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Energies 2013, 6(10), 5259-5282; doi:10.3390/en6105259

Article
Building Agro-Energy Supply Chains in the Basilicata Region: Technical and Economic Evaluation of Interchangeability between Fossil and Renewable Energy Sources
School of Agricultural Sciences, Forestry, Food and Environment—SAFE, University of Basilicata, Potenza 85100, Italy
*
Author to whom correspondence should be addressed.
Received: 5 August 2013; in revised form: 24 September 2013 / Accepted: 8 October 2013 / Published: 15 October 2013

Abstract

:
In this study, we present a model for the implementation of agro-energy chains based on the actual availability of forest biomass and the real demand for energy (heat) in the area of the Basilicata region, Italy. The demand for energy has been estimated by drawing on the database of the Ministry of Economic Development or by calculating the Annual Energy Requirement (AER) index, while for the estimate of the available forest biomass, reference was made to the public forest lands managed according to forestry management plans. The collected data were cross-checked with a view to detecting the technical and economic feasibility of district heating systems. The technical evaluation has mainly focused on the energetic and plant aspects, while the economic assessment was directed to defining the cost effectiveness criteria [Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period] that can measure the profitability of the investment. In the economic evaluation we also included the national public incentives, designed to encourage the production of energy from renewable sources in compliance with the international agreements signed by Italy for the reduction of greenhouse gases (GHGs).
Keywords:
biomass; agro-energetic chain; district heating systems; cost-effective use of biomass; green economy

1. Introduction

The energy market is experiencing profound changes as a result of the liberalization process, the tensions in the oil markets and the impulse given to the production of energy from renewable sources. In particular, the actions and the emission reduction obligations entered into force with Italy’s ratification of the Kyoto Protocol (the implementation of which should have ended in 2012) are expected to continue until 2020 without major changes; this is the outcome of the last climate summit held in Doha, Qatar, which gave birth to the second phase, known as Kyoto 2, in view of an international agreement for 2020.
The forecasts proposed in the last report, “OECD Environmental Outlook to 2050” [1] point out an 80% increase in energy consumption by 2050; the expected population growth from seven billion to nine billion, combined with the quadrupling of global gross domestic product (GDP), will result in an increased energy use and a rise in CO2 concentration, which is expected to reach 685 ppm by 2050. The consequences of these trends will be mainly reflected in the annual mean temperature that is projected to rise by three to six degrees [1] or by four degrees by the end of the 21st century according to the latest scenarios presented by the World Bank [2], as compared to the pre-industrial times.
Hence it is crucial to implement proactive policies aimed at improving energy use efficiency, by allocating new and increasingly large areas to alternative and renewable energy sources, reducing pollution and the impact of human activities on the environment.
In the framework of renewable energy sources (RES), biomasses are perhaps the most striking example of growth factor related to the green economy, which tends to combine the return on investment with beneficial impacts in terms of land protection, sustainable management of agroforestry resources, startup of new enterprises in the area, etc. Actually, in contrast to the solar and wind energy investments, which are typically capital intensive, highly profitable and low labor intensive, biomasses, due to their extreme diversification (by sector of origin of the raw material) and their strong link with the territory may generate positive impacts at the local level, in terms of employment, land care and maintenance and optimal use of agro-forestry resources.
The use of biomasses, however, imposes plenty of constraints, such as their temporal availability (biomasses are not available at any time of the year) and the spatial density (biomass production generally involves very large areas). Furthermore, debates over the competition for land use between food and energy crop production have recently heated up at the international level. Actually, the development of energy crops has involved some modifications, with the subsequent change in the use of the soil that instead of supplying food products has become supplier of products destined for energy [3,4]. Moreover, the installation of biomass-based plants has triggered in some cases social conflicts between the local population and the promoters of biomass-based plants [5,6,7]. In fact, the local people’s perceived risk to the potential negative impacts on the landscape and the ecological aspects of the area, exceeds the merely economic benefits associated with the installation of biomass-based systems, thus engendering a hostile approach towards biomass-based plants and renewable energy sources, in a more general sense. The term “biomass” designates a wide range of products, ranging from plants and plant materials derived from agriculture, forestry and food industry, to household waste [8,9]. Over the last few years the use of biomass for energy has become an issue of primary importance, both in the fight against climate change and as a strategy for energy supply security. Currently, biomasses are the main source of renewable energy, so that at the European level they supply 14% of primary energy consumption. In Italy, the total share of renewable energies has increased (from 20% to 22%), and biomasses, in particular, are among the sources that have most contributed to this growth.
The future scenarios point out those biomasses could provide significantly to the production of primary energy. The estimates for 2050 [10,11] indicate a range between 200 and 470 EJ/year (Exajoules = 1018 joule).
The use of biomass energy may be an important option both for environmental and economic sustainability, considering that the cost per unit energy of firewood is lower than that of methane [12]. Moreover, the enhancement of biomass could trigger processes of environmental improvement and socio-economic development resulting from the building of micro agro-energy supply chains. This could have multiple impacts at the territorial level (defense of the associated agricultural activities, startup of businesses specialized in the collection and transfer of biomass, new jobs, “active” management of forest areas, etc.), as well as on the economic sectors concerned (agriculture, forestry, industry, etc.).
To ensure highly efficient energy conversion and the sustainable use of biomass, however, it is necessary to use modern equipment and technologies and plan the reasoned use of biomass [13,14].
The literature includes different studies and research on the entire agro-energy chain that have addressed the problem from the spatial pattern point of view through the integrated use of decision support systems (DSSs) by Geographic Information Systems [15,16,17,18,19]. Other research works deal with the problem from a technical-engineering point of view, for the optimization of agricultural machinery and energy conversion plants [20], from the economic perspective [21,22,23] and in terms of environmental impacts [24,25,26].
The applications for the exploitation of the potential energy contained in biomass are diversified and constantly evolving. Currently, the largest energy consuming sectors in Italy are the domestic or industrial heating that make use of individual or network systems (district heating), the production of electricity and of liquid fuels for motor-vehicles. Biomass is, however, a limited resource, so it should be used as efficiently as possible. In order to maximize the mitigation of CO2, many authors believe that it is more efficient to use biomass for heating or for combined heat and power rather than for the combined production of biofuels [27,28]. In particular, district heating is not only more efficient (high ratio of thermal energy produced to biomass energy) compared to other uses, but it also contributes to mitigate the use of fossil fuels for the heating of buildings, which accounts for about one-third of the total energy demand of our country that is mostly met by the use of methane. Part of this demand is met through the use of methane gas that, compared to other fossil fuels, has a reduced environmental impact, as it does not contain sulfur, so it does not produce sulfur dioxide, preventing the so-called “acid rain” pollution. In addition, methane gas is characterized by a CO2 emission factor that is about 25%–30% less than oil and about 40%–50% less than coal to produce the same amount of energy.
The purpose of the present work is indeed to identify a model for the development of renewable energy chains of high local value added. The setting up of the model requires defining the supply area, as well as carrying out the technical and economic evaluations of the total or partial substitution potential of the energy source. The product resulting from the analysis, if based on the actual energy demand and the real local availability of biomass, represents an objective model, easily transferable to other land areas.
On this basis, the analysis was carried out in the Basilicata region, chosen as pilot area because its percentage of forest cover is in line with the national average. This choice is also related to the fact that in the area under analysis there is no forest-wood system that can ensure the environmental and economic sustainability of wood production.
Therefore, for the purpose of the technical and economic feasibility study, it was decided to implement, at the municipal level, district heating systems powered by the biomass of public forests. The applied reference criterion was the 25% possible substitution of the thermal energy produced from methane.

