Productivity Analysis in Power Generation Plants Connected to the National Grid: A New Case of Bio Economy in Nicaragua.

The purpose of this paper was to study the productivity where renewable energy resources and non-renewable resources for generating electricity in power plants connected to the national grid of Nicaragua were used. This article analyzed the total factor productivity of Bioeconomy for the generation of electricity from plants using sugarcane bagasse (biomass) as a renewable resource and petroleum. The data envelopment analysis (DEA) and the Malmquist index were used to measure the total factor productivity of power generation utilities connected to the national grid of Nicaragua. The results obtained by comparing sugar mills connected to the SIN was that Monte Rosa mill has a higher rate of increase in productivity due to the change of total factor productivity and when comparing thermal plants that employ petroleum products in power generation, the more efficient were ALBANISA, GECSA and TIPITAPA POWER; but when comparing thermal plants and some using renewable energy San Antonio sugar mill and ALBANISA were more efficient.


Introduction
Electrical energy is produced and consumed in Nicaragua from a matrix that uses renewable energy including biomass (sugarcane bagasse), with small percentage in relation to the inputs of the thermal plants. Thermal plants that operate based on petroleum account for 60% of the installed capacity, the hydro11%, the geothermal 4.1%, the wind power 7.12%, the biomass of the sugar cane 11.79% and the isolated systems 1.2% (Nicaraguan Institute of energy, 2012).
Moreover, the process of generating electrical energy using thermal generation systems based on fossil fuels causes negative environmental impacts with heat and greenhouse gases emissions produced by the combustion of oil (CONNUE, 2009). Indeed, the negative environmental impacts come from the combustion of petroleum used to generate electricity in thermal power plants, so that, the pollutants emitted with the exception of CO 2 , fall to the ground surface. As evidence of the negative effects of the use of fossil fuels in power generation, we can refer to the case of air pollution receiving direct emissions as dust particles and gaseous pollutants, and depending on the fuel used to generate power plant (type, composition and calorific value) and combustion technology, the gases can take different amounts of pollutants such as nitrogen oxides (NOx), sulfur dioxide (SO 2 ), carbon monoxide (CO ), carbon dioxide (CO 2 ), unburned hydrocarbons (HC), particulate matter dust, heavy metals and organic compounds (Laguna, 2007).
Similarly, the use of fossil fuels in generating electricity emits dust particles and gaseous pollutants and also contaminates with atmospheric transformation products such as NO 2 and nitrate emissions that return to earth through precipitation and dry deposition constituting a pollution load to the water and soil that can harm vegetation and fauna (Estrucplan, 2012). Therefore, efforts are needed to encourage the implementation of new plants planned based on renewable energy to transform the energy matrix that is still dependent on fossil fuels to avoid degradation of the natural environment.
The purpose of this paper is to study the productivity of using renewable and non-renewable energy resources, as petroleum, for generating electricity in power plants connected to the national grid of Nicaragua. This article is organized into five sections. The introductory section presents the environmental problems of the use of fossil fuels in power generation. The second section is a review of literature that constitutes the conceptual framework of the methodology, based on data envelope analysis;in the third section it is presented the methodology used for data envelope analysis. The fourth section reports the data used in researching and in the fifth section are presented the research results and finally the conclusions.

