Influence of Extreme Events in Electric Energy Consumption ... - MDPI

sustainability Article

Influence of Extreme Events in Electric Energy Consumption and Gross Domestic Product Fabrício Vieira * , Maurício Aparecido Ribeiro, Antonio Carlos de Francisco Giane Gonçalves Lenzi *

and

Federal Technological University of Paraná, Ponta Grossa 84016-210, Paraná, Brazil; [email protected] (M.A.R.); [email protected] (A.C.d.F.) * Correspondence: [email protected] (F.V.); [email protected] (G.G.L.) Received: 7 December 2018; Accepted: 19 January 2019; Published: 28 January 2019

Abstract: The objective of this paper was to identify how extreme events can indicate periods of economic instability in variables from the economic and environmental context (per capita Gross Domestic Product (GDP), per capita electric energy consumption, and per capita carbon dioxide (CO2 ) emission). The research is limited to the population of the country (Brazil) and five cities of Paraná (Curitiba, Londrina, Maringá, Ponta Grossa, and Cascavel). Therefore, the major research interest was focused on finding information related to extreme events and other techniques that are used for interpretation of complex systems currently. The development was based on data collection. The results indicated that extreme events have influence in periods of economic instability. They also evidenced that there is greater correlation in GDP data/electric energy consumption than in GDP data/CO2 emissions or electric energy consumption/CO2 emissions. Keywords: extreme events; electric energy consumption; CO2 emission; Domestic Product—GDP

1. Introduction The world has witnessed several nations facing economic and financial crises. The causes of these crises are the most varied and differ according to the particularities of each country [1]. Nations considered exponents in financial terms, named advanced economies, due to their degree of security and return of investments, saw their finances pointing downward given the financial crises, which demonstrates the importance of the analysis and studies focusing on countries affected by instabilities [2]. Economic crises can be understood as temporary imbalances that significantly impact a nation and can even affect the financial stability of neighboring countries, besides influencing trading partners, businesses, employment and unemployment, inflation and others [3]. Several precise events can directly affect the economic and environmental variables. Therefore, extreme events directly affect the electric energy consumption. The global demand and consumption of energy, even though being able to reflect the heating up of the economy, are issues that have been calling the attention of several researchers, because, in terms of energy supply, electricity is a strategic resource for the development of cities and regions as a whole [4]. The Extreme Value Theory is a statistical and probabilistic technique used, in the analysis and interpretation of the so-called ‘extreme events’, in a time series [5]. In this context, extreme events are considered the values, in the time series, which stand out and differ, considerably, in absolute terms, from the average. This analysis includes mathematical observation of specificities and properties of these values that diverge, the so-called extreme events, also regarding the possibility of cyclical repetitions of these events in the course of time.

Sustainability 2019, 11, 672; doi:10.3390/su11030672

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Sustainability 2019, 11, 672

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2 The self-organized criticality (SOC) analysis is based on cellular automata to annalize the behavior statistical behavior. The analyzed systems of occurrence Energy consumption and CO2 emissions statistical are not in of the complex structures of the system. The of SOC is related to stationary steady behavior due to their dynamic behavior [6–8]. The theory of extreme values is basically the behavior. The analyzed systems of Energy consumption and CO2 emissions are not in steady behavior tendency of rare or extreme events to occur based on the distance between the mean and the median due to their dynamic behavior [6–8]. The theory of extreme values is basically the tendency of rare or of the system. analyzes thethe sampling rare values and the of their [9– extreme events It toalso occur based on distanceof between the mean andbehavior the median of thedistributions system. It also 11]. analyzes the sampling of rare values and the behavior of their distributions [9–11]. PeakOver OverThreshold Threshold(POT) (POT)isisaamethod methodthat thataddresses addressesthe thetail tailestimation estimationproblem, problem,through throughthe the Peak generalized Pareto distributions, so that we can analyze the extreme events [12,13]. generalized Pareto distributions, so that we can analyze the extreme events [12,13]. In this this context, context, this this study study aims aims to to evaluate evaluate the the periods periods of of economic economic and and financial financial instability instability In (GDP—Gross Domestic Domestic Product), Product), and and the the electric electric energy energy consumption consumption in in Brazil’s Brazil’s cities cities and and in inthe the (GDP—Gross country as a whole, evaluating changes in these indexes, relating them to relevant global and regional country as a whole, evaluating changes in these indexes, relating them to relevant global and regional contemporary events. events. The will bebe thethe analysis of Extreme Events (EE) (EE) in a time contemporary Thetechnique techniqueused used will analysis of Extreme Events in a series time that outstands considerably in absolute terms of ofaverage used, being being series that outstands considerably in absolute terms averageororother other parameters parameters used, unprecedentedin inthis thistype typeof ofapplication. application. unprecedented Materialsand andMethods Methods 2.2.Materials 2.1. 2.1.Data DataPresentation Presentation The Thesearch searchwill willbe beconducted conductedto toverify verifythe thecorrelation correlationbetween betweenextreme extremeevents eventsand andthe theelectric electric energy energydemand demandof ofthe thefive fivecities cities(Curitiba, (Curitiba,Londrina, Londrina,Maringá, Maringá,Ponta PontaGrossa Grossaand andCascavel) Cascavel)of of Paraná, Paraná, Brazil, Brazil, in interms termsof ofpopulation, population, and and Brazil, Brazil, as as shown shown in in Figure Figure 1A) 1A) Cities Cities of of Paraná, Paraná, research research object. object. Source: [14]. Figure 1B) Brazil’s map emphasizing the Brazilian system of electric energy transmission. Source: [14]. Figure 1B) Brazil’s map emphasizing the Brazilian system of electric energy Source: [15]. Source: [15]. transmission.

