dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion This article has provided a theoretical and empirical investigation into the uneven environmental transformations associated with different modes of insertion within global trade. We analyzed compossible development patterns within GVCs for 133 countries between 1995 and 2015, using data covering six key dimensions: GVC participation, GVC value capture, productive development, socio-economic development, domestic environmental impact and international environmental balance. Our results confirm that GVCs are a major driver of unequal socio-ecological developments. While integration within GVCs may enhance socio-economic indicators for some countries there is a general trend towards increasing polarization and ecologically unequal exchange. In other words, while in socio-economic terms GVC participation is a positive-sum game but asymmetrical (some countries benefit more than others), in ecological terms we do find some zero-sum game dynamics.
Conversely, given that is unlikely that the exporting country produces all the parts of the exported intermediary input locally, the trade of this intermediary input is to be considered GVC for the exporting country as it is the result of transnational division of labour to produce a non-generic good. Third, instead of dividing GVC trade by gross exports, we divide it by GDP. This difference responds to the particularities of our research question. We want to assess the environmental and developmental effects of GVCs as a form of division of labour, rather than the share of trade that can be explained by GVCs. Calculating GVC trade relative to the size of a countries' economy gives us a more accurate picture of the relevance of GVCs for a country's economy. In order to build this indicator, we recurred to three datasets.
The first one is EORA26  (Lenzen et al. , 2012 (Lenzen et al. , , 2013)) , which we use to obtain data on trade. This dataset was chosen over alternative databases, notably OECD'S TIVA since EORA26 has a considerable larger country coverage: 189 countries and a "rest of the world" region. OECD's TIVA 2018 edition includes 64 countries and a "rest of the world" region. Counting with a large panel of countries is critical to our research question, which can only be approached empirically by observing a large number and variety of countries in terms of their insertion in GVCs and ecological characteristics. The latter is particularly important, as a sample with several missing countries could obfuscate ecologically unequal exchange dynamics through which the ecological impact correlate of the value captured by a country through GVC participation is offloaded to another one.
The methodology behind the two datasets has been assessed to be solid and their results consistent. Studies comparing traditional GVC indicators values between EORA26 and TiVA have shown strong correlation between the two  (Aslam et al. , 2017; Casella et al. , 2019). We have calculated our indicators of GVC participation and GVC value capture with both datasets and the consistency between the two for the countries for which they overlap is confirmed. EORA26's dataset contains input-output data aggregated to a common 26-sector classification. For each country, we aggregated all the sectors for each variable. In order to obtain gross intermediate imports (ipM in Equation  1 ) we summed, for each country (row), the columns of the transactions matrix T 4  and subtracted the diagonal of the matrix to avoid counting within-country flows.
In order to obtain domestic value added in gross exports, we used EORA's 'domestic value added' (DVA) indicator, which is identical to TIVA's XDVA indicator, as explained in  Casella et al (2019, p. 122-123). The shares of intermediary products in gross exports and imports were calculated using UNCTADStat data, which provides information of gross exports broken down by products using the SITC system. We chose this database because SITC is a more precise alternative to other sources that use ISIC classification. Indeed, the latter treats many primary products as processed intermediates  (Radetzki, 2008, p. 23). Using the UNCTADStat data, therefore, primary commodities, precious stones and non-monetary gold (SITC 0 + 1 + 2 + 3 + 4 + 68 + 667+ 971) as total volumes in US dollars of exports and imports for each country were made into shares by dividing by total exports and imports in US dollars from the same database.
Investment rate The investment rate was taken from the IMF's World Economic Outlook database (April 2021 release). It provides total investment per country-year as a share of GDP. There was missing data for 10 countries in 1995 and for 7 countries in 2015. Missing data were treated using different methods. If data existed for the year in the alternative data source (The World Bank), this data was used as the preferred option. If not, a nearby year from the original source was used with a maximum threshold of +/-3 years. Finally, if none of these two options were feasible, we statistically imputed the missing data. We preferred using World Bank's data as the preferred option not only because of the reliability of the source, but also because investment rates are calculated using national accounting variables for which there are standardized calculation procedures.
