Life Cycle and Emergy Based Design of Energy Systems in Developing Countries: Centralized and Localized OptionsBerrin Kursuna,b, Bhavik R. Bakshia, Manoj Mahatac, Jay F. Martind

aWilliam G. Lowrie Department of Chemical and Biomolecular Engineering,The Ohio State University, Columbus, OH 43210bDepartment of Chemical Engineering Marmara University Goztepe, Istanbul, Turkey 34722cDevelopment Alternatives New Delhi, India 110016dDepartment of Food, Agricultural and Biological Engineering The Ohio State University, Columbus, OH 43210

The supporting information on designing sustainable energy systems for Rampura village is organized as follows:

1. Background process information related to localized energy technologies analyzed. Background information related to clean coal technologies can be found in Kursun et al and in its supporting information (Kursun et al, 2014).

2. Fundamental knowledge related to life cycle assessment, emergy analysis and their joint use.

3. Details of application of life cycle assessment and emergy analysisregarding each technology.

4. Economic assessment details5. Linear programming underlying data, calculations and summary of results6. Emergy analysis of domestic sector in Rampura7. GHG mitigation potential details

1 Processes1.1 Floating-Drum Biogas Digester

The plant we evaluated is a floating drum biogas digester of 60 m3 biogas production capacity which is presented in figure 1.1. The main body of plant is built using bricks and the floating drum is made of steel. In a floating drum digester, the pressure of biogas is kept constant (Kalia and Singh, 1999). Floating drum digesters generally produce 1 m3 biogas per 25 kg wet manure fed to the digester under ideal conditions. Hence for 10 m3 capacity biogas digester 250 kg wet cow dung should be fed to the digester (Kalia and Singh, 1999). If enough cow dung is not added, then it necessitates more than 25 kg manure to produce 1 m3 biogas.

The digester evaluated is situated in a cowshed in Jhansi, Uttar Pradesh in India. Since, cows are holy in India, scrap cows are not slaughtered, but kept in cowsheds after they are discarded. Our partner Development Alternatives established such a cowshed and utilizes the manure from those cows to produce electricity from biogas to supply energy needs for the income generating activities in their center.

Currently, 345 kg cow dung is fed to the digester everyday with the same amount of water producing 8.5 m3 of biogas and slurry which is 50-75 % of the dry weight of cow dung fed by weight (Kalia and Singh, 1999). The slurry produced can be utilized as organic fertilizer. Biogas is 60-65 % consists of methane, rest being CO2 (Zhou et al, 2010).Produced biogas is combusted in a 100% biogas operated generator producing 6 kWh of electricity per day on average. However, 850 kg cow dung should be fed to be able to supply all the energy demand of the center which constitutes the requirement case of 20 kWh of electricity generation from 28.5 m3 biogas daily. A third scenario investigated is full capacity case in which 1500 kg wet cow dung is fed to the digester and 60 m3 of biogas is produced. In this case, 42 kWh of electricity can be generated daily.

Figure 1.1: Floating drum biogas digester in cowshed in Jhansi and 7.5 VA electricity generator.

1.2 Down-draft Biomass Gasifier

The biomass gasification system under study is a 100 kW capacity downdraft biomass gasifier utilizing locally available woody biomass ipomea. A down-draft gasifier is shown in1.2. The synthesis gas produced contains 15-30% CO, 10-20% H2, 2-4% CH4, 5-15% CO2, 6-8% H2O and the remainder is N2 (Development Alternatives,2011). In our calculations, the content of producer gas is assumed as 23 % CO, 15 % H2, 3 % CH4, 10% CO2, 7 % H2O and 42% N2 by volume. These are the average values. The density of producer gas is 1100 g/m3 and energy content is 4.7 MJ/m3. The system under study is an air blown gasifier which is the reason of

high N2 content and low calorific value of the producer gas. Producer gas is then combusted in a diesel engine in dual fuel mode to generate electricity (Development Alternatives, 2011).We consider three operation schemes in analysis of biomass gasification technology. Current case represents the current operating scheme in Development Alternatives campus in Orccha. A diesel engine generates electricity, utilizing producer gas from the gasifier and diesel in dual fuel mode and produces 17420 kWh electricity utilizing 20295 kg of ipomea and 1665 liters of diesel per year. Second scenario is ideal case operation in dual fuel mode. In this scenario, we assumed the biogas plant operates with 70% efficiency and 6 hrs per day generating 420 kWh electricity daily, resulting in 153300 kWh of electricity generation per year. In this scenario, the plant utilizes 184000 kg of ipomea and 15330 liter of diesel per year. 153300 kWh of electricity is also generated in third scenario, however utilizing a natural gas engine operating with producer gas only in single fuel mode which utilizes 261000 kg of ipomea is utilized per year.

Figure 1.2: A down-draft biomass gasifier (APL(a)).

1.3Multi-crystalline Solar PV Plant

The solar system studied is an open ground mounted mc-crystalline silicon photovoltaic (PV) power plant with a-8.7 kWp generation capacity which is shown in figure 1.3. This system has a life time of 20 years. PV power plant consists of 60 panels, each having 50 solar cells that are connected in series and parallel to form a 67.5 m2 photo-sensitive area and a total area of 74 m2 after being framed and mounted. The PV system captures solar radiation and produces direct current electricity which is then converted to alternate current by the inverter. There are two inverters of 5 kW capacity and a battery bank of 24 batteries, each of 2 V to provide a back-up of 3 days. The electricity generated is distributed to the village Rampura via a 0.75 km mini-transmission line. This system has electrified the village since January 2009. It provides the energy for lighting of the village and for a small flourmill enterprise of power 3 HP. 44 out of 69 households are connected to the solar grid. Utilization of solar

power in the village has resulted in 2000 liter/year kerosene savings which was used for lighting in the village prior to solar electricity (Development Alternatives, 2011).

Figure 1.3: 8.7 kWp capacity mc-Si solar PV in Rampura village.

2 Analysis Techniques2.1 Life Cycle Assessment (LCA)

LCA ,taking its roots from traditional energy analysis, LCA has evolved into “environmental burden” analysis in 1970s further improving into a “life cycle impact assessment” in 1980s and 1990s (Guinee et al, 2010). Starting from resource extraction, material processing, transportation, use, maintenance and disposal stages and their impacts are considered in LCA (Guinee and Heijungs, 2005).

In development of LCA, 1970-1990 were the “decades of conception”. From 1990 to 2000 has been the decade of “standardization by the efforts of Society of Environmental Toxicology and Chemistry (SETAC) and International Organization of Standardization (ISO) to create a framework for LCA. 2000-2010 has been the decade of “elaboration” in which many support tolls for LCA were developed (Guinee et al, 2010).

2.1.1 Life Cycle Assessment Framework

According to ISO14040 standards, LCA framework consists of four stages. These are goal and scope definition, inventory analysis, impact assessment and interpretation (ISO, 2006). Figure 2.1presents the conceptual framework of LCA and its applications.

In goal and scope definition stage, LCA practitioner should define the motivation to perform the LCA and what kinds of outcomes are intended to be obtained. After the intended outcome is defined; system boundary to be analyzed, impact categories to be calculated and functional unit based on which impacts per functional unit are calculated should be defined. The assumptions made for the analysis should also be defined in goal and scope definition stage (Curran, 2012).

Figure 2.1: LCA framework and its applications (ISO, 2006).

Inventory analysis is the “phase of life cycle assessment involving the compilation and quantification of inputs and outputs for a product throughout its life cycle” (ISO, 2006). In this stage, raw data related to the processes are gathered and presented in tables. Raw data includes the amounts of inputs utilized, products produced and emissions released into air, water or soil. Generally, these data represents the amounts per day or per year. By this way, life cycle inventory (LCI) for the processes or products considered is constructed (Curran, 2012 and Guinée and Heijungs, 2005). However, LCA is a data intensive and complex methodology. Creating life cycle inventory for numerous processes and products requires

long time and intensive human effort. Moreover, since these data gathered from multiple sources by multiple people, uncertainties occur in data resulting in a decline in data quality (Guinee and Heijungs, 2005).

According to ISO, impact assessment is the “phase of life cycle assessment aimed at understanding and evaluating the magnitude and significance of potential environmental impacts of a product system throughout the life cycle of the product”. The calculated environmental impacts can be life cycle global warming potential (GWP), acidification potential, land use or water use etc (Guinee and Heijungs, 2005 and ISO, 2006).

Last stage of LCA is interpretation in which the methods to improve the current state of the system analyzed is defined according to the results of inventory analysis and impact assessment. Interpretation stage is defined as the “phase of life cycle assessment in which the findings of either the inventory analysis or impact assessment, or both, are evaluated in relation to the defined goal and scope in order to reach conclusions and recommendations” (ISO, 2006). Based on LCA analysis results and interpretation of these results, recommendations for process improvement for an existing process, favoring one production scheme over the other or policy making decisions can be made.

Applications of LCA

LCA can be employed to evaluate the total impacts (attributional LCA) related to a production process or to evaluate the effect of changes (consequential LCA) in an application (Rehl et al, 2012). Furthermore, LCA results can be utilized to determine inefficient process or life cycle steps as we do in clean coal technologies. Here, we determine that the energetic inefficiency of CLP compared to conventional process is because of the calcination energy consumed for regeneration of the sorbent and recommend the efforts to be directed to reduce the energy consumption in this step. Or, LCA can be utilized to compare two products or processes based on their impacts and to favor one over the other (Xie et al, 2011 and Hurst et al, 2012).

2.1.2 Boundary Selection and Models Utilized in LCA

According to the boundary they account for, LCA can be applied at two scales utilizing two major models. These are process-LCA model and economic input- output (EIO) model (Urban and Bakshi, 2009). While process-LCA accounts for the process itself and most important life cycle steps as the analysis boundary, EIO model considers the system boundary as a region or a country since it uses economic data belonging to that region or country (Zhang et al, 2010 (a) and Zhang et al, 2010 (b)). Below, pros and cons of these models are discussed.

Process-LCA Model

For LCA at the process scale, the process itself and most important life cycle steps are considered, secondary and tertiary effects are neglected. Hence, system boundary includes the process and significant steps in the supply chain of the process such as resource extraction and transportation and waste disposal. In this model, the process can be analyzed in a network of life cycle steps which are related to each other in sequence and process information is evaluated within this network. Then, lifecycle resource consumptions and emissions can be calculated. Although process-LCA model gives detailed information about the process, it underestimates secondary or tertiary effects (truncation errors) in the process life cycle. The underestimation of these indirect effects can cause large errors, furthermore wrong process data can reflect with greater impact in lifecycle results (Zhang et al, 2010 (a) and Zhang et al, 2010 (b)).

EIO Model

The algebraic representation of input-output model is presented in figure 2.2.This model, developed by Leontief, utilizes monetary values of flows (inputs and outputs) belonging to a process and the system boundary is chosen as a region or a country since it uses economic data belonging to that region or country. Considering the system of interest within a network, the flows among the sectors for production of a certain amount of good can be related with this model, which means that total input is equal to the total output from a sector. The algebraic representation of relations among sectors opens many other doors for lifecycle study. Not only monetary flows but also energetic or mass flows among sectors can be related by using input-output model. (Zhang et al, 2010 (a) Cruze, 2013).

Despite its intensiveness, input-output model suffers from high aggregation of data within sectors and errors resulting from this aggregation. The sectors in EIO are defined on the basis of product similarities not on the “similarities of production pathways, resource consumption or emissions” (Cruze, 2013). As a result, the life cycle inventory values given for a sector may not be representative of impacts related to a specific product.

Figure 2.2: Linear algebraic representation of input-output model.

By applying LCA at different scales, how the analysis results vary with changing analysis boundary can be determined. As it is stated, each method (process LCA or economy scale) has its own plusses or minuses. Instead of employing only one method, performing analysis at all scales can give a better insight about the processes analyzed and enable benefiting from strengths of these methods (Urban and Bakshi, 2009).With this motivation, we utilized process scale and economy scale LCA in life cycle assessment of clean coal technologies since EIO data are available for GWP, water and land use impacts for US. However, LCA of localized energy options is performed at process-LCA scale only since EIO data for these impacts are not available for India.

2.1.3 Allocation in LCA

In multi- product processes, allocation is utilized to allot emissions or resources utilized among products in LCA studies. Besides, allocation enables to compare different processes producing the same products based on the impacts allocated to those products. Process producing the same product with higher impacts becomes less favorable (Cruze, 2013). Allocation can be performed based on market value, mass, energy content or a common property that the products share. Allocation type chosen can change analysis results. Analyses of which results are robust with changing allocation strategy are desirable (Urban and Bakshi, 2009 and Cruze, 2013).

