Littérature scientifique sur le sujet « Regional analysi »

Research conducted aims to: 1) to identify the potential economic sectors as the base sector as a determinant of the central region of growth in the Region Development Unit (RDU) Kediri East Java; 2) to identify economic sectors that have a competitive advantage in the Region Development Unit (RDU) Kediri East Java; (3) to classify the region in each Regency / City in the Region Development Unit (RDU) Kediri East Java; (4) to identify region based on the availability of service facilities in Region Development Unit (RDU) Kediri East Java; and (5) to identify region with potential interaction strength among regions as the central development areas in Region Development Unit Kediri East Java'. An analysis method that used a method of quantitative descriptive analysis by using secondary data obtained from the Statistic Indonesia and other institutions needed. The analysis is use location quotient analysis, shift share analysis, typologi klassen analysis, scalogram analysis, dan gravitation model analysi. The Analysis showed Kediri City emerges as a growth poles in Region Development Unit Kediri East Java with regional advantages from two aspects namely regional capability (Location Quotient analysis) and high per capita income (Tipology Klassen analysis) with hinterland area such as Kab. Nganjuk, Kab. Trenggalek, and Kab. Kediri and Kab. Tulungagung. Keywords: Gravitation, Growth Poles, Location Quotient, Scalogram, Shift Share, Typologi Klassen.

The conditions of life and the levels of living of the population present one of the most important indicators of the overall economic development of a given geographic area. Recording and assessment of trends in this domain offers an important information as the strategical management and decision making about regional development are concerned.. Assessment of the state of levels of living then presents a comparatively complex task, because containing a very broad system of indicators, containing a very broad system of indicators of both the quantitative and qualitative nature. The official statistics are aimed at quantifying and univariate analysis of some selected components of the levels of living, particularly from the areas of population incomes and consumption. Such an analysis offers a very important information on the levels of living but it is a partial information only purely. Besides those indicators the values of which can be quantified, also some other factors have to be included into the levels of living. The subjective opinion of the population concerning their levels of living and the state of the conditions of life are to be assessed here, and moreover also the factors explaining and complementing the quantitative indicators mentioned. The paper deals with analysi of subjective assessment of living conditions of population in regions CR.

One of the most important characteristics of the labour market is labour mobility that allows assessing the economic efficienc y o f labour . A comparativ e analysi s i s necessar y fo r determinin g th e degre e o f mobility . I n term s o f spatia l and sectoral characteristics, the paper assesses the degree and dynamics of mobility in the Russian labour market based on previously published studies, as well as the authors’ findings. To determine the degree of mobility, the research uses various approaches, applying both direct (mobility costs, transition matrices) and indirect indicators (structural unemployment, wage differentiation, unemployment rate, gross regional product (GRP)). The analysis uses the data of the Russia Longitudinal Monitoring Survey — Higher School of Economics (RLMS-HSE) and Federal State Statistic Service (Rosstat) for 2000– 2016. The obtained results demonstrate a relatively low intersectoral and interregional mobility in Russia compared to Organisation for Economic Co-operation and Development (OECD) countries. Low intersectoral mobility may indicate weak exchangeability of the sectors and high mobility costs. The largest number of transitions is observed in trade, where employees do not need any specific knowledge. Generally, other transitions are made between related sectors that require similar knowledge from employees. The lowest intersectoral mobility is characteristic for the education and health sectors. According to the Shorrocks index, in Russia, interregional mobility is lower than intersectoral mobility. Low spatial mobility is explained by high migration costs, including those associated with “poverty traps”, the peculiarity of statistical accounting of migrants and the size of Russian regions. The obtained results are correct for the examined period and the applied criteria. The changes in labour mobility in Russia caused by global digitalisation of the economy and the transition to remote working require a separate study.

One of the most important characteristics of the labour market is labour mobility that allows assessing the economic efficienc y o f labour . A comparativ e analysi s i s necessar y fo r determinin g th e degre e o f mobility . I n term s o f spatia l and sectoral characteristics, the paper assesses the degree and dynamics of mobility in the Russian labour market based on previously published studies, as well as the authors’ findings. To determine the degree of mobility, the research uses various approaches, applying both direct (mobility costs, transition matrices) and indirect indicators (structural unemployment, wage differentiation, unemployment rate, gross regional product (GRP)). The analysis uses the data of the Russia Longitudinal Monitoring Survey — Higher School of Economics (RLMS-HSE) and Federal State Statistic Service (Rosstat) for 2000– 2016. The obtained results demonstrate a relatively low intersectoral and interregional mobility in Russia compared to Organisation for Economic Co-operation and Development (OECD) countries. Low intersectoral mobility may indicate weak exchangeability of the sectors and high mobility costs. The largest number of transitions is observed in trade, where employees do not need any specific knowledge. Generally, other transitions are made between related sectors that require similar knowledge from employees. The lowest intersectoral mobility is characteristic for the education and health sectors. According to the Shorrocks index, in Russia, interregional mobility is lower than intersectoral mobility. Low spatial mobility is explained by high migration costs, including those associated with “poverty traps”, the peculiarity of statistical accounting of migrants and the size of Russian regions. The obtained results are correct for the examined period and the applied criteria. The changes in labour mobility in Russia caused by global digitalisation of the economy and the transition to remote working require a separate study.

- By means of different econometric techniques, this paper estimates the relationship between the knowledge capabilities (i.e. the extent of R&D activities and higher education) and the GDP per capita growth of European developed regions. Along with structural features and initial income levels, our estimations account for the presence of spatial dependence. We find that regional growth is positively affected by the intensity of R&D and the share of adults with tertiary education. These findings are robust to alternative estimation procedures, as they arise from both OLS regressions with country demeaned variables and ML estimations of different spatial models. Keywords: Regional growth, knowledge capabilities, spatial dependence.Keywords: Crescita economica regionale, disuguaglianze regionali, regioni italiane.Parole chiave: Crescita regionale, capacitŕ tecnologiche, dipendenza spaziale.JEL classification: R11, O33, C31

- In this paper we analyse the innovative capacity of the most productive and most industrialised Italian regions by comparing them with a set of European regions that in the past two decades followed an industrial restructuring path towards knowledgebased sectors, doing so via the formation of a Regional Innovation System. Even though the European benchmarking regions now specialise in high-tech sectors and are characterised by high innovative activity, they share an industrial past based on heavy and traditional industries. In this respect, the two groups of regions are not so different in nature, and comparing them yields strategic insights for the Italian regional transformation process and suggests interesting local policy implications.Keywords: Knowledge economies, regional development, regional innovation systems.Parole chiave: economie della conoscenza, sviluppo regionale, sistemi regionali d'innovazioneJEL classification: R11, R58.

