A Multi-Criteria Modelling for Ranking Co2 Emitting G20 Countries from the Kaya Identity and Their Impacts on Elderly Health

A MULTI-CRITERIA MODELLING FOR RANKING CO2 EMITTING G20 COUNTRIES FROM THE KAYA IDENTITY AND THEIR IMPACTS ON ELDERLY HEALTH

L.M. Abreu, H. R. M. da Hora, J.J. A. Rangel, M. Erthal Jr, N. Razmjooy, Vania V. Estrela, Thierry Edoh, G.G. de Oliveira, Y. Iano,

Instituto Federal de Educação Ciência e Tecnologia Fluminense Campos dos Goytacazes, RJ, Brazil [email protected] [email protected] [email protected] [email protected]

Department of Electrical Engineering, Tafresh University Tafresh, Iran [email protected]

Dep. of Telecommunications, Fluminense Federal University (UFF) Niteroi, RJ, Brazil [email protected]

Department of Pharmacy University, Bonn, Germany, Department of Applied Software Engineering Technical University of Munich (TUM) Munich, Germany [email protected] [email protected]

School of Electrical and Computer Engineering (FEEC), UNICAMP Campinas, SP, Brazil [email protected] [email protected]

Abstract. Understanding the factors driving CO2 emissions and their spatial behavior are vital to lessen natural as well as anthropogenic emissions. Lessen CO2 emissions can impact elderly health, especially those living in affected regions. The constant search for sustainable development motivated the elaboration of this work, whose purpose is to build a ranking of the most sustainable countries concerning CO2 emissions, according to the criteria established in the Kaya identity indicator. Furthermore, this study aims to push to enact policy for improving the population health, elderly ones. For the study, the economic block G20 was selected due to its representativeness in the world scenario, with the application of the multi-criteria method PROMETHEE II for the country order, whose data were obtained from the International Energy Agency. As a result, the five best positions in the ranking are represented by countries with excellent economic projections and energy efficiency according to the criteria.

Keywords: CO2 emissions, Kaya identity, G20, PROMETHEE II, green design, smart cities, pollution surveillance.

1. INTRODUCTION

The issues regarding sustainable development, which intends to achieve a balance between economic, social and environmental aspects, became relevant to humanity from the reflexes of its anthropic actions. With industrialization and the advance of the economy, one of the main hurdles for the planet sustainability is Greenhouse Gases, carbon dioxide in particular. However, recent and harsher environmental and economic programs have been implemented, aiming to reduce these emissions. Current studies are demonstrating a decoupling trend between various countries CO2 emissions and their GDP. This behavior is observed when pressure or environmental impact is lower than their economic strength, pointing to economic growth in developed countries without a compelling growth in their CO2 emissions [13]. The G20, or Group of Twenty, assembled on September 25, 1999, in the face of the 1990’s financial crises, is an international forum of economic and financial cooperation between developed and emergent countries, aiming to establish a broader dialogue about the global economy. These countries integration aim to promote sustainable growth coupled with economic stability, through financial programs and regulations that aim to reduce the risk of crisis in an international scenario [6]. This group consists of the European Union and the nineteen major world economies: Argentina, Australia, Brazil, Canada, China, France, Germany, India, Indonesia, Italy, Japan, Mexico, Russia, Saudi Arabia, South Africa, South Korea, Turkey, United Kingdom and the United States of America. Together, the member nations of G20 represent approximately 90% of the world GDP, 80% of international trade, two-thirds of the world population and 84% of the greenhouse gases emissions, granting them significate input on the financial system management and global economy [9]. The Multi-Criteria Decision Making Methods (MCDMs) consist of methodological formulations or theories, with well-defined axiomatic structures, that can be utilized for the elaboration of a decision model aiming to solve a problem [1]. The Kaya identity modeling provides robust utility by allowing the calculation of emissions based on populational growth, economic growth, energetic consumption by GDP per unit and CO2 emissions by energy per unit predictions [4]. This tool has been utilized to project greenhouse gases future emissions in various climatic projections. In this context, this article ranks the most sustainable countries regarding their CO2 emissions and according to the criteria addressed by the Kaya identity indicator, with analyses of economic, populational, energetic and carbon dioxide emissions criteria. The MCDM model used was the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II) to ordinate the member nations of G20, due to their representativeness in the global scenario. 3D GIS modeling practices integrating geospatial methodologies, e.g., photogrammetry and laser scanning, help to intensify the effectiveness of environmental documentation and preservation. This struggle brings up technological approaches that eventually will lead to better administration of CO2. The present study will be employed in emission inventories, along with maps, remote sensing techniques and a modification of the Kaya identity in a further study about municipalities. The authors will need to combine these components, incorporating the applicable criteria that follow. Section 2 discourses about the Kaya identity methodology. Section 3 shows some experimental results and discusses them. Section 4 concludes in this paper.

