4.1. Description
The Catalan economy has evolved in recent years towards a clear process of deindustrialization and progress in the service sector. This is clearly seen in
Table 1 and
Table 2. It is important to observe this economic structure to be able to correctly analyze the question of new metrics in economic analysis, which is pursued in this research.
The agricultural sector has lost weight in the total GDP, going from 1.7% in 2000 to 0.9% in 2016. The industry, which in 2000 generated 26.9% of Catalan GDP, fell to 19.8% in 2016. Between 2010 and 2016, it gained weight slightly (up to 20.0%). Construction fell from a high of 11.9% in 2006 (and values above 10% during the period 2002–2009) to 4.9% in 2016. Since then, it has regained weight slightly, up to at 5.3% in 2019. The services sector gained 13.1 percentage points in the productive structure of the Catalan economy between 2001 and 2013 (up to 74.8%). The process where the services sector dominates is obvious. However, as indicated, this new orientation of the Catalan economy (with a loss of industrial prominence) does not eliminate the negative environmental externalities, although the indicators we have built suggest improvements in energy efficiency.
To answer the objectives raised in this paper, eight indicators were processed for the period 2000–2016. This interval of years includes a stage of remarkable economic growth that ended in 2008 with the beginning of the Great Recession, and a stage of stagnation that lasted until practically the last two years of the period, when the Catalan economy began a slow recovery. Some comments are peremptory:
(a) The indicators should help us to infer the evolution of biophysical data in relation to the intensity of the economic development of Catalonia and thus have an initial idea of Catalan economic development in relation to variables related to natural resources;
(b) It is in this sense that variables specific to the analysis of economic growth are incorporated, such as GDP, GDP per capita or the Gini Index, with environmental variables;
(c) The period considered, which includes different economic cycles, helps us to better understand the behavior of this relationship in changing scenarios; the data allow a study that, while not providing a very long-term view, includes a time interval long enough to make inferences from the object of study of this research with consistent robustness;
(d) The authors are aware they are working with aggregate data from Catalonia; that is, they are not broken down according to the Catalan provinces, a project that would require much more time and dedication than available.
The sources available for Catalonia allow us to work with the following indicators (see
Appendix A at the end of the text):
These indicators have been chosen according to two important parameters. In the first place, because of its explanatory capacity, which also links with other case studies [
34,
36]. Waste and gas emissions are emphasized, while determining consumption such as energy and water are underlined. The indicators are therefore highly explanatory if, in addition, they are related to the evolution of GDP. The chrematistic gives way, thus, to the economy.
The eight indicators and their reciprocal relationships are characterized, always taking into account demographic evolution, because:
They do not present unattainable methodological difficulties for data collection and subsequent calculation;
The chrematistic variables (GDP and GDP per capita) can be integrated with the environmental variables (CO2 emissions, energy use and water consumption) to obtain descriptive indicators;
They help to identify some ecological effects of economic growth;
They provide a different reading of the growth process, as they specify and systematize scattered variables that do not usually appear in the periodic diagnoses of different agents.
The basic idea, made later for all of them and others, that can be incorporated is to create an indicator as synthetic as possible that provides an environmental vision of the Catalan economy. It must be emphasized that a certain degree of imperfection is assumed and that the indicator could be more complete and incorporate more variables; however, the work performed shows how this approach can represent a first step towards future deeper research that expands the analytical framework and understanding of the results presented in it.
Table 3 and
Table 4 show the absolute data and index numbers to make the comparison easier.
Figure 1 and
Figure 2, in turn, mark the evolution; although
Figure 2, which synthesizes a grouped processing of the variables, allows us to differentiate the chronological phases.
Reading these data allows us to determine two specific stages in our series: 2000–2007 and 2008–2016. The figures show:
GDP grew robustly between 2000 and 2007, before the outbreak of the Great Recession. The data are in line with an increase in energy consumption (GDP per capita: 5.37; energy consumption: 2.64), while CO2 emissions increase at a rate of 0.88;
The Great Recession laminates the advance of GDP per capita, which is in a steady state between 2008 and 2016; eight years, therefore, of anemic growth in the production of material wealth. At that time, the biophysical indicators experienced a significant decrease: −1.94 energy, −3.57 water consumption and −2.93 CO2 emissions;
The aggregate data for the two different periods provide more information: GDP per capita 2000–2016 has increased at a fairly acceptable rate, which is explained by the growth momentum in the first of the phases. It is significant that the rest of the variables considered are below the growth of GDP per capita, with the sole exception of water consumption.
These three considerations are complemented by the arrangement of growth rates, which is shown in
Table 5.
A survey of the evolution of these indicators suggests the following:
The Catalan GDP in 2016 was 23.78% higher than that in 2000, while the GDP per capita was 4.3%. The increase in the population of Catalonia stood at 18.53%. This circumstance shows an increase in GDP, driven, among other factors, by an increase in population; therefore, the data indicate how this increase in production was insufficient to maintain the GDP per capita in stable and constant terms during the period studied. The productive structure of Catalonia, therefore, has not been able to generate a growth rate of GDP above the growth rate of the population. The constant GDP (Base = 2000) from Idescat, in millions of EUR. The result is derived from multiplying the deflator of the year 2000 by real GDP. The GDP per capita (Base = 2000) based on Idescat, is obtained by dividing the constant GDP (Base = 2000) by the population;
Environmental data show some behaviors that, in some cases, are surprising because they show a clearly downward trend. Water consumption was ostensibly reduced, and energy use had a residual increase of approximately 1% for the entire period, while CO
2 emissions were reduced by 20.22% (see
Table 3 again).
