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China's marine economy occupies an important position within the national economy, and its contribution thereto is constantly improving. The overall operation of the marine economy shows positive developmental trends with potential for further growth. The purpose of this research is to analyse the prosperity of China's marine economy, reveal trends therein and forecast the likely turning point in its operation.

Based on the periodicity and fluctuation of China's marine economy development, China's marine economic prosperity indicator system is established from five perspectives. On this basis, China's marine economic operation prosperity index can be synthesised and calculated, then a dynamic factor model is constructed. Using the filtering method to calculate China's marine economic operational Stock–Watson index, Markov switching has been used to determine the trend to transition. Furthermore, China's current marine economic prosperity is evaluated through analysis of influencing factors and correlation analysis.

The analysis shows that, from 2017 to 2019, the operation of the marine economy is relatively stable, and the prosperity index supports this finding; meanwhile it also exposes problems in China's marine economy, such as an unbalanced industrial structure, low marine economic benefits and insufficient capacity for sustainable development.

Through the analysis of the prosperity of China's marine economy, the authors reveal the trends in China's marine economy and forecast its likely future turning point.

When comparing and evaluating performance, decision-makers are concerned with providing a range of effective, efficient, and fair measures that can yield representative relative rankings for the units being evaluated. In this chapter, we apply three multicriteria benchmarking modeling techniques – weighted linear combination, data envelopment analysis (DEA), and ordered weighted average (OWA) – to an example dataset to provide a quantitative assessment of performance. Evaluation of the results demonstrates that each of these techniques has relative strengths and shortcomings. To take advantage of the relative strengths, and avoid some of the shortcomings that we observed, we develop and assess a promising new methodological approach, the order rated effectiveness (ORE) model. ORE uses the OWA unit ratings within a DEA optimization framework to provide an overall relative performance assessment.

The purpose of this paper is to collectively measure and compare the efficiency of Brazilian and American soybean transport corridors, from farmers to export ports, using the data envelopment analysis (DEA).

This paper aims to determine routes from main producing micro-regions to main export ports, specifically using slack-based measure and variables that represent the three pillars of sustainability (economic, social, and environmental). The choice of variables was guided by literature review and analyzed through the principal component analysis. After the application of the model, the quantitative tiebreaking method of the composite index is applied.

The findings are coherent with a global report that compares soybean transportation in both countries (Brazil and USA). Efficient routes and corridors tend to present short distance truck trips and long distance train or barge trips. The efficiency of the inland waterway trips depends on how many barges are used in the same expedition. Routes with more than three modes tend to be inefficient which suggest that there is a limit for multimodality.

Corridor benchmarking is a rare topic in the literature and previous works normally focus on some specific and limited corridor performance characteristics, such as cost. The main contribution of this research is that it expands the discussion regarding corridor benchmarking and it focuses on efficiency as a whole. The paper also proposes a method that can be applied in different logistics contexts, like expanding the study to different countries. More specifically, this method could be used in infrastructure investments programs.

The purpose of this paper is to assess the public health risk factors of internally displaced households and suggest appropriate measures and strategies for health risk reduction in the context of IDPs.

The composite Index (CI) method was used to compare the public health risk factors at the household level in three IDP camps. A set of 22 indicators were studied in 326 households. Households were selected by using a two-stage cluster sampling technique.

The findings indicate that the Shangil Tobaya camp is at the highest risk for communicable diseases (63.6%) followed by Zamzam (52.4%) and Abu Shouk (42.7%) at the household level. Eight indicators appeared to have made differential impacts between Abu Shouk and Shangil Tobaya, these include: level of education, walking time to health facilities, water source, latrines type, safe disposal of child feces, frequency of visit by pregnant women to antenatal care services, place of delivery and women delivering their children with the help of skilled birth attendants.

Since the selection criteria of the camps were predefined; there are variations in the number of samples between the camps. Therefore, the generalizability may be compromised.

Increased access to healthcare services particularly reproductive health services to the most vulnerable groups (women). Community involvement in services management to promote ownership.