Fig. 1. Research steps.

Wei Criteria Description ght This criterion will evaluate the amount of carbon dioxide emissions by fossil CO 2 fuel. The country with the least amount C1 emission 1,0 of emissions will be considered the most s sustainable. It is a minimization

criterion, that is, the least the better.

This criterion will evaluate the

economic growth concerning the

GDP per population. The country with the best C2 1,0 capita relation will be considered the most

sustainable. It is maximization criteria,

that is, the bigger the better. This criterion is represented by the ratio between the total amount of energy generated by a country and its GDP. It Energeti shows the degree of efficiency between C3 c the energy utilization and the country's 1,0 Intensity wealth. The country with the best efficiency will be considered the most sustainable. It is a minimization criterion, that is, the lower, the better. This criterion is represented by the ratio between emissions and total energy consumption, evaluating the energetic Carbon efficiency. The country with lower C4 1,0 Intensity results will be considered most sustainable. It is a minimization criterion, that is, the least, the lower, the better. TABLE I. EVALUATION CRITERIA

2. METHODOLOGY

This article details some steps to build a rank of the most sustainable countries in light of established criteria. The research was organized and structured with the following sequential steps presented in Fig. 1. With the objectives determined, for the application of the decision analysis method, we selected as alternatives member nations from G20 and the criteria was determined according to the Kaya identity indicator. Data were obtained in the public database from IEA – International Energy Agency, for the year of 2016 [8, 11]. Kaya identity is an extension of the IPAT identity, in which population (P), affluence (A) and technology (T), impacts the environment (I). Affluence is related to the consumption or income/product per capita [5]. The Kaya identity [10], is a mathematical decomposition used to establish a relationship between carbon dioxide emissions produced by human activity and four important factors: demographic, economic, energy intensity and carbon intensity, as seen in (1) [12].

PIB E C C  P   . (1) P PIB E

In which C is the carbon dioxide emission rate (Mt CO2); P is the population by millions of people; PIB/P is the ratio per capita of the GDP (1 billion dollars in 2010 per person); and the E/PIB ratio is the energetic intensity, the generation of primary energy (Mtoe) per GDP unit (toe/a thousand dollars in 2010), with toe – tonne of oil equivalent; and the C/E ratio is the carbon intensity, the carbon emissions per energy unit generated (tCO2/toe).

CO GDP Carbo Criterions/ 2 Energy per n Alternativ Emissi Intensity capita Intensity es on (min) (max) (min) (min) Argentina 191 10.11 0.19 2.22 Australia 392 60.88 0.09 3.02 Brazil 417 10.81 0.13 1.46 Canada 541 50.78 0.15 1.93 European 1.4 29.26 Union 70 0.10 2.06 France 293 41.96 0.09 1.20 Germany 732 46.12 0.08 2.36 India 2.0 1.86 77 0.35 2.41 Indonesia 455 3.98 0.22 1.98 Italy 326 34.11 0.07 2.16 Japan 1.1 47.66 47 0.07 2.69 Korea 589 25.61 0.22 2.09 Mexico 445 10.32 0.15 2.41 China 9.0 6.89 57 0.31 3.06 Russian 1.4 11.31 39 0.45 1.97 Saudi 527 21.59 Arabia 0.30 2.51 South 414 7.50 Africa 0.33 2.96 Turkey 339 14.38 0.12 2.47 United 371 41.79 Kingdom 0.06 2.07 United 4.8 52.38 States 33 0.13 2.23

