A traditional approach to developing a GHG mitigation strategy and policy relies heavily on marginal abatement cost curves (MACCs). A MACC describes the relationship between emission levels and the (economic) cost of additional GHG reduction. Total abatement on the horizontal axis is plotted against the cost of abatement technology options on the vertical axis. Points near the origin have lower, or even negative marginal cost with abatement growing more expensive per unit moving out along the curve. Underlying segments of the curve are the abatement technologies employed. Efficient abatement activities are identified by falling below a cost threshold, usually the shadow price of carbon (corresponding to permit prices or taxes). These curves can be derived at the firm, national, regional, or global level and are good at directing the adoption of available technical abatement measures over the short-term. However, when determining long-term strategies for abatement, such as where to invest in developing new technology, the MACC has some weaknesses.
First, such an approach is static in terms of economic structure. Changes in economic structure can be driven by many factors such as economic growth, population change or even the adoption of abatement technologies and instruments. The resulting structural changes will impact the relative importance of abatement technologies and instruments, and be a major driver in terms of where to focus the development of new technology. Economic sectors are interlinked such that a change in one sector will have an economy-wide impact. For example, energy production and uses are the main sources of anthropogenic GHG emissions. A change in energy consumption is likely to affect its production as well as the sectors used as inputs in its production in the long run.
Second, the MACC does not capture the linkages between economic sectors and some important economic relationships in the major emission drivers. Energy efficiency is often regarded as an important tool in improving the economic and environmental efficiency. However, recent studies suggest that technical efficiency and environmental efficiency related to energy use is not always consistent (Hanley, McGregor et al. 2009). This is because even if demand for energy itself decreases, it is still an important input to produce other goods in the economy that may experience a simultaneous increase in demand.
Finally, the MACC is not related to the decision-making process. Unless under a command and control regime, agents in the economy (i.e. households and firms) respond to price signals established by environmental policy. For some specifications of policy mechanisms, agents may reduce output (McKitrick 1999) or consumption (Kilian 2008) instead of investing in abatement technology leading to a re-allocation of factors in the economy and hence structural shift that is not captured in the MACC framework. However, in this paper we will focus mainly on addressing the first issue.
Approaches that capture economic structural change are those based on input-output tables, such input-output analysis and general equilibrium analysis. These models represent the structure of a (usually national) economy by a system of equations that account for each sectors output, and maps where it is consumed in the economy. If certain assumptions hold, the coefficients of the system can be subjected to matrix operations to determine various multiplier effects, undergo optimization problems using linear programming techniques, or simply provide a “snapshot” of the economy (Raa 2005).
IO analysis was extended to address environmental concerns by Wassily Leontief as early as 1970 so that “any reduction or increase in the output level of pollutants can be traced either to changes in the final demand for specific goods and services, changes in the technical structure of the economy, or to some combination of the two” (Leontief 1970). Other approaches to incorporating environmental goods and services in an I-O framework have been developed employing “ecological commodities” either through ecosystem sub-matrices or by adding additional rows and columns for sector-specific ecological inputs and outputs, but the “generalized I-O model” dominates the literature mostly due to the extensive data demands of the more recent ecological approaches (Hawdon and Pearson 1995) and provides the base for the approach adopted in this analysis.
For the case of GHG emissions specifically, there are three main approaches taken by general equilibrium studies (input-output or computable general equilibrium) to emissions. Some do not attempt to model emissions explicitly, but instead rely on the implicit value of energy efficiency gains (Allan, Hanley et al. 2007) or afforestation (Dhubhain, Flechard et al. 2009). The majority of studies including physical emission values are limited to carbon dioxide attached to energy inputs due to the greater availability of data, and because the type of combustion has no impact on the amount of emissions produced (Hanley, McGregor et al. 2006; Wissema and Dellink 2007; Druckman and Jackson 2009; Hanley, McGregor et al. 2009) and some additional output related emissions attached to industrial processes only. Another approach is to use top-down emission inventory data to allocate multiple gases expressed in global warming potential (GWP) as fixed output/expenditure coefficients (Gay and Proops 1993; Hawdon and Pearson 1995; Learmonth, McGregor et al. 2007; McGregor, Swales et al. 2008). The benefit of such an approach is the potential inclusion of non-energy related emissions and sequestration. The downside is that technological, efficiency and energy-input substitution changes are not captured, making the approach more useful in a static, rather than dynamic framework. However, given this research aims to provide information on structural change and abatement, not path-dependent abatement time-lines, and aims to provide a descriptive analysis of energy and non-energy emissions, the emission-coefficient approach is deemed more appropriate.
One advantage of applying input-output analysis is in identifying the productive linkages between different activity branches in terms of GHG emissions. Moran and Gonzalez (2007, MG approach hereafter) developed a framework to explore the optimal technical solution for GHG mitigation from the demand side. In this study, we attempt to combine traditional IO analysis and the MG approach to examine potential GHG mitigation strategies for Northern Ireland in the context of structural changes to the economy.