Project Selection Research Articles

Site selection of sludge pyrolysis projects based on improved interactive multi-indicators decision-making method under vague linguistic environment: A decision framework from the perspective of low-carbon

Site selection of sludge pyrolysis projects based on improved interactive multi-indicators decision-making method under vague linguistic environment: A decision framework from the perspective of low-carbon

Responses of teachers in Scotland to the reintroduction of the practical project in the advanced higher chemistry curriculum

Abstract Many pupils who study chemistry in Scotland and intend to progress to university undertake a final capstone practical research project as part of their Advanced Higher chemistry qualification. This project work was suspended for several years due to COVID-19, then reintroduced in 2023/24. We surveyed 47 Scottish teachers during this first project year using a mixed-methods survey, and found that resource availability had the biggest impact on projects, with over half of respondents reporting that they asked pupils to buy materials and a third committing their own money. Technician support was seen as critical to success, but project selection was heavily curtailed by the availability of existing equipment or chemicals, even as teachers made good use of available professional development and other forms of support. We finally make recommendations around the resourcing of project work and teacher/technician CPD provision.

Exploring Greenium and the Determinants of Green Bond Performance in Asia

This study examines the presence and determinants of the ‘greenium’ in Asian green bonds. The dataset includes green bonds issued by governments and corporations in Asian countries from 2016 to 2022. To analyse the determinants of the greenium, we applied a coarsened exact matching (CEM) model, an advanced form of the matching method. Ordinary least squares (OLS) regression was employed to assess the factors influencing the greenium. Our findings indicate that a greenium exists in green bonds issued in Asia, with significant effects observed in China, Korea, and Japan. Key drivers of the greenium include coupon rates, issuer type, oil prices, and the federal funds rate. This study contributes practical insights by highlighting that a greenium can be achieved through strategic project selection, transparent communication, and strong environmental commitment. Additionally, investors should account for coupon rates, issuer sector, and macroeconomic factors such as oil prices and the federal funds rate when investing in green bonds.

Impact of compliance with project requirements on projects value outcomes: Evaluation from TASAF-III beneficiaries’ perspectives in Tanzania

The study sought to evaluate the impact of compliance with projects requirements on projects value outcomes from Tanzania Social Action Fund (TASAF)-III beneficiaries’ perspectives in Tanzania. Specifically, the study evaluated the impact of compliance with project requirements on households’ relief from transitional income poverty (as a measure of projects value outcomes) in Tanzania. The study was a cross sectional design with multiple cases approach undertaken in twelve TASAF-III supported community economic development (CED) projects. Under multistage sampling procedure, the said CED projects, their six hosting districts and regions (Kilosa–Morogoro; Handeni–Tanga; Misungwi–Mwanza; Uyui–Tabora; Kibondo–Kigoma; and, Rungwe–Mbeya) were identified using stratified sampling. Simple random sampling enhanced to determine a sample of 480 households. Data were collected using research schedules and were descriptively and inferentially analyzed using STATA software. Propensity score matching (PSM), coupled with logistic model was considered the apt test statistic. The average treatment effect on the treated (ATT) observation results shown that, the mean differences in incomes [TZS. 7 794.30; t=0.61(t<2.00)] and spending [TZS 11 388.50; t=0.96 (t<2.00)] between the treated and control households’ are non-significant at t<2.00. This implied that, compliance with TASAF-III project requirements has no significant impact on households’ income poverty relief in Tanzania. Moreover, compliance with requirements does not necessarily lead to desired projects value outcomes—if requirements are not well defined. The study stresses on the selection of right projects’ requirements with positive outcomes rather than having a set of requirements which are non-predictive to the desired targets’ felt needs—when in compliance with.