TABLE II. PAYMENT MATRIX

This research utilized the LTI method, which is an MCDM of the PROMETHEE family, in particular, developed by PROMETHEE II [2], which establishes a complete order between alternatives destined to sorting problems. The software we utilized for the application was Visual PROMETHEE Academic Edition. As established, the model consists of twenty alternatives and four criteria. Table I presents the evaluation criteria with their respective descriptions and weights. There is no weight distinction between criteria. Table II shows the data from the payment matrix of the alternatives - the member nations of G20 - with a group of four criteria, them being the quotient of the determining factors of Kaya identity. This study considered four of the seven criteria from the indicator, not analyzing population, GDP and primary energy in isolation, considering that these criteria are part of the ratios analyzed in the indicator, such as GDP per capita, energetic intensity, and carbon intensity. Only carbon emissions were utilized individually, considering they are the central part of the problem. The method calculation consists of 4 steps [3]. Firstly, the difference between the performances (δik) of alternative xi with alternative xk regarding criteria j and the relative preference function (P) of each j criteria is determined. Relative preference is given by

P(xi, xk) = Pj(uj(xi)-uj(xk)) = Pj(δik). (2)

The difference between each pair of alternatives is denoted by

δik = uj(xi)-uj(xk)). (3)

Next, the preference index (Sik) of the alternative xi is compared to the other alternatives xk as seen in Equation (4). wj is the weight of each criterion that is the im- portance given to the criteria.

 w j .P( ik ) j . (4) Sik   w j j

The alternative ranking is done based on the decreasing order of their respective liquid flows, in which the more prominent the flow, the better is the alternative performance and if the liquid flows are the same, the alternatives are neutral between themselves. The third step represents the overflow. Equation (5) has the positive overflow, or outflow, which represents the preference intensity of an alternative over all other alternatives. Equation (6) has the negative overflow, or inflow, which represents the preference intensity of all other alternatives over one of the alternatives. The bigger the Φ+, the better the alternative and the lower the Φ-, the better the alternative.

   S i  ik , and (5) k

   S i  ki . (6) k

TABLE III. ALTERNATIVES RANK AND PROMETHEE II LIQUID FLOW

Rank Alternatives ϕ 1 France 0.7113 2 United Kingdom 0.5875 3 Italy 0.5049 4 Canada 0.3285 5 Australia 0.2680 6 Brazil 0.2348 7 Germany 0.1773 8 Japan 0.0921 9 Turkey 0.0830 10 European Union 0.0631 11 Argentina 0.0542 12 United States 0.0244 13 Korea -0.0755 14 Indonesia -0.1462 15 Mexico -0.1902 16 Russian -0.3029 17 Saudi Arabia -0.3188 18 South Africa -0.4874 19 India -0.7481 20 China -0.8600

The fourth and last step allows us to obtain the general order of the alternative, utilizing the overflow or liquid flow given in Equation (7), representing the results between power and weakness of the alternative.

(a)   (a)   (a). (7)

