Authors : Anjana Tripathi ; Divya Natarajan ; Gautam Kumar Mishra ; Soumili Rakshit
Abstract
India’s Smart Cities Mission (SCM), launched in 2015, aims to improve urban livability through targeted investments in infrastructure, service delivery, and digital governance. However, empirical evidence on the household-level economic implications of these interventions remains limited. This study examines the impact of the Smart Cities Mission on household cost of living, access to urban services, and economic well-being in selected Indian cities using a comprehensive secondary data approach.
The analysis integrates unit-level data from the National Sample Survey (NSS) Consumer Expenditure and Housing Conditions rounds to assess changes in household consumption patterns, housing affordability, and expenditure on utilities and transportation. Employment outcomes and income-related proxies are examined using the Periodic Labour Force Survey (PLFS), while demographic characteristics, housing quality, and access to basic amenities are drawn from the Census of India. City-level information on project type, investment size, and sectoral focus is sourced from the Ministry of Housing and Urban Affairs (MoHUA) Smart Cities Mission dashboards and official reports. A comparative framework is employed to analyze trends across Smart Cities and non-Smart Cities, as well as pre- and post-SCM implementation periods.
By triangulating multiple nationally representative datasets with administrative project data, the study evaluates whether Smart City investments have translated into measurable improvements in household welfare or have contributed to rising costs and uneven access to services. The findings aim to provide evidence-based insights for strengthening the inclusiveness, affordability, and effectiveness of urban development policies under the Smart Cities Mission.
Keywords : Smart Cities Mission; Secondary Data Analysis; National Sample Survey; Periodic Labour Force Survey; Census of India; Urban Services Access; Household Cost of Living; Economic Well- Being; Urban Policy; Indian Cities
Introduction
With increase in rapid urbanization, Indian cities are under enormous pressure to deliver key services such as affordable housing, reliable infrastructure, and essential urban services with ensuring essential economic opportunities and quality of life of residents. Inadequate water supply, poor sanitation, rising transport cost, congestion with uneven access to public services. These all directly or indirectly affect the household cost of living and economic well-being (Jha, 2021). The Government of India launched The Smart City Mission (SCM) in June 2015, with the objective of transforming 100 cities into inclusive, sustainable and efficient urban centers through modernization of infrastructure, governance reform and the application of smart technologies (Centre for Financial Accountability, 2019).
The Smart Cities mission conceptualises a “smart city” not merely as a technology-driven urban space but as one that provides core infrastructure, a clean and sustainable environment, and improved quality of life through effective service delivery and citizen participation (CFA, 2019). Area Based Development (ABD)-focussed on retrofitting, redevelopment or greenfield development in the selected urban zones and Pan-City initiatives, which deploy information and communication technologies (ICT) across the entire city to improve services such as transport management, water supply and governance systems (CFA, 2019).
Smart cities mission has generated substantial investment in urban infrastructure, concerns have emerged regarding its implications for affordability, equity, and household welfare. A Capital- Intensive, with significant reliance on the public-private partnerships and cost-recovery mechanisms such as urban services (CFA, 2019). The financing arrangements may influence household expenditure patterns, particularly for low and middle-income groups, by increasing the cost of access to basic services like water, sanitation and electricity with urban mobility. A disproportionate share of investments is concentrated in limited urban pockets under area-based development, raising concerns about spatial inequality and uneven distribution within cities (Jha, 2021).
The present study undertakes a field-based analysis of the Smart Cities Mission in four Indian cities – Patna, Lucknow, Chennai and Bangalore. These cities were selected to reflect diverse regional contexts, levels of urban development and governance capacities. Large metropolitan cities like Chennai and Bangalore have experienced extensive smart infrastructure and digital governance interventions, while Patna and Lucknow represent emerging urban centers where institutional capacity and service delivery constraints remain significant (CFA, 2019). Comparing household experience across cities, this study aims to assess whether smart cities mission, the study aims to assess whether smart cities mission interventions have translated into reduced household costs, improved access to essential services, and enhanced economic well- being, thereby contributing to evidence-based urban policy and inclusive smart city development in India.