2.1.4 Environmental Impacts

In calculation of life cycle impacts, firstly amounts of inputs to and outputs from the system studied are quantified. After inputs and outputs are quantified for a specified time period (per day or per year); energy consumed, emissions and resources utilized for production of these inputs and for disposal of system wastes can be calculated. The energy consumption, emissions and resources utilized in each life cycle step are then added up to calculate cumulative life cycle impacts. Impacts calculated in a life cycle study can be global warming

potential (GWP), eutrophication, acidification, ozone depletion potentials; land use, water use and energy consumption in the life cycle of a product.

Eutrophication potential is measured in g PO4- eq. / functional unit. Acidification potential is

measured in g SO2 eq. / functional unit. Unit of ozone depletion potential is g chloro flouro carbon eq. / functional unit. Land use is expressed as the land area utilized in the life cycle of a product for production of its unit amount. Water volume utilized for unit production represents the life cycle water use. GWP is expressed as g CO2 eq. / functional unit (Ulgiati et al, 2010).

Greenhouse gases having global warming potential are CO2, CH4 and N2O. To convert CH4 and N2O into CO2 equivalents amount of CH4 is multiplied by 21and amount of N2O is multiplied by 310 for 100 years time interval of effects (EPA, 2013).

In our study, we calculate life cycle GWP because of its contribution to climate change. We also calculate life cycle land and water resource uses due to importance and degradation of these natural resources(Steffen et al, 2011 and Rockstrom et al, 2009).

2.2 Emergy Analysis

2.2.1 Fundamentals of Emergy Analysis

Emergy is “the total amount of available energy of one kind that is directly or indirectly required to make a given product or to support a given flow” (Odum, 96). Therefore, we can employ different emergy types like coal emergy, oil emergy or solar emergy (Ulgiati et al, 2010). We utilize solar equivalent joules (sej) as our emergy unit since solar energy is the driving force for all transformations in the nature and human activities. According to Odum (1996), solar energy is transferred among living things (producers, primary consumers, predators) or in formation of energy sources. All ecosystems or human created economic systems take part in a network of energy transformations by concentrating and increasing the quality of lower quality energy into higher quality energy and waste heat (Hau, 2005). For instance, green plants convert solar energy into chemical energy via photosynthesis or a higher quality of energy, electricity is generated in coal power plants by combustion or gasification of coal. By this way, biological and economic systems sustain themselves and support each other by creating an “energy hierarchy” (Odum, 1996 and Hau, 2005).

Specific emergy or transformity of a resource or product is the solar emergy required for unit amount of production of that resource or product (Brown and Ulgiati, 2004). Transformity can be in units of sej/J, sej/g or sej/$. The higher a resource in the energy hierarchy, the higher its transformity will be since in energy transformations, the energy is concentrated and its quality is increased. Hence, transformity is accepted as an indicator of energy quality (Odum, 1996 and Hau, 2005). In other words, it takes more solar equivalent joules of energy or environmental work to make a higher quality product. Figure 2.3 elaborates these energy transformations within ecosystems and economy related systems.

Figure 2.3: Transformation of sun energy in nature (Adapted from Odum, 1996 and Hau and Bakshi, 2004).

Emergy of each input flow to a system can be calculated by multiplying specific emergy or transformity of the input by its quantity (energetic or material quantity). Then, total emergy input to the system can be calculated simply by adding emergy of all input flows (Odum, 1996 and Pizzigallo et al, 2008). Utilizing solar emergy as a common denominator to represent emergy of all inputs makes it possible to account for quality differences among the resources (Odum, 1996 and Ulgiati et al, 2010). All other energy inputs to the earth which do not have solar origin such as crustal heat and tidal energycan be converted to solar equivalents by appropriate equivalence factors. Emergy accounts for all the environmental work spent back in time for formation of natural resources, creating an “energy memory”. In short, emergy quantifies environmental support demand by a process or product (Odum, 1996). In emergy analysis, emergy input flows are classified as locally available renewable (R) and non-renewable (N) inputs and external purchased (feedback flows, P) inputs. With this classification, it becomes possible to calculate emergy ratios as indicators of environmental performance of the process evaluated. These emergy ratios provide information about how much environmental support is required for the product or process in question, system renewability, system efficiency, load of system to environment and dependency of system on external purchased inputs (Odum, 1996 and Ulgiati et al, 2010).In the following subsection, emergy analysis procedure will be introduced followed by a subsection regarding emergy algebra. Lastly, current improvements and application of emergy analysis will be discussed shortly.

2.2.2 Emergy Analysis Procedure

Emergy analysis is performed at three stages. Firstly, a systems diagram is drawn to identify emergy flows entering and leaving the system and emergy flows among system components. Then, an emergy table is constructed in which all input quantities, transformity values and calculated emergy values for each input are listed. In the last stage of analysis, emergy ratios are calculated according to ratios of the inputs classified as renewable (R), non-renewable (N) and purchased (P) (Odum, 1996, Paoli et al, 2008 and Brown et al, 2012).

Energy System Diagrams

Energy system diagram is a pictorial representation of the system under study. The aim of beginning an emergy analysis with drawing an energy system diagram is to understand and to define energy and material flows entering the system outside the analysis boundary, to determine the material and energy exchange flows and interactions among system components and understand interaction between the system investigated and its surroundings (Brown, 2004 and Odum, 1996). In other words, a pictorial model of the system analyzed is prepared by drawing an energy systems diagram.

Figure 2.4 lists important symbols utilized in drawing energy systems diagrams. Here, arrows represent the material or energy flows, circles represent external forcing functions such as sun and rain or purchased inputs such as diesel or gasoline. Producer symbol generally represents green plants or items like algae. For instance, agriculture is represented by a producer symbol in energy diagramming of Rampura village. Storage represents any entity that accumulates within the analysis boundary. In energy systems diagramming of Rampura underground water and topsoil storages are represented as storages. Consumers are represented with hexagons and in Rampura livestock and domestic sector are the consumers represented by hexagons. Interaction symbol is utilized to identify interactions and transformations among system components (Odum, 1996 and Brown, 2004).

Figure 2.4: Energy systems diagramming symbols (UFCEP, 2013).

Emergy Evaluation TableEmergy evaluation tables are kind of accounting sheets in emergy analysis. An inventory of all inputs, outputs, their quantities for a specific time interval, transformity values for each input and calculated emergy values are all listed in an emergy evaluation table (Odum, 1996).

In table 2.1, a generic emergy evaluation table is presented. Total emergy input to the system is calculated by addition of all the emergy inputs and the transformity of each output is calculated by division of dedicated emergy input by the quantity of that output (Odum, 1996).

Table2.1: A generic emergy evaluation table.

ItemItem

(name)Data

(flow/time) Units

Transformity

(sej/unit)

Solar Emergy

(sej/time)

1 First item xxxx J/year xxxx Em1

2Second

item xxxx g/year xxxx Em2

n.. nth ite xxxx J/year xxxx Emn

O Output xxxxJ/year or g/year xxxx

∑1

n

Emi

Calculation of Emergy Ratios

In classifying emergy inputs to a system, free inputs which are provided by the nature such as solar radiation, wind, rain are categorized as renewable inputs. The amount of renewable inputs entering a system is flow-limited, one can not intervene to increase or decrease the amount of these flows. Non-renewable inputs are also local resources. However, they are not necessarily free. Minerals such as quartz, ground water and topsoil are examples of non-renewable inputs. If the withdrawal rate of these inputs is slower than their regeneration rate by the nature, then these items are classified as renewable inputs. Purchased or feedback emergy flows are external inputs which are neither free nor locally available (Ulgiati and Brown, 1998).

In figure 2.5, classification of emergy inputs and interaction of natural systems with economic human systems are summarized. By classification of emergy inputs which contribute a system, calculation of emergy ratios becomes possible and inferences about system`s environmental performance can be made (Ulgiati et al, 2010).

Figure 2.5: Classification of emergy flows to a system which is the basis for calculation of emergy ratios (Ulgiati and Brown, 1998).

The total emergy consumption of process or yield (Y) is equal to summation of all emergy inputs to the system (Y). Percent renewability (% Re) is the percentage of renewable emergy inputs to total emergy consumption. Emergy yield ratio (EYR) is the ratio of total emergy(Y) to purchased emergy (or feedback, F). EYR is an indicator of process efficiency in terms of process` ability to use purchased inputs to produce a product. Environmental loading ratio (ELR) is the ratio of non-renewable and purchased inputs to renewable inputs. A high ELR value indicates high stress on the environment due to the production process evaluated.

Environmental sustainability index (ESI) is the ratio of EYR to ELR. A high ESI indicates high output with low environmental loading. Lastly, environmental investment ratio (EIR) is ratio of purchased inputs to renewable (R) and non-renewable (N) local inputs. A high EIR value can indicate system fragility due to high dependence to external resources (Odum, 1996, Paoli et al, 2008, Pizzigallo et al, 2008 and Ulgiati et al 2010). Calculation of emergy ratios according to the classification of emergy inputs is presented mathematically below.

Emerg yYield (Y )=R+N +F(1)

% Renewablity= RY

(2)

Emergy Yield Ratio ( EYR )=YF

(3)

Environmental Loading Ratio ( ELR )=(N+F )R

(4)

Environmental Sustainability Index ( ESI )= EYRELR

(5 )

Environmental Invenstment Ratio ( EIR )= F(R+N )

(6)

2.2.3 Emergy Algebra

Co-products and Splits

As discussed in section 2.1.3, allocation in LCA can be performed based on market value, mass, energy content or a common property that the products share for a multi-product process (Zhang et al, 2010 (a) and Zhang et al, 2010 (b)). Emergy analysis avoids allocation for co-products except splits and allocates all the emergy input to all the co-products or partitions total emergy input according to the available energy content of outputs if all the outputs of the system analyzed are known (Hau and Bakshi, 2004).

The rules in assigning total emergy yield to outputs of a system are known as “emergy algebra” (Brown and Herendeen, 1996). In emergy analysis, co-products are products which can only be produced jointly and have different purposes of use and properties (Bastionani et al, 2009). For instance, outputs of husbandry sector in Rampura which are animal draft, milk and cow dung are classified as co-products. When, emergy inputs are invested into husbandry sector, cows produce these products and it is not possible to produce these items separately. As a result, milk, animal draft and cow dung are co-products of husbandry sector in our analysis.

On the other hand, splits are products of similar characteristics with similar functionality and they can be produced separately (Odum, 1996 and Bastionani et al, 2009). In emergy

analysis of agricultural sector in this work, agricultural products and residues are classified as splits since different crops can be grown independently from each other and they have similar functionality. Here, total emergy is allocated among them according to their available energy content.

Likewise; rain, wind, earth cycle are the co-products of earth processes. As a result, total emergy available to earth in a year is assigned to each of these co-products. Their transformity is calculated by division of this total emergy by their available energy content (Odum, 2000).

Rules of Emergy Algebra

The first rule of emergy algebra states that “All source emergy is assigned to the process outputs.” meaning the total emergy yield is assigned to each of the co-products (Brown and Herendeen, 1996).

The second rule of emergy algebra is regarding splits. It states that “When a pathway (co-product) splits, emergy is assigned into each leg based on its percentage of the total energy flow on the pathway.” meaning splits obtain their share of emergy based on their available energy content (Brown and Herendeen, 1996).Figure 2.6 elaborates partitioning of total emergy yield among co-products and splits according to the rules of emergy algebra. Here a total emergy of 500 sej is assigned to each of the co-products. Then, 500 sej is partitioned between two splits according to their energy content. The first leg has an energy content of 7 J and 350 sej is assigned to this leg. Remaining 150 sej is assigned to the second leg.

The third rule of emergy algebra sets the conditions to avoid double counting in emergy analysis. Third rule states that “Emergy cannot be counted twice within a system. Co-products, when they are reunited, cannot be added to equal to a sum greater than the source emergy from which they are derived.” (Brown and Herendeen, 1996 and Bastionani et al, 2009).Hence emergy of splits are additive whereas emergy of co-products are not. In case of co-products the emergy of co-product with maximum magnitude is chosen (Odum, 2000).

In our analysis, we choose emergy of rain and do not account for emergies of wind or earth cycle in calculating the total emergy input to our systems to avoid double counting since they are co-products of earth processes and rain has the emergy content with maximum magnitude (Odum, 2000).

Figure 2.6: Partitioning of total emergy among co-products and splits (Herendeen , 2004).

