dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of enabling factors through sensitivity analysis To explore which actors and policy and regulatory context options are key enabling factors in the uptake of SI The "actor group" describes how actors are classified in the sensitivity analysis (section Identification of enabling factors through sensitivity analysis). The ranks indicate the order of importance of the actors for the different SI measures, where the most important actor is ranked as 1 measures, we did a sensitivity analysis using the BBN. By doing four different sets of sensitivity runs, we inferred how the case study will evolve if specific groups of actors take a supportive or unsupportive role. The uptake levels of the outputs were recorded, and for the runs where all actors are assumed supportive or unsupportive, the output for each location-farm size-age combination was calculated and linked to the spatial data on farm and field distribution, for visualization and interpretation.
Funding This research was funded through the  ERA-Net/FACCE-JPI projects VITAL  (grant agreement  652615 ) and TALE, nationally funded by the  Netherlands Organization for Scientific Research (NWO) ,  ANR (France) , and  German Federal Ministry of Education and Research (BMBF). Furthermore, this research was supported by  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation Program through the projects PROVIDE  (grant agreement  633838 ) and  CONSOLE  (grant agreement  817949 ). This work does not necessarily reflect the view of the  EU  and in no way anticipates the Commission's future policy.
Introduction: The objective was to conduct a systematic review of economic assessment of interventional strategies, in the prevention of frailty in elderly of 60 years and over living at home. Methods: The keywords were searched in databases such as Pubmed, ScienceDirect, Google Scholar and Embase. Articles published in English and French between 31/10/2010 and 31/12/2021 were included. The CHEERS statement reading grid was used to assess the quality of the studies in terms of economic assessments. Results: The search had identi ed nine relevant research studies, including seven randomised controlled trials and two quasi-experimental studies. Of these studies, we classi ed them into three programs: ve studies on frailty screening, three studies on falls prevention and one study on the analysis of drugs and treatments prescribed and delivered.
Research strategies We have used all the terms associated with the PICO elements. The keywords were: « frailty » AND « qaly » OR « cost-effectiveness analysis » OR « cost-utility analysis » OR « personalized care plan » OR « e-health » OR « elderly » OR « community-dwelling » OR « intervention ». The research strategy was carried out from 31/10/2010 to 31/12/2021 on the databases: Pubmed, ScienceDirect, Google Scholar and Embase. The lower limit of 2010 was chosen in consultation with the authors. We manually searched the references of all identi ed systematic reviews, as well as the included studies to identify other potentially relevant articles.
Inclusion criteria The studies included should meet the following criteria: elderly of 60 years and over living at home, prevention of frailty, identi cation of an interventional strategy to optimise the use of adapted digital tools (allowing to measure or transmit parameters remotely, organizational solution for data analysis for remote medical monitoring of the elderly as well as the organization and management of alerts and interactive system for personalized interactions between health professionals and the elderly), or support through a personalized or mixed care plan and complete economic assessments. Cost-effectiveness assessment methods for estimating e ciency, as well as analyzes for estimating a cost differential and a health outcome differential between several compared interventions were included.
Data extraction One of the authors (KG) proposed to all authors (MLG, CG, MLL, MDC and AT) a list of categories containing variables extracted from the studies. For overall study characteristics, we extracted: rst author, country, year of publication, country, study design, intervention group and comparator sample sizes, intervention group and the comparator descriptions, outcomes and measures. For the complete economic assessments data, we extracted: time horizon, perspective, cost assessment, incremental cost-effectiveness ratios (ICER), economic methods, economic results and sensitivity analysis.
Conclusions This literature review, carried out in four databases and over a period of 11 years, had the objective to identify e cient interventional strategies, combined with an economic assessment, in the prevention of frailty in elderly of 60 years and over living at home. Only two multidimensional and interdisciplinary intervention strategies were highly e cient, cost-effective and improved the quality of life of elderly. Economic results were more mixed. Many methodological weaknesses were present in these studies. Figures.
