Hasil untuk "Geography"

P. Krugman

A. T. A. Learmonth, R. Johnston

P. Haggett

Edward E. Leamer, M. Storper

A. Scott

M. Hesse, J.P. Rodrigue

J. Howells

W. Nordhaus

C. Malloy

Yuri Takhteyev, A. Gruzd, B. Wellman

B. Anderson, M. Kearnes, Colin Mcfarlane et al.

R. Boschma, K. Frenken

Nathan Nunn, D. Puga

Rob Kitchin

Tobias Heimann, Lara-Sophie Wähling, Tomke Honkomp et al.

Bioenergy with carbon capture and storage (BECCS) is a crucial element in most modelling studies on emission pathways of the Intergovernmental Panel on Climate Change to limit global warming. BECCS can substitute fossil fuels in energy production and reduce CO _2 emissions, while using biomass for energy production can have feedback effects on land use, agricultural and forest products markets, as well as biodiversity and water resources. To assess the former pros and cons of BECCS deployment, interdisciplinary model approaches require detailed estimates of technological information related to BECCS production technologies. Current estimates of the cost structure and capture potential of BECCS vary widely due to the absence of large-scale production. To obtain more precise estimates, a global online expert survey ( N = 32) was conducted including questions on the regional development potential and biomass use of BECCS, as well as the future operating costs, capture potential, and scalability in different application sectors. In general, the experts consider the implementation of BECCS in Europe and North America to be very promising and regard BECCS application in the liquid biofuel industry and thermal power generation as very likely. The results show significant differences depending on whether the experts work in the Global North or the Global South. Thus, the findings underline the importance of including experts from the Global South in discussions on carbon dioxide removal methods. Regarding technical estimates, the operating costs of BECCS in thermal power generation were estimated in the range of 100–200 USD/tCO _2 , while the CO _2 capture potential was estimated to be 50–200 MtCO _2 yr ^−1 by 2030, with cost-efficiency gains of 20% by 2050 due to technological progress. Whereas the individuals’ experts provided more precise estimates, the overall distribution of estimates reflected the wide range of estimates found in the literature. For the cost shares within BECCS, it was difficult to obtain consistent estimates. However, due to very few current alternative estimates, the results are an important step for modelling the production sector of BECCS in interdisciplinary models that analyse cross-dimensional trade-offs and long-term sustainability.

Song Yang

The Southeast Asian monsoon is characterized by many features that are distinct from those of the East Asian monsoon, including monsoon intensity and evolution. They are also influenced differently by external factors and affect global climate in diverse ways. Studies that consider these factors should yield a better understanding of both monsoon components.

Hao He, Timothy P Canty, Russell R Dickerson et al.

Between June 6 and 8, 2023, wildfires in Quebec, Canada generated massive smoke plumes that traveled long distances and deteriorated air quality across the Northeastern United States (US). Surface daily PM _2.5 observations exceeded 100 µ g m ^−3 , affecting major cities such as New York City and Philadelphia, while many areas lacked PM _2.5 monitors, making it difficult to assess local air quality conditions. To address this gap, we developed a WRF-CMAQ-BenMAP modeling system to provide rapid, spatially continuous estimates of wildfire-attributable PM _2.5 concentrations and associated health impacts, particularly benefiting regions lacking air quality monitoring. CMAQ simulations driven by two wildfire emissions datasets and two meteorological drivers showed good agreement with PM _2.5 observations, with linear regression results of R ^2 ∼0.6 and slope ∼0.9. We further quantified uncertainties introduced by varying emissions and meteorological drivers and found the choice of wildfire emissions dataset alone can alter PM _2.5 simulations by up to 40 µ g m ^−3 (∼40%). Short-term health impacts were evaluated using the BenMAP model. Validation against asthma-associated emergency department (ED) visits in New York State confirmed the framework’s ability to replicate real-world outcomes, with ED visits increased up to ∼40%. The modeling results identified counties most severely affected by wildfire plumes, the majority of which lack regulatory air quality monitors. Our approach highlights the value of integrated modeling for identifying vulnerable populations and delivering timely health burden estimates, regardless of local monitoring availability.

Zhao Liu, Wei Liu, Huajie Zhu et al.

The next Point-of-Interest (POI) recommendation task aims to predict users' next destinations based on their historical movement data and plays a key role in location-based services and personalized applications. Accurate next POI recommendation depends on effectively modeling geographic information and POI transition relations, which are crucial for capturing spatial dependencies and user movement patterns. While Large Language Models (LLMs) exhibit strong capabilities in semantic understanding and contextual reasoning, applying them to spatial tasks like next POI recommendation remains challenging. First, the infrequent nature of specific GPS coordinates makes it difficult for LLMs to model precise spatial contexts. Second, the lack of knowledge about POI transitions limits their ability to capture potential POI-POI relationships. To address these issues, we propose GA-LLM (Geography-Aware Large Language Model), a novel framework that enhances LLMs with two specialized components. The Geographic Coordinate Injection Module (GCIM) transforms GPS coordinates into spatial representations using hierarchical and Fourier-based positional encoding, enabling the model to understand geographic features from multiple perspectives. The POI Alignment Module (PAM) incorporates POI transition relations into the LLM's semantic space, allowing it to infer global POI relationships and generalize to unseen POIs. Experiments on three real-world datasets demonstrate the state-of-the-art performance of GA-LLM.

Ethan Jasny, Christopher T. Kenny, Cory McCartan et al.

Changes in political geography and electoral district boundaries shape representation in the United States Congress. To disentangle the effects of geography and gerrymandering, we generate a large ensemble of alternative redistricting plans that follow each state's legal criteria. Comparing enacted plans to these simulations reveals partisan bias, while changes in the simulated plans over time identify shifts in political geography. Our analysis shows that geographic polarization has intensified between 2010 and 2020: Republicans improved their standing in rural and rural-suburban areas, while Democrats further gained in urban districts. These shifts offset nationally, reducing the Republican geographic advantage from 14 to 10 seats. Additionally, pro-Democratic gerrymandering in 2020 counteracted earlier Republican efforts, reducing the GOP redistricting advantage by two seats. In total, the pro-Republican bias declined from 16 to 10 seats. Crucially, shifts in political geography and gerrymandering reduced the number of highly competitive districts by over 25%, with geographic polarization driving most of the decline.

Daniël Andel, Benjamin Rin

Hive is an abstract strategy game played on a table with hexagonal pieces. First published in 2001, it was and continues to be highly popular among both casual and competitive players. In this paper, we show that for a suitably generalized version of the game, the computational problem of determining whether a given player in an arbitrary position has a winning strategy is PSPACE-hard. We do this by reduction from a variant of Generalized Geography we call Formula Game Geography.