Suchergebnisse

Interpelación abierta a aquellos que en un futuro cercano son la esperanza de un mundo distinto: nuestros jóvenes.

Apuntes para la comprensión de la pobreza en las sociedades desarrolladas. La pobreza no es una fatalidad ni una característica natural de la sociedad. Mucho menos, corresponde al "orden" social o a la "naturaleza" de la sociedad. La pobreza, hoy más que nunca en la historia de la humanidad, es evitable puesto que hay en el mundo y entre nosotros recursos suficientes para remediarla. El problema no es de medios, sino de prioridades políticas y de objetivos: de querer o no querer. La pobreza supone un sufrimiento tal para las personas que la padecen que ninguna de ellas podría desearla para sí o para los suyos I. Esto nos exige desmentir y denunciar toda afirmación que responsabilice únicamente a estas personas de su situación de pobreza o que, todavía peor, las culpabilice. Tal responsabilización o culpabilización tiende a servir y perpetuar las distintas formas de poder y privilegios y, por ello, plantean como inevitables la pobreza y la injusticia, puesto que aísla la pobreza y sus causas del resto de la sociedad como si nada ni nadie más que los pobres tuviera nada que ver con las situaciones en las que viven éstos. La causa última de la pobreza es la desigual, y por ello injusta, distribución de los recursos de todo tipo (vivienda, sanidad, educación y cultura, muy especialmente la capacitación profesional y el empleo, ingresos, posibilidades de ocio y de utilización del tiempo libre, seguridad y estabilidad vitales, relaciones humanas y afectivas, autorrealización personal y sentido vital.) y de las oportunidades de promoción personal y familiar de desarrollo humano.

QM-AI-11-001-EN-C (print)/QM-AI-11-001-EN-N (online) ; THINK Policy Briefs are abbreviated versions of THINK Reports. ; Substantial investments in RD&D in new low-carbon technologies are required to reach the EU climate objectives. Given existing market failures affecting clean innovation, developing a balanced portfolio of existing and new clean technologies will require both demand pull support measures – namely carbon pricing and the Renewables Directive, and direct public support to innovation. Innovation activities should comprise research, development and demonstration and be aimed at both (i) accelerating the decarbonization of energy systems to reach mid-term 2020 objectives by pushing especially more mature technologies and (ii) developing a diversified technology mix enabling the achievement of long-term 2050 objectives by supporting also still immature technologies. Cooperation and coordination among Member State and EU support policies have to be improved. The initiation of European Energy Research Alliances is a step into the right direction; their successful implementation should be fostered and progress monitored. The form of direct public support needs to be tailored to the features of each innovation project – depending on both the technology targeted and its level of maturity – and to the type of entity best placed to undertake the respective RD&D. Financing instruments need to be implemented in a way that encourages efficiency while not discouraging participation by the private sector. Competition for funds should be used and public funding should be output-driven whenever possible; the institutions set up to allocate funds should be flexible enough to avoid institutional inertia and lock-in. ; The THINK project (2010-2013) is funded by the European Commission under the Seventh Framework Programme, Strategic Energy Technology Plan. (Call FP7-ENERGY-2009-2, Grant Agreement no: 249736). Coordinator: Prof. Jean-Michel Glachant and Dr. Leonardo Meeus, Florence School of Regulation, Robert Schuman Centre for Advanced Studies, European University Institute

Each semester the THINK project publishes two research reports based on topics proposed by the European Commission ; QM-31-11-489-EN-C (print) QM-31-11-489-EN-N (online) ; The THINK project (2010-2013) is funded by the European Commission under the Seventh Framework Programme, Strategic Energy Technology Plan. (Call FP7-ENERGY-2009-2, Grant Agreement no: 249736). Coordinator: Prof. Jean-Michel Glachant and Dr. Leonardo Meeus, Florence School of Regulation, Robert Schuman Centre for Advanced Studies, European University Institute

