11.6.5 Capital stock and inertia determinants of transitions in the second quarter of the century
The scope for change, and the rate of transition, will be constrained by the inertia of the relevant systems. The IPCC SAR Summary for Policymakers noted that ‘the choice of abatement paths involves balancing the economic risks of rapid abatement now (that premature capital stock retirement will later be proved unnecessary) against the corresponding risk of delay (that more rapid reduction will then be required, necessitating premature retirement of future capital stock).’ Capital stock is therefore a central consideration.
The time scales of stock turnover vary enormously between different economic sectors, but appear to be very long for most greenhouse-gas emitting sectors. Typical investment time scales are several decades for forestry, coal mining and transporting facilities, oil & gas production, refineries, and power generation. On the demand side, observed time scales for typical industrial stock using energy are estimated to range from decades to a century (Worrell and Biermans, 2005; see Table 11.17). The time scales for other end-use infrastructure (e.g. processes, building stock, roads and rail) may be even longer, though components (such as heaters, cars) may have considerably faster turnover.
Table 11.17: Observed and estimated lifetimes of major GHG-related capital stock
|Typical lifetime of capital stock ||Structures with influence > 100 years |
|Less than 30 years ||30-60 years ||60-100 years |
Water heating and HVAC systems
However, Lempert et al. (2002) caution against overly simplistic interpretations of nameplate lifetimes, emphasizing that they ‘are not significant drivers [of retirement decisions] in the absence of policy or market incentives’ and that ‘capital has no fixed cycle’. This can be crucial to rates of decarbonization. A study of the US paper industry found that ‘an increase in the rate of capital turnover is the most important factor in permanently changing carbon emission profiles and energy efficiency’ (Davidsdottir and Ruth, 2004). Similarly, emission reductions in the UK power sector were largely driven by the retirement of old, inefficient coal plant during the 1990s, through sulphur regulations which meant plant owners were faced with the choice of either retrofitting stock or retiring it (Eyre, 2001). Such micro-level ‘tipping points’ at which investment decisions need to be taken may offer ongoing opportunities for lower cost abatement.
Energy system inertia provides another dimension to the time scales involved. It has taken at least 50 years for each major energy source to move from 1% penetration to a major position in global supplies. Such long time scales – and the even longer periods associated with interactions between systems – imply that, for stabilization, higher inertia brings forward the date at which abatement must begin to start meeting any given constraint, and lowers the subsequent emissions trajectory (Ha-Duong et al., 1997). In the context of stabilization at 550 ppm CO2, van Vuuren et al. (2004) and Schwoon and Tol (2006) demonstrate that higher inertia in the energy system brings forward mitigation.
However, beyond a certain point, inertia can also dramatically increase the cost of stabilization, particularly when infrastructure constraints are likely to limit the growth of new industries more than established ones. Manne & Richels (2004) illustrate that if global total contributions from new (renewable) power sources are limited to 1% by 2010 and treble each decade thereafter, the world has little choice other than to continue expanding carbon-intensive power systems out to around 2030. This feature appears to drive their finding of high costs for 450 ppm CO2 stabilization, since much of this stock then has to be retired in subsequent decades to meet the constraint. This pattern contrasts sharply with some other studies, such the MIT study (McFarland et al., 2004) that states an opposing time profile based partly upon the rapid deployment of natural gas plant, including CCS. Crassous et al. (2006) also find high costs by assuming that long-lived infrastructure construction continues without foresight over the century. If low-carbon transport technologies do not become available quickly enough, the economy is squeezed as carbon controls tighten. They also show that the early adoption of appropriate infrastructure avoids this squeeze and allows lower costs for carbon control. Drawing partly on more sociological literature, and the systems innovation literature (Unruh, 2002), tends to support a view that we are now ‘locked in’ to carbon-intensive systems, with profound implications: ‘Carbon lock-in arises through technological, organizational, social and institutional co-evolution ... due to the self-referential nature of [this process], escape conditions are unlikely to be generated internally.’
Lock-in is less of a problem for new investment in rapidly developing countries where the CDM is currently the principal economic incentive to decarbonize new investments. The Shrestha (2004) study cited above illustrates how the structure of power sectors could be radically different depending upon the value of Certified Emission Reduction (CER) units. Their finding that an effective CER price of 20 US$/tCO2 from 2006 onwards could drive a radical switch of investment from new coal plants and primarily to natural gas and renewables in the three Asian countries studied would not only represent a large saving in CO2 emission, but a totally different capital endowment that would sustain far lower emission trajectories after 2030. Again, this supports the conclusion that carbon prices of this order play a very important role, with their potential to forestall the construction of carbon-intensive stock in developing countries. Diverse policies that deter investment in long-lived carbon-intensive infrastructure and reward low-carbon investment may maintain options for low stabilization levels in Category I and II at lower costs.
At a global scale, van Vuuren et al. (2004) present a systematic set of results showing the effects of different time profiles for carbon prices in studies that combine the representation of inertia and induced innovation. A carbon price that rises linearly to 82 US$/tCO2 by 2030 reduces emissions by 40% by 2030 if the tax is introduced in 2020 and raised sharply, but by 55% if it is introduced in 2000 and increased more slowly. Van Vuuren et al. do not describe the impact on subsequent trajectories, but clearly the capital stock endowment differs substantially. Moreover, Lecocq et al. (1998) demonstrate that, in the face of uncertainty, an efficient approach may include greater effort directed at reducing investment in longer-lived carbon intensive infrastructure, over and above the incentives of any uniform carbon price.
Chapter 3 (Section 3.6) emphasizes the importance of ‘hedging’ strategies based upon sequential ‘act-then-learn’ decision-making. Mitigation over the next couple of decades that would be consistent with enabling stabilization at lower levels (Categories I, II or III) does not irrevocably commit the world to such levels. The major numerical addition to the literature in this vein appears to be that of Mori (2006). Using the MARIA model, he analyses optimal strategies to limit the global temperature increase to 2.5 ºC given uncertainty about climate sensitivity in the range of 1.5-4.5 ºC per doubling of CO2-equivalent. When there is no uncertainty, only the above-average sensitivities require significant mitigation in the next few decades. In the context of uncertainty, however, the optimal strategy is to keep global emissions relatively constant at present levels until the uncertainty is resolved, after which they may rise or decline depending upon the findings.