Suchergebnisse

Este artigo explora a literatura da avaliação económica de bens ambientais em Portugal com o objetivo de identificar oportunidades para reforçar os contributos para a definição de políticas. A análise considera quatro questões: o que tem sido feito neste domínio; quais as características comuns aos diferentes estudos; o que sabemos sobre a validade das estimações; e quais as tendências mais recentes. Conclui-se que a avaliação ambiental em Portugal tem uma aplicação regional relevante com destaque para parques naturais e paisagens. A avaliação contingente é o método mais utilizado. O preço, o rendimento e o uso do recurso para recreio estão entre as variáveis explicativas mais influentes. Os resultados confirmam a validade dos métodos e o seu potencial para fins de política local/regional.

Texto da comunicação apresentada a International Society for Ecological Economics (ISEE) 2012 Conference, Rio de Janeiro, Brasil, 16-19 de junho de 2012 ; Environmental valuation techniques are intended to provide valuable insights in helping scientists and decision-makers to make informed choices about the trade-offs that are inherent to the scarcity restrictions of our daily decisions. However, among other limitations, values obtained on the assumption of a rational behaviour may only be of use for policy guidance if people make consistent and systematic choices. This research embraces the challenge of contributing to organize the complexities of valuation of non-market goods, rather than just ignoring them. We address the ongoing debate on symptoms of bounded rationality in studies applying stated or revealed preferences methods by examining the theoretically consistency of preferences using observed and intended behaviour without monetary values being directly asked. The environmental good considered in our empirical approach is a national wood (Mata do Bussaco), located in a European country (Portugal). Overall, the results reveal that visitors are sensitive to both price and quality changes. In the deterioration scenario, the intended number of trips would be seriously reduced and respondents would suffer an important welfare loss. Another key finding is the apparent inconsistency between preferences expressed by revealed and observed behaviour. As inconsistencies are detected for changes resulting from manager's action, it is argued that they are likely the outcome of strategic behaviour.

Comunicação apresentada em 19th APDR Congress on Place-Based Policies and Economic Recovery, Braga, 2013 ; In Portugal, the public debate at regional level is typically engaged in the discussion of asymmetries amongst the 'interior' and the 'coast'. What is often discussed, with political and social relevance, is the extent of the interior's delay (in terms of development) comparatively to the coastal region, and into what extent the dynamics of the economy, or eventually the 'bias' introduced by public policies, contributes to this drawback. Interestingly, however, the Portuguese regional science has miscarried this debate, largely on the grounds that the official statistics do not include this cleavage. Indeed, the design of the NUTS II in Portugal splits the country horizontally, forgetting the vertical gap that splits the interior regions from the coastal ones. The first objective of this paper is therefore to refocus the debate - in scientific terms – on the actual territorial disparity in Portugal: the contrast Coast-Interior. Accordingly, this paper starts by presenting the structure of a bi-regional Input-Output (IO) model for the Portuguese Economy. We consider a rectangular IO model (431 products by 125 industries), decomposing the Portuguese economy into two regions with comparable territorial sizes (the Coastal Region, comprising 44% of the Portuguese continental area, and the Interior Region). the model is 'closed' for the private consumption of households below 65 (which is supposed to be endogenous, as it depends on regional employment and therefore on households' earnings. Multi-regional IO models describe the inter-sectoral dependencies both within the region and between the regions. The main aim is then to assess how the effects of a shock that hits only one of the regions are 'distributed' among the two regions. In particular, we intend to analyse at a greater detail the role of the agri-food sector in the Interior Region. Overall results illustrate the dependence of the Interior on the ...

Purpose
This paper aims to explore the potential contribution of integrated traffic and parking management strategies to ensure more rational use of available parking spaces and to reduce fuel consumption and greenhouse gas emissions by commuters traveling to the University of Coimbra (UC) main campus.


Design/methodology/approach
An integrated modelling approach is used, including the characterization of supply and demand for parking and public transport, the creation and implementation of a survey to campus users and a life-cycle approach to assess six transportation and parking strategy scenarios.


Findings
This comprehensive analysis demonstrates the importance of integrated management measures to greening commuters' transportation and parking within a University campus, identifying and quantifying opportunities for successfully making the transitions toward a more sustainable future, namely, increasing well-being and reducing environmental impact.


Practical implications
Results demonstrate that effective control of illegal parking and different forms of modal shift toward public transportation may contribute to important reductions in environmental impacts.


Social implications
Local population reveals willingness to participate in collective efforts to tackle traffic and parking problems, challenging authorities to take action and empowering ever more people to engage in such cathartic changes.


Originality/value
This comprehensive approach is highly valuable for the management of parking and traffic within the UC campus, providing innovative lessons on the social and environmental impacts that would result from this policy approach to urban areas (e.g. historical centers) facing the typical problems of a carbon society, such as traffic congestion, non-regulated parking and intensive car use.

