University of Pisa
Sant’Anna School of Advanced Studies
Master of Science in Economics
Exploring the relationship between social capital and carbon footprint:
empirical evidence from the EU regions
Candidate: Leonardo Giannetti
Supervisor: Prof. Angela Parenti
ABSTRACT ... 3
INTRODUCTION ... 4
DATA DESCRIPTION... 7
DESCRIPTIVE STATISTICS ... 9
ANALYSES AND RESULTS ... 14
DOES SOCIAL CAPITAL RELATE TO THE HOUSEHOLD CARBON FOOTPRINT? ... 14
Model ... 14
Results ... 15
Discussion ... 18
IS THERE A PLACE FOR SPATIAL ANALYSIS? ... 19
Model ... 19
Results ... 22
Discussion ... 23
CONCLUSIONS ... 24
APPENDIX 1 – BUILDING THE DATASET ... 25
APPENDIX 2 – SOCIAL CAPITAL (REGIONAL SCORES) ... 27
BIBLIOGRAPHY ... 28
ABSTRACT
Environmental sustainability is today one of the most concerns. Not many acknowledge our word is in jeopardy and carbon emission is one of the most prominent reasons. Consumption choices cause carbon dioxide emissions, so individual behaviour might be crucial to turn the tables. This paper combines the data on emissions from Ivanova and co-authors (2017) with the data from the European Values Study and investigates the link between household carbon footprint and social capital at subnational level within the EU, by means of regression and spatial analysis. Results show the spatial error model is the one that best fits the purpose, and that social capital exerts just a mild influence on carbon footprint. On the other side, socio-economic factors, together with the intensity of the electricity mix, seem to have the most remarkable effect over the environment.
INTRODUCTION
Climate change is a global issue, and it is shaking our world. Rise in temperatures and in sea levels are two of the most dangerous threats we are dealing with today. Greenhouse gas (GHG) emissions (e.g., carbon dioxide – CO2) are the driving force behind climate change, and, in the context of their actual increase, human behaviour has not to be neglected, rather it is the key element a turnaround can be ascribed to. Indeed, it is no more than utopian the attempt to solve environmental issues if no public support is recognised. Hence, taking action is crucial and the Paris Agreement and the Agenda 2030 (Pillar 13) are just a few results of a common understanding of the issue. While the Paris Agreement draws the line on global warming, the Agenda 2030 encourages initiatives at all level of decision.
At European level, instead, the European Green Deal is one of the last policies that has been set by the European Commission (EC) and a reduction in greenhouse gas emissions of at least 55% within 2030 is one of the main objectives.
All of this to say that goals and reasons are clear, it is important what are the means to achieve what it is supposed to be achieved. In this context, national policies are necessary but are not sufficient and this is why the EC has pointed out that an effort should be made at subnational level too.
To better understand the policies that need to be implemented it is important to carefully comprehend what the determinants of greenhouse gas emissions are. Part of the literature analyses the impact of households on the environment starting from socio-economic measures (e.g., income) to technical ones (e.g., electricity mix), through geographical factors (e.g., temperature).
As mentioned, to safeguard the environment, citizens’ concern is crucial. In this context, a stream of literature studies the interaction between the society and the environment, trying to understand, whether or not, the reasons of the emergency we are facing today have to be found behind the relationship concerning the two. Here, it is important the notion of social capital. Indeed, political scientists like Putnam (1993, 1995, 2000) and Fukuyama (1995) argue that social capital not only has a private aspect, but also a public side, hence it can have a positive impact on the wider society within which is lodged. Unfortunately, due to its multifaced nature, there is no common definition of social capital. Anyway, the Organisation for Economic Co-operation and Development (OECD) ascribes social capital to ‘network together with shared norms, values and understanding that facilitate cooperation within and among groups.’ Therefore, in a simple way, social capital is about networking, trustiness, compliance with norms and civic engagement.
one about environmental awareness, meaning the flow of information about environmental issues can hold people responsible in making an effort toward the social welfare. However, at the same time, active participation, meaning unpaid voluntary work, brings along those elements of impact that are proper of working outside home, while passive participation, that is membership, can concern activities or events that have a downside over the environment. For what regards trustiness it is important to discriminate among social trust and institutional trust. Social trust has to do with people trusting other people, while institutional trust is about trusting the main authorities in the society. Higher level of social trust may minimise the occurrence of ‘free-riding’ behaviours and encourage people working for the common good, even when it comes to the management of natural resources. Moreover, social trust can improve the efficiency of collective action and it is key to develop justice consciousness, meaning, in our case, environmental protection. Nonetheless, the literature argues that people who are not inclined to trust others tend to surround themselves with a smaller circle of people with whom they interact and to be more solitary. In turn, less interaction might implicate less emissions. Institutional trust is important too. Higher level of institutional trust means institutions are likely to be more trustworthy and less prone to abuse their powers. As a consequence, individuals feel like they have to play their role too, also sticking to those rules and recommendations concerning the environment. Despite that, some authors, among them Sønderskov and Dinesen (2016), found that there is a causal relationship between institutional and social trust. Indeed, institutional trust may spread a feeling of security, which, in turn, fosters interaction among people. Hence, on the same line as social trust, this might trigger a rise in emissions. When it comes to the compliance with norms, instead, it can be assumed that if people play by the rules and struggle to legitimise careless conducts, it is likely to be the same for the environment as well. Lastly, for what is about civic engagement, it can be assumed that people who are interested in political matters are more informed and concerned on environmental issues. This may also entail a higher level of participation in networks. Table 1, at the end of this paragraph, summarises what has been described so far, together with the expected effect that each variable the analysis will consider might play over the environment.
