Published papers

We present a global dataset of anthropogenic carbon dioxide (CO ) emissions for 343 cities. The dataset builds upon data from CDP (187 cities, few in developing countries), the Bonn Center for Local Climate Action and Reporting (73 cities, mainly in developing countries), and data collected by Peking University (83 cities in China). The CDP data being self-reported by cities, we applied quality control procedures, documented the type of emissions and reporting method used, and made a correction to separate CO emissions from those of other greenhouse gases. Further, a set of ancillary data that have a direct or potentially indirect impact on CO emissions were collected from other datasets (e.g. socio-economic and traffic indices) or calculated (climate indices, urban area expansion), then combined with the emission data. We applied several quality controls and validation comparisons with independent datasets. The dataset presented here is not intended to be comprehensive or a representative sample of cities in general, as the choice of cities is based on self-reporting not a designed sampling procedure. 2 2 2

This study attempts to simulate evaluation flow after the flooding in the Kanto-Tohoku heavy rain in September 2015, using a mobile GPS data. A simple Monte Carlo approach is introduced to simulate micro-scale people flow from spatially sparse GPS information. The simulation result shows that GPS data effectively recovers people flow after flooding even if the simulation approach is very simple.

Land use has changed dramatically in the Inner Mongolia Autonomous Region because of rapid economic growth and human disturbances. However, little information is available about the medium- and long-term land use changes in this region. The effects of ecological recovery policies have also been evaluated rarely. In this study, we employed the self-organizing map neural network method to identify the land cover changes in Inner Mongolia between 2000 and 2014. MOD13Q1, Landsat, and DMSP/OLS night-time light data were used as the data resources. The dynamic change map was characterized using the grid cell method. The results showed that urban area of Inner Mongolia increased by more than five times during the 15-year study period, while the mining area also increased. In addition, 35.3% of the farmland was changed into grassland, which may have been caused by the “Grain to Green” policy. The most significant environmental issue in Inner Mongolia is the loss of wetland. >40% of the wetland was converted into other land use types between 2000 and 2014. Grassland increased by 6.05%, but areas of open water and woodland remained about the same. In terms of the geographical distribution, cropland increased in the eastern and middle parts of the region. The transformation from wetland to grassland mainly occurred in the north. Grassland degradation occurred in the west. Thus, environmental policy has resulted in some ecological improvements in Inner Mongolia. However, new environmental problems associated with rapid economic development should be addressed in a timely manner.

Nighttime data from the Defense Meteorological Satellite Program Operational Linescan System have been widely used to map urban/built-up areas (hereafter referred to as “built-up area”), but to date there has not been a geographically comprehensive evaluation of the effectiveness of using nighttime lights data to map urban areas. We created accurate, convenient, and scalable grid cells based on Defense Meteorological Satellite Program/Operational Linescan System nighttime light pixels. We then calculated the density of Landsat-derived built-up areas within each grid cell. We explored the relationship between Defense Meteorological Satellite Program/Operational Linescan System nighttime lights data and the density of built-up areas to assess the utility of nighttime lights for mapping urban areas in 50 cities across the globe. We found that the brightness of nighttime lights was only in moderate agreement with the density of built-up areas; moreover, correlations between nighttime lights and Landsat-derived built-up areas were weak. Even in relatively sparsely populated urban regions (where the density of the built-up area is less than 20%), the highest correlation coefficient (R ) was only 0.4. Furthermore, nighttime lights showed lighted areas that extended beyond the area of large cities, and nighttime lights reduced the area of small cities. The results suggest that it is difficult to use the regression model to calibrate the Defense Meteorological Satellite Program/Operational Linescan System nighttime lights to fit urban built up areas. 2

To achieve low carbon cities or green smart city, it is very important to foresee how we can reduce the number of cars in the residential communities without losing convenience and comfort of people. For that purpose, walkability is one of the key performance indicators expressing the environmental quality of a district. As the first step for creating a low-carbon smart community, this study attempts to evaluate the influence of walkability on traffic behavior of people by using mobile GPS data. Specifically, we statistically analyze the relationship between various walkability indices (centrality, betweenness, angularity, etc.) evaluated by road network data, and pedestrian movement estimated by mobile GPS data in the six main wards in Tokyo, Japan. The result suggests the usefulness of our approach for low-carbon smart community design rousing people's walking activity. The walkability results and data are then compared to the results of a macrosimulation traffic model for the Sumida Ward of Tokyo to understand the impact that walkability may have on emissions if built environment conditions are improved in favor of a lesser automobile mode share.