Capital stock The data on capital stock is retrieved from the Penn World Tables version 10. 0. This data is in current 2017 USD PPP. Given that our analysis spans over 20 years, we converted the data to constant 2017 USD PPP using a GDP implicit deflator equal to the ratio between current 2017 USD GDP and constant 2017 USD GDP PPP for each country. In order to obtain that deflator, we retrieved current USD PPP GDP and constant 2017 USD PPP GDP data from the World Bank for each country. Before computing the above-mentioned ratio, we transformed current year USD GDP data into current 2017 USD GDP using the United States' implicit GDP deflator, also retrieved from the World Bank. Finally, for each of the years under analysis (1995 and 2015), we divided each country's constant 2017 USD PPP by its population that year.
Employment rate The employment rate is taken from the International Labor Organization's ILOStat, specifically, their measure Unemployment rate by sex and age -ILO modeled estimates. The advantage of the database is that the ILO uses several cross-validation methods to produce maximal comparability across countries for a large country selection. Since we want the employment rate rather than the unemployment rate, we simply subtract ILOStat's unemployment rate figures from 100. Here it should be noted that we consider the employment rate simply as the employed population over active working-age population. We therefore depart from the OECD definition of the employment rate as the ratio of employed labour force to working age population.
Palma ratio As a measure of inequality, we opted for the Palma ratio as a standard metric. This indicator is defined as the ratio between the income of the 9 th decile and that of individuals below the 4 th decile in the income distribution. Its advantage over the Gini index is that it is less sensitive to changes in the middle of the distribution and more responsive to changes between the rich (top 10%) and the poor (bottom 40%). For the same reason, the indicator has the disadvantage of not reflecting changes in income distribution taking place within the 4 th and 9 th decile. There was also a better country coverage available for the Palma ratio, with the World Inequality Database providing data for 139 out of the 153 countries retained in the final dataset from the Eora26 dataset. That said, we observed a correlation of above 0.
g. , both 1994 and 1996 were available), the latest possible year was taken. When there appeared to be large changes between survey years on both sides of the target year, we interpolated the data to ensure accuracy  (e. g. , 1992 and 1997). This procedure gave us data for only 107 countries for 1995 and 119 countries for 2015. To increase our country coverage, we found additional estimates in other datasets, principally Thomas Piketty and Lucas Chancel's "Carbon and Inequality" dataset (they provide average incomes by decile, so we took the median decile) and the Lakner-Milanovic "World Panel Income Distribution" dataset. The same principles were followed for adjusting surrounding years to the target year as with the Povcalnet data. The Carbon inequality dataset added ten countries for 1995 and six for 2015; the World Panel Income dataset added two more for 1995.
Labor share As a measure of inequality focused on the division between capital and labor, we also include the labor share of net value-added. The calculation of a comparable labor share for countries with different income levels is not a simple task. If mixed income is available in the national accounts, assumptions have to be made about what proportion to assign as labor income. These assumptions may be more or less accurate depending on whether the non-corporate sector employs capital in a greater or lesser proportion than the corporate sector, which is likely to vary depending on the employment structure and income level of a country (van Treeck, 2020). There is therefore no consensus adjustment to deal with mixed income in the literature. Nonetheless, the Penn World Tables argues for assuming the labor share of mixed income is the same as the labor share of the corporate sector wherever mixed income data is available  (Feenstra et al.
Domestic material extraction In order to account for the ecological impact of GVC participation in terms of extraction of materials, we measure domestic material extraction as a share of a country's size. Domestic material extraction corresponds to biomass, fossil fuels, metal ores and non-metallic minerals extracted by a country within a year, all measured in tons. This data is directly retrieved from UNEP's Global Material Flows database  (Schandl et al. , 2016). Given that countries that are richer in natural resources will, ceteris paribus, likely extract more tones of materials than other countries, we normalized the data by the country's land area measured in square kilometers. The latter is retrieved from the World Bank. It constitutes a proxy for a variable of countries' endowment in materials.
