Harvesting versus Biodiversity: An Occam's Razor Version

The point of departure for this paper is the familiar prototype fisheriesmodel where a fictitious sole owner harvests a fish population to maximizepresent discounted profits. The paper answers analytically the followingquestion. ``What happens to a policy when the sole owner also valuesbiodiversity, as well as profits?'' It turns out that the size of the steady-state stock and the number of species preserved are both higher, whenspecies diversity is positively valued. This paper provides a sharpcharacterization of the optimal policy in terms of the usual economicparameters and an exogenously introduced willingness-to-pay function forspecies preservation.     

Forecasting high resolution electricity demand data with additive models including smooth and jagged components

Short-Term Load Forecasting (STLF) is a fundamental instrument in the efficient operational management and planning of electric utilities. Emerging smart grid technologies pose new challenges and opportunities. Although load forecasting at the aggregate level has been extensively studied, electrical load forecasting at fine-grained geographical scales of households is more challenging. Among existing approaches, semi-parametric generalized additive models (GAM) have been increasingly popular due to their accuracy, flexibility, and interpretability. Their applicability is justified when forecasting is addressed at higher levels of aggregation, since the aggregated load pattern contains relatively smooth additive components. High resolution data are highly volatile, forecasting the average load using GAM models with smooth components does not provide meaningful information about the future demand. Instead, we need to incorporate irregular and volatile effects to enhance the forecast accuracy. We focus on the analysis of such hybrid additive models applied on smart meters data and show that it leads to improvement of the forecasting performances of classical additive models at low aggregation levels.     

Identifying resident preferences for bus-based and rail-based investments as a complementary buy in perspective to inform project planning prioritisation

Much of the debate associated with the development of new public transport infrastructure appears to have an emotional bias with communities in favour of one mode, especially rail. This, in turn, carries much sway at the political level as if there is no budget constraint or consideration of value for money and coverage. This paper presents a stated choice experiment to investigate this context as two unlabelled options described by 20 potential drivers of community preferences for improved public transport. Each choice scenario is conditioned on a given route length but with different costs, reflecting different modal investment options for the same route length. To establish whether a modal bias exists within and between geographical jurisdictions, the choice scenario is followed by a labelling of each investment option to reveal whether the option is bus rapid transit (BRT) or light rail transit (LRT). Data from all eight capital cities of Australia, collected in mid-2014, form the empirical setting. Mixed logit random regret models provide new evidence on the nature and extent of community modal bias in this choice setting. The paper also proposes a complementary tool to benefit-cost analysis that uses the residence preferences model to show, through scenario analysis, the potential gains in public support for BRT over LRT. The results suggest that BRT should be in the mix of candidate projects if more than one mode is considered and not ignored, as is so often the case in developed economies.     

Delimiting port hinterlands based on intermodal network flows: Model and algorithm

This study aims to propose a tangible approach to delimiting the probabilistic hinterland of a port of interest. We first build a geometric model for the probabilistic port hinterland based on intermodal network flows jointly using discrete choice analysis and geographical information of shippers. We further design an algorithm that can efficiently determine the hinterland boundaries using the sample approximation of shippers’ choice probabilities. We provide theoretical results that characterize the minimum computational effort required to achieve a certain degree of accuracy in the sample approximation. We also offer two numerical case studies to justify the proposed approach.     

浅析高压气流引射防尘雨幕在煤矿安全生产中的应用

矿尘始终是困扰井下安全生产的最大因素之一。长期从事采掘和粉尘作业环境的职工,易引起尘肺病,在高度粉尘作业场所工作的职工,视线易受阻挡,操作中易造成人身事故。其最大危害在于煤尘具有爆炸危险的矿井,煤尘在一定的条件下易发生煤尘爆炸事故,给矿井安全生产带来很大威胁。     

车削细长丝杆的技术应用

车细长轴是车削的难点,而加工细长丝杆就更困难,车削细长丝杆中根据工艺要点,抓住跟刀架与中心架的使用。解决工件的热度形伸长及合理选择几何参数,问题就可解决。     

Do precious metal prices help in forecasting South African inflation?

