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Keywords:

  • delinking;
  • European Union;
  • evaluation;
  • sustainable development;
  • synergy measurement;
  • trade-off

ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Conclusions
  7. Acknowledgements
  8. References

This paper presents a new assessment tool developed for the analysis of synergies and trade-offs between selected development trends. We suggest new quantitative measures for the concepts of synergy, trade-off and delinking. The tool is developed to analyse the synergy between two different trends, but it can be used to analyse simultaneously the synergy between three trends representing the three different dimensions of sustainable development. The use of the tool is demonstrated with several examples from European Union countries. Copyright © 2012 John Wiley & Sons, Ltd and ERP Environment


Introduction

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Conclusions
  7. Acknowledgements
  8. References

This paper presents a new assessment tool developed for the analysis of synergies and trade-offs between selected development trends. We suggest new quantitative measures for the concepts of synergy, trade-off and delinking. The tool is developed to analyse the synergy between two different trends, but it can equally be used to analyse simultaneously the synergies between three trends representing the three different dimensions of sustainable development.

The article is in response to the need to provide quantitative estimates for the concepts of synergy and trade-off, which are central in policy planning and implementation. The concepts of synergy and win–win strategies have been widely discussed in, for example, the fields of economic growth, well-being and social policy (see Andor et al., 2011), European policies (see European Policies and Politics, 2011), work (Guillen and Dahl, 2009), regional strategies (Alfonsi, 2011), community strategy for rural development (see European Council, 2006), EU Eastern policy (see Duleba and Bilčík, 2010), Baltic Sea plans (see Lindholm, 2010), migration (Koeb and Hohmeister, 2010), human development (UNDP, 2011), environment and poverty (see Netherlands Commission, 2012), the synergy between China's 12th Five-Year Plan and Europe's 2020 Strategy (Zhe, 2011), poverty and tropical forests (see Wunder, 2001), climate policy (see Hanh et al., 2003; Commission Communication, 2005; Chhatre and Agrawal, 2009) and land use (see Cowie et al., 2010). Thus, the concept of synergy is seen to be a useful theoretical concept in many fields of social and natural sciences.

In the field of sustainable development policy planning, synergies are often described with the concept of win–win strategies or winning strategies (see, for example, UNDP, 2011, Chapter 4 - Positive synergies—winning strategies for the environment, equity and human development). For example, the EU 2020 strategy needs to ensure full recovery from the current economic crisis and allows the transition to a renewed growth model which fosters the synergies between the economic, social and environmental dimensions (Notre Europe, 2012).

The concept of synergy has been used in several disciplines. Corning (1998) discusses synergy in the fields of quantum physics, physics, thermodynamics, biophysics, chemistry, biochemistry, molecular biology, developmental biology, neurobiology, ecology, behavioural biology and anthropology, but recognizes that it is mostly used in such “hard sciences” such as endocrinology, neurochemistry and pharmacology. For instance in epidemiology the concept of a synergy index is used to indicate the relationship of joint effects from any two exposures compared with the effects of single exposures (Li and Chambless, 2007).

One online dictionary defines synergy as: (1) the interaction of two or more agents or forces so that their combined effect is greater than the sum of their individual effects, and (2) cooperative interaction among groups, especially among the acquired subsidiaries or merged parts of a corporation, that creates an enhanced combined effect. In systems theory synergy is defined as behaviour of whole systems that is unpredicted by the behaviour of their parts taken separately.

According to a business definition synergy is an increase in the value of assets as a result of their combination (http://business.yourdictionary.com/synergy). In this field synergy emerged as a key concept in efforts to conquer business cycles. It was hoped that by building multi-industry conglomerates, companies could create synergies that would result in constantly rising earnings through all economic cycles. The term was later used to describe the gains in revenues or cost savings arising from takeovers or mergers, gains stemming from, for example, reduced marketing costs and a need for fewer employees (Linner, 2006).

A concept related to synergy is statistical interaction. Interaction can be specified in several ways, according to the statistical model under study. The conventional definition of interaction states simply that it is the product of two independent variables Xi and Xj, i.e. XiXj (Southwood, 1978). This is often accompanied by a coefficient to denote the sign and the extent of the interaction. Furthermore, the statistical significance of an interaction can be tested.

The term synergy, as applied in the method described in this article, refers to an interaction that is mathematically defined in this conventional way, although there is as yet no statistical test provided to test its significance. Instead, synergy ranges from −1 to 1, enabling the comparisons of intensities between the different combinations of variables. As a result, the method provided here is more explorative than a strictly statistical tool.

