Regional Assessment of Agricultural Residues for Bioenergy Production in Ghana
Bibliographic record
Abstract
1. INTRODUCTIONEnergy is an important component of socio-economic development (Carley et al., 2011). In every country of the world, energy is needed to provide a range of services including, but not limited to, lighting, heating, cooking, and ensuring mobility. Industrial development cannot happen without access to energy. It has been shown by the Human Development Index (HDI) that access to energy somewhat matches extent of economic growth of a country (Chontanawat et al., 2008). Per capita energy consumption is highest in those countries and regions that are regarded as most developed. These include the United States, Canada, Japan, Europe, China and other notable countries in Asia and Latin America. The countries/regions with the least development are also the regions with the lowest per capita energy consumption. Majority of countries in Sub-Saharan Africa and South East Asia fall into this category. For example, per capita electricity consumption in sub-Saharan Africa [minus South Africa] was 180 kWh in 2010 compared to a world average of 2500 kWh (Bazilian et al., 2012). Only about 31% of the population in sub-Saharan Africa has access to electricity (Suberu et al., 2013). Apart from poor access to electricity, there is also poor access to modern fuels for cooking and heating. Close to 80% of the population in sub-Saharan Africa still rely on woodfuel for cooking and heating (Prasad, 2011) due to a lack of access to modern fuels such as Liquefied Petroleum Gas (LGP) and Natural Gas. This has implications for forestry as rural households resort to the use of charcoal and firewood, which are sourced from or processed from forestry resources.Many of the countries that do not have high access to energy services often rely on expensive energy imports that weighs negatively on their trade balance and leave little for other infrastructural developmental. To curb this situation and to advance home grown fuels, renewable energy has emerged as an alternative source of energy that is promoted globally. Technologies for assessing renewable energy may have higher start-up costs but levelised costs keep decreasing as efficiencies improve. Among the more popular renewable energy sources are solar energy, wind energy and biomass energy. For many countries in Sub-Saharan Africa (SSA), biomass energy is already the most consumed energy source but comes in traditional forms, such as firewood and charcoal. Advancement in technology has provided an opportunity to modernize biomass into cleaner energy carriers, such as liquid biofuels or biogas for transportation, cooking and electricity generation. To meet increasing demand for energy as well as ensure a low carbon future, it is anticipated that renewable energy forms, including modern biomass energy will be integrated into the global energy mix. For most developing countries with high agricultural potentials, the idea of modernizing biomass, especially for application in rural communities, is one that sounds very appealing. Residues from crop production, which come at virtually no cost to rural communities, can be used for the generation of electricity to address rural lighting challenges or to produce biogas to decrease the reliance on traditional biomass. Decreasing traditional biomass use is especially desirable because it has the potential to reduce deforestation. Large-scale deployment of modern biomass energy (which could also mean a lot of small-scale deployment in several rural communities) may help diversify fuel supply in many situations, which in turn may lead to a more secure energy supply with important environmental benefits (Fernandes and Costa, 2010). Like many other developing countries, Ghana is also seeking to become more sustainable in its use of energy resources. In this regard, the country is planning to increase the use of renewable energy in its energy mix. Already biomass dominates the energy mix but it is used in unsustainable combustion routes with very low conversion efficiencies. …
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".