dataesr/scientific-paragraphs-categorization huggingface dataset:
Challenges and uncertainties Our two datasets document, for the first time, the spatialtemporal-sectoral gas supply sources and potential solutions (at the time of publication) from both the demand and supply sides that can alleviate the Russian gas shortage in EU countries, with a relatively high temporal resolution. However, our estimates do not contend with the social, economic, https://doi. org/10. 5194/essd-15-949-2023 Earth Syst. Sci. Data, 15, 949-961, 2023 or political factors; with the international gas/LNG market; nor with other international cooperation. Our proposed solutions are highly country dependent. For example, some countries can easily overcome small shortfalls in Russian gas (the less Russian-gas-dependent countries in Fig. 7 ), while other countries might be able to use less gas because of their particular energy structure (e.
Data availability We published the two datasets (EUGasSC and EUGasRP) as CSV files, and they are hosted on the Zenodo platform: https://doi. org/10. 5281/zenodo. 7549233 (Zhou et al. , 2022). The datasets are open access and are licensed under a Creative Commons Attribution 4. 0 International license. The column headings of the data dictionary files as well as the unit of each variable are listed in Table  S3. Our datasets provide daily gas supply-storageconsumption of five consumption sectors for the EU27 and UK as well as the potential gas reduction capacity from the heating and power sectors of each country as solutions https://doi. org/10. 5194/essd-15-949-2023 Earth Syst. Sci. Data, 15, 949-961, 2023 for resolving Russian gaps. They can be used as either input or reference datasets for further research in various fields, such as gas/energy modeling, carbon emission, climate change, geopolitical policy discussions, and the international gas/energy market.
Conclusions We presented two datasets for the EU27 and UK at daily resolutions: (1) the EUGasSC dataset, describing the sectoral and country-based daily natural gas supply-storageconsumption, and (2) the EUGasRP dataset, describing the daily sectoral and country-based natural gas reduction capacities for the heating and power sectors, increased EU production, and foreign imports. They can be applied to various fields and topics for future research, such as gas/energy consumption and market modeling, carbon emission and climate change research, and policy decision-making. We used these two datasets to analyze the gas supplyconsumption patterns and trends, thereby quantifying how the Russian gas gap could be alleviated if Russian imports were to terminate. Our results indicate that a full and sudden loss of Russian gas for the EU could be theoretically filled with short-term solutions, including plausible demand reductions for heating; higher power generation from nuclear and coal sources; and intra-EU and international coordination, particularly with the UK, the USA, Australia, and Norway, albeit with numerous challenges and uncertainties.
Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-15-949-2023-supplement. Author contributions. CZ contributed to model development, the ENTSOG and climate data collection, analysis of result, and drafting the paper. BZ and ZL contributed to the ENTSOE data collection and cleaning. SBA and HdAR contributed to partial ENTSOG data collection. SJD and AH reviewed the paper. PC contributed to the methodology and to drafting and reviewing the paper. All authors have read and approved the paper. Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Recent evidence has shown that hybrid models for credit ratings are important when assessing the risk of firms. Within this stream of literature, we aim to provide novel evidence on how hard (quantitative), soft (qualitative), and market information predict corporate defaults for unlisted firms by implementing the Cox proportional hazard model. We address this research question by exploiting a unique proprietary dataset comprising of detailed information on internal credit ratings of European unlisted mid-sized firms and compute their Merton's distance-to-default (DD) measure of credit risk with market data collected on comparable publicly listed companies. Our results show that the bank's use of hard, soft, and market information when assessing the credit ratings of borrowers has a significant influence on the prediction of their defaults.
, 2009; Doumpos et al. , 2015; Gadenne & Iselin, 2000; Hillegeist et al. , 2004). Moreover, some recent empirical findings in the literature show that the hybrid models integrating market and accounting information can significantly improve the predictability of default risks of SMEs and unlisted firms  (Andrikopolous & Khorasgani, 2018; Roy & Shaw, 2021). However, none of these studies effectively combine firmspecific accounting (quantitative) and soft (qualitative) information with public-peer market information. 1  Therefore, in our best knowledge, this study is the first to integrate market information simultaneously with hard and soft information in predicting default probabilities of European unlisted firms. We address this research question by exploiting a unique proprietary dataset comprising of detailed loanlevel information on 437 European unlisted firms belonging to the mid corporate segment of the market.
