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This article conceptualizes and constructs a comprehensive framework that can better help to answer that question – Who is accountable for social and public problems? – theoretically and practically.

Tracing the drivers behind two phenomena “accountability hole” and “accountability black hole”, stemming from “pushing power game” and “pulling power game”, respectively, this study considers (1) the three actors of society: citizens (civil society), corporations (market) and civil servants (government), and (2) the principal-agent relationship between the three actors in the face of social and public problems. As a result, the 4CAs framework that contains the three actors’ collaborative accountabilities to one another is presented.

The 4CAs model emphasizes (1) all three actors function as agents that are accountable to one another, (2) collaborative accountability beyond collaborative governance and (3) repowering citizens and corporations beyond just empowering them, i.e. returning their inherent rights and obligations to serve one another.

The 4CAs model may function as a descriptive and prescriptive lens through which the trilemma between market failure, government failure and citizen failure can be re-assessed and balanced. The model can also be used as a set of indicators for assessing and helping a society to better resolve the social and public problems collectively.

Research on solar energy adoption offers a multidimensional scope and warrants exploration from multiple perspectives, including political, economic, management, behavioral, policy and innovation aspects. The aim of this paper is to comprehensively consolidate major research findings on the premise of solar energy adoption and to disclose gaps in the existing literature.

A bibliometric analysis of the vast literature is conducted on 1,009 meticulously shortlisted articles following the semi-systematic literature review methodology. A text analytics tool named BibExcel is used for synthesizing the literature, and the results are visualized using Gephi, Pajek and a spreadsheet application.

This paper reports the evolution of research in the selected domain. It is noted that research in this domain was primarily concentrated on four broad themes, namely, peer effects and spatial patterns, public perceptions, policies and economics and technological evolution. The analysis further reveals the merging of two of these themes as a result of transdisciplinary research and also projects future research trends emphasizing political interventions in technological evolution and diffusion.

Research trends and future research scope are identified and discussed in detail. The information revealed from the analysis, along with the research implications, will assist policymakers in noting the flaws in the current doctrines and practices, entrepreneurs in understanding potential enablers and barriers influencing solar energy adoption and budding scholars in comprehending the current research status and framing promising research objectives to close the existing research gaps.

The purpose of this paper is to develop a blockchain-based data capture and transmission system that will collect real-time power consumption data from a household electrical appliance and transfer it securely to a local server for energy analytics such as forecasting.

The data capture system is composed of two current transformer (CT) sensors connected to two different electrical appliances. The CT sensors send the power readings to two Arduino microcontrollers which in turn connect to a Raspberry-Pi for aggregating the data. Blockchain is then enabled onto the Raspberry-Pi through a Java API so that the data are transmitted securely to a server. The server provides real-time visualization of the data as well as prediction using the multi-layer perceptron (MLP) and long short term memory (LSTM) algorithms.

The results for the blockchain analysis demonstrate that when the data readings are transmitted in smaller blocks, the security is much greater as compared with blocks of larger size. To assess the accuracy of the prediction algorithms data were collected for a 20 min interval to train the model and the algorithms were evaluated using the sliding window approach. The mean average percentage error (MAPE) was used to assess the accuracy of the algorithms and a MAPE of 1.62% and 1.99% was obtained for the LSTM and MLP algorithms, respectively.

A detailed performance analysis of the blockchain-based transmission model using time complexity, throughput and latency as well as energy forecasting has been performed.