Urban amenity and urban economic resilience: evidence from China.

Under the influence of multiple uncertain factors at home and abroad, urban amenities, as the underlying support for urban renewal activities, are of great significance in enhancing urban economic resilience. The panel data of Chinese cities from 2011 to 2019 is used in this study. Urban amenity is measured from artificial amenities and climate amenities, respectively. By using a two-way fixed effects model, we empirically test the impact of urban amenities on urban economic resilience. The key findings of this study are as follows. (1) Urban amenities can significantly enhance urban economic resilience. (2) Heterogeneity analysis shows that there are regional differences in the role of urban amenities in promoting urban economic resilience, with cities in the eastern region, strong environmental regulations, and high urbanization rates benefiting more. (3) We further find that urban amenities mainly enhance economic resilience by promoting population agglomeration, attracting labor migration, improving the quality of human capital, and stimulating urban innovation. Our conclusions recommend to rationally allocate and optimize urban amenity resources, strengthen urban planning and construction management, and create a more livable urban environment, thereby enhancing urban economic resilience.

Urban transport infrastructure planning and the public interest: a public health perspective.

OBJECTIVE: Transport infrastructure impacts public health. WestConnex in Sydney, New South Wales (NSW), is Australia's largest and most expensive transport infrastructure project. Concerns about the motorway project resulted in a NSW parliamentary inquiry into the project's impacts. Submissions to the inquiry were analysed to investigate their emphasis on health impacts and the cost-benefit analysis underpinning the project's business case. STUDY TYPE: Quantitative content and qualitative thematic analysis. METHODS: There were 556 submissions made to the inquiry into the impact of the WestConnex project. The content of a random sample of 93 (20%) of the individual submissions was analysed to identify health concerns. A purposive sample of 81 submissions by named groups including political parties and organisations was analysed separately (15% of the total submissions). RESULTS: Most individual submissions (63%) mentioned at least one aspect of health. Air pollution and children's health were the most frequently mentioned health issues. In the purposive sample, most submissions (64%) concerned the cost-benefit analysis (CBA), including concerns that the health impacts were being underestimated and economic benefits overestimated in the CBA. CONCLUSIONS: This study on the WestConnex project demonstrates how health impacts require early consideration within business cases for urban infrastructure projects, and later during environmental impact assessment. Systems for communicating and involving the public in decision making need to be improved, alongside greater transparency in CBA early in the project planning cycle.

Black Rock City versus Manhattan: An economist's view.

Urban street networks take many forms, from the circular streets in Black Rock City (which is built and removed every year as part of the Burning Man Festival) to the streets and avenues in the Manhattan grid. This paper compares the traits of cities with different street networks using the tools of urban economics. When both cities have commuting arteries of the same length, cities with circular streets have higher population densities unless access to these arteries is expensive and the number of arteries is large. Cities with arteries set at a 45o angle to the grid have smaller population densities under all circumstances.

Spatial interactions in urban scaling laws.

Analyses of urban scaling laws assume that observations in different cities are independent of the existence of nearby cities. Here we introduce generative models and data-analysis methods that overcome this limitation by modelling explicitly the effect of interactions between individuals at different locations. Parameters that describe the scaling law and the spatial interactions are inferred from data simultaneously, allowing for rigorous (Bayesian) model comparison and overcoming the problem of defining the boundaries of urban regions. Results in five different datasets show that including spatial interactions typically leads to better models and a change in the exponent of the scaling law.

Learning from urban form to predict building heights.

Understanding cities as complex systems, sustainable urban planning depends on reliable high-resolution data, for example of the building stock to upscale region-wide retrofit policies. For some cities and regions, these data exist in detailed 3D models based on real-world measurements. However, they are still expensive to build and maintain, a significant challenge, especially for small and medium-sized cities that are home to the majority of the European population. New methods are needed to estimate relevant building stock characteristics reliably and cost-effectively. Here, we present a machine learning based method for predicting building heights, which is based only on open-access geospatial data on urban form, such as building footprints and street networks. The method allows to predict building heights for regions where no dedicated 3D models exist currently. We train our model using building data from four European countries (France, Italy, the Netherlands, and Germany) and find that the morphology of the urban fabric surrounding a given building is highly predictive of the height of the building. A test on the German state of Brandenburg shows that our model predicts building heights with an average error well below the typical floor height (about 2.5 m), without having access to training data from Germany. Furthermore, we show that even a small amount of local height data obtained by citizens substantially improves the prediction accuracy. Our results illustrate the possibility of predicting missing data on urban infrastructure; they also underline the value of open government data and volunteered geographic information for scientific applications, such as contextual but scalable strategies to mitigate climate change.

