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Adinrayanane Ramamurthy et al., International Journal of Advanced Engineering Technology E-ISSN 0976-3945

Int J Adv Engg Tech/Vol. VII/Issue III/July-Sept.,2016/146-157

Research Paper PLANNING FOR ENERGY EFFICIENT SUSTAINABLE

DEVELOPMENT OF CHENNAI METROPOLITAN CITY, INDIA: A DYNAMIC SIMULATION MODELING APPROACH

Adinrayanane Ramamurthy1, Monsingh D. Devadas2 Address for Correspondence

1Ph.D. Research Scholar, School of Architecture and Planning, Anna University, Chennai-600 025, India 2Professor and Dean, School of Architecture and Planning, Anna University, Chennai-600 025, India

ABSTRACT System concept is employed in planning to overcome inadequacy of traditional methods in addressing increasingly complex problems, which require holistic approach, and where emphasis is put mainly on interrelationships of individual subsystem within the system by various Scholars. In this present research, System Dynamic models for various subsystems were developed by employing STELLA software 9.1.4 Version, to recognize the functions of the system. An integrated System Dynamic model for energy efficient sustainable development was developed to look at the dynamic functions, under different alternative conditions. The validated model has been employed to project the control parameters, which decide the functions of the system and thereby developed the projected year model for the year 2041 A.D. Further, alternative plausible scenarios were developed and tested in the forecasted year model 2041 A.D., by employing simulation techniques for arriving at plausible decisions. The results of optimal scenario chosen were analyzed, and recommended for the sustainable development of the system to evolve plausible policy planning guidelines. KEYWORDS:Urban System, Energy consumption, System Dynamics Model, Integrated Planning Approach, Sustainable Urban Development.

1. INTRODUCTION Climate change is widely recognized as the most serious environmental threat facing mankind and has diverse local, regional and global consequences. Human activities stimulate the climate to change. In other words, there is overwhelming evidence for human-made global warming, due to the substantial increase in greenhouse gas (GHG) concentrations. Underlying causes of climate change implies the human activities, primarily energy-use and land-use practices are increasing the atmospheric concentrations of greenhouse gases and, in some regions, aerosols. The balance of scientific evidence suggests a discernible human influence on the Earth's climate. The main source of GHG emissions generated in the urban areas are from consumption of fossil fuels; energy use in residential and commercial buildings; transportation; industrial production. The world has seen an uncontrollable pace of urbanization, and a consequent rise in energy demand for private and public consumption and for economic activities leading to greater emission of GHGs. This has led to an urgent need for incorporation of energy efficiency measures in urban planning and development. Urbanization is one of the major demographic and economic trends occurring in developing countries, with important consequences for development, energy use, and well-being. Long term projections of future energy use, land use, and greenhouse gas emissions have typically focused on the role of technological change and economic growth as the principal drivers of future emissions, whereas, less attention has been given to changes in the composition of the population by demographic or socio-economic characteristics (O'Neill et al., 2010). Urbanization is a major demographic trend in much of the world, particularly in Asia and Africa (Montgomery, 2008), with potentially major consequences for development and the environment (Hardoy et al., 2001). It is particularly important for energy policy and planning. Urbanization plays a key role in what has become known as the “energy transition” (Leach, 1992). The process of economic development is generally accompanied by a shift within developing country households towards an increasing use of modern fuels, and decreasing