Le deformazioni lente di versante in roccia (DGPV e grandi frane) sono fenomeni diffusi che interessano interi versanti e mobilizzano volumi di roccia anche di miliardi di metri cubi. La loro evoluzione è legata a processi di rottura progressiva sotto forzanti esterne e di accoppiamento idromeccanico, rispecchiate da un complesso processo di creep. Sebbene caratterizzate da bassi tassi di spostamento (fino a pochi cm / anno), queste instabilità di versante danneggiano infrastrutture e ospitano settori potenzialmente soggetti a differenziazione e collasso catastrofico. È quindi necessaria una robusta caratterizzazione del loro stile di attività per determinare il potenziale impatto sugli elementi a rischio e anticipare un eventuale collasso. Tuttavia una metodologia di analisi finalizzata a questo scopo è ancora mancante. In questa prospettiva, abbiamo sviluppato un approccio multiscala che integra dati morfostrutturali, di terreno e tecniche DInSAR, applicandoli allo studio di un inventario di 208 deformazioni lente di versanti mappate in Lombardia. Su questo dataset abbiamo eseguito una mappatura geomorfologica e morfostrutturale di semi dettaglio tramite immagini aeree e DEM. Abbiamo quindi sviluppato un pacchetto di procedure oggettive per lo screening su scala di inventario delle deformazioni lente di versante integrando dati di velocità di spostamento, cinematica e di danneggiamento dell’ammasso roccioso per ogni frana. Utilizzando dataset PS-InSAR e SqueeSAR, abbiamo sviluppato una procedura mirata a identificare in maniera semiautomatica la velocità InSAR rappresentativa, il grado di segmentazione e l'eterogeneità interna di ogni frana mappata identificando la presenza di possibili fenomeni secondari. Utilizzando la tecnica 2DInSAR e tecniche di machine learning, abbiamo inoltre sviluppato un approccio automatico caratterizzare la cinematica di ciascuna frana. I dati così ottenuti sono stati integrati tramite analisi di PCA e K-medoid per identificare gruppi di frane caratterizzati da stili di attività simili. Partendo dai risultati della classificazione su scala regionale, ci siamo poi concentrati su 3 casi di studio emblematici, le DGPV di Corna Rossa, Mt. Mater e Saline, rappresentativi di problematiche tipiche delle grandi frane (segmentazione spaziale, attività eterogenea, sensibilità alle forzanti idrologiche). Applicando un approccio DInSAR mirato abbiamo indagato la risposta del versante a diverse baseline temporali per evidenziare le eterogeneità spaziali e, tramite un nuovo approccio di stacking su basline temporali lunghe abbiamo estrattoi segnali di spostamento permanenti ed evidenziato i settori e le strutture con evoluzione differenziale. Lo stesso approccio DInSAR è stato utilizzato per studiare la sensibilità delle deformazioni lente di versante alle forzanti idrologiche. Il confronto tra i tassi di spostamento stagionale e le serie temporali di precipitazioni e scioglimento neve per il monte. Mater e Saline hanno delineato complessi trend di spostamento stagionale. Queste tendenze, più evidenti per i settori più superficiali, evidenziano una risposta maggiore a periodi prolungati di precipitazione modulati dagli effetti dello scioglimento della neve. Ciò suggerisce che le DGPV, spesso considerate non influenzate dalla forzante climatica a breve termine (pluriennale), sono sensibili a input idrologici, con implicazioni chiave nell'interpretazione del loro fallimento progressivo. I nostri risultati hanno dimostrato l'efficacia della metodologia multi-scala proposta, che sfrutta i prodotti DInSAR e l'analisi mirata per identificare, classificare e caratterizzare l'attività delle deformazioni lente di versante includendo dati geologici in tutte le fasi dell'analisi. Il nostro approccio, è applicabile a diversi contesti e dataset e fornisce gli strumenti per indagare processi chiave in uno studio finalizzato alla definizione del rischio connesso alle deformazioni lente di versante.
Slow rock slope deformations (DSGSDs and large landslides) are widespread, affect entire hillslopes and displace volumes up to billions of cubic meters. They evolve over long time by progressive failure processes, under variable climatic and hydro-mechanical coupling conditions mirrored by a complex creep behaviour. Although characterized by low displacement rates (up to few cm/yr), these slope instabilities damage sensitive structures and host nested sectors potentially undergoing rockslide differentiation and collapse. A robust characterization of the style of activity of slow rock slope deformations is required to predict their interaction with elements at risk and anticipate possible failure, yet a comprehensive methodology to this aim is still lacking. In this perspective, we developed a multi-scale methodology integrating geomorphological mapping, field data and different DInSAR techniques, using an inventory of 208 slow rock slope deformations in Lombardia (Italian Central Alps), for which we performed a geomorphological and morpho-structural mapping on aerial images and DEMs. On the regional scale, we developed an objective workflow for the inventory-scale screening of slow-moving landslides. The approach is based on a refined definition of activity that integrates the displacement rate, kinematics and degree of internal damage for each landslide. Using PS-InSAR and SqueeSAR datasets, we developed an original peak analysis of InSAR displacement rates to characterize the degree of segmentation and heterogeneity of mapped phenomena, highlight the occurrence of sectors with differential activity and derive their characteristic displacement rates. Using 2DInSAR velocity decomposition and machine learning classification, we set up an original automatic approach to characterize the kinematics of each landslides. Then, we sequentially combine PCA and K-medoid cluster analysis to identify groups of landslides characterized by consistent styles of activity, accounting for all the relevant aspects including velocity, kinematics, segmentation, and internal damage. Starting from the results of regional-scale classification, we focused on the Corna Rossa, Mt. Mater and Saline DSGSDs, that are emblematic case studies on which apply DInSAR analysis to investigate typical issues in large landslide studies (spatial segmentation, heterogenous activity, sensitivity to hydrological triggers). We applied a targeted DInSAR technique on multiple temporal baselines to unravel the spatial heterogeneities of complex DSGSDs and through a novel stacking approach on raw long temporal baseline interferograms, we outlined the permanent displacement signals and sectors with differential evolution as well as individual active structures. We then used DInSAR to investigate the possible sensitivity of slow rock slope deformations to hydrological triggers. Comparison between seasonal displacement rates, derived by interferograms with targeted temporal baselines, and time series of precipitation and snowmelt at the Mt. Mater and Saline ridge outlined complex temporally shifted seasonal displacement trends. These trends, more evident for shallower nested sectors, outline dominant controls by prolonged precipitation periods modulated by the effects of snowmelt. This suggests that DSGSDs, often considered insensitive to short-term (pluri-annual) climatic forcing, may respond to hydrological triggering, with key implication in the interpretation of their progressive failure. Our results demonstrated the effectiveness of the proposed multi-scale methodology that exploits DInSAR products and targeted processing to identify, classify and characterize the activity of slow rock slope deformation at different levels of details by including geological data in all the analysis stages. Our approach, readily applicable to different settings and datasets, provides the tools to solve key scientific issues in a geohazard-oriented study of slow rock slope deformations.

Estendendo l’approccio di decomposizione dell’Indice di Malmquist proposto da Fare et al. (1992) secondo cui la variazione della produttività è descritta come variazione della tecnologia e dell’efficienza, Simar e Wilson (1999) hanno fornito per la prima volta un’interpretazione statistica dell’indice e dei suoi componenti, proponendo un algoritmo basato sul bootstrap per stimare gli intervalli di confidenza degli indici sopra definiti. In questa tesi si è, per la prima volta, proposto un nuovo metodo di stima della densità, basato su una selezione più accurata della bandwidth, partendo e adattando i recenti sviluppi introdotti da Simar e Wilson (2007) nel caso univariato al caso bivariato. Inoltre, per la prima volta è stata testata la performance delle procedure per stimare gli indici, attraverso l’implementazione di simulazioni Monte Carlo. Essi hanno mostrato un basso livello di performance del modello proposto da Simar e Wilson nel 1999 rispetto a quello proposto in questo lavoro. In particolare, essi hanno evidenziato che la procedura di stima della densità è molto sensibile alla presenza di valori unitari dell’efficienza, tanto da fornire seri problemi nella valutazione della stima della funzione di densità continua. Inoltre, sono stati applicati e adattati i data driven methods, che hanno evidenziato risultati diversi rispetto alla procedura originale, lasciano ampi spazi a ricerche future. Da un punto di vista empirico, è stata analizzata la crescita delle regioni italiane attraverso la Total Factor Productivity (TFP), nel periodo 1980-2001. Quindi sono stati stimati l’indice di Malmquist e i suoi componenti come pure i loro rispettivi intervalli di confidenza, applicando la procedure migliore, identificata nella fase di ricerca precedente. E’ stato registrato un guadagno complessivo della variazione della produttività, corretta nella bias, del 2.1%, dell’efficienza del 0.5% e della tecnologia del 1.6%. L’analisi di sensibilità, basata su tecniche bootstrap, ha rilevato che per la maggior parte delle regioni italiane l’efficienza e la tecnologia non hanno mostrato cambiamenti statisticamente significativi. Secondo questi risultati, l’approccio inferenziale ha fornito un’analisi più accurata e rigorosa rispetto all’approccio tradizionale, adottato da Leonida et al.(2004 ,Table 1, pg. 2190) nella quale le stime sono state valutate come miglioramenti o recessioni, trascurando sia la correzione della bias che il loro significato statistico.
Improving the Fare et al. (1992) approach on Malmquist index of productivity, which can be decomposed into indices describing changes in technology and changes in efficiency , Simar and Wilson (1999) provided a statistical interpretation to their Malmquist productivity index and its components, and presented a bootstrap algorithm to estimate confidence intervals for the indices. Extending the recent developments introduced by Simar and Wilson (2007) in the bandwidth specification in the univariate case, we propose new methods of density estimation, based on more accurate bandwidth specification. Monte Carlo experiments have been computed for the first time in this context. They have shown a low quality of performance of the Simar and Wilson (1999)'s bootstrap approximations, and high level of quality for the proposed methods. In particular, they have found out as best performer method the procedure based on the density estimation without considering the ones, revealing the severe problem of deteriorating the estimation of the continuous density of the efficiency scores. Moreover, data driven methods have been applied to the Malmquist Index framework and at this stage of research they have shown different results from those provided by Simar and Wilson (1999). From an empirical point of view, Total Factor Productivity (TFP) growth of the Italian regions over the period 1980-2001 has been analyzed. Malmquist Productivity Index (MPI) and its components (namely Efficiency Change and Technical Change) as well as confidence intervals have been estimated by applying the best performed procedure, previously determinated. Including human capital among inputs, we estimated an overall bias-corrected productivity gain of 2.1 percent, an efficiency gain of 0.5 and a technical gain of 1.6 percent. The bootstrap analysis revealed that for most Italian regions efficiency and technical changes did not show a statistically significant change. According to these results, the inferential approach has provided a more rigorous and accurate insights on the Italian regional TFP than the traditional Data Envelopment Analysis (DEA) estimation carried out by Leonida et al.(2004 ,Table 1, pg. 2190) in which all the estimated values are interpreted as progress or regress without taking into account the bias of the estimated values and their statistical significance.

