Local Bioenergy System for Heat and Electricity

Executive Summary

In this report it seeks to develop a visual model of a Local Bioenergy system (LBS). Going up empirical cases and analyzing what has been found in literature and interviews, the LBS borders are outlined; moreover, a list of relevant input and output variables upon the LBS is detected. Such variables are sorted based on four categories: Physical, Customs, Policies and Markets.

Further on, some insights from literature have been used to build up a cognitive framework made up by three sub-systems, respectively a Farm Sub-system (FS), a Biomass Sub-system (BMS) and a Bioenergy Sub-system (BES). Each of them tries to provide a logical cause-effect pattern among detected variables. The purpose is to perform a tool settle to investigate potential externalities and socio-economic effects related to the LBS.

Some output variables have been outlined as possible socio-economic indicators, namely local GDP, wealth distribution and standard of living. Therefore attempts have been done to provide some numerical figures related to job creation, environmental and economic effects, and added value creation upon each sub-system and the overall model. Finally a brief framework has been worked out to show how the three sub-systems integrate with each other.

Table of Contents

1. Introduction
   1. Problem statement and research questions
   2. Methodology
   3. Limitations
2. Literature review
   1. A quick look at the literature background LBS modelling
3. Borders and actors of the LBS
   1. Sub-system definitions and analyses
      1. Farm Sub-system
      2. Biomass Sub system
      3. Bioenergy Sub system
      4. Sub-systems integration
4. Conclusions and Recommendations
    
5. List of Figures
6. List of Tables
    
7. Acknowledgements
8. Bibliography
9. Abbreviations
10. Interviewees

1 Introduction

This report seeks to provide a wide descriptive overview of relevant socio-economic input-output variables (employment rate, local GDP, wealth distribution, standard of living, etc.) and their relative interlinks related to Local Bioenergy Systems (LBS). Even though attempts have been done in order to provide economic measures of some of the detected variables, the report does not have the intent to build up an algorithm that quantifies outcomes. Such work remains for the future.

1.1 Problem statement and research questions

Potential positive and negative externalities resulting from LBS have been repeatedly described in literature (c.f. Seppälä, 1992; Vlasblom et al., 1998; Faaij, 1998; Börjesson 1999 and 2000, Azar et al., 2001). Nevertheless, such authors just partially provide an establishment of interlinks between physical, customs, policies and markets.

Most of the literature investigated in this study examines the connection between bioenergy systems and local regional systems and discuss what problems may exist; however that provides neither a clear picture of critical interrelations nor does it attempt to quantify externalities. Two main research gaps (or problems) are addressed in this study:

• Even if relevant aspects of the LBS are touched upon, interconnections between variables remain hidden and no formal presentation is provided to render such relationships explicit.

• Some of literature analyzes specific aspects (environmental impacts, employment creation, etc.) providing quantitative outcomes; nonetheless that represent just a partial investigation of the bioenergy system and other relevant interlinked factors are excluded from the evaluation.

Addressing the two previous points, an integrated and complete descriptive overview of the variables and their expressed interconnections may represent a foundation for the generation of models to start more quantitatively assessing positive and negative externalities associated with LBS.

As such, the following focusing questions have guided this work:

• What are interlinks between variables?

• Which of these can be described in quantitative as well as in qualitative terms?

• How could a visual model be built up that both describes relevant variables as well as showing their interlinkages?, and

• How may such a model be developed in the future to provide a tool for qualitative and quantitative socio-economic evaluation of LBS?

1.2 Methodology

The generation of this study relies mainly on two types of sources:

• Information that has been derived from formal/semi-structured interviews and research interactions with people who are involved at different levels in local bioenergy systems functions: academic staff, farmers, farm consultants, bioenergy supply plants/corporate managers and owners, corporate consultants and municipal officers.

• Information sources comprising a wide and heterogeneous literature composed of academic articles, specialized reviews and corporate magazine articles. These have predominantly been sourced from university databases and corporate/institutional Internet websites.

The work was conducted in an official exchange program involving the IIIEE and Centro Studi e Formazione Villa Montesca (Italy). The exchange program was scheduled, as part of the EMSES course funded by the UE Social Fund and taught in Cittá di Castello (Italy). The researcher was located at IIIEE and relied on its material and immaterial resources to conduct a four months research project.

The work schedule can be summarized in five steps:

1. The extraction and documentation of exhaustive background knowledge on local bioenergy systems, focusing on aspects such as the state of the art, opportunities and barriers for a further local bioenergy systems development.

2. Detecting input and output variables relevant to better expose and define the contents of the existing "black box" as an embryo for a potential socio-economic model of the LBS.

3. Producing a first draft of the framework using the information gathered through the literature findings.

4. Interviewing a list of preselected relevant participants to obtain information from the "real-life" conditions and the most "up to date" context.

