dataesr/scientific-paragraphs-categorization huggingface dataset:
Since the first phase of EU ETS was experimental and featured mainly government quotas (and weak volatility), and CER futures had not been introduced yet in that phase, here we only look at the time-frequency dependent performance of the second  (2008) (2009) (2010) (2011) (2012) (2013)   and third (2013-2021 ) phases of EU ETS. Carbon futures is one of the best performing commodity-based assets of recent years, with an increase of 31% in 2020, and plenty of room for further development in the international carbon finance market. Here, we select the European Union Allowance (EUA) futures data as representative of the EU carbon finance market, and the carbon emissions reduction (CER) futures data as representative of the world carbon finance market, regulated by the United Nations Clean Development Mechanism (CDM).
The variables for the two markets are shown in Table  1. Carbon finance data is retrieved from the Wind database (https://www. wind. com), and shipping market data from Clarksons. All data are weekly, from  March 28, 2008 to March 19, 2021. After removing inconsistent data on some trading days, our sample size comprised 633 data points. To make the data more stable, we have transformed it into logarithmic first differences, for better stationarity, and we rescaled it to reduce calculation errors. Table  2  reports the descriptive statistics of the selected variables. Similar to most financial assets, the series of carbon finance futures and shipping freight rates present both such characteristics as typical peak and thick tail, non-normal, and fluctuation aggregation. Specifically, the standard deviation between the carbon finance market and the middleshipping market is large, showing relatively obvious fluctuation characteristics, while both the front-end-and the back-end shipping markets are relatively stable.
068 -0. 186 0. 537 0. 058 0. 569 1. 000 0. 049 Bulker -0. 061 -0. 124 0. 061 0. 016 0. 161 0. 086 0. 516 -0. 228 0. 042 0. 049 1. 000 Notes:The EUA and CER futures are published in the Wind database (https://www. wind. com). Newindex, GRU; BDI, BDTI, BCTI, CCFI, MGO, Capesizet, Bulker spot prices are published by Clarksons (https://www. clarksons. com). All data are weekly, with a time range from March 28, 2008 to March 19, 2021. The EUA and CER are futures regulated by the European Union's Emissions Trading System (EU ETS) and the United Nations Clean Development Mechanism (CDM). Newindex is newbuilding prices; GRU is the Global Steel Price Index; BDI is the Baltic Exchange Dry Index; BDTI is the Baltic (Exchange) Dirty Tanker Index; BCTI is the Baltic (Exchange) Clean Tanker Index; CCFI is Clarksons Composite Container Freight Rate Index; MGO is the Marine Light Diesel Price; Capesizet and Bulker are Clarksons secondhand ship prices and scrap prices (details in Table.
However, in practice, achieving justice in valuation is challenging because of the political context in which valuation studies are conducted, and practical limitations  [21, 27]. This systematic literature review focuses on the extent to which valuation studies meet environmental justice objectives in their design and implementation. As the largest review to date of the academic valuation literature on nature, NCP and ecosystem services, we use a set of criteria developed in the IPBES VA database to assess valuation studies along distributive, procedural and recognition justice dimensions. Based on the review, we recommend approaches to better address justice in valuation studies.
Data The data are drawn from a systematic review, developed for the IPBES VA. The dataset represents the most extensive review of academic, peer-reviewed papers written in English and reporting on global valuation studies. Details of the method can be found in Appendix B. Our results are based on an analysis of 1163 studies that presented empirical valuation evidence. The sampled studies covered different valuation methods from various disciplines, which use monetary, biophysical and sociocultural indicators to assess values (see Appendix C).
The link between entrepreneur's network centrality and innovation performance has been broadly studied in knowledge-intensive industries such as biotechnology. However, little research has been focused on the social mechanisms that allow innovators to reach such a central position. We contribute to the existing literature by exploring the factors that may lead or prevent entrepreneurs from reaching a central position in their professional networks of knowledge exchange and social support in French biotech milieu. We use a unique quantitative and qualitative database of 138 and 126 biotech entrepreneurs observed, respectively, in 2008 and 2013. When accounting for entrepreneurs' position in the social (friendship) and knowledge (advice) domain, we draw on three dimensions through which entrepreneurs build their position: their professional experience, their inter-organizational (or political) engagement, and the financial and geographical situation of their company.