The subjekt of my diploma work is socioeconomic development of region Plzeň, its goals and priorities and areas of development. The work consists of two parts. The subjekt of first part is the characteristic of the region. I'm dealing with the settlement structure, population, economic structure, labour market, transport, tourism , culture and health and social security. The sekond parts is concerning with proposals of developing goals and priorities of the region. I'm comparing apportionable datas with datas for the whole Czech republic. In the SWOT analysis I am summarizing datas that I have found out, In the final part I am suggesting solving problemes, which region has those problemes and wchich region could have them in the future.

Studies on water resources are rarely developed in basins where anthropic impact is negligible; therefore, the flow values are seldom ''natural'' and are often significantly affected by the interference of human works. These alterations of the natural discharges can be due to the presence, along the river network, of interfering hydraulic structures. Interactions between these infrastructures and the natural streamflow are certainly in need of further attention. Two main issues have been studied here in detail: i) the impact on water availability and the variability caused by water abstractions and ii) the flood attenuation effect induced by existing reservoirs, that produces flood hazard mitigation downstream. When working in a regional analysis framework, input data and results should represent the natural conditions. Hence, the alterations due to hydraulics structures embedded into the river network should be removed or introduced carefully. For instance, regional estimates of flood peak discharges do not consider the flood attenuation operated by the existing reservoirs. Nevertheless dams have a significant impact, especially in the nearest areas downstream, that should be accounted for. For the Piemonte and Valle d'Aosta Regions, this flood peak attenuation effect has been studied in detail to provide a correct starting point for further flood hazard studies. On the other hand, to assess the impact of water abstractions on daily discharge data, basic indices of alterations have been defined and an innovative correction model to reconstruct the natural streamflow statistics has been proposed. Non-impacted discharge characteristics are needed to provide a correct regional estimate of water resources availability and, consequently, of the gross hydropower potential. For water use planning purposes or to define the residual hydropower potential, the correction model proposed can also be used backward, adding anthropic effects to non-impacted values of discharge. The relevance of the proposed approach and methods in regional analysis of extreme and average flows is something that can be better recognized only with the effort of systematically characterizing the infrastructures that interfere with the natural hydrology along the river network. The applications made in this work were made possible by a cooperative effort addressed to the realization of a comprehensive Hydrological and Infrastructures Information System for the Regione Piemonte, that constitutes an important laboratory for hydrological simulations in a real world of heavily altered natural processes.