Raw materials trade balance This variable is calculated as the materials embodied in net imports as a percentage of total material consumption (material footprint). It corresponds to the variable raw trade balance of the UNEP Global Material Flows Database from which it is extracted. There was missing data for 13 out of the 186 countries in the database (7% of the total): Comoros, Djibouti, Equatorial Guinea, Grenada, Guinea-Bissau, Kiribati, Marshall Islands, Micronesia, Palau, Solomon Islands, Sudan, Timor Leste, Tonga and Tuvalu. None of them remain in the final dataset.
Local pollutants Data on domestic local air pollutants were taken from the European Commission's Emissions Database for Global Atmospheric Research (EDGAR). Aside from CO2 emissions, EDGAR v5 provides data on a range of air pollutants from 1970-2015 for all of the countries in our dataset  (European Commission, 2020). The data includes (1) ozone precursor gases: Carbon Monoxide (CO), Nitrogen Oxides (NOx), Non-Methane Volatile Organic Compounds (NMVOC) and Methane (CH4); (2) acidifying gases: Ammonia (NH3), Nitrogen oxides (NOx) and Sulfur Dioxide (SO2); and (3) primary particulates: Fine Particulate Matter (PM10) and Carbonaceous speciation, 'Black Carbon' (BC). All data are expressed in gigagrams and can thus be aggregated into a domestic local air pollutant variable for each country. In this way, the load of local pollutants can be understood as a factor linked to a particular level of integration within GVCs.
CO2 emissions Domestic per 2017 PPP GDP CO2 emissions for both 1995 and 2015 come from World Bank World Development Indicators (EN. ATM. CO2E. PP. GD. KD). CO2 imports and footprint (consumption-based) data used to compute CO2 emissions embodied in net imports as a share of total CO2 consumption come from EORA. For 1995, we use the series from EORA computed using domestic CO2 data from the Carbon Dioxide Information Analysis Center. For 2015, these series are not available so we use the series from EORA computed using PRIMAPHIST domestic emissions data, itself built upon CDIAC and Edgar data.
Final sample Based on the availability of the GVC variables in the Eora26 tables and UNCTAD-Stat, we have 153 potential countries. However, data are not available across the other 16 variables for all these countries in both 1995 and 2015. To decide how many countries to drop and how much remaining missing data to impute statistically, we decide to draw the line at more than four missing values for a given country. That is, any country with at least 28 out of the 32 data points (16 variables for 1995; 16 variables for 2015) is retained in the dataset. Any missing values across the remaining data panel are imputed by performing multiple imputations based on principal component analyses. Only median income for 1995 and 2015 still exhibits missing values. We use the missMDA package in R  (Josse and Husson, 2016).
We generate 1000 imputed datasets to assess the quality of the imputation. Along the way we eliminate a few more countries as their presence substantially decreases the quality of our imputation while they are not key to our investigation: Barbados, Fiji, Vanuatu, Seychelles, Suriname, Trinidad and Tobago, Laos, Papua New Guinea and Haiti. We end up with an imputation of very good quality, as can be seen in Figure  1  illustrating the position of variables, countries and axes across the 1000 imputed datasets. We obtain a final dataset of 133 countries that is very balanced across income categories: at the beginning point in 1995, 48 countries were considered high income (30) or upper middle income (18) by the World Bank's classifications, 42 countries were considered lower middle income and 43 countries were considered low income.
