Project Risk Assessment Research Articles

The Role of Technology in Promoting Green Finance: A Systematic Literature Survey and the Development of a Framework

Green finance, defined as channeling money into sustainable development activities, is still far lower than needed to achieve net-zero emissions objectives. In this paper, we discuss the role of technologies in developing green finance. We identify that green finance faces three major challenges, including the risk management of green projects, the scarcity of innovative green financing products, and compliance with the regulations. Then, in the context of the existing literature, we explore recent technologies, including blockchain, artificial intelligence (AI), machine learning (ML), data analytics, Internet of Things (IoT), and robotics that are helping to deal with the challenges in green finance. We show that data-driven approaches utilizing AI and ML help in the risk assessment of green projects; FinTech-based crowdfunding platforms provide innovative green financial products and regulatory technologies (RegTech) support in compliance with regulations. We also identify that the environmental footprint of cryptocurrencies is an emerging area in the technologies and green finance domain. Our framework could be helpful to further extend the debate on the role of technology in green finance.

The risk assessment of public-private partnership projects using artificial intelligence algorithms

Purpose: is to develop an innovative approach to risk management in public-private partnership (PPP) projects using advanced artificial intelligence technologies that allow creating the risk assessment model that takes into account non-linear relationships between various risk factors.Methods: in addition to traditional methods of scientific knowledge, interdisciplinary approaches of risk management and established practice of machine learning were used in the work. The methodological basis of the study was formed by works on the risk assessment and the application of AI algorithms in this area. The empirical basis of the study was the data of the official portal of ROSINFRA on public-private partnership projects.Results: the practice of applying AI algorithms to the task of assessing the risks of PPP-projects in Russia and abroad was studied. It is established that the most effective result is shown by the models based on Random-Forest-Classifier. However, the presented solutions do not take into account Russian economic realities. The authors have structured a database of implemented PPP-projects suitable for risk modeling. The model for assessing the risk of failure to achieve the objectives of Russian PPP-projects has been developed and its quality has been assessed. Recommendations on implementation of the model in the operational loop of PPP projects realization processes are offered.Conclusions and Relevance: the developed model allows, according to the general parameters of PPP project (region, authority, term of agreement, industry and scope of implementation, etc.) with the accuracy of 93% (according to the ROC\AUC metric), to assess the risk that the project will end incorrectly (due to a failed tender, refusal to launch, termination by court decision, cancellation/annulment of the tender). With the help of the model the executive authorities of the Russian Federation can build risk management for PPP projects management in the regions and thus contribute to their efficiency improvement. The article may also become useful for project management practitioners and appraisers.

Multisource information fusion for safety risk assessment in complex projects considering dependence and uncertainty.

The success of tunneling projects is crucial for infrastructure development. However, the potential leakage risk is particularly challenging due to the inherent uncertainties and fuzziness involved. To address this demanding challenge, a hybrid approach integrating the copula theory, cloud model, and risk matrix, is proposed. The dependence of multiple risk-related influential factors is explored by the construct of the copula-cloud model, and the diverse information is fused by applying the risk matrix to gain a crisp risk result. A case study is performed to test the applicability of the proposed approach, in which a risk index system consisting of nine critical factors is developed and Sobol-enabled global sensitivity analysis (GSA) is incorporated to investigate the contributions of different factors to the risk magnitude. Key findings are as follows: (1) Risk statuses of the studied three tunnel sections are perceived as under grade I (safe), II (low-risk), and III (medium-risk), respectively, and the waterproof material aspect is found prone to deteriorating the tunnel sections. Furthermore, the proposed approach allows for a better understanding of the trends in the risk statuses of the tunnel sections. (2) Strong interactions between influential factors exist and exert impacts on the final risk results, proving the necessity of studying the factor dependence. (3) The developed neutral risk matrix presents a strong robustness and displays a higher recognition capacity in risk assessment. The novelty of this research lies in the consideration of the dependence and uncertainty in multisource information fusion with a hybrid copula-cloud model, enabling to perform a robust risk assessment under different risk matrices with varying degrees of risk tolerance.

