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While global attention to zero-energy building (ZEB) has surged as a sustainable countermeasure to high-energy consumption, a congruent expansion in research remains conspicuously absent. Addressing this lacuna, our study harnesses public research and development grant data to decipher evolving trajectories within ZEB research. Distinctively departing from conventional methodologies, we employ state-of-the-art natural language processing (NLP) artificial intelligence models to meticulously analyze grant textual content pertinent to ZEB. Our findings illuminate an expansive spectrum of ZEB-related research, with a pronounced focus on the holistic continuum of energy supply, demand, distribution, and actualization within architectural confines. Theoretically, this work delineates key avenues ripe for future empirical exploration, fostering a robust academic foundation for subsequent ZEB inquiries. Practically, the insights derived bear significant implications for practitioners, informing optimal implementation strategies, and offering policymakers coherent roadmaps for sustainable urban development. Collectively, this study affords a panoramic perspective on contemporary ZEB research contours, enhancing both scholarly comprehension and practical enactment in this pivotal domain.

Biogas is one of the most attractive renewable resources due to its ability to convert waste into energy. Biogas is produced during an anaerobic digestion process from different organic waste resources with a combination of mainly CH4 (~50 mol/mol), CO2 (~15 mol/mol), and some trace gasses. The percentage of these trace gases is related to operating conditions and feedstocks. Due to the impurities of the trace gases, raw biogas has to be cleaned before use for many applications. Therefore, the cleaning, upgrading, and utilization of biogas has become an important topic that has been widely studied in recent years. In this review, raw biogas components are investigated in relation to feedstock resources. Then, using recent developments, it describes the cleaning methods that have been used to eliminate unwanted components in biogas. Additionally, the upgrading processes are systematically reviewed according to their technology, recovery range, and state of the art methods in this area, regarding obtaining biomethane from biogas. Furthermore, these upgrading methods have been comprehensively reviewed and compared with each other in terms of electricity consumption and methane losses. This comparison revealed that amine scrubbing is one the most promising methods in terms of methane losses and the energy demand of the system. In the section on biogas utilization, raw biogas and biomethane have been assessed with recently available data from the literature according to their usage areas and methods. It seems that biogas can be used as a biofuel to produce energy via CHP and fuel cells with high efficiency. Moreover, it is able to be utilized in an internal combustion engine which reduces exhaust emissions by using biofuels. Lastly, chemical production such as biomethanol, bioethanol, and higher alcohols are in the development stage for utilization of biogas and are discussed in depth. This review reveals that most biogas utilization approaches are in their early stages. The gaps that require further investigations in the field have been identified and highlighted for future research.

Spent coffee grounds (SCG) are industrial biowaste resulting from the coffee-brewing process, and they are often underutilized and end up in landfills, thereby leading to the emission of toxic gases and environmental damage. Hydrothermal carbonization (HTC) is an attractive approach to valorize wet biomass such as SCG to valuable bioproducts (i.e., hydrochar). Thus, in this work, the HTC of SCG was carried out in a 500 L stainless steel vessel at 150, 170, 190, 210, and 230 °C for 30 min, 60 min, 90 min, and 120 min and a feedstock to water weight ratio of 1:5, 1:10, and 1:15, and the use of the resulting hydrochar as a solid fuel was evaluated. The results showed that a high energy recovery (83.93%) and HHV (23.54 MJ/kg) of hydrochar was obtained at moderate conditions (150 °C, 30 min, and feedstock to water weight ratio of 1:5) when compared with conventional approaches such as torrefaction. Following this, the surface morphology, functionality, and combustion behavior of this hydrochar were characterized by SEM, FTIR, and TGA, respectively. In short, it can be concluded that HTC is an effective approach for producing solid fuel from SCG and the resulting hydrochar has the potential to be applied either in domestic heating or large-scale co-firing plants.

In 2009, the ETC Group estimated that some 70% of the food that people globally consume originates in the ‘peasant food web’. This figure has been both embraced and critiqued, and more recent critiques have focussed on analysing farm productivity to offer some more precise estimates. Several analyses suggest that the proportion of small farms’ contributions to total food production is closer to one-third, arguing that the role of small food producers in food security are grossly exaggerated. We challenge this argument by re-tabulating the available farm productivity data to demonstrate that smaller farms continue to provide a significant proportion of food and are consistently more productive than their larger counterparts. We further posit that even our own interpretation falls short of estimating the full extent of small farms’ contributions, including non-monetary ones, like ecosystem services and community life, many of which run counter to the productivist model that drives large-scale industrial agriculture. We conclude that policies that support small farms are a global necessity for food security, as well as for transitions to more sustainable and more equitable food systems.

