Ecological Indicators 2 (2002) 197–210
A system oriented integrated indicator for sustainable development in Italy E. Ronchi a,∗ , A. Federico b,1 , F. Musmeci b a
b
Istituto per lo Sviluppo Sostenibile Italia (ISSI), via dei Laghi 12, Rome 00189, Italy ENEA, Agenzia Nazionale per lo Sviluppo Sostenibile, Igt. Thaon de Revel 72, Rome 00169, Italy
Abstract The scientific community currently deals with the challenge of measuring sustainable development by using long lists of core set indicator. These are then used in one of two ways: either shortened sets of headline indicators are selected (these vary by sector or theme), or an aggregation procedure is adopted that allows the creation of unique integrated indices, such as the well known ISEW, HDI, PPI. Recent work has demonstrated that most countries prefer to develop their own approaches over and above the accepted international standards, in some cases adapting these standard approaches, whilst seeking to strike a delicate balance between, on the one hand, globally accepted environmental and socio-economic indicators and, on the other, national and local peculiarities. The imperative in such cases is to avoid losing international comparability. The choice must take account of the varying space and time scales of each country with regard to sustainable development, as well as the role of the national policymakers in selecting the approach to sustainability. This paper introduces the ISSI index which is based on three baskets: welfare, environmental quality and resource use each one defined by a limited set of headline indicators. A combination algorithm is then proposed which gives a global, integrated non-dimensional index, which allows comparisons between the Italian regions as well as between countries. A multidimensional vector presentation tool is introduced that allows the trends of each index component, as well as the index as a whole to be monitored. The index sections relating to all of the three components require a system approach based on models rather than on simple groupings and combinations of variables. Modelling introduces the capability to manage quantitative indicators together with non-numeric evaluations of quality of life. The resource use component makes use of the material flow model to evaluate the overall performance in the area of production and consumption in Italy by assessing eco-efficiency and by calculating total material input and resulting waste generation. © 2002 Elsevier Science Ltd. All rights reserved. Keywords: Sustainability indicators; National sustainability; Indices aggregation; Sustainable development; System dynamics models
1. Introduction Since the Rio de Janeiro UNCED in 1992, the international debate, the impressive initiatives provided ∗ Corresponding author. Fax: +39-6-84-186-21. E-mail addresses:
[email protected] (E. Ronchi),
[email protected] (A. Federico). 1 Fax: +39-6-30-486-410.
by institutions and NGOs and the large contribution of scientific research work to the problem of developing indices for the health of the environment and for the sustainable development have been so extensive that it would be wholly inappropriate to attempt to summarise them in this paper. Nevertheless, no contribution could be made nowadays to a further implementation of sustainable development indicators without a solid knowledge of the whole framework of
1470-160X/02/$ – see front matter © 2002 Elsevier Science Ltd. All rights reserved. PII: S 1 4 7 0 - 1 6 0 X ( 0 2 ) 0 0 0 4 5 - 6
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current scientific understanding and of projects that are taking place throughout the world. This will be the assumption for the purposes of this paper. Indicators are not necessarily numbers; in many cases they can be informational signs or labels. They generally do not increase our understanding of what is going on, but we certainly trust them when taking certain decisions or for planning actions. “Green” for a traffic light or “beautiful” for a landscape are very common descriptions of familiar phenomena. However, to know the state of pressure on potential climate changes, we have no choice but to carefully measure the GHG emissions and concentrations in hard physical units. These are examples, respectively, of qualitative, subjective and instrumental variables, all of comparably great importance for our lives (UK, 1999). So we have had to adapt our formal languages, mathematics, statistics or logic to manage and combine attributes of all the processes relevant to us, regardless of how we have chosen to measure them. For the purposes of this study, we have formulated all the algorithms relevant to our application. This is not to say that the complexity of the human and environmental processes can be simplified or reduced by the use of numbers, colours or qualities. A profound understanding and the ability to model the processes involved in sustainable development mathematically (i.e. formally), cannot be achieved without an adequate research effort. Not all of us need to develop a deep understanding of many and varied complex phenomena. We do, however, need appropriate information to drive our decisions. It is this aid to common judgement that is the prime role of indicators. The vexed question of whether the availability or otherwise of indicators is indeed a bottleneck has been the subject of considerable controversy. It is our contention that we cannot develop a strategy for sustainable development without a quantitative knowledge of the state of the system, a quantitative exploitation of the targets and a quantitative assessment of their implementation. In such a complex area as this, there are necessarily a multitude of indicators which cannot be collected without a coherent strategy. However, we must be wary of misuse of these indicators as the evidence provided even by a common indicator could potentially influence private and public decisions. This latter phenomenon is seen on a daily basis in the role of the information in modern democratic
