dataesr/scientific-paragraphs-categorization huggingface dataset:
Institutional arrangements Institutional arrangements should be formalized by establishing data-sharing protocols or memorandums of understanding with the relevant institutions to facilitate data sharing (UNFCCC CGE, 2019). The institutional arrangements and relevant mandates should be supported by the creation of a legal instrument mandating the preparation of the national GHG inventory and the continuous disclosure of activity data by major GHG emitters to the designated entity responsible for the national GHG inventory (UNFCCC CGE, 2019). This will contribute to making the process more sustainable (PATPA, 2019.
Data collection and management and procedures When providing support for GHG inventory improvements, the establishment of national GHG inventory data management systems are central. The systems should also function as an archive and make accessible to the relevant stakeholders the background data and information, procedures and steps undertaken, assumptions made, and functions performed by key stakeholders (UNFCCC CGE, 2019). When providing data to the system, users should be enabled to document the steps in the data collection process to help maintain institutional memory and create a basis for a larger data depository (UNFCCC CGE, 2019). There is a need to improve the data collection system, storing of data and QA/QC procedures in LDCs and SIDS.
National Arrangements The capacity-building support should aim to develop in-house expertise and arrangements to make the process sustainable (PATPA, 2019). The strengthening of coordination with enforcement entities, including ministries and other stakeholders at different governance levels, can facilitate data collection and use of policy instruments (UNFCCC CGE, 2019). There is a need to develop or strengthen a mechanism for tracking and verifying GHG emission reductions resulting from various mitigation actions across all sectors (UNFCCC CGE, 2019). This system should build upon the lessons learned and existing mechanisms in the GHG inventory process. There needs to be institutional arrangements in place to properly identify and prioritise adaptation actions, which are included in almost all NDCs from LDCs (IIED, 2019).
final report is being commented on by country representatives in the NKL Steering Group. ICAT has commented that only half of ICAT countries are LDCs and/or SIDS. A specific analysis of gaps and needs for this group of countries has not been done. The ICAT initiative focuses on the establishment of MRV/transparency systems in countries with a focus on tracking mitigation policies and actions in the context of NDC implementation. Most of the work done under ICAT is related to "The ETF reporting component NDC progress and achievement". Some countries have, however, identified activities under ICAT focusing explicitly on GHG inventory. In general, country activities related to inventories are considered in the broader context of the ETF. For example, some countries are working on improving GHG data inventories but it is usually in the context of the broader ETF.
A few countries have asked for support regarding GHG inventory methodologies, including Peru and Dominican Republic --but only as a minor activity in their work plans. In terms of sectors, Agriculture and Forestry were not the most targeted sectors under ICAT. Under ICAT, cross-sectoral and energy-related activities are the most common, followed by transport and waste. CBIT was not available to respond within the consultation period, however, in their progress report to GEF (November 2019), the following priorities for CB support to transparency were reported: Overall, the project activities proposed by LDCs and SIDS are similar to the overall portfolio for all countries (as shown in Appendix 3, Figure  1 ). However, there are some differences in the proportion of countries prioritizing certain activities, namely: 1) GHG inventory data collection and management tools, and overall GHG inventory improvements, 2) tracking climate finance, and 3) NDC transparency and policy design.
Results so far In 2019: -3 regional hands-on training workshops on institutionalizing data management for national GHG inventory were organised, and 106 national experts (38 female and 68 male) from 91 developing country Parties were trained through these workshops. -3 pre-workshop webinars were organised to prepare the nominated national experts for the workshop. Reporting on support needed and received: 1. Allocation of responsibilities for MRV of support 2. Data collection process (including establishment of a database, data-sharing system, web-based knowledge management platform) 3. Process for the coordination of support received 4. Availability of data 5. Process or approach for integrating processes to report to various donors on support received 6. Data management process (including documentation, archiving, QA/QC protocols) 7.
