Search

Between 60% and 80% of global energy is consumed in urban areas and given the projected increase in world׳s urban population, this share is expected to further increase in the future. Continuity of energy supply in cities is affected by climate change and a growing array of other threats such as cyber attacks, terrorism, technical deficiencies, and market volatility. Determined efforts, acknowledging the interactions and interlinkages between energy and other sectors, are needed to avoid adverse consequences of disruption in energy supply. Resilience thinking is an approach to management of socio-ecological systems that aims to develop an integrated framework for bringing together the (often) fragmented, diverse research on disaster risk management. The literature on urban resilience is immense and still growing. This paper reviews literature related to energy resilience to develop a conceptual framework for assessing urban energy resilience, identify planning and design criteria that can be used for assessing urban energy resilience, and examine the relationship of these criteria with the underlying components of the conceptual framework. In the conceptual framework, it is proposed that in order to be resilient, urban energy system needs to be capable of “planning and preparing for”, “absorbing”, “recovering from”, and “adapting” to any adverse events that may happen in the future. Integrating these four abilities into the system would enable it to continuously address “availability”, “accessibility”, “affordability”, and “acceptability” as the four sustainability-related dimensions of energy. The paper explains different resilience principles associated with these abilities and sustainability dimensions. Also, different planning and design criteria were extracted from the literature and categorized into five themes: infrastructure; resources; land use, urban geometry and morphology; governance; and socio-demographic aspects and human behavior. Examination of the relationship of these criteria with the underlying components of the conceptual framework highlighted the complexity and multi-faceted nature of energy resilience. Exploration of the relevance of the identified criteria to climate change mitigation and adaptation revealed that most of the identified criteria can provide both mitigation and adaptation benefits. [ABSTRACT FROM AUTHOR]

An assessment of residential CO 2 emissions is typically performed through the intensity method, in which total energy consumption is estimated by multiplying floor space by intensity value. Although spatially detailed intensity data is required for an accurate estimation, the finer spatial resolution will result in a less stable estimated value due to the small sample size; hence, existing studies in Japan created intensity data at a regional or prefectural level. The objective of this study is to create municipality level intensity data via a statistical approach, using the household level micro data from the National Survey of Family Income and Expenditure, of the Ministry of Internal Affairs and Communications, Japan, by focusing on electricity. First, this study builds several (spatial) statistical models, where per household electricity expenditure is regressed on housing types (two categories), household types (seven categories), and other household specific variables. Second, by substituting averaged municipality level explanatory variables from official statistics into the model, it estimates municipality level intensity data. The obtained results suggest that conventional intensity data in Japan, created by the simple average of samples in each unit (prefecture), may suffer from an upward bias, suggesting a danger of overestimation of residential CO 2 emissions. [ABSTRACT FROM AUTHOR]

This paper systematically reviews literature on passive heating and cooling of buildings using roof ponds. The main aims were to gain a detailed understanding of different roof pond configurations and their performance, compare effectiveness of different roof pond variants, evaluate performance of different roof pond variants relative to other passive design techniques, explore effects of climatic conditions and various design configurations on the performance of roof ponds, and identify gaps in knowledge and data. Overall, 19 roof pond cooling and 4 roof pond heating systems were identified. This review suggests that, in some cases, roof ponds can provide year-round thermal comfort while reducing demand for active heating and cooling systems. Therefore, they can be utilized in efforts towards the goal of carbon–neutral design. Roof ponds with wet gunny bags, shaded roof ponds, ventilated roof ponds, and roof ponds with movable insulation proved to be more effective relative to other variants of roof pond cooling systems. Comparing performance relative to other passive strategies showed that, in many cases, roof pond cooling systems are about equally effective in maintaining indoor thermal comfort. Meteorological conditions, water depth, roof deck material, and thickness of the insulating panel are the main factors affecting performance of roof ponds. Several areas of weakness in the literature are identified. Future research should, among other things, provide more detailed knowledge on performance of all roof pond variants, further explore suitability of roof ponds relative to other passive design measures, examine effectiveness of roof ponds when combined with other passive design measures, analyze life-cycle costs of roof ponds, and provide more knowledge on their real-world application. [ABSTRACT FROM AUTHOR]

Scenarios of future population at the grid level (e.g., 1 km 2 ) is very important for various types of regional planning, including energy planning. Though the most often used method for population projection is the so-called cohort-component method, if our focus is world level, not country or finer level, we must rely on much simpler methods. In this study, we compare the performance of several typical simple population projection methods using the residential electricity demand derived from each population scenario, especially focusing on spatial patterns. We show that different projection methods may produce different spatial patterns such as “compact” or “dispersed” urban form, which is quantified using the Gini coefficient. Also, we show that the projection method for gravity-based residential electricity demand is very flexible in that it can create the most compact and the most dispersed urban form among the focused methods. Furthermore, we point out the usefulness of model ensemble in projection. [ABSTRACT FROM AUTHOR]