Acknowledgements This article was generated from discussions at the 17th Global Cardiovascular Clinical Trialists (CVCT) Forum held online in December 2020 (https://www. globalcvctforum. com/). The CVCT is a strategic workshop for high level dialogue between clinical trialists, industry representatives, regulatory authorities, and patients. The authors would like to thank  Pauline Lavigne  and  Steven Portelance  (unaffiliated, supported by the CVCT) for contributions to writing, and editing the manuscript.
These serious medical conditions could compromise the diagnostic test performance. Survey procedures. MUPAC holds monthly meetings at its headquarters in each of the six districts of the city of Douala. Participants were recruited from 5 districts at MUPAC meetings between first January 2019 and 31th May 2019. At each MUPAC meetings, the study purpose was presented to the participants. Then, they were enrolled in the study if only they met all eligibility criteria and provide oral and written consent. Data were collected on (1) Socio-demographic characteristics: age, sex, occupation, education level, marital status; (2) Medical histories: hypertension, diabetes, chronic alcoholism, smoking; (3) Sarcopenia using the SPPB (Short Physical Performance Battery) that includes three subtests: balance test, Gait speed and 5-chair lift test.
In addition, using the SOF index (Study of Osteoporotic Fractures also based on three indices: weight loss, 5-chair lift test and reduction in energy level) we collected data related to physical frailty. The assessment procedure for these tests is presented in the Appendices 1 and 2. (4) Finally, cognition status was assessed using the Mini Mental-State Examination. Other data collected included ADL (Activities of daily living), IADL (instrumental activities of daily living), depression (CES-D scale: Center for Epidemiologic Studies-Depression), falls and sensory impairment (vision and hearing). Participants were asked whether they had been diagnosed with a VI by an ophthalmologist. If the participants answered "Yes" to this question then the were considered as having a VI. We use a similar approach for hearing impairment and for the correction of these two sensory deficits.
Moreover, the quality of the data is reliable because the data were collected by clinicians trained in geriatric assessment. In addition, we achieved a good completeness of the data, which maximized the statistical power of sound analyses. Another strength of this work was the important number of variables that were collected. This allowed us to account for numerous potential confounding factors, although all psycho-social factors that can influence the association were not assessed. Despite these strengths, our study has some limitations. The first was it cross-sectional design. This does not allow to assess the sequence of happening between the onset of VI and sarcopenia/frailty. Nevertheless, some cohort studies have assessed the longitudinal relationship between VI and frailty and the results were similar  14, 17.
A new indicator of the energy mix, which we refer to as the index of energy sources diversification, is something that our study suggests in light of this context. Numerous articles have used a variety of econometric approaches to study the link between alternate forms of energy and economic growth using time-series and panel datasets  [13, 37, [41] [42] [43] [44]. Previous research has yielded a variety of empirical findings, which may be broken down into the following four categories: the conservation, the growth, the feedback, the growth, and the neutrality hypotheses  [45]. The conservation theory suggests that increases in economic activity are caused by increases in energy indicators. In relation to this scenario, as nations develop, they will look for alternate sources of energy. The growth hypothesis posits that there is a causal link between increases in energy indicators and increases in economic growth.
The long-term asymmetric effect is denoted by the GDP i,t-1 , the long-term positive impact of energy diversification is denoted by the ED + i,t-1 value, and the long-term negative impact of energy diversification is denoted by the ED - i,t-1 value. The short-term asymmetric effect is denoted by ∆GDP i,t-k , the short-term positive impact of energy diversification is denoted by ∆ED + i,t-k , and the short-term negative impact of energy diversification is denoted by ∆ED - i,t-k. The error term is denoted by the symbols i and t. Table  1  provides the descriptive properties of the variables used in the study. The index of energy diversification is computed in this research using the data on energy consumption acquired from the source mentioned above. Our sample coverage spans the years 1998 to 2018, and it includes all four Nordic nations.
