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This paper contributes to the development of improved guidelines for cost evaluation of Carbon Capture and Storage (CCS) from industrial applications building on previous work in the field. It discusses key challenges and factors that have a large impact on the results of cost evaluations, but are often overlooked or insufficiently addressed. These include cost metrics (especially in the context of industrial plants with multiple output products), energy supply aspects, retrofitting costs, CO2 transport and storage, maturity of the capture technology. Where possible examples are given to demonstrate their quantitative impact and show how costs may vary widely on a case-by-case basis. Recommendations are given to consider different possible heat and power supply strategies, as well as future energy and carbon price scenarios, to better understand cost performances under various framework conditions. Since retrofitting CCS is very relevant for industrial facilities, further considerations are made on how to better account for the key elements that constitute retrofitting costs. Furthermore, instead of using a fixed unit cost for CO2 transport and storage, cost estimates should at least consider the flowrate, transport mode, transport distance and type of storage, to make more realistic cost estimates. Recommendations are also given on factors to consider when assessing the technological maturity level of CCS in various industrial applications, which is important when assessing cost contingencies and cost uncertainties. Lastly, we urge techno-economic analysis practitioners to clearly report all major assumptions and methods, as well as ideally examine the impact of these on their estimates.

The Engineering and Construction (E&C) sector of the global economy accounts for $10 trillion in expenditures annually and creates more than 100 million jobs around the world. In the United States alone, $1.25 trillion were spent, and 8.4 million individuals were employed by the E&C sector in 2017. Yet despite the economic significance of this industry, repeated studies have shown that productivity is at best holding steady, and at worst declining relative to the industry’s performance in previous decades, and the present performance of comparative non-farm industries. Since 1964, construction industry productivity has been steadily declining, with the industry now operating at only 80% of its 1964 productivity, an annual decline of -0.7%. This is largely attributed to the fragmented nature of the industry, a lack of innovation and adoption of disruptive technologies, strict regulations on construction performance, excessive risk transfer, and lack of coordination between different contracting parties. The impetus for this research effort is the industrywide problem of declining and stagnating productivity, which frequently leads to delays in construction. Worldwide, some of the largest owners and clients of building construction are in the public-sector (i.e. state, local, and federal governments and their agencies). Traditionally, public building construction procurement operates on a low-bid model, using the DesignBid-Build (DBB) delivery method. The method ensures transparency in selection of the prime general contractor and focuses on the initial construction cost, often crucial to annual budgets and public oversight. But DBB is criticized for not considering total cost of construction and ownership, which can be much higher due to delays, quality issues, etc. The Traditional DBB delivery method can be divided into three types: • DBB Multiple-Prime (MP): the state solicits bids for each major type of work on a project and engages in separate contracts with each of the winning firms. • DBB Single-Prime: the state solicits bids for the entire project and contracts directly with a general contractor, who in turn contracts with the pertinent subcontractors or suppliers. • DBB Hybrid Single-Prime (SP): the approach in current use by the State in which the State solicits bids from MEP contractors 1 week before the GC bid is due. The state then selects the lowest specialty contractors and the GC is bound to contract with the winning MEP contractor. Other innovative project delivery methods (PDMs) have been developed that can increase early participation of the contractor(s) in the design stage and encourage collaboration. These include, for example, Design-Build (DB), Construction Manager at Risk (CMAR), and Integrated Project Delivery (IPD). These PDMs involve collaboration among all stakeholders, including the general contractors and subcontractors, much earlier in the process than under the DBB system. This allows for more effective problem-solving. Furthermore, studies have shown that in some situations those delivery methods can reduce change orders, delays, and cost overruns. This thesis seeks to investigate the delivery methods used in Wisconsin public building projects and make recommendations that will enable state decision makers to improve outcomes on future projects. The data collected in this study shows that the majority of the building projects were executed under Hybrid Single-Prime (SP) and DBB Multiple-Prime (MP). However, some of Wisconsin’s public building projects used other delivery methods. This research compares the results of these delivery methods to the Traditional DBB method using a number of performance metrics. The initial step was a comprehensive data collection effort. The objective of this phase was to eliminate coincidental inconsistencies, maximize sample size, and capture as much project diversity as possible. Data was collected from 189 capital projects completed between 2000 and 2017, with a total construction value of $5.2 billion (in 2018 dollars). This amounts