区域层面温室气体清单不确定性量化研究

基于《IPCC国家温室气体清单优良做法指南和不确定性管理》中不确定性分析的理论和方法,以编制包括省级、地市级在内的区域层面温室气体清单为背景,就不确定性分析的难点、来源和组成进行分析,并通过实例研究对适用于区域层面温室气体清单的不确定性量化方法进行说明,进而提升不确定性分析的可操作性和透明性,为完善温室气体清单提供有价值的参考。     

Co-benefits and additionality of the clean development mechanism: An empirical analysis

The Clean Development Mechanism (CDM) allows industrialized countries to comply with the Kyoto Protocol by using carbon offsets from developing countries. There are two puzzles within this carbon market: additionality (the proposed activity would not have occurred in its absence) and co-benefits (the project has other environmental benefits besides climate mitigation). This paper proposes an econometric approach to evaluate the CDM effect on sulfur dioxide emission reductions and assess its additionality indirectly. Our empirical model is applied to China's emissions at the prefecture level. We found that the CDM does not have a statistically significant effect in lowering sulfur dioxide emissions. This result casts doubt on additionality of these CDM activities, that is, they would have happened anyway.     

Probability distributions and statistical inference for axial data

Observations on axes which lack information on the direction of propagation are referred to as axial data. Such data are often encountered in enviromental sciences, e.g. observations on propagations of cracks or on faults in mining walls. Even though such observations are recorded as angles, circular probability models are inappropriate for such data since the constraint that observations lie only in [0, π) needs to be enforced. Probability models for such axial data are argued here to have a general structure stemming from that of wrapping a circular distribution on a semi-circle. In particular, we consider the most popular circular model, the von Mises or circular normal distribution, and derive the corresponding axial normal distribution. Certain properties of this distribution are established. Maximum likelihood estimation of its parameters are shown to be surprisingly, in contrast to trigonometric moment estimation, numerically quite appealing. Finally we illustrate our results by several real life axial data sets. Received: September 2004/ Revised: December 2004     

Bootstrap methods for simultaneous benchmark analysis with quantal response data

A primary objective in quantitative risk assessment is the characterization of risk which is defined to be the likelihood of an adverse effect caused by an environmental toxin or chemcial agent. In modern risk-benchmark analysis, attention centers on the “benchmark dose” at which a fixed benchmark level of risk is achieved, with a lower confidence limits on this dose being of primary interest. In practice, a range of benchmark risks may be under study, so that the individual lower confidence limits on benchmark dose must be corrected for simultaneity in order to maintain a specified overall level of confidence. For the case of quantal data, simultaneous methods have been constructed that appeal to the large sample normality of parameter estimates. The suitability of these methods for use with small sample sizes will be considered. A new bootstrap technique is proposed as an alternative to the large sample methodology. This technique is evaluated via a simulation study and examples from environmental toxicology.

Modelling and analysis of recruitment distributions

Recruitment data for 18 marine fish stocks are smoothed using 10 parametric families of probability distributions. Comparative fit of the 10 families is assessed by means of the maximized log-likelihood. Results indicate that the gamma distribution provides an overall good fit in the right-hand tail of the data, but that some adjustment to the gamma distribution is called for in the left-hand tail. Weight functions and weighted distributions are suggested as one means of achieving the needed adjustment.     

Shifting Baselines and The Extinction of The Caribbean Monk Seal

The recent extnction of the Caribbean monk seal Monachus tropicalis has been considered an example of a human‐caused extinction in the marine environment, and this species was considered a driver of the changes that have occurred in the structure of Caribbean coral reef ecosystems since colonial times. I searched archaeological records, historical data, and geographic names (used as a proxy of the presence of seals) and evaluated the use and quality of these data to conclude that since prehistoric times the Caribbean monk seal was always rare and vulnerable to human predation. This finding supports the hypothesis that in AD 1500, the Caribbean monk seal persisted as a small fragmented population in which individuals were confined to small keys, banks, or isolated islands in the Gulf of Mexico and the Caribbean Sea. This hypothesis is contrary to the assumption that the species was widespread and abundant historically. The theory that the main driver of monk seal extinction was harvesting for its oil for use in the sugar cane industry of Jamaica during the 18th century is based primarily on anecdotal information and is overemphasized in the literature. An analysis of reported human encounters with this species indicates monk seal harvest was an occasional activity, rather than an ongoing enterprise. Nevertheless, given the rarity of this species and its restricted distribution, even small levels of hunting or specimen collecting must have contributed to its extinction, which was confirmed in the mid‐20th century. Some sources had been overlooked or only partially reviewed, others misinterpreted, and a considerable amount of anecdotal information had been uncritically used. Critical examination of archaeological and historical records is required to infer accurate estimations of the historical abundance of a species. In reconstructing the past to address the shifting baseline syndrome, it is important to avoid selecting evidence to confirm modern prejudices. Puntos de Referencia Cambiantes y la Extinción de la Foca Monje Caribeña     

Estimating influence of stocking regimes on livestock grazing distributions

Livestock often concentrate grazing in particular regions of landscapes while partly or wholly avoiding other regions. Dispersing livestock from the heavily grazed regions is a central challenge in grazing land management. Position data gathered from GPS-collared livestock hold potential for increasing knowledge of factors driving livestock aggregation patterns, but advances in gathering the data have outpaced advancements in analyzing and learning from it. We fit a hierarchical seemingly unrelated regression (SUR) model to explore how season of stocking and the location where cattle entered a pasture influenced grazing distributions. Stocking alternated between summer on one side of the pasture one year and fall on another side of the pasture the next year for 18 years. Waypoints were recorded on cattle for 50 d each year. We focused our analysis on the pasture's 10 most heavily grazed 4-ha units, because these units were the most prone to negative grazing impacts. Though grazing of the study units was always disproportionately heavy, it was much heavier with the summer than fall stocking regime: Bayesian confidence intervals indicate summer grazing of study units was approximately double the average fall grazing value. This is our core result, and it illustrates the strong effect stocking season or date or both can have on grazing distributions. We fit three additional models to explore the relative importance of stocking season versus location. According to this analysis, stocking season played a role, but stocking location was the main driver. Ostensibly minor factors (e.g. stocking location) can greatly influence livestock distributions.