We show that economic conditions when managers enter the labor market have long-run effects on their career paths and managerial styles. Managers who began their careers during recessions become CEOs more quickly, but at smaller firms. They also have more conservative styles, such as lower investment in capital expenditures and research and development, more cost cutting, and lower leverage and working capital needs. These recession effects appear to be largely driven by the characteristics of the CEO’s first job (recession CEOs tend to start in smaller or private firms), which suggests that the early work environment is important to the formation and selection of managers.
The article discusses a methodology for selecting the optimal composition of technological equipment for production areas of machine-building enterprises. The methodology includes the development of an algorithm that takes into account technical and organizational aspects of production. Using the example of the “Fork” part, the article demonstrates the selection of milling equipment taking into account technological preparation, machine placement, and production organization. The methodology takes into account the flow of production, minimization of motions, efficient use of space, service support, and personnel qualifications. The algorithm provides a comprehensive assessment of production processes, including equipment utilization, economic indicators, and organizational requirements. The main focus is on optimizing capital investments while maximizing productivity. The application of this methodology allows you to improve technical and economic indicators, optimize processes, improve working conditions, reduce costs, and increase productivity.
Saeid Ario Vaghefi, Aymane Hachcham, Veronica Grasso
et al.
Tracking financial investments in climate adaptation is a complex and expertise-intensive task, particularly for Early Warning Systems (EWS), which lack standardized financial reporting across multilateral development banks (MDBs) and funds. To address this challenge, we introduce an LLM-based agentic AI system that integrates contextual retrieval, fine-tuning, and multi-step reasoning to extract relevant financial data, classify investments, and ensure compliance with funding guidelines. Our study focuses on a real-world application: tracking EWS investments in the Climate Risk and Early Warning Systems (CREWS) Fund. We analyze 25 MDB project documents and evaluate multiple AI-driven classification methods, including zero-shot and few-shot learning, fine-tuned transformer-based classifiers, chain-of-thought (CoT) prompting, and an agent-based retrieval-augmented generation (RAG) approach. Our results show that the agent-based RAG approach significantly outperforms other methods, achieving 87\% accuracy, 89\% precision, and 83\% recall. Additionally, we contribute a benchmark dataset and expert-annotated corpus, providing a valuable resource for future research in AI-driven financial tracking and climate finance transparency.
Franziska Klaucke, Karsten Neuhoff, Alexander Roth
et al.
To ensure security of supply in the power sector, many countries are already using or discussing the introduction of capacity mechanisms. Two main types of such mechanisms include capacity markets and capacity reserves. Simultaneously, the expansion of variable renewable energy sources increases the need for power sector flexibility, for which there are promising yet often under-utilized options on the demand side. In this paper, we analyze how a centralized capacity market and an advanced reliability reserve with a moderately high activation price affect investments in demand-side flexibility technologies. We do so for a German case study of 2030, using an open-source capacity expansion model and incorporating detailed demand-side flexibility potentials across industry, process heat, and district heating. We show that a centralized capacity market effectively caps peak prices in the wholesale electricity market and thus reduces incentives for investments in demand-side flexibility options. The advanced reliability reserve induces substantially higher flexibility investments while leading to similar overall electricity supply costs and ensuring a similar level of security of supply. The advanced reliability reserve could thus create a learning environment for flexibility technologies to support the transition to climate neutral energy systems. Additionally, an advanced reliability reserve could be introduced faster and is more flexible than a centralized capacity market. While concrete design parameters are yet to be specified, we argue that policymakers should consider the reliability reserve concept in upcoming decision on capacity mechanisms in Germany and beyond.
Amund Norland, Lasse Skare, Ole Jakob Viken
et al.
The global energy transition toward net-zero emissions by 2050 is expected to increase the share of variable renewable energy sources (VRES) in the energy mix. As a result, industrial actors will encounter more complex market conditions, characterized by volatile electricity prices, rising carbon costs, and stricter regulations. This situation calls for the industry to capitalize on opportunities in both spot-price arbitrage and reserve market participation, while also meeting future regulatory demands. This paper presents a multi-stage optimization framework that supports investment decisions in flexible assets and enables reserve market participation by delivering ancillary services. The framework incorporates investment decisions, spot- and reserve-market bidding, and real-time operation. Uncertainty in market prices and operational conditions is handled through a nodal formulation. A case study of a large industrial site in Norway is performed, comparing the investment decisions with future technology- and carbon pricing scenarios under varying market conditions.
The research aims to measure the level of application of growers to smart farming techniques to confront climate change. The study population included all farmers in Sheikhhan district, totaling 300 farmers. A simple random sample of 40% was selected, resulting in 120 respondents. A questionnaire form consisting of three parts was developed. The first part included the independent variables: age, educational attainment, land area, type of land tenure, and participation in training courses. The second part comprised 50 items to measure the application level, using a three-point scale with three options: (applied extensively, applied moderately, not applied), with weights assigned as (3, 2, 1) accordingly. The third part aimed to identify the problems hindering the application of smart agriculture techniques. Data were collected through personal interviews. Subsequently, the data were coded and prepared for statistical analysis using SPSS software. The results indicated that 69.2% of respondents had a weak application level. Additionally, the results revealed a significant correlation between the independent variables (attitude toward applying smart agriculture, access to information sources, participation in extension activities) and the application level. However, no significant correlation was found between age or educational attainment and the application level. The issue of high costs ranked first with the highest percentage (2.63%). The researcher recommends attracting capital investments into smart agriculture and establishing dedicated communication channels related to food security.