The methods used in this study is original and flexible and can be replicated for other emergency areas and risks.

This paper describes the use of an analytical hierarchy process (AHP) in determining the rational weights of importance of pavement maintenance priority ranking factors. These weights were obtained by capturing the local people’s perception towards this vital part of the pavement management system (PMS). In this regard, different groups of individuals were asked to estimate the weight of importance in pavement maintenance of different factors for ranking pavement sections. These factors were road class, pavement condition, operating traffic, riding quality, safety condition, maintenance cost, and the overall importance of the road section to the community. The AHP method of pair‐wise comparison was employed to get the factor weights, which were compared with the weights obtained from the direct assignment method. It was concluded that the two methods were statistically similar which confirms that the results of the direct assignment method can be used safely with a sound reliability and consistency. This conclusion comes from the fact that the AHP method has a high reputation and applications, and it uses a high‐precision technique for obtaining the weights (priorities) of alternatives or items. Priority factor weights were used in developing a pavement maintenance priority ranking procedure for a road network. This procedure was validated by real case studies, and found to be logically and efficiently able to handle the ranking of a huge number of pavement sections for maintenance and repair.

This paper aims to quantitively assess the resilience of residential properties to urban flooding in Port Harcourt, Nigeria, and assess whether they vary at spatially aggregated scales relative to the level of flood exposure.

The study synthesizes theoretical constructs/indicators for quantifying property level resilience, as a basis for measuring resilience. Using a two-stage purposive/stratified randomized sampling approach, 407 questionnaires were sent out to residents of 25 flood-prone areas, to solicit information on the resilience constructs as indicated by the adaptation behaviors of individual households and their property attributes. A principal component analysis approach is used as a mechanism for weighting the indicators, based on which aggregated spatial-scale resilience indices were computed for the 25 sampled areas relative to their levels of flood exposure.

Area 11 located in the moderate flood zone has the lowest resilience index, while Area 20 located in the high flood zone has the highest resilience index. The resilience indices for the low, moderate and high flood zone show only minimal and statistically insignificant differences indicating maladaptation even with incremental levels of flood exposure.

The approach to resilience measurement exemplifies a reproducible lens through which the concept of “living with floods” can be holistically assessed at the property level while highlighting the nexus of the social and technical dimensions.

The study moves beyond theoretical conceptualization, to empirically quantify the complex concept of property-level flood resilience.

The purpose of this study is that on the basis of the competitive edge theory, source mechanism and evaluation approaches of industrial cluster competitiveness, combined with international trends in the automobile industry and the features of Chinese automobile industrial cluster development, an evaluation index system about cluster competitiveness of auto industry is built with comprehensive consideration of factors such as cluster development environment, external scale effect and internal competitiveness from the perspective of value chain of automobile industry.

An evaluation index system for automobile industrial cluster competitiveness was realized by integrating current strengths and future growth capacities with multidimensional, dynamic and comprehensive characteristics, which included 3 second-level, 10 third-level and 16 fourth-level indices. In the light of evaluation methods, a group intelligence optimization algorithm – (cuckoo search) – and traditional methods of complex decision-making system – analytic hierarchy process (AHP) – were combined to propose the cuckoo-AHP evaluation method. It was applied for the calculation and optimization of weight values in an automobile industrial cluster competitiveness evaluation index for the purpose of obtaining better scientific and more reliable results.

The research might further enrich the evaluation theory of automobile industrial cluster competitiveness and also can be useful for showing how traditional evaluation methods can be combined with intelligent algorithms to carry out better automobile industrial cluster competitiveness evaluations. In addition, studies of channels for kick-starting Chinese auto industrial cluster competitiveness are expected to provide references for how to enhance the cluster competitiveness of the Chinese automobile industry.

