“Data for Good” can address pressing global challenges, but its impact is hindered by limited access, weak governance, skill gaps, and unprepared institutions. The COVID-19 pandemic highlighted these barriers, with fragmented data systems and missed opportunities for action. Closing this gap requires inclusive infrastructure, ethical standards, skilled “data bilinguals,” and institutional readiness to turn data into real-world impact.
Banking modernization today goes beyond technical upgrades; it’s a strategic shift toward using data ethically and inclusively for societal benefit, aligned with ESG principles. Financial institutions can unlock real-time, responsible services like microloans and fraud alerts by replacing legacy systems with agile, cloud-native infrastructures and operational data layers (ODLs). These upgrades reduce costs, improve decision-making, and expand access to underserved communities. However, challenges persist, including poor data quality, outdated systems, talent shortages, and ethical governance gaps. To overcome these, banks must invest in interdisciplinary talent, scalable tech, and stakeholder-centered change management, ensuring digital transformation drives positive impact, not just efficiency and profit.
In a world increasingly focused on sustainability, the healthcare industry is no exception. With the growing demand for high-quality care and the urgent need to reduce environmental impact, healthcare systems are turning to Artificial Intelligence (AI) and data-driven solutions to achieve these dual goals. In a recent webinar hosted by the ESG and Data for Good Center of Excellence, Dr. Vibhor, an expert in healthcare transformation with over 18 years of experience, shared groundbreaking insights on how AI is transforming healthcare into a more sustainable, efficient, and patient-centric industry.
The Intersection of AI, Sustainability, and Healthcare
Dr. Vibhor began by defining sustainability in healthcare as the ability to deliver affordable, high-quality care while minimizing environmental impact. He emphasized that sustainability is not just about reducing carbon emissions but also about creating a healthcare system that is financially viable, patient-focused, and less burdensome for providers.
According to Dr. Vibhor, AI and data analytics are key to achieving these goals. By leveraging AI, healthcare systems can optimize resource utilization, reduce waste, and improve patient outcomes—all while contributing to a greener planet.
AI-Driven Initiatives in Sustainable Healthcare
Dr. Vibhor shared several real-world examples of how AI is being used to drive sustainability in healthcare:
Reducing Carbon Footprint through E-Visits:
One of the most impactful initiatives discussed was the use of AI to reduce patient travel by converting in-person appointments to telemedicine visits.
Dr. Vibhor explained how an AI algorithm was developed to identify which appointments could be conducted virtually without compromising the quality of care. This initiative not only reduced the carbon emissions associated with patient travel but also improved access to care for patients in remote areas. The project resulted in 15 million tons of carbon savings over 18 months, showcasing the potential of AI to drive both environmental and healthcare benefits.
Digital Twins of Hospitals:
Another innovative approach was the use of digital twins—virtual replicas of hospitals—to simulate and optimize energy consumption.
By creating a digital twin, healthcare systems can experiment with different energy-saving measures, such as switching to more efficient ventilation systems, before implementing them in real life. This approach not only reduces emissions but also ensures that hospitals operate more efficiently.
Solar Panel Dashboards and Energy Forecasting:
Dr. Vibhor also highlighted the use of AI in solar energy management. By building a forecasting model, healthcare facilities can predict energy generation and consumption from solar panels, allowing them to optimize their use of renewable energy.
This initiative not only reduces reliance on non-renewable energy sources but also contributes to significant cost savings for healthcare organizations.
Challenges in Implementing AI for Sustainability:
While the potential of AI in sustainable healthcare is immense, Dr. Vibhor acknowledged that there are significant challenges to overcome:
Data Quality: Inaccurate or incomplete data can hinder the effectiveness of AI models. For example, incorrect patient addresses can affect the accuracy of carbon emission calculations.
Executive Sponsorship: Without strong support from leadership, AI projects may remain as proofs of concept rather than being implemented at scale.
Resistance to Change: Healthcare professionals may be hesitant to adopt AI-driven solutions, fearing that they could replace human roles.
To address these challenges, Dr. Vibhor emphasized the importance of skill development programs and cultural shifts within organizations. He advocated for training healthcare professionals to embrace AI, ensuring that it is seen as a tool to assist rather than replace them.
The Future of AI in Healthcare:
Looking ahead, Dr. Vibhor expressed optimism about the role of AI agents in healthcare. He believes that AI will increasingly take over administrative tasks, allowing healthcare providers to focus more on patient care. However, he stressed that AI should always be used responsibly, with a focus on patient safety and ethical considerations.
Dr. Vibhor also highlighted the importance of aligning AI initiatives with Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being) and SDG 13 (Climate Action). By integrating AI into healthcare, organizations can not only improve patient outcomes but also contribute to global sustainability targets.