This master thesis starts from the work “Mapping the carbon footprint of EU regions” (Ivanova et al., 2017) and compile a set of questions from the European Values Study, in order to detect whether or not social capital has an impact on the household carbon footprint in the regions of the European Union (EU). What it is really important to stress is that it is not just about how people behave towards the environment, but why they do or do not behave in its favour. This work is going to address this line of reasoning. Not a great stream of literature faces this issue due to either a feeling of scepticism towards the phenomenon or due to a lack of data at regional level. In the second part of the thesis a spatial analysis is performed with the objective of understanding whether adjacent regions affect each other in terms of greenhouse gas emissions.
Data description is the first part of this paper, then the second part is about the analysis, together with the results it brings along. The final section concludes.
Table 1. Summary of exploratory hypotheses on relevant dimensions for household carbon footprint. Dimension Sign Explanation Source
S oc io -e con om ic
Net income (+) Income positively determines household consumption. Higher consumption implicates higher greenhouse gas emissions
Wilson et al. (2013), Tukker et al. (2010), Peters and Hertwich (2008), Jackson and
Papathanasopoulou (2008), Lenzen et al. (2006)
Household size
(-) Household members share electrical appliances and living space. This triggers economies of scale in different consumption domains
Tukker et al. (2010), Lenzen et al. (2006), Wilson et al. (2013), Minx et al. (2013)
Predominantly urban
(+/-) Urban typology is linked to more compact development and larger availability of public transport. On the other side, the literature also shows urban inhabitants' consumption to be associated with higher impacts
Marcotullio et al. (2014), Tukker et al. (2010), Lenzen et al. (2006), Minx et al. (2013), Wiedenhofer et al. (2013)
Tertiary education
(+/-) More educated people tend to change consumption habits with more or less emission-intensive behaviours
Chancel and Piketty (2015)
Basic need expenditure
(-) Spending more on necessities might be associated with less expenditure on high-emission goods or services
Ivanova et al. (2015), Steen-Olsen et al. (2016)
Number of rooms
(+) Increasing the dwelling size implicates a higher space to be heated or cooled and a need for building material use, hence higher emissions
Lenzen et al. (2006), Newton and Meyer (2012) G eogr ap h ic al Heating degree days, monthly
(+/-) Lower average temperatures implicate higher impact, but rising in temperature may also drive energy use for cooling
Minx et al. (2013), Wiedenhofer et al. (2013), Chancel and Piketty (2015)
Forest and semi-natural area
(+/-) Access to forest and semi-natural area may promote low-carbon leisure activities, but also encourages the consumption of available resources
Ivanova et al. (2015) T ec h n ic al Electricity mix intensity
(+) The local electricity mix directly influences the carbon intensity of products produced and consumed locally
Tukker et al. (2010) S oc ial c ap it al Active participation
(+/-) Active participation can lead to a sense of collectivism and environmental awareness. At the same time working outside home might produce a rise in emissions
-
Passive participation
(+/-) Passive participation can have its positive effect on carbon emissions through the same channels that active participation triggers. However, be a member of an organisation might mean taking part in activities or events that are of non-zero emissions
-
Social trust (+/-) Social trust may mean working efficiently for the common good and environmental protection, but higher levels of trust also means more interactions
-
Institutional trust
(+/-) Trust in institutions might influence people to comply with norms, also when it comes to the environment. Anyway, it could also lead to more interactions
DATA DESCRIPTION
As mentioned, the starting point is the paper “Mapping the carbon footprint of EU regions” (Ivanova et al., 2017). Over the course of the paper the authors conduct a multiregional input-output analysis (MRIO) combining information from both the consumer expenditure surveys (CESs) – spanning in the years 2006-2014 – and the EXIOBASE 2.3 database. The core of the output is an EU-NUTS 2 measure of household carbon footprint and this is the dimension that it is going to be used in this thesis as dependent variable, meaning the variable the analysis is about. In addition, the authors consider socio-economic factors, but also some geographical and technical elements in order to be helped in explaining what they are meant to explain, meaning what really moves the level of GHG emissions. This thesis will use these factors too and, as mentioned, each of them is highlighted in Table 1.
The second dataset that it is going to be used is the one from the 1999 wave of the European Values Study (EVS). “The European Values Study is a large-scale, cross-national, and longitudinal survey research program on basic human values. It provides insights into the ideas, beliefs, preferences, attitudes, values, and opinions of citizens all over Europe. It is a unique research project on how Europeans think about life, family, work, religion, politics, and society.” Moreover, the EVS is arranged to bring a standardised questionnaire to about 70000 Europeans every 9 years. Some of the questions of the survey will be organised and used as independent variables in an attempt to shed lights on the main variable the thesis is concerned with.
Before moving to the description of the data we have, it is worth saying the year 1999 of the EVS is not by chance. This choice deserves to be explained. As mentioned, the data of the CESs span from the year 2006 to the year 2014, meaning that the data for, let us say, Italy may not be available for the same time period as those for, let us say, Germany or France. Anyway, in our case the year 2006 is the first year ever, all the others come after. Making this point is necessary for what is next. Indeed, the literature on social capital not only stresses the measure of social capital has to be taken from a period prior to the year considered for the response variable in order to control for reverse causality, but also that social capital is affected by a stability issue. Therefore, acting considering the EVS for the year 1999 is an attempt to hold off reverse causality problems, even if the stability issue pushes the other way. The choice did not fall instead neither on the EVS waves prior to 1999 to not sacrifice too much the sample of countries, nor on the EVS waves after 1999 due to the fact that the first wave after that year is the wave 2008 and, in our case, it would not make any sense to explain a phenomenon for the year, let us say, 2006, considering a set of explanatory variables for the year, let us say, 2008.