societies (particularly in politics!) (Ronchi, 2000). Over-simplified information may therefore be just as misleading as publishing inappropriate indicators or appropriate indicators with false values (Bossel, 1999). However, system engineers know very well that, given a process, there is a minimum set of state variables that define its dynamic and evolution, given the inputs and the outputs. Therefore, here too, the role of scientific research is crucial in guaranteeing the effectiveness of a given set of indicators in representing any process and in leading to a decision. Meadows (1998) wisely says that an environmental indicator may become a sustainability indicator only with the addition of a time scale and a target. Furthermore, despite continuing controversy over the question of how to develop targets for sustainability, the four Daly principles seem today to be widely accepted (Daly, 1991). They establish four limiting equations for a sustainable development model, summarised briefly in terms of flows as: impact must be less than carrying capacity, renewables used must be less than reproduction rate, waste must be less than natural sinks and non-renewables must be less than the resource substitution rate. Given that the planet is managed under these constraints, sustainable development policies have a time scale determined by the intrinsic time constants of the natural processes, i.e. the reaction times to the pressure steps, the remaining time during which the resources (such as water, minerals and land) will be available at current rates of exploitation. So the targets have values and dates of fulfilment even though they must be dynamic and adaptive both to fit in with the limitations of the policy-making process and to take into account phenomena like ecosystem adaptation, resource substitution and technological improvement. The Daly equations overshadow the quality of life that is now recognised as the non-physical and non-ecosystem counterpart of any suitable model of sustainable development. Some work was recently devoted to indicators and measurements of quality of life (Cobb, 2000). As recently observed (Bologna, 2000), the very delicate dynamic of the relationships between the environment and the socio-economic world cannot be reduced to the assessment of the ecological/ economical balances. Furthermore, any attempt to reduce the question of the well being of mankind and of the environment to quantities measurable by money, energy or other equivalents (Wakernagel and Rees,
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1996) is fruitless without the new assumption of what can be called a “Green Aesthetic”. This is where the close connection between sustainability, development and quality of life should be achieved (ibid.). If, as already stated, quality can be judged taking mankind as the arbiter, is it still the case that we need targets and times for the aesthetic as well? The answer must be affirmative for many reasons. Consider, for example, that an artistic heritage, of which Italy is so rich but not always so responsible (after all, Italy has a responsibility to the world to maintain its heritage). This heritage has its natural rates of decay and is subjected to increasing environmental and social pressures which increase the urgency of fixing protection targets in terms of time and resources. Consider furthermore that some achievements in terms of peace, civil rights, equity, childhood conditions or democracy have a potentially increasing cost in human lives. Such pressing issues cannot—or at least should not—be ignored and passed on to future generations (Federico, 1998).
2. Indices for the sustainable development Economic growth and development are no longer accepted as being synonymous with one another. In colloquial language growth has its paradigms and its indicators, but this is not yet the case for sustainable development (Spangenberg and Bonniot, 1997). The initial attempts to overcome the hegemony of GDP (Jesinghaus, 1999a; Bologna, 2000), by far the dominant index of economic growth, were the greening of GDP and the introduction of general welfare indicators like ISEW (Daly and Cobb, 1989; Guenno and Tiezzi, 1998) and HDI (UNDP, 1999; Spangenberg, 1996). The former approach has been largely abandoned, due to the awkward insertion of the environmental items into the GDP monetary aggregates. In its place, increased use has been made of the more promising method of National Satellite Environmental Accounting (EC, 1994). Among the integrated indices, the HDI has enjoyed considerable success, introducing longevity, knowledge, standard of living and, in subsequent updates, gender and income disparities to adjust the GDP. The HDI normalisation formula is of particular relevance because it allows non-homogeneous indicators to be combined, introduces the concept of distance to target and gives rise to figures related to
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all patterns of national development. These concepts have since been improved with the addition of performance indices from the OECD and the World Bank (OECD, 1993; World Bank, 1995, 1999; Serageldin, 1996). These rivals to GDP also use the so-called “top-down approach”, where the ultimate significance of the index derives from a subjective conception of welfare. They are compiled by seeking out appropriate indicators which best reflect the opinions of the index designer. As an example, consider that the otherwise optimum HDI takes no account of the environmental dimension. This is why a further adjustment, introducing the per capita total material input, has been proposed in order to arrive at an environmentally sustainable HDI (Hinterberger et al., 1999). The state variable approach to system analysis, mentioned above, suggests that a thorough description of the environment inherently requires a large number of descriptors. Prior to Rio de Janeiro, the OECD put forward a new conceptual framework for environmental analysis, the well-known Pressure State Response model (PSR). This is based on a very straightforward causality chain that, with some major improvement, is still in use today as the main reference for environmental analyses. The UN CSD received from Agenda 21, §8 and §40, a mandate to develop sustainability indicators (UNCED, 1992). The Rio Principles on