Energy production and consumption contribute to 76% of the European greenhouse gas (GHG) emissions in 2018, and 90% of global GHG emissions with land use, land use change and forestation (LULUCF) in the same year. By applying energy efficiency (EE) and renewable energy (RE) technologies, the GHG emission intensity of the energy sector reduced by 1.3% in 2018 compared to the previous year. The current climate change policy aims at decarbonization, sustainable environment, economic prosperity and social equity. It requires the deep decarbonisation of the economies, meaning that the energy and power systems as well as other emission intensive sectors need to transform into zero-emission ones. It also requires the minimization of the environmental impacts while ensuring the economic development and meeting the need of the population growth. This thesis quantifies and evaluates the life cycle environmental impacts with focus on GHG emissions of the power sector, as consequences of changes in the environmental policy. Specifically, the thesis will answer five research questions: 1. What are climate change and energy/ power development policies in Italy? 2. What are changes in the energy/ power systems as consequences of energy climate policies? 3. What are the methods and approach for quantifying and evaluating life cycle environmental impacts as consequences of changes? 4. What are the life cycle environmental impacts of the Italian energy/ power system, with focus on GHG emissions, as consequences of changes in environmental and power policies? 5. The interactions between the energy climate policies and the environmental impacts/ GHG emissions of the Italian power system? The thesis is structured into six chapters, including two chapters of introduction and conclusion, and four chapters of answering five above-mentioned research questions. Chapter 2 provides the answers for two questions (Question 1 and Question 2) on climate and energy policies and changes in the Italian energy/power system due to climate and energy policies. Climate change and energy/ power development policy in Italy is presented in five main documents: FIT for 55, Integrated national energy and climate plan (NECP), national energy strategy (SEN), national energy efficiency action plan (PAEE), and national renewable energy action plan (NREAP). The four national documents set out the targets for EE and RE. Specifically, the targets of energy savings by 2030 include 43% reduction in primary energy consumption, 0.8% reduction in annually final energy consumption without transportation sector and 10 MTOE final energy consumption reduction. For RE, by 2030, the target is 28% ~ 30% of share of RE in total energy consumption, 55% of RE share in electricity consumption and 21% ~ 22% of RE share in transportation sector. It is expected that the electricity generation technology mix will change in order to meet the requirement on RE and EE targets set out in the Italian energy and climate policies. In this thesis, the energy scenarios called National Trend Italia (NT Italia) will be used. The NT Italia was developed by Terna and Snam, for the horizon years 2025, 2030 and 2040, using modelling tools for electricity demand, gas demand and market simulation. In these scenarios, the installed capacity of electricity by natural gas, which is slightly increased by 2040. The installed capacity of coal-based electricity and other fossil fuels-based electricity reduce from 7GW currently to 2GW by 2025, and will not change then. The scenarios also see a constant growth of electricity by RE, reaching 64 GW for solar and 25 GW for wind power (including 4.2 GW offshore) by 2040, while the installed capacity of hydropower and other renewable electricity will be stable. Chapter 3 and Chapter 4 of this thesis will deal with the research question 3, in which Chapter 3 is about the methodology and Chapter 4 focuses on the applied framework. In Chapter 3, the state of the art of consequential life cycle assessment (C-LCA) in the energy and power sectors has been reviewed. The review was conducted on 43 case studies of C-LCA in energy sector and 31 C-LCA papers in power sector. It was identified that economic models are frequently applied in combination with life cycle assessment (LCA) to conduct a C-LCA study in energy and power sectors. The identified economic models include equilibrium (partial and general equilibrium), input-output, and dynamic (agent based and system dynamic) models. Out of these, the equilibrium model is the most widely used, showing some strengths in availability of data and energy system modelling tools. The input-output model allows for describing both direct and indirect effects due to changes in the energy sector, by using publicly available data. The dynamic model is less frequently applied due to its limitation in availability of data and modelling tools, but has recently attracted more attention due to the ability in modelling quantitative and qualitative indicators of sustainability. The review indicates that the most suitable approach to conduct the study is combining one or several economic models and LCA to assess the consequential life cycle impacts of the power system. As each economic model has their own strengths and limitations, the choice of the applied models in combination with LCA largely depends on the goal of the study, the nature of the changes due to market mechanisms, economic or social origins, and the availability of data. In Chapter 4, a framework of combining Input Output Analysis (IOA) and process-based LCA for conducting the study was proposed. Moreover, this chapter provides detailed information on data collected for the model. There are several weighting points for proposing this framework. Firstly, the goal of the study is to assessing the consequential life cycle impacts of energy/ power systems. It requires the comprehensive overview of all economic sectors, as energy is connected all economic activities. The comprehensiveness will be ensured by applying IOA. At the same time, the process-based LCA will provide the detail of a sector/ a product system, which is normally a limitation of economic-wide tool such as IOA. Secondly, the change in the power system originates from economic activities (supply and demand of energy) as well as the environmental requirement to GHG emission reduction and zero carbon emissions. This change can be well modelled with an economic analysis tool (IOA) in combination with an environmental management tool (processed-based LCA). Finally, data for these tools is publicly available. The IOA depends on the input output tables (IOT), which is published every five years by the Italian Statistics (Istat). Data on energy sector is collected from Energy Balance Table, published annually by Ministry of Economic Development, the data from Terna and Snam, the database of the International Energy Agency (IEA), International Renewable Energy Agency (IRENA) and European Commission. Data on environmental aspects includes the National Accounting Matrix with Environmental Accounts (NAMEA), being collected from Istat. Data for process-based LCA is taken from ecoInvent 3. Some global database for IOA are available such as World Input Output Database (WIOD), EXIOBASE, and ect. Followings is the general framework for combining IOA and processed-based LCA to conduct a C-LCA. Consequential life cycle impact is the subtraction of the life cycle impact ‘after change’ and the life cycle impact ‘before change’. The life cycle impact ‘before change’ is quantified by applying IOA. The life cycle impact ‘after change’ depends on the change of pollutant amount, technological coefficient and the final demand due to the inclusion of renewable energy into the Italian energy system. In this thesis, multiregional input output (MRIO), a variant of IOA is used to cover several regions or countries. The application of hybrid MRIO and process-based LCA (hereinafter being called as H-MRIO) is described as followings: • First, two types of data, including MRIO and hybridization data are collected. MRIO data such as the Italian and multiregional IOTs and air emissions accounts are collected from Istat and EXIOBASE. Hybridization data is collected from Italian power/energy suppliers for power development scenarios, and from the ecoinvent database for direct air emissions of power generation technologies • From MRIO data, the MRIO model with two regions of Italy and Rest of the World (RoW) and 36 economic sectors will be constructed. • In combination with the power development scenarios, the Italian electricity sector is disaggregated into seven power generation technologies, for both intermediate flow matrices and final demand vectors in Italian IOT. Similarly, in the environmental burden matrices, the air emissions of electricity sector are disaggregated into those of seven power generation technologies, with data taken from ecoinvent. At this time, the H-MRIO model composes of 42 sectors (36 economic sectors - 1 electricity sector + 7 power technologies). • The model is calculated with historical data of 2010 and 2017 (reference scenario) and replicated for the future scenarios of 2025, 2030 and 2040. Chapter 5 focuses on applying the proposed H-MRIO framework on the Italian context, to obtained the answers for the last two research questions (Question 4 and 5). The total GHG emissions to meet global final demand in 2017 calculated in the study is at 47.69 GtCO2e, which is slightly higher than the global GHG emissions estimated by Climate Watch, at 47 GtCO2e excluding Land use change and forestation (LUCF). The difference in the obtained results of this model and other models is caused by the difference in scope of air emissions being studied. This model quantified actual anthropogenic emissions of CO2, CH4 and N2O, excluding emissions from LULUCF and biomass burning as a fuel. Meanwhile the Climate Watch’s model takes into account all GHGs (CO2, CH4, N2O, and F-gases such as HFCs, PFCs, and SF6), excluding LUCF. This causes a difference of around 1 GtCO2eq of F-gases and 2.8 Gt CO2eq of CH4. The exclusions of emissions from land use (mostly CH4), biogenic CO2 and F-gases in this model leads to an insignificant difference of around 0.69 GtCO2e (less than 1.5%). In order to look into details of the sources of the change in the air emission, a decomposition analysis has been conducted. With the change in final demand and electricity sector composition of Italy, consumption-based GHG emissions appear to decrease in the period 2010-2040. Specifically, due to changes in production structure, emission coefficients, and final demand, the annual CO2 emission reduction embodied in production activities during the period 2017- 2025 will be up to 7.1 MtCO2, which makes up 57.1 MtCO2 emission reduction in the whole period. The increased final demand of Italy causes an annual increase of 4.8 MtCO2. While the change in production structure, including electricity sector and corresponding change in other economic sectors, helps to reduce 6.1 MtCO2 annually. The change in emission flow coefficients brings an annual reduction credit of about 5.8 MtCO2. During the period of 2025-2030 and 2030-2040, the annual change in emission reduction will be much smaller, at 2.3 MtCO2 and 33.9 ktCO2 respectively. Due to the change in power supply technologies and power consumption, the future air emissions dramatically reduce in electricity sector. Most of the emissions of the domestic electricity production come from fossil fuel based electricity, e.g. electricity by coal and natural gas. A smaller part comes from other renewable electricity, including geothermal and biomass based electricity. The productions of solar and wind power do not generate any air-borne emission, and that of hydropower emits an amount of N2O. The reduction in electricity from fossil fuels such as coal and natural gas help to reduce the emissions of the domestic electricity production nearly four times from 97.5 MtCO2 in 2017 to 25.9 MtCO2 by 2040. Besides, the CO2 emission of final consumption of electricity is 34.9 MtCO2 in 2017, which reduces by more than half, at 13.7 MtCO2 by 2040. The CO2 emission of final electricity consumption is divided among technologies by their production structure. As it can be observed, low-carbon technologies such as solar and wind power technologies contribute to emissions, because of the manufacturing of their infrastructures. The emissions of final electricity consumption are smaller than that of domestic electricity production, as they are shared by other economic sectors as intermediates for production activities. The changes in electricity consumption induce changes in other economic sectors, which are clearly shown in coke and petroleum, pharmaceuticals, water transportation, education, and healthcare, either increase or decrease their emissions. Particularly, electricity sector accounts for 11.6% of the total CO2 emissions in 2017, which reduces to 5.9% by 2040. The CO2 emission shares of some other economic sectors also decrease during the period 2017-2040, such as construction and healthcare (reducing around 1 percent point). Meanwhile, the CO2 emission shares of some sectors increases, such as food and beverage (increasing less than 1 percent point). It should be noted that the CO2 emission contributions of these sectors to the national final consumption emissions do not show the correspondingly absolute increase (or decrease). Instead, they relatively present the changes in the identified ‘hotspot’ sectors over years. The absolute values of the CO2 emissions decrease in all economic sectors between 2017 and 2040. The decrease is clearly presented in economic sectors such as construction, decreasing from 20.99 MtCO2 in 2017 to 13.4 MtCO2 by 2040, at about 0.33 MtCO2 annually; or food and beverage, decreasing from 15 MtCO2 to 12.5 MtCO2, or 0.1 MtCO2 annually; or healthcare, decreasing from 17.7 MtCO2 to 11.43 MtCO2 or 0.27 MtCO2 annually in the same period. Five economic sectors holding larges shares out of total CO2 emission of final consumption includes: wholesale and retail, healthcare, food and beverage, electricity and construction (‘hotspot’ sectors). In 2017, wholesale and retail contribute to more than 12% of the total CO2 emission of the Italian final consumption. The four remaining sectors account for an average CO2 emission, from 6% to 10% of the total CO2 emissions. By 2040, the shares of emissions of these sectors remain in the same range. This emission pattern suggests that between 2017 and 2040, in order to reduce the national CO2 emissions, effort should be focused on these ‘hotspot’ sectors. Besides, the different contributions of domestic and import emissions to the total emissions suggest that Italy should have proper strategies to reduce its emissions in term of geographical effort. CO2 emissions of Italian trade partners for food and beverage, health, construction, and wholesale and retail should be taken into account because their emissions largely depends on import. The effort should be taken either to reduce their trade partners’ emission intensity, or to move away from trade partners that having high emission intensities. Meanwhile equal effort should be shared between local manufacturers and trade partners being relevant to renewable power technologies such as solar, wind and other renewable.