A)

B)

Figure1.1.(A) A) Cities Cities of of Paraná, Paraná, research research object. object. Source: Source: IBGE IBGE -- Brazilian Brazilian Institute Institute of ofGeography Geographyand and Figure Statistics(2017). (2017). (B) B) Brazil’s map emphasizing the Brazilian system of of electric electric energy energy transmission. transmission. Statistics Source:ANEEL—National ANEEL—NationalAgency Agencyof ofEnergy EnergyElectricity Electricity(2017). (2017). Source:

Data energy consumption of these locations comprise the period from from 1980 to 2014. Dataofoftotal totalelectric electric energy consumption of these locations comprise the period 1980 to The electric energy consumption includes the following categories: residential, industrial, commercial, 2014. The electric energy consumption includes the following categories: residential, industrial, and rural energy power; and total power; consumption. Dataconsumption. were obtainedData fromwere the commercial, andconsumption; rural energypublic consumption; public and total

obtained from the Energy Company of Paraná (COPEL—Companhia Paranaense de Energia) [16].


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Energy Company of Paraná (COPEL—Companhia Paranaense de Energia) [16]. Figure 1B shows the Brazilian system of electrical energy transmission in 2015. Gross Domestic Product data from these places were obtained from the World Bank [17]. 3

2.2. Methodology

1B shows the Brazilian system of electrical energy transmission in 2015. Gross Domestic TheFigure analysis to verify the candidates to the points of the time series to extreme events is conducted Product data from these places were obtained from the World Bank [17]. in three steps: i) adjustment of a polynomial of degree N, 5 in this case, to the time series data; ii) calculation of the points of difference between the adjusted by the polynomial and that obtained by 2.2. Methodology the time series. we have equation in which The Therefore, analysis to verify the the candidates to the points of the time series to extreme events is conducted in three steps: i) adjustment of a polynomial of degree N, 5 in this case, to the time series δ(t) = Pobetween (t) − Pathe (t) adjusted by the polynomial and that data; ii) calculation of the points of difference obtained by the time series. Therefore, we have the equation in which

(1)

Po (t) is the observed point (time series) and Pa (t) is the point obtained by adjusting the polynomial; (1) δ(𝑡) = 𝑃 (𝑡) − 𝑃 (𝑡) iii) calculation of the probability distribution of the sequence of points obtained by the difference of is the observed point (time series) and 𝑃 (𝑡) is the point obtained by adjusting the Po (t) and Pa𝑃(t(𝑡) a probability distribution, the calculation of the full width at half maximum, will ). Such polynomial; iii) calculation of the probability distribution of the sequence of points obtained by the be a parameter of identification of candidates to extreme events in the time series. difference of 𝑃 (𝑡) and 𝑃 (𝑡). Such a probability distribution, the calculation of the full width at half Data analysis through application of data in the program and gnuplot and maximum, willoccurred be a parameter of identification of candidates to extreme events inGrace the time series. elaborationData of Figures. The nextthrough sectionapplication shows and discusses results obtained in the analysis occurred of data in the the program Grace and gnuplot andresearch of extreme Figures. The nextin section shows and discusses the results obtained in the research throughelaboration analysis of events the data pool. through analysis of extreme events in the data pool.

3. Results and Discussion 3. Results and discussion

In Brazil, data from 1970 to 2014 indicate values for per capita GDP, per capita carbon dioxide In Brazil, data from 1970 to 2014 indicate values for per capita GDP, per capita carbon dioxide (CO2 ) emissions and per electric energy time series, as shown in Figure 2. (CO2) emissions and capita per capita electric energyconsumption consumption inin time series, as shown in Figure 2.

1.6

2 00 0

CO2 Emission

Electricity consumption, CO2 Emission, GDP

3 00 0

1.2

0.8

1980

1990

2 000

2 01 0

20 20

Ye ars

1 00 0

0 1 9 60

1980

2 0 00

20 20

Y e a rs E le c tricity c o n s u m p tio n k w h (p e r c a p ita ) C O 2 E m iss io n (m e tric : to n s p e r c a p ita ) G D P p e r ca p ita ( U S $ c u rre n t - d e c 2 0 1 7 ) Figure 2. Gross Domestic Profit (GDP), CO2emissions, emissions, and energy consumption in Brazil Figure 2. Gross Domestic Profit (GDP), CO andelectric electric energy consumption ininBrazil in 2 per capita values in a time series (1970 to 2014). Source: own authorship (2018). per capita values in a time series (1970 to 2014). Source: own authorship (2018).

Data indicate a growth, over the years, to all variables studied, indicating an influence among

Data indicate a growth, over the years, to all variables studied, indicating an influence among them. In addition, there is a drop in CO2 and a decrease in GDP around 2011 to 2014. On the other them. In addition, there is a drop a decrease in GDP around 2011 to 2014. On the 2 and hand, this is not observed for in theCO electric energy consumption. There are studies that establish a other hand, this is not observed for the electric energy consumption. There are studies that establish a correlation between energy and the development of groups or institutions in general [18]. Figure 3 shows data of electric energy consumption in kW/h divided by the population, from correlation between energy and the development of groups or institutions in general [18]. 1970 to 2015, fordata the cities of Curitiba, Londrina, Maringá, Cascavel, and Ponta Grossa. Figure 3 shows of electric energy consumption in kW/h divided by the population, from 1970 to 2015, for the cities of Curitiba, Londrina, Maringá, Cascavel, and Ponta Grossa.


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4

Electric Energy Consumption (KW/h) [per capita]

1.0

Curitiba Londrina Maringa Cascavel Ponta G rossa

0.9 0.8 0.7 0.6 0.5 0.4

2005

0.3 0.2

2000

0.1

1994

0.0 1970

1980

1990

2000

2010

2020

T im e (years) 3. Total electric energy consumption (per capita) per time. Source: own authorship (2018). Figure Figure 3. Total electric energy consumption (per capita) per time. Source: own authorship (2018).

