Climate change continues to be high on the international political agenda. As the world's top carbon emitter, China is a key player in climate negotiations and has been facing mounting domestic and international pressure to commit to a mandatory emission target. A sound understanding of the level and heterogeneity of marginal carbon abatement cost (MAC) across localities, sectors or even firms would inform policy makers about the potential cost advantage of a market based approach over the traditional command and control approach (Newell and Stavins, 2003).
The literature on the mitigation costs of greenhouse gases (GHG) has been extensively reviewed (Repetto and Austin, 1997; Weyant and Hill, 1999; Lasky, 2003; Fischer and Morgenstern, 2005; Kuik et al., 2009). A large part of this existing literature is based on the use of integrated systems forecasting models that derive the MAC of GHG emissions as shadow prices representing the economic growth that would be forgone in the pursuit of a Kyoto-based mitigation or stabilization target. These prices are often estimated for various future time horizons and for different sets of constraints and assumptions about the economic system. Such shadow price information is most useful for long-term planning and policy-making.
Another strand of the literature estimates the MACs as the shadow price of pollution mitigation within a distance function framework. The approach is simpler. The distance function is a generalization of the production function and provides a means of representing a technology that produces multiple outputs (in this case, good and bad outputs). The estimated function captures the trade-offs or marginal rate of transformation (MRT) between outputs. Reduction in undesirable outputs (or abatement) is costly to the producer as it would require the use of more inputs or reductions in desirable outputs as inputs are diverted to abatement activities. Thus one can measure the cost of abatement as the desirable output that would be forgone in the process of reducing an undesirable output by one unit. This shadow price can be multiplied by the price of the desirable output to provide an empirical and monetary measure of the cost of abatement. The distance function models use historical data and do not need to make widely varying and strong assumptions about future economic development and technological progress. As these estimates reflect recent evolutions in marginal abatement costs (MACs), they are more useful for identifying existing low cost opportunities for carbon reduction and for evaluating the potential cost savings from market-based policy instruments. That is, they are more relevant for immediate use or policy design exercises.
However, existing empirical estimates of the MAC of GHG mitigation in China obtained from these approaches vary widely and range from a few to thousands of US dollars per metric tonne. This variability in cost estimates undermines the scientific support for policy change as policy makers are usually reluctant to implement a mandatory GHG mitigation policy without a firm understanding of the true costs. In the last few years, there have appeared a few studies investigating China's GHG MAC estimates. Du et al. (2013) provides a thorough review of this literature which has mainly focused on carbon dioxide with a minor proportion of it investigating sulfur dioxide mitigation costs. Our study builds on this literature but makes a number of original contributions. We show that such variability can be explained by the differences in the coverage of inputs and outputs, the set of assumptions made on the production technology, the constraints imposed by various distance functions, and whether the MAC estimated is conditional or unconditional. We also compare estimated MACs with observed carbon prices from China's recently piloted carbon trading markets.
Firstly, given China's heavily coal-dependent energy structure and the way that carbon emissions were calculated in empirical literature, one would expect energy consumption and carbon emissions to be highly correlated. This high correlation would have significant distorting impact on the MAC estimates in studies including energy as a good output and carbon as a bad output. In cases where the correlation is high, it would be difficult to reliably estimate MACs. However, one can get around this problem by aggregating inputs or removing the energy variable to allow for estimation and comparison across different approaches.
Secondly, all previous studies have either used primary energy use as the energy input or not provided a clarification of their energy input definitions. In this paper, we define energy input as final rather than primary energy consumption. It is important to make this clarification and use final rather than primary energy as the former is a more appropriate measure of the actual energy contributing to production. Using primary energy would overestimate the actual energy input in some provinces and underestimate it in others because energy demand and supply are not well matched across Chinese provinces. Some provinces produce and export while others import substantial secondary energy. In energy exporting provinces, primary energy used to produce secondary energy that is then exported should be counted as a raw material rather than as energy input. At the same time, imported secondary energy should be counted as part of actual energy input use in energy-importing provinces. In short, it is the final consumption, not the primary use, which defines the amount of energy that contributes to the final economic production of a province. Studies also differ in the calculation of GHG emissions. Some only account for energy-related GHG emissions while others also include emissions from production processes. The scope of emissions considered also influences the estimated MAC.
Thirdly, in recent years, economists have started to move beyond evaluating regulatory effects on a pollutant-by-pollutant basis since the interaction between different pollutant mitigation activities is important (Greenstone, 2003; Burtraw et al., 2003; Gamper-Rabindran, 2006; Considine and Larson, 2006). However, all previous studies on China's MAC of GHG mitigation have focused on a single undesirable output--either carbon dioxide or sulfur dioxide. Environmental policies often require simultaneous reduction of several pollutants. The MAC estimated from a distance function including a single bad output would be less informative about the overall compliance cost of such policies. The MAC estimated as such is unconditional marginal abatement cost and it is not appropriate to consider the sum of MACs estimated individually as the overall compliance cost of simultaneous mitigation targets. Some airborne pollutants are highly correlated (i.e. jointly produced). For example, given China's heavily coal-dependent energy structure, a policy aiming to mitigate carbon emissions will often have the co-benefit of mitigating other pollutants such as sulfur dioxide, soot and dust. This will have a significant impact on the estimation of MAC. If multiple pollutants are jointly produced then the productivity impact that we associated with one pollutant should also be associated with other pollutants. The unconditional MAC of a pollutant may be very different from the MAC estimated conditional upon the emissions of other correlated pollutants remaining unchanged. A distance function including multiple bad outputs, on the other hand, allows estimation of conditional marginal costs and the overall cost of meeting simultaneous mitigation targets.
Lastly, the choice of distance function in the empirical literature is largely arbitrary. However, the MACs estimated are shown to be very much sensitive to the parameterization, the assumptions and constraints imposed, and the mapping schemes which are the paths in which the inputs or outputs are scaled toward the technology frontier in various distance functions (Vardanyan and Noh, 2006). Studies that do provide justifications for their choices often fail to consider the nature of the policy environment and associated interpretation of their results. Because the estimated MACs can be interpreted as the value of a pollution permit or allowance in a market environment (Coggins and Swinton, 1996), one can always compare estimated MACs with observed carbon prices in the market to assess the appropriateness of the production technology specification and other parameters of an empirical estimation. This was impossible in the past but is feasible now because of China's recently piloted carbon trading markets. This study provides the first comparison between observed and estimated carbon prices and reflects on the implications of the choice of production technology specification and mapping schemes within the distance function framework for shadow price estimation.
The paper is organized into five sections. The next section presents a review of the literature. The methods and data used in the study are described in the third section. The final two sections discuss the results and provide conclusions.
In spite of the size of China's carbon emission contributions and the significance of compliance cost that mitigation policies could impose, there is only a small number of studies investigating the MAC of carbon mitigation using a distance function approach. Table 1 summarizes empirical estimates of China's MAC for carbon obtained using various distance functions. As shown in Table 1, all studies were conducted fairly recently. The results from these studies are not directly comparable as the studies differ in the chosen distance function, the period covered and the level of decision management unit (DMU). Nevertheless, the empirical estimates of the MAC of carbon emissions in China based on the distance function approach vary widely from merely a...