City Scale Research Articles

Evaluating Urban Green Space Inequity to Promote Distributional Justice in Portland, Oregon

Access and exposure to urban green space—the combination of parks and vegetative cover in cities—are associated with various health benefits. As urban green space is often unequally distributed throughout cities, understanding how it is allocated across socio-demographic populations can help city planners and policy makers identify and address urban environmental justice and health equity issues. To our knowledge, no studies have yet combined assessments of park quality, park availability, and green cover to inform equitable urban green space planning. To this end, we developed a comprehensive methodology to identify urban green space inequities at the city scale and applied it in Portland, OR, USA. After auditing all public parks in Portland and gathering green cover data from publicly accessible repositories, we used a suite of statistical tests to evaluate distribution of parks and green cover across Census block groups, comprising race, ethnicity, income, and educational attainment characteristics. Right-of-way tree canopy cover was the most significant urban green space inequity identified in bivariate analysis (rs = −0.73). Spatial autoregressive models identified that right-of-way, private, and overall tree canopy cover (Nagelkerke pseudo-R2 = 0.66, 0.77, and 0.67, respectively) significantly decreased with the proportion of minoritized racial population and increased with median income. The results were then used to identify priority locations for specific urban green space investments. This research establishes a process to assess intra-urban green space inequities, as well as identify data-informed and spatially explicit planning priorities to promote health equity and environmental justice.

Does new-type infrastructure improve total factor carbon productivity? Experimental evidence from China

The implementation of new-type infrastructure plays a crucial role in driving low-carbon economic development in China and worldwide. This work employs fixed-effects model, mediated-effects model, spatial Durbin model, and meta-analysis to thoroughly examine the influence of new-type infrastructure on total factor carbon productivity using panel data of 274 Chinese cities spanning from 2011 to 2021. The findings indicate that: firstly, new-type infrastructure has a notable and beneficial impact on total factor carbon productivity, but there is heterogeneity in resource endowment, city scale, and policy intensity. New-type infrastructure can only promote total factor carbon productivity enhancement in non-resource cities and large cities. Under the influence of smart city policies, new-type infrastructure has a stronger effect on total factor carbon productivity enhancement. Secondly, digital finance and marketization are important ways for new-type infrastructure to improve total factor carbon productivity. Thirdly, via spatial spillover effect, new-type infrastructure may boost total factor carbon productivity not only in the local region but also in places that are spatially connected. Accordingly, this paper offers policy recommendations for the government to encourage the investment and development of new-type infrastructure to support total factor carbon productivity growth.

The synergistic decarbonization potential from construction industry and upstream sectors with a city-scale: A case study of hangzhou, China

How to reduce Carbon Emissions from the Construction Industry (CECI) is one of the key issues in China's efforts to achieve its “dual-carbon target”. The Carbon Emission of the building Materialization Process (CEMP) is the main source of CECI, originating from both the upstream sector and the construction industry itself. Researching the synergistic decarbonization potential and reduction pathways of the construction industry and upstream sectors can help guide sustainable development. Focusing on the city scale, this study establishes a bottom-up framework based on system dynamics and carbon intensity methods to explore the possibilities of CEMP. The framework comprises a dynamic material flow analysis module and a carbon emission accounting module. The Logarithmic Mean Divisia Index (LMDI) method is employed to elucidate the emission reduction potentials of various factors. The results show that in the case of Hangzhou: (1) Without control measures, CEMP will reach a maximum of 25.1 million tons, and a substantial scale of 14.9 million tons remaining in 2060. (2) Through synergistic decarbonization, the cumulative emission reduction over the next 40 years could reach 323.4 million tons, and by 2060, CEMP could decrease to 3.7 million tons of CO2. Achieving carbon neutrality in the construction industry still requires carbon offsets from other sectors. (3) The decarbonization potential mainly comes from controlling the new construction area and carbon emission reduction in material production process, contributing 45.4% and 36.9%, respectively. This study reveals the significant pressure faced by the construction industry in achieving carbon neutrality. It also underscores that further controlling material demand and reducing the carbon intensity of materials is a prerequisite for Hangzhou's construction industry to achieve carbon neutrality.