sustainable development clearly give three founding domains to sustainability in the environment, the economy and society. For the latter two the PSR model is not effective. The CSD, in its Project SCOPE (Moldan and Billharz, 1997), which pointed out the incompatibility of the PSR model with the social and economic system, introduced the concept of driving force and, as a result, the DSR model. The driving forces are the areas of human activities and, therefore, the pressure generating processes relate to them. The risk that the clear causal link of the PSR model could be partially lost at the economy/environment interface has necessitated the reintroduction, in many leading indicator projects (like that of the EU-EEA; Jesinghaus, 1999b), the pressures in a somewhat mixed model called DPSR. In the PSR framework, the primary attention of the expert groups working on the environmental indicators was devoted to the completeness of their indicator lists, mainly because they attempted to provide comprehensive descriptions of the environmental processes. Many of these processes were approached
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during those years, with many uncertainties and a generic lack of dependable data and historical series. Many new environmentally relevant processes were added along the way, requiring additional new indicators. This enlargement process is on-going. Increasing the variables may not, however, be an endless process because, systemic completeness aside, too large indicator sets may give rise to confusing messages to the public and to decision makers. Furthermore, from the system analysis viewpoint, the largely unexplored correlations existing between variables, that reduce the marginal informational value added by any new indicator, may suggest that the trend should be reversed and the number of indicator contained. In order to achieve a balance between concision and completeness, the main world-wide institutes and projects (despite the absence of any analytical proof) have developed the so-called “core sets” of environmental indicators, limiting the core lists around numbers slightly larger than 100. Today we have these core sets from UN CSD, EU, OCSE and others, including many at regional levels, where the core indicator selection should be capable of giving value to local peculiarities. The same approach is now being tried also for the economic and social domains, with a view to giving a solid, quantitative basis to the three areas of sustainable development.
3. The key indicators and the integration process A core set of indicators may be sufficient and efficient to model and control its reference domain but the set remains too large to fulfil the goal of informing the public and the decision makers in a simple and clear fashion. The acknowledgement that the popular indicators of economic and social growth, such as GDP and the unemployment rate, are better suited to achieving this objective creates a further need for the selection of a few key indicators which refer to the environment (EC, EEA, 1999). Some headline indicator lists have already been developed for the whole area of sustainability (OECD, 1993; UK, 1998). In many cases, the key indicator is in fact an index, calculated from a subset of similar core indicators with suitable algorithms. This is the case for the “potential for global warming” from the GHG emissions. The
key indicator lists are typically lower by an order of magnitude or more than the corresponding core sets. Meadows (1998) states, jokingly, that 10 is the right number for them. Both headline and core sets are dynamic in nature: they can be adjusted, changed or substituted whenever necessary. These approaches depict a sort of information pyramid (Jesinghaus, 1999a) built up by successive integration steps. Starting with raw data, the application of algorithms, models and statistics produces regional and national indicators, some groups of which constitute the national core sets of environmental, social and economic indicators. At the top, the key headline indicators are selected for each domain. Following a bottom up integration process, inverse with respect to the aforementioned indices like HDI, a suitable combination of the key indicators may give a global index for each domain. A further integration of ecological, economic and social markers can give a unique overall index for sustainable development at the top of the pyramid. With two opposite approaches, the two methods have produced a result that may be considered similar in terms of information and decision driving power. To manage such a complex pyramid, computerised, network based, information systems are necessary. The bottom up integration in no way obliges the end user to definitively select the level of observation required. It is very likely that a domain expert will be interested in a level of detail superior to that required by a stakeholder or a decision maker—provided he/she has confidence in the data. The latter will probably prefer to be informed by integrated indices at the same level as GDP. These integrated information systems, often called “dashboards”—the name derives from its computer graphic output—allow easy access to the desired level of detail (with a click), to read and correct an algorithm, to update or modify the data and also to interactively experiment with new solutions like those proposed by IISD, by EC JRC of Ispra (Jesinghaus, 1999a,b,c) and by others.
4. Data failure and uncertainty Although the objective of this communication is different, we cannot avoid taking this opportunity to call for an increasing world-wide effort in standardising,
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measuring and collecting data that form the base for the entire system of indicators. If the data layer is missing any part of the necessary information, the poor quality of the results is assured. No algorithm or mathematical trick can restore missing information. The overall efficiency of a global information system where the low end quality is very poor, as is the case when a number of countries or regions are not able to provide adequate information, is also questionable.
