dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of local climate change mitigation plans and strategies For the identification of local climate change mitigation plans and strategies (M-LCPs), an approach similar to the one described in Reckien et al. [26]  was used: • Between November 2016 and January 2017, a team of native or fully proficient language authors compiled a database of M-LCPs for each sample city and EU-28 country. This database was facilitated using desk/web review and prominent keyword searches in each national language  [26, 32] , complemented with direct contact with local authorities (when further information and/or clarifications were needed). The list of plans  [32]  was re-checked and if necessary, updated between November 2018 and November 2019. All relevant plans and strategies were downloaded and saved.
Statistical analysis To investigate the relationships between mitigation targets and urban population, climate membership, type of plan and integration level (combination of adaptation and mitigation aspects in one plan) descriptive statistics and correlation analysis were conducted. For the correlation analysis bivariate, non-parametric, two-tailed tests were employed based on Kendalls-tau-b  [45]. Additionally, partial, twotailed correlations were used to control for potential confounding factors, i. e. covariates  [46] , which is necessary for variables that seem highly correlated with the descriptors. The statistical analysis was conducted with IBM SPSS Statistics Package 2.
In addition, the dataset does not reveal whether smaller cities that have joined these networks are less ambitious or just more realistic than their counterparts that remain outside. Giest and Howlett  [48]  suggest that "high-profile" climate networks (such as the C40) could be less effective in fostering "concrete actions compared to regional "in-depth" networks"  [49]. It may therefore be the case that different networks play different roles and further research is therefore needed into how they may shape climate policy ambition. The results also show that local motivation is a good stimulus for climate ambition, as demonstrated by its correlation with A1 M-LCPs and also by the lower level of ambition of French cities that only seek to meet national obligations (see below). However, Southern European cities with less capacity/ GDP may already find it difficult to reach slightly lower mitigation targets.
Thus, B and C plans were examined only in the absence of A1, A2 or A3 plans. Moreover, it may be more difficult to find B or C plans online than to find A plans (because the keywords are less clear), so the number of cities with B or C plans may be underestimated. Also, plans labelled as A1 (autonomously developed) or A2 (nationally induced) may have been led by an international climate network. To investigate these aspects, data on the availability of M-LCPs in this sample was cross-checked with detailed information on each city's current stage of commitment under the Covenant of Mayors for Climate and Energy. This comparison distinguish between two different groups of cities: (a) Cities that generally develop a plan after (and enhanced by) their membership of the CoM. This represents the most frequent case in which the only available M-LCP is a SEAP/SECAP (A3 Type) -51% of the cities in our sample fall into this category; (b) Cities that are members of the CoM but developed their plan autonomously and then entered it into the system through the CoM.
Acknowledgements We thank council representatives and local technicians that supported the data collection. We also thank the  EU COST Action   TU0902  (led by  Richard Dawson ) that established the core research network and the positive engagement and interaction of the members of this extended group. The paper reflects only the views of the authors. The  European Union , the  European Environment Agency  or other supporting bodies are not liable for any use that may be made of the information that is provided in this manuscript.
The aim of the Fault2SHA European Seismological Commission Working Group Central Apennines laboratory is to enhance the use of geological data in fault-based seismic hazard and risk assessment and to promote synergies between data providers (earthquake geologists), end-users and decision-makers. Here we use the Fault2SHA Central Apennines Database where geologic data are provided in the form of characterized fault traces, grouped into faults and main faults, with individual slip rate estimates. The proposed methodology first derives slip rate profiles for each main fault. Main faults are then divided into distinct sections of length comparable to the seismogenic depth to allow consideration of variable slip rates and the exploration of multi-fault ruptures in the computations. The methodology further allows exploration of epistemic uncertainties documented in the database (e.
g. , main fault definition, slip rates) as well as additional parameters required to characterize the seismogenic potential of fault sources (e. g. , 3D fault geometries). To illustrate the power of the methodology, in this paper we consider only one branch of the uncertainties affecting each step of the computation procedure. The resulting hazard and typological risk maps allow both data providers and end-users 1) to visualize the faults that threaten specific localities the most, 2) to appreciate the density of observations used for the computation of slip rate profiles, and 3) interrogate the degree of confidence on the fault parameters documented in the database (activity and location certainty). Finally, closing the loop, the methodology highlights priorities for future geological investigations in terms of where improvements in the density of data within the database would lead to the greatest decreases in epistemic uncertainties in the hazard and risk calculations.
