Public Disclosure Programs vs. traditional approaches for environmental regulation: Green goodwill and the policies of the firm

A Public Disclosure Program (PDP) is compared to a traditional environmental regulation (exemplified by a tax/subsidy) in a simple dynamic framework. A PDP aims at revealing the environmental record of firms to the public. This information affects its image (goodwill or brand equity), and ultimately its profit. A firm polluting less than its prescribed target would win consumer’s sympathy and raise its goodwill, whereas it is the other way around when the firm exceeds its emissions quota. The evolution of this goodwill is assumed to depend also on green activities or advertising expenditures. Within this framework, we analyze how a PDP affects the firm’s optimal policies regarding emissions, pricing and advertising as compared to a traditional regulation. We show that advertising acts as a complementary device to pricing and that emissions are increasing in goodwill. The role of a standard or target level for emissions turns out to be totally different under both policy regimes. In the case of a tax/subsidy approach, this target level only acts as constant who increases or decreases profit by a fixed amount, but it does not affect the policy of the firm. On the contrary, if a PDP is implemented, the target value for emissions enters in an important way in the goodwill accumulation mechanism and determines how the firm reacts to the regulation and what is the time path for the economic and environmental variables. Moreover, this value is also crucial to determine the possibility that a PDP is profit improving. A policy implication of this fact is that regulators should be particularly careful in fixing the emission standard when a PDP is applied. The theoretical results are complemented with a numerical illustration.     

An uncertain contract model for rural migrant worker’s employment problems

This paper studies the implementation of the employment relationship problem between the enterprise (he) and the rural migrant worker (she) in labor market. An uncertain contract model is established to maximize the expected utility of the enterprise under incentive feasible mechanism, in which the enterprise’s assessment of the rural migrant worker’s own income at home is subjective, and characterized as an uncertain variable. The crisp equivalent model is then presented and the optimal solution for the equivalent model is obtained. The results show that if the rural migrant worker’s own income at home is higher, she is less willing to pay much effort on work, i.e., the rural migrant worker’s optimal effort level decreases with her income at home. Finally, a numerical example is presented to show the effectiveness of the model.     

A note on issues of over-conservatism in robust optimization with cost uncertainty

We identify and discuss issues of hidden over-conservatism in robust linear optimization, when the uncertainty set is polyhedral with a budget of uncertainty constraint. The decision-maker selects the budget of uncertainty to reflect his degree of risk aversion, i.e. the maximum number of uncertain parameters that can take their worst-case value. In the first setting, the cost coefficients of the linear programming problem are uncertain, as is the case in portfolio management with random stock returns. We provide an example where, for moderate values of the budget, the optimal solution becomes independent of the nominal values of the parameters, i.e. is completely disconnected from its nominal counterpart, and discuss why this happens. The second setting focusses on linear optimization with uncertain upper bounds on the decision variables, which has applications in revenue management with uncertain demand and can be rewritten as a piecewise linear problem with cost uncertainty. We show in an example that it is possible to have more demand parameters equal their worst-case value than what is allowed by the budget of uncertainty, although the robust formulation is correct. We explain this apparent paradox.     

THE PROBLEM OF NONMALLEABLE CAPITAL REVISITED; A STUDY OF THE PACIFIC HALIBUT FISHERY

It has been suggested in the literature on commercial fisheries that fishing capital may be nonmalleable, i.e., not easily moved from one fleet to another, and that the socially optimal rate of capitalization of boats may depend on the degree of malleability (irreversibility) of the fishing fleet. To find out how irreversibility affects optimal regulation, two of the many possible regulatory tools, unallocated quotas and catch taxes, are examined. These optimal policies are derived, alternatively assuming malleable and nonmalleable fishing capital. Using a simulation of the Pacific halibut fishery, the results obtainable through catch quotas are shown to be inferior to those obtainable through taxes, but that the degree of malleability of capital has a surprisingly small impact on policy. A sensitivity analysis is performed, rerunning these simulations over a variety of parameter values. The previous results are largely borne out.     

Stochastic competitive entries and dynamic pricing

How should firms price new products when they do not know the timing, nor the nature of the next competitive entry? To guide managers’ pricing decisions in such contexts, we propose a dynamic pricing model with two types of randomly timed entry, i.e. imitative and innovative. The characterization of the equilibrium strategies reveals how optimal prices vary with the manager’s knowledge about the timing of future competitive entries. We show that price skimming is not always optimal when entry dates are unknown to managers. Everything else equal, we demonstrate that the randomness of competitive entries make forward looking managers to choose constant prices, even though the characteristics of the market would have justified skimming the demand in the normal course. Moreover, we show that the constant pricing policy remains optimal even when the incumbent’s optimal pricing strategy influences the probability of facing a competitive entry. Finally, we find that uncertainty does not necessarily hurt firms’ profits.     

