Advanced LCA Research Group

５Researchers:
　Naoya Kojima , Sana Mikami, Kyoko Ono (concurrent), Keiichiro Sakurai (concurrent)

８Contract Employees

Outline

We are conducting risk assessments to evaluate the potential issues posed by new technologies, with the aim of providing evidence to support better decision-making for building a sustainable society. Specifically, we are conducting a risk trade-off analysis considering the product life cycle of chemicals, as well as risk assessments related to disasters and accidents. Additionally, we are developing evaluation methods to analyze socio-economic impacts, spillover effects, and social acceptability. Our research covers a wide range of topics, including carbon-neutral and energy-related technologies, nitrogen cycle technologies, and the introduction of marine biodegradable plastics. Furthermore, we are advancing research on mathematical methods for supplementing data gaps in risk assessment, as well as statistical methods that form the foundation of many industries.

Research Highlights

Risk Management of Plastic for the Circular Economy

To accelerate the transition to a circular economy for plastic, the health impacts of additives and contaminants specific to recycled plastic must be assessed. Towards the final goal of proposing a management strategy, we aim to understand the entire life cycle of plastic by conducting exposure assessments and risk evaluation of its additives and contaminants.

Risk assessment framework for recycled plastics

Risk Assessment/Management for Ammonia Fuel

We conduct risk assessments for the leakage of ammonia fuel, the use and storage of which is expected to increase. Notably, we applied the Bayesian theorem to estimate the leak frequency for ammonia-fueled vessels based on data from onshore ammonia handling facilities extracted from the Japanese accident database.

Overview of leak frequency estimation for ammonia-fueled vessels

Data Imputation Methods for Risk Assessment

Risk assessment of chemical substances is becoming more difficult as the number of substances increases and regulations on animal testing tighten. Limited time, cost, and access to experimental data make risk assessment challenging. To address this, our research develops data-imputation methods based on statistical modeling and machine learning. To improve data-imputation methods, we explore mathematical approaches, including discrete optimization, topology, and graph theory, to extract multifaceted features of chemical substances. We are also actively involved in ISO/TC 69, where we contribute to international standards on statistical terminology, data interpretation, and methods for evaluating measurement accuracy and precision. Through these efforts, we aim to improve the efficiency and rationality of chemical risk assessment.

Examples of multifaced features of chemical substances

Finding Pathways for the Accelerated Deployment of Decarbonization Technologies

To achieve a sustainable society, it is not enough to just develop new technologies such as energy-efficient buildings, renewables, and electric vehicles. Strategies for smooth deployment are also required. We research these strategies to maximize the societal benefits of new technologies.

A proposed scenario for global deployment of photovoltaics (PV) to achieve 75TW of cumulative capacity by 2050. Historical and projected installation data suggest this scenario is achievable.

(N.M. Haegel et al., Science 380, Issue 6640, pp. 39-42, 6 Apr 2023)