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The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

Given the importance of style allocation strategy under the outsourced chief investment officer (OCIO) structure, the authors examine the validity of style allocation strategies in the Korean stock market. The authors find that external investment agencies can improve performance by using newly suggested investment styles such as high dividend yield and low volatility as well as traditional styles. In addition, the authors find that the style combination strategies create economically large and statistically significant returns. Finally, empirical results indicate that factor timing strategies suggested in this study can improve the reward-to-risk ratio. In sum, the empirical findings indicate that external investment agencies under the OCIO structure can improve performance using active style allocation strategies.

This paper aims to replicate 148 anomalies and to examine whether the performance of the Korean market anomalies is statistically and economically significant. First, the authors observe that only 37.8% anomalies in the universe of the KOSPI and the KOSDAQ and value-weighted portfolios have t-statistics that exceed 1.96. When the authors impose a higher threshold (an absolute value of t-statistics of 2.78), only 27.7% of the 148 anomalies survive. Second, microcaps have large impacts. The results vary significantly depending on whether the sample included stocks in the KOSDAQ and whether value-weighted or equal-weighted portfolios are used. The results suggest that data mining explains large portion of abnormal returns. Any tactical asset allocation strategies based on market anomalies should be applied very cautiously.

The role of clusters in the development and growth of local and national economies has been extensively studied and discussed in global literature. Different methodologies are used for analysing the impact these have in national and regional economies, such as the input–output (IO) and gravity models. This paper aims to detail the methodologies present in the literature and propose a new robust theoretical framework, which facilitates the evaluation and comparison among maritime clusters in terms of attractiveness assisting stakeholders to devise strategies, which will attract companies.

An index is created composed of five key categories, namely, infrastructure, financing, governance, manpower and institution/legislation. For the analysis of the index, multi-attribute utility theory (MAUT) is used as a tool to evaluate the importance and performance of the different attributes using both quantitative and qualitative criteria. The methodology has been tested via the use the Piraeus maritime cluster.

The framework has been tested on its robustness and friendliness to the user providing useful insights to the stakeholders. Among the results has been the importance of the finance, manpower and infrastructure attributes, which appear to promote the cluster’s attractiveness. In addition, legislation and institutional partnerships, along with Government support, need to take place improve the performance of the cluster.

A key limitation is the fact that the methodology has been tested in a single case. Applying the methodological framework in a wider sample of clusters will significantly improve the present work.

The proposed model takes further existing research in the field via adopting the philosophy of the World Bank’s Logistics Performance Index. Among the benefits of the proposed index is that it offers the flexibility and robustness to compare among different maritime clusters globally and can be readily used as a benchmarking policy tool at national, regional and global levels at any given point in time and attribute dimension.

The purpose of this study is to identify the most vulnerable households and districts in Northwest Ethiopia and help decision-makers in developing and prioritising effective adaptive strategies and actions.

A multi-scale analytical tool and hazard-generic socio-economic indicators were developed to identify and prioritise the most vulnerable households and districts in Northwest Ethiopia. Categorical principal component analysis with 36 indicators was used to develop weights for different indicators and construct a household intrinsic vulnerability index. Data were collected through key information interviews, focus group discussions and a household survey with 1,602 randomly selected households in three districts of Northwest Ethiopia.

Drawing on intrinsic vulnerability index computation, this study highlights that low levels of education, low access to climate information and credit services, long distance travelled to fetch water and frequent food shortages are the dominant factors contributing to high levels of intrinsic vulnerability at district level, while lack of livelihood support and income diversification are the key drivers of vulnerability at household level. The findings of this study further show that the majority of households (78.01%) falls within the very high to moderately high vulnerable category. Disaggregating the data according to agro-climatic zones highlights that the prevalence of high intrinsic vulnerability is most widespread in the lowland agro-climatic zone (82.64%), followed by the highland (81.97%) and midland zones (69.40%).

From a policy intervention vantage point, addressing the drivers of vulnerability provides a reliable approach to reduce the current vulnerability level and manage potential climate change-induced risks of a system. Specifically, reliable information on inherent vulnerability will assist policymakers in developing policies and prioritising actions aimed at reducing vulnerability and assisting in the rational distribution of resources among households at a local level.

