Explore the vast range of titles available.

The role of cities in achieving more sustainable regional environmental and economic development has been increasingly recognized, and quantifying the process of city sustainable transformational development has become a hot topic of research. This paper extends the spatial general equilibrium model, moistens environmental capital into production factors, and conducts theoretical research and empirical analysis on the sustainable transformational development patterns (STDP) and heterogeneity of cities. The study shows that the level of industrial agglomeration and environmental productivity interact with each other to influence the level of STDP, and that two types of factors, factor structure and industrial linkage, market size and industrial linkage, influence the heterogeneity of STDP. The analysis of the empirical results of STDP shows that the level of STDP of Chinese cities is low, and there are large differences in regional development; the number of cities with general sustainable transformational development pattern (GSTDP) occupies more than 50% of the number of the total number of cities, the cities with weak sustainable transformational development pattern (WSTDP) are concentrated in central and western regions such as Gansu, Shaanxi, and Ningxia, while the cities with strong sustainable transformational development pattern (SSTDP) form a cluster mainly in the Shandong–Jiangsu–Zhejiang–Fujian–Guangdong border. Further, the heterogeneity analysis of cities with SSTDP, GSTDP, and WSTDP is conducted, respectively, which provides some theoretical basis and empirical support for policy makers to formulate reasonable sustainable transition development regulation strategies.

Poland is the most coal-dependent economy and one of the biggest polluters in the EU. In order to alleviate this problem, meet CO2 emission requirements set by EU, and improve the country’s energy security, Poland decided to introduce nuclear power to its energy mix. So far, several potential locations for nuclear power plants have been officially proposed, mainly based on technical parameters, but no comparisons of the economic impact of such locations have been considered. Consequently, the main goal of this paper is to compare the national and regional economic effects of investments in nuclear power plants—for both the construction and exploitation phases—in the four most probable locations, which are similarly beneficial from a technical point of view. In order to simulate these effects, the spatial recursive dynamic Computable General Equilibrium model was calibrated until 2050 including agglomeration effects and featuring the regional economies of all Polish regions. The results show that although the construction phase is beneficial for economic development in all four regions, the exploitation phase is good for only one. The economies of the other regions suffer, to a greater or lesser extent, from the Dutch disease. The paper argues that the regional economic effects of such an investment differ significantly, due to differences in the regions’ economic structures; hence, they should always be taken into account in the final decisions on the power plants’ locations.

The use of separate transport and economic models in urban planning provides a limited view of economic impacts, restricts the testing of network design options and lengthens the planning process. Furthermore, the standard methodology for economic appraisal assumes partial economic equilibrium and cannot determine the distribution of impacts from the transport sector to households. Computable general equilibrium (CGE) models can capture general equilibrium effects and measure welfare at the household level, but mostly lack integration with transport models and do not represent all trip generators. This paper develops an integrated traffic assignment and spatial CGE model in nonlinear complementarity form, casted as a framework for economic appraisal of urban transport projects. The CGE submodel generates commuting, shopping and leisure trips as inputs into the transport submodel, which then assigns trips to the network according to user equilibrium. The resulting travel times then feed back into household prices and freight margins. Households and firms fully account for travel times in decisions on where to shop, how much labour to supply and where to source production inputs. Calibration and applications of the model are demonstrated for 14 regions and 2 industries across Sydney using GAMS/PATH on the NEOS server. The welfare of various network improvements is measured using equivalent variations. The model can be calibrated to external strategic transport models, and be extended to simulate additional trip generators and land-use.

En este artículo se muestra el desarrollo de un modelo conceptual del tipo new economics geography de dos ciudades; este modelo ayuda a analizar cómo la importancia relativa (peso) que los individuos asignan a las economías y las deseconomías de aglomeración afectan los lugares en los que se celebran actividades económicas. Se muestra que el peso asignado a las externalidades puede cambiar drásticamente la localización poblacional. Se ilustra la importancia de cuantificar la magnitud de estas externalidades, y cómo su incorporación simultánea en los análisis de sistemas urbanos puede evitar conclusiones engañosas. I develop a New Economics Geography model of two-cities, to analyse how the relative importance (weight) that individuals assign to agglomeration economies and diseconomies affects the places where economic activities take place. It is shown that the weight assigned to externalities can drastically change the population location. We illustrate the importance of quantifying the magnitude of these externalities, and how urban systems analysis that simultaneously incorporates agglomeration effects can avoid misleading conclusions.

