Unpacking the determinants of cross-border private investment in renewable energy in developing countries

Private finance has emerged as a fundamental catalyst of the clean energy transition, an urgent and necessary step that must be taken in order to avert catastrophic climate change. Yet, private investment in renewable energy, although gaining momentum, remains limited in reaching some developing countries, where it is most needed. Previous research has provided some insights into the drivers and barriers faced by investors in this sector; however, these remain understudied in the context of developing country markets. This study contributes to this body of knowledge by systematically testing the effects that a variety of factors have on foreign investment in renewable power generation in developing countries, and by investigating how these effects may vary according to the source of finance. The determinants include the implementation of domestic renewable energy policies, the provision of international public finance and the wider business environment. Using panel data covering 62 countries over a 7-year period, this analysis relied on linear and logistic fixed effects models to determine what best explains the decision to invest and the volume of foreign private capital flows in the renewable energy sector. Results suggested that the provision of international public finance, regulatory support measures and feed-in tariffs, coupled with political stability, are strong drivers of cross-border investment in renewable energy in developing countries. Finally, evidence was presented that the effects of public interventions and business environment factors on investment may vary according to the source of finance, shedding light on the importance of breaking down investment flows to fully understand private financing decisions in renewable energy

Regression toward the mean – a detection method for unknown population mean based on Mee and Chua's algorithm

<p>Abstract</p> <p>Background</p> <p>Regression to the mean (RTM) occurs in situations of repeated measurements when extreme values are followed by measurements in the same subjects that are closer to the mean of the basic population. In uncontrolled studies such changes are likely to be interpreted as a real treatment effect.</p> <p>Methods</p> <p>Several statistical approaches have been developed to analyse such situations, including the algorithm of Mee and Chua which assumes a known population mean <it>μ</it>. We extend this approach to a situation where <it>μ </it>is unknown and suggest to vary it systematically over a range of reasonable values. Using differential calculus we provide formulas to estimate the range of <it>μ </it>where treatment effects are likely to occur when RTM is present.</p> <p>Results</p> <p>We successfully applied our method to three real world examples denoting situations when (a) no treatment effect can be confirmed regardless which <it>μ </it>is true, (b) when a treatment effect must be assumed independent from the true <it>μ </it>and (c) in the appraisal of results of uncontrolled studies.</p> <p>Conclusion</p> <p>Our method can be used to separate the wheat from the chaff in situations, when one has to interpret the results of uncontrolled studies. In meta-analysis, health-technology reports or systematic reviews this approach may be helpful to clarify the evidence given from uncontrolled observational studies.</p

Cancer patients as &apos;experts&apos; in defining quality of life domains. A multicentre survey by the Italian Group for the Evaluation of Outcomes in Oncology (IGEO)

Although the subjective nature of quality of life is generally accepted, less attention has been paid to the procedure of selecting domains to be explored with questionnaires. To explore what contributes to cancer patients' quality of life, a survey was conducted with the aim of identifying contents of quality of life using cancer patients as 'experts'. A questionnaire with open-ended items aimed at exploring the meaning of quality of life and at determining the contents of health and not health related quality of life, was submitted to a sample of cancer patients stratified by residence, cancer site and stage of disease. The 248 questionnaires received were transcribed and broken down into phrases to allow coding. A content analysis was performed, using as a conceptual framework, the domains identified by the Italian Society of Psycho-Oncology. Overall, 43 domains and a list of symptoms were identified. The two most frequently reported symptoms were pain (21.4% patients) and fatigue (14.1% patients). Social relationships and psychological domains were heavily represented. Twenty sub-domains related to the domain `psychological well-being'. This study suggests that information on the content of quality of life questionnaires to be submitted to people affected by a specific disease, should be derived by studying people suffering the specific disease. These results reinforce the criticism that available quality of life instruments are more likely to reflect the perspective of health professionals than patients

Cancer patients as “experts” in defining quality of life domains. A multicentre survey by the Italian Group for the Evaluation of Outcomes in Oncology (IGEO).

Although the subjective nature of quality of life is generally accepted, less attention has been paid to the procedure of selecting domains to be explored with questionnaires. To explore what contributes to cancer patients' quality of life, a survey was conducted with the aim of identifying contents of quality of life using cancer patients as 'experts'. A questionnaire with open-ended items aimed at exploring the meaning of quality of life and at determining the contents of health and not health related quality of life, was submitted to a sample of cancer patients stratified by residence, cancer site and stage of disease. The 248 questionnaires received were transcribed and broken down into phrases to allow coding. A content analysis was performed, using as a conceptual framework, the domains identified by the Italian Society of Psycho-Oncology. Overall, 43 domains and a list of symptoms were identified. The two most frequently reported symptoms were pain (21.4% patients) and fatigue (14.1% patients). Social relationships and psychological domains were heavily represented. Twenty sub-domains related to the domain `psychological well-being'. This study suggests that information on the content of quality of life questionnaires to be submitted to people affected by a specific disease, should be derived by studying people suffering the specific disease. These results reinforce the criticism that available quality of life instruments are more likely to reflect the perspective of health professionals than patients