City Scale Research Articles

Multi-Scale Analysis of Carbon Emissions in Coastal Cities Based on Multi-Source Data: A Case Study of Qingdao, China

Coastal cities, as centers of economic and industrial activity, accommodate over 40% of the national population and generate more than 70% of the GDP. They are critical centers of carbon emissions, making the accurate and long-term analysis of spatiotemporal carbon emission patterns crucial for developing effective regional carbon reduction strategies. However, there is a scarcity of studies on continuous long-term carbon emissions in coastal cities. This study focuses on Qingdao and explores its carbon emission characteristics at the city, county, and grid scales. Data from multi-source are employed, integrating net primary production (NPP), energy consumption, and nighttime light data to construct a carbon emission estimation model. Additionally, the Tapio model is applied to examine the decoupling of GDP from carbon emissions. The results indicate that the R2 of the carbon emission inversion model is 0.948. The central urban areas of Qingdao’s coastal region are identified as hotspots for carbon emissions, exhibiting significantly higher emissions compared to inland areas. There is a notable dependence of economic development on carbon emissions, and the disparities in economic development between coastal and inland areas have resulted in significant geographical differentiation in the decoupling state. Furthermore, optimizing and transitioning the energy structure has primarily contributed to carbon reduction, while exceptional circumstances, such as the COVID-19 pandemic, have led to passive fluctuations in emissions. This study provides a scientific reference for coastal cities to formulate targeted carbon reduction policies.

A scaling law for predicting urban trees canopy cooling efficiency

Urban heat mitigation is a pressing concern for cities. Intense urban heat poses a threat to human health and urban sustainability. Tree planting is one of the most widely employed nature-based heat mitigation methods worldwide. Therefore, city policy makers require knowledge of how much temperature will be reduced by increasing urban tree canopy (UTC). Cooling efficiency (CE), which was been proposed to quantify the magnitude of temperature reduction associated with a 1% increase in UTC, has been primarily investigated at smaller scales previously. However, such small-scale results cannot be used to develop policy at the whole-city scale. This study developed a method that reveals the scaling relations of CE so as to predict its effects at the city scale. CE was found to follow the form of a power law as spatial scale increased from the small analytical units through intermediate size units up to the extent of a whole city. The power law form appeared consistently across cities with different climate backgrounds during summer daylight hours. Furthermore, the power law form was robust within cities under different summer weather conditions. The power-law scaling approach can thus be used to predict CE at the whole-city scale, providing a useful tool for managers to set UTC goals to mitigate extreme urban heat.

Modeling the Spatial Flows of Nitrogen: The Case of Xiamen

Rapid city expansion and intensive human activities have remarkably affected nitrogen flow, leading to increasingly intricate spatial heterogeneity of nitrogen flow. Focused on the temporal characteristics of nitrogen flow at certain city scales, the existing research has missed comprehensive grid-scale spatial models for nitrogen flow. To address this gap, this study develops a comprehensive spatial model for nitrogen flow by incorporating both natural and anthropic processes. Taking Xiamen as its research case, this study utilizes grid technology and spatial analysis to build a detailed spatial model for nitrogen flow at the grid scale. The results of spatial characteristics of Xiamen in 2015 revealed that hotspots of nitrogen input were primarily located in the surrounding areas outside and east of Xiamen, with the maximum nitrogen input reaching 20.07 × 104 kg/ha. However, the hotspots of nitrogen load in the atmosphere were concentrated in the urban center (i.e., Xiamen Island) and the nearby sea areas. The maximum nitrogen outputs can reach 18.32 × 104 kg/ha, which is 18 times the total nitrogen output to the water environment. Additionally, it was found that a significant gradient correlation exists between nitrogen flow and population density. These findings provide support for low-nitrogen spatial planning and emission reduction policymaking.

Cultural, Economic, or Transport Link: Does Carbon Emissions Trading Promote “Good Neighbor” Carbon Emission Reduction?

