5 Feasibility and uses of the NAMEA-Type Framework Appliedat Local Level
Case Studies in North-Western Italy
Introduction
The National Accounting Matrix including Environmental Accounts (NAMEA) is currently applied at national level in most industrialized countries in compliance with the SEEA 2003 (Handbook of National Accounting: Integrated Environmental and Economic Accounting – UN, 2003) international standard. Environmental accounting at local government level cannot yet rely on a comparably established methodology. Its development is to be tracked to the Local Agenda 21 process and its aim tends to point to the practical understanding of environmental information for communicating the objectives and the results of local policies rather than to the suitability of the statistical standard for strategic planning and policy-making. This bottom-up approach has generated a number of different green budgeting schemes in different countries, the most important of which is probably the Eco-Budget method, developed by the International Council for Local Environmental Initiatives (ICLEI), a worldwide network of local government units that supports sustainable development initiatives. It consists of a budgeting system for natural resources that conforms to the existing financial budgeting procedures of local government and is based on environmental indicators that offer aggregate information already processed with the purpose of providing insights into the trend of given environmental issues or the effectiveness of specific policies (e.g. ambient concentration of a given pollutant, the quantity of municipal waste per inhabitant, the average age of circulating motor vehicles, and so on). The focus is typically on the municipality or local jurisdiction, the indicators to be included are subjectively defined according to the interests of local authorities and the temporal span generally includes a few sample years so that identification of the direction of the trend is possible.
At local level, the implementation of integrated environmental and economic accounts – that is, systems based on proper accounting schemes of the kind standardized in the SEEA approach, with a regular, long-term structure that integrates the pre-existing standard economic accounting – is still experimental and until now has only been experienced occasionally, in isolated contexts. It appears, however, to be the natural direction for the development of organic environmental accounts harmonized between national and sub-national levels. Policies for many environmental and natural resources are, in most countries, designed and implemented at sub-central levels of government. Municipalities, for instance, are often in charge of urban pollution-control policies and the control of land use and protected areas is often assigned at intermediate levels (regional/provincial). Making environmental accounting an operational tool not only for reporting results, but also for setting objectives and designing policies, requires detailed accounts of a kind that allows analysts to trace the origin of the emission or resource consumption by sector and sub-sector of economic activity. This ought to be compiled not only on a national but also on a local scale, and thus made available to local planners and decision-makers who have the responsibility of administering and regulating natural resources, local development actions and conservation policies (Dalmazzone and La Notte, 2009).
There is virtually still no experience of a comprehensive system of environmental accounting systematically extended to all levels of government. In several countries, however, environmental accounting modules based on some kind of rigorous accounting framework have been tested at local level. In Italy, the national statistical office (ISTAT) has tested the compilation of NAMEA for air emissions in all Italian regions.1 Within the European project GROW, five European regions have started to compile NAMEAs for air emissions. In the Netherlands, some applications related to water accounts have been implemented with respect to river basins. In Sweden, there has been work on regional accounts for the Stockholm area and several studies have been made at district level for water accounts. In Germany, the statistical offices of the Länders compile material flow accounts at regional level, and generally the results for the 16 Länders sum up to the results for Germany as a whole. In Canada, there are two NGOs producing environmental accounts at provincial level, but there is no coordination activity between the national statistical office and these organizations. The same occurs in the United Kingdom where a local NGO produces environmental accounts for Wales. In New Zealand, physical stock accounts for water are compiled by the central statistical office on both a national and a regional scale. In the Philippines, in the Cordillera Administrative Region and in the province of Palawan, all asset accounts compiled at national level are also compiled at regional and provincial levels.
There is no comprehensive research to date that compares the experiences of these different experimental applications and draws lessons and methodological implications. In this chapter, we present indications stemming from pilot applications of NAMEA accounts in the Piedmont Region (Italy) at regional, provincial and municipal level conceived with the specific purpose of testing the feasibility and reliability of integrated environmental and economic accounts at all sub-national levels of government. The aim is to show how to apply NAMEA at different scales, identifying criticalities and possible solutions, and checking to what extent the obtained results prove to be useful.2
General Features of a Local NAMEA-Type Account
In local environmental accounting, the data for filling both the economic and environmental components of NAMEA should be gathered locally: using proxies calculated at national level fails to serve the very purposes of compiling local environmental accounts. Data should in fact reflect the peculiarity of local contexts, and this does not happen when specific local values are averaged on a larger scale. The first step is therefore to investigate whether there are datasets at local level and whether they are suitable to be used for this purpose.
