論文

This study focuses on carbon emissions of the building sector in relation to local climate zone (LCZ) classification, concentrating on two major parts. First, we estimated carbon emissions in the building sector, which were cal- culated for weekdays and weekends real-time daily energy consumption patterns. The estimations were divided into direct (from petroleum products consumption) and indirect emissions (from electricity consumption). Sec- ond, we examined urban carbon emissions mapping in relation to LCZ. Bangkok Metropolitan Administration (BMA) was used as the case study and 2016 as the base year for examination. The results illustrate that indirect emissions in Bangkok can be up to ten times higher than direct emissions. The analysis indicates that LCZ, such as compact high-rise, large low-rise, light industry, and warehouse zones had a relatively higher carbon emission intensity than others. Additionally, we identified that the compact high-rise zone has the highest indirect emission intensity, while the light industry and warehouse zone have the greatest direct emission intensity. These results provide insights into the dynamics of carbon emission characteristics in the building sector and the methodology purported here can be used to support low carbon city planning and policymaking in Bangkok.

Globally, urbanization constitutes one of the major underlying drivers of global ecological degradation. Hence, deurbanization, i.e., demographic shift from urban to distant rural areas in a way that increases quality of life (QoL), can be one of the key pathways to address this global challenge. In this study, we investigated the contribution of nature and other types of rural capital to QoL and to people’s decision to migrate from urban to rural areas by studying residents in Hokuto City, a popular urban-to-rural migration destination in Japan. An integrated analysis of the 414 responses to a questionnaire survey and open and commercial geospatial datasets representing natural, built, human, cultural, and financial capital revealed the contributions of specific elements of rural capital to people’s QoL. These included natural capital (farmland, symbolic natural sites, mountain peak view, lower temperature, and tranquility), built capital (highways, railway stations, shops, and restaurants), and financial capital (employment). Many of these are related to the reasons that migrants, including return and one-way migrants, chose their present home location in Hokuto City, indicating their intention to increase QoL by migration. Particularly, one-way migrant homes were located predominantly on higher up mountain slopes with lower temperatures, higher forest cover, near natural parks, and symbolic natural sites, and yet with easier access to railway stations and employment. These results provide a valuable evidence base for rural spatial planning for increased QoL and attracting migrants that considers ecological–social feedbacks, and hence supports deurbanization.

This paper deals with application of cutting-edge information technology represented by Digital Transformation, DX, for maintaining critical infrastructures such as water and city-gas supply systems. Among many stakeholders related to such infrastructure, we have focused on system maintenance for the end-user by focusing on manufacturing, wholesale and retail, logistic, and on-site construction that formulate a very complex System of System. First, this paper describes a successful example of DX application to the water supply system. Second, the approach and means of this example were applied to an obsolete system of a city-gas supply system with successful results in relatively short period of time. In this DX application, there were several issues to overcome during the execution of reformation but finally the goal of this attempt has been attained by improving the process and increasing the profit. This has proven that appropriate application of the DX is useful and provides a powerful tool for improving business. Typical successful results are: 1) Service time has increased to 24 hours a day and 365 days a year. 2) Number of workforce has decreased from 37 to 27, and 3) Number of errors decreased to 1 to 4 out of 200 orders.

This study focuses on carbon emissions of the building sector in relation to local climate zone (LCZ) classification, concentrating on two major parts. First, we estimated carbon emissions in the building sector, which were calculated for weekdays and weekends real-time daily energy consumption patterns. The estimations were divided into direct (from petroleum products consumption) and indirect emissions (from electricity consumption). Second, we examined urban carbon emissions mapping in relation to LCZ. Bangkok Metropolitan Administration (BMA) was used as the case study and 2016 as the base year for examination. The results illustrate that indirect emissions in Bangkok can be up to ten times higher than direct emissions. The analysis indicates that LCZ, such as compact high-rise, large low-rise, light industry, and warehouse zones had a relatively higher carbon emission intensity than others …

Designing waterfront redevelopment generally focuses on attractiveness, leisure, and beauty, resulting in various types of building and block shapes with limited considerations on environmental aspects. However, increasing climate change impacts necessitate these buildings to be sustainable, resilient, and zero CO2 emissions. By producing five scenarios (plus existing buildings) with constant floor areas, we investigated how buildings and district forms with building integrated photovoltaics (BIPV) affect energy consumption and production, self-sufficiency, CO2 emission, and energy costs in the context of waterfront redevelopment in Tokyo. From estimated hourly electricity demands of the buildings, techno-economic analyses were conducted for rooftop PV systems for 2018 and 2030 with declining costs of rooftop PV systems. We found that environmental building designs with rooftop PV system are increasingly economical in Tokyo with CO2 emission reduction of 2–9% that depends on rooftop sizes. Payback periods drop from 14 years in 2018 to 6 years in 2030. Toward net-zero CO2 emissions by 2050, immediate actions are necessary to install rooftop PVs on existing and new buildings with energy efficiency improvements by construction industry and building owners. To facilitate such actions, national and local governments need to adopt appropriate policies.