Literature Review
The pace of urbanization in India has created opportunities and challenges for sustainable development, particularly in the areas of urban services, economic well-being, and overall quality of life for urban households. To address this, the Government of India initiated the Smart Cities Mission (SCM) with the objective of enhancing urban infrastructure, facilitating access to basic services, and fostering economic development. Although there have been numerous studies evaluating the SCM, there is still a debate on its specific contributions to the cost of living, service access, and economic well-being of Indian households. This literature review distills recent peer-reviewed studies to contextualize these issues within the global and Indian literature on urban studies.
Smart Cities: Conceptual Foundations and Critiques
The literature on smart cities has grown considerably in the past decade. Initial conceptual foundations by Caragliu, Del Bo, and Nijkamp (2011) presented a multidisciplinary approach, conceptualizing smart cities in relation to technology-driven enhancements in urban infrastructure and quality of life. This was followed by empirical studies by Neirotti et al. (2014) on smart city projects around the world, reaffirming the multidisciplinary nature of the construct “smartness” that cuts across governance, economy, environment, and citizen services. This is further supplemented by the conceptual mapping of smart cities by Mosannenzadeh and Vettorato (2014), which highlights the complexity involved in the conceptual definition of smart cities.
However, some scholars have challenged the prevailing discourses on smart cities. For instance, Hollands (2020) suggested that the implementation of smart city projects may embody more entrepreneurial visions than people-centered urban development. On the same note, Söderström, Paasche and Klauser (2014) indicate that smart cities are a social construction of corporate narratives. These arguments suggest that the impact of the Smart Cities Mission possibly should be critically evaluated rather than assumed to be positive.
Smart Cities Mission in India: Governance and Policy Dynamics
In the Indian context, the SCM has been examined in terms of governance and multilevel institutions. For instance, Datta (2015) examined the postcolonial smart urban utopias of India and argued that the SCM presents a new paradigm of urban planning in which digital infrastructure and global competitiveness are emphasized. Recently, Datta (2023) argued that the digitalization of cities through the SCM has been transformative for urban governance in India, although it has often been criticized in terms of inclusivity, transparency, and equity. Chatterji and Mukkai (2024) also examined the multilevel governance of data-driven smart cities in India and emphasized the importance of national and local levels in governing the smart cities of the country.
Reardon et. al (2024), for instance, examined the governance structures of the Smart Cities Mission, thus creating an understanding of the impact of hierarchical structures of governance and policy in the achievement of the objectives of the SCM. In addition, the significance of systemic linkages, like technological, social, and institutional linkages, in the analysis of the outcomes of the smart cities, as noted by Komninos and Mora (2018), underscores the importance of the quality of governance in the analysis of the outcomes of the SCM.
Urban Services and Infrastructural Change
The major aim of the Smart Cities Mission is the provision of access to basic urban services like
water, sanitation, waste management, and transportation. Mondal (2021), for instance, analyzed the provision of access to basic urban services in peri-urban India and noted the existence of deficiencies in the provision of basic urban services, despite the development of infrastructures. Mondal and Sen (2020), in their analysis of the methodologies of demarcation of peri-urban areas, noted the wide variations in the accessibility of basic urban services. Shaw and Das (2018), using GIS, noted the existence of peri-urban sprawl, thus surpassing the provision of basic urban services. In addition, Kar et al. (2018) noted the impact of changes in the landscape of peri-urban areas, thus creating challenges in the provision of continuous urban services, despite the development of infrastructures in the urban areas. Chettry (2022) further clarified that urban sprawl makes urban planning more challenging, making it harder to provide continuous services such as water, electricity, and sanitation.
Coluzzi et. al (2022) examined the environmental aspect of urban service transformation, illustrating how the rapid expansion of settlements leads to land degradation, which in turn influences service sustainability. Brinkmann et al. (2012) provided a comparative example, illustrating that landscape changes surrounding growing cities can lead to the degradation of ecological services, a finding that can be applied to the Indian urbanization context. Butsch and Heinkel (2020) examined peri-urban changes and their effects on water-based livelihoods, highlighting the nexus between urban services and economic activities.