This comprehensive dataset allows us to collect information not only on borrowers' internal credit ratings but also on the different components of intermediate ratings. We implement semiparametric Cox regression models 2 which are widely used in finance for survival analysis  (Henebry, 1997; Lane et al. , 1986; Buehler et al. , 2006)  to investigate the association between hard, soft, and market information of a given borrowing firm, and its probability of survival after the bank disburses a loan. Next, we further examine the influence of soft information in predicting corporate defaults by distinguishing between two routes loan officers can use to 'harden' soft information in the rating process 3 : questionnaire and override. Questionnaire reflects that type of soft information which is mandatory for the loan officer when assessing borrowers' credit risk and is based on the bank's standardised and codified numerical scale in the qualitative questionnaire which is mandatory as part of the rating process; as such, the answers provided by loan officers in the obligatory qualitative questionnaire are not subject to authentication by the bank's senior managers.
On the other hand, it demonstrates that private and unlisted firms are the most informationally opaque firms due to limited availability of hard information as they have no track record and often operate in emergent industries  (Liberti & Petersen, 2019). Therefore, we contribute to the current theoretical and empirical literature on financial intermediation by exploiting a unique proprietary dataset comprising of European unlisted mid-sized firms to study whether the inclusion of market information in banks' hybrid credit scoring models that make use of both hard and soft information can significantly improve the predictability of corporate defaults for unlisted firms. Further, we also contribute to the theoretical literature on corporate finance that highlights the role of asymmetric information in accessing external debt financing  (Demetriades & Devereux, 2000; Fazzari et al.
Moreover, existing studies (such as  Das et al. , 2009; Doumpos et al. , 2015)  are focused on integrating, rather than comparing, accounting with market information in forecasting corporate defaults. Hence, this study contributes to the literature by arguing that, in addition to hard and soft information, market information collected from publicly traded peers can be useful in predicting the defaults of their comparable unlisted firms. This is also the first study that attempts to use market information to assess the credit risks of unlisted firms through a survival analysis, since the literature so far has used the  Merton (1974)  model for measuring credit risks of private listed firms  (Andrikopolous & Khorasgani, 2018; Rikkers & Thibeault, 2009). We calculate the DD for unlisted firms with market data collected from their comparable publicly listed firms to proxy unlisted firms' asset price volatility.
| The bank's environment and credit scoring We address our research question by employing a unique and proprietary dataset collected from the Corporate and Investment Banking (CIB) Division of a major European banking player in the period September 2011 and 2012. 6  Such dataset comprises 437 loan applications, approved or denied. The multinational European banking group (which our data-providing bank belongs to) has total assets of about EUR 646 bn, market capitalization of about EUR 50 bn, and foreign subsidiaries operating in 12 European and Mediterranean countries. This European banking group owns 14 affiliated banks with more than 4000 branches and a market share of roughly 15% in the loans and deposits market in its home country of operations. Specifically, our data-providing bank is the lead of the aforementioned banking group and conducts its banking activity in its home country with around 1900 traditional bank branches spread out in 16 regions over the home country's territory.
| Banking data We use a banking proprietary dataset comprising of detailed information on the bank's internal ratings of mid-corporate borrowing firms as well as their different intermediate rating components. The bank implements a hybrid credit scoring methodology to rate borrowers' creditworthiness in which the final credit ratings are calculated on the basis of both quantitative and qualitative information. We now briefly present the hybrid credit scoring process adopted by our bank. To the purpose of this paper, this is relevant for two reasons. First, the bank's internal ratings reflect our measure of the bank's accuracy in predicting corporate defaults. Second, understanding the way internal ratings are generated is relevant to derive the underlying mechanism turning purely hard information-based statistical ratings into hybrid information-based final ratings simultaneously accounting for both hard and soft information.
| Market data The estimation of the DD encompasses several steps. First, we obtain the daily series for equity market prices and number of outstanding shares for the listed firms which are more closely associated with the sample of our unlisted firms. As previously mentioned, our mapping approach relies on peer analysis provided by Refinitiv's Starmine Peers based on a proprietary algorithm that provides accurate selection of comparable peers. 8 We therefore construct eight portfolios of equally weighted daily returns for the market value of equity of our peers that represent the eight sectors of the equity market in which our sample of unlisted firms operate based on their domestic industry classification. The detailed descriptions of the sectors of our peer group of publicly listed firms as well as the selected firms in that group are reported in the Appendix (Table  A2 ).
Definitions of the variables and their descriptive statistics are provided in Table  1. Through this process, we estimate the DD that is unique for each unlisted firm, since the accounting-based F I G U R E 1 The bank's credit rating process. Source: Credit rating policy adopted by our data provider. To test the predictive power of bank's internal ratings and Merton's DD for firm defaults, we perform several specifications of the Cox proportional hazard model. Cox (1972)  proposed a hazard regression model that many studies have widely used in medical research and in the analysis of survival data in finance  (Buehler et al. , 2006; Henebry, 1997; Lane et al. , 1986). In this paper, we implement the Cox proportional hazard model to examine the association between the survival time of borrowing firms after the loan is disbursed and their relative hard, soft, and market information.
