Search results

The purpose of this paper is to outline an integrative modelling approach that includes agricultural and forestry process chains in an energy system model, on a regional scale. The main focus is on land use for biomass production, aimed at satisfying the demands for energy, food, and materials.

The described model combines geographic modelling with a linear optimisation approach. The cost‐based optimisation of the energy system includes agricultural and forestry process chains. The system's commodities and processes are identified and these are linked appropriately in the specifications of the reference system. Spatial models provided geographically specific input data for the optimisation; these spatial models were based on publicly available data, regional heat and electricity demands, and regional biomass potentials. The optimisation tool was applied in two case studies.

The optimisation results allow an improved understanding of the interdependencies between regional agricultural and forestry structures and the regional energy system. Future developments of the energy system can be quantified. The application of the model in the case studies has revealed the limits on biomass availability, even in rural areas, and the fossil fuel price sensitivity of an optimal system setup.

Geographic models linked to a forecast model approach and based on publicly available data allow a high spatial resolution by taking into account the region‐specific conditions and mean that the modelling approach is transferrable to other regions. This paper provides an initial insight into the linkage between bottom‐up optimisation and spatial modelling, representing an innovative approach that is yet to be well explored.

The purpose of this paper is to investigate regional impact of macroeconomic and regional policy impulses, using our newly developed multi-regional computable general equilibrium (CGE) model for three, structurally distinctive Polish macro-regions.

In this study, we build an interregional social accounting matrix for Poland and use it to develop a small scale, three-region CGE model, reflecting the size of regional economies and cross-regional differences in industrial structures, while also explicitly accounting for the dynamics of main economic relationships across regions, such as interregional flows in commodities, labor and capital. The model is subsequently use to simulate regional effects of various policy impulses.

We demonstrate important cross-regional differences in the transmission mechanism of macro-level policies, which either affect regional output and its individual components (as in the case of imposing shocks to VAT or PIT rates) or are limited to the components, while preserving a rather uniform impact on output (as in the case of imposing shocks to wages). Furthermore, we contribute to the regional policy equity-efficiency trade-off debate, by illustrating quantitatively how, due to structural differences, spatially targeted expenditure measures might promote either regional convergence or aggregate output growth at the country-level.

Prior to our study, regional CGE models have not been used to simulate spatial distribution of aggregate shocks in Poland or in any other CEE country. Another originality of our study lies in comprehensive evaluation of various policy impulses, from the perspective of their impact on the respective region, spillovers to the other regions and its overall, country-level effect.

Market integration in China is still progressing, while the border effects of trade among regions still exist. The question of whether eliminating or weakening regional bias can promote of China's agricultural trade still remains an important issue. This paper analyzes the impact of regional bias on China's agricultural trade.

This paper constructs a pure exchange computable general equilibrium model of nine regions and three sectors, and analyzes the impact of regional bias on China's regional agricultural trade; Comparing the differences of regional bias on China's inter-regional and external agricultural trade, the paper especially analyzes the impact of the agricultural imports and exports in eight regions of China.

The results show that regional bias has had substantial impacts on China's agricultural trade. Elimination of regional bias would therefore increase China's agricultural exports and imports by factors of 1.32 and 1.63, respectively while its agricultural trade deficit would increase by 84%. Inter-regional agricultural trade in China would increase by 3.53 times. With the elimination of regional bias, the Northern coastal, Central and Northwestern regions would have the largest increase in inter-regional agricultural trade. Unlike the Northern coastal region, inter-regional agricultural import in the Central and Northwestern regions tends to be greater than inter-regional agricultural exports.

This paper thus aims to fill existing gap in investigating the impacts of regional bias on China's agricultural trade. Firstly, the model proposed in this paper does not only consider the linkage between the agricultural and non-agricultural sectors, but also the inter-regional agricultural trade linkages of the different regions in China. Secondly, the authors decompose home bias into national and regional biases and assess how regional bias affects agricultural trade of the various regions of China.

The purpose of this article is to analyze the supply (technology, education, labour, unemployment and real estate development) and demand (fiscal revenue and resident income) factors that influence regional entrepreneurial activity in China. Entrepreneurship develops at a rapid pace in China with significant differences among the country’s regions.

Statistics of 31 Chinese provinces from 2005 to 2010 were collected, and an econometric model of the panel data was established.

Empirical results show that technology and employment positively impact on regional entrepreneurial activity. A subsequent analysis comparing data from 2005-2008 to 2009-2010 showed that different variables on regional entrepreneurship weaken during a period of financial crisis, with technology remaining as the only significant variable across all models. Finally, the study summarizes China’s entrepreneurial activity as primarily supply-driven.

This study is limited by the data sources and index design, which may not fully capture all influences on regional entrepreneurship to determine whether an inflection point or other interaction mechanisms exist.

The study demonstrates a differential emphasis on the impact of economic supply factors in a developing economy to positively affect entrepreneurial activities and sustained economic growth at the regional level. Conversely, it can be inferred that increased government spending during an economic crisis positively influences regional entrepreneurial activities.

The study contributes toward the development of a theoretical framework that emphasizes the relationship between entrepreneurial activities and its regional supply and demand factors. The overall model and findings highlight technology’s importance on the development of innovation clusters that spur industrial agglomeration.

This chapter examines whether the supply of food is large enough to feed an increasing world population for the 2012–2050 period. Special attention is given to the implications of bioenergy production on global and regional food security.

