新时代耕地占补平衡的逻辑根源、模式探索与管理创新——基于“新时代耕地占补平衡方式改进与管理创新研讨会”的思考

研究目的:对耕地占补平衡的发展阶段与政策绩效、存在问题与逻辑根源进行分析,对改进方式进行探讨,进而提出管理创新建议,为新时代耕地占补平衡政策实施提供科学依据,为促进城乡融合、扶贫攻坚和乡村振兴提供参考。研究方法:综合分析与系统推理。研究结果:(1)耕地占补平衡存在实质性不平衡、时空性不平衡、政策性弱化等微观、中观及宏观层面问题;(2)在数量方面,通过盘活宅基地增量、存量和完善易地调剂方式,统筹城乡和区域平衡来实现数量占补平衡。(3)在质量方面,通过优化建设用地约束指标、完善耕地质量评价、规范耕作层保护与再利用等促进耕地质量占补平衡。(4)在生态方面,通过完善指标调剂的价格内涵、扩大耕地补偿标准范围、转变耕地补充途径等实现耕地生态占补平衡。研究结论:建议做好顶层设计,抓好规划引领,构建耕地占补平衡与城乡要素流动的平衡机制,建立耕地占补平衡后续生态管护长效机制,提升软硬实力等。     

Global energy forecasting competition 2017: Hierarchical probabilistic load forecasting

The Global Energy Forecasting Competition 2017 (GEFCom2017) attracted more than 300 students and professionals from over 30 countries for solving hierarchical probabilistic load forecasting problems. Of the series of global energy forecasting competitions that have been held, GEFCom2017 is the most challenging one to date: the first one to have a qualifying match, the first one to use hierarchical data with more than two levels, the first one to allow the usage of external data sources, the first one to ask for real-time ex-ante forecasts, and the longest one. This paper introduces the qualifying and final matches of GEFCom2017, summarizes the top-ranked methods, publishes the data used in the competition, and presents several reflections on the competition series and a vision for future energy forecasting competitions.     

Collaborative delay management towards demand-capacity balancing within User Driven Prioritisation Process

The concept of User Driven Prioritisation Process (UDPP) was introduced to give Airspace Users (AUs) more flexibility under demand-capacity imbalance. This paper presents two UDPP-DCB models, built on the UDPP principle, using the Selective Flight Protection (SFP) approach to minimize the total delay cost. AUs are enabled to adapt their operations in a more cost-efficient way in the presence of capacity constraints in airspace, optimizing their flights to keep the priorities track. Then, ATFM integrates the AUs’ decision to reassign the time slots and the preferred rerouting trajectories. Results suggest that the delay cost for AUs can be largely reduced through applying the UDPP-DCB models proposed in this paper, while allowing rerouting proves effective in reducing the system delay cost.     

The Load Model: an alternative to QALY

Background: QALYs are widely used in health economic evaluation, but remain controversial, largely because they do not reflect how many people behave in practice. This paper presents a new conceptual model (Load Model) and illustrates it in comparison with the QALY model.

Methods: Load is the average annual weight attributed to morbidity and mortality over a defined period, using weightings based on preference judgements. Morbidity Load is attributed to states of illness, according to their perceived severity. When people are in full health, Load is zero (no morbidity). Death is treated as an event with negative consequences, incurred in the year following death. Deaths may be weighted equally, with a fixed negative weight such as ?100, or differ according to the context of death. After death, Load is zero. In a worked example, we use the standard gamble method to obtain a weighting for an illness state, for both Load and QALY models. A judge is indifferent between certainty of 1.5 years’ illness followed by death, or a 50/50 chance of 1.5 years’ full health or 1-year illness, each followed by death. The weightings calculated are applied to a hypothetical life, 72 years in full health followed by 3 years with illness then death, using both models. Three other hypothetical outcomes are also compared.

Results: For an example life, the relative size of the morbidity component compared with the mortality component is much higher in the Load model than in the QALY model. When comparing alternative outcomes, there are also substantial differences between the two models.

Conclusions: In the Load model the weight of morbidity, relative to mortality, is very different from that in the QALY model. Given the role of the QALYs in economic evaluation, the implications of an alternative, which generates very different results, warrant further exploration.     

