Sensitivity of modeling results to technological and regional details: The case of Italy's carbon mitigation policy

Abstract

Model differences in technological and geographical scales are common, but their contributions to uncertainties have not been systematically quantified in the climate policy literature. This paper carries out a systematic assessment on the sensitivity of Computable General Equilibrium models to technological and geographical scales in evaluating the economic impacts of carbon mitigation policies. Taking Italy as an example, we find that the estimation for carbon price and the economic cost of a de-carbonization pathway by means of a model with technological and regional details can be lower than a model without such details by up to 40%. Additionally, the effect of representing regional details appears to be far more important than the effect of representing the details of electricity technology in both the estimated carbon prices and the estimated economic impacts. Our results for Italy highlight the importance of modeling uncertainties of these two key assumptions, which should be appropriately acknowledged when applying CGE models for policy impact assessment. Our conclusions can be generalized to different countries and policy scenarios not in terms of absolute numbers but in terms of economic explanations. In particular, intra-national trade and the sub-national sectoral/technological specialization are important variables for understanding the economic dynamics behind these outcomes.

Exploring spatial patterns of carbon dioxide emission abatement via energy service companies in China

“…The results reveal a huge potential for reducing CO 2 emissions in the building, industrial, and transport sectors (Xu et al, 2017). Previous research has long focused on exploring the underpinnings of CDEA, including energy efficiency technology innovation (Wen and Li, 2014;Xiao et al, 2014;Xu et al, 2016), clean energy usage (Murphy and McDonnell, 2017;Nduagu and Gates, 2016), and policy interventions (Murphy and McDonnell, 2017;Standardi et al, 2017). In China, the growing research on CDEA has focused on the driving factors (Ouyang and Lin, 2017;Ren et al, 2014;Zhou et al, 2017), mitigation policies (Jiang et al, 2016;Lu et al, 2016) and technology innovation .…”

Equilibrium Modeling for Environmental Science: Exploring the Nexus of Economic Systems and Environmental Change

“…Surprisingly, we found that select high income countries (e.g., France and the United Kingdom) are disproportionately aggregated relative to their economic output due to frequent inclusion within a European Union aggregate (Table S2). While seemingly harmless, evidence suggests that the geographic scale of CGE models has a considerable effect on policy impacts, even when comparing national with subnational resolutions (Standardi et al, 2017). Future integrated assessments focusing on the environmental implications of trade and consumption would benefit by refraining from spatial aggregation when computationally feasible, particularly when the underlying countries have diverse sectoral specializations and primary factor compositions despite geographic proximity.…”