5. Refining the framework based on information gathered through interviews and new literature findings.

In addition, workgroups have been periodically set up with some of IIIEE staff members. Feedbacks and references provided by Philp Peck, Åke Thidell, Kes McCormick, Thomas B. Johansson and Tomas Kåberger have provided contributions to the implementation of the present work. The following framework shows how every step relates to the each other.

1.3 Limitations

Our challenge has been to produce a visual representation of a socio-economic model of LBS that would be sufficiently generic so that it may be adapted to a wide range of geographic and environmental conditions as well as social contexts. However, it has been chosen to constrain the analysis to bioenergy systems dealing with solid biomass for supplying heat and electricity. This important limitation has two main motivations: firstly difficulty in detecting all relevant variables when it comes to some specific kind of more complex biofuels (for instance ethanol and methanol), secondly geographic difficulties where potential actors involved may be part of a global bioenergy system rather than a LBS, this is not a matter of analysis in the present work.

Attention has been mainly focused on some specific energy crops (primarily Salix), forestry primary production (timber) and by products. Secondly, the economic evaluation of the Greenhouse Gases (GHG) impact has been touched just superficially, being hard to define a direct cause-effect relationship between emission produced and environmental changes within the LBS.

Moreover, this study has mostly taken in consideration the Nordic countries´ experiences partially excluding other potential innovative cases such as some observed in the UK, North America and Australia.

Despite their relevance in the LBS, farmers interviewed count as a minority within the contacts list, mostly because of difficulty in arranging interviews and, in a few cases, difficulties for the farmers´ with the working language for this study (English).

Priority has been given to Swedish actors, especially in the case of farmers. Finding suitable contacts within the Skåne Region was significantly helped and facilitated due to the availability of references from members of IIIEE staff and a body of previous research work conducted by IIIEE students and staff.

2 Literature review

2.1 A quick look at the literature background LBS modelling

To our knowledge, no detailed attempt had been made to provide a wide overview of the financial effects and the positive and negative social and economic externalities associated with LBSs. Still, there are many references related to partial models of a bioenergy system, that provide input that is both quantitative as well as qualitative. Such references have been used to build up a more complete representation that seeks to provide a significantly more complete view of the key variables and their interconnections regarding bioenergy systems at a local scale.

Due to the number of actors directly and indirectly involved, the complexity of the model is high and there are several partial tools detected in the literature, each trying to analyse and evaluate specific aspects of the LBS. A short précis of some of the important reference sources found and their relevance to this work is included in the following paragraphs.

In Azar et al. (2001) and Vlasbom et al. (1998) interaction in price terms and economic effects between food crops and energy crops are analyzed. The former analyzes the cause-effect pattern of food crop price as the land requirement for bioenergy crops growing increases worldwide; in the latter such price effects are taken in consideration in term of changes in added value related to the Dutch national and regional economies. Insights from these sources has been taken in consideration to investigate land competition and its potential socio-economic implications within the LBS.

In De La Torre Urgante et al. (2000), the Policy Analysis System (POLYSYS) is presented as a national simulation model of the US agricultural sector. This system can incorporate agricultural supply and demand and related modules to estimate agricultural production response, resource use, price, income and environmental impacts of projected changes from an agricultural baseline. The framework incorporates linear programming, econometric and process models to estimate an impact path resulting from changes imposed on a baseline scenario and its underlying assumptions. This work has provided this report with hints about the framework architecture and the dynamic relationship between energy crops price and supply, as showed in the FS framework in section 3.1.1.

Some valuable insights into price-demand relationships have been found in Ankarhem (2005), where the dynamic effects on the forest sector of an increased demand for biofuels is estimated developing a partial adjustment model of the forest sector that enables short, intermediate and long run price elasticity to be estimated. Such evaluation concerns the forestry industry in Sweden within the period 1967-1994. Some of the variables and flow on effects listed in this work appears in the model documented in Section 3.1.2.

In ETSU (1998) the objective is to construct a quantitative economic model capable of capturing the income and employment effects arising from the deployment of bio-energy plants in rural communities; such estimation is done taking in consideration a 1 MW combined heat and electricity plant in North Ireland, UK. From this source, insights in all further sub-models performed have been provided concerning income multipliers and the evaluation of direct, indirect and induced job creation.

In Caputo et al. (2004), the effects of main logistic variables such as specific vehicle transport costs, vehicles capacity, specific purchased biomass costs and distribution density, have been examined in order to evaluate the impact of logistics on the bioenergy plants profitability and flow on effects to LBSs. Such evaluations have been integrated into the LBS with the part concerning the biomass supply (Section 3.1.2).

In Seppälä (1992), based on the sensitivity analyses done with the MERSU-model, it is shown how one of the key factors affecting the performance and structure of the Finnish forest industries (and so far of the biomass availability) at that time was the price of electricity which mainly depended on nuclear power decisions and generally even more so on the national energy policy. In the present report, such aspects have been relevant in investigating interlinks between national policy, forestry industry and bioenergy yield, especially in term of price effects due to feedstocks scarcity and competition (Section 3.1.3).