How to optimize government R&D budget allocation? A review of recent studies and research gaps

Purpose Research and Development (R&D) activities are important for technological innovation and present opportunities for entrepreneurship. These activities depend on the flow of funding. This paper aims to review approaches used in R&D project selection and budget allocation. Design/methodology/approach This study conducts a systematic review, examining the content of 60 relevant papers (spanning 2000–2022) concerning public R&D budget allocation. The analysis focuses on allocation methodology, R&D output evaluation, budget allocation efficiency and the management of uncertainty in the allocation process. Findings The systematic review reveals different methods proposed for allocating government R&D budgets. These methods range from classical optimization, multi-criteria analysis and hierarchical analysis to techniques such as balanced scorecard, data envelopment analysis and analytic hierarchy process, including fuzzy approaches. Recent trends indicate an increase in the use of advanced optimization, integration and simulation algorithms. Performance indicators for reflecting R&D project outputs or goals can be categorized into four main groups: output (e.g. publications, patents, graduates), outcome, productivity (e.g. citations, patent references, articles and patents per capita) and sector-specific metrics. Practical implications Future research directions in government R&D budget allocation may include optimizing allocation to maximize social, economic and political benefits, developing ranking models, decision-making frameworks, simulations and evaluations of factors influencing allocation type and strategy. Additionally, there is a growing interest in novel budget allocation algorithms leveraging artificial intelligence and self-adjusting meta-heuristic algorithms. Originality/value The systematic review showed that some important research gaps in (government) R&D budget allocation could be considered in future studies; for example, long-term social, economic and political benefits in budget allocation optimization models, comprehensiveness of allocating government R&D budgets to universities, higher education and research institutes, R&D budget allocation to strategic technology development, e.g. renewable energy sector, supply chain issues and renewable energy value chain; new budget allocation algorithms based on artificial intelligence and self-adjusting meta-heuristic algorithms; methods for optimizing the structures of government budget allocation to R&D, considering executive and regulatory conflicts.

Finding robustly fair solutions in resource allocation

In this study, we consider resource allocation settings where the decisions affect multiple beneficiaries and the decision maker aims to ensure that the effect is distributed to the beneficiaries in an equitable manner. We specifically consider stochastic environments where there is uncertainty in the system and propose a robust programming approach that aims at maximizing system efficiency while guaranteeing an equitable benefit allocation even under the worst scenario. Acknowledging the fact that the robust solution may lead to high efficiency loss and may be over-conservative, we adopt a parametric approach that allows controlling the level of conservatism and present the decision maker alternative solutions that reveal the trade-off between efficiency and the degree of conservatism when incorporating fairness. We obtain tractable formulations, leveraging the results we provide on the properties of highly unfair allocations. We demonstrate the usability of our approach on project selection and shelter allocation applications.

Implementation of Green Lean Six Sigma in Dutch Healthcare: A Qualitative Study of Healthcare Professionals' Experiences.

Introduction: The healthcare sector significantly contributes to environmental degradation, highlighting the need for sustainable practices. Green Lean Six Sigma (GLSS) offers a relevant and impactful approach to reduce healthcare's environmental footprint while improving efficiency. By incorporating environmental considerations into Lean Six Sigma, GLSS has the potential to mitigate healthcare's environmental impact and promote environmental sustainability. This study aims to gain insight into healthcare professionals' experiences with GLSS at their workplace. Materials and Methods: This qualitative exploratory study employed semi-structured surveys based on theory of training evaluation from Kirkpatrick with Dutch healthcare professionals in the first six to eight months after completing GLSS training. Results: Even though 76% (N = 16) of trained healthcare professionals applied GLSS at their workplace and 43% of them (N = 9) completed a project within the first six to eight months after training, they all experienced one or more barriers. The most frequently reported barriers were lack of time, difficulties with project selection and a lack of management support. GLSS project results included reduction of products, energy, costs and travel, green choices in procurement and sustainable food choices. GLSS also helped to create awareness on the environmental impact of healthcare and to optimize processes by reducing costs, waiting time, workload and defects. Discussion: This is the first study to report experiences from applying GLSS in healthcare. Furthermore, it is the first study presenting GLSS training evaluation results in terms of participant behaviour and organizational outcomes. Conclusions: Results of this study can be used to enhance GLSS deployment programs and to optimize organizational settings for successful GLSS implementation in healthcare.

Optimizing Multi-Stage Project Portfolio Selection Considering Integrating Lifecycle and Interactions

Project portfolio selection is essential for a company to achieve its strategic goals. Due to constraints such as budget and manpower, companies cannot undertake all projects simultaneously and must prioritize those offering the highest value. Projects often interact and progress through various phases, adding complexity to the selection process. To address these challenges, this study introduces a model that accounts for the multi-stage execution of projects, their interactions, and multiple objectives. A novel multi-objective optimization algorithm is developed to solve this problem, along with a refined project selection method designed to offer decision-makers enhanced insights. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed approach.

