Opportunities and Challenges of Allocation of Alternative Energy Resources in Japan
Bibliographic record
Abstract
In the case of Japan, which lacks any significant fossil fuel reserves (Gasparatos & Gadda, 2009, pp. 4038-4048) some alternative forms of energy production have been implemented. Japan has implemented alternative energy options such as nuclear power, photovoltaic power, hydroelectric power, and other various energy alternatives and renewable resources. These alternatives have the potential to lessen climate change. Japan is becoming an example of the economic and environmental outcomes associated with alternative energy sources. Following the Fukushima disaster, the Japanese government addressed the possibility of removing nuclear power from the energy mix entirely by 2040 (Hong et al., 2013, pp. 418-424). In May of 2012, a governmental advisory board announced its plans to replace nuclear power with a combination of renewable sources and imported fossil fuels by 2030 (Hong et al., pp. 2013, 418-424). Japan intends to supply ten percent of the country’s primary energy using renewable resources by 2020, under the Strategic Energy Plan of Japan (Japan Energy Report, 2013, pp. 1-31). As reported by the Japan Forum on International Relations in 2006, increased energy demands in Asia, accompanied with pressure to reduce carbon emissions, has created uncertainty surrounding Japan’s energy security since it has a large reliance on imported fossil fuels (Nasu & Faunce, 2013, pp. 68-74). The major goals of energy policy in Japan are energy security, economic development and environmental sustainability. To reach these goals, feed-in tariffs were approved by the Japanese government in the summer of 2012, in order to incentivize the use of renewable resources, especially photovoltaic power, and accelerate investment in renewable energy technologies (Frishberg, 2013, pp. 5-6). Since implementing these tariffs, Japan’s supply of solar generated power has grown by forty percent (Frishberg, 2013, pp. 5-6). There are many criteria that need to be met for efficient and useable sustainable energy technologies, such as accessibility to remote locales, user friendliness, adaptability to local conditions, efficiency and reliability (Balachandra et al., 2010, pp. 1842-1851). This criterion can be difficult to attain for large, growing populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".