Toyoaki Washida

Bio: Toyoaki Washida is an academic researcher from Sophia University. The author has contributed to research in topics: Climate change & Global warming. The author has an hindex of 5, co-authored 14 publications receiving 201 citations. Previous affiliations of Toyoaki Washida include Kobe University & Toyohashi Sozo College.

Weighting across safeguard subjects for LCIA through the application of conjoint analysis

Abstract: Background Many types of weighting methods, which have integrated the various environmental impacts that are used for life-cycle impact assessment (LCIA), were proposed with the aim of developing the methodology as a useful information resource for decision making, such as in the selection of products. Economic valuation indexes, in particular, have attracted attention, as their assessment results are easy to understand and can be applied in conjunction with other assessment tools, including life-cycle costing (LCC) and environmental accounting. Conjoint analysis has been widely used in market research, and has recently been applied to research in environmental economics. The method enables us to provide two types of assessment results; an economic valuation and a dimensionless index. This method is therefore expected to contribute greatly to increasing the level of research into weighting methodology, in which an international consensus has yet to be established. Conjoint analysis, however, has not previously been applied to LCIA.

Assessment of mitigation strategies as tools for risk management under future uncertainties: a multi-model approach

Abstract: Although the world understands the possible threat of the future of climate changes, there remain serious barriers to be resolved in terms of policy decisions. The scientific and the societal uncertainties in the climate change policies must be the large part of this barrier. Following the Paris Agreement, the world comes to the next stage to decide the next actions. Without a view of risk management, any decision will be "based on neglecting alternatives" behavior. The Ministry of the Environment, Japan has established an inter-disciplinary research project, called Integrated Climate Assessment-Risks, Uncertainties, and Society (ICA-RUS) conducted by Dr. Seita Emori, National Institute for Environmental Studies. ICA-RUS consists of five research themes, i.e., (1) synthesis of global climate risks, (2) optimization of land, water, and ecosystem for climate risks, (3) analysis of critical climate risks, (4) evaluation of climate risk management options, and (5) interactions between scientific and social rationalities. We participated in the fourth theme to provide the quantitative assessment of technology options and policy measures by integrating assessment model simulations. We employ the multi-model approach to deal with the complex relationships among various fields such as technology, economics, and land use changes. Four different types of integrated assessment models, i.e., MARIA-14 (Mori), EMEDA (Washida), GRAPE (Kurosawa), and AIM (Masui), participate in the fourth research theme. These models contribute to the ICA-RUS by providing two information categories. First, these models provide common simulation results based on shared socioeconomic pathway scenarios and the shared climate policy cases given by the first theme of ICA-RUS to see the ranges of the evaluation. Second, each model also provides model-specific outcomes to answer special topics, e.g., geoengineering, sectoral trade, adaptation, and decision making under uncertainties. The purpose of this paper is to describe the outline and the main outcomes of the multi-model inter-comparison among the four models with a focus upon the first and to present the main outcomes. Furthermore, in this study, we introduce a statistical meta-analysis of the multi-model simulation results to see whether the differently structured models provide the inter-consistent findings. The major findings of our activities are as follows: First, in the stringent climate target, the regional economic losses among models tend to diverge, whereas global total economic loss does not. Second, both carbon capture and storage (CCS) as well as BECCS are essential for providing the feasibility of stringent climate targets even if the deployment potential varies among models. Third, the models show small changes in the crop production in world total, whereas large differences appear between regions. Fourth, the statistical meta-analysis of the multi-model simulation results suggests that the models would have an implicit but common relationship between gross domestic product losses and mitigation options even if their structures and simulation results are different. Since this study is no more than a preliminary exercise of the statistical meta-analysis, it is expected that more sophisticated methods such as data mining or machine learning could be applicable to the simulation database to extract the implicit information behind the models.

Computable general equilibrium analyses of global economic impacts and adaptation for climate change: the case of tropical cyclones

Abstract: Computable general equilibrium models have been widely used for simulating global warming and evaluating economic damages caused by climate change However, to date little research has focused on the economic consequences incurred across several industry sectors at a global level This article uses the evaluation model for environmental damage and adaptation (EMEDA) to simulate direct economic damages caused by tropical cyclones and losses that are offset through growth in other sectors to measure the global economic impacts arising from climate change Simulated results by EMEDA indicate that: 1) several regions experience economic growth, with four regions offsetting economic damages in the primary industry sector whilst the other regions increase their damages; 2) seven regions show economic growth whilst only North America neutralises damage in their secondary sectors, with the other regions revealing more severe losses; 3) several regions are able to offset their tertiary sector losses yet the other regions show an increase in damages; 4) the equivalent variation in all regions except East Asia decreases as temperature increases

Assessing the environmental impacts of freshwater consumption in LCA

Abstract: A method for assessing the environmental impacts of freshwater consumption was developed. This method considers damages to three areas of protection: human health, ecosystem quality, and resources. The method can be used within most existing life-cycle impact assessment (LCIA) methods. The relative importance of water consumption was analyzed by integrating the method into the Eco-indicator-99 LCIA method. The relative impact of water consumption in LCIA was analyzed with a case study on worldwide cotton production. The importance of regionalized characterization factors for water use was also examined in the case study. In arid regions, water consumption may dominate the aggregated life-cycle impacts of cotton-textile production. Therefore, the consideration of water consumption is crucial in life-cycle assessment (LCA) studies that include water-intensive products, such as agricultural goods. A regionalized assessment is necessary, since the impacts of water use vary greatly as a function of location. T...

