dataesr/scientific-paragraphs-categorization huggingface dataset:
As shown in Fig. 2 a ), according to IEA's report on Task 12: PV Sustainability Activities  [78] [79] [80] , the open ground PV panels market mix in France 2019 is shared by three types of PV panels: multi-crystal Si PV panel (average, 62. 35 %), mono-crystal Si PV panel (average, 33. 04 %), and thin-film PV panel (average, 4. 83 %). And the thin-film PV panel market of France in 2019 comprises mainly Cadmium telluride PV panels (CdTe, 54. 77 %) and Copper indium gallium selenide PV panels (CIGS, 45. 23 %). The PV panel production market has ushered in tremendous technological progress and efficiency improvement since 2005, namely, the time node in ecoinvent v3. 6 database, as discussed in detail by Besseau  [81]  and the Methodology Guidelines for PV LCA released by IEA  [82]. The latest LCI database for PV panels released by IEA  [78]  is used in this report to overcome the time hysteresis of the ecoinvent v3.
PV plant construction: network connection & auxiliaries Following the protocol in Fig. 3 , the network connection unit & auxiliaries apply the identical life cycle boundaries and LCI as defined in the ecoinvent v3. 6 database, where the site preparation process, PV panel installation service, PV mounting system, and the DC/AC inverter, tap water consumption during PV plant installation and operation should be taken into consideration: The DC\AC inverter used has a rated capacity of 500 kW. The LCI of the inverter, the PV panel installation service, and the tap water supplement are directly taken from the ecoinvent v3. 6 database. The LCI of the PV mounting system used in this research is derived from IEA's report on Task 12: PV Sustainability Activities  [78] , overcoming time hysteresis of the ecoinvent v3.
6 database as stated above. The requirement of the site preparation process (including mainly the diesel consumption for land preparation), PV panel installation service, tap water consumption during PV plant operation was recalculated from the data in the ecoinvent v3. 6 database to match the installed capacity of the PV plant to satisfy the FU. The installed capacity of the inverter equals the PV model's installed capacity. The amount of the PV mounting system, in m 2 , equals the land area needed for the PV electricity plant, which is 100 m 2 as assumed. Equation (  1 ) below calculates the 100 m 2 PV plant's installed capacity accessed in the solar-to-H2 pathway. PVplant peak PVplant A P P GCR    (1) Where, PVplant P : Nominal maximum power of the 100 m 2 French national benchmark PV plant.
PV plant operation: solar irradiance and PV electricity generation mix in France The daily or yearly solar irradiance determines the PV electricity generation. The accumulative yearly solar irradiance (Wh/m 2 ) for 13 administrative regions of mainland France 2019 are taken from the CAMS radiation service dataset "AGATE" with McClear version 3 and CAMS radiation bias correction  [86] , as shown in the supplementary materials. To utilize the maximum annual solar irradiance potential, the tilt angle is fixed at 30 °and azimuth at 180 °for PV panels installed in the French national benchmark PV plant. With France's yearly solar irradiance data, we can calculate the total electricity production during the whole PV power plant lifespan with Equation (  2 ), as standardized by IEA  [76]. PV PVplant PV HTC Irr Elec P PR Life ALR I      (2) Where,.
90 %) electricity production. One can calculate the specific ratio from France's annual total wind electricity production and the production amount by type. The annual accumulative wind electricity production in France 2019 is 34. 1 TWh from both off-shore and onshore wind power  [94]. And total off-shore wind electricity production can be calculated by Equation (  4 ) below. Besides the off-shore wind electricity, other electricity comes from the onshore wind turbines. Wind Power Database (28th July 2021 last version)  [95]  summarized the technical details for all wind power plants (farm location, turbine model and capacity, hub height, turbine number, etc. ) in France. On average, the market share of onshore wind turbine assembly mix is 11. 27 % (<1MW wind turbine): 82. 16 % (1-3 MW wind turbine): 6.
Wind plant construction: plant network connection & auxiliaries Based on the capacity level of the wind turbine and onshore/off-shore features, the types and amount of network connection unit & auxiliaries needed, i. e. , the DC\AC transformer, cables, tower, foundation, lubricating oil, will vary. In the wind-to-H2 pathway, the LCI of network connection & auxiliaries to each corresponding wind turbine assemblies under different capacity levels are exported from the ecoinvent v3. 6 database. We can directly find the amount needed for 1 kWh wind electricity production.
