Russian behaviour in the market for permits under the Kyoto Protocol

After the US withdrawal from the Kyoto Protocol and the extension of national quotas in the Bonn and Marrakesh agreements, meagre environmental effects and a low price of emission permits are likely to be the outcome of implementation. This paper attempts to analyze this scenario, mainly in relation to the Russian case. I discuss on the basis of certain key assumptions the strategic options open to the supply side of the permit market and Russia's potentially incompatible interests as a producer of oil and gas on the one hand and a dominating seller of emission permits under the Kyoto Protocol on the other. The analysis shows that Russian oil and gas interests are likely to boost Russia's inclination to sell permits, ultimately resulting in lower permit prices.     

多情景粮食安全底线约束下的中国耕地保护弹性空间

科学解析中国耕地数量保多少与如何保等问题对保障国家粮食安全与资源环境可持续发展具有重要意义。面向共享社会经济路径（SSPs）的多情景粮食安全底线约束,本文试图利用耕地保有量、人口预测、粮食需求估算、单产提升潜力衰减等模型方法,依据产能特征—健康状态—耕作条件—利用水平的多维度耕地保护紧迫性评估框架,通过耦合时间序列的数量边界控制与空间尺度的区域规则约束刻画出中国的耕地保护弹性空间。在满足个人基本营养安全与作物单产条件干扰变量恒定的假设前提下,研究发现：① 中国多情景耕地保有量在2020—2100年间呈下降态势,现有耕地数量足以维持长期的粮食安全,但短期内则仅勉强达到理论粮食安全的临界区间1.40×10⁸~1.59×10⁸ hm²。② 不同维度耕地保护紧迫性在九大农业区划内呈现差异化分布特征,自然条件优越且适宜耕作的黄淮海平原区与长江中下游地区是综合紧迫性高值地块集聚区域。③ 可持续情景与区域冲突情景分别代表粮食安全底线约束下的最小与最大耕地保有量情景,在当前国际形势下,中国应保证短期（2035年内）可休耕量不高于9.29%。④ 基于时空耦合划定的耕地保护弹性空间可分为优先保护、严格管控、休养生息、战略储备、整治修复和特殊保护6类,不同空间的耕地保护弹性强度、目标与任务各有侧重。整体而言,耕地保护弹性空间的划定思路有助于农业结构调整、休耕政策完善等耕地空间规划体系构建,对中国耕地保护制度转型具有借鉴价值。     

Integrated Futures for Europe＇s Mountain Regions： Reconciling Biodiversity Conservation and Human Livelihoods

Europe＇s mountains cover nearly half of the continent＇s area and are home to one fifth of the European population. Mountain areas are hotspots of biodiversity and agriculture has played a multifunctional role in defining and sustaining mountain biodiversity. Ongoing trends of agricultural decline are having negative impacts on mountain biodiversity. This paper presents results from an interdisciplinary European research project, BioScene, which investigated the relationship between agriculture and biodiversity in six mountain study areas across Europe to provide recommendations for reconciling biodiversity conservation with social and economic activities through an integrated rural development strategy. BioScene used scenario analysis and stakeholder participation as tools for structuring the analysis of alternative mountain futures. Three main BioScene scenarios were evaluated： Business as Usual （BAIT）, Agricultural Liberalisation （Lib）, Managed Change for Biodiversity （MCB）. BioScene brought together ecologists, economists, sociologists and rural geogra- phers, to carry out interdisciplinary analysis of the scenarios： identifying key drivers of change, assessing the biodiversity consequences and evaluating costeffectiveness. BioScene used a sustainability assessment to integrate the research outputs across natural and social science disciplines to assess the broader sustainability of the scenarios in terms of biodiversity, natural resources, rural development, social development, economic development and institutional capacity. The sustainability assessment showed that the MCB scenario was potentially the most sustainable of the three BioScene scenarios. Through the reconciliation of potentially conflicting objectives, such as conservation, economic development and human livelihoods, and with a strong participatory planning approach, the MCB scenario could represent an alternative approach to BaU for sustainable rural development in Europe＇s mountains. BioScene confirms the necessity for natural and social scientists to work together to seek solutions to environmental problems. Interdisciplinary research can assist with the definition of integrated strategies with the potential to reconcile the ecological, social and economic parameters that determine a sustainable future for European mountain areas.     

Temperature in lowland Danish streams: contemporary patterns,empirical models and future scenarios

Continuous temperature measurements at 11 stream sites in small lowland streams of North Zealand, Denmark over a year showed much higher summer temperatures and lower winter temperatures along the course of the stream with artificial lakes than in the stream without lakes. The influence of lakes was even more prominent in the comparisons of colder lake inlets and warmer outlets and led to the decline of cold‐water and oxygen‐demanding brown trout. Seasonal and daily temperature variations were, as anticipated, dampened by forest cover, groundwater input, input from sewage plants and high downstream discharges. Seasonal variations in daily water temperature could be predicted with high accuracy at all sites by a linear air‐water regression model (r²: 0·903–0·947). The predictions improved in all instances (r²: 0·927–0·964) by a non‐linear logistic regression according to which water temperatures do not fall below freezing and they increase less steeply than air temperatures at high temperatures because of enhanced heat loss from the stream by evaporation and back radiation. The predictions improved slightly (r²: 0·933–0·969) by a multiple regression model which, in addition to air temperature as the main predictor, included solar radiation at un‐shaded sites, relative humidity, precipitation and discharge. Application of the non‐linear logistic model for a warming scenario of 4–5 °C higher air temperatures in Denmark in 2070‐2100 yielded predictions of temperatures rising 1·6–3·0 °C during winter and summer and 4·4–6·0 °C during spring in un‐shaded streams with low groundwater input. Groundwater‐fed springs are expected to follow the increase of mean air temperatures for the region. Great caution should be exercised in these temperature projections because global and regional climate scenarios remain open to discussion. Copyright © 2006 John Wiley & Sons, Ltd.     

