dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions In this paper, we use a unique dataset that combines information on carbon emissions, imports, imported emissions and environmental policy stringency, all at the firm level, on a panel of around 5,000 firms operating in the French manufacturing sector to show that imported carbon emissions cause a decrease in French firms' domestic emission intensity. Most importantly, we provide evidence suggesting that this carbon import effect is not due primarily to a pollution haven effect (except perhaps in energy-intensive sectors) but to a general increase in the propensity to import of French companies. Finally, we find that the stringency of domestic carbon pricing policies (as proxied by energy prices) has a larger effect on firms' domestic emission intensity than carbon offshoring. However, these results should be taken cautiously because the identification of energy price effects on emission presents some unresolved challenges.
and the  25th EAERE  conference for useful comments. This work was supported by  Horizon 2020 Framework Programme , project  INNOPATHS  (grant number  730403 ). This work uses confidential microdata from the  Institut national de la statistique et des études économiques (INSEE) , made available through the  Centre d'accès sécurisé aux données (CASD) Secured Data Access Center. The CASD work was also supported by a public grant overseen by the  French National Research Agency (ANR)  as part of the  Investissements d'avenir program  (reference:  ANR-10-EQPX-17 -CASD ). This version of the paper supersedes a previous version entitled "Carbon  Offshoring: Evidence from French Manufacturing Companies. " The opinions expressed and arguments employed in this paper are those of the authors and should not be reported as representing the official views of the OECD or of its member countries.
Two related papers using firm-level data find that the EU-ETS increased outward FDI in Italy (Borghesi et al. 2020)  and Germany (Koch and Mama 2019). However, they do not directly focus on the pollution leakage effect of the EU-ETS. 15405982, 0, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/caje. 12653 by Test, Wiley Online Library on [27/04/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License.
In the EACEI survey, large firms are always surveyed while small companies are randomly surveyed. Thus, the panel dimension of the data is often more available for large firms than for small ones. 15405982, 0, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/caje. 12653 by Test, Wiley Online Library on [27/04/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License.
Les trajectoires démographiques récentes des villes petites et moyennes Les études consacrées aux villes petites et moyennes montrent donc le lien très fort entre évolution démographique et évolution de l'emploi industriel. Pour observer la distribution de ces dynamiques entre l'ensemble des villes petites et moyennes, nous construisons une base de données à partir de laquelle nous pouvons étudier les spécialisations industrielles et les trajectoires démographiques. Cette base de données comprend toutes les villes pour pouvoir y contextualiser les villes petites et moyennes avant de les en extraire. Or, il n'y a pas, en France, de définition unifiée de la petite ville  (Édouard, 2012 ; Laborie, 1979)  ou de la ville moyenne  (Santamaria, 2012) , de même qu'on peut constater l'absence de cadre théorique unifié pour définir les villes en décroissance  (Fol et Cunningham-Sabot, 2010).
Notre démarche consiste donc plutôt à opter construire notre base de données à partir des périmètres statistiques. Comme périmètre urbain, nous avons donc retenu l'unité urbaine (ou agglomération). Fondée sur un nombre d'habitants aggloméré (2 000) et une continuité du bâti, elle possède l'avantage, par rapport à des périmètres fonctionnels telles que l'aire urbaine ou l'aire d'attraction des villes, de comprendre l'ensemble des communes urbaines. L'aire urbaine (comme l'aire d'attraction) retire les plus petites communes urbaines car elles n'atteignent pas un seuil d'emploi (ou une densité de population) suffisant pour être intégrées au périmètre. Au contraire, de nombreuses communes rurales sont prises en compte à cause de l'intégration des couronnes périurbaines. Pour distinguer les villes petites et moyennes parmi les 2 410 unités urbaines définies dans le périmètre 2020 2 , nous avons également recours à des seuils de population courants : moins de 20 000 habitants pour les petites villes et moins de 100 000 habitants pour les villes moyennes.
Pour suivre les recompositions de l'emploi des villes petites et moyennes durant les dernières décennies, nous analysons les agglomérations grâce à une typologie de l'évolution du taux d'emplois industriels réalisée par méthode d'analyse de séquences par appareillement optimal. Cette analyse est complétée par le calcul d'un indice de spécialisation globale. Formalisation de l'emploi industriel dans les bases de données de l'emploi.
Contemporary governments are awash in quantification, as numbers and algorithms play an ever-greater role in public decision-making processes. In the field of defense and diplomacy, the development of armed conflict databases holds out the promise of improved predictive capacities and early warning systems. While this dynamic can be witnessed across a range of European countries, notably Germany and the Netherlands, it appears to be much less present in France. Diplomats and militaries make only marginal use of the available databases, judged both inadequate in their own right and ill-adapted to strategic analysis. This article proposes two explanatory variables to explain this specific appreciation: not only is a relatively limited training in quantitative methods offered to French diplomats and militaries, which leads to a specific use of numbers, but there is also a broader picture link to the will of protecting "digital sovereignty" in the international realm.
