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Keywords:

  • economic development;
  • energy consumption;
  • integrated analysis;
  • Italy;
  • societal metabolism

ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Methodological and Empirical Framework
  5. Study Areas and Data Sources
  6. Presentation of Results
  7. Discussion and Concluding Remarks
  8. References

In the light of Italy's current economic stagnation, characterized by mass unemployment, especially in the south, the development of effective energy policies should be seen as an opportunity of reducing energy dependency, so as to increase competitiveness and sustainability. However, due to imbalances in development in the country, focus at the national level does not reveal particular characteristics of the different regions. In this context, the definition of specific energy actions should be based on information and analyses that refer to integrated and multi-level perspectives, such as regional and sectoral levels. Here we investigate, by applying a multi-scale integrated analysis of societal metabolism approach, the imbalanced nature of development, coupled with energy consumption, of two Italian regions, Veneto (northern Italy) and Abruzzo (southern Italy). Changes over time (1995–2007), related to both socio-economic and biophysical indicators, are analysed at multiple levels: region, economic sectors, and production and consumption. The results show that different structural changes and challenges are needed to meet energy efficiency, depending on the diverse level of regional development. They provide support for the future development of effective Regional Energy Plans, as well as to identify potential local barriers to achieving regional competitiveness and sustainability. Copyright © 2012 John Wiley & Sons, Ltd and ERP Environment.


Introduction

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Methodological and Empirical Framework
  5. Study Areas and Data Sources
  6. Presentation of Results
  7. Discussion and Concluding Remarks
  8. References

Developed and developing countries are characterized by an increasing link between energy consumption and economic development (Asafu-Adjaye, 2000; Soytas and Sari, 2003). A report by the Ministero Attività Produttive (2005) shows an increasing trend of the Italian energy requirement, from 195.5 Mtoe (millions of tonne of oil equivalent) of energy consumed in 2004 to an estimate of 243.6 Mtoe for 2020. To meet EU energy reduction targets, Italy, as with other European countries, has to develop a detailed energy policy. In the light of the country's current economic stagnation, characterized by mass unemployment, especially in the south, the development of effective policies to achieve EU targets should be seen as an opportunity to reduce energy dependency, and thereby increase competitiveness and enhance economic development. However, due to the imbalanced nature of development of the country, focus at the national level does not reveal particular characteristics that the different regions may have. Because of this, Italian regional governments are elaborating energy programmes, namely Regional Energy Plans (REPs). These plans are aimed to provide a framework for the definition of specific energy actions. REPs usually comprise two parts: (1) evaluation of trends and projections of energy demands for different sectors; and (2) formulation of objectives and policies to guide the region towards energy efficiency and sustainability. Therefore, the elaboration of effective REPs entails a clear understanding of the evolution of energy demands, as well as of the relationship between energy consumption and socio-economic development. In this context, the definition of energy policies should be based on information and analyses that refer to integrated and multi-level perspectives (Giampietro et al., 2006). The use of integrated frameworks, able to tackle the complex interactions within evolving socio-ecological systems in the realm of energy policy development, has become one of the main points of discussion in the policy agenda on both European and national levels (EU Climate and Energy Package, 2007). Moreover, several theoretical and empirical studies have shown that conventional single-criterion approaches, based on energy intensity calculations only, are insufficient to tackle the many aspects related to the use of energy resources coupled with socio-economic development (Iorgulescu and Polimeni, 2009; Recalde and Ramos-Martin, 2011).

With the aim of providing useful information for the definition of local energy strategies, and identifying key factors impeding regional sustainable development, here we investigate the imbalanced nature of development, coupled with energy consumption, of two Italian regions, namely Veneto (northern Italy) and Abruzzo (southern Italy). This is done by applying the so-called Multi Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM) approach (Giampietro and Mayumi, 1997, 2000a,2000b; Giampietro, 2003). Societal and ecosystem metabolism concepts have been utilized in different fields: ecological economics (Martínez-Alier, 1987; Andenberg, 1998), industrial ecology (Ayres and Simonis, 1994), material and energy flow analysis (Adriaanse et al., 1997; Fischer-Kowalski, 1998; Matthews et al., 2000), economic structural analysis (Duchin, 1998) and social ecology (Schandl et al., 2004). In all these fields, the metabolism approach is employed by analysing interactions between human societies and their natural environment in terms of flows of energy and materials, viewing socio-economic systems as a subsystem of a larger physical system.

The MuSIASEM approach has been extensively applied to the analysis of the metabolism of different countries, for example Ecuador, Spain, Vietnam, Argentina, China, and Central and Eastern Europe. However, few studies have focused their analysis on the regional level and compared the performance of different regional systems (Ramos-Martin et al., 2009; Geng et al., 2011). This paper provides a further study showing the potential of the MuSIASEM approach to analyse development perspectives coupled with energy consumption at the regional level in Italy.

The results show that different structural changes and challenges are needed to meet energy efficiency, depending on the diverse level of regional socio-economic development.