Changsha and Liuzhou, the Guangxi automobile industrial clusters as the two empirical analysis objects selected for this paper, are geographically adjacent to each other. The automobile industries of the two cities are local pillar industries with the strong support of the local government. Both clusters have their own advantages and weak points with different characteristics of cluster development, and they enjoy a representative significance amongst China’s numerous auto industrial clusters that are taking shape. Comparative analysis of both clusters serves as a good reference for the objective evaluation of the competitiveness of Chinese automobile clusters in terms of their real and practical developments and in respect of the success of reasonable scientific and industrial cluster policies.

Multidimensional, dynamic, integrated evaluation index systems are constructed around automobile industrial cluster competitiveness, which has taken into account developments in current strengths and future growth capacity. The cuckoo-AHP evaluation method has been formed by combining the traditional decision-making method known as AHP with a new meta-heuristic optimization algorithm called “cuckoo search”. Both have been used in evaluations of automobile industrial cluster competitiveness in Liuzhou and Changsha, which will be beneficial for enriching automobile industrial cluster competitiveness evaluation theory and new evaluation methods that will enable better evaluations of automobile industrial cluster competitiveness.

The study aims to analyze the impact of market participation on small paddy farmers' income and consumption expenditure. The study also estimates various determinants affecting the market participation of smallholders. Further, the study computes the efficiency of different paddy marketing channels and identifies the determinants that impact the marketing channel selection of paddy growers in Eastern India.

The study used the propensity score matching (PSM) approach to measure the impact of market participation on farm income and per capita consumption. Further, the study employed Acharya and Aggarwal's composite index approach to estimate the marketing efficiency of various paddy marketing channels. Further, a multinomial logit model was used to determine the marketing channel selection constraints.

The outcomes indicate that market participation positively impacts farm income and consumption expenditure. Education, membership in farmers' organizations, price information and distance to the marketplace significantly affect farmers' market participation. The results show that the producer–retailer marketing channel is the most efficient compared to others. However, most paddy farmers sell paddy to farmgate collectors due to a lack of market information, vehicle ownership, storage system, and inability to take the risk of venturing out of the farmgate into markets.

The study uses primary data and captures only farmers' perspectives to measure the impact of market participation, marketing channel efficiency and determinants for market channel selection. The other stakeholder's perceptions can be included in future studies.

Rarely does any study identifies the efficiency of different marketing channels for paddy farmers in India and includes cognitive factors like risk perception and trust in buyers as constraints for market channel selection.

This paper aims to present the concepts of two different ways of generating a dynamic structure of the urban system to further allow in understanding specific urban behavior facing against flood and further evaluate the potential effect of specific resilience strategies aiming to decrease the exposure and vulnerability of the system.

Two system dynamics model structures are presented in form of Casual Loop Diagrams.

The main differences among the tow approaches are the time horizon and the approach that regulates the assessment of the resilience through a dynamic composite indicator: the first model refers to baseline at initial simulation time; the second model is focused on the ratio service supply to demand.

Within the approach, the purpose is to properly and efficiently evaluate the effect of different Flood Risk Management strategies, i.e. prevention, defence, mitigation, preparation and recovery for consistent and resilient flood governance plans with different type of resilience scenarios.

The need for such tool is underlined by a lack on the assessment of urban resilience to flood as whole, considering the physical and social dimensions and the complex interaction among their main components. There are several assessment tools based on an indicator approach that have been proposed to meet this need. Nevertheless, indicator-based approach has the limitation to exclude the complexity of the system and its systemic interaction in terms of feedbacks’ effects among the identified components or variables selected for the system description. This peculiarity can be provided by System Dynamics modeling.

The most common technique to determine the predicted final cost of a Department of Defense (DoD) acquisition contract, or the Estimate at Completion (EAC), involves the use of performance indices to adjust the EAC. Other methods including simple linear regression and time series analysis have been developed to predict the final cost, but these methods are not widely publicized or have limited applicability. As a potential remedy, this research utilizes the historical contract data reported in the Defense Acquisition Executive Summary database and provides to the analyst a set of five working multiple regression models. Useful over the life of the contract, they accurately predict the final cost of the average major weapons system contract using contractor Cost Performance Report data.