Assessing the multiple benefits of clean energy policies in Asian mega-cities

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： https://doi.org/10.1016/j.ecmx.2025.100874

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： https://doi.org/10.1016/j.ecmx.2024.100713

DOI： https://doi.org/10.1016/j.ecmx.2024.100713

Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)  

Publishing type：Research paper (scientific journal)  

Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： https://doi.org/10.1016/j.isci.2023.106978

Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

Quantifying the co-benefits of renewable energy investments can aid policymakers in identifying technologies capable of generating significant social, economic, and environmental benefits to effectively offset mitigation costs. Although there has been a growing body of work evaluating co-benefits, few studies have compared the potential co-benefits of several technologies across different regions in key countries. This study fills this gap by formulating a new modeling structure to assess the environmental–health–economic co-benefits of hybrid renewable energy systems (HRESs) in different parts of Indonesia. The proposed model is unique in that it incorporates various techno-economic activities to assess air quality, health, and economic benefits and then presents results as part of a cost–benefit analysis. From the intervention scenario, the modeling results show that installing 0.5 GW grid-connected solar PV, 100 MW of wind turbines, and a 100 MW biomass generator to cover a total of 1.64 million residential load units in the Bali province can avoid GHGs, PM2.5, disability-adjusted life years (DALYs), and provide health savings of 1.73 Mt/y, 289.02 t/y, 1648, and 6.16 million USD/y, respectively. In addition, it shows that the payback period is enhanced by one year, while the net present value is increased by 28%. In Jakarta, a 3 GW solar PV plant and a 100 MW biomass generator that supply 5.8 million residential load units can deliver 32,490 averted DALYs and 652.81 million USD/y of health care savings. Nationally, the contribution of renewable energy to the electricity supply mix could grow from the 2020 baseline of 18.85% to 26.93%, reducing dependence on oil and coal contribution by 5.32%.

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

In liberalized electricity markets, formulating the techno-economic functions of each market's participant restricts the resulting market clearing mechanism (MCM) into the market equilibrium. This study aims to synthesize the MCM based on a new perspective that correlates the nationwide energy supply and demand equilibrium with the wholesale market's prices. The nationwide energy capacities are collected from all the Japanese transmission and distribution utilities from 2016 to 2022. The demand curve is structured based on estimating the price elasticity of demand (PED) considering regional and hourly cross-sections using two-stage econometrics. The supply curve is structured based on the merit order effect of all existing energy production technologies. A long short-term-memory forecasting model is employed to capture the non-stationarity and -linearity of the prices. The estimated PED varies spatiotemporally from −0.017 to −0.25. From the synthesized MCMs, Kyushu region's price drop to nearly zero is justified by the 93.5% clean supply and the use of regional interconnections. Nationally, nuclear, hydroelectric, and hydro-storage contributions to the supply mix and the balancing market launching have mitigated the price spike of 2022. In addition, the planned share increase of baseload resources in 2030 drives the prices lower with higher stability versus the current electricity mix.

Language：English   Publishing type：Research paper (scientific journal)  

Real-time pricing demand response programs (RTP-DRPs) are practical measures that ensure the end user’s profitability from using electricity by adjusting the supply and demand equilibrium without activating costly solutions. This study explores the potential of RTP-DRPs by developing and applying a region-wise modeling approach based on maximizing the end user’s social welfare in the wholesale electricity market in Japan. The regions of the wholesale market are classified based on their response into regions with excess supply, regions with high demand burden, and regular suppliers of inter-regional connections. The results revealed that the RTP-DRPs could potentially reduce the peak demand of the residential sector in Chubu, Chugoku, Kansai, Kyushu, Tokyo, and Tohoku by 1.91%–7.81%. Meanwhile, in Hokkaido, Hokuriku, and Shikoku, by 16.13%–22.9%. The avoided greenhouse emission (GHG) in Tokyo is estimated to be 82.6 and 192.2 tons in summer and winter, respectively.