Methodological and Empirical Framework

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Methodological and Empirical Framework
  5. Study Areas and Data Sources
  6. Presentation of Results
  7. Discussion and Concluding Remarks
  8. References

Methodological Framework

The MuSIASEM approach has been developed as a framework for the integrated assessment of sustainability issues across various scales. It integrates biophysical, economic, social and demographic analyses. Theoretical discussions of this method are available in two books (Giampietro, 2003; Polimeni et al., 2008) and two special issues (Giampietro, 2000, 2001). Moreover, empirical analyses based on this approach have been realized on the national level to study the metabolism of different countries, for example Ecuador (Falconí-Benítez, 2001), Spain (Ramos-Martin, 2001), Vietnam (Ramos-Martin and Giampietro, 2005), China (Ramos-Martin et al., 2007), Argentina (Recalde and Ramos-Martin, 2011), and Central and Eastern Europe (Iorgulescu and Polimeni, 2009), as well as at the regional level in Catalonia and China (Ramos-Martin et al., 2009; Geng et al., 2011).

The basic principle of the MuSIASEM approach can be found in the concept of societal metabolism. Societal metabolism is based on the notion that economic systems can be analysed in terms of material and energy transformations with metabolic pathways that evolve over time (Ayres and Simonis, 1994; Fischer-Kowalski, 1998; Fischer-Kowalski and Haberl, 2007). It has been extensively applied to the analysis of the links between social activities and resource use, becoming a key concept in sustainability science (Kuskova et al., 2008). The integrated analysis of socio-economic systems metabolism found its theoretical basis on the bioeconomics discipline suggested by Georgescu-Roegen (Georgescu-Roegen, 1971, 1975; Mayumi and Gowdy, 1999), as well as by the work done in the energy analysis of economic systems by various researchers (Leach, 1976; Pimentel and Pimentel, 1979; Martínez Alier and Schlüpmann, 1987). To better comprehend the nature of the MuSIASEM method, it is important to distinguish between two different concepts of metabolism, exosomatic and endosomatic energy metabolism, which can be applied to understand the biophysical characteristics of human societies and their technological development (Georgescu-Roegen, 1971). Exosomatic metabolism refers to the energy metabolized outside the human body useful for the production of goods and services, such as that used to move a tractor or a machine in an industry. Endosomatic metabolism refers to the energy embodied in food, fundamental to support human life in terms of its physiological processes (Mayumi, 2001). To study exosomatic and endosomatic energy metabolism of human societies means exploring the efficiency of the use of technology and natural resources to support the production and consumption of goods and services from a metabolism perspective, instead of using energy intensity as the sole indicator (Iorgulescu and Polimeni, 2009). Based on these concepts, the MuSIASEM approach analyses nature–society interaction through an integrated representation of social systems, referring to: (1) the biophysical characteristics of the system, such as energy intensity and consumption; and (2) the socio-economic characteristics, such as human time and monetary variables (gross domestic product, GDP). The application of this approach makes it possible to analyse the evolution of human societies over time in relation to their socio-economic development coupled with the allocation of scarce resources such as human time and energy (Recalde and Ramos-Martin, 2011).

Method of Analysis and Description of the Variables

We have analysed the regional socio-economic systems under investigation at different hierarchical levels. Within the MuSIASEM approach this means first dividing each region's economy (level n) into two sectors (production and consumption), which belong to the same hierarchical level (n-1), such as: (i) the paid-work (PW) sector, responsible for the generation of added value of the system under analysis; and (ii) the household sector (HH), which is responsible for the consumption of the added value. The paid-work sector is then disaggregated into subsectors (level n-2), such as: the productive sector (PS, which includes energy, building and manufacturing), services and government (SG), and finally the primary sector (AG, including agriculture, husbandry, forests and hunting). In the MuSIASEM approach, the variables used are: primary energy consumption, human time allocated to different activities and added value generated. The variables are calculated for the different hierarchical levels considered in the analysis: the regional level (n), sector level (n-1) and subsectoral level (n-2). They also denote two distinct groups of variables, extensive and intensive. The former represents the size of the different hierarchical levels in absolute terms, such as total human activity expressed in hours (THA, Total Human Activity), primary energy consumption measured in joules (TET, Total Energy Throughput) and added value (GDP) measured in constant euros. These variables are used to describe the state of the system. The intensive variables refer to indicators or benchmark values, which are used to represent changes in the system over time (Recalde and Ramos-Martin, 2011) and to make comparisons between the different hierarchical levels or for the same level for systems of different sizes (Iorgulescu and Polimeni, 2009). A detailed description of the variables and indicators measured in MuSIASEM are indicated in Table 1.

Table 1. Variables and indicators used in MuSIASEM
VariableDefinition (units)Description and calculation
THATotal human activity (h)=HApw + HAhh
Total available human time a society has to perform different activities, measured in hours (population*8760, hours available in a year)
TETTotal energy throughput (J)=ETpw + EThh
Total primary energy used in an economy in one year, measured in Joules
GDPGross domestic product (€)Added value generated by an economy in one year, measured in constant euros, 2000
AViAdded value in sector i (€)Added value generated in sector i, measured in constant euros, 2000
HAiHuman time in sector i (h)Human time allocated to sector i per year measured in hours
ETiEnergy throughput in sector i (J)Primary energy used in one year measured in Joules
GDPhGDP per hour in the society= GDP/THA (GDP/8760*population)
GDP per hour of human activity in the society
EMRSAExosomatic metabolic rate, average of the society (MJ/h)= TET/THA
Energy consumption per hour of human time available to society
EMRiExosomatic metabolic rate (MJ/h)= ETi /HAi
Energy consumption per working hour in sector i
ELPiEconomic labour productivity (€/h)= GDPi/HAi
Added value per hour of working time in sector i
EISAEnergy intensity (MJ/€) for the whole economy=TET/GDP
Energy consumption per unit of added value (GDP)
ELPi/EMRiEnergy efficiency of production (€/GJ) for an economic sector= GDPi/ETi
Added value generated per unit of energy consumption in sector i

The indicators used in the application of the MuSIASEM approach reflect both the use of exosomatic energy by a society with respect to the human time allocated to different activities (paid work, HApw, and non-paid work activities, HAhh) and sectors (Exosomatic Metabolic Rate, EMRi), as well as the economic and energy efficiency with respect to monetary values (Energy Intensity, EI) and human time allocation (Economic Labour Productivity, ELP, and GDP per hour of Human Activity, GDPh).