The synergy measure defined here is different from the concept of correlation. The familiar measure of dependence between two quantities used in statistics is the Pearson product–moment correlation coefficient, called usually “Pearson's correlation”. It is obtained by dividing the covariance of the two variables by the product of their standard deviations The correlation reflects the noisiness and direction of a linear relationship, but not the slope of that relationship, nor many aspects of non-linear relationships. In the case where the variance of either X or Y is zero, the slope between two variables is zero and the correlation coefficient is undefined as are many aspects of non-linear relationships.

Synergy can also be compared to the concept of elasticity. The latter is mainly used in the field of economics with respect to different categories, such as price elasticity of demand, income elasticity of demand and cross price elasticity of demand. Elasticity is a measure of responsiveness of behaviour measured by variable X to a change in environment variable Y. In the field of economics elasticity is often connected to changes in prices and quantities as well as changes is market supply and demand (e.g. Gillespie, 2007). Elasticity measures the percentage change in demand (or supply) caused by a percentage change in price or income, etc. The concept of elasticity is different from the concept of synergy determined here. Elasticity can have values from zero to infinity while synergy can vary between −1 and +1 (see Figure 1).

image

Figure 1. Relationship between elasticity and synergy

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The study of synergies and trade-offs conducted here builds on previous work carried out by our research team and its Advanced Sustainability Analysis (ASA) approach (see Kaivo-oja et al., 2001a,2001b; Kaivo-oja and Luukkanen, 2002, 2004; Hoffren et al., 2001; Luukkanen and Kaivo-oja, 2002a,2002b, 2003, 2005; Tapio et al., 2007; Vehmas et al., 2007). Here we use indicators to analyse different dimensions of sustainable development [e.g. gross domestic product (GDP), CO2, employment, poverty and income distribution] as examples to illustrate the methodology.

To explore the synergies and trade-offs between different trends we need to provide definitions for the terms. We can say that there is synergy between two factors when their combined effect is greater (or smaller) than the sum of their separate effects. Trade-off can be defined as a balance achieved between two desirable but incompatible features or as a situation where the selection of one feature results in the loss of another feature. In addition to synergy and trade-off, delinking can describe the situation between the variables and in this case the increase or decrease of one variable does not have an effect on the other variable. The mathematical definitions of synergy, trade-off and delinking are given in the next section.

Two different kinds of analysis are carried out. First, we determine whether there is synergy, trade-off or delinking between two trends under investigation. Secondly, we determine the same for three trends simultaneously in order to include the different dimensions of sustainable development in the same analytical framework. Ideally the trends investigated represent different dimensions of sustainable development, although synergies and trade-offs within any of the dimensions can be equally interesting.

Materials and Methods

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Conclusions
  7. Acknowledgements
  8. References

It can be said that there exists synergy between two factors when their combined effect is greater or smaller than the sum of their separate effects. In mathematical form this can be expressed as:

  • display math

where x, y and z are variables and a, b, c and d are coefficients that determine how the output z depends on inputs x and y. In this case we assume a time-invariant system, where the parameters remain constant. If y is 0, the output is determined by x and the coefficients a and d. Coefficients a, b and d determine the impact of the single inputs on the output. The synergy of the inputs x and y is determined by the component cxy, i.e. the co-effect of both the inputs. The idea of synergy indicates choosing variables x and y such that an increase in the value of both variables x and y is desirable and refers to a commonly accepted direction of sustainable development.

If we look at a change from A to B in the Figure 1 (from the original state x0y0 to x1y1) we can determine the change in the area (Δz) to be

  • display math

We can interpret the synergy of the inputs to be determined by the shaded area in Figure 2, which equals ΔxΔy.

image

Figure 2. Synergy between two variables x and y determined by their changes ΔxΔy

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The synergy can also be negative, as is shown in the Figure 3 where the change in y is negative and ΔxΔy becomes negative. This is a trade-off situation: when one factor increases the other factor decreases.

image

Figure 3. Negative synergy or trade-off between x and y in the case where Δy is negative

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Figure 4 shows a case where synergy equals 0 in a case where Δy is 0. This is a delinking situation between the variables: the change in one variable does not impact the other variable.

image

Figure 4. Synergy between x and y equals 0, which is a delinking situation

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This type of calculation of synergy (or trade-off) does not imply a causal relationship between the variables. The calculation results indicate only possible (potential) causality.

