dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, it briefly debates the use of LCSA methods in assessing the stage of product/ service life cycle during manufacturing enterprises' activities and operations. "Theoretical backgrounds" section gives some definitions of EPS method and impact monetization. Moreover, it provides an overview of the BDA tools and techniques used in this study. While "BDA-based LCSA approach" section presents the proposed approach which combines BDA and LCSA methods. Besides, "Experiments results and analyses" section demonstrates the applicability of the developed approach, where a case study from the corporate environmental impact database is used, and the numerical results are analyzed. Finally, "Conclusion" section concludes the paper with some challenges and upcoming perspectives.
This involves considering sustainability at every stage, from product design and development to production, distribution, consumption, and disposal  [29]. Collaboration and coordination across different business fields are essential to foster sustainable practices and drive positive environmental and social impacts while ensuring long-term economic viability  [61]. In this regard, Jamwal et al. [29]  presented a systematic literature review study based on well-known scientific databases to discover and evaluate the evolution of contributions in Industry 4. 0 technologies in achieving manufacturing enterprises' sustainability. Consequently, the authors concluded that: "most of the conducted research in this field focused on general theatricals and conceptual, and just a few of them have studied the real applications of different technologies for achieving manufacturing enterprises' sustainability".
Big Data Analytics models and techniques Nowadays, enterprises generate a great variety of data along their operations and activities. This critical velocity of data generation creates challenges for enterprises in terms of storage, processing, and computing  [59]. Thus, specific and powerful data storage platforms were developed to support a new kind of database architectures and computing capabilities, such as Apache Spark, Hadoop, NoSQL (i. e. Cassandra and MongoDB), Enterprise Data Warehouses, Big Data Warehouse, Talend for Big Data, and Cloud computing and storage (i. e. Amazon Web Services, Google Cloud, Microsoft Azure). In addition, BD technologies show their efficiency and potential to manage BD that can come from a variety of sources in structured, semi-structured, and unstructured formats  [7, 20] , such as textual data, images, videos, online blogs, IoT sensors data, climate data, environmental data, social media data, consumer reviews, to name a few.
BD integration and management (DIM) During their businesses, enterprises are generating a great amount of data from heterogeneous and various sources. In this regard, DIM is enterprises' ability to integrate, transform, and store the different types of heterogeneous raw data collected across their activities and processes in near/real-time into a single database. Moreover, this process of integration can be defined as the acquisition, pre-processing, and storage of data, respectively  [11]. Thus, DIM is the most important step in the BD value chain because, it solves the inconsistencies and conflicts in terms of semantic, structure, and syntactic issues during the data fusion process for consistent business intelligence or analytics  [76]. For instance, the integration and loading of the data in a BD warehouse for online analytical processing OLAP systems is performed using tools, such as Extract Transform Load (ETL)/Extract Load Transform (ELT).
BD advanced analytics and visualization After the data get integrated and prepared in a single and unified database, advanced analytics skills are needed to derive valuable insights and information for decisionmaking. To this end, enterprises need to leverage the analytical tools and techniques to analyze and process their BD either using batch size or real-time/near-time splitting. According to Souza,  [54] , the analytics is performed through four critical levels, namely descriptive, diagnostic, predictive, and prescriptive analytics. Therefore, descriptive analytics provides information on what happened based on historical data. Descriptive analytics can be usually performed using classical statistics methods and data mining (DM) techniques to see what happened. At the same time, diagnostic analytics enables one to evaluate what happened and give meaningful information by studying the causes of trends and correlations between variables.
Environmental and social impacts monetization To demonstrate the efficiency of BDA in the evaluation of manufacturing enterprises' sustainability, a real database is used (to access the database, the reader can connect by using the link  [22] ). Thus, the inputs of the proposed approach are data from the Impact Weighted Accounts Project (IWAP) developed by a team at Harvard Business School  [50]. They adopted a methodology that makes use of a number of reputable academic sources and publicly available data to determine monetized estimates and measures of the organizational environmental and social impacts from processes and activities around the globe. The main goal behind this methodology is to show business leaders how to calculate the environmental impacts and rectify day-to-day decision-making.
Adapted Apache Spark ecosystem In order to execute, maintain, and create reliable pipelines and algorithms for largescale data analysis across a diversity of workloads, BDA requires efficient frameworks. Fortunately, in order to solve BD challenges using a single processing tool and multifunctional and flexible languages, Apache Spark for BDA has come to light as a unifying engine for data science and engineering  [49]. Additionally, Apache Spark is renowned for its distributed computing and cutting-edge in-memory processing programming tool. Additionally, it is regarded as the most active BD open-source project and is a quick and scalable framework  [51]. In contrast to disk-based models like Hadoop's MapReduce, Apache Spark is an in-memory multistage programming tool. Moreover, it integrates a number of languages to provide extensive and effective APIs (i.
e. Python, Java, Scala, SQL, and R). This integration and merging between APIs enable Apache Spark to perform complex distributed computing and storage. The Apache Spark engine is designed to exceed batch applications by including powerful computations that require separate distributed systems, such as iterative algorithms, interactive queries, and streaming  [32]. Consequently, Apache Spark is more suitable for BD analysis than Apache Hadoop because its system is composed of two levels, namely, Spark core and Upper-Level Libraries, where, each level includes several components.