Mitchell et al. (1995) have developed the Bioenergy Assessment Model (BEAM) to allow the techno-economic assessment of biomass to electricity schemes, including investigation of the interfacing issues. This model has provided our framework with relevant information concerning the relationship between bioenergy prices and technological options, (Section 3.1.3).

Finally in Domac et al. (2002), a meaningful overview of socio-economic aspects of the LBS is presented, some of which have been partially used to build up our descriptive model. Social, cultural, institutional and environmental variables are taken in consideration in addition to more standard economic and employment implications. Such information has been provided by Task 29 as an international collaboration within the IEA Implementing Agreement on Bioenergy and is based on several national and regional experiences from participating countries.

3 Borders and actors of the LBS

Complexity of the LBS involves several actors (stakeholders). The actors themselves define the borders of the LBS. Due to the limitations mentioned in Section 1.3, the LBS is made up by three interlinked sub-systems, namely a Farm sub-system (FS), a Biomass Supply sub-system (BMS) and a Bioenergy Supply sub-system (BES).

The actors, which are considered to belong to the FS in this analysis, are farmers, EU and national/local institutions, R&D institutions and local communities. Forestry farmers and industries, EU and national/local institutions, R&D institutions and local communities have been categorized as belonging to the BMS; finally the energy industry, EU and national/local institutions, R&D institutions and local communities have been allocated to the BES. Such listed stakeholders have been grouped based on information from case studies found in literature (cf. Helby et al., 2004; Tomescu, 2005).

3.1 Sub-system definitions and analyses

A certain number of physical, institutional and socio-economic variables have been detected throughout the literature and the interviews. Such variables have been grouped in four categories, namely physical, customs, policies and markets. The physical category concerns variables related to environmental and geographic conditions (climate, land characteristics, environmental impact, etc.); the customs category refers to variables such as farmers´ age or farmers´ risk attitude more afferent to cultural and social backgrounds; policies concerns variables that define the level of institutional activities affecting the LBS (EU food crops regulation, national energy policy, etc.); finally economic variables such as forestry residues price or extra border energy dependency belong to the markets category.

3.1.1 Farm Sub-system

In order to build up the FS, a list of relevant variables based on the former classification, namely physical, customs, policies and markets, is outlined (Table 2). The variables´ sorting has been made on logical bases. It results in intricate interlinks between variables either between categories or within themselves occur as shown further on.

Figure 1 · The FS; variables used are caught from the table above. Each color identifies the variable categories.

As shown in the previous framework, markets variables play a primary role in the overall sub-system functioning. The domestic energy crops price is one of the key variables within the markets category. As a matter of fact, price sums up all relevant information, such as productivity per employee and level of openness in the market, becoming the main driver for the energy crops yield.

As the Swedes have experienced (Helby et al., 2006), imported energy crops and forestry residue prices can markedly affect domestic energy crops prices, the former being a perfect substitute for the latter. This aspect has serious implications in terms of energy crop investment feasibility as long as in an unprotected agriculture market a too low energy crops price discourages farmers to plant them.

Productivity per employee is also a crucial aspect (IEA, 2003). As claimed in some interviews (Rosenqvist, Åsheim, Bjorn and Kåberger) and reported in Helby (2006), investing in know-how and R&D is key to ensuring a competitive energy crop production. This concerns both business management and good agronomic practices. Shortcomings faced with Salix in the 1990´s Swedish experience show clearly how the lack in agronomic knowledge and management has partially compromised the success of this energy crop´s growth. At least in the first stages, support has to be provided by public/private institution (universities, category associations, public administration, etc.), due to their human and financial resource endowment (OECD, 2004). In addition it is worth noting how productivity countervails the expressed desire in EU policy for job creation in so called disadvantaged areas, which has claimed to be one of the main drivers of LBS´s development (IEA, 2002 and 2003); in fact, growing competitive energy crops requires a low labor-intensive business. It turns out that in a mature well functioning FS (where each task is mechanized) the main socio-economic benefits should not be expectedto come from direct job creation (Kåberger and Bjorn). If it looks at the projection up to 2010 related to the solid and liquid biofuel market in Finland (Zhang, 2005) job creation concerning energy crops production is much less relevant than the one concerning other biomass production industries (Table 3).

Still, such employment trend could still constitute an important social concern due to the high unemployment rate of some rural areas.

Further, the R&D that underlies biomass system implementation can provide a local system with competitive advantages through biotechnology innovation so that new markets can be set up and further economic benefits would be gained (OECD, 2004).

Other variables affect the energy crops yield. As reported in Helby et al. (2006), the ratio between leased and owned lands has effects on the final crops production as long as the majority of energy crops require a long-term commitment; this means that where leasing of agricultural land prevails, then serious barriers will occur to large volume implementation and to overall energy crops yield.