Les gouvernements contemporains se retrouvent inondés de quantifications, car le rôle des chiffres et des algorithmes n'a jamais été aussi important dans les processus de prises de décisions publiques. Dans le domaine de la défense et des politiques stratégiques, l'élaboration de bases de données sur les conflits armés s'accompagne d'une promesse, celle d'une amélioration des capacités de prédiction et des systèmes d'alerte précoce. Bien que cette dynamique soit visible dans de nombreux pays d'Europe, notamment en Allemagne ou aux Pays-Bas, il semble qu'elle soit bien moins présente en France. Les diplomates et militaires n'utilisent les bases de données disponibles que de façon marginale, les estimant par nature inadéquates et mal adaptées à l'analyse stratégique. Cet article propose deux variables explicatives pour éclairer cette appréciation.
He is the author of numerous books including La bienveillance dans les relations internationales. Un essai politique (Paris, CNRS Éditions, 2022). Louise Beaumais is a PhD candidate at Sciences Po/CERI Paris, France, and member of the Datawar research project. She works on the use of quantitative data in foreign policy in France and in the United Kingdom. But is this trend consistent in all spheres of public actionincluding foreign policy and defense-and is it consistent across all national contexts? This paper focuses on a specific quantitative tool developed in strategic studies, namely armed conflict databases (ACD). ACD refer, in this study, to any database that furnished quantitative data on issues related to armed conflict. These can be databases dealing with the direct consequences of conflict (deaths, casualties, etc.
), state capacity of the belligerent powers (defense expenditures and military capabilities), or the risk of conflict itself (early warning systems and risk indicators). Various in origin-academia, institutes for strategic studies, international initiatives within the European Union (EU) or the United Nations (UN), and think tanks-ACD have proliferated over the past decade. They are presented as a means to help practitioners make better decisions, a decisive asset for apprehending the international environment. These databases may, for example, provide raw materials in the form of quantitative dataquantitative data, as understood here, are any information that can be counted or measured and given a numerical value-to enhance analysis. They may also be aggregated or modeled to uncover new causal relationships, to control black swans-events that are unforeseeable by definitionor to forecast potential crises and in a monitored region.
In what follows, the focus is on how practitioners use quantitative data from ACD, not the activity of designers or data scientists. Despite these perceived assets, there is considerable variation in actual uses of ACD in foreign policy. Some countries, such as Germany, the Netherlands, the United Kingdom, and the United States, invest heavily in the exploitation of conflict data. However, in countries such as France, it does not seem institutionalized yet. Practitioners even seem to have put up real resistance not only to the possibilities offered by ACD but also to quantitative data in general. This "exception" comes as a surprise: France has about the same resources as Germany and is even more committed to international crisis management. What is more, following Brexit, France is the only state in the EU with a permanent seat on the United Nations Security Council and an extensive military presence abroad.
These factors invite us to examine in greater detail the attitudes of diplomats and military officers in the French ministry of foreign affairs (MEFA) and the French ministry of armed forces (MAF) 3 toward the prospect of using databases both to predict and comprehend conflicts and to prepare operations: Why has France resisted the integration of ACD, and more generally of quantitative data, into foreign policy processes? Is this resistance linked to policy officers' lack of awareness of ACD and other quantitative tools? Is it a lack of interest and training? Is it the result of an institutional mistrust toward quantitative data? In other words, is relative "backwardness" in the use of quantitative tools the result of an intentional choice? This article does not undertake a systematic comparison.
I. Explaining (Non-)Conversion to Numbers Historically, the quantification of conflict takes its roots in the academic field, with the so-called behavioralist turn in international relations (IR) of the 1960s ( Dieckhoff,  Martin, and Tenenbaum 2016 ), and in the construction of the first generation of databases "measuring" conflict dynamics and potential causal indicators. Since then, work based on large-N datasets (e. g. , those produced by the Correlates of War [COW] project and the Peace Research Institute of Oslo [PRIO]) has bolstered and refined a range of propositions, such as the decline of interstate conflict since 1990 ( Harbom, Melander, and Wallensteen 2008 ) and the role of foreign aid in explaining the success of post-conflict reconstruction ( Girod 2015 ). The importance of this research has grown in tandem with the depth and quantity of available databases expanded.