reliance on biomass, even in the absence of policies explicitly aimed at achieving this outcome (Pauchari and Jiang, 2008). The design of effective policy interventions, and the anticipation of future demand necessary for planning supply options, depends on understanding the determinants and consequences of this transition. Lately, analysts have begun to investigate the role of urbanization, and differences in energy-related consumption between urban and rural households (Jiang and O'Neill, 2004), as part of long-term scenario analyses (Eckholm et al., 2010; O'Neill et al., 2010; van Ruijven et al., 2008). The increase in the world urban population are concentrated in a few countries, with China and India together projected to account for about a third of the increase in the urban population in the coming decades. Between 2011 and 2030, the urban areas of the world are expected to gain 1.4 billion people, including 276 million in China and 218 million in India, which account together for 37 per cent of the total increase. In addition to the above, another nine countries including Nigeria and the Democratic Republic of the Congo in Africa; Bangladesh, Indonesia, Pakistan and the Philippines in Asia; Brazil and Mexico in Latin America, and the United States of America are projected to contribute 26 per cent of the urban increment, with increases ranging from 22 million to 76 million. Among them, those in Africa and Asia will experience high rates of urban population growth, usually surpassing 2 per cent or even 3 per cent per annum. This phenomenal increase in population growth of developing and less developed regions of the world would require vast transformation of resources into goods and services and massive infrastructure for their sustenance. These transformations of natural resources into goods and services and all kinds of Physical, Social and Economic infrastructures to cater the economic needs of growing population would stimulate the surge of urban energy demand. As a consequence, the rise of urban energy demand is highly responsible for excessive greenhouse emissions, which leads to environmental deterioration in the system. Having the aforesaid knowledge, Chennai Metropolitan City, India has been chosen for the present investigation. System thinking refers specifically to the conceptualization, development, and use of system



dynamics models. System dynamics is a simulation methodology that was specifically developed to support the study of dynamic behavior of the complex systems. The goal of a modeling effort is to improve and understanding of the relationship between feedback structure and dynamic behavior of a system, so that policies for improving problematic behavior may be developed'' (Richardson and Pugh, 1981). System dynamics techniques most widely employed dynamic assessment to formulate, simulate, and validate the sustainable land use and urban development. System dynamics is especially designed for large-scale, complex socio-economic systems. A detailed description of the methodology is presented by the author (Forrester, J. W., 1969).This methodology, blending the art of traditional management with the science of feedback control, has been applied into various fields, including but not confined to, global environmental sustainability (Forrester, 1961, 1968; Meadows, Meadows, and Randers, 1992), regional sustainable development issues (Bach and Saeed, 1992; Saeed, 1994), environmental management (Mashayekhi, 1990), water resource planning (Ford, 1996) and ecological modeling (Wu, Barlas and Wankat, 1993), agricultural sustainability (Saysel, Barlas, and Yenigun, 2002), regional environmental planning and management (Guo et al., 2001), national development programs (Wolstenholme, 1983), transport and land use (Heimgartner, 2001). Basically, all system dynamics models are made of three kinds of variables: stock, rate, and auxiliary, and two kinds of flows, physical/ material and information, only through both of which could variables interact and respond to others. Variables, together with flows, consist of the basic structure of one dynamics system, called stock-flow diagram, in which feedback loops, the foremost concept and pivotal role in simulation of the model, could be observed. In system dynamics, simulation is governed entirely by the passage of time and is referred to as ‘‘time-step’’ simulation (Coyle, 1977, 1996). The typical purpose of a system dynamics study is to understand how and why the dynamics of concern are generated and to search for managerial policies to improve the situation. These policies refer to the long-term, macro-level decision rules used by upper management (Saysel et al., 2002; Forrester, J.W., 1994). A system dynamics approach facilitates the identification of important information about the structure and behavior of systems required for the development of effective policies and actions. A system dynamics approach, specifically group model building efforts also support learning and shifts in mental models of decision makers and development planners. Eventually, system dynamics offers a common language to support interdisciplinary communication and cooperative planning also required for the design and implementation of sustainable development strategies. Over the past four decades, the process of modeling dynamic urban development has changed dramatically. From Forrester’s Urban Dynamics to cellular automata and Geographical Information Systems (GIS), these attempts illustrate that urban changes are both challenging and complex to define. One of the first contemporary works that attempted to model urban development was Jay Forrester’s Urban

