Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 109 https://internationalpubls.com Environment Analysis and Standard of Living: A Case of Member Nations of Indian Ocean RIM Association Manbir Singh1, Jasdeep Kaur Dhami2 1 CT University, Ludhiana, Punjab 2 CT Institute of Engineering, Management & Technology, Jalandhar Article History: Received: 12-11-2024 Revised: 25-12-2024 Accepted: 05-01-2025 Abstract: The world economy is experiencing rapid changes with advancement of technology, globalization and liberalization. All these factors also influence the standard of living, well being and quality of life of a particular nation. Indian Ocean is the third largest ocean of the world. The Indian Ocean RIM Association is an inter governmental organization, which established in the year 1997 to promote the regional economic corporation, consisting of 23 members and 10 dialogue partners. This study analysed the impact of GDP, Trade Openness and Urban Population, on CO2 Emissions of members of Indian Ocean RIM Association for the period of 1990 to 2019 and standard of living and quality of life of member nations of Indian Ocean RIM Association. Keywords: Indian Ocean RIM Association (IORA), CO2 Emissions, Trade Openness, Population, Standard of Living ,Quality of Life, Human Development Index Introduction The world economy is experiencing rapid changes with advancement of technology, globalization and liberalization. All these factors also influence the standard of living, well being and quality of life of a particular nation. Indian Ocean is the third largest ocean of the world. The Indian Ocean RIM Association is an inter governmental organization, which established in the year 1997 to promote the regional economic corporation, consisting of 23 members and 10 dialogue partners. In the recent times debate on CO2 emissions has gained a great matter of concern. International trade also influenced the CO2 emissions. . But the outbreak of the Covid-19 pandemic has highly impacted the economic, social and other activities, which further influenced the standard of living and quality of life. Review of Literature: Islam & Huda (1999) explained that even a densely populated country can live quite comfortably with a solar energy input of 79) and all the amenities that modern technology can provide[This does not even require any novel technology[Rich countries can afford to buy fossil fuel and are accustomed to a wasteful way of life[They will not initiate research for large-scale solar energy utilization[It is critical that poor countries in tropical regions mobilise their resources to develop technology . Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 110 https://internationalpubls.com Mazumdar (2002) examined the test approach is used in this study, which expands the study to look at convergence in quality of life across nations from 1960 to 1995. It evaluates convergence of those variables for other income categories, such as high, middle, and poor human development nations, in addition to the whole sample of 91 countries. The findings indicate divergence in all cases, showing that global economies are getting increasingly distinct in terms of human development index and per capita real gross domestic product throughout the period 1960-1995. Voth (2004) narrated that most fresh research findings have offered growing but not clear credence to gloomy perspectives during the previous two decades. Importantly, real wages did not rise at anywhere near the rate predicted by previous forecasts. Between 1760 and 1830, overall improvements were moderate, with periods of reversal. Even by 1850, actual earnings had barely increased little. Longer hours of more strenuous work, conducted in more risky and unpleasant environments, were most likely purchased by Englishmen and women, many of whom lived in the unsanitary, disease-ridden, dark, wet, and congested circumstances of British cities throughout the mid-nineteenth century. Bérenger & Chouchane (2007) examined about health and education standards are related with excellent functioning (or quality) in each sector using Sen's capabilities approach. In