Land availability is another constraining factor. As far as a good quality energy crop supply calls for good quality land, competition can and will take place with food crop production with a number of potential of social and economic implications or problems (Azar et al., 2001; IEA, 2005; OECD, 2004).

Therefore a high energy crop demand would result in a reduction in food crops production and a relative increase in price. Based on the magnitude of that increase, the commercial balance can be negatively affected (European Commission, 2005).

Addressing another parameter, Vlasblom et al. (1998) document information related to the changes in added value, when energy crops substitute grain production in Holland. Evidences is provided that such substitution can have negative effects in terms of added value creation (Figure 2).

Another effect that has importance has been experienced in Sweden, where the land demand for energy crops growing has been boosted by a high carbon fuel price (EEA, 2007).

The farmers´ risk attitude is also relevant in driving the energy crop production. In this case socio-cultural and political elements are involved. As has turned out in the Swedish Salix experience, the inclination to grow crops increased with the age of the farmer, until a turning point. A clearly reduced interest in energy crops was documented in younger farmer groups - this occurs because young farmers want more labor-intensive crops and quicker cash flows (Helby et al., 2006). Moreover, most or all farmers prefer crops with guaranteed "floor prices" and at present traditional crop subsidy systems offer this but bioenergy crop systems do not.

Further, research by Rosenqvist et al. (2000) has indicated, farm size could be relevant in influencing risk attitude. In fact in Sweden willow is cultivated on relatively larger farms, as the size allows the farmers to mitigate risks in terms of new crops; furthermore these farmers are said to be more knowledgeable in terms of cultivation practices and available subsidies.

EU and national energy/agriculture policies affect the risk attitude as well (EEA, 2007). As Åsheim claimed, uncertainty in energy/agriculture policy is an important factor in preventing new investment in energy crops.

Therefore planting food crops will continue to be more secure than energy crops as long as a minimum guaranteed price (as in the case of cereals) is not put on the latter as well (Helby et al., 2006).

Due to what has been previously mentioned, not surprisingly an unbalanced subsidy ratio in favour of other crops will not help to expand the growth of energy crops. (McCormick et al., 2006); so the subsidy scheme is also a crucial factor. As one way of mitigating such effects, Rosenqvist claims that it would be much more effective to provide low establishment subsidies for energy crops combined with high maintenance ones instead of high establishment subsidies alone (Helby et al., 2006).

Finally the Environmental/economic impact related to the energy crops management plays a considerable role in affecting (directly and indirectly) the three main outlined socio-economic variables, namely wealth distribution, local GDP variation and effect on standard of living (purple boxes in the previous model). Regardless of the reliability of an economic evaluation of such environmental effects, their influence cannot be discounted. As stated in Börjesson (1999), several different and positive effects could be or can be achieved by replacing food crops with perennial energy crops (see Table 4). The positive environmental effects listed include reduced nutrient leaching and heavy metal reduction in soil, erosion reduction, fertility increases, some minor improvements in biodiversity (as also claimed by Rosenqvist and Åsheim) and even significant applications for low energy effective wastewater treatment (EEA, 2007) with concomitant benefits such as fertilization and biodiversity increases.

Table 4 · Economic evaluations of environmental changes occurring when perennial energy crops replace annual food crops cultivated with current agriculture practices (Börjesson, 1999)

The three variables earlier mentioned have been detected as synthetic socio-economic indicators of the LBS. All variables within the system are directly or indirectly related to them. While no relevant problems exist in measuring the first two, namely wealth distribution and local GDP variation, it could be controversial to estimate the effect on standard of living, as its interpretation is highly subjective.

To date, some attempts have been made to measure how and to what extent changes in the LBS influence the GDP (Faaij, 1998). Direct, indirect and induced job creation is considered one of the most influential factors in the FS wealth generation.

Not least, the environmental impact related to energy crops management is a relevant concern since running intensive cultivation strongly affects the ecosystem and the security of communities. In fact a potential trade-off occurs among the need for social security through the employment creation and maintenance in the short term and the need of a sustainable socio-economic development in the long term (Hall et al., 1998).

If we look at the short term, energy crops prices are certainly the most crucial aspect. The investment decisions of farmers are to a large extent driven by good profit opportunities and perceptions of such opportunities (Rosenqvist et al. 2006).

Nonetheless policy making at European and national level does provide a powerful lever toward the energy crops profitability (Rosenqvist et al. 2006). At present, direct agriculture subsidies are crucial as are indirect financial supports, engagement in R&D and access to, and dissemination of knowledge. Further on, a functioning FS calls for a quick policy making adjustment in order to match dynamic agricultural market conditions, such as that which occurs in the case of food crops.

3.1.2 Biomass Sub system

The aspects of concern detailed here that are related to the activities of biomass gathering, logging and supply to market. Great attention is placed on the forestry industry because many interlinks of this sector have been detected with the LBS.

Figure 3 · The BMS

As in the previous section, biofuel/raw material price plays a crucial rule in the entire sub-system equilibrium. It primary leads (and is partially led by) the biofuel supply.