Moreover, energy efficiencies of the different economic sectors are analysed dividing the economic labour productivity of each sector by its exosomatic metabolic rate (ELPi/EMRi), which reflects the amount of monetary flow a unit of exosomatic energy is producing in the sector analysed.

Regarding the socio-economic and biophysical interpretation of the main indicators used in the MuSIASEM approach, EMRi reflects the combination of the use of technologies in different economic sectors and their level of capitalization1 (Hall et al., 1986; Pastore et al., 2000). With social development EMR usually tends to increase. This increasing trend reflects the decoupling effect between technological development and human time allocated to the production sector of modern societies (Mayumi, 2001). In other words, this indicator is used as a proxy for capital accumulation. ELP reflects the efficiency of the productive sector to produce goods and services with respect to the human time allocated to the different activities of the paid-work sectors (Agriculture, AG; Industry, PS; Services and Government, SG).

Study Areas and Data Sources

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Methodological and Empirical Framework
  5. Study Areas and Data Sources
  6. Presentation of Results
  7. Discussion and Concluding Remarks
  8. References

Study Areas: General Description

In the context of the imbalanced Italian development, it is interesting to analyse and compare the key factors which characterize the different regional energy and socio-economic profiles. This kind of analysis is particularly meaningful in Italy, where the difference in the level of socio-economic development between the north, the south and the centre of the country is one of the main points of discussion in the policy agenda. The two regions selected can be considered representative of the Italian imbalanced economic development mentioned above, for both their economic characteristics and their geographical location, north (Veneto) and south (Abruzzo) (Figure 1).

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Figure 1. Geographical location of the case study areas within Italy.

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Veneto is a region of north-eastern Italy. It has experienced strong economic expansion since World War II, and today represents the base of major industrial and service sectors. In 2003, Veneto contributed 67% of the wealth produced by the entire North East and 9% by the country as a whole. Its population increased from approximately 4.4 million in 1995 to 4.8 million in 2007 (ISTAT – Istituto Nazionale di Statistica, 2010a), representing 8.17% of the Italian population. Veneto is a well-developed region, contributing 9.6% of Italian GDP, with a per-capita GDP of 22 819 in 2007 (ISTAT – Istituto Nazionale di Statistica, 2010b). It also represented 6.2% of Italy's primary energy consumption in 2007. It is dependent on imported energy, as it produces only 10% of its internal needs (mostly renewables used by the residential sector). It has a total surface area of 18 399 km², and a population density of 268 hab/km2 or people per Km2 in 2007.

Abruzzo is in the south-east. It is located between one of the less favoured areas of southern Italy and the more developed regions of the centre-north. GDP per capita in the region is higher than in the south of the country and basic infrastructure is well developed. The unemployment rate is also lower than the national average (9.6% compared with 12.3%). Until a few decades ago, Abruzzo was one of the poorest regions of southern Italy, but since 1950 it has enjoyed steady growth in GDP. In 2006 the region showed for the first time the highest per-capita GDP of southern Italy, and for the period 1951–2006 exceeded the per-capita GDP rate of growth of any Italian region.

In 2007 it had a population of 1.3 million people (ISTAT – Istituto Nazionale di Statistica, 2010a), representing 2.2% of the Italian population. In 2007 the region contributed 1.8% of Italian GDP and had a GDP per capita of 15 906 (ISTAT – Istituto Nazionale di Statistica, 2010b). In the same year Abruzzo also represented 1.4% of Italy's primary energy consumption. It is less dependent than Veneto on energy imports; in 2007 it produced approximately 23% of its energy requirements, from natural gas and renewables. Natural gas production satisfies 40% of the region's final needs, mostly in the transport and production sectors. It has a total surface area of 10 753 km², and a population density of 124 hab/km2 or people per Km2 in 2007.

Data Used in the Analysis

Energy data have been obtained from the Energy Balances of Italy, Veneto and Abruzzo regions for the period 1995–2007 as provided by the Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA) in March 2011. Demographic data came from national statistics for both total population (ISTAT – Istituto Nazionale di Statistica, 2010a) and labour statistics, such as working hours and labour force for the different economic sectors (ISTAT – Istituto Nazionale di Statistica, 2010c). As data on working hours per economic sectors were lacking for the regional level, we estimated them on the basis of the working hours for each sector on the national level and the number of people employed in the different sectors on the regional and national levels.

Regarding added value (at constant 2000 prices), we used data generated by the National Statistics Institute on its Regional and National Accounts (ISTAT – Istituto Nazionale di Statistica, 2010b). Based on the data collected, we have built historical series on socio-economic and energy variables for the period 1995–2007.

