中国省级区域初始碳配额分配方案研究——基于责任与目标、公平与效率的视角

制定公平有效的省级初始碳配额分配方案,是中国统一碳市场健康稳定运行的基石。公平原则方面,本文将碳减排责任区分为历史排放责任和省际转移责任。其中历史排放责任主要利用与经济发展水平相接近的年份作为责任核定基期,从而体现"共同而有区别的责任";而转移责任主要通过与区域发展规模、技术水平和经济结构相契合的"合理碳排放"与实际碳排放的差异来表征。效率原则方面,本文建立了考虑区域之间分配博弈的零和DEA模型,通过多次调整得到所有地区效率最大化时的初始配额分配方案。同时,通过熵值法将公平原则与效率原则的分配方案耦合形成综合配额分配方案,并通过比较分析3种不同配额分配方案的地区减排成本效应差异,甄选出兼顾责任与目标、公平与效率的省级初始碳配额分配方案。研究结果表明:(1)1995～2016年间中国省际间贸易造成了大量的地区碳转移,转移的方向主要从能源生产地向能源需求地、从经济高值和中值地区向经济低值地区流动。由于历史排放过高,经济高值地区和一些能源产业密集地区应该承担更多减排责任;(2)在综合考虑历史排放责任和地区转移责任的初始配额分配方案中,经济高值地区的配额盈亏表现出异质性,而经济低值地区则表现出同质性,这表明经济高值地区的减排责任由于能源结构和发展模式的不同而有所差异,而经济低值地区则统一表现为需要承担较少的减排责任;(3)基于公平原则的配额分配方案将会导致"鞭打快牛"而损失效率,从而导致地区发展成本过高;而基于效率原则的配额分配方案虽然成本较低但也会产生"马太效应"问题,从而进一步加剧地区发展不平等;只有兼顾公平与效率的综合配额分配机制,才能既减缓地区发展不平等,又能实现全国平均减排成本最小化。本文的研究结论对于建立公平有效的初始碳配额分配机制、实现国家碳排放总量控制目标、激发统一碳市场活力、推动地区协同减排等方面具有重要的政策启示。     

多目标条件下企业碳配额分配和政府公平——基于(p,α)比例公平的视角

本文在企业和政府主从博弈框架下,研究政府兼顾社会福利和减排成本的多目标条件下企业最优碳配额分配方式,并采用（p,α）比例公平建立了最优分配与政府公平态度之间的对应关系,进一步分析企业间减排效率差异对碳配额最优分配以及政府所持公平态度的影响。采用遗传算法模拟结果显示,在政府最大化社会福利和最小化减排总成本的多目标下,政府始终给予高效率减排企业更多的配额,在企业间减排效率差异较小和较大的行业,减排效率对最优配额分配和政府所持公平态度的影响是完全不同的。当行业内企业减排效率差异较小时,随减排效率差异的增加,低效率企业所得配额逐渐减少,政府公平性逐渐降低;当企业减排效率差异较大时,情况刚好相反,低效率企业的配额逐渐增加,政府变得越来越注重公平。除此之外,政府公平性并不意味着低效率企业得到更多的碳配额,当企业间减排效率差异增加到某一区间,随减排效率差异的增加,低效率企业所得配额逐渐减小,而政府公平逐渐增加。我们的结论为相关部门多目标下的最优碳配额分配决策以及分析政府的公平性提供了有益参考。     

供应链企业碳减排投资策略选择与行为演化研究

基于当下气候变暖的背景中有社会责任感的消费者对低碳产品有特殊偏好的情境,研究上游企业主导的供应链在面对具有低碳产品偏好的市场消费者时,上下游企业的减排投资行为与策略。依照Stackelberg博弈模型,构建上下游企业采用不同的行为策略的支付矩阵。进而,应用演化博弈理论中双种群演化博弈模型分析得到上下游企业减排投资行为的演化稳定策略:当需要较大的投资或者下游企业分担减排成本意愿较强时,由处于主导地位的上游企业实施投资减排是稳定的;当需要较小的减排投资或者下游企业分担减排成本意愿较弱时,上下游企业组成的供应链中必然会有一个企业实施减排。最后,指出了减排投资系数和下游企业分担投资成本比例的不同对演化博弈稳定均衡的影响。     