Identifying best existing practice for characterization modeling in life cycle impact assessment

Abstract: Life cycle impact assessment (LCIA) is a field of active development. The last decade has seen prolific publication of new impact assessment methods covering many different impact categories and providing characterization factors that often deviate from each other for the same substance and impact. The LCA standard ISO 14044 is rather general and unspecific in its requirements and offers little help to the LCA practitioner who needs to make a choice. With the aim to identify the best among existing characterization models and provide recommendations to the LCA practitioner, a study was performed for the Joint Research Centre of the European Commission (JRC). Existing LCIA methods were collected and their individual characterization models identified at both midpoint and endpoint levels and supplemented with other environmental models of potential use for LCIA. No new developments of characterization models or factors were done in the project. From a total of 156 models, 91 were short listed as possible candidates for a recommendation within their impact category. Criteria were developed for analyzing the models within each impact category. The criteria addressed both scientific qualities and stakeholder acceptance. The criteria were reviewed by external experts and stakeholders and applied in a comprehensive analysis of the short-listed characterization models (the total number of criteria varied between 35 and 50 per impact category). For each impact category, the analysis concluded with identification of the best among the existing characterization models. If the identified model was of sufficient quality, it was recommended by the JRC. Analysis and recommendation process involved hearing of both scientific experts and stakeholders. Recommendations were developed for 14 impact categories at midpoint level, and among these recommendations, three were classified as “satisfactory” while ten were “in need of some improvements” and one was so weak that it has “to be applied with caution.” For some of the impact categories, the classification of the recommended model varied with the type of substance. At endpoint level, recommendations were only found relevant for three impact categories. For the rest, the quality of the existing methods was too weak, and the methods that came out best in the analysis were classified as “interim,” i.e., not recommended by the JRC but suitable to provide an initial basis for further development. The level of characterization modeling at midpoint level has improved considerably over the last decade and now also considers important aspects like geographical differentiation and combination of midpoint and endpoint characterization, although the latter is in clear need for further development. With the realization of the potential importance of geographical differentiation comes the need for characterization models that are able to produce characterization factors that are representative for different continents and still support aggregation of impact scores over the whole life cycle. For the impact categories human toxicity and ecotoxicity, we are now able to recommend a model, but the number of chemical substances in common use is so high that there is a need to address the substance data shortage and calculate characterization factors for many new substances. Another unresolved issue is the need for quantitative information about the uncertainties that accompany the characterization factors. This is still only adequately addressed for one or two impact categories at midpoint, and this should be a focus point in future research. The dynamic character of LCIA research means that what is best practice will change quickly in time. The characterization methods presented in this paper represent what was best practice in 2008–2009.

Valuing ecosystem services: Theory, practice, and the need for a transdisciplinary synthesis

Abstract: The concept of ecosystem services has shifted our paradigm of how nature matters to human societies. Instead of viewing the preservation of nature as something for which we have to sacrifice our well-being, we now perceive the environment as natural capital, one of society's important assets. But ecosystem services are becoming increasingly scarce. In order to stop this trend, the challenge is to provoke society to acknowledge the value of natural capital. Ecosystem services valuation (ESV) is the method to tackle such a challenge. ESV is the process of assessing the contributions of ecosystem services to sustainable scale, fair distribution, and efficient allocation. It is a tool that (1) provides for comparisons of natural capital to physical and human capital in regard to their contributions to human welfare; (2) monitors the quantity and quality of natural capital over time with respect to its contribution to human welfare; and (3) provides for evaluation of projects that will affect natural capital stocks. This review covers: (1) what has been done in ESV research in the last 50 years; (2) how it has been used in ecosystem management; and (3) prospects for the future. Our survey of the literature has shown that over time, there has been movement toward a more transdisciplinary approach to ESV research which is more consistent with the nature of the problems being addressed. On the other hand, the contribution of ESV to ecosystem management has not been as significant as hoped nor as clearly defined. Conclusions drawn from the review are as follows: first, ESV researchers will have to transcend disciplinary boundaries and synthesize tools, skills, and methodologies from various disciplines; second, ESV research has to become more problem-driven rather than tool-driven because ultimately the success of ESV will be judged on how well it facilitates real-world decision making and the conservation of natural capital.

Tackling Climate Change with Machine Learning

Abstract: Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.