2.2.4 Applications of Emergy Analysis

Emergy analysis does not have a standardized analysis framework creating problems of inaccuracy and reproducibility. Main area of criticism for emergy analysis is calculation of specific emergy values of inputs. It is claimed that they do not have fixed values and calculated by vague, over simplified models (Rugani and Benetto, 2012 and Hau and Bakshi, 2004). Bastoniani et al studied the transformity calculation of petroleum fuels and found only 1.7% higher transformity values than values calculated by Odum (Bastionani et al, 2009). To create a framework and increase the reproducibility of emergy studies, efforts to integrate emergy within LCA framework and adapt emergy algebra to LCA calculation schemes have been made (Rugani and Benetto, 2012 and Marvuglia et al, 2013). Marvuglia et al also study on a software to calculate emergy of systems and products by utilizing life cycle inventories (Marvuglia et al, 2013).

Emergy analysis can be utilized to evaluate self-sufficiency and dependence of a region on external inputs (Zhang et al, 2007) or it can be utilized to evaluate renewability and sustainability of an energy system (Ciotola et al 2011). Furthermore, emergy analysis can be utilized to compare the relative sustainability and environmental performance of different energy technologies as we did to compare five different energy technology options in this work. Comparison of solar electricity and electricity generated from fossil fuels has also been performed utilizing emergy analysis (Brown et al, 2012 and Paoli et al, 2008).

2.3 Joint Use of LCA and Emergy Analysis

In essence, emergy analysis and life cycle assessment (LCA) are two complementary analysis techniques. One technique can account for the factors the other cannot, so that using them jointly provides a more complete picture of the systems under study (Pizzigallo et al, 2008).Here, comparison of these two techniques will be presented in terms of their underlying assumptions, methods, results obtained and related strengths and weaknesses that these techniques have. Then, we try to explain how they will be used in this research for designing sustainable energy systems.

2.3.1 Underlying assumptions

Comparison of LCA and emergy analysis can be done in terms of their underlying assumptions under three aspects: main considerations of the techniques, system boundary and allocation strategies.

LCA takes into account all life cycle stages starting from resource extraction, transportation, distribution, disposal/ recycle of a product and analyzes related impact and resource use in each stage, in other words, inputs and outputs to the system and their impacts are accounted for in LCA. In that sense, LCA is a “human-side” or user side evaluation technique ignoring the work biosphere done to form the natural resources. Whereas, emergy analysis accounts for work nature has done to form all the inputs or natural resources to the system analyzed on the common denominator of solar equivalent joules (sej). So, in emergy analysis, solar emergy is assumed to be the driving force for all transformations in nature and human economic activities (Pizzigallo et al, 2010 and Rugani and Benetto, 2012). Emergy analysis is nature- oriented or donor-side analysis technique, not being able to account for impacts of emissions from the system.

System boundary for LCA is either value-chain of the process analyzed (process-LCA) or the economy in which that product is produced if LCA framework is integrated with economic input-output models. Economy considered can be at regional, national or global scales. On the other hand, system boundary is the ecosystem which is the production process or product is integrated in for emergy analysis (Pizzigallo et al, 2010 and Rugani and Benetto, 2012). It can be said that application of emergy analysis enlarges analysis boundary from vale chain or economy to ecosystems.

LCA adopts partitioning impacts, inputs or outputs to products according to the mass, energy content or monetary value of the outputs as allocation strategy(Rugani and Benetto, 2012). Emergy analysis avoids allocation for co-products except splits and allocates all the emergy input to all co-products if all the outputs from the system is not known or partitions emergy input according to the exergy (available energy) content of outputs if all system outputs are known (Hau and Bakshi, 2004).

2.3.2 Methods

Life cycle assessment methodology has a four-stage framework; namely goal and scope definition, life cycle inventory analysis, impact assessment and interpretation (Guinee and Heijungs, 2005).Whereas, emergy analysis is performed at three stages. Firstly, a systems diagram is drawn to identify emergy flows of inputs and among system components using energy systems language. Then, an emergy table is constructed in which all input quantities, specific emergy values (emergy needed for formation of per unit of product or input) and emergy values calculated by multiplying specific emergy and input quantities are listed for each of the inputs. In the last stage of analysis, emergy ratios are calculated by which conclusions about the investigated system can be withdrawn (Odum, 1996). Unlike LCA in emergy analysis, inputs to the system can be classified as locally available renewable (R) and non-renewable (N) inputs and external purchased (P) inputs. With this classification, it becomes possible to determine how the system analyzed is dependent on external resources and how self-sufficient (Odum,1996).

2.3.3 Analysis Results

LCA provides results related to the emissions and resource use per functional unit chosen such as grams of product, kwh of power generated vice versa. Results regarding emissions can be life cycle GWP in g of CO2 equivalents per functional unit, eutrophication potential in g PO4

3- equivalents per functional unit, acidification potential in g SO2 equivalents per functional unit and so on. Results regarding resource use can include life cycle water use (volume/functional unit), land use (area/functional unit), fossil fuel consumption (such as gallons of oil/functional unit or tons of coal/ functional unit) (Guinee and Heijungs, 2005 and Ulgiati et al, 2010). Life cycle energy consumption and a metric called energy return on investment (EROI) can also be among results of a LCA study.

Emergy analysis results include total emergy consumption and emergy ratios providing information about how much environmental support is required for the product or process in question, system renewability, system efficiency, load of system to environment and dependency of system on external purchased inputs (Odum, 1996 and Ulgiati et al, 2010).The total emergy consumption of a process is equal to summation of all emergy inputs to the system (Y) and represents extent of natural support invested into the system. Percent renewability (% Re) is the percentage of renewable inputs to total emergy consumption. High % Re is an indicator of an environmentally sustainable system. Emergy yield ratio (EYR) is the ratio of total emergy(Y) to purchased emergy (P) and is an indicator of process efficiency in terms of process` ability to use purchased inputs to produce a product. Environmental loading ratio (ELR) is the ratio of non-renewable and purchased inputs to renewable inputs. A high ELR value indicates high stress on environment due to the production process evaluated. Lastly, environmental investment ratio (EIR) is ratio of purchased inputs to renewable (R) and non-renewable (N) local inputs. A high EIR value can indicate system fragility due to high dependence of system to external resources (Odum, 1996 and Ulgiati et al, 2010).

As a result, LCA results provide information about emissions and amount of resources consumed, emergy results can provide information about how much environmental work is needed for formation of these resources, thus can account for role of biosphere in human activities.

2.3.4 Strengths and Weaknesses

Emergy analysis and LCA are two techniques which aid and complement each other especially in terms of embracing characteristic of a system when they are utilized jointly (Pizzigallo et al, 2008 and Ulgiati et al, 2010).

LCA is a well standardized analysis technique which can evaluate multiple downstream environmental impacts (multi-criteria analysis technique) due to resource use and emissions. However, it cannot account for the environmental work invested into natural resources for their formation. Since LCA is a human oriented or user-side analysis technique, it cannot evaluate and quantify inputs having no economic value such as sun, rain, labor etc. Moreover, LCA cannot account for energy quality differences among natural resources utilized (Pizzigallo et al, 2008 and Guinee and Heijungs, 2005). Here comes emergy analysis into aid. With the main assumption that solar energy is the driving force for all transformations in nature, it can account for quality differences among all inputs to the system by evaluating them on the common denominator of solar equivalent joules since it takes different amounts of solar available energy for each input to form (Odum, 2000) Emergy analysis is a nature oriented or donor-side thermodynamic analysis technique. Emergy analysis can evaluate natural capital, sources not having monetary or market value. In other words, emergy analysis can quantify environmental work invested in natural resources and can relate natural systems to human economic systems (Hau and Bakshi, 2004). However, emergy analysis cannot address issues regarding user preferences since it cannot evaluate and quantify downstream impacts related to emissions and resource consumption. So, this is the point where LCA aids emergy analysis (Pizzigallo et al, 2008 and Hau and Bakshi, 2004).

2.3.5 Application

To design sustainable human systems; social, economic and environmental aspects should to be considered simultaneously. Furthermore, these systems must be in harmony with the society and biosphere they exist in (Griggs et al, 2013). And, sustainability can be achieved only if these systems are implemented in a way satisfying necessities arising from these aspects (Martin et al, 2010). Here, emergy analysis and LCA can capture and evaluate factors related to environmental dimension of sustainability (Pizzigallo et al, 2008 and Rugani and Benetto, 2012). When emergy analysis and LCA are used separately, they cannot take a complete picture for environmental sustainability, since emergy analysis can capture upstream effects and LCA downstream effects only. However, if they are utilized jointly a decent evaluation of system can be preformed (Pizzigallo et al, 2008 and Ulgiati et al, 2010).

Figure 2.7: Integration of emergy analysis and LCA (Rugani and Benetto, 2012).

The underlying assumptions of LCA and emergy analysis make it possible to analyze effects at value-chain, economy (LCA) or ecosystem (emergy) scales given the system boundaries they consider. Starting from formation of natural resources to extraction, use and disposal related impacts and costs to the nature can be evaluated. And, allocation of these impacts and costs to products can lead understanding weaknesses and strengths of processes in terms of their environmental performance (Pizzigallo et al, 2008, Rugani and Benetto, 2012 and Ulgiati et al, 2010). Figure2.7 expresses this analysis boundary expansion via integration and joint use of LCA and emergy analysis.

These methodologies can create a basis for a systematic analysis leading to calculation ofmetrics to evaluate system`s environmental performance and comparison of different

products and processes(Pizzigallo et al, 2008, Rugani and Benetto, 2012 and Ulgiati et al, 2010).

Resulting metrics or methodology outcomes makes it possible to quantify system efficiency, renewability, dependency on external resources, polluting emissions and so on. With this information; inefficient, polluting or over-consuming system elements can be detected and process improvements, impact reductions can be performed via optimization. Or, system options having better environmental performance can be chosen over options having lower environmental performance for implementation(Pizzigallo et al, 2008 and Ulgiati et al, 2010).

By aiding LCA with emergy analysis for factors LCA cannot account for or the reverse, we think that the weaknesses of the analysis methods can be compensated and a close-to-complete picture can be taken in terms of important system factors, their impacts and costs to nature.

3 LCA and EA Application Details

3.1 Life Cycle Assessment

3.1.1Clean Coal Technologies

(a) (b)

Figure 3.1: Process LCA scale boundaries for (a) conventional, and (b) calcium looping processes.

(a)

(b)

Figure 3.2: Hybrid model for (a) conventional, and (b) calcium looping processes.

3.1.2 Biogas Digester

Life Cycle Assessment of Electricity Generation via Biogas Digestion

Figure 3.3 presents the life cycle steps considered in LCA of electricity generation from biogas. Considered life cycle starts with cultivation of fodder for cows to eat. Then, cows produce manure. Per kg of manure, scrap cows in cowshed of Development Alternatives are fed 0.9 kg fodder. Fodder is mainly wheat straw. In this region, 1800 kg of wheat straw and 1800 kg of wheat are obtained from 1 acre of agricultural land. To allocate land and water utilized between wheat and wheat straw; mass, monetary value based allocation strategies are adopted (Zhang et al, 2010(a) and 2010(b)). Case where manure is accounted as waste is also considered. Manure is then fed to the biogas digester. There are two products of bio-digestion, slurry and biogas. This slurry or digestate can be utilized as fertilizer reducing chemical fertilizer use. In LCA of the system, emissions and resource use (land and water) are allocated among these products according to their energetic content (Zhang et al, 2010 (a) and (b)). The portion of impacts and resource use allocated to biogas is attributed to electricity generation, since electricity is generated by combustion of biogas. 1% of biogas produced is assumed to leak. In last step of life cycle, produced biogas is combusted to generate electricity by a natural gas engine.

Figure 3.3: Life cycle steps considered for electricity generation from biogas.

The greenhouse gas (GHG) emissions emitted during biogas production and electricity generation are sequestered by the fodder while it is growing. During photosynthesis wheat captures CO2 and produces the biomass. However in biogas production stage, 1 % of the biogas leaks of which 60% is CO2 and 40% is CH4. CH4 has a GWP 21 times stronger than

CO2 (Guinee, 2011). The leaked CH4 is accounted as GHG emissions from biogas generation step. In addition, manure produced by cows can be applied to fields as fertilizer or the slurry from biogas digester can be applied to fields. This practice change creates reduction in GHG emissions (Jorgesson and Berlund, 2007). Furthermore, application of slurry reduces the need for chemical fertilizers. And, we take credit for these reductions in evaluation of our system.

3.1.3 Biomass Gasifier

Life Cycle Assessment of Electricity Generation via Biomass Gasification

Figure 3.4 presents the life cycle steps considered in the LCA of electricity generation from producer gas. As stated in emergy analysis section, ipomea is a naturally growing local plant around riverbanks and marshy areas. The life cycle of electricity generation from biomass via gasification starts with growth of feedstock ipomea. Ipomea is then collected by villagers and transported to Development Alternatives campus by tractor. Each load of tractor can carry 2000 kg ipomea. However, 25% of green plant is lost during cutting. Wet ipomea is sold to Development Alternatives at a cost of 0.7 Rs/kg. Ipomea is dried to have 15% moisture and to be ready to be used in the gasifier. The cost of ready to use ipomea is 1.2 Rs/kg. The distance that ipomea is transported is approximately 10 km and 1 liter of diesel is consumed by the tractor for this transportation. 15% wet ipomea is then gasified and producer gas is produced. Producer gas and diesel are co-fired in a diesel engine in dual fuel mode (DFM).

