The global transport sector currently contributes 23% of carbon dioxide emissions, a figure set to double by 2050 without mitigation efforts. While Scope 3 emissions, notably in container transport, are challenging to address, they constitute over half of all emissions. This study explores cargo owners' perspectives on Scope 3 emissions and identifies opportunities for mitigation. With impending Scope 3 reporting obligations in 2024, the shipping sector lacks a clear path for emissions reduction. The research aims to bridge this gap by examining how carbon emission information in port environments can enhance the sustainability of logistics processes. The study combines supply chain performance theory, decision-making processes, and decarbonization of logistics into a conceptual framework. A multiple case study, incorporating diverse cases based on product and market segments, validates the framework. Interviews with logistical decision-makers and experts reveal challenges such as lack of data availability, data quality, responsibility, and intrinsic motivation. The study identifies legislative pressure, consumer and shareholder influence, and comparison functions as potential drivers for prioritizing carbon emissions in decision making. Improvements suggested include enhanced data quality through responsibility agreements, standardized emission tools with mode comparison functions, and the facilitation role of port authorities. Technological development is seen as crucial for increasing sustainable options, with legislative and stakeholder pressures necessary for cargo owners to prioritize carbon emissions in decision making. The study concludes that emission information tools, supported by port authorities, can contribute to a more sustainable approach in logistics decision making.

The textile and clothing industry is one of the most polluting industries worldwide and requires the use of many resources.Textile-to-textile recycling can be crucial for closing the material loop and transition towards a circular textile and clothing industry. Currently, only 1% of textile waste is recycled into new high-quality textile items. The Netherlands has ambitious circularity goals and textile-to-textile recycling should be scaled up to reach a 100% circular industry in 2050. This research explores the textile-to-textile recycling niche in the Netherlands from a transition perspective.

The modern world is under great pressure to fight climate change and limit global temperature rises to a sustainable level. As part of new mitigation strategies, the Voluntary Carbon Market (VCM) emerged after the compliance markets materialized. Via the voluntary carbon market companies can offset their carbon emissions. The captured carbon is transformed into ’carbon credits’ representing one tonne of Greenhouse gas (GHG) emissions captured or reduced. One of the leading quality and integrity concerns is the potential double counting of credits. In a nutshell, the voluntary carbon market is based on voluntary carbon offset projects, compliance schemes require governments to report their national mitigation activities in Nationally Determined Contributions (NDC)s, and the private sector needs to report on their environment, governance, and sustainability responsibilities. Double counting occurs among these three activities when captured carbon is allocated to more than one actor. This thesis consists of an exploratory, single case study based on the blockchain solution of Acorn Rabobank. Acorn is both a project developer of carbon credits and the enabler of its own digital marketplace. The data for the case study is comprised of in-house research provided by Acorn, interview transcripts, reports, and news articles. This data is used to answer the research question: “What framework can be developed to evaluate the benefits and limitations of blockchain-based platforms to prevent double counting in the voluntary carbon market?

To answer this research question several research activities were performed. The research activities consisted of selecting and defining the case, data collection, data interpretation, and theory building. Three forms of double counting were established. To further enrich the knowledge base the stakeholder dimension was mapped based on secondary sources and empirical knowledge gained through preliminary interviews. Several mapping techniques were employed to map the interrelations between the market mechanisms and double counting.

The MLGBE framework allows carbon market actors to evaluate and, if applicable, reason about the various available blockchain-based infrastructures as tokenizing carbon credits and the digitized trading of these credits. By doing so, they can identify the stakeholder dynamics with all policies, interrelated treaties, and community plans in place, to develop or evaluate sustainable blockchain-based platforms tailored to the market to prevent double counting.

Scientifically, new theory was built through the development of the MLGBE framework. By integrating multi-level governance theory and business & governance information to the particularities of asset tokenization in blockchain applications, a new approach to evaluate blockchain designs is presented. Secondly, the research delineates the issue of double counting in the voluntary carbon market, Lastly, the research corroborates earlier findings in the logistics domain of the importance of reliability of data elements of blockchain-based applications used for audit trails.

