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Climate change is a significant challenge that the medical community must address. Hospitals are large facilities with high water and energy consumption, as well as high levels of waste generation, which makes it important to pursue green hospital initiatives. Neurosurgery requires substantial energy for surgeries and tests. Based on the keywords "Climate change," "green hospital," "neurosurgery," "energy consumption," "environmental impact" listed in this paper, we extracted representative manuscripts, and the practices employed in the authors' hospital were assessed. The "Guidelines for Environmental Consideration in Hospitals" and "Guidelines for the Sustainability of Hospital Environments" have been developed; however, they are not implemented in most hospitals in Japan. Inhalational anesthetics were found to contribute significantly to greenhouse gas emissions. Educating patients and staff and employing the "8 Rs" (rethink, refuse, reduce, reuse, recycle, research, renovation, and revolution) showed promise in achieving green hospital standards. The advent of 'green hospitals' in Japan is imminent. The active participation of neurosurgeons can play a crucial role in diminishing the environmental footprint of health care while simultaneously enhancing medical standards. Given the pressing challenges posed by climate change, there is a critical need for an overhaul of medical practices. It is imperative for neurosurgeons to pioneer the adoption of new, sustainable medical methodologies. [ABSTRACT FROM AUTHOR]

Scheduling workload is a demand response strategy for data centers to reshape electricity usage, which provides an opportunity for them to utilize renewable energy. Enhancing the flexibility of workload scheduling would promote the data center to integrate renewable energy. Considering that the improvement of flexibility in workload scheduling is tightly related to the Quality of Service (QoS) required by IT consumers (ITCs), it becomes desirable for the data center to collaborate with them to further facilitate renewable energy integration in demand response programs. This paper proposes a QoS‐based bi‐level demand response model for the data center, where game theory is adopted to resolve the conflict of interest in the collaboration between the data center and ITCs. At the upper level, the data center is a leader who provides differential monetary incentives to ITCs while dispatching workload to integrate renewable energy. At the lower level, ITCs act as followers who adjust the workload's QoS to enhance the flexibility of workload scheduling. Simulation results show that the proposed model can decrease the operating cost of the data center while increasing the utilization of renewable energy. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

With the world population growth, energy consumption and the rapid development of industrial economy, a large amount of carbon emissions has brought destruction and threats to the earth's environment on which human beings depend. The development of low-carbon economy has become the consensus of governments all over the world and has been vigorously advocated & promoted. This paper focuses on the top five global GDP nations in 2022: The United States, China, Japan, Germany, and Britain. A comprehensive evaluation index system of urban low-carbon economic development level is constructed from four dimensions: economic development level, environmental quality, energy consumption emission intensity and social development speed by using literature review and field interview. The evaluation measures are determined using the TOPSIS evaluation method with entropy weight and the grey relational model, providing a comprehensive assessment of the low-carbon economy's development level in these five countries." Judging from the comprehensive evaluation score, the overall development of low-carbon economy in American cities is in good condition and has reached the development standard of low-carbon economy; Germany and Japan rank second and third, and they are low-carbon economies. Britain ranks fourth in comprehensive evaluation, although it belongs to a low-carbon economy country, but there is still a certain gap with Germany and Japan; There is still a big gap between China and the other four countries. Based on the measurement and evaluation outcomes, it presents recommendations and strategies to foster the growth of low-carbon economies, offering valuable insights for the advancement of such economies across different nations. The research results guide countries all over the world to reduce carbon emissions in the process of economic development, protect the earth environment on which human beings depend, and make a better tomorrow for sustainable development. [ABSTRACT FROM AUTHOR]

This paper proposes an energy management strategy (EMS) based on double‐deep Q‐Networks (DDQN) with demand torque prediction (DTP) to optimize the fuel consumption of hybrid electric vehicles (HEVs) by online utilizing vehicle‐to‐vehicle (V2V) information. The main framework of the EMS is designed as DDQN, combining Q‐learning with the deep neural network to realize real‐time training and online optimization of the torque distribution between the internal combustion engine and the electric motor, and given that the model training easily falls into local minima and overestimation, two different Q‐networks are designed to decouple action selection and target evaluation. Meanwhile, BP‐network predicts future demand torque using the ego vehicle speed, front vehicle speed, and distance between the two vehicles to reduce the burden of model training due to the introduction of traffic information. The predicted demand torque is introduced into the DDQN‐based EMS as states, together with the distance traveled in the driving cycle and the current information on HEV. The effectiveness and adaptability to actual driving cycles of the proposed strategy are verified by comparison with DDQN‐based EMS without DTP and rule‐based EMS. The results show that the fuel consumption using DDQN‐based EMS without DTP is reduced by 4.1% compared with rule‐based EMS. The introduction of demand torque prediction resulted in a further 3.9% reduction in fuel consumption. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Energy internet zones (EIZ) supply multiple energy carriers to customers in certain regions. They can achieve high energy efficiency by energy cascade utilization. In mid‐term, they participate in the wholesale market and retail market simultaneously. Customers can shift their energy use among different hours and energy carriers to satisfy demand according to the retail prices. Unfortunately, multiple energy systems and markets bring various uncertainties on demand and prices. In this paper, the weekly bidding strategy for the EIZ is proposed to determine the optimal bilateral contracts to sign and the optimal energy retail prices. In retail market, the satisfaction degree and elasticity matrix describe customers' response. Distributionally robust optimization is adopted to consider the distributional uncertainties of random variables and Worst‐case Conditional Value‐at‐Risk is employed for risk management. The distributional uncertainties of pool prices and elasticity matrix are discussed in this paper. An EIZ in Shanghai, China is adopted to illustrate the proposed strategy. The results have great resistance to distributional uncertainties. The risk aversion and satisfaction degree affect the EIZ's strategy in wholesale market and retail market, respectively. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