We observe that the curves of the total electric energy consumption have a similar behavior to

We observe that the curves of the total electric energy consumption have a similar behavior to all all cities studied from 1970 to 2015. This means that this consumption is affected by the events in a cities studied 1970Therefore, to 2015. This means thatthat thissome consumption is affected by the similar from manner. we can indicate factors affect an increase or events decreaseinina similar manner.consumption. Therefore, These we can indicate that some factors affect an increase or decrease in consumption. events can be regional or global. Figure 4 shows data These events can be regional or relating global. a period-range and the extreme events/GDP/electric energy consumption in function of time. We observe that theand extreme influence the period in which energy Figure 4 shows data relating a period-range the events extreme events/GDP/electric they occur, i.e., they are not a precise phenomenon. consumption in function of time. We observe that the extreme events influence the period in which they occur, i.e., they are not a precise phenomenon. 5

Extreme Events

E le c tric C o n su m p tio n GDP

19 7 0

19 75

1 98 0

1 98 5

1 990

1 99 5

20 00

20 0 5

201 0

T im e (y e a rs) Figure 4. Extreme events (electric energy consumption/GDP). Source: own authorship (2018).

Figure 4. Extreme events (electric energy consumption/GDP). Source: own authorship (2018). In particular, GDP values show a greater oscillation in specific periods: after 1980, after 1990,

In particular, GDP values show a greater oscillation in specific periods: after 1980, after 1990, between 2000 and 2005, and discreet oscillation before 2010 and after 2010. between 2000 and 2005, and discreet oscillation before 2010 and after 2010. 3.1. Analysis of extreme events The data were acquired in the five largest cities in the state of Paraná, Brazil (Curitiba—Figure 5, Londrina—Figure 6, Maringá—Figure 7, Ponta Grossa—Figure 8, Cascavel—Figure 9), see Table 1. These data of total consumption of Electric Energy include the following uses: Residential, Industrial, Commercial, Rural, Public, Public Lighting, Public Service, Own (Energy Company). To


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Analysis of Extreme Events The data were acquired in the five largest cities in the state of Paraná, Brazil (Curitiba—Figure 5, Londrina—Figure 6, Maringá—Figure 7, Ponta Grossa—Figure 8, Cascavel—Figure 9), see Table 1. These data of total consumption of Electric Energy include the following uses: Residential, Industrial, Commercial, Rural, Public, Public Lighting, Public Service, Own (Energy Company). To observe in general, the study was conducted using data from Brazil. In this data the emission of CO2 was also included (Figure 10). 6

Figure 5. Acquisition of extreme (1) Adjustment Adjustment of 5. Acquisition extreme events events for forCuritiba. Curitiba. (A) A) GDP; (B) B) Energy. (1) polynomial; (2) (2) points points of ofdifference differencebetween betweenthat thatadjusted adjustedbyby polynomial and obtained thethe polynomial and thatthat obtained by by time series; (3) probability distribution of sequence of points obtained by difference the difference Po (t) time series; (3) probability distribution of sequence of points obtained by the fromfrom 𝑃 (𝑡)and and authorship (2018). (t). Source: own own authorship (2018). 𝑃 (𝑡).PaSource:


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7

Figure Figure 6.6. Acquisition of of extreme extreme events events for forLondrina. Londrina. (A) A) GDP; GDP; (B) B) Energy. Energy. (1) Adjustment of of polynomial; between that adjusted by the polynomial and and that obtained by time polynomial;(2) (2)points pointsofofdifference difference between that adjusted by the polynomial that obtained by series; (3) probability distribution of sequence of points obtained by theby difference from Pfrom Pa (t). o ( t ) and time series; (3) probability distribution of sequence of points obtained the difference 𝑷𝒐 (𝒕)and Source: own authorship (2018). 𝑷𝒂 (𝒕). Source: own authorship (2018).


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Figure (1) Adjustment Figure 7.7. Acquisition Acquisition of of extreme extreme events events for forMaringá. Maringá. (A) A) GDP; GDP; (B) B) Energy. Energy. (1) Adjustment of of polynomial; (2) points points of of difference differencebetween betweenthat thatadjusted adjustedbybythe the polynomial and that obtained polynomial; (2) polynomial and that obtained by by time series; probability distribution of sequence of points obtained by difference the difference from Po (t) time series; (3) (3) probability distribution of sequence of points obtained by the from 𝑃 (𝑡)and and P t . Source: own authorship (2018). ( ) a 𝑃 (𝑡). Source: own authorship (2018).

8


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Figure Figure 8.8. Acquisition Acquisition of of extreme extremeevents eventsfor forPonta PontaGrossa. Grossa.(A) A) GDP; GDP; (B) B) Energy. Energy. (1) (1) Adjustment Adjustment of of polynomial; between that adjusted by the polynomial and that by time polynomial;(2) (2)points pointsofofdifference difference between that adjusted by the polynomial and obtained that obtained by series; (3) probability distribution of sequence of points obtained by the by difference from Pfrom Pa (t). time series; (3) probability distribution of sequence of points obtained the difference 𝑃 (𝑡)and o ( t ) and Source: own authorship (2018).(2018). own authorship 𝑃 (𝑡). Source:

9


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Figure Energy. (1) (1) Adjustment Adjustment of Figure 9. 9. Acquisition Acquisition of of extreme extreme events events for forCascavel. Cascavel. (A) A) GDP; GDP; (A) B) Energy. of polynomial; (2) points of difference between that adjusted by the polynomial and that obtained by time polynomial; (2) points of difference between that adjusted by the polynomial and that obtained by series; (3) probability distribution of sequence of pointsofobtained by the difference from Po (t)from and P t ). time series; (3) probability distribution of sequence points obtained by the difference 𝑃a ((𝑡) Source: own authorship (2018). and 𝑃 (𝑡). Source: own authorship (2018).