Research on the relationship between Urban economic development level and urban spatial structure-A case study of two Chinese cities.

The characteristics of urban spatial structure and the objective evaluation of the development level of urban economy have always been the concern of urban researchers, However, the spatial relationship between urban spatial structure and urban economic development level is often deliberately ignored. Through the point of interest (POI), the identification framework is constructed, the spatial structure of the city is identified and evaluated, and the Geographically Weighted Regression analysis is carried out with the distribution of unit GDP (Gross Domestic Product) in this study. The research shows that Kunming and Guiyang are polycentric spatial structures and Kunming's structure is more significant. Kunming's economic level is generally higher than Guiyang, but the unit area cannot be compared. The city center will promote the development of the central area in this city, and the more urban centers are distributed within the geographical and spatial range, the greater contribution would have to economic development. In addition, the results of this study will have a positive impact on urban planning and construction, and will also provide a new perspective for the study of cities and related disciplines.

Assessing alternative methods for unsupervised segmentation of urban vegetation in very high-resolution multispectral aerial imagery.

To analyze types and patterns of greening trends across a city, this study seeks to identify a method of creating very high-resolution urban vegetation maps that scales over space and time. Vegetation poses unique challenges for image segmentation because it is patchy, has ragged boundaries, and high in-class heterogeneity. Existing and emerging public datasets with the spatial resolution necessary to identify granular urban vegetation lack a depth of affordable and accessible labeled training data, making unsupervised segmentation desirable. This study evaluates three unsupervised methods of segmenting urban vegetation: clustering with k-means using k-means++ seeding; clustering with a Gaussian Mixture Model (GMM); and an unsupervised, backpropagating convolutional neural network (CNN) with simple iterative linear clustering superpixels. When benchmarked against internal validity metrics and hand-coded data, k-means is more accurate than GMM and CNN in segmenting urban vegetation. K-means is not able to differentiate between water and shadows, however, and when this segment is important GMM is best for probabilistically identifying secondary land cover class membership. Though we find the unsupervised CNN shows high degrees of accuracy on built urban landscape features, its accuracy when segmenting vegetation does not justify its complexity. Despite limitations, for segmenting urban vegetation, k-means has the highest performance, is the simplest, and is more efficient than alternatives.

Can health and health equity be advanced by urban planning strategies designed to advance global competitiveness? Lessons from two Australian case studies.

BACKGROUND: The purpose of this article is to investigate whether the neo-liberal goal of global economic competitiveness when included alongside image-inspired social goals such as liveability and environmental goals such as sustainability can lead to policies that impact positively on health and health equity. The paper presents an analysis of the content and intent of strategic planning and transport plans from two Australian state governments. METHODS: The analysis was undertaken using a thematic document analysis of each plan and interviews with agents (n = 21) directly involved in the preparation of each document. FINDINGS: Key strategic documents formulated under a neo-liberal hegemony simultaneously provided and reduced opportunities to promote and advance health. The policies viewed goals like liveability and sustainability as means of enhancing their cities' image in global competition for exogenous capital flows. Although liveability has many definitions, one definition was able to be used in one jurisdiction as an avenue to include a broad array of social determinants of health into urban planning policy. However, a productivity or a narrowly focussed image narrative can undermine the social determinants of health credentials of liveability. Overemphasising immediate city problems like road congestion as mechanisms to enhance global competitiveness can undermine necessary long-term strategies for city planning that are known to improve liveability and human health. Even where liveability is at the fore, there is a high risk of exacerbating spatial inequities through liveability investments for competitive advantage because they tend to flow to parts of cities with the greatest connections to the global economy, not those with the greatest social need. CONCLUSIONS: A neo-liberal-inspired competitive city paradigm provides opportunities for the advancement of health in urban development. However, when driven by the goals of productivity and/or liveability as image enhancement it can potentially exacerbate health inequities.

Economic value of regulating ecosystem services: a comprehensive at the global level review.

