Departmental Research Output Prize 2023

The Quiet Violence of Empire traces USAID’s long and bloody history of development work in Afghanistan to reveal the transnational entanglements of imperialism and racial capitalism, deftly underscoring the notion of development as a form of slow violence. Thinking relationally across human geography, global studies, and critical ethnic studies, Attewell uncovers the explicitly racial underpinnings of international development theory and praxis.

Although a burgeoning literature has focused on greenspace quantity and relevant contribution to life satisfaction, little effort has been devoted to greenspace quality and nuanced differences in shaping the nexus of urban greenness and life satisfaction across social groups. Taking Beijing as an example, this study attempts to (1) explore the effects of both the quantity (availability and accessibility) and the quality (attractiveness and natural aesthetics) of neighborhood greenspaces on residents’ subjective life satisfaction, and (2) examine the heterogeneity in these effects across different social groups defined by household registration and housing status. Combining geo-coded, street-level residential greenness exposure data and city-scale social survey data in a multilevel ordered logit model, the results reveal that greenness quality play more pronounced roles, while both quantity and quality features could contribute positively to life satisfaction. When interacting quantity dimensions with quality ones, evident complementary roles of greenspace quality are detected. The impacts of four greenspace dimensions on life satisfaction are stronger for residents with Beijing hukou than those without. While availability becomes insignificant for residents in private-housing, and natural aesthetics is insignificant for those in social-housing. This study enriches our understanding of the nexus between multi-dimensions of
urban greenspaces and life satisfaction, and complements existing research on green inequality to how unequal greenness in terms of quantity and quality affects individuals’ life satisfaction across different social strata. Our findings offer useful insights for fleshing out urban greening as central to crafting livable cities that can improve population life satisfaction and narrow inequalities.

Urban forests provide multiple ecosystem services for city dwellers, amongst which improving public health via mitigating mental stresses and providing attractive spaces for diverse physical activities has attracted increasing attention from scholars and policy makers within the context of the ongoing COVID-19 pandemic as well as urgently-needed post-pandemic urban transformation towards healthy cities. This short communication summarizes existing empirical evidence pertinent to the linkage between urban forests and public health maintenance and improvement, highlights three underlying mechanisms, i.e., physiological, psychological, and immunological pathways, and outlines practical implications for the establishment and management of urban forests as a strategy for planning healthy cities.

The birth of the world’s great megacities is the surest and starkest harbinger of the “urban age” inaugurated in the twentieth century. As the world’s urban population achieves majority for the first time in recorded history, theories proliferate on the nature of urban politics, including the shape and quality of urban democracy, the role of urban social and political movements, and the prospects for progressive and emancipatory change from the corridors of powerful states to the routinized rhythms of everyday life. At stake are both the ways in which the rapidly changing urban world is understood and the urban futures being negotiated by the governments and populations struggling to contend with these changes and forge a place in contemporary cities. Transdisciplinary by design, Monstrous Politics first moves historically through Mexico City’s turbulent twentieth century, driven centrally by the contentious imbrication of the Institutional Revolutionary Party (PRI) and its capital city. Participant observation, expert interviews, and archival materials demonstrate the shifting strategies and alliances of recent decades, provide the reader with a sense of the texture of contemporary political life in the city during a time of unprecedented change, and locate these dynamics within the history and geography of twentieth-century urbanization and political revolution. Substantive ethnographic chapters trace the emergence and decline of the political language of “the right to the city,” the establishment and contestation of a “postpolitical” governance regime, and the culmination of a century of urban politics in the processes of “political reform” by which Mexico City finally wrested back significant political autonomy and local democracy from the federal state. A four-fold transection of the revolutionary structure of feeling that pervades the city in this historic moment illustrates the complex and contradictory sentiments, appraisals, and motivations through which contemporary politics are understood and enacted. Drawing on theories of social revolution that embrace complexity, and espousing a methodology that foregrounds the everyday nature of politics, Monstrous Politics develops an understanding of revolutionary urban politics at once contextually nuanced and conceptually expansive, and thus better able to address the realities of politics in the “urban age” even beyond Mexico City.

