Published papers

The concept of resilience is increasingly used in scientific and political discourses on sustainable urban development and urban disaster risk reduction. It has its roots in disciplines such as physics, psychology and ecology and is a relatively new concept in the field of urban planning. This chapter aims to explore the implications of integrating resilience thinking into urban planning. It introduces the concept of resilience-oriented urban planning and discusses how it is distinct from conventional urban planning. Extending the theory of adaptive cycle, it is argued that urban planning should not be considered as a static process. Urban systems are dynamic entities characterized by non-equilibrium dynamics and constantly go through the four phases of ‘exploitation’, ‘conservation’, ‘release’, and ‘reorganization’. Resilience-oriented planning is needed to address dynamics and complexities of urban systems. This chapter provides discussions on paradigm shifts that are needed to integrate resilience thinking into urban planning. These paradigm shifts are discussed in the context of different planning themes, namely, strategy making and visioning, public participation, equity and empowerment, learning from traditional local knowledge, institutional reforms, social networks, sectoral and spatio-temporal dynamics, land use planning, and urban infrastructure. The chapter concludes with some discussions on how these paradigm shifts contribute to integrating principles that underpin the concept of resilience into urban planning and design.

Resilience is widely recognized as a pre-requisite for achieving sustainability. Despite the wealth of research on urban resilience, no comprehensive work has been published on the association between urban form and resilience. Form of cities can affect their capacity to survive and thrive in the face of adverse events. Better understanding of the concept of ‘resilient urban form’ and ‘resilient urban form typologies’ is, therefore, essential for achieving more advances in urban resilience. It is essential to elucidate the meaning of ‘resilient urban form’ and explain what issues should be considered to achieve resilient urban morphologies. This chapter elaborates on the concept of ‘resilient urban form’ and introduces a conceptual framework that can be used for its assessment. The conceptual framework emphasizes paying attention to the following issues: ‘resilience of what?’, ‘resilience at what geographic and temporal scale?’, ‘resilience to what’, and ‘resilience for what?’. To answer the first question, various underlying components of urban form should be identified. The issue of ‘resilience at what geographic scale?’ implies that dynamics and interactions across various geographic scales need to be considered. Certain urban form components may be relevant to multiple scales and have different and even opposing affects across different scales. The question of ‘resilience at what temporal scale?’ indicates that, for example, certain urban forms may be desirable when the growth speed of the city is high, but detrimental after it reaches its growth limit. The question of ‘resilience to what?’ is important as certain forms may be favorable in terms of resilience to some hazards but undesirable with respect to others. Finally, elaboration on the question ‘resilience for what?’ helps better understand what specific resilience qualities can be improved through promoting favorable urban forms. The conceptual framework can be utilized to further elucidate the nature of relationships between different components of urban form and resilience of cities. It can also be used to assess resilience of different urban forms across different geographical and temporal scales.

We have assessed the risks associated with setting 1.5, 2.0, or 2.5 °C temperature goals and ways to manage them in a systematic manner and discussed their implications. The results suggest that, given the uncertainties in climate sensitivity, “net zero emissions of anthropogenic greenhouse gases in the second half of this century” is a more actionable goal for society than the 2 or 1.5 °C temperature goals themselves. If the climate sensitivity is proven to be relatively high and the temperature goals are not met even when the net zero emission goal is achieved, the options left are: (A) accepting/adapting to a warmer world, (B) boosting mitigation, and (C) climate geoengineering, or any combination of these. This decision should be made based on a deeper discussion of risks associated with each option. We also suggest the need to consider a wider range of policies: not only climate policies, but also broader “sustainability policies”, and to envisage more innovative solutions than what integrated assessment models can currently illustrate. Finally, based on a consideration of social aspects of risk decisions, we recommend the establishment of a panel of “intermediate layer” experts, who support decision-making by citizens as well as social and ethical thinking by policy makers.

The objective of this study is to map direct and indirect seasonal urban carbon emissions using spatial micro Big Data, regarding building and transportation energy-use activities in Sumida, Tokyo. Building emissions were estimated by considering the number of stories, composition of use (e.g., residence and retail), and other factors associated with individual buildings. Transportation emissions were estimated through dynamic transportation behaviour modelling, which was obtained using person-trip surveys. Spatial seasonal emissions were evaluated and visualized using three-dimensional (3D) mapping. The results suggest the usefulness of spatial micro Big Data for seasonal urban carbon emission mapping; a process which combines both the building and transportation sectors for the first time with 3D mapping, to detect emission hot spots and to support community-level carbon management in the future.

