Transforming Mumbai city: removing the bottlenecks

Chatterjee Amit

Transforming Mumbai City

´52nd ISOCARP Congress 2016´

Transforming Mumbai city: removing the bottlenecks to achieve future sustainability Amit CHATTERJEE, School of Planning and Architecture, Bhopal, India Soumendu CHATTERJEE, Presidency University, Kolkata, India R.N.CHATTOPADHYAY, Indian Institute of Technology, Kharagpur, India Abstract: Growth of Mumbai city (population 12.4 million, 2011) has almost saturated and problems such as housing shortage, infrastructure deterioration, environmental degradation, transportation, and scarcity of land resources have attracted good deal of attention among policy makers. Further, declining growth rate, low floor area ratio, income inequalities, geographical constraints (three sides bounded by sea) etc. prevalent in the city call for redefining the ways of future urban development to achieve sustainability. This paper provides a clue for policymakers to take careful decision for removing bottlenecks and plan for a sustainable city. 1. Research Background Urban India accommodated 377 million people (31.2% of total population), the second largest urban population in the world (Census of India, 2011a). According to United Nation’s estimate by the year 2050, half of India’s populations are expected to live in urban areas (United Nations, 2014). Many Indian large cities specially the large metropolises and metropolitan regions are facing problems with respect to their growth, composition, spatial spread, congestion, environmental factors, housing aspects, infrastructure availability as well as accessibility. New challenges such as globalization, demographic change and shortage of future developable land make it necessary to tackle metropolitan growth in a rational manner particularly in Indian context. Mumbai (administered by Municipal Corporation of Greater Mumbai) is no longer an exception. Mehta (2012) in his research termed Mumbai as ‘Maximum city’ through highlighting everyday problems of people who inhabit the stunning metropolis. Mumbai continues to see population increases although its carrying capacity already exceeded (Mumbai HDR, 2009). The over-concentration of population and overdevelopment beyond carrying capacity has created adverse impact on sustainability for Mumbai and Mumbai Metropolitan Region (MMR) as a whole. This paper proceeds in six sections. Following introduction, Section 2 presents theoretical aspects and past research related with sustainability and economic forecasting models. Section 3 specifies the methodological framework and how it was applied. Section 4 presents current state of metropolitan growth, population overconcentration, lack of future developable land, rapidly decreasing natural areas, low FAR (Floor Area Ratio) etc. in Mumbai. Section 5 focuses on assessing the urban carrying capacity of Mumbai city through worker based ‘Relative Employment Potential (REP) model’ and carrying capacity based ‘Sustainable Accommodation through Feedback Evaluation (SAFE)’ model. After validation of REP model, scenarios for two forwarding decades have been forecasted. Further, SAFE model has been tested with various FAR options (with existing FAR and with increased FAR) to find out future FAR requirements for Mumbai. Finally, Section 6 synthesizes the findings and presents policy implications. 1

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2. Literature Review Initial regional development and growth model includes stages of economic growth by Rostow (1960), circular and cumulative causation by Myrdal (1966), relative income potential by Isard (1962), modified neoclassical growth model for the regional context by Borts and Stein (1964), change in market potential by Difiglio (1968). Richardson (1974) discusses that income potential and gravity models are members of the same family and its application includes regional economic projections (Isard and Freutel, Isard and Bramhall, and Difiglio), the measurement of market accessibility in the analysis of location of location of industry or agriculture (Harris, Dunn, Clark, Wilson and Bradely), spatial price theory (Warntz), income potential contour mapping (Stewart and Warntz), as a determinant of migration (Vanderkamp) and as the main component of investment in a regional growth model (Olsen and Peaker). More recent empirical studies on the regional economic growth includes geographic clustering for national industrial competitiveness by Porter (1990), relationship between public investment and regional economic growth by Button (1998) and interregional convergence by Barro and Sala-i-Martin (1999). Population forecasting empirical studies on model includes cohort analysis by Wunsch and Termote (1978), an econometric model using cross-sectional data of 131 Dutch cities and villages by Bierens and Hoever (1985), expertbased stochastic population forecasting method by Billari,M.,et.al.(2012), the Bayesian paradigm by Guimarães (2014). There is always scope to revisit the existing economy based population forecasting models and suggesting new or modified models applicable for developing world. The concept of carrying capacity originated from ecology and mainly focused of environmental and man-made physical factors over a long period of time (Rees (1992); Abernethy (2001); Schneider et al (1978); Liu (2012) Oh et al. (2005)). Researchers worked on other non-environmental factors determining carrying capacity particularly last four decades and accordingly many factors included in carrying capacity assessment. It includes technical, socio-economic and cultural components by Schroll (2012), human attitudes, values, and behaviour by Godschalk DR, Axler N (1977), economic, social, environmental, and institutional (Liu (2012); Downs et al. (2008)). Several evaluation methods and tools evolve for assessing carrying capacity such as infrastructure and land use based by Oh et al. (2005), Visual threshold carrying capacity by Oh (1998), relative carrying capacity based on grey relevant degree by Xu et al. (2010), environmental carrying capacity theory and ubiquitous technology by Lee and Oh (2012). According to Wei (2015), carrying capacity is an evolving tool for monitoring sustainable development. 3. Methodology REP Model developed based on principles of Walter Isard’s Relative Income Potential (RIP) model (1962). Since estimation of income potential at regional level, especially at metropolitan level, is quite difficult and unreliable due to information gaps on income, it is considered, in this study, that employment of urban sector can serve the role of a good surrogate for urban income. In current study the model is assumed to remain same structurally as RIP model presents, but the principal variable is replaced by employment variable and the modified model is designated in this study as REP model. Thus REP model estimates growth by two relevant components namely; i. Proportionality Effect (A) ii. Effect of Region’s Change in Interregional Position (B)

