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This paper investigates the spillover effect of government locations on economic growth by exploring more than 180 relocation events of city governments in China. Empirical results demonstrate that government relocations improve regional economic growth measured by night lights. This positive effect decreases with the distance to city governments' new locations. Government relocations are also found to accelerate urbanization. Potential mechanisms include the changes in land conversion and firm birth patterns in the move-in areas. These results indicate that the relocation of local governments can be an effective place-based policy to improve economic growth and urbanization. [ABSTRACT FROM AUTHOR]

Urbanization and the release of carbon emission are two significant features of contemporary economic and social progress in China. However, there are few studies that consider how urbanization policy goals affect carbon emission. This paper aims to study the nonlinear effects of urbanization policy goals on carbon emission in 13 cities of the Beijing-Tianjin-Hebei region. In this paper, a multi-dimensional spatial disturbance grey model is proposed. Based on matrix perturbation theory, this paper explores the spatial perturbation bounds of the novel model and verifies that the spatial property of the model is consistent with the viewpoint of the First Law of Geography. Then, the novel model is used to predict the urban carbon emission under different urbanization policy goal scenarios. The results showed that the novel model has spatial regional correlation in spatial region prediction. With the development of urbanization level, carbon emissions show a downward trend in more than 50% of cities from 2022 to 2025. In addition, the improvement of urbanization policy goals will promote the carbon peak time of most cities. The carbon emission intensity will exhibit an inverted U-shaped trend distribution in space. The research results are conducive to providing scientific guidance for the formulation of new-type urbanization policies and helping to achieve the dual carbon goals. • A multi-dimensional spatial disturbance grey model is proposed. • The novel grey model is consistent with the viewpoint of the First Law of Geography. • The spatial distribution of carbon intensity showed an inverted U-shaped pattern. [ABSTRACT FROM AUTHOR]

The Western Himalayan Region (WHR) is under constant threat from Land Use and Land Cover Change (LULCC) brought on by various socioeconomic activities such as urbanization, agricultural expansion, and population growth. Detailed research into the scope of these changes is necessary to comprehend the challenges they represent. This study aimed at (1) examining and quantifying spatiotemporal patterns of LULCC from 1975 to 2015, (2) mapping the hotspots of forest loss, and (3) modelling future LULCC for the year 2055 using medium spatial resolution multispectral satellite imagery (MSS, TM, LISS-I, LISS-III, AWiFS), field data, visual interpretation and logistic regression model in the WHR. Logistic regression integrated various drivers of LULCC, including climatic, topographic, and socio-economic factors and past LULCC trends to forecast potential LULC in the WHR by 2055. The results highlight an outrageous 184% increase in regional urbanization. Expansion of urban and cropland (12%) led to deforestation which reduced the area of natural habitats, specifically forests (−11%), water bodies (−8%) and scrubland (−6%). A significant rise in barren land by 30% was noted, while there was a decrease of 20% in glacier/snow melt. The future projection for 2055 anticipates continuing current trends, emphasizing an intensified urban expansion of 63% and an approximate 9% reduction in overall forest coverage. The accuracy of the predicted 2015 LULC map was assessed using ROC/AUC (Receiver Operating Characteristic/Area under Curve) which was found to be 0.79. [ABSTRACT FROM AUTHOR]

The global increase in urbanization has coincided with a rise in depression prevalence. However, the effect of urbanization on depression remains controversial, especially for the elderly. To clarify how urbanization impacts depression in the elderly from a network perspective. This sectional cohort study used data from China Health and Retirement Longitudinal Study (CHARLS). The occurrence of depressive symptoms in urban and rural elderly residents. Network metrics of depressive symptoms. Of the 13,993 participants, lower incidence of depressive symptoms was observed in urban (26.3 %, 95 % CI, 24.7 %–27.8 %) than in rural (40.4 %, 95 % CI, 39.5 %–41.3 %, P < 0.0001) residents. However, higher incidence of depressive symptoms was observed in urban (26.3 %, 95 % CI, 25.2 %–28.4 %) than in rural (17.5 %, 95 % CI, 16.1 %–18.9 %, P < 0.0001) residents in a subset of 2898 pairs of participants after PSM. No difference in the network structure and metrics between urban and rural residents before (M = 0.071, p = 0.054, S = 0.037, p = 0.80) and after (M = 0.085, p = 0.133, S = 0.086, p = 0.47) PSM was detected. The networks structure revealed that negative affect was strongly connected to somatic symptoms and that the two anhedonic symptoms were also strongly connected. The current study indicated the homogeneity of the ultimate nature of depression between rural and urban residents from the network perspective, supporting the viewpoint that urbanization might not impose influence on depression. Further researches delving deeper into the complexity of the issue may provide new insights into our understanding of depression in an urban environment among the elderly. • The association between urbanization and depressive sympotoms in the elderly were investigated through network analysis. • It might be insufficient to decipher the influence of urbanization on depression in the elderly just from the aspect of prevalence • No difference in the network structure and metrics of depressive symptoms was observed between urban and rural residents. [ABSTRACT FROM AUTHOR]

The frequency, intensity, and geographical reach of natural disasters, fueled in part by factors such as climate change, population growth, and urbanization, have undeniably been escalating concerns around the world. This is a retrospective analysis of natural disasters recorded in the Emergency Events Database from 1995 to 2022. Between 1995 and 2022, 11,360 natural disasters occurred, with a mean of 398 per year. Asia experienced the most disasters (4390) and the highest number of casualties (918,198). Hydrological disasters were the most common subgroup (4969), while geophysical disasters led in terms of deaths (770,644). Biological disasters caused the most injuries (2544), particularly in Africa. Recognizing the historical impacts of the various subtypes of natural disasters may help different regions better risk analyze and mitigate the unique risks associated with such events. [ABSTRACT FROM AUTHOR]

• We studied the urban–rural gradient of urban vegetation phenology changes (△UVP) and its drivers across 1500+ cities in China. • Along the urban–rural gradient, the SOS advance and EOS delay increase first, decrease later, and finally steady. • The △UVP is contributed more by climate change than by urbanization level for urban subzones. A comprehensive investigation on the long-term changes of urban vegetation phenology (△UVP) is critical for a better understanding of changes in urban ecosystems. However, the urban–rural gradient of the △UVP across cities with diverse climate zones and city sizes, as well as the associated drivers, remains largely unclear. Here, using multi-source remote sensing data from 2001 to 2019, we investigated the urban–rural gradient (including six urban–rural subzones, i.e., the old urban core, new urban core, new urban area, suburban area, exurban area, and rural area) of the △UVP across 1500 + cities in China and analyzed the impacts of urbanization level and climate change on the △UVP. First, over the past two decades, the △UVP during spring (i.e., the changes of the start of growing season, termed △SOS) is generally negative (advanced SOS; 73 % ± 4 %) and that during autumn (i.e., changes of the end of growing season, termed △EOS) is mainly positive (delayed EOS; 63 % ± 3 %) in all urban–rural subzones. The spatial distribution of the △UVP based on enhanced vegetation index (EVI) data is relatively consistent with that based on solar-induced chlorophyll fluorescence (SIF) data. Along the urban–rural gradient, both the SOS advance and EOS delay exhibit an increasing first, decreasing later, and finally steady curve, with the maximum occurring in the new urban area at − 12.4 ± 18.5 days (for △SOS) and 3.8 ± 9.9 days (for △EOS). Second, the SOS changes from an advance to a delay from the middle temperate to southern subtropical zones, while the EOS changes from a delay to an advance. The magnitudes of both △SOS and △EOS show a general trend of increasing first and then decreasing with decreasing latitude. Third, the SOS advance and EOS delay generally decrease with city size for the old urban core, increase initially and decrease later with city size for the three suburban subzones, while they show little deviations with city size for the new urban core and new urban area. Finally, in the three urban subzones, the mean △UVP across all the cities is contributed more by climate change (mean: △SOS: 73 %; △EOS: 78 %) than by urbanization level (mean: △SOS: 27 %; △EOS: 22 %). Our findings can improve the understanding of urban vegetation changes in the context of rapid urbanization and climate change. [ABSTRACT FROM AUTHOR]