Presentation of Results

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Methodological and Empirical Framework
  5. Study Areas and Data Sources
  6. Presentation of Results
  7. Discussion and Concluding Remarks
  8. References

Level n: Regional Level

The first result that can be seen for the Veneto and Abruzzo economies is the correlation between energy consumption and GDP (Figure 2). In the period under investigation (1995–2007), both regions show a yearly average increase in energy consumption and GDP, as is explained in detail in the following sections.

image

Figure 2. TET and GDP in the Veneto and Abruzzo regions, 1995–2007.

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Veneto Region

Over the period of analysis (1995–2007) GDP grew in Veneto at a yearly average rate of 1.7%, from 90 000 to 110 194 million euros. This represented an increase in GDP per capita from 20 302 euros in 1995 to 22 800 in 2007, with a yearly growth rate of 1%. Population grew by about 400 000 in the period, reaching 4.8 million in 2007. Primary energy consumption rose more slowly than GDP, at about 1% a year, but slightly faster than population, 0.7%, going from 447 PJ in 1995 to 550 PJ in 2007 (see Table 2 and Table 4 for main data and results). Therefore, while energy intensity decreased due to the faster growth of GDP with respect to primary energy consumption (TET), by contrast energy consumption per capita grew in that period given the slower growth of the population (Figure 3). The former decreased from 5 MJ/€ in 1995 to 4.5 MJ/€ in 2007, which is below the figure for Italy as a whole, 6 MJ/€ for the same year. The latter increased from 101 GJ per head in 1995 to 104 GJ per head in 2007 (and 116 in 2006), below the figure for Italy in 2007 (140 GJ per head). Note that if we take from the analysis the breakdown in energy consumption registered in 2007, TET grew faster that GDP in the period 1995–2006, 2 and 1.7%, respectively, with energy intensity remaining approximately the same in 1995 and 2006 (Table 2).

Table 2. Main data and results – Veneto Region
Level n variable (units)PopulationGDP, (million € year 2000)GDP AG, (million € year 2000)GDP PS, (million € year 2000)GDP SG, (million € year 2000)THA (h)TET (MJ)Energy intensity (MJ/€)EMRsa (MJ/h)   
  1. Source: own elaboration based on data from Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), ISTAT – Istituto Nazionale di Statistica (2010a, 2010b, 2010c).

19954433 06090 000250333 62353 8743.88E + 104.47E + 115.011.5   
19964452 79391 712270334 23654 7733.90E + 104.51E + 114.911.6   
19974469 15694 048278735 20556 0563.91E + 104.48E + 114.811.5   
19984487 56094 445283834 78056 8273.93E + 104.65E + 114.911.8   
19994511 71495 714296535 12757 6223.95E + 104.89E + 115.112.4   
20004540 853100 588283536 49961 2543.98E + 104.79E + 114.812.0   
20014529 823101 176282836 04762 3013.97E + 104.84E + 114.812.2   
20024577 408100 417249436 23461 6904.01E + 104.81E + 114.812.0   
20034642 899101 807227335 96263 5724.07E + 104.97E + 114.912.2   
20044699 950104 723262236 60865 4934.12E + 105.17E + 114.912.6   
20054738 313105 757261936 91666 2214.15E + 105.23E + 114.912.6   
20064773 554108 197249738 39567 3054.18E + 105.51E + 115.113.2   
20074832 340110 194251839 17568 5024.23E + 105.00E + 114.511.8   
Level n-1 variable (units)HApw (h)HAhh (h)ETpw (MJ)EThh (MJ)EMRpw (MJ/h)EMRhh (MJ/h)ELPpw (€/h)     
19953.61E + 093.52E + 103.03E + 111.44E + 1184.14.124.9     
19963.68E + 093.53E + 103.06E + 111.45E + 1183.04.124.9     
19973.71E + 093.54E + 103.14E + 111.34E + 1184.63.825.3     
19983.78E + 093.55E + 103.17E + 111.48E + 1184.04.225.0     
19993.83E + 093.57E + 103.35E + 111.55E + 1187.44.325.0     
20003.91E + 093.59E + 103.38E + 111.42E + 1186.33.925.7     
20013.92E + 093.58E + 103.35E + 111.49E + 1185.44.225.8     
20023.93E + 093.62E + 103.42E + 111.39E + 1187.03.825.5     
20034.01E + 093.67E + 103.46E + 111.51E + 1186.44.125.4     
20044.03E + 093.71E + 103.67E + 111.50E + 1191.14.026.0     
20054.02E + 093.75E + 103.58E + 111.65E + 1189.04.426.3     
20064.09E + 093.77E + 103.95E + 111.57E + 1196.64.226.5     
20074.21E + 093.81E + 103.49E + 111.51E + 1182.94.026.1     
Level n-2 variable (units)HAag (h)HAps (h)HAsg (h)ETps (MJ)ETsg (MJ)ETag (MJ)EMRps (MJ/h)EMRsg (MJ/h)EMRag (MJ/h)ELPag (€/h)ELPps (€/h)ELPsg (€/h)
19952.23E + 081.40E + 091.99E + 091.43E + 111.50E + 111.10E + 10102.175.349.211.224.127.1
19962.13E + 081.42E + 092.05E + 091.45E + 111.50E + 111.06E + 10102.573.149.612.724.226.7
19972.25E + 081.44E + 092.05E + 091.46E + 111.57E + 111.15E + 10101.376.651.112.424.527.3
19982.14E + 081.46E + 092.10E + 091.48E + 111.57E + 111.17E + 10101.274.954.813.223.827.1
19992.14E + 081.47E + 092.15E + 091.59E + 111.63E + 111.26E + 10108.475.858.913.923.926.8
20002.05E + 081.47E + 092.23E + 091.62E + 111.63E + 111.22E + 10110.073.159.513.924.827.4
20012.00E + 081.47E + 092.26E + 091.61E + 111.61E + 111.26E + 10109.971.563.014.124.627.6
20021.88E + 081.45E + 092.29E + 091.67E + 111.66E + 119.90E + 09114.672.452.613.224.926.9
20031.83E + 081.46E + 092.36E + 091.65E + 111.70E + 111.11E + 10112.972.060.512.424.626.9
20041.76E + 081.48E + 092.37E + 091.82E + 111.75E + 111.07E + 10122.773.760.814.924.727.6
20051.59E + 081.47E + 092.39E + 091.69E + 111.78E + 111.15E + 10114.374.572.216.425.027.7
20061.68E + 081.50E + 092.42E + 092.00E + 111.82E + 111.20E + 10133.475.571.614.925.627.8
20071.78E + 081.55E + 092.48E + 091.62E + 111.75E + 111.23E + 10104.570.369.214.125.327.6
image