2. Materials and Methods

The proposed work is divided into four phases. The first phase concerned the estimate of building heat demand, obtained from official statistics and through the calculation of annual energy requirement (AER). In the second phase, the optimal power of biomass conversion plants was calculated as the ratio of the overall volume of buildings to their energy use. The third phase involved the estimate of the available biomass from the Municipal forests provided with Management Plans. In the last phase, a technical and economic evaluation was proposed so as to indicate the actual cost-effectiveness related to the replacement of methane gas with forestry biomass, through the creation of district heating plants.

2.1. Study Context

According to the data published by the Authority for Electricity and Gas (Autorità per l’Energia Elettrica e il Gas, AEEG), the national methane consumption in 2010 amounted to 71,959 million m3, that is 7% more than in the previous year.
In Basilicata, the methane gas consumption for 2010 amounted to 538.2 million m3, showing a substantial increase as compared to the period of 2006–2008. Despite some mismatch between the sources (the main sources are the Ministry of Economic Development and the Authority for Electricity and Gas), the regional consumption by sector shows an increase in natural gas consumption for the domestic sector and a fluctuating trend for the industry, the electricity generation and in the service sector (Figure 1).
Figure 1. Methane gas consumption by sector in Basilicata. Source: Authority for Electricity and Gas (AEEG), statistical data of natural gas consumption for Italian regions, various years (2011 provisional data).
Figure 1. Methane gas consumption by sector in Basilicata. Source: Authority for Electricity and Gas (AEEG), statistical data of natural gas consumption for Italian regions, various years (2011 provisional data).
Energies 06 05259 g001
The rise of natural gas demand is differentiated across the territory because only recently the southern Italy regions have been involved in a gradual development of methane gas distribution networks. The wide geographic variability also reflects two distinct phenomena: on the one hand, different climate patterns, which influence domestic consumption, on the other hand, the different degree of industrial development, which affects the volumes consumed by industries.
In Basilicata region, according to the data released by the Ministry of Economic Development (Ministero dello Sviluppo Economico, MSE), out of 131 municipalities, only six are not supplied with methane gas distribution networks, and the number of users served is 172,442 for a total length of 2395 km of pipelines.
From the socio-demographic perspective, Basilicata is the second Italian region with the lowest population density, equal to 59 people/km2, against the national average of 199.3 people/km2. Its economy features a large service sector with an employment rate of 63.2%, followed by the industrial sector with 27.1% and agriculture with 9.5% [29]. Crop production mainly includes cereals, olives and grapes. The per capita GDP recorded in the region in 2011 was €18,639.5, lower than the national average equal to €21,980.9 [30].
With regards to the geographical aspects, the region of Basilicata is situated in the South of Italy, with 47% of its area covered by mountains, whereas 45% is hilly and 8% is made up of plains. The presence of geomorphologically and climatically diversified environments has favored the development of large woodland areas through the years. The forest cover, mapped by the National Institute of Energy and Environment (Istituto Nazionale Energia e Ambiente, INEA), includes 354,895 ha of forest area with a forest area index of 35.6%; about 11% of the forest area is managed in accordance with the regional regulations on “sustainable management” for a total of 45 forest management plans (FMPs), involving 33 municipal and 12 regional plans.

2.2. Estimate of Methane Gas Consumption

The estimate of the building heating requirement was necessary to check the energy consumption of municipalities and hence start up the design of district heating plants. For the intended purpose, we considered only the consumption of methane gas (for heating) in the 33 municipalities with FMPs. The data of energy consumption of methane for heating have been extrapolated from the databases of the Ministry of Economic Development, and were only available for 26 municipalities.
For the remaining ones, a proxy variable was estimated, based on the calculated AER. This needs some input data [31], including:
  • the residential surface of the built-up area (ba) of the Municipality;
  • the residential surface of the housing clusters (hc);
  • the residential surface of scattered houses (sh);
  • the resident population, divided into ba, hc and sh.
The surface areas were multiplied by a coefficient of 2.7, namely the average height floor (in meters) of individual homes, as provided for by national building legislation; then the residential volume was calculated per housing typology using the following formulae:
Vba = A h [ Pba / ( Pba + Phc + Psh ) ]
Vhc = A h [ Phc / ( Pba + Phc + Psh ) ]
Vsh = A h [ Psh / ( Pba + Phc + Psh ) ]
where Vba, Vhc, Vsh are the residential built volumes of built-up areas, housing clusters and scattered houses, respectively; A is the total residential area; h is the average height of 2.7 m; Pba, Phc, Psh are the populations living in built-up areas, housing clusters and scattered houses, respectively.
The inferred values were used for the calculation of AER from Equation (4) [32] for the seven Municipalities lacking methane gas consumption data:
AER ( kJ ) = [ H V ( HDD + Δ c Hd ) ] λ 86.4
where H is the overall heat loss [W/(m3 °C)]; V is the heated volume (m3); HDD are the degree days [the degree days are a unit of measurement that indicates the heat requirement of a building in a particular location; they are calculated as the sum, extended to all days of an annual period of conventional heating, of the daily differences (only the positive ones) between the optimal conventional temperature for heated environments (20 °C) and the average daily temperature outside the building. In particular, the whole country has been divided into six climate zones identified on the basis of the degree days and mainly depending on the latitude and altitude that establish the running hours per day and the annual operating period of thermal plants] (°C); Δc is the variation coefficient compared to 20 °C; Hd are the heating days; and λ is the daily heating coefficient.
H, defined as the thermal power dispersed per cubic meter assuming a 1 °C temperature difference between inside and outside, is determined by the sum of two terms, namely Ht and Hv.
Ht, namely the specific heat capacity is an index reflecting the building insulation level. The maximum allowed value of Ht (Ht max) varies depending on the municipality in which the property is situated and on a characteristic parameter, called the shape factor, S/V, where V is the volume calculated by measuring its external surfaces; and S is the total area of surfaces, referred to the heated portions of the building (Table 1). Since the calculation was extended to an entire municipality rather than to a single house, we assumed a single volume equal to a parallelepiped for each type of housing, of which the shape coefficient (S/V) was calculated. Subsequently, due to the lack of sufficient data to calculate the Ht of each unit, it was decided to calculate the Ht max.
Table 1. Threshold values of the Ht coefficient. Source: [33,34].
Table 1. Threshold values of the Ht coefficient. Source: [33,34].
Climate zoneABCDEF
HDD≤60060190090114001401210021013000≥3000
S/V≤ 0.20.490.490.460.460.420.420.340.300.300.30
S/V≥ 0.91.161.161.081.080.950.950.780.780.730.73
The heat loss caused by ventilation (Hv) measures the heat loss by fresh air supply and was calculated as the product of the number n of air changes per hour times the heat loss rate from buildings, established by the regulations in force, equal to 0.35 W/(m3 °C). For buildings of category E.1 (buildings used as residences and similar), n is established by national rules to 0.5 and, therefore, the Hv is reported to be 0.175 W/(m3 °C).
For the calculation of AER and H, it was necessary to identify other parameters, including the degree days. According to the location of the municipality and on the basis of the national legislation in the field of domestic heating [33,34], the number of degree-days was determined following the reverse process and considering the climate zone of the municipalities under examination. Based on the altitude range of the municipality and the type of plant, the following values of Hd, λ and Δc were identified and then used for calculating the AER (Table 2).
Table 2. Values of Hd, λ and Δc used for calculating the annual energy requirement (AER) by plant. Source: [33,34].
Table 2. Values of Hd, λ and Δc used for calculating the annual energy requirement (AER) by plant. Source: [33,34].
Altitude rangeHeating daysDaily heating coefficientPlant typeVariation coefficient compared to 20 °C
m a.s.l.Hdλ-Δc
0–1501000.66Centralized0
Independent−1
Individual−1
Undefined0
151–3001200.71Centralized0.25
Independent−0.75
Individual−0.75
Undefined0
301–5001500.76Centralized0.5
Independent−0.5
Individual−0.5
Undefined0
501–10001800.84Centralized0.75
Independent−0.25
Individual−0.25
Undefined0
>10002000.93Centralized1
Independent0
Individual0
Undefined0

