区域能源碳足迹计算模型比较研究——以湖北省为例

能源消费排放是最主要的碳排放来源,研究能源碳足迹重要理论和现实意义。如何准确计算和表征能源碳足迹的大小成为该研究领域的重要问题。在文献回顾的基础上,文章总结了3种目前应用较广泛的计算区域能源消费碳足迹的方法和模型,它们分别是碳汇法、净初级生产力改进模型和净生态系统生产力改进模型。分别介绍了其计算公式、输入参数和计算过程。以湖北省1998-2009年能源消费碳足迹的计算为例,分别用这几种方法计算了能源消费碳足迹的总量和人均碳足迹。得到的主要结论有：研究区域能源消费碳排放量增长较快,3种方法和模型计算得到的碳足迹总量和人均量从时间序列上看,整体变化趋势一致,碳足迹亦呈现快速增长。但3种模型计算出的碳足迹大小差异较大,碳汇法模型足迹最大,净生态系统生产力改进模型次之,净初级生产力改进模型结果最小,并且碳汇法的计算结果明显大于其他两种模型。计算结果差异的原因在于,传统碳汇法模型只考虑林地的碳吸收能力,忽略了区域其他用地类型的碳吸收能力。净初级生产力改进模型考虑了各种用地类型的吸收能力,但从生态系统来说忽略了异氧呼吸的碳释放,高估了区域的碳吸收能力。几种模型都运用了固定值或者平均统计量,未能考虑地域差异,同时未考虑各统计量随时间和气候等变化而变化的可能,存在一定的不合理性,这也是今后研究值得深人研究的方向。     

Performance of a Bayesian state-space model of semelparous species for stock-recruitment data subject to measurement error

Measurement errors in spawner abundance create problems for fish stock assessment scientists. To deal with measurement error, we develop a Bayesian state-space model for stock-recruitment data that contain measurement error in spawner abundance, process error in recruitment, and time series bias. Through extensive simulations across numerous scenarios, we compare the statistical performance of the Bayesian state-space model with that of standard regression for a traditional stock-recruitment model that only considers process error. Performance varies depending on the information content in data, as determined by stock productivity, types of harvest situations, and amount of measurement error. Overall, in terms of estimating optimal spawner abundance S_MSY, the Ricker density-dependence parameter β, and optimal harvest rate h_MSY, the Bayesian state-space model works best for informative data from low and variable harvest rate situations for high-productivity salmon stocks. The traditional stock-recruitment model (TSR) may be used for estimating α and h_MSY for low-productivity stocks from variable and high harvest rate situations. However, TSR can severely overestimate S_MSY when spawner abundance is measured with large error in low and variable harvest rate situations. We also found that there is substantial merit in using h_MSY (or benchmarks derived from it) instead of S_MSY as a management target.     

太湖渔业环境优化技术初探

根据对东太湖养殖区主要水化学指标进行的跟踪监测结果,系统分析研究了N、P等主要营养物质的变化趋势。结果显示,TP网围内的周年平均值为0.034mg/L,网围外周年平均值为0.042mg/L,TN网内周年平均值为0.72mg/L,网外周年平均值为0.73mg/L。并依据试验及监测结果,估算了太湖养殖渔业对水体的营养贡献份额(N、P),现有养殖状况每年通过水产品可带出湖中19.51tN和6.62tP。通过调整、优化养殖模式,大力推广应用轮牧式网围养殖,大幅降低放养量,合理搭配滤食性鱼类,在养殖区普及种植太湖优势水生植物,移植本湖底栖动物,利用水生植物和底栖动物既可有效净化养殖环境,又能作为养殖对象的适口饲料这种作用和功能,同时充分利用湖区低值鱼资源作为河蟹的饲料,最终将水草、螺、低值鱼等转化成水产品,并通过水产品的形式将营养物质带出湖区,从而达到优化改善渔业环境的目的。     