The error term is denoted by the symbols  and. Table  1  provides the descriptive properties of the variables used in the study. The index of energy diversification is computed in this research using the data on energy consumption acquired from the source mentioned above. Our sample coverage spans the years 1998 to 2018, and it includes all four Nordic nations. As was suggested by earlier research, the values of all the variables have been represented using natural logarithms  [44, 58]. We utilize various panel data estimation techniques to obtain the short-term and long-term parameters. The overview of the methodology used in this article is depicted in the Figure  2    The POLS approach is less reliable than the PFMOLS and PDOLS methods because the POLS method uses an endogenous estimating process, which might result in biased conclusions.
The PFMOLS and PDOLS methods are more resilient than the POLS method  [59]. Due to the fact that there are normally distributed asymptotic standard errors, the PFMOLS estimator, which was presented by  [60, 61] , offers evidence that is free from bias. This problem gives elastic and efficient parameters over the long term. Phillips and Hansen [62]  demonstrates that the semi-parametric correction of the FMOLS may eliminate the possibility of endogeneity as well as residual autocorrelation in the data. On the other hand, when all of the indicators in the model are cointegrated, we should think about using the PFMOLS technique  [63]. The PFMOLS methodology uses either the group mean or the between-group estimator as its foundation. It makes it possible for the panel datasets to have a high degree of variability  [64].
Conclusions and Policy Implications The purpose of this study is to investigate the impact that the diversity of energy sources has on economic performance across the panel datasets of four Nordic nations using data dating from 1998 to 2018. In order to accomplish this goal, we use the data on yearly energy consumption as a novel measurement of the diversity of energy sources. According to the findings of the NPARDL estimate model, energy diversification does not contribute to improvements in short-term economic performance. However, in the short term, a decline in energy diversification promotes economic activity. On the other hand, there is no evidence that shows that energy diversification has a detrimental effect on economic performance over the long term. In light of these data, we argue that the adverse effects of energy diversification on economic growth are relatively temporary.
We use GEMINI-E3, a model of the European and World economy, we construct different simulations to estimate the implications for energy prices of such an embargo, the economic cost to European citizens and whether the impact on the Russian economy would be significant. GEMINI-E3 is a dynamic model in the framework of a computable general equilibrium (CGE), which is a framework that is frequently used to assess the impact of economic sanctions  [9, 24]. This general equilibrium approach captures supply and demand by a consistent representation of different sectors, households, markets, and interdependencies; so adjustments in quantities and prices following the implementation of the energy an embargo could be adequately measured. Particularly for GEMINI-E3, GHG emissions are adequately calibrated from the most up-to-date policy databases.
The GEMINI-E3 model We use the latest modification of GEMINI-E3 based on the study by Bernard and Vielle  [53]. GEMINI-E3 is a multi-country, multisector, recursive dynamic, general equilibrium model with backwardlooking (adaptive) expectations and total flexibility in macroeconomic and microeconomic markets. International trade is represented by the Armington assumption  [54] , which assumes that goods from different regions are imperfect substitutes. For fossil energy imports, we use an elasticity higher than 1, meaning that fossil energy goods are highly substitutable. The current version is built on the GTAP-Power database version 10 with the year 2014 as a Ref. [55] , and with countries aggregated into eleven regions. In this version of GEMINI-E3, European Union refers to EU28, including the United Kingdom.
The ESR price also applies to all GHG emissions of methane, nitrous oxides and fluorinated gases. 4 The databases used to calibrate these non-CO 2 GHG are described in Appendix A, while marginal abatement curves implemented in the model are derived from the work done by the US Protection Agency  [57]  following the methodology described in  [58]. The abatement targets are 61% for the ETS sector and 40% for non-ETS emissions compared to 2005 emissions [59]. As shown in Table  1 , the ''Fit For 55 Package'' increases the ETS price, reaching 113 US$ in 2030. This is in line with the estimation by Cornago  [60]  of 100 e per tonne of CO 2 in early February. 5 The price in non-ETS sectors (that is, the sectors included in ESR) increases rapidly after 2025 and reaches 189 US$ in 2030, showing how stringent the new ''Fit For 55 Package'' is, especially in the transport sector and for non-CO 2 GHGs.
