Big Green InnovationsInnovation that Matters – for our company and for the world Barbara Eckman Senior Technical Staff Member Member, IBM Academy of Technology Big Green Innovations IBM
1971 1996 In remarks at a White House briefing on climate change, Vice President Gore applauded IBM's new PFC emissions reduction goal. "These developments send a strong message: A healthy environment and a healthy economy go hand in hand," the Vice President said. "Through technology and innovation, we can turn this challenge into a huge opportunity for business and for America. And the sooner we act, the easier it will be." IBM’s commitment to energy & environment 2005 1992 2006 IBM becomes charter member of EneryStar program IBMers “jam” on innovations for a better planet, and IBM invests $20M 40% reduction in IBM’s total worldwide CO2 emissions attributable solely to its energy conservation efforts between 1990 and 2005. 1990 Big Green Innovations Intell. Utility Network IBM formally establishes a Corporate policy on Environmental Affairs Collaborating to solve problems
Big Green Innovations • Genesis in the “Innovation Jam”, November 2006 • Big Green Innovation: a new business unit in IBM focused on applying the company's technology and innovation expertise to emerging environmental opportunities, such as advanced water modeling, water filtration via nanotechnology, and efficient solar power systems. • Mission was broadened in February 2007 • Through collaborative innovation with our partners and clients we will use IT to optimize the use of energy and water in enterprises, industries and countries, leveraging our deep computational, science & technology expertise.
Big Green Innovations portfolio Alternative Energy ** Green Operations and Supply Chain Advanced Water Management • “Minimizing the carbon footprint of energy creation, storage and transmission via alternative energies”. • PV technologies • Scenario modeling: • Environmental • C02 • Demand/availability • New technologies • Power grid management • Sensors • Systems integration • Analytics • Consulting services • “Maximizing the efficiency & effective-ness of water provision for human, agricultural & industrial consumption”. • Scenario modeling: • Weather • Demand • Availability • New technologies • Water network management • Sensors • Systems integration • Filtration technologies • Consulting services • “Minimizing the carbon footprint of companies’ operations and supply chains”. • Diagnosis • Modeling/analysis • Process and product redesign • Consulting services • (Green buildings • Data centers++ • Fabs++ • Offices++) ++Separate but related developments **Working with Energy and Utilities team ComputationalModeling • Support for computational and business/process modeling associated with improving global environmental health • “Center for Earth Systems Intelligence” • Support for the above and areas such as weather, climate, hydrology, pollution, pandemics…. • Support for simulation & optimization of energy systems, including biofuels
Water • 41% of the Earth’s population (2.3 billion) live in water-stressed areas • Another billion by 2025 • Water scarcity & toxicity are significant issues • 1B today do not have clean, piped water (WHO) • Emerging geographies areexpected to have 40% of the world’s population by 2025 • China's Yellow River has been diverted to the point that it no longer flows to the sea. Meanwhile the water tables of Beijing and other large northern cities are falling dramatically as a result of the pumping of groundwater. Sustainable Developments: The Challenge of Sustainable Water - December 2006 Scientific American Magazine (Jeffrey D. Sachs)
IBM teams with The Nature Conservancy Great Rivers Partnership (1 of 3) Paraguay-Parana River Basin, Brazil Mississippi River, USA Yangtze River, China Photos courtesy of The Nature Conservancy
IBM teams with The Nature ConservancyGreat Rivers Partnership (2 of 3) • Combine science-driven conservation with I/T expertise and computing power • Build a new software modeling framework that will allow users to simulate the behavior of river basins around the world • Inform policy and management decisions that conserve the natural environment and benefit the people who rely on these resources
IBM teams with The Nature ConservancyGreat Rivers Partnership (3 of 3) • Sample questions: • What impact will development have on water quality for a village downstream? • Will clear-cutting a forest in the upper part of a river's watershed imperil fish stocks local people depend on for food? • Data: climate, rainfall, land cover, vegetation and biodiversity • Software: • Integrative modeling framework • 3-D visualization • Scenario forecasting tools
Building a flexible modeling framework: Goals and Challenges Model coupling issues • Define multi-step analytic workflow • “Plug and play” models of choice • Make use of all relevant input data • Different formats • Different locations • Different semantics • Run and rerun simulations representing different scenarios • Building a dam at different sites • Allowing agricultural land use vs. forest vs. development • Ease of use for a wide variety of stakeholders • Research scientists, government agencies, land managers, … Data Integration, Data Quality Performance Implications Visualization is Key!
Model coupling/workflow issues • Analytic workflows are typically hard-coded into single programs (“models”) • Models have “hidden” assumptions that can conflict with other models’ • Can lead to bad simulations, incorrect predictions, bad land management decisions! Proposed Solution: • Divide models conceptually into atomic components • Via wrappers and interfaces, eg wsdl • Express workflows as pipelines of components • Provide metadata for components • Input parameters, input data • Output data • Semantics of component (including assumptions) • Express metadata in XML format • Emerging standards • CUAHSI WaterML • OpenMI (Open Modeling Interface and Environment)
Comparison of IT issues: Eco-Informatics 2004 and Global Climate Change Today • Modeling and Simulation • Standards-based interfaces • Modeling infrastructure • Performance supporting interative predictive scenarios • Data quality problems • Same • Information Integration • Same • Social and human aspects • Same • Visualization • Wide variety of users