Evolution and genetic characterization of Seoul virus in wild rats Rattus norvegicus from an urban park in Lyon, France 2020-2022.

Seoul virus (SEOV) is an orthohantavirus primarily carried by rats. In humans, it may cause hemorrhagic fever with renal syndrome (HFRS). Its incidence is likely underestimated and given the expansion of urban areas, a better knowledge of SEOV circulation in rat populations is called for. Beyond the need to improve human case detection, we need to deepen our comprehension of the ecological, epidemiological, and evolutionary processes involved in the transmission of SEOV. We performed a comprehensive serological and molecular characterization of SEOV in Rattus norvegicus in a popular urban park within a large city (Lyon, France) to provide essential information to design surveillance strategies regarding SEOV. We sampled rats within the urban park of 'La Tête d'Or' in Lyon city from 2020 to 2022. We combined rat population genetics, immunofluorescence assays, SEOV high-throughput sequencing (S, M, and L segments), and phylogenetic analyses. We found low structuring of wild rat populations within Lyon city. Only one sampling site within the park (building created in 2021) showed high genetic differentiation and deserves further attention. We confirmed the circulation of SEOV in rats from the park with high seroprevalence (17.2%) and high genetic similarity with the strain previously described in 2011 in Lyon city. This study confirms the continuous circulation of SEOV in a popular urban park where the risk for SEOV transmission to humans is present. Implementing a surveillance of this virus could provide an efficient early warning system and help prepare risk-based interventions. As we reveal high gene flow between rat populations from the park and the rest of the city, we advocate for SEOV surveillance to be conducted at the scale of the entire city.

Examining nonlinear effects of socioecological drivers on urban solar energy development in China using machine learning and high-dimensional data

In the context of carbon neutrality target, renewable energy sources have been transforming from “supplementary energy” to “main energy”, which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the “resource-environment-demand” comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry.

Study on the impact of administrative division management on the regional economy—From the perspective of 281 prefecture-level cities

Merging counties into districts has significant implications for optimizing administrative structures, enhancing regional governance efficiency, and promoting socio-economic development. By incorporating the experiences of previous studies and summarizing corresponding advantages and disadvantages, we utilized data from 1999 to 2018. Employing a double-difference model, we constructed an index system covering economic, employment, ecological, and other aspects. We selected 22 indicators from 281 cities to assess the high-quality development level at the city scale. Based on whether the policy of merging counties into districts was implemented, we divided all samples into treatment and control groups to delve into the economic impact of administrative division adjustment policies. The study empirically demonstrates that the policy of merging counties into districts has a significantly positive impact on economic development in the regions where this policy is implemented. We found that administrative division management can have a positive impact on the economy, and this result has been verified, passing the significance test.

Deriving Input Variables through Applied Machine Learning for Short-Term Electric Load Forecasting in Eskilstuna, Sweden

As the demand for electricity, electrification, and renewable energy rises, accurate forecasting and flexible energy management become imperative. Distribution network operators face capacity limits set by regional grids, risking economic penalties if exceeded. This study examined data-driven approaches of load forecasting to address these challenges on a city scale through a use case study of Eskilstuna, Sweden. Multiple Linear Regression was used to model electric load data, identifying key calendar and meteorological variables through a rolling origin validation process, using three years of historical data. Despite its low cost, Multiple Linear Regression outperforms the more expensive non-linear Light Gradient Boosting Machine, and both outperform the “weekly Naïve” benchmark with a relative Root Mean Square Errors of 32–34% and 39–40%, respectively. Best-practice hyperparameter settings were derived, and they emphasize frequent re-training, maximizing the training data size, and setting a lag size larger than or equal to the forecast horizon for improved accuracy. Combining both models into an ensemble could the enhance accuracy. This paper demonstrates that robust load forecasts can be achieved by leveraging domain knowledge and statistical analysis, utilizing readily available machine learning libraries. The methodology for achieving this is presented within the paper. These models have the potential for economic optimization and load-shifting strategies, offering valuable insights into sustainable energy management.

