МОДЕЛЬ ОЦЕНКИ ОБЩЕСТВЕННОЙ ЭФФЕКТИВНОСТИ КРУПНОМАСШТАБНЫХ ИНВЕСТИЦИОННЫХ ПРОЕКТОВ: ДЕРЕВО ЦЕЛЕЙ И КОЭФФИЦИЕНТЫ ОТНОСИТЕЛЬНОЙ ВАЖНОСТИ

The implementation of large-scale projects affects the gross domestic product, the end-use fund, and making a decision on investing in a particular project is an extraordinary task. Projects of this scale are associated with a high degree of uncertainty and traditional methods are unacceptable as a tool for assessing their effectiveness. In the process of research, the author uses both well-known methods of system analysis and methods of neo-systemic analysis (it is proposed to use a hybrid model of verbal and non-verbal assessment models). The objects of comparative assessment are railway projects: the Subpolar Mainline, the Lensko-Kamchatka Mainline and the railway crossing to the island Sakhalin. To assess the achievement of the general goal, sub-goals were formed: military-strategic, social and economic. At the same time, the goal tree was considered in the framework of three scenarios: optimistic, intermediate and pessimistic. As a result of processing the opinions of experts, it was possible to obtain the coefficients of the relative importance (priorities) of each sub-goal in three different scenarios. At the next stage, the experts were asked to compare compositions - “alternative-scenario”, within the framework of the constructed goal tree. This approach to processing the results of the goal tree allows one to take into account the “scale” of the project, since the projects under consideration have a significant impact on the entire economy, thereby influencing the scenarios themselves, in contrast to the widely used method of hierarchy analysis, which does not take into account such impact.KeywordsProject appraisalTransport infrastructureRailway projectProject managementLarge-scale projectsMajor projectsMegaprojectAnalytic hierarchy processScenarios

The transition toward sustainability in the energy sector necessitates innovative approaches to material management, particularly in large-scale infrastructure projects. This paper explores strategies for enhancing cost efficiency through the reuse of materials, emphasizing their economic and environmental benefits. It provides a theoretical framework grounded in sustainable material management principles and examines the challenges associated with inconsistent material quality, logistical complexities, and regulatory constraints. Opportunities for progress are highlighted, including emerging technologies like additive manufacturing, scalable strategies such as steel recycling, and cross-industry collaborations that expand the scope of material reuse. Practical recommendations for stakeholders focus on integrating reuse into project design, investing in advanced technologies, fostering partnerships, and advocating for supportive policies. The paper also identifies future research directions, including the development of recyclable materials, scalable recovery technologies, and standardized reuse guidelines. The energy sector can harness material reuse to achieve cost efficiency and sustainability by addressing these issues, contributing to the global push for a circular economy.

Teargas, taboo and transformation: A neo-institutional study of community resistance and the struggle to legitimize subway projects in Amsterdam 1960–2018

Aim. The presented study aims to identify factors that have a significant impact on the implementation of large-scale projects.Tasks. The authors analyze major project management practices; investigate problems arising in the management of such projects; describe factors leading to an increase in the cost of major projects; examine the features of infrastructure projects.Methods. This study uses general scientific methods of cognition and analysis, as well as the tabular method.Results. In recent decades, mega-projects that are crucial for economic growth and social prosperity have become increasingly common across the world. The characteristic features of most large-scale and infrastructure projects include cost overruns, time delays, and often failure to achieve expected revenues compared to the original estimates. The implementation of major projects is associated with various problems, such as extreme technical and social complexity, poor implementation, low construction safety indicators, and significant impact on society, people’s living conditions, and the environment. One of the reasons for the current situation is the lack of scientifically based methodological research on the issues of major project management in a changing external environment and with a risk of uncertainty. The success of the project as a multidimensional structure includes technical and managerial aspects, as well as economic and strategic goals.Conclusion. Analysis of current major project management practices shows the shortcomings of existing approaches manifested in organizational and planning errors, as well as economic, geopolitical, and environmental risks. Mega-projects require complex management strategies to ensure smooth implementation, such as introduction of a risk management system, promotion of partnerships, and creation of information platforms. It can be concluded that there is significant international experience in the implementation of major projects. However, the shortcomings identified in this study suggest that a number of issues in the theory and practice of managing such projects remain underdeveloped methodologically.

