LEIDEN UNIVERSITY
FACULTY OF GOVERNANCE AND GLOBAL AFFAIRS
INSTITUTE OF PUBLIC ADMINISTRATION
COURSE OF PUBLIC ADMINISTRATION
Economics and Governance Track
MASTER THESIS
A
NALYSIS OF THEE
UROPEANU
NIONM
EMBERS
TATES’
R
EADINESS FOR THET
RANSITIONT
OWARDS THEC
IRCULARE
CONOMYConstruction of the Circular Economy Readiness Index and Determination of the Influencing Factors
Prepared for: P.W. van Wijck
by
Anastasia Gnusina Student ID: 1904124
Due Date 10 January 2018
Abstract
This study is devoted to the empirical investigation of the circular economy. It has become a relevant topic at the EU level in the last years with the main explanation that the transition towards the circular economy would solve a lot of contemporary economic problems such as dependence on the scarce resources, environmental damages, resource efficiency in production and consumption as well as development of eco-friendly technologies and job creation. Even though the concept of the circular economy is a widely applied by policy-makers, there is no common measurement framework elaborated that could define the progress in transition, however it is an urgent issue that is addressed by many non-governmental organisations and governmental agencies. That is why this study is focusing on the constructing a composite indicator, called for the Circular Economy Readiness Index (CERI), that measures the readiness of the EU countries for the transition towards the circular economy as well as empirically tests the factors that influence this readiness. Adopting the methodological framework and design from the studies on the Environmental Kuznets Curve hypothesis, the economic growth, environmental policy stringency and environmental public expenditures were considered to be the factors influencing the Circular Economy Readiness Index. Hence, it was discovered that in terms in circular economy, the economic growth is neither necessary, not sufficient in general, whereas a simultaneous interaction of environmental policies and financial support for environmental protection is both necessary and sufficient factor for CERI increase.
iii Table of Contents
List of Tables ... iv
List of Figures ... v
1. INTRODUCTION ... 1
1.1. RELEVANCE OF THE RESEARCH ... 1
1.2. RESEARCH QUESTION ... 2
1.3. STRUCTURE OF THE RESEARCH... 3
2. LITERATURE OVERVIEW ... 3
2.1. ENVIRONMENTAL KUZNETS CURVE ... 3
2.1.1. Formation of the Hypothesis ... 3
2.1.2. Initial Explanations and Empirical Evidence of the EKC ... 5
2.1.3. Econometric Framework ... 7
2.1.4. Extended Argumentation of the EKC hypothesis ... 8
2.1.5. Critique of the Econometric Approach ... 11
2.2. UNDERSTANDING THE CIRCULAR ECONOMY ... 12
2.2.1. Definition and Origin ... 12
2.2.2. Dimensions of the Circular Economy ... 14
2.2.3. Measurement ... 15
2.2.3.1. The Ellen MacArthur Foundation ... 15
2.2.3.2. European Environmental Agency... 17
2.2.3.3. The European Academies’ Science Advisory Council ... 18
2.2.3.4. PBL Netherlands Environmental Assessment Agency ... 22
3. RESEARCH DESIGN ... 23
3.1. VARIABLES AND OPERATIONALISATION ... 23
3.1.1. Dependent Variable ... 23
3.1.2. Independent Variable ... 24
3.2. METHODOLOGY ... 25
3.2.1. Literature Review ... 25
3.2.2. Construction of the Composite Indicator ... 25
3.2.3. Regression ... 27
3.3. SAMPLE ... 27
4. PART I OF THE STUDY:DEVELOPMENT OF THE DEPENDENT VARIABLE ... 27
4.1. BUILDING UP A COMPOSITE INDICATOR ... 28
4.2. RESULTS ... 37
5. PART II OF THE STUDY:REGRESSION ... 40
5.1. SELECTION OF THE INDEPENDENT VARIABLES ... 40
5.2. REGRESSION MODEL ... 45 5.2.1. Data ... 45 5.3. EMPIRICAL RESULTS ... 46 6. CONCLUSION ... 50 7. DISCUSSION ... 51 7.1. STRENGTHS ... 51 7.2. LIMITATIONS ... 52 7.3. POLICY IMPLICATIONS ... 53 8. REFERENCES ... 54
iv List of Figures
Figure 1: The Environmental Kuznets Curve (generalized) ... 4
Figure 2: Models of Linear and Circular Economy ... 13
Figure 3: Scheme of Material Flow Measured by the MCI ... 16
Figure 4: Hierarchy of the Requirements for the CE Indicator ... 28
Figure 5: The Scheme of Deriving the Sub-Indicators for CERI ... 30
Figure 6: CERI by the EU Member States, 2000-2011 ... 38
Figure 7: Comparison of CERI with Different Amount of Sub-Indicators ... 40
v List of Tables
Table 1: Structure of the CE measurement by EEA and PBL ... 30
Table 2: The Principles of the CE with Corresponding Sub-Indicators ... 32
Table 3: Clusters of Countries ... 35
Table 4: Descriptive Statistics ... 46
Table 5: Correlation between Dependent and Independent Variables ... 46
Table 6: Estimation Results ... 47
1
1. I
NTRODUCTIONThe environmental policy is one of the most important and targeted competences of the European Union (EU). With the rise of the environmental international treaties and relevance of the Sustainable Development Goals, the EU actively participates in passing the regulations on the environmental protection. Its success is reflected in the widest emission trading scheme (ETS) in the world and the strong facilitation and support of the research and development for the renewable energy sources. However, in the last two years the European Commission1 actively disseminates the ideas of the circular
economy2, where “the value of products, materials and resources is maintained in the
economy for as long as possible, and the generation of waste minimised” (EC, 2015). By the end of 2015, the Commission published the EU Action Plan for the circular economy3 showing a high concern on the preservation of natural resources and their efficient exploitation. These worries have economic cores because the resource scarcity causes serious market failures in the fair pricing of raw materials (EC, 2015). Apart from the negative production externalities that arise from the non-inclusion of social costs generated by the production processes (Lodge and Wegrich, 2012), the volatile prices of resources undermine the stability of the businesses (EC, 2015).
Relating to the resource scarcity, the EU is highly dependent on the oil imports. By 2015, 89% of consumed oil products were imported and more than a half of these imports were supplied by four countries. The statistics on the air pollution is better because the amount of emissions have been reduced by 50% since the 1990s. However, from 2013 there is a slight increase in particular air pollutants (Eurostat, 2017c).