2.1Basic Literature
factors of production according to a technological process of transformation, in which shall be considered different combinations of technical factors. Also, the production function can be understood as a series of factors of production and production techniques that can be managed to select efficient combinations in order to get the best results. Meanwhile, Farrel (1957) considered the importance of knowing how an industry can increase its production output, by simply increasing its efficiency. Thus, he proposes to analyze the productivity of a production unit in terms of its production function compared to the efficient production frontier; this efficient production function means that the technical efficiency of a production unit is compared to other units according to which the frontier function is estimated. Coelli (2008) refers that the efficiency of a production unit can be divided into two components: technical efficiency that reflects the ability of the unit to obtain maximum production output given an input quantity and the allocation efficiency that reflects the ability to use the inputs in optimal proportions given a fixed price; these two efficiencies are combined to provide a measure of total economic efficiency. Similarly, Zuniga (2010) refers that the frontier variable means the limit of a function, and that it would represent a production function maximization (maximum output) given a set of inputs, or in a cost function, it would represent a cost function minimization (minimum cost); given the prices and outputs, in a profit function, it represents the maximum benefit. Also, a frontier function represents the best running production unit, which in turn is a reflection of the technology that is being implemented in it. The frontier function represents the best technology practice, serving it to compare the efficiency of other production units in the sector under analysis.
Meanwhile, the stochastic production frontiers Cobb-Douglas are used to obtain the conditional demand functions that minimize the cost of production and can be used to calculate the efficiency in the use of energy inputs (Armanda, Scarpellini, and Feijoo, 2003). The inefficiency can be measure in a relative sense as the deviation from the best results achieved by companies in a specific sector; thus, the analysis results in a production frontier with the intention of representing the efficient behavior of a production unit and this involves using a direct inputs, usually labor, capital and raw materials, with the maximum output that can be obtained, given the state of technology (Armanda et al., 2003;Farrell, 1957).
Moreover, productivity is regularly referred to technological changes. By contrast, the efficiency is explained when in a production unit the procedures are implemented to improve education and training to ensure that manpower produces more efficiently. Thus, productive growth can be achieved through technological progress or improvement in the efficiency of manpower.
Similarly, according to Leal (2012) in economics, the possibility production frontier (PPF) reflects the options and the need to choose between them, adjusted to our study, we can see this in the case of the Bio Economy. To produce more of one of the goods (in our study electricity is considered) means reducing energy production in the sugar mill or in the Thermal Plant, and vice versa. So that, the points outside the PPF Bio Economy curve are called "Impossible Bio Economy region FPP". Since given a number of fossil or biomass resources (i.e., if they are fully and efficiently employed and does not occur an increase of Journal of Agricultural Studies ISSN 2166-0379 2013 www.macrothink.org/jas 84 these thereof) you cannot increased the energy production of a particular power plant without reducing the energy production of other. And the points within the boundary, i.e. inside, indicate that there are unemployed resources, so this area is called the "economic inefficiency". The points on the curve are in the area is called "maximum economic efficiency" see figure 1.

Figure 1. Production possibility frontier in the bioeconomy
To measure the productivity of energy production units there are two methods to estimate the efficiency frontiers of power plants, such as: data analysis envelopment (DEA) and stochastic frontier, these methods involve mathematical programming and econometric methods, respectively (Boris Bravo-Uretra, 2007;Zuniga,2010;Ludena, 2012;O'Donnell,2012) The DEA Malmquist method uses panel data to estimate changes in the rates of total factor productivity (TFP) which may be: technological change, technical efficiency change and scale efficiency change. The method can be used under the Malmquist Output-oriented measure to investigate how many quantities of inputs can be proportionally reduced, with no change in the quantities produced. In addition, you can investigate how much can be proportionately increased the quantity outputs if the quantity inputsamounts are altered using the input-oriented analysis.
On the other hand, oriented output measures provide equivalent measures of technical efficiency where there are constant returns to scale. In the measurement of output-oriented efficiency, the distance AB shown in the figure 2 indicates the technical inefficiency, expressing the amount by which the outputs may be increased without additional input required. Therefore, the efficiency measure output-oriented technique is defined by the ratio:

=
(1) If we have price information then we can plot the iso-income line DD ', and assigned define efficiency as: And total economic efficiency can be represented as the product of these two measures: These three measures are limited by values between zero and one.

