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Publications
Publications (1,153)
Technological progress can help reduce the flood adaptation gap, but its sector-specific impacts remain unclear. We propose a text-mining-based structural equation modeling approach to analyze cross relationships between technology and perceived flood losses across multi-sectors, using 3.58 million flood-related posts during 1000-year-return extrem...
Household finance is an important branch of finance that studies the behavior and decision-making of households in financial markets. As financial markets become more complex and the global economy develops, the importance of household finance has become increasingly prominent. Households are not only consumption units but also key participants in...
Images, with their direct content experience, have become essential in tourists’ narratives of accommodation sharing. Although visual content plays an important role in influencing user decisions, research on hotel images often neglects the significance of sensory elements. Sensory elements in visual content help shape tourists’ perceptions and eva...
In practice, market timing is a well-known trading strategy among investors, and different indicators have been proposed for market timing methods. This paper compares three indicators used in market timing strategy: the return, the 0–1 binary index, and the realized probability index. It is shown that realized probability index is more informative...
With the rising labor costs and increasing resource and environmental constraints in China, coupled with geopolitical conflicts, related industries or production processes are shifting to emerging economies such as Southeast Asia, South Asia, and Mexico. Among these, India’s development potential has garnered significant attention, and the “China-t...
By treating intervals as inseparable sets, this paper proposes sparse machine learning regressions for high-dimensional interval-valued time series. With LASSO or adaptive LASSO techniques, we develop a penalized minimum distance estimation, which covers point-based estimators are special cases. We establish the consistency and oracle properties of...
The article identifies key principles of the important role played by low-carbon generation for ecosystems, which explores studies from perspectives of technology, strategy, and policy. In doing so, it provides a scientific basis for policymakers to formulate efficient and feasible plans to reduce emissions via low-carbon generation to revive ecosy...
The gradual adjustment of fertility and retirement policies in China has social benefits in terms of coping with population aging. However, the environmental consequences of these policies remain ambiguous. Here we compile environmentally extended multiregional input–output tables to estimate household carbon footprints for different population age...
The tragic consequences of the COVID-19 epidemic highlight to governments the importance of keeping sufficient medical supplies. Given that the physical stocks are subject to considerable obsolescence risk, existing studies have considered combining holding safety stocks with keeping production capacity or capital reserve. Pioneering the exploratio...
Church tourism has gained popularity due to the historical, artistic, and religious significance of churches, contributing significantly to the social and economic sectors. The destination image of churches plays a crucial role in tourism marketing. However, existing literature predominantly relies on traditional methods to study tourists' percepti...
Online food delivery has become a popular mode of urban food consumption in China as its underlying business mechanism, Online To Offline (O2O), gaining popularity. However, the environmental impacts of a rapidly expanding online food delivery industry and its potential to mitigate environmental burdens remained unexplored in China. Our research fo...
With the diversity of shopping styles, reference prices have become a consideration in consumers’ purchasing decisions. It is critical to understand how manufacturers and retailers make optimal decisions based on consumer behavior. To this end, we develop a manufacturer-led Stackelberg (M-Stackelberg) game model to investigate the impact of consume...
Purpose
The purpose of this paper is to study the importance of image data in hotel selection-recommendation using different types of cognitive features and to explore whether there are reinforcing effects among these cognitive features.
Design/methodology/approach
This study represents user-generated images “cognitive” in a knowledge graph throug...
This study investigates the extent and persistence of major crisis events in the crude oil market and economy and searches for general rules of event impact. Although the short-term effects of such crises may quickly become evident, their long-term implications can be challenging to uncover. To this end, we analyzed 50 major crisis events across fo...
The establishment of a long-term and accurate forecasting approach of urban air pollution is conducive to the implementation of pollution prevention and control policies. However, existing research has not fully taken into account the spatial-temporal pattern characteristics of long-distance air pollution transport. Multistep ahead forecasting face...