QM-AI-11-005-EN-C (print)/QM-AI-11-005-EN-N (online) ; THINK Policy Briefs are abbreviated versions of THINK Reports. ; In the current context, where public budgets are overstretched due to the economic crisis, there is a pressing need to understand the fiscal implications of climate policies. Policies intended to achieve decarbonization will impact both sides of a country's budget via changes in the tax levels and composition of taxes on the one hand, as well as transfer payments and direct investments on the other. Back-of-the-envelope calculations – comparing net public revenues in 2020 for a Baseline and an Enhanced Policy scenario – show that the additional revenues from carbon pricing and the reduction in revenues from excise taxes on fossil fuels clearly dominate other direct and indirect effects of policies on public budgets such as the additional expenditures dedicated to RD&D targeting low-carbon technologies. The aggregated net budget impact of all direct and indirect effects of new climate policies implemented in the Enhanced Policy Scenario on public budgets in 2020 for the EU-27 as a whole – given our simplyfying assumptions – amounts to additional net public revenues of about €12.6bn (0.09% in terms of the EU-27 GDP) under medium-level abatement costs. This makes a non-negligible impact which is nevertheless much lower than the impact on public accounts from changes in main macroeconomic variables over time. Differences among Member States mainly depend on the additional revenues they will obtain from carbon pricing, which are driven by three main factors: the carbon intensity of the economy, which is positively correlated with the absolute value of the net budget impact of new policies; the share of non-ETS GHG emissions, which is positively correlated with the net budget impact; and the reduction in GHG emissions resulting from new policies, which is negatively correlated with this impact. Countries most significantly affected, both positively and negatively, are among the "new" Member States in the EU-27. In contrast, the impact of new climate policies on large EU-15 economies would be generally positive and typically in line with average EU values. Therefore, authorities from the EU-15 may consider the option of sharing the economic burden of the transition to a low-carbon economy among EU countries, taking into account their economic strength. ; The THINK project (2010-2013) is funded by the European Commission under the Seventh Framework Programme, Strategic Energy Technology Plan. (Call FP7-ENERGY-2009-2, Grant Agreement no: 249736). Coordinator: Prof. Jean-Michel Glachant and Dr. Leonardo Meeus, Florence School of Regulation, Robert Schuman Centre for Advanced Studies, European University Institute

Carbon Capture and Storage (CCS) might be a central technology to reach the decarbonisation goals of the European energy system. However, CCS deployment faces multiple economic, technological, and infrastructure challenges. Related literature tends to only focus on certain aspects of the CCS technology or to be limited to a particular sector perspective. In contrast, this paper presents a holistic modelling framework to analyse the long-term perspectives of CCS in Europe by extending the typical analysis from the electricity sector to the industry sector, and by including the CO2 infrastructure level with CO2 pipelines and storage. To this end, we use state-of-the-art models of the electricity sector (generation investment and electricity grid models), the industry sector, as well as the CO2 infrastructure sector. This unique modelling framework analyses the feasibility and costs of CCS deployment in the European Union towards 2050 in three scenarios with the same ambitious climate policy target (~85% CO2 emissions reduction). The main insights on the deployment of CCS in Europe hinges on two factors: i) the development of low-cost power generation technologies with carbon capture (coal and/or gas-fired), and ii) a sufficiently high CO2 price to compensate for the costs of deploying the CO2 transport infrastructure. Once CO2 transport infrastructure is available, CCS will be a preferred mitigation option for the industry sector emissions. The joint use of CO2 infrastructure by the electricity and the industry sector allows for economies of scale and economies of density. In the long term, CCS cannot achieve the 100% decarbonisation target of the energy sector because the technology can only capture 80–90% of the CO2 emissions of thermal power plants. Moreover, the advantages of CCS in terms of energy system costs compared to a system without CCS is rather small, in the range of 2%. It crucially depends on the costs of renewables and the costs of their integration in the electricity grid. ; publishedVersion

Carbon Capture and Storage (CCS) might be a central technology to reach the decarbonisation goals of the European energy system. However, CCS deployment faces multiple economic, technological, and infrastructure challenges. Related literature tends to only focus on certain aspects of the CCS technology or to be limited to a particular sector perspective. In contrast, this paper presents a holistic modelling framework to analyse the long-term perspectives of CCS in Europe by extending the typical analysis from the electricity sector to the industry sector, and by including the CO2 infrastructure level with CO2 pipelines and storage. To this end, we use state-of-the-art models of the electricity sector (generation investment and electricity grid models), the industry sector, as well as the CO2 infrastructure sector. This unique modelling framework analyses the feasibility and costs of CCS deployment in the European Union towards 2050 in three scenarios with the same ambitious climate policy target (~85% CO2 emissions reduction). The main insights on the deployment of CCS in Europe hinges on two factors: i) the development of low-cost power generation technologies with carbon capture (coal and/or gas-fired), and ii) a sufficiently high CO2 price to compensate for the costs of deploying the CO2 transport infrastructure. Once CO2 transport infrastructure is available, CCS will be a preferred mitigation option for the industry sector emissions. The joint use of CO2 infrastructure by the electricity and the industry sector allows for economies of scale and economies of density. In the long term, CCS cannot achieve the 100% decarbonisation target of the energy sector because the technology can only capture 80-90% of the CO2 emissions of thermal power plants. Moreover, the advantages of CCS in terms of energy system costs compared to a system without CCS is rather small, in the range of 2%. It crucially depends on the costs of renewables and the costs of their integration in the electricity grid.