As usual, several other variables are linked to the price. Related to that, it is worth noticing how aspects such as residues/by-product gathered, environmental-economic impact related to the forestry residues gathering, integration level of the agro-forestry industry, forestry raw material processed by forestry industry and productivity per employee directly affect the price.

Due to favorable environmental conditions, residues and forestry industry by-products are cheaply available. Costs come up mainly in logging, gathering and moving biomass to recoveries/plants rather than in compensating nutrient loss related to the residues gathering (Börjesson, 2000; Caputo et al., 2005).

Table 6 · Estimated direct costs of logging residue recovery and nutrient compensation in different parts of Sweden (Börjesson, 2000)

The logistics of biomass fuel supply is likely to be complex owing to the intrinsic feedstock characteristics, such as the limited period of availability and the scattered geographical distribution over the territory (Mitchell et al.1995; Caputo et al., 2005; Madlener et al., 2005). These aspects are summed up by two physical variables, namely feedstock distance from stocking place/wideness of stocking place and transport modes level. So far logistics comes out as a keen constraining factor in the economic feasibility of biomass supply (Figure 4).

Figure 4 · Diagrams below (see also further down) show respectively maximum specific biomass transport costs (CVT), maximum specific purchased biomass costs (CB), minimum biomass distribution density (DB) and minimum vehicle capacity (VC) associated to a zero Net Present Value (NPV) of an investment in combustion- (C/ST) and gasification (G/CC) -based technology for bioenergy production. According to this performance, if the ranged costs are too low or too high for being included in the sections among the dotted lines, bioenergy production is economically unfeasible.

Benefits to the local community could come up from engaging investments in supply infrastructure settled to develop the bioenergy industry, in particular new transport modes. Such infrastructure could provide a strategic service to the existing local industries, easing or empowering industrial integration; therefore positive economic externalities would occur, especially for remote rural areas (Ontario Ministry of Energy, 2006). In fact, as Lunnan has reported (Lunnan, 2002), based on the extent of investments further socio-economic effects would come from indirect and induced jobs creation through the income multiplier.

Still, developing infrastructures could generate negative effects as well. A rise in moves of goods and people related to the biofuel supply implies defensive/health costs for communities belonging to the LBS (Schmid et al., 2001; OECD, 2004); it is noticeable that these affect negatively both the local GDP and the standards of living. So a trade-off comes up between the need to decrease delivery costs and preserving the community´s well being.

Table 7 · Monetary evaluation of ills related to pollution effects of transports emission in Europe (Schmid et al., 2001). This may represent a range of possible health costs related to the increase in biomass transport within the LBS.

As mentioned earlier, through vertical and horizontal integration the agro-forestry industry can exploit synergies, across different activities supplying feedstock, such as agriculture, forestry, and municipal waste, and also develop institutional infrastructures for research and development, transport, marketing and sales networks (European Commission, 2000; OECD, 2004); this results in price bias, being production costs partly depending on the industry integration level (Figure 2).

Agro-forestry industry integration has been observed in Sweden. Despite a first hostility by pulp and paper industry against the biomass utilization in energy production, many forestry companies started to run a private forest business in roundwood and by-products supply for energy production. Such hostility was the consequence of the misperception that there was a shortage of wood feedstock, with potential negative effects on raw material price for forestry industry itself (Hilliring, 1997).

Still, according to Kåberger, the new business strategy at a certain extent avoided national forestry industry to suffer from the East European countries competition (OECD, 2004).

Forestry industry hostility against biomass utilization for energy production experienced in Sweden allows us to detect a further price-affecting variable labeled as forestry raw material processed by forestry industry (Figure 2). In fact competition between bioenergy and traditional forestry industry cannot be excluded (European Commission, 2005; Ontario Ministry of Energy, 2006).

Productivity per employee affects the price too; concerning skilled workers applying good practices in forestry management, like recycling ashes, improve the biomass supply efficiency (Börjesson, 2000).

Moreover, as Börjesson (2000) has found out such good practices are key in ensuring potential environmental and economic benefits related to an improved nitrogen balance and a reduction in soil acidification (Table 8).

Table 8 · Estimated environmental costs/benefits of logging residue recovery (LRR) and nutrient compensation in different parts of Sweden, compared with no logging residue recovery and no nutrient compensation (Börjesson, 2000). The environmental impact costs variation refers to a situation where no LRR occurs.

In regard to direct jobs creation the BMS shows a better trend than the FS. As reported by Zhang (2005) good performances occur either in harvesting or in transporting wood fuel. Besides the 85% of the workers involved in the entire production chain are employed in these two stages.

As in the case of the LBS, the social relevance of the job creation is related to the local unemployment level. As far as a positive employment variation rate concerns a reduction in local employment (especially where that is pretty high), socio-economic benefits are gained in terms of a local GDP rise and a better wealth distribution.