• Preparation of Spectral, thermal and pollution indices. • Examining the relationships between spectral and thermal indices with LST. • Prediction of land surface temperature (LST) using Markov and CA-Markov chain. • Prediction of urban pollution using MLP, RBF, LSTM neural networks. Urbanization has led to the emergence of surface urban heat islands (SUHI) and air pollution, necessitating comprehensive investigations into the urbanization process. This study explores the impact of urbanization on temperature trends in northern Iran using remote sensing and neural networks. MODIS images from 2001, 2010, and 2019 were employed to determine climatic features and vegetation levels. SUHI indices and the Urban Thermal Field Variance Index (UTFVI) were utilized to assess ecological comfort and urban heat islands. Markov chain methods were used for forecasting future thermal pollution. The relationship between temperature, air pollutants, spectral indices, and thermal indices was investigated through regression analysis. Furthermore, multilayer perceptron (MLP), radial basis function (RBF), and long short-term memory (LSTM) neural networks were employed for predicting indicator values. The findings reveal an increase in thermal pollution indicated by elevated Land Surface Temperature (LST), SUHI, and UTFVI values, along with a decrease in Normalized Difference Vegetation Index (NDVI) values between 2001 and 2019. The Urbanization Index (UI) values demonstrate a 5% increase in urban areas during the same period. The UTFVI values suggest a decline in ecological comfort due to urbanization and reduced vegetation, with the majority of the region experiencing the poorest thermal comfort in 2019. The SUHI index peaked at 11.72 in 2019, with the highest values observed in spring and the lowest in winter. Correlation analysis highlights a significant relationship between LST and various factors, including SUHI, UTFVI, CH 4 , SO 2 , and CO 2 , underscoring the influence of temperature increase on pollution escalation in the study area. Air pollution assessment indicates the highest concentration of CO gas in the northern and southern parts of the region. Tehran exhibits the highest CO gas levels at 0.051 ppm, while Golestan records the lowest at 0.012 ppm. SO 2 levels reach their peak in northern regions (0.0088 ppm) and are lowest in southern regions (0 ppm). The Markov and CA-Markov chain methods predict that by 2050, approximately 50% of the region will experience high temperatures. Neural network results demonstrate that the LSTM method outperforms MLP and RBF methods, with R2 values of 0.99 and 0.98, respectively, indicating higher accuracy in LST prediction during testing and validation stages. Overall, this study highlights the potential of neural networks in predicting thermal pollution and facilitating effective land management strategies in urban areas. [ABSTRACT FROM AUTHOR]

The systematic decline of rural areas in the process of rapid urbanization has become a global trend, creating greater challenges for sustainable rural development. As the spatial projection of socio-economic development and living environment in rural areas, the continuous tracking of rural settlements (RUS) is crucial to quantify the imbalance of rural development. However, consistent information on RUS is highly needed but is quite deficient in current research. In this study, a cost-effective mapping model was proposed to produce an annual RUS dataset in the rapid urbanization region of Beijing-Tianjin-Hebei (BTH) in North China during 1990–2020, and the temporal-spatial regularity of RUS changes was further analyzed. The location-based and the area-based comparison verified the effectiveness of our model, with a mean overall accuracy of 85% and a mean correlation value of 0.88, respectively. The total area of RUS in the BTH region increased by 2561 km2 from 1990 to 2020, while the average size of RUS remained stable after 2005. The annual change trends in RUS appeared with increasing and decreasing accounting for 76.33% and 23.67%, respectively. The centroids of RUS in Tianjin and Hebei have moved closer to Beijing, while those in Beijing have moved away from the former. Notably, we have identified 56.3% counties in the BTH region belong to the "Convex-I" change type in RUS. In general, our work can help to consistently quantify the spatiotemporal patterns of RUS in a cost-effective way, providing more explicit spatial information and continuous temporal information for rural residential land management. [ABSTRACT FROM AUTHOR]

This article aims is to create a liveability index that combines traditional population-based datasets and non-traditional data sources from Uber services, potentially supporting when pursuing new perceptions of a city. For that, a comprehensive study was performed focusing on Natal, a Brazilian urban center that has important socioeconomic and public/private transport services inequalities. In order to assess intra-municipal disparities, the units of analysis were neighborhoods and intra-neighborhoods (the Human Development Unit - HDU), which represents small homogeneous areas in socioeconomic terms. A data-driven workflow was conducted to process more than 4 million trips and to retrieve the Estimated Time of Arrival (ETA), considered in this work as a proxy for the Uber quality service. In order to evaluate variations, associations and spatial autocorrelation between ETA and sociodemographic features, different methods were applied, notably the Exploratory Data Analysis (EDA), the Exploratory Spatial Data Analysis (ESDA), and the Regression Analysis. From the results obtained by these methods and the application of a Principal Component Analysis (PCA), a Composite Indicator of Liveability (CIL) could be created and validated as a measure fitted to sociodemographic characteristics, as it describes high values in wealthier regions and lower values in the poorest areas of a city. • A data-oriented method is proposed to make a Composite Indicator of Liveability (CIL). • Exploratory Data Analysis, Spatial Statistics and Factor Analysis were applied. • Estimated Time of Arrival (ETA) from Uber services is a proxy for liveability. • CIL showed the potential of technology-led data to be as coherent as traditional data. [ABSTRACT FROM AUTHOR]

To identify potential barriers to care, this study examined the general psychiatry outpatient new appointment availability in the US, including in-person and telepsychiatry appointments, comparing results between insurance types (Medicaid vs. private insurance), states, and urbanization levels. This mystery shopper study investigated 5 US states selected according to Mental Health America Adult Ranking and geography to represent the US mental health care system. Clinics across five selected states were stratified sampled by county urbanization levels. Calls were made during 05/2022–07/2022. Collected data included contact information accuracy, appointment availability, wait time (days), and related information. Altogether, 948 psychiatrists were sampled in New York, California, North Dakota, Virginia, and Wyoming. Overall contact information accuracy averaged 85.3%. Altogether, 18.5% of psychiatrists were available to see new patients with a significantly longer wait time for in-person than telepsychiatry appointments (median = 67.0 days vs median = 43.0 days, p < 0.01). The most frequent reason for unavailability was provider not taking new patients (53.9%). Mental health resources were unevenly distributed, favoring urban areas. Psychiatric care has been severely restricted in the US with low accessibility and long wait times. Transitioning to telepsychiatry represents a potential solution for rural disparities in access. • <20% of psychiatrists were available to see new patients. • Median wait times for in-person and telepsychiatry appointments were 67 days and 43 days. • Fewer local mental health resources were available in rural areas compared to urban areas. • Psychiatric care has been restricted in the US with low accessibility and long wait times. • Barriers to timely psychiatric care need to be identified and addressed. [ABSTRACT FROM AUTHOR]

Changes in land use and cover (LULC) are now considered as a significant concern because they have a great impact on biodiversity loss and climate changes. Therefore, the present study was applied to assess the effect of land use dynamic on land surface temperature (LST). LULC maps of years; 1991, 2001, 2014, and 2022 were applied using Maximum likelihood classification MLC) while the LST was developed using thermal bands of Landsat images. LULC maps showed a considerable development and improvement in vegetation cover and urbanization. Results showed a negative and positive correlation between LST with NDVI and NDBI respectively. It was revealed that the estimated and predicted LST confirm the reliability of the present model with R2 = 0.95. The study calculated that the maximum LST in 2022 to simulate current state of Riyadh, Saudi Arabia and the results were 32.1, 45.5, 38.7 and 41.3 in winter, summer, autumn and spring respectively. We are now sounding the alarm because the state must act quickly and decisively to stop intensive forestation if it is to avoid very high temperatures by 2030. Our findings point to the need for environmental protection in response to the study area's quick conversion of LULC and increasing trend of LST. [ABSTRACT FROM AUTHOR]

