Due to its ability to recover both material and energy from organic waste, biogas technology is considered one of the best technology for treating organic waste. While in many emerging Asian countries more than 50% of municipal waste is organic waste, the amount of organic waste treated with biogas technology remains very limited. This study identified key challenges faced by practitioners in sustaining biogas plants from literature and interviewed a number of sustainably operating biogas plant managers and, based on the findings, developed an implementation framework to help decision makers and practitioners in planning a sustainable municipal organic waste biogas plant facility.

In pursuing Japan’s target of net zero greenhouse gas (GHG) emissions by 2050, decarbonization strategies at the regional level have been taking place nationally. Some successes have been achieved in the residential sector in achieving decarbonization at the regional level due to improvements in the advancement of energy-saving technologies. An important prerequisite to achieving further decarbonization in the residential sector is understanding household electricity consumption of power demand objects. This study constructed a method for predicting residential electricity consumption in a case study region. First, we set up six models of household composition for scenario exercises. Then, we used the residential energy estimation based on daily activities (REEDA) method to calculate the hourly electricity consumption of each household composition in the four seasons based on the duration of daily life activity. Finally, we separately explore the impact of housing performance (insulation, airtightness), air-conditioning patterns (intermittent operation method in a habitable room/continuous operation method in a habitable room/continuous operation method in all rooms), and geographical location on residential air conditional electricity consumption. The output is a regional residential energy estimate method that can consider multiple key variables. We verified the developed model by (1) comparing the estimated output with the Japan Energy Database and (2) testing the method for various residential areas in Japan. The results showed an accuracy level greater than 75% and nationwide applicability.

Japan has set a decarbonisation goal in the Plan for Global Warming Countermeasures, which aims to reduce greenhouse gas emissions by 46% compared with those in 2013 by 2030. To achieve this goal, the Japanese government established a regional decarbonisation roadmap in 2021. In pursuing the roadmap, 74 municipalities were appointed as Japan's decarbonisation‐leading regions . In this study, we make the projection to clarify whether the goal will be met by performing the following steps. First, we classified the regions on the basis of their specific energy structure and examined their decarbonisation policies. Next, we performed a cluster analysis of their energy structure and energy‐saving methods in the civil sector, residential sector, sectors outside the civil sector and transportation sector. The results of the analysis indicate that in terms of energy‐use characteristics, the proportion of the municipalities selected is representative of Japan. However, we find that if the carbon neutrality plans of these regions are implemented and extended nationwide, the 2030 goal will not be met. Moreover, we found that within the civil sector, regions are more inclined to achieve carbon neutrality in business and public facility areas than in residential areas. This study urges Japan to be more aggressive in its global warming mitigation strategies and identifies residential areas as the most promising sector to put more effort into.

Green power from renewable resources is essential for low-carbon development, but its life-cycle management is constrained by fragmented data, complex monitoring tasks, traceability requirements, and greenwashing risks. This paper develops a conceptual framework for applying large language models (LLMs) to green power life-cycle accounting and monitoring. Unlike task-specific AI models, the framework positions LLMs as a cross-stage coordination layer that integrates multi-source data management, digital twins, multimodal fusion, edge-cloud deployment, and carbon-footprint tracking. It shows that LLMs can support resource assessment, forecasting, equipment diagnosis, green power traceability, market optimisation, and carbon accounting through a closed loop of perception, modelling, analysis, decision-making, and evolution. It also identifies challenges related to data governance, interpretability, computing emissions, and regulatory accountability, and proposes policy recommendations for data sharing, power-domain models, green computing, and AI-enabled carbon-accounting standards.