PRIO, for instance, now offers more than thirty datasets on topics ranging from religious cleavages to urban social disorder. 2 esearch institutes, charities, nongovernmental organizations, and other institutions have likewise all drawn on insights from quantitative studies to forecast armed conflict and adapt their analytical and normative stances ( De Franco  and Meyer 2011 ; Ward et al. 2013 ; Colonomos 2016 ; Meyer,  De Franco, and Otto 2019 ). These initiatives have emerged as a "second wave" in recent decades. Some databases are interested in the quantification of political violence, such as the Armed Conflict Location Event Data (ACLED) project or the Action on Armed Violence (AOAV) initiative, while others specialize in defense and security matters, such as the annual reports on military expenditures produced by the Stockholm International Peace Research Institute (SIPRI) or the International Institute for Strategic Studies (IISS).
Others still have focused on establishing world performance indexes, such as the Fragile State Index (FSI) or the Global Peace Index (GPI). In the field of foresight and anticipatory action, the EU and the UN have also begun to implement projects based on quantitative data, such as INFORM or UN Global Pulse. One may wonder: What is driving the multiplication of such initiatives? What is the real influence of these databases? Are they used by conflict practitioners, beyond the academic sphere? These questions are all the more important insofar as quantitative data are thought to be a significant asset in policymaking. An extensive scholarly effort to come to grips with these questions dates back decades, with new impetus from the turn of the millennium. One branch has sought to reconstruct how statistics came to be considered a "science of the state" ( Staatskunde ) in the nineteenth century ( Mann 1986 ; Stigler 1986 ; Porter 1995 ;  Desrosières 2008a , 2008b ).
g. , Goldstone et al. 2010 ; Ward et al. 2013 ), not to mention genocides (see, e. g. , Heidenrich and  Heidenrich 2001 ; Goldsmith et al. 2013 ; Verdeja 2016 ;  Nanlohy, Butcher, and Goldsmith 2017 ), via the exploitation of quantitative models and databases with and the aid of ever more sophisticated tools (such as machine-learning algorithms). Part of the academic sphere remains quite cautious on what could realistically be achieved at a policy level. Voices were raised quite early on regarding the many barriers to be removed to make models more consistent at an academic level-not to mention policymaking ( Cederman and  Weidmann 2017 ; Chadefaux 2017 ; Hegre et al. 2017 ; Blair  and Sambanis 2020 ; Hegre, Nygard, and Landsverk 2021 ). The same authors often even question the desirability and utility of such methods at a political level.
Yet, these words of precaution are barely audible amid the clamorous excitement surrounding new avenues to be opened up by quantitative data. This is even more the case since the "second wave" of data-driven initiatives arrived, and with it a serious drive to market these databases. However, while there is a lot of research on the supply side-scholars working with and on quantitative data and models, the demand side-practitioners who could use these databases-remains understudied. What is more, the existing literature spends a lot of time discussing if data should, or should not, be used in policymaking; however, there is very little research looking at actual uses, and even less research trying to explain why there might be variation in those uses. By exploring the different uses and feelings of two types of French practitioners regarding ACD and quantitative data, this cross-institutional study is a first step to fill the gap.
II. Methodology: Two Paths for Describing the French Approach Our study is based on two broad types of empirical evidence: a range of approximately thirty interviews conducted within the MEFA and the MAF from September to December 2021 (see appendix 1 for details) and a survey circulated at the Centre des Hautes Études Militaires (CHEM-Centre for Advanced Military Studies), an organism that provides training at the strategic level. This section specifies how these data were constituted. Due to the explorative nature of our research, we proceeded in an inductive fashion, relying mainly on interviews with French foreign policy practitioners in various departments. We thus identified directorates, sections, and subsections within the MEFA and the MAF we considered representative of the use of quantitative data (and therefore ACD) for analytical or foresight purposes in both ministries.
An additional survey at the CHEM-a major component of the Enseignement militaire supérieur (EMS) de la défensewas conducted in order to observe how conflict-related databases are discussed at the highest level. 5 Coming from the army, air force, navy, gendarmerie, and the defense procurement agency, the thirty yearly auditors are experienced senior officers who have held command responsibilities. For one year, participants are embedded in a political-military and strategic training program that covers operational areas, preparation for the future, management and organization of the armed forces, and interministerial and international aspects of defense and security issues. CHEM is the last training program for senior officers who will be called upon to perform major management functions in the French army.
III. On the Traces of Armed Conflict Databases: Targeted Use, Specificity, and Rhetoric Use The first finding of this study is that, within French ministries, the use of ACD is very limited, mainly because the use of quantitative data in general is minimal. In fact, one could argue that the French data consumption system in foreign policy is confined to either highly specific or opportunistic uses. This part of the paper describes this limited data consumption system to better demonstrate how ACD may be incorporated into it.
