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The use of fossil fuels in developing countries places increasing economic, health, and environmental costs on the population. In decentralized and rural communities without existing grid systems, direct solar technologies provide the basis for electricity production, for water pumping and hot water, and for heating of houses. Examples and case studies for each of these direct solar technologies are presented which may be directly applicable or potentially modified for rural development in countries such as Uzbekistan and Turkmenistan, which have ample direct solar resources. Related design involving both daylighting and passive cooling are described as part of the incorporation of passive solar heating techniques.

The technique of graphical modelling (Whittaker, 1990) can be used to identify the dependence relationships between variables representing characteristics of recorded road accidents. It allows large multi-dimensional tables to be analysed by looking for conditional independence relationships among the variables. The variables under study can often be divided into groups that are ordered in time or by a hypothesised causal assumption. For these situations graphical chain models (Whittaker, 1990) are used to explore causal relationships between the variables. Some examples are given for a six-dimensional and a ten-dimensional contingency table.

This chapter focuses on the physical work environment, in particular, the possibilities and limitations of biophilic design in hotel office space. Biophilic design has already gained attention in directly influencing the guest experience, but little focus has been given to workspace. Traditionally, the hospitality industry places employees' office spaces in parts of the hotel not appropriate as guest space due to lack of daylight and windows. Many studies have shown that improving the workplace with nature-based features can play a role in increasing employee well-being. Data collected from employees with both extensive managerial and back-of-house hotel experience established that employees would welcome the opportunity to work in such an environment and currently find back-of-house design basic and lacking any aesthetically pleasing elements. An understanding of the obstacles in initiating such changes was shown as well as employee initiatives to improve work spaces. This initial exploratory study concludes that the needs of employees in terms of comfort, wellbeing, motivation and work satisfaction are often overlooked when considering back-of-house spaces. There is a general understanding and expectation that management needs to develop awareness and practical initiatives to address the deficits of the physical work environment. Biophilic design can be influential in promoting a calming and restorative environment at the workplace and thus positively impacting employee motivation and performance.

One of the most neglected sources of energy loss is streetlights that generate too much light in areas where it is not required. Energy waste has enormous economic and environmental effects. In addition, due to the conventional manual nature of operation, streetlights are frequently seen being turned ‘ON’ during the day and ‘OFF’ in the evening, which is regrettable even in the twenty-first century. These issues require automated streetlight control in order to be resolved. This study aims to develop a novel streetlight controlling method by combining a smart transport monitoring system powered by computer vision technology with a closed circuit television (CCTV) camera that allows the light-emitting diode (LED) streetlight to automatically light up with the appropriate brightness by detecting the presence of pedestrians or vehicles and dimming the streetlight in their absence using semantic image segmentation from the CCTV video streaming. Consequently, our model distinguishes daylight and nighttime, which made it feasible to automate the process of turning the streetlight ‘ON’ and ‘OFF’ to save energy consumption costs. According to the aforementioned approach, geo-location sensor data could be utilised to make more informed streetlight management decisions. To complete the tasks, we consider training the U-net model with ResNet-34 as its backbone. Validity of the models is guaranteed with the use of assessment matrices. The suggested concept is straightforward, economical, energy-efficient, long-lasting and more resilient than conventional alternatives.

Purpose – The purpose of this chapter is to investigate the relationship between emotion and European energy forward prices of oil, gas, coal and electricity during normal times and periods of extreme price movements.

Methodology/Approach – We use a biorhythm approach characterized by the seasonal affective disorder (SAD) variable to study the impact of emotion on energy markets. Normal times and periods of extreme price movements are approximated by OLS and quantile estimations, respectively.

Findings – We use European energy forward prices of oil, gas, coal, and electricity. European equity future index (Dow Jones Euro Stoxx 50) and euro/dollar US exchange rate are used as control variables for economic and financial environment. Estimating OLS and quantile regressions, we find that seasonal patterns have a significant impact during extreme volatility periods only. Further investigations reveal that the SAD effect is significant during periods of price decrease, but insignificant during price increase times. The out-of-sample predictive ability properties show that our “SAD model” outperforms significantly the pure “macroeconomic” one.

Originality/Value of chapter – This topic is novel in energy finance since I use psychological background theory to understand energy price dynamics. I illustrate the relevance of our approach by comparing the out-of-sample predictive ability of our model against macroeconomic one. My results could be considered to improve energy porfolio allocation.