4 Development and Use of a Regional NAMEA in Emilia-Romagna (Italy)
Introduction
The development and use of environmental accounting tools in Emilia-Romagna Italy starts by considering the need to integrate conventional economic indicators when drawing up sustainability reports and monitoring the effects of regional policies. So far, this study has been focused mainly on RAMEA (a regional NAMEA), its updating and related developments. Research activities have focused on two main fields: the extension of the framework to new environmental issues and the possibility of updating data and the use of RAMEA as a tool for regional environmental reports and environmental assessments of regional plans.
This chapter attempts to show the structure of the RAMEA matrix as developed in Emilia-Romagna and introduce two further developments: the results of an application of a statistical analysis (shift-share) and the extensions made with environmental taxes, industrial waste production and energy consumptions. The strategic aim is to support policy-making, also at regional level, providing an evidence base for sustainable policies and thus integrating sustainable development concerns at all levels.
There has been increasing worldwide interest in developing a broader set of statistics that gives values to aspects left outside the traditional economic system. Countries and governments need to develop a more comprehensive view of progress rather than focusing mainly on essential economic indicators such as gross domestic product (GDP). Non-market factors like environmental externalities are not counted in the GDP and conventional economic indicators. From international to local scales, there is a growing emphasis on ‘evidence-based policy-making’ which needs better measures of the current outcomes of programmes and policies, thus requiring statistical and analytical approaches that go beyond national borders and the conventional reporting system.1 The Revised European Strategy for Environmental Accounting (ESEA; Eurostat, 2008) will help to ensure the availability of important environmental accounts data from all European countries and will enable these data to be harmonized, timely and of adequate quality in order to facilitate their use in developing and informing policy. In addition, the ESEA Task Force recommends that the priority for environmental accounts focuses primarily on physical and monetary flows including hybrid accounts such as NAMEA,2 economic information on the environment and economic activities and products related to the environment and other environmentally related transactions such as taxes and subsidies.
State of the Art of RAMEA Matrix in Emilia-Romagna
If we consider GDP Emilia-Romagna is one of the richest regions in Italy. Nevertheless in Emilia-Romagna, as in many other developed regions, there is a critical growth of pollutant emissions (in particular greenhouse gases – GHG); transport, industries, agriculture and residential consumptions are the main drivers of this growth. To tackle these problems, a low-carbon and green economy is included in the new Regional Development Strategy. The research activities of compilation and development of a Regional NAMEA (RAMEA) matrix in Emilia-Romagna starts with the European project ‘RAMEA – Regionalized NAMEA-type matrix’ financed by the INTERREG IIIC Program 2005–2007 under the Regional Framework Operation ‘GROW’. The project, promoted by the Emilia-Romagna Region and led by Arpa Emilia-Romagna (the Regional Environment Agency), involved seven partners from four European regions, who cooperated for two years to build four regional NAMEAs: Emilia- Romagna in Italy, South-East England, Noord Brabant in the Netherlands and Malopolska in Poland (Bonazzi et al., 2008; Sansoni et al., 2010). The main outcomes of this project in Emilia-Romagna3 are the following: two RAMEA air emission account matrices extended with input–output tables (for 1995 and 2000); a guideline book describing the compiling process and regional case studies (RAMEA, 2007); a shift-share analysis built on the year 2000 matrix (Dosi et al., 2008). In detail, the RAMEA project can be considered the first example of four EU regions that work together to build a regional NAMEA by following a shared methodology and improving a knowledge base for regional sustainable development policies: the regional scale for economic-environmental accounting seems to demonstrate a crucial role in building a pathway for sustainable development.
Based on these premises, the development of RAMEA matrices is encouraged by the European Commission Environment DG (European Commission, 2008) and now funded in Emilia-Romagna by the regional government. The research activity of Arpa Emilia-Romagna focuses on two main fields: the extension of the framework to new environmental issues and the possibility of updating data and the use of RAMEA as a tool for Regional Environmental Reports and Environmental Assessments.
Linking environmental and economic indicators could encourage and facilitate the involvement of the decision-makers who are likely to be more familiar with socio-economic concepts, but who are going to pay an increasing amount of attention to the effects of economic activities on the environment.
Since application to policies is a fundamental requisite for environmental accounting tools that aspire to be more than just a mere compilation of data, RAMEA has been conceived as ‘a multi-purpose information system which is able to inform the public and policy-makers about the status quo of the environmental assets and environmental pollution’, useful for organizing and analysing economic and environmental data in relation to policy objectives (Goralczyk and Stauvermann, 2007).