Now it is time to describe the data we are going to use in this work. As mentioned, we start considering two sources of data: the dataset from Ivanova et al. (2017) – hereinafter GHG dataset – and the data from the 1999 EVS wave – hereinafter EVS dataset. For what regards the two datasets just mentioned, two issues have to be addressed. The first concerns the different level by which data are compiled. In fact, the GHG dataset contains information at regional level, while the EVS lists information at individual level. This mismatch is solved applying an aggregation procedure to the EVS dataset. The second is about the different classification regions
follow. The GHG dataset sticks with the NUTS classification, while the EVS dataset responds to the one published by the International Organization for standardization (ISO). In addition, things complicate due to the engagement of different years. To solve this problem, regions are simply combined with each other. Anyway, in some cases regions are defined differently, so it turns out they have different boundaries, which is why a manual adjustment is here performed. Once those adjustments are made, the two datasets are merged, and a new set of data is obtained – hereinafter Final Dataset. A full explanation of the procedure used to get the Final Dataset is debated in Appendix 1.
Before showing some descriptive statistics about our data, it is time to introduce a dummy variable for each country, taking either zero, if a region does not belong to that specific country the dummy is designed for, or 1, if otherwise. These additional variables capture country-specific characteristics, meaning features a region has due to the fact that it belongs to a specific country. These dummies will be added to the analysis because it can be assumed that a country can have some characteristics that influence the level of greenhouse gas emissions.
DESCRIPTIVE STATISTICS
Table 2 lists a set of descriptive statistics concerning those dimensions we are interested in. Of course, calculations are based on the sample under analysis. Those statistics are the mean, the standard deviation (SD), the minimum value and the maximum one.
Table 2. List of definitions and descriptive statistics for the variables included in the analyses.1
Variable Description Mean SD Min Max 1 Household carbon
footprint (HCF) Kilograms carbon dioxide eq per capita (kgCOee/cap) 10901 3104 4720 21880
2 Net income (INC) Thousand Euro per capita (1000 EUR/cap) 14,2 7,43 2 29,2
3 Household size
(HHSIZE) Number of household members 2,47 0,318 1,46 3,31
4 Predominantly urban
(URBAN) Predominantly urban region dummy 0,301 0,46 0 1
5 Tertiary education
(EDUC)
Percentage of the population aged 30-34 with tertiary
education 30,3 10,9 13 59,8
6 Basic need
expenditure (BASIC) % of expenditure on necessities 52,3 10,3 31,8 72,6
7 Number of rooms
(NROOMS) Average number of rooms per person 1,5 0,363 0,9 2,3
8 Heating degree days,
monthly2 (HDD) (18°C – Tm) × d if Tm ≤ 15°C, nil if Tm > 15°C 217 79,2 0,333 518
9 Forest and
semi-natural area (FORESTAREA)
Thousand square meters per capita (1000 m2/cap) 5,43 8,66 0,00739 85,5
10 Electricity mix
intensity (EMIX)
From 1, for intensity 0-0.20 kgCO2e/kWh, to 6, for
intensity 1.0-1.2 kgCO2e/kWh 2,82 1,15 1 6
11 Active participation
(ACT) Proportion of people stating active participation 0,244 0,145 0 0,748
12 Passive participation
(PASS) Proportion of people stating passive participation 0,41 0,197 0,07 0,879
13 Social trust
(STRUST) Mean level of social trust 1,28 0,147 1 1,73
14 Institutional trust
(ITRUST) Mean level of institutional trust 6,52 0,877 3,8 9,02
15 Compliance with
norms (NORMS) Mean level of compliance with social norms 24,6 1,44 18,5 27,7
16 Civic participation
(CIVIC) Mean level of civic participation (after standardisation) -0,214 0,825 -2,31 1,83
1Country dummies are left out by purpose to not weight down too much the table.
2Tm is the mean (Tmin + Tmax
Among all, the household carbon footprint deserves to be further analysed. For this reason, Figure 1 shows the distribution of the character in the sample.
Figure 1. Regional sample distribution of household carbon footprint (kgCO2e/cap)
Since the frequency of the mode value is of 6 in such a big distribution, it can be misleading to use the mode in order to analyse to what extent the distribution is skewed. Hence, it is worth going for the median and we cannot say the distribution is such skewed, since the mean and the median are almost the same. As a consequence, the distribution is not so far from being normal. This is counterchecked by the result of the Shapiro-Wilk test for normality, which assesses the distribution of the data is not significantly different from a normal distribution (p-value > 0.05), meaning normality can also be assumed.
Now, it might be interesting to get a visual feeling on how the household carbon footprint behaves in the EU. Figure 2 fills this gap.
Figure 2. Household carbon footprint in the EU measured in kgCO2e/cap.
The map shows that the most hit regions, meaning those coloured with the darkest shade of pink, are Luxembourg and South West in England. On the other side, the regions that are better off, meaning those falling into the first class of the distribution (4720-5500), are Vest Oltenia, Est, Nord-Est, Sud-Muntenia, Centru and Nord-Vest in Romania and Severen tsentralen in Bulgaria.
Since this paper is mostly about social capital, it is now appropriate to show its behaviour in Europe for the regions under examination. Figure 3 does this job, and the resulting patterns are quite in line with those of Van Oorschot et. al (2006).