With rapid industrialization, rising fossil fuel consumption, and environmental concerns, developing clean and green energy is an inescapable option. Hydrogen has emerged as a significant potential energy carrier and a viable future replacement fuel for fossil fuels due to its renewable and pollution-free properties. Previous review papers have significantly contributed to the body of literature on the various technologies for producing hydrogen by revealing key insights into their working principles and conditions, as well as the economic and environmental aspects. In addition, they also highlighted the potential pathways to enable the application of these technologies in the context of carbon neutrality. However, these studies have not broken down the evolutionary patterns and developmental progress of either fossil fuel-based or renewable energy-based technologies used to produce hydrogen. In addition, the currently available literature does not contain the most recent research that focuses on the evolution and life cycle of each technology category from a chronological point of view. The key drivers, countries/regions, and their contributions to the field's development have received little attention. As a result, it is critical to monitor technological advances in hydrogen energy production and investigate the key enablers of these advancements. Against this backdrop, the current study employs patent analysis tools to achieve four primary goals: (1) to track the development trends in the field of hydrogen production from 2000 to 2019; (2) to identify and compare the recent development trends in the last five years according to the feedstock, i.e., fossil fuel, water, and biomass-based technologies; (3) to predict the technology life cycle of the two main groups of hydrogen production technologies (fossil and renewable); (4) to identify and compare the key drivers of hydrogen production technologies from a statistical standpoint. The findings of the study may aid in identifying technical prospects in the field of fossil and renewable-based hydrogen production, and decision-makers may use them as a reference in developing a strategic plan for future technological growth. • Patent review on hydrogen production technologies from 2000 to 2019. • Japan, US, China are the leading countries in the field. • Recent developments are focused on renewable energy-based technologies especially water. • Technology maturity rate of renewable-based technologies is about 57%. • R&D expenditure strongly promotes hydrogen production innovations. [ABSTRACT FROM AUTHOR]

The integration of vast quantities of clean energy causes intense impact on the power grid reliability. Digital Twin can be used to handle the high‐growth data flow generated in the power grid, and thus accelerate its digitalization process to fulfill the clean energy consumption target. As the traditional central cloud network structure can no longer effectively support the operation of digital twin power grid, the mobile edge network can be used as a reasonable substitute. To support the information interaction process of the mobile edge network, considering total profit, time delay and energy consumption, this paper proposes the mobile edge network communication and computing resource joint allocation model. On the basis, the GA‐PSO algorithm is proposed to solve the resource allocation optimization function. The feasibility and effectiveness of the proposed model is verified by the related simulation experiment. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper compares the energy consumption of elderly and non-elderly households, recognizing the ever-increasing importance of the aging population for energy policy. Decisions of the domestic (in-home) and transport (out-of-home) sectors at the household level are jointly incorporated by building a multivariate Hierarchical Archimedean Copula model with intra-sector and inter-sector dependencies. Data from a life-oriented behavioral survey in Japan are used, which includes 532 elderly households and 340 non-elderly households. A variety of life-related factors, together with built environment factors, are introduced into the above model. Modeling analysis results show that inter- and intra-sector dependencies are heterogeneous across households in terms of these factors. It is found that almost equal numbers of influential factors are sensitive and insensitive to age-based sample segmentation. Various similarities and dissimilarities across energy types between elderly and non-elderly households are revealed. The paper concludes with a discussion of the policy implications of the results. [ABSTRACT FROM AUTHOR]

Energy‐efficient train operation (EETO) in high‐speed railways (HSRs) is an extra cost‐effective and flexible means to promote energy‐saving. This paper first examines the energy consumption sources and energy‐saving measures of high‐speed trains (HSTs). Then presents the EETO in HSRs, including three categories: energy‐efficient train control, energy‐efficient train timetabling, and EETO considering train timetabling and driving strategy. Next, the current research status and progress on three aspects of EETO in HSRs, namely optimization algorithm, constraint condition, and oriented scenario, are sorted out. Finally, the research on EETO in HSRs has prospected and proposed directions that can study in depth in the future. By summarizing and reviewing the research on EETO in HSRs, it is hoped to furnish a reference for the railway sector to enhance quality, efficiency, and green, low‐carbon development. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper reports demonstration results of non‐contact out‐of‐plane vibration energy harvesting using an electric‐double‐layer electret (EDLE). Unlike contact types or non‐contact in‐plane types, the proposed operation type can be useful in terms of expanding the operating frequency range and simplifying the device structures. A demonstration system is developed, and power generation is confirmed using an EDLE with a surface potential of −1 V under an atmospheric condition. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In order to construct an efficient and reliable global path for unmanned surface vehicles (USVs), this paper proposes a USV global path planning algorithm based on an optimized ant colony algorithm (OACA). The algorithm constructs a USV global path planning model by comprehensively considering energy consumption costs and turning control costs. In order to ensure the adaptability of the model to the task environment, the USV's motion characteristics are analyzed with full consideration of external interference, and on this basis, the characterization of energy consumption costs and turning control costs is completed. In order to obtain the global optimal solution of the model and accelerate the convergence speed of the model, in the initialization process of the ant colony algorithm, the initial pheromone is distributed unevenly by introducing the distance relationship among the intermediate node, the starting point, and ending point to improve the initial search efficiency. In the iterative process of searching for the optimal solution, enhancing the guidance effect of the current optimal solution on the offspring ants by introducing a weight factor to improve the update rule of pheromone, which accelerates the convergence speed of the algorithm. In the last two steps of the deadlock path, the number of lost ants is reduced by introducing a penalty factor to punish the pheromone which can guarantee the diversity of ants, and the algorithm's premature convergence is overcome. The simulation results prove the effectiveness of this algorithm. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In order to provide guidance for demand‐side management and improve energy efficiency, the accuracy of residential electricity demand forecasting plays a significant role. Data‐driven methods and deep learning network methods have been proved an effective methods for time series forecasting. In the context, the current research work proposes a novel neural network model based on convolutional neural network (CNN)‐attention‐bidirectional long‐short term memory (BiLSTM) to predict residential energy consumption. The proposed approach combines CNN, attention mechanism, and BiLSTM, initially implementing CNN to extract the effective features of the original data. Subsequently, the extracted segments are employed as the input of attention‐BiLSTM to predict energy consumption, where the attention mechanism comprehensively considers the output of BiLSTM neurons and assigns weights to neurons at each timestamp. In this article, we evaluate the proposed method using household electricity consumption data. This paper selects different input timestamp lengths (10/60/120 min, respectively) to validate the model's performance. We compare evaluation indicators (root mean square error, mean absolute error, mean absolute percentage error [MAPE]) between the proposed method and the state‐of‐the‐art prediction methods. The experimental result shows that the proposed method achieves higher energy consumption forecasting accuracy and has the lowest average MAPE (3.7%). © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The induction machine (IM) has advantages of simple structure, low cost, reliable operation, and convenient maintenance, and the application of ultra‐high efficiency IM could effectively improve the energy utilization rate and realize the emission reduction. Therefore, in this paper, the radial skewed rotor and the stator energy‐saving winding are first designed to improve the efficiency of IM. Then the Taguchi method, differential evolution algorithm, sequential subspace multi‐objective optimization, and 6σ evaluation criterion are integrated to realize the multi‐objective robust optimization design (MOROD). In the proposed MOROD scheme, the combined surrogate model is also researched to reduce the calculation burden during the optimization process. Furthermore, the MOROD scheme is applied to the design of a 22 kW, four poles ultra‐high efficiency IM to validate its effectiveness. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper deals with load extrapolation methods and algorithms for the estimation of the energy efficiency of induction motors, starting from the electrical data generally available after parameter identification. The main aim of this paper is to help the readers move from the theoretical analysis to the experimental verification, which is suitable to be included in its own testing. For this reason, step‐by‐step algorithms are included in this paper. The first part deals with the determination of energy efficiency based on an original load extrapolation. Finally, experimental verification of the high‐power and high‐voltage induction motors is provided. Comparison between the values of the in–out method and load extrapolation method confirms the validity of the proposed algorithms. Compared to the direct in–out method, the error of the load extrapolation proposed in this paper is less than 1%.The load extrapolation method is mainly used for the high‐voltage and high‐power asynchronous motor. The high‐voltage and high‐power motor can be used to drive a low‐power motor to achieve coaxial twin trawling in the absence of a suitable power supply and matched load. The energy efficiency of a high‐power motor under different loads are measured and estimated using a mathematical fitting method. The estimated energy efficiency curves in the whole load range can be used to evaluate the high‐power and high‐voltage induction motor. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