Abstract: Designing a future smart city (FSC) that copes with the reduction of CO2 has become one of the urgent tasks of the next 20years. One promising approach to achieve FSC is to combine appropriate land use (compact city with energy efficient buildings and photovoltaic panels (PVs)), transportation (electric vehicles (EVs) and public transportation system) and energy systems (smart grid systems), because of the interaction between these elements. However, there are few models which simulate these elements in an integrated manner. This paper presents the concept of the integrated model, and shows the land use-energy part of the model created for the Tokyo metropolitan area, which is the largest Mega city in the world. Firstly, a spatially explicit land use model (urban economic model) is constructed for the study area, and the model is calibrated using existing statistical data. Secondly, possible future compact/dispersion city scenarios for the year 2050 are created using the model. Thirdly, intra-day dynamics (hourly) of electricity demand and supply from PVs, which is assumed to be installed to the roofs of all detached houses in the study area, under two urban scenarios is simulated. The obtained results suggest that [1] “compact” urban form may contribute to the reduction of electricity demand from the residential sector, but [2] PV-supply under the scenario may also be reduced because of the decreased share of detached houses. Hence in the compact city scenario, it is important to discuss the effective use of vacant areas in suburbs, which may be used for large PV installations, or be re-vegetated to mitigate urban heat island effects. [Copyright &y& Elsevier]

Abstract: The estimates of above-ground forest biomass (AGB) in Northern Eurasia are highly uncertain, despite the global importance of AGB for ecosystem services and its role as carbon stores. In this paper, we demonstrate the potential of ALOS/PALSAR (Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar), for the estimation of AGB in the range of 10–190tons (dry matter)/ha in mixed and deciduous forests at the southern edge of the boreal region in Western Siberia. Various regression models were tested to determine the relationship between forest biomass derived from field measurements and radar backscatter. The best results were obtained using HV-polarized backscatter with the Water Cloud model, giving estimation errors in terms of root mean square errors (RMSE) between 25% and 32% of the mean biomass, and coefficient of determination (R 2) between 0.35 and 0.49 for the whole range of SAR backscatter used in the analysis. The method displayed a higher prediction accuracy with RMSE of 15%, and the R 2 between 0.55 and 0.72 when restricted to SAR backscatter (σ 0)<−12.6dB where the model was clearly defined. The SAR-based estimates offer a potential of rapid, high resolution and low cost mapping of the lower biomass woody vegetation (sparse or young forests on shallow peat) in Siberia, the area where more accurate national or large scale forest inventories hardly exist. [Copyright &y& Elsevier]

In the present study, a large-scale geographically explicit land-use model was developed for projecting the geographical distribution of urban environmental variables, such as population density and fraction of urban and green vegetation land cover, for different urban forms. These variables form key inputs for regional climate models, yet they are sometimes addressed in an ad hoc manner. This study employs a land-use equilibrium model based on urban economic theory, which endogenously projects the geographical distribution of households, residential floor space/rent and land area/rent. The model can deal with not only urban growth but also urban shrinkage, which is becoming an important issue for developed countries, including Japan, confronting population decrease. The model is calibrated for the Tokyo Metropolitan Area at the micro-district level. Using the model, this paper demonstrates an extreme urban compact city scenario for the year 2050, and it is compared to the business as usual (BAU) scenario. [ABSTRACT FROM PUBLISHER]

Abstract: Combining remote sensing data and socio-economic data to quantitatively analyze urban growth is a topic growing in importance. We used square grid cells to investigate the spatial and temporal dynamics of urban growth in the Tokyo, Japan, metropolitan area by using remote sensing imagery from 1972 to 2011 and census population data from 1970 to 2010. First, we used the subspace classification method to produce land-cover maps by using Landsat images from 1972, 1987, 2001, and 2011. Next, we integrated the land-cover maps with basic grid cell maps (using the standard 1km2 grid cell system of Japan) to represent the proportion of each land-cover category within each 1km2 grid cell area. Finally, we combined the proportional land-cover maps and population census data to investigate the relationship between land-cover changes and population density change based on grid cells. By using grid cells it is straightforward to (i) integrate remote sensing, geographic information system (GIS), and statistical data within the cells; (ii) quantify land-cover changes in terms of the percentage of area affected and rates of change and compare them with population census data; and (iii) analyze the spatial-temporal dynamics of urban growth patterns. Between 1972 and 2011 the rapid expansion of the urban area was accompanied by extensive shrinking of the agricultural area around the new settlements. As a result, the urban growth rate exceeded the population growth rate by more than a factor of 2.6. We used the grid cells to investigate the spatial relationship between the changes of land-cover classes and population density change, and then calculated the correlation coefficients of land-cover categories and population density changes for 3 intervals between 1972 and 2011 (1972–1987, 1987–2001, and 2001–2011). The results showed that the urban/built-up density decreased in the metropolitan inner core as the city center experienced depopulation. Spatial correlation analysis showed a strong positive correlation between urban expansion and population density change (r =0.59), and that urban expansion was strongly negatively correlated with cropland change (r =−0.77). The results also demonstrated that grid cells allow remote sensing and statistics data to be combined, improving the knowledge, understanding, and analysis of urban dynamics. [Copyright &y& Elsevier]