The term 'exaptation' was coined by Gould and Vrba 40 years ago, yet the role that character states which evolved under prior conditions play in novel adaptation to contemporary environments is still a matter of debate. The need to adopt the term 'exaptation' was to replace the term 'preadaptation', which still persists in the literature and may contribute to confusion of the accurate process by which evolution shapes new character states. The emerging discipline of urban evolutionary ecology provides a new lens for the framework by which exaptation and adaptation interplay in helping us to identify phenotypes that are the result of selective pressures in novel environments. The contribution of pre-existing phenotypic variation to evolution in novel environments has long been appreciated. Nevertheless, evolutionary ecologists have struggled with communicating these aspects of the adaptive process. In 1982, Gould and Vrba proposed terminology to distinguish character states shaped via natural selection for the roles they currently serve ('adaptations') from those shaped under preceding selective regimes ('exaptations'), with the intention of replacing the inaccurate 'preadaptation'. Forty years later, we revisit Gould and Vrba's ideas which, while often controversial, continue to be widely debated and highly cited. We use the recent emergence of urban evolutionary ecology as a timely opportunity to reintroduce the ideas of Gould and Vrba as an integrated framework to understand contemporary evolution in novel environments. [ABSTRACT FROM AUTHOR]

Nighttime light (NTL) remote sensing data offer unique capabilities to characterize both the extent and intensity of human activities and have been extensively used to understand urbanization since 1992. The recent proliferation of NTL sensors, algorithms, and products creates new opportunities to understand contemporary urbanization and the associated socioeconomic and environmental changes. We conducted a comprehensive literature review of 688 peer-reviewed papers published between 1992 and 2022 to understand the trends in how NTL data have been used to study urbanization (e.g., with which data products, during which time span, and in which geographies) and to synthesize the progress and challenges of key urban application topics. Based on our review, we identified four research directions for future NTL-based urban applications: (1) a better understanding of scale effects and sources of variations in NTL data; (2) integrating multi-source NTL data and synergizing NTL data with other types of geospatial data for improved NTL utilization; (3) more research on the Global South; and (4) developing new urban applications with new NTL data products. Addressing research gaps in these areas will generate new insights into the urbanization process under different geographical and socioeconomic settings. [ABSTRACT FROM AUTHOR]

The need for a carbon-free ecosystem is a quest that cannot be answered without the engagement, participation, commitment, and deployment of sophisticated countermeasure strategies by national and international governing bodies and institutions. Therefore, the current study explores the context of ASEAN countries because of their significance in the global economy, whereas the role of structural transformation, urbanization, and renewable energy consumption was explored in determining the possible effects of sustainable energy technologies innovations from 1995 to 2020. Using Methods of Moments Quantile Regression, the current study's findings show that structural transformation, urbanization, and renewable energy consumption significantly derive sustainable energy technology innovations. However, the marginal contribution towards innovation-driven technologies is substantially varied at lower and higher quantiles. Similar findings are echoed in alternative estimators and offer substantial policy implications for driving green innovation. • MMQR applied to ASEAN countries' sample. • Structural transformation and renewable energy consumption significantly derive sustainable energy technologies. • Urbanization rate is also positively associated with green transformation. • Bidirectional causality exists between model variables. [ABSTRACT FROM AUTHOR]

Analysis of the accessibility impacts of logistics suburbanization is critical for logistics land-use planning practices. To understand these impacts, this paper conceptualizes logistics service accessibility from two perspectives in terms of urban logistics infrastructure networks (ULINs) and logistics land use stakeholders in the network, and proposes a novel quantitative approach for their measurement. We measure the service accessibility of ULINs via solving a bi-objective binary integer program which considers the facility spatial coverage and vehicle path planning. Further, cumulative-opportunity freight accessibility models are proposed to measure the service accessibility of logistics land use stakeholders while accounting for their different logistics demands. The proposed method is capable of measuring the spatiotemporal impacts of logistics suburbanization on the service accessibility of ULINs and the corresponding stakeholders. We use the real data of Beijing as a testbed and our results reveal that (1) there is considerable variation in the service accessibility of ULINs and logistics firms' service accessibility with respect to different logistics facility locations and (2) choosing logistics facilities' locations requires careful consideration of the differences in ULINs' service accessibility to mitigate uneven service availability on the receiving side. The paper highlights the potential for freight accessibility in urban freight planning and provides effective quantitative evaluation approaches to support better practices in logistics land use and transport planning. • Logistics service accessibility has been conceptualized from two views of infrastructure network and land-use stakeholders. • Infrastructure-based freight accessibility model has been proposed to measure the service performance of ULINs. • The impacts of logistics suburbanization on the service accessibility of ULINs, goods suppliers and receivers are examined. • Policy suggestions for planning attractive logistics facility locations to encourage logistics clusters are provided. [ABSTRACT FROM AUTHOR]

Cities across the USA are implementing urban policies to mitigate unwanted effects of urbanization. These policies may inadvertently promote the proliferation of mosquito vectors. Limited evidence exists regarding how urban policies impact arbovirus transmission risk. Models can evaluate public health interventions aimed at reducing arbovirus-related risks caused by urban policies. [ABSTRACT FROM AUTHOR]

Recent advances in artificial intelligence (AI) and data analytics (DA) could provide opportunities for the fault management and the decision-making of the urban wastewater treatment systems (UWS) operations. The UWS is typically a large system, including Sewer networks (SNs), Wastewater Treatment plants (WWTPs) and also considering the Receiving media (RM). However, applications of AI and DA in the UWS can be challenging due to the complexities and size of systems, the large variation in the level of UWS instrumentation, and the relatively poor data quality. This review goes beyond the state of the art by critically analyzing previous work on AI-based data-driven methodologies to system-wide fault detection, life cycle fault management and transformation of big and small data into analytics, particularly, considering two different points of view: process faults (such as bulking sludge, sewer corrosion & technology specifics) and instrumentation faults (such as sensors and actuators), thereby offering more opportunities to distinguish complex patterns and dynamics. Our analysis reveals the relative strengths and weaknesses of the different approaches to design fault diagnosis tools and to apply these in the UWS. Finally, the opportunities and challenges about the inter-play among UWS, data and AI are discussed. [ABSTRACT FROM AUTHOR]

Urban green spaces (UGS) play an important role in understanding of urban ecosystems, climate, environment, and public health concerns. Satellite derived UGS maps provide an efficient and effective tool for urban studies and contribute to targets and indicators of the sustainable development goals, at the global level, set by the United Nations. However, clouds create a challenging issue in optical satellite image processing, leading to significant uncertainty in UGS mapping. In this study, we propose an automatic UGS mapping method by integrating satellite images with crowdsourced geospatial data while aiming to reduce the uncertainty caused by cloud contamination. The proposed method consists of three parts: (1) auxiliary data pre-processing module; (2) cloud attention intelligent network (CAI-net); and (3) non-cloud scenes classification module. The auxiliary data pre-processing module was used to convert crowdsourcing geospatial data into auxiliary maps. The CAI-net was proposed to retrieve detailed UGS classes within clouds from satellite image patches and auxiliary maps, while non-cloud scenes classification module was used to extract UGS from satellite image patches. The proposed method was applied to generate spatial continuous global UGS map products, considering the uncertainty caused by cloud contamination. The results show the proposed method yielded a high-quality global UGS map with average overall accuracy as high as 92.96% when satellite images had cloud coverage ranging from 0% to 50%. The geospatial AI, specifically CAI-net, can provide more accurate UGS mapping regardless of different geographical and climatic conditions of the study areas, which is especially significant for humid tropical and subtropical regions with frequent clouds and rains. [ABSTRACT FROM AUTHOR]