the area of health, we may examine if public spending, access to water, and the number of physicians allow women and children to live in healthy environments, and the population to have a high birth rate (as in Algeria, Egypt, Mauritius, Morocco, the Seychelles, Swaziland, and Tunisia). The inefficiency of the two sectors is due to their functioning (poor quality). Antony & Rao (2007) described that the genuine level of life is determined by demographic, socioeconomic, health, and nutritional factors. Poverty, living standards, and human growth are all influenced by a variety of circumstances. Existing indices, such as the HDI and HPI, utilise income data to determine a person's level of living but ignore food and nutritional health. Because it is a complete index of income and non-income variables, the suggested index was found to be more suited for gauging the genuine quality of living and human development. Validation for diverse populations might be done in the future. Winiarczyk & Razniak (2011) mentioned that according to the living conditions index, many of Poland's main metropolitan centres are not adequately suited to accommodate a growing population. Sub-regions bordering ód and the lskie Province had significant drops in the comprehensive index. The Kraków urban sub-region, as well as the sub-regions of Ek, Lublin, and Szczecin, saw the biggest gains in the index. During the study period, community infrastructure was also determined to be insufficient. In this example, the areas around significant cities like ód, Szczecin, and Katowice (more generally - Upper Silesia) had rather large declines. In terms of healthcare, the previously described shadow zone deepens surrounding Poland's largest cities. Kraków, Katowice, and the Tri- City are included. According to the living conditions index, several large urban centres in the United States are in poor shape. In terms of education, the future is brighter. The education index decreased the most in northern Poland, while it grew little in central Poland. This was notably true in the Tarnobrzeg, Radom, and Sieradz sub-regions. In terms of the culture index, the picture does not appear to be promising. Access to cultural services is similar to access to healthcare in that the comprehensive index has dropped practically everywhere in Poland. Only six sub-regions performed Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 111 https://internationalpubls.com better, including Biaystok, Sosnowiec, and Katowice. For the Jelenia Góra sub-region, the highest reduction in the cultural services index was computed. Poland is ill-equipped to deal with the situation. Schoch & Pfister (2012) specified that the average height series for each social stratum has a distinct pattern when compared to the underlying job profiles' respective real-wage series, which indicates a certain income bracket. Lower-class conscripts' height is most strongly connected with their real-pay series, whereas skilled workers' wage series is significantly less so. The average height series of upper-class conscripts, on the other hand, is strongly connected to the salary series of carpenters, that is, employees with substantial abilities, reasonably steady working circumstances, and long-term employment contracts. As a result, we find that the biological standard of living yearly average for each class is extremely strongly connected to the associated real-wage series and hence reflects underlying economic realities Madzík & Daňková (2015) expressed that the goal of this research was to look at the relationship between a country's competitiveness and its population's level of life. Long-term statistics show that there is a link between GDP per capita and population living standards, as evidenced by the strongest correlation between income and other relevant variables in our study. One of the study's most important contributions is the use of empirical research findings to theory creation and the identification of future scientific research areas. Brambert & Kiniorska (2018) expressed that the standard of living is not a uniform category, and its diversity is mostly