Number of workshop/training/dialogue activities provided to LDCs/SIDS LDCs and SIDS LDCs (including LDC-SIDS) SIDS (including LDC-SIDS) Figure7shows the number of workshop-type support activities provided to LDCs and SIDS (both as one group, blue in colour, and as separate groups, orange and grey). The figure shows that most countries have received two or three workshop-type support activities, and only eight countries have received just one workshop activity (Afghanistan, Bahrain, Cook Islands, Mauritania, South Sudan, Suriname, Yemen). All countries have received at least one workshoptype support activity, leaving no country without support. Further details on which countries have received how much support is provided in Appendix 2. 3 0 5 10 15 20 25 30 35 40 Figure 7: 0 Workshop 45 1 Workshop 2 Workshops 3 Workshops 4 Workshops Source: Own analysis based on 'MRV Group of Friends' database and the respective initiatives' own websites, 2019.
Since 2017, GGGI has hosted the MRV forum in Latin America to transfer lessons learned for potential replication and prompt joint mitigation actions with enhanced transparency for Latin American countries. The CGE consists of five members from each of the regions of non-Annex 1 Parties (Africa, Asia and the Pacific, and Latin America and the Caribbean), six members from Annex 1 Parties, and three members from international organisations. At COP24 in Katowice 2018, its mandate was extended for eight years. The current work plan of the CGE covers the year 2019 and includes, among others, the identification of collaborations with the LDC Expert Group and/or the Adaptation Committee. Participation Open to all developing country Parties, no specification for LDCs and SIDS N/A Countries involved (All LDCs and SIDS were invited to join the regional training workshop on institutionalization of data management for GHG inventory) Burkina Faso, Colombia, Ethiopia, Lao PDR, Marshall Countries involved Islands, Mexico, Mongolia, Myanmar, Peru, Qatar, Knowledge delivery: Regional hands-on training Solomon Islands, Thailand and Uganda workshops; webinars; in-country support, upon request by countries; training of technical experts; MRV/ Mode of work Mode of work Transparency Helpdesk; expert database Support includes: Technical support for sector-specific MRV guidelines and MRV Masterplan; Practical policy Knowledge products; E-Learning; development and dissemination of tools and handbooks.
Moreover, this phenomenon of mortality deceleration is consistent with various theoretical explanations  (Beard 1959; Vaupel et al. 1979; Wachter and Finch 1997; Wilmoth and Horiuchi 1999; Steinsaltz and Wachter 2006; Mueller et al. 2011). Nevertheless, its validity has recently been challenged by those who assert that the Gompertz model is appropriate even at the very oldest ages  (Gavrilov and Gavrilova 2011; Brouard 2012; Gavrilova and Gavrilov 2015). The question of the trajectory of mortality at very old ages was in fact the main driver in the development of an International Database on Longevity (IDL), gathering as many verified cases of supercentenarians as possible in countries where the quality of vital records allows it. France has been a contributor since the beginning of the IDL project.
We will discuss the validity of these sources and how we can make the most of their respective strengths for the purpose of mortality measurements, with the help of age validation procedures and the extinct generation method to estimate populations at risk. We will first focus on the RNIPP, which stands out as a crucial source of data for the identification of supercentenarian and semi-supercentenarian deaths in France (Sect. 9. 2). We will then describe our age validation procedures for each of these two categories of old-age deaths (Sect. 9. 3), and provide a comparison between the RNIPP records and "public" lists of individual supercentenarians (Sect. 9. 4). The much-awaited analytical result of this research is presented in Sect. 9. 5, where we will calculate mortality indicators using vital statistics and RNIPP data to compare them within selected appropriate births cohorts.