12 As one officer explained, "use of quantitative data is not that frequent, except in support of a statement, an analysis, or a hypothesis, but it is more momentary and distended than continuous. "  13 In other words, quantitative data are invoked in support of conclusions arrived at by other means. Their function is not so much analytical as a rhetorical: Numbers distort our perception, but sometimes we use numbers to influence the perception of others. It's political. 14 pending on the data [and]what we do with it, we can make it say whatever we want. 15 e power of persuasion and the credibility associated with numbers is thus a decisive factor, notwithstanding recent arguments to the contrary ( Baele, Coan, and Sterck 2018 ). Here, the source of the quantitative data mobilized may be an ACD, especially since these are readily available and tender a vast array of definitions, dates, and categories.
However, institutional databases (e. g. , EU, UN) are also summoned in connection with thematic issues and may possess an additional aura of legitimacy-especially for practitioners unfamiliar with the academic world. 16 Finally, it is important to highlight that this rhetorical use of quantitative data paradoxically reinforces practitioners' mistrust in quantitative analysis: since it is known that such data can be malleable, they are also thought to be manipulable by others in their use-whether discursive or analytical-and subject to politicization. 17.
Within the MAF, for example, there is a specific office in charge of analyzing, comparing, and cross-checking international defense expenditures. Practitioners from this office mobilize a variety of sources, internal and external alike (e. g. , North Atlantic Treaty Organization (NATO) and EU external action service reports). Data are regularly drawn from the most well-known ACD, such as the Military Balance + of the IISS and the SIPRI arms transfers, arms industry, and military expenditure databases. This finding echoes the CHEM survey results: among the respondents, the (few) military professionals who use opensource databases also consult IISS and SIPRI data (chosen from a list of twenty-three databases; see appendix 2 ). Based in London, the IISS generates an annual assessment of the military capabilities and defense budgets of more than 171 countries as well as topical information on particularly momentous subjects.
Highlights in 2021 included the effects of the coronavirus pandemic, progress (or lack thereof) of European defense, and military modernization in China and Russia, all relevant to France's foreign policy interests. This is not a surprise, since the IISS works closely with NATO and the EU. The institute is, in fact, more than a producer of data: it also organizes colloquia and publishes books, blogs, and analytical tools. 19 Since 2017, it has also offered an online database, Military Balance + , available at any time and updated in real time. In exchange for a (nonpublic) subscription fee, it provides defense budget data, procurement data, information on military deployments, and policy and capability analyses by region and even by country. SIPRI, based in Stockholm, is best known for its yearly report on "armament, disarmament and international security," derived from its own online databases.
The institute also generates analyses of conflict management and chemical and biological warfare. Thus, when mobilizing quantitative data from those ACD, the main ambition of French military officers is two-fold: to gauge the global strategic situation (especially in terms of balance of power) and thereby to justify future defense outlays. It is not so much the raw facticity of the data that counts as the documentation of change over time: as one practitioner said, "what matters to us is the long-term trend, [. ]  what it says about a country's ambitions and tactical operational effectiveness. " 20 Once practitioners have established a strong trend, they typically go on to unpack the issue in more qualitative terms. On the other hand, diplomatic practitioners from the MEFA have a very different use of ACD-far less structuring.
d. 20 Interview 12 (2021). 21 Interview 17 (2021). of verifying analyses of "fragile states" and the allocation of humanitarian funds, particularly when integrated into European projects. This process is facilitated by internal indicators; MEFA practitioners exploit ACD to gain perspective on quantitative data collected in the field. Outside ACD, however, they do use quantitative data from international organizations (mostly UN bodies), which relate to displaced populations, immigration, or economic matters (GDP, imports, exportations, market share, etc. ).
The Effects of Using Quantification The use of ACD within the French ministries in charge of foreign policy and defense is quite delimited. Quantitative data derived from it are mainly mobilized as a rhetorical strategy, not as a tool of analysis-except for defense expenditures, and, to some extent, "fragile states. " That said, purely rhetorical use does not exclude concrete effects at a cognitive level, most obvious in the reinforcement of preexisting biases. Quantitative data may thereby promote wishful thinking, with the risk that practitioners themselves fall prey to the mirage of certitude conjured by subjective and politicized interpretations of quantitative data. As Andreas and McGreen explain, when information supports a person's preconceived notions, he or she is less apt to question it, while when the opposite is true, he or she can be quite adept at summarily dismissing it (…).
31 wever, this is slightly different for military officers. For instance, the CHEM survey shows how the integration of quantitative data in their educational background runs from marginal (45 percent of respondents) to moderate (27 percent) to significant (27 percent). Most (64 percent) had significant previous experience with the manipulation of quantitative data, but not conflict databases as such. Finally, when choosing between "optimistic," "neutral," "inexperienced," or "pessimistic" to qualify their attitude toward quantitative data, only one person admitted to being "inexperienced" and the majority (64 percent) proclaimed themselves to be neutral. Thus, militaries are more used than other practitioners to handle quantitative data. This can be explained by previous experiences at an operational level, where quantitative data-notably through the digitization of the battlefield-have been increasingly used since the 2000s ( Lefeez 2017 ).