Figure 3. Representation of the dimensions of social capital in the EU. Measurement units stick with those
For a moment, let us analyse social capital at national level. The main purpose is to get an immediate feeling on how good countries score in those dimensions of social capital the study is concerned with, without rambling on subnational scores for which Appendix 2 is basically designed. The position of Finland and Denmark is prominent whenever either a measure of participation or a measure of trust is considered, while, in the same areas of investigation, the majority of the Eastern countries seems to score bad. When it comes to compliance with norms, Denmark keeps its position of privilege, Eastern countries overall gain more ground and France goes down. Finally, Denmark, Germany and Greece jump out to perform high in the rank of civic participation, while UK and Spain struggle with scoring great. It may be that those that Ronald Inglehart (1995) classifies as “postmaterialist” values – meaning those emphasizing self-expression and quality of life – play a role in shaping those elements of social capital that are deemed to be relevant for the environment. Additionally, the maps make the presence of clusters clear, where less, where more. At times, they do not cross the country boundaries, as in the case of Spain when social trust is of concern, at others they do, and this is the case of most Eastern countries when the attention is on passive participation. Anyway, a comparison between those pictures of social capital and the one about the carbon footprint does not sketch any obvious relationship, except for the inverse connection that household emissions and active participation seem to have. So, now more than before, it is important to investigate whether or not social capital relates with the environment. Before moving to the core of this paper, the correlation matrix needs to be examined. In here, all the variables entering the forthcoming analysis are considered and the relationships of dependence are the objects that need to be tested. The output does not show any worrying interaction.
ANALYSES AND RESULTS
DOES SOCIAL CAPITAL RELATE TO THE HOUSEHOLD CARBON FOOTPRINT?
MODEL
The main objective of the first part of the analysis is to detect the relationship between household emissions and social capital, controlling for a set of dimensions that are assumed to play a role over the environment (e.g., income).
We set up an ordinary least squares (OLS) model.
Theoretically, we have 𝑁 observations and we observe the dependent variable 𝑦𝑖 and a row-vector of
explanatory variables 𝒙𝑖 = (𝑥𝑖1, … , 𝑥𝑖𝑘) with 𝑖 = 1, … , 𝑁, where, in our case, 𝑁 are the regions, 𝑦𝑖 is the
household carbon footprint and 𝒙𝒊 is a set of dimensions that changes as the model specification changes. An
additional ingredient that plays a role in the model is the error 𝑢𝑖. The error 𝑢𝑖 is a measure that accounts for
residual elements affecting 𝑦𝑖.
The model specifies 𝑦𝑖 as a linear function of the regressors and the error, through a set of coefficients 𝜷 =
(𝛽1, … , 𝛽𝑘):
𝑦𝑖= 𝒙𝑖𝑇𝜷 + 𝑢𝑖, 𝑖 = 1, … , 𝑁
In this context, it is common discipline to assume 𝑥𝑖1 = 1 for all 𝑖 = 1, … , 𝑁 in order to allow 𝛽1 to be the
constant of the model.
So now that the model is set, we want to investigate the relationship between 𝑥𝑖𝑗 and 𝑦𝑖, meaning we want to
estimate 𝛽𝑗, i.e., we will look for 𝛽̂ .𝑗3
In practice, our model is specified as below:
For the sake of completeness, it is worth saying that, at some point, the relationship between the household carbon footprint and income is expected to wear off. For this reason, a squared term in income enters our model specification. Moreover, before estimation is carried out, the dimensions of active participation, passive participation and social trust are made as percentages. This is going to ease the interpretation of the coefficients we are about to estimate.
Two additional model specifications will be debated: the first one replaces those dimensions concerning social capital with that set of dichotomous variables that have been introduced to account for country characteristics, while the second one mixes the original model together with the last specification that has been just mentioned. In these two models the country dummy for Spain is dropped to not fall in what is called the dummy variable trap. As a consequence, the omitted category goes to the constant and the coefficients of the other dummies need to be read accordingly, meaning in terms of difference with respect to the Spain’s level of greenhouse gas emissions.
Multicollinearity is kept under control on a trial-and-error basis, meaning models are implemented and Variance Inflation Factor (VIF) assessment is the key tool to decide whether or not a variable needs to be ruled out from the model. As a rule of thumb, a threshold of 10 is set, therefore 𝑉𝐼𝐹(𝛽̂ ) < 10, ∀ 𝑗 = 1, … , 𝑘 is 𝑗
what is required to not have unreliable coefficients. As a result, the dimensions of basic need expenditure (BASIC), number of rooms (NROOMS) and electricity mix intensity (EMIX) are not included in the second and third model.
RESULTS
Table 3 shows the results of the models under analysis.
Table 3. Regional determinants of household carbon footprint across the model specifications under
analysis. The dimensions of active participation, passive participation and social trust are measured in %. The dummy for Spain goes to the constant.