Energy production and consumption contribute to 76% of the European greenhouse gas (GHG) emissions in 2018, and 90% of global GHG emissions with land use, land use change and forestation (LULUCF) in the same year. By applying energy efficiency (EE) and renewable energy (RE) technologies, the GHG emission intensity of the energy sector reduced by 1.3% in 2018 compared to the previous year. The current climate change policy aims at decarbonization, sustainable environment, economic prosperity and social equity. It requires the deep decarbonisation of the economies, meaning that the energy and power systems as well as other emission intensive sectors need to transform into zero-emission ones. It also requires the minimization of the environmental impacts while ensuring the economic development and meeting the need of the population growth. This thesis quantifies and evaluates the life cycle environmental impacts with focus on GHG emissions of the power sector, as consequences of changes in the environmental policy. Specifically, the thesis will answer five research questions: 1. What are climate change and energy/ power development policies in Italy? 2. What are changes in the energy/ power systems as consequences of energy climate policies? 3. What are the methods and approach for quantifying and evaluating life cycle environmental impacts as consequences of changes? 4. What are the life cycle environmental impacts of the Italian energy/ power system, with focus on GHG emissions, as consequences of changes in environmental and power policies? 5. The interactions between the energy climate policies and the environmental impacts/ GHG emissions of the Italian power system? The thesis is structured into six chapters, including two chapters of introduction and conclusion, and four chapters of answering five above-mentioned research questions. Chapter 2 provides the answers for two questions (Question 1 and Question 2) on climate and energy policies and changes in the Italian energy/power system due to climate and energy policies. Climate change and energy/ power development policy in Italy is presented in five main documents: FIT for 55, Integrated national energy and climate plan (NECP), national energy strategy (SEN), national energy efficiency action plan (PAEE), and national renewable energy action plan (NREAP). The four national documents set out the targets for EE and RE. Specifically, the targets of energy savings by 2030 include 43% reduction in primary energy consumption, 0.8% reduction in annually final energy consumption without transportation sector and 10 MTOE final energy consumption reduction. For RE, by 2030, the target is 28% ~ 30% of share of RE in total energy consumption, 55% of RE share in electricity consumption and 21% ~ 22% of RE share in transportation sector. It is expected that the electricity generation technology mix will change in order to meet the requirement on RE and EE targets set out in the Italian energy and climate policies. In this thesis, the energy scenarios called National Trend Italia (NT Italia) will be used. The NT Italia was developed by Terna and Snam, for the horizon years 2025, 2030 and 2040, using modelling tools for electricity demand, gas demand and market simulation. In these scenarios, the installed capacity of electricity by natural gas, which is slightly increased by 2040. The installed capacity of coal-based electricity and other fossil fuels-based electricity reduce from 7GW currently to 2GW by 2025, and will not change then. The scenarios also see a constant growth of electricity by RE, reaching 64 GW for solar and 25 GW for wind power (including 4.2 GW offshore) by 2040, while the installed capacity of hydropower and other renewable electricity will be stable. Chapter 3 and Chapter 4 of this thesis will deal with the research question 3, in which Chapter 3 is about the methodology and Chapter 4 focuses on the applied framework. In Chapter 3, the state of the art of consequential life cycle assessment (C-LCA) in the energy and power sectors has been reviewed. The review was conducted on 43 case studies of C-LCA in energy sector and 31 C-LCA papers in power sector. It was identified that economic models are frequently applied in combination with life cycle assessment (LCA) to conduct a C-LCA study in energy and power sectors. The identified economic models include equilibrium (partial and general equilibrium), input-output, and dynamic (agent based and system dynamic) models. Out of these, the equilibrium model is the most widely used, showing some strengths in availability of data and energy system modelling tools. The input-output model allows for describing both direct and indirect effects due to changes in the energy sector, by using publicly available data. The dynamic model is less frequently applied due to its limitation in availability of data and modelling tools, but has recently attracted more attention due to the ability in modelling quantitative and qualitative indicators of sustainability. The review indicates that the most suitable approach to conduct the study is combining one or several economic models and LCA to assess the consequential life cycle impacts of the power system. As each economic model has their own strengths and limitations, the choice of the applied models in combination with LCA largely depends on the goal of the study, the nature of the changes due to market mechanisms, economic or social origins, and the availability of data. In Chapter 4, a framework of combining Input Output Analysis (IOA) and process-based LCA for conducting the study was proposed. Moreover, this chapter provides detailed information on data collected for the model. There are several weighting points for proposing this framework. Firstly, the goal of the study is to assessing the consequential life cycle impacts of energy/ power systems. It requires the comprehensive overview of all economic sectors, as energy is connected all economic activities. The comprehensiveness will be ensured by applying IOA. At the same time, the process-based LCA will provide the detail of a sector/ a product system, which is normally a limitation of economic-wide tool such as IOA. Secondly, the change in the power system originates from economic activities (supply and demand of energy) as well as the environmental requirement to GHG emission reduction and zero carbon emissions. This change can be well modelled with an economic analysis tool (IOA) in combination with an environmental management tool (processed-based LCA). Finally, data for these tools is publicly available. The IOA depends on the input output tables (IOT), which is published every five years by the Italian Statistics (Istat). Data on energy sector is collected from Energy Balance Table, published annually by Ministry of Economic Development, the data from Terna and Snam, the database of the International Energy Agency (IEA), International Renewable Energy Agency (IRENA) and European Commission. Data on environmental aspects includes the National Accounting Matrix with Environmental Accounts (NAMEA), being collected from Istat. Data for process-based LCA is taken from ecoInvent 3. Some global database for IOA are available such as World Input Output Database (WIOD), EXIOBASE, and ect. Followings is the general framework for combining IOA and processed-based LCA to conduct a C-LCA. Consequential life cycle impact is the subtraction of the life cycle impact ‘after change’ and the life cycle impact ‘before change’. The life cycle impact ‘before change’ is quantified by applying IOA. The life cycle impact ‘after change’ depends on the change of pollutant amount, technological coefficient and the final demand due to the inclusion of renewable energy into the Italian energy system. In this thesis, multiregional input output (MRIO), a variant of IOA is used to cover several regions or countries. The application of hybrid MRIO and process-based LCA (hereinafter being called as H-MRIO) is described as followings: • First, two types of data, including MRIO and hybridization data are collected. MRIO data such as the Italian and multiregional IOTs and air emissions accounts are collected from Istat and EXIOBASE. Hybridization data is collected from Italian power/energy suppliers for power development scenarios, and from the ecoinvent database for direct air emissions of power generation technologies • From MRIO data, the MRIO model with two regions of Italy and Rest of the World (RoW) and 36 economic sectors will be constructed. • In combination with the power development scenarios, the Italian electricity sector is disaggregated into seven power generation technologies, for both intermediate flow matrices and final demand vectors in Italian IOT. Similarly, in the environmental burden matrices, the air emissions of electricity sector are disaggregated into those of seven power generation technologies, with data taken from ecoinvent. At this time, the H-MRIO model composes of 42 sectors (36 economic sectors - 1 electricity sector + 7 power technologies). • The model is calculated with historical data of 2010 and 2017 (reference scenario) and replicated for the future scenarios of 2025, 2030 and 2040. Chapter 5 focuses on applying the proposed H-MRIO framework on the Italian context, to obtained the answers for the last two research questions (Question 4 and 5). The total GHG emissions to meet global final demand in 2017 calculated in the study is at 47.69 GtCO2e, which is slightly higher than the global GHG emissions estimated by Climate Watch, at 47 GtCO2e excluding Land use change and forestation (LUCF). The difference in the obtained results of this model and other models is caused by the difference in scope of air emissions being studied. This model quantified actual anthropogenic emissions of CO2, CH4 and N2O, excluding emissions from LULUCF and biomass burning as a fuel. Meanwhile the Climate Watch’s model takes into account all GHGs (CO2, CH4, N2O, and F-gases such as HFCs, PFCs, and SF6), excluding LUCF. This causes a difference of around 1 GtCO2eq of F-gases and 2.8 Gt CO2eq of CH4. The exclusions of emissions from land use (mostly CH4), biogenic CO2 and F-gases in this model leads to an insignificant difference of around 0.69 GtCO2e (less than 1.5%). In order to look into details of the sources of the change in the air emission, a decomposition analysis has been conducted. With the change in final demand and electricity sector composition of Italy, consumption-based GHG emissions appear to decrease in the period 2010-2040. Specifically, due to changes in production structure, emission coefficients, and final demand, the annual CO2 emission reduction embodied in production activities during the period 2017- 2025 will be up to 7.1 MtCO2, which makes up 57.1 MtCO2 emission reduction in the whole period. The increased final demand of Italy causes an annual increase of 4.8 MtCO2. While the change in production structure, including electricity sector and corresponding change in other economic sectors, helps to reduce 6.1 MtCO2 annually. The change in emission flow coefficients brings an annual reduction credit of about 5.8 MtCO2. During the period of 2025-2030 and 2030-2040, the annual change in emission reduction will be much smaller, at 2.3 MtCO2 and 33.9 ktCO2 respectively. Due to the change in power supply technologies and power consumption, the future air emissions dramatically reduce in electricity sector. Most of the emissions of the domestic electricity production come from fossil fuel based electricity, e.g. electricity by coal and natural gas. A smaller part comes from other renewable electricity, including geothermal and biomass based electricity. The productions of solar and wind power do not generate any air-borne emission, and that of hydropower emits an amount of N2O. The reduction in electricity from fossil fuels such as coal and natural gas help to reduce the emissions of the domestic electricity production nearly four times from 97.5 MtCO2 in 2017 to 25.9 MtCO2 by 2040. Besides, the CO2 emission of final consumption of electricity is 34.9 MtCO2 in 2017, which reduces by more than half, at 13.7 MtCO2 by 2040. The CO2 emission of final electricity consumption is divided among technologies by their production structure. As it can be observed, low-carbon technologies such as solar and wind power technologies contribute to emissions, because of the manufacturing of their infrastructures. The emissions of final electricity consumption are smaller than that of domestic electricity production, as they are shared by other economic sectors as intermediates for production activities. The changes in electricity consumption induce changes in other economic sectors, which are clearly shown in coke and petroleum, pharmaceuticals, water transportation, education, and healthcare, either increase or decrease their emissions. Particularly, electricity sector accounts for 11.6% of the total CO2 emissions in 2017, which reduces to 5.9% by 2040. The CO2 emission shares of some other economic sectors also decrease during the period 2017-2040, such as construction and healthcare (reducing around 1 percent point). Meanwhile, the CO2 emission shares of some sectors increases, such as food and beverage (increasing less than 1 percent point). It should be noted that the CO2 emission contributions of these sectors to the national final consumption emissions do not show the correspondingly absolute increase (or decrease). Instead, they relatively present the changes in the identified ‘hotspot’ sectors over years. The absolute values of the CO2 emissions decrease in all economic sectors between 2017 and 2040. The decrease is clearly presented in economic sectors such as construction, decreasing from 20.99 MtCO2 in 2017 to 13.4 MtCO2 by 2040, at about 0.33 MtCO2 annually; or food and beverage, decreasing from 15 MtCO2 to 12.5 MtCO2, or 0.1 MtCO2 annually; or healthcare, decreasing from 17.7 MtCO2 to 11.43 MtCO2 or 0.27 MtCO2 annually in the same period. Five economic sectors holding larges shares out of total CO2 emission of final consumption includes: wholesale and retail, healthcare, food and beverage, electricity and construction (‘hotspot’ sectors). In 2017, wholesale and retail contribute to more than 12% of the total CO2 emission of the Italian final consumption. The four remaining sectors account for an average CO2 emission, from 6% to 10% of the total CO2 emissions. By 2040, the shares of emissions of these sectors remain in the same range. This emission pattern suggests that between 2017 and 2040, in order to reduce the national CO2 emissions, effort should be focused on these ‘hotspot’ sectors. Besides, the different contributions of domestic and import emissions to the total emissions suggest that Italy should have proper strategies to reduce its emissions in term of geographical effort. CO2 emissions of Italian trade partners for food and beverage, health, construction, and wholesale and retail should be taken into account because their emissions largely depends on import. The effort should be taken either to reduce their trade partners’ emission intensity, or to move away from trade partners that having high emission intensities. Meanwhile equal effort should be shared between local manufacturers and trade partners being relevant to renewable power technologies such as solar, wind and other renewable.