As in case of emergy analysis, three operation schemes are considered in LCA of biomass gasifier and electricity generation from producer gas. These are current case, ideal case in dual fuel (DFM) mode and ideal case in single fuel mode (SFM). Life cycle global warming potential (GWP), land use and water use for these three cases have been calculated.

Figure 3.4:Life cycle steps considered for electricity generation from producer gas.

Two factors have been taken into account in ipomea growth step for GWP calculations. First is the CO2 sequestered by the ipomea and second is the CO2 sequestered by soil. It is assumed that 50% of dry ipomea weight is carbon and 15 g of carbon per m2 per year is sequestered by soil (Lal, 2004). In transportation stage, emissions caused by consumption of diesel are accounted for. CO2 content of producer gas is the emissions from gasification stage, since it will not be combusted further in electricity generation. In Electricity, generation emissions originating from diesel and combustible portion of producer gas are summed up to find the total life cycle GWP. In ideal SFM, no diesel is utilized, consequently emissions originate from combustion of producer gas only.

3.1.4 Solar Life Cycle Assessment

Figure 3.5 shows all life cycle steps taken into account in the LCA of multi-crystalline solar PV. Production of a silicon based solar plant starts with purification of SiO2 via reduction with carbon. Coal, woodchips, charcoal and coke are the sources of carbon. Silicon with 98.5-99.5% purity (metallurgical grade silicon (MG-Si)) is produced as a result of this process. Then, MG-Si is further purified into electronic grade silicon (EG-Si) and solar grade silicon (SG-Si) to form silicon mix for PVs. This silicon mix contains 85 % SG-Si and 15 % EG-Si. Difference between EG-Si and SG-Si is in their purity. SG-Si contains impurities 0.01 part per million by weight (ppmw) and EG-Si contains impurities 0.0001 ppmw. The multi-crystalline silicon is then melted and cast as blocks from which the multi-crystalline wafers are cut as layer in certain thicknesses. Wafers are treated with chemicals (NaOH, HCl) to eliminate any damages on their surface and are then doped to create p/n junction in the wafer.

The production of solar cell is completed after adding parts for electronic connections and applying anti reflection coating. These solar cells are connected to form the solar panels. After mounting of solar panels and electronic connections with inverter and battery bank is completed, solar plant takes form (Ecoinvent, 2009)

Figure 3.5: Life cycle stages considered for LCA of solar PV.

LCA of solar PV encompasses evaluation of impacts related to the life cycle steps considered. Balance of system (BOS) components are responsible for a significant portion of environmental impacts related to solar power plants (Hsu et al., 2012). BOS components include battery bank, inverter, mounting structures such as support rack, wires and switches for electronic connections. According to Ecoinvent report, BOS related environmental impacts are in the range of 30%-50% related environmental impacts (Ecoinvent, 2009).

3.2 Emergy Analysis

3.2.1 Clean Coal technologies

For emergy analysis of clean coal technologies, we utilized the Eco-LCA software (OSU, 2013). Eco-LCA software has money/emergy ratios for all economic sectors of 2002 Producer Price Model. Economic value of each action for coal gasification per year is multiplied by these money/ emergy ratios to calculate emergy flow related to each activity.

The emergy of coal is the emergy for coal to be utilized in a process for engineering purposes (Odum, 1996). In other words, the emergy spent for coal mining and transportation is calculated in this analysis. It is likewise for limestone. Furthermore, emergy spent for power plant construction, production of equipment utilized for gasification, production of chemicals, disposal of wastes from the coal power plants are all calculated. Except water input all the emergy flows are purchased emergy flows. Water is classified as 100% renewable local input. The energy system diagrams for conventional and CL processes is presented in figure 3.6.The monetary data fed as an input to Eco-LCA softwarefor corresponding sectors can be found in the SI of Kursun et al (Kursun et al, 2014). For instance, sector 33329A other industrial machinery manufacturing sector is chosen for equipment production activity in ECO-LCA and the 35.58 M$/year money flow is entered as an input.Eco-LCA multiplies the emergy/money ratio of sector 33329A with 35.58 M$ and calculates the emergy flow belonging to equipment production activity.

Figure 3.6: Energy system diagrams for conventional and CL processes.

3.2.2 Biogas Digester

Figure 3.7 is the energy system diagram drawn for representing energy and material inputs to and outputs from the biogas production phase. Among these inputs, solar energy is renewable. Water is classified as a non-renewable local input since it is withdrawn faster than it replenished, as indicated by depletion of underground water level in Rampura. As in case of solar panel, labor is categorized as 20% renewable and 80% purchased, the percentages

calculated in our study. Steel, PVC piping and fuel are 100% purchased external inputs. Although manure is assumed to be 100% renewable originally,there are non-renewable inputs that cows consume to produce cow dung. So, it is highly optimistic to assume cow dung as100% renewable. In their analysis, Ciotola et al take renewability of manure as 68% according to the renewable emergy input ratio ofCosta Rican economy.(Ciotola et al, 2011). The renewable emergy input to Indian economy is not known as far as we are aware. However, assuming similar development levels but more purchased input ratio to Rampura based on our educated guess about the region, we think that assuming as 60% renewability for manure in our analysis is reasonable.

Figure 3.7: Emergy system diagram for electricity generation from biogas.

3.2.3 Biomass Gasifier

Figure 3.8 represents the energy systems diagram drawn to determine the inputs and outputs for electricity generation via biomass gasification. Different from producer gas production system; electricity generator, lubricant and diesel are included in the system for DFM operations in current and ideal cases. In SFM operation, only electricity generator is included since diesel and lubricant are not utilized. Categorization of inputs in producer gas production phase is also the same in electricity generation phase. Diesel, lubricant and electricity generator inputs are 100% purchased external inputs to the system considered.

Figure 3.8: Energy systems diagram of electricity generation from producer gas

3.2.4 Emergy Analysis of Solar PV

In emergy analysis of the solar power plant, we utilized Ecoinvent V2.1 dataset for quantification of material and energy inputs to the system (Ecoinvent 2009). These data are modified according to specifications of our system. The process data regarding the operation of solar PV are obtained from Development Alternatives. The solar radiation data for Jhansi is assumed to be the same for Bhopal, the closest meteorological station to Rampura as 18.65 MJ/m2day (Tyagi, 2009). The system generates around 8350 kWh electrical energy per annum. Figure 3.9 presents the energy systems diagram drawn for determining energy and material inputs to and outputs from the solar system. Materials and fuels are utilized in the production of solar panels. Gravel is used for mounting of the panels to the ground. Services include monetary value of maintenance work done by professionals and materials used. Labor represents the work at the time of mounting of solar panels, their wiring and the work for maintenance and operation of solar panels. And, solar radiation is the source of energy for electricity generation.

Figure 3.9: Energy systems diagram of multi-crystalline solar PV.

In classifying inputs to a system, free inputs which are provided by nature such as solar radiation, wind, rain are categorized as renewable inputs. The amount of renewable inputs entering a system is flow-limited, one can not intervene to increase or decrease the amount of these flows. Non-renewable inputs are not necessarily free. Minerals such as quartz, ground water and topsoil are examples of non-renewable inputs. If the withdrawal rate of these inputs is slower than their regeneration rate by the nature, then these inputs are classified as renewable inputs. Purchased or feedback inputs are external inputs that a neither free nor locally available (Ulgiati and Brown, 1998). Based on these principles, solar radiation is classified as a 100% renewable input. The inputs purchased from the economy are classified as purchased external inputs (100% F). These include fuels, metals, plastics for packaging and polymers utilized in supporting structures (HDPE) and solar cell surface protection (EVA). Inverter, battery bank and maintenance inputs are expressed in terms of their monetary values which covers both human and material content of these inputs and consequently classified as purchased inputs (100% F). Quartz (silica sand), the raw material for solar panel production, is classified as a non-renewable local input (100% N). Since labor regarding mounting and maintenance takes place in Rampura, labor is classified as 20% renewable and 80 % purchased. These percentages are calculated by emergy analysis of Rampura village domestic sector. The related analysis results are presented in table 6.1 of this SI. The transformity for labor is also obtained from the domestic sector emergy analysis of Rampura. Lastly, wood and water inputs are classified as renewable inputs since the solar panels are produced in Norway and donated to Rampura by Scatec Solar, a Norwegian solar company and water and wood sources are not degrading resources in Norway (Ge et al., 2013).

4 Economic Assessment

4.1Economic Assessment of Electricity Generation from Clean Coal Technologies

Total annualized cost of electricity generation in CLP and conventional process encompasses capital and operating costs. Capital cost includes power plant construction and cost of equipment utilized for coal gasification. Plant and equipment costs are annualized by dividing by plant lifetime (20 years).Operating cost includes cost of raw materials and their transportation costs, cost of chemicals used in gasification and cost of waste disposal. The annual cost of these items is calculated by multiplication of annual input quantities by their unit price. For wastes, annual waste quantity is multiplied by unit cost of corresponding disposal activity. Economic data regarding operating and capital cost are obtained from NETL study (NETL, 2010) and The Ohio State University team (Ramkumar, 2010). The electricity generated in these centralized coal power plants should be transmitted and distributed to the final consumer. The cost data regarding transmission cost in Uttar Pradesh, India is obtained from Uttar Pradesh Electricity Regulatory Commission (UPERC) report for year 2009 (UPERC, 2010). Cost of electricity distribution in India is 0.75 Rs/kWh (IMA, 2013). Addition of total annualized cost of generation per kWhe, distribution and transmission costs per kWhe constitutes the final cost of electricity delivered to the consumer per kWhe.

Tables 4.1 and 4.2 present the economic assessment results for CLP and conventional process. In conventional process, cost of electricity is slightly lower. CLP has higher plant construction and equipment costs and it consumes more resources, hence has a higher operating cost. However, CLP generates unit (1 kWhe) power almost at the same price since CLP has higher production capacity than conventional process (5.89E+09 kWhe/year versus 4.30E+09 kWhe/year).

Table 4.1: Cost of electricity generation via calcium looping process.

Capital CostTotal

Cost(M$)Annualized Cost(M$)    

Power Plant Construction Cost 485.00 24.25    

EquipmentCost 1142.25 57.11    

         

Operating Cost        

Amount

(Tonnes/day)

Amount

(tonnes/year)Unit

Price($/unit)Annualized Cost(M$)

Coal 7990 2916350 30.47 88.85

Coal Transportation 7990 2916350 15.23 44.43

Limestone 1699 620135 8.3 5.15

Limestone Transportation 1699 620135 11.7 7.26

Water 17374 6341510 0.10 0.76

Activated C 0.047 17.155 2288 0.04

Water Treatment Chemicals 6.18 2255.7 374 0.84

OUTPUTS        

Solid Disposal 617 225205 16 3.60

Solid Purge 1569 572685 8 4.58

       

   Total Annualized Cost(M$) 236.87  

   Annual Elect Generation(kWhe) 5.89E+09  

   Cost per kWhe($/kWhe) 0.04

   Cost per kWhe(Rs/kWhe) 1.94  

   Transmission Cost(Rs/kWhe) 0.22  

   Distribution Cost(Rs/kWhe) 0.75  

  Total (Rs/kWhe) 2.91  

Table 4.2: Cost of electricity generation via conventional process.

Capital CostTotal

Cost(M$)Annualized Cost(M$)    

Power Plant Construction Cost 327.10 16.36    

Equipment Cost 711.54 35.58    

         

Operating Cost        

Inputs

Amount

(Tonnes/day)

Amount

(tonnes/year)

Unit Price

($/unit)Annualized Cost(M$)

Coal 5426 1980490 30.47 60.34

Coal Transportation 5426 1980490 15.23 30.17

Water 21787 7952255 0.10 0.95

Shift Catalyst 0.0018 0.657 1086800 0.71

Selexol Solution 0.043 15.695 29524 0.46

Activated C 0.047 17.155 2288 0.04

Water Treatment Chemicals 6.18 2255.7 374 0.84

OUTPUTS        

Solid Disposal 617 225205 16 3.60

         

         

   Total Annualized Cost(M$) 149.06  

   Annual Elect Generation(kWhe) 4.30E+09  

   Cost per kWh($/kWhe) 0.035

   Cost per kWh(Rs/kWhe) 1.67  

   Transmission Cost(Rs/kWhe) 0.22  

   Distribution Cost(Rs/kWhe) 0.75  

   Total (Rs/kWhe) 2.64  

4.2 Economic Analysis of Electricity Generation from Biogas

As stated in emergy analysis and LCA sections 5.2.1 and 5.2.2, three cases are also considered for economic assessment of biogas plant and electricity from biogas. The installed biogas digester and electricity generator costs are the capital costs related for electricity generation from biogas. Feedstock manure cost and labor related to maintenance and operation of biogas digester constitutes the operating cost for the biogas electricity generation. In terms of operation of biogas digester three scenarios are considered.