• Decoupling of GDP from all energy sources under multiple principles are examined. • World economy achieved weak energy decoupling in most years during 2000–2011. • World economy coupled with coal use and tended to couple with cleaner energy use. • None of studied economies achieved long-term GDP-embodied energy decoupling. • Decoupling delusions occurred when only considering direct and total energy use. Decoupling indicators are widely used to understand links between economic growth and energy use. However, traditional decoupling analyses mostly focus on domestic energy consumption (i.e., the production-based principle) and neglect off-site energy use across global supply chains to satisfy an economy's final consumption (i.e., the consumption-based principle). Moreover, analyses for total primary energy conceal an economy's preference for different energy sources. Therefore, this paper evaluates decoupling states of GDP from all types of primary energy use under consumption-based principle, for world economy and eight typical economies during 2000–2011. Regarding total primary energy, world economy witnessed weak decoupling in most years, and most economies studied (e.g., USA, Japan and China) achieved decoupling initially but performed negative decoupling finally. For EU, USA, Japan, Russia and India, production-based decoupling performances were generally better than consumption-based ones. Decoupling phenomena detected under production-based principle even became coupling or negative decoupling under consumption-based principle in some cases. As for each energy source, world economy decoupled from oil use, but still coupled with coal use, and gradually showed a trend to couple with natural gas and renewables use. Different energy sources showed distinct decoupling degrees from GDP, affected by individual embodied energy requirement structure. This paper uncovers potential energy decoupling delusions to deepen the understanding of relationships between energy use and economic growth. [ABSTRACT FROM AUTHOR]

This paper provides a detailed analysis of the Tokyo Metropolitan Emissions Trading Scheme (Tokyo ETS), Japan’s first emissions trading scheme with mandatory cap initiated by the government of Tokyo. Unlike trading schemes in other countries, the Tokyo ETS covers indirect emissions from the commercial sector. It is well known that a variety of market barriers impede full realization of energy efficiency opportunities, especially in the commercial sector. Experiences with the Tokyo ETS should therefore provide important lessons for the design of climate change mitigation policies, especially when targeting the commercial sector. The emissions from covered entities have been drastically reduced from those at the scheme’s outset, with an average 14% reduction as of the end of the first commitment period of five years (2010-2014) compared with 2009 levels. This paper shows that the Tokyo ETS alone did not cause these reductions; there were other drivers. Among them, the energy savings triggered by the Great East Japan Earthquake in 2011 were crucial. The contribution of credit trading, in contrast, was limited since most of the covered entities reduced emissions by themselves. Through an investigation of official reports, an assessment of the emissions data from the covered entities compared to those of uncovered entities and in-depth interviews with firms covered by the scheme, this paper confirms that the main drivers of emissions reductions by covered entities were separate from the ETS. In fact, the advisory aspect of the scheme seems to be much more important in encouraging energy-saving actions. Key policy insights Most of the observed emission reductions were not caused by the Tokyo ETS alone. An advisory instrument was crucial to the effectiveness of the Tokyo ETS. The experience of the Tokyo ETS suggests that making full use of the advantages of emissions trading is difficult in the case of the commercial sector. Price signals have not provided a stimulus to climate change mitigation actions, which implies that establishing a cap to yield effective carbon prices poses a challenge. [ABSTRACT FROM AUTHOR]

In this study, we clarify the effectiveness of the energy‐effective techniques for the occupancy lighting control that is based on an occupancy sensor. The time delay from detecting no occupant to turning off the light is used for performing the occupancy control to avoid erroneously turning off the light (hereafter referred to as false‐off). Our experiment was conducted at an actual home environment over 8 days. We evaluated the performance of an image‐based sensor, which detects the occupants using image‐processing techniques and time‐delay adaptation, which decreases the time delay without false‐off. Compared with a passive infrared sensor, the image‐based sensor increased the average sensing accuracy for occupants with small movements by 21.58% or more. In addition, compared with the no‐adaptation case, the time‐delay adaptation increased the rate of average lighting energy saving by 3.94% or more. However, the experimental results suggested that the parameter of the time‐delay adaptation should be carefully set in terms of the energy‐saving effect and false‐off. The analysis and its practical discussions are included in this paper to achieve further energy efficiency. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

A high‐voltage direct‐current circuit breaker is a key equipment to construct a flexible direct current transmission system. Most of the existing design schemes have some shortcomings such as slow fault‐breaking speed, poor current‐limiting effect, large capacity of the arrester, and long reclosing operation time. To solve the above problems, this paper proposes an H‐type high‐voltage direct current circuit breaker with a current‐limiting function. The current‐limiting inductance is put into operation by judging the fault condition. It has good current‐limiting ability in the process of fault‐breaking and can maintain the dynamic performance of the power grid in the normal operation of the system. It has the advantages of fast fault‐breaking speed, buffered energy consumption, early separation of fault points, and small capacity of the arrester. During reclosing operation, the transfer branch can be turned on at no‐voltage. The different working states in the process of fault‐breaking are analyzed theoretically based on the description of the topological structure and working principle. Finally, using the PSCAD/EMTDC simulation platform, the functional verification and system verification of fault‐breaking and the reclosing operation process is conducted. Compared with the typical scheme, the simulation analysis shows the effectiveness and applicability of the function of fast fault‐breaking and fault current‐limiting. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