2.3. Sizing of Processing Plants

Once the energy values relative to the consumption of municipal methane gas (please note that for seven municipalities only the AER was used for estimating the potential thermal energy demand) were estimated, and considering a conversion factor of 38.1 MJ/m3 as reported by the Ministry of Economic Development (MSE), the annual requirement of fuel needed to replace 25% of the thermal energy produced by methane gas was determined. [District heating plants need to serve users with high population density and/or high consumption (buildings, schools, public buildings) in order to reduce installation costs and thermal losses in the network. Through a survey conducted by our Research Unit (RU), we analyzed the social and demographic characteristics of the Municipalities concerned and we estimated the percentage of potential users of district heating plants. The survey showed an average degree of substitution of 25% of thermal energy from natural gas that coincides with the consumption of users located in the residential area of the municipalities.] In the case of a chain not perfectly organized [35] both from a logistical and chronological point of view, this would lead to the use of green wood with water contents (WCs) above 30% which would involve a fuel requirement, as calculated by the following formula:
ARF = Energy required / ( ɳ Hv f w )
where ARF (t/year) is the annual requirement of fuel (amount of biomass needed to produce the required energy); Energy required is the annual energy requirement (kW h/year), corresponding to 25% of energy from methane gas consumed annually; ɳ is the plant performance (80%); and Hvfw is the heating value of fresh wood (2230 kW/t f.w. with WCs of 45%–50%).
In the case of an organized chain, which includes measures for the use of forest products during cold and rainy periods as well as the curing and storage of the products during summer, this would lead to a change in the initial WCs from 45%–50% to 25%–30% in the month of September, before wood chipping. This procedure allows an increase in the calorific value (about 3700 kW/t, WC = 25%) of the biomass compared to the calorific value of fresh biomass, with a significant decrease (about 30%) of the amount of biomass required to produce the same quantity of energy. In the second case, the annual requirement of fuel needed to replace 25% of the thermal energy produced from methane gas is equal to:
ARF = Energy required / ( ɳ Hv )
The sizing of the heating systems to be installed in each municipality was related to the volumes to be heated, assuming, for the area under analysis, a required power of 30 W/m3 [36]. The first step consisted in building a relationship between the AER and the heated volumes, in the 6 municipalities in which the AER had been previously calculated (Figure 2). The results show a good correlation between the variable AER and the variable V, with R2 of 0.84. The interpolating function has then been used to estimate the volumes of all the municipalities in the examined area (Figure 2).
Figure 2. Correlation of municipal built volume (m3)-AER. Source: elaboration of the Research Unit (RU).
Figure 2. Correlation of municipal built volume (m3)-AER. Source: elaboration of the Research Unit (RU).
Energies 06 05259 g002
The estimate of the built volume has actually allowed for the sizing of plants, assuming the replacement of 25% of the energy currently supplied by methane gas. The results showed very different plant powers, ranging from 250 kW in San Costantino Albanese to 10 MW in Venosa.

2.4. Municipal Forests

To characterize the bio-energetic supply, we considered the municipal forests currently provided with FMPs, assuming the recovery of all of forest residues and a maximum of 50% of the annual allowable cut so as to leave the remaining part for normal uses that are largely focused on the firewood market (Figure 3). (A survey conducted by our RU among timber enterprises operating in Basilicata has identified different issues concerning firewood market. In particular, they complained about the poor demand for firewood, high stumpage value, and low supply of forests, too much competition, over-fragmentation and insufficient forest road services; as for the demand side, some recovery in the use of firewood for domestic heating has been recorded over the last few years, although the required quantities are significantly below the supply. In particular, this is reflected in a reduced use of forests and, among these, public forests are the least used. Taking into account the above issues and trends, we have envisaged a 50% maximum utilization of the annual allowable cut, considering this value as being representative and well below the maximum usable value.)
Figure 3. Characterization of bio-energetic supply. Source: elaboration of the RU.
Figure 3. Characterization of bio-energetic supply. Source: elaboration of the RU.
Energies 06 05259 g003
The municipal forests with FMPs are currently 33, of which 85% in the province of Potenza, and covering a total area of 22,614.60 ha. 79% of this area is covered with forests, while the remainder, namely 3942.29 ha, is made up of clearings, roads, streams, rock outcrops, etc. In our study, we considered only 30 municipalities, excluding from the analysis the forest area of three municipalities in which the forest utilization was not provided for.
In order to consider the varying residue supply from different forest formations (in relation to the species and the silvicultural system), the database was reorganized and classified into eight types of forests that are the most representative of the regional context: beech, oak, chestnut, hornbeam, riparian hygrophilous formations, maquis shrubland with holm oak communities, mountain pine forests and Mediterranean pine forests.
As regards the definition of the parameters related to the percentage of residues (branches and treetops), reference was made to the literature relating to similar experiences in national contexts [37] and to the values of bundle and brushwood reported by the stereometric and growth tables built for the Italian forests [38]. These data have been validated by a direct survey conducted in collaboration with some timber harvesting and processing enterprises operating in different regional contexts, resulting in the definition of the parameters shown in Table 3.
Lastly, for the parameters of volume density used for the conversion of fresh wood into the commercial form, reference was made to what is reported by Giordano [39], considering these values as being representative of the regional context, thus applicable to the present study (Table 4).
The estimated final figure represents the average annual quantity of biomass with 25% WCs potentially derivable from uses of managed woods, and it ranges between 29.24 t/year for the town of Palazzo San Gervasio and 1,542.25 t/year for the Municipality of Forenza (Table 5).
Table 3. Percentage of waste by type of wood. Source: elaboration of the RU on [37,38].
Table 3. Percentage of waste by type of wood. Source: elaboration of the RU on [37,38].
SpeciesTall TreesCoppice
% Residues
Beech8%25%
Oak15%20%
Chestnut15%16%
Hornbeam15%20%
Hygrophilous formations100%100%
Holm oak25%32%
Mountain pine forests100%-
Mediterranean pine forests100%-
Plantations and reforestation15%-
Table 4. Density of different types of woods in Basilicata. Source: [39].
Table 4. Density of different types of woods in Basilicata. Source: [39].
SpeciesFresh woodCommercial Moisture Content (12%–15%)Commercial/Fresh
Kg × 102/m3m3/Kg × 102Kg × 102/m3m3/Kg × 102
(1)Beech10.50.0957.50.1330.7
(2)Oak11.00.0919.00.1110.8
(1)Chestnut10.00.1005.80.1720.6
(3)Hornbeam10.00.1008.00.1250.8
(4)Hygrophilous formations8.60.1165.60.1790.7
(1)Holm oak11.00.0919.60.1040.9
(5)Mountain pine for.9.00.1115.60.1790.6
(6)Mediterranean pine for.9.50.1056.40.1560.7
(1): Beeach reference value; (2) Turkey oak reference value (most frequent); (3) hornbeam reference value; (4) black alder reference value; (5) black pine reference value; (6) value averaged for Aleppo pine, Pinus pinea and maritime pine.
Table 5. Municipal available biomass. Source: Elaboration of the RU.
Table 5. Municipal available biomass. Source: Elaboration of the RU.
MunicipalityAllowable cutForest residuesTotal
Total 50% WCs (t)Annual 50% WCs (t) *Total 50% WCs (t)Annual 50% WCs (t)50% WCs (t)25% WCs (t)
Acerenza (PZ)7,339.51366.981,100.93110.09477.07357.80
Albano di L. (PZ)12,840.94642.051,926.14192.61834.66626.00
Aliano (MT)1,924.3096.22288.6528.86125.0893.81
Cancellara (PZ)6,401.97320.10960.2996.03416.13312.10
Castelmezzano (PZ)7,001.77350.091,050.27105.03455.11341.33
Castronuovo di S. (PZ)14,185.44709.272,127.82212.78922.05691.54
Cersosimo (PZ)7,808.20390.411,171.23117.12507.53380.65
Fardella (PZ)13,539.43676.971,143.36114.34791.31593.48
Forenza (PZ)31,635.871,581.794,745.38474.542,056.331,542.25
Francavilla sul S. (PZ)14,565.54728.281,472.70147.27875.55656.66
Ginestra (PZ)15,232.72761.642,284.91228.49990.13742.60
Gorgoglione (MT)4,447.33222.372,197.05219.70442.07331.55
Lagonegro (PZ)18,787.27939.361,502.98150.301,089.66817.25
Latronico (PZ)23,834.571,191.733,029.03302.901,494.631,120.97
Noepoli (PZ)20,899.961,045.002,471.66247.171,292.16969.12
Palazzo S. G. (PZ)599.8429.9989.989.0038.9929.24
Pietragalla (PZ)10,228.61511.433,409.36340.94852.37639.28
Pietrapertosa (PZ)20,111.291,005.563,016.69301.671,307.23980.42
Pignola (PZ)29,564.051,478.203,273.40327.341,805.541,354.16
Rotonda (PZ)27,874.601,393.732,229.97223.001,616.731,212.55
San Chirico R. (PZ)3,845.48192.27576.8257.68249.96187.47
S. Costantino A. (PZ)15,151.58757.582,017.26201.73959.30719.48
San Mauro F. (MT)2,859.39142.97428.9142.89185.86139.40
San Severino L. (PZ)20,720.921,036.051,657.67165.771,201.81901.36
Sasso di Castalda (PZ)15,570.90778.541,284.65128.47907.01680.26
Spinoso (PZ)7,268.64363.43604.8560.49423.92317.94
Stigliano (MT)2,395.37119.77359.3135.93155.70116.78
Tito (PZ)7,705.13385.26843.6084.36469.62352.22
Tricarico (MT)13,272.44663.621,990.87199.09862.71647.03
Venosa (PZ)5,861.47293.07879.2287.92381.00285.75