[Display omitted] • Despite higher density in the urban population, woodpigeon individuals were characterised by lower chewing louse abundance. • Birds with lower weights showed higher parasitic loads, which became more noticeable in urban woodpigeons. • Different from rural woodpigeons, parasites typical of feral pigeons were found on urban individuals. • Most of the species found were chewing lice, with Columbicola claviformis and Campanulotes bidentatus being dominant. The type of habitat occupied by avian populations has a marked effect on the parasitises they host. The growth of cities and urban areas in recent decades has favoured some species of birds adapted to these types of habitats – urban exploiters – although the effects of urbanisation on the parasitism of wildlife are not always well known. This study compares the ectoparasites characteristic of two differentiated populations of woodpigeons, one located in a predominantly urban environment and the other in a rural one. Most of the species found were chewing lice, with Columbicola claviformis and Campanulotes bidentatus being dominant. Despite the higher density of the urban population, woodpigeon individuals were characterised by a lower abundance of chewing lice, as well as the presence of ectoparasites typical of feral pigeons such as Hohorstiella lata and the hippoboscids fly , Pseudolynchia canariensis. Similarly, birds with lower weights showed a higher parasitic load, which became more noticeable in urban woodpigeons. The lower ectoparasite load of urban hosts represents a health advantage compared with rural populations, which could be one of the causes of greater growth and reproductive success in urban populations of woodpigeons. [ABSTRACT FROM AUTHOR]

The vertical distribution of air pollutants in urban street canyons is closely related to residents' health. However, the vertical air quality in urban street canyons has rarely been assessed using field observations obtained throughout the year. Therefore, this study investigated the seasonal and annual concentrations of particulate matter (PM 2.5 and PM 10), CO, NO 2 , SO 2 , O 3 , air quality index, and their responses to microclimatic factors at three height levels (1.5, 27, and 69 m above street level) in an urban street canyon. The PM concentration was higher at 27 m than at 1.5 m in winter, whereas the situation was reversed in other seasons. It was found that photochemical pollutants such as NO 2 and O 3 were the primary pollutants in the urban street canyon. The days on which O 3 was the primary pollutant at the height of 1.5 m accounted for 81.07% of the entire year. The days on which NO 2 was the primary pollutant at the height of 27 and 69 m accounted for 82.49% and 72.33% of the entire year, respectively. Substantially higher concentrations of NO 2 and O 3 were found at the height of 27 m than at 69 m. In-canyon concentrations of NO 2 and O 3 were strongly correlated with air temperature, wind speed, and wind direction, which played important roles in photochemical reactions and pollutant dispersion. [Display omitted] [ABSTRACT FROM AUTHOR]

Urban freight transport is an important issue in sustainable mobility discussions. It constitutes a significant proportion of urban traffic, and expected negative impacts for urban freight transport can be arguments against implementing restrictive measures targeting passenger traffic. The scarcity of empirical studies might lead to over- or underestimation of consequences for urban freight transport. This might slow shifts towards more sustainable mobility or cause unintended negative consequences. A long-planned 14-month capacity reduction in a main road tunnel in Oslo, Norway, causing significantly increased congestion, offered an excellent opportunity to study urban freight transport adaptations, effects and consequences. With truck drivers and logistics professionals as key informants, the study amplifies voices not often heard in research. Truck drivers adapted by avoiding the tunnel during rush hours only to a limited degree, and less than general traffic did. They reported limited flexibility, as routes and trip timing are strongly defined by customer contracts. The wider consequences for drivers were more stress and less predictable workdays. The findings might improve understandings of how truck drivers can and do adapt, and what consequences they experience. This will help authorities and freight companies plan for changes in urban transport systems aimed at sustainable mobility. • Empirical study of how truck drivers adapted to increased delays in main road tunnel. • Analyses of traffic data, surveys to and interviews with truck drivers. • Truck drivers adapted less than other traffic by avoiding the tunnel. • Consequences for drivers were mainly more stress and frustration. [ABSTRACT FROM AUTHOR]

This study proposes a new method for monitoring land cover change in urban areas using all available Landsat time series data, named the Ensemble of Bidirectional Time Series Analysis (EBTSA). In this method, the bidirectional Continuous Change Detection and Classification (CCDC) and the Chow Test are combined to improve the robustness against data scarcity in earlier times and reduce break detection errors and refine classification results. There are three key stages in this method: break detection using bidirectional CCDs, break refinement using the Chow Test, and bidirectional model integration and classification. The EBTSA method was evaluated over the Tianjin metropolitan area in China using Landsat data from 1989 to 2018. Results show that the proposed method improved spatial and temporal accuracies of both land cover classification and change detection, by reducing the influence of sparser Landsat data in the earlier years and the break detection errors. Using the land cover change results in the Tianjin area obtained using the EBTSA method, we analyzed the spatio-temporal distribution of land cover classification and change detection. It is found from the results that the Tianjin area experienced dramatic urban land changes, characterized by rapid urban expansion and noticeable transition from vegetation to urban land, with diverse changes from urban land to various nonurban land cover types. Results of this study showcase the effectiveness of the proposed method in land cover change monitoring in urban areas, which facilitates a more comprehensive understanding of urban land change dynamics and diversity. [ABSTRACT FROM AUTHOR]

Plasticity-led evolution is central to evolutionary theory. Although challenging to study in nature, this process may be particularly apparent in novel environments such as cities. We document abundant evidence of plastic behavioral changes in urban animals, including learning, contextual, developmental, and transgenerational plasticities. Using behavioral drive as a conceptual framework, our analysis of notable case studies suggests that plastic behaviors, such as altered habitat use, migration, diurnal and seasonal activity, and courtship, can have faciliatory and cascading effects on urban evolution via spatial, temporal, and mate-choice mechanisms. Our findings highlight (i) the need to incorporate behavioral plasticity more formally into urban evolutionary research and (ii) the opportunity provided by urban environments to study behavioral mechanisms of plasticity-led processes. Urbanization elicits diverse plastic behavioral responses in animals, which may lead to rapid evolutionary changes. Behavioral shifts in city-living animals can drive evolutionary divergence in populations at different scales through several spatial, temporal, and mate-choice mechanisms. Behavioral drivers of divergence are present in adaptive, neutral, and maladaptive contexts. Divergence may occur at urban-nonurban, within-city, and across-city scales. Cities provide biologists with a unique opportunity to enhance understanding of how plastic behaviors can precede and guide evolutionary change. [ABSTRACT FROM AUTHOR]

Once an urban rail transit (URT) system breaks down or is deliberately damaged, it will cause tremendous pressure on the whole urban transportation system. Hence, its ability to deal with incidents has become an important research field. Existing studies seldom consider the entire cycle of an incident and ignore changes in passenger travel behavior during emergency events. In this paper, resilience is defined as the ability of absorbing, resisting and recovering when the URT network fails. We propose a multi-dimensional evaluation model for measuring the resilience by calculating multiple abilities with consideration of multi-source data including passenger flow, train diagram, passenger travel choice behavior, network topology, etc. Finally, we present a case study based on real-world data from the Beijing Subway network to illustrate the effectiveness and applicability of the proposed model. Through the resilience analysis, we recognize the critical stations, measure emergency recovery ability and provide necessary information support for reducing the risk of URT incidents. • The proposed multi-dimensional model evaluates the network resilience from three aspects: absorption ability, resistance ability and recovery ability. • Multi-source data including passenger flow, train diagram, passenger travel choice behavior, network topology, etc., are considered in the proposed evaluation model. • A real-world large-scale case study of the Beijing Subway network is analyzed in the paper. [ABSTRACT FROM AUTHOR]

Many regions, such as coastal communities, are often located at the end of large transmission lines, leaving them susceptible to power outages and reliant on conventional fuels. We propose a multi-disciplinary, multi-objective optimization method to design a hybrid renewable energy system mix including solar, wind, and marine energy for these regions. The method first simulates demand for Eastport – a prototypical coastal community in Maine (USA) – using urban building energy modeling, and subsequently develops an hourly optimization and time series load-balancing approach to derive an optimal mix of renewable energy systems to meet the region's demand. Our results suggest that while different renewable energy system mixes can be viable, optimizing for cost, energy utilization, and power deficit can be increasingly challenging as higher annual demand hours (>90%) are met. This suggests that the final percentages require a disproportionate effort to achieve, and it will be challenging to meet 100% renewable energy deployment for all demand hours across a year. The proposed method is scalable – it can be applied to other regions to help policymakers and city planners make informed, data-driven decisions for various energy and city planning policy analyses and interventions. • Multi-objective optimization can be used with physics-based urban building energy modeling. • Energy storage is important in renewable energy system optimizations. • Optimizing for the final percentage points towards full renewables will be challenging. [ABSTRACT FROM AUTHOR]