Concerning double counting further research is recommended on the potential of blockchain solutions to link schemes together to facilitate a global carbon market, how these link to the eventual implementation of Article 6, and the potential nesting within countries’ NDCs. For
the MLGBE framework, future research should look into the interaction between the standardization/fragmentation dependent on policy layers and the different rights in the governance
requirements.

Since the introduction of the shipping container as early as 1968, the shipping industry experienced great increase in trade and efficiency. The shipping container revolutionised the way goods are handled within the ports and enabled the growth of international supply chains. However, as this growth of trade continued into the late 1900s and early 2000s, both customs and the organisations within this chain experienced an increased administrative burden. This administrative burden was experienced within the different information streams to get the goods smoothly from the selling actor to the buying actor. Additionally, many actors within the supply chain perceive opportunities to greatly increase trade efficiency. Improving their business model through the addition of information based new services, expanding business through integrating other actors, and lowering costs through the development of cost-effective in-house capabilities. Early digitisation efforts have only produced limited results, as these mainly focused on automating internal business processes. These internal processes were often not able to automate communication with other actors. Processes still relied on a significant amount of paper communication.
The introduction of digital platforms has greatly affected different industries, for example enabling direct booking within the air travel business. Replacing paper documentation with an electronic equivalent can enable an increase in efficiency, through reduced administration costs and improved planning and operational capabilities. Efforts in introducing a digital platform within the shipping industry have been taking place using different governmental research efforts. However, as a possible additional effect, the digital platform may put reconfiguration of the network in motion. This reconfiguration enables certain actors to partake a bigger role, whereas other actors might lose control of the supply chain process.

The TradeLens platform launched in 2018, is such a digital platform. This platform allows sharing both documentation (e.g. the commercial invoice, packing list, bill-of-lading) and supply chain events (e.g. lodging ENS, the actual time of arrival) with the other actors. The platform uses a blockchain infrastructure. This structure is used to increase the trustworthiness of the data. Firstly, the auditability hinders documentation fraud as the actors within the network can trace the exact moment and actor that placed uploaded a document. The immutability of the blockchain infrastructure allows the automation of information processes. When the information is uploaded it cannot be changed. It was developed by a co-operation between a shipping carrier and a technology developer. The platform was tasked with alleviating the pressure on the administrative systems of the different actors within the supply chain. This research investigated possible scenarios due to the introduction of a digital platform using the TradeLens platform as the main research case.

Research Question and Objectives
This research aims to address the different possible future configurations of the network and roles within the container supply chain. To address this, the following main research question was developed: What is the possible supply chain configurations that come with a digital information infrastructure?.
In addressing this research question a number of research steps and clarifications have to be answered. Firstly, the research has to determine what is considered an actor within the supply chain ecosystem and what are the key activities performed in that ecosystem. Secondly, the research aims to perform an analysis of each actor and thus explain the different roles to be able to perform this analysis a theoretical model has to be developed. Thirdly, the research will evaluate the ecosystem using this model. Fourthly, the different scenarios will be developed using the developed model.
Research Method
The research employed three main methods of data collection. Firstly, a literature review is performed to identify the important information and innovation concepts to be used throughout the research. Secondly, through analysing many different public sources, the research gains company insights and information for constructing different roles, activities, and resources. Thirdly, through interviews with experts on the TradeLens platform where careful attention is given to the shifting activities, resources and control within the configuration of the network.

