IPCC Fourth Assessment Report: Climate Change 2007
Climate Change 2007: Working Group III: Mitigation of Climate Change Technological change and the costs of achieving climate targets

Rates of technological change are also critical determinants of the costs of achieving particular environmental targets. It is widely acknowledged that technological change has been a critical factor in both cost reductions and quality improvements of a wide variety of processes and products.[27] Assuming that technologies in the future improve similarly to that observed in the past enables experts to quantify the cost impacts of technology improvements in controlled modeling experiments. For instance, Edmonds et al. (1997, compare Figure 3.36 in Chapter 3) analyzed the carbon implications of technological progress consistent with historical rates of energy technology change. Other studies have confirmed Edmonds’ (1997) conclusion on the paramount importance of future availability and costs of low-emission technologies and the significant economic benefits of improved technology that, when compounded over many decades, can add up to trillions of dollars. (For a discussion of corresponding ‘value of technological innovation’ studies see Edmonds and Smith (2006) and Section 3.4, particularly Figure 3.36 in Chapter 3). However, to date, model calculations offer no guidance on the likelihood or uncertainty of realizing ‘advanced technology’ scenarios. However, there is an increasing number of studies (e.g. Gerlagh and Van der Zwaan, 2006) that explore the mechanisms and policy instruments that would need to be set in place in order to induce such drastic technological changes.

The treatment of technological change in an emissions and climate policy modeling framework can have a huge effect on estimates of the cost of meeting any environmental target. Models in which technological change is dominated by experience (learning) curve effects, show that the cost of stabilizing GHG concentrations could be in the range of a few tenths of a percent of GDP, or even lower (in some models even becoming negative) – a finding also confirmed by other modelling studies (e.g. Rao et al., 2005) and consistent with the results of the study by Gritsevskyi and Nakicenovic (2000) reviewed above, which also showed identical costs of ‘high’ versus ‘low’ long-term emission futures. This contrasts with the traditional view that the long-term costs[28] of climate stabilization could be very high, amounting to several percentage points of economic output (see also the review in IPCC, 2001).

Given the persistent uncertainty of what constitutes ‘dangerous interference with the climate system’ and the resulting uncertainty on ultimate climate stabilization targets, another important finding related to technology economics emerges from the available literature. Differences in the cost of meeting a prescribed CO2 concentration target across alternative technology development pathways that could unfold in the absence of climate policies are more important than cost differences between alternative stabilization levels within a given technology-reference scenario. In other words, the overall ‘reference’ technology pathway can be equally, if not more, important in determining the costs of a given scenario as the stringency of the ultimate climate stabilization target chosen (confer Figure 2.2).

In a series of alternative stabilization runs imposed on the SRES A1 scenarios, chosen for ease of comparability as sharing similar energy demands, Roehrl and Riahi (2000) confer also IPCC (2001) have explored the cost differences between four alternative baselines and their corresponding stabilization targets, ranging from 750 ppmv all the way down to 450 ppmv. In their calculations, the cost differences between alternative baselines are also linked to differences in baseline emissions: advanced post-fossil fuel technologies yield both lower overall systems costs as well as lower baseline emissions and hence lower costs of meeting a specified climate target (confer the differences between the A1C and A1T scenarios in Figure 2.2). Their findings are consistent with the pattern identified by Edmonds et al. (1997) and Gerlagh and Van der Zwaan (2003). Cost differences are generally much larger between alternative technology baselines, characterized by differing assumptions concerning availability and costs of technologies, rather than between alternative stabilization levels. The IEA (2004) World Energy Outlook also confirms this conclusion, and highlights the differential investment patterns entailed by alternative technological pathways.[29] The results from the available literature thus confirm the value of advances in technology importance in lowering future ‘baseline’ emissions in order to enhance feasibility, flexibility, and economics of meeting alternative stabilization targets, in lowering overall systems costs, as well as in lowering the costs of meeting alternative stabilization targets.

Figure 2.2

Figure 2.2: The impacts of different technology assumptions on energy systems costs and emissions (cumulative 1990–2100, systems costs (undiscounted) in trillion US$) in no-climate policy baseline (reference) scenarios (based on the SRES A1 scenario family that share identical population and GDP growth assumptions) and in illustrative stabilization scenarios (750, 650, 550 and 450 ppm respectively). For comparison: the total cumulative (undiscounted) GDP of the scenarios is around 30,000 trillion US$ over the 1990–2100 time period.

Source: Roehrl and Riahi (2000).

A robust analytical finding arising from detailed technology-specific studies is that the economic benefits of technology improvements (i.e. from cost reductions) are highly non-linear, arising from the cumulative nature of technological change, from interdependence and spillover effects, and from potential increasing returns to adoption (i.e. costs decline with increasing market deployment of a given technology).[30] (A detailed review covering the multitude of sources of technological change, including the aforementioned effects, is provided in Chapter 11, Section 11.5, discussing so-called ‘induced technological change’ models).

  1. ^  Perhaps one of the most dramatic historical empirical studies is provided by Nordhaus (1997) who has analyzed the case of illumination since antiquity, illustrating that the costs per lumen-hour have decreased by approximately a factor of 1,000 over the last 200 years. Empirical studies into computers and semiconductors indicate cost declines of up to a factor of 100,000 (Victor and Ausubel, 2002; Irwin and Klenov, 1994). Comparable studies for environmental technologies are scarce.
  2. ^  Note here that this statement only refers to the (very) long term, i.e. a time horizon in which existing capital stock and technologies will have been turned over and replaced by newer vintages. In the short term (and using currently or near-term available technologies) the costs of climate policy scenarios are invariably higher than their unconstrained counterparts.
  3. ^  The IEA (2004) ‘alternative scenario’, while having comparable total systems costs, would entail an important shift in investments away from fossil-fuel-intensive energy supply options towards energy efficiency improvements, a pattern also identified in the scenario study of Nakicenovic et al. (1998b).
  4. ^  This is frequently referred to as a ‘learning-by-doing’ phenomenon. However, the linkages between technology costs and market deployment are complex, covering a whole host of influencing factors including (traditional) economics of larger market size, economies of scale in manufacturing, innovation-driven technology improvements, geographical and inter-industry spillover effects, as well as learning-by-doing (experience curve) phenomena proper. For (one of the few available) empirical studies analyzing the relative contribution of their various effects on cost improvements see Nemet (2005). A more detailed discussion is provided in Chapter 11.