In this paper we test whether the key metals prices of gold and platinum significantly improve inflation forecasts for the South African economy. We also test whether controlling for conditional correlations in a dynamic setup, using bivariate Bayesian-Dynamic Conditional Correlation (B-DCC) models, improves inflation forecasts. To achieve this we compare out-of-sample forecast estimates of the B-DCC model to Random Walk, Autoregressive and Bayesian VAR models. We find that for both the BVAR and BDCC models, improving point forecasts of the Autoregressive model of inflation remains an elusive exercise. This, we argue, is of less importance relative to the more informative density forecasts. For this we find improved forecasts of inflation for the B-DCC models at all forecasting horizons tested. We thus conclude that including metals price series as inputs to inflation models leads to improved density forecasts, while controlling for the dynamic relationship between the included price series and inflation similarly leads to significantly improved density forecasts.     

弹性材料3J21微槽的微细电解加工研究

为解决高硬度、高强度弹性材料3J21微结构的成形加工难题,在分析进给速度、微细电极、加工电压、电解液浓度等工艺参数对弹性材料3J21加工精度的影响规律基础上,进行了微槽的微细电解铣削加工工艺实验,加工出了宽度约为40μm的微槽,具有较好的加工质量。     

Biophysical and socioeconomic determinants of tea expansion: Apportioning their relative importance for sustainable land use policy

Tea expansion, a typical process of regional land use and cover change (LUCC), has raised great concerns on regional sustainability. In this regard, exploring the determinants of tea expansion should provide critical implications for land use policy. It has been widely recognized that LUCC interacts nonlinearly with a set of determinants and their feedbacks should be rather complex. Policy makers are now facing the challenge to identify, apportion, and compare the determinants of regional tea expansion for designing more targeted political intervenes. Our paper utilizes a robust tool, the random forest (RF) regression in particular, to explore the determinants of tea expansion across two periods (1985–2007 and 2007–2016) in Anji County, a typical region of tea production in subtropical China. More specifically, tea is extracted from Landsat imageries and total tea cultivated area acts as the dependent variable. Exploratory variables include 38 potential determinants and these determinants are divided into two categories (biophysical and socioeconomic) at two levels (pixel and village). We obtain some similar findings, though the relative importance of determinants varies with the two periods. In general, biophysical determinants (e.g., topography, soil type, land use in the neighborhood) present greater relative importance than the socioeconomic determinants in both periods. In period 1985–2007, biophysical determinants at pixel level are more essential in governing tea expansion. In period 2007–2016, the relative importance of pixel level biophysical determinants is comparable with that of the village level determinants. Comparisons of the two periods indicate that relative importance of soil type and socioeconomic proximity becomes greater in period 2007–2016, while that of the total employees and non-agricultural population proportion becomes lower. Partial dependency plots are further drawn to visualize the marginal effect of each determinant. We finally propose three options for land use policy towards sustainability. Our study demonstrates that the RF regression is efficient for policy makers to understand the determinants of tea expansion with a nonlinear and complex nature.     

The right speed and its value

Slow investments cause substantial revenue losses, yet acceleration increases costs. This tradeoff implies that an optimal investment speed usually exists; it is faster the higher a firm's intrinsic speed capability. We hypothesize that it is a firm's intrinsic speed capability, rather than its speed relative to industry competitors per se, that boosts firm value. Using data on oil and gas facilities (1996–2005), we find that intrinsic speed capabilities augment firm value in a varied way: their value is larger with better corporate governance, lower cost of capital, and higher ability to draw value from R&D investment. Our work elevates the discussion of speed from a project‐level consideration to a firm‐level competitive advantage issue and raises the need to further explore its strategic value. Copyright © 2013 John Wiley & Sons, Ltd.