Brazil hosts the largest expanse of tropical ecosystems within protected areas (PAs), which shelter biodiversity and support traditional human populations. We assessed the vulnerability to climate change of 993 terrestrial and coastal-marine Brazilian PAs by combining indicators of climatic-change hazard with indicators of PA resilience (size, native vegetation cover, and probability of climate-driven vegetation transition). This combination of indicators allows the identification of broad climate-change adaptation pathways. Seventeen PAs (20,611 km2) were highly vulnerable and located mainly in the Atlantic Forest (7 PAs), Cerrado (6), and the Amazon (4). Two hundred fifty-eight PAs (756,569 km2), located primarily in Amazonia, had a medium vulnerability. In the Amazon and western Cerrado, the projected severe climatic change and probability of climate-driven vegetation transition drove vulnerability up, despite the generally good conservation status of PAs. Over 80% of PAs of high or moderate vulnerability are managed by indigenous populations. Hence, besides the potential risks to biodiversity, the traditional knowledge and livelihoods of the people inhabiting these PAs may be threatened. In at least 870 PAs, primarily in the Atlantic Forest and Amazon, adaptation could happen with little or no intervention due to low climate-change hazard, high resilience status, or both. At least 20 PAs in the Atlantic Forest, Cerrado, and Amazonia should be targeted for stronger interventions (e.g., improvement of ecological connectivity), given their low resilience status. Despite being a first attempt to link vulnerability and adaptation in Brazilian PAs, we suggest that some of the PAs identified as highly or moderately vulnerable should be prioritized for testing potential adaptation strategies in the near future. 相似文献
With China's rapid economic growth, ecological construction and environmental protection become increasingly important. The regenerated resources industry is an effective way to solve problems, such as resources depletion, energy shortage, and pollution, and it also has strategic importance for the construction of a resource-conserving and environment-friendly society. The regenerated resources industry has been established in Miluo for long time, which includes a recycling system, a processing and utilization system, and a refuse decontamination system. An industrial cluster is in its early stage of development. In order to solve current problems, such as short industrial chain, low processing rate, and low added value, the industrial cluster should be dynamically upgraded by means of technology innovation, chain nucleus creation, and chain extension. We think the industrial cluster of regenerated resources will become a local brand for Miluo, from which other regions or cities will gain valuable experiences and inspirations. 相似文献
This article aims to evaluate municipal solid waste (MSW) management schemes in order to promote sustainability and eco-efficiency, core elements in global mitigation strategies in both public and private policies. A discrete event simulation (DES) approach was used to integrate the economic, environmental, and social aspects related to aseptic carton packages (ACP) in Itajuba, Brazil. The simulated scenarios consider three alternatives for disposing ACP: landfills, recycling, and incineration with energy recovery. According to our findings, incineration alternatives are preferred from an eco-efficiency perspective, given the potential greenhouse gas (GHG) emissions reductions and due to the possibility of energy recover, which reinforces the contribution of this technology to promote sustainability as largely found in the international literature. Given the context of MSW management in Brazil, this represents a significant opportunity to increase the effectiveness of mitigation strategies adopted in the country. Taking into account that this is by far the least applied technology, the authors strongly advocate that global strategies for mitigation consider different approaches to integrate carbon dioxide (CO2) emission reductions related to the entire MSW management system and its alternatives, thus advancing from a waste disposal-oriented system to a life cycle–oriented system.
Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems. 相似文献