Total Factor Productivity Index
Progress in total factor productivity (TFP) indicates an improvement in technology and organization of production. To measure productivity, we must focus first on the quantification of the output and the production factors used for their generation and, secondly, in the study of the relationship between the two. The approach to measuring productivity through index numbers, TFP, is defined as the ratio of an index of output (Q) with respect to an index of inputs (P) representative equation is the same equation 1.
The Malmquist index is defined using the distance function. The distance function describes a production technology multi-input, multi-output. An input distance function characterizes the production technology looking minimum proportions of input vector given an output vector; the output distance function considers proportionally expanding and maximizingthe output vector, given an input vector.
A production technology can be defined using the output: = : The output distance function is defined in the output, P (x), as: The distance function, d 0 (x, y) takes a value less than or equal to 1, if the output vector Y is an element of the desirable energy output, P (x). In addition, the distance function will take the value of unity if Y is located outside the desirable production set.
The DEA Malmquist index is a method of estimating frontiers functions (maximization, minimization, etc.,) using data envelope analysis (Zuniga, 2010). These distances are: a) The DEA frontier technologies to constant returns to scale run, b) the DEA frontier with constant returns to scale technologies for the current period, c) DEA frontier technologies constant returns to scale to the next period, and d) the DEA frontier technologies of diminishing returns to scale.

The Model of Constant Returns to Scale (REC)
The model considers that there is data on K inputs and M outputs for each N production units (PU) for the ith PU which are represented by the vector x 1 and y 1 , respectively. The input matrix K x N, X, and the product matrix M x N, Y, represents the data for all output units. DEA's purpose is to build a surround border on nonparametric data observed as those under production frontier.
The best way to introduce DEA is by way of reasons. For each unit of production the producers would like to get the reason of all products on all inputs, as u' yi / v' xi, where u is an M x 1 vector of weights output and v is a vector weights inputs. To select the optimal weights we specify a mathematical programming problem: It involves finding values of u and v, such that the measure of efficiency of the ith maximized farm, subject to the constraint that all efficiency measures must be less or equal to one. One problem with the development of this particular reason is that it has infinite number of solutions (Coelli, 1996), to avoid this restriction can pose v 'xi = 1, which provides: This reflects the mathematical transformation procedure known as the multiplier of form linear programming problem. Using linear programming duality one can derive an equivalent Journal of Agricultural Studies ISSN 2166-0379 2013 surrounding form of this problem (Coelli: 1996).
Where θ is a scalar and λ is a constant N x 1 vector. This form of involvement implies fewer restrictions than the multiplier form (K + M <N +1); hence, these is usually the preferred solution. The value of θ obtained will be the measure of efficiency for the ith production unit under the condition θ ≤ 1, with a value of 1 indicating a point on the frontier and consequently the technologically efficient production unit, according to the definition of Farrell (1957). The linear programming problem must be solved N times, once for each production unit in the sample. A value of θ is then obtained for energy production unit or power plant.
Working with a panel data, DEA can be used as a form of linear programming (input or output oriented) Malmquist index of TFP (Total input) to measure productivity change, and to decompose this productivity change in technology and technical efficiency change.
Färe et al (1994) specified an output based on the rate of change of Malmquist productivity as: This represents the productivity of the production at the point (X t+1 , Y t+1 ) relative to the production point (X t , Y t ). A value greater than one indicates TFP growth from period t to period t +1. This index is actually the geometric mean of two outputs based on Malmquist TFP index, an index used in period t technology and other technology period t +1. A value greater than one indicate positive TFP growth from period t to period t +1.
To calculate the linear programming we must calculate distance functions of four components which engage linear programming problems; it is assumed REC and the programming approaches are: Production points are compared with different types of technologies periods, the parameter need not be θ ≥ 1, as it should be when we calculate Farrell efficiency. The point should be located under feasible production set that most probably occur in LP7 where the point of production period t 1 is compared to technology in period t. If technological progress has occurred, then a value of θ <1 is possible. Note that it is possible to occur in LP8 if the technological back has occurred, but this is less likely.
The θ and λ can take four different values approach to linear programming (LP). In addition, the four linear programming problems must be calculated for each production unit in the  (UP). So if you take 20 PU and 2 times period should be calculated 80 LP. You must calculate three LP for each PU (to build an index changed); for T periods, calculate (3T-2) for each PU of the sample, i.e. for N production units is required to calculate NX (3T-2) linear programs.