The RNIPP Data Three sources of data can be used to enumerate semi-and supercentenarians in France: (1) the vital statistics system operated by the French National Institute of Statistics and Economic Studies (Institut national de la statistique et des études économiques, or INSEE), (2) the National Directory for the Identification of Natural Persons (Répertoire national d'identification des personnes physiques, or RNIPP), also administered by INSEE, and (3) public lists of long-lived individuals compiled from personal archives, local media coverage, and to some extent the IPSEN survey. Among these, the RNIPP stands out as the most reliable source  (Meslé et al. 2010). In a nutshell, the RNIPP was created in 1945 and initially consisted of paper listings that were kept at INSEE's regional offices.
In the early 1970s, the listings of persons born after 1890 in metropolitan France (1900 for French overseas départements) were computerized and centralized by INSEE. The RNIPP is considered exhaustive by INSEE for cohorts born 1890 (1900 for overseas départements) and beyond, but incomplete for earlier birth cohorts. It covers all persons born in France, as well as persons born abroad and living in France who have requested a social security number (numéro d'identification personnel). For further details on the RNIPP, see  Meslé et al. (2010, p. 126-127). Access to the RNIPP is limited because it gathers data at the individual level and it includes nominative information that is deemed confidential. In 2004, our research group was granted permission by the Commission nationale de l'information et des libertés  (CNIL)   ).
Our total RNIPP universe therefore sums up to 9,100 individuals (8,284 females and 816 males). 1 INSEE provided us with all cases where the difference between alleged years of death and birth was 105 or greater. The data set therefore included some deaths of individuals that passed away at age 104, namely those who had not yet celebrated their 105th anniversary that year. 2 For married females, both married and maiden names are provided. 3 Mayotte is excluded because it was too recently established as an overseas département. Other French overseas territories of various statuses were also excluded since the quality of their civil registration system was still questionable at the beginning of the twentieth century. It should be noted, however, that until they became overseas collectivités in 2007, the small islands of Saint Martin and Saint Barthélemy were included as parts of Guadeloupe, while Saint-Pierre et Miquelon is excluded altogether despite having been an overseas département until 1985.
Semi-supercentenarians We now turn to the validation of alleged semi-supercentenarians that was conducted a few years ago using the 2004 RNIPP data extract. This was prior to the 2014 agreement between INED and INSEE (see Sect. 9. 2) that resulted in the RNIPP data upon which the validation of alleged supercentenarians presented above rests. The validation period for alleged semi-supercentenarians is thus shorter and it covers years of death that range from 1988 to 2002. Out of the 3,272 persons included in the RNIPP data set at our disposal in 2004, all of whom had died in France between 1988 and 2002 and were born between 1883 and 1897, 2,031 had supposedly lived to ages 105-109, excluding those who died before their 105th birthday or after their 110th birthday, or were born outside French départements.
The last erroneous case is a woman born in 1895 for which we found a minor 10-day error on her date of death (she passed away on 8 Nov. 2001 instead of 29 Oct. 2001), but that left her age at death unchanged at 106. It should be noted that among these three erroneous cases, the one with a 10-year error on her year of birth was born and died outside metropolitan France, namely in Guadeloupe. Table  9. 3b displays the results limited to females (959 cases out of 1,050), which are highly similar to those presented in Table  9. 3a for both sexes. The high quality of RNIPP data was further confirmed by an additional age validation study of 100 cases (20 for each year of age from 105 to 109), randomly selected among the 10,305 cases from the 2014 RNIPP extract. No error was found either in the dates of birth or death among the 99 cases which could be validated (for the one remaining case, we were unable to find the birth and death certificates).
RNIPP Data Comparison with Public Lists of French Supercentenarians Besides official sources of information such as the RNIPP, that are subject to statistical confidentiality and for which permission by the CNIL must be obtained to grant data access, there are several public lists of individual supercentenarians in France. These public lists rely essentially on French communes' newsletters and information sites, local media coverage (e. g. , press reports), research led by genealogical societies, and to a smaller extent the IPSEN survey. See, for instance, the Wikipedia list of French supercentenarians, available at https://fr. wikipedia. org/ wiki/Liste_de_supercentenaires_français. Previous work by  Meslé et al. (2010)  singles out the RNIPP data as the most reliable source for enumerating supercentenarians in France.