• Operating time of SMEs is estimated using a SOM and hourly demand data. • The estimated operating time is used for hourly AC demand disaggregation. • The average errors of yearly AC demand estimation are better than 10%. • Hourly disaggregation highlights season and time period of AC demand peaks. Despite the growing penetration of smart meters and energy management systems in recent years, few tools have been proposed for energy demand data analysis and visualization to promote energy conservation in small- and medium-sized enterprises (SMEs). In this paper, we propose a new method consisting of operating time estimation and simplified disaggregation based only on the SME's hourly electricity demand data and actual outdoor temperature data. The method first estimates the operating time using a self-organizing map, which categorizes the hourly data as operating, half-operating, or non-operating time and eliminates manual parameter adjustment that requires user trial and error. Then, using the estimated operating time, our simplified disaggregation method estimates hourly air conditioning (AC) demand considering the variation in each office's temperature at which air conditioners are switched on. The method was evaluated using a public business energy management system dataset from Japan, and the average errors of yearly AC demand estimation were better than 10%. The outputs of the proposed method may provide information that is more relevant, reliable, and specific to each customer and efficiently promote energy conservation measures in SMEs. [ABSTRACT FROM AUTHOR]

Highlights • Material structure significantly affects lifecycle vehicle energy consumption. • Vehicle energy efficiency increases in the order of EV, FCV, CV, and HV. • Transition of material structure may not significantly mitigate energy consumption. • Overall vehicle energy efficiency may deteriorate in 2050. Abstract Given a fundamental role of automobiles in human society, evaluation of vehicle energy efficiency is of utmost importance. Various reports have been published hitherto concerning well-to-wheel (WTW) fuel consumption at the vehicle operation phase. On the other hand, WTW energy consumption at other lifecycle phases has been scarcely integrated in the assessment of vehicle energy efficiency. Particularly, WTW energy consumption for material structure is significantly associated with fuel economy. As such, this paper firstly analyzes the lifecycle WTW vehicle energy efficiency from the perspective of both material structures at the manufacture phase and fuel consumption at the operation phase for conventional vehicle (CV), electric vehicle (EV), hybrid vehicle (HV) and fuel cell vehicle (FCV). Then, an expected transition of vehicle weight and energy consumption arising from material structural shift through the replacement of steel with aluminum is evaluated. Finally, the overall vehicle energy efficiency in Japan in 2020–2050 is projected. It is discovered that the inclusion of energy consumption for material structure has a significant impact on the determination of the vehicle energy efficiency, particularly for new generation vehicles. WTW analysis at the multiple lifecycle phases may be of use in establishing more comprehensive principles of vehicle energy efficiency. [ABSTRACT FROM AUTHOR]

• Multi-source models help solve energy prediction for different types of buildings. • LSTM networks with multi-kernel maximum mean difference build multi-source prediction models. • Multi-source transfer learning models achieve better average prediction performance compared with single-source models. • Dynamic time warping relatively accurately measures the similarity between building energy consumption datasets. Transfer learning can use the knowledge learned from the operating data of other buildings to facilitate the energy prediction of a target building. However, most of the current research focuses on the transfer of a single source building of the same type or from the same region. A single source domain produces domain shift due to the difficulty of aligning the distribution with the target domain. To address this problem, this paper proposes a novel multi-source transfer learning energy prediction model based on long short-term memory (LSTM) and multi-kernel maximum mean difference (MK-MMD) domain adaptation. This model was used for the short-term energy prediction of different types of buildings lacking historical data. In addition, dynamic time warping (DTW) was used to select the source domain. Multiple multi-source models and corresponding single-source models were compared on a collection of buildings in the Higashida area of Fukuoka Prefecture, Japan. On the experimental datasets, the results showed that DTW relatively accurately measured the similarity between building energy consumption datasets. Compared with the results of the single-source transfer learning models, the multi-source transfer learning models achieved better average prediction performance, and their mean absolute percentage error (MAPE) improved the prediction accuracy by 6.88–15.37%. [ABSTRACT FROM AUTHOR]

This paper aims to offer a quantitative contribution to energy-environment policy in Japan in the aftermath of the Fukushima nuclear power accident. Since then, nuclear power energy supply has almost entirely been banned, and consequently, an intensive search for alternative forms of energy supply (ranging from fossil fuels to renewable forms of energy, such as solar or thermal power) has started. There is no clear-cut direction for energy policy, as each option involves costs and CO consequences; Japan has even withdrawn from the Kyoto protocol. A balanced energy-environment policy is difficult to achieve in the short term, and therefore, there is an urgent need for a comprehensive efficiency and performance analysis of the Japanese energy sector. A popular standard tool to judge the efficiency or performance of a decision-making unit (DMU) is a data envelopment analysis (DEA). The existence of multiple efficiency-improvement solutions has prompted much criticism in recent years. In the meantime, much progress has been made in extending this approach in several directions. An example is the euclidean distance minimisation (EDM) method, based on a generalised distance function, which serves to improve a DMU's performance by tracing the most appropriate movement towards the efficiency frontier. The EDM model is able to calculate an optimal input reduction value and an optimal output increase value in order to reach an efficiency score of 1.0, even though in reality this may be hard to achieve for low-efficiency DMUs. In contrast, high-efficiency DMUs might find it easier to reach an efficiency score of 1.0, or in the case of a super-efficiency DEA, a score of above 1.0. This paper aims to present a newly developed adjusted DEA model, emerging from a blend of the EDM and the target-oriented (TO) approach based on a super-efficiency model, for generating an appropriate efficiency-improving projection model. The TO approach specifies a target-efficiency score (TES) for inefficient DMUs. This approach can compute an input reduction value and an output increase value in order to achieve a higher TES. The abovementioned TO-EDM model will be applied to an efficiency analysis of the energy-environment interface for 10 regions in Japan. The focus will be on one input cost criterion (viz. expenditures) and two output performance criteria (viz. electricity generation and CO emission). Based on an extensive database, a comparative performance analysis of the 10 Japan regions under consideration will be pursued. [ABSTRACT FROM AUTHOR]