Acknowledgements We are deeply indebted to our interlocutors and archivists in Geneva, Oman, Tanzania and  Kerala. We also acknowledge here the crucial ethnographic work of  Claudia Lang  ( University of Leipzig ) in Kerala and  Fanny Chabrol  ( CEPED-IRD ) in Tanzania. We are grateful to  Martin Gorsky  and  John Manton  for their comments on an earlier version of this piece. We also would like to thank  Ruth Prince ,  Janina Kehr  and  Jacinta Victoria Syombua Muinde  for their energy and support while spearheading the collective effort of this special issue about 'Health for all in the 21st century?Past and present aspirations for  Universal Health Care and Universal Health Coverage '.
The change in concentrations of CH4 and N2O are function of the atmospheric concentrations, the sum of anthropogenic and natural emissions, and their lifetime. Then, radiative forcing for CH4 and N2O are parameterized separately, taking into account both the saturation effect of the absorption bands resulting from increasing GHG concentration, as well as the radiative effect of the absorption bands that are shared among the two gases. 42. Finally, uncertain parameters in ACC2 are calibrated with a Bayesian approach  13, 22. The model is developed in the GAMS programming language and numerically solved using CONOPT3 and CONOPT4, two nonlinear optimization solvers included in the GAMS software package.
M. M. acknowledges financial support from the  Italian Ministry of the University and Research , as well as the  European Union  for the  Erasmus+ Traineeship scholarship. This research was conducted as part of the  Achieving the Paris Agreement Temperature Targets after Overshoot (PRATO) Project  under the  Make Our Planet Great Again (MOPGA ) Program  and funded by the  National Research Agency in France  under the  Programme d'Investissements d'Avenir , grant number  ANR-19-MPGA-0008. We also acknowledge funding from the  European Union 's  Horizon Europe research and innovation programme  under Grant Agreement N°  101056939  ( RESCUE -Response of the Earth System to overshoot, Climate neUtrality and negative Emissions).
Data collection Data collection entailed a survey of a total of 411 sweet potato consumers (198 from Homa Bay and 213 from Nairobi). Focus group discussions provided information on consumers' preference for OFSP products, including OFSP chapati and consumption pattern. Face-to-face interviews with individual consumers were conducted using semi-structured questionnaires. This survey method was preferred since it allowed for further probing and clarification of the questions to the respondents. 20 he survey questionnaire comprised three sections seeking information on sweet potato purchase and consumption pattern, socio-demographic characteristics and CE questions on WTP for OFSP puree composite chapati. The respondents were provided with a hypothetical scenario as a basis for eliciting the amount of money that they would be willing to pay for OFSP puree composite chapatis.
ACKNOWLEDGEMENTS This research was undertaken as part of the  CGIAR Research Program on Roots, Tubers, and Bananas (RTB). Funding for this work was provided by RTB Cluster 4. 4 and the  African Economic Research Consortium (AERC)  through the provision of a research grant to the first author, who was a student in the Collaborative Masters in Agricultural and  Applied Economics  ( CMAAE) program. The authors are also grateful to grant opportunity  INV-008567  (formerly OPP1178942):  Breeding RTB Products for End User Preferences (RTBfoods) , to the  French Agricultural Research Centre for International Development (CIRAD), Montpellier, France , by the  Bill & Melinda Gates Foundation  (BMGF): https://rtbfoods. cirad. fr. Previous versions of this paper and the ideas in it benefited greatly from suggestions and comments by the editorial team.
We expect the calibrated model to reproduce empirically observed annual and transport costs, which in turn are assumed to represent an implicit societally accepted equitable balance between school efficiency and proximity. The methods to simulate school locations, estimate school sizes and assess accessibility were developed for GeoDMS. Cost estimations were done in R. All used software is open source, and all scripts and data are available upon request. The remainder of this paper proceeds as follows. In the next section, we review the literature relevant to the presented method. We then present the methods to simulate school locations and estimate their pupil populations and costs, followed by considerations regarding the application of our approach in empirical case studies. We subsequently compare the simulated school placement, access, size and costs of primary schools with observed distributions that are described in comprehensive, publicly available databases from Portugal, England and France.