We assume that other regions implement a subset of current climate policies as defined in the CD-Links policy database, documented in Roelfsema et al. [62]. The resulting GHG emissions are given and compared with other integrated assessment models in Sognnaes et al. [63]. The current policies scenario differs from a more binding scenario where the nationally determined contributions (NDC) are supposed to be implemented. In 2030, Sognnaes et al. [63]  evaluate the difference to 2 Gt CO 2 worldwide.
Green house gas (GHG) emissions covered GHG emissions in GEMINI-E3 are calibrated from the most upto-date policy databases that cover country to the sectoral level of disaggregation. Historical inventories for CO 2 and methane, are based on the Community Emissions Data System (CEDS) detailed in Hoesly et al. [75]. Nitrous oxide is aligned with the PRIMAP Dataset  [76] , and F gases are calibrated from the U. S. Environmental Protection Agency  [57]. The non-CO 2 gases come from diverse sources such as agriculture, industries, transport, etc. , and where emissions and mitigation options must be represented at the bottom-up level. These non-CO 2 gases represent 19% of EU28 GHG emissions in 2016  [77]. The agriculture sector contributes the most (52%), followed by the waste and waste-water sector (18%) and the energy sector (15%)  [78].
Materials and methods To provide an accurate a view of Saudi Arabia's CO 2 emissions, data from several major sources was reviewed, focusing on the following databases: the International Energy Agency's (IEA) World CO 2 Emissions from Fuel Combustion, Enerdata's Global Energy and CO 2 , BP's Statistical Review of World Energy, and the European Union's Emissions Database for Global Atmospheric Research (EDGAR) Fossil CO 2 database  (IEA, 2020a; Enerdata, 2020; BP, 2020; EDGAR, 2020). All data used for this study was accessed in October 2020. Both BP and EDGAR provide publicly-available data through 2019 at an aggregated level, while the IEA and Enerdata offer by subscription more detailed CO 2 estimates by sector and fuel. At the time of writing, Enerdata included CO 2 estimates up to 2019, while 2018 was the latest year of data provided by the IEA for Saudi Arabia.
The Saudi government does not publish regular data on national GHG emissions, and the inventories it has submitted to the UNFCCC  (for 1990, 2000, 2010 and 2012)  do not allow for analysis of recent emissions trends (e. g. DNA, 2018). Each of the above-mentioned international datasets has different advantages in terms of accessibility, scope, timeliness, frequency of updates, and how detailed sectoral and fuel use-related emissions are presented. CO 2 estimates from all providers can change several times a year when updates are made. The timing of such updates can result in significant differences in estimates, especially for recent years. Differences also arise between the international providers of emissions data owing to different assumptions regarding the attribution of fuels to end use sectors, although this is not necessarily visible from aggregate emissions estimates.
For this, we used energy and CO 2 data from Enerdata, GDP in 2015 constant purchasing power parity US dollars, and population data from the World Bank. Equation 1: The Kaya Identity DCO 2 = D CO 2 TPEC + D TPEC GDP + D GDP POP + DPOP To assess sectoral emissions, we compared data from the IEA with Enerdata. This was important as there are differences between these two sources in sectoral allocations of emissions. Of note, is the falling diesel consumption, which has been one of the most significant drivers behind the Kingdom's stabilisation in emissions. Enerdata allocates most of the decline in diesel consumption to the transport sector, while the IEA allocates it to the power sector. A significant amount of diesel used in power generation may be purchased through the same channels as that for transport, which may be the likely explanation for this.
Our assessment is that it is more likely that the majority of the decline in diesel emissions was in the power sector. For example, it is unlikely that diesel transport emissions could realistically fall 37 megatonnes (Mt) CO 2 , from 65 MtCO 2 in 2015-28 MtCO 2 (or 57%) in 2018, as suggested by Enerdata. We triangulated our view on diesel with data from Saudi Arabia's Electricity and Cogeneration Authority (ECRA), which reflects a corresponding large fall in diesel used in the power sector (shown in the Appendix) (ECRA, 2020). As a further check, and to provide the most recent view of possible emission trends, we also used data from the Joint Organization Data Initiative (JODI), which provides monthly fuel consumption data for Saudi Arabia with only a three-month lag. In our case, the latest data point was for August 2020.
