DEPARTMENTAL RESEARCH OUTPUT PRIZE 2024

ADLER Patrick J

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UK

5 (IF2019-2023)

13/172 (Top 7 %)

ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD

Adler, P. (2024). Brand on the run: place brands as judgement devices and sources of local advantage in the music industry. Regional Studies, 58(10), 1904–1920. https://doi.org/10.1080/00343404.2024.2306326

Products fromcertain areas are assumed to share qualities by virtue of where they aremade. This article considers the economic significance of such place brands in the wider market for symbolic goods. It forwards a theory of these as judgement devices, whereby place reputation serves to lower search costs in symbolic goods markets with excess supply. This theory is investigated through a study of an online music platform where a weak form of place branding is available to producers. Results suggest that branding is associated with musical success at the individual level and that place brands may act as strategic resources for producers from creative clusters. Branding effects do not necessarily depend on the content of the place brand signal (i.e., country acts branded from Nashville are not especially privileged) and may be based in simpler heuristic mechanisms where a listed origin is a stamp of quality or an aid in recognition.

ATTEWELL Wesley L

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UK

8.3 (IF2019-2023)

6/172 (Top 3%)

SAGE PUBLICATIONS LTD

Attewell, Wesley. (2024). "Empire Redux: Towards a New Political Geography of Race War." Progress in Human Geography. 48.6: 826-842.

This essay revisits geographical debates on empire to clarify how broader geopolitical economies of power and violence have always been experienced at the scale of the everyday as an intimate politics of relation- and difference-making. It is guided by two questions that promise to stretch geographical writing on empire in new ways. They are: how has empire always been a racial project? And how has imperial race-making historically gone hand-in-hand with imperial place-making? Both questions force us to reckon with empire as a multi-scalar project that entangles the foreign and the domestic, the intimate and the global, and so on.

CHEN Yan Wendy

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Germany

11.4 (IF2019-2023)

1/89 (Top 1%)

SPRINGER

Wang, C., Jin, J., Davies, C., & Chen*, W. Y. (2024) Urban forests as nature-based solutions: a comprehensive overview of the National Forest City action in China. Current Forestry Reports, 10, 119-132.

In tandem with China’s rapid urbanisation and economic growth, some negative impacts on the ecoenvironment and human wellbeing have arisen, such as the urban heat island effect, air pollution and lack of recreational spaces. To address the degradation of urban eco-environment and improve residents’ quality of life simultaneously, China’s central government launched the National Forest City action in 2004, which essentially promotes urban forests as nature-based solutions (UF-NBS) and contributes to achieving sustainable development goals. Whilst this key national action has been implemented for about two decades, it has received limited scholarly attention within and beyond China. This paper is the very first to summarise comprehensively the development of the action, focusing on its rationale, evaluation and management.

CHEN Yan Wendy

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Netherlands

8.6 (IF2019-2023)

31/358 (Top 8%)

ELSEVIER

Han, W., & Chen*, W. Y. (2024) Survival of biomass and waste power generation: A global overview. Science of the Total Environment 940: 173593.

Biomass and waste power generation holds the promise to secure electricity supply for a growing population and mitigate global warming simultaneously. Along with the increasing commission and installation of biomass/ waste power units (BWPUs) across the globe, some BWPUs failures have been observed, including the cancellation of planned/commissioned BWPUs and the termination of those in operation before reaching their natural retirement. While empirical evidence suggests that factors like feedstock accessibility and policy instruments might affect the feasibility and performance of BWPUs, there is a lack of comprehensive investigation about why some BWPUs failed at the global scale. To fill this knowledge gap, this study quantifies the hazard ratio of BWPUs via a parametric survival analysis using a panel dataset covering a total of 12,829 BWPUs (relying on woody, non-woody, and waste biomass as raw feedstocks) located in 164 countries/regions worldwide for the period of 2001–2021. The analytical results suggest that large unit size is conducive to BWPUs failure, while feedstock accessibility and the implementation of policy instruments (including Feed-in-Tariff and carbon pricing) could largely reduce the hazard ratio of BWPUs, with varying impacts on BWPUs at the planned/commissioned stage or the operation stage, located in developed or developing countries. Our findings not only shed additional light on the fate of BWPUs, which is crucial to enriching our understanding about the development of the bioenergy sector worldwide, but also provide salient empirical evidence for policy-making in terms of ensuring feedstock accessibility, overcoming diseconomies of scale, and making fiscal instruments available and transparent to boost the confidence of investors and entrepreneurs in support of BWPUs development.

CHEN Yan Wendy

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Netherlands

6.4 (IF2019-2023)

2/89 (Top 2%)

ELSEVIER

Jin, J., Chen*, W. Y., Jia, B., & Wang, C. (2024)Cooling effect of urban greenery: A bibliometric analysis. Urban Forestry & Urban Greening 99: 128453

The mechanism and effectiveness of urban greenery in mitigating urban heat islands, regulating microclimate, and enhancing thermal comfort has been extensively studied during the last decades. While sporadic empirical evidence has been generated, the trends and patterns of existing scholarship pertinent to urban greenery’s cooling effect have been rarely summarized and synthesized. To bridge this knowledge gap, the present paper systematically reviewed 310 relevant publications in the Web of Science database (1998–2022) and conducted a bibliometric analysis to depict a comprehensive profile of urban greenery’s cooling effect, focusing on global research trends, prevalent research topics, and future prospects. Our analytical results reveal (1) a steady increase in publications, active journals, and knowledge-generating institutions since 2008 that might be attributed to the free accessibility of diverse remote sensing data; (2) a significant increasing trend of transdisciplinarity and interdisciplinarity, expanding from Environmental Science and Ecology to various subjects such as Engineering, Remote Sensing, Construction & Building Technology, Urban Forestry, and Urban Studies; (3) four influential publication outlets including Urban Forestry & Urban Greening, Science of the Total Environment; Building and Environment, and Sustainable Cities and Society; (4) core research themes focusing on the association of urban greenery’s biophysical characteristics with cognate cooling effect, urban heat island mitigation, and land surface temperature; and (5) several new research themes that have not yet well-developed in the extant literature, including the integration of various analytical approaches to up-scale empirical studies from micro-scale to mesoand global scales, extending urban greening-thermal comfort to public health and social thermal justice, and coupling urban greenery’s cooling effect with other environmental/ecological benefits to inform the design of urban greenery for biodiverse, climate-resilient and sustainable cities. Findings of this synthetic review offer a reference for the research focusing on urban greenery’s cooling effect, and provide clear direction for further development of cognate scholarship that is urgently needed facing more frequent urban climate extremes along with global warming.