Figure 3. Energy intensity and energy consumption per capita for the Veneto and Abruzzo regions, 1995–2007.

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Finally, exosomatic metabolic rate, societal average (EMRsa), increased from 11.5 MJ/h in 1995 to 11.8 MJ/h in 2007 with an average yearly growth rate of 0.3% and of 1.3% for the period 1995–2006.

Abruzzo Region

For Abruzzo, in the period 1995–2007, GDP grew at a yearly rate of 1.2%, from 18 205 to 21 050 million euros. This represented an increase in per-capita GDP from 14 500 euros in 1995 to 15 899 in 2007, with a yearly growth rate of 0.8%. Population grew more slowly, about 68 400 in the period, reaching 1.3 million in 2007. Primary energy consumption rose slightly faster than GDP, at about 1.3% a year, and faster than population (0.4%), increasing from 99 PJ in 1995 to 113 PJ in 2007 (see Table 3 and Table 4 for main data and results). Regarding energy intensity and energy consumption per capita, we obtained different results from Veneto if we consider the period 1995–2007 (Figure 3) but approximately similar results if we consider the period 1995–2006. In particular, energy intensity remained the same, 5.4 MJ/€ in 1995 and 2007. Energy consumption per capita rose from 79 GJ in 1995 to 86 GJ in 2007. Finally, EMRsa increased from 9.0 MJ/h in 1995 to 9.8 MJ/h in 2007 with an average yearly growth rate of 0.9%, not significantly different from that obtained for the period 1995–2006. Table 4 summarizes the different annual growth rates, for the period 1995–2007, with regard to GDP, TET and population for both Veneto and Abruzzo.

Level n-1: Production And Consumption

To provide an explanation of the level of energy consumption and increase in EMRsa, a detailed interpretation of the data can be provided by disaggregating the information at the level of production and consumption. This can be done by looking at the metabolic behaviour and distribution of time for the paid work (PW) and household (HH) sectors.

Veneto Region

For the Veneto region, both EMRpw and EMRhh show an average yearly growth rate that is close to zero, although values for the paid work sector are slightly higher than for the household sector. In general, the paid work sector seems to have performed best despite the reduction of EMRpw in 2006. If we look at the evolution of EMRpw before 2007 (Figure 4), it shows an average yearly growth rate of 1.3%, which reflects the capitalization of the paid work sector, which is more realistic if we consider that Veneto is one of the most developed regions of Italy. The decreasing trend of the value of EMRpw between 2006 and 2007 may be related to the evolution of oil prices and in particular to the large increase for the period 2006–2008 when oil prices increased from $70 per barrel to $98 (Figure 5 shows the evolution of oil prices from 1995 to 2008).2 The same decreasing trend is seen for the Abruzzo region, although it is of lower intensity.

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Figure 4. Exosomatic metabolic rate (energy consumption per hour) of sectors generating added value (EMRpw) and sectors consuming added value, households (EMRhh), in MJ/h, for the Veneto and Abruzzo regions.

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image

Figure 5. Crude oil prices 1995–2008. (Source: BP, 2010.)

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Abruzzo Region

In Abruzzo, the energy consumed per hour of work (EMRpw) in the paid work sector increased in the period analysed at an average yearly rate of 1.4%, from 77 MJ/h in 1995 to 89 MJ/h in 2007. In the houshold sector, the energy consumed per non-working hours (EMRhh) decreased, at a negative average yearly rate of −0.8% (Figure 4). So, the increasing rate of EMRsa for the entire region can be explained mostly by an increase of the level of capitalization of the productive sector as compared with the consumption side. Moreover, if we consider that the average yearly growth rate of energy consumption in the productive sector (ETpw) has been 2.1% and that of the working population (HApw) 0.7%, we can conclude that the EMR growth of the productive sector has been based essentially on new investments in technology rather than a change in the working population. Likewise, on the consumption side, the average yearly growth rate of EThh presents a negative value of −0.4%, with an increasing rate of hours, i.e. HAhh, of 0.4%, resulting in a decreasing level of capitalization of the household sector. Given that EMRhh can be considered as a proxy for the material standard of living of the population (Iorgulescu and Polimeni, 2009), we can conclude that, for the Abruzzo region, the increase in the population did not link to new investments in technologies in the household sector. Disaggregating the data, lower level analyses will show, in the following section, which sectors have most influenced the capitalization of the paid work sector in Abruzzo for the period analysed.