长江流域蒸发皿蒸发量及影响因素变化趋势

采用MannK-endall非参数检验方法和反距离权重插值法IDW对长江流域近40年来20cm蒸发皿蒸发量及其影响因素进行了时空变化趋势分析,研究结果表明:近40年来,长江流域年平均蒸发皿蒸发量在全流域和不同区域内均呈现显著的下降趋势,且中下游地区(99%的置信度)比上游地区(95%的置信度)下降趋势显著,夏季(99%的置信度)比其它季节下降趋势显著。影响蒸发皿蒸发量的主要气象因子太阳净辐射和风速呈现显著下降趋势,气温表现为显著升高趋势,但中下游地区夏季温度微弱下降,降水微弱增加,但中下游地区夏季降水显著增加。因此,长江流域年平均太阳净辐射和风速的显著下降是年平均蒸发皿蒸发量显著下降的主要原因,而中下游地区夏季气温的微弱下降和降水的显著增加使得中下游地区夏季蒸发皿蒸发量下降趋势尤其显著。     

Nutrient criteria for lakes,ponds, and reservoirs: A Bayesian TREED model approach

We develop regional-scale eutrophication models for lakes, ponds, and reservoirs to investigate the link between nutrients and chlorophyll-a. The Bayesian TREED (BTREED) model approach allows association of multiple environmental stressors with biological responses, and quantification of uncertainty sources in the empirical water quality model. Nutrient data for lakes, ponds, and reservoirs across the United States were obtained from the Environmental Protection Agency (EPA) National Nutrient Criteria Database. The nutrient data consist of measurements for both stressor variables (such as total nitrogen and total phosphorus), and response variables (such as chlorophyll-a), used in the BTREED model. Markov chain Monte Carlo (McMC) posterior exploration guides a stochastic search through a rich suite of candidate trees toward models that better fit the data. The Bayes factor provides a goodness of fit criterion for comparison of resultant models. We randomly split the data into training and test sets; the training data were used in model estimation, and the test data were used to evaluate out-of-sample predictive performance of the model. An average relative efficiency of 1.02 between the training and test data for the four highest log-likelihood models suggests good out-of-sample predictive performance. Reduced model uncertainty relative to over-parameterized alternative models makes the BTREED models useful for nutrient criteria development, providing the link between nutrient stressors and meaningful eutrophication response.     

Quantifying Eradication Success: the Removal of Feral Pigs from Santa Cruz Island, California

Abstract:  A major challenge facing pest-eradication efforts is determining when eradication has been achieved. When the pest can no longer be detected, managers have to decide whether the pest has actually been eliminated and hence to decide when to terminate the eradication program. For most eradication programs, this decision entails considerable risk and is the largest single issue facing managers of such programs. We addressed this issue for an eradication program of feral pigs ( Sus scrofa ) from Santa Cruz Island, California. Using a Bayesian approach, we estimated the degree of confidence in the success of the eradication program at the point when monitoring failed to detect any more pigs. Catch-effort modeling of the hunting effort required to dispatch pigs during the eradication program was used to determine the relationship between detection probability and searching effort for different hunting methods. We then used these relationships to estimate the amount of monitoring effort required to declare eradication successful with criteria that either set a threshold for the probability that pigs remained undetected (type I error) or minimized the net expected costs of the eradication program (cost of type I and II errors). For aerial and ground-based monitoring techniques, the amount of search effort required to declare eradication successful on the basis of either criterion was highly dependent on the prior belief in the success of the program unless monitoring intensities exceeded 30 km of searching effort per square kilometer of search area for aerial monitoring and, equivalently, 38 km for ground monitoring. Calculation of these criteria to gauge the success of eradication should form an essential component of any eradication program as it allows for a transparent assessment of the risks inherent in the decision to terminate the program.     