HUANG Bo

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5 Year Impact Factor:

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Netherlands

4.8 (IF2019-2023)

15/172 (Top 8%)

ELSEVIER

Wenting Zhang, Haochun Guan, Shan Li, Bo Huang, Wuyang Hong, Wenping Liu,

The impact of street-scale built environments on urban park visitations: A case study in Wuhan, Applied Geography, Volume 171, 2024, 103374.

The COVID-19 pandemic has changed human life globally. Existing studies have revealed that citizens’ visitations to urban parks varied before and after the COVID-19 outbreak. However, few studies have examined how street-scale built environments (SBEs) on routes affect visitations to urban parks at varying COVID-19 risk levels. In this study, a stated-preference survey was conducted to investigate 3,218 visitors’ changes in urban park visitation under various COVID-19 risk levels. In addition to park visit influencing factors, including park features, neighborhood built environment, socio-demographic attributes, and travel distances, multiple SBE indexes on visitors’ routes to parks were obtained from 34,780 Baidu Map street view images using a deep neural network (DeepLabv3+) method. The results suggest that a high GVI and high traffic congestion on the route from the visitor’s home to the urban park led to an increased probability of visiting the urban park by 188.1% (p = 0.044, OR = 2.881) and a decreased probability by 32.3% (p = 0.049, OR = 0.677), respectively. The high probability of visitation was also associated with socio-demographic attributes (including male gender, high income, high and medium education levels, and the elderly) and short travel distances.

HUANG Bo

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5 Year Impact Factor:

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Netherlands

8.6 (IF2019-2023)

31/358 (Top 8%)

ELSEVIER

Chao Wu, Shuo Yang, Donglai Jiao, Yixiang Chen, Jing Yang, Bo Huang,

Estimation of daily XCO2 at 1 km resolution in China using a spatiotemporal ResNet model, Science of The Total Environment, Volume 954, 2024,176171.

Carbon dioxide (CO2) serves as a crucial greenhouse gas that traps heat and regulates the Earth’s temperature. High spatiotemporal resolution CO2 estimation can provide valuable information to understand the characteristics of fine-scale climate change trends and to formulate more effective emission reduction strategies. This study presents a spatiotemporal ResNet model (ST-ResNet) specifically developed to estimate the highest resolution (1 km × 1 km) daily column-averaged dry-air mole fraction of CO2 (XCO2) in China from 2015 to 2020. The ST-ResNet model excels in estimating XCO2 by comprehensively considering the complex relationships between XCO2 and its various influencing factors, while efficiently capturing both temporal and spatial correlations, thereby demonstrating remarkable generalization capability. The results show that the ST-ResNet generates a highly accurate XCO2 dataset, outperforming the traditional ResNet. Ground-based validation results further confirm the high accuracy and spatiotemporal resolution of our estimated data product. Using this dataset, the spatial and temporal characteristics of XCO2 across the entire China and several urban agglomerations have been analyzed. The high spatiotemporal resolution estimated XCO2 dataset for China is made publicly

HUANG Bo

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5 Year Impact Factor:

Ranking within the subject:

Netherlands

6.8 (IF2019-2023)

5/77 (Top 6%)

ELSEVIER

Zheyan Chen, Bo Huang,

Achieving urban vibrancy through effective city planning: A spatial and temporal perspective, Cities, Volume 152, 2024, 105230.

Achieving urban vibrancy is a critical goal for urban planning and policy-making, and the city's physical plan plays an important role in shaping and supporting urban vibrancy. However, the effectiveness of urban planning strategies in optimizing resource allocation and fostering a vibrant city remains unclear. This study aims to contribute to this understanding by exploring the spatial and temporal effects underlying the association between urban vibrancy and urban forms. Using mobile-phone location-request data along with urban nighttime light data as a proxy for urban vibrancy in Shenzhen, China, we conducted a geographically and temporally weighted regression (GTWR) analysis to examine the relationship between land use, street configuration, and urban vibrancy. The results showed that a diverse land use mix and the proportions of land allotted for sports and cultural usage, parks, and greenspace generally have a positive impact on urban vibrancy. We noted the facilitating role of degree centrality and closeness centrality on urban vibrancy, compared to the negative impact of betweenness centrality. Our analysis of local spatial variations in the influence of road networks suggests a potential connectivity threshold for attracting people to specific areas. Finally, we identified a divergence between weekdays and weekends in terms of temporal variations.

IAQUINTO Benjamin L

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5 Year Impact Factor:

Ranking within the subject:

Netherlands

5.5 (IF2019-2023)

9/172 (Top 5%)

ELSEVIER

Barry, K., Iaquinto, BL. and Azeredo, R. (2024). From tourists to essential workers: The multifaceted presence of backpackers in rural Queensland, Australia. Journal of Rural Studies 112, 103469 https://doi.org/10.1016/j.jrurstud.2024.103469

Although there is notable scholarship on backpackers and their part in tourism cultures, there has been little reflection on their status as long term essential workers in rural areas and what this means for the communities who receive them. We address this gap by investigating the evolution of Australia’s Working Holiday Maker program and how it has shaped the presence of backpackers in farming communities. Contemporary backpacking in Australia now involves a culturally and ethnically diverse cohort, which has become essential for farming communities’ economic and cultural livelihoods. We argue that the ongoing modifications to the visa program have transformed the presence of backpackers in farming towns, from highly transient tourists to essential workers who may stay for longer periods as temporary migrants, and this has transformed the people and places that host them. Through the lens of mobilities, we outline useful lessons and insights from this example of a backpacker visa, which are relevant for future research and debates around rural livelihoods, labour migration, and farming communities.