Criteria for effective site selection of direct air capture and storage projects

Criteria for effective site selection of direct air capture and storage projects

Solving Transport Infrastructure Investment Project Selection and Scheduling Using Genetic Algorithms

The development of transport infrastructure is crucial for economic growth, social connectivity, and sustainable development. Many countries have historically underinvested in transport infrastructure, necessitating more efficient strategic planning in the implementation of transport infrastructure investment projects. This article addresses the selection and scheduling of transport infrastructure projects, specifically within the context of utilizing pre-allocated funds within a multi-annual budget investment program. The current decision-making process relies heavily on expert judgment and lacks quantitative decision support methods. We propose a genetic algorithm as a decision-support tool, framing the problem as an NP-hard 0–1 multiple knapsack problem. The proposed genetic algorithm (GA) is unique for its matrix-encoded chromosomes, specially designed genetic operators, and a customized repair operator to address the large number of invalid chromosomes generated during the GA computation. In computational experiments, the proposed GA is compared to an exact solution and proves to be efficient in terms of quality of obtained solutions and computational time, with an average computational time of 108 s and the quality of obtained solutions typically ranging between 85% and 95% of the optimal solution. These results highlight the potential of the proposed GA to enhance strategic decision-making in transport infrastructure development.

Enhancing IT Project Portfolio Management Through Dynamic Dashboard Visualization and Integrated Chatbot and Mathematical ML Model for Strategic Decision Making

This project paper presents an innovative approach to enhance IT Project Portfolio Management (IT PPM) using dynamic dashboard visualization and machine learning (ML) techniques. Effective IT PPM is essential for organizations to align IT projects with business objectives and optimize resource allocation. However, conventional methods often lack innovation and real-time visibility, leading to suboptimal decision-making and costly software investments. The objective of this project is to develop a dynamic dashboard visualization system integrated with a machine learning model derived from the Meta-Portfolio Method (MPM). The MPM leverages advanced analytics and historical project data to dynamically allocate resources and prioritize projects based on risk-return profiles. By incorporating the MPM into the dashboard, organizations gain real-time insights into project status, progress, and resource allocation, enabling informed decision-making and proactive portfolio optimization. The ML model derived from the MPM analyzes historical project data, including success rates, resource intensity, criticality, regulatory compliance, and portfolio diversification, to predict optimal project selection and resource allocation. By dynamically adapting to changing market conditions and project dynamics, the ML model facilitates strategic decision-making and enhances portfolio performance. Through the integration of dynamic dashboard visualization and ML-driven decision support, organizations can streamline IT PPM processes, improve project outcomes, and achieve strategic objectives effectively and efficiently. This innovative approach empowers organizations to navigate the complexities of IT project portfolios with agility, foresight, and confidence.

Core‐flooding experiments of various concentrations of CO2/N2 mixture in different rocks: II. Effect of rock properties on residual water

AbstractDuring CO2 storage in deep saline aquifers, the presence of residual water has an important influence on the storage efficiency and safety. In this study, natural rock cores taken from deep reservoirs in the Ordos Basin and Fukang, Xinjiang are used as research objects. Nine groups of core‐flooding experiments are performed under different CO2/N2 gas mixture ratios to study the influence of rock properties (mineral composition, permeability, porosity and pore structure) on the residual water. Furthermore, the geophysical and chemical properties of rock cores are analyzed by X‐ray diffraction, electron microscopy, field emission scanning electron microscopy and piezoelectric mercury method. The results show that residual water saturation is a quantitative power function of drainage time. The residual water saturation is positively correlated with the total amount of quartz and feldspar and increases with increasing permeability. Moreover, both the average and median pore throat radius show a strong inverse correlation with irreducible residual water saturation; as these radius increase, the residual water saturation decreases. In contrast, the porosity and maximum pore throat radius display a weaker correlation with irreducible residual water saturation. This study is of great value for engineering practices such as the site selection of CO2 storage projects in saline aquifer and improvement of CO2 storage efficiency. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