Wind plant construction: transportation process Transportation of all components involved in wind plant construction and operation, e. g. , wind plant network connection & auxiliaries, each type of wind turbine assembly, as shown in Table  2 , from the production site to the installation site, are not covered by the ecoinvent database. Thus, following the logic of Fig. 3 , to cover the transportation from the production site to the installation site, this research applied the same two-stage land transportation process as defined in Section 2. 3. 1 of each component involved, as shown in Table  2. For off-shore wind turbine assembly, this research applies overseas transportation to 10 km (on average). The mass of each component summarized in Table  2  can be found in their corresponding LCI shown in the supplementary materials.
H2 compressor and compression process This research uses the most common H2 compressor type, the diaphragm compressor, to model the H2 compression process in the RE-to-H2 pathway. LCI per unit diaphragm compressor can be found in the premise database  [105]. Since no exact data for diaphragm compressor lifespan inconsistent with the diaphragm compressor LCI is found during the research. To better represent the actual situation in France 2019, we estimate the diaphragm compressor unit needed in the RE-to-H2 pathway with Equation (8).
H : Total amount of H2 can be compressed during the entire lifespan of one compressor unit, which is 5906475. 3 kg/unit on average, as shown in the premise database  [105, 106]. Furthermore, this research applies the two-stage transportation process defined above to each component involved in the AE and PEM electrolysis unit (e. g. , the electrolysis stack, BOP, and H2 compressor) to cover the transportation process from the component production site to the installation site. The mass of each component can be found from their corresponding LCI as shown in the supplementary materials.
Parametric LCA model & scenarios analyzed Compared with other LCA software, due to their opensourced features, the Python framework for LCA: Brightway2 (BW2) and the algebraic parametrization BW2 layer: lca_algebraic library  [107, 108]  enable a more flexible environment for LCA research innovation, e. g. , dynamic LCA, life cycle model simplifying  [108] , as well as the quick check for impact categories correlation  [107]. To perform a complete and systematic life cycle interpretation, thus meeting the goal and scope of the research, this research builds the French national benchmark parametric LCA model of all the pathways studied together with the corresponding LCI and data source in BW2 based lca_algebraic library. The LCA results and the related interpretation are then generated by simulating the parametric LCA model.
6 database  [115]  indicated), which will produce a large pile of radioactive waste during PWR unit operation. Inside the wind electricity supply chain, the production process of copper (the primary component for 1-3 MW wind turbine assembly) is the main contributor to the FTA score. Under the current French context, major copper production technology is flash smelting furnaces reducing and refining, which is sulfur dioxide emission-intensive. The main contributor for IR can be further attributed to the production of electricity consumed during wind turbine assembling, mounting, and turbine end-of-life (EoL) removal. FD and REI mainly contributed from massive low-alloyed steel consumed during the construction of 1-3 MW wind turbine assembly. This low-alloyed steel consumption also ranked as the second significant contributor of IR and FTA.
(7)]]: Net water splitting electricity demand with a 100 % efficiency in LHV kWh/ kg H2, which is 33. 3 kWh/kg H2 (literally equals LHV of 1 kg H2)[103]. Water electrolysis unit global efficiency covering all facilities in the system, e. g. , stack, electronics, pumps, safety equipment, infrastructure. Both for PEM, AE unit (average, 60 %) are summarized based on data from Bareiß et al. [99]and ecoinvent v3. 6 database. One can calculate the total H2 production amount in the RE-to-H2 pathway, e. g. , total H2 produced during the entire lifespan of one unit French national benchmark wind farm or PV plant utilizing Equation(7).
Acknowledgments This research is supported by the  National Natural Science Foundation of China  (No. 51576087 ), the  Foundation of State Key Laboratory of Coal Combustion  (No. FSKLCCA1902 ), and the Double first-class research funding of  China-EU Institute for Clean and Renewable Energy  (No. 3011120016 ). We also would like to express our sincere thanks to professor Philippe BLANC, doctor  Romain BESSEAU , and all the colleagues in  Centre Observation, Impacts, Energie  for their selfless help and valuable suggestions during the research and paper revision.