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Hence, the model to be developed consists of two elements or two terms. The first express the proportionality effect on market sensitive activities (as the nation grows). In this case as MMR grows, all of its satellite towns will also grow due to proportionally effect. Proportionality effect (A) equation is as follows;

A=

[Eqn.-1]

Where: E = number of workers f = factors which converts number of employment into number of population or dependency ratio t+θ = forecasting year t= base year i= city i (urban units of MMR) r = specific region (here MMR) The second element or term of the model refers to a region’s change in interregional position, that is, to an improvement or deterioration in a region’s total access to the employment market. The second set covers the forces that generate improvement or deterioration in a region’s (here any urban component of MMR) interregional position, such position being relative. Proportionality effect not expected as it overstates or understates growth, hence urban components change in interregional position modifies the REP when composed with Proportionality effect. Thus any urban component’s change in interregional position (B) can be derived by the reconstituted model as follows;

B=

[Eqn.-2]

Where: b= positive constant ρ= ratio (dependency ratio) t+θ= forecast period t= base year i= city i (urban units of MMR) r= specific region (here MMR) p = Population

Where:

[Eqn.-3]

[Eqn.-4]

Where interacting urban units are designated as (1,2,…..n) and E1= Employment of urban unit1, En= Employment of urban unit n, di1= distance through public transport (bus) of urban unit 1, din= distance through public transport (bus) of urban unit n and so on. It is clear that we must eliminate from this ratio of employment potentials the general effect of regional growth or decline of employment. Such a task is easily done by multiplying the denominator of the ratio by a factor called dependency ratio. 3

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Finally, [Eqn.-5] Where, Pt+θ= Forecasted Population A= Proportionality Effect, B= Urban Components change in inter-regional position. In the above model the proportionality effect and the factor of urban components change in interregional position are considered as additive. Since the two terms are additive, each is expressed in the same units, namely population numbers. This model is used here for future population allocation of all urban units of MMR. Applying this model for 2001 census data, the population of 2011 has been validated. After validation and necessary modification scenarios for two forwarding decades 2021 and 2031 could be forecasted. Further SAFE model can be used in any urban area for assessing the carrying capacity (Sharma et al, 2012) and the same applied for Mumbai to estimate future land and FAR requirements. The carrying capacity of the area can be calculated using the following equation: CC= AU - (AND + AIF) x FAR/S

[Eqn.-6]

where, CC= Carrying Capacity, AU = total urban area, AND= net non-developable area, AIF = area for infrastructure development, FAR = Floor Area Ratio and S = Floor area requirement per head. Due to non-availability of more recent data, land use survey conducted in 2008 by Mumbai Metropolitan Regional Development Authority (MMRDA) is considered as base period source of information for present study. Municipal boundary expansion in future is not considered as scope of present study because Mumbai has geographical constraints for expansion. This paper describes the method by which future population can be forecasted for Mumbai city through REP and carrying capacity based SAFE model. 4. Mumbai : the case study Mumbai has not only become the biggest city in India, population-wise, but it is also the core of the biggest urban agglomeration in the country and is poised to be the world’s third largest agglomeration after Tokyo and Maxico city (Mumbai HDR, 2009). It is seen from Table-1 that since 1901 there is a continuous growth of population in Mumbai in absolute number till 2011. On the contrary the annual average growth rate has drastically been reduced from that of 2.37% during 1901-11 to 0.44% during 2001-2011. It is clear that a significant rise in growth rate (4.28%) had taken place only during 1961-1971 but after that the growth rate has indicated a steady falling trend over the last four decades. It is presumed that the same trend may possibly continue for the coming decades also.