KOH Keumseok Peter

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Netherlands

4.8 (IF 2019-2023)

15/172 (Top 8%)

ELSEVIER

Ng, K. Y., Hong, A., Higgins, C. D., Widener, M. J., & Koh, K. (2024). Beyond distance: Measuring spatial accessibility to healthy food for older adults in Hong Kong using a 3D least-effort method. Applied Geography, 169, 103336.

An age-inclusive built environment is essential for promoting an accessible food landscape for the elderly population. However, previous research has focused on least-distance/time travel metrics in a 2D environment, potentially overlooking travelers’ physical constraints and underestimating actual walking distances. In contrast, this study employs advanced geocomputational methods that leverage 3D building models, 3D pedestrian networks, and elevation data to appraise the fine-scale spatial accessibility to healthy food in Hong Kong. Guided by the principles of least effort, our findings suggest that 95% of older adults can access healthy food within 913.3m due to Hong Kong’s compact and transit-oriented built environment. However, nearly half (47%) of older adults may encounter difficult pedestrian paths even with the least-effort route. Subsequently, the Aggregated Accessibility Index (AAI) is devised to identify communities that require improvement in promoting active living for older people. Site visits were also conducted to validate the AAI and present real-world situations to better articulate the mobility challenges imposed on older adults. Our study underscores the instrumental role of advanced spatial data computation in shaping age-friendly communities that prioritize and enhance spatial accessibility to healthy food, advocating for nuanced urban planning approaches that address the diverse needs of aging populations.

KOH Keumseok Peter

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5 Year Impact Factor:

Ranking within the subject:

Netherlands

6.8 (IF 2019-2023)

5/77 (Top 6%)

ELSEVIER

Tang, K. C., Shi, C., & Koh, K. (2025). Using GeoAI to examine infectious diseases spread in a hyperdense city: A case study of the 2022 Hong Kong COVID-19 Omicron wave. Cities, 158, 105600.

This study utilizes self-organizing maps (SOMs) to investigate the spatiotemporal diffusion patterns and clusters of the 2022 COVID-19 Omicron variant in Hong Kong, incorporating various sociodemographic and environmental datasets. A large dataset necessarily creates a higher dimension in structure, making it challenging for humans to explore the complex associations among many variables and observations. SOMs effectively reduce data dimensions while preserving the topological structure of the original data through unsupervised artificial neural network approaches. We found that many non-centric residential areas repeatedly exhibited similar diffusion patterns over time after the relaxation of anti-pandemic measures. Notably, several non-centric localities with fewer commercial establishments and transportation hubs often became infection and transmission clusters due to temporary increase in crowds during the anti-epidemic measures. Areas with more older housing and industrial facilities were also identified as vulnerable to COVID-19 diffusion due to outdated building structures and equipment. Findings emphasize the need for tailored interventions in local neighborhoods, as well as densely populated commercial and business districts, to effectively manage and prevent infectious diseases in dense urban areas. This study showcases the utility of geospatial AI techniques in analyzing spatial and temporal diffusion patterns of infectious diseases and designing proper measures for their control and prevention.

LIANG Shunlin

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Germany

12.1 (IF2019-2023)

4/254 (Top 1%)

COPERNICUS PUBLICATIONS

Li, B., S. Liang, Ma, H., Dong, G., Liu, X., He, T., and Zhang, Y.: (2024),Generation of global 1 km all-weather instantaneous and daily mean land surface temperatures from MODIS data, Earth Syst. Sci. Data, 16, 3795–3819

Land surface temperature (LST) serves as a crucial variable in characterizing climatological, agricultural, ecological, and hydrological processes. Thermal infrared (TIR) remote sensing provides high temporal and spatial resolutions for obtaining LST information. Nevertheless, TIR-based satellite LST products frequently exhibit missing values due to cloud interference. Prior research on estimating all-weather instantaneous LST has predominantly concentrated on regional or continental scales. This study involved generating a global allweather instantaneous and daily mean LST product spanning from 2000 to 2020 using XGBoost. Multisource data, including Moderate-Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) observations, surface radiation products, and reanalysis data, were employed. Validation using an independent dataset of 77 individual stations demonstrated the high accuracy of our products, yielding root mean squared errors (RMSEs) of 2.787K (instantaneous) and 2.175K (daily). The RMSE for clear-sky conditions was 2.614K for the instantaneous product, which is slightly lower than the cloudy-sky RMSE of 2.931 K. Our instantaneous and daily mean LST products exhibit higher accuracy compared to the MODIS official LST product (instantaneous RMSE D3.583 K; daily 3.105 K) and the land component of the fifth generation of the European ReAnalysis (ERA5-Land) LST product (instantaneous RMSED4.048 K; daily 2.988 K). Significant improvements are observed in our LST product, notably at high latitudes, compared to the official MODIS LST product. The LST dataset from 2000 to 2020 at the monthly scale, the daily mean LST on the first day of 2010 can be freely downloaded from https://doi.org/10.5281/zenodo.4292068 (Li et al., 2024), and the complete product will be available at https://glass-product.bnu.edu.cn/ (last access: 22 August 2024).