Streets classification models by urban features for road traffic noise estimation

Road traffic is the primary source of environmental noise pollution in cities. This problem is also spreading due to inadequate urban expansion planning. Hence, integrating road traffic noise analysis into urban planning is necessary for reducing city noise in an effective, adaptable, and sustainable way. This study aims to develop a methodology that applies to any city for the stratification of urban roads by their functionality through only their urban features. It is intended to be a tool to cluster similar streets and, consequently, traffic noise to enable urban and transportation planners to support the reduction of people's noise exposure. Three multivariate ordered logistic regression statistical models (Model 1, 2, and 3) are presented that significantly stratify urban roads into five, four, and three categories, respectively. The developed models exhibit a McFadden pseudo-R2 between 0.5 and 0.6 (equivalent to R2 >0.8). The choice between Model 1 or 2 depends on the scale of the city. Model 1 is recommended for developed cities with an extensive road network, while Model 2 is most suitable in intermediate and growing cities. On the other hand, Model 3 could be applied at any city scale but focused on local management of transit routes and for designing acoustic sensor installations, urban soundwalks, and identification of quiet areas. Urban features related to road width and length, presence of transport infrastructure, and public transport routes are associated with increased traffic noise in all three models. These models prove useful for future action plans aimed at reducing noise through strategic urban planning.

How can low-carbon help high-quality urban development?-Empirical evidence from low-carbon city pilot policies.

High-quality development is the paramount task for comprehensively building a socialist modernized country. The low-carbon city pilot policy, with cities as the unit of action, provides new opportunities for high-quality economic transformation. Based on panel data from 261 cities between 2005 and 2018, this study calculates the level of high-quality economic development in Chinese cities, constructs a multi-period difference-in-differences model, analyzes the impact of the low-carbon city pilot policy on high-quality economic development, and explores the policy's heterogeneous effects on high-quality development in different types of cities and its transmission mechanism. The research findings show that the low-carbon city pilot policy can significantly promote high-quality economic development in cities and has heterogeneous effects in terms of regional differences, city types, and city scale. The effects are relatively greater in the eastern region, non-resource-based cities, and mega-cities. The low-carbon city pilot policy promotes high-quality economic development through mechanisms such as technological progress effects, resource agglomeration effects, and government action improvement effects. Combining theoretical analysis with empirical results, this study proposes policy recommendations to enhance the effectiveness of the low-carbon city pilot policy in promoting high-quality development.

Unlock city-scale energy saving and peak load shaving potential of green roofs by GIS-informed urban building energy modelling

Urban green roofs have emerged as a significant trend in urban architecture worldwide, offering numerous benefits, including enhanced energy performance, improved urban microclimate, and public health. This study proposes a holistic framework for assessing the green roofs' energy-saving potential at the city scale, achieved by scaling up building-scale energy simulations to city-scale energy demands. Firstly, massive building information is collected using Geometric Information System (GIS) technologies. Subsequently, prototype buildings are generated to accurately represent the geometric characteristics of buildings at the city scale. Building performance simulation is further conducted considering three types of plants on roofs. A case study in Xiamen, China, demonstrates the effectiveness of the proposed framework to efficiently quantify the city-scale energy-saving potential of green roofs. By implementing green roofs in Xiamen, energy savings of 1.62–1.83% and peak load shaving of 1.10–1.63% can be achieved for the whole city. Overall, the proposed framework has the potential for widespread application in other cities with minor adjustments to accommodate variations in climate and building parameters.