Risk assessment of public-private partnership water projects in Indonesia

Public-private partnerships (PPPs) are vital for infrastructure development in developing countries, integrating private efficiency with public oversight. However, PPP models often face risks, particularly in Indonesia’s water sector, due to its unique geographical and regulatory challenges. This study aims to identify and evaluate risk factors specific to drinking water PPP projects in Indonesia. Using a quantitative approach, structured questionnaires were distributed to experts in the sector, and the data was analyzed using a fuzzy evaluation method. Risks were categorized into location, design and construction, financial, operational, revenue, and political. The study emphasizes that effective risk management, including identification, analysis, and mitigation, is essential for project success. It highlights the importance of stakeholder involvement and flexible risk management strategies. Comprehensive and proactive risk management is key to the success of drinking water infrastructure projects. The research suggests that an integrated and collaborative approach among stakeholders can enhance risk management effectiveness. These findings provide valuable insights for policymakers, project managers, investors, and other stakeholders, underscoring the necessity for adaptable regulatory frameworks and robust policy guidelines to improve the sustainability and efficacy of future water-related PPPs.

A Framework for Investment and Risk Assessment of Agricultural Projects

A Framework for Investment and Risk Assessment of Agricultural Projects

Critical success factors frameworks, and models for risk assessment of eGovernment projects: A systematic literature review

Critical success factors frameworks, and models for risk assessment of eGovernment projects: A systematic literature review

Structural equation modeling and Fuzzy set theory: Advancing risk assessment in oil and gas construction projects

This research seeks to evaluate the critical risks associated with Oil and Gas Construction Projects (O&GCPs) in terms of their level of significance and quantifiable effects on Project Performance (PP) by surveying O&GCPs experts in the USA. A multiphase research methodology was adopted, including Systematic Literature Review (SLR) to identify the key O&GCPs risks; semi-structured interviews to identify PP indicators and establish hypotheses on the effect of O&GCPs risks on PP; surveys to assess the perceived agreement of O&GCPs experts regarding the effects of O&GCPs risks on PP, and to rank the probability and impact of the identified O&GCPs risks; Structural Equation Model (SEM) to quantitatively assess the effects of O&GCPs risks on PP; Fuzzy Set Theory (FST) to assess the level of significance of O&GCPs risks; and interviews to verify the developed assessment models and their outputs. The SLR analysis identified 35 articles on O&GCP risks from 2013 to 2023, revealing 51 risk factors in eight categories based on their sources. The SEM analysis indicated that the oil and gas safety risks category had the strongest direct impact on PP. Furthermore, the FST analysis revealed that non-compliance with PPE emerged as the most significant risk factor across all categories of O&GCP risks. This study enhances knowledge and practice by aiding researchers and practitioners in understanding how key O&GCP risks impact PP. This improved understanding is expected to facilitate the formulation of effective mitigation strategies during the early stages of the projects.

Integrating Dimensions in Predictive Risk Models for Construction Projects

Abstract This review explores the evolution of predictive risk models in construction, emphasizing the integration of key dimensions—quality, safety, schedule delay, cost overrun, and environmental risk. Synthesizing diverse literature, it highlights findings, methodologies, and implications. From a historical perspective, it outlines criteria and search strategies, addressing limitations. Thematic sections delve into individual risk dimensions, and cross-theme analysis identifies overarching trends. Methodological approaches are scrutinized, challenges and controversies are discussed, and future directions suggest areas for exploration. In conclusion, the paper advances holistic risk management, aiding researchers, practitioners, and stakeholders in construction project risk assessment.

Multi-attribute perceptual fuzzy information decision-making technology in investment risk assessment of green finance Projects