LIANG Shunlin

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USA

15.6 (IF2019-2023)

2/101 (Top 2%)

IEEE

Jia, A., S. Liang， et al., (2024), Advances in Methodology and Generation of All-Weather Land Surface Temperature Products from Polar-Orbiting and Geostationary Satellites: A Comprehensive Review, IEEE Geoscience and Remote Sensing Magazine， 12（4）： 218-260

Land surface temperature (LST) is crucial for understanding surface energy budgets, hydrological cycling, and land–atmosphere interactions. However, cloud cover leads to numerous data gaps in existing remote sensing thermal infrared (TIR) LST products, seriously restricting their applications. This article provides a comprehensive review concerning both LST recovery methodologies and 26 emerging all-weather products derived from polar-orbiting and geostationary (GEO) satellites. Clarifying product distinctions will enable end users to select suitable options for diverse research. Methodologies are categorized into spatiotemporal interpolation, surface energy balance (SEB)-based physical estimation, passive microwave (PMW)-based methods, and simulated temperature-based approaches. Historical research trajectories, strengths, limitations, and potential research directions of the methodologies and products are discussed. The review reports that existing all-weather LST products generally exhibit root-meansquare errors (RMSEs) of <4 (2.5) K at instantaneous (daily mean) scales based on extensive ground measurements, comparable to clear sky retrievals. Deep learning (DL) models prominently feature in state-of-the-art interpolation and fusion approaches [e.g., long short-term memory (LSTM) and extreme gradient boosting (XGBoost)]. Product intercomparisons in various application scenarios reveal that interpolation-based products offer better texture details; however, noticeable biases exist compared to fusion-based products, especially in arid and semiarid regions, despite the high availability of clear sky samples. The bias shifts to negative at higher latitudes, due to ignored cloud radiative effects. The review emphasizes the underexplored recovery of diurnal temperature cycles (DTCs) from GEO satellites. This focus will benefit heat exposure monitoring for public health, understanding circadian rhythm responses of ecosystems to environmental changes, and harmonizing existing and forthcoming high-resolution TIR missions.

LIANG Shunlin

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Ranking within the subject:

Netherlands

12.7 (IF2019-2023)

2/63 (Top 3%)

ELSEVIER

Zhang, G*., Liang, S., Ma, H., He, T., Yin, G., Xu, J., Liu, X., & Zhang, Y. (2024). Simultaneous estimation of five temporally regular land variables at seven spatial resolutions from seven satellite data using a multi-scale and multi-depth convolutional neural network. Remote Sensing of Environment, 301, 113928

Various satellite sensors have provided a huge amount of observations of Earth’s environment at variable spatial and temporal resolutions. Many global coarse-resolution land products have been generated from single-satellite data, but global temporally regular land products at fine spatial resolutions (say 10-30 m) are scarce because of infrequent observations. An ideal inversion framework can estimate multiple global continuous land variables at different spatial resolutions by combining all sources of satellite data. This paper proposes a new framework that can estimate five land variables simultaneously from the top-of-atmosphere (TOA) reflectance acquired by seven satellite sensors based on a multi-scale and multi-depth convolutional neural network (MSDCNN). This framework enables us to estimate temporally regular land variables at as fine as 10 m spatial resolution by transforming information from satellite data at coarser spatial resolutions. The estimated land variables include Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), shortwave albedo, visible albedo, and spectral reflectance. Seven sensors that acquire data at different spatial resolutions, include Visible Infrared Imaging Radiometer Suite (VIIRS) (750 m), Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m), Fengyun-3 (FY-3) Medium Resolution Spectral Imager (MERSI) (250 m), China-Brazil Earth Resources Satellite 04 (CBERS-04) Wide Field Imager (WFI) (73 m), Landsat 8 Operational Land Imager (OLI) (30 m), Gaofen-1 (GF-1) Wide-Field-of-View (16 m) and Sentinel 2 A/B Multispectral Imager (MSI) (10 m). This framework is mainly composed of four steps. First, a Shuffled Complex Evolution (SCE) optimization method is adopted to estimate these variables from VIIRS TOA reference. Second, a joint-output random forest regression (RF) method is used to link the satellite observations and the estimated values from step 1. Third, the six-type multi-resolution satellite observations are used to downscale the VIIRS TOA reflectance to six different spatial resolutions by using the MSDCNN. Finally, the downscaled six-resolution VIIRS TOA reflectance is fed into the multiple-variable RF model to estimate land variables. The framework was validated, and the results based on high-resolution reference maps from ImagineS network and time series shortwave albedo field values from Surface Radiation (SURFRAD) and Integrated Carbon Observation System (ICOS) network show that the retrieved variables had high validation accuracy, with root mean square error (RMSE) ranges of 0.361–0.489 (LAI), 0.023–0.120 (FAPAR), 0.013–0.026 (snow-free shortwave albedo), respectively. Comparison results of the retrieved multi-scale variables with the Sentinel 2 A/B (10 m), Landsat 8 (30 m), Global LAnd Surface Satellite (GLASS, 250 m, 500 m), MODIS (500 m), and VIIRS (750 m) products show that their values were close, with RMSE ranges of 0.107–0.273 (LAI), 0.015–0.027 (FAPAR), 0.003–0.007 (shortwave albedo), 0.001–0.007 (visible albedo), and 0.003–0.025 (spectral reflectance), respectively. The results of the direct validation as well as the product intercomparison show that this novel framework has the potential to be used in estimating global land variables at various spatial scales using a variety of satellite data sources.

LOO P. Y. Becky

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Netherlands

10 (IF2019-2023)

3/92 (Top 3%)

ELSEVIER

Feng, X., Zeng, F., Loo, B.P.Y.*, & Zhong, Y. (2024). The evolution of urban ecological resilience: An evaluation framework based on vulnerability, sensitivity and self-organization. Sustainable Cities and Society, 105933. DOI: 10.1016/j.scs.2024.105933

Ecological resilience assessment has become a key link in urban sustainable governance. This study introduces a new evaluation framework to inform policy-making and practical applications. Based on the structural and functional dimensions of landscape patterns, it integrates the vulnerability, sensitivity and self-organization of resilience to point to desirable directions of ecological resilience. A composite ecological resilience index is compiled based on six indices of landscape diversity, landscape disturbance, source-sink patch distance, habitat quality, minimum cumulative resistance, and landscape restoration. The framework is particularly applicable to cities located in ecologically sensitive areas. Hence, Nanchang City, China was selected as a case study. Using 1km2 hexagonal grids, the framework is applied to map spatiotemporal changes and to analyze various natural and anthropogenic driving forces of ecological resilience in Nanchang from 2000 to 2020. Research findings confirm the feasibility and value of the urban ecological resilience analysis framework. They also highlight the advantages of the framework in revealing spatially dynamic processes and ecological resilience contributing factors, making it a valuable and practical tool for sustainable urban planning and refined management decisionmaking.

