One of the important goals of the EU is to ensure a secure, sustainable, and competitive energy system that is less dependent on external energy supply. Greater independence is planned to be achieved by diversifying energy sources, as well as investing in renewables and energy efficiency. One of the mechanisms is the demand response (DR) that provides a high level of energy independence for the consumer. In this paper, we explore perspectives of the development of DR with a mediating effect of the independent aggregators from an EU member state standpoint. We use a hybrid research methodology that combines instruments of strategic analysis, i.e., PESTLE framework and SWOT analysis, along with the integrated risk management framework in order to identify, evaluate and rank prominent risks to which this initiative is exposed. Interdependencies between the identified risk factors are also included and efficient mitigation measures are proposed. The findings of this exploratory research are aimed at developing the policies and strategies for the aggregators’ development in the medium term. The most emphasized risks detected in analysis are the investment risk, the legal risk, the risk of substitute technologies, the consumer behavior risk, the risk of opportunistic behavior and the risk of entry barriers.

High risk-adjusted returns, low correlation with financial asset classes and inflation hedging are investment characteristics that make forests a desirable investment opportunity. To examine returns on forestry investments (from 2011 to 2020), we focused solely on 48 forest companies (across the globe) that were listed on stock exchanges. Results indicate the economic justification of investing in publicly traded forestry companies. The positive five-year beta coefficients (β) range from 0.21 to 3.46, amounting to 1.15 on average. Taking the last 10-year comparison of the world’s most common capital market benchmarks, the highest return was achieved by the S&P500 (13.8% on average) followed by forestry companies (9.1%), U.S. Treasury bonds (4.4%), and gold (3.0%). Forestry companies, along with their associated business activities (sawmilling, final products production, and paper production), show the best historical performance from an investor’s point of view (total return of 13.2%).

This paper assesses the profitability of battery storage systems (BSS) by focusing on the internal rate of return (IRR) as a profitability measure which offers advantages over other frequently used measures, most notably the net present value (NPV). Furthermore, this study proposes a multi-objective optimisation (MOO) approach to IRR estimation instead of relying on the simple linear optimisation and compares the results to the popular linear optimisation with battery cycle-cost penalty. The analysis is conducted under perfect foresight conditions by considering multiple revenue streams: arbitrage trading in the day-ahead and intraday markets, peak shaving, participating in the primary reserves market, and from photovoltaic (PV) power-generation unit. Data are collected for the German power market for 2017 and 2021. The results show that MOO approach yields similar IRR estimates to the cycle-cost model in 2017. However, higher market volatility and increased electricity prices in 2021 resulted in tangible differences. The analysis shows that, if such conditions are coupled with a low battery capacity price, the MOO method significantly outperforms the cycle-cost model. The effects of battery calendar lifetime and state of charge which decrease profitability are also considered. Nevertheless, a noticeable rise in profitability in 2021 relative to 2017 could provide enough compensation to address the issue of relatively poor viability track record.

The paper provides, for the first time, the analysis of the quality of the GDP growth and inflation forecasts by multiple forecasters for the Croatian economy. Forecast data of 6 different institutions in the 2006–2015 period are analysed. Efficiency and biasedness test are conducted following the Davies and Lahiri econometric framework based on a three-dimensional panel dataset which includes multiple individual forecasters, target years and forecast horizons. In order to assess directional accuracy we follow the approach by Pesaran and Timmermann. Based on MAE values we find the forecasts to be accurate on a scale comparable to the European Commission’s forecast reported in 2016 for the EU and the euro area. GDP growth forecasts exhibit a strong bias related to a notable tendency to over-predict GDP growth. In the case of inflation forecasting the bias is still present for all forecasters, albeit less pronounced and not statistically significant for all of them. There is evidence of forecast inefficiency regarding both analysed variables. Overall, inflation forecasting presents less of a challenge due to specific monetary policy strategy and inaccurate national accounts data accompanied by extended revision process of the GDP data by the government’s statistics office.

This paper focuses on designing a methodological workflow to fill a knowledge gap for determining the cost of capital for commercial forestry projects. Upon reviewing the literature, a method to determine the cost of capital for profit-oriented forestry seems to be lacking. Accordingly, we selected and analyzed 42 companies that do businesses worldwide, are present on the stock exchange, and possess or lease forest land. Based on their business activities (growing forest, sawmilling, final production, paper production), these companies are classified into four subgroups. An algorithm has been devised using the concept of risk diversification and the capital asset pricing model for three groups of investors and four forestry subgroups. In doing so, the real risk-free rate (0.43%) is set as the difference between an average return on 10-year US government bonds (2.59% nominal) and the 10-year average US inflation rate (2.16%). The measure of forestry systematic risk (beta coefficient) varies between 0.83 and 1.41, while the equity (stock exchange market) risk premium is set to 6%. Unsystematic risk is determined using a process of mapping which takes into account all risk elements marked as relevant for the forestry sector. This approach provides results that reveal the cost of capital varying between 5.41% and 16.55% based on the current level of an investor's portfolio diversification and the risk characteristics of the forestry subgroup. Finally, the forestry companies meeting the investor's expectations are noted as preferable investment opportunities.