3. RESULTS AND DISCUSSION

The PROMETHEE II approach has produced a rank of the most sustainable countries in liquid flow decreasing order, that is, from the most sustainable to least sustainable according to the established criteria, as seen in Table III. Taking the spotlight in the first position, France is considered the most sustainable country according to the model applied, with a liquid flow of 0.7113. The twentieth and last position on the rank is China’s, is considered the least sustainable country with a liquid flow of -0.8600. Fig. 2 represents a graphic of dispersion with an analysis of the 20 countries rank. The axis-ordinates represents the rank of the world’s major economies, and in the axis- of-abscissas, the sustainability rank was obtained by the PROMETHEE model. Amongst the twenty alternatives, France obtained the best results, with the best performance in all criteria, taking the spotlight with the least CO2 emissions and least carbon intensity. It is possible to observe in the graph that France is the sixth major world economy and the most sustainable country. The United Kingdom and Italy followed France with the best energetic intensity, both countries with positive performance in all criteria. On the other hand, China provides the worst performance in all criteria, with the most carbon dioxide emissions and carbon intensity. China takes second place in the GDP rank and the twentieth and last position in the rank of sustainable countries. India and Saudi Arabia also obtained negative performances in all criteria, emphasizing India as the worst economic relation per habitant. Analyzing the economy, the United States of America has the world’s largest economy by GDP, and it is the second major carbon dioxide emitter, only behind China, but occupies the twelfth place in the sustainability rank, due to its GDP per capita relation. Regarding the CO2 emission criteria, the most relevant to this study, apart from the countries already mentioned, the countries with the best performance in the order of liquid flow were Australia, Brazil, Turkey, Argentina, Indonesia, Mexico, and South Africa. Although they emit fewer greenhouse gases in the atmosphere, all of them presents strengths and weaknesses in the remaining criteria. Argentina had the most sustainability relevance regarding emissions, with the lowest emissions in the group and Australia stands out in the GDP per capita relation. On the other hand, the countries with negative performance regarding CO2 were Canada, Germany, Japan, the European Union, South Korea, and Russia. They presented positive and negative behaviors in the remaining criteria, even though they are the biggest offenders regarding emissions, with Russia having the worst energetic intensity. For the European Union, 28 countries are considered, excluding the ones with double participation on G20, like Germany, France, Italy, and the United Kingdom. Besides that, the country with the lowest GDP between the 20 economies, is South Africa, occupying the 18th spot on the sustainability rank, as we can see in Fig. 2. Argentina is the country with the lowest amount of CO2 emission amongst the 20 countries, but occupies the 11th spot in the flow rank, considering its weak performance regarding its GDP per capita and energetic intensities, taking the second to last position amongst the twenty economies.

Fig. 2. Dispersion graphic between the GDP rank and the PROMETHEE II rank.

(a)

(b)

Fig. 3. Kaya indicators development for France and China.

(a) (b)

Fig. 4. (a) CO2 emissions intensity, (b) Energetic matrix. Kaya identity allows us to analyze the development of these indicators, being an essential tool to the implementation of actions that contribute to the reduction or limitation of Greenhouse Gases emission in a more intense and less expensive manner, aiming to revert the effects of the populational and economic growth. Analyzing the results of the model with the intensity indicator, we can verify the ascending or descending behavior of the curves. This behavior indicates the relative situation of a country in a specific moment concerning the base-year of 1990 (index = 1). The period analyzed ranges from 1990 to 2016, with the data available on the IEA database. Below, on Image 3, we present the behavior of countries that occupied the first and last positions of our rank with the Kaya identity model. France in 2016 was one of the countries with the least amount of CO2 emissions, regarding the twenty analyzed countries, second only to Argentina, with 1.12% of the emissions. Hence, we can observe in Fig. 3 (a) the reduction in emissions, carbon and energetic intensities throughout the years, maintaining its economic growth. On the other hand, China, with its relevant economic development and industrialization was responsible in 2016 for 34.76% of carbon dioxide emissions, taking the lead on harmful emissions amongst all countries in the world. This increases all analyzed factors, as shown in Fig. 3 (b), especially their GDP per capita and carbon intensities. In both cases, populational growth is constant. The economic growth of a country implies the increase of energy consumption and pressure growth in the environment. The efficiency of the energy sector and the substitution of fossil fuels to renewable energy is a necessary measure. The environment is suffering from economic growth. The population health is in turn severely impacted. Health conditions like chronic respiration diseases, asthma, are the consequences. Elderly people are more affected by such condition. This tool will therefore help to choose living place for elderly people and thus prevent them from any health complication.

4. CONCLUSION

Analyzing the aspects that result in CO2 emissions and their spatial comportment are crucial tasks to minimize both the natural as well as anthropogenic emissions. This study aimed to rank the G20 member nations from most sustainable to least sustainable, regarding their CO2 emissions according to economic, populational, energetic and CO2 emissions criteria of the Kaya identity indicator. The objective was achieved through the use of the PROMETHEE II method, showing France in the first place and China in the last position. With this result, we can verify that France adopts techniques that assure them the first place in sustainability amongst the world’s largest economies, with an energetic matrix mostly consisting of nuclear energy and potential growth of renewable energies, along with politics and programs that promote environmental impacts reduction in climate change. These practices are part of the French project “Energy Transition for the Green Growth,” which aims to make the country a world leader in sustainable development, reducing Greenhouse Gases emissions, diversifying their energetic model and increasing the development of sustainable energies [7]. On the other hand, China, being the world’s largest population and with a notable economic growth, faces the big challenge of maintaining its economic development 13