This study develops a comprehensive analytical framework based on Fuzzy Interpretive Structural Modeling (Fuzzy ISM) to identify and analyze factors that influence transmission line construction schedules. Addressing the limitations of traditional risk assessment approaches in capturing the inherent uncertainties and complex interdependencies within large-scale infrastructure projects, this study integrates fuzzy logic principles with interpretive structural modeling to create a robust analytical tool. An empirical analysis was conducted on a 500 kV transmission line project in southwestern China, where 42 influencing factors were systematically evaluated across technical, management, environmental, and resource categories through expert assessments from 18 industry professionals. The Fuzzy ISM analysis reveals a five-level hierarchical structure, identifying project planning, risk assessment, and regulatory frameworks as fundamental driver factors that cascade their influence through multiple intermediate levels to ultimately impact schedule outcomes. Regulatory approvals emerge as the critical bottleneck with the highest amplification factor of 2.31, while the strongest direct impact relationship exists between construction delays and schedule delays (0.94). The MICMAC analysis demonstrates that most factors cluster in the linkage category, indicating a highly interconnected system requiring careful management of cascading effects. Critical path analysis identifies key influence routes with cumulative impacts ranging from 0.334 to 0.892, providing precise intervention targets for project managers. The methodology successfully bridges the gap between qualitative expert knowledge and quantitative analytical rigor, offering practical insights that could potentially reduce project delays by 30–40% through targeted interventions at identified critical nodes. These findings contribute to both theoretical advancement in project risk assessment methodologies and practical applications in infrastructure project management.

Large-scale energy infrastructure projects such as oil and gas developments, power generation facilities, and transmission networks are characterized by high hazard intensity, complex socio-technical interactions, and multi-contractor delivery environments. These conditions create persistent challenges for effective occupational safety risk control, particularly when traditional safety management approaches remain reactive, compliance-driven, and reliant on lagging indicators. In response, this paper develops a conceptual model for data-driven occupational safety risk control tailored to the operational and governance realities of large-scale energy projects. The proposed model conceptualizes safety risk as a dynamic and measurable system property that can be proactively managed through the systematic integration of heterogeneous data sources. These include operational process data, workforce and competency records, incident and near-miss reports, environmental conditions, and real-time monitoring technologies. The model is structured around four interrelated layers: (1) risk sensing and data acquisition, (2) analytics and risk interpretation, (3) decision support and governance integration, and (4) risk control and intervention mechanisms. Emphasis is placed on the use of leading indicators, predictive analytics, and feedback loops to enable early identification of emerging hazards and weak signals across project phases. By explicitly linking data-driven insights to decision authority, escalation pathways, and safety governance structures, the model addresses a critical gap between analytical capability and practical risk control. It also highlights the importance of organizational learning, continuous model recalibration, and cross-contractor data integration in complex project ecosystems. The conceptual contribution of this work lies in framing occupational safety management not merely as an operational function, but as an adaptive, intelligence-enabled control system embedded within project governance. The model provides a foundation for future empirical research and offers practical guidance for project owners, EPC contractors, and regulators seeking to enhance safety performance, improve risk predictability, and strengthen resilience in large-scale energy infrastructure delivery.

Project design, implementation and evaluation are undergoing major change in line with new concepts and practices of community participation, consultation and stakeholder analysis. Social capital is the key in this change process. This paper surveys recent literature on social capital and derives guidelines for development projects, especially large-scale projects. The paper offers a micro-economic method for measuring social capital derived from interdependent utility functions.