Summarizing the results obtained above, we have seen that a huge part of the increase in the Veneto and Abruzzo economies to consume more exosomatic energy reflects a capitalization process which took place within the productive sector rather than the household sector.

This result contrasts with evidence from a similar study in Catalonia for the period 1990–2005 (Ramos-Martin et al., 2009). In that period, the authors showed that the increase in energy consumption has not been driven by a progressive capitalization of the productive sector, but rather by: (1) an increase in the active population (simply by an increase in the number of hours of work); and (2) the increase in energy consumption of the household sector. This difference can be partially explained by a moderate population growth of the two Italian regions in comparison with Catalonia, as well as the by differences in economic terms. For instance, in the period 1995–2005 Catalonia showed a yearly GDP growth rate of 3.6%, which is higher than that of Veneto for the same period (1.7%), and a similar difference applies for the population growth.

At this point, an analysis at level n-2 is needed to shed light on the contribution of each economic sector to the capitalization of the paid work compartment in the two regions under analysis, as explained in the following section.

Level n-2: Evolution of the Productive Sector

This section provides an analysis of the change of the structure of the labour market in the period under investigation and of the different compartments comprising the productive sector: agriculture, industry and services. This kind of analysis is useful in determining which sectors of the paid-work compartment are capitalizing and which are de-capitalizing between 1995 and 2007. It also reveals similarities and dissimilarities of the model of development of the two regions.

According to the data presented in Table 2, in Veneto the agricultural sector lost 20% of working hours in 2007 as compared with 1995, the industrial sector gained 10% and services gained 25%. In the Abruzzo region (Table 3) the agricultural sector lost 8% of working hours in 2007 as compared with 1995, the industrial sector gained 14% and services gained 9%. To see how this structural change explains changes in EMRpw as presented in the previous section, we need to discuss the exosomatic metabolic rates of each sector. This is shown in Figure 6. EMRpw depends on the behaviour of each individual EMRi, and the profile of distribution of working time among sectors, HAi.

Table 3. Main data and results – Abruzzo region
Level n variable (units)PopulationGDP, (million € year 2000)GDP AG, (million € year 2000)GDP PS, (million € year 2000)GDP SG, (million € year 2000)THA (h)TET (MJ)Energy intensity (MJ/€)EMRsa (MJ/h)   
  1. Source: own elaboration based on data from Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), ISTAT – Istituto Nazionale di Statistica (2010a, 2010b, 2010c).

19951255 50418 205687574711 7711.1E + 109.88E + 105.49.0   
19961257 42718 406709575311 9441.102E + 109.87E + 105.49.0   
19971259 15018 635773579312 0691.103E + 101.02E + 115.59.2   
19981260 49718 616804577012 0421.104E + 101.05E + 115.69.5   
19991261 13418 922763604912 1101.105E + 101.07E + 115.79.7   
20001261 30020 081758668112 6421.105E + 101.06E + 115.39.6   
20011262 37920 553731674813 0731.106E + 101.09E + 115.39.8   
20021273 28420 587756657513 2561.115E + 101.07E + 115.29.6   
20031285 89620 151686626113 2041.126E + 101.27E + 116.311.3   
20041299 27219 681721619512 7661.138E + 1051.22E + 116.210.8   
20051305 30720 179714623213 2331.143E + 101.14E + 115.610.0   
20061309 79720 688729647013 4901.147E + 101.16E + 115.610.1   
20071323 98721 050643687313 5341.16E + 101.13E + 115.49.8   
Level n-1 variable (units)HApw (h)HAhh (h)ETpw (MJ)EThh (MJ)EMRpw (MJ/h)EMRhh (MJ/h)ELPpw (€/h)     
19958.69E + 081.013E + 106.68E + 103.20E + 10773.220.9     
19968.97E + 081.012E + 106.50E + 103.37E + 10733.320.5     
19978.80E + 081.015E + 106.79E + 103.40E + 10773.421.2     
19988.89E + 081.015E + 107.32E + 103.19E + 10823.120.9     
19998.80E + 081.017E + 107.52E + 103.21E + 10853.221.5     
20008.96E + 081.015E + 107.62E + 103.00E + 10853.022.4     
20019.30E + 081.013E + 107.71E + 103.18E + 10833.122.1     
20029.39E + 081.022E + 107.69E + 103.05E + 10823.021.9     
20039.32E + 081.033E + 109.27E + 103.42E + 10993.321.6     
20049.03E + 081.048E + 108.92E + 103.33E + 10993.221.8     
20059.16E + 081.052E + 108.38E + 102.99E + 10912.822.0     
20069.20E + 081.055E + 108.51E + 103.10E + 10932.922.5     
20079.42E + 081.066E + 108.34E + 103.00E + 10892.822.4     
Level n-2 variable (units)HAag (h)HAps (h)HAsg (h)ETps (MJ)ETsg (MJ)ETag (MJ)EMRps (MJ/h)EMRsg (MJ/h)EMRag (MJ/h)ELPag (€/h)ELPps (€/h)ELPsg (€/h)
19959.58E + 072.63E + 085.10E + 082.78E + 103.55E + 103.50E + 09105.669.636.57.221.823.1
19961.04E + 082.73E + 085.19E + 082.83E + 103.31E + 103.60E + 09103.763.734.56.821.023.0
19979.75E + 072.72E + 085.11E + 082.90E + 103.56E + 103.32E + 09106.769.734.17.921.323.6
19989.75E + 072.76E + 085.16E + 082.99E + 103.99E + 103.41E + 09108.577.335.08.220.923.4
19999.32E + 072.79E + 085.08E + 083.14E + 104.06E + 103.21E + 09112.679.934.48.221.723.8
20008.25E + 072.84E + 085.30E + 083.23E + 104.07E + 103.21E + 09113.676.738.99.223.523.9
20019.34E + 072.94E + 085.42E + 083.32E + 104.06E + 103.28E + 09112.674.935.17.822.924.1
20029.57E + 072.87E + 085.56E + 083.19E + 104.16E + 103.42E + 09111.374.835.77.922.923.8
20039.21E + 072.82E + 085.59E + 084.27E + 104.53E + 104.63E + 09151.781.150.37.522.223.6
20048.95E + 072.80E + 085.33E + 084.02E + 104.54E + 103.64E + 09143.785.040.78.122.123.9
20058.46E + 072.79E + 085.53E + 083.51E + 104.44E + 104.31E + 09125.880.451.08.422.324.0
20068.02E + 072.79E + 085.60E + 083.78E + 104.33E + 104.01E + 09135.677.350.09.123.224.1
20078.84E + 073.00E + 085.54E + 083.44E + 104.50E + 103.98E + 09114.881.245.07.322.924.4
Table 4. Average yearly growth rates (%) of GDP, TET and population – Veneto and Abruzzo (1995–2007)
RegionGDPGDP per capitaPrimary energy consumption (TET)Population
Veneto1.71.01.00.7
Abruzzo1.20.81.30.4
image