The worst situation is with waste. In 2014, the EU generated 2,503 million tonnes of waste that was the highest amount recorded since 2004. The hazardous waste represented 3.8% of the total amount. Moreover, the waste per capita has increased with an average annual growth rate 2% for more than a half of the EU countries (Eurostat, 2017c).
A mechanism of a traditional linear economy has no capacity to resolve these negative consequences of unsustainable production and consumption and requires a new approach of the economic growth (Ghisellini et al., 2016; Geissdoerfer, 2017; Nasir et al., 2017).
1.1. Relevance of the Research
The transition towards the circular economy solves problems of dependence on the scarce resources, diminishes the environmental damages, increases efficiency in production and consumption as well as it is expected to accelerate eco-friendly technologies and create jobs (EC, 2014). However, the transition towards the CE is a long-term process.
1
Hereafter, the Commission.
2 Hereafter, the circular economy or the CE, where appropriate.
3 It is a working document of the Commission that presents the aim, principles and actions related to the
transition towards circular economy in general terms (Eur-Lex, 2017; EC, 2015); hereafter, the Action Plan.
2 The benefits of launching the circular economy are recognized by both policy-makers
and scholars. While the definition and wide principles of the CE are elaborated, a unite approach to measure the CE is not accomplished. All proposed methods are either theoretical and require a selection of the new data in order to create indicators, or are based on the cross-indicator comparison that show the changes in the particular indicators and not the progress of the circular economy itself. The Commission (2015) recognizes the urgency of creation of the CE assessment techniques that are needed to (i) monitor the changes in the process of the transition, and (ii) control for the effectiveness of the EU actions and countries’ incentives.
The transition process covers such fields as production design and processes, consumption patterns, waste management and application of secondary raw materials (EC, 2015). At the moment, the EU is at the preparation stage before the actual transition towards the circular economy. The Commission has already amended legislative proposals on waste and some proposals on issues mentioned in the Action Plan are still in the process of revision (EC, 2017).
According to Lodge and Wegrich (2012), the composition of an appropriate regulation content and choosing for a right enforcement tool as well as monitoring approaches define a successful application of the regulation. In the context of the CE, the Commission has to take into consideration that the process of shifting the economy towards the circularity is a long-term operation that requires a sufficient support from the public and private sector. The factors that accelerate the transition are primarily the financial tools (EASAC, 2015). Moreover, the EU member states differ in other macro-economic factors. Hence, the EU has to understand and consider these differences for better formulation of the regulation before the starting the transition period to the circular economy. However, a comprehensive reflection of current position regarding the circular economy is impeded due to the lack of the common measurement framework.
1.2. Research Question
The aim of this study is to develop an approach that allows measuring the level of the circular economy among the EU member states before the transitional period, empirically investigate the factors that influence the level of the circular economy before the transition period and discuss policy implications of the results.
Therefore, the research question is the following: How does the economic development of the EU member states influence the conditions of the initial stage of the circular economy at the beginning of its transition? In order to answer this question, it is first necessary to define the framework for the CE measurement. Then, before assessing the causal relationship of the research question, it is required to define potential economic and policy-related factors that influence the CE.
The circular economy is a new phenomenon of the environmental policy and this situation causes several barriers before answering the research question. That is why the research question has to be supported by several sub-questions related to the process of
3 development of the framework for the CE and choosing appropriate determinants of the
CE before the transition period. These sub-questions are:
How is it feasible to measure the circular economy at the moment?
What is meant by the initial stage of the circular economy and how can it be reliably assessed?
Which indicators can measure the initial stage of the circular economy?
Which factors can explain the differences between countries at the initial stage
of the circular economy?
These sub-questions provide the guidelines for this study and define its course. Answering them step by step allows to empirically test the main research question.
1.3. Structure of the Research
Section 2 provides a broad overview on the fundamental concepts of this study: Environmental Kuznets Curve hypothesis and Circular Economy. The literature on the former reveals the principles of the theory that is used further and the latter is represented from different perspectives giving a priority to the measurement approaches. The explanation of variables, their causal relationship mentioned in the research question and methods to answer it, are explained in Section 3, whereas Section 4 is devoted to the construction of the composite indicator by analyzing previous literature on the CE measurement. Section 5 includes the analysis of the independent variables in terms of the EKC hypothesis and, finally, applies econometric tools for the research question assessment. Section 6 gives a brief conclusion, whereas Section 7 is about discussion of strengths and limitations of the study, and policy application of the results.
2. L
ITERATUREO
VERVIEW2.1. Environmental Kuznets Curve
This Section explains an empirically derived Environmental Kuznets Curve hypothesis. It is further adapted and used in order to answer the research question. The description is focused on showing the operationalized variables, method of assessment and significance of the results.
2.1.1. Formation of the Hypothesis
The Environmental Kuznets Curve (EKC) is a relatively new phenomenon that formulates a systemic relationship between the level of income and pollution and is visualized on Figure 1 as an inverted U-shape curve. The income level is put on the horizontal axis, and vertical axis reflects the pollution level. It hypothesizes that with the economic development the pollution amounts increase, however, at particular income level (turning point) the pollution is suspended and further income growth results in diminishing levels of pollution (de Bruyn, 1997).4
4
The pollution is a collective meaning for all types of the environmental problems such as particular air pollutants, degradation (soil), waste generation, etc., and is often substituted by a general term “environmental quality”.
4 The name of the EKC was coined
from so called Kuznets curve that was developed by Simon Kuznets in 1955 and described a similar relationship between the income level and inequality (Torras and Boyce, 1998) where an early stage of an economic development is accompanied by unequal income distribution but with growth the inequality diminishes (Kuznets, 1955).
The EKC stems from the empirical works that tested the income
growth influence on the
environmental degradation. The pioneers in the field are Grossman and Krueger (1991),5 Shafik and
Bandyopadhyay (1992),6 and
Panayotou (1993).7 The results of these studies triggered the formulation of different movements. The one is an academic application where all the subsequent studies used the patterns of the EKC and were based on the methodology of these authors. The EKC hypothesis has opponents and proponents where the latter extend the initial findings, whereas the former examine the validity of the results by analyzing the methods of estimation, data availability and generalization possibility (Yandle, Bhattarai and Vijayaraghvan, 2004). The second application of the initial EKC results was realized by international organizations and policy-makers.
In its annual development report, the World Bank (1992) implicitly used the EKC hypothesis for promoting further economic growth. In contrary to dominating idea of scarce resources that is reflected in the “The Limits to Growth” (1972), the EKC allowed to justify the current environmental degradation, stating that the economic growth is not a threat to the environment but a mean to an eventual environmental improvement. The EKC was a devious instrument in promoting the economic growth in terms of sustainability. As Stern (2004:1419) remarks: “The possibility of achieving sustainability without a significant deviation from business as usual was an obviously enticing prospect for many”.