Dependent variable: --- HCF (1) (2) (3) --- INC 507,878*** 678,499*** 717,904*** (85,174) (80,645) (78,456) INC2 -10,162*** -10,729*** -11,535*** (2,644) (1,955) (1,820) HHSIZE -1048,742* -1978,649*** -2004,444*** (543,370) (387,957) (376,855) URBAN -538,659* -466,009*** -508,652***
(318,671) (148,828) (148,460) EDUC 61,472*** 1,211 -5,845 (12,481) (11,121) (11,603) BASIC -40,941** (16,568) NROOMS -150,244 (641,856) HDD -0,905 4,385** 5,564*** (1,948) (1,740) (1,553) FORESTAREA -1,414 -26,099*** -30,502*** (14,311) (8,696) (8,634) EMIX 752,774*** (135,910) ACT 38,997*** -5,805 (13,189) (9,342) PASS 12,385 -7,923 (12,185) (7,113) STRUST 12,326 5,773 (8,426) (4,112) ITRUST 99,875 59,374 (184,876) (94,052) NORMS -139,061 -83,172 (113,326) (51,265) CIVIC -92,501 -161,260 (206,881) (111,321) AUSTRIA -2780,095*** -2616,761*** (456,434) (531,420) BELGIUM -1838,887*** -1682,183*** (400,214) (420,310) BULGARIA 36,025 324,452 (553,467) (520,093) CZECH_REPBLIC -202,883 280,485 (376,064) (365,079) GERMANY -1478,175*** -1421,807*** (394,505) (467,781) DENMARK -1121,280*** -863,552* (399,015) (488,531) ESTONIA 2859,400*** 2892,273*** (397,795) (366,168) FINLAND -876,131 -827,683 (560,895) (662,308) FRANCE -4110,458*** -4139,261*** (255,032) (329,557) GREECE 4923,492*** 5625,404*** (362,559) (465,723) HUNGARY -947,212 ** -958,968** (464,252) (416,757) ITALY -2417,715*** -2302,385*** (317,486) (315,962) LITHUANIA 487,321 562,273
PORTUGAL -289,866 -272,489 (371,779) (400,452) ROMANIA -532,584 -329,381 (505,943) (437,861) SLOVENIA 3535,213*** 3654,281*** (205,758) (203,723) SLOVAKIA 1595,715*** 2123,352*** (438,190) (451,986) UNITED_KINGDOM 1874,412*** 1875,978*** (373,326) (341,850) Constant 8435,063** 8532,129*** 9940,969*** (3449,869) (1460,257) (1865,372) --- Observations 163 163 163 R2 0,775 0,964 0,967 Adjusted R2 0,750 0,956 0,958 Residual Std. Error 1551,362 (df = 146) 647,969 (df = 134) 634,593 (df = 128) F Statistic 31,403*** (df = 16; 146) 127,963***(df = 28; 134) 110,215***(df =34; 128) ================================================================================ Note: *p<0,1; **p<0,05; ***p<0,01
The results of the regressions aim at explaining the regional variations in household carbon footprint using a set of dimensions that have been hypothesized to influence the environment. Of course, a ceteris paribus approach is here applied. The coefficient for income is estimated to be positive and significant (p < 0,01) in all model specifications. However, with a turning point of about 24,989, 31,619 and 31,120 respectively, the trend levels off, other things equal. Hence a thousand-EUR rise in income results in an increase in household carbon footprint of 507,878 kgCO2e/cap in model (1), 678,499 kgCO2e/cap in model (2) and 717,904 kgCO2e/cap in model (3). Once the annual income reaches the threshold, the effect weakens. However, it turns out that in model (2) and (3) the turning points go beyond the maximum value we observe in the distribution of income, and so, in these cases, the much-debated inverted-U relationship between environmental degradation and income does not show up in the sample. The estimated coefficient for household size is negative and significant in all the three models, although in the first one significance is just at 10% level. Increasing the average household size of a region by one person leads to a drop in the average person’s emission of 1048,742 kgCO2e/cap in the first model, 1978,649 kgCO2e/cap in the second model and 2004,444 kgCO2e/cap in the third model. The coefficient for urban typology follows the same pattern as the coefficient for household size does. Predominantly urban regions entail, on average, 538,659 kgCO2e/cap less in model (1), 466,009 kgCO2e/cap less in model (2) and 508,652 kgCO2e/cap less in model (3). When it comes to education, a one-percent point rise in the regional population with tertiary education is associated with an increase of 61,472 in kgCO2e/cap (p < 0,01) in model (1), while no significance is shown in the other two model specifications. The parameter that has been estimated for basic need is negative and significant at 5% in model (1). In here, a one-percent point increase in the regional household expenditure on basics brings about a reduction in emissions of 40,941 kgCO2e/cap. On the contrary, increasing the dwelling size does not entail any important effect on the carbon footprint. The estimate for the coefficient of heating degree days experiences a change in sign between the estimate of the first model and the others and the dimension gains
significance when model (1) is not considered anymore, being in fact statistically different from zero at 5% in model (2) and at 1% in model (3). The effect on the environment of an increase in the severity of the cold by one degree is between 4 and 6 kgCO2e/cap in both cases. In the case of forest area, the coefficient is always negative, but significant at all levels only in model (2) and (3). A rise in the forest area of a region by a thousand square meters per capita is associated with a 26,099 kgCO2e/cap drop in the second model and a 30,502 kgCO2e/cap drop in the third model. The computation of the coefficient for the electricity mix intensity in model (1) leads instead to a value that is positive and significant at all level of significance. A rise in the electricity mix of 0,2 kgCO2e/kWh brings to an increase in household carbon footprint of 752,774 kgCO2e/cap. When it comes to social capital, the coefficient for active participation is estimated to be positive and significant (p < 0,01) in (1) and a one-percent increase in this dimension is associated with an increase in emissions of 38,997 kgCO2e/cap. The other measures of social capital do not seem to be significant in (1). In model (3), instead, no coefficient estimate for social capital is statistically different from zero. To proceed, country dummies coefficients deserve a comment too. It is important to say that signs keep the same moving from (2) to (3), except for what concerns the coefficient of Czech Republic, and that statistical significance does not change, apart from a very few cases, where the variation is not much. Neither magnitudes really change from the second model to the third. Finally, it is worth discussing extreme behaviours. Being a region in Luxembourg leads to an increase in household carbon footprint of 7398,778 kgCO2e/cap (p < 0,01) in (2) and 7418,798 kgCO2e/cap (p < 0,01) in (3), while being a region in France leads to a drop in emissions of 4110,458 kgCO2e/cap (p < 0,01) in model (2) and 4139,261 kgCO2e/cap (p < 0,01) in model (3).