非对称情形下R & D竞争项目的策略性投资

在非对称情形下,考虑具有技术不确定和未来收益不确定的竞争研究与开发(R&;D)项目的不可逆策略性投资.利用期权博弈理论和随机优化方法给出了高效研发公司(主导者) 的最优投资阈值和最优投资规则的解析表达式,并证明了由于两公司的竞争使投资阈值下降.其次讨论了两公司的混合投资策略,并给出每个公司执行投资期权的概率和两公司同时执行投资期权的概率. 在最后给出了数值模拟算例来说明该文结论的合理性.     

Input-output dynamic model for optimal environmental pollution control

This paper describes the allocation of a wastewater treatment fund within a region based on a dynamic input-output model. Considering the complexity of the input-output process, many indeterminate factors must be included in the model. For example, with the aging of machines, an unexpected loss will be caused by the retention of raw materials during an operation; this can be realistically considered as a random variable, because of the sufficiently large amount of historical data. By contrast, actions such as a temporary transfer or inexperienced operators can only be regard as uncertain variables, because of a lack of historical data. First, the pollution control model is formulated in an uncertain environment by including both human uncertainty and objective randomness. Second, an optimal control model subject to an uncertain random singular system is established; this model can be transformed into an equivalent optimization problem. To solve such a problem, recurrence equations are presented based on Bellman’s principle, and these were successfully applied to address the optimal control problem in two special cases. Moreover, two algorithms are formulated for solving the pollution control problem. Finally, the optimal distribution strategies of the pollution control fund used to control the emissions of COD and NH₃-H, which are two indicators of wastewater in China, were obtained through the proposed algorithms.     

Induced effects and technological innovation with strategic environmental policy

This paper investigates an environmental policy designed to reduce the emission of pollutants under uncertainty, where the agents’ problem is formulated as an optimal stopping problem. We first analyze the single-agent’s case according to Pindyck [Pindyck, R.S., 2002. Optimal timing problems in environmental economics. Journal of Economic Dynamics and Control 26, 1677–1697]. We then extend the model to the case in which there are two competing agents. Therefore, we consider the external economic effects that are peculiar to an agent’s environmental policy decision. Finally, we consider the effect of technological innovation. The results of the analysis suggest that if there are two competing agents, they implement environmental policy simultaneously. Furthermore, the threshold for implementing environmental policy is higher when there are two agents, and how long these two agents take to implement environmental policy depends on the magnitude of the external economic effect. Furthermore, when we consider the effect of technological innovation, we show that the incentive to be the leader occurs if an additional condition is satisfied.     

Rollover risk and endogenous network dynamics

Using a dynamic network formation model, solved numerically, we study banks’ rollover decisions. We find that when the existence of linkages between market participants generates an informational externality, the newly formed network is conditioned by past architectures. Moreover, this inertia is strongly dependent on macroeconomic conditions, such as investors’ risk appetite. Simulations show that for intermediate values of the risk appetite’s parameter the financial network exhibits tipping points, i.e., the inability to maintain a threshold number of linkages may push the market into a gridlock. In this context, we study also how policy instruments, such as taxes and subsidies, affects debt rollover. Since a reduction in the policy level plays the same role as an improvement in economic fundamentals, the creation of interbank connections can be stimulated by it. Thus, in order to restart lending after a major stress situation in the interbank market a considerable reduction in the policy level is required, advising a counter-cyclical policy similar to the ones recently proposed with respect to capital requirements.     

An approximate dynamic programming framework for modeling global climate policy under decision-dependent uncertainty

Analyses of global climate policy as a sequential decision under uncertainty have been severely restricted by dimensionality and computational burdens. Therefore, they have limited the number of decision stages, discrete actions, or number and type of uncertainties considered. In particular, two common simplifications are the use of two-stage models to approximate a multi-stage problem and exogenous formulations for inherently endogenous or decision-dependent uncertainties (in which the shock at time t+1 depends on the decision made at time t). In this paper, we present a stochastic dynamic programming formulation of the Dynamic Integrated Model of Climate and the Economy (DICE), and the application of approximate dynamic programming techniques to numerically solve for the optimal policy under uncertain and decision-dependent technological change in a multi-stage setting. We compare numerical results using two alternative value function approximation approaches, one parametric and one non-parametric. We show that increasing the variance of a symmetric mean-preserving uncertainty in abatement costs leads to higher optimal first-stage emission controls, but the effect is negligible when the uncertainty is exogenous. In contrast, the impact of decision-dependent cost uncertainty, a crude approximation of technology R&D, on optimal control is much larger, leading to higher control rates (lower emissions). Further, we demonstrate that the magnitude of this effect grows with the number of decision stages represented, suggesting that for decision-dependent phenomena, the conventional two-stage approximation will lead to an underestimate of the effect of uncertainty.     