Abstract: To develop a scenario, easily traceable even for ordinary citizens, toward the national challenge target of 80% CO2 reduction by 2050, we have proposed a new model that calculates the total CO2 emission based on the final consumption. Using the model, we have tested appropriate (locally available) technology based sub-scenarios: (1) energy saving through electrification of all transportation; (2) promotion of wood utilization for housing; (3) introduction of renewable energies; and (4) efficient energy recovery from wastes. Applying the scenarios to the Kyoto-city as a case study, we have found that local 80% CO2 emission reduction could be possible using appropriate technologies including emission reduction from construction of private and public infrastructures, and shifting our final consumption mode into low CO2 emission has a significant impact. We have also argued that it is of interest to examine the future socio-economic change that would influence on people’s consumption behavior. [Copyright &y& Elsevier]

Abstract: We suggests that temperature response of plant productivity can be modeled by the Arrhenius function modified to describe the effect of temperature on enzyme activity: G A(T)=2f(T)/(1+ f 2(T)), where f(T)=exp(E a/RT opt − E a/RT), R the universal gas constant, E a the activation energy and T opt is the optimal temperature. In common with other functions used for modeling the temperature response of plant productivity, the curve of function G is almost symmetrical and bell-shaped. The special convenience of G A is that it relates the width of the “bell” to thermodynamic concepts, such as activation energy of chemical reactions converting carbon dioxide and water to carbohydrates. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

By using household-level micro data captured through the National Survey of Family Income and Expenditure for 2004, this study evaluates the residential parking rent price elasticity of car ownership in Japan. It analyzes the number of cars owned by a household, using various attributes including expenditure for renting a parking space on a monthly basis. The estimation results derived from the IV-ordered probit model show that the absolute value of parking rent price elasticity of car ownership is, at most, 0.48, which is fairly small (i.e., inelastic). The elasticity value varies depending on city size; for megacities, elasticity is always negative for car ownership, whereas for middle-sized or small cities, towns, and villages, elasticity is positive for one-car ownership and negative for the ownership of more than one car. Hence, when the price of parking increases, some people may switch from more than one car to one car and some people in megacities may switch from one to zero cars. Indeed, the net effect of a price increase may be that non-car ownership increases in megacities and one-car ownership increases in other cities. [ABSTRACT FROM AUTHOR]

Using a high-resolution regional climate model coupled with urban canopy model, the present study provides the first attempt in quantifying the impact of metropolitan-scale urban planning scenarios on moist thermal environment under global warming. Tokyo metropolitan area is selected as a test case. Three urban planning scenarios are considered: status quo, dispersed city, and compact city. Their impact on the moist thermal environment is assessed using wet-bulb globe temperature (WBGT). Future projections for the 2070s show a 2–4°C increase in daytime mean WBGT relative to the current climate. The urban scenario impacts are shown to be small, with a −0.4 to +0.4°C range. Relative changes in temperature and humidity as the result of a given urban scenario are shown to be critical in determining the sign of the WBGT changes; however, such changes are not necessarily determined by local changes in urban land surface parameters. These findings indicate that urban land surface changes may improve or worsen the local moist thermal environment and that metropolitan-scale urban planning is inefficient in mitigating heat-related health risks for mature cities like Tokyo. [ABSTRACT FROM AUTHOR]