Current case represents the current operation scheme in Development Alternatives cowshed with 345 kg daily wet cow dung input in 300 days of a year. In this operation scheme, 6 kWh of electricity is generated per day. In this scenario, cow dung is free and supplied from the scrap cows in the campus. Electricity generation price with 0.2 Rs/kg collection fee is also calculated for the current case. Ideal case represents the operation scheme with 850 kg wet cow dung feed with daily 20.13 kWh of electricity generation. Ideal case operation scheme represents the case which produces enough electricity to meet the energy demand in the cowshed. In ideal case, scenarios where cow dung is free, with 0.2 Rs/kg collection fee and at a cost of 0.4 Rs/kg cost are considered. Full capacity operation scenario represents full capacity production capacity of 60 m3 biogas production per day and around 12700 kWh of electricity generation per year. 1500 kg cow dung is fed to the digester per day in 300 days of a year in this scenario. Likewise, cases where cow dung is free, with a collection fee and at a cost of 0.4 Rs/kg are considered in price calculations of biogas electricity.

Installed biogas digester cost data is obtained from IRENA for year 2009 (IRENA, 2012b). The installed biogas digester cost is annualized by dividing this cost to 25 years, the life time of the biogas plant. The electricity generator set cost changes from $500 to $1000 for a 7.5 kW capacity with 10 years life time (Alibaba, 2013). In the analysis, $750 price is assumed for the electricity generator as an average and this price is leveled to year 2009 (1$=48.3 Rs) as in case of solar electricity (X-rates, 2013). Cost of labor (22 Rs/hr) and manure (0.4 Rs/kg) cost data is supplied by Development Alternatives.

Table 4.3: Cost of biogas electricity for the ideal case with 0.2 Rs/kg collection fee.

Operating Costs Amount/year Unit Cost(Rs)Annual Cost(Rs/year)

Manure (kg) 450000 0,2 69300Maintenance Lab(hr/year) 750 21,875 16406,25                     

Capital Cost Total Cost(Rs)Annual Cost(Rs/year)  

Biogas Digester 255748,5 10229,94  Electrcity Generator  33689 3369           Total Annual Cost(Rs) 99305    Electricity(kWh) 12706  

 Cost per kWhe(Rs/kWh) 7,8  

4.3 Economic Analysis of Electricity Generation from Producer Gas

In economic assessment of biomass gasifier, three scenarios have also been considered. Current case represents the current operating scheme in Development Alternatives campus in Orchha. A diesel engine generates electricity, utilizing producer gas from the gasifier and diesel in dual fuel mode and produces 17420 kWh electricity utilizing 20295 kg of ipomea and 1665 liters of diesel per year. Second scenario is ideal case operation in dual fuel mode. In this scenario, we assumed the biogas plant operates with 70% efficiency and 6 hrs per day generating 420 kWh electricity, resulting in 153300 kWh of electricity production per year. In this scenario, plant utilizes 184000 kg of ipomea and 15330 liter of diesel per year.153300 kWh of electricity is also generated in third scenario, however utilizing a natural gas engine operating with producer gas only in single fuel mode. In this mode, 261000 kg of ipomea per year is utilized.

The cost data related to capital and operating costs of biomass gasifier is obtained from Development Alternatives. As in case of solar electricity and electricity from biogas, these costs are annualized and total annualized cost is divided by the electricity generated in kWh in a year in each corresponding scenario.

Table 4.3 summarizes the cost of electricity from producer gas for ideal DFM operating scheme. When generation capacity is increases cost of electricity reduces from 26.1 Rs to 8.8 Rs and when generation is shifted from dual fuel mode to single fuel mode, price of electricity reduces further.

Table 4.4: Cost of biomass electricity for ideal DFMOperating Costs Amount/year Unit Cost(Rs) Annual Cost(Rs/year)Feedstock Preperation Labor(hr) 836 21,875 18288Feedstock(kg) 184000 1,2 220800Diesel (lt) 15330 43 659190Lubricant (lt) 1159 100 115900       Capital Cost Total Cost(Rs) Annual Cost(Rs/year)  Biomass Gasifier 6650000 266000  Electrcity Generator (Diesel Eng) 1100000 73333  Cutting Machine 15200 1520    Total Annual Cost(Rs) 1355031    Electricity(kWh) 153300  

 Cost per kWhe(Rs/kWh) 8,8  

Figure 4.1 shows the breakdown of cost for biomass electricity. As in case of electricity generation from biogas, share of capital cost which contains biomass gasifier, electricity generator, and cutting machine costs reduces as production capacity increases. In current case, cost of biomass gasifier dominates all other contributing factors to the cost of electricity. In ideal case operating in dual fuel mode, diesel cost becomes more significant than other inputs and share of capital costs diminishes. In ideal case operating in single fuel mode, no diesel or lubricant is utilized and feedstock ipomea cost dominates.

Current Case Ideal Case DF Ideal Case SF0

5

10

15

20

25

30

Cutting MachineElectrcity Generator Biomass GasifierLubricant Diesel FeedstockFeedstock Preparation Labor

Rs/k

Wh

Figure 4.1: Breakdown of cost of electricity from producer gas under different scenarios considered.

4.4 Economic Analysis of Solar Electricity Generation

The economic assessment of solar panel is performed based on the data supplied by International Renewable Energy Agency (IRENA, 2012a). The total annualized cost for the solar plant ($2877) which consists of operating cost and annualized capital cost is calculated and this number is divided by annual electricity generation to find the cost of electricity per kWh. As in LCA, life time of the solar plant is 20 years. All the prices are leveled to year 2009, which the solar plant started to operate in the village Rampura.

Capital cost encompasses the panel, panel mounting, inverter, battery bank costs. These costs are divided by the life time of related equipment to be annualized for one year of operation. Panel cost per watt capacity is calculated as $ 1.736 for year 2009 in Europe. Mounting cost was 6% of the total capital cost. Inverter cost is $ 0.67/W capacity and there are 5 kW inverters with a life time of 10 years. Battery bank price is obtained from Wholesale Solar and this price is adjusted to year 2009 with present value of money (US Inflation Calculator, 2013).

Operating cost includes the maintenance cost. This cost is $ 522/ kW capacity. Multiplied by the capacity of our plant (8.7 kWp) and divided by plant life time (20 years), annualized maintenance cost is calculated.

As a result, the price of solar electricity per kWh is found to be $ 0.34 and its conversion to Rupees is Rs 17 for year 2009 (X-Rates, 2013). These results are presented in table 4.5 altogether.

Table 4.5: Cost of solar electricityCapital Cost Total Cost($) Annual Cost($/year)Panels 15103 755Inverter 13386 669Mounting 3462 173Battery Bank 16740 1116  48691  Operating Costs Total Cost($) Annual Cost($/year)Maintenance 3267 163Total 51958 2877 Electricity Generated(kWh/year)   8353Price($/kWh)   0,34Price(Rs/kWh)   17

5 Multi-objective Linear Programming

5.1Energy Demandin Rampura

In reality, there is nothing that can be considered as waste in a rural setting. Every residue or resource which at first sight may be perceived as waste is used for some purpose. People of Rampura utilize most of agricultural residues either as fodder for their animals or as cooking fuel. Likewise, solar dried cow dung is an important energy source for cooking all year around. However, the way these energy sources are utilized is very primitive and inefficient.

Figure 5.1: A regular cook stove in Rampura.

The picture in figure 5.1 that is taken in Rampura presents a regular cook stove utilized. Cook stoves are either located in open air in gardens or inside the houses in kitchens. The insulation of these stoves is very poor. If the food is cooked using cow dung, the efficiency of the stove is around 5 % and if the stove is operated with wood or agricultural residues, efficiency is around 10% (Kaygusuz, 2011, DA). In other words, only 5% of the energy in cow dung or 10 % of the energy in wood or agricultural residues is absorbed by the food cooked, rest of the available energy in the fuels is wasted. Especially, women who cook using these traditional stoves expose pollutants and in general indoor smoke is an important problem in rural areas like Rampura (Alteri and Masera, 1993 and Zheng et al, 2010). Likewise using kerosene for lighting is not a satisfactory method. It does not provide sufficient light and people in

Rampura reported that they have itchy eyes when they use kerosene. With solar electricity use, this situation has improved (Development Alternatives, 2011).

According to the village level data obtained from Development Alternatives, 520 kg of cow dung, 275 kg of agricultural residues, 975 kg of wood per day are utilized for cooking in the village. Additionally, 3.5 liters of kerosene and 19 kWh of electricity are used for lighting per day. To irrigate fields, diesel pumps are utilized. For total irrigation hours of 3580, around 4350 liters of diesel is consumed per year in the village. Table 5.1 presents the consumption of each energy source in Rampura annually.

Table5.1: Energy use in Rampura village annually.

Energy Use In Rampura Amount/year

Electricity (kWh) 6935

Kerosene (liter) 1278

Wood(kg) 355875

Agricultural Residues(kg) 100375

Dung Cake(kg) 189800

Diesel (Irrigation) (liter) 4350

The other energy needs in Rampura include gasoline use for transportation of milk to the market in Jhansi by motorbike and diesel use in tractors for field ploughing and transportation of agricultural products to the market. However, the technologies which can replace transportation fuels are outside the scope of our research.

For calculating the annual total energy demand in Rampura, we convert all energy needs into electricity equivalents. However, utilizing biogas directly for cooking can be another option.

5.1.1 Irrigation Energy Requirements

Submersible diesel pumps are used in Rampura to irrigate the fields. These pumps utilize 1.25 liter diesel per hour of pump operation. For around 3480 pump hours at the village level, 4350 liter diesel is utilized annually. According to diesel engine operation data in Rampura, 1 liter diesel is consumed to generate 2.25 kWh of electrical energy (Development Alternatives, 2011). As a result, 9810 kWh electricity is needed to meet the irrigation energy requirements per year in Rampura.

5.1.2 Lighting Energy Requirements

Prior to solar electricity use, kerosene was the only energy source utilized for lighting in Rampura. Currently, 44 of the households out of 69 are connected to the solar grid. Rest of

the households (25) still utilizes kerosene for lighting. As stated in table 1, 6935 kWh of solar electricity is utilized in Rampura annually. Additionally, 1278 liter ofkerosene (1022 kg) is consumed annually. Kerosene is a mixture of heavy hydrocarbons which has similar chemical and physical characteristics to diesel. Its chemical formula can be approximated as C12H26. Diesel can be converted to electricity with 22.2% efficiency (Development Alternatives, 2011). Assuming, 4.5 J of kerosene can generate 1 J of electricity as in case of diesel, kWh of electricity which can be generated is calculated by the formula 7.

Electrcity (kW h )=EnergyContentofKerosene4.5∗3.6 E+06

(7)

Kerosene has energy content of 44 kJ/g and density of 800 g/liter (Bastionani et al, 2009). As a result, 2780 kWh electricity equivalents energy is needed for households which are not connected to the solar grid. Cumulatively, 9715 kWh of electricity is needed in Rampura for lighting purposes.

5.1.3 Cooking and Heating Energy Requirements

In Rampura, sun dried cow dung (20% moisture), agricultural residues and wood (15% moisture content) are used for cooking (Beck, 2003 and Zhang et al, 2007). Moisture content of fuels utilized is important since wet wood or cow dung do not burn properly and produce smoke. In table 1, the annual amounts of cow dung, wood and agricultural residues utilized for cooking are presented. As it is earlier stated here efficiency of stoves utilized by burning cow dung is 5 % and it is 10 % if agricultural residues or wood are used. If biogas operated cook stoves are utilized, this efficiency raises to 60% (Marchaim, 1992). Electric stoves have an average efficiency of 70% (Kaygusuz, 2011). Improved biomass stoves operating with cow dung, agricultural residues or wood have higher efficiency than traditional cook stoves (Bhattacharya and Salam, 2002). The efficiency of these improved cook stoves are 19%, 21% and 24 % if they are operated with cow dung, agricultural residues and wood respectively (Bhattacharya and Salam, 2002 and Panwar et al, 2009).