This provides an indication of the impact that declining economic activity due to COVID-19 has had on energy consumption and emissions. We obtained our forward estimates from Enerdata's EnerFuture database, using the EnerBase (consistent with 5°C to 6°C of average global warming by the end of the century) and EnerGreen (consistent with 2°C of average global warming) scenarios, which project Saudi Arabia's CO 2 emissions from fuel combustion in 2050 to stand at 482 and 163 Mt, respectively.
Total CO 2 emissions include both fuel combustion and industrial processes, such as cement manufacturing. The IEA's most recent 2018 estimate for CO 2 from fuel combustion was 492 MtCO 2 in 2018, while the corresponding figure from Enerdata was 530 MtCO 2. In 2018, Saudi Arabia experienced its first ever large decline in total CO 2 emissions with a 22. 61 MtCO 2 decline (3. 93%) reported by Enerdata, 19. 23 MtCO 2 (3. 24%) decline from BP and a 3. 54 MtCO 2 (0. 58%) decline from EDGAR. In 2018, CO 2 emissions from fuel combustion were down 24. 97 MtCO 2 (4. 83%) according to the IEA and by 21. 69 MtCO 2 (3. 93%) according to Enerdata. As discussed in the previous section, differences in the estimates relate to different attribution of fuels to end use sectors, among other factors. The Kaya identity enables an assessment of the drivers of this decline and potential stabilisation at the macroeconomic level, with the results presented in Figure  3.
Sectoral emissions (2015-2019) are shown Figure  4  and annual percentage changes in these emissions in Figure  5. The IEA and Enerdata differ with respect to the relative sizes of the power and industrial sectors, which is likely to reflect the attribution of industrial autoproducers of electricity to either sector. Enerdata attributes the stabilisation primarily to the transport sector, with falls of 4% (5. 64 MtCO 2 ) in 2016, 9% (12. 21 MtCO 2 ) in 2017, and 18% (21. 94 MtCO 2 ) in 2018. This differs from IEA, which shows the largest falls in the power sector, with 2% (5. 10 MtCO 2 ) in 2016, 5% (10. 19 MtCO 2 ) in 2017, and 9% (19. 33 MtCO 2 ) in 2018. Although their attribution of CO 2 between transport and the power sector differs, both sources agree in attributing the fall to a substantial decline in diesel consumption (Figure  A1 , Appendix).
This paper introduces a dataset that gathers information on whether and how Latin America and the Caribbean (LAC) have complied with or deviated from implemented fiscal rules. It provides annual data on fiscal rules for 14 LAC countries from 2000 to 2020, and it considers the design features of the rules and information about numerical compliance. It provides descriptive statistics reflecting the panorama of the fiscal rules implemented in LAC countries. Additionally, it calculates compliance rates across countries, years, and rules. On average, this study finds that compliance with rules aiming to constrain debt ratios and structural balances is the highest, while compliance with fiscal balance and expenditure rules is the lowest. Furthermore, the data collection process revealed that LAC countries still have room for discretion even when they subject their fiscal policy to rules.
In order to achieve the best outcome in returning to fiscal rules, countries must consider the extent to which the initially established has worked its purpose and what has not. This paper focuses on emerging economies proposing a novel dataset that gathers information on whether and how Latin American and the Caribbean (LAC) countries have complied with or deviated from implemented fiscal rules. This dataset provides annual data on fiscal rules for 14 LAC countries from 2000 to 2020 and considers the design features of the rules and information about numerical compliance. We calculate compliance rates across countries, years, and rules by contrasting the objectives set by the rules and their executed or observed values. The results show that the region's numerical compliance with fiscal rules stands at 66%, indicating that, on average, countries breached their rules only one-quarter of the years of implementation.