Comparing model averaging with other model selection strategies for benchmark dose estimation

Model averaging (MA) has been proposed as a method of accommodating model uncertainty when estimating risk. Although the use of MA is inherently appealing, little is known about its performance using general modeling conditions. We investigate the use of MA for estimating excess risk using a Monte Carlo simulation. Dichotomous response data are simulated under various assumed underlying dose–response curves, and nine dose–response models (from the USEPA Benchmark dose model suite) are fit to obtain both model specific and MA risk estimates. The benchmark dose estimates (BMDs) from the MA method, as well as estimates from other commonly selected models, e.g., best fitting model or the model resulting in the smallest BMD, are compared to the true benchmark dose value to better understand both bias and coverage behavior in the estimation procedure. The MA method has a small bias when estimating the BMD that is similar to the bias of BMD estimates derived from the assumed model. Further, when a broader range of models are included in the family of models considered in the MA process, the lower bound estimate provided coverage close to the nominal level, which is superior to the other strategies considered. This approach provides an alternative method for risk managers to estimate risk while incorporating model uncertainty.

Ranking and selecting dangerous crash locations: correcting for the number of passengers and Bayesian ranking plots

INTRODUCTION: In this paper a sensitivity analysis is performed to investigate how big the impact would be on the current ranking of crash locations in Flanders (Belgium) when only taking into account the most serious injury per crash instead of all the injured occupants. RESULTS: Results show that this would lead to a different selection of 23.8% of the 800 sites that are currently considered as dangerous. CONCLUSIONS: Considering this impact quantity, the researchers want to sensitize government that giving weight to the severity of the crash can correct for the bias that occurs when the number of occupants of the vehicles are subject to coincidence. Additionally, probability plots are generated to provide policy makers with a scientific instrument with intuitive appeal to select dangerous road locations on a statistically sound basis. Impact on industry Considering the impact quantity of giving weight to the severity of the crash instead of to all the injured occupants of the vehicle on the ranking of crash sites, the authors want to sensitize government to carefully choose the criteria for ranking and selecting crash locations in order to achieve an enduring and successful traffic safety policy. Indeed, giving weight to the severity of the crash can correct for the bias that occurs when the number of occupants of the vehicles are subject to coincidence. However, it is up to the government to decide which priorities should be stressed in the traffic safety policy. Then, the appropriate weighting value combination can be chosen to rank and select the most dangerous crash locations. Additionally, the probability plots proposed in this paper can provide policy makers with a scientific instrument with intuitive appeal to select dangerous road locations on a statistically sound basis. Note that, in practice, one should not only rank the crash locations based on the benefits that can be achieved from tackling these locations. Future research is also needed to incorporate the costs of infrastructure measures and other actions that these crash sites require in order to enhance the safety on these locations. By balancing these costs and benefits against each other, the crash locations can then be ranked according to the order in which they should be prioritized.     

基于故障树与贝叶斯网络的钻井井塌事故的定量分析

井塌事故在钻井过程极其常见,由于钻井液、井身结构等设计不合理,以及钻井过程的误操作均会造成井塌的发生,其后果主要是造成卡钻、卡套管等事故,严重时造成井眼毁坏。为了对井塌事故进行危险性分析,采用贝叶斯网络与故障树相结合的方法,避免传统故障树方法的局限性。首先对井塌事故的所有危险因素进行辨识建立故障树,然后将故障树转化为贝叶斯网络,建立条件概率表,运用贝叶斯网络的推理能力对井塌事故进行危险性分析。对井塌事故各基本事件概率分布的计算分析结果表明,考虑事件的多态性,综合利用故障树分析法和贝叶斯网络法能有效提高井塌事故分析的有效性,推算出井塌事故概率分布更为准确。     

A density projection approach for non-trivial information dynamics: Adaptive management of stochastic natural resources

We demonstrate a density projection approximation method for solving resource management problems with imperfect state information. The method expands the set of partially-observed Markov decision process (POMDP) problems that can be solved with standard dynamic programming tools by addressing dimensionality problems in the decision maker's belief state. Density projection is suitable for uncertainty over both physical states (e.g. resource stock) and process structure (e.g. biophysical parameters). We apply the method to an adaptive management problem under structural uncertainty in which a fishery manager's harvest policy affects both the stock of fish and the belief state about the process governing reproduction. We solve for the optimal endogenous learning policy—the active adaptive management approach—and compare it to passive learning and non-learning strategies. We demonstrate how learning improves efficiency but typically follows a period of costly short-run investment.