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The purpose of this paper is to model housing price temporal variations and to predict price trends within the context of land use–transportation interactions using machine learning methods based on longitudinal observation of housing transaction prices.

This paper examines three machine learning algorithms (linear regression machine learning (ML), random forest and decision trees) applied to housing price trends from 2001 to 2016 in the Greater Toronto and Hamilton Area, with particular interests in the role of accessibility in modelling housing price. It compares the performance of the ML algorithms with traditional temporal lagged regression models.

The empirical results show that the ML algorithms achieve good accuracy (R² of 0.873 after cross-validation), and the temporal regression produces competitive results (R² of 0.876). Temporal lag effects are found to play a key role in housing price modelling, along with physical conditions and socio-economic factors. Differences in accessibility effects on housing prices differ by mode and activity type.

Housing prices have been extensively modelled through hedonic-based spatio-temporal regression and ML approaches. However, the mutually dependent relationship between transportation and land use makes price determination a complex process, and the comparison of different longitudinal analysis methods is rarely considered. The finding presents the longitudinal dynamics of housing market variation to housing planners.

This study aims to determine shoreline change statistics and net erosion and accretion, along the Kuakata Coast, a magnificent sea beach on Bangladesh’s southernmost point.

The research follows a three stages way to achieve the target. First, this study has used the geographic information system (GIS) and remote sensing (RS) to detect the temporal observation of shoreline change from the year 1991 to 2021 through satellite data. Then, the digital shoreline analysis system (DSAS) has also been explored. What is more, a prediction has been done for 2041 on shoreline shifting scenario. The shoreline displacement measurement was primarily separated into three analytical zones. Several statistical parameters, including Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), End Point Rate (EPR) and Linear Regression Rate (LRR) were calculated in the DSAS to quantify the rates of coastline movement with regard to erosion and deposition.

EPR and LRR techniques revealed that the coastline is undergoing a shift of landward (erosion) by a median rate of 3.15 m/yr and 3.17 m/yr, respectively, from 1991 to 2021, 2.85 km² of land was lost. Naval and climatic influences are the key reasons for this variation. This study identifies the locations of a significantly eroded zone in Kuakata from 1991 to 2021. It highlights the places that require special consideration while creating a zoning plan or other structural design.

This research demonstrates the spatio-temporal pattern of the shoreline location of the Kuakata beach, which would be advantageous for the region’s shore management and planning due to the impacts on the fishing industry, recreation and resource extraction. Moreover, the present research will be supportive of shoreline vulnerability. Hence, this study will suggest to the local coastal managers and decision-makers for particularizing the coastal management plans in Kuakata coast zone.

This paper aims to explore the spatio-temporal patterns of Chinese foreign direct investment (FDI) since the inception of the Belt and Road Initiative (BRI) in 2013 as an extended version of geographically weighted regression.

The panel data are used to examine spatial and temporal dynamics of the magnitude and the direction of China's outward FDI stock and its flow from 2011 to 2015 at a country level. Using the geographically and temporally weighted regression (GTWR), spatio-temporal distribution of FDI is explained through Logistic Performance Index, the size of gross domestic product (GDP), Shipping Linear Connectivity Index and Container Port Throughput.

A comparative analysis between participating and non-participating countries in the BRI shows that the size of GDP and Container Port Throughput of the participating countries have a positive effect on the increases of China's outward FDI Stock to Asia especially after 2013, while non-participating countries, such as North America, Western Europe and Western Africa, have no significant effect on it before and after the implementation of the BRI.

The findings, however, will not necessarily provide insight into the needs of China's outward FDI in certain countries to develop their economy. The findings provide the evidence to inform policy making to help identify the winners and losers of the investment, scale and direction of investment and the key drivers that shape the distributive investment patterns globally.

The study provides the empirical evidence to inform investment policy and strategic realignment by quantifying scale, direction and drivers that shape the spatio-temporal shifts of China's FDI.

The analysis also guides the Chinese government improve bilateral trade, build infrastructure and business partnerships with preferential countries participating in the BRI.

There is an urgent need to adopt a new perspective to unfold the spatial temporal complexity of FDI that incorporates space and time dependencies, and the drivers of the situated context to model their effects on FDI. The model is based on GTWR and an extended geographically weighted regression (GWR) allowing the simultaneous analysis of spatial and temporal decencies of exploratory variables.

This study aims to explore the use of time series analyses to examine changes in travelers’ preferences in accommodation features by disentangling seasonal, trend and the COVID-19 pandemic’s once-off disruptive effects.