The quality of a supervised classification map depends on the quality of the ground reference data and the classification method used. However, training samples for agriculture landscapes are often mixed with noise. Therefore, the classification of agriculture regions using remotely sensed data requires the use of classification methods with good generalization capabilities. In this study, the performance of the subspace method in land cover classification of a complex cropping mix area is explored. Landsat-5 thematic mapper (TM) data were used to classify 12 different land cover classes in the study area, located between Tianjin and Tangshan cities in northern China. We compared the classification maps obtained using the subspace method with those obtained using the self-organizing map neural network (SOM) and maximum likelihood classification (MLC) methods. The results of this comparative study confirm that the subspace method performed better than both the SOM and MLC methods. Furthermore, a comparison of the sensitivity of these methods to the reduction in the training sample size shows that the subspace method has a lower sensitivity to variations in the number of training pixels used than the other two methods. Our results demonstrate the ability of the subspace method to distinguish between different crop types over a large area. Moreover, the subspace method is less sensitive to small training sample sizes than the other two methods.

Section 5.1 of this chapter provides a brief review of spatial econometrics. Section 5.2 describes various types of spatial econometric models. Impact measures are also explained here. Section 5.3 explains representative parameter estimation methods for spatial econometric models. Sections 5.4 and 5.5 introduce likelihood-based tests for spatial autocorrelation and spatial heterogeneity, respectively. Section 5.6 explains the remaining important models that were not explained in Section 5.2. Finally, Section 5.7 explores the methods that seem useful when we apply spatial econometric models to a large dataset.

This chapter introduces a twitter data application to a heatwave analysis. Section 8.1 introduces our discussion emphasizing on statistical modeling combining different types of spatial data. Section 8.2 introduces the twitter data and their processing. Section 8.3 explains our analysis method, and Section 8.4 applies this method to a heatwave analysis. Section 8.5 concludes our discussion.

This chapter introduces a spatial econometric model application to a downscaling of global socioeconometric scenarios. Section 10.1 provides a brief review of relevant literature. Section 10.2 describes our downscale approach and Section 10.3 employs it to estimate populations and GDPs by 0.5-degree grids. Finally, Section 10.4 concludes our discussion.

Climate resiliency is a key topic for cities across the world especially after the Paris Agreement. By combing socio-economic situations, carbon emission reduction, disaster risk management, and other factors determine sustainability of cities, we need to understand trade-offs among these factors. In other words, wise urban systems design are required in cities in the world. Especially in the developed countries like Japan which are expected to experience unprecedented pollution decrease, we need to achieve “wise shrink” of cities that are desirable from multiple viewpoints. With such a background, we introduce our spatially-explicit urban land-use model (SULM) as a tool to analyze the trade-offs among climate resilient sustainability. The SULM is applied to an analysis in the Tokyo metropolitan area to analyze the implications of Business-As-usual (BAU), Compact city, and Wise shrink land use scenarios. SULM could be a useful tool for assessing urban resiliency against climate extreme events for eco-urbanism oriented land forms.

Urban heatwave is increasingly severe as the global warming advances. Even worse, especially in Japan, an increasing proportion of residences are vulnerable against heats under the aging society. Today, heatwave monitoring is an emergent task toward climate adaptive urban development. Our final goal is developing a smart navigation system to monitor real-time and micro-scale heatwave risk in Tokyo. To achieve it, we performed the following observation experiments: collection people location data with age information and people sentiment data by twitter, ground surface temperature monitoring by airborne and from the Tokyo Sky Tree. A method to combine these multi-scale information is developed to estimate micro-scale spatiotemporal behavior on heatwave risks. Based on the result, it is discussed how we can achieve the real-time and micro-scale heatwave monitoring, and make Tokyo more heat risk mitigative.

Urban heatwave is increasingly severe as the global warming advances. Even worse, in Tokyo, an increasing proportion of residences are vulnerable against heats under the aging society. Today, heatwave monitoring is an emergent task toward climate adaptive urban development. Our final goal is developing a system to monitor real-time and micro-scale heatwave risk in Tokyo. To achieve it, we performed the following observation experiments: airborne monitoring, monitoring from the Tokyo Sky Tree, and micro-scale monitoring censoring inside and outside comforts. A method to combine these multi-scale information is developed to estimate micro-scale spatiotemporal behavior on heatwave risks. Based on the result, it is discussed how we can achieve the real-time and micro-scale heatwave monitoring, and make Tokyo more risk adaptive.