Maximum synergy can be obtained when relative changes Δx and Δy are equal (x0 and y0 are first normalized, i.e. the initial year values are normalized to 1 or 100). This means that synergy between two variables can be measured in the scale −1 … +1 as

  • display math

where Δy is in this case the larger change. If Δy is smaller than Δx the quotient must be inverted. The synergy can be also measured geometrically by the ratio of the area of the real change (ΔxΔy) to the area of the maximum change, i.e. the ratio of the area of rectangular ABEF to the area of ABCD in Figure 5.

image

Figure 5. Measuring synergy as a ratio of the area ABEF to the maximum areas ABCD (max ΔxΔy)

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Synergy between three variables can be calculated in a similar way. In Figure 6 the synergy can be calculated as a ratio of the volume of the cube ΔxΔyΔz (A′B′C′D′E′F′G′) to the volume of the maximum cube ABCDEFG, where the changes in x, y and z would be equal.

image

Figure 6. Determination of the synergy between three variables

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In the three-dimensional case the sign of the synergy indicator depends on the signs of the pairwise comparisons. If all the pairwise synergies are of the same sign (positive or negative) the three-dimensional synergy is positive. If one of the pairwise synergies is of different sign the three-dimensional synergy is negative.

To make the interpretation of the three-dimensional calculation results easier we have modified the data so that the positive direction of change of the indicator is connected to positive development in relation to sustainability. This is why we do not use, for example, CO2 emissions as an indicator but the reduction of CO2 emissions from the base year of analysis (e.g. Kyoto base year 1990). Analogously, we use employment as an indicator and not unemployment.

To illustrate the synergy calculations we use some examples from the European Union (EU). GDP, population and CO2 emission data for the case studies were taken from the International Energy Agency (IEA), employment data from the International Labour Organization (ILO) and World Bank, and at-risk-of poverty, inequality of income distribution and household saving rate data from Eurostat. It was decided to use data from Eurostat for two main reasons: (1) the coherence of data among different countries and data availability for the EU area and (2) to test the applicability of the Eurostat sustainable indicator data set in sustainability analysis with the aid of the new method.

All the calculations were done for selected EU countries because of good data availability and to illustrate the possibilities of the developed method.

Results

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Conclusions
  7. Acknowledgements
  8. References

The synergy calculations are presented for a group of EU countries to indicate the possibilities of the framework of analysis for comparative purposes. The selection of the cases was based on two main points of departure: (1) the availability of sufficiently long time series and comparatively reliant data of different dimensions of sustainable development and (2) adequate differences in the countries compared to better illustrate the developed methodology.

Figure 7 shows the change in GDP and CO2 emissions reduction in EU15 countries from 1990 to 2009. The data have been normalized to 100 in the year 1990. GDP increased by about 38% over this period while CO2 emissions decreased by more than 5%. As the decrease in CO2 emissions is seen to improve the rather than instead of emissions. Figure 7 indicates that there has been synergy between CO2 reductions and GDP during this time period because both of the indicators have been increasing.

image

Figure 7. Changes in CO2 emission reductions and GDP in EU15 countries from 1990 to 2009 (normalized to 100 in the year 1990)

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To analyse changes over a longer time period we have carried out synergy calculations for CO2 emission reductions and GDP in EU15 countries with 1960 as the base year. Figure 8 illustrates the results. In the 1960s and 1970s CO2 emissions increased hand in hand with economic growth and thus the synergy calculation for emission reductions and GDP has values close to −1. There has been considerable development towards delinking the emissions and economic growth.

image

Figure 8. Synergy between CO2 emission reductions and GDP in EU15 countries for 1965–2009

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Figures 9-12 illustrate results from synergy calculations in four EU Member States, namely Greece, Germany, the UK and Italy. These figures include the possible synergy calculations based on three variables describing the different dimensions of sustainable development, i.e. GDP (GDP, economic dimension), employment (EMP, social dimension) and CO2 emissions reduction (CO2, environmental dimension). In Greece, a positive synergy can be found for one pair of variables only (between GDP and employment), while in Germany a positive synergy exists in all pairwise comparisons and between all three variables. This difference reflects the diverging development in CO2 emissions in these countries. In Greece, CO2 emissions have not decreased but increased over the whole period, due to the country's relatively traditional industrial development and without significant changes in the energy mix, which is largely based on the use of fossil fuels. In Germany the situation is different. Both the industrial structure and energy mix have been changing and CO2 emissions have decreased due to a switch to a lighter industrial structure and less CO2-intensive energy sources. Reducing CO2 emissions has also been a clear target in German energy and environmental policy.

image

Figure 9. Synergies of variable pairs and three-variable synergy for Greece. Base year for synergy calculation 1990