Local governments and agencies usually maintain accurate and in-depth environmental databases with locally gathered, bottom-up information. As far as economic data are concerned, national statistical offices generally produce statistics at national and sometimes regional levels. The only statistical data that systematically reach the level of municipalities are census surveys which usually take place only every ten years. However, data at sub-regional levels can be collected by integrating statistical databases with local administrative archives and other minor sectoral and business registers.
Even when available, local data must be precise, accurate and homogeneous to be useful. What should be known and checked is their sources,3 processing procedure,4 scale5 and timing.6 The methodological issues that arise when compiling the NAMEA-type framework at local level concern the lack of data at detailed territorial level and the fact that data may not be classified in an appropriate way to fill the accounting module.
In the case studies presented in the following sections, NAMEA for air emissions is compiled at regional and sub-regional levels and refers to the year 2005.
In Italy, economic data can be obtained from the register of active enterprises (named ASIA). The sources of ASIA are both statistical (derived from ISTAT) and administrative data (derived by chambers of commerce, the institute for social security, the revenue office, telephone subscriptions, banking and insurance institutes, and so on). The integration and harmonization of statistical and administrative data is undertaken by ISTAT in order to accomplish what is required by European legislation (Council Regulation (EEC) No. 2186/93). Thanks to ASIA, we can retrieve data on the number of employees and local units at regional, provincial and municipal level according to the NACE (Nomenclature générale des Activités économiques dans les Communautés Européennes, Rev. 1.1) classification from 2004 to 2007. However, data on value added and production are not available at present from ASIA although ways of calculating them using the ASIA input data are being tested (Faramondi, 2008). Primary and public sectors are also not currently included, but their inclusion is planned for the near future.
Environmental data can be acquired from the Regional Inventory of Air Emissions (IREA) maintained by the regional Environmental Protection Agency (EPA). Data are estimated according to the CORe INventory AIR emissions (CORINAIR) method, the framework supported by the European Environment Agency and adopted by the national environmental protection agency to compile the national inventory. IREA, however, retains a bottom-up focus: all the information on economic activities refers to a municipality or a geographically identified location. IREA records data according to the SNAP (Selected Nomenclature for Air Pollution) classification.7 This poses a reclassification problem: the SNAP process classification must be turned into NACE sector classification and emissions generated by natural processes have to be excluded. Reclassification implies a qualitative assignment when a correspondent SNAP production process is assigned to each NACE economic activity, and a quantitative assignment whenever the link to the process involves multiple economic activities and emission allocations must be estimated.8 In both qualitative and quantitative assignments, it is important to know how emissions were estimated for each SNAP process. To this end, cooperation must be established between the environmental accounting analyst and the statisticians or technical staff that built the inventory and have first- hand knowledge of territorial characteristics such as land use and the distribution of productive settlements.
Once the emissions have been attributed to each NACE activity, three indicators are calculated, as suggested in the NAMEA handbook (European Commission, 2009): greenhouse gases (based on ‘global warming potential’),9 acidification10 and tropospheric ozone (based on troposphericozone formation potential).11
In the next section, we are going to present an application of NAMEA at regional and sub-regional levels and discuss the information content that this accounting module can provide to local policy-makers.
Case Studies: Descriptive Analysis
A Comparison Between Two Main Northern Regions: Lombardia and Piemonte
The first application of NAMEA is tested at regional and provincial levels in two bordering regions in North-Western Italy, Piemonte and Lombardia. The application is then used to undertake comparisons at both horizontal level (i.e. between the two regions) and vertical level (i.e. between the region and one of its provinces). The two regions are characterized by well developed industrial sectors: in Piemonte, the automotive industry (the Fiat group and its induced activities) is the dominating compartment followed by chemical, food, textile, clothing, electronics and editorial compartments. The tertiary sector is also well developed with banking, insurance, trade and tourism. In Lombardia, the secondary sector is important in the mechanics, electronics, metallurgic, textile, chemical and petrochemical, pharmaceutical, food, shoes and furniture compartments whereas trade and finance dominate the tertiary sector.