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Year

Population (in Million)

Annual Average Growth Rate (in %)

Year

Population (in Million)

Annual Average Growth Rate (in %)

1901

0.92

-

1961

4.15

3.87

1911

1.14

2.37

1971

5.93

4.28

1921

1.38

2.03

1981

8.24

3.90

1931

1.39

0.12

1991

9.92

2.04

1941

1.80

2.89

2001

11.91

2.00

1951

2.99

6.62

2011

12.44

0.44

Table No 1– Population Growth and Growth rate of Mumbai (1901-2011). Source: Census of India, 1901-2011b

It is obvious that the population influx in Mumbai is an obvious result of continuous flow of migrants to Mumbai from surrounding areas and other regions. Nearly half (43.7 per cent) of the population had been categorised as migrants in the 2001 Census (HDR,2009). Mumbai covers a space of 10% geographical area and has a population share of almost 60% of MMR. Over 1971-2011 period, the gross density of Mumbai increased from 13,391 persons per sq.km. to 28,420 persons per sq.km. This puts a tremendous pressure on existing land use, environment and infrastructure. Built-up land has more than doubled from being 25% of total area in 1971(MMRDA, 2008) to 60.59% in 0212 (Draft DP, 2014-2034). Natural areas and open spaces (forest, water body, coastal wetlands etc.) have been rapidly decreasing from 61% of total land in 1971(MMRDA, 2008) to 31.5% of the same in 2012 (Draft DP, 2014-2034) (See Fig. 1). Considering the very high population density prevalent in Mumbai, the low per capita open space availability (1.24 sqm. per person) is an expected outcome.

Figure 1: Temporal Change of Built up area, Natural areas and Open Spaces at Mumbai (1971-2012). Souce: Transform Study, MMRDA, Draft DP (2014-2034).

An analysis of urbanizable land potential at Mumbai shows that only 9.47 sq.km. of land is available for future development (MMRDA, 2008). According to Bertaud (2008), Mumbai FAR

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values are low, uniform over very large areas. In planning for majority of Metropolitan cities the maximum residential FAR values considered is 3.5 [Sridhar, (2010)] whereas cities like Mumbai, the permitted FSI is uniform and in 1991 was fixed at 1.33 for the Island City and 1.00 for the suburbs, although some higher FAR has been allowed in some isolated lots outside the Island City area through the program called Tradable Development Rights (TDR) (Fig 2) (Bertaud (2011)).

Figure 2: Distribution of FSI values in Mumbai. Souce: Bertaud, 2011.

City like Mumbai where geographical constraints exists for horizontal expansion (three side bounded by sea and northern side limited expansion possible because of hills and reserve forest), the following strategy can be adopted for areas where future developable land is insufficient;  Re-densification of space depending upon maximum permissible FAR  Increase FAR for accommodating future population  Channelizing the excess population to satellite towns of Mumbai 5. Results and discussion 5.1 Application of REP and SAFE model for rationality of existing population distribution Based on past population trend and REP model developed above, 2011 population has been validated to find out whether the model works in real situation or not. In this, forecasting exercise (REP) t and t+θ are taken as 2001 and 2011 and based on MMR data source, dependency ratio (f) is considered as 2.51. Distance matrix has been prepared through primary survey based on road distance through public transport routes. Constant value (b=0.37) could be derived through regression analysis (by least square method) of available existing census (2011) information. Mean Standard Error calculated for the model value is 9.56 and population variation for MMR is 12.60%. For applying the SAFE model required land and other infrastructure details have been taken from ‘Transform‘ study conducted by MMRDA. Floor area required per head has been calculated from 2011 census. Population estimates (validation) and carrying capacity has 6

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been calculated with with existing FAR for Mumbai and the same are represented in Table no-2. For Mumbai with FAR 1, the maximum carrying capacity has been estimated to be 8.27 million. In Mumbai, particularly in island city the maximum FAR has been found to be 1.33 and with 1.33 FAR, the carrying capacity has been to be estimated 11.10 million. Population Allocation Census Variation with respect based on REP Model- Population- 2011 to Census population 2011 (in million) (in million)+ and REP Model (in million) 15.82 12.44 3.38

Carrying Carrying Capacity Capacity (in million) with (in million) with existing FAR increased FAR 1.5 8.27* 12.42

Table No 2– Population estimates based on REP model and carrying capacity based SAFE model for Mumbai. Note: *with FAR 1.33 carrying capacity 11.10 million. Source: + Census of India, 2011b.