El tema "Pasado, Presente y Futuro de las Operaciones de Paz. Análisis a través del Tiempo". El Embajador Mulet inició su disertación con un recuento respecto a la evolución de las operaciones del mantenimiento de la paz, recordando que desde que el Consejo de Seguridad envió los primeros cascos azules al Monte Sinaí hace sesenta años, 69 operaciones de paz han sido desplegadas, 56 de ellas desde 1988. Actualmente existen 16 operaciones de mantenimiento de la paz alrededor del mundo, con aproximadamente 123,560 efectivos entre civiles y militares y un presupuesto total para el actual período fiscal que alcanza los 8.6 mil millones de dólares. ; Telefónica y APM Terminals.

AbstractA growing number of studies have linked mindfulness with the adoption of environmentally friendly behaviors. We aim to contribute to this emergent research by putting forward a model in which the relationship between mindfulness and a specific pro‐environmental behavior, water conservation, is indirect. In this pursuit, we draw on the hierarchical model for the influence of psychological characteristics on individuals' behaviors. We propose that the relationship between mindfulness and water conservation is mediated by environmental beliefs, namely water utilitarian beliefs, and consumer abilities, specifically water‐related perceived consumer effectiveness. To collect the data, we relied on a pretested self‐report questionnaire that was distributed in a Portuguese municipality. We retained the responses from 876 individuals, for a net response rate of 54.8%. The research model was tested with structural equation modeling. The results indicate that mindfulness is negatively related to water utilitarian beliefs, that these are negatively related to perceived consumer effectiveness, which, in turn, is positively associated with water conservation behavior. In addition to these direct relationships, the results show that mindfulness is indirectly related to water conservation behavior and to perceived consumer effectiveness, and that water utilitarian beliefs are indirectly related to water conservation behavior. These novel results are used to derive managerial implications.

AbstractPublic health measures enacted to mitigate the spread of coronavirus disease 2019 (COVID‐19) have dampened economic activity by shuttering businesses that provide 'nonessential' goods and services. Not surprisingly, these actions directly impacted demand for nonessential goods and services, but the full impact of this shock on the broader economy will depend on the nature and strength of value chains. In a world where production chains are increasingly fragmented, a shock in one industry (or a group of industries) in one country will affect other domestic industries as well as international trade, leading to impacts on production in other countries. We employ the World Input–Output Database to depict the interdependencies among both industries and countries, which provides a full representation of global value chains. By assuming a homogeneous impact on demand for nonessential goods and services around the world, we demonstrate asymmetric effects on production by industry and international trade, leading to asymmetric relative impacts on national economies. Our results indicate that if demand for nonessential goods and services decreases by 50%, the global gross domestic product will decline by 23%, leading to relative impacts that are larger in China, Indonesia, and some European countries. Also, international trade declines by almost 30%, largely due to a reduction in economic activity associated with the production of raw materials and certain types of manufacturing. This work highlights the relevancy of going beyond measuring the direct effects of COVID‐19 and provides insights into how international trade linkages will induce broader economic impacts across the globe.

Este trabalho centra-se no cluster Engineering & Tooling (E&T), apresentando três análises complementares: (i) a concentração geográfica e avaliação da sua importância económica, do ponto de vista regional e nacional, suportada nos quocientes de localização baseados no volume de negócios e análise shift-share para compreender a evolução do emprego; (ii) as interdependências intersetoriais, analisando a evolução dos indicadores das Contas Nacionais do INE; e (iii) a integração do cluster no contexto do comércio internacional e das cadeias de valor global através da análise multirregional Input-Output com base na World Input-Output Database.
Conclui-se que o E&T apresenta relevância especial na Região Centro, mas também no contexto da economia nacional e respetiva integração internacional, devido à elevada especialização da Região (destacando-se as NUTS-III Regiões de Leiria e de Aveiro) e à componente diferencial ou regional, que estimulou um aumento do emprego nestes setores na Região Centro, mais significativo do que no resto do país. Ao nível nacional, os setores associados ao cluster revelaram resiliência e capacidade de ligeiro crescimento (assentando também nas exportações), mesmo num contexto particularmente adverso à economia. O comércio internacional mostrou igualmente ser um fator determinante para a evolução do cluster, demonstrando a existência de dependências internas e externas.

International audience ; With the growing availability of large-scale datasets, and the popularization of affordable storage and computational capabilities, the energy consumed by AI is becoming a growing concern. To address this issue, in recent years, studies have focused on demonstrating how AI energy efficiency can be improved by tuning the model training strategy. Nevertheless, how modifications applied to datasets can impact the energy consumption of AI is still an open question. To fill this gap, in this exploratory study, we evaluate if datacentric approaches can be utilized to improve AI energy efficiency. To achieve our goal, we conduct an empirical experiment, executed by considering 6 different AI algorithms, a dataset comprising 5,574 data points, and two dataset modifications (number of data points and number of features). Our results show evidence that, by exclusively conducting modifications on datasets, energy consumption can be drastically reduced (up to 92.16%), often at the cost of a negligible or even absent accuracy decline. As additional introductory results, we demonstrate how, by exclusively changing the algorithm used, energy savings up to two orders of magnitude can be achieved. In conclusion, this exploratory investigation empirically demonstrates the importance of applying data-centric techniques to improve AI energy efficiency. Our results call for a research agenda that focuses on data-centric techniques, to further enable and democratize Green AI.