2.5. The Incentive Scheme: White Certificates and Energy Saving Companies

Rational use of energy has been promoted in Italy since the 1980s. The first incentives concerned the construction industry, agriculture and industry, with capital funding and interest aimed to promote the use of renewable energy sources and reduce energy consumption. Twenty years later a new scheme was enacted by White Certificates (Wc) or Energy Efficiency Credits (EECs) that is regulated and managed by the Authority for Electricity and Gas, while the Energy Market Managing Board (Gestore Mercati Energetici, GME) is entrusted with the management of the EEC market.
The innovation lies in the fact that the promotion of energy efficiency is pursued through a gradual shifting from fossil towards renewable and more environmentally friendly energy sources, promoting energy efficiency by more performing technologies and systems, according to an integrated approach consistent with the development of the liberalized market. The scheme integrates innovative and original elements of tariff and market direct control. The element of direct control concerns above all the energy saving obligations imposed to electricity and gas distribution companies, as well as the activity carried out by the Authority for Electricity and Gas for the certification of savings achieved and the verification of compliances. Tariff regulation concerns the system of aids recognized in the bill to partially cover the costs of energy saving measures. The market-related element, finally, is constituted by the creation of an artificial and regulated market for energy saving, known as “market for energy efficiency credits” or “white certificates,” where market participants may trade the EECs.
The demand for EECs is generated by the obligation imposed to electricity and gas distribution companies to “stick” to binding targets of primary energy savings that are to be achieved through energy efficiency measures at the level of final consumers. The national targets are allocated every year by the Authority for Electricity and Gas among electricity and methane gas distributors on the basis of their respective market shares. The proposals are submitted to the above Authority that assesses and certifies the energy savings and authorizes the Energy Market Managing Board to issue the Wc based on the certified savings [for each tons of oil equivalent (TOE) saved, one Wc is issued for a period of five years]. As an alternative to carry out their own energy saving measures at the level of final consumers, the “obliged” distributors (i.e., those who supply a number of customers above 50,000 units) may opt to satisfy their obligations by purchasing, in whole or in part, EECs by other parties that represent the side of the supply of credits or certificates, including smaller distributors who are not subject to the obligations, companies monitored by electricity and methane gas distributors and companies operating in the field of energy services [Energy Service Companies (ESCO)]. The EECs may be sold either by bilateral contracts or on an organized market managed by the GME on the basis of specified and transparent rules established in agreement with the Authority for Electricity and Gas.
The white certificate scheme identifies three methods for the evaluation of the proposals:
  • standardized assessment methods, whose savings associated with the procedure are determined on the basis of the number of physical reference units (PRUs) involved in the proposed intervention;
  • analytical evaluation methods that quantify the energy savings on the basis of a specific algorithm for each type of intervention;
  • ex-post evaluation methods applied to the proposals that include heterogeneous interventions in relation to the evaluation method and to which the above methods are not applicable.
The recognition of energy efficiency certificates by the Authority for Electricity and Gas (AEEG) requires the achievement of a minimum level of energy savings, as reported in Table 6.
Table 6. Minimum threshold values. Source: Authority for Electricity and Gas (AEEG).
Table 6. Minimum threshold values. Source: Authority for Electricity and Gas (AEEG).
Project typeMinimum project size
Obliged subjects—Energy managerVoluntary subjects
Standardized25 toe/year
Analytical100 toe/year50 toe/year
Ex-post200 toe/year100 toe/year
Depending on the type of energy saving, we can have four types of recognized certificates:
  • type I: certifying the achievement of primary energy savings through a reduction of power consumption;
  • type II: certifying the achievement of primary energy savings through a reduction of natural gas consumption;
  • type III: certifying the achievement of savings of primary energy forms other than electricity and natural gas not intended for use in motor vehicles;
  • type IV: certifying the achievement of savings of primary energy forms other than electricity and natural gas intended for use in motor vehicles.
As this paper envisages the setting up of district heating systems to replace the thermal energy produced from methane gas, there is the possibility of access to type II certificates, assessed by analytical methodology.
As mentioned previously, only the obliged and non-obliged entities operating in energy services can access the white certificate scheme; in our specific case, for having access to this market it has been suggested to set up ESCO companies in public-private partnership, for each municipality under examination. The setting up of this type of company could represent a valuable tool of long-term territorial energy planning. In particular, the advantages of public bodies’ involvement include the energy planning of their territory and the long-term management of action plans that would be integrated into the political and business local system; moreover, the presence of the public component in ESCO companies can ensure the fair distribution of the economic and social benefits resulting from the implementation of biomass-based plants. The main advantages for the population may be summarized as follows:
  • more employment, associated with energy production-related activities (cut, harvest, logging, transportation, operation of the system, etc.);
  • reduction of energy costs, with regards to energy supply for housing.
On the other hand, the private sector participation in the ESCO could compensate for the lack of adequate skills and insufficient equipment and financial asset resources needed to run major investment plans in the area that, in this case, actually involve the setting up of district heating plants.
In a recent study [40], carried out in 38 countries, a positive correlation was found between the ESCO indicators (age of ESCO market, number of ESCO companies, total value of ESCO projects and the percentage of the sectors targeted by ESCOs) and socioeconomic indicators (countries per-capita GDPs, energy consumptions and CO2 emissions). Moreover, in richer countries, ESCOs find more opportunities in the commercial and municipal sectors [40].