For the goals of this paper, these school location databases are considered actual and comprehensive sources of school provision. In addition, the English department of education has provided a sample of school locations including financial records, covering 60% of primary schools with 2,727,656 pupils; and the UK department of transport has provided stated distances travelled to school, averaged by rural-urban classification, collected in the UK National Travel Survey (DfT 2021). To apply the proposed methods, road travel distances were obtained from road network data (TomTom, 2018). To measure demand, pupil population distributions were obtained from 1 km 2 LUISA population age grids for 2011  (Goujon et al. 2021; Jacobs-Crisioni et al. 2020 ). These describe total local population per 5-year age group and are based on the GEOSTAT census-based, 1km 2 population grid (EUROSTAT 2021) and regional population projections prepared for the 2015 ageing report  (EC, 2015).
Results Results are split into four sections, subsequently discussing the accuracy of location-allocation, pupil assignment, school size and school cost simulation. We use four sources of data in this section (they are marked by single letters for brevity), representing data from England and Portugal on which parts of the method were fitted, and data on school locations from France, which is used as a partial validation set. Where relevant, we present results for: (1) simulated locations (marked by an s), (2) actual locations (a), (  3 ) the subset of English schools with financial record data (f), and (4) the UK national travel survey (t), see  DfT (2021). In most material, the focus will be on England, as it provides the most comprehensive dataset including locations, partial costs and aggregate distances travelled.
As the distances travelled to both actual and simulated locations depend on the balancing method discussed before, only comparisons with stated distances can be used as an empirical validation here. The obtained data describing travelled distances is classified using the UK urban-rural classification, which is not identical, but somewhat similar to the Degree of Urbanisation. We link the UK 'urban conurbation' class with 'cities'; UK 'urban city and town' with 'suburbs and towns'; and UK 'rural villages, hamlets and isolated' with the 'dispersed rural' and 'villages' classes. The UK 'rural town and fringe' category is ignored here as it seems to have no clear counterpart in the degrees of urbanisation classification. The discrepancies in the territorial classifications and the inherent differences between stated distances and the results of network analyses  (Rietveld et al.
Such small schools may not be viable in England given that such extremely small schools are not recorded in the full dataset of actual school locations. Figure  7  shows box plots of the distribution of estimated costs per pupil for actual and simulated school locations in England. Costs distributions for primary schools again confirm the accuracy of estimates for those schools. In addition, for both the actual and simulated distributions, it is evident that there are significant geographical differences in school costs, with high demand density in cities enabling substantially higher cost efficiency both for the actual schools in England and the simulated placements. Table  8  shows the estimated costs per pupil for the full actual placement and pupil counts, as well as for simulated school locations, by degree of urbanisation.
Annex B: Additional detail on cost estimation using pupil counts only We assess school costs on the assumption that costs vary primarily through number of teaching staff, which in turn depends on pupil counts. As the cost estimations hinge on those assumptions, we go through alternative explanations and provide evidence that supports our assumptions in this annex. To start, it is worth verifying that the distribution of costs per teacher follows a normal distribution; see Fig. 9. Note that costs are given in GBP in this annex, and EUR in the paper; for the paper we have centred our estimates around European averages, while we discuss the results from the UK DfE here. The primary school data shows that the mean gross salary for teachers is GBP 38 716, and that costs are fairly normally distributed.
Using the capital asset pricing model, this article critically assesses the relative importance of computing 'realized' betas from high-frequency returns for Bitcoin and Ethereum-the two major cryptocurrencies-against their classic counterparts using the 1-day and 5-day return-based betas. The sample includes intraday data from 15 May 2018 until 17 January 2023. The microstructure noise is present until 4 min in the BTC and ETH high-frequency data. Therefore, we opt for a conservative choice with a 60 min sampling frequency. Considering 250 trading days as a rolling-window size, we obtain rolling betas < 1 for Bitcoin and Ethereum with respect to the CRIX market index, which could enhance portfolio diversification (at the expense of maximizing returns). We flag the minimal tracking errors at the hourly and daily frequencies.