Carbon emission reduction was a crucial objective for China’s green transformation, yet establishing regional cooperation with neighboring areas posed a significant challenge in carbon governance. We investigated the spillover effects of carbon emissions trading (CET) on carbon emissions among Chinese cities, employing spatial difference-in-differences (DID), Callaway and Sant’Anna DID (CSDID), and other methodologies. Our paper integrated datasets on high-speed rails, dialects, and carbon emissions at the prefecture-level city scale. The findings revealed that (1) CET reduces emissions by 11.55% in pilot regions, although the impact of the second policy pilot was not significant. (2) In terms of spillover effects, CET increased emissions in neighboring cities with economic and geographic ties, creating a “beggar-thy-neighbor” reduction model. Conversely, it reduced emissions in cities with shared cultural ties, fostering a “good neighbor” reduction model. (3) The policy spillover effect exhibited an “inverted N” curve, first decreasing, then increasing, and finally decreasing as economic and geographic distance increased. (4) Technological advancement was identified as the primary mechanism driving the spillover effects of CET. To achieve the dual-carbon goal, avoiding harmful economic competition and promoting low-carbon cultural exchanges were essential.

Indicators of Cultural Ecosystem Services for Peri-Urban Agricultural and Natural Areas at an Intermediate Scale: A Case Study of Arequipa, Peru

The evaluation of management in peri-urban agricultural and natural areas (PANAs) constitutes a complex topic, particularly when considering its scale in cities where landscape planning is a priority. Therefore, indicators are required to assess the non-material well-being, biodiversity, and quality of life of society, which are criteria of cultural ecosystem services (CESs). This research develops identification and subclassification CESs and tangible and intangible indicators that facilitate the management of the PANA landscape, using the city of Arequipa as a case study—a dynamic cultural city with a declared cultural heritage, in turn located in an artificial oasis in the Atacama Desert. Therefore, the results will enable the selection of indicators that are practically applicable to planning instruments, serving as support systems for decision-making regarding interventions in PANAs. The methodology began with a systematic and meta-analytic (PRISMA) review, identifying 25 articles containing 230 indicators across 19 subclassifications. Using the Multiscale Indicators Framework (MSIF), the literature was integrated with a participatory workshop to validate these findings. The level of impact was assessed for 115 indicators across 13 subclassifications, applying criteria such as diversity, sustainable management, quality of life, and scalability—factors necessary to comply with urban sustainability policies.

A Multi-Scale Evaluation Model of Sustainable Development Goal 11.7 for Problem-Solving at Different Levels

Sustainable Development Goal 11.7 (SDG 11.7) aims to promote the improvement of urban public spaces. However, the localization process of SDG 11.7 mainly relies on a bottom-up problem-solving approach, which fails to fully encompass the connotation of SDG 11.7. Additionally, existing evaluations primarily focus on a single scale, neglecting the impact of scale issues. These limitations can lead to imbalanced development or misallocation of responsibilities when guiding governments at different levels in promoting the sustainable development of public spaces. Therefore, this article introduces a multi-scale assessment model of SDG 11.7. It employs a top-down problem-solving approach to construct a sustainable development indicator framework, setting appropriate sustainable development indicators for various levels of government based on the connotation of SDG 11.7, and generates city-scale results by integrating three scales: apartment complexes, street blocks, and counties. Testing this model in Xi’an, China, revealed that it adequately captures four key aspects of SDG 11.7—safety, inclusiveness, accessibility, and greenness—through 11 indicators. The evaluation outcomes at the apartment complex, street block, and county levels effectively guide future development directions for various levels of government. Ultimately, the synthesis of these scales reveals the spatial pattern of SDG 11.7 at the city scale and identifies focal areas for development. Overall, this exploratory model demonstrates high accuracy and robustness, providing a comprehensive understanding of the essence of SDG 11.7. It also alleviates challenges posed by scale issues, offering decision support for monitoring SDG 11.7 across different levels of government in Chinese cities and promoting the process of sustainable development.

