Regional Science Policy and Practice最新文献

The present study maps and classifies industrial districts in Portugal, identifying the features of specialization and the current capacity of industrialization. It employs an adaptation of the algorithm developed by Canello and Pavone (2016) in 308 Portuguese municipalities based on six phases. In terms of methodological innovation, a new concentration ratio was introduced, making it possible to identify homogeneity in the industrial structure and to consider different sizes of companies, regarding the classification of Industrial Districts. It provides guidelines for policymakers in order to promote cross-fertilized industrial districts, considering the specificities of low-density industrial regions.

This article emphasizes the radically original character of the Urban Metric System: use of vector fields, a single parameter, several types of urban areas, a single input (the distribution of populations and jobs), no political boundaries taken into account and no density as input, abandonment of urban-rural distinctions and of the "commuting" criterion to distinguish the central city and its metropolitan area, and estimation of the centers, boundaries and densities of urban areas as outputs. The genesis of this approach is presented here for the first time. It leads not only to the calculation of a synthetic urban sprawl criterion (average distance to the center), but also to the estimation of four functions for calculating urban sprawl standards for central Canada, which was the authors’ ultimate research objective in terms of policy and planning.

Recently, the concerns about climate change have heightened the need for effective methods for estimating and mapping Biomass and Carbon stock at local, national, continental, and global scales. Reliable Biomass and Carbon stock quantification and spatialization is a challenge, especially in degraded Mediterranean Cork oak forest. To estimate and map Biomass (B_tree−Total) and Carbon stock (C_st−total), we explored an improved approach using extracted metrics collected by Lidar-UAV (unmanned aerial vehicles Lidar), combined with forest inventory data. We approach three types of models for data analysis: Simple linear regression, multiple linear regressions, and stepwise multiple linear regression. The best Biomass and Carbon stock model fit is the Stepwise multiple linear regressions, involving the following metrics: maximum elevation, canopy cover and point cloud density and intensity. Our finding provides a quantification and spatialization Biomass and Carbon stock model based on Lidar-UAV metrics in Cork Oak Mediterranen forest and the results confirm the degraded state of Maamora Forest with a Biomass and Carbon stock relatively medium to low.

The New Economic Geography (NEG) has over the past decades become a leading paradigm in regional science and geography. This paper seeks to provide a retrospective and prospective look into its achievements and current challenges. Clearly, regions are not oases of tranquility. Uncertainty and shocks in regional and urban development are omnipresent and call for adaptive and resilient strategies on spatial dynamics. The spatial arena is governed by many factors; this is mapped out in the present paper by means of a ‘Pentagon’ model of critical territorial capital inputs which form interdependent anchor points for policy and action based on the principle of confluence. The space-economy is multi-faceted and three prominent challenges in particular, are highlighted in this study: (i) the influence of digital technology on the standard NEG framework; (ii) the livability and proximity conditions in urban agglomerations, critically assessed through the lenses of the currently popular 15-minute city framework; (iii) the implications of social wellbeing and happiness motives for regional and urban planning in an NEG context. The conclusion of the study is that the NEG legacy and principles continue to be prominent signposts for regional and urban analysis and policy, conditional upon a flexible adaptivity of this legacy to new framework conditions in the ever-changing space-economy.

This paper aims to build and evaluate long-term scenarios within the economy of the state of Sergipe so as to identify sectors with a greater capacity to leverage the state's economic growth and mitigate regional disparities. We use a dynamic and inter-regional CGE model, calibrated for the year 2015 and 41 sectors. Our simulations capture he effects of sectorial shocks in the state and explore the direct and indirect effects of such shocks throughout the economy. The main results show that sectors such as Agriculture, Transport, Financial intermediation, and Public Utility Industrial Services all have an above average impact on the state’s GDP and contribute to a reduction in regional disparities.

This research investigates the factors influencing managers' willingness to adopt Environmental, Social, and Corporate Governance (ESG) practices in small and medium-sized enterprises (SMEs) in Bosnia and Herzegovina. As these practices are becoming increasingly important, it is crucial to understand what factors can increase managers' acceptance of them for both theory and practice. Our analysis was conducted on a sample of 306 managers from Bosnia and Herzegovina. Anchored in the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), we tested three models to identify predictors of intention to adopt ESG practices and to explore the moderating role of facilitating conditions (FC) on the relationship between performance expectancy and intention to adopt ESG practices. Our findings indicate that there are consistent factors that predict the managers’ intention of adopt ESG practices, including Performance Expectancy (PE), Social Influence (SI), and Habitual Tendency (HT). We found that the FC negatively moderate the link between PE and intention to adopt social and governance practices within ESG framework. We argue that the nature of each domain within ESG, ceiling effect, and resource constraints and trade-offs might be possible explanations for these results.

Consumer decisions are essential in reducing CO₂ emissions released by human activities. Climate policies aimed at reducing consumers' carbon footprint require societal embrace and leaving no one behind; however, they often negatively affect low-income families and rural households with limited access to low-carbon consumption choices. In this paper, we estimate households' carbon footprint in Castilla-La Mancha (Spain) by urban-rural location and expenditure attributes and simulate the impacts of a carbon tax on the disposable income of different types of households. A novel multiregional input-output model is proposed to assign global carbon footprints to products (instead of industries), leading us to identify the main driving goods in the carbon footprint of each type of household, evaluating the household's vulnerability after carbon taxation. Our findings suggest that high-spending households would face the taxation strongest effects on car fuels and transport services, as they would have an impact of 2 % of their total expenditure by the taxation on these products, while this effect in lower-income households would be 0,75 %. A tax on basic housing services (electricity and heating) would have a regressive impact, undermining the consumption level of vulnerable households. They would be affected by 2,5 %, while higher income households would spend between 1 and 1,5 % of their total expenditure on paying the carbon tax on this product. This heterogeneity across households leads us to recommend taxes and compensation mechanisms charged on adequate products to reduce the carbon footprint of households while avoiding the regressivity of climate policies and reducing urban-rural inequalities.

This study aims to assess the economic impacts of the National Family Farming Strengthening Program (Pronaf) in the various Brazilian biomes. The impacts at the sectoral, regional, and macroeconomic levels, arising from increases in rural credit to farmers in Brazilian territory – family and non-family farmers separately considered –, are carried out using the Computable General Equilibrium model, TERM-Biomas, specially built for analysis of the rural sectors in the Brazilian biomes. The key conclusion is that all regions of the model win, in the most varied magnitudes. Regions with higher participation in family farming production earn more than others. Up to the year 2020, some regions – from the Caatinga biome – show comparative losses. This may be because traditionally family farming regions would have their demands for primary factors more drastically increased, investments and jobs migrate, for example, from those regions of Caatinga. When the results are analyzed in a wider time limit, however, the results suggest general gains. The more vulnerable and specialized in family farming sectors are the regions, the greater the gains.