Population and Data Our network analysis is based on two waves of observations of friendship and advice exchanges among entrepreneurs in the French biotech industry in 2008 and 2013. The 2008 wave included 138 entrepreneurs from 78 companies, while the 2013 wave included 126 entrepreneurs from 92 firms. In total, 68 entrepreneurs were present in both waves. Approximately half of the companies were in Paris while the rest were located in regional biotech clusters. In addition to data availability, the sample choice was justified by the fact that France has one of the strongest biotech communities in Europe, especially in clinical drug assets. Around 1800 French companies are focused on healthcare aspects, including Sanofi, Servier, Ipsen, and Pierre Fabre. The focus on the biotech sector to investigate network centrality is explained by the fundamental role played by network structure, in addition to knowledge, in determining R&D productivity in the sector  (Jain 2023 ) and, thus, advancement in science and in society more in general, also thanks to dispersion to other innovative sectors  (Ma 2023).
2018 ). This study is focused on samples collected in 2008 and 2013, thus the results may be affected by the time period considered, encouraging future research for different sectors, countries, and time-frames. The observed population of entrepreneurs from the 2008 dataset was carefully selected through preliminary ethnographic research. First, lists of biotech companies from biotech specific website were explored. Companies self-defined as biotech but providing service without proprietary technology or providing advice only, were excluded together with firms created as subsidiary companies, or outside of France. Only biotech companies working in 'human health' and that had raised more than 500K$ at that time were kept. We identified 94 biotech companies within this profile and a range of 1 to 4 entrepreneurs per company were selected to obtain a population of 220 entrepreneurs, of which 138 were interviewed for 9 months to obtain qualitative information about their potential relationship with other entrepreneurs of the industry.
We collected qualitative and quantitative data in 2008 and 2013 on 138 and 126 biotech entrepreneurs, respectively, and perform a series of regression analyses. In line with the idea that upper echelons' work experience, especially from the biopharmaceutical sector, affects their ability to receive generous endorsements  (Higgins and Gulati 2006) , our results show the elements, on the individual and organizational track of a venture, significantly predicts the likelihood of becoming a central friend and advisor. Being a company's founder is a strong predictor of social and epistemic status. This connects to the idea that founding a company may create a legitimacy to which individuals may want to partake as well as build an activity undertaken by a competitor  (Hegeman and Sørheim 2021). This could play a role in identifying a founder as a friend, especially in the biotech industry, where competition is fierce  (Yu et al.
We also test the robustness with respect to the returns to scale assumption. Third, we extend the analysis to EU agriculture using official production data from Eurostat (Eurostat, 2023) over the period 2000-2020, which provide country-level information about the same agricultural outputs and inputs as contained in the US state-level data. Finally, since the periods covered by the US and EU data sets differ, we employ the International Agricultural Productivity (IAP) data set, also provided by USDA, which covers the years 1961-2020 and is the globally most complete data on agricultural output and inputs. Besides  O'Donnell (2012b) , several studies have decomposed TFP change in US agriculture into individual components (e. g. ,  Andersen et al. , 2012; Capalbo, 1988; Mugera et al. , 2016; O'Donnell, 2014; Plastina & Lence, 2018).
Data O'Donnell (2012b) employs agricultural price and quantity data provided by the Economic Research Service (ERS) of the USDA. This data set has been widely used to measure US agricultural productivity (e. g. ,  Ball et al. , 2004; Ball et al. , 2016; Chambers & Pieralli, 2020; Njuki et al. , 2018; O'Donnell, 2014; Plastina & Lence, 2018). For each of 48 contiguous US states over the period 1960-2004, prices and implicit quantities for three outputs (crops, livestock, and other farm outputs) and four inputs (capital, labor, land, and materials) are provided. Implicit quantities are obtained by dividing revenues and costs by Fisher price indices 4 with bases equal to unity in Alabama in 1996. Importantly, the data accounts for quality changes in inputs such as chemicals, capital, or land.
Data One of the article's objectives is to compare sources of productivity change between US and EU agriculture. Ideally, we would like to employ European agricultural data (a) on the same outputs and inputs, (b) constructed with the same methods, and (c) observed over the same period as in the US data set. Unfortunately, the data collection methods vary across national statistical agencies. The primary data source for EU agricultural inputs and outputs is the Eurostat database (see below). The provided input and output categories resemble the ones in the state-level US data, and therefore allow us calculating the same productivity components including scale and mix efficiency. However, the Eurostat data set does not correct for changes in input qualities 10 and is reasonably complete only from year 2000 onwards.
Eurostat database We obtain panel data on agricultural outputs and inputs at the country level from the Eurostat (2023) database. Specifically, we extract data on three outputs (crops, livestock, and other output) and four inputs (capital, labor, land, and materials) for 25 EU countries for the years 2000-2019. 11 All variables except for labor and land use are sourced from the "Economic Accounts for Agriculture" data set, whose main purpose is the analysis of primary income in the agricultural sector, rather than productivity measurement as in the USDA data set. 12 Other output includes both agricultural services and secondary activities (e. g. , transformation of agricultural outputs) that are inseparable from agricultural activities. While the selection of input variables is in line with previous literature on productivity measurement in EU agriculture  (Agri, 2016; Baležentis et al.