International audience ; With the growing availability of large-scale datasets, and the popularization of affordable storage and computational capabilities, the energy consumed by AI is becoming a growing concern. To address this issue, in recent years, studies have focused on demonstrating how AI energy efficiency can be improved by tuning the model training strategy. Nevertheless, how modifications applied to datasets can impact the energy consumption of AI is still an open question. To fill this gap, in this exploratory study, we evaluate if datacentric approaches can be utilized to improve AI energy efficiency. To achieve our goal, we conduct an empirical experiment, executed by considering 6 different AI algorithms, a dataset comprising 5,574 data points, and two dataset modifications (number of data points and number of features). Our results show evidence that, by exclusively conducting modifications on datasets, energy consumption can be drastically reduced (up to 92.16%), often at the cost of a negligible or even absent accuracy decline. As additional introductory results, we demonstrate how, by exclusively changing the algorithm used, energy savings up to two orders of magnitude can be achieved. In conclusion, this exploratory investigation empirically demonstrates the importance of applying data-centric techniques to improve AI energy efficiency. Our results call for a research agenda that focuses on data-centric techniques, to further enable and democratize Green AI.

International audience ; With the growing availability of large-scale datasets, and the popularization of affordable storage and computational capabilities, the energy consumed by AI is becoming a growing concern. To address this issue, in recent years, studies have focused on demonstrating how AI energy efficiency can be improved by tuning the model training strategy. Nevertheless, how modifications applied to datasets can impact the energy consumption of AI is still an open question. To fill this gap, in this exploratory study, we evaluate if datacentric approaches can be utilized to improve AI energy efficiency. To achieve our goal, we conduct an empirical experiment, executed by considering 6 different AI algorithms, a dataset comprising 5,574 data points, and two dataset modifications (number of data points and number of features). Our results show evidence that, by exclusively conducting modifications on datasets, energy consumption can be drastically reduced (up to 92.16%), often at the cost of a negligible or even absent accuracy decline. As additional introductory results, we demonstrate how, by exclusively changing the algorithm used, energy savings up to two orders of magnitude can be achieved. In conclusion, this exploratory investigation empirically demonstrates the importance of applying data-centric techniques to improve AI energy efficiency. Our results call for a research agenda that focuses on data-centric techniques, to further enable and democratize Green AI.

International audience ; With the growing availability of large-scale datasets, and the popularization of affordable storage and computational capabilities, the energy consumed by AI is becoming a growing concern. To address this issue, in recent years, studies have focused on demonstrating how AI energy efficiency can be improved by tuning the model training strategy. Nevertheless, how modifications applied to datasets can impact the energy consumption of AI is still an open question. To fill this gap, in this exploratory study, we evaluate if datacentric approaches can be utilized to improve AI energy efficiency. To achieve our goal, we conduct an empirical experiment, executed by considering 6 different AI algorithms, a dataset comprising 5,574 data points, and two dataset modifications (number of data points and number of features). Our results show evidence that, by exclusively conducting modifications on datasets, energy consumption can be drastically reduced (up to 92.16%), often at the cost of a negligible or even absent accuracy decline. As additional introductory results, we demonstrate how, by exclusively changing the algorithm used, energy savings up to two orders of magnitude can be achieved. In conclusion, this exploratory investigation empirically demonstrates the importance of applying data-centric techniques to improve AI energy efficiency. Our results call for a research agenda that focuses on data-centric techniques, to further enable and democratize Green AI.

project grant (PTDC/OCE-ETA/31250/2017) with financial support from FCT/MCTES through national funds and co-financed by FEDER, under the Partnership Agreement PT2020 (UID/QUI/50006/2019-POCI/01/0145/FEDER/007265). Financial support was also obtained from the Spanish Ministry of Science, Innovation and Universities (CTQ2015-69021-R and RTI2018-102212-B-I00), the Xunta de Galicia (GRC2014/040, ED431C 2018/30, and Centro Singular de Investigacion de Galicia Accreditation 2016-2019, ED431G/09) and the European Union (European Regional Development Fund-ERDF). ; The interaction of two anthocyanins with a water-soluble polyanionic dendrimer was studied through UV/Vis, stopped-flow, and NMR spectroscopy. Cyanidin-3-glucoside (cy3glc) revealed a stronger interaction than malvidin-3-glucoside (mv3glc) at pH 1 according to their apparent association constants. A higher color increased was also obtained for cy3glc at pH 3.5 as a result of this stronger interaction. A high-frequency chemical shift of the cy3glc aromatic protons suggest the formation of ionic pairs. The interaction parameters (K≈700 m−1, n≈295) indicated the binding of approximately two anthocyanin molecules by each sulfate group. The equilibrium and rate constants of cy3glc in the presence of dendrimer showed an increased stability of the flavylium cation and a higher protection of this species from hydration (pK′a and pKh increased almost one pH unit). The tuning and color stabilization of anthocyanins by using this dendrimer allow novel applications as colorimetric sensors for food packaging. ; authorsversion ; published