Life Cycle Costing and Food Systems: Concepts, Trends, and Challenges of Impact Valuation

Our global food systems create pervasive environmental, social, and health impacts. Impact valuation is an emerging concept that aims to quantify all environmental, social, and health costs of food systems in an attempt to make the true cost of food more transparent. It also is designed to facilitate the transformation of global food systems. The concept of impact valuation is emerging at the same time as, and partly as a response to, calls for the development of legal mechanisms to address environmental, social, and health concerns. Information has long been understood both as a necessary precursor for regulation and as a regulatory tool in and of itself. With global supply chains and widespread impacts, data necessary to produce robust and complete impact valuation requires participation and cooperation from a variety of food system actors. New costing methods, beyond basic accounting, are necessary to incorporate the scope of impacts and stakeholders. Furthermore, there are a range of unanswered questions surrounding realizations of impact valuation methods, e.g. data sharing, international privacy, corporate transparency, limitations on valuation itself, and data collection standardization. Because of the proliferation of calls for costing tools, this article steps back and assesses the current development of impact valuation methods. In this article, we review current methods and initiatives for the implementation of food system impact valuation. We conclude that in some instances, calls for the implementation of costing have outpaced available and reliable data collection and current costing techniques. Many existing initiatives are being developed without adequate consideration of the legal challenges that hinder implementation. Finally, we conclude with a reminder that although impact valuation tools are most often sought and implemented in service of market-based tools for reform, they can also serve as a basis for robust public policies

Scaling out Big Data Distributed Pricing in Gaming Industry

Game companies have millions of customers, billions of transactions and petabytes of other data related to game events. The vast volume and complexity of this data make it practically impossible to process and analyze it using traditional relational database models (RDBMs). This kind of data can be identified as Big Data, and in order to handle it in efficient manner, multiple issues have to be taken into account. It is more straightforward to answer to these problems when developing completely new system, that can be implemented with all the new techniques and platforms to support big data handling. However, if it is needed to modify an existing system to accommodate data volumes of big data, there are more issues to be taken into account. This thesis starts with the clarification of the definition 'big data'. Scalability and parallelism are key factors for handling big data, thus they will be explained and some of the conventions to do them will be reviewed. Next, different tools and platforms that do parallel programming, are presented. The relevance of big data in gaming industry is briefly explained, as well as the different monetization models that games have. Furthermore, price elasticity of demand is explained to give better understanding of a Dynamic Pricing Engine and what does it do. In this thesis, I solve a bottleneck that emerges in data transfer and processing when introducing big data to an existing system, a Dynamic Pricing Engine, by using parallel programming in order to scale the system. Spark will be used to deal with fetching and processing distributed data. The main focus is in the impact of using parallel programming in comparison to the current solution, which is done with PHP and MySQL. Furthermore, Spark implementations are done against different data storage solutions, such as MySQL, Hadoop and HDFS, and their performance is also compared. The results for utilizing Spark for the implementation show significant improvement in performance time for processing the data. However, the importance of choosing the right data storage for fetching the data can't be understated, as the speed for fetching the data can widely variate.Peliyhtiöillä on miljoonia asiakkaita, miljardeja maksutapahtumia ja petatavuja pelin tapahtumiin liittyvää dataa. Tämän datan suuri määrä ja kompleksisuus tekevät sen prosessoimisesta sekä analysoimisesta lähes mahdotonta tavallisilla relaatiotietokannoilla. Tällaista dataa voidaan kutsua Big Dataksi, ja jotta sen käsittely olisi tehokasta, useita asioita on otettava huomioon. Uuden järjestelmän toteutuksessa näihin ongelmiin pystytään vastaamaan melko johdonmukaisesti, sillä uusimmat tekniikat ja alustat voidaan ottaa tällöin helposti käyttöön. Jos kyseessä on jo olemassa oleva järjestelmä, jota halutaan muuttaa vastaamaan big datamaisiin datamääriin, huomioon otettavien asioden määrä kasvaa. Tämän diplomityön aluksi selitetään termi 'Big Data'. Big Datan kanssa työskentelyyn tarvitaan skaalautuvuutta ja rinnakkaisuutta, joten nämä termit, sekä näiden yleisimmät käytännöt käydään läpi. Seuraavaksi esitellään työkaluja ja alustoja, joilla on mahdollista tehdä rinnakkaisohjelmointia. Big Datan merkitys peliteollisuudessa selitetään lyhyesti, kuten myös eri monetisaatiomallit, joita peliyritykset käyttävät. Lisäksi kysynnän hintajousto käydään läpi, jotta lukijalle olisi helpompaa ymmärtää, mikä seuraavaksi esitelty Apprien on ja mihin sitä käytetään. Tässä diplomityössä etsin ratkaisua Big Datan siirrossa ja prosessoinnissa ilmenevään ongelmaan jo olemassa olevalle järjestelmälle, Apprienille. Tämä pullonkaula ratkaistaan käyttämällä rinnakkaisohjelmointia Sparkin avulla. Pääasiallinen painopiste on selvittää rinnakkaisohjelmoinnilla saavutettu hyöty verrattuna nykyiseen ratkaisuun, joka on toteutettu PHP:llä ja MySQL:llä. Tämän lisäksi, Spark toteusta hyödynnetään eri datan säilytysmalleilla (MySQL, Hadoop+HDFS), ja niiden suorityskykyä vertaillaan. Tulokset, jotka saatiin Spark toteutusta hyödyntämällä, osoittavat merkittävän parannuksen suoritusajassa datan prosessoimisessa. Oikean tietomallin valitsemisen tärkeyttä ei pidä aliarvioida, sillä datan siirtämiseen käytetty aika vaihtelee myös huomattavasti alustasta riippuen