Spark core Using a straightforward programming interface and a cluster of nodes, spark core enables the processing of massive BD sets. This interface offers effective fundamentals for data sharing between computations and is known as the Resilient Distributed Datasets (RDDs) concept. Indeed, a read-only, and partitioned collection of records is what an RDD is, according to Zaharia,  [73]. Hence, users can directly store data on disk or in memory, manage its partitioning, and manipulate it using a wide range of operators thanks to RDDs' fault-tolerant, parallel data structures. Thus, to support functions like data transformations and in-memory cluster computing, Java, Python, and R APIs were integrated into the development of the spark core, which was created using the Scala programming language.
Upper-level libraries Upper-level libraries within Apache Spark include Spark's MLlib, GraphX, Spark Streaming, and Spark SQL. Spark's MLlib enables the implementation of large-scale ML algorithms based on data or model parallelism using Spark core  [41]. Moreover, building ML algorithms require implementing machine-usual tasks like extracting and transforming features, training the model, evaluating the model, etc. For doing so, to create such effective algorithms and pipelines, Spark's MLlib is developed as a distributed ML library. Regarding this, Spark's MLlib is made up of two main packages, namely, spark. mllib and spark. ml  [49]. Whereas, spark. mllib is constructed on top of RDDs to provide APIs for setting up, debugging, and tuning ML pipelines. While spark. ml, which has packages for linear algebra, statistics, and other fundamental ML functions, is developed on top of DataFrames.
Finally, the approach facilitates a comprehensive understanding of the monetary aspects and benefits associated with the pursuit of SDGs. In this part of the predictive analytics, MLlib Library for Apache Spark is used. MLlib is Spark's open-source distributed ML library, as discussed in "Upper-level libraries" section. It provides a number of fundamental statistics, optimization, and linear algebra primitives which give Apache Spark effective functionality for a variety of learning scenarios. Besides, these learning scenarios are supported by a number of programming languages and high-level APIs using Apache Spark robust ecosystem. Hence, these functionalities simplify the use of Apache Spark to create end-to-end ML algorithms and pipelines. In this research work, two ML algorithms are adopted, namely, multiple linear regression (MLR)  [1]  and artificial neural network (ANN)  [15].
Conclusion This paper provided a study of BDA applicability in enhancing manufacturing enterprises' life cycle sustainability assessment (LCSA). It started by giving what LCSA means to manufacturing enterprises in designing and making sustainable products by integrating environmental and social thinking into their business models. Then, it discussed some methods of how the environmental and social impacts can be measured in order to be evaluated and optimized. Thus, based on the EPS method for impacts monetization, a real database that provides impacts measurements of many enterprises around the world was chosen to validate the proposed approach. These impacts measurement concern the impacts monetization on the safeguard subjects as defined by the EPS method during the life cycle of products from their design to the end use (from the cradle to grave).
As solution methodologies, they presented five heuristic algorithms where the most effective on average is within 10% of the optimal solution. Another relevant work on the vertiport selection problem is the study of  Jeong et al. (2021) , who proposed a methodology for establishing vertiport locations contemplating the effect of aircraft noise in residential zones of the Seoul metropolitan area. First the authors analyzed and clustered demand data from the commuter population identifying potential vertiport locations over the region under study. Next they selected the final vertiport locations under two scenarios: the first one considers the shortest route between commuters' origin to destination (business scenario); and the second scenario selects the vertiport location minimizing the amount of noise exposure in residential areas.
Locating vertiports for EMS in new facilities Another possibility for locating vertiports for EMS in a healthcare network is to establish them as bases outside the healthcare facilities. In this way, the emergency eVTOL aircraft could be near to the hard-to-reach zones. However, this option requires a different OS which is presented in Fig. 4. This OS is similar to OS2 of the previous subsection. However, in this case, the emergency eVTOL departs from an auxiliary base, travels to the emergency site (A-B), transports the patient to the nearest emergency department (C-A), and returns to the vertiport. To determine the location of the auxiliary bases we coupled a data analysis procedure with a clustering algorithm. The outline of this solution procedure is the following. First we collect the information of the potential emergency sites and the healthcare network as presented in the first part of Subsect.