However, it is important to mention that the World Bank (1992) was careful in its formulation regarding the environmental recovery. In considered the caveats mentioned by Grossman and Krueger (1995) of possible misinterpretation of the EKC hypothesis
5
The research was prepared for the conference relating to the NAFTA negotiations (Grossman and Krueger, 1991).
6 The research was a background study for the World Development Report used by the World Bank
(Stern, 2004).
7 The research was conducted on the request of the World Labour Organization (Panayotou, 1993).
turning point Economic Development, GDP per capita L e v el o f E n v ir o n m e n tal Deg rad atio n
Figure 1: The Environmental Kuznets Curve (generalized)
Source: self-elaborated based on Panayotou (1993:1151); Yandle, Bhattarai and Vijayaraghavan (2004:3).
Industrialization period
5 (Stern, Common and Barbier, 1996; Torras and Boyce, 1998). Thus, the report
emphasized that the environmental issues are not “transitional phenomena” and cannot be automatically solved by economic growth. The EKC has to be supported by stringent environmental standards and strict policy enforcement (Torras and Boyce, 1998).
Due to the fact that the EKC is an empirical phenomenon, it is essential to reflect not only the results of the previous studies, but also to represent the evolution of rationale for the EKC hypothesis, methodology and its scientific criticism.
2.1.2. Initial Explanations and Empirical Evidence of the EKC
The empirical investigation of the EKC hypothesis indicates an existence of the curve as such, however, it does not give any explanations on why it is shaped as an inverted U. That is why every study is guided by analysis of potential causes and assumptions of the EKC that advocate the income level as an explanatory variable.
Factors supporting the EKC hypothesis
Panayotou (1993) creates a framework of assumptions that is based on the factors that influence the stock of natural resources and environment in the country.
An intuitive reason for higher pollution levels results from the increased production volumes and is called for (i) the scale effect. Another common way to explain the inverted U-shape of the EKC is (ii) a structural change in the economy that appears in the process of economic development. Figure 1 shows the division of the economic development steps according to the pollution level. During the pre-industrialisation period the initial stages of development are dominated by agriculture and primary industries that lead to a slight decline in natural resources stock. Higher income level is followed by a take-off of industrialisation that extensively generates pollution and waste. With further development, an economy that stabilizes and shifts towards more information-intensive industries finds itself in the post-industrialisation period.
Even though the countries may have the same structure of the economy, the levels of pollution depend on the (iii) quality of technology. It is assumed that modern technologies are cleaner due to the awareness of environmental issues. Thus, allowing a free trade of environmentally adjusted technology facilitates the discontinuity of pollution.
Further factor that determines the pollution is (iv) strictness and enforcement of the environmental regulations, where tougher environmental standards constrain the activities that cause environmental degradation. However, it depends on (v) society’s demand to protect the environment and the ability to invest in it. If people are poor, it is less likely to collect appropriate taxes to provide a good basis for the environmental expenditures. According to Panayotou (1993), all these factors depend on the income level so it is plausible to use income level as a proxy variable for all of them.
Continuing the list of these potential causes of the EKC existence, the conditions of trade derive further factors. Grossman and Krueger (1991) were the first who estimated the relationship between income level and environmental quality (Suri and Chapman, 1998). The goal of their study was to explore whether the free-trade agreement between
6 USA and Mexico, North American Free Trade Agreement (NAFTA), would cause any
environmental pressure induced by industrialisation. Their argumentation of adopting the income level as a main explanatory variable relies on the effects created by trade liberalization and direct foreign investments described further.
The trade is linked to the scale effect, when (vi) increased demand for imports causes a higher intensity of exploitation of natural resources. Complementing the factor (iv) the trade creates the composition effect resulting from the competitive advantage. A trading country may face the problem of environmental regulation that induces the country to specialize in economic activities that are less regulated by the government and “shift out the production in industries where the local costs of pollution abatement are relatively great” (Grossman and Krueger, 1993:4). Thus, (vii-a) the comparative advantage of a lenient environmental regulation caused by trade liberalization is damaging the environment. In traditional terms of comparative advantage every country takes into consideration the cross-country differences and (vii-b) specializes in the fields where it makes an efficient use of abundant factors of production. Here the effects cannot be purely estimated because it depends on the sectors of production covered by the country. Furthermore, the technique effect incorporates the factor (iii) and also states that better technology is beneficial to less developed countries since it increases the output per unit of pollution. This may cause the economic growth leading to the factor (v).
In addition, Shafik and Bandyopadhyay (1992) discuss the impact of political and civil liberties. They assume that there is higher level of freedom of expression in more democratic countries with free elections, existence of multiple parties, and decentralization of power. It results into a higher pressure from interest groups on governments to provide measures for environmental protection.
Empirical Results
The early empirical results of the EKC hypothesis are ambiguous. The divarication appears due to many reasons. The studies were limited in data availability and that is why some data lack the accuracy. It also influences the variable choice for the econometric estimation. The regression equations differ in the structure, since apart from dependent and independent variables they include various control variables. Grossman and Krueger (1994) evaluated the impact of GDP per capita on three different air pollutant including site-related variables8, time trend and trade intensity variables in cities of different countries. The coefficients on the income levels confirmed the EKC hypothesis at 0.001 significance levels. The time trend and trade intensity had negative estimates on the significant level only for one air pollutant type.
Shafik and Bandyopadhyay (1992) applied the EKC hypothesis on ten indicators of environemental degradation such as lack of clean water, lack of urban satination, river quality, air pollutants, carbon emissions and waste. The explanatory variable also was expressed in GDP per capita. The sample consisted on 149 countries covering the years
8 Included city and site characteristics such as population density, location (coast or desert), or type of area
7 from 1961 to 1986. The regression was supplemented by site-related variables, location
as well as policy variables (trade policy, electricity prices, etc.). The results were difficult to interpret on the majority of the variables of environmental degradation. However, similar to the results of Grossman and Krueger (1994), the EKC approved to exist for air pollutants, whereas amount of waste, deforestation and emissions tend to grow with increasing income.
Panayotou (1993) confined his study with four variables of environmental degradation,
that is, SO2, NOx, SPM and deforestation. The sample includes cross-sectional data on
68 countries. The independent variable represents the nominal level of GDP, including a control variable on population density and dummy variable on the tropical countries. The results confirm the previous estimates for these variables.
2.1.3. Econometric Framework
The regression structure and choice of variables depend on the theoretical background and assumptions. This section is devoted to the representation of relevant components of the regression equation used in various studies.
