Quantitative data analysis

Quantitative data analysis includes the use of a computer program, software and models which consume skill, time and cost. Hence, before the start of quantitative data analysis, the researcher should revisit the hypothesis, research questions, aims and objectives to assess precision, predictivity and to be aware of how much effect will occur if any methodological shortcomings appear (O'Leary, 2013). The researcher is like an instrument for data collection and analysis, hence the quality of the result depends on the researcher's data management performance, understanding of variables and an appropriate use of statistical tests (O'Leary, 2013). However, results should be presented in a systematic and logical fashion so that the research questions

could be addressed consistently and increase the reader’s understanding.

The quantitative data analysis approach in this study involved: (i) analysis of household characteristics and energy use patterns; (ii) assessment of the potential for improving biogas production efficiency; (iii) financial assessment of biogas systems; and (iv) impact analysis on energy consumption and corresponding environmental emissions.

Household characteristics and energy use pattern analysis

Descriptive statistics were used to analyse the characteristics of the surveyed households. This included the socio-economic characteristics of the households in

Problem identification

Information collection

Data collection in typical two rural communities (biogas household survey, key informants’

interview, Informal discussion/observations, document analysis)

Expected outcomes Biogas replacing traditional fuels Increase in biogas production through co-digestion Lower cost of energy after biogas Reduction in energy consumption and GHG emissions

Benefits Greater energy security and gender benefits

Cost-effectiveness: Lower per unit cost of energy Energy demand

and consumption pattern analysis

Analysis of potential for improving biogas production efficiency

Data Analysis Financial

assessment of biogas system Insufficient biogas production Slower replication of biogas technology Impact analysis on energy consumption and emissions Reduced impacts on health and environment

110 terms of existing practices of biogas production and utilisation, such as energy use practices, size and age of biogas plants installed, use of biogas, feedstock availability,

and reduction in women’s workload. Basic statistical tools, such as frequency distribution, mean, standard deviation and range were used to analyse the data. A comprehensive analysis of household energy consumption patterns for cooking before the use of biogas, and changes in the consumption and quantity of fuels after the use of biogas, were also analysed in order to examine the role of biogas in replacing conventional fuel sources. Analysing the changes in energy use patterns after the use of biogas is important for measuring the potential and real benefits from biogas systems. The data was analysed using the Microsoft Excel and Statistical Package for Social Sciences (SPSS), and the results were summarised and presented descriptively using tables or charts. Such analysis was also used to interpret the results of the quantitative outcomes.

In order to identify the daily biogas deficit, and examine how the users coped with lower gas production, the demand and consumption patterns of biogas were analysed both for the summer and winter seasons. Although it was difficult to measure the actual daily biogas demand, the average daily biogas demand was measured by multiplying gas consumption rate of the stove (R) with average daily required operating time (T) (Equation 4.1) (Werner, Stöhr, & Hees, 1989). It is assumed that the stoves were used at their full capacity (based on the respondents' responses), and the gas consumption rate of the standard biogas stove at full capacity is 0.4m3/hour (BSP-Nepal, 2012). The actual daily biogas consumption was calculated by multiplying the gas consumption rate of the appliances (R) with the daily actual operating time (t) (Equation 4.2). The average daily biogas demand and consumption was recorded for major uses of biogas29, e.g., cooking two meals, breakfast and snacks, based on the respondents' daily practice. The responses were also verified by the researcher herself in a few households during observation/informal discussion. The difference between the demand and consumption gives the biogas deficit (Equation 4.3). The use of other energy sources to cope with the biogas deficit was then analysed.

Average daily biogas demand (D) = R1T1 + R2T2+ ………+ RnTn (Equation 4.1)

Actual daily biogas consumption (C) = R1t1 + R2t2+ ………+ Rntn (Equation 4.2)

Daily biogas deficit = D – C (Equation 4.3)

(Biogas is surplus, if C >D)

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111 where,

R1, R2, ……,Rn are the consumption rate of the appliances 1, 2, …., n;

T1, T2, ……..,Tn are the required operating time of the appliances; and

t1, t2, ………, tn are the actual operating time of the appliances.