Energy consumption shows a constant decrease for the entire period, with the exception of the rise of 2006 and 2007, while the number of liters per inhabitant and day decreases systematically and is quite detached from the economic cycle. Energy consumption, however, experienced a rather significant cumulative increase for the period 2000–2007 and a decline in subsequent years; there is a strong relationship with the economic cycle, expansive for the first half of the series and recovery for the second. However, we can see that in the first years of recovery, after the Great Recession, consumption remained at levels below the pre-crisis period. CO2 emissions, on the other hand, showed a decreasing trend in the period 2000–2016. Although they increased in the years of greatest economic dynamism, with the recession, emissions fell significantly and, with the recovery, they have increased but not at pre-economic crisis levels.
There is, then, a tendency towards an improvement in environmental indicators. Despite the economic growth experienced for the entire period, these indicators have, in almost all of them, a downward behavior. The most obvious manifestation is the intensity of the use of resources, which is shown in
Figure 3. In fact, energy intensity decreased considerably from 2000 to a base of 100 to 58 in 2016, a very significant reduction that was constant throughout virtually the entire period (except for the years 2003 and 2004). This contrasts with the evolution of the curves in
Figure 4: the so-called “productivity” of the resources inserted in the production processes. The evolution is, logically, the inverse of that observed in
Figure 3. All this supports the idea of a better, more efficient use of biophysical inputs for the achievement of Catalan GDP, which is also linked to its demographic variable, GDP per capita.
(a)
Figure 5 shows the relationship between GDP per capita and CO
2 emissions: positive increases (decreases) in GDP which leads to increases (decreases) in CO
2 emissions. However, emissions systematically increase less than GDP per capita. For example, in the total period 2000–2016, GDP per capita increased by 45%, while CO
2 emissions decreased by 20%. This is mainly because, since the 2007 crisis, decreases in GDP per capita imply proportionally larger decreases in CO
2 emissions than increases in the period 2000–2007 (for example, between 2007 and 2013, GDP per capita decreased by 9%, while CO
2 emissions decreased by 28%). Therefore, we can suggest that, during the economic recession, greater efficiency lowered CO
2 emissions.
(b) As with CO
2 emissions, the ratio between GDP per capita and energy consumption in
Figure 6, is positive (increases (decreases) in GDP infer increases (decreases) in energy consumption). As in the previous case, energy consumption systematically increases less than GDP per capita; for example, in the period 2000–2016, GDP per capita expanded by 45% and energy consumption remained unchanged. Again, this is mainly because, since the 2007 crisis, decreases in GDP per capita have in turn lowered proportionally larger energy consumption than increases in the period 2000–2007 (for example, between 2007 and 2013, GDP per capita decreased by 9%, while energy consumption decreased by 19%). Therefore, we hypothesize that, during the economic recession, efficiency in energy consumption increased.
(c) The case of water consumption—
Figure 7—is different from that of CO
2 emissions and energy consumption. The figure shows that water consumption has not stopped declining (with the exception of the period 2005–2007). Thus, GDP per capita increased by 45% in 2000–2016, while water consumption fell by 40%.
4.2. Analysis of Main Components
This study will use the analysis of the main components (ACP) method to capture the general characteristics and trends of the use of natural resources in Catalonia (we explain in
Appendix B). This method makes it possible to synthesize the information contained in W, E and CO
2 and avoids the subjectivity and rigidity of the weights given to each variable when synthesizing various indicators of the use of natural resources.
We will use the ACP method to develop a synthetic indicator of the use of natural resources, which we will call CI_PC. ACP is based on a linear transformation of variables; therefore, they are orthogonal to each other [
39]. Thus, the main component is one that maximizes the variance of the data, rather than minimizing the minimum square distance between them. In short, the ACP method transforms data into new variables (i.e., major components) that are not correlated with each other. The ACP method can be applied to the original values of the variables, to the deviations of their means or to the standardized variables. In this study, we adopt the last procedure because W, E and CO
2 are not measured in the same units. Therefore, we transform the variables into logarithms.
Before using the ACP method, we perform some preliminary data testing. To ensure the use of ACP, we applied the Bartlett sphericity test (BTS), adequacy test and Kaiser–Meyer–Olkin (KMO) adequacy test. As expected, the results of the BTS test suggest the rejection of the null hypothesis that the correlation matrix is an identity matrix (all zeros except those of the main diagonal) at the significance level of 1%. The result of the KMO measurement of sampling adequacy is 0.70 (greater than 0.50), indicating that there are major common factors between the variables and a strong linear relationship between them, which is suitable for the ACP method. Both tests support the suitability of the ACP method.
Once we verified the suitability of the use of ACP in our study, we applied the method. First, we obtained the number of components using the Kaiser–Guttman rule.
Table 4 shows the proportions of the variance of the eigenvalue to select the optimal number of components. According to the Kaiser–Guttman rule, only one component can be retained because only the first component has an eigenvalue greater than one.
As seen in
Table 6, the first component represents 78% of the cumulative contribution rate, which can explain most information from the original variables. This indicates that it is only necessary to use the first main component when synthesizing our index of resource use in Catalonia (CI_PC).
CI_PC is highly correlated with each original variable (W, E and CO2). We also observe a high correlation between the original variables (W, E and CO2), which helps to explain why the first main component alone explains 94% of the total variation in W, E and CO2, which makes the reduction of the data highly effective. We can see that CI_PCi GDP per capita is negatively correlated. This may be indicative of a negative relationship between economic growth and the use of natural resources. However, since our research is merely descriptive, more research is needed in this regard.