Assessment of Urban Resilience to Floods: A Spatial Planning Framework for Cities

Urbanization-led economic growth drives infrastructure investments and population accumulation in cities, hence exploiting natural resources at an extreme rate. In this context, coastal cities have become vulnerable to climate change-induced extreme weather events and human-made disasters in recent history, where effective measures to improve the resilience of cities are pivotal for developing sustainable living environments. This study proposes a framework for assessing urban resilience to natural disasters (floods) using bottom-up spatial interactions among natural, physical, and social systems within cities and regions. It is noted that seminal studies focus on either the mitigation or adaptation strategies within urban environments to assess disaster resilience, where limited multidisciplinary and operational models hinder evaluations at the city scale. Therefore, urban system interactions and quantifiable parameters proposed in this framework are essential for policymakers and disaster management agencies in the timely allocation of resources to optimize the recovery process. Moreover, spatial planning agencies can adopt resilience mapping to identify the potential risk zones and orient sustainable land use management. Urban resilience can be embodied in spatial strategies with the operational framework proposed here, and future urban growth scenarios can be tested in multiple disaster conditions.

The Implementation and Implications of Sustainable Development Goals at City Scale and Sustainable Urban Development Path

Cities were products of human social development, but the environmental pollution problems brought by high-density buildings and high-density populations in cities had become increasingly serious. The 17 Sustainable Development Goals proposed by the United Nations in 2015 were closely related to urban sustainable development issues. Based on it, this article analyzed the impact of SDGs and related theories on urban sustainable development and possible development directions. Starting from the impact of SDGs on urban sustainable development, elaborated on their impact on already implemented urban sustainable projects and relevant theories. It concluded that the future development of sustainable cities should be based on existing cities, with different focuses on developed and developing countries, but both require the protection of historical value landscapes and the greening of existing facilities. By explaining the theories and practical cases related to SDGs and sustainable cities, hoping to provide a reference and guidance for the path of urban sustainable development.

Estimation of Regional Electricity Consumption Using National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite Night-Time Light Data with Gradient Boosting Regression Trees

With the rapid development of society and economy, the growth of electricity consumption has become one of the important indicators to measure the level of regional economic development. This paper utilizes NPP-VIIRS nighttime light remote sensing data to model electricity consumption in parts of southern China. Four predictive models were initially selected for evaluation: LR, SVR, MLP, and GBRT. The accuracy of each model was assessed by comparing real power consumption with simulated values. Based on this evaluation, the GBRT model was identified as the most effective and was selected to establish a comprehensive model of electricity consumption. Using the GBRT model, this paper analyzes electricity consumption in the study area across different spatial scales from 2013 to 2022, demonstrating the distribution characteristics of electricity consumption from the pixel level to the city scale and revealing the close relationship between electricity consumption and regional economic development. Additionally, this paper examines trends in electricity consumption across various temporal scales, providing a scientific basis for the optimal allocation of energy and the effective distribution of power resources in the study area. This analysis is of great significance for promoting balanced economic development between regions and enhancing energy efficiency.

Measuring Linkages of Place-Related Constructs at City Scale—A SEM Approach to Perception

Measuring Linkages of Place-Related Constructs at City Scale—A SEM Approach to Perception

Response Mechanism and Evolution Trend of Carbon Effect in the Farmland Ecosystem of the Middle and Lower Reaches of the Yangtze River

The farmland system in the global terrestrial ecosystem has dual attributes as both a carbon source and a carbon sink, playing a crucial role in controlling carbon emissions and mitigating global warming. Using carbon source and sink accounting of farmland ecosystems, we applied methods such as standard deviation ellipse, Tapio decoupling theory, and Markov chain to analyze the spatiotemporal changes, response mechanisms, and evolutionary trends of regional carbon effects. The results indicated that from 2011 to 2021, the farmland ecosystem in the middle and lower reaches of the Yangtze River consistently acted as a carbon sink. However, the net carbon sink showed slight fluctuations and significant spatial differences. The migration range of the net carbon sink center in the farmland ecosystem of the middle and lower reaches of the Yangtze River was relatively small, ranging from 115.52 to 115.77° E and 30.14 to 30.27° N. The decomposition of the Tapio decoupling index between the net carbon sink of the farmland ecosystem and agricultural output value showed the order of effects on their coupling relationship as follows: agricultural mechanization level > agricultural mechanization efficiency > agricultural output value > planting scale. The probability of maintaining the original state of net carbon sink in various cities in the middle and lower reaches of the Yangtze River (over 77%) was much higher than the probability of transfer, making it difficult to achieve a leapfrog growth in net carbon sink. The net carbon sink at the city scale exhibits the Matthew effect and spatial spillover effect. The above research results clarify the spatiotemporal changes in carbon effects in agricultural production at multiple levels, including city, province, and region. They also provide a theoretical basis for formulating differentiated regional emission reduction and sink enhancement strategies in the middle and lower reaches of the Yangtze River, promoting the rapid development of low-carbon agriculture in China.

