Effect of Environmental Optimism on Responsible Electricity Consumption with Price Concern as a Moderator

The study aimed to determine the effect of environmental optimism, as a cognitive–emotional factor, on the responsible use of electricity. Furthermore, it investigated the moderating effect of consumer concern on the price of electricity. An online survey was conducted on 345 young adults in Jakarta selected through the snowball sampling method. Data were analyzed using JASP version 15.0 and IBM SPSS Statistics version 23 reinforced with PROCESS macro. Simple linear regression analysis demonstrated that environmental optimism significantly explains the variance in electricity conservation behavior. The moderating effect of price concern was also substantiated by the result of the data analysis, thus the interaction between environmental optimism and dichotomous predictors of price concern (i.e. high vs low) was found to be statistically significant in moderating the effect of environmental optimism toward electricity conservation behavior. In conclusion, when consumers are initially dominated by price concern (a rational extrinsic motivator), then it reduces the effect of environmental optimism (an emotional intrinsic motivator) on responsible electricity consumption.

The Effects of Extreme High Temperature Day-Off on Electricity Conservation

The continuously increasing temperatures worldwide indicate the frequently extreme heat in summer will become a new normal. The extreme high temperature (EHT) could be dangerous to human health, especially for outdoor workers or commuters, and increase the risk of grid collapse. Thus, the possibility of a day-off due to EHT has started to be discussed in Taiwan, based on the experience of typhoon day-off, but not yet concluded. In this study, the effects of the EHT day-off on electricity consumption in the industrial, service, and residential sectors was investigated through two determinants: First, high temperature would increase the electricity consumption in space cooling. Second, a day-off would change people’s behavior of electricity consumption from workday to non-workday modes. Combining the effects of cooling hours and non-workdays, the net influence of the EHT day-off on electricity consumption can be evaluated. Estimated results indicated that an EHT day-off can reduce aggregate electricity consumption by between 0.41% and 1.08%. The reduction of electricity consumption due to the off-day offsets the increase driven by high temperatures. Thus, an EHT day-off will mitigate the pressure of power grid and be of benefit to electricity conservation.

Investigating Carbon Emissions from Electricity Generation and GDP Nexus Using Maximum Entropy Bootstrap: Evidence from Oil-Producing Countries in the Middle East

The maximum entropy bootstrap for time series is applied in this study to investigate the nexus between carbon emissions from electricity generation and the gross domestic product, using a bivariate framework for eight Middle Eastern countries between 1995 and 2017. The sample under study includes oil-producing countries such as Bahrain, Iran, Iraq, Kuwait, Oman, Qatar, Saudi Arabia, and United Arab Emirates. As the electricity generation in these economies relies mainly on oil and gas, finding out the existence and direction of the relationship between the two considered variables has remarkable implications for policymakers and governments in these countries to achieve both higher economic growth and environmental protection. As expected, this nexus is validated for all countries in the sample but not in all models, time periods, and lags. Therefore, policymakers can set appropriate electricity conservation policies based on these varied empirical findings to boost economic growth with minimum environmental degradation.

SMART ELECTRICITY CONSERVATION SYSTEM USING EFFICIENTNET

Conservation of electric resource has been one of the important challenges over the decades. Worldwide, many nations are struggling to conserve and to bridge the gap between the demand and production of the resource. Though many measures like several Government acts, replacing existing products with energy conserving products and many solar based systems are being invented and used in practise, the demand and the need to preserve the resource still persists. Hence, this paper focuses on a novel technique to conserve the electric resource using a deep learning technique. The system uses Convolutional Neural Networks to identify and localize humans in the CCTV footages using EfficientNet, a deep transfer learning model. The classifier processes and yields its output to an embedded Arduino microcontroller, after detecting the presence/absence of human. The microcontroller enables/disables the electric power supply in the area of human’s presence/absence, based on the classifier’s output respectively. The system achieves an accuracy percentage of 84.2% in detecting humans in the footages with the subsequent enabling/disabling of electric power resource to conserve electricity.

Real-time human detection for electricity conservation using pruned-SSD and arduino

Electricity conservation techniques have gained more importance in recent years. Many smart techniques are invented to save electricity with the help of assisted devices like sensors. Though it saves electricity, it adds an additional sensor cost to the system. This work aims to develop a system that manages the electric power supply, only when it is actually needed i.e., the system enables the power supply when a human is present in the location and disables it otherwise. The system avoids any additional costs by using the closed circuit television, which is installed in most of the places for security reasons. Human detection is done by a Modified-single shot detection with a specific hyperparameter tuning method. Further the model is pruned to reduce the computational cost of the framework which in turn reduces the processing speed of the network drastically. The model yields the output to the Arduino micro-controller to enable the power supply in and around the location only when a human is detected and disables it when the human exits. The model is evaluated on CHOKEPOINT dataset and real-time video surveillance footage. Experimental results have shown an average accuracy of 85.82% with 2.1 seconds of processing time per frame.

Analyzing the role of values, beliefs and attitude in developing sustainable behavioral intentions: empirical evidence from electric power industry

Energy conservation is an economic, social, and environmental issue that offers various academic, practical, and policy implications. The growing magnitude, complexity, and relevance of energy wastage have attracted the attention of scholars and practitioners. There is a lack of consumer awareness towards electricity conservation practices of household consumers in the context of developing countries. This research utilized the theory of planned behavior to explain sustainable behavioral intentions in the context of electricity conservation. This research aims to measure the effect of beliefs, values, and attitudes on sustainable behavioral intentions in the energy conservation context. This research hypothesized awareness, compatibility, perceived value, resistance to change, and actual gain as predictors of attitude towards energy conservation and sustainable behavioral intentions. Using survey methodology, purposive sampling techniques were used to collect the data from young household consumers. Data of 246 electricity consumers of Pakistan were analyzed using confirmatory factor analysis (CFA) and structural equation modeling (SEM). Results of the study validate a significant relationship between attitude towards energy conservation and sustainable behavioral intentions. Based on results, it has been established that the amount of actual gain intentions and awareness are the main contributors to attitude towards energy conservation and sustainable behavioral intention. Compatibility and resistance to change were also significant precipitators of attitude towards energy conservation and sustainable behavioral intention. Perceived value was found a significant predictor of attitude towards energy conservation while insignificant towards sustainable behavioral intention. The study findings have a significant impact on government, policymakers, marketers, and academics interested in developing strategies to mitigate the effects of energy wastage. Prevention of resource wastage depends upon the awareness and consumption practices of the customers.