Calculations of Scale Efficiencies
Technical efficiency can be decomposed into two components, one due to scale efficiency and the other due to ¨ pure ¨ technical efficiency. If there is a difference in specific registers of units of electricity production in terms of technical efficiency, then this indicates that the unit has inefficiency scale production. So, technical efficiency to constant returns to scale (REC) is decomposed into "pure" efficiency and scale efficiency. The scale efficiency indicates whether the production unit is operating in an area of performance of increasing or decreasing scales.
The technical efficiency (TE) can be interpreted as a relative measure of the capability of managing a given technology and is derived from improved decision making, which in turn, is related to a number of variables, such as knowledge, experience and education. While, in the analysis of technological change (TC) the effect of productivity from the adoption of new production practices is evaluated and referred to investments in research and technology (Uretra -Bravo,2007).

Methodology
As part of the research process, information related to the input and output components of the generation of electricity in power plants that use biomass from sugarcane bagasse as a renewable resource and fossil fuel currently operating in Nicaragua and connected to the national grid was compiled. The information collected from San Antonio and Monte Rosa mills corresponds to the input of energy cogeneration process as bagasse consumption in tons. The bagasse is burned to produce steam at high pressure; the steam is transformed into mechanical energy in turbines and then transformed into electrical energy in synchronous generators. The output data corresponds to the electricity generated in GW-hr; this energy is used in the sugar mills and part is sold to the national electricity market.
With respective to the power plants based on fossil fuels, it was documented the bunker fuel input that is used for high-pressure steam. The steam is converted into mechanical energy in the turbines, and then electrical energy is generated in Synchronous electrical generators. The output of the thermal plants based on fossil fuels is detailed in electrical energy in GW-hr; this energy is sold to the national electricity market.
After gathering the information input and output of energy in the power generation systems using biomass and petroleum, we proceeded to the analysis of productivity in the exploitation of these energy resources using the methodological tool data envelope (DEA), and Malquist index to compare productivity between different power generations plants connected to the national grid of Nicaragua.
For the analysis of the efficiency of electricity production using biomass as bagasse and Journal of Agricultural Studies ISSN 2166-0379 2013 petroleum the data analysis method (Data Envelopment Analysis, DEA) was used. Also, panel data were used to calculate changes in the rates of total factor productivity (TFP), technological change and technical efficiency change in efficiency scaling. For the calculation, we made use of the simulation program DEAP 2.1 that contains the algorithms of the methodological process of Malmquist method and the Output-Oriented analysis.

Data panel DEA
The data analyzed in this section were obtained from the State institutions websites such as the Energy Nicaraguan Institute (INE), the National Load Dispatch (CNDC) and the Ministry of Energy and Mines of Nicaragua (MEM). We considered three Power Generation plants cases of study of Nicaragua that are connected to the National Interconnected System (SIN).

Case # 1
The first scenario considers the comparison of the sugar mills connected to the SIN and they are the San Antonio and Monte Rosa mills. The data analyzed were: Out puts: net generation in GW-HR was used during the study period.
Inputs: Bagasse consumption measured in 103 tons of bagasse during the study period.

Case #3
The third scenario considered was the Case # 3: comparison of the thermal plants and renewable energy connected to the SIN, with contracts in the electricity market, the plants under study are detailed in the following

Results and Discussion
The main purpose of this article is the analysis of total factor productivity and technical efficiency of the bio economy in power generation plants connected to the national grid, comparing the productivity of using renewable energy resources such as biomass with the use of petroleum in power generation. So, it is intended to provide a comprehensive assessment of the resources used to generate electricity and that the study results can be considered for making decisions about what kind of energy sources foster for the energy matrix change in Nicaragua that is still dependent on fossil fuels.
We studied three cases, The first was to compare the Bio Economy of total factor productivity with the use of biomass in electricity generation, the second case was to assess the productivity of the use of petroleum in the power generation and the latter case was intended to compare the productivity of the use of renewable and nonrenewable energy together to generate electric power.
In the first case of study the comparison of the Bio Economy of total factor productivity betweenMonte Rosa and San Antonio mills yielded the results shown in table 2:  ISSN 2166-0379 2013 In geometric mean the annual growth rate of total factor productivity during the study period for both sugar mills was 0.002%, this low growth rate is reasonable in our country where the Bio Economy is not known and applied to production processes as an epistemological alternative to fossil fuel. The TFP in the Bio Economy is mainly explained by the rate of growth of changes in technical efficiency rather than technology (bio technology, bagasse and biomass) with the result of 0.067%.In turn, we can assess that this efficiency is due the growth rate of 0.55% of the pure efficiency, indicating the capacity and technical assistance of the workers in the process of bio economy and likewise it is shown an economy of scale of 0.01% in the rate of growth of technical efficiency scale.