This study contributes to the existing vulnerability literature by showing how hazard-generic socio-economic indicators in the vulnerability assessment adopted by the IPCC (2014) are important to identify drives of vulnerability which ultimately may feed into a more fundamental treatment of vulnerability.

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

This study aims to assess readiness for climate change mitigation in the Kingdom of Bahrain.

Two stages were followed aiming at understanding the situation related to climate change mitigation in Bahrain and assessing the mitigation readiness. Baseline and mitigation scenarios for the period 2019–2040 were developed using the Low Emissions Analysis Platform software based on historical emissions and energy data for the period 1990–2018. Using the analytic hierarchy process, the mitigation readiness was assessed by 13 experts, and priority areas for mitigation action were identified.

CO₂e emissions are projected to grow continuously. However, no explicit climate change strategy is in place yet. Mitigation is tackled implicitly through energy efficiency and renewable energy initiatives. These initiatives can make 23% reduction in CO₂e emissions by 2040. Adopting additional measures is needed to achieve the recently set emission reduction target of 30% by 2035. The findings revealed potential areas for improving mitigation efforts in Bahrain. Priority areas for mitigation actions, as identified by experts, were mainly related to policy and governance. Focus needs to be paid to the social aspect of climate change mitigation.

Literature on mitigation readiness in developing countries is sparse. Knowledge of the requirements for climate change mitigation and assessment of the country’s performance can prioritize areas for improving mitigation action. Several lessons can be learnt from the case of Bahrain. In addition, the adopted methodology can be applied to other developing or Arab countries at local or institutional levels. However, its application to specific sectors may require adjustments.

Crop suitability analysis is vital for identifying a piece of land’s potential for sustainable crop production and aids in the formulation of an effective agricultural management plan. This study aims to conduct crop suitability analysis of prominent Kharif (rice and maize) and Rabi (potato and wheat) crops in Sirajganj district, a flood-prone area of Bangladesh, and recommend a suitable cropping pattern to mitigate the detrimental effects of flooding.

Various factors such as soil drainage, soil depth, soil moisture, soil texture, soil permeability, soil pH, erosion hazard, nutrient status and flooding risk were considered for this study. For all four crops, the weights of each factor were determined using the analytical hierarchy process approach, and the scores of each subfactor were assigned on the basis of favorable circumstances of crop cultivation. Using the weighted overlay analysis in the ArcGIS 10.3 environment, the crop suitability maps were generated and were divided into four suitable classes. Geographic information system integration of crop suitability for all the crops determined the suitable cropping pattern of the study area in Kharif and Rabi seasons.

A vast portion of the study area covering 64.80% of the total land is suitable for cultivating either rice or maize in Kharif season followed by either potato or wheat in Rabi season. Other suitable cropping pattern for Kharif and Rabi seasons found in the study area are rice-wheat, rice-wheat/potato, rice/maize-wheat and rice/maize-potato, which covers a little portion of the study area.

This research validates the suitable location of crop cultivation on the basis of flooding occurrences in the locality.

The establishment of partnerships between companies, government and universities aims to enhance innovation and the technological development of institutions. The biotechnology sector has grown in recent years mainly driven by its cooperative business model. Compared to other countries, this sector is slowly advancing in Brazil, with delays in science, technology and innovation, especially in the private sector. This paper aims to examine, through social network analysis, the collaborative networks between institutions that filed patents in biotechnology – medicinal preparations from plants – whose inventions had Brazil as the priority country.

The study of technological cooperation using patent documents is a reliable approach as they serve as good indicators of the interactions between organizations that focus on innovation and development of new product. Social network analysis of cooperation networks helps to understand the connections between patent assignees, and how they establish relationships.

Results show that public universities are the institutions that most deposit patents, as well as those that co-operate the most, especially Universidade of Campinas. The study also reveals the critical role of Research Support Agencies in stimulating research and technological development, which result in new technologies.

The study applied the social network analysis to provide an overview of the interactions among Brazilian institutions with the purpose of helping in decision-making and inciting public policies to leverage the biotechnology sector.