基于成本优化的中国省际碳减排目标分配

中国政府大力提倡生态文明建设,重要措施之一是建立全国碳交易市场。如何将全国碳减排目标分摊到各省区从而确定初始排放配额,则是确保碳交易市场有序运行的前提和基础。在效率分析的理论框架下,对每一省级区域拟合出其GDP损失与二氧化碳减排量之间的回归方程。将减排引起的GDP下降视为减排成本,提出一种优化全国整体减排成本的规划方法,实现对碳减排总目标的省际分配、制定区域低碳发展目标。分摊结果显示了方法的有效性和易操作性,表明不同区域承担与其经济发展、产业结构、能源消费等现状相符合的减排任务;逐步回归分析下,GDP、人均GDP、二氧化碳排放量、第二产业比重和第三产业比重五个因素对于减排目标分配结果均具有显著影响。     

两种分成契约下供应链企业合作减排决策机制研究

本文考虑由一个受政府碳排放规制且处于供应链上游的产品供应商和处于下游的零售商所组成的低碳供应链系统。由于低碳消费需求增加所引起的零售商利润的提高,下游零售商可采取不同的契约形式促进上游供应商扩大减排投资。为此,本文考虑了分享减排所增收益与分担减排投资成本两种契约。通过建立零供两主体的Stackelberg博弈模型,得出了在两种契约下主体的最优减排水平及最优分成比例,以及两主体在不同契约形式下的最优利润值。结果表明:分担减排投资成本契约能够使减排更加彻底,得到的减排水平较高;在分担减排投资成本契约下,两主体的利润值均会有所提高;在分享减排所增利润契约下,两主体的利润变化要依据参数来确定。最后,文章依据合理数据对上述结论进行了验证,并对碳交易价格等相关参数的敏感性进行了分析。     

促进低碳产品需求的供应链减排成本分担模型

减排成本的存在,导致低碳产品的零售价格偏高,市场需求偏低,从而制约低碳产品的推广。以此为背景,针对单一制造商和单一零售商构成的双边垄断供应链,建立了基于减排成本分担契约的Stackelberg模型,分析比较了有、无减排成本分担契约两种情况下订货量、供应链各成员利润及供应链整体利润变化。研究发现,引入减排成本分担契约后,低碳产品最优订货量增加,零售价格降低,且在一定条件下制造商和零售商的利润得到帕累托改进,并推导出了制造商和零售商的参与约束和最优分担率。在此基础上,进一步分析了消费者对低碳产品的认知与零售商的产品订货量、供应链整体利润呈正相关关系,与制造商承担的减排成本分担率呈负相关关系。最后结合数值分析验证了成本分担契约的有效性。     

碳配额交易政策下闭环供应链的减排策略选择

本文以单一制造商、单一零售商和消费者组成的闭环供应链为研究对象,同时考虑新产品和再制造品的价格差异及消费者低碳偏好,分别构建了在碳配额交易政策下制造商进行减排技术投入、回收再制造、同时进行减排技术投入与回收再制造三种模式的Stackelberg博弈模型,分析了减排成本系数、再制造减排系数、低碳价值系数等对供应链节点企业利润和碳排放总量的影响。研究结果表明,碳配额交易价格小于某一临界值时,与制造商的减排技术投入呈正相关;当再制造减排系数满足一定条件时,碳配额交易政策会激励制造商进行回收再制造;制造商同时采用减排技术投入与回收再制造模式可以获得最大利润,但是不能保证供应链碳排放总量最小。     

不同碳交易配额分配方法下的鲁棒减排运作策略研究

本文结合当前碳交易配额分配方式多样化和需求分布信息不确定的现象,研究历史法和基准线碳配额分配方式下企业的鲁棒减排运作策略。通过构建和求解两种碳配额政策下企业减排运作决策的鲁棒优化模型,本文给出不同碳配额政策下企业进行减排投资的必要条件。不同碳配额政策下最优减排运作决策、经济和环境绩效的对比分析揭示了两种碳配额分配方法对最差分布下企业利润和碳排放量的影响。通过理论分析结合数值实验发现,相同减排水平下,基准线法碳配额下的最优生产量高于历史法碳配额下的最优生产量;虽然基准线法碳配额下的碳排放总量高于历史法碳配额下的值,但单位产品碳排放量在一定条件下却可以低于历史法碳配额下的值。用基准线法设定碳配额能充分调动企业的生产积极性,政府可以通过调整基准线法碳配额的数值使企业实现高利润低排放,从而实现经济和环境协调发展的目标。     