This paper aims to estimate the costs and CO 2 emissions of wood biomass co-firing in the Tohoku region of Japan. A woody biomass supply system is designed, which comprises of four processes: collection, resource transportation, preprocessing, and fuel transportation. Preprocessing is further divided into two processes: drying and fuel production. Sawmill residue, construction waste, forest residue, thinning residue, and fruit-tree pruning residue are treated as raw materials. Wood chips, pellets, and torrefied pellets (known as black pellets) are regarded as bio-fuels. The raw-material and bio-fuel transportation pathways are optimized using a geographical information system (GIS). In the Tohoku area, the availability of raw-material is 3.6 PJ/y, and the results indicate that the preprocessing plant locations should be widely distributed in that region, because the resource transportation costs have a significantly greater influence on the costs and emissions than the scale effect of the preprocessing facilities. The chip production case exhibits the smallest energy consumption of 3.8 GJ/t, the lowest supply cost of 1558 JPY/GJ, and the largest CO 2 reduction of 252 × 10 3 t CO 2 /y, whereas the pellet production case exhibits a supply cost and CO 2 reduction of 1643 JPY/GJ and 229 × 10 3 t CO 2 /y, respectively. The energy consumption, energy waste, and cost of torrefaction are greater than the reductions in the transportation energy and cost due to wider distribution of the preprocessing plants; thus, the black pellet case has a larger supply cost than the chip and pellet cases. [ABSTRACT FROM AUTHOR]

This paper aims to advance a model able to explain the synchronization tendency in energy consumption, under the impact of international technology transfers. The key argument is related to the differences in energy consumption efficiency of local and foreign technologies. One direct testable consequence of the model is that there might be a certain correlation between the energy consumption between countries and/or economic areas where a free movement of capital goods and technologies takes place. Hence, it is further tested, in the framework of a multivariate spectrum analysis, the case of energy consumption synchronization between Europe and, respectively, United States and Japan for a time span between 1981 and 2011 based on the data provided by U.S. Energy Information Administration. There are evidences of a leading behavior of energy consumption in United States and Japan in respect to Europe as well as of short-run phase delays of 2 up to 5 months. [ABSTRACT FROM AUTHOR]

Time-of-use electricity tariffs may become more widespread as smart meters are installed across deregulated domestic electricity markets. Time-of-use tariffs and other methods of time-dependant pricing can be mutually beneficial, realising a cost reduction for both energy companies and customers if the customer responds to the price signalling. However, such tariffs are likely to create positive and negative financial outcomes for individuals because of customer engagement and potential peak shifting capacity. Identifying potential reducers or non-reducers beforehand can optimise a time-of-use programme design, in turn maximising the outcome of the programme. This paper provides a statistical model to identify the characteristics of so-called winners and losers - or households that would be better or worse off under a time-of-use tariff - using only ex ante information. The model's accuracy reaches a reliable level using historical electricity load and basic household characteristics. This accuracy can be further improved if online activity data is available - providing justification for digital interaction and gamification in time-of-use programmes. This paper also publishes a new public dataset of 1423 households in Japan, including historical smart meter data, household characteristics and online activity variables during the time-of-use intervention period in 2017 and 2018. • A model to identify household outcomes under a Time-of-Use tariff. • Reliable model accuracy using historical electricity load and basic characteristics. • Created public dataset of energy consumption with online activity variables. [ABSTRACT FROM AUTHOR]

Energy is one of the important elements in the production of goods and satisfies the required services. The economy of Japan is basically dependent on imported energy resources for 97% of its total domestic energy consumption per year, the highest percentage of any major industrialized nations. Thus, reducing dependency and import expenditures are always main issues in the energy policy of Japan. In this study, four indices are constructed to measure Energy Supply Security: Dependency Index, Intensity Index, Local Production Index, and Composite Index. This paper investigates the causal relationship between aggregated and disaggregated levels of energy supply security indices and the input parameters that are: petroleum prices, total primary energy supply, energy consumption per capita, share of renewable energy sources, carbon dioxide emissions, human development index, and the mean of democracy indices of energy supplier countries of Japan for the period of 1975–2007. [ABSTRACT FROM PUBLISHER]

Abstract: This paper quantifies the environmental impacts of the different rail ticketing options available in Japan. It is found that, in terms of their increasing impacts, the ranking is the rechargeable smart card system, the limited use plastic card, and the one-use paper ticket. Touch-and-go ticketing impacts are higher because of limited ticket reuse levels and higher gate energy consumption, especially during standby periods. [Copyright &y& Elsevier]

Abstract: This study examines the economics of energy-efficiency strategies for reducing CO2 emissions in the residential sector in Japan from the perspective of regional characteristics. For this study, the residential sector in Iwate prefecture was selected as representative of rural areas in Japan. In order to promote purchases of energy-efficient consumer appliances, the prefectural government is presumed to reimburse purchasers a part of the cost difference between energy efficient and conventional appliances. This paper begins with a discussion of the prefecture’s financial support for purchasers of energy efficient appliances and assumes that the payments come from prefectural government funds. This paper then looks at the effect of a carbon-tax refund on the reduction of CO2 emissions. The results show that, if half of the households use energy-efficient appliances, then CO2 emissions in the residential sector in the year 2020 will decreases from the BAU scenario, 0.726Mt-C to 0.674Mt-C. However, the Iwate prefectural government expends $105 million annually, which is 1.5% of the total tax revenue in the year 2003. The carbon-tax refund effectively encourages further reductions in CO2 emissions. Under the $20/tC carbon tax, proposed by the Ministry of the Environment, the carbon-tax refund leads to a reduction in residential CO2 emissions from 0.726Mt-C to 0.712Mt-C. [Copyright &y& Elsevier]

While many studies have estimated the energy saving potential of office equipment power management, there is no recent study about the energy saving potential of shortening the power management delay time. In this paper, we estimated the energy saving potential of the complete saturation of power management, and also the additional energy saving potential of shortening the power management delay time for PCs, displays, copiers, and laser printers in Japanese offices. To obtain key data, we conducted the surveys and field measurements of usage in business hours and the turn-off rate at night. We found that office equipment power management can save as much as 3.5 TWh per year, which is nearly equal to 2% of commercial electricity consumption in Japan. We also found that the energy use of office equipment in Japanese offices with no use of power management is even lower than that in US offices with maximum use of power management. This is primarily because the portable computer is much more popular in Japan, and also because the manual-off rate at night is more than twice that in the US. [Copyright &y& Elsevier]