3. Results

3.1. Technical Evaluation

After estimating the annual fuel requirements and the plant power required to meet the energy needs of each municipality, the next step was to assess the possible replacement of methane energy source with forest biomass.
The results of the analysis have shown how the poor supply of biomass obtainable from the management of public forests in some municipalities and the reduced natural gas consumption in others, constitute the two limiting factors for the development of district heating systems. In fact, it results that in only 50% of the examined municipalities, the replacement of the energy source is feasible (Table 7).
Table 7. Availability and evaluation of forest biomass energy (in gray the municipalities for which the AER was calculated, because the natural gas consumption data were not available). Source: elaboration of the RU.
Table 7. Availability and evaluation of forest biomass energy (in gray the municipalities for which the AER was calculated, because the natural gas consumption data were not available). Source: elaboration of the RU.
MunicipalityMunicipalityABCDEFGHILMN
Methane gas consumption (m3 × 1000)AER (GJ)Energy produced from methane gas (MW h)25% energy from methane gas D = C × 25% (MW h)Climate zoneEstimated built volume (m3 × 1000)25% built volume potentially supplied by district heating G = F × 25% (m3 × 1000)Plant power 0.03 KW/m3 × G (MW)ARF (annual requirements of fuel) (t_WC = 25%)Annual available biomass (t_WC = 25%)Deficit/surplus of available biomass of ARF M = L – I (t_WC = 25%)Feasibility
Acerenza (PZ)917.00-9,704.642,426.16E250.5662.641.88819.65357.80−461.85Insufficient biomass
Albano di Lucania (PZ)453.00-4,794.121,198.53E127.7931.950.96404.91626.00221.09Positive
Aliano (MT)-11,128.553,091.18772.79D85.2221.310.64261.0893.81−167.27Insufficient biomass
Cancellara (PZ)312.00-3,301.91825.48E90.4922.620.68278.88312.1033.22Positive
Castelmezzano (PZ)-10,051.312,791.95697.99E77.7419.430.58235.81341.33105.53Positive
Castronuovo di S. A. (PZ)-12,998.643,610.63902.66D98.2124.550.74304.95691.54386.59Positive
Cersosimo (PZ)110.00-1,164.13291.03D37.049.260.2898.32380.65282.33Positive
Fardella (PZ)130.00-1,375.79343.95E42.3410.580.32116.20593.48477.28Positive
Forenza (PZ)848.00-8,974.422,243.60E232.3058.081.74757.971,542.25784.27Positive
Francavilla sul Sinni (PZ)594.00-6,286.321,571.58D165.1041.271.24530.94656.66125.72Positive
Ginestra (PZ)188.00-1,989.61497.40D57.6814.420.43168.04742.60574.56Positive
Gorgoglione (MT)-14,152.153,931.04982.76E106.2226.550.80332.01331.55−0.46Insufficient biomass
Lagonegro (PZ)1,436.00-15,197.243,799.31E387.8796.972.911,283.55817.25−466.31Insufficient biomass
Latronico (PZ)693.00-7,334.041,833.51E191.2947.821.43619.431,120.97501.54Positive
Noepoli (PZ)245.00-2,592.84648.21E72.7618.190.55218.99969.12750.13Positive
Palazzo S. Gervasio (PZ)1,743.00-18,446.234,611.56D469.10117.273.521,557.9629.24−1,528.72Insufficient biomass
Pietragalla-Filiano (PZ)912.00-9,651.732,412.93E249.2362.311.87815.18639.28−175.90Insufficient biomass
Pietrapertosa (PZ)-18,328.195,091.021,272.76E135.2233.801.01429.98980.42550.44Positive
Pignola (PZ)1,708.00-18,075.834,518.96E459.84114.963.451,526.671,354.16−172.52Insufficient biomass
Rotonda (PZ)718.00-7,598.621,899.66E197.9149.481.48641.781,212.55570.77Positive
San Chirico Raparo (PZ)-14,906.284,140.521,035.13E111.4527.860.84349.71187.47−162.24Insufficient biomass
San Costantino A. (PZ)100.00-1,058.30264.58D34.408.600.2689.38719.48630.09Positive
San Mauro Forte (MT)336.00-3,555.90888.98D96.8424.210.73300.33139.40−160.93Insufficient biomass
San Severino L. (PZ)257.00-2,719.84679.96E75.9418.980.57229.72901.36671.64Positive
Sasso di Castalda (PZ)214.00-2,264.77566.19E64.5616.140.48191.28680.26488.98Positive
Spinoso (PZ)-18,991.515,275.271,318.82D139.8234.961.05445.55317.94−127.61Insufficient biomass
Stigliano (MT)2,188.00-23,155.685,788.92E586.83146.714.401,955.72116.78−1,838.94Insufficient biomass
Tito (PZ)2,405.00-25,452.206,363.05D644.25161.064.832,149.68352.22−1,797.46Insufficient biomass
Tricarico (MT)2,132.00-22,563.035,640.76D572.02143.004.291,905.66647.03−1,258.63Insufficient biomass
Venosa (PZ)5,065.00-53,603.081,3400.77D1,348.02337.0010.114,527.29285.75−4,241.54Insufficient biomass