Mumbai has already crossed its carrying capacity and the same requires immediate attention for policy makers. Mumbai, as per 2011 census, had 12.44 million population and accordingly suggested FAR should be 1.5. With 1.5 FAR the carrying capacity has been worked out to be 12.42 million and naturally the prime target remains as to decentralize additional population from Mumbai. 5.2 Population forecasting through REP model and sustainability through SAFE model After validation of REP model the same method has been applied for projecting population for the years 2021 and 2031. According to REP model, the population predicted for Mumbai is seen to be 17.35 million and 17.86 million for 2021 and 2031 census years, respectively. Population allocation for future decades has been based on space and floor area requirements for Mumbai. For the same, various FAR options (with increased FAR) are tested to find out the optimum FAR requirements for Mumbai city. Table-3 depicts population allocation for the census year 2021 and 2031 and accordingly spatial sustenance has been worked out with various FAR combinations (FAR 2.0 and FAR 2.5) for Mumbai. Population Allocation Population through REP Model- Allocation through 2021 REP Model-2031 (in million) (in million) 17.35 17.86

Carrying Capacity (in million) with increased FAR 2.0 16.55

Carrying Capacity (in million) with increased FAR 2.5 20.69

Table No 3– Population allocation through REP model and sustainability through SAFE model for Mumbai

It may also be noted that the increase in FAR will directly call for immediate improvement of infrastructural conditions of Mumbai. FAR increase with supporting infrastructural augmentation remains as the sole but immediate solution for already saturated Mumbai. The increase of FAR depends on and affect both the physical form of Mumbai and its functioning and present research is just one of many inputs which could justify the actual changes in policy making. Accordingly for 2031, FAR 2.0 is suggested for Mumbai. With 2.0 FAR, the carrying capacity has been worked out to be 16.55 million and naturally the prime focus remains as to decentralize 1.30 million populations from Mumbai. This calls for channelizing this excess population to satellite towns of MMR. For Mumbai, since scope of urban boundary expansion is limited, accordingly channelizing the excess population to satellite towns of MMR is the ultimate long term solution. Additional population of Mumbai can be distributed to Kalyan-Dombivali, Navi Mumbai, Vasai-Virar City and Bhiwandi-Nizampur 7

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where more space will be available after meeting their own population demand. Some industries should be reallocated outward Mumbai through stimulatory subsidies under a decentralization policy. Regional linkages through public transport (bus and rail based) need to be improved for better interaction not only for Mumbai with other satellite towns but also among satellite towns each other. 6. Conclusion To overcome urbanization challenges in Mumbai, the emphasis should be on compact sustainable urban form (shape, density and land use) that reduce over exploitation of natural resources, accelerate economic viability, assure livability, promote environmental quality and confirm social equality. Urban compaction aims to increase built-up area and residential population densities, to intensify urban economic social and cultural activities and to achieve sustainable benefits. Linkage of spatial aspects of urban development with economic, social and environmental components, in particular to achieve mixed use call for both vertical and horizontal integration. The rapid influx of urban population is the immediate cause for the over development of Mumbai. From this research, it appears that carrying capacity of Mumbai is already saturated and only 9.4 sq.km of future developable land will not be able to take care of the urban load of Mumbai in future. As FAR value of Mumbai is very low, the same can be increased from 1.0 (island city 1.33) to 1.5 to accommodate existing residential demand and to 2.0 t o accommodating future population demand. Satellite towns like Navi Mumbai, Thane, Vasai-Virar city etc. did not fulfill their expected role in sharing Mumbai’s over concentrated population and activities. It is also necessary to frame a policy aiming at decentralization of metropolitan growth, particularly from Mumbai, and allocation of surplus population to the capable satellite towns for balanced development of entire MMR. Present research provides a clue for policymakers which could justify actual changes of policy making with regards to the extent to which the urban population should be decentralized. This paper can play a pivotal role of examining the problem of metropolitan growth and developing a systematic model encompassing economic growth applicable for metropolitan cities in developing world. Acknowledgements We gratefully acknowledge valuable comments and suggestions received from Malgorzata Hanzl, Technical University of Lodz, Poland for paper revision.

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