Multi-dimensional Prosperity Index

Transcription

1 National Institute of Urban Affairs Multi-dimensional Prosperity Index Review of the first 20 Smart Cities of India

2 TITLE Multi-dimensional Prosperity Index PUBLISHER National Institute of Urban Affairs, Delhi YEAR OF PUBLISHING 2017 DISCLAIMER While effort has been made to ensure the correctness of data/information used in this report, neither the authors nor NIUA accept any legal liability for the accuracy or inferences every drawn from the material contained therein or for any consequences arising from the use of this material. No part of this report may be reproduced in any form (electronic or mechanical) without prior permission from or intimation to NIUA. THE FULL REPORT SHOULD BE REFERENCED AS FOLLOWS NIUA (2017) Multi-dimensional Prosperity Index Delhi, India. Text from this report can be quoted provided the source is acknowledged.

3 MULTI-DIMENSIONAL PROSPERITY INDEX Review of the first 20 Smart Cities of India

4 Contents 1 SMART CITIES NEW APPROACH FOR URBAN PROSPERITY INTRODUCTION 1.2 URBAN PROSPERITY 1.3 POST 2015 AGENDA 1.4 URBAN SDG AND NEW URBAN AGENDA 1.5 DATA DRIVEN GOVERNANCE 1.6 INTERNATIONAL APPROACH IN SMART CITY PLANNING 1.7 INDIA S URBANISATION 1.8 INDIAN SMART CITY MISSION 1.9 SUMMARY 2 SMART CITY MISSION FIRST PHASE CITIES INTRODUCTION 2.2 OVERVIEW OF CITIES 2.3 SOCIO-ECONOMIC SCENARIO 2.4 PROFILE, KEY ISSUES AND INTERVENTIONS UNDER SMART CITY MISSION BHUBANESWAR PUNE JAIPUR SURAT KOCHI AHMEDABAD JABALPUR VISHAKHAPATNAM SOLAPUR DAVANAGERE INDORE 2 I MULTI-DIMENSIONAL PROSPERITY INDEX

5 NDMC COIMBATORE KAKINADA BELAGAVI UDAIPUR GUWAHATI CHENNAI LUDHIANA BHOPAL 2.4 SUMMARY 3 MULTI-DIMENSIONAL PROSPERITY INDEX INTRODUCTION 3.2 METHODOLOGY 3.3 RESULTS PHYSICAL AND FINANCIAL INFRASTRUCTURE INDEX HEALTH AND EDUCATIONAL INFRASTRUCTURE INDEX COMMUNITY ASSET INDEX HOUSEHOLD AMENITIES INDEX MULTI-DIMENSIONAL PROSPERITY INDEX 3.10 SUMMARY AND POLICY IMPLICATION REFERENCES MULTI-DIMENSIONAL PROSPERITY INDEX I 3

6 Acronyms AMRUT AP AS BRGF CPI CPI DL GDP GJ GNP HRIDAY IAEG-SDGs ICT ITMS JNURM KE KR MH MoUD MP MPI NDMC NIUA NUHM NULM OD PRASAD RJ SC/ST SCP SDG SPMRM TN ToD UN URJA Atal Mission for Rejuvenation and Urban Transformation Andhra Pradesh Assam Backward Regions Grant Fund Programme City Prosperity Index Consumer price index Delhi Gross Domestic Product Gujarat Gross National Product Heritage City Development and Augmentation Yojana Inter-Agency and Expert Group on SDG Indicators Information and Communication Technology Intelligent Transport Management System Jawaharlal Nehru National Urban Renewal Mission Kerala Karnataka Maharashtra Ministry of Urban Development Madhya Pradesh Multi-Dimensional Prosperity Index New Delhi Municipal Council National Institute of Urban Affairs National Urban Health Mission National Urban Livelihood Mission Odisha Pilgrimage Rejuvenation and Spirituality Augmentation Drive Rajasthan Scheduled Caste/Scheduled Tribe Smart City Proposals Sustainable Development Goals Shyama Prasad Mukherji RURBAN Mission Tamil Nadu Transit Oriented Development United Nations Urban Jyoti Abhiyan 4 I MULTI-DIMENSIONAL PROSPERITY INDEX

7 List of Figures 1.1 The Wheel of Urban Prosperity 2.2 Literacy Level among Smart Cities 2.3 Level of Work Force Participation among Smart Cities 3.1 Physical and Financial Infrastructure Index of Smart Cities 3.2 Health and Social Infrastructure Index of Smart Cities 3.3 Community Index of Smart Cities 3.4 Household Amenity Index of Smart Cities List of Maps 2.1 Locations of First Phase Smart Cities 2.2 Literacy and Work Force Participation Rate 3.1 SC/ST, Slum Poverty in Smart Cities 3.2 Multi-Dimensional Prosperity Index List of Tables 1.1 Dimensions of City Prosperity Index 2.1 Nature of the First Phase Smart Cities 2.2 Spatial Distribution Smart Cities 2.3 Demographic Profile of First Phase Smart Cities 3.1 Physical and Financial Infrastructure 3.2 Results from the Factor Analysis of Physical and Financial Infrastructure 3.3 State of Health and Educational Infrastructure 3.4 Results for Factor Analysis of Health and Educational Infrastructure 3.5 State of Community Assets 3.6 Results from Factor Analysis of Community Assets 3.7 State of Housing in Smart Cities 3.8 State of Sanitation among Smart Cities 3.9 State of Electricity, Clean Fuel, Kitchen and Electronic Media in Smart Cities 3.10 Results from Factor Analysis of Household Amenities 3.11 Multi-dimensional Prosperity Index 3.12 Results from Factor Analysis on the Four Indices with other Variables 3.13 Results of Factor Analysis for Multi-dimensional Prosperity Index MULTI-DIMENSIONAL PROSPERITY INDEX I 5

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9 Acknowledgements I would like to take this opportunity to extend my sincere gratitude to the Asian Development Bank (ADB) for allowing me to take up this study under the South Asia Urban Knowledge Hub. I am thankful to Prof. Jagan Shah, Director, NIUA, for providing me this opportunity to use a new framework to review the preparedness of Indian Smart cities towards planned prosperity. My sincere thanks and appreciation is also extended to Dr. Debjani Ghosh, Team Leader, South Asia Urban Knowledge Hub at NIUA, and my colleagues Dr. Debolina Kundu and Ms. Paramita Dutta in facilitating my efforts. I would like to thank Prof. Arup Mitra of the Institute of Economic Growth, New Delhi, for giving me the critical intellectual inputs for computing the index as well as sharing insights from his past work on Indian cities. I am grateful to Dr. Rajnish Kumar who supported me in collecting the relevant data and in the analysis. My sincere gratitude to Prof. R.B.Bhagat, International Institute for Population Sciences, Mumbai for reviewing and advising on this report before finalisation. I would also like to acknowledge the scholars and practitioners who participated in the roundtable and other meetings organised as part of the preparation of this report. My sincere thanks to the ADB team comprising Dr. Shekhar Bonu, Ms. Vivian Castro, Mr. Keiichi Tamaki, Mr. Mingyuan Fan, Ms. Michelle Laurie and Mr. Ron Slangen for their sustained support in completing this task. Finally, I would like to acknowledge the support of the NIUA staff and design team in compiling this report. Ajith Kaliyath, PhD Environment Specialist South Asia Urban Knowledge Hub MULTI-DIMENSIONAL PROSPERITY INDEX I 7

10 Chapter 1 SMART CITIES A NEW APPROACH TO URBAN PROSPERITY 1.1 INTRODUCTION The evolution of cities is a result of powerful cultural processes, which are further enhanced by a variety of tools that elicit the imagination of people in various social strata. In recent years, global society has undergone several transformations leading to a shrinkage of space and increased intensity of real as well digital experiences. Governments and public bodies around the world have been formulating plans to respond to these changes. The Smart city mission of the government of India is one such programme envisaged to increase the competitiveness and livability of Indian cities through planned development and strategic investments. This, along with a slew of other measures taken by the government is currently creating a paradigm shift in India s urban policy and planning arena. This paper reviews existing discourses, and proposes a new index related to measuring prosperity in the context of the Smart Cities Mission (SCM) in India. India, as the home of many of the oldest surviving cities of the world such as Varanasi, Ujjain and Madurai, has experienced a palpable urban transition over the centuries. Urban development in the country has recently assumed a central place in politico-economic processes. This is pertinent at a time when all over the world, populations are moving towards urban centres. Recently, the policy discourses in the country have moved away from national and regional perspectives to focus on cities as drivers of growth agenda (Wong, 2015:3). Measuring growth and development in the past has led to the publication of various indices, which have further given impetus to a healthy competition among policy makers. The World Bank has been preparing annual World Development Indicators to monitor development goals. UNDP has been publishing a Human Development Index (HDI). Government of India has followed this with state level HDIs for Indian states. The development gap between rural and urban areas has further pushed this discourse towards creating indices that the development of cities. In 2012, UN-HABITAT came out with the City Prosperity Index. The Asian Development Bank (ADB, 2001) endeavored to compile city data by publishing Urban Indicators for Managing Cities. Various stakeholders have contributed to the thinking and refining of indices that could be used to review the progress of urban societies. These include Social Progress Index by Social Progress Imperative, Environmental Performance Index as the joint initiative of Yale and Columbia Universities, Networked Society City Index prepared by Ericsson and the Arcadis Sustainable Cities Index. The concept of Smart cities is of recent genesis. According to the Ministry of Urban Development, Government of India (2015), the Smart Cities Mission aims to provide core infrastructure and give a decent quality of life to its citizens, a clean and sustainable environment and application of smart solutions in cities. Several Indian cities are currently embarking on a mission to enhance their institutional and societal capabilities to become Smart cities in three years. This calls for an array of structural, physical and social measures taken by the city administration. Eventually, the efficacy of these measures will need to be reviewed through established methods to check how the new strategies have brought all round prosperity to each particular city. After reviewing various Indices used for measuring prosperity and related aspects of urbanisation, a new Index called Multidimensional Prosperity Index (MPI) was prepared. It is a tool to measure the overall progress of the city based on the quality of infrastructure, socio-cultural opportunities and resources available for collective well-being. The parameters and sub indices used to compute are given in detail at section 3.3. This index is based on published data for the first 20 cities identified by the Ministry of Urban Development, Government of India, for developing as Smart cities. This is expected to generate primary interest in policy makers at the national, state and city levels involved in Smart city initiatives and broader urban development programmes. Additionally, scholars and academicians teaching urban planning, urban studies and related disciplines might find interest in this index. It is also expected to invite the attention of businesses and community organizations, which are striving in their own ways to achieve prosperity in their respective cities and local spaces. This report has three chapters. The first chapter has attempted to knit key ideas from prevailing debates on prosperity, technology, urban planning, data driven urban governance, sustainable development goals, and the Smart cities approach in leading world economies and in India. The second chapter provides an overview of the first 20 cities, their key challenges, 8 I MULTI-DIMENSIONAL PROSPERITY INDEX