With growing regional economic integration, transportation systems have become critical to regional development and economic vitality but vulnerable to disasters. However, the regional economic ripple effect of a disaster is difficult to quantify accurately, especially considering the cumulated influence of traffic disruptions. This study explored integrating transportation system analysis with economic modeling to capture the regional economic ripple effect. A state-of-the-art spatial computable general equilibrium model is leveraged to simulate the operation of the economic system, and the marginal rate of transport cost is introduced to reflect traffic network damage post-disaster. The model is applied to the 50-year return period flood in 2020 in Hubei Province, China. The results show the following. First, when traffic disruption costs are considered, the total output loss of non-affected areas is 1.81 times than before, and non-negligible losses reach relatively remote zones of the country, such as the Northwest Comprehensive Economic Zone (36% of total ripple effects). Second, traffic disruptions have a significant hindering effect on regional trade activities, especially in the regional intermediate input—about three times more than before. The industries most sensitive to traffic disruptions were transportation, storage, and postal service (5 times), and processing and assembly manufacturing (4.4 times). Third, the longer the distance, the stronger traffic disruptions’ impact on interregional intermediate inputs. Thus, increasing investment in transportation infrastructure significantly contributes to mitigating disaster ripple effects and accelerating the process of industrial recovery in affected areas.

The EU decarbonization strategy foresees deep cuts in CO2 in the transport sector. Investment in infrastructure, manufacturing of new technology vehicles and production of alternative fuels induce macroeconomic changes in activity and employment for both national and regional economies. The objective of the paper is to present a newly built macroeconomic-regional model (GEM-E3-R general equilibrium model for economy, energy and environment for regions) for assessing impacts of transport sector restructuring on regional economies of the entire EU, segmented following NUTS-3 (nomenclature of territorial units of statistics). The model combines general economic equilibrium theory with location choice and New Economic Geography and implements a dynamic, fully endogenous agglomeration-dispersion mechanism for people and industries coupled with a gravity model for bilateral interregional flows. A novelty of the model is a two-layers structure: (i) the country-wide layer formulated as a global multi-sector, multi-country and multi-period computable general equilibrium (CGE) model; and (ii) the regional economy layer, which simulates impacts on regional economies, while considering country-wide economic trends as boundary conditions. The paper presents a use of the model in the assessment of regional economic effects of electrification of car mobility in Europe and wide use of domestically produced advanced biofuels.

The European Fund for Strategic Investments (EFSI) is the financial pillar of the Investment Plan for Europe. It tackles the post-crisis investment gap in the European Union (EU) and aims to revive investment in key areas in all the EU Member States. EFSI was launched in 2015 jointly by the European Investment Bank (EIB) Group and the European Commission. Every year, macroeconomic impact assessments are carried out using the spatial dynamic RHOMOLO-EIB model in order to gauge jobs and growth impact of the EFSI-supported operations in the EU. This article illustrates the methodology used for the assessment and reports the result of the latest set of simulations, corresponding to the portfolio of all approved EFSI-supported operations as of the June 13th EIB Board of Directors meeting, 2019. According to the results, EFSI is contributing significantly to job creation and growth. The estimates suggest that, by 2019, more than 1 million jobs are expected to be created thanks to the approved operations (1.7 million by 2022), with a positive contribution to GDP of 0.9% (1.8% expected by 2022) over the baseline. El Fondo Europeo para Inversiones Estratégicas (FEIE) es el pilar financiero del Plan de Inversiones para Europa. Aborda la brecha de inversión posterior a la crisis en la Unión Europea (UE) y tiene como objetivo revivir la inversión en áreas clave en todos los Estados miembros de la UE. El FEIE se lanzó en 2015 conjuntamente por el Banco Europeo de Inversiones (BEI) y la Comisión Europea. Cada año, las evaluaciones de impacto macroeconómico se llevan a cabo utilizando el modelo espacial dinámico RHOMOLO-EIB para medir el empleo y el impacto de las operaciones respaldadas por el FEIE en la UE. Este artículo ilustra la metodología utilizada para la evaluación e informa el resultado del último conjunto de simulaciones, correspondiente a la cartera de todas las operaciones aprobadas respaldadas por el FEIE a partir de la reunión de la Junta Directiva del BEI del 13 de junio de 2019. Según los resultados, FEIE está contribuyendo significativamente a la creación de empleo y al crecimiento. Las estimaciones sugieren que, para 2019, se espera que se hayan creado más de 1 millón de empleos gracias a las operaciones aprobadas (1,7 millones para 2022), con una contribución positiva al PIB del 0,9% (1,8% esperado para 2022) respecto al escenario base.

The paper describes the dynamic spatial general equilibrium model, Regional Holistic MOdeL(RHOMOLO), developed by the European Commission, where the labour market equilibrium is determined by firms' labour demand, unemployment and interregional labour migration. We carry out simulations on the effects of transportation costs on regional labour markets. Our results confirm that wages and unemployment are by far the most important channels of labour market adjustment in the EU. In contrast, labour migration is found to play a secondary role. Also, the complexity and spatially inter-dependent relationship among market access, labour demand, and labour supply is found. It underlines the significance of dynamic spatial general equilibrium approach.