In the last decade, retrofitting strategies have been reviewed to improve energy efficiency and reduce the environmental impact of existing buildings. One retrofitting strategy consists of innovating building envelopes with the adoption of high-performance materials or systems. Despite the potential performance enhancement, opportunities for new envelopes have been constrained because various envelope options are difficult to be evaluated synthetically. Also, the decisions should consider the optimization of multiple objectives as well as uncertainties. In this respect, this paper aims to support the decision of selecting building envelopes to meet multiple objectives under uncertainties while considering possible envelope options. A multi-objective optimization model is developed considering the existing built form, uncertainties in performance predictions, and incorporating newly developed façade systems. The optimal selection includes emerging materials and technologies that are provided with building envelope renovation options to satisfy indoor thermal comfort, energy balance, environmental emissions, and economic aspects. The decision-support framework is also devised to add any envelope options. This adaptable framework enables decision makers to accommodate new system materials and proactively evaluate their feasibility. The optimization model and framework proposed in this research will contribute to providing a roadmap for transforming existing buildings into smart and sustainable built systems.

This study proposes an approach to estimate quasi-real-time electricity/energy demand in each commercial building using Google's Populartime data. The Populartime data records real-time human locations/activities that are collected from users of Google's maps on smartphones. The proposed approach considering changes by hour and by day of the week is applied to Sumida-ward, Tokyo, Japan. The result suggests the usefulness of our approach for energy demand monitoring considering quasi-real-time human activities.

Negative emission technologies such as bioenergy with carbon capture and storage (BECCS) are regarded as an option to achieve the climatic target of the Paris Agreement. However, our understanding of the realistic sustainable feasibility of the global lands for BECCS remains uncertain. In this study, we assess the impact of BECCS deployment scenarios on the land systems including land use, water resources, and ecosystem services. Specifically, we assess three land-use scenarios to achieve the total amount of 3.3 GtC year (annual negative emission level required for IPCC-RCP 2.6) emission reduction by growing bioenergy crops which requires huge use of global agricultural and forest lands and water. Our study shows that (1) vast conversion of food cropland into rainfed bio-crop cultivation yields a considerable loss of food production that may not be tolerable considering the population increase in the future. (2) When irrigation is applied to bio-crop production, the bioenergy crop productivity is enhanced. This suppresses the necessary area for bio-crop production to half, and saves the land for agricultural productions. However, water consumption is doubled and this may exacerbate global water stress. (3) If conversion of forest land for bioenergy crop cultivation is allowed without protecting the natural forests, large areas of tropical forest could be used for bioenergy crop production. Forest biomass and soil carbon stocks are reduced, implying degradation of the climate regulation and other ecosystem services. These results suggest that without a careful consideration of the land use for bioenergy crop production, a large-scale implementation of BECCS could negatively impact food, water and ecosystem services that are supporting fundamental human sustainability. −1

This study downscales the population and gross domestic product (GDP) scenarios given under Shared Socioeconomic Pathways (SSPs) into 0.5-degree grids. Our downscale approach has the following features. (i) It explicitly considers spatial and socioeconomic interactions among cities, (ii) it utilizes auxiliary variables, including road network and land cover, (iii) it endogenously estimates the influence from each factor by a model ensemble approach, and (iv) it allows us to control urban shrinkage/dispersion depending on SSPs. It is confirmed that our downscaling results are consistent with scenario assumptions (e.g., concentration in SSP1 and dispersion in SSP3). Besides, while existing grid-level scenarios tend to have overly-smoothed population distributions in nonurban areas, ours does not suffer from the problem, and captures the difference in urban and nonurban areas in a more reasonable manner. Our gridded dataset, including population counts and gross productivities by 0.5 degree grids by 10 years, are available from http://www.cger.nies.go.jp/gcp/population-and-gdp.html.