11 and interventions proposed to be taken up through the Smart City Mission. The third chapter explains the background for preparing the multi-dimensional prosperity index, methodology, and the results of the statistical analysis in detail. 1.2 URBAN PROSPERITY A review of relevant literature confirms that prosperity is one of the most widely followed themes by policy makers, planners, scholars and entrepreneurs. According to the Leghatum Institute, prosperity is a condition of physical and material security combined with personal and social well-being. One of the key determinants of prosperity is prevailing economic conditions which are linked to productivity. Mobility of skilled workers becomes critical for enhancing productivity (Kerr et al., 2016, 2). This was also the view of Cortright (2008) when he observed that human capital is a key determinant of urban prosperity and cities succeed because they attract talented workers and further develop the talent of their labour force. Urbanisation has been offering several avenues for advancing human creativity and entrepreneurship. The availability of a large number of people in a few locations creates competition for resources, but also results in creation of multitude of opportunities. A higher stock of human capital generates better performance of business and facilitates organisational change, expansion and adoption of new techniques, and of course has a major impact on social and political life (Prager, 2008, 2). The aim of the Smart Cities Mission launched by Government of India in 2014 is to drive economic growth and improve the quality of life by enabling local area development and harnessing technology, especially technology that leads to smart outcomes (MoUD, 2017). A review of evidence from various urban regions around the world has confirmed that innovative FIGURE 1.1 THE WHEEL OF URBAN PROSPERITY Source: UN-Habitat (2012) MULTI-DIMENSIONAL PROSPERITY INDEX I 9

12 TABLE 1.1: DIMENSIONS OF CITY PROSPERITY INDEX Level 1 Level 2 No of Parameters Productivity Productivity 8 Employment Agglomeration Infrastructure Housing Infrastructure 19 Information Communication Technology Mobility Quality of Life Safety and Security 13 Public Space Equity Social Inclusion 7 Gender Inclusion Environment Air Quality 8 Waste Management cities have managed to invite and retain the best talent. Cities aiming to achieve sustainable prosperity by harnessing new technology need to focus on improving livability and inclusion by prioritising investments in social infrastructure. The observation of Feldman et al. (2014, 1), while economic growth is simply an increase in aggregate output, economic development is concerned with quality improvements, the introduction of new goods and services, risk mitigation and the dynamics of innovation and entrepreneurship, confirms that the choice of the pathway needs to be visualised from the very early stages of planning. It is certain that rapidly urbanising regions competing for a fair share of the global capital and technology are keen to position their economy on a higher-quality growth trajectory (Feldman et al., 2014:7). Glaeser (2011) observed that ideas are the ultimate creator of wealth and cities that have adapted to the age of information will succeed. The factors that contribute to the success of urban economies have long been of interest to policy makers and scholars. However, the challenge for policy is to create the environment which harnesses the potential of cities as places to work and live (Venables, 2015, 1) and for the policy makers to discern those appropriate for the particular situation confronted by their city (Cortright, 2008). Energy The technological progress that the world is witnessing at a rapid pace is Governance Institutional Capacity 10 now accessible to all areas, thanks to communication technology. However, Governance of Urbanisation its implementation and utilisation at the fullest level is possible only when the Municipal Finance support structure in terms of highly skilled manpower, adequate infrastructure Total 65 facilities and overall governance exists significantly (Mitra et al., 2002 and Source: UN Habitat (2012). Mitra, 2014, 25). At a time when cities around the world are competing for attracting talent, capital and technology to enhance their quality of life and economic buoyancy, the expectations from policy makers at all levels are extremely high whereas their jobs have become more complex. It is essential that the multiple layers of information are made visible and comprehensive for them to take correct decisions in a time-bound manner. This calls for greater economic intelligence (Winden and Carvalho, 2015, 46) enabled by spatially disaggregated data. A simple and relatively cheap way to obtain economic intelligence is to collect available statistics and present them in a systematic way. An increasing number of cities and regions have set up economic dashboards consisting of key indicators that show how the economy is doing through time and in comparison with other cities (Winden and Carvalho, 2015, 46). The focus on prosperity as a key indicator of economic growth at the city level was initiated through UN-Habitat s City Prosperity Index (CPI) which was launched in September According to this report, the prevailing views on prosperity did not consider dimensions other than economic growth and thereby could not offer a comprehensive framework that could facilitate sustained well-being of individuals and societies. The vitality and transformative dynamics of cities became a very significant development agenda and thus their importance as the world moves into the urban age (UN-Habitat, 2012, v) for a new type of city that is a good, people-centred, shedding off the inefficient, unsustainable forms of functionalities of the city of the previous century (UN-Habitat, 2012, iv). These five dimensions of prosperity (see Fig. 1.1) are regarded as the spokes of the wheel of prosperity, each of which is measured by a number of indicators or sub-indices. The detailed list of the indices and number of parameters in each category used for constructing the CPI is provided in Table 1.1. According to Venables (2015, 7), urbanisation will occur in the coming decades, regardless of the policy. This makes it all the more important that it is done right, harnessing the potential advantages of cities to generate economic growth. It will require smart policy that is joined-up across all parts of the urban agenda. UNDP s Sustainable Urbanisation Strategy (2016) notes that the availability of a wide range of resources and opportunities for exchange of ideas in urban centres ensures a platform for germination of new possibilities and nurtures innovation that can equip institutions to respond to ever growing developmental challenges effectively. Recent years have witnessed several international commitments to address disaster risk, climate change, and the Post 2015 Agenda leading to Sustainable Development Goals (SDG) and the New Urban Agenda. All these have called for several collective actions especially focusing on cities. They have further emphasised the need to constantly monitor the impact of these actions through agreed means. One of the detailed deliberations that reviewed the institutional mechanism to monitor the impact of new policies and investments was the Post 2015 Agenda. It had several layers of consultations that shaped a robust approach to deal with the developmental challenges of the new age society. 10 I MULTI-DIMENSIONAL PROSPERITY INDEX

13 1.3 POST 2015 AGENDA The Post 2015 Agenda (2030 Agenda as it is called now) was formally adopted by the United Nations General Assembly on 25 September, This has enabled convergence of Sustainable Development Goals (SDGs) with planning for growth and prosperity. Smart cities is one approach, which offers potential to shape urban economies into prosperous, yet inclusive ones. However, one key factor that has emerged from related discourses is the need for monitoring the effectiveness of the policies and the changes with the help of local level spatial data. The High Level Panel of Eminent Persons on the Post 2015 Agenda called for a data revolution. The UN Secretary General s Synthesis Report on the Post 2015 Agenda emphasised the need for an evidence-based course for realising sustainable development and highlighted the need for data literacy. The Synthesis Report has further detailed the requirements to achieve data literacy as: 1) enhanced statistical capacities; 2) rigorous indicators; 3) reliable and timely data sets; 4) new and non-traditional data sources; and 5) broader and systematic disaggregation that can uncover inequities. In order to achieve results from the actions that will follow implementation of SDGs, both the reports have called for a range of engagements. These are broadly: 1) Global Partnership for Sustainable Development Data; 2) World Fora on Sustainable Development Data; 3) Network of Data Innovation Networks; and 4) SDG Data Lab. The Independent Expert Advisory Group has emphasised the need for matching the data cycle and decision cycle. The New Urban Agenda adopted by HABITAT-III (October 2016) in Quito shares a vision of cities for all, referring to the equal use and enjoyment of cities and human settlements, seeking to promote inclusivity and ensure that all inhabitants, of present and future generations, without discrimination of any kind, are able to inhabit and produce just, safe, healthy, accessible, affordable, resilient, and sustainable cities and human settlements, to foster prosperity and quality of life for all. This has invigorated the efforts by leading stakeholders to develop and institutionalise credible means for monitoring the impact of their respective programmes. 1.4 URBAN SUSTAINABLE DEVELOPMENT GOALS AND THE NEW URBAN AGENDA The recently announced United Nations Global Goals or Sustainable Development Goals (SDGs) provide a framework to improve planning, financing and governance mechanisms so that they can contribute to sustainable prosperity. The Urban SDG (Goal.11) along with parameters under other goals that have an urban component is certain to impact the planning and governance of cities around the world. SDG.11 focuses on making cities inclusive, safe, resilient and sustainable. It has 10 targets and 15 indicators. These include issues related to: 1) affordable housing and basic services; 2) access to public transport; 3) inclusive and participatory urban planning; 4) safeguard cultural and natural heritage; 5) death and direct economic losses due to disasters and risks; 6) environmental impact of cities including air quality and waste management; 7) access to green and public spaces. The means of implementation of Goal.11 has addressed urban-rural linkages within the context of regional dynamics along with resilience and resource efficiency. These formulations have generated the need for a new approach to the generation of reliable data at disaggregate level and establishment of enabling protocols. The Cape Town Global Action Plan for Sustainable Development Data adopted on 15 January, 2017 has called for action on several issues including 1) integrating geospatial data into statistical production programmes at all levels, and 2) strengthening and expanding data on all groups of population to ensure that no one is left behind. Undeniably, the successful implementation of the New Urban Agenda will depend largely on the skills and capacity of urban stakeholders to understand urbanization, transform propositions into actions, develop and make use of tools and knowledge to guide cities towards a sustainable future (UN-HABITAT, 2015). The SDGs, with their emphasis on indicators and data collection, offer local governments the opportunity to strengthen local data systems (SDSN & GIZ, 2016, 58). The New Urban Agenda, which has embedded key ideas from SDGs has made a commitment to strengthen the capabilities for data generation and use for public policies. It states that we will support the role and enhanced capacity of national, sub-national, and local governments in data collection, mapping, analysis, and dissemination, as well as in promoting evidence-based governance, building on a shared knowledge base using both globally comparable as well as locally generated data, including through census, household surveys, population registers, community-based monitoring process and other relevant sources, disaggregated by income, sex, age, race, ethnicity, migration status, disability, geographic location and other characteristics relevant in national, sub-national, and local contexts. The review of the progress towards SDGs will be made based on the global indicator framework developed by the Inter-Agency and Expert Group on SDG Indicators (IAEG- SDGs) which was agreed upon at the session of UN Statistical Commission held in March Through the Ministry of Statistics and Programme Implementation, India has been participating in these deliberations and has initiated steps to orient the national planning (NITI Aayog) and statistical system to respond effectively to the global commitments. MULTI-DIMENSIONAL PROSPERITY INDEX I 11

