EMERGY SYNTHESIS 3: Theory and Applications of the Emergy Methodology

Transcription

1 EMERGY SYNTHESIS 3: Theory and Applications of the Emergy Methodology Proceedings from the Third Biennial Emergy Conference, Gainesville, Florida Edited by Mark T. Brown University of Florida Gainesville, Florida Managing Editor Eliana Bardi University of Florida, Gainesville, Florida Associate Editors Daniel E. Campbell US EPA Narragansett, Rhode Island Vito Comar State University of Mato Grosso do Sul Dourados, Brazil Shu-Li Haung National Taipei University Taipei, Taiwan Torbjorn Rydberg Centre for Sustainable Agriculture Uppsala, Sweden David Tilley University of Maryland College Park, Maryland Sergio Ulgiati University of Siena Siena, Italy November 2005 The Center for Environmental Policy Department of Environmental Engineering Sciences University of Florida Gainesville, FL ii

2 36 Emergy Analysis of Selected Local and National Transport Systems in Italy Mirco Federici, Franco Ruzzenenti, Sergio Ulgiati, and Riccardo Basosi ABSTRACT In this work we analyze and compare the local transport systems in two different Italian provinces, Brescia and Siena, with the most important National freight and passenger transport system, the Milan-Naples transportation axis, by means of the emergy synthesis method. The latter is composed of three sub-systems: Highway, Railway and High-speed Railway. Each transport system has been investigated in relation to the landscape characteristics and final use. The specific emergies of passenger transportation by car were calculated in the range of *10 11 sej/p-km, by bus in the range of *10 10 sej/p-km and by train in the range of *10 11 sej/p-km. The specific emergies of commodity transportation were calculated in the range of *10 11 sej/t-km (for transport by truck) and in the range of *10 11 sej/t-km (for transport by train). Specific emergies are calculated, accounting for materials, labor, energy and fuels used in the construction, maintenance and yearly use of roads and railways as the major sources of emergy input. After investigating the factors that affect the calculated emergy intensities, the sustainability of each transportation subsystem is discussed. Finally, the combined use of global-scale emergy-based indicators and a local-scale exergy indicator (the so-called Second Order Exergy Efficiency) is suggested, in order to help quantify the potential for emergy savings and better use of available resources. INTRODUCTION The goal of this work is to obtain a comprehensive picture of the environmental support needed as well as the environmental loading generated by each transport type: car, bus and train for passenger transport, truck and train for goods transport. In two previous papers (Federici et al., 2003 a,b), we jointly applied Mass Balance (Hinterberger and Stiller, 1998), Embodied Energy Analysis (Herendeen, 1998), Exergy Analysis (Szargut et al., 1988) and Emergy Synthesis (Odum, 1996) to investigate the efficiency and the sustainability of local transport systems in Italy. The provinces of Brescia (industrial economy, Northern Italy) and Siena (tourist and service economy, Central Italy) were considered as case studies. Results identified the presence of a strong correlation between the environmental performance of transport systems (considered globally as vehicles, infrastructures and management) and the economic and territorial structure of local societal systems. Results seem also to indicate that the thermodynamic performance of vehicles does not play a major role in determining the environmental impact, which instead seems to be affected by use factors, linked to local specificities (orography, intensity of use, load factor per trip, welfare, etc.). In order to get a deeper picture of the energy and environmental problems of the transportation sector, application of a similar analysis to a wide and highly traffic-intensive system became a necessary step. The Milan-Naples axis is the -449-