determined by a region's character, structure, and, above all, socioeconomic situations. Fast growth is a characteristic of rural regions located near metropolitan centres; yet, it is dependent to a considerable extent on labour resources and the potential of a specific zone connected to big cities. Njiru & Letema (2018) mentioned that Kirinyaga residents are facing energy poverty as manifested in reliance on traditional fuels by households, educational institutions, and agroprocessing industries, coupled with low access to electricity. Energy access directly or indirectly influences availability and consumption of goods and services and hence affects peoples’ standard of living. Low energy access thus lowers people’s living standards. The low access to electricity has led reliance on traditional fuels. Use of solid fuels leads to indoor air pollution which is associated with premature death caused by respiratory and heart ailments. Fuel wood scarcity and associated high cost have affected the dietary patterns of Kirinyaga residents through switching from their traditional foods that take long time to cook, to foods that demand less fuel, thus impacting on calorific intake. Some residents have resorted to street foods exposing themselves to health risks associated with street food. Diseases resulting from consumption of street food can lower living standards if household income is largely diverted to health issues. Regions with higher electrification rates have more residents with higher educational levels and life expectancy. Electricity access improves school completion rates, thus enabling the residents get well-paying jobs which in turn improves their purchasing power eventually raising living standards. Electricity access improves provision of health care and reduces maternal and child morbidity and mortality, thus raising the life expectancy. Access to modern energy services therefore has a positive impact on standard of living. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 112 https://internationalpubls.com Research Methodology To make an analysis of the standard of living of member nations of IORA RIM Association five variables have been taken into consideration. The variables are life quality index, safety index, healthcare index, pollution index and climate index. . The Indian Ocean RIM Association is an inter governmental organization, which established in the year 1997 to promote the regional economic corporation, consisting of 23 members and 10 dialogue partners. But the present research concentrated on 11 members. For find out the relationship between CO2 Emissions , GDP, Trade Openness and Urban Population of members of Indian Ocean RIM Association, Unit Root, Johnson Cointegration test and Granger Causality test has been applied. Karl Pearson correlation coefficients between Life Quality Index, Safety Index, Healthcare Index, Pollution Index and Climate Index to find out the association exist among them. Proximity Matrix of Life Quality Index, Safety Index, Healthcare Index, Pollution Index and Climate Index of Select Member Nations of Indian Ocean RIM Association, calculate the similarity of standard of living indexes between Australia, United Arab Emirates proximities, Singapore, Malaysia, India, Thailand, Indonesia, Sri Lanka, Iran, Kenya and Bangladesh. Environmental Analysis Ho1: D(LOG(GDP)) has a unit root Ho2: D(LOG(TRADEOPENESS)) has a unit root Ho3:D(LOG(URBAN_POPULATION_IORA)) has a unit root Ho4: D(LOG(CO2_EM)) has a unit root Table 1 Augmented Dickey-Fuller test statistic(At First Difference) Variable t-Statistic Prob.