Background: We aimed to clarify the potential of spaceborne light detection and ranging (LiDAR) to meet the increased demand for large-scale monitoring of forest resources. Results: We developed empirical models to estimate aboveground biomass (AGB) and canopy height in Borneo from Ice, Cloud, and land Elevation Satellite (ICESat)/Geoscience Laser Altimeter System (GLAS) data, and obtained root-mean-square errors of 38.7 Mg ha–1 and 4.0 m, respectively. GLAS-estimated AGB averaged 191.8 Mg ha–1. From 2004 to 2007, AGB decreased by an average of 33.1 Mg ha–1, and the rate of forest loss was 2.4% year–1. The total AGB in Borneo was estimated as 10.34 Gt. Conclusions: The results demonstrate the potential of spaceborne LiDAR for monitoring forest resources, and its potential to play an important role in REDD+ implementations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper purposed to reduce total electricity consumption of households in a cold area, Japan. [•] We implemented an information provision system to install four households for one year. [•] The system provided the information of electricity consumption 15min interval via websites. [•] We considered temperature effects to electricity demand and made a benchmark for evaluation. [•] Consequently, a tendency of reduction of electricity consumption could be seen in two households. [Copyright &y& Elsevier]

Global land cover maps are widely used for assessment and in research of various kinds, and in recent years have also come to be used for socio-economic forecasting. However, existing maps are not very accurate, and differences between maps also contribute to their unreliability. Improving the accuracy of global land cover maps would benefit a number of research fields. In this paper, we propose a methodology for using ground truth data to integrate existing global land cover maps. We checked the accuracy of a map created using this methodology and found that the accuracy of the new map is 74.6%, which is 3% higher than for existing maps. We then created a 0.5-min latitude by 0.5-min longitude probability map. This map indicates the probability of agreement between the category class of the new map and truth data. Using the map, we found that the probabilities of cropland and grassland are relatively low compared with other land cover types. This appears to be because the definitions of cropland differ between maps, so the accuracy may be improved by including pasture and idle plot categories. [ABSTRACT FROM AUTHOR]

Recently, model averaging techniques have been employed widely in empirical investigations as an alternative to the conventional model selection procedure, a procedure criticized because it disregards a major component of uncertainty, namely, uncertainty regarding the model itself, and, thus, it leads to the underestimation of uncertainty regarding the quantities of interest. Bayesian model averaging ( BMA) is one of the most popular model averaging techniques. Some studies indicate that BMA has cumbersome aspects. One of the major practical issues of using BMA is its substantial computational burden, which obstructs the process of obtaining exact estimates. A simulation method, such as Markov chain Monte Carlo ( MCMC), is required to resolve this problem. Weighted-average least squares ( WALS) estimation has been proposed as an alternative to BMA. The computational burden of WALS estimation is negligible; therefore, it does not require the MCMC method. Furthermore, WALS estimation has theoretical advantages over BMA estimation. This article presents two contributions to the WALS literature. First, it applies WALS to spatial lag/error models in order to consider spatial dependence. Second, it extends WALS in order to consider explicitly the problem of multicollinearity by employing the technique of principal component regression. The small sample properties of the estimators of the proposed models are examined using Monte Carlo experiments; the results of these experiments suggest that the standard WALS may produce biased estimates when the underlying data-generating process is a spatial lag process. Results also indicate that when the correlation among the regressors is high, the standard WALS estimators may suffer from large variances and root mean squared errors. Both of these problems are significantly mitigated by using the proposed models. Las técnicas de promediado de modelos (model averaging) vienen siendo empleadas con creciente frecuencia en las investigaciones empíricas como una alternativa a los procedimientos convencionales de selección de modelos estadísticos. Dichos procedimientos convencionales han sido criticados por no tomar en cuenta un componente clave de la incertidumbre: la incertidumbre del modelo en sí, y por lo tanto, conducen a la subestimación de la incertidumbre en la cuantificación de las valores estimados. El promediado bayesiano de modelos ( Bayesian Model Averaging-BMA) es una de las técnicas de promediado más usadas. Algunos estudios indican que BMA tiene aspectos engorrosos: uno de los principales aspectos prácticos a considerar en su uso es su pesada carga computacional, la cual obstruye el proceso de obtención de estimaciones exactas. Esta limitación hace necesario el uso de métodos de simulación, como el de la cadena de Markov de Monte Carlo ( Markov Chain Monte Carlo-MCMC). La estimación de mínimos cuadrados usando un promediado ponderado ( Weighted-Average Least Squares-WALS) ha sido propuesta como alternativa a BMA. La carga computacional de la estimación WALS es mínima y por lo tanto no requiere del uso de MCMC. Más aun, la estimación WALS posee ventajas teóricas sobre BMA. Este artículo presenta dos contribuciones a la literatura especializada de WALS. En primer lugar, aplica WALS a modelos espaciales tipo lag/error con el fin de incorporar la dependencia espacial. En segundo lugar, modifica el método WALS, a fin de considerar explícitamente el problema de la multicolinealidad entre variables mediante el empleo de la técnica de regresión de componentes principales ( Principal Component Regression-PCR). Luego los autores utilizan experimentos Monte Carlo para examinar las propiedades de tipo 'muestra pequeña' ( small simple) de los estimadores de los modelos propuestos. Los resultados de los experimentos sugieren que el método WALS estándar puede producir estimaciones sesgadas cuando el proceso generador de datos subyacente ( Data Generating Process-DGP) es un proceso de retardo espacial ( Spatial Lag Process-SLP). Los resultados también indican que cuando la correlación entre las variables es alta, los estimadores estándar de WALS pueden padecer de varianzas y errores cuadráticos medios ( root mean squared errors-RMSEs) atípicamente grandes. Ambos problemas son mitigados significativamente mediante el uso de los modelos propuestos en el presente artículo. 近来，模型平均技术作为与传统模型选择流程可替换的方法，在经验调查中得到广泛应用。传统的模型选择流程忽视了模型本身的不确定性，进而低估了感兴趣样本数量的不确定性而受到批评。贝叶斯模型平均技术(BMA)是最为流行的模型平均技术之一。但已有研究表明，BMA在某些方面较为繁琐复杂，一个最主要的问题是其巨大的计算负荷阻碍模型了精确估计的过程，因此需要利用马尔可夫-蒙特卡洛(MCMC) 之类的模拟方法进行解决。加权平均最小二乘(WALS)估计可作为BMA的可替换方法，其优点在于计算负荷可以忽略不计，因此不需要采用MCMC方法解决计算负荷问题。此外，WALS估计相比于BMA估计在理论上有一定的优势。本文针对WALS的贡献有两点：将WALS应用于空间滞后/空间误差模型以考虑空间依赖性，并利用主成分回归(PCR)拓展WALS以明确考虑多重共线性问题。本文利用蒙特卡洛实验对所提模型估计的小样本特征进行测试，结果显示当潜在数据生成过程(DGP)是一个空间滞后过程时，标准WALS可能产生有偏估计；此外，当回归量的相关性较高时，标准WALS估计量可能有较大的方差和根均方差(RMSEs).而本文提出的加权平均最小二乘估计模型能很好地缓解这两个问题。 [ABSTRACT FROM AUTHOR]