Dynamics in 1969. Forrester creates a dynamic model of the growth and decay of a city. Starting from open land, the model illustrates the development of businesses and housing, and the growth of a population, and then represents its decline with the aging of its industry and population. Forrester’s model lends insight into the construction of such a system and the importance of a multi-leveled model for shared understanding. Although the present model evolved by the authors focuses on land area consumed and the effects on quality of life, rather than the life cycle of the city as in Forrester’s model, his contribution helps to evolve this new dynamic development models. In this present research, System Dynamic Models have been developed for various subsystems and employed to understand the functions of the system, likewise all the subsystem models were developed and combined together for evolving a suitable integrated system dynamic model for energy efficient sustainable development and presented in Fig. 4. STELLA 9.1.4. Software is employed to develop the System Dynamics model. The evolved System Dynamics model is validated by employing simulation technique, to understand the reliability of the model for further investigation. 1.1 URBAN SYSTEM CONCEPT

Figure 1: Functions of Urban system along with its

subsystems Urban System concept developed by considering

the seven subsystems and is employed in this present research. This concept explains that “the system functions as a whole with the interaction of several sub system. All the subsystems of the systems are interlinked and inter dependent on each other. If one of the sub systems of the system defunct or partly function or takes a lead role during its functions, its effects can also be observed in the whole system over a period of time". In this present investigation the study area is considered as system, since it has several subsystems, which include physical, social, economics, ecology, environment, infrastructure, and institutions, and all these sub-systems are interlinked and interdependent to each other, and function as a whole. The functions of urban system along with its subsystems are presented in Figure.1 2. STUDY AREA AT A GLANCE

Chennai, formerly known as ‘Madras’ is the capital city of Tamil Nadu State, India, lies on the Coromandel Coast of the Bay of Bengal, and its latitude and longitude are 13.04º N 80.17º E, respectively. Chennai city is blessed with good



amount of industries, which include automobiles, computer technology, hardware manufacturing, and healthcare industries. Chennai Metropolitan City contributes 39 per cent of the Tamil Nadu State’s GDP.

The study area has been facing multidimensional problems in almost all aspects, which include physical, socio-economic, infrastructure, and environment. The exponential population growth has wreaked havoc on human life in the city environment. The doubling and tripling of urban population creating strain on the existing systems, which has manifested in environmental chaos. Chennai city faces the proliferated typical planning problems of urban expansion, inadequate housing, poor transportation, poor sewerage, erratic electric supply, and insufficient water supplies. An increasing number of trucks, buses, cars, three-wheelers and motorcycles all spewing uncontrolled fumes, all competing for space on city streets already jammed with jaywalking pedestrians, auto-rickshaws and cattle. The phenomena of rapid urban economic growth and urbanization are the main perpetrators, which besides bringing higher standards of living, has also brought problems related to the growth of dense and unplanned residential areas, environmental pollution, lack of services and amenities, solid waste generation, and growth of slums. Population growth and in-migration of poor people, industrial growth, inefficient and inadequate traffic corridors, and poor environmental infrastructure are the main factors that have deteriorated the overall quality of the city’s environment. Having the aforesaid knowledge, Chennai Metropolitan City, India has been chosen for the present investigation. The geographical location of the Chennai Metropolitan City is presented in Figure.2.

Figure 2: Geographical location of Chennai

Metropolitan City, India Note: The extent of Chennai Metropolitan Area (CMA) is 1189 Sq.km. CMA covering of Chennai City and its Urban Agglomeration, comprising of administrative divisions such as, Chennai city, 16 Municipalities, 20 Town Panchayats and 214 Village Panchayats in 10 Panchayat Unions. 3. OBJECTIVES The present investigations have few objectives to carry out the research, and are presented as below in the sequel. They are:

a. To identify the potentials for energy efficient and CO2 emission reduction mechanism in the system.

b. To forecast the demand and supply of infrastructure services for 2041 A.D and quantify the functions of the system in different alternative conditions.

c. To evolve an integrated urban system dynamic model for energy efficient sustainable development of the system.

d. To evolve a set of policy guidelines, and their implications in the system (study area).

