城市土地储备制度绩效评价研究——以武汉市为例

城市土地储备制度绩效评价对保障土地储备制度健康发展有重要意义.在对城市土地储备制度绩效评价内涵界定的基础上,构建绩效评价指标体系,运用层次分析法和综合指数法.以武汉市为例,对城市土地储备制度绩效进行评价研究.结果表明,武汉市城市土地储备制度绩效评价综合得分为78.89,取得了良好制度绩效.其中,城市经济发展和土地利用配置效益明显提高,综合指数分别为80.50和83.16,而公平配置效益得分较低,为70.18.依据评价结果,提出了兼顾"效益"和"公平"的政策建议.     

中国工业废水治理效率评价

基于投入产出的思想,应用DEA模型对2008年中国工业废水治理效率进行静态评价,得出各地区当前工业废水治理效率.用Malmquist指数对中国2003-2008年工业废水治理效率进行动态评价,分别得出近几年中国工业废水治理的名义全要素生产率和实际全要素生产率.结果表明,中国工业废水平均治理效率仅为0.630,2008年达到DEA有效的地区仅占16.7％,几乎所有地区都处在规模收益递减;2003-2008年间的中国工业废水治理名义T下P为0.922,实际TFP为0.943,即分别在此期间平均每年下降7.8％和5.7％,造成逐年下降的主要原因是技术的落后.     

Incorporating low-resolution historic species location data decreases performance of distribution models

Developing robust species distribution models is important as model outputs are increasingly being incorporated into conservation policy and management decisions. A largely overlooked component of model assessment and refinement is whether to include historic species occurrence data in distribution models to increase the data sample size. Data of different temporal provenance often differ in spatial accuracy and precision. We test the effect of inclusion of historic coarse-resolution occurrence data on distribution model outputs for 187 species of birds in Australian tropical savannas. Models using only recent (after 1990), fine-resolution data had significantly higher model performance scores measured with area under the receiver operating characteristic curve (AUC) than models incorporating both fine- and coarse-resolution data. The drop in AUC score is positively correlated with the total area predicted to be suitable for the species (R² = 0.163-0.187, depending on the environmental predictors in the model), as coarser data generally leads to greater predicted areas. The remaining unexplained variation is likely to be due to the covariate errors resulting from resolution mismatch between species records and environmental predictors. We conclude that decisions regarding data use in species distribution models must be conscious of the variation in predictions that mixed-scale datasets might cause.     

Evolving Expectations of Dam Removal Outcomes: Downstream Geomorphic Effects Following Removal of a Small,Gravel‐Filled Dam1

Kibler, Kelly, Desiree Tullos, and Mathias Kondolf, 2011. Evolving Expectations of Dam Removal Outcomes: Downstream Geomorphic Effects Following Removal of a Small, Gravel‐Filled Dam. Journal of the American Water Resources Association (JAWRA) 1‐16. DOI: 10.1111/j.1752‐1688.2011.00523.x Abstract: Dam removal is a promising river restoration technique, particularly for the vast number of rivers impounded by small dams that no longer fulfill their intended function. As the decommissioning of small dams becomes increasingly commonplace in the future, it is essential that decisions regarding how and when to remove these structures are informed by appropriate conceptual ideas outlining potential outcomes. To refine predictions, it is necessary to utilize information from ongoing dam removal monitoring to evolve predictive tools, including conceptual models. Following removal of the Brownsville Dam from the Calapooia River, Oregon, aquatic habitats directly below the dam became more heterogeneous over the short term, whereas changes further downstream were virtually undetectable. One year after dam removal, substrates of bars and riffles within 400 m downstream of the dam coarsened and a dominance of gravel and cobble sediments replaced previously hardpan substrate. New bars formed and existing bars grew such that bar area and volume increased substantially, and a pool‐riffle structure formed where plane‐bed glide formations had previously dominated. As the Brownsville Dam stored coarse rather than fine sediments, outcomes following removal differ from results of many prior dam removal studies. Therefore, we propose a refined conceptual model describing downstream geomorphic processes following small dam removal when upstream fill is dominated by coarse sediments.     

Factors Affecting Stream Nutrient Loads: A Synthesis of Regional SPARROW Model Results for the Continental United States1

Preston, Stephen D., Richard B. Alexander, Gregory E. Schwarz, and Charles G. Crawford, 2011. Factors Affecting Stream Nutrient Loads: A Synthesis of Regional SPARROW Model Results for the Continental United States. Journal of the American Water Resources Association (JAWRA) 47(5):891‐915. DOI: 10.1111/j.1752‐1688.2011.00577.x Abstract: We compared the results of 12 recently calibrated regional SPARROW (SPAtially Referenced Regressions On Watershed attributes) models covering most of the continental United States to evaluate the consistency and regional differences in factors affecting stream nutrient loads. The models – 6 for total nitrogen and 6 for total phosphorus – all provide similar levels of prediction accuracy, but those for major river basins in the eastern half of the country were somewhat more accurate. The models simulate long‐term mean annual stream nutrient loads as a function of a wide range of known sources and climatic (precipitation, temperature), landscape (e.g., soils, geology), and aquatic factors affecting nutrient fate and transport. The results confirm the dominant effects of urban and agricultural sources on stream nutrient loads nationally and regionally, but reveal considerable spatial variability in the specific types of sources that control water quality. These include regional differences in the relative importance of different types of urban (municipal and industrial point vs. diffuse urban runoff) and agriculture (crop cultivation vs. animal waste) sources, as well as the effects of atmospheric deposition, mining, and background (e.g., soil phosphorus) sources on stream nutrients. Overall, we found that the SPARROW model results provide a consistent set of information for identifying the major sources and environmental factors affecting nutrient fate and transport in United States watersheds at regional and subregional scales.     

