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.     

关键驱动力作用下的新疆生态系统服务时空格局分析

[目的]恶劣的生态环境和有限的资源驱动着新疆特殊生态系统服务的形成,人类又通过对生态系统服务的消费来满足和提高自身福祉,需要研究其时空变化并辨识主要驱动因素,支持区域可持续发展。[方法]文章构建了融合不同土地利用类型生态系统服务的价值量排序及服务功能强度矩阵的生态系统服务评估体系,并耦合3种关键驱动因子以评估各个因子的驱动强度,实现对生态系统服务热点与冷点变化的驱动及迁移分析。[结果]2000—2010年全疆生态系统服务总体增加,这一时期生态系统服务的热点区大部分转变为2010—2015年的冷点区,且15年来生态系统服务总体呈现小幅下降趋势。3个时期北疆和南疆人为干扰强度均呈增加趋势,由其驱动的生态系统服务则相应减小;南疆地区植被覆盖范围极其有限,故对生态系统服务驱动强度低;降水在北疆和南疆都能够产生高生态系统服务,但降水量的不稳定性能够使生态系统服务产生较大波动。相比于南疆地区,北疆生态系统在外界干扰下能够体现出更强的抗性和整体性,从而实现相对高效的生态系统服务输出。[结论]对关键驱动因子作用下的生态系统服务进行时空变化分析,有助于理解当地生态系统产生变化的原因,从而为当地生态环境保护提供参考。     

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.     

Cox Point Processes: Why One Realisation Is Not Enough

We review a rich class of point process models, Cox point processes, and illustrate the necessity of more than one observation (point patterns) in performing parameter estimation. Furthermore, we introduce a new Cox point process model by treating the intensity function of the underlying Poisson point process as a random mixture of normal components. The behaviour and performance of the new model are compared with those of popular Cox point process models. The new model is exemplified with an application that involves a single point pattern corresponding to earthquake events in California, USA.     

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.     

Beyond symbolic and substantive: Strategic disclosure of corporate environmental information in China

Under increasing regulatory pressure to disclose environmental impacts, corporate environmental information disclosure (EID) is becoming more prevalent. However, the motives for EID and disclosure approaches (i.e., symbolic or substantive) remain controversial. Using a sample of data for 742 listed manufacturing firms in China across 3 years, we provide evidence to show that the level of EID is significantly positively associated with a firm's advertising intensity and that firms in moderately competitive industries have a higher propensity for adopting an EID‐based differentiated strategy than firms in stronger or weaker competitive industries. We also find that firms that are more sensitive to environmental issues strategically understate their environmental impact when directly facing consumers. Our results suggest that the implementation of corporate EID is strategic and that firms have strong market motives, even under regulatory pressure to demonstrate legitimacy. We discuss this study's contributions to research on EID theory and implications for strategic EID management.     

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.     

Size and value effects in high-tech industries: The role of R&D investment

We use monthly US stock data over 55 years from 1962 to 2017 to show that the R&D intensity at firms adds another important dimension to the size and value effects in describing stock returns, especially for small high-tech firms. A trading strategy that double sorts on R&D intensity and size or book-to-market ratio outperforms a simple small-minus-big (SMB) or high-minus-low (HML) strategy in producing higher and more significant portfolio returns. The most profitable schemes involve triple sorts by size, BM, and R&D intensity: the payoffs of buying high-BM/R&D-Active portfolio and selling low-BM/R&D-Inactive portfolio in the small-size/high-tech group and that of buying high-tech/high-BM and selling low-tech/low-BM in the small-size/R&D-active group generate a return of more than 2% on a monthly basis. Our results are robust to alternative classification method of assigning stocks in portfolios.     

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.