BACKGROUND: Heart failure (HF) is the leading cause of hospitalization in people over age 65. Predictive hospital admission models have been developed to help reduce the number of these patients. AIM: To develop and internally validate a model to predict hospital admission in one-year for any non-programmed cause in heart failure patients receiving primary care treatment. DESIGN AND SETTING: Cohort study, prospective. Patients treated in family medicine clinics. METHODS: Logistic regression analysis was used to estimate the association between the predictors and the outcome, i.e. unplanned hospitalization over a 12-month period. The predictive model was built in several steps. The initial examination included a set of 31 predictors. Bootstrapping was used for internal validation. RESULTS: The study included 251 patients, 64 (25.5%) of whom were admitted to hospital for some unplanned cause over the 12 months following their date of inclusion in the study. Four predictive variables of hospitalization were identified: NYHA class III-IV, OR (95% CI) 2.46 (1.23-4.91); diabetes OR (95% CI) 1.94 (1.05-3.58); COPD OR (95% CI) 3.17 (1.45-6.94); MLHFQ Emotional OR (95% CI) 1.07 (1.02-1.12). AUC 0.723; R2N 0.17; Hosmer-Lemeshow 0.815. Internal validation AUC 0.706.; R2N 0.134. CONCLUSION: This is a simple model to predict hospitalization over a 12-month period based on four variables: NYHA functional class, diabetes, COPD and the emotional dimension of the MLHFQ scale. It has an acceptable discriminative capacity enabling the identification of patients at risk of hospitalization. ; This study was financed by the Health Research Fund (FIS), grant no. PI 14/01677 and co-financed with ERDF funds from the European Union: REDISSEC - Project ISCIII (Red de Investigación en Enfermedades Crónicas del Servicio de Salud - Instituto de Salud Carlos III) concession no. RD16/0001/0004. The Foundation for Biosanitary Research and Innovation in Primary Health Care (FIIBAP) financed the costs of publishing the article. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ; Sí

Background Heart failure (HF) is the leading cause of hospitalization in people over age 65. Predictive hospital admission models have been developed to help reduce the number of these patients. Aim To develop and internally validate a model to predict hospital admission in one-year for any non-programmed cause in heart failure patients receiving primary care treatment. Design and setting Cohort study, prospective. Patients treated in family medicine clinics. Methods Logistic regression analysis was used to estimate the association between the predictors and the outcome, i.e. unplanned hospitalization over a 12-month period. The predictive model was built in several steps. The initial examination included a set of 31 predictors. Bootstrapping was used for internal validation. Results The study included 251 patients, 64 (25.5%) of whom were admitted to hospital for some unplanned cause over the 12 months following their date of inclusion in the study. Four predictive variables of hospitalization were identified: NYHA class III-IV, OR (95% CI) 2.46 (1.23–4.91); diabetes OR (95% CI) 1.94 (1.05–3.58); COPD OR (95% CI) 3.17 (1.45–6.94); MLHFQ Emotional OR (95% CI) 1.07 (1.02–1.12). AUC 0.723; R2N 0.17; Hosmer-Lemeshow 0.815. Internal validation AUC 0.706.; R2N 0.134 Conclusion This is a simple model to predict hospitalization over a 12-month period based on four variables: NYHA functional class, diabetes, COPD and the emotional dimension of the MLHFQ scale. It has an acceptable discriminative capacity enabling the identification of patients at risk of hospitalization. ; This study was financed by the Health Research Fund (FIS), grant no. PI 14/01677 and co-financed with ERDF funds from the European Union: REDISSEC - Project ISCIII (Red de Investigación en Enfermedades Crónicas del Servicio de Salud - Instituto de Salud Carlos III) concession no. RD16/0001/0004. The Foundation for Biosanitary Research and Innovation in Primary Health Care (FIIBAP) financed the costs of publishing the article