RAMEA is based on an internationally accepted methodology (UN, Eurostat), reliable data (official statistical accounts) and standardized systems (SEEA, SNA and ESA). These conditions ensure its consistency with similar tools at national level (NAMEA). The economic activities follow NACE classification and the Household categories COICOP nomenclature. The RAMEA framework proposed for Emilia-Romagna is shown in Figure 4.1.
Figure 4.1 RAMEA framework (Arpa Emilia-Romagna).
RAMEA could be scheduled for different kinds of analyses to explore some of the possibilities that this type of tool offers to regional planning/reporting (e.g. monitoring regional economic performance, air emissions and eco-efficiency, comparing regional eco-efficiency with national eco-efficiency and understanding the indirect effects/responsibilities of production and consumption chains on the environment).
Shift-Share Analysis
If we consider RAMEA for the year 2000, the intensity of emission of GHG in the regional economic system, compared with the national average, has been analysed. The indicator ‘intensity of emission’, explained as the ratio between GHG emissions and value added, has been used as a measure of eco-efficiency.4
By means of shift-share analysis, the role of the productive structure as a cause in the average gap between Emilia-Romagna and Italian efficiency of emissions has been isolated and quantified and a measure of the role of the specific efficiency of emissions of productive fields obtained in a complementary way (Bonazzi et al., 2008; Bonazzi and Sansoni, 2008; Dosi et al., 2008; Sansoni et al., 2010). The approach on how to derive and analyse the shift-share signs, focusing on pollution-related issues, follows Mazzanti et al. (2007) and Maz zanti and Montini (2009). The choice of this methodology derives from a search for effects and factors that explain the relative eco-efficiency of Emilia-Romagna, compared with Italy, which could be shown in a more exhaustive way than a descriptive statistic analysis (the differences between the regional and national indicators of intensity of emission). The deviation matrix between the regional and national average, generated by a descriptive statistic analysis, can be investigated by application of shift-share analysis to carry out detailed considerations on these differentials (Table 4.1). In this study, the total differentials of efficiency for GHG do not remain in favour of Emilia-Romagna for every sector since otherwise there would be an advantage for the whole regional economic system when compared with the national one. It is explained by both an industry mix effect and a differential one.
Table 4.1 Shift-Share Analysis Applied in Emilia Romagna: simplified Matrix 2000. (Mg CO2eq/Meuro); 
Source: own calculations.
The regional average intensity of emission (Xe) for GHG is the summation of sector intensity of emission (Xse), weighted for the sector ratios of the total value added (Pe). The national average intensity (X) is defined in the same way. The region can show a total higher or lower intensity of emission compared with the national average caused by the combination of the three shift-share effects: Industry mix (me), Differential (pe), Allocative (ae). The total difference between regional and national average intensity of emission equals the summation of the three effects 
. The Industry mix estimates the part of higher/ lower intensity of emissions that is due to the sector structure of the economic system. The difference between regional and national average intensity of emission could depend on differences in the specific intensity of emissions of some or all considered fields marking out the Differential effect. Finally, the Allocative component adds further analytic information: the covariance between sector structure (assuming parity of efficiency) and difference between sector intensity of emission (assuming parity of sector structure) indicates how much and if the system has a productive specialization in the fields where it carries out a comparative advantage of efficiency. This is reflected in the interpretation of differential between Emilia-Romagna and Italy; therefore, if Xe − X > 0, Emilia-Romagna is relatively less efficient (i.e. produces more emissions for unit of value added than the national average). The same is true for the signs of the three effects: when they are algebraically negative, they mark an advantage of efficiency for the Emilia-Romagna region. These effects show influences deriving from the sector structure and from ‘the history of development’ of the economic system, or may refer to the average state of productive technologies (and of emissions) in the region compared with the national average. For example, a higher value of regional intensity of emission may only be due to productive structure reasons in terms of sectors in which an environmental policy does not directly have a substantial influence; instead, it could have a greater chance of action if the relative total regional inefficiency were due to specific environmental inefficiency of the sectors caused by the technologies used or by inefficient public regulation. As a result of this reasoning and processing, a pilot Decision Support Matrix is proposed as an aid to policy-makers: it shows the scenarios, depending on the possible combination of shift-share effects and identifies strategies for sector policy.
