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Internet-of-Things (IoT) technologies have been steadily adopted and embedded into energy infrastructure following the rapid transformation of energy grids through distributed consumption, renewables generation, and battery storage. The data streams produced by such energy IoT infrastructure can be extracted, processed, analyzed, and synthesized for informed decision-making that delivers optimized grid operations, reduced costs, and net-zero carbon emissions. However, the voluminous nature of such data streams leads to an equally large number of analysis outcomes that have proven ineffective in decision-making by energy grid operators. This gap can be addressed by introducing artificial intelligence (AI) chatbots, or more formally conversational agents, to proactively assist human operators in analyzing and identifying decision opportunities in energy grids. In this research, we draw upon the recent success of generative AI for optimized AI chatbots with natural language understanding and generation capabilities for the complex information needs of energy IoT infrastructure and net-zero emissions. The proposed approach for optimized generative AI chatbots is composed of six core modules: Intent Classifier, Knowledge Extractor, Database Retriever, Cached Hierarchical Vector Storage, Secure Prompting, and Conversational Interface with Language Generator. We empirically evaluate the proposed approach and the optimized generative AI chatbot in the real-world setting of an energy IoT infrastructure deployed at a large, multi-campus tertiary education institution. The results of these experiments confirm the contribution of generative AI chatbots in simplifying the complexity of energy IoT infrastructure for optimized grid operations and net-zero carbon emissions. [ABSTRACT FROM AUTHOR]

This research aims to find the best carbon tax regime for the achievement of net-zero carbon emissions and promotion of social welfare level. We discuss two regimes in this study, including carbon tax on total social welfare level (CTTW) and carbon tax on single social welfare level (CTSW). Findings show that the best regime depends on product substitution and product price elasticity of demand. Industrial transformation not only causes changes in product substitution and product price elasticity of demand, but also makes these two regimes have different effects on the achievement of net-zero carbon emissions and promotion of social welfare level. [ABSTRACT FROM AUTHOR]

This study uses the tree stumps of the three representative trees in Taiwan (Leucaena leucocephala, Syzygium samarangense, and Ziziphus jujuba) as the material source and recyclable oyster shell powder as an activator. A carbonization process for upgrading and recycling the tree stumps was developed with our homemade, digital-controlled, energy-saving carbonization system. First, the tree stumps are carbonized at a medium temperature of 500 °C and then heated to 900 °C for high-temperature carbonization, followed by the activation procedure as required. With our method, we can produce biochar with a high proportion of fixed carbon and a high proportion of meso- and macropores while maximizing the yield of wood vinegar. The specific surface area of the meso- and macropores can reach up to 70 m2/g or more. The effect of different activation materials on the pore characteristics and specific surface area of biochar was carefully examined. It was found that both KOH and oyster shell powder is the ideal activator for producing biochar with a high proportion of meso- and macropores. The FTIR spectrum, CEC, and contents of the ordinary elements and heavy metals of the biochar were also reported. It is clear from the FTIR data that the absorption peaks of the overall spectrum of the three types of biochar after carbonization at high temperature are cleaner than those of biochar carbonized at low temperature. This research can promote the recycling of agricultural residues, enhance soil carbon sequestration, preserve fertilizers, and suppress diseases and pests, moving towards approaching the goal of net-zero carbon emissions. [ABSTRACT FROM AUTHOR]

Extensive studies have examined the financial performance of green buildings in recent years. The results have frequently observed that the premium of green buildings is time-varying and dependent on the study period and markets being examined. Further, virtually no dedicated study has been devoted to examine the role of mandatory building energy rating disclosure policies on green building price premium. This raises the question of whether the mandatory energy rating disclosure policies would have an influence on the financial performance of green buildings. This study assesses the premium of green buildings by considering the role of mandatory energy efficiency of commercial building disclosure program (CBDP) using the MCSI/IPD NABERS data over 2005–2020. The results of the study showed that, in Australia, buildings with NABERS rating of 4 stars and above delivered a higher total return compared with buildings with lower NABERS ratings. This also supports the Freeman's (1984) social impact hypothesis in which favorable social performance will ultimately lead to favorable financial performance. In addition, our empirical modelling results also demonstrated the premium of green buildings is stronger since the launch of CBDP, reflecting the importance of mandatory building efficiency disclosure. The policy implications of our studies have also been discussed as buildings play a crucial role in achieving the United Nations Sustainable Development Goals (SDGs), particularly net-zero carbon emissions. [ABSTRACT FROM AUTHOR]