5. The project of an Italian index Although Italy does not have much acknowledged scientific work in the sustainable development indicators field, the recent inception of the network of Environment Agencies and of the Environment Department of the Statistical Office should rapidly bridge this gap. Unfortunately, no national programme in the area of sustainable development indicators has so far been sponsored. Two reports on sustainable development were published in Italy after Rio de Janeiro, the first, approved in 1993 by the Economic Planning Ministerial Committee (CIPE, 1993) followed the EU 5th Environmental Action Plan “Towards Sustainable Development”, the second, published in 2000 by the Environment Ministry, was already designed using the format of the EU 6th Action Plan approved by the EU Council at Gothenburg in 2001 (Ministero Ambiente, 2001b). Both publications contain a large and, for the purposes of our research, overly-exhaustive list of indicators that nonetheless remain in the early stages of development, where the indicators are neither yet elaborated as a core set, nor integrated in any way. With strong encouragement from the Environment Ministry, the latter document was first aligned, as far as the indicators are concerned, with the 2000 Report on the State of the Environment in Italy (Ministero Ambiente, 2001a). Subsequently, of this larger list, the shorter EU Headline Indicators list was highlighted. At the turn of the century one of the authors2 founded the Institute of Sustainable Development, ISSI, Italy. The institute is substantially based on a large board made up of most of the environment and development experts at national level either from 2
E. Ronchi is the former Environment Minister of Italy.
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academia and public agencies or from the private and NGO sectors. One of the initial research projects has been that of building an integrated sustainability index for Italy with the aim of bridging a chronic gap in the country’s knowledge base. This has now been partially remedied by the recent initiative undertaken in the area of urban sustainability (Bianchi and Zanchini, 2001). The ISSI Project aims to provide a formal and reliable background to the regular report on the sustainable development in Italy, which will be issued by the institute as one of its primary roles. The project took the same acronym, ISSI, as the institute and this will be the name assigned to the general sustainability index for Italy. Some kick-off seminars, held by ISSI Project, traced the pathways to be followed. The ISSI will be a unique index obtained by the integration of three performance indices scoring the sustainability in the economic, social and environmental domains in Italy. The ISSI index will be the top level of a fully integrated indicator informational system for our country. The system, based on a choice of indicators which is coherent with the main international projects currently running, is designed to be completely transparent to users at every level. The selection of indicators will, as a minimum, take account of the key lists issued by the EU, the CSD, the OECD and others. This will allow benchmarking at international level. For each domain a national headline indicators list will be implemented, reflecting the country-specific vision of sustainable development as formulated by the institute. The whole indicators hierarchy of the ISSI informational system will be aimed at assessing Italian regional models of development. This is a cause of great concern as the lack of regional cohesion is a typical characteristic of our country. Regional cohesion will be one of the leading criteria of analysis of the sustainability reports that will be issued by the ISSI. This way, ISSI may evolve to become a group of regional indicators for sustainable development. The project has decided to treat sustainable development in the economic social and environmental fields separately. This is consistent with approaches adopted internationally following Rio de Janeiro. However, we have made some modifications aimed at improving the role of the resource use element. The sector experts have come up with a new definition of the three domains:
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• The socio-economic development, that combines the social and economic concerns, income and employment, with equity and civil rights introducing the concepts of quality of life and quality of environment, public health and artistic and cultural heritage. • The environment, that assumes the well-established paradigms and indicators of ecological sustainability, mainly with reference to the arguments laid out in the recent 6th EU Environmental Action Plan and to the core set and headline indicators developed by the EC (EC, EUROSTAT, 2001). The framework of the DPSR model is adopted. • The use of resources, that, in keeping with the central European approaches to material flows and eco-efficiency (Hinterberger, 1993; Von Weizsacker et al., 1995; Adriaanse et al., 1997; Schmidt-Bleek, 2000; Federico et al., 2001), again includes paradigms of eco-efficiency—in terms of materials, energy, water and land use—as inputs to the economic system, and waste as one of the outputs of the system.
6. The socio-economic development and the environment The socio-economic development may be roughly considered sustainable if the pressures generated on the environment do not impair its ecological quality. Furthermore, the use of resources must be compatible with their long-term preservation. It seems to us very useful for supporting a sustainable development strategy to make available an indicators system that, relating the socio-economic development parameters with the pressure on the environment and the natural resources consumption, may be capable of representing, assessing and evidencing the route of a country towards a sustainable development, on the required time frame. The system of indices here being presented, is consistently based on a larger core set of indicators. It is structured in three subsets, respectively measuring the level and quality of the Italian socio-economic development model, the actual pressure factors on the environment, the efficiency and intensity of use of the natural resource (eco-efficiency). Each subset will be composed by 10 key indices. Really they will neither quantify nor represent all the complexity of the referenced processes, as it is