Extension to New Environmental Issues
The idea for an extension is based on the ESEA (Eurostat, 2008) and the European Commission proposal on environmental economic accounts (2010) which suggest developing data collection in the areas of energy, waste accounts and environmental taxes. The release of 2005 regional NAMEA air emission accounts for Italian regions by ISTAT (Italian National Statistics) is thus taken as a robust base for studying the opportunities of extending the framework to new environmental issues: (a) environmental taxation scheme, by downscaling national statistics data; this research also investigates the use of eco taxes (which have long been an instrument used to boost the behaviour change of citizens by giving monetary values to negative externalities on the environment) coordinated with RAMEA; (b) energy consumptions of industries and households by processing regional data provided by ENEA (Italian National Agency for Energy) and TERNA SpA (Italian company responsible for electricity transmission); (c) waste production of industries, using regional datasets by Arpa Emilia-Romagna.
The extension methodology follows two main steps, applied to the link between the RAMEA (which uses NACE and COICOP codes) classification system and the other available datasets: (a) the analysis of the qualitative link; (b) the quantitative allocation of data.
While with the first step we can make a distinction between one-to-one and one-to-several correspondence (between environmental data and RAMEA categories), in the second one, we have to quantitatively assign data from environmental issues to RAMEA categories. In the one-to-one correspondence, the allocation is easy (100 per cent of the environmental data goes to the corresponding RAMEA category) whereas in the one to several link, the use of proxy variables (e.g. value added, CO2 emissions) is fundamental for splitting the value of environmental data between the several RAMEA categories.
Environmental Taxes
Following EU directions, environmental taxes5 have long been an instrument for boosting the behaviour change of citizens by giving monetary values to negative externalities on the environment, such as polluting, and also by increasing the costs of certain products which have a negative impact on the environment and an instrument for adjusting revenues in national budget spending or reducing other taxes. European efforts, such as the Lisbon Strategy, emphasize that environmental taxes are an important tool, not only for the protection of the environment but also for competitiveness and growing economies. The green tax reform6 should lead to decreasing labour taxes and more weight being placed on environmental taxes. Environmentally related taxes can often be usefully implemented in the context of instrument mixes in combination with other policy tools such as command and control regulations, tradable permits, voluntary approaches and environmental accounting tools. Of the environmental policy tools, environmental taxes are considered to be environmentally effective and economically efficient. The OECD has supported the use of these tools and has carried out an analysis of their implementation (2001). The Sixth Community Action Programme on the Environment, approved in 2002, and the European Commission’s Green Paper (2007) recommend the use of economic instruments (energy taxes, taxes on resources) to mitigate climate change and promote sustainable use of resources.
Regional data for environmental taxes are not available in Italy yet, but Eurostat and ISTAT provide environmental taxes split up into economic activities and household consumption (following NACE classification) at national scale.7 In Italy, three kinds of environmental taxes are now available: energy taxes, pollution taxes and transport taxes. In particular, the CO2 taxes are included under energy taxes rather than under pollution taxes, and the second one includes taxes on measured or estimated emission to air and water and management of solid waste.8
In order to build a RAMEA matrix integrated with eco-taxes, regional eco-taxes had to be estimated by downscaling the national data: value added and household consumptions were identified as good proxies to perform the analysis.
A very good statistical correlation (0.94) was obtained between total regional and national values added (historical series 2000–2006) and an excellent correlation (0.96) between regional and national household final consumption (Bonazzi et al., 2009).
Using the above findings, the three environmental taxes available at national level (energy, pollution, transport) were downscaled at regional level and split up in economic activities and household using the following formulas:
for economic activities, where ETER,I is the regional environmental tax for the i-th sector, VAER,i is the regional value added for the i-th sector, VAIT,i is the national value added for the i-th sector and ETIT,i is the national environmental tax for the i-th sector and
for household, where ETER,H is the regional environmental tax for household, HER is the regional household consumption, HIT is the national household consumption and ETIT,H is the national environmental tax for households.
As explained above, thanks to the high statistical correlation verified among regional and national value added and household consumptions, we downscaled eco-taxes at regional level referring to these proxies. So it is important to remark that the outcomes are estimations. Therefore, attention when evaluating the quality of numbers obtained is strongly recommended; in this case the attempt is essentially to show the structure of an environmental economic tool, on a regional scale, useful for monitoring, controlling and addressing the effects of environmental fiscal policies. It is important to take care of the structure proposed and the relevance in addressing statistical offices to provide data at local scale in order to support sustainable local policies.
