Search results

Green-space-triggered gentrification, wherein original residents are displaced by wealthier individuals owing to the creation of new green spaces, has been criticized for exacerbating environmental injustice. While previous studies have explored green-space-triggered gentrification in individual cities, few have examined heterogeneities across multiple cities, especially in developing countries where cities are at different stages of expansion. The roles of green space in different stages of urbanization can produce varying outcomes of gentrification. This study investigated the relationship between green space and gentrification in Chinese cities from 2012 to 2020. The normalized difference vegetation index (NDVI), distance to adjacent parks, and area of adjacent parks were used as indicators of green space, while nighttime lights and residential land prices were used as proxies for gentrification. Nationwide analyses indicated that both increasing NDVI and building new parks nearby could lead to gentrification, but the park area had a marginal effect. Stratified analyses further showed that the effect of green space on gentrification was related to the different roles of green space associated with the stage of urbanization. Cities with higher urbanization rates were more affected by NDVI but less affected by park distance. Our findings provide insights for urban planners and decision-makers on developing localized strategies that mitigate the varying outcomes of gentrification at different stages of urban development.

AbstractChina's railways were restructured in 2013. The number of regulatory practitioners has decreased significantly, making real‐time regulation more difficult. Regulatory transfers from inside to outside the railway industry increases information risks. A more reasonable regulation mechanism is needed. The article considers introducing a public supervision mechanism into the railway transportation safety regulation system, which includes two regulators and one regulatee. As the government regulator, the State Railway Administration (SRA) regulates the safety of China Railway Corporation (CR) and encourages the public to act as supervisors to expose the CR's unsafe production information. To analyze the risks and effectiveness of the system, a multiplayer evolutionary game and system dynamics‐based model for railway transportation safety regulation is established. The decision processes of players under different conditions are simulated. The results show that improving the public supervision ratio is conducive to improve the CR's safe production ratio. However, there is no evolutionarily stable strategy (ESS) in the system. Strategies and evolutionary processes have large fluctuations, which represent high risk. Excessive penalty and reward coefficients can aggravate the amplitude and frequency of fluctuations, causing uncertainty in regulation and making it more difficult to control the actual problems. A dynamic reward and punishment mechanism is proposed to control these fluctuations. The system finally achieves an ESS that results in the lowest regulation investment for the SRA, a safe production ratio for the CR of 95%, and a public supervision ratio of 95.2%. Introducing public supervision and dynamic reward and punishment mechanisms help to stabilize and improve the CR's safe production ratio and to decrease the SRA's regulatory investment.

The demand for the sensor-based detection of camouflage objects widely exists in biological research, remote sensing, and military applications. However, the performance of traditional object detection algorithms is limited, as they are incapable of extracting informative parts from low signal-to-noise ratio features. To address this problem, we propose Camouflaged Object Detection with Cascade and Feedback Fusion (CODCEF), a deep learning framework based on an RGB optical sensor that leverages a cascaded structure with Feedback Partial Decoders (FPD) instead of a traditional encoder–decoder structure. Through a selective fusion strategy and feedback loop, FPD reduces the loss of information and the interference of noises in the process of feature interweaving. Furthermore, we introduce Pixel Perception Fusion (PPF) loss, which aims to pay more attention to local pixels that might become the edges of an object. Experimental results on an edge device show that CODCEF achieved competitive results compared with 10 state-of-the-art methods.

Abstract
Climate change has posed inequitable risks to different communities. Among communities of color in the United States, Native Americans stand out because 1) they desire resources to sustain resilient nations and 2) they have developed nature-based solutions through experiences with local climate-related challenges, which can provide deep insight for the whole society. Projection of climate risks for Native Americans is essential to assess future risks and support their climate-ready nations, yet there has been lack of useable information. In this study, we projected three climate hazards—heavy rainfall, 2-yr floods, and flash floods—for tribal nations in Oklahoma. To break down into tribal jurisdictions, we utilize a coupled regional climate model at 4 km and flash-flood forecast model at 1 km. A hazard–exposure–vulnerability risk framework is applied to integrate both climate and demographic changes in a high-emissions scenario. It is found that 1) Indigenous people are the most vulnerable community in Oklahoma; 2) heavy rainfall and 2-yr floods have marked increases in risks at 501.1% and 632.6%, respectively, while flash floods have a moderate increase (296.4%); 3) Native Americans bear 68.0%, 64.3%, and 64.0% higher risks in heavy rainfall, 2-yr flooding, and flash flooding, respectively, than the general population in Oklahoma; 4) in comparing climate and demographic changes, it is seen that population growth leads to greater climate hazard risks than does climate change; and 5) emerging tribal nations are projected to have 10 times as much population, resulting in great exposures to climate extremes. This study can raise awareness of the impact of climate changes and draw attention to address climate injustice issues for minoritized communities.

Significance Statement
This study examines the impact of climate change on a marginalized community—Native Americans in Oklahoma, home to 39 federally recognized tribal nations. We utilized the high-resolution climate simulation at 4-km resolution and hydrologic simulation at 1-km resolution to aggregate three climate extremes to tribal jurisdictions. We find that climate and demographic changes disproportionately put many Native Americans at risk. The heavy rainfall, 2-yr floods, and flash floods are all projected to have increased risks by 501.1%, 632.6%, and 296.4%, respectively. Those risks are 68.0%, 64.3%, and 64.0% higher than the state average for the general population, respectively. We urge proper attention to tribal nations to address climate injustice issues as a whole with the acknowledgment of their distinct relationships to their homelands as sovereign peoples.