Journal of Agricultural Studies
The sugar mill with the highest rate of growth of total factor productivity is Monte Rosa. This sugar mill presented an index of 1,116, equivalent to 11.6% increase in productivity growth rate, which is due to the change in technical efficiency of 18% interannual, compared to 0.9, which represents a 10% reduction of total factor productivity of San Antonio. However, the highest rate of productivity of Monte Rosa is not due to technological change. The change in technology (bagasse as biomass) is based on the development of the activities of electricity generation using bagasse as biomass, and since both mills have the same rate of technological change (bio technology) throughout the study period, then, the highest rate of productivity is based on training of the workforce of Monte Rosa expressed in an index of 1,188, or an increase of 18.8% equivalent, technical efficiency compared to0.958 of the San Antonio mill, which has a deficit of 4.2%.
The Increase in technical efficiency for Monte Rosa mill is explained by a greater change in pure efficiency (1,138, 13.8% increase) and the efficiency in the scale of power generation (1,044, 4.4% increase). The pure efficiency change represents the difference between the change in efficiency and technology change; in our case is the difference between the Bio technology and the ability of workers, as was explained before both mills have the same characteristic Bio Technology (bagasse as biomass), with the difference that the Monte Rosa mill has developed higher performance in labor. Therefore, the change in efficiency can be explained by technical scale efficiency by the fact that on average the percentage of growth of the electric power generation is greater Monte Rosa mill with a 0.44% decrease compared to the 0.021% of San Antonio mill.
Summarizing, we can assess that during the study period the Bio Economy had a low growth, highlighting the Monte Rosa mill.In both mills is clear that this growth rate is not explained by the bio technology used, but rather by technical efficiency. It implies that scientifically, it should be reviewed the bio technological production processes in order to increase more rapidly the growth rate.
In case 2 a comparison of the thermal power plants that use petroleum in power generation connected to the SIN is made using the DEAP program and the results are shown below: ISSN 2166-0379 2013  In geometric mean during the study period it is noted a decline on the pace of growth of total factor productivity (0.04%). However it shows an improvement for thermal power plant ALBANISA, GECSA and TIPITAPA POWER COMPANY.The results show that the plant that has the highest rate of total factor productivity with a 2.6% increase in its rate of growth was ALBANISA followed by GECSA plants with 1.9% and TIPITAPA POWER COMPANY with 0.6%. The highest rate of change in total factor productivity of ALBANISA, TIPITAPA POWER COMPANY and GECSA is not explained with technological change in fossil fuel use, because all plants studied have virtually the same deficit (worsen) in increasing technological change, on the contrary, the highest rate of total factor productivity of these plants is due to the change in technical efficiency, that is, in the training of human talent working in the plants.