The two regions show very similar characteristics and both host important capitals (Turin and Milan) where most of the population is concentrated. The data sources utilized for air emissions are IREA for the Piemonte region and INEMAR for the Lombardia region. Once data on pollutant emissions have been properly reclassified, the NAMEA-type framework is compiled and the greenhouse gases, acidification and tropospheric ozone indicators calculated.Table 5.1 shows the impact of each economic macro-sector in terms of each pollutant and the three aggregate environmental indicators.
Table 5.1 Impact of Air Emissions by Macro-Sectors in Piemonte and Lombardia, 2005 (%)
Some sectors have an almost identical impact in the two regions, but for some forms of pollution – consider, for instance, nitrous oxide (N2O), non-methane volatile organic compounds (NMVOC) and nitrogen oxides (NOx) – there also appear to be important differences. In order to understand why, the macro-sector (industry) must be broken down into more detailed economic activities and the origin of each pollutant traced.
N2O and NMVOC emissions in Piemonte are generated by the activities ‘Manufacture of other non-metallic mineral processing’, ‘Manufacture of pulp, paper and paper products, printing and publishing’ and ‘Manufacture of wood, rubber, plastics and other manufacturing’, respectively. NOx emissions in Lombardia are generated by the activities ‘Manufacture of basic metals and fabricated metal products’ and ‘Production and distribution of electricity, gas, steam and water’ (Table 5.2).
Table 5.2 NAMEA-type Emissions of some Economic Activities from the Secondary Sector in Piemonte and Lombardia Regions, 2005
NOx emissions in Lombardia are generated by the ‘Manufacture of basic metals and fabricated metal products’ and ‘Production and distribution of electricity, gas, steam and water’ activities (Table 5.3).
Table 5.3 Impact of Air Emissions by Macro-Sectors in Turin and Milan Provinces, 2005 (%)
However, if we consider data in absolute terms and not in terms of their percentage impact, Lombardia records emissions which are almost double those in Piemonte (Figure 5.1). The number of jobs in the secondary and tertiary sectors (thus excluding the public sector) in Lombardia is 4,013,655 units, whereas it is 1,434,764 units in Piemonte.
Figure 5.1 Pollutant emissions in Piemonte and Lombardia regions, 2005.
A comparison between the Turin and Milan provinces and their respective region reveals important differences (vertical comparison in Table 5.1 and Table 5.3), and so does a comparison between the two provinces (horizontal comparison in Table 5.3).
Contrary to what occurs at regional Piemontese level, in the province of Turin, the primary sector is not the biggest methane polluter; the service sector, and specifically the disposal of waste in landfills, is the one that contributes most to CH4 emissions. Similarly, the shares of N2O emissions ascribed to each sector vary substantially compared with that identified at regional level because of a non uniform spatial location of source activities among the provinces. In the province of Turin, for example, households emit more SO2 than the secondary sector.
In the province of Milan the agricultural sector is also no longer the main source of CH4 and the provincial NAMEA table highlights that households and the tertiary sector are responsible for a much larger share of the total impact, compared with the secondary sector, than at regional level.
A horizontal comparison of the two provinces reveals that the main differences concern emissions of CO2, NOx and SO2. Once again, the disaggregation of the secondary sector allows us to understand which activities are responsible for these differences.
Most CO2 emissions in the province of Turin are due to the ‘Production and distribution of electricity, gas, steam and water’ activity. This activity appears more efficient in terms of CO2 emissions in the province of Milan, where it is, however, the main emitter of SO2.
Total emissions by the secondary sector in the two provinces are more or less equivalent (top part of Table 5.4). However, the total emissions generated by household consumption are, for most pollutants, substantially higher in the province of Milan – between 25 per cent (CO2) and 160 per cent (NMVOC) higher –much more than justified by demographic variables alone (the province of Milan counts 3,170,000 inhabitants while the province of Turin has 2,295,000).