Industrial Waste Production
Some interesting experiences already exist in Italy concerning the application of the NAMEA approach to industrial waste production (Dalmazzone and La Notte, 2009). The integration of RAMEA with the production of industrial waste is effected using data provided by the regional thematic centre for integrated management of waste (Arpa Emilia-Romagna). Data on industrial waste production is collected annually from waste producers using specific surveys (MUD – declaration of industrial waste production) and collected in a regional database (waste cadastre). Even if the data collected by MUD has some limitations (the use of MUD could lead to an underestimation of the actual amount of total waste generated, taking into account that not all manufacturers are obliged to submit the MUD declaration, not all types of waste have to be declared and a number of individuals do not fulfil the obligation to submit the declaration), it should be noted that they are to be considered official data. Thanks to the availability of production of industrial waste already divided into NACE codes, the processing of data for RAMEA purposes is easier than for other environmental issues and all correspondences fall into the one-to-one category.
Energy Consumption
Integration with the energy consumption issue is performed using data from regional energy balances (provided by ENEA for overall energy consumptions in toe – tons of oil equivalent – and by TERNA SpA for electricity consumptions in GWh). TERNA is the electricity transmission system operator and annually analyses electricity consumption according to different types of users at national, regional and provincial level. The TERNA classification is a modified version of NACE classification and therefore one-to-one correspondences to RAMEA sectors (and electricity consumptions in GWh) can be easily found. ENEA is the Italian National Agency for New Technologies, Energy and Sustainable Economic Development and, among its other duties, is responsible for drawing up national and regional energy balances. The energy balance contains information on supply, transformation and use of different energy sources (solid, liquid and gaseous fuels, renewables and electricity in toe) in four macro-sectors (Agriculture and fishing, Industry, Residential and Transportation). Each macro-sector is then divided into sub-sectors. The ENEA classification slightly differs from NACE in that most of the correspondences are one-to-one, but some one-to-several can be found.
First, the ENEA Textile sector energy consumption is split into DB and DC NACE codes using the following formulas:
where ENDB and ENDC are the energy consumption in toe for DB and DC regional sectors, VADB and VADC are the regional value added for DB and DC sectors and ENtextile is the energy consumption in toe of the Textile sector as reported in the ENEA regional energy balance.
Second, energy consumptions from ENEA sector Transportation has to be distributed to all NACE and COICOP activities (and summed to the energy consumption already distributed). In a first estimation, RAMEA data on CO2 emitted by each NACE and COICOP codes are selected as a proxy variable to distribute energy consumption using the following formula:
where ENtransportation,i is the quantity of energy consumption in toe from ENEA Transportation sector to be distributed to the RAMEA i-th sector (when i covers all NACE and COICOP codes used), CO2i is the tons of CO2 emitted by the i-th sector of RAMEA, CO2 is the total emission from all RAMEA sectors and ENtransportation is the energy consumption in toe of the Transportation sector as reported in the ENEA regional energy balance.
Using the above assumptions, a first draft of RAMEA air emissions extended to environmental taxes, industrial waste production and energy (electricity plus total energy) consumptions can be drawn up (Table 4.2). The structure of RAMEA can be used to identify the different contributions of economic sectors and households to the economy and the environment. If values are calculated as a percentage of the total, it becomes immediately obvious how much each sector contributes to the economy and to environmental pressures respectively (Figure 4.2).
Table 4.2 RAMEA Air Emissions Extended to Eco-Taxes, ind. Waste Production and Energy Consumptions (2005)
Legend
VA: value added basic prices (source: ISTAT); FIN CONS: final consumption (source: ISTAT); FTE: full-time equivalents (source: ISTAT); GHG: greenhouse gases (source: ISTAT); ACID: acidification (source: ISTAT); NOx: nitrous oxides (source: ISTAT); PM10: particulate matter (source: ISTAT); ETAX – EN: environmental taxes – energy (source: own calculations on Eurostat data); ETAX – POLL: environmental taxes – pollution (source: own calculations on Eurostat data); ETAX – TR: environmental taxes – transport (source: own calculations on Eurostat data); IND WASTE: industrial waste (source: Arpa Emilia-Romagna); ELECTRICITY: electricity consumptions (source: own calculations on TERNA SpA data); ENERGY: total energy consumptions (source: own calculations on ENEA data).