Population amount risk assessment of extreme precipitation-induced landslides based on integrated machine learning model and scenario simulation

In this study, the future landslide population amount risk (LPAR) is assessed based on integrated machine learning models (MLMs) and scenario simulation techniques in Shuicheng County, China. Firstly, multiple MLMs were selected and hyperparameters were optimized, and the generated 11 models were cross-integrated to select the best model to calculate landslide susceptibility; by calculating precipitation for different extreme precipitation recurrence periods and combining the susceptibility results to assess the landslide hazard. Using the town as the basic unit, the exposure and vulnerability of the future landslide population under different Shared Socioeconomic Pathways (SSPs) scenarios in each town were assessed, and then combined with the hazard to estimate the LPAR in 2050. The results showed that the integrated model with the optimized random forest model as the combination strategy had the best comprehensive performance in susceptibility assessment. The distribution of hazard classes is similar to susceptibility, and with an increase in precipitation, the low-hazard area and high-hazard decrease and shift to medium-hazard and very high-hazard classes. The high-risk areas for future landslide populations in Shuicheng County are mainly concentrated in the three southwestern towns with high vulnerability, whereas the northern towns of Baohua and Qinglin are at the lowest risk class. The LPAR increased with the intensity of extreme precipitation. The LPAR differs significantly among the SSPs scenarios, with the lowest in the “fossil-fueled development (SSP5)” scenario and the highest in the “regional rivalry (SSP3)” scenario. In summary, the landslide susceptibility model based on integrated machine learning proposed in this study has a high predictive capability. The results of future LPAR assessment can provide theoretical guidance for relevant departments to cope with future socioeconomic development challenges and make corresponding disaster prevention and mitigation plans to prevent landslide risks from a developmental perspective.     

El Niño influence on streamflow forecasting

Stochastic models are often fitted to historical data in order to produce streamflow scenarios. These scenarios are used as input data for simulation/optimization models that support operational decisions for water resource systems. The streamflow scenarios are sampled from probability distributions conditioned on the available information, such as recent streamflow data. In this paper we introduce a procedure for further conditioning the probability distributions by considering the recent measurements of climatic variables, such as sea temperatures, that are used to describe the occurrence of El Ni?o. We adopt an auto-regressive model and use the “El Ni?o information” to refine the parameter estimation process for each time step. The corresponding methodology is tested for the monthly energy time series, “inflowing” to the power plants of Colombia. This is a linear combination of streamflow values for the 18 most important rivers of the country.     

Modeling the potential impact of climate change in northern Mexico using two environmental indicators

Modeling the deterioration of natural resources, especially water and soil that results from the global effects of climate change has become a powerful tool in the search for mitigation and adaptation measures. The objectives of this research were: (1) to model the potential impact of climate change for the period 2010-2039, and (2) to offer advice about future risks based on local radiative forcing or critical areas and taking into account two indicators of environmental quality, the aridity index (AI) and laminar wind erosion (LWE). Evaluation techniques for natural resources, similar to those applied by the Instituto Nacional de Ecología y Cambio Climático (National Institute of Ecology and Climate Change) were used for studies of ecological land use. The inputs include climate information (current and future), soil cover and edaphic properties related to the municipality of Gómez Palacio, Durango, Mexico (25.886° N, 103.476° W). According to calculations estimated from the anomalies for the mean annual rainfall and mean annual temperature, in a future climate change scenario, an average impact of approximately 63% would be caused by LWE, and the AI would change from its historical value of 9.3 to 8.7. It is estimated that the average impact on the AI in the future will be 0.53 ± 0.2.     

LAMOST观测控制系统用户界面设计

位于国家天文台兴隆观测基地的大天区面积多目标光纤光谱天文望远镜(LAMOST)是世界上最大的、可操控的反射施密特望远镜。为了便于控制望远镜,提高观测效率,设计了观测控制系统图形用户界面。首先分析了LAMOST望远镜的控制软件体系结构,采用基于场景的以用户为中心的设计方法,对界面进行了分析和设计。使用Qt、CORBA、多线程技术和MVC模式,实现了用户界面,并对界面进行了测试。最后,用户界面得到了用户的认可,成功应用于LAMOST观测。     

Changing maritime scenarios. The geopolitical dimension of the EU Atlantic Strategy

The European Union׳s Integrated Maritime Policy (EU IMP) is being developed in the framework of a process that is reshaping maritime geographical scenarios and world geopolitics. This process is characterised by a change in States׳ territoriality resulting from their jurisdictional expansion. Over 60% of coastal States in the world possess territory with a more extensive maritime component than the terrestrial component which helps to reaffirm the fragmentation of maritime space and stresses the national vision of the oceans. Unlike the process of ocean space fragmentation that results from its nationalisation the EU Integrated Maritime Policy has a global and cooperative vision, which is particularly evident in the Atlantic Strategy. In this context, this article aims to: (i) provide a first estimate of the size and extent of the territorial changes resulting from jurisdictional claims available to date and their mapping; (ii) consider the case of the European Union as one of special significance with its express policy of maritime spatial projection. The conclusions point to a redefinition of the large territorial pieces of the global geopolitics puzzle and the emergence of a new Atlanticism associated with the EU׳s maritime policy.