Most pansharpening methods refer to the fusion of the original low-resolution multispectral (MS) and highresolution panchromatic (PAN) images acquired simultaneously over the same area. Due to its good robustness, multiresolution analysis (MRA) has become one of the important categories of pansharpening methods. However, when only MS and PAN images acquired at different times can be provided, the fusion results from current MRA methods are often not ideal due to the failure to effectively analyze multitemporal misalignments between MS and PAN images from different times. To solve this issue, MRA pansharpening based on variation factor for MS and PAN images from different times is proposed. The MRA pansharpening based on dual-scale regression model is first established, and the variation factor is then introduced to effectively analyze the multitemporal misalignments by using the alternating direction method of multipliers (ADMM), yielding the final fusion results. Experiments with synthetic and real datasets show that the proposed method exhibits significant performance improvement compared to the traditional pansharpening methods, as well as the state-of-the-art MRA methods. Visual comparisons demonstrate that the variation factor introduces encouraging improvements in the compensation of multitemporal misalignments in ground objects and advances pansharpening applications for MS and PAN images acquired at different times.

Poisson noise is one of the significant sources of noise present in hyperspectral imagery (HSI). In most of the existing denoising methods, Poisson noise is first transformed into Gaussian noise through the Anscombe transform and then removed. However, the use of Anscombe transform can give rise to transform errors that affect the final denoising results. In addition, blurs often contaminate the HSI during the imaging procedure, which makes it more difficult to remove the Poisson noise. In view of the above problems, under the maximum a posteriori (MAP) model, we propose a Poissonian blurred HSI denoising based on variable splitting and penalty technique (named as VSPT) to directly remove the Poissonian blurred HSI noise without using the Anscombe transform. By finding the minimum value of the negative logarithmic Poisson log-likelihood combined with the total variation (TV), the proposed method transforms the problem into two subproblems, which are easier to solve: 1) a TV regularized deconvolution problem and 2) an ordinary convex optimization problem. The experimental results show that the proposed VSPT method can effectively remove Poisson noise in HSI contaminated by blurs during the imaging procedure.

Since the mid-2000s, drilling and production of oil and gas activities have grown exponentially in the southwestern United States. The clearing of pre-existing vegetation and topsoil to build well pads is known to have a broad range of ecological, biological, hydrological, and health impacts, therefore ecosystem restoration of the well pads is generally required. This process, however, is often complicated by limited funding, various governing bodies and ownership, and frequent extreme weather events. To ensure that well pad construction does not result in damaging, irreversible environmental change in the region, a prioritization strategy is needed to maximize the effectiveness of restoration efforts. The objective of this study is to develop a methodology to prioritize well pads where ecosystem restoration is urgently needed. In this methodology, a set of locational soil (e.g., soil fragility, wind and water erodibility) and land cover (e.g., land cover, proximity to streams) attributes were derived from publicly available datasets and a restoration priority score system along with a weighting factor were assigned to individual attributes. Accordingly, a total restoration priority score (TRPS) was calculated for individual well pads. This methodology was applied to a dataset of >10,000 well pads located in the Permian Basin and the surrounding area. This method effectively filtered out a large number of sites with low TRPS, and identified a small portion of high-score, clustered well pads. The identification of such well pads makes the logistical challenge of targeted restoration much easier for stakeholders tasked with maximizing the effectiveness of restoration efforts with limited funding. Despite some known limitations and inaccuracies, this method is lowcost and can be easily adaptable to humid and sub-humid systems, and even to restoration relevant to non-oil and gas exploration activities, such as solar and wind development, in the southwestern United States and many other areas worldwide.