A multi-stakeholder engagement framework for material-building-city synergy through circular transformation

Scholars, industrial stakeholders, and governmental institutions are developing the circular economy paradigm. However, the emergence of multiple perspectives has challenged its implementation. As the industry that is the biggest contributor to the negative impacts on the environment, the construction industry stakeholders are paving the way for more sustainable as well as circular and regenerative construction by considering all actors in the system. Yet, the construction industry has a complex supply chain that requires clear strategies and stakeholder engagement across materials, buildings, and cities for efficient flows in the supply chain. Nonetheless, there is a need for improvement in the engagement of construction stakeholders for circular transformation. Therefore, this study aims to develop a multi-stakeholder engagement framework through circular transformation to guide the decision-makers for circular city governance. It has identified critical success factors by considering the construction stakeholders. The framework includes strategies at the micro (material), meso (building), and macro (city) scales to strengthen the material-building-city synergy. It's a significant step toward advancing circular city governance by bridging the gap between theoretical understanding and practical implementation and establishing a robust engagement for material-building-city synergy. The study employs a systematic literature review to extract strategies and natural language processing to analyze the strategies by topic modeling and defines critical success factors for multi-stakeholder engagement at multiscale. The outcome introduces the REVERT framework, bridging resource, envisagement, validation, entity, regulation, and technology, to facilitate a seamless transition by material-building-city synergy advancing circular city governance.

Impacts of On‐Road Vehicular Emissions on U.S. Air Quality: A Comparison of Two Mobile Emission Models (MOVES and FIVE)

AbstractOn‐road vehicles are significant contributors to air pollution globally, particularly to nitrogen oxides (NOx) and ozone (O3). Quantifying their contribution to air quality is crucial to understanding the trends of vehicle emissions as low‐ and “zero” emission vehicles join the fleet. Modeling on‐road emissions is complex due to various factors like fleet activities, traffic patterns, and meteorological conditions. We compare on‐road emissions from two mobile models: the National Oceanic and Atmospheric Administration Fuel‐based Inventory of Vehicle Emission (FIVE) and the US Environmental Protection Agency Motor Vehicle Emission Simulator (MOVES), finding they contribute 4%–33% to volatile organic compounds (VOCs), NOx, and fine particulate matter (PM2.5) in the contiguous United States (CONUS). Using a regional chemical transport model, we assess air quality effects under different emission scenarios. Both emission data sets yield satisfactory performance, with MOVES showing lower biases in ozone (O3) and PM2.5 over CONUS, while FIVE performs better at city scales due to higher urban NOx emissions. In January, on‐road emissions increased surface O3 over western and southern US by up to 9.1%–13.1% but decreased by 2.5% over the northeastern US, while PM2.5 predictions vary across the US (−85% to 24%). In July, on‐road emissions elevate O3 and PM2.5 concentrations by 15%–20% across CONUS, except in some west coast cities. They also greatly contribute to nitrogen dioxide (NO2) by more than 80% near roads and in urban areas. This study highlights the significant impact of on‐road emissions on urban air quality and provides insights for improving air quality forecasting and management.