Journal of Agricultural Studies
So, for example, ALBANISA exhibits a change in technical efficiency of 3.4% which is explained in increasing scale efficiency of 3.4%, and not pure efficiency growth. Similarly, GECSA and TIPITAPA POWER COMPANY present changes in the technical efficiency of 3.1% and 1.02%, an increase of 2.9% pure efficiency for GECSA and without increased to TIPITAPA POWER COMPANY. In the scale efficiency GECSA increased 2 % and 1.2% for TIPITAPA POWER COMPANY. Therefore, the increase of productivity change factors of the power plants ALBANISA, GECSA and TIPITAPA POWER COMPANY is explained by the technology management capacity, improved decision making, increased knowledge, and experience and training of the personnel.
In summary, we note that the total factor productivity recorded geometric mean deterioration in the study period. However, ALBANISA GECSA and TIPITAPA POWER COMPANY were the only ones that recorded a growth rate of between 2.6% and 0.6%.  The results of the comparison of power plants based on fossil fuel and plants using renewable sources show that the power plants with increase in total factor productivity were ALBANISA with 19.3%, CORINTO ENERGY COMPANY with 6.3% and TIPITAPA POWER COMPANY with 0.6%. About the power plants using renewable energy sources studied, highlights San Antonio sugar mill with 8.8%. The increased rate of total factor productivity of the plants specified above is explained by the increase of technological change and in turn for change in the efficiencies of scale, thus in the way of participation in the electricity market in Nicaragua.
Therefore, the increased productivity of the POWER plants ALBANISA, CORINTO ENERGY COMPANY, TIPITAPA POWER COMPANY and SAN ANTONIO SUGAR Mill is due to high technology management capabilities, improved decision making, and greater investment in training to acquire the knowledge that make them more productive. Similarly, the increase of technological change (TC) that affects productivity growth is explained in adopting new production practices and investments in research and technology. Consequently, power plants based on fossil fuel and renewable resources of energy analyzed and that resulted in low rates of productivity must take action to improve the training of human resources in their companies and improve managing the technology used in the generation of electricity.
The table 5 summarizes the results of the main estimates. The Bio Economy compared to conventional power generation was more representative highlighting the Monte Rosa sugar mill in its improvement of PTF with 11.6 % in annual growth. Conventional economics using fossil fuels resulted with a lower growth rate in TFP, highlighting, ALBANISA with 2.6%; this shows the effort of the government of reconciliation and national unity for contributing to the conversion of the energy matrix. Studying the power plants by the level of investment made in their economy of scale, ALBANISA stands in conventional economics with an increase in TFP of 19.3%. Similarly the San Antonio mill highlights for their investment in the bio economy production Bio Technology energy (bagasse from sugar cane) with an increase in TFP of 8.8%, but when tested with Biotechnology presented no improvement.
These results show the need to continue improving the total factor productivity not only in terms of investment, but in improving the bio technology processes as in the case of Monte Rosa sugar mill and decrease the growth rate of total factor productivity factors in conventional economics. These results are acceptable if we compare with other authors who have investigated the total factor productivity in Conventional Economics (Boris Bravo-Urethra, 2007;Zuniga, 2010;Ludena, 2012;O'Donnell, 2012).
Likewise, it is important to appreciate that these growth rates are above the growth rate of electricity demand; therefore the Bio-technology processes should be reviewed in terms of raising at least a 4.5% interannual to meet demand with a Bio Economy environmentally friendly considering the trend of exhaustion of non-renewable resource.
The author Bravo-Uretra used the DEA method to examine the impact of the various attributes in a study (i.e., estimation technique, functional form, sample size) to estimate the technical efficiency, for which a meta-regression analysis of 167 studies of frontier technical efficiency in agriculture published in scientific articles was made. Notably, meta-regression analysis is a quantitative method that allows assessing the effect of methodological and other specific studies on the characteristics of published empirical estimates as an indicator of technical efficiency. The results of this study agree to apply the methodology that the author Zuniga (2013) proposed to measure the total factor productivity of Bio Economy using Malmquist index methods. The author Zúniga quoting Trigo (2011) defines Bio Economy as new ways of linking natural resources to the processing of goods and services through increasing knowledge intensity as the common denominator of new value chains. Zuniga used the efficiency measure oriented in biofuels to construct an efficiency frontier based on the concept of the Malmquist index. Zúniga examined productivity growth of the Bio Economy in 7 countries during the period 1980-2007, finding an annual growth of productivity growth ISSN 2166-0379 2013 www.macrothink.org/jas of the Bio Economy of 1.1 percent, with change bio economy efficiency (or recovery bio economy) provided 0.03 percent per year and Bio Economy technical change (frontier shift or bioethanol) yielding 0.09%. In terms of country performance, the most extraordinary performance was published by Belize, with an average annual growth of 4 percent during the study period and other countries with a strong performance are average in Guatemala and Nicaragua.

