As we approach 2026, sustainability is no longer just a desirable goal—it has become a crucial business necessity. Companies are increasingly pressured by regulators, investors, and consumers to demonstrate their environmental and social impacts.
In this context, analytics has become essential to sustainability efforts, allowing organizations to accurately measure, predict, and optimize their actions. The combination of analytics and sustainability is a strong force driving change across industries globally.
This guide walks through what sustainability analytics actually means today, the latest trends, a concrete real-world case study, future directions, and practical takeaways.
Why Data is the New Green
Sustainability, once a peripheral concern, has moved firmly onto the boardroom agenda. The global mandate to accelerate the net-zero transition, coupled with increasing climate and nature risks, requires organizations to fundamentally change their operations.
Analytics—the process of examining data to draw conclusions and inform decision-making—is the catalyst for this transformation. It allows companies to:
- Measure Impact: Accurately quantify their carbon footprint (Scope 1, 2, and 3 emissions), resource usage, and social performance.
- Optimize Operations: Use real-time data to find inefficiencies, reduce waste, and lower energy consumption.
- Ensure Compliance & Transparency: Meet rigorous new reporting standards, providing auditable and credible data to investors and regulators.
In essence, you can’t manage what you don’t measure, and for sustainability, analytics provides the most reliable yardstick.
Key Trends and Insights in 2025-2026
1. Regulation-Driven Data Quality and Assurance
- Shift from Spreadsheets to Platforms: Manual reporting is giving way to specialized ESG platforms that integrate IoT, ERP, and cloud systems for automated data collection.
- Focus on Double Materiality: Analytics helps assess both financial materiality (how sustainability affects the company) and impact materiality (how the company affects society and the environment).
- Blockchain for Supply Chain Trust: Blockchain is emerging as a tool for tamper-proof, auditable records, ensuring transparency in ethical sourcing and emissions tracking.
2. AI and Predictive Analytics for Climate Mitigation
- Energy Optimization: AI models predict energy demand and fine-tune HVAC systems, cutting costs and emissions.
- Climate Risk Forecasting: Machine learning analyzes climate datasets to anticipate extreme weather events, guiding resilient infrastructure planning.
- Precision Agriculture: AI-powered sensors and satellite imagery optimize irrigation, fertilization, and pest control, reducing waste and emissions in farming.
3. Supply Chain and Nature-Related Analytics
- Enhanced Supply Chain Transparency: Regulations like the EU’s Corporate Sustainability Due Diligence Directive (CSDDD) require companies to track sustainability practices across their entire value chain. Analytics enables visibility into Scope 3 emissions and supplier risks.
- Biodiversity Integration: Frameworks such as the Task Force on Nature-related Financial Disclosures (TNFD) are driving businesses to measure and manage their impacts on ecosystems, embedding biodiversity into corporate risk models.
Using Data to Solve Issues of Sustainability
Case 1: AI + HVAC Optimization in a Manhattan Office Tower
Overview: The owners of 45 Broadway in Manhattan deployed an AI-driven system to autonomously manage its Heating, Ventilation, and Air Conditioning (HVAC) systems. This was a direct response to New York City’s Local Law 97 (LL97), which sets strict, data-driven emissions caps for large buildings.
The system uses predictive analytics to ingest real-time sensor data and weather forecasts, then continuously micro-tunes thousands of HVAC components to maintain comfort with minimum energy.
This resulted in a 15.8% reduction in HVAC-related energy use and a substantial drop in CO₂e emissions, demonstrating how AI turns existing infrastructure into a continuously optimized, lower-carbon asset.
Case 2: Chain-Wide Energy Analytics at Dollar Tree
Overview: Dollar Tree, a major US retailer, piloted a large-scale, autonomous HVAC optimization system across over 600 stores (6.6 million sq. ft.) to meet its goal of cutting Scope 1 and 2 emissions by 50% by 2032.
The machine learning system pulls operational data from each store’s Building Management System (BMS) and external weather forecasts. It automatically adjusts rooftop units, shortens runtimes, and optimizes system behavior.