In a similar vein, we control for the country's area and the industry in which the given borrower operates according to the domestic industry classification. 14  To strengthen our claim that market related information improves the banks' predictive power for defaults, we perform an out-of-sample prediction and demonstrate that adding a certain type of information causes the model's predictive power to improve. To divide the modelling dataset into the training set and the testing set, we assign 80% of the data points to the training set (243 observations) and the other 20% to the test set (61 observations). 15 We then use the model with the training set and apply it to the test set in order to evaluate the performance of Equation (  1 ). We gather predictions Note: The table presents the results of out-of-sample prediction where we assign 80% of the data points to the training set (243 observations) and the remaining 20% to the test set (61 observations).
We employ a unique proprietary dataset that contains detailed information on internal credit ratings and on the different intermediate credit ratings for European unlisted mid-sized corporations. Using the semiparametric Cox proportional hazard model, we evaluate the predictive power of hard (quantitative), soft (qualitative), and market information on corporate default probabilities. We proxy asset price volatility of our sample of unlisted firms by computing their Merton's DD with market data collected from comparable publicly listed firms. We find that the simultaneous use of hard, soft, and market information in the bank's credit rating process significantly improves its predictive ability of firm defaults. Further, we show that in addition to hard and market information, codified and uncodified discretions have significant effects on improving the bank's ability to predict corporate defaults.
This relates to the fear of the abrupt fall of the talented tightrope walker who strives to keep the populace united around the Chinese Dream, which is omnipresent among Chinese political elites much like the fear of a blockage in the Malacca straits  (Duarte & Leandro, 2020). Such fear has been aggravated since 2019 by the successive Hong Kong protests and the controversial Huawei issue. Interestingly, as reported by Harvard University's Ash  Center (2020) , the Chinese people's satisfaction with and support for Xi Jinping-led government and the CCP have increased significantly as a result of improvements achieved in the three key area, notably social security, anti-corruption and environment. Equally important, the consolidation of political legitimacy brought with it the rise of trust and optimism among the population about the future of their country, according to The Edelman Trust Barometer's data for the past years (2022).
Acknowledgments The authors acknowledge that this study was conducted at the  Research Center in Political Science  ( UIDB/CPO/00758/ 2020 ),  University of Minho/University of Évora , and that it was supported by the  Portuguese Foundation for Science and Technology and the Portuguese Ministry of Education and Science , through national funds. The authors would like to thank  Li Xing ,  Sabrina Evangelista Medeiros  and  Sérgio Ribeiro  for their valuable comments on earlier drafts.
, 2021)  and the FAP processing industry has been only slightly affected by the pandemic  (Pititto et al. , 2021). Thus, the French framework offers a relevant approach to studying the fresh consumption of FAPs during the Covid-19 crisis. This paper investigates how far the Covid-19 crisis has been an opportunity for French consumers to contribute to environmental and food sovereignty issues by increasing the part of fresh domestic production in their at-home consumption of fresh FAPs. Based on a large dataset of FAP at home consumption purchases from 2017 to 2021, it reports the detailed features of fresh FAPs home consumption in 2020 compared to the previous years. As the origin of each product is not recorded in the database, we will consider the origin of the products by estimating their primary domestic production (see Tab.
However, FAPs can be gathered according to common features in terms of purchase variables. Further to the descriptive approach, it is suggested to consider clusters of FAPs according to purchase variables and compare the evolution of these clusters between 2017-2019, and 2020. The Principal Component Analysis (PCA) is a multivariate statistical method, which consists of transforming related variables into new variables that are decorrelated to each other. This allows information to be summarised by reducing the number of variables. FactoMineR R package was used to perform the different PCA  (Husson and Le, 2011). In this case, patterns are captured within clusters as well as trends between clusters created with 2017-2019 data and 2020 while dimensions (different variables used) are greatly reduced allowing for easier interpretations.
According to  Ugaglia et al. (2021)  the answer could be found on the supply side and its institutional structures. According to  (Villasante et al. , 2021) , the pandemic led to abrupt shifts in demand and supply in the Galician seafood sectors. It is therefore hardly surprising to observe the same phenomenon in France. We believe that both effects are present and have contributed to the changes observed in the  database. The increase in the consumption of pre-packaged food is not a new phenomenon 30. In the past, generalised distribution channels have probably tried to increase their supply of pre-packaged food without being followed by consumers. They also most certainly have access to the Kantar panel together with sale points checkout data. It is reasonable to assume that many distribution channels adjust their strategies in the short term according to their observations on demand 31.