Research on the evolutionary ecology of urban areas reveals how human-induced evolutionary changes affect biodiversity and essential ecosystem services. In a rapidly urbanizing world imposing many selective pressures, a time-sensitive goal is to identify the emergent issues and research priorities that affect the ecology and evolution of species within cities. Here, we report the results of a horizon scan of research questions in urban evolutionary ecology submitted by 100 interdisciplinary scholars. We identified 30 top questions organized into six themes that highlight priorities for future research. These research questions will require methodological advances and interdisciplinary collaborations, with continued revision as the field of urban evolutionary ecology expands with the rapid growth of cities. The impact of urbanization on biodiversity has been well documented, yet research into the complex dynamics of ecological and evolutionary processes in urban areas is still in its infancy. When novel research challenges emerge, a horizon scan exercise is an integrated approach that brings together global interdisciplinary-minded individuals to identify future research questions that can influence new collaborations and funding agenda. Our horizon scan identified 30 questions for future research in urban evolutionary ecology covering themes in fundamental ecological and evolutionary processes, temporal and spatial scales, sustainability, climate change, sociopolitical and ethical considerations, and innovation in technology. [ABSTRACT FROM AUTHOR]

In this paper, an improved mathematical model is proposed to analyze a special internal relationship between rainfall intensity and gas pollutants in urban atmospheric system, where the nonlinear relationship between precipitation and the concentration of pollutants in the air is considered, as well as the influence of random disturbances from the outside world. After the approximate solution of the system is obtained, the stability and bifurcation characteristics of the system with and without random disturbance are both analyzed. Theoretical analysis and numerical simulation results show that there is a close covariant relationship between rainfall and gaseous pollutants; the system has abundant bifurcation characteristics, which, in the physical sense, represents sudden changes of the gaseous pollutants in the urban atmospheric system; the urban atmospheric system can always be kept at a lower pollution level by applying slight disturbances to offset external disturbances. [ABSTRACT FROM AUTHOR]

Ride-sourcing services have become an integral part of urban transportation systems. In a cooperative relationship with the platform, ride-sourcing drivers have the flexibility to work according to their habits or preferences, which could constitute heterogeneous working pattern(s) among drivers. Based on real-world ride-sourcing data, we analyze drivers' working pattern(s) via their work slices (WSs). A WS is defined as a time window during which a driver is continuously online to take ride-sourcing orders. We use the Gaussian mixture model to classify the WSs according to the start time/start place attributes and obtain 9 clusters with significant spatiotemporal differences. It is found that WSs starting from rural areas that are closed to urban areas show no significant spatiotemporal grouping and mediocre performance in terms of fare income and fare-cost ratio, while WSs starting from distant rural areas show the worst performance; WSs starting from urban/suburban areas reflect more diverse spatiotemporal groupings and significant differences in attributes of delivery time and fares earned. We also analyze drivers' working pattern(s) by summarizing their habitual WS clusters. Findings include that drivers with suburban-dominant/rural-dominant patterns have a disadvantage in working efficiency in terms of the shorter order distance and longer cruising time. If geometrical distance allows, they can improve their income by switching to urban WS clusters occasionally. However, frequently switching between WS clusters can lead to a decrease in fares income. According to our results, the platform can introduce differentiated policies, including subsidies and order-matching algorithms, to better handle the spatiotemporal inequality, ultimately improving the service performance and overall benefits. • We analyze ride-sourcing drivers' working pattern(s) via their work slices (WSs) based on real-world data. • We use the Gaussian mixture model to classify the WSs according to their attributes and obtain 9 clusters. • WSs starting from distant rural areas show lowest earnings, but WSs from urban/suburban areas reflect higher earnings. • Drivers with suburban/rural working patterns have a disadvantage in efficiency with short order distance and long cruise. [ABSTRACT FROM AUTHOR]

Despite their economic success, the BRICS countries have yet to achieve environmental sustainability. On the other hand, these countries will not be able to maintain their economic expansion if they do not protect the natural environment. In this regard, this paper intends to assess the effects of financial integration (globalization) on ecological footprint. The paper also considers other ecological footprint drivers, including economic expansion, urbanization, renewable energy and technological innovation. The research utilizes a dataset spanning from 1990 to 2018. The study employed the method of moments quantile regression (MMQR) approach, which is robust to cross-sectional dependence and slope homogeneity issues. The outcomes from the MMQR disclose that the effect of financial globalization on ecological footprint is positive across all quantiles (0.1–0.90), thus validating the pollution haven hypothesis (PHH) in the BRICS nations. Furthermore, both renewable energy and technological innovation curb ecological footprint across all quantiles (0.1–0.90). Moreover, technological innovation is proven to impact the environment positively across all quantiles (0.1–0.90) via the path of urbanization. As a result, technological innovation is projected to help the BRICS nations achieve sustainable urbanization. The DOLS, FMOLS, FE-OLS outcomes also validate the outcomes of MMQR. Based on these results, policies were proposed. • MMQR applied to integrate asymmetric effects. • Renewable energy consumption and technological innovation derive ecological sustainability. • Financial integration-led Pollution-haven-hypothesis is confirmed. • Economic growth and urbanization lead to higher ecological footprints. • Technology innovation neutralizes the negative impact of urbanization on ecological sustainability. [ABSTRACT FROM AUTHOR]

Researchers typically use performance analysis tools to support the design decisions of PV systems on urban scales. However, performance simulation tools are mostly used for analysis only, providing feedback to confirm the system performance rather than influencing the system design to respond to the intended performance goals. Hence, it is essential to use simulation for synthesis and generation instead of analytical simulation in designing PV systems. Employing evolutionary algorithms (AEs) seems to be a great way to solve multi-objective optimization problems that aim to design PV systems in accordance with the electricity demand and peak times of neighborhoods. This study proposes a novel design decision-making framework to develop, evaluate, and interact with the design of the PV system at the neighborhood scale in one interface to better achieve the intended objectives. The design objectives of this study include increasing the self-consumption (SC), decreasing the payback period (PB), maintaining higher self-sufficiency (SS) of the PV system, and reducing the net load variance over the grid. The study aims to increase the correlation between the PV system's electricity production and the aggregate electricity demand of buildings in neighborhoods. This is done by optimizing the range of orientation of the PV panels and the number of panels on each orientation. The design decision-making framework is guided by multiple scenarios, and a residential neighborhood located in Los Angeles, California, is considered a case study. The results show how the PV production pattern differs when various design objectives are considered in the optimization process. In general, the optimized PV system in all tested scenarios has more PV panels in the south-west orientations as a response to the late demand and the higher electricity prices in the evening in the studied neighborhood. [ABSTRACT FROM AUTHOR]

Although the relevance of establishing low-carbon economic growth has been extensively highlighted in the Paris Agreement and the Sustainable Development Goals declarations, the analysis of the macroeconomic determinants of carbon productivity has remained overlooked in the literature. Therefore, this study makes a novel attempt to evaluate whether energy efficiency gains, along with renewable energy use, financial inclusivity, economic growth, globalization, and urbanization, improve carbon productivity in the emerging seven countries between 2007 and 2018. Moreover, the study contributes to the literature by predicting the net moderating and mediating effects of energy efficiency improvements on carbon productivity. The findings support that enhancing the level of energy use efficiency by 1% helps to improve carbon productivity by around 0.3% in the long run. In addition, the predicted net effects reveal that energy efficiency gains exert a moderating effect on the level of carbon productivity and reverse the negative impact of financial inclusivity, trade globalization, and urbanization on carbon productivity. However, energy efficiency gains cannot moderate to neutralize the carbon productivity-inhibiting impact associated with economic growth. Moreover, the analysis shows that energy efficiency gains mediate to jointly boost carbon productivity alongside higher renewable energy use. Lastly, financial globalization is evidenced to enhance carbon productivity in the emerging seven countries in the long run. Accordingly, a set of relevant policies are recommended. • 1% improvement in energy efficiency enhances carbon productivity by 0.3%. • Energy efficiency improvement exerts moderating and mediating effects. • Renewable energy use does not directly influence carbon productivity. • Financial inclusivity, economic growth, and urbanization reduce carbon productivity. • Globalization exerts heterogeneous impacts on carbon productivity. [ABSTRACT FROM AUTHOR]