4. CONCEPT The above discussed urban system concept is employed in this present investigation. 5. METHODOLOGY

The Chennai Metropolitan City area is divided into few segments for development administration, and is: a Corporation, 16 Municipalities, 20 Town Panchayats and 214 Village Pachayats, excluding the cantonment area of 12.92 Sq. Km, which lies under the location of Saint Thomas Mount and Pallavaram (CMDA, 2012).Representative samples have been chosen by employing stratified random sampling technique covering all the aforesaid segments of the study area, the households are listed with their size of income, with various ranges of monthly income viz., < Rs.40,000, Rs.40,000-80,000, Rs.80,000-120,000, Rs.120,000-160,000, Rs.160,000-200,000 and > Rs.200,000, by employing house-listing schedules and thereby the households are stratified by income group-wise. Thereafter, representative samples are chosen from all strata of income by employing simple random sample technique, and the total of 301 households have been chosen for conducting the investigation at the grassroots level. There are two reasons for using stratified random sampling technique, and are: at first, the researcher is interested in obtaining data among different segments of the study area; and secondly, this sampling technique is more efficient, as it increases the representatives of the sample households to reflect the population (universe).The entire population is stratified in such a way that the elements of homogeneous within the different income groups are considered. For example, if the interest is to estimate the expenditure of the households on utility services, the appropriate criteria for stratification would be household income, since the expenditure on utility services and household income are highly correlated. The area-wise stratification of the study area, and the samples collected in different area (total of 301 samples) are presented in Fig.3. Besides these, opinion survey was done among the officers who are well connected with the planning and development activities in the system. There are 18 Government Departments /agencies have been considered to conduct the investigation. Simple random sampling technique was employed to choose the Class I officers from the chosen Government Departments/Agencies, and the total 101 Class-I officers were considered for conducting the investigation. The research methodology followed in this research is presented in Fig.4.

An integrated urban system dynamics model was developed by considering all the subsystem of the urban system and in the present model energy is considered as a constraint for development in the developing countries, and particularly in the study area. At the outset, different sub-models are developed, which include population model, city area model, educational infrastructure model, health infrastructure model, transportation infrastructure model, water supply model, financial institutional model, electricity model, employment model,



pollution model, etc., separately, and integrated all together to evolve an integrated model by employing STELLA software. Thereafter, this model was validated and then used for projection. Then the projected year model was used for simulation. The survey research methodology, which has been employed to conduct this investigation at the grassroots level, is presented in the form of flow model in Figure.5. 5.1 SURVEY SCHEDULE

Pretested household survey schedules are used for conducting survey at the grassroots level. These schedules have covered the following aspects, such as physical, socio-economic, and environmental quality of the households in the system. Besides this, professional survey schedule was developed, pretested and used for conducting survey among the government officials who involve in planning and development activities in the study area. 5.2 METHODS OF ADMINISTERING THE SURVEYS

The household survey and the professional surveys were conducted in the year 2013. To conduct the household survey, the Investigator approached the households directly, had detailed discussion with the members of the households, obtain prior appointments from the respondents for conducting survey at the household level. Thereafter, the Investigator conducted the survey at the household level based on the availability of the respondents.

The Investigator himself had approached the Government officials in person (Class-I officers) from about 18 Government departments/agencies in the Chennai Metropolitan Area, which involved in planning and development process, to conduct the opinion survey. Since the Investigator himself conducted both surveys, he could gain more insight about the function of the system (the study area).

Figure 3: Sample Distribution in the Study Area

Figure 4: Research Methodology



Figure 5: Methodology for Primary Survey

6.1. BASE YEAR MODEL RESULTS (2011 A.D.) The Investigator developed the System Dynamic (S.D.) Model by employing the STELLA Software 9.1.4 version, to quantify the functions of the system. In this model, the following important parameters, which have more direct bearings in the system, are considered as control parameters. They are: Population, Corporation Area, Housing Density, GDP Per Capita, Primary Schools, High Schools, Higher Secondary Schools, Colleges for General Education, College for Technical Education, Number of Beds in Hospital, Solid Waste Generation,

Aggregate CO2 Emission, Atmospheric Temperature, SO2 Residential, SO2 Commercial, SO2 Industrial, NOx Residential, NOx Commercial, NOx Industrial, SPM Residential, SPM Commercial, SPM Industrial, Population Density, House Units, Personal Vehicles, Public Transport Vehicles, Electricity Generation, Per Capita Income, Employment, Water Supply Capacity, Number of Banking Offices, Road Length, and Per Capita Electricity Consumption, Chennai Metropolitan Area (CMA)Attractiveness Index.

