Nino Javakhishvili-Larsen (p), Irena Stefaniak , Albert Kwame Osei-Owusu

Andre Carrascal-Incera

There are ongoing political and scientific debates regarding pollution from the economic activities and its consequences on nature and society in the global terms. The climate change seems inevitable and the natural resources keep depleting. One of the reasons can be because we lack understanding how to treat the resources and how to decrease pollution without creating economic disasters, hunger and further conflicts. Another reason can also be the fact that we look the environmental issues mainly globally with the national political checklist of solutions, however, we fail to understand that the pollution and utilisation of the resources are result of the locally bounded economic activities and therefore, solutions for the global environmental issues should be studied and solved locally. The aim of this paper is to develop methodological approaches for integrating environmental data to the local economic data and construct multiregional IO and CGE model bottom-up, for small geographical units, such as municipalities in Denmark. The research is based on the Danish Interregional economic model SAM-K/LINE for Danish municipalities. In this mode, the interregional SAM is built on two-by-two-by-two approach, involving two sets of actors (production units and institutions / households), two types of markets (commodities and factors) and two locations (origin and destination). While LINE is based on the Leontief and Miyazawa formulations of the Interrelational Income Multiplier Model that incorporates SAM data input and the two-by-two-by-two approach.
This paper describes the methodology that we use to hybridize the economic cycle of LINE with the emission cycle at the product (commodity) and the municipality (NUTS3) level.
In this paper we explain how we intend to extend SAM-K/LINE model by creating environmental-economic accounting for production and final demand by commodity and geography. This will allow us to study both producer and consumer actions and test how changes in the production at one side and the consumption on the other side can support the reduction of carbon footprint, by maintaining economic growth. Linking the demand-side approach to supply-side approach and developing the economic-environmental hybrid IO-CGE at the local (municipality) level will contribute to the previous similar attempts, which should create better understanding of how to solve environmental issues at the local level in order to save the planet globally.

Marco Bianchi (p), Carlos Tapia , Ikerne Del Valle

Andre Carrascal-Incera

---------See extended abstract----------
The Circular Economy (CE) is the far-reaching strategy adopted by the European Commission in response to concerns about the long-term viability of the prevailing resource-intensive economic model (European Commision 2015; Macarthur 2015). This new approach is expected to contribute to a fundamental de-linking between economic growth and resource depletion. Understanding how these systemic changes might occur and how regional economies could converge towards circular and sustainable trajectories is one of the main challenges that policy-makers and regional planners face (Ghisellini et al. 2016; Kalmykova et al. 2018), often in a poor data environment.
Recently, new CE monitoring frameworks have been proposed by European and national institutions (European Commission 2018; PBL 2018) to track progress towards the various priorities of the CE, including material consumption. However, the available data are highly aggregated, and offer limited information on the diversity of conditions at sub-national scales. Moreover, the few material consumption studies available at sub-national scales differ in their purposes and approaches, thus limiting comparability of results (Rosado et al. 2014).
The availability of harmonised and disaggregated data is crucial to support decision makers in coming to understand the metabolism of regions. Indeed, this is the scale where material analysis results have been more often translated into successful policies (Binder et al. 2009). Given the impossibility to directly gather or systematically produce harmonised regional data on material consumption – especially for past years – this paper presents a stage-based econometric approach that makes use of available socioeconomic data and spatial techniques to disaggregate national material consumption data of 31 EU countries down to their regional level. The method provides a pragmatic but reliable solution to deal with data scarcity at sub-national levels and can be efficiently used to build time series for past years where disaggregated data do not exist nor can be collected. It provides enough data resolution for many applications and contributes to the identification of regional patterns of resource use that would otherwise remain unknown.

Sofía Jiménez (p), Alfredo Mainar

Andre Carrascal-Incera

The analysis of the functioning of the regional economies is fundamental to understand basic aspects in the economic development. Knowing the specific behavioural mechanisms of regional economies is key, both for the individual study of each region in particular, and for the analysis of interactions in a global economy, due to the mechanisms of translation of the effects between economies that operate in the same context.
To capture the full economic impact, a significant amount of data is necessary, especially to use multisectoral models, which fit in a particularly suitable way to the regional analysis (use of linear multipliers or computable general equilibrium models (CGE), for example). But the application of these tools requires the use of Input-output frames or, whenever possible, the construction of Social Accounting Matrices (SAM). Therefore, one of the biggest problems when carrying out regional economic analysis is the absence of adequate databases for the empirical study of the theoretical models proposed or for the ex-ante evaluation of regional economic policy proposals.
The objective of this paper is to get some more insight about the different methods available for regionalizing SAMs. In order to do that, we apply some existing methods in the literature for the updating and temporal projection of Input output tables, but adapted here to Social Accounting matrices (SAMs) and to a spatial projection or extrapolation. For this application, a European Union SAM will be used and its systematic regionalization will be applied to each of the 28 member states.
The methods that we have chosen are going to test are the following: EURO (Eurostat, 2008c; Beutel and Rueda-Cantuche, 2012), RAS (Günlük-Senesen and Bates, 1988; McDougall, 1999), Cross Entropy Method -CEM- (Robinson et al., 2001) and Flegg's location quotient (Flegg and Tohmo, 2013). The EU28 SAM will be regionalized for each member state, using all methods indicated and making a subsequent comparison with the single country SAM directly estimated from its national data.
For further information 'see extended abstract'.

Dmitry Kovalevsky (p), Richard J. Hewitt

Andre Carrascal-Incera

Successful implementation of environmental policies, including climate adaptation and mitigation policies, requires careful consideration of regional and local conditions. Consequently, there is growing understanding that regional models are needed to support climate and environmental policy making. Such models need to take into account the dynamics of geographical space as well as historic and expected future land use change patterns. One relevant geographical modelling approach is based on cellular automata (CA) which has a prominent track record of successful application to a diverse range of geographical problems. Traditionally, CA models are calibrated to reproduce the footprint of actor decision-making manifested in historical land use dynamics, and then projected forward to explore the effect of the observed dynamics on future periods. However, this is a poor representation of the way the world actually works, since policy decisions reflect current needs and priorities, not historic ones. Such a model cannot help us understand how decision-making actors might respond spontaneously to emerging land use outcomes. For these reasons, we believe there is considerable scope for existing CA-based geographical models to be improved by introducing realistic representations of the dynamic behaviour of decision-making actors. We present a modelling approach which retains the well-attested benefits of CA land use models, but which allows greater flexibility in modelling the dynamic behaviour of actors for particular “policy driven” land uses. To implement our approach, we integrate the APoLUS model (APoLUS stands for Actor, Policy and Land Use Simulator) – an open-source, multi-platform model based on geographical CA – with a system dynamics (SD) model describing the actor dynamics. The SD model is tailored to reproduce the dynamics of interaction (and possible conflicting interests) of a number of aggregate actors that might influence regional development in general and might affect (either in positive or in negative way) the implementation of policy under study in particular. In the present paper, we describe new developments in the actor dynamics model family, progressing beyond earlier work in three key ways: (i) incorporating the possible ‘regime shifts’ that might be related, in particular, to election cycles; (ii) describing in more detail the economic drivers of actor dynamics; (iii) introducing the stochasticity in a SD model.