10


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11

Figure 10. 10. Acquisition Acquisition of of extreme extreme events eventsfor forBrazil. Brazil.(A) A) GDP; GDP; (B) B) Energy. C) CO CO2.. (1) Adjustment of Figure Energy. C) 2 (1) Adjustment of polynomial; (2) points of difference between that adjusted by the polynomial and that obtained by polynomial; (2) points of difference between that adjusted by the polynomial and that obtained by time (𝑡)and time series; (3) probability distribution of sequence of points obtained by the difference from 𝑃 series; (3) probability distribution of sequence of points obtained by the difference from Po (t) and Pa (t). own authorship 𝑃 (𝑡). Source: Source: own authorship (2018).(2018). Table 1. Extreme Events indicated by city and variable studied. Locality

Extreme Events

Years that Presented EE above the

Years that Presented EE below the


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Table 1. Extreme Events indicated by city and variable studied. Locality

Curitiba

Londrina

Maringá

Ponta Grossa

Cascavel

Extreme Events (EE) x Variable

Years that Presented EE above the Width at Half Height:

Years that Presented EE below the Width at Half Height:

EE(Extreme Events) GDP

1991–1996

1970, 1982–1986, 199–2006, 2013–2014

EE electricity consumption

1995–2000, 2010–2013

1991–1995, 2004–2008

EE GDP

1970, 1980–1985 (discreet), 2002–2007 (strong), 2012–2013 (strong)

1972–1975 (leve), 1990–1995 (leve), 2008–2011 (strong), 2014 (strong)


1995–1999, 2009–2011

1991–1993, 2002–2008, 2014

EE GDP

1970, 1979–1985 (discreet), 2000–2007 (strong), 2013–2014(strong)

1973–1976, 1990–1997, 2010–2011, 2015


1997–2000, 2007, 2009

1992–1995, 2004–2007, 2015

EE GDP

1970, 1980–1984, 2000–2005, 2013–2014

1971–1975, 1989–1996 (discreet), 2008–2010 (strong), 2015


1995–2000, 2011–2014

1992–1994, 2004–2009, 2015

EE GDP

2005–2010, 2015

1998–2002 (discreet),


1996–2000, 2012

1991–1993, 2004–2006, 2015

EE GDP

1986, 1989–1992, 2010–2014,

1961–1963, 1973–1986, 1991–1992, 2005–2010, 2015–2016


1960, 1967–1970, 1980–1982, 2000–2002, 2010

1962–1965, 1971–1976, 1996–2000, 2007–2010

EE Emissions of CO2

1973–1980, 1997–2003, 2011–2012

1965–1969, 1982–1985, 1990–1993, 2006–2009

Brazil

4. Discussion 4.1. Paraná The 1970s were single period for Paraná, particularly due to the decline in the agricultural activities involving coffee production, a cultivation prevalent in the State in this period, and to the industrialization process, which began at this time. [19]. With the results obtained in Figure 5 to Figure 10, we can verify the occurrence of Extreme Events coinciding with the socioeconomic crises in the 1970s and 1980s, especially the crises that occurred in this period and which affected the price of oil barrels significantly, affecting the economy and the energy consumption, and these events occur and spread over a period. Based on this industrialization, the State was expected to achieve economic, technological, and social development [20]. The main sources of income and development explored were agriculture, extractivism, and processing of yerba mate and wood, related to exportation and domestic consumption, mainly for the Brazilian Southeast [21]. The coffee production, developed in the North of the State, was in decline due to factors such as the modernization of agriculture, drops in market value and losses due to the weather, which resulted in unemployment and rural exodus [22]. In 1970, according to [23], p. 96 “the coffee still represented 27% of the cultivated area; in 1981, it fell to 8.5%; in the same period, the area cultivated with the six main grains (soybean, corn, bean, wheat, rice and peanut) increased from 61% to 85%.” In the 1970s, the remarkable event is that in October 1973 the disputes involving petroleum exporting countries (PEC) began, which forbade the oil supply to the United States, Japan, and parts of Europe, that, at the time, imported from the Paraná State. This was, among others, the first crisis that occurred and went by the 1970s. This crisis also affected Paraná, since the activities of agriculture and importation/exportation suffer influence when the price of the oil oscillates.


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We emphasize that the global events from this period, which significantly affected the GDP of Paraná, are the increase in the price of oil, the drop in exportations and the difficulties related to agriculture. 4.1.1. 1979 to 1987 In 1979, the significant economic and financial instability related to oil occurred. The Iran–Iraq war started in this period, in addition to the Iranian revolution previously started, convulsed the Arab world, and consequently affected the oil exports, a commodity that hit records in terms of price in this period. The 1979 crisis contributed to form the scenery of the 1980s in Brazil, marked by reduction of GDP, high interest rates, and inflation at high levels. At the time when the first instabilities related to oil that surrounded the 1970s and 1980s began, Brazil, as well as other nations, had enormous dependence on this commodity. Since Brazil needed to import about 70% of the oil consumed in the country, in the most acute periods of the crisis, the value of this product had an increase of about 40%. These factors naturally impacted the State of Paraná and its GDP, given that the State had as primordial economic activities, in general, agriculture, vegetal extractivism, and exportation [11], activities that are economically influenced by the price of a barrel of oil. With the increase in petroleum, there is consequently an increase in expense of related fuels and other expenditures related to the petroleum consumption. The oil crisis affected the State in such a way that rationing measures were adopter for fuels such as gasoline, which accompanied the rise of the oil prices. Some highlighted global events from this period, which significantly affected the GDP of Paraná, are rises in the price of oil, which had already begun in the 1970s; the drop in exportations (the main countries that imported from Paraná were in financial difficulties); and excessive dependence in relation to oil. 4.1.2. 2000s In the 2000s, Paraná was still remarkable considering exportations, extractivism, and agribusiness. Also, the agribusiness and extractivism increasingly had more promising prospects due to the rising foreign market and appreciation of these products in the market. However, this State, as well as Brazil, was affected by instabilities in periods related to extreme events pointed in the figure, such as the Asian financial crisis (1997), the Russian crisis (1998), the Brazilian crisis (1999), and the American crisis (2000), in the year after the terrorist attacks on the USA (2001) and the crisis in Argentina (2001–2002) that were predominant factors affecting exportations and agribusiness in the State and causing oscillations in the GDP of Paraná. 4.1.3. 2009 In 2009, Paraná suffered the influences of global instabilities initiated with European countries and with the United States. With the most prominent activities in the State being agriculture and vegetal extractivism, the greatest economic crisis in the US since 1929, and intense periods of economic instability in the European Union, Paraná suffered the impacts of these crises, reflected also in its GDP. Exportations were specially affected since nations importing goods from the State, as European countries were undergoing severe financial instability. 4.2. Brazil 4.2.1. 1979 to 1987 After 1980, Brazil, within the global context, suffered the effects resulting from conflicts in the East (in 1979). In addition, this region was an exponent of oil, which resulted in a global crisis. Nations, in general, felt the impact of this crisis; however, developing countries were the most affected, as they had to handle the high expenses due to oil prices, subsequent rises of inflation, and burden of external