Longwave radiation components, including downward, upward, and net longwave radiation (DLR, ULR, and NLR, respectively), are essential parameters in heat flux exchange across the ocean–atmosphere interface. However, few long-term, high-resolution, and accurate sea–surface longwave radiation (SSLR) products are available. We generated the first high-resolution (5-km) all-sky daily SSLR product from Advanced Very High- Resolution Radiometer (AVHRR) top-of-atmosphere observations, combined with the European Center for Medium-Range Weather Forecasts Reanalysis V5 near-surface meteorological variables and National Oceanic and Atmospheric Administration sea–surface temperatures from 1981 to 2018. We coupled the densely connected convolutional neural network and bidirectional long short-term memory neural network as a retrieval algorithm. The training dataset was generated using integrated SSLR samples from 2002 to 2012 at 437 globally distributed locations. The archived product, SSLR_AVHRR, showed a high accuracy against 81,546 buoy-based observations from eight observation networks, with an R2 of 0.96 (1.00, 0.77), root mean square error of 10.27 (4.51, 9.27) Wm - 2, and mean bias error of - 1.30 (0.30, - 0.72) Wm- 2 for DLR (ULR, NLR) retrievals. Based on SSLR_AVHRR, the global DLR (ULR) flux exhibited a significantly (p-value < 0.05) increasing trend of 1.03 (1.08) Wm - 2/decade during 1982–2018. The trend was 0.24 (0.34) Wm - 2/decade during 1982–2000, which increased to 1.79 (1.45) Wm - 2/decade during 2001–2018. This globally increasing trend was dominantly impacted by the significant increases in high latitude, particularly in the Arctic Ocean. Trend variations at low latitudes, which were more frequent than at middle and high latitudes because of the El Ni˜no-Southern Oscillation, mitigated the increasing rates of global DLR and ULR after strong El Ni˜no years, whereas the global NLR flux remained relatively stable throughout the study period. This method can be extended and applied to estimate other air-sea fluxes based on a unified estimating framework to help mitigate imbalanced energy and freshwater budgets at the air-sea interface to some degree.

Land surface temperature (LST) plays a dominant role in the surface energy budget (SEB) and hydrological cycling. Thermal infrared (TIR) remote sensing is the primary method of estimating LST globally. However, cloud cover leaves numerous data gaps in satellite LST products, which seriously restricts their applications. Efforts have been made to produce gap-free LST products from polar-orbiting satellites (e.g., Terra and Aqua); however, satellite data from limited overpasses are not suitable for characterizing the diurnal temperature cycle (DTC), which is directly related to heat waves, plant water stress, and soil moisture. Considering the high temporal variability in LST and the importance of the DTC, we refined the SEB-based cloudy-sky LST recovery method by improving its feasibility and efficiency and produced a global hourly, 5 km, all-sky land surface temperature (GHA-LST) dataset from 2011 to 2021. The GHA-LST product was generated using TIR LST products from geostationary and polar-orbiting satellite data from the Copernicus Global Land Service (CGLS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). Based on ground measurements at the 201 global sites from the Surface Radiation Budget (SURFRAD), Baseline Surface Radiation Network (BSRN), Fluxnet, AmeriFlux, Heihe River basin (HRB), and Tibetan Plateau (TP) networks, the overall rootmean-square error (RMSE) of the hourly GHA-LST product was 3.31 K, with a bias of 􀀀0:57K and R2 of 0.95. Thus, this product was more accurate than the clear-sky CGLS and MODIS MYD21C1 LST samples. The RMSE value of the daily mean LST was 1.76 K. Validation results at individual sites indicate that the GHALST dataset has relatively larger RMSEs for high-elevation regions, which can be attributed to high surface heterogeneity and input data uncertainty. Temporal and spatial analyses suggested that GHA-LST has satisfactory spatiotemporal continuity and reasonable variation and matches the reference data well at hourly and daily scales. Furthermore, the regional comparison of GHA-LST with other gap-free hourly datasets (ERA5 and Global Land Data Assimilation System, GLDAS) demonstrated that GHA-LST can provide more spatial texture information. The monthly anomaly analysis suggests that GHA-LST couples well with global surface air temperature datasets and other LST datasets at daily mean and minimum temperature scales, whereas the maximum temperature and diurnal temperature range of LST and air temperature (AT) have different anomalous magnitudes. The GHA-LST dataset is the first global gap-free LST dataset at an hourly, 5 km scale with high accuracy, and it can be used to estimate global evapotranspiration, monitor extreme weather, and advance meteorological forecasting models. GHA-LST is freely available at https://doi.org/10.5281/zenodo.7487284 (Jia et al., 2022b) and http://glass.umd.edu/allsky_LST/GHA-LST (last access: 10 February 2023; Jia et al., 2022c).