: (7)]]: Net water splitting electricity demand with a 100 % efficiency in LHV kWh/ kg H2, which is 33. 3 kWh/kg H2 (literally equals LHV of 1 kg H2)[103]. Water electrolysis unit global efficiency covering all facilities in the system, e. g. , stack, electronics, pumps, safety equipment, infrastructure. Both for PEM, AE unit (average, 60 %) are summarized based on data from Bareiß et al. [99]and ecoinvent v3. 6 database. One can calculate the total H2 production amount in the RE-to-H2 pathway, e. g. , total H2 produced during the entire lifespan of one unit French national benchmark wind farm or PV plant utilizing Equation(7). E3S Web of Conferences 350, 01021 (2022) E3S Web of Conferences 350, 01021 (2022) https://doi. org/10. 1051/e3sconf/202235001021 https://doi. org/10. 1051/e3sconf/202235001021 EREGCE 2022 EREGCE 2022 H 2 Elec compress : Compressor electricity demand for compressing H2 from stack outlet pressure (30 bar) to 200 bar, which is 3.
Onshore wind turbine capacity factor ( onshore Cf , %) PD type: Normal Default: 25 Std: 0. 01 Default and Std fitted by oracle crystal ball software based on capacity factor from Statista dataset[96] and corresponding land area[113] of 13 administrative regions in France. Wind farm lifespan PD type: Triangle Min: 15 Default stands for the average[81]. Min[81] stands for frequently overload turbine with long ( wind Life , year) Default: 20 working time. Max[81] stands for turbine under Max: 25 good maintenance.
Observations SST observations from the HadSST4 data set  (Kennedy et al. , 2019)  are used. These observations have the advantage of providing an estimate of the measurement uncertainty, which encompasses the treatment of incomplete data coverage, homogenization uncertainty etc. through an ensemble of 200 equiprobable realisations over the 1870-2021 period. Compared to other datasets, this ensemble allows a more comprehensive estimation of the measurement error with the possibility of calculating the temporal covariance of uncertainty with the 200 members. Note that I do not consider other datasets because they usually fall within the uncertainty of HadSST4. The median of the 200-member ensemble will be considered as the best-estimate, while all realisations are used to estimate the measurement uncertainty.
Acknowledgements. This work was supported by the  European Union 's  Horizon 2020 Research and Innovation Programme in the framework of the   EUCP project  (Grant Agreement  776613 ), the  CONSTRAIN  project (Grant Agreement  820829 ) and  Météo-France. I thank  Hervé Douville ,  Aurélien Ribes  for fruitful discussions about this work. I thank the climate modeling groups involved in CMIP6 exercises for producing and making available their simulations. I thank the  ETH Zurich  for providing CMIP6 data through their cmip6-ng interface (http://dx. doi. org/10. 5281/zenodo. 3734128). The analyses and figures were produced with the R software (https://www. R-project. org/) and the  NCAR Command Language Software  (http://dx. doi. org/10. 5065/D6WD3XH5).
Code and data availability. The datasets generated and analysed during the current study are available at: https://doi. org/10. 5281/zenodo. 6952546. The HadCRUT5 dataset is available at https://www. metoffice. gov. uk/hadobs/hadcrut5/index. html. The HadSST4 dataset is available at https://www. metoffice. gov. uk/hadobs/hadsst4/index. html. The CMIP6 datasets are available at https://esgf-node. llnl. gov/projects/ esgf-llnl/. All required programs to run the statistical method are in the associated KCC R package, which is available under a GNU General Public License, version 3 (GPLv3), at https://doi. org/10. 5281/zenodo. 5233947.
Methods Input data. Occurrence data collection. For the eight species, we compiled contemporary occurrence data from three available public databases: The Ocean Biogeographic Information System (OBIS, http:// www. iobis. org), the Global Biodiversity Information Facility (GBIF, https:// www. gbif. org) and Fishbase (http:// www. fishb ase. org). Occurrence data may be collected from various sources (e. g. , scientific surveys, on-board observers, geo-referenced fisheries catch or diving observations), independently of the sampling protocol (e. g. gear, mesh size). However, because such data are not mainly based on scientific surveys (e. g. MEDITS, ICES trawling surveys), they may suffer from spatial heterogenous sampling effort (e. g. due to accessibility or survey equipment  49  ), potentially leading to a risk of spatial niche truncation (i.
e. , when only a subset of the environmental conditions experienced by a species across its full range is characterized  18  ). To alleviate this risk and build the most up-todate datasets, (i) we also retrieved all available species observations from the literature (Supplementary Material 1), especially in limiting environmental conditions (i. e. at the edge of the environmental niche) and (ii) evaluated the species response curves to detect any niche truncation (see "Species distributions models and environmental variables" and "Ensemble model selection"). The resulting occurrence data ranged from 1950 to 2017. Recent records (i. e. , since 1990) represented 72. 82 ± 6. 12% of the total species occurrences. Past records (i. e. , before 1990) represented 10. 79 ± 3. 7% of the total species occurrences.