Figure 6. EMRi (MJ/h) for the Veneto and Abruzzo regions.

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Veneto Region

Looking at the performance of Veneto, two aspects of the evolution of the exosomatic metabolic rates of the paid work sector are particularly interesting: (1) the de-capitalization of the services and government sector and (2) the average annual rate of growth of EMR in the agricultural sector. The de-capitalization of the services sector reflects the fact that energy consumption in the sector (ETsg) increased more slowly than working population (HAsg), at an annual rate of 1.3 and 1.9%, respectively. The working population in this sector increased between 1995 and 2007 by 25%, indicating a movement of the working force from the agricultural and industrial sectors to services. However, the most impressive change relates to the agricultural sector, in which EMRag increased at an average annual growth rate of 3.3%, in comparison with the 0.6% increase for the industrial sector and −0.5% for the services sector, as explained above. Again, if we do not take into consideration the decrease in energy consumption in Veneto in 2007 (see Figure 2), the average yearly growth rates indicated above become, for the period 1990–2006, 3.9% for the agricultural sector, 2.6% for the industrial sector and 0% for the services sector. This demonstrates that the 2007 decrease in energy consumption has affected the industrial sector more than the other economic sectors in Veneto. The high annual rate of growth of EMR in the agricultural sector is due mainly to the huge reduction of the working hours in the sector, –20%, with a negative average annual rate of −1.7%. This result can be explained by the high mechanization and the spread of intensive agricultural production in Veneto, which generally requires fewer working hours than less intensive practices. The evolution of EMRsa can be also analysed with respect to the productivity of labour (ELPi) and energy efficiency indicators (ELPi/EMRi). In terms of productivity of labour, which is the ratio between the GDPi generated and hours of work of the different sectors, the agriculture sector shows the lowest values. This is not surprising, as this sector in developed society is always less productive than the industry and services sectors in terms of wealth produced per hour of work. Moreover, while the agricultural sector shows an increasing ELP in the period 1995–2007, the services and industrial sectors remain more or less constant (Figure 7). The increasing trend of ELPag demonstrates an increasing capacity of the sector to produce wealth per hour of work, which is probably related, as previously explained, to the increasing mechanization along with reduced need of human labour in the sector.

image

Figure 7. ELP for the agriculture, industry and services sectors, 1995–2007, Veneto and Abruzzo regions.

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To complement the analysis for Veneto, we can look at the energy efficiency of production represented by the ratio between ELPi and EMRi, which indicates the amount of wealth produced by consuming 1 MJ of energy in a particular sector (Figure 8). According to the results shown, the agricultural and industrial sectors are the less efficient in terms of the generation of added value per unit of energy. The services and government sectors show the best performance.

image

Figure 8. ELPi/EMRi for the agriculture, industry and services sectors, 1995–2007, Veneto and Abruzzo regions.

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Abruzzo Region

In the case of Abruzzo, the most interesting aspect of the evolution of the exosomatic energy consumption of the paid work sectors is for the services and government sectors. In the period analysed, it is interesting that EMRsg shows an average yearly growth rate that is higher than the industrial sector (see Figure 6).

Human activity in this sector has also increased by 9% over the period analysed, a value that is not as high if we consider the increase in the industrial sector of 14%. Moreover, similarly to the case for Veneto, but with lower values, the agriculture sector has reduced the amount of working hours by −8%. This movement of working hours from the agricultural sector to industry and services is in line with the positive economic development registered by the region during the period analysed, with large investments in the industry and services and government sectors.

