Quantifying landscape externalities of renewable energy development: Implications of attribute cut-offs in choice experiments

Renewable energy is worldwide seen as a key element necessary to address climate change. However, finding socially acceptable locations for renewable energy facilities and the accompanying infrastructure increasingly often faces fierce opposition. This paper quantifies the landscape externalities of renewable energies employing a choice experiment. In addition, it is investigated how accounting for non-compensatory choice behavior, i.e. attribute cut-offs, affects welfare measures and subsequently policy recommendations. The empirical application is Germany where we conducted a nationwide survey on the development of renewable energies. We first show that cut-off elicitation questions prior to the choice experiment at least partially influence preferences. We further find that most participants state cut-off levels for attributes. Many are, however, at the same time willing to violate the self-imposed thresholds when choosing among the alternatives. To account for this effect, stated cut-offs are incorporated into a mixed logit model following the soft cut-off approach. Model results indicate substantial taste heterogeneity in preferences and in the use of cutoffs. Also, welfare estimates are substantially affected. We conclude that welfare changes from renewable energy development could be strongly underestimated when cut-offs are ignored.     

Self serving reference points in double auctions

We present a novel approach for modelling self serving bias by way of reference dependence. Bias is modelled as a systematic individual level deviation of the endogenous reference point from the expected price of a good in a two person k-double auction. We find that bias decreases the efficiency of the model, and that efficiency is decreasing in both the level of bias and reference dependence.     

Advancing environmental productivity: Organizational mindfulness and strategies

This study investigates the importance of mindfulness‐based organization systems and policies on the organization's continuous efforts to become a greener company. The study is devised as a pooled cross‐sectional time series analysis of productivity in energy consumption, water utilization, and waste generation and recycling of U.S. companies. The data set taken from U.S. Newsweek 500 recorded 600 companies dispersed into 63 industries during the period of 2012–2014 and provided 1,498 company–industry–year observations. The study found that systems and policies (e.g., deployment of third‐party auditors for its environmental metrics report; linkage of the remuneration of company's senior executive team with the achievement of environmental performance targets; and establishment of a committee at the Board of Directors' level whose mandate includes sustainability of the company in environment‐ and resource‐related matters) connect the company's mindfulness to its effort in improving the environmental quality through the company's productivity in energy consumption, water utilization, and waste generation and recycling. The relationships are positive and significant. Also, systems and policies serve as a motivator for the firm to continue to improve upon the current achievements. The study emphasizes the importance of translating organizational and individual mindfulness into organization policies and systems in order to motivate the organization to continuously improve its productivity in energy consumption, water utilization, and waste generation and recycling leading to improvements in environmental quality.     

探地雷达在公路改扩建工程检测中的应用

公路改扩建工程在广东省正处于快速发展时期,如何准确得到路面病害情况,是旧路处治方案制定面临的主要问题.依托开阳高速改扩建工程,采用三维探地雷达对旧路进行病害检测,得到道路表面及内部的病害分布情况,并分别与人工徒步调查路况及开挖后内部实际情况进行比对.结果表明:当三维探地雷达检测采样间距设置为2.0 cm、驻波时间设置为0.6 ms、时窗设置为85 ns时,可达到较好的检测精准度;三维探地雷达可实现对路面结构内部不同深度位置的裂缝病害进行探测,精确识别3 mm宽裂缝,并了解裂缝的发展方向及性质.     

Probabilistic access forecasting for improved offshore operations

Improving access is a priority in the offshore wind sector, driven by the opportunity to increase revenues, reduce costs, and improve safety at operational wind farms. This paper describes a novel method for producing probabilistic forecasts of safety-critical access conditions during crew transfers. Methods of generating density forecasts of significant wave height and peak wave period are developed and evaluated. It is found that boosted semi-parametric models outperform those estimated via maximum likelihood, as well as a non-parametric approach. Scenario forecasts of sea-state variables are generated and used as inputs to a data-driven vessel motion model, based on telemetry recorded during 700 crew transfers. This enables the production of probabilistic access forecasts of vessel motion during crew transfer up to 5 days ahead. The above methodology is implemented on a case study at a wind farm off the east coast of the UK.     

泰州市动力和储能电池产业发展研究

梳理国内外动力和储能电池产业发展情况,从产业规模、重点企业、产业链、培养方向等角度剖析了泰州市动力和储能电池产业的现状问题,并针对性地提出了发展建议。     

Fuel consumption for various driving styles in conventional and hybrid electric vehicles: Integrating driving cycle predictions with fuel consumption optimization*

Improving fuel economy and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel economy in laboratory-controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This paper aims to estimate fuel consumption for a conventional vehicle and a HEV using personalized driving cycles extracted from real-world data to study the effects of different driving styles and vehicle types on fuel consumption when compared to the estimates based on standard driving cycles. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale U.S. survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: volatile, normal, or calm. Then, the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate fuel consumption for conventional vehicles and HEVs with power-split powertrain. To further optimize fuel consumption for HEVs, the Equivalent Consumption Minimization Strategy (ECMS) is applied. The results show that depending on the driving style and the driving scenario, conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel consumption was 13% lower in calm urban driving, but almost 34% higher for volatile highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on the application of case-based reasoning and used to tune the power distribution in a hybrid electric vehicle, its fuel consumption can be reduced by up to 12% in urban driving. Implications and limitations of the findings are discussed.     

地下综合管廊混凝土爆灰检测及加固

针对某市钢筋混凝土地下综合管廊的现浇顶板、侧壁出现的混凝土爆灰质量缺陷,为确保管廊结构的安全使用,采用钻心法、超声波法、沸煮试验、爆灰物质的化学成分分析等检测方法,对爆灰现象进行了检测。结果显示,混凝土中存在的块状氧化钙与水熟化后体积增大1倍~2.5倍,使得混凝土在强度未达到设计抗拉强度前在块状氧化钙体积膨胀应力作用下被拉裂,导致了此类质量缺陷的发生。分析了质量缺陷对管廊结构的影响,并对管廊结构进行了加固设计及处理,满足了结构的正常安全使用要求。研究结果可为类似爆灰缺陷的设计及处理提供参考。     

一种经验模式分解下的海杂波小目标检测方法

利用海杂波有效探测海上小目标是目前雷达探测领域的热点问题，具有重要的应用价值。鉴于海杂波是一种非线性非平稳性的雷达回波信号，充分发挥整体平均经验模式分解的优势，将海杂波分解为若干个不同尺度的独立分量。通过研究发现有目标时，分解出的前5个分量与未分解前信号的相关系数明显减小，因此提出了一种新的海杂波背景下的目标检测方法。通过实测和模拟的海杂波数据进行训练和测试，研究结果表明，该方法能有效地实现海杂波下目标的探测，性能优于经典时域下、分数阶傅里叶变换域下以及平均经验模式分解后的广义Hurst指数的目标检测方法。     

广义空间调制系统的正则化OMP检测算法

在广义空间调制（GSM）系统中，最大似然（ML）检测可以取得最优的检测性能，然而其计算复杂度随激活天线数的增加急剧增长。针对这一问题，提出了一种基于稀疏重构理论的低复杂度检测算法——正则化正交匹配追踪（ROMP）算法。该算法首先根据信道矩阵和当前残差的内积选取多个候选激活天线索引，接着对候选天线索引按正则化标准进行可靠性验证，剔除错误索引，缩小信号的搜索空间，最后通过求解最小二乘问题估计信号。仿真结果表明，与经典的正交匹配追踪（OMP）算法相比，所提算法以少许复杂度的增加为代价极大提升了检测性能，能够在检测性能与复杂度之间取得更好的折中。