Different energy sources have different available energy contents, in other words, different work performing capacity. For instance, 1 J of oil energy does more work than 1 J of coal. When coal energy is converted into oil energy equivalents, coal energy is multiplied by a conversion factor originating from this different work performing capacity or their efficiency in generating the same amount of electricity (Cleveland, 1992). Based on this fact, the energy quality of cow dung, wood, agricultural residues, biogas and electricity can be evaluated based on their effectiveness to cook food or on their work performing capacity. When this is done, the equality given in equation 8 can be assumed for cooking performances of different fuels utilized.

1Jofelectrcity=1.17 Jofbiogas=7Jofwood=7 Jofagriculturalresidue=14 Jofcowdu ng(8)

Based on this assumption and utilization amount of each energy source, energy demand for cooking in Rampura is found to be 302500 kWh electricity equivalents per year if all the cooking energy needs are supplied by electricity . Table 5.2 presents the annual electricity needs for all the energy requirements including lighting and irrigation.

Table 5.2: Annual energy demand in Rampura in kWhe equivalents

Cooking (kWhe) 302500

Irrigation(kWhe) 9810

lighting(kWhe) 9715

Total(kWhe) 322025

Another option to meet cooking energy needs would be to use biogas directly without converting it into electricity. The efficiency of stoves operating with biogas or electricity is similar (60% versus 70%). Here, 1.17 J of biogas energy can perform the work 1 J of electrical energy can. On the other hand, if we choose to generate electricity from biogas and use it for cooking, it takes8.9 J of biogas energy to generate 1 J of electricity which will be explained in more detail in the following biogas and electricity potential subsection. Consequently, most of the available energy potential in biogas is lost in transformation from biogas to electricity, although these two energy sources can perform similar useful work (cooking) without any conversion. As a result, utilizing biogas directly to meet cooking needs and converting excess biogas into electricity to meet other energy demands can make more sense. If we choose this second option, 1.27 E+12 J of biogas energy would be needed cumulatively for cooking. Then, energy needs in Rampura can be listed as in table 5.3. It is important to be aware here that cooking energy need is in kWh biogas equivalents.

Table 5.3: Annual energy demand in Rampura with direct use of biogas for cooking.

Cooking (kWhbiogas) 396520

Irrigation(kWhe) 9810

lighting(kWhe) 9715

If we convert biogas into electricity equivalents but keep in mind that it is utilized directly as biogas, total energy demand in Rampura can be represented in kWhe as in table 5.4.

Table 5.4: Annual energy demand in Rampura in kWhe equivalents for direct use of biogas for cooking.

Cooking & Heating(kWhe) 44550

Irrigation(kWhe) 9810

lighting(kWhe) 9715

Total(kWhe) 64075

Remarkable reduction in energy demand with direct use of biogas also supports our insight that utilizing biogas directly to meet cooking needs and converting excess biogas into electricity can make more sense.

In analysis of the different scenarios to meet energy demand in Rampura, modifications into these two basic cases of utilizing biogas directly for cooking or utilizing electricity for all energy needs are investigated as separate scenarios. Pros and cons of each case are discussed in detail.

5.2 Energy Potential in Rampura

The energy needs in Rampura is to be met by the energy generated by the five energy technology options investigated. These are two clean coal technologies (conventional and CLP), biomass gasification, anaerobic digestion and solar power generation. In this section, we present the potential of local energy resources which can be utilized by localized energy options to generate electricity. The clean coal technologies are centralized high capacity energy technologies of which electricity can be transmitted to Rampura.

5.2.1 Solar Electricity Potential

Rampura is in Bundelkhand region of India and has a semi arid climate with high temperatures except the month of January. The average solar insolation is 18.65 MJ/m2.day (Tyagi, 2009). The magnitude of solar insolation and number of sunny days are significantly higher compared to Northern European countries (Ecoinvent, 2009). In general, being a tropical country, India has a high solar potential.

The solar plant in Rampura is a 8.7 kWp capacity multi-crystalline solar plant with 67.5 m2 photo-sensitive and 74 m2 framed area. A total area of 100 m2 is dedicated to the solar plant including battery bank, inverters, charge controllers and other electronic equipment. This plant provides around 8350 kWh electricity per year.

Theoretically, all the energy need in Rampura can be met by solar electricity. However, we limit solar electricity potential to 20000 kWh for ground mounted solar panels option since

land is a valuable asset in Rampura. A solar plant providing all the energy demand would occupy around 3800 m2 land area which is an unreasonable number for a village of which major income is agriculture and of which total land area is 133.5 hectares. Rampura village cannot dedicate that large area to solar electricity generation. A plant generating 20000 kWh of electricity per year would cover a land area of around 250 m2 which is manageable.

If the solar panels were to be mounted on the roofs of the 44 houses currently utilizing solar electricity, 40000 kWh of electricity generation would be possible. Around 11 m2 area on each rooftop is occupied by the panels in this case. Mounting solar panels on rooftops alleviates the dedicated land area limitation for solar panels. Rooftop mounted solar panels constitute a second scenario for energy options considered to meet the energy demand in Rampura village.

5.2.2 Biogas and Electricity Potential

In Rampura, there are 436 individuals from which 3.98E+07 (wet) g of human feces are obtained annually. From 408 different animals, 5.55E+08 g of manure (dry) are obtained annually. Human feces have an energetic content of 8000 J/g on wet basis (Murphy et al, 1991), and manure has an energetic content of 17613 J/g on dry basis (Fan et al, 1985). Based on these values, the potential of biogas energy and potential of electrical energy from biogas in Rampura village are listed in table 5.5.

Table 5.5: Biogas and biogas electricity potential in Rampura village.

 Mass(g/year)

Energy Content(J)

Biogas(J)

Electricity(kWhe)

Human Feces (Wet) 3.98E+07 3.18E+11

5.44E+10 1.70E+03

Manure (Dry) 5.55E+08 9.78E+121.67E+1

2 5.22E+04

Total   1.01E+131.73E+1

2 5.39E+04

The conversion factors from manure to biogas and biogas to electricity are based on emergy analysis performed for ideal case of the biogas digester. There is a demand of 1.27E+12 J of biogas in Rampura and a potential of 1.73E+12 J of biogas. After supplying the cooking energy demand, excess biogas (4.56E+11 J of biogas) can be converted to electricity (1.42E+04 kWh of electricity) to meet other energy requirements.

5.2.3 Biomass Electricity Potential

In Rampura, 2.95E+08 g of agricultural residues are available annually. Availability of ipomea changes between 6.50E+07 and 8.00E+07 g of ipomea per year (Development Alternatives, 2011). On average, agricultural residues have an energy content of 14400 J/g (Zhang et al, 2007) and ipomea has an average energy content of 16000 J/g on dry basis (Pandey et al, 2012). From 6 J of agricultural residue or wood, 1 J of electrical energy can be generated by gasification. Based on these values, the electricity potential in Rampura by gasification is presented in table 5. 6. On average, Rampura has a total potential of 242000 kWh of electricity generation through biomass gasification.

Table 5.6: Potential of electricity through biomass gasification in Rampura.

Availability of Feedstock Mass (g)Energy Content(J)

Electricity(kWhe)

Agricultural Residues 2.95E+08 4.25E+12 1.97E+05

Ipomea(wood) 6.50E+07 8.84E+11 4.09E+04

Ipomea(wood) 8.00E+07 1.09E+12 5.04E+04

Total up   5.34E+12 2.47E+05

Total low   5.13E+12 2.38E+05

5.3 Problem Formulation

To find the optimum energy mix to supply the energy demand in Rampura village, an optimization problem is set up by using linear programming approach. As in all optimization problems, the problem we present is subject to some constraints for realizing the objectives we set to meet a certain amount of energy demand.

Decision Variables: The decision variables are the kWhe (kWh electricity) supplied by each of five energy technologies analyzed.

Decision Variables:

Xi = kWh electricity provided by each energy technology option, i = 1,…,5.

X1= kWh electricity provided by conventional clean coal technology,

X2= kWh electricity provided by calcium looping clean coal technology,

X3= kWh electricity provided by biogas digester,

X4= kWh electricity provided by biomass gasification,

X5 = kWh electricity provided by multi crystalline solar PV.

Constants: The constants utilized are land use (m2/kWhe), water use (L/kWhe), GWP (gCO2eq./kWhe), total annualized cost (Rs/kWhe), electricity transformity (sej/kWhe, which is the yield for production of 1 kWhe); renewable, non-renewable, purchased emergy contents (each in sej/kWhe) per unit (1 kWhe) electricity production. Values of these constants are given in table 5.7.

Constants:

Yi = Land use in m2/ kWhe for different technology options, i =1, ..,5.

Zi = Water use in liters/ kWhe for different technology options, i =1,...5.

Wi = GWP in g CO2 equiv. / kWhe for different technology option, i =1,...5.

Mi = Total annualized cost for different technology options in Rupees/kWhe, i =1,...5.

Ti = Transfromity of electricity for different energy technology options in sej/kWhe, i=1,...5.

Ri= Renewable emergy content of electricity for different energy technology options in sej/kWhe, i=1,...5.

Pi =Purchased emergy content of electricity for different energy technology options in sej/kWhe, i=1,...5.

Ni= Non-renewable emergy content of electricity for different energy technology options in sej/kWhe, i=1,...5

Table 5.7: Values of constants used in solving the LP problem.

Constants X1 X2 X3 X4 X5

Land Use(m2/kWh)1.43E-

021.77E-

021.80E+0

02.71E-

017.02E-

03

Water Use(liter/kWh)2.84E+0

02.21E+0

01.07E+0

22.02E+0

22.19E+0

0

GWP(CO2eq/kWh)1.61E+0

24.48E+0

11.18E+0

22.38E+0

29.48E+0

1Cost(Rs/kWh) 2.64 2.91 7.80 8.80 17.0Transformity (Sej/kWh)

5.04E+11

3.34E+11

4.69E+12

9.48E+10

6.57E+10

Renewable(Sej/kWh)1.85E+0

94.66E+0

82.43E+1

24.73E+0

91.46E+0

9

Purchased(Sej/kWh)4.25E+1

12.86E+1

12.25E+1

27.56E+1

06.37E+1

0Nonrenewable(Sej/ 7.75E+1 4.81E+1 8.50E+0 1.46E+1 5.15E+0

kWh) 0 0 9 0 8

Objectives

Our aim is to find the energy technology mix having minimum land and water use, GWP, cost; maximum %Re, EYR and minimum ELR. Hence, we have the objectives of minimum land use, minimum water use, minimum GWP, minimum total annual cost, maximum %Re, maximum EYR and minimum ELR which are formulated below:

Objective Functions:

1. Minimum Land Use: Min z =∑1

5

YiXi /∑1

5

Xi

2. Minimum Water Use: Min z=∑1

5

ZiXi /∑1

5

Xi

3. Minimum GWP: Min z =∑1

5

WiXi /∑1

5

Xi

4. Minimum Total Annualized Cost: Min z =∑1

5

MiXi /∑1

5

Xi

5. Maximum % Renewability (%Re): Max z =¿

6. Maximum Emergy Yield Ratio (EYR): Max z =¿

7. Minimum Environmental Loading Ratio (ELR): Min z =¿

Constraints:

Energy Demand Constraint =∑1

5

Xi

Capacity Constraints:

1. Electricity from biogas ≤ X3 kWhe

2. Electricity from biomass gasification ≤ X4 kWhe

3. Solar electricity ≤ X5 kWhe

4. Xi ≥ 0

This problem is solved for four different scenarios. In each scenario, energy demand and potential change according to the assumptions made.

The Scenarios

Four different scenarios are analyzed to meet the energy demand in Rampura. Cooking energy is the largest chunk in the total energy demand. Different applications in cooking have the greatest impact on the overall system and its sustainability. In the first scenario; irrigation, lighting and cooking energy needs are all to be met with electrical energy. Thus, electrical stoves with 70% efficiency are used for cooking. Solar energy potential is limited to 20000 kWhe based on the dedicated land area constraint of the village for ground mounted solar panels. In the second scenario, everything except the solar potential is kept the same. Considering rooftop mounted solar panels option, solar power potential is taken as 40000 kWhe. In the third scenario, all cooking energy is supplied by biogas energy use directly. Excess biogas can then be utilized to generate electricity to meet other energy demands (irrigation, lighting). In this scenario, at least 39700 kWhe equivalents (cooking energy portion) of the total energy demand have to be provided by direct use of biogas. In the fourth scenario, 70% of cooking energy is met by direct use of biogas. 30 % of cooking is performed by using improved biomass cook stoves utilizing cow dung, agricultural residues and wood as fuel. In this scenario, at least 27790 kWhe equivalents of energy needs have to be supplied by direct use of biogas (70% of cooking needs). Improved biomass cook stoves operate with 19%, 21% and 24% efficiency if they utilize cow dung, agricultural residues or woody biomass respectively. Table 5.8 summarizes the total energy demand and local energy availability from different resources in kWhe equivalents for four different scenarios considered.