To further analyse the economic and biophysical performance of the different sectors, we can look, as for Veneto, at the productivity of labour and energy efficiency. Similarly to Veneto, the agricultural sector shows the lowest values of ELP in comparison with the industry and services sectors (Figure 7), but with a smoother evolution of ELPi for all the sectors than Veneto over the period analysed. Finally, concerning the energy efficiency of the different sectors, none of the sectors was able to increase the generation of value added per unit of energy between 1995 and 2007 in Abruzzo. This means that, despite the increase in energy consumption coupled with economic development, the efficiency in the use of energy of the different sectors is very low, and the region is even becoming more inefficient. This result is particularly important in the context of the need on the regional level to meet the European and national energy reduction targets for 2020. In this respect, Abruzzo needs to explore and invest in increasing its efficiency in the use of energy in relation to all the productive sectors.

Discussion and Concluding Remarks

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Methodological and Empirical Framework
  5. Study Areas and Data Sources
  6. Presentation of Results
  7. Discussion and Concluding Remarks
  8. References

In the field of governance for sustainability towards a low-carbon economy, government decision-makers are increasingly aware of their ability to improve economic and environmental conditions through local energy actions.

Acknowledging the importance of implementing meaningful interventions designed for specific areas, Italy has delegated on the regional level the preparation of REPs. However, to our knowledge energy planners in Italy do not take into consideration the relationship between energy consumption and socio-economic profiles from a multi-level perspective. Analysis of the evolution of energy demands coupled with socio-economic development requires complementing conventional energy analysis with the use of variables that are able to deal with economic productivity, as well as environmental pressure at different levels of analysis (Recalde and Ramos-Martin, 2011). Here we have applied a multi-scale integrated approach (MuSIASEM) to look at the metabolism of two regional socio-economic systems from an integrated perspective, with the specific purpose of providing useful information to support the development of effective local energy policies. By employing this method we have identified the main aspects and dissimilarities that characterize the socio-economic development and energy consumption of the richest northern-east region of Italy, Veneto, and the most developed southern Italian region, Abruzzo. First, we have seen that both regions show a positive correlation between energy consumption and economic development over time. Secondly, we have seen that, differently from other developed societies, such as Catalonia in Spain, the two regions show that the increase in energy consumption was driven mainly by a progressive capitalization of the productive sector. Thus, the two regions appear to perform quite similarly. However, at lower hierarchical levels and with reference to the different sectors, we have identified differences. As one of the richest regions of Italy, Veneto has shown better performance than Abruzzo, with higher economic labour productivities of the productive sectors (€/h) (Figure 7). However, this aspect was not coupled with energy efficiencies (i.e. the value added generated per MJ of energy consumed), especially in relation to the agricultural sector. In this sector, the increase in labour productivity over time has been linked to a decreasing efficiency in the use of energy. This result is particularly worrying if we consider that the agricultural sector is generally the most dependent on the consumption of oil products, which are expensive (see Figure 5), with consumption in Veneto representing approximately 64% over total consumption of all energy sources. By contrast, the services and government sector was the most competitive in terms of labour productivity and energy efficiency. However, if compared with one of the most dynamic regions of Spain, Catalonia, Veneto shows very low GDP growth rates, together with a moderate growth in population. These two aspects characterize not only the Veneto region but the Italian economy in general. In this situation, the implementation of eco-efficiency strategies to reduce the energy dependence of the productive sectors (and therefore reducing production costs), together with the use of a larger fraction of renewable energy sources, especially in agriculture, should be seen by the Veneto government as a unique opportunity to enhance the competitiveness of the region and stimulate its economy.

For Abruzzo, the lower competitiveness of the productive sector as compared with Veneto, is also associated with a decreasing trend in energy efficiency for all the different sectors (see Figure 8). This result indicates that, even if Abruzzo has recently shown encouraging economic results as compared with other southern Italian regions, its development is not sustainable in the long run in terms of eco-efficiency. In the light of the energy reductions that the regions have to meet by 2020, Abruzzo will probably require structural changes and huge investments in all the productive sectors. Thus, while Veneto should consider a moderate increase in the energy efficiency of the productive sector in general, and of the agricultural sector in particular, in the case of Abruzzo larger structural changes and investments are needed for all the productive sectors. In the light of our analysis and given the imbalances in Italian development, we argue that the Italian Government should put more effort into developing specific measures to support the achievement of eco-efficiency goals for the less developed regions, necessitating huge restructuring interventions, such as in Abruzzo.

By employing the MuSIASEM approach we have shown how the socio-economic evolution of a particular system, coupled with its energy consumption, can be analysed from an integrated and multi-level perspective. This kind of analysis has been particularly useful here in highlighting dissimilarities in the eco-efficiency and socio-economic development of two distinct Italian regions, as well as in identifying potential local key barriers to the achievement of future energy targets. In future work, the MuSIASEM approach can be applied to all the Italian regions to provide a comprehensive picture of the socio-economic and biophysical characteristics of Italy in order to discuss energy actions and scenarios.

  • 1

    The level of capitalization refers to both the consumption and the production compartments. In the former it reflects the increase in the material standard of living of the household sector (Pastore et al., 2000), such as an increase in the use of home appliances. By contrast, the capitalization of the productive compartment reflects the investment in machinery and tools for the production of goods and services (Hall et al., 1986).

  • 2

    However, to better understand the underlying reasons for the drop in energy consumption in 2007, further analyses are needed, especially with regard to the industrial sector, which according to our analysis has been hit more by the consumption drop than agriculture, government and services (see level n-2 for a detailed explanation).

References

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Methodological and Empirical Framework
  5. Study Areas and Data Sources
  6. Presentation of Results
  7. Discussion and Concluding Remarks
  8. References
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