Thus, making an assessment on the length of a given distribution channel and a given product may be delicate. Which is why 32 Due to the closure of some hyper/supermarket fish stalls, problems of fishing landings, panic buying, etc. 33 Most likely imported. some authors speak of 'zero km food strategies', between producers and the final consumer  (Aldaco et al. , 2020). A fairly simple way to check whether French home consumption is becoming more sustainable would be to explore the evolution of FAPs home consumption labelled with the Marine Stewardship Council (MSC; https://www. msc. org/uk) and/or Aquaculture Stewardship Council (ASC; https:// www. asc-aqua. org/). This could provide very interesting insights, however, this information is not contained in the Kantar dataset. Due to lack of data, sustainable issues related to home consumption are evaluated here solely through an approximation of primary domestic supply.
Analysing more appropriate data such as short distribution channels or sustainable certifications may nuance our findings. The development of online distribution channels along with proximity shops for pre-packaged fresh fish and frozen fish, the recent renewed interest in consumers for fresh and smoked trout as well as fresh sardines and fresh mackerel in Specialised distribution channels should be monitored beyond the Covid-19 crisis. Indeed, weak signals suggest new trends regarding those products in those distribution channels. However, these early-stage trends are not yet enough represented in the dataset to assess with certainty the rise of one or several new trends.
Acknowledgments. The writing of this paper was supported by the  COPECO  (https://www. umr-amure. fr/projets-scientifiques/projet_copeco/) research programme. We thank FranceAgriMer (https://www. franceagrimer. fr/),partner in the  COPECO  project, for providing the dataset. More particularly we thank  GUILLOT Cécile ,  NOUVEL Guillaume, MARCINKOWSKA Grazyna, SAMSON Caroline and OLRY-CHIFFOLEAU Agnès  for this collaboration and useful discussions. We thank Kantar (https://www. kantarworldpanel. com/global) and  GARDILLOU Franck  for assistance in understanding the construction of the dataset, as well as their permission to use the dataset. We thank  LE GRAND Christelle (IFREMER) and MERZEREAUD Mathieu (IFREMER)  for assistance in the construction of PCA and the COMEXT dataset.
must be distributed globally. This requires global efforts to strengthen and use LMIC research capacity and regionally distributed biopharmaceutical and diagnostic manufacturing capacity. A very promising initiative that includes both of these features is the WHO mRNA TT Hub. The Hub is developing a Moderna-like mRNA vaccine (an improved, more heat stable version of the same), and additional applications of mRNA technologies to address other pressing health needs. 21 The Hub's innovations, commercial manufacturing know-how, and clinical trial data will be shared with at least 15 spoke companies in other underserved LMIC regions who will be authorised to clone the relevant technologies. The model combines open access initiatives to expedite and coordinate research and to share knowledge with a commitment to democratise supply and ensure more equitable access to more affordable medical countermeasures.
Acknowledgements The authors gratefully acknowledge the financial assistance provided for this publication through the  University of Gothenburg. We would also like to thank the  Binghamton University Philosophy Department  for their support. Moreover, we deeply appreciate the feedback we received from  Kaushik Basu ,  Larry Gostin ,  Pedro Villarreal ,  Steven Hoffman ,  Johnathan Wolff , and  José Szapocznik  as well as audiences and colleagues at  Drugs for Neglected Disease Initiative , the  Pandemic Action Network, Rutgers Center for Population Bioethics, Cornell University ,  CRADLE/ Institute of Economic Growth (Dehli) Roundtable on Law and Economic Development, Bowling Green State University, Regis University, Oxford University ,  Merrimak College , and a series of virtual workshops organized in conjunction with the  Blavatnik School of Government at Oxford University, Center for Global Development, and IRG-GHJ  (https://www.
The agencies selected were also representative of different approaches to conducting HTAs, some using the cost per QALY approach and others relying on an interpretation of the clinical data alone (see  Table 1). A data extraction form was developed based on the template first presented by Drummond et al. [4]  following verification of updates needed through the targeted review (see Table  2 ). The data extraction form included three sections: the first relates to clinical effectiveness assessments, including items such as sample size and duration of pivotal clinical trials used to approve the therapies, and whether trials were single-arm or uncontrolled; the second section relates to the valuation of benefits for gene therapies, including the treatment of severe disease, value to caregivers, scientific spillovers, and substantial improvements in life expectancy; the third section relates to additional considerations, such as discounting or consideration of alternative payment models.
The checklist has been previously used by Huygens et al. [16]  in a smaller study of a single gene therapy in 3 jurisdictions. We applied it by recording whether each item was considered to be relevant in the jurisdictions concerned (Yes/No/Not available), together with illustrative quotes from the HTA reports analysed. Data extraction was performed by one researcher and a random sample of the reports (n = 10) was checked for accuracy of data extraction by another member of the research team. We also conducted a more detailed analysis of the comments made in cases where a particular item was considered, by classifying the comments as 'positive' , 'neutral' or 'negative'. Positive assessments were those where the HTA body considered that the manufacturer had made a reasonable attempt at dealing with the issue, or where the HTA body recognised the importance of the item in considering the technology.