This study analyses the environmental and economic benefits of integrating renewable energy sources (RES), biogas and solar energy into urban wastewater treatment plants (WWTPs). The energy intensity (kWh/m3) of the selected 456 WWTPs in Turkey was evaluated according to type and treatment process. The energy intensity of primary WWTPs (0.066–0.335 kWh/m3) is lower than that of secondary (0.075–1.361 kWh/m3) and advanced WWTPs (0.147–0.988 kWh/m3). RES integration is investigated using energy system optimization software for a sample of 25 WWTPs. Six scenarios were developed based on different CO 2 emission penalties (0 and 16 USD/ton) and prices for selling electricity to the grid (0.000, 0.050, and 0.133 USD/kWh) to demonstrate the impact of market-based instruments. RES integration is cost-effective for secondary and advanced WWTPs with a flow capacity above 1 million m3/y. Market-based instruments are found to significantly influence the decision to integrate, generate and use renewable electricity at WWTPs. The RES integration can meet up to 88% of the annual energy demand of the WWTPs at an electricity selling price of 0.133 USD/kWh. When no market-based instruments are implemented, RES generation can reach up to 23% of the WWTPs' annual energy demand while reducing CO 2 emissions by 15%. [Display omitted] • Operational data of over 450 wastewater treatment plants are collected. • The range of energy intensities is determined between 0.066 and 1.361 kWh/m3. • Renewable energy integration is cost-effective for plants sized more than 1 M m3/y. • Renewable energy integrations meet 88% of energy demand and reduce 62% of emissions. • Sell-back to the grid reduces the payback period of renewable energy integration. [ABSTRACT FROM AUTHOR]

• VR environments mirroring the streetscape of a real Dutch city are designed. • Bicycle infrastructure, traffic volume, urbanisation degrees are manipulated. • Safety, enjoyment, attractiveness of cycling is measured real time. • Various attributes impact with different magnitude the cognitive (safety) and affective (enjoyment, attractiveness) component. • Providing understanding on determinants of cycling and to create optimal cycling conditions. Understanding the determinants of cycling and thus creating optimal cycling conditions is still a challenge. The current study addresses this challenge by providing in-depth exploration of attributes of bicycle infrastructure, traffic volume, gradients, urbanisation degrees in stimulating cycling for various population categories. Participants had to cycle in a simulated VR environment mirroring the streetscape of a real Dutch city. The cognitive (e.g., safety perception) and affective (e.g., enjoyment, attractiveness) response was measured, real time. The results suggest that various attributes impact the cognitive and affective components to different extents. In particular, bicycle path presence and intersection absence had a positive impact on safety perception. Greenness of the environment contributed for lifting the attractiveness of the cycling experience. Hight car traffic had a negative impact on the way safety, enjoyment and attractiveness of cycling was perceived. Current outcomes should be implemented in creating bicycle infrastructure that appropriately meets the demand for attractive cycling experience that is safe and enjoyable for all. [ABSTRACT FROM AUTHOR]

Developing countries with rapid urbanization face challenges in providing sufficient energy to both rural and urban households in an efficient manner. However, few empirical studies explored both energy sufficiency and efficiency inequality in rural and urban households at the sub-national level within a nation during a rapid urbanization period to inform equitable energy transitions. Therefore, this research collected 2000–2019 subnational-level data in China to explore the pattern of rural-vs-urban inequality in energy use per person (indicating sufficiency) and energy use intensity (EUI, indicating efficiency). Gini coefficients and disparity ratios, two social inequality measurements, were used to quantify social inequality in energy use. Fixed effect models with interaction terms were adopted to explore the impact of income, energy end-use structure, and floor area on energy use differences between rural and urban households. Gini coefficients of both energy measurements decreased by 43%–47% from 2000 to 2019, suggesting a more equitable distribution of end-use energy sufficiency and efficiency between urban and rural households. Rural-vs-urban disparity ratios showed that rural households in more provinces used more energy per person with higher EUI than urban households in 2019, compared to the pattern observed in 2000. Income in rural households had a statistically significant larger impact on energy use per person and EUI than that in urban households. By evaluating energy inequality in both sufficiency and efficiency from an overlooked rural-vs-urban perspective, this study enhances the understanding of social inequality in energy use and provides valuable insights into just energy transitions in urbanizing and developing countries. • Inequality in energy use sufficiency & efficiency explored on rural-vs-urban basis. • Gini index of energy sufficiency & efficiency reduced from 2000 to 2019 in China. • Rural-vs-urban disparity ratios increased across 30 provinces from 2000 to 2019. • Income and floor area have a significant impact on rural-urban energy disparities. • Energy structure is important to reduce energy use in both urban and rural areas. [ABSTRACT FROM AUTHOR]

The impact of urbanization on weather events has received increasing attention. Taking Typhoon Hato as an example, the impact of anthropogenic land cover change on typhoon landfall and sequential rainfall is quantified in this study. A numerical model is first built based on the Weather Research and Forecast model. Then, the model is validated, the performance of parameters for the different meteorological and microphysical processes is assessed and a set of optimal parameters are selected. After that, the responses of typhoon and the corresponding rainfall to land cover patterns under three conditions of no urbanization, current urbanization and future urbanization in Pearl River Delta are simulated and compared. Verification experiments show that it is necessary to consider the microphysical process to enhance the accuracy of typhoon simulation. The dramatic change of land cover has limited influence on typhoon tracks. However, the urbanized land surface promotes typhoon rainfall significantly. The hourly rainfall rate under the urbanization condition is about double that under the no urbanization condition. With the intensifying urbanization in the future, the maximum hourly rainfall rate will increase by 21%. The urban heat island effect almost disappears as Typhoon Hato approaches the inland region, and the urban dynamic effect plays an important role in typhoon rainfall. The intensified rainfall is related to the retention of typhoon caused by rough surface of the city. The wind speed under the condition of no urbanization is in general 2 m/s greater than that under the urbanization condition. The increase in surface roughness reduces the typhoon movement speed and more rainfall is generated. With the development of urbanization, intensified rainfall at typhoon landfall needs to be paid more attention to. • Urbanization process enhances the rainfall rate after the typhoon landfall. • Urban heat island effect gradually disappears during typhoon landfall. • Rough urban surface significantly slows down the moving wind speed by about 2 m/s. • The maximum hourly rainfall rate will increase by 21% in the future urbanization scenario. [ABSTRACT FROM AUTHOR]

[Display omitted] • Urban and rural populations of P. rapae both display beneficial acclimation in heat and cold tolerance traits. • Heat and cold tolerance traits show evolutionary potential that is population-specific and temperature-dependent. • Despite displaying evolutionary potential, tolerance traits have not evolved between rural and urban P. rapae populations. Despite the generally negative impact of urbanization on insect biodiversity, some insect species persist in urban habitats. Understanding the mechanisms underpinning the ability of insects to tolerate urban habitats is critical given the contribution of land-use change to the global insect decline. Compensatory mechanisms such as phenotypic plasticity and evolutionary change in thermal physiological traits could allow urban populations to persist under the altered thermal regimes of urban habitats. It is important to understand the contributions of plasticity and evolution to trait change along urbanization gradients as the two mechanisms operate under different constraints and timescales. Here, we examine the plastic and evolutionary responses of heat and cold tolerance (critical thermal maximum [CT max ] and critical thermal minimum [CT min ]) to warming among populations of the cabbage white butterfly, Pieris rapae , from urban and non-urban (rural) habitats using a two-temperature common garden experiment. Although we expected populations experiencing urban warming to exhibit greater CT max and diminished CT min through plastic and evolutionary mechanisms, our study revealed evidence only for plasticity in the expected direction of both thermal tolerance traits. We found no evidence of evolutionary divergence in either heat or cold tolerance, despite each trait showing evolutionary potential. Our results suggest that thermal tolerance plasticity contributes to urban persistence in this system. However, as the magnitude of the plastic response was low and comparable to other insect species, other compensatory mechanisms likely further underpin this species' success in urban habitats. [ABSTRACT FROM AUTHOR]