3 selected national case study. In so doing, it is possible to compare road and railway energy and environmental performance both at the same territorial scale and across different scales. In the last section, an integration of Emergy and Exergy based indicators is presented, in order to highlight the role of thermodynamic losses and inefficiency in amplifying emergy demand. The new integrated indicator appears capable of quantifying the misuse of emergy or, in other words, the amount of emergy that can be saved and used elsewhere. This may help in implementing suitable improvement strategies. THE SYSTEMS INVESTIGATED Investigation was performed on two different scales: 1) a local scale, where transport systems are used mainly for short transport trips, and 2) a national scale where trips are characterized by long distances and high speed. The Local Systems: Siena and Brescia The cities of Brescia (Northern Italy) and Siena (Central Italy) are presented as case studies on the local scale. The main characteristics of these two areas are shown in Table 1. Brescia is located on a very important traffic, rail and freight axis of Italy, the Turin-Venice axis, and its economy is characterized by widespread industrialization. In contrast, Siena is situated in a less accessible zone and its economy is based mainly on agriculture and services. Although different from a geographical, morphological and economical point of view, these two provincial districts are similar from the point of view of some macroeconomic variables, like per capita income, and are also comparable with respect to the size of their two main towns (Federici et al., 2003,b). The economic structure of Brescia is mainly based on a well-developed industrial sector (iron and steel manufacturing, machinery, textile and local clusters specialized in producing components for big industries). This intense economic activity generates critical levels of chemical and dust emissions, production of waste, and road traffic. In the urban area, the attention and alarm thresholds of airborne chemical concentrations are very often exceeded (Camera di Commercio di Brescia, 2000), especially in winter, requiring the city administration to forbid car use for several days. The transport sector accounts for about 28% of the total Brescia energy consumption, while it is 33% of total national consumption). Road and railway subsystems are the main means of transportation in the area. The province of Siena has a surface of 3,820 km 2, dominated by a hilly landscape (92%). The economic structure of the district is centered on a well-developed and high added value agricultural activity, as well as on a service sector of banking, university, tourism and health care activities. A low population density (Table 1) and little industrial activity, make the level of pollution (traffic, noise, production of waste, release of chemicals, etc.) low and quite acceptable (i.e., people perceive it as acceptable). The transport sector represents about 39% of total energy consumption and related airborne emissions for the province (ARPAT, Environmental Protection Agency of the Tuscany Region, 2000). The railway system is based on an old fleet of diesel-powered trains, mainly used for transporting daily commuters to their villages outside of Siena. Due to recent incentives offered by the Italian government to favor the decommissioning of old cars, in both areas the automobiles provide improved control of air quality. Old cars replaced by new models in the last five years represent 35% of the total circulating fleet in Siena and 33% in Brescia (ACI, 2003). However, the transport system is perceived by the population as the main environmental problem in both areas, although Brescia is also heavily affected by industry-related pollution

4 Table 1. Characteristics of the local systems. Chapter 36. Emergy Analysis of Selected Brescia (a) Siena (b) Surface (km 2 ) Population density (persons/km 2 ) Energy use that is transport 28% 39% Energy use that is residential 28% 23% Total road length (km) Total passenger transport (p-km/yr) 1.01E E+09 Passenger by car 89.70% 91.25% Passenger by bus 4.30% 4.16% Passenger by train 6% 4.59% Total commodity transport (t-km/yr) 3.28E E+09 Commodity fraction transported by truck 98% 87.80% (a) (b) Camera di Commercio di Brescia, 2000, Camera di Commercio di Siena, 2000, The National System: The Milan-Naples Axis The Milan-Naples axis is the most important traffic line in Italy connecting the economic core of Northern Italy, the Milan area, with the biggest and more populated city of Southern Italy, Naples. Rome, Florence and Bologna are also served by this transportation infrastructure. The axis is composed by three parallel sub-systems: the A1 Toll-Highway, the present electric railway, and the high-speed railway, TAV, still in construction. Each sub-system covers a length of about 800 km. In the year 2001, the A1 highway supported traffic of 1.19 E10 v-km (vehicle-km) for a total passenger traffic of 2.10 E10 p-km; commodity transport was 4.09 E9 v-km for 3.6 E10 t-km. In the period the total traffic on this highway faced an increase of 27% (Autostrade SpA, 2002). In the same period, passenger transport by railway decreased by 2.32% while the railway commodity transport increased by 8.32% (Trenitalia SpA, 2003). The TAV railway is still in construction and therefore no traffic data are available. Our calculations were performed according to two hypotheses: a) a use rate similar to the one on the existing line (TAV, 2000, 2003), and b) the maximum possible utilization rate (max load factor). The latter assumption was also tested for the existing line. Passenger traffic ranges is between 1.09 E10 p- km and 1.52 E10 p-km, while the commodity transport ranges is between 3.84 E9 t-km and 5.84 E9 t-km. Differences between the TAV and existing electric railway are: higher power of the locomotive (6-8 MW vs 4-6 MW) and a much higher number of tunnels; the latter are required to prevent losses of velocity. TAV trains also have a maximum load capacity equal to 70% of existing low trains. THE APPROACH At either the local or national scale, the investigated transportation systems can be divided into two main sub-systems, i.e. road and railway. For each of these several sub-steps were considered: (a) constructions of infrastructure and machinery (roads, tracks, cars, trains, etc.), (b) maintenance, and (c) use for transport of commodities and passengers. The systems diagram in Figure 1 shows the main -451-