This paper engages with the ongoing theoretical enquiry into the actual functioning and diverse practices of informal property rights. Departing from the prevailing neoliberal perspective and the credibility thesis, this paper develops a framework of institutional analysis to understand the functioning and regional variation of informal property rights. It takes the institutions-as-equilibria perspective and situates both structural and endogenous institutional variables in actors’ strategic choices. We test our conceptual framework through a multisite empirical study of extralegal and untitled housing in China. Building on game theory, archival research, and fieldwork, we compare the key variables contributing to the divergent fortunes of extralegal and untitled housing property rights in three Chinese cities: prospering in Shenzhen, selectively interfered with in Beijing, and eliminated in Sanya. The functioning of informal property rights is found to be contingent upon diverse strategic interactions of local governments and key local stakeholders. Our paper highlights the formation of local actors’ behavioral beliefs and their adjustment through a nonlinear feedback mechanism. It suggests the necessity and significance of integrating structural and endogenous institutional variables, elaborating on the interplay of formal and informal institutions, and applying the microeconomic and dynamic perspectives to better understand the functioning and diverse practices of informal property rights in the developing world.

This study contributes to the worldwide efforts of adopting innovative low carbon construction methods to reduce net zero embodied carbon of buildings and to minimize local environmental impacts during the construction period. It uses agent-based models (ABM) to simulate multi-threaded and multi-objective construction activities through collecting detailed project data of two case studies (one using the modular integrated construction (MiC) method and the other one using the cast-in-situ method) in Hong Kong. Considering that the construction process is dynamic and site issues are complex in nature, three major environmental impacts of carbon emissions, air pollution and noise are modelled at fine spatial and temporal resolutions. The findings show that the MiC project has significant environmental advantages over the cast-in-situ project in terms of reducing CO2 emissions, harmful emissions (including sulphur dioxide, nitrogen oxides, and organic volatile compounds), PM2.5 and PM10 emissions, and noise pollution levels, consequently reducing health risk associated with people in surrounding communities near construction sites. In terms of per square meter of construction floor area (per m2 CFA), the MiC project produced 17.60% less CO2, 42.54% less harmful gases, 32.15% less PM2.5 and 27.41% less PM10. The duration of noise exceeding 70 dB (minutes) in the neighbourhood of the MiC project was 81.25% shorter than the cast-in-situ project. The total health damage (in DALYs per m2 CFA) of the former was 63.18% lower than the latter. The research concludes that the adoption of low carbon construction methods like MiC can help to foster sustainable buildings and healthy communities in an urban context.

Transport systems are confronting challenges under disruptive events. In transit-oriented cities, the heavy reliance on urban railways makes the transport system ever more vulnerable. Extant literature has paid much attention to urban rail transit vulnerability and resilience against natural disasters. Yet, less focus has been put on disturbances caused by people. In this study, by integrating the on- and off-road situations, we assess the urban rail transit vulnerability under the Anti-extradition Law Amendment Bill (Anti-ELAB) movement in Hong Kong in 2019, during which the Mass Transit Railway (MTR) services were severely disrupted. Our results show that very highly vulnerable stations are spatially concentrated in traditional urban core areas. It is also observed that roads around some new town stations became more congested during service disruption. When disruptive events occurred during non-peak hours, the level of congestion in more than half of stations’ ambient roads did not show significant changes. This study sets forth a rail transit vulnerability assessment matrix and suggests that future assessments of transport vulnerability should take a more holistic approach and consider both on- and offroad traffic situations. The findings also provide directions for building resilient cities in the future.