Longitudinal data are retrieved by online traveler reviews (n = 519,200) from the Canary Islands, Spain, over a period of seven years (2015 to 2022). A time series analysis decomposes the seasonal, trend and disruptive effects of six prominent accommodation features (view, terrace, pool, shop, location and room).

Single accommodation features reveal different seasonal patterns. Trend analyses indicate long-term trend effects and short-term disruption effects caused by Covid-19. In contrast, no long-term effect of the pandemic was found.

The findings stress the need to address seasonality at the single accommodation feature level. Beyond targeting specific features at different guest groups, new approaches could allow dynamic price optimization. Real-time insight can be used for the targeted marketing of platform providers and accommodation owners.

A novel application of a time series perspective reveals trends and seasonal changes in travelers’ accommodation feature preferences. The findings help better address travelers’ needs in P2P offerings.

In this “Info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activity and e-commerce. The purpose of this paper is to model users' preference evolution to recommend potential items which users may be interested in.

A novel recommendation system, namely evolution-learning recommendation (ELR), is developed to precisely predict user interest for making recommendations. Differing from prior related methods, the authors integrate the matrix factorization (MF) and recurrent neural network (RNN) to effectively describe the variation of user preferences over time.

A novel cumulative factorization technique is proposed to efficiently decompose a rating matrix for discovering latent user preferences. Compared to traditional MF-based methods, the cumulative MF could reduce the utilization of computation resources. Furthermore, the authors depict the significance of long- and short-term effects in the memory cell of RNN for evolution patterns. With the context awareness, a learning model, V-LSTM, is developed to dynamically capture the evolution pattern of user interests. By using a well-trained learning model, the authors predict future user preferences and recommend related items.

Based on the relations among users and items for recommendation, the authors introduce a novel concept, virtual communication, to effectively learn and estimate the correlation among users and items. By incorporating the discovered latent features of users and items in an evolved manner, the proposed ELR model could promote “right” things to “right” users at the “right” time. In addition, several extensive experiments are performed on real datasets and are discussed. Empirical results show that ELR significantly outperforms the prior recommendation models. The proposed ELR exhibits great generalization and robustness in real datasets, including e-commerce, industrial retail and streaming service, with all discussed metrics.

This study aims to examine the moment-to-moment (MTM) effects of in-consumption dynamic comments on consumers' responses to digital engagement and the underlying mechanisms involved, as well as the interactive role of advertisements embedded in short-form online video.

This study uses data extracted from 2,081 videos posted on the prominent Chinese online live platform, Bilibili. The hypotheses are tested using regression models and natural language processing.

The results indicate that the intensity of live comments at the beginning negatively affects users' digital engagement, while a corresponding increase in live comments at the end elicits a positive effect. A linear trend and peak difference in live comments intensity positively affect digital engagement, while the variability of live comment intensity exerts a negative effect. These MTM effects were driven by sentiments of live comments. Furthermore, in-video advertisements are likely to amplify the negative beginning effect on users' digital engagement and mitigate the negative variability of live comments.

This study is the first to examine the direct effects of MTM comments from the online temporal sequence perspective, differentiating the process- and performance-based engagement. The mechanism and interactive role of in-video advertisements were identified. These findings contribute to literature on interactive marketing and provide valuable guidance for influencer marketing.

The purpose of this study is to examine whether PV uptake is associated with key housing market determinants and linked to socio-economic profiles. An abundance of extant literature has examined the role of solar photovoltaic (PV) adoption and user costs, with an emerging corpus of literature investigating the role of the determinants of PV uptake, particularly in relation to the built environment and the spatial variation of PV dependency and dissimilarity. Despite this burgeoning literature, there remains limited insights from the UK perspective on housing market characteristics driving PV adoption and in relation spatial differences and heterogeneity that may exist.

Applying micro-based data at the Super Output Area-level geography, this study develops a series of ordinary least squares, spatial econometric models and a logistic regression analysis to examine built environment, housing tenure and deprivation attributes on PV adoption at the regional level in Northern Ireland, UK.

The findings emerging from the research reveal the presence of some spatial clustering and PV diffusion, in line with several existing studies. The findings demonstrate that an urban-rural dichotomy exists seemingly driven by social interaction and peer effects which has a profound impact on the likelihood of PV adoption. Further, the results exhibit tenure composition and “economic status” to be significant and important determinants of PV diffusion and uptake.

Housing market characteristics such as tenure composition across local market structures remain under-researched in relation to renewable energy uptake and adoption. This study examines the role of housing market attributes relative to socio-economic standing for adopting renewable energy.