Taken together, these studies indicate that despite the efforts of infrastructure development projects under the Smart Cities Mission to improve accessibility, the spatial disparities and structural issues frequently impede the provision of basic services to all households. Household Impact: Cost of Living and Economic Well-Being Household cost of living is a composite outcome influenced by changes in infrastructure, service provision, mobility, and access to employment. Brandano et al. (2023) studied the digital divide and regional inequality, arguing that technological investments may inadvertently widen socio-economic gaps unless accompanied by inclusive policies. Suh (2025) analyzed the income impact of the digital divide, showing how the lack of equal access to digital services can affect economic opportunities. Xie, Deng, and Chong (2019) demonstrated the impact of rapid urbanization on the environment including air pollution that subsequently impact on household well-being and health expenditures. Arku and Marais (2021) addressed the issue of sustainability in urbanisms of the Global South, where economic well-being is closely intertwined with city management and access to services. Sen (2016) introduced gendered aspects to the discussion of peri-urban labor markets, which showed how structural exclusions influence economic participation and household wellbeing of women. Gowda et al. (2012) examined the economics of peri-urban agriculture, and the importance of livelihood. patterns are altered due to urban growth and development of infrastructure. These studies jointly preempt the concept that the infrastructural improvements of the Smart Cities Mission can. have mixed effects on household cost of living and economic well-being. While improved services would lower daily expenses and increase the number of jobs, these gains can be neutralized by environmental pressures and digital divides.
City-Level and Spatial Case Studies Relevant to India
Case studies are empirical and offer the necessary background to comprehend localized effects. Khan et al. (2024) studied the urban growth in Lucknow between 1991 and 2021, revealing both spatial growth and mounting strains on infrastructure. Although Liu and Jiang (2021) are concerned with sustainability metrics, which offers methodology to evaluate urban growth patterns, it can be modified to Indian cities such as Chennai and Bengaluru. Sun et al. (2017) used satellite remote sensing to track urbanization and showed how infrastructure stress is affected by spatial dynamics. Samat et al. (2021) explored urban development boundaries, shedding light on planning mechanisms that may limit or guide service delivery. Rajendran et al. (2024) approached the peri-urban futures in a systems-thinking manner, focusing on the urban-rural interfaces of fast urbanizing cities, which is applicable for Patna’s peri-urban areas. These case studies demonstrate that urban development trends, coupled with smart city projects, have different impacts on households both in terms of cost and access to services.
Methodology
Population, Sample, and Sample Size
Population: The study population comprises all 100 cities selected under India’s Smart Cities Mission launched by the Ministry of Housing and Urban Affairs in 2015.
Sample: Four cities have been purposively selected for this study:
● Chennai (Tamil Nadu)
● Bangalore (Karnataka)
● Patna (Bihar)
● Lucknow (Uttar Pradesh)
These cities represent different geographical regions, economic development levels, and implementation phases of the Smart Cities Mission.
Sample Size: The study analyzes secondary data from these four cities covering the period from 2015 (baseline year) to 2024, encompassing approximately 9 years of Smart Cities Mission implementation.
Rationale for Selection: The cities were selected based on geographical diversity (South, East, and North India), varying economic development stages, data availability from government sources, and representation of different implementation timelines under the mission.
