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This paper aims to present a conceptual framework for the emerging field of green energy platform economics.

The authors develop a conceptual framework based on a careful review of the existing literature, and research into the current provider landscape and insights from academic and industry experts. The authors also examine the implications for the energy sector’s value chain and derive a research agenda based on those areas where research still needs to be pursued.

The framework combines the spatial characteristics of platform models (residential/mobile) with the different types of platform business model (B2C/C2C/C2Grid). Using this framework, the authors illustrate how green energy platforms can fundamentally disrupt the conventional electricity value chain by enabling prosumers to market their assets, creating new arenas for trading and collaboration, and by increasing transparency and competition in the sector. The authors also identify areas where more research is required, particularly empirical studies into energy forms other than electricity and analyses of currently underrepresented aspects such as user interfaces and social interactions.

Green energy platforms have the potential to contribute meaningfully to the energy sector’s decarbonization, digitalization and decentralization, and hence to the deceleration of climate change.

This paper is among the first to provide a holistic perspective on platformization in the energy sector. It also offers a new perspective on platform economics in general that is based on the unique characteristics of energy as an economic good (intangibility, homogeneity, credence good).

This paper aims to introduce the current-state of the energy grid, to reviews new and enabling peer-to-peer and blockchain-based solutions and to propose a strategic go-to-market framework, populated with energy companies strategically positioned to capture the unique opportunities present across the energy industry.

Hundreds of use-cases worldwide have been reviewed, and 50 worldwide blockchain energy companies and initiatives were included in this study based on the publicly accessible information that indicates their go-to-market strategies. As a result, these initiatives were classified into three main types of go-to-market strategies. Each company and its portfolio of blockchain-based solutions was described under these three categories.

Based on the research conducted in this review paper, it is evident that the adoption of blockchain-based technologies, solutions and services is accelerating at a rapid pace within the global energy industry to meet the needs and challenges that exist within it. Given the companies outlined in this paper, the opportunity to leverage blockchain technology while aligning to a social driver like green energy is perceived to be the most promising go-to-market strategy within this sector.

This study explores the apparent business plans of different blockchain initiatives around the world. Although there are a few other review papers recently published, to the best of the authors’ knowledge, this approach has not been taken in other studies in terms of the categorization of available use-cases.

The platform economy reflects the business model of some of the largest and fastest-growing firms in the economy. Platform business models emerge and thrive because of the potential profit in taking advantage of transactions cost reductions to connect people for mutual benefit, and this value creation is best understood by thinking about the epistemology of decentralized market processes. Three essential aspects of knowledge are relevant to platform business models: (1) knowledge can be private and diffuse; (2) knowledge can be contextual; and (3) knowledge may not exist outside of the economic process. After defining and analyzing the technology, economic, and institutional aspects of platforms the author defines and applies market epistemology to explore how platforms harness technological and organizational features to create value-enhancing market platforms by exploiting the epistemic benefits of technology-enabled decentralized market processes. The author concludes by using this epistemic framework to propose an electricity distribution platform business model – the retail electricity industry is undergoing a process of technological dynamism, and as a regulated infrastructure industry, evolving into a decentralized market industry is presenting challenges to which this epistemic framework can bring increased understanding.

The purpose of this paper is to design a sustainable development platform for water and energy peer-to-peer trading that is financially and economically feasible. Water and other resources are becoming scarcer every day, and developing countries are the neediest for an immediate intervention. Water, as a national need, is considered to be one of the most precious commodities, but it is also one of the main causes for conflicts in the 21st century. Rainwater harvesting and peer-to-peer trading of the harvested water is one of the most convenient, scalable and sustainable solutions but faces organization challenges such as the absence of suitable business models motivating normal users to sell their generated resources (such as water and energy), currency and financial settlement complexities and single utility markets.

This paper proposes a multi-utility trading platform based on the blockchain technology which can address the challenges faced by peer-to-peer trading for resources such as energy and water.

This paper presents a peer-to-peer multi-utility trading platform that solves the shortcomings of existing utility frameworks reported in the current literature.

This proposed platform meets the needs of developing countries as well as rural areas of developed countries. The open nature of the proposed design makes it suitable for adoption and use by various stakeholders.

The purpose of this study is to examine private households’ preferences for service bundles in the German energy market.

This investigation is based on survey data collected from 3,663 customers of seven mainly municipal energy suppliers in the German energy market. The data set was analyzed via a binary logistic regression model to identify the most prospective customers and their preferences regarding bundles of energy services.

The results indicate that potential adopters of energy-related service bundles have greater prior knowledge about service bundles; place higher importance on simplified handling, flat rates and long price guarantees; prefer to purchase a service bundle from an energy supplier; live in urban areas and have a gas tariff; are both less likely to have a green electricity tariff and to support the German energy transition; have a greater intention to purchase a smart home product; are less likely to already be prosumers; and prefer customer centers and social media as communication channels with energy providers.

This paper offers several implications for decision-makers in developing marketing strategies for bundled offerings in a highly competitive energy market.

This paper contributes to the sparse research on service bundles in the energy sector, despite the growing interest of energy suppliers and consumers in this topic. It expands the research focusing on the telecommunications sector.

