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.     

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.     

Is bank staff interaction associated with customer saving behavior in banks?

Major changes are underway in the U.S. retail banking sector toward heavy investments in technology and fewer in personnel. Using the 2017 survey of household economics and decision‐making (SHED) (n = 11,359), we examine the relationship between saving behavior related to emergency, long‐term and periodic expenses and personal, technological, and hybrid bank account access methods. Binary logistic regression models were used to estimate the odds of reporting various saving behaviors in relation to various banking access methods. Findings suggest that the personal access method is positively associated with savings behavior for periodic expenses for the general population, and negatively associated with emergency savings in people with lower education attainment. Technology is associated with all types of saving behavior, while the hybrid access method is associated only with saving for periodic expenses. As investments in self‐service technology increase, the importance of access methods to savings behavior must be considered.     

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.     

Peer effects in the diffusion of water‐saving agricultural technologies

We investigate the role of peer effects in the diffusion of an important water‐saving irrigation technology: low energy precise application (LEPA). Using detailed irrigation behavior data for growers in the High Plains Aquifer region of Kansas covering 1990–2014, we find clear evidence of peer influence in adoption of LEPA, net of environmental factors. Specifically, an additional neighboring LEPA installation within 1 km increases the probability of adoption by about 0.3 percentage points, on average, and this effect diminishes with distance. Our empirical estimates indicate that in the absence of peer effects, LEPA adoption would have been about 10% lower (1,000–1,600 fewer installations) per year. In addition, we find that growers install LEPA in response to higher energy prices.     

On the rebound: estimating direct rebound effects for Australian households

Reducing dependence on fossil fuels by decreasing energy consumption is a common environmental policy. One mechanism used to achieve this is to encourage increased energy efficiency. However, improving efficiency may have an opposing effect and cause an increase in energy consumption if the intensity of use changes. This phenomenon is known as the rebound effect. We estimate direct rebound effects for energy use in Australia based on both aggregate residential energy use data and on household energy expenditure data. Our approach implements a new methodology developed by Hunt and Ryan (2014, Catching on the rebound: Why price elasticities are generally inappropriate measures of rebound effects. Surrey Energy Economics Discussion Paper Series SEEDS 148; 2015, Energy Economics 50, 273) that explicitly relates energy service use with energy source demand and directly incorporates measures of efficiency changes. The results indicate that the rebound effect is relatively high for energy use by Australian households. Due to the unique nature of our household data set, we can examine the influence of demographic and housing characteristics. We find that low‐income households and households with vulnerable members have the largest rebound effects. The relatively large rebound effects found here suggest that consumers gain from efficiency by improved energy services, and thus, policy targeting energy efficiency is not likely to be successful at reducing energy consumption.     

基于运行规划的能源综合服务系统能效管理研究

以浙江电网企业转型现代能源综合服务的具体战略与发展规划为研究背景，针对能源综合服务系统的运行规划目标，开展基于运行规划的能源综合服务系统能效管理研究。从系统工程的视角出发，以能源流为纽带，基于多尺度能源系统建模方法，构建了能源综合服务系统模型，并结合系统综合能效，采用粒子群优化算法来优化系统各设备单元的运行策略，以实现基于运行规划的最优能效管理。通过能源综合服务系统模型计算表明，大规模清洁能源和可再生能源的接入，可以明显提高系统综合能效；随着区域用户总冷量和/或总热量需求占总电力需求的比值增大，系统最优综合能效逐渐增大；与使用内燃机、燃气轮机及汽轮机为原动机的热电联产系统相比，使用燃料电池为原动机的热电联产系统可以使能源综合服务系统综合能效更优。     

碳排放交易制度的节能减排路径：效率提升还是结构转型？——来自中国省级数据的准自然实验

以中国2013年以来实行的碳排放交易试点政策作为准自然实验事件，基于2000-2017年中国30个省市的面板数据，运用双重差分法实证检验碳排放交易制度的节能减排效应及影响机制。研究发现，碳排放交易制度有利于实现中国经济“节能”与“减排”的双重目标，并通过了一系列稳健性检验；作用机制检验表明，能源效率提升和能源结构转型都是碳排放交易制度实现节能减排目标的重要路径；能源效率提升在碳排放交易制度实现“节能”和“减排”中分别发挥了10.19%和5.93%的作用，而能源结构转型分别发挥了48.87%和52.95%的作用。这意味着中国碳排放交易制度实现节能减排的主要动力来自能源结构转型，而非能源效率提升。能源结构转型涉及问题更加宏观和深层，这为中国加快推进节能减排进程，特别是完成2030年碳达峰和2060年碳中和的国际承诺提供政策启示。