DATA SOURCES
This research is based entirely on secondary data collected from authenticated government sources. The key data sources include:
Press Information Bureau (PIB)
● Official press releases and updates on Smart Cities Mission
● Policy announcements and progress reports
Ministry of Housing and Urban Affairs (MoHUA)
● Smart Cities Mission official website (smartcities.gov.in)
● Annual reports and mission progress data
● City-wise project details and financial allocations
NITI Aayog
● SDG Urban India Index
● Ease of Living Index
● Municipal Performance Index
Census of India
● Population and demographic data
● Household amenities and infrastructure statistics
National Sample Survey Office (NSSO)
● Household Consumer Expenditure data
● Urban amenities and service access reports
Economic Survey Reports
● Central and State-level economic data
● Urban sector performance indicators
Ministry of Statistics and Programme Implementation (MoSPI)
● Price indices and cost of living data● Urban development statistics
Tools and Techniques
Sampling Techniques
Purposive Sampling has been employed to select the four cities from the 100 Smart Cities. This non-probability sampling method was chosen because:
● It allows selection of cities representing diverse geographical and economic contexts
● It ensures data availability and accessibility from government sources
● It enables meaningful comparative analysis across different regional and developmental scenarios
Data Analysis Techniques
The study employs comparative analysis as the primary analytical approach:
Descriptive Analysis
● Compilation and summarization of key indicators related to household cost of living,
urban service access, and economic well-being for each city
Comparative Analysis
● Cross-city comparison of Smart Cities Mission implementation and outcomes
● Benchmarking of the four cities against each other on parameters such as:
○ Changes in cost-of-living indicators
○ Improvements in urban service delivery
○ Economic well-being measures
Trend Analysis
● Examination of pre- and post-implementation trends (2015-2024)
● Year-wise progression of key development indicators
Graphical Representation
● Use of diagrams, charts, and graphs (bar charts, line graphs, comparative tables) to
visually represent:
○ Inter-city comparisons
○ Time-series trends
○ Performance variations across different parameters
The analysis will be conducted using Microsoft Excel for data organization, statistical calculation, and visual representation through charts and diagrams.
Limitations
● The study is limited to secondary data available from government sources
● Findings are specific to the four selected cities and may not represent all 100 Smart Cities
● Analysis depends on the availability and timeliness of published government data
Data Analysis and Interpretation
This chapter presents a comprehensive analysis of the impact of Smart Cities Mission on household cost of living, access to urban services, and economic well-being across the four selected cities: Chennai, Bangalore, Patna, and Lucknow. The analysis is based on secondary data collected from various government sources and is organized according to the three key dimensions of the study.
Overview of Smart Cities Mission Implementation
Before examining the specific impacts, it is essential to understand the implementation status and financial progress of the Smart Cities Mission in the selected cities.
Project Implementation Status
Table Chart is available at Click Here
Project Completion Rate (%)
Chart is available at Click Here
Interpretation:
The data reveals that Bangalore leads in project completion with 84.3% of sanctioned projects completed, followed closely by Lucknow (81.4%) and Chennai (80.3%). Patna, being selected in the later round (September 2017), shows a lower completion rate of 69.7%, which is expected given its relatively shorter implementation timeline. All four cities demonstrate substantial progress in executing Smart Cities projects, indicating active implementation of the mission objectives.
Financial Allocation and Utilization
Table Chart is available at Click Here
Figure 4.2: Fund Utilization Comparison
Table Chart is available at Click Here
Interpretation:
Bangalore demonstrates the highest fund utilization rate at 87.2%, indicating efficient financial management and project execution. Chennai follows with 85.8%, while Lucknow and Patna show utilization rates of 83.6% and 81.0% respectively. All four cities have achieved utilization rates above 80%, suggesting effective deployment of allocated resources. The slightly lower utilization in Patna can be attributed to its later entry into the mission and consequently shorter implementation period.
Impact on Household Cost of Living
The cost of living is a critical indicator of urban affordability and household economic burden. This section analyzes changes in key cost of living parameters across the four cities.
Consumer Price Index Trends
Table 4.3: Consumer Price Index for Urban Areas (Base Year 2012 = 100)
Table Chart is available at Click Here
Figure 4.3: CPI Trend Across Four Cities (2015-2024)
Table Chart is available at Click Here
Interpretation:
The CPI data reveals that all four cities experienced inflation during the study period, with Bangalore showing the highest absolute increase (46.3 points, 34.2% growth) and Patna the lowest (38.4 points, 30.4% growth). Bangalore and Chennai, being more economically developed cities, demonstrate higher cost of living levels throughout the period. However, the growth rate is relatively similar across all cities, ranging from 30.4% to 34.2%. While the Smart Cities Mission has improved infrastructure and services, it has not significantly altered the natural inflation trajectory in these urban centers. The data suggests that cost of living pressures remain a challenge across all four cities despite mission interventions.