With the collapse of the bubble economy in the early 1990s, economic disparities have arisen both among the people and in the different regions of Japan. Advancements in spatial analysis have led to the development of strong tools to explicitly consider spatial dependence and heterogeneity; however, no significant research has been carried out using such tools for analyzing the economic disparity at the municipal level in Japan. The objective of this study is to analyze the regional income disparities in Japan during the period known as the 'lost decade.' The data used in this research are annual data collected at the municipality level during 1998-2007. Exploratory spatial data analysis and local indicators of spatial association have been used to classify municipalities into categories and to identify local spatial clustering and spatial outliers leading to income disparity in Japan, respectively. [ABSTRACT FROM AUTHOR]

Abstract: Biomass energy is one of mitigation method of CO2 reduction. In Japan, it aimed to reduce fossil fuels supply 670,000kL of crude oil equivalent in thermal power plants and 340,000kL of crude oil equivalent in the utilization of heat by biomass. It was decided to use 25% or more of the forestry products such as logging residues. Japanese government aim to supply 634PJ of woody biomass for power generation in 2010. This amount of energy accounts for 2.8% of total primary energy. More than 68% of Japan is covered by forests, and more than 40% of these forests are plantations. But the use of woody biomass is limited because it is still not seen as economically viable. In this article, we developed a large scale forestry economic model which can estimate the wood chips supply for coal thermal power plants across all around Japan. By using this model, wood chips supply potential is currently 32,000m3/year and supply will increase drastically when wood chips price increase or carbon credit is installed and we found that biomass production of 15PJ that is the numeric target of Japanese government is possible. Especially, the lengthening of rotation period of forestry and the decrease of wood chips transportation cost is important for wood chips use in coal thermal plant. [Copyright &y& Elsevier]

The purpose of this study was to contribute to filling the knowledge gap in public opinion and knowledge about forest and its certification in Japan, as well as to identify key elements and the possible role of public opinion within integrated bottom-up policies, bridging the sectors of forest, environment and energy. For the study 1930 questionnaires were disseminated in a small town in early 2007. Results from the statistical analysis indicated that forest was perceived as an ecosystem with a protective function against e.g. soil erosion or flooding, rather than a place that might serve for wood production and providing jobs. Forest certification and bioenergy from forest were identified as key elements for future integrated bottom-up policies that need to concentrate on facilitating the linkage between forestry and renewable energy as well as on promoting environmentally sound management and forest certification. [Copyright &y& Elsevier]