The used approach can be defined in four steps. First, the researchers developed an initial meta-framework using the findings from the literature review. Secondly, the different concepts within the meta-framework were combined into constructing a model for the assessment of the ecosystem. Thirdly, a generic container shipping case is construed from the information gained from the different public and academic sources. Lastly, a comparison is performed between the construed case and a test case of a Dutch tyre importer. The main findings within these steps will be discussed in the following paragraph.
Main Findings
The research identified five key theories to be of importance within the model. These are 1. Ecosystem theory, 2. Stakeholder theory, 3. Diffusion theory, 4. Control Point theory and lastly, 5. Barriers and Stimulating Factors. Firstly, the concept of the business ecosystem. An ecosystem describes how different actors within a business domain influence and interact with the other actors outside and within the direct business network. This research investigates the effect of digital platforms on ecosystem reconfiguration. The chosen system of analysis for this ecosystem reconfiguration is the blockchain-enabled platform, TradeLens. This platform enables information sharing between the different actors using a trusted blockchain structure. The TradeLens ecosystem can be considered a service ecosystem, as the main value creation is intangible and the many different actors within the ecosystem co-produce the final value within the system. Secondly, stakeholder theory describes when someone can be considered a stakeholder and how to evaluate motivations and incentives. Thirdly, diffusion theory described how an innovation such as TradeLens goes through different phases before mass-market adoption. Fourth, the control points theory explain how different actors within a business process are able to exercise control on that process. Control points were used to describe how different actors are able to perform certain roles within the ecosystem. Lastly, barriers and stimulating factors describe how certain factors can enable or disable a certain development to progress further. In the case of TradeLens this was used to investigate further growth barriers and stimulating factors.
This meta-framework was converted into a six-point assessment model. This model uses a comparison between different states of an ecosystem, to evaluate possible scenarios. The first case is that of the generic constructed benchmark. This benchmark has been developed from cross-referencing a selection of public and academic sources. The main task of the constructed case was to show a generic and common supply chain structure. To assess the enhanced version of the supply chain, a case study of a Dutch tyre importer was selected. This case was selected due to the extensive documentation around this case. Additionally, this case has ships of non-hazardous and non-perishable goods that do not require additional certificates and documentation that might be applicable for other goods. The constructed benchmark case and the tyre importer case were both evaluated using the six-point assessment. With regards to key activities, the main difference found was that the tyre importer self-organises its land transport as the organisation owns its own transportation vehicles. Secondly, the tyre importer case performed the import declaration itself. This in contrast with the benchmark case, where this task was delegated to a freight forwarder who organises both the land transport and the lodging of the import declaration. This main difference becomes more visible when assessing the second point, the key actors. Here it was observed that the freight forwarder was missing on the importing side within the tyre case. This was possible as the buyer/tyre importer performed the activities of the freight forwarder. Within the value exchanges and the key information, it was observed that the buyer was able to directly lodge the required data for the import declaration. This automated the customs lodging process and increased cost-effectiveness. Secondly, within the TI case, the buyer had its own land transport capabilities and did not rely on an intermodal operator to collect the goods from the port. This allowed the buyer to redevelop its strategy with regard to the supply chain. The effects of the digital platform allowed the process of lodging the customs declaration to be more efficient as the commercial invoice and HS codes could be directly gathered from this platform. This was made possible due to the API and blockchain data pipeline architecture of the digital platform. The API-structure allowed the data to be automatically collected, whereas the blockchain structure enhanced the trustworthiness of the submitted data. Regarding, intermodal transport. The digital platform allowed the buyer to have an accurate and actual time of release and arrival of the container. This allowed the buyer to improve the planning of the collection of the container. When observing the control points it was identified that the main control points of the freight forwarder are two-fold. First, it has the expertise and capabilities to be able to perform the customs lodging. Secondly, it has the capability of gathering and forwarding logistics data within the network. Within the tyre case, both of these control points were absorbed by the buyer.

Using the control point evaluation a set of four different scenarios were identified. These developed scenarios are not comprehensive, but a combination of these scenarios are likely to be observed in the near future. For every scenario, it is evaluated how the actor could use its current control points and the digital infrastructure to increase its control on the process and thus enable reconfiguration. Firstly, the status-quo scenario. In this scenario, there is not a clear actor who absorbs the activities of other actors. The main benefits of the digital infrastructure are experienced throughout the chain as the different actors increase their efficiency using automation and digitisation of the communication processes. In this case, no reconfiguration is thus observed. The second scenario is the development of capabilities to perform more logistical tasks within the supply chain by either the buyer, the seller or both. As observed within the tyre case, the buyer is able to more efficiently perform the customs lodging and the arrangement of land transport due to having access to the commercial invoice and the actual time of arrival and release of the container. An identified stimulating factor within the capability development of the buyer/seller is the standardisation of the data and the development of a market solution to booking and tracking logistical transport. The third scenario is where the carrier becomes a one-stop shop for logistics. Using their central position within the supply chain, they are able to redevelop their value offering. This offering is expanded with the logistical support of lodging customs data and providing intermodal transport. The fourth scenario is that of the freight forwarder expanding its value offering. Here the freight forwarder expands into managing the customer’s warehouse and perform a larger set of logistic services towards the customers.