Abstract In the investment risk assessment of green finance (GF) projects, the application of multi-attribute perceptual fuzzy information decision technology is taken as the main research object. With the promotion of the concept of environmental protection and the development of green economy, the investment risk assessment of GF projects has become more and more important. However, this requires dealing with a large amount of fuzzy information and multi-attribute decision problems, which is a big challenge for traditional decision techniques. Based on this background, a new decision model, intuitionistic fuzzy preference theory-based tomada de decisão interativa multicritério (IF-PT-TODIM), is adopted, which can better deal with fuzzy information and multi-attribute decision problems by taking two different choices as reference. By knowing the weight distribution of experts, the model can better assess the influence of various factors on the decision. In the research results, the calculated results of expert weights are 0.2796, 0.2221, 0.1914, 0.1328, and 0.1745, respectively, showing that each expert has different degrees of influence on decision-making. In addition, the application of IF-PT-TODIM model can effectively reduce the investment risk. Compared with national bank of Kuwait, systematic review, evolutionary algorithm, the improved method can reduce the risk of venture capital by 28.14, 15.47, and 11.05%, respectively. This result further confirms the advantage of the IF-PT-TODIM model in dealing with fuzzy information and multi-attribute decision problems. This study has practical implications for understanding and improving the investment risk assessment of GF projects. It not only provides a new decision model for risk assessment, but also provides an effective method to deal with fuzzy information and multi-attribute decision problems. This provides new ideas and methods for the risk management of GF projects and also provides a new perspective and reference for research in related fields.

Traffic-Related Air Pollution and Childhood Asthma—Are the Risks Appropriately Mitigated in Australia?

Childhood asthma is a major health issue in Australia, and traffic emissions play a causative role. Two urban planning policies that impact children’s exposure to traffic emissions are considered in terms of the potential health risks to children in a Melbourne suburb with high truck volumes and hospital attendances for childhood asthma. Firstly, the health impact assessment component of the state planning approval of a major road project, and secondly, local government placement of childcare centres and schools in relation to freight routes. Three sources of air quality monitoring data were examined: (i) a Victorian EPA reference site; (ii) a site with planning approval for development into a childcare centre; and (iii) five sites within the boundary of the West Gate Tunnel Project, an AUD 10 billion road and tunnel project. The Australian Urban Research Infrastructure Network data was utilised to assess distances of childcare centres and schools from major truck routes. A range of cconcentration–response functions for childhood asthma (0–18 years) from international systematic meta-analyses and a smaller Australian cross-sectional study were applied to comparative elevations in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations between the EPA reference monitor (used for project risk assessment) and local roadside data. It was found that comparative elevations in NO2 concentrations were associated with the following risk increases: developing asthma 13%, active asthma 12%, and lifetime asthma 9%. Overall, 41% of childcare centres (n = 51) and 36% of schools (n = 22) were ≤150 m to a high-density truck route. Truck emissions likely make a substantial contribution to childhood asthma outcomes in the project area. This study exemplifies how current practices may not be commensurate with guiding policy objectives of harm minimisation and equitable protection.

Leveraging Artificial Intelligence for Enhanced Performance in Matrix Organizations: A Research Perspective

In today's complex business landscape, matrix organizations represent a prevalent structural framework characterized by overlapping reporting structures and cross-functional project teams. This organizational model, while offering flexibility and specialization, also presents unique challenges in resource allocation, decision-making, collaboration, and project management. This research paper investigates the potential of artificial intelligence (AI) to address these challenges and enhance the performance of matrix organizations. Drawing upon an extensive review of existing literature and relevant case studies, the paper provides a comprehensive analysis of AI's role in optimizing various aspects of matrix organization operations. Specifically, it examines how AI technologies can streamline resource allocation processes by analyzing project requirements, team capabilities, and individual skill sets to ensure optimal staffing levels and skill matching across projects. Additionally, the paper explores AI's capabilities in providing decision support to managers and team leaders by leveraging data analytics to offer insights into project progress, risk assessment, and performance evaluation.

Communication of project risk assessment information through visuals

Risk assessment is aimed at providing necessary risk-related information to decision-makers for formulating risk mitigation strategies. Poor communication of its context and outputs may lead to misperceptions and wrong judgements. In this research, the role of visualization as an enabler of better communication of risk assessment information is explored using a design science approach. A questionnaire was carried out with project management professionals using the risk report of a case project to understand challenges in interpretation of risk assessment outputs, information and visualization requirements for making better sense of project risk. Based on the findings from the questionnaire, a visual representation, the Risk Box was developed and further tested by semi-structured interviews. Findings reveal that visuals are not only needed for better understanding of probabilistic information but also the context of risk assessment such as relations between individual risks, risk-prone variables, assumptions, scenarios and mitigation strategies.