Data collection We obtained the latitude and longitude of the sites of the region under study from the data. gouv. fr portal, which is extracted from (OpenStreetMap 2021). These data consist of multiple cartographic layers of households, working places, commerce sites, roads, railways, etc. , for each department in the region. We assumed that these cartographic layers correspond to the potential emergency sites in the region. Using the QGIS TM application, we established the centroids of the layers of interest and exported them. In total, we had 5,495,903 pairs of coordinates. After a cleaning process that corrected the erroneous (coordinates outside of the department) and duplicated data, we ended up with 5,495,574 pairs of coordinates. Next we collected and marked the locations of the SMUR units in the region.
• For the OS1 and OS2 studied using the methodology of Subsect. 3. 1. 1, we generated 10,000 clusters of potential emergency sites. • We did not consider transferring times or admission times to hospitals. We assumed that emergency departments are ready to receive the patients being transported by the emergency eVTOL. • For computing the eVTOL operational service time, we assumed an on-site treatment time of 45. 9 min based on the study of  Aboueljinane et al. (2014)  conducted in the French Val-de-Marne department. Lastly, to evaluate the impact of the eVTOL range parameter , we considered four eVTOL propulsion types as seen in Table  1. We determined as the average between the minimum and maximum range based on the work of  Goyal and Cohen (2022). The processing and data analysis of the large amount of data (millions of data points) was performed using the software Python.
As a second step in the quantification process, employees would be asked to record their creative ideas, in a daily or weekly log, for example, in the case context of creative ideas they used to improve the online customer interface syntax. These logbooks could provide a measure of the employee's IC conversion ratio. In the third step, the yield from their IC conversion ratio would be measured in terms of the number and quality of the implementation of their creative ideas. Increased customer traffic or repeat usage on the company's web platform would provide one measure of successfully yielded IC implementations and represent the value of the outputs of employees  (Housel & Bounfour, 2011). This conversion ratio data could be used to help predict a firm's capacity for SA in times of significant market disruptions.
Methodology As a first attempt to empirically explore the potential effect of the IC conversion ratio, we exploited a recent natural experiment using a firm-level survey carried out under the H2020 project GlobalInto focused on the initial (i. e. 2019) and a later period (i. e. 2020) of the COVID-19 crisis. The implied relationship between SA, IC and performance was tested using OLS and ordered logistic regressions, a statistical design which is particularly appropriate when dealing with survey data and with Likert-type interval scale dependent variables. The analysis consists of two parts: (1) First, we tested the relationship between SA, DT and performance during the COVID-19 crisis. Here, SA was measured by several organizational level questions, H1. Strategic agility helped firms that invested more in digital technologies, than firms that did not, in order to tackle the negative effects of the crisis.
Data The GlobalInto (2021) questionnaire was conducted between the end of 2020 and the beginning of 2021 on a sample of 1796 firms from seven countries (Denmark, Finland, France, Germany, Greece, Slovenia and the UK). The survey focused on the role of intangibles in the COVID-19 economic crisis. The survey included 51 questions that covered general aspects of firms' activities, their investment in several categories of IC, their performance and their response to the COVID-19 crisis. Because the study focused on intangible assets, very small companies, i. e. with less than 20 employees, were not selected for the questionnaire, as they do not typically make substantial investments in IC compared to their investments in tangible assets. Most of the firms in the sample (about 75%) were classified as small and medium enterprises (SMEs), with less than 250 employees and were selected from the ORBIS database.
Indeed, the growing discontent with globalization has crystallized in the aftermath of the Great Recession, and the perception that it has widened inequalities between elites -who benefit greatly from it -and the rest of society -who face increasing pressure from international competition -has intensified with recent scandals such as LuxLeaks and Panama Papers. In a context where globalization is increasingly perceived as an unfair process in which the equality of individuals and companies is trampled before taxation, public discontent towards tax optimization intensifies with every crisis episode. Interestingly, one of the first claims of the civil society with respect to the reforms of international taxation was the implementation of a public database shedding light country by country on the economic activity and corresponding taxes paid by MNEs.
6 Furthermore, we evaluate the dynamic effects by asking whether the productivity differential for firms with presence in tax havens evolves over time after the entry in tax haven and checking that the pre-trends do not exhibit specific patterns. Noting that firms self-select in tax havens, the entry in tax havens cannot be seen as purely exogenous and it could itself be linked to strategic decisions influencing firms' productivity. Even if our estimates are likely to be biased toward zero, providing, therefore, a lower bound of the true productivity mismeasurement, we reinforce the identification of the contribution of profit-shifting to the productivity measure by exploiting the 2006 Cadbury-Schweppes decision of the European Court of Justice. 7 This decision restricts the application of Controlled-Foreign Company by member States and therefore loosens legal constraints for European MNEs with a presence in a European tax haven before 2006, which we observe in our dataset.