GIS-Based Regional Seismic Risk Assessment for Dubai, UAE, Using NHERI SimCenter R2D Application

Over the last two decades, the UAE’s construction sector has grown significantly with the development of tall buildings, but the region faces seismic risks. Similar concerns in China led to earthquake simulation research on a city scale. The objectives include developing programming for parallel computing and creating simplified models for estimating losses. The challenges include computational complexity and uncertainties in various modules. In 1995, the structural engineering community adopted performance-based engineering principles, shifting to a probabilistic design process. The Computational Modeling and Simulation Center (SimCenter) implemented this into a generic software platform, with the 2010 release of Regional Resilience Determination (R2D) automating the methodology. A research plan aims to advance realistic seismic simulation in the UAE, integrating studies and custom developments. The goal is to create an end-to-end seismic risk assessment framework aligned with digital trends, such as BIM and GIS. The investigation focuses on a virtual dataset for tall buildings, considering variations in location, material properties, height, and seismic activity. For the studied archetypes, the average expected losses include a 3.6% collapse probability, a 14% repair cost, 22 days repair time per asset, and almost 1.5% total population injuries, ranging from 1% for the lowest severity to 0.15% for the highest.

The impact of capital goods trade liberalization on regional labor market in China

The impact of trade liberalization on regional employment dynamics remains a topic of ongoing debate and scrutiny. This study aims to provide a nuanced understanding by focusing specifically on the effects of imported capital goods trade. Our study analysis reveals that capital goods trade liberalization predominantly leads to a substitution effect between capital and labor, resulting in a reduction in overall regional employment levels. However, we also find complementary effects between capital and high-skilled labor, alongside substitution effects on low-skilled labor, which collectively influence the regional labor market's skill structure. Furthermore, our study identifies the heterogeneous spillover effects of capital goods trade liberalization on regional labor markets, varying across different regions and city scales. We argue that investing in region skill development resources can help align the workforce with global factors, fostering positive interaction between regions.

Mapping dynamic human sentiments of heat exposure with location-based social media data

Understanding urban heat exposure dynamics is critical for public health, urban management, and climate change resilience. Near real-time analysis of urban heat enables quick decision-making and timely resource allocation, thereby enhancing the well-being of urban residents, especially during heatwaves or electricity shortages. To serve this purpose, we develop a cyberGIS framework to analyze and visualize human sentiments of heat exposure dynamically based on near real-time location-based social media (LBSM) data. Large volumes and low-cost LBSM data, together with a content analysis algorithm based on natural language processing are used effectively to generate near real-time heat exposure maps from human sentiments on social media at both city and national scales with km spatial resolution and census tract spatial unit. We conducted a case study to visualize and analyze human sentiments of heat exposure in Chicago and the United States in September 2021. Enabled with high-performance computing, dynamic visualization of heat exposure is achieved with fine spatiotemporal scales while heat exposure detected from social media data can be used to understand heat exposure from a human perspective and allow timely responses to extreme heat. HIGHLIGHTS Near real-time and high spatial resolution mapping of human sentiments of heat exposure with Twitter data An integrated cyberGIS and machine learning framework for visualizing heat exposure with Twitter data Human sentiment of heat exposure mapping in the City of Chicago and the United States

Advancing Urban Building Energy Modeling: Building Energy Simulations for Three Commercial Building Stocks through Archetype Development