Table 5.4 NAMEA-type Emissions of some Economic Activities from the Secondary Sector in the Provinces of Turin, Milan, Alessandria and Cuneo, 2005
The above examples clearly show how critical information on a local scale may prove to be in order to correctly target pollution control policies. It helps decision-makers understand, for instance, whether they should act primarily on the production or on the consumption side, which sectors and productive compartments need to be targeted and at which jurisdictional level.
The choice, in this case study, of the provinces of Turin and Milan, the most important cities in Northern Italy in terms of economic activity and population density, hints at the importance of extending this exercise to a municipal level, particularly in view of the administrative, fiscal and management independence that metropolitan areas are in the process of acquiring in the decentralization and federalism process.
Inter-Provincial Environmental Accounts: NAMEA-Type Air Emissions Accounts in Cuneo and Alessandria12
While the first case study concerned provinces driven by the secondary and tertiary sectors, in this section we consider two provinces (both in the Piemonte region) where the primary sector is the leading economic activity.
The first province we consider is Alessandria. It covers a surface area of 3,560 km2 with a population of 413,000 inhabitants, and three-quarters of the territory is mountainous and hilly. The main economic activities are agriculture (herbaceous and woody crops) and mechanical, chemical and plastics processing. The second province we consider is Cuneo: the territory is mainly mountainous and covers a surface area of 6,903 km2 with a population of roughly 583,000 inhabitants (almost double the territorial dimension of the province of Alessandria, with about half the population density). The leading economic sector is agriculture (crops and pastures) followed by confectionery and dairy products.
Comparing the percentage polluting impact of economic macro-sectors in the two provinces (bottom part of Table 5.4) highlights that most of the CH4 emissions are due to agriculture in the province of Cuneo and to the service sector in the case of Alessandria. Within the service sector, this is due mainly to the presence of landfills and energy production. In addition, the module reveals that the percentage impact of polluting emissions in the case of Cuneo is much larger than the percentage impact at the regional level, not only for CH4 but also for N2O, PM10 and NH3, pollutants mainly due to the use of chemical fertilizers (Table 5.5). Figure 5.2 shows the amount of pollutants in absolute terms.
Table 5.5 Impact of Air Emissions by Macro-Sectors in Piemonte Region and Alessandria and Cuneo provinces, 2005 (%)
Figure 5.2 Pollutant emissions by macro-sectors in Alessandria and Cuneo provinces, 2005.
In the case of Alessandria province, the analysis shows the increase in emissions ascribable to the higher population density: more urban waste directed to landfills (CH4) and higher impact of transport (CO, CO2, NMVOC, NOx) when compared to Cuneo. A more detailed analysis of the impact from households (Figure 5.3) allows us to attribute around 30 per cent of NMVOC, NOx and PM10 emissions and 50 per cent of CO emissions to the transport sector. Transport and heating account for 70 per cent of emissions of these pollutants.
Figure 5.3 Household emissions in Alessandria and Cuneo provinces.
In the province of Cuneo, although the total ratio of CO emissions is almost equal to that of Alessandria, we can see how heating weighs more than proportionally if we consider the total population living in this province. This can be explained by the residential sprawl that characterizes the area: scattered housing requires more dispersive heating systems.
In Table 5.4, the NAMEA for the province of Alessandria disaggregated for the secondary sector shows high emissions of CO2 due to the use of combustion engines for the processing of cement; combustion processes using fossil fuels generate high emissions of SO2.
In Table 5.4, the NAMEA for the province of Cuneo disaggregated for the secondary sector shows high emissions of CO2 and SO2 generated by ‘Manufacture of other non-metallic mineral processing’, while the main sector in terms of NMVOC emissions is ‘Manufacture of wood, rubber, plastics and other manufacturing’.
A horizontal comparison within the Piemonte region of the three considered provinces (Turin, Alessandria and Cuneo) clearly highlights very different environmental issues and, equally importantly, a very different sectoral allocation of responsibility. Again, uniform policies designed on a regional scale on the basis of aggregate information can only be inefficient.
Intra-Provincial and Inter-Municipal Environmental Accounts: NAMEA-Type Air Emissions Accounts in the Municipalities of Robilante and Morozzo in the Province of Cuneo13
Let us now consider the municipal level. We present the case of two small municipalities within the province of Cuneo, similar in terms of surface area, about 20 km2, and number of inhabitants, about 2,000, but very different in terms of economic activity.