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

With the aim of realizing energy-efficient, personalized, and safe mobility, a novel lane-changing motion planning strategy for personalized energy-efficient autonomous driving is proposed in this research. The key technologies consist of trajectory planning and trajectory tracking. Taking the quintic polynomials as the general trajectory cluster generator, the overall trajectory planning is converted into a constrained optimization problem using the lane-changing duration. The feasible and safe lane-changing trajectories can be extracted from the general trajectory cluster by introducing a stable handling envelope and a safe lane-changing area considering the constraints of vehicle dynamics limitation and surrounding traffic vehicles. A driving style identification module is developed based on multi-class Gaussian process classification utilizing real driving data to determine the trajectories that can characterize personalized features. Reflecting the constraints of feasibility, safety, and personalization on the boundaries of lane-changing duration, an energy-optimal lane-changing trajectory representing a specific driving style can be found and regarded as a reference. To precisely and rapidly control the vehicle to track the reference trajectory, a real-time nonlinear model predictive controller is designed and solved utilizing the parallel method. The algorithms proposed above are integrated and Driver-in-the-Loop experimental verifications are conducted. Experiment results demonstrated that the proposed strategy is able to realize lane change with an energy saving rate of 2.87% to 5.73% compared with human drivers’ maneuver. Comparative simulation with a typical automatic lane-change model also shows the effectiveness of the proposed approach, which is capable of not only accomplishing the energy-efficient lane change but also satisfying human driver’s personalized driving preferences.

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

Other Link： https://www.mdpi.com/journal/sustainability/special_issues/zero_emission_energy_intelligent_buildings?fbclid=IwAR2pQZK6KqSbgPlYo4obOsX6Xa9MGMZzyJ-KRGwh_jdTzuJop2rVMDp85gE

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

Microgrids are essential for creating next-generation energy systems because they allow loads, energy storage systems (ESS), and distributed energy resources (DER) to be efficiently and seamlessly integrated. This study presents the dynamic modeling and simulation of an off-grid direct current (DC) microgrid consisting of the photovoltaic (PV) panel, wind turbine, battery, and a DC load incorporating simple, comprehensible, and well-established component-based power control strategies for quality power output. In addition, the main emphasis is on the real-time experimental validation of the dynamic simulation model achieved by designing and developing an indoor test system architecture. A 48-hourly meteorological dataset from Fukuoka, Japan, was used to validate the developed model. The results show a reasonable range of Root-mean-square deviation (RMSE), suggesting that the simulation model can precisely depict the model the dynamic operation of a hybrid DC microgrid system. Furthermore, detailed scenario analysis for sunny, windy, rainy, and cloudy considering real-time meteorological conditions for 72 h of simulation reveals that the proposed microgrid system can effectively meet the load in any situation with a sufficiency factor above 1, making it a self-sustaining hybrid renewable microgrid for residential areas in Japan. The study highlights the significance of understanding microgrid operation energy management with actual implementation.

DOI： https://doi.org/10.1016/j.enconman.2022.116462

Language：English   Publishing type：Research paper (other academic)  

Language：English   Publishing type：Research paper (other academic)  

Language：English   Publishing type：Research paper (other academic)  

Language：English   Publishing type：Research paper (other academic)  

Language：English   Publishing type：Research paper (scientific journal)  

DOI： https://doi.org/10.1016/j.egyr.2022.09.027

Language：English   Publishing type：Research paper (scientific journal)  