Key Findings Summary
Strengths Observed:
All four cities have successfully improved basic urban service delivery (water, sanitation, waste management)
Digital infrastructure has witnessed transformative growth across all cities
Employment generation and economic growth indicators show positive trends
Project completion rates exceed 80% in three out of four cities
Challenges Identified:
Cost of living has increased across all cities, potentially offsetting benefits for lower-income households
Significant inter-city disparities persist, with tier-1 cities (Bangalore, Chennai) outperforming tier-2 cities (Lucknow, Patna)
Source segregation and waste processing remain below optimal levels 4. Traffic congestion persists despite transport infrastructure investments
Differential Impact:
● Already developed cities (Bangalore, Chennai): Enhanced existing strengths, achieved near-universal service coverage, attracted significant investments
● Emerging cities (Lucknow, Patna): Made substantial progress from lower baselines but continue to face infrastructure and economic development gaps
Summary
The data analysis reveals that the Smart Cities Mission has had a multifaceted impact on the selected cities across the three dimensions studied:
On Household Cost of Living: The mission has been associated with moderate increases in cost of living across all cities, with CPI rising by 30-34% and utility costs increasing by 30-47%. While improved services justify some cost increases, affordability remains a concern, particularly for lower-income households.
On Access to Urban Services: This dimension shows the most significant positive impact, with marked improvements in water supply coverage (+8.7% to +17.2%), sanitation infrastructure, waste collection efficiency (+15-19 percentage points), and digital connectivity (broadband penetration doubling or tripling). Smart Cities projects have successfully enhanced service delivery infrastructure.
On Economic Well-Being: The mission has contributed to positive economic outcomes, including unemployment reduction (0.4-0.6 percentage points), substantial per capita income growth (53.7-67.8%), and significant investment attraction. However, benefits have been more pronounced in already developed cities.
Overall Assessment: The Smart Cities Mission has achieved notable success in infrastructure development and service delivery enhancement. Bangalore and Chennai have leveraged the mission to consolidate their positions as leading urban centers, while Lucknow and Patna have made substantial progress despite starting from lower baselines. However, the mission’s impact on affordability and economic inclusion requires more targeted interventions to ensure equitable urban development outcomes.
Suggestions For Future Research
While this study provides evidence on the household-level impacts of the Smart Cities Mission
using nationally representative secondary data, several avenues remain open for deeper and more
nuanced inquiry.
First, future research could incorporate primary household surveys and qualitative fieldwork to capture lived experiences, perceptions of service quality, and intra-household impacts that are not fully observable in secondary datasets. In-depth interviews and focus group discussions could help assess how different socio-economic groups interpret and benefit from Smart City interventions.
Second, there is scope for applying stronger causal identification strategies, such as difference- in-differences, synthetic control methods, or matched city-pair comparisons, to more precisely isolate the effects of Smart Cities Mission investments from broader urban and macroeconomic trends. Linking city-level project timelines with high-frequency household data could further strengthen causal inference.
Third, future studies could explore intra-city spatial disparities by using ward-level or neighbourhood-level data, combined with geospatial analysis. This would allow researchers to examine whether Smart City investments are concentrated in select areas and whether peripheral or informal settlements experience differential outcomes in terms of cost of living and access to services.
Fourth, additional research could focus on long-term and dynamic effects of Smart City interventions, particularly on housing markets, rental inflation, urban land values, and household indebtedness. As many projects are recent or ongoing, longitudinal analysis over a longer time horizon would be valuable in understanding sustainability and unintended consequences.
Fifth, comparative research across different models of urban governance and financing including public–private partnerships, municipal borrowing, and state-led initiatives could shed light on how institutional arrangements influence household welfare outcomes. Cross-country comparisons with smart city initiatives in other developing economies may also offer useful policy lessons.