This chapter illustrates the results of applications of geographically weighted regression modeling (Chapter 6) for assessing goodness of walking environment, that is, walkability. Section 9.1 briefly introduces some representative measures of walkability. Section 9.2 explains about using data and walkability measures. The settings for geographically weighted regression modeling are also explained here. Sections 9.3 and 9.4 show the estimation results. The former focuses on the impact of pedestrian network structure on the number of pedestrians, whereas the latter focuses on heatwave risk for pedestrians at an intra-urban scale. Finally, Section 9.5 concludes this chapter with a discussion.

This research proposes a flood area estimation method in urban areas using personal location data. Many studies have investigated the estimation of flood levels; however, the majority of these previous works are based on the flood monitoring data. The main cause of flood disaster death is drowning due to evacuation delay in the area where observation equipment is not installed, so it is difficult to estimate flood occurrence by the previous method based only on monitoring data. In this research, we propose an estimation method that does not rely on monitoring data and instead estimates flood areas using GPS data collected from smartphones owned by the affected people. This method detects anomaly areas to analyze temporal and spatial changes of an area where a personal movement during a flooding event differs from that during regular times from personal location data. For anomaly detection, we use a dynamic time warping method with fixed window size and inequality metrics to estimate the area where an anomaly event occurred in 2 km grids. We applied this method to Kurashiki city, Okayama Prefecture, where there 52 people died during a flood that occurred in Japan in 2018. Our method found that, at the flooding time, the anomaly occurrence was estimated correctly in the area where inundation actually occurred.

In this chapter, Section 4.1 briefly introduces the history of geostatistics. Section 4.2 explains stationary spatial processes and the basic geostatistical model to describe the process; Section 4.3 explains how to estimate the model parameters. After introducing Kriging, which is the spatial interpolation approach, in Section 4.4, Section 4.5 introduces its extensions including linear and non-linear kriging approaches. Then, Sections 4.6, 4.7, and 4.8, respectively, explain semiparametric, Bayesian, and spatiotemporal extensions of the geostatistical approach. Finally, Section 4.9 explains geostatistical approaches for large samples.

The carbon emission pattern of the built environment is closely associated with its morphological and functional structures. Based on the online volunteered geographic information and some publicly available official data sources, this study intends to provide a standardized framework for estimating the indirect building carbon emissions within the boundaries of various types of Local Climate Zones (LCZs), to better forecast the LCZ carbon emission patterns and assist district wide energy management. The whole research is devised into four sequential sections: First, the statistics of energy use intensity of different building uses (including residential and non-residential buildings) are retrieved from official data sources using a down-scaled approach; then a random forest machine learning method is applied to automatically identify building uses based on the training samples; next, a GIS method is developed to delineate the LCZs in Shanghai utilizing calculated urban form and land cover parameters; finally, the building carbon emission values are linked to the LCZs to determine the emission coefficient of different LCZ categories in Shanghai.

Section 2.1 of this chapter defines the typical notations. Section 2.2 explains the classical linear regression model with typical assumptions and discusses what may happen when these assumptions are violated. Estimation techniques such as ordinary least squares, two-stage least squares, and generalized method of moments are briefly explained. Sections 2.3 and 2.4 introduce two advanced regression models, the generalized linear model and the additive model. Section 2.5 explains the basics of Bayesian statistics and the Markov chain Monte Carlo in terms of a regression model.

This chapter introduces approaches in quantitative geography. After the introduction in Section 6.1, Sections 6.2 and 6.3 explain the geographically weighted regression and the spatial filtering approaches, respectively. Section 6.4 describes extensions of these approaches for large spatial dataset.