[Tørsløv et al. , 2018]  document how MNEs are systematically more profitable in low-tax jurisdiction countries than in other places, and they are even much more profitable than domestic firms in low tax countries. Exploiting these tax generated anomalies, they estimate that around 40% of global profits in 2015 are shifted to tax havens and revise official statistics adjusted by profit-shifting. Their proposed database reports adjusted GDP, trade balance and capital shares, which are all underestimated in countries from where profits are shifted away and overestimated for low tax jurisdiction countries. For instance, in the case of the French trade balance in 2015, the trade deficit disappears with a surplus of 0. 4% (equivalent to a 1. 1 pp difference with the official statistics). Our contribution is twofold.
Sources and cleaning Our main data sources for firms domiciliated in France come from the FICUS and FARE bases and are made available by the French national statistical institute (INSEE) and the public finances directorate (DGFiP). These bases are drawn from fiscal files and no firm size threshold determining the inclusion/exclusion is applied. Hence, there is full coverage of French firms given that every firm is subject to compulsory reporting with fiscal authorities. 10 The FICUS-FARE base contains balance sheet information on value added, employment, capital, depreciation, investment, the wage bill, materials, four-digit sector the firm belongs to, etc. that are important in estimating productivity and labor share. In addition, a unique firm identifier is associated to each firm (siren number) which is used to link it to other French databases (LIFI and DADS) which we use in order to get yearly information on the firms' bilateral international trade, the firms' bilateral presence in a foreign country (and in a tax haven), and on the detailed composition of the firms' workforce and wage bill in France.
The LIFI database is the "financial linkages base" (Liaisons Financières) which comes from the INSEE. More specifically, it provides information about the composition of economic groups through firm's ownership relations (foreign and domestic) of companies residing in Metropolitan France and French overseas departments. Although the base has a good coverage, it is not exhaustive in the sense that it is constructed by applying different thresholds. More specifically, it includes firms verifying at least one of the following conditions: having more than 500 employees, holding equity securities above 1. 2 million euro, having a turnover of more than 60 million euro, being the parent of a group or being held by foreign capital in the previous year. The survey is complemented with additional administrative sources (DIANE) in order to ensure a better coverage of smaller groups.
For this reason, we provide alternative lists of tax havens based on academic work such as the Dharmapala & Hines list (  [Dharmapala and Hines, 2009] ) or NGOs list of tax haven such as the one provided by Oxfam (see C. 3). In order to reduce the sensitivity of our result to the definition of tax haven, we finally construct a "consensus" list that reports a country as a tax haven if this country is present in a least two of the three lists mentioned above. Finally, the DADS database (Déclaration annuelle de données sociales) which is provided by the INSEE, is based on mandatory annual reports filled by all firms with employees; it contains annual hours paid in a firm, as well as the number of workers employed by different socio-professional occupation types. The relevant information that we extract from these data is the annual number of firm employees by socio-professional category, which we use to compute a firm-year share of skilled workers.
provide evidence of decrease of effective tax rate for Europeans MNEs (since the ECJ ruling applies in all member States) after 2005. In our case, the question we ask is whether the profit-shifting-related mismeasurement of productivity is more severe for Europeans MNEs based in France (and therefore present in our administrative database) with affiliates located in European tax havens after 2006 compared to other MNEs in our database. Thus, this difference-in-difference exercise exploits the difference of measured productivity from 2006 onwards between firms with investments in European tax havens before the ruling ("treated group") and other MNEs ("control group"). Because firms can endogenously decide to open-up affiliates in European tax havens following this decision, we add a specification where the control group is restricted to MNEs without presence in European tax havens.
The same qualitative message is found when analysing aggregate productivity by focusing on TFP (table 9 in Appendix B. 2) with an even bigger effect. ∆ Aggregate Within-firm Between-firm ALP term term Exitors Entrants All firms (1997-2015) 21. 51 4. 71 19. 42 3. 90 -6. 52 Excl. tax havens (1997-2015) 17. 65 4. 46 13. 82 2. 08 -2. 71 Source: Authors' calculations using LIFI and FICUS-FARE databases.
Based on these insights, we mapped key programme stages and activities over time by using the generic model of a TD research funding programme as developed by  Schneider et al. (2019)  (see Table  1 ). To assess the benefits and challenges of the programme's activities (RQ3), we used data gathered through participatory observation, formal post-activity evaluations (e. g. evaluation of a single training course by grantees), programme monitoring (e. g. annual programme progress reports), a survey conducted with all grantees at project end (total 27), as well as the interviews with the programme representatives mentioned above. This data was assessed using qualitative content analysis and descriptive statistics (doi. org/10. 5281/zenodo. 8238102), and attributed to the programme stages identified before.