Finally, future research could integrate environmental and social indicators, such as air quality, climate resilience, gendered access to services, and digital inclusion, to develop a more holistic assessment of urban well-being under the Smart Cities Mission. Together, these research directions can help deepen understanding of the distributional, spatial, and long-term impacts of smart urban development initiatives and support more inclusive and evidence-driven urban policymaking in India.
Conclusion
The study was conducted to examine the impact of the Smart Cities Mission in India on the cost
of living, urban services, and economic wellbeing of households in Indian cities. From a
comparative study of secondary data gathered in Chennai, Bangalore, Lucknow and Patna, the
findings reveal that the Smart Cities Mission has had a positive effect on urban infrastructure and
service delivery, and at the same time posing affordability problems and economic disparities.
The greatest and the most beneficial impact of the Smart Cities Mission is the improvement of access to basic urban services. In the four cities, advancements were made in water supply, hygiene, solid waste disposal, transport, and internet access. The greatest improvements were made in the new cities of Patna and Lucknow, where service delivery coverage rose significantly at the beginning of the lower levels. These results confirm that The Smart Cities Mission has succeeded in enhancing the physical and technological infrastructure of urban service delivery.
Nevertheless, these are positive results of better service delivery that have been coupled with a raise in the cost of living of households, service fees, and housing prices which grew in all four cities throughout the Smart Cities Mission.
The uniform increase in prices in metropolitan and emerging cities prove that the issue of affordability is systemic rather than city-specific. Even though better service delivery can justify higher service fees, the net outcomes prove that the infrastructure development of the Smart Cities Mission has not led to a decrease in the cost of living of households, particularly low- and middle-income households. Cities like Bangalore and Chennai have benefited more from the mission, as they have attracted more investment and created more jobs. However, cities like Patna and Lucknow, although making rapid progress, are still lagging behind in absolute economic performance.
This is an indication that the Smart Cities Mission has been favoring already existing regional and institutional advantages rather than closing the gap between different cities. From the above analysis, it is clear that the Smart Cities Mission has been successful in achieving infrastructure and service-related outcomes but has been making uneven progress in terms of affordability and inclusive economic development. The Smart Cities Mission can thus be said to be technically successful but socially uneven. For smart urban development to make a positive impact on the welfare of households, future policies should focus more on affordability, equitable pricing of services, balanced development, and institutional capacity building along with technological development.
Limitations of the study
Despite providing valuable insights into the household-level effects of India’s Smart Cities
Mission (SCM), this study faces a number of limitations that must be kept in mind while
considering the results.
First, this study exclusively uses secondary data sources such as NSSO, PLFS, Census of India, MoHUA dashboards, and NITI Aayog indices. Although these sources are representative and methodologically sound, they may not be able to provide a complete picture of the subjective experiences of households and the variations in service delivery quality and coping mechanisms developed by urban households in response to cost increases. Thus, the subjective experience of well-being and the lived realities of households are not considered in this study.
Second, this study only considers four purposively selected Smart Cities of India, namely Bangalore, Chennai, Lucknow, and Patna. Although these cities were selected to provide a representative picture of different regions and development levels, the results cannot be generalized to the remaining 96 Smart Cities, especially the smaller ones with less institutional capacity and different implementation levels.
Third, the study takes a comparative and descriptive research design rather than a causal identification approach. Although the pre- and post-SCM trends are analyzed, the study cannot completely separate the effects of SCM interventions from the overall macroeconomic factors such as inflation, national urbanization trends, and state-level policy variations.
Fourth, the intra-city spatial disparities could not be analyzed because of the lack of data at the ward or neighborhood level. Although the Smart Cities investments are often focused in the Area-Based Development (ABD) zones, the study may underestimate the disparities between the upgraded urban cores and the peripheral or informal settlements.
Finally, some of the SCM projects are recent or ongoing, especially in cities like Patna. Therefore, the long-term effects of SCM interventions on housing markets, household debt, environmental sustainability, and intergenerational economic mobility could not be analyzed in this study.
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