The need for domestic hot water in regions with cool climate represents a significant proportion of domestic energy consumption. The steady increase in the electricity costs and environmental concern from use of fossil-based fuel raises the interest in the search for alternative energy sources. In the case of domestic hot water provision, many governments have initiated a gradual switch to more environmentally friendly systems powered by renewable energy such as solar. Solar water heating (SWH) is a mature technology and is gaining popularity in many countries with increasing number of affluent population in society. The increasing adoption of these systems and technologies is a welcome development; however the robust methods of assessment of their thermal performances are required. This paper presents a comparative study of the methods of evaluation of SWH systems' thermal performance in three countries with increasing hot water systems penetration: Australia, Taiwan and Japan. The aim of this comparative study is to discuss merits and weaknesses of each approach and to explore possible common approach that will improve the existing methodologies. [ABSTRACT FROM AUTHOR]

Japan's energy consumption in 2018 was about 2.5 times that in 1975, with the increase in the household sector being the largest at 28%. Most of primary energy is still fossil fuel, and it is urgent to reduce energy consumption in the household sector. The purpose of this paper was to identify ways to reduce household energy consumption without compromising the quality of life in residence. However, the reduction methods vary by region, building specifications, household type, equipment specifications, season, and weather. The value of this paper is based on a systematic analysis of home energy management systems (HEMS) data from about 50,000 households under various conditions. We are analyzing ways to reduce energy consumption. Few studies have analyzed this much back-up data, which is likely to lead to a reduction in CO2 emissions across the household sector. To explore ways to reduce energy consumption in this sector, the company has introduced and provided services for home energy management systems (HEMS) since 2011 and is currently collecting HEMS data for up to 50,000 households. In order to grasp the actual state of energy consumption in each household, HEMS data are systematically analyzed, necessary conditions for energy reduction and self-sufficiency rate (SSR) improvement are analyzed, and energy consumption under certain conditions is estimated using storage batteries (SB) and heat pump water heaters (HPWH). In addition, energy consumption was investigated by actual measurement and simulation for several hundred households. Since power generation and consumption vary greatly depending on the region, building specifications, household type, equipment specifications, season, weather, etc., it is necessary to analyze these factors systematically. As a conclusion, in order to improve SSR, it is necessary to (1) reduce surplus power consumption and energy consumption of heat pump water heaters (HPWHs), (2) increase solar power generation, and (3) increase the size of SB. This study contributes to the spread of advanced housing and the reduction of CO2 emissions in the household sector. [ABSTRACT FROM AUTHOR]

SUMMARY This paper evaluates the impact of an extensive introduction of electric vehicles (EVs) and plug-in hybrid vehicles (PHEVs) on the energy supply mix in Japan. Energy consumption in Japan's transport sector has been dominated by petroleum, and CO2 emissions in this sector account for 20% of Japan's CO2 emissions. Therefore, the deployment of clean energy vehicles, such as EV and PHEV, is expected to play a significant role in tackling energy security and environmental concerns. In order to assess the energy and environmental benefits of EV and PHEV, we develop an energy system model integrating an optimal power generation mix model and a vehicle penetration model, with consideration of the optimal electricity charging profile of those advanced vehicles. The calculated results suggest that massive EV penetration serves as an energy saving measure in Japan's whole energy system due to significant petroleum reductions exceeding the growth of fuel input into the power generation sector derived from significant EV and PHEV penetration. Massive EV deployment is estimated to contribute to CO2 mitigation in the energy system as well. Evaluating CO2 emissions by mileage by automobile, however, carbon emissions by mileage for EVs is almost equivalent to that of gasoline hybrid vehicles (HEVs) in the case of decommissioning of nuclear power plants, which eventually causes a higher carbon intensity of the electricity supply. [ABSTRACT FROM AUTHOR]

Manufacturing accounts for 37% of the global energy demand. Few researchers have conducted a holistic energy assessment of factories where energy beyond production equipment is accounted for, and environmental impact assessments of machine tool manufacturing are even more limited. All the while, factories need to understand where energy is used, how to allocate it to their products, and where savings lie. This paper quantifies the energy consumption and greenhouse gas emissions of a machine tool manufacturer located in Iga, Japan. Six plants within the factory are investigated whose primary operations range from precision machining to product assembly. Furthermore, a model of the energy consumption of each plant's HVAC system is developed and used to evaluate the environmental impacts and operating costs at alternative sites in China, Germany, and the US. Location was found to have a significant influence on the environmental impacts and resulted in changes in the annual energy consumption relative to the Iga baseline that ranged from −10% to 8%. Additionally, opportunities for improvement were identified by comparing the energy intensities of the equipment within the plants to industrial averages. Since the plants in this study use widespread manufacturing processes and varied climates were investigated, the methods and strategies that were developed can be extended to other discrete manufacturing facilities to identify opportunities for cleaner production. [ABSTRACT FROM AUTHOR]

This paper presents a novel review and laboratory test results of four standard test methods for determining the energy consumption of microprocessor controlled gas tankless water heaters. The test standards examined are from Australia, Europe, Japan and North America. The European test standard is found to be technically the most comprehensive in measuring all aspects of energy consumption for these types of appliances. Four water heaters, both conventional and condensing type, ranging in capacity from 28 to 55kW are tested to each of the test standards. The European and North American tests deliver similar results. Those to the Australian standard yield higher energy use while the Japanese method produces the lowest consumption. The relative difference in energy use between water heater models tested to any single standard is consistent, with the exception that the Australian test appears to favour the smaller water heater. Therefore, while consumers cannot use standard test results to calculate their actual running costs, in general, any of the standards will reasonably predict relative running cost differences between models if used as part of the product selection process. While the correlation between the standards varies according to the capacity of the water heater, a simple approximation is developed to convert from the efficiency measured by one standard to another. [ABSTRACT FROM AUTHOR]

SUMMARY This paper focuses on residential photovoltaic systems in Japan. It projects the installation and installed capacity of residential photovoltaic systems by analyzing the correlation between their payback period and annual installation in new and existing homes. Exponential and hyperbolic curves are used for correlation analysis. However, the exponential curve gives a better fit than the hyperbolic one. PV installation in new houses shows a stronger correlation with the payback period than with existing houses. For the installation projections, target and delayed scenarios are assumed. In the target scenario, the target price is achieved as planned. However, it is achieved later in the delayed scenario. Ongoing subsidies and feed-in tariffs are also assumed. Based on the results of the analysis, the target cumulative installation and installed capacity are achieved in the target scenario. They are not significantly affected even without subsidies and feed-in tariffs. But they are reduced by almost half in the delayed scenario compared with the target scenario. This means that systemic price reductions resulting from research and development as well as mass production are more effective than ongoing dissemination promotion programs. To achieve the target capacity, not only cost reductions but also conversion efficiency improvements through research and development are indispensable. [ABSTRACT FROM AUTHOR]