* Null Hypothesis Log(GDP) -3.535407 0.0144 Rejected Log(TRADEOPENESS) -4.325332 0.0021 Rejected Log(URBAN_POPULATION_IORA) -3.731961 0.0088 Rejected Log(CO2_EM) -3.038911 0.0439 Rejected Source: Authors Calculation Table 1 describes the ADF Unit Root Test on GDP, Trade Openness , Urban Population, CO2 Emissions of members of Indian Ocean RIM Association. The value of ADF is -3.535407 for GDP, -4.325332 for TRADEOPENESS, -3.731961 for URBAN_POPULATION_IORA and -3.038911 for CO2_EM . In all cases p value is less than 0.05. The null hypothesis is rejected and it is concluded that series is stationery at first difference for GDP, Trade Openness , Urban Population, CO2 Emissions of members of Indian Ocean RIM Association. Cointegration Test Assumption: Quadratic deterministic trend Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 113 https://internationalpubls.com H0: No deterministic Trend Table 2: Unrestricted Cointegration Rank Test (Trace) Hypothesized Trace 0.05 Null Hypotheses No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.571601 65.28168 55.24578 0.0051 Rejected At most 1 * 0.564441 42.39379 35.01090 0.0069 Rejected At most 2 * 0.368421 19.95344 18.39771 0.0301 Rejected At most 3 * 0.243826 7.546064 3.841466 0.0060 Rejected Source: Authors Calculation Trace test indicates 4 cointegrating eqn(s) at the 0.05 level . * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Co-integration test examine the existence of long run relationship between GDP, Trade Openness , Urban Population, CO2 Emissions of members of Indian Ocean RIM Association. The p value is less than 0.05 and this explains the rejection of null hypothesis. A long run association exists in case of GDP, Trade Openness , Urban Population, CO2 Emissions of members of Indian Ocean RIM Association. Table 3:Granger Causality Null Hypothesis F- Statistic Prob Result LOG(GDP) does not Granger Cause LOG(CO2_EM) 0.43196 0.6546 Accepted LOG(CO2_EM) does not Granger Cause LOG(GDP) 3.80893 0.0380 Rejected LOG(TRADEOPENESS) does not Granger Cause LOG(CO2_EM) 0.27448 0.7625 Accepted Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 114 https://internationalpubls.com LOG(CO2_EM) does not Granger Cause LOG(TRADEOPENESS) 4.95339 0.0167 Rejected LOG(URBAN_POPULATION_IORA) does not Granger Cause LOG(CO2_EM) 0.28751 0.7529 Accepted LOG(CO2_EM) does not Granger Cause LOG(URBAN_POPULATION_IORA) 6.84996 0.0049 Rejected LOG(TRADEOPENESS) does not Granger Cause LOG(GDP) 3.82641 0.0368 Rejected LOG(GDP) does not Granger Cause LOG(TRADEOPENESS) 3.30450 0.0548 Rejected LOG(URBAN_POPULATION_IORA) does not Granger Cause LOG(GDP) 0.23265 0.7943 Accepted LOG(GDP) does not Granger Cause LOG(URBAN_POPULATION_IORA) 8.39052 0.0018 Rejected LOG(URBAN_POPULATION_IORA) does not Granger Cause LOG(TRADEOPENESS) 0.36077 0.7010 Accepted Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 115 https://internationalpubls.com LOG(TRADEOPENESS) does not Granger Cause LOG(URBAN_POPULATION_IORA) 5.18965 0.0138 Rejected The results show that there is bidirectional causal relationship between CO2 emissions, GDP , Trade Openness and Urban Population. logCo2𝑖𝑗𝑡 = 𝛼0 + 𝛼1 log 𝑌𝑖𝑡 + 𝛼2 log T𝑖𝑡 + 𝛼3 logP𝑖t + u𝑖t Where Co2= Carbon dioxide emissions of IORA nations in time period t Yit= Gross Domestic Product of IORA nations in the time period t Tit= Trade Openness Pit=Urban Population Uit= Error term Table4: Ordinary Least Square Dependent Variable: LOG(CO2_EM) Variable Coefficient Std. Error t-Statistic Prob. LOG(GDP) 0.713621 0.062560 11.40706 0.0000 LOG(TRADEOPENESS) -0.111323 0.040714 -2.734254 0.0111 LOG(URBAN_POPULATIO N_IORA) 0.099764 0.168067 0.593593 0.5579 R-squared 0.954211 Mean dependent var 19.26108 Adjusted R-squared 0.950689 S.D. dependent var 0.217017 S.E. of regression 0.048191 Akaike info criterion -3.129581 Sum squared resid 0.060382 Schwarz criterion -2.988137 Log likelihood 48.37892 Hannan-Quinn criter. -3.085282 Durbin-Watson stat 0.449363 Source: Authors calculations The OLS results reveal that a 1% increase in GDP leads to a %0.713621increase CO2 emissions and a 1% increase in trade openness leads to % 0.111323 decrease in CO2 emissions. Standard of living of Indian Ocean RIM Association Member Nations: The Standard of Living in India varies from member nation to member nation. All the member nations have different Rate of poverty, per capita income and income inequality, education system, healthcare amenities, Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 116 https://internationalpubls.com life quality, safety parameters, environmental concerns, Cultural resources etc. According to United Nations Human Development Index (HDI), the standard of living refers to the level