Undated-and therefore discarded-species occurrences represented 16. 37 ± 4. 58%) of the total species occurrences. To avoid a biased estimation of the niche due to low quality occurrence records  50  , past or undated occurrence were only kept along the distribution edge when confirmed by recent records from the literature (Supplementary Material 6). For each species, we preprocessed the data and improved the quality of the eight occurrence record datasets by removing (i) unreliable observations-such as preserved specimen-and incorrect taxonomic identifications, (ii) duplicate occurrences and (iii) locational errors, such as geographical outliers. The resulting number of observations ranged from 1211 for gilthead seabream to 15,827 for common sole. For each species, we aggregated occurrences on a 0.
Environmental filter and pseudo-absence selection. Spatially biased sampling effort in presence-only species datasets-i. e. , when data sources are not comprehensive across the study area-can induce a bias in the environmental space in which the spatial distribution of species is modelled 54. To consider this potential effect for each species, we used an environmental filter to keep only a single observation among a group of occurrences characterized by a similar combination of environmental values (Supplementary Material 3), as performed in the GARP (Genetic Algorithm for Range Prediction) modelling system (program RASTERIZ  55  ). We determined the following resolution for environmental filtering: 0. 5 °C for temperature-related variables, 0. 5 psu for SSS and 0. 5 mol C. m -2. s -  for PP (used in logarithm).
Ensemble model selection. For each species and combination of environmental variables, the algorithms that best reflected the observed distribution were selected according to the Continuous Boyce Index (CBI), an evaluation metric specifically designed for presence/pseudo-absence datasets. We retained algorithms with a CBI value over 0. 5  58. To ensure the ecological realism of our models, we discarded spurious responses to environmental factors (e. g. , bimodal response to temperature) and selected the simulations for which response curves matched a priori expectations (see  22  for further details). The inter-algorithm divergence in responses curves (i. e. a major criticism of ensemble modelling procedure  19  have been quantified by means of the standard deviation (SD) computed from all retained simulations (Figure  S2.
For each GCM and RCP scenario, we then corrected model-based temperature data according to their difference with observation-based data for each geographical cell. This procedure ensured a perfect correlation (Pearson coefficient r = 1), no RMSD and the same SD between model-and observation-based datasets for a common period  61. Projected changes in species environmental suitability index. For each future period, we estimated the occurrence of each species per geographical cell (0. 1° × 0. 1°) by combining our ensemble modelling method with environmental data originating from the five GCMs and the three RCP scenarios. For each species and future period, we calculated the proportion (in km 2 ) of the studied area that was projected to contain a suitable habitat in order to quantify (as percentage) potential changes in the spatial extent of species, relative to the contemporary (1990-2017) period.
As an index of the potential consequences of distributional shifts at the scale of each Mediterranean and European EEZs-we assessed the potential consequences of distributional shifts at the scale of each Mediterranean and European EEZs under the assumption of uniform spatial distribution of catches in EEZs. We calculated ESI by EEZ as the mean by pixel (0. 1° × 0. 1°) in each EEZ  62  , stretching from the coastline out to 200 nautical miles over which a country has special rights regarding the use of marine resources. We also calculated the total catch landings for each species over the period 1990-2017 (in logarithm). For each species and EEZ, we downloaded the mean catch data from the Sea Around Us database (http:// www. seaar oundus. org/) for the period 1990-2017 (i. e. , the most recent available information).
Data availability These data were derived from the following resources available in the public domain: the Ocean Biogeographic Information System Mapper (OBIS, http:// www. iobis. org/ mapper/), the Global Biodiversity Information Facility (GBIF, https:// www. gbif. org/) and Fishbase (http:// www. fishb ase. org/). Received: 14 February 2022; Accepted: 13 May 2022.