Centralized Intermediation in a Decentralized Web3 Economy: Value Accrual and Extraction

The advent of Web3 has ushered in a new era of decentralized digital economy, promising a shift from centralized authority to distributed, peer-to-peer interactions. However, the underlying infrastructure of this decentralized ecosystem often relies on centralized cloud providers, creating a paradoxical concentration of value and power. This paper investigates the mechanics of value accrual and extraction within the Web3 ecosystem, focusing on the roles and revenues of centralized clouds. Through an analysis of publicly available material, we elucidate the financial implications of cloud services in purportedly decentralized contexts. We further explore the individual's perspective of value creation and accumulation, examining the interplay between user participation and centralized monetization strategies. Key findings indicate that while blockchain technology has the potential to significantly reduce infrastructure costs for financial services, the current Web3 landscape is marked by a substantial reliance on cloud providers for hosting, scalability, and performance

The Valuation of Digital Intangibles: Technology, Marketing, and the Metaverse

This book offers an updated primer on the valuation of digital intangibles, a trending class of immaterial assets. Startups like successful unicorns, as well as consolidated firms desperately working to re-engineer their business models, are now trying to go digital and to reap higher returns by exploiting new intangibles. This book is innovative in its design and concept since it tackles a frontier topic with an original methodology, combining academic rigor with practical insights. Evaluation issues are increasingly based on an analytical comprehension of augmented business models and virtual function analysis, nurtured by real-time big data. The impact of digitalization on scalable business models is the main competitive advantage factor of the BigTechs and other Unicorns, representing a target for startups and the reengineering of traditional firms. The transition from the Internet to the metaverse represents the last frontier, showing how 3D virtual and augmented reality impacts social networking. The second edition of this book updates the contents of the first edition while comprehensively introduces these innovative topics--such as the metaverse, cloud storage, multi-sided digital platforms, ESG-compliance, and value co-creation patterns of digitized stakeholders--and demonstrates how best practices can be applied to specific asset appraisals, making it of interest to researchers, students, and practitioners alike. Tackles a frontier topic with an original methodology, combining academic rigor with practical insights Demonstrates how best practices can be applied to specific asset appraisal

This changes everything : climate Shocks and sovereign bonds

Climate change is already a systemic risk to the global economy. While there is a large body of literature documenting economic consequences, there is scarce research on the link between climate change and sovereign risk. This paper investigates the impact of climate change vulnerability and resilience on sovereign bond yields and spreads in 98 countries over the period 1995–2017. We find that the vulnerability and resilience to climate change have a significant impact on the cost government borrowing, after controlling for conventional determinants of sovereign risk. That is, countries that are more resilient to climate change have lower bond yields and spreads relative to countries with greater vulnerability to climate change. Furthermore, partitioning the sample into country groups reveals that the magnitude and statistical significance of these effects are much greater in developing countries with weaker capacity to adapt to and mitigate the consequences of climate change.info:eu-repo/semantics/publishedVersio

PUBLIC PRIVATE PARTNERSHIPS, BIG DATA NETWORKS AND MITIGATION OF INFORMATION ASYMMETRIES

Public Private Partnerships (PPP) represent an increasingly frequent investment pattern where composite stakeholders interact in joint initiatives. Alignment of interests and consequent composition of conflicts is driven by the business purpose of the shared corporation, represented by a private Special Purpose Vehicle (SPV) within a Project Financing (PF) investment package. Corporate governance implications go beyond the traditional contra position between ownership and control, showing cooperative patterns where the value is co-created and distributed. Big data-driven networks represent a trendy issue that connects public and private stakeholders through digital platforms where data are shared in real time. Information asymmetries and governance concerns are consequently softened

Data Monetization – Miten organisaatio voi tuottaa liikevaihtoa datan avulla?

While digitalization evolves and distinct technologies are developed further, the role of data and data analytics has grown and become more important in the eyes of organizations. Simultaneously data is not considered anymore as insignificant raw material but an important ingredient in developing business activities and enabling innovation. Nowadays, due to the enhanced information technologies, an organization does not need to create data or its more refined forms itself but data can be sold or purchased in the same way as tangible goods or services. Despite an idea of business focused on selling data is rather novel and it has not been yet researched extensively. This thesis work studies Data Monetization phenomenon which refers to business built on data and furthermore revenue generated with data and its derivatives. The terminology related to Data Monetization has not stabilized yet and no unambiguous definition was found from the scientific literature. Hence this study aspires to clarify the phenomenon, its definition and terminology. The main research question of this study is following: “What kind of factors are behind of and affect Data Monetization?” In order to answer the previously described question, the definition of Data Monetization is studied as well as the distinct options and measures an organization may take to enable revenue generation with data. Furthermore this study pursues to discover and identify other phenomena that associate with Data Monetization and moreover to recognize different strategic options to implement Data Monetization business. The thesis work was executes as a systematic literature review and literature sources were searched from scientific libraries and databases, such as Scopus and Google Scholar. Since it seems that this topic is not yet studied extensively and hence only few relevant pieces of literature were found, the literature sample was not confined too much. As a result of this study an unambiguous and justifiable definition of Data Monetization was established. Such definition could not be found from the pieces of literature utilized in the systematic literature review. Additionally distinct components and aspects of Data Monetization business were recognized as well as a variety of different business and revenue generation models. From the perspective of Data Monetization it is essential to identify the valuable data and to be able to, if needed, refine and develop it further in order to enable the business transaction which in turn generates revenue. Since Data Monetization can be either indirect or direct and furthermore the organization’s main or supplementary offering, Data Monetization is a multidimensional, diverse and complex phenomenon and form of business. Hence Data Monetization can be executed in variety of ways and it may offer distinct strategic purposes for the organizations