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debt. Within the internal political context, Brazil began the redemocratization of the country, 14 which hitherto was under the regime of the Military Dictatorship, since 1964. affected, as they had to handle due Republic to oil prices, rises of and inflation, andthat this In 1979, again, conflicts in the thehigh Eastexpenses (the Islamic of subsequent Iran was created) the fact burden of external debt. Within the internal political context, Brazil began the redemocratization region is exponent, in terms of oil supply, resulted in a global financial crisis. Nevertheless,ofthe 1973 the country, which hitherto was under the regime of the Military Dictatorship, since 1964. crisis was a warning to Western countries to question their dependence on oil. The drop in the offer of In 1979, again, conflicts in the East (the Islamic Republic of Iran was created) and the fact that this commodity resulted, again, in the significant rise of the prices. Nations, in general, felt the impact this region is exponent, in terms of oil supply, resulted in a global financial crisis. Nevertheless, the of 1973 this crisis; however, developing were the most as they handle crisis was a warning to Westerncountries countries to question their affected, dependence on oil.had Theto drop in thethe high expenses due commodity to oil prices, subsequent inflation,rise andofburden of external offer of this resulted, again, rises in theofsignificant the prices. Nations, debt. in general, felt 1980s was marked by two financial crises of greater significance. 1980, again, a conflict theThe impact of this crisis; however, developing countries were the most affected, as In they had to handle between and Iran caused a quite expressive high in theand price of the barrel (US$ 40 a barrel, the highIraq expenses due to oil prices, subsequent rises of inflation, burden of oil external debt. The 1980s was marked financial crisesThis of greater significance. In caused 1980, again, a conflict index never achieved in anyby of two the 1970s crises). new high in prices Western countries to between Iraq and for Irantheir caused a quite expressive high in the price of the oil barrel (US$ 40 a barrel, search oil reserves own exploitation. index never aachieved in anyout of the 1970s crises). This newexchanges high in prices Western countries to In 1987, crisis broke in the American stock andcaused expanded to other regions, such search oil reserves for their own exploitation. as Europe and Asia, and affected significantly developing economies, such as Brazil, that were not In 1987, a crisis broke out in the American stock exchanges and expanded to other regions, such able to pay the external debt. as Europe and Asia, and affected significantly developing economies, such as Brazil, that were not The following figure represents the variation in GDP of some countries over the years. GDP able to pay the external debt. annualThe growth rate at market prices based on local currency. “GDP is the gross value following figure represents the variation inconstant GDP of some countries over the sum years.ofGDP added by all resident producers in the economy plus any product taxes and minus any subsidies not annual growth rate at market prices based on local constant currency. “GDP is the sum of gross value included inall the value producers of the products. It is calculated making for depreciation of added by resident in the economy plus anywithout product taxes anddeductions minus any subsidies not included assets in the value the products. is calculated of without making deductions of intense fabricated or forofdepletion andItdegradation natural resources” [24]. for Wedepreciation highlight the fabricated assets or forof depletion and degradation natural resources” [24]. We highlight the intense oscillations in periods instability, same periodofin which this oil crisis broke out Figure 11. oscillations in periods of instability, same period in which this oil crisis broke out Figure 11. 80

Gross Domestic Product

60

M e x ic o B ra z il Ira q E u ro p e a n U n io n

A r g e n tin a USA Ira n

40 20 0 -2 0 -4 0 -6 0

1970

1980

1990

2000

2010

2020

Y e a rs

Figure 11. Variation of GDP (annual %) in selected countries. Source: own authorship (2017). Figure 11. Variation of GDP (annual %) in selected countries. Source: own authorship (2017).

After 1990, Brazil had rising values for variables of per capita CO2 emissions and per capita 1990, Brazil had rising for variables of per capita COvalues, 2 emissions and per capita electricAfter energy consumption, with values little noise. As for per capita GDP we observed a remarkable electric energy consumption, with little noise. As for per capita GDP values, we a as the drop in values after 1993, a probable influence of the financial crises taking place observed at the time, remarkable in values after 1993, a probable influence of the financial crises taking place at the Mexican crisisdrop broke out notably in 1994. time, as the Mexican crisis broke out notably in 1994.