The development of Robilante has been driven by the industrial sector: a ceramic and a cement factory, processing the raw materials which come from an adjacent municipality. In addition it hosts a ski resort. The economy of the town of Morozzo, on the other hand, is based mainly on breeding (capons) and on the presence of a natural reserve. The two municipalities are thus very different from an economic, social and environmental point of view. The impact of emissions disaggregated by macro-sector for the municipalities of Robilante and Morozzo is shown in Table 5.6.
Table 5.6 Air Emissions by Macro-Sectors in the Municipalities of Robilante and Morozzo
The different productive structure is reflected in very different patterns of environmental impact: the municipality of Robilante records CO and CO2 emissions which are ten times higher than those of Morozzo, and NOx emissions which are 70 times those of Morozzo. On the other hand, Morozzo records high emissions of CH4 and NH3.
A more detailed analysis, disaggregating the secondary sector, confirms that most of CO, CO2 and NOx emissions in the jurisdiction of Robilante are generated by the compartment to which the ceramic and cement factories belong. The indicators reported in Table 5.6 turn out to be closely linked to ‘Manufacture of other non-metallic mineral processing’ that emits 98 per cent of greenhouse gases (CO2) and contributes to acidification and formation of trophospheric ozone with emissions of NOx and CO.
This third case study draws attention to the existence of local contexts, even within provinces, that may require targeted polices. Although the province of Cuneo is considered mainly agricultural when compared with other provinces such as Turin or Alessandria, there may still be municipalities within it dominated by a manufacturing sector which is the source of serious local impacts, as demonstrated in the case of Robilante. These contexts will require different environmental management and policies compared with the surrounding areas.
Further Analyses Enabled by Local NAMEA-Type Accounts
Shift-Share Analysis
The case studies presented in the previous section are based on very simple descriptive analyses that nonetheless may already represent a powerful information tool. More structural analyses are possible, even though at present the limiting factor of NAMEA-type analytic applications at sub-regional level is the lack of time series. Not all the analyses currently undertaken at national level in EU member states can be conducted on a sub-regional scale, but some can, and, in our opinion, with useful results. Here we propose the application of a vertical shift-share analysis for the Piemonte region, the province of Turin and the municipality of Turin14 for the year 2005.
Shift-share analysis has already been undertaken employing NAMEA outcomes (Mazzanti et al., 2007; Dosi et al., 2008; van Rossum and van de Grift, 2009; Bonazzi, 2009). The main policy issues that this kind of technique is supposed to address are those related to the analysis of economic growth/decline of an area, the state of a community compared to other communities, the economic sectors to be monitored or subsidized, and so on. Through shift-share analysis, the role of economic activities can be isolated and the gap between emission efficiency in the different sectors explained at different administrative levels. In this application, we compare the regional, provincial and municipal levels (instead of national and regional levels as is usually done) and we use the number of employees instead of value added in the calculation of emission intensity.15
Following the application proposed by Esteban (2000) we calculate three indicators: (a) the industry mix, which describes how specialized the economic system is in some economic activities; when negative, this indicator indicates that, at a sub-hierarchical level, the sectors that employ more workers are less polluting; (b) the productivity differential, which compares the efficiency of a sub-hierarchical level with the superior one; when negative, this indicator indicates that, at sub-hierarchical level, economic activities pollute less than at the higher hierarchical level; (c) the allocative component, which presents the contribution of sub-hierarchical levels to economic activity where the higher one shows a higher performance; when negative, this indicator indicates that the sub- hierarchical level is specialized in the economic activities that pollute less.
The composition of economic sectors in terms of workers employed varies substantially across the three administrative levels. In some activities the number of employees grows in relative terms from the regional to the municipal level (real estate, information technology, etc.), in other activities it decreases (manufacture of machinery) and in others it remains almost unchanged (e.g. wholesale and retail).
Sectoral emissions at the regional and provincial levels are related to the number of employees in Table 5.7. Here the analysis highlights how, in terms of emissions of CH4 and CO per unit of labour, the economic activities in the province of Turin are less efficient than those in the Piemonte region as a whole.
