The GRM Departmental Award for Outstanding Research Output is to recognize the outstanding efforts of the department’s full-time faculty members in publishing high-quality research outputs in top-tier academic journals. The award is given annually to faculty members with one or more papers published in a journal ranked in the top 10% of a subject category based on the Journal Impact Factor published in Clarivate’s Journal Citation Reports.
2022-23
Teo, S. S., Chung, C. K. L., & Wang, Z. (2023). Theorising with urban China: Methodological and tactical experiments for a more global urban studies. Dialogues in Human Geography, 0(0). https://doi.org/10.1177/20438206231156656
2022 Journal Impact Factor (JIF): 27.5
2022 JIF Rank in Subject Category: 1/86 (Top 1%)
Despite global academic interest, the field of urban China continues to be dominated by exceptionalist theorising. Given that the unique properties of Chinese urbanisation present rich cases for an engaged pluralism in urban studies, we argue for theorising with urban China based on two methodological grounds: ‘thinking cities through elsewhere’ and conjunctural analysis. This opens space for mid-level theorisation, which has the potential to contribute to the revision of existing theoretical frameworks and/or create new starting points for analysis and conceptualisation between urban China and a wider range of contexts. We propose three tactics for mid-level conceptualisation with urban China cases: generating concepts through bespoke comparisons between cases in urban China and elsewhere; conceptualising from a single urban China case by placing two theoretical frameworks into conversation; and launching concepts developed from inductive research in urban China to develop novel analytical frameworks. We conclude by arguing that theorising with urban China can benefit from collaborative research across borders, with the need to include researchers who are deeply embedded in the field.
Ka Kit Sun, Sylvia Y. He, John Thøgersen (2022). The purchase intention of electric vehicles in Hong Kong, a high-density Asian context, and main differences from a Nordic context. Transport Policy,128, 98-112. https://doi.org/10.1016/j.tranpol.2022.09.009
2022 Journal Impact Factor (JIF): 6.8
2022 JIF Rank in Subject Category: 26/380 (Top 7%)
In this paper, we aim to examine people’s EV purchase intention in Hong Kong, an Asian compact city, and how the influential factors are different from the Western context, for which a low-density Nordic context, Denmark, was chosen. To achieve this, we conducted a survey in Hong Kong to understand the effects of several key factors, including subjective norms, personal norms, and perceptions and knowledge about EVs. Structural equation modelling was employed and multi-group analysis was conducted. Our findings show that EV purchase intentions in Hong Kong are highly value-driven with the assimilation to social expectations directly and strongly encouraging individuals’ EV acceptance, which is different from the European case where the adoption desire is built upon the assessment of usage difficulty. In addition, range anxiety poses a direct deterrence to EV adoption even in the compact city. Availability of charging opportunities, be it the public ones or home-based ones, plays a critical role in an individual’s decision of EV adoption in the high-density city. Our study reveals a strong disparity in the pathways towards EV adoption between the Asian and the Nordic contexts, thereby illustrating the necessity to differentiate e-mobility promotion policies in compact cities from low-density cities.
Sylvia Y. He, Xueying Chen, Murat Es, Yuanyuan Guo, Ka Kit Sun, Zeli Lin (2022). Liveability and migration intention in Chinese resource-based economies: Findings from seven cities with potential for population shrinkage. Cities, 131, 103961. https://doi.org/10.1016/j.cities.2022.103961
2022 Journal Impact Factor (JIF): 6.7
2022 JIF Rank in Subject Category: 3/43 (Top 7.0%)
Resource-based economies often face the challenge of resource depletion and population shrinkage. After reaching a production peak, cities confront slower economic development and the inability to attract skilled workers, often leading many resource-based cities to experience urban shrinkage. This prompts questions regarding whether residents of resource-based cities are concerned about liveability indicators beyond job opportunities and whether residents would stay if a city were more liveable. This research has employed a mixed-methods approach to examine the relationship between urban liveability and migration intention. We collected data via a questionnaire survey and in-depth interviews in seven resource-based cities in the Northeast and Northwest regions of China characterised by slow or negative population growth. Our mixed-methods approach identifies five main dimensions of factors affecting an individual’s migration intention: job opportunities, employability and prospects with the mining industry; transport; public facilities; age, life cycle stage and family consideration; income and financial considerations. Our findings confirm that certain aspects of liveability significantly affect migration intention. Our subsequent analysis suggests that there is a skills gap between newly created employment opportunities and the labour force in resource-based cities. These issues call for government action to improve the liveability of resource-based cities and retain skilled workers.
Shuli Luo, Sylvia Y. He, Susan Grant-Muller, Linqi Song (2023).Influential factors in customer satisfaction of transit services: Using crowdsourced data to capture the heterogeneity across individuals, space and time. Transport Policy,131, 173-183. https://doi.org/10.1016/j.tranpol.2022.12.011
2022 Journal Impact Factor (JIF): 6.8
2022 JIF Rank in Subject Category: 26/380 (Top 6.8%)
In a rapidly evolving and highly competitive transportation market, increasing customer satisfaction and ridership retention are essential for transit agencies. Understanding how different market segments perceive transit services can help service providers to identify potential priority areas and develop specialised strategies to address their varying travel concerns. However, traditional studies have predominantly investigated the variations across socioeconomic cohorts or travel characteristics but ignored the complex effect of spatial, temporal, and user heterogeneity. Recently social media data has attracted growing interest from academia as an alternative way to compensate public attitude surveys with a high volume of semantic, spatial, and temporal information. This study collected 177,807 microblogs from Sina Weibo to understand how customer satisfaction varies among different market segments characterised by social, temporal, and spatial heterogeneity. Methodologically, this study applies sentiment analysis as a real-time measurement of customers’ satisfaction towards transit services, covering safety, crowdedness, reliability, personnel behaviour, and comfort. A beta regression model is then applied to identify the most important explanatory factors and the extent to which explanatory factors affect customers’ satisfaction, respectively. The result indicates that age, gender, travel mode, time, and space significantly contribute to customers’ satisfaction with the transit system. Their varying impacts on different service attributes are also identified in this study. This study also reveals the highly polarised nature of online sentiment, explaining gendered attitudes. Our research framework could be used as a benchmark for other service industries to conduct similar market segment analysis and integrate it into the policy decision process.