This study investigates the co-benefits from the utilization of the battery-electric bus (BEB) fleet in the Delhi public transportation system as a part of the Delhi electric vehicles policy 2020. To this aim, an integrated quantitative assessment framework is developed to estimate the expected environmental, health, and economic co-benefits from replacing the currently existing public bus fleet with the new BEBs in Delhi. First, the model estimates the avoided emissions from deploying the BEB fleet, using a detailed battery energy simulation model, considering the impact of the battery capacity loss on the annual operational time (hours of service) of the BEB. The annual operational time of the BEB is greatly affected by its battery degradation, which results in time lost due to charging the battery. This indicates that the annual passenger-kilometer (PKM) delivered by the BEB is less than the regular bus, under the same traveling condition. Second, considering fine particles (PM2.5) as the most health-harming pollutant, the model calculates the near roadway avoided PM2.5 exposure in the selected traffic zones of 11 major districts of Delhi, using a Gaussian dispersion model. Third, the near roadway avoided PM2.5 exposure is further used in a health impact assessment model, which considers concentration-response functions for several diseases to evaluate the public health benefits from introducing the BEB fleet in Delhi. The research findings indicate that, the utilization of the new BEB fleet leads to a 74.67% reduction in the total pollutant emissions from the existing bus fleet in Delhi. The results of the integrated co-benefits assessment reveal a significant reduction in PM2.5 emissions (44 t/y), leading to avoidance of mortality (1370 cases) and respiratory diseases related hospital admissions (2808 cases), respectively, and an annual savings of about USD 383 million from the avoided mortality and morbidity cases in Delhi.

Language：English   Publishing type：Research paper (other academic)  

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

This article quantifies the environmental, health, and economic co-benefits from the use of solar electricity and heat generation in the Ger area (a sub-district of traditional residences and private houses) in Ulaanbaatar (UB), Mongolia. The quantification of the featured co-benefits is based on calculating emissions reductions from the installation of the solar photovoltaic (PV) and solar water heaters. A user-friendly spreadsheet tool is developed to shed much-needed light on the steps involved in estimating these co-benefits. The tool simulates the hourly electricity and thermal energy generation, taking into account local meteorological conditions, local geographical data, and technical specifications of the solar power and heat generation systems. The tool is then employed to evaluate two intervention scenarios: (1) Installing 100 MW solar electricity, including both rooftop PV and community grids, to reduce the peak-load burden on the grid; (2) Providing solar thermal heaters for 20,000 households to replace the heating load demand from the existing heat only boilers (HOBs) in UB. The modelling results reveal a significant reduction in GHG emissions and fine particulate matter (PM2.5) (PM that is 2.5 microns or less in diameter) by 311,000 tons and 767 tons, respectively, as well as nearly 6500 disability-adjusted life years (DALYs) and an annual saving of USD 7.7 million for the local economy. The article concludes that the mainstreaming spreadsheet-based estimation tools like the one used in this article into decision-making processes can fill important research gaps (e.g., usability of assessment tools) and help translate co-benefits analyses into action in Mongolia and beyond.

Language：English   Publishing type：Research paper (scientific journal)  

Modern power grids face the challenge of increasing renewable energy penetration that is stochastic in nature and calls for accurate demand predictions to provide the optimized power supply. Hence, increasing the self-consumption of renewable energy through demand response in households, local communities, and micro-grids is essential and calls for high demand prediction performance at lower levels of demand aggregations to achieve optimal performance. Although many of the recent studies have investigated both macro and micro scale short-term load forecasting (STLF), a comprehensive investigation on the effects of electrical demand aggregation size on STLF is minimal, especially with large sample sizes, where it is essential for optimal sizing of residential micro-grids, demand response markets, and virtual power plants. Hence, this study comprehensively investigates STLF of five aggregation levels (3, 10, 30, 100, and 479) based on a dataset of 479 residential dwellings in Osaka, Japan, with a sample size of (159, 47, 15, 4, and 1) per level, respectively, and investigates the underlying challenges in lower aggregation forecasting. Five deep learning (DL) methods are utilized for STLF and fine-tuned with extensive methodological sensitivity analysis and a variation of early stopping, where a detailed comparative analysis is developed. The test results reveal that a MAPE of (2.47–3.31%) close to country levels can be achieved on the highest aggregation, and below 10% can be sustained at 30 aggregated dwellings. Furthermore, the deep neural network (DNN) achieved the highest performance, followed by the Bi-directional Gated recurrent unit with fully connected layers (Bi-GRU-FCL), which had close to 15% faster training time and 40% fewer learnable parameters.