The common EKC regression equation is:
log 𝑃𝑖𝑡 = 𝛼𝑖 + 𝛾𝑡+ 𝛽1log 𝑌𝑖𝑡+ 𝛽2log 𝑌𝑖𝑡2+ 𝛽3log 𝑌𝑖𝑡3+ 𝛽4𝑍𝑖𝑡+ 𝜀𝑖𝑡, (1)
where 𝑃𝑖𝑡 represents a particular type of environmental degradation for country i in the
year t, and 𝑌𝑖𝑡 is per capita GDP for the country i in year t. Sometimes the studies neglect the logarithmic expression of dependent variable, however, Stern (2004:1422) argues that “regressions that allow levels of indicators to become zero or negative are inappropriate except in the case of deforestation where afforestation can occur”. The quadratic function of 𝑌𝑖𝑡 is used in order to find the inverted u-shape EKC, where 𝛽1 > 0, 𝛽2 < 0, and 𝛽3 is insignificant (Bruyn, 1997). A cubic function of the income level is used by occasion since there is not an adequate interpretation of the coefficient (McConnell, 1997).
A variable 𝑍𝑖𝑡 represents the covariates that supposed to have an impact on the environment such as population density, openness to trade or other geographical
covariates (Torras and Boyce, 1998; Suri and Chapman, 1998; Bruyn, 1997). An 𝜀𝑖𝑡 is
the error term.
A special attention has to be paid to the first two parameters, 𝛼𝑖 and 𝛾𝑡. The former represents the country or site-specific effect, whereas the latter stands for the time- specific effects. It is assumed that an environmental quality may differ over the countries at any particular income level but simultaneously the income elasticity is the same in all countries at a given income level. Thus, the time specific parameters consider the time-varying omitted variables and other shocks common to all countries (Stern, 2004).
The EKC is usually assessed with the panel data. That is why some studies include random and fixed effect models in the regression. In the fixed effect model 𝛼𝑖 and 𝛾𝑡 are perceived as parameters, but random effect model treats them as components of random disturbance (Stern, 2004).
8 Another important aspect to reveal refers to the occasions where reduced-form or
structural models are applied.9 The equation (1) represents the example of the mostly used reduced-form model. In the context of the EKC, it is assumed that the income level is a direct determinant of the pollution level. It encompasses other variables such as changes in economy or institutions that potentially could influence the dependent variable (de Bruyn, 1997). McConnell (1997:385) lists these variables that are subject to interpretation for the EKC hypothesis and takes the income per capita as a proxy for all the changes they cause:
“ (…) the establishment of governmental agencies responsible for pollution control, increases in education and contaminant increase in awareness of the effects of pollutants, change in the product mix of GDP from agriculture to industry or to services, reductions in the inequality of income distribution and expenditures on the research and development”.
The structural model implies that there is an indirect relationship between the income level and emissions levels, and other variables have a theoretical background. It is recognized that reduced-form model creates a problem of omitted variable bias (OVB) because neglected variables are significant (de Bruyn, 1997).
However, Grossman and Krueger (1995) clarify why they initially chose reduced-form model. They emphasize that equation (1) estimates the net effect of the income level, whereas a structural model would be complicated by estimation of biases and precision. To add, by the time when the studies were conducted, there were not sufficient data on additional variables such as technological innovation or environmental policy.
2.1.4. Extended Argumentation of the EKC hypothesis
After the first wave of investigations of the EKC hypothesis, the scholars saw the possibility for interpretation and a deeper research of the EKC. They used new instruments and ideas, discovering a context even though the econometric analysis of income-pollution relationship was weak or was not even acknowledged. Besides, the variable estimation became more theoretically rationalized.
Energy Consumption and Trade
Thus, Suri and Chapman (1998) noticed that the trade was included into the regression as a proximate factor but the effect of the movement of goods and services that cause pollution was never estimated. They offer to measure an environmental stress (pollution) through the consumption of primary commercial energy per capita that depends on the energy source and its emissions per unit of energy expressed as:
𝑝𝑖𝑡 = 𝑎𝑖𝑡𝐸𝑖𝑡, (2)
where 𝐸𝑖𝑡stands for the amount of energy used, 𝑎𝑖𝑡 defines the emissions per unit of the particular source. Hence, the increase of energy consumption 𝐸𝑖𝑡 results in increased
9 The structural model estimates the relationship that can be explained by the economic theory but
reduced-form model allows to construct a probabilistic causality about the phenomena being tested (Sant, 1975).
9 pollution 𝑝𝑖𝑡.
The main explanatory variable from previous studies (income level) is supplemented by the variables that reflect the combination of trade and structural change. The authors argue that it is necessary to include such trading variables that embody the consumption of the energy for both energy-intensive and non-energy intensive goods. The independent variables are ratio of imports of all manufacture goods to domestic production of all manufactures, ratio of exports of all manufacture goods to domestic production of all manufactures, interaction variable of income and manufacturing imports, the share of total manufacturing share in GDP.10 The estimated coefficients satisfied the expectations. The imports of manufacturing goods decrease the level of consumption, whereas exports increase, as well as a general share of manufacturing sector is positively related with the consumption level. It was also confirmed that industrialized countries benefit more from the substitution effect of imports by shifting pollution-intensive production abroad (Suri and Chapman, 1998).
Socio-economic Inequalities and Pollution
Torras and Boyce (1998) propose an interesting theoretical background that allows to consider the level of literacy, political rights and civil liberties to be significant factors in the EKC hypothesis. They support the opinion of Grossman and Krueger (1991) that the economic growth alone cannot improve the environmental quality and the environmental policies facilitate the resource preservation. However, the fact that the demand for environmental quality is associated with richer countries is put in doubt and challenged by “power-weighted social decision rule” (PWSDR) that maximizes the net benefits weighted by power of those to whom they accrue:
𝑚𝑎𝑥 ∑ 𝜋𝑖 𝑖𝑏𝑖, (3)
where 𝑏𝑖 represent maximized net benefits from the pollution-generating activities11 but
𝜋𝑖 is a net power. Following the equation, if beneficiaries of pollution generation have
more power, then there are expected high levels of pollution, and vice versa. It is expected that net benefits and power are correlated with the income level. Hence, the distribution of power is affected by income inequality, political rights or other civil liberties that, in turn, influence the pollution level. This is reflected in the equation (4):
𝑃𝑂𝐿 = 𝑓(𝑌, 𝜋, 𝑍).12 This theoretical basis allows adding such variables as Gini
coefficient, literacy rate and political rights and civil liberties to the reduced-form model.