5 Figure 1. Systems diagram of road transport. components as well as the flows of energy and materials among them for the road transport subsystem. A similar diagram can be drawn for the railway systems. An average passenger weight of 65 kg was assumed in order to be able to compare the total transported passenger mass and commodity mass and allocate infrastructure and maintenance inputs accordingly. On the basis of the assumption that 1 t corresponds to about 13.4 passengers, p-km units were converted into t-km units, resulting into a total passenger traffic of 1.41E+09 t-km for highway transport (3.76% of total weight transported) compared with a commodity traffic of 3.60E+10 t-km (96.24% of total weight transported). In a similar way, TAV passengers were calculated in the range 7.08E E+08 t-km (15%-16% of total weight transported) while commodities amounted to 3.84E E+09 t-km (84%-85% of total weight transported). Tables 2 to 5 list the most important flows considered in the Emergy Analysis of the A1 highway and TAV trains: similar procedures were used for local systems. The transformities used refer to a total emergy flow supporting the Biosphere equal to 9.44E+24 sej/yr (Odum, 1996). This baseline was recalculated in the year 2000 (Odum et al., 2000) and the total flow set to E+24 sej/yr. We used the previous baseline value to ensure that results for 2002 were easily compared with those from already published analyses (e.g., Odum and Odum, 1994). Transformities for global flows are from Odum (1996), while transformities for Italy are from Ulgiati et al. (1994) and Cialani et al. (2004). Converting the old transformities to the new updated values would require that they be multiplied by 1.68 (the ratio of 15.83/9.44). In particular, Tables 2 and 3 show the emergy analyses of the Milan-Naples highway and TAV railway. Data refer to construction and maintenance of the infrastructure. Foundations of roads and tracks are built up in a very similar way: there is an internal layer of stabilized gravel and rock that support respectively the upper layer of asphalt for the highway, and the ballast and the steel line for the railway. Differences occur in the different depth of foundation to support the higher weight of trains. In both cases the average life-time of lower layers is taken as 50 years. The emergy supporting the -452-

6 Table 2. Emergy analysis of Milan-Naples Highway: Construction and maintenance. Item Unit Annual Amount Solar Transformity Solar Emergy (sej/unit) (sej/year) Sunlight J/year 9.62E E E+16 Rain water (chemical potential) J/year 9.44E E E+18 Deep heat J/year 6.60E E E+17 Construction of road infrastructure Gravel kg/year 2.46E E E+20 Top soil J/year 8.04E E E+19 Ballast kg/year 6.80E E E+20 Asphalt J/year 1.72E E E+20 Concrete kg/year 4.48E E E+19 Reinforced concrete kg/year 4.45E E E+18 Reinforced concrete traffic divider kg/year 2.64E E E+19 Diesel J/year 1.21E E E+16 Steel in machinery kg/year 8.53E E E+15 Steel in tunnel reinforcement kg/year 1.54E E E+20 Steel in guardrail kg/year 4.30E E E+19 Steel in traffic divider kg/year 2.15E E E+19 Labor J/year 4.47E E E+16 Service /year 2.20E E E+20 Maintenance Diesel J/year 5.75E E E+18 Steel in machinery kg/year n.d. 6.70E E+00 Labor J/year 4.06E E E+17 Service /year 1.86E E E+18 Self consumption of Highway society Methane J/year 9.69E E E+17 Diesel J/year 6.23E E E+18 Gasoline J/year 4.22E E E+18 LGP J/year 6.10E E E+16 Crude oil J/year 1.60E E E+17 Electricity J/year 1.34E E E+19 Total Emergy for Highway Infrastructures sej/yr 1.67E

7 construction of road infrastructure is much lower than the actual emergy of transport operations (due to the large input of fuel and services related to vehicles). Instead, in the railway system the emergy of infrastructure is the dominating input, due both to the very large use of gravel and sand for track support and to the large amount of emergy in topsoil used up, compared to the lower input of electricity and services. One may claim that these inputs are not actually driving the process and that gravel is not degraded but simply moved from the mining site to the railway line. Notwithstanding this, we feel justified in including these items in the analysis for two main reasons. First, gravel and ballast used to build roads and railway are not mined and moved from hills and mountains to infrastructure sites at no cost. As a consequence of mining and removal, the useful work made by nature to develop local ecosystems (with local climax, flora and fauna) is destroyed and lost forever and this loss represent a cost that must be charged to the infrastructure itself. Secondly, the infrastructure materials are not eternal; due to weathering and vehicle traffic they are degraded and no longer useful after a relatively short period of time. Therefore, they are an unavoidable input to the process. Table 3. Emergy analysis of TAV, Milan-Naples axis: Construction and maintenance. Item Unit Annual Amount Solar Transformity Solar Emergy (sej/unit) (sej/year) Sunlight J/year 5.30E E E+16 Rain water (chemical potential) J/year 4.32E E E+17 Deep heat J/year 2.89E E E+17 Construction of railway infrastructure Sand and gravel kg/year 5.27E E E+21 Top soil J /year 4.42E E E+21 Concrete kg /year 7.34E E E+20 Reinforced concrete kg /year 5.53E E E+19 Diesel J/year 8.87E E E+18 Steel in machinery kg/year 2.65E E E+16 Steel in track kg/year 1.85E E E+20 Steel in electric poles kg/year 1.25E E E+18 Steel in tunnel reinforcement kg/year 5.00E E E+20 Copper in electric cables kg/year 3.86E E E+16 Service /year 3.68E E E+20 Labor J/year 5.16E E E+17 Maintenance Electricity J/year 2.10E E E+18 Steel in machinery kg/year 3.67E E E+17 Service /year 3.81E E E+18 Labor J/year 8.30E E E+18 Total Emergy for TAV Infrastructures sej/yr 7.70E