Table 5.7 Shift-Share Coefficients for the Economic System Region-Province, 2005
By comparing region-municipality (Table 5.8) and province-municipality (Table 5.9), the three indicators emerge as negative only for SO2. For all other pollutants the sub-hierarchical level does not prove to be more efficient when compared to the superior hierarchical level. This is probably driven by the choice of Turin as case-study province and municipality.
Table 5.8 Shift-Share Coefficients for the Economic System Region-Municipality, 2005
Table 5.9 Shift-Share Coefficients for the Economic System Province-Municipality, 2005
In the case of CH4 and CO neither the sector specialization nor pollutant emissions are ever efficient – we have a positive sign for the indicators ‘industry mix’ and ‘productivity differential’ in almost all the administrative levels considered (Table 5.7, Table 5.8 and Table 5.9). The economic activities in the municipality of Turin are not specialized in sectors with the highest environmental efficiency for CH4, CO and NH3 and are less efficient than the regional and provincial levels for the emissions of CH4, CO and NMVOC. Local decision-makers could act in several different directions: from promoting eco-efficiency techniques in the activities that play a leading economic role at municipal level to supporting the development of sectors that prove to be more environmentally friendly.
Linking Emissions and Concentrations through Chain Modelling
The compilation of NAMEA-type accounts entails working with local technical and policy units since this enables the analyst not only to understand the real demands of policy-makers and, based on those, to focus on data that provide an answer, but also to combine the proposed environmental accounting tools with local knowledge and expertise to develop practically implementable integrated tools.
Local authorities are likely to aim at acting on specific air pollutants. In this case, policy-makers should consider both emission sources and pollutant concentrations. The EU Air Quality Framework and Directives make it compulsory for member states to assess air quality within their territories. Air Quality Assessment is undertaken through local observation of pollutant concentrations at sampling sites, but jurisdictions are not fully covered by the sampling network. Local authorities therefore use air-quality models whose reliability and accuracy is verified by comparing modelling results with real measurements. In Piemonte, the regional environmental protection agency (Arpa) has developed a 3-D modelling system that simulates air pollution emissions, transport, diffusion and chemical reactions, and concentrations for the pollutants CO, NOx, SO2, PM10, PM2.5, O3 and benzene (C6H6). The simulation is based on a deterministic modelling system which consists of an emission processing system (named EMMA; see Arianet, 2005), a diagnostic meteorological model (named MINERVE; see Aria Tech. 2001), an atmospheric turbulence and dispersion parameter interface module (named GAP/SurfPRO; see Finardi et al., 2005) and a Eulerian chemical transport model (named FARM; see Cost728, 2006).16 The territorial units employed are municipalities and the results of simulation refer to grid cells. The common ground that allows us to compare the NAMEA-type outputs with the chain modelling results is the IREA database which constitutes the data source for both. Given the complexity of the processes that lead to pollutant concentrations, the relationship we can identify between emissions and concentrations is non-linear and the measurement units will not be the same. Maps showing where the most polluting source is located and where the highest concentrations are recorded can, however, provide local policy-makers with some input for planning. They enable, for example, seeing where the target does not have to be attained for each pollutant, which administrative level has to be involved and, especially at the borders between regions, where inter-regional cooperation should be sought.
Figures 5.4–5.7 allow us to visually compare emissions based on IREA and concentrations resulting from chain modeling for four pollutants (in 2005).17
Figure 5.4 Emissions (a) and concentrations (b) of NO2.
Notes
Emissions: tons per year.
Concentrations: yearly average of μg/mc.
Figure 5.5 Emissions (a) and concentrations (b) of NOx.
Notes
Emissions: tons per year.
Concentrations: yearly average of μg/mc.
Figure 5.6 Emissions (a) and concentrations (b) of PM10.
Notes
Emissions: tons per year.
Concentrations: yearly average of μg/mc.
Figure 5.7 Emissions (a) and concentrations (b) of SO2.
Notes
Emissions: tons per year.
Concentrations: yearly average of μg/mc.
The impacts of NO2 (Figure 5.4) and NOx (Figure 5.5) affect large areas around the emission source, and emission sources in one municipality may affect many provinces. Pollution control policies in one municipality, therefore, will have beneficial effects spilling over several other jurisdictions.
