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Blockchain for energy trading is a trending research area in the current context. However, a noticeable gap exists in the review articles focussing on solar energy trading with blockchain technology. Thus, this study aims to systematically examine and synthesise the existing research on implementing blockchain technology in sustainable solar energy trading.

The study pursued a systematic literature review to achieve its aim. The data extraction process focussed on the Scopus and Web of Science (WoS) databases, yielding an initial set of 129 articles. Subsequent screening and removal of duplicates led to 87 articles for bibliometric analysis, utilising VOSviewer software to discern evolutionary progress in the field. Following the establishment of inclusion and exclusion criteria, a manual content analysis was conducted on a subset of 19 articles.

The results indicated a rising interest in publications on solar energy trading with blockchain technology. Some studies are exploring the integration of new technologies like machine learning and artificial intelligence in this domain. However, challenges and limitations were identified, such as the absence of real-world solar energy trading projects.

This study offers a distinctive approach by integrating bibliometric and manual content analyses, a methodology seldom explored. It provides valuable recommendations for academia and industry, influencing future research and industry practices. Insights include integrating blockchain into solar energy trading and addressing knowledge gaps. These findings advance societal goals, such as transitioning to renewable energy sources (RES) and mitigating carbon emissions, fostering a sustainable future.

This study aims to examine how audit committee (AC) attributes influence quality and quantity disclosure of energy consumed by the listed nonfinancial firms for the period of five years (2016–2020). The study aims at providing empirical evidence on how board of director’s independence influences the relationship between AC attributes and firms’ energy in achieving sustainable development goals (SDGs) on world climate policy.

The study obtained data from a sample of 83 listed nonfinancial firms, content analysis technique was used to compute energy disclosure indexes using global reporting initiative standards, while regression analysis was conducted to test the relationship among research variables.

The study revealed that AC independence, diversity and meetings were significantly related with energy disclosure. Also, the study found that other variables were insignificantly related with energy disclosure.

The study is constrained for not considering all listed firms in the country. Furthermore, the study considered selected attributes, other important audit-committee size attributes such as audit-committee size, audit-committee size tenure could be study in by the future study.

The study’s findings would have practical implications for corporations and other business organizations seeking to actively involve the energy-related SDGs 7 and 13 in their business models and successfully communicate these efforts to stakeholders.

To the best of author’s knowledge, this is the first study that provides empirical evidence on the effect of AC attributes on the energy disclosure using effect of board independence as moderator in Nigeria.

This study aims to evaluate the quality of the energy efficiency disclosures made by Australian cities. As cities are significant energy users, and energy use is a crucial source of greenhouse gas emissions, energy efficiency initiatives can play an essential role in addressing climate change. Yet, little is understood about the energy efficiency disclosures being made.

The authors developed an original energy efficiency disclosure index to assess the reporting quality of the eight largest Australian cities. The websites of these cities were analysed for information on energy efficiency measures from December 2018 to June 2019. Annual reports, environmental reports, climate action plans and any other material related to energy plans were downloaded and then coded using the index.

While all cities provided energy efficiency information, little financial information was provided, limited forward-looking information was disclosed, key challenges were not disclosed, and each city provided energy efficiency disclosures differently. Collectively, these findings demonstrate that public accountability is limited.

An important implication is the need to standardise and improve cities’ energy efficiency reporting, especially concerning financial information. Cities, governments and the Carbon Disclosure Project (formerly the CDP) could achieve this, perhaps as part of the broader update of the CDP city-focused guidelines for greenhouse gas (GHG) reporting.

Although some studies on GHG reporting by cities have already been undertaken, including energy efficiency as part of their disclosure index, no study has focused on energy efficiency disclosures. The authors provide original insights concerning these practices. The study also provides an energy efficiency disclosure index that can be used in further research.

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.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

The purpose of this study is to assess the influence of institutional quality (IQ), fossil fuel efficiency, structural change and renewable energy (RE) consumption on carbon efficiency.

This research uses an econometric approach, more specifically the Autoregressive Distributed Lag model, to examine the relationship between structural change, RE consumption, IQ, fossil fuel efficiency and carbon efficiency in India from 1996 to 2019.

This study finds the positive contributions of variables like fossil fuel efficiency, technological advancement, structural transformation, IQ and increased RE consumption in fostering environmental development through enhanced carbon efficiency. Conversely, this study emphasises the negative contribution of trade openness on carbon efficiency. These findings provide concise insights into the dynamics of factors impacting carbon efficiency in India.

This study's exclusive focus on India limits the generalizability of findings. Future studies should include a broader range of variables impacting various nations' carbon efficiency. Furthermore, it is worth noting that this study examines renewable and fossil fuel efficiency aggregated. Future research endeavours could yield more specific policy insights by conducting analyses at a disaggregated level, considering individual energy sources such as wind, solar, coal and oil. Understanding how the efficiency of each energy source influences carbon efficiency could lead to more targeted and practical policy recommendations.