sustainably. One of the things that most contribute to the emissions of CO2 is the use of coal in its energetic matrix, considering that China is the world’s biggest coal producer and the world’s biggest coal importer. However, China has the most number of installations for renewable energies, with almost 30% of all renewable energies such as solar, hydroelectric and eolic installations being in China according to the Key World Energy Statistics report [11]. Finally, this study is capable of providing a tool to ordinate results like these and analysis that aim to contribute to sustainability, such as determining better energetic sources, in the face of economic, environmental and social aspects. In future applications, the authors intend to use this type of analysis to map and quantify urban carbon emissions using the Cyber-Physical System paradigm and the Internet of Things [14, 15, 16].

REFERENCES

[1] Almeida, A. T. Processo de decisão nas organizações: construindo modelos de decisão multicritério, São Paulo: Atlas, 2013.

[2] Brans, J. P., and Vincke, P. A Preference Ranking Organisation Method: (The PROMETHEE Method for Multiple Criteria Decision- Making). Management Science, 31(6), 647–656, 1985.

[3] Brans, J. P., Vincke, P., and Mareschal, B. How to select and how to rank projects: The PROMETHEE method. European Journal of Operational Research, 24(2), 228–238, 1986. doi: 10.1016/0377- 2217(86)90044-5

[4] Budzianowski, W. M. Modelling of CO_{2 content in the atmosphere until 2300: influence of energy intensity of gross domestic product and carbon intensity of energy. International Journal of Global Warming, 5(1), 1, 2013. doi:10.1504/IJGW.2013.051468}

[5] Ehrlich, P. R., and Holdren, J. P. Impact of Population Growth. Science, New Series, 171, 1212–1217, 1971.

[6] G20, 2018. Retrieved Aug 15 2018 from https://g20.org/en/

[7] Gouvernement.fr. 2018 Retrieved Aug 15 2018 from https://www.gouvernement.fr/en/energy-transition

[8] IEA - International Energy Agency, 2018. Retrieved Jan 19 2019 from https://www.iea.org/statistics/

[9] Itamaraty, 2018. Retrieved Aug 15 2018 from http://www.itamaraty.gov.br/pt-BR/politica-externa/diplomacia- economica-comercial-e-financeira/15586-brasil-g20 15

[10] Kaya, Y. Impact of carbon dioxide emission control on GNP growth: Interpretation of proposed scenarios. Paper presented at the IPCC Energy and Industry Subgroup, Response Strategies Working Group, Paris, France, 1989.

[11] Key World Energy Statistics 2018. (2018) (p. 51). Retrieved Jan 19 2019 from https://www.iea.org/statistics/kwes/

[12] Tavakoli, A. A journey among top ten emitter country, decomposition of “Kaya Identity”. Sustainable Cities and Society, 38, 254–264, 2018. doi: 10.1016/j.scs.2017.12.040

[13] Wang, Q., Li, R., Liao, H. Toward Decoupling: Growing GDP without Growing Carbon Emissions. Environmental Science & Technology, 50(21), 11435–11436, 2016. doi: 10.1021/acs.est.6b05150

[14] Estrela, V.V., Saotome, O., Loschi, H.J., Hemanth, D.J., Farfan, W. Aroma, R.J. et al., Emergency response cyber-physical framework for landslide avoidance with sustainable electronics, Technologies, vol. 6, 42, 2018. doi:10.3390/technologies6020042

[15] Estrela, V.V., Monteiro, A.C.B., França, R.P., Iano, Y., Khelassi, A. and Razmjooy. N. Health 4.0: Applications, Management, Technologies and Review. Med Techn J, Vol. 2, no. 4, 2019, pp. 262-76, http://medtech.ichsmt.org/index.php/MTJ/article/view/205. doi: 10.26415/2572-004X-vol2iss1p262-276

[16] Razmjooy, N. and Estrela, V.V., Applications of Image Processing and Soft Computing Systems in Agriculture, IGI Global, 2019. doi: 10.4018/978-1-5225-8027-0