Conceptual framework for measuring carbon footprint in supply chains

Over the last few years, the fight against climate change has become one of the main topics of international debate. Hence, consumer behaviour has begun to change as they have started to assess the environmental impacts of the products and services they buy. Although various methods exist for measuring environmental (e.g. carbon) impacts, there is no international consensus about the most appropriate one. In addition, calculations can also be affected by limited data availability and uncertainty surrounding the value of key variables. This paper proposes a conceptual framework for measuring and analysing the carbon footprint in supply chains. This research contributes to the knowledge and practice of green supply chain management, at the corporate level, by providing robustness. This aids the decision-making process by identifying strategies in order to reach the efficiency that can be achieved by reducing CO₂ emission over the supply network. The framework is validated using real data from a supply chain belonging to the agro-industrial sector. Finally, these results and experience is generalised in order to show the difficulties and challenges in the measuring task.     

民族古籍与民族古籍学

中国的民族古文字创制于不同时期,记录了大量的史实,还有不少用汉字写作的文献,它们都是古代不同民族的智慧结晶。整理与研究民族古籍,其文化财富才会有现实意义,民族古籍学亦在此基础上得以创立。     

工业化过程中碳排放消费建设比的演变规律研究

本文从历史发展角度动态地考察了由投资和消费引发CO₂排放的变化规律,提出了碳排放中的消费型排放、建设型排放和消费建设比的概念,利用环境投入产出分析模型(EE-IOA)估计了处于不同工业化发展阶段的32个国家86组数据的消费型排放和建设型排放,得到了消费建设比在工业化过程中(包括后工业化时期)的演变规律。实证结果表明:随着工业化水平的提高,消费建设比呈上升趋势;应根据消费型排放和建设型排放的减排潜力,科学制定减排目标,更加注重引导消费者行为向绿色消费转变、依靠消费者力量促进企业节能减排。本研究对正确评价不同工业化发展阶段国家的排放水平和减排潜力,科学制定减排政策和目标具有重要意义。     

Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain

We develop results for the use of Lasso and post‐Lasso methods to form first‐stage predictions and estimate optimal instruments in linear instrumental variables (IV) models with many instruments, p. Our results apply even when p is much larger than the sample size, n. We show that the IV estimator based on using Lasso or post‐Lasso in the first stage is root‐n consistent and asymptotically normal when the first stage is approximately sparse, that is, when the conditional expectation of the endogenous variables given the instruments can be well‐approximated by a relatively small set of variables whose identities may be unknown. We also show that the estimator is semiparametrically efficient when the structural error is homoscedastic. Notably, our results allow for imperfect model selection, and do not rely upon the unrealistic “beta‐min” conditions that are widely used to establish validity of inference following model selection (see also Belloni, Chernozhukov, and Hansen (2011b)). In simulation experiments, the Lasso‐based IV estimator with a data‐driven penalty performs well compared to recently advocated many‐instrument robust procedures. In an empirical example dealing with the effect of judicial eminent domain decisions on economic outcomes, the Lasso‐based IV estimator outperforms an intuitive benchmark. Optimal instruments are conditional expectations. In developing the IV results, we establish a series of new results for Lasso and post‐Lasso estimators of nonparametric conditional expectation functions which are of independent theoretical and practical interest. We construct a modification of Lasso designed to deal with non‐Gaussian, heteroscedastic disturbances that uses a data‐weighted ℓ₁‐penalty function. By innovatively using moderate deviation theory for self‐normalized sums, we provide convergence rates for the resulting Lasso and post‐Lasso estimators that are as sharp as the corresponding rates in the homoscedastic Gaussian case under the condition that logp = o(n^1/3). We also provide a data‐driven method for choosing the penalty level that must be specified in obtaining Lasso and post‐Lasso estimates and establish its asymptotic validity under non‐Gaussian, heteroscedastic disturbances.     

碳限额与交易机制下零售商模糊库存模型优化求解

为探讨不确定条件下碳交易机制对库存管理中排放控制和成本优化的影响,用梯形模糊数描述各参数和决策变量,建立碳交易机制下模糊经济订货批量模型和排放模型,对比分析不同决策下经济订货量的关系,讨论碳交易机制对模糊成本与碳排放的影响效果。研究结果表明:模糊模型能有效降低决策的不确定性;模糊参数满足一定条件时,考虑碳交易机制前后和仅考虑碳排放最小时的经济订货量不相同,适宜进行碳交易;碳限额增大能有效降低成本,对经济订货量和排放无直接影响;碳价与排放负相关,而对成本的作用则取决于给定碳限额的范围。     

碳减排约束下我国产业结构变迁的路径选择

建立我国29个省份1995—2010年的面板数据模型,从产业结构合理化和高级化两个维度实证研究产业结构变迁对碳排放强度的影响。从全国范围来看,产业结构的合理化和高级化均对碳排放强度有显著影响。具体而言,东部地区的产业结构高级化有助于碳减排目标的实现,而中西部地区则是产业结构合理化对碳减排有明显作用。因此,各地区应该因地制宜选择产业结构变迁的路径,积极推进碳排放交易市场建设和碳排放指标的分解。     

低碳视角下中国造纸产业国际竞争力分析

低碳经济时代低碳成为各国产业获得国际竞争优势的重要手段,从低碳的角度对中国造纸产业的竞争力进行重新评估具有重要的理论和现实意义。基于对现有文献关于产业竞争力和低碳竞争力评价指标和方法的回顾与综述,选取国际市场占有率、贸易竞争指数、相对贸易优势指数三个传统国际竞争力评价指标和碳排放强度、碳排放强度变化率、碳生产率三个低碳竞争力指标对低碳视角下中国造纸产业国际竞争力进行评价与国际比较。研究结论表明:中国造纸产业的传统国际竞争力和低碳竞争力相对比较弱,与世界造纸强国相比差距较大。     

主权财富基金如何进入东道国市场?——基于共容性投资的动态信号博弈分析

针对主权财富基金投资壁垒提出共容性投资理念,即主权财富基金在投资过程中关注自身收益的同时,让渡价值或者给东道国带来正经济外部性,以实现共赢。建立了多阶段动态信号博弈模型,主权财富基金发出共容性投资信号,并据以约束自身行为,东道国会根据这一信号对主权财富基金进行识别和监督。结果表明:共容性投资程度越高,越有利于双方合作的开展,主权财富基金可获得投资的主动性,并实现长期收益,而东道国获得共容性投资所带来的收益。     

中外女子马拉松训练理念比较研究

中外女子马拉松教练训练理念存在较大的差异,主要表现在对马拉松项目本质特征的认识上存在差异而导致训练思路不同,特别是在提高有氧与无氧耐力、专项速度耐力、运动员比赛中后半程保持高速度耐力的能力方面的训练存在差异较大.中国女子马拉松竞技水平徘徊不前的关键原因,可能与中国项群训练理论把马拉松划分为＂体能主导类耐力项群＂有重要关系.     

博士生教育的个性化记录——《柳叶集·续编》序

博士、硕士研究生导师对于学生的培养是一项复杂的工作,要根据同学的实际情况,有针对性地在培养学术思维、优化学术观念、锻炼学术方法、端正学术态度、遵守学术规范等方面下工夫。《柳叶集·续编》的材料来自于学习生活,是教育实践的产物,当然是有实际启发意义的。