of capital, comfort, requirements, and material goods available to a particular geographic area. Quality of life is the standard of health, comfort, and happiness experienced by a group. The quality of life index is based on purchasing power, safety, health care, cost of living, traffic commuting time, climate and level of pollution. Table 5 Standard Of Living Index of Indian Ocean RIM association member nations Country lifeQuality Rank lifeQualityI ndex safetyIn dex healthcareI ndex pollutionI ndex climateIn dex Australia 4 185.03 58.33 77.81 23.22 94.2 Oman 12 173.08 - - - - United Arab Emirates 24 155.41 84.55 67.92 50.66 45.23 Singapore 34 142.25 68.47 70.97 33.3 57.45 Malaysia 51 117.42 41.45 68.87 62.81 59.21 India 59 105.86 55.58 66.21 79.56 64.74 Thailand 69 101.64 59.99 78.04 75.26 69.45 Indonesia 73 91.63 53.94 60.51 66.9 68.48 Sri Lanka 77 80.36 59.77 72.44 60.03 59.11 Iran 79 70.32 51.09 52.23 76.69 70.99 Kenya 80 66.6 38.27 55.58 76.56 99.79 Bangladesh 81 66.29 36.18 43.08 85.83 72.91 Source:worldpopulationreview.com Table 5 explains life Quality Rank, life Quality Index, safety Index, healthcare Index, pollution index, climate index. In case of IORA member nations in case of life Quality Rank only Australia, Oman, UAE and Singapore are in better position. Bangladesh attained the lower middle economy status in the year 2015. There is a reduction in poverty from 43.8 per cent recorded in 1991 to 14.8 per cent recorded in year 2016. Kenya congregated various Millennium Development Goals, by reducing child mortality, primary school enrollment, investment in healthcare sector etc. In case of Kenya there are severe rural/urban differences in poverty rate, rural areas witnessed 27 per cent and 6 per cent in urban areas. Sri Lanka is a lower middle income economy. There is a decline in head count poverty from 15.6 per cent in the Year 2006-07 to 4.1 per cent in the year 2016. Before the outbreak of pandemic, Indonesia was in a better position. But due to lack of social assistance 5.5 to 8 million people in Indonesia have been pushed in to poverty. Thailand witnessed lower poverty rate as compare to 80’s. There is a decline in poverty from last 3 decades from 65.2 per cent to 9.85 per cent in the year 2018. Since the 2000s, there has been reduction in absolute poverty. Poverty levels Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 117 https://internationalpubls.com recorded in 2015 declined, lifted 90 million people out of extreme poverty. The impact of pandemic on Indian economy is also very severe. The majority of the population in India engaged in informal sector. This further deteriorated the standard of living of the people. The Covid-19 has impacted the Malaysia. Less than 1 per cent of the population living in extreme poverty. The low income group is the main victim of pandemic, because there is increase in cost of living. Income inequality in case of Malaysia is relatively higher as compare to other East Asian economies. (worldbank.org) Fig 1 Standard Of Living Index of Indian Ocean RIM association member nations Source:worldpopulationreview.com Table 6 Correlations between Life Quality Index, Safety Index, Healthcare Index, Pollution Index and Climate Index lifeQualityIn dex safetyInd ex healthcareIn dex pollutionIn dex climateInd ex lifeQualityIn dex Pearson Correlati on 1 .634* .682* -.871** -.144 Sig. (2- tailed) .036 .021 .000 .672 safetyIndex Pearson Correlati on .634* 1 .577 -.559 -.581 Sig. (2- tailed) .036 .063 .074 .061 healthcareInd ex Pearson Correlati .682* .577 1 -.636* -.187 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 118 https://internationalpubls.com on Sig. (2- tailed) .021 .063 .036 .582 pollutionInde x Pearson Correlati on -.871** -.559 -.636* 1 .042 Sig. (2- tailed) .000 .074 .036 .902 climateIndex Pearson Correlati on -.144 -.581 -.187 .042 1 Sig. (2- tailed) .672 .061 .582 .902 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). Source: Author’s Calculations There is positive correlation between life quality index ,safety index and health care index. But a negative association exists between life quality index, pollution index and