Finding economical and sustainable pathways for the deployment of renewables is critical for the success of decarbonizing energy systems. Because of the variable nature of renewable energy, however, the integration of renewables becomes increasingly costly and difficult as the renewable penetration becomes higher. The recent rise of electric vehicles (EVs) provides us with an opportunity to increase self-consumption of solar photovoltaic (PV) at households with substantially lower additional costs. In this paper, an environmental techno-economic assessment of residential PV systems is conducted with battery or EVs allowing charging and discharging (Vehicle to Home: V2H) in Kyoto, Japan, and Shenzhen, China, incorporating the cost projections of these technologies towards 2030. It is found that “PV + EV” (V2H) becomes highly cost-effective towards 2030 in Kyoto in comparison with “PV only”, “PV + battery”, and “EV charge”. In the region of lower electricity costs such as Shenzhen, switching to EVs from gasoline vehicles is the most cost-effective option during 2020s. Finally, it is found that “PV + EV” has the highest CO emission reduction potential across all the technology combinations considered, owing to EV charging of carbon-free electricity from PV. To facilitate a rapid household decarbonization, policy makers should reinforce policies to enhance the penetration of combined technologies of “PV + EV” (V2H) towards 2030. 2

Cities are responsible for a large share of global energy consumption and CO2 emissions. Significant reduction of urban energy consumption and CO2 emissions is essential for meeting the ambitious climate stabilization targets. This hinges on having adequate understanding of the patterns and trends of emissions. It is essential to create datasets of cities emissions and utilize mapping techniques to inform planners and decision makers about the dynamics of urban carbon emissions. Due to the openness of cities and issues related to availability and accessibility of urban energy consumption data, this is a challenging task. It is critical to develop consistent methods for mapping emissions. Such methods and frameworks should enable cities to map their emissions with minimum data requirements. They should also be applicable to different cities across the world. As a preliminary effort, this study introduces a framework for synthesizing building and transport energy consumption data with the Local Climate Zones (LCZs) classification system. Drawing on preliminary results from applying the framework to Bangkok, Shanghai, and Tokyo, we explain how this approach can provide opportunities for standardizing urban carbon accounting. The paper concludes with suggestions for improving granularity and accuracy of emissions accounting. The concept of Local Energy Zones (LEZs) is introduced and is suggested to be used as a potentially suitable concept for analysing CO2 emissions dynamics of cities.

The spatial and spectral heterogeneity of urban areas makes land cover classification a challenging process. In this study, we highlight the potential of combined multi-spectral Sentinel-2 and fully polarimetric PALSAR-2 data for land cover classification in dense urban areas, based on the Local Climate Zone (LCZ) scheme. We classified differently combined spectral and back-scattering characteristics using the subspace method in comparison with the Support Vector Machine (SVM) and Maximum Likelihood Classifier (MLC) methods. Results show that, (i) the overall accuracy (OA) was 65.9% for the Sentinel-2 data, (ii) higher OA (71.9%) was achieved by adding four intensity images of PALSAR-2 to Sentinel-2, (iii) the inclusion of decomposed components increased OA to 72.8%, and (iv) the highest OA (73.3%) was achieved using all features. These results suggest that the inclusion of different backscattering characteristics disproportionately improved classification accuracy from using multi-spectral data alone. The results of comparison between different methods show that the subspace method performed better than SVM and MLC, particularly when high-dimensional data were used. The subspace method classified particularly well for some specific LCZ classes which are easily mixed between each other. It provides a promising option for LCZ mapping.

This study attempts to simulate people flow before and after the flooding in the West Japan heavy rain on July 2018, using a mobile GPS (global positioning system) data. We focus on Mabi-Cho, Kurashiki-City, which is one of the most damaged area on the flood disaster. A Monte Carlo simulation approach is introduced to generate micro-scale people's evacuation flows from spatially sparse GPS information. The result shows that GPS data effectively simulate people flow after flooding even if the simulation approach is very simple, and how people flow changed before and after the disaster.