Blockchain Technology for Tracking and Reporting of Carbon Dioxide Emission (A case study on its possible adoption on the container shipping industry)
Author
Tabirao, Michelle Anne
Term
4. Semester
Publication year
2018
Pages
101
Abstract
Formålet med dette speciale er at undersøge, om blockchain-teknologi kan bruges til at spore og rapportere CO2-udledninger i container-skibsfarten, og hvad der kan påvirke en mulig fremtidig anvendelse. Arbejdet er guidet af teorier om innovation, adoption og diffusion og bygger på både empiriske data og litteratur. Specialet leverer tre hovedbidrag: For det første kortlægges den nuværende praksis for Monitoring, Reporting og Verification (MRV) af CO2-udledninger. For det andet vurderes, hvordan blockchain—særligt distribueret hovedbogsteknologi (DLT) og smarte kontrakter—kan imødekomme behov, udfordringer og forbedringsområder i de nuværende MRV-ordninger. På den baggrund udvikles en procesinnovation i form af en forretningsprocesmodel (BPM), der viser dokumentflow, interessenter, procesmilepæle og, hvor det er relevant, blockchainens roller og kapabiliteter. Tre varianter af modellen præsenteres for at afspejle forskellige interessenters præferencer for både skibs- og last/TEU-specifik MRV. Endelig analyseres innovationens opfattede egenskaber og implementeringsudfordringer, som kan påvirke udbredelsen i branchen. Analysen peger på centrale problemer i dag: en blanding af frivillige og obligatoriske bæredygtigheds- og MRV-programmer uden en fælles CO2-metodologi for skibs- og lastspecifik sporing; rederier, der arbejder i flere forskellige MRV-systemer; og usikkerhed om datatroværdighed og gennemsigtighed i brændstofforbrug og andre CO2-målinger. Specialet skitserer derfor et blockchain-baseret koncept, identificerer relevante interessenter og deres forpligtelser og intentioner, og vurderer, om blockchain er et egnet valg, herunder hvilken type der i givet fald kunne passe bedst. Arbejdet befinder sig i en præ-diffusionsfase og udvikler ikke en fungerende prototype af et blockchain-baseret MRV-system. Specialet gør derfor ikke krav på, at løsningen vil blive fuldt accepteret eller adopteret. I stedet præsenteres de faktorer, som kan påvirke en mulig fremtidig udbredelse.
This thesis examines whether blockchain technology could be used to track and report CO2 emissions in the container shipping industry, and what might influence its future adoption. The study is guided by theories of innovation, adoption, and diffusion, and draws on both empirical data and literature. There are three main outputs. First, a review of current Monitoring, Reporting, and Verification (MRV) practices for CO2 emissions. Second, an assessment of how blockchain—especially distributed ledger technology (DLT) and smart contracts—could address needs, challenges, and improvement areas in existing MRV implementations. Based on this, a process innovation is proposed as a Business Process Model (BPM) that maps document workflows, stakeholders, process milestones, and, where relevant, the roles and capabilities of blockchain. Three versions of the model are presented to reflect stakeholder preferences for vessel- and cargo/TEU-specific MRV. Finally, the study analyzes the perceived characteristics of the innovation and the practical challenges that could affect industry uptake. The analysis highlights current issues: a mix of voluntary and mandatory sustainability/MRV programs without a standardized CO2 methodology for vessel- and cargo-specific tracking; ship operators dealing with multiple MRV systems; and concerns about data reliability and transparency in fuel consumption and other CO2 monitoring methods. The thesis therefore outlines a blockchain-based concept, identifies the stakeholders and their obligations and intentions, and evaluates whether blockchain is a suitable fit, including what type might be most appropriate. The research is in a pre-diffusion stage and does not build a working prototype of a blockchain-based MRV system. It does not claim that the innovation will be fully accepted or adopted; instead, it presents factors that could shape possible future adoption.
[This abstract was generated with the help of AI]
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