The main finding of this study is that the income parameter decreases significantly by the inclusion of inequality variables. The coefficients on the inequalities vary across low-level and high-income countries. However, the hypothesis that greater inequalities in the distribution of power lead to higher pollution levels is confirmed.
10 See detailed explanations of hypotheses in Suri and Chapman (1998).
11 The individuals are divided in those who benefit from pollution generation, for example, producers that
receive pollution subsidies or consumers that obtain advantage from that subsidy in a form of lower prices, and losers that bear the cost of pollution. Under the cost-benefit analysis it is required to define the level of pollution that maximizes the net benefit for all individuals (Torras and Boyce, 1998).
12 𝑃𝑂𝐿 stands for pollution level, 𝑌 is income level, 𝜋 is aggregate variable for inequalities and 𝑍
10 Environmental Policies and Emissions
Another study substituted the econometrical approach of the EKC hypothesis estimation by decomposition analysis.13 The motivation behind such decision lies in the limits of reduced-form models and multicollinearity problems.14 It was found that structural changes fail to explain the downward sloping part of the EKC, whereas environmental policy is a relevant instrument in decreasing the amounts of pollution (de Bruyn, 1997). The regression estimates the environmental policy targets defined by Second Sulfur
Protocol15 with the regard to the income level of the countries. The question of interest
was whether the environmental ambitions belong to the higher-income countries. The variable Zit from equation (1) includes a variety of factors that could influence the
reduction target. Those were: population density, emissions per capita, dummies for former communist countries, and dummies for countries with an eastern coast line. The results were reasonable. The higher rates of reduced emissions are associated with higher incomes. The countries with higher emission concentration are subject to tighter restrictions. Furthermore, the former USSR countries agreed to reduce emissions in addition because of the drop in the economic activity. The results are statistically significant (de Bruyn, 1997).
Relative Income Effect and Environmental Expenditures
For Magnani (2000) the interpretation and building the foundations for the EKC hypothesis starts with the decomposition of pollution. The actual level of pollution
depends on incipient pollution 𝐼𝑖𝑡 (pollution level created by production if
environmental costs were zero) and abatement 𝐸𝑖𝑡, expressed as:
𝑚𝑖𝑡 = 𝐼𝑖𝑡− 𝐸𝑖𝑡. (5)
Taking into consideration the income-pollution relationship it appears that both 𝐼𝑖𝑡 and
𝐸𝑖𝑡 depend on the economic growth, meaning that economic development is
accompanied by increase in abatement. In other words, “if income elasticity of demand for environmental amenities16 is large, the demand for pollution abatement policies is likely to rise with GDP per capita” (Magnani, 2000:434). However, the author expands this statement by investigation of the relative income effect on the consumption decisions of individuals in rich countries. The hypothesis states that the environmental protection depends on two factors: absolute income effect (rise in income per capita leads to the increasing capacity to pay for environment amenities) and relative income effect where high income inequality reduces the willingness to pay for environment. Equation (6) represents the utility function of individuals 𝑈𝑙 that depends on the
consumption of private good 𝑐𝑙 and public good 𝑄 (environmental quality):
𝑈𝑙= 𝑐𝑙+ 𝛿𝑙𝑄, (6)
13 See the details in de Bruyn (1997). 14
See p. 12.
15
An agreement on reducing Sulphur Emissions and undertaking other energy related measures (UNECE, 1994).
16 Implies that with the increase in income, the environmental quality is subject to the higher demand by
assumption that environment quality is a normal good (Franklin and Ruth, 2012). J-curve for abatement is characterized by stricter abatement policies and environmental awareness (Selden and Song, 1995).
11 The 𝛿𝑙 stands for the degree of preference to care about environmental quality. It is
assumed that environmental quality is a public good and represents a market that has to be treated by public intervention, that is, 𝑄 = 𝑄(𝐸). The environmental quality 𝑄 depends on the public expenditure 𝐸 allocated for the environmental care, but 𝐸 is directly financed by the tax payers. Assuming that all individuals have different income levels, the income inequality appears if the majority of individuals have income below
the average. That is why preferences for the public good 𝛿𝑙 positively correlate with the
individual’s relative income.17
“The larger is the income inequality, the lower is the relative income of the individual, and more he is willing to spend in consumption of private good rather than financing public environmental expenditure” (Magnani, 2000:437).
In regression, the dependent variable is expressed as per capita research and development expenditure for the environmental protection and an independent variable of GDP per capita is supported by inequality ratio. The coefficients for income per capita and income inequality have expected signs, the former is positive and the latter is negative, both statistically significant. In her conclusions, Magnani (2000) emphasizes that the results of the study are applied to high-income countries because the estimations were explaining the downward sloping part of the EKC that implies that at that level the countries already achieved higher levels of income. The study contributed to the EKC hypothesis because it showed that “income growth per capita is not necessary not sufficient to guarantee a decline in the environmental damage” (Magnani, 2000:437).
2.1.5. Critique of the Econometric Approach
Due to the ambiguity of empirical evidence on the EKC hypothesis, it was subject to a strong critique of both theoretical assumptions and econometric approaches.
The core argument of Arrow et al. (1995) is that the EKC hypothesis is generalized to all pollutants, whereas the evidence is valid only to local pollutants that have short-term costs. Such heavy pollutants as CO2 or waste generation have long-term and more dispersed costs that intend to rise with the income growth. Moreover, it was noted that the EKC hypothesis was estimated with the assumption that natural resource stock has no limits. This one-dimensional causality neglected the fact that economic growth can be reduced by the resource depletion.
Furthermore, Arrow et al. (1995) and Stern (2004) believe that the EKC relationship appear only due to the effect of open trade, formulated within the Hekscher-Ohlin trade theory.18 Hence, the reduction of pollution in one country is a result of shifting the pollution-intensive production to another country.
The main econometric weakness of the EKC is the disregard of fixed and random effect models in many studies. The difference in estimated parameters for fixed effect model and random effect model implies that there is omitted variable bias. If there is a correlation between parameters and independent variable, then the random effect model
17 The relative income is expressed as a ratio of individual income and average income, 𝑅
𝑙= 𝑦 𝑌, but
parameter 𝛿𝑙= 𝛿(𝑅𝑙).
12 cannot be applied because of consistent estimation. If this is the case, the estimations of
the regression are conditional on the country and cannot be extrapolated to another sample (Stern, 2004).
Another problem appears from the correlation of explanatory variables (multicollinearity). The results of such regression are difficult to interpret since the individuals effects of the variables cannot be disentangled (de Bruyn, 1997).