• Future DHW intensity rises over 4 times, population exposed increases by over 1.5 times. • The Yangtze and Yellow River basins undergo a significant DHW increase. • Future DHW-exposed population increase 1.61–2.41 times over the historical baseline. • The temperature increase is the main driver of DHW event escalation. Global warming has accelerated the interregional hydrological cycle, resulting in a significant increase in the frequency and intensity of extreme events worldwide. These events often involve a combination of spatial and temporal factors, giving rise to compound events. Among them are rapid transitions from dry and hot conditions to wet (DHW) events, which can have more severe impacts on human societies and ecosystems than individual extreme events. Urbanization not only heightens the likelihood of disasters but also exacerbates the exposure of affected populations. However, there has been insufficient attention given to understanding the connections between these successive compound events and the heightened risks posed by the increasing urbanization. In this study, we used bias-corrected daily precipitation and maximum temperature data simulated by the Coupled Model Intercomparison Project Phase 6 (CMIP6) model, along with historical and future daily runoff data simulated by the Variable Infiltration Capacity (VIC) model. We systematically investigated the spatiotemporal changes (i.e., frequency, duration, intensity, and compound probability) in DHW events in China during the upper historical period (1979–2014) and under two medium and high emission scenarios (2015–2100) − Shared Socioeconomic Pathways (SSP245 and SSP585). Furthermore, we examined how extreme runoff responds to variations in maximum temperature and daily precipitation during DHW events. We also evaluated the potential population and urban exposure to DHW events using dynamic future population and urban expansion data to assess the potential risks. Our results indicate that the multi-model ensemble predicts varying spatial patterns of future DHW events under SSP245 and SSP585 scenarios. The frequency and duration of these events are expected to decrease by approximately 20–25% in the Northwest region under both scenarios. At the same time, the middle and lower plains of the Yangtze and Yellow River Basins experience increased occurrence, broader geographical impact, and higher DHW event intensity alongside urban expansion and population growth in these regions. Specifically, event intensity is anticipated to increase by a factor of approximately 7–11. Temporally, we expect short-duration, high-intensity DHW events to occur in the Yangtze and Yellow River basins around 2023, 2038, and 2058, respectively. The primary driver for the future rise in population exposure to DHW events is the expected increase in event frequency in the middle and lower plains of the Yangtze and Yellow River basins, concurrent with population growth in these regions. Under the medium emission scenario, the total exposed population to DHW events in China is predicted to be 1.61 times higher than the historical baseline. In contrast, the high-emission scenario estimates a total population of 2.41 times higher than the historical baseline period. Most of the DHW events occurred in regions that exhibit a positive dependence between high temperature and high runoff events, serving as the primary driver of DHW events. Urbanization has a positive impact on DHW events, with the effect under high emissions being approximately 30% higher than during the historical base period. Additionally, it is indicated that future DHW events will exhibit higher sensitivity to global warming, with intensity projected to increase approximately fourfold and the exposed population to rise by about 1.5 times for every 1 °C of global warming. This research enhances our understanding of forecasting future compound hydrometeorological extreme events under different scenarios and provides insight into the role of climate change and urbanization in shaping these events in China. [ABSTRACT FROM AUTHOR]

• A BPROME method to quantify the impact of number of events on model performance and uncertainty is constructed. • A regular influence of the model uncertainty decreases and the model performance increases with the number of events increases. • At an appropriate range of numbers of events can achieve better parameter calibration effect of the model in data-scare areas. Rainfall-runoff data in drainage systems in urban areas is the essential input variable for urban hydrological modeling. Obtaining high quality and sufficient size of urban flood events is always difficult due to the inconvenient underground observation and the untimely capture of the rapid rising and recession stages of urban runoff. It largely constrains the efficiency of model calibration and brings large uncertainty in urban rainfall-runoff simulation. Therefore, the evaluation of the number of events' influence on model performance and uncertainty is of great significance, which can provide useful information to help the decision makers select sufficient useful observation data for model calibration with relatively fewer events. In this study, we constructed a comprehensive method of behavioral parameter ranking of multi-events (BPROME) based on coupling the Generalized Likelihood Uncertainty Estimation (GLUE) algorithm and the Storm Water Management Model (SWMM). The results in the two small demonstration areas (named Case #A and Case #B) in Shenzhen city of China proved the good performance of the BPROME in uncertainty assessment in urban areas. We found the model uncertainty (average bandwidth (B) and average deviation amplitude (D)) and model performance (containing Ratio (CR) and the maximum value of behavioral samples' NSE (NSEmax)) became stable at a certain number of events in both two case areas. The B and D decrease and the CR and NSEmax increase as the number of event increases. In particular, the model performance and uncertainty reach their appropriate state concerning the limited observations at a certain range of numbers of events (both three to five in our two case areas). Our results demonstrate the potential influence of the numbers of events for the urban rainfall-runoff modeling calibration which can balance the model efficiency and data collection cost (the number of input rainfall-runoff data). The findings could help decision-makers seek a trade-off between data investment and acceptable model performance. [ABSTRACT FROM AUTHOR]

• Coupled control strategies balance calculation time and effect of flood control. • Model predictive control holds promise for application in urban river systems. • Performance relies on prediction range of surrogate model and optimization variable. Model Predictive Control (MPC) has the potential to enhance flood control in urban river systems. However, the computational burden of online optimization hinders the application of MPC to large-scale areas. To address this, we propose a method incorporating both Rules-Based Control (RBC) and MPC based on surrogate models. MPC optimizes the operation of gates and pumping stations in the area of interest, while RBC is implemented in a specific region that does not participate in online optimization, thereby reducing calculation time. We have successfully validated this approach in coastal urban river systems in China, reducing the decision variables from 109 to 24 and narrowing the simulation area from 165 km2 to 15 km2. During the Typhoon Lupit rainfall test, the total computation time and the highest online optimization time were only 33.9 % and 38.1 % of the full MPC system, respectively. Additionally, the method demonstrated impressive results, including a significant 94.62 % reduction in flood volume and a remarkable 96.5 % decrease in flood duration. These findings demonstrate that our method effectively addresses the challenge of long computation times for online optimization in urban river systems and presents a novel approach for implementing urban flood mitigation strategies. [ABSTRACT FROM AUTHOR]

City planners face a significant challenge with the issue of solid waste disposal, especially in developing countries because of urbanization and rapid population growth. The local environment and public health may suffer immensely because of inappropriate or un-scientific selection of landfill locations. This research was carried out for future scientific landfill site selection in Ambernath and environs, India. In this regard, twelve environmental, physical, and socio-economic factors have been considered to maintain sustainability and tackle the solid waste disposal problems. Integrated GIS (Geographic Information System) based on Multi-Influencing Factor (MIF) technique were adopted with an emphasis on structuring the decision-making processes to locate appropriate landfill locations that are cost effective, environmentally sustainable, and acceptable in a public context. The delineated landfill suitability results show that 16.37% of the research area is falling under very highly suitable for optimal landfill sites selection. The remaining 20.58%, 29.32%, and 15.28% of the area are highly, moderately, and less suitable, whereas 18.43% of the area encompasses the unsuitable zonation. The effectiveness of each variable was determined by sensitivity analysis, which shows that vegetation density, location of drainage density, and water bodies are the dominant factors to delineate suitable landfill sites. Further analysis of the results showed that MIF is the effective approach, with an Area Under Curve (AUC) value of 0.78. The findings of this study offer reliable geospatial outputs that waste management authorities and city planners can use to recommend new landfill sites that ensure environmental and socio-economic sustainability. • Integrated GIS-MIF for landfill site selection. • Results show that the 16.37% highly suitable, 18.43% unsuitable areas in area. • In this study, Dominant factors: vegetation, drainage, water bodies etc. included. • Reliable geospatial outputs for sustainable landfill placement. [ABSTRACT FROM AUTHOR]