The market-based dissemination of energy-access technologies as a business model for rural entrepreneurs: Evidence from Kenya

Improving access to more modern forms of energy requires supply chains that reach further into rural areas. This paper studies a supply-side intervention intended to foster last-mile distribution of energy-access technologies through local small-scale entrepreneurship. We use a staggered-implementation evaluation design to assess the impact on employment and income outcomes of the intervention, which is a large-scale program in Kenya that supports the diffusion of improved cookstoves and small solar products. The results demonstrate how trained entrepreneurs intensify and diversify their income-generating activities, often by shifting away from subsistence farming as a main source of income. For cookstove entrepreneurs, this goes along with improvements in individual and household incomes as well as perceived economic well-being. Our estimates suggest that impacts do not only differ between the two technologies but also across subgroups including gender, age, and baseline occupation. Our findings substantiate that market-based interventions can foster energy access in rural areas by supporting the establishment of local businesses. We highlight several contextual factors that are of relevance when considering the adoption of this approach.     

Quantile regression for the qualifying match of GEFCom2017 probabilistic load forecasting

We present a simple quantile regression-based forecasting method that was applied in the probabilistic load forecasting framework of the Global Energy Forecasting Competition 2017 (GEFCom2017). The hourly load data are log transformed and split into a long-term trend component and a remainder term. The key forecasting element is the quantile regression approach for the remainder term, which takes into account both weekly and annual seasonalities, such as their interactions. Temperature information is used only for stabilizing the forecast of the long-term trend component. Information on public holidays is ignored. However, the forecasting method still placed second in the open data track and fourth in the definite data track, which is remarkable given the simplicity of the model. The method also outperforms the Vanilla benchmark consistently.     

Estimation of interregional input–output table using hybrid algorithm of the RAS method and real-coded genetic algorithm

In this paper, we propose a method for improving the accuracy of the estimation of interregional input–output tables, by combining the RAS method and the real-coded Genetic Algorithm (GA); these are simple representative methods for the estimation of an interregional input–output table. By comparing the performance evaluation results obtained using the proposed method, the RAS method, and Simulated Annealing, we verified that the combination of the genetic algorithm and the RAS method can enhance the estimation accuracy of an interregional input–output table. In addition, performance is further enhanced by adjusting GA parameters.     

Exploring the relationship between truck load capacity and traffic accidents in the European Union

Applying econometric techniques to EU28 panel data and controlling for explanatory variables such as road types, we find that increased truck load capacity does not necessarily aggravate road traffic safety. Specifically, heavy trucks do not seem to be linked with greater numbers of traffic fatalities/accidents, medium trucks appear to be the worst performers in terms of fatalities, and light trucks seem to be the worst for accidents. In summary, our results clarify the complex relationship between truck load capacity and road safety, pointing to the existence of a negative correlation for accidents per capita and an inverse U-shaped curve for fatalities per capita.     

A multi-objective sustainable load planning model for intermodal transportation networks with a real-life application

Growing importance of intermodal transportation necessitates modeling and solving load planning problems by taking into account various complex decisions simultaneously like transportation mode/service type selection, load allocation, and outsourcing. This paper presents a mixed-integer mathematical programming model for a multi-objective, multi-mode and multi-period sustainable load planning problem by considering import/export load flows to satisfy transport demands of customers and many other related issues. Several multiple objective optimization procedures are utilized in order to handle conflicting objectives simultaneously under crisp and fuzzy decision making environments. A real-life case study is also performed to present application and usefulness of the proposed model.     

Stakeholder Theory and Managerial Decision-Making: Constraints and Implications of Balancing Stakeholder Interests

Stakeholder theory is widely recognized as a management theory, yet very little research has considered its implications for individual managerial decision-making. In the two studies reported here, we used stakeholder theory to examine managerial decisions about balancing stakeholder interests. Results of Study 1 suggest that indivisible resources and unequal levels of stakeholder saliency constrain managers’ efforts to balance stakeholder interests. Resource divisibility also influenced whether managers used a within-decision or an across-decision approach to balance stakeholder interests. In Study 2 we examined instrumental and normative implications of these two approaches. We conclude by considering the contributions of this research.