Urban building energy models (UBEMs), developed to understand the energy performance of building stocks of a region, can aid in key decisions related to energy policy and climate change solutions. However, creating a city-scale UBEM is challenging due to the requirements of diverse geometric and non-geometric datasets. Thus, we aimed to further elucidate the process of creating a UBEM with disparate and scarce data based on a bottom-up, physics-based approach. We focused on three typically overlooked but functionally important commercial building stocks, which are sales and shopping, healthcare facilities, and food sales and services, in the region of Pittsburgh, Pennsylvania. We harvested relevant local building information and employed photogrammetry and image processing. We created archetypes for key building types, designed 3D buildings with SketchUp, and performed an energy analysis using EnergyPlus. The average annual simulated energy use intensities (EUIs) were 528 kWh/m2, 822 kWh/m2, and 2894 kWh/m2 for sales and shopping, healthcare facilities, and food sales and services, respectively. In addition to variations found in the simulated energy use pattern among the stocks, considerable variations were observed within buildings of the same stock. About 9% and 11% errors were observed for sales and shopping and healthcare facilities when validating the simulated results with the actual data. The suggested energy conservation measures could reduce the annual EUI by 10–26% depending on the building use type. The UBEM results can assist in finding energy-efficient retrofit solutions with respect to the energy and carbon reduction goal for commercial building stocks at the city scale. The limitations highlighted may be considered for higher accuracy, and the UBEM has a high potential to integrate with urban climate and energy models, circular economy, and life cycle assessment for sustainable urban planning.

Institutional change for the development of urban sanitation in the Global South: A social science review

This paper analyses knowledge in social sciences about the conditions for and the process of institutional change in the urban sanitation sector in the Global South. We conducted a literature review of 148 papers. This review is organised around the six steps of institutional change proposed by Greenwood et al. (2002). Most studies focus on conditions and barriers to change, on the “legitimacy” of social or technological innovations, and the actors promoting change. The change process is rarely analysed from a long-term perspective, nor at the city scale, calling for further research.

From Block to City Scale: Greenery’s Contribution to Cooling the Urban Environment

Urban greenery is a strategy to improve the thermal environment in urban areas affected by heat islands and global warming. These phenomena can harm the citizens’ quality of life. Researchers have investigated the thermal benefits of urban vegetation, but only a few have explored its complexities across diverse urban scales. Understanding these variations is critical for precise analysis, customized solutions, efficient resource allocation, and enhancing urban living quality while promoting sustainability and climate resilience. This paper reviews 250 scientific articles about the relationship between greenspace and the urban thermal environment published between 2010 and 2023 through urban scales. It summarizes the parameters and findings of greenery’s contribution to cooling the urban environment. The data reveal that most studies concentrated on the block scale, public open spaces, neighborhoods, parks, grouped vegetation, mixed arrangements, high vegetation, spatial parameters, and the use of air temperature data to report their findings. The cooling-effect evidence shows that the block scale has an average mitigation range of 0.7–2.7 °C, the neighborhood scale of 1.1–2.9 °C, and the city scale of 0.5–2.2 °C. Furthermore, it is critical to define reliable research methods and perform thorough software validation to assess model performance and establish guidelines for urban-landscape design accurately.

Smart District Through a Study of Urban Area Usage onHigh-Speed Internet Area: A Case Study of Chiang Mai City

Towards becoming Smart city, Internet has become an important infrastructure in every city. Since 2018 Chiang Mai has forced as Smart city which focuses on city traffic and transportation. Without learning from people and city on the reality, an idea of smart city conceptually requires building up the relation of people and city development with high-speed Internet provider. To elevate quality of people life, smart city is not only holistically on city scale but also community as district scale. According to National Strategy 2018-2037, Smart city presently run by government and internet provider service as Top-down development. The shape of Chiang Mai city is sprawled by City development, Government has served only centralized infrastructure without understanding and elevating existing social and community learning. Then, City development in the past usually overrun and cause to conflict of local community. As 3 ring roads or superhighways which provides city spreading to other suburban districts. But why center of the city still gets potentially to raise businesses and happen new kind of convergence activities. The strategy of Smart city must be collaboratively planned by learning on smart lifestyle and grounding on people. To understand new city area wheresupports new smart people and society is the key to elevate and integrate City development. Several active areas in the city have been appeared as “Smart district” where creates new convergence activities by locals, entrepreneur, digital nomad. By wireless city and Smart city forcing, this leads to a question where Chiang Mai city area could potentially be ready to this upcoming development. Smart district is a new study to study urban area through high-speed internet area where ground up the topic of smart city specifically.