4.2.2. 2000s

4.2.2. 2000s

The Mexican crisis was the first among the four crises that occurred in the 1990s. In 1994, Mexico adopted a system named crawling peg. However, there was a sharp devaluation of the peso, which, together with the rising Mexican inflation, culminated in the crisis that spread to other nations through

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The Mexican crisis was the first among the four crises that occurred in the 1990s. In 1994, Mexico a system named crawling peg.“Tequila However,effect”. there was sharp devaluation of were the peso, debt adopted instruments, a consequence named Thea effects of this crisis felt which, worldwide together and in Brazil.with the rising Mexican inflation, culminated in the crisis that spread to other nations throughindebt instruments, consequence named “Tequila effect”. The effects of this crisisdevaluation were felt Brazil, 1999, would be aprominent when viewing its currency (real) suffer strong in worldwide and in Brazil. the international market, episode known as the Brazilian financial crisis. The Real Plan, implemented Brazil, in 1999, would be prominent when viewing its currency (real) suffer strong devaluation in 1994, managed to stabilize what was, at that time, a lasting Brazilian difficulty and that significantly in the international market, episode known as the Brazilian financial crisis. The Real Plan, affected the country’s economy: the implemented in 1994, managed to inflation. stabilize what was, at that time, a lasting Brazilian difficulty and In 1997, according to [25], the one thateconomy: became known as “the crisis of the Asian giants” was started that significantly affected the country’s the inflation. with successive of the of nations this of block, starting by Thailand In 1997, devaluations according to [25], the currencies one that became knownforming as “the crisis the Asian giants” was and extending Korea, Malaysia, and of others. The effects of this forming crisis spread and affected several started withtosuccessive devaluations the currencies of nations this block, starting by Thailand and extending to Korea, Malaysia, andBrazil), others. in Theaddition effects ofto this crisis spread and affected countries (Australia, Mexico, England, Japan, causing bankruptcy of banks several countries Mexico, England, Brazil), in to causes causingof bankruptcy and indebtedness of (Australia, companies. [26] Added toJapan, that, among theaddition possible this crisis,ofthere banks and indebtedness of companies. [26] Added to that, among the possible causes of this crisis, are highlighted: the plan, used by the ‘Asian Tigers’, to be linked to the American currency; external there are highlighted: the plan, used by the ‘Asian Tigers’, to be linked to the American currency; indebtedness; and exchange of favors between the government and the private initiative. This crisis external indebtedness; and exchange of favors between the government and the private initiative. was contained only after interventions by the International Monetary Fund (IMF). Figure 12 shows the This crisis was contained only after interventions by the International Monetary Fund (IMF). Figure variation in the valueinin some nations in this period. 12 shows theGDP variation the GDPselected value in some selected nations in this period.

C h in a In d ia P a k is ta n R e p u b lic o f K o re a R u s s ia U n it e d A r a b E m ir a te s USA

GDP per capita

40

20

0

-2 0 1960

1970

1980

1990

2000

2010

2020

Y e a rs Figure GDP variation(in (inUS$) US$)in in some some countries. own authorship (2017). Figure 12. 12. GDP variation countries.Source: Source: own authorship (2017).

According to [27], in 1998, Russia,that thathad had already already suffered impacts of previous crisescrises and and According to [27], in 1998, Russia, sufferedthe the impacts of previous having as main causes the monetary issues and currency devaluation, underwent a period of having as main causes the monetary issues and currency devaluation, underwent a period of economic economic turmoil. The Russian stock exchange showed deficits as well as others around the world. turmoil. The Russian stock exchange showed deficits as well as others around the world. Only in 1999 Only in 1999 was there a stabilization of this situation and signs of improvement of the Russian was there a stabilization of this situation and signs of improvement of the Russian economy and the economy and the nation in general. nation in The general. stock exchange, National Association of Securities Dealers Automated Quotations The stock exchange, Association of Securities Dealers (NASDAQ), which National has great representativeness among Automated computing, Quotations technology(NASDAQ), and whichtelecommunications has great representativeness among computing, technology and telecommunications companies suffered a sharp drop in 2000, being the first of six financial companies crises that the worlddrop experienced the 21st only three years, thisthat crisis ofworld companies in the fieldin the suffered a sharp in 2000,inbeing thecentury. first ofInsix financial crises the experienced of technology led to financial losses bankruptcy of thousands small in led addition to 21st century. In only three years, thisand crisis of companies in theoffield ofcompanies, technology to financial some giant companies in the area of computing and telecommunications. The attack on the Twin losses and bankruptcy of thousands of small companies, in addition to some giant companies in the area of computing and telecommunications. The attack on the Twin Towers of the World Trade Center in New York would occur in the next year, with 3,000 deaths. This fact culminated in the 2001 crisis with expressive drops in stock exchanges of several nations. Within the same period, from 2001 to 2002, Argentina suffered the adjustment measures adopted by the president. These measures would generate high unemployment rates and an international


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climate of mistrust. In the end of 2001, Argentina suspended the payment of the foreign debt of 100 billion dollars. Figure 13 shows the unemployment percentage in Brazil and in other countries in this period of imbalance. Unemployment, according to the [28], refers to the supply of labor of unemployed people, but available for and seeking work. We highlight in Figure 13 the expressive variation of this index in cycles of financial instability.

Figure 13. Total unemployment in % related to the total workforce in some countries. Source: own authorship (2017).

4.2.3. 2009 In 2008, the US were at the center of the major economic crisis since the 1929 crisis. The highlights in this crisis were the so-called ‘housing bubble burst’ (lessening in evaluation for concession of mortgages, generating highs in nonpayments) and the massive declaration of insolvency by financial institutions. Among the causes of this crisis, there is the negligence in risk assessments in financial investments. This crisis went by some American governments (George W. Bush and Barack Obama), who invested by injecting massive amounts of money into economic recovery programs. The European Union, a social, political, and economic cluster, which accounts for a significant portion of the global GDP, suffered a monetary crisis in 2009. It was initially motivated by Greece’s sharp and successive deficits, which generated distrust on the capacity of several member countries, significantly and directly affecting countries as Portugal, Spain, Ireland, and Italy, which caused a significant drop in the appreciation of the euro against other currencies. According to the World Bank data, at the peak of the crisis, countries, as Italy, had accumulated percentage deficits in relation to GDP of 11.5% in Spain and 8.5% in Portugal. The percentages of unemployment rates reached 18% in Greece, 15% in Ireland and 21% in Spain. Finally, in 2015, the Brazilian economic crisis would be characterized by certain factors which showed marks of economic instability. The Table 2 below summarizes the EE in the sample by location, year, and variable studied.