Regional perspective on prospects and risks of the renewable energy in Russia

AbstractThe expansion of renewable energy (RE) relies on both natural factors and socioeconomic conditions fostered at the regional level. This paper examines the key factors influencing RE development in selected Russian regions. We conducted a literature review, surveyed 250 industry experts, and analyzed reports from energy and rating agencies, along with natural resource maps, to identify specific risks and indicators. Our expert survey revealed that the most significant technical risks are low resource potential (68%), energy system surplus (68%), and the absence of transport logistics with neighboring regions (58%). Politically, the lack of additional sector initiatives (6%) was noted. Economically, the high cost of RE (56%) and insufficient experience with “green” investments (54%) were highlighted. Social factors included low unemployment rates (74%), and the absence of remote areas without power supply (72%). Environmentally, the lack of enterprises for energy unit utilization (84%), low greenhouse gas emissions (60%), and insufficient accessible territories for facilities (58%) were significant concerns. We developed risk profiles for nine Russian regions, categorizing them into leaders, intermediates, and nascent stages of RE market formation. Our findings indicate that the most impactful factors include resource potential, space limitations for energy facilities, proximity to equipment manufacturers, regional support measures, and the development of “green” investment instruments. Conversely, regional investment attractiveness had a lesser influence. On the basis of these insights, we recommend enhancing RE development through refined federal and regional support programs. We propose additional criteria for project selection: (1) minimum construction costs covered by “green” investments and (2) quantitative restrictions on new capacities, considering regional energy system status. Practical recommendations also include developing regional centers for servicing generating facilities and increasing the sector's investment attractiveness to stimulate private investment.

A generalized behavioral-based goal programming approach for decision-making under imprecision

The body of literature on goal programming (GP) approaches in modeling preferences and the satisfaction philosophy in multi-objective programming (MOP) decision-making processes is extensive. However, there has been little focus on how preferences change in relation to the decision-maker's (DM) behavior within this satisfaction philosophy, particularly in situations involving risk. To address this challenge, we propose introducing a behavior-type utility function into the GP model using the concept of a behavior function. This idea offers an innovative perspective for modeling DM's behavioral preferences in the imprecise GP approach by integrating a risk-aversion parameter specific to each objective. We then formulate a generalized behavioral-based GP approach for decision-making based on this new behavior-type utility function. To validate our proposed approach, we present an illustrative example of project selection in health service institutions, followed by a sensitivity analysis and comparisons with other approaches. The results demonstrate that DM's behavioral preferences significantly impact the decision-making process, and the proposed model provides more reasonable and convenient decisions for DMs with varying degrees of risk aversion.

Pathways to success: a machine learning approach to predicting investor dynamics in equity and lending crowdfunding campaigns

AbstractCrowdfunding has evolved into a formidable mechanism for collective financing, challenging traditional funding sources such as bank loans, venture capital, and private equity with its global reach and versatile applications across various sectors. This paper explores the complex dynamics of crowdfunding platforms, particularly focusing on investor behaviour and investment patterns within equity and lending campaigns in Italy. By leveraging advanced machine learning techniques, including XGBoost and LSTM networks, we develop predictive models that dynamically analyze real-time and historical data to accurately forecast the success or failure of crowdfunding campaigns. To address the existing gaps in crowdfunding analysis tools, we introduce two novel datasets—one for equity crowdfunding and another for lending. Moreover, our approach extends beyond traditional binary success metrics, proposing novel measures. The insights gained from this study could support crowdfunding strategies, significantly improving project selection and promotional tactics on platforms. By enhancing decision-making processes and providing forward-looking guidance to investors, our computational model aims to empower both campaign creators and platform administrators, ultimately improving the overall efficacy and sustainability of crowdfunding as a financing tool.

Exploring decision-making: experimental observations on project selection and the impact of justification pressure

AbstractIn this experimental investigation, we explore the impact of justification on project choices. Introducing a novel element, we implement asymmetric payoff schemes commonly employed in business, signifying distinct payoff distributions for the firm (principal) and the manager (agent). The agent has to choose one project from two options that differ in their risk-return profiles. The outcomes of our experiment substantiate our hypothesis, indicating that a mandate for justification decreases the probability of agents selecting the project with higher risk and return. The degree of this reduction appears to hinge on the nature of justification. Increased profit shares for the agent or a project recommendation from the principal can partially counterbalance the distortion in the project choice.