In addition, critical pollutant concentrations may be located in areas where there is no relevant emission source, as shown in the case of PM10 (Figure 5.6) and SO2 (Figure 5.7). Especially where more than one region is involved, citizens’ health protection will require inter-regional cooperation.
Information on the drivers of polluting emissions made available to local policy-makers would allow them to assess the relative merits of alternative policy options properly (promotion of eco-efficiency practices in production activities, enhancement of infrastructures and transport facilities, choice of sectors to be supported with incentives, and so on) and implement better targeted, cost-efficient and effective actions for protecting environmental quality.
Final Remarks
The range of information that can be obtained from the application of integrated environmental and economic accounts of the NAMEA type is large, and it is difficult to establish a priori which local administrative level is ideal for its implementation. Each administrative level, from regional to provincial and municipal, provides many inputs for useful horizontal and vertical analyses both in descriptive and structural terms.
The examples presented in this chapter show that hybrid accounts at local government level allow us to highlight crucial differences (for example, in terms of emissions per worker employed) between regions that may appear, from aggregate data, substantially similar. They provide information that can help decision-makers choose whether to direct environmental policies to producers or households. They draw attention to the territorial differences in terms of prevailing economic activities and consequent environmental impact, both among and within provinces. They identify spillovers between neighbouring jurisdictions.
NAMEA compiled at national level is obviously a precious tool for a number of uses, but the top-down estimates that can be derived from it cannot properly approximate the local information that can help policy-makers who act locally. The various territorial contexts are so different that only bottom-up analyses on a local scale, we argue, can provide detailed and reliable information for local planners.
Structural analytical techniques can be useful for investigating the spatial production structure and help effective policies for local development and environmental protection.
The validity of the assessment tools provided by environmental accounting can be further strengthened by combining them with knowledge and expertise of the technical units working in closer contact with the territory, in local government environment departments and environmental protection agencies.
There are still many shortcomings in the application of NAMEA-type accounts at local level: database incompleteness (ASIA does not provide value added and production data), lack of time series (IREA is only available for a few years so far and ASIA has only been available since 2004), non-automation of the compilation process (mainly because in some phases local technical units must provide information in order to make meaningful assignments of emissions to sources). The advantages in terms of informational content of the output, however, turn out to be potentially extremely interesting for the design, management, implementation and assessment of environmental policies at local level, since the subsidiary approach applied to environmental policies at the EU level assigns the large part of competences and responsibilities to local authorities.
Notes
1 See www.istat.it/dati/dataset/20090401_00.
2 All data and accounting modules that have been omitted from the chapter due to space contraints are available from the authors on request.
3 Source of data should specify whether the responsibility of providing the information lies with a public institution, a voluntary based initiative, a private organization and so on and the purpose for which data are collected.
4 Processing procedures describe the way data are collected, classified, processed and stored and they have to be transparent.
5 Aggregating small consistent units in order to obtain a larger administrative/territorial unit is possible and advisable, but the disaggregation from a larger scale aimed at estimating values for smaller units exposes results to several criticisms.
6 Sources must be able to provide up-to-date data and to maintain a regular, adequate periodicity.
7 It includes 11 macro-sectors, 75 sectors and 430 activities and it is available for the years 2001, 2005 and 2007. The pollutants recorded are methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (N2O), ammonia (NH3), non- methane volatile organic compounds (NMVOC), oxides of nitrogen (NOx), sulphur dioxide (SO2) and particulate matters (PM10).
8 For more details on the methodology, see Dalmazzone and La Notte (2009).
9 The formula for calculation of Global Warming Potential is CO2*1000 + 310 *N2O + CH4*21.
10 The formula for calculation of acidification is SO2 + 0.7*NOx + 1.9*NH3.
11 The formula for calculation of tropospheric ozone formation is NMVOC + 1.22NOx + 0.11*CO + 0.014*CH4.
12 Data collected and partially processed by Diano, Locatelli and Roatta, University of Turin.
13 Data collected and partially processed by Belgero, Fregnan and Nada, University of Turin.
14 The following is an extract from a previously published case study (La Notte et al., 2009).
15 We cannot use value added because data are not available in the ASIA dataset. Previous studies (e.g. Harrison and Antweiler, 2003; Hettige et al., 1995) have used the number of employees as an acceptable proxy for similar calculations.
