THE CONTEXT: India may add approximately 270 million new urban residents over the next two decades. The 2011 Census recorded India’s urbanization rate at 31.2%, up from 27.8% in 2001. Current estimates suggest that around 40% of India’s population now resides in urban areas. This urban growth necessitates innovative approach to city planning and management, with Artificial Intelligence (AI) emerging as a powerful tool to address complex challenges.
ROLE OF ARTIFICIAL INTELLIGENCE IN URBAN SUSTAINABILITY
- Data Collection and Coordination: AI can transform how cities collect and manage data. For example, AI systems can integrate data from multiple sources—such as sensors, public records, and satellite imagery—to provide real-time insights on traffic patterns, energy consumption, air quality, and water management. This enables city authorities to make informed decisions quickly and efficiently.
- Decision Support Systems: AI-based decision support systems can simulate various scenarios to help urban planners predict the outcomes of policy decisions. For instance, when deciding on zoning changes for wetlands or green spaces, AI can model the environmental and economic impacts of such decisions. This reduces reliance on speculation and allows for more evidence-based urban planning.
- Public Transport Optimization: AI can enhance last-mile connectivity by analyzing transit data in real time to optimize bus routes, metro schedules, and ride-sharing services. Cities like Chennai have already started using AI-driven systems to coordinate traffic management across different modes of transport. This not only reduces congestion but also makes public transport a more viable alternative to private vehicles.
- Routine Task Automation: AI excels at performing routine tasks with high precision and reliability. In city management, this means automating functions such as waste collection scheduling, streetlight maintenance, and even monitoring air quality. For example, Surat has implemented AI-driven wastewater recycling systems that operate autonomously to ensure water security.
GOVERNMENT INITIATIVES:
- National AI Centers for Sustainability: In the Union Budget 2023-24, the Government of India allocated Rs 990 crore for three National AI Centers focusing on agriculture, health, and urban sustainability. The Airawat Consortium, led by IIT Kanpur, was selected as the National Centre for AI for Sustainable Cities. This center aims to integrate AI into urban planning through projects like energy distribution optimization and multimodal transit planning.
- IndiaAI Mission: The IndiaAI Mission is another significant initiative aimed at bolstering the country’s AI ecosystem with a budget allocation of Rs 10,300 crore over the next five years. This mission focuses on developing indigenous AI capabilities that can be applied across sectors including urban governance.
AIRAWAT’S FOCUS AREAS:
- Energy Distribution Networks: Energy efficiency is a critical component of sustainable cities. Airawat is collaborating with industry leaders like Adani Industries to develop AI-driven models that optimize energy distribution networks.
- Multimodal Urban Transit Planning: Airawat aims to create an open-source platform for multimodal transit planning that integrates buses, metros, bicycles, and pedestrian pathways.
- Traffic and Road Infrastructure: AI-driven decision support interfaces will assist in designing road infrastructure that accommodates future growth while minimizing environmental impact.
- Air and Water Quality Management: Low-cost sensors powered by AI will provide high-precision estimates of air and water quality in real time. These data points can be used by local governments to implement timely interventions aimed at improving public health.
- Digital Transformation of Local Governance: Airawat is working on creating a “digital twin” for municipal functions—essentially a virtual model of a city that can be used for real-time monitoring and decision-making.
INDUSTRY PARTNERSHIPS:
- Energy Distribution: Adani Industries is collaborating with Airawat to enhance the efficiency of energy distribution networks through AI models that predict demand patterns based on historical data.
- Urban Metabolism Models: TCS is helping develop models that simulate how changes in land use affect factors like flood risk and air quality. These models are essential for understanding the long-term sustainability impacts of urban development projects.
- DIGIT Platform: The e-Governance Foundation has co-developed plans with Airawat to upgrade its DIGIT platform—a digital tool used by municipalities across India for governance tasks such as tax collection and service delivery.