A study on cross-project fault prediction through resampling and feature reduction along with source projects selection

A study on cross-project fault prediction through resampling and feature reduction along with source projects selection

Pore pressure prediction based on rock physics theory and its application in seismic inversion

Formation overpressure seriously affects drilling and downhole operation. Accurate prediction on the formation pore pressure can not only reduce the probability of drilling accidents, but also quantitatively evaluate the original formation pressure of underground pore space, which provides an important reference for site selection of carbon sink projects using underground space resources such as CO2 geological storage. It is therefore necessary to set up a widely applicable method that is based on rock physics theory and conforms to the characteristics of rock mechanics and fluid mechanic. This method is suitable for both logging prediction and seismic inversion of pore pressure. The traditional method of predicting pore pressure based on P-wave velocity has multiple solutions, and the prediction based on S-wave velocity which is not sensitive to fluid has new significance. Based on the Hertz-Mindlin petrophysical model that considering pressure variation and the Gassmann fluid substitution equation that addresses the change in fluid saturation, this paper firstly derived rock physical formulas for predicting pore pressure in logging, and then derived the intrinsic power function relationship between the effective pressure (Pe) and S-wave velocity (Vs) as well as S-wave impedance (Is). Based on this, a set of geophysical methods integrating S-wave velocity prediction, pore pressure prediction in well and seismic inversion is finally established. The efficacy of this method has been well validated, with an average error of 2.35% in S-wave velocities prediction, 4.5% in single-well pore pressure prediction. The results of seismic inversion of pore pressure are consistent with the phenomenon of overpressure development in actual working area. This method can be further extended to other areas, providing invaluable reference for underground operation such as oil and gas exploration and CO2 geological storage.

Multicriteria approach for evaluating biowaste-valorization cases

The selection of waste-valorization projects is a complex decision process that involves many factors and variables to guarantee an appropriate definition of the best waste-management strategy to be applied for a specific waste typology. Different technological approaches can be identified based on the diverse waste types, such as fish byproducts, spent coffee grounds, used cooking oils, and sewage sludge. To identify the most suitable and sustainable method for processing each residue type, one must consider multiple criteria in the decision-making process (i.e., economic, technological, environmental, social, and political) to define a specific waste-management strategy. In this study, the analytical-hierarchy-process model is applied to evaluate waste-treatment projects based on the criteria above. This methodological approach involves several steps. First, existing state-of-the-art projects are critically reviewed to identify more advanced solutions for waste management. Second, biowaste projects are characterized by the application of multiple criteria and subcriteria. Finally, prioritized solutions are generated to elucidate the ranking of biowaste based on a hierarchical structure.

Prioritising water-related regional development project ideas based on stakeholder involvement activities: A case study from Szigetköz, Hungary

Szigetköz—a large island of the Danube in Hungary—is attracting a new wave of interest in water-related regional development projects from the public and private sectors alike. The revived interest in the Szigetköz floodplain area, which has historically endured the adverse effects of large-scale water management initiatives, draws attention to the necessity for careful consideration and the active involvement of local stakeholders in the decision-making process.In our research, we conducted various stakeholder engagement activities, including interviews, questionnaires, and workshops. These activities helped define the problems and objectives of each stakeholder group. Through collaborative consultations, we gathered over 100 proposed water related development project ideas. Additionally, to address key issues in project development and selection, we evaluated the integrative potential of each proposed project, considering the number of distinct water management issues it addressed. We also assigned an attitude score to each project, derived from the diverse objectives of various stakeholder groups, and analysed these quantities using a multiple-criteria analysis.This paper details a novel approach to assess and rank the proposed water-related regional development project ideas based on the calculated integrative potential and attitude scores. The goal was to prioritise and further develop these proposed projects to benefit Szigetköz and its surrounding areas.Main results of the study have shown the average project scored between 0.33–0.50 in integrative potential out of a maximum of 1.00 For positive attitude score the average was between 0.28–0.43, out of a maximum of 1.00 while for the negative attitude score the average project scored between -0.14–0.00 out of a minimum of -1.00.The ranking of projects that was based on these scores, highlighted three types of proposed projects that need to be further developed in different ways. The top-ranked positive attitude integrative projects need cost-benefit calculations involving all ecosystem services to justify their economic sustainability. Top-ranked opposing attitude integrative projects need conflict resolution to be socially sustainable. Finally, project ideas that scored low on integrative potential need to be developed to make use of blue-green infrastructure, and circular economical advancements to foster their environmental sustainability.