This paper engages with the thesis of the mobile border and the growing interest in cross-border mobilities and practices to understand how borders provide a raison d’être for the organization of everyday life for people living in China’s Southwest borderlands. Through a conversation between the literatures on cross-border mobilities, practices and livelihoods, this study moves beyond the mobile border thesis’s lopsided focus on diffused and dispersed practices that strengthen state sovereignty and border security, but instead emphasizes the kaleidoscopic everyday practices asserting and erasing borders at the same time, the plural rationalities of state governance, and grassroots actors’ agencies and skills in appropriating or transgressing borders. In sum, this study re-appropriates the mobile border thesis to argue that borders are mobile because of their permeation into the textures of everyday life. The empirical study elucidates this argument by investigating two border regions in China, Hekou, and Ruili in Yunnan Province. Specifically, it unpacks four sets of cross-border practices – cross-border socialities and kinship ties, cross-border marriage, labor mobilities, and everyday spaces of exchange – to reveal how what is possible at the everyday level is overdetermined by official territorialities, bottom-up negotiations, and the flexibility of state governance.

Soil conservation is of global importance, as accelerated soil erosion by human activity is a primary threat to ecosystem viability. However, the significance and role of soil conservation in reshaping landscape carbon (C) accounting has not been comprehensively integrated in the terrestrial C sink. Here, we present the first integrated assessment of the modified terrestrial C sink and aquatic C transport due to soil conservation for the semiarid Chinese Loess Plateau (CLP), the world's most vulnerable region to soil erosion. We show a surprisingly low terrestrial-aquatic C transfer that offset the terrestrial net ecosystem productivity by only 7.5%, which we attribute to the effective implementation of soil conservation practices. Despite the highest soil erosion, the semiarid CLP acts as effective C sink at 43.2 ± 22.6 g C m −2 year −1, which is comparable to temperate forest in absorbing atmospheric CO2. Moreover, C burial in reservoirs has created an additional anthropogenic C sink of 2.9 ± 1.1 g C m −2 year −1. Our findings indicate that effective soil conservation can significantly increase landscape C sequestration capacity. The co-benefits of soil conservation in erosion control and C sequestration have important implications for policy makers in other regions undergoing increasing erosion intensity to pursue environmental sustainability.

Inland waters have been increasingly viewed as hotspots for greenhouse gas (GHG) emissions owing to their strong capability to intercept and mineralize carbon from the terrestrial environment. Although small waterbodies in humid subtropical climates have the potential to emit considerable amounts of GHG, their emission patterns have remained understudied. This study involved intensive measurements of carbon dioxide (CO2) emissions from a small reservoir and its upstream and downstream reaches located in subtropical Hong Kong. Our results revealed that a variety of metabolic, hydrological, and hydrochemical processes play a critical role in regulating its CO2 dynamics. The reservoir was an overall source of CO2 to the atmosphere with an average areal flux of 24.6 mmol m−2 d−1, and it occasionally functioned as a sink for atmospheric CO2 under intense solar radiation when primary productivity was high. This flux is on the low side relative to that of global (sub)tropical reservoirs, which was likely attributable to the prolonged history of the reservoir (>150 years) and the occasional undersaturation of CO2 in thewater column.We also noticed pronounced differences in the underlying controls of CO2 dynamics between the reservoir and its upstream and downstream reaches, emphasizing the importance of taking into account the distinct characteristics of both lentic and lotic waters when evaluating catchment-scale CO2 fluxes.