rather the scope of the embedded indicators core set. They are requested to drive globally our country towards sustainability answering questions like “Are we in a situation of socio-economic development or recession?”; “Are we going to perform a real protection of the environment?”; “Are we fair with future generations?”. The integrated global sustainable development index, named ISSI, will result from a suitable combination of the key indicators. This indicators structure is addressed to get the maximum level of clarity and simplicity and to make the index understandable and straightforward. This is the same approach of most of the similar projects all around the world. In the first approach, the relative weight of all the key indices may be the same, so that they are simply combined by addition after the normalisation. The normalisation will take into account the actual value, a reference year and a performance index, or target, that must be carefully justified. The combination coefficients may be modulated as functions of the distance to targets so that the single index contribution will correspond to the actual sustainability gap in the addressed field. This point to be effective, the relative importance of the key indicators selected must be equivalent. A similar weighting approach must be followed combining the eventually weighted averages of the three subsets. Averaging the three subsets indices gives, as it is very simple to see, an ISSI more oriented to the environment than if averaging in two steps, environment and resource use first, the result with the socio-economic index after. The list of the socio-economic development indices, the physical units and the targets are listed in Table 1. It includes not only the classical measures of the economic growth, but gives a multiple reference to the quality of the economic development and to the quality of life. This is seen through the quantification of civil rights like personal income, education, employment, health, social security and the diffusion of beneficial technologies. The equity problem, particularly critic in a country like Italy where the level of social and economic cohesion is still unsustainable, is addressed and rated by the Gini index that could be improved later by means of a further combination of indicators. Table 2 lists the environmental key indicators. Most of them appear in the international environmental key indicators lists, mainly those from the EU, EEA,
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Table 1 Red set: socio-economic development key indicators S&E
Socio-economic development key indicators
Target
1 2 3 4 5 6 7 8 9 10
Life expectancy (male–female weighted average, years) Per capita income (∈) % GDP of public aid to development (ODA, %) Unemployment rate (%) Income inequality, Gini index (%) Educational level (% of high school and university graduates) Per capita health, social security and instruction expenditure (∈) Computers households ownership (%) Cultural and recreational households % expenditure (%) Scientific research % expenditure (%)
Best country Best country Rio commitment 3 Best country Best country Best country 100 Best country Best country
Table 2 Green set: environmental key indicators E
Environmental key indicators
Target
1 2 3 4 5 6 7 8 9 10
Greenhouse gases total equivalent emissions (tonnes CO2 eq.) Air pollution in the eight largest towns in Italy (% time exceeding limits) Dioxins and Furans emissions (I-T g eq.) Coastal water quality (Km of bathing interdiction) Purified polluting discharges (no. of inhabitants equivalent) Burnt forests (ha) Pesticides per cultivated hectare (kg/ha) Building abuses (no. of built illegal units) High hydro-geological disaster risk areas (sites without measures) Terrestrial and marine protected areas (% of total surface)
Kyoto Null Null Null 111.2 Ml Null 7.5 Null Null 20
UN CSD (2001a) and OECD (2001). The guide indices of the UN Conventions from UNCED on Climate Changes and Biodiversity are included. Some quantifiers of the environmental crises of our country are more emphasised. They address the biological quality of the coastal line, that is longer than elsewhere and crowded by urbanised areas and human settlements and infrastructures; the loss of forest area by human induced fire; the pollution level of liquid discharges; the excess of chemicals in agriculture and, with particular relevance, the number of abuses, crimes and law violations that affect the land use with unsustainable buildings. Probably, the most dangerous environmental hazard in Italy is the hydro-geological distress. Most land is subjected to risk deriving either from increasing pressures, like climate changes and desertification, or by the misuses in the fragile areas. The substitution of the key indices in this subset may be possible in the future because some new critical processes may arise and some indices could go
very close to the targets and/or stabilise themselves to sustainable levels.
7. The use of resources The list of proposed indices is in Table 3. The current unsustainable production and consumption patterns in our economy are characterised by a massive use of materials, water, land and energy. The waste generation from the production–consumption chain is the counterpart of the material flow in input and the effect of an overall system efficiency very far from sustainability. The material flow can be used as a proxy for estimating the weight of human pressure on the environment. The index of resources use is therefore made up by the combination of families of indicators of material flows, energy, land use and waste. The indicators could be estimated by the combination of several sources of information that must
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Table 3 Blue set: resource use key indicators R
Resource use key indicators
Target
1 2 3 4 5 6 7 8 9 10
Primary energy consumption per unit GDP (kep/∈) Renewable primary energy production (Mtep) Total material requirement per capita (kg eq.) Water per capita consumption (m3 ) Marine demersal resources (kg/h) Special waste per unit PIL (kg/∈) Municipal waste per capita (kg) % selective collection of municipal waste (%) Urbanised surface and road infrastructure length (%) Rail transport total demand share (%)
0.167 Doubling (EU white paper) Factor 4 −20% Stable Halving −20% 70 Stable Doubling to 2000