Results In this section, we present the main results for the measurement and decomposition of TFP in European agriculture based on the A-DEA and the global Malmquist indices. We restrict our attention to the assumption of constant returns to scale as they are theoretically and empirically more consistent considering the use of country-level data. We first present the TFP change and components for the period 2000-2019, which are based on the Eurostat EAA database. Next, we directly compare TFP change between EU and the United States based on the IAP data set, covering the longer period 1961-2020.
EU versus US productivity from 1961 to 2020 Finally, we turn to the results for the period 1961-2020 based on the IAP data set. With this data, we estimate the technology jointly for all available 24 EU countries or regions and the United States. Having only one observation for the entire United States, we cannot separate distinct production environments there (as done with the 10 distinct regions in the state-level analysis), and hence do not decompose productivity change into its components. 17 Panel a in Figure  4  shows the productivity change for selected European countries as well as for the United States based on the A-DEA index. As seen in Panel b, average TFP growth in EU and US agriculture has been similar until about 2005, after which the EU experienced a slightly negative TFP change, which was not found for in the country-level EU data from Eurostat.
As in the US case, output-oriented technical efficiency is high and stable, a result which is also supported by the global Malmquist index. Furthermore, we find a strong overlap in the list of the most productive countries identified by the A-DEA and global Malmquist indices. The comparability of the described results on TFP development in EU and US agriculture, based on the country-level data from Eurostat and the state-level data from USDA, is limited due to different data periods covered in the respective data sets as well as due to different methodologies for the measurement of input and output quantities. To overcome this limitation, we employed a third data set-the International Agricultural Productivity data set-which provides the globally most complete and consistent information on agricultural output and input use.
4 Although the EKS procedure was applied to make the price indices transitive, they do not satisfy the unity property (O'Donnell, 2012b). 5 The computations largely rely on the productivity package  (Dakpo et al. , 2018). Codes are available in supplementary material online and on Github (https://github. com/swimmer008/Ag-Productivity-US-EU). 6 The variables were used exactly as prepared and published by USDA. As the data undergo quality checks prior to publication, neither we nor the original author undertook further data cleaning steps. 7 The input weights are constructed using the reversed signs of the input coefficients. frontier analysis resulted in negative elasticity estimates, which is inconsistent with economic theory. We also employed a Bayesian regression technique to impose monotonicity, but did not achieve convergence for the posteriors.
A possible reason may be the much smaller sample size compared to the state-level US data. 10 As discussed in  Ball et al. (2016) , there is a debate whether quality improvements should be regarded as input growth or productivity growth, although economic theory guides to the former. Given the data availability, we are not able to control for such quality changes in EU agriculture, and this limitation must be kept in mind when interpreting the results. 11 The countries include all EU-27 member states except  Bulgaria (missing data in 2000 , 2001 , and 2005 ) and Cyprus (missing data in 2000 -2002). For Italy in 2009, France in 2006 and 2009, the utilized agricultural area is imputed as the average of prior and posterior years. 12 Output data and input data on capital and materials come from Eurostat data table aact_eaa03, and land and labor variables come from Eurostat data tables apro_cpsh1 and aact_ali01, respectively.
13 The data sources and methodologies are described in detail in  Fuglie (2012 Fuglie ( , 2015)). 14 Some countries are summarized to groups depending to ensure long-term data availability. 15 We merged mountain and continental regions because the separation at the country level was not clear-cut. Additionally, we re-estimated the technologies for the five regions identified in Bar ath and Fert} o (2017) using cluster analysis based on output shares and weather variables, which yielded very similar results. 16 O'Donnell (2012a) describes that points on the unrestricted frontier may have different output and input mixes, although all of them are mix-efficient. As a result, OSME encompasses both scale and residual mix effects. 17 The necessary assumption would be that both the European countries and the US have access to the same technology.
the resulting values at constant prices are converted by Eurostat to purchasing power standards using Purchasing Power Parities of each country. TableS1in the online appendix presents descriptive statistics for the all variables next to the state-level USDA data introduced above. USDA International Agricultural Productivity databaseOur second source of agricultural production data is the IAP data set compiled by USDA-ERS. This data set provides agricultural output and input data for 172 countries over the period 1961-2020. The majority of the variables are sourced from the FAOSTAT database of the United Nations Food and Agriculture Organization (FAO), supplemented with national statistics data. 13Output is measured as gross agricultural output at constant 2004-2006 average international prices (by contrast, the state-level US and country-level EU data use prices received by farmers).