LOO P. Y. Becky

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Netherlands

12.5 (IF2019-2023)

1/58 (Top 1%)

ELSEVIER

Lian, T., & Loo, B.P.Y.* (2024). Cost of travel delays caused by traffic crashes. Communications in Transportation Research, 4, 100124. DOI: 10.1016/j.commtr.2024.100124

This study proposes a method for measuring travel delays caused by traffic crashes based on taxi GPS data and other open-source spatial data. Travel delays caused by traffic crashes are quantified according to the difference between the post-crash and typical travel speeds on affected road segments. Based on multiple sources of data in Hong Kong, we also develop a generalized linear model with explanatory variables including crash characteristics, temporal attributes, road network features, traffic indicators, and built environment features, to unveil the relationship between travel delays and these factors. The findings show that crash characteristics alone inadequately explain variations in delays. The model performance improves after factors about the built environment and the dynamic road conditions are incorporated. This underscores the importance of urban factors in traffic delay analysis. Furthermore, we estimate the total travel delays caused by traffic crashes in the city. It is estimated that Hong Kong has suffered from a total delay of 713,877 vehicle-hours in 2021. The associated economic loss amounts to US$11.02 million. This study contributes to methodological advances in estimating crash-induced travel delays. The explanatory model considers factors which help policy makers and planners to identify risky factors and hot spots for devising more targeted and effective strategies of shortening crash-induced traffic congestion in the future. In addition, the findings highlight the significance and magnitude of another negative externality of traffic crashes – traffic delays – in a complex urban road network.

LOO P. Y. Becky

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Netherlands

6.2 (IF2019-2023)

7/172 (Top 4%)

ELSEVIER

Gao, W., Cui, M., Pan, E., & Loo, B.P.Y.* (2024). Green commuting within the x-minute city: Towards a systematic evaluation of its feasibility. Journal of Transport Geography, 121, 104003. DOI: 10.1016/j.jtrangeo.2024.104003

In cities, carbon emissions associated with commuting transport is large and significant. This study integrates data about the jobs-housing relationship, road network configurations, public transport availability, and realtime traffic conditions during peak hours to evaluate the commuting feasibility and performance of green travel modes (walking, cycling and public transport) and explore the potential determining factors. In the context of the x-minute city, the green travel commuting feasibility (GTCF) indicator measures the percentage of the working population who can commute via green travel modes within specific x-minute thresholds. 15, 30, 45 and 60 min have been considered. In comparison, the car travel commuting feasibility (CTCF) indicator is developed to evaluate the corresponding commuting performance by car. Nanshan district of Shenzhen in China is taken as a case study. Results show that distinct gaps exist between GTCF and CTCF. Public transport performs well only for long-distance (> 8 km) commuting trips, and cycling does well for short (< 3 km) and medium-distance trips. Geographically, areas with large differences of GTCF and CTCF are identified for improving green travel modes with priority. Potential factors influencing GTCF are explored with a regression model and case-based analysis. Smaller street blocks, bus route realignment and better jobs-housing balance should be targeted. Designing cycling short-cuts and public transport routes that avoid traffic jams are also recommended to promote green commuting. The findings demonstrate that real-time trip-planning information is of great value in evaluating the commuting feasibility of multimodal travel and identifying influential factors for achieving the x-minute city.

PENG Liqing

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Germany

6.7 (IF2019-2023)

12/254 (Top 4%)

COPERNICUS PUBLICATIONS

Peng, L., Sheffield, J., Wei, Z., Ek, M., and Wood, E. F.: An enhanced Standardized Precipitation–Evapotranspiration Index (SPEI) drought-monitoring method integrating land surface characteristics, Earth Syst. Dynam., 15, 1277–1300, https://doi.org/10.5194/esd-15-1277-2024, 2024.

Atmospheric evaporative demand is a key metric for monitoring agricultural drought. Existing ways of estimating evaporative demand in drought indices do not faithfully represent the constraints imposed by land surface characteristics and become less accurate over nonuniform land surfaces. This study proposes incorporating surface vegetation characteristics, such as vegetation dynamics data, aerodynamic parameters, and physiological parameters, into existing potential-evapotranspiration (PET) methods. This approach is implemented across the continental United States (CONUS) for the period from 1981–2017 and is tested using a recently developed drought index, the Standardized Precipitation–Evapotranspiration Index (SPEI). We show that activating realistic maximum surface conductance and aerodynamic conductance could improve the prediction of soil moisture dynamics and drought impacts by 29 %–41% on average compared to more simple, widely used methods. We also demonstrate that this is especially effective in forests and humid regions, with improvements of 86 %–89 %. Our approach only requires a minimal amount of ancillary data while allowing for both historical reconstruction and real-time drought forecasting. This offers a physically meaningful yet easy-to-implement way to account for vegetation control in drought indices.

QIAN Junxi

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UK

4.6 (IF2019-2023)

12/172 (Top 6%)

SAGE PUBLICATIONS LTD

Qian JX, Ma Y, Tang XQ. (2024). In the frontier zone of market transition: Economic possibilities across the market/non-market divide. Environment and Planning A: Economy and Space, 56(6): 1710-1730.

This paper engages with two important approaches theorising the co-existence and even close entanglement between the market and non-market to rethink the making of actually existing market economies. The first, that is, the diverse/community economies approach, underscores alternative relations and ethics to capitalism but often views community economies as external to market processes. A second approach on market frontiers rejects the idea of the non-market domain as a Utopian space but re-imagines it as a constituent part within capitalism, while powerful actors manage and utilise non-market differences to configure particular regimes of accumulation. However, it says relatively little about how the market/nonmarket divide is navigated and appropriated to suit the wellbeing of grassroots people and communities. This paper calls for a dialogue between the two approaches and argues that community economies provide important ‘background conditions of possibility’ for ordinary people to advance their needs, interests, and wellbeing by negotiating or traversing the market/non-market divide. Our empirical study investigates recent socioeconomic transformations in two villages, Lolong and Nyiru, located within the Potatso National Park, Yunnan Province, China. In both villages, local people keep alive communal norms of reciprocity and mutual support. The persistence of the non-marketised community economies is partly attributed to a state-capital coalition that outlaws grassroots participation in local tourism economy. Subsequently, villagers devise a number of tactics to penetrate the market realm and meet emerging lifestyle and consumer needs. Three of such tactics are discussed in this study.