INTRODUCTION Computing and communicating seismic hazard and risk based on faults has remained a long-standing challenge. Ideally it requires: 1) recognition of which faults are active (or not) and characterizing their seismic potential; 2) developing models (physical or statistical) and algorithms for assessing seismic hazard that can consider all the available data; 3) representing and communicating the results to geologists, geophysicists, seismologists, risk management professionals and practitioners, governments, and the at-risk population. In 2016, the Fault2SHA working group was established within the European Seismological Commission ESC (fault2sha. net; Scotti and Peruzza, 2016; Pace et al. , 2018)  in the attempt to develop the next generation of fault-based PSHA founded on three pillars: 1) novel methodologies that can lead to informative, community-participated fault databases 2) shared open-source codes that allow creating alternative fault models and multi-fault ruptures in collaboration with the data providers; 3) fault-based PSHA linking the database and the outcomes.
In regions with high levels of tectonic activity such as California  (Haller et al. , 2004) , Turkey  (Emre et al. , 2018) , Japan (Active fault database of Japan, https://gbank. gsj. jp/ activefault/index_e_gmap. htm) and New Zealand  (Lichfield et al. , 2014)  great advances in seismic hazard assessments have been made thanks to the building of "community-participated fault databases. " On the contrary, in Europe, where the tectonic environment and data collection procedures are not uniform, such "community" efforts to trace the available geological information for use in seismic hazard are slowly emerging. A first effort in Europe is that of the European Database of Seismogenic Faults  (EDSF, Basili et al. , 2013) , a collation of regional information with the aim of providing a list of already interpreted seismogenic sources deemed to be capable of generating earthquakes of magnitude equal to or larger than Mw 5.
5, including blind fault sources. The advantage of EDSF is that it provides a useful tracking of the literature supporting the definition of each seismic source, and it ensures a common reference input to assess ground-shaking hazard in the Euro-Mediterranean area. The disadvantages are that the criteria used to define seismic sources, the conceptual frame of what a "fault source" is and the level of information in EDSF are not uniform. Most of all, tracing data and uncertainties used to build the sources is not readily accessible to the end-users. To fill in this knowledge gap in the community it is fundamental to first build geological databases that are easily accessible to end users. The Fault2SHA effort thus focused on building community databases at the scale of two test areas (laboratories), one representative of transpressive tectonics in the Betic of Spain  (Gomez-Novell et al.
, 2020)  and the other representative of normal faulting tectonics in the Central Apennines of Italy (see Fault2SHA Central Apennines Laboratory (CA-Lab) database,1 1Faure Walker, J. P. ,  Boncio, P. , Pace, B. , Roberts, G. , Benedetti, L. , Scotti, O. , et al. (2021). Fault2SHA Central Apennines database and structuring active fault data for seismic hazard assessment, Scientific Data (under review). Faure  Walker et al. , 2020). To ensure the coherence between the database content and PSHA modelling, the CA-Lab brought together researchers across multiple institutions comprising field geologists, seismic hazard modellers and practitioners. The detailed geological data available in the database prompted us to develop a new methodology that can account for slip rate variability and the occurrence of multi fault ruptures.
In this paper we present this new generation of fault-based models aimed at obtaining a high spatial resolution of hazard and risk assessment. We also propose novel schemes for the disaggregation of hazard and risk in fault-based approaches that we believe will improve the link between data providers and endusers. Figure  1  shows a summary of the 4-step methodology that allows visualizing seismic hazard and risk assessment results at specific localities and at the same time visualize the data that was extracted from the Fault2SHA database and used to build the fault model. Each step involves user choices that are briefly summarized in the flowchart, to remind readers that results are conditioned on the choices made. In this methodological paper, only a few choices are discussed to illustrate their impact.
A complete exploration of the uncertainties involved in computing seismic hazard and risk assessment is beyond the scope of this paper. Finally, we provide in a GitHub repository (https://github. com/fault2shaESCWG/CentralApenninesLabFAULT2RISK), the data from the Fault2SHA Central Apennines Database (Fault2SHA CAD) which are used in this paper, and the codes used to parametrize slip rate profiles, building multi-fault ruptures and visualizing hazard and risk results. In Supplementary Table  S1  we provide a short summary for each Matlab code that needs to be run to use directly the Fault2SHA CAD contents for computing a seismic risk output.
The Fault2SHA Central Apennines Database The Central Apennines Lab has delivered the Fault2SHA Central Apennines Database (See https://doi. org/10. 1594/PANGAEA. 922582, Faure  Walker et al. , 2020). It was developed so as to provide a resource for hazard and risk modellers. The details in and design of the database facilitates seismic hazard uncertainty Step 1 From primary data From Fault2SHA CAD to seismogenic sources to seismogenic Uncertainties in primary data Section dimensions (vvidth and length) Choice of Main fault option Step 2 From seismogenic sources to Earthquake rates   calculations in three ways. First, the Fault2SHA CAD provides four levels of fault activity and four levels of location certainty accompanied by the geological and geomorphic criteria that led to these designations, thus the levels and data that led to their assignment can be traced back to the observations made  (Faure Walker et al.
