David Hampel

Introduction: Large-scale forest disturbances caused by bark beetle outbreaks, windstorms, and climate change have created extensive forest clearings that require rapid reforestation. In practice, however, reforestation is frequently delayed by shortages of seedlings, labor, and financial resources. Such delays accelerate soil degradation, reduce the productivity of future forest stands, and increase long-term restoration costs. Although pioneer tree species are widely recognized for their ecological benefits, their role as an economically efficient emergency reforestation strategy has received little attention. Methods: This study develops a simulation model to evaluate the long-term economic performance of alternative reforestation strategies under post-disturbance conditions. Using detailed data from Czech forestry, the model simulates 160 years of forest development while incorporating silvicultural operations, harvesting, timber revenues, varying levels of soil degradation caused by delayed reforestation, and stochastic variation in forest characteristics and market conditions. Eight alternative reforestation strategies, including direct sowing of birch, planted birch, and two-phase regeneration combining birch with beech, are evaluated using free cash flow, net present value, and internal rate of return. Results: The results demonstrate that direct sowing of birch substantially reduces establishment costs while maintaining favorable long-term economic performance. Among the broadleaved alternatives, birch consistently provides the highest economic returns and remains financially attractive even under increasing soil degradation associated with delayed reforestation. Although Norway spruce generates higher expected timber revenues under historical price conditions, these returns are accompanied by substantially greater ecological and production risks that are not reflected in conventional financial indicators. The simulation further shows that establishing pioneer birch stands followed by subsequent conversion to beech represents a viable strategy that combines rapid site stabilization with long-term forest diversification. Discussion: These findings suggest that birch should not be regarded merely as a temporary pioneer species but as a strategic component of climate-resilient forest management. The proposed simulation framework provides a practical decision-support tool for selecting economically efficient emergency reforestation strategies following large-scale forest disturbances and can readily be adapted to forest ecosystems beyond the Czech Republic.

This research examines the difficulties in estimating the price elasticity of energy demand during periods of extreme market volatility, with a focus on the Czech Republic as a small open economy. Utilising a detailed dataset of monthly electricity and natural gas consumption from January 2021 to December 2024, we evaluate the consistency of regression models over different time periods and the impact of auxiliary variables. Our results indicate that models based on three- and four-year datasets are notably more dependable than those using only two-year data. Monthly data analysis reveals low absolute short-term price elasticities for electricity (0.062−0.167) and natural gas (0.091−0.205), indicating that energy demand remains highly inelastic even amid sharp price increases. This suggests that price signals alone are not sufficient to drive significant short-term reductions in energy use.

This study investigates the impact of international accounting and valuation standards (IFRS 13, IVS 2020, IVS 2022, RICS 2022, IPSM 2023) on information asymmetry in the residential real estate markets of the UK, Germany, and Spain. Given the frequent use of subjective inputs in fair value measurements, this sector is particularly susceptible to higher levels of information asymmetry, making it an apt context for analysis. The research analyses changes in selected standards and includes control variables such as company size and market capitalization, using two indicators over the period 2019–2023. Revisions to IFRS 13, IVS 2022, RICS 2022, and IPSM 2023 significantly reduced valuation variability (as measured by the VCVT indicator), confirming their positive effect on market transparency. In contrast, the IVS 2020 update was associated with an increase in the bid–ask spread, suggesting a rise in perceived uncertainty. By jointly examining the bid–ask spread and VCVT, the study provides a comprehensive assessment of how accounting and valuation standards influence liquidity and information asymmetry in the residential property sector - a field that has received limited attention in prior literature. The findings are consistent with earlier research indicating that reduced disclosure requirements can enhance stock market liquidity. Practical implications include recommendations for stricter guidance on Level 3 fair value models and the adoption of best practices from EPRA and REIT frameworks to enhance transparency and credibility in capital markets.

Agribusiness is currently faced with the challenge of providing sufficient quality food for a growing population with limited natural resources. Egg production, as an important source of nutrition, is a dynamically developing sector of livesto...

This paper aims to verify the relationship between minimum wage growth and the financial indicators of automotive companies. As a hi-tech sector, the automotive industry is usually not expected to be affected by minimum wage policies. The introduction of the minimum wage in Germany and the dynamic development of the minimum wage in Eastern European countries make it possible to assess this relationship. German, Czech, Polish, and Slovak automotive company data was obtained from the Orbis database. Panel regression models were applied to test for dependencies. The paper detects the association between the growth of the minimum wage and the increase in personnel cost, which is next to the associations with several financial indicators at the company level. The identified impact is specific to small and medium-sized companies.

In a world of intense competition, automotive manufacturers are continually increasing their outsourcing activities and, as a result, automotive companies have built?extensive cost engineering departments within their organisations. Staff in t...

The forestry sector is facing critical challenges due to climate change. Decision-making support based on efficiency evaluation using non-parametric methods could provide important information for both forest managers and policymakers. However, such advanced technical analysis is scarce in forestry science. When applied, its application has been primarily based on aggregated, macro-level data, and efficiency was analysed for the forestry sector as a whole. There is a lack of studies from the company-level perspective, which are needed to provide sound decision support.In this paper, we focus on the micro-data level and offer the data envelopment analysis model settings and interpretations for an efficiency evaluation based on the financial data of individual forestry companies. The aim is to provide an original analysis of the company-level driving forces of forestry sector efficiency. The results for central European countries show that efficiency is driven by company size and country of operation. The study also confirms that, generally, German companies are the »efficiency leaders« in the region, while Czech companies may serve as an efficiency reference for east-central European forestry companies.