Figure 6: An Integrated System Dynamic Model for Sustainable Development of

Chennai Metropolitan City, India



The results of the model were validated by employing simulation technique. To validate the model, the year 2001 values of the more important control parameters were considered. The chosen control parameters values for the year 2001 and 2011 were further closely analyzed in the model; and observed that the population of the 2011 census., and the model results were matching very closely, i.e., 98,52 per cent, which confirms that the model is reflecting the real system. Further, values of few more important parameters were also tested and observed that they are also very closely matching this model results. The model results demonstrates that the population in the city is 8,805,068; the city corporation area is 392.94 Sq.km; Housing Density is 2,228; GDP Per Capita is 36,524; Primary Schools are1,583; High Schools are 1,066; Higher Secondary Schools are 678; Colleges for General Education are 132; Colleges for Technical Education are143; Number of Beds in Hospital are14,992; Solid Waste Generation is 660 grams, Aggregate CO2 Emission is 16,378,408.96 Metric Tons; Atmospheric Temperature is 30.39 ◦C; SO2 Residential is 9.13; SO2 Commercial is 10.81; SO2 Industrial is 22.37; NOx Residential is 24.30; NOx Commercial is 30.47; NOx Industrial is 23.14; SPM Residential is 178.33; SPM Commercial is 419.22; SPM Industrial is

170.03;Population Density is 7,405; House Units are 2,649,532; Personal Vehicles are 2,933,311; Public Transport Vehicles are 306,899; Electricity Generation is 5,935,396 in MWH; Per Capita Income is Rs.48,924; Employment is 411,462; Water Supply Capacity is 282,819 ML; No of Banking Offices are1,715; Road Length is 4,680 in KM and Per Capita Electricity Consumption is 1,012 MW and CMA Attractiveness Index is 44.64. The integrated urban dynamics model is presented in Figure 6. The values of the most important parameters, which were considered for model validation and their values are presented in Table.1. The table1 reveals that the population parameter, which is one of the most important parameter, has just 1.26 per cent differ between the literature value and the model value. Similarly, the following values such as, Corporation area, Solid Waste Generation, Atmospheric Temperature, SO2 Residential, NOx Commercial, NOx Industrial, SPM Commercial, SPM Industrial, Personal Vehicles, and Public Transport Vehicles are also closely matching the model values. Thus the model was validated and thereafter the validated model is used for forecasting. The year 2041 A.D. is considered for forecasting, and its results are presented in the sequel.

Table 1: Model Validation

Model Validation Results

S.N0. Parameter Literature Values

Model Values

% Difference

1 Population 8917000 8805068 1.26 2 Corporation Area in Sq.km 426 392.34 7.90 3 Solid Waste Gen PC/day 650 659.75 1.50 4 Atmospheric Temperature in ◦C 32.30 30.39 5.91 5 SO2 Residential 9.00 9.13 1.44 6 NOx Commercial 28.55 30.47 6.73 7 NOx Industrial 23.61 23.14 1.99 8 SPM Commercial 398.67 419.22 5.15 9 SPM Industrial 169.45 170.03 0.34