Based on climate issues and carbon emissions, this study aims to promote low-carbon consumption and compel consumers to actively shift to energy-saving appliances. In this big data era, online reviews in social and electronic commerce (e-commerce) websites contain valuable product information, which can facilitate firm business strategies and consumer comparison shopping. This study is designed to advance existing research on energy-saving refrigerators by incorporating machine learning models in the analysis of online reviews to provide valuable suggestions to e-commerce platform managers and manufacturers to effectively understand the psychological cognition of consumers.

This study proposes an online e-commerce review mining and management strategy model based on “data acquisition and cleaning, data mining and analysis and strategy formation” through multiple machine learning methods, namely, Bayes networks, support vector machine (SVM), latent Dirichlet allocation (LDA) and importance–performance analysis (IPA), to help managers.

Based on a case study of one of the largest e-commerce platforms in China, this study linguistically analyzes 29,216 online reviews of energy-saving refrigerators. Results indicate that the energy-saving refrigerator features that consumers are generally satisfied with are, in sequential order, logistics, function, price, outlook, after-sales service, brand, quality and space. This study also identifies ten topics with 100 keywords by analyzing 18 different refrigerator models. Finally, based on the IPA, this study allocates different priorities to the features and provides suggestions from the perspective of consumers, the government and manufacturers.

In terms of limitations, future research may focus on the following points. First, the topics identified in this study derive from specific points in time and reviews; thus, the topics may change with the text data. A machine learning-based online review analysis platform could be developed in the future to dynamically improve consumer satisfaction. Moreover, given that consumers' needs may change over time, e-commerce platform types and consumer characteristics, such as user profiles, can be incorporated into the model to effectively analyze trends in consumers' perceived dimensions.

This study fills the gap in previous research in this field, which uses small-sample data for qualitative analysis, while integrating management ideas and proposes an online e-commerce review mining and management strategy model based on machine learning methods. Moreover, this study considers how consumers' emotional and thematic preferences for products affect their purchase decision-making from the perspective of their psychological perception and linguistically analyzes online reviews of energy-saving refrigerators using the proposed mining model. Through the improved IPA model, this study provides optimizing strategies to help e-commerce platform managers and manufacturers.

The objective of this chapter is to identify the key characteristics of Global Services businesses that will thrive and achieve success in the future. These factors are integrated into three main pillars, which we refer to as the Triple-Win. The first and most obvious pillar is technology as a tool. The second pillar is the design and sustainability of the business model, without which the previous factor would be merely a cost and not an investment. And last but not the least, there is the purpose which gives meaning to the proposal, focusing on the human being and their environment. The DIDPAGA business model sits at the intersection of these three elements.

The purpose of this study is to propose a decentralized multi-party cross-trading scheme based on a certificate transaction mechanism for the transaction of excess consumption certificates (ECCs) of renewable energy. The aim is to address the problems associated with the existing centralized transaction mode and to promote the development of the green electricity industry.

The proposed scheme involves calculating the quotation difference for the same type of certificate transaction based on the quotations of all users of both buyers and sellers. The transaction volume is then determined based on the order of quotation difference from large to small, and the total interests of cooperation are calculated. The nucleolus method is adopted to allocate the total interests to each member of the alliance and calculate the final transaction price. The blockchain technology is used for the transaction to achieve accurate traceability and efficient supervision, and a corresponding smart contract is designed and simulated in the Ethereum consortium chain.

The results of the simulation show the rationality and effectiveness of the proposed scheme. The decentralized multi-party cross-trading scheme can overcome the problems associated with the existing centralized transaction mode, such as low transaction efficiency, difficulty in obtaining the optimal transaction strategy and efficient supervision. The proposed scheme can promote the development of the green electricity industry by stimulating users' demand potential for green electricity.

The proposed scheme is original in its use of a certificate transaction mechanism to facilitate the trading of ECCs of renewable energy. The scheme adopts a decentralized multi-party cross-trading approach that overcomes the problems associated with the existing centralized transaction mode. The use of the nucleolus method for the allocation of total interests to each member of the alliance is also original. Finally, the use of blockchain technology for accurate traceability and efficient supervision of the transaction is an original contribution to the field.

The European Union’s Digital Markets Act (DMA) calls for far-reaching changes to the way economic activity will occur in EU digital markets. Before its remedies are imposed, it is critical to assess their impacts on individual markets, the digital sector, and the overall European economy. The European Commission (EC) released an Impact Assessment in support of the DMA that purports to evaluate it using cost/benefit analysis.

An economic evaluation of the DMA should consider its full impacts on dynamic competition. The Impact Assessment neither assesses the DMA's impact on dynamic competition in the digital economy nor evaluates the impacts of specific DMA prohibitions and obligations. Instead, it considers benefits in general and largely ignores costs. We study its benefit assessments and find they are based on highly inappropriate methodologies and assumptions. A cost/benefit study using inappropriate methodologies and largely ignoring costs cannot provide a sound policy assessment.

Instead of promoting dynamic competition between platforms, the DMA will likely reinforce existing market structures, ossify market boundaries, and stunt European innovation. The DMA is likely to chill R&D by encouraging free riding on the investments of others, which discourages making those investments. Avoiding harm to innovation is critical because innovation delivers large, positive spillover benefits, driving increases in productivity, employment, wages, and prosperity.

The DMA prioritizes static over dynamic competition, with the potential to harm the European economy. Given this, the Impact Assessment does not demonstrate that the DMA will be beneficial overall, and its implementation must be carefully tailored to alleviate or lessen its potential to harm Europe’s economic performance.