Based on data of the manufacturing sector of China and Japan from 2003 to 2016, this paper attempts to measure the progresses in energy-biased technology and energy efficiency by constructing a threshold panel regression model with variables including foreign direct investment (FDI) and energy consumption structure to explain energy efficiency using energy-biased technology as the key explaining variable. The estimation indicates significant differences in the energy efficiency of China's and Japan's manufacturing industries. In general, Japan's total energy efficiency is higher than China's. The industry with more intensive technology has higher energy efficiency which rises much faster. The paper finds that the energy efficiency of China's manufacturing sector shows an upward trend in general, while Japan's fluctuates more, showing two peaks and two troughs. Our empirical results show that there is a threshold value of progress in energy-biased technology; below this, progress in energy-biased technology will have a positive effect on energy efficiency and beyond it, the effect will be negative. Since this effect is not one-way, we define it as a 'double-edged effect'. It is estimated that the level of energy-biased technology progress of most manufacturing industries in China is below the threshold value, indicating that the technology progress in China's manufacturing sector has not been excessively biased towards energy consumption, and the impact on energy efficiency is still positive. The China-Japan comparison shows that the threshold value for Japan's manufacturing sector is significantly lower than that for China's, indicating a marginal effect on the 'double-edged effect': The threshold value will decrease when energy efficiency reaches a certain level. Therefore, it is necessary to offset these negative externalities from technological progress with other factors such as by increasing FDI and improving energy consumption structure. • This paper examines the influence of energy-biased technological progress alone on energy efficiency. • It proves the influence of energy-biased technological progress on energy efficiency to be nonlinear. • It measures the energy-biased technological progress and energy efficiency of some industries of the manufacturing sector of China and Japan. • It proves the rebound effect to be conditional in the manufacturing sector of China and Japan. • It also proves the threshold effect in the influence of energy-biased technological progress on energy efficiency. [ABSTRACT FROM AUTHOR]

The equipment energy efficiency improvement policy (EEEIP) is one of the important measures of energy conservation and emission reduction in various countries. However, due to the simultaneous implementation of variety policies, the effect of the single policy cannot be clearly reflected. In this paper, a method of identification and evaluation of EEEIP was proposed, and the application was verified by analyzing the example of EEEIP in Japan (Top Runner policy, TRP). Firstly, through the factor decomposition model, this paper studied the energy conservation and emission reduction potential of this policy area in Japan. Then, the TRP was identified by using moving windows and correlation analysis, and the impact of specific equipment in TRP was analyzed. Finally, through the calculation of the rebound effect of the carbon footprint (REC), this paper analyzed the energy consumption and emission reduction effects of TRP in the short-term and whole life cycle. It showed that the policy has a good effect in tertiary industry and transportation, while the effect in residential is poor. For life cycle, the TRP of air conditioning and passenger car can bring better CO2 emission reduction effect, but the emission reduction effect of lighting is basically offset. [ABSTRACT FROM AUTHOR]

SUMMARY Higher demand for energy consumption and importance of environmental issues has encouraged researchers and policy makers to consider renewable energies more seriously. Geothermal resources are a green energy source that can make a considerable contribution in some countries. Japan has the third ranking geothermal energy potential, and its geothermal electricity production is currently eighth in the world. Since the nature of geothermal resources dictates its method of utilization, it is important to categorize available resources. There is no consensus on classification of geothermal resources. Most scientists, from geologist to engineers, agree on the term temperature. However, temperature or enthalpy alone cannot describe the nature of fluids; they can have same temperature with different phases, such as saturated water or saturated steam. Using exergy for resource classification benefits their comparison, according to their ability to do work. In this paper, exergetic classification of geothermal resources was applied to 18 under-operating geothermal power plants in Japan. Six geothermal fields have high exergy resources according to their SExI values in excess of 0.5. The remaining geothermal fields in Japan are classified in the medium resources zone. Classification results can be used by decision makers as a reference for future geothermal development. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY Reducing the rates of nuclear power generation for all electric power sources has been seriously discussed in Japan since the accident at the Fukushima No. 1 nuclear power plant in March 2011. Thus, the distributed power supply is expected to expand. The local production of safe and clean energy for local consumption is greatly needed in Japan. In this paper, the Saroma Lake green microgrid (SLMG), a fuel cell microgrid using tidal power generation and photovoltaics, has been planned. Energy balance equations were used to investigate this system's method of operation. Further simulated analysis of the facility, its operation cost, and the electric power quality of the network were conducted using MATLAB/Simulink, and the relationships among the capacity of a facility, the cost, and supply rate of green energy and the electric power quality (interphase voltage, frequency, and higher harmonic wave) of the power network were clarified. The total projected cost of the equipment and operation for introducing the proposed SLMG is ¥1,500,000,000/10 years. Increasing the supply rate of green energy and reducing the facility cost will require the introduction of biofuels and the reduction of the facility costs of solid oxide fuel cells in the SLMG. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Decomposition methodologies are requisite to identify the sources of changes in energy use or carbon dioxide emissions. This paper is an inquiry into the theoretical properties of such decomposition methodologies. The study first presents our new decomposition methodology – the Multiple Calibration Decomposition Analysis (MCDA) – as a tool for the investigation. Then, it theoretically reexamines an established decomposition methodology – the Structural Decomposition Analysis proposed by . Subsequently, the study empirically investigates the properties of both methodologies, applying them to an actual case: the changes in energy use and carbon dioxide emissions in Japan during the oil crises period, when the oil price had a significant influence on the economy. The result shows that understanding the theoretical properties of decomposition methodologies is essential for a precise interpretation of empirical results. [Copyright &y& Elsevier]

Abstract: This paper reports the development of a methodology for identifying and improving occupant behavior in existing residential buildings. In this study, end-use loads were divided into two levels (i.e. main and sub-category), and they were used to deduce corresponding two-level user activities (i.e. general and specific occupant behavior) indirectly. The proposed method is based on three basic data mining techniques: cluster analysis, classification analysis, and association rules mining. Cluster analysis and classification analysis are combined to analyze the main end-use loads, so as to identify energy-inefficient general occupant behavior. Then, association rules are mined to examine end-use loads at both levels, so as to identify energy-inefficient specific occupant behavior. In order to demonstrate its applicability, this methodology was applied to a group of residential buildings in Japan, and one building with the most comprehensive household appliances was selected as the case building. The results show that, for the case building, the method was able to identify the behavior which needs to be modified, and provide occupants with feasible recommendations so that they can make required decisions. Also, a reference building can be identified for the case building to evaluate its energy-saving potential due to occupant behavior modification. The results obtained could help building occupants to modify their behavior, thereby significantly reducing building energy consumption. Moreover, given that the proposed method is partly based on the comparison with similar buildings, it could motivate building occupants to modify their behavior. [Copyright &y& Elsevier]

