Search results

The purpose of this paper is to assess model risk with regard to wind energy output in monthly cash flow models for the purpose of valuation and risk assessment of wind farm investments, where only a few approaches exist in the literature.

This paper focuses on the risk-return characteristics of this investment from the perspective of private and institutional investors and takes into account several risks, in particular the resource risk related to the uncertainty of the monthly wind energy produced. To this end, this paper presents different approaches for modeling monthly wind power output and assesses the impact of three selected models with different properties on the investment’s risk-return characteristics by means of a stochastic discounted cash flow model. In addition, the model considers the possibility of a joint operation of the wind farm with a pumped hydro storage system to reduce risk and improve profits.

The results show that the (non-)consideration of seasonality of the monthly wind energy produced considerably influences the risk-return characteristics, but that principal developments dependent on input parameters and model variables remain similar.

This paper contributes to the literature by presenting different approaches for modeling the monthly wind energy produced based on direct models of the wind energy output, which are rare in the existing literature. Further, their impact on risk-return characteristics of a wind farm investment is analyzed, and thus, related model risk is assessed.

This study aims to examine the relationship between environmental, social and governance (ESG) disclosure, firm risk and stock market returns within the Chinese energy sector. Using a variety of econometric techniques, the study seeks to uncover the impact of ESG disclosure on risk mitigation and its influence on stock market performance.

Benchmark regression models were used to explore the associations between ESG disclosure, firm risk and stock returns. To address potential endogeneity, a generalised method of moments estimator is used. Quantile regression was used for robustness analysis.

The study reveals a negative relationship between ESG disclosure and firm risk, indicating that companies with greater ESG disclosure tend to experience reduced risk exposure. In addition, a positive association is observed between ESG disclosure and stock market returns, suggesting that companies with more comprehensive ESG disclosure practices tend to perform better in the stock market.

This study implies that investors appreciate sustainable investment and incorporate ESG practices and disclosure in decision-making. Policymakers can promote transparent ESG reporting through regulatory frameworks, fostering sustainable practices in the energy sector.

Despite the mounting concerns over carbon dioxide emissions and the energy industry’s environmental footprint, this study pioneers a comprehensive analysis of ESG disclosure within this critical sector. Delving into the relationship of ESG practices, firm risk and market returns, this research uniquely examines both risk mitigation and return enhancement, shedding new light on sustainable strategies in the energy domain.

This study aims to investigate the response of green investments of emerging countries to own-market uncertainty, oil-market uncertainty and COVID-19 effect/geo-political risks (GPRs), using the tail risks of corresponding markets as measures of uncertainty.

This study employs Westerlund and Narayan (2015) (WN)-type distributed lag model that simultaneously accounts for persistence, endogeneity and conditional heteroscedasticity, within a single model framework. The tail risks are obtained using conditional standard deviation of the residuals from an asymmetric autoregressive moving average – ARMA(1,1) – generalized autoregressive conditional heteroscedasticity – GARCH(1,1) model framework with Gaussian innovation. For out-of-sample forecast evaluation, the study employs root mean square error (RMSE), and Clark and West (2007) (CW) test for pairwise comparison of nested models, under three forecast horizons; providing statistical justification for incorporating oil tail risks and COVID-19 effects or GPRs in the predictive model.

Green returns responds significantly to own-market uncertainty (mostly positively), oil-market uncertainty (mostly positively) as well as the COVID-19 effect (mostly negatively), with some evidence of hedging potential against uncertainties that are external to the green investments market. Also, incorporating external uncertainties improves the in-sample predictability and out-of-sample forecasts, and yields some economic gains.

This study contributes originally to the green market-uncertainty literature in four ways. First, it generates daily tail risks (a more realistic measure of uncertainty) for emerging countries’ green returns and global oil prices. Second, it employs WN-type distributed lag model that is well suited to account for conditional heteroscedasticity, endogeneity and persistence effects; which characterizes financial series. Third, it presents both in-sample predictability and out-of-sample forecast performances. Fourth, it provides the economic gains of incorporating own-market, oil-market and COVID-19 uncertainty.

Recent research on the energy system highlights the need for understanding the bandwidth of drivers and inhibitors of household investor's behaviour in rooftop PV (or photovoltaic power system) and to fit the broader socio-economic context in which they are deployed. However, apart from few exceptions, these newer perspectives have not been duly applied in the research on rooftop PV. This paper aims to fill this gap and to shed new light on rooftop PV investment decisions.

This study has been conducted with the primary data collected using two data sets of 237 households and 387 households of Indian southern state Kerala using survey-based questionnaire. The findings from first data set revealed that households considering the adoption of PV were likely influenced by six distinct factors, three motivators and three inhibitors. Second data set for multi-state analytic approach was proposed whereby the research model was tested using structural equation modelling (SEM). The outcomes of SEM were used as inputs for an artificial neural network (ANN) model for forecasting investor investment decision in in renewables. The ANN model was also used to rank the relative influence of significant predictors obtained from SEM.

In line with the risk–return framework, government subsidies act as primary motivator which helps in overcoming the initial risk of investment in the new technology. Further, low prices and low cost of maintenance are some of the financial motivators which may likely mitigate the long-term apprehension of returns and maintenance cost. Lastly, the strongest motivators of PV investment come from the environmental and financial motivator in the form of PV subsidies, which further solidifies the role of policy interventions in investment decision. The ANN model identified the technical barrier and knowledge and awareness factors play a significant role in forcasting the investor investing decision.

The study results will be useful for policymakers for framing strategies to attract and influence their investment in renewable energy.

Building upon behavioural finance and institutional theory, this paper posits that, in addition to a rational evaluation of the economics of the investment opportunities, various non-financial factors affect the household's decision to invest in renewables.

Briefly reviews previous literature by the author before presenting an original 12 step system integration protocol designed to ensure the success of companies or countries in their efforts to develop and market new products. Looks at the issues from different strategic levels such as corporate, international, military and economic. Presents 31 case studies, including the success of Japan in microchips to the failure of Xerox to sell its invention of the Alto personal computer 3 years before Apple: from the success in DNA and Superconductor research to the success of Sunbeam in inventing and marketing food processors: and from the daring invention and production of atomic energy for survival to the successes of sewing machine inventor Howe in co‐operating on patents to compete in markets. Includes 306 questions and answers in order to qualify concepts introduced.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.