One important caveat to say that, in all the models, standard errors are adjusted to be robust to heteroscedasticity.
DISCUSSION
Notwithstanding the results of the first model seem to validate, although weakly, the initial idea that social capital matters for the environment, once country dummies are brought into play, it is no longer so. A country dummy may capture some policy components that play some role over the emissions. These policies may influence people and so the effect of social capital over the environment can be channelled through their implementation. This might be why the net effect of social capital components is not statistically different from zero in the last model. Of course, since country dummies account for country characteristics, many other conjectures are allowed. And so, by way of example, these variables might also capture institutional quality. At the same time, it might be that those proxies of social capital that have been picked are not acting as expected, meaning they are not behaving as social capital would behave. Therefore, once we add country
of the electricity mix exerts a positive and significant effect on the environment. Moreover, other than significant, the role that income plays over the environment seems to be kind of hybrid, inasmuch it is positive up to a certain threshold and then becomes negative. However, in model (2) and (3) no fraction of the regional sample lies beyond the turning point that has been estimated, and so no evidence is in favour of an Environmental Kuznets Curve (EKC), meaning an inverted U-shape relationship between environmental decay and income per capita.4
IS THERE A PLACE FOR SPATIAL ANALYSIS?
MODEL
In what follows, we are going to further analyse model (1). This choice is due to the fact that the VIF of each estimated coefficient of this model obeys to a more stringent definition of the threshold, according to which multicollinearity is detected. This assures us that the estimates of model (1) are more reliable than those of model (2) and (3).
In our setup, there may be that observations are not spatially independent across space and so they tend to cluster geographically. Indeed, the model that has been examined so far does not account for the fact that what happens in one area may be related to what happens in another area. However, when areas are adjacent in space, it could be that independency is violated. In this case, the need to call on a spatial component is prominent, and the model specification cannot fail to consider it.
Many interaction effects can be examined. In any case, before moving to the model implementation, it is useful to test whether or not geographical dependence is here the case. As a first step, since Figure 2 does not clarify things, let us explore how OLS’ residuals behave in space. To accomplish this task, we are going to express residuals in terms of standard deviations away from the mean. Then it is time to map those values. Figure 4 displays the results.
Figure 4. Representation of model (1)’s residuals .
The map above shows us that spatial dependence may be present. Indeed, cluster of overprediction (red tones) and underprediction (blue tones) are easy to recognise.
An additional test that may be performed to further investigate this idea is the Moran’s I test for residual spatial autocorrelation. A key ingredient of this assessment procedure is the so-called spatial weights matrix, here W, meaning that matrix describing the spatial arrangement of the geographical units in the sample. Many spatial
choice falls on a matrix, having 𝑤𝑖𝑗= 1 when 𝑖 and 𝑗 belong to the same country, 0 otherwise. Down here a
preview of matrix W is presented.
𝐖 = [ 0 1 … 0 1 ⋱ … 0 ⋮ ⋮ ⋱ ⋮ 0 0 … 0 ]
This is consistent with the evidence that in most of the cases observations cluster within the countries. Once
W has been chosen, it is row-normalised, and then it is time to run the Moran’s I test. The result is positive
and significant at all level of significance and this is a promising result towards spatial dependence. As mentioned, different choices can be made in order to include the spatial component in our regression model. Theoretically, the full model, i.e., the one considering all types of interaction, takes the form:
𝐘 = δ𝐖𝐘 + α𝐢N+ 𝐗𝛃 + 𝐖𝐗𝛉 + 𝐮, 𝐮 = λ𝐖𝐮 + 𝛜
where 𝐘 is a N × 1 vector of one observation of the household carbon footprint for every unit in the sample (i = 1, … , N), 𝐢N is a N × 1 vector of ones, 𝐗 is an N × K matrix of our exogenous explanatory variables and 𝛜 is a N × 1 vector of disturbances. 𝐖𝐘, 𝐖𝐗 and 𝐖𝐮 denote the interaction effects among the dependent variable, the interaction effects among the independent one and the interaction effects among the error term respectively. Finally, δ, α, 𝛃, 𝛉 and λ are the coefficients to be estimated, of which 𝛃 and 𝛉 are N × 1 vectors, while the others are constant terms. This is the so-called general nesting spatial (GNS) model. Now, imposing restrictions on one or more parameters it is possible to set up different models. Thanks to the Lagrange Multiplier test and additional simulations we end up saying the most appropriate model that fits our case is the spatial error model (SEM). SEM is defined as:
𝐘 = α𝐢N+ 𝐗𝛃 + 𝐮, 𝐮 = λ𝐖𝐮 + 𝛜
and it treats spatial autocorrelation as a nuisance. This model is consistent with a situation where independent variables omitted from the model are spatially autocorrelated, or with a situation where unobserved shocks follow a spatial pattern. Estimation relies on maximum likelihood (ML) theory.
