Evaluating Smart Cities Mission and COVID-19 Response

1. Policy Background

The Smart Cities Mission (SCM) is an urban renewal initiative by the Indian government aimed at improving urban infrastructure, sustainability, and quality of life through technology. During the COVID-19 pandemic, 45 cities repurposed their Integrated Control and Command Centres (ICCCs) into COVID-19 War Rooms. These centers enabled real-time monitoring, data tracking, contact tracing, and public health support services, including outreach to vulnerable groups and use of drones, telemedicine, and sanitization stations.

2. Literature Review

Mullick et al. (2022): Highlights the digital divide in smart cities, stressing the exclusion of marginalized populations.
Deloitte (2020): Analyzes case studies of Indian smart cities’ COVID-19 response across themes like War Rooms and social services.

3. Research Design

A cross-sectional analysis comparing 100 Smart Cities with 100 non-Smart Cities. The key goal is to measure the impact of smart infrastructure on COVID-19 outcomes like case and death rates. Data comes from government databases and smart city reports.

4. Outcome Variables

COVID-19 death rates
Total number of positive cases

5. Control Variables

To isolate the effect of smart infrastructure, we control for:

Smart City selection score (2015)
Income, population density, government funding
Education, vaccine coverage, age, gender
Policies on lockdowns, social distancing, and masks

6. Main Regression Equation

We elect a difference-in-differences model to compare COVID-19 outcomes between cities with and without the Smart Cities Mission.

Our main regression equation reads:


    Y_i = β₀ + β₁SmartCity_i + β₂Scores_i + β₃AverageIncome_i + β₄PopulationDensity_i + β₅GovernmentFunding_i + β₆EducationProportion_i + β₇VaccineCoverage_i + β₈AverageAge_i + β₉GenderDistribution_i + β₁₀LockdownPolicy_i + β₁₁SocialDistancing_i + β₁₂MaskMandates_i + ε_i

Where σ_c includes control variables and SmartCity_i is a binary indicator.

For easier readability, the equation can be simplified to:


    Y_i = β₀ + β₁SmartCity_i + σ_c + ε_i

Where σ_c is a vector of control variables that includes:

Smart City selection scores (2015)
Average income
Population density
Government funding
Education levels
Vaccine coverage rate
Average age
Gender distribution
Binary variables for lockdown policies, social distancing, and mask mandates

σ_c effectively acts as the city fixed-effects vector that controls for relevant demographic, economic, and policy-related factors.

7. Additional Analyses

Sensitivity analysis (varying bandwidths, robustness)
Cost-benefit analysis (comparing infrastructure costs with public health benefits)
Time-series trends (infection rates, response efficiency over time)

8. Policy Discussion

If the treatment effect is significant, it supports further investment in SCM and smart public health infrastructure. If not, refinement—not abandonment—of current interventions is needed.