論文

Earth’s atmospheric CO level has increased beyond 400 ppm and still continuing to rise. In fact, this is the highest level in the last 2 million years. It appears we are heading towards an overshoot of CO concentration before stabilizing green house gases (GHGs) to keep global warming below dangerous level. In Paris on December 2015, the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) agreed to strengthen the global response to the threat of climate change by keeping a global temperature rise this century well below 2° celsius above preindustrial levels and to pursue efforts to limit the temperature increase even further to 1.5° celsius. Can this level of CO stabilization still be achieved? One core strategies in the mitigation mix are negative emissions technologies (NETs) which are also explicitly described as important options in many IPCC (AR5) CO emission scenarios. Among others, BioEnergy with Carbon Capture and Storage (BECCS) are shown their potential for CO removable from the atmosphere. BECCS as the NETs are most widely selected by integrated assessment models (IAMs) to meet the requirements of temperature limits of 2° and below. It is based on assumed carbon-neutral bioenergy combined with capture of CO produced by combustion and its subsequent storage in geological or ocean repositories. BECCS is a net transfer of CO from the atmosphere into geological layers. However, its credibility as a climate change mitigation option and their impacts to global and regional ecosystems are still unknown. To explore the ecosystem impacts of the large-scale deployment of negative emission land use scenarios, we review some papers that are analyzing global impacts of the land use scenarios on various factors of ecosystem services to look at the sustainability limits to their global large scale deployments. Ecological services implications of the NETs especially land demanding BECCS land use scenarios need to be addressed before implementing such projects not only for achieving global low carbon target but also achieving more comprehensive ecological sustainabilities to meet the sustainable development goals (SDGs). 2 2 2 2 2 2 2

Future changes in the climate system could have significant impacts on the natural environment and human activities, which in turn affect changes in the climate system. In the interaction between natural and human systems under climate change conditions, land use is one of the elements that play an essential role. On the one hand, future climate change will affect the availability of water and food, which may impact land-use change. On the other hand, human-induced land-use change can affect the climate system through biogeophysical and biogeochemical effects. To investigate these interrelationships, we developed MIROC-INTEG-LAND (MIROC INTEGrated LAND surface model version 1), an integrated model that combines the land surface component of global climate model MIROC (Model for Interdisciplinary Research on Climate) with water resources, crop production, land ecosystem, and land-use models. The most significant feature of MIROC-INTEG-LAND is that the land surface model that describes the processes of the energy and water balance, human water management, and crop growth incorporates a land use decision-making model based on economic activities. In MIROC-INTEG-LAND, spatially detailed information regarding water resources and crop yields is reflected in the prediction of future land-use change, which cannot be considered in the conventional integrated assessment models. In this paper, we introduce the details and interconnections of the submodels of MIROC-INTEG-LAND, compare historical simulations with observations, and identify various interactions between the submodels. By evaluating the historical simulation, we have confirmed that the model reproduces the observed states well. The future simulations indicate that changes in climate have significant impacts on crop yields, land use, and irrigation water demand. The newly developed MIROC-INTEG-LAND could be combined with atmospheric and ocean models to develop an integrated earth system model to simulate the interactions among coupled natural-human earth system components.

This chapter investigates a model to integrate performance measure and urban design in the context of a high-density urban environment. The objective of this research is to discuss how performance-based modeling can be used for addressing problems of energy self-sufficiency and thermal comfort in a vertical and complex urban environment, in which increasing density may cause climate and ecological consequences. An urban neighborhood in Misono of Tokyo’s metropolitan region is studied to test the relationship between vertical urban form and energy performance. Benchmarking the performance behind urban form and neighborhood typologies leads to criteria of high performance and low carbon emission. The chapter discusses the policy implications of performance-based urban design and development in the context of the Misono Smart City Project, a site for Tokyo’s 2020 Olympics, and proposes a transformative approach to generate better performance for an energy resilient and sustainable urban future.

Consensus on carbon accounting approaches at city-level is lacking and analytic frameworks to systematically link carbon mitigation with the Sustainable Development Goals are limited. A new accounting approach anchored upon key physical provisioning systems can help to address these knowledge gaps and facilitate urban transitions.

Objectives: To evaluate a predictive model for robust estimation of daily out-of-hospital cardiac arrest (OHCA) incidence using a suite of machine learning (ML) approaches and high-resolution meteorological and chronological data. Methods: In this population-based study, we combined an OHCA nationwide registry and high-resolution meteorological and chronological datasets from Japan. We developed a model to predict daily OHCA incidence with a training dataset for 2005-2013 using the eXtreme Gradient Boosting algorithm. A dataset for 2014-2015 was used to test the predictive model. The main outcome was the accuracy of the predictive model for the number of daily OHCA events, based on mean absolute error (MAE) and mean absolute percentage error (MAPE). In general, a model with MAPE less than 10% is considered highly accurate. Results: Among the 1 299 784 OHCA cases, 661 052 OHCA cases of cardiac origin (525 374 cases in the training dataset on which fourfold cross-validation was performed and 135 678 cases in the testing dataset) were included in the analysis. Compared with the ML models using meteorological or chronological variables alone, the ML model with combined meteorological and chronological variables had the highest predictive accuracy in the training (MAE 1.314 and MAPE 7.007%) and testing datasets (MAE 1.547 and MAPE 7.788%). Sunday, Monday, holiday, winter, low ambient temperature and large interday or intraday temperature difference were more strongly associated with OHCA incidence than other the meteorological and chronological variables. Conclusions: A ML predictive model using comprehensive daily meteorological and chronological data allows for highly precise estimates of OHCA incidence.

The global population is concentrating unprecedentedly into urban areas, raising concerns on global sustainability and human well-being. There also exists a niche trend of migration from urban to rural areas particularly in countries with post-industrial economies. This paper investigated values of migration expressed by the migrants arrived in Hokuto City, a Japanese rural municipality experiencing pervasive population decline but is also a popular destination for migrants from urban areas. Statistical analyses of 868 responses to a Hokuto City's migrant survey between April 2017 and January 2019 identified their common values of migration, i.e., nature, housing and food. In addition to these common values, households with different demographic characteristics had different priorities: employment for singles in ages between 16 and 29; favorable environment for raising children for married couples in their 30s and 40s; not specific or ‘lifestyle’ for migrants in 50s; and staying with or near family for retirees over 60 years. Knowledge of heterogeneity in migrants and of their values, as described herein, will enable targeted policies and public services concerning migration. Widespread acceptance of teleworking after experiencing the COVID-19 pandemic might offer people of working age, particularly in their 20s and younger, a wider range of options of places to work and live, and thus is likely to influence future urban-rural population flow. A more detailed analysis of the region's natural attributes which are central to the values of migration to rural areas, such as Hokuto City, will be useful to inform regional land use planning that is salient to the values of migrants.