be selected not only by their importance but also by the data availability. To overcome the lack of data we often made use of proxies. 7.1. Material flow and water consumption The total material requirement (TMR), developed by the Wuppertal Institute and adopted by the EEA (EEA, 2001), would be exhaustive for this index. Some difficulties persist in the evaluation of the TMR for Italy, especially because the infrastructures give an important as well as ill-defined contribution to the flow. A feasible solution is to give a proxy made by the sum of the consumption of steel, coal, cement and aluminium. This choice overlaps the energy and land use, keeping track of embedded energy. On the other side, the concrete component incorporates information on pressure on land use. Each material flow includes the ecological rucksack, that is the sum of the primary natural resources and their “hidden flows” (Hinterberger, 1993). The unit used is kg eq. The water consumption involves problems of use the fossil water reservoir, pollution, agricultural strategies and household’s behaviours. In the south of Italy the lack of water is an historical problem. While, in the north, the pollution of ground waters caused several wells and water facilities to be often out of service. 7.2. Energy In Italy the energy production is mainly based on fossil sources. The consumption from the transport sector seems to be the least governable phenomena with a steady increase of the demand. The main
indicators for this area is the per capita consumption of energy. The information is correlated with the energy embedded in the materials rucksacks that is, as matter of fact, a not negligible consumption of hidden energy, partly embedded into imported components. 7.3. Waste A complementary point of view is to look at the material flow exiting the system, i.e. the production of waste. While computing these indicators does not take into account the ecological rucksack of the single waste component, waste are also carrying information on land used for land-filling, on emissions and on lifestyles. 7.4. Land In Italy it is difficult to speak about natural ecosystems in terms of “wilderness”. Nevertheless, we have inherited an environment made by a network of habitats where historical settlements, agriculture and other activities found an equilibrium with the hosting environment. They also played a role of integration, maintenance and enhancement of the country. This equilibrium was kept up to the beginnings of the XX century. The industrial revolution before and the green revolution in agriculture later, started a pattern where the consumption of land played an important role. As a consequence, many of the ancient relationships were weakened or destroyed, important habitats were challenged or simply disappeared, erosion and soil losses became an issue. Illegal behaviours had also an important role in the aggression to land in
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the post-war period with many abusive buildings. In this area, it is difficult, more than elsewhere, not to take the beauty of sights as the first important guide in the search of a sustainable land use. On the other hand, several difficulties arise when trying to measure or to quantify something like cultural and personal points of views or judgements. Some physical pressure parameters, like the road network length and the urbanised areas are of great concern in land use.
8. Combination algorithms and models ENEA implemented and proposed to the ISSI Project a specific methodology to support the construction of a unique index of sustainable development from a set of key indicators. An information system has been made available to compute and display the indices, their time history and their distance to targets. The system takes full care of the large amount of research work available today (UN CSD, 2001b). The methodology keeps the process as transparent as possible. All the data and the assumptions are easily accessible for checking and displaying at every stage. Several system tools are provided to support the user. The problems addressed by the informational system are discussed in the following paragraphs with the suggested solutions. A methodology sheet, strictly close to the formats adopted by the main current projects, has been prepared. The sheets collection was published (Federico et al., 2002). 8.1. Data collection and missing data Data may be numbers, x, or qualitative categories, y, that instance the indicators. We will assume both of them to be random variables:3 the randomisation is the right approach to manage data uncertainty and their variability (Hovanov et al., 1997). The same author very clearly indicates the way of managing the 3 The indicators raw data set X is composed by semi-random matrices x hk = |xij |hk ; h = 1, . . . , 3; k = 1, . . . , 10; i = 1, . . . , nhk ; j = 0, m that give the values (some of them may be missing) of the m xij series, or vectors, needed to compute the kth indicator, belonging to the hth sector. The variable xi 0 is the overall continuous time sequence in years.
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subset of qualitative variables by suitable relations x = ϕ(y). Data on every listed indicator are collected on an average national basis, unless otherwise requested. The sources of data and their reliability levels are stored in the methodology sheets database. The optimal time series should extend over a period of at least 10 years, however some exceptions will occur. A polynomial interpolation of the time series has been made possible for missing data. The missing data can be better estimated either from strongly correlated time series, or from the series of the same indicator in foreign but similar countries (i.e. EU countries). Short extrapolations beyond the temporal limits of the time series are also available on a statistical basis. The interpolated, extrapolated and simulated data are clearly marked as such. Some indicators require operations on multiple time series: they may, for example, refer to per capita values or to unit of GDP. In order to maintain the consistency, all the original time series are stored, while these indicators are computed at run time. The system provides operations like (weighted) sums and differences, division, product and functional transformations. 8.2. Key indicators The calculation of the reduced set of q hk key indices, proposed in the three aforementioned lists, is performed after the raw data collection:4 q hk = f (x hk ) = |qhkij |, h = 1, . . . , 3; k = 1, . . . , 10; i = 1, . . . , nhk ; j = 0, 1. The system implements a first normalisation of the indices with the aim of gaining a better comparison of their trends: a reference year is defined for each indicator and the percentage difference to the reference year value is computed. 8.3. Setting the targets As previously stated, to become a candidate for sustainable development, an index must have a target value. The Kyoto Protocol, after the EU internal 4 Here n hk is the cardinality of the minimum subset of years of availability of the group of indicators that give rise to index qhk .