Sui Tao, Long Cheng, Sylvia He, Frank Witlox (2023). Examining the non-linear effects of transit accessibility on daily trip duration: A focus on the low-income population. Journal of Transport Geography,109,103600. https://doi.org/10.1016/j.jtrangeo.2023.103600
2022 Journal Impact Factor (JIF): 6.1
2022 JIF Rank in Subject Category: 7/86 (Top 8.1%)
Public transit provides an affordable and reliable transport option especially to the vulnerable groups. However, the relevance of transit accessibility to the daily mobility of different social strata has not been fully understood. It remains unclear to what extent the low-income may benefit from enhanced transit accessibility compared to others. Focusing on an Asian metropolis—Hong Kong, this study investigates the interplay between transit accessibility and daily trip duration with a particular focus on the low-income population via a machine-learning approach (Gradient Boosting Decision Tree). Our findings indicate that network accessibility by Mass Transit Rail (MTR) exerts a weaker effect on the duration of mandatory and discretionary trips of the low-income than for the non-low-income for these trips. This implies the presence of possible barriers of using MTR among the low-income. Moreover, marked threshold effects are identified for both MTR and bus accessibility especially in relation to the mandatory and maintenance trips of the low-income. Based on these findings, policy recommendations are proposed to help strengthen the linkage between improvement of transit accessibility and equitable mobility conditions across society.
Yu Pan, Sylvia Y. He (2023). An investigation into the impact of the built environment on the travel mobility gap using mobile phone data. Journal of Transport Geography,108, 103571. https://doi.org/10.1016/j.jtrangeo.2023.103571
2022 Journal Impact Factor (JIF): 6.1
2022 JIF Rank in Subject Category: 7/86 (Top 8.1%)
The travel mobility gap is among the indicators that can be used to evaluate the level of social and transport inequity. To achieve a large and representative sample for this investigation of the different impacts of the built environment on travel mobility of various income and migrant groups, we have utilized big data from mobile phones for over 10 million users in Shenzhen, China. Travel mobility was measured by non-commute travel frequency and activity space. Our descriptive analysis demonstrates lower-income groups and migrant workers have lower levels of travel mobility than higher-income groups and non-migrant workers. The results produced by our linear regression models also reveal a significant travel mobility gap between different income and migration groups. That gap appears to be positively impacted by job density and bus stop distance and negatively impacted by residential density and metro station distance. Our modeling results also demonstrate that the travel mobility gap is larger in the outer suburbs than in the city center and inner suburbs. Our research findings reveal that the built environment influences the travel mobility gap, which implies that marginalized groups experience some degree of social inequality and exclusion. Based on these findings, we provide policy recommendations that aim to reduce the travel mobility gap between the marginalized and reference groups.
Sui Tao, Sylvia Y. He, Xueying Chen, Jeongwoo Lee, Meng Liu (2023). Walking accessibility to non-work facilities and travel patterns in suburban new towns. Cities,137,104324. https://doi.org/10.1016/j.cities.2023.104324
2022 Journal Impact Factor (JIF): 6.7
2022 JIF Rank in Subject Category: 3/43 (Top 7.0%)
Previous research on the self-containment of new towns has focused on access to employment and commute travel; little research has been documented regarding access to non-work facilities and its relation to non-work active travel, overlooking an important aspect to assessing the self-containment of new towns. Drawing on multiple sources of data in Hong Kong, we measure walking accessibility to three main types of non-work facilities (markets, restaurants and parks) as major destinations for non-work travel. Through a series of statistical analysis, we investigate in detail the effects of accessibility on the probability and duration of home-based walking trips. Our findings indicate that: (1) new towns were more disadvantaged and less equitable in terms of the accessibility to non-work facilities than urban areas; and (2) accessibility increased both the likelihood and duration of new town residents’ walking trips particularly for grocery-shopping and dining-out purposes. The findings indicate that enhancing accessibility to non-work facilities in new towns can be beneficial in terms of both achieving self-containment and promoting more sustainable travel behaviour. Recommendations are proposed accordingly to help create more pedestrian-friendly environment, and balanced provision of non-work facilities in and around new towns and other suburban developments.
Yuqi Bai, Yutong Zhang, Olena Zotova, Helen Pineo, José Siri, Lu Liang, Xiangyu Luo, Mei-Po Kwan, John Ji, Xiaopeng Jiang, Cordia Chu, Na Cong, Vivian Lin, William Summerskill, Yong Luo, Hongjun Yu, Tinghai Wu, Changhong Yang, Jing Li, Yixiong Xiao, Jingbo Zhou, Dejing Dou, Hui Xiong, Lee Ligang Zhang, Lan Wang, Shu Tao, Bojie Fu, Yong Zhang, Bing Xu, Jun Yang, Peng Gong (2022). Healthy cities initiative in China: Progress, challenges, and the way forward. The Lancet Regional Health – Western Pacific, 27,100539. https://doi.org/10.1016/j.lanwpc.2022.100539
2022 Journal Impact Factor (JIF): 7.1
2022 JIF Rank in Subject Category: 7/106 (Top 6.6%)
China implemented the first phase of its National Healthy Cities pilot program from 2016-20. Along with related urban health governmental initiatives, the program has helped put health on the agenda of local governments while raising public awareness. Healthy City actions taken at the municipal scale also prepared cities to deal with the COVID-19 pandemic. However, after intermittent trials spanning the past two decades, the Healthy Cities initiative in China has reached a crucial juncture. It risks becoming inconsequential given its overlap with other health promotion efforts, changing public health priorities in response to the pandemic, and the partial adoption of the Healthy Cities approach advanced by the World Health Organization (WHO). We recommend aligning the Healthy Cities initiative in China with strategic national and global level agendas such as Healthy China 2030 and the Sustainable Development Goals (SDGs) by providing an integrative governance framework to facilitate a coherent intersectoral program to systemically improve population health. Achieving this alignment will require leveraging the full spectrum of best practices in Healthy Cities actions and expanding assessment efforts.
Jiangyu Song, Suhong Zhou, Mei-Po Kwan, Shen Liang, Junwen Lu, Fengrui Jing, Linsen Wang (2022). The effect of eye-level street greenness exposure on walking satisfaction: The mediating role of noise and PM2.5. Urban Forestry & Urban Greening, 77, 127752. https://doi.org/10.1016/j.ufug.2022.127752
2022 Journal Impact Factor (JIF): 6.4
2022 JIF Rank in Subject Category: 2/69 (Top 2.9%)
While there are plenty of studies on the effects of neighborhood and park greenness on personal overall satisfaction and walking behavior, the relationship between street greenness exposure and walking satisfaction has received limited attention. Also, the possible pathways by which street greenness exposure affects walking satisfaction need to be further examined. To fill these research gaps, we measured eye-level street greenness using street view images, machine learning techniques and global position systems. A structural equation model was used to examine the mediating effects of objective noise and PM2.5 exposure and related subjective annoyance, on the relationship between street greenness exposure and people’s walking satisfaction. The results showed that street greenness exposure not only had a significant direct effect on walking satisfaction, but also has a significant indirect effect on walking satisfaction through subjective environmental annoyances (including noise and PM2.5 annoyances) rather than through objective noise and PM2.5 exposures. Besides physical activity and social interaction, the indirect effect of street greenness exposure on walking satisfaction through subjective environmental pollution annoyance accounted for about 17.39% of the total effect and cannot be ignored. These results suggest that the urban greenness layout policy should not only consider residential greenness but should improve people’s environmental perception and walking satisfaction by allocating more greenness on streets with high noise and PM2.5 levels.