Language：English   Publishing type：Research paper (scientific journal)  

Overcapacity is regarded as an inevitable problem for rapid economic developing countries like China, which also causes serious adverse impacts on the environment and public health. However, few studies have quantified the overcapacity feature and corresponding co-benefit from de-capacity policy. To fill such research gaps, this study constructed a comprehensive assessment model by combining the Data Envelopment Analysis (DEA) model, the GAINS-China (Greenhouse gas - Air pollution Interactions and Synergies) model, and a meta-analysis and health impact assessment module, to measure the capacity utilization rate of 41 industrial sectors in 31 Chinese provinces and forecast the environmental and health co-benefits from de-capacity policy in 2050. Results showed that the capacity utilization rate of China's industry is 64.13% in 2018, which is much lower than the threshold value of 75%, indicating serious overcapacity in China's industry. Capacity utilization rates of light industries are higher (around 70%) than heavy industries (50%–60%), and the capacity utilization rate in East and South-Central China is higher (70%–96%) than West China (below 40%). Under a de-capacity scenario in 2050, China's CO2 and PM2.5 emissions are reduced by 1.05 billion tons (9.6%) and 57.8 kilotons (5.8%), respectively. This reduction in PM2.5 emissions results in a substantial health co-benefit, reducing national premature mortality cases by approximately 792,100 (1.6%). Finally, it is recommended that de-capacity priority be given to industries with low capacity utilization rate, as well as regions with intensive heavy industry or high levels of greenhouse gas emissions, severe air pollution, and dense population.

Language：English   Publishing type：Research paper (scientific journal)  

This paper proposes a real-time dynamic predictive cruise control (PCC) system to minimize the energy consumption for electric vehicles (EVs) under integrated traffic situations with synthetic driving scenarios, considering both constraints from the preceding vehicle and the influence of traffic signal lights. The proposed PCC system is working based on the bi-level model predictive control (MPC) algorithm. The Signal Phase and Timing (SPaT)-oriented MPC calculates a desired acceleration command as the optimal control signal at each sampling step based on the forthcoming SPaT information with the purpose of passing the nearest signalized intersection during the green light interval without stop. The car-following-oriented MPC executes preceding vehicle tracking task through maintaining a safe inter-distance using a customized variable time headway (VTH) strategy. The instantaneous energy consumption for EV in different traffic scenarios was quantified by a data-driven model. The developed system was validated through comparison with IDM and human driver's maneuver in both suburban and urban areas road in the city of Fukuoka, Japan, during off-peak and peak hours, using the real traffic system and SPaT data. To further evaluate the performance of the proposed PCC system in high speed driving situation, another case study with transitions from highway to urban road was conducted. The simulative results showed that the proposed PCC system can realize the energy-saving rates by 8.5%–15.6%. And it was working well and robustly under high speed driving situation.

Language：English   Publishing type：Research paper (other academic)  

Language：Japanese   Publishing type：Research paper (other academic)  

Language：English   Publishing type：Research paper (other academic)  

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

Pakistan’s dependence on energy imports, inefficient power generation and distribution, and lack of planned investment have made the country’s economy vulnerable. Low carbon and resilient climate development in Pakistan can help to ensure climate action and reduce the chronic energy deficit ailing the country’s economy, society, and environment. This study focuses on developing and applying an integrated energy supply-demand modeling framework based on a combination of microeconomics and system integration theories, which can be used to address policies that could dramatically change the future course of Pakistan toward a low emission energy system. The methodology involves medium-term forecasting of energy demand using an integration of top-down and bottom-up modeling approaches. The demand-side model is interlinked with a bottom-up technology assessment supply model. The objective of the supply-side model is to identify the optimal combination of resources and technologies, subject to satisfying technical, institutional, environmental, and economic constraints, using the cost minimization approach. The proposed integrated model is applied to enable a complete perspective to achieve overall reductions in energy consumption and generation and better analyze the effects of different scenarios on both energy demand and supply sides in Pakistan. The results revealed that, in the baseline case, the energy demand is expected to increase from 8.70 Mtoe [106.7 TWh] to 24.19 Mtoe [297.2 TWh] with an annual average growth rate of 6.60%. Increasing the share of renewable energy power generation by 2030 can help to reduce emissions by 24%, which is accompanied by a 13% increase in the total cost of power generation.

