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Short-term forecasting of electricity generation from variable renewable energy sources is not an end in itself but should provide some net benefit to its user. In the case of electricity trading, which is in the focus of this paper, the benefit can be quantified in terms of an improved economic outcome. Although some effort has been made to evaluate and to improve the profitability of electricity forecasts, the understanding of the underlying effects has remained incomplete so far. In this paper, we develop a more comprehensive theoretical framework of the connection between the statistical and the economic properties of day-ahead electricity forecasts. We find that, apart from the accuracy and the bias, which have already been extensively researched, the correlation between the forecast errors and the market price spread determines the economic implications - a phenomenon which we refer to as 'correlation effect'. Our analysis is completed by a case study on solar electricity forecasting in Germany which illustrates the relevance and the limits of both our theoretical framework and the correlation effect. • The economics of short-term electricity forecasts are studied analytically and empirically. • The value and the risk of forecast-based electricity trading are evaluated. • We find that the economics depend on the correlation of forecast errors with prices. • Hence, the most accurate forecast is not necessarily the most accurate. [ABSTRACT FROM AUTHOR]

In contemporary times, where most academic research mainly focuses on the factors of economic and environmental sustainability and emissions reduction. Yet, very little attention has been paid to the identification of the factors of renewable energy, which requires appropriate policy-level attention. Consequently, this research investigated two developed economies, i.e., Germany and the USA, from 1991 to 2021 where, the objective of the study includes using novel and robust empirical methods to test the causal relationship between renewable energy and CO2 emissions, economic growth, technological innovations and oil prices. Using the normality and unit root estimators, this study observed that non-normal data distribution, yet all the variables are stationary. Using time-series and panel cointegration tests, the results validate the cointegration between economic growth, oil prices, carbon emission, technological innovation, and renewable energy adoption in the United States whereas Germany does not show cointegration between the variables. This study employ 's the Morlet-Wavelet approaches and key findings show that all these variables have a significant role in improving renewable energy adoption in both the region. Furthermore, results show a unidirectional and bidirectional causal association between the variables via the panel-stacked Granger causality test. This study recommends effective policy ramifications concerning improved investment in technological innovation, improved low-carbon production, and diverting economic growth to renewable energy transition. Use of improved new time-series method of wavelet coherence show the key contribution in this paper with new evidence of time frequency analysis on how external variables affect renewable energy consumption in developed countries of US and Germany. The objective includes understanding the effects of CO2 emissions, economic growth, technological innovations and oil prices on renewable energy which would give evidence to policy makers and environmentalists on how developed countries should improve clean energy adoption. [ABSTRACT FROM AUTHOR]

Abstract: The development of geothermal energy application as a part of Germany’s renewable energy portfolio is a multidimensional process with fast growing improvements. In this paper basic information on geothermal application in low-enthalpy regions, such as Germany and Abu Dhabi are given. [Copyright &y& Elsevier]