• Discussed why can certain rural areas withstand the external impact of urbanization and thus maintain the vitality of development, while others cannot and eventually decline. • Use Coupled Infrastructure System framework to the internal mechanisms of the response of the rural social-ecological system to the impact of urbanization. • The key to ensuring the robustness of the rural social-ecological system under the impact of urbanization is to provide institutional rules to guarantee the operational processes through the collective-choice process. Urbanization is essential for achieving rapid development. In this process, the labor outmigration from rural areas to cities caused by urbanization has diversified the impacts on the robustness of the rural social-ecological system. However, existing research has rarely discussed the internal mechanism of the response of the rural social-ecological system to the impact of urbanization. Based on the coupled infrastructure systems framework, in this paper, the internal interactions of the rural social-ecological system responding to the impact of urbanization are discussed. An empirical test is conducted using survey data from 123 villages of 14 Chinese provinces. Focusing on the specific practice of China, this paper further discusses how the robustness of the rural social-ecological system can be ensured under external impact. The main conclusions are as follows: under the impact of urbanization, the internal interaction paths with which the rural social-ecological system maintains robustness at the operational level are not unique. These different internal interaction paths may not only make the rural social-ecological system automatically realize a virtuous and robust cycle under the impact of urbanization, they may also cause a gradual collapse of the rural social-ecological system. Further, the discussion of China's specific practice shows that it is key to provide institutional rules to guarantee the operational processes through the collective-choice process, thus ensuring the robustness of the rural social-ecological system under the impact of urbanization. The inspection of the interaction mechanisms in this study may obtain some restricted views on the general laws that may be contained in the complex interaction within the rural social-ecological system, and provide a theoretical basis developing countries can apply to realize rural revitalization when faced with the irreversible wave of urbanization. [ABSTRACT FROM AUTHOR]

[Display omitted] • Spatiotemporal dynamic ESV reconstruction framework for urban ecosystem services. • GI network covers 1/5th area, boosts 1/3rd ESs value in Xining. • Case study in Xining optimizes urban planning for ES conservation. • Optimized Xining City planning integrates GI, preserving 59.3 million CNY ESV. • Study informs sustainable development in ecologically vulnerable plateau cities. The construction of green infrastructure networks is an effective measure to guide orderly city expansion and ensure ecological security. However, limited research has been conducted to explore practical strategies for enhancing ecosystem services threatened by urban development, particularly in plateau cities with constrained natural resources. Xining, the largest and most densely urbanized city on the Tibetan Plateau in China, faces a significant conflict between urban expansion and ecosystem service conservation that is limited by an uninhabitable climate and mountainous topography. It is the only central city on the Tibetan Plateau, with a population of 2.47 million (2020). From 2000 to 2020, the urban area expanded by 37.42%; thus, reconciling the escalating demand for natural resources driven by urban development and promoting synergistic development between ecosystem services and urbanization remains a challenge. Here, we propose a modeling framework to reconstruct ecosystem service values using improved temporal (p i) and spatial (d i) difference indices, highlighting the temporal sensibility and spatial aggregation of multiple ecosystem services. The material quantities (d i) and monetary values (p i) of seven ecosystem services were estimated using experimentally verified local parameters as a case study in Xining from 2000 to 2020. The green infrastructure network and its interactions were identified based on the reconstructed ecosystem service value using the gravity modeling toolbox proposed in our previous study. The results illustrated that the identified green infrastructure network comprised 48 ecological sources and 96 corridors, covering an area of 162318.3 hm2; while this includes approximately one-fifth of the total study area, but it contributes to one-third of the overall ecosystem service value. The conservation priority and planning construction areas in the Xining City Master Planning (2021–2035) outlines were then optimized based on the identified green infrastructure network. Overall, these optimized urban planning scenarios would reserve 59.3 million CNY of the reconstructed ecosystem service value encroached on by urban sprawl. The opportunity cost is transferring 14.7% of the planned construction area overlapping with the green infrastructure network. This study provides a distinctive example for establishing a green infrastructure network based on the spatiotemporal dynamic sensitivity of ecosystem services and provides a strong reference for sustainable development and ecosystem service conservation, particularly in plateau cities. [ABSTRACT FROM AUTHOR]

• Urbanization impacts stream health through complex pathways in the Southeast Piedmont. • Flow and pesticides were major mediators of urbanization impact on macroinvertebrates. • Potential interactions existed among pesticides, nutrients, and basic water quality. • Structural equation modeling has potential in informing watershed management. • Landscape development intensity index is a useful urban indicator. Urbanization has greatly degraded stream health, particularly in the rapidly urbanizing southeastern United States. Recent studies identified primary instream stressors affecting biological communities along urban gradients in the Southeast Piedmont. However, a comprehensive understanding of the complex mechanisms is still lacking, which impedes effective management actions. To improve our understanding, we investigated the potential pathways by which urbanization, as represented by the landscape development intensity index (LDII), and important environmental characteristics affected instream stressors and macroinvertebrate communities using structural equation modeling (SEM) and the U.S. Geological Survey Southeast Stream Quality Assessment dataset. We investigated the direct, indirect, and total effects of LDII and environmental characteristics on instream stressors and macroinvertebrate communities as well as the interactions of instream stressors. Moreover, we explored alternative reasonable relationships between variables by using an alternative model approach. We found that LDII had moderate to strong total effects on all instream stressors and macroinvertebrates with complex impact pathways revealed in SEM models, demonstrating the dominant impact of urban landscape development on stream ecosystems in the Southeast Piedmont. Flow disturbance and pesticides were major mediators transmitting most of LDII's effects on macroinvertebrates. Furthermore, we found potential interactions among instream stressors, which were not widely discussed in previous studies. Compared with agricultural watersheds, urban watersheds represented different land use impact mechanisms. Our findings contributed to an improved understanding of urbanization's impact on stream ecosystems and demonstrated the usefulness of an alternative model approach that provided insights into the underlying mechanisms. In addition, we demonstrated the utility of LDII in characterizing landscape development and improving SEM models. Finally, we explored the potential of SEM in global watershed management activities by discussing the practical implications of impact pathways and the innovative use of SEM as a decision-making tool. [ABSTRACT FROM AUTHOR]

[Display omitted] • Combined with PED model and GCAM, CO 2 emissions under different urbanization scenarios are predicted. • China's unique urbanization process is reflected – the urban–rural dual system and the birth policy. • China can reach its carbon peak by 2030 with slow urbanization. • The peak time of energy consumption is generally later than that of CO 2. • In the long run, more rapid urbanization has a stronger emission reduction effect. The urbanization process has an impact on greenhouse gas emissions, but little research has linked specific processes of urbanization to CO 2 emissions. Particularly, for China, the urban–rural dual system and unique fertility policy play an important role in its urbanization process. A large population base and a sharp urban–rural divide need to be taken into account in CO 2 emission forecast. Based on three urbanization processes corresponding to Shared Socioeconomic Pathways (SSPs) scenarios, this article combined the Population-Development-Environment (PDE) model and the Global Change Assessment Model (GCAM) to forecast energy consumption and CO 2 emissions in China in this century. The results show as follows. 1. China's CO 2 emissions and energy consumption will experience an inverted U-shaped trend. In the rapid, moderate and slow urbanization scenarios, CO 2 emissions peak in 2045, 2035 and 2030 at 11.55 billion tons, 10.88 billion tons and 10.65 billion tons, respectively. 2. The peak time of energy consumption is generally later than that of CO 2 emissions. In the rapid, moderate and slow urbanization scenarios, the primary energy consumption peaks at 212.03 EJ in 2055, 190.65 EJ in 2055 and 171.80 EJ in 2045, respectively; the final energy consumption peaks at 192.34 EJ in 2055, 174.47 EJ in 2055 and 157.25 EJ in 2045, respectively. 3. More rapid urbanization corresponds to a later peak in CO 2 emissions and energy consumption, but the decline is faster after the peak. In the future, China should vigorously implement intensive and sustainable policies to avoid excessive emissions in the urbanization process. [ABSTRACT FROM AUTHOR]