This paper examines whether voluntary actions can lead to manufacturing sector investment that reduces environmental impact. Our approach relies on unique firm-level panel data. First, we investigate the effects of the Keidanren's (Japanese Business Federation) voluntary action plans (VAP) on investment in energy efficiency and environmental preservation. We also consider the type of VAP used by a firm, whether absolute or relative targets. We conclude that firms with absolute targets are more likely to invest in energy-efficiency processes than those with relative targets. Our simulation confirms that the use of absolute targets can increase investment in energy efficiency by 7%. Hence, we confirmed that voluntary action plans with absolute targets are effective in increasing investment related to energy efficiency. Second, we examine whether VAP promotes investment in low-emission processes at the expense of productive investment. We conclude that VAP promotes investment in energy efficiency and protecting the environment by reducing productive investment. Finally, our results did not verify the existence of a liquidity constraint for emission reduction investment for large firms. [ABSTRACT FROM AUTHOR]

This paper presents a societal level exergy analysis approach developed to analyse transitions in the way that energy is supplied and contributes to economic growth in the UK, the US, Austria and Japan, throughout the last century. We assess changes in exergy and useful work consumption, energy efficiency and related GDP intensity measures of each economy. The novel data provided elucidate certain characteristics of divergence and commonality in the energy transitions studied. The results indicate that in each country the processes of industrialization, urbanisation and electrification are characterised by a marked increase in exergy and useful work supplies and per capita intensities. There is a common and continuous decrease in the exergy intensity of GDP. Moreover for each country studied the trend of increasing useful work intensity of GDP reversed in the early 1970s coincident with the first oil crisis. [Copyright &y& Elsevier]

Abstract: A model is developed that simulates nationwide energy consumption of the residential sector by considering the diversity of household and building types. Since this model can simulate the energy consumption for each household and building category by dynamic energy use based on the schedule of the occupants’ activities and a heating and cooling load calculation model, various kinds of energy-saving policies can be evaluated with considerable accuracy. In addition, the average energy efficiency of major electric appliances used in the residential sector and the percentages of housing insulation levels of existing houses is predicted by the “stock transition model.” In this paper, energy consumption and CO2 emissions in the Japanese residential sector until 2025 are predicted. For example, as a business – as-usual (BAU) case, CO2 emissions will be reduced by 7% from the 1990 level. Also evaluated are mitigation measures such as the energy efficiency standard for home electric appliances, thermal insulation code, reduction of standby power, high-efficiency water heaters, energy-efficient behavior of occupants, and dissemination of photovoltaic panels. [Copyright &y& Elsevier]

In this paper we draw up future roadmaps for technologies, policies, and optimal investment timing toward the achievement of a low-carbon society (LCS) in Japan by 2050. Future pathways for Japan to follow are calculated using an analytical model based on a backcasting methodology. Early actions can lead to pathways for minimizing the costs toward a LCS in Japan. However, to take early actions, large investments will be needed at the initial stages. The investment requirements for the residential and commercial sector and transportation sector will be especially costly: The former reaching 2.5 trillion JPY annually from 2010 to 2025 and the latter reaching 2.5 trillion JPY annually from 2010 to 2015. There are four main advantages to early actions. (1) Technologies have learning-by-doing effects: The additional cost of reducing CO2 emissions will fall as the technologies spread. (2) If actions are delayed, learning-by-doing effects may fail to work sufficiently, resulting in higher total investment requirements for achieving a LCS. (3) No infrastructure can be built immediately; hence it would be difficult to switch suddenly to a LCS in the years just before 2050. (4) Future technological development has several uncertainties. If the development of one of the currently dominant technologies falls behind schedule, it will fail to spread as expected and CO2 emission targets will not be met. Early actions will open up new opportunities for the spread of alternative actions toward the LCS should a dominant technology fail in some way. [ABSTRACT FROM AUTHOR]

The aim of this paper is to define low carbon society (LCS) based on the phase diagram with the variables from the IPAT [Human Impact (I) on the environment equals the product of population (P) affluence (A) and technology (T)], starting from the problem definitions: (1) Economy is not everything for LCS and (2) it is not clear if speed on the constraint is more important than liberation from it. To tackle these problems, two research questions are set: (1) What are the basic indicators, objects, and constraints to shape the argument of LCSs and (2) what are the historical paths of several countries and what can be said for their future paths toward carbon societies? Time series data from 1900 or before are used, while the phase diagram with the variables from the IPAT equation is used as the core methodology. In addition to the IPAT variables, the importance of land per capita shall be considered as another basic indicator for LCS related to the carrying capacity. The three different kinds of objects [i.e., total gross domestic product (GDP), GDP per capita, and social indicator] and the two constraints (i.e., total emission and emission per capita) are considered, and it is shown that the combination of these objects and constraints strongly affects the argument of the carrying capacity of climate and land, development of economic system and economic man, and other human developments. Among the four cases analyzed in the phase diagram, the case where GDP per capita is object and total emission is constraint is given the most attention and requires the further research in the future since it does not contradict with a definition of LCS. It is also suggested that if changing the direction of the path with low carbon technology development takes time and cost, institutional arrangement would be additionally necessary in addition to market mechanism where optimal solutions are found on constraint. [ABSTRACT FROM AUTHOR]

Abstract: China''s demand for energy has grown to fuel its rapidly expanding industrial, commercial and consumer sectors. At the same time, China has become the second largest consumer of petroleum products having surpassed Japan for the first time in 2003. The environmental consequences of a continuation of these trends will have global implications. Government policies and consumers have become more environmentally aware, but the ability of governments to formulate policies has been hindered by the lack of data on inter-factor and inter-fuel substitution possibilities. In this paper Allen partial elasticities of factor and energy substitution, and price elasticities of energy demand are calculated for China''s industrial economy using a two-stage translog cost function approach for the period 1995–2004. The results suggest that energy is substitutable with both capital and labor. Coal is significantly substitutable with electricity and slightly complementary with oil, while oil and electricity are slightly substitutable. China''s energy intensity is increasing during the study period and the major driver appears to be due to the increased use of energy-intensive technology. [Copyright &y& Elsevier]