1


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Table 2. Extreme Events indicated by city, period (intervals of 5 years) and variable studied. Location/ Year

1970–1975

1975–1980

1980–1985

1985–1990

1990–1995

1995–2000

2000–2005

2005–2010

2010–2015

Curitiba

EE GDP: 1970 EE electricity consumption: absent

EE GDP: absent EE electricity consumption: absent

EE GDP: 1982–1985 EE electricity consumption: absent

EE GDP: 1986 EE electricity consumption: absent

EE GDP: 1991–1995 EE electricity consumption: 1991–1995

EE GDP: 1995–1996, 1999–2000 EE electricity consumption: 1995–2000



EE GDP: – EE electricity consumption: 2010–2013

Londrina

EE GDP: 1970, 1972–1975 EE electricity consumption:





EE GDP: absent EE electricity consumption: 1995–1999


EE GDP: 2005–2007, 2008–2010 EE electricity consumption: 2005–2008, 2009–2010

EE GDP: 2010–2011, 2012–2013 EE electricity consumption: 2010–2011, 2014

Maringá

EE GDP: 1970, 1973–1975 EE electricity consumption: absent

EE GDP: 1975–1976, 1979–1980 EE electricity consumption: absent






EE GDP: 2005–2007 EE electricity consumption: 2005–2007, 2009

EE GDP: 2010–2011, 2013–2014, 2015 EE electricity consumption: 2015

Ponta Grossa

EE GDP: 1970, 1971–1975 EE electricity consumption: absent





EE GDP: 1996 EE electricity consumption: 1995–2000



EE GDP: 2013–2014, 2015 EE electricity consumption: 2011–2014, 2015

Cascavel




EE GDP: absent EE electricity consumption:

EE GDP: absent EE electricity consumption: 1991–1993



EE GDP: 2005–2010 EE electricity consumption: 2006

EE GDP: 2012–2014, 2015 EE electricity consumption: 2012, 2015

Brazil

EE GDP: 1973–1975 EE electricity consumption: 1970, 1971–1975 EE Emissions of CO2 : 1973–1975,

EE GDP: 1975–1980 EE electricity consumption: 1976 EE Emissions of CO2 : 1975–1980

EE GDP: 1980–1985 EE electricity consumption: 1980–1982 EE Emissions of CO2 : 1982–1985

EE GDP: 1986, 1989–1990 EE electricity consumption: absent EE Emissions of CO2 : absent

EE GDP: 1990–1992 EE electricity consumption: absent EE Emissions of CO2 : 1990–1993

EE GDP: absent EE electricity consumption: 1996–2000 EE Emissions of CO2 : 1997–2000

EE GDP: absent EE electricity consumption: 2000–2002 EE Emissions of CO2 : 2000–2003

EE GDP: 2005–2010 EE electricity consumption: 2007–2010 EE Emissions of CO2 : 2006–2009

EE GDP: 2010–2014, 2015–2016 EE electricity consumption: 2010 EE Emissions of CO2 : 2011–2012

Historical context: Paraná: decline of some agrarian activities (coffee cultivation), unemployment, rural exodus, industrialization, extractivism, maté and wood processing, export oriented.

Historical context: Paraná economic instability due to excessive dependence on fossil fuels and because the main oil suppliers are involved in wars. Fall in exports. Brazil: Since the 1970s, the country was influenced by fluctuations in the price of oil, oscillations arising from wars between oil suppliers. Crisis in the American and European stock exchanges (1987).

Historical context: Paraná: prominent in extractivism, agribusiness, and exports. Russian Crisis (1998), North American Crisis (2000), terrorist attacks USA (2001). Brazil: Mexican Crisis (1994). Crisis of the Asian giants (1997). Brazilian exchange rate crisis (1999). NASDAQ Crisis (2000).

Historical context: Paraná: The state maintains among its sources of income the agriculture and the extractivism, being influenced by importing countries that faced economic crises (Europe, USA, etc.). Brazil: Crisis Argentina (2001), American Crisis (2008). European Crisis (2009). Brazilian Crisis (2015).


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It is still relevant to present the EE in the sample in a different way so that a better understanding of the results can be obtained. It is imperative to point out that the most significant EE observed are contemporaneous with periods of financial economic mismatch, as show in Table 3. Table 3. Mapping of the Extreme Events indicated by year, place and variable. 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986

Curitiba −

Londrina −

Maringá +

− − − −

− − − −

Cascavel

Brazil + − − − − − −

− − − − − − −

+ + + + + + +

+ + + + + +

− − − − −

Ponta Grossa + − − − − −

−

+ + + + +

Historical-Economic Context North American Crisis

+ + + + + + +

+

− −

+

Oil Crisis High in the price of oil barrel

+

−

+

− − −

− − − − Crisis in the American and European stock exchanges

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

−

− − − − + + + + +

2000

−

+

− + + + + + +

2008 2008 2001 2008 2009 2002 2009 2003 2010 2009 2004 2010 − 2005 2011 2010− 2011 − 2006 2012 2011 2007 2012 2013 2008 2012 2013 2009 2014 2013 2010 2014 2015 2014 2011 2015 2012 2015− 2013 2014 2015

Oil Crisis—OPEC

− − −

− − −

−

− − −

− − − −

−

− − 2008 − 2008 + 2009+ + 2009 −2010 ++ −2010 ++ ++ −2011 ++ −2011 + 2012 +− −2012 + 2013 −2013 +− +−2014− +−2014− +2015 +2015 −

+ + + + +

− − −− − −− − −− −− −− − −+ + −+ − + − ++ − +− − +− + +

−

− − −

− − − − − −

− − − −

− − − − −

− −

− − − + + + + +

+ + +

+

+

− −− + − −+− − + + + +− + + − −+ +++ −− − +++ −− − +++ −− − +++ −+ − + + + ++ + ++ +− + ++ − −− + − − −− + − + − +