, 1998; Bravard, 1999). Today, 106 river flow obstacles are documented for the Bienne Basin (Fig. 1b , ROE database, www. data. gouv. fr). Spatial density of river infrastructures is similar in the upper and the lower parts of the Bienne basin with 0. 3 and 0. 4 low-head dams or sills per km of river straight, respectively. However, in the upper part, these obstacles are concentrated near and in the city of Morez (Hauts de Bienne municipality), while they are more dispersed in the lower part. Two large dams also influence the Bienne River flow: the Etables Dam (27 m), built between 1930 and 1932 downstream of the city of Saint-Claude and the Coiselet dam (37. 5 m high), built across the Ain River between 1968 and 1970 which controls 2. 6 km of the Bienne River level at its outlet (Fig. 1b ).
Considering the effect of dams, water levels are anthropogenically controlled on 14% of the river drop and 15% of the river course. Consequently, sedimentary transport in the Bienne River has been highly altered  (Landon et al. , 1998 (Landon et al. , , 2000)). With data from the monitoring of the river system since 1993 and given by the Haut-Jura Natural Park as the main stakeholder (www. parc-haut-jura. fr) predominant manifestations of these disturbances can be detailed as follows: (i) a dramatic decrease in sediment transport from 25,000 to 8000 m 3 /y since the 1950 s, (ii) a lack of>550,000 m 3 of riverbed sediments in the lower part of the river, (iii) a deep incision of the riverbed, up to 2 m in some sections, (iv) a channel narrowing of about 40% to 50%, (v) an armoring of the stream over most of the river course (partially or totally over the channel width, depending on reaches).
These hydro-sedimentary issues also affect the Ain River downstream of the confluence  (Marston et al. , 1995; Piégay et al. , 2008; Dufour and Piégay, 2010). The population density of the Bienne Basin is low, with an average of 60 inhabitants/km 2 (www. insee. fr). The two most important populated areas are the Hauts-de-Bienne / Morbier area (approximately 7,700 inhabitants) and Saint-Claude (9,500 inhabitants, population in 2018, www. insee. fr). Many industries are historically present in the Bienne Basin as nail factories, wireworks, edge-tool factories, undercutting, polishing, coating, eyewear, watchmaking, plastic industries, etc. A total of 1032 industrial plants (historical and currently operating) are documented in this basin (Fig. 1b , BASIAS database, www. georisques. gouv. fr).
Spatial and temporal influence of trace element sources To explain the strong decrease in TE enrichments along the river, we intended to quantify the provenance of sedimentary contaminants recorded in the core sampled at the river outlet. For this purpose, we applied the fingerprinting procedure defined in the FingerPro R package  (Lizaga et al. , 2018 , Lizaga et al. , 2020; Gaspar et al. , 2019). In this approach, all the samples from stations 1 to 5 were considered as potential sources and those of station 6 were considered as a mixture of these sediments. We integrated the normalized TE-Al ratio dataset for the most enriched TEs (Bi, Cu, Hg, Pb, Sn, and Zn with EF > 5, c. f. §4. 3). The first step of this procedure consists in identifying which geochemical tracers are the most discriminant to characterize anthropogenic sources.
To this end, a stepwise multivariate discriminant function analysis (DFA) was used to determine the TE-Al ratio, which passed a nonparametric Kruskal-Wallis H test. In our dataset, Hg, Bi, and Sn were excluded with this procedure (p > 0. 05). The second step corresponds to multivariate linear discriminant analysis (LDA) performed with the remaining tracers  (Cu, Pb, and Zn). This procedure allows the clustering of three well-defined endmembers as samples of station 1, station 2, and a group composed of all samples from stations 3, 4, and 5 (Fig. 7a ). These results are consistent with the spatial distribution of the EF defined in Section 4. 3. In the last step of this procedure, a standard multivariate mixing model was used to calculate the geochemical fingerprinting index quantifying the relative weights of these three endmembers in the composition of the mixture sediments (sedimentary core from station 6; Fig.
Acknowledgments This research program received financial and technical support from many stakeholders engaged in the preservation of the Bienne Basin: the  Haut-Jura Natural Park , the  Bourgogne-France-Comté region , the  Rhône-Méditerranée-Corse Water Agency , the  Jura Department , the Saône-Doubs local public basin establishment, fishing federations of the Jura and national, and the speleology club of Saint Claude.