The Belt and Road Initiative (BRI) tremendously impacts the Sustainable Development Goals (SDGs) of the countries involved. However, the BRI's impacts and impact pathways on the synergies and trade-offs in these countries' SDGs remain unclear. Using global data from 2005 to 2021, we find that the BRI has dramatically promoted low-income countries' SDGs and the synergies between high-income countries' SDGs. Continuing to prioritize economic growth as the main focus of the BRI may not be the optimal path for achieving mutual development that benefits all of humanity. Although there is environmental pressure involved with the BRI, this factor does not force involved countries to make trade-offs between improving SDGs and environmental resources. Furthermore, the BRI affects involved countries' SDGs through two key channels: fostering industries, innovation, and infrastructure (SDG 9) by enhancing people's well-being (SDG 3) and promoting zero hunger (SDG 2) through sustainable cities (SDG 11).

Sustainable land use is crucial for achieving Carbon Neutrality goals, which requires a scientific identification of optimized pathways for land use patterns across multiple scales. Yet, current land use studies predominantly focus on single scales but lack system thinking and fail to establish complementary cross-regional carbon neutrality collaboration schemes. Applying life-cycle thinking to analyze land use sustainability and carbon neutrality potential at multiple scales could address this challenge. This study aims to present China’s first multiscale spatiotemporal optimization pathway for sustainable land use to improve carbon neutrality potential. It systematically integrates the complex spatial coupling relationships between land use intensity and efficiency. We integrate multi-scale sustainable land use pathways, spanning grid, basin, and administrative levels, and unveil significant variations in land use sustainability and carbon neutrality potential across China. Sixty-three percent of China’s land is in low sustainability, and the overall carbon neutrality potential in China is relatively low, with regions accounting for <30 % facing more carbon neutrality missions. Implementing sequential and partitioned governance modes can effectively support China in achieving sustainable land use and advancing Carbon Neutrality goals. Our sustainable land use pathways for China provide valuable insights for systematically undertaking carbon neutrality actions across different scales.

Rising sea level caused by global climate change may increase extreme sea level events, flood low-lying coastal areas, change the ecological and hydrological environment of coastal areas, and bring severe challenges to the survival and development of coastal cities. Hong Kong is a typical economically and socially developed coastal area. However, in such an important coastal city, the mechanisms of local sea-level dynamics and their relationship with climate teleconnections are not well explained. In this paper, Hong Kong tide gauge data spanning 68 years was documented to study the historical sea-level dynamics. Through the analysis framework based on Wavelet Transform and Hilbert Huang Transform, non-stationary and multi-scale features in sea-level dynamics in Hong Kong are revealed. The results show that the relative sea level (RSL) in Hong Kong has experienced roughly 2.5 cycles of high-to-low sea-level transition in the past half-century. The periodic amplitude variation of tides is related to Pacific Decadal Oscillation (PDO) and El Niño-Southern Oscillation (ENSO). RSL rise and fall in eastern Hong Kong often occur in La Niña and El Niño years, respectively. The response of RSL to the PDO and ENSO displays a time lag and spatial heterogeneity in Hong Kong. Hong Kong's eastern coastal waters are more strongly affected by the Pacific climate and current systems than the west. This study dissects the non-stationary and multi-scale characteristics of relative sea-level change and helps to better understand the response of RSL to the global climate system.

Fine-grained building heights are indispensable in various aspects of urban energy consumption, environment, and climate. The highly accurate and refined building height product is expected to be produced by high-resolution remote sensing images in this paper. To obtain this product painlessly, we aim to design a framework that automatically extracts building heights within cities. Specifically, high-resolution remote sensing images are shadow corrected, on which the algorithm is applied to generate the digital surface model. Then the model is masked using OSM’s building data. Finally, the height of each building is obtained by zonal statistics. The abovementioned data in Hong Kong are collected. The experimental results show that the highest accuracy is achieved in the medium building density area, outperforming the low and high-density areas by 1.42 and 4.06 m in RMSE. At the same time, the extracted building heights with shadow correction are closer to the reference heights. It can be concluded that extracting building heights using high-resolution remote sensing satellite imagery is feasible; the accuracy varies for buildings of different heights and densities; and correcting the shadows can significantly improve the accuracy of building height extraction. The innovations are the automatic process and the shadow correction. It allows for dynamic building height monitoring when long time series of remote sensing images are available and proves that shadow may harm height estimation performance. Our research sheds light on the design of building height estimation algorithms and provides height validation data in developed cities and height estimation solutions for developing cities.