This analytics at scale approach delivered quantifiable results, including over 7.9 million kWh reduction in electricity consumption and over 5,600 tCO₂e reduction in emissions in a single year, leading to a planned expansion to thousands more stores.
Case 3: Solar + Data-Driven Planning at the University of New Orleans
Overview: The University of New Orleans (UNO) installed a large solar array on campus in 2024 as part of a 25-year energy partnership. While the solar panels are a physical asset, the success of the project is entirely quantified through energy and emissions modeling.
UNO used analytics to size the array, forecast long-term kWh production (offsetting $\sim 17\%$ of annual consumption), and calculate the long-term financial return ($\$ 2.1$ million in annual savings projected).
This case highlights how institutions use analytics for capital planning and climate communications, translating kilowatt-hours and dollars into relatable impact metrics for stakeholders.
Case 4: Healthcare Emissions Dashboards at UCLA Health
Overview: UCLA Health, a large US health system, developed a public-facing, multi-tab Sustainability Dashboard, including a dedicated Greenhouse Gas (GHG) tracker. This system uses data visualization (via platforms like Power BI) to track and publicly share progress toward national commitments, such as the White House Health Sector Climate Pledge (a commitment to cut emissions $50\%$ by 2030).
The dashboards track complex data, broken out by Scope 1 and Scope 2 emissions, and extend to metrics like water use and “Greening the OR” protocols. This is a classic example of “analytics for accountability,” making performance transparent to the public, regulators, and clinical staff to drive operational change.
Case 5: Campus-Wide Sustainability Dashboards at the University of Pittsburgh
Overview: In 2025, the University of Pittsburgh (Pitt) expanded its Pitt Sustainability Dashboards to include building-centric metrics for energy and water use intensity. The university uses this system to track progress toward its ambitious targets, such as achieving carbon neutrality by 2037.
This case exemplifies “analytics as infrastructure,” where raw utility, transportation, and waste data are continuously fed into interactive dashboards. These tools provide a clear line of sight from data to institutional goals and support ongoing operational decision-making, treating sustainability data as a core system, not a one-off report.
Key Points to Run With
Sustainability and analytics are now closely linked. Real-world examples, including 45 Broadway, Dollar Tree, UNO, UCLA Health, and Pitt, demonstrate how organizations leverage data to reduce energy consumption, lower emissions, save costs, and demonstrate progress to regulators and stakeholders.
Analytics plays a key role in various aspects of this process—optimizing existing systems, managing performance across multiple locations, planning large capital investments, and increasing transparency around emissions and resource use.
As regulations become stricter and expectations grow, successful organizations will treat sustainability data as essential infrastructure rather than an afterthought—by investing in data quality, advanced tools, and personnel skilled in both ESG and analytics.
While analytics cannot replace leadership or culture, it serves as the driving force that enables meaningful sustainability initiatives to expand, transforming net-zero commitments into tangible, measurable, and verifiable outcomes.
Frequently Asked Questions
Data analytics and data science contribute significantly to environmental, social, and economic sustainability. These fields offer new career paths for those interested in using their analytical skills to benefit the planet
Sustainability focuses on three main pillars: environmental, economic, and social. It aims to meet current needs without compromising the ability of future generations to meet theirs, balancing present actions with future consequences
Data can support sustainability by informing business practices that consider environmental, economic, and social impacts. This growing awareness is driving companies to integrate analytics into their strategic planning to meet sustainability goals
IBM is a notable example. They acquired Envizi, a software company specializing in environmental data analytics, to enhance their sustainability initiatives. IBM’s Environmental Intelligence Suite exemplifies how data can be used to assess and improve environmental business practices
Companies are increasingly committing to sustainability, with large investments like JPMorgan Chase’s plan to finance over $2.5 trillion in sustainability solutions by 2030. Technological innovations, such as Equinix’s generator-less data centers and renewable energy initiatives, are setting new standards in sustainable practices
The UN’s Sustainable Development Goals (SDGs) highlight the critical role of big data in tackling environmental and economic challenges. Data science and analytics provide valuable insights for achieving these goals, emphasizing the importance of data in addressing global issues