This study is the first meta-regression analysis of the economic value of regulating ecosystem services at the global level. Most of the regulating ecosystem services have not been properly estimated in terms of economic value and are also ignored in the everyday decision-making process. This study has reviewed 100 publications and included 275 economic value estimates. This study includes explanatory variables in the meta-analysis to account for these influences on the estimated economic value of regulating ecosystem services. This study has estimated the economic value of regulating ecosystem services at US$29.085 trillion for 2015. This study also has found that the values of climate and water regulations are the highest contributors to the total value of regulating ecosystem services. This study indicates that the results of meta-analysis might be helpful to decision-making with respect to three aspects: first, planning and management of urban green cover for sustainable cities; second, integration of the economic value of all the regulating ecosystem services; third, budget allocation for conservation and improvement of regulating ecosystem services for the present and future generations.

Do street-level scene perceptions affect housing prices in Chinese megacities? An analysis using open access datasets and deep learning.

Many studies have explored the relationship between housing prices and environmental characteristics using the hedonic price model (HPM). However, few studies have deeply examined the impact of scene perception near residential units on housing prices. This article used house purchasing records from FANG.com and open access geolocation data (including massive street view pictures, point of interest (POI) data and road network data) and proposed a framework named "open-access-dataset-based hedonic price modeling (OADB-HPM)" for comprehensive analysis in Beijing and Shanghai, China. A state-of-the-art deep learning framework and massive Baidu street view panoramas were employed to visualize and quantify three major scene perception characteristics (greenery, sky and building view indexes, abbreviated GVI, SVI and BVI, respectively) at the street level. Then, the newly introduced scene perception characteristics were combined with other traditional characteristics in the HPM to calculate marginal prices, and the results for Beijing and Shanghai were explored and compared. The empirical results showed that the greenery and sky perceptual elements at the property level can significantly increase the housing price in Beijing (RMB 39,377 and 6011, respectively) and Shanghai (RMB 21,689 and 2763, respectively), indicating an objectively higher willingness by buyers to pay for houses that provide the ability to perceive natural elements in the surrounding environment. This study developed quantification tools to help decision makers and planners understand and analyze the interaction between residents and urban scene components.

Distributed forecasting and ant colony optimization for the bike-sharing rebalancing problem with unserved demands.

Bike-sharing systems (BSS) have widely spread over many cities in the world as an environmentally friendly means to reduce air pollution and traffic congestion. This paper focuses on the bike-sharing rebalancing problem (BRP), which consists of two aspects: determining desired demands at each station and designing routes to redistribute bikes among stations. For the first task, we firstly apply the random forest, a very efficient machine learning algorithm, to forecast desired demands for each station, which can be easily implemented with distributed computing. For the second task, it belongs to the broad class of the vehicle routing problem with pickup and delivery (VRPPD). In most existing settings, all of the demands being strictly satisfied can lead to longer routes and add operational costs. In this paper, we propose a new model with unserved demands by relaxing demands satisfying constraints. Then, we design a distributed ant colony optimization (ACO) based algorithm with some specific modifications to increase its efficiency for the proposed model. We propose to use the percentage of average cost saving per bike as a metric to evaluate the performance of our method on cost-reducing and compare with existing methods and best-known values. Computational results on benchmarks show the advantage of our approach. Finally, we provide a real case study of BSS in Hangzhou, China, with insightful elaborations.

Evaluating the Hydrologic Performance of Low Impact Development Scenarios in a Micro Urban Catchment.

As urbanization progresses, increasingly impervious surfaces have changed the hydrological processes in cities and resulted in a major challenge for urban stormwater control. This study uses the urban stormwater model to evaluate the performance and costs of low impact development (LID) scenarios in a micro urban catchment. Rainfall-runoff data of three rainfall events were used for model calibration and validation. The pre-developed (PreDev) scenario, post-developed (PostDev) scenario, and three LID scenarios were used to evaluate the hydrologic performance of LID measures. Using reduction in annual runoff as the goal, the best solutions for each LID scenario were selected using cost-effectiveness curves. The simulation results indicated that the three designed LID scenarios could effectively reduce annual runoff volumes and pollutant loads compared with the PostDev scenario. The most effective scenario (MaxPerf) reduced annual runoff by 53.4%, followed by the sponge city (SpoPerf, 51.5%) and economy scenarios (EcoPerf, 43.1%). The runoff control efficiency of the MaxPerf and SpoPerf scenarios increased by 23.9% and 19.5%, respectively, when compared with the EcoPerf scenario; however, the costs increased by 104% and 83.6%. The reduction rates of four pollutants (TSS, TN, TP, and COD) under the MaxPerf scenario were 59.8-61.1%, followed by SpoPerf (53.9-58.3%) and EcoPerf (42.3-45.4%), and the costs of the three scenarios were 3.74, 3.47, and 1.83 million yuan, respectively. These results can provide guidance to urban stormwater managers in future urban planning to improve urban water security.