Conclusion:
The insights shared by Dr. Vibhor underscore the transformative potential of AI in creating a more sustainable healthcare system. From reducing carbon emissions to optimizing energy use, AI is proving to be a powerful tool in addressing some of the most pressing challenges in healthcare today. As the world moves toward a net-zero future, the integration of AI and sustainability in healthcare will be crucial. By embracing these technologies, healthcare systems can not only improve the quality of care but also contribute to a healthier planet for future generations.
Wildfires in the United States are intensifying in both frequency and severity, with climate change acting as both a driver and consequence of these escalating disasters. Over the past decade, wildfire seasons have lengthened, economic damages have soared, and vulnerable populations have faced increasing risks. The latest Los Angeles wildfire is not an anomaly, it is the new normal.
This report provides a data-driven analysis of U.S. wildfires, using real fire data from NASA’s Fire Information for Resource Management System (FIRMS) to examine wildfire trends from 2016 to 2023. A time series analysis using SAS Viya software shows that the U.S. experiences an average of 95,137 wildfires annually, with projections indicating further increases in the coming years. Additionally, wildfire intensity—measured by Fire Radiative Power (FRP)—suggests that most U.S. wildfires fall within an intermediate intensity range, though certain regions experience significantly higher fire energy outputs.
The report also highlights regional disparities in wildfire risk, with states in the Interior West and Pacific Northwest coast facing the highest combination of wildfire frequency and intensity. However, risk is not solely determined by fire frequency; Wyoming, despite experiencing fewer fires, has the highest average FRP, making it particularly vulnerable. California stands out as the most wildfire-prone state, leading both in wildfire occurrence and FRP values.
Beyond environmental impact, wildfires disproportionately affect certain demographic groups, making data-driven decision-making essential for mitigation and response efforts. By linking U.S. Census data with wildfire records, this analysis identifies the most at-risk counties in California— Lassen, Trinity, Butte, Shasta, Inyo, El Dorado, and Glenn. Within these counties, Hispanic and Latino populations, particularly children (5–17) and seniors (65+), face heightened risks due to socioeconomic vulnerabilities, limited healthcare access, and pre-existing health disparities.
To effectively allocate resources, improve evacuation strategies, and protect the most vulnerable populations, data must be at the core of wildfire mitigation and adaptation plans. Decision-makers need timely, accurate data to ensure that support reaches the right people at the right time. As wildfires become more intense and unpredictable, leveraging data for proactive planning is no longer an option, it is a necessity.
Join the SAS and AAMBFS Forecasting Workshop and Hackathon for Sustainability and Data for Good , powered by ESG Data for Good Center of Excellence. Unlock your potential to dive into the world of advanced forecasting. This unique event is your gateway to mastering the art and science of forecasting, exploring real-world applications, and shaping the future of decision-making.
What you’ll learn:
Large Scale Forecasting – What, How and what’s next ?
Sample Use Cases
ATM Cash Forecasting (Banks)
Network Capacity Forecasting(Telco)
Energy (Re Energy) Forecasting (Utilities)
Spare Parts Forecasting (Airlines, Automotives, Heavy Vehicle)
Baggage and Passenger Forecasting (Airlines)
Demand Forecasting (O&G, Retail, Pharma, Govt)
Resource Forecasting (Contact Center, Hospitals, Service Industry)
What’s in for You?
Learn foundations of Forecasting: Understand the basics of time series and machine learning-based forecasting.
Build simple forecasting models.
Develop advanced models using explanatory variables.
Evaluate the impact of events such as natural disasters, public policy changes, or sales promotions.
Hands-On Workshop:
Get practical experience with SAS Visual Forecasting, a low-code/no-code solution, using real-world retail data.
Why this event is significant?
Hackathons like this are more than just competitions; they are innovation incubators.
Participants will:
Learn: Gain exposure to cutting-edge tools like SAS Visual Forecasting, directly from industry experts.
Apply: Develop hands-on experience with forecasting techniques to solve real challenges.
Engage: Collaborate with peers, industry leaders, and academics in a stimulating, fast-paced environment.
Win: Compete for recognition and prizes that underscore the value of their innovative solutions.
Previous events have shown that hackathons spark groundbreaking ideas. For instance, similar initiatives have resulted in solutions like improved water distribution models in arid regions and optimized resource allocation for non-profit organizations.
Who Can Participate?
Egyptian students: Undergraduate & Postgraduate
Working Professionals
Pre requisites:
Participants are free to bring open datasets for Forecasting under Sustainability or Data4good.
Bring your own gadgets. (Laptops)
Should be familiar with SAS Viya, Python or similar.