14 1.5 DATA DRIVEN GOVERNANCE Since the early 20th century, social and economic indicators, such as unemployment rate, gross domestic product (GDP), gross national product (GNP), balance of payments, inflation, and the consumer price index (CPI) have been used by governments to assess how a nation is performing (Kitchin, 2015, 6). This paved the way for monitoring by various national and international agencies for their respective programmes, eventually leading to formulation of more sophisticated tools such as indices. The key challenges identified by the UN Secretary General s Independent Expert Advisory Group on data revolution for sustainable development are related to the following areas. 1. Availability of good quality place-based data or spatially and temporally disaggregated data. 2. Comparability and standardisation of data generated through different methods. 3. Aggregating data from various geographical regions or spatial frameworks. In order to eliminate the barriers for shaping a vibrant global data ecosystem, the Expert Advisory Group has recommended three strategies which are: 1) data for everyone, 2) data for now, and 3) data for the future. The advent of Smart cities along with Internet of Things offer many new opportunities to source real time information that can significantly improve livability and sustainability of urban systems. The 100 Data Innovations released by Centre for Data Innovation (2014) has documented path-breaking urban applications triggered by availability of big data in cities around the world. These include management of traffic jams in Dublin, use of acoustic data to locate gunshots in Washington, DC, automatic detection of polluting vehicles in Madrid, improving fire safety in New York, and use of chemical sensors in sewers to monitor public health in Europe. The Birmingham Smart City Commission formulated 10 guiding principles that could institutionalise agile planning processes through integration of technology. Reflecting on the penetration of embedded sensors in society and its impact, Pentland (2012, 1) stated that the new lens that lets us examine society in such fine-grain details is known as big data. According to Keller et al. (2012, 4), big data is bringing a revolution in science and technology, but will bring a revolution still more profound when it interacts with people s decisions resulting in re-ordering of our cities and our lives. Big data is the aggregate of several geo-coded human operations including smart card transactions, information captured through networked cameras, messaging through social networking sites, information exchanged through smart phones, and use of search engines on internet. It is characterised by volume, velocity and variety. 1.6 INTERNATIONAL APPROACH IN SMART CITY PLANNING In recent years, a few of the leading economies around the world have been promoting smart cities as a new approach to create economic vibrancy through technology based innovation. Since the advantages of digital technologies are widely appreciated, this is seen as a means to achieve prosperity. Smart cities promote advanced planning with the help of technology and innovation. The Smart Cities Council defines a Smart city as one that uses information and communication technology (ICT) to enhance its livability, workability and sustainability. They use technological advancement to address the complex realities of urban systems and create effective interfaces for authorities as well as citizens. For national governments, the Smart city is attractive because it represents an opportunity to improve its towns and cities and to access a large global market, estimated to be in the order of 1.3 trillion and growing by 17% each year (ARUP,2016,6). ARUP s research has indicated that there are five main requirements to enable Smart city activity in a country. They are: 1) municipal capacity; 2) investment certainty; 3) skills and research; 4) public acceptance and digital literacy; 5) sharing of data. The RTPI (2017,13) defines the Smart city approach as an urban development vision to integrate information and communication technology (ICT) and Internet of things technology in a secure way to manage a city s assets. This is a new paradigm in urban planning which evolved from the need to have capability to oversee and address the ever increasing complexities in cities. The constant struggle of urban administrations to keep pace with the needs of citizens and at the same time to optimise expenditures invoked the need for technologically enabled governance. A Smart city initiative must first review the nature of the urban environment in the recipient city which can often be found in the very essence of the place, its urban culture, national traditions, heritage and political landscape as well as any economic drivers, aspirations, technological capabilities and the dynamics of society itself (Calzada and McAdam, 2016, 3). The development of high quality, but low-cost sensors along with digital technologies created a new wave in developed and emerging economies. The growth of the middle class along with increased mobility and inter-cultural exchanges of a large number of professionals further accelerated the permeation of technology enabled living. Governments around the world responded to this trend by 12 I MULTI-DIMENSIONAL PROSPERITY INDEX

15 engaging with technology companies as well as entrepreneurs. Emerging economies such as India, which are propelled by the aspirations of a large middle class, considered this as a major opportunity to address their urban challenges. According to Calzada and McAdam (2016, 3) smartness must first start with the city not the smart, and the key objective of smartness must be to foster prosperity. The essence of the idea revolves around the need to coordinate and integrate technologies that have hitherto been developed separately from one another but have clear synergies in their operation and need to be coupled so that many new opportunities which will improve the quality of life can be realised (Batty, 2012, 483). 1.7 INDIA S URBANISATION With per cent urban population (Census 2011), India is well poised to transform into an urban society. The recent estimates by the World Bank (2016,51) using Agglomeration Index has suggested that the extent of India s urbanisation is about 55.3 per cent. Irrespective of these two estimates, the trends of urban growth at sub national level offers evidence of more rapid growth. States such as Goa, Mizoram, Tamil Nadu, Kerala and Maharashtra have more than 45 per cent urban population. Kerala, Uttar Pradesh and Maharashtra together have 20 of the 53 million plus cities. There are 81 districts in India which have more than 50 per cent urban population. Among these, 47 belong to 6 states such as Tamil Nadu (13), Kerala (6), Maharashtra (5), Punjab (5), Uttar Pradesh (5), Madhya Pradesh (4) and Delhi (9). However, the data informs us that the urban growth in the country was not contributing to inclusive prosperity as the average work participation rate (WPR) across metropolitan cities increased very marginally, while it declined in other Class I cities (Kundu, 2017, 14). This scenario juxtaposes the position of Liu (2016, 9) as growth, prosperity and inclusion are complementary not contradictory goals for meaningful economic development. Thus there is a need for policy makers in India to embark upon a series of initiatives that can accelerate the economy as the work to stay on a high growth trajectory does not end but is iterative and designed to bring about continuous improvement, addressing market failures and increasing the economic participation of more people and assets (Liu, 2016, 9). Since 2004, the Government of India started giving prominence to planned investments in cities. This shift in the focus from rural to urban based economic development was enabled through the Jawaharlal Nehru National Urban Renewal Mission (JNNURM). Under this, 63 cities were covered for a period of 10 years. The mission uncovered several new challenges which did not capture the imagination of policy makers until then. One key response which emanated from the JNNURM was to bring more cities into the next phase of planned urbanisation. Thus, with the change of government in 2014, it was decided to address urban challenges through specialised missions. Currently, there are 11 national missions in India focusing on urban areas. They are Smart Cities, Atal Mission for Rejuvenation and Urban Transformation (AMRUT), Swachh Bharat-Urban, Heritage City Development and Augmentation Yojana (HRIDAY), Pilgrimage Rejuvenation and Spirituality Augmentation Drive (PRASAD), National Urban Health Mission (NUHM), National Urban Livelihood Mission (NULM), Prime Minister s Housing for All Mission, Solar Cities, Urban Jyoti Abhiyan (URJA), and Shyama Prasad Mukherji RURBAN Mission (SPMRM). 1.8 INDIAN SMART CITY MISSION Since the most recent urban growth strategy adopted by leading cities around the world was ICT enabled planning and governance, Government of India also decided to embed this into its newly calibrated urban strategy. The presence and rapidly growing number of internet users (34.8%) and smart phone users (17%) in India, along with the growing presence of technology companies and a large pool of educated youth, prompted the Government of India to adopt Smart cities as the game changer scheme. In 2014, the Government of India announced its intention to develop 100 cities as Smart cities. This brought India into a small group of countries which are harnessing the power of digital connectivity to improve their urban systems. Since the Smart Cities Mission has attracted maximum attention both nationally and internationally, the government has made efforts to ensure it facilitates a positive turn in India s urban future. Hence, the selection of the cities was done through a competitive process and in multiple phases. Until now, 60 cities have been selected through three phases of 20, 13 and 27 cities. The Smart Cities Mission aims to transform India s urban landscape by enabling local innovation ecosystems. These newly wired systems can constantly support the knowledge, skills, products and service needs of the cities in their attempt to provide better services to citizens. Reviewing the key urban challenges in the country and the factors that have made Indian cities underperforming systems, the Smart City Mission has suggested the following two types of interventions in each city. Proposals from aspiring cities have been elicited to undertake projects for strategic investments therein. MULTI-DIMENSIONAL PROSPERITY INDEX I 13

16 Area Based Developments: This is to identify one of the most promising infrastructure projects within a small, contiguous area of the city. It follows three tracks such as Retrofitting, Redevelopment and Greenfield development. Retrofitting means to develop an area of upto 500 acres within the existing urban fabric so that it can generate investments apart from testing ideas, and thereby stimulate a positive engagement within the community. Redevelopment projects call for replacement of an existing infrastructure with a newly developed set of buildings and street elements. The maximum area that can be covered under this is upto 50 acres. It will result in mixed land use, higher density and higher FSI in these newly developed areas. The third type of development that will be supported by the mission is Greenfield development. This will cater to planned development in an area within the city limits that is either controlled by the municipal authority or the local urban development authority. The maximum area that can be covered under this is 250 acres. These projects are ideal for cities which are experiencing constant pressure on their built-up areas (retail as well as housing) as more and more people move into them. Pan-city Developments: These are the types of projects in which a city-wide upgradation of selected infrastructure will be undertaken. They include new mobility infrastructures including integrated public transport, installation of sensors and smart meters to constantly gauge the city s dynamics and invisible trends. Through the Smart cities, Government of India has attempted to bring in some fundamental transformation in the urban planning process. This includes promoting mixed land use in area-based developments, widening access to the formal housing market to all, encouraging walkability in communities through a series of connected measures, recognising the central role of public spaces and parks in building vibrant communities, ensuring good quality public transport and last mile connectivity, promoting local culture and heritage to enhance the cultural connect within the city, and advancing the established identity of the city. These are in addition to a range of initiatives to embed high-end technologies and smart solutions that enable the key stakeholders to respond to the human-environment dynamics in these cities. The Indian Smart Cities Mission began by identifying potential cities by each state through an intra-state competition. The cities selected by the State-level High Powered Committee were recommended to the Ministry of Urban Development, Government of India. This was followed by a City Challenge round. At this stage, each of the 100 potential cities was to prepare its Smart City Proposal (SCP) explaining its approach to the mission such as pan-city or area based development. These proposals were evaluated by a committee comprising national and international experts. At the end of this process, the first set of 20 cities was selected. In the order of the ranking, these were: Bhubaneswar, Pune, Jaipur, Surat, Kochi, Ahmedabad, Jabalpur, Visakhapatnam, Solapur, Davanagere, Indore, New Delhi Municipal Council, Coimbatore, Kakinada, Belagavi, Udaipur, Guwahati, Chennai, Ludhiana and Bhopal. 1.9 SUMMARY In the age of urban diplomacy, cities around the world are constantly striving to increase their global visibility by enhancing their competitiveness. There are several ideas and pathways being tried by various levels of government to increase prosperity and attract new opportunities. The recent shift in the social processes and discourses enabled by the penetration of digital technology has offered powerful opportunities. Contemporary society provides a combination of unprecedented possibilities, such as generation of real time spatially enabled digital data, easy ways for sharing, development of analytical tools for handling large data sets, and large pools of technical and professionally qualified people with increased access to funding. Hence, it is essential that cities establish procedures to converge these for harnessing prosperity for all. The available evidence from leading Smart cities around the world and from India shows that the ongoing interventions will produce more legible urban forms and vibrant communities. 14 I MULTI-DIMENSIONAL PROSPERITY INDEX