Journal of Agricultural Studies
According to Trigo (2011) the bio economy relates with the conception to move economies based on petroleum and petroleum products to biofuels and energy sources sustainable, environmentally friendly and with greater availability. However, it is noted that in the immediate future, biotechnology will coexist with hybrid technologies in a gradual process of change of current energy-intensive technologies to energy-efficient alternatives, with increased productivity and at the same time generating benefits in terms of natural resource management. Meanwhile, Mohammadian (2005) conceived the Bio Economy as the science that determines the threshold of socioeconomic activity to use the biological system without destroying the conditions necessary for regeneration and therefore seek sustainability. Finally, Blaschek (2008) notes that the economy, and in particular oil prices and environmental sustainability are pushing for the use of alternative feedstock materials such as biomass; it is expected that biomass could be satisfy between 25-50 % of global energy demand for the XXI century. For this reason, we compared the efficiency of the power plant based on fossil fuels with those using biomass knowing that they will operate for many years on with the purpose of promote the gradual substitution of fossil fuel with renewable energy resources.
Finally, based on the results discussed in this article is recommended to improve the overall productivity of the factors in improving power generation processes of Bio technology, and that power plants based on fossil fuels and renewable resources for generating electricity must take action to improve the management of bio technology used in power generation and thus achieve meet demand with a Bio Economy alternative.

Social Impact
We assume that the average growth of the technology used in the study period with records below 4.5% in the rate of population growth has a negative impact socially and economically because it involves not meet the demand for this type of service.
The production units used Bio Economy identifies the Monte Rosa and San Antonio mils, with annual growth rates above average population growth rate, therefore these companies reported a positive social and economic impact.
Companies that used conventional economics to identify the companies studied in case # 2 is below the population growth rates therefore recorded a negative impact. However in the case of group companies # 3 only Tipitapa Power Company registration no rate of population growth above the rate of population growth.

Conclusion
In this paper we studied the bio economy of power generation by measuring the total factor productivity in electrical plants which use non-renewable energy resources (fossil fuel derivates of petroleum) and renewable resources for electricity generation in Nicaragua and that are connected to the national grid of (SIN). To measure the total factor productivity of these power plants dataanalysis envelopment (DEA) and Malmquist indices were used.
We have considered three case studies: the first was to compare the total factor productivity of the sugar mills connected to the SIN. We found that the Monte Rosa sugar mill had a higher rate of increase in total factor productivity compared to the San Antonio sugar mill. The highest productivity Monte Rosa mill is based on the training of labor, expressed in a higher rate of technical efficiency; this mill has developed a higher performance in labor in the process of generating electricity.
In the second case studied, we compared thermal power plants connected to SIN and found that plants with higher rates of total factor productivity are ALBANISA, GECSA and TIPITAPA POWER COMPANY. The highest rate of change in total factor productivity of these plants is due to the change in technical efficiency based on the training of human talent working in the plants.
In the third case study, we compared thermal power plants that use petroleum in with power generation plants using renewable energy sources and found that plants with increased change in total factor productivity are ALBANISA CORINTO ENERGY COMPANY, TIPITAPA POWER, and among the plants using renewable energy sources highlighted the San Antonio sugar mill.
Also, it was determined that the increased productivity of the thermal power plants described above and in the case of San Antonio mill that used biomass to generate electricity, the increased in productivity is due to improved manageability of technology, improved decision making, and greater investment in training to acquire the knowledge that make them more productive. Moreover, the increase of technological change (TC) is explained in the adoption of new production practices and investments in research and technology.
However, when comparing the productivity increase in electricity generation by type of energy resources, renewable and non renewable, the result was in average that thermal power plants using fossil fuels have a deficit in productivity growth which is a disadvantage to make a commitment to supply the demand for electricity grows with an annual rate of over 4% (see table5). Meanwhile, the use of biomass energy resource for power generation has a low productivity rate averaged 2%, which means that the productivity can be improved using this resource to meet the annual growth in demand for electricity.