Lizhi Miao, Sheng Tang, Xinting Li, Dingyu Yu, Yamei Deng, Tian Hang, Haozhou Yang, Yunxuan Liang, Mei-Po Kwan, Lei Huang (2023). Estimating the CO2 emissions of Chinese cities from 2011 to 2020 based on SPNN-GNNWR. Environmental Research, 218, 115060. https://doi.org/10.1016/j.envres.2022.115060
2022 Journal Impact Factor (JIF): 27.5
2022 JIF Rank in Subject Category: 8.3 (Top 7.7%)
Global warming is a serious threat to human survival and health. Facing increasing global warming, the issue of CO2 emissions has attracted more attention. China is a major contributor of anthropogenic CO2 emissions and so it is essential to accurately estimate China’s CO2 emissions and analyze their changing characteristics. This study recalculates CO2 emissions from Chinese cities from 2011 to 2020 using the SPNN-GNNWR model and multiple factors to reduce the uncertainty in emission estimates. The SPNN-GNNWR model has excellent predictions (R2: 0.925, 10-fold CV R2: 0.822) when cross-validation is used. The results indicate that the total CO2 emissions in China calculated by the model are close to those accounted for by other authorities in the world, with the total CO2 emissions increasing from 9.122 billion tonnes in 2011 to 9.912 billion tonnes in 2020. The city with the largest increase in CO2 emissions is Tianjin, and the city with the largest decrease is Beijing. The study also reveals the regional differences in CO2 emissions in Chinese mainland, including emissions, emission intensity and per capita emissions. Capturing and understanding the emissions and the related socioeconomic characteristics of different cities can help to develop effective emission mitigation strategies.
Wang, Q., Fan, J., Kwan, MP. et al. (2023). Examining energy inequality under the rapid residential energy transition in China through household surveys. Nature Energy, 8, 251–263. https://doi.org/10.1038/s41560-023-01193-z
2022 Journal Impact Factor (JIF): 56.7
2022 JIF Rank in Subject Category: 2/344 (Top 0.6%)
Since 2013, China has initiated a rapid energy transition that replaces traditional solid fuels with modern clean energy. Despite the tremendous success of the energy transition, its impacts on household energy costs and associated energy inequality remain largely unexplored. Here we use data from a large nationwide household survey to investigate these trends. We find that about two-fifths (43.0%) of surveyed households switched from traditional solid fuels to clean energy during 2013–2017. However, 56.1% to ~61.0% of them were from extremely poor or poor households, causing deep concern for increasing household energy burden. Accordingly, the share of surveyed households in energy poverty increased from 30.1% to 34.2%. Despite the declining inequality in energy cost, a growing inequality in energy burden was revealed during 2013–2017. Our results demonstrate that the energy burden on rural households increased due to the dramatic rise in the cost of clean energy, while urban households tend to spend a lower and decreased proportion of their income on energy.
Jian Lin, Bo Huang, Mei-Po Kwan, Min Chen, Qiang Wang (2023). COVID-19 infection rate but not severity is associated with availability of greenness in the United States. Landscape and Urban Planning, 233, 104704. https://doi.org/10.1016/j.landurbplan.2023.104704
2022 Journal Impact Factor (JIF): 9.1
2022 JIF Rank in Subject Category: 7/171 (Top 4.1%)
Human exposure to greenness is associated with COVID-19 prevalence and severity, but most relevant research has focused on the relationships between greenness and COVID-19 infection rates. In contrast, relatively little is known about the associations between greenness and COVID-19 hospitalizations and deaths, which are important for risk assessment, resource allocation, and intervention strategies. Moreover, it is unclear whether greenness could help reduce health inequities by offering more benefits to disadvantaged populations. Here, we estimated the associations between availability of greenness (expressed as population-density-weighted normalized difference vegetation index) and COVID-19 outcomes across the urban–rural continuum gradient in the United States using generalized additive models with a negative binomial distribution. We aggregated individual COVID-19 records at the county level, which includes 3,040 counties for COVID-19 case infection rates, 1,397 counties for case hospitalization rates, and 1,305 counties for case fatality rates. Our area-level ecological study suggests that although availability of greenness shows null relationships with COVID-19 case hospitalization and fatality rates, COVID-19 infection rate is statistically significant and negatively associated with more greenness availability. When performing stratified analyses by different sociodemographic groups, availability of greenness shows stronger negative associations for men than for women, and for adults than for the elderly. This indicates that greenness might have greater health benefits for the former than the latter, and thus has limited effects for ameliorating COVID-19 related inequity. The revealed greenness-COVID-19 links across different space, time and sociodemographic groups provide working hypotheses for the targeted design of nature-based interventions and greening policies to benefit human well-being and reduce health inequity. This has important implications for the post-pandemic recovery and future public health crises.
Min Chen, Christophe Claramunt, Arzu Çöltekin, Xintao Liu, Peng Peng, Anthony C. Robinson, Dajiang Wang, Josef Strobl, John P. Wilson, Michael Batty, Mei-Po Kwan, Maryam Lotfian, François Golay, Stéphane Joost, Jens Ingensand, Ahmad M. Senousi, Tao Cheng, Temenoujka Bandrova, Milan Konecny, Paul M. Torrens, Alexander Klippel, Songnian Li, Fengyuan Zhang, Li He, Jinfeng Wang, Carlo Ratti, Olaf Kolditz, Hui Lin, Guonian Lü (2023). Artificial intelligence and visual analytics in geographical space and cyberspace: Research opportunities and challenges. Earth-Science Reviews, 241, 104438. https://doi.org/10.1016/j.earscirev.2023.104438
2022 Journal Impact Factor (JIF): 12.1
2022 JIF Rank in Subject Category: 5/202 (Top 2.5%)
In recent decades, we have witnessed great advances on the Internet of Things, mobile devices, sensor-based systems, and resulting big data infrastructures, which have gradually, yet fundamentally influenced the way people interact with and in the digital and physical world. Many human activities now not only operate in geographical (physical) space but also in cyberspace. Such changes have triggered a paradigm shift in geographic information science (GIScience), as cyberspace brings new perspectives for the roles played by spatial and temporal dimensions, e.g., the dilemma of placelessness and possible timelessness. As a discipline at the brink of even bigger changes made possible by machine learning and artificial intelligence, this paper highlights the challenges and opportunities associated with geographical space in relation to cyberspace, with a particular focus on data analytics and visualization, including extended AI capabilities and virtual reality representations. Consequently, we encourage the creation of synergies between the processing and analysis of geographical and cyber data to improve sustainability and solve complex problems with geospatial applications and other digital advancements in urban and environmental sciences.