For this analysis, a global food security simulation model was developed to determine if the global and regional supply of food, in terms of calories, is large enough to meet the demand and also to estimate the impact on food prices.

This chapter found that the global supply of food in terms of calories is insufficient to satisfy food demand in 2050, with food shortages especially significant in Africa.

The estimated shortage of food may result in significant food-price inflation by 2050.

The purpose of this paper is to establish a combined forecasting model to predict regional logistics demand, which is an important procedure on decision making of regional logistics planning.

There are several kinds of mathematical models often used in forecasting regional logistics demand. Trend extrapolation method extrapolates the future development trends bases on the hypothesis that the regional logistics will develop steadily. Grey system method predicts the change of logistics demand by the generation and development of original data sequence and excavation of inherent rules of the original data. Regression method obtains the change rules through the analysis between explained variable and explanatory variables. Each method has unique characteristics. In order to improve the accuracy of the prediction, combined methods are established. Genetic algorithm is used to determine the weights of different single models.

The results show that the combined forecasting model optimised by genetic algorithm can improve the accuracy.

Combined forecasting model can integrate the advantages of different single forecasting models. The key of improving the accuracy is to determine the weights of single forecasting models. Genetic algorithm can do well in finding suitable weights of each single forecasting model.

The paper succeeds in providing a combined forecasting model using genetic algorithm to determine the weights of each single prediction model, which helps to the decision making of regional logistics demand.

Previous studies on tourism input-output (IO) primarily focus on a single year’s snapshot or utilize outdated IO coefficients. The purpose of this paper is to analyze the multi-period development of regional tourism capacities and its influence on the magnitude of the industry’s regional economic contribution. The paper highlights the importance of applying up-to-date IO coefficients to avoid estimation bias typically found in previous studies on tourism’s economic contribution.

For the period 2008-2014, national IO tables are regionalized to estimate direct and indirect economic effects for output, employment, income and other value-added deffects. A comparison of Leontief inverse matrices is conducted to quantify estimation bias when using outdated models for analyzing tourism’s economic contribution.

On the one hand, economic linkages strengthened, especially for labour-intensive sectors. On the other hand, sectoral recessions in 2012 and 2014 led to an economy-wide decline of indirect effects, although tourists’ consumption was still increasing. Finally, estimation bias observed after applying an outdated IO model is quantified by approximately US$4.1m output, 986 jobs full-time equivalents, US$24.8m income and US$14.8m other value-added effects.

Prevailing assumptions on IO modelling and regionalization techniques aim for more precise survey-based approaches and computable general equilibrium models to incorporate net changes in economic output. Results should be cross-validated by means of qualitative interviews with industry representatives.

Additional costs for generating IO tables on an annual base clearly pay off when considering the improved accuracy of estimates on tourism’s economic contribution.

This study shows that tourism IO studies should apply up-to-date IO models when estimating the industry’s economic contribution. It provides evidence that applying outdated models involve the risk of estimation biases, because annual changes of multipliers substantially influence the magnitude of effects.

UK regional data on GDP and the GDP deflator are analysed to extract information on underlying demand and supply shocks as well as aggregate demand and supply shocks. Identification is achieved using long run restrictions, based on a theoretical model. The main results are that the supply shocks are almost completely symmetric across UK regions and that there is no evidence of these shocks being propagated slowly across the regions.

The UK construction industry faces unprecedented skills demands which have been fuelled by sustained sectoral growth and a concurrent downturn in the number of young people entering the industry. However, patterns of supply and demand are not uniform across the country, with regional and local skills shortages being determined by the specific socio‐economic context of the area under consideration. Thus, developing effective labour market policy demands spatially‐oriented labour market information which can be reconciled against industry growth forecasts within a particular region or locality. This paper explores the potential of Geographic Information Systems (GIS) in providing such a mechanism for enhancing the labour market planning process. The paper details how GIS can aid construction labour market planning through its ability to integrate disparate labour market information efficiently, thereby placing analysts in a better position to understand specific spatial patterns. A range of datasets were strategically combined in order to reveal regional nuances in labour demand and supply which would be difficult to discern without the use of such a tool. Although the GIS output would need to be considered in combination with a range of other forecasting techniques if robust projects of labour demand and shortage are to be generated, it nevertheless offers an effective decision‐support tool for informing labour market policy in the future.

Motivated by a problem in the context of DiDi Travel, the biggest taxi hailing platform in China, the purpose of this paper is to propose a novel facility location problem, specifically, the single source capacitated facility location problem with regional demand and time constraints, to help improve overall transportation efficiency and cost.

This study develops a mathematical programming model, considering regional demand and time constraints. A novel two-stage neighborhood search heuristic algorithm is proposed and applied to solve instances based on data sets published by DiDi Travel.

The results of this study show that the model is adequate since new characteristics of demand can be deduced from large vehicle trajectory data sets. The proposed algorithm is effective and efficient on small and medium as well as large instances. The research also solves and presents a real instance in the urban area of Chengdu, China, with up to 30 facilities and demand deduced from 16m taxi trajectory data records covering around 16,000 drivers.

This study examines an offline and single-period case of the problem. It does not consider multi-period or online cases with uncertainties, where decision makers need to dynamically remove out-of-service stations and add other stations to the selected group.

Prior studies have been quite limited. They have not yet considered demand in the form of vehicle trajectory data in facility location problems. This study takes into account new characteristics of demand, regional and time constrained, and proposes a new variant and its solution approach.