to over half of state-funded building construction projects (by value) over this period. There was a negligible difference in the population of projects by state agency (i.e. UW-System, Department of Corrections, etc.), thus the sample collected is considered an appropriate representation of Wisconsin public building construction projects executed under the supervision of the Department of Administration (DOA). Next, projects were evaluated in an objective, quantifiable manner. Eight metrics were identified from the literature and used to assess performance in schedule, communication, change management, and spending – both individually and collectively. From these metrics, a mathematical model, the Project Performance Index (PPI), was developed. The PPI is a single numeric score that ranges from 1 to 10, facilitating the comparison of projects that may significantly differ in their scope, cost, length of contract, etc. The scoring and analysis were then validated using two independent statistical techniques: Confirmatory Factor Analysis (CFA) and Partitioning Clustering Analysis, ensuring consistency of the findings and the confidence level of the conclusions. Finally, the project performance was broken down by UW-System campus (if applicable) to determine the impact of this factor on the performance of a project. The results show that 97% of projects in this study cost more than the bidder originally proposed. This cost growth averaged 10.3%. However, it should be noted that some cost data includes contingency while others do not. The degree to which the cost growth observed has resulted from the normal use of contingency is unknown. Specifically, the data for DB and CMAR contain contingency, whereas the data for DBB does not. In terms of schedule, 69% of projects were delivered behind schedule. These delays extend the schedule by an average of 39%, increasing indirect costs of administration of the projects, and the opportunity costs of projects delivered later than initially committed. However, these costs were not available and are therefore not considered in this thesis. A review of current literature shows that these costs can be substantial and that PDMs other than DBB manage the costs resulting from these delays more favorably. This thesis compares the performance of DBB, DB and CMAR based on the metrics defined in this study. It was found that DB statistically outperformed SP and MP in all eight of the performance metrics. CMAR outperformed SP in 5 metrics and MP in 6. It was further found that SP outperformed MP in terms of 3 metrics, and DB outperformed CMAR in terms of only one metric. These findings were validated statistically at the 95% confidence level. Nevertheless, these findings should be used with the understanding that multiple factors were not captured by the analysis, including the complexity of projects, market conditions, unforeseen conditions, as well as other intangible factors. Application of the PPI model developed in this research found that Collaborative delivery methods—that is, Design-Build (DB) and Construction Manager at Risk (CMAR)— performed 40% better on average than the more traditional Hybrid Single-Prime (SP) and Multiple-Prime (MP) methods. That is, Collaborative projects are 40% more capable of reducing changes, executing quickly, meeting schedule, and maintaining communication among vested parties than Traditional methods. Furthermore, due to the lower performance of both SP and MP as compared to DB and CMAR (based on the metrics), this research recommends the State investigate the potential benefits from the use of alternative and innovative delivery methods. These methods may be particularly suitable for advanced projects such as hospitals and research labs, or complex, large-scale projects which may require early coordination of multiple trades and design. More Collaborative delivery methods have the potential to improve communication and allow for innovative practices. One possible approach would be to develop a pilot program that allows for the selection of more appropriate methods on specific projects, those characterized by high levels of system complexity and a high risk of schedule slippage, and evaluate the benefits for the State of Wisconsin as a proof of concept.

Summary The hierarchical routing algorithm is categorized as a kind of routing method using node clustering to create a hierarchical structure in large-scale mobile ad hoc network (LMANET). In this paper, we proposed a new hierarchical clustering algorithm (HCAL) and a corresponded protocol for hierarchical routing in LMANET. The HCAL is designed based on a cost metric in the form of the link expiration time and node's relative degree. Correspondingly, the routing protocol for HCAL adopts a reactive protocol to control the existing cluster head (CH) nodes and handle proactive nodes to be considered as a cluster in LMANET. Hierarchical clustering algorithm jointly utilizes table-driven and on-demand routing by using a combined weight metric to search dominant set of nodes. This set is composed by link expiration time and node's relative degree to establish the intra/intercommunication paths in LMANET. The performance of the proposed algorithm and protocol is numerically evaluated in average end-to-end delay, number of CH per round, iteration count between the CHs, average CH keeping time, normalized routing overhead, and packet delivery ratio over a number of randomly generated benchmark scenarios. Furthermore, to corroborate the actual effectiveness of the HCAL algorithm, extensive performance comparisons are carried out with some state-of-the-art routing algorithms, namely, Dynamic Doppler Velocity Clustering, Signal Characteristic-Based Clustering, Dynamic Link Duration Clustering, and mobility-based clustering algorithms.