, 2020). Second, the database provides three levels of fault mapping: 1) Traces (Figure  3A ), along with the observations made that lead to determination of the trace location scale and trace activity scale, are presented at the scale that primary observations are made so there is transparency for the modeller and end-user regarding which observations have led to the location and activity scale designation. 2) Faults (Figure  3B ) represent how the traces are connected at the surface and/or at depth. 3) Main Faults (Figure  3C ), the Frontiers in Earth Science | www. frontiersin. org S1  in Supplementary Material for additional options). Third, the Fault2SHA CAD includes primary slip-rate data, i. e. direct measurements from the field with the locations and methods used for inferring both the displacement and time periods over which the slip-rates are calculated together with uncertainties in these and reference to the papers where the data were originally published.
Fault2SHA CAD: Point Measurements and Main Faults Points of measurement are represented by geologic and/or topographic offset and age at specific sites. The Fault2SHA CAD provides slip rate uncertainties based on the age of the offset, the error in the vertical offset and in the dip angle if converting vertical offset into the orientation of the plunge, i. e. the slip orientation. In this work, we recalculate slip rates based only on errors of slip value (preferred, minimum and maximum) and the preferred age provided in the database. Here we consider a time period of 15 kyrs (Last Glacial Maximum) to estimate the slip rates for all sections, with the exception of the Pizzalto slip rate data point for which 3 kyrs was considered (oldest age of the fault scarp,  Tesson et al. , 2016). Frontiers in Earth Science | www.
frontiersin. org We assume that the geological slip rate of faults estimated at the surface can be used to estimate the overall deformation rate accommodated by earthquakes in the fault system. This is the more cautious end-member hypothesis: accounting for the aseismic component of the tectonic deformation process (e. g. Field et al. , 2013)  that could potentially affect individual faults will be the object of future work. As regards the main faults, the database documents two options for each of the three main faults (see Supplementary Figure  S1  in Supplementary Material for alternatives and Supplementary Table  S2  for the full list of faults affected by alternative main fault definitions). For simplicity, the analysis presented herein is done on one of the main fault option configurations, namely the A1B1C1 scenario.
A full hazard and risk analysis should account for the uncertainty in the main fault option choices, which is beyond the scope of this methodological paper. We used main faults with at least one slip-rate measurement resulting in the use of 31 of the 43 main faults within the A1B1C1 Main Fault option configuration. In some instances, a main fault trace can locally deviate from the individual fault traces that comprise it. Due to this, some slip rate measurements may not appear to lie exactly on the main fault trace. In the present paper we exclude slip rate data points located at more than 500 m from main fault traces.
, 2017)  distribution and thermo-mechanical analyses  (Boncio et al. , 2009) , thus exploring two different deformation models. For the other parameters, we consider simplifying assumptions: we assign to each main fault the average of the dip measurements contained in the database, with a constraint that dips do not exceed 55° to account for the fault plane geometries imaged by recent seismic sequences (e. g. ,  Chiaraluce et al. , 2011; Improta et al. , 2019). Again, we note that a full uncertainty analysis should account for the range of surface dip values presented in the database and consider different hypotheses for how the surface dip propagates at depth. In order to define rupture scenarios that preserve the slip rate variability as much as possible, main faults were subdivided into sections (hereafter named MF-sections), here considered to be 10 km long.
6 < Mw < 7. 0, although individual publications usually refer to M greater than ~6. 5/7. 0. The seismicity rates implied by both deformation models (either with 10 km or 15 km thick seismogenic layer) are in general agreement with the data. Not surprisingly, assuming a 15 km thick seismogenic layer leads to a higher overall moment rate budget and higher possible maximum magnitudes along each MF-section. All the data and codes used for this step of the computation are available in the A_SHERIFS_CAD folder of the Github repository.
The fragility model is empirically derived by statistically processing a database including damage data from the Irpinia (1980) and L'Aquila (  2009 ) earthquakes  (Michelini et al. , 2008). Rosti et al. (2020)  defined 12 macro-categories by considering two classes of building heights (i. e. L: 1-2 stores and MH: >2 stores) and six classes of construction age (i. e. <1919, 1919-45,1946-61,1962-71,1972-81, >1981) , accounting for the building code evolution. Our computations are based on the convolution of the hazard curve at each locality with the fragility curves for an L type building built before 1919. The seismic hazard and typological risk calculations are performed using OpenQuake, an open source hazard and risk package  (Pagani et al. , 2014). In Figure  7A ,B we simplified the distribution of seismic hazard and risk by grouping values in four classes, to highlight the internal ranking of hazard and risk amongst the different localities.