Language：English   Publishing type：Research paper (other academic)  

Language：English   Publishing type：Research paper (scientific journal)  

In order to reduce the load demand of buildings in Japan, this study proposes a grid-tied hybrid solar–wind–hydrogen system that is equipped with a maximum power point tracking (MPPT) system, using a fuzzy logic control (FLC) algorithm. Compared with the existing MPPTs, the proposed MPPT provides rapid power control with small oscillations. The dynamic simulation of the proposed hybrid renewable energy system (HRES) was performed in MATLAB-Simulink, and the model results were validated using an experimental setup installed in the Chikushi campus, Kyushu University, Japan. The techno-economic analysis (TEA) of the proposed system was performed to estimate the optimal configuration of the proposed HRES, subject to satisfying the required annual load in the Chikushi campus. The results revealed a potential of 2% surplus power generation from the proposed HRES, using the FLC-based MPPT system, which can guarantee a lower levelized cost of electricity (LOCE) for the HRES and significant savings of 2.17 million yen per year. The TEA results show that reducing the cost of the solar system market will lead to a reduction in LCOE of the HRES in 2030.

Language：English   Publishing type：Research paper (scientific journal)  

Air pollution is a major public health problem. A significant number of epidemiological studies have found a correlation between air quality and a wide variety of adverse health impacts emphasizing a considerable role of air pollution in the disease burden in the general population ranging from subclinical effects to premature death. Health risk assessment of air quality can play a key role at individual and global health promotion and disease prevention levels. The Air Pollution Health Risk Assessment (AP-HRA) forecasts the expected health effect of policies impacting air quality under the various policy, environmental and socio-economic circumstances, making it a key tool for guiding public policy decisions. This paper presents the concept of AP-HRA and offers an outline for the proper conducting of AP-HRA for different scenarios, explaining in broad terms how the health hazards of air emissions and their origins are measured and how air pollution-related impacts are quantified. In this paper, seven widely used AP-HRA tools will be deeply explored, taking into account their spatial resolution, technological factors, pollutants addressed, geographical scale, quantified health effects, method of classification, and operational characteristics. Finally, a comparative analysis of the proposed tools will be conducted, using the SWOT (strengths, weaknesses, opportunities, and threats) method.

Language：English   Publishing type：Research paper (scientific journal)  

The emerging concept of smart buildings, which requires the incorporation of sensors and big data (BD) and utilizes artificial intelligence (AI), promises to usher in a new age of urban energy efficiency. By using AI technologies in smart buildings, energy consumption can be reduced through better control, improved reliability, and automation. This paper is an in-depth review of recent studies on the application of artificial intelligence (AI) technologies in smart buildings through the concept of a building management system (BMS) and demand response programs (DRPs). In addition to elaborating on the principles and applications of the AI-based modeling approaches widely used in building energy use prediction, an evaluation framework is introduced and used for assessing the recent research conducted in this field and across the major AI domains, including energy, comfort, design, and maintenance. Finally, the paper includes a discussion on the open challenges and future directions of research on the application of AI in smart buildings.

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

The government of Indonesia has pledged to meet ambitious greenhouse gas mitigation goals in its Nationally Determined Contribution as well as reduce water pollution through its water management policies. A set of technologies could conceivably help achieving these goals simultaneously. However, the installation and widespread application of these technologies will require knowledge on how governance affects the implementation of existing policies as well as cooperation across sectors, administrative levels, and stakeholders. This paper integrates key governance variables--involving enforcement capacity, institutional coordination and multi-actor networks--into an analysis of the potential impacts on greenhouse gases and chemical oxygen demand in seven wastewater treatment scenarios for the fish processing industry in Indonesia. The analysis demonstrates that there is an increase of 24% in both CH4 and CO2 emissions between 2015 and 2030 in the business-as-usual scenario due to growth in production volumes. Interestingly, in scenarios focusing only on strengthening capacities to enforce national water policies, expected total greenhouse gas emissions are about five times higher than in the business-as-usual in 2030; this is due to growth in CH4 emissions during the handling and landfilling of sludge, as well as in CO2 generated from the electricity required for wastewater treatment. In the scenarios where there is significant cooperation across sectors, administrative levels, and stakeholders to integrate climate and water goals, both estimated chemical oxygen demand and CH4 emissions are considerably lower than in the business-as-usual and the national water policy scenarios.