Table 5.8: Energy demand and potential in kWhe equivalents in Rampura village.

  Electricity-GM Electricity-RMBiogas

Cooking70% Biogas

Cooking

Total energy demand (kWhe) 322025 322025 59225 47315

Solar energy potential (kWhe) 20000 40000 20000 20000

Biogas energy potential (kWhe) 53800 53800 53800 52725

Biomass gasification potential (kWhe) 242000 242000 242000 204747

5.4 Summary of LP Results

5.4.1 Scenarios and Energy Combination List

A. Electricity-GM- Land Use Objective: 302025 kWhe Conventional+20000 kWhe Solar- Water Use Objective: 302025 kWhe CLP+20000 kWhe Solar- GWP Objective:268225 kWhe CLP+53800kWhe Biogas-Cost Objective: 322025 kWhe Conventional-%Re: 242000 kWhe Biomass+53800 kWhe Biogas+20000kWhe Solar+6225 kWhe Conventional-EYR:242000 kWhe Biomass+53800 kWhe Biogass+26225 kWhe Conventional-ELR: 242000 kWhe Biomass+53800 kWhe Biogas+20000kWhe Solar+6225 kWhe Conventional

B. Electricity-RM- Land Use Objective: 282025 kWhe Conventional+40000 kWhe Solar- Water Use Objective: 282025 kWhe CLP+40000 kWhe Solar-GWP Objective:268225 kWhe CLP+53800kWhe Biogas-Cost Objective: 322025 kWhe Conventional-%Re: 242000 kWhe Biomass+53800 kWhe Biogas+26225 kWhe Solar-EYR:242000 kWhe Biomass+53800 kWhe Biogass+26225 kWhe Conventional-ELR: 242000 kWhe Biomass+53800 kWhe Biogas+26225 kWhe Solar

C. Biogas Cooking- Land Use Objective: 39700 kWhe Biogas+19525 kWhe Solar- Water Use Objective: 39700 kWhe Biogas+19525 kWhe Solar- GWP Objective:53800 kWhe Biogas+5425 kWhe CLP -Cost Objective: 39700 kWhe Biogas+19525 kWhe Conventional -%Re: 53800 kWhe Biogas+5425 kWhe Biomass-EYR: 53800 kWhe Biogas+5425 kWhe Biomass-ELR: 53800 kWhe Biogas+5425 kWhe Biomass

D. 70% Biogas Cooking- Land Use Objective: 27790 kWhe Biogas+19525 kWhe Solar- Water Use Objective: 27790 kWhe Biogas+19525 kWhe Solar- GWP Objective: 47315 kWhe Biogas -Cost Objective: 27790 kWhe Biogas+19525 kWhe Conventional -%Re: 47315 kWhe Biogas-EYR: 47315 kWhe Biogas-ELR: 47315 kWhe Biogas

5.4.2 Raw Data and Modified Data for Scenario and Energy Combination Selection

For the scenarios examined, the greater (maximized objectives) are the values of %Re, EYR and the smaller (minimized objectives) are the values of land, water use, GWP, cost and ELR objectives, the better.To determine the most sustainable scenario and the best energy combination with in the selected scenario, we modified the data in a way that the larger the area of the spider diagram the more sustainable the scenario becomes. For this, each objective value is divided (normalized) by the greatest value of that objective within the four different

scenarios. Then, minimized objective values are multiplied by -1 to make their higher value better. Because of positive and negative values of conflicting objectives, the axis of the spider graph changes between 1 and -1, -1 being at the center of the graph.

Original Data for Scenario Selection

Table 5.9: Originaldata for drawing scenario selection graph (figure 9 in the paper).

  Electricity-GM Electricity-RMBiogas

Cooking70% Biogas

CookingLand Use(m2/kWhe) 1.43E-02 1.34E-02 1.21E+00 1.06E+00Water Use(L/kWhe) 2.21E+00 2.21E+00 7.52E+01 6.38E+01GWP(gCO2eq./kWhe) 1.76E+01 1.76E+01 -1.03E+02 -1.18E+02Cost(Rs/kWhe) 2.64 2.64 6.10 5.69%Re 12.55% 12.59% 47.50% 51.82%EYR 1.39 1.39 2.00 2.08ELR 22.4 18.1 2.59 0.93

Modified Data for the Bigger Area Better

Table 5.10: Modified data for drawing scenario selection graph (figure 9 in the paper).

  Electricity-GM Electricity-RMBiogas

Cooking70% Biogas

CookingLand Use(m2/kWhe) -0.01 -0.01 -1.00 -0.88Water Use(L/kWhe) -0.03 -0.03 -1.00 -0.85GWP(gCO2eq./kWhe) -0.15 -0.15 0.87 1.00Cost(Rs/kWhe) -0.43 -0.43 -1.00 -0.93%Re 0.24 0.24 0.92 1.00EYR 0.67 0.67 0.96 1.00ELR -1.00 -0.81 -0.12 -0.04

Original Data for Energy Combination Selection

Table 5.11: Original data for drawing energy combination selection graph (figure 10 in the paper).

  Combination 1 Combination 2 Combination 3Land Use(m2/kWh) 1,06E+00 1,80E+00 1,06E+00Water Use(liter/kWh) 6,37E+01 1,07E+02 6,40E+01GWP(CO2eq/kWh) -3,02E+01 -1,18E+02 -2,87E+00Cost(Rs/kWh) 11,6 7,8 5,67%Re 31,35% 51,82% 30,59%EYR 1,65E+00 2,08E+00 1,71E+00

ELR 8,41E+00 9,31E-01 1,13E+02Modified Data for the Bigger Area Better

Table 5.12: Modified data for drawing energy combination selection graph (figure 10 in the paper).

  Combination 1 Combination 2 Combination 3Land Use -0,59 -1,00 -0,59Water Use -0,60 -1,00 -0,60GWP -0,26 -1,00 -0,02Cost -1,00 -0,67 -0,49%Re 0,60 1,00 0,59EYR 0,79 1,00 0,82ELR -0,07 -0,01 -1,00

6 Emergy Analysis of Domestic and Husbandary Sectors in Rampura

6.1 Domestic Sector

Domestic sector provides the human labor required in husbandry and agricultural sectors. Human feces can also be a source of organic fertilizer despite not being utilized in the village currently. Food crops and agricultural residues are 20% renewable, 80 % purchased inputs classified according to the analysis results of agricultural sector. Milk and manure are 10% renewable, 90% purchased inputs classified according to the analysis results of husbandry sector. Wood and groundwater are non-renewable local inputs which are utilized faster than their replenishment rate by nature. Sun, rain and wind are local renewable inputs. Lastly, kerosene and inputs for solar electricity are purchased emergy inputs contributing to domestic sector.

Emergy flow belonging to each emergy input is calculated. The emergy flow for each input and references for related transformity values are presented in table 6.1. As it can be seen in this table, cow dung and milk are two co-products of husbandry sector. According to the emergy algebra rules, emergy flows for co-products cannot be added but the emergy flow having the maximum value is picked (Odum, 1996 and Basianoni et al, 2009). For that reason, emergy flow of milk is accounted for in our emergy evaluation. On the other hand, food crops and agricultural residues are the products of agricultural sector. However, their emergy flows can be added up since they are splits not co-products (Bastianoni et al, 2009).

Table 6.1: Emergy evaluation table for domestic sector in Rampura.

Inputs AmountUni

tTransformity(Sej/Unit)

Emergy

(Sej/year) Reference

Sun(land)(100%R) 1.86E+15 J 1 1.86E+15  Odum,2000

Sun(field)(100%R) 4.64E+15 J 1 4.64E+15  Odum, 2000

Rain(100%R) 5.61E+12 J 3.06E+04 1.71E+17  Odum,2000

Wind(100%R) 6.56E+08 J 2.52E+03 1.65E+12  Odum, 2000

Earth Cycle(100%R) 1.34E+12 J 4.28E+04 5.71E+16  Odum,2000

         

1.Lighting          

Solar Electricity(100%F) 3.01E+10 j 1.81E+05 5.46E+15 Self Calculation

Kerosene(100%F) 1.02E+06 g 2.88E+09 2.94E+15Bastionani, 2009

2.Cooking          

Cow Dung(10%R,90%F) 2.67E+12 J 2.98E+05 7.97E+17 Self Calculation

Agricultural Residues (20%R,80%F) 1.00E+08 g 2.37E+09 2.38E+17 Self Calculation

Wood(100%N) 3.56E+08 g 6.79E+08 2.42E+17 Pizzigallo, 2008

Ground Water(100%N) 3.15E+09 g 3.98E+05 1.25E+15 Zhang, 2012

Food (20%R,80%F) 6.96E+07 g 2.57E+09 1.79E+17 Self Calculation

Milk(10%R,90%F) 6.20E+07 g 1.40E+10 8.74E+17 Self Calculation

      Total 1.71E+18  

Outputs          

Human Labor 1.25E+12 J 1.37E+06 1.71E+18  

Human Feces 3.13E+11 J 5.46E+06 1.71E+18  

Table 6.2: Emergy indicators for domestic sector in Rampura.

%Re 20%

EYR 1.52ELR 3.99

6.2 Husbandary Sector

Sun, rain and wind are the renewable emergy inputs to the system. Groundwater is the local non-renewable input since underground water level declines. Human labor is categorized as 20 % renewable and 80 % purchased input of which calculation is presented in domestic sector emergy analysis. Likewise agricultural residues are 20 % renewable and 80% purchased inputs. Fodder and gasoline for the transportation of milk to the market is 100% purchased inputs in the husbandry sector.

Animal draft, manure and milk are the products obtained from the husbandry sector. Outputs of the husbandry sector are co-products which cannot be produced independently. For that reason, the total emergy yield is assigned to all products without allocation according to the emergy algebra rules (Odum, 1996 and Bastianoni, 2009). Product transformities are calculated by division of total emergy yield by product available energy (exergy) or product mass.

Emergy flow belonging to each emergy input is calculated. The emergy flow for each input and references for related transformity values for husbandary sector are presented in table 6.3.Emergy ratios and index are calculated following this step.

Sensitivity analysis is performed to detect the effect of changes in renewability of fodder since it is the input constituting 75 % of all emergy inputs to the husbandry subsystem. Table 6.4 presents the results for emergy indicators for the base case (fodder 100%F) and the results for the sensitivity analysis performed (fodder 20% and 40 % e or 80% and 60% F).

Table 6.3: Emergy evaluation table for husbandry sector in Rampura village.

Inputs Amount UnitTransformity(S

ej/Unit)

Emergy

(Sej/year) Reference

Sun(land)(100%R)1.86E+1

5 J 1 1.86E+15  Odum,2000

Sun(field)(100%R)4.64E+1

5 J 1 4.64E+15  Odum,2000

Rain(100%R)5.61E+1

2 J 3.06E+04 1.71E+17  Odum,2000

Wind(100%R)6.56E+0

8 J 2.52E+03 1.65E+12  Odum,2000

Earth Cycle(100%R)1.34E+1

2 J 4.28E+04 5.71E+16  Odum,2000

Fodder(100%F)1.07E+0

9 g 2.07E+09 2.21E+18  Zhang, 2007

Human Labor(20%R,80%F)1.72E+1

1 J 1.37E+06 2.36E+17 Self Calculation

Ground water(100%F)3.99E+0

9 g 3.98E+05 1.59E+15  Zhang,2012

Ag. Residue Fodder(20%R,80%F)

1.19E+08 g 2.37E+09 2.81E+17

 Self Calculation

Milk Transportation To Market          

Gasoline(100%F)4.34E+0

6 g 2.92E+09 1.27E+16Bastionani,2009 

      Total 2.92E+18  

Outputs          

Milk2.08E+0

8 g 1.40E+10 2.92E+18  

Animal Draft8.60E+1

1 J 3.38E+06 2.92E+18  

Manure1.85E+0

9 g 1.58E+09 2.92E+18  

Table 6.4: Emergy indicators for base case and their variation due to change in renewability of fodder input.

Fodder 100%F Fodder 80%F Fodder 60%F

%Re 9.42% 24.6% 39.7%

EYR 1.10 1.33 1.66

ELR 9.62 3.07 1.52

7 Greenhouse Gas (GHG) Mitigation

7.1 Current Green House Gas Emissions(GHG) in Rampura

Table 7.1 summarizes the current energy use and GHG emissions for cooking activity in Rampura. The incomplete combustion in traditional stoves causes high GHG emissions combined with high resource use (Panwar, 2009 and Bhattacharya and Salam, 2002). Utilization of efficient cook stoves such as biogas cook stoves and improved biomass cook stoves can help mitigating GHG emissions caused by cooking activities.