Longxu Yan, Rui Zhu, Mei-Po Kwan, Wei Luo, De Wang, Shangwu Zhang, Man Sing Wong, Linlin You, Bisheng Yang, Biyu Chen, Ling Feng (2023). Estimation of urban-scale photovoltaic potential: A deep learning-based approach for constructing three-dimensional building models from optical remote sensing imagery. Sustainable Cities and Society, 93, 104515. https://doi.org/10.1016/j.scs.2023.104515
2022 Journal Impact Factor (JIF): 11.7
2022 JIF Rank in Subject Category: 1/68 (Top 1.5%)
Building-integrated photovoltaics are increasingly used to build low-carbon buildings and promote energy transition. However, the absence of three-dimensional (3D) building models may hinder accurate estimation of photovoltaic (PV) potential on 3D urban surfaces. This study develops a detail-oriented deep learning approach, which for the first time constructs 3D buildings from high-resolution satellite images and estimates PV potential. Specifically, two convolutional neural networks, i.e., the Rooftop Segmentation Model and Height Prediction Model, were developed by advancing the basic DeepLabv3+ architecture and integrating dedicated layers, adaptive activation functions, and hybrid losses. Next, the two models were trained and tested on a self-made dataset targeted at Shanghai and an open datasets under standard data augmentation and transfer learning strategies. Then, morphological post-processing procedures were developed to cluster and regularize individual rooftops with estimated heights. Finally, PV potentials in typical areas were estimated and compared. Accuracy assessments suggest satisfactory rooftop segmentationand building height estimation. The absolute relative error between the PV potentials derived from the actual and predicted building models showed little difference, implying the reliability of the extracted buildings. The proposed model is novel and effective for constructing 3D building models that can facilitate PV penetration and urban studies in various fields.
Ping Yang, Derrick Y.F. Lai, Hong Yang, Yongxin Lin, Chuan Tong, Yan Hong, Yalan Tian, Chen Tang, Kam W. Tang (2022). Large increase in CH4 emission following conversion of coastal marsh to aquaculture ponds caused by changing gas transport pathways. Water Research, 222,118882. https://doi.org/10.1016/j.watres.2022.118882
2022 Journal Impact Factor (JIF): 12.8
2022 JIF Rank in Subject Category: 1/103 (Top 1.0%)
Methane emissions from aquatic ecosystems play an important role in global carbon cycle and climate change. Reclamation of coastal wetlands for aquaculture use has been shown to have opposite effects on sediment CH4 production potential and CH4 emission flux, but the underlying mechanism remained unclear. In this study, we compared sediment properties, CH4 production potential, emission flux, and CH4 transport pathways between a brackish marsh and the nearby reclaimed aquaculture ponds in the Min River Estuary in southeastern China. Despite that the sediment CH4 production potential in the ponds was significantly lower than the marsh, CH4 emission flux in the ponds (17.4 ± 2.7 mg m−2 h−1) was 11.9 times higher than the marsh (1.3 ± 0.2 mg m−2 h−1). Plant-mediated transport accounted for 75% of the total CH4 emission in the marsh, whereas ebullition accounted for 95% of the total CH4 emission in the ponds. CH4 emission fluxes in both habitat types were highest in the summer. These results suggest that the increase in CH4 emission following the conversion of brackish marsh to aquaculture ponds was not caused by increased sediment CH4 production, but rather by eliminating rhizospheric oxidation and shifting the major transport pathway to ebullition, allowing sediment CH4 to bypass oxidative loss. This study improves our understanding of the impacts of modification of coastal wetlands on greenhouse gas dynamics.
Tan, L., Ge, Z., Ji, Y., Lai, D. Y. F., Temmerman, S., Li, S., Li, X., & Tang, J. (2022). Land use and land cover changes in coastal and inland wetlands cause soil carbon and nitrogen loss. Global Ecology and Biogeography, 31, 2541–2563. https://doi.org/10.1111/geb.13597
2022 Journal Impact Factor (JIF): 6.4
2022 JIF Rank in Subject Category: 4/49 (Top 8.2%)
Natural wetlands are widely considered important for mitigation of climate change, but they have been impacted by land use and land cover change (LULCC), often resulting in ecosystem degradation and significant changes in soil carbon (C) and nitrogen (N) dynamics. However, the impact of various LULCC types on wetland soil C and N dynamics remains unclear.
Ping Yang, Kam W. Tang, Chuan Tong, Derrick Y.F. Lai, Linhai Zhang, Xiao Lin, Hong Yang, Lishan Tan, Yifei Zhang, Yan Hong, Chen Tang, Yongxin Lin (2022). Conversion of coastal wetland to aquaculture ponds decreased N2O emission: Evidence from a multi-year field study. Water Research, 227,119326. https://doi.org/10.1016/j.watres.2022.119326
2022 Journal Impact Factor (JIF): 12.8
2022 JIF Rank in Subject Category: 1/103 (Top 1.0%)
Land reclamation is a major threat to the world’s coastal wetlands, and it may influence the biogeochemical cycling of nitrogen in coastal regions. Conversion of coastal marshes into aquaculture ponds is common in the Asian Pacific region, but its impacts on the production and emission of nitrogen greenhouse gases remain poorly understood. In this study, we compared N2O emission from a brackish marsh and converted shrimp aquaculture ponds in the Shanyutan wetland, the Min River Estuary in Southeast China over a three-year period. We also measured sediment and porewater properties, relevant functional gene abundance, sediment N2O production potential and denitrification potential in the two habitats. Results indicated that the pond sediment had lower N-substrate availability, lower ammonia oxidation (AOA and comammox Nitrospira amoA), nitrite reduction (nirK and nirS) and nitrous oxide reduction (nosZ Ⅰ and nosZ Ⅱ) gene abundance and lower N2O production and denitrification potentials than in marsh sediments. Consequently, N2O emission fluxes from the aquaculture ponds (range 5.4–251.8 μg m–2 h–1) were significantly lower than those from the marsh (12.6–570.7 μg m–2 h–1). Overall, our results show that conversion from marsh to shrimp aquaculture ponds in the Shanyutan wetland may have diminished nutrient input from the catchment, impacted the N-cycling microbial community and lowered N2O production capacity of the sediment, leading to lower N2O emissions. Better post-harvesting management of pond water and sediment may further mitigate N2O emissions caused by the aquaculture operation.