Rooftop photovoltaics (PVs) integrated with electric vehicles (EVs) has the potential to deeply decarbonize urban energy systems in a cost-effective way. The SolarEV City Concept suggested that the rooftop PV plus EV systems can supply up to 95% of electricity demand within cities in Japan. However, it was not clear which district in city could consume, generate, and store the PV electricity, as each district has different load patterns, building structures, and number of parked cars. In this study, we performed techno-economic analyses on rooftop PV systems integrated with stand-alone batteries or EVs in residential and commercial districts in Japan from 2020 to 2040. We found that rooftop PV systems in 2020 are already cost competitive relative to existing energy systems. However, “PV + EV” systems in residential houses rapidly increases its economic advantage over commercial districts due to greater rooftop space and higher number of available vehicles. Moreover, energy sharing significantly improved the decarbonization potential. By 2025, energy cost savings, payback periods, and internal rate of return (IRR) of residential “PV + EV” systems respectively reached 23%, 9 years, and 11%, and continued improving in subsequent years. CO2 emissions from electricity and gasoline consumption was reduced by 88%, and the system was capable to supplying 89% of electricity demand. The results indicate that residential “PV + EV” systems are a potential source for significant renewable energy generation and storage that can also produce increasingly dispatchable electricity. Policy makers, industries, and communities should prepare to establish these systems through regulatory reform and demonstration projects to scale-up after 2025.

Building energy models have under predicted actual electricity consumption by about 30%. These models are typically based on physical attributes of an actual building and simulate its operation at static indoor conditions. The attributes of building designs and systems may not represent accurate properties of the building because the data becomes available only as architectural and engineering design progresses. The lack of building data details creates uncertainties in the model. Internet of things (IoT) data enable us to obtain reliable building operational data. This research addresses a major challenge for using IoT data during a building energy prediction and operation. IoT data currently pose two major challenges: 1) IoT data has been generated in existing buildings, but it is often unused, and 2) uncertainties in building energy predictions have resulted in unreliability of the simulation results. The research objective is to present a methodology of IoT-initiated building energy modeling and monitoring to increase the reliability of energy predictions and energy management. The uncertainty-integrated energy modeling enables for reliable energy planning prior to building construction, and for resilient monitoring during post construction and facility operation.

Bioenergy has been promoted in Japan with ambitious targets. However, the incentive schemes excluded renewable heat and overlooked synergies with local forest management, leading to the development of large-scale biomass plants that heavily rely on overseas biomass supplies. This case report discussed an alternative scenario of decentralised bioenergy systems supported with local biomass through five important questions. The currently available knowledge indicates that such a scenario is feasible with integrative forest management that considers both ecosystem services and multiple uses of wood. In addition to various environmental benefits, replacing imported fossil fuels with local biomass can also enhance energy security. Realising this scenario requires careful consideration of local context, empowerment of local governments and encouragement of both public and private initiatives.

The purpose of this study is to quantify (a) spatiotemporal deforestation patterns, and (b) the relationships between changes in the main land-cover types in the Chapare region of Bolivia. We applied subspace classification methods to LANDSAT data from 1986, 1999, and 2018 and used grid cells at scales of 150, 300, 600, and 900 m to measure deforestation trajectories. The 150 m grids provided better detail to determine deforestation trajectories than coarser-scale grid cells. Differences in grid-cell scale did not influence the statistical trends in land-cover changes significantly. Changes in forest area were negatively correlated with changes in cropland (r = −.44), grassland (r = −.34), swamp grassland (r = −.38), and regrowth (r = −.32) areas. Correlations between forest losses in cropland, grassland, and regrowth change analyses were weaker between 1999 and 2018 compared to 1986 to 1999. Forest cover declined from 6,635 km (1986) to 3,800 km (2018), and the deforestation rate increased from an annual average of 1.36% between 1986 and 1999 to 2.0% between 1999 and 2018. The key proximate drivers of forest clearance rates and patterns were increasing population, agricultural expansion, and road building. While coca is an economically important crop in Chapare, its direct and indirect effects on deforestation could not be determined unambiguously. It is probable that the expansion of agriculture will lead to further deforestation and forest fragmentation and, along with decreases in forest cover, further changes will take place between non-forest categories. 2 2