La présente thèse a pour objet d'étudier la contribution de la région dans le développement régional. En s'appuyant sur une argumentation financière et juridique, et en faisant appel à des approches historique, comparative et prospective, cette thèse étudie le développement en tant que fonction principale de la collectivité territoriale régionale. Le rôle peut-être résumé dans la primauté régionale en matière de développement économique, consacrée par le législateur à travers la reconnaissance à la région du rôle de coordination et de chef de file. Ces configurations sont pour autant entravées par un contexte financier qui rend le rôle de la région particulièrement complexe
This thesis is intended to study the contribution of the region to regional development. Based on the financial and legal analysis, referring to historical, comparative and perspective approaches, the thesis examines the development as a major function of administrative territorial structures. The nature of this function can be summarized in the regional primacy in economic development, dedicated by the legislature through the recognition of the role of regional coordination and leadership. In the meantime, these configurations are hampered by a financial context which makes the region's function of a particularly complex nature

This diploma thesis deals with complex evaluation of tourism competitiveness of five Moravian regions. First chapter explains basic definitions related to the tourism and regional competitiveness. Second chapter analyzes in detail natural conditions, economics, transport, people and tourism of the regions. In the third chapter, the competitiveness of Moravian regions in three areas (economics, people and tourism) and as a whole is examined through the method of the economic n-angle.

The paper studies the regional integration as the unique process which depends on the degree of cooperation and interchange among regions. The generalisation of existing approaches for regional integration has been classified by the criterions. The data of the main economic indicators have been analysed. The economic analysis proves the differences in production endowments, the asymmetry in fixed capital investment, the disproportional income, and foreign direct investment distribution in 2001 – 2005 in Ukrainian regions. Econometric modelling depicts the existence of the division for the industrial regions with high urbanisation and backward agrarian regions in the Ukraine, the industrial development disparities among regions; the insufficient infrastructure (telecommunications, roads, hotels, services and etc.), the low labour productivity in industrial sector, and insufficient regional trade.