17 Chapter 2 SMART CITY MISSION FIRST PHASE CITIES 2.1 INTRODUCTION This chapter is aimed at providing an overview of the process of selection of the first 20 Smart cities and the unique characteristics of these selected cities. Their detailed profiles have been written to inform readers about the current stage of urban development in each of them and their associated challenges. The promise of Smart cities is that they institutionalise procedures to harness high-end technology for increasing the city s governance effectiveness while propelling new economic and innovation cultures. These could be through policy directives or through entrepreneurial measures. Each of the selected cities done a detailed documentation on current challenges and planned interventions through Smart Mission. Since the discourses on urban prosperity in these cities and among related stakeholders are expected to stay alive, it is important that the current strength, dynamism and resilience of these cities is studied and integrated into the Multi-dimensional Prosperity Index (MPI). The MPI scores gained by each of them reflect their levels of prosperity. However, these scores can be effectively understood only if the key characteristics of these cities are part of the document. The barriers to growth need to be understood before initiating various interventions. Since the proposals formulated in the Smart cities have also been shared with citizens through public consultations, the engagement has begun. The investments coming through the Smart city projects are expected to invoke greater public participation and positive responses. These would help in mobilising civil society s support in managing the new assets and infrastructure, identifying new projects and ensuring the city is able to build a cohesive strategy towards its own prosperity. It was noted earlier that the key determinant of MPI is human capital. This can also enable future enquiries to be more informed and effective as the baseline is documented. 2.2 OVERVIEW OF CITIES The type, function and size of the first phase Smart cities can be understood from Table 2.1. The majority of the first phase Smart cities have been classified as business and industrial centres. There were 11 cities identified under this category. Capital cities occupy second position with 5 capitals (New Delhi, Chennai, Jaipur, Bhopal and Bhubaneswar). Three cities, namely Kochi, Vishakhapatnam and Kakinada have been classified as port cities. Udaipur is the only city under the cultural and tourism city category. New Delhi and Chennai have been notified as Tier 1 cities whereas Davanagere, Udaipur and Kakinada come under Tier 3 cities. It can be said that the first phase of the Smart Cities Mission is more focused towards Tier II business and industrial cities. Though there is a growing call for data driven urban governance and increasing acceptance of the benefit of technology in breaking down the urban complexities, only a few cities have had the opportunity to benefit from this under the first phase City Type Business and Industrial Centre Capital New Delhi (DL), Chennai (TN) Tier I II III Total Ludhiana (PB), Coimbatore (TN), Ahmedabad (GJ), Pune (MH), Surat (GJ) Indore (MP), Belgaum (KR), Guwahati (AS), Solapur (MH), Jabalpur (MP) Jaipur (RJ), Bhopal (MP), Bhubaneswar (OD) Davanagere (KR) 11 Cultural & Tourism Udaipur (RJ) 1 Port Kochi (KE), Vishakhapatnam (AP) Kakinada (AP) 3 Total Source: India Smart City Profile, MoUD TABLE 2.1: NATURE OF THE FIRST PHASE SMART CITIES 5 Note: AP=Andhra Pradesh, AS=Assam, DL=Delhi, GJ=Gujarat, KE=Kerala, KR=Karnataka, MH=Maharashtra, MP=Madhya Pradesh, OD= Odisha, RJ=Rajasthan, TN= Tamil Nadu MULTI-DIMENSIONAL PROSPERITY INDEX I 15

18 MAP 2.1: LOCATIONS OF FIRST PHASE SMART CITIES TABLE 2.2: SPATIAL DISTRIBUTION OF SMART CITIES State/UTs Smart City Number Assam Guwahati 1 Punjab Ludhiana 1 NCT of Delhi NDMC 1 Rajasthan Jaipur, Udaipur 2 MP Bhopal, Indore, Jabalpur 3 Odisha Bhubaneswar 1 Gujarat Ahmedabad, Surat 2 Maharashtra Pune, Solapur 2 Andhra Pradesh Kakinada, Vishakhapatnam 2 Karnataka Belgaum, Davanagere 2 Tamil Nadu Chennai, Coimbatore 2 Kerala Kochi 1 Total 20 Source: Smart City Profile, MoUD of the Smart Cities Mission. Madhya Pradesh has had the maximum share with 3 cities i.e. Bhopal, Indore and Jabalpur. The states of Andhra Pradesh, Gujarat, Karnataka, Maharashtra, Rajasthan and Tamil Nadu had 2 cities each. The most populous states like Uttar Source: Author Pradesh, Bihar and West Bengal could not register their presence in the first phase. The spatial distribution of 20 Smart cities is depicted in Map 2.1. Out of 29 states and 7 Union Territories only 11 states and UTs were able to avail of this opportunity in the first phase. The population size and density of these cities are considered as major determinants of their level of development. Figure 2.1 depicts population size and density of the first phase mission cities. Density indicates the population pressure on the land resource. As already known, the Smart City Mission considers the urban local body (ULB) as its local implementation partner to promote healthy competition among aspiring cities. Five ULBs have reported a relatively high population of more than three million, namely Ahmedabad, Chennai, Surat, Pune and Jaipur. Contrary to this, New Delhi and Kochi have FIGURE 2.1: POPULATION SIZE AND POPULATION DENSITY Source: Census 2011 (Note: Primary Y axis: Population in numbers, Secondary Y axis: density in persons per square kilometers) 16 I MULTI-DIMENSIONAL PROSPERITY INDEX

19 reported relatively lower populations which in fact represent their respective urban agglomerations. With the exceptions of Visakahapatnam, Guwahati and Belagavi, all Smart cities in the first phase have a population density of higher than 5,000 person per square kilometre. Chennai has reported a very high density (26,553) followed by Surat (13,304). Ahmedabad, Pune, Indore and Ludhiana have also experienced a population density of more than 10,000 person per square kilometre. The household size in these cities vary from 3.7 to 5.1 persons per household. Coimbatore (3.7), Kakinada (3.8), Kochi (3.85), GVMC (3.9) and Surat 3.89) have reported relatively low household sizes among these cities. Jaipur has the highest household size of 5.1. Indore, Jabalpur and Solapur have also reported more than 4.8 persons per household. 2.3 SOCIO-ECONOMIC CONDITIONS Policies and programmes are envisaged based on the established needs of respective societies. The social dynamics of cities can be understood by reviewing some key parameters. Gender inclusion is one such parameter. Sex ratio and child sex ratio (0 6 years) have been considered as parameters of gender equality. In this analysis, Surat emerged as the city with the lowest sex ratio followed by New Delhi and Ludhiana. Bhubaneswar and Ludhiana too have a very low sex ratio with respect to other Smart cities of Phase I. Smart cities of the southern states have shown a higher sex ratio indicating a positive inclination towards gender equality. The port cities of Kakinada and Kochi have more than 1030 females per 1000 males. Belagavi, Chennai, Coimbatore, Davanagere, Visakhapatnam and Solapur also seem to have a higher sex ratio compared to many other cities. The child sex ratio also followed the general trend of the adult sex ratio. Cities in the south of India, namely Coimbatore, Davanagere, Kakinada and Kochi reported a high child sex ratio of more than 950 females per 1000 males. With the exception of Indore and Surat, north Indian cities viz. Jaipur, Ludhiana and New Delhi have registered a sex ratio of less than 900 females per 1000 males. The quality of human capital and skills is not uniform across the urban centres. In general, the southern states have a better human capital level than the rest of the India. Figure 2.2 gives the level of adult and female literacy among the cities. Kakinada, Solapur, Jaipur, Bhopal and Vishakhapatnam have reported a relatively lower literacy rate where adult literacy is around 83 per cent or less. Kochi reported a literacy rate of 97 per cent. Chennai, Coimbatore, Guwahati and Bhubaneswar also reported a literacy rate higher than 90 per cent. In terms of female literacy, there exists a similar pattern with few exceptions. The female literacy rate of Kochi is at par with the male rate i.e. 97 per cent. Less than 80 per cent female literacy rates have been observed in Kakinada, Vishakhapatnam, Jaipur, Bhopal, Solapur and Surat. The workforce participation rate is used to measure an economically active population. Figure 2.3 gives the state of workforce participation for TABLE 2.3: DEMOGRAPHIC PROFILE OF FIRST PHASE SMART CITIES Name hh_size sex_r child_ sex per_06 p_scst Lit f_flit Wpr fwpr Ahmeda-bad Belgaum Bhopal Bhubanes-war Chennai Coimba-tore Davana-gere Guwahati GVMC Indore Jabalpur Jaipur Kakinada Kochi Ludhiana NDMC Pune Solapur Surat Udaipur Source: Calculated from Census of India, 2011 Note: hh_size= Household Size, sex_r= Sex ratio, child sex= Child sex ratio (0-6 Year), per_06= population of 0-6 years old child, p_sc_st= Population of SC and ST, Lit= Adult literacy rate, f_lit= Female literacy rate, wpr= workforce participation rate fwpr=female workforce participation rate MULTI-DIMENSIONAL PROSPERITY INDEX I 17

20 FIGURE 2.2: LITERACY LEVEL AMONG SMART CITIES Source: Calculated from Census of India, 2011 (Note: Y axis: In Percent to Total Population) FIGURE 2.3: LEVEL OF WORK FORCE PARTICIPATION RATE AMONG SMART CITIES Source: Calculated from Census of India, 2011 (Note: Y axis: In Percent to Total Population) the entire population. Some of the cities like Surat, New Delhi and Surat show brighter prospects with the highest workforce participation rates. Udaipur, Kakinada and Jaipur reported the lowest workforce participation rates. Solapur reported the best female workforce participation rate followed by Pune and Kochi. Jaipur, Ahmedabad and Ludhiana emerged as the poor performers in relation to women s participation in employment PROFILES, KEY ISSUES AND INTERVENTIONS UNDER THE SMART CITY MISSION The following section provides a detailed account of the cities that have been taken under the first phase of the Smart Cities Mission. Though varied in several aspects, these cities are now driven by the common goals of the SCM. The details of their key characteristics and developmental challenges have been reviewed bringing out their similarities and differences in the 18 I MULTI-DIMENSIONAL PROSPERITY INDEX