Recalling the factors that influence the environmental quality described in Section 2.1.2., the interplay of those factors reflects the complexity of the EKC phenomenon. For example, the scale and time effects (increased income over the time) may be different in developed and developing countries, explaining the evidence of the EKC hypothesis particularly in high-income countries because their growth rates are lower so that pollution reduction efforts can overcome the scale effect (Stern, 2004).
2.2. Understanding the Circular Economy
This Section covers the concept of the circular economy. It is relatively new in a scientific world. Even though the term is backing to the 1970s, the circular economy became popular among policy-makers and scholars in the last decade (Smol, Kulczycka and Avdiushchenko, 2017). Due to the variety of the scientific opinions and approaches to represent circular economy, it is necessary to create a comprehensive description of the concept including all perspectives.
Thus, the circular economy is discussed in general terms and classified by different aspects. The emphasis is put on the the CE measurement aspects.
2.2.1. Definition and Origin
A prevailing way to present a circular economy is to juxtapose it to the linear economy. Figure 2 shows the models of material flow movement for both approaches. The linear economy is optimizing the components, whereas the circular economy strives to improve the system (EMF, 2015a; Murray, Skene and Haynes, 2017). The linear economy is a leading economic growth model since the end of World War II, which follows the logic of “extract-produce-use-dump” (Korhonen, Honkasalo and Seppälä, 2017). The scarce resources are used for production of goods and services that are further consumed and by the end of the usage period are disposed taking a form of waste. The value is maximized through the production and consumption (Het Groene Brein, 2017). In contrary, the circular economy maximizes the value by keeping the resources within the system as long as possible and prolonging the life-cycle of the product by delaying its disuse (Nasir et al., 2017). In other words, there are two value creation cycles (Moreno et al., 2016). The (i) bio-cycle is responsible for the regeneration of retrieved natural resources in order to keep the bearing capacity of the natural systems with or without people’s help. The (ii) technical cycle is in charge of drawing out the maximum value of product and minimizing the amount of waste (EMF, 2015b). In the ideal CE system the material loop is closed19 and there is no inflow of
19 In a closed loop, used products come back to the original manufacturer and components or materials are
13 new natural resources and outflow of landfill waste, however, as Potting et al. (2017a)
claim, it is not feasible in practice. Nevertheless, it is expected that the value is created through the retaining of natural resources and processing of the existing products made from already extracted natural resources. More extensively, the technological cycle of the circular economy has strategies, known as R-strategies, that define the strength of the circular economy (Potting, 2017a). Ordered from low to high levels of circular economy, these strategies are: Recover, Recycle, Repurpose, Remanufacture, Refurbish, Repair, Reuse, Reduce,
Rethink and Refuse
(Potting, 2017a).
Up to date, the recycling stays for the most widely used strategy all over the world which contributes to the CE principles (Haas et al., 2015). In many studies and also in practice, the CE economy is perceived as an efficient system to deal with waste where recycling takes a leading position in CE strategies20 (Maio and Rem, 2015;
Ghisellini, Cialani and
Ulgiati, 2016; Huysman et al., 2017). But the CE is more than an instrument used in a combat with
waste. The waste
elimination is one of many CE goals (Banaite, 2016).
The publication of French Ministry of the Environement (2017:6) “10 Key Indicators for Monitoring the Circular Economy” defines the CE as: “economic system based around the exchange and production methods that, at every stage of the product life cycle (goods and services), aims to increase the efficiency of resource usage and diminish environemental impact, while also improving the well-being of individuals citizens”. This definition reveals the sustainable development cores of the CE. But CE creates conditions for stronger sustainability (Geissdoerfer et al., 2017), where economic growth is decoupled from scarce resource usage and production of environmental damage (EASAC, 2015; Elia, Gnoni and Tornese, 2017).
20 Depends on the material (advanced level for metal, paper or glass, whereas stays primitive for
demolition or construction) (Haas et al., 2015). Natural Resource Input
Waste
Figure 2: Models of the Linear and Circular Economy
14 There is a discourse about the relationship between sustainability and the CE. Some
consider the CE as a result of evolution of sustainable development principles because it goes deeper into the issue of production and environmental impacts. “Sustainable development requires balanced and simultaneous consideration of the economic, environmental, technological and social aspects of an investigated economy, sector or individual industrial process as well as of the interaction among all these aspects” (Ghisellini, Cialani and Ulgiati, 2016:12). Sauve, Bernard and Sloan (2016:53) argue that sustainable development is focusing on the “downstream processes of production and consumption”. That implies that it is acting within the linear economy mechanisms with a strong emphasis on the waste reduction, recycling and reduction of pollution. Meanwhile, the CE encompasses the entire production processes, from the choice for the product or service design up to its disposal stage (Korhonen, Honkasalo and Seppälä, 2017). However, when scrutinizing the CE in its gains or “wins” from the sustainable development three pillar perpective (economic, social and environmental), there is a compatibility and consistency between these two conceptualisations. To mention some of the environmental benefits estimated by the Commission (2015), the CE leads to the reduction of natural resources extraction, diminishing amounts of waste and causes less environmental damage because of use of renewable energy. In economic terms, more circular production approaches curtail production costs associated with raw material purchase, “value leaks and losses” and other expenditures that refer to the environemental regulations and taxation. One of the social “wins”, also emphasized by Commission (2015), refers to the new employment opportunities which appear along with the new production processes. Besides, the CE creates a social order where goods and services are shared between people rather than used for consumption of one individual (Korhonen, Honkasalo and Seppälä, 2017).
2.2.2. Dimensions of the Circular Economy
The concept of the CE is a multi-dimensional so the sole definition itself cannot reveal all the processes related to the circular economy. Therefore, it is better to systemize all the relevant aspects. Elia, Gnoni and Tornese (2017) distinguish between different faces of the CE by organizing a four-level framework to reflect its aspects. They are: (i) processes, (ii) actions, (iii) requirements, and (iv) implication levels.
Processes
The processes reflect the life-cycle of the product and circular movement of the resources starting from the material input for the product or service, followed by its design and production approach/delivery, consumption phase and its end-of-life resource management that should continue the resource loop and return to the material input phase.