8 The top asphalt layer has a five-year lifespan, and a similar lifespan was assumed for steel rail. Results show that emergy required to construct and efficiently maintain the TAV system is about 5 times higher than the emergy required for the A1 highway; this is mainly due to the tremendous amount of rock, steel 1 and concrete required to build the railway. Tables 4 and 5 show the emergy evaluation of the A1 highway and TAV operation: annual maintenance, as well as construction and support to vehicles. The emergy required for the construction of infrastructure (Tables 2 and 3) and vehicles was divided by their average lifespan (10 and 30 years for cars and trains respectively). The emergy supporting the trucks used for road commodity transport (including the emergy of fuel) was calculated on the basis of the average distance covered each year, average load factor per trip, average lifespan for each class of trucks and the annual traffic intensity for commodity transport (tkm/year) 2. This is because, in general, trucks are used over a much wider territory than cars, which requires assumptions on average performance and tasks. In a similar way we calculated the emergy stored in cars and railway vehicles running on the Milan-Naples axis. The specific intensities calculated in Tables 4 and 5 also include the annual fraction of infrastructures calculated in Tables 2 and 3. These performance parameters are crucial for the evaluation and comparison of the different subsystems, because they are intensive variables independent of the scale and the size of the investigated systems. The amount of emergy required for one unit of service is capable of indicating the total environmental support supplied in order to generate one unit of transportation service. RESULTS Emergy intensities are used to rank environmental support supplied to existing or planned local and national systems for passenger and commodity transport. We believe that these parameters are the most suitable to evaluate the efficiency and the ecological footprint of transport systems. These systems are very often investigated on the basis of the energy that they use directly to move people and goods. If transport policy is the goal, direct energy analysis is not a proper tool, since it does not account for free environmental sources, ignores labor and services, and finally does not properly account for the indirect energy embodied in machinery and infrastructure. In fact, the latter are only evaluated on the basis of the commercial energy invested for their construction, completely disregarding the considerable environmental work performed by nature to provide minerals and fuels. Emergy synthesis takes into account these non-commercial flows. In so doing it is able to answer questions about the environmental support that is globally needed for a given process to actually occur, and about the pressure of a process on biosphere resources and equilibria (ecological footprint). The Local Scale: Siena and Brescia Results from the local scale evaluation are shown in Table 6, where Siena and Brescia are compared by means of the emergy requirement intensity for passenger and commodity transportation. A significant difference between the two systems emerges because of the larger size and power of cars in Brescia, while other factors are not notably different between the two areas (number of persons per trip, trip average length, etc.). Diesel trains running in Siena and Brescia are identical, and the same can be said for the infrastructure of the railway system. The problem with railways in Brescia is that they are largely underutilized, compared with Siena. Results show that passenger bus transport provides the best performance among the available alternatives, and that it is much more efficient in Brescia than Siena. This result can be attributed to the higher load factor of buses compared with cars and trains, as well as, to a lesser extent, to the 1 The steel required to reinforce tunnels is about 12,400 tons per km. 2 (t-km per year)/ (tons per trip)* (truck weight)/(truck life time) = [(t-km/year)/(t-km)*(kg)/(year)] = kg/year -455-