21 conflicts and barriers to development that each of them faces. This section informs about the current thinking of these cities and their plans to improve competitiveness through strategic investments. As noted in the earlier sections, the SCM is not going to transform the entire urban area in each of these cities. The key conflict areas and opportunities have been reviewed and it was decided to rebuild or upgrade areas that can offer higher social and economic returns. The types of projects identified are based on the types of local conflicts. Even when the cities have been asked to restrict their plans within Pan City or Area Based Development, they have come up with a wide range of innovative projects. These are all going to facilitate new investments resulting in short term as well as long term job creation, retail opportunities, technology adoption, new social contracts and improved quality of place enabling overall prosperity. This review is based on the Smart city profiles prepared by individual cities for submission to the Ministry of Urban Development during the Smart city challenge. Since the last Census (2011), several new developments have taken place in the cities contributing to new dynamics and conflicts. The following sections capture the key issues from the cities. MAP: 2.2: LITERACY AND WORKFORCE PARTICIPATION IN THE SMART CITIES Source: Author BHUBANESWAR Bhubaneswar secured top position in the first round of the Smart city challenge. It is the capital of Odisha. It is also known as a temple city. The city is one of the growing hubs for education, health and information technology. Since Bhubaneswar is located near Puri and Konark (UNESCO World Heritage Site) as well as Chilika Lake, it is also a destination for domestic and international tourists. As per the Census 2011, Bhubaneswar had a total population of 0.84 million and an average population density of persons per square kilometre. The literacy rate in the city was per cent per cent of the population lived in slums per cent of the households had access to tap water (from treated sources) within their premises. Households with access to electricity constituted per cent and those having toilets within their premises was per cent. a. Identified Challenges Multi-hazard Risks Bhubaneswar is vulnerable to cyclonic storms, earthquakes, flooding and has heat island. Institutional Capacities Existing institutional capacities within public agencies are inadequate to charter a high growth strategy. This is compounded due to the barriers in hiring external experts for supporting new initiatives taken up by public agencies. Migration The disparity between the city and its rural hinterland is expected to result in more migration creating increasing pressure on the urban infrastructure especially on housing. Environmentally Sensitive Zones The city is enveloped by environmentally sensitive areas which have already started experiencing development pressure. This is a growing concern. Private Vehicle Ownership There was a 23 per cent increase in the private vehicle ownership between 2014 and This puts severe pressure on the physical infrastructure and challenges all efforts towards building good quality public mobility systems. b. Smart City Strategies Build capacities of the public institutions. Promote planning based on the principles of Transit oriented Development (ToD) Respond to the regional dynamics by focusing on strategic nodes along the transit priority corridors. Achieve self-sufficiency by efficient management of revenue streams supplemented by seeking diversified financing mechanisms and responsible monetisation of assets. Ensure resilience of public infrastructure and reduce risks by increased investments in disaster risk reduction strategies. Enhance access to universal and equitable public health and welfare programmes. Strategic positioning as a regional growth centre through improving the Ease of Doing Business. MULTI-DIMENSIONAL PROSPERITY INDEX I 19

22 2.4.2 PUNE Pune has an area of 700 square kilometres. It is the second largest city in the state of Maharashtra. As per Census 2011 it had a population of 3.12 million (3,124,458) and population density of 11,304 persons/square kilometre. Literacy rate was per cent per cent of the population lived in slums per cent of the households had access to tap water within the premises per cent households had access to electricity and per cent had toilet facilities within premises. a. Identified Challenges Needs an efficient mobility infrastructure Since 22 per cent of the population now lives in slums, there is a serious need to address the housing challenge. It requires a hierarchy of open spaces to improve civic engagement. It requires street beautification including water front development. Effective implementation of the identified 36 local area development ideas requires sustained engagement by the public system with higher levels of competencies. b. Smart City Strategies Improve mobility through a combination of efficient use of current infrastructure and harnessing strategic investments for newly identified projects. Provide water supply to all through minimisation of waste and by placing a modern monitoring mechanism. Upgrade slums with the active participation of civil society institutions. Enhance health care facilities in the city through effective deployment of technology JAIPUR Jaipur is the capital of Rajasthan. It is also the largest city in the state. The city owes its planned origin to Maharaja Jai Singh II in It commands an area of square kilometres. In 2011, Jaipur had a population of 3.04 million (3,046,163) with a density of 6285 persons per square kilometre. According to Census 2011, the literacy rate of Jaipur was per cent per cent of the population of the city lived in slum areas per cent of households had access to tap water (from a treated source) within premises whereas per cent had access to electricity per cent of households had a wastewater outlet connected to drainage. The percentage of households with toilet facilities within premises was a. Identified Challenges Inadequate and disconnected transport infrastructure. Erratic electricity supply. Inadequate street lights and lighting tourist infrastructure. Water shortage Sanitation and solid waste issues in the city. b. Smart City Strategies An Implement an integrated mobility infrastructure with balanced share of non-motorised transport, pedestrian infrastructure, bike sharing systems, barrier free public spaces, electric vehicles and smart parking and signage. Provide uninterrupted electricity supply. 10 per cent of the energy in the Smart city area to be sourced from solar installations. Modernise the lighting infrastructure in streets as well as monuments using energy efficient sensors. Install adequate infrastructure in public building for wastewater recycling and rainwater harvesting. Address sanitation and solid waste issues in the city. Embed sustainability principles into the city s planning and governance SURAT Surat is located in Gujarat. It is the second largest city in the state. The city has won national and international recognition. This includes being rated the fourth fastest developing city in the world by City Mayors Foundation in 2011 and first Smart IT city in India by Microsoft in Surat covers an area of square kilometres. As per Census 2011 it had a population of 4.46 million (4,467,797) with a density of 13,304 persons per square kilometre per cent of the total population lived in slum areas. The city had a literacy rate of More than 80 per cent of households had access to tap water (from a 20 I MULTI-DIMENSIONAL PROSPERITY INDEX

23 treated source) within premises per cent of the households had access to electricity whereas per cent had toilet facilities within premises. a. Challenges A weak public transport causing frequent traffic congestions. Flood prone urban areas. Bad air quality resulting from intense industrial activities. Local economy heavily dependent on diamond and textile sectors. Large share of migrant population. Inadequately developed high skills market. Absence of major corporate organisations in the city. b. Smart City Strategies Strive for a global city status. Effectively manage human and material flows through improved mobility infrastructure. Establish a comprehensive flood management system. Enhance the energy grid by harnessing alternate sources such as solar, biogas and waste to energy. Use retrofitting to create a smart infrastructure KOCHI Kochi is a coastal city and is located in the state of Kerala. The city is set over square kilometres and had a population of 0.6 million (602,046) residents (Census 2011). The population density is was 5620 persons per square kilometres. It was the largest urban agglomeration in the state of Kerala. Kochi had a literacy rate of per cent and a slum population of 0.86 per cent. The percentage of households having tap water and toilet facilities was per cent and per cent respectively. Electricity was accessed by per cent of households per cent of the households had a waste water outlet connected to drainage. a. Identified Challenges Inadequate transport infrastructure resulting in traffic bottlenecks and congestion. Inadequate parking facilities compounding the congestion issues. Only certain areas within the city covered by sewerage network. Water bodies and canals are polluted. Shortage of public and open spaces. Being a coastal city interspersed with water bodies, there is shortage of land for development projects. b. Smart City Strategies Connect Fort Kochi and the central city through an integrated water transport project. Uplift the central city area through redensification. Harness the heritage assets of Fort Kochi and Mattancherry for tourism. Create new housing stock in Mattancherry. Upgrade the basic infrastructure. Augment the canal system. Ensure 24x7 water supply services AHMEDABAD Ahmedabad is the largest city in Gujarat. The city assumes a significant place both in the historic and contemporary economic as well as industrial development spheres. In 2010, Forbes reported it as one of the fastest growing cities of the decade. The city region has a large number of textile, pharmaceuticals and automobile industries. Ahmedabad commands an area of square kilometres. As per Census 2011 it had a total population of 5.57 million (5,577,940) with a density of 11,895 persons per square kilometre per cent of the population lived in slums. The city had a literacy rate of per cent. The percentage of households having tap water and toilet facilities was and respectively per cent of households had electricity connections per cent of the households had a waste water outlet connected to drainage. MULTI-DIMENSIONAL PROSPERITY INDEX I 21

24 a. Identified Challenges Existing infrastructure is close to 100 years old. Ineffective traffic management. Costs are a barrier to developing trench-less technologies. Public spaces need uplifting. Shortage of green and open areas. b. Smart City Strategies Focus on sustainable development. Enhance urban resilience and disaster preparedness. Use ICT for effective citizen engagement and collaborative governance. Develop intelligent infrastructure to improve service delivery. Improve the efficiency of implementation JABALPUR The city is located in the east of the state of Madhya Pradesh and on the banks of the river Narmada. Jabalpur has been a hub of educational institutions and manufacturing industries particularly related to defence operations. The city has a geographical area of square kilometres. Jabalpur had a total population of 1,055,525 (Census 2011)_with a density of 6,920 persons per square kilometre per cent of the population lived in slum areas. Jabalpur city had a literacy rate of per cent. The percentage of households having tap water and toilet facilities was and respectively. Electricity was accessed by per cent of households per cent of household waste water outlets were connected to drainage. a. Identified Challenges Inadequate employment opportunities contributing to out-migration of youth. During the last 20 years, the economic footprint of the industrial landscape has reduced. The unplanned growth of the city in an environmentally sensitive area has been offering severe challenges to the natural resources. Large number of informal settlements. Jabalpur falls within earthquake Zone IV. b. Smart City Strategies Use urban regeneration as an opportunity for economic revival. Shape an environmentally sound development model for the city region. Promote frugal innovation to increase the agility and effectiveness of the city system VISHAKHAPATNAM Vishakhapatnam is a coastal city in the state of Andhra Pradesh. In terms of population as well as economy, it is the largest city in the state. The city is well connected by air and water. The port is the country s fifth busiest sea port. The presence of SEZs have made the city an industrial hub. Visakhapatnam commands an area of square kilometres. According to Census 2011 it had a population of 1,728,128 with a density of 3,365 persons per square kilometre per cent of the city s population lived in slums. The city had a literacy rate of per cent per cent of households had access to tap water (from a treated source) within premises per cent of households had access to electricity whereas per cent had toilet facilities within premises. a. Identified Challenges Owing to high increase in the number of vehicles in the region combined with the operation of steel and diesel plants, there is a high level of air pollution. Improper management of solid waste has resulted in blocking of drains and water logging during monsoons. The hill slopes in the city region have been proliferated by of illegal housing. Occurrence of natural hazards such as cyclones offer major risks to human life and critical infrastructure. Unchecked withdrawal of ground water to meet the escalating water needs in the region offers a major risk of saline water ingression in the near future. 22 I MULTI-DIMENSIONAL PROSPERITY INDEX

