As one of the few countries in Latin America to have reversed persistent losses in tree cover, Chile may hold important insights for forest transition theory. However, existing studies have not provided methodologically consistent analyses at sufficient temporal and spatial scales to properly assess the state of Chile's forest transition. In the current study, we generate high-resolution maps of Chilean land use change between 1986, 2001 and 2011. We couple remote sensing with a review of historic assessments of Chile's forest resources to document long-term trends in forest extent. This historical review identified multiple discrete forest transitions throughout Chile's history. These fluctuations in forest clearing emphasize that the cultural, economic and political forces that precipitate forest transitions can all be reversed. The remote sensing analysis calls into question official statistics indicating an expansion of native forests between 1986 and 2011. We find that increases in forest cover were largely driven by the expansion of forest plantations, rather than through native forest regeneration. Plantation forests directly displaced native forests in many locations, especially during the 1986-2001 period. Nevertheless, declines in the rate of forest conversion during the 2001-2011 period may suggest that plantations are beginning to ease pressure on native forests.
This paper aims to analyse the economy-wide implications of a carbon tax applied on the Chilean electricity generation sector. In order to analyse the macroeconomic impacts, both an energy sectorial model and a Dynamic Stochastic General Equilibrium model have been used. During the year 2014 a carbon tax of 5 US$/tCO2e was approved in Chile. This tax and its increases (10, 20, 30, 40 and 50 US$/tCO2e) are evaluated in this article. The results show that the effectiveness of this policy depends on some variables which are not controlled by policy makers, for example, non-conventional renewable energy investment cost projections, natural gas prices, and the feasibility of exploiting hydroelectric resources. For a carbon tax of 20 US$/tCO2e, the average annual emission reduction would be between 1.1 and 9.1 million tCO2e. However, the price of the electricity would increase between 8.3 and 9.6 US$/MWh. This price shock would decrease the annual GDP growth rate by a maximum amount of 0.13%. This article compares this energy policy with others such as the introduction of non-conventional renewable energy sources and a sectorial cap. The results show that the same global OPEN ACCESSEnergies 2015, 8 2675 greenhouse gas (GHG) emission reduction can be obtained with these policies, but the impact on the electricity price and GDP are lower than that of the carbon tax.
BACKGROUND Pneumococcal conjugate vaccines (PCVs) prevent invasive pneumococcal disease and pneumonia. However, some low- and middle-income countries have yet to introduce PCV into their immunization programs due, in part, to lack of certainty about the potential impact. Assessing PCV benefits is challenging because specific data on pneumococcal disease is often lacking, and it can be difficult to separate the effects of factors other than the vaccine that could also affect pneumococcal disease rates. METHODS We assess PCV impact by combining Bayesian model averaging with change point models to estimate the timing and magnitude of vaccine-associated changes, while controlling for seasonality and other covariates. We applied our approach to monthly time series of age-stratified hospitalizations related to pneumococcal infection in children under 5 years of age in the United States, Brazil, and Chile. RESULTS Our method accurately detected changes in data in which we knew true and noteworthy changes occurred, i.e. in simulated data and for invasive pneumococcal disease. Moreover, 24 months after the vaccine introduction, we detected reductions of 14%, 9%, and 9% in the U.S., Brazil, and Chile, respectively, in all-cause pneumonia (ACP) hospitalizations for age group 0 to <1 years of age. CONCLUSIONS Our approach provides a flexible and sensitive method to detect changes in disease incidence that occur after the introduction of a vaccine or other intervention, while avoiding biases that exist in current approaches to time trend analyses.
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