Cohort Probabilities of Death and Death Rates at Older Ages In the present section, we take advantage of the validation work described above to investigate patterns and trends in probabilities of death and death rates at very old ages in France. Our first set of calculations is based on data from the national vital  , both sexes Sources: RNIPP and https://fr. wikipedia. org/wiki/Liste_de_supercentenaires_français statistics system, which is the most commonly available source of mortality data in France. As stated earlier, it suffers, however, from data quality problems at advanced ages. Here, we take these data as they are published, making no corrections whatsoever. In a second step, we compute probabilities of death and death rates using both raw and verified data from the RNIPP. Finally, we take advantage of these various probabilities of death series obtained from vital statistics and from the verified RNIPP data to derive and compare curves of life expectancy at ages 100 and above.
For probabilities of dying calculated from vital statistics data, the denominator was obtained using the method of extinct generations  (Vincent 1951 ). In the absence of migration, the outcome is the same as counting the exact number of survivors at the beginning of each age interval from a data set consisting of individual records, as it was done with the RNIPP data. With regard to death rates, death counts are divided by the population's exposure to the risk of death. For a given cohort, the death rate in the age interval [x, x + 1), written m x , is equal to the number of deaths to the cohort between exact ages x and x + 1 divided by the number of person-years lived by the cohort in the same age interval. The outcome is a number of events per person-year. For the RNIPP data, the number of person-years lived were directly counted by adding the contribution of each individual in each age interval, thereby taking full advantage of the information on the exact date of birth and date of death of the semi-and supercentenarians.
Vital Statistics Data for Ages 100 and Above Figure  9. 2 displays one-year probabilities of death, q x , starting from age x = 100 for female cohorts born between 1883 and 1901, as well as for an earlier group of female cohorts born during the period 1868-1882. All these cohorts are extinct today and the vital statistics data allow us to follow each one starting from age 100 onward. More specifically, the calculations are based on the vital statistics data received from INSEE, where death counts are aggregated by sex, year of birth, year of death, and single years of age at death 6. All deaths that occurred in metropolitan France are included here but we cannot distinguish between deaths of individuals born in France and deaths of individuals born abroad. Figure  9. 2 reveals that for the latest group of birth cohorts born between 1883 and 1901, the q x series seems reasonable, both in terms of level and trend, up to about age 108.
After age 108, the series starts to decline slightly and becomes irregular. For the q x series for the earlier group of cohorts born between 1868 and 1882, however, the declining trend and fluctuations are much more pronounced beyond age 108. This goes to show that the quality of vital statistics data has greatly improved over time in France even if some part of this apparent progress is due to the increase in the number of survivors at age 100. At the most advanced ages, the q x 's used to be largely underestimated, most probably due to age exaggeration problems. The underestimation may persist to some lesser extent past the age of 108 for the latest set of birth cohorts, and the RNIPP can be used to provide us with better information on this matter. 6 Vital statistics routinely published by INSEE include death counts by single years of age up to 104, followed by an open age group starting at 105.
RNIPP Data for Ages 105 and Above We used the RNIPP data to compute age-specific probabilities of death beyond age 105 among semi-and supercentenarians, all of whom were born and died in metropolitan French départements. We decided to focus on female cohorts born during the period 1883-1901 7  : 1901 is the most recent extinct birth cohort as of 31 December 2016 while individuals born in 1883 reached age 105 in 1988, which correspond to the first year where we can consider that death registration above age 105 is complete in the RNIPP, as suggested by Fig. 9. 3. First, we used the original RNIPP data as they were provided to us by INSEE, that is without applying any correction emerging from our age validation work presented in Sect. 9. 3. The second set of calculations uses only verified RNIPP data for ages 110 and above, for which an exhaustive check has been done (Table  9.