环境税费政策下快递包装材料回收利用的最优决策及社会福利分析

本文在一个两阶段的决策模型中研究了政府环境税收政策下快递包装材料的回收利用问题,通过比较快递服务商在不同经营策略下的总利润和碳排放总量,刻画出不同环境税费水平下的最优经营策略选择。研究结果表明,当快递包装材料回收再利用的固定投资成本过高时,随着环境税费水平的不断升高,快递服务商并不一定总是选择绿色经营策略。此外,在这种情况下快递服务商即使选择了绿色经营策略也并不能总是带来碳排放的减少和环境的改善。而如果政府能够进行适当的固定投资补贴,则可有效地促进快递服务商选择并保持绿色经营策略,并且能够实现经济发展与环境保护的双赢。在引入单位碳排放的外部性成本后,本文分析了社会福利最大化下的最优环境税费水平。算例分析显示,政府的政策干预有可能同时实现社会福利最大化、快递服务商总利润的增加和碳排放总量的减少。     

Advance selling to strategic consumers

Advance selling of goods and services is a form of separating purchase from consumption. It is often employed when consumers are uncertain about their consumption utilities until a short time period before consumption. A book to be released, a concert to attend, or a cruise to take are some examples. Invariably, in consumers’ mind inventory availability (of copies, seats, or rooms) is a concern. In this paper we study a retailer’s inventory and pricing decisions in an advance selling scenario that involves consumers who are strategic. Some consumers not only consider advance and spot prices, but also the uncertainty in future availability of the product (during the spot period) and in their consumption utility from it. We characterize the optimal inventory management and pricing policies, and discuss several interesting aspects of the solution. For example, it can be optimal for the retailer to limit advance sales even if there is more demand for it, and it can be optimal for the retailer to limit its inventory even though there is more capacity to keep it, but not both.     

政府奖惩下供应链合作减排与低碳宣传的动态优化

为减少生产过程中碳排放对环境带来的不利影响,考虑到政府对制造商采取奖惩措施,以此为背景,针对供应链长期合作减排、低碳宣传的问题,构建微分博弈模型。考虑到产品需求受碳排放量和零售商低碳宣传努力的共同影响下,得出并分析了分散式和集中式决策下制造商和零售商的反馈均衡策略及产品碳排放量的最优轨迹,发现产品碳排放量的最优轨迹具有多种敛散性,且在集中式决策下实现了供应链利润和环境绩效的双赢。此外,还分析了政府奖惩力度对产品碳排放量的影响。最后结合算例对模型进行分析,为供应链的低碳化管理和政府奖惩政策的制定提供了理论参考。     

Uncertain programming model for uncertain optimal assignment problem

This paper employs uncertain programming to deal with uncertain optimal assignment problem in which profit is uncertain. Within the framework of uncertain programming, it gives the uncertainty distribution of the optimal assignment profit, and the concept of α-optimal assignment for uncertain optimal assignment problem is proposed. Then α-optimal model is also constructed. Taking advantage of properties of uncertainty theory, α-optimal model can be transformed into a corresponding deterministic form, which can be solved by Kuhn–Munkres algorithm.     

Demand uncertainty and supplier's returns policies for a multi-store style-good retailer

As demand uncertainty grows in the marketplace, a critical issue today in most purchase contract negotiations between an independent retailer of a style-good and its supplier is the provision of a returns policy, i.e., a commitment by the supplier to buy back unsold inventory of the good at the end of its selling season. Management science research on the strategic role and optimal design of returns policies has grown in recent years but so far offers little treatment of how exactly the retailer's optimal order quantity decisions are affected by demand uncertainty and how a supplier's returns policy can influence these decisions. Employing the traditional “newsboy problem” modeling framework, the authors investigate these issues considering a supplier who faces a retailer with two or more store outlets with normally distributed and possibly correlated demands. To facilitate their analyses, the authors employ a methodology based on special error function representations of the highly nonlinear objective functions of the retailer and supplier. Utilizing this approach, the authors are able to provide explicit insights into how: (a) the buyer's total order quantity decision is affected by the variability in demand; (b) buyback prices in combination with wholesale prices can influence the buyer's order quantity response to demand uncertainty; (c) demand uncertainty moderates the effects of the buyback and wholesale prices; (d) supplier's optimal combination of actions are affected by demand variability; (e) retailer's and supplier's expected profits behave in response to changes in the supplier's actions under different levels of demand variability.     

Modelling and computing the peaks of carbon emission with balanced growth

In this paper, we assume that under the balanced and optimal economic growth path, the economic growth rate is equal to the consumption growth rate, from which we can obtain the ordinary differential equation governing the consumption level by solving an optimal control problem. Then, a novel numerical method, namely a so-called discontinuous Galerkin method, is applied to solve the ordinary differential equation. The error estimation and the superconvergence estimation of this method are also performed. The model’s mechanism, which makes our assumption coherent, is that once the energy intensity is given, the economic growth is determined, followed by the GDP, the energy demand and the emissions. By applying this model to China, we obtain the conclusion that under the balanced and optimal economic growth path the CO₂ emission will reach its peak in 2030 in China, which is consistent with the U.S.-China Joint Announcement on Climate Change and with other previous scientific results.     