Actions
Next aspect – actions, also called for building blocks (EMF, 2012) – represent technical or economic schemes that promotes and strengthen the CE paradigm. These include: circular product design and production (eco-design), developing novel business models (widen consumer-to-consumer channels), promoting cascading (when high quality
15 recycling is not possible, to use other R-strategies) and widening collaboration among
sectors (knowledge sharing). Requirements
Requirements are also perceived as characteristics or principles of the CE. Adopted from the EEA report (2016), the CE is described as the system that:
reduces the use of virgin natural resources and minimizes the amount of
input attained by deriving maximum material value from fewer natural resources;
increases the share of renewable and recyclable resources and energy to achieve closure of material loops;
minimizes emission volumes caused by lesser use of raw materials and cleaner material cycles;
diminishes the material losses/residuals by minimizing the generation of waste, its incineration and landfill;
keeps the value of products, components and materials, that is, increases
the duration of the products lifetime by their components reuse and a high-quality recycling.
Implication Level
The last but not least aspect of the CE – an implication level – is the most mentioned and discussed issue relating to CE measurement approaches (Saidani et al., 2017). Due to the complexity and scale of the CE, its implementation is performed on three levels, which are commonly acknowledged by all the scientific researchers (Saidani et al., 2017; Banaite, 2016): micro, meso and macro. The micro level applies to individual companies or consumers, but meso level refers to the activity of industrial parks or industry networks. The macro level contains policies and programs that are operating within the city province, region or nation.
2.2.3. Measurement
Apart from reconstructing the origins and analyzing the benefits to the economy of running the circular economy, the policy-makers are searching for appropriate means to measure the progress made towards the circular economy. This section presents the proposed frameworks by scholars, various non-governmental organisations (NGOs) and governmental agencies.
2.2.3.1. The Ellen MacArthur Foundation
The Ellen MacArthur Foundation (EMF), the leading British non-profit organization that promotes circular economy, is one of the first who constructed the CE indicator. It is called for Material Circularity Indicator (MCI). Due to the fact that EMF (2015a:15) defines the CE as “regenerative and restorative by design (…), and that aims to keep products, components and materials at their highest utility and value, at all times”, the MCI measures the material flows through the production process of one particular good. The assessment of the circularity is based on the actual data of material usage (not theoretical estimations). Figure 3 shows the material flows that are exchanged between
16 the manufacturer and consumer as well as describes the movement of material flows
followed after this exchange. It reflects not only a simplified input and output flows like in linear economy (encompassed in the shared area), but establishes more complicated material movements such as, for example, “waste from recycling process” that appear after collecting material for recycling from the users. The material flows are expressed in variables that are further used for calculations.
The MCI consists on the linear part of the flow and restorative part of the flow. The linear economy steps are shaded and other steps represent the restorative processes of the resources. The idea of the MCI is reflected in formula:
𝑀𝐶𝐼 = 1 − 𝐿𝐹𝐼 ∗ 𝐹(𝑋), (7)
where 𝐿𝐹𝐼 is a linear flow index and 𝐹(𝑋) is the utility of the product. The 𝐿𝐹𝐼 reflect the ratio of restorative and linear part of the flows, whereas utility is derived from the two components, namely, the lifetime of the product and intensity to use. These components reflect the usage of resources through the regenerative part.
As MCI is based on the material flows, it is does not consider what kind of material is used and its impact on the environment. To support the MCI in its validity there are two additive indicators created. One of them is Complementary Risk Indicator that determines the scarcity and toxicity of the resources. Another indicator is Complementary Impact Indicator based on energy and water impacts of a given setup (EMF, 2015a).
The MCI is user-friendly, so that in a short period of time it gives a first overview on the circularity of the product. However, it is not adequate since it considers only the material flows of the product, without taking into consideration other important aspects of the CE. Saidani et al. (2017:10) hardly criticizes a disregard towards systemic
Figure 3: Scheme of the Material Flow Measured by the MCI
17 thinking where “modularity, upgradability, connectivity, easy disassembly or design of
preventative maintenance of products enable the CE”. 2.2.3.2. European Environmental Agency
Being aware and acknowledged with goals and strategies related to the circular economy, the European Environmental Agency (EEA) recognizes the need to provide a framework for monitoring the transition towards the CE. In its report “Circular economy in Europe”, the EEA (2016) analyzes the challenges and solutions in measuring the CE. This is done from the policy-makers point of view. There are five fields that are relevant for the CE. Every field contains suggestive policy questions on what has to be measured and discusses the availability and convenient indicators. These fields are: (i) material input, (ii) eco-design, (iii) production, (iv) consumption, and (v) waste recycling.
For measuring the (i) material input there are several indicators. The most common is domestic material consumption.21 For measuring the material losses and a share of recycled materials, there are data on generated and treated waste as well as on the waste recovered or recycled. However, the EEA assesses that estimates are not matching and small errors occur. This leads to the conclusion that these data are not yet competent to be used at the macro level.
The indicators on (ii) eco-design are perceived to be the key measures in evaluating the CE. They relate to the monitoring of “full life cycles of products, processes, services, organizations and systems” (EEA, 2016:26). The longevity, the degree of re-usage and recycling, and secondary material inputs are challenging measurement aspects at the macro-level. Contemporary, these estimates are available at micro-level with the help of Material Circularity Indicator, constructed by the Ellen MacArthur Foundation. The collection of these data on the macro-level has difficulties because these indicators are product- and industry-specific.
The (iii) production is measured in terms of material use, amount of hazardous waste and waste in general. The data are available but the EEA (2016) states that these measures are subject for changes caused by structural changes in the economy and not by transition towards the CE. It is offered to define whether the companies involve into the CE networks and monitor the share of remanufacturing business.
The (iv) participation of consumers in realization of the CE also has to be monitored. There are data on the environmental footprint of consumption that show a social sharing of assets, choosing longer-lasting products or reusing products. The longevity of usage is also important to follow. However, there are no available data yet.
Finally, the (v) waste recycling measures recycling rates, quality and environmental effects. The data on waste recycling rate are available differentiating between municipal wastes and packaging waste. There is no indicator which defines the quality of the waste recycled. One of the requirements of circular economy is the recycling with no
21
It measures the total amount of materials directly used by an economy and is defined as the annual quantity of raw materials extracted from the domestic territory, plus all physical imports minus all physical exports (European Communities, 2001). See in detail on p. 19-20.
18 quality loss. This is still a challenge since the technology of recycling does not allow
separating all types of waste. That is why there is a high degree of contamination in the recycled products.
2.2.3.3. The European Academies’ Science Advisory Council
The European Academies’ Science Advisory Council (EASAC) (2016) reports on the already existing indicators that oversee the environmental policies and comments on their potential application on measuring the transition towards the CE. All of them are generated by different institutions, and follows various approaches. There are several frameworks generated for sustainability monitoring at the international level, however, it is more relevant to mention the ones that have more concentrated implication – the ones which are used within Europe.