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agreement, for example, requests for Italy a −6.5% GHG emissions reduction in 2012 with respect to 1990: this target is provisional, but clearly shows the path to sustainability in the near future. The ISSI Project has defined all the target values thk at 2012 (Rio + 20). Targets, years and reference values may sometimes be indicated either in protocols or in international standards or rules. They may be otherwise determined by national interests or visions, taking into account either the amount of deliverable effort in a given period or ecological limits imposed by definitions like the Daly’s principles. The targets thk for every index may be dynamically updated and delayed in time. There are many methods to estimate the total effort needed to fulfil a target. The system supports the calculation of an effort estimate, that, given the actual trend of an index, is assumed proportional to the length of a cubic spline from the actual value (actual time is zero) to the proposed target. The first derivative is supposed continuous at the origin and zero at the target. This effort factor will somehow give an idea of the effort needed to correct the trend of an index towards the target, in a given time interval, with a pre-defined universal approaching pattern. 8.4. Integration of the ISSI index by weighted averaging A straightforward representation of the indices that makes feasible their comparison, has been obtained by a slightly modified HDI normalisation approach. Two classes of indicators are defined: if the preference is increasing we suppose the target to be above most of the values assumed by the index; if the preference is decreasing the target is supposed to be under most of the values assumed by the index. These assumptions imply either that an index that stably reached its target is no more critical and may be eliminated from the list or that the target must be updated. The normalised index time series rhk in the interval 0 ≤ rhk ≤ 1 is computed assigning a target thk to be attained in a given year: rhki =
qhki − thk min|i qhki − thk
(for increasing preference),
rhki =
qhki − thk max|i qhki − thk
(for decreasing preference).
Fig. 1. E1 as r21 : normalised GHG total equivalent emissions in Italy. The worst emissions (year 2000) are equal to 546,341 Gg.
This index is a-dimensional and zeroes when the target is fulfilled, i.e. when the sustainability debt is paid. Occasionally negative—beyond the target—values are set to zero. See Fig. 1 for the index E1, the GHG emissions. The current values of the integrated indices Qh , respectively for the socio-economic development, the environment and the use of resources are obtained by weighted averaging the indicators of the three subsets, red, green and blue. The weights are interactively free. In the following examples, the weights are assumed to be 0.1 for the sake of simplicity. The three sub-indices are again averaged to give ISSI, the integrated Italian sustainability index. The averaging formula is: ISSI = h wh Qh . See in Fig. 2 a case example of combination of the three sectoral indices with wh = 1/3. 8.5. Trend and fluctuation We consider any index as the output of a system driven by external and internal variables. Therefore, an index time series may be regarded as a sum of a trend, determined by the input exogenous variables, plus an independent random fluctuation due to the system endogenous variability. If a suitable model of
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Fig. 2. ISSI general (black), socio-economic development (red), environment (green) and resource use (blue) time series obtained by weighted averages.
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Fig. 3. Normalised indicators (S&E4: unemployment rate; E3: furans and dioxins emissions; R10: fraction of rail transport) with modelled trends (thin lines).
8.6. Correlation models the process is lacking, the information system tries to fit the time series with linear, polynomial, exponential, logarithmic or logistic trend, maximising the Akaike’s Information Criteria: a model with more parameters will be justified only by a worthwhile increasing of the goodness of fit. The fluctuation parameters are estimated from the residuals. The variance σhk = σ (qhk ) of the fluctuation may be seen as a direct function of the responsiveness of the index to actions policies and measures. The actual first and second derivatives of an index trend give valuable indications about the chances of approaching its target. The second derivative was recently indicated (G. Cannata, 1999, private communication) as a hint of possible changes from an exponential to a logistic model of growth, with a chance for a future stabilisation. The system assigns a qualitative score to the indicators trends by a facet code: bad, if both the derivatives are unfavourable with respect to the target; quiet, if at least one is favourable; good, if both them are reducing the distance to target. Fig. 3 shows the trend–fluctuation decomposition of some indices and the quality of these trends towards the sustainability.
Most indicators are driven by common processes. Their time series may be therefore highly correlated. High levels of statistical correlation may be desirable when selecting a proxy for a given index. Otherwise, a new index will profitably augment a set of indicators only in the case of low correlations with the existing set. When combining indices by a simple weighted summation, the correlation information is lost. Then a risk of overweighing some part of the information may arise. It could be useful to spot the highly correlated time series, so that, when entering a new indicator, the system will check and mark the highest and the lowest correlations as well as the coherence of the new candidate to the existing integrated indices. The correlation models are receiving increasing attention in recent literature (World Economic Forum, 2001).5 A 30-dimensional square correlation matrix R arranges all the correlations coefficients for the ISSI indicators set. 5 A complete correlation model is defined by the Covariance 2 and Correlation matrices (Mardia, 1979) as: Σ = |σhk h k | = |i (qhki −E(qhki ))(qh k i −E(qh k i ))/(n −1)| and R = |ρhk h k | = 2
|σhk h k /(σhk hk σh k )|.