Source and Delivery of Nutrients to Receiving Waters in the Northeastern and Mid‐Atlantic Regions of the United States1

Moore, Richard B., Craig M. Johnston, Richard A. Smith, and Bryan Milstead, 2011. Source and Delivery of Nutrients to Receiving Waters in the Northeastern and Mid‐Atlantic Regions of the United States. Journal of the American Water Resources Association (JAWRA) 47(5):965‐990. DOI: 10.1111/j.1752‐1688.2011.00582.x Abstract: This study investigates nutrient sources and transport to receiving waters, in order to provide spatially detailed information to aid water‐resources managers concerned with eutrophication and nutrient management strategies. SPAtially Referenced Regressions On Watershed attributes (SPARROW) nutrient models were developed for the Northeastern and Mid‐Atlantic (NE US) regions of the United States to represent source conditions for the year 2002. The model developed to examine the source and delivery of nitrogen to the estuaries of nine large rivers along the NE US Seaboard indicated that agricultural sources contribute the largest percentage (37%) of the total nitrogen load delivered to the estuaries. Point sources account for 28% while atmospheric deposition accounts for 20%. A second SPARROW model was used to examine the sources and delivery of phosphorus to lakes and reservoirs throughout the NE US. The greatest attenuation of phosphorus occurred in lakes that were large relative to the size of their watershed. Model results show that, within the NE US, aquatic decay of nutrients is quite limited on an annual basis and that we especially cannot rely on natural attenuation to remove nutrients within the larger rivers nor within lakes with large watersheds relative to the size of the lake.     

CO2 capture and storage from a bioethanol plant: Carbon and energy footprint and economic assessment

Biomass energy and carbon capture and storage (BECCS) can lead to a net removal of atmospheric CO₂. This paper investigates environmental and economic performances of CCS retrofit applied to two mid-sized refineries producing ethanol from sugar beets. Located in the Region Centre France, each refinery has two major CO₂ sources: fermentation and cogeneration units. “carbon and energy footprint” (CEF) and “discounted cash flow” (DCF) analyses show that such a project could be a good opportunity for CCS early deployment. CCS retrofit on fermentation only with natural gas fired cogeneration improves CEF of ethanol production and consumption by 60% without increasing much the non renewable energy consumption. CCS retrofit on fermentation and natural gas fired cogeneration is even more appealing by decreasing of 115% CO₂ emissions, while increasing non renewable energy consumption by 40%. DCF shows that significant project rates of return can be achieved for such small sources if both a stringent carbon policy and direct subsidies corresponding to 25% of necessary investment are assumed. We also underlined that transport and storage cost dilution can be realistically achieved by clustering emissions from various plants located in the same area. On a single plant basis, increasing ethanol production can also produce strong economies of scale.     

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.     

Predicting modes of spatial change from state-and-transition models

State-and-transition models (STMs) can represent many different types of landscape change, from simple gradient-driven transitions to complex, (pseudo-) random patterns. While previous applications of STMs have focused on individual states and transitions, this study addresses broader-scale modes of spatial change based on the entire network of states and transitions. STMs are treated as mathematical graphs, and several metrics from algebraic graph theory are applied—spectral radius, algebraic connectivity, and the S-metric. These indicate, respectively, the amplification of environmental change by state transitions, the relative rate of propagation of state changes through the landscape, and the degree of system structural constraints on the spatial propagation of state transitions. The analysis is illustrated by application to the Gualalupe/San Antonio River delta, Texas, with soil types as representations of system states. Concepts of change in deltaic environments are typically based on successional patterns in response to forcings such as sea level change or river inflows. However, results indicate more complex modes of change associated with amplification of changes in system states, relatively rapid spatial propagation of state transitions, and some structural constraints within the system. The implications are that complex, spatially variable state transitions are likely, constrained by local (within-delta) environmental gradients and initial conditions. As in most applications, the STM used in this study is a representation of observed state transitions. While the usual predictive application of STMs is identification of local state changes associated with, e.g., management strategies, the methods presented here show how STMs can be used at a broader scale to identify landscape scale modes of spatial change.     

Modelling the fate of marine turtle hatchlings

The SLIM oceanographic model was used to examine the fate of hatchling flatback turtles (Natator depressus) in the first two weeks of their dispersal starting at Wild Duck Island, a major turtle-nesting site in the central Great Barrier Reef region of Australia. We ran simulations to investigate the effects of spring versus neap tides, hatchling's swimming behaviour during their first three days at sea, and the location of nesting beaches. The model predicted that up to 50% of the turtle hatchlings entering the sea from the windward, southern beach remained after 14 days in shallow, nearshore waters, irrespective of tides and swimming. These waters are turbid and may be inhospitable to hatchlings. In contrast >80% of hatchlings dispersing from the leeward, northern beach were dispersed to deeper water (10-40 m) in a quasi-stationary dispersion core centred around 10 km north of the nesting beach after 14 days and the offshore spread of the turtle plume was enhanced by the hatchling's seaward swimming during the first three days. This was due to the presence of a coastal boundary layer and a stagnation zone around the northern side of island, but not the southern side. The model confirmed that dispersal from eastern Queensland flatback turtle rookeries is restricted to the lagoons and coastal waters, and that water circulation and hatchling's swimming control dispersion. The model explains why more turtles nest on the northern than the southern side. This study highlights the usefulness of oceanographic models to increase knowledge about a cryptic life stage of marine turtles.