+

− − + + + + + +

+ +

− +− − +− − + + −+ − − − −

+ ++ − ++ −+ + − ++ −− − − − + − −

+

+

+

− − −

− Mexican crisis

− −

+ + + +

−

+

− − −− − −

−− − − − −

− −− −− + + − ++ − + + + − + + + + − + ++ + ++ −+ + +− −+ − −+ − +

− − −

+ + +

−

− − − − −

− − − +

−+ +

−+

− − − − −

+ − +− + − ++− + −− +− − − +− −+ + + −+ + −+ − + −+ +− + ++ −+ −+ +− −+ −+ −+ − −+ + −+ − + − + + − +

− − − − +

− +− +−− +−− +− + +− − −−

+ + +

Crisis of the Asian giants Russian Crisis 21 21 21 Brazilian Crisis 21 21 North American Crisis + American crisis (biggest crisis since the (NASDAQ) American crisiscrisis, (biggest crisis sincecrisis the since the American crisis (biggest terrorist “Great” crisis of 29) − − Argentina +− American crisis (biggest crisis since the American crisis (biggest crisis “Great” crisis of 29) attacks on the USA “Great” crisis of 29) since the − − − Crisis European Union “Great” crisis of 29)crisis of 29) “Great” + −+ − − Crisis European Crisis Union European Union

− − −− − +− − − + + + + + −+ + + − ++ + +− ++ − + −+ + − + −+− + −+ + −+ + + + − + + + − + + +

− Crisis European Crisis Union European Union + + + + +American crisis (biggest crisis +since the “Great” crisis of 29) Crisis European Union

Brazilian Crisis Brazilian Crisis Brazilian Crisis Brazilian Crisis Brazilian Crisis

− Events) − + + − above the width + EE (Extreme GDP− at half-heightBrazilian Crisis EE (Extreme GDP above EE Events) (Extreme Events) GDP + + the width above at thehalf-height width at half-height EE(Extreme (Extreme Events) GDP above width at EEEvents) (Extreme Events) GDP + + the above abovethe thehalf-height width at at half-height half-height EE GDP width EE electricity consumption below the width at half-height EEelectricity electricity consumption below width the at EEconsumption electricity consumption - thebelow below thehalf-height width at athalf-height half-height EE width EE electricity consumption below width at EE electricity consumption - the below thehalf-height width at half-height EECO CO2emissions emissions EE EE CO2 2 emissions EE CO2 emissions EE CO2 emissions EE CO2 emissions 5. Conclusions 5. Conclusions 5. Conclusions 5. Conclusions Under a global perspective, culture, economy, imports/exports oscillated and were influenced 5. Conclusions 5. Conclusions Under a global perspective, culture, economy, imports/exports oscillated and were influenced by these periods of instability. These oscillations justify the extreme events in the sample and,were influenced Under a global perspective, culture, economy, imports/exports oscillated and wereand influenced Under ainstability. global perspective, culture, economy, by these periods of perspective, These oscillations justify theimports/exports extreme eventsoscillated in the sample and, Under a global culture, economy, imports/exports oscillated and were influenced Under a global perspective, culture, economy, imports/exports oscillated and were influenced therefore, correspond with the global context at These the time. They also evidenced that there is greater by these periods of instability. These oscillations justify thejustify extreme events inevents the sample and, by these periods of instability. oscillations the extreme inis the sample and, therefore, correspond with the global context at the time. They also evidenced that there greater by these periods of instability. These oscillations justify the extreme events in the sample and, by these periods of instability. These oscillations justify the extreme events in the sample and, correlation in GDP data/electric consumption than inatGDP data/CO electric therefore, correspond with energy the global context atcontext the time. They alsoThey evidenced thatorthere is greater therefore, with the global the time. also2 evidenced that there is greater 2 emissions correlation in GDP correspond data/electric energy consumption than in GDP data/CO emissions or electric therefore,therefore, correspond with the global context at the time. They also evidenced that there is greater correspond with the global context at the time. They also evidenced that there is greater correlation in GDP data/electric energy consumption than in GDP 2 data/CO emissions or electricor electric correlation in GDP data/electric energy events consumption than data/CO in GDP 2 emissions energy consumption/CO 2 emissions. The consumption extreme in the variables per capita GDP, perelectric capitaor electric correlation in GDP data/electric energy than in GDP 2 data/CO emissions or correlation in GDP data/electric energy consumption than data/CO in GDP 2 emissions energy consumption/CO 2 emissions. The extreme events in events the variables per capitaper GDP, perGDP, capitaper capita energy consumption/CO 2 emissions. The extreme in the variables capita electric energy consumption, and per capita CO 2 emissions, broke out in periods related to economic energy consumption/CO 2 emissions. The extreme in events the variables per capitaper GDP, perGDP, capitaper capita energy consumption/CO 2 emissions. Theevents extreme in the variables capita electric energy consumption, and per capita COcapita 2 emissions, broke outbroke in periods torelated economic electric energy consumption, and per CO 2 emissions, out inrelated periods to economic crises. It observed that developing countries have a more pronounced influence in the studied electric energy consumption, and per capita COcapita 2 emissions, broke outbroke in periods torelated economic electric energy consumption, and per CO2 emissions, out inrelated periods to economic crises. It crises. observed that developing countries countries have a more pronounced influence influence in the studied It observed that developing have a more pronounced in the studied variables, and these are related. crises. It crises. observed that developing countriescountries have a more influence influence in the studied It observed that developing have pronounced a more pronounced in the studied variables,variables, and theseand are these related. are related. variables, and theseand are related. variables, these are related. Author Contributions: F.V., M.A.R., A.C. de F. and G.G.L. contributed to the design and implementation of the


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energy consumption/CO2 emissions. The extreme events in the variables per capita GDP, per capita electric energy consumption, and per capita CO2 emissions, broke out in periods related to economic crises. It observed that developing countries have a more pronounced influence in the studied variables, and these are related. Author Contributions: F.V., M.A.R., A.C.d.F. and G.G.L. contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript. Funding: This research received no external funding. Conflicts of Interest: The authors declare that there is no conflict of interest regarding the publication of this paper.

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