DOI： https://doi.org/10.1016/j.jenvman.2020.111241

Language：English   Publishing type：Research paper (scientific journal)  

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NATIONAL ACTION ON CLIMATE CHANGE and international negotiations are interlinked and mutually reinforcing. The international negotiations in the past years have stimulated national action, especially on Low Emission Development Strategies (LEDS) meaning development with the minimal output of emissions. This research will try to develop effective science-policy interaction to discuss the opportunities where LEDS can be used to support energy system, environmental, and economic development planning strategies in the city of Shanghai, China. In this paper, we argue that the urgency of bold and timely LEDS coupled with the social, environmental, and economic opportunities. With this in mind, we elaborate an interest-oriented approach to mobilizing multiple benefits of the Shanghai Master Plan as one of the essential LEDS in this city and argue that multiple benefits assessments can be important drivers of ambitious and effective social policy.

DOI： https://doi.org/10.30852/sb.2020.1006

Language：English   Publishing type：Research paper (bulletin of university, research institution)  

Language：English   Publishing type：Research paper (scientific journal)  

Climate change mitigation strategies oer significant societal co-benefits such as
improvement in public health, air quality, local economy, and even safety. By considering these
co-benefits during the transportation planning process, local governments would be able to link their
local appreciate mitigation actions into the Sustainable Development Goals (SDGs), where diverse
objectives should be achieved simultaneously. This study first clarifies the co-benefits approach to
climate change mitigation in the transport system, by introducing an integrated multiple-impact
framework known as A-S-I (Avoid-Shift-Improve) to evaluate the co-benefits. Thereafter, it applies
the quantitative modeling approach to assess public health and environmental co-benefits of the
implementation of the Tehran Transportation Master Plan, “the TTMP” in the city of Tehran, Iran,
which includes targeted interventions such as shifting from private vehicles to the urban transport
system, improving vehicle technologies and introducing alternative fuels. The results from the
application of “the TTMP” reveal a significant reduction of CO2 and other local air pollutant emissions
by 12.9 and 1.4 million tons, respectively, prevention of about 10,000 mortality cases, and more than
USD 35 million savings by 2030

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

This paper proposes an innovative hydrogen-based hybrid renewable energy system
(HRES), which can be used to provide electricity, heat, hydrogen, and water to the small community
in remote areas. The HRES introduced in this study is based on the integration of solar power
generation, hydrogen generation from supercritical water gasification (SCWG) of wet biomass
feedstock, hydrogen generation from solar water electrolysis, and a fuel cell to convert hydrogen to
electricity and heat. The wet biomass feedstock contains aqueous sludge, kitchen waste, and organic
wastewater. A simulation model is designed and used to investigate the control strategy for the
hydrogen and electricity management through detailed size estimation of the system to meet the
load requirements of a selected household area, including ten detached houses in a subject district
around the Shinchi station located in Shinchi-machi, Fukushima prefecture, Japan. As indicated by
results, the proposed HRES can generate about 47.3 MWh of electricity and about 2.6 ton of hydrogen
per annum, using the annual wet biomass consumption of 98 tons, with a Levelized Cost of Energy
(electricity and heat) of the system at 0.38 $/kWh. The implementation of the proposed HRES in the
selected residential area has GHG emissions reduction potential of about 21 tons of CO2-eq per year.