Syftet med denna uppsats är att analysera fyra statliga offentliga dokument för att se vad problemen är representerade att vara som leder politiken mot en riktning där den kommunala planeringen, i form av den fysiska planeringen och bostadsförsörjningsplaneringen, i större utsträckning ska ordnas i- och ta hänsyn till ett regionalt perspektiv. Syftet med denna uppsats blir således också att analysera villkoren för det kommunala självstyret när en av dess grundbultar, den kommunala planeringen, i ökad utsträckning ska ordnas i- och ta hänsyn till den regionala nivån. Den här uppsatsen teoretiska- och metodologiska ansats bygger på ett vetenskaplig paket av Carol Bacchi. Hennes ansats studerar problemrepresentationer och för att besvara den här uppsatsen övergripande frågeställningar använder jag mig av ett diskursanalysverktyg som är inspirerad utifrån hennes ansats. Den empiriska analysen visar att en av problemrepresentationerna är att det pågår en regionförstoring med växande funktionella regioner. En annan problemrepresenation som framträder i de undersökta dokumenten är att det anses vara betydande av en ökad samordning av den kommunala planeringen. Det kommunala planmonopolet, som är en del av det kommunala självstyret, problematiseras att vara av stor vikt i de undersökta dokumenten, men att det finns brister i tillämpningen m.m. Alla dessa problematiseringar ovan som funnits i de undersökta dokumenten kan spåras till en diskurs där hållbar utveckling, konkurrenskraft och tillväxt är det självklara och eftersträvansvärda. Villkoren för kommunalt självstyre kan förstås på det sättet att en av dess grundbultar, den kommunala planeringen, i större utsträckning ska ordnas i- och ta hänsyn till en nyregional logik som bygger på en viss typ av politik med en konsensustradition.
The purpose of this essay is to analyze four public document published by the Swedish state to see what the problems is represented to be, which makes the politics move to a certain direction where municipal planning, more specifically spatial and social planning and housing planning, should take into account regional perspectives. Also, the purpose of this essay is to analyze the terms of the local self-government in a context where the municipal planning is increasingly to be arranged in a regional perspective. This essays theoretical- and methodological approach contains of a scientific package created by Carol Bacchi. Her theoretical- and methodological approach studies problem representations and to answer this essay overall questions I use a discourse analysis tool inspired of Bacchis approach. The empirical analysis shows that there is a problem representation about an ongoing regional expansion, thus expanding functional regions. Another problem representation is that it is considered necessary to have a greater coordination of the municipal planning. The municipal monopoly, which is a part of the local self-government, is problematized in the examined document to be of great importance, but there are some shortcomings in the application. All of these problem representations and problematisations can be traced to a discourse in which sustainable development, competitiveness and growth is desirable. The terms of the local self-government can be understood in the sense that one of its cornerstones, the municipal planning, greater will be arranged in a new regional logic, which builds upon a certain kind of politics with a consensus tradition

Frequency analysis is a vital tool when nding a well-suited probability distributionin order to predict extreme rainfall. The regional frequency approach have beenused for determination of homogeneous regions, using 11 sites in Skane, Sweden. Todescribe maximum annual daily rainfall, the Generalized Logistic (GLO), GeneralizedExtreme Value (GEV), Generalized Normal (GNO), Pearson Type III (PE3),and Generalized Pareto (GPA) distributions have been considered. The method ofL-moments have been used in order to nd parameter estimates for the candidatedistributions. Heterogeneity measures, goodness-of-t tests, and accuracy measureshave been executed in order to accurately estimate quantiles for 1-, 5-, 10-, 50- and100-year return periods. It was found that the whole province of Skane could beconsidered as homogeneous. The GEV distribution was the most consistent withthe data followed by the GNO distribution and they were both used in order toestimate quantiles for the return periods. The GEV distribution generated the mostprecise estimates with the lowest relative RMSE, hence, it was concluded to be thebest-t distribution for maximum annual daily rainfall in the province.

AbstractThe concept of competitiveness is today a central element for regional development, European cohesion policies and smart specialisation strategies. Despite being born for firm-level analyses, competitiveness is indeed commonly used at the territorial level, mainly at the regional or urban scale, normally measured with different composite structural indicators. However, since territorial competitiveness is unevenly distributed in space, territorial units smaller than a full NUTS-2 region might be differently competitive and hence suited to implement differentiated cohesion policies and smart specialisation strategies. To test the hypothesis that these firm-level indicators can characterize the intraregional differences in aggregate performance, the paper sets up a meta-analysis framework between these indicators and structural indicators (employment growth and specialisation index) measured at the NUTS-3 level. For the meta-analysis at this novel intraregional level, the paper exploits the Lombardy region as a case study. Lombardy is well suited for the aims of this paper, being a large and competitive European region, whose territory—as well as its labor market—is highly differentiated, from peripheral and mountainous areas to many medium and small cities, second-tier large cities and a large metropolitan area—the city of Milan. All these territories are characterized by different economic and social vocations, but all share the same regional administration. The results of the meta-analysis show that firm-level indicators correlate with the aggregate performance of regions and that the structural measures selected can characterize different territories in different conditions. Hence, the competitiveness of firms seems to translate into aggregate territorial performance at small spatial scales. This implies that territorial specificities are also relevant inside regions and should be considered in designing regional policy interventions, such as those of the Smart Specialisation Strategy (S3).

At present, at a time of incoming recession in the global and domestic economy, wages are an important tool in the labor market. The paper aims to analyze the development of average and minimum wages from the perspective of the Slovak Republic and its regions. At the same time, using correlation analysis, we analyze the relationship between wages and the educational structure of the region's population and registered unemployment in the region. The analyzes showed that average wages are constantly growing. The highest wage is in the Bratislava region, while in the Prešov region it was up to 38% lower. While in the Bratislava region the ratio of the minimum wage to the regional average wage was at the level of 38%, in the Prešov region it reached almost 62%. Based on the results of the correlation analysis, it can be stated that the impact of the monitored attributes on the average monthly wage varies from region to region. We observe the most significant differences for the Bratislava region in comparison with the remaining Slovak regions.

This article examines regional innovation systems in Central and Eastern Europe, with particular attention to the regions with the highest innovation success. The articles also include a discussion of the presence of elements and dynamic of regional innovation systems in these countries. The Innovation performance of regions in Central and Eastern Europe is analyzed and selected progressive regions are further examined in terms of partial innovation and socio-economic indicators. Data on regions were obtained from the Regional Innovation Scoreboard 201at the NUTS II level. Detailed analysis is possible through a case study of the Malopolskie region. The analysis focuses primarily on the causes of the region's innovative progress, including the setting of favorable conditions for SMEs and the associated high cluster activity, the commercialization of research and the dissemination of external knowledge. On the basis of the overall analysis, RIS in Central and Eastern Europe are far below RIS in Western and Northern Europe and their results are very low compared to these regions.

Purpose – Tourism is one of the fastest-growing sectors in the world, and many developing countries saw it as the chance of economic development accelerating. However, tourism potential is different among countries as well as within one country. The scope of this paper is to reveal the regional differences in business conditions considering tourism development in Serbia. Methodology – Instead of using a wide number of different individual indicators measuring regions’ tourism performances, it is more appropriate to use one composite indicator for depicting complex issues in regional tourism development - a composite index was developed using Data Envelopment Analysis. Data Envelopment Analysis is a sort of methodology that constructs an ‘efficiency frontier’ based on each region’s individual data using mathematical linear programming. It determines the best practice by measuring the relative position of each of the regions in terms of the value of the set of observed indicators. Such presentation of the existing region’s development in the tourism field and recommendations for possible improvement are clearer to the general public and non-scientific audience. The composite index is calculated as the weighted sum of the corresponding individual indicators, where the weights are endogenously determined by mathematical linear programming to obtain the maximum possible value. Findings – The results of DEA showed which parts of Serbia have the best business conditions for tourism development, considering the different factors (regional development index and subindexes). The results have been contrasted to chosen tourism statistics on the regional level, with a reference to the limitations during the research process. The proposed composite index is used to point the differences in regional business conditions which could be further influenced by government policies to tackle the specificities and needs of each separate region during tourism development. Contribution – DEA is a known methodology in regional development analysis, but it hasn’t been so much used in business conditions regional analysis before. Having different indicators which shaped business climate within one region, using DEA one tried to analyze the entrepreneurship possibilities with a spatial reference to the tourism development. This is an important contribution in the context of local economic and social development especially within poorly developed regions in Serbia

During the years of independence, the unevenness (asymmetry) of the economic development of the regions of the Kyrgyz Republic sharply increased, the multiple gap between the regions increased according to the most important indicators of regional production, income level and poverty, the quality of life of the population. Regions of the country vary significantly in size, population size and density, level of economic development, natural and climatic conditions, national and historical features. All this causes significant differences in the needs of budget financing and tax bases of individual regions. Smoothing economic asymmetry is one of the most important issues of public administration. The core of the research toolkit should be the economic and mathematical model of economic growth in the region. In regional studies, the following factors of economic growth can be identified: natural resources; labor resources; main capital; volume of investment. In addition, new methods of overcoming socio-economic asymmetry include working out the mechanism of effective public-private partnership and everywhere to introduce long-term planning for the development of the region's economy. The preservation and development of the country as a state largely depends on the effective regional policy implemented in the country.