25 b. Smart City Strategies Focus on health and liveability. Build a resilient urban system that can withstand both the vagaries of nature and human induced challenges such as terrorism. Harness technology to improve the governance process as well as ensure participation of citizens. Develop well planned sub systems for increasing economic buoyancy and recreational opportunities. Some of these are shore protection projects, Vibrant Vizag and beautification of the beach front SOLAPUR Solapur is located in the south-east of Maharashtra. It is the administrative headquarters of Solapur district and a Tier II city. Since it shares a border with Karnataka and Andhra Pradesh, it is a hub of migrant population across southern India. Solapur has the highest number of sugar factories in India. Until recently, it was an active centre of cotton textile manufacturing. Solapur set up the first waste-to-energy electricity plant in Maharashtra. The city covers an area of square kilometres. It had a population of 0.9 million (951,558) with a density of 5,329 persons per square kilometre according to Census Out of the total population of Solapur, per cent of lived in slum areas. The city had a literacy rate of per cent per cent of households had access to tap water (from a treated source) within premises per cent of households had access to electricity whereas per cent had toilet facilities within premises per cent of households had a waste water outlet connected to drainage. a. Identified Challenges The natural environment is polluted. Air and water quality in the city region is very bad offering serious health challenges to the citizens. Lack of assured water supply for industrial activities has prevented the economic growth of the city. The declining industrial base has resulted in shrinkage of the labour market. This has been causing out-migration from the region especially of educated youth. b. Smart City Strategies Revive the economic base of the city through provision of a clean and efficient infrastructure. Protect the natural resources for achieving sustainable prosperity. Ensure efficient management of natural resources. Strengthen the city s business system through innovative and new age opportunities DAVANAGERE Davangere is located in central Karnataka. It was accorded the status of Municipal Corporation in January In 2006, the Ministry of Panchayati Raj recognised Davanagere as one of India s 250 most backward districts (in terms of poverty). The city s economy is boosted by textile and agro-processing industries. It commands an area of square kilometres. It had a population of 0.43 million (434,971). The density of population was 5,640 persons per square kilometre per cent of the city s lived in slum areas. The prevailing literacy rate (2011) was per cent. More than 82 per cent of the households had access to tap water (from a treated source) in their premises per cent of households had access to electricity and per cent had toilet facilities within premises per cent of households had a waste water outlet connected to drainage. a. Identified Challenges Declining of the inner city (old city) area. Improper management of waste resulting in increase of the stray animal population as well as mosquitoes, causing further health risks. Poorly managed public transport and congestion. Inadequately equipped rice factories causing air pollution and respiratory diseases. b. Smart City Strategies There are disparities between the northern and southern parts of the city. In order to reduce the stress on the infrastructure, a planned urban development is required. MULTI-DIMENSIONAL PROSPERITY INDEX I 23

26 Implement efficient and affordable public transport supported by an intelligent transport management system. Promote low carbon technologies and maximise the use of renewable energy sources INDORE Indore is located in Madhya Pradesh. It covers an area of 172 square kilometre. As per Census 2011 the city had a population of 1.96 million (1,964,086) and population density of 11,393 persons per square kilometre. The literacy rate in the city was per cent. The share of slum population in Indore was 30 per cent per cent of the city s households had access to tap water from treated source and in their own premises. 98 per cent households had access to electricity and 90 per cent had toilet facilities within the premises. a. Identified Challenges Building by-laws need amendment. There are planning conflicts as in redrafting the zonal plan for a commercial hub. There are barriers to bringing in a Transit oriented Development Policy to promote high density, mixed land use in the city region. There is a need to create walkability in urban communities through connected measures. b. Smart City Strategies Retrofit 500 acres of land in Rajwada area to enhance its economic and cultural impact. Use redevelopment to improve land utilisation in a 50 acre area. Develop 250 acres of vacant land as a super corridor and areas adjoining the railway station. The pan city approach would include measures to improve safety in public spaces and transport infrastructure NEW DELHI MUNICIPAL COUNCIL The New Delhi Municipal Council (NDMC) is one of the five urban local bodies that govern the city of Delhi. NDMC commands an area of square kilometres. As per Census 2011 the total population in the Council area was 0.25 million (257,803) and the population density was 7,000 person per square kilometre. 30 per cent of the population in the area lived in slums. The literacy rate within the NDMC was per cent per cent of the households had access to tap water within the premises per cent of the households had access to electricity. 85 per cent households had toilet facilities within their own premises. a. Identified Challenges Higher time and costs for commuting to other parts of the city. Inadequate systems to manage the high load of traffic in the area resulting in multiple conflicts. Poor air quality arising out of poorly governed activities in the greater city region. b. Smart City Strategies Effect a balanced mobility plan. Improve services related to water supply, waste management and energy outages. Provision of adequate housing stock. Establish government-citizen interfaces for effective communication. Enhance safety and security through planned measures COIMBATORE The city is located in the state of Tamil Nadu. Since 2011, it commands an area of square kilometers. As per Census 2011, it had a population of 1.6 million. The population density was 63 person per square kilometers. In the city, it was recorded that 7.5 per cent of the households were without toilet facilities. (However, the initiatives for decentralised waste management won Coimbatore the Skoch Smart Governance Award in 2015.) 22 per cent of the city s population lived in slum areas. 24 I MULTI-DIMENSIONAL PROSPERITY INDEX

27 a. Identified Challenges Owing to sustained industrial activity and unplanned urbanisation, both the air and water quality in the city region is bad. The city has been recording high noise pollution. Inadequate blue-green infrastructure contributes to high temperature in the city. b. Smart City Strategies Strengthen the green cover along drainage lines, canals and major water bodies. Develop children s park in 74 locations. Improve water supply and storage capacity and augment the sources. Establish a proper solid waste management system with 100 per cent source segregation. Create capacity for bio-methanation. Pave underground electric cable system. Redevelop Mettupalayam and Sundarapuram markets. Integrated the new bus terminal KAKINADA Kakinada is in Andhra Pradesh. The city has a geographical area of 40 square kilometers. Its population was 0.32 million (325,985) as per Census 2011 and population density 4,370 persons per square kilometers. It had a slum population of 132,185 which constituted 41 per cent of the population. The literacy rate within the city was 72 per cent per cent of the households has had access to tap water in their own premises per cent households had access to electricity and per cent had toilet facilities within their premises. a. Identified Challenges Inadequate systems for water management. Lack of capacity to quickly address the new policy directives coming under the Smart City Mission. b. Smart City Strategies Lay down physical infrastructure to ensure uninterrupted water supply, sewage collection and recycling of treated water. Create underground cabling for electricity supply. Ensure PNG supply for households and commercial establishments through underground pipeline. Build a storm water drainage system for the growing city BELAGAVI Belagavi is located in Karnataka and has geographical coverage of square kilometers. As per Census 2011 it had a population of 0.48 million (488,157). The population density was 5000 persons per square kilometre. The literacy rate in the city was per cent. 10 per cent of the city s population lived in slums per cent of the households had access to tap water within the premises per cent households had access to electricity. The percentage of families with toilet facilities within own premises was around 91. a. Identified Challenges Since National Highway-4 passes through the city, there are a minimum of 4000 heavy vehicles moving through the city in a day. Old and central part of the city is very congested. Less than 50 per cent of the city only is covered under the sewerage network. Water supply is intermittent in most of the city areas. There is a serious shortage of housing stock in the city especially for the middle and lower income groups. b. Smart City Strategies Improve all road network, junction, public utilities, amenities like water supply, sewerage, sanitation and solid waste management. Improve transportation facilities, mobility and basic support system like hawkers, vending areas, parking and bus terminals. MULTI-DIMENSIONAL PROSPERITY INDEX I 25

28 Encourage non-motorised transport, conservation and preservation of central areas. Facilitate good health and education facilities for residents of the areas. Provide affordable housing for people belonging to EWS and LIG. Improve energy efficiency with e-governance UDAIPUR The city of Udaipur has a geographical area of square kilometers. The total population was 0.47 million (474,531) as in Census Population density was 8,000 person/square kilometers. Literacy rate in the city was 89.6 per cent per cent of Udaipur s population lived in slums. 80 per cent of the households had access to tap water within own premises per cent of the households had access to electricity and per cent had toilet facilities within the premises. a. Identified Challenges Management of waste especially solid waste. Heritage conservation. Urban mobility. Air pollution. Increasing energy needs. b. Smart City Strategies Ensure round the clock water supply Install a complete sewerage network. Provide uninterrupted power supply. Improve mobility infrastructure in the Walled city area. Install solar power systems. Install Intelligent Transport Management System (ITMS). Install citywide smart utilities GUWAHATI Guwahati is the capital of Assam. It is major port on the banks of the river Brahmaputra. It is the urban hub of the North East. The city has a geographical area of 219 square kilometers. Its total population was 0.95 million (957,352) (Census 2011). The density of population within the city was 4370 person per square kilometre. The city s literacy rate was per cent. 2.7 per cent of the population lived in slums. Only per cent of the households had access to tap water from treated sources. 93 per cent of the household had access to electricity and 85 per cent have toilet facilities within the premises. a. Identified Challenges Traffic congestion Flooding b. Smart City Strategies Build ecological infrastructures along the contiguous network of storm water channels of Bharalu, Morabhralu, Pamohi and Borsala Beel as a measure to mitigate flash floods in the city. Develop the Brahmaputra River Front Promenade. Establish an ICT based Traffic Information System. Enhance subscription of public transport by improving their service quality CHENNAI Chennai is India s fourth largest city by the size of the economy as well as population. It is the capital of Tamil Nadu. The city commands an area of 175 square kilometres. It had a total population of 4.6 million as per Census The population density was 26,553 person per square kilometre. Literacy rate within the city was per cent per cent of the city s population lived in slum areas per cent of the households had access to tap water within the premises per cent of the households had access to electricity per cent had toilet facilities within the premises. 26 I MULTI-DIMENSIONAL PROSPERITY INDEX

29 a. Identified Challenges Coastal city with a high risk of recurring urban flooding. Scarcity for of drinking water. Inadequate systems to manage the waste. Traffic congestion. b. Smart City Strategies Establish an integrated transport system encompassing non-motorised means and multi-level parking enabled by a modern surveillance system. Harness advanced technologies for disaster risk reduction and improve urban resilience. Institute plan development and technology enabled monitoring of a busy commercial district in the city. The newly developed facility will also be integrated with the mass rapid train stations LUDHIANA Ludhiana is located in Punjab. It is the largest city in the region in terms of area and population. The city commands an area of square kilometres. The city is located on the banks of the river Sutlej. The largest agricultural university in Asia is located in the city region. It is one of the well-developed manufacturing and commercial hubs of northern India. Ludhiana is also an important pilgrimage centre for Sikhs. As per Census 2011 it had a population of 1.6 million. Ludhiana s population density was 10,158 persons per square kilometre. Out of the total population, per cent lived in slum areas. The city had a literacy rate of per cent per cent of the households had access to tap water within the premises per cent had access to electricity per cent had toilet facilities within the premises per cent had a waste water outlet connected to drainage. a. Identified Challenges Bad air quality due to high number of diesel vehicles. Highest per capita vehicles in India with 21,23,215 cars daily on the roads. High accident rate on arterial roads is a deterrent to using Non Motor Transport (NMT). Ludhiana falls in seismic Zone IV which is a high earthquake risk zone. 90 per cent of the city is not covered by a storm water network. b. Smart City Strategies Envisioned to be the most bicycle friendly city in the country by the year Establish a clean and green city supported by efficient infrastructure. Develop multi-modal transportation options. Institute GIS based property mapping in the municipal area. Aesthetically designed public spaces, adequate lighting and video surveillance to enhance attractiveness and safety of public spaces BHOPAL Bhopal is the capital of Madhya Pradesh. It is the 19th largest city in India. The city commands an area of square kilometres. It has a population of 1.79 million (1,798,218). The density of population is 6,290 persons per square kilometre per cent of the city s population lives in slums. Literacy rate is per cent of the households have access to tap water within the premises per cent of households have access to electricity per cent have toilet facilities within premises per cent of the households have drainage connections. a. Identified Challenges Urban sprawl. Need effective engagement between academia-industry to enhance the quality of education. Lack of clean and efficient infrastructure. Need to establish innovative funding mechanism. b. Smart City Strategies Upgrade the urban infrastructure and urban fabric. MULTI-DIMENSIONAL PROSPERITY INDEX I 27