9 Table 4. Emergy analysis of the Milan-Naples Highway: Passengers and commodity transport. Unit Annual Amount Solar Transformity (sej/unit) Solar Emergy (sej/year) Individual passenger transport (car) Steel of vehicles kg/year 7.04E E E+20 Gasoline J/ year 2.52E E E+21 diesel J/ year 3.52E E E+20 LPG J/ year 8.72E E E+19 Tires J/ year 1.50E E E+18 Lubricant J/ year 1.10E E E+18 Driving J/ year 3.98E E E+20 Labor for vehicle maintenance J/ year 1.04E E E+18 Vehicle cost / year 9.80E E E+21 Total Emergy for Passenger Transport sej/yr 4.22E+21 Commodity transport Steel of vehicles kg/ year 8.45E E E+19 Gasoline J/ year 3.63E E E+20 Diesel J/ year 2.89E E E+21 Tires J/ year 7.75E E E+19 Lubricant J/ year 2.07E E E+19 Driving J/ year 1.78E E E+20 Labor for vehicle maintenance J/ year 2.43E E E+20 Vehicle cost / year 9.56E E E+20 Total Emergy for Commodity Transport sej/yr 2.90E+21 Passenger traffic 2.16E+10 p-km/yr Commodity traffic 3.60E+10 t-km/yr Specific intensity for passengers (*) 1.98E+11 sej/p-km Specific intensity for commodities (*) 1.25E+11 sej/t-km (*) Calculated intensities include the emergy of infrastructures, from Table 2. thermodynamic performance of vehicles, most of which were purchased recently. Passenger transport by car and by diesel train results in higher specific emergies per p-km, with better performance in Siena. Freight transportation by truck performs well in Brescia and a little worse in Siena, while electric and diesel railway transportation is not competitive. The specific emergy values are composed of two fractions: direct emergy consumption (fuels, electricity, tires and steel vehicles) and indirect emergy consumption for infrastructures (cement, asphalt, ballast and steel of tracks). For commodity transport, allocation of the infrastructure emergy plays a key role: in fact, the road system in Brescia supports a commodity flow ten times higher than the Siena system, so that the emergy of the infrastructure is allocated in proportion to a higher number of units transported. In so doing, notwithstanding a lower average load factor (8.78 t vs t per trip in Siena), road transport in Brescia shows lower emergy intensity per t-km

10 Table 5. Emergy analysis of TAV Milan-Naples: Passengers and commodity transport. Unit Annual Amount Solar Transformity (sej/unit) Solar Emergy (sej/year) Passenger transport Steel of vehicle kg/ year 1.74E E E+19 Electricity J/ year 4.15E E E+20 Service / year 7.35E E E+19 Labor J/ year 5.77E E E+19 Total Emergy 8.04E+20 Commodity transport Steel of vehicle kg/ year 4.39E E E+18 Electricity J/ year 8.44E E E+20 Service / year 2.85E E E+18 Labor J/ year 4.78E E E+19 Total Emergy 1.95E+20 Option 1: Maximum use rate(*) Passenger traffic 1.52E+10 p-km/yr Commodity traffic 5.48E+09 t-km/yr Specific intensity for passengers 1.30E+11 sej/p-km Specific intensity for commodities 1.23E+12 sej/t-km Option 2: present use rate (*) Passenger traffic 1.09E+10 p-km/yr Commodity traffic 3.84E+10 t-km/yr Specific intensity for passengers 1.84E+11 sej/p-km Specific intensity for commodities 1.75E+12 sej/t-km (*) Calculated intensities include the emergy of infrastructures, from Table 3. Railway transport suffers from the high emergy stored in the steel within tracks and trains in both provinces: even in this case diesel trains are identical in Siena and Brescia so the different values are due solely to the different use. Electric trains in Brescia, with a value of E+11 sej/t-km, are oversized with respect to their utilization rate. Details of emergy allocation are given in Table 7. The rows of the table show the % composition of the emergy input to each subsystem (the sum of all fractions makes up 100% of total emergy input, apart from negligible effects of rounding). The emergy of infrastructure clearly appears to be a significant fraction of the total input and its fraction is always much higher in commodity transport than in people transport, due to the allocation of the input in proportion to the mass -457-

11 Table 6. Emergy accounting at local scale. n.a.: Not applicable to Siena (only diesel railway) Chapter 36. Emergy Analysis of Selected Siena Brescia Passenger transport unit (10 11 sej/unit) (10 11 sej/unit) Road individual transport (car) (p-km) Road mass transport (bus) (p-km) Railway (diesel) (p-km) Railway (electric) (p-km) 1.87 Commodity transport Road (by truck) (t-km) Railway (electric) (t-km) n.a Railway (diesel) (t-km) transported. In contrast, the emergy cost of direct energy expenditures is much higher in road than in railway, which usually makes people believe that railway is much more environmental friendly than road. The Milan-Naples Axis Results of the emergy analysis applied to the national scale are presented in Table 8. Passenger transport by car shows higher emergy intensity, while the best performance is shown by bus transport, confirming the results obtained at the local scale. Trains perform better than cars, with lower emergy intensities in the maximum load factor scenario. The different performances between the existing railway line and the TAV are due to the different power of trains (with consequent higher energy and emergy input to TAV) and the larger material requirement (mainly steel) for tunnels and vehicles (with additional emergy input). Even in the case of long distance railway, the excess size of infrastructure and excess power of machinery still is a significant problem. Results for commodity transport are clearly negative for both railway options: the expected shift of fractions of road traffic to the railway systems, in order to decrease the environmental impact of commodity transport, does not appear to be a sustainable alternative. In fact, the specific emergy of railway transport is ten to fifteen times higher than for the road system (Table 6 and 8). DISCUSSION Results of both investigations on local and global scales suggest unexpected conclusions. First of all, the railway system does not show the environmentally friendly performance expected when assessment is only based on direct energy use. The amount of resources indirectly required for machinery, infrastructures and labor, measured in emergy terms, expose the hidden costs of this transportation pattern, which can be reduced and made comparable with car transportation only if the load factor is increased by maximizing the number of users. In any case, buses appear to be the less resource intensive way of moving people and trucks are the best option for freight transportation. This -458-