eCommerce, Brexit, new safety and security concerns are only a few examples of the challenges that government organisations, in particular customs administrations, face today when controlling goods crossing borders. To deal with the enormous volumes of trade customs administrations rely more and more on information technology (IT) and risk assessment, and are starting to explore the possibilities that data analytics (DA) can offer to support their supervision tasks. Driven by customs as our empirical domain, we explore the use of DA to support the supervision role of government. Although data analytics is considered to be a technological breakthrough, there is so far only a limited understanding of how governments can translate this potential into actual value and what are barriers and trade-offs that need to be overcome to lead to value realisation. The main question that we explore in this paper is: How to identify the value of DA in a government supervision context, and what are barriers and trade-offs to be considered and overcome in order to realise this value? Building on leading models from the information system (IS) literature, and by using case studies from the customs domain, we developed the Value of Data Analytics in Government Supervision (VDAGS) framework. The framework can help managers and policy-makers to gain a better understanding of the benefits and trade-offs of using DA when developing DA strategies or when embarking on new DA projects. Future research can examine the applicability of the VDAGS framework in other domains of government supervision.

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The last decade saw the rise of e-commerce trade and the shift of the manufacturing industry to the emerging economies, China first of all. In this context, the European Customs Authorities experienced an explosion of small parcels coming from e-commerce websites, often from China, and faced difficulties to detect fiscal frauds and security threats using their conventional risk management systems. To address this problem, the European project PROFILE brings together the customs administrations of Netherlands, Belgium, Sweden, Norway, and Estonia, aiming to provide the EU with a shared platform for: (1) accurately assessing customs risks; (2) optimizing operation and logistics by integrating multiple sources of information; (3) developing a shared data platform to share customs risk management (CRM) practices.

As part of this project, the Dutch Customs Administration (DCA) and International Business Machines (IBM) Corporation are collaborating to deploy the cutting-edge technologies of artificial intelligence to automatically cross-check the customs declarations coming from Chinese e- commerce against online information. Through a Design Science approach, I carried out this research for the Delft University of Technology, written in collaboration with IBM Netherlands, aiming to deliver a preparatory study for the developing team before the PROFILE project begins. This includes knowledge brokering between the Dutch Customs Administration and IBM Netherlands so that a more precise problem scope can be defined, and the requirements elicited. In particular, this research focuses on the first part of the project: the development of an adaptive web-crawler for e-commerce, able to compare the declarations documents against online information.

According to the Dutch Customs Administration, the web-crawling system should gather the description of the goods from declarations, search the product on the web, find its price of sale on the e-commerce platforms, compare it with the value declared in the declaration, and return a risk indicator of green/red flag to the targeting officer. The design process of this system follows approaches coming from the systems engineering discipline, starting with the requirement analysis, addressing them with the state-of-the-art big data analytics, and finally deriving the logical components of the system, whose design is presented through a logical architecture.

First, the application domain is investigated. When goods entry the Netherlands need an entry declaration. These goods arrive at the harbor of Rotterdam or airport of Schiphol, where some of these are imported into the country and become import/export, and others stop temporarily as transit waiting to be shipped somewhere else. The Dutch Customs Administration monitors these processes through risk management systems aiming to stop non-compliant goods. This research describes these practices, with a higher focus on the e-commerce risk targeting. About the e- commerce world, a study of the e-commerce processes behind an online purchase is also carried out through a real purchase on Chinese e-commerce. This was used to observe how the Chinese sender described the item, and how the Dutch Customs assessed the risk and decided on the duties to be paid. This led to reflect on the possible frauds scenarios and how to address them. Finally, the Dutch Customs also reported that the products descriptions are often vague and ambiguous, and a more accurate formulation of the problem is described.