30 Harness the natural advantages of the city for increasing impact of business and tourism. Identify and strategically use high value land in the city region. Develop model townships in the city region through analysis of the economic potential. 2.5 SUMMARY Review of the key characteristics, challenges and proposed interventions through Smart city programmes suggests the emerging response to one of the flagship programme of the Government of India in the urban sector. The cities that selected do not belong to one category by size, regional affiliation, socio-economic issues, and environmental challenges or in any of their current states of urban development. However, the type of projects being identified brings them all into one class. Irrespective of the diverse challenges these cities experience, they have embarked upon a planned process to enhance their urban fabric and economic systems which again vary in scale and typology. The observed common thread observed is related to establishment local systems to improve service delivery so that the cities become both investor friendly as well as liveable. 28 I MULTI-DIMENSIONAL PROSPERITY INDEX

31 Chapter 3 MULTI-DIMENSIONAL PROSPERITY INDEX 3.1 INTRODUCTION The Multi-dimensional Prosperity Index (MPI) has been developed in an effort to address the need for monitoring Indian Smart cities. The other indices being based on global data could not be directly applied to the Indian context. Table.1.1 (Ref. page.11) has shown the key indicators and number of parameters required for preparing the City Prosperity Index. Such detailed data is not available for Indian cities especially for the Smart cities. Hence a new index which can help in capturing the real dynamics of Indian cities was attempted. This index has given emphasis to issues such as physical and financial infrastructure, health and educational infrastructure, housing and amenities and community assets. The key consideration is to review the physical and cultural strength of the city to support development of human capital. All these four dimensions have been given equal weightage while constructing the index. The index has been computed based on the published data for the above 20 cities on the themes of demography, city infrastructure, quality of life of the people, social justice and equity in the city. It would have been useful to have had the environmental dimension included in the index. However, the lack of published data on key variables such as air quality, water quality, open spaces and generation and disposal of solid and liquid waste for all these cities prevented us from exploring this dimension. In the first instance, the available sources of data from the central government and municipal level have been consulted. These include Government of India sources such as Census of India and unit level data of the National Sample Survey Organisation (NSSO), and also reports on socioeconomic indicators of recent rounds from the National Sample Survey Office and data from various ministries especially the Ministry of Urban Development. 3.2 METHODOLOGY After reviewing the available secondary data, only those variables which were available for all the 20 cities have been selected for statistical analysis. The MPI was computed from 28 parameters clubbed into four groups, namely, physical and financial infrastructure, health and educational infrastructure, community assets, and household amenities. All the four indices have been assigned 25 per cent weightage. Subsequently, another factor analysis was done by clubbing parameters such as percentage of Caste and Scheduled Tribe population, per capita income, unemployment rate and urban poverty, along with the four indices to generate the MPI. The number of parameters has varied between different dimensions. The parameters used in each index have been shown below the respective tables in the following sections. 3.3 RESULTS The statistical analysis has revealed the relationship between the studied issues and their variance across the 20 cities. Broadly, the results have shown the most influential issues and their bearing on the various indices. The analysis also helped in knowing the strength and weaknesses of each city. The specific details of the parameters used for each index as well as the results of the analysis are in the related sections PHYSICAL AND FINANCIAL INFRASTRUCTURE INDEX In order to measure the overall status of physical infrastructure, road density, water capacity of the tank, intensity of connection of commercial electricity, intensity of street lights, availability of banks and working women s hostels have been considered. Table 3.1 gives state of infrastructure for all the 20 Smart cities. Road density has been found very high in New Delhi, followed by Kochi. Jabalpur has reported very low road density. Huge city level variations in the physical and financial infrastructure exists. Udaipur also reported low road density. Water tank capacity has been found highest in Bhopal which is also known for surface water storage and city tanks. In Ludhiana, water storage capacity was at a critical level. Kochi and Vishakhapatnam have reported better electricity connections whereas cities such as Kakinada, Guwahati and Jabalpur reported very low levels of electricity connection. The condition of street lights have has been found best in Kochi and Delhi. Udaipur did not MULTI-DIMENSIONAL PROSPERITY INDEX I 29

32 TABLE 3.1: PHYSICAL AND FINANCIAL INFRASTRUCTURE Name Road Watercap Comm_ elele Street_ Light Bank W_work_ host Ahmedabad Belgaum Bhopal Bhubaneswar Chennai Coimbatore Davanagere Guwahati Vishakhapatanam Indore Jabalpur Jaipur Kakinada Kochi Ludhiana New Delhi Pune Solapur Surat Udaipur Source: Town Directory, Census of India, 2011 road= road density per 10,000 population, watercap=water capacity in litre per ten thousand population, comm_ele= commercial electricity connection per 10,000 population, streetlight= streetlight=number of Street light per 10,000 population, bank= availability of bank per 10,000 population, w_work_host= working women hospital per 10,000 female population. TABLE 3.2: RESULTS FROM FACTOR ANALYSIS OF PHYSICAL AND FINANCIAL INFRASTRUCTURE report much intensity of street lights. Availability of banking facilities was once again best in New Delhi and Kochi. Surat and Jabalpur have low banking infrastructure. Working women s hostels were absent in many cities. Kochi took the top spot in this category. Proportion of exp Factor 1 explains 73 Source: Author per cent observation. Roads and banks have taken more factor loading than others indicators. Water tank capacity and working women s hostels seem to have less impact. Kochi, New Delhi and Ludhiana occupied the first three places in the physical and financial infrastructure index. Jabalpur, Guwahati and Belgaum reported the lowest level of infrastructure HEALTH AND EDUCATIONAL INFRASTRUCTURE INDEX Health and education are very important for formation of human capital. The number of hospital beds per one lakh population was found highest in Ludhiana city followed by Guwahati. Ludhiana reported the lowest level of availability of beds. Availability of doctors per one lakh population was observed highest in Guwahati followed by Jaipur. Availability of maternity beds was found best at New Delhi. Kakinada and Udaipur reported a high incidence of primary and middle schools. The same was true for New Delhi and Udaipur for secondary and higher secondary schools. Higher educational institutions have been reportedly high in Guwahati and Kochi. Engineering and medical colleges per one lakh population high in number in Bhopal whereas poly-techniques were found highest in Belagavi. The first two factors explain more than 80 per cent observation. Primary and middle schools along with secondary and higher Variable Factor1 Road 0.94 Watercap comm_elec Streetlight Bank w_work_host Eigen FIGURE 3.1: PHYSICAL AND FINANCIAL INFRASTRUCTURE INDEX OF SMART CITIES Source: Author (Note: Y axis: Index in number) 30 I MULTI-DIMENSIONAL PROSPERITY INDEX

33 secondary schools take maximum factor loading for Factor 1. Availability of doctors and institutes of higher education took greater weightage in Factor 2. The results of the factor analysis translated into construction of the health and social index. Guwahati and Ahmedabad performed well on this scale whereas Surat and Ludhiana had the lowest ranking COMMUNITY ASSETS INDEX In order to understand the level of community assets in the city, three variables, namely availability of cinema halls per 10,000 population, availability of public library per 10,000 population, and availability TABLE 3.3: STATE OF HEALTH AND EDUCATIONAL INFRASTRUCTURE Name bed Doc mat_doc mat_bed pri_middle sec_hrsec hr_edu eng_med polytec Ahmedabad Belgaum Bhopal Bhubaneswar Chennai Coimbatore Davanagere Guwahati Vishakhapatanam Indore Jabalpur Jaipur Kakinada Kochi Ludhiana New Delhi Pune Solapur Surat Udaipur Source: Town Directory, Census of India, 2011 Note: bed= no of bed per one lakh population, doc= no of doctors per lakh one population, mat_doc= no of doctors per lakh female population, mat_ bed= no of beds in maternity hospital per 10,000 female population, pri_middle= no of primary and middle schools per 10,000 population, sec_hr sec= no of secondary and higher secondary schools per 10,000 population, hr_edu= no of higher educational institutes per one lakh population, eng_med= no of engineering and medical colleges per one lakh population, polytech= no of poly-techniques n per one lakh population. of community hall per 10,000 population were taken. Kochi reported the highest concentration of cinema halls followed by Surat. Cities such as Bhopal, Guwahati, Jabalpur and Udaipur reported very low availability of cinema halls. As far as the availability of public libraries is concerned, once again Kochi emerged as the front runner followed by NDMC. The number of public libraries were very less in Ludhiana, Jabalpur and Udaipur. Kochi, Kakinada and Solapur seem to be more prosperous in terms of community halls whereas Bhopal and Ludhiana did not have many community halls. TABLE 3.4: RESULTS FROM FACTOR ANALYSIS OF HEALTH AND EDUCATIONAL INFRASTRUCTURE Variable Factor 1 Factor 2 Bed Doc Mat_doc Mat_bed_ Pri_middle Sec_hr sec Hr_edu HOUSEHOLD AMENITIES INDEX 16 variables have been taken for calculating the amenity index among the cities. These include percentages of good housing, houses with concrete roof, houses with brick wall, houses with cemented floor, houses with two or more rooms. Owned houses were used to generate this index. Surat (80.2) and Chennai (81.7) have shown a higher proportion of houses with good living conditions whereas Ludhiana and Bhubaneswar were found at the bottom of the list where around 40 per cent houses were not in good condition. New Delhi and Ludhiana have shown very high share of concrete roof i.e. more than 80 per cent of the total houses. Cities such as Guwahati and Solapur showed a very low Eng_med Poly Eigen Proportion Weight Source: Author proportion of concrete roofs 30.2 and 38.3 per cent respectively. As in the case of concrete roofs, brick walls were found higher in New Delhi, Ahmedabad, Surat and Ludhiana with more than 90 per cent houses with brick walls. Contrary to this, in cities of Kakinada, Kochi and Vishakhapatnam, the share of brick walls was relatively low. Cemented floors in urban houses have been varied in number across the cities. New Delhi, Pune and Chennai have reported more than 90 per cent houses with cemented floors. In Udaipur, the incidence of cemented floors does not seem to be much in practice. The assumption was that good prosperous houses will have at least two rooms or more. Only Kochi has shown MULTI-DIMENSIONAL PROSPERITY INDEX I 31

