Search Results (6)

In the face of economic, social, and environmental pressures, the issue of sustainable development has garnered widespread attention in the Logistics Service Supply Chain (LSSC) with risk attitudes under Technical Output Uncertainty. In this regard, this paper first constructs an optimal emission reduction investment game model for an LSSC composed of Logistics Service Integrators (LSIs) and Logistics Service Providers (LSPs) against the backdrop of Technical Output Uncertainty. To this end, it quantifies the participants’ risk attitudes using a mean-variance model to analyze optimal emission reduction investment decisions for centralized and decentralized LSSC under different levels of risk tolerance. Subsequently, it designs a joint contract with altruistic preferences for sharing emission reduction costs in the LSSC. This contract analyzes the parameter constraints for achieving Pareto optimization within the supply chain. Finally, the study employs a case simulation to analyze the changes in expected revenues for centralized LSSC and joint contracts under different risk tolerance levels. The study reveals that (1) in a centralized LSSC, under risk-neutral attitudes, there exists a unique optimal emission reduction investment, which yields the highest expected return from emission reduction. However, under risk-averse attitudes, the expected return is always lower than the optimal expected return under risk neutrality. (2) In a decentralized LSSC, the emission reduction investment decisions of the Logistics Service Providers are similar to those in a centralized LSSC. (3) Under risk-neutral attitudes, the cost-sharing and altruistic preference-based joint contract can also coordinate the risk-averse LSSC under certain constraints, and by adjusting the cost-sharing and altruistic preference parameters, the expected returns can be reasonably allocated. Full article

Construction machinery operations are intrinsically linked to critical societal challenges, including safety risks and carbon emissions. In response to the high incidence of fatal accidents during installation and dismantling phases, the Chinese government has officially promoted integrated installation–dismantling services to enhance construction safety since 2023. However, the economic viability of this policy for leasing companies remains largely underexplored. To address this gap, this paper develops a leasing-oriented closed-loop construction machinery supply chain model that incorporates integrated installation–dismantling services under an industrial internet platform. The study first compares and analyzes the product leasing demand, installation and dismantling demand, and supply chain profits under both centralized and decentralized decision-making scenarios. Based on these analyses, a profit–cost sharing joint contract is designed to coordinate the supply chain. Furthermore, the interrelationships among key parameters are examined through a sensitivity analysis and numerical simulation. The results reveal that enhancing leasing information services increases both the demand for construction machinery and the platform’s operating costs. These costs are positively correlated with the product’s selling price, leading to higher purchasing costs for lessees. Similarly, improving information services for installation and dismantling raises the platform’s operating costs and enhances service levels, which in turn increases installation and dismantling costs for lessees. The findings demonstrate that within a certain range of cost-sharing and leasing-sharing proportional coefficients, the joint contract enables the supply chain to achieve Pareto optimization. This approach simultaneously alleviates economic pressure on lessees, improves construction safety, and promotes the integration of installation and dismantling services. Full article

With the sustainable development of the global economy, environmental problems and the green economy are increasingly becoming points of concern for the community. However, the large amount of capital invested in green technology and the high price of green products have become the key problems hindering the development of a risk-averse green supply chain. In order to promote the supply chain to increase green investment level, improve the green degree of products, and reduce the impact of risk aversion on green investment, this paper studies a two-echelon green supply chain composed of a risk-averse manufacturer and a risk-neutral retailer, in which the retailer is the leader and the manufacturer is the follower. We construct the wholesale price contract model, cost-sharing contract model, and two-part contract model, respectively, and use the Optimization Theory and Methods to discuss the impact of the three contracts on the green degree, expected utility of supply chain, retail price, consumer surplus, and social welfare. The results show that in the cost-sharing contract, compared with the wholesale price contract, the green degree of the product has been significantly improved, but the expected utility of the supply chain enterprises cannot achieve Pareto improvement, and the higher consumer environmental awareness will cause the manufacturer’s expected utility to decline. In the two-part tariff contract, compared with the wholesale price contract, the expected utility of supply chain enterprises achieves Pareto improvement, and the green degree of products is the highest in the three contracts; more importantly, in the two-part contract, the product green degree, the retail price, and the expected utility of the supply chain are not related to the manufacturer’s risk aversion; meanwhile, the retail price in the two-part tariff is the lowest among the three contracts, and the consumer surplus and social welfare are the highest. Our main contribution is that the two-part contract eliminates the influence of the manufacturer’s risk aversion on the above decision variable and realizes the unification of manufacturers, retailers, consumers, and social benefits. Finally, this paper uses numerical examples to verify the above conclusions and then analyzes the sensitivity of the supply chain system. Full article