10 Personal Vehicles 3,183,045 2,933,311 7.85 11 Public Transport Vehicles 300,308 306,899 2.19

Source: Compiled by the Authors based on the model validation results

6.2. FORECASTED YEAR MODEL RESULTS (2041 A.D.) The validated System Dynamic model was used for forecasting up to the year 2041 A.D and the forecasted year model has been used for testing the different kinds of scenarios to understand the functions of the system under different alternative conditions to arrive at plausible policy decisions. The results of the forecasted year model were carefully analyzed and presented in Table No.2 and Fig. 7(a), 7(b), 7(c), 7(d), 7(e), 7(f), 7(g), 7(h) and 7(i) respectively, are presented in Appendix-I. In this model, the population in the city for the year 2041 would be 2, 12, 89,787; Corporation area of the city would be 1138 Sq. Km; Housing Density would be 4,471; GDP Per Capita would be 103,619.75; Primary Schools would be 2,105 ; High Schools would be 1,278; Higher Secondary Schools would be 723; Colleges for General Education would be 195; Colleges for Technical Education would be 228 ; Number of Beds in Hospital would be19,342; Solid Waste Generation would be 2,549 grams, Aggregate CO2 Emission would be 92,092,376.51 Metric Tons; Atmospheric Temperature would be 36.25 ◦C; SO2 Residential would be 43.99; SO2 Commercial would be 26.83 ; SO2 Industrial would be 27.67 ;NOx Residential would be 32.12 ; NOx Commercial

would be 38.11; NOx Industrial would be 27.66 ; SPM Residential would be 237.10; SPM Commercial would be 334.09 ; SPM Industrial would be 183.52 ; Population Density would be 17,906; House Units would be 5,316,102; Personal Vehicles would be 52,743,146; Public Transport Vehicles would be 2,844,592; Electricity Generation would be 26,600,599 in MWH; Per Capita Income would be Rs.165,944; Employment would be 507,612 ;Water Supply Capacity would be 761,131 ML; No of Banking Offices would be 38,617 ; Road Length would be 9,026 in KM and Per Capita Electricity Consumption would be 2,490 MW and CMA Attractiveness Index would be 43.24. To evolve Development Plan (Five year plans) the Investigator has given option in the model to calculate the results once in quinquennial period, and the results show that the population is increasing 13.68 per cent constantly from 2011 to 2016, 2016 to 2021, 2021 to 2026, 2026 to 2031, 2031 to 2036 and 2036 to 2041, whereas the values of other parameters are changing from time to time, during the same period, considerably. It has been observed from this model that the city has 88 lakhs population in the year 2011; whereas it would be 21.2 million in the year 2041 which is 242 per cent increase in population in the city. The city has been facing



numerous kinds of problems pertaining to infrastructure services, and other socio-economic problems like poverty, unemployment, under employment, malnutrition, housing shortage, etc., and these problems would be further aggravated in the system, like problem like population explosion in the year 2041. Therefore, the institutional mechanisms, which govern the city, should evolve plausible development plans to afford the required amount of infrastructure, and also to satisfy the socio-economic programs, projects/schemes for sustainable development of the city. There are 120 scenarios developed based on the historical behavior of the system, survey results of expert’s opinion, and trend analysis, and tested in the forecasted year model 2041 A.D. by employing simulation techniques, and observed that 25 scenarios are closely behaving with the system. The results of the chosen 25 scenarios are analyzed again to identify the optimal scenario, which could be considered for policy decisions. The Scenario-22 is chosen after thorough analysis and recommended for policy

decision, which comprises of “70% reduction in Personal Vehicles Travel, 50% reduction in Domestic Electrical Energy consumption, 60% replacement of Thermal Power generation by Wind, Solar and Municipal Solid Waste energy, 15% increase in GDP growth, 100% replacement of Public Transport Vehicle by CNG,170% increase in Hospital Beds,170 % increase in Water Supply,170% increase in Housing Density, 30% decrease in Corporation Area, 170% increase in all Educational infrastructures, 120% increase in Solid Waste Generation,170% increase in Per Capita Electrical Energy consumption, 170% increase in Employment, and 250% increase in Electricity Generation capacity” were together tested in the model and led to very interesting findings. The results of this particular scenario have been carefully analyzed and overall analysis reveals that the city would require huge quantity of infrastructure services, socio-economic development programs, projects/schemes, etc., to navigate towards the sustainable development.