Averaged learning subspace methods (ALSM) have the advantage of being easily implemented and appear to outperform in classification problems of hyperspectral images. However, there remain some open and challenging problems, which if addressed, could further improve their performance in terms of classification accuracy. We carried out experiments mainly by using two kinds of improved subspace methods (namely, dynamic and fixed subspace methods), in conjunction with the [0,1] and [-1,+1] normalization methods. We used different performance indicators to support our experimental studies: classification accuracy, computation time, and the stability of the parameter settings. Results are presented for the AVIRIS Indian Pines data set. Experimental analysis showed that the fixed subspace method combined with the [0,1] normalization method yielded higher classification accuracy than other subspace methods. Moreover, ALSMs are easily applied: only two parameters need to be set, and they can be applied directly to hyperspectral data. In addition, they can completely identify training samples in a finite number of iterations. [ABSTRACT FROM AUTHOR]

• Combining PV and EV in a city scale can reduce CO 2 emission by 60–74%. • The PV + EV systems can save energy costs by 22–37% in Kyoto City in 2030. • EV penetration policy should be reinforced integrating with PV systems. • Roof-top PV + EV system in a city-scale is defined as Solar-EV City. • Citizen's decision-making process is key to build PV + EV systems. To minimize the impacts of climate change, it is increasingly clear that global CO 2 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 2 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study provides the first attempt in quantifying the uncertainties in future urban climate projections due to regional climate models and metropolitan-scale urban planning scenarios. Targeted for the 2050s Augusts in Tokyo, Japan, dynamical downscaling simulations are conducted using two regional climate models, the WRF and NHRCM models, both downscaled from the global climate model MIROC5 under the RCP4.5 scenario. Both regional climate models are coupled with appropriate urban canopy models to accurately evaluate the urban climate. The projected 10-year mean temperature increases for the 2050s Augusts in the central Tokyo are roughly 2.4 °C and 2.2 °C, for the WRF and NHRCM models, respectively, with a roughly 0.2 °C difference between the two. Urban scenario experiments with the WRF model indicate that the compact city urban scenario can reduce the August mean temperature of surrounding residential areas by 0.4 °C, while the dispersed city scenario can increase the temperature by 0.1 °C. On the other hand, impact of urban scenarios on the temperature increase in central Tokyo is comparative or less than the surrounding areas. The impacts of urban scenario and regional climate model differences are larger in nighttime than in daytime, but are at most 0.6 °C. The results indicate that the uncertainties with the regional climate models and urban scenario are significantly less than those in emission scenarios or global climate model projections. [ABSTRACT FROM AUTHOR]

Eigenvector-based spatial filtering is one of the often used approaches to model spatial autocorrelation among the observations or errors in a regression model. In this approach, a subset of eigenvectors extracted from a modified spatial weight matrix is added to the model as explanatory variables. The subset is typically specified via the selection procedure of the forward stepwise model, but it is disappointingly slow when the observations n take a large number. Hence, as a complement or alternative, the present article proposes the use of the least absolute shrinkage and selection operator ( LASSO) to select the eigenvectors. The LASSO model selection procedure was applied to the well-known Boston housing data set and simulation data set, and its performance was compared with the stepwise procedure. The obtained results suggest that the LASSO procedure is fairly fast compared with the stepwise procedure, and can select eigenvectors effectively even if the data set is relatively large ( n = 104), to which the forward stepwise procedure is not easy to apply. [ABSTRACT FROM AUTHOR]

Spaceborne light detection and ranging (LiDAR) sensors can accurately observe forest parameters, such as canopy height and forest biomass. In this study, we investigated the potential of spaceborne LiDAR to quantify the effects of forest disturbances caused by typhoons on Hokkaido Island, Japan, after typhoon Songda in 2004. We developed a model to estimate canopy height from the Geoscience Laser Altimeter System (GLAS) of the Ice Cloud and land Elevation Satellite. GLAS waveforms are broadened by slopes (“pulse broadening”), so we used the lead10 and trail10 values (edge extents referring to 10% and 90% of the cumulative energy returned) to correct for this effect, because they were the parameters most strongly correlated with pulse broadening. We developed the estimation model by dividing GLAS data between gentle and steep slopes, using an empirically determined threshold, and obtained a root-mean-square error of 3.5 m for canopy height. We then applied the model to GLAS data in the area damaged by typhoon Songda to estimate changes in canopy height caused by the typhoon. We used a wind damage map produced in a previous study to identify the damage severity. The model showed that canopy height decreased by an average of 2.7 ± 1.8 m (95% confidence interval) in heavily damaged areas. The canopy height decreased most in coniferous forests, and especially in Japanese larch forests, that they have very shallow roots. We also assessed the geographic factors that most strongly influenced wind damage risk by means of multivariate analysis. We found serious damage in areas with a gentle slope that sustained strong winds. Understory plants and topography type also affected the risk. Our results show that spaceborne LiDAR can be used to quantify the severity of wind damage caused by forest disturbances, thereby providing guidance for forest management. [ABSTRACT FROM AUTHOR]