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Figure 10. Synergies of variable pairs and three-variable synergy for Germany. Base year for synergy calculation 1990

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Figure 11. Synergies of variable pairs and three-variable synergy for the UK. Base year for synergy calculation 1990

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Figure 12. Synergies of variable pairs and three-variable synergy for Italy. Base year for synergy calculation 1990

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In the UK the synergies between the three trends have been positive since the late 1990s. The results from the UK can be partly explained as for Germany: the late 1990s and the first decade of the new Millennium show an increase of economic performance in terms of GDP, an increased employment, and at the same time a clear reduction of CO2 emissions. The reduction in CO2 emissions has been mainly due to the continuation of economic structural change and – together with the reduction of traditional industrial sectors – the continuous emergence of a post-industrial service economy. In addition, the shift to the use of more natural gas instead of coal has helped decrease emissions.

The result from Italy resembles the result from Greece and the synergy value is negative. It is higher than in Greece as it reflects a smaller trade-off between the years 1997 and 2003. In 2001–2002 the situation in Italy is close to delinking.

Figure 13 presents the comparative results of the three-dimensional synergy calculation for Greece, Germany, Italy and the UK by using the same variables as above (GDP, employment and CO2 reductions). The comparison shows that the three-variable synergy results from the UK are rather similar to the results from Germany, but the synergy value is lower during the whole period investigated (except in 2004). The result from Italy resembles that from Greece and the synergy value is negative. It is higher than in Greece as it reflects a smaller trade-off between the years 1997 and 2003. In 2001–2002 the situation in Italy is close to delinking. The result from the UK can be explained partly by the same reasons as for Germany, but there are also differences.

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Figure 13. Three-variable synergy of employment, CO2 reduction from 1990 and real GDP. Comparison of UK, Germany, Greece and Italy

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To illustrate differences between European countries we have analysed the synergy/trade-off between per-capita GDP and household saving rate. These results are shown in Figure 14 for a group of EU countries. It clearly shows the differences in national circumstances and policies. Italy shows the highest and most constant synergy between the selected variables of household savings and GDP. This means that increases in GDP are reflected in savings rate, indicating constant consumption behaviour. This is in contrast to the results for the same indicators in Finland, Spain, the UK and the Netherlands, where the situation is very close to complete delinking for each of these countries. The reasons for this can be linked to cultural differences: while it is common in Italy to save more as soon as there is a greater amount of money available, such behaviour is probably different in other EU countries.

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Figure 14. Synergies of household saving rate and per-capita GDP in selected EU countries

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In the case of Romania, it is possible to see how the data reveal a situation that is very close to a trade-off: it can be assumed that this is due, at least partly, to an inequality of income distribution, which, as described further below, is quite high for Romania for the investigated period.

Figure 15 shows the synergy between per-capita GDP and at-risk-of-poverty rate. The declining GDP in Italy does not appear to be synergetic with poverty, but in fact the opposite: GDP decreased by some 15% during the period examined time, while at the same time at-risk-of-poverty rates remained steady. In Romania an increase in per-capita GDP shows high synergy with at-risk-of-poverty rate and, as pointed out for Figure 14, this is linked to the inequality in income distribution. In Finland per-capita GDP and risk of poverty seem to be totally delinked, reflecting the Nordic welfare state policy, where social policy takes care of the poorer population and changes in GDP do not impact their situation (see Nordic Council, 2011).

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Figure 15. Synergies of GDP and at-risk-of-poverty rates in selected EU countries

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The synergy between GDP and inequality of income is examined in Figure 16. The situation appears to be steady in Finland, where a constant delinking throughout the period is seen. This can again be interpreted to be the result of the Nordic welfare state policy, which delinks GDP and income inequality (see Nordic Council, 2011).

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Figure 16. Synergies of GDP and inequality of income distribution

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The slightly rising synergy trend of the UK (especially in the late 1990s) indicates that increasing GDP has started to be related to increasing inequality of income distribution. This can be interpreted to be a result of less emphasis on traditional Labour welfare politics. The results for the Netherlands present a rather low average synergy (about 0.3) so it would appear that a weak relationship exists also in this case.

The results for Spain and Italy indicate a trade-off between GDP and inequality in income distribution. In Italy, decreasing GDP has caused an increase in inequality, especially during the early 2000s. For Spain the trade-off has been a prevalent phenomenon for the whole 10-year examination period.