This study investigates the impact of external environmental uncertainty on technological innovation from the perspective of key resource acquisition. Using data from A‐share listed companies in Shanghai and Shenzhen stock markets from 2008 to 2019, we empirically test the relationship between external environmental uncertainty faced by firms and technological innovation input and output. Our findings reveal that external environmental uncertainty has a negative impact on the input and output of corporate technological innovation. In addition, key resource acquisition, namely, capital resource acquisition and information resource acquisition, plays a mediating role in this relationship. As an asset for obtaining resources, corporate social capital is a crucial means for firms to obtain external resources. This study further incorporates corporate social capital into the analysis, which shows that corporate social capital plays a positive moderating role in the impact of external environmental uncertainty on corporate technological innovation. Furthermore, research shows that compared with nonhigh‐tech industry firms, the technological innovation of high‐tech industry firms is more sensitive to external environmental uncertainty.

The article is devoted to the issue of the possibility of economic development of the mining industry in the subjects of the Far Eastern Federal District (FEFD) and the impact of investment projects. With the vast wealth and diversity of mineral resources, their involvement in the national economy is an essential factor in the industrialization and successful development of the regions. In the case of the establishment of processing plants, for a number of minerals, the region could provide its own needs, as well as the countries, finished products from it, and develop an export focus. The article considers the export orientation of the mineral and raw materials complex of Russia and the constituent entities of the Russian Federation, as well as the commodity structure of exports of the regions of the FEFD. Special attention is paid to the implementation of major regional investment projects, which are important for the development of the regions. So, it is proposed to implement a major project in the form of an integrated mining and metallurgical complex due to the fact that the region needs its own ferrous metallurgy. The calculations carried out (in various versions: basic, in the conditions of the territory of advanced development (TAD), without the costs of infrastructure creation) showed that the implementation of the project is cost-effective, but due to the high costs, the support of the state is necessary.

The article presents a critical analysis of the main economic programs adopted in Brazil in the first year and a half of Lula da Silva's rule (2023 – the first half of 2024), and which will determine the country's development strategy for the next decade. Particular attention is paid to diagnosing the current state of industry and infrastructure in Brazil, analyzing the causes of deindustrialization, assessing the opportunities and risks of strengthening the role of the state in the modernization of industry and the implementation of large-scale infrastructure projects. The success of new development programs and their geopolitical impact will depend on the effectiveness of public resource management, improved planning and implementation of projects, and increased interaction and control at the federal and sub-regional levels.

Introduction. Some features of the organization of the actions of the inspector of the department of supervision and preventive work are considered. It is proposed to use a model for solving a transport problem as a formal model for streamlining the preventive work of a supervisory activity unit in conditions of simultaneous planning and execution. The solution of the transport problem is illustrated by an example of one of the divisions of the Moscow region. The development of a decision support system based on the hierarchy analysis method as input data for the design and training of a neural network is adapted and illustrated. The training data is based on the results of an expert survey, which includes a sample of the importance of criteria (preventive measures) and alternatives based on expert survey data. Goals and objectives. The purpose of the article is to develop a plan of preventive measures. It is based on the idea of the transport task of choosing the necessary preventive measure depending on the sample parameters. To achieve the goal, the task of developing a sequence of actions to optimize labor costs using the method of analyzing hierarchies and machine learning was implemented. The sequence of actions for training the developed neural network on a specific example is described, where the data were obtained as a result of an expert survey based on the developed questionnaire. Methods. The article uses the method of mathematical processing of statistical data, the method of algorithmizing, visualization and machine learning. Adaptive control methods are also used. Results and its discussion. It is proposed to use a model for solving the transport problem as a formal model for streamlining the preventive work of the supervisory activity unit. Key algorithms and mathematical underpinnings have also been developed for the application of neural network technologies using the properties of a decision support system based on expert survey data. Conclusion. The developed plan system for planning preventive work includes both a regulatory approach and modern machine learning methods, which is one of the priority areas in the development of modern science. Keywords: transport model; paired comparisons; objective function; organization of supervisory activities; optimal distribution; preventive measures.