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

In the aftermath of the Paris Agreements, many countries around the globe have pledged to reduce the amount of greenhouse gas emissions being released into the atmosphere. To do so, it is important that the amount of renewable energy in the electricity grid increases. However, there are worries of the capacity of the grid to cope with intermittent energy sources. To assess the feasibility of a 100% renewable energy system in Japan, the authors conducted an hourly simulation of future electricity production based on wind, solar and tidal data. The system was shown to be stable, and the authors calculated the required capacity of electrical batteries that would be necessary to balance such a system.

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DOI： 10.14456/ajmi.2017.16

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Language：Japanese   Publishing type：Research paper (scientific journal)  

Adrian Bejan's remarkable career is a continuous stream of original ideas that earn him recognition time and again. Detailed assessments of his life and scientific contributions were conducted in 2008 [1] and in 2018 [2]. Most recently, he was honored with the Humboldt Research Award for Lifetime Achievement, Turkish Academy of Sciences Prize (2019), Lifetime Achievement Award (IAGE 2021), Kimberly-Clark Distinguished Lectureship Award (InterPore, 2023), and Nautilus Book Award Winner 2023 for his book “Time and Beauty” [3].

As we mark his 75th birthday, we celebrate the enduring evolution of an exceptional career characterized by creativity and profound scientific impact, as well as a multitude of groundbreaking scientific methods and designs. What sets Adrian apart is his consistent effort to build bridges between seemingly disparate fields and individuals. He achieves this through brilliant theories and designs that unify and fortify the scientific landscape.

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Responsible for pages：Routledge Taylor & Francis   Language：English  

Responsible for pages：Institute for Global Environment Strategies (IGES); Ministry of the Environment, Japan   Language：English  

Responsible for pages：Taylor & Francis Group   Language：English  

DOI： 13:978-77188-311-5

Event date： 2022.1

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：Online   Country：Japan  

Event date： 2022.1

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：Online   Country：Japan  

Event date： 2021.12

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：Chikushi Campus   Country：Japan  

Event date： 2021.1

Language：English   Presentation type：Symposium, workshop panel (public)  

Country：Japan  

Event date： 2021.1

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：Ito Campus, Inamori Hall   Country：Japan  

Other Link： http://farzaneh-lab.kyushu-u.ac.jp/Events.html

Event date： 2020.1

Language：English   Presentation type：Oral presentation (general)  

Venue：Ito Campus, Inamori Hall   Country：Japan  

Other Link： http://farzaneh-lab.kyushu-u.ac.jp/Events.html

Event date： 2020.10

Language：English   Presentation type：Oral presentation (general)  

Venue：Tokyo, Japan   Country：Japan  

Event date： 2020.10

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：IGES, Tokyo, Japan   Country：Japan  

Event date： 2020.10

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：Chikushi Campus   Country：Japan  

Event date： 2020.10

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：France   Country：Japan  

Event date： 2020.10

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：Kyoto University   Country：Japan  

Event date： 2020.10

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：IGES, Tokyo, Japan   Country：Japan  

Event date： 2020.10

Language：English   Presentation type：Oral presentation (general)  

Venue：Kuala Lumpur, Malaysia   Country：Japan  

Event date： 2020.10

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：Beijing, China   Country：Japan  

Event date： 2020.10

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：Porto, Portugal   Country：Japan  

Event date： 2020.10

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：Kyoto University, Japan   Country：Japan  

Event date： 2020.10

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：Osaka, Japan   Country：Japan  

Event date： 2020.10

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：Kyoto, Japan   Country：Japan  

Event date： 2016.6 - 2018.6

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：5th International Advisers’ Conference (GSS-Kyoto University)   Country：Japan  

Language：English   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Venue：Energy seminar   Country：Korea, Republic of  

Audience：General,　Scientific,　Company,　Civic organization,　Governmental agency

Type：Seminar, workshop

This opening workshop aims to enable policymakers and researchers in Thailand
to achieve co-benefits in air pollution, climate change and other relevant policies and practices.
It is followed up by two small-group Technical Training Workshops on how to use the tools
presented in the workshop to understand how different technologies can contribute to
sustainable development in the region. The opening section will be broadly organized around
three themes: 1) identifying, 2) quantifying and 3) integrating co-benefits into decisions.