Measuring biogas production efficiency

In order to evaluate the performance of the biogas plants under study, the efficiency of the biogas plants was calculated. The efficiency of a biogas plant is defined as the ratio of output to input, where feedstock added to the digester is considered as input and gas produced from anaerobic digestion is the output (Equation 4.4) (Ghimire, 2005; Postel, Schumacher, & Liebetrau, 2012). The efficiency was calculated in terms of theoretical biogas production based on: (i) plant size (feeding of prescribed quantity of feedstock); and (ii) actual daily feeding. It was considered for this calculation that one kg of dung produces 40 litres (0.04 m3) of biogas (BSP-Nepal, 2012; Ghimire, 2005; Werner, et al., 1989). The actual biogas output was calculated based on the gas being used per day, considering that a biogas stove burn in full capacity consumes 400 litres (0.4 m3) of gas per hour30 (BSP-Nepal, 2012).

Actual biogas production (output)

Efficiency (%) = --- x100 (Equation 4.4) Theoretical gas production based on feedstock input

In order to assess the potential for improving the biogas production efficiency, this study considered co-digestion of agricultural residues with animal dung as a way to enhance biogas yield and supplement feedstock deficit experienced in the biogas households. Biogas production from manure-only digestion as practised in Nepal has a relatively low gas yield (IEA Bioenergy, 2005) and was not sufficient to meet the energy demand for cooking. The possibility of using other organic wastes, such as agricultural residues as co-substrate, could improve biogas production efficiency and provide a viable option fulfilling the energy demand (Jingura & Matengaifa, 2009; Lungkhimba, et al., 2011). The literature suggests that co-digestion can increase biogas yield by 50- 200% more than single feedstock digestion, depending on the operating condition and co-substrate used (Alvarez, et al., 2010; Amon et al., 2006) by providing an opportunity to optimise biogas production by improving nutrient balance from a variety of substrates (e.g., Alvarez, Mace, & Llabres, 2000; Jingura & Matengaifa, 2009; Poschl, et al., 2010; Rao & Braral, 2011). In this study, three different methods of methane yield prediction, namely, (a) elemental composition analysis; (b) chemical oxygen

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112 demand (COD) stabilisation; and (c) organic composition analysis were used to estimate biogas yields from individual feedstocks (dung, crop residues and human excreta) and different proportions of co-digestion mixtures as co-substrates (Hansen, 2005; Labatut & Scott, 2008; Shanmugan & Horan, 2009; Tchobanoglous, Burton, & Stensel, 2003). The yields were then compared to estimated increases in biogas production as a result of co-digestion. VMP was calculated to predict how much methane could be produced per day. The methods are described in detail in Chapter 6.

Financial assessment of biogas system

Co-digestion of crop residues to supplement feedstock deficit and improve biogas production efficiency might incur additional cost. Therefore, a financial assessment of a biogas system was conducted in order to ensure that biogas production is a viable and affordable option, even after the co-digestion, in terms of total annual cost of energy compared to the cost before biogas or without co-digestion. The basic underlying assumption of financial assessment is that households will adopt co-digestion practices if it is cost-effective and at the same time reduces the demand for unsustainable (e.g., fuelwood) and expensive (e.g., LPG) energy sources. The analysis looked at what was the total annual cost of energy before the installation of biogas plants, total existing annual cost of energy after the use of biogas, and the expected total cost of energy after co-digestion of crop residues with dung.

In this study, all the costs were valued from the user’s point of view, because the costs

depend upon the use of inputs and outputs by a particular user. The evaluation was carried out assuming that all capital costs for the systems were expended in the first year, while the operation and maintenance costs were constant over the life of the system. The results of the assessment are presented in Chapter 7.

Impact of biogas on energy consumption and associated emissions

In order to ensure that biogas is a cheap, efficient and adequate source of renewable energy to provide energy security for rural households in Nepal, it is important to look at the impact of biogas use on energy demand and its environmental dimensions. A comprehensive analysis was made of the potential of biogas to reduce energy demand and environmental emissions using the LEAP model under different technical and policy scenarios. LEAP, a widely-used modelling tool for the study of integrated energy policy analysis and climate change mitigation assessment, was selected as the most suitable model for this study from among the existing similar models (see Chapter 8). Four different scenarios were generated based on energy use information and related

113 policy conditions such as biogas and subsidy policy: reference or business-as-usual, improved production efficiency, increased subsidy, and integrated scenario (with cumulative effect of improved biogas production and increased subsidy scenarios) to examine how much conventional fuels, particularly fuelwood and corresponding emissions, could be reduced under different scenarios. The analysis was also focused on the potential of biogas to reduce non-energy emissions and save social costs, including externality costs and carbon credit earning. Details of policy options/conditions and various assumptions for the scenario generation and the empirical results of the LEAP model are described in Chapter 8.