Stuart McIntyre

Incorporating Jurisdiction Issues into Regional Carbon Accounts under Production and Consumption Accounting Principles

Despite increased public interest, policy makers have been slow to enact targets based on limiting emissions under full consumption accounting measures (such as carbon footprints). We argue that this may be due to the fact that policy makers in one jurisdiction do not have control over production technologies used in other jurisdictions. We use a regional input–output framework and data derived on carbon dioxide emissions by industry (and households) to examine regional accountability for emissions generation. In so doing, we consider two accounting methods which permit greater accountability of regional private and public (household and government) final consumption as the main driver of regional emissions generation, while retaining focus on the local production technology and consumption decisions which fall under the jurisdiction of regional policy makers. We propose that these methods permit an attribution of emissions generation that is likely to be of more use than a full global footprint analysis to regional policy makers.

Local and global spatial effects in hierarchical models

ABSTRACT Hierarchical models have a long history in empirical applications; recognition of the fact that many datasets of interest to applied econometricians are nested; counties within states, pupils within school, regions within countries, etc. Just as many datasets are characterized by nesting, many are also characterized by the presence of spatial dependence or spatial heterogeneity. Significant advances have been made in developing econometric techniques and models to allow applied econometricians to address this spatial dimension to their data. This article fuses these two literatures together and combines a hierarchical model with the two general spatial econometric models.

Hierarchical Spatial Econometric Models in Regional Science

Hierarchical econometric models have a long history in applied research. Recent advances have seen the development of spatial hierarchical econometric models, fusing the advantages of hierarchical modeling with those of spatial econometrics. Many datasets used to investigate key questions in regional science are inherently nested: individuals within counties, counties within states, regions within countries, etc. Being able to reflect this nesting within the econometric framework will be essential to future applied work in regional science. This chapter begins by introducing the key elements of spatial and non-spatial hierarchical econometric models before briefly reviewing existing econometric work using these models. Thereafter, we focus on different types of future development of these models and their uses in regional science.

Computable General Equilibrium Modelling in Regional Science

Computable General Equilibrium (CGE) modelling has a long and distinguished history in regional science. In the past decade or so, improvements in computation have led to more elaborate and detailed CGE models being developed and used in a range of different policy areas. Against a backdrop of these advances, this chapter seeks to identify and review a number of areas where we see the potential for significant developments in CGE modelling in the years ahead. Specifically, we first consider potential improvements in computation, model specification and methodology, before looking in more detail at three areas where these models are used, or could be used, with a view to identifying avenues where model improvements would be valuable. These three areas are: urban and spatial modelling, model integration with other systems and models, and regional fiscal issues. CGE modelling has a bright future in regional science, but to remain at the forefront of economic research in regional science it must continue to adapt and evolve as, historically it has done, and we hope that the directions identified in this chapter are helpful to the future direction of this field.