Green Growth Indicators
Since 2011 the Organisation for Economic Co-operation and Development (OECD) (2017a) uses Green Growth Indicators (GGI) in order to measure the progress in economic growth and development while conserving natural resources and increasing their efficient usage during the production processes (OECD, 2011). The process described is called Green Growth (GG).
The GGI include up to thirty separate indicators which are grouped into four areas. They are: (i) environmental and resource productivity; (ii) natural asset base; (iii) environmental dimension of quality of life; and (iv) economic opportunities and policy responses.
The area (i) focuses on the indicators that capture the efficiency and footprints of the resource exploitation. Related to pollution, there are indicators that show productivity
in terms of CO2 emissions. The production-based CO2 productivity reflects the
economic value per one unit of CO2 emissions, whereas demand-based CO2
productivity accounts for emissions that appeared due to energy usage for “production of goods and services consumed in domestic final demand”. That is, it includes CO2
emissions that were created by production of imported goods and extracts the emissions from exported goods (OECD, 2017b).
Next indicators in this area relates to the energy usage. The energy productivity indicator measures economic value generated per one unit of the total primary energy supply (TPES) but energy intensity computes the amount of energy used per capita. Apart from the assessment of fossil fuels consumption, the Green Growth Indicators measure the share of renewable energy resources – renewable energy supply indicator, expressed as a percentage of TPES. Other indicators on energy resources classify its usage by economic sectors (agriculture, services, etc.) (OECD, 2017b:5).
The last group of indicators represented in area (i) deals with non-energy material productivity that includes data on biomass, metals and waste. In the framework of this study, it is crucial to clarify the data provided on the waste treatment. Expressed as the share of total waste treated there are assessments on material recovery that include recycling and composting processes, as well as incinerated waste and waste disposed to landfills (OECD, 2017b).
19 The second area (ii) indicators estimates the utilization of natural resources such as
water, land, forest as well as counts for wildlife inhabitants.
The area (iii) relates to a social dimension and accounts for people’s life condition in terms of environment consisting of people’s exposure to pollution and accessibility to water and treatment.
The last area (iv) is devoted to the economic and financial incentives directed to accelerate the Green Growth. There are such indicators that measure the financial flows for environmental development assistance, environmental taxes and expenditures for research and development of technology to diminish pollution. Moreover, assessing a wide range of environment-related technological domains and data on patents, there is an indicator on development of environment-related technologies, presented as a share of all the technological innovations (OECD, 2017b).
The GGI encompasses a variety of indicators and their primary concerns are environmental issues while the progress of economic growth is neglected. The indicators can be used separately for cross-country comparison.
Economy-Wide Material Flow Accounts
An older approach of managing the material flows is called for the economy-wide material flow accounts (EW-MFA). It was developed by the United Nations in the 1970s and officially applied in several EU countries. The management of material flow was put on the political agenda within the framework of “eco-efficiency”, that, in turn, was subject to the Fifth Environmental Action Programme. After the European Council meeting in 1999, the EW-MFA became a part of environmental accounting on the national level. The Eurostat emphasized the importance of statistical processing of the material flows for the development and generation of sustainability indicators (European Communities, 2001).
The EW-MFA supplies the information on resource inputs inflowing or outflowing the country. Adopting the book-keeping approach, the EW-MFA works within the material balance principle. That means, that all the material inputs entered the system take a form of output or are accumulated into the system after the transformation processes (production or consumption). In mathematical terms, it can be expressed as:
𝑡𝑜𝑡𝑎𝑙 𝑖𝑛𝑝𝑢𝑡𝑠 = 𝑛𝑒𝑡 𝑎𝑐𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑜𝑛 + 𝑡𝑜𝑡𝑎𝑙 𝑜𝑢𝑡𝑝𝑢𝑡𝑠, (8)
Two main sources of inputs are domestically extracted materials (fossil fuels, minerals and biomass) and material imports (raw materials, finished or semi-finished product, waste, etc.). The outputs are classified as emissions, waste, and exports (the same content as with imports). The infrastructure, buildings and material losses represent the accumulations or, in other words, stock of the material flows. The water and air flows as well as recycling amounts are not taken into consideration in none of the flows’ accounting.
This scheme allows deriving the indicators according to the three “pillars” (input, consumption and output). With time, the terminology of the indicators was updated due to the improvements of the EW-MFA methodology.
20 The main purpose of the EW-MFA is to monitor the material flow interplay between the
domestic economy and earth’s system of natural resources and the rest of the world. Even though the EW-MFA is represented as a systematic approach, some indicators are used separately within other conceptually different measurement initiatives. For example, Domestic Material Consumption (DMC) is a relevant indicator of the
Resource Efficiency Scoreboard (Eurostat, 2017a).22
Thus, the EW-MFA provides a general overview of material flows as well as serves as the basis for contemporary measurement framework related to the resource efficiency. EU Resource Efficiency Scoreboard
By 2010, the Commission launched the Europe 2020 Strategy that stands for “smart, sustainable and inclusive growth” (Eurostat, 2016). By 2020, it aims to transform the economy in several ways. The established goals affect social and environmental issues. The flagship initiatives touch upon the youth employment, provision of the support for research and developments of innovations, helping the small- and medium-sized enterprices to enter the international markets. The only flagship initiative related to the environmental policy is called for “Resource efficient Europe” (EC, 2010).
The Communication on Resource Efficiency Roadmap (EC, 2011b) mentions several reasons why the issue of resource efficiency takes the place of the flagship initiative. Besides the environmental problems such as unsustainable consumption and increased amounts of landfilled waste, exhausted stock of raw materials and danger of climate change, there are economic issues that cause instability. For example, the prices of fundamental raw materials are soaring due to their scarcity and leading to costs increase, whereas other natural resources are estimated below their true costs. Thus, the supply of natural resources has to be guarded by efficient consumption encouraged by product redesign, sustainable management resources and creation of the greater economic value through reuse, recycling and substitution of materials. Finally, the resource-efficiency makes it possible to decouple the economic growth from the resource consumption, however, the transition of economy is required.
The Commission (2011a) specifies four fields that are relevant for this transformation. The first covers the (i) consumption and production patterns. The buyer has to be appropriately informed about the resources that are used during the production, their impact and costs. The knowledge is a guide for decision-making in favor of goods and services that can be reused or recycled. The producers are encouraged to exercise the resource-efficiency and share the knowledge with other chain suppliers.
The second field of transformation belongs to the (ii) waste management. The Commission realizes that the amounts of waste has to be diminished in particular areas, however, the main goal is to turn the waste into energy that implies the change in product design. A better waste management, modern facilities for waste processing as well as innovations has to be supported in order to achieve a high-quality recycling.
