coverage.

The research contributes to the literature on corporate governance, financial regulation, and capital formation by evaluating whether COWs enhance financing efficiency or undermine shareholder interests. Findings will provide actionable insights for policymakers, investors, and scholars concerned with balancing regulatory flexibility and managerial accountability.

This study systematically examines structural flaws in next-generation automated market maker (AMM) protocols like UniswapX, where arbitrageurs exploit high gas fees for risk-free profits via transaction front-running, shifting systemic losses to liquidity providers (LPs). Validators monopolize arbitrage rents through gas auctions, creating centralized value extraction, while high-frequency arbitrage and liquidity imbalances risk market paralysis. To address these issues, the research proposes a triple-layered innovation framework. Theoretically, it develops an AMM analytical model integrating on-chain constraints with dynamic game theory, capturing validator rent extraction and liquidity fragility thresholds. Technologically, it introduces a hybrid off-chain/on-chain architecture resistant to miner extractable value (MEV) and a zero-knowledge proof (ZKP)-driven decentralized governance model, achieving tripartite equilibrium among fairness, efficiency, and stability. Practically, it designs volatility-sensitive dynamic liquidity range adjustments, liquidity option-based hedging instruments, and cross-chain MEV tax redistribution protocols to mitigate the “tragedy of the commons” in decentralized liquidity systems. The study transcends conventional DEX designs focused on efficiency optimization by establishing a novel DeFi protocol methodology, architectural innovation, and market resilience mechanisms. These contributions collectively resolve incentive misalignments, infrastructure centralization, and systemic instability, providing a sustainable framework for decentralized exchange ecosystems through integrated theoretical, technological, and practical advancements.

The advent of generative AI technologies has profoundly impacted consumers in multifaceted ways. Traditionally, consumers have relied on search engines (e.g., Google) and word-of-mouth from both online and offline platforms to gather information for their decision-making processes (Lynch & Zauberman, 2007). The emergence of AI technologies offers a novel decision-making aid (Duan, Edwards, & Dwivedi, 2019). GenAI is capable of providing personalized itineraries and precise recommendations through deep analysis of vast travel data. For instance, travel companies like Expedia and Kayak are integrating their services into generative AI platforms, enabling consumers to solicit travel advice, plan, and book trips directly through these platforms. This research project aims to investigate the factors influencing consumer adoption of and trust in generative AI technologies, and how these technologies impact decision quality and satisfaction.

Guided by the consumer decision-making process frameworks of Engel, Blackwell, and Miniard (1986), and modified by Darley, Blankson, and Luethge (2010), consumer decision-making involves five stages: need recognition, information search, evaluation of alternatives, purchase, and post-purchase evaluation. Generative AI technologies are expected to predominantly influence the information search and alternatives evaluation stages by streamlining these processes through comprehensive and pertinent information provision. This, in turn, may impact decision quality through consumers’ perceived value, satisfaction, and regret after using AI technologies as decision-making aids, compared to traditional aids like online reviews and personal consultations.

This research hypothesizes that assistance from generative AI technologies will lead to better-informed decisions, higher satisfaction, and reduced regret due to its efficient and targeted information provision. However, it may also reduce users’ sense of agency and autonomy, impacting their overall happiness. This research project will provide a nuanced understanding of generative AI technologies’ role in enhancing consumer decision-making and overall well-being and provide actionable suggestions to practitioners in Smart Tourism industry.

Drawing experience from Macao’s smart tourism development, this research proposal focuses on the exploration of legal and ethical implications inhabiting health research data sharing within the smart medical tourism industry. This research primarily aims at understanding the dichotomy between advancing health research and upholding data security, while navigating the challenges of various regulatory frameworks. The first element of this research involves identifying the legal barriers affecting health data sharing. These constraints could potentially hinder international health research and limit the quality and accessibility of healthcare services within the smart medical tourism industry. The goal is to provide a foundation for standardization and reforms. Based on the legal analysis in the first element, the second element will explore the establishment of an international health research data sharing mechanism. This mechanism should ensure that it includes countries from the Global South to achieve more comprehensive cooperation, thereby increasing the inclusivity and diversity of global health research data. This part will focus on discussing data visiting and related governance structures and standards. The final aspect addresses the paradox between the necessity for openness in health research to accelerate scientific discovery and the requirement to protect data security and patient confidentiality. Given the escalating growth of the medical tourism industry, the study’s outcomes may build a robust foundation for prospective ethical and legal guidelines, become a cornerstone for policy-makers and stakeholders in this domain, and then establish a safe, transparent, and sustainable data-sharing environment for the medical tourism industry. This research also emphasizes the importance of technological integration, inclusivity, and user-centric approaches in enhancing service quality and accessibility. This study highlights the need for greater public awareness and deeper technological adoption to ensure the effective and ethical use of health data in smart medical tourism.