QIAN Junxi

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UK

7.4 (IF2019-2023)

8/172 (Top 4%)

OXFORD UNIVERSITY PRESS

Qian JX, Zeng Y, Tang XQ, Hu XH. (2024) Empowering left-behind places in Southwest China: Participation in coffee value chains as place-based development. Cambridge Journal of Regions, Economy and Society, 17(2), 375-392.

Geographical scholarship has advocated the importance of endogenous and place-sensitive development to levelling up left-behind places, by means of reactivating untapped potentials and recuperating a sense of belonging. Drawing on the approaches of global value chain (GVC), and to a lesser extent, global production network (GPN), this paper rethinks how GVC/GPN participation articulates with endogenous assets and enhances local actors’ capacities to achieve economic and social upgrading. We present a case study of the coffee economy in Lujiang Township, Yunnan Province, China. We find that local villagers are able to tap into opportunities of learning and upgrading, but these processes are mediated by institutions and moral economies. Ultimately, economic empowerment translates into the revival of a sense of belonging.

QIAN Junxi

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Netherlands

7.1 (IF2019-2023)

4/77 (Top 5%)

ELSEVIER

Qian JX, Lu YH, Li XP, Tang X. 2024. Counterurban sensibilities in the global countryside: The relational making of rurality and heritage in Xizhou Town, Southwest China. Habitat International, 149, 103109.

This paper investigates the series of works that Brian Linden, an American citizen and the founding president of the cultural tourism brand the Linden Centre (LC), has undertaken in Xizhou, a rural township in Dali Bai Autonomous Prefecture, Yunnan Province, Southwest China. Drawing on the counterurbanisation scholarship, this study recognises the place-based and endogenous qualities and assets of rural place that motivate the migration of Linden and the LC staff to Xizhou. However, we concurrently argue that the counterurbanisation thesis needs to be enriched through a conversation with the ascendancy of relational rurality and the global countryside, as counterurbanisation inevitably entail rich circularities of discourses, meanings, materials, and cultural knowledge. This has resulted in new ways that rural places are valued and appropriated by different actors on the move. Probing into a miscellany of cultural practices and educational programmes masterminded by the LC, this paper analyses how they deviate from the “authentic” meanings and uses of the heritage buildings but respond to broader visions and aspirations for a more fulfilling life vis-`a-vis modernity. This study contributes to the counterurbanisation debate by revealing how counterurban movements can be mobilised to provide cultural, discursive, and material resources that address cultural sensibilities, aspirations, and projects articulated at broader scales.

RAN Lishan

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USA

5.9 (IF2019-2023)

2/22 (Top 9%)

AMER GEOPHYSICAL UNION

Chen, Shuai, Lishan Ran, Clément Duvert, Boyi Liu, Yongli Zhou, Xiankun Yang, Qianqian Yang, Yuxin Li, and Si‐Liang Li. "Anthropogenic and hydroclimatic controls on the CO2 and CH4 dynamics in subtropical monsoon rivers." Water Resources Research 61, no. 1 (2025): e2024WR038341.

Anthropogenic perturbations have substantially altered riverine carbon cycling worldwide, exerting influences on dissolved carbon dioxide (CO2) and methane (CH4) dynamics at multiple levels. However, the magnitude and role of anthropogenic activities in modulating carbon emissions across entire river networks, as well as the influence of climatic controls, remain largely unresolved. Here, we explore the controlling factors of riverine CO2 and CH4 dynamics across 62 subtropical, monsoon‐influenced streams and rivers through basin‐wide seasonal measurements. We found that land use and aquatic metabolism played significant roles in regulating the spatial and temporal patterns of both gases. Increased nutrient levels and organic matter contributed to higher partial pressure of CO2 (pCO2) and CH4 (pCH4). Dissolved oxygen, stable carbon isotope of dissolved inorganic carbon, the proportion of impervious surface, catchment slope, and river width were the major predictors for pCO2. For pCH4, the major predictors were Chlorophyll a and water temperature, which influence organic matter availability and methanogenesis. Seasonal variations in pCO2 and pCH4 were strongly modulated by hydroclimatic conditions, with temperature markedly regulating river ecosystem metabolism. These findings highlight the likelihood of significant changes in riverine carbon emissions as climate changes and land use patterns evolve, thereby profoundly affecting the global carbon cycle.

RAN Lishan

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Netherlands

12.2 (IF2019-2023)

1/128 (Top 1%)

ELSEVIER

Hou, Yongmei, Si-Liang Li, Fu-Jun Yue, Shuai Chen, Xiaolong Liu, and Lishan Ran. "Carbon transfer from land to fluvial networks in a typical karst river-reservoir system." Water Research 271 (2025): 122899.