This paper proposes a novel kernel‐based generalized random interval multilayer perceptron (KG‐iMLP) method for predicting high‐volatility interval‐valued returns of crude oil. The KG‐iMLP model is constructed by utilizing the distance based on a kernel function, which outperforms the conventional Euclidean distance. Additionally, the optimal kerne...
Purpose
This study explores the contagion of greenwashing strategies among ESG mutual funds. It investigates how the greenwashing behaviors of peer funds within the same family influence a fund’s decision to engage in greenwashing. The research also examines the impact of greenwashing on genuine ESG funds and explores the mechanisms through which g...
Mining diamond poses significant and potentially underestimated risks to the environment worldwide. Here, we propose a Diamond Environmental Impacts Estimation (DEIE) model to forecast the environmental indicators, including greenhouse gas (GHG) emissions, mineral waste, and water usage of the diamond industry from 2030 to 2100 in the top diamond p...
Combatting climate change depends on demand-side mitigation strategies related to food, which is in turn contingent on explicit estimation and management of dish-level emissions. Here, on the basis of a bottom-up integrated emissions framework, we first estimate the greenhouse gas emissions of 540 dishes from 36 cuisines using data from over 800,48...
Chinese cities need independent but synergetic dual-carbon abatement roadmaps to mitigate climate change and achieve carbon neutrality. Using source-level data, we develop a time-series, full-scale emission inventory for all Chinese cities from 2005 to 2020, exploring associated heterogeneous and homogeneous patterns. We find that 31% of cities hav...
Many countries have cut their corporate tax rates in the past decades to attract foreign investment. To prevent this, a global minimum tax policy was approved by OECD countries in 2021. Global changes in corporate tax rates could reshape production and investment networks while impacting welfare and global emission patterns. Here we develop a theor...
The authors aim to interpret human and AI interactions from the decision perspective. The authors decompose the interaction analysis into the following main components in the context of interactions: Individual behavior patterns, interaction relationships, and comprehensive analysis. The authors interpret intertemporal decisions from a physical per...
The forecasting and trading of exchange rates is a prevalent and intricate undertaking within the realm of economics. While prior research has mostly concentrated on enhancing the accuracy of predictions through various methodologies, this study places greater emphasis on providing practical suggestions for effective trading. Hence, we propose a do...
Developing effective strategies to earn excess returns in the stock market is a cuttingedge topic in the field of economics. At the same time, stock price forecasting that supports trading strategies is considered one of the most challenging tasks. Therefore, this study analyzes and extracts news media data, expert comments, social opinion data, an...
By conducting a bibliometric analysis of 1997 scholarly publications on carbon neutrality and zero carbon emissions from 2019 to 2022, it is found that reviews of quantitative socioeconomic modeling research remain limited. To address this issue, a comprehensive review of carbon neutrality research, specifically, a systematic and synergistic review...
As the second-largest oil producer and natural gas exporter, Russia’s war with Ukraine has severely impacted the energy market. To what extent has the war influenced crude oil prices, and has it altered the long-term dynamics of oil prices? An objective analysis of the effects of the Russia–Ukraine war on the crude oil market can assist relevant en...
To mitigate the impact of market uncertainty on trading investments, this paper proposes a forecasting and investing framework for crude oil market by integrating interval models and machine learning models. Firstly, natural language processing technique is employed to analyze text information from social and news media, enabling the capture of mar...
The internet of things presents a significant economic value potential, where sensors play a crucial role in its infrastructure. However, the restricted electricity of sensors limits the lifespan of the entire network. The development of modern charging technology has made it possible to realize simultaneous one-to-many charging using drones. To ma...
To explain the volatility of the Bitcoin price, a total of 23 elements from four domains (Bitcoin-related indicators, financial market, exchange rates and commodities, and social sentiment) were collected. With the application of machine learning and game theory, experimental results demonstrate that S&P 500 is the most significant factor on the Bi...