The transformation of the electricity market structure from a monopoly model to a competitive market has caused electricity to be exchanged like a commercial commodity in the electricity market. The electricity price participants should forecast the price in different horizons to make an optimal offer as a buyer or a seller. Therefore, accurate electricity price prediction is very important for market participants. This paper investigates the monthly/seasonal data clustering impact on price forecasting. To this end, after clustering the data, the effective parameters in the electricity price forecasting problem are selected using a grey correlation analysis method and the parameters with a low degree of correlation are removed. At the end, the long short-term memory neural network has been implemented to predict the electricity price for the next day. The proposed method is implemented on Ontario—Canada data and the prediction results are compared in three modes, including non-clustering, seasonal, and monthly clustering. The studies show that the prediction error in the monthly clustering mode has decreased compared to the non-clustering and seasonal clustering modes in two different values of the correlation coefficient, 0.5 and 0.6.

This paper proposes a novel peer-to-peer (P2P) decentralized energy market consisting of retailers and prosumers considering integrated demand response (IDR). Retailers can trade electrical energy and gas with prosumers in a P2P way to maximize their welfare. Since they are equipped with electrical storage and power self-generation units, they can benefit from selling power not only to the upstream network but also to prosumers. In peer-to-peer transactions, the prosumers purchase electricity as well as gas from retailers. Because of their access to the competitive retail market, including some retailers, they enjoy more freedom to reduce their energy supply cost. In addition, the prosumers are equipped with an energy hub consisting of combined heat and power (CHP) units and electric pumps, allowing them to change their energy supply according to price fluctuations. Furthermore, they have some changeable electrical and thermal load enabling them to change their load if needed. To clear the proposed P2P decentralized market, a fully decentralized approach called the fully decentralized alternating direction method of multipliers (ADMM) is applied. This method does not require a supervisory entity and, thus, preserves the players’ private information. The numerical studies performed on a system with two retailers and multiple prosumers demonstrate the feasibility and effectiveness of the proposed decentralized market. The results also show that the proposed decentralized algorithm achieves the optimal global solution, compared with the centralized approach.

This article is motivated by a conundrum: How can shale gas development be encouraged and managed without complete knowledge of the associated risks? To answer this question, we used back propagation (BP) neural networks and expert scoring to quantify the relative risks of shale gas development across 12 provinces in China. The results show that the model performs well with high predictive accuracy. Shale gas development risks in the provinces of Sichuan, Chongqing, Shaanxi, Hubei, and Jiangsu are relatively high (0.4~0.6), while risks in the provinces of Xinjiang, Guizhou, Yunnan, Anhui, Hunan, Inner Mongolia, and Shanxi are even higher (0.6~1). We make several recommendations based on our findings. First, the Chinese government should promote shale gas development in Sichuan, Chongqing, Shaanxi, Hubei, and Jiangsu Provinces, while considering environmental, health, and safety risks by using demonstration zones to test new technologies and tailor China’s regulatory structures to each province. Second, China’s extremely complex geological conditions and resource depths prevent direct application of North American technologies and techniques. We recommend using a risk analysis prioritization method, such as BP neural networks, so that policymakers can quantify the relative risks posed by shale gas development to optimize the allocation of resources, technology and infrastructure development to minimize resource, economic, technical, and environmental risks. Third, other shale gas industry developments emphasize the challenges of including the many parties with different, often conflicting expectations. Government and enterprises must collaboratively collect and share information, develop risk assessments, and consider risk management alternatives to support science-based decision-making with the diverse parties.

The field crop industry in Canada is a source of both significant economic benefits and environmental impacts. Environmental impacts include land and energy use, as well as greenhouse gas (GHG) and other emissions. Impacts also accrue upstream of the field in the product supply chain, from the production of such inputs as fertilizers and pesticides. There are currently two types of environmental life cycle assessment (LCA)—attributional LCA (ALCA) and consequential LCA (CLCA)—that may be used to study the life cycle impacts of products such as field crops. ALCA is a retrospective methodology that presents a snapshot of average, “status quo” conditions. CLCA is a prospective methodology that presents the potential implications of changes in a product system, including any associated market-mediated changes in supply or demand in other product systems. Thus, CLCAs can be used to assess large-scale changes in the field crop industry, including its relationship to other sectors and processes, such as the production of biofuel or of food for both human and animal consumption. The aim of this paper is to review and curate the knowledge derived through published CLCA studies that assessed the impacts of changes to field crop production systems on the life cycle resource use and emissions associated with the agricultural products, with a focus on their relevance to temperate climate conditions. The current study also highlights how previous studies, including ALCAs and farm management recommendations, can be used to inform the changes that should be studied using CLCA. The main challenges to conducting CLCAs include identifying the system boundaries, marginal products and processes that would be impacted by changes to field crop production. Marginal markets and product systems to include can be determined using economic equilibrium models, or information from local experts and industry reports. In order to conduct ISO-compliant CLCAs, it is necessary to include multiple relevant environmental impact categories, and to perform robust data quality and uncertainty analyses.