The question of what cities can contribute to mitigation and adapting to climate change is gaining traction among researchers and policy makers alike. However, while the field is rich with case studies, methods that provide rich data across municipalities and potentially at global scale remain underdeveloped, and comparative insights remain scarce. Here we summarize contributions to the focus issue on 'Systematizing and Upscaling Urban Climate Solutions', also drawing from presentations given at an accompanying conference in 2018. We highlight four core areas for systematizing and upscaling urban climate mitigation solutions. First, with more and better (big) data and associated machine learning methods, there is increasing potential to compare types of cities and leverage collective understanding. Second, while urban climate assessments have mostly emphasized urban planning, demand-side action as related to both behavioral change and modified social practices relevant to urban space deserve more academic attention and integration across a diverse set of social sciences. Third, climate mitigation would be intangible as a single objective at the urban scale, and measures and solutions that coordinate mitigation coherently with adaptation and broader sustainable development goals require explicit conceptualization and systematization. Forth, all insights should come together to develop governance frameworks that translate scientific exercises into concrete, realistic and organized action plans on the ground, for all cities.

Over the last several decades developing countries, especially in Asia, have undergone large scale rapid urbanization, impacting their developmental patterns. Compounded with increased economic prosperity and social mobility attracting individuals and companies, to urban centres, has led to severe administrative challenges that can negatively affect the citizens, nation, and environment. In efforts to mitigate those adverse side effects cities have instituted, or citizens naturally adopted, a variety of strategies to address those issues. These strategies result in the typologies of rapid urbanization that are most prevalent in developing Asian cities. By focusing on Asian and Chinese cities, using shanghai as a test case, this research proposes several major typological categories associated with rapidly urbanizing areas. Shanghai's rapid urbanization has been shaped by seven main typologies of urban growth and expansion, since the implementation of China's economic growth policies and its resurgence as an influential global city. Spatial typological patterns of urban growth in Shanghai are analyzed to explain how the economic prosperity and social mobility have been managed. Typological adaptations learned from the historical urban expansion provide the subsequent strategies for future sustainable development in a rapidly urbanizing area.

Cities produce over 70% of global carbon emissions and are thus crucial in driving climate change. Urban carbon emissions may continue to increase especially in those less-developed countries and regions which are still under rapid urban development. Policymakers need to find ways to effectively control and reduce carbon emissions. Thus, spatial modeling methods to map and predict urban carbon emissions have been developed to meet these needs. This paper examines the progress of the spatial modeling of carbon emissions and the relationship between urban form and carbon emissions in China by reviewing more than 100 peer-reviewed journal articles in the Scopus database. The latest prediction methods and techniques are described in the paper. Their advantages and limitations are then discussed. Urban forms have a significant influence on carbon emissions and have been applied in spatial modeling studies in other countries. However, this review has identified the lack of urban form data and high-resolution inventories from existing studies in China. Future developments in the spatial modeling in China should therefore have a fine spatial resolution and incorporate open and high-quality urban form data, including urban morphology and land use/land cover.

Technical advancements such as building-integrated renewable systems and autonomous vehicles (AVs) require existing electricity grids to be transformed into adaptable microgrids considering future energy supply and demand. Interactions between energy demanders and suppliers should be predicted to quickly respond and adapt to future demands or uncertain events. However, communities consisting of buildings, transports, and people are complex and their interactions are difficult to be recognized. This research investigates the interactions among elements in communities featuring AVs and solar photovoltaics (PV)-integrated buildings. A framework for establishing smart energy networks that can be adjustable spatially and temporally is developed by communicating buildings and vehicles. The methodology is tested in buildings and road networks in Kyojima 1, North Sumida, Tokyo, Japan. Existing energy demands and transportation systems are reviewed to establish future scenarios for applying AVs and solar PV buildings. The scenarios are analyzed using community clustering and Voronoi diagram algorithms, in which sustainable community boundaries are examined using prediction data from parametric energy modeling and spatial network analysis. The framework can support decision-making for future community boundaries, thereby distributing energy securely and efficiently. This research will also contribute to preparing communities to share energy information and to achieve resilient and sustainable infrastructure systems.

Energy models should be simplified to handle data limitations and should predict reliable energy use. Currently, it remains challenging to ensure an appropriate level of detail for simplifying building energy models and to avoid performance gaps when predicting electricity consumption. In this respect, this research proposes to identify an appropriate level of simplifying a building energy model, predict electricity demands and performance gaps using the simplified energy model, and expand the model usability through the operational stage. Building electricity demands predicted through EnergyPlus (version 8.7.0) simulation are compared with actual electricity data collected through Internet of Things (IoT) sensors. Consideration of performance gaps increases the predictability of electricity consumption of a simplified energy model. Also, the Bayesian multilevel additive model updates the performance gaps along with the collection of new IoT data. The findings of this study contribute to forecasting electricity demands with a simplified energy model by predicting performance gaps that can be applied to predicting the electricity needs of similar buildings in the design stage and controlling operational electricity use in the operational stage by comparing sensor measurement with reference data provided by the energy model.