3.2. Economic Evaluation

Once established the energy feasibility, meant as the availability of the biomass obtainable from managed forest areas to replace part of methane gas, the economic viability of the investment was evaluated. For the cost analysis, considering the large number of cases and the technological and system types currently available on the market, reference was made both to the estimates provided by some companies operating in the sector and some bibliographic data [36,41] that were then interpolated so as to obtain the unit investment costs of the thermal plant and of the district heating network. This was integrated by the annual management cost, estimated in proportion to the size and equal to 3% of the plant cost; unit costs take account of mechanical and electrical equipment, including the installation. For the machinery room the estimated average costs were, respectively, €40,000 for biomass plants with a power exceeding 500 kW and €30,000 for the others, because the cost of the latter is highly dependent on the volume and type of applied material (Table 8).
Table 8. Summary of panel installation costs and biomass supplies. Source: elaboration of the RU on [36,41].
Table 8. Summary of panel installation costs and biomass supplies. Source: elaboration of the RU on [36,41].
Thermal plant biomassDistrict heating networkMachinery roomAnnual running costs as a percentage (%)Chip fuel cost (€/t)
WCs = 25%
Power (kWt)Cost (€/kWt)Power (kWt)Network length (m)Cost (€/m)Cost (€)
≤100400.00≤100500190.0030,000330.00
100 < P ≤ 300300.00100 < P ≤ 250800190.0030,000330.00
300 < P ≤ 450260.00250 < P ≤ 5001200190.0030,000330.00
450 < P ≤ 700170.00P > 5001500190.0040,000 *330.00
P > 700120.00190.0040,000330.00
* For plants with a power <500 kW the cost of machinery room has been estimated to €30,000.
Regarding the cost of the fuel, we considered the costs for the processing stages in the forest, for transportation and chipping. These costs have been estimated to €9/t for chipping, €11/t for in-forest practices (cut, preparation and logging) and €8/t for transportation. The above data represent the averaged observations made by the RU across the regional territory. Finally, considering that the public institution, owner of the forests, might be involved in the ESCO companies, we could presumably exclude the stumpage charges from our assessment of costs. This can be justified by the creation of new business opportunities, new jobs and new local value added, in addition to the savings in energy bills. Additional benefits may be derived from the revenues obtained by the ESCO that could benefit the entire local community.
Once the total costs were obtained, the price of Break Even Point (the point at which cost or expenses and revenue are equal) was estimated as the selling price of the energy produced from forest biomass, at which the discounted total revenues and discounted total costs are equal. In this way it was possible to identify the difference between the price of the thermal energy produced from forest biomass and the price of thermal energy produced from methane gas. Because of the considerable differences in investment costs of plants in different cases, the price of Break Even Point of the thermal energy produced from forest biomass varies from a minimum of €31/MW ht to a maximum of €119/MW ht. Compared to the price of €80/MW ht of methane gas, it is evident that the difference, and therefore the economic viability of substitution, is largely dependent on the installation costs (biomass power plant and district heating network). In fact, a breakdown of the costs, shown in Table 8, indicates higher unit costs for low power plants compared to more powerful plants.
The economic evaluation of the investment was conducted making use of the major economic indicators, namely Net Present Value (NPV), Internal Rate of Return (IRR) and Payback Period applying the criterion of the discounted cash flow (DCF), based on the discounting of cash flow and assuming the lifetime of the power plant is 20 years. In view of the high number of plant types and the resulting wide range of Break Even Point prices of thermal energy produced from biomass, the reference selling price was taken to €95/MW ht assuming also the access to the market of EECs. Based on the information contained in the databases of the AEEG, the above credits were evaluated at an average price of €86.98/toe, which corresponds to a value per MW h equal to €7.48. From the energy selling price we may also deduct a tax credit equal to €25.82/MW h, as provided for in the national legislation; this would result in a consumer energy price of €69.18/MW ht.
It is also assumed that users can gradually get connected to district heating plants in the first four years, considering that in the first year only public structures are served [35]. Based on a survey carried out by our RU on the average consumption of electricity by public users, it resulted that such consumption is, on average, equivalent to 30% of the thermal energy produced from biomass. The estimated distribution of energy in the first 4 years is reported in Table 9.
Table 9. Share of energy from biomass. Source: elaboration of the RU.
Table 9. Share of energy from biomass. Source: elaboration of the RU.
Year% energy supplied from biomassServed users
Year 130%Public users
Year 255%Public and private users
Year 380%Public and private users
Year 4–20100%Public and private users
Before carrying out the economic evaluation, it was necessary to choose the discount rate to be applied, which is largely influenced by the goals and the financial conditions of the investor, as well as the estimated level of risk of the initiative, etc.
In our analysis a discount rate of 5% was applied, taking into account the above factors and assuming a loan of seven years at a real interest rate of 4.2% (on the basis of information provided by the Loan and Deposit Bank).
The NPV analysis was conducted using the formula below:
NPV = k = 0 n FC ki ( 1 + r ) k
where FCki is the flow at year ki obtained from revenues minus costs; k is the length of the project in years; r is the capital cost.
In parallel we also calculated the IRR on investment, namely the discount rate for which the equation is satisfied:
NPV = k = 0 n [ R ki C ki ( 1 + r ) k ] = 0
where Rki are the revenues at year k; Cki are the costs at year k; r is the capital cost; k is the length of the project in years.
Table 10. Table summarizing the economic evaluation of facility investments. Source: Elaboration of the RU.
Table 10. Table summarizing the economic evaluation of facility investments. Source: Elaboration of the RU.
CountryABCDEFGHILMNOP
-25% energy from methane gas (kW h)TOE saved per yearTOE Subsidised by national legislationTotal cost (€)Annual payment (4.2%) (€)Annual running cost (€)Cost of wood chips (€30/t)Break Even Point (€/KW h)Selling price of energy (€/kW h)Revenues from energy sales (€)Revenues from White Certificates (€)NPV (€)IRR (%)Payback Period (years)
Albano di Lucania1,198,528.9490.60304.42440,014.5073,853.4212,000.4412,147.250.0420.095113,860.25132,390.52742,435.96>50%4.6
Cancellara825,476.8962.40209.66440,373.9073,913.7412,011.228,366.320.0560.09578,420.3087,535.56311,462.9619.3%7.0
Castelmezzano697,988.0952.76177.28424,118.2671,185.3411,523.557,074.200.0620.09566,308.8774,016.34183,656.6713.6%8.2
Castronuovo di S. A.902,658.06--413,386.1369,384.0311,201.589,148.560.0490.09585,752.52-353,481.0619.8%6.3
Cersosimo291,033.5222.0073.92341,349.7957,293.229,340.492,949.660.1110.09527,648.1822,503.47NEGATIVE--
Fardella343.948.7026.0087.36340,554.9557,159.789,316.653,485.970.0950.09532,675.1326,595.00NEGATIVE--
Forenza2,243,603.84169.60569.86534,071.2589,640.2014,822.1422,739.230.0310.095213,142.37247,830.371,802,114.08>50%2.8
Francavilla sul Sinni171,580.99118.80399.17473,589.1979,488.7013,007.6815,928.190.0360.095149,300.19173,598.161,120,700.82>50%3.7
Ginestra497,402.7437.60126.34370,478.4862,182.2710,214.355,041.240.0740.09547,253.2649,449.1731,947.796.6%10.7
Latronico1,833,511.16138.60465.70497,162.9083,445.3913,714.8918,582.880.0340.095174,183.56202,531.191,386,291.01>50%3.3
Noepoli648,211.0249.00164.64417,771.6870,120.1111,333.156,569.710.0650.09561,580.0564,441.74137,801.6911.1%8.9
Pietrapertosa1,272,755.3396.21323.27446,694.8874,974.6712,200.8512,899.550.0400.095120,911.76140,589.63817,699.58>50%4.3
Rotonda1,899,655.14143.60482.50503,116.0084,444.5713,893.4819,253.260.0330.095180,467.24209,837.511,453,359.08>50%3.2
San Costantino A.264,575.9320.0067.20335,396.8356,294.069,161.902,681.510.1190.09525,134.7116,366.16NEGATIVE--
San Severino L.679,960.1351.40172.70421,819.6970,799.5411,454.596,891.490.0630.09564,596.2172,104.61172,432.0912.8%8.5
Sasso di Castalda566,192.4842.80143.81340,314.3257,119.439,309.435,738.440.0620.09553,788.2956,287.29154,568.9613.6%8.2
Finally, we calculated the Payback Period, i.e., the time which actually defines the level of risk of an investment, because it measures the time within which the proceeds achieved through the investment can restore the capital employed. In fact, the impossibility of a rapid restoration of the invested capital can induce the investor, even in the presence of positive NPV, to stop the project. It is clear that the payback period does not measure the risk of a given investment, but only the length of exposure to the capital risk. The Payback Period is expressed in years and is obtained by the formula:
Payback Period = Initial Investment Net Benefits
where the initial investment is given by the ratio of the total cost (letter D, Table 10) to the revenues, obtained by adding the annual energy revenues (L) and the revenues of white certificates distributed over twenty years (M/20), and subtracting the management costs (G) and the cost for the supply of wood chips (F).
The results of the economic evaluation reported in Table 10 show that out of a total of 33 initial municipalities, the implementation of district heating plants powered by biomass produced from the management of public forests is technically and economically viable in 13 municipalities. For these municipalities the results of the economic analysis show positive values both in terms of NPV and IRR, with Payback Periods between three and eleven years.