基于两阶段博弈模型的钢铁行业碳强度减排机制研究

减少温室气体排放,高耗能行业将承担主要的减排任务,不同的减排机制对减排任务在不同技术水平的企业间分配和减排成本影响不同,而且对企业竞争力和社会福利有影响。本文构造了一个两阶段动态博弈模型,以两个代表性钢铁企业(东部和西部)为研究对象,来考察在完成一定减排目标前提下,政府设置统一碳税和差异化碳税对减排成本、社会经济福利、企业竞争力等的影响。实证结果表明:统一碳税下,钢铁行业产量降幅较小,行业减排任务分解的更加平均,西部钢铁企业竞争力损失较小;差异税下,实现既定减排目标的减排成本较小;社会经济福利损失较小,且随着减排目标的不断升高,两种碳税下社会经济福利损失之差有拉大的趋势;东部钢铁企业竞争力提高幅度较大,但对西部钢铁企业的负面影响十分明显。因此,在碳税机制设计时应充分考虑不同税率模式对减排任务分解、钢铁行业产量、社会经济福利以及企业竞争力的具体影响,需要在社会成本较低和个体企业的竞争力受影响较小之间进行权衡。     

信息不对称下基于区块链驱动的供应链减排信息共享机制研究

信息不对称是阻碍供应链多主体协同减排的主要因素,针对消费者隐匿低碳偏好导致的供应链协同减排效率损失问题,通过与基于委托-代理理论的供应链减排信息甄别机制比较,设计了基于区块链驱动的供应链协同减排信息共享机制。研究表明,消费者获得额外的信息租金是导致供应链减排效率损失的主要因素,生产商和供应商通过分担一定比例的“区块链+协同减排”投资成本,可以获得额外的信息共享收益。“区块链+协同减排”信息共享机制在消除信息租金的同时,可以有效提高供应链收益,并且存在一最优收益分配比例使得生产商与供应商收益达到均衡以及供应链收益达到最优。“区块链+协同减排”信息共享机制可以有效提高供应链上下游企业协同减排效率,是推进我国“十四五”时期减排任务的重要路径之一。     

A Framework for Linking Cybersecurity Metrics to the Modeling of Macroeconomic Interdependencies

Hierarchical decision making is a multidimensional process involving management of multiple objectives (with associated metrics and tradeoffs in terms of costs, benefits, and risks), which span various levels of a large-scale system. The nation is a hierarchical system as it consists multiple classes of decisionmakers and stakeholders ranging from national policymakers to operators of specific critical infrastructure subsystems. Critical infrastructures (e.g., transportation, telecommunications, power, banking, etc.) are highly complex and interconnected. These interconnections take the form of flows of information, shared security, and physical flows of commodities, among others. In recent years, economic and infrastructure sectors have become increasingly dependent on networked information systems for efficient operations and timely delivery of products and services. In order to ensure the stability, sustainability, and operability of our critical economic and infrastructure sectors, it is imperative to understand their inherent physical and economic linkages, in addition to their cyber interdependencies. An interdependency model based on a transformation of the Leontief input-output (I-O) model can be used for modeling: (1) the steady-state economic effects triggered by a consumption shift in a given sector (or set of sectors); and (2) the resulting ripple effects to other sectors. The inoperability metric is calculated for each sector; this is achieved by converting the economic impact (typically in monetary units) into a percentage value relative to the size of the sector. Disruptive events such as terrorist attacks, natural disasters, and large-scale accidents have historically shown cascading effects on both consumption and production. Hence, a dynamic model extension is necessary to demonstrate the interplay between combined demand and supply effects. The result is a foundational framework for modeling cybersecurity scenarios for the oil and gas sector. A hypothetical case study examines a cyber attack that causes a 5-week shortfall in the crude oil supply in the Gulf Coast area.     