Assessment of Project Risks in the Russian Economy

Assessment of Project Risks in the Russian Economy

Assessing invasion risks using EICAT-based expert elicitation: application to a conservation translocation

Conservation translocations are widely used to recover threatened species, but can pose risks to recipient ecosystems, particularly in the case of conservation introductions. Because of limited data and uncertainty, risk assessments for such projects often rely on extrapolated evidence and expert opinion, further complicating decision making. The Environmental Impact Classification for Alien Taxa (EICAT) serves to classify the realised impacts of invasive species. We developed a protocol combining EICAT and formal expert elicitation to predict these impacts. We applied our protocol to the extinct-in-the-wild sihek (Guam kingfisher; Todiramphus cinnamominus), for which introduction outside the known historical range is being considered. We elicited from multiple experts probability estimates of impact levels across four impact mechanisms and five candidate release sites. We aggregated estimates using simulation-based and Bayesian approaches, with and without accounting for expert confidence. Experts generally agreed that sihek introduction might impact the recipient ecosystem through predation, competition, and disease, although they disagreed about the likely impact levels. Releases to Palmyra Atoll were considered to pose the lowest risk across candidate sites, so this site was selected for further detailed ecological assessments and risk mitigation efforts. EICAT, with its standardized impact mechanisms and definitions, helped reduce the linguistic uncertainty and subjectivity common to expert-based assessments. Expressing judgments as probabilities allowed us to evaluate uncertainty transparently and to assess the weight of expert confidence on the overall risk estimation. Formal quantitative elicitation and aggregation then allowed a transparent evaluation of results, facilitating communication with stakeholders and decision-makers.

Big Data and Precise Identification Will Be Applied to Public-Private Partnership Projects

Public-Private Partnership (PPP) projects represent a pivotal strategy in addressing infrastructure funding shortages. Originating in the 1980s, the PPP model gained traction globally, with China entering its standardization phase in 2014. However, the extensive scale, prolonged construction periods, and diverse stakeholders involved in PPP projects introduce multifaceted risks. To navigate these challenges, the Ministry of Finance emphasized the need for accurate risk identification in 2019. This paper integrates the "accurate identification" theory with big data technology, providing a theoretical foundation and technical support for precise risk assessment in PPP projects. The 5V characteristics of big datascale, diversity, low-value density, data validity, and rapid processingenable cost-effective and efficient risk analysis. Key risks, including financial, government credit, pricing mechanism, force majeure, and approval delays, are identified. The "accurate identification" theory and big data facilitate not only risk identification but also preventive measures. This comprehensive approach aims to enhance decision-making, reduce conflicts, attract social capital, and improve the overall efficiency of PPP projects.

Long-term risk assessment of subsurface carbon storage: analogues, workflows and quantification

A key aspect of transferrable oil and gas expertise to subsurface CO 2 storage (SCS) relates to risk assessment. While initial subsurface risk and volume assessments for SCS projects are similar to oil and gas prospect evaluations, full life-cycle risk assessment requires evaluation of the current knowledge of the storage complex and future potential events that may have impacts over long time frames. It is important to learn from past water, gas, and CO 2 injection and storage projects, examples of which are reviewed here. The concepts of aleatory and epistemic risk and uncertainty are discussed, and the use of standard risk matrices for evaluation of long-term, low-frequency but potentially high-impact events is challenged. Drawing on the large volume of previous work, this paper highlights key elements for development of a robust risking framework, including thorough project framing and implementation of a staged approach to project execution. The paper assesses methods for ensuring all potential hazards are captured and addresses the challenges of defining a quantitative risking scale suitable for long-term SCS projects. Example quantitative risk profiles can be used to calculate the timing and duration of ‘Peak Risk’, augment monitoring and mitigation planning for management of risk and capital exposure, and help to ensure successful project outcomes. Thematic collection: This article is part of the Geoscience workflows for CO 2 storage collection available at: https://www.lyellcollection.org/topic/collections/geoscience-workflows-for-CO2-storage

Passive Buildings—Big Opportunities or Big Risks? Quantitative Risk Assessment for Passive Buildings Projects

Passive Buildings—Big Opportunities or Big Risks? Quantitative Risk Assessment for Passive Buildings Projects

Influential factors for risk assessment and allocation on complex design-build infrastructure projects; the Texas experience