RESULTS
Results are reported in Table 4.Table 4. Comparison between the determinants of OLS and the determinants of SEM. The dimensions of
active participation, passive participation and social trust are measure as %. Dependent variable: --- HCF OLS5 SEM --- INC 507,878*** 513,351*** (85,174) (101,796) INC2 -10.162*** -10,144*** (2,644) (3,029) HHSIZE -1048,742* -1232,065** (543,370) (575,310) URBAN -538,659* -607,552** (318,671) (291,366) EDUC 61,472*** 68,696*** (12,481) (15,242) BASIC -40,941** -58,075*** (16,568) (17,462) NROOMS -150,244 -455,206 (641,856) (794,588) HDD -0,905 -2,733 (1,948) (2,023) FORESTAREA -1,414 12,973 (14,311) (16,900) EMIX 752,774*** 678,277*** (135,910) (122,557) ACT 38,997*** 18,349 (13,189) (12,581) PASS 12,385 22,495** (12,185) (10,895) STRUST 12,326 16,167** (8,426) (7,966) ITRUST 99,875 -23,041 (184,876) (183,421) NORMS -139,061 -145,516 (113,326) (94,657) CIVIC -92,501 -117,984 (206,881) (208,233) Constant 8435,063** 11405,570*** (3449,869) (3440,208) ---
When it comes to the Akaike’s information criterion (AIC), SEM scores better than OLS. Therefore, since in both cases coefficients results can be interpreted as marginal changes, it is possible to compare SEM’s coefficients with those from OLS. As we can see, once the spatial component comes into play results experience a change. In particular, the coefficient for household size, but also the one for urban typology falls and becomes significant at 5% level. For what regards basic consumption, now a one-percent point increase in the regional household expenditure on basics brings about a reduction in emissions of 58,075 kgCO2e/cap against a smaller reduction we observe in case of OLS. Even in this case, the estimate increases its significance up to 1% level. To move forward, in SEM, active participation completely loses its significance, while passive participation, other than experiencing a rise in magnitude, gains significance up to 5%. Concerning social trust, a one-percent point more in this dimension leads to an increase in carbon footprint of 16,167 kgCO2e/cap, while in OLS the score is lower. Once again, significance increases up to 5%. One last remark is to say that what is left over does not go through any important change, neither in significance nor in magnitude.
DISCUSSION
Focusing on the further analysis that has been conducted just now and judging the model specification as to be the best fitting the data available, it is worth saying that, in somehow, social capital affects the household carbon footprint, even though this effect seems to be low. Once again, socio-economic factors, together with the level of intensity of the local electricity mix still have their role. In any case, the spatial error model has been of help in the attempt to draw the right line of connection between social capital and household carbon footprint. Unfortunately, this line is just blurred, since some components of social capital does not play any role in our analysis, and, for those elements affecting the environment, magnitudes are just mild. In this respect, as mentioned, proxy misspecification can be the matter to work on.
Log Likelihood -1414,718
sigma2 1954698,000
Akaike Inf. Crit. 2875,7 2867,436
Residual Std. Error 1551,362 (df = 146) F Statistics 31,403*** (df = 16; 146) Wald Test 23,691*** (df = 1) LR Test 10,270*** (df = 1) =================================================== Note: *p<0,1; **p<0,05; ***p<0,01
CONCLUSIONS
This study investigates the relationship between social capital and environmental distress at subnational level within the European Union. The underlining purpose sticks with the concern of the EC, saying action is needed at this level of decision. Different model specifications have been tested, mostly based on OLS and ML theory, precisely regression and spatial models has been the instruments on which this study has been developed through. Results show that some components of social capital exert a mild influence on the environment. In particular, according to the spatial error model, whose implementation seems to be the most proper, passive participation and social trust positively affect the household carbon footprint. However, when it comes to explain what mostly affect the environment, these factors are not the most important ones, while, on the contrary, socio-economic elements, but also what is known as the carbon intensity of the electricity mix, have by far the most prominent role. Lastly, the results of the remaining variables included in the analysis turn out to be in line with those reported in the previous literature.
Of course, there is a number of limitations that need to be addressed. First of all, if we consider the EVS dataset, it is possible to notice that the sample size changes highly across the regions within which the questionnaire is handed out. So, once aggregation is performed, small sample sizes can result in huge standard errors, and so accuracy is negatively affected. Nevertheless, in some cases, sampling weights turns out to mitigate this problem. Another issue that has been mentioned many times concerns the proxies that have been used as components of social capital. It might be that those dimensions the choice fell upon are not entirely suitable for the purpose they have been assigned to.
One last point to stress the lack of regional data concerning carbon emissions. An effort should be made toward the collection of this type of data so as to benefit the implementation of subnational policies in favour of the environment. Unfortunately, environmental emergency is not equally acknowledged among people, but data might encourage research and research might encourage policies and common understanding.
APPENDIX 1 – BUILDING THE DATASET
This section is devoted to the explanation of the dataset the analysis considers. At first, we have what has mentioned to be the GHG dataset and the EVS dataset. The former dataset is provided for the EU and it is aggregated at NUTS 2 level, so each observation is a region. In a few cases the observation is made available at NUTS 1 level. The observations are 184 and the variables that are going to be useful in the analysis are compiled in Table 2 (rows from 1 to 10). Table 2, as we can see, lists all the variables of interest for the analysis, combined with a short description of the corresponding meaning. As mentioned, we have 184 observations, but the sample is going to be subject to a reduction up to 163 observations in order to stick with the regional specification of the EVS dataset, meaning if a region is not in the EVS dataset, it is going to be removed from the GHG dataset. Moreover, two additional adjustments have to be made in order to control for the regional mismatches being present in the two data sources and be able to map the estimates. The first change is about Italy. A measure for Trentino-Alto Adige is created, by averaging the observations for Trento and Bolzano and substituting this aggregate to each of them. This is done to take care of the fact that in the EVS dataset Trentino-Alto Adige turns to be an observation. Once this observation of the EVS dataset is duplicated, a record for both Trento and Bolzano is available in the EVS dataset too, and so it is possible to map the estimates for the two regions the initial problem arises from. The second change is about Czech Republic. The idea is that the GHG dataset and the EVS dataset have the same regional boundaries for what concerns Prague and Central Bohemian, but it is not the case for the other regions in the two datasets. Also in this case, to match the two data sources, it is worth starting from the GHG dataset, and substituting to each region, but Prague and Central Bohemian, the average value of those regions the boundaries do not overlap. Eventually, in the EVS, the same procedure is implemented.