34 MAP 3.1: INDIA SC/ST, SLUM AND POVERTY IN THE SMART CITIES more than 90 per cent houses with two or more rooms. On the other side Surat emerged as the only city where only 48 per cent houses had two or more rooms. Security of tenure among urban housing demonstrated huge differences. We have on the one hand a city like New Delhi where only 8.9 per cent houses were owned by the residents. This is possible because New Delhi Municipal Corporation has mainly government accommodation. Some of the cities like Bhopal, Vishakhapatnam, Jaipur and Ahmedabad reported more than 75 houses under owned category. Under sanitation and hygiene, the variables such as safe drinking water viz. tap water connection in the household, availability of water within the household premises, availability of latrine, availability of bathroom and availability of closed drain were taken. Supply of safe drinking water is the responsibility of urban local bodies (ULBs). There was a huge gap in terms of availability of tap water among the Smart cities. Visakhapatanam reported as low as 15 per cent houses covered under piped water, followed by Guwahati (39.1 %). Pune (97.7%) did extremely well in terms of tap water supply followed by NDMC (93.6%). In terms of availability of water within the premise, New Delhi, Ahmedabad, Pune, and Udaipur reported more than 90 per cent houses with water Source: Author within the premise. It was found worst at coastal cities of Kakinada and GVMC. Availability of latrine in the houses gives the status of personal hygiene and sanitation which has a direct relationship with public health. The majority of Smart cities viz. New Delhi, Ahmedabad, Surat, Belgaum, Kochi, Indore, Ludhiana, Jaipur, Udaipur and Guwahati reported availability of latrine in more than 90 per cent houses. It was worse at GVMC. Availability of bathrooms was found to be more than 90 per cent in cities like Ahmedabad, Belgaum, Davanagere, Indore, Pune, Ludhiana, Jaipur, Udaipur, Chennai and Coimbatore. The status of closed drains was reported to be best in Pune followed by Chennai. NDMC) and Chennai reported more than 99 per cent electricity connections. These were found lowest in Bhubaneswar where more than 86 per cent households had electricity connection. Availability of clean cooking fuel has shown a significant FIGURE 3.2: HEALTH AND SOCIAL INFRASTRUCTURE INDEX OF SMART CITIES Source: Author (Note: Y axis: Index in number) 32 I MULTI-DIMENSIONAL PROSPERITY INDEX

35 TABLE 3.5: STATE OF COMMUNITY ASSETS Name Cinema Pub_lib Comm_hall Ahmedabad Belgaum Bhopal Bhubaneswar Chennai Coimbatore Davanagere Guwahati Vishakhapatnam Indore Jabalpur Jaipur Kakinada Kochi Ludhiana New Delhi Pune Solapur Surat Udaipur Source: Town Directory, Census of India, 2011 Note: cinema= no of cinema hall per 10,000 population, pub_lib= no of public library per 10,000 population, comm_hall= no of community hall per 10,000 population. TABLE 3.6: RESULTS FROM FACTOR ANALYSIS FOR COMMUNITY ASSETS Variable Factor 1 Cinema Public Library Community Hall Eigen Proportion Source: Author variation across the cities. Some of the cities, namely Belagavi, Davanagere, Kochi, Chennai and Coimbatore registered a very high level of LPG availability i.e. more than 90 per cent. Kakinada and Visakhapatanam have reported a lower incidence of LPG availability. Availability of internet connection in the houses has not been found very high among the Smart cities. Around one-third houses of New Delhi reported internet connections whereas it was around one-fifth for Pune and Chennai. In terms of the availability of television or computer at households, New Delhi and Pune have around one-third households with this facility. In cities such as Jaipur, Udaipur and Chennai around one- fourth households have television or computer. METHODOLOGY FOR AMENITIES INDEX The percentage good housing condition to total houses (good_house), per cent concrete roof to total (conc_roof), per cent brick wall to total (bric_conc_wall), per cent cemented mosaic floors to total (cemt_mos_floor), per cent houses with 2 or more rooms to total (room 2), per cent owned houses to total houses (owned house), per cent tap water in the household to total (tapwater), per cent water within the household to total (water_premise), per cent latrine in the household to total (latrine), per cent bathroom in the household to total (bathroom), per cent closed drain in the household to total(closed_drain), per cent commercial electricity connection in the household to total (electricity), LPG connection within the household to total (lpg), per cent kitchen in the household to total (kitchen), per cent internet in the household to total (internet), television or computer in the household to total (tv_computer) were included to estimate the index related to amenities. Since these variables are quite different in nature any kind of standard aggregation (simple or weighted average) would be highly subjective and faulty. In order to resolve this issue, recourse to factor analysis using the factor loadings as weights was done. However, before estimating the index it was desirable to first verify the factor analysis results if they are consistent. Four factors were found to be statistically significant (Eigen value being greater than 1). In Factor 1 which was the most significant one, the factor loadings of all the variables have positive signs except the following two: per cent cement and per cent owned houses. But the magnitude of factor loading of cemented floor housing is highly negligible. Even for the other variable, percent of owned houses, the magnitude is quite low. However, the negative sign would mean that FIGURE 3.3: COMMUNITY INDEX OF SMART CITIES Source: Author (Note: Y axis: Index in number) MULTI-DIMENSIONAL PROSPERITY INDEX I 33

36 City TABLE 3.7: STATE OF HOUSING IN THE SMART CITIES good_ house conc_ roof bric_ conc_wall cem_mos_ floor room> =2 own house Kakinada New Delhi Ahmedabad Surat Belgaum Davanagere Kochi Bhopal Indore Jabalpur Solapur Pune Bhubaneswar Ludhiana Jaipur Udaipur Chennai Coimbatore Guwahati Vishakhapatnam Source: Town Directory, Census of India, 2011 Note: good_house= % good housing condition to total house, conc_roof= % concrete roof to total, bric_conc_wall= % brick wall to total, cemt_mos_floor= % cemented mosaic floors to total, room>=2= % houses with 2 or more rooms to total, owned house= % owned houses to total houses. City TABLE 3.8: STATE OF SANITATION AMONG SMART CITIES tapwater Source: Census of India, 2011 water_ premise latrine bathroom closed_ drain Kakinada New Delhi Ahmedabad Surat Belgaum Davanagere Kochi Bhopal Indore Jabalpur Solapur Pune Bhubaneswar Ludhiana Jaipur Udaipur Chennai Coimbatore Guwahati Vishakhapatnam Note: tapwater= % tap water in the household to total, water_premise= % water within the household to total, latrine= % latrine in the household to total, bathroom= % bathroom in the household to total, closed_drain= % closed drain in the household to total. MAP 3.2: INDIA MULTI-DIMENSIONAL PROSPERITY INDEX IN THE SMART CITIES there were cities which are better off in terms of most of the attributes but do not necessarily have a higher percentage of households which have self-owned houses. In other words there are households living in rented houses but are better off in terms of other characteristics. This is evident from Factor 2 as well as Factor 3. Different variables turn out to be significant (factor loading being different from 0 and closer to 1) in different factors though a few of them could be significant in more than one factor. The following variables in Factor 1 take relatively high factor loadings (greater than 0 and closer to unity). On the other hand, in Factor 2 water premises, LPG, internet and owned houses there are relatively higher factor loadings. Using the factor loadings as the weights, the variables have been combined to generate an index. New Delhi emerged as the most prosperous city in terms of availability of household amenities, followed by Pune and Chennai. On the other side, Vishakhapatnam, Kakinada and Solapur were found at the bottom among the Smart cities in terms of availability of household amenities. Source: Author 34 I MULTI-DIMENSIONAL PROSPERITY INDEX

37 TABLE 3.9: STATE OF ELECTRICITY, CLEAN FUEL, KITCHEN AND ELECTRONIC MEDIA IN SMART CITIES City Electricity Lpg Kitchen internet tv_computer Kakinada New Delhi Ahmedabad Surat Belgaum Davanagere Kochi Bhopal Indore Jabalpur Solapur Pune Bhubaneswar Ludhiana Jaipur Udaipur Chennai Coimbatore Guwahati Vishakhapatnam TABLE 3.10: RESULTS FROM FACTOR ANALYSIS OF HOUSEHOLD AMENITIES Variable Factor1 Factor2 Factor3 Factor4 good_house conc_roof bric_conc_wall cemt_mos_floor Room Owned house Tap water water_premise Electricity Latrine Bathroom closed_drain LPG Kitchen Internet tv_computer Eigen value Prop Weight Source: Author Source: Census of India, 2011 Note: electricity= % commercial electricity connection in the household to total, lpg= % lpg connection within the household to total, kitchen= % kitchen in the household to total, internet= % internet in the household to total, tv_computer= % television or computer in the household to total. 3.4 MULTI-DIMENSIONAL PROSPERITY INDEX Since there are four significant factors four indices have been generated. Later, using the Eigen values as the weights four indices have been averaged to get one index value for each of the cities. In the table below (Table 3.12), the factor analysis has been conducted on the four indices (representing four groups of variables) and the following demographic and other variables: population size (log transformation), percentage of SC/ST population, per capita income (taken from smart city profiles), unemployment rate, percentage of work force engaged in regular wage and salaried jobs, and incidence of urban poverty. Two factors turned out to be significant. In Factor 1 the infrastructure index, amenity index and social infrastructure index take moderate factor loadings. Only the community index is highly insignificant. The most surprising point is while the physical cum financial infrastructure index and the amenity index FIGURE 3.4: HOUSEHOLD AMENITY INDEX FOR SMART CITIES Source: Author (Note: Y axis: Index in number) MULTI-DIMENSIONAL PROSPERITY INDEX I 35

38 MPI Rank City Source: Author TABLE 3.11: MULTI-DIMENSIONAL PROSPERITY INDEX (MPI) Household Amenity Index Physical/Finance Infrastructure Index Social/Health Index Community Asset Index 1. Kochi New Delhi Ludhiana Davanagere Coimbatore Jaipur Chennai Vishakhapatnam Ahmedabad Pune Bhopal Surat Udaipur Indore Belgaum Bhubaneswar Guwahati Solapur Kakinada Jabalpur are positively associated, the social infrastructure index has a negative coefficient. In other words, cities with higher amenity and physical cum financial infrastructure index are not necessarily endowed with higher social infrastructure (health and education) index. Per capita income and regular wage employment are positively associated and with a rise in income and regular wage employment both urban poverty and unemployment rate decline. However, city size and percentage SCST are both statistically insignificant. The next significant factor is both the physical cum financial infrastructure and community index take very high factor loadings m_wpr f_fpr log_pop urbanpover~i compositei~x Source: Author while the other two are highly insignificant. Both the significant indices are not positively associated with population size or the percentage of SC/ST population, though per capita income is associated. MPI TABLE 3.12: RESULTS FROM FACTOR ANALYSIS ON THE FOUR INDICES WITH OTHER VARIABLES Variable Factor1 Factor2 log_pop p_scst percapitai~n unemp~ regularwag~s urbanpover~i infra_index soc_hea_in~x comm_index Amenityindex Eigen Proportion of exp Source: Author TABLE 3.13: RESULTS OF FACTOR ANALYSIS FOR MULTI-DIMENSIONAL PROSPERITY INDEX Variable Factor 1 Factor 2 Percent SC/ST percapitai~n unemp~ regularwag~s FIGURE 3.5: MULTI-DIMENSIONAL PROSPERITY INDEX FOR SMART CITIES Source: Author (Note: Y axis: Index in number) 36 I MULTI-DIMENSIONAL PROSPERITY INDEX