To promote Bio-Energy with Carbon dioxide Capture and Storage (BECCS), which aims to replace fossil fuels with bio energy and store carbon underground, and Reducing Emissions from Deforestation and forest Degradation (REDD+), which aims to reduce the carbon emissions produced by forest degradation, it is important to build forest management plans based on the scientific prediction of forest dynamics. For Measurement, Reporting and Verification (MRV) at an individual tree level, it is expected that techniques will be developed to support forest management via the effective monitoring of changes to individual trees. In this study, an end-to-end process was developed: (1) detecting individual trees from Unmanned Aerial Vehicle (UAV) derived digital images; (2) estimating the stand structure from crown images; (3) visualizing future carbon dynamics using a forest ecosystem process model. This process could detect 93.4% of individual trees, successfully classified two species using Convolutional Neural Network (CNN) with 83.6% accuracy and evaluated future ecosystem carbon dynamics and the source-sink balance using individual based model FORMIND. Further ideas for improving the sub-process of the end to end process were discussed. This process is expected to contribute to activities concerned with carbon management such as designing smart utilization for biomass resources and projecting scenarios for the sustainable use of ecosystem services.

Sharing successful practices with other stakeholders is important for achieving SDGs. In this study, with a deep-learning natural language processing model, bidirectional encoder representations from transformers (BERT), the authors aimed to build (1) a classifier that enables semantic mapping of practices and issues in the SDGs context, (2) a visualizing method of SDGs nexus based on co-occurrence of goals (3) a matchmaking process between local issues and initiatives that may embody solutions. A data frame was built using documents published by official organizations and multi-labels corresponding to SDGs. A pretrained Japanese BERT model was fine-tuned on a multi-label text classification task, while nested cross-validation was conducted to optimize the hyperparameters and estimate cross-validation accuracy. A system was then developed to visualize the co-occurrence of SDGs and to couple the stakeholders by evaluating embedded vectors of local challenges and solutions. The paper concludes with a discussion of four future perspectives to improve the natural language processing system. This intelligent information system is expected to help stakeholders take action to achieve the sustainable development goals.

In sustainability science (SS), it is difficult to identify what needs to be solved, and it is also not clear how to solve the problems that are identified. There has been no consensus on the underlying question of “What is structuring knowledge in SS?” This paper focuses on knowledge structuring accompanied by supporting of thinking. It addresses the key challenges associated with knowledge structuring in SS, identifies the requirements for the structuring of knowledge, proposes a reference model, and develops an ontology-based mapping tool as a solution to one layer of the reference model. First, we identify the important requirements for SS knowledge structuring. Second, we develop a reference model composed of five layers based on three of the requirements. Third, we develop an ontology-based mapping tool at Layer 2 of the reference model for meeting the two major challenges for SS, namely, identifying what problems should be addressed in SS itself and proposing solutions for those problems. The tool is designed to store and retrieve information regarding SS, to provide access to a prototype ontology for SS, and to create multiple maps of conceptual chains depending on a user’s interests and perspectives. Finally, we assess whether the developed tool successfully realizes the targeted part of the reference model for SS by examining the tool’s conformity to the reference model, as well as its usability, effectiveness, and constraints. Although several issues were identified in the prototype ontology and the mapping tool, the study concluded that the mapping tool is useful enough to facilitate the function of Layer 2. In particular, the mapping tool can support thinking about SS from the viewpoint of: (a) finding new potentials and risks of technological countermeasures studied in SS; (b) helping users to get a more comprehensive picture of problems and their potential solutions; and (c) providing an effective opportunity to come up with new ideas that might not be thought of without such a tool.