Yalan Tian, Ping Yang, Hong Yang, Huimin Wang, Linhai Zhang, Chuan Tong, Derrick Y.F. Lai, Yongxin Lin, Lishan Tan, Yan Hong, Chen Tang, Kam W. Tang (2023). Diffusive nitrous oxide (N2O) fluxes across the sediment-water-atmosphere interfaces in aquaculture shrimp ponds in a subtropical estuary: Implications for climate warming. Agriculture, Ecosystems & Environment, 341, 108218. https://doi.org/10.1016/j.agee.2022.108218
2022 Journal Impact Factor (JIF): 6.6
2022 JIF Rank in Subject Category: 2/58 (Top 3.4%)
Emissions of the potent greenhouse gas nitrous oxide (N2O) from aquaculture remain a large knowledge gap in the global N2O budget. The water column and the sediment of aquaculture ponds present very different environmental conditions, but their relative contributions to N2O production and emission are poorly resolved. We sampled three aquaculture ponds in the Min River Estuary in southeastern China monthly throughout the farming season. Based on the dissolved N2O concentrations within the water column and in sediment porewater, we calculated the diffusive N2O fluxes across the water-atmosphere interface (WAI) and sediment-water interface (SWI). The diffusive N2O flux averaged 216.9 nmol m−2 h−1 across WAI and 16.0 nmol m−2 h−1 across SWI. The estimated N2O production rate under steady-state condition was 0.13 nmol L−1 h−1 in the water column and 1.07 nmol L−1 h−1 in sediment porewater. Hence, the water column compartment and the sediment compartment of the aquaculture ponds played different roles in N2O dynamics. Based on our data, it is calculated that China’s coastal aquacultural ponds would emit 0.2 Gg N2O yr−1, or less than 1% of all aquaculture N2O emission in China. Therefore, coastal shrimp aquaculture has a relative minor climate impact compared to other aquaculture operations. Future studies should examine the role of N-cycling functional genes on N2O production and the mechanisms regulating N2O emission from aquaculture ecosystems.
Ping Yang, Kam W. Tang, Hong Yang, Chuan Tong, Linhai Zhang, Derrick Y.F. Lai, Yan Hong, Lishan Tan, Wanyi Zhu, Chen Tang (2023). Contrasting effects of aeration on methane (CH4) and nitrous oxide (N2O) emissions from subtropical aquaculture ponds and implications for global warming mitigation. Journal of Hydrology, 617, Part A,128876. https://doi.org/10.1016/j.jhydrol.2022.128876
2022 Journal Impact Factor (JIF): 6.4
2022 JIF Rank in Subject Category: 15/202 (Top 7.4%)
The increasing number of small-hold aquaculture ponds for food production globally has raised concerns of their emission of greenhouse gases (GHGs) such as methane (CH4) and nitrous oxide (N2O). Aeration is commonly applied to improve oxygen supply for the farmed animals, but it could have opposite effects on GHG emission: It may inhibit anaerobic microbial processes that produce GHGs; it may also increase water-to-air GHG exchange via physical agitation. To resolve the overall effect of aeration on GHG emissions, this study analyzed and compared the monthly CH4 and N2O emissions from earthen shrimp ponds with and without aeration, in the farming period for two consecutive years, in an estuary in subtropical southeastern China. CH4 flux was mainly influenced by water temperature and dissolved oxygen, and it was significantly higher in non-aerated pond (7.6 mg m-2h−1) than in aerated ponds (4.5 mg m-2h−1), with ebullition accounting for >90 % of the emission. Conversely, non-aerated pond had ca. 50 % lower N2O flux than aerated ponds, and dissolved nitrate was the main driving factor. The combined CO2-equivalent emission in aerated ponds (avg. 10,829 kg CO2-eq ha−1 yr−1) was substantially lower than that in non-aerated pond (avg. 17,627 kg CO2-eq ha−1 yr−1). While aeration may increase diffusive flux of GHGs via physical agitation, it remains a simple and effective management practice to decrease the overall climate impact of aquaculture ponds.
Yingsheng Zheng, Chao Ren, Yuan Shi, Steve H.L. Yim, Derrick Y.F. Lai, Yong Xu, Can Fang, Wenjie Li (2023). Mapping the spatial distribution of nocturnal urban heat island based on Local Climate Zone framework. Building and Environment, 234, 110197. https://doi.org/10.1016/j.buildenv.2023.110197
2022 Journal Impact Factor (JIF): 7.4
2022 JIF Rank in Subject Category: 6/139 (Top 4.3%)
A spatial understanding of street-scale urban heat island (UHI) is essential but challenging in Hong Kong, due to its highly heterogeneous urban environment and a limited weather station monitoring network. Night-time mobile measurements were conducted during the summertime of 2014 to monitor UHI variation at local level. Three measurement routes and a total of 80 sample sites were selected according to the Local Climate Zone (LCZ) framework. The measured climatic data and urban morphology data were synergized and analyzed at LCZ scale through Geographical Information System (GIS). Stepwise Multiple Linear Regression (MLR) and Partial Least Square Regression (PLSR) were applied to quantify the connections between urban form and local UHI conditions of LCZ. Mean sky view factor, total street length, and pervious surface fraction of LCZ sites have been found to be the most explanatory variables of local UHI intensity, and over 50% of UHI variations can be explained by both statistical models of stepwise MLR and PLSR. An UHI evaluation map of urban areas in Hong Kong has been developed based on the statistical models, through which UHI hotspots have been identified. LCZ-based UHI mitigation strategies were further developed for climatic planning of Outline Zoning Plan areas. The results indicate that urban forms have significant influences on UHI development at local scale, and an optimal design of urban morphology is necessary for UHI mitigation and climate adaptation.
Yan Hong, Linhai Zhang, Ping Yang, Chuan Tong, Yongxin Lin, Derrick Y. F. Lai, Hong Yang, Yalian Tian, Wanyi Zhu, Kam W. Tang (2023). Responses of coastal sediment organic and inorganic carbon to habitat modification across a wide latitudinal range in southeastern China. CATENA, 225, 107034. https://doi.org/10.1016/j.catena.2023.107034
2022 Journal Impact Factor (JIF): 6.2
2022 JIF Rank in Subject Category: 17/202 (Top 8.4%)
Coastal wetlands are important to the global carbon (C) budget and climate regulation. Plant invasion and aquaculture reclamation have drastically transformed China’s coastal wetlands, but knowledge of the effects on sediment carbon remains limited. We sampled top layer sediments (0–20 cm) in 21 coastal wetlands in southeastern China across the tropical-subtropical climate gradient, that have experienced the same sequence of habitat transformation from native mudflats (MFs) to Spartina alterniflora marshes (SAs) then to aquaculture ponds (APs). We measured the sediment carbon contents and ancillary physicochemical parameters. Landscape change from MFs to SAs increased sediment organic carbon (SOC) but decreased sediment inorganic carbon (SIC) content, whereas conversion of SAs to APs resulted in the opposite changes. Based on stepwise regression analysis, ammonium concentration and particle size distribution were the common factors that affected changes in SOC between habitat types, whereas for SIC it was ammonium and chloride concentrations. Habitat change affected SOC to a larger degree than SIC. Overall, invasion of MFs by SAs increased total carbon storage in the top sediment by 22%, or 6.6 × 106 g C ha−1; conversion of SAs to APs decreased it by 9.7%, or 3.5 × 106 g C ha−1. Our results showed the differential effects of different habitat modification scenarios on the sediment carbon pools and help assess how landscape-scale change affects terrestrial carbon budget and emission in the context of global climate change.
