全球气候变化模式和多基因遗传规划在逐日参考作物腾发量预报中的应用

提出并评测了基于全球气候变化模式与多基因遗传规划(MGGP)人工智能方法的逐日参考作物腾发量(ET0)预报方法(GCM-MGGP)。分别采用该方法、全球气候变化模式的原始数据法(GCM法)和全球气候变化模式与传统单基因遗传规划方法(GCM-SGGP)对该庄河地区的逐日ET0序列进行计算。结果显示,GCM-MGGP法性能最优,具有非常好的推广价值。

Preliminary Assessment of Simulations of Climate Changes over China by CMIP5 Multi-Models

Based on 18 global climate models' simulations of the 20th century climate, a set of experiments within phase five of the Coupled Model Inter-comparison Project (CMIP5), the performances of simulating the present climate over China are assessed. Compared with observations, models can capture the dominant features of the geographic distributions of temperature and precipitation during 1961-2005. For the temporal changes of temperature, models appear to have a good performance on reproducing the warming tendency but show limited skills for precipitation. For the regional mean temperature and precipitation over the whole of China, most models underestimate the actual temperature and overestimate precipitation. Concerning the standard deviations of simulations by the 18 models, they are larger for temperature in the western part of China, while the standard deviations are larger for precipitation in the South.

Environmental Capital,Negative Externality and Carbon Finance Innovation

In the context of global climate change,the internalization of negative externality,which is brought about by the traditional mode of economic growth,has become an inevitable choice.In order to achieve the internalization,it is necessary to make innovations on the market mechanism and system,find the value of environmental capital,establish a new mode of economic growth based on environmental capital,and then transform the environmental capital,an exogenous factor of economic growth,into an endogenous factor.Of this,the key of market mechanism and system innovation is the financial innovation that is based on environmental capital and negative externality;the government defines the initial property right of environmental resources and establishes environment energy trading market,so as to guide enterprises to trade environmental resources(represented by carbon emission permit trading) based on the Clean Development Mechanism,and to vigorously develop environmental finance and carbon finance.

Pesticide Packaging Waste Management Model for Thailand

Pesticide packaging waste in Thailand has recently been valued as a kind of commercial product. Selling empty pesticide packaging to reuse/recycling business has been increased in all over the country. Subject to the resource conservation and global climate change, this could be defined as sustainable or green consumption for the most beneficial use of natural resources. However, on the point of environmental and public health, this is the way to disperse harmfulness of pesticide into non-target areas. Human and environmental health risk of exposure due to pesticide residue in such packaging waste could be a co-incident. This paper was aimed to determine a practical model for pesticide packaging waste management appropriate for Thailand based on the context of country＇s circumstances and the best practices reported around the world. Establishing some necessary components has been proposed, i.e. multiple rinse method applied by farmers conscientiously, central collecting site driven by local community, waste recovery program offered by pesticide production enterprises, and sanitary glass bottle washing plant registration and regulations launched by the government. In conclusion, governmental initiative and all stakeholders including in pesticide packaging waste supply chain should be encouraged to contribute their participation towards sustainable management.

Industrial Carbon Emissions Status and Analysis Based on Energy Consumption

With frequent disastrous weathers and increasingly prominent GHG effects in recent years, normal existence and development of mankind are facing unprecedented threats and challenges. GHG emissions mitigation for the global climate changes has been the focus of concern of the world. As the biggest developing country and the second largest country of carbon-emission, China attaches importance to the carbon emission reduction. The major GHG component is carbon dioxide and in China, the emis- sion of carbon dioxide is mainly from industrial production. In the paper, the status and trend of Coz emission from industrial departments, high-carbon emission and its specific industries are shown in statistics. Meanwhile, the policy environment, industrial organization structure and technology of carbon high emission are all discussed based on practical situations in these departments and industries. At the end, through the analysis of gray correlation, correlativity is explored for both fossil energy consumption and total carbon emission, and also for the production value and carbon emission of each industrial sector. Some policy proposals for the establishment of low-carbon industries and transition of economic development pattern are set forth.

A comparative study on three EOF analysis techniques using decades of Arctic sea-ice concentration data

Change in Arctic sea ice extent is one of the indicators of global climate changes. Spatio-temporal change and change patterns can be identified using various methods to facilitate human understanding global climate changes. Three empirical orthogonal function(EOF) techniques are discussed and applied to decades of sea-ice concentration(SIC) dataset in Arctic area for identifying independent patterns. It was found that: 1) discrepancies exist in magnitude and scope for each EOF pattern, however, the first two leading EOFs of variability possess high similarities in structure and shape; 2) Even though there are somewhat differences in amplitude of each PC mode, the first two leading PC modes maintain consistent in overall trend and periodicity; 3) There are significant discrepancies and inconsistencies in the third and fourth leading EOF and PC modes. The accuracies of three techniques are further validated in representing the physical phenomena of SIC anomaly patterns.