The Impact of Smart City Policies on City Resilience: An Evaluation of 282 Chinese Cities

This study uses four dimensions, namely social resilience, economic resilience, infrastructure resilience, and ecological resilience, to construct an index system for urban resilience. The subject data came from the panel data of 282 prefecture-level cities in China from 2006 to 2020. We selected a multiperiod double-difference model to study the effects of smart city pilot policies on macro-urban resilience. By conducting parallel-trend tests and selecting appropriate robustness tests, this study drew the following relevant conclusion: smart city pilot policies can have a positive effect on the urban resilience level. These policies exert their influence by facilitating industrial structure upgrading, which plays a partial mediating role. Considering different city area distributions and city scales, smart city pilot policies can have significant heterogeneity in their enhancement of urban resilience. The effect is pronounced in the “east > central > west” states and is more likely to have a significant impact on small- and medium-sized cities. Therefore, promoting the scope of smart city pilots, strengthening the intermediary role of industrial structure upgrading, and implementing differentiated policies for different regions and city sizes are important for sustainable urban development.

Research on energy saving effect of spatial correlation network of digital infrastructure: based on the analysis of network centrality.

Accurately portraying the spatially linked network characteristics of digital infrastructure and exploring its energy-saving effects are highly valuable for enhancing the synergy in digital infrastructure development and expanding its network spillover effects on energy conservation. This paper uses panel data at the city level in China and employs a modified gravity model to calculate the centrality of digital infrastructure spatial correlation network nodes. Based on this, an econometric model is constructed, incorporating variables such as digital infrastructure spatial correlation network node centrality and urban green total factor energy efficiency. The model is used to analyze the effects and transmission paths of digital infrastructure network node centrality on urban green total factor energy efficiency. The analysis yields the following conclusions: (1) Digital infrastructure spatial correlation network node centrality significantly improves urban green total factor energy efficiency, with considerable variability due to city geographic location, city scale, and city attributes. (2) Nonlinear testing results indicate that as digital infrastructure construction advances, its impact on urban green total factor energy efficiency shifts from inhibitory to promotional. (3) The impact mechanism shows that digital infrastructure node centrality enhances urban green total factor energy efficiency through green technology innovation. Additionally, it promotes advanced industrial structures and reduces capital mismatch, further influencing energy efficiency. (4) Digital infrastructure node centrality not only boosts urban green total factor energy efficiency but also facilitates regional convergence, increasing the convergence rate from 0.094 to 0.170%. The findings of the research offer policy guidance for the government on advancing digital transformation initiatives and enhancing energy efficiency.

Unraveling nonlinear and spatial non-stationary effects of urban form on surface urban heat islands using explainable spatial machine learning

Under global warming, surface urban heat islands (SUHI) threaten human health and urban ecosystems. However, scant research focused on exploring the complex associations between urban form factors and SUHI at the county scale, compared with rich studies at the city scale. Therefore, this study simultaneously examined the nonlinear and spatial non-stationary association between SUHI and urban form factors (e.g., landscape structure, built environment, and industrial pattern) across 2321 Chinese counties. An explainable spatial machine learning method, combining the Geographically Weighted Regression, Random Forest, and Shapley Additive Explanation model, was employed to deal with nonlinearity, spatial non-stationary, and interpretability of modeling. The results indicate the remarkable spatial disparities in the relationship between urban form factors and SUHI. Landscape structure contributes the most in southern counties, while the built environment is more important in northeastern counties. The impact of building density and building height increases with the county size and becomes the main driver of urban heat in mega counties. Most urban form factors exhibit nonlinear impacts on SUHI. For example, urban contiguity significantly affects SUHI beyond a threshold of 0.93, while building density does so at 0.17. By comparison, the influence of shape complexity remains stable above a value of 7. Factors such as industrial density and diversity have a varied influence on SUHI between daytime and nighttime. The results of local explanations and nonlinear effects provide targeted regional mitigation strategies for urban heat.