CHALLENGES IN USING AI FOR SUSTAINABLE URBAN DEVELOPMENT IN INDIA:
- Data Quality and Availability: The absence of reliable data on urban infrastructure, population dynamics, and resource consumption patterns hinders the effective deployment of AI solutions in Indian cities. A study by the Indian Council for Research on International Economic Relations (ICRIER) found that only 30% of Indian cities have digitized land records, making it difficult to implement AI-based urban planning tools.
- Digital Divide and Technological Inequality: According to the Digital India Report 2023, while urban internet penetration stands at 67%, there are stark disparities between tier-1 and tier-3 cities, with the latter having only 38% internet penetration. The digital divide in India risks creating ‘smart enclaves’ within cities, exacerbating existing inequalities and potentially leading to biased AI outcomes that favor already privileged urban areas.
- Privacy and Data Security Concerns: A 2023 survey by the Internet and Mobile Association of India (IAMAI) revealed that 78% of urban Indians are concerned about the privacy implications of smart city initiatives.
- Algorithmic Bias and Fairness: AI systems trained on historical data may perpetuate or exacerbate existing urban inequalities. A study by the AI Ethics Lab at IIT Bombay found that AI-based traffic management systems in Mumbai were disproportionately prioritizing routes through affluent neighborhoods, potentially reinforcing spatial segregation.
- Integration with Existing Infrastructure: The McKinsey Global Institute estimates that India needs to invest $1.2 trillion in urban infrastructure by 2030 to support its growing urban population. Integrating AI systems with this massive infrastructure upgrade is a complex task.
- Skill Gap and Capacity Building: A 2023 report by NASSCOM indicates that India faces a shortage of over 800,000 AI and big data analytics professionals. Building AI capacity within urban governance structures is crucial. We need a comprehensive program to train urban planners, municipal officials, and local administrators in AI applications for sustainable development.
THE WAY FORWARD:
- Implement Data Governance Frameworks: Develop clear guidelines for data collection, storage, and sharing, ensuring privacy and security. The Digital Personal Data Protection (DPDP) Act, 2023 can serve as a foundation for this. It enables evidence-based policymaking and fosters innovation in urban services.
- Invest in Digital Infrastructure: Allocate funds for last-mile connectivity in tier-2 and tier-3 cities. The BharatNet project can be leveraged and expanded for this purpose. Mandate multilingual interfaces and accessibility features in AI-driven urban services.
- Mandate Algorithmic Audits: Require regular audits of AI systems used in urban governance to detect and mitigate biases. In Loomba vs. Union of India (2021), the Supreme Court emphasized the need for algorithmic transparency in public services.
- Establish AI Centers of Excellence: Set up specialized centers in each state, following the model of the National Center for AI in Sustainable Cities. The K. Kasturirangan Committee on AI (2020) emphasized the need for interdisciplinary AI education and research.
- Foster Interoperability: Ensure AI systems can communicate across different urban departments and services. Singapore’s Virtual Singapore platform demonstrates the potential of integrated urban digital twins for planning and decision-making.
- Regulatory Sandbox for Urban AI: Implement adaptive regulatory frameworks that can evolve with technological advancements. Involve citizens in the testing and feedback process of new AI-driven urban solutions.
THE CONCLUSION:
AI simulations can help Indian cities better manage urban sprawl by providing “evidence-based insights” into the long-term consequences of development decisions. By integrating data from multiple sources—such as satellite imagery, IoT sensors, and public records—AI systems provide a comprehensive view of a city’s infrastructure needs. This enables more informed decision-making on issues ranging from housing policies to transportation planning.
UPSC PAST YEAR QUESTIONS:
Q.1 Account for the huge flooding of million cities in India including the smart ones like Hyderabad and Pune. Suggest lasting remedial measures. 2020
Q.2 The growth of cities as I.T. hubs has opened new avenues employment but has also created new problems. Substantiate this statement with examples. Urbanization 2017
MAINS PRACTICE QUESTION:
Q.1 “AI-based decision support systems can revolutionize urban planning in India by addressing challenges of rapid urbanization and resource management.” Discuss
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