This study aims to investigate the evolving use of Chinese vocabulary in Macao’s hotel and food and beverage industries, particularly in the context of smart tourism and multilingual communication. In recent years, Macao’s tourism industry saw a significant recovery, welcoming receiving 28.23 million visitors in 2023, which amounted to approximately 70% of pre-pandemic levels. Notably, tourists from mainland China accounted for 67.5%, making up the majority of visitors. As Macao’s economy heavily relies on tourism, its GDP increased by 80.5% in 2023, largely due to the surge in tourism revenue. In terms of language, Macao implements a “trilingual written and quadrilingual spoken languages” policy, and with the increasing influx of tourists, the use of Chinese, English, and Portuguese in the tourism industry has gradually increased. This trend is especially prominent with the advancement of smart tourism, where digital tools and big data technologies are widely applied. This multilingual environment provides rich linguistic data for studying Chinese vocabulary in Macao’s tourism, while also presenting new challenges and opportunities for Chinese vocabulary research. This study, based on the Generative Lexicon Theory, analyzes the qualia structure of key Mandarin in Macao’s Hotel Industry and Food and Beverage Industry, including the constitutive role, formal role, telic role, agentive role, and evaluative role. Additionally, the project will present vocabulary information in the form of digital labels and publish it on popular new media platforms such as Xiaohongshu, Youku, Bilibili, and YouTube. This project helps to reveal the usage contextual use of these vocabulary.

Tourism interpretation is widely recognized as an effective tool for helping destinations achieve sustainable tourism development goals (Staiff, 2016). It can enhance tourist experience, preserve cultural values, and protect the environment (Hughes et al., 2013; Ruan et al., 2024). Originating from eco-tourism, it has been extensively applied and investigated in heritage tourism research, because interpretation of culture heritage and artifacts can help visitors understand heritage tourism and encourages responsible behaviors. The officer-in-charge of culture unit in UNESCO has once emphasized that “heritage interpretation is a valuable tool to help World Heritage sites articulate UNESCO’s values, making interactions with heritage more inclusive, accessible, and meaningful as an educational experience in sustainable development and global citizenship” (Dimitrovska, 2024). Despite the recognized importance of heritage interpretation, there is still a need to explore specific strategies that can guide practitioners. This project aims to investigate how heritage interpretation strategies can effectively facilitate cultural transmission and preservation. A mixed methods approach will address this question from three key areas: content of artifact restoration, the role of interpreters, and cross-cultural transmission of artifacts memes via social media. The findings are expected to provide actionable recommendations for cultural destinations, focusing on innovative interpretation and promotion strategies that enhance the educational and preservation of cultural artifacts. Ultimately, this research will deepen our understanding of tourism interpretation’s significance in tourism development and facilitate the preservation of cultural heritage amid contemporary globalization challenges.

This project addresses China’s unprecedented demographic shift by investigating how psychological interventions can enhance tourism experiences for seniors. Focusing on Mainland China—where the elderly population is projected to exceed 28% by 2040 (WHO, 2025)— this research aims to unlock the “silver economy” potential by transforming seniors from passive consumers into active contributors by exploring the relationship between subjective age perception and travel behavior. Using mixed methods, we will: (1) analyze the link between subjective age and social engagement in tourism contexts; (2) develop innovative interventions to promote active aging through travel, and (3) validate their impact on seniors’ happiness and community participation in real-world settings. This research bridges critical gaps in understanding how to design age-inclusive tourism experiences that foster both personal well-being and social contribution.