Although terrestrial ecosystems have been widely recognized as an important atmospheric carbon (C) sink, the net C sink capacity may have been overestimated due to C loss through aquatic ecosystems, particularly in catchments with fragile landscapes and intense human disturbances. Here, we integrated the three primary pathways of aquatic C export, including C burial, gaseous C emissions, and downstream C export, into the terrestrial-aquatic C assessment within the Wujiang River basin (WRB) in Southwest China, a typical karst riverreservoir system with cascade reservoirs. The assessment reports a net landscape C sink of 12.0, 13.8, 14.0, and 16.1 Tg C/yr in the WRB in the years 2000, 2006, 2013, and 2017, respectively, with the aquatic C export counteracting 10.6%, 11.9%, 14.6%, and 14.1% of the terrestrial C sink in these years. The aquatic C export exhibited a discernible increasing trend, indicating that dam construction and ecological restoration have profoundly altered the C biogeochemical processes and terrestrial-aquatic C transfer dynamics. Particularly, downstream C export contributed 61.8%–82.1% to the aquatic C export with approximately 72% occurring during the wet season, due largely to enhanced rock weathering and allochthonous C supply under severe soil erosion in this karst region. Organic C burial in reservoirs accounted for 0.7%–2.0% of the terrestrial C sink, which was primarily regulated by autochthonous C biogeochemical processes and terrestrial C input. Simultaneously, CO2 and CH4 emissions counteracted 1.2%–3.7% of the terrestrial C sink, and this counteracting effect was intensified if the gaseous emissions from depth-profile waters that are characterized by elevated microbial degradation and anoxic conditions were considered. This study emphasizes the substantial role of terrestrialaquatic C transfer in offsetting the terrestrial C sink, which underscores the need of integrating aquatic C export for a holistic understanding of the net C sink capacity at the landscape scale.

XU Zhenci

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UK

16.1 (IF2019-2023)

8/134 (Top 6%)

NATURE COMMUNICATIONS

Xiao, H., Bao, S., Ren, J., Xu, Z., Xue, S., & Liu, J. (2024). Global transboundary synergies and trade-offs among Sustainable Development Goals from an integrated sustainability perspective. Nature Communications, 15(1), 500.

Domestic attempts to advance the Sustainable Development Goals (SDGs) in a country can have synergistic and/or trade-off effects on the advancement of SDGs in other countries. Transboundary SDG interactions can be delivered through various transmission channels (e.g., trade, river flow, ocean currents, and air flow). This study quantified the transboundary interactions through these channels between 768 pairs of SDG indicators. The results showed that although high income countries only comprised 14.18% of the global population, they contributed considerably to total SDG interactions worldwide (60.60%). Transboundary synergistic effects via international trade were 14.94% more pronounced with trade partners outside their immediate geographic vicinity than with neighbouring ones. Conversely, nature-caused flows (including river flow, ocean currents, and air flow) resulted in 39.29% stronger transboundary synergistic effects among neighboring countries compared to non-neighboring ones. To facilitate the achievement of SDGs worldwide, it is essential to enhance collaboration among countries and leverage transboundary synergies.

XU Zhenci

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UK

3.7 (IF2019-2023)

13/267 (Top 5%)

HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS

Li, C., Zhao, G., Koh, K. P., Xu, Z., Yue, M., Wang, W., ... & Wu, L. (2024). Impact of China’s financial development on the sustainable development goals of the Belt and Road Initiative participating countries. Humanities and Social Sciences Communications, 11(1), 1-12.

China’s Belt and Road Initiative (BRI) aims to strengthen regional economic and policy cooperation and achieve the rapid development among the participating countries. While the impact on the financial development of the economic growth and energy environment of BRI participating countries has garnered close attention among scholars, few studies focus on the impact of financial development on the sustainable development goals (SDGs) of the BRI participating countries. To address this gap, we utilized panel regression models to quantitatively assess the impact of China’s financial development scale, structure, and efficiency on the SDGs of the BRI participating countries, and adopted Geographically and Temporally Weighted Regression (GTWR) model to explore the spatial-temporal effects of China’s financial development scale, structure, and efficiency on the SDGs of the BRI participating countries. Our findings indicate that China’s financial development has significantly promoted the SDGs of the BRI participating countries. This study further reveals that the scale and efficiency of China’s financial development have had a more pronounced impact on the SDGs of Asian countries, low- and middle-income countries, and the Land Silk Road participating countries, compared to those of European countries, high-income countries, and the Maritime Silk Road participating countries, respectively. In contrast, the structure of financial development primarily promotes the SDGs of European and high-income BRI participating countries in the land silk belt. The role of China’s financial development in promoting the SDGs of most BRI participating countries has gradually increased over time. This study provides valuable insights for decision-makers in China to facilitate the sustainable development of BRI participating countries and foster a shared community within the BRI framework.

ZHANG Hongsheng

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Netherlands

7.2 (IF2019-2023)

8/182 (Top 4%)

ELSEVIER

Ling, J., Yip, K. H. A., Wei, S., Sit, K. Y., Sun, L., Meng, Q., ... & Zhang, H.(2024). Multiscale rooftop greening and its socioeconomic implications in Hong Kong. Building and Environment, 259, 111643.

Classification of urban land use and land cover is vital to many applications, and naturally becomes a popular topic in remote sensing. The finite information carried by unimodal data, the compound land use types, and the poor signal-noise ratio caused by restricted weather conditions would inevitably lead to relatively poor classification performance. Recently in remote sensing society, multimodal data fusion with deep learning technology has gained a great deal of attention. Existing research exhibit integration of multimodal data at a single level, while simultaneously lacking exploration of the immense potential provided by popular transformer and CNN structures for effectively leveraging multimodal data, which may fall into the trap that makes the information fusion inadequate. We introduce SoftFormer, a novel network that synergistically merges the strengths of CNNs with transformers, as well as achieving multi-level fusion. To extract local features from images, we propose an innovative mechanism called Interior Self-Attention, which is seamlessly integrated into the backbone network. To fully exploit the global semantic information from both modalities, in the featurelevel fusion, we introduce a joint key–value learning fusion approach to integrate multimodal data within a unified semantic space. The decision and feature level information are simultaneously integrated, resulting in a multi-level fusion transformer network. Results on four remote sensing datasets show that SoftFormer is able to achieve at least 1.32%, 0.7%, and 0.99% performance improvement in overall accuracy, kappa index, and mIoU, compared to other state-of-the-art methods, the ablation studies show that multimodal fusion outperforms the unimodal data on urban land cover and land use classification, the highest overall accuracy, kappa index as well as mIoU improvement can be up to 5.71%, 10.32% and 7.91%, and the proposed modules are able to boost performance to some extent, even with cloud cover. Code will be publicly available at https://github.com/rl1024/SoftFormer.