Secondly, an in-depth literature on the fields of web-crawling and big data analytics techniques is carried out. The possible technologies that could be useful to address the requirements and the problem formulation are investigated. Starting with an analysis of the existing literature on the field of big data analytics, this research also covers the recent trends of machine learning and artificial intelligence. To avoid reporting a too big literature, the topics reported have been accurately chosen, for instance describing only the techniques for web analytics and text analytics.

This literature on big data analytics is further broken in two sub-topics, one more theoretical, which classifies the types of analytics methods and defines the technology of machine learning and natural language processing, including the last paradigms of deep learning and reinforcement learning, and one more practical, where guidelines for the design, development, and implementation of machine learning techniques are proposed. It is here that a theoretical framework to systematically reflect on the challenges of the field of big data analytics has been identified. This framework is then used to systematically collect the main technological challenges of the use case under analysis and translate them into non-functional requirements.

Finally, the last part of the literature describes what a web-crawler is and what web- crawling/web-craping means. This later extends to the concepts of focused web-crawling and smart, intelligent, adaptive web-crawling, where machine learning techniques are deployed to improve performance. The literature concludes by providing related works of machine learning techniques implemented in smart web-crawling of the e-commerce websites and stating the knowledge gap that needs to be bridged to address the use case under analysis.

After the application domain and the literature review, the knowledge from these previous phases combines in a continuous iterative process according to the design science methodology (Hevner, 2014). Through unstructured interviews with the DCA and IBM experts, the requirements elicitation is carried out. The approach by Armstrong and Sage (2000) deriving from the field of systems engineering is used. The main objective of the system to be developed is broken down into a series of sub-activities that must be carefully structured to formulate the requirements. About the non-functional requirements, instead of reflecting on the different domains – technological, environment, law compliance, etc. – as it is proposed by the same systems engineering approach mentioned earlier, this research uses the framework identified in the literature review about the main challenges of big data project (Sivarajah, 2016).

To derive the components of the architecture from the requirements and customer needs, the methodology proposed by Suh (1998) called Axiomatic Design has been used, mapping the requirements into architectural components in a rigorous manner. In this way, the design domains proposed by this methodology – customer, functional, physical and process domains – are taken as the reference point for the design process: first, the business needs are identified, then these are translated into requirements, which are mapped into design features. The process domain is left out of this research and will be addressed by the IBM development team in Ireland.
The design cycle leads to the design of a web-crawling system represented through a service- oriented architecture (SOA). Its block diagram and black-box description of each application service are provided. Furthermore, the architecture functionality is described with an architecture walk-through and a sequence diagram in the unified modeling language (UML). The result is an innovative real-time web-crawling system to identify the value of a given product on the e-commerce websites. It deploys natural language process models to filter the non-relevant search results, and other machine learning models to best matching the remaining relevant results with a given item description.

The design and architecture description of this innovative web-crawling system is the main artifact of this research, while the mixed methodology of systems engineering methodologies and big data frameworks is another important scientific contribution.

Today, shipping industry is a complex system involving multiple actors, multiple interests and intricate interactions occurring at different levels.With any disruption to interaction occurring there would be multiple impacts and accumulation of costs on individual actors operating in the system.Unless these risks are well understood and prioritized based on the situational demands, the businesses will find it difficult operate in such complex actor-intensive system.In energy industry, Risk Based optimization (RBO) approach has been in use to manage the large infrastructures and multiple assets. RBO approach has been effective in identifying and prioritising the risks affecting the performance of the assets.Research was thus taken up to investigate this idea and thereby to develop a risk based decision making (RBDM) approach for international shipping domain. Further, to improve visibility in supply chain, development of a large scale Digital Trade Infrastructure (DTI) called Data Pipeline is in progress under the project called CORE. This research also tried to evaluate the extent to which both the concepts could reinforce each other and improve the supply chain quality.In the process, a prototype of RBDM Tool was developed and used to analyze the trade lane of JamboFresh, a fruit importing company in the Netherlands. By coping with the challenges posed by RBO approach of energy industry, by narrowing down the limitations offered by current risk management practices, and by incorporating the relevant multi-actor theories a RBDM Appraoch was developed. Based on the findings and discussions several reccomendations were laid down.