4. Conclusions

International conventions on research and climate protection have induced several countries to commit themselves to reduce emissions of greenhouse gases by encouraging, inter alia, the use of renewable energy. In this context, a strategic priority is the use of multiple sources of energy, potentially available in the area, along with the decentralization of production and the development of small networks of local users. At the national level, due to the wide availability of forest land, a viable alternative to fossil fuels could be represented by the biomass that enables the achievement of a twofold objective: the enhancement of local resources (raw materials and labor) and savings in energy costs for families and public bodies. This would ensure positive social and economic outcomes for local communities.
Biomass is a renewable source of energy, whose main peculiarity is its intrinsic link with the territory, as it is available and widespread everywhere, obviously to varying extents and with different characteristics. Thanks to its versatility, many conversion technologies that can produce as many final forms of energy may be applied. This clearly indicates the need to build up a sound agro-energetic chain and choose, at the same time, the most efficient systems of energy production from biomass. In fact, a correct use of biomass involves coherent planning actions combined with adequate technical know-how in the energy field. It is extremely useful to assess, on the other hand, the negative aspects associated with biomasses, such as the competition for land use between energy production and food production, the social rejection of biomass-based systems and the local population’s perceived risks relating to health, environment and landscape. These concerns may often cause social conflicts between the concerned parties. Therefore, information, education and training, combined with public consultation initiatives, might offer more guarantees among all concerned parties both for environmental and more strictly economic issues.
In our specific case, based on the comparison of thermal energy demand and biomass energy supply, we tested the possibility to substitute methane gas for heating with thermal energy from biomass; the results have shown how the feasibility of this replacement in both energy and economic terms depends, respectively, on the biomass availability and investment costs.
More specifically, as to energy, the availability of biomass in the area is of primary importance, as it represents the distinctive element of the supply chain. In fact, the use of off-site biomass (i.e., biomass supplied by external areas) would only increase the negative externalities of the investment with uneconomic impacts related to transportation (costs and energy); so the analysis excluded all the municipalities that did not have an FMPs, as well as the Municipalities that, despite the presence of managed forest lands, showed quantities of potential biomass well below the minimum amounts required to ensure the goals of energy sustainability related to the substitution of methane gas.
From the economic point of view, however, the analysis has highlighted how the higher unit costs that characterize small-power plants make their implementation inefficient. To this end, the tax credit provided by the current national legislation and the new incentives on the production of thermal energy from renewable sources and on energy efficiency could give significant impetus to expand the market of thermal energy from renewable sources.
The proposed model shows a possible strategy for the use of biomass tailored to the characteristics of the territory, in terms of energy demand and actual supply of biomass. A limitation to the application of this model is constituted, however, by the inability to correctly identify the transformation center and the subsequent inaccuracy in defining the size of district heating networks. This would result in a variation of plant implementation costs, either their reduction or increase. For this purpose, it would be necessary to carry out a more detailed analysis, focusing on each geographical area with a view to properly siting the transformation center and sizing more accurately the district heating network based on site-specific needs.

Conflicts of Interest

The authors declare no conflict of interest.

List of Acronyms:

AEEG
Authority for Electricity and Gas
AER
Annual Energy Requirement
ARF
Annual Requirement of Fuel
DCF
Discounted Cash Flow
EECs
Energy Efficiency Credits
ESCO
Energy Service Companies
FMPs
Forest Management Plans
GDP
Gross Domestic Product
GHGs
Greenhouse Gases
GME
Energy Market Managing Board
Hvfw
Heating value of fresh wood
INEA
National Institute of Energy and Environment
IRR
Internal Rate of Return
MSE
Ministry of Economic Development
NPV
Net Present Value
PRU
Physical Reference Units
RES
Renewable Energy Sources
RU
Research Unit
TOE
Tons of Oil Equivalent
WCs
Water Contents
Wc
White certificates