We collected data on the first five of these measure from an online questionnaire and ethnographic interviews. We collected data on attitudes toward vaccination from interviews conducted by telephone and online discussions. In the first section, we use quantitative and qualitative data as well as data collected from monitoring various media to study the experience of feeling out of step with the majority of the French population, in terms of attitudes toward protective measures. In the second section, we use quantitative and qualitative data to examine differences within the Chinese diaspora in France, in particular, differences between Chinese migrants and their descendants.
This article describes a software environment called HybroGen , which helps to experi-1 ment binary code generation at run-time. As computing architectures are getting more complex, 2 the application performances become data-dependent. The proposed experimental platform is 3 helpful in programming applications that can be reconfigured at run-time in order adapted for a 4 new data environment. HybroGen platform is adapted to heterogeneous architectures and can 5 generate instructions for different target. This platform allows to go farther than classical JIT 6 compilation in many directions: the code generator is smaller by three orders of magnitude, faster 7 by three orders of magnitude compared to JIT (Just-In-Time) platforms and allows making code 8 transformation that is impossible in traditional compilation scheme like code generation for non 9 Von Neumann accelerators or dynamic code transformations for transprecision.
The latter will 10 be illustrated in a code example: the square root with Newton's algorithm. We also illustre the 11 proposed HybroGen platform with two others examples: a multiplication with a specialization on 12 a value determine at run-time and a convertion of degree Celcius to degree Fahrenheit. This article 13 presents a proof of concept of the proposed HybroGen platform in terms of its functionalities, and 14 demonstrate the working status.
23 From the classical application development point of view, the priority is to make 24 programmers efficient by providing richer programming environments. As an illustra-25 tion the Java SDK environment contained around 100 classes in the 1. 0 release  (1995)  26 and 13,367 for the 1. 5 (J2SE 5. 0 2004) release (by counting the. class objects). These two 27 orders of magnitude in complexity gives a very rich programming environment which 28 makes programmers very efficient because of the "job oriented API" (JDBC for database 29 applications, Graphic for gaming, etc).
Install Time Many works have tried to do detect possible optimization during the program install on a new machine. ATLAS in 2001  [5]  is a BLAS implementation with semi-automatic optimization detection. Other works including source code generation push the limit farther for other mathematical kernels: FFTW in 2005  [6]  for FFT implementations and SPIRAL for linear algebra kernels  [7]. Interestingly, FFTW has a dynamic scheduler which chooses the best implementation at run-time, depending on the FFT signal size.
Compiler: When Code Generation Should Arise ? There are different code generation times to generate a code for an application. In a standard development flow, the code is generated at static compilation time. Data values are resolved at run-time and with only one step of compilation before the execution, some optimization cannot be applied. Usually programmers want to write a small code with good performance on all computers. This is not the case in the real life, programmers need to specialize code for a specific architecture. Moreover, programmers develop different version of the code to adapt programs to data types like float, integer and different word size. With HybroGen we propose to generate instructions during the execution to take advantage of data values resolved at run-time. Our platform allows experimentations on multiple scenarios of code generation time.
Language In this paper, we propose HybroLang is a new language with syntax close to C programming language. We develop HybroLang to add support for dynamic compilation of applications with different targeted architectures. This language uses specific data types which are defined with a triplet type, vector size and word size. This language is used only to describe the part of kernel that we want to optimize, we named this part a compilette. The other part of the program is written with the language targeted, in this paper we have chosen C language, but we can imagine other languages like JavaScript or python.
Data and Code Generation Interleaving The main characteristics of our HybroGen environment are: • The possibility to delay the code generation and have versatile code generation scheme that will be demonstrated later in this article, Those characteristics will be demonstrated in the later examples. The input is a kernel described with HybroLang language described previously. Our compiler HybroGen implements different passes of compilation, like a classic compiler but at the output it produces a code in an existing programming language, for this paper it produces C code. HybroLang requests the database to construct a code generator which writes the correct encoding at run time. After using HybroLang , we use a compiler for the second compilation time (c) like gcc or clang, in this paper we have used gcc.
Experimental Platform To run demonstrations a system-level simulator and real platform are used. Qemu  [12]  is used to simulate POWER8 and RISC-V architectures. This simulator exists for different architectures like x86, MIPS or ARM and support different variants. For example, there is a version for RISC-V 32 bits and another for the 64 bits architecture. In this paper we use qemu-riscv32 version 5. 0. 0 and qemu-ppc64le version 5. 0. 0. We also verify our results on a physical POWER8. For the static compilation, e. g. , the source to binary compilation, we use riscv32-elf-gcc-9. 3. 0 and powerpc64le-linux-gnu-gcc-8. The figure  3  shows the different steps of our compilation chain from the figure  2  section (b) which rewrites the HybroLang section of the application to a C version which will be able to generate multiple binary version.