Finally, a policies variable calls for investigation. As Seppälä (1991) has found out in Finland, the national energy policy can potentially affect the biofuel/raw material price. In fact, high electricity price pushes the pulp and paper industry to reduce the amount of raw material processed, this industry being the energy intensive one that relies on improvements in the production system. Therefore the fewer the raw materials processed the cheaper the wood available to sawmills (characterized by a low electricity demand) and bioenergy industry.

As mentioned earlier, biomass price drives the demand for biofuel. Biofuel refers to forestry biomass after having been logged, transported and stocked for the energy production.

The most relevant factors in influencing the biofuel price are biomass scarcity and transport modes level (Caputo et al., 2005). While the former could be overcome through imports, the latter is more problematic to meet.

Transport modes and, more in general, infrastructure endowments provide the BMS with keen constraints, being those certainly relevant in making up biofuel market price. This explains why the most successful biofuel productions take place nearby the power plant.

Moreover transports involve positive or negative externalities. Which of these prevails depends on the context. However, negative externalities could engage relevant concerns affecting local GDP and standard of living (Schmid et al., 2001). Some experiences showed how such aspect has been a relevant constraint to further bioenergy supply projects (Sinclair et al., 2001; Upreti et al., 2003).

3.1.3 Bioenergy Sub system

This concerns all aspects related to the bioenergy yield, namely heat and electricity.

Figure 5 · The BES

In the BES, electricity and heat prices mainly drive (and are partially driven by) bioenergy supply.

As the totality of experts interviewed claimed, in turn one of their most leading factors is the relative biofuel/carbon fuel price (Figure 6).

Energy and environmental national policies (labeled as GHG policy in Figure 5) can strongly affect such a ratio (McCormick et al., 2005; Ericsson et al.). Carbon tax has been proven being a powerful economic incentive to shift energy production toward biomass utilization, especially in the case of heat production, as experienced in Sweden and Finland (Börjesson, 2000; Ericsson et al.).

Figure 6 · Diagrams show the Swedish and Finnish relative biofuel/carbon fuel price trends (graph above) and the energy supply trends respectively in Finland (bottom- left) and Sweden (bottom-right) sorted by source. If relative bioenergy price and supply trends are compared it can notice that as the former decrease, the latter rise.

Biofuel supply strategy could be also a relevant aspect in changing the relative biofuel/carbon fuel price. As mentioned in Roos et al. (1999), when a small bioenergy industry with limited resources is threatened by tough competition from the traditional energy industry, the best strategy for individual bioenergy companies may be to co-operate with other bioenergy enterprises in order to meet the threat of the competing energy technology.

Another key factor is the integration level of the bioenergy industry. As observed in the BMS, integration between bioenergy and agro-forestry industry contributes to develop synergies reducing input factors price, transaction costs and risks (Roos et al.1999); therefore it results in decreasing of biofuel/carbon fuel relative price.

Research and Development is crucial to improve the energy production efficiency. It concerns both workers´ know-how and technology.

As some studies show (Mitchell et al., 1995; Roos et al., 1999; OECD, 2004), bioenergy price can sharply vary by using different technology options, as far as it affects the productivity per employee (Table 11).

What it has been stated previously came out from some interviews as well. As claimed by Feurstein (C4) and Johansson (ENA Energi), investing financial resources to inform power plant employees and biomass suppliers is strategic to improve the energy supply efficiency, so that energy delivering cost cuts down (McCormick et al., 2005).

Moreover the improved efficiency implies benefits on the environmental impact side, as far as a reduction in greenhouse gases is achieved (European Commission, 2005).

At the bottom line it is worth evaluating the potential socio-economic effects of a low price bioenergy supply.

One of the positive externalities related to the LBS concerns the magnitude of the extra border energy dependency. According to Feurstein (C4), Johansson (ENA Energi) and Kivela (Lahti Energia), as far as a local plant is fueled with biomass belonging to the local area and the energy yield meets the community demand, the community itself controls the energy supply so that it contributes to developing a sense of security against instability of traditional fuel delivering and price (OECD, 2004; Sinnov, 2006).

Further, as long as local enterprises gain from a low energy price, competitive advantages occur (IEA, 2002). It turns out that the reduced extra border energy dependency and the increased local economy competitiveness could result in a positive effect on local commercial balance (OECD, 2004).

When it comes to the economic advantages for local companies, potential increased revenues for local government are to be taken into consideration. Gaining from a rise in competitiveness allows a company to expand their business and profits. It results in more revenues through taxes for the local government; if well reinvested, these resources can strongly affect the wealth distribution (Varela et al., 1999).

Even though electricity and heat prices are the main drivers in boosting the bioenergy supply, it cannot be denied that the environmental problems perception by companies and communities could be relevant to the LBS functioning. As mentioned in Segon (2004) this is one of the keenest social barriers to the implementation of the LBS.

It is crucial to develop an educational program through the use of success stories and demonstration projects, in order to overcome the community distrust towards bioenergy supply and its concerns.