Localities with highest hazard and risk are in close proximity to main fault traces that are in highest slip rate classes (FucinoOvindoliPezza and Liri in Figure  7A, B ). There are however exceptions in the centre of the study region where localities close to faults categorized in the lower slip rate class show high seismic hazard and risk values. This is due to the presence of closely spaced faults. Hence, the importance of mapping hazard based on faults compared to seismotectonic zoning approaches that smooth out hazard and risk, ignoring geological information and providing lower resolution results. All the data and codes used for this step of the computation are available in the B_OQ_JOB_GMPE_FRAGILTY_EXPOSURE folder of the GitHub repository.
Furthermore, this representation shows that both MF-sections rely only on one observation point and thus require further field investigation. In Table  2  we show that the majority of the MF-sections participating the most to the typological risk at L'Aquila are parametrized on the basis of few data points and should therefore become priority data collection targets before future seismic hazard/assessments. Although codes presented here are written to read the Fault2SHA CAD structure, they can be easily adapted to read other database structures as long as geo-localized main fault traces and geo-localized fault slip rate information is provided.
DISCUSSION The purpose of this paper is to illustrate the potential of the new generation of fault-based PSHA to link detailed databases of active faults, with fault modellers, seismic hazard practitioners and end-users. The assumptions made at each step of the methodology are traced and can be easily re-questioned in future modelling approaches. The applicability of this new generation of fault-based PSHA, however, requires the availability of detailed data. Building such database is challenging. In Europe such endeavors can only be made at a regional scale, for the time being. The existing EDSF database has the advantage of proposing source models at the Euro-Mediterranean area scale but the disadvantage that the information provided is not traceable. For example, in France, the EDSF fault sources are represented as simplified trace of the information provided in the BDFA (Base de Données des Failles potentiellement Actives, http://bdfa.
irsn. fr/), a compilation of published fault data  (Jomard et al. , 2017). In Italy, the EDSF is based on an already parametrized Database   (Peruzza and Pace, 2002; Boncio et al. , 2004; Roberts and Michetti, 2004; Pace et al. , 2006; Akinci et al. , 2009) , but not considered seismogenic sources in the DISS releases published at the time of those earthquakes. Thus the challenge today to improve assessment of seismic hazard and risk is to incorporate in the pan-European effort such detailed traceable geological information where available. The Faultt2SHA-CAD is a first effort in such a direction. Thanks to the availability of Fault2SHA-CAD, we can propose a new generation of fault-based PSHA with codes that are capable of analyzing complex database structures, linking the variability in the available geological data to the variability in hazard and risk assessment.
This notwithstanding, the results presented here provide a good first order measure of key issues that require urgent action. For geologists, we recommend the pursuit of the acquisition of well-constrained data to improve the parametrization of faults that contribute the most to hazard and risk at specific localities (for some faults there is actually no slip data available in the database). For endusers: we note that fault-based hazard and risk estimates are extremely variable in this region. Thus, depending on the specific needs of the end-users, a much wider range of epistemic uncertainties will need to be explored (combinations of main fault hypotheses presented in the Fault2SHA CAD, different GMPE and fragility curves, to mention just a few) to quantify a fault's contribution to hazard/risk and set priorities accordingly.
Hazard and risk have been estimated here only on the basis of faults. Note that not all faults in the region are equally well informed and it will always be difficult to assume completeness of knowledge in any given area. Thus, future studies will need to discuss the potential contribution of off-fault seismicity which is another source of uncertainty in seismic hazard assessment  (Field et al. , 2013)  as well as the contribution of possible blind faults. Clearly, informing the model with the best data available for a given area will be vital in future studies. In the short-term, we Frontiers in Earth Science | www. frontiersin. org therefore encourage geologist to trace their data in structured databases including locations and certainties in the activity of fault traces, slip-rate paleoseismic measurements and main fault options.
CONCLUSION The new generation of fault-based PSHA proposed here provides a transparent methodology to account for the best geological information available in a given region for seismic hazard and risk studies. In the framework of the ESC FAULT2SHA working group (fault2sha. net), the Central Apennines laboratory (CA-lab) developed a database, which provides geologic data in the form of characterized fault traces, grouped into faults and main faults, with individual slip rate estimates. Using this database, we were able to build detailed fault deformation models, including variable slip rate profiles and multi fault ruptures, and develop novel visualization schemes of the results. The approach developed empowers end-users and decision-makers with the capacity to identify main fault sections that participate the most to the seismic risk of a site as well as understanding and tracing differences of interpretation documented in the database.
Furthermore, data-providers can easily visualize the impact of their data on seismic hazard and risk at specific localities and identify priority locations that require further data collection. It is hoped that this work will motivate other fault-communities to adopt the Fault2SHA CAD structure and the methodology for their own region. The Open Source codes provided in the electronic supplement represent an important deliverable that will facilitate this link, paramount to ensure that model assumptions reflect as much as possible the geological interpretations.