Fully automated simplification of urban drainage models on a city scale

ABSTRACT The article presents an innovative method for simplifying urban drainage models. This approach strategically reduces complexity while preserving accuracy in large-scale, high-resolution models such as those of the city of Graz. It involves the selective removal and aggregation of sewer network elements like subcatchments and conduits, ensuring vital features like storage nodes and flow controls remain intact for precise simulations. Despite significant reductions in model components, the simplified version maintains high accuracy in hydrological and hydraulic aspects, including water balance components like infiltration loss, surface runoff, and external outflow. The method proves equally effective across both land cover and sewer shed-based models, offering computational efficiencies that speed-up processing by 20–45 times for the study site. This is particularly beneficial for rapid decision-making and resource optimization in urban planning. The model also adeptly predicts flood events, especially from larger, infrequent rainfall, although an overly restrictive flow capacity can refine flood predictions at the expense of other flow characteristics. Ultimately, this streamlined approach allows for the quick creation of simplified, yet accurate, models from high-resolution city-scale hydrodynamic data or digital twins, facilitating efficient and timely analyses in urban drainage management.

Mapping the health impacts of transport noise in the densely populated area of the Île-de-France region

The European Noise Directive (END) requires agglomerations of more than 100,000 inhabitants to produce strategic noise maps. In the Ile-de-France region, 14 agglomerations with a total population of 10,5 million inhabitants are concerned. Bruitparif applied the health impact assessment methodology recommended by the WHO to carry out a detailed territorial diagnosis of the issues related to transport noise. For this purpose, population exposure was assessed on the basis of strategic noise maps produced according to the 4th round of the END and the associated risks were calculated using the exposure-response functions published by the WHO in October 2018. The work continued with the valuation of healthy life years lost due to transport noise. Two types of maps have been produced with a 200 m2 grid as well as at the city scale: maps of collective health impacts related to population noise exposure and maps of average individual risk corresponding to the potential impact per individual. The combination of these two types of information has made it possible to highlight significant territorial disparities and to identify the sectors most concerned by the health issues related to noise, a prerequisite for prioritizing public action.

Impact of car electrification on urban noise pollution: a microscopic traffic simulation study

Noise pollution is a major health and well-being concern, being associated with, e.g., sleep disturbance, annoyance, and cardiovascular diseases. Road traffic is, by far, the largest contributor to this noise pollution in urban areas. With the growing electrification of cars in developed countries, it is commonly believed that road traffic noise pollution and the associated problems will disappear shortly in urban areas. However, at high speeds, the difference in noise emissions between internal-combustion-engine and electric vehicles is not significant, and at low speeds, the propulsion noise of electric cars is not negligible. Few works have quantitatively estimated the actual impact of passenger cars electrification on population exposure to noise, especially considering such aspects as the gradual transition towards a fully electric fleet. The present contribution thus aims to assess the evolution of population exposure to noise pollution, using a microscopic traffic simulation on the scale of an entire city, noise mapping, and various indicators, at multiple stages of this transition.

Development of a Multi‐Scale Meteorological Large‐Eddy Simulation Model for Urban Thermal Environmental Studies: The “City‐LES” Model Version 2.0

AbstractTo bridge the gaps between meteorological large‐eddy simulation (LES) models and computational fluid dynamics (CFD) models for microscale urban climate simulations, the present study has developed a meteorological LES model for urban areas. This model simulates urban climates across both mesoscale (city scale) and microscale (city‐block scale). The paper offers an overview of this LES model, which distinguishes itself from standard numerical weather prediction models by resolving buildings and trees at the microscale simulations. It also differs from standard CFD models by accounting for atmospheric stratification and physical processes. Noteworthy features of this model include: (a) the calculation of long‐ and short‐wave radiations in three dimensions, incorporating multiple reflections within urban canopy layers using the radiosity method, and accounting for building and tree shadows in the simulations; (b) the provision of various heat stress indices (Universal Thermal Climate Index, Wet Bulb Globe Temperature, MRT, THI); (c) the assessment of the efficacy of heat stress mitigation measures such as dry‐mist spraying, roadside trees, cool pavements, and green/cool roofs strategies; (d) the capability to run on supercomputers, with the code parallelized in a three‐dimensional manner, and the model can also run on a graphics processing unit cluster. Following the introduction of this model, the study confirms its basic performance through various numerical experiments, including simulations of thermals in the convective boundary layer, coherent structure of turbulence over urban canopy, and thermal environment and heat stress indices in urban districts. The model developed in this study is intended to serve as a community tool for addressing both fundamental and applied studies in urban climatology.