Some British cases have shown that the lack of reliable information to the local community can strongly compromise the social acceptability of bioenergy projects (Sinclair et al., 2001; Upreti et al., 2003). As far as this prevents from gaining of bioenergy project benefits (job creation, environmental and economic benefits, etc.), it leads to negative bias on the standard of living.

Again several benefits related to a well-structured commitment between public institutions, companies and community have been experienced in the Enköping bioenergy system building (McCormick et al., 2005). So it has been widely proven that when local stakeholders jointly work to build up a functioning LBS this results in a local community empowerment. In turn it concerns a decrease in social conflicts and an outcome of positive externalities affecting the standard of living so far (IEA, 2003).

Due to what was stated earlier, is the environmental impact a relevant concern within the BES? The environmental impact magnitude depends mainly on two factors: first of all the technology used, secondly the power generation capacity and the biomass engaged to fuel the plant. A bioenergy plant shows better environmental performances with respect to a coal/oil power plant, mostly due to the CO2 neutrality (European Commission, 2000).

Instead the evaluation of such benefits is controversial if other aspects are taken in consideration such as NOx emission and particulates (European Commission, 2000).

The bioenergy districts heating examined in Finland, Norway, Denmark and Sweden, seem to gain from a local air quality improvement since the power supply has been shifted from the traditional power plant to the biomass fueled ones. However, roughly speaking, a site-specific evaluation should be undertaken when it comes to running a bioenergy plant.

As it has been observed in the BMS, expanding the bioenergy production involves infrastructural adjustments. As some of the interviewed plants experts claimed, investments mostly concern the expansion of the grid/plant in the district heating and the renewing of the supply infrastructure (see also section 3.1.2). Both are respectively run to improve the efficiency of the bioenergy supply and to enrich scale economies.

As Roos (1999) has reported a bigger and more efficient bioenergy supply contributes to reduce production costs; besides a growing market leads to positive externalities such as stimulating the R&D that in turn improve the cost effectiveness of the bioenergy yield. Such infrastructural adjustments engage potentially relevant direct and indirect jobs creation (ETSU, 1998; Lunnan, 2002).

As in the previous sub models, price is the first driver of the overall bioenergy supply. A mix of incentives and disincentives pushes the energy yield toward biofuels use.

In Nordic countries it has been observed how carbon taxes and electricity certificates have provided the energy producers with such stimulating factors (Ericsson et al.).

Further incentives are aspected to come up with the development of a CO2 tradable permits market in the next years.

Such policy variables affect bioenergy market price making it economically attractive respect to traditional carbon fuels. No economic feasibility would occur without those policy features.

Moreover bioenergy production requires a critical mass in terms of market demand. Scale economies need to be achieved to make infrastructural investment paybacks convenient (Roos et al. 1999).

Even though building up a steering committee among stakeholders and funding pilots project are key in managing the supply system at the first implementing stages, energy price is with no doubt the leading element that allows actors to cooperate and the overall sub system to function so far

Nonetheless there is a strategic issue with relying on policy leviers to provide a competitive bioenergy price market. As has been found in literature and further confirmed by interviewed experts, facilitating communities to reduce traditional energy dependency is a keen concern of political class.

3.1.4 Sub-systems integration

As mentioned in section 3, sub systems are integrated with each other (Figure 7). Therefore any the changes that would affect the interconnection variables are aspected to bias the whole model equilibrium.

Figure 7 · Sub models integration through the interconnection variables namely energy crops yield and biomass supplied

Some estimates have been provided trying to quantify economic effects of biomass use along the LBS. Faaij (1998) has compared added value coming from the biomass fuel cycle with the one related to the coal cycle in power generation referred to the Netherlands (Table 13).

Table 13 · Changes in added value, costs of fuel import, value added tax and GDP caused by total expenditures (annualized per year) for both the biomass and coal fuel cycle as a function of the interest rate (Faaij, 1998)

Relevant are also estimation related to the direct and indirect job creation (Table 14).

Table 14 · Overview of the direct and indirect employment generated by the biomass and coal fuel cycle in the Netherlands (Faaij, 1998)

It´s worth noticing that in this case the biomass fuel cycle performs better outcomes in term of added value and direct/indirect job creation.

More controversial is the benefits evaluation of the biomass fuel cycle respect to the coal cycle in term of damages coming from air emissions, as it is showed in Table 15 and Table 16.

Table 15 · Summary of the damage costs of emissions to air based on generic data from Hohmeyer (Faaij, 1998)

Table 16 · Summary of the damage costs of emissions to air based on generic data from Dorland (Faaij, 1998)

4 Conclusions and Recommendations

In the previous sections attempts have been done to make up fragmented information related to the socio-economic effects of the LBS. Relying on the analysis of ongoing experiences of LBS, some insights have been found and matched together to built up a cognitive framework that performs all the detected key variables along with their interlinkages.