The most widespread approach to transport appraisal is to combine cost-benefit analysis (CBA) with environmental assessments and public consultations. However, large-scale transport projects such as the HS2 high-speed rail system in the UK seem to have pushed this approach beyond its limits, leading to broad discontent with the appraisal process. There is a need both to develop new methods capable of integrating a wide range of perspectives in a systematic manner and to test these for large-scale projects. Multicriteria analysis (MCA) has proven useful in supporting transport decision-making by including a broader set of criteria in the appraisal process. Multiactor multicriteria analysis (MAMCA) has extended this approach to include multiple actors and stakeholders in the judgment and decision-making process. This paper builds on the MAMCA method and demonstrates its practicability and usability by applying it to the case of HS2. The purpose of this paper is not to reach a definitive conclusion on the desirability of various project options, but to complement existing transport appraisal methods by making different perspectives explicit. For example, the results for this case show contrasting views for different groups of transport professionals: a favorable assessment of HS2 among transport planners employed in government, but an unfavorable assessment among transport researchers with a background in sustainability. In terms of contribution to the development of data collection methods, this research demonstrates the usefulness of conducting semistructured interviews in conjunction with an online questionnaire for the assessment and weighting process within MCA. Because MCA results are expressed in terms of relative desirability of projects, the approach also effectively systematizes the inclusion and assessment of multiple options. Overall, the proposed method enhances the capacity to analyze conflicting views in large-scale transport project appraisal processes.

The problem of assessing the social effectiveness of large-scale railway projects (hereinafter referred to as the CPAW) is considered in this article in the light of J. Kornais system paradigm and its instrumental specification as a non-system approach. In this context, the CPCR is analyzed as large and complex socio-technical systems. The objects of research are the Lensko-Kamchatskaya and Subpolar railway lines, which are at the stage of pre-investment studies in the first case and partially implemented in the second, when the uncertainty of costs and results is extremely high. To assess the comparative effectiveness of these projects, computer products were used – foreign and domestic, designed to work within the framework of the theory of making complex decisions with semiverbal models and expert information. Computational experiments have shown that different computer products, as a means of supporting investment decision-making procedures, give analysts different recommendations about the preference of compared projects. It is shown that the reason is inadequate accounting for foreign products (as applied to large-scale projects) of the uncertainty factor. The conclusion is made about the productivity of the tools of the neosystemic approach implemented by the authors of the article as an integrator of verbal and mathematical models in evaluating large-scale projects in a situation of uncertainty.

Purpose The purpose of this study is to examine the impact of servant leadership (SL) on project success (PS) in large-scale infrastructure projects. This study uniquely integrates social exchange theory (SET), social learning theory (SLT) and organizational learning (OL) theory to explore the under-researched role of SL, OL and PS in multinational projects involving diverse stakeholders and high complexity. Design/methodology/approach This quantitative study used a survey across 66 projects of the China Pakistan economic corridor (CPEC) while collecting data from 507 participants through structured questionnaires and employed structural equation modeling (SEM) for analysis and testing the study hypotheses. Findings The results showed a high correlation between the study variables. Moreover, other than the project’s nature and OL, the relationship between individual characteristics and the learning process was found to be significant. Originality/value SL is a crucial determinant of PS. Also, the results advocate that type of project and OL strongly affect the role of SL in PS, especially for the large-scale and diverse projects like CPEC.

Large-scale infrastructure projects often use reactive approaches to manage construction risks. This can result in expensive delays and increased budgets. This study creates and tests a risk prediction framework that uses machine learning, specifically Gradient Boosting Decision Trees (GBDT), to help identify and address risks early in urban infrastructure construction. Data from 220 infrastructure projects, spanning from 2015 to 2024 and located in North America, Europe, and Asia, were analyzed. These projects had values between $50 million and $2 billion USD. The approach combined Principal Component Analysis and GBDT, handling 47 variables related to risk across six different risk areas. To test the model, 5-fold cross-validation was used, along with temporal validation, which involved setting aside the most recent 20% of projects. The GBDT model reached an overall prediction accuracy of 87.3%. It outperformed traditional methods by 23%. The ability to detect risks early on improved significantly, from 45% to 78%, and this led to an average cost reduction of 12.4%. Technical risks had the highest prediction accuracy, at 89.4%, while resource optimization saw a 25.7% improvement in equipment use. This machine learning-based framework is considered to significantly improve construction risk management. It offers better accuracy, earlier risk detection, and cost savings, suggesting it could be widely used in urban infrastructure construction.