There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, the proposed model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, point identification is achieved by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. It is also shown how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.

Although extensive studies have examined the link between tourism and carbon emissions, the impact of tourism on carbon emissions remains controversial. In contrast to prior studies, this study aims to investigate the effects of tourism on carbon emissions at the city level and the underlying moderating mechanism.

This study designs an econometric model drawing on panel data for 313 city-level regions in China from 2001 to 2019. This study also performs rigorous robustness tests to support the regression results. In addition, the temporal and spatial heterogeneity is analyzed based on which this study discusses the moderators of the effects of tourism on carbon emissions.

The results show that both tourist arrivals and tourism revenue significantly impact carbon emissions. Also, there exists a significant temporal and spatial heterogeneity of these effects. Economic development significantly enhances while green technology and tertiary industry development suppress the positive relationship between tourism and carbon emissions. Moreover, regarding the impact on carbon emissions, an explicit substitution exists between tourism and tertiary industry development.

For the first time, this study quantitatively estimates the moderators of tourism’s impact on carbon emissions and concludes the moderating effects of economic growth, technological progress and industrial structure, thus furthering the theoretical understanding of the heterogeneity of tourism’s association with carbon emissions. The study also fills a technical gap in previous studies by demonstrating the reliability of the findings through various robustness tests. This is also the first empirical study to systematically examine the relationship between tourism and carbon emissions in China.

尽管已经有大量的研究考察了旅游和碳排放之间的联系, 但旅游对碳排放的影响仍有争议。与之前的研究相比, 本研究旨在研究城市层面上旅游业对碳排放的影响以及潜在的调节机制。

本研究基于2001-2019年中国313个城市层面的面板数据, 设计了一个计量经济学模型。本研究还进行了各种严格的稳健性检验以支持基准回归结果。本研究还分析了时空异质性, 并在此基础上讨论了旅游对碳排放影响的调节因素。

研究结果显示, 旅游者人次和旅游收入都对碳排放有明显影响。同时, 这些影响存在明显的时间和空间异质性。经济发展明显增强但是绿色技术和第三产业发展抑制了旅游业与碳排放之间的正向关系。此外, 旅游业和第三产业发展在对碳排放的影响方面存在显著的替代关系。

本研究首次定量估计了旅游业对碳排放影响的调节因素, 并总结出经济增长、技术进步和产业结构的调节作用, 从而进一步推动了对旅游业与碳排放关联的异质性的理论认识。文章还填补了以往研究的技术空白, 通过各种稳健性检验证明了研究结果的可靠性。本研究还是第一个系统地研究中国旅游业与碳排放关系的实证研究。

Este estudio diseña un modelo econométrico basado en datos de panel para 313 regiones a nivel de ciudad en China desde 2001 hasta 2019. Este estudio también aplica rigurosas pruebas de robustez para apoyar los resultados de la regresión. Además, se analiza la heterogeneidad temporal y espacial en base a la cual este estudio discute los moderadores efectos del turismo en las emisiones de carbono.

Aunque numerosos estudios han examinado la relación entre el turismo y las emisiones de carbono, su impacto sigue siendo controvertido. A diferencia de los estudios anteriores, este estudio pretende investigar los efectos del turismo en las emisiones de carbono a nivel de ciudad y el mecanismo moderador subyacente.

Los resultados muestran que tanto las llegadas de turistas como los ingresos por turismo influyen significativamente en las emisiones de carbono. Además, existe una importante heterogeneidad temporal y espacial de estos efectos. El desarrollo económico aumenta significativamente, mientras que la tecnología verde y el desarrollo de la industria terciaria suprimen la relación positiva entre el turismo y las emisiones de carbono. Además, en lo que respecta al impacto sobre las emisiones de carbono, existe una sustitución explícita entre el turismo y el desarrollo de la industria terciaria.

Por primera vez, este estudio estima cuantitativamente los moderadores del impacto del turismo en las emisiones de carbono y concluye los efectos moderadores del crecimiento económico, el progreso tecnológico y la estructura industrial, lo que permite avanzar en la comprensión teórica de la heterogeneidad de la asociación del turismo con las emisiones de carbono. El artículo también resuelve una carencia técnica de los estudios anteriores al demostrar la fiabilidad de las conclusiones mediante diversas pruebas de solidez. Este es también el primer estudio empírico que examina sistemáticamente la relación entre el turismo y las emisiones de carbono en China.

The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.