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.     

Effect of Energy Prices on Economic Growth in the ECOWAS Sub-Region: Investigating the Channels Using Panel Data

The article empirically examines the effect of energy prices on economic growth within the Economic Community of West African States sub-region by acknowledging that the effect of energy prices on growth is quintessentially indirect and hence can be tracked through some channels. Exploiting the System Generalized Methods of Moments estimation technique for the period spanning 2002–2015, the results indicate that the overall effect of energy prices on economic growth is significantly negative. This effect propagates mainly through government consumption expenditure and investment, albeit its effect through real interest rate is positive. However, its negative effects on government consumption, investment, and exchange rate significantly overwhelm the positive effect from real interest rate.     

Globalisation,economic growth and energy consumption in the BRICS region: The importance of asymmetries

This paper examines the asymmetric impact of globalisation and economic growth on energy consumption in BRICS countries, applying the NARDL bounds approach to explore the presence of asymmetric cointegration across variables. The empirical results reveal that energy consumption is positively and negatively affected by the positive and negative globalisation shocks, respectively. A positive shock in economic growth promotes energy consumption, while a negative shock reduces energy consumption.     

Energy,unemployment and trade

This article investigates the dynamic relationships among sectoral economic activities, macro expenditure patterns, renewable and non-renewable energy consumption and unemployment in 41 countries from 1980 to 2014. The state of the art econometric techniques, both linear and non-linear panel and time series estimation techniques are used. The results show that industrialization, services sector, government expenditure and trade openness play a positive role in reducing unemployment, while agriculture and renewable energy consumption increase unemployment. This might be, in part, due to recent technological advancements and large capital intensive investments in agriculture and renewable energy sectors. Therefore, dedicated social and labour market policies need to be adopted to complement greening economic policies.     

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.     

一种基于地理信息系统的异构网络多目标算法

地理信息系统（Geographic Information System,GIS）和多目标算法为异构网络基站部署问题带来新的突破。ArcGIS系列是GIS专业化的管理软件，采用ArcGIS软件对包含学校、医院等场景的中国某市密集城区进行建模分析，并且提出基于目标权重因子的多目标离散粒子群算法（Multi-objective Discrete Particle Swarm Optimization algorithm based on Target Weight Factor,MDPSO/TWF），基于多个目标函数整体最优研究异构网络基站部署问题。仿真结果表明，所提的MDPSO/TWF算法具有可行性和先进性，可以综合考虑网络负载和网络能效多个优化目标，将业务需求与实际基站选址相结合，在满足业务需求的基础上尽可能达到节能的目的。     

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.     

一种高能效低时延的LLN路由修复算法

由于低功耗有损网络(LLN)中无线链路的不稳定性和有损性，外部环境的干扰极易导致网络出现故障，从而严重影响网络性能，而LLN网络中现有路由修复算法存在控制开销冗余和修复时延较大等问题。为此，提出了一种高能效低时延的LLN路由修复算法(EELDR-RPL)。该算法通过采用“零额外控制开销通告链路故障及邻居节点信息”机制，使得链路故障节点的子节点能够及时获知链路故障以及链路故障节点的邻居情况；通过采用“自适应调整节点网络深度值”机制，使得链路故障节点能够快速地重新接入网络；通过采用“链路故障节点子节点自适应切换”机制，能够达到优化网络拓扑的目的。仿真结果表明，与现有路由修复算法相比，EELDR-RPL算法能够有效地降低路由修复时延和减少控制开销。