Exploring transitions of sewer wastewater infrastructure towards decentralisation using the modular model TURN-Sewers

We present a new modular model called TURN-Sewers for exploring different adaptations of centralised wastewater infrastructure towards more decentralised wastewater systems under different urban development scenarios. The modular model is flexible and computationally efficient in exploring transitions at the city scale, allowing for the comparison of different policies and management strategies for sanitary wastewater infrastructure. TURN-Sewers includes independent modules that simulate the generation, dimensioning, deterioration, management, and calculation of performance indicators for different wastewater systems. This model can use readily available spatial information to support infrastructure planners and other stakeholders in exploring different transition pathways from centralised to decentralised wastewater infrastructure. An illustrative example demonstrates how TURN-Sewers can generate multiple future alternatives, define different infrastructure management strategies regarding system expansion, rehabilitation and transition, and assess the economic, hydraulic and structural impacts.

Mobile measurement background quantification methods for determining local traffic emissions and background source contributions to ammonia

On-road ammonia (NH3) could be critical for public health in high-population areas due to its contribution to fine particulate matter (PM2.5) formation. Therefore, quantifying the impact of vehicular NH3 emissions on pollution levels is vital from both policy and exposure perspectives. In this study, we conducted night- and daytime mobile measurements during a pollution episode in March 2021, explored common data processing strategies for background concentration estimation and calculated the vehicle contribution in urban Beijing. Percentile and time–frequency methods were evaluated and applied to separate local vehicle and background concentrations. The spatial distributions differed between the night- and daytime periods. However, vehicular emissions were an important source of NH3 both at night and during the day. In the pollution accumulation process, the vehicle contribution increased to 27.4%. Even though local traffic emissions notably impacted NH3 levels, the background concentration, including regional transport, was the largest contributor to NH3 at the city scale. This study has implications for local and regional NH3 control strategies aimed at reducing pollution in densely populated urban areas.

Transfer payments and carbon reduction in China: The effect of the national comprehensive demonstration city of energy saving and emission reduction fiscal policy

To address climate change and achieve high-quality economic development, China began implementing the Comprehensive Demonstration City of Energy Saving and Emission Reduction Fiscal Policy (ESERFP) in 2011. This policy relies on special transfer payments to increase the integration of various energy conservation and emission reduction fiscal policies, thereby accelerating institutional and mechanism innovation, actively promoting economic structure adjustment and development mode transformation, and achieving energy conservation and emission reduction goals in various regions. This study uses a multi-period difference-in-difference (DID) model to estimate the ESERFP's CO2 emission reduction effect and paths. The findings demonstrate the following: (1) The ESERFP has lessened the CO2 emission intensity of the demonstration city and effectively played a role in reducing CO2 emissions. (2) Mechanism analysis demonstrates that the ESERFP has significantly improved the green travel level of residents in pilot cities and can also reduce cities' total energy consumption. Therefore, the ESERFP can effectively suppress CO2 emissions by improving transportation systems and optimizing energy consumption structures. (3) Further analysis shows that the ESERFP can reduce the CO2 emission scale of the demonstration cities and effectively promote the increase of actual per capita gross domestic product in pilot cities, bringing economic dividends and thus achieving a “win-win” situation of environmental protection and economic growth. (4) Heterogeneity analysis demonstrates that the CO2 emission reduction effect is more significant in non-old industrial-based cities, non-resource-based cities, and urban agglomerations. This study not only evaluates the CO2 reduction effect of the demonstration city but also provides reference significance for the improvement of subsequent environmental regulations. In addition, clarifying the implementation effect of the ESERFP can help alleviate ecological and environmental problems in the process of urban economic development in China and achieve the sustainable development goal of green transformation.