This study investigated the moderation of the urban heat island via changes in the urban form in the Tokyo metropolitan area (TMA). Two urban scenarios with the same population as that of the current urban form were used for sensitivity experiments: the dispersed-city and compact-city scenarios. Numerical experiments using the two urban scenarios as well as an experiment using the current urban form were conducted using a regional climate model coupled with a single-layer urban canopy model. The averaged nighttime surface air temperature in TMA increased by ~0.34°C in the dispersed-city scenario and decreased by ~0.1°C in the compact-city scenario. Therefore, the compact-city scenario had significant potential for moderating the mean areal heat-island effect in the entire TMA. Alternatively, in the central part of the TMA, these two urban-form scenarios produced opposite effects on the surface air temperature; that is, severe thermal conditions worsened further in the compact-city scenario because of the denser population. This result suggests that the compact-city form is not always appropriate for moderation of the urban-heat-island effect. This scenario would need to combine with other mitigation strategies, such as the additional greening of urban areas, especially in the central area. This study suggests that it is important to design a plan to adapt to higher urban temperatures, which are likely to ensue from future global warming and the urban heat island, from several perspectives; that is, designs should take into account not only climatological aspects but also impacts on urban inhabitants. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this study is to analyze the in situ BECCS capacity for green-field bioenergy plants in South Korea. The technical assessment is used to support a policy discussion on the suitability of BECCS as a mitigation tool. We examined the technical potential of bioenergy production from domestic forest biomass. In a first step, the biophysical global forestry model (G4M) was applied to estimate biomass availability. In a second step, the results from G4M were used as input data to the engineering model BeWhere, which optimizes scaling and location of combined heat and power plants (CHP). The geographically explicit locations and capacities obtained for forest-based bioenergy plants were then overlaid with a geological suitability map for carbon storage. From this, a theoretical potential for in situ BECCS was derived. Results indicate that, given the abundant forest cover in South Korea, there is substantial potential for bioenergy production, which could contribute not only to substituting emissions from fossil fuels but also to meeting the targets of the country's commitments under any climate change mitigation agreement. However, there seems to be only limited potential for direct in situ carbon storage in South Korea. [Copyright &y& Elsevier]

• "PV only" system in 2020 is already cost competitive relative to existing systems. • Electricity sharing between houses enhances decarbonization potential of "PV + EV". • Decarbonization potentials of "PV + EV" rapidly increase in residential district. • Decarbonization potentials of "PV + EV" become highly cost-competitive after 2025. • Residential "PV + EV" system cuts CO 2 emission by up to 94 %. 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. CO 2 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. [ABSTRACT FROM AUTHOR]

Abstract: Spaceborne light detection and ranging (LiDAR) enables us to obtain information about vertical forest structure directly, and it has often been used to measure forest canopy height or above-ground biomass. However, little attention has been given to comparisons of the accuracy of the different estimation methods of canopy height or to the evaluation of the error factors in canopy height estimation. In this study, we tested three methods of estimating canopy height using the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice, Cloud, and land Elevation Satellite (ICESat), and evaluated several factors that affected accuracy. Our study areas were Tomakomai and Kushiro, two forested areas on Hokkaido in Japan. The accuracy of the canopy height estimates was verified by ground-based measurements. We also conducted a multivariate analysis using quantification theory type I (multiple-regression analysis of qualitative data) and identified the observation conditions that had a large influence on estimation accuracy. The method using the digital elevation model was the most accurate, with a root-mean-square error (RMSE) of 3.2m. However, GLAS data with a low signal-to-noise ratio (⩽10.0) and that taken from September to October 2009 had to be excluded from the analysis because the estimation accuracy of canopy height was remarkably low. After these data were excluded, the multivariate analysis showed that surface slope had the greatest effect on estimation accuracy, and the accuracy dropped the most in steeply sloped areas. We developed a second model with two equations to estimate canopy height depending on the surface slope, which improved estimation accuracy (RMSE=2.8m). These results should prove useful and provide practical suggestions for estimating forest canopy height using spaceborne LiDAR. [Copyright &y& Elsevier]

