2.1. GHG Emissions by Productive Sector
Before presenting the modeling carried out and the results, the data used are described, specifying the sources, and a descriptive–comparative analysis is made of the GHG emissions in Spain by each productive sector and in the years mentioned (2005 and 2015). A reduction is seen after the economic crisis despite an increase in production.
Regarding data, information from 2005 was considered because it was a year of economic growth representing the pre-crisis level, in addition to data from 2015, when the crisis ended, representing post-crisis levels. Therefore, the model used the symmetric input–output (IOT) tables for years 2005 and 2015 as databases. These were published by the National Institute of Statistics [
22]. These tables gather 73 and 64 activities or productive sectors, respectively, according to the Classification of Products by Activity (CPA), and private household consumption was disaggregated for each of the sectors identifying the types of households grouped by age brackets. This disaggregation was performed based on the total household expenditure by age brackets for the reference years. In addition, the data were grouped into 12 consumption groups that come from the Household Budget Survey, according to the COICOP classification variables (3 digits), and are also provided by the NIS [
22].
The data arrangement from the different classifications means that the correspondence between the expenditure groups and codes, and the productive sectors is not direct or univocal. This causes difficulties in the distribution of final household demand in the productive sectors, differentiating it by age group. Therefore, it was necessary to construct a conversion matrix that related consumption groups with the sectors on the basis of the matrix created by Cai and Vandick [
23] to achieve a correspondence between the household consumption groups (as classified by COICOP) and the sectors (as classified by CPA).
Moreover, the total greenhouse gas emissions (in thousands of tons of CO
2 equivalent) for each productive sector of the IO tables of the years studied, obtained from the Air Emission Accounts provided by the National Institute of Statistics [
24], were used.
The symmetrical input–output tables used were aggregated to 20 productive sectors (
Table 1), according to the Statistical Classification of Products by Activities [
25] of Eurostat.
The reference years under study facilitate a comparison of GHG emissions before and after the 2008 crisis, for each productive sector. The following table shows the total production of the Spanish economy with total GHG emissions.
As shown in
Table 2, there was an inverse relationship between GHG emissions and production in the Spanish economy after the 2008 crisis. While emissions fell by 37%, the total production of the economy grew by 15%. This was due to the important role played by the increase in renewable energies and improvements in energy efficiency promoted by national and international plans to mitigate climate change.
Below (
Table 3) is a breakdown of the total CO
2 equivalent emissions of the referenced years produced in each productive sector.
Emissions were observed to have reduced throughout all sectors except in the real estate activities sector (L). In particular, one of the sectors that reduced its emissions the most was extractive industries (B) with 76.6%. This significant decrease was due to the increased use of renewable energies as a primary energy source, to the detriment of the extraction of raw materials used to generate electricity. The construction sector (F) reduced its GHG emissions the most, 72.1%, largely due to the fact that the sector was the most affected by the 2008 crisis. In addition, GHG emissions caused by other services (S); manufacturing (C); the wholesale and retail trade; the repair of motor vehicles and motorcycles (G); and transportation and storage (H) reduced by 54%, 43.1%, 41.7% and 40.4%, respectively.
Moreover, the productive sectors that reduced their emissions the least during the economic crisis period were education (P), with a reduction of 2.2%; household activities (T), with a reduction of 11.8%; artistic, recreational, and entertainment activities (R), with a reduction of 13.8%; and financial and insurance activities (K), with a reduction of 16.7%.
Once the total GHG emissions and their distribution in each of the productive sectors were analyzed, the impact of variations in the consumption of Spanish households on GHG emissions was modeled. It was differentiated by age group, with emphasis made on the “young person” age group.
2.2. Methology
National accounting systems have come to be widespread in most economies, providing valuable information on a country’s economic situation. Thus, input–output tables were used and served as the main database for the economic analysis carried out. Using multisectoral modeling [
26] on the input–output tables (IOT) published by the NIS [
22], the impact of household consumption, differentiated by age bracket, on greenhouse gas (GHG) emissions was determined. A demand model was used, which was expressed in physical units, namely greenhouse gases (GHGs) measured in tons of CO
2 equivalent.
The input–output table can distinguish between the intermediate consumption matrix, the primary factor matrix, and the final demand matrix. Each column of the intermediate consumption matrix shows the intermediate products used by each productive sector to carry out its productive activity. The final demand matrix breaks down into different transactions (private consumption, public consumption, gross capital formation, and exports) the excess of resources of each sector over the intermediate demand made by all sectors. From these matrices, an input–output model was developed, wherein factor demands are independent of their prices, primary factor prices are exogenous, final demand is also exogenous, and product prices are independent of the structure of demand.
An input–output model defines sectoral production (
by assuming a linear structure of intermediate consumptions
plus an exogenous sectoral final demand (
), where
is sector
j’s intermediate consumption of products from sector
n. The input–output model used consists of a system of linear equations, each of which describes the distribution of a sector’s products throughout the economy. These models are linear multisectoral models, where productive sectors are expressed as linear functions of the demand matrix. By defining the input–output technical coefficients (
) as the relationship between the intermediate consumption (
) and the total sectoral output (
) (
), the total production of any sector can be expressed as the sum of the transactions with the rest of the sectors, and the transactions through final demand. Thus, the matrix equation is as follows:
where
is an
type of matrix (where
n is the number of productive sectors) that includes final demand,
is an
type of matrix composed of the total output of the sectors, and
is an
type of matrix formed by the average spending trends of the productive sectors (matrix of input–output technical coefficients).
Solving the equation:
where
is the inverse matrix of Leontief. Each
element of the inverse matrix shows the change in the output of sector
i if sector
j receives an additional monetary unit since the final demand. The resulting matrix
shows the degree to which an exogenous variation in the system may affect the total income of sectors.
Starting from the matrix in Equation (2), any variation in the income of the sectors (due to a variation in their final demand) is reflected in a variation of the production matrix, as described in the following equation:
Expression includes direct and indirect impacts on production when there is a change in final demand. An increase/decrease in final demand in a sector generates an increase/decrease in its production to meet the new demand (direct impact). This, in turn, causes that sector to increase/decrease its purchases from other sectors (indirect impact).
In addition to the impact on production, these models facilitate learning the impact on other macro magnitudes, as well as to express the impact in physical units, such as, in the case of this study, greenhouse gas (GHG) emissions.
To this end, the above modeling is used to assess the impact of changes in household consumption on GHG emissions caused by changes in economic activity (changes in production), implied by the modeled changes in household consumption.
This information is obtained using the unit coefficients of GHG emissions defined by the relationship between the emissions in physical terms (
Ei) and the
ith sector’s total output in monetary terms (
Xi). Using this definition, we can rewrite the Expression (3), premultiplying the inverse matrix of the model by a diagonalized vector of unit coefficients of atmospheric emissions,
, which shows the atmospheric emissions of a sector per unit of production. This yields the environmental effects of changes in household consumption.
Thus, it is possible to calculate changes in GHG emissions , both direct and indirect, caused by the variation in economic activity associated with changes in household consumption.