The financial management of self-governing regions is often examined, but less so their property management. The aim of the paper is to analyze the situation in individual self-governing regions of the Slovak Republic in the time series of 15 years (2005 - 2019) and to identify differences in the method of financing and also in the efficiency of property management. To achieve the goal, two hypotheses were set: H1: The increase in property strength is achieved by increasing the region's debt and H2: The income effect from the region's assets increases with the reproductive power of the assets. The method property creditworthiness was used for the analysis. The results confirmed H1, stating that the source of asset growth is also debt growth (Žilina, Trnava, Prešov region), but several local authorities also significantly use other sources (Nitra, Košice, Trenčín). H2 was only partially confirmed – the regions increased the value of the property, but the income from it was not decisive. This indicates that they have built the infrastructure to provide entrusted services to citizens. The analysis also pointed to the phenomenon that the total value of assets per capita shows significant regional differences over the whole period.

Purpose – The purpose of this paper is to discuss the potential of geocaching as an informative, educational, and promotional tool that can be implemented and managed in a particular area. This paper concentrates on the features of the game and the potential benefits of geocaching for the region. The article also explains the reason why geocaching can be treated as an attractive tool of regional communication and promotion. Methodology – Desk research method and own research including semi-structured interviews among Officers of Polish Marshal’s Offices (14 out of 16 in Poland) were applied to achieve the paper’s purpose. In addition, the data obtained from the websites: www.geocaching.pl and www.geocaching.com was analysed as a source for market information. Findings – Findings from the literature review show that players expect geocaching to play an informative, educational, and promotional role. Geocaching is perceived as an attractive tool to showcase the region. Findings from interviews show that regional Officers treat geocaching as a modern tool that can be used to build the region’s image and present its values. Contribution – This article proved that geocaching could become a useful and an attractive informative, educational, and promotional tool for regions. Since this kind of game is not commonly used by regional managers in Poland, it is important to analyse its possibilities. As such, this paper is of value to region management seeking solutions to present and make the region more recognizable.

The Integrated Regional Operational Program provides financial assistance from European Union funds to contribute to improving the quality of life and ensuring the sustainable provision of public services with an impact on balanced and sustainable territorial development, economic, territorial and social cohesion of regions, cities and municipalities. The target group includes state and local government authorities, social partners, civil society organizations and NGOs. The main objective of this paper is to analyse the implementation Integrated Regional Operational Program in particular regions of the Slovak Republic. In the analysis, we focused on evaluating the success of individual regions in obtaining projects, the current status of projects in implementation and completed projects, which is also related to the amount of approved non-repayable financial contribution. The analysis showed that over 60% of the total number of submitted projects had been successfully implemented in each region.

The aim of this paper is to analyse the mobility of researchers from the six Western Balkan Countries, Albania, Bosnia and Herzego-vina, Kosovo*1, Montenegro, North Macedonia, and Serbia (abbr. WB6) within structured regional and European mobility programmes. We want to identify geographical patterns with a view on mobility-based training from the WB6 region to the EU, but also within the WB6 region. The following structured regional European programmes provide the basis for this comparative analysis • CEEPUS • ERASMUS + • Marie Skłodowska-Curie Actions (MSCA) • COST

Located a 45-minute drive apart from each other in WA’s Great Southern Region, Albany and Denmark attract creative practitioners who are drawn to the region’s natural beauty and country lifestyle. A regional services hub, Albany has a robust creative services presence with a legacy media sector that functions as a hub for public and commercial media organisations servicing Great Southern and the Wheatbelt. Denmark, while a much smaller town, is renowned nationally as an enclave for locally, nationally, and internationally acclaimed artists and creatives.

State of the Nordic Region 2022 has its point of departure in the Covid-19 pandemic and examines how it has affected demography, labour market and economy in the Nordic countries, regions, and municipalities. State of the Nordic Region is published every two years and provides a comprehensive account of regional development trends in the Nordic countries based on the latest statistical data. Charts and maps on different geographical scales are accompanied by expert analysis, providing policymakers and practitioners with a solid evidence base for informed decision making. In writing this edition of State of the Nordic Region, it was, of course, vital to acknowledge the impact of the Covid-19 pandemic. Given the magnitude of this crisis, a conscious decision was taken early in the process to make the pandemic the explicit focus of the publication. State of the Nordic Region 2022 is produced and published by Nordregio.

The objective of this study is to investigate Saudi Arabia’s industrial electricity consumption at the regional level. We apply structural time series modeling to annual data over the period of 1990 to 2019. In addition to estimating the size and significance of the price and income elasticities for regional industrial electricity demand, this study projects regional industrial electricity demand up to 2030. This is done using estimated equations and assuming different future values for price and income. The results show that the long-run income and price elasticities of industrial electricity demand vary across regions. The underlying energy demand trend analysis indicates some efficiency improvements in industrial electricity consumption patterns in all regions.

The objective of this study is to investigate Saudi Arabia’s industrial electricity consumption at the regional level. We apply structural time series modeling to annual data over the period of 1990 to 2019. In addition to estimating the size and significance of the price and income elasticities for regional industrial electricity demand, this study projects regional industrial electricity demand up to 2030. This is done using estimated equations and assuming different future values for price and income. The results show that the long-run income and price elasticities of industrial electricity demand vary across regions. The underlying energy demand trend analysis indicates some efficiency improvements in industrial electricity consumption patterns in all regions.

Induced (anthropogenic) earthquakes are caused by reactivation of pre-existing faults due to human activities, including hydraulic fracturing and saltwater disposal. The Montney play, the pre-eminent shale gas play in Canada, contains localized regions with relatively high geological susceptibility to induced seismicity. Identification of potential seismogenic structures is hindered because published fault maps in the Montney play are often focused on small areas, leading to inconsistencies when these are compiled across the region. We have developed a regional framework based on structural corridors: complex, multi-strand faults systems. A three-step structural interpretation workflow was used, including seismic and structural interpretation along with trend surface analysis (TSA). This approach was used to identify 34 structural corridors in NE BC and NW Alberta.

Studying earthquakes in Baffin Bay and the surrounding regions is challenging. There is no knowledge of earthquake activity in this region prior to 1933 when a moment magnitude (MW) 7.4 earthquake occurred in Baffin Bay. With improved instrumentation, increased seismograph coverage in the north, and modern analysis techniques, knowledge and understanding of earthquakes in the Baffin region is improving. Active seismic zones include Baffin Bay, the east coast of Baffin Island, and the Labrador Sea, separated by areas of low seismicity. Focal-mechanism solutions show a mix of faulting styles, predominantly strike-slip and thrust. Regional stress-axes orientations show more consistency, which suggests that activity is occurring on previously existing structures in response to the current stress field. There is little correlation between earthquake epicentres in Baffin Bay and mapped structures. Glacial isostatic adjustment may be a triggering mechanism for earthquakes in the Baffin region, but modelling efforts have yielded equivocal results.