To minimize the impacts of climate change, it is increasingly clear that global CO emissions should be eliminated by 2050 and that leading low-carbon cities should reach net zero emissions by 2040. However, the precise pathways by which they can reach such ambitious goals have yet to be identified. As costs of photovoltaics (PV), batteries, and electric vehicles (EVs) are likely to keep falling, they can jointly play a key role for deep decarbonization. Here, we conduct a techno-economic analysis of a city-scale energy system with roof-top PV, batteries, and EVs for Kyoto City, Japan. We find that aggressive EV adoption and the use of EVs for electricity storage could help roof-top PV penetration in the city with substantially lower costs than just deploying PV and batteries alone or allowing EV to charge only. CO emissions from vehicle and electricity usage in the city could be reduced by 60–74% if the entire current car fleet is replaced by EVs while also reducing energy costs by 22–37% by 2030. The largest challenge of a city-wide “PV + EV” system (named as “Solar-EV city”) is its implementation. We explore how it could be realized in Kyoto through peer-to-peer (P2P) power trading/blockchain technology initially on a community scale as smart microgrids, then gradually expanding/converging into a city-wide. For the transition to decentralized power systems, citizen's decision-making process is one of the keys to overcome social, institutional, and regulatory barriers. 2 2

This study develops an approach for optimizing the size/scale of microgrids used in electricity sharing around each residence by considering the uncertainty between the electricity supply from photovoltaics and electricity demand. Uncertainties are quantified using simulations that consider actual daily variations in supply and demand. The developed approach is applied to microgrid optimization in Sumida-ku, Tokyo, Japan, and results suggests that large-scale microgrids are required in central districts but small microgrids are sufficient in old residential areas. Results also show the statistically significant effect of optimizing microgrids to enhance energy self-sufficiency.

The WUDAPT (World Urban Database and Access Portal Tools project goal is to capture consistent information on urban form and function for cities worldwide that can support urban weather, climate, hydrology and air quality modeling. These data are provided as urban canopy parameters (UCPs)as used by weather, climate and air quality models to simulate the effects of urban surfaces on the overlying atmosphere. Information is stored with different levels of detail (LOD). With higher LOD greater spatial precision is provided. At the lowest LOD, Local Climate Zones (LCZ)with nominal UCP ranges is provided (order 100 m or more). To describe the spatial heterogeneity present in cities with great specificity at different urban scales we introduce the Digital Synthetic City (DSC)tool to generate UCPs at any desired scale meeting the fit-for-purpose goal of WUDAPT. 3D building and road elements of entire city landscapes are simulated based on readily available data. Comparisons with real-world urban data are very encouraging. It is customized (C-DSC)to incorporate each city's unique building morphologies based on unique types, variations and spatial distribution of building typologies, architecture features, construction materials and distribution of green and pervious surfaces. The C-DSC uses crowdsourcing methods and sampling within city Testbeds from around the world. UCP data can be computed from synthetic images at selected grid sizes and stored such that the coded string provides UCP values for individual grid cells.

This chapter illustrates a geostatistic model (Chapter 4) application to estimate quasi real-time energy consumption in each building using Google's Popular Times data. After the introduction in Section 11.1 where our research motivations are explained, Section 11.2 describes the empirical methods, and we adopt compositional kriging approach for this analysis. Section 11.3 shows the estimation results of estimating building-level energy consumption. Effective visualization is attempted with the results. Section 11.4 concludes this chapter with a discussion.

To conserve energy in a residential sector, showing action plans how to conserve energy is important. However, we need to consider both energy efficiency and indoor comfort. The usage patterns in the residential sector change according to their life style and some people would try to maintain the indoor comfort at high level even paying high energy cost. It is necessary to understand home energy usage patterns in terms of the relationships between indoor comfort and electricity consumption in order to provide appropriate energy saving action plans to the residential sector. For this purpose, in this study we have collected real time data of electricity consumption and indoor temperature using IoT devices and sensors installed to 13 households for about one month in Summer at Sumida ward in Tokyo, Japan. Then, we have analyzed the data using three types of method, which are a clustering algorism to delineate the electricity usage pattern which characterize the household life style, conducted correlation analysis to look at the relationship between electricity consumption, and the indoor temperature for households living in condominium apartments and detached houses with different building structure and ages.