To mitigate climate change, the governments of various countries have formulated and implemented corresponding low-carbon emission reduction policies. Meanwhile, consumers’ awareness of the necessity of environmental protection is gradually improving, and more consumers pay attention to the environmental attributes of products, which all encourages enterprises to have great power to implement low carbon technology. As rational decision makers, members tend to show the characteristics of risk aversion. How to meet the needs of consumers and reduce their own risks has become a key point of low-carbon supply chain management. Considering carbon quota policy, in this paper, the optimal pricing decision-making process of a supply chain system is discussed under risk-neutral and risk-avoidance decision-making scenarios by game theory, and a cost-sharing contract is used to coordinate the decision-making process of a supply chain system. By analyzing the influence of the risk aversion coefficient on the optimal strategies of participants, we find that when the manufacturer has the risk aversion characteristic, the risk aversion coefficient will further reduce the carbon emission rate, the wholesale price of the product and the manufacturer’s profit but increase the product order quantity and the retailer’s profit. In addition, if consumers have a high preference for low-carbon products, the manufacturer’s risk-aversion coefficient will lead to a lower selling price than in the centralized decision-making situation, and the profit of the supply chain system will also be further reduced. When the cost-sharing contract is adopted for coordination, the Pareto improvement of supply chain members’ profits can be achieved when the parameters of the cost-sharing contract are appropriate, regardless of the manufacturer’s risk-neutral decision or risk-aversion decision. Full article

In this study, we examine how Battery Storage (BES) and Thermal Storage (TES) combined with solar Photovoltaic (PV) and Concentrated Solar Power (CSP) technologies with an increased storage duration and rental cost together with diversification would influence the Moroccan mix and to what extent the variability (i.e., adequacy risk) can be reduced; this is done using recent (2013) cost data and under various penetration scenarios. To do this, we use MERRA-2 climate reanalysis to simulate hourly demand and capacity factors (CFs) of wind, solar PV and CSP without and with increasing storage capabilities—as defined by the CSP Solar Multiple (SM) and PV Inverter Loading Ratio (ILR). We adjust these time series to observations for the four Moroccan electrical zones over the year 2018. Our objective is to maximize the renewable (RE) penetration and minimize the imbalances between RE production and consumption considering three optimization strategies. We analyze mixes along Pareto fronts using the Mean-Variance Portfolio approach—implemented in the E4CLIM model—in which we add a maximum-cost constraint to take into account the different rental costs of wind, PV and CSP. We propose a method to calculate the rental cost of storage and production technologies taking into account the constraints on storage associated with the increase of SM and ILR in the added PV-BES and CSP-TES modules, keeping the mean solar CFs fixed. We perform some load bands-reduction diagnostics to assess the reliability benefits provided by each RE technology. We find that, at low penetrations, the maximum-cost budget is not reached because a small capacity is needed. The higher the ILR for PV, the larger the share of PV in the mix compared to wind and CSP without storage is removed completely. Between PV-BES and CSP-TES, the latter is preferred as it has larger storage capacity and thus stronger impact in reducing the adequacy risk. As additional BES are installed, more than TES, PV-BES is favored. At high penetrations, optimal mixes are impacted by cost, the more so as CSP (resp., PV) with high SM (resp., ILR) are installed. Wind is preferably installed due to its high mean CF compared to cost, followed by either PV-BES or CSP/CSP-TES. Scenarios without or with medium storage capacity favor CSP/CSP-TES, while high storage duration scenarios are dominated by low-cost PV-BES. However, scenarios ignoring the storage cost and constraints provide more weight to PV-BES whatever the penetration level. We also show that significant reduction of RE variability can only be achieved through geographical diversification. Technological complementarity may only help to reduce the variance when PV and CSP are both installed without or with a small amount of storage. However, the diversification effect is slightly smaller when the SM and ILR are increased and the covariances are reduced as well since mixes become less diversified. Full article

We reconsider costs in insurance, and suggest a new type of cost function, which we argue is a natural choice when there are relatively small, but frequent, claims. If a fixed cost is incurred each time a claim is made, we obtain a Pareto optimal deductible even if the cost function does not vary with the indemnity. The classical result says that deductibles appear if and only if costs are variable. This implies that when the claims are relatively small, it is not optimal for the insured to be compensated, since the costs outweigh the benefits and a deductible will naturally occur. When we constrain the contract to contain a cap, a non-trivial deductible is Pareto optimal regardless of the assumptions about the cost structure, which is what is known as an XL-contract. Full article