References

  1. OECD. OECD Environmental Outlook to 2050: The Consequences of Inaction; OECD Publishing: Paris, France, 2012. [Google Scholar]
  2. The World Bank. Turn down the Heat: Why a 4 °C Warmer World Must be Avoided; World Bank: Washington, DC, USA, 2012. [Google Scholar]
  3. Kerckow, B. Competition between agricultural and renewable energy production. J. Int. Agric. 2007, 46, 333–347. [Google Scholar]
  4. Rathmann, R.; Szklo, A.; Schaeffer, R. Land use competition for production of food and liquid biofuels: An analysis of the arguments in the current debate. Renew. Energy 2010, 35, 14–22. [Google Scholar] [CrossRef]
  5. Upreti, B.R.; van der Horst, D. National renewable energy policy and local opposition in the UK: The failed development of a biomass electricity plant. Biomass Bioenergy 2004, 26, 61–69. [Google Scholar] [CrossRef]
  6. Van der Horst, D.; Evans, J. Carbon claims and energy landscape: Exploring the political ecology of biomass. Landsc. Res. 2010, 35, 173–193. [Google Scholar]
  7. Van der Horst, D. UK biomass energy since 1990: The mismatch between project types and policy objectives. Energy Policy 2005, 33, 705–716. [Google Scholar] [CrossRef]
  8. McKendry, P. Energy production from biomass: Overview of biomass. Bioresour. Technol. 2002, 83, 37–63. [Google Scholar] [CrossRef] [PubMed]
  9. Klass, D.L. Biomass for Renewable Energy, Fuels, and Chemicals; Academic Press: San Diego, CA, USA, 1998. [Google Scholar]
  10. Fischer, G.; Schrattenholzer, L. Global bioenergy potentials through 2050. Biomass Bioenergy 2001, 20, 151–159. [Google Scholar] [CrossRef]
  11. Johansson, T.B.; Kelly, H.; Reddy, A.K.N.; Williams, R.H. Renewable Fuels and Electricity for a Growing World Economy: Defining and Achieving the Potential. In Renewable Energy-Sources for Fuels and Electricity; Island Press: Washington, DC, USA, 1993; pp. 1–72. [Google Scholar]
  12. Forest Products Laboratory. Wood Biomass for Energy; U.S. Department of Agriculture: Madison, WI, USA, 2004.
  13. Combs, J. Biomass energy—An industry waiting for growth. The technology to use almost any organic matter as an energy source is now available, yet the sector is growing only slowly. Renew. Energy World 2002, 5, 132–145. [Google Scholar]
  14. Rosch, C.; Kaltschmitt, M. Energy from biomass—Do non-technical barriers prevent an increased use? Biomass Bioenergy 1999, 16, 347–356. [Google Scholar] [CrossRef]
  15. Cozzi, M. Agro-energie in Basilicata: Un approccio analitico per la valutazione dei costi di trasporto. Aestimum 2008, 53, 51–74. (in Italian). [Google Scholar]
  16. Fiorese, G.; Guariso, G. A Gis-based approach to evaluate biomass potential from energy crops at regional scale. Environ. Model. Softw. 2009, 25, 702–711. [Google Scholar] [CrossRef]
  17. Kinoshita, T.; Inoue, K.; Iwao, K.; Kagemoto, H. A spatial evaluation of forest biomass usage using GIS. Appl. Energy 2009, 86, 1–8. [Google Scholar] [CrossRef]
  18. Perpina, C.; Alfonso, D.; Perez-Navaro, A.; Penalvo, E.; Vargas, C.; Cardenas, R. Methodology based on Geographic Information System for biomass logistic and transport optimization. Renew. Energy 2009, 34, 555–565. [Google Scholar] [CrossRef]
  19. Romano, S.; Cozzi, M.; Luongo, V.; Pesce, F. La Valutazione dei Costi di Trasporto Delle Biomasse Agroforestali: Funzioni e Mappatura dei Costi su Base Geografica (in Italian); Atti del III Congresso Nazionale di Selvicoltura: Taormina, Italy, 2009; pp. 902–908. [Google Scholar]
  20. Abdallah, R.; Auchet, S.; Meausoone, P.J. Experiment study about the effects of disc chipper settings on the distribution of wood chip size. Biomass Bioenergy 2011, 35, 843–852. [Google Scholar] [CrossRef]
  21. Manzone, M.; Airoldi, G.; Balsari, P. Energetic and economic evaluation of a poplar cultivation for the biomass production in Italy. Biomass Bioenergy 2009, 33, 125–126. [Google Scholar] [CrossRef]
  22. Spinelli, R.; Magagnotti, N.; Nati, C.; Cantini, C.; Sani, G.; Picchi, G.; Biocca, M. Integrating olive grove maintenance and energy biomass recovery with a single-pass pruning and harvesting machine. Biomass Bioenergy 2011, 35, 808–813. [Google Scholar] [CrossRef]
  23. Wiskerke, W.T.; Dornburg, V.; Rubanza, C.D.K.; Malimbwi, R.E.; Faaij, A.P.C. Cost/benefit analysis of biomass energy supply options for rural smallholders in the semi-arid eastern part of Shinyanga Region in Tanzania. Renew. Sustain. Energy Rev. 2010, 14, 148–165. [Google Scholar] [CrossRef]
  24. Caserini, S.; Livio, S.; Giugliano, M.; Grosso, M.; Rigamonti, L. LCA of domestic and centralized biomass combustion: The case of Lombardy (Italy). Biomass Bioenergy 2010, 34, 474–485. [Google Scholar] [CrossRef]
  25. Gasol, C.M.; Gabarell, X.; Rigola, M.; Gonzales-Garcia, S. Environmental assessment: LCA and spatial modeling GIS of Energy crop implementation on local scale. Biomass Bioenergy 2011, 35, 2975–2985. [Google Scholar] [CrossRef]
  26. Valente, C.; Spinelli, R.; Hillring, B.G. LCA of environmental and socio-economic impacts related to wood Energy production in alpine conditions: Valle di Fiemme (Italy). J. Clean. Prod. 2011, 19, 1931–1938. [Google Scholar] [CrossRef]
  27. Azar, C.; Lindgren, K.; Andersson, B. Global energy scenarios meeting stringent CO2 constraints—Cost effective fuel choices in the transportation sector. Energy Policy 2003, 31, 961–976. [Google Scholar] [CrossRef]
  28. Wahlund, B.; Yan, J.Y.; Westermark, M. Increasing biomass utilization in energy systems: A comparative study of CO2 reduction and cost for different bioenergy processing options. Biomass Bioenergy 2004, 26, 531–544. [Google Scholar] [CrossRef]
  29. ISTAT, Statistiche flash, Occupati e disoccupati. October 2012. Available online: http://www.istat.it/it/files/2012/11/Occupati-e-disoccupati_30_nov_2012.pdf (accessed on 9 September 2013).
  30. SVIMEZ (Associazione per lo Sviluppo dell’industria nel mezzogiorno), Rapporto SVIMEZ Sull’Economia del Mezzogiorno 2011, Schede Regionali. Available online: http://lnx.svimez.info/images/RAPPORTO/materiali2012/rapporto_2012_schede_regionali.pdf (accessed on 9 September 2013).
  31. ISTAT, 14° Censimento Generale Della Popolazione e Delle Abitazioni. Available online: http://dawinci.istat.it/MD/dawinciMD.jsp (accessed on 18 March 2013).
  32. Bernetti, I.; Ciampi, C.; Sacchelli, S.; Marinelli, A. La pianificazione dei distretti agro-energetici. Un modello di analisi per la regione Toscana. L’Ital. For. Mont. 2011, 66, 305–320. (in Italian). [Google Scholar]
  33. Regulation Laying Down Rules for the Design, Installation, Operation and Maintenance of Heating System in Buildings in order to Reduce Energy Consumption; Presidential Decree No. 412 of 26/08/93; Italian Official Gazette: Rome, Italy, 1993.
  34. Regulation Laying Down Modifications to the Presidential Decree No. 412 of 26/08/93 on the Design, Installation, Operation and Maintenance of Heating System in Buildings in order to Reduce Energy Consumption; Presidential Decree No. 551 of 21/12/99; Italian Official Gazette: Rome, Italy, 1999.
  35. Marinelli, A.; Fagarazzi, C.; Tirinnanzi, A. La biomassa ad uso energetico: Valutazione della sostenibilità economica di alcune filiere foresta-legno-energia in Toscana. Econ. Dirit. Agroaliment. 2012, 17, 291–315. (in Italian). [Google Scholar]
  36. Bernetti, I.; Fagarazzi, C. Valutazione Della Domanda di Biocombustibili Solidi (Legno Cippato) Nell’Area Dell’Appennino Pistoiese (in Italian); Centro Editoriale Toscano: Firenze, Italy, 2008. [Google Scholar]
  37. Pettenella, D.; Favero, M. Disponibilità di Sottoprodotti Legnosi Impiegabili a Fini Energetici. In I Sottoprodotti di Interesse del DM 6.7.2012—Inquadramento, Potenzialità e Valutazioni; Comitato Termotecnico Italiano (CTI): Milano, Italy, 2013; Volume 1, pp. 21–33. [Google Scholar]
  38. Castellani, C. Tavole Stereometriche ed Alsometriche Costruite per Boschi Italiani (in Italian); Istituto Sperimentale per l’Assestamento Forestale e l’Alpicoltura (ISAFA): Trento, Italy, 1980. [Google Scholar]
  39. Giordano, E. Tecnologia del Legno; UTET: Torino, Italy, 1971. [Google Scholar]
  40. Okay, N.; Akman, U. Analysis of ESCO activities using country indicators. Renew. Sustain. Energy Rev. 2010, 14, 2760–2771. [Google Scholar] [CrossRef]
  41. Energy & Strategy Group-2012. Biomass Energy Executive Report; Le bioenergie in Italia alla prova del Decreto Rinnovabili: Milano, Italy, 2012. [Google Scholar]
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