Toshihiko Nakata

A novel approach for analyzing the food-energy nexus through on-farm energy generation

To find a balance between food and energy security, this research presents the design of an energy self-sufficient farm in electricity, heat and bioethanol, which is produced by energy crops and agricultural residues. The farm proposed is evaluated by two models: Land Optimization and Cost Optimization. Due to the food–fuel debate over land and the detriment of food security, this research proposes utilizing the current abandoned land and increasing the food self-sufficiency ratio (FSSR) of the crops analyzed (rice, wheat and maize). The farm is optimized for several food and fuel demands, with a maximum farm unit size of 100xa0ha. The result is a myriad of farms of different sizes, each optimized for a certain demand. Subsequently, the amount and variety of such farms are optimized maximizing the food and fuel produced for each city of the case study (Miyagi Prefecture, Japan). The results suggest that the establishment of energy self-sufficient farms in the abandoned land can stimulate the biofuel industry and increase food security simultaneously. The FSSR of maize and wheat can be improved in approximately 10–25 and 7–9xa0%, respectively. The estimated bioethanol potential is 3.2–3.8xa0ML. Additionally, a surplus of electricity and heat, approximately 61–65xa0GWh and 60–165xa0MJ, respectively, is obtained. As the Land Optimization model proposed is sensitive to crop yields, a simultaneous evaluation is recommended. The results also suggest that the farms must be larger than 8xa0ha to achieve self-sufficiency; therefore, the policies involved need further evaluation.

Induced technological change and the timing of public R&D investment in the Japanese electricity sector considering a two-factor learning curve

The UNFCCC has stated that energy policies and measures to address climate change should be cost-effective to ensure global benefits at the lowest possible cost. To mitigate the bulk of carbon emission from the electricity sector, a large market penetration of renewable energy technologies with as low cost as possible is a key research topic. The energy-related R&D policy in Japan aims to achieve a green economy. In our study, based on this context, we demonstrated how to optimize the timing of public R&D investment within the framework of a bottom-up partial equilibrium model. The developed optimization model represents the Japanese electricity sector and minimizes the total system cost subject to an accumulated carbon emission constraint. Our main research focus is the role of R&D activity, especially in the innovation stage of renewable technologies. We employ a two-factor learning curve and quantify the impact of the learning effect on the dynamic diffusion of major renewable technologies. The study shows a dynamic technology transition in the Japanese electricity sector and the optimized R&D investment schedule for each renewable technology. With the first-best energy policy, an R&D budget of more than 2000 million USD would be allocated to PV in 2050, which corresponds to 45% of the energy-related Japanese R&D budget in 2050. Because some have criticized the uncertainty problems with dynamic simulations and learning models, supplemental sensitivity analyses are included.