There is a lack of understanding of the complex spatiotemporal patterns of heat exposure during heat waves, and
the impact of urbanization intensity and urban morphology on heat exposure in urban thermal environments. To
address these issues, this study used mean radiant temperature (Tmrt) as an index to indicate human exposure to
extreme heat, and generated hourly heat exposure maps at a 1-m spatial resolution in Summer 2020 for heat
wave and non-heat wave days across three diversely urbanized and climatically different U.S. cities (Riverside,
CA; Des Moines, IA; and Boston, MA) using the SOlar LongWave Environmental Irradiance Geometry (SOLWEIG)
model and multi-source remote sensing and GIS data. Based on these high-frequency and microscale maps, we
found that heat exposure in urban canyons of downtown areas was high due to relatively low building’s height to street’s width (H/W) ratio, which resulted in a limited shading effect in the studied cities. Heat exposure during heat waves was enhanced by 6 ◦C to 10 ◦C compared to non-heat wave conditions, with the main differences occurring in the early afternoon between 12 pm and 2 pm. We found that hot cities (Riverside, 63 ◦C) had higher heat exposure than warm cities (Des Moines and Boston, 53 ◦C and 51 ◦C) during heat waves. Heat exposure in urban core areas was approximately 5C higher than that in rural areas during heat waves. Additionally, we found that sky view factor was the most important urban morphology factor influencing heat exposure, with a relative importance of over 67% in these cities, but the role of impervious surface and trees varied among these cities. Impervious surface area (ISA) contributed more to heat exposure than trees in dry and hot regions (Riverside), but not in humid and warm cities (Des Moines and Boston). This study is the first to generate hourly heat exposure maps at a 1-m resolution for heat wave and non-heat wave days, and to investigate spatiotemporal patterns and the impacts of urbanization intensity and urban morphology on heat exposure in multiple cities. The findings of this study can be useful in developing urban policies to improve urban thermal environments in diverse urban settings, and our transferable framework can potentially be applied to other cities for heat exposure studies.

The information of global spatially explicit urban extents under scenarios is important to mitigate future environmental risks caused by global urbanization and climate change. Although future dynamics of urban extent were commonly modeled with conversion from non-urban to urban extent using cellular-automata (CA)-based models, gradual changes of impervious surface area (ISA) at the pixel level were limitedly explored in previous studies. In this paper, we developed a global dataset of urban fractional changes at a 1 km resolution from 2020 to 2100 (5-year interval), under eight scenarios of socioeconomic pathways and climate change. First, to quantify the gradual change of ISA within the pixel, we characterized ISA growth patterns over the past decades (i.e., 1985–2015) using a sigmoid growth model and annual global artificial impervious area (GAIA) data. Then, by incorporating the ISA-based growth mechanism with the CA model, we calibrated the statespecific urban CA model with quantitative evaluation at the global scale. Finally, we projected future urban fractional changes at 1 km resolution under eight development pathways based on the harmonized urban growth demand from Land Use Harmonization2 (LUH2). The evaluation results show that the ISA-based urban CA model performs well globally, with an overall R2 of 0.9 and a root mean square error (RMSE) of 0.08 between modeled and observed ISAs in 2015.With the inclusion of temporal contexts of urban sprawl gained from GAIA, the dataset of global urban fractional change shows good agreement with 30-year historical observations from satellites. The dataset can capture spatially explicit variations of ISA and gradual ISA change within pixels. The dataset of global urban fractional change is of great use in supporting quantitative analysis of urbanizationinduced ecological and environmental change at a fine scale, such as urban heat islands, energy consumption, and human–nature interactions in the urban system. The developed dataset of global urban fractional change is available at https://doi.org/10.6084/m9.figshare.20391117.v4 (He et al., 2022).