Table 2: Forecasted year model results

6.3. RESULTS OF OPTIMAL SCENARIO-22 The results of the Scenario-22 were closely analyzed and are presented in Table No.3 and Fig. 8(a), 8(b), 8(c), 8(d), 8(e), 8(f), 8(g), 8(h) and 8(i) respectively, are presented in Appendix-II. Population in the city for the year 2041would be 21,290,699; Corporation area of the city would be 910Sq.km; Housing Density would be 12,072; GDP Per Capita would be 393,803; Primary Schools would be 7,486; High Schools would be 4,112; Higher Secondary Schools would be 2,079; Colleges for General Education would be 768; Colleges for Technical Education would be 987; Number of Beds in Hospital would be 52,224; Solid Waste Generation would be 5,609 grams, Aggregate CO2 Emission would be 140,353,659,004,310 Metric Tons; Atmospheric Temperature would be 41.91◦C; SO2 Residential would be 52.02; SO2 Commercial

would be 28.56; SO2 Industrial would be 36.24; NOx Residential would be 33.52; NOx Commercial would be 39.67; NOx Industrial would be 33.45; SPM Residential would be 255.22; SPM Commercial would be 344.93; SPM Industrial would be 190.73; Population Density would be 17,906; House Units would be 5,316,193; Personal Vehicles would be 52,743,571; Public Transport Vehicles would be 2,844,451; Electricity Generation would be 218, 168, 796, 840, 848 in MWH; Per Capita Income would be Rs.626,843; Employment would be 1,370,553;Water Supply Capacity would be 2,055,054 ML; No of Banking Offices would be 38,618; Road Length would be 9,026 in KM and Per Capita Electricity Consumption would be 8,547 MW and CMA Attractiveness Index would be 74.71.



Table 3: Results of Scenario 22

6.4 CITY ATTRACTIVENESS INDEX Authors have made an attempt to develop the City Index. A city index is the measurement of City’s attractiveness in the system. The attractiveness of the study area has been carefully analyzed by considering six attractiveness factors such as (i).Attractiveness due to employment, (ii). Educational Attractiveness factor, (iii) Attractiveness due to CO2 emission, (iv) Attractiveness due to hospital, and (v) Attractiveness due to water. All these attractiveness factors were

identified based on the present trend and closely analyzed to have clear understanding about the real system. Based on the trend analysis, the weightage have been given for each attractiveness factor to arrive the Attractiveness Index for the study area. Authors have developed the Chennai Metropolitan Area (CMA) Attractiveness Index sub-model by employing System Dynamics and are presented in Figure.9.

Attractiveness due to Employment

~

Attractiveness

due to Water

~

Attarctiveness factor

due to Hospital

~

Educational

Attractiveness Factor

Attarctiveness

due to CO2 in PPM

~

Attractiveness of CMACMA Attractiveness

Index

Figure 7: Attractiveness Index of Chennai Metropolitan City

In the Forecasted year model, decreasing trend has been observed in the City Attractiveness Index, for the year 2011 to 2016, and it would be decreased by 2.55 per cent, the shifts for 2016 to 2021 would be 3.55 per cent, for 2021 to 2026, it would be 3.66 per

cent, for 2026 to 2031, it would be 4.39 per cent, and then slowly started to picked up for 2031 to 2036 would be 0.60 per cent and for 2036 to 2041, it would be 1.16 per cent only in the system. Results of the optimal scenario-22 demonstrate that CMA