Table 7.1: Cooking energy use and related GHG emission currently in Rampura.

  Amount(g)Energy(MJ)

GHG(gCO2 eq.)

Cow Dung 1.90E+08 2.67E+06 2.02E+07

Agricultural Residues 1.00E+08 1.45E+06

1.09E+07

Wood 3.56E+08 4.84E+06 5.84E+07

7.2 70 % Biogas Cooking Mitigation Potential

Bhattacharya and Salam studied the GHG emissions caused by cooking activities using different cook stoves. Table 7.2 summarizes their results and emissions per MJ of fuel utilized in cooking. Since biomass cook stoves utilize bio-fuels, only CH4 and N2O emissions have a net GWP. CO2 emitted is sequestered during photosynthesis while the plants grow.

Table 7.3 summarizes the cooking energy use and related emissions in scenario 4. Here, 1.10E+07 g CO2 equivalents of GHG are emitted. By utilization of biogas and improved biomass cook stoves for cooking, 7.85E+07 g CO2 equivalents of GHG emission can be mitigated annually. That is 88% of cooking related GHG emissions can be avoided.

The mitigation by utilization of electric cook stoves depends on the source of electricity and how it is generated. However, our result regarding biogas or biogas-biomass cook stove use reveals that utilization of these efficient cooking schemes has a substantial potential to mitigate cooking related GHG emissions in Rampura

Table 7.22: Cooking related GHG emissions using different cook stoves and fuels. (Adapted from Bhattacharya and Salam,2002).

Traditional Stove

CH4

(kg/TJ fuel)

N2O

(kg/TJ fuel) gCO2 eq./MJ Fuel

Wood 519.6 3.74 12.07

Residue 300 4.00 7.54

Dung 300 4.00 7.54

Improved Stoves      

Biogas 57.8 5.20 2.83

Wood 408 4.83 10.07

Residue 131.8 4.00 4.01

Dung 300 4.00 7.54

Table 7.3: GHG emissions related to biogas and improved biomass cook stove use for cooking in 70% Biogas cooking scenario

 Energy (MJ)

gCO2 eq. Emissions

Biogas 8.89E+05 2.51E+06

Cow Dung 2.11E+05 1.59E+06

Agricultural Residues 2.06E+05 8.28E+05

Wood 6.05E+05 6.09E+06

Supporting Information References

Alibaba,2013.http://www.alibaba.com/trade/search?fsb=y&IndexArea=product_en&CatId=&SearchText=generator+7.5+kva Accessed January, 2013.

All Power Labs (APL) (a).Gasifier types http://www.gekgasifier.com/info / gasification-basics/gasifier-types. Accessed May, 2013.

Assessment of Hydrogen Production with CO2 Capture, Volume 1:Baseline State-of-the-Art Plants; DOE/NETL 2010/1434; U.S. Department of Energy, National Energy Technology Laboratory: Washington, DC, August 30, 2010.

Alteri MA, Masera O. Sustainable rural development in Latin America: Building from the bottom-up. Ecological Economics. 1993;7:93-121.

Bhattacharya S.C, Salam PA. Low greenhouse gas biomass options for cooking in developing countries. Biomass and Bioenergy. 2002; 22:305-317.

Bastianoni S, Chambell DE, Rudolfi R, Pulselli FM. The solar transformity of petroleum fuels. Ecological Modelling. 2009; 220: 40-50.

Beck RW. Review of Biomass Fuels and Technologies. Yakima County Public Works Solid Waste Division. 2003. 47 pp.

Brown MT. A picture is worth thousand words: Energy systems language and simulation. Ecological Modelling. 2004; 178: 83-100.

Brown MT, Herendeen RA. Embodied energy analysis and emergy analysis: A comperative view. Ecological Economics. 1996; 19:219-235.

Brown MT, Ulgiati S. Energy quality, emergy and transformity: H.T Odum`s contributions to quantifying and understanding systems. Ecological Modelling. 2004; 178: 201-213.

Brown MT, Raugei M, Ulgiati S. On boundaries and investments in emergy synthesis and LCA: A case study on thermal versus electricity. Ecological Indicator. 2012; 15: 227-235.

Ciotola RJ, Langsing S, Martin J.K. Emergy analysis of biogas production and electricity generation from small-scale agricultural digesters. Ecological Engineering. 2011, 37: 1681– 169.

Cleveland CJ. Energy quality and energy surplus in the extraction of fossil fuels in the U.S. Ecological. Economics. 1992; 6:139-162.

Cruze NB. Addressing Allocation and Disparity in Methods Life Cycle Inventory. Columbus, Ohio: The Ohio State University; 2013.

Cost of electricity distribution in India. http://www.ima-india.com/solarising-rural-india.pdf. Accessed January, 2013.

Curran MA eds, 2012. Life Cycle Assessment Handbook. Massachusetts, Scrivener. 641 pp.

Development Alternatives (2011). (Personal Communication, Survey, Organization Data).

Ge ZM, Kellomaki S, Zhou X, Wang KY, Vaisanen H, Strandman H. Effects of climate change on evapotranspiration and soil water availability in Norway spruce forests in southern Finland: an ecosystem model based approach. Ecohydrology. 2013; 6: 51-63. Guinée, J. B. and Heijungs, R. Life Cycle Assessment. Kirk-Othmer Encyclopedia of Chemical Technologt. 2005; 14:805-831.

Guinée, JB, Heijungs R, Huppes G. Life cycle assessment: Past, present and future Environmental Science and Technology. 2011; 45: 90–96.

Hau LH, Bakshi B.R. Promise and problems of emergy analysis. Ecological Modelling 2004; 178: 215–225.

Herendeen RA. Energy analysis and emergy analysis: A comparison. Ecological Modelling. 2004; 178: 227-237.

Hurst TF, Cockerill TT, Florin HN. Life cycle greenhouse gas assessment of a coal-fired power station with calcium looping CO2 capture and offshore geological storage. Energy and Environmental Science. 2012; 5: 7132-7150.

Hsu DD, O’Donoughue P, Fthenakis V, Heath GA, Kim HC,Sawyer P, Choi JK, Turney DE. Life cycle greenhouse gas emissions of crystalline silicon photovoltaic electricity generation. Journal of Industrial Ecology. 2012; 16: S122-S135.

International Energy Agency (IEA). World Energy Outlook 2012. International Energy Agency. http://www.iea.org/publications/freepublications/publication / English.pdf. Published November, 2012. Accessed February 27, 2013.

International Renewable Energy Agency (a). Biomass for Power Generation. Renewable Energy Technologies: Cost Analysis Series. Volume: 1, Issue 1/5. 2012

International Renewable Energy Agency (b). Solar Photovoltaics. Renewable Energy Technologies: Cost Analysis Series. Volume: 1, Issue 4/5. 2012.

ISO 14040 International Standard. Environmental management- Life cycle assessment - Principles and framework; International Organization for Standardization: Geneva, Switzerland, 2006.

Jungbluth N., Stucki M., Frischknecht R. Photovoltaics. In Dones R. (Ed.) et al., Sachbilanzen von Energiesystemen: Grundlagen fur den Okologischen Vergleich von Energiesystemen und den Einbezug von Energiesystemen in Okobilazen fur die Schweiz. Ecoinvent Report No:6-XII, Swiss Centre for Life Cycle Inventories, Dubendorf, CH, 2009.

Kalia KK, Singh SP. Case study of 85 m3 floating drum biogas plant under hilly conditions. Energy Conversion and Management. 1999; 40: 693-702.

Kaygusuz K . Energy services and energy poverty for sustainable rural development. Renewable and Sustainable Energy Reviews. 2011; 15:936-947.

Kursun B, Ramkumar S, Bakshi BK, Fan LS. Life cycle comparison of coal gasification by conventional versus calcium looping processes. Industrial and Engineering Chemistry Research. 2014. Asap:A-J.

Marchaim U. Biogas Processes for Sustainable Development. Chapter 8. FAO, 1992. <http://www.fao.org/docrep/T0541E/T0541E0b.htm#Use of biogas in India> Accessed January, 2013.

Marvuglia A, Benetto E, Rios G, Rugani B. Scale: Software for calculating emergy based on life cycle inventories. Ecological Modelling.2013; 248:80-91.

Odum, HT, 1996. Environmental Accounting: Emergy and Environmental Decision Making. Wiley, New York. 370 pp.

Odum HT. 2000. Emergy of Global Processes. Folio No. 2: Handbook of Emergy Evaluation: a compendium of data for emergy computation issued in a series offolios. Center for Environmental Policy, University of Florida, Gainesville, GL, 28 pp.

Panwar NL, Kurchania AK, Rathore NS. Mitigation of greenhouse gases by adoption of improved biomass cookstoves. Mitigation and Adaptation Strategies for Global Change. 2009; 14: 569-578.

Paoli C, Vassallo P, Fabiano M. Solar power: An approach to transformity evaluation. Ecological Engineering. 2008; 34:191-206.

Pizzigallo ACI, Granai C, Borsa S. The joint use of LCA and emergy evaluation for the analysis of two Italian wine farms. Journal of Environmental Management. 2008; 86: 397-406.

Ramanathan R, Ganesh LS. A Multi-objective evaluation of decentralized energy electricity generation options available to urban households. Energy Conversion and Management. 1994; 8: 661-670.

Ramkumar S. Calcium Looping Processes for Carbon Capture. Columbus, Ohio: The Ohio State University; 2010.

Rehl T, Lansche J, Muller J. Life cycle assessment of energy generation from biogas: Attributional versus consequential approach. Renewable and Sustainable Energy Reviews. 2012; 16: 3766-3775.

Rockstrom J , Steffen W, Noone et al. A safe operating space for humanity. Nature. 2009; 461: 472-475.

Rugani B, Benetto E. Improvements to emergy evaluations by using life cycle assessment. Environmental Science and Technology.2012, 46: 4701−4712.

Steffen W, Persson A, Deutsch L, et al. The Anthropocene: from global change to planetary stewardship. Ambio.2011; 40:739-761.

University of Florida Center for Environmental Policy (UFCEP). http://www.cep.ees.ufl.edu/emergy/resources/symbols_diagrams.shtml. Accessed May, 2013.

Ulgiati S, Brown MT. Monitoring patterns of sustainability in natural and man-made ecosystems. Ecological Modelling.1998; 108: 23-36.

Ulgiati S, Ascione M, Bargigli ., Cherubini F, Federici M, Franzese PP, Raugei M, Viglia S, Zucaro A. Multi-method and multi-scale analysis of energy and resource conversion and use. NATO Science for Peace and Security Series C: Environmental Security. 2010: 1-36.

Urban RA, Bakshi BR.1,3-propanediol from fossils versus biomass: A life cycle evaluation of emissions and ecological resources. Industrial and Engineering Chemistry Research. 2009; 48: 8068-8082.

Uttar Pradesh Electricity Regulatory Commission (UPERC). Determination of Annual Revenue Requirement and Tariff for FY 2009-2010. 2010: 311pp.

The Ohio State University (OSU). Ecologically based life cycle assessment. The Ohio State University. http://resilience.eng.ohio-state.edu/ecolca-cv/. Accessed January 13, 2013.

Tyagi PA. Solar Radiant Energy over India. Government of India, India Meteorological Department, Ministry of Earth Sciences, 2009.

Xie X, Wang M, Han J. Assessment of fuel-cycle energy use and greenhouse gas emissions from coal and cellulosic biomass. Environmental Science and Technology. 2011, 45: 3047-3053.

X-Rates. http://www.x-rates.com/average/?from=INR&to=USD&amount= 1&year=2009. Accessed January, 2013.

Zhang Y, Singh S, Bakshi BR. Accounting for ecosystem services in life cycle assessment part 1: A critical review. Environmental Science and Technology. 2010 (a); 44: 2232-2242.

Zhang Y, Baral A, Bhavik BR. Accounting for ecosystem services in life cycle assessment, part 2: Toward an ecologically based LCA. Environmental Science and Technology. 2010 (b): 44: 2624-2631.

Zheng YH, Li ZF, Feng SF, Lcas M, Wu GL, Li Y, Li CH., Jiang GM. Biomass energy utilization in rural areas may contribute to alleviating energy crisis and global warming: A case study in a typical agro-village of Shandong, China. Renewable and Sustainable Energy Reviews. 2010; 14: 3132-3139.

Zhou SY., Zhang B, Cai ZF. Emergy analysis of a farm biogas project in China: A biophysical perspective of agricultural ecological engineering. Communications in Nonlinear Science and Numerical Simulation.2010; 15: 1408–1418.