To the best of the authors’ knowledge, this study addresses a significant gap in the existing literature by being the first empirical investigation into the effects of IQ, fossil fuel efficiency, structural change and RE consumption on carbon efficiency. Unlike prior research, the authors consider a comprehensive IQ index, providing a more holistic perspective. The use of a comprehensive composite index for IQ, coupled with the focus on fossil fuel efficiency and structural change, distinguishes this study from previous research, contributing valuable insights into the intricate dynamics shaping India's path towards enhanced carbon efficiency, an area relatively underexplored in the existing literature.

Purpose: The study aims to extract insights into five significant industries, pharmaceutical, space, defence, renewal energy, and information technology (IT), which have huge potential to make India achieving a five trillion-dollar economy in the future.

Design/methodology/approach: The authors focus on future-driven industries which are not only making India a third highest gross domestic product (GDP) producer country but also reviewing the different aspects of these industries and how they can assist India in achieving a five trillion-dollar economies along with determining India’s self-reliance through different governments initiatives in this direction.

Findings: The findings highlight the importance of inclusiveness of policymakers, stakeholders, private players, foreign investors, and the masses. Their significant contributions especially in the pharmaceutical, space, defence, renewal energy, and IT sectors in terms of creativities, innovations, intellect, executions, implementations, and improvements can assist India in achieving its five trillion-dollars economy soon.

Practical implications: This study offers (1) convincing insights into five key industries, pharmaceutical, space, defence, renewal energy, and IT, which have huge potential to increase total GDP volume shortly and (2) the investment areas for the masses where they can see their world not only self-reliant but also will see huge growth in their invested amount in these industries in future.

Originality/value: The insights of five key industries, pharmaceutical, space, defence, renewal energy, and IT, highlight that India has the potential to achieve a five trillion-dollar economy in the future; however, it does not ignore the significant contribution of other industries in making of total GDP.

The purpose of this paper is to analyse the influence of economic growth, digitalisation, eco-innovation, energy consumption and patents on environmental technologies on the volume of greenhouse gas emissions (GHG) recorded in European countries for a period of nine years (2010–2018).

Two empirical methods were integrated into the theoretical approach developed based on the analysis of the current scientific framework. Multiple linear regression, an extended version of the OLS model, and a non-causal analysis as a robustness method, Dumitrescu–Hurlin, were used to achieve the proposed research objective.

Digitalisation described by the number of individual Internet users and patents on environmental technologies determines the amount of GHG in Europe, and economic growth continues to have a significant effect on the amount of emissions, as well as the consumption of renewable energy. European countries are not framed in well-established patterns, but the economic growth, digitalisation, eco-innovation and renewable energy have an impact on the amount of GHG in one way or another. In many European countries, the amount of GHGs is decreasing as a result of economic growth, changes in the energy field and digitalisation. The positive influence of economic growth on climate neutrality depends on its degree of sustainability, while patents have the same conditional effect of their translation into environmentally efficient technologies.

This study has a number of limitations which derive, first of all, from the lack of digitalisation indicators. The missing data restricted the inclusion in the analysis of variables relevant to the description of the European digitalisation process, also obtaining conclusive results on the effects of digitalisation on GHG emissions.

A similar analysis of the relationship among the amount of greenhouse gas emissions and economic growth, digitalisation, eco-innovation and renewable energy is less common in the literature. Also, the results can be inspirational in the sphere of macroeconomic policy.

This study aims to investigate the daily performance of the proposed microgrid (MG) that comprises photovoltaic, wind turbines and is connected to the main grid. The load demand is a residential area that includes 20 houses.

The daily operational strategy of the proposed MG allows to vend and procure utterly between the main grid and MG. The smart metre of every consumer provides the supplier with the daily consumption pattern which is amended by demand side management (DSM). The daily operational cost (DOC) CO2 emission and other measures are utilized to evaluate the system performance. A grey wolf optimizer was employed to minimize DOC including the cost of procuring energy from the main grid, the emission cost and the revenue of sold energy to the main grid.

The obtained results of winter and summer days revealed that DSM significantly improved the system performance from the economic and environmental perspectives. With DSM, DOC on winter day was −26.93 ($/kWh) and on summer day, DOC was 10.59 ($/kWh). While without considering DSM, DOC on winter day was −25.42 ($/kWh) and on summer day DOC was 14.95 ($/kWh).

As opposed to previous research that predominantly addressed the long-term operation, the value of the proposed research is to investigate the short-term operation (24-hour) of MG that copes with vital contingencies associated with selling and procuring energy with the main grid considering the environmental cost. Outstandingly, the proposed research engaged the consumers by smart meters to apply demand-sideDSM, while the previous studies largely focused on supply side management.