ZHANG Hongsheng

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Netherlands

11.8 (IF2019-2023)

1/65 (Top 2%)

ELSEVIER

Liu, R., Ling, J., & Zhang, H. (2024). SoftFormer: SAR-optical fusion transformer for urban land use and land cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 218, 277-293.

Classification of urban land use and land cover is vital to many applications, and naturally becomes a popular topic in remote sensing. The finite information carried by unimodal data, the compound land use types, and the poor signal-noise ratio caused by restricted weather conditions would inevitably lead to relatively poor classification performance. Recently in remote sensing society, multimodal data fusion with deep learning technology has gained a great deal of attention. Existing research exhibit integration of multimodal data at a single level, while simultaneously lacking exploration of the immense potential provided by popular transformer and CNN structures for effectively leveraging multimodal data, which may fall into the trap that makes the information fusion inadequate. We introduce SoftFormer, a novel network that synergistically merges the strengths of CNNs with transformers, as well as achieving multi-level fusion. To extract local features from images, we propose an innovative mechanism called Interior Self-Attention, which is seamlessly integrated into the backbone network. To fully exploit the global semantic information from both modalities, in the featurelevel fusion, we introduce a joint key–value learning fusion approach to integrate multimodal data within a unified semantic space. The decision and feature level information are simultaneously integrated, resulting in a multi-level fusion transformer network. Results on four remote sensing datasets show that SoftFormer is able to achieve at least 1.32%, 0.7%, and 0.99% performance improvement in overall accuracy, kappa index, and mIoU, compared to other state-of-the-art methods, the ablation studies show that multimodal fusion outperforms the unimodal data on urban land cover and land use classification, the highest overall accuracy, kappa index as well as mIoU improvement can be up to 5.71%, 10.32% and 7.91%, and the proposed modules are able to boost performance to some extent, even with cloud cover. Code will be publicly available at https://github.com/rl1024/SoftFormer.

ZHANG Hongsheng

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UK

8.4 (IF2019-2023)

11/254 (Top 4%)

COMMUNICATIONS EARTH & ENVIRONMENT

Wei, S., Zhang, H., Xu, Z., Lin, G., Lin, Y., Liang, X., ... & Gong, P. (2024). Coastal urbanization may indirectly positively impact growth of mangrove forests. Communications Earth & Environment, 5(1), 608.

Coastal urbanization is a key driver ofmangrove loss, yet its global impacts on mangroves have yet to be thoroughly understood. Here we present a fine-scale assessment of the hidden impacts of urbanization on mangroves mediated by climate, and the joint effects of urbanization and climate at the global scale. Surprisingly, both urbanization and climate had positive impacts on mangrove growth and carbon stock in some regions, which is different from the general belief of the adverse impacts from previous research. In total, 27.3% of global mangroves received positive impacts from urbanization regarding their extent and carbon stock, among which 59.5% are indirectly mediated by climate. Moreover, mangroves in subtropical/temperate climate zones experienced more indirect positive impacts from urbanization, which enhances local climate conditions for growth by altering temperature, rainfall and sea levels. These findings suggest the feasibility of facilitating mangrove conservation through effective urban planning to achieve coastal sustainability.

ZHOU Yuyu

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Netherlands

12.7 (IF2019-2023)

16/358 (Top 4%)

ELSEVIER

Xia, H., L. Qiao, Y. Guo, X. Ru, Y. Qin, Y. Zhou & C. Wu (2024) Enhancing phenology modeling through the integration of artificial light at night effects. Remote Sensing of Environment, 303, 113997.

Spring vegetation phenology is closely influenced by photoperiod, and the presence of artificial light at Night (ALAN) therefore substantially impacts the phenological response of plants to climate change. How ALAN impacts spring phenology in relative to warming and what are the drivers regulate these impacts are not well understood. Here we focused on the extra-tropical terrestrial ecosystem (>30◦N) of China where the highest urbanization has experienced using satellite images to extract the start of the growing season (SOS) from three independent datasets, as well as ALAN data from harmonized global nighttime light (NTL over 2001–2018. We found that ALAN caused earlier SOS both at the ecosystem level and for the major climate zones, and this advanced effect weakened at lower latitude regions and for the high-altitude ecosystems. Further, we discovered that the advanced effect of ALAN on SOS was strengthened in areas with lower chilling days and with the increased distance from the city center. We therefore derived a new model for the estimation of SOS including the effects of ALAN and the new model provided improved representation of SOS in terms of higher proportions of significant pixels between model estimates and observations, higher correlation coefficients, lower root mean square error, Akaike information criterion and higher Kling-Gupta efficiency. Our results highlight that the effects of ALAN on SOS were influenced by latitude, elevation, and winter chilling. Overall, our study sheds light on the impact of human activities on plant spring phenology and provides insights for predicting plant growth patterns under future urbanization and global climate change.

ZHOU Yuyu

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Netherlands

12.7 (IF2019-2023)

16/358 (Top 4%)

ELSEVIER

Chen, G., Y. Zhou, J. A. Voogt & E. C. Stokes (2024) Remote sensing of diverse urban environments: From the single city to multiple cities. Remote Sensing of Environment, 305, 114108.

Remote sensing of urban environments has unveiled a significant shift from single-city investigations to the inclusion of multiple cities. Originated from the ideas of the Remote Sensing of Environment special issue entitled “Remote Sensing of the Urban Environment: Beyond the Single City,” this paper offers a comprehensive examination of the state of the science in multi-city remote sensing, and aims at fostering the rapid advancement of this emerging field to address global sustainability challenges and support knowledge development needed for a new discipline – urban sustainability science (USS). Through a synthesized review of eight key research fields within urban remote sensing [i.e., land use and land cover (LULC) and change, urban vertical structure, urban heat islands, hazards, energy use and emissions, air quality, carbon budgets, and green space], the paper provides insights into the underlying rationale for conducting multi-city studies, the criteria employed in the selection of cities, the societal applications, as well as the opportunities and future directions for expanding the scope of assessments in multi-city remote sensing.