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Spatial evolution of traditional waterside settlements south of the Yangtze River and the distribution of settlement heritage: evidence from the Nanxi River Basin

The study of ancient settlements in the traditional waterside towns of Jiangnan is an important part of scientific research on architectural heritage. This study examines ancient settlements in the Nanxi River Basin during various historical periods, such as the Neolithic Age, Eastern Han Dynasty, Tang-Five Dynasty, Song-Yuan Dynasty, Ming Dynasty, and Qing Dynasty. It investigates their temporal and spatial evolution and the factors that influence their distribution, with a particular focus on the role of intangible cultural heritage. This study focuses on the relationship between the spatial evolution of traditional waterside settlements in the Nanxi River Basin and the distribution of intangible heritage and analyzes the driving factors of their development. How settlements changed over time and space was examined with geographic information systems (GIS) software and by using kernel density, elliptical variance, and spatial autocorrelation methods on 204 ancient settlement points. This study also employs buffer and data overlay methods to analyze the factors that affect settlement distribution by elevation, slope, water system distance, and distance to intangible cultural heritage points. The study reveals the following. (1) During the Ming and Qing Dynasties, clans, culture, and the economy drove the expansion of early settlements, which relied on water systems and flat terrain, to form a multicenter distribution. (2) The settlement distribution in the Nanxi River Basin has undergone a transformation from single-point distribution to multipoint aggregation and divergence during the evolution from the Neolithic Age to the Qing Dynasty. The overall center of gravity of the settlements shifts from south to north and east, and the overall distribution of the settlements is in a state of aggregation. (3) The spatial and temporal evolution of settlements is jointly influenced by the natural environment and cultural factors. The natural environment determines the spatial distribution of early settlements, while cultural factors promote the evolution and development of the settlement space. This study further clarifies the key role of intangible cultural heritage in the formation and development of settlements and provides a reference framework for future heritage protection policies.

Carbon emissions from urban takeaway delivery in China

Online food delivery has become a popular mode of urban food consumption in China as its underlying business mechanism, Online To Offline (O2O), gaining popularity. However, the environmental impacts of a rapidly expanding online food delivery industry and its potential to mitigate environmental burdens remained unexplored in China. Our research found that Chinese cities generated 1.67 MtCO2-equivalent (CO2e) from 13.07 billion times of deliveries in 2019, including transport and packaging. The transportation-related GHG emissions were 745 KtCO2e in 2019, with an average of 0.057 kg CO2e per order and an average of 0.011 kg CO2e per capita. These emissions have surged from 0.31 MtCO2e in 2014 to 2.74 MtCO2e in 2021. We predict that this figure will increase further to 5.94 MtCO2e by 2035. However, with a range of policies such as replacing motorcycles with electric bikes and optimizing traffic routes, it is possible to mitigate such GHG emissions by 4.39–10.97 MtCO2e between 2023 and 2035. These findings highlight the need for further research into the environmental impact of online food delivery and the potential for mitigating it.

Integration of multiple machine learning approaches develops a gene mutation-based classifier for accurate immunotherapy outcomes

In addition to traditional biomarkers like PD-(L)1 expression and tumor mutation burden (TMB), more reliable methods for predicting immune checkpoint blockade (ICB) response in cancer patients are urgently needed. This study utilized multiple machine learning approaches on nonsynonymous mutations to identify key mutations that are most significantly correlated to ICB response. We proposed a classifier, Gene mutation-based Predictive Signature (GPS), to categorize patients based on their predicted response and clinical outcomes post-ICB therapy. GPS outperformed conventional predictors when validated in independent cohorts. Multi-omics analysis and multiplex immunohistochemistry (mIHC) revealed insights into tumor immunogenicity, immune responses, and the tumor microenvironment (TME) in lung adenocarcinoma (LUAD) across different GPS groups. Finally, we validated distinct responses of different GPS samples to ICB in an ex-vivo tumor organoid-PBMC co-culture model. Overall, our findings highlight a simple, robust classifier for accurate ICB response prediction, which could reduce costs, shorten testing times, and facilitate clinical implementation.

Towards a public policy of cities and human settlements in the 21st century

Cities and other human settlements are major contributors to climate change and are highly vulnerable to its impacts. They are also uniquely positioned to reduce greenhouse gas emissions and lead adaptation efforts. These compound challenges and opportunities require a comprehensive perspective on the public policy of human settlements. Drawing on core literature that has driven debate around cities and climate over recent decades, we put forward a set of boundary objects that can be applied to connect the knowledge of epistemic communities and support an integrated urbanism. We then use these boundary objects to develop the Goals-Intervention-Stakeholder-Enablers (GISE) framework for a public policy of human settlements that is both place-specific and provides insights and tools useful for climate action in cities and other human settlements worldwide. Using examples from Berlin, we apply this framework to show that climate mitigation and adaptation, public health, and well-being goals are closely linked and mutually supportive when a comprehensive approach to urban public policy is applied.

Bank lending and environmental quality in Gulf Cooperation Council countries

To achieve economies with net-zero carbon emissions, it is essential to develop a robust green financial intermediary channel. This study seeks empirical evidence on how domestic bank lending to sovereign and private sectors in Gulf Cooperation Council (GCC) countries impacts carbon dioxide and greenhouse gas emissions. We employ PMG-ARDL model to panel data comprising six countries in GCC over twenty years for carbon dioxide emissions and nineteen years for greenhouse gas emissions. Our findings reveal a long-term positive impact of both bank lending variables on carbon dioxide and greenhouse gas emissions. In addition, lending to the government shows a negative short-term effect on greenhouse gas emissions. The cross-country results demonstrate the presence of a long-run effect of explanatory variables on both types of emissions, except for greenhouse gas in Saudi Arabia. The sort-term impact of the explanatory variables on carbon dioxide and greenhouse gas emissions is quite diverse. Not only do these effects differ across countries, but some variables have opposing effects on the two types of emissions within a single country. The findings of this study present a new perspective for GCC economies: neglecting total greenhouse gas emissions and concentrating solely on carbon dioxide emissions means missing critical information for devising effective strategies to combat threats of environmental degradation and achieve net-zero goals.

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