Supply chain capabilities for customisation: a case study

Abstract

Despite the growing interest in supply chain management (SCM), there is little literature on knowledge and competences needed to properly configure and manage a supply chain. There are some interesting methodologies to map the knowledge and the techniques used in the supply chain of a sector and transfer them in another sector. This is particularly important when cross-fertilisation of practices can support sectors facing global competition as the footwear industry. New business opportunities can in fact be exploited addressing the needs of specific target groups searching for customised fashion and healthy shoes. Based on literature review and case study analysis of twelve companies within the fashion and orthopaedic footwear supply networks, this work compares the relevant processes and the distinctive capabilities necessary in managing supply networks to implement customisation, identifying and highlighting the interchangeable best practices that could serve as a new common knowledge base to be shared by the two sectors.     

中国A股市场金融板块间风险相依关系及动态演化研究

将中国金融板块细分为国有大型银行、全国性股份制银行、城商行、证券、保险和信托等6个金融子板块，并以2015年中国股市异动和2019年新冠肺炎疫情为研究背景，分析在两个场景下的不同时期内，6个金融板块间的风险相依关系及其动态演化。通过计算各板块间波动指数的互信息，构建金融板块风险相依关系网络，并使用最大生成树刻画该相依关系的核心结构。研究发现，在市场处于相对平静时期，银行类金融板块与非银行类金融板块二者彼此之间的风险关联较弱，处于相对割裂状态；在市场走势波动较大时，银行类金融板块与非银行类金融板块之间的风险关联程度增强，且保险板块成为重要的中间节点；在两个场景下的异动期和疫情期，国有大型银行板块和城商行板块分别成为最大的风险节点。     

Modeling the demand reduction input-output (I-O) inoperability due to terrorism of interconnected infrastructures.

Interdependency analysis in the context of this article is a process of assessing and managing risks inherent in a system of interconnected entities (e.g., infrastructures or industry sectors). Invoking the principles of input-output (I-O) and decomposition analysis, the article offers a framework for describing how terrorism-induced perturbations can propagate due to interconnectedness. Data published by the Bureau of Economic Analysis Division of the U.S. Department of Commerce is utilized to present applications to serve as test beds for the proposed framework. Specifically, a case study estimating the economic impact of airline demand perturbations to national-level U.S. sectors is made possible using I-O matrices. A ranking of the affected sectors according to their vulnerability to perturbations originating from a primary sector (e.g., air transportation) can serve as important input to risk management. For example, limited resources can be prioritized for the "top-n" sectors that are perceived to suffer the greatest economic losses due to terrorism. In addition, regional decomposition via location quotients enables the analysis of local-level terrorism events. The Regional I-O Multiplier System II (RIMS II) Division of the U.S. Department of Commerce is the agency responsible for releasing the regional multipliers for various geographical resolutions (economic areas, states, and counties). A regional-level case study demonstrates a process of estimating the economic impact of transportation-related scenarios on industry sectors within Economic Area 010 (the New York metropolitan region and vicinities).     

Interactions among the human and organizational factors within the public sector regarding epidemic prevention and control

The management of human and organizational factors (HOFs) within the public sector directly concerns the efficacy of epidemic prevention and control (EPC). Insufficient examination of such HOFs has led to defective countermeasures. This study attempts to comprehensively identify the HOFs within the public sector critical to EPC and investigate their interactions with the weighted network theory. A total of 55 HOFs were identified, and their interactions were assessed and visualized in the Chinese context. Then, the established weighted network was analyzed to investigate the interactions and diagnose critical factors and sectors. The analysis shows that there are strong interactions among HOFs, and that the human and organizational risks emerging from administrative departments of public health, centers for disease control and prevention, and medical institutions act as the key risk sources in the complex interconnected EPC system, exacerbating risk and causing a significant domino effect. It is recommended that the authorities devote more resources to the core sectors and endeavor to reinforce those critical HOFs by implementing closer risk communication, collaboration, and response. This study may deepen and broaden the authorities’ awareness and understanding of interactions among HOFs regarding epidemic mitigation, and strengthen their capacity to perceive, evaluate, and manage these factors in a proactive and effective way, thereby facilitating the success of EPC.     

