ORCID Profile
0000-0001-6370-1240
Current Organisations
Technische Universität Berlin
,
James Cook University
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Publisher: MDPI AG
Date: 26-08-2021
DOI: 10.3390/JRFM14090404
Abstract: Do foreign banks enjoy a competitive edge in the Chinese banking market or are they disadvantaged vis-à-vis domestic banks? This is the question that the present paper seeks to answer. The issue is important since on the one hand, these banks face the challenges the liability of foreignness brings, but at the same time, they have bank-specific advantages. We examine this issue in light of the literature of the liability of foreignness. In our path-breaking study, we found that due to the cost of foreignness, foreign banks’ performance was not as good as that of the local banks. Furthermore, despite the same amount of location- and bank-specific advantages, they performed badly as compared to their local counterparts. It was found that the cost of location-based disadvantages outweighed the cost of bank-specific disadvantages for foreign banks, and recent policy changes may help them overcome some of the cost of foreignness.
Publisher: Elsevier BV
Date: 2018
DOI: 10.2139/SSRN.3221991
Publisher: American Association for the Advancement of Science (AAAS)
Date: 31-12-2022
DOI: 10.1080/20964129.2022.2130094
Abstract: In recent years, there has been widespread concern regarding the carbon footprint (CF) of food waste due to the key impact of CF on climate change, particularly as China’s food waste is rising with its economic development. China has the largest scale of higher education in the world, and the amount of food waste in university canteens is considerable and cannot be ignored. This study attempts to assess the carbon footprint (CF) of food waste at Chinese universities for the first time based on a national survey. It is estimated that 1.55 million tons of food were wasted in Chinese university canteens in 2018, based on 9,192 s les covering 29 provinces in China. The associated CF was 2.51 Mt CO2eq. The top two food categories contributing to the total CF were meat and grains, accounting for 46.28% and 36.52%, respectively. Furthermore, the location of the university was significantly associated with the CF of plate waste. It also indicated that household income, meal satisfaction, sex, education, meal days, and food-saving c aigns were important factors influencing the CF of food waste. This study highlights areas that can help reduce the environmental impact of plate waste. It also provides targeted measures to reduce the associated CF of food waste in Chinese universities.
Publisher: Elsevier BV
Date: 10-2017
Publisher: Wiley
Date: 05-09-2014
DOI: 10.1002/TIE.21649
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-112
Abstract: & & An increasing number of urban residents are affected by the urban heat island effect and water scarcity as urbanization and climate change progress. Evapotranspiration (ET) is a key component of urban greening measures aimed at addressing these issues, yet methods to estimate urban ET have thus far been limited. In this study, we present a novel approach to model urban ET at a half-hourly scale by fusing flux footprint modeling, remote sensing (RS) and geographic information system (GIS) data, and artificial intelligence (AI). We investigated this approach with a two-year dataset (2018-2020) from two eddy flux towers in Berlin, Germany. Two AI algorithms (1D convolutional neural networks and random forest) were compared. The land surface characteristics contributing to ET measurements were estimated by combining footprint modeling with RS and GIS data, which included Normalized Difference Vegetation Index (NDVI) derived from the Harmonized Landsat and Sentinel-2 (HLS) NASA product and indicators of 3D urban structure (e.g. building height). The contribution of remote sensing and meteorological data to model performance was examined by testing four predictor scenarios: (1) only reference evapotranspiration (ETo), (2) ETo and RS/ GIS data, (3) meteorological data, and (4) meteorological and RS/ GIS data. The inclusion of GIS and RS data extracted using flux footprints improved the predictive accuracy of models. The best-performing models were then used to model ET values for the year 2019 and compute monthly and annual sums of ET. A variable importance analysis highlighted the importance of the NDVI and impervious surface fraction in modeling urban ET. The 2019 ET sum was considerably higher at the site surrounded by more urban vegetation (366 mm) than at the inner-city site (223 mm). The proposed method is highly promising for modeling ET in a heterogeneous urban environment and can bolster sustainable urban planning efforts.& &
Publisher: Pageant Media US
Date: 31-01-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: World Scientific Pub Co Pte Lt
Date: 03-2021
DOI: 10.1142/S0219091521500065
Abstract: While the fund performance management literature has clearly documented that the fund size, fund family size, and net cash flow are important antecedents of equity fund performance, prior empirical studies have revealed mixed results that have not been adequately explained. Through the lens of the contingency perspective, we developed a conceptual model that examines how the expense ratio and management compensation as contextual factors interact with the fund size, fund family size, and net cash flow to affect equity fund performance. The empirical analyses were based on panel data including 690 equity funds in China over a 7-year period from 2009 to 2015. The results show that the expense ratio and management compensation moderate the effects of the fund family size and net cash flow on fund performance, and management compensation also moderates the relationship between the fund size and fund performance.
Publisher: Elsevier BV
Date: 11-2021
Publisher: Informa UK Limited
Date: 03-07-2020
Publisher: Springer Science and Business Media LLC
Date: 16-03-2022
Publisher: MDPI AG
Date: 08-10-2021
DOI: 10.3390/JRFM14100474
Abstract: Risk management has been a topic of great interest to Michael McAleer. Even as recent as 2020, his paper on risk management for COVID-19 was published. In his memory, this article is focused on bankruptcy risk in financial firms. For financial institutions in particular, banks are considered special, given that they perform risk management functions that are unique. Risks in banking arise from both internal and external factors. The GFC underlined the need for comprehensive risk management, and researchers since then have been working towards fulfilling that need. Similarly, the central banks across the world have begun periodic stress-testing of banks’ ability to withstand shocks. This paper investigates the machine-learning and statistical techniques used in the literature on bank failure prediction. The study finds that though considerable progress has been made using advanced statistical and computational techniques, given the complex nature of banking risk, the ability of statistical techniques to predict bank failures is limited. Machine-learning-based models are increasingly becoming popular due to their significant predictive ability. The paper also suggests the directions for future research.
Publisher: Elsevier BV
Date: 11-2017
Location: Netherlands
No related grants have been discovered for Alby Duarte Rocha.