Technology largely determines economic development and its impact on the environment; yet technological change is one of the least developed parts of existing global change models. This paper reviews two approaches developed at the International Institute for Applied Systems Analysis, both of which use the concept of technological learning and aid modeling of technological change. The first approach is a micromodel ("bottom-up") of three electricity generation technologies that rigorously endogenizes technological change by incorporating both uncertainty (stochasticity) and learning into the model＇s decision rules. This model, with its endogenous technological change, allows radical innovations to penetrate the energy market and generates S-shaped patterns of technological diffusion that are observed in the real world. The second approach is a macro ("top-down") model that consists of coupled economic- and technological-system models. Although more stylistic in its representation of endogenous technological change, the macro model can be applied on a worldwide scale and can generate long-term scenarios that are critical for policy analysis. Both the micro- and macro models generate radical departures from currently dominant technological systems ("surprises"), including long-term scenarios with low carbon and sulfur emissions. Our focus is modeling, but for policy, the work underscores the need for huge investments before environmentally superior technologies can compete in the market.