Comprehending the multiple 'values' of green infrastructure - Valuing nature-based solutions for urban water management from multiple perspectives.

The valuation of urban water management practices and associated nature-based solutions (NBS) is highly contested, and is becoming increasingly important to cities seeking to increase their resilience to climate change whilst at the same time facing budgetary pressures. Different conceptions of 'values' exist, each being accompanied by a set of potential measures ranging from calculative practices (closely linked to established market valuation techniques) - through to holistic assessments that seek to address wider concerns of sustainability. Each has the potential to offer important insights that often go well beyond questions of balancing the costs and benefits of the schemes concerned. However, the need to address - and go beyond - economic considerations presents policy-makers, practitioners and researchers with difficult methodological, ethical and practical challenges, especially when considered without the benefit of a broader theoretical framework or in the absence of well-established tools (as might apply within more traditional infrastructural planning contexts, such as the analysis of transport interventions). Drawing on empirical studies undertaken in Sheffield over a period of 10 years, and delivered in partnership with several other European cities and regions, we compare and examine different attempts to evaluate the benefits of urban greening options and future development scenarios. Comparing these different approaches to the valuation of nature-based solutions alongside other, more conventional forms of infrastructure - and indeed integrating both 'green and grey' interventions within a broader framework of infrastructures - throws up some surprising results and conclusions, as well as providing important sign-posts for future research in this rapidly emerging field.

Urban sustainability in an age of enduring inequalities: Advancing theory and ecometrics for the 21st-century city.

The environmental fragility of cities under advanced urbanization has motivated extensive efforts to promote the sustainability of urban ecosystems and physical infrastructures. Less attention has been devoted to neighborhood inequalities and fissures in the civic infrastructure that potentially challenge social sustainability and the capacity of cities to collectively address environmental challenges. This article draws on a program of research in three American cities-Boston, Chicago, and Los Angeles-to develop hypotheses and methodological strategies for assessing how the multidimensional and multilevel inequalities that characterize contemporary cities bear on sustainability. In addition to standard concerns with relative inequality in income, the article reviews evidence on compounded deprivation, racial cleavages, civic engagement, institutional cynicism, and segregated patterns of urban mobility and organizational ties that differentially connect neighborhood resources. Harnessing "ecometric" measurement tools and emerging sources of urban data with a theoretically guided framework on neighborhood inequality can enhance the pursuit of sustainable cities, both in the United States and globally.

Are Urban Stream Restoration Plans Worth Implementing?

To manage and conserve ecosystems in a more sustainable way, it is important to identify the importance of the ecosystem services they provide and understand the connection between natural and socio-economic systems. Historically, streams have been an underrated part of the urban environment. Many of them have been straightened and often channelized under pressure of urbanization. However, little knowledge exists concerning the economic value of stream restoration or the value of the improved ecosystem services. We used the contingent valuation method to assess the social acceptability of a policy-level water management plan in the city of Helsinki, Finland, and the values placed on improvements in a set of ecosystem services, accounting for preference uncertainty. According to our study, the action plan would provide high returns on restoration investments, since the benefit-cost ratio was 15-37. Moreover, seventy-two percent of the respondents willing to pay for stream restoration chose "I want to conserve streams as a part of urban nature for future generations" as the most motivating reason. Our study indicates that the water management plan for urban streams in Helsinki has strong public support. If better marketed to the population within the watershed, the future projects could be partly funded by the local residents, making the projects easier to accomplish. The results of this study can be used in planning, management and decision making related to small urban watercourses.

Planning the City Logistics Terminal Location by Applying the Green p-Median Model and Type-2 Neurofuzzy Network.

The paper herein presents green p-median problem (GMP) which uses the adaptive type-2 neural network for the processing of environmental and sociological parameters including costs of logistics operators and demonstrates the influence of these parameters on planning the location for the city logistics terminal (CLT) within the discrete network. CLT shows direct effects on increment of traffic volume especially in urban areas, which further results in negative environmental effects such as air pollution and noise as well as increased number of urban populations suffering from bronchitis, asthma, and similar respiratory infections. By applying the green p-median model (GMM), negative effects on environment and health in urban areas caused by delivery vehicles may be reduced to minimum. This model creates real possibilities for making the proper investment decisions so as profitable investments may be realized in the field of transport infrastructure. The paper herein also includes testing of GMM in real conditions on four CLT locations in Belgrade City zone.