This master’s project explores the issue of energy poverty using micro-level sample survey data collected from Indian households between November 2020 and October 2022.
Energy poverty, the lack of access to sufficient volumes of efficient energy for daily use, is a significant challenge for poorer households. In this study, we leverage the power of artificial intelligence (AI) to analyze and address the factors contributing to energy poverty.
Using advanced statistical analysis combined with AI techniques, we identify variables that play a crucial role in explaining energy poverty within households. Our model utilizes data from the Indian Human Development Survey (IHDS), a comprehensive source of information on living standards, poverty, and inequality in Indian households, gathered through direct interview questionnaires. Collaboratively designed by the University of Maryland and the National Council of Applied Economic Research (NCAER), New Delhi, this survey provides a rich dataset for our analysis.
To address the issue of energy poverty, we employ AI algorithms that can predict fuel type choices based on various influencing factors. These algorithms take into consideration household consumption (as a proxy for income), education levels of adult female and male members, poverty levels, household size, and place of residence. The AI model not only identifies significant predictors but also determines the interplay between these variables. Consumption data emerges as a pivotal factor influencing fuel type choice. Additionally, education, place of residence, and household size are found to be statistically significant modifiers of these choices. The AI-powered model draws a key conclusion: polluting fuels are more commonly used in poorer households, households with lower education levels, and rural households. Furthermore, the model highlights that adopting cleaner fuels faces the greatest challenge in larger family setups.
To address the health impacts associated with traditional biomass stoves and the smoke they produce, AI comes to the rescue once again. By controlling for variables such as education of adult men and women, place of cooking, ventilation, and household consumption levels, our AI model assesses the mortality and morbidities caused by smoke exposure. This assessment is performed separately for men, women, children, and younger children. Leveraging the power of AI, we identify statistically significant explanatory variables that mitigate the impact of smoke exposure on morbidity levels. Notably, the economic status of the household and the education of females emerge as critical factors. Additionally, we find that the education of children is adversely affected by the health consequences of exposure to smoke from biomass-based stoves.
This study demonstrates the immense potential of AI in addressing energy poverty and its related health impacts. By accurately predicting fuel type choices and assessing the health risks associated with traditional cooking methods, AI empowers us to make informed decisions and implement targeted interventions to alleviate energy poverty and enhance overall well-being.
As we look toward the future, AI opens up a realm of possibilities for tackling energy poverty on a larger scale. Here are a few ways AI can play a transformative role:
Predictive Analytics for Energy Demand
AI can be employed to forecast energy demand patterns based on various socio-economic factors. By understanding when and where energy demand is likely to surge, governments and organizations can proactively allocate resources and develop targeted energy infrastructure projects.
Smart Energy Distribution
AI-powered smart grids can optimize energy distribution by dynamically rerouting energy to areas with higher demand. This not only minimizes energy wastage but also ensures equitable distribution, particularly during peak usage times.
Renewable Energy Integration
AI algorithms can analyze local environmental conditions to determine the most suitable renewable energy sources for a region. This empowers communities to harness solar, wind, or other clean energy sources, reducing reliance on polluting fuels.
Efficient Household Energy Consumption
AI-driven smart home systems can learn the energy consumption patterns of households and suggest ways to optimize energy use. From adjusting thermostat settings to managing lighting, these systems help families conserve energy and reduce their bills.
Targeted Subsidy Programs
By analyzing household characteristics and energy usage patterns, AI can identify households most in need of energy subsidies. This enables governments to allocate resources more efficiently and ensure that assistance reaches those who require it the most.
Health Impact Mitigation
AI can be employed to develop predictive models that identify regions with high health risks due to traditional cooking practices. This information can guide healthcare interventions and awareness campaigns to reduce the health impacts of indoor air pollution.
Behavioral Change Interventions
AI-powered personalized recommendations can encourage behavioral changes that promote energy efficiency. Whether through mobile apps or online platforms, these interventions can guide individuals toward adopting cleaner energy practices.
By embracing these AI-driven solutions, we can create a future where energy poverty becomes a thing of the past. The combination of data analysis, predictive modeling, and proactive intervention can revolutionize the way we approach energy access and consumption. As we continue to innovate and implement AI solutions, we take a significant step toward a more sustainable and equitable energy landscape.
In the next phase of our research, we intend to delve deeper into the application of AI in addressing energy poverty challenges. We will explore how machine learning algorithms can refine predictive accuracy and enhance the effectiveness of interventions. Stay tuned for more insights into how AI is shaping the future of energy access and well-being.
Thank you for joining us on this journey as we harness the power of AI to create positive change in the lives of millions affected by energy poverty.