Attractiveness Index, it would be increased by 31.47 per cent for the projected year, i.e., 43.24 per cent from the forecasted year model 2041 to 74.71 per cent by the optimal scenario-22 recommended for the study area. The authors believe that if the recommendations based on the optimal scenario have been implemented in time, definitely energy efficient sustainable development would be anticipated in the study area. 7. FINDINGS The major findings observed in this investigation are analyzed and few of them are presented in the sequel as below: The variables which are having more bearing in the system have been identified and termed as control parameters, which would increase in size from Base year (2011) to Forecasted year (2041). They are: (i) Population (142 per cent); (ii) Population Density (142 per cent);(iii) Corporation Area (190 per cent);(iv) House Units(100 per cent) and (v) House unit Density (100 per cent); (vi) Employment (23.37 per cent); (vii) GDP Per Capita (184 per cent); (viii) Per Capita Income (239 per cent); (ix) Primary Schools (33 per cent); (x) High Schools (19.90 per cent); (xi) Higher Sec Schools (6.70 per cent); (xii) Colleges for General Education (47.53 per cent); (xiii) College for Technical Education (59.06 per cent); (xiv) Number of Beds in Hospital (29 per cent); (xv) Solid Waste Generation Per Capita (286 per cent);; (xviii) Electricity Generation Capacity (348 per cent); (xix) Electricity Consumption Per Capita would increase by (146 per cent); (xx) Road Length (93per cent); (xxi) Number of Banking Offices (2152 per cent); (xxii) Water Supply Capacity (170 per cent); (xxiii) Personal Vehicles (1698 per cent); (xxiv) Public Transport Vehicles (827 per cent); (xvi) Aggregate CO2 Emission (462 per cent); (xvii) Atmospheric Temperature (11.47 per cent) and (xxv) CMA Attractiveness Index (decrease by 3.14 per cent). 8. POLICY IMPLICATIONS AND RECOMMENDATIONS The following policy implications are arrived based on the investigation and the model results. Population of the system would be increased by 142 per cent by the year 2041 A.D., whereas the employment opportunities would increase just 23 per cent. It has been observed from the other analytical work that unemployment, underemployment and disguised unemployment are increased rampantly over the years in the system. If this population and the employment trend continue as per the model, then the existing grave situation would be aggravated further and there would be a chaotic situation pertaining to occupation and employment in the system by the year 2041 A.D. Based on the above, it is recommended to have the following for sustainable development of the system. The present city corporation area is 426 Sq.km. The model results into that the city corporation area would increase 190 per cent, i.e., almost twofold increase in the corporation area in the year 2041 A.D. The model results show that the entire study area would be turned into the corporation area by the year 2041 A.D. The present corporation area has been facing numerous amounts of problem, including shortage of housing, mushrooming of slums, shortage of all kinds of infrastructures, which include Physical infrastructure, Social infrastructure and economics

infrastructure. Further, Urban Heat Island (UHI) and its associated problems shall be aggravated, since the study area lies in the humid arid zone and also does not have enough green lush space. As a consequence, the problem of UHI would be aggravated in the system. In this alarming situation, it is very much imperative to have a close look at the system, in general, and the physical subsystem in particular. Since all the subsystems are interconnected and interdependent to each other in the system, the physical subsystem should be attended thoroughly along with all subsystems. Therefore, conducive policies and development plans pertaining to the development of physical, social, economics, ecology, environment, infrastructure, and institutions subsystems must be evolved and to be implemented in the system for achieving sustainable urban development. 9. CONCLUSION In this present research, it has been attempted to analyze energy scenarios, energy indicators and their functions in sustainable development, impact of energy applications in socio-economic and environmental aspects, and energy scarcity in the system by employing secondary sources of data. Further, domestic energy consumption, energy consumption in physical, social, economic aspects of the system (study area), all kinds of infrastructure, etc., are quantified among the sample households level by employing survey research methods. Besides this environmental conditions are also quantified related to energy usage and its impact in the system by employing survey research methods. Thereafter, system concept is analyzed thoroughly and the functions of the study area are analyzed with system’s concept. Different dynamic sub-models were developed including population; area, housing; transportation; health; solid waste; water; educational institutions; financial institutions; electricity generation; energy consumption in domestic and transportation sub-systems; employment generation; increase in GDP; environment sub-model; and attractiveness index sub-models were developed; and finally all these sub-systems are combined together, and thereby developed an integrated urban dynamics model, where energy is considered as the catalyst for development. The developed integrated model was validated to find out its reliability by employing simulation techniques. Subsequently, the validated model has been used for projecting the functions of the system in the year 2041 A.D. Further, plausible scenarios were developed and tested in the projected year model to understand the functions of the system under various alternative conditions to arrive at plausible decisions. There are about 100 scenarios developed and tested in the model, and identified 22 scenarios are having more bearings in the system and are presented in the thesis. Of these 22 scenarios, a scenario was chosen based on maximum output with minimal input along with environmental conditions, where Attractiveness Index is much higher i.e., under this situation. The Author hopes that the system would be energy efficient and more conducive for sustainable development. Finally the study concludes with plausible recommendations for achieving energy efficient sustainable development in the system, based on the findings of this research.



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