1b). For ages 105-109, the check was not exhaustive but it has been demonstrated that errors are quite negligible (Table  9. 3b) and original RNIPP data were used. Note that correcting for age-at-death errors among alleged supercentenarians could lead to differences in q x values beyond age x = 110, as well as prior to that age because of possible changes in the remaining size of the birth cohorts at the beginning of each age intervals serving as denominator for q x. The results are illustrated in Fig. 9. 4, along with the vital statistics q x series from age 100 for birth cohorts  1883 -1901 (from Fig. 9. 2) (from Fig. 9. 2). Caution is advised when contrasting the latter with the two RNIPP q x series because it includes the foreign-born female population, while the RNIPP data focuses exclusively on the native population.
Despite this, the three curves practically overlap up to age 108, suggesting that the vital statistics is as reliable as RNIPP up to that age for cohorts born during the period 1883-1901. After age 108, however, the three curves start diverging. Probabilities of death derived from vital statistics data, which are known to be of poorer quality compared to the RNIPP data, show the lowest values and those calculated from the verified RNIPP data have the highest values. Such hierarchy between the curves was expected since age misstatement, especially when due to age overstatement, biases mortality estimates downwards at the most advanced ages  (Preston et al. 1999). Probabilities of death beyond age 108 derived from the original set of RNIPP data fall between the two other curves, but they remain closer to the values obtained from verified RNIPP data.
So far in our analyses, we have focused solely on age-specific probabilities of death. Figure  9. 5 presents these probabilities, together with age-specific death rates, m x , according to the RNIPP verified data set 8. As expected given the definition of q x 8 Recall that in the denominator of these death rates, the number of person-years lived were directly counted by adding the contribution of each individual. Calculations based on an aggregated version of the RNIPP data (by single years of age, death and birth) and resting on the assumption that deaths are distributed uniformly within each age interval leads to almost identical results (not shown here but available on request from nadine. ouellette@umontreal. ca) Fig. 9. 4 Age-specific probabilities of death, q x , calculated using two versions of the RNIPP data and the vital statistics data for French female cohorts born between 1883 and 1901 who died in metropolitan France Note: The size of birth cohorts 1883-1901 at age 105 for the original and verified RNIPP data sets upon which the figure is based are N = 3,492 and N = 3,485, respectively.
Life Expectancies After Age 100 We now use the q x series discussed above to derive values of life expectancy at the oldest ages with standard life table techniques. For female cohorts born between 1883-1901, life expectancy at age 100 given by vital statistics is 1. 57 years (Fig. 9. 6). At age 105, when the verified RNIPP data becomes available, we obtain a barely higher value of female expectation of life from vital statistics (1. 36 vs. 1. 34 years). Afterwards, however, life expectancy values tend to diverge increasingly with age according to the two sources of data. At age 113, the vital statistics Fig. 9. 5 Age-specific probabilities of death, q x , and death rates, m x , calculated using the verified RNIPP data for French female cohorts born between 1883 and 1901, metropolitan France Note: The size of birth cohorts 1883-1901 at age 105 upon which the figure is based is N = 3,485 Source: RNIPP result appears to be quite exaggerated (1.
50 years, compared to 1. 23 if using verified RNIPP data), obviously due to the growing number of false cases of supercentenarians with age included in vital statistics. Perhaps more surprising is the sudden reversal of life expectancy values from vital statistics toward an unexpected increasing trend after age 108. The downward trend in the more trustworthy RNIPP life expectancy values also comes to a halt, but only temporarily between ages 108 and 110. Is this unexplainable bump at 109-111 years due to some undetected problem with the RNIPP data or is it attributable to random variation? The only way to know is to wait for additional years' worth of data. On the bright side, the vital statistics trend for life expectancy from age 100 to age 104 appears credible as it is in a perfect agreement with the life expectancy at 105, given by RNIPP data.