16 For further details, see Arduino et al. (2007) and La Notte et al. (2009).
17 The software used is ArcGIS 9.3 and for the visual representation we adopt the natural break classification criteria in five classes.
References
Arduino, G., Contardi, C., Bovo, S., Bande, S., Clemente, M., Calori, G., De Maria, R., Finardi, S., Muraro, M. and Silibello, C. (2007) ‘Utilizzo dei GIS nell’ambito della Valutazione sulla Qualità dell’Aria’, paper presented at Atti 11° Conferenza Nazionale ASITA, Centro Congressi Lingotto, Turin, 6–9 November.
ARIANET (2005) EMMA (EMGR/Make) User’s Guide Version 3.5, Rapporto Arianet R2005.08, Milan.
ARIA Technologies (2001) MINERVE Wind Field Model General Design Manual, ARIA Tech. Report, Paris.
Bonazzi, E. (2009) ‘Emissioni di gas serra ed eco-efficienza: il vantaggio dell’Emilia-Romagna’, Arpa Rivista, 1: 48–49.
COST728 (2006) ‘Cost 728/732 model inventory’. Online: www.mi.unihamburg.de/cost-modin.
Dalmazzone, S. and La Notte, A. (2009) ‘Environmental accounting at different levels of government: the state of the art’, in A. Breton, G. Brosio, S. Dalmazzone and G. Garrone (eds), Governing the Environment: Salient Institutional Issues, Cheltenham: Edward Elgar.
Dosi, M.P., Bonazzi, E. and Sansoni, M. (2008) ‘Progettare la sostenibilità nello sviluppo di un territorio: l’analisi Shift-Share su aggregati economico-ambientali’, paper presented at the XXIX Conferenza Italiana di Scienze Regionali, Bari, September.
Esteban, J. (2000) ‘Regional convergence in Europe and the industry mix: a shift-share analysis’, Regional Science and Urban Economics, 3: 353–364.
European Commission (2009) Manual for Air Emissions Accounts, KA-RA-09-004-EN, Brussels: European Commission.
Faramondi, A. (2008) ‘Valore aggiunto comunale: integrazione tra fonti e approccio bottom-up’, Rivista Internazionale di Scienze Sociali, 116: 179–209.
Finardi, S., De Maria, R., D’Allura, A., Cascone, C., Calori, G. and Lollobrigida, F. (2005) ‘A deterministic air quality forecasting system for Torino urban area’, Environmental Modelling and Software, 23: 344–355.
Harrison, K. and Antweiler, W. (2003) ‘Incentives for pollution abatement: regulation, regulatory threats, and non-governmental pressures’, Journal of Policy Analysis and Management, 3: 361–382.
Hettige, H., Martin, P., Singh, M. and Wheeler, D. (1995) ‘IPPS: The Industrial Pollution Projection System,’ World Bank, Policy Research Department Working Paper, February, Washington, DC: World Bank.
La Notte, A. (2008) ‘Integrated environmental and economic accounts applied at local level: Results of NAMEA flow accounts at sub-regional level in Piemonte (Italy) – air emission origin and destination through an atmospheric modeling system’, Proceedings of the 10th Biennial Conference of the International Society for Ecological Economics, Nairobi, Kenya, 7–11 August.
La Notte, A., Arduino, G., Sordi, F. and Truffo, G. (2009) ‘La contabilizzazione di emissioni e concentrazioni degli inquinanti in atmosfera utilizzando conti ambientali e chain modeling’, Economia delle Fonti di Energia e dell’Ambiente, 3.
Mazzanti, M., Montini, A. and Zoboli, R. (2007) ‘Struttura produttiva territoriale ed indicatori di efficienza ambientale: un’analisi attraverso la NAMEA regionale’, Economia delle Fonti di Energia e dell’Ambiente, 1: 35–64.
United Nations (UN) (2003) SEEA: System of Integrated Environmental and Economic Accounting (an Operational Manual), New York: United Nations Publications.
Van Rossum, M. and van de Grift, M. (2009) ‘Regional analysis: differences in emission intensity due to differences in economic structure or environmental efficiency?’ Journal of Sustainable Development, 2: 43–56.