Figure 16 shows quite different results for Romania, for which synergy is present for the whole period, with an increase in the last 5 years and an average value of more than 0.8. Note also that in Romania the inequality of income distribution rises sharply toward the end of the investigated period, even if the average for the same indicator during the whole period is not much higher than that of the other countries considered.

In Romania the synergy between the selected indicators may be so strong because the economic structure of the country is still developing and newly generated wealth is distributed unevenly among the population: this raises an interesting point regarding the social policies of the country and the future development of this trend should be monitored closely. The fact that a synergy exists between an increasing GDP and an increasing inequality in income distribution gives insight into the economic situation of the country and points to the need for measures to avoid an ever growing rate of people at risk of poverty.

Conclusions

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Conclusions
  7. Acknowledgements
  8. References

This study introduces a new method that can be applied to the analysis of potential synergies and trade-offs between different unsustainable trends. We have used this new approach to determine whether there is synergy or trade-off between different trends by calculating the ratio between changes in these trends. This type of quantitative synergy analysis has not been carried out before in the field of social sciences, whereas in epidemiology, for example, the concept of a synergy index has been used to indicate the relationship of joint effects of two exposures. In the social sciences it is, however, very difficult to apply the exposure approach using different policy measures, at least with the sample sizes necessary to ensure statistical reliability. This is why the developed method calculates the potential synergy between the actualized trends describing development patterns in different dimensions of sustainable development.

Interpretation of the results is straightforward: the closer the calculated synergy factor is to 1 the stronger the synergy between the two (or three) variables can potentially be, and the closer the ratio is to −1 the potential for a trade-off is stronger. When the synergy factor is close to 0 there is delinking between the trends. This kind of analysis does not imply that synergy is necessarily good and trade-off is bad, or vice versa. Such interpretation is case specific; to interpret the results in more depth, we need to determine how we would like the trends to involve. For example, if we consider per-capita GDP and per-capita CO2 and find no synergy between the two, this can be interpreted to be a positive situation as increasing per-capita GDP does not increase per-capita CO2. This means that a trade-off between per-capita GDP and per-capita CO2 is a desirable situation. To make the comparisons easier in three dimensions we have modified the indicators so that the increase in CO2 reductions indicates development towards sustainability.

Based on the results of our study we can conclude that the results of potential synergies and trade-offs between different dimensions of sustainable development and between different unsustainable trends are highly case specific. It is not possible to draw generalized policy recommendations based on the results, but every case has to be analysed separately. The developed tool could be seen as a policy evaluation tool, which makes it possible to carry out comparative analyses between different countries or different thematic areas. One of the main strengths of the tool is the ability to analyse the three different dimensions of sustainable development simultaneously and to see whether the applied policies have resulted in synergetic development in the different dimensions of sustainability. The synergy evaluation tool can be used in the large policy programme evaluation, for example in the EU's Environmental Agency or in the United Nations sustainability planning and programmes. For example, the Millennium Development Goals strategy could be evaluated by this kind of synergy evaluation tool. However, it must be remembered that the developed tool does not provide a simple answer for policy comparisons as the development outcomes are dependent on numerous interrelating factors and no single indicator can capture all the information. The presented synergy method should be understood as the first step in identifying interesting phenomena that should be analysed in more detail with other methods.

Sustainability planning is a multi-dimensional process which normally requires integration of plans made under different sectoral planning organisations. The task is then to try to formulate a coherent sustainability plan that takes into account the different development trends in different thematic fields and tries to utilize potential synergies between them as well as to avoid potential trade-offs. Planning combines forecasting of developments with the preparation of scenarios of how to react to them. From this perspective, for policy-makers, reliable knowledge about the synergies of trends is an important starting point for policy programming and planning.

Development of the new methodology for quantitative synergy measurement has been in response to the needs of sustainability policy planning. This type of tool can be used to compare countries with different policy approaches and assessment of the policy results as a function of the potential synergies or trade-offs between the development trends. As the tool does not provide explanatory details of the causes of differences in the development trends, further analysis will be required to reveal the detailed case-specific drivers behind the trends.

Acknowledgements

  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Conclusions
  7. Acknowledgements
  8. References

This work has been carried out within EU FP6 project 044428 “Development and comparison of sustainability indicators” (DECOIN), EU FP7 project 217213 “Synergies in multi-scale eco-social systems” (SMILE) and the Academy of Finland project “Land use: Synergies and trade-offs between energy and food production?”. Financial support from the European Commission and the Academy of Finland is kindly acknowledged.

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  1. Top of page
  2. ABSTRACT
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Conclusions
  7. Acknowledgements
  8. References
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