climate index. Table 7 Proximity Matrix of Life Quality Index, Safety Index, Healthcare Index, Pollution Index and Climate Index of Select Member Nations of Indian Ocean RIM Association Correlation between Vectors of Values 1:Austr alia 3:Unit ed Arab Emira tes 4:Singap ore 5:Malay sia 6:Ind ia 7:Thail and 8:Indone sia 9:Sr i Lan ka 10:Ir an 11:Ken ya 12:Bangla desh 1:Australia 1.000 .835 .950 .852 .697 .788 .829 .799 .119 .092 -.020 3:United Arab Emirates .835 1.000 .956 .800 .738 .756 .718 .813 -.027 -.352 -.173 4:Singapor e .950 .956 1.000 .836 .699 .782 .754 .864 -.049 -.204 -.194 5:Malaysia .852 .800 .836 1.000 .946 .993 .946 .887 .385 .150 .285 6:India .697 .738 .699 .946 1.00 0 .950 .949 .734 .582 .197 .488 7:Thailand .788 .756 .782 .993 .950 1.000 .920 .890 .396 .142 .312 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 119 https://internationalpubls.com 8:Indonesi a .829 .718 .754 .946 .949 .920 1.000 .696 .608 .369 .496 9:Sri Lanka .799 .813 .864 .887 .734 .890 .696 1.00 0 -.064 -.208 -.144 10:Iran .119 -.027 -.049 .385 .582 .396 .608 - .064 1.00 0 .786 .985 11:Kenya .092 -.352 -.204 .150 .197 .142 .369 - .208 .786 1.000 .807 12:Bangla desh -.020 -.173 -.194 .285 .488 .312 .496 - .144 .985 .807 1.000 This is a similarity matrix Source: Author’s calculations 1. Australia proximities: Australia They are lowest with Iran , Kenya and Bangladesh , highest with Singapore, United Arab Emirates, Malaysia, Thailand, Sri Lanka and Indonesia, and in between with India. 2. United Arab Emirates proximities: They are lowest with Iran , Kenya and Bangladesh , highest with Australia, Singapore, Malaysia , Thailand, , Sri Lanka, Indonesia and India. 3. Singapore: They are lowest with Iran, Kenya and Bangladesh, highest with Australia, United Arab Emirates, Malaysia, Thailand, Sri Lanka and Indonesia, and in between with India. 4. Malaysia: They are lowest with Iran, Kenya and Bangladesh, highest with Australia, Singapore, United Arab Emirates, Thailand, Sri Lanka, Indonesia and India. 5. India: They are lowest with Kenya and Bangladesh, highest with Malaysia, Thailand, Indonesia, Sri Lanka, UAE and in between with Australia, Singapore and Iran. 6. Thailand: They are lowest with Iran , Kenya and Bangladesh , highest with Australia, Singapore, Malaysia , Thailand, , Sri Lanka, Indonesia and India. 7. Indonesia: They are lowest with Kenya and Bangladesh, highest with Malaysia, India, Thailand, Australia, UAE, Singapore and in between with Sri Lanka and Iran. 8. Sri Lanka: They are lowest with Iran , Kenya and Bangladesh , highest with Australia, United Arab Emirates, Malaysia, Thailand, Sri Lanka and India, and in between with Indonesia. 9. Iran: They are lowest with Australia, UAE, Singapore and Sri Lanka highest with Bangladesh, Kenya and Indonesia and in between with Malaysia, India and Thailand. 10. Kenya: They are lowest with Australia, UAE, Singapore, Malaysia, India, and Thailand, highest with Iran and Bangladesh and in between with Indonesia and Sri Lanka. 11. Bangladesh: They are lowest with Australia, UAE, Singapore, and Sri Lanka, highest with Iran and Kenya and in between with Malaysia, India, Thailand and Indonesia. Conclusion(s): The results show that there is bidirectional causal relationship between CO2 emissions, GDP , Trade Openness and Urban Population. The OLS results reveal that a 1% increase in GDP leads to a %0.713621increase CO2 emissions and a 1% increase in trade openness leads to Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 120 https://internationalpubls.com % 0.111323 decrease in CO2 emissions. The outbreak impacted all the economies badly in the world. But in case of Indian Ocean RIM Association, majority of member nations are low income or middle income economies with social assistance from Government. Main part of the population in India is engaged in informal sector, any package announced by the government for the support may not be beneficial for them. In case of IORA member nations, life Quality Rank is in better position only for Australia, Oman, UAE and Singapore. References [1] Akin, C. S. (2014). 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