Representative Concentration Pathway 6.0 (RCP6) is a pathway that describes trends in long-term, global emissions of greenhouse gases (GHGs), short-lived species, and land-use/land-cover change leading to a stabilisation of radiative forcing at 6.0 Watts per square meter (Wm) in the year 2100 without exceeding that value in prior years. Simulated with the Asia-Pacific Integrated Model (AIM), GHG emissions of RCP6 peak around 2060 and then decline through the rest of the century. The energy intensity improvement rates changes from 0.9% per year to 1.5% per year around 2060. Emissions are assumed to be reduced cost-effectively in any period through a global market for emissions permits. The exchange of CO between the atmosphere and terrestrial ecosystem through photosynthesis and respiration are estimated with the ecosystem model. The regional emissions, except CO and NO, are downscaled to facilitate transfer to climate models. [ABSTRACT FROM AUTHOR]

Abstract: We conducted a spatial evaluation of forest biomass usage using a geographic information system (GIS) for the Japanese town of Yusuhara. In Japan, over 60% of the land is covered with forest, of which at least 40% is artificial forest. However, because of high labor costs, the profitability of forestry is decreasing, so timber cultivation is not done to the extent that it could be, and thinning has to be subsidized. Under these circumstances, much of the forest is deteriorating. Most of the thinning is accounted for by throwaway thinning, in which the resulting wood is not used. However, with the steep rise in oil prices and the intensification of global warming concerns, expectations are rising for the use of biomass energy from thinned timber that has previously been discarded. If thinned timber, logging residues, and offcuts are utilized for biomass energy and their economic value becomes apparent, profitability will improve for both final cutting and thinning. And in addition to forestry activities being invigorated, it will be possible for some of the deteriorating forests (which have associated dangers such as landslides) to recover. However, using thinned timber and logging residues is problematic in that profitability is affected by harvesting costs. Harvesting costs are largely determined by geographic factors and are higher for more distant stands. Accordingly, in this article, operational costs for different stands are calculated using GIS and matched with total demand in the subject region. In addition, stands with lower operational costs are identified and an investigation of a highly feasible use of forest biomass is carried out. [Copyright &y& Elsevier]

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. • The spatial modeling studies of urban carbon emission in China are reviewed. • The available data and methods are summarized. • The strengths and weaknesses of the methods are compared. • Urban forms can affect urban carbon emissions. • Future developments will require a finer spatial resolution and urban form data. [ABSTRACT FROM AUTHOR]

Abstract: We have provided a framework for identifying least-cost sites for afforestation and reforestation and deriving carbon sequestration cost curves at a global level in a scenario of limited information. Special attention is given to country risk in developing countries and the sensitivity to spatial datasets. Our model results suggest that within 20 years and considering a carbon price of US$50/tC, tree-planting activities could offset 1 year of global carbon emissions in the energy sector. However, if we account for country risk considerations–associated with political, economic and financial risks–carbon sequestration is reduced by approximately 60%. With respect to the geography of supply, illustrated by grid-scale maps, we find that most least-cost sites are located in regions of developing countries such as the Sub-Sahara, Southeast Brazil and Southeast Asia. [Copyright &y& Elsevier]

Local climate zone (LCZ) maps have been used widely to study urban structures and urban heat islands. Because remote sensing data enable automated LCZ mapping on a large scale, there is a need to evaluate how well remote sensing resources can produce fine LCZ maps to assess urban thermal environments. In this study, we combined Sentinel-2 multispectral imagery and dual-polarized (HH + HV) PALSAR-2 data to generate LCZ maps of Nanchang, China using a random forest classifier and a grid-cell-based method. We then used the classifier to evaluate the importance scores of different input features (Sentinel-2 bands, PALSAR-2 channels, and textural features) for the classification model and their contribution to each LCZ class. Finally, we investigated the relationship between LCZs and land surface temperatures (LSTs) derived from summer nighttime ASTER thermal imagery by spatial statistical analysis. The highest classification accuracy was 89.96% when all features were used, which highlighted the potential of Sentinel-2 and dual-polarized PALSAR-2 data. The most important input feature was the short-wave infrared-2 band of Sentinel-2. The spectral reflectance was more important than polarimetric and textural features in LCZ classification. PALSAR-2 data were beneficial for several land cover LCZ types when Sentinel-2 and PALSAR-2 were combined. Summer nighttime LSTs in most LCZs differed significantly from each other. Results also demonstrated that grid-cell processing provided more homogeneous LCZ maps than the usual resampling methods. This study provided a promising reference to further improve LCZ classification and quantitative analysis of local climate. [ABSTRACT FROM AUTHOR]

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 2 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. • PV + EV becomes highly cost-effective towards 2030 in Kyoto. • Switching to EV is the most cost-effective option in Shenzhen during 2020s. • PV + EV has the highest decarbonization potentials. [ABSTRACT FROM AUTHOR]