The assessment of credit risk for P2P lending platform applicants is critical to investors. Feature engineering is an essential technique in distilling classification knowledge during the credit risk prediction data preprocessing stage. Although previous literature used feature selection methods to identify key features, feature transformation is m...
This study analyzes the performance of the Shanghai Composite Index, S&P 500 index, WTI oil price, and LBMA gold price when wars took place, especially the Russia-Ukraine conflict. We employ empirical methods to explore the stability, instantaneous shock, and short-term shock regarding the abovementioned financial assets. We first adopt the event s...
Statistical analysis of COVID-19 mortality is challenging due to its non-stationarity and cross-sectional instability. In this paper, the authors introduce a unified method to evaluate the fatality rate of COVID-19 across countries, whose method provides more reliable information for cross-country comparison than the traditional case-fatality rate...
The need to make more accurate grain demand (GD) forecasting has become a major topic in the current international grain security discussion. Our research aims to improve short-term GD prediction by establishing a multi-factor model that integrates the key factors: shifts in dietary structures, population size and age structure, urbanization, food...
Due to the strong nonlinearity and high complexity of foreign exchange rate data, how best to accurately analyze their trends and forecast them is regarded as a challenging research topic. While such research has developed rapidly and a large number of publications have appeared, few programmatic quantitative and qualitative overviews have been com...
A popular self-normalization (SN) approach in time series analysis uses the variance of a partial sum as a self-normalizer. This is known to be sensitive to irregularities such as persistent autocorrelation, heteroskedasticity, unit roots and outliers. We propose a novel SN approach based on the adjusted-range of a partial sum, which is robust to t...
Tourism has become a major driver of China’s economic growth and consumes much energy causing environmental pollution problems. This paper combines the LMDI (logarithmic mean Divisia index) method and K-means clustering to analyze the factors influencing tourism energy consumption in seven Chinese provinces and discusses strategies for energy consu...
Industry redistribution is a common economic phenomenon that involves a dynamic configuration of the production location across a region, country, or the world. However, measurements of the associated pollutant emission effects have not been well conducted at the domestically regional level. Here, we calculate the CO2 emission changes induced by Ch...
The global tourism industry is struggling to recover from the COVID-19 pandemic. During the COVID-19 pandemic, daily tourism forecasting is more critical than ever before in supporting decisions and planning. Considering the changes in tourist psyche and behaviour caused by COVID-19, this study attempts to investigate whether the statistical modell...
The security of credit card fraud detection (CCFD) models based on machine learning is important but rarely considered in the existing research. To this end, we propose a black-box attack-based security evaluation framework for CCFD models. Under this framework, the semisupervised learning technique and transfer-based black-box attack are combined...
The security of credit card fraud detection (CCFD) models based on machine learning is important but rarely considered in the existing research. To this end, we propose a black-box attack-based security evaluation framework for CCFD models. Under this framework, the semi-supervised learning technique and transfer-based black-box attack are combined...
The coronavirus disease (COVID-19) pandemic has already caused enormous damage to the global economy and various industries worldwide, especially the tourism industry. In the post-pandemic era, accurate tourism demand recovery forecasting is a vital requirement for a thriving tourism industry. Therefore, this study mainly focuses on forecasting tou...
Counterfeit and low-quality products pose huge challenges to brand owners. Consumer trust in brands requires facilitation from product information disclosure. A traceability system enabled by blockchain technology (BCT) presents one of the most promising solutions to these issues and contributes to two effects: total market potential improvement (T...
This paper explores whether the government's control of refined oil prices can slow down this impact and play a “shock absorber” function. Adopting the SVAR model and divides the sample into two sub-samples according to the level of price regulation, it is found that the degree of price regulation of refined oil is very high before May 2009. At thi...
Studies have shown that the soaring demand for air conditioners in recent years is closely related to the worsening global warming; however, little evidence has been provided for China. This study uses weekly data of 343 Chinese cities to investigate how air conditioner sales respond to climate variability. We detected a U-shaped relationship betwe...