Now, it is time to move to the EVS dataset, keeping in mind what we have just taken care of. The EVS dataset contains information at individual level, so a process of adjustment is here necessary to stick with the same level of specification we have in the GHG dataset. Before aggregating, it is important to select the variables, and so the questions of the EVS, in accordance with the elements of social capital that have been defined. So, as a starting point we consider the question ‘Please look carefully at the following list of voluntary organisations and activities and say ... b) which, if any, are you currently doing unpaid voluntary work for?’. A measure of active participation is obtained, by, first, summing up across answers and then assigning a zero whether the result of the summation is equal zero, and a one elsewhere. This procedure results in a dichotomous variable, taking either zero, if the individual is not actively involved in any form of organisation, or one, if otherwise. Applying the same procedure to the question ‘Please look carefully at the following list of voluntary organisations and activities and say ... a) which, if any, do you belong to?’ leads to a measure of individual passive participation and, again, this measure can take zero if the individual is not a member of an organisation and one otherwise. Reversing the scores to the question ‘Generally speaking, would you say that most people can be trusted or that you can't be too careful in dealing with people?’ allows us to define a measure of social trust, taking higher values the higher is the level of social trust. Now, it is time to consider the question ‘Please
look at this card and tell me, for each item listed, how much confidence you have in them, is it a great deal, quite a lot, not very much or none at all?’ for the items police, parliament, civil service and justice system. Adding up across answers and reversing the final scale it is possible to construct a measure of institutional trust that is higher the higher is the level of confidence in those authorities the question mentions. The aforementioned procedure is also used to deal with the questions ‘Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card’ for the statements ‘claiming benefits’, ‘cheating on taxes’ and ‘accepting a bribe’. A score for the variable ‘compliance with norms’ is the output and its values are higher the more people follow the rules. Finally, a slightly different procedure is applied to the answers to the questions ‘How important in your life: politics?’, ‘When you get together with your friends, would you say you discuss political matters frequently, occasionally or never?’ and ‘How often do you follow politics in the news on television or on the radio or in the daily papers?’. Since here the answers span different scales, first these scales are reversed, in order to assign higher values to better results, and then dimensions are standardised, so that the final indicator is not affected by one of the three in particular. A measure of civic engagement is what it is eventually obtained by adding all the variables together.
For the sake of completeness, it is worth saying that, for each specific variable of social capital missing values are replaced by their regional sample mean.
At this point observations are aggregated at regional level, taking care of what are called sampling weights, meaning measures of how many individuals in the population each sampled individual represents. We end up having a dataset with 406 observations, where an observation can be at any level of NUTS and dimensions are in terms of mean.
Once aggregation is carried out, the EVS dataset needs to be adjusted to stick with the regional classification we have in the GHG dataset. To do that, it is appropriate to consider the NUTS classifications used to create the GHG dataset, meaning those in force on the date of the CESs. In those uncommon cases where the regional classification of the EVS dataset does not stick precisely with that in the GHG dataset, regions are adjusted manually to meet the standard. Finally, those observations of the EVS dataset non-overlapping at all with the GHG dataset are cancelled out. The EVS dataset flows into having 163 observations. Table 2 gives us a compilation of all the variables the analysis brings into play, including also those concerning social capital. The GHG dataset and the EVS dataset contain now the same number of observations, so it is possible to merge them and work on the same dataset, to which the name of Final dataset is assigned.
APPENDIX 2 – SOCIAL CAPITAL (REGIONAL SCORES)
6This paragraph is designed to further investigate the behaviour of those elements of social capital a choice was made upon. Of course, the ground tested is cantered on the regions of Europe here available. Table 5 contains the regions scoring at the top of those dimensions of social capital under analysis, but also the regions lying at the bottom of the ranks.
Table 5. Best and worst regions per element of social capital.
Dimension Top three regions with the highest scores (>) Top three regions with the lowest scores (<)
1 Active participation • Sterea Ellada (GR)
• Ionia Nisia (GR) • Valle d'Aosta (IT)
• Algarve (PT) • Hamburg (DE) • (⋀) Umbria (IT)
2 Passive participation • Midtjylland (DK)
• Ita-Suomi (FI) • Lansi-Suomi (FI)
• Hamburg (DE) • Podlaskie (PL) • Severoiztochen (BG)
3 Social trust • Hovedstaden (DK)
• Hamburg (DE) • Midtjylland (DK)
• Nord-Est (RO) • Switokryskie (PL) • Voreio Aigaio (GR)
4 Institutional trust • Saarland (DE)
• Rheinland-Pfalz (DE) • Nordjylland (DK)
• Notio Aigaio (GR) • Voreio Aigaio (GR) • Bucuresti-Ilfov (RO)
5 Compliance with norms • Saarland (DE)
• Malta (MT) • Balears (ES)
• Est (FR) • Kriti (GR)
• Sterea Ellada (GR)
6 Civic engagement • Ionia Nisia (GR)
• Rheinland-Pfalz (DE) • Molise (IT)
• Valle d'Aosta (IT) • Balears (ES) • Algarve (PT)
6“>” means the order is decreasing, “<” means the order is increasing and (⋀) means that element scores on a par with the one just
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ACKNOWLEDGEMENTS
I would like to thank everyone who has helped undertake this dissertation. Every recommendation, critique and observation has been of very help for its completion.
Before anyone, a special thanks to my supervisor, Professor Angela Parenti, for her great availability and expertise. She has supported me since the very first discussion of the topic of this thesis.
I also thank my family and my girlfriend for believing in me and backing me up during all my academic career. I am finally gratitude to my friends for encouraging me and being present in all these years.