Ping Yang, Kam W. Tang, Linhai Zhang, Xiao Lin, Hong Yang, Chuan Tong, Yan Hong, Lishan Tan, Derrick Y.F. Lai, Yalan Tian, Wanyi Zhu, Manjing Ruan, Yongxin Lin (2023). Effects of landscape modification on coastal sediment nitrogen availability, microbial functional gene abundances and N2O production potential across the tropical-subtropical gradient. Environmental Research, 227, 115829. https://doi.org/10.1016/j.envres.2023.115829
2022 Journal Impact Factor (JIF): 8.3
2022 JIF Rank in Subject Category: 16/207 (Top 7.7%)
Wetland sediment is an important nitrogen pool and a source of the greenhouse gas nitrous oxide (N2O). Modification of coastal wetland landscape due to plant invasion and aquaculture activities may drastically change this N pool and the related dynamics of N2O. This study measured the sediment properties, N2O production and relevant functional gene abundances in 21 coastal wetlands across five provinces along the tropical-subtropical gradient in China, which all had experienced the same sequence of habitat transformation from native mudflats (MFs) to invasive Spartina alterniflora marshes (SAs) and subsequently to aquaculture ponds (APs). Our results showed that change from MFs to SAs increased the availability of NH4+-N and NO3−-N and the abundance of functional genes related to N2O production (amoA, nirK, nosZ Ⅰ, and nosZ Ⅱ), whereas conversion of SAs to APs resulted in the opposite changes. Invasion of MFs by S. alterniflora increased N2O production potential by 127.9%, whereas converting SAs to APs decreased it by 30.4%. Based on structural equation modelling, nitrogen substrate availability and abundance of ammonia oxidizers were the key factors driving the change in sediment N2O production potential in these wetlands. This study revealed the main effect patterns of habitat modification on sediment biogeochemistry and N2O production across a broad geographical and climate gradient. These findings will help large-scale mapping and assessing landscape change effects on sediment properties and greenhouse gas emissions along the coast.
Yue Li, Tageui Hong, Yefu Gu, Zhiyuan Li, Tao Huang, Harry Fung Lee, Yeonsook Heo, and Steve H. L. Yim (2023). Assessing the Spatiotemporal Characteristics, Factor Importance, and Health Impacts of Air Pollution in Seoul by Integrating Machine Learning into Land-Use Regression Modeling at High Spatiotemporal Resolutions. Environmental Science & Technology, 57 (3), 1225-1236. https://doi.org/10.1021/acs.est.2c03027
2022 Journal Impact Factor (JIF): 11.4
2022 JIF Rank in Subject Category: 19/275 (Top 6.9%)
Previous studies have characterized spatial patterns of air pollution with land-use regression (LUR) models. However, the spatiotemporal characteristics of air pollution, the contribution of various factors to them, and the resultant health impacts have yet to be evaluated comprehensively. This study integrates machine learning (random forest) into LUR modeling (LURF) with intensive evaluations to develop high spatiotemporal resolution prediction models to estimate daily and diurnal PM2.5 and NO2 in Seoul, South Korea, at the spatial resolution of 500 m for a year (2019) and to then evaluate the contribution of driving factors and quantify the resultant premature mortality. Our results show that incorporating the random forest algorithm into our LUR model improves the model performance. Meteorological conditions have a great influence on daily models, while land-use factors play important roles in diurnal models. Our health assessment using dynamic population data estimates that PM2.5 and NO2 pollution, when combined, causes a total of 11,183 (95% CI: 5837–16,354) premature mortalities in Seoul in 2019, of which 64.9% are due to PM2.5, while the remaining are attributable to NO2. The air pollution-attributable health impacts in Seoul are largely caused by cardiovascular diseases including stroke. This study pinpoints the significant spatiotemporal variations and health impact of PM2.5 and NO2 in Seoul, providing essential data for epidemiological research and air quality management.
Will W. Qiang, Haowen Luo, Yuxuan Xiao, David W.H. Wong, Alex S. Shi, Ziwei Lin, Bo Huang, Harry F. Lee (2023). Can urban polycentricity improve air quality? Evidence from Chinese cities. Journal of Cleaner Production, 406, 137080. https://doi.org/10.1016/j.jclepro.2023.137080
2022 Journal Impact Factor (JIF): 11.1
2022 JIF Rank in Subject Category: 22/275 (Top 8.0%)
This study utilizes high-resolution population grid data from 2004 to 2018 to assess the extent of urban polycentricity in 284 Chinese prefecture-level cities. A new method for measuring urban polycentricity is devised using the Clauset-Newman-Moore grey modularity maximization algorithm. Additionally, the Spatial Durbin Model is applied to investigate the effect of urban polycentricity on the mean and spatial variation of PM2.5 concentrations. Our statistical results show that the increased distance between urban centers and the increased size disparity between the large and small centers effectively reduces air pollution within the city. On the other hand, in cities with a strong clustering of urban centers and significant size disparity between main and sub-centers, the polycentric urban structure cannot help reduce air pollution but merely shift it to other urban centers within the city. Hence, interpreting the severity of urban air pollution solely on the basis of the citywide average of air pollution may be insufficient. It is necessary to consider the possible relocation of air-polluting activities associated with polycentric urban structures. Policymakers should take into account the potential spatial inequality of air pollution driven by urban polycentricity.
Yiang Chen, Jimmy C.H. Fung, Dehao Yuan, Wanying Chen, Tung Fung, Xingcheng Lu (2023). Development of an integrated machine-learning and data assimilation framework for NOx emission inversion. Science of The Total Environment, 871, 161951. https://doi.org/10.1016/j.scitotenv.2023.161951
2022 Journal Impact Factor (JIF): 9.8
2022 JIF Rank in Subject Category: 26/275 (Top 9.5%)
As major air pollutants, nitrogen oxides (NOx, mainly comprising NO and NO2) not only have adverse effects on human health but also contribute to the formation of secondary pollutants, such as ozone and particulate nitrate. To acquire reasonable NOx simulation results for further analysis, a reasonable emission inventory is needed for three-dimensional chemical transport models (3D-CTMs). In this study, a comprehensive emission adjustment framework for NOx emission, which integrates the simulation results of the 3D-CTM, surface NO2 measurements, the three-dimensional variational data assimilation method, and an ensemble back propagation neural network, was proposed and applied to correct NOx emissions over China for the summers of 2015 and 2020. Compared with the simulation using prior NOx emissions, the root-mean-square error, normalized mean error, and normalized mean bias decreased by approximately 40 %, 40 %, and 60 % in NO2 simulation using posterior NOx emissions corrected by the framework proposed in this work. Compared with the emissions for 2015, the NOx emission generally decreased by an average of 5 % in the simulation domain for 2020, especially in Henan and Anhui provinces, where the percentage reductions reached 24 % and 19 %, respectively. The proposed framework is sufficiently flexible to correct emissions in other periods and regions. The framework can provide reliable and up-to-date emission information and can thus contribute to both scientific research and policy development relating to NOx pollution.