12 Table 7. Breakdown of emergy input per unit of product of each typology of transport. (Values are given as % of the total input supporting each category) Sub-System Renewable Structure Infrastructure Directly Energy Use Labor and services ( ) (*) (**) ( ) (#) Road transport, Siena Individual transport, car 0.008% 12.68% 3.18% 49.43% 33.77% Road mass transport, bus 0.032% 6.76% 13.31% 64.28% 14.80% Road goods transport, truck 0.097% 0.37% 40.48% 34.02% 22.54% Road transport, Brescia Individual transport, car 0.002% 13.28% 0.89% 52.15% 33.59% Road mass transport, bus 0.014% 29.05% 5.92% 25.68% 38.73% Road goods transport, truck 0.044% 1.72% 17.83% 69.94% 8.63% Diesel railway transport, Siena Railway mass transport 0.01% 6.48% 28.31% 26.59% 38.61% Railway goods transport 0.02% 0.16% 70.12% 1.70% 27.99% Diesel railway transport, Brescia Railway mass transport 0.01% 20.83% 53.32% 7.99% 17.82% Railway goods transport 0.02% 0.24% 74.28% 2.97% 22.45% Electric railway transport, Brescia Railway mass transport 0.01% 0.34% 36.62% 13.53% 49.15% Railway goods transport 0.02% 0.07% 62.16% 18.60% 19.13% ( ) Only the direct solar radiation impinging on the interested area is accounted for as Renewable Emergy. This corresponds to the solar emergy that supported the sustainable ecosystem previously existing in this area before the system of roads and railway were constructed. (*) Only vehicles (cars, trains, trucks) are included in this item. Emergy supporting labor and services is not included. (**) All kinds: roads, bridges, railway, etc are included. Emergy supporting labor and services is not included. ( ) Fuel and electricity. (#) Includes direct labor as well as indirect labor quantified as services and measured by the economic value of the items supplied. is probably a consequence of the high load factor that can be reached in these two sub-sectors, coupled with lower demand for infrastructure compared with railway. We do not claim here that other advantages of railway are negligible (comfort, lack of noise, speed, etc). Notably, electric trains help prevent the diffusion of pollution generated by fuel combustion in millions of small engines and offer the possibility of uptaking at least a fraction of pollutants at power plant chimneys and recycling them when possible (e.g. ash and sulphur). In spite of these advantages, the double conversion fuel to electricity and electricity to motion pays an unavoidable entropy tax, which translates into an additional emergy demand for the delivery of electricity. The latter is added to the emergy support to labor, services and infrastructure and results in an unexpected high emergy cost per unit of service. Increasing the load factor as well as decreasing power and infrastructure, by rejecting the High Velocity Option (TAV) seems to be the only feasible way to decrease these costs

13 Table 8. Specific emergies of the Milan-Naples axis. Sub-system (Unit) Specific Emergy (10 11 sej/unit) Passenger transport Highway (car) p-km 1.98 Highway (bus) p-km 0.28 Electric train (*) p-km TAV (*) p-km Commodity transport Highway, trucks t-km 1.25 Electric train (*) t-km TAV (*) t-km (*) Results refer to two scenarios: an assumed maximum load factor and the present use rate. A suitable integration between train and bus may result in a lower emergy cost per unit of transport service and still keep the comfort and flexibility of traveling at an acceptable level, while being competitive with the car option. In contrast, the transport of commodities by systems other than trucks cannot be provided at competitive emergy costs. The problem is not a thermodynamic one, nor is it simply a matter of optimization. The economic systems, at the local, national and international scales, are such that they generate and require these kinds of transport options. It is only by re-thinking the way and the scales at which commodities are produced and traded that a feasible alternative to this problem may be found. A PROPOSAL FOR A COMBINED EXERGY AND EMERGY APPROACH An interesting synergy can be obtained by combining emergy results (donor-side based, global scale) and an efficiency measure derived from the exergy approach (user-side evaluation, local scale). The proposal stems from the consideration that the use of inappropriate tools requires an excess investment of resources, which could be avoided if more attention were paid to the efficiency and the effectiveness of the tool used. The exergy method provides, in addition to the traditional First Law energy efficiency, a so-called second order exergy efficiency, which measures the adequateness of the tool to the goal, from the point of view of the user. If vehicles and infrastructures were weighted by means of the second order exergy efficiency, inappropriate tools could be replaced by means of already existing appropriate solutions. A significant fraction of the emergy support could be saved and diverted to other uses, thus increasing the overall sustainability on the larger scale of the economic system. The Exergy Concept According to Szargut et al. (1988) and Szargut (1998) exergy (hereafter EXA) is defined as the amount of work obtainable when some matter is brought to a state of thermodynamic equilibrium with the common components of the natural surroundings by means of reversible processes, involving interaction only with the above-mentioned components of nature". The emergy synthesis method already uses exergy as a numeraire to measure flows of energy and matter on the basis of their user