Variables detected have been classified as Physical, Customs, Policies and Markets and afterward entered in a framework.

The result is a visual model that seeks to provide a wide overview of all possible interconnections and sets down a cause-effect pattern between variables engaged.

Due to its complexity, the framework is presented as made up by three sub-systems, namely the Farm sub-system, the Biomass Supply sub-system, and the Bioenergy Supply sub-system. This breakdown has the purpose to ease the analysis of the model itself.

The partition has been made on logical bases, trying to pool homogeneous variables in the same sub-system. Nonetheless the three sub-systems are strongly related to each other.

Some variables have been detected as more influent than other in driving the LBS and in affecting socio-economic factors, based on what it has been found out in literature and most of all in the interviews. More specifically markets and policies variables appear to be crucial in setting up the overall LBS equilibrium.

European and National policies in the energy/environmental and agriculture sectors bias prices of energy crops/biofuel and bioenergy (heat and electricity). So far price becomes the main leader to the economic actors in an ongoing LBS.

At the bottom line, while market prices are strongly affected by the national and international policy making, most of the externalities locally occur.

In the first place, except the GHG emission that has been not investigated in the present work, other environmental concerns have exclusively a local dimension. Those occur in all the three sub-systems and could involve positive as well as negative externalities.

Growing willow has been claimed to provide the communities with environmental advantages respect to traditional food crops such as nutrient leaching and heavy metal reduction in soil, erosion reduction, fertility increase and some minor improvements in biodiversity. Positive effect are met also in forestry biomass exploitation, on condition that good practices are engaged in forestry management. Further benefits in terms of air quality improvements come into the local area when biofuel substitutes oil and coal in power generation.

On the other hand, negative externalities for communities belonging to the LBS may come from a rise in biofuel transportation to the power plant. Moreover the evaluation of the environmental impact is controversial when a bioenergy plant is built up ex novo or replaces an existing plant fueled with natural gas.

Regarding socio-economic effects, employment creation is one the main factor that is looked at. However expectations in that sense need to be reorganized as far as competitive LBS require a low labor rate.

Again, wealth creation may be locally relevant as long as the three sub-systems physically belong to same area at a local level. In turn, that allows developing a sense of community based on mutual trust and sound long-term relationships among local stakeholders, as experienced in the Enköping district heating.

At this point relevant decision-makings become local. Building and running LBS call for reliable information to the local community. Furthermore an institutional steering committee (regional or local public administrations, category associations, etc.) should coordinate the start up, providing stakeholders with a tool to cooperate at the first stages of the LBS implementation.

The model performed in the present work challenges to outline some brief output variables. Local GDP, wealth distribution and standard of living have been thought as the most suitable socio-economic indicators in the evaluation of the LBS. Such indicators may well summarize the complexity of the LBS allowing a cost-benefit estimation of externalities.

While the GDP and the wealth distribution are quite easily measurable, problems arise when it comes to the standard of living. This indicator may capture the complexity of the realty; however, up to date, none has been jointly recognized as the universal one.

Roughly speaking, the present work tried to investigate all relevant aspects involved by the LBS. Nonetheless, due to its complexity, some other key variables could be missing. Further research is aspected to come up with new contribution in that sense.

Finally the intended purpose of this paper is to provide researchers with a valid reference point for a complete socio-economic analysis of LBS. Many aspects touched upon in the present work calls for further investigation. One of the desirable future steps would be to test the model in the real life so that feedbacks could be gained from it to improve the model itself.

List of Figures

• Figure 1 · The FS
• Figure 2 · The shares of added value and import
• Figure 3 · The BMS
• Figure 4 · Bioenergy costs
• Figure 5 · The BES
• Figure 6 · Price trends
• Figure 7 · Sub models integration

List of Tables

• Table 1 · The LBS borders
• Table 2 · The FS variables
• Table 3 · Job creation trends
• Table 4 · Economic evaluations
• Table 6 · Estimated direct costs
• Table 7 · Monetary evaluation
• Table 8 · Environmental costs/benefits
• Table 9 · Employment effect
• Table 10 · The BES variables
• Table 11 · Electricity cost by technology
• Table 12 · Direct and indirect job creation
• Table 13 · Changes in added value
• Table 14 · Direct and indirect employment
• Table 15 · Damage costs of emissions (Hohmeyer)
• Table 16 · Damage costs of emissions (Dorland)

Acknowledgements

I would like to express my gratitude to my supervisors Åke Thidell and Philip Peck for providing me with great support and guidance during the whole training period. I would also like to thank Kes McCormick, Tomas Kåberger and Thomas B. Johansson for their advice and suggestions that allowed me to focus on the core of my research. Special thanks also to Mihai Tomescu, Helen Nilsson, Luis Mundaca and Hanna Savola for having providing me with useful references. I am thankful to the IIIEE staff for having made me feel at home during these four months spent here in Lund. Last, but not least, thank you to all my interviewees.

Bibliography

Abbreviations

Interviewees

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