Wanying Chen, Xingcheng Lu, Dehao Yuan, Yiang Chen, Zhenning Li, Yeqi Huang, Tung Fung, Haochen Sun, and Jimmy C.H. Fung (2023). Global PM2.5 Prediction and Associated Mortality to 2100 under Different Climate Change Scenarios. Environmental Science & Technology, 57 (27), 10039-10052. https://doi.org/10.1021/acs.est.3c03804
2022 Journal Impact Factor (JIF): 11.4
2022 JIF Rank in Subject Category: 19/275 (Top 6.9%)
Ambient fine particulate matter (PM2.5) has severe adverse health impacts, making it crucial to reduce PM2.5 exposure for public health. Meteorological and emissions factors, which considerably affect the PM2.5 concentrations in the atmosphere, vary substantially under different climate change scenarios. In this work, global PM2.5 concentrations from 2021 to 2100 were generated by combining the deep learning technique, reanalysis data, emission data, and bias-corrected CMIP6 future climate scenario data. Based on the estimated PM2.5 concentrations, the future premature mortality burden was assessed using the Global Exposure Mortality Model. Our results reveal that SSP3-7.0 scenario is associated with the highest PM2.5 exposure, with a global concentration of 34.5 μg/m3 in 2100, while SSP1-2.6 scenario has the lowest exposure, with an estimated of 15.7 μg/m3 in 2100. PM2.5-related deaths for individuals under 75 years will decrease by 16.3 and 10.5% under SSP1-2.6 and SSP5-8.5, respectively, from 2030s to 2090s. However, premature mortality for elderly individuals (>75 years) will increase, causing the contrary trends of improved air quality and increased total PM2.5-related deaths in the four SSPs. Our results emphasize the need for stronger air pollution mitigation measures to offset the future burden posed by population age.
Liao, K., Zhang, J., Chen, Y., Lu, X., Fung, J.C.H., Ying, Q., and Yu JZ (2023). Regional source apportionment of trace metals in fine particulate matter using an observation-constrained hybrid model. npj Climate and Atmospheric Science 6, 65. https://doi.org/10.1038/s41612-023-00393-4
2022 Journal Impact Factor (JIF): 9.0
2022 JIF Rank in Subject Category: 5/94 (Top 5.3%)
Trace metals in fine particulate matter (PM2.5) are of significant concern in environmental chemistry due to their toxicity and catalytic capability. An observation-constrained hybrid model is developed to resolve regional source contributions to trace metals and other primary species in PM2.5. In this method, source contributions to primary PM2.5 (PPM2.5) from the Community Multiscale Air Quality (CMAQ) Model at each monitoring location are improved to align better with the observation data by applying source-specific scaling factors estimated from a unique regression model. The adjusted PPM2.5 predictions and chemical speciation data are then used to generate observation-constrained source profiles of primary species. Finally, spatial distributions of their source contributions are produced by multiplying the improved CMAQ PPM2.5 contributions with the deduced source profiles. The model is applied to the Pearl River Delta (PRD) region, China using daily observation data collected at multiple stations in 2015 to resolve source contributions to 8 trace metals, elemental carbon, primary organic carbon, and 10 other primary species. Compared to three previous methods, the new model predicts 13 species with smaller model errors and 16 species with less model biases in 10-fold cross validation analysis. The source profiles determined in this study also show good agreement with those collected from the literature. The new model shows that during 2015 in the PRD region, Cu is mainly from the area sources (31%), industry sector (27%), and power generation (20%), with an annual average concentration as high as 50 ng m−3 in some districts. Meanwhile, major contributors to Mn are area sources (40%), emission from outside PRD (23%) and power generation (17%), leading to a mean level of around 10 ng m−3. Such information is essential in assessing the epidemiological impacts of trace metals as well as formulating effective control measures to protect public health.
W. Li, P. Ma, H. Wang and C. Fang (2023). SAR-TSCC: A Novel Approach for Long Time Series SAR Image Change Detection and Pattern Analysis. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-16, 5203016. https://doi.org/10.1109/TGRS.2023.3243900
2022 Journal Impact Factor (JIF): 8.2
2022 JIF Rank in Subject Category: 5/87 (Top 5.7%)
Change detection has played an increasingly important role in multitemporal remote sensing applications recently. Long time series analysis is providing new information of land cover changes and improving the quality and accuracy of the change information being derived from remote sensing. The purpose of this study is to dig for more change temporal information and change pattern information from synthetic aperture radar (SAR) image time series (ITS), which is of great significance for monitoring urban area changes, conducting land use surveys, and renovating illegal constructions. In the study, a novel unified framework for long time series SAR image change detection and change pattern analysis (SAR-TSCC) was proposed for land cover change mapping. To obtain the most notable change time rapidly, a fast SAR ITS change point search method based on pruned exact linear time (SAR-PELT) algorithm was adopted. Meanwhile, the deep time series classification network, named SAR time series transformer (SAR-TST), was implemented to recognize the change patterns, which is based on time series transformer (TST) architecture. Considering the lack of real training data, a novel synthetic data generation method is developed. The combination of the synthetic and real data enhanced the generalization of the classifiers. The proposed framework was used for monitoring a large urbanization area in the northwest of Hong Kong, China. The Cosmo Skymed (CSK) time series data acquired from 2013 to 2020 were exploited for land cover change analysis. Experiment results showed that our approach achieved the state-of-the-art performance, as the time accuracy reached 86% and the classification accuracy on the four main change patterns (impulse, step, cycle, and complex) is over 99%. In particular, the proposed SAR-TST model showed remarkable advantages in the presence of insufficient real data.