A new distributionally robust p-hub median problem with uncertain carbon emissions and its tractable approximation method

The p-hub median problem is to determine the optimal location for p hubs and assign the remaining nodes to hubs so as to minimize the total transportation costs. Under the carbon cap-and-trade policy, we study this problem by addressing the uncertain carbon emissions from the transportation, where the probability distributions of the uncertain carbon emissions are only partially available. A novel distributionally robust optimization model with the ambiguous chance constraint is developed for the uncapacitated single allocation p-hub median problem. The proposed distributionally robust optimization problem is a semi-infinite chance-constrained optimization model, which is computationally intractable for general ambiguity sets. To solve this hard optimization model, we discuss the safe approximation to the ambiguous chance constraint in the following two types of ambiguity sets. The first ambiguity set includes the probability distributions with the bounded perturbations with zero means. In this case, we can turn the ambiguous chance constraint into its computable form based on tractable approximation method. The second ambiguity set is the family of Gaussian perturbations with partial knowledge of expectations and variances. Under this situation, we obtain the deterministic equivalent form of the ambiguous chance constraint. Finally, we validate the proposed optimization model via a case study from Southeast Asia and CAB data set. The numerical experiments indicate that the optimal solutions depend heavily on the distribution information of carbon emissions. In addition, the comparison with the classical robust optimization method shows that the proposed distributionally robust optimization method can avoid over-conservative solutions by incorporating partial probability distribution information. Compared with the stochastic optimization method, the proposed method pays a small price to depict the uncertainty of probability distribution. Compared with the deterministic model, the proposed method generates the new robust optimal solution under uncertain carbon emissions.     

Robust suboptimal control of nonlinear systems

In this paper, we consider a nonlinear dynamic system with uncertain parameters. Our goal is to choose a control function for this system that balances two competing objectives: (i) the system should operate efficiently; and (ii) the system’s performance should be robust with respect to changes in the uncertain parameters. With this in mind, we introduce an optimal control problem with a cost function penalizing both the system cost (a function of the final state reached by the system) and the system sensitivity (the derivative of the system cost with respect to the uncertain parameters). We then show that the system sensitivity can be computed by solving an auxiliary initial value problem. This result allows one to convert the optimal control problem into a standard Mayer problem, which can be solved directly using conventional techniques. We illustrate this approach by solving two example problems using the software MISER3.     

Optimal scheduling policies in time sharing service systems

We consider optimal scheduling problems in a TSSS (Time Sharing Service System), i.e., a tandem queueing network consisting of multiple service stations, all of which are served by a single server. In each station, a customer can receive service time up to the prescribed station dependent upper bound, but he must proceed to the next station in order to receive further service. After the total amount of the received services reaches his service requirement, he departs from the network. The optimal policy for this system minimizes the long-run average expected waiting cost per unit of time over the infinite planning horizon. It is first shown that, if the distribution of customer's service requirement is DMRL (Decreasing Mean Residual Life), the policy of giving the highest priority to the customer with the most attained service time is optimal under a set of some appropriate conditions. This implies that any policy without interruptions and preemptions of services is optimal. If the service requirement is DFR (Decreasing Failure Rate), on the other hand, it is shown that the policy of giving the highest priority to the customer with the least attained service time, i.e., the so-called LAST (Least Attained Service Time first) is optimal under another set of some appropriate conditions. These results can be generalized to the case in which there exist multiple classes of customers, but each class satisfies one of the above sets of conditions.     

碳交易规制下信息不对称供应链的减排和低碳推广博弈研究

论文在碳交易规制下,研究单一制造商和双零售商组成的供应链减排与低碳推广决策以及零售商对制造商的成本信息分享问题,分析了零售商对制造商分享成本信息的条件,及不同情形下的制造商减排和零售商低碳推广策略。研究发现:零售商的最优低碳推广水平只与自身相关参数有关,零售商只有在自身低碳推广效率足够高时才会与制造商分享信息;制造商最优减排量随消费者低碳意识、碳交易价格、零售商低碳推广效率及其不确定性增大而提高,随零售商之间竞争程度提高而降低;无论零售商是否对制造商分享成本信息,供应链成员的最优利润随零售商之间竞争程度提高而增加,随消费者低碳意识、碳交易价格提高而提高;零售商低碳推广效率的不确定性越大则制造商的最优利润越低,零售商的最优利润越高;仅一家零售商分享成本信息时,制造商无法通过转移支付使另一家零售商与其分享信息。