Introduction: The design-build (DB) delivery method is used to deliver increasingly complex transportation infrastructure projects associated with higher uncertainty. As such, allocating risks in the contract between the owner and design-builder becomes challenging and often leads to higher initial bids, increased contingency, or claims. Learnings from implementation worldwide have underlined the need for improving risk allocation in DB contracts. Most existing studies address risk allocation mechanisms to manage contingency at the contract level. Other studies have recognized the need for owners to adapt their processes to better allocate risks in DB contracts. This study explored the influential factors for risk assessment and allocation for complex DB infrastructure projects, addressing the opportunity to improve transportation owners’ risk allocation processes before the design-builder is selected and the DB contract is awarded.Method: The objectives of this work were achieved by utilizing empirical data collected through 20 interviews with Texas Department of Transportation and private sector experts. The interview data were analyzed using inductive and axial coding. Inductive coding allowed themes to emerge without a pre-existing framework, identifying six influential factors and six pertinent risks on complex DB projects.Results: These factors include the (i) Quality of DB teams, (ii) Level of up-front investigation, (iii) Limitations on the timing of letting, (iv) Design optimization opportunities, (v) Project-specific requirements, and (vi) Relationships with third parties. Through axial coding, the interaction and frequency between the factors and risks were also examined. The coded interactions demonstrated how the identified factors influence allocation for six pertinent risks including right-of-way acquisition, stakeholder approval, site conditions, permits and third-party agreements, railroad interaction, and utility adjustments and coordination. Findings indicate that the evaluation of these interactions can shift the risk allocation from baseline norms established by an agency to correspond to project-specific needs.Contribution: In contributing to the infrastructure project management, this is the first study to examine the factors that influence risk allocation in complex DB projects and examine interactions with pertinent risks, setting the foundation for optimizing allocation based on project-specific needs. In practice, the findings presented in this study can guide owners in adapting their allocation practices, managing, and developing their strategic plan for delivering complex DB projects. The findings can also assist contractors in pricing risks more efficiently and increase competitive bidding.

A case study on the relationship between risk assessment of scientific research projects and related factors under the Naive Bayesian algorithm

This paper delves into the nuanced dynamics influencing the outcomes of risk assessment (RA) in scientific research projects (SRPs), employing the Naive Bayes algorithm. The methodology involves the selection of diverse SRPs cases, gathering data encompassing project scale, budget investment, team experience, and other pertinent factors. The paper advances the application of the Naive Bayes algorithm by introducing enhancements, specifically integrating the Tree-augmented Naive Bayes (TANB) model. This augmentation serves to estimate risk probabilities for different research projects, shedding light on the intricate interplay and contributions of various factors to the RA process. The findings underscore the efficacy of the TANB algorithm, demonstrating commendable accuracy (average accuracy 89.2%) in RA for SRPs. Notably, budget investment (regression coefficient: 0.68, P < 0.05) and team experience (regression coefficient: 0.51, P < 0.05) emerge as significant determinants obviously influencing RA outcomes. Conversely, the impact of project size (regression coefficient: 0.31, P < 0.05) is relatively modest. This paper furnishes a concrete reference framework for project managers, facilitating informed decision-making in SRPs. By comprehensively analyzing the influence of various factors on RA, the paper not only contributes empirical insights to project decision-making but also elucidates the intricate relationships between different factors. The research advocates for heightened attention to budget investment and team experience when formulating risk management strategies. This strategic focus is posited to enhance the precision of RAs and the scientific foundation of decision-making processes.

Lessons identified from applications of the Risk Analysis Quality Test Release 1.0.

The Risk Analysis Quality Test Release 1.0 (RAQT1.0) was developed as a framework to encourage mutual understanding between technical risk analysts and risk management decision makers of risk assessment quality indicators. The initial version (release 1.0) was published by the Society for Risk Analysis (SRA) in 2020 with the intent of learning from early test applications whether the approach was useful and whether changes in approach or contents would be helpful. The results of applications across three diverse fields are reported here. The applications include both retrospective evaluations of past risk assessments and prospective guidance on the design of future risk assessment projects or systems. The fields represented include Quantitative Microbial Risk Assessment, Cultural Property Risk Analysis, and Software Development Cyber Risk Analysis. The RAQT1.0 proved helpful for identifying shortcomings in all applications. Ways in which the RAQT1.0 might be improved are also identified.