14 side quality. Transformities provide these flows with an additional property, their donor-side quality, which is discussed in detail in Brown and Ulgiati (2004 a,b,c). The second order efficiency points out that a significant fraction of the work potential of the input flows is not converted into the work potential or the actual work of the products, due to the use of conversion tools that are not appropriate to the goal. The second order efficiency is defined as the ratio between the net output work and the maximum reversible work: ε = W W output ideal (1) In order to evaluate the rational use of energy, it can be expressed as the ratio between the minimum exergy required for the goal and the exergy actually used: ε = Ex Ex min real (2) The differences between first and second order information are very significant: the first order efficiency (based on the first law of thermodynamics) is just a measure of the engine yield, while the second order efficiency measures how far our process is from an ideal behavior. The lower the second order exergy efficiency, the higher the potential energy savings that could be obtained. For practical purposes, the reference is not made to an ideal device, but instead to the best available technology able to achieve the desired goal. We will not therefore refer to an ideal and impossible performance, but instead to the best vehicles actually available on the Italian market, used in the best way (max load factor). Average second order exergy efficiencies for vehicles used in our case studies are shown in Table 9. The Concept of Emergy Loss By using appropriate tools, i.e. by replacing our tools by means of the best available technologies (arbitrarily assumed as the ideal ones), the emergy investment required, E min, would be lower than those actually calculated in our case studies, E, by an extent at least proportional to the adequateness of the new tool. The difference (E E min ) can be interpreted as an avoidable emergy loss, E loss. The total emergy, E, supporting a system, characterized by a given ε value, can be expressed as the sum of two terms: E = E min + E loss = E*(ε) + E (1-ε) where: (3) E is the actual flow of emergy supporting the process; ε is the Second Order Exergy Efficiency; E min is the minimum emergy required for the goal; E loss is the emergy loss due the inefficient performance of the system. E loss represents the surplus of the emergy requirement that can be usefully diverted to other uses, thanks to a system s exergetic optimization. The results from applying Equation 3 to our case studies are shown in Table 9. Results from Table 9 indicate that some sub-systems are already very close to the best available performance and that there is little room for improvement. Other systems (such as the individual transport by car) show a potential for further improvement that is very large and may suggest an urgent policy for system reorganization. The emergy inefficiency in Brescia and Siena represents respectively the 83% and 82% of the total emergy requirement of province transport systems. The emergy squandered in Brescia would be sufficient to support the whole Siena transport system. This is a very important result that indicates the potential emergy savings obtainable by system optimization

15 Table 9. Minimum emergy requirement and emergy Loss. Milan Naples axis E ε E min E loss (sej/p-km) (sej/p-km) (sej/p-km) Highway 1.98E+11 21% 4.16E E+11 TAV (a) 1.30E+11 80% 1.04E E+10 TAV (b) 1.84E+11 57% 1.05E E+10 Railway (a) 1.04E+11 90% 9.36E E+10 Railway (b) 1.39E+11 80% 1.11E E+10 Siena Road (individual) 1.66E+11 16% 2.66E E+11 Road (bus) 6.00E+10 40% 2.40E E+10 Railway 7.40E+10 60% 4.44E E+10 Brescia Road (individual) 2.47E+11 16% 3.95E E+11 Road (bus) 3.70E+10 97% 3.59E E+09 Railway 3.58E+11 22% 7.88E E+11 (a) Maximum use rate (b) Present use rate CONCLUSION Emergy accounting indicators offer interesting tools for the evaluation of the systems under study, both on the local and national scales. In particular, specific emergy (sej/p-km and sej/t-km) allows comparison of the different performances of each subsystem investigated. Transportation patterns which were expected to show the best performance on the basis of their direct energy consumption are unexpectedly penalized by large loading from infrastructure and inefficient use, as clearly shown by the emergy evaluation. By accounting for the environmental support to input flows other than direct energy, as well as by assigning donor-side quality factors to all input flows, the emergy approach is able to highlight hidden costs that are not usually accounted for. A comparison of all feasible alternatives (best available technologies) was performed by means of the combined use of emergy intensities and Second Order Exergy Efficiency. The emergy savings due to avoidable inefficiencies, calculated by means of an emergy-exergy indicator, could be diverted to support other natural or human-dominated systems, in order to increase the overall system sustainability. REFERENCES ACI, Automobile Club Italia, Annual Report, (in Italian) ARPAT, Azienda Regionale Protezione Ambiente per la Toscana. Rapporto sullo stato dell Ambiente in Toscana. Regione Toscana Editrice. (in Italian) -462-