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To face the problem of missing data a multivariate linear model will be set up. The missing data qhki at time qhki 0 will then be recovered by a linear combination of all the indicators available at that time. Actually, the simulated data are derived from the modelled trend. 8.7. Distance from the target The discussed HDI-like normalisation method partially introduces the concept of distance to target. The normalised value of single indicator is a distance to target. The integration of indices by (weighted) averaging, however, causes the loss of the formal geometric properties of the distance and a generic loss of information. An actual distance method will restore the capability of a full multidimensional vectorial representation of the indicators. The correlation/covariance model may be used to compute a meaningful distance between our 30-dimensional key indicators vector and the target. In this framework, the indicators set is seen as a multivariate unique index moving towards a unique multivariate target. The same model may also be a straightforward basis for a coherent solution of the problem of weighing the indices. The covariances of the model are suitable weighing factors that give the distances as decreasing functions of the responsiveness (elasticity) of the indicators. To this end the fluctuations multivariate covariance matrix Σ takes into account either scaling or correlations. The indicator variance gives the natural fluctuation around its trend and may be assumed as a measure of feasibility of the future changes towards sustainability. Provided that Σ is a systemic parameter, that depends on the intrinsic dynamic of the process, we can estimate its entries also by pooling data coming from different countries with similar economies. The distance metric we assume is a generalisation of the multivariate Mahalanobis distance D2 (Mardia, 1979). If Q is an integrated index built up combining a given subset q of the key indicators qhk , and if T is the related target, this distance is: D 2 (Q − T ) = (q − t) −1 (q − t). The running distance may be computed on a year by year basis (see Fig. 4). However, the actual gap versus the sustainability should be assumed as a function
Fig. 4. Subsample of “ISSI running distance to target” time series (black), socio-economic development (red), environment (green), resource use (blue).
of the difference between the target and the expected value of the global ISSI index at the target time. It is obtained by extrapolating the time series trends, with the so-called “business as usual” approach. The gap is then computed as a distance to give a measure of the residual effort needed to achieve the target. We assume it as a the absolute “ISSI distance from the target”. The economic effort to be provided to get a target could furthermore be properly discounted reducing the effort due as the target goes farther. The preferred discounting may be embedded in the covariance matrix simply by a further scaling of the basic covariance matrix, Σ ∗ . The addition of new indicators will not affect the final distance if the new entries and the new targets are strongly correlated, whereas, if new information is carried by the new indicators, the distance will fully register their contribution. The Mahalanobis distance, beyond its intrinsic normalising capability, allows the further weighting of the single key indices based, as commonly happens, on the preferences expressed by expert panels. These weighing coefficients may be easily introduced in the model modifying the matrix Σ ∗ in the same way as the discount rate.
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8.8. Displaying ISSI: an Italian dashboard The dashboard displays, very popular today, are addressed to simplify the large public understanding the sustainability while not shadowing the details. The dashboard imaging does not depend on the particular combination algorithm. The most popular projects, like the IISD and PPI dashboards, the SD Barometers, the ESI Polygonal and many others largely use computer graphic and internet technology. An Italian dashboard should by not be different. However, to give value to the trends, the displays of the time trajectories (the routes) of the single and of the integrated indices approaching the sustainable development dynamic targets must also be taken into account (Figs. 2 and 4). The ISSI dashboard is realised by a colour code representation similar to that of the European Project EPI (Jesinghaus, 1999a) (Figs. 2 and 5). The figure displays the actual state of the sustainability in Italy, as a case study, for a reduced set of the key indicators. The integration used in Fig. 5 is the linear combination of the indicators (§8.4) Nevertheless, the method of computing the ISSI at is target time, discussed in §8.7, is adopted also in this display. The component indicators are represented in two levels: the outer circle represents the actual value on a three-level colour scale. The sectors angles are
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proportional to the numeric values of the indicators. The intermediate circle gives the quality of the trends, either with colours or with facet codes. The inner circle is divided in the three sectors corresponding to socio-economic, environmental and resource use integrated sub-indices. Their angular widths are proportional to the values of the three sectoral indices. The central facet/colour represents the distance from the target of ISSI indicator at the year 2000 on a three-level scale.
9. Work to be done The indicator lists have now a quite stable configuration but will be further implemented. Many time series needed for a suitable computation of the key indices are either not yet or definitively not available or too scarce for an effective model extraction. The informational system and the displays must be fully developed and implemented. The correlations/covariances estimates must be improved. The uncertainty management must be further investigated. Some counterintuitive effects of the combination methods must be understood.
Acknowledgements The authors acknowledge A. Giuliobello, the ISSI Project manager, G. Onufrio and C. Donolo, the Chairmen of the ISSI expert groups on the use of resources and on the social and economic development, that provided the lists of the key indicators and the related data for ISSI. Our acknowledgements go to Ministero Ambiente, ENEA, ANPA, ICRAM, ISTAT, University of Rome, Banca d’Italia and all the institutions that delivered the basic data for the indicators. References
Fig. 5. The proposed Italian dashboard for linear weighted averaging of an ISSI “distance to target” subsample.
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