Sectoral crediting mechanisms such as sectoral no-lose targets have been proposed as a way to provide incentives for emission reductions in developing countries as part of an international climate agreement, and scale up carbon trading from the project-level Clean Development Mechanism to the sectoral level.

Countries would generate tradable emission credits (offsets) for reducing emissions in a sector below an agreed crediting baseline. However, large uncertainties in the regulator's predictions of the counterfactual business-as-usual baseline are likely to render sectoral no-lose targets an extremely unattractive mechanism in practice, at least for the transportation case study presented here. Given these uncertainties, the regulator faces a tradeoff between efficiency (setting generous crediting baselines to encourage more countries to opt in) and limiting transfer payments for non-additional offsets (which are generated if the crediting baseline is set above business-as-usual).

The first-best outcome is attainable through setting a generous crediting baseline. However, this comes at the cost of either increased environmental damage (if developed country targets are not adjusted to account for non-additional offsets), or transfers from developed to developing countries that are likely to be too high to be politically feasible (if developed country targets are made more stringent in recognition that many offsets are nonadditional). A more stringent crediting baseline still generates a large proportion of non-additional offsets, but renders sectoral no-lose targets virtually irrelevant as few countries opt in.

The majority of rural residents in China are dependent on traditional fuels, but the quality and quantity of existing data on the process of fuel switching in rural China are insufficient to have a clear picture of current conditions and a well-grounded outlook for the future.

Based on an analysis of a rural household survey data in Hubei province in 2004, we explore patterns of residential fuel use within the conceptual framework of
fuel switching using statistical approaches. Cross-sectional data show that the transition from biomass to modern commercial sources is still at an early stage, incomes may have to rise substantially in order for absolute biomass use to fall, and residential fuel use varies tremendously across geographic regions due to disparities in availability of different energy sources. Regression analysis using logit and tobit models suggest that income, fuel prices, demographic characteristics, and topography have significant effects on fuel switching.

Moreover, while switching is occurring, the commercial energy source which appears to be the principal substitute for biomass in rural households is coal. Given that burning coal in the household is a major contributor to general air pollution in China and to negative health outcomes due to indoor air pollution, further transition to modern and clean fuels such as biogas, LPG, natural gas and electricity is important. Further income growth induced by New Countryside Construction and improvement of modern and clean energy accessibility will play a critical role in the switching process.