16 Autostrade SpA, Annual Report. Brown, M.T., and Ulgiati, S., 2004a. Emergy and Environmental Accounting. In: Encyclopedia of Energy, C. Cleveland Editor, Academic Press, Elsevier, Oxford, UK, in press. Brown, M.T., and Ulgiati, S., 2004b. Emergy, Transformity, and Ecosystem Health. In: Handbook of Ecosystem Health. Sven E. Jorgensen Editor. CRC Press, in press. Brown, M.T., and Ulgiati, S., 2004c. Energy Quality, Emergy, and Transformity: H.T. Odum s contribution to quantifying and understanding systems. Ecological Modelling, in press. Camera di Commercio di Brescia, (in Italian) Camera di Commercio di Siena, (in Italian) Cialani, C., Russi, D., and Ulgiati, S., Investigating a 20-year national economic dynamics by means of emergy-based indicators. In: Brown, M.T., Campbell, D., Comar, V., Huang, S.L., Rydberg, T., Tilley, D.R., and Ulgiati, S., (Editors), Emergy Synthesis. Theory and Applications of the Emergy Methodology 3. Book of Proceedings of the Third International Emergy Research Conference, Gainesville, FL, January, The Center for Environmental Policy, University of Florida, Gainesville, FL. Federici, M., Ulgiati, S., Verdesca, D., and Basosi, R., 2003a. Efficiency and sustainability indicators for passenger and commodities transportation systems. The case of Siena, Italy. Ecological Indicators 3: Federici, M., Ruzzenenti, F., Ulgiati, S., and Basosi, R., 2003b. A Thermodynamic and Economic Analysis of Local Transport Systems. In: Advances in Energy Studies. Reconsidering the Importance of Energy, S. Ulgiati, M.T. Brown, M. Giampietro, R.A. Herendeen, and K. Mayumi, Editors. SGE Publisher, Padova, Italy, 2003, pp Herendeen, A. R., Embodied energy, embodied everything...now what? In: Advances in Energy Studies. Energy Flows in Ecology and Economy. S. Ulgiati, M.T. Brown, M. Giampietro, R.A. Herendeen, and K. Mayumi (Eds). MUSIS Publisher, Roma, Italy, pp Hinterberger, F., and Stiller, H., Energy and Material Flows In: Advances in Energy Studies. Energy Flows in Ecology and Economy. S. Ulgiati, M.T. Brown, M. Giampietro, R.A. Herendeen, and K. Mayumi (Eds). MUSIS Publisher, Roma, Italy, pp Odum, H.T Environmental Accounting: Emergy and Environmental Decision Making. John Wiley and Sons. New York. Odum, H.T., and Odum, E.C. (Eds.) Emergy Evaluation of Texas Highway. In: Ecology and Economy. Emergy Analysis and Public Policy in Texas. Lyndon B. Johnoson School of Public Affairs and Texas Department of Agriculture, Policy Research Project Report, Number 78, University of Texas, Austin, Texas, pp Odum, H.T., M.T. Brown and S.B. Williams Handbook of Emergy Evaluation: A Compendium of Data for Emergy Computation Issued in a Series of Folios. Folio No.1 - Introduction and Global Budget. Center for Environmental Policy, Environmental Engineering Sciences, Univ. of Florida, Gainesville, pp Szargut, J., Exergy Analysis of Thermal Processes: Ecological Cost. In: Advances in Energy Studies. Energy Flows in Ecology and Economy. S. Ulgiati, M.T. Brown, M. Giampietro, R.A. Herendeen, and K. Mayumi (Eds). MUSIS Publisher, Roma, Italy, pp Szargut, J., Morris, D.R., and Steward, F.R., Exergy analysis of thermal, chemical and metallurgical processes, Hemisphere Publishing Corporation, London. TAV, Treni Alta Velocità. Rome, Italy. TAV, Press Agency, personal communication. Trenitalia SpA, Annual Report. Tuttotrasporti, Vol Editoriale Domus. (in Italian) Ulgiati S., Energy Flows in Ecology and in the Economy. Encyclopedia of Physical Science and Technology. Academic Press, Vol.5, pp

17 Ulgiati, S., Evaluation of energy and environmental indicators based on Emergy Accounting, for selected electricity production processes in Italy. Final Report to ENEA - National Agency for New Technologies, Energy and the Environment, Research Contract No.2780/95. Ulgiati S., Odum H.T., Bastianoni S., Emergy use, Environmental loading and sustainability. An emergy analysis of Italy. Ecological Modelling 73, pp