Zherong Wu, Peifeng Ma, Yi Zheng, Feng Gu, Lin Liu, Hui Lin (2023). Automatic detection and classification of land subsidence in deltaic metropolitan areas using distributed scatterer InSAR and Oriented R-CNN. Remote Sensing of Environment, 290,113545. https://doi.org/10.1016/j.rse.2023.113545
2022 Journal Impact Factor (JIF): 13.5
2022 JIF Rank in Subject Category: 11/275 (Top 4.0%)
Multi-temporal interferometric synthetic aperture radar (InSAR) is an effective tool for measuring large-scale land subsidence. However, the measurement points generated by InSAR are too many to be manually analyzed, and automatic subsidence detection and classification methods are still lacking. In this study, we developed an oriented R-CNN deep learning network to automatically detect and classify subsidence bowls using InSAR measurements and multi-source ancillary data. We used 541 Sentinel-1 images acquired during 2015–2021 to map land subsidence of the Guangdong-Hong Kong-Macao Greater Bay Area by resolving persistent and distributed scatterers. Multi-source data related to land subsidence, including geological and lithological, land cover, topographic, and climatic data, were incorporated into deep learning, allowing the local subsidence to be classified into seven categories. The results showed that the oriented R-CNN achieved an average precision (AP) of 0.847 for subsidence detection and a mean AP (mAP) of 0.798 for subsidence classification, which outperformed the other three state-of-the-art methods (Rotated RetinaNet, R3Det, and ReDet). An independent effect analysis showed that incorporating all datasets improved the AP by 11.2% for detection and the mAP by 73.9% for classification, respectively, compared with using InSAR measurements only. Combining InSAR measurements with globally available land cover and digital elevation model data improved the AP for subsidence detection to 0.822, suggesting that our methods can be potentially transferred to other regions, which was further validated this using a new dataset in Shanghai. These results improve the understanding of deltaic subsidence and facilitate geohazard assessment and management for sustainable environments.
Li, C., Ng, M. K., Xu, Y., & Yow, T. C. (2023). Environmental justice and ecological civilization in the Pearl River Delta, China. Eurasian Geography and Economics, 1–30. https://doi.org/10.1080/15387216.2023.2220340
2022 Journal Impact Factor (JIF): 3.8
2022 JIF Rank in Subject Category: 4/84 (Top 4.8%)
The central government of China aims to transform the Pearl River Delta (PRD) from a polluting and low value-added global factory to a sustainable region. The transformative vision of the PRD is underpinned by the ecological civilization concept. While ecological civilization represents a significant initiative in moving toward sustainable development, China’s top-down and command-and-control mode of environmental governance may fall short of addressing multi-dimensional and multi-scalar social and environmental justice issues of the PRD cities. The authors therefore suggest embracing the concept of environmental justice to enhance ecological civilization, with the aim of benefiting every stratum of society. The concept of environmental justice is multi-faceted, embracing recognition, procedural, distribution and compensatory justice at different geographical scales, often calling for institutional changes. Without environmental justice, a harmonious and sustainable PRD would not be possible. In recent years, China has made unprecedented institutional adjustments to deal with environmental issues, such as conducting annual “health screening” and five-yearly thorough evaluations to monitor conservation and development. To embrace environmental justice, legislative changes are required to recognize individual environmental rights through collective action and public participation, incorporating citizens’ inputs to the appraisal of cadres, and establishing intergovernmental institutionalized organizations.
Yiling Luo, Jianfa Shen (2022). Urban entrepreneurialism, metagovernance and ‘space of innovation’: Evidence from buildings for innovative industries in Shenzhen, China. Cities,131, 104067. https://doi.org/10.1016/j.cities.2022.104067
2022 Journal Impact Factor (JIF): 6.7
2022 JIF Rank in Subject Category: 3/43 (Top 7.0%)
Supporting innovation is among the priorities for entrepreneurial cities globally. Dominant urban studies examine the developmental process, power dynamics, and political-economic impacts of industrial spaces that foster high-tech development. Scant research unfolds the governance transformation of entrepreneurial cities and illustrates how the entrepreneurial cities transform the governing pattern to guide the governance process for supporting innovation space production. A new spatial policy entitled ‘Building for Innovative Industries’ (BII) has been formed in Shenzhen, China. Based on policy review, data analysis and interviews, this study investigates metagovernance arrangements of the entrepreneurial state. Research finds that the Chinese entrepreneurial city introduces flexible governance over innovation space production by inviting broader actors, increased autonomy of operative agency and contextualised operation.
Chenglong Wang, Jianfa Shen, Ye Liu (2023). Hukou transfer intention of rural migrants with settlement intention in China: How cities’ administrative level matters. Journal of Rural Studies, 99, 1-10. https://doi.org/10.1016/j.jrurstud.2023.01.022
2022 Journal Impact Factor (JIF): 5.1
2022 JIF Rank in Subject Category: 9/86 (Top 10.5%)
This paper is the first to investigate the role of China’s top-down model captured by cities’ administrative level in the hukou transfer intention of rural migrants with settlement intention. We develop a conceptual framework and attribute rural migrants’ decision on hukou transfer to a comprehensive evaluation of the benefit, risk, and opportunity. Using a Multilevel Logistic Regression, the empirical study uncovers that the impact of cities’ administrative level comprises direct effect and moderating effect. The direct effect highlights the benefit of hukou transfer, through which cities’ administrative level positively affects rural migrants’ hukou transfer intention. The moderating effect underlines both the benefit and risk of obtaining urban local hukou, through which cities’ administrative level shapes the relationship between marital status/migration type/arable land/homestead/household income and hukou transfer intention. Our work offers an international insight into rural migrants’ settlement by revealing the role of cities’ political system in shaping the stratification of the distribution of permanent migrants at the city level. It also contributes to the knowledge of the neoclassical perspective in migration studies by considering rural migrants’ long-term cost-benefit balance on settlement.
Balasubramanian Sambasivam, Yuan Xu (2023). Reducing solar PV curtailment through demand-side management and economic dispatch in Karnataka, India. Energy Policy,172, 113334. https://doi.org/10.1016/j.enpol.2022.113334
2022 Journal Impact Factor (JIF): 9.0
2022 JIF Rank in Subject Category: 8/380 (Top 2.1%)
India has set 2070 as the target year to achieve carbon neutrality, while carbon-intensive fossil fuels are still dominating its energy system. In the next five decades, economically optimized energy transition towards renewables is crucial for India to reduce CO2 emissions in an affordable manner. India has installed a large fleet of solar PV, and thus, maximizing their capacity factors plays an influential role in energy transition. This study examines how the state of Karnataka managed to enhance solar PV capacity factor by two-thirds with substantially reduced curtailment from 2017 to 2019. We built, calibrated, and validated a Mixed-Integer Linear Programming (MILP) model with detailed hourly data to quantify the impacts of two major policy changes, being shifting electricity consumption of irrigation from night-time to daytime (load shift) and dispatching electricity generation units by their merit order (economic dispatch). Our results indicate that these two measures could explain about 20% and 70% of the capacity factor increase, respectively, which is equivalent to reducing the cost of solar electricity by about 40%. India and other countries may further expand these policies for accelerating and optimizing energy transition.