Figure 4.2 Contribution of different sectors to the economy and the environment, (2005, %).
Conclusion
In the long term, the overall aim of this study is to outline RAMEA as a policy tool to support sustainable regional policies and possibly environmental assessments of regional plans and programmes (the most important being the Regional Territorial Plan). In this context, the opportunity of building a scenario analysis based on the extended RAMEA should be studied and regional sustainable development steered by means of a more complete environmental accounting system.
The pilot extended RAMEA needs to be updated, using the most appropriate economic and environmental data sources: the air emission accounts issue, in particular, could be built using data coming from the regional air emissions inventory by Arpa Emilia-Romagna and following Eurostat guidelines. The availability of up-to-date RAMEA matrices will enhance the opportunity to study the integrated economic-environmental performances of the region and possibly answer important policy questions, as highlighted by Eurostat (2009).
Summing up, RAMEA framework, in its most recent comprehensive version and in its future developments, could be regarded in the Emilia-Romagna region as: (a) a monitoring system which analyses the pressure placed on the environment by the economic sectors and households, helps to identify the ‘hot spots’ in terms of environmental pressures and potential decoupling patterns, allows processing of eco-efficiency indexes, uses the knowledge base on the economic and environmental performances of regional sectors and enforces the role of policy tools in promoting sustainable behaviour(e.g. regarding eco-taxes ‘to make the polluter pay’); (b) a tool that allows scenario analysis to evaluate the economic-environmental effects of the policies; (c) a benchmarking tool that can be used to compare European regions and countries; (d) an evaluation tool that helps to assess policy effects on the economic system, identify which are the most efficient (eco-efficient) sectors in the region and, together with an input–output matrix, could be helpful in verifying environmental-economic inter-relations between the sectors; (e) a motivating tool that should strengthen the final goal of environmental taxes by creating incentives for producers and consumers to move away from environmentally damaging behaviour; thanks to RAMEA, environmental taxes could also be applied more efficiently in the long term, by acting in proper economic sectors.
Acknowledgements
The authors are particularly grateful for their suggestions and data provided to: Paolo Acciari (Ministero dell’ Economia e delle Finanze), Cecilia Cavazzuti, Barbara Villani, Giacomo Zaccanti (Arpa Emilia-Romagna), Massimiliano Mazzanti (University of Ferrara), Anna Montini (University of Bologna), Martina Ruffilli (University of Bologna student), Angelica Tudini (Istat). All errors are our own.
Notes
1 See Kennedy (1968), Hall (2005), Matthews (2006), Almunia (2007), Commission of the European Communities (2009), Stiglitz et al. (2009), the Beyond GDP International Conference (2007; www.beyond-gdp.eu) and the Global Project on Measuring the Progress of Societies – OECD (2008; www.wikiprogress.org/index.php/Global_Project).
2 Communication COM (94) 670 (Commission of the European Communities, 1994).
3 The application in Emilia-Romagna benefited from previous pilots of regional NAMEA for two Italian regions, Toscana (by IRPET – Tuscan Regional Institute for Economic Planning) and Lazio (by ISTAT – Italian National Statistics), together with the compilation of national and regional NAMEA for Italy by ISTAT.
4 The term eco-efficiency was coined by the World Business Council for Sustainable Development (WBCSD) in its 1992 publication ‘Changing Course’. It is based on the concept of creating more goods and services while using fewer resources and creating less waste and pollution. Following this indicator, the quantity of emissions is related to the gross value added of an economic sector. The lower the indicator, the more eco-efficient the sector; additionally, regional eco-efficiency can be compared with the overall eco-efficiency of the country or with other national or foreign regions. Policy-makers are able to use these indicators to find which sectors of the economy should increase their eco-efficiency.
5 Eco-taxes are those whose tax base has a proved harmful effect on the environment, e.g. a process or product which pollutes the environment. The aim of environmental taxes is to internalize external environmental costs by focusing on limitation of environmental burden and responsible use of natural resources by producers as well as consumers. They are divided into the following categories: energy taxes, transport taxes, pollution taxes, resource taxes.
6 www.eea.europa.eu/highlights/green-tax-reform-can-boost-eco-innovation-andemployment.
7 http://epp.eurostat.ec.europa.eu/portal/page/portal/environmental_accounts/data/database.
8 See Eurostat (2001).
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