Modeling Uncertainties in Workforce Disruptions from Influenza Pandemics Using Dynamic Input‐Output Analysis

Influenza pandemic is a serious disaster that can pose significant disruptions to the workforce and associated economic sectors. This article examines the impact of influenza pandemic on workforce availability within an interdependent set of economic sectors. We introduce a simulation model based on the dynamic input‐output model to capture the propagation of pandemic consequences through the National Capital Region (NCR). The analysis conducted in this article is based on the 2009 H1N1 pandemic data. Two metrics were used to assess the impacts of the influenza pandemic on the economic sectors: (i) inoperability, which measures the percentage gap between the as‐planned output and the actual output of a sector, and (ii) economic loss, which quantifies the associated monetary value of the degraded output. The inoperability and economic loss metrics generate two different rankings of the critical economic sectors. Results show that most of the critical sectors in terms of inoperability are sectors that are related to hospitals and health‐care providers. On the other hand, most of the sectors that are critically ranked in terms of economic loss are sectors with significant total production outputs in the NCR such as federal government agencies. Therefore, policy recommendations relating to potential mitigation and recovery strategies should take into account the balance between the inoperability and economic loss metrics.     

燃油税、融资约束与企业行为——基于DSGE模型的分析

征收燃油税在实现节能减排的同时也会增加企业的财务负担。如何在保护环境的同时减少对经济的冲击,有赖于对燃油税的科学评估。本文构建了一个包含燃油税和融资约束的随机动态一般均衡模型,并基于1995年第1季度至2018年第2季度的数据对相关参数进行了校准和估计,系统考察了融资约束下征收燃油税对环境经济以及企业行为的影响。研究结果发现：征收燃油税对促进节能减排有显著效果;但同时也会抑制消费、投资和产出,增加失业,对经济产生负影响。此外,融资约束会通过金融加速器的作用放大燃油税冲击的影响。而且,当融资约束越强时,降低燃油税对经济的刺激作用也越明显。     

Why Do People from Southern Italy Seek Jobs in the Public Sector?

This paper analyses the choice open to a worker seeking a job in the public and private sectors of the labour market. The private sector is identified by a steeper wage profile and by lower job security than the public sector. The reservation wage for the two sectors is calculated in the first part of the paper. The results reveal that the reservation wage for the public sector is higher than that for the private sector. The effect of career prospects, job riskiness and labour demand on optimal time allocation between the search in the two sectors is then analysed. Finally, an empirical analysis is made in order to study Italian workers’ search strategies. It highlights relevant geographical differences which can be interpreted through the theoretical results obtained in the paper.     

Risk‐Based Input‐Output Analysis of Influenza Epidemic Consequences on Interdependent Workforce Sectors

Outbreaks of contagious diseases underscore the ever‐looming threat of new epidemics. Compared to other disasters that inflict physical damage to infrastructure systems, epidemics can have more devastating and prolonged impacts on the population. This article investigates the interdependent economic and productivity risks resulting from epidemic‐induced workforce absenteeism. In particular, we develop a dynamic input‐output model capable of generating sector‐disaggregated economic losses based on different magnitudes of workforce disruptions. An ex post analysis of the 2009 H1N1 pandemic in the national capital region (NCR) reveals the distribution of consequences across different economic sectors. Consequences are categorized into two metrics: (i) economic loss, which measures the magnitude of monetary losses incurred in each sector, and (ii) inoperability, which measures the normalized monetary losses incurred in each sector relative to the total economic output of that sector. For a simulated mild pandemic scenario in NCR, two distinct rankings are generated using the economic loss and inoperability metrics. Results indicate that the majority of the critical sectors ranked according to the economic loss metric comprise of sectors that contribute the most to the NCR's gross domestic product (e.g., federal government enterprises). In contrast, the majority of the critical sectors generated by the inoperability metric include sectors